Farr_etal_2024_MEE

Add rep information of Farr_etal_2024_MEE

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@@ -72,7 +72,32 @@ <h2>About</h2>        <p class="summary">
 <h2 id="dataintegration">Data integration models</h2>
 <p> Population data are often collected by different sources, in different locations, or on different life stages. We are developing and applying novel techniques for integrating multiple data types to estimate the distribution, abundance, and dynamics of populations and predict future trajectories. </p>
 
-<p><h3><em><a href="#dataintegration2023D">Davis_etal_2023_BioCons</a></em> | <em><a href="#dataintegration2022Z">Zylstra_etal_2022_GCB</a></em> |  <em><a href="#dataintegration2021D">Doser_etal_2021_MEE</a></em> | <em><a href="#dataintegration2020F">Farr_etal_2021_Ecol</a></em> | <em><a href="#dataintegration2021Z">Zylstra_etal_2021_NEE</a></em> | <em><a href="#dataintegration2019S2">Saunders_etal_2019_PNAS</a></em> | <em><a href="#dataintegration2019S">Saunders_etal_2019_Ecol</a></em> | <em><a href="#dataintegration2018S">Saunders_etal_2018_JAE</a></em> | <em><a href="#dataintegration2017Z">Zipkin_etal_2017_Ecol</a></em></h3></p>
+<p><h3> <em><a href="#dataintegration2024F">Farr_etal_2024_MEE</a></em> |<em><a href="#dataintegration2023D">Davis_etal_2023_BioCons</a></em> | <em><a href="#dataintegration2022Z">Zylstra_etal_2022_GCB</a></em> |  <em><a href="#dataintegration2021D">Doser_etal_2021_MEE</a></em> | <em><a href="#dataintegration2020F">Farr_etal_2021_Ecol</a></em> | <em><a href="#dataintegration2021Z">Zylstra_etal_2021_NEE</a></em> | <em><a href="#dataintegration2019S2">Saunders_etal_2019_PNAS</a></em> | <em><a href="#dataintegration2019S">Saunders_etal_2019_Ecol</a></em> | <em><a href="#dataintegration2018S">Saunders_etal_2018_JAE</a></em> | <em><a href="#dataintegration2017Z">Zipkin_etal_2017_Ecol</a></em></h3></p>
+
+<br><br>
+<h3 id="dataintegration2024F">Farr_etal_2024_MEE</h3>
+  <section class="example">
+    <section class="title">
+      <h1>Overcoming data gaps using integrated models to estimate migratory species’ dynamics during cryptic periods of the annual cycle</h1>
+      <img class="modal-img" src="assets/images/Davis_BioCons_2023.png" alt="Farr et al. 2024" width="200" style="background-color:white;" >
+	      <div class="modal" id="Farr et al. 2024">
+		      <span class="close">&times;</span>
+		      <img class="modal-content">
+		      <div class="modal-caption"></div>
+	      </div>
+     </section>
+    <section class="content">
+      <p>
+	      <strong>Citation</strong> - <a href="https://github.com/farrmt">Farr M.T.</a>, <a href="https://github.com/ezylstra">Zylstra, E.R.</a>, Ries, L., and <a href="https://github.com/ezipkin">Zipkin E.F.</a> (2024) Overcoming data gaps using integrated models to estimate migratory species’ dynamics during cryptic periods of the annual cycle. <em>Methods in Ecology and Evolution</em>. <a href=https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.14282>DOI: 10.1111/2041-210X.14282</a>
+      </p>
+      <p>
+	<strong>Abstract</strong> - 1.	Environmental and anthropogenic factors affect the population dynamics of migratory species throughout their annual cycles. However, identifying the spatiotemporal drivers of migratory species' abundances is difficult because of extensive gaps in monitoring data. To estimate population abundance and distribution at broad spatiotemporal extents, we developed an integrated model that incorporates unstructured data during time periods and spatial locations when structured data are unavailable. Data for widespread and migratory species are often fragmented across multiple monitoring programs. Our integrated model can estimate population abundance at broad spatiotemporal extents despite structured data gaps during the annual cycle by leveraging opportunistic data.
+      </p>
+      <p>
+	<strong>Code and Data</strong> - <a href="https://github.com/zipkinlab/Farr_etal_2024_MEE">Link to repo</a>
+      </p>
+            </section>
+          </section> 
 
 <br><br>
 <h3 id="dataintegration2023D">Davis_etal_2023_BioCons</h3>