i

A Multi-Temporal Remote Sensing Approach to Freshwater Turtle Conservation

by

Amy B. Mui

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Department of Geography University of Toronto

© Copyright by Amy B. Mui 2015

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A multi-temporal remote sensing approach to

freshwater turtle conservation

Amy Mui

Doctor of Philosophy

Department of Geography

2015

Abstract

Freshwater turtles are a globally declining taxa, and estimates of population status are not

available for many species. Primary causes of decline stem from widespread habitat loss and

degradation, and obtaining spatially-explicit information on remaining habitat across a relevant

spatial scale has proven challenging. The discipline of remote sensing science has been

employed widely in studies of biodiversity conservation, but it has not been utilized as frequently

for cryptic, and less vagile species such as turtles, despite their vulnerable status. The work

presented in this thesis investigates how multi-temporal remote sensing imagery can contribute

key information for building spatially-explicit and temporally dynamic models of habitat and

connectivity for the threatened, Blanding’s turtle (Emydoidea blandingii) in southern Ontario,

Canada.

I began with outlining a methodological approach for delineating freshwater wetlands from high

spatial resolution remote sensing imagery, using a geographic object-based image analysis

(GEOBIA) approach. This method was applied to three different landscapes in southern Ontario,

and across two biologically relevant seasons during the active (non-hibernating) period of

Blanding’s turtles. Next, relevant environmental variables associated with turtle presence were

extracted from remote sensing imagery, and a boosted regression tree model was developed to

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predict the probability of occurrence of this species. Finally, I analysed the movement potential

for Blanding’s turtles in a disturbed landscape using a combination of approaches. Results

indicate that (1) a parsimonious GEOBIA approach to land cover mapping, incorporating texture,

spectral indices, and topographic information can map heterogeneous land cover with high

accuracy, (2) remote-sensing derived environmental variables can be used to build habitat

models with strong predictive power, and (3) connectivity potential is best estimated using a

variety of approaches, though accurate estimates across human-altered landscapes is challenging.

Overall, this body of work supports the use of remote sensing imagery in species distribution

models to strengthen the precision, and power of predictive models, and also draws attention to

the need to consider a multi-temporal examination of species habitat requirements.

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Acknowledgments

First and foremost, I would like to thank my advisor Dr. Yuhong He, who has been

instrumental in my academic success, but has also contributed so much as a mentor, a

confidante, and a great friend. She has always answered my frantic queries with a lightning

quick response time, troubleshoots my problems with such insight and apparent ease, and

does all of this with an eternal optimism that has kept me going all these years. Yuhong, by

your example I am a better researcher, writer, and teacher, and I move forward with strong

confidence in my ability to balance a healthy work life with a thriving family life. This

much I owe to you, and more. Thank you for your unrelenting faith in me, and for a

journey that has been so enjoyable because I had you in my corner.

Many graduates have told me that having a good supervisory committee, is paramount in

successfully and enjoyably navigating a PhD, and I could not have been more fortunate in

this regard. Dr. Jing Chen, and Dr. Nathan Basiliko, your positive energy, encouragement,

and support have meant a lot to me. A special thank you to Dr. Marie-Josée Fortin for

providing such insightful suggestions regarding my research, for guiding the biologist in

me, and for always treating me such care and kindness. To Bob Johnson, and Julia Phillips

from the Toronto Zoo; I was lucky to have once worked at the zoo and made your

acquaintance earlier on, but never would I have guessed that it would have led to such a a

long and rewarding relationship. I would like to express my appreciation for your support,

given in so many ways, and through so many opportunities. I hope what I have produced

here will make you proud.

To those wild and courageous souls who donned such fashionable waders and trekked

through the wetlands with me, I extend my sincere thanks and appreciation. Through rain

or shine, clouds of blackflies and biting toads, while dragging a canoe through dried up

wetlands, or falling out of them into leech-prone waters, I could not have accomplished

what I have without you, but more importantly you made it all so much fun! A special

thank you to Brennan Caverhill and James Paterson for introducing me to the less travelled

world of wetlands and the wonders held therein; to Kelly Wong for making field

campaigns fun and never tedious, and to all my other adventurous field assistants. Thank

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you also Christopher Edge, James Paterson, and Dr. Jackie Litzgus of Laurentian

University for sharing your data, and also to Brad Steinberg of the Ministry of Natural

Resources, Parks and Protected Areas, Dr. Leo Cabrera of Parks Canada, and Frank

Dorombozi (Brant County) for such generous field support and interest in this work.

Primary funding for this research was provided by an NSERC IPS grant sponsored by the

Toronto Zoo, an Ontario Graduate Scholarship, the William G. Dean scholarship from the

University of Toronto, and the generous support of the Department of Geography at UTM.

To my fellow graduates, past and present, you have enriched my time at the University of

Toronto and made it about so much more than just an overwhelming workload. Special

thank you to Randy Bui, Kelly Wong, Varun Gupta, and Carolyn Winsborough, for all the

laughs and the impromptu stress relief.

To my family, thank you for supporting me unconditionally, and most especially

throughout the past few years. After returning to my studies following maternity leave,

when I was struggling mightily with a young child and the dual demands of work and

family life, you helped to lighten my load by bringing food, helping with day care pick-ups,

and lending a sympathetic ear when I felt I was at my lowest. Thank you especially to my

sister for all the home-cooked meals, and to my dad for always showing such a keen

interest in my research, even though I was terrible at explaining it. To my mom, you left us

much too early but you have always been close by and I hope you can see what I have

accomplished!

To my husband, you showed your support in so many ways other than words, and I thank

you for single-parenting all those nights so that I could work late, and for waiting for my

call no matter the time, so you could talk to me as I walked across the dark parking lot to

my car. I could not have made it here without you. Last of all, but first in my heart, to my

little Georgie who reminds me that life is beautiful and simple. Around you, I (literally)

forget about R codes, statistical assumptions, and grant applications past due, and instead

take pleasure in the most important things in life; like hiding from zombies, and climbing

up slides.

Finishing this PhD was not just about returning back to academia and earning another

degree, but about growing intellectually and emotionally. I learned how to read and how to

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write (naively having thought that I already knew how to do those two things), and also

how to teach, how to lead, and how to disseminate. But even more than that, I learned that

my limits are further than I thought they were, and that my family and friends will never let

me fall.

Like others before me, I stood on the shoulders of giants and now I can see a little further

than I did before.

Thank you to everyone who walked beside me along this journey.

Amy

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Table of Contents

Acknowledgments.......................................................................................................................... iv

Table of Contents .......................................................................................................................... vii

List of Tables ...................................................................................................................................x

List of Figures ............................................................................................................................... xii

List of Appendices ....................................................................................................................... xvi

Chapter 1 ........................................................................................................................................1

Introduction ....................................................................................................................................1

Global Status of Chelonians.................................................................................................1

Remote Sensing and Biodiversity ........................................................................................4

Wetlands and Freshwater Turtles .........................................................................................5

Thesis Structure ...................................................................................................................7

1.4.1 Chapter Two: An object-based approach to delineate wetlands across

landscapes of varied disturbance with high spatial resolution satellite imagery .....8

1.4.2 Chapter Three: Modelling seasonal wetland habitat suitability for Blanding’s

turtles (Emydoidea blandingii) using optical satellite remote sensing imagery ......9

1.4.3 Chapter Four: Estimating seasonal landscape connectivity for Blanding’s

turtles in a fragmented agricultural landscape .......................................................10

Statement of research collaboration and manuscript submission ......................................11

References .................................................................................................................................11

Chapter 2 ......................................................................................................................................15

An object-based approach to delineate wetlands across landscapes of varied

disturbance with high spatial resolution satellite imagery ..................................................15

Introduction ........................................................................................................................15

Study Area .........................................................................................................................18

Data and Methods ..............................................................................................................19

2.3.1 Satellite Imagery and Preprocessing ......................................................................21

2.3.2 Development of Input Layers .................................................................................22

2.3.3 Image Segmentation ...............................................................................................24

2.3.4 Classification Approach .........................................................................................28

2.3.5 Accuracy assessment ..............................................................................................33

Results ................................................................................................................................34

2.4.1 Multi-scale segmentation .......................................................................................34

2.4.2 Classification ..........................................................................................................41

2.4.3 Comparison of Sample Attribute Separation between Classes ..............................46

Discussion ..........................................................................................................................47

2.5.1 Segmentation and the GEOBIA approach .............................................................48

2.5.2 Classification Accuracy ..........................................................................................49

2.5.3 Landscape heterogeneity ........................................................................................52

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Conclusions ........................................................................................................................53

References .................................................................................................................................54

Chapter 3 ......................................................................................................................................60

Modelling seasonal wetland habitat suitability for Blanding’s turtles (Emydoidea

blandingii) using optical satellite remote sensing imagery ..................................................60

Introduction ........................................................................................................................60

Materials and Methods .......................................................................................................63

3.2.1 Study Area ..............................................................................................................63

3.2.2 Satellite Imagery ....................................................................................................64

3.2.3 Study Population & Telemetry Data ......................................................................65

3.2.4 Temporal Partitioning ............................................................................................66

3.2.5 Environmental Input Variables ..............................................................................67

3.2.6 Pseudo-absence (Background) Sampling ...............................................................69

3.2.7 Linking Biophysical Variables to Satellite Data ....................................................71

3.2.8 Calculating Landscape Metrics ..............................................................................75

3.2.9 Extracting Topographic Data .................................................................................77

3.2.10 Models and Model Fitting ......................................................................................78

3.2.11 Evaluation and Evaluation Criteria ........................................................................79

Results ................................................................................................................................80

3.3.1 Regression Models in Biophysical Variable Estimation ........................................80

3.3.2 BRT and Logistic Regression Model Results ........................................................82

3.3.3 Comparison of variable contribution ......................................................................84

3.3.4 BRT model comparison .........................................................................................86

3.3.5 Probability of occurrence maps ..............................................................................90

3.3.6 Map accuracy .........................................................................................................90

Discussion ..........................................................................................................................94

3.4.1 Biophysical Analysis ..............................................................................................94

3.4.2 Satellite-derived predictors ....................................................................................95

3.4.3 Model comparison ..................................................................................................96

3.4.4 Seasonal change .....................................................................................................96

3.4.5 Landscape Comparison ..........................................................................................99

Conclusions ......................................................................................................................100

References ...............................................................................................................................101

Chapter 4 ....................................................................................................................................106

Estimating seasonal landscape connectivity for Blanding’s turtles in a fragmented

agricultural landscape ..........................................................................................................106

Introduction ......................................................................................................................106

Methods............................................................................................................................109

4.2.1 Study Area and Blanding’s Turtle Population .....................................................109

4.2.2 Seasonal Land cover Maps and Habitat Nodes ....................................................111

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4.2.3 Resistance Layers .................................................................................................112

4.2.4 Connectivity Modelling Overview .......................................................................114

4.2.5 Evaluation .............................................................................................................117

Results and Discussion ....................................................................................................117

4.3.1 Least-Cost Pathways & Patch-based Indices .......................................................117

4.3.2 Circuit-based corridors .........................................................................................120

4.3.3 Barrier Mapping ...................................................................................................122

Conclusions ......................................................................................................................125

References ...............................................................................................................................127

Chapter 5 ....................................................................................................................................132

Thesis Summary and Conclusion .............................................................................................132

Chapter Synthesis.............................................................................................................132

Management Applications and Future Direction .............................................................134

APPENDIX ..................................................................................................................................137

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List of Tables

Table 2-1. Satellite Image data information ................................................................................. 21

Table 2-2. Land cover class descriptions adapted from Anderson et al., (1976) and the Canadian

wetland classification system (National Wetlands Working Group, 1997). ................................. 30

Table 2-3. Wetland land cover class descriptions according to the Canadian Wetland

Classification System (NWWG, 1997). ........................................................................................ 31

Table 2-4. Hierarchical segmentation scale for each study site and corresponding target land

cover class ..................................................................................................................................... 34

Table 2-5. Accuracy statistics for land cover classes at each study site (PA = producer’s

accuracy, UA = user’s accuracy). ................................................................................................. 42

Table 2-6. Error matrix for the landcover classification of the Brant County agricultural site (7

classes) using GeoEye1 MS data .................................................................................................. 43

Table 2-7. Error matrix for the landcover classification of the Algonquin park study site (8

classes), using GeoEye1 MS data. ................................................................................................ 43

Table 2-8. Error matrix for the landcover classification of the east Toronto urban site (8 classes)

using WorldView2 MS data.......................................................................................................... 44

Table 2-9. Accuracy statistics of producer’s accuracy (PA), user's accuracy (UA), overall

accuracy and Kappa statistic for merged wetland, upland, and water classes across all study sites.

....................................................................................................................................................... 45

Table 2-10. Error matrices for grouped water, wetland, and upland classes over each study area.

Numbers denote image objects (not individual pixels)................................................................. 45

Table 3-1. Summary of satellite imagery acquired over study sites and temporal periods

examined in this chapter ............................................................................................................... 65

Table 3-2. List of biophysical variables measured over presence and pseudo-absence points at

the park site (Algonquin Provincial Park), and the agricultural site (Brant County study) during

the spring and summer seasons. .................................................................................................... 72

Table 3-3. Summary of significant biophysical variables identified through a t-test comparison

of means between biophysical measurements at observed turtle presence points and random

pseudo-absence points (random values in brackets). Based on 70±5 total sample points for each

variable at each site and each season. ........................................................................................... 73

Table 3-4. Summary of landscape and topographic derived variables used as model inputs ...... 78

Table 3-5. Summary of regression models developed from field-measured biophysical

parameters and satellite-derived data for use in mapping target variables ................................... 81

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Table 3-6. Accuracy assessment of regression models developed for biophysical variable

estimation #add units to RMSE .................................................................................................... 81

Table 3-7. Comparison of model test statistics for the park study site (Algonquin Provincial

Park) .............................................................................................................................................. 82

Table 3-8. Comparison of response variables included in final models of habitat selection

developed for Blanding’s turtles of the park study site (Algonquin Provincial Park) in Ontario,

Canada (*p < 0.01). ....................................................................................................................... 83

Table 3-9. Response variables included in final BRT models of seasonal habitat selection for

Blanding’s turtles of the agricultural study site (Brant County), Ontario, Canada. ...................... 85

Table 3-10. Map accuracy statistics based upon all telemetry (presence) data partitioned by

season and overlaid against binary BRT maps set with threshold value of 0.6. Map accuracy

value represents the estimated probability of occurrence of Blanding’s turtles. .......................... 91

Table 4-1. Summary of habitat patch, LCPs and overall landscape indices for the spring and late

summer connectivity models († p < 0.05) ................................................................................... 118

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List of Figures

Figure 1-1. Examples of Chelonian status worldwide depicting (a) the critically endangered

Leatherback sea turtle (Dermochelys coriacea), (b) the critically endangered Ploughshare

tortoise (Astrochelys yniphora) from Madagascar, (c) turtles supplying the food market in Asia,

and (d) Lonesome George, the last of the now extinct Pinta Island Galápagos tortoise

(Chelondoidis nigra abingdonii) on display at the American Museum of Natural History. Images

licensed under CC 2.0 via Wikimedia Commons [(a) Tinglar, (b) Hans Hillewaert, (c) Allison

Meier, (d) Vmenkov]. ..................................................................................................................... 2

Figure 1-2. Overview of thesis structure and process demonstrating source data, research

objectives by chapter, and final outcomes (products). .................................................................... 7

Figure 2-1. Study areas located in Ontario (a) Algonquin Provincial Park relatively undisturbed

site, (b) Brant County agricultural site, and (c) east Toronto urban site. Images displayed in false

colour (RGB=NIR-Red-Green). ................................................................................................... 19

Figure 2-2. Process workflow for segmentation and classification of wetland landscapes ......... 20

Figure 2-3. Subset of input layers from the urban site of eastern Toronto showing the (a) digital

elevation model (a), standard deviation texture layer (b), and NDVI layer (c). ........................... 24

Figure 2-4. Multi-scale segmentation process used to segment images at three levels, using a

hierarchical parent-child relationship between wetlands and within wetland components at the

medium (level 2) and fine (level 1) scale. ..................................................................................... 27

Figure 2-5. Example of reference data used in sample selection. (a) Algonquin park site

electronic Forest Resource Inventory (eFRI) imagery (OMNR, 2005) and wetland thematic layer

(pink), and (b) subset – note reference thematic layer does not capture all wetlands in the area.

(b) Brant county agricultural site South Western Ontario Orthoimagery Project (SWOOP, 2005)

and reference wetland thematic layer (pink) from the Grand River Conservation Authority

(GRCA) downloaded from the Grand River Information Network (GRIN). ............................... 32

Figure 2-6. Example of object sample selection for accuracy assessment of the park site

(Algonquin Provincial Park) land cover map. .............................................................................. 33

Figure 2-7. Quantitative evaluation of selected scale parameter with the modified ED3 algorithm

at the coarse (diamond), medium (square), and fine (triangle) levels for the (a) Algonquin park

site, (b) Brant county agricultural site and (c) east Toronto urban site. Hollow circles denote the

selected scale value through visual assessment. ........................................................................... 36

Figure 2-8. Comparison of coarse level segmentation results over the Brant County agricultural

scene (a) with subset shown in red square and (b). Results of segmentation at scale 200 using all

seven input layers shown in (c), and at the same scale 200 with the NDVI layer excluded in (d).

White arrows in (d) show locations of over-segmentation that do not correspond with crop field

boundaries. .................................................................................................................................... 37

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Figure 2-9. Comparison of medium level segmentation results over the Algonquin park scene (a)

with subset a wetland complex shown in the red square and (b). Results of segmentation at scale

40 using all seven input layers (c), and at the same scale 40 with the DEM layer excluded (d).

White arrow in (c) shows improved segmentation of a wetland boundary (to the right of the

indicated line) with the inclusion of the DEM layer. .................................................................... 38

Figure 2-10. Comparison of fine level segmentation results over the east Toronto urban scene (a)

and with subset of a marsh complex shown in the red square and (b). Results of segmentation at

scale 15 using all seven input layers (c), and at the same scale 15 with the texture layer excluded

(d). Note the significant over-segmentation of the texture-excluded image in (d). ...................... 39

Figure 2-11. Sample view of wetlands enclosed by object boundaries created by the FNEA

multiresolution segmentation algorithm (yellow), and its corresponding reference boundary

(white) showing improved delineation of wetland boundaries (a), under segmentation (b), better

detection of within wetland components (c, d); and examples of over segmentation, particularly

of treed wetlands (e, f). Wetlands in the top row are from the natural park site, middle row

wetlands are from the rural site, and bottom row wetlands are from the urban site. Reference

polygons were provided by the OMNR (park site: a, b), Grand River Conservation Authority

(agricultural site: c, d), and the Toronto Region Conservation Authority (urban site: e, f). ........ 40

Figure 2-12. Comparison of mean object layer values providing the best separation between

wetlands and all other classes at each study site. Y-axis shows number of times a layer provided

the best separation distance between classes, normalized out of 1. Average values are

standardized across total number of land cover classes at each site. ............................................ 46

Figure 2-13. Classification results showing original satellite image (a, d, g) (RGB: NIR-Red-

Green), final classified map (b, e, h) with subsets in the red polygons expanded in (c, f, i) over

the Algonquin Park natural site (first row), the Brant county agricultural site (middle row), and

the east Toronto urban site (bottom row). ..................................................................................... 47

Figure 3-1. Study regions in a (a) relatively undisturbed park landscape in Algonquin Provincial

Park, and (b) a fragmented agricultural landscape in Brant County. Images acquired from (a)

GeoEye1 on May 25, 2013 and (b) WorldView2 on April 9, 2012. ............................................. 63

Figure 3-2. Seasonal change in wetland vegetation and standing water in the park study area

(Algonquin Provincial Park) during the early spring (May; top left), and late summer (August;

top right), and the agricultural (Brant County) study area during the early spring (April; bottom

left), mid-season (June; bottom centre) and late summer (August; bottom right).. ...................... 67

Figure 3-3. Workflow demonstrating source data, extraction of environmental input variables,

and development of final raster layers used in model building. ................................................... 68

Figure 3-4. Sampling design for identification of significant biophysical variables correlated

with turtle presence. A subset of present points (blue circles) are selected from the pool of

temporally partitioned telemetry points (pink circles), and paired with a pseudo-absence point

(yellow circles) constrained to a 90m (± 10m) distance, any direction from the selected presence

point. ............................................................................................................................................. 69

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Figure 3-5. Background points generated across study landscape (park site, spring) and stratified

by dominant landcover type shown as pink circles. Temporally partitioned (spring) telemetry

points show in blue circles. ........................................................................................................... 70

Figure 3-6. Change in wetland composition, vegetation extent, and available standing water

across the spring and late summer periods over the park site. Land cover maps were developed

from high spatial resolution GeoEye1 imagery during the spring (May 2013; top) and late

summer (September 2012; bottom). ............................................................................................. 75

Figure 3-7. Change in wetland composition, vegetation extent, and available standing water

across the spring and late summer periods over the agricultural site (subsets). Land cover maps

were developed from high spatial resolution WorldView2 imagery during the spring (April 2012;

top) and late summer (September 2013; bottom). ........................................................................ 76

Figure 3-8. Comparison of variable influence and contribution as habitat predictors for the park

study site (Algonquin Provincial Park). BRT variables are shown on the left as relative influence

and logistic regression on the right as ROC plots of each contributing variable. Lines curving

towards 1 on the sensitivity y-axis represent variables most capable of accurately detecting turtle

presence (true positives). Lines curving towards 1 on the 1-specificity x-axis represent variables

most capable of detecting turtle absence (true negatives). Diagonal line indicates reference line

for which variable provides no discriminatory power. ................................................................. 84

Figure 3-9. BRT model results over the park study site (Algonquin Provincial Park) and the

agricultural (Brant County) study site, over both spring and late summer seasons. Bars depict the

area under the curve receiver operator characteristic value. Orange line displays model predictive

deviance. ....................................................................................................................................... 86

Figure 3-10. Partial dependence fitted function curves for variables retained in final BRT models

for the park study site (Algonquin Provincial Park), spring model (left) and late summer model

(right). Relative influence values in brackets. Y axes are on a logit scale and centred to have a

zero mean over the data distribution. Graphed lines (or dashes) above 0 on the y-axis indicate

higher selection probability over the range indicated by the x-axis, while functions below 0 on

the y-axis indicate lower selection probability (avoidance) over the range indicated on the x-axis.

....................................................................................................................................................... 88

Figure 3-11. Fitted function curves for predictors retained in final BRT models for agricultural

study site (Brant County) spring model (left) and late summer model (right). Relative influence

values in brackets. Y axes are on a logit scale and centred to have a zero mean over the data

distribution. Graphed lines (or dashes) above 0 on the y-axis indicate higher selection probability

over the range indicated by the x-axis, while functions below 0 on the y-axis indicate lower

selection probability (avoidance) over the range indicated on the x-axis. .................................... 89

Figure 3-12. Predicted potential probability surface for the occurrence of Blanding’s turtle in the

agricultural (Brant County) study area (left) and the park (Algonquin Provincial Park) study area

(right) developed using boosted regression trees. Seven final environmental predictors were used

in each model. Red areas indicate the highest probability of occurrence. .................................... 90

Figure 3-13. Threshold map based on habitat suitability ≥ 0.6 for the occurrence of Blanding’s

turtles in the park study area (Algonquin Provincial Park) during the spring (top) and late

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summer (bottom) seasons with a map accuracy of 90.8% and 87.8% respectively. Subset (at

right) show pooled turtle presence locations plotted over binary habitat map to demonstrate

overlap with predicted areas. ........................................................................................................ 92

Figure 3-14. Threshold map based on binary habitat suitability (≥ 0.6) for the occurrence of

Blanding’s turtles in the agricultural (Brant County) study area over the spring (top) and late

summer (bottom) seasons with a map accuracy of 94.1% and 65.5% respectively. Subset (images

on right) show pooled turtle presence locations overlaid with predicted suitable habitat for each

season. White circles indicate areas of suitable habitat which have disappeared in the subsequent

late summer season. ...................................................................................................................... 93

Figure 4-1. Map depicting Blanding’s turtle range in North America, and study site in southern

Ontario. Map image licensed under Creative Commons 3.0. ..................................................... 109

Figure 4-2. Examples of wetland habitat found in the agriculturally-modified landscape

depicting habitat patches bisected by roads (A), isolated wetlands surrounded by farmland (B),

man-made irrigation ponds serving as temporary refuge (C), and natural corridors bisected by

multiple roads (D). Imagery: © Digital Globe 2015, Google Map data 2015. ........................... 110

Figure 4-3. Seasonal landcover maps over the agricultural site developed from multispectral

satellite imagery acquired in the spring (left) by GeoEye1 (April 2012) and late summer (right)

by WorldView2 (September 2013). Images were classified using a multi-scale geographic

object-based image analysis (GEOBIA) approach, and the nearest neighbour classifier. .......... 111

Figure 4-4. Resistance maps developed from expert-based knowledge. Habitat nodes shown in

pink for the early spring (left) and late summer (right). Higher resistance is shown in lighter

colours, and lower resistance in darker colours. ......................................................................... 113

Figure 4-5. Predicted least-cost pathways (dashed lines) connecting spatially shifting seasonal

habitat nodes during the spring (left) and late summer (right) season. Nodes coloured according

to relative contribution to connectivity of the network (more important nodes are red, least

important nodes are blue. Top three nodes with highest dIIC scores (∆ Integral Index of

Connectivity; nodes for which removal would most strongly reduce connectivity) are labelled.

