references.bib

@article{fleming_correcting_2018,
	title = {Correcting for missing and irregular data in home-range estimation},
	url = {https://esajournals.onlinelibrary.wiley.com/doi/10.1002/eap.1704},
	doi = {10.1002/eap.1704},
	abstract = {Home-range estimation is an important application of animal tracking data that is frequently complicated by autocorrelation, sampling irregularity, and small effective sample sizes. We introduce a novel, optimal weighting method that accounts for temporal sampling bias in autocorre-lated tracking data. This method corrects for irregular and missing data, such that oversampled times are downweighted and undersampled times are upweighted to minimize error in the home-range estimate. We also introduce computationally efficient algorithms that make this method feasible with large data sets. Generally speaking, there are three situations where weight optimization improves the accuracy of home-range estimates: with marine data, where the sampling schedule is highly irregular, with duty cycled data, where the sampling schedule changes during the observation period, and when a small number of home-range crossings are observed, making the beginning and end times more independent and informative than the intermediate times. Using both simulated data and empirical examples including reef manta ray, Mongolian gazelle, and African buffalo, optimal weighting is shown to reduce the error and increase the spatial resolution of home-range estimates. With a conveniently packaged and computationally efficient software implementation, this method broadens the array of data sets with which accurate space-use assessments can be made.},
	author = {Fleming, C H and Sheldon, D and Fagan, W F and Leimgruber, P and Mueller, T and Nandintsetseg, D and Noonan, M J and Olson, K A and Setyawan, E and Sianipar, A and Calabrese, J M},
	year = {2018},
	keywords = {animal tracking data, autocorrelation, home range, irregular sampling, kernel density estimation, marine tracking data, utilization distribution},
	file = {PDF:C\:\\Users\\Francisco\\Zotero\\storage\\K3RRJII5\\full-text.pdf:application/pdf},
}

@article{montano_stable_2022,
	title = {A stable home: {Autocorrelated} {Kernel} {Density} {Estimated} home ranges of the critically endangered {Elongated} tortoise},
	volume = {32},
	issn = {02680130},
	doi = {10.33256/32.3.120129},
	abstract = {Home range analysis is a standard and fundamental concept in ecology used to describe animal space use over their lifetimes. Connecting home range sizes with animal characteristics, location, and habitat can be used to inform conservation decisions. Reptiles are frequently lacking robust estimates of space use, particularly reptiles in tropical regions. Here we analyse a publicly available dataset, collected by the authors of this study, describing the movements of Critically Endangered Elongated tortoises Indotestudo elongata. The tortoise data included the locations of 17 tortoises (12 females, 5 males) collected on average once every three days for an average duration of 353.76 SE ± 33.10 days. We use these data to estimate the home range of Elongated tortoise, and explore how tortoise size and sex influences home range size. To mitigate issues resulting from low effective sample sizes and low temporal resolution of the data, we used a modern home range estimation method – Autocorrelated Kernel Density Estimators (AKDE). We found 14 of 17 individuals appear to be occupying a stable home range (using variograms to determine range residency). The average AKDE home range for all 14 individuals with range residency was 44.81 ± 10.44 ha. Bayesian Regression Models suggest comparable size estimates between male and female home ranges, despite males being physically larger than females in both mass and carapace length. These AKDE home range estimates have the added utility of being more comparable with other studies, less susceptible to errors from a suboptimal tracking regime, and are well positioned for inclusion in future meta-analyses.},
	number = {3},
	journal = {Herpetological Journal},
	author = {Montano, Ysabella and Marshall, Benjamin Michael and Ward, Matt and Silva, Ines and Artchawakom, Taksin and Waengsothorn, Surachit and Strine, Colin Thomas},
	month = jul,
	year = {2022},
	note = {Publisher: British Herpetological Society},
	keywords = {autocorrelated kernel density estimator, Indotestudo elongata, space use, spatial ecology, testudine, Thailand},
	pages = {120--129},
	file = {PDF:C\:\\Users\\Francisco\\Zotero\\storage\\K6H8WUSP\\Montanoetal_2022_Astablehome-Autocorrelatedkerneldensityestimatedhomerangesofthecriticallyendangeredelongatedtortoise.pdf:application/pdf},
}

@article{silva_autocorrelation-informed_2022,
	title = {Autocorrelation-informed home range estimation: {A} review and practical guide},
	volume = {13},
	issn = {2041210X},
	doi = {10.1111/2041-210X.13786},
	abstract = {Modern tracking devices allow for the collection of high-volume animal tracking data at improved sampling rates over very-high-frequency radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under- or overestimate home range areas. The autocorrelated kernel density estimation (AKDE) family of estimators was designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user-friendly guide on how they may be combined to reduce multiple biases simultaneously. We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs. Finally, we provide guidelines for researchers to choose among alternative estimators and an R script to facilitate the application and interpretation of AKDE home range estimates.},
	number = {3},
	journal = {Methods in Ecology and Evolution},
	author = {Silva, Inês and Fleming, Christen H. and Noonan, Michael J. and Alston, Jesse and Folta, Cody and Fagan, William F. and Calabrese, Justin M.},
	year = {2022},
	keywords = {home range, tracking data, kernel density estimation, movement process, telemetry},
	pages = {534--544},
	file = {PDF:C\:\\Users\\Francisco\\Zotero\\storage\\3I2QYE6X\\Silva et al 2021 Autocorrelation‐informed home range estimation A review and practical guide.pdf:application/pdf},
}

