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

Wilson, Tammy L.; Schmidt, Joshua H.

doi:10.1890/es15-00250.1

Cited by 21 publications

(19 citation statements)

References 37 publications

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“…Our results showing that OA relationships are unaffected by spatial sampling scale, contrasted with our predictions based on previous work that implicitly considered only areal sampling (He and Gaston , Hui and McGeoch , Wilson and Schmidt ). We reconcile this disagreement by considering the sampling unit.…”

Section: Discussioncontrasting

confidence: 64%

Sampling scales define occupancy and underlying occupancy–abundance relationships in animals

Steenweg

Hebblewhite

Whittington

et al. 2017

Ecology

114

130

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Abstract. Occupancy-abundance (OA) relationships are a foundational ecological phenomenon and field of study, and occupancy models are increasingly used to track population trends and understand ecological interactions. However, these two fields of ecological inquiry remain largely isolated, despite growing appreciation of the importance of integration. For example, using occupancy models to infer trends in abundance is predicated on positive OA relationships. Many occupancy studies collect data that violate geographical closure assumptions due to the choice of sampling scales and application to mobile organisms, which may change how occupancy and abundance are related. Little research, however, has explored how different occupancy sampling designs affect OA relationships. We develop a conceptual framework for understanding how sampling scales affect the definition of occupancy for mobile organisms, which drives OA relationships. We explore how spatial and temporal sampling scales, and the choice of sampling unit (areal vs. point sampling), affect OA relationships. We develop predictions using simulations, and test them using empirical occupancy data from remote cameras on 11 medium-large mammals. Surprisingly, our simulations demonstrate that when using point sampling, OA relationships are unaffected by spatial sampling grain (i.e., cell size). In contrast, when using areal sampling (e.g., species atlas data), OA relationships are affected by spatial grain. Furthermore, OA relationships are also affected by temporal sampling scales, where the curvature of the OA relationship increases with temporal sampling duration. Our empirical results support these predictions, showing that at any given abundance, the spatial grain of point sampling does not affect occupancy estimates, but longer surveys do increase occupancy estimates. For rare species (low occupancy), estimates of occupancy will quickly increase with longer surveys, even while abundance remains constant.Our results also clearly demonstrate that occupancy for mobile species without geographical closure is not true occupancy. The independence of occupancy estimates from spatial sampling grain depends on the sampling unit. Point-sampling surveys can, however, provide unbiased estimates of occupancy for multiple species simultaneously, irrespective of home-range size. The use of occupancy for trend monitoring needs to explicitly articulate how the chosen sampling scales define occupancy and affect the occupancy-abundance relationship.

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Section: Discussioncontrasting

confidence: 64%

Sampling scales define occupancy and underlying occupancy–abundance relationships in animals

Steenweg

Hebblewhite

Whittington

et al. 2017

Ecology

114

130

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“…We developed a competing model set (Appendix S1) using the same covariates as above, and we used Program Mark for fitting the models, estimating C-hat, and performing model averaging of the real parameters. We found minimal literature guidance on topics related to the size of plots for population monitoring (Efford andDawson 2012, Wilson andSchmidt 2015) and almost nothing on two-phase sampling (Conroy et al 2008). The multistate occupancy modeling approach was hierarchical using the Nichols et al (2007) formulation implemented in MARK.…”

Section: Data Analysesmentioning

confidence: 99%

“…Hence, we included all sightings for analyses, given future objectives could be to use occupancy approaches to survey large areas of the lagoon for further study. We found minimal literature guidance on topics related to the size of plots for population monitoring (Efford andDawson 2012, Wilson andSchmidt 2015) and almost nothing on two-phase sampling (Conroy et al 2008). We used the following real parameters for the models: Ѱ1 [i] = probability the site (i) is occupied, regardless of abundance state (probability that the true abundance = few or many, conditional on the true state being few or many), Ѱ2 [i] = probability that the site is occupied by many, given that the site is occupied (probability that true abundance is many, conditional on the true state being few or many), p1 [i] = probability that few or many were detected for site i during a period, conditional that the site was occupied by few (probability that the true state is few or many, conditional on true state is few), p2 [i] = probability that few or many were detected for site i, during a period given the site was occupied by many (probability that the true state is many, conditional on true state being many), d [i] = probability that many were observed, given that few or many were detected during the period and true state was many (classified state is many, conditional on true state being many).…”

Section: Data Analysesmentioning

confidence: 99%

Sampling rare and elusive species: Florida east coast diamondback terrapin population abundance

et al. 2019

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The Florida east coast terrapin (Malaclemys terrapin tequesta) is a rare and potentially endangered species that is difficult to survey because of poor detection probability and a patchy distribution. Like many rare species sampling programs, we apply a density and multistate occupancy sampling approach that considered the impacts of imperfect detection. We separated the detection process into availability of the animal within the sampling area (e.g., coming to surface) and perceptibility (actually seeing it). Our study employed a density estimation approach originally developed for birds, which combined time to detection and distance sampling within a Bayesian N-mixture model. Our study also estimated two abundance occupancy states (few and many terrapins). We were able to estimate large differences in terrapin densities between sites through functions of site-specific environmental covariates (water depth, distance to mangrove, and distance to land). The detection probability was poor (0.28) for the few terrapin occupancy state, but was much greater for many (0.75). The time to detection and distance-sampling approach for this aquatic animal should be useful for other aquatic organisms that regularly surface. Terrapins were generally available to be sighted within four minutes but detection declined rapidly to a low probability of terrapin detection >30 m. The approach of estimating density as a function of habitat covariates to identify habitat associations can provide an effective method that combined with adaptive sampling should be useful to investigate the distribution of terrapins in open water.

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“…The relationship between occupancy and density has garnered considerable attention in recent years (Tempel and Gutiérrez , Clare et al. , Wilson and Schmidt , Linden et al. , Parsons et al.…”

Section: Introductionmentioning

confidence: 99%

The influence of movement on the occupancy–density relationship at small spatial scales

et al. 2019

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The global decline of large carnivores demands effective and efficient methods to monitor population status, particularly using non-invasive methods. Density is among the most useful metrics of population status because it is directly comparable across space and time. Unfortunately, density is difficult to measure reliably, especially for mobile, cryptic species. Recently, efforts have turned to approximating density based on its relationship to more readily estimable indices of occurrence. However, the relationship between density and such indices is contingent on several key assumptions that field studies often violate. Recent research has shown that these relationships are unreliable where sampling units are not independent, as is often the case when estimating density or occurrence of large carnivores. Here, we use the largest data set thus far collected for leopards (Panthera pardus)-88 camera-trap surveys undertaken in 24 protected areas between 2013 and 2018-to explore how density and other population characteristics relate to parameter estimates in occupancy and Royle-Nichols abundance models. We show how home-range size confounds underlying relationships, with larger home ranges inflating the proportion of area used (PAU) and resulting in double counting in abundance models. Relativizing estimates of occupancy and abundance by home-range size improved their relationship with density, but the relationship remained weak and largely uninformative for management. Our findings illustrate the pitfalls of using the PAU or abundance as implicit proxies for density and highlight the challenges of assessing population status for wide-ranging, cryptic species across fragmented landscapes.

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

Cited by 21 publications

References 37 publications

Sampling scales define occupancy and underlying occupancy–abundance relationships in animals

Sampling scales define occupancy and underlying occupancy–abundance relationships in animals

Sampling rare and elusive species: Florida east coast diamondback terrapin population abundance

The influence of movement on the occupancy–density relationship at small spatial scales

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