Higher dIIC scores indicate higher importance. ......................................................................... 119

Figure 4-6. Results of circuit-based models showing corridors of highest conductance during the

spring (left) and late summer (right). Areas of higher conductance are shown in red, which

denote predicted corridors for Blanding’s turtles. Boxed regions (a, b, c) show areas of

significant change in conductance between maps. ..................................................................... 122

Figure 4-7. Barrier map depicting intersect points between LCPs and paved roads in the spring

(left) and late summer (right) connectivity models. .................................................................... 123

Figure 4-8. Spring Circuitscape map combined with road network (a) and spring (April-June)

Blanding’s turtle telemetry points (b) showing turtle locations on either side of the road. The

image in (c) depicts the estimated movement pathway for a single adult Blanding’s turtle

demonstrating that turtles must cross roads to access preferred habitat. .................................... 124

xvi

List of Appendices

Figure A1. Continuous % vegetation cover raster map derived from high spatial resolution

GeoEye1 imagery (NDVI) and field-based measurements over the spring Algonquin park

study site (a) and subset in white square and (b). ...................................................................137

Figure A2. Continuous % vegetation cover raster map derived from high spatial resolution

GeoEye1 imagery (NDVI) and field-based measurements over the spring Algonquin park

study site (a) and subset in white square and (b). ...................................................................137

138

Figure A3. Continuous water depth raster map derived from high spatial resolution

GeoEye1-imagery (relative water depth algorithm) and field-based measurements. Late

summer Algonquin Provincial Park study area (left) and subset locations shown in white

squares, and boxes (a) and (b). ................................................................................................138

138

Figure A4. Continuous water depth raster map derived from high spatial resolution

GeoEye1-imagery and field-based measurements. Algonquin Provincial Park study area,

late summer. ............................................................................................................................138

139

Figure A5. Continuous map of percent vegetation cover estimated from high spatial

resolution GeoEye1-derived NDVI over the Brant County agricultural study area during

the spring season. ....................................................................................................................139

Table B1. Sensitivity Analysis on map accuracy of habitat suitability maps set at threshold

values of 0.4 - 0.8 ....................................................................................................................139

Table B2. Expert-based resistance values for the spring and late summer season used in

least-cost and circuit theory models. .......................................................................................140

1

Chapter 1

Introduction

Global Status of Chelonians

Turtles are one of the most distinctive and instantly recognizable of all organisms. They possess

a unique outer shell of dermal bone that has remained relatively unchanged since the Triassic

Period when they first appeared in the fossil record (Pough et al., 1998). Out of this extant order

Chelonia, are maintained some of the most ancient reptiles alive in modern times, and a legacy

worth preserving.

Turtles are surprisingly cosmopolitan in their distribution given their ectothermic physiology,

and they occupy every terrestrial habitat apart from Antarctica and the high Arctic (Böhm et al.,

2013; Lesbarreres et al., 2014). However, with nearly 50% of known species listed as threatened

by the International Union for the Conservation of Nature (IUCN; www.iucnredlist.org), turtles

represent one of the most highly threatened groups of vertebrates.

The most cosmopolitan and wide-ranging groups is the marine turtles; inhabitants of the vast

Atlantic, Pacific, and Indian Oceans as well as the Mediterranean Sea. Of the seven species in

existence today, six have been designated as endangered or critically endangered, with the

remaining species absent from this listing only due to a lack of information on population status

(IUCN: www.iucnredlist.org). All major threats to marine turtles originate from human actions

such as fisheries by-catch, global warming, coastal development, and pollution. For critically

endangered species such as the Leatherback marine turtle (Dermochelys coriacea), extinction is

expected within the next eighty years or three generations if effective action is not taken (Figure

1-1a). In other regions of the world terrestrial turtles fare no better, and have also experienced

widespread decline as a result of anthropogenic threats. Ploughshare tortoises (Astrochelys

yniphora) of Madagascar are under such intense pressure from habitat loss, invasive species, and

illegal collection from the pet trade that poaching levels as low as three animals every two years

corresponds to a twenty-five percent population decline over one generation (Leuteritz &

Pedrono, 2014; Figure 1-1b). The consumption of turtles for food, exceeds any possible

sustainable levels, and extinction in the wild can be expected within the next decade for many of

2

these long-lived species (Gibbons et al., 2000; Figure 1-1c). In another part of the world, the

process of extinction occurred inevitably and in plain view as the last male Pinta Island

Galápagos tortoise (Chelondoidis nigra abingdonii) died in June of 2012. Lonesome George

embodied the perils of threatened species, particularly turtles and tortoises, worldwide (Figure 1-

1d). His prolonged path towards inevitable extinction underscores the fate of many other turtle

species as extreme longevity and delayed sexual maturity are traits that do not breed success for

these animals in this rapidly changing world.

Figure 1-1. Examples of Chelonian status worldwide depicting (a) the critically endangered

Leatherback sea turtle (Dermochelys coriacea), (b) the critically endangered Ploughshare

tortoise (Astrochelys yniphora) from Madagascar, (c) turtles supplying the food market in

Asia, and (d) Lonesome George, the last of the now extinct Pinta Island Galápagos tortoise

(Chelondoidis nigra abingdonii) on display at the American Museum of Natural History.

Images licensed under CC 2.0 via Wikimedia Commons [(a) Tinglar, (b) Hans Hillewaert,

(c) Allison Meier, (d) Vmenkov].

3

Due to this discordant relationship between Chelonian life history, and generally short-sighted

anthropogenic activities, impacts of current threats will have pronounced effects on turtle

populations long before demographic effects are detectable.

The global decline of the approximately 300 described turtle species can be linked almost

exclusively to anthropogenic activities (Lesbarreres et al., 2014; Gibbons et al., 2000), while data

deficient species suffer from a lack of basic knowledge on their distributions, and their spatial

and temporal dynamics (Lesbarreres et al., 2014). This is especially true of Canadian reptiles and

amphibians which reach their northern range limits here, and for which information on existing

populations is sorely needed to avoid species loss or decline before spatial distribution and

geographic variation is even known (the ‘Wallacean shortfall’) (Richardson and Whittaker,

2010). Along with amphibians, reptiles have the highest proportion of threatened and data

deficient species globally (Baillie et al., 2010), thus better methods of assessing species status

would contribute greatly towards effective management strategies.

In this dissertation I explore an approach that directs Earth-orbiting satellite remote sensing

technology towards a landscape level analyses of seasonal land cover heterogeneity, habitat, and

connectivity of a threatened freshwater turtle species. The Blanding’s turtle (Emydoidea

blandingii) is endemic to North America, with one of the most restricted ranges of all freshwater

turtles on this continent (Herman et al., 1995). Blanding’s turtles are designated as at-risk across

17 of the 18 jurisdictions that encompass its range in the United States and Canada (NatureServe,

2010) due primarily to habitat loss, road mortality, and illegal collection for the pet trade

(Congdon et al., 2008). The application-based objective of this work is to provide conservation

practitioners with a sound method of obtaining population estimates, to support the identification

and protection of remaining habitat, and to provide information on the landscape level needs of

freshwater turtle species living in highly altered environments. Towards this goal, I have

collaborated with research partners from the Toronto Zoo, Parks Canada, the Ontario Ministry of

Natural Resources, and the Toronto Region Conservation Authority. Results of this work will be

provided to all partners and disseminated in a manner that will address direct conservation

actions and long-term planning. The overall higher order aim is to promote a remote sensing

approach to biodiversity conservation that fully utilizes the spatio-temporal, and multispectral

information of satellite imagery, and to further the knowledge in the arena of conservation

remote sensing.

4

Remote Sensing and Biodiversity

Remote sensing (RS) is a discipline concerned primarily with deriving information from Earth’s

surface for the broad purpose of studying the human dimensions of global environmental change.

Remote sensing is broadly defined as the art and science of obtaining information about an

object or phenomenon without having any direct contact with that object or phenomenon

(Campbell, 2002). In today’s technological world, this approach to obtaining information is a

logical move towards remotely connecting researchers with inaccessible regions through a far-

reaching synoptic view. Environmental RS applications are as diverse as the technology upon

which they are built, and include the mapping of net primary productivity across the Canadian

landmass (Liu et al., 2002), combatting wildfires in the tropics (Eva & Lambin, 2000),

forecasting natural disasters (Joyce et al., 2009), assessing water quality (Ritchie et al., 2003),

and mapping oil spills (Brekke & Solberg, 2005).

In the context of this thesis I was most interested in the use of remote sensing for the

conservation of threatened species, an association that has been eagerly explored since the first

satellite imagery became available in the early 1970s. The availability of such far-ranging

datasets resulted in a dramatic increase in both the scope and amount of plant and animal-related

studies (Rushton, Merod, & Kerby, 2004). De Wulf and Goossens (1988) used early 80 m

Landsat MSS imagery to map the fragmented habitat of the Giant Panda at a time when digital

image processing software was rarely justified for investment, resulting in basic visual

interpretation as the primary means of image analysis. Other early studies further utilised this

new resource towards analysing alligator habitat in alluvial floodplains (Zhujian et al., 1985),

assessing the utility of RS data for managing migratory waterfowl habitat (Colwell et al., 1978),

and delineating broad vegetation patterns related to caribou habitat in the Northwest Territories

(Thompson, Klassen and Cihlar, 1980). The appearance of RS data provided the opportunity to

obtain ecologically relevant spatial and temporal data across a broader scale compared with

traditional field-based methods. This allowed researchers to address fundamental questions about

why organisms are found where they are and more importantly, where limited conservation

funds should be invested (Turner et al., 2003).

While direct observation of individual organisms via RS imagery is possible, an indirect

approach employing environmental parameters as proxies for species presence, has been

5

considerably more popular (Turner et al., 2003). Successes in habitat studies based on this

approach have been achieved with wide-ranging mobile species such as grizzly bears (Linke et

al., 2005), ungulates (Leimgruber et al., 2001), and birds (Osborne et al., 2001; Melles et al.,

2001; Guo et al., 2009). However, less work has been conducted on smaller, cryptic species

which utilize a small home range, such as reptiles and amphibians.

Not all species exhibit characteristics that allow analysis at broad regional scales and there is no

one universal ecological scale that can be applied across all species (Elith and Leathwick, 2009),

but this does not mean that smaller species should be excluded from remote sensing studies. In

fact, the availability of remote sensing data across a range of spatial scales suggests that an

appropriate resolution can be found to match the needs of most animals including turtles.

Fortunately, continuing advances in satellite technology have resulted in improved sensors that

may further widen the scope of species and habitats that can be studied.

Wetlands and Freshwater Turtles

An estimated 40% of the value of global ecosystem services is provided by wetlands (Zedler,

2003), even though they cover only about 4 to 6% of Earths’ terrestrial surface (Mitsch &

Gosselink, 1993). Globally, wetlands cover an estimated 5.3 to 12.8 million km2, and an

estimated 14% of the land area of Canada (Environment Canada, 2010). Yet despite their

obvious importance to ecosystem resilience and benefits to society, more than 50% of the

world’s wetlands have already been lost with little sign of abatement (Verhoeven & Setter, 2010;

Zedler & Kercher, 2005). Humans are the dominant agents of wetland loss and degradation, and

our relationship with these ecosystems is complex. Quantitative estimates of wetland loss include

more than half of peatlands, depressional wetlands, riparian zones, lake littoral zones, and

floodplains, primarily through conversion to agricultural uses (Verhoeven & Setter, 2010).

Meanwhile, many remaining wetlands are not pristine, and are subjected to hydrological

modifications including damming and pumping that alter the natural timing of water fluctuations

responsible for the diversity of vegetation communities and habitat types found in wetlands

(Brock et al., 1999). Conversely, as landscapes become sufficiently developed to be considered

as urban centres, the value of wetlands to residents increases (Boyer & Polasky, 2004), and may

result in wetland restoration activities where formerly wetlands had already existed but were

removed (Ehrenfeld, 2000).

6

It is across this gradient of human-constructed landscape heterogeneity where many wetlands,

and wetland-associated species persist. The high rate of biological productivity of wetlands,

along with strong natural selection pressures connected with these diverse aquatic environments,

have produced many species of both plants and animals that are not found in other habitats

(Gibbs, 1993). Freshwater turtles are completely dependent on aquatic habitats, which makes

them particularly sensitive to changes in wetland hydroperiod, productivity, size and turbidity

(Bodie et al., 2000). Blanding’s turtles inhabit all wetland types containing shallow open water

and abundant vegetation, while generally avoiding terrestrial uplands and fast-flowing water

features (Edge et al., 2010; Paterson et al., 2014; Ross et al., 1990), except during nesting

migrations when uplands are used extensively by gravid females searching for terrestrial nesting

sites (Congdon et al., 2011).

On a microhabitat scale, Blanding’s turtles are often found in close association with emergent

and floating aquatic vegetation (Hamernick, 2000; Millar & Blouin-Demers, 2011), submergent

vegetation (Edge et al., 2010), cold waters and bog mats (Millar & Blouin-Demers, 2011),

floating logs (Barker & King, 2012), and permanent pools (Joyal, McCollough, & Hunter, 2001).

Soft organic substrates and an abundance of sedge tussocks and muskrat mounds for basking are

also preferred (Pappas & Brecke, 2009). Non-natural disturbances to water regimes such as

draining for agriculture alter both the total amount and character of available wetland habitat

(Brock et al., 1999). Furthermore, small and ephemeral wetlands represent critical habitat that

support activities such as foraging (Congdon et al., 2011), and refuge (Grgurovic and Sievert,

2005). Consequently, the loss of these small ecosystems has been found to result in an elevated

extinction risk for many wetland-dependent species, suggesting that their role in the dynamics of

metapopulation stability is greater than their modest area might imply (Gibbs, 1993).

In this dissertation, I was interested in examining wetlands across landscapes of varying

heterogeneity, through a lens of remote sensing and freshwater turtle needs. In the context of

remote sensing, delineating these small (< 0.2 ha) and ephemeral wetlands represents a key

challenge (Ozesmi and Bauer, 2002) towards identifying relevant habitat and landscape features

of import to turtles. Meanwhile the turtles themselves most certainly exhibit a complex

relationship with their surroundings which requires a comprehensive examination of both biotic

and abiotic drivers of habitat selection.

7

Thesis Structure

This thesis is comprised of three independent research chapters which each build upon results

obtained in the preceding study. Research was conducted to improve our understanding of the

habitat needs of threatened freshwater turtles, and to forge a connection between these needs and

the data available in remotely sensed imagery. Ultimately, I was interested in understanding the

natural and anthropogenic processes that affect Blanding’s turtle persistence across typical

landscapes found worldwide. As current research on predictive models of species occupancy do

not often consider the temporal nature of species needs, nor fully exploit the temporal and

spectral information of remote sensing imagery, the main objective of this research was:

To determine if the spatio-temporal aspects of remote sensing imagery could be

used to predict a previously uninvestigated dimension of habitat needs for an

imperilled freshwater turtle species.

Specific hypotheses are described in the following chapter sections and summarized graphically

in Figure 1-2.

Figure 1-2. Overview of thesis structure and process demonstrating source data,

research objectives by chapter, and final outcomes (products).

8

Terminology regarding the practice of predicting where a species may be found, can vary

between disciplines and individual studies. Herein, the terms habitat suitability, species

occupancy, and species distribution modelling are taken to be synonymous with the process of

predicting species presence based upon a suite of environmental variables. A general conclusion

is provided at the end of this dissertation to synthesize overall findings, and discuss future

directions.

1.4.1 Chapter Two: An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution satellite imagery

Land use and land cover information represents a basic unit of information necessary for

predicting species occupancy (Kerr & Ostrovsky, 2003). The object-based approach has been

used extensively in image analysis since the start of the 21st century with hundreds of studies

conducted on this topic (Blaschke, 2010). Meanwhile wetlands as a target ecosystem have been

studied using all major satellite systems and a variety of classification methods (Ozesmi and

Bauer, 2002). Notable studies include 1) the large-scale mapping of aquatic vegetation and

habitat features across the Great Lakes shoreline in Ontario, Canada using IKONOS data with an

overall accuracy of 90% (Wei & Chow-Fraser, 2007); 2) a multi-temporal SPOT-5 classification

tree approach to monitoring aquatic marsh vegetation in southern France using a variety of

vegetation indices with accuracies greater than 80% (Davranche et al, 2010); 3) using the

GEOBIA (geographic object-based image analysis) approach, Grenier et al (2007) mapped

wetlands in the context of the Canadian Wetland Inventory (CWI) using a combination of

RADARSAT-1 and Landsat ETM images in Quebec, Canada with global accuracy values

between 67-80%, and 4) Dingle-Roberson and King (2014) mapped wetlands in the context of

the Ontario Wetland Evaluation System (OWES) using multi-temporal WorldView2, Landsat-5,

and RADARSAT-2 data. My study differs from previous work in that a primary objective was to

develop a wetland classification approach that was robust across landscapes of varied

heterogeneity due to human disturbance, and to further investigate the effect of this variation on

wetland classification accuracy.

In this chapter, three landscapes of varying disturbance due to human activities were selected,

including; (i) a relatively undisturbed site in Algonquin Provincial Park, (ii) a fragmented

9

agricultural landscape characteristic of the southern Ontario farming regions in Brant County,

and (iii) an urban landscape consisting of dense urban development (built structures) and isolated

green spaces located in east Toronto which is representative of a typical metropolitan area. In

this chapter I develop a parsimonious method for characterizing landscapes of varying

heterogeneity, with emphasis on accurate wetland delineation. I assert the importance of

monitoring wetlands in all landscape types, and hypothesize that landscape heterogeneity affects

the accuracy of both land cover and wetland classification. I further demonstrate that the

GEOBIA approach is particularly effective for wetland detection and delineation.

1.4.2 Chapter Three: Modelling seasonal wetland habitat suitability for Blanding’s turtles (Emydoidea blandingii) using optical satellite remote sensing imagery

This chapter follows a species-centred approach to modelling Blanding’s turtle occurrence based

on remote sensing derived environmental predictors. Data availability is often a constraint in

building models of species’ distribution, particularly for large-scale studies (Osborne et al,

2001), thus many studies are limited to establishing models from available data rather than

ecologically and biologically relevant data. To address this knowledge gap, some studies have

employed novel methods of extracting relevant spatial information from remote sensing imagery.

For example Jeganathan et al (2004) used all Landsat 7 ETM bands and NDVI (normalised

difference vegetation index) to correlate environmental variables around tracking strips for

Jerdon’s courser (Rhinoptilus bitorquatus) to estimate habitat suitability across a wildlife reserve

in India. Osborne et al (2001) used 12-month AVHRR data to calculate monthly NDVI which

was used alongside other inputs to develop a province-wide species distribution map for great

bustards (Otis tarda) in Madrid, Spain. In regards to Blanding’s turtles, two notable studies have

sought to map habitat based upon either single-date aerial imagery, or a combination of available

spatial (GIS) data. Millar and Blouin-Demers (2012) examined the effect of background data

selection on building a province-wide species distribution model for Blanding’s turtles in Ontario

using a provincial land cover product, a digital elevation model (DEM), a provincial road

network vector layer, and derivative layers derived from each. Receiver Operating Characteristic

(ROC) results in the study ranged from 0.73 to 0.91 based on boosted regression tree and Maxent

models. Barker and King (2012) used single-date 20 cm aerial orthophotographs to map wetlands

10

and within wetland habitat features believed to be correlated with habitat selection for

Blanding’s turtles, with an accuracy > 80%.

Building on these previous studies, this chapter takes a landscape-scale approach aimed at

providing habitat information relevant to informing ground-level conservation action. Unlike the

previous studies, I derive relevant environmental predictors from remotely sensed data, and

incorporate a multi-temporal perspective which does not assume the static environment of

traditional habitat models. I further identify relevant biophysical variables associated with habitat

selection of Blanding’s turtles through field surveys and radio-telemetry information.

Furthermore, this study focuses on two different landscapes (the park site and agricultural site)

across two seasons in order to capture the intra-annual variation of wetland habitat and the

heterogeneity of different landscapes. Following a species-centred approach to habitat modelling,

I compile a suite of relevant environmental predictors derived from remote sensing imagery and

predict the probability of occurrence across the two landscapes using a boosted regression tree

approach.

I hypothesize that preferred habitat shifts temporally and spatially as a result of (i) fluctuating

wetland habitat, and (ii) changing behavioural and physiological needs of Blanding’s turtles.

From a methodological standpoint, I further hypothesize that meaningful environmental

variables derived from remote sensing imagery, can improve the realism and precision of

predictive models.

1.4.3 Chapter Four: Estimating seasonal landscape connectivity for Blanding’s turtles in a fragmented agricultural landscape

This chapter retains the temporal dimension, but focuses on the agricultural study site as a model

for a fragmented landscape. Here, I employ multiple measures of connectivity to describe the

movement potential of Blanding’s turtles within a shifting mosaic of land use that includes

actively managed crop fields, residential areas, and a moderately dense road network. I

hypothesize that connectivity is a construct of physiological and behavioural needs which change

temporally along with available habitat. At the time of this study, to my knowledge no previous

work has been conducted on spatial modelling of landscape connectivity for Blanding’s turtles.

Validation of connectivity models can be challenging as empirical data on species movement is

generally difficult to acquire, and genetic validation methods are reserved for species with a

11

relatively fast generation time, and where multiple populations across broader scales are the

focus of the study.

Statement of research collaboration and manuscript submission

I am the primary author of this thesis and I developed the experimental design, carried out field

data collection and data analysis, and established collaborative agreements with research

partners. Any errors and omissions in this work are my own. Chapter Two has been submitted to

the International Society of Photogrammetry and Remote Sensing (ISPRS) and is currently under

review. Co-authors of this chapter are Dr. Yuhong He (PhD supervisor) and Dr. Qihao Weng

(Centre for Urban and Environmental Change, Indiana State University, IN) who provided

advice on methodology, structure of results, and manuscript construction. The success of Chapter

Three depended heavily on the Blanding’s turtle telemetry data shared by the Toronto Zoo (Bob

Johnson, Curator of Reptiles and Amphibians) as well as Christopher Edge and James Paterson

of Laurentian University, Sudbury, Ontario (Supervisor, Dr. Jackie Litzgus). Chapter Four was

developed with the guidance of Bob Johnson, Julia Phillips, and Brennan Caverhill from the

Toronto Zoo who provided expert advice on the behaviour and ecology of Blanding’s turtles and

who provided the landscape resistance values used in connectivity modelling. Dr. Marie-Josée

Fortin provided invaluable advice on the selection of modelling approaches for Chapters Three

and Four, and my supervisor Dr. Yuhong He has been pivotal in all aspects of this research.

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district of Keewatin, NWT: An application of digital Landsat data. Journal of Applied

Ecology, 17, 125-138.

Turner, W., Spector, S., Gardiner, N., Fladeland, M., Sterling, E., & Steininger, M. (2003).

Remote sensing for biodiversity science and conservation. Trends in Ecology &

Evolution, 18(6), 306-314.

Verhoeven, J.T.A., and Setter, T.L. (2010) Agricultural use of wetlands: opportunities and

limitations. Annals of Botany, 105, 155-163.

Wei, A., & Chow-Fraser, P. (2007). Use of IKONOS Imagery to Map Coastal Wetlands of

Georgian Bay. Fisheries, 32(4), 164–173.

Zedler, P. H. (2003). Vernal pools and the concept of “isolated wetlands”. Wetlands, 23(3), 597-

607.

Zedler, J.B., and Kercher, S. (2005) Wetland resources: Status, trends, ecosystem services, and

restorability. Annual Review of Environmental Resources, 30, 39-74.

15

Chapter 2

An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution

satellite imagery

Introduction

Mapping wetlands across natural and human-altered landscapes is important for understanding

their responses to natural and anthropogenic activities, for developing strategies to conserve

wetland biodiversity, and to prioritise areas for restoration or protection. While public perception

of the conservation value of wetlands has increased over the past century (Brock et al., 1999),

wetland loss appears to continue with little abatement and this change requires ongoing

monitoring.

The ability to delineate wetlands and monitor changes in a semi-automated, and ongoing manner

is important to the management of these ecosystems. A viable approach is the use of satellite

remote sensing data, which provides advantages of large area coverage, ongoing data collection,

and improved spatial resolution for wetland detection. While a variety of methods to delineate

wetlands have been used with varying success (Davranche et al., 2010; Hirano et al., 2003;

Schmidt & Skidmore, 2003; Shanmugam et al., 2006), less attention has been given to the

applicability of such methods across different landscapes. Urban and rural landscapes represent

uplands subjected to disturbance related to increased surface heterogeneity, changes to

hydrologic regime, and land cover composition which may affect wetland detection accuracy.

Previous research has demonstrated that wetlands can be detected within upland surroundings,

yet a unified approach to mapping wetlands across landscapes of varying complexity has not

been identified. Further, fewer studies have included the detection of small and ephemeral

wetlands even though pools as small as 0.2 ha represent important, often critical habitat

(Semlitsch & Bodie, 1998). In some areas such as the glaciated prairie pothole region of central

Canada, almost 88% of wetlands are less than 0.4 ha in area (Halabisky, 2011). Coarser 30 m

data such as those from the Landsat series require a minimum of 9 pure pixels (0.81 ha) to

consistently identify a feature (Ozesmi & Bauer, 2002), resulting in many mixed pixels and small

16

wetlands below this threshold being missed (Klemas, 2011; Powers et al., 2011). At the local

scale, protection of small wetlands is vital, particularly for the maintenance of biodiversity

(Gibbs, 1993; Semlitsch & Bodie, 1998), and many wetlands in altered landscapes are

significantly reduced in size from their former coverage. While the current cost associated with

obtaining high spatial resolution satellite data can be high, the cost is still significantly lower

than field surveying or aerial photographs (see Wei and Chow-Fraser, 2007 for a cost

breakdown) and provides the advantage of repeat coverage for monitoring temporal trends and

the addition of data outside of the optical range (e.g., in the near infrared region). Current work

with high spatial resolution sensors has been used to successfully monitor the change in aquatic

vegetation in coastal marshes (Wei & Chow-Fraser, 2007), to discriminate between submerged

and emergent wetland vegetation (Davranche et al., 2010), and to estimate marshland

composition and biomass in riparian marshes (Dillabaugh & King, 2008).