@article{kays_terrestrial_2015,
	title = {Terrestrial animal tracking as an eye on life and planet},
	volume = {348},
	issn = {10959203},
	doi = {10.1126/science.aaa2478},
	abstract = {Moving animals connect our world, spreading pollen, seeds, nutrients, and parasites as they go about the their daily lives. Recent integration of high-resolution Global Positioning System and other sensors into miniaturized tracking tags has dramatically improved our ability to describe animal movement. This has created opportunities and challenges that parallel big data transformations in other fields and has rapidly advanced animal ecology and physiology. New analytical approaches, combined with remotely sensed or modeled environmental information, have opened up a host of new questions on the causes of movement and its consequences for individuals, populations, and ecosystems. Simultaneous tracking of multiple animals is leading to new insights on species interactions and, scaled up, may enable distributed monitoring of both animals and our changing environment.},
	number = {6240},
	journal = {Science},
	author = {Kays, Roland and Crofoot, Margaret C. and Jetz, Walter and Wikelski, Martin},
	year = {2015},
	pmid = {26068858},
	pages = {aaa2478},
	file = {PDF:C\:\\Users\\Francisco\\Zotero\\storage\\3HAX97JK\\Kays et al 2015 Terrestrial animal tracking as an eye on life and planet.pdf:application/pdf},
}

@article{castellanos_andean_2011,
	title = {Andean bear home ranges in the {Intag} region, {Ecuador}},
	volume = {22},
	issn = {1537-6176, 1938-5439},
	url = {http://www.bioone.org/doi/abs/10.2192/URSUS-D-10-00006.1},
	doi = {10.2192/URSUS-D-10-00006.1},
	abstract = {I estimated home ranges of 5 female and 4 male Andean bears (Tremarctos ornatus) inhabiting the Intag region in Ecuador between September 2001 and December 2006, using 1,439 and 412 telemetry locations for females and males, respectively. Multi-annual and seasonal home ranges were estimated using 2 methods: minimum convex polygon (MCP) and nearest-neighbor convex hull (k-NNCH) analyses. I considered k-NNCH analysis the best method for estimating Andean bear home ranges in fragmented landscapes such as those across the study site. Annual home range of males (59 km2) were larger than those for females (15 km2) using the k-NNCH method. During the rainy season home ranges of males were 23 km2 versus 10 km2 for females, and in the dry season, 27 km2 versus 7 km2. All bears in this study showed some degree of home range overlap, indicating intra-specific tolerance. The mean annual kNNCH home range of males overlapped home ranges of females by 32\%, and among females, overlap was 22\%. No evidence of territorial behavior was observed in this study.},
	language = {en},
	number = {1},
	urldate = {2024-02-28},
	journal = {Ursus},
	author = {Castellanos, Armando},
	month = apr,
	year = {2011},
	pages = {65--73},
	file = {Castellanos - 2011 - Andean bear home ranges in the Intag region, Ecuad.pdf:C\:\\Users\\Francisco\\Zotero\\storage\\RG4JFT82\\Castellanos - 2011 - Andean bear home ranges in the Intag region, Ecuad.pdf:application/pdf},
}

@article{fleming_new_2017,
	title = {A new kernel density estimator for accurate home-range and species-range area estimation},
	volume = {8},
	issn = {2041210X},
	doi = {10.1111/2041-210X.12673},
	abstract = {Kernel density estimators are widely applied to area-related problems in ecology, from estimating the home range of an individual to estimating the geographic range of a species. Currently, area estimates are obtained indirectly, by first estimating the location distribution from tracking (home range) or survey (geographic range) data and then estimating areas from that distribution. This indirect approach leads to biased area estimates and difficulty in deriving reasonable confidence intervals. We introduce a new kernel density estimator (and associated confidence intervals) focused specifically on area estimation that applies to both independently sampled survey data and autocorrelated tracking data. We test our methods against simulated movement data and demonstrate its use with African buffalo data. The area-corrected kernel density estimator produces much more accurate area estimates, particularly at small sample sizes, and the newly derived confidence intervals are more reliable than existing alternatives. This new method is the most efficient nonparametric home-range estimator for animal tracking data and should also be considered when calculating nonparametric range estimates from survey data. This estimator is now the default method in the ctmm r package.},
	number = {5},
	journal = {Methods in Ecology and Evolution},
	author = {Fleming, Christen H. and Calabrese, Justin M.},
	year = {2017},
	keywords = {home range, autocorrelation, tracking data, utilization distribution, kernel density estimation, geographic range, species range},
	pages = {571--579},
	file = {PDF:C\:\\Users\\Francisco\\Zotero\\storage\\NUVNVEAU\\full-text.pdf:application/pdf;PDF:C\:\\Users\\Francisco\\Zotero\\storage\\4WRZWQJ7\\Fleming and Calabrese 2017 A new kernel density estimator for accurate HR.pdf:application/pdf},
}

