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Dejeante R, Valeix M, Chamaillé-Jammes S. Time-varying habitat selection analysis: A model and applications for studying diel, seasonal, and post-release changes. Ecology 2024; 105:e4233. [PMID: 38180163 DOI: 10.1002/ecy.4233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 11/13/2023] [Accepted: 12/04/2023] [Indexed: 01/06/2024]
Abstract
Resource selection functions are commonly used to evaluate animals' habitat selection, for example, the disproportionate use of habitats relative to their availability. While environmental conditions or animal motivations may vary over time, sometimes in an unknown manner, studying changes in habitat selection usually requires an a priori segmentation of time in distinct periods. This limits our ability to precisely answer the question "When is an animal's habitat selection changing?" Here, we present a straightforward and flexible alternative approach based on fitting dynamic logistic models to used/available data. First, using simulated datasets, we demonstrate that dynamic logistic models perform well in recovering temporal variations in habitat selection. We then show real-world applications for studying diel, seasonal, and post-release changes in the habitat selection of the blue wildebeest (Connochaetes taurinus). Dynamic logistic models allow the study of temporal changes in habitat selection in a framework consistent with resource selection functions but without the need to segment time in distinct periods, which can be a difficult task when little is known about the process studied or may obscure interindividual variability in timing of change. These models should undoubtedly find their place in the movement ecology toolbox. We provide R scripts to facilitate their adoption. We also encourage future research to focus on how to account for temporal autocorrelation in location data, as this would allow statistical inference from location data collected at a high frequency, an increasingly common situation.
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Affiliation(s)
- Romain Dejeante
- CEFE, Université de Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Marion Valeix
- CEFE, Université de Montpellier, CNRS, EPHE, IRD, Montpellier, France
- CNRS, Université de Lyon, Université Lyon1, Laboratoire de Biométrie et Biologie Evolutive UMR 5558, 69622, Villeurbanne, France
| | - Simon Chamaillé-Jammes
- CEFE, Université de Montpellier, CNRS, EPHE, IRD, Montpellier, France
- Department of Zoology and Entomology, Mammal Research Institute, University of Pretoria, Pretoria, South Africa
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2
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McClintock BT, Lander ME. A multistate Langevin diffusion for inferring behavior-specific habitat selection and utilization distributions. Ecology 2024; 105:e4186. [PMID: 37794831 DOI: 10.1002/ecy.4186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 06/29/2023] [Accepted: 09/07/2023] [Indexed: 10/06/2023]
Abstract
The identification of important habitat and the behavior(s) associated with it is critical to conservation and place-based management decisions. Behavior also links life-history requirements and habitat use, which are key to understanding why animals use certain habitats. Animal population studies often use tracking data to quantify space use and habitat selection, but they typically either ignore movement behavior (e.g., foraging, migrating, nesting) or adopt a two-stage approach that can induce bias and fail to propagate uncertainty. We develop a habitat-driven Langevin diffusion for animals that exhibit distinct movement behavior states, thereby providing a novel single-stage statistical method for inferring behavior-specific habitat selection and utilization distributions in continuous time. Practitioners can customize, fit, assess, and simulate our integrated model using the provided R package. Simulation experiments demonstrated that the model worked well under a range of sampling scenarios as long as observations were of sufficient temporal resolution. Our simulations also demonstrated the importance of accounting for different behaviors and the misleading inferences that can result when these are ignored. We provide case studies using plains zebra (Equus quagga) and Steller sea lion (Eumetopias jubatus) telemetry data. In the zebra example, our model identified distinct "encamped" and "exploratory" states, where the encamped state was characterized by strong selection for grassland and avoidance of other vegetation types, which may represent selection for foraging resources. In the sea lion example, our model identified distinct movement behavior modes typically associated with this marine central-place forager and, unlike previous analyses, found foraging-type movements to be associated with steeper offshore slopes characteristic of the continental shelf, submarine canyons, and seamounts that are believed to enhance prey concentrations. This is the first single-stage approach for inferring behavior-specific habitat selection and utilization distributions from tracking data that can be readily implemented with user-friendly software. As certain behaviors are often more relevant to specific conservation or management objectives, practitioners can use our model to help inform the identification and prioritization of important habitats. Moreover, by linking individual-level movement behaviors to population-level spatial processes, the multistate Langevin diffusion can advance inferences at the intersection of population, movement, and landscape ecology.
