Joint species distribution models with imperfect detection for high-dimensional spatial data

Determining spatial distributions of species and communities are key objectives of ecology and conservation. Joint species distribution models use multi-species detection-nondetection data to estimate species and community distributions. The analysis of such data is complicated by residual correlations between species, imperfect detection, and spatial autocorrelation. While 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 compare the proposed model performance to five candidate models, each addressing a subset of the three complexities. We implemented the proposed and competing models in the spOccupancy software, designed to facilitate application via an accessible, well-documented, and open-source R package. Using simulations, we found ignoring the three complexities when present leads to inferior model predictive performance. 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 candidate models. Our proposed framework, together with its implementation in spOccupancy, serves as a user-friendly tool to understand spatial variation in species distributions and biodiversity metrics while addressing common complexities in multi-species detection-nondetection data.Comment: 27 pages, 3 figure

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

Occupancy modeling is a common approach to assess spatial and temporal species distribution patterns, while explicitly accounting for measurement errors common in detection-nondetection data. Numerous extensions of the basic single species occupancy model exist to address dynamics, multiple species or states, interactions, false positive errors, autocorrelation, and to integrate multiple data sources. However, development of specialized and computationally efficient software to fit spatial models to large data sets is scarce or absent. We introduce the spOccupancy R package designed to fit single-species, multi-species, and integrated spatially-explicit occupancy models. Using a Bayesian framework, we leverage P\'olya-Gamma data augmentation and Nearest Neighbor Gaussian Processes to ensure models are computationally efficient for potentially massive data sets. spOccupancy provides user-friendly functions for data simulation, model fitting, model validation (by posterior predictive checks), model comparison (using information criteria and k-fold cross-validation), and out-of-sample prediction. We illustrate the package's functionality via a vignette, simulated data analysis, and two bird case studies, in which we estimate occurrence of the Black-throated Green Warbler (Setophaga virens) across the eastern USA and species richness of a foliage-gleaning bird community in the Hubbard Brook Experimental Forest in New Hampshire, USA. The spOccupancy package provides a user-friendly approach to fit a variety of single and multi-species occupancy models, making it straightforward to address detection biases and spatial autocorrelation in species distribution models even for large data sets.Comment: 20 pages, 2 figure

spAbundance: An R package for single-species and multi-species spatially-explicit abundance models

Numerous modeling techniques exist to estimate abundance of plant and wildlife species. These methods seek to estimate abundance while accounting for multiple complexities found in ecological data, such as observational biases, spatial autocorrelation, and species correlations. There is, however, a lack of user-friendly and computationally efficient software to implement the various models, particularly for large data sets. We developed the spAbundance R package for fitting spatially-explicit Bayesian single-species and multi-species hierarchical distance sampling models, N-mixture models, and generalized linear mixed models. The models within the package can account for spatial autocorrelation using Nearest Neighbor Gaussian Processes and accommodate species correlations in multi-species models using a latent factor approach, which enables model fitting for data sets with large numbers of sites and/or species. We provide three vignettes and three case studies that highlight spAbundance functionality. We used spatially-explicit multi-species distance sampling models to estimate density of 16 bird species in Florida, USA, an N-mixture model to estimate Black-throated Blue Warbler (Setophaga caerulescens) abundance in New Hampshire, USA, and a spatial linear mixed model to estimate forest aboveground biomass across the continental USA. spAbundance provides a user-friendly, formula-based interface to fit a variety of univariate and multivariate spatially-explicit abundance models. The package serves as a useful tool for ecologists and conservation practitioners to generate improved inference and predictions on the spatial drivers of populations and communities

An environmental habitat gradient and within-habitat segregation enable co-existence of ecologically similar bird species.

Peer reviewed: TrueFunder: This work was supported by the WCS Graduate Scholarship Program, a program of the Wildlife Conservation Society, and the Beinecke African Conservation Scholarship; WWF's Russell E. Train Education for Nature Program (EFN); and the National Science Foundation through DBI-1954406.Niche theory predicts that ecologically similar species can coexist through multidimensional niche partitioning. However, owing to the challenges of accounting for both abiotic and biotic processes in ecological niche modelling, the underlying mechanisms that facilitate coexistence of competing species are poorly understood. In this study, we evaluated potential mechanisms underlying the coexistence of ecologically similar bird species in a biodiversity-rich transboundary montane forest in east-central Africa by computing niche overlap indices along an environmental elevation gradient, diet, forest strata, activity patterns and within-habitat segregation across horizontal space. We found strong support for abiotic environmental habitat niche partitioning, with 55% of species pairs having separate elevation niches. For the remaining species pairs that exhibited similar elevation niches, we found that within-habitat segregation across horizontal space and to a lesser extent vertical forest strata provided the most likely mechanisms of species coexistence. Coexistence of ecologically similar species within a highly diverse montane forest was determined primarily by abiotic factors (e.g. environmental elevation gradient) that characterize the Grinnellian niche and secondarily by biotic factors (e.g. vertical and horizontal segregation within habitats) that describe the Eltonian niche. Thus, partitioning across multiple levels of spatial organization is a key mechanism of coexistence in diverse communities

The transcription factors SIX3 and VAX1 are required for suprachiasmatic nucleus circadian output and fertility in female mice

The homeodomain transcription factors sine oculis homeobox 3 (Six3) and ventral anterior homeobox 1 (Vax1) are required for brain development. Their expression in specific brain areas is maintained in adulthood, where their functions are poorly understood. To identify the roles of Six3 and Vax1 in neurons, we conditionally deleted each gene using Synapsincre , a promoter targeting maturing neurons, and generated Six3syn and Vax1syn mice. Six3syn and Vax1syn females, but not males, had reduced fertility, due to impairment of the luteinizing hormone (LH) surge driving ovulation. In nocturnal rodents, the LH surge requires a precise timing signal from the brain's circadian pacemaker, the suprachiasmatic nucleus (SCN), near the time of activity onset. Indeed, both Six3syn and Vax1syn females had impaired rhythmic SCN output, which was associated with weakened Period 2 molecular clock function in both Six3syn and Vax1syn mice. These impairments were associated with a reduction of the SCN neuropeptide vasoactive intestinal peptide in Vax1syn mice and a modest weakening of SCN timekeeping function in both Six3syn and Vax1syn mice. Changes in SCN function were associated with mistimed peak PER2::LUC expression in the SCN and pituitary in both Six3syn and Vax1syn females. Interestingly, Six3syn ovaries presented reduced sensitivity to LH, causing reduced ovulation during superovulation. In conclusion, we have identified novel roles of the homeodomain transcription factors SIX3 and VAX1 in neurons, where they are required for proper molecular circadian clock function, SCN rhythmic output, and female fertility