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

   https://doi.org/10.1111/2041-210X.13897  

   Doser, Jeffrey W.; Finley, Andrew O.; Kéry, Marc; Zipkin, Elise F. (May 2022, Methods in Ecology and Evolution)

   Abstract Occupancy modelling is a common approach to assess species distribution patterns, while explicitly accounting for false absences in detection–nondetection data. Numerous extensions of the basic single‐species occupancy model exist to model multiple species, spatial autocorrelation and to integrate multiple data types. However, development of specialized and computationally efficient software to incorporate such extensions, especially for large datasets, is scarce or absent.We introduce thespOccupancy Rpackage designed to fit single‐species and multi‐species spatially explicit occupancy models. We fit all models within a Bayesian framework using Pólya‐Gamma data augmentation, which results in fast and efficient inference.spOccupancyprovides functionality for data integration of multiple single‐species detection–nondetection datasets via a joint likelihood framework. The package leverages Nearest Neighbour Gaussian Processes to account for spatial autocorrelation, which enables spatially explicit occupancy modelling for potentially massive datasets (e.g. 1,000s–100,000s of sites).spOccupancyprovides 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.ThespOccupancypackage provides a user‐friendly platform 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 datasets. 

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2. Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor–immune hubs

   https://doi.org/10.1038/s41587-024-02173-8  

   He, Siyu; Jin, Yinuo; Nazaret, Achille; Shi, Lingting; Chen, Xueer; Rampersaud, Sham; Dhillon, Bahawar S; Valdez, Izabella; Friend, Lauren E; Fan, Joy Linyue; et al (February 2025, Nature Biotechnology)

   Abstract Spatially resolved gene expression profiling provides insight into tissue organization and cell–cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference. Starfysh improves the characterization of spatial dynamics in complex tissues using histology images and enables the comparison of niches as spatial hubs across tissues. Integrative analysis of primary estrogen receptor (ER)-positive breast cancer, triple-negative breast cancer (TNBC) and metaplastic breast cancer (MBC) tissues led to the identification of spatial hubs with patient- and disease-specific cell type compositions and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC. 

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3. spAbundance: An R package for single‐species and multi‐species spatially explicit abundance models

   https://doi.org/10.1111/2041-210X.14332  

   Doser, Jeffrey_W; Finley, Andrew_O; Kéry, Marc; Zipkin, Elise_F (May 2024, Methods in Ecology and Evolution)

   Abstract Numerous modelling techniques exist to estimate abundance of plant and animal populations. The most accurate methods account 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 thespAbundance Rpackage 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 Neighbour 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 highlightspAbundancefunctionality. 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 above‐ground biomass across the continental USA.spAbundanceprovides 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 abundance in populations and communities. 

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4. The geodiv r package: Tools for calculating gradient surface metrics

   https://doi.org/10.1111/2041-210X.13677  

   Smith, Annie C.; Dahlin, Kyla M.; Record, Sydne; Costanza, Jennifer K.; Wilson, Adam M.; Zarnetske, Phoebe L. (January 2021, Methods in Ecology and Evolution)

   Goslee, Sarah (Ed.)

   1. The geodiv r package calculates gradient surface metrics from imagery and other gridded datasets to provide continuous measures of landscape heterogeneity for landscape pattern analysis. 2. geodiv is the first open-source, command line toolbox for calculating many gradient surface metrics and easily integrates parallel computing for applications with large images or rasters (e.g. remotely sensed data). All functions may be applied either globally to derive a single metric for an entire image or locally to create a texture image over moving windows of a user-defined extent. 3. We present a comprehensive description of the functions available through geodiv. A supplemental vignette provides an example application of geodiv to the fields of landscape ecology and biogeography. 4. geodiv allows users to easily retrieve estimates of spatial heterogeneity for a variety of purposes, enhancing our understanding of how environmental structure influences ecosystem processes. The package works with any continuous imagery and may be widely applied in many fields where estimates of surface complexity are useful. 

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5. Temporal rarity is a better predictor of local extinction risk than spatial rarity

   https://doi.org/10.1002/ecy.3504  

   Wilfahrt, Peter_A; Asmus, Ashley_L; Seabloom, Eric_W; Henning, Jeremiah_A; Adler, Peter; Arnillas, Carlos_A; Bakker, Jonathan_D; Biederman, Lori; Brudvig, Lars_A; Cadotte, Marc; et al (August 2021, Ecology)

   Abstract Spatial rarity is often used to predict extinction risk, but rarity can also occur temporally. Perhaps more relevant in the context of global change is whether a species is core to a community (persistent) or transient (intermittently present), with transient species often susceptible to human activities that reduce niche space. Using 5–12 yr of data on 1,447 plant species from 49 grasslands on five continents, we show that local abundance and species persistence under ambient conditions are both effective predictors of local extinction risk following experimental exclusion of grazers or addition of nutrients; persistence was a more powerful predictor than local abundance. While perturbations increased the risk of exclusion for low persistence and abundance species, transient but abundant species were also highly likely to be excluded from a perturbed plot relative to ambient conditions. Moreover, low persistence and low abundance species that were not excluded from perturbed plots tended to have a modest increase in abundance following perturbance. Last, even core species with high abundances had large decreases in persistence and increased losses in perturbed plots, threatening the long‐term stability of these grasslands. Our results demonstrate that expanding the concept of rarity to include temporal dynamics, in addition to local abundance, more effectively predicts extinction risk in response to environmental change than either rarity axis predicts alone. 

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