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1. Integrated community occupancy models: A framework to assess occurrence and biodiversity dynamics using multiple data sources

   https://doi.org/10.1111/2041-210X.13811  

   Doser, Jeffrey W. ; Leuenberger, Wendy ; Sillett, T. Scott ; Hallworth, Michael T. ; Zipkin, Elise F. ( February 2022 , Methods in Ecology and Evolution)

   1. The occurrence and distributions of wildlife populations and communities are shifting as a result of global changes. To evaluate whether these shifts are negatively impacting biodiversity processes, it is critical to monitor the status, trends and effects of environmental variables on entire communities. However, modelling the dynamics of multiple species simultaneously can require large amounts of diverse data, and few modelling approaches exist to simultaneously provide species and community‐level inferences.

   2. We present an ‘integrated community occupancy model’ (ICOM) that unites principles of data integration and hierarchical community modelling in a single framework to provide inferences on species‐specific and community occurrence dynamics using multiple data sources. The ICOM combines replicated and nonreplicated detection–nondetection data sources using a hierarchical framework that explicitly accounts for different detection and sampling processes across data sources. We use simulations to compare the ICOM to previously developed hierarchical community occupancy models and single species integrated distribution models. We then apply our model to assess the occurrence and biodiversity dynamics of foliage‐gleaning birds in the White Mountain National Forest in the northeastern USA from 2010 to 2018 using three independent data sources.

   3. Simulations reveal that integrating multiple data sources in the ICOM increased precision and accuracy of species and community‐level inferences compared to single data source models, although benefits of integration were dependent on the information content of individual data sources (e.g. amount of replication). Compared to single species models, the ICOM yielded more precise species‐level estimates. Within our case study, the ICOM had the highest out‐of‐sample predictive performance compared to single species models and models that used only a subset of the three data sources.

   4. The ICOM provides more precise estimates of occurrence dynamics compared to multi‐species models using single data sources or integrated single‐species models. We further found that the ICOM had improved predictive performance across a broad region of interest with an empirical case study of forest birds. The ICOM offers an attractive approach to estimate species and biodiversity dynamics, which is additionally valuable to inform management objectives of both individual species and their broader communities.

    

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2. Joint species distribution models with imperfect detection for high‐dimensional spatial data

   https://doi.org/10.1002/ecy.4137  

   Doser, Jeffrey W. ; Finley, Andrew O. ; Banerjee, Sudipto ( July 2023 , Ecology)

   Determining the spatial distributions of species and communities is a key task in ecology and conservation efforts. Joint species distribution models are a fundamental tool in community ecology that use multi‐species detection–nondetection data to estimate species distributions and biodiversity metrics. The analysis of such data is complicated by residual correlations between species, imperfect detection, and spatial autocorrelation. While many methods exist to accommodate each of these complexities, there are few examples in the literature that address and explore all three complexities simultaneously. Here we developed a spatial factor multi‐species occupancy model to explicitly account for species correlations, imperfect detection, and spatial autocorrelation. The proposed model uses a spatial factor dimension reduction approach and Nearest Neighbor Gaussian Processes to ensure computational efficiency for data sets with both a large number of species (e.g., >100) and spatial locations (e.g., 100,000). We compared the proposed model performance to five alternative models, each addressing a subset of the three complexities. We implemented the proposed and alternative models in thespOccupancysoftware, designed to facilitate application via an accessible, well documented, and open‐source R package. Using simulations, we found that ignoring the three complexities when present leads to inferior model predictive performance, and the impacts of failing to account for one or more complexities will depend on the objectives of a given study. Using a case study on 98 bird species across the continental US, the spatial factor multi‐species occupancy model had the highest predictive performance among the alternative models. Our proposed framework, together with its implementation inspOccupancy, serves as a user‐friendly tool to understand spatial variation in species distributions and biodiversity while addressing common complexities in multi‐species detection–nondetection data.

    

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3. From backyard to backcountry: changes in mammal communities across an urbanization gradient

   https://doi.org/10.1093/jmammal/gyad110  

   Hansen, Christopher P. ; Kays, Roland ; Millspaugh, Joshua J. ; Goheen, ed., Jacob ( November 2023 , Journal of Mammalogy)

   Urbanization often results in biodiversity loss and homogenization, but this result is not universal and there is substantial variability in the spatiotemporal effects of urbanization on wildlife across cities and taxa. Areas with lower population and housing density are some of the fastest-growing regions in the western United States; thus, more research in these areas could offer additional insight into the effects of urbanization on wildlife and the potential importance of wild spaces in maintaining a diverse biotic community surrounding developed areas. To address this need, we conducted a study to identify the effects of urbanization (i.e. housing density) on mammals along a housing density gradient from wilderness to suburbia in Missoula, Montana. We deployed 178 motion-activated trail cameras at random sites within urban/suburban, exurban, rural, and wild regions from May to October 2019 to 2020. We identified all mammals >150 g, then evaluated how housing density influenced: (i) occupancy and (ii) species richness using multispecies occupancy models; (iii) relative abundance using Poisson models; and (iv) diel activity patterns using kernel density estimation and logistic regression. Urbanization was the strongest driver of mammal distribution, with a linear decline in mammal species richness as housing density increased. Urbanization also had strong effects on occupancy and detection rates, with larger-bodied mammals generally having stronger negative associations. Overall, mammal relative abundance was highest in suburban regions; however, this effect was largely driven by White-tailed Deer. Natural environmental factors explained most changes in mammal nocturnal activity; however, urbanization strongly affected nocturnality in some species, with Black Bear and White-tailed Deer becoming more nocturnal and Red Fox and Northern Raccoon becoming less nocturnal as housing density increased. While our study confirms that some mammals can live and thrive in developed areas, it emphasizes the importance of maintaining wild areas for those species that cannot.