@article{noonan_comprehensive_2019,
	title = {A comprehensive analysis of autocorrelation and bias in home range estimation},
	volume = {89},
	issn = {15577015},
	doi = {10.1002/ecm.1344},
	abstract = {Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ((Formula presented.)) to quantify the information content of each data set. We found that AKDE 95\% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the hold-out sets by AKDE 95\% (or 50\%) estimates was 95.3\% (or 50.1\%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing (Formula presented.). To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small (Formula presented.). While 72\% of the 369 empirical data sets had {\textgreater}1,000 total observations, only 4\% had an (Formula presented.) {\textgreater}1,000, where 30\% had an (Formula presented.) {\textless}30. In this frequently encountered scenario of small (Formula presented.), AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.},
	number = {2},
	journal = {Ecological Monographs},
	author = {Noonan, Michael J. and Tucker, Marlee A. and Fleming, Christen H. and Akre, Thomas S. and Alberts, Susan C. and Ali, Abdullahi H. and Altmann, Jeanne and Antunes, Pamela Castro and Belant, Jerrold L. and Beyer, Dean and Blaum, Niels and Böhning-Gaese, Katrin and Cullen, Laury and de Paula, Rogerio Cunha and Dekker, Jasja and Drescher-Lehman, Jonathan and Farwig, Nina and Fichtel, Claudia and Fischer, Christina and Ford, Adam T. and Goheen, Jacob R. and Janssen, René and Jeltsch, Florian and Kauffman, Matthew and Kappeler, Peter M. and Koch, Flávia and LaPoint, Scott and Markham, A. Catherine and Medici, Emilia Patricia and Morato, Ronaldo G. and Nathan, Ran and Oliveira-Santos, Luiz Gustavo R. and Olson, Kirk A. and Patterson, Bruce D. and Paviolo, Agustin and Ramalho, Emiliano Esterci and Rösner, Sascha and Schabo, Dana G. and Selva, Nuria and Sergiel, Agnieszka and Xavier da Silva, Marina and Spiegel, Orr and Thompson, Peter and Ullmann, Wiebke and Zięba, Filip and Zwijacz-Kozica, Tomasz and Fagan, William F. and Mueller, Thomas and Calabrese, Justin M.},
	month = may,
	year = {2019},
	note = {Publisher: Ecological Society of America},
	keywords = {minimum convex polygon, tracking data, kernel density estimation, space use, telemetry, animal movement, local convex hull, range distribution},
	file = {PDF:C\:\\Users\\Francisco\\Zotero\\storage\\39SICP2T\\noonan2018.pdf:application/pdf},
}

@article{fleming_comprehensive_2020,
	title = {A comprehensive framework for handling location error in animal tracking data*},
	doi = {10.1101/2020.06.12.130195},
	abstract = {Animal tracking data are being collected more frequently, in greater detail, and on smaller taxa than ever before. These data hold the promise to increase the relevance of animal movement for understanding ecological processes, but this potential will only be fully realized if their accompanying location error is properly addressed. Historically, coarsely-sampled movement data have proved invaluable for understanding large scale processes (e.g., home range, habitat selection, etc.), but modern fine-scale data promise to unlock far more ecological information. While location error can often be ignored in coarsely sampled data, fine-scale data require much more care, and tools to do this have been lacking. Current approaches to dealing with location error largely fall into two categories—either discarding the least accurate location estimates prior to analysis or simultaneously fitting movement and error parameters in a hidden-state model. Unfortunately, both of these approaches have serious flaws. Here, we provide a general framework to account for location error in the analysis of animal tracking data, so that their potential can be unlocked. We apply our error-model-selection framework to 190 GPS, cellular, and acoustic devices representing 27 models from 14 manufacturers. Collectively, these devices are used to track a wide range of animal species comprising birds, fish, reptiles, and mammals of different sizes and with different behaviors, in urban, suburban, and wild settings. Then, using empirical data on tracked individuals from multiple species, we provide an overview of modern, error-informed movement analyses, including continuous-time path reconstruction, home-range distribution, home-range overlap, speed and distance estimation. Adding to these techniques, we introduce new error-informed estimators for outlier detection and autocorrelation visualization. We furthermore demonstrate how error-informed analyses on calibrated tracking data can be necessary to ensure that estimates are accurate and insensitive to location error, and allow researchers to use all of their data. Because error-induced biases depend on so many factors—sampling schedule, movement characteristics, tracking device, habitat, etc.—differential bias can easily confound biological inference and lead researchers to draw false conclusions. \#\#\# Competing Interest Statement The authors have declared no competing interest.},
	journal = {bioRxiv},
	author = {Fleming, C. H. and Drescher-Lehman, J. and Noonan, M. J. and Akre, T. S. B. and Brown, D. J. and Cochrane, M. M. and Dejid, N. and DeNicola, V. and DePerno, C. S. and Dunlop, J. N. and Gould, N. P. and Hollins, J. and Ishii, H. and Kaneko, Y. and Kays, R. and Killen, S. S. and Koeck, B. and Lambertucci, S. A. and LaPoint, S. D. and Medici, E. P. and Meyburg, B.-U. and Miller, T. A. and Moen, R. A. and Mueller, T. and Pfeiffer, T. and Pike, K. N. and Roulin, A. and Safi, K. and Séchaud, R. and Scharf, A. K. and Shephard, J. M. and Stabach, J. A. and Stein, K. and Tonra, C. M. and Yamazaki, K. and Fagan, W. F. and Calabrese, J. M.},
	year = {2020},
	keywords = {GPS, animal tracking, ARgos Doppler-shift, DOP, location error, VHF},
	pages = {2020.06.12.130195},
	file = {PDF:C\:\\Users\\Francisco\\Zotero\\storage\\GKJY8IL6\\Flemming et al Acomprehensiveframeworkforhandlinglo cationerrorinanimaltrackingdata.pdf:application/pdf},
}

