Ralph W. Tingley III1, Dana M. Infante

1 , Richard A. MacKenzie

2, Robert

Nishimoto3, James Parham

4

1 Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI

2 Institute of Pacific Island Forestry, Pacific Southwest Research Station, Hilo, HI

3 Division of Aquatic Resources, Honolulu, HI

4 Parham and Associates Environmental Consulting, Gallatin, TN

Hawai’i’s fluvial systems: Using GIS to assess

current conditions and identify management

strategies in a changing climate

Degraded habitat and aquatic biodiversity

• Habitat degradation is the main cause of decline and

extinction of freshwater fishes (Helfman 2007)

– Contributed to 71% of all known fish extinctions

• Other organisms’ declines assumed related to habitat

– In US and Canada, 72% of mussel and 47% of crayfish

taxa imperiled (Abell et al. 2000, Taylor et al. 2000)

– 43% of stoneflies, 36% of amphibians, and 18% of

dragon/damselflies imperiled in U.S. (Stein et al. 2000)

www.fishhabitat.org

Mission: To protect, restore and enhance the

nation’s fish and aquatic communities through

partnerships that foster fish habitat conservation

Initial objectives (Partial list)

• Conduct a condition analysis of all fish habitats

• Identify priority fish habitats

Completed for conterminous U.S., Alaska and

Hawai’i in 2010

• Followed a landscape approach

The landscape approach

• Natural and

anthropogenic landscape

factors influence habitat

• Habitat condition directly

influences stream biota

• Landscape disturbances

are assumed to impact

stream biota

Human land uses

Habitat

Stream organisms

Natural factors

• For conterminous U.S….

The landscape approach

Human land uses

Habitat

Stream organisms

Natural factors

% Network Urban Med

% Network Pasture

Esselman et al. 2011

Assessing the relative risk of degradation

• Catchment landscape factors are good predictors

of aquatic biology and physical habitat (Gergel et al. 2002;

Allan 2004)

• Useful for assessing large regions across which

site-specific data may be unavailable or

inconsistently collected

• Can compare relative risk of degradation across

stream reaches

Norris et al. (2001))

Danz et al. (2007))

• Created spatial framework at two scales, local and network

catchment

• Identified variables indicating major sources of disturbance

• Attributed and aggregated data local and network catchments

• Grouped variables and created sub-indices for specific

disturbance categories, relativized and summed into a

cumulative index (follows Danz et al. 2007)

– Urban land cover and population density

– Agriculture

– Barriers to fish movement (fragmentation)

– Point source disturbances

– Length of ditches

– Former plantations

– 303D listed streams

Hawai’i 2010 initial assessment overview

Spatial framework and database development

• National Hydrography Dataset (1:24k NHD)

– Stream arcs only

– Canal/ditches and pipelines not included

– New breaks implemented based on

elevation (Parham and Lapp 2006)

Spatial framework and database development

• National Hydrography Dataset (1:24k NHD)

– Local and network

catchments delineated

using ArcHydro

– Represents an improvement

to any existing spatial

framework

– Each reach had landscape

data summarized at both

scales

– Data must be continuous

and available state-wide NHD 1:24K Reach Local

catchment

Network catchment

Disturbance variables:

Urban and point source inputs of toxics and other pollutants

Disturbance variables:

Former plantation land, current agriculture, stream fragmentation,

ditches, 303D listings

• At local and network scale, sub-index scores generated

using Principal Components Analysis (PCA) or additive

methods

• Disturbance scores at local and network catchments

summed to obtain relative condition score

• Several agencies are using the spatial framework

to organize and attribute data.

• Scores are being incorporated into the Atlas of

Hawaiian Watersheds

• Condition assessment of coastal habitat and

reefs

Current applications

©2008 Atlas of Hawaiian Watersheds & Their Aquatic Resources.

What’s next for the assessment?

• Streams respond to

disturbances differently

due to differences in

natural factors (Melles et al.

2011, Utz et al. 2011)

• Accounting for these

natural differences

allows for an increased

understanding of relative

condition

Human land uses

Habitat

Stream organisms

Natural factors

• Develop an ecological classification of Hawaiian

stream reaches that characterizes their natural

potential for supporting native species

1. Identify biologically relevant landscape-scale

variables

2. Identify biologically relevant climate variables

3. Identify where changes in species composition

occur in relationship to climate and biological

variables

Classification of ecological potential:

Goal and objectives

Step 1: Identify biologically relevant

landscape variables

• Presence/absence data

is available across

islands

• Only the most

influential landscape

variables will be

included in analysis

Summarize local/network

catchments

Identify initial natural

landscape variables

Compile biological data

Output: Biologically relevant landscape

variables

Use multivariate approach to

select variables

Presence/ absence data

Natural landscape variables and datasets

Variable Description Scale/Res. Scale of summary Source

Drainage area Total area draining to stream reach 1:24k Upstream catchment HFHP modified NHD

Stream order Strahler stream order 1:24k Local reach HFHP modified NHD

Slope Precent slope 4 m grid All reach scales IFSAR elevation data-NOAA

Maximum slope drop Maximum drop in slope downstream of reach 4 m grid Downstream reach IFSAR elevation data-NOAA

Distance Inland Linear distance from marine input 1:24k Downstream reach HFHP modified NHD

Elevation zones Biologically significant breaks in elevation 1:24k Local reach Parham and Lapp 2006

Groundwater delivery Mean groundwater delivery to reach Local/upstream catchmentCWRM data/IFSAR elevation data-

NOAA; In development

*Warm groundwater input Area of catchment with warm groundwater input 10m grid Local/upstream catchment GeothermEx, Inc.