    

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4. An integrated model of population genetics and community ecology

   https://doi.org/10.1111/jbi.13541  

   Overcast, Isaac ; Emerson, Brent C. ; Hickerson, Michael J. ( March 2019 , Journal of Biogeography)

   Quantifying abundance distributions is critical for understanding both how communities assemble, and how community structure varies through time and space, yet estimating abundances requires considerable investment in fieldwork. Community‐level population genetic data potentially offer a powerful way to indirectly infer richness, abundance and the history of accumulation of biodiversity within a community. Here we introduce a joint model linking neutral community assembly and comparative phylogeography to generate both community‐level richness, abundance and genetic variation under a neutral model, capturing both equilibrium and non‐equilibrium dynamics.

   Global.

   Our model combines a forward‐time individual‐based community assembly process with a rescaled backward‐time neutral coalescent model of multi‐taxa population genetics. We explore general dynamics of genetic and abundance‐based summary statistics and use approximate Bayesian computation (ABC) to estimate parameters underlying the model of island community assembly. Finally, we demonstrate two applications of the model using community‐scale mtDNAsequence data and densely sampled abundances of an arachnid community on La Réunion. First, we use genetic data alone to estimate a summary of the abundance distribution, ground‐truthing this against the observed abundances. Then, we jointly use the observed genetic data and abundances to estimate the proximity of the community to equilibrium.

   Simulation experiments of ourABCprocedure demonstrate that coupling abundance with genetic data leads to improved accuracy and precision of model parameter estimates compared with using abundance‐only data. We further demonstrate reasonable precision and accuracy in estimating a metric underlying the shape of the abundance distribution, temporal progress towards local equilibrium and several key parameters of the community assembly process. For the insular arachnid assemblage, we find the joint distribution of genetic diversity and abundance approaches equilibrium expectations, and that the Shannon entropy of the observed abundances can be estimated using genetic data alone.

   The framework that we present unifies neutral community assembly and comparative phylogeography to characterize the community‐level distribution of both abundance and genetic variation through time, providing a resource that should greatly enhance understanding of both the processes structuring ecological communities and the associated aggregate demographic histories.

    

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5. An empirical evaluation of camera trap study design: How many, how long and when?

   https://doi.org/10.1111/2041-210X.13370  

   Kays, Roland ; Arbogast, Brian S. ; Baker‐Whatton, Megan ; Beirne, Chris ; Boone, Hailey M. ; Bowler, Mark ; Burneo, Santiago F. ; Cove, Michael V. ; Ding, Ping ; Espinosa, Santiago ; et al ( April 2020 , Methods in Ecology and Evolution)

   Camera traps deployed in grids or stratified random designs are a well‐established survey tool for wildlife but there has been little evaluation of study design parameters.

   We used an empirical subsampling approach involving 2,225 camera deployments run at 41 study areas around the world to evaluate three aspects of camera trap study design (number of sites, duration and season of sampling) and their influence on the estimation of three ecological metrics (species richness, occupancy and detection rate) for mammals.

   We found that 25–35 camera sites were needed for precise estimates of species richness, depending on scale of the study. The precision of species‐level estimates of occupancy (ψ) was highly sensitive to occupancy level, with <20 camera sites needed for precise estimates of common (ψ > 0.75) species, but more than 150 camera sites likely needed for rare (ψ < 0.25) species. Species detection rates were more difficult to estimate precisely at the grid level due to spatial heterogeneity, presumably driven by unaccounted habitat variability factors within the study area. Running a camera at a site for 2 weeks was most efficient for detecting new species, but 3–4 weeks were needed for precise estimates of local detection rate, with no gains in precision observed after 1 month. Metrics for all mammal communities were sensitive to seasonality, with 37%–50% of the species at the sites we examined fluctuating significantly in their occupancy or detection rates over the year. This effect was more pronounced in temperate sites, where seasonally sensitive species varied in relative abundance by an average factor of 4–5, and some species were completely absent in one season due to hibernation or migration.

   We recommend the following guidelines to efficiently obtain precise estimates of species richness, occupancy and detection rates with camera trap arrays: run each camera for 3–5 weeks across 40–60 sites per array. We recommend comparisons of detection rates be model based and include local covariates to help account for small‐scale variation. Furthermore, comparisons across study areas or times must account for seasonality, which could have strong impacts on mammal communities in both tropical and temperate sites.

    

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