@article{castellanos_pilot_2022,
	title = {A pilot study on the home range and movement patterns of the {Andean} {Fox} {Lycalopex} culpaeus ({Molina}, 1782) in {Cotopaxi} {National} {Park}, {Ecuador}},
	volume = {86},
	issn = {18641547},
	doi = {10.1515/mammalia-2020-0195},
	abstract = {This study reports movement patterns and home range estimates of an Andean fox (Lycalopex culpaeus) in Cotopaxi National Park in Ecuador, representing the first GPS-tagging of the species. The GPS functioned well during the 197-day tracking period. Home range sizes ranged between 4.9 and 8.1 km2, depending on the estimation method. Movement speeds averaged 0.17 km/h at day versus 0.87 km/h at night, and distance traveled averaged 0.23 km at day versus 0.89 km at night. These preliminary results highlight the importance of collecting unbiased, high-quality data which enables an enhanced understanding on mammal behavior and human/animal interaction.},
	number = {1},
	journal = {Mammalia},
	author = {Castellanos, Armando and Castellanos, Francisco X. and Kays, Roland and Brito, Jorge},
	year = {2022},
	keywords = {Andean fox habits, continuous-time movement model, mammal tracking, non-parametric methods, straight-line displacement},
	pages = {22--26},
	file = {PDF:C\:\\Users\\Francisco\\Zotero\\storage\\6UQRWRQW\\Castellanosetal2021_Apilotstudyonthehomerangeoflycalopex.pdf:application/pdf},
}

@article{silva_movedesign_2023,
	title = {movedesign: {Shiny} {R} app to evaluate sampling design for animal movement studies},
	volume = {14},
	copyright = {© 2023 The Authors. Methods in Ecology and Evolution published by John Wiley \& Sons Ltd on behalf of British Ecological Society.},
	issn = {2041-210X},
	shorttitle = {movedesign},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.14153},
	doi = {10.1111/2041-210X.14153},
	abstract = {Projects focused on movement behaviour and home range are commonplace, but beyond a focus on choosing appropriate research questions, there are no clear guidelines for such studies. Without these guidelines, designing an animal tracking study to produce reliable estimates of space-use and movement properties (necessary to answer basic movement ecology questions), is often done in an ad hoc manner. We developed ‘movedesign’, a user-friendly Shiny application, which can be utilized to investigate the precision of three estimates regularly reported in movement and spatial ecology studies: home range area, speed and distance travelled. Conceptually similar to statistical power analysis, this application enables users to assess the degree of estimate precision that may be achieved with a given sampling design; that is, the choices regarding data resolution (sampling interval) and battery life (sampling duration). Leveraging the ‘ctmm’ R package, we utilize two methods proven to handle many common biases in animal movement datasets: autocorrelated kernel density estimators (AKDEs) and continuous-time speed and distance (CTSD) estimators. Longer sampling durations are required to reliably estimate home range areas via the detection of a sufficient number of home range crossings. In contrast, speed and distance estimation requires a sampling interval short enough to ensure that a statistically significant signature of the animal's velocity remains in the data. This application addresses key challenges faced by researchers when designing tracking studies, including the trade-off between long battery life and high resolution of GPS locations collected by the devices, which may result in a compromise between reliably estimating home range or speed and distance. ‘movedesign’ has broad applications for researchers and decision-makers, supporting them to focus efforts and resources in achieving the optimal sampling design strategy for their research questions, prioritizing the correct deployment decisions for insightful and reliable outputs, while understanding the trade-off associated with these choices.},
	language = {en},
	number = {9},
	urldate = {2024-03-06},
	journal = {Methods in Ecology and Evolution},
	author = {Silva, Inês and Fleming, Christen H. and Noonan, Michael J. and Fagan, William F. and Calabrese, Justin M.},
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.14153},
	keywords = {home range, space use, biologgers, experimental design, GPS sampling, GPS tracking, simulations},
	pages = {2216--2225},
	file = {Full Text PDF:C\:\\Users\\Francisco\\Zotero\\storage\\YPXSJB4J\\Silva et al. - 2023 - movedesign Shiny R app to evaluate sampling desig.pdf:application/pdf;Snapshot:C\:\\Users\\Francisco\\Zotero\\storage\\ANKVGAI4\\2041-210X.html:text/html},
}

