Estimating wolverine<i>Gulo gulo</i>population size using quadrat sampling of tracks in snow

Golden, Howard; Henry, Jason; Becker, Earl F.; Goldstein, Michael I.; Morton, John M.; Frost, Dennis J.; Poe, Aaron J.

doi:10.2981/0909-6396(2007)13[52:ewggps]2.0.co;2

Cited by 21 publications

(13 citation statements)

References 9 publications

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“…The WEAR population covers an area of approximately 100,000 km 2 . Although population density for this area is unknown, Golden et al (2007b) reported densities of approximately 10 animals/1,000 km 2 for a Canadian population in a similar environment, which would give an approximate population of 1,000 animals in our area. However, because this estimate is highly uncertain, we used a conservative range of 300-1,500 individuals, which reflects a realistic range of animals within the area.…”

Section: Population Model and Model Parametersmentioning

confidence: 84%

Estimating Sustainable Harvest in Wolverine Populations Using Logistic Regression

2008

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Population viability analysis (PVA) is a common tool to evaluate population vulnerability. However, most techniques require reliable estimates of underlying population parameters, which are often difficult to obtain and PVA are, therefore, best used in a qualitative context. Logistic regression is a powerful alternative to traditional PVA methods but has received surprisingly limited attention. Logistic regression fits regression equations to binary output from PVA models at a specific point in time to predict probability of a binary response over a range of parameter values. We used logistic regression on output from stochastic population models to evaluate the relative importance of demographic parameters for wolverine (Gulo gulo) populations and to estimate sustainable harvest in a wolverine population in Alaska. Our analysis indicated that adult survival is the most important demographic parameter to reliably estimate in wolverine populations because it had a greater effect on population persistence than did both fecundity and subadult survival. In accordance with this, harvest rate had a greater effect on population persistence than did any of the other harvest‐ and migration‐related variables we tested. Furthermore, a high proportion of harvested females strengthened the effect of harvest. Hypothetical wolverine populations suffered high probabilities of both extinction and population decline over a range of realistic population sizes and harvest regimes. We suggest that harvested wolverine populations must be regarded as sink populations and that source populations in combination with sufficient dispersal corridors must be secured for any wolverine harvest to be sustainable.

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Section: Population Model and Model Parametersmentioning

confidence: 84%

Estimating Sustainable Harvest in Wolverine Populations Using Logistic Regression

2008

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“…This facultative scavenger occupies a cold, low-productivity niche (Copeland et al, 2010;Inman et al, 2012a,b) that results in sparse population densities ($5/1000 km 2 ) and low reproductive rates (0.7 young/female > 3 yrs/yr) across its range (Golden et al, 2007;Inman et al, 2012a;Persson et al, 2006). As a result, wolverine populations are relatively vulnerable due to their small size and limited capacity for growth (Brøseth et al, 2010;Persson et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Developing priorities for metapopulation conservation at the landscape scale: Wolverines in the Western United States

Inman

Brock²,

Inman

et al. 2013

Biological Conservation

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a b s t r a c tWildlife populations are often influenced by multiple political jurisdictions. This is particularly true for wide-ranging, low-density carnivores whose populations have often contracted and remain threatened, heightening the need for geographically coordinated priorities at the landscape scale. Yet even as modern policies facilitate species recoveries, gaps in knowledge of historical distributions, population capacities, and potential for genetic exchange inhibit development of population-level conservation priorities. Wolverines are an 8-18 kg terrestrial weasel (Mustelidae) that naturally exist at low densities ($5/1000 km 2 )in cold, often snow-covered areas. Wolverines were extirpated, or nearly so, from the contiguous United States by 1930. We used a resource selection function to (1) predict habitat suitable for survival, reproduction and dispersal of wolverines across the western US, (2) make a rough estimate of population capacity, and (3) develop conservation priorities at the metapopulation scale. Primary wolverine habitat (survival) existed in island-like fashion across the western US, and we estimated capacity to be 644 wolverines (95% CI = 506-1881). We estimated current population size to be approximately half of capacity. Areas we predicted suitable for male dispersal linked all patches, but some potential core areas appear to be relatively isolated for females. Reintroduction of wolverines to the Southern Rockies and SierraNevadas has the potential to increase population size by >50% and these regions may be robust to climate change. The Central Linkage Region is an area of great importance for metapopulation function, thus warranting collaborative strategies for maintaining high survival rates, high reproductive rates, and dispersal capabilities. Our analysis can help identify dispersal corridors, release locations for reintroductions, and monitoring targets. The process we used can serve as an example for developing collaborative, landscapescale, conservation priorities for data-sparse metapopulations. Ó

