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@@ -329,7 +329,7 @@ <h1>Integrated community models: A framework combining multi-species data source
 	<strong>Citation</strong> - <a href="https://github.com/n-a-gilbert">Gilbert, N.A.</a>, Blommel, C.M.,  <a href="https://github.com/farrmt">Farr M.T.</a>, Green, D.S., Holekamp, K.E., <a href="https://github.com/ezipkin">Zipkin E.F.</a> (2024) A multispecies hierarchical model to integrate count and distance sampling data. <em>Ecology</em>. <a href=https:// >DOI: TBD</a>
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-	<strong>Abstract</strong> - Integrated community models combine the benefits of integrated and community modeling approaches. Here, we developed a multi-species model that combines distance sampling and single-visit count data, in which information is shared among data sources (via a joint likelihood) and species (via a random effects structure) to estimate abundance patterns across a community. Simulations demonstrate that the model provided unbiased estimates of abundance and detection parameters even when detection probabilities varied between the data types. We applied the model to a herbivore community in the Masai Mara National Reserve (Kenya) and found considerable interspecific variation in response to local wildlife management practices.
+	<strong>Abstract</strong> - Integrated community models combine the benefits of integrated and community modeling approaches. Here, we developed a multi-species model that combines distance sampling and single-visit count data, in which information is shared among data sources (via a joint likelihood) and species (via a random effects structure) to estimate abundance patterns across a community. Simulations demonstrate that the model produced unbiased estimates of abundance and detection parameters even when detection probabilities varied between the data types. We applied the model to a herbivore community in the Masai Mara National Reserve (Kenya) and found considerable interspecific variation in response to local wildlife management practices.
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 	<strong>Code and Data</strong> - <a href="https://github.com/zipkinlab/Gilbert_etal_2024_TBD">Link to repo</a>
@@ -353,7 +353,7 @@ <h1>Integrated community models: A framework combining multi-species data source
 	<strong>Citation</strong> - <a href="https://github.com/ezipkin">Zipkin E.F.</a> , <a href="https://github.com/doserjef">Doser J.W.</a>, <a href="https://github.com/CourtneyLDavis">Davis C.L.</a>, <a href="https://github.com/wleuenberger">Leuenberger W.</a>, <a href="https://github.com/Samwiry">Ayebare S.</a>, <a href="https://github.com/davisk93">Davis K.L.</a> (2023) Integrated community models: A framework combining multi-species data sources to estimate the status, trends, and dynamics of biodiversity. <em>Journal of Animal Ecology</em>. <a href=https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/1365-2656.14012>DOI: 10.1111/1365-2656.14012</a>
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-	<strong>Abstract</strong> - Evaluating the dynamics of whole communities is critical to understanding the responses of biodiversity to ongoing environmental pressures. However, available data vary substantially in quantity and information content, which must be carefully reconciled for meaningful analysis. We highlight the emerging "integrated community modelling" (ICM) framework that combines both data integration and hierarchical community modelling to derive inferences on species- and community-level dynamics. We illustrate the framework with three worked examples to demonstrate how ICMs can be used to extend the geographic scope when evaluating species distributions and community richness; discern population and community trends over time; and estimate demographic rates and population growth for communities of sympatric species.  
+	<strong>Abstract</strong> - Evaluating the dynamics of whole communities is critical to understanding the responses of biodiversity to ongoing environmental pressures. However, biodiversity data vary substantially in quantity and information content, requiring careful reconciliation. We highlight the emerging "integrated community modelling" (ICM) framework that combines both data integration and hierarchical community modelling to derive inferences on species- and community-level dynamics. We illustrate the framework with three worked examples to demonstrate how ICMs can be used to: extend the geographic scope when evaluating species distributions and richness; discern population and community trends over time; and estimate demographic rates and population growth for communities of sympatric species.  
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 	<strong>Code and Data</strong> - <a href="https://github.com/zipkinlab/Zipkin_etal_2023_JAE">Link to repo</a>