@article{abrahms_emerging_2021,
	title = {Emerging {Perspectives} on {Resource} {Tracking} and {Animal} {Movement} {Ecology}},
	volume = {36},
	issn = {0169-5347},
	url = {https://www.cell.com/trends/ecology-evolution/abstract/S0169-5347(20)30310-4},
	doi = {10.1016/j.tree.2020.10.018},
	language = {English},
	number = {4},
	urldate = {2024-03-06},
	journal = {Trends in Ecology \& Evolution},
	author = {Abrahms, Briana and Aikens, Ellen O. and Armstrong, Jonathan B. and Deacy, William W. and Kauffman, Matthew J. and Merkle, Jerod A.},
	month = apr,
	year = {2021},
	pmid = {33229137},
	note = {Publisher: Elsevier},
	keywords = {movement ecology, foraging theory, phenological variation, resource landscape},
	pages = {308--320},
	file = {Full Text PDF:C\:\\Users\\Francisco\\Zotero\\storage\\SIYRZWSW\\Abrahms et al. - 2021 - Emerging Perspectives on Resource Tracking and Ani.pdf:application/pdf},
}

@article{hays_implications_2001,
	title = {The implications of location accuracy for the interpretation of satellite-tracking data},
	volume = {61},
	issn = {00033472},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0003347201916859},
	doi = {10.1006/anbe.2001.1685},
	language = {en},
	number = {5},
	urldate = {2024-03-06},
	journal = {Animal Behaviour},
	author = {Hays, G.C. and Åkesson, S. and Godley, B.J. and Luschi, P. and Santidrian, P.},
	month = may,
	year = {2001},
	pages = {1035--1040},
	file = {Hays et al. - 2001 - The implications of location accuracy for the inte.pdf:C\:\\Users\\Francisco\\Zotero\\storage\\3JBJR7MM\\Hays et al. - 2001 - The implications of location accuracy for the inte.pdf:application/pdf},
}

@article{fleming_estimating_2016,
	title = {Estimating where and how animals travel: an optimal framework for path reconstruction from autocorrelated tracking data},
	volume = {97},
	issn = {1939-9170},
	shorttitle = {Estimating where and how animals travel},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1890/15-1607.1},
	doi = {10.1890/15-1607.1},
	abstract = {An animal's trajectory is a fundamental object of interest in movement ecology, as it directly informs a range of topics from resource selection to energy expenditure and behavioral states. Optimally inferring the mostly unobserved movement path and its dynamics from a limited sample of telemetry observations is a key unsolved problem, however. The field of geostatistics has focused significant attention on a mathematically analogous problem that has a statistically optimal solution coined after its inventor, Krige. Kriging revolutionized geostatistics and is now the gold standard for interpolating between a limited number of autocorrelated spatial point observations. Here we translate Kriging for use with animal movement data. Our Kriging formalism encompasses previous methods to estimate animal's trajectories—the Brownian bridge and continuous-time correlated random walk library—as special cases, informs users as to when these previous methods are appropriate, and provides a more general method when they are not. We demonstrate the capabilities of Kriging on a case study with Mongolian gazelles where, compared to the Brownian bridge, Kriging with a more optimal model was 10\% more precise in interpolating locations and 500\% more precise in estimating occurrence areas.},
	language = {en},
	number = {3},
	urldate = {2024-03-07},
	journal = {Ecology},
	author = {Fleming, C. H. and Fagan, W. F. and Mueller, T. and Olson, K. A. and Leimgruber, P. and Calabrese, J. M.},
	year = {2016},
	note = {\_eprint: https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/15-1607.1},
	keywords = {autocorrelation, Brownian bridge, tracking data, utilization distribution, CRAWL, Krige, Mongolian gazelle, Procapra gutturosa, telemetry error, tracking data gaps},
	pages = {576--582},
	file = {Full Text PDF:C\:\\Users\\Francisco\\Zotero\\storage\\KW4D5643\\Fleming et al. - 2016 - Estimating where and how animals travel an optimal framework for path reconstruction from autocorre.pdf:application/pdf;Snapshot:C\:\\Users\\Francisco\\Zotero\\storage\\PDJY4ZME\\15-1607.html:text/html},
}

