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Final Report on the Rocky Mountain Tailed Frog Pilot Monitoring Project, 2005, 2007-2009

March 15, 2010

Prepared By:

Cordilleran Geoscience,Box 612, Squamish BCV8B 0A5

And

ESSA Technologies Ltd. Suite 300 – 1765 West 8th AveVancouver, BCV6J 5C6

For:Ecosystems BranchMinistry of EnvironmentVictoria BC

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TABLE OF CONTENTS1 INTRODUCTION........................................................................................................12 SPECIES INFORMATION..........................................................................................1

2.1 Distribution & Phylogeny................................................................................................................12.1.1 Ascaphus spp............................................................................................................................12.1.2 Ascaphus montanus..................................................................................................................2

2.2 Habitat Associations.........................................................................................................................32.2.1 Lotic Environment....................................................................................................................32.2.2 Terrestrial Environment............................................................................................................4

2.3 Threats..............................................................................................................................................52.4 Status In British Columbia...............................................................................................................52.5 Previous Inventory Studies...............................................................................................................52.6 Wildlife Habitat Areas......................................................................................................................6

3 PROJECT BACKGROUND.........................................................................................73.1 Purpose.............................................................................................................................................73.2 Objectives.........................................................................................................................................73.3 Indicators..........................................................................................................................................83.4 Project History & Status...................................................................................................................8

4 STUDY AREA...........................................................................................................114.1 Climate...........................................................................................................................................114.2 Hydrology.......................................................................................................................................114.3 Physiography..................................................................................................................................13

5 METHODS.................................................................................................................135.1 Study Design..................................................................................................................................135.2 Field work Effort and Schedule......................................................................................................135.3 Sampling Period.............................................................................................................................145.4 Rationale for Selection of Sample Reach Length...........................................................................145.5 Naming Conventions and Locational Data.....................................................................................155.6 Channel and Habitat Condition Sampling......................................................................................15

5.6.1 Topographic Surveys..............................................................................................................155.6.2 Pebble Counts.........................................................................................................................175.6.3 Embeddedness Estimates........................................................................................................205.6.4 Pole Photography....................................................................................................................20

5.7 Tailed Frog Abundance Sampling..................................................................................................215.7.1 Basic Rubble Rousing Method...............................................................................................215.7.2 Time Constrained Searches (TCS).........................................................................................215.7.3 Area Constrained Searches (ACS).........................................................................................225.7.4 Cohort Classification..............................................................................................................225.7.5 Weights and Length Measures...............................................................................................235.7.6 Animal Care............................................................................................................................23

5.8 Pre & Post Ram-Cabin Fire Sampling, 2003-2005.......................................................................235.9 Sentinel Samples, 2005, 2007, and 2008........................................................................................23

5.9.1 Site Selection..........................................................................................................................235.9.2 Tasks conducted each sampling year.....................................................................................245.9.3 Pebble Counts and Embeddedness Ranks..............................................................................245.9.4 ACS animal sampling.............................................................................................................25

5.10 Calibration Samples, 2008-2009....................................................................................................255.10.1 Site selection...........................................................................................................................255.10.2 Tasks executed at each site.....................................................................................................265.10.3 Pebble counts and embeddedness ranks.................................................................................265.10.4 TCS and ACS animal sampling..............................................................................................26

5.11 Stream Thermal Regime, 2008-2009.............................................................................................265.12 Data Review and Synthesis............................................................................................................295.13 Data Summary................................................................................................................................305.14 Baseline Estimates and Pilot Variance Estimates..........................................................................30

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5.14.1 Target population....................................................................................................................315.15 Compare Estimates of ACS and TCS.............................................................................................315.16 Demographics and Length/Weight Data........................................................................................315.17 Statistical Tests...............................................................................................................................32

5.17.1 Trends.....................................................................................................................................325.17.2 Linking habitat and geomorphology to abundance and/or occupancy...................................32

5.17.2.1 Channel condition...........................................................................................................325.17.2.2 Habitat condition............................................................................................................325.17.2.3 Multiple indices..............................................................................................................32

5.17.3 Threats....................................................................................................................................336 RESULTS...................................................................................................................33

6.1 Data review and processing............................................................................................................336.2 Channel condition...........................................................................................................................33

6.2.1 Time Requirements................................................................................................................336.2.2 Data Summary........................................................................................................................346.2.3 Pole Photography....................................................................................................................35

6.3 Habitat condition............................................................................................................................356.3.1 Pebble Counts.........................................................................................................................35

6.3.1.1 Time Requirements............................................................................................................356.3.1.2 Sentinel site summaries......................................................................................................356.3.1.3 Statistical summary............................................................................................................366.3.1.4 Baseline across all sites......................................................................................................37

6.3.2 Embeddedness........................................................................................................................386.3.2.1 Time Requirements............................................................................................................38

6.4 Aquatic population condition.........................................................................................................396.4.1 Time Requirements................................................................................................................396.4.2 Data summary.........................................................................................................................396.4.3 Metrics of population size......................................................................................................416.4.4 Comparison between ACS and TCS......................................................................................416.4.5 Occupancy..............................................................................................................................426.4.6 Length / Weight data..............................................................................................................43

6.4.6.1 Time Requirements............................................................................................................436.4.6.2 Data Summary....................................................................................................................43

6.4.7 Demographics.........................................................................................................................456.4.7.1 Time Requirements............................................................................................................456.4.7.2 Data Summary....................................................................................................................45

6.5 Linking habitat and geomorphology to abundance and/or occupancy...........................................466.5.1 Channel condition...................................................................................................................466.5.2 Habitat condition:...................................................................................................................46

6.6 Pre & Post-fire Sampling................................................................................................................486.6.1 Fire severity and Tailed Frog distribution..............................................................................486.6.2 Immediate fire effects.............................................................................................................496.6.3 Delayed fire effects.................................................................................................................49

6.7 Organisation of Data Files..............................................................................................................527 DISCUSSION.............................................................................................................52

7.1 Channel condition...........................................................................................................................527.2 Habitat condition............................................................................................................................537.3 Aquatic population condition.........................................................................................................54

7.3.1 Population Estimation............................................................................................................547.3.2 Occupancy..............................................................................................................................54

7.4 Effects post fire...............................................................................................................................567.5 Review of Sampling Design...........................................................................................................56

7.5.1 Target population....................................................................................................................567.5.2 Selection of sites in space.......................................................................................................567.5.3 Selection of sites in time, within a year..................................................................................57

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7.5.4 Selection of sites in time, between years................................................................................577.5.5 Selection of homogeneous reaches within a site....................................................................57

7.6 Tradeoffs in field methods..............................................................................................................587.6.1 Effort.......................................................................................................................................587.6.2 Probability of Detection & bias..............................................................................................587.6.3 Ability to expand the estimate................................................................................................597.6.4 Repeatability...........................................................................................................................597.6.5 Geomorphic Effects of Repeat Sampling...............................................................................59

7.7 Review of Tailed Frog WHA monitoring objectives.....................................................................608 RECOMMENDATIONS............................................................................................61

8.1 Refine questions and objectives.....................................................................................................618.2 Use of existing data........................................................................................................................618.3 Routine Monitoring........................................................................................................................618.4 Extensive Monitoring.....................................................................................................................62

8.4.1 Stratified random sampling....................................................................................................628.4.2 Ensuring spatial balance.........................................................................................................628.4.3 Target population, sampling frame, and sampling unit..........................................................628.4.4 Allocation of effort.................................................................................................................638.4.5 Field protocol for rapid assessment indicator of occurrence & relative abundance...............638.4.6 Selection of sites in time, within a year..................................................................................648.4.7 Selection of sites in time, between years................................................................................648.4.8 Channel and Habitat Condition Indicators.............................................................................65

8.5 Intensive Monitoring......................................................................................................................658.5.1 Post Fire Data set....................................................................................................................65

8.6 Monitoring outside WHAs.............................................................................................................668.7 Overall data management strategy.................................................................................................66

9 REFERENCES............................................................................................................6610 APPENDICES.........................................................................................................71

10.1 Directions to Sentinel Sites............................................................................................................7110.1.1 Boyd.......................................................................................................................................7110.1.2 Norge......................................................................................................................................7110.1.3 Sprucetree...............................................................................................................................7110.1.4 Cabin.......................................................................................................................................7210.1.5 Couldrey.................................................................................................................................7210.1.6 Storm......................................................................................................................................72

10.2 Example of t-tests results comparing counts between years or locations......................................7210.3 Confidence intervals for an individual site, extracted from Pickard (2008)..................................7310.4 Progression of study designs: Excerpted from Pickard and Porter (2008).....................................74

10.4.1 Descriptive study:...................................................................................................................7510.4.2 Observational study................................................................................................................7510.4.3 Analytical survey....................................................................................................................7510.4.4 Impact and control-impact surveys........................................................................................7510.4.5 Designed experiments............................................................................................................77

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LIST OF TABLESTable 1 Fieldwork schedule, 2005-2009.........................................................................................14Table 2 I-button site locations (UTM, NAD 83, Zone 11U), date and time of retrieval, and

number of I-buttons per site....................................................................................................28Table 3 Summary of revised datasets.............................................................................................29Table 4 Summary of script files......................................................................................................29Table 5 Summary of available data................................................................................................30Table 6. Reach averaged hydraulic geometry and channel condition indices (2005)....................34Table 7. Velocity and discharge estimates based on reach averaged hydraulic geometry.............34Table 8. Annual estimates of the proportion of refuge filling substrate for each watershed by year.

.................................................................................................................................................37Table 9. Estimates of the proportion of refuge filling substrate for each Sentinal site across all

years (estimates of year to year variability within a site)........................................................38Table 10. Annual estimates of embeddedness for each watershed.................................................38Table 11. Estimates of the embeddedness for each Sentinal site across all years (estimates of year

to year variability within a site)...............................................................................................38Table 12. Calibration sample results by year and region. Cobble embeddedness ranks and late

summer stream temperature are reported for comparisons. TCS is total number of animals caught in 30-min search; ACS is total animals caught in aggregate 15-m belt sample. Occ. Rate is frequency of positive occurrence in belts as a ratio of total number of belts. Sentinel samples from 2008 show TCS and five 3-m belts. Cases with zero counts are shaded and potentially responsible indicator is boxed in green.................................................................39

Table 13. Baseline estimates of tadpole abundance by watershed, using the ACS protocol..........41Table 14 Estimates of tadpole abundance over time at a fixed site, using the ACS protocol........41Table 15. Coefficient estimates for regression model: sqrt(ACS)=sqrt(TCS)...............................41Table 16. Tadpole mean length and weight values by cohort and watershed. 2008-2009 data.....44Table 17. Frog mean length and weight values by cohort and watershed. 2008-2009 data...........44Table 18. Regression estimates for sqrt(ACS)= ∑h/H...................................................................46Table 19. Regression estimates for sqrt(ACS)= ∑Runs/L..............................................................46Table 20. Regression estimates for sqrt(ACS)=D50........................................................................47Table 21. Regression estimates for sqrt(ACS) = % Refuge filling substrate.................................47Table 22 Regression estimates for sqrt(ACS)=embeddedness rank...............................................48Table 23. Pre and post fire conditions within the fire boundary.....................................................51Table 24. Pre and post fire conditions at selected control site outside the fire boundary...............52Table 25. Proposed stratification categories...................................................................................62

LIST OF FIGURESFigure 1 Range of Ascaphus spp. (from Carstens et al 2005)...........................................................1Figure 2 Phylogeny of Ascaphus spp. (from Carstens et al 2005). Northern Rocky Mountains

inhabited by Rocky Mountain Tailed Frog (Ascaphus montanus). North and South Cascades inhabited by Coastal Tailed Frog (Ascaphus truei)...................................................................2

Figure 3 Global range of Ascaphus montanus. Modified after AMPHIBIAWEB...........................2Figure 4 Watershed level distribution of A. montanus in the Flathead River (above) and Yahk

River (below) (source: Dupuis and Friele 2006).......................................................................3Figure 5 Distribution of Rocky Mountain Tailed Frog and stream habitat stratification in the

Yahk (left) and Flathead (right) River watersheds, southeast British Columbia (source Dupuis and Friele 2005)............................................................................................................6

Figure 6 Rocky Mountain Tailed Frog WHAs in the Yahk (left) and Flathead (right) River watersheds, southeast British Columbia (source Dupuis and Friele 2005a).............................7

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Figure 7 Temperature and Precipitation Climate Normals at Cranbrook Airport, 1971-2000.......11Figure 8 Monthly discharge (m3/s) for Cabin Creek near mouth (Station 08NP004), 1978-2008

(Environment Canada 2009)....................................................................................................12Figure 9 Left column: step-pool channel morphology and bedform resistance changes caused by

deep flow or sedimentation (after Chin 2003), where H, total reach height; h, individual step height. Right column: step-geometry (top box) and watershed level distribution of geomorphic process domains and sedimentary processes (two bottom boxes; from Montgomery and Buffington 1997).........................................................................................16

Figure 10 Square hole template, or “Gravelometer” for standardizing grain size measurements for pebble count samples. (website: http://fisp.wes.army.mil/......................................................18

Figure 11 Qualitative embeddedness ranks based on cobble thickness (256 mm). Grey indicates refuge filling sediment.............................................................................................................20

Figure 12 Histogram of time to first detection for 135 TCS searches conducted during reconnaissance work (Dupuis and Friele 2002, 2004). TCS searches employed two people searching for 15 minutes each, for a combined 30 minute TCS search..................................22

Figure 13. Grain size histograms from Sentinel sites, 2005, 2007, 2008, 2009.............................36Figure 14 The D50 particle mean diameter (mm) from sentinel sites for: 2005, 2007, and 2008.

The horizontal dotted lines represent the breaks between ‘Refuge Filling, Refuge Forming, and Step Forming’ habitats......................................................................................................37

Figure 15 Box plots summarizing the proportion of each pebble count sample in each of 3 habitat categories: Refuge filling (0-32 mm), Refuge forming (32-256 mm), and Step forming (>256 mm). The box plots show the results from all samples across all years for both the Flathead and Yahk watershed................................................................................................................37

Figure 16. Diagnostic plots for fit of straight line regression model to raw ACS and TCS counts..................................................................................................................................................42

Figure 17. Diagnostic plots for fit of straight line regression model to sqrt(ACS) and sqrt(TCS) counts.......................................................................................................................................42

Figure 18. Average length (mm) and weight (g) by life-stage for all sites where Tailed Frogs were found, summarized by watershed and sub-basin....................................................................44

Figure 19. Observed age-classes, Sentinel sites only, standardized to 15-m..................................45Figure 20. Observed age-classes, All ACS sites, standardized to 15m..........................................46Figure 21. Diagnostic plots for fit of straight line regression model to sqrt(ACS) and % Refuge

filling substrate........................................................................................................................48Figure 22. Diagnostic plots for fit of straight line regression model to sqrt(ACS) and

embeddedness rank..................................................................................................................48Figure 23. The Ram-Cabin fire (Fire N10689) Sept 2003, with fire intensity areas derived from

pre-fire forest cover maps and site observations. Tailed Frog habitat mapping and Tailed Frog sample sites are indicated................................................................................................50

Figure 24. This figure illustrates the effect of sampling unit size on the estimate of occupancy, copied from MacKenzie et al. (2006)......................................................................................54

Figure 25. This hypothetical example illustrates the risk of not sampling the population annually, if the year to year variability is large. Say we sampled once every 5 years: if we considered 2000 vs. 2005 we would assume the population was in decline; yet if we sampled 2003 vs. 2008 we would assume the population was increasing. In reality, the population is stable, but has significant year to year variability.....................................................................................65

Figure 26. Relationship between degree of control, strength of inference (and ability to determine causation), and type of study design (from Schwarz 2006)...................................75

Figure 27. Value of BACI design for inferences - A) Hypothetical time series of mainstem river and Hayden Creek (control area) parr-to-smolt survival rates, with habitat restoration actions assumed to happen simultaneously in 2015 (dark arrow) in the mainstem river. Time series for both impact and control streams track erratically over time. B) Hypothesized

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difference between mainstem river and Hayden Creek parr-to-smolt survival rates over the time series. D(PRE) and D(POST) represent average survival difference pre- and post-impact in 2015 in the mainstem river. Difference between control and impact streams shows much greater parr-smolt survival in the treatment stream vs. the control, indicating a benefit of the restoration action over time that would not have been apparent given annual variation without a control system for comparison (source CSMEP 2006)...........................................76

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ACKNOWLEDGEMENTSThe Rocky Mountain Tailed Frog reconnaissance and monitoring initiative is a cumulative effort spanning the time period 1996 – 2009. The work has been supported since inception by BC MoE at the provincial and regional level. Reconnaissance, preliminary monitoring design and conservation analysis, spanning the years 1996-2005, were a collaboration between Linda Dupuis, R.P. Bio. and Pierre Friele, P.Geo. Since 2006 Pierre Friele has been responsible for project implementation.

Field Assistance over the years was ably provided by Krista Wilson, Leo Frid, Charlenne Hamm, Kari Stuart-Smith, John Krebs, Dennis Knopff, Jean-Marc Bell, Ian Bell, Randy Morris, Sparky, Alex Brandt, Jared Hobbs, Joanna Burgar, Julie Tyrell, Laura Parkinson, Dan Kerslake, Cait Nelson, Kristen Murphy, Melissa Todd, Purnima Govindarajulu, and Line Gillespie.

The project has benefited from the support and guidance of Kathy Paige, Ted Antifeau, Darcy Pickard and Pauline Hubregtse.

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1 INTRODUCTIONThe purpose of this report is to summarize findings of a multi-year Rocky Mountain Tailed Frog (Ascaphus montanus) monitoring pilot study. The goals of this project were to collect baseline information and develop monitoring methods for conducting effectiveness evaluations of Rocky Mountain Tailed Frog Wildlife Habitat Areas (WHAs) in British Columbia.

This project was completed for the Wildlife Resource Value Team (WRVT) of the British Columbia Forest and Range Evaluation Program (FREP). More information on the Wildlife Resource Value is available at:http://www.for.gov.bc.ca/hfp/frep/values/wildlife.htm

2 SPECIES INFORMATIONThe species of interest is the Rocky Mountain Tailed Frog (Ascaphus montanus - Nielson, Lohman & Sullivan, 2001).

2.1 Distribution & Phylogeny

2.1.1 Ascaphus spp.The modern range of Tailed Frog is shown in Figure 1. Following the ancient vicariance hypothesis (Carstens et al 2005), 20,000,000 years ago the Pacific Northwest Cordillera was characterized by low relief (~600m) topography covered by mesic equitemperate forest (Carstens et al 2005). Tailed Frog occupied this ancestral landscape. Population divergence began with the Coast Mountain Orogeny, 6-10 million years ago (Farley et al 2001). The rise of the Coast Mountains created a leeward windshadow, a dispersal barrier that isolated Coast and Rocky Mountain populations (Nielson et al 2001). Two to three million years ago, Pliocene and Pleistocene glaciations caused isolation and divergence within Coast and Rocky Mountain populations (Nielson et al 2006). This is supported by the phylogeny Figure 2 presented by Carstens et al (2005).

Figure 1 Range of Ascaphus spp. (from Carstens et al 2005).

Final report on the Rocky Mountain Tailed Frog WHA Monitoring Pilot 1

http://www.for.gov.bc.ca/hfp/frep/values/wildlife.htm

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Figure 2 Phylogeny of Ascaphus spp. (from Carstens et al 2005). Northern Rocky Mountains inhabited by Rocky Mountain Tailed Frog (Ascaphus montanus). North and South Cascades inhabited by Coastal Tailed Frog (Ascaphus truei).

2.1.2 Ascaphus montanusThe Rocky Mountain Tailed Frog Figure 3 occupies montane areas of Idaho, western Montana, northeastern Oregon, eastern Washington, and southeastern British Columbia. The species range extends only about 25 km north of the International border into British Columbia at two locations: the Yahk River watershed, west of the Rocky Mountain Trench, and the Flathead River, east of the Trench. The Yahk and Flathead populations have not been examined genetically but form discrete geographically isolated populations, being separated by the Rocky Mountain Trench. There are no population estimates available for either population (see Section 7.7).

Figure 3 Global range of Ascaphus montanus. Modified after AMPHIBIAWEB.


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2.2 Habitat Associations

2.2.1 Lotic Environment Rocky Mountain Tailed Frogs occupy montane watersheds generally less than 100 km2 in size Figure 4. These watersheds support between first and fourth order montane creeks. Montane creeks have a step-pool morphology (Chin 1989, 2003) characterized by steep channel slope (≥0.03 m/m), heterogeneous bed material & low sediment supply relative to transport capacity (Montgomery and Buffington 1997), highly dynamic flow regime (Wohl and Merritt 2008; Wohl and Thompson 2000; Scheuerlein 1999), and periodic debris flow activity (Chin 1998; De Scally et al 2001). Substrates are clean and coarse-grained, arranged in stable aggregations of stones forming step-like sequences. By causing flows to tumble, steps reduce water velocity and lower the tractive force on the bed during peak flow events. Fine gravel is winnowed from coarse bed material, leaving matrix pore space to serve as refuge and foraging sites for aquatic organisms, including the Tailed Frog.

Tailed Frogs are strongly adapted to the conditions found in step-pool systems (see Scheuerlein 1999; Wohl and Merritt 2008). Specific adaptations include (1) larvae possess an oral sucker to withstand high tractive forces; (2) all life stages make use of substrate pore space as refugia and foraging sites; and (3) egg strings are attached to the underside of large stones (Karraker et al 2006) where probability of physical disturbance is the least. There are behavioral adaptations as well; adult frogs will quickly retract their limbs in response to sharp vibrations. (This has been observed when captured animals are kept in a water-filled bucket. If the side of the bucket is rapped, the frogs will retract their limbs). The vibration is apparently perceived as bedload movement, and the limbs are retracted to prevent them from being pinched off between rolling or saltating stones.

Figure 4 Watershed level distribution of A. montanus in the Flathead River (above) and Yahk River (below) (source: Dupuis and Friele 2006).

Sedimentation from natural or anthropogenic disturbance can result in a measurable decline in channel condition (Wood-Smith and Buffington 1996). Low tadpole densities have been documented in streams channels dominated by fine substrates (Dupuis and Friele 1996; Welsh and Ollivier 1998; Diller and

Final report on the Rocky Mountain Tailed Frog WHA Monitoring Pilot

Yahk River

Flathead River

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Wallace 1999; Wilkins and Peterson 2000). In a typical headwater creek, disturbed habitat is gradually re-established as fines are winnowed away and the streambed is re-armoured (Scheuerlein 1999).

Although step bedforms are stable over a wide range of flows, they are subject to collapse at critical flows with 5-50 year recurrence intervals (Chin 1998) depending on local geomorphic conditions. With this pattern of disturbance (i.e., dynamic flow regime and occasional critical flow or debris flow), stream inhabitants die directly through channel erosion and bedload movement, but a proportion of individuals will survive in refuge sites.

Farther downstream, the step-pool bedform gives way to plane bed and then to pool-riffle bedforms. These reaches have lower gradients (<0.03 m/m), larger discharges, and modulated flow regimes. Such third and fourth order mainstem channels typically support floodplains because sediment supply exceeds transport capacity (Montgomery and Buffington 1997). Larger flows and finer sediments make the bedforms of mainstem channels less resilient to change. Tailed Frog abundance falls off sharply in this setting, possibly because matrix habitat availability and channel bed stability are both reduced; eggs and hatchlings in particular lack large stable rocks for “safe” nest sites.

A watershed is a system of channels that drains a catchment basin defined by the surrounding height of land. The channel system represents an energy continuum (Vannote et al. 1980) that integrates watershed processes (Innis et al 2000) and must be considered not as a linear system, but as a network (Gomi et al 2002). Since headwater channels occupy a major portion (70-80%) of the watershed, they are an important source of sediment, water, nutrients, and organic matter for the mainstem channel downstream (Gomi et al. 2002). In this context, management of Tailed Frog habitat necessitates watershed-level considerations.

2.2.2 Terrestrial EnvironmentAlthough the fluvial environment exerts a strong influence on Tailed Frog distribution patterns (Dupuis and Friele 2002, 2003, 2004a, 2004b), the riparian environment also plays an important role in the ecology of this species.