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“…(), we found that modelling spatial correlation between adjacent trail segments increased occupancy estimates and failing to account for the spatial correlation would result in negatively biased occupancy estimates with increased type I error associated with narrow CIs. Given the prevalence of winter snow‐based tracking studies in monitoring (Stephens et al ., ; Golden et al ., ; Linnell et al ., ; Magoun et al ., ; Gardner et al ., ; Aing et al ., ; Webb & Merrill, ), spatially and temporally replicated occupancy surveys are a promising monitoring technique.…”

Section: Discussionmentioning

confidence: 99%

“…Snow‐tracking surveys have been used extensively to estimate species distribution and relative abundance using both ground (Thompson et al ., ; Stanley & Bart, ; Hayward et al ., ; Stephens et al ., ; Linnell et al ., ) and aerial‐based surveys (Becker, Spindler & Osborne, ; Golden et al ., ; Magoun et al ., ; Gardner et al ., ; Aing et al ., ; Webb & Merrill, ). Aerial surveys usually focus upon single species within open landscapes, whereas ground surveys detect multiple species within both open and forested landscapes.…”

Section: Introductionmentioning

confidence: 98%

Estimating occupancy using spatially and temporally replicated snow surveys

Whittington

Heuer²,

Hunt

et al. 2014

Animal Conservation

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Occupancy modelling is increasingly used to monitor changes in the spatial distribution of rare and threatened species. Occupancy methods have traditionally relied upon temporally replicated surveys to estimate detection probability. Recently, occupancy models with spatial replication have been used to estimate detection probabilities over large geographical areas that are difficult to survey repeatedly. We developed occupancy models that combine spatially and temporally replicated data and applied them to snow-tracking surveys of six species, including wolverine Gulo gulo and Canadian lynx Lynx canadensis. We surveyed thirty-nine 100-km 2 cells and used 1-km trail segments within cells as spatial replicates. We surveyed 56% of the cells once and 44% of the cells between 2 and 14 times, resulting in a total of 872 km surveyed. We compared four occupancy models that incorporated spatial correlation in detection probability and hierarchically estimated occupancy at two spatial scales: cell occupancy and segment presence. We detected strong serial correlation in probability of detection for all species. Our models with serial correlation had higher occupancy estimates with larger confidence intervals than models assuming segments were independent and exchangeable. Spatial and temporal replicates have identical power to detect decreases in occupancy when survey segments are independent, but spatial correlation in detection probability can reduce the power of spatial replicates. The effects of spatial correlation are more pronounced when detection probability is low. Application of temporal replicates to spatial replicated surveys increases the precision of occupancy estimates, but sampling design trade-offs between number of sites and spatial versus temporal replicates need to balance levels of spatial correlation in detection probability with costs to visit sites. bs_bs_banner Animal Conservation. Print ISSN 1367-9430 Animal Conservation 18 (2015) 92-101

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Estimating wolverineGulo gulopopulation size using quadrat sampling of tracks in snow

Abstract: BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses.

Cited by 21 publications

References 9 publications

Estimating Sustainable Harvest in Wolverine Populations Using Logistic Regression

Estimating Sustainable Harvest in Wolverine Populations Using Logistic Regression

Developing priorities for metapopulation conservation at the landscape scale: Wolverines in the Western United States

Estimating occupancy using spatially and temporally replicated snow surveys

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