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Affiliation(s)
- Brett T McClintock
- Marine Mammal Laboratory, Alaska Fisheries Science Center, NOAA, National Marine Fisheries Service, Seattle, Washington, USA
| | - Michelle E Lander
- Marine Mammal Laboratory, Alaska Fisheries Science Center, NOAA, National Marine Fisheries Service, Seattle, Washington, USA
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3
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Hewitt J, Gelfand AE, Schick RS. Time-discretization approximation enriches continuous-time discrete-space models for animal movement. Ann Appl Stat 2023. [DOI: 10.1214/22-aoas1649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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4
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Gardner B, McClintock BT, Converse SJ, Hostetter NJ. Integrated animal movement and spatial capture-recapture models: Simulation, implementation, and inference. Ecology 2022; 103:e3771. [PMID: 35638187 PMCID: PMC9787507 DOI: 10.1002/ecy.3771] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 03/18/2022] [Accepted: 04/19/2022] [Indexed: 12/30/2022]
Abstract
Over the last decade, spatial capture-recapture (SCR) models have become widespread for estimating demographic parameters in ecological studies. However, the underlying assumptions about animal movement and space use are often not realistic. This is a missed opportunity because interesting ecological questions related to animal space use, habitat selection, and behavior cannot be addressed with most SCR models, despite the fact that the data collected in SCR studies - individual animals observed at specific locations and times - can provide a rich source of information about these processes and how they relate to demographic rates. We developed SCR models that integrated more complex movement processes that are typically inferred from telemetry data, including a simple random walk, correlated random walk (i.e., short-term directional persistence), and habitat-driven Langevin diffusion. We demonstrated how to formulate, simulate from, and fit these models with standard SCR data using data-augmented Bayesian analysis methods. We evaluated their performance through a simulation study, in which we varied the detection, movement, and resource selection parameters. We also examined different numbers of sampling occasions and assessed performance gains when including auxiliary location data collected from telemetered individuals. Across all scenarios, the integrated SCR movement models performed well in terms of abundance, detection, and movement parameter estimation. We found little difference in bias for the simple random walk model when reducing the number of sampling occasions from T = 25 to T = 15. We found some bias in movement parameter estimates under several of the correlated random walk scenarios, but incorporating auxiliary location data improved parameter estimates and significantly improved mixing during model fitting. The Langevin movement model was able to recover resource selection parameters from standard SCR data, which is particularly appealing because it explicitly links the individual-level movement process with habitat selection and population density. We focused on closed population models, but the movement models developed here can be extended to open SCR models. The movement process models could also be easily extended to accommodate additional "building blocks" of random walks, such as central tendency (e.g., territoriality) or multiple movement behavior states, thereby providing a flexible and coherent framework for linking animal movement behavior to population dynamics, density, and distribution.
