spOccupancy: An R package for single‐species, multi‐species, and integrated spatial occupancy models

Doser, Jeffrey W.; Finley, Andrew O.; Kéry, Marc; Zipkin, Elise F.

doi:10.1111/2041-210x.13897

Cited by 55 publications

(79 citation statements)

References 35 publications

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“…For these reasons, we treated the combined data as one larger investigation and elected not to fit a categorical variable for “study” in our analyses, which would have made our already complex models more complicated. To fit our BMSOD model, we used the spOccupancy (Doser, Finley, & Banerjee, 2022; Doser, Finley, Kéry, & Zipkin, 2022) package in R version 4.1.2 (R Core Team, 2021), which uses a Pólya‐Gamma data augmentation (Polson et al, 2013) and Markov Chain Monte Carlo algorithm for computational efficiency, the details of which are beyond the scope of this paper (see https://www.jeffdoser.com/files/spoccupancy-web/articles/modelfitting and the references therein). The model quantified the probability of occupancy for each species while taking into account factors that influence detection, such as wind or time of day (MacKenzie et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Context dependency in interference competition among birds in an endangered woodland ecosystem

Lindenmayer

Blanchard

Evans

et al. 2023

Diversity and Distributions

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Aim: Much research has quantified species responses to human-modified ecosystems.However, there is limited work on how human-modified ecosystems may reshape competitive interactions between species. Using a 19-year study across 3 million ha,we aimed to answer the question: Are levels of interference competition between bird species context dependent and influenced by habitat structure and productivity? We focussed on the hyper-aggressive behaviour of the Noisy Miner (Manorina melanocephala), which is recognized as a key threatening process for other woodland bird species in Australia. Whether environmental conditions such as amount of forest cover and net primary productivity (NPP) mediate the Noisy Miners' impact remains untested at large spatiotemporal scales.Location: Temperate woodlands of south-eastern Australia. Methods:We gathered data on bird site occupancy from repeated surveys of field sites and assembled satellite data on tree cover and NPP. We constructed Bayesian multi-species occupancy/detection models of bird species in woodland patches and tested the fixed and interactive effects of Noisy Miner presence, the amount of tree cover, NPP, and time. We quantified the responses of 31 species, many with known interactions with the Noisy Miner documented previously at fine spatial scales. Results:We identified negative associations between the Noisy Miner and 18 bird species, including, unexpectedly, both small and large bodied taxa. Site occupancy in some species was influenced by interactions between Noisy Miner presence and increasing amounts of tree cover or productivity. For some species, interference competition by the Noisy Miner is context-dependent and mitigated by increasing tree cover and/or increasing NPP.Main Conclusions: Our analyses revealed that woodland bird conservation in our study region will be promoted by protecting refugia characterized by areas of high NPP and high tree cover. Preventing vegetation clearing that reduces tree cover could reduce interference competition by the Noisy Miner on parts of the remaining woodland bird community, including species of conservation concern.

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Section: Discussionmentioning

confidence: 99%

Context dependency in interference competition among birds in an endangered woodland ecosystem

Lindenmayer

Blanchard

Evans

et al. 2023

Diversity and Distributions

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show abstract

“…Thus x(s j ) may be identical to x(s j ) if all covariate effects are assumed to vary spatially, or a subset of x(s j ) if some effects are assumed to be constant across space. Note that the model reduces to a traditional single-species occupancy model when all covariate effects are assumed constant across space and a spatial occupancy model (Johnson et al, 2013;Doser et al, 2022b) when only the intercept is assumed to vary across space.…”

Section: Svc Single-season Occupancy Modelmentioning

confidence: 99%

“…We implement single-season and multi-season Bayesian SVC SDMs with new functionality in v0.5.2 of the spOccupancy R package (Doser et al 2022b; see Appendix S2: Table S1 for function names). We assign Gaussian priors to all non-spatial regression coefficients, inverse-Gamma priors for the temporal variance parameter, and uniform priors for all correlation parameters.…”

Section: Software Implementation and Predictionmentioning

confidence: 99%

“…Below we present a set of five practical guidelines for practitioners to consider when fitting SVC SDMs. We provide this set of guidelines along with user-friendly and computationally efficient implementations of single-season and multi-season SVC SDMs in the spOccupancy R package (Doser et al, 2022b) and an associated vignette that provides a detailed walk through for fitting these models (Appendix S3).…”

