Create README.md

Author: farrmt

README.md

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+# Overcoming data gaps using integrated models to estimate migratory species dynamics during cryptic periods of the annual cycle
+
+### [Matthew T. Farr](https://farrmt.github.io/), [Erin R. Zylstra](https://erinzylstra.weebly.com/), [Leslie Ries](https://www.butterflyinformatics.org/), & [Elise F. Zipkin](https://zipkinlab.org/)
+
+### *In Review*
+
+### Code/Data DOI
+
+### Please contact the first author for questions about the code or data: Matthew T. Farr ([email protected])
+__________________________________________________________________________________________________________________________________________
+
+## Abstract:  
+1. Environmental and anthropogenic factors affect the population dynamics of migratory species throughout their annual cycles. However, identifying the spatiotemporal effects of abiotic variables on migratory species’ abundances is difficult because of extensive gaps in monitoring data. The collection of unstructured opportunistic data by volunteer (citizen science) networks provides a solution to address data gaps for locations and time periods during which structured, design-based data are difficult or impossible to collect.  
+2. 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. We validated our approach through simulations and then applied the framework to the eastern North American migratory population of monarch butterflies during their spring breeding period in eastern Texas. Spring climate conditions have been identified as a key driver of monarch population sizes during subsequent summer and winter periods. However, low monarch densities during the spring combined with very few design-based surveys in the region have limited the ability to isolate effects of spring weather variables on monarchs.
+3. Simulation results confirmed the ability of our integrated model to accurately and precisely estimate abundance indices and the effects of covariates during locations and time periods in which structured sampling are lacking. In our case study, we combined opportunistic monarch observations during the spring migration and breeding period with structured data from the summer Midwestern breeding grounds. Our model revealed a nonstationary relationship between weather conditions and local monarch abundance during the spring, driven by spatially-varying vegetation and temperature conditions.
+4. Data for widespread and migratory species are often fragmented across multiple monitoring programs, potentially requiring the use of both structured and unstructured data sources to obtain complete geographic coverage. Our integrated model can estimate population abundance at broad spatiotemporal extents despite structured data gaps during the annual cycle by leveraging opportunistic data.
+
+## Repository Directory
+
+### [DataAnalysis](./DataAnalysis): Contains code for modeling, analysis, and results for both the simulation and case studies
+### [DataFormat](./DataFormat): Contains non-proprietary data, code to format raw data for analysis
+### [PostAnalysis](./PostAnalysis): Contains code to create tables & figures
+### [SupportingInformation](./SupportingInformation): Contains supporting information and code to generate supporting information
+
+## Data
+See the following subdirectories for data and metadata: [DataFromatting](./DataFormatting)  
+
+## Code
+See the following subdirectories for code and metadata: [DataAnalysis](./DataAnalysis), [DataFormatting](./DataFormatting), [PostAnalysis](./PostAnalysis), [SupportingInformation](./SupportingInformation)