Several authors have reported strong ties to old forests (see Bury and Corn 1988; Gomez and Anthony 1996; Matsuda 2001; Biek et al. 2002; Dupuis and Friele 2002; Stoddard 2002; Wahbe et al. 2004) presumably because these structurally diverse forests provide a stable microclimate. The cover of tall trees and understory plants helps to maintain cool, moist air and soil conditions, by reducing wind speed and solar radiation. Terrestrial amphibian activity is strongly related to soil and atmospheric moisture because their skin is susceptible to water loss (Feder 1983; Keen 1984). Without protection from prolonged hot, dry conditions, amphibians become severely constrained in their activity during the growing season (e.g., Johnston and Frid 2002). Juveniles are particularly vulnerable to desiccation (Feder 1983) yet juveniles move much greater distances than adults do (Sheldon and Daugherty 1982; Bury and Corn 1988; Wahbe et al. 2004). Prolonged adverse climatic conditions may lower individual as well as population fitness levels.

Amongst anurans (frogs), the Tailed Frog is the least tolerant of desiccation (Claussen 1973) and heat (Brattstromm 1963; de Vlaming and Bury 1970). In support of this, Wahbe et al. (2004) trapped significantly more Coastal Tailed Frogs within 25 m of streams in clearcuts compared to old growth; clearcut movement was predominantly upstream whereas in old growth, most of the movement was towards streams from uplands (indicative of between stream movement). Maxcy (2000) reported movement rates of 12.3 ± 3.5 m/day in forested stream sites, and 8.5 ± 5 m/day in buffered sites. In the Interior, where annual precipitation is much less and conditions much drier than on the coast, Daugherty and Sheldon (1982) reported rates not exceeding 1 m/day. This represents a ten-fold difference in movement between the Coast and Interior.

Aquatic life stages also benefit from riparian buffers because they help to maintain channel structure (Dupuis and Steventon 1999) and water quality (Gilliam 1994). Once individuals metamorphose and become terrestrial, they are strongly dependent on riparian habitats for survival (e.g., foraging, thermal and predatory refuge, movement, mating). Riparian zones also provide landscape connectivity (Naiman et al. 1993), which facilitates juvenile and adult dispersal and meta-population dynamics.


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2.3 Threats Ascaphus montanus is vulnerable to management practices that alter the riparian or aquatic zones of streams, especially those that increase stream sediment load and temperature regimes, degrade stream bank integrity, and increase moisture stresses in the riparian. Protection of headwater streams is particularly important for this species (Hallock and McAllister 2005).

Possible reasons for Tailed Frog declines include land-use impacts (e.g., forestry), and natural habitat impact caused by insect infestation, fire, and climate change (Walker and Pellat 2008)(climate change may affect forest cover, fire frequency, and watershed hydrology). Although logging is the predominant historical land use, future land use activities that could pose a threat are run-of-river power development and mining.

Disease is another important threat. Chytrid fungus (Batrachochytrium dendrobatidis) has been implicated in global amphibian declines. This fungus occurs throughout the range of Tailed Frog. Its detection on Tailed Frog is rare – Daszak et al (1999) did not detect it, while on a sample of 452 Tailed Frogs from throughout the US Pacific Northwest, Hossack et al (2010) detected Chytrid on only 3 adult Ascaphus truei from 1 stream in Oregon. Purnima Govindarajulu (BC MoE) directed a study in August 2009 to collect swabs from Tailed Frog adults in the Yahk and Flathead populations: chytrid was not detected. It is possible that cold stream temperatures inhibit the effect of the fungus (Rollins-Smith et al 2002) in streams of the Pacific Northwest.

2.4 Status In British Columbia The Rocky Mountain Tailed Frog is on the provincial Red List in British Columbia. It is designated as Endangered in Canada (COSEWIC 2002).

2.5 Previous Inventory Studies The Rocky Mountain Tailed Frog range in BC has been well documented through several wide-ranging inventories in the Southern Interior Mountains, covering a block from the Creston Valley east to the Alberta Border and from the International Border north to Cranbrook and Sparwood (Dupuis and Bunnell 1997; Dupuis and Wilson 1999; Dupuis and Friele 2002, 2004a, b). In the process of cataloguing these occurrences, habitat associations were described from the site to watershed level and preliminary habitat stratification is available Figure 5; Dupuis and Friele 2002, 2004a, 2006.


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Figure 5 Distribution of Rocky Mountain Tailed Frog and stream habitat stratification in the Yahk (left) and Flathead (right) River watersheds, southeast British Columbia (source Dupuis and Friele 2005).

2.6 Wildlife Habitat Areas Wildlife Habitat Areas (WHAs), consisting of 100m buffers alongside all known subbasins containing Tailed Frog occurrences were proposed in 2005 and established in 2006 Figure 6. A conservation analysis (Dupuis and Friele 2005a) was prepared to examine the conservation status of Rocky Mountain Tailed Frog in relation to various forms of habitat protection (parks, fish streams, various reserves, non-harvesting land base).


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Figure 6 Rocky Mountain Tailed Frog WHAs in the Yahk (left) and Flathead (right) River watersheds, southeast British Columbia (source Dupuis and Friele 2005a).

3 PROJECT BACKGROUND3.1 Purpose Wildlife are species or plant communities that have been determined to require special management under Forest and Range Practices Act (FRPA). Wildlife habitat areas (WHAs) were established under the Identified Wildlife Management Strategy for areas of important habitat or areas containing critical habitat features for Identified Wildlife.

The Forest and Range Practices Act and Regulations apply a results-based management approach. As a consequence, monitoring is required to evaluate the management success. An effectiveness evaluation program, referred to as the Forest and Range Resource Evaluation Program (FREP), was initiated to assess the effectiveness of 11 resource values managed for under FRPA (for more information visit

The Forest and Range Resource Evaluation Program (FREP) implements a three-tiered approach to conducting effectiveness evaluations. The three levels of assessment are referred to as routine, extensive and intensive evaluations defined as follows:

Routine evaluations: Low intensity overview evaluations that use indicators obtained through direct consultation with local industries and/or governing agencies, and through GIS queries.

Extensive evaluations: Moderate intensity evaluations that involve systematic, standardized categorical and quantitative data collection of specified indicators, along randomly selected transects/reaches.

Intensive evaluations: Detailed evaluations that involve intensive quantitative data collection and analysis (with comparison to controls, where applicable), to answer specific research questions.

This project was intended to develop baseline information and explore monitoring methods, contributing to the development of a protocol for Tailed Frog wildlife habitat area effectiveness monitoring.

3.2 Objectives The main objective of WHA effectiveness evaluations is to determine whether an established or proposed WHA is achieving its intended goals and objectives. Generally, the goals of a WHA are to minimize disturbance within important habitats, often related to critical life stages, and maintain the habitat conditions and features necessary for the species to continue to successfully use the area, and in these ways meaningfully contribute to the conservation of the species. Critical habitats for Tailed Frogs include breeding reaches Figure 5 to generate adult recruits, and dispersal reaches Figure 5 to maintain gene flow and facilitate adaptation to climate change (Dupuis and Friele 2002, 2004a). Specific monitoring objectives of a Rocky Mountain Tailed Frog WHA effectiveness evaluation include:

Confirming the continued presence of Tailed Frog tadpoles and juveniles within WHAs, and documenting trends in relative abundance within these WHAs;

Confirming continued suitability or improvements to aquatic and terrestrial habitats and habitat features that are important for reproduction and larval development within WHAs;

Documenting and evaluating threats/risks to Tailed Frog populations within WHAs and the sub-basins where WHAs are located;

Evaluating accessibility of important habitat features and habitats that may not be within WHAs. Assessing mortality risk and population security (viability) within WHAs.

Through effectiveness evaluations it will be possible to assess the effectiveness of WHAs within the context of regional (Flathead and Yahk River) populations. If WHAs are deemed ineffective or only partially effective at conserving Tailed Frogs in southeast B.C., results of the effectiveness evaluation can be used to improve management standards (e.g., General Wildlife Measures). In some areas, improving WHA effectiveness could involve changes in the design and/or number of WHAs, strategic WHA


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relocation, or the use/addition of other conservation strategies. Thus, effectiveness monitoring is used in the context of adaptive management.

WHAs aim to maintain important stream reaches and riparian forests, by establishing 100m wide riparian buffers areas (non-harvestable) along natal reaches, and by guiding forest and range management practices along core area boundaries and in encompassing sub-basins. Specific questions to consider in a Tailed Frog WHA effectiveness evaluation are as follows:

Is the Tailed Frog terrestrial and aquatic habitat maintained in WHAs sufficient to meet adult, juvenile and larval life stage requirements?

Are populations stable, increasing or decreasing within WHAs? Do WHAs and land use practices within the watershed minimize disturbances to in-stream habitats

required by the larval stages? Do WHAs and adjacent land use practices minimize disturbance to riparian habitats required by

terrestrial life stages (e.g., home ranges in natal reaches, mating/dispersal sites in headwaters)? Do characteristics of the sub-basins containing WHAs (e.g., relief, headwater OGMAs) facilitate

or impede movement of dispersing juveniles and adults? Within the sub-basin, are key processes (e.g., hydrology, microclimate) that create and maintain

suitable aquatic and terrestrial habitat conditions functioning within their natural range of variability?

3.3 Indicators Key indicators include lotic habitat condition (both physical and thermal), riparian habitat condition, and population occurrence and abundance.

3.4 Project History & Status Rocky Mountain Tailed Frog WHA effectiveness monitoring was initially conceived to track indicators and establish methods to detect cause and effect between indicators and population trends, as cited from Maxcy (2004), below:

“Monitoring programs typically require the measurement of indicators over time … Trends in indicators are important for predicting future status of environmental indicators such as species/habitat decline or recovery.”

“Finally, an important concept in effectiveness monitoring programs is establishing cause/effect relationships to explain patterns. Monitoring programs can be ineffective when they are not simultaneously designed to record environmental patterns and the causes of those patterns; without cause-effect relationships, it is impossible to link monitoring programs back to management practices. As a result, managers will not know which management practices are effectively contributing to achieving monitoring goals and which need to be altered. Causation can be examined in effectiveness monitoring programs by linking disturbances to changes in the status and/or trends of indicators.”

This approach is consistent with the recommendations of the United States Department of Agriculture (1999):

“To be most meaningful, a monitoring program should provide insights into cause-and-effect relations between environmental stressors and anticipated ecosystem responses. Indicators should be chosen based on a conceptual model clearly linking stressors and indicators with pathways leading to effects on ecosystem structure and function. This process enables the monitoring program to investigate the relations between anticipated stressors and environmental


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consequences, and provides the opportunity to develop predictive models to anticipate trends instead of waiting until trends have been demonstrated.”

The initial monitoring protocol design (Dupuis and Friele 2005b) followed this guidance and laid out a set of indicators and methods for routine, extensive and intensive monitoring. The implementation of an assessment protocol for routine indicators was piloted by Gyug (2006) for Coastal Tailed Frog in the Merritt Forest District, however no routine evaluation has yet been conducted for Rocky Mountain Tailed Frog. A design for terrestrial habitat monitoring is under development by MoE staff.

To detect trends and discern cause and effect (as per Maxcy 2004; USDA 1999), the field sampling methods developed in the initial monitoring protocol design were designed for statistical precision. As a result, the methods were detailed and labour intensive, and this limited how many sites could be surveyed within the budget available. In 2005, it was only possible to sample six sites, three each in the Yahk and Flathead watersheds. These sites were named “Sentinel” sites based on the concept that any changes affecting the watershed would be observed at the sample sites.

At each Sentinel site, sampling was conducted within a 100 m reach. The tasks trialed included (1) detailed survey of channel morphology to document hydraulic geometry and channel condition, (2) pole photography to document channel condition and/or provide samples for photo-sieving of streambed sediment, (3) 100-stone pebble count samples and embeddedness estimates to document streambed texture, and (4) ten 3m belt area constrained searches to document animal abundance. Some methods were ineffective (e.g., photographic documentation) or simply too intensive given the available budget (e.g., detailed channel surveys to monitor channel condition). Results of the first field season concluded the most feasible tasks were monitoring of trends in animal abundance and streambed texture.

No funding was provided in 2006 so there was no field season that year.

In 2007, based on the 2005 pilot sampling results, a three-year monitoring study was initiated at the six sentinel sites established in 2005. The objectives of this work were to collect baseline data and refine the sampling methodology further. Area-constrained searches and embeddedness estimates were repeated at each of the 2005 sentinel site reaches, and at one additional 100 m reach per sentinel site, at the same sampling intensity and following the same methods as in 2005. Pebble count transects were completed using the same methods as in 2005 but at double the sampling intensity. Channel condition sampling was limited to gradient and water temperature in each reach, as well as seasonal active channel width and depth for each population belt transect.

In 2007, shortcomings with the Sentinel site approach became apparent. An early critique was offered by Gyug (2005a, p 12):

“…trying to measure these indicators in one spot on the WHA where changes have not occurred (and may never occur) will be a fruitless exercise that will not yield results except by luck, and even then it may be many years until before-after comparisons can be made.”

In 2007, MoE expressed the opinion that although the Sentinel site approach yielded good baseline information, it did not track the occurrence and abundance of Tailed Frog within the entire study area, therefore it was not meeting a critical objective. Nevertheless, the initial study design was adhered to for the 2007 field season. Finally, in her comments on the 2007 field season, Pickard (2008) expressed the same concerns and suggested a more spatially extensive approach be followed, employing concepts such as “double sampling” and “rotating panel” design (Pickard and Porter 2008). Double sampling referred to calibrating sampling methods that differ in cost and accuracy, and then using the cost effective but less accurate method to collect a greater number of samples (Thompson 2002; Pollock et al. 2002). Rotating panel referred to sampling designs that allowed the estimation of both status and trends, but employed more sample sites distributed over a larger area, without resampling previous sites in succeeding sample years (McDonald 2003).


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This spatially extensive approach aligns with the United States Geological Survey’s Amphibian Research and Monitoring Initiative (ARMI) (http://armi.usgs.gov/programbackground.asp#amphibianmonitoring). ARMI states that different intensities of baseline data are needed (e.g., routine, extensive, intensive), but ultimately monitoring has to be conducted at the landscape level. According to the ARMI website, an amphibian monitoring program follows the following tenets:

“A defined range of inference. Investigators delineate the geographic range of inference, divide it into sampling units, and select a subset of units to sample via a probabilistic scheme. This includes decisions on species to monitor and habitat data to collect and analyze as covariates.

Development of regionally meaningful response variables that can be integrated at a national level. Currently, one such response variable is being tested: proportion of sites (or area) occupied (PAO). Regional investigators will produce annual estimates of PAO and PAO variance for a subset of species of their choosing for each midlevel monitoring area.

Disease screening as an integral part of data collection. Data are being collected at each mid-level monitoring area.

Stressors and Causal Research. Research to date indicates that a host of environmental variables including climate change, competition, contamination, disease, habitat destruction, parasitism, predation, and ultraviolet radiation may cause amphibian population declines or malformations. It is likely that amphibians are subject to combinations of these stressors, and that the combinations vary by region and time. Physical and fiscal limitations of ARMI preclude monitoring for all potential stressors at all amphibian monitoring locations, but to the extent possible, the ARMI Program will link collection of statistically robust amphibian monitoring data with environmental data to support covariate analyses into potential causal relationships.”

Consequently, the 2008 and 2009 sampling seasons were designed to satisfy the needs of a more spatially extensive monitoring approach, with the aim to provide a first test of PAO after the 2009 field season. This required a shift in the data collection methods.

To sample a greater number of sites over a wider area, a more rapid sampling method was required. Time constrained searches (TCS) were used in previous reconnaissance work (Dupuis and Friele 2002, 2004a, 2004b), and in a post-fire assessment (Friele and Dupuis 2006), so this method was the logical rapid assessment procedure to employ. To follow up on Pickard’s “double sampling” recommendation, a calibration of TCS and ACS sampling techniques (Section 5.10) was required. The goal of 2008 and 2009 field seasons was to collect as many calibration samples as possible. In the two seasons, 44 calibration samples were collected.

In 2008, an additional baseline objective was introduced, based on the observation that cold streams within the Flathead study area Figure 5 were unoccupied (Dupuis and Friele 2006). The goal was to characterize the annual stream thermal regime, to define the threshold for critically cold habitat, and relate growing season length to Tailed Frog productivity via biomass (animal number, length and weight measures). In 2008, stream temperature dataloggers were installed to measure the length of the growing season at different sites for approximately one year.

In summary, the detailed trend monitoring conducted at the Sentinel sites in 2005, 2007, and 2008, and the stream thermal regime data collected in 2008-2009, represents intensive baseline information. This information will contribute to understanding Tailed Frog population dynamics (year-to-year fluctuations, relationship between habitat quality and animal occurrence/abundance), definition of critical thresholds, and habitat stratification (geomorphic and thermal). Thermal classification of individual streams may provide a basis for assessing potential impacts caused by future climate change in the region (see Walker


http://armi.usgs.gov/programbackground.asp#amphibianmonitoring

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and Pellat 2008). The calibration sampling conducted in 2008 and 2009 will provide a relationship between ACS and TCS methods, thereby increasing the utility of the more cost effective TCS method for estimating animal abundance. The TCS data collected in 2008 and 2009 can be compared against TCS samples collected in 2001 (Yahk reconnaissance; Dupuis and Friele 2002), 2003 (Flathead reconnaissance; Dupuis and Friele 2004) and 2005 (Flathead post fire; Friele and Dupuis 2006). A PAO comparison is now possible.

In addition to the tasks outlined above, the occurrence of the RamCabin wildfire provided the opportunity to monitor the impact of fire on Tailed Frog. The RamCabin fire burnt over much of the Storm and Leslie drainages one week after reconnaissance sampling was completed in 2003. In 2004, Friele (on his own initiative) resampled (using same TCS methods as the reconnaissance work) several sites in the Storm drainage. Then in 2005, 13 sites within the fire boundary and 11 sites outside the fire boundary were resampled, again using the same TCS methodology as in the reconnaissance work. Preliminary results were presented by Friele and Dupuis (2006). The 2005 sampling was requested by Kathy Paige and is part of the pilot project.

4 STUDY AREA4.1 Climate Climate in the study area is continental. At Cranbrook Figure 7; elevation 940 m, mean annual precipitation is about 400 mm/year, with about 30% falling as snow between the months of September and April (Environment Canada 2004). Maximum precipitation occurs as rain from May to July inclusive as a result of convective storms. A secondary precipitation peak occurs from November to January. Winter precipitation results in the accumulation of a thin snow pack. Mean annual temperature is about 5.5 oC (Environment Canada 2004). Freezing temperatures occur from November to March inclusive. April, September, and October are cool, with average daily temperature of 0–10 oC; average daily temperatures exceeding 10 oC occur from May to August inclusive. July is the hottest month of the year, with an average daily temperature of 18 oC.

Figure 7 Temperature and Precipitation Climate Normals at Cranbrook Airport, 1971-2000.

4.2 Hydrology Stream runoff Figure 8 reflects the continental climate, with an annual runoff peak in May and June resulting from a combination of snowmelt and rain-on-snow. There may be a secondary runoff peak in the fall, but this is subdued because most precipitation falls as snow. Very rarely, a fall storm may result in a severe flood. The low flow period occurs from August to March.


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Figure 8 Monthly discharge (m3/s) for Cabin Creek near mouth (Station 08NP004), 1978-2008 (Environment Canada 2009).


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4.3 Physiography Physiographic differences between the Yahk and Flathead cause differences in local climate. Timberline in the region is situated at about 2,000 m elevation. The Yahk River mainstem flows at 1,200 m elevation, and valley-bottom to drainage divide relief is a maximum of 600 m. Thus, its tributaries drain entirely forested catchments. Local climate results in predominantly moist–warm interior cedar hemlock (ICHmw) and Engelmann spruce–subalpine fir (ESSFwm) biogeoclimatic forest variants (Meidinger and Pojar 1991). Late summer headwater stream temperatures are typically cool (8–16oC), and within the range of the Tailed Frog. In the Flathead, the base level of the Cabin and Couldrey mainstems is about 1,500 m elevation. With a local relief of up to 600–700 m, some drainage basins reach into the alpine. Alpine ridges form the northern (29-mile Leslie Ridge) and western (Couldrey Ridge) boundaries of the mapped range, and Inverted Ridge separates the Cabin and Couldrey drainages Figure 5. This local climate supports primarily dry–cool ESSF (ESSFdk) and montane spruce (MSdk) biogeoclimatic forest variants (Meidinger and Pojar 1991). In this setting, late summer temperatures of creeks range from 5 to 17 oC, with some being colder than optimal for Tailed Frog. Both regions contain sedimentary rock (e.g., argillite, siltstone, and quartzite; refer to Leech 1960; Holland 1976; Journeay et al. 2000) that range from reasonably hard but brittle, to poorly consolidated and very soft.

5 METHODS5.1 Study Design Post metamorphic Tailed Frogs breed instream and spend a significant proportion of their 10+ year lifespan instream, while tadpoles reside instream for 1-4 years. Due to this dependency on the lotic habitat, tadpole abundance has been typically used as an indicator for Tailed Frog inventory and habitat association research (e.g., Welsh and Olivier 1998). Monitoring terrestrial frogs is more time consuming, less productive and not suited to an expedient monitoring strategy. The study of terrestrial movement of Tailed Frog is considered an intensive investigation (e.g., Daugherty and Sheldon 1982; Wahbe et al 2004; Burkholder and Diller 2007). Therefore, the monitoring approach herein is based on instream, or lotic indicators.

In this section, the distinction is made between Sentinel samples (collected at Sentinel sites) and Calibration samples (collected at both Sentinel and Calibration sites). Sentinel samples are based on 100m reaches; while Calibration samples are based on 50m reaches (see Sections 5.7-5.10 for detailed description of animal sampling at Sentinel and Calibration sites).

In 2005 and 2007, sampling consisted of developing the Sentinel sampling approach at the six Sentinel sites. As discussed Section 3.4, in 2008 the emphasis changed to a spatially extensive approach, with a focus on collecting calibration samples. In 2008, typical Sentinel samples were collected at Sentinel sites, and Calibration samples were collected at other sites within the study area. In 2009, Calibration samples were collected at Sentinel sites and at new Calibration sites within the study area. A total of 44 Calibration samples were collected.

5.2 Field work Effort and Schedule Annual budgets typically allowed for 12-16 field days Table 1, plus 4 days travel and logistics, which included planning (1 day), field preparation (1 day), and travel return from Lower Mainland (1.5 days each way) to southeastern BC and between Flathead and Yahk regions (one day travel and logistics). In 2005, 4 days (Aug 30 - Sept 2) were allocated to a post-fire assessment and 12 days to Sentinel sampling. In 2007, 2 days were allocated at each Sentinel site. In 2008, 6 days were allocated to Sentinel samples (1day each) and 10 days to Calibration samples. In 2009, 15 days were allocated to Calibration samples and 2 days to retrieving stream temperature dataloggers.

Fieldwork in 2005 and 2007 employed a two-person field team, and in 2008 and 2009 a four-person field team. In all years, a two-person team (Friele and Assistant) was per the MoE contract. In 2008, Jared


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Hobbs supplied BC Conservation Corps staff to make up the additional two-person team; while in 2009, MoE and MFR supplied the additional two-person team as part of a training initiative.

The annual work plan was designed according to what could be accomplished given the available budget/time and personnel.

Fieldwork involved camping in remote locations, bushwhacking, and long days hunched over to move and flip cobbles and boulders. Field workers must have proven resiliency in the bush and healthy backs.

Table 1 Fieldwork schedule, 2005-2009. Year Flathead Yahk Water temp Comments2005 Aug 30-Sept 2,

Sept 3-6, 15-16Sept 7-10, 13-14 Stream conditions at

Sentinel samples were cold, with water temperatures between 4-7ºC in all creeks but Storm.

Post Fire Sampling was conducted Aug 30-Sept 2. Between September 10-11, 30-45 cm of snow fell in the study area.

2007 Aug 20-25 Aug 27-30 All Sentinel samples > 7ºC

Consistently clear and dry.

2008 August 9-18 Aug 22-26 All Sentinel samples > 7ºC, select cold creeks sampled for calibration.

Clear and dry on all work days except Aug 26 when it rained and tarps were required. Rain also between sessions on Aug 19-21.