While high resolution data provide the needed spatial resolution to capture smaller wetlands, it

also results in greater within-class spectral variance, making separation of mixed and similar land

cover classes more difficult than with coarser-resolution imagery (Klemas, 2011; Hu and Weng,

2011). To address this increased variance an appropriate classification method must be

employed. In recent decades object based image analysis (OBIA), or geographic object based

image analysis (GEOBIA), has gained much attention as an alternative to traditional pixel-based

methods. The packaging of pixels into discrete objects minimizes the variance (noise)

experienced by high spatial resolution images, allowing the objects, rather than individual pixels

to be classified. Past work has found that the object-based approach is preferred over the pixel-

based approach for classifying urban areas (Myint et al. 2011; Hu and Weng, 2011), mapping

land cover (Whiteside & Ahmad, 2005; Yan, Mas, Maathuis, Xiangmin, & Van Dijk, 2006), and

land cover change (Dingle Robertson & King, 2011). The object-based approach has also been

successfully used in wetland research for classifying macrophyte communities in coastal marsh

habitat (Midwood & Chow-Fraser, 2010; Rokitnicki-Wojcik, Wei, & Chow-Fraser, 2011),

evaluating the structure of patterned peatlands (Dissanska, Bernier, & Payette, 2009), and

mapping multiple classes of wetlands according to the Canadian Wetland Inventory (Grenier et

al., 2007). Fournier et al. (2007) reviewed wetland mapping methods to be applied to the

Canadian Wetlands Inventory program and identified the object-based approach as most

appropriate due to its flexibility and ability to address the spatial heterogeneity of wetlands.

17

Despite past successes in mapping wetland classes and vegetative communities, the majority of

previous research has examined wetlands in isolation from the surrounding landscape. Yet the

ability to delineate wetlands from regions where a previous wetland inventory does not exist, is

important for monitoring trends and mitigating further wetland losses.

Wetland classification approaches have ranged from traditional unsupervised (Sawaya et al.,

2003; (Jensen, Rutchey, Koch, & Narumalani, 1995) and supervised algorithms (Wang, Sousa, &

Gong, 2004; Yu et al., 2006) including fuzzy methods (Benz, Hofmann, Willhauck,

Lingenfelder, & Heynen, 2004; Townsend & Walsh, 2001) and object-based approaches

(Blaschke, 2010; Blaschke et al. 2014) to more complex machine learning algorithms such as

classification tree methods (Midwood & Chow-Fraser, 2010; Wright & Gallant, 2007) including

random forest classification (Corcoran, Knight, & Gallant, 2013) with some complex models

drawing from numerous data layers to discriminate among wetland types (Wright & Gallant,

2007). As a result, it is not surprising that many studies have been devoted entirely to comparing

the utility of these different methods (Dingle Robertson & King, 2011; Duro, Franklin, & Dubé,

2012; Harken & Sugumaran, 2005; Shanmugam et al., 2006) with no general consensus being

reached on a universal methodology. Similarly, the use of ancillary data in improving wetland

mapping accuracy has been demonstrated by the inclusion of LIDAR (Hopkinson et al., 2005)

and RADAR data (Grenier et al., 2007) to characterize vegetation height, hyperspectral data for

discriminating between aquatic plant species (Becker, Lusch, & Qi, 2007; Hirano et al., 2003;

Zomer, Trabucco, & Ustin, 2009), time series image data for wetland boundary and change

detection (Davranche et al., 2010; Johnston & Barson, 1993), and passive microwave data to

map flooded areas (Prigent, Matthews, Aires, & Rossow, 2001).

This paper employs a GEOBIA supervised-classification approach to wetland land cover

mapping across three different landscapes in southern Ontario, Canada using high spatial

resolution WorldView2 and GeoEye1 imagery. The same approach to classifying wetlands is

applied across three landscapes varying in disturbance from human activity representing a semi-

natural park, agricultural, and urban landscape, to determine the robustness of this method across

scenes of varying heterogeneity and composition. While a methodological design is emphasized,

results of this study are intended for use in operational applications to help improve the

management of wetlands in all landscapes.

18

Study Area

Three study sites were selected and categorized as park (semi-natural), agricultural (moderately-

disturbed), and urban (heavily disturbed). As most natural areas have undergone some level of

alteration or disturbance, the park landscape was defined based on criteria adapted from Fahrig et

al. (2011) as areas where (1) most primary production is not consumed by humans either directly

or indirectly, (2) the main species of the cover type has an evolutionary or long-term association

with that area, and (3) the frequency and intensity of anthropogenic disturbances are low relative

to those in agricultural and urban regions. Study sites were further categorized based on

population density with an urban area supporting over 400 people/km2, an agricultural area of

less than 400 people/km2, and a natural site with no permanent human population

(http://www.statcan.gc.ca/subjects-sujets/standard-norme/sgc-cgt/notice-avis/sgc-cgt-06-

eng.htm).

The natural study site was located in the northeast corner of Algonquin Provincial Park (Ontario,

Canada) which represents a protected and relatively undisturbed landscape (Figure 2-1a). The

Park was established in 1893 and encompasses 7,630 km2 including approximately 340 ha of

wetlands of all classes as defined by the Canadian Wetlands Classification System (NWWG,

1997). Logging activity occurs in the study area as well as recreational use by park visitors,

though the study site is located in a less heavily visited section. The agricultural site was situated

in the County of Brant (Ontario, Canada) which sits within the Grand River watershed, located

approximately 130 km west of Toronto, and supporting a population of 35,000 people, or a

population density of 42.3 person per square kilometre (Statistics Canada, 2011). Provincial and

private roads bisect the agriculturally dominated landscape and surround the Oakland Swamp, an

890 ha wetland of provincial significance (Figure 2-1b, outlined in yellow). Several smaller

wetlands of variable size and shape were also distributed throughout the study area. The urban

study site encompassed the eastern portion of Toronto and the adjacent city of Pickering (Figure

2-1c). Toronto is the largest city in Canada and supports a population of 2.79 million people, and

a greater Toronto area (GTA) population of 5.5 million, the latter of which supports a population

density of 945.4 persons per square kilometre (Statistics Canada, 2011). The study site included

the Rouge Urban National Park (Figure 2-1c, outlined in yellow), a federally-governed urban

recreation area covering roughly 6,300 ha that is bounded along its western and eastern border by

dense urban development including roadways that cross over and through the park interior.

19

Pockets of wetlands can be found throughout the study area, including several recently restored

wetlands. This urban park receives thousands of visitors annually and its interior is bisected with

pedestrian and bike pathways.

Figure 2-1. Study areas located in Ontario (a) Algonquin Provincial Park relatively

undisturbed site, (b) Brant County agricultural site, and (c) east Toronto urban site. Images

displayed in false colour (RGB=NIR-Red-Green).

Data and Methods

A multi-scale GEOBIA approach was used to segment images, which were then classified using

a supervised nearest neighbour algorithm (Figure 2-2). Multiple input layers were utilised during

20

image segmentation with both qualitative and quantitative measures used to select and evaluate

the resultant image objects.

Figure 2-2. Process workflow for segmentation and classification of wetland landscapes

21

2.3.1 Satellite Imagery and Preprocessing

High spatial resolution images from WorldView2 and GeoEye1 sensors were acquired over the

study sites (Table 2-1) and cover 40 km2. Both sensors captured image data at the same high

spatial resolution (1.85 m multispectral) and covered the same bandwidths over the blue and

green regions, and similar bandwidths over the red and near infrared regions. All effort was

made to acquire imagery from the same sensor over all study sites, but coverage from high

resolution satellites is rarely complete and imagery from two sensors was required to capture all

three study areas.

Table 2-1. Satellite Image data information

Sites Rouge Park

(urban site)

Algonquin Park

(natural site)

Brant County

(rural site)

Acquisition date 25 July 2012 25 May 2013 9 April 2012

Sensor WorldView2 GeoEye1

Nominal ground

pixel size 1.85 m multispectral

Spectral bandwidths

Blue (450-510nm)

Green (510-580nm)

Red (630-690nm)

Near infrared (770-895nm)

Blue (450-510 nm)

Green (510-580nm)

Red (655-690nm)

Near infrared (780-920nm)

Radiometric

Quantization 16 bits

Each image contained natural segments (unmanaged forests, wetlands, open water), built

segments (paved roads, commercial, residential and urban structures), and altered natural

components (agricultural crops, dirt roads) in varying proportions. All three study areas were

located in southern Ontario (44o00’N 80o00’W) which covers a core area of 126,819 km2. Full

deciduous leaf-on conditions are typically reached by the end of the month of May or beginning

of June. Leaf-off conditions generally occur by late October or early November. The timing of

crop growth is more variable as crop types which are typical to this region (e.g. corn, soy,

tobacco, ginseng, wheat) follow different schedules of planting, growth, and harvest which are

further determined by the timing, configuration, and conditions of planting as well as annual

climate variations (OMAFRA, 2014). Length of the crop growth from time of planting to full

22

maturity or harvest are variable and can range from 25 days (soy) to 149 days (corn) (OMAFRA,

2014).

All multispectral images were radiometrically normalized using an atmospheric correction

method for broad-band visible/NIR imagery (Richter et al. 2006; Richter, 1996) and for thin

cloud contamination across diverse surfaces (Zhang et al. 2002). Atmospheric correction is

generally not needed for classification of single-date images as long as training data and the

image to be classified are on the same relative scale (of corrected or uncorrected) (Song et al.,

2001). Assuming a homogenous horizontal atmosphere, applying radiometric correction has little

effect on classification accuracy (Song et al., 2001), however the correction was applied as

subsequent work (chapter three) relates ground-based measurements to vegetation indices

derived from the satellite imagery, for which atmospheric correction is required. Radiometric

normalization was implemented in PCI Geomatica, (ATCOR3; PCI Geomatica, 2014) though

cloud cover and haze was minimal across all images. This atmoshperic correction approach was

deemed most appropriate for this study as the algorithm was optimized for high spatial resolution

imagery (Richter, 1997), and only required three visible and one near infrared band for

normalization (Richter et al., 2006) which is a common band configuration for many high spatial

resolution Earth-observing satellites (e.g. GeoEye1, SPOT6) and for economical (reduced-band)

options of imagery such as those from WorldView2 which provides data at 8 (full) or 4 (reduced)

bands.

After atmospheric correction, all images were projected to the Universal Transverse Mercator

projection datum (NAD83, UTM Zone 17) and georeferenced to a root mean squared error

(RMSE) of less than 2 pixels using a 1st order polynomial transformation and a nearest neighbour

resampling method, corresponding to less than 4 m ground error. Processed images were clipped

to a 40 km2 boundary using ArcGIS version 10.2 (Environmental Systems Research Institute,

Redlands, CA, USA). The panchromatic layer was not used, as it increased processing time to

unrealistic lengths.

2.3.2 Development of Input Layers

Seven features were used for image segmentation including four multispectral layers (blue,

green, red, and near infrared), a DEM layer, an NDVI (normalized difference vegetation index)

23

layer, and a standard deviation texture layer. A 10-m digital elevation model (DEM) was

acquired from the Ontario Ministry of Natural Resources (Figure 2-3a) which was interpolated

from a DTM (digital terrain model), a contour map, spot height data, and a water virtual flow

map to a ±10 m vertical precision. This dataset was the best available over all three study areas,

and was expected to be useful at the accuracy level provided (±10 m). The DEM for each image

scene was resampled to 2 m to match the resolution of the other input layers. Resampling the

DEM did not provide any additional information but ensured continuity in pixel size across all

input layers and avoided a coarser resolution affecting the boundaries of image objects. Elevation

was included as an input layer because wetlands and water bodies are known to sit

topographically low in the landscape due to their close association with ground water and surface

run-off (Mitsch & Gosselink, 2000). Other elevation-related input layers such as slope and aspect

were originally included, but were discarded as they did not contribute any additional

information.

Texture information refers to the spatial variation in the spectral brightness of a digital image,

and has a high potential for revealing differences between classes in remotely sensed imagery

(Berberoğlu, Curran, Lloyd, & Atkinson, 2007). Texture measures can be derived directly from

satellite imagery, and do not require the acquisition of additional data. For this study a first-order

texture layer was created (Figure 2-3b) based on the standard deviation within a 3 pixel by 3

pixel moving window.

NDVI is a well-established indicator of live green vegetation (Rouse et al., 1974) and was

created from the red and near infrared bands of the multispectral data according to the following

equation:

���� =ρnir − ρred

ρnir + ρred

where (ρ) is the reflectance of the visible (red) and near-infrared (nir) bands of the

electromagnetic spectrum. NDVI values range from -1 to 1, where higher NDVI values indicate

a greater coverage of photosynthetically-active vegetation, while values less than zero typically

do not have any ecological meaning. NDVI has been used to separate water from dry land, and

for delineating wetland boundaries (Ozesmi & Bauer, 2002) (Figure 2-3c). All final image

layers were weighted equally in the multiresolution segmentation process.

24

Figure 2-3. Subset of input layers from the urban site of eastern Toronto showing the (a)

digital elevation model (a), standard deviation texture layer (b), and NDVI layer (c).

2.3.3 Image Segmentation

Segmentation is a key aspect of GEOBIA relating to the ultimate quality of the final

classification (Baatz et al., 2008) and its optimal result is a scene segmented into objects that

reflect real-world features of interest. In the object-oriented approach, both spectral and spatial

(or contextual) parameters are used to define an image object, whereas traditional per-pixel

classifiers treat each individual pixel independently of its neighbours. This study employed the

fractal net evolution approach (FNEA) to segmenting images (Baatz & Schape, 2000) which was

25

implemented through the multiresolution segmentation algorithm in Definiens Developer 7.0

(Munich, Germany; Definiens, 2008, formerly eCognition).

Three key segmentation parameters of scale, colour, and shape control the size, shape and

spectral variation of segmented image objects. Weights of colour and shape sum to 1, while

shape is further divided into smoothness (relating to the smoothness of object edges) and

compactness (relating to the closeness of an object shape with a circle) which sum to 1

(Definiens, 2008). The colour parameter was set to 0.9 to place greater emphasis on pixel values

of input layers, and shape parameters were set to 0.5 each to balance both the compactness and

smoothness of object boundaries equally. The most critical step is the selection of the scale

parameter (unitless) which controls the size of the image objects by sequentially merging pixels

pairwise with the intent of minimising the heterogeneity within (Blaschke & Hay, 2001;

Mallinis, et al., 2008). The scale parameter sets a threshold of homogeneity which determines

how many neighbouring pixels can be merged together to form an image object (Benz et al.,

2004) and is given by the equations:

where h is the heterogeneity for a d-dimensional features space, f is the selected object feature

(equation 1), and h1 and h2 describes the homogeneity of two adjacent regions before the merge,

and after (hm) the merge, with hdiff relating to the change in the object after a virtual merge

whose aim is to reduce heterogeneity with growth (Blaschke & Hay, 2001).

In this paper, we applied a multi-scaled segmentation approach that utilized three levels of scale

parameterization to capture different land cover classes (Figure 2-4). Dominant land cover

classes that covered the majority of the scene were segmented at the coarse level, while

remaining classes were delineated at the medium level. Entire wetlands were segmented and

defined as objects at the mid-range scale, and further segmented at the finest scale level to

delineate components within wetlands and classify these as marsh, swamp, fen or bog. These

smaller (child) objects retain links to their larger (parent) class which employs a true multi-scale

approach through applying vertical constraints in segmentation and classification. Classification

26

for specific land cover classes was thus completed at each scale level, with remaining

unclassified objects undergoing further segmentation, followed by classification. A thematic road

network layer was available for each scene and was used in the segmentation process.

We first employed a qualitative visual approach to select the scale parameter at each level

(coarse, medium, and fine). At the medium and fine segmentation level this ensured that the

optimal scale parameter for wetlands was selected by drawing upon knowledge of the study

areas, and based on the premise that the human eye is best capable of interpreting and

recognizing complex patterns in conjunction with neighbourhood context (Benz et al., 2004;

Myint et al., 2011). This approach is especially fitting for wetlands that can be highly variable in

both size and shape. Scale values ranging from 5 to 250 with an interval of 5 were evaluated for

each image and final scale selection was guided by field knowledge, thematic maps and aerial

imagery.

27

Figure 2-4. Multi-scale segmentation process used to segment images at three levels, using

a hierarchical parent-child relationship between wetlands and within wetland components

at the medium (level 2) and fine (level 1) scale.

28

This selection was then quantitatively assessed using the modified ED3 discrepancy measure of

Yang, He, and Weng (2015) which is based on global geometric and arithmetic relationships

(e.g. over-under segmentation) between hand-digitized reference polygons and corresponding

segments produced by the multiresolution segmentation algorithm. This multi-band scale

parameter evaluation method allows identification of multiple appropriate scale parameters based

on the equation:

where ri is a reference polygon, and I is the number of reference polygons, sj is the corresponding

segment for the reference polygon i, and Ji is the number of its corresponding segments. In this

modified equation, a candidate segment will be labelled as the corresponding segment of a

reference polygon only when the overlapping area is over 50%. Results are normalized between

zero and 0.71, with lower values indicating a higher segmentation quality (Yang et al., 2015).

Multiresolution segmentation results between scale values of 5 to 200 (intervals of 5) were

compared to a set of manually delineated reference polygons at each scale level (coarse, medium,

fine), and for each image. A total of 30 reference polygons per scale level were used in the

analysis. There are multiple quantitative and automated approaches to selecting the scale

parameter including automated parameterisation using the potential of local variance to detect

scale transitions (Drăguţ et al., 2014; Drǎguţ et al., 2010), supervised methods that use various

indices to describe the discrepancy between reference polygons and corresponding image objects

(Clinton et al. , 2010; Liu et al., 2012), and a comparison index using both topological and

geometric object metrics (Moller et al. 2007). However, there is no perfect algorithm that is

appropriate for all images (Munoz et al., 2003) and a certain element of trial, error, and repetition

is inherent to the overall process of scale selection and evaluation.

2.3.4 Classification Approach

A non-parametric nearest neighbour classifier was used to place image objects into defined land

cover classes. This iterative process involved selecting training samples, comparing sample

attributes, and refining training samples until a satisfactory result was achieved. The nearest

29

neighbour classifier is advantageous when image data are composed of spectrally similar classes

that are not well separated using a few features (Definiens, 2008) or when training sample sizes

may be uneven (Myint et al., 2011; Yu et al., 2006). The mean feature values of pixels in each

object (calculated from the input layers), were used to quantify separation distance between

classes following the equation:

where d is the separation distance between feature values of an object (vo) with its nearest

training sample (vs), over the standard deviation of the feature attribute.

The nearest neighbour, or k-NN approach as it is often called, is a simple yet efficient

classification algorithm that has been shown to perform as well as more complicated methods

such as support vector machines (SVM) under constant conditions (Im et al., 2008). There are

many attributes that can be used to train the nearest neighbour classifier, but the contribution of

each varies and constraints such as processing time often dictates that only a few features be

employed. A parsimonious model was developed based upon only the mean object value and

standard deviation for each input layer in order to maintain a realistic processing time and an

efficient model that can be compared across landscapes. A spatially representative sample of

training objects were selected to inform the classifier.

Land cover classes. The final classification scheme (Table 2-2) was based on the land cover and

land use classification system developed by Anderson et al. (1976) and the Canadian Wetland

Classification System (NWWG, 1997). Specifically, study areas were classified into the

following categories: agricultural land, barren land, forested upland, herbaceous upland, urban

matrix, water, and wetland. Wetlands were broadly defined as land that is saturated with water

for a period of time sufficient to promote wetland or aquatic processes resulting in characteristics

such as poorly drained soils, hydrophytic vegetation, and other biological activity adapted to wet

environments (NWWG, 1997). According to the Canadian Wetland Classification System

(NWWG, 1997) wetlands were further classified as marsh, swamp, bog, or fen (Table 2-3). All

wetland classes were found in the park study site, while only marshes and swamps were found in

the agricultural and urban locales.

30

Table 2-2. Land cover class descriptions adapted from Anderson et al., (1976) and the

Canadian wetland classification system (National Wetlands Working Group, 1997).

Class Description

Agricultural Land Land used primarily for production of food and fiber (e.g., Row

crops, bare (idle) fields, shaded crops; groves; orchards)

Barren Land Land of limited ability to support life; less than one-third of the area

has vegetation or other cover (e.g., sands, rocks, thin soil)

Forested Upland Closed canopy deciduous , coniferous, or mixed forests

Herbaceous

Upland

Land where vegetation is dominated by a mix of grasses, grass-like

plants, forbs, shrubs or bush; either naturally-occurring or modified

(e.g. old fields, roadside vegetation, meadows, mixed composition

short vegetation upland)

Urban or Built

Matrix

Areas of intensive use with much of the land covered by man-made

structures (e.g., residential, commercial, industrial, utility, and

transportation sites such as those found in cities, towns, rural

communities and strip developments)

Water All areas that are persistently water-covered (e.g., lakes, reservoirs,

streams, bays, estuaries)

Wetland Bog, fen (or wet meadow), swamp, marsh, shallow open water

31

Table 2-3. Wetland land cover class descriptions according to the Canadian Wetland

Classification System (NWWG, 1997).

Class Description

Bog A peat landform, raised or level with the surrounding terrain and

isolated from runoff and groundwater, receiving water primarily

from precipitation, fog, and snowmelt. Water table sits at or slightly

below the bog surface. Treed or treeless, and usually covered with

Sphagnum spp. and shrubs, or woody remains of shrubs.

Fen A type of peatland which receives both surface and groundwater flow

due to its topographic position which is level with the surface of the

fen (+/- a few centimetres). Vegetation can include graminoids,

bryophytes, shrubs, and also trees (in drier fens).

Marsh A shallow water wetland with water levels that can fluctuate daily,

annually, or seasonally resulting in highly variable hydrology.

Receives water from the surrounding catchment as well as

precipitation. Marsh vegetation is comprised of emergent aquatic

macrophytes such as graminoids (e.g. rushes, reeds, sedges),

floating-leaved species (e.g. lilies) and submergent species (e.g.

water milfoil). Marsh plant communities are seasonal and dynamic,

often shifting with water levels.

Swamp Forested or wooded wetland, dominated by minerotrophic

groundwater and a water table below the ground surface of the

swamp for the majority of the year. Vegetation dominated by

coniferous or deciduous trees or tall shrubs (generally over 30%).

Sample Selection. Training sample objects were selected using aerial photographs, thematic

maps and reference data collected during field campaigns (Figure 2-5). A minimum of 50

training sample objects were chosen for each non-wetland class, with some exceptions for

classes which only covered a small proportion of the scene such as the urban/built class in the

park site. A minimum of 35 samples for wetland classes were selected where possible, though all

wetland types were not present in all study areas. An advantage of the multi-scale approach is the

ability to adequately sample rarer classes such as wetlands by segmenting these landforms into

smaller image objects. Sample image objects for wetlands were grouped into the classes

described in Table 2-3. In some cases, a dominant class such as ‘marsh’ was further separated

into emergent marsh and wet meadow in order to capture the spectral and textural variation in

32

heterogeneous marsh communities. These groups were later merged into one marsh class for

comparison across landscapes. Samples were selected to represent the range of spatial and

spectral variability across each landscape.

Figure 2-5. Example of reference data used in sample selection. (a) Algonquin park site

electronic Forest Resource Inventory (eFRI) imagery (OMNR, 2005) and wetland thematic

layer (pink), and (b) subset – note reference thematic layer does not capture all wetlands in

the area. (b) Brant county agricultural site South Western Ontario Orthoimagery Project

(SWOOP, 2005) and reference wetland thematic layer (pink) from the Grand River

Conservation Authority (GRCA) downloaded from the Grand River Information Network

(GRIN).

33

2.3.5 Accuracy assessment

A minimum of 35 independently selected image objects per class were used for accuracy

assessment. Sample selection was based upon very high resolution (VHR) aerial photographs

over each site, reference thematic maps, and ground truth data collected in June 2011 from each

study area (Figure 2-5). Validation and training samples did not overlap. Accuracy was assessed

based on the error matrix and associated statistics of overall accuracy, kappa statistic, producer’s

accuracy (1 - errors of omission) and user’s accuracy (1 - errors of commission). Object-based

assessment was preferred over pixel-based methods as I was most interested in determining if

wetland boundaries and marsh vegetation communities were accurately classified (Figure 2-6).

With this objective, accuracy was better assessed using individual objects which have a clearly

defined boundary.

Figure 2-6. Example of object sample selection for accuracy assessment of the park site

(Algonquin Provincial Park) land cover map.

34

Results

2.4.1 Multi-scale segmentation

Final scale values selected through visual assessment varied between study areas (Table 2-4).

Dominant land cover classes of mixed forest (park site), crop field (agricultural site), and urban

matrix (urban site) were most accurately delineated at a scale of 125, 200, and 75 respectively. In

general, boundaries were clearly defined with minimal absorption of smaller classes. Similarly,

whole wetland boundaries were mostly well defined and often included greater spatial detail than

reference thematic maps, although specific depiction varied across each landscape. Medium level

scale values of 40, 60, and 50 were selected at the park, agricultural, and urban site, respectively.

Whole wetlands were further segmented at the finest scale level (20 [park], 10 [agricultural], 15

[urban]) to further classify these objects into marsh, swamp, bog, fen, or water. This parent-child

relationship maintained a hierarchical constraint which limited classification of the five wetland

classes to only those objects defined earlier as wetlands. Scale values of 20 (park), 10

(agricultural) and 15 (urban) were selected at the finest level. Segmentation scales varied across

all scenes, and no segmentation scale mirrored those of the other sites at any level.