*Soil erodability Mean soil erodability within catchment variable Local/upstream catchment SSURGO

Dataset

*Major land resource areas Terrestrial based landscape descriptions 10m grid Local/upstream catchment USDA/NRCS/NHQ/RAD

*Geologic age Age of hawaiian islands soils/rock variable Local/upstream catchment USGS

*Geologic type Geolocial type of all Hawaiian islands variable Local/upstream catchment USGS

*Soil type Type of Hawaiian soils variable Local/upstream catchment SSURGO

*Datasets aquired through the Hawai'i statewide GIS program

Step 2a: Identify biologically relevant

climate variables: linking biology to flow

Identify reaches with

flow data

Use multivariate approach to

select variables

Summarize local/network

catchments

Regional species

datasets

Identify initial natural

landscape variables

Compile biological data

Output: Biologically relevant landscape

variables

Use multivariate approach to

select variables

Biologically relevant flow

variables

Presence/ absence data

Atyoida bisulcata of the Hilo/Hamakua Coast

• Extreme precipitation gradient

• 9-15 sites

– Flow data

– Body condition

– Length

– Relative abundance

– Fecundity

On-going research of: Richard MacKenzie, Ralph Tingley, Ayron

Strauch, Dana Infante, Greg Bruland, Patra Foulk, Therese Frauendorf

Atyoida bisulcata of the Hilo/Hamakua Coast • Variables that show declines

with decreasing annual flow:

– Length

– Body condition

– Relative abundance

Step 2b: Identify biologically relevant climate variables: linking flow to climate

Identify climate

variables

Identify reaches with

flow data

Use multivariate approach to

select variables

Output: Biologically relevant climate

variables

Link climate to flow variables

Summarize local/network

catchments

Regional species

datasets

Identify initial natural

landscape variables

Compile biological data

Output: Biologically relevant landscape

variables

Use multivariate approach to

select variables

Biologically relevant flow

variables

Summarize local/network

catchments Presence/ absence data

Climate variables

• Initial climate variables are being selected based

on availability and anticipated predictions

Variable Description Scale/Res. Scale of summary Source

*Solar radiation Estimated daily solar insulation contours 10m grid Local/network catchment Department of Planning

Mean annual precipitation Mean annual precipitation 250 m grid Local/network catchment 2011 Rainfall Atlas of Hawaii

Mean dry season precipitation April through October 250 m grid Local/network catchment 2011 Rainfall Atlas of Hawaii

Mean wet season precipitation November through March 250 m grid Local/network catchment 2011 Rainfall Atlas of Hawaii

Variance of monthly precipitation Entire water year 250 m grid Local/network catchment 2011 Rainfall Atlas of Hawaii

Variance of monthly precipitation

wet seasonApril though October 250 m grid Local/network catchment 2011 Rainfall Atlas of Hawaii

Variance of monthly precipitation

dry seasonNovember through March 250 m grid Local/network catchment 2011 Rainfall Atlas of Hawaii

Mean annual air temperature Mean annual air temperature 10 m grid Local/network catchment PRISM data

Daily rainfall metrics Number of consecutive days with no rain

*Datasets aquired through the Hawai'i statewide GIS program

Biologically relevant landscape variables

Classification of ecological potential

Establish breaks through: 1. CART analysis

2. Regional studies

Step 3: Identify breaks in relevant

variables and classify streams

Biologically relevant climate variables

Presence/absence

Regional datasets

• Increased utility of the NFHAP current condition

assessment

– Reference of community structure at reach scale

• Contribution of a new scale of classification to the

Atlas of Hawaiian Watersheds

• Act as a baseline for understanding vulnerability to

changes in climate

Benefit and application of results

Species specific vulnerability to climate change: Atyoida bisulcata

Species specific vulnerability to climate change: Atyoida bisulcata

Acknowledgements

• Dr. Christian Giardina, Dr. Gordon Smith, Dr. Ayron Strauch, Dr. Greg Bruland

• Patra Foulk, Therese Frauendorf • Arthur Cooper, Dan Wieferich, Jacqui Fenner, Ali David,

Dr. Yin-Phan Tsang • USDA Forest Service • Division of Aquatic Resources • Hawai’i Fish Habitat Partnership • University of Hawai’i at Manoa • Kamehameha Schools • Michigan Sate University • USGS • USFWS

Mahalo!