Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance

Wilson, Tammy L.; Odei, James B.; Hooten, Mevin B.

doi:10.1111/j.1365-2664.2009.01766.x

Cited by 21 publications

(23 citation statements)

References 29 publications

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“…This makes sense because larger areas necessarily have more dens, and the Royle and Nichols (2003) model incorporates abundanceinduced heterogeneity into the detection model. A possible avenue for further development includes: modeling den persistence using a capture-mark-recapture approach (e.g., Wilson et al 2014), paired with the incorporation of additional animal signs that would improve confidence in annual use (e.g., Wilson et al 2010). Our estimates of den abundance were about twice the expected density.…”

Section: Discussionmentioning

confidence: 93%

Scale dependence in occupancy models: implications for estimating bear den distribution and abundance

Wilson

Schmidt

2015

Ecosphere

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Monitoring programs are typically designed to identify long-term trends in animal abundance, however estimating abundance at a relevant scale can be logistically prohibitive. This is particularly true for species that occur at low densities or those with large home ranges. In such cases, occupancy surveys are often employed in place of more expensive abundance estimation techniques such as mark-recapture because precise estimation of occupancy probability generally requires fewer data. Although choice of plot size is a critical design element of occupancy monitoring, relatively little effort has been expended to develop or test plot size recommendations. Animal movement between surveys can complicate efforts to obtain an optimal plot size, but surveys of fixed objects, such as nests, dens, or burrows can provide insight about scale effects because the population exposed to sampling does not change during the duration of the survey. We used repeated aerial occupancy surveys to obtain estimates of brown bear (Ursus arctos) den distribution and abundance in a portion of Katmai National Park and Preserve in Alaska. We then used these data to assess the importance of plot size selection and highlight the effects of spatial grain on the resulting inference and utility for monitoring. Scale effects in estimates of mean den-based site occupancy, but not total den abundance demonstrated that careful selection of sample unit size is important if estimating occupancy probability is a primary monitoring objective. We expect occupancy surveys based on important structures such as nests or dens could have wide applicability for many species.

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Section: Discussionmentioning

confidence: 93%

Scale dependence in occupancy models: implications for estimating bear den distribution and abundance

Wilson

Schmidt

2015

Ecosphere

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“…While it is difficult to identify underlying mechanisms with such Wilson et al 2010). However, modern statistical methods provide an opportunity to increase ecological realism in posthoc analyses of species' distributions and abundances.…”

Section: Discussionmentioning

confidence: 99%

Incorporating ecological principles into statistical models for the prediction of species’ distribution and abundance

et al. 2012

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Understanding the determinants of species' distributions and abundances is a central theme in ecology. The development of statistical models to achieve this has a long history and the notion that the model should closely reflect underlying scientific understanding has encouraged ecologists to adopt complex statistical methods as they arise. In this paper we describe a Bayesian hierarchical model that reflects a conceptual ecological model of multi-scaled environmental determinants of riverine fish species' distributions and abundances. We illustrate this with distribution and abundance data of a small-bodied fish species, the Empire gudgeon Hypseleotris galii, in the Mary and Albert Rivers, Queensland, Australia. Specifically, the model sought to address; 1) the extent that landscape-scale abiotic variables can explain the species' distribution compared to local-scale variables, 2) how local-scale abiotic variables can explain species' abundances, and 3) how are these local-scale relationships mediated by landscape-scale variables. Overall, the model accounted for around 60% of variation in the distribution and abundance of H. galii. The findings show that the landscape-scale variables explain much of the distribution of the species; however, there was considerable improvement in estimating the species' distribution with the addition of local-scale variables. There were many strong relationships between abundance and local-scale abiotic variables; however, several of these relationships were mediated by some of the landscape-scale variables. The extent of spatial autocorrelation in the data was relatively low compared to the distances among sampling reaches. Our findings exemplify that Bayesian statistical modelling provides a robust framework for statistical modelling that reflects our ecological understanding. This allows ecologists to address a range of ecological questions with a single unified probability model rather than a series of disconnected analyses.

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“…Previous efforts have generated spatial models of pygmy rabbit habitat for several regions within the species range, including Idaho (Rachlow and Svancara 2006), northern Utah (Wilson et al 2010), and southwestern Montana (Burkholder 2016). Other regional studies also have produced model equations of habitat suitability for the species (Gabler et al 2001, Himes and Drohan 2007, Larrucea and Brussard 2008.…”

Section: Overlap Between Sage-grouse and Pygmy Rabbitsmentioning

confidence: 99%

Habitat specialists as conservation umbrellas: Do areas managed for greater sage‐grouse also protect pygmy rabbits?

et al. 2019

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Sagebrush ecosystems are affected by a variety of factors, and loss and degradation of sagebrush habitat threatens many wildlife species. Federal land management and wildlife agencies have invoked the umbrella species concept to protect this ecosystem, designating >580,000 km 2 as Habitat Management Areas (HMAs) for greater sage-grouse (Centrocercus urophasianus; hereafter "sage-grouse") putatively benefiting multiple wildlife species. The pygmy rabbit (Brachylagus idahoensis) also is a species of conservation concern due to its obligate relationship with the sagebrush ecosystem, and our goal was to evaluate the degree to which grouse-focused HMAs might serve as a conservation umbrella for pygmy rabbit habitat. We acquired 18,598 records of pygmy rabbit occurrence from all eight range states (excluding Washington because that population is undergoing reintroduction); after screening for reliability, we retained 10,420 records, which we used to estimate minimum occupied area (MOA) and to create an inductive species distribution model (SDM) for pygmy rabbits across their full geographic range. We used the program Maxent to build models of varying complexity, incorporating topographic, vegetation, fire, climate, and soil information. The pygmy rabbit MOA is estimated at 28,367 km 2 , and~92% of this area is included within sage-grouse HMAs. We identified 224,820 km 2 of suitable habitat for pygmy rabbits (maximum test sensitivity plus specificity threshold of 0.3167) and 145,725 km 2 of primary habitat (equal test sensitivity and specificity threshold 0.4661), with concentrations in four distinct core areas. Overlap with sage-grouse HMAs was high (87% of suitable habitat and 91% of primary habitat), suggesting that the sage-grouse umbrella has the potential to conserve habitat for pygmy rabbits. Two of the largest HMAs are Priority Habitat Management Areas (PHMAs; which have the most habitat protection) and General Habitat Management Areas (GHMAs; which have less protection). Our results suggest that PHMAs encompass 59% and GHMAs encompass of 34% of primary habitat for pygmy rabbits. The SDM of habitat for pygmy rabbits can be used by land managers and biologists to prioritize survey locations for pygmy rabbits and to identify areas for habitat management, conservation, or restoration.

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Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance

Cited by 21 publications

References 29 publications

Scale dependence in occupancy models: implications for estimating bear den distribution and abundance

Scale dependence in occupancy models: implications for estimating bear den distribution and abundance

Incorporating ecological principles into statistical models for the prediction of species’ distribution and abundance

Habitat specialists as conservation umbrellas: Do areas managed for greater sage‐grouse also protect pygmy rabbits?

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