This repository hosts the analysis for the Greater Sage-grouse in Nevada. The project employs a Resource Selection Function (RSF) analysis, leveraging FOSS4G technologies and advanced statistical methods in R for efficient, scalable, and replicable data processing and analysis. It aims to predict fine-scale lek site selection by analyzing various environmental and anthropogenic factors.
function_inputs/: Input data and parameters for various functions.functions/: Modular R scripts for specific analysis tasks, including data preprocessing, model fitting, and validation.results/: Output data and results from the analysis.main.r: Main R script that orchestrates the analysis workflow.Utilities/: Additional scripts and tools for data management and analysis.
- Requirements: R (with packages like
car,MuMIn,data.table), PostgreSQL/PostGIS for spatial data management. environment.yml: Contains information for setting up the analysis environment.- Database setup instructions are provided separately, focusing on efficient raster data management in PostGIS.
- Detailed processing of raster data using PostGIS for spatial calculations like slope, aspect, and ruggedness.
- Efficient extraction of raster values and spatial feature generation in R.
- Comprehensive RSF analysis using a modular approach in R.
- Functions include model fitting with
glm, model selection, and variance inflation factor (VIF) analysis. - Emphasis on efficient data handling, modular coding, and parallel processing for large datasets.
This project is licensed under the MIT License - see the LICENSE file for details.
This work is part of a collaborative effort involving multiple stakeholders and contributors dedicated to the conservation and study of the Greater Sage-grouse in Nevada.