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Affiliation(s)
- Beth Gardner
- School of Environmental and Forest SciencesUniversity of WashingtonSeattleWashingtonUSA
| | - Brett T. McClintock
- Marine Mammal LaboratoryNOAA‐NMFS Alaska Fisheries Science CenterSeattleWashingtonUSA
| | - Sarah J. Converse
- U.S. Geological Survey, Washington Cooperative Fish and Wildlife Research Unit, School of Environmental and Forest Sciences and School of Aquatic and Fishery SciencesUniversity of WashingtonSeattleWashingtonUSA
| | - Nathan J. Hostetter
- U.S. Geological Survey, North Carolina Cooperative Fish and Wildlife Research Unit, Department of Applied EcologyNorth Carolina State UniversityRaleighNorth CarolinaUSA
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McClintock BT, Abrahms B, Chandler RB, Conn PB, Converse SJ, Emmet RL, Gardner B, Hostetter NJ, Johnson DS. An integrated path for spatial capture-recapture and animal movement modeling. Ecology 2022; 103:e3473. [PMID: 34270790 PMCID: PMC9786756 DOI: 10.1002/ecy.3473] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 01/25/2021] [Accepted: 03/15/2021] [Indexed: 12/30/2022]
Abstract
Ecologists and conservation biologists increasingly rely on spatial capture-recapture (SCR) and movement modeling to study animal populations. Historically, SCR has focused on population-level processes (e.g., vital rates, abundance, density, and distribution), whereas animal movement modeling has focused on the behavior of individuals (e.g., activity budgets, resource selection, migration). Even though animal movement is clearly a driver of population-level patterns and dynamics, technical and conceptual developments to date have not forged a firm link between the two fields. Instead, movement modeling has typically focused on the individual level without providing a coherent scaling from individual- to population-level processes, whereas SCR has typically focused on the population level while greatly simplifying the movement processes that give rise to the observations underlying these models. In our view, the integration of SCR and animal movement modeling has tremendous potential for allowing ecologists to scale up from individuals to populations and advancing the types of inferences that can be made at the intersection of population, movement, and landscape ecology. Properly accounting for complex animal movement processes can also potentially reduce bias in estimators of population-level parameters, thereby improving inferences that are critical for species conservation and management. This introductory article to the Special Feature reviews recent advances in SCR and animal movement modeling, establishes a common notation, highlights potential advantages of linking individual-level (Lagrangian) movements to population-level (Eulerian) processes, and outlines a general conceptual framework for the integration of movement and SCR models. We then identify important avenues for future research, including key challenges and potential pitfalls in the developments and applications that lie ahead.
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Affiliation(s)
- Brett T. McClintock
- Marine Mammal LaboratoryNOAA‐NMFS Alaska Fisheries Science CenterSeattleWashingtonUSA
| | - Briana Abrahms
- Department of BiologyUniversity of WashingtonLife Sciences Building, Box 351800SeattleWashingtonUSA
| | - Richard B. Chandler
- Warnell School of Forestry and Natural ResourcesUniversity of Georgia180 E. Green St.AthensGeorgiaUSA
| | - Paul B. Conn
- Marine Mammal LaboratoryNOAA‐NMFS Alaska Fisheries Science CenterSeattleWashingtonUSA
| | - Sarah J. Converse
- U.S. Geological SurveyWashington Cooperative Fish and Wildlife Research UnitSchool of Environmental and Forest Sciences & School of Aquatic and Fishery SciencesUniversity of WashingtonBox 355020SeattleWashingtonUSA
| | - Robert L. Emmet
- Quantitative Ecology and Resource ManagementUniversity of WashingtonSeattleWashingtonUSA
| | - Beth Gardner
- School of Environmental and Forest SciencesUniversity of WashingtonSeattleWashingtonUSA
| | - Nathan J. Hostetter
- Washington Cooperative Fish and Wildlife Research UnitSchool of Aquatic and Fishery SciencesUniversity of WashingtonSeattleWashingtonUSA
| | - Devin S. Johnson
- Marine Mammal LaboratoryNOAA‐NMFS Alaska Fisheries Science CenterSeattleWashingtonUSA
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Conn PB, Ver Hoef JM, McClintock BT, Johnson DS, Brost B. A
GLMM
approach for combining multiple relative abundance surfaces. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Paul B. Conn
- Marine Mammal Laboratory Alaska Fisheries Science Center NOAA, National Marine Fisheries Service Seattle WA USA
| | - Jay M. Ver Hoef
- Marine Mammal Laboratory Alaska Fisheries Science Center NOAA, National Marine Fisheries Service Seattle WA USA
| | - Brett T. McClintock
- Marine Mammal Laboratory Alaska Fisheries Science Center NOAA, National Marine Fisheries Service Seattle WA USA
| | - Devin S. Johnson
- Pacific Islands Fisheries Science Center NOAA, National Marine Fisheries Service Honolulu HI USA