Table 2-4. Hierarchical segmentation scale for each study site and corresponding target land

cover class

Natural Landscape

Rural Landscape

Urban Landscape

Scale Target Land

cover

Scale Target Land

cover

Scale Target Land cover

125

Forested upland,

water

200

Agricultural

fields

75 Urban matrix,

agricultural fields

40 Wetland, barren

land, herbaceous

upland

60

Wetlands, water,

urban matrix,

meadow

50 Wetlands, water,

forests, herbaceous

upland, barren land

20 Wetland classes 10 wetland classes 15 Wetland classes

Modified ED3 results showed a consistent positive evaluation for all scale parameters selected

by visual assessment (Figure 2-7). ED3 results range from 0 to 0.71 with lower values

corresponding to better quality segments that more closely match with reference polygons (Yang

35

et al., 2015). In the corresponding graphs, the selected scales fell within the lowest dip in the data

points, which characterises scale parameters with the greater fitness in matching with reference

polygons (Yang et al., 2015). Across all scale levels (coarse, medium, fine) and image scenes

(park, agricultural, urban), scale values selected through visual assessment fell within this region

indicating a robust selection. Interestingly, at the coarse (first) level, results across all three

scenes do not demonstrate a pronounced trough but rather a gradual descent in values indicating

that several scale values are appropriate at this level.

The contribution of additional input layers (DEM, NDVI, texture) improved overall

segmentation results across all scenes. At the coarse level, it was found the inclusion of the

NDVI layer improved delineation of vegetated boundaries. For example, in the agricultural

scene, NDVI data improved crop field segments such that object boundaries more closely

followed the outer edges of each field (Figure 2-8). At the medium level, elevation data from the

DEM resulted in improved segmentation of wetland boundaries (Figure 2-9). Texture

information was useful at the finest scale level for segmenting within wetland features (Figure 2-

10). The inclusion of this layer resulted in larger image objects more representative of distinct

vegetation communities.

A comparison with reference thematic maps (Figure 2-11) indicates that the segmentation

captured a greater level of variation in wetland components such as floating vegetation, islands,

and water (Figure 2-11 a, b, c), yet suffered from a varying degree of over and under

segmentation when the swamp class was present (Figure 2-11 d, e, f). Multispectral data used in

this study did not capture information from the mid infrared water-absorbing regions, therefore if

visible standing water was not evident at the time of image acquisition, swamps could be easily

confused with upland forests. In both the rural and agricultural study areas, some wetlands were

identified that were missing from provincial reference datasets indicating that this approach is

not only able to capture additional wetlands, but also able to provide a greater level of detail

concerning within wetland boundaries and components.

36

.

Figure 2-7. Quantitative evaluation of selected scale parameter with the modified ED3

algorithm at the coarse (diamond), medium (square), and fine (triangle) levels for the (a)

Algonquin park site, (b) Brant county agricultural site and (c) east Toronto urban site.

Hollow circles denote the selected scale value through visual assessment.

37

Figure 2-8. Comparison of coarse level segmentation results over the Brant County

agricultural scene (a) with subset shown in red square and (b). Results of segmentation at

scale 200 using all seven input layers shown in (c), and at the same scale 200 with the

NDVI layer excluded in (d). White arrows in (d) show locations of over-segmentation that

do not correspond with crop field boundaries.

38

Figure 2-9. Comparison of medium level segmentation results over the Algonquin park

scene (a) with subset a wetland complex shown in the red square and (b). Results of

segmentation at scale 40 using all seven input layers (c), and at the same scale 40 with the

DEM layer excluded (d). White arrow in (c) shows improved segmentation of a wetland

boundary (to the right of the indicated line) with the inclusion of the DEM layer.

39

Figure 2-10. Comparison of fine level segmentation results over the east Toronto urban scene

(a) and with subset of a marsh complex shown in the red square and (b). Results of

segmentation at scale 15 using all seven input layers (c), and at the same scale 15 with the

texture layer excluded (d). Note the significant over-segmentation of the texture-excluded

image in (d).

40

Figure 2-11. Sample view of wetlands enclosed by object boundaries created by the FNEA

multiresolution segmentation algorithm (yellow), and its corresponding reference boundary

(white) showing improved delineation of wetland boundaries (a), under segmentation (b), better

detection of within wetland components (c, d); and examples of over segmentation, particularly

of treed wetlands (e, f). Wetlands in the top row are from the natural park site, middle row

wetlands are from the rural site, and bottom row wetlands are from the urban site. Reference

polygons were provided by the OMNR (park site: a, b), Grand River Conservation Authority

(agricultural site: c, d), and the Toronto Region Conservation Authority (urban site: e, f).

41

Segmentation of non-wetland classes varied across sites. The acquisition of early spring imagery

resulted in better overall segmentation of dominant crop land in the rural-agricultural scene

likely due to the fact that the majority of fields were bare, and borders were clearly visible.

However, some smaller features such as hedgerows and isolated irrigation ponds suffered from

absorption into these larger agricultural fields. In contrast, the urban scene which also included

agricultural fields suffered from a greater over and under-segmentation, as boundaries were not

as distinct in this landscape. This is attributed to variation in land use patterns and a greater

proportion of mixed vegetation classes adjacent to managed agricultural fields (see final

classification maps, Figure 2-13).

There were no agricultural fields, or isolated ponds in the natural Algonquin Park study area and

the dominant forest class was segmented with a high accuracy. While forest cover was relatively

continuous across most of the scene in the natural landscape, forested uplands in the urban and

rural sites were highly fragmented resulting in comparatively lower segmentation accuracy.

2.4.2 Classification

GeoeEye1 and WorldView2 data were classified initially into 8 classes for the park and urban

sites, and 7 classes for the agricultural site. All scenes were then merged into three common

classes of wetland, upland, and water for comparison across study sites.

Overall Classification. Overall classification accuracy was the highest over the park landscape

(overall accuracy 0.90, kappa 0.88), followed by the urban landscape (overall accuracy 0.86,

kappa 0.84) and lowest over the agricultural landscape (overall accuracy 0.81, kappa 0.78)

(Table 2-5). Producer’s accuracies were high across all landscapes (> 0.80) with the exceptions

of herbaceous upland (0.57), and forested upland (0.74) in the agricultural site, and swamp

wetlands in both the agricultural (0.69) and urban (0.67) sites. Similarly, user’s accuracies were

high across all landscapes (> 0.80) with the exceptions of the swamp class (0.65), forested

upland (0.66), and agricultural land (0.79) for the agricultural land cover map, as well as the

herbaceous upland class for both the agricultural (0.50) and urban (0.74) sites. The most poorly

achieving classes were the swamp class, and the herbaceous upland class in the agricultural

landscape. For all study sites, water received the highest classification accuracy.

42

Table 2-5. Accuracy statistics for land cover classes at each study site (PA =

producer’s accuracy, UA = user’s accuracy).

Park Site Agricultural Site Urban Site

(Algonquin Park) (Brant County) (East Toronto)

Land cover Class

PA

UA

PA

UA

PA

UA

Marsh 0.91 0.94 0.81 0.93 0.81 0.90

Swamp 0.83 0.81 0.69 0.65 0.67 0.95

Fen 0.89 0.83 - - - -

Bog 0.87 0.84 - - - -

Water 0.97 0.94 0.98 0.96 0.97 0.88

Forested Upland 0.94 0.91 0.74 0.66 0.89 0.96

Herbaceous Upland 0.80 0.82 0.57 0.50 0.84 0.74

Agricultural Land - - 0.81 0.79 0.91 0.83

Built/Urban Matrix - - 0.95 0.95 1.0 0.89

Barren Land 0.92 0.95 - - 0.85 0.85

Overall (kappa)

0.90 (0.88) 0.81 (0.78) 0.86 (0.84)

The error matrix for the park site is shown in Table 2-6. In this landscape with relatively low

heterogeneity due to human disturbance, notable errors include the misclassification of swamps

as forested upland, and herbaceous upland as forested upland and to a lesser extent, marshes as

fen, and herbaceous upland as forested upland. In the agricultural landscape of greater

disturbance due to human activities, most significant misclassification occurred with forested

upland objects being erroneously committed to the swamp class and vice versa (Table 2-7). To a

lesser extent, marshes were misclassified as swamps, agricultural land was incorrectly classified

herbaceous upland, and herbaceous upland was misclassified as agricultural land. The error

matrix for the classification map produced for the highly disturbed urban scene (Table 2-8),

included substantial misclassification of herbaceous upland as forested upland, and herbaceous

upland classified as marsh wetland. Lesser instances of erroneous classification include water

image objects classified as marsh, agricultural land committed to herbaceous upland, and built-

urban matrix objects misclassified as barren land.

43

Table 2-7. Error matrix for the landcover classification of the Algonquin park

study site (8 classes), using GeoEye1 MS data.

Table 2-6. Error matrix for the landcover classification of the Brant County

agricultural site (7 classes) using GeoEye1 MS data

44

Wetland-Upland Classification. Individual classes were merged into wetland (marsh, fen, bog,

swamp), upland (forest, meadow, agricultural field, built, barren), and water categories in order

to compare wetland accuracy amongst non-wetland classes (Table 2-9). Accuracy for the merged

classification map was the highest for the park study site (overall accuracy 0.90, kappa 0.86),

followed by the urban site (overall accuracy 0.86, kappa 0.81) with the agricultural landscape

receiving the lowest accuracy (overall accuracy 0.76, kappa 0.71). Producer’s accuracy was high

across all study sites (> 80%) with the exception of uplands in the agricultural landscape. Map

user accuracies were generally high (> 80%) with the exception of wetlands, and water classes in

the agricultural and urban landscape (66-77%).

Table 2-8. Error matrix for the landcover classification of the east Toronto urban

site (8 classes) using WorldView2 MS data.

45

Table 2-9. Accuracy statistics of producer’s accuracy (PA), user's accuracy (UA),

overall accuracy and Kappa statistic for merged wetland, upland, and water classes

across all study sites.

Park Site

(Algonquin Park)

Agricultural Site

(Brant County)

Urban Site

(East Toronto)

Land cover Class PA UA PA UA PA UA

Wetland 0.86 0.95 0.80 0.71 0.85 0.74

Upland 0.92 0.90 0.64 0.91 0.97 0.84

Water 0.95 0.97 1.00 0.66 0.98 0.77

Overall (kappa) 0.90 (0.86) 0.76 (0.71) 0.86 (0.81)

Across the merged classification map of the park site, only minimal errors occurred between

wetland and upland classes while greater errors were found in the agricultural and urban merged

maps (Table 2-10). Focussing on wetlands, there was a high error of commission of uplands into

the wetland class, and a slightly lower omission of wetland objects into the water class in the

agricultural landscape. Over the urban study area wetland objects were erroneously classified as

both water and upland, while only minimal errors of commission occurred.

Table 2-10. Error matrices for grouped water, wetland, and upland classes over each study area.

Numbers denote image objects (not individual pixels).

46

2.4.3 Comparison of Sample Attribute Separation between Classes

Since the nearest neighbour classifier selects the most suitable attributes to classify a land cover

class, I further investigated the most suitable attributes used for class separation by examining

overlap values between classified polygons. The mean object value of the red band, near infrared

band and NDVI provided the greatest separation between wetlands and all other classes (Figure

2-13). The mean object value from the NDVI layer was used most frequently in discriminating

wetland classes from other land cover groups over the urban east Toronto site. The near-infrared

layer was used most frequently to separate between wetland and upland classes over the natural

Algonquin Park and rural Brant County sites. Texture was used to separate wetlands from built

areas in the rural site disproportionately more than in the natural and urban landscapes.

Figure 2-12. Comparison of mean object layer values providing the best separation

between wetlands and all other classes at each study site. Y-axis shows number of times

a layer provided the best separation distance between classes, normalized out of 1.

Average values are standardized across total number of land cover classes at each site.

47

Figure 2-13. Classification results showing original satellite image (a, d, g) (RGB: NIR-Red-

Green), final classified map (b, e, h) with subsets in the red polygons expanded in (c, f, i) over

the Algonquin Park natural site (first row), the Brant county agricultural site (middle row), and

the east Toronto urban site (bottom row).

Discussion

In this study the accuracy of a multi-scale GEOBIA approach was examined in correctly

classifying wetlands across three different landscapes. Despite the variability in study areas,

overall wetland class accuracy across scenes was greater than 80% indicating that this approach

is efficient across scenes of varying heterogeneity due to human-disturbance.

48

2.5.1 Segmentation and the GEOBIA approach

The multi-scale object-based approach provided an effective method of partitioning wetlands,

and other land cover classes. The class accuracy of wetlands (marsh, swamp, bog, fen) was

higher than grouped upland-wetland accuracy across all sites, which is attributed in part, to the

use of the hierarchical parent-child segmentation approach. The segmentation of whole wetland

objects into smaller objects for within wetland classification allowed this process to be

constrained to its parent class which minimized the potential for misclassification with other

groups. Repeatedly modifying training objects to achieve the best classification also contributed

to improving final map results. Specifically, a sample of incorrectly classified objects should be

iteratively selected as training samples to retrain the classifier so that subsequent classifications

can target areas of demonstrated spectral overlap or confusion. The visual approach to selecting

the scale parameter proved to be a robust method demonstrating the inherent ability of the human

eye to distinguish between landscape elements and neighbourhood context. The use of the

modified ED3 algorithm to evaluate the scale parameter provided important quantitative support

for scale selection, as well as further information on the range of appropriate scale values. In

general, the combination of both quantitative and qualitative measures are recommended as each

is important for scale parameter selection. It should also be noted that the identified scale at each

level cannot be interpreted as a universal value that can be applied to any image of similar

composition or resolution (spectral, spatial, and radiometric). The size and shape of image

objects is greatly affected by the extent, composition, spectral heterogeneity and type of

segmentation algorithm used. For example in preliminary segmentation tests, it was found that a

subset of a larger image segmented at a scale value of 100, would create very different image

objects than those created by segmenting the entire image at the same scale of 100. Here, the

extent alone alters the resultant objects relative to the scale value which remains constant.

Nevertheless, the scale values reported here are for the purpose of comparison of targeted land

cover classes within the study sites, and not as a recommendation for optimal scale values to use

for other images composed of similar elements as ours.

At the coarse segmentation level, the addition of the NDVI layers resulted in a general

improvement in delineating boundaries of classes which were comprised of, or adjacent to

vegetation such as crop fields bordered by hedgerows, or mixed herbaceous vegetation.

Multispectral indices have been shown to improve models of wetland discrimination (Bradley

49

and Fleishman, 2008) due to their sensitivity to vegetation surface roughness and phenological

stage (Davranche et al., 2010). Elevation information improved segmentation of whole wetland

boundaries at the medium scale, and particularly in palustrine (inland) wetlands as opposed to

lacustrine (lake-associated) wetlands. This is likely a result of a greater difference in elevation

between inland wetlands and the terrestrial uplands which completely surround them. The NDVI

layer contributed more to the segmentation of lacustrine wetlands, which were present in small

proportions in the park and urban scenes.

Texture contributed most to segmentation at the finest scale where the spatial information

improved delineation of wetland vegetation communities. Resultant image objects were larger

than those segmented without textural information, and they also more accurately captured edges

where macrophyte communities transitioned. Previous work has shown that texture analysis can

improve classification accuracy by reducing the confusion between permanent crops and

perennial meadows (Peña-Barragán et al., 2011). For future work, higher order texture measures

should be explored such as those derived from the grey-level co-occurrence matrix (Haralick et

al.,1973), which has shown success in discriminating between deciduous and evergreen tree

species (Kim et al., 2009) and may improve classification accuracy between treed uplands and

swamps (treed wetlands).

2.5.2 Classification Accuracy

Wetlands in landscapes of varying heterogeneity were classified with an accuracy between 81%

(kappa 0.78) and 90% (kappa 0.88) with the least disturbed site achieving the highest accuracy.

While what is considered acceptable for mapping accuracy may vary, the recommended target of

85% overall accuracy (Foody, 2002; Thomlinson, Bolstad, & Cohen, 1999) was achieved by two

of the three classification maps. Not unexpectedly, differences in upland complexity resulted in

varied outcomes with regard to both segmentation and classification accuracy. When comparing

overall classification accuracy, the more disturbed sites consistently demonstrated higher errors

compared to the less disturbed park site, with the agricultural landscape performing the poorest

overall. An analysis of the error matrix for this site suggests that a dominant contributor to

mapping error was the confusion between the forested upland and swamp class. The high

proportion of swamp areas present in this landscape likely contributed to the lower classification

error. Here, the lower accuracy results were partly attributed to the presence of facultative tree

50

species such as Red maple (Acer rubrum) which can grow in both saturated wetland soils and dry

upland soils and would show spectral and textural similarity if above ground reflectance does not

reveal the hydrologic state beneath (Sader et al., 1995). Confusion between agricultural land and

herbaceous upland further reduced accuracy. Notably, all confused classes belonged to groups

containing an abundance of vegetation, indicating the need for better measures to separate these

similar classes. Similarly, the urban land cover map demonstrated reduced accuracy with the

swamp class, despite (or partly as a result of) the low proportion of swamps in this scene. The

use of advanced texture measures such as the grey level co-occurrence matrix, multi-date MS

imagery or data from active sensors would likely help to improve accuracy over this class and

should be investigated further.

Overall, it is generally accepted that mapping error on less frequent classes like wetlands will be

higher than error on dominant classes (Cunningham, 2006) and the relative rarity of wetlands at

each site likely contributed to the over/under classification and spectral confusion of wetlands

classes. Wright & Gallant (2007) documented a similar error for palustrine wetland mapping in

Yellowstone National Park for which wetlands comprised less than 6% of the total cover.

The use of temporal imagery has been shown to improve wetland detection, and utilises a major

advantage of Earth-observing satellite data. Dechka et al. (2002) classified prairie wetland

habitats in southern Saskatchewan using two IKONOS images acquired 24 May 2000 and 29

July 2000 to maximize seasonal variation in vegetation growth from minimal spring conditions

to mid-summer growth. The multi-temporal combination of May and July imagery produced the

highest accuracy (95.9%), although compared to results using the only the July image (84.4%)

authors concluded that the increase in accuracy may not be enough to justify the high cost of

additional multi-temporal image acquisitions. Interestingly, in this study the earlier season (May)

image produced the lower classification accuracy (50.5%), while others found that spring

imagery was most optimal for wetland discrimination (Ozesmi & Bauer, 2002; Gilmer et al.,

1980). Dingle-Robertson (2014) examine Ontario wetland classification according to the Ontario

Wetland Evaluation System across three seasons using WorldView2, Landsat5, and Radarsat2

data and found that high spatial resolution WorldView2 data from spring or summer acquisitions

produced the highest accuracies. Other multi-temporal work has found improvement in the

identification of wetland plant species using a combination of field spectral data, LIDAR top of

canopy data, and multi-date Quickbird imagery (Gilmore et al., 2002), as well as improved

51

accuracy in mapping seasonally flooded forested wetlands using multi-date RADARSAT data

(Townsend, 2001). In this study, multi-date imagery was only available for two of the three sites

(the urban east Toronto site only had high spatial resolution data available in July) therefore a

multi-date evaluation was not possible. However, future work requiring higher classification

accuracy, for example in wetland change detection studies, should consider employing a multi-

temporal approach.

Other factors may have influenced final classification accuracy such as the difference in timing

of image acquisitions across study areas, and the difference in sensors. The Brant County

agricultural site and the Algonquin park site were both acquired in the spring from the GeoEye1

sensor (25 May 2013, and 9 April 2012 respectively), albeit in different years, whereas the east

Toronto urban site was acquired in the summer (25 July 2012) from the WorldView2 sensor.

From an operational standpoint, acquiring satellite imagery that perfectly matches the required

timing and conditions mandated by the study, can present one of the greatest challenges in

remote sensing research. Shifting priorities in commercial tasking orders, limited availability of

archived imagery, presence of cloud cover, and high cost, can collectively contribute to

mismatches in sensor and temporal continuity. Thus results from the urban scene may not be

directly comparable to results from the agricultural and park scenes, as the presence of

vegetation further along in development and growth, as well as the slightly narrower bandwidth

of the red channel (630-390 nm WorldView2 compared to 655-690 nm GeoEye1), and the near

infrared channel (770-895 nm WorldView2 compared to 780-920 nm GeoEye1) may have

influenced final results. Yet, these differences can also have a positive effect if identifying

potential benefits or disadvantages of one sensor configuration in comparison with the other. For

example, it is interesting that for the landscapes represented by GeoEye1 imagery (park and

agricultural), which operates with a wider NIR bandwidth, the contribution of this band to

classification is greater than the NDVI layer. Conversely, for the urban site based on the

narrower NIR band of WorldView2, the opposite is true. This raises the possibility that different

sensors may utilise spectral layers differently in the classification process as a result of their

bandwidth, and is a topic warrants further investigation. However, despite this discontinuity in

sensor and image acquisition timing, accuracy results over the urban site were neither higher nor

lower than the accuracy over the other two sites with matching sensors and dates. While this

uncertainty should be recognized, I do not believe it negates the results provided in this study.

52

2.5.3 Landscape heterogeneity

A primary objective of this study was to examine if this methodological approach was robust to

variability in scene heterogeneity caused by human disturbance. Overall, with wetland accuracy

results above 80% across all scenes, it can be concluded that this method was indeed well-suited

to classifying wetlands from landscapes of varied heterogeneity, but there was a slight pattern of

decreased accuracy with increasing scene complexity. Fahrig et al. (2011) made an important

distinction between compositional heterogeneity (the variation of land cover types) and

configurational heterogeneity (the variation in spatial patterning of land cover types) with which

to describe a landscape. However, it should be noted that there is no universally accepted

description of ecological heterogeneity (Cadenasso et al. 2007), which makes it difficult to

identify those regions for which special considerations should be taken.

In terms of land cover heterogeneity, Smith et al. (2002) quantified both land cover patch size

and heterogeneity over a large portion of the eastern US and demonstrated an almost continuous

decrease in accuracy as heterogeneity increased, suggesting that landscape characteristics should

be afforded the same consideration in accuracy assessments as those conducted on land cover

classes. We noticed a similar relationship between land cover heterogeneity and map accuracy

among our study sites with the natural site achieving the best results, and more disturbed sites

performing worse. The decreased accuracy over the agricultural site was not anticipated since

built features were considered more complex than agricultural fields, and coverage of built areas

was considerably higher in the urban region. Yet, the relative ease of segmentation and

classification of residential parcels in both disturbed landscapes indicated that this class may not

contribute as much to classification confusion as originally thought. Upon further examination of

results, urban-built features have greater spectral and textural distinction which separate them

more easily from spectrally and texturally similar vegetated land cover classes. A careful review

of misclassified objects indicated that the wide range of upland vegetation classes of both human

and natural origin, may be responsible for the lower map accuracy in the rural site. This was

especially true along transitional areas where one dominant class transitioned into another.

Cingolani et al. (2004) experienced a similar challenge in mapping heterogeneous rangeland

ecosystems where the influence of grazed lands combined with natural environmental gradients

to create complex vegetated patterns that were difficult to separate.

53

Conclusions

A simple yet efficient methodology for mapping wetlands across landscapes of varied

heterogeneity was presented in this chapter. High spatial resolution satellite data and the

GEOBIA approach can be combined to provide a sound methodology for characterizing whole

wetlands and individual wetland classes. The GEOBIA approach specifically, was very

appropriate for wetland detection as it allowed for a nested multi-scale approach to constrain

classification of wetland components to within defined wetland boundaries. In regards to

landscape variations, a more heterogeneous landscape may negatively affect accurate wetland

classification due to increased spatial and compositional complexity. Specifically, rural

landscapes presented special challenges due to the large proportion of vegetated upland classes

of both anthropogenic and natural origin that reduced segmentation accuracy and resulted in

greater spectral overlap during the classification.

Future work in wetland mapping of treed swamp wetlands should include SAR data which can

capture the presence of standing water underneath tree canopies. In all cases, image acquisition

during early spring leaf-off conditions are recommended to aid in discriminating between

confounding vegetation classes, though from an operational standpoint reliance on archived

imagery often means that this is not possible.

Overall, the trend of reduced wetland coverage with increasing landscape complexity due to

human disturbance creates ongoing challenges for accurate wetland delineation. Wetlands are

important ecosystems contributing an estimated 40% of the value of global ecosystem services

(Zedler, 2003) and a better recognition of their value should be demonstrated through stricter

legislation for wetland protection, particularly where new developments are concerned. In many

areas a previous wetland inventory may not exist, making identification, evaluation and

monitoring of wetlands a challenge. This study demonstrated a robust approach to delineating

wetlands across variable landscapes which can provide starting information for better

management of these ecosystems. The multi-temporal aspect of satellite sensors can be exploited

to provide repeat coverage allowing for change detection and evaluation of wetland health over

time. However, the greater issue at hand is the ongoing loss and degradation of wetlands

worldwide, including the vast peatlands of the north which will have serious consequences for

global climate as well as the maintenance of biodiversity.

54

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Chapter 3

Modelling seasonal wetland habitat suitability for Blanding’s turtles (Emydoidea blandingii) using optical satellite remote

sensing imagery

Introduction

An animal’s ability to adjust to changes in its environment is a testament to evolutionary

adaption and species resilience (Gotthard & Nylin, 1995). This is especially true for species

whose distribution falls across temperate latitudes that undergo significant seasonal changes.

Changes to intra annual habitat preference and use are driven by changes in environmental

factors such as shifting food resources (Nielsen et al., 2010; Wallace, 2006), fluctuating water

availability (Humphrey & Zinn, 1982), and seasonal climate (Millar & Blouin-Demers, 2011).

Behavioural characteristics may also act as strong drivers which alter the way an animal uses the

environment. Behaviours driven by reproduction were found to seasonally alter use of habitat

such as the defence of larger territory in Lesser Spotted woodpeckers (Dendrocopos minor)

(Wiktander et al., 2001) and extensive movements by both mink and river otters during mating

season (Humphrey & Zinn, 1982). In Cutthroat trout (Oncorhynchus clarki) competition with

other species is thought to be responsible for the abandonment of larger pools in the spring

(Heggenes et al., 1991).

In species habitat modelling, traditional approaches use environmental predictors representing a

single snapshot in time, resulting in habitat maps that imply a static environment. However, the

boundaries of species’ ranges are probabilistic entities, and they shift in space and time (Fortin et

al., 2005). In circumstances where habitat needs vary according to time of year, such snapshot

habitat maps are not appropriate for making decisions on land development, policy, or for setting

conservation priorities. Remote sensing (RS) science is a rapidly-advancing field and provides a

wealth of data capable of increasing the spatio-temporal resolution of species occurrence models.

