From 10e407f87ab0599336d330a7257cd462ab57ac5e Mon Sep 17 00:00:00 2001
From: Elise Zipkin <elise.zipkin@gmail.com>
Date: Tue, 21 May 2024 17:51:17 -0400
Subject: [PATCH] Update index.html

---
 index.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/index.html b/index.html
index b5e0eac..e8abe38 100644
--- a/index.html
+++ b/index.html
@@ -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>
       </p>
       <p>
-	<strong>Abstract</strong> - Integrated community models—an emerging framework in which multiple data sources for multiple species are analyzed simultaneously—offer opportunities to expand inferences beyond the single-species and single-data source approaches common in ecology. We developed a novel integrated community model that combines distance sampling and single-visit count data; within the model, 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 demonstrated 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 community of 11 herbivore species 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—an emerging framework in which multiple data sources for multiple species are analyzed simultaneously—offer opportunities to expand inferences beyond the single-species and single-data source approaches common in ecology. We developed a novel 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 demonstrated 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.
       </p>
       <p>
 	<strong>Code and Data</strong> - <a href="https://github.com/zipkinlab/Gilbert_etal_2024_TBD">Link to repo</a>