The package “adehabitat” for the R software: A tool for the analysis of space and habitat use by animals

By Clément Calenge. 2006

4,034 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Genetics

View Paper: https://www.semanticscholar.org/paper/a32728cf2a8baf408e3270da5c6ced7aba9e4068

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Citations in the corpus

Habitat modelling of tracking data from multiple marine predators identifies important areas in the Southern Indian Ocean (2018, methodology): “For visualization, we calculated kernel utilization distributions (Worton 1989) for each species (‘adehabitatHR’ package; Calenge 2006), with h-values selected”
What shapes Eurasian lynx distribution in human dominated landscapes: selecting prey or avoiding people? (2009, methodology): “All statistical analyses were conducted using R 2.7.1 (R Development Core Team 2008) and the R-package ‘‘adehabitat’’ (Calenge 2006).”
Trophic cascades in the western Ross Sea, Antarctica: revisited (2015, methodology): “D utilization distributions (UDs) with only foraging locations as described above and a fixed-kernel density estimation (Worton 1989) using the ‘adehabitatHR’ package (Calenge 2006) in R.”
Do fences create an edge-effect on the movement patterns of a highly mobile mega-herbivore? (2010, methodology): “We used the Compana function (ADEHABITAT package; Calenge, 2006) in the statistical software R 2.”
Prey preference of the common vampire bat (Desmodus rotundus, Chiroptera) using molecular analysis (2015, methodology): “The resource selection function ( w i ) was computed using adehabitat 1.8.12 package with design I data type (Calenge 2006) in the R computing environment 2.15.3 (R Development Core Team 2013).”
Using species distribution models to inform IUCN Red List assessments (2014, methodology): “Minimum convex polygons (MCPs) were calculated using the adehabitat package (Calenge 2006) within the statistical software R (version 2.11; R Development Core Team 2010).”
Disentangling biotic interactions, environmental filters, and dispersal limitation as drivers of species co‐occurrence (2018, positive): “3.1.2, R Core Team) using the libraries ‘ade4’ (Dray et al. 2007), ‘adehabitatHR’ (Calenge 2006), ‘ecospat’ (Di Cola et al. 2017), ‘maptools’ (Bivand and Lewin-Koh 2014), ‘raster’ (Hijmans 2015), ‘rgdal’ (Bivand et al. 2017), ‘rgeos’ (Bivand and Rundel 2015) and ‘vegan’ (Oksanen et al. 2014).”
Ecologically based definition of seasons clarifies predator–prey interactions (2013, methodology): “All statistical analyses were conducted using R 2.11.1 (R Development Core Team), with the R-packages adehabitat (Calenge 2006) for the extraction of movement characteristics and clusterSim (Walesiak and Dudek 2010) for the K-means analysis.”
Functional connectivity within conservation networks: Delineating corridors for African elephants (2013, methodology): “Home ranges were created using R software (R Development Core Team 2011), along with the packages “adehabitat” (Calenge 2006) and “NNCH” (Wayne Getz Lab 2005).”
Multi-scale habitat selection and foraging ecology of the eurasian hoopoe (Upupa epops) in pine plantations (2008, methodology): “Compositional analysis was performed using the R package ‘adehabitat’ with missing values replaced by 0.01%, and randomisation tests (1000 permutations) to assess for the signiWcance of habitat selection ( Calenge 2006 ).”
Behavioural adjustments of a large carnivore to access secondary prey in a human‐dominated landscape (2012, methodology): “…analyses (Aebischer, Robertson & Kenward 1993), a method that uses individuals as sampling units, using the package “adehabitat” for R software (Calenge 2006) to test the hypothesis that resident lions preferentially used areas located inside the national park in the migratory prey periods…”
Annual and seasonal consistency in the feeding ecology of an opportunistic species, the yellow-legged gull Larus michahellis (2014, methodology): “The nonparametric fixed kernel density (FKD) estimator was used to calculate the 25, 50, 75 and 95% density contour areas of each trip using functions (‘kernelUD’, ‘getvolumeUD’, ‘getverticeshr’ and ‘kernel.area’) of the adehabitat package (h = 0.05, grid = 500; Calenge 2006) under R 2.15.2.”
Home range size of adult Indo-Pacific bottlenose dolphins (Tursiops aduncus) in a coastal and estuarine system is habitat and sex-specific (2016, methodology): “However, to date, the method cannot be applied to study areas with complex barriers, such as a detailed coastline (Calenge 2006).”
Sexual segregation in a wide-ranging marine predator is a consequence of habitat selection (2015, methodology): “156 For each year and sex, we estimated 95% and 50% utilization distributions (UD) 157 using kernel analysis conducted with the R package adehabitatHR (Calenge 2006).”
Evaluating marine protected areas for the conservation of tropical coastal sharks (2012, methodology): “MCPs were calculated with the adehabitat package in R (Calenge, 2006) and plotted in ArcGIS 9.3 to show the spatial distribution of individuals inside MPAs.”