This is particularly true of satellite data with vast archives (e.g. Landsat continuity mission)

allowing studies to extrapolate into the past and identify trends. The use of satellite imagery is

not new to species distribution modelling and has been used to predict the preferred habitat for

grizzly bears (Franklin et al., 2002; Linke et al., 2005), birds (Osborne et al., 2001; Melles et al.,

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2011; Shen et al., 2013), ungulates (Leimgruber et al., 2001), and marine turtles (Hays et al.,

2001; Kelle et al., 2007; Wabnitz et al., 2008). In fact, the field of conservation biology was

revolutionized during the 1990’s when satellite-derived data became widely available, resulting

in a dramatic increase in both the scope and amount of species distribution studies conducted

(Rushton, Merod, & Kerby, 2004). However, the wealth of information that can be derived from

remote sensing imagery is not often fully exploited for use in species distribution models nor

used to provide biologically relevant answers to pressing conservation questions. Land cover

maps are the most often used remote sensing product in species distribution models (Miller,

2010), however maps are often created for multiple purposes by third-party government or non-

government organisations, and may not necessarily match the scale of observation or land cover

types relevant to the target species physiological and behaviour needs. Moreover, the use of

spectral values and vegetation indices as environmental predictors are not commonly employed

for use in habitat models for species other than birds (Gottschalk et al., 2005; Miller, 2010),

despite the wealth of biophysical information they can provide. Possibly one of the most

important characteristics of satellite imagery is the multi-temporal feature of space-borne sensors

which can not only allow studies to extrapolate into the past using archived imagery, but also

provide ongoing repeat coverage over the same ground location across a range of temporal

scales, allowing us to identify patterns, trends, and changes in habitat.

This study used environmental variables derived from multi-temporal satellite imagery, and

seasonal high spatial resolution land cover maps, to predict suitable habitat for a threatened

freshwater turtle, which represents a globally declining taxon. The Blanding’s turtle (Emydoidea

blandingii) is a medium-sized turtle whose primary geographic distribution covers portions of

southern Ontario, and Quebec as well as some states in the north, central and eastern United

States where they are considered at-risk in the majority of the political boundaries in which they

occur (NatureServe, 2010). They inhabit all wetland types containing shallow open water and

abundant vegetation, while uplands and lotic (fast-flowing) habitats are generally avoided (Edge,

Steinberg, Brooks, & Litzgus, 2010; Paterson, Steinberg, & Litzgus, 2014; Ross, Anderson,

Journal, & Mar, 1990), except during nesting migrations when uplands are used extensively

(Congdon, Kinney, & Nagle, 2011). On a microhabitat scale, Blanding’s turtles have been

associated with emergent and floating aquatic vegetation (Hamernick, 2000; Millar & Blouin-

Demers, 2011), submergent vegetation (Edge et al., 2010), cold waters and bog mats (Millar &

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Blouin-Demers, 2011), floating logs (Barker & King, 2012), permanent pools (Joyal,

McCollough, & Hunter, 2001), soft organic substrates and an abundance of sedge tussocks and

muskrat mounds (Pappas & Brecke, 2009). While wetland ecosystems can change rapidly

throughout a single season, Blanding’s turtle movement, activity, and physiological needs also

demonstrate cyclical shifts in habitat selection related to foraging, and mating, nesting, and the

onset of hibernation (Beaudry, deMaynadier, & Hunter, 2009; Edge et al., 2010). Declining

across much of their range, Blanding’s turtles suffer pressures from pervasive habitat loss, and

degradation, as well as increased mortality of nesting females due to road barriers fragmenting

the landscape (Beaudry et al., 2008; Steen et al., 2006). Alongside the trend of decline in turtle

species worldwide, global wetland loss also continues without apparent abatement despite

increased awareness of their importance over the last century (Brock, Smith, & Jarman, 1999)

and the association of many declining species with these ecosystems.

The aim of this study was to investigate whether turtles select habitat based on different

characteristics throughout the year, and if this difference could be captured by remote-sensing

based modelling. To achieve this objective, habitat models were developed over two study areas

at two different periods during the active season, related to significant changes in both

physiological and behavioural needs of Blanding’s turtles as well as changes to wetland

hydrology and vegetation communities. This ability is tested across two landscapes varying in

alteration due to human-related activities. The effectiveness, and versatility of boosted regression

trees (BRT) and logistic regression in correctly identifying Blanding’s turtle preferred habitat

was also compared. Results are evaluated in the context of strengths and limitations of this

approach for threatened species conservation planning.

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Materials and Methods

3.2.1 Study Area

This study was conducted in two locations in Ontario, Canada, (Figure 3-1) representing a

relatively undisturbed landscape in Algonquin Provincial Park (hereafter referred to as the park

site), and a landscape highly disturbed by human farming and settlement activities located in

Brant County (hereafter referred to as the agricultural site). The park study site was located in the

northeast corner of Algonquin Provincial Park, which encompasses 7,630 km2 in its entirety

including approximately 340 ha of wetlands of all classes as defined by the Canadian Wetlands

Classification System (NWWG, 1997).

Figure 3-1. Study regions in a (a) relatively undisturbed park landscape in Algonquin

Provincial Park, and (b) a fragmented agricultural landscape in Brant County. Images

acquired from (a) GeoEye1 on May 25, 2013 and (b) WorldView2 on April 9, 2012.

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Logging activity occurs in the study area as well as recreational use by park visitors, though the

study site was located in a less heavily visited section. The agricultural site was located in the

County of Brant in southwestern Ontario and supports a population of ~35,000 people.

Provincial and private roads bisect the agriculturally dominated landscape and surround the

Oakland Swamp, an 890 ha wetland of provincial significance as determined by the Ontario

Wetland Evaluation System (OWES, 2002); a designation which precludes development and site

alteration. Several smaller wetlands of variable size and shape were also distributed throughout

the study area.

3.2.2 Satellite Imagery

Four satellite images from WorldView-2 and GeoEye-1 sensors were acquired for developing

seasonal remote-sensing based environmental predictors. Both high spatial resolution sensors

capture data across multiple spectral regions (red, green, blue, and near infrared) (Table 3-1).

Four images were acquired over each study site, and across two seasons during the spring (April-

May) and late summer (September). High spatial resolution sensors were selected to match the

habits of the study species, which utilizes a relatively small home range compared to mammals

and birds, and therefore required image data capable of resolving fine details. Satellite images

were geometrically and radiometrically corrected using PCI Geomatica version 2014. Geometric

correction achieved an accuracy of 2 pixel RMSE (root mean square error) or better, representing

approximately 4 m error on the Earth’s surface. Atmospheric correction was completed using the

ATCOR2 top of the atmosphere reflectance calibration algorithm (Richter et al. 2006; Richter,

1996; Zhang et al. 2002). All images were projected to the Universal Transverse Mercator

(UTM) system (zone 17).

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Table 3-1. Summary of satellite imagery acquired over study sites and temporal periods

examined in this chapter

Specifications Park Site (Algonquin Park) Agricultural Site (Brant County)

Acquisition Date 25 May 2013 12 Sept 2012 9 April 2012 5 Sept 2013

Sensor GeoEye1 GeoEye1 WorldView2 WorldView2

Nominal Ground

Pixel Size (m)

1.84 m multispectral

Spectral

Bandwidth

Blue (450-510nm)

Green (510-580nm)

Red (630-690nm)

Near infrared (770-895nm)

Blue (450-510 nm)

Green (510-580nm)

Red (655-690nm)

Near infrared (780-920nm)

Radiometric

Quantization

16 bits

16 bits

3.2.3 Study Population & Telemetry Data

Locational data from a subset of two populations of adult Blanding’s turtles were obtained from

ongoing radio-telemetry studies conducted by research partners from Laurentian University and

the Metro Toronto Zoo. Turtles from the park site population (n = 17) were radio-tracked from

2006 to 2007, and from 2010 to 2011 for the agricultural site population (n = 20) throughout the

active season (April – September), and less frequently once turtles settled into their winter

hibernacula (October-November). Transmitters (Holohil models SI-2F, AI-2 and PD-2, Holohil

Inc, Carp, Ontario, Canada) were affixed to rear marginal scutes, and locations pinpointed using

a 3-element Yagi antenna and receiver (Lotek Suretrack STR-1000, Lotek Engineering Inc.,

Newmarket, Ontario, Canada; Telonics receiver, Telonics, Mesa, Arizona; Wildlife Materials

model TRX-1000S, Wildlife Materials Inc. Illinois, USA). Each study site accumulated a

minimum of 600 locations, which were recorded using a hand held GPS (positional accuracy

±5m), then partitioned by season. Telemetry data was not subsampled to account for spatial

autocorrelation as Fortin and Dale (2005) note that reduction of the dataset does not necessarily

remove the effect of autocorrelation, but only reduces the power to detect it, and it is in fact this

signal of serial correlation that habitat models attempt to detect. Cushman and Huettman (2010)

further assert that reducing the dataset to achieve a level of perceived statistical independence

incurs heavy costs in terms of information loss. A comparison of home range estimate using a

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full and subsampled telemetry data found that the reduced dataset did not improve results, and

home range size and movement patterns were better represented by the full set auto correlated

observations (De Solla et al., 1999).

3.2.4 Temporal Partitioning

Changes in the behaviour and physiological needs of Blanding’s turtles during the active season

can be partitioned into periods corresponding with distinct activities. At the onset of the active

period (April-May), turtles emerge from hibernation, forage widely, bask often and search for

mates. During the late spring and mid-season summer (June-July), females leave wetlands in

search of terrestrial nesting sites. Towards the end of the summer and fall (July-October), turtle

activity and consumption is reduced and the onset of hibernation begins (Beaudry et al., 2009;

Edge et al., 2010). This timing is generally applicable across the extent of the Blanding’s turtle

range that falls within the latitudes of southern Ontario (44-45oN). For this study, each active

period was partitioned into two seasons; spring (April-June) and late summer (July-Sept) which

captured primary activities as well as major changes in wetland habitat. Nesting behaviour and

nesting habitat was not considered in this study as it represents a distinct temporal period limited

to the reproductively active female cohort which was not representative of the population. In

terms of the wetland habitat, spring corresponds with maximum inundation extent (Ozesmi &

Bauer, 2002) and early growth of wetland vegetation (Figure 3-2; a, c). As the season progresses

and temperature increases, water levels drop and precipitation decreases, while wetland

vegetation reaches its maximum coverage and gradually begins to senesce (Figure 3-2; b, d, e).

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3.2.5 Environmental Input Variables

All candidate input variables were selected based upon ecologically meaningful habitat and

landscape characteristics for Blanding’s turtles, guided by existing literature and expertise gained

from field surveying. This species-centred approach endeavours to recognize that different

species perceive a landscape in different ways (Betts et al., 2014). In keeping with these

principles, three broad variables likely to affect habitat selection in Blanding’s turtles were

considered. Variables were categorised as (a) biophysical characteristics, (b) landscape-related

metrics (i.e., distance to landscape elements, proportion of land cover type) and (c) topographic

(elevation-related) variables (Figure 3-3). A preliminary field study was conducted to identify

significant biophysical variables correlated with habitat selection for Blanding’s turtles (see

following section 3.2.7) to target key variables to extract from multispectral remote sensing data.

Figure 3-2. Seasonal change in wetland vegetation and standing water in the park study area

(Algonquin Provincial Park) during the early spring (May; top left), and late summer (August;

top right), and the agricultural (Brant County) study area during the early spring (April;

bottom left), mid-season (June; bottom centre) and late summer (August; bottom right)..

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Landscape variables were derived from land cover maps produced in Chapter Two, while

topographic variables were calculated from a provincial DEM product. The 10 m DEM was

produced by the Ontario Ministry of Natural Resources which was interpolated from a DTM

(digital terrain model), a contour map, spot height data, and a water virtual flow map to a ±10 m

vertical precision. Climatic data was not considered due to the smaller landscape scale of this

study, and because the inclusion of vegetation characteristics likely act as proxies for climate at a

more detailed resolution.

Figure 3-3. Workflow demonstrating source data, extraction of environmental input variables, and

development of final raster layers used in model building.

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3.2.6 Pseudo-absence (Background) Sampling

Pseudo-absence or background locations were selected following two sampling schemes to serve

two different purposes. The first scheme was related to identifying significant field-based

biophysical data correlated with turtle presence. Pseudo-absence points were selected by

randomly choosing a direction and a distance within 90 m (+/- 10 m) of a turtle presence point

(Figure 3-4). The 90 m constraint represents the average daily movement of Blanding’s turtles

(Gibbs & Shriver, 2002) and ensured that random locations represented areas that were

accessible to the turtles and not remote locations where the species would not reasonably be

found. These pseudo-absence locations were created to allow comparison with biophysical

habitat data measured over presence points. A t-test comparison of means was used to determine

which biophysical habitat variables differed significantly between turtle and random points (e.g.

water depth at turtle presence locations representing habitat selected by the turtle, vs water depth

over random pseudo-absence points representing habitat (assumed) not to be selected by the

turtles). Field-based biophysical variables were measured over a total of 70 (±5) turtle

(presence) locations and 70 (±5) random (pseudo-absence) locations.

Figure 3-4. Sampling design for identification of significant biophysical variables correlated with

turtle presence. A subset of present points (blue circles) are selected from the pool of temporally

partitioned telemetry points (pink circles), and paired with a pseudo-absence point (yellow

circles) constrained to a 90m (± 10m) distance, any direction from the selected presence point.

70

The second pseudo-absence (background) sampling scheme was designed to sample the entire

study landscape by overlaying these points across all continuous raster inputs (Figure 3-3, far

right column), and was therefore not constrained to the 90 m limit of the biophysical sampling

scheme. Background points were generated across the entire study area using the Generate

Random Points tool in ArcMap (10.2) and stratified by the proportion of each land cover type to

determine the amount of background points to be generated (Barbet-Massin et al., 2012). A total

of 828 telemetry locations from the park site, and 688 points from the agricultural site were used

to represent presence points at each site, while background points were generated at a ratio of 1:2

for presence to pseudo-absence points (Figure 3-5). Background points were not excluded from

land cover types containing presence points to avoid arbitrary assumptions about what is

considered unsuitable habitat for the target species (Stokland, 2011). These sample points were

overlaid across all environmental input layers to build the dataset used for training the model.

Points were partitioned by season, with 15% withheld for validation.

Figure 3-5. Background points generated across study landscape (park site, spring)

and stratified by dominant landcover type shown as pink circles. Temporally

partitioned (spring) telemetry points show in blue circles.

71

3.2.7 Linking Biophysical Variables to Satellite Data

Identifying relevant biophysical variables associated with habitat selection. Biophysical habitat

characteristics such as water depth, temperature, or vegetation cover are important for defining

microhabitat selection, as they exist at a scale of observation most immediately relevant to the

animal. Yet this information is not often available in a spatially explicit format, and in many

cases variables that are most relevant to the study species are often the most data-poor (Aarts et

al., 2008), resulting in models being built from whichever datasets are available, rather than

those that are most biologically relevant. In this study, biophysical data related to water and

vegetation characteristics were obtained by linking field-measured data with satellite data using

multispectral indices as the independent (predictor) variable to extract the target biophysical

(response) data across the entire study area. The underlying assumptions were that: i) selected

variables displayed biophysical properties that were detectable by remote sensors (i.e., through

detectable variations in surface reflectance), and ii) these variables drive the distribution and

abundance of species across landscapes (Turner et al., 2003). Field measured biophysical

variables included vegetation and water characteristics such as percent vegetation cover, leaf

area index, water depth, dissolved oxygen and pH (see Table 3-2 for a complete list).

LAI (Leaf Area Index) was collected using an Accupar LP-80 probe (Decagon Devices, Inc.,

Pullman, WA, USA) held horizontally above ground level. Water chemistry variables were

measured using a YSI water quality sonde (Model 6820, YSI Inc., Yellow Springs, OH, USA)

placed directly above the surface of the wetland or aquatic water body. Measurements were

collected over 5 points within each 1.84 m quadrat (conforming to one pixel of the satellite

image) over each sample point, and averaged to represent the sampling point condition.

A t-test comparison of means was conducted to identify which variables significantly differed

between presence and pseudo-absence points and thus were associated with Blanding’s turtle

habitat preference. Highly correlated variables were eliminated, and those remaining were

extracted from the remote sensing image for the corresponding site and season. The null

hypothesis is stated as no difference between biophysical measurements over turtle (presence)

and random (pseudo-absence) points, indicating that turtles select habitat randomly. Based on the

results of the t-test, the null hypothesis was rejected as there was a significant statistical

72

difference between means of multiple habitat variables between turtle and random points

including percent vegetation cover, vegetation height, leaf area index, and water depth (Table 3-

3).

Table 3-2. List of biophysical variables measured over presence and pseudo-absence points at

the park site (Algonquin Provincial Park), and the agricultural site (Brant County study) during

the spring and summer seasons.

Variable Description

LAI

Leaf area index, a dimensionless measure of the one-sided green

leaf area per unit ground surface area

Emergent Vegetation Cover Percent (%) emergent vegetation cover estimated within a 1.84m

quadrat. Emergent refers to vertically-oriented vegetation for

which at least a portion of the plant exists above the surface of

the water.

Floating Vegetation Cover

Submergent Vegetation

Cover

Percent (%) floating vegetation cover estimated within a 1.84m

quadrat. Floating refers to horizontally-oriented vegetation

which sits across the surface of the water.

Percent (%) submerged vegetation estimated within a 1.84m

quadrat. Submerged refers to any vegetation that does not break

through the surface of the water.

Total vegetation cover Percent (%) total vegetation cover estimated within a 1.84m

quadrat; includes emergent and floating aquatic vegetation

Vegetation height Average maximum height of vegetation (cm)

Water depth Average water depth (cm)

Water temperature Water temperature (Celsius); collected at bottom substrate level

which represents where Blanding’s turtles would be found at

rest.

Dissolved oxygen A measure of the amount of dissolved oxygen in the water

(mg/L); collected at bottom substrate.

Specific conductivity A measure of the amount of dissolved solids (e.g. salt) in the

water (S/cm3)

pH A measure of the acid and base chemistry of water

73

Blanding’s turtles were found to select sites with greater vegetation cover, LAI, and height

compared with random points during the early spring season, while late summer selection

corresponded with selection of different water depths at each site. Shallower water was selected

by turtles in the park site during the late summer which is likely a result of the large lake

connected to many of the wetlands in the study area. Since deeper water was available year

round, the turtles had a wider range of depths to utilise. Therefore, relative to what was available,

turtles selected shallower regions Conversely, at the highly disturbed agricultural site, there was

no comparable body of water available, and open deeper water was restricted to creeks and

irrigation ponds. During the late summer, water depth selection by turtles was confined to deeper

depths relative to what was available. The smaller size of wetland and aquatic bodies at the

agricultural site may also relate to higher overall water temperatures, which could explain why

turtles utilised deeper, and thus cooler waters to regulate body temperature.

Table 3-3. Summary of significant biophysical variables identified through a t-test

comparison of means between biophysical measurements at observed turtle presence points

and random pseudo-absence points (random values in brackets). Based on 70±5 total sample

points for each variable at each site and each season.

Park site (Algonquin Provincial Park)

Variable

Mean

Std. Error

Mean

p*

Spring Percent vegetation

cover†

51.52 (27.04) ±4.82 (±7.13) 0.009

Late

Summer

Vegetation height (cm)†

Leaf area index

73.62 (48.97)

0.63 (0.01)

±5.66 (±7.89)

±0.12 (±0.00)

0.013

0.000

Water depth (cm) † 16.31 (37.27) ±2.68 (±8.17) 0.003

Agricultural site (Brant County)

Spring Vegetation height (cm) 76.96 (31.79) ±12.59 (±8.05) 0.007

Vegetation cover (%)† 78.66 (43.88) ±4.62 (±7.85) 0.000

Leaf area index 0.93 (0.11) ±0.18 (±0.06) 0.001

Late

Summer

Water depth (cm)†

96.06 (56.93)

±15.26 (±12.46)

0.041

† selected variables for model building

*significance value between observed (presence) and random (pseudo-absence) points

74

Extracting significant biophysical variables from satellite data. The normalized difference

vegetation index (NDVI) was extracted from satellite imagery and used to establish a

relationship with identified vegetation parameters (from Table 3-3). Leaf area index did not

demonstrate a robust relationship with NDVI and was not retained for use particularly as

equivalent information was provided by other variables such as percent vegetation cover. In

cases where more than one vegetation variable was identified as significant by the t-test, a

muticollinearity test was employed to identify and discard highly correlated biophysical

variables. NDVI is a widely-used indicator of live green vegetation that uses the reflectance (ρ)

of the visible (red) and near-infrared bands of the electromagnetic spectrum to estimate

vegetation characteristics (Rouse et al., 1974):

���� =ρnir − ρred

ρnir + ρred

NDVI values range from -1 to 1, where higher NDVI values indicate a greater coverage of

photosynthetically-active vegetation, while values less than zero typically do not have any

ecological meaning. An NDVI layer was created from the red and near infrared bands of the

satellite data, and values over sample points were extracted and input into a linear regression

model with field-measured vegetation parameters as the dependent variable.

Water depth was derived from remote sensing imagery using the relative water depth algorithm

developed by Stumpf, Holderied and Sinclair (2003) and given by the equation:

Where, Rw is the above surface water reflection, Lw is the water-leaving radiance, Ed is the

downwelling irradiance entering the water, and � represents each of the spectral bands. Rw is

found by correcting for the total reflectance (aerosol, surface reflectance, and Rayleigh

scattering) using the blue and near-infrared bands, and is therefore applied to the non-

atmospherically corrected satellite image. The algorithm was implemented in ENVI version 5.1

(Exelis Visual Information Solutions, Boulder, Colorado) which outputs a spatial map of

continuous relative water depth values (range 0-1). Relative water depth values (predictor

variable) were extracted at field-sampled locations, and regressed with field-measured data

(response variable) to build the regression model.

�� =���(�)

��(�)

75

3.2.8 Calculating Landscape Metrics

Land cover maps are the most often used remote sensing product in species distribution models

(Miller, 2010), yet maps acquired from secondary sources can result in products that may not

accurately reflect the spatial and temporal scale, nor the actual land cover types appropriate to

the target species. To illustrate this point, the third-party provincial land cover map available

over the study areas was created from 30m Landsat data and only included wetlands as one

general class, which would homogenize much of the variation important in capturing habitat

selection. To overcome this issue, land cover maps were produced in Chapter Two using

categories relevant to Blanding’s turtle biology and behaviour. Land cover maps were developed

for the early spring and late summer period to capture dramatic changes in wetland vegetation

composition which represents preferred habitat for Blanding’s turtles (Figures 3-6 and 3-7).

Figure 3-6. Change in wetland composition, vegetation extent, and available standing

water across the spring and late summer periods over the park site. Land cover maps were

developed from high spatial resolution GeoEye1 imagery during the spring (May 2013;

top) and late summer (September 2012; bottom).

76

Four classes of wetlands were categorized according to the Canadian Wetland Classification

System (NWWG, 1997) including marsh, swamp, fen, and bog. Preferred marsh wetland habitat

was further classified into emergent and wet meadow categories to reflect dominant vegetation

community types potentially related to habitat selection. Over the agricultural site, bare and full

canopy crop fields were classified separately to reflect the species-eye view of the landscape

(i.e., turtles may utilise exposed habitats very differently from areas with full canopy coverage).

Roads present a major landscape barrier to most turtles (Gibbs & Shriver, 2002; Congdon et al.,

1993) and were classified independently from other built features such as residential properties.

The latter were grouped into ‘parcels’ which included residences, driveways, lawns, and other

common elements of suburban domiciles. This grouping reflected the relative risk or aversion

that may be associated with habitat use near areas of increased human activity.

Figure 3-7. Change in wetland composition, vegetation extent, and available standing

water across the spring and late summer periods over the agricultural site (subsets). Land

cover maps were developed from high spatial resolution WorldView2 imagery during the

spring (April 2012; top) and late summer (September 2013; bottom).

77

Metrics based upon these targeted land cover maps were obtained from GIS environments. The

distance to a particular land cover type was measured using the Near tool in ArcMap 10.2 (ESRI,

2011, Redlands, CA). The proportion of a land cover type within a 90 m radius buffer around

sample points was extracted using the isectpolyrst tool in Geospatial Modelling Environment

(Beyer, 2012). A circular buffer of 90 m radius was selected to reflect the average daily

movement of Blanding’s turtles (Gibbs & Shriver, 2002). Distance and proportion metrics were

compiled from seasonally partitioned telemetry points and randomly generated background

points (see section 3.2.9 Presence and Pseudo-Absence Sampling).

3.2.9 Extracting Topographic Data

Blanding’s turtles are a semi-aquatic species, yet aside from nesting forays by gravid females,

and occasional dispersals by adults, individuals spend the majority of their time in wetlands.

During terrestrial travel, they tend to prefer low-lying areas and avoid steep rises and higher

elevations (B. Johnson, pers comm). For this reason, environmental variables (Table 3-4)

including slope, aspect, and topographic wetness index (TWI) were also included as initial model

inputs. TWI, also called compound topographic index (CTI), describes the propensity of a

location for saturation given the contributing area and local upslope values (Beven & Kirby,

1979). Higher TWI values represent (wetter) drainage depressions, while lower values represent

(dryer) crests and ridges. A TWI raster was produced from the provincial DEM layer according

to the equation:

��� = ln(�

tan �)

where a is the upstream (or catchment) contributing area in m2 and β is the slope in degrees. As

the topographic information at each site does not differ between seasons, one common layer was

used at each study site to characterize the two seasons. Results were normalized to a range of 0-

100.