| | - Brian Brost
- Marine Mammal Laboratory Alaska Fisheries Science Center NOAA, National Marine Fisheries Service Seattle WA USA
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7
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Butts DJ, Thompson NE, Christensen SA, Williams DM, Murillo MS. Data-driven agent-based model building for animal movement through Exploratory Data Analysis. Ecol Modell 2022. [DOI: 10.1016/j.ecolmodel.2022.110001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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8
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Potts JR, Börger L, Strickland BK, Street GM. Assessing the predictive power of step selection functions: how social and environmental interactions affect animal space use. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13904] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jonathan R. Potts
- School of Mathematics and Statistics University of Sheffield, Hicks Building, Hounsfield Road Sheffield UK
| | - Luca Börger
- Department of Biosciences College of Science Swansea University, Singleton Park Swansea Wales UK
- Centre for Biomathematics College of Science Swansea University, Singleton Park Swansea Wales UK
| | - Bronson K. Strickland
- Department of Wildlife, Fisheries, and Aquaculture Mississippi State University Mississippi State MS USA
| | - Garrett M. Street
- Department of Wildlife, Fisheries, and Aquaculture Mississippi State University Mississippi State MS USA
- Quantitative Ecology and Spatial Technologies Laboratory Mississippi State University Mississippi State MS USA
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Bergen S, Huso MM, Duerr AE, Braham MA, Katzner TE, Schmuecker S, Miller TA. Classifying behavior from short-interval biologging data: An example with GPS tracking of birds. Ecol Evol 2022; 12:e08395. [PMID: 35154643 PMCID: PMC8819645 DOI: 10.1002/ece3.8395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 10/28/2021] [Accepted: 11/03/2021] [Indexed: 11/17/2022] Open
Abstract
Recent advances in digital data collection have spurred accumulation of immense quantities of data that have potential to lead to remarkable ecological insight, but that also present analytic challenges. In the case of biologging data from birds, common analytical approaches to classifying movement behaviors are largely inappropriate for these massive data sets.We apply a framework for using K-means clustering to classify bird behavior using points from short time interval GPS tracks. K-means clustering is a well-known and computationally efficient statistical tool that has been used in animal movement studies primarily for clustering segments of consecutive points. To illustrate the utility of our approach, we apply K-means clustering to six focal variables derived from GPS data collected at 1-11 s intervals from free-flying bald eagles (Haliaeetus leucocephalus) throughout the state of Iowa, USA. We illustrate how these data can be used to identify behaviors and life-stage- and age-related variation in behavior.After filtering for data quality, the K-means algorithm identified four clusters in >2 million GPS telemetry data points. These four clusters corresponded to three movement states: ascending, flapping, and gliding flight; and one non-moving state: perching. Mapping these states illustrated how they corresponded tightly to expectations derived from natural history observations; for example, long periods of ascending flight were often followed by long gliding descents, birds alternated between flapping and gliding flight.The K-means clustering approach we applied is both an efficient and effective mechanism to classify and interpret short-interval biologging data to understand movement behaviors. Furthermore, because it can apply to an abundance of very short, irregular, and high-dimensional movement data, it provides insight into small-scale variation in behavior that would not be possible with many other analytical approaches.
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Affiliation(s)
- Silas Bergen
- Department of Mathematics and StatisticsWinona State UniversityWinonaMinnesotaUSA
| | - Manuela M. Huso
- U.S. Geological SurveyForest and Rangeland Ecosystem Science CenterCorvallisOregonUSA
- Statistics DepartmentOregon State UniversityCorvallisOregonUSA
| | - Adam E. Duerr
- Bloom Research Inc.Los AngelesCaliforniaUSA
- West Virginia UniversityMorgantownWest VirginiaUSA
- Conservation Science Global, Inc.West Cape MayNew JerseyUSA
| | | | - Todd E. Katzner
- U.S. Geological SurveyForest and Rangeland Ecosystem Science CenterBoiseIdahoUSA
| | - Sara Schmuecker
- U.S. Fish and Wildlife ServiceIllinois‐Iowa Field OfficeMolineIllinoisUSA
| | - Tricia A. Miller
- West Virginia UniversityMorgantownWest VirginiaUSA
- Conservation Science Global, Inc.West Cape MayNew JerseyUSA
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10
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Glennie R, Adam T, Leos‐Barajas V, Michelot T, Photopoulou T, McClintock BT. Hidden Markov Models: Pitfalls and Opportunities in Ecology. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13801] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Richard Glennie