Section: Guidelines When Fitting Spatially-varying Coefficient Specie...mentioning

confidence: 99%

“…Using simulations and an empirical case study, we present guidelines and recommendations for fitting SVC SDMs via the spOccupancy R package (Doser et al, 2022b) while explicitly accounting for imperfect detection. We focus our guidelines on practical issues that are likely to be encountered by practitioners when using SVCs in a species distribution modeling framework.…”

Section: Introductionmentioning

confidence: 99%

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Guidelines for the use of spatially-varying coefficients in species distribution models

Doser¹,

Kéry²,

Finley³

et al. 2023

Preprint

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Species distribution models (SDMs) are increasingly applied across macroscales using widely available detection-nondetection data sources. However, assumptions of stationarity in speciesenvironment relationships or population trends inherent to most SDM techniques are frequently violated at broad spatial scales. Bayesian spatially-varying coefficient (SVC) models can readily account for nonstationarity, yet their use is relatively scarce, due, in part, to a gap in understanding both the data requirements needed to fit SVC SDMs, as well as the inferential benefits of applying a more complex modeling framework. InnovationUsing simulations, we present guidelines and recommendations for fitting single-season and multi-season SVC SDMs. We display the inferential benefits of SVC SDMs using an empirical case study assessing spatially-varying trends of 51 forest birds in the eastern US from 2000-2019. We provide user-friendly and computationally efficient software to fit SVC SDMs in the spOccupancy R package. Main conclusionsWhile all datasets are unique, we recommend a minimum sample size of ∼500 spatial locations when fitting single-season SVC SDMs, while for multi-season SVC SDMs, ∼100 sites is adequate for even moderate amounts of temporal replication (e.g., 5 years). Within our case study, we found 88% (45 of 51) of species had strong support for spatially-varying occurrence trends.Further, SVC SDMs revealed spatial patterns in occurrence trends that were not evident in simpler models that assumed a constant trend or separate trends across distinct ecoregions. We suggest five guidelines: (1) only fit single-season SVC SDMs with more than ∼500 sites; (2) consider using informative priors on spatial parameters to improve spatial process estimates; (3) use data from multiple seasons if available; (4) use model selection to compare SVC SDMs with simpler alternatives; and (5) develop simulations to assess the reliability of inferences. These guidelines provide a comprehensive foundation for using SVC SDMs to evaluate the presence and impact of nonstationary environmental factors that drive species distributions at macroscales.

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Habitat and climatic associations of climate‐sensitive species along a southern range boundary

Wilson

Cousins

Etter

et al. 2023

Ecology and Evolution

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Climate change and habitat loss are recognized as important drivers of shifts in wildlife species' geographic distributions. While often considered independently, there is considerable overlap between these drivers, and understanding how they contribute to range shifts can predict future species assemblages and inform effective management. Our objective was to evaluate the impacts of habitat, climatic, and anthropogenic effects on the distributions of climate-sensitive vertebrates along a southern range boundary in Northern Michigan, USA. We combined multiple sources of occurrence data, including harvest and citizen-science data, then used hierarchical Bayesian spatial models to determine habitat and climatic associations for four climate-sensitive vertebrate species (American marten [Martes americana], snowshoe hare [Lepus americanus], ruffed grouse [Bonasa umbellus] and moose [Alces alces]). We used total basal area of at-risk forest types to represent habitat, and temperature and winter habitat indices to represent climate. Marten associated with upland spruce-fir and lowland riparian forest types, hares with lowland conifer and aspen-birch, grouse with lowland riparian hardwoods, and moose with upland spruce-fir. Species differed in climatic drivers with hares positively associated with cooler annual temperatures, moose with cooler summer temperatures and grouse with colder winter temperatures. Contrary to expectations, temperature variables outperformed winter habitat indices. Model performance varied greatly among species, as did predicted distributions along the southern edge of the Northwoods region. As multiple species were associated with lowland riparian and upland spruce-fir habitats, these results provide potential for efficient prioritization of habitat management. Both direct and indirect effects from climate change are likely to impact the distribution of climate-sensitive species in the future and the use of multiple data types and sources in the modelling of species distributions can result in more accurate predictions resulting in improved management at policy-relevant scales.

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spOccupancy: An R package for single‐species, multi‐species, and integrated spatial occupancy models

Cited by 55 publications

References 35 publications

Context dependency in interference competition among birds in an endangered woodland ecosystem

Context dependency in interference competition among birds in an endangered woodland ecosystem

Guidelines for the use of spatially-varying coefficients in species distribution models

Habitat and climatic associations of climate‐sensitive species along a southern range boundary

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