78

Table 3-4. Summary of landscape and topographic derived variables used as model inputs

Source Data Variable Description

Land cover

Map

Distance

Proximity to the nearest land cover type (m)

Proportion

land cover

Proportion of land cover type within a 90m buffer

around sample points

Digital

Elevation

Model

Elevation

Slope

Height (m) above sea level

A measure of the steepness of a change in elevation

(degrees)

Aspect

A measure of the compass direction that a slope faces

(degrees)

TWI Topographic wetness index; a measure of the wetness

of an area based upon flow accumulation and the total

catchment area.

3.2.10 Models and Model Fitting

Boosted regression trees combine two powerful statistical techniques of boosting, a type of

ensemble machine learning, and regression trees which are models that relate a response to their

predictors by creating a binary split similar to the branching of a tree (Elith et al., 2008; Jafari et

al., 2014). Unlike other decision-tree methods which produce a single top model, boosting

merges the results of numerous simple trees which collectively boost the overall predictive

performance of the model compared to any one single tree (Elith et al., 2006; Jafari et al., 2014).

The final ensemble model is used to predict, and map the probability of species occurrence.

Despite its relative infrequent use in ecology, BRT demonstrates strong predictive performance

and consistent identification of relevant predictors and pairwise interactions while providing

simple numerical interpretations of complex relationships (De'ath, 2007; Elith et al., 2008).

BRT modelling was implemented through the gbm package (Ridgeway, 2006) and the gbm.step

package (Elith et al., 2008) within R (R Development Core Team, 2014). Following the

recommendations of Elith, Leathwick, and Hastie (2008) for determining appropriate settings for

model fitting options, an exploratory analysis was conducted on an independent test set to

identify the following settings. A Bernoulli distribution was utilised which is a discrete

distribution having two possible outcomes of success (1) or failure (0) of whether a specified

79

event occurs (in this case, turtle presence or absence). A tree complexity of 5 was employed,

which controls the allowable level of interaction among variables. Bagging fraction which

controls the amount of training data randomly selected for building each tree was set to 0.5 (i.e.,

50% of the training dataset was randomly selected during each repeated cross-validation step).

The learning rate controls the speed of the algorithm with lower learning rates corresponding to a

more stable reduction in prediction error (Elith et al., 2008). In all cases, the learning varied

slightly between 0.0075 and 0.01 and was adjusted to reach for a minimum of approximately

1000 trees as recommended by Elith et al. (2008).

To compare with BRT results, binary logistic regression models were also fitted using the same

training data over the park study site. Logistic regression uses the simple maximum likelihood

approach to fitting models (Wintle & Bardos, 2006) and is built around predicting probabilities

(the odds ratio) using a logit link where a perfect relationship falls along an S-shaped curve

rather than a straight line as in linear regression. Unlike BRT models, the logistic regression

output includes a single model equation which is established using a forward step-wise approach.

While BRT has been used often in other disciplines but is relatively new to ecology (Elith et al.,

2008), logistic regression models are one of the most widely used modelling approach for

mapping species distribution (Rushton et al., 2004), and have been used to map habitat for

mountain caribou (Johnson et al., 2004), wolves (Mlandenoff et al., 1995), wood turtles

(Compton et al., 2002), and rattlesnakes (Moore & Gillingham, 2006). In BRT models, the top

seven variables were retained in order to provide a fair comparison of predictive power

compared to variables selected by the logistic regression models. A top model type was selected

and applied to the second Brant County agricultural study site.

3.2.11 Evaluation and Evaluation Criteria

Model performance was evaluated using an independent test set created by a random hold-out of

15% of the total telemetry points from each population in each season. BRT model performance

was assessed using measures of specificity, sensitivity, and the Area Under the Curve (AUC)

Receiver Operating Characteristic (ROC) value. Logistic regression model performance was

assessed using Nagelkerke’s pseudo-R squared value, and the model deviance. The contribution

of final predictors was compared using measures of relative influence for BRT variables, and

ROC plots for logistic regression variables. Final map accuracy was estimated by applying a

80

threshold value to the continuous probability of occurrence maps to define binary categories of

habitat and non-habitat. Map accuracy was then assessed by quantifying the proportion of

telemetry (presence) points overlapping with polygons defined as habitat. A sensitivity analysis

was performed using threshold values of 0.4 - 0.8 to assess their effect on map accuracy (see

Appendix B1).

Results

3.3.1 Regression Models in Biophysical Variable Estimation

Different biophysical variables were associated with habitat selection by Blanding’s turtles

during the spring and late summer seasons. NDVI was used as the explanatory variable for

vegetation biophysical parameters associated with turtle preference (percent vegetation cover and

vegetation height). Water depth was estimated from satellite imagery using the relative water

depth algorithm (Stumpf et al., 2003). Results of regression models are shown in Table 3-5. The

red and near infrared bands are used in the estimation of the vegetation variables using NDVI,

and all spectral bands are used in the estimation of water depth using the relative water depth

algorithm.

A cross-validation analysis, withholding one third of the dataset at each fold, was applied to test

the fit of the model to a rolling subset of the total sample points. RMSE was calculated to

compare relative error across each grouping and results demonstrate that no obvious outliers

were present in the dataset (Table 3-6) indicating that the model was not overfit and shows

general applicability across sampled data. All sampled data was therefore retained for model

building.

81

Table 3-5. Summary of regression models developed from field-measured biophysical

parameters and satellite-derived data for use in mapping target variables

Park site (Algonquin Provincial Park)

Biophysical variable

(response)

Multispectral

Index

(predictor)

Regression

Relationship

R2

p

Spring % Vegetation cover NDVI y = 117.46x + 9.0 0.77 < 0.01

Late

Summer

Vegetation height

Water Depth

NDVI

RWDA

y = 127.59x + 43.24

y = 129.03x + 10.72

0.63

0.56

< 0.01

< 0.05

Agricultural site (Brant County)

Spring % Vegetation cover NDVI y = 180.21x + 24.88 0.53 < 0.05

Late

Summer

Water depth (cm)

RWDA

y = 199.42x + 21.74

0.75

< 0.01

NDVI (Normalised Difference Vegetation Index)

RWDA (Relative Water Depth Algorithm, Stumpf et al., 2003)

Table 3-6. Accuracy assessment of regression models developed for biophysical

variable estimation #add units to RMSE

Study Site

Season Biophysical

Variable

(response)

RMSE

Park Site

(Algonquin Provincial Park)

Spring % Vegetation Cover

15.74

18.49

12.94

Late

Summer

Vegetation Height 23.08

25.26

24.803

Water Depth 35.31

25.85

29.13

Agricultural Site

(Brant County)

Spring % Vegetation Cover

15.89

18.96

17.02

Late

Summer

Water Depth 31.92

34.11

22.06

82

All biophysical variables listed in Table 3-5 were produced as a continuous raster depicting the

target parameter across the entire study area (see Appendix, Figures A1-5). Vegetation-related

maps (i.e., percent vegetation cover and vegetation height) generally represented vegetation areas

well with higher cover associated with forested regions, and lower coverage corresponding with

open water, bare crop fields, and logged regions. Water depth maps reflected logical assumptions

of shallower water near the edge of water bodies, and deeper water further from upland

boundaries, though some maps displayed an unexplained speckled appearance that was possibly

due to reflection over a non-smooth water surface at the time of image acquisition, or surface

vegetation. All continuous raster layers were used as inputs alongside landscape-related and

topographic layers used to build BRT and logistic regression habitat models.

3.3.2 BRT and Logistic Regression Model Results

Both models were tested over the park site and demonstrated strong predictive power in

discriminating between turtle presence and absence (Table 3-7). The BRT model for the spring

and late summer correctly predicted Blanding’s turtle presence and absence with an area under

the curve ROC value of 0.98 and 0.98 respectively. Values closer to 1 indicate high sensitivity

(correctly identifying species presence; true positives) and low specificity (false positives

calculated as 1-sensitivity), while values close to 0 represent no predictive power. Predictive

deviance, is a measure of the unexplained variance in the model, where lower values represent a

larger proportion of the variation in the data being explained by the model. Model predictive

deviance (0.32) was higher in the late summer, and slightly improved (0.26) in the spring model.

Logistic regression models produced Nagelkerke R2 values of 0.86 for the spring model, and

0.88 for the late summer.

Table 3-7. Comparison of model test statistics for the park study site (Algonquin

Provincial Park)

Model Type Test Statistic SPRING model FALL model

Boosted Regression Tree

AUC ROC

0.98

0.98

Logistic Regression

Nagelkerke R2

0.86

0.88

83

Each BRT model was dominated by one environmental predictor that contributed between 38 -

53% of the model power out of a total of 100% (Table 3-8). Percent emergent vegetation within

a 90 m average daily movement buffer (peremerg) contributed the strongest to model prediction

(38.83) early in the active season, while the proximity to wet meadow wetlands (distwmead)

demonstrated the strongest influence later in the active season (60.76). The logistic regression

equation for the spring is given by:

P = 1/1 + e –(27.19 – 0.154(x1) – 0.102(x

2) – 2.139(x

3) – 0.016(x

4) – 0.005(x

5) – 0.016(x

6))

while the equation for the late summer is denoted by:

P = 1/1 + e –(17.79 – 0.011(x1

) – 0.026(x2

) – 0.042(x3

) – 0.053(x4

) – 1.678(x5

) – 1.882(x6

) – 5.224(x10

))

Table 3-8. Comparison of response variables included in final models of habitat selection

developed for Blanding’s turtles of the park study site (Algonquin Provincial Park) in

Ontario, Canada (*p < 0.01).

PARK SITE (Algonquin Provincial Park)

Model Type

SPRING

FALL

Relative

Influence

Relative

Influence

Boosted

Regression

Tree

Percent (Emergent Veg)

Proximity (Water)

Land Cover Type

Proximity (Bog)

Proximity (Emerg Veg)

Elevation

Proximity (Upland)

38.83

11.82

11.31

6.89

5.68

5.34

4.91

Proximity (Wet meadow)

Percent (Wet Meadow)

Elevation

Proximity (Emergent Veg)

Proximity (Water)

Percent (Emergent Veg)

Land Cover Type

60.76

11.56

7.27

6.60

4.79

4.67

4.33

B

B

Logistic

Regression

Constant

Slope

Elevation

Land Cover (Forest)

Proximity (Water)

Proximity (Bog)

Proximity (Emerg Veg)

27.19*

-0.154*

-0.102*

-2.139*

-0.016*

-0.005*

-0.016*

Constant

Proximity (Water)

Vegetation Height

Proximity (Wet Meadow)

Elevation

Land Cover (Water)

Land Cover (Swamp)

Percent Bog

17.79*

-0.011*

-0.026*

-0.042*

-0.053*

-1.678*

-1.882*

-5.224*

84

3.3.3 Comparison of variable contribution

Variables related to emergent vegetation (percent or proximity) within a radius of average daily

movements for Blanding’s turtles was selected as a common predictor in both the BRT and

logistic regression models for turtles early in the spring at the park study site (Figure 3-8). Other

common predictor variables included the distance to water, bog, emergent vegetation, and

elevation. The logistic regression model further selected the distance to the nearest upland

variable.

Figure 3-8. Comparison of variable influence and contribution as habitat predictors for the park

study site (Algonquin Provincial Park). BRT variables are shown on the left as relative

influence and logistic regression on the right as ROC plots of each contributing variable. Lines

curving towards 1 on the sensitivity y-axis represent variables most capable of accurately

detecting turtle presence (true positives). Lines curving towards 1 on the 1-specificity x-axis

represent variables most capable of detecting turtle absence (true negatives). Diagonal line

indicates reference line for which variable provides no discriminatory power.

85

Later in the active season, proximity to wet meadow marshes was the strongest term in the BRT

model, and the percent of bog wetlands in a 90m radius was most important in the logistic

regression model. Elevation, distance to water, and wet meadow wetlands were common across

both late summer models. Despite some similarities in variable selection, and overall strong

model performance, the BRT model was selected due to a higher model accuracy (Table 3-7) and

greater model interpretability. Coefficient values for all logistic regression variables were

relatively low and it was difficult to decipher the contribution of each selected explanatory

variable. Further, the selection of the forest class in the spring logistic regression model is

unsupported by field data or existing knowledge on physiological and behavioural needs of

Blanding’s turtles, who were rarely found in this land cover class except during travel. Late

summer model selection of the swamp land cover class, and the percentage of the bog class also

seemed to be a poor fit as turtles were found most often in marsh (emergent/wet meadow)

wetlands. Based on these conclusions, BRT modelling was subsequently applied to the

agricultural study site (Table 3-9).

Table 3-9. Response variables included in final BRT models of seasonal habitat selection for

Blanding’s turtles of the agricultural study site (Brant County), Ontario, Canada.

AGRICULTURAL SITE (Brant County)

Model

Type

SPRING

FALL

Relative

Influence

Relative

Influence

Boosted

Regression

Tree

Proximity (emergent veg)

Elevation

Proximity (water)

Land cover type

Proximity (road)

Percent (forest)

Proximity (wet meadow)

76.18

10.31

8.62

1.58

1.20

1.10

1.01

Percent (emergent veg)

Land cover type

Proximity (emergent veg)

Percent (wet meadow)

Elevation

Proximity (wet meadow)

Proximity (forest)

47.42

25.96

8.52

6.19

5.11

4.38

2.42

Similar to the relatively undisturbed park study site, environmental variables related to emergent

vegetation, elevation, and water were also selected for the fragmented agricultural study site.

Proximity to emergent vegetation was the strongest predictor by a wide margin, followed by

elevation and proximity to water. The late season model selected variables tending towards the

two dominant marsh communities (emergent vegetation and wet meadow), as well as elevation,

86

land cover type and proximity to forest. The proportion of emergent vegetation within a radius of

average daily movement was the strongest contributor followed by land cover type.

3.3.4 BRT model comparison

The four final BRT models were tested using 15% holdout validation points and all showed a

strong discriminatory ability in predicting the probability of Blanding’s turtle presence and

absence. Park study site models produced an area under the curve ROC value of 0.98 for the

spring and late summer seasons respectively, while agricultural site models demonstrated an

even stronger predictive power at 0.99 for both seasons (Figure 3-9). Predictive deviance was

lower in the agricultural site models in both the spring (0.19) and late summer (0.19) compared

to deviance form the park site in the spring (0.26) and summer (0.32).

Figure 3-9. BRT model results over the park study site (Algonquin Provincial Park) and

the agricultural (Brant County) study site, over both spring and late summer seasons.

Bars depict the area under the curve receiver operator characteristic value. Orange line

displays model predictive deviance.

87

The effect of a predictor on the dependent variable can be visualized by plotting fitted or partial

dependence functions which show the effect of a variable on the response. While these graphs

account for the average effects of all other variables in the model, they are not a perfect

representation but rather a basis for general interpretation (Elith et al., 2008). Responses for the

seven retained model variables indicate that at the start of the active season, Blanding’s turtles

occur in locations with a higher proportion of emergent vegetation, in close proximity to water,

uplands, emergent and bog wetlands (Figure 3-10, left). As wetlands in our study sites were

generally small, proximity to uplands was likely a results of this spatial characteristic (i.e. being

in a wetland is generally always close to uplands). They also occur more frequently at lower

elevations. Later in the season, partial dependence plots indicate that Blanding’s turtles still

occur in close proximity to water, and emergent aquatic vegetation, but also select areas near to

wet meadow marshes and areas which have a greater proportion of wet meadow and emergent

vegetation (Figure 3-10, right). Further, late season occurrence is associated with a strong

positive response towards lower elevations, as was mirrored in the spring model, but now a

strong negative response or avoidance of high elevation areas is also demonstrated for the late

summer model.

Partial dependence plots of environmental variables in the agricultural study site demonstrate

some subtle differences from the park population. Blanding’s turtles in this agricultural region

show a strong positive association with proximity to emergent vegetation and water, and a

similar but weaker association with proximity to wet meadow wetlands in the spring (Figure 3-

11, left). Unexpectedly, agricultural site turtles appear to select higher elevations and avoid lower

elevations. Lower forest cover is weakly avoided, and a complex response to roads is displayed.

Land cover types include a strong selection for wetlands and water, and a weak relationship with

forests while built areas, crop fields, and roads are avoided. Later in the season, agricultural site

turtles still occurred close to marsh wetlands, but also in areas with a higher percent of emergent

and wet meadow marsh vegetation (Figure 3-11, right). The same association with elevation

occurs, and there is a moderate selection for closer proximity to forests.

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SPRING MODEL LATE SUMMER MODEL

Figure 3-10. Partial dependence fitted function curves for variables retained in final BRT models

for the park study site (Algonquin Provincial Park), spring model (left) and late summer model

(right). Relative influence values in brackets. Y axes are on a logit scale and centred to have a

zero mean over the data distribution. Graphed lines (or dashes) above 0 on the y-axis indicate

higher selection probability over the range indicated by the x-axis, while functions below 0 on

the y-axis indicate lower selection probability (avoidance) over the range indicated on the x-axis.

89

SPRING MODEL LATE SUMMER MODEL

Figure 3-11. Fitted function curves for predictors retained in final BRT models for agricultural

study site (Brant County) spring model (left) and late summer model (right). Relative influence

values in brackets. Y axes are on a logit scale and centred to have a zero mean over the data

distribution. Graphed lines (or dashes) above 0 on the y-axis indicate higher selection

probability over the range indicated by the x-axis, while functions below 0 on the y-axis

indicate lower selection probability (avoidance) over the range indicated on the x-axis.

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3.3.5 Probability of occurrence maps

Model results were output to a raster map depicting probability of occurrence along a continuous

scale of 0 to 1.0 with the latter corresponding to regions of greatest suitability and thus highest

probability of occurrence (Figure 3-12).

Figure 3-12. Predicted potential probability surface for the occurrence of Blanding’s turtle

in the agricultural (Brant County) study area (left) and the park (Algonquin Provincial Park)

study area (right) developed using boosted regression trees. Seven final environmental

predictors were used in each model. Red areas indicate the highest probability of

occurrence.

3.3.6 Map accuracy

A binary threshold ≥ 0.6 was selected as best representative of Blanding’s turtle habitat with

regions equal to or above this value indicating suitable habitat, and values below 0.6

corresponding with unsuitable habitat (Figures 3-13 and 3-14; also see threshold sensitivity

analysis in Appendix Table B1). This value was deemed to be the most biologically accurate as it

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closely aligned with borders of preferred wetland aquatic vegetation communities. Using all

telemetry data (partitioned by season), map accuracy was calculated as the percentage of points

correctly located within areas defined as suitable by the BRT binary models. Map accuracy was

high across spring models (> 90%) suggesting BRT models are especially robust during this

early period (Table 3-10). Accuracy decreased in later season models from both study sites with

the late summer park site binary map correctly capturing 87.8% of telemetry points within

suitable habitat, and the agricultural site BRT map correctly predicting 65.5%.

Table 3-10. Map accuracy statistics based upon all telemetry (presence) data

partitioned by season and overlaid against binary BRT maps set with

threshold value of 0.6. Map accuracy value represents the estimated

probability of occurrence of Blanding’s turtles.

Study Area Season Map accuracy (%)

Park Site

(Algonquin Provincial Park)

Spring

Late Summer

90.8

87.8

Agricultural Site

(Brant County)

Spring

Late Summer

94.1

65.5

Regions of suitable habitat in the park study site depict an overall reduction in suitable habitat

from the spring to late summer. Likewise, suitable regions in the agricultural site not only

diminished by late season, but demonstrated complete loss of some habitat patches by late

summer. The total area defined as habitat in the spring park site model was 1.17 square

kilometres (3.35% of total study area) , and had reduced to 1.06 square kilometres (2.97% of

total study area) by late summer, corresponding with a 9.57% reduction in habitat. Agricultural

site spring habitat covered 2.18 square kilometres (2.35% of total study area) in the spring, and

had contracted to 2.04 square kilometres (2.20% of total study area) by the late summer. This

corresponded with a 6.42% reduction in suitable habitat.

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Figure 3-13. Threshold map based on habitat suitability ≥ 0.6 for the occurrence of

Blanding’s turtles in the park study area (Algonquin Provincial Park) during the spring

(top) and late summer (bottom) seasons with a map accuracy of 90.8% and 87.8%

respectively. Subset (at right) show pooled turtle presence locations plotted over binary

habitat map to demonstrate overlap with predicted areas.

93

Figure 3-14. Threshold map based on binary habitat suitability (≥ 0.6) for the

occurrence of Blanding’s turtles in the agricultural (Brant County) study area over the

spring (top) and late summer (bottom) seasons with a map accuracy of 94.1% and 65.5%

respectively. Subset (images on right) show pooled turtle presence locations overlaid

with predicted suitable habitat for each season. White circles indicate areas of suitable

habitat which have disappeared in the subsequent late summer season.

94

Discussion

This study began with an observation that Blanding’s turtles utilise their environment differently

depending on the time of year and the underlying mechanism, leading to an investigation into the

spatial and temporal dynamics of habitat use. Turtles are especially well-suited to multi-temporal

definitions of habitat due to shifting behavioural and physiological requirements, based in part on

an ectothermic lifestyle, as well as their dependence on freshwater wetlands which themselves

are characterized by recurring hydrologic fluctuations. This led to the development of seasonal

habitat models which demonstrated that suitable habitat does change across time. Therefore

remote sensing data, which can provide multi-temporal analysis of habitat, is critical in properly

assessing baseline habitat information needed for determining a species’ population health and

viability.

3.4.1 Biophysical Analysis

The biophysical input layers developed, represented field-measured variables corresponding to

turtle selection, and were derived from the same remote sensing imagery as landscape-derived

metrics of proximity and proportion of land cover type. While biophysical input layers were

modelled with an acceptable level of agreement with field-observed data, none contributed

strongly in final models which retained only the seven variables highest in predictive power. It is

possible that correlation with other predictors resulted in the attenuation of the biophysical

variable signal relative to all others. Water depth was expected to be highly significant, however,

proximity to uplands, and wetland classes such as emergent and wet meadow marshes, also

indirectly represent shallower waters which are preferred by Blanding’s turtles. Another possible

explanation is that the vegetation biophysical variables had a stronger signal within the wetland

habitat, and extrapolating to the entire study area may have reduced the strength of this signal. If

the investigation was constrained to areas within wetlands, it is likely that these biophysical

predictors would greatly increase in influence, and contribute more strongly to the models’

discriminatory power.

In all BRTs, the relative influence of the biophysical variables was low but still contributed to

the model. Some landscape category predictors such as the proportion of meadow, and barren

land within a 90 m average daily movement buffer did not contribute at all to model power. The

identification of variables that differed significantly between turtle and random points,

95

demonstrated that Blanding’s turtles select habitat based on different habitat variables during

different seasons. Results also confirm that these biophysical variables directly associated with

habitat selection, can be extracted from multispectral satellite data. Overall, the addition of

satellite-derived predictors has been found to be beneficial for modelling rare species when

compared with bioclimatic models (Zimmermann, Edwards, Moisen, Frescino, & Blackard,

2007) and thus should be considered in future modelling studies. Continuing work is necessary

to improve methods of biophysical feature extraction, and to identify the optimal scale for

inclusion into predictive models.

3.4.2 Satellite-derived predictors

While climatic and topographic data are more often used for predicting species distributions

(Bradley & Fleishman, 2008), such data are generally provided by third parties and at a

resolution too coarse for cryptic species such as reptiles and amphibians which respond to finer-

scale variations in their environment. Thus, it was not surprising that topographic information of

slope, aspect, and TWI, did not contribute strongly to any model. Yet interestingly, elevation was

retained as a top contributor in all final models. It is possible that at the landscape level, the

species overall preference for water and wetlands created a strong topographic signal that was

captured by the digital elevation model, despite the coarser (10 m) spatial resolution.

Landscape-level variables of proximity and proportion of a land cover type comprised the

majority of final predictors in each model, and were consistently associated with emergent and

wet meadow marshes across all landscapes and seasons. Several other studies have associated

Blanding’s turtle occupancy with these wetland components (Edge et al., 2010; Millar & Blouin-

Demers, 2011; Pappas & Brecke, 2009; Ross & Anderson, 1990) suggesting that the habitat

models developed in this study have captured general trends in habitat preference. It is important

to acknowledge that estimated errors identified from land cover maps produced in Chapter Two,

will impact variables derived from the final land cover products. The herbaceous upland class

demonstrated the lowest producer’s (0.57) and user’s accuracy (0.50) over the agricultural site

indicating that objects would have been misclassified as herbaceous upland (errors of

commission), as well as actual herbaceous upland objects that would have been classified as

another class (errors of omission). Fortunately, the majority of proximity and proportion-related

variables derived from these land cover maps in this current habitat suitability study, were

96

associated with emergent and wet meadow communities which were classified with a relatively

high accuracy ranging from 0.81-0.91 (PA) and 0.90-0.94 (UA). Still, a level of uncertainty

should be expected due to errors inherent to the mapping process. For future work, sensitivity

analyses should be conducted to better understand the impacts of mapping error on final habitat

suitability maps. Methods of improving classification accuracy should also be further examined

in order to minimize errors related to map-derived metrics.

The environmental predictors represent one out of a series of components which together make

up the predictive model. Efforts were made to ensure measurements of variables were both

representative of the population, time and space, but it is generally recognized that only in an

ideal world is the target species sedentary in locations that represent actual core habitat, and its

ecological requirements well known and measurable at the desired spatio-temporal scales

(Rushton et al., 2004).

3.4.3 Model comparison

While both the BRT and logistic regression approach performed well as models for ecological

forecasting, the BRT method allowed for easier interpretation of model terms and explanation of

interactions between terms. The ability to view the relative influence of each variable and to drop

those beyond a user-defined threshold was advantageous as was the fitted function graphs which

visually depict the relationship of each variable to the model outcome. Moreover, the

attractiveness of modelling techniques to conservation practitioners is in reality, affected by the

complexity (and availability) of the technique as well as the data required, and the BRT approach

is both widely available and relatively simple to implement.

