A short post demonstrating how to use the R package auk to extract eBird data from within a study area defined by a polyon or Shapefile.
As part of the eBird Status & Trends team, I often find myself having to quickly summarize large rasters across layers. For examples, we might summarize weekly relative abundance layers for a single species across the weeks to estimate year round abundance or, for a given week, we might summarize across species to produce a species richness layer.
In this post, I examine how the size of blocks impacts the efficiency of block processing raster data in R
Investigating how raster data are processed in R to improve the efficiency of processing large raster files.
This open source book outlines a set of best practices for using eBird data to estimate species distributions in R. It covers accessing and pre-processing eBird data, preparing satellite-derived habitat variables for use as model covariates, and best practices for model encounter rate, occupancy, and abundance using eBird data.
This R package provides access to [eBird Status and Trends](https://ebird.org/science/status-and-trends) data for over 600 North American bird species. These data include weekly estimates of relative abundance and occurrence at high spatial resolution across the entire Western Hemisphere.
An R package to access and analyze bird observation data from [eBird](https://ebird.org/), the largest biodiversity-related citizen science project in the world. Observations can be extracted based on a variety of taxonomic, spatial, and temporal filters. `auk` also includes tools for importing and processing eBird data in R.
The `prioritizr` R package provide a flexible interface for building and solving systematic conservation prioritization problems using integer linear programming (ILP). It supports a broad range of objectives, constraints, and penalties that can be used to custom-tailor prioritization problems to the specific needs of a conservation planning exercise. For more details, watch [this talk](https://www.youtube.com/watch?v=da7r3Qyn6ag) by lead developer [Jeff Hanson](https://jeffrey-hanson.com/).
Analyzing dynamical systems in R. Using the Lotka-Volterra predator prey model as a case-study, I use the R packages deSolve and FME to solve a system of differential equations and perform a sensitivity analysis.
Exploring the benefits of hexagonal grids relative to square grids for spatial sampling and analysis, and generating hexagonal grids in R.