- Centre for Research into Ecological and Environmental Modelling University of St Andrews St Andrews KY16 9LZ UK
| | - Timo Adam
- Centre for Research into Ecological and Environmental Modelling University of St Andrews St Andrews KY16 9LZ UK
| | | | - Théo Michelot
- Centre for Research into Ecological and Environmental Modelling University of St Andrews St Andrews KY16 9LZ UK
| | - Theoni Photopoulou
- Centre for Research into Ecological and Environmental Modelling University of St Andrews St Andrews KY16 9LZ UK
| | - Brett T. McClintock
- Marine Mammal Laboratory NOAA‐NMFS Alaska Fisheries Science Center Seattle USA
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11
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Mews S, Langrock R, Ötting M, Yaqine H, Reinecke J. Maximum approximate likelihood estimation of general continuous-time state-space models. STAT MODEL 2022. [DOI: 10.1177/1471082x211065785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Continuous-time state-space models (SSMs) are flexible tools for analysing irregularly sampled sequential observations that are driven by an underlying state process. Corresponding applications typically involve restrictive assumptions concerning linearity and Gaussianity to facilitate inference on the model parameters via the Kalman filter. In this contribution, we provide a general continuous-time SSM framework, allowing both the observation and the state process to be non-linear and non-Gaussian. Statistical inference is carried out by maximum approximate likelihood estimation, where multiple numerical integration within the likelihood evaluation is performed via a fine discretization of the state process. The corresponding reframing of the SSM as a continuous-time hidden Markov model, with structured state transitions, enables us to apply the associated efficient algorithms for parameter estimation and state decoding. We illustrate the modelling approach in a case study using data from a longitudinal study on delinquent behaviour of adolescents in Germany, revealing temporal persistence in the deviation of an individual's delinquency level from the population mean.
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Affiliation(s)
- Sina Mews
- Department of Business Administration and Economics, Bielefeld University, Bielefeld, Germany
| | - Roland Langrock
- Department of Business Administration and Economics, Bielefeld University, Bielefeld, Germany
| | - Marius Ötting
- Department of Business Administration and Economics, Bielefeld University, Bielefeld, Germany
| | - Houda Yaqine
- Department of Business Administration and Economics, Bielefeld University, Bielefeld, Germany
| | - Jost Reinecke
- Faculty of Sociology, Bielefeld University, Bielefeld, Germany
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Fieberg J, Signer J, Smith B, Avgar T. A 'How to' guide for interpreting parameters in habitat-selection analyses. J Anim Ecol 2021; 90:1027-1043. [PMID: 33583036 PMCID: PMC8251592 DOI: 10.1111/1365-2656.13441] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 02/02/2021] [Indexed: 11/29/2022]
Abstract
Habitat‐selection analyses allow researchers to link animals to their environment via habitat‐selection or step‐selection functions, and are commonly used to address questions related to wildlife management and conservation efforts. Habitat‐selection analyses that incorporate movement characteristics, referred to as integrated step‐selection analyses, are particularly appealing because they allow modelling of both movement and habitat‐selection processes. Despite their popularity, many users struggle with interpreting parameters in habitat‐selection and step‐selection functions. Integrated step‐selection analyses also require several additional steps to translate model parameters into a full‐fledged movement model, and the mathematics supporting this approach can be challenging for many to understand. Using simple examples, we demonstrate how weighted distribution theory and the inhomogeneous Poisson point process can facilitate parameter interpretation in habitat‐selection analyses. Furthermore, we provide a ‘how to’ guide illustrating the steps required to implement integrated step‐selection analyses using the amt package By providing clear examples with open‐source code, we hope to make habitat‐selection analyses more understandable and accessible to end users.
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Affiliation(s)
- John Fieberg
- Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, St. Paul, MN, USA
| | - Johannes Signer
- Wildlife Science, Faculty of Forestry and Forest Ecology, University of Goettingen, Göttingen, Germany
| | - Brian Smith
- Department of Wildland Resources and Ecology Center, Utah State University, Logan, UT, USA
| | - Tal Avgar
- Department of Wildland Resources and Ecology Center, Utah State University, Logan, UT, USA
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