2009 Aug 18-24 Aug 6-13 All > 7ºC Rain on Aug 12-13 when sampling required tarps.

5.3 Sampling Period To ensure consistency in animal detectability and phenology, it is important that sampling in any given year be conducted within the same month. August is preferred because it is the driest summer month Figure 7, with the lowest stream flows Figure 8, and therefore has the highest animal detectability: streams are narrower and shallower and therefore easier to search, and metamorphosed frogs are typically confined to the stream (or other wet sites). September is an unreliable month for favourable temperatures, and streams may become too cold, especially in the Flathead area.

In 2005, there were delays in issuing the contract and the work was executed in September. The water temperatures were cold, and this made sampling challenging for the field crew. Second, it snowed 40 cm in the middle of the field season. In subsequent years, sampling was conducted in August.

5.4 Rationale for Selection of Sample Reach Length Stream segments identified for study must be of an adequate length such that the phenomena being studied are properly captured in the study reach. The channel morphology, the length of morphological units (i.e., meanders, step-pools, etc), the periodicity of those units, and practical considerations determined the sample length chosen. In fluvial studies, the sample reach length is typically defined in terms of multiples of channel width. Recommendations for reach length range from 5-7 channel lengths to 10-20 channel lengths (Bunte and Abt 2001; pg 325).

Channel width of Tailed Frog breeding reaches are typically 3 to 5 m wide, but may be up to 10m wide, or more. Based on the range of recommended channel width multiples, a 5m wide reach could have a sample length of 25-100 m. Based on the need for large pebble counts (100-200 stones) and the need to distribute ten 3m area constrained belt samples for an aggregate of 30m, a 100m reach length was chosen for Sentinel samples. A 50m reach length was selected for Calibration samples because half the coverage was required (three 5m belts for an aggregate of 15m) and 50m captured geomorphic conditions adequately.


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The criteria for selecting a sample reach for a Sentinel or a Calibration sample was a homogeneous alluvial channel, with no bedrock (if feasible), no coarse woody debris jams, and no channel braiding. Because it was often difficult to define a homogeneous sample that was 100m long, sometimes several attempts were required before these criteria could be met.

5.5 Naming Conventions and Locational Data Creek names used in this report follow those used in the reconnaissance projects (Dupuis and Friele 2002, 2004). Formal creeks names were used where they existed and informal names were applied where they did not. These creek names are provided on the maps that accompany the reconnaissance reports. All the creek names used herein are recorded under Creek ID in the master spreadsheet - Pilot 2005-09 Sampling Summary. Site names follow a two-part system, using a two-letter code for Creek ID followed by a hyphen and unique alphanumeric identifier (e.g., Cabin site 10, CA-10). The alphanumeric for Sentinel sites is R1 or R2 depending on which 100m reach was sampled in a given year (e.g., ST-R1, ST-R2). For Calibration sites, the alphanumeric identifiers were established when directions were provided to Jared Hobbs prior to his installation of I-buttons. Sites were selected where reconnaissance sample locations existed, and with some data mining the calibration sites could be correlated with sites from the spreadsheets in the Reconnaissance data folder.

Sentinel and Calibration site locations (master spreadsheet - Pilot 2005-09 Sampling Summary - and in the calibration spreadsheet - Calib_TCS-ACS_2008-2009.xls) were collected using a handheld Garmin GPSmap 60cx global positioning unit. Generally, location data was recorded only if better than 10m accuracy was reported, but in some cases that was not possible due to poor horizon visibility or satellite configuration. Monitoring site locations have not been verified and checked for accuracy. Sometimes, but rarely, sites plot in erroneous locations: one such error was noted in preparing this document, the start location for Cabin Sentinel (e.g., CA-R1). The error was corrected using previous site data, knowledge of the area and Google Earth. Other slight inconsistencies might exist that can only be corrected through plotting which was beyond the scope if this project.

Exact field locations of Sentinel sites are marked in the field with blazes and tags on trees and can be found with the directions provided in Appendix 1. Field locations for Calibration sites were not marked in the field, because the geo-referenced location is accurate enough for a sample representing a much longer alluvial reach (i.e., the sample is 50m long, but is a subset of an alluvial reach that may be several hundred metres long).

5.6 Channel and Habitat Condition Sampling

5.6.1 Topographic SurveysDetailed topographic surveys can be applied to measure hydraulic conditions of the channel (Wood-Smith and Buffington 1996; Zimmerman and Church 2001; Wooldridge and Hickin 2002; Chin 2003). One measure of condition is the potential energy dissipated by steps at low flow (Chin 2003). This is indicated by the sum of step heights divided by total height of the reach (Figure 9, left column). By this method, channel condition is a measure of how well formed the step-pool geometry is at a site. Steps and pools are linked morphological units Figure 9; step-pool geometry. Wood-Smith and Buffington (1996) have demonstrated that the ratio of pools to runs can differentiate between disturbed and pristine channels, with pristine channels having a high percentage of the total reach height formed by steps rather than runs.

Good channel condition, with well formed step pool channel morphology, results in turbulent flow and reduced streamflow velocities, thereby reducing the tractive force on the stream bed and on the animals therein. Tailed Frogs are adapted to this condition.


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Figure 9 Left column: step-pool channel morphology and bedform resistance changes caused by deep flow or sedimentation (after Chin 2003), where H, total reach height; h, individual step height. Right column: step-geometry (top box) and watershed level distribution of geomorphic process domains and sedimentary processes (two bottom boxes; from Montgomery and Buffington 1997).

Topographic surveys of channel morphology and hydraulic geometry were conducted at each Sentinel site in 2005. The proposed survey method (Dupuis and Friele 2005b) was a precise survey employing a total station instrument. Due to the short preparation time available due to late contract allocation, the surveying method adopted was an open traverse using an Impulse® laser rangefinder mounted on a monopod. The rangefinder measured vertical and horizontal distance. Bearings were measured using a compass. The survey program consisted of measuring the long profile of the sample reach and a number of channel cross-sections.

Prior to surveying, brush overhanging the banks was cleared, the channel was walked, and channel steps were defined and labeled with flags on the bank. Each step was described, including number and size of stones or wood pieces, and orientation of the step to the bank. Steps and some intermediate positions to be used as “turning points” were marked with blue paint.

The long profile survey followed the channel centreline (thalweg). Three bedform units were identified: steps, pools and runs. Steps are semi-stable features oriented transverse to the flow, and formed of interlocked (imbricate) groupings of large stones or large woody debris. Runs are long sections of uniform, intermediate slope, composed of intermediate-sized substrate; they begin at a pool outlet, and may end in a pool or at a step. Pools are areas of reversed channel slope, and may be of two types: plunge pools downstream of steps, or pools at the downstream end of runs.

Surveying proceeded from downstream to upstream along the center of the wetted channel, with the goal of producing a detailed description of the bed topography Figure 9. Survey points included step crests, the deepest part of pools, pool outlet and run surfaces. For each long-profile, about 50 shots were taken, or about one every 2m.


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Stream cross-section geometry (i.e., hydraulic geometry) measured at “turning points” targeted specific survey points including top of bank, bottom of bank, vegetation trimline, water’s edge, bed topography, and the turning point. The Impulse® laser was positioned on one bank, and 10-15 shots were taken for a typical cross-sectional profile. Ten to fifteen cross sections were surveyed per 100 m reach.

In the office, survey data for each creek was entered into an Excel spreadsheet (folders Pilot_2005-2009 - Sentinel samples - Sent_Reach Surveys_2005) and reduced to a vertical datum estimated from the TRIM map.

Long profiles and cross sections were then drafted at a scale of 1:400 on tabloid size paper. Reach plans show the location of turning points, the location and orientation of cross-sections, the top of bank and vegetation trimline, and channel centerline (thalweg). Channel cross-sections show the profile and imaginary water surface extending from trimline to trimline.

In 2005, all drafting was done using a Freehand graphics program (FH9). In 2009, the Freehand files were converted to a single file using Adobe Illustrator, version CS4 format. Individual reach plans are available as pdf files. Excel spreadsheets and graphics files are included in the Data folder – Sentinel Samples – Sent_ReachSurveys_2005.

Following the drafting of plans and profiles, step geometry and hydraulic geometry were calculated. Total height of the reach (H) divided by the total length (L) yielded average slope (SR). Step heights (h) were calculated for each step as the elevation from a step crest to the next step crest or pool outlet crest. The total elevation drop produced by steps was derived as the sum of all step heights (∑h). The effective slope (SE) was then calculated as the residual height (H-∑h) divided by the reach length (L). Run lengths (r) were measured from pool outlet crest to the crest of the next step. The proportion of the reach occupied by runs was the sum of all run lengths divided by the total length (∑r/L).

Hydraulic geometry is the channel cross-sectional geometry used in calculating flow velocity and discharge. Standard parameters include width, depth, hydraulic radius, and channel slope. For each cross section, the area (A) and wetted perimeter (WP) contained between the top of bank (subscript B; i.e., AB), and vegetation trimlines (subscript TL; i.e., ATL) was digitized and calculated by MINICAD software. Then the hydraulic radius (R) for each section was calculated as A/WP. Finally, a reach-averaged hydraulic radius was computed as the average of all cross-sections. Using Manning’s equation, velocity was calculated as 1/n*(R2/3)*(S1/2), where “n” equals Manning’s coefficient, which varies with bed roughness. Reach-averaged velocity was calculated using a Manning’s “n” value of 0.06 (Desloges and Gardner 1984) for both average slope (SR) and effective slope (SE). Discharge for both bankful and vegetation trimline flow, using both average and effective slope, was then calculated by the continuity equation: discharge (Q) equals velocity (V) times cross sectional area (A).

5.6.2 Pebble CountsTailed Frogs respond negatively to increasing levels of fine sediment in the streambed. Thus habitat quality is inversely proportional to the percentage of fines (e.g., <= 32 mm).

To measure the change in refuge habitat quantity accurately, the grain size distribution of the entire armour layer must be measured using volumetric samples. To be statistically accurate, the size of a volumetric sample must be about 100 times the weight of the largest stone in the sample (Church et al 1987). In Tailed Frog streams, with stones exceeding 256 mm in size, the bulk sample required to represent the size distribution would be approximately 500 kg or more. Clearly, this is impractical for monitoring purposes.

Pebble counts can be used to measure grain size distribution (texture) of the surface layer. Because the subsurface layer is not measured, pebble counts provide an index of change in refuge habitat quality only (not quantity). Although this method is a quick and efficient method to measure surface fining, it has several limitations: (1) large sample sizes are required for poorly sorted substrates, as in Tailed Frog creeks; (2) they are subject to operator error; and (3) the fine tail of the distribution is poorly represented because of the difficulty of selecting smaller particles.


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Streambed surface textures were sampled by the Pebble Count method (Bunte and Abt 2001, pg 145), which is a systematic sample of stones selected along a linear transect. Whether the sample site was a 100m Sentinel sample reach, or a 50m Calibration sample reach, a nylon tape measure was laid out, taut over the active channel surface along the length of the sample reach. To follow the thalweg, the tape was anchored by stones at bends, creating a kinked transect. Note that the active channel area is the area free of vegetation that is subject to flooding more or less annually. Since the sample is conducted at low flow (August, Figure 8), parts of the sampled bed may were dry during sampling.

Stones were selected for measurement at 0.5 m or 1.0 m increments. The increment used depended on the size of the stones on the bed. The idea is to use an increment size that reduces the potential of double sampling. If the grain size distribution appeared coarse, then a 1m increment was used; whereas if the distribution was finer, then the 0.5 m increment was used. Where the desired sample could not be achieved with the increment and reach length selected, then the tape was shifted laterally, and the process repeated to achieve the desired sample size. In some cases, double sampling large stones was unavoidable. Where wood formed a channel element (i.e., a step) then it was recorded as wood and its diameter noted. Similarly, if bedrock was encountered, it was recorded as rock. Note however that alluvial reaches were selected for sampling, so rock was avoided if possible.

Stones were selected by suspending a pointed metal rod “plumb bob” next to the tape increment and lowering it to the streambed. The stone identified by the tip of the plumb bob was then removed from the bed by hand and fitted through successively smaller “gravelometer” holes Figure 10 until the stone was retained by a hole (Bunte and Abt 2001; pg 25). This hole size was then recorded. If the stone could not be removed from the streambed, either because it was too large to lift or was too embedded, then its b-axis1 was identified and measured with a folding ruler. Measurements were called out to a field assistant who recorded the data, for a total of 100 or 200 particles, depending on the sample being collected.

Figure 10 Square hole template, or “Gravelometer” for standardizing grain size measurements for pebble count samples. (website: http://fisp.wes.army.mil/.

1 Particles have three perpendicular axes (a, b, c). In a sphere, they are all the same length. However, in a rock fragment they are all different lengths. The a-axis is defined as the longest, the b-axis as the intermediate, and the c-axis as the shortest. The b-axis typically determines whether the particle will pass through a sieve, and hence it is the relevant axis to measure (Bunte and Abt 2001, pg 14).


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For each sampling year, all raw pebble count data, data manipulation, chart (histogram, cumulative frequency) plotting and statistical calculations are included in a spreadsheet (e.g., Sent_texture_year.xls). All the spreadsheets are included in the Data folder - Sent_pebble count_2005-2008. The folder contains a metadata file - Sent_Texture_metadata.doc - discussing the data sets. Statistical parameters (grain size percentiles, graphic mean and standard deviation) calculated for a given year were carried forward to the succeeding year’s spreadsheet so that between year trends could be analysed.

Pebble count data were grouped into grain size categories based on the phi scale (i.e., -Log2): <2 mm; 2-4 mm; 4-8 mm; 8-16 mm; 16-32 mm; 32-64 mm; 64-128 mm; 128-256 mm; 256-512 mm; 512-1024 mm; and 1024-2048 mm. In 2005 and 2007, raw data were sorted and the Excel COUNT function was used to tally particle size frequencies. (Sorting cannot be undone. To regain order, raw data would need to be re-entered.) In 2008, the raw data was left as entered and Excel COUNTIF was used to tally particle size frequencies.

Histograms were plotted using the grain size categories as above. Based on lab and field observations that Tailed Frogs select coarser gravels (>50 mm) (Altig and Brodie 1972; Karraker et al 2006), these categories were then lumped into refuge filling (2-32 mm), refuge forming (32-256 mm), and step forming (256-2048 mm) categories. These histograms are useful for judging habitat quality at a site – the more refuge filling sediment in a reach, the poorer the habitat.

To calculate simple statistics (grain size percentiles, graphic mean and standard deviation) pebble count data was plotted on cumulative frequency diagrams. From the frequency distributions, the graphic mean (MZ) and standard deviation (SI) of the samples were derived as follows (after Folk and Ward 1957):

MZ = (D16+D50+D84)/3

SI = (D16-D84)/4+(D5-D95)/6.6

where Di is the grain size in phi units of the ith percentile on the cumulative frequency plot (Folk and Ward 1957).

The graphical standard deviation provides a measure of sediment sorting, with six classes of sorting based on graphical standard deviation: SI = 0-0.5, well sorted (WS); 0.5-1, moderately sorted (MS); 1-2, poorly sorted (PS); 2-3, very poorly sorted (VPS); 3-4, extremely poorly sorted (EPS); >4, unsorted (US) (Blair and McPherson 1999).

Mean and standard deviation were then used to calculate the required sample size with 90% confidence intervals 0.3 phi about the mean, following methods outlined in Bunte and Abt (2001, pg 241).

For Tailed Frog work, as for fish, we are really interested in the fine tail of the distribution. However, due to limitations of the sampling method and operator errors, which both make tests of the fine tail less reliable (Bunte and Abt 2001), shifts of the mean were used for analysis, with significant shifts toward the fine tail causing concern. Differences between means were calculated using the T-test as outlined by Sokal and Rolf (1987, pg. 170-172, eqtns 8.3 & 8.4), using formulas for both equal and unequal sample sizes:

Eqtn 8.3 - Equal n: Ts=(mean1-mean2)/((1/n)*(stdev1+stdev2))1/2, or

Eqtn 8.4 - unequal n: Ts=(mean1-mean2)/((stdev1/n1+stdev2/n2))1/2

Where mean and stdev are measured in phi units, and Ts and Tc refer to the sample T and the critical T value, respectively.

T tests for difference between D50 particle mean diameter (mm) follow Sokal and Rolf (1987). When Ts>Tc then the result is significant. Tc selected from Sokol and Rolf 1987, Appendix 2, Table III, for alpha 0.1 and n=100 or n=200.


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5.6.3 Embeddedness EstimatesEmbeddedness, or the degree to which cobbles are buried in fine sediment Figure 11, is a qualitative measure of the armour layer thickness and refuge space available (e.g., the higher the embeddedness the lower the available refuge space). It is estimated during Tailed Frog abundance searches for each belt subsample, using the following categories: none, 1; low, 2; moderate, 3; and high, 4 (Dupuis and Friele 2002, 2004),

Cobbles are used as the reference grainsize for embeddedness because cobbles (32-256 mm diameter) form refuge habitat. No embeddedness indicates that the substrate is free of fines (<32 mm diameter) to a depth exceeding 256 mm. Low indicates cobbles are buried in fines up to 25%. Moderate indicates cobbles are buried 50%. High embeddedness indicates cobbles are more than 75% buried in fine sandy substrate, or only pebbles (<64 mm diameter) are loose at the surface to provide refuge space.

For each belt subsample, the embeddedness rank is qualitatively judged while conducting animal searches. Since embeddedness may vary across the sample belt, each worker judges the embeddedness rank for the areas they surveyed, and at the completion of the sample the workers pool their opinions for the final rank that is recorded. One or two-letter descriptors were recorded (e.g., L, L-M, N-L, N, etc). The qualitative (e.g., L) and numeric (e.g., 2) rank is entered in the data sheet, and the reach averaged embeddedness value is calculated as the mean of the numeric ranks. Translating from a qualitative rank to numerical rank allows simple numerical summaries that may be useful. If embeddedness translates directly to habitat quality, then the numerical version will be easier to interpret. However, if it is possible that Tailed Frogs prefer the middle categories, it may be more useful to maintain the categorical response.

Figure 11 Qualitative embeddedness ranks based on cobble thickness (256 mm). Grey indicates refuge filling sediment.

Because embeddedness ranks were conducted during animal sampling, the results are included in the animal sampling Data folder - Sentinel Samples - Sent_Animal abundance_2005-2008 - Sent_Abun_05-08.xls, or Calibration Samples - Calib_TCS-ACS_2008-2009.xls

5.6.4 Pole PhotographyIn 2005, pole photography was employed in an attempt to document streambed conditions. Photos were taken at each defined step location and at some intermediate locations, for about 15 shots per reach. At each photo location, a pair of metre sticks set at right angles to one another were laid on the bed for scale. A 35mm SLR camera with a 50 mm lens and polarizer was fitted with a ball joint on the end of a 2m long pole. The 50 mm lens was chosen to provide resolution of particle sizes for photo-sieving analysis. High-speed film (800 ASA) was selected so that maximum depth of field could be utilized. To take the photo, the ball joint was adjusted so the camera was at approximately at a 45º angle to the pole, the self-timer was activated, and the camera held 2-3 m above the bed. The camera was set on automatic mode, the lens aperture was set on its smallest setting, and the focus was adjusted for the predicted height above the bed. The side of the stream (left or right bank), station number, and photo number were recorded for each photo. Photos were not analysed further in the office, because upon examination, reflection from the water’s surface, even with use of a polarizer, rendered them unusable for photo sieving (see Bunte and Abt 2001; pg 178).


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5.7 Tailed Frog Abundance Sampling

5.7.1 Basic Rubble Rousing MethodWhether conducting time constrained searches (TCS) or area constrained searches (ACS) the basic search method is the same. It is referred to as “rubble rousing”, but as defined herein is more thorough than the “rubble rousing” method described by Quinn et al (2007). Sampling consists of systematically simplifying the channel bed by removing all loose and partially embedded boulders and cobbles and placing them on the channel bank. Boulders that are too large to lift are tilted up. These large stones are refuge sites selected by Tailed Frogs (e.g., Karraker et al 2006), and moving them often yields numerous animals (frogs and tadpoles) and nest sites (i.e., hatchlings or egg masses). The coarse substrate is removed down to the plane at which all voids are filled, thereby removing all available pore space. Remaining pebble gravel deposits are raked by hand to dislodge remaining tadpoles. A small dip net is held downstream of material being dislodged, allowing tadpoles and frogs to be captured as the work proceeds. The tadpoles are chased into the nets by hands and fingers, and frogs are caught by hand. Banks and undercut spaces are visually and manually inspected for adult frogs. All caught animals are placed in a bucket of creek water and kept until the end of the search when they are tabulated and released. To keep the water cool, the bucket is kept in the shade, or partially submerged in the creek.

Successive year-to-year animal fluctuations at Sentinel sample sites, both positive and negative, suggests that this method of sampling does not cause systematic indirect lethal effects resulting in persistent declines.

5.7.2 Time Constrained Searches (TCS)When a TCS is being conducted, the searcher works a small area thoroughly using the rubble rousing method described above, and if no animals are found or the water column becomes turbid, then the searcher moves to a new location to optimize the chance of discovery within the allotted time. In each location, the area investigated is simplified in the way described above (Section 5.7.1). After the 30 minute search (i.e., 2 people for 15 minutes each) is terminated, the searcher conducts a visual inspection to tally any “missed” animals. When conducting a TCS, the searcher must be focused and work hard for the allotted time. It is also important that they have some experience with TCS and catching tadpoles, before their results can be considered valid. It is believed (but not tested) that searching “lightly” (e.g., Quinn et al 2007) will result in a lower detection rate than reported herein.

Based on reconnaissance work (Dupuis and Friele 2002, 2004), where “time to first detection” was diligently recorded, the sample time frame of 15 minutes, for a combined 30 minute search with a two-person crew, was found to optimize detectability for time spent. Figure 12 shows results from 135 positive detection searches within Yahk and Flathead ranges: in about 65% of searches detection occurred within the first 5 minutes, while in 95% of cases detection occurred within the first 10 minutes.


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Figure 12 Histogram of time to first detection for 135 TCS searches conducted during reconnaissance work (Dupuis and Friele 2002, 2004). TCS searches employed two people searching for 15 minutes each, for a combined 30 minute TCS search.

5.7.3 Area Constrained Searches (ACS)When an ACS is being conducted, sampling consists of searching channel wide belts of a defined length. Prior to sampling each belt, a 5 mm wire mesh fence is placed across the channel and supported with aluminum ski poles driven vertically into the substrate with a hammer. The bottom 0.2 m of the screen is folded and placed flat on the streambed and anchored with cobbles. Once the belt is established, then the entire area is simplified in the manner described above (Section 5.7.1.). This can take 10-20 minutes or up to several hours depending on the width of the creek and the abundance of animals in the belt.

Gyug (2005b) conducted a series of trials to test the accuracy and precision of 1m, 3m and 5m long channel wide area constrained searches replicated to achieve 15-45% (average 30%) coverage of 100m long study reaches (i.e., for total sample length varying between 15-45 m). Some trials were extended to cover 200-300 m long reaches. Based on his results, Gyug (2005b) differentiated between sparsely populated sites (<100 animals/100m) and more populous sites (>100 animals/100m). His main conclusions were 1) in more populous reaches, for an aggregate 30m sample, 3m samples did not perform as well as 1m or 5m sample lengths; 2) in sparsely populated sites an aggregate 30m sample is too small to achieve good accuracy and precision with standard error within 10% of the mean - best achievable standard error was 20% with 18, 5m long samples (i.e., 90m aggregate); and 3) sub-sampling 200-300 m reaches produced similar standard error to the 100m reach, thus standard error is not a function of reach length but is based on sample size within any given ecologically/geomorphically homogeneous channel reach. Although 3m segments apparently did not perform well, Gyug (2005b) could provide no intuitive ecological reason for the result, and assumed it was a statistical artifact.

5.7.4 Cohort Classification To ensure efficiency of data collection at Sentinel sites, animals were tallied by morphological classes (modified after Bury and Corn 1991) based on the following traits:

nest site: an egg mass (several strings with one attachment point) or cluster of hatchlings (small tadpoles with yolk sacks);

cohort 1, small tadpole with no leg buds; cohort 2, intermediate-sized tadpole with rear leg buds; cohort 3, large tadpole with well-developed hind legs; cohort 4 tadpole with developing forelimbs but no biting mouth metamorph; transforming tadpole with biting mouth and various stages of tail absorption;

Final report on the Rocky Mountain Tailed Frog WHA Monitoring Pilot

0

10

20

30

40

50

60

70

80

90

0-4 5-9 10-15Time to first detection (min)

Num

ber

of s

earc

hes

(n)

22

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juvenile frog; adult frog.