3.4.4 Seasonal change

Our examination into the temporal shift in preferred habitat, demonstrated that turtles seek out

and utilise different environmental conditions throughout the year. This shifting selection

confirms the importance of temporal considerations in predictive habitat models, particularly for

species dependent upon rapidly changing ecosystems such as wetlands. Traditional habitat

models which present a single snapshot product may be missing out on important areas of habitat

that are needed at different times of the year. Landscape-related (distance, proximity, land cover

97

type) and topographic (elevation) predictors selected by the models did not differ remarkably

between seasons, but the majority of the change was captured by the seasonal land cover maps

which modelled the shift in the spatial distribution and extent of key wetland land cover types

(emergent marsh vegetation, wet meadow wetlands). Land cover maps depicted significant

transitions in wetland vegetation communities, particularly emergent and wet meadow marshes

which increased in coverage and density, in parallel with a decrease in open standing water as

the season progressed. The Blanding’s turtles dependence on open shallow water in the spring

will therefore shift spatially with availability as this association with water continues into the late

summer, but locations of remaining water change.

Wetlands reach their maximum inundation during the early spring after winter thaw, during

which time boundaries are clearly visible and wetlands are most easily classified (Ozesmi &

Bauer, 2002). This early leaf-off green-up period likely contributed to the higher map accuracy

achieved during the spring seasons over both study sites due to better visibility and subsequent

classification of wetlands. In the agricultural spring landscape, vegetation characteristics (height,

cover, and leaf area index) were identified as significant predictors during the early spring

season. This period corresponds with the start of the active period for Blanding’s turtles with the

onset of spring thaw and emergence from hibernation. During this time individuals forage widely

and search for mates as hydrologic connectivity is highest during this time of year. Blanding’s

turtles are opportunistic carnivores and animal prey (e.g. snails, crayfish, earthworms and

insects) typically comprises at least 85% of their diet with the remainder consisting of plant

matter (Rowe, 1992). Ephemeral wetlands hold abundant reservoirs of vegetation seed banks and

invertebrate eggs, that germinate and hatch with spring thaw and snowmelt (Gibbs, 1993; Zedler,

2003), while stands of floating, submergent and emergent macrophytes are often found in close

association with invertebrate communities (Voigts, 1976). Thus, wetland vegetation parameters

may act as a proxy for food resources. These ephemeral wetlands represent preferred sites for

foraging since they do not support larger aquatic carnivores (such as fish), allowing invertebrates

and larval amphibians to flourish in relative isolation (Semlitsch & Bodie, 1998). These

observations are supported by data from both sites which suggest that foraging behaviour, is

most likely the primary driver for early season selection of vegetation-related parameters by

adults, and also that the availability of water was not limiting at this time of year.

98

Conversely, late summer wetlands at both study sites were characterised by a decrease in water

availability and depth, and an increase in water temperature and vegetation growth which closed

early season aquatic passages. This reduction in open standing water, and increase in density and

extent of wetland vegetation probably contributed to the lower late summer map accuracy over

both study sites. Accurate wetland classification becomes challenging as the distinction between

wetland and upland boundaries blurs as water recedes and above ground vegetation thickens. The

mismatch between the timing of radio-telemetry observations, measurement of field biophysical

data, and satellite image acquisition also contributed to lower summer map accuracy for the

agricultural site due to a moderate drought experienced in southern Ontario in 2012. The earlier

spring months (April-June) were not largely affected, but the summer months (July-September)

experienced lower precipitation than normal, and higher average temperatures which would have

altered selection of habitat by Blanding’s turtles. However, this change in habitat selection was

not captured by the radio-telemetry data which had already been completed the year before.

Additional causes of the lower map accuracy across the agricultural late summer landscape could

be from hydrological modifications including pesticide and fertilizer run-off, and pumping for

crop irrigation that altered the natural timing of water fluctuations. Roads also likely negatively

affected the habitat quality of wetlands in this disturbed landscape through changes to hydrology

and water quality (Jones et al., 2000), introduction of chemical pollutants (Grant et al., 2003),

facilitation of the invasive species spread (Coffin, 2007), and an increase in human-induced edge

effects (Reed et al., 1996).

Physiologically, this late summer period represents a time of relative inactivity for Blanding’s

turtles, as mating and nesting activities have been completed and the relative abundance of

invertebrates has subsided. As ambient temperatures increase, the need to forage to sustain

metabolism and body temperature has decreased. Late summer also corresponded with the

maximum drying extent and for populations living in seasonal wetlands, permanent pools

become refuges for both adults and juveniles. Aestivation is a mechanism for coping with

wetland drying in freshwater turtles, and it involves the choice of remaining in a dried

environment and entering into a period of extended dormancy until the area re-floods (Roe and

Georges, 2008). While this strategy is sometimes practiced by smaller freshwater turtles such as

Painted turtles (Chrysemys picta), it is rare in Blanding’s turtles and has not been observed in the

northern (Edge et al., 2010) nor southern (Beaudry et al., 2009) range of their distribution. This

99

preference for aquatic habitats likely explains their close association with permanent pools in the

late summer (Joyal et al., 2001) as residency in these pools would reduce the risk of seasonal

drying. This dependency on water later in the active season, was mirrored by biophysical

measurements over both study sites which indicated that water depth was an important predictor

of late summer habitat. Aside from the prevention of desiccation, water also provides a medium

for thermoregulation, during the hot summer months. Previous studies on reptiles support the

selection of microhabitat based on structural and physical environmental characteristics that

affect thermoregulation (Rasmussen & Litzgus, 2010; Row & Blouin-Demers, 2006; Millar,

Graham, and Blouin-Demers, 2012).

3.4.5 Landscape Comparison

The role of landscape type on habitat availability was more complex. A greater overall decrease

in available habitat was expected by late summer in the agricultural site due to altered water

regimes resulting from intensified agricultural practices. While it has been documented that

overall wetland coverage in southern Ontario has been severely reduced from historic conditions

(Bardecki, 1982), less is known about wetland hydrological responses to anthropogenic

alterations at the landscape level. The argument has been made that wetlands in agricultural

landscapes have been subjected to water regimes that increase stability to be either continuously

wet or dry (Brock et al., 1999), which lead to a reduction in both the number and hydrological

diversity of wetlands in the landscape (Kath, LeBrocque, Miller, & Conservation, 2010).

Naturally occurring wetlands in the agricultural study site experienced extreme drying during the

late summer season resulting in some wetlands being completely devoid of any standing water

by August. However, man-made irrigation ponds retained water throughout the active season,

providing some form of refuge for turtles though conditions may not have been ideal. Combined

with water extraction for irrigation from both man-made ponds and natural wetlands, a practice

which itself is subject to local climate and rotating crop types, means that water-dependent

species in this area likely demonstrate a complex relationship with both the landscape and

ongoing agricultural practices. Telemetry data showed that turtles retreated to remaining areas of

deep water during the late summer, while a significant portion of wetland marsh habitats with

shallow open water during the spring season, experienced either complete or partial drying.

Further work examining the connectivity of the habitat nodes between preferred natural

100

wetlands, and irrigation ponds may yield important information on the permeability of the upland

matrix.

Conclusions

The use of satellite data proved to be highly valuable for building probability of occurrence (and

habitat suitability) maps for this threatened freshwater turtle species. Predictors derived from

species-centred land cover maps comprised the majority of selected model terms and

demonstrate that base information such as land cover can be highly informative if it matches the

scale and biology of the target species. Still further work using higher spatial or spectral

resolution sensors aboard airborne platforms may provide even clearer data on available habitat,

as well as locations of preferred nesting or hibernation habitat which could not be adequately

modelled by this study. Biotic relationships such as predation, or competition can also affect the

target species use of the habitat, but were not explicitly considered in this study. The inclusion of

co-occurrence data, if available, would more closely represent the realized niche of the study

species (Betts et al., 2014) as previous work has found that direct or indirect interactions between

turtle species may affect assemblage structure (Bodie et al., 2000).

The temporal shift in preferred habitat draws attention to the dynamic relationship between

species and landscapes over time. Drivers of such change include abiotic factors of both natural

and anthropogenic origins such as farming practices, and increased evaporation of water during

the summer, as well as biotic factors such as the behaviour and physiological needs of

Blanding’s turtles. Future studies therefore, should incorporate a multi-temporal approach to

mapping species occupancy. The cost of satellite remote sensing imagery may deter public land

management or non-profit conservation agencies from its use, however the cost is still

significantly lower than field surveying or aerial photographs (see Wei and Chow-Fraser, 2007

for a cost breakdown) and the advantages provided by multi-temporal analysis are significant

enough to justify the costs. For large mammals and wide-ranging avian species that operate at a

larger spatial scale of observation, publically available multispectral (30 m) Landsat data is

available at no cost and can provide a viable alternative to commercial high spatial resolution

imagery. Since neither plants, animals, nor the abiotic components of ecosystems are invariable

in space or time, it is important to consider alternatives to traditional static environment habitat

maps to better model the dynamic nature of Earth’s biodiversity.

101

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Chapter 4

Estimating seasonal landscape connectivity for Blanding’s turtles in a fragmented agricultural landscape

Introduction

Effective species conservation must include critical aspects of physiology, behaviour, and habitat

required for a species to persist. Consideration must include both time and space in order to

come to an understanding that reflects the true ecology of the species. Models which predict

species occurrence based on environmental variables are important for identifying key habitat

regions to inform conservation policies, prioritize for surveying of rare or cryptic species, and to

monitor changes over time. However, with the increasing urbanization and widespread alteration

of the landscape by humans, it cannot be assumed that a species can exist in isolation in patches

of suitable habitat. Connectivity therefore, defined as the ease with which individuals can move

within the landscape (Merriam, 1984), is considered of paramount importance for species

survival (Fahrig & Merriam, 1994; Calabrese & Fagan, 2004). At the immediate time frame, the

ability to move across the landscape can be driven by basic physiological needs such as the

search for food, water, or nesting sites, as well as behavioural drivers such as mate searching, or

predator avoidance. On a more intermediate time-scale, stochastic events such as a drought, can

drive animals to search for alternate habitat, while the long-term trend of warming, may push

species to shift entire ranges (Nuñez et al., 2013).

One of the most commonly encountered human-constructed landscape features that affect

wildlife movement are roads. Roads present a significant barrier to movement for many species

(Forman, 1998; van der Ree et al., 2011), although slow-moving animals suffer greater negative

effects due to increased mortality with vehicle collisions (Coffin, 2007). Roads are described as

the single most destructive element in the process of habitat fragmentation (van der Ree et al.,

2011) and are the only spatial element that can be found in essentially all landscapes (Forman,

1998). The negative effects of roads extend beyond the impediment of movement, and include

changes to hydrology and water quality ( Jones et al., 2000), introduction of chemical pollutants

(Grant et al., 2003), facilitators of the invasive species spread (Coffin, 2007), and an increase in

human-induced edge effects (Reed et al., 1996). While there are a range of modified landscapes

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that can affect movement probability in different ways, agricultural landscapes present additional

complexity due to their temporally dynamic nature, both within and between years (Burel &

Baudry, 2005). Short term annual changes include the rapid growth of crops and varied

harvesting schedules, while inter-annual crop succession and rotation will change spatially as

well as in extent, presenting an ever shifting mosaic of land cover types for wildlife to navigate.

Moreover, the agricultural landscape also includes farming structures, and farming systems (e.g.

irrigation, fertilization, harvesting equipment) that interact closely with the landscape (Burel &

Baudry, 1995). Agricultural land occupies approximately half of earth’s habitable area, or 38%

of the planet’s land surface (Clay, 2004), and the effect on species habitat loss may be

disproportionately larger than the physical loss in area since agricultural spread tends to occur in

regions with particularly high biodiversity (Scharlemann, et al., 2004).

Efforts to quantify such complex landscapes in terms of both structural and functional

connectivity have resulted in a multitude of modelling approaches, though there is little

consensus on the best general approach (Calabrese & Fagan, 2004). Simple measures of

connectivity have been estimated through the use of patch-based indices which focus on the

distribution of habitat rather than the underlying matrix . Measures of nearest neighbour distance

are easy to obtain but can be over simplistic (Calabrese & Fagan, 2004) and generally do not

perform well compared to other metrics (Bender et al., 2003). Spatial pattern indices quantify the

spatial arrangement as well as the number, size, and shape of habitat patches under the

assumption that these metrics affect species movement (Calabrese & Fagan, 2004) and are easy

to apply across large regions. Popular graph-theoretic approaches include resistance based

models such as least-cost pathways (Adriaensen et al., 2003) and circuit theory (McRae et al.,

2008) which analyse the cost of movement between patches to identify optimal linkages or

corridors. Likely the most accurate estimates of connectivity are provided by empirical data on

actual observed movements of individuals of a species (e.g., Meegan & Maehr, 2000) but these

methods are labour-intensive and only applicable to species where movement rates are

sufficiently high to allow adequate data sampling (Calabrese & Fagan, 2004). Regardless of the

selected methodology, the same landscape can yield differing degrees of connectivity depending

on the characteristics of the habitat, matrix, and species (Schumaker, 1996; Tischendorf &

Fahrig, 2000). As disturbances derived from both natural and anthropogenic sources can operate

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at more than one spatial and temporal scale (Fortin & Agrawal, 2005) analyses of connectivity

must also consider these important dimensions.

From a conservation and management perspective, information on landscape connectivity is

sorely needed for sensitive and declining species persisting in disturbed environments.

Identifying priority areas for improving connectivity is needed to decrease mortality, and

increase the potential for dispersal, and immigration which drive basic processes such as gene

flow and recolonization after disturbances and are critical to the maintenance of population

stability.

In this study, the focus shifts to estimating movement potential of Blanding’s turtle (Emydoidea

blandingii) in the fragmented agricultural study area which represents the dominant landscape of

southern Ontario. Extensive drainage of wetlands for agricultural purposes has occurred

historically with an estimated minimum of 80% of wetlands lost since European settlement

(Bardecki, 1982). This region is also considered a significant “hotspot” of herpetofaunal

diversity and species at risk (Lesbarrères et al., 2014) and represent a much understudied taxa in

the field of urban wildlife research (Magle et al., 2012). As wetlands typically occur in discrete

patches surrounded by a varying matrix of upland habitat, many wetland-dependent organisms

consequently live in multiple local populations sustained through movement of individuals

among patches (Gibbs, 2001). Reptiles and amphibians represent a unique taxa in this regard as

they are limited in their ability to disperse or migrate to more suitable environs compared to birds

and mammals. Turtles exhibit habits that make them especially sensitive to landscape

modification (Fahrig & Rytwinski, 2009; Gibbs & Shriver, 2002) including delayed sexual

maturity, extreme longevity, and low annual fecundity (Congdon et al., 2008; Marchand &

Litvaitis, 2004). The Committee on the Status of Endangered Wildlife in Canada (COSEWIC)

has given a designation of at-risk to seven of eight native Ontario turtles with some form of

habitat fragmentation and development identified as a primary cause of decline for every species.

Past work on Blanding’s turtle habitat use and ecology has found they can utilise as many as 20

unique wetlands annually, and as few as one (Beaudry et al., 2009), and travel extensively across

the landscape where they frequently overlap with areas of human activity (Congdon et al., 2011),

making them an appropriate model for examining landscape connectivity.

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The goal of this study is to examine the seasonal change in landscape connectivity for Blanding’s

turtles in a typical southern Ontario landscape to better understand how freshwater turtles move

in heavily disturbed environments. To achieve this objective, predictive models are developed

across two biologically meaningful periods during the active (non-hibernating) season. Three

different measures of connectivity are applied to characterize the connectivity of the landscape,

and to compare results amongst different approaches. Finally, a set of recommendations for

improving connectivity for turtles in the study area are compiled, taking into account shifting

core habitat, and behavioural and physiological drivers of movement.

Methods

4.2.1 Study Area and Blanding’s Turtle Population

This study focused on the connectivity of remnant wetlands located in my agricultural study site,

which represents the dominant landscape type overlapping with the Blanding’s turtle range in

Ontario (Figure 4-1). Along with the provincially significant Oakland Swamp, several smaller

wetlands of variable size and shape were also distributed throughout the study area including

marshes, swamps, and shallow open water surrounded by varying configurations of upland

matrix (Figure 4-2).

Figure 4-1. Map depicting Blanding’s turtle range in North

America, and study site in southern Ontario. Map image licensed

under Creative Commons 3.0.

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The study area was dominated by active agricultural crops such as corn, soy, tobacco, ginseng

and millet embedded in a matrix of semi-natural cover types such as mixed forest buffers,

hedgerows, mixed meadows, riparian vegetation, and aquatic features, including remnant natural

wetland (ponds, emergent, sedge and wet meadow marshes, scrub-shrub and willow swamps),

and man-made irrigation ponds. Roads bisect the entire study area which encompassed 90 km2.

A population of Blanding’s turtles persists in the landscape, though road mortality has been

reported in the study region. Total population size and status are unknown, but estimated at over

100 individuals including over 30 reproductive females which makes this one of the larger

documented populations in the province. Paved and unpaved roads bisect the entire study area,

including the 840 ha wetland, and high traffic volumes (>400 vehicles per hour during peak

daylight hours) have been documented across the main provincial highway that bisects the study

region.

Figure 4-2. Examples of wetland habitat found in the agriculturally-modified landscape

depicting habitat patches bisected by roads (A), isolated wetlands surrounded by

farmland (B), man-made irrigation ponds serving as temporary refuge (C), and natural

corridors bisected by multiple roads (D). Imagery: © Digital Globe 2015, Google Map

data 2015.

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4.2.2 Seasonal Land cover Maps and Habitat Nodes

Seasonal land cover maps over the agricultural site were developed using the GEOBIA approach

from Chapter Two and are shown in Figure 4-3. Major changes from spring to late summer

include a reduction in wetland habitat, particularly emergent marsh and open water habitats as

well as a rapid increase in crop growth from barren soil in the spring (yellow) to closed canopy

plots of land by the late summer (orange). Some misclassification occurred in the late spring

landcover map (Figure 4-3, right) between the grown crop field and forest classes (white arrows).

Figure 4-3. Seasonal landcover maps over the agricultural site developed from multispectral

satellite imagery acquired in the spring (left) by GeoEye1 (April 2012) and late summer (right)

by WorldView2 (September 2013). Images were classified using a multi-scale geographic

object-based image analysis (GEOBIA) approach, and the nearest neighbour classifier.

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Habitat nodes (or patches) were identified in Chapter Two using a boosted regression tree

approach to model species occupancy across the spring (April-June) and late summer (July-

September) periods defined in this study. Species presence was identified through radio-

telemetry observations, and environmental variables were derived from high spatial resolution

satellite-data, seasonal land cover maps and a provincial digital elevation model. Habitat nodes

were defined by setting a thresholds of estimated probability of occurrence (≥ 0.6) following a

sensitivity analysis conducted in the previous chapter (Figure 4-4, pink polygons). Wetlands

below an area threshold of 25 m2 were filtered out of the map as they generally represented

mapping error and did not constitute actual wetland habitat. The amount of available habitat

decreased by the late summer predominantly due to loss of available standing water as well as

significant changes in wetland vegetation that closed off early spring aquatic passages.

4.2.3 Resistance Layers

In the context of animal movement, the term ‘resistance’ characterizes the willingness of an

organism to cross a particular environment, and ideally should incorporate contributing factors

such as physiological costs, and features that reduce survival (Zeller et al., 2012). The basic

assumption is that species will move across the path of least resistance (Beier et al., 2009) , and

that this selection can be accurately estimated and modelled. Methods of estimating resistance

surfaces include expert-based approaches (Chardon et al., 2003; Moore et al., 2011), empirical

methods such as detection (Bartelt et al., 2010; Wang et al., 2008), and genetic routes (Epps et

al., 2007; Castilho et al., 2011). Empirical methods of estimation can be challenging because

animal movement is one of the most difficult behaviours to quantify (Zeller et al., 2012). While

genetic data has been used for investigating mobile species such as wolverines (McRae & Beier,

2007) and, also for species with low vagility, but with a high generational turnover time such as

amphibians (Moore et al., 2011), the life history characteristics of freshwater turtles are not well-

suited to this approach. Traits such as delayed sexual maturity, high nest mortality, and extreme

longevity (Congdon et al., 2008; Marchand & Livaitis, 2004) make them poor candidates for

understanding connectivity through gene flow. In this study resistance surface parameters were

estimated using an expert-literature based approach. Three experts from the Toronto Zoo and

Acadia University were presented with data on landscape features encountered in the non-habitat

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matrix in the study area, and were asked to assign a value between 0 and 100 relating to two

aspects of Blanding’s turtle movement.

behaviour (see Appendix B2). First, the likelihood of travel or the animals’ willingness to cross

through a landscape feature. This category addressed the seasonal aspect of the species’

behaviour by considering the likelihood of whether an individual would traverse a region given

its behavioural and physiological needs of that period of the active season. Secondly, the

probability of success or the turtle’s physical ability to successfully cross the landscape feature

was estimated. For example, if a turtle has prior knowledge of a habitat patch that it wants to

access, it will willingly cross a road barrier in a relatively straight line trajectory to access said

patch. While the willingness to travel may be high, the ability to physically cross this road

feature would be low as road mortality is very probable for slow-moving turtles.

Final resistance values were averaged across all respondents for each land cover type. Land

cover maps were reclassified according to resistance values to produce new base maps for each

season (Figure 4-4).

Figure 4-4. Resistance maps developed from expert-based knowledge. Habitat nodes shown

in pink for the early spring (left) and late summer (right). Higher resistance is shown in

lighter colours, and lower resistance in darker colours.

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4.2.4 Connectivity Modelling Overview

Three metrics to quantify, predict and visualize ecological connectivity for turtles in this

landscape are employed. No single measure of connectivity can reliably reflect all dispersal

characteristics of the species, which may vary by age cohort, gender, and prior experience

(knowledge of the landscape). Older individuals with prior knowledge of alternate habitat

patches may follow a direct pathway towards that patch, which relates to models based on

Euclidean distances (e.g., least cost paths). On the other hand, juveniles which do not emerge

from the shelter of aquatic habitats until much later in life, would follow a less purposeful and

meandering path before encountering a favourable patch. Even if environmental cues such as

water orienting (McNeil et al., 2000) were used, animals would still need to reach a location in

the landscape (e.g., higher elevation, or reduced cover) in order to use such signals. Moreover,

from an application perspective, there is a relatively poor ability to compare between different

metrics (Kindlmann & Burel, 2008), therefore a multi-technique approach can provide more

information to allow for comparison with other studies and landscapes. Nesting females require

special considerations as a result of extended searching behaviour and selection of terrestrial

oviposition sites. As nesting site selection is dictated by habitat characteristics other than those

governing core wetland selection, the movements of nesting females were not considered in this

study.

Functional connectivity was mapped using the graph theory approach to predict least-cost

pathways, general corridors across the study site, and patch-based indices to assess the relative

influence of specific nodes and links. Functional spatial graphs consider both the structural

characteristics (e.g. distance) and quality of the intervening non-habitat matrix (e.g. road barriers

) that may impede movement for a species (Fortin et al., 2012). Connectivity was analysed over

two periods corresponding with early active season emergence from hibernation, foraging, and

mating (April-June), and later season aestivation and migration to deeper pools for

thermoregulation and preparation for hibernation (July-Sept). The ability to move across the

landscape is important for short-term needs such as finding food, in response to inter annual

variation in precipitation (e.g., drought years), and as a result of long-term climate change

forecasts which may drive populations slowly northwards. As all types of movements were being

evaluated, a threshold of maximum distance beyond which the turtles would not disperse, was

not set. Long distance (> 1 km) movements by Blanding’s turtles have been documented in the

115

literature, yet are not a common occurrence. Instead turtles will often use small patches of water

(e.g. irrigation ponds) as ‘stepping stones’ which increase the overall distance they can travel,

allowing connectivity to be assessed across our entire study area.

Least-Cost Modelling. In least-cost modelling, a single path of least resistance between two

nodes, or habitat patches was identified based on an underlying resistance surface which

characterizes the non-habitat areas (Adriaensen et al., 2003). This approach assumes an

individual has prior knowledge of the landscape being traversed (McRae et al., 2008), which

characterizes the behaviour of older adults Blanding’s turtles in the study area who have

accumulated a greater familiarity with the landscape. This allows the selection of the shortest

(based on Euclidean distance) path of least resistance towards a chosen destination. Identification

and mapping of LCPs was implemented in Linkage Mapper 1.0.8 (McRae & Kavanagh, 2011)

through ArcGIS 10.2. A resistance raster was developed from land cover maps at a spatial

resolution of 2.0 m which characterized the landscape in early spring (April, 2012) and late

summer (September, 2013). Land cover maps were developed in Chapter Two, using a

geographic object-based image analysis (GEOBIA) approach to segmenting and classifying

multispectral imagery. While the LCP method has been used often, it is generally accepted that

individuals may not travel through a single pathway, nor would they always traverse the shortest

direct route possible. Therefore, a complementary alternative to the single pathway approach, is

the modelling of multiple corridors using circuit theory.

Circuit Theory Corridor Modelling. Circuit theory is based in Markovian random walk theory

which describes every movement as a random choice with an equal probability of moving in any

direction (McRae et al., 2008) and is particularly suited to species with random dispersal patterns

(Rayfield et al., 2011). With this approach, total resistance or conductance across the landscape

is characterized, as opposed to single linear pathways. Within this framework, an individual is

not assumed to have prior knowledge of the landscape, which appropriately models the

behaviour of both inexperienced juvenile Blanding’s turtles, immigrants into the study area, and

long-distance dispersers in response to stochastic events. Similar to LCP modelling, this

approach maintains the premise that movement can be impeded by various landscape factors, but

it also considers characteristics that may facilitate or conduct movement (Howey, 2011). Thus, a

circuit theory model predicts an array of circuits, or corridors that facilitate movement, which

incorporates additional spatial information about the non-habitat matrix (Rayfield et al., 2011)

116

while not requiring adherence to the shortest Euclidean distance (McRae et al., 2008). Circuit

modelling was implemented through Circuitscape 4.0.5 (McRae & Shah, 2009) using the

pairwise mode which considers conductance across all pairs of nodes in the study area.

Resistance-conductance surface layers were resampled to 10 m due to processing constraints,

though all relevant landscape elements were maintained.