@article{gurarie_what_2016,
	title = {What is the animal doing? {Tools} for exploring behavioural structure in animal movements},
	volume = {85},
	copyright = {© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society},
	issn = {1365-2656},
	shorttitle = {What is the animal doing?},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1365-2656.12379},
	doi = {10.1111/1365-2656.12379},
	abstract = {Movement data provide a window – often our only window – into the cognitive, social and biological processes that underlie the behavioural ecology of animals in the wild. Robust methods for identifying and interpreting distinct modes of movement behaviour are of great importance, but complicated by the fact that movement data are complex, multivariate and dependent. Many different approaches to exploratory analysis of movement have been developed to answer similar questions, and practitioners are often at a loss for how to choose an appropriate tool for a specific question. We apply and compare four methodological approaches: first passage time (FPT), Bayesian partitioning of Markov models (BPMM), behavioural change point analysis (BCPA) and a fitted multistate random walk (MRW) to three simulated tracks and two animal trajectories – a sea lamprey (Petromyzon marinus) tracked for 12 h and a wolf (Canis lupus) tracked for 1 year. The simulations – in which, respectively, velocity, tortuosity and spatial bias change – highlight the sensitivity of all methods to model misspecification. Methods that do not account for autocorrelation in the movement variables lead to spurious change points, while methods that do not account for spatial bias completely miss changes in orientation. When applied to the animal data, the methods broadly agree on the structure of the movement behaviours. Important discrepancies, however, reflect differences in the assumptions and nature of the outputs. Important trade-offs are between the strength of the a priori assumptions (low in BCPA, high in MRW), complexity of output (high in the BCPA, low in the BPMM and MRW) and explanatory potential (highest in the MRW). The animal track analysis suggests some general principles for the exploratory analysis of movement data, including ways to exploit the strengths of the various methods. We argue for close and detailed exploratory analysis of movement before fitting complex movement models.},
	language = {en},
	number = {1},
	urldate = {2024-03-07},
	journal = {Journal of Animal Ecology},
	author = {Gurarie, Eliezer and Bracis, Chloe and Delgado, Maria and Meckley, Trevor D. and Kojola, Ilpo and Wagner, C. Michael},
	year = {2016},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2656.12379},
	keywords = {telemetry, behavioural change points, hidden Markov models, partitioning, segmentation, state space models},
	pages = {69--84},
	file = {Full Text PDF:C\:\\Users\\Francisco\\Zotero\\storage\\J7VQLHFE\\Gurarie et al. - 2016 - What is the animal doing Tools for exploring behavioural structure in animal movements.pdf:application/pdf;Snapshot:C\:\\Users\\Francisco\\Zotero\\storage\\V6QD9RLM\\1365-2656.html:text/html},
}

@article{pretorius_movement_2020,
	title = {Movement patterns of lesser flamingos {Phoeniconaias} minor: nomadism or partial migration?},
	shorttitle = {Movement patterns of lesser flamingos {Phoeniconaias} minor},
	abstract = {Waterbirds in stochastic environments exhibit nomadism in order to cater for the unpredictable availability of water resources. Lesser flamingos Phoeniconaias minor have long been thought to be nomadic waterbirds. In southern Africa, con-servation efforts for lesser flamingos are hampered by a lack of knowledge about their movement trajectories. To investigate their movement ecology in southern Africa, we fitted GPS–GSM transmitters to 12 adults and tracked their movements over four years, from March 2016 to February 2020. Net squared displacement (NSD) was used in nonlinear least squares models classifying trajectories as nomadic, migratory, mixed-migratory, home range restricted or dispersal movement types. Data from eight of the 12 birds met the criteria for the NSD analysis. Model success was good; only 8 out of 120 (6.7\%) movement type models failed to reach convergence. Goodness of fit statistics from the NSD models supported migratory and mixed migratory movement types (concordance criteria coefficient (CC) = 0.78) for more than half of the annual trajectories investigated (57.2\%). Dispersal, home range-restricted and nomadic movements best described 28.6, 9.5 and 4.8\% of annual trajectories, respectively, but all resulted in a mean CC of {\textless} 0.4 and thus did not fit observed NSD pat-terns as well as the migratory movement types. We then used nonlinear mixed effects models to account for annual and individual differences in migration parameters. Variation in the timing and duration of all migrations were more important than variation in migration distance, indicating well-established summer and winter ‘ranges’ and routes between Kamfers Dam (South Africa) and Sua Pan (Botswana). We propose that lesser flamingos in central southern Africa may be partial migrants, not true nomads, as most of their movements followed a regular, repeated pattern between two primary locations. 

(PDF) Movement patterns of lesser flamingos Phoeniconaias minor: nomadism or partial migration?. Available from: https://www.researchgate.net/publication/343736462\_Movement\_patterns\_of\_lesser\_flamingos\_Phoeniconaias\_minor\_nomadism\_or\_partial\_migration [accessed Aug 19 2020].},
	journal = {Wildlife Biology},
	author = {Pretorius, Mattheuns and Leeuwner, Lourens and Tate, Gareth and Botha, Andre and Michael, Michael and Durgapersad, Kaajial and Chetty, Kishaylin},
	month = aug,
	year = {2020},
	file = {Full Text PDF:C\:\\Users\\Francisco\\Zotero\\storage\\YR52M2GD\\Pretorius et al. - 2020 - Movement patterns of lesser flamingos Phoeniconaias minor nomadism or partial migration.pdf:application/pdf},
}