At stream temperature sites where length and weight data were collected cohort classification was slightly expanded to account for variations in tadpole growth between sites. Cohort 3 was expanded into three subclasses based on body bulk and rear limb development: Cohort 2/3 tadpoles with under-developed knees, and smallest in size and weight; Cohort 3 tadpoles with well-developed knees but intermediate in size and weight; and Cohort 3“Uber tads”, Cohort 3 tadpoles largest in size and weight.

5.7.5 Weights and Length MeasuresAll animals collected at stream temperature datalogger sites were tallied by morphological class (Section 5.7.4). For biomass estimation, the aim was to document representative lengths and weights of animals by class. At sites where abundances were less than 30 animals all were weighed and measured, but at sites with high abundance, only a subsample were measured. This was done to save time and because variability within each class appeared limited and large sample sizes were not required to capture the variability. Total length of tadpole and metamorphs, and snout vent length of frogs was recorded in centimeters using a ruler. Animals were weighed on a digital scale (Shanghai Yousheng Weighing Apparatus Company Ltd., BS600+ balance, 600g capacity, 0.01 g divisions) using a water-filled tare. Weight was recorded as the average weight (to 0.1 g).

All weight and length data is included in the Data folder - Calibration Samples in the Calib_CohortL&W_2008-2009.xls spreadsheet.

5.7.6 Animal CareAnimal handling corresponds to Invasiveness Category C of the Canadian Council on Animal Care (2003). Although a record was not kept of the direct mortality caused by searching, it is very low. Of the hundreds of animals caught each year, only one or two tadpoles were killed by trauma (crushed by stones or boots). One frog was injured in 2005 and had to be euthanised. Pharmaceutical and physical methods of euthanasia are recommended by the Canadian Council on Animal Care (2003). Given the rarity of the need for euthanasia, a physical method is most practical in the field setting.

5.8 Pre & Post Ram-Cabin Fire Sampling, 2003-2005. In 2003, the Ram-Cabin fire burnt over much of the Cabin, Storm and Leslie Creek watersheds. In 2004 and 2005, several pre-fire reconnaissance sample sites were revisited and 30min TCS samples were repeated and embeddedness rank recorded. The sampled sites consisted of samples upstream and downstream of sumps excavated in Tailed Frog channels, samples within the burn area, and a set of select control samples outside the burn area. Pre and Post burn sample data is compiled in a spreadsheet, Pre&postfire_2003-2005.xls, and is included in the Data folder - Ram-Cabin Fire Pre-post Samples_2003-2005.

5.9 Sentinel Samples, 2005, 2007, and 2008.

5.9.1 Site SelectionIn the Flathead area (Refer to Figure 5), Sentinel sample sites were selected in Storm Creek, Couldrey Creek and Cabin Creek, as per the rationale below:• Storm Creek was selected because it is the northern source colony for the Bighorn and Leslie creeks satellite populations. This stream is located at mid elevation, bordered by a 200-300 m wide moist riparian meadows, and characterized by low relief, low risk of debris flow, and a warm stream temperature regime. As a result, it is a secure and productive watershed.

• Couldrey Creek drains Couldrey and Inverted Ridges, alpine ridges reaching 2200 m elevation. The Couldrey watershed is typified by cold creeks unsuitable or marginally suitable for Tailed Frog (Dupuis and Friele 2006). The Couldrey Sentinel site is a warm enclave, with the stream temperature regime locally cool to warm because of the influence of a subalpine tarn lake and meadow system. Thus, this site is a source population for an otherwise sparsely populated watershed.


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• The Cabin Sentinel site is located along an assumed dispersal pathway from founding populations in the US. Much of the headwater forms a low elevation (1750-1800 m), gentle pass between Cabin and Burnham creeks. The watershed does not extend above 2000 m into the alpine ecotone, and as a result stream temperatures are cool to warm. The Sentinel sample site is located along a reach vulnerable to debris flow impact from steep 400m relief hillslopes, crossed by degrading forest roads, and supporting potentially unstable terrain. Severe disturbance on this channel would sever the link between Storm Creek and founding populations to the south. This WHA was designated to monitor the impacts of potential severe disturbance following fire or forestry operations.

In the Yahk area (Refer to Figure 5) Sentinel sample sites were chosen in Boyd Creek, Norge Creek and Sprucetree Creek, as per the rationale below:• Yahk headwaters do not extend above timberline, and the stream temperature regime is generally warm.• Yahk River flows from the upper Yahk subbasin, due south 25 km to the International boundary, and is fed from the east and west by 2nd –3rd order tributary subbasins. The range is composed of two spatially discrete areas (Boyd, Yahk headwater).

• The tributary subbasins support Tailed Frog populations linked by the riparian network. • At the northern range limit, sections of upper Yahk and Norge subbasins support warm and clean coarse habitat, and are very productive. Norge is the most readily accessed and was chosen to represent this northern population.

• At the south end of the Yahk headwater population, Sprucetree Creek also supports warm and clean coarse habitat and is productive. Adjacent subbasins (No Name, Cedartree) have lower flows and poorer habitat, and are less productive. Sprucetree was selected because it was productive and potentially vulnerable to road-related sedimentation, sediment flood and debris flow activity.

• Boyd Creek supports an isolated subpopulation with headwater links to the US founding population. It was selected for this reason. It supports poor habitat and low abundance, and is vulnerable to sedimentation impacts, such as from post fire effects.

Monitoring locations were targeted so that they (1) fell within the breeding zone of the population Figure 5, determined by previous sampling (Dupuis and Friele 2002, 2004); (2) were located in areas with reasonably good access; and if possible (3) were located in gentler reaches, or deposition zones, where sedimentation effects would be pronounced. Within these general locations, 100m long sample reaches were situated such that they were alluvial (flowing on gravel, not rock), homogeneous in character, with a single thread channel (i.e., not braided), and free of large woody debris jams that would prevent effective sampling.

Once selected, the downstream and upstream end of the reaches were marked by a blaze on a nearby tree. A metal tag noting the sample name and date of establishment was nailed to the blaze. On the opposite bank, a metal rod was driven into the stream bank. UTM locations were recorded for both upstream and downstream locations using a handheld GPS unit and are reported in the spreadsheet – ASMO 2005-2009 Sampling Summary.

5.9.2 Tasks conducted each sampling yearIn 2005, Sentinel sampling consisted of four tasks, in the following sequence: (1) detailed surveying of channel morphology and hydraulic geometry; (2) stream bed texture sampling; (3) pole photography of the streambed; and (4) Tailed Frog area constrained, channel wide belt sampling. The indicators were channel condition (via topographic surveys, pebble counts, and embeddedness estimates) and animal abundance (via area constrained searches). The sampling program was designed so that it could be completed in two field days for each creek.

In 2007 and 2008, a refined set of tasks were executed at the Sentinel sites, again targeting the indicators of channel condition and animal abundance. Sampling consisted of (1) pebble counts and embeddedness estimates, and (2) animal abundance sampling. In 2007, the sample size was doubled by defining an additional 100m reach.


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5.9.3 Pebble Counts and Embeddedness RanksIn 2005, sampling was exploratory and the standard recommended sample size of 100 particles was collected. After sample size analysis, a 200-particle pebble count was found to be the appropriate sample size (see below). Therefore, in 2007 the sample size was doubled by selecting a second 100 m reach at each sentinel site. After conducting a 100 pebble count in each reach at Storm Creek, it was realized that there would be enough time to sample 200 particles within each reach at the remaining sentinel sites. Thus in 2007, pebbles counts yielded a grand total of 400 stones for all sentinel sites, except Storm. In 2008 and 2009, sampling was consisted of one 200-stone count in Reach 1 of each sentinel site. These sampling trials will allow power analysis between 100, 200 and 400-particle samples. Embeddedness ranks were collected while animal sampling, as previously described.

5.9.4 ACS animal samplingBecause Tailed Frog distribution can be patchy (at all scales), a longer channel-wide belt sample will result in fewer cases of “not detected”. For Sentinel samples, we considered 1m, 3m and 5m belts based on a detailed trial by Gyug (2005b): it was decided that 1m belts would yield too many zero counts while 5m belts would involve too much work and reduce sample size (# of belts). Therefore, 3m belts were chosen to optimize detectability, sample size and feasibility given all Sentinel sample tasks had to be accomplished in one day. For Sentinel samples, ten 3m channel-wide belts were located within the 100m long sample reach. Following terminology of Gyug (2005b), this represented 30% coverage of the sample reach.

Gyug (2005b) stated that sub-samples should be randomly selected within a subject reach. However, systematic sampling does not produce biased estimates of means and confidence limits if there are no periodicities in the data (Krebs 1989; p. 234). Since Tailed Frog distribution is determined by lotic habitat, and step-pool distribution is a random phenomenon (Zimmerman and Church 2001), it is argued that systematic and random belt locations are both acceptable.

In 2005, samples were located systematically along the sample reach. In other years, they were located randomly, without overlap, along the sample reach. Occasionally start positions were shifted if deep pools or woody debris prevented a consistent sampling effort (as per Bury and Corn 1991). Once the belts were established, the search was done as described in Section 5.7.1. The embeddedness rank was estimated for each belt during animal sampling as described in Section 5.6.3.

For all years, Sentinel sample data (Sample ID, date year, stream temperature, belt sample increment, number of animals by cohort class (see below), summary totals, and embeddedness ranks) was entered into an Excel spreadsheet (Sent_Abun_05-08.xls) contained in the Data folder - Sentinel Samples - Sent_Animal abundance_2005-2008. The Spreadsheet contains variable description worksheets that describe all metadata.

Tadpole abundance data from TCS and ACS sampling is skewed by numerous zero counts. Lognormal or square root transformations do not normalize the raw data. Chi square tests yield high values (chi>300) and indicate use the negative binomial distribution for statistical analysis (White and Bennetts 1996). Due to the inapplicability of the normal distribution, sample size could not be estimated from the mean and standard deviation.

The non-parametric Mann-Whitney U test (SPSS), which makes no assumptions about normality, was used to test for differences in tadpole abundance between years (n=10 per site, per year) (Appendix 10.2).

5.10Calibration Samples, 2008-2009.

5.10.1 Site selectionCalibrations samples (n=44) were collected at Sentinel sites (n=6), and Calibration sites (n=38). Calibration sites were selected to ensure a wide distribution of samples across the study area, with at least one sample collected in each main subbasin (see Figure 5). To optimize field effort, the calibration samples were collected at 28 stream temperature (I-button) sites. Therefore, site selection for I-buttons sites determined the location of 28 of 44 Calibration sample sites.


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5.10.2 Tasks executed at each siteThe four field members worked in teams of two: one team conducted texture sampling and reach measurements; while the other team laid out the ACS sample belts and then conducted TCS. Once the texture sampling and reach measurements were completed, all members worked together to conduct ACS.

At all sample sites, the UTM location, stream temperature, reach gradient, and active channel width and depth were recorded. Active channel width and depth were measured for each belt and the average of the three measures was reported.

5.10.3 Pebble counts and embeddedness ranksPebble counts were based on 50m reach length, with 100-particle sample size, typically sampled at 0.5 m increments, and sampled as previously described. In the spreadsheet Calib_pebblecount_2008-2009.xls contained in the Data folder - Calibration Samples, the raw pebble count data were left ordered as sampled and the Excel COUNTIF function was used to tally particle size frequencies. Embeddedness ranks were estimated while animal sampling, as previously described.

5.10.4 TCS and ACS animal samplingCalibration samples were based on a selected 50m reach of homogeneous, alluvial, step-pool habitat.

The change in belt length from 3m to 5m reaches was done partly because three 5m reaches is a commonly used ACS method (Bury and Corn 1991), but also it was more efficient in set up time and longer belts result in higher detectability.

At calibration sites in 2008, three 5m reaches were randomly selected for ACS, without overlap, and the search was done as described in Section 5.7.1. Because Sentinel samples were conducted at Sentinel sites in 2008, the abundance sampling consisted of ten 3m reaches. Collecting three 5m samples would have created additional work and channel disturbance. Thus for 2008 five 3m belts were used to produce a 15m aggregate sample. In 2009 Sentinel samples were discontinued and standard Calibration samples were collected at Sentinel sites.

Because the habitat was homogeneous, the TCS search area was conducted outside the three 5m belts, across the remainder of the 50m reach. On second and third order channels (1-6+ m wide) (e.g., wider streams) the “remainder” is large enough that it does not provide a constraint to the TCS. However, first order channels are much narrower (< 1m wide), and TCS may require extra length. Thus, the Calibration method is not applicable to some small channels.

For each Calibration sample, location, stream temperature, reach gradient, seasonal active channel width and depth, number of I-buttons, Y/N statement whether length and weight measures were collected, animal abundance, embeddedness rank, and pebble count summary data are reported in the spreadsheet Calib_ACS/TCS_2008-2009.xls within the Data folder Calibration Samples. The spreadsheet contains Variable Description worksheets.

5.11Stream Thermal Regime, 2008-2009. The stream temperature regime is known to affect Tailed Frog life history, governing time to metamorphosis (1-4 yrs), seasonal dormancy (<6oC), and mortality (18-21oC) (Altig and Brody 1972; Claussen 1973; deFlaming and Bury, 1970). Stream temperature patterns along a stream may drive behavioural thermoregulation (Adams and Frissel 2001), with animals seeking refuge from lethal temperatures, or seeking warm productive sites. Previous work (Dupuis and Friele 2002, 2004, 2006) has provided evidence of striking thermal habitat patterns in the stream network. A better understanding of these will aid habitat stratification (see Figure 5) and predict potential habitat shifts under climate change (Walker and Pellat 2008).


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During 2008-2009, baseline data on stream annual thermal regime was collected. Sites Table 2 were chosen to span a range of thermal conditions from too cold to warm, and were located in a variety of physical settings from 1st to 3rd order streams, low to high elevation, various aspects and canopy cover types. The aim was not to establish a rigorous experimental design, such as investigating the effect of clearcutting on water temperature, but to document the range of existing conditions across the species range in BC.

The stream temperature datalogger (I-button) sites were located at Sentinel sites, and at 22 additional sites across a cline of creek temperature regimes ranging from too cold to thermally suitable, while also covering a wide range of elevation, aspect, and creek size conditions. Based on reconnaissance work Figure 5, six creeks were selected that were too cold to sustain Tailed Frog; while 22 sites were warm enough. At sites with I-buttons, length and weight measurements were taken where abundance permitted (>1-2 animals in the entire sample). These biomass measures will allow temperature thresholds to be defined for Tailed Frog suitability.

Growing season is based on degree-day calculations. The method selected for calculating degree-days is defined by the data. Where the data is near continuous (i.e., 15minute intervals), the calculation can be based on the formula for a sine curve. Where sparse observations exist, calculation relies on maximum and minimum daily temperatures. An examination of 15minute interval stream temperature data for other Kootenay creeks (raw data provided by Peter Jordan, MoF, Nelson) showed that the timing of maximum and minimum daily stream temperature is not consistent: within and between creeks it varies from day to day and shifts seasonally. To adequately capture the maximum and minimum daily temperatures, a minimum two-hour sampling interval was required.

I-buttons were provided and installed by MoE. Ideally the I-buttons would be programmed to collect a reading every 15 minutes and would be left in the stream for one year. In this case, the I-buttons were recycled from another project, and lacked sufficient memory so “creative” programming and placement was required. Due to data logger memory limitations, only a four-hour sampling interval was possible, and despite this wide interval, only an 11month sampling period could be obtained. Thus, to capture maximum and minimum temperatures, two I-buttons were placed at each site, offset by two hours. In addition, two additional I-buttons (also offset by two hours) were placed at each sentinel site, with maximum start delay to bridge the one month data logging/memory storage gap. At sites where only two I-buttons were placed, the data gap will need to be patched. The creation of a synthetic annual temperature curve for these sites is satisfactory, as the main interest is the characterization of the difference between sites, rather than year-to-year comparison.

The programmed I-buttons were coated in Plasti-dip rubber compound and then fixed with epoxy on a boulder 25 cm diameter, or greater. Boulder placement was in the channel thalweg (deepest section along centerline of flow), sometimes in a plunge pool, and stacked in an imbricate fashion against other boulders. The boulder was positioned so that the I-button was on the underside and was shaded. Locations were marked using GPS Table 2 and flagged for visual recognition the following season. All of the I-buttons were recovered in 2009. A few buttons failed to record any data.


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Table 2 I-button site locations (UTM, NAD 83, Zone 11U), date and time of retrieval, and number of I-buttons per site.

Region Sample-Site ID*Easting

(m)Northing

(m) Date Time # of I-butt.Flathead CA-6 679525 5440755 Aug 24 10:15 am 2

CA-10* 672083 5438811 Aug 24 11:30 am 2CA-9* 670352 5439295 Aug 24 11:55 am 2CA-R1* 671588 5444000 Aug 19 9:55 am 4ST-R1* 668329 5445186 Aug 18 2:50 pm 4ST-3* 672722 5441410 Aug 19 4:27 pm 2CA-4b* 667809 5442389 Aug 24 1:00 pm 2CA-4* 665905 5443113 Aug 24 1:10 pm 2CA-7* 670888 5440224 Aug 24 12:15 pm 4CA-5* 672375 5441008 Aug 19 5:45 pm 2FL-19* 670727 5430594 Aug 23 12:15 pm 4FL-20 672712 5430459 Aug 23 1:00 pm 2FL-21 674954 5431976 Aug 23 5:35 pm 2CO-R1* 667719 5434263 Aug 20 9:30 am 4CO-15* 673267 5432725 Aug 23 4:00 pm 4CO-16 674980 5432245 Aug 23 5:10 pm 2CO-17* 674801 5434202 Aug 23 6:35 pm 2CO-14* 670194 5433493 Aug 23 3:45 pm 2CO-11* 667309 5434228 Aug 23 3:00 pm 4CO-13* 668421 5435103 Aug 23 2:15 pm 2CO-18 680171 5434788 Aug 23 6:50 pm 2LE-23* 670685 5447110 Aug 24 4:00 pm 4LE-1* 668961 5449048 Aug 24 4:55 pm 2BH-22 * 665040 5447891 Aug 24 6:30 pm 2BH-010 663247 5447424 Aug 24 1:50 pm 2BH-009 663376 5447769 Aug 24 1:40 pm 2BH-012 661718 5448491 Aug 24 6:45 pm 2

Yahk SP-5* 589459 5438691 Aug 11 8:10 am 4NO-3* 590557 5445185 Aug 13 9:36 am 4NO-R1* 590888 5442475 Aug 6 9:00 am 4NO-11* 590289 5447263 Aug 13 10:00 am 2SP-R1* 591394 5439018 Aug 6 1:35 pm 4UY-1 592306 5447581 Aug 13 11:20 am 2UY-2 593268 5447531 Aug 12 9:38 am 2UY-6 591347 5441959 Aug 9 9:25 am 2YA-8 592326 5436282 Aug 7 8:30 am 2YA-9 595479 5430992 Aug 14 9:55 am 2MA-014* 588527 5443139 Aug 8 11:00 am 2MA-013* 587771 5443883 Aug 8 8:40 am 2NN-12* 592425 5438448 Aug 10 9:10 am 2BO-R1* 595350 5429903 Aug 7 8:56 am 4

* Indicates Calibration sample.


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5.12Data Review and Synthesis A unique site identification code was adopted based on the ‘Reach #’ identified in the original ‘Sampling Summary.xls’. The same identification code was used throughout the analyses. Each dataset was reviewed and, if necessary, reorganized to produce a simpler format that could be more readily used by analytical software, now and in the future. We also described any limitations of the datasets. A list of the revised datasets created is provided Table 3. All data processing and analyses were completed using a combination of: Excel, SAS, and R software. A summary of the script files for analyses are provided Table 4. All datasets and script files will be provided electronically.

Table 3 Summary of revised datasets.File name DescriptionFull_Pebble.csv Complete dataset containing all raw pebble count data, in one spreadsheet.Habitat results.csv Summary by site and year of the percent of substrate in each of 3 categories:

refuge filling, refuge forming, and step formingFull raw abund data.csv

Complete record of all raw abundance data, organized into a single spreadsheet. Each record is identified by: Watershed, SiteID, Date, Year, Method (ACS/TCS), Type (Sentinel/Calibration).

Full abund summary.csv

Contains the total count of Tailed Frogs across all belts within a site, by age-class; also records the total length of stream (sum of belt lengths) measured.

Full embed_summary.csv

Summary of embeddedness ranks by site and year.

Length-weight-summary-final.csv

Length and weight cohort data summarized by site.

Cohort by site.xls Counts of tadpoles by cohort, site, and year. Standardized to 15m.

Table 4 Summary of script filesFile name DescriptionAbundance.sas - some processing of raw data

- produces annual estimates for sites & watersheds (abundance and embeddedness)

- also produces summary file of age-class data by site and yearAbundance.r - Some processing of raw data

- ACS vs TCS comparisonPebble counts.r - Reads in and processes all of the raw pebble count data, to produce a

single spreadsheet with all of the pebble count data. Works with ‘data processing pebble.sas’ to do so.

- Groups the pebble counts into three habitat categories and reports the proportion in each category by site and year.

Data processing pebble.sas

- Assists ‘Pebble counts.r’ in processing the raw data.

D50.r - produces D50 barplots- produces pebble count box plots


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5.13Data Summary

Table 5 Summary of available data.Channel condition Metrics6 sentinel sites (2005)

Detailed survey of channel morphology.

- ∑Runs/L, sum of runs divided by total reach length- ∑h/H, sum of step heights divided by total reach height- Velocity/discharge

38 calibration sites (2008-2009)

Assessment of general channel features

Width, depth and slope

Habitat condition6 sentinel sites (2005, 2007-2009),

Pebble counts, embeddedness rank

- D50, mean pebble diameter (mm)- proportion of substrate in each of 3 habitat categories- numerical or categorical embeddedness rank


Pebble counts, embeddedness rank

- proportion of substrate in each of 3 habitat categories- numerical or categorical embeddedness rank

Aquatic Population condition6 sentinel sites (2005, 2007-2009),

Area constrained search (ACS) count of individuals by lifestage

- density- demographics


ACS and Time Constrained Search (TCS) counts of individuals by lifestage

- density- demographics

6 sentinel sites (2008)

Length and weight data, by age-class


12 Calibration sites Length and weight data, by age-class


5.14Baseline Estimates and Pilot Variance Estimates Data collected during the pilot project were used to produce baseline estimates of: channel condition, habitat condition, and aquatic population condition for the Flathead and Yahk WHA’s. These baseline estimates can be used for future assessments of the effectiveness of the WHA’s at maintaining Tailed Frog habitat and populations. Variance estimates obtained from the pilot data will inform future WHA monitoring design decisions, as reasonable estimates of between-year and between-site variation are necessary to enable required power analyses and sample size calculations. In addition, these estimates provide a basis for evaluating tradeoffs between various field methods in regards to sampling intensity vs. desired accuracy and precision.

Detailed channel morphology surveys were only completed in 2005 at the sentinel sites. Both habitat condition and aquatic population condition data were collected with varying intensities throughout the study period, with a minimum of three sites surveyed in each watershed each year (with the exception of 2006, where no sampling occurred). For each of the metrics available we calculated annual site specific estimates and report these electronically. We also produced simple summary figures of the complete dataset.

We then calculated annual estimates of each metric with available data by watershed. We used all available data (i.e., both sentinel sites and calibration sites) from each watershed to generate these estimates. We also


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present annual estimates for the sentinel sites alone, which provide information about the expected year-to-year variability within a site. However, because neither the sentinel sites nor the calibration sites were selected randomly, such an analysis is not entirely valid. Arbitrarily selected samples should not be used to make inference to the broader population; in this case, the entire watershed. Such an assumption is particularly poor during the years where only sentinel sites were assessed. The sampling undertaken does, however, represent the best data assembled to date for evaluating baseline watershed estimates and year-to-year variability.

