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@@ -91,7 +91,7 @@ <h1>Overcoming data gaps using integrated models to estimate migratory species
 	      <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>
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-	<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.
+	<strong>Abstract</strong> - 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.
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 	<strong>Code and Data</strong> - <a href="https://github.com/zipkinlab/Farr_etal_2024_MEE">Link to repo</a>