An empirical test of DNA mark–recapture sampling strategies for grizzly bears

Boulanger, John; Proctor, Michael F.; Himmer, Stefan; Stenhouse, Gordon B.; Paetkau, David; Cranston, Jerome

doi:10.2192/1537-6176(2006)17[149:aetodm]2.0.co;2

Cited by 67 publications

(105 citation statements)

References 12 publications

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“…While the theory is sound, implementation has at least 3 disadvantages. First, to ensure that all individuals encounter traps, users often scale grid cells to match the average home range size of the target species (Boulanger et al, 2004a;Boulanger et al, 2006). Problems arise when biologists lack home range information for their population under study, home range sizes vary between surveys, or home range sizes vary widely within a population.…”

Section: Grid and Targeted Samplingmentioning

confidence: 99%

“…Problems arise when biologists lack home range information for their population under study, home range sizes vary between surveys, or home range sizes vary widely within a population. When home range data are lacking, then practitioners can rely on home range sizes from the same or related species elsewhere, hoping that the cell size used is appropriate (Boulanger et al, 2006;Sawaya et al, 2012). If animals change their behavior in response to concentrated or dispersed resources, making their home ranges shrink or expand, then home range sizes will vary between years (i.e.…”

Section: Grid and Targeted Samplingmentioning

confidence: 99%

“…This makes it challenging for a defined grid size to always be appropriate. If home range sizes have PrePrints much variation in the population, then a mean value has little biological relevance (McNab, 1963;Powell, 2000;Boulanger et al, 2006). By relying on the home range mean, a segment of the population will have home range sizes below the cell size, so traps may not be available to capture them.…”

Section: Grid and Targeted Samplingmentioning

confidence: 99%

“…Therefore, we used a CMR framework and evaluated sampling designs which varied by configuration, effort (trap amount) and complexity (stationary or moved). The goal was to identify informative and affordable sampling designs that biologists could use for estimating or indexing abundance.For grid-based sampling, it is often recommended that cell size corresponds to the average area of an animal's home range, or smaller, to reduce capture heterogeneity (Boulanger et al, 2004;Boulanger et al, 2006;Sawaya et al, 2012). However, animal home ranges usually have much variability, which can change within and across seasons based on resource pulses and their distribution (McNab, 1963;Boulanger et al, 2006, Nielsen et al, 2010.…”

mentioning

confidence: 99%

“…Second, since traps occur along streams, they are more accessible by walking or rafting. Third, the lure (fish) is provided naturally, reducing time, expense and costs for trap set-up, and concerns that the target species may lose interest in the trap over time (Boulanger et al 2004a; Boulanger et al 2008b).The conventional assumption is that moving traps raises capture probabilities, by increasing the proportion of bears captured, and therefore reducing bias (Boulanger et al,2002;Boulanger et al, 2004a;Boulanger et al, 2006). Our simulation confirmed this assumption for grid and targeted sampling, as moving traps always increased capture probabilities.…”

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confidence: 99%

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Sampling designs matching species biology produce accurate and affordable abundance indices