As described in the field methods, some of the methods (e.g., ACS) involved sub-sampling a site. For these metrics mean and variance by site were calculated and are available electronically. Confidence intervals2 for an individual site and year could be calculated if necessary, but we assume that in general WHA objectives will be focused on the average behaviour of sites in the watershed, or the watershed as a whole. Sub-sampling is also known as two-stage sampling where there is a primary sampling unit (psu) and within each psu, a sub-sample is taken of secondary sampling units (ssu). In this case, the population of interest is the watershed, and the sites are the primary sampling unit. The 3-5m belts within each site are the secondary sampling units. The sample size for the watershed estimates is the number of ‘sites’ not the number of ‘belts’. Cochran (1977) describes how to calculate estimates for a two-stage sample. Roughly speaking the variance of a two stage estimate is equal to the variance of the means (between psus) + the mean of the variance (within each psu). However, since the second stage variance is typically small relative to the first stage (i.e., often the precision is determined by the number of psus) (Schwarz 2009), we simply use the first stage variance estimate. Hence, the watershed estimates reported here were generated by first calculating the mean value per site, and then finding the mean and variance of the sites.

An exception was made for the ACS data. In this case we summarized the counts in 15m units. So, if there were five, 3m belts we simply summed the counts and recorded this as the ‘site count/15m’. In the few cases where ten, 3m belts occurred we again summed the counts and then divided by 2, to standardize with the 15m counts. Adjusting the counts by expanding to 100m or reducing to 1m, changes the distribution of the data. This is particularly problematic where the counts are small and there are frequent zeros.

5.14.1 Target populationThe ultimate target population is probably intended to be the entire WHA. However, sampling at this time has focused on the ‘core breeding area’. In future, each site should be identified by relevant strata, and estimates made across multiple strata. The pilot study surveys were restricted to the breeding core and sampling of Tailed Frog populations was not undertaken in areas with deep pools or woody debris. Hence, all baseline estimates are only intended to reflect the ‘sampled population’ as defined by these restrictions. While presence of all Tailed Frog life-stages was documented, the stream-based field methods undertaken for the pilot focused on sampling the stream-confined tadpole populations. As a result we used total tadpole counts as the general indicator of Tailed Frog population size throughout our analyses.

5.15Compare Estimates of ACS and TCS A key objective of the pilot project was to compare the ACS and TCS field methods. We used regression analyses to determine whether or not TCS was correlated with ACS. We used all sites from 2008 and 2009 that had both ACS and TCS samples. The response variable was the square root of the mean ACS at each site and the predictor variable was the square root of TCS at each site. The ACS values were standardized to 15m.

5.16Demographics and Length/Weight Data We report the observed demographic data (i.e., count per life-stage) by site, creek, and watershed. We report mean length and weight data by site and life-stage, as well as reporting the average values by creek and watershed. These data were collected at sites with stream temperature loggers with the intent of

2 Some complications arise when calculating confidence intervals for highly clustered data, such as has been observed with Tailed Frog counts. Appendix 10.3, provides several strategies and examples if necessary.


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determining whether temperature based indicators such as ‘growing season’ could be related to the size or development of Tailed Frogs. This analysis is beyond the scope of this report.

5.17Statistical Tests It is important to have a clear question or hypothesis in mind before completing a statistical test. After four years, we have data from about 40 sites across two WHAs. There are many questions/hypotheses that could now be tested. However, not all possible questions will provide much insight. For example: we could use a two-sample t-test to compare between any two sites, or years. But how would we interpret a statistically significant result? All that we could say is that those two sites or two years differ. Unless we are interested in a single site for some specific reason, this is not very helpful. If instead, we compared all sites with a landslide nearby versus sites that did not have a landslide, then a significant result could provide some useful insight. You could replace ‘landslide’ with other land-use changes of interest: fire, forestry activity, restoration activity, etc… We describe three general types of questions that may be of interest and provide examples of how these might be analyzed.

5.17.1 TrendsOne objective of the Tailed Frog WHAs is to maintain or increase populations and improve habitat over time. This can be tested using simple trend analyses, where time is used as the predictor variable and Tailed Frog densities or habitat condition indices are the response.

5.17.2 Linking habitat and geomorphology to abundance and/or occupancyAssessing cause-and-effect relationships is a critical component to effectiveness monitoring (Maxy 2004; USDA 1999). While maintaining a healthy population condition is the ultimate objective management of Wildlife Habitat Areas is most likely to have direct influence only on channel and habitat condition, with only indirect affects on the populations themselves. As described above tadpoles can be used as the index of Tailed Frog population size within such analyses. Once again clear hypotheses should be described. While formal cause-and-effect3 studies are preferable (e.g., BACI), a regression analysis of population condition as it relates to channel and habitat condition may provide useful insights, with an added benefit that key habitat metrics, once identified, are usually less costly to track for long term monitoring than direct population metrics.

5.17.2.1 Channel conditionWe test the hypothesis that the step-pool geometry (as described by ∑Runs/L, sum of runs divided by total reach length and ∑h/H, sum of step heights divided by total reach height) relate to the abundance of Tailed Frogs. We use the 2005 tadpole abundance data as the response variable and the channel condition indices as the predictors in a regression analysis. The intent of this analysis is to validate the indicators.

5.17.2.2 Habitat conditionWe test the hypotheses that there will be fewer Tailed Frogs in sites with: smaller mean particle size (D50); or high proportions of refuge filling material, by relating D50 and the proportion of refuge filling material to Tailed Frog counts, again using regression with tadpole counts as the response variable and D50 or proportion of refuge filling material as the predictors.

5.17.2.3 Multiple indicesMultiple regression or Classification and Regression Tree (CART) analysis might be a useful way to identify the relative importance of varied potential indices and thresholds in habitat and channel condition metrics that relate to Tailed Frog abundance (Pickard and Porter 2008). At this time there are few data (at least on-the-ground data) available to analyze this way.

3 It is very difficult to truly establish causal relationships, see Appendix 10.4 for a progression of study designs.


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5.17.3 ThreatsAssessing the effects of threats on Tailed Frog populations as well as the effectiveness of WHAs in mitigating such threats is another potential objective of the WHA monitoring program. The spatial scale of interest for these questions may be quite different from that described so far.

The Ram-Cabin fire is an example of a specific scenario that may be of interest. It may be of interest to compare habitat and Tailed Frog (abundance and cohort structure) from pre and post fire. We suggest several hypotheses:

1) elevated water temperature would kill all stream cohorts 2) if adults were affected then eggs in 2004 or age1 in 2005 would be reduced3) an increase in refuge filling substrate post fire

You could use the non-parametric Mann Whitney U test to compare the abundance of tadpoles before and after, as the count data are non-normal. You could also use a chi-squared test to compare the proportion of individuals in each age-class before and after. Finally you could use a simple t-test to compare the proportion of refuge filling material. At this time we have simply reported the summary pre/post data available.

6 RESULTS

6.1 Data review and processing In general the raw datasets summarized in: ‘Data Folder Organization.doc’ are well organized. In most cases the raw data are available and the variables are well defined. However, as is typical of data collected over multiple years with changing protocols, there are some inconsistencies that make it difficult to synthesize. Several common problems were encountered, essentially as a result of the data being stored in Excel spreadsheets rather than in a database format: – inconsistent naming of both columns and rows– complex formats such as: multiple data types in the same column, headers in more than one rows,

merged cells– data from different years stored in multiple tabs or files

It is difficult to write automated scripts or read data into other software packages when it is formatted this way. Where necessary we revised the datasets to produce a single spreadsheet with all of the data in the same format. Table 3 lists all of the revised datasets. The length and weight dataset had additional limitations. The summary weights and lengths are readily available but the raw data are not. The only way to obtain the raw length data from the spreadsheet is to view each cell. The raw data have been entered as a formula, for example: “=(3.4+3.6+2.7)/3”, as a result variation among individuals is not readily available. To improve efficiency weights were assessed in batches (i.e., several individuals were weighed simultaneously), so variability among individuals is not available, only the mean weights. When not all individuals were measured or weighed, it is not clear that a random sub-sample was taken. While it is not easy to obtain a random sample of individuals in the field (Hilborn and Walters 1992), this should be the goal to ensure your sample is not biased. I reviewed the values in a sample of the cells and found that in most cases all of the frogs had been measured and so the reported mean values are likely accurate.

6.2 Channel condition

6.2.1 Time RequirementsOne field day, one day of data reduction and plotting, and 1/ day of calculation was required for each 100-m long Sentinel site reach, totalling 2 1/3 days for field measurement, data reduction and preparation of reach average summary indices for each 100m reach. Further statistical analysis (e.g., after Wood-Smith and Buffington 1996) would require considerably more effort.


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6.2.2 Data SummarySentinel sample reach averaged hydraulic geometry and channel condition indices are provided in Table 6. Reaches were typically 3.5-5.0 m wide between vegetation trimlines and 5.0-8.0 m wide measured bank to bank. Average depths were 0.14-0.27 m between trimlines and 0.48-0.65 m at bankful. Average channel slope varied between reaches from a low of 3% on Storm to a high of 11% on Cabin. Average sample reach slopes compared well with that estimated from TRIM maps for the entire reach. However, effective slope was 1-2% for all reaches except Cabin, which was a little higher at 4%. The proportion of total reach height occupied by steps was between 57-62% for Boyd, Norge, Cabin and Storm Creeks, but was higher for Sprucetree and Couldrey creeks. Sprucetree also had a low percentage of the total reach occupied by runs, while Cabin and Boyd creeks had the highest. Based on cumulative step height and cumulative run length, Boyd has the poorest channel condition and Sprucetree has the highest channel quality. Couldrey had a high cumulative step height, but average cumulative run length, possibly resulting from the relatively steep gradient and large mean stone size.

Table 6. Reach averaged hydraulic geometry and channel condition indices (2005).Width

(m)Average

depth (m)Maximumdepth (m)

Slope(m/m)

Sentinel WB WTL DB DTL DB DTL SR SE ∑h/H ∑Runs/LBoyd 5.03 3.6

60.56 0.19 0.8

50.38 0.05 0.0

20.57 0.41

Norge 5.57 3.93

0.50 0.20 0.79

0.37 0.05 0.02

0.62 0.34

Sprucetree

4.92 3.44

0.65 0.27 0.92

0.39 0.04 0.01

0.88 0.11

Cabin 8.16 3.67

0.49 0.14 0.80

0.25 0.11 0.04

0.62 0.37

Couldrey 5.23 3.87

0.48 0.24 0.77

0.40 0.10 0.02

0.76 0.32

Storm 6.52 4.91

0.51 0.23 0.78

0.39 0.03 0.01

0.59 0.32

Where: Subscripts B, bankful; TL, trimline; R, reach; E, effective; ∑h/H, sum of step heights divided by total reach height; ∑Runs/L, sum of runs divided by total reach length.

The method of surveying and analyzing stream morphology outlined in this study provides a useful way of characterizing the condition of sediment-supply limited channels (see Wood-Smith and Buffington 1996; Chin 2003) in terms that are intuitively relevant to Tailed Frogs (e.g., see Scheuerlein 1999). As the data show (Table 6), the presence of steps reduces the average channel slope to an effective channel slope, so that typical flood velocities (i.e., those resulting from flows producing the vegetation trimline) and bed stresses are reduced (Table 7), producing hydraulic conditions (Scheuerlein 1999) tolerable to Tailed Frogs. These data provide a useful verification that the stream morphology results were meaningful. The calculated velocities are consistent with measured flood velocities reported for similar streams in Colorado (Wohl and Thompson 2000).

Table 7. Velocity and discharge estimates based on reach averaged hydraulic geometry.


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Using reach slope Using effective slopeVelocity

(m/s)Discharge

(m3/s)Velocity

(m/s)Discharge

(m3/s)Sentinel Basin

area(km2)

VB VTL QB QTL VB VTL QB QTL

Boyd 13.3 2.3 1.2 6.3 0.8 1.4 0.7 4.0 0.5Norge 13 2.2 1.2 5.9 0.9 1.4 0.8 3.7 0.6Sprucetree 21 2.3 1.3 7.2 1.2 1.1 0.7 3.6 0.6Cabin 4.0 3.3 1.5 13.4 0.8 2.0 0.9 8.1 0.5Couldrey 4.8 3.0 1.9 7.6 1.7 1.3 0.8 3.4 0.8Storm 8.0 1.7 1.0 5.9 1.1 1.0 0.6 3.4 0.7Where: Subscripts B, bankful; TL, trimline.

6.2.3 Pole PhotographyEven with the use of a polarizing filter, reflection from the water’s surface rendered the photos from pole photography useless for photo sieving (Bunte and Abt 2001). Thus, the method cannot be used to detect trends in substrate quality for Tailed Frog monitoring.

6.3 Habitat condition

6.3.1 Pebble Counts

6.3.1.1 Time RequirementsIn the field pebble counts were conducted by a two-person crew. The time required for pebble counts varied by operator experience and by sample size, as detailed below: pebble counts at Sentinel samples (n=200) ranged from 40-135 minutes, and at Calibration samples (n=100) they ranged from 17-62 minutes. The time was markedly different between crews: an inexperienced crew required 135 minutes to complete a 200 stone count and 33-62 minutes for a 100-stone count, while an experienced crew required 40-44 minutes to complete a 200 stone count and 17-28 minutes for a 100-stone count. For time budgeting, a medium between these two extremes is probably appropriate: 60 minutes for a 200-stone count and 30 minutes for a 100-stone count. A person-day per year was required to compile data and calculate percentiles and sorting for six 100-m reaches.

In terms of analytical effort, summarizing the data by size categories and then by habitat categories is relatively straight forward. However, the current manual procedure for calculating the D50 is time consuming, although it may be able to be automate the procedure if it is determined to be valuable.

6.3.1.2 Sentinel site summaries

Histograms illustrate the size distribution observed in the Sentinel sites over the study period. Boyd creek appears to support the finest substrate (Figure 13). The D50, particle mean diameter (mm) for all creeks belongs to the ‘refuge-forming’ category (Figure 14). Boyd and Storm Creeks have the smallest D50 and Norge Creek has the largest. Both the histograms of pebble sizes (Figure 13) and barplots of D50 (Figure 14) indicate substantial year to year variability within a site and between sites. It is not surprising then that t-tests comparing the D50 between years find several significant results. T-tests (Spreadsheet Sent_texture_08.xls, T-test worksheet) suggest a significant difference in mean grain size on Boyd and Couldrey between 2005 and 2007, and on Boyd, Cabin and Storm creeks between 2007 and 2008. Boyd, which experienced fining between 2005 and 2007, became coarser in 2008. Cabin Creek became slightly finer, but remains a coarse creek, and no concern is warranted. Storm Creek experienced considerable fining, with mean grain size dropping from 58 m to 40 mm. In 2007, reach 2 in Norge and Cabin creeks was finer than reach 1. The finer texture of reach 2 at both sites is likely related to the existence of sediment aggradations behind coarse woody debris jams on reach 2.


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Figure 13. Grain size histograms from Sentinel sites, 2005, 2007, 2008, 2009.

6.3.1.3 Statistical summarySample size is both a function of 1) the standard deviation, or sorting, and 2) the acceptable level of error about the mean (Bunte and Apt 2001). Without knowledge of the grain size distributions before hand it is not possible to determine an adequate sample size, and an initial sample of 100 particles is usually used. Based on the data collected in 2005, the sample size (n) required to produce a 15% and 30% error about the mean was calculated assuming an alpha level of 0.05, following the method outlined in Bunte and Abt (2001) The data from 2007 and 2008 indicate that a sample size of 200 is somewhat larger than required for 90% confidence limits of 25% about the mean.

Figure 14 The D50 particle mean diameter (mm) from sentinel sites for: 2005, 2007, and 2008. The horizontal dotted lines represent the breaks between ‘Refuge Filling, Refuge Forming, and Step Forming’ habitats.


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6.3.1.4 Baseline across all sitesFile ‘habitat-results.csv’ provides a complete summary of the proportion of substrate in each habitat class by site and year, for all sites. Figure 15 illustrates that refuge forming habitat is dominant in both watersheds (median: FH=50; YK=62.8), but they both have fairly large amounts of refuge filling sediment as well (median: FH=37.6; YK=25.5).

As described earlier, refuge filling sediment is believed to be an indicator of poor habitat for Tailed Frogs. This indicator can be compared across years and sites to assess habitat quality. Annual estimates for this indicator are shown by watershed in Table 6. These may be used as a baseline for assessing future performance. The sentinel sites provide the best data for assessing the between year variability within a site (Table 9). Between year variability at a watershed scale can be evaluated by comparing the variability in the watershed annual estimates (Table 6). The best data available for assessing between site variability, within a year is from 2008 (Flathead) and 2009 (Yahk) when the calibration sites were added. These are reported in Table 6 as the standard deviation for each year. Caution should be used when interpreting the watershed estimates, particularly in years with only three sites, as the sites were not selected at random and therefore should not be used make inference to the whole population.

Figure 15 Box plots summarizing the proportion of each pebble count sample in each of 3 habitat categories: Refuge filling (0-32 mm), Refuge forming (32-256 mm), and Step forming (>256 mm). The box plots show the results from all samples across all years for both the Flathead and Yahk watershed.

Table 8. Annual estimates of the proportion of refuge filling substrate for each watershed by year.

Watershed Year n (sites) mean sdMean (sd) across years

Flathead 2005 3 16.70 7.13Flathead 2007 6 27.65 8.59Flathead 2008 19 47.98 16.95*


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Flathead 2009 8 35.26 15.19 31.9 (13.15)Yahk 2005 3 21.00 8.09Yahk 2007 6 20.22 13.58Yahk 2008 6 23.53 8.73Yahk 2009 17 30.31 11.34* 23.8 (4.59)

*Best estimate of between site variability within a year.

Table 9. Estimates of the proportion of refuge filling substrate for each Sentinal site across all years (estimates of year to year variability within a site)

SiteID n (yrs) mean sdCA.R1 4 18.80 6.74CO.R1 4 24.03 8.44ST.R1 4 33.73 6.33BO.R1 4 36.63 5.29NO.R1 4 12.38 2.17SP.R1 4 16.58 4.21

6.3.2 Embeddedness

6.3.2.1 Time RequirementsEmbeddedness was estimated during animal sampling and required no extra time. Mean embeddedness ranks for each site and year are reported in Full embed_summary.csv.

Table 10. Annual estimates of embeddedness for each watershed.

Watershed Year n (sites) Mean sdMean (sd) across years

Flathead 2005 3 2.22 0.79Flathead 2007 6 2.84 0.31Flathead 2008 19 2.75 0.68Flathead 2009 8 3.33 0.72 2.8 (0.46)Yahk 2005 3 1.75 1.26Yahk 2007 6 2.41 0.90Yahk 2008 6 2.59 0.69Yahk 2009 17 2.80 0.99 2.4 (0.45)

Table 11. Estimates of the embeddedness for each Sentinal site across all years (estimates of year to year variability within a site)

SiteID n (yrs) Mean sdCA.R1 4 2.70 0.21CO.R1 4 1.91 0.66ST.R1 4 2.85 0.18BO.R1 4 3.28 0.16NO.R1 4 1.25 0.39SP.R1 4 1.78 0.48

6.4 Aquatic population condition

6.4.1 Time RequirementsSentinel samples consisting of ten 3-m belts were conducted by a two-person crew. The complete 30-m aggregate sample required approximately 6-8 hours to complete. Calibration samples consisting of three 5-m belts were conducted by a four-person crew. The time required was highly variable depending on two


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factors: creek size and animal abundance. On small creeks and/or those with low abundance, up to three calibration samples were conducted each field day; whereas on larger creeks and/or those with high abundance only one sample was achieved per day.

6.4.2 Data summaryThe raw data and summary table from area constrained searches for sentinel sites is supplied in an Excel spreadsheet (Spreadsheet Sent_Abun_05-08.xls). Animal sampling data for calibration sites and 2009 Sentinel sites are presented in the folder Calibration Samples/calib_TCS-ACS_2008-2009.xls. Raw data were compiled in a worksheet, and the reach averaged indicators embeddedness rank, stream temperature and total abundance per TCS and ACS 15-m aggregate sample was compiled in a summary table worksheet (Table 12). Two additional spreadsheets are provided: one containing the complete set of raw abundance data, the other summarized by site (full raw abund data.csv; full abund_summary.csv).

Calibration of ACS and TCS methods was conducted at the six Sentinel sites and 38 additional sites (in 2008, sixteen in the Flathead and three in the Yahk; in 2009, eight in the Flathead and 17 in the Yahk for a total of 44 standard calibration samples). Four more samples were conducted in the Flathead because it is the larger area and contains a greater diversity of stream temperature regimes. In addition there were six non standard (i.e., five 3-m belt samples) Calibration samples collected at Sentinel sites in 2008.

Considering just the Sentinel sites, the number of animals caught per 3-m belt ranged from 0-41 tadpoles and 0-9 frogs. A surprising result in 2008 and 2009 was both the large numbers on some creeks and the detection of five nest sites each year. In past reconnaissance and monitoring years 0-2 nests might be discovered per season.

Table 12. Calibration sample results by year and region. Cobble embeddedness ranks and late summer stream temperature are reported for comparisons. TCS is total number of animals caught in 30-min search; ACS is total animals caught in aggregate 15-m belt sample. Occ. Rate is frequency of positive occurrence in belts as a ratio of total number of belts. Sentinel samples from 2008 show TCS and five 3-m belts. Cases with zero counts are shaded and potentially responsible indicator is boxed in green.

Abundance (#Tailed Frog)Region Sample-Site-ID Embed. Rank Temp (oC) TCS (30-min) ACS (15-m) Occ. Rate 2008Flathead ST-R1 M 16 6 45 5/5

“ CO-R1 L 9 5 121 5/5“ CA-R1 M 10 13 83 5/5“ CA-10 L-M 9 19 36 2/3“ CA-9 M 8 1 0 0/3“ ST-3 L 10 1 7 3/3“ CA-4b H 9 4 9 3/3“ CA-4 H 13 10 14 2/3“ CA-7 M 6 0 1 1/3“ CA-5 M 10 2 9 2/3“ FL-19 L-M 13 2 0 0/3“ CO-15 L-M 6 0 0 0/3“ CO-17 M-H 6 0 0 0/3“ CO-14 L 7 0 0 0/3“ CO-11 M-H 8 1 21 3/3“ CO-13 L 8 0 0 0/3“ LE-23 M-H 13 7 6 2/3“ LE-1* M 12 0 0 0/3“ BH-22 L 15 26 85 3/3

Yahk BO-R1 M 9 5 8 4/5


DRAFT

“ NO-R1 L 9 16 61 5/5“ SP-R1 L 9 4 57 5/5“ SP-5 M-H 9.5 2 1 1/3“ NO-3 L 9 40 245 3/3“ NO-11 M 6 0 0 0/3

*Occurrence in this reach is suspect, subbabsin may be isolated.

Table 12 continued. Calibration sample results by year and region. Cobble embeddedness ranks and late summer stream temperature are reported for comparisons. TCS is total number of animals caught in 30-min search; ACS is total animals caught in aggregate 15-m belt sample. Occ. Rate is frequency of positive occurrence in belts as a ratio of total number of belts. Sentinel samples from 2007 show TCS and five 3-m belts. Cases with zero counts are shaded and potentially responsible indicator is boxed in green.

Abundance (#Tailed Frog)Region Site # Embed. Rank Temp (oC) TCS (30-min) ACS (15-m) Occ. Rate2009Yahk NO-R1 N 13 12 27 3/3

“ SP-R1 L 9 11 31 3/3“ BO-R1 M-H 12 3 8 2/3“ BO-356 M-H 12 0 3 1/3“ CK-357 L 13 4 20 3/3“ MA-013 M 9 1 0 0/3“ MA-014 L-M 10 12 48 3/3“ MA-358 M-H 11 1 8 3/3“ CC-359 M-H 10 17 44 3/3“ UY-360 N-L 14 4 23 3/3“ NN-12 H 8 1 1 1/3“ NN-361 M-H 8.5 1 2 1/3“ CE-362 H 10 1 0 0/3“ BR-363 H 10 3 1 1/3“ MA-364 N 11 16 69 3/3“ UY-365 L-M 10 15 234 3/3“ SC-372 M 10 6 13 3/3

Flathead ST-R1 L-M 8.5 7 72 3/3“ ST-374 H 11 0 0 0/3“ CA-R1 M 7.5 9 77 3/3“ CO-R1 L .. 19 130 3/3“ CO-375 H 12 2 2 2/3“ BU-376 M-H 10 0 6 3/3“ BU-377 M-H 9 1 4 2/3“ BU-378 H 9 3 2 1/3

ACS TCSAverage Occupancy - Flathead 0.58 0.70

Average Occupancy - Yahk 0.70 0.91

6.4.3 Metrics of population size

Table 13. Baseline estimates of tadpole abundance by watershed, using the ACS protocol.