@incollection{gregory_home_2017,
	title = {Home {Range} {Estimation}},
	isbn = {978-0-470-67337-9},
	abstract = {Understanding the extent of the area in which an animal or group of animals lives, or the home range, is important to understanding ecology. There are many methods available for estimating the extent of a home range. The following, which are frequently used in primatology, are described in this entry: the grid cell approach, minimum convex polygon (MCP), kernel density estimators (KDE), and low convex hull (LoCoH). In recent decades, GIS technology has allowed researchers to estimate home ranges very easily. However, there is no standard method for making estimations, and when estimating and reporting home ranges, it is important to use multiple methods and to describe the data in detail to avoid misinterpretation.},
	author = {Gregory, Tremaine},
	month = apr,
	year = {2017},
	doi = {10.1002/9781119179313.wbprim0177},
	file = {Full Text PDF:C\:\\Users\\Francisco\\Zotero\\storage\\TQ7HZ3VF\\Gregory - 2017 - Home Range Estimation.pdf:application/pdf},
}

@article{signer_fresh_2021,
	title = {A fresh look at an old concept: home-range estimation in a tidy world},
	volume = {9},
	shorttitle = {A fresh look at an old concept},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8048401/},
	doi = {10.7717/peerj.11031},
	abstract = {A rich set of statistical techniques has been developed over the last several decades to estimate the spatial extent of animal home ranges from telemetry data, and new methods to estimate home ranges continue to be developed. Here we investigate home-range ...},
	language = {en},
	urldate = {2024-03-08},
	journal = {PeerJ},
	author = {Signer, Johannes and Fieberg, John R.},
	year = {2021},
	pmid = {33954027},
	note = {Publisher: PeerJ, Inc},
	file = {Full Text:C\:\\Users\\Francisco\\Zotero\\storage\\IU6U9Q4V\\Signer and Fieberg - 2021 - A fresh look at an old concept home-range estimation in a tidy world.pdf:application/pdf;Snapshot:C\:\\Users\\Francisco\\Zotero\\storage\\V6PYVFYB\\PMC8048401.html:text/html},
}

@article{fleming_fine-scale_2014,
	title = {From {Fine}-{Scale} {Foraging} to {Home} {Ranges}: {A} {Semivariance} {Approach} to {Identifying} {Movement} {Modes} across {Spatiotemporal} {Scales}.},
	volume = {183},
	issn = {0003-0147},
	shorttitle = {From {Fine}-{Scale} {Foraging} to {Home} {Ranges}},
	url = {https://www-journals-uchicago-edu.lib-e2.lib.ttu.edu/doi/10.1086/675504},
	doi = {10.1086/675504},
	abstract = {Understanding animal movement is a key challenge in ecology and conservation biology. Relocation data often represent a complex mixture of different movement behaviors, and reliably decomposing this mix into its component parts is an unresolved problem in movement ecology. Traditional approaches, such as composite random walk models, require that the timescales characterizing the movement are all similar to the usually arbitrary data-sampling rate. Movement behaviors such as long-distance searching and fine-scale foraging, however, are often intermixed but operate on vastly different spatial and temporal scales. An approach that integrates the full sweep of movement behaviors across scales is currently lacking. Here we show how the semivariance function (SVF) of a stochastic movement process can both identify multiple movement modes and solve the sampling rate problem. We express a broad range of continuous-space, continuous-time stochastic movement models in terms of their SVFs, connect them to relocation data via variogram regression, and compare them using standard model selection techniques. We illustrate our approach using Mongolian gazelle relocation data and show that gazelle movement is characterized by ballistic foraging movements on a 6-h timescale, fast diffusive searching with a 10-week timescale, and asymptotic diffusion over longer timescales.},
	number = {5},
	urldate = {2024-03-10},
	journal = {The American Naturalist},
	author = {Fleming, Chris H. and Calabrese, Justin M. and Mueller, Thomas and Olson, Kirk A. and Leimgruber, Peter and Fagan, William F.},
	month = may,
	year = {2014},
	note = {Publisher: The University of Chicago Press},
	keywords = {autocorrelation function, characteristic timescales, continuous movement models, movement modes, semivariance function, variogram, variogram regression},
	pages = {E154--E167},
	file = {Full Text PDF:C\:\\Users\\Francisco\\Zotero\\storage\\YSQV9CBH\\Fleming et al. - 2014 - From Fine-Scale Foraging to Home Ranges A Semivariance Approach to Identifying Movement Modes acros.pdf:application/pdf},
}