Patch-based Indices. In patch-based models, it is the relationship among patches of habitats or

nodes, that is modelled (Galpern et al., 2011) and not the composition or structure of the non-

habitat matrix. In patch-centric approaches, links between habitat patches commonly represent

geographic distance with the probability of connection decreasing or disappearing below a

movement threshold relevant to the target species (Galpern et al., 2011). Previous applications

of this approach include the identification of patches which play a higher role in connectivity,

which can be used to assign conservation priority for a declining species (Pascual-Hortal &

Saura, 2008). In this study the importance of each wetland habitat patch in the network was

assessed using a measure of the relative change in the Integral Index of Connectivity (dIIC:

Pascual-Hortal and Saura, 2006) given by the equations:

�� =

∑ ∑ "# × "%&%'(

&#'(

(()&*#%)

+,-

.�� / (%) = �� − �� 123452,/

�� × 100

In equation (1), n is the total number of nodes in the landscape, ai and aj represent the area of

the wetlands i and j, nlij is a measure of the number of shortest connecting links between

wetlands, and AL is the total size of the landscape. If two wetlands are not connected, then nlij =

∞, and IIC = 1 if the entire landscape is occupied by habitat. In Equation (2), IICremove,k

represents the IIC value when wetland k is lost from the habitat network. The value of dIICk is

the percent reduction in IIC that occurs when wetland k is lost (i.e., the importance of wetland k

in the network). The dIIC model is a binary connection model which considers two nodes as

connected or not, based on a specified threshold dispersal distance (Saura & Torné, 2009). The

threshold was set to the maximum length of the study area to allow all connections to be

considered since turtles generally do not disperse according to a one time long-distance event for

which a threshold can be clearly identified, but rather move shorter distances among patches

(2)

(1)

117

which are used as stepping stones. The binary approach was chosen over the probabilistic

version of the connection model as the use of interpatch distance as a decreasing exponential

function did not appear to emulate the behaviour of our study species, for which distance is not

necessarily a limiting factor to movement. IIC and dIIC values were calculated using Conefor 2.6

software (Saura & Torné, 2009). These indices represent the importance of each individual

habitat patch for maintaining overall landscape connectivity, which allows the ranking of patches

according to their contribution.

An overall index of connectivity was also calculated to compare between seasons over the study

site using an Equivalent Connectivity value of the dIIC (Saura et al., 2011). In this metric, the

area of the patch was used as the node attribute, and was calculated as the square root of the

numerator of the IIC index (equation 1, above). The EC(IIC) metric is defined as the size of a

single habitat patch that would provide the same value of the IIC metric corresponding to the

actual habitat pattern in the landscape (Saura & Torné, 2009).

4.2.5 Evaluation

Animal movement is one of the most difficult behaviours to quantify and observe (Zeller et al.,

2012), therefore evaluating the accuracy of connectivity models can be challenging. Due to their

cryptic nature and low vagility compared to more mobile taxa, empirical data on movement

selection can be difficult to assemble for turtles. Moreover, the longevity of many turtle species

means that long-distance movements may occur only a few times in the animals’ lifetime; an

infrequent event which radio-telemetry studies of typically one or two active seasons will likely

not be able to capture. In this study, site specific findings pertaining to known barriers, field

survey sightings, and radio-telemetry data are used to provide support or contrast results of

connectivity models and indices.

Results and Discussion

4.3.1 Least-Cost Pathways & Patch-based Indices

Overall, the number of habitat patches and links (LCPs) was higher during the spring compared

to late summer (Table 4-1). Average patch area was 2.28 ha for the spring, and decreased to 1.20

118

ha in the late summer (p < 0.05), while average spring LCP length (998 m) increased by late

summer (1185 m; p < 0.05). LCPs differed in the number, length, and directionality of links

with the spring model demonstrating a more complex network (Figure 4-6).

Table 4-1. Summary of habitat patch, LCPs and overall landscape indices for

the spring and late summer connectivity models († p < 0.05)

Spring Late Summer

Number of Patches 101 56

Average Patch Area 2.28 ha (SE±0.44) 1.20 ha (SE±0.29) †

Number of LCPs 209 119

Average LCP Length 998 m (SE±61.5) 1185 m (SE±84.9) †

Overall Index of

Connectivity (EC[dIIC])

130.59

37.15

This change in seasonal LCP network complexity is partly a logical consequence of the greater

number of habitat patches existing during the early season, since ephemeral wetlands are most

abundant during this time of year (Zedler, 2003), and Blanding’s turtles are known to exploit this

resource for mating and foraging (Beaudry et al., 2009). Behavioural drivers of movement

across the landscape are also higher in the spring due to the turtles’ physiological requirement of

quickly increasing body mass through consumption after long months spent in hibernation. As

the season progresses, and temperatures increase, food requirements of ectothermic turtles will

decrease along with activity and these physiological and behavioural aspects were incorporated

into resistance surfaces from which final LCP models were derived.

Patch-based indices were determined using the overall index of connectivity (EC[dIIC]), which

was higher in the spring season (130.59) indicating a higher level of connectivity compared to

the late summer (37.15). Interestingly, dIIC values identified two different habitat patches from

each season, as contributing the highest to overall network connectivity. During the spring, a

patch in the northern region was identified (Figure 4-5; left (a)), while influence shifts

dramatically to the far south of the study area by late summer (Figure 4-5: right (c)). This

southern node is known to support open marsh habitat and permanent deeper water throughout

the active season. It is also the only other patch where consistent observations of a smaller

population of Blanding’s turtles have been confirmed.

119

In both seasons, the primary habitat patch known to support the source population in this study

area was identified as one of the top three patches positively influencing connectivity (Figure 4-

5: left and right (a)). This is likely a result of the larger size of this patch compared to others as

well as its close proximity to many other identified patches. Preferred habitat of open shallow

water, and marsh habitat with abundant aquatic vegetation as well as standing water throughout

the active season were characteristic of this node.

Figure 4-5. Predicted least-cost pathways (dashed lines) connecting spatially shifting

seasonal habitat nodes during the spring (left) and late summer (right) season. Nodes

coloured according to relative contribution to connectivity of the network (more

important nodes are red, least important nodes are blue. Top three nodes with highest

dIIC scores (∆ Integral Index of Connectivity; nodes for which removal would most

strongly reduce connectivity) are labelled. Higher dIIC scores indicate higher

importance.

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A puzzling finding from the dIIC analysis was that two spatially disparate habitat nodes were

found to contribute the strongest to connectivity across each season. This discrepancy cannot be

easily explained however, it could be postulated that the change in spatial configuration of nodes

between seasons contributed to this result. The dIIC values utilise a measure of Euclidean

distance between nodes in the analysis, therefore a change in distance, and quantity of links

between nodes, would result in different nodes becoming central to the network. The circuit-

based model may elucidate some possibilities for this result, as the early season model

demonstrated a marked increase in conductance in the north western quadrant of the study area

which decreased by late summer and shifted instead towards the southern region. These

complementary findings highlight the advantage employing multiple metrics to assess

connectivity.

If connectivity potential was higher in the southern patch during the late summer, this would

explain the shift in importance. Another possible explanation is the acquisition of satellite

imagery during different years. The spring image was acquired in April of 2012 while the late

summer image was taken in September 2013, allowing for either further human or climate-

mediated changes to the landscape, to alter the character of resistance surfaces or habitat nodes.

Regardless of the underlying cause, this finding does lend support to the notion that landscapes

are highly dynamic in nature.

4.3.2 Circuit-based corridors

Current maps show several corridors of high conductance (red areas) that represent potential

movement routes for Blanding’s turtles (Figure 4-6). The spring corridors closely match the

shape of both the Oakland swamp, and the creek systems that sustain this wetland, indicating

primary routes that may facilitate movement. In this context, roads that bisect these natural areas

are highlighted as linear features of lower conductance (yellow or blue linear features). Circuit-

based corridors paralleled least-cost paths in relation to natural areas, but there was less

agreement between paths and corridors crossing the upland matrix.

Overall, the spring circuit model displays a greater proportion of the landscape categorized as

high conductance, particularly in two regions to the northwest and southeast of the study regions

(Figure 4-6, boxed regions a and b) while the late summer model identifies one region of higher

121

conductance in the southwest quadrant of the map (Figure 4-6, boxed region c). The majority of

the disturbed (or altered) portions of landscape are categorized as moderate to low conductance

while sections of semi-natural features (e.g., creeks, riparian vegetation) are depicted as having

high conductivity.

These results appear to more closely approximate what are believed to be actual corridors

through natural landscape features bordering waterways. Notably, there were no clear corridors

defined through the patchwork of agricultural crops, indicating poor selection of cover types for

movement. Paths of high conductance suggest a central artery running through the Oakland

swamp and southwards into the tributary and larger ponds. A secondary corridor can be

visualized following the creek system across the eastern half of the study area and southwards to

connect with the same ponds in that region. Circuit-based current maps have been used

frequently in mapping gene flow for large mammals across large regions such as mountain lions

in southern Brazil (Castilho, Marins-Sá, Benedet, & Freitas, 2011), and wolverines across the

northern U.S. Rockies (Schwartz et al., 2009), however these results demonstrate that this

approach can have equal value when applied across the landscape scale, and for species that

demonstrate a year-round close association to the landscape.

122

Figure 4-6. Results of circuit-based models showing corridors of highest conductance

during the spring (left) and late summer (right). Areas of higher conductance are shown in

red, which denote predicted corridors for Blanding’s turtles. Boxed regions (a, b, c) show

areas of significant change in conductance between maps.

4.3.3 Barrier Mapping

143 instances were identified where LCPs crossed over a paved road in the spring connectivity

model, and 111 instances in the late summer model (Figure 4-7) using the Intersect tool (ArcMap

10.2). Spring LCPs also crossed through 82 residential/suburban parcels with an average overlap

length of 12 m (SE±1.5m). Late summer LCPs crossed through 158 parcels with a greater

average overlap length of 30.9 m (SE±4.09 m). There were an average of 143 intersections with

LCPs per road during the early season period, and 111 by the end of the season. Roads represent

a significant barrier for turtles (Gibbs & Shriver, 2002), particularly for gravid (nesting) females

123

(Steen et al., 2006; Gibbs & Steen, 2005), which also represent the most important cohort for

maintaining population stability (Congdon et al., 1993).

Data obtained from radio-telemetry plotted over spring circuit-based corridors shows a direct

intersection of a major highway with a correctly identified corridor as well as confirmation of

road crossing through analysis of individual movement based on telemetry data (Figure 4-8).

This intersection of road, corridor, and significant habitat nodes (as identified through patch-

based indices and field-telemetry data) was the site of multiple road mortality incidences prior to

the initiation of an ecopassage project which resulted in the installation of exclusion fencing in

2011. The fencing effectively acted as a funnel to coerce turtles through the existing aquatic

culver to prevent futher road mortality. Although public awareness and scientific research on the

Figure 4-7. Barrier map depicting intersect points between LCPs and paved roads in the

spring (left) and late summer (right) connectivity models.

124

effect of roads on wildlife has increased, proven methods for identifying target locations for

mitigation (e.g., ecopassage or exclusion fencing installation) are still lacking.

Overall, the utility of the LCP approach in identifying locations where a road segment presented

a significant barrier was limited, its production of discrete linear pathways allowed the

calculation of supporting metrics such as average path length, and the intersection with built

features. The actual location of intersections provided little usable information but the quantity of

intersect points illustrated on a larger scale that access to all habitat patches in this landscape

would require the crossing of numerous roads. Data from field surveys and local residents

support the observations of numerous road mortality incidences in the study site. Moreover, data

from radio-telemetry also identified multiple road crossings from tagged individuals, including

Figure 4-8. Spring Circuitscape map combined with road network (a) and spring (April-June)

Blanding’s turtle telemetry points (b) showing turtle locations on either side of the road. The

image in (c) depicts the estimated movement pathway for a single adult Blanding’s turtle

demonstrating that turtles must cross roads to access preferred habitat.

125

one adult male who crossed a road at least thirteen times within one active season. Other

freshwater species such as the Painted turtle (Chrysemys picta) have also demonstrated a

propensity for crossing roads, and in straight-line trajectories towards known habitat patches,

which has been found to negatively affect connectivity for this species (Bowne, Bowers, &

Hines, 2006).

Conclusions

In this study, three methods to quantify and visualize connectivity potential for Blanding’s turtles

were used across two seasons to provide a better understanding of how turtles may move through

an agriculturally modified landscape. Circuit theory, least-cost modelling, and patch-based

indices each provided different yet complementary information towards this understanding.

Results indicate that that connectivity changed temporally and spatially across the two time

periods examined, and most likely this change in movement potential occurred along a gradient

rather than across a clear threshold.

Overall, the least-cost approach had limited general applicability as there was no empirical

evidence to support the use of such extensive networks of LCPs. In addition, the multitude of

intersections with barriers would point towards a requirement for an equally numerous quantity

of ecopassage structures, which is an infeasible approach. The least-cost approach has been

criticised for incorrectly assuming that animals will always make movement choices that are the

most optimal, and for indirectly suggesting that if optimal routes are protected, development may

continue elsewhere (Fahrig, 2007). Moreover, various factors such as temporary blockages,

presence of predators, or individual plasticity in travel preferences can cause divergences from

optimal path selection (Howey, 2011). Other studies comparing connectivity measures have

converged upon a similar conclusion when modelling movement for amphibians (Moore,

Tallmon, Nielsen, & Pyare, 2011) and wolverines (McRae & Beier, 2007), although in

circumstances where species’ are naturally distributed along narrow linear stretches of habitat,

they may exhibit movement characteristics more suited to this single pathway LCP approach

(Schwartz et al., 2009). However, used in conjunction with patch-based indices and barrier

mapping, LCPs can expose general trends towards characterizing the landscape. The circuit-

based approach appeared to best model the potential routes that turtles might traverse, and

provided a better overall characterization of the entire study area. The use of multiple approaches

126

to understanding landscape connectivity for Blanding’s turtles is strongly supported by this

study, though accurately capturing the processes that affect movement selection in such a

dynamic landscape, remain challenging.

Despite achieving support for connectivity measures applied in this study, the larger goal of

deriving accurate spatial information to inform actual ground level mitigation actions will be

challenging in this type of landscape. Human-modified landscapes tend to undergo continual

modifications, which can rarely be predicted. This is certainly true of agricultural landscapes

where land use decisions rest primarily on the shoulders of farmers and landowners (Burel &

Baudry, 1995). The rate at which human-related modification occurs is often at a speed and

frequency too great for animals to adapt behaviourally to select the safest paths (Fahrig, 2007).

This may result in the opposite effect whereby a mismatch in cues results in the animal selecting

non-optimal human-altered cover types, and falling victim to an ecological trap (Robertson &

Hutto, 2006). Turtles in fragmented landscapes already demonstrate this behavioural

characteristic when gravid females choose to nest along roadside shoulders, resulting in high

mortality for both the female, and her offspring (Steen et al., 2006). Challenges related to

methods of connectivity analysis include the need for more information on the selection of an

appropriate scale for modelling turtle movements.

From a management perspective, the baseline action that must first occur is the enforcement of

biologically relevant wetland-protection laws that conserve wetland mosaics and facilitate

movement between them. Protection must include small and ephemeral wetlands which represent

critical early spring foraging and breeding habitat for many reptiles and amphibians, and for

which a current weakness in the Ontario Wetland Evaluation System (OWES) is the exclusion of

wetlands < 2 ha from evaluation, regular monitoring, and hence protection (Schulte-Hostedde et

al., 2007). Roads should not be constructed across larger habitat patches which tend to support

the larger source populations of wetland-associated species such as Blanding’s turtles. Where

roads already exist, ecopassages should be installed along with exclusion fencing to funnel

turtles into these structures and increase their effectiveness. Concurrently, natural corridor

enhancement and creation should be planned to connect patches at multiple spatial scales

ensuring animals have the ability to disperse across a larger area as needed.

127

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Chapter 5

Thesis Summary and Conclusion

Chapter Synthesis

A central objective of this dissertation was to unify the strengths of remote sensing science with

knowledge of species biology to obtain information on the population status of a declining and

data deficient species. Baseline information such as this has never been more important as

Earth’s surface is being increasingly altered and stressed by anthropogenic activities. Continual

modifications to the environment ensure that the relationship between organisms and their

surroundings will also be modified, and thus novel approaches are needed to model these

changes.

In Chapter Two I described a simple yet efficient methodology for mapping wetlands across

landscapes of varied heterogeneity. High spatial resolution satellite data and the GEOBIA

approach can be combined to provide a sound methodology for characterizing whole wetlands

and individual wetland classes. The GEOBIA approach specifically, was very appropriate for

wetland detection as it allowed for a nested multi-scale approach to constrain classification of

wetland components to within defined wetland boundaries. In regards to landscape variations, a

more heterogeneous landscape may negatively affect accurate wetland classification due to

increased spatial and compositional complexity. Specifically, rural landscapes presented

challenges due to the large proportion of vegetated upland classes of both anthropogenic and

natural origin that reduced segmentation accuracy and resulted in greater spectral overlap during

the classification. Multi-temporal data may be able to improve identification of spectrally similar

classes such as crops and wetland vegetation. As the science and value of wetland restoration

continues to grow, an effective method of delineating small wetlands from heterogeneous

landscapes is a necessity, particularly as wetland loss continues despite our increased

understanding of their ecological and societal importance.

With the ongoing global loss of habitat and continuing trend of species decline, novel methods of

obtaining information on species’ habitat requirements is warranted. Chapter Three evaluated the

statistical strength and usefulness of seasonal predictive habitat models, and found a high degree

133

of accuracy using the boosted regression tree approach and predictors derived from satellite-

imagery. Results further demonstrated that Blanding’s turtles exhibit a distinct shift in preferred

habitat over seasons and across both natural and altered landscapes, likely related to

physiological requirements and behavioural traits. Habitat associated with emergent and wet

meadow wetlands, as well as lower elevation was consistently selected across all models and

seasons. Biophysical characteristics related to microhabitat included selection of regions of high

vegetation cover and height during the early season and a closer association with standing water

and deeper waters during the late summer period. Overall findings from this study highlight the

dynamic choices that species make when selecting habitat locations that meet their needs, and

further draw attention to the dangers of assuming a static environment when modelling species

distribution.

Movement potential represents another aspect of species persistence on the landscape that is

affected by both intra-annual variation in land cover, and heterogeneity from human-oriented

disturbances. Chapter Four demonstrates that there are many dynamic variables and processes

that affect connectivity for Blanding’s turtles in fragmented landscapes. These changes in

movement potential and habitat importance are likely a consequence of shifting habitat and

changing behavioural drivers that are especially noticeable in altered landscapes, and for species

who are tied to dynamic ecosystems such as wetlands. This study supports the application of

multiple approaches to visualizing and quantifying connectivity to produce results that can

address different aspects of a species’ behavioural, and physiological needs including individual

plasticity, and age-specific and gender specific traits. Regardless of season, roads bisected a large

proportion of estimated least-cost pathways and circuit-based corridors. Roads represent a major

barrier to wildlife movement (Coffin, 2007) which greatly reduce the permeability of any

landscape where they are found. While Blanding’s turtles exhibit a relatively tolerant range to a

variety of environmental conditions, suggesting they could be resilient in the face of a warming

trend, the extensive modification of the landscape that has occurred and continues to occur

across much of southern Ontario indicates that dispersal events may be highly restrictive and

generally unsuccessful.

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Management Applications and Future Direction

There is little doubt that organisms on this planet are declining at a rate faster than we are able to

detect, and results from this work substantiates the capability of remote sensing technology for

contributing information needed to assess data deficient and declining species. A major gap

exists however, in linking the results of research such as this with conservation practitioners and

government and non-government stakeholders who are responsible for managing landscape level

policy that will ultimately decide the fate of species such as Blanding’s turtles. To that end, I

have attempted to bridge this divide through collaboration with relevant organizations in need of

results from work such as this, however challenges still remain in reconciling conflicting agendas

and lack of funds for conservation work. While all efforts were made to use tools that are

publically available, the nature of remote sensing and GIS research is that numerous programs

are often employed to complete various tasks, and some of these will be proprietary (e.g.,

ArcMap, ENVI). While this may be daunting, the increasing availability of open source software

means that conservation practitioners can gain access to an increasing array of tools to apply to

their work.

Overall, several general conclusions and recommendations can be derived from results of this

work and applied to land management decisions and conservation strategies. In areas where a

wetland inventory does not previously exist, a GEOBIA approach to mapping these ecosystems

is recommended to provide baseline information from which to answer basic land management

questions. The ability to detect small and ephemeral wetlands through this approach may provide

an avenue for the inclusion of wetlands < 2 ha into the Ontario Wetland Evaluation System

(OWES) which is the first step in assigning protection to these currently unprotected wetlands.

From an even broader perspective, land cover mapping is not limited to wetlands, and this

approach can be applied to virtually any type of key habitat and using any type of digital

imagery. The key is to employ a species-centred approach to land cover mapping such that

appropriate landscape features are captured, and that scale of the map matches that of the species

in question. This base information can be used to derive a whole suite of complementary

information needed to adequately address issues of threatened species decline.

The habitat identified in Chapter Three represents an effective method of providing baseline

information on species occupancy, and for monitoring changes to available habitat. But

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identification of habitat is only the first, though critical, step in managing wildlife populations.

Greater protection of preferred habitat is required along with an understanding of the importance

of the surrounding landscape. The use of the term buffers, or strips of land surrounding a target

habitat such as wetlands, may be misleading as these areas can actually represent locations of

biological importance for semi-aquatic species which carry out critical life-history functions such

as nesting, overwintering or foraging. Similarly, upland linkages between wetland mosaics

characterize equally important habitat needed for dispersal, immigration, recolonization and gene

flow, without which many meta populations would be doomed to isolation and eventual local

extinction.

The general trend in connectivity-related mitigation is the construction of an ecopassage after

high incidences of animal mortality are noted along a particular area, most often a road. While

the installation of the ecopassage and associated structures such as exclusion fencing fulfil a

necessary action to mitigate further mortality, we need to shift our policy to either (i) install the

ecopassage structure before high incidences of mortality occur, and for which species occupancy

models and patch-based connectivity indices can help identify such locations, or (ii) to divert

transportation networks away from critical habitat, as road mortality is only one of a series of

negative consequences of roads. In order to drive this shift in policy, road planning agencies

should place a higher priority on maintaining natural landscape connectivity, and ecologists need

to work more closely with planners to provide the necessary scientific information (e.g.,

developing habitat maps, modelling movement, estimating population viability) to guide road-

construction. Furthermore, landowners need to better understand how their properties can play a

larger role in providing critical habitat and linking corridors for an array of wildlife species in all

types of landscapes. These are daunting tasks, as conflicting priorities, lack of funds, and

understanding can generally impede successful collaboration. Fortunately, awareness of the

importance and inherent value of biodiversity and green spaces in our society continues to rise,

and we can only hope that public perspectives and government attitudes can shift in time to

prevent the loss of further species.

Future work building on results of this dissertation include the estimation of population viability

analysis for turtle populations in my fragmented study sites. A logical next step after identifying

habitat and landscape connectivity potential is to predict population stability and time to

extinction under different management scenarios. The ability to simulate different changes to

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landscapes (both positive and negative) and the resulting effect on turtle populations can provide

further science-based information for setting conservation priorities.

So, does slow and steady win the race?

It may be too early to tell, but turtles have lumbered across this planet for almost 300 million

years proving they are far more resourceful than their cumbersome shell may imply. With just a

little bit of help, they may persist long into the future.

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APPENDIX

Figure A1. Continuous % vegetation cover raster map derived from high spatial

resolution GeoEye1 imagery (NDVI) and field-based measurements over the spring

Algonquin park study site (a) and subset in white square and (b).

Figure A2. Continuous % vegetation cover raster map derived from high spatial

resolution GeoEye1 imagery (NDVI) and field-based measurements over the spring

Algonquin park study site (a) and subset in white square and (b).

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Figure A3. Continuous water depth raster map derived from high spatial resolution

GeoEye1-imagery (relative water depth algorithm) and field-based measurements. Late

summer Algonquin Provincial Park study area (left) and subset locations shown in white

squares, and boxes (a) and (b).

Figure A4. Continuous water depth raster map derived from high spatial resolution

GeoEye1-imagery and field-based measurements. Algonquin Provincial Park study

area, late summer.

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Table B1. Sensitivity Analysis on map accuracy of habitat suitability maps set at

threshold values of 0.4 - 0.8

Binary

Threshold

Spring Map

Accuracy (%)

Summer Map

Accuracy (%)

Park site (Algonquin

Park)

0.4 95.97 78.17

0.5 93.08 72.56

0.6 90.78 65.49

0.7 85.01 50.73

0.8 78.67 36.38

Agricultural site (Brant

County)

0.4 98.74 88.11

0.5 98.74 88.11

0.6 97.49 87.80

0.7 95.73 87.80

0.8 91.46 86.89

Figure A5. Continuous map of percent vegetation cover estimated from high spatial

resolution GeoEye1-derived NDVI over the Brant County agricultural study area during the

spring season.

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Table B2. Expert-based resistance values for the spring and late summer season used in least-cost

and circuit theory models.

Land cover Expert

1

Expert

2

Expert

3

Spring

Average

Expert

1

Expert

2

Expert

3

Summer

Average

Wetland

(emergent marsh 0 0 0 0 0 50 40 30

Wetland (wet

meadow marsh) 30 2.5 15 16 50 45 50 48

Wetland

(swamp) 2.5 2.5 15 7 20 26 20 22

Open water 0 0 0 0 0 0 0 0

Mixed forest 35 60 40 45 57.5 60 40 52

Mixed meadow 40 53 35 42 57.5 52.5 40 50

Crop field

(closed canopy) 40 40 30 36 84.5 49 65 66

Crop field

(barren) 20 100 30 50 52.5 100 30 61

Road 75 94 58 76 90 96.5 90 92

Residential

parcel 63 58 93 71 64.5 62 75 67