Watershed Year Method n (sites) Mean sdMean (sd) across years


DRAFT

Flathead 2005 ACS 3 23.0 23.92Flathead 2007 ACS 6 32.7 27.37Flathead 2008 ACS 19 22.1 34.00Flathead 2009 ACS 8 33.3 47.65 27.8 (6.02)Yahk 2005 ACS 3 6.5 3.12Yahk 2007 ACS 6 22.7 13.18Yahk 2008 ACS 6 58.3 89.48Yahk 2009 ACS 17 24.8 43.22 28.1 (21.72)

Table 14 Estimates of tadpole abundance over time at a fixed site, using the ACS protocol.Watershed SiteID n (yrs) Mean sdFlathead CA.R1 4 42.5 35.91Flathead CO.R1 4 69.6 50.76Flathead ST.R1 4 53.0 29.89Yahk BO.R1 4 8.4 6.63Yahk NO.R1 4 30.3 20.76Yahk SP.R1 4 32.3 19.20

6.4.4 Comparison between ACS and TCS

We first attempted to fit a straight line regression model using raw TCS tadpole counts and raw ACS tadpole counts. The residuals from this fit did not meet the assumption of constant variance. The plot of residuals versus fitted values shows that the variance in residuals increased with increasing predicted values (Figure 16). We then tried a square root transformation for both the ACS and TCS counts. The residual diagnostics from this fit, confirm that the model assumptions and hence estimates are valid (Figure 17). There was significant evidence (p<.001) to reject the hypothesis that the slope parameter is equal to zero. The model coefficients indicate that on average the square root of the ACS count is 2.04 times that of the square root of the TCS count (Table 15). For example if the raw TCS tadpole count was 5, then the predicted raw ACS tadpole count would be: ACS = (2.042)*5=20.8.

Table 15. Coefficient estimates for regression model: sqrt(ACS)=sqrt(TCS)Estimate Std. Error t value Pr(>|t|)

(Intercept) 0.328 0.433 0.758 0.452sqrt(TCS) 2.040 0.186 10.955 <.001


DRAFT

Figure 16. Diagnostic plots for fit of straight line regression model to raw ACS and TCS counts.

Figure 17. Diagnostic plots for fit of straight line regression model to sqrt(ACS) and sqrt(TCS) counts.

6.4.5 Occupancy


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Observed occupancy4 was greater on average using the TCS protocol vs. the ACS protocol (Flathead: 0.70 vs. 0.58 & Yahk: 0.91 vs. 0.70). This is likely due to the fact that determining occupancy is directly related to the size of the sampling unit. The ACS counts were undertaken in either 3m or 5m belts, while the TCS protocol allows one to search a larger area. Detectability appears similar between the two protocols, when the ACS counts were standardized to 15m. Often both methods (ACS or TCS) failed to detect occupancy, while in others only one method failed to detect:

• In 2008 six creeks in the Flathead were sampled that were classed as too cold (Figure 5) but lotic growing season length and abundance needed documentation. On four of these creeks both sampling methods failed to detect (Table 12), while on the two other creeks TCS and ACS each detected when the other failed. Thus, the methods appeared to have similar detectability.

• In 2008 at site LE-1 in the Flathead both methods failed to detect; however the habitat indicators were considered good. This site was recorded as positive once by Krista Wilson, but has never been verified by follow up samples (2003, 2009). This subbasin may be isolated.

• In 2008 TCS detected when ACS failed at a low abundance site (CA-19), but the indicator variables do not suggest why abundance was low.

• In 2008 in the Yahk, the site where both samples failed to detect (NO-11) demonstrated poor reasonable temperature but poor substrate quality.

• In 2009 creeks were sampled where water temperatures were not limiting. On four creeks TCS and ACS each detected twice when the other failed, and on one creek both failed to detect. Thus, the methods had similar detectability. The indicator likely responsible for zero counts was mod-high to high embeddedness. In one case (MA-WP013) the zero count may be related to the small creek size and winter freezing.

6.4.6 Length / Weight data

6.4.6.1 Time RequirementsThe time required for this task was not recorded. At least one individual, sometimes with the aid of a second to record, was required from the four-person crew. It is judged that about 15-45 minutes were allocated to this task per ACS belt or TCS sample, the exact amount depending on the number of animals measured.

6.4.6.2 Data SummaryThe length and weight data summarized by site are reported in ‘length-weight-summary-final.xls’. Simple watershed summaries do not indicate any obvious differences in length by age-structure between sub-basins within a region (Figure 18; Table 16; Table 17). These data should be analyzed along side temperature data. The hypothesis of interest is that stream temperature will affect length and weight of Tailed Frogs. The Flathead region in particular contains sub-basins with strong contrasts in stream temperature, whereas the sub-basins within the Yahk are similar. If stream temperature is related to length and weight, we’d expect to see greater variation among sub-basins in the Flathead than in the Yahk. The simple bar plots do not support this, but formal analyses should be completed to test the hypothesis.

4 True occupancy should account for the probability of detection as well as the observed occupancy. We do not have this information at this time and so have simply reported the observed occupancy.


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Figure 18. Average length (mm) and weight (g) by life-stage for all sites where Tailed Frogs were found, summarized by watershed and sub-basin.

Table 16. Tadpole mean length and weight values by cohort and watershed. 2008-2009 data.Watershed Metric Cohort 1 Cohort 2 Cohort 2/3 Cohort 3 Uber tad5 Cohort 4Flathead Length 2.72 3.64 4.97 5.64 6.28 5.31Yahk Length 2.80 3.35 4.74 5.64 6.37 0.00Flathead Weight 0.16 0.45 1.12 1.64 2.60 1.46Yahk Weight 0.26 0.39 1.02 1.63 2.40 0.00

Table 17. Frog mean length and weight values by cohort and watershed. 2008-2009 data.Watershed Metric Metamorph Juvenile frog Adult male Adult femaleFlathead Length 5.55 3.84 3.86 4.49Yahk Length 4.55 3.31 3.86 4.47Flathead Weight 1.77 6.03 7.32 10.53Yahk Weight 1.83 3.62 6.14 8.56

5 Cohort 3 was expanded into three subclasses based on body bulk and rear limb development: Cohort 2/3 tadpoles with under-developed knees, and smallest in size and weight; Cohort 3 tadpoles with well-developed knees but intermediate in size and weight; and Cohort 3“Uber tads”, Cohort 3 tadpoles largest in size and weight.


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6.4.7 Demographics

6.4.7.1 Time RequirementsThe time required for tallying animals by morphological class was not recorded, but is a relatively rapid procedure requiring one person less than five minutes per belt.

6.4.7.2 Data Summary

Figure 19 shows the demographic breakdown of the observed Tailed Frogs by year and watershed for the Sentinel sites alone, while Figure 20 shows the annual demographic summary for all sites by watershed. These figures could suggest questions related to demographics that may be worth testing more formally in the future. The plots restricted to sentinel site data are easier to track through time as they are considered to lack the confounding created by additional spatial variation. The Flathead watershed estimates are generally higher than the Yahk watershed when only the Sentinel sites are compared. However, a comparison of 2008 values for the Flathead when just using the Sentinel sites (Figure 19) versus using the full set of data (Figure 20, n=19) shows that the average number of tadpoles drops by roughly 2/3rds. This suggests that the Sentinel sites within the Flathead are disproportionately good habitat for Tailed Frogs and hence do not provide a good reflection of the overall watershed.

Figure 19. Observed age-classes, Sentinel sites only, standardized to 15-m.


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Figure 20. Observed age-classes, All ACS sites, standardized to 15m.

6.5 Linking habitat and geomorphology to abundance and/or occupancy

6.5.1 Channel condition

Neither ∑h/H, sum of step heights divided by total reach height, nor ∑Runs/L, sum of runs divided by total reach length were found to relate to the ACS tadpole abundance estimates. There was no evidence found to reject the hypothesis that the slope parameter was zero using a simple straight-line regression between the square root transformed tadpole abundances and the channel condition indices (Table 18; Table 19). Given the small sample size of only n=6 sites, this is not surprising and should not be taken to mean that the channel condition indices do not relate to the tadpole abundances, simply that there is no evidence that they do.

Table 18. Regression estimates for sqrt(ACS)= ∑h/HEstimate Std. Error t value Pr(>|t|)

(Intercept) 5.381 5.197 1.035 0.359∑h/H -2.888 7.617 -0.379 0.724

Table 19. Regression estimates for sqrt(ACS)= ∑Runs/LEstimate Std. Error t value Pr(>|t|)

(Intercept) 3.750 2.926 1.282 0.269∑Runs/L -1.009 8.976 -0.112 0.916

6.5.2 Habitat condition:


DRAFT

No evidence was found to suggest the mean pebble diameter (D50) relates to tadpole abundance. There was no evidence to reject the hypothesis that the slope parameter was zero, using a simple straight-line regression between the square root transformed tadpole abundance and the n=18 D50 values (Table 20).

Table 20. Regression estimates for sqrt(ACS)=D50

Estimate Std. Error t value Pr(>|t|)(Intercept) 5.155 2.015 2.559 0.021mean_mm 0.002 0.022 0.106 0.917

The percent of the each pebble count that fell in the ‘refuge filling’ category was found to be negatively correlated with tadpole abundance. There was significant evidence to reject the null hypothesis that the slope parameter was zero, using a simple straight-line regression between the square root transformed tadpole abundance and the percent of sediment in the refuge filling habitat category (p=0.017). This result should be interpreted cautiously however as the model assumptions are only loosely met. The standardized residuals appear reasonably normal and there are not an unusual number of outliers (Figure 21). However, there may be some evidence of non-constant variance in the plot of predicted values versus the residuals. We also considered a square root transformation on the independent variable and adding a quadratic term. Neither of these substantially improved the model fit. The model coefficients indicate that for every percent increase the square root of the ACS counts decrease by 0.062 (Table 21). For example, if there was an increase in the percent of refuge filling substrate from 40% to 60%, we’d expect that the raw ACS tadpole counts would decrease by: 12.6 to 5.3 tadpoles per 15m.

Table 21. Regression estimates for sqrt(ACS) = % Refuge filling substrateEstimate Std. Error t value Pr(>|t|)

(Intercept) 6.024 0.931 6.470 0.000pct_32 -0.062 0.025 -2.449 0.017


DRAFT

Figure 21. Diagnostic plots for fit of straight line regression model to sqrt(ACS) and % Refuge filling substrate.

The numerical embeddeness rank was correlated with tadpole abundance. There was significant evidence to reject the null hypothesis that the slope parameter was zero, using a simple straight-line regression between the square root transformed tadpole abundance and the embeddedness rank (p<0.001). Generally the model assumptions appear reasonable; the residuals appear normal and there are not an unusual number of outliers (Figure 22). There is slight indication that there may be some curvature remaining in the residuals; this might support the initial hypothesis that Tailed Frogs prefer the middle categories over the rank 4 category (i.e,. no embeddedness). Including a squared ‘embeddedness’ term did not improve the model fit, however it may be worth exploring other analyses including treating the independent variable as categorical. The model coefficients indicate that for every unit increase in embeddedness the square root of the ACS counts decrease by 1.859 (Table 22). For example, if there was an increase in the embeddeness rank from 3 to 4 (Medium to High) we’d expect that the raw ACS tadpole counts would decrease from: 11.6 to 2.4 tadpoles per 15m.

Table 22 Regression estimates for sqrt(ACS)=embeddedness rank.Estimate Std. Error t value Pr(>|t|)

(Intercept) 8.993 1.334 6.741 <0.001embed_mean -1.859 0.468 -3.971 <0.001

Figure 22. Diagnostic plots for fit of straight line regression model to sqrt(ACS) and embeddedness rank.

6.6 Pre & Post-fire Sampling


DRAFT

6.6.1 Fire severity and Tailed Frog distributionAssessing the effects of threats on Tailed Frog populations is another potential objective of the WHA monitoring program. The Ram fire in Aug/Sept 2003 provided an unusual opportunity to evaluate the effect of a specific impact, as data had been collected by chance before the fire began. The Ram fire, started on the west-facing slope of the North Fork of Bighorn Creek and spread east covering about 5000 ha., overlapping parts of Leslie, Storm and Cabin creeks (Figure 23). Fire intensity was not uniform within the fire boundary. Where older forests existed the fire was severe, resulting in complete stand replacement, partial consumption of forest floor coarse woody debris, and total consumption of forest floor duff. However, in recently logged, not satisfactorily restocked, rocky or wet meadow sites the burn intensity was low, with only candling of a proportion of the trees in the forest stand and low impact to the duff layer.

Due to the presence of old logging in valley bottom areas, and the presence of extensive wet organic soils along middle Storm and Bighorn creeks, the high severity burn areas did not directly overlap core Tailed Frog breeding habitat (Figure 23). The most severely burnt areas overlap with drier upland areas on the divide between Storm, Bighorn and Leslie creeks. These areas are considered potential Tailed Frog dispersal corridors.

6.6.2 Immediate fire effectsImmediate fire effects to Tailed Frogs may have included direct mortality of terrestrial frogs in high or low severity areas, and mortality of frogs and tadpoles in stream channels in high severity areas. In low severity areas, stream temperatures would not have been lethal to frogs and tadpoles. Chemical effects in high severity areas are also possible.

In the high severity areas, since there were no pre-fire sample sites with more than zero abundance, post fire sampling of these areas was not considered worthwhile. However, it is assumed that any animals that were in those zones were killed. As the high severity areas were located away from core breeding reaches (Figure 23), so the immediate effect to the core population was considered low.

6.6.3 Delayed fire effectsComparison of 2003, 2004 and 2005 data within the burned area (Table 23), and between the burned and control areas (Tables 3, 4) suggests that there was no significant difference in channel substrate or Tailed Frog abundance attributable to the Ram-Cabin fire (see spreadsheet - Pre&postfire_2003-2005.xls – in the Ram-Cabin Fire folder. The degree and direction of change shown by burnt (Table 23) and control (Table 24) areas are similar: a similar occurrence rate, but slightly lower abundance was noted in both data sets. This could be attributed to either natural variation or a sampling artifact. Formal analyses should be completed on these data to determine whether or not the fire was likely to have impacted the abundance, cohort structure, or percent fines.


DRAFT

Figure 23. The Ram-Cabin fire (Fire N10689) Sept 2003, with fire intensity areas derived from pre-fire forest cover maps and site observations. Tailed Frog habitat mapping and Tailed Frog sample sites are indicated.


DRAFT

Table 23. Pre and post fire conditions within the fire boundary.

Rip. burn Water temp Substrate Abundance/30 minSite (Figure 23) Date %cover (ºC) Embed. %Fines Frogs Tads ChangeST-014 03 100 -- Low 35 1 8

04 100 -- High 28 0 0 -9ST-003 8/13/03 0 8 High 70 1 0

8/30/05 0 9 High 65 0 1 0ST-006 8/13/03 0 15.5 None 40 0 31

04 0 -- Low 45 0 7 -248/30/05 0 10 Low 40 1 8 -22

ST-005 8/13/03 0 14.5 Low 35 0 2504 0 -- Mod 60 0 23 -28/30/05 0 10 Low 35 0 11 -14

ST-007 8/13/03 0 14 Mod 65 5 095 -- Mod 70 0 0 -5

8/30/05 95 9 High 65 0 0 -5ST-010 8/14/05 0 9 Low -- 0 1

04 0 -- Mod 65 0 1 08/31/05 0 13 Mod 60 0 2 1

ST-011 8/14/03 0 10 Mod 55 0 504 0 -- Mod 55 3 0 -28/31/05 0 12 Low 50 2 0 -5

ST-002 03 25 0 004 40 0 10 +10

ST-013 03 0 Low 30 0 1204 0 -- Mod 43 0 0 -12

LE-005 8/15/03 0 13 Mod 65 0 138/31/05 50 8 High 65 0 1 -12

LE-021 8/18/03 0 15.5 Low 40 0 38/31/05 0 7 Mod 55 0 0 -3

CA-033 8/16/03 0 12 Mod 70 1 59/1/05 0 9 High 80 0 4 -2

CA-034 8/16/03 0 12 Mod 50 0 19/1/05 0 9 Mod 75 0 6 5


DRAFT

Table 24. Pre and post fire conditions at selected control site outside the fire boundary.Rip. burn Water temp Substrate Abundance/30 min

Site (Figure 23) Date %cover (ºC) Embed. %Fines Frogs Tads ChangeCA-017 8/11/03 0 8 Low 30 0 42CA-017 9/1/05 0 6 Mod 40 3 13 -26CA-015 8/11/03 0 11 Low 50 1 22CA-015 9/1/05 0 8 Mod 60 4 21 2CA-018 8/11/03 0 12 Low 45 3 6CA-018 9/1/05 0 8 Low 46 2 7 0CA-024 8/12/03 0 8 Mod 30 0 1CA-024 9/1/05 0 10 None 50 0 0 -1CA-023 8/12/03 0 9 Mod 2 2CA-023 9/1/05 0 9 Low 50 0 16 12CO-014 8/5/03 0 13 Low 30 2 8CO-014 9/2/05 0 11 Mod 30 1 1 -8CO-015 8/5/03 0 8 Low 35 0 14CO-015 9/2/05 0 6 None 35 1 10 -3CO-016 8/5/03 0 9 Low 37 1 5CO-016 9/2/05 0 8 None 30 0 4 -2CO-017 8/5/03 0 10 None 35 0 10CO-017 9/2/05 0 8 None 37 0 10 0CO-018 8/5/03 0 11 Low 52 0 23CO-018 9/2/05 0 9 None 65 3 18 -2CO-019 8/5/03 0 11 Low 30 2 18CO-019 9/2/05 0 9 None 30 1 8 -11

6.7 Organisation of Data Files All files are presented on a disk. The first folder contains a metadata document file, a report folder, and a data folder. The metadata file contains the layout and description of files, including all major phases of Rocky Mountain Tailed Frog field work in BC: Reconnaissance; Post-fire sampling; and Monitoring. In the - Pilot_2005-2009 - folder is a Master Spreadsheet - Pilot 2005-09 Sampling Summary – that documents the Monitoring Pilot sample design. That spreadsheet provides sample location and sample design summary for each sample year from 2005-2009. The folder - Sent_pebble count_2005-2008 – in the - Sentinel Samples – folder contains a metadata file - Sent_Texture_metadata.doc - that gives a brief overview of the evolution of pebble count spreadsheets.

Data sets have been compiled as much as is feasible given the evolving project design, and the multiplicity of goals and objectives. One requested item (Hubregste, Dec 1, memo) was that location (i.e., UTM) data be provided in “each and all data sets”. This has not been done, but all datasets have been compiled into single spreadsheets using a unique site identification (SiteID). Another spreadsheet links this site identification number to the UTM and other locational data. Linking the name file to any of the data files can easily be done using the unique SiteID.

7 DISCUSSION7.1 Channel condition

Detailed surveys could be used to measure and classify channel morphology. Based on the data presented in Section 6.2, channel condition indices could be developed. For example, ∑h/H categories >75%, 50-75% and <50% might be termed good, fair and poor, respectively, and assigned ranks 1 to 3; ∑r/L


DRAFT

categories <20%, 20-35% and >35% might be termed good, fair and poor, respectively and assigned ranks 1 to 3. Scores from both measures could be tallied to produce a final ranking: Boyd, 5; Norge, 4; Sprucetree, 2; Cabin, 5; Couldrey, 3; and Storm, 4.

These methods are very labour intensive, and may be subject to error introduced by operator subjectivity when describing and surveying channel geometry (Hayward 1980). As Gyug (2005a, p.20) points out, the significance of channel condition indicators to Tailed Frogs has not yet been studied, the degree of change detrimental to Tailed Frogs is unknown, and threshold values above which to trigger extensive monitoring cannot be set with confidence. Specific research aimed to answer these questions is required before intensive measures of channel condition can be applied to Tailed Frog monitoring.

In a detailed comparison of methods, Wooldridge and Hickin (2002) concluded that visual identification of coarse-grained bedforms provided more information about bedform geometry and type than three survey based methods (zero-crossing, bedform differencing, or power spectral analysis). Detailed surveys are costly and have utility in hydraulic analyses and classification, but operator error may limit use for trend detection. Further, channel condition indices have not been related to Tailed Frog abundance. For these reasons detailed surveys were not continued.

If either of the channel condition metrics were found to be good indicators for Tailed Frog abundance, then it may be possible to obtain a rough measure of ∑r/L by walking the site with a tape. ∑h/H is likely more difficult to do without the proper equipment.

7.2 Habitat condition

In this work we used a sample size of 200-stones for Sentinel samples, providing 90% confidence limits for an error of 25% about the mean; operator error is controlled somewhat by use of a systematic increment on a fixed transect, a plumb-bob to select stones, and the use of a template, or gravelometer, to classify stones. Calibration sites used 100 stone counts.

Surveyor errors result from two main factors: 1) incorrect measurement of particle size, and 2) biased particle selection. Incorrect measurement of particles can be controlled by requiring that surveyors use templates for grain size determinations. Surveyor bias is more difficult to control, and is critical because unlike statistical error that can be reduced by increasing sample size, surveyor bias remains constant with sample size. In particular there may be sampling bias when sampling very small material. Experiments have shown that standard deviations may double when multiple surveyors conduct sampling, and therefore it becomes more problematic to detect texture changes through time. For comparable results it is critical that surveyors understand the limitations, and employ a strict and consistent methodology.

Statistical tests focusing on shifts of the mean (i.e. D50), avoid problems with sampling the fine tail of the distribution. However, calculating the D50 requires substantial analytical effort. A simpler metric using the same data is the proportion of the count in each of three habitat categories: refuge filling, refuge forming, and step forming. Refuge filling substrate is believed to be less favourable Tailed Frog habitat and so an indicator that focuses on the tail of the distribution rather than the center of the distribution may be more valuable. Embeddedness is another indicator of Tailed Frog habitat quality that requires very little effort once at a site. Pebble counts are less subjective than assessments of embeddedness, but the effort required for pebble counts is far greater than that for embeddedness. There was evidence that both refuge filling material and embeddedness rank are related to Tailed Frog abundance. However, neither metric is correlated strongly enough to justify use as a surrogate of abundance. If Tailed Frogs prefer the middle embeddedness categories rather than either extreme, it may be better to summarize the data categorically.

7.3 Aquatic population condition


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7.3.1 Population EstimationAt this time it is not possible to produce a population estimate for Tailed Frogs. To achieve a population estimate the entire stream network would need to be stratified according to appropriate criteria and then each stratum sub-sampled to produce an estimate for that reach. The current data can however produce an estimate of density for the sampled population, which consisted of core-breeding area streams but excluded hard to sample areas with deep pools and woody debris.

7.3.2 Occupancy

Proportion of Area Occupied (PAO) was a concept formalized by the USGS’s Amphibian Research and Monitoring Initiative (ARMI). The idea began when they found that obtaining wide-spread estimates of absolute abundance with sufficient accuracy to estimate changes over time was unrealistic. The PAO refers to the number of sites that are occupied, where the sites might be: ponds, wetlands, trees, or a unit defined by area or time. An important component of estimating occupancy is accounting for the probability of detecting an animal given that it is there. MacKenzie et al. (2006) synthesized the current research on occupancy estimation into a single text. ARMI is focusing on POA as a metric that can be used and integrated at a National scale (ARMI 2009). While occupancy estimation was developed for amphibian monitoring, early use focused on pond based amphibians. The first paper that focused attention on PAO for headwater stream amphibians was Kroll (2008). Kroll also provided a review of several metrics including occupancy, for stream based amphibians in 2009.

Observed occupancy of Tailed Frogs during this pilot project was greater using the TCS protocol. This is likely due to the fact that occupancy is directly related to the size of the sampling unit (Figure 24). The exact same density of animals translates to a different occupancy rate, depending on the size of the unit sampled. The ACS counts were either 3m or 5m belts, while the TCS method allowed the observer to cover more ground if necessary.

