From cbc1e37c18422cce95aaf18adf52f9961542abe1 Mon Sep 17 00:00:00 2001 From: Bruna Amaral <74007922+br-amaral@users.noreply.github.com> Date: Fri, 8 Mar 2024 14:20:19 -0500 Subject: [PATCH] Farr_etal_2024_MEE Add rep information of Farr_etal_2024_MEE --- index.html | 27 ++++++++++++++++++++++++++- 1 file changed, 26 insertions(+), 1 deletion(-) diff --git a/index.html b/index.html index afdadd9..9ff5e4a 100644 --- a/index.html +++ b/index.html @@ -72,7 +72,32 @@

About

Data integration models

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.

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Davis_etal_2023_BioCons | Zylstra_etal_2022_GCB | Doser_etal_2021_MEE | Farr_etal_2021_Ecol | Zylstra_etal_2021_NEE | Saunders_etal_2019_PNAS | Saunders_etal_2019_Ecol | Saunders_etal_2018_JAE | Zipkin_etal_2017_Ecol

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Farr_etal_2024_MEE |Davis_etal_2023_BioCons | Zylstra_etal_2022_GCB | Doser_etal_2021_MEE | Farr_etal_2021_Ecol | Zylstra_etal_2021_NEE | Saunders_etal_2019_PNAS | Saunders_etal_2019_Ecol | Saunders_etal_2018_JAE | Zipkin_etal_2017_Ecol

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Farr_etal_2024_MEE

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Overcoming data gaps using integrated models to estimate migratory species’ dynamics during cryptic periods of the annual cycle

+ Farr et al. 2024 + +
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+ Citation - Farr M.T., Zylstra, E.R., Ries, L., and Zipkin E.F. (2024) Overcoming data gaps using integrated models to estimate migratory species’ dynamics during cryptic periods of the annual cycle. Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.14282 +

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+ Abstract - 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. +

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+ Code and Data - Link to repo +

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Davis_etal_2023_BioCons