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@Article{LaPoint2013,
author="LaPoint, Scott
and Gallery, Paul
and Wikelski, Martin
and Kays, Roland",
title="Animal behavior, cost-based corridor models, and real corridors",
journal="Landscape Ecology",
year="2013",
month="Oct",
day="01",
volume="28",
number="8",
pages="1615--1630",
abstract="Corridors are popular conservation tools because they are thought to allow animals to safely move between habitat fragments, thereby maintaining landscape connectivity. Nonetheless, few studies show that mammals actually use corridors as predicted. Further, the assumptions underlying corridor models are rarely validated with field data. We categorized corridor use as a behavior, to identify animal-defined corridors, using movement data from fishers (Martes pennanti) tracked near Albany, New York, USA. We then used least-cost path analysis and circuit theory to predict fisher corridors and validated the performance of all three corridor models with data from camera traps. Six of eight fishers tracked used corridors to connect the forest patches that constitute their home ranges, however the locations of these corridors were not well predicted by the two cost-based models, which together identified only 5 of the 23 used corridors. Further, camera trap data suggest the cost-based corridor models performed poorly, often detecting fewer fishers and mammals than nearby habitat cores, whereas camera traps within animal-defined corridors recorded more passes made by fishers, carnivores, and all other non-target mammal groups. Our results suggest that (1) fishers use corridors to connect disjunct habitat fragments, (2) animal movement data can be used to identify corridors at local scales, (3) camera traps are useful tools for testing corridor model predictions, and (4) that corridor models can be improved by incorporating animal behavior data. Given the conservation importance and monetary costs of corridors, improving and validating corridor model predictions is vital.",
issn="1572-9761",
doi="10.1007/s10980-013-9910-0",
url="https://doi.org/10.1007/s10980-013-9910-0"
}
@article{signer2019animal,
title={Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses},
author={Signer, Johannes and Fieberg, John and Avgar, Tal},
journal={Ecology and Evolution},
volume={9},
number={2},
pages={880--890},
year={2019},
publisher={Wiley Online Library}
}
@article{avgar2016integrated,
title={Integrated step selection analysis: bridging the gap between resource selection and animal movement},
author={Avgar, Tal and Potts, Jonathan R and Lewis, Mark A and Boyce, Mark S},
journal={Methods in Ecology and Evolution},
volume={7},
number={5},
pages={619--630},
year={2016},
publisher={Wiley Online Library}
}
@article{avgar2017relative,
title={Relative Selection Strength: Quantifying effect size in habitat-and step-selection inference},
author={Avgar, Tal and Lele, Subhash R and Keim, Jonah L and Boyce, Mark S},
journal={Ecology and evolution},
volume={7},
number={14},
pages={5322--5330},
year={2017},
publisher={Wiley Online Library}
}
@article{thurfjell2014applications,
title={Applications of step-selection functions in ecology and conservation},
author={Thurfjell, Henrik and Ciuti, Simone and Boyce, Mark S},
journal={Movement ecology},
volume={2},
number={1},
pages={4},
year={2014},
publisher={BioMed Central}
}
@book{fahrmeir2013regression,
title={Regression: models, methods and applications},
author={Fahrmeir, Ludwig and Kneib, Thomas and Lang, Stefan and Marx, Brian},
year={2013},
publisher={Springer Science \& Business Media}
}
@article{wickham2017,
title={Tidyverse:
Easily install and load’tidyverse’packages},
author={Wickham, Hadley},
journal={R package version},
volume={1},
number={1},
year={2017}
}
@article{lele2013selection,
title={Selection, use, choice and occupancy: clarifying concepts in resource selection studies},
author={Lele, Subhash R and Merrill, Evelyn H and Keim, Jonah and Boyce, Mark S},
journal={Journal of Animal Ecology},
volume={82},
number={6},
pages={1183--1191},
year={2013},
publisher={Wiley Online Library}
}
@article{duchesne2015equivalence,
title={Equivalence between step selection functions and biased correlated random walks for statistical inference on animal movement},
author={Duchesne, Thierry and Fortin, Daniel and Rivest, Louis-Paul},
journal={PloS one},
volume={10},
number={4},
pages={e0122947},
year={2015},
publisher={Public Library of Science}
}
@article{avgar2016iSSA,
author = {Avgar, Tal and Potts, Jonathan R. and Lewis, Mark A. and Boyce, Mark S.},
title = {Integrated step selection analysis: bridging the gap between resource selection and animal movement},
journal = {Methods in Ecology and Evolution},
volume = {7},
number = {5},
pages = {619-630},
keywords = {conditional logistic regression, dispersal, habitat selection, movement ecology, random walk, redistribution kernel, resource selection, step selection, telemetry, utilisation distribution},
doi = {10.1111/2041-210X.12528},
url = {https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.12528},
eprint = {https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.12528},
abstract = {Summary A resource selection function is a model of the likelihood that an available spatial unit will be used by an animal, given its resource value. But how do we appropriately define availability? Step selection analysis deals with this problem at the scale of the observed positional data, by matching each ‘used step’ (connecting two consecutive observed positions of the animal) with a set of ‘available steps’ randomly sampled from a distribution of observed steps or their characteristics. Here we present a simple extension to this approach, termed integrated step selection analysis (iSSA), which relaxes the implicit assumption that observed movement attributes (i.e. velocities and their temporal autocorrelations) are independent of resource selection. Instead, iSSA relies on simultaneously estimating movement and resource selection parameters, thus allowing simple likelihood-based inference of resource selection within a mechanistic movement model. We provide theoretical underpinning of iSSA, as well as practical guidelines to its implementation. Using computer simulations, we evaluate the inferential and predictive capacity of iSSA compared to currently used methods. Our work demonstrates the utility of iSSA as a general, flexible and user-friendly approach for both evaluating a variety of ecological hypotheses, and predicting future ecological patterns.},
year = {2016}
}