Harris

Farley

Russell

et al. 2013

Preprint

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Wildlife biologists often use grid-based designs to sample animals and generate abundance estimates. Although sampling in grids is theoretically sound, in application, the method can be logistically difficult and expensive when sampling elusive species inhabiting extensive areas. These factors make it challenging to sample animals and meet the statistical assumption of all individuals having an equal probability of capture. Violating this assumption biases results. Does an alternative exist? Perhaps by sampling only where resources attract animals (i.e. targeted sampling), it would provide accurate abundance estimates more efficiently and affordably. However, biases from this approach would also arise if individuals have an unequal probability of capture, especially if some failed to visit the sampling area.Since most biological programs are resource limited, and acquiring abundance data drives many conservation and management applications, it becomes imperative to identify economical and informative sampling designs. Therefore, we evaluated abundance estimates generated from grid and targeted sampling designs using simulations based on geographic positioning system (GPS) data from 42 Alaskan brown bears (Ursus arctos). Migratory salmon drew brown bears from the wider landscape, concentrating them at anadromous streams. This provided a scenario for testing the targeted approach. Grid and targeted sampling varied by trap amount, location (traps placed randomly, systematically or by expert opinion), and traps stationary or moved between capture sessions. We began by identifying when to sample, and if bears had equal probability of capture. We compared abundance estimates against seven criteria: bias, precision, accuracy, effort, plus encounter rates, and probabilities of capture and recapture. One grid (49 km2 cells) and one targeted configuration provided the most accurate results. Both placed traps by expert opinion and moved traps between capture sessions, which raised capture probabilities. The grid design was least biased (-10.5%), but imprecise (CV 21.2%), and used most effort (16,100 trap-nights). The targeted configuration was more biased (-17.3%), but most precise (CV 12.3%), with least PeerJ PrePrints | https://peerj.com/preprints/129v1/ | v1 received: 27 Nov 2013, published: 27 Nov 2013, doi: 10.7287/peerj.preprints.129v1PrePrints effort (7,000 trap-nights). Targeted sampling generated encounter rates four times higher, and capture and recapture probabilities 11% and 60% higher than grid sampling, in a sampling frame 88% smaller. Bears had unequal probability of capture with both sampling designs, partly because some bears never had traps available to sample them. Hence, grid and targeted sampling generated abundance indices, not estimates. Overall, targeted sampling provided the most accurate and affordable design to index abundance. Targeted sampling may offer an alternative method to index the abundance of other species inhabiting expansive and inaccessible landscapes elsewhere, provided their attrac...

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Section: Grid and Targeted Samplingmentioning

confidence: 99%

Section: Grid and Targeted Samplingmentioning

confidence: 99%

Section: Grid and Targeted Samplingmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Sampling designs matching species biology produce accurate and affordable abundance indices

Harris

Farley

Russell

et al. 2013

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References

2011

Molecular Ecology

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Alaskan brown bears (Ursus arctos) aggregate and display fidelity to foraging neighborhoods while preying on Pacific salmon along small streams

Wirsing

Quinn

Cunningham

et al. 2018

Ecology and Evolution

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The interaction between brown bears (Ursus arctos) and Pacific salmon (Oncorhynchus spp.) is important to the population dynamics of both species and a celebrated example of consumer‐mediated nutrient transport. Yet, much of the site‐specific information we have about the bears in this relationship comes from observations at a few highly visible but unrepresentative locations and a small number of radio‐telemetry studies. Consequently, our understanding of brown bear abundance and behavior at more cryptic locations where they commonly feed on salmon, including small spawning streams, remains limited. We employed a noninvasive genetic approach (barbed wire hair snares) over four summers (2012–2015) to document patterns of brown bear abundance and movement among six spawning streams for sockeye salmon, O. nerka, in southwestern Alaska. The streams were grouped into two trios on opposite sides of Lake Aleknagik. Thus, we predicted that most bears would forage within only one trio during the spawning season because of the energetic costs associated with swimming between them or traveling around the lake and show fidelity to particular trios across years because of the benefits of familiarity with local salmon dynamics and stream characteristics. Huggins closed‐capture models based on encounter histories from genotyped hair samples revealed that as many as 41 individuals visited single streams during the annual 6‐week sampling season. Bears also moved freely among trios of streams but rarely moved between these putative foraging neighborhoods, either during or between years. By implication, even small salmon spawning streams can serve as important resources for brown bears, and consistent use of stream neighborhoods by certain bears may play an important role in spatially structuring coastal bear populations. Our findings also underscore the efficacy of noninvasive hair snagging and genetic analysis for examining bear abundance and movements at relatively fine spatial and temporal scales.

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An empirical test of DNA mark–recapture sampling strategies for grizzly bears

Cited by 67 publications

References 12 publications

Sampling designs matching species biology produce accurate and affordable abundance indices

Sampling designs matching species biology produce accurate and affordable abundance indices

References

Alaskan brown bears (Ursus arctos) aggregate and display fidelity to foraging neighborhoods while preying on Pacific salmon along small streams

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