Figure 24. This figure illustrates the effect of sampling unit size on the estimate of occupancy, copied from MacKenzie et al. (2006).

Designing a study to estimate occupancy requires consideration of the following (MacKenzie et al. 2006): o How to define a site?o How to select a site?o How to define a season?o How to incorporate repeated surveys?o How to allocate effort?

A site or sampling unit is usually defined by size, but may also be defined by effort. If we use an ACS protocol, then we’ll need to define an appropriate belt length. If we use a TCS protocol, we need to define an appropriate search time. MacKenzie et al. (2006) recommend defining a sampling unit such that the occupancy rate is between 0.4 and 0.8.


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Selecting which sites to sample should be accomplished using some form of probabilistic sampling design and is discussed in the sampling design section of the report. Defining a season refers to the within year temporal component of the sampling design and is also discussed in the sampling design section.

Repeated surveys are strongly recommended for occupancy estimation, to estimate the probability of detection (MacKenzie et al. 2006). There are several ways in which repeat surveys may occur, each with their own limitations:

o Multiple visits to the site (i.e., on different days)o Multiple surveys per visito Multiple surveyors sampling the same siteo Sub-sample a larger site

We review each of the possible strategies with Tailed Frog WHA monitoring in mind. Multiple visits to the same site would be cost prohibitive and given that detectability is thought to be dependent on the timing (5.3 Sampling Period), it is better to select a short window to complete all of the monitoring. ‘Multiple surveys per visit’ and ‘Multiple surveyors sampling the same site’ are not practical given the destructive nature of the substrate removal sampling. This leaves the choice of taking a sub-sample of a larger site. This adds in additional noise because the probability of detection will now be confounded with the sampling error. This method could easily be implemented for Tailed Frogs with an area based method. This method would also be feasible using a time based method, by using several different starting points for the time constrained search.

Allocation of resources depends on:o Desired precision for the occupancy estimateo Initial estimates of the probability of occupancy and detectiono An upper limit to the feasible cost

By assuming that the probability of detection is 1, we can estimate the minimum number of sites required for a given occupancy level, and desired precision. Detailed methods are described in MacKenzie et al. (2006), but a quick example is provided here.

If the probability of occurrence (ψ) = 0.7 as in the Yahk according to the ACS data (Table x) and the desired standard error of the estimate is 0.05 (i.e., 95% CI of +/- 0.1), and we assume the probability of detection (p) = 1, then we can estimate the necessary sample size by solving Equation 1 for n.

Equation 1.

se=√ ψ (1−ψ )n

n=0.7×0 .30 .052

=84

If we reduce the desired precision to a standard error of 0.1 (corresponds to a 95% CI of roughly +/-0.2), the number of sites required drops substantially from 84 to 21.

n=0. 7×0 .30. 12

=21

If the probability of detection is less than 1, the number of samples required to maintain the same precision will increase. However, if the probability of detection is reasonably high (i.e., > 0.5) then two or three repeat surveys per site may be sufficient (MacKenzie et al. 2006). The ACS protocol is likely as close to a complete census as is possible, although it is still possible that some individuals are missed. When the TCS protocol was compared with the group of ACS belts at a site, the detectability was similar (Table x). In each case there were times when one of the methods detected an individual and the other did not. Of


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course, this discrepancy could also be caused by the spatial sampling error (i.e., sampling did not occur on exactly the same locations). It seems very likely that the probability of detection using either method is >0.5 and so 2-3 repeat surveys would likely be sufficient. For the TCS protocol, it might be easiest to simply use a single start point with two observes moving away from each other.

7.4 Effects post fire

Several papers have now reported on the effects of fire in the early 2000s on Tailed Frog populations. As with the Ram-Cabin fire, although impacts were noted, they were not reported to be severe. Hossack et al. (2006) reported overall reduced abundance with disproportional impact on number of Class 1 tadpoles. No long term or severe impact was expected. Dunham et al (2007) reported persistence of Tailed Frog several years after fire and suggest populations may be resilient to fire.

There are only a few papers out on this subject. Landscape response to fire is highly variable and many factors interact to determine if severe impacts occur. The impact due to the Ram-Cabin fire appeared to be minor because the intense burn area did not overlap with the core of the population, and because the watershed is gentle and no extreme sedimentation impacts occurred. In the Canadian Range of Rocky Mountain Tailed Frog there are several basins that support steeper slopes and dense forest. Fire in sites such as these could have both high direct lethality and short to longer-term sedimentation impacts.

The prediction for increased fire frequency with human induced global warming indicates fire is a potential threat that merits further attention.

7.5 Review of Sampling Design

7.5.1 Target population

The target population needs to be clarified. While four strata have been identified and mapped in each region, the pilot program only sampled from the ‘core breeding area’ stratum. The field protocol excludes deep pools and areas with woody debris from the population. As a result density estimates are only valid for areas without deep pools and woody debris within the ‘core-breeding area’. The delineation of ‘core-breeding area’ is unlikely to change dramatically between years, but the number of deep pools and woody debris may vary annually which might confound the sampling (e.g. sampled population in a dry year versus a wet year may be quite different). The target population should also describe the time of year of interest. For aquatic population sampling, the life-stage of interest should be defined for each question of interest.

7.5.2 Selection of sites in space

Three Sentinel sites were arbitrarily selected from each watershed. Data from these sites should only be used to make inference to these sites, not to the broader population. The temperature loggers were also placed arbitrarily to capture the ‘range of conditions’. This shouldn’t affect regression analyses, but will affect the ability to assess the average condition. The Calibration sites were selected to ensure spatial coverage and at least one site in each main sub-basin. Some of the sites selected overlapped with temperature logger locations. While the underlying concepts of: ensuring spatial coverage, and a range of stream temperatures are valid, the lack of a formal design limits the inference possible.

While we have presented the best baseline estimates available to date, these should be used with caution. Watershed estimates based on the Sentinel sites alone are not valid and should only be used as preliminary estimates. The calibration sites are still arbitrary, but are less so, and simply as a result of the increased sample size it is more reasonable to assume they are ‘pseudo-random’ and hence make inference to the watershed. Unfortunately the increased number of samples only occurs during a single year in each watershed. For a pilot study, the data are very rich and even though the design was not probabilistic, the baseline estimates and estimates of variability are still useful. There are several tradeoffs to consider when


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deciding which estimates to use as the baseline moving forward. The best estimate of site to site variability within a watershed is provided by the calibration sites. This also provides the best estimate of the watershed status, as it is least likely to be biased and the increased sample size improves the confidence of the estimates. However, the calibration sites only occurred during a single year, and so the results are confounded with any year effect (e.g., fire, rainy vs. dry, early or late breeding…). The repeated sentinel site data provide very good estimates of the between year variability at a single site. They also provide good estimates of the status of those 6 sites. They do not provide a good estimate of the status of the watersheds as a whole. The best estimate of the variation in the watershed between years is the variance in the watershed means. However, this is likely very rough as the sample size in most years is small. This should only be used as a preliminary estimate.

7.5.3 Selection of sites in time, within a year

As described in the methods, detectability of Tailed Frogs depends on the time of year. The current sampling design targets the time of year with the lowest flows and when metamorphosed frogs are typically confined to the stream or other wet sites. This is an important component of the sampling design. Even within the short sampling period there will be fluctuations in detectability. To prevent confounding abundance results with sampling date, the order in which sites are sampled should be randomized.

7.5.4 Selection of sites in time, between years

Generally speaking, repeat sampling of the same sampling unit improves the ability to detect a trend. However, repeat sampling of the same sites reduces the overall sample size. It is important to clarify the scale of interest for your question when deciding whether or not to use repeat visits, and if so, at what scale (e.g., revisit the exact same site, or revisit the watershed?). It is also important to consider whether the sampling is either destructive (e.g., destroys the habitat), or inviting (e.g., uses a feeder) then the results in following years may be confounded by the effect of the sampling (Section 7.6.5). A more detailed discussion of these ideas can be found in: McDonald (2003) or Pickard (2008).

McDonald (2003) provides several useful definitions that may be helpful in clarifying the study objective and hence determining the best design:

Net change: measurement of total change in a Tailed Frog density arising from all sources change in mean or total response

o get an estimate of mean Tailed Frog density over all sites each year and assess this over time

individual change can happen without causing net changeo Tailed Frogs could move from one stream reach to another. So individual stream

reach could experience a trend while the overall population of the watershed does not.

Individual change: change experienced by an individual or particular member of the population, this can be further divided into three categories:

gross change: change in response of a particular population unit (e.g. change in a particular stream reach)

average gross change: if all reaches monitored have higher densities (so the same change occurs to many individual units)

instability: variance of responses from individual population units (this doesn’t sound like it is of as much interest to this study)

7.5.5 Selection of homogeneous reaches within a site

Reducing the sites sampled to hand picked homogeneous reaches restricts the ability to make inference beyond the sample. This strategy is typical of stream sampling protocols, but is not a valid sampling approach. There is valid reasoning behind this kind of ‘representative sample’ strategy, which explains their prevalence, but better methods are available that do allow inference. It is very difficult to actually choose a ‘representative sample’ without knowing what the whole population looks like. In this case,


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selecting a homogeneous reach had two objectives: 1) to select an area without too many deep pools or woody debris that can’t be sampled; 2) to account for local scale geomorphological differences.

The vision was to map the entire watershed into homogeneous reaches. Then we could use the homogeneous reaches as sampling units; these would not necessarily be the same size. This is essentially a valid stratified sampling design with each homogeneous reach as its own stratum. However, you would then have to sample every stratum (all homogeneous reaches) to get a population estimate. Results for an individual stratum would not be transferable to other strata. A more effective and affordable strategy is to use a smaller set of strata with randomly selected sites.

As for restricting sampling to areas without bedrock, woody jams, and deep pools, this needs to be made clear in the definition of the target population. It is also worth distinguishing between areas that are not sampled because they are too difficult versus being unlikely habitat for Tailed Frogs. Instead of looking for areas that don’t have any of these features, you could stratify on these features or simply remove them from your sample when you arrive at the site. For example if a randomly selected site of 50m length contains 10m of bedrock, woody jams, and deep pools, then just sample the 40m that falls in your target population and use density rather than absolute count as the metric to correct for the shorter sample unit. If you are reporting a density in the stream of areas without ‘bedrock, woody jams, and deep pools’ this is valid. If you are interested in making inference to an actual population size for the entire stream, you would then have to map or sample the whole stream to know what portion of the stream doesn’t have ‘bedrock, woody jams, and deep pools’.

7.6 Tradeoffs in field methods

There are several basic field methods available for assessing aquatic populations of Tailed Frogs: Area Constrained Search (ACS), Time Constrained Search (TCS), ‘Rubble Rousing’ (RR), and Light Touch (LT). In this report, RR is used to describe the physical search technique of removing substrate used by both the ACS and TCS. The difference is that ACS is constrained by area while the TCS is constrained by time. Quinn et al. (2007) uses a RR to describe an area based method similar to ACS in this report. The LT method is area constrained but uses less intense search methods than either: ACS, TCS, or RR. Results from this pilot project used ACS and TCS, Quinn et al. (2007) compared RR and LT. None of these methods is perfect; we present some of the tradeoffs here.

7.6.1 Effort

All of the methods require the same effort to get to the site. TCS requires a fraction of the time that ACS requires. With two people, a 30-min TCS takes 15-minutes plus perhaps another 30-45 minutes to collect pebble count, stream temperature and channel geometry information. On the other hand, with 2-4 people, a three 5-m belt ACS sample can take several hours to most of a day. Four to ten TCS samples can be executed in a day, whereas 1-3 ACS samples can be accomplished in a day. Quinn et al. (2007) reported rubble rousing methods to take 12 times as long as the LT method. The LT method would likely be more time consuming than the TCS method simply because there is no set-up involved in the TCS method.

7.6.2 Probability of Detection & bias

Gyug (2005, pg. 2) suggests that “area constrained searches are the closest thingto an absolute census” that there is, but emphasized the “closest” part, by noting multiplesweeps often turn up more tadpoles. Individuals may be deep in the substrate, or may escape when the nets aren’t able to completely block the stream. The results of this pilot indicate that the probability of detecting at least one individual at a site is similar using the TCS and the ACS (i.e., 3 x 5m belts). The results also indicate that TCS counts are a good predictor of ACS counts. TCS will underestimate abundance where difficult search conditions (large bouldery channel, deep pools) make quick location and capture difficult, and where there are many Tailed Frogs (>20-30 animals per 5-m belt) as the TCS becomes limited by how many can be caught in the allotted time. The LT methods were found to be correlated with RR counts for


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most life stages. Probability of detection was estimated to be 0.51 for LT when compared with RR counts. While LT and TCS have not been formally compared, field experience suggests that the probability of detection would be greater for the TCS method.

7.6.3 Ability to expand the estimate

The area based methods are actually based on length, not area as the name implies. A belt of a fixed length is identified and blocked off using nets. These methods are easier in principle to expand to the population. We can present the estimates as counts per area (or stream length). These can then be expanded to the population based on the total stream length in the watershed. However, variation in stream width can affect these estimates as the area by change greatly even though the length is the same. Appropriate use of stratification could mitigate this to some degree.

The time constrained method can’t be used to estimate the population or even the density. This is because the sampling unit is defined by time and we have no way of translating that to a spatial unit. We can use the estimates as a measure of relative abundance and can compare the relative abundance over time. There are precedents for using indices of relative abundance based on counts per unit time, usually called ‘Catch Per Unit Effort’ (CPUE) (Porter et al. 2008; Hilborne & Walters 1992). It might also be possible to obtain a measure of area/distance covered during the time based search so that we could make an estimate of density.

7.6.4 Repeatability

All of the methods will have some degree of observer error, as experienced individuals will be better at spotting and catching frogs than inexperienced individuals.

The area constrained searches (ACS, LT) have an advantage in regard to how the sampling area is selected. Searching within a well defined area is less confusing than the somewhat looser method described by the TCS. The TCS method allows for the individual to move between locations within a site. It would be hard to write a clear protocol for when it is ‘ok’ to move on and when it isn’t. It would also be hard for an experienced observer to stop themselves from keying on the ‘best’ areas. This introduces a potentially large subjectivity. While results from one individual may be quite consistent, it may be difficult to use results from multiple observers.

The ACS method should be the most repeatable. Even if individuals are inexperienced they are allowed as much time as necessary to catch all of the animals. The LT protocol is simpler than the TCS in how the site is selected in space. However, the method for actually searching for animals is less clear and would likely be quite sensitive to observer experience (i.e., an experienced observer might be able to find and catch tadpoles quite easily with only light touch methods, but an inexperienced observer might be too slow to catch many without removing the substrate). Formalizing the methods and implementing training will reduce these concerns for all of the protocols described here.

7.6.5 Geomorphic Effects of Repeat Sampling

Tailed Frog breeding streams are characterized by sequences of semi-stable, interlocked aggregations of cobble and boulder sized stones forming steps and associated plunge pools. Animal searching completely rearranges the stone aggregations. Thus, rubble rousing is a destructive sampling process. The question is what are the short and long term effects of repeat sampling, are they significant and how do they bear on the results or the suitability of the method.

Gyug (2005b, p 28) observed that repeat sampling in the same year results in reduced encounter rates, probably because dislodged animals drift out of the sample area after the initial sampling. This effect was not observed in successive annual searches, probably because over the course of a year there is a natural


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flux of animals through the sample reach. In fact animal abundance fluctuated widely between years, both up and down.

Dupuis and Friele (2006b) expressed concern about the effect of destructive sampling on channel stability, postulating that the destruction of a step, or series of steps, during sampling may weaken the armour layer and cause temporary bedform instability. Stones forming the armour layers are stacked like shakes on a roof: when a mobile cobble or boulder stops tumbling during a flood event, then others pile up against it in a stacked arrangement called imbrication. Rubble rousing destroys imbrication and when the stones are returned to the creek after the sample, they are randomly arranged not imbricate. Based on successive annual sampling 2007 & 2008 it appears that the average annual flood recreates imbrication within 1-2 years of sampling. Because Tailed Frog numbers were seen to be stable or increase, and fluctuate up and down, the effect of sampling on Tailed Frog tadpoles is unknown.

It is possible, that there is an increased potential for trauma during the first post sampling flood that reorganizes the bed. An example supporting this supposition was an adult frog found in 2008 on Norge Creek. This animal had a crushed hind leg. The trauma was an old injury (not caused by the sample just conducted) and the frog was thin and suffering (it was euthanised). It clearly had had its leg pinched by a stone during the previous spring freshet flood event. Although this type of trauma occurs naturally, it is possible that 2007 sampling may have increased the risk of injury in this case.

Clearly, repeat sampling of ten belt samples over a 100-m reach represents a high disturbance factor. Although results did not quantify a negative effect on animal abundance, it seems unduly disruptive, and might increase the trauma risk for animals in the reach. For this reason repeat Sentinel sampling, although yielding good baseline information about population densities and animal fluctuations, is not desirable for long-term abundance monitoring.

Further, repeat sampling of Sentinel samples alters the step-pool morphology. Thus, repeat channel morphology surveys at Sentinel sites where repeat abundance sampling has occurred has limited use in that the channel is defacto altered by the method.

7.7 Review of Tailed Frog WHA monitoring objectives

In the draft monitoring protocol Dupuis and Friele (2005a) took the view, after Maxcy (2004) that it was not sufficient to just sample the population of interest, but to derive and sample critical indices of terrestrial and aquatic habitat condition, so that if a population change was detected, it could be ascribed to a cause. Only then could a decline be managed. However, as pointed out by Gyug (2005a), it is not possible to foresee when or where an impact will take place; thus it becomes fruitless to extensively monitor habitat variables in a small area over extended time periods. Gyug (2005a) also noted that although we know from research experience what variables Tailed Frogs are positively or negatively correlated with, we really do not understand the mechanisms, the degree of change that is significant, and/or the threshold values over which red flags must be raised. This is confounded by the fact that the fluvial system is a complex multivariate system, and in one setting a certain variable (or sets of variables) may be of significance, but in another setting an entirely different interaction may be important (Dupuis and Friele 2006). It may be very difficult to tease out cause and effect, especially over intra-basin and regional scales, and the conclusions drawn from a single monitoring reach may not be applicable to changes basin wide, or regionally for that matter (Dupuis and Friele 2006). Finally, both the habitat and the animal populations show high degrees of spatial and temporal variability, and large samples sizes are required to produce an error level about the mean that will allow change to be detected in independent or response variables.

In light of all these complex issues, the questions, “what is trying to be accomplished by monitoring?” and “what are we willing to invest?” need to be carefully examined, because trying to detect local and/or regional trends requires considerably more sampling effort than there appears to be the will or capacity to support currently. If funds are limited, it may be wiser to focus on animal sampling only, using the ARMI PAO approach, and only when and if significant population changes are noted, then retrospective studies can be designed to evaluate the cause(s).


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Thus, one of the main conclusions resulting from this work is that the original goals of trend monitoring and monitoring indicators so as to be able to discern cause and effect should a change be detected is far too lofty a goal given the size of the range, the number of WHAs within the range, the number of samples required to yield the statistical precision required to establish cause and effect, and finally, the amount of money available. Consequently, recognizing these limitations, we would suggest that the scope of the monitoring objective be revised to something more attainable. We believe that a monitoring program similar to that advocated by ARMI is appropriate whereby populations within a defined range are tracked by application of a “percent of area occupied” (PAO) variable. Ideally extensive monitoring studies to discern cause and effect (e.g. BACI studies) or at least correlative relationships (e.g., regression) would be completed to assess relationships between habitat condition and/or various threats and Tailed Frogs. However, given the monetary constraints it may only be feasible to initiate detailed studies if/when significant changes in PAO are noted. The risk of waiting until a change has been observed is that the power to discern cause/effect relationships is greatly improved by having ‘before’ data.

The precedent for this is the post fire assessment (Friele and Dupuis 2006b) that compared before and after TCS samples from sites within and outside the 2003 Ram-Cabin fire perimeter. The 50 calibration samples collected in 2008 & 2009 can be compared against the baseline data provided by the reconnaissance mapping (Dupuis and Friele 2002, 2004a), yielding the first pass PAO test.

Whether or not the monitoring objectives are revised they need to be clarified to explicitly define the target populations and the scale of interest in both time and space.

8 RECOMMENDATIONS

8.1 Refine questions and objectives Define the spatial and temporal scale of interest for each question and objective. For objectives related to status and trends of habitat or Tailed Frog population size in the WHA’s, the ‘net change’6 or the ‘average gross change’7 are probably most appropriate. Assessing shifts in range, either expansion or contraction may be worth adding to the list of monitoring objectives. Refine or prioritize objectives related to establishing cause and effect relationships between specific land-use activities to more realistic levels.

8.2 Use of existing data Caution should be used in using the pilot data to answer questions they were not designed to address. The data provide a very useful source of information for designing future studies, but only provide adequate information to answer very specific questions (e.g., did relative abundance in Cabin Creek Reach 1 change between 2007 and 2008?). Given limited budgets, it is common to try to use old data collected for different objectives to answer a variety of questions, but this is not as effective as designing an appropriate study in the first place and may be inappropriate (Kroll 2009).

8.3 Routine Monitoring Conduct a routine monitoring exercise, per Gyug (2007) or later version. Update routine indicators periodically. Updating the routine evaluation more frequently will aid tracking threats from land-use (logging, run-of-river and mining) or natural disturbance (fire, infestation, catastrophic flood, debris flow). Where threats are not apparent and year-to-year variability is less a longer monitoring interval is sufficient.

6 Net change: measurement of total change in the indicator, arising from all sources, some sites may decrease so long as the net result is an increase.

7 Average gross change: when all sites increase (i.e., the same change occurs to many individual sites)


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8.4 Extensive Monitoring Develop a monitoring scheme around a Percent Area Occupied (PAO) metric, in coordination with other Pacific Northwest Tailed Frog studies. The elements of the monitoring design (e.g., spatial and temporal selection of sites and field protocol) are outlined below.

8.4.1 Stratified random samplingUse two levels of stratification: 1) habitat based: cold, frontier, core-breeding, mainstem, and 2) local scale geomorphology: reach slope and substrate. Refer to reconnaissance mapping for preliminary stratification (Figure 5). The secondary stratification should also be defined during the reconnaissance mapping, or using remote sense data from routine evaluations. A maximum of 2-3 categories should be defined for the secondary strata. Too many categories will require too many samples, plus they will be too difficult to map. If the secondary level of stratification is too difficult to map ahead of time, a different approach will need to be taken. For example: the points could be selected at random from the primary strata, and the local geomorphology could be recorded as a covariate; the target population could be re-defined to exclude certain areas (e.g. bedrock) and the field protocol could allow the individual to move to the nearest alluvial reach; the sampling units could be increased in size and mapped once arriving at the randomly selected site, then each geomorphic type could be sub-sampled. If cost is limiting the sampling design, it may be necessary to prioritize among primary strata to ensure that at least the ‘core-breeding area’ is adequately sampled. If this occurs, the results need to clearly indicate the sampled population.

Table 25. Proposed stratification categories.Primary stratification ColdFrontierCore-breedingMain-stemSecondary Stratification Bedrockalluvial

8.4.2 Ensuring spatial balanceIn order to ensure good spatial coverage as per the original methods, a spatially balanced design could be implemented. Generalized Random Tesselation Stratified (GRTS) designs have been employed extensively for sampling water quality and fish populations from stream networks (Pickard and Porter 2008). A GRTS design ensures that samples are spatially balanced and yet still random, so as to enable inference to the population (Stevens & Olsen 2004). This is a practical and yet statistically valid solution for sampling natural resources where a simple random sample may result in a cluster of sites (Theobald et al. 2007). So called representative sites are often used by biologists because they have recognized and are trying to address the limitations of a simple random sample.

8.4.3 Target population, sampling frame, and sampling unitThe target population should be clearly defined. For most questions it should probably be defined as the WHA and since there are only a small number of Rocky Mountain Tailed Frog WHAs, they should all be monitored. The target population must be split into strata and sampling units. Every sampling unit must be assigned to a stratum. The sampling unit should either be a unit of length or a point along the stream network. There are trade-offs between sampling a unit of length and sampling a point in the network. Sampling a point in the network is relatively straight-forward given a GIS map of the stream network. From the randomly selected ‘start-point’ the protocol can define whether the sample is time or area based. However, there is some risk of drawing overlapping samples (e.g., 2 points that are very close together so that the area or time based samples would overlap). Sampling a ‘reach’ from the population of reaches prevents having overlapping samples but it is more difficult to define the sampling frame in the first place. EPA has developed extensive probabilistic methods for sampling from a stream network and generally uses a random starting point (Phil Larsen, pers. comm.). The use of homogeneous reaches was useful for


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defining a unit where ACS and TCS methods could be compared, but they should be discontinued in the future.

