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@@ -846,9 +846,7 @@ <h1>spAbundance: An R package for single-species and multi-species spatially exp
 <strong>Citation</strong> - <a href="https://github.com/doserjef">Doser J.W.</a>, Finley A.O., K&eacute;ry M., and <a href="https://github.com/ezipkin"> Zipkin E.F.</a> (2024) spAbundance: An R package for single-species and multi-species spatially explicit abundance models. <em>Methods in Ecology and Evolution</em>. <a href="https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.14332">DOI: 10.1111/2041-210X.14332</a>
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-       <strong>Abstract</strong> - The spAbundance R package provides a user-friendly platform to fit spatially explicit single-species and multi-species hierarchical distance sampling models, N-mixture models, and generalized linear mixed models using a Bayesian approach. We provide three vignettes and three case studies that highlight spAbundance functionality. 
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-	      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 (<em>Setophaga caerulescens</em>) abundance in New Hampshire, USA, and a spatial linear mixed model to estimate forest above-ground 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 abundance in populations and communities.
+       <strong>Abstract</strong> - The spAbundance R package provides a user-friendly platform to fit spatially explicit single-species and multi-species hierarchical distance sampling models, N-mixture models, and generalized linear mixed models using a Bayesian approach. We provide three vignettes and three case studies that highlight spAbundance functionality. The package serves as a tool for ecologists and conservation practitioners to generate inferences and predictions on the spatial drivers of abundance in populations and communities.
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        <strong>Code and Data</strong> - <a href="https://github.com/zipkinlab/Doser_et_al_2024_MEE">Link to repo</a>