@article{fleming_correcting_2018-1,
	title = {Correcting for missing and irregular data in home-range estimation},
	volume = {28},
	copyright = {© 2018 by the Ecological Society of America},
	issn = {1939-5582},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/eap.1704},
	doi = {10.1002/eap.1704},
	abstract = {Home-range estimation is an important application of animal tracking data that is frequently complicated by autocorrelation, sampling irregularity, and small effective sample sizes. We introduce a novel, optimal weighting method that accounts for temporal sampling bias in autocorrelated tracking data. This method corrects for irregular and missing data, such that oversampled times are downweighted and undersampled times are upweighted to minimize error in the home-range estimate. We also introduce computationally efficient algorithms that make this method feasible with large data sets. Generally speaking, there are three situations where weight optimization improves the accuracy of home-range estimates: with marine data, where the sampling schedule is highly irregular, with duty cycled data, where the sampling schedule changes during the observation period, and when a small number of home-range crossings are observed, making the beginning and end times more independent and informative than the intermediate times. Using both simulated data and empirical examples including reef manta ray, Mongolian gazelle, and African buffalo, optimal weighting is shown to reduce the error and increase the spatial resolution of home-range estimates. With a conveniently packaged and computationally efficient software implementation, this method broadens the array of data sets with which accurate space-use assessments can be made.},
	language = {en},
	number = {4},
	urldate = {2024-03-10},
	journal = {Ecological Applications},
	author = {Fleming, C. H. and Sheldon, D. and Fagan, W. F. and Leimgruber, P. and Mueller, T. and Nandintsetseg, D. and Noonan, M. J. and Olson, K. A. and Setyawan, E. and Sianipar, A. and Calabrese, J. M.},
	year = {2018},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/eap.1704},
	keywords = {animal tracking data, autocorrelation, home range, irregular sampling, kernel density estimation, marine tracking data, utilization distribution},
	pages = {1003--1010},
	file = {Full Text PDF:C\:\\Users\\Francisco\\Zotero\\storage\\3PDWY57M\\Fleming et al. - 2018 - Correcting for missing and irregular data in home-range estimation.pdf:application/pdf;Snapshot:C\:\\Users\\Francisco\\Zotero\\storage\\R74BHURV\\eap.html:text/html},
}

@article{kays_movebank_2022,
	title = {The {Movebank} system for studying global animal movement and demography},
	volume = {13},
	copyright = {© 2021 The Authors. Methods in Ecology and Evolution published by John Wiley \& Sons Ltd on behalf of British Ecological Society.},
	issn = {2041-210X},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13767},
	doi = {10.1111/2041-210X.13767},
	abstract = {Quantifying movement and demographic events of free-ranging animals is fundamental to studying their ecology, evolution and conservation. Technological advances have led to an explosion in sensor-based methods for remotely observing these phenomena. This transition to big data creates new challenges for data management, analysis and collaboration. We present the Movebank ecosystem of tools used by thousands of researchers to collect, manage, share, visualize, analyse and archive their animal tracking and other animal-borne sensor data. Users add sensor data through file uploads or live data streams and further organize and complete quality control within the Movebank system. All data are harmonized to a data model and vocabulary. The public can discover, view and download data for which they have been given access to through the website, the Animal Tracker mobile app or by API. Advanced analysis tools are available through the EnvDATA System, the MoveApps platform and a variety of user-developed applications. Data owners can share studies with select users or the public, with options for embargos, licenses and formal archiving in a data repository. Movebank is used by over 3,100 data owners globally, who manage over 6 billion animal location and sensor measurements across more than 6,500 studies, with thousands of active tags sending over 3 million new data records daily. These data underlie {\textgreater}700 published papers and reports. We present a case study demonstrating the use of Movebank to assess life-history events and demography, and engage with citizen scientists to identify mortalities and causes of death for a migratory bird. A growing number of researchers, government agencies and conservation organizations use Movebank to manage research and conservation projects and to meet legislative requirements. The combination of historic and new data with collaboration tools enables broad comparative analyses and data acquisition and mapping efforts. Movebank offers an integrated system for real-time monitoring of animals at a global scale and represents a digital museum of animal movement and behaviour. Resources and coordination across countries and organizations are needed to ensure that these data, including those that cannot be made public, remain accessible to future generations.},
	language = {en},
	number = {2},
	urldate = {2024-03-11},
	journal = {Methods in Ecology and Evolution},
	author = {Kays, Roland and Davidson, Sarah C. and Berger, Matthias and Bohrer, Gil and Fiedler, Wolfgang and Flack, Andrea and Hirt, Julian and Hahn, Clemens and Gauggel, Dominik and Russell, Benedict and Kölzsch, Andrea and Lohr, Ashley and Partecke, Jesko and Quetting, Michael and Safi, Kamran and Scharf, Anne and Schneider, Gabriel and Lang, Ilona and Schaeuffelhut, Friedrich and Landwehr, Matthias and Storhas, Martin and van Schalkwyk, Louis and Vinciguerra, Candace and Weinzierl, Rolf and Wikelski, Martin},
	year = {2022},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13767},
	keywords = {animal behaviour, animal tracking, bio-logging, cyberinfrastructure, FAIR data, GPS, live data, movement},
	pages = {419--431},
	file = {Full Text PDF:C\:\\Users\\Francisco\\Zotero\\storage\\UQ5S2UJU\\Kays et al. - 2022 - The Movebank system for studying global animal movement and demography.pdf:application/pdf;Snapshot:C\:\\Users\\Francisco\\Zotero\\storage\\PPW3DYJR\\2041-210X.html:text/html},
}