As described earlier, any portions of the stream which cannot be sampled or are not of interest (e.g., deep pools; bedrock reaches), should be clearly removed from the target population. If the randomly selected site cannot be sampled due to access or safety, this should be documented along with some discussion of whether or not this could bias the results.

8.4.4 Allocation of effortMcDonald (in Sampling Rare and Elusive Species 2004) describes many examples where allocating some effort outside the supposedly core areas provided vast improvements in their understanding and estimates of rare and elusive populations. In addition, information about the areas outside the core-breeding areas would be of interest as there may be a loss of connectivity, contraction or expansion in the headwaters, or loss of habitat. These shifts may be accelerated by climate change impacts both within and beyond current WHAs. Even if abundance or density estimates are the only metric of interest, there should be some sampling effort applied to each of the strata.

The allocation of effort required in each stratum depends on the variability within each stratum and the overall importance of the stratum. Strata with greater variance require more samples. For example, if a high density site has a mean of 100 +/- 20, and the low density site has a mean of 10, then even relatively large errors for the low density site would have little impact on the overall estimate. The appropriate allocation of effort across strata can be calculated given preliminary estimates of variability within each stratum, detailed methods are described in Cochran (1977) and Thompson (2002).

Based on the biology of Tailed Frogs, most of the population is expected to occur in the ‘core-breeding area’, and so most of the effort should be allocated to this stratum. A small proportion of the effort should be put into the other three strata. Given that no data exist currently to calculate the optimal allocation, we’d recommend starting with a minimum of three samples per strata, per WHA or sub-basin depending on how the WHAs are structured.

8.4.5 Field protocol for rapid assessment indicator of occurrence & relative abundance

ACS is too time consuming and destructive to use extensively. Use either TCS or Light Touch methods to enable more samples and greater spatial coverage. Evaluate the tradeoffs between TCS and LT and for whichever method is ultimately selected establish a rigorous protocol and training regime to improve repeatability. Consider how well each of these methods would work for Tailed Frog and for a variety of questions when selecting between them. In addition, it would be worthwhile to coordinate monitoring strategies with other researchers in the Pacific North West. It would be preferable to use a consistent strategy across all questions and species to enable results to be easily compared.

The probability of detection for LT was estimated at 0.51 and TCS is thought to be at least as good. As per MacKenzie et al. (2006), when probability of detection is >.5, 2-3 repeat samples per site is usually sufficient to optimize the design. As described earlier, the field protocols don’t lend themselves to true repeat sampling as they disturb the site. For the LT protocol, a random sample of 2-3 belts could be sampled at each site. For the TCS protocol, it would probably be easiest to simply have the two observers use the same starting point and work away from eachother for 15 minutes, treating each as a site replicate for the purpose of estimating the probability of detection.

While the proposed metric is PAO, the TCS and LT methods do correlate to the ACS abundance estimates and so given that you are already at a site, it is still worth estimating the relative abundance. In addition, detectability is related to abundance and so may aid in interpreting results.


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8.4.6 Selection of sites in time, within a year

Adopt the existing methods which target the period in the year with the lowest flows and highest detectability. Typically, it is a good idea to randomize order where logistically feasible to prevent confounding of results with sampling date. In this case however, it may not be practical or even sensible to randomize as different regions have different elevations and local climates which influence the logistics (e.g., safety) and the detectability. As a minimum, a discussion should be provided to justify the order in which the monitoring occurs and to identify any potential biases that result. This is important to ensure that an observed difference between watersheds isn’t simply an artifact of one always being sampled earlier in the season.

8.4.7 Selection of sites in time, between years

Monitoring should occur annually to avoid interpreting a natural fluctuation as a significant shift in the population (Figure 25). There are tradeoffs between revisiting the same sites and re-randomizing every year. Rotating panel designs provide a nice alternative where a subset of sites are revisited (perhaps only for two years), and new sites are always being added. These designs have been shown to optimize the ability to assess both status and trends simultaneously, especially when the question of interest is for broad scale (e.g. WHA-wide) trends. If consistent funding is expected, then a rotating panel design is probably the best option. However, if there are concerns about funding a simpler design where sites are selected at random from each stratum each year is probably best. In the case of Rocky Mountain Tailed Frog since there are only two WHAs it is likely sufficient to re-randomize annually. In the case of Coastal Tailed Frogs where the range is much larger and hence there are many more WHAs, there will be an additional stage to the sampling design (i.e., selection of WHAs) which may make a rotating panel design more important. The question of how often to sample is not a simple one. It depends heavily on the question(s) of interest and the time-frame. Dr. Lowell Diller of the Green Diamond Resource Company in California has recommended against annual sampling for POA estimates (Lowell Diller pers. comm.), although he does recommend annual monitoring for population trend detection. It may be that the annual variability in POA is fairly small without substantial changes in abundance, even though the annual fluctuations in abundance (even relative abundance) are quite large as seen by the pilot data. In cases where the annual variability is small it is less important to obtain annual estimates and the frequency may be reduced. This issue should be reviewed in more detail by the team developing the final monitoring protocol. As we don’t yet have estimates of between year variability for the POA of BC populations of Rocky Mountain Tailed Frogs, these should either be obtained or at a minimum the results from other Tailed Frog studies should be reviewed.

70

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Figure 25. This hypothetical example illustrates the risk of not sampling the population annually, if the year to year variability is large. Say we sampled once every 5 years: if we considered 2000 vs. 2005 we would assume the population was in decline; yet if we sampled 2003 vs. 2008 we would assume the population was increasing. In reality, the population is stable, but has significant year to year variability.

8.4.8 Channel and Habitat Condition IndicatorsStand/reach level monitoring of riparian microclimate and channel morphology are considered an intensive evaluation. Detailed surveys are costly and have utility in hydraulic analyses and classification, but operator error may limit use for trend detection. Further, channel condition indices have not been related to tailed frog abundance. For these reasons continued detailed surveys are not recommended.

A combination of air photo vegetation mapping (existing product) and routine update of land-use operations would provide extensive evaluation of terrestrial and riparian condition.

Rapid and robust stream substrate indicators are pebble count and embeddedness samples. These are considered good channel condition indicators for extensive sampling. If D50 estimates are determined to be useful summaries for assessing changes over time, the routine to generate the metric should be automated. Percent fines (<=32 mm) is a simple metric with proven validity.

8.5 Intensive Monitoring Trend detection designed to discern cause and effect is deemed impractical for extensive monitoring. Initiate intensive research projects at a smaller spatial scale to assess cause and effect of specific land-use activities or when the PAO and / or relative abundance decline. Refer to Pickard and Porter (2008) for a summary of different study design approaches required for different types of questions.

8.5.1 Post Fire Data setThe opportunistic before/after sampling for the Ram Cabin Fire is an example of intensive monitoring. Further analysis of the post fire response to the Ram Cabin Fire is possible. The preliminary report did not look at age class structure, only total number of animals. Age class structure should be examined. The data is compiled in the - Pre&postfire_2003-2005.xls – spreadsheet in the - Ram-Cabin Fire – folder. Further, the sentinel data could be examined to assess if Storm Creek has responded differently than the others, in both animal abundance and stream texture. Again, age class structure should be examined.

8.6 Monitoring outside WHAs There are several questions which may require additional monitoring outside of the WHAs. Using paired controls of a WHA and a non-WHA within the species range, or if possible a full BACI (before, after, control, impact design) would improve the ability to determine if the WHA as a whole is effective or not. This may not be appropriate in the case of the Rocky Mountain Tailed Frog as the entire BC range is essentially within these two regions and are mostly covered by WHAs. It is probably more relevant to Coastal Tailed Frogs. However, given the limited budgets it is not a priority to address this question. Perhaps a more important question is monitoring range expansion and contraction. Based on the biology of Tailed Frogs, likely expansion corridors can be identified. These regions could be monitored to assess the ability of the Tailed Frog to colonize new areas and to adapt to habitat changes resulting from climate change.

8.7 Overall data management strategy For any long-term monitoring project, it is critical to use good data management. Access databases can be very useful but are probably excessive for this project. Several basic principles should be adopted to manage the excel spreadsheet data.


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o Continue to maintain a single spreadsheet which uniquely identifies all sites and documents any location specific information such as GPS coordinates.

o Continue to maintain a document that summarizes all data files.o Use a consistent unique identification number for every site, replicate, strata, and watershed

throughout the dataset. Small differences in format (e.g., ST versus Storm) can create significant time sinks later when trying to synthesize results.

o Rather than using separate spreadsheets or tabs for different years try to maintain the data in a single spreadsheet.

o Maintain one set of completely raw data files. Don’t include any extra formatting. Don’t merge cells. Try to have a single type of data in each column. Don’t complete any summary statistics in the raw data file.

o The raw datafiles are for record purposes only. From them each analyst can pull what they would like into another working file. Any summaries or re-formatting, should be completed on the working file.

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10APPENDICES

10.1Directions to Sentinel Sites To access the Yahk watershed take the Sunrise Forest Service Road (FSR) off of Highway 95 just south of Moyie Lake, between Yahk and Cranbrook. Follow the Sunrise FSR up to the pass and into the Yahk River drainage. Access to the Flathead watershed is along the Lodgepole FSR off Highway 3, south of Fernie. Follow the Lodgepole FSR over the divide to Harvey Creek and south along the Flathead FSR. At 74 km take the west branch onto the Border FSR. The Cabin and Couldrey creek drainages branch off within a few kilometers of each other.

10.1.1 BoydTo get to the Boyd Creek sample site follow the Yahk FSR south to the junction of the Boyd Creek FSR, which branches west. Immediately after the junction the road crosses Yahk River. Take a left at the next junction and follow it to the first Boyd Creek crossing. The sample reach is located on the upstream side of the FSR.

10.1.2 NorgeTo get to the Norge Creek sample site follow the Yahk FSR to the sharp bend where the road crosses Malpass Creek. Within the next few hundred metres look for an overgrown road cutting off to the east toward the creek. This road can be driven with a 4x4 vehicle to a ford crossing over the Norge/Malpass Creek. On foot from the ford follow the east bank for a few hundred metres to the study site, using GPS for guidance if required.

10.1.3 SprucetreeTo get to the Sprucetree Creek sample site follow the Yahk FSR to the sharp bend where the road crosses Sprucetree Creek. Within the next few hundred metres the road comes close to the creek where it is incised in a ~10m deep rock canyon. Park at the nearest pullout on the north side of the road. To the north is a meadow and the sample reach.

10.1.4 CabinTo get to the Cabin Creek sample site follow the Cabin Creek FSR west for approximately 9 km, looking for a branch with a bridge across the Cabin Creek mainstem on the south side of the road. Cross the bridge and follow the road east. At the first junction take the north branch. Follow this overgrown road to an overgrown landing, and continue on a north branch. Just before the sampled stream the road has been blocked by a large berm. Beyond the berm, follow the stream to the confluence of two tributaries and take the east fork. The sample reach is about 30min hike upstream. Use a handheld GPS for guidance.

10.1.5 CouldreyTo get to the Couldrey Creek sample site follow the Border FSR to the Couldrey/Frozen Lake FSR junction and head west along Couldrey Creek. Continue past the Frozen Lake junction up the Canadian (or North) Fork of Couldrey Creek for about 8 km, to a ford across Couldrey Creek. The sample site is on a south tributary joining the Couldrey Creek mainstem just upstream of the ford. Cross the ford and follow the road for a few hundred metres to where it crosses the tributary. The 2005 sample reach is between this crossing and the mouth of the tributary.


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10.1.6 StormTo get to the Storm Creek sample site follow the Cabin Creek FSR west to the junction of the Storm Creek FSR. Follow the Storm Creek FSR to the ford over Storm Creek. The sample is just downstream of the ford.

10.2Example of t-tests results comparing counts between years or locations

Results of area constrained searches in 100-m sample reaches (n=10, 3-m belts). Nest, egg mass or hatchling site; Tad, tadpoles; frog, metamorphosed animals; Tailed Frog, combined nest, tadpoles and frogs.

Creek Tota

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N0 1-3 4-9 >10BO-R1-2005 0 6 11 17 1 40 3.2 7 3 0 0 10BO-R1-2007** 0 36 4 40 4 240 3.5 1 5 3 1 10BO-R1-08** 0 11 1 12 1 73 3.2 4 6 0 0 10NO -R1-2005 0 18 2 20 2 120 1.0 3 6 1 0 10NO -R1-2007** 0 36 4 40 4 240 1.2 1 2 6 1 10NO-R1-2008 0 116 6 122 12 774 1.8 0 0 6 4 10SP -R1-2005 0 15 1 16 2 100 1.1 4 5 1 0 10SP -R1-2007** 1 83 9 93 8 554 2.1 0 3 4 3 10SP-R1-2008 0 104 7 111 10 694 2.0 0 0 7 3 10CA-R1-2005 0 14 1 15 1 93 2.7 4 5 1 0 10CA-R1-2007 0 34 6 40 3 227 2.6 3 2 4 1 10CA-R1-2008** 1 160 37 198 16 1067 2.6 0 0 1 9 10CO -R1-2005 0 23 3 26 2 153 1.3 1 7 2 0 10CO -R1-2007** 0 94 3 97 9 627 2.8 0 1 3 6 10CO-R1-2008** 0 186 7 193 19 1241 1.6 0 0 2 8 10ST -R1-2005 0 101 28 129 10 674 2.7 0 5 1 4 10ST -R1-2007** 1 26 3 30 3 173 3.0 4 5 0 1 10ST-R1-2008** 4 167 11 182 17 1114 3.0 1 1 1 7 10Grand Total/Mean 7 1230 144 1381 7 8204 2.3 33 56 43 48 180** Significant change (P<0.05) from previous year according to Mann Whitney U.

10.3Confidence intervals for an individual site, extracted from Pickard (2008).

Possible Distributions Poisson: Typically used for count data, assumes that individuals are randomly distributed. The

mean is equal to the variance.


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Negative binomial: This distribution has an extra parameter allowing the heterogeneity to be accounted for. The negative binomial distribution simplifies to the Poisson distribution when the heterogeneity is zero or the mean equals the variance. It is commonly used in ecological studies when count data has more variability than one would expect from a Poisson distribution. There is some controversy around this approach as the heterogeneity parameter does not have any clear ecological property (Seber 1986).

Zero-inflated Poisson: There are a couple of approaches that have been developed to model zeros differently with count data that have more zeros than one would expect with a Poisson distribution.

Zero-inflated negative binomial: Likewise this approach is used when there is both excess variability and more zeros than one would expect with a negative binomial distribution.

Confidence intervals for the mean:There are several possibilities:

1. non-parametric bootstrap from the observed data o This strategy is not recommended for small sample sizes – less than 10, like we have here

2. parametric confidence interval o requires assumption about distributiono In this situation that means we need to be able to make an assumption about the distribution of

the data from 10 data points. We have enough information from the data to say that there is more variability than would be expected from a Poisson distribution and this seems to be consistent across the reaches, so it wouldn’t be totally unreasonable to assume a negative binomial distribution.

a. Friele draft 2007 chi-squared test, b. Plus, if you plot log (variance) x log (mean) for all of the different reaches, you see

the slope is >1.o zero inflated models are generally not recommended for small sample sizes (UCLA 2008)

Negative binomial Confidence Intervals: A negative binomial distribution is a reasonable assumption for the 2007 data. We use a

parametric bootstrap to calculate the 90% CI for the mean This may not actually be the most interesting statistic for the negative binomial, but this is

presumably what would be used for summaries over time. Another statistic could be calculated, but I’m not sure what is of most interest biologically speaking.

I used a parametric bootstrap method (Devore 1995) in R code to calculate 90% confidence intervals for the mean of the data, assuming a negative binomial distribution. This code could also be used to assess the impact on the confidence interval width of adding additional samples.

Table: 90% confidence intervals for the mean of the data, assuming a negative binomial distribution.creek reach year mean var sdev lcl uclBoyd 1 2005 1 1.6 1.2 0.4 1.7Boyd 1 2007 3.7 10.7 3.3 2.2 5.5Boyd 2 2007 0.8 0.8 0.9 0.3 1.3Norge 1 2005 1.9 5.9 2.4 0.8 3.3Norge 1 2007 6.9 32.3 5.7 4.3 10.2Norge 2 2007 3.6 4.9 2.2 2.5 4.8Sprucetr 1 2005 1.5 3.6 1.9 0.7 2.6Sprucetr 1 2007 8.5 28.7 5.4 5.9 11.5Sprucetr 2 2007 5.5 60.3 7.8 2.1 10.7Cabin 1 2005 1.4 4.5 2.1 0.5 2.7Cabin 1 2007 3.5 11.6 3.4 2 5.4Cabin 2 2007 4.7 36.2 6.0 1.9 7.8Couldrey 1 2005 2.4 3.8 2.0 1.4 3.4


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Couldrey 1 2007 9.6 20.0 4.5 7.3 11.9Couldrey 2 2007 16.8 80.8 9.0 12.3 22Storm 1 2005 11.5 144.3 12.0 5.7 18.2Storm 1 2007 4.7 54.5 7.4 1.6 9.1Storm 2 2007 3.2 5.1 2.3 2.1 4.4

10.4Progression of study designs: Excerpted from Pickard and Porter (2008).

There are many possible ways in which to approach an environmental field study (see Eberhardt and Thomas 1991 for an overview). Choosing the right approach requires careful consideration of the: study objectives, the degree of control required, the desired level of inference, the effect size of interest, and the tradeoffs surrounding issues of cost and feasibility of the various approaches. Cochran (1977) describes two broad types of survey: descriptive and analytical. The objective of descriptive surveys is to obtain information about general categories of objects (e.g., the frequency of large woody debris pieces in a watershed); whereas, analytical surveys are used to make comparisons among groups within the population in order to test hypotheses (e.g., are there fewer large woody debris pieces in FSWs than in undesignated watersheds?). Hurlbert (1984) categorises studies as either: manipulative experiments or mensurative experiments, where manipulative studies are those where the investigator has control over the factors in the study and mensurative studies are those where only passive observations is used. Eberhardt and Thomas (1991) include replication as a key requirement for improving the strength of inference and describe eight categories of environmental studies that range from the preferred approach of a controlled experiment with replication to a simple descriptive sampling approach. Schwarz (2006) provides an excellent summary of the tradeoffs between different study approaches ranging from descriptive surveys to designed experiments. Figure 26 illustrates the relationship between the degree of control and the strength of inference possible for an array of study designs.

Figure 26. Relationship between degree of control, strength of inference (and ability to determine causation), and type of study design (from Schwarz 2006).

Based on definitions from Schwarz (2006), we provide a Fisheries Sensitive Watershed (FSW) specific example of each study design identified in Figure 26. These examples were generated to inform the Fisheries Sensitive Watershed monitoring framework, but the concepts are applicable to FREP WHA’s as well.


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10.4.1 Descriptive study: A FSW is selected and an indicator, e.g., road density, is measured. The information collected is only relevant to the watershed sampled.

10.4.2 Observational studyA non-designated watershed and a FSW are selected; road density is measured in both. Comparisons between the two watersheds can be made, but the results are only applicable to the two watersheds sampled. Using this approach, it is not possible to conclude whether any observed differences are representative of the differences between the two categories of watersheds. Descriptive and observational studies involve non-randomly selected sampling units; as a result the information obtained is limited to the sites actually observed.

10.4.3 Analytical surveyA random sample of watersheds from each category is selected and road density is measured in each watershed. An estimate of the mean road density with known precision can be obtained for each category. The estimates from the two categories can be compared; however, it is possible that another unknown factor (besides FSW designation) is actually responsible for the difference.

10.4.4 Impact and control-impact surveysThe goal of this approach is to assess the impact of some change, in this case the designation of a watershed as ‘Fisheries Sensitive’. A variety of impact designs with increasing levels of effort and increasing degrees of inference exist. Mellina and Hinch (1995) provide a summary of different impact designs and describe how each might be used to assess watershed restoration. Schwarz (2006) and Underwood (1994) provide a good description along with examples for a range of impact studies, as well as evaluating their respective strengths and weaknesses. The simplest impact studies look at a single location before and after some event. Obtaining multiple observations before and after an event improves the ability to determine if an observed change is ‘real’ by taking into account the natural year to year variability. Since obtaining ‘before’ samples is often difficult, it may be possible to get variance estimates by randomly sampling from similar but undisturbed habitats (Underwood 1994). This approach can be considerably improved by adding a control site, where the control watershed (an undesignated watershed) is similar to the treatment watershed (i.e., FSW) with respect to general watershed characteristics (e.g., region, annual precipitation, size, etc.). This approach is termed a ‘Before After Control Impact’ (BACI) design. BACI designs are intended to address the question of whether a particular action has resulted in a change at the treatment/impact site relative to the control site, while simultaneously adjusting for extraneous co-variables that might be similarly affecting both impact and control areas. In most cases, the use of controls greatly increases the power of detecting treatment/impact effects; however, poorly chosen control sites can decrease the power of detecting an effect (Korman and Higgins 1997; Roni et al. 2003). For example, a lack of randomization in assigning impact sites prevents us from inferring whether the impact will occur elsewhere. Alternatively, if there is only a single impact/control pair, how do we know that the results are not just a consequence of the choice of sites?

The example in Figure 27 illustrates the value of including a control site for assessing the effects of an impact/treatment on populations which are highly variable naturally over time. In this hypothetical example the ability to detect improved salmon parr to smolt survival after a habitat restoration treatment would not be possible without a control (Hayden Creek), due to high variability in survival (5A). Evaluation of the average annual difference between fish survival in the treatment vs. the control stream (5B), however, indicates that survival in the treatment stream is much greater relative to the control in the years subsequent to the restoration action.


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Figure 27. Value of BACI design for inferences - A) Hypothetical time series of mainstem river and Hayden Creek (control area) parr-to-smolt survival rates, with habitat restoration actions assumed to

happen simultaneously in 2015 (dark arrow) in the mainstem river. Time series for both impact and control streams track erratically over time. B) Hypothesized difference between mainstem river and Hayden Creek

parr-to-smolt survival rates over the time series. D(PRE) and D(POST) represent average survival difference pre- and post-impact in 2015 in the mainstem river. Difference between control and impact

streams shows much greater parr-smolt survival in the treatment stream vs. the control, indicating a benefit of the restoration action over time that would not have been apparent given annual variation without a

control system for comparison (source CSMEP 2006).

BACI designs can be grouped into three types; BACI, BACIP, and MBACI (Downes et al. 2002). In simple BACI designs, measurement are made before an impact at control and treatment locations and then after the impact. However paired measurements through time pre- and post impact are better able to avoid spurious results (Green 1979, Stewart-Oaten et al. 1986). Downes et al. (2002) refers to this as BACIP (P for paired measurement through time). In BACIP paired measurements are taken in both the impact and control sites at multiple random times pre- and post- impact. Keough and Mapstone (1995) further extended the BACI design to contain multiple controls and if possible multiple impact sites, referred to as MBACI (Downes et al. 2002). MBACI designs have been developed to address questions about the impacts of an action across broader scales. Multiple treatment and control locations are chosen randomly from a group of potential locations, thereby providing the means to extrapolate to a larger area. If it is not possible to randomly assign treatments and controls, but the same pattern is observed in multiple pairs, it is reasonable to assign a causal relationship (Schwarz 2006). The MBACI design compares a fixed period of time before the manipulation to (in ideal situations) a similar period of time after the manipulation.


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10.4.5 Designed experimentsIn a designed experiment, the investigator has control over the treatment and can randomly assign experimental units to treatments. The degree of control the investigator has on a study affects the ability to show causation. The ability to make inference to other sampling units depends on random selection of samples or assignment of treatments. There are many good text books available on the subject of designing experiments including: Schwarz 2006, these online course notes geared to the environmental scientist and are probably the best place to start; Montgomery 1997, is a solid introductory book which is probably more than enough for most environmental studies; Box, Hunter, & Hunter 1978, is a traditional reference; Wu & Hamada 2000, is a more recent and extensive reference.


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