Useful tools for constructing species distribution models
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Updated
Oct 14, 2026 - R
Useful tools for constructing species distribution models
population and community dynamics on spatial graphs, in julia.
Abundance-based species distribution models
Tools for the assessment of the cumulative impacts of anthropogenic features in ecological studies
Classic Habitat Condition Assessment System (HCAS)
Functions to estimate the relative importance of various processes structuring aggregation, for example in tropical forest trees.
R package for generating and classifying ecological landscape patterns with neural networks using landscape metrics or raster pixels
An R {targets} pipeline for cleaning up and analyzing satellite telemetry data of GPS-collared Roosevelt elk on Vancouver Island, BC.
Agent-based bee colony simulation featuring genetic modeling, foraging algorithms, disease tracking, and comprehensive CLI tools for pollinator population research.
R-based tiger prey-resource suitability modelling using Maxnet, GIS and spatial environmental data
Shiny application. Map displays the extent of sage cover and volepocalypse intensity overlaid with herbaceous functional group cover and known thinning polyons. Users may plan thinning efforts around the cover of undesirable herbaceous species in the vicinity.
Assessment of individual specialization in the use of space by animals.
Contains scripts for Amey Danole's MS thesis project
A step-by-step guide to vegetation classification and calculation of unvegetated - vegetated ratio of salt marshes with public aerial imagery
R workflow for analysing land-cover transitions and human–elephant conflict (HEC) intensity using a Gaussian GLMM.
Arteeri spatial population dynamics tool (formerly 'Artemis').
Seasonal sighting map and humpback whale density surface model from NOAA PIFSC cetacean surveys, Hawaiian Islands EEZ.
A collection of tools for measuring individual specialization in the use of space by organisms.
Segment aerial line-transect marine mammal survey data (NARWC format) into effort segments and detection distances for distance sampling and density surface modeling. On-effort determination, great-circle effort, Becker segmentation, right-angle distances from all four sources the archive records, and a Distance-ready flatfile.
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