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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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Vanderley-Silva I, Valente RA. Landscape resistance index aiming at functional forest connectivity. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1224. [PMID: 37725180 DOI: 10.1007/s10661-023-11749-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 08/18/2023] [Indexed: 09/21/2023]
Abstract
Resistance models may quantify the ability of the landscape to impede species' movement and represent suitable habitats. Moreover, the performance of resistance models parameterized by land-use/land cover attributes evidence that the integrity of the environments subject to urban sprawl is poorly understood. In this sense, the study assumed we could identify the forest functional connectivity in a landscape considering the disparity in the landscape mosaic. In this context, we sought to develop a landscape resistance index through structural equation modeling (SEM), supported by the criteria of heat emission, biomass, and anthropogenic barriers, obtained by remote sensing, called observed variables. The landscape studied in the Green Belt Biosphere Reserve of São Paulo has significant remnants of the Atlantic Forest, a biodiversity hotspot. However, our results indicated criteria variability in the landscape modeled through the SEM, obtaining a significant adjustment of the landscape resistance index, with comparative fit index (CFI) of 1.00 and root mean square error of approximation (RMSEA) of 0.00. The index reflects the resistance levels of the land use/land cover, expressed by the class interval, ranging from 0% (1.73) to 100% (493.88), with the highest values associated with the anthropized uses and forest isolation. Thus, our index based on environmental attributes reflects the structure of functional forest connectivity and offers a framework to design forest corridors across landscapes.
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Affiliation(s)
- Ivan Vanderley-Silva
- Program in Planning and Use of Renewable Resources (PPGPUR), Federal University of São Carlos (UFSCAR-Sorocaba), João Leme dos Santos Highway (SP-264), km 110, Sorocaba, SP, Brazil.
| | - Roberta Averna Valente
- Environmental Sciences Department, Federal University of São Carlos (UFSCAR-Sorocaba), João Leme dos Santos Highway (SP-264), km 110, Sorocaba, SP, Brazil
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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: 16] [Impact Index Per Article: 8.0] [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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Nelms SE, Alfaro-Shigueto J, Arnould JPY, Avila IC, Bengtson Nash S, Campbell E, Carter MID, Collins T, Currey RJC, Domit C, Franco-Trecu V, Fuentes MMPB, Gilman E, Harcourt RG, Hines EM, Hoelzel AR, Hooker SK, Johnston DW, Kelkar N, Kiszka JJ, Laidre KL, Mangel JC, Marsh H, Maxwell SM, Onoufriou AB, Palacios DM, Pierce GJ, Ponnampalam LS, Porter LJ, Russell DJF, Stockin KA, Sutaria D, Wambiji N, Weir CR, Wilson B, Godley BJ. Marine mammal conservation: over the horizon. ENDANGER SPECIES RES 2021. [DOI: 10.3354/esr01115] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Marine mammals can play important ecological roles in aquatic ecosystems, and their presence can be key to community structure and function. Consequently, marine mammals are often considered indicators of ecosystem health and flagship species. Yet, historical population declines caused by exploitation, and additional current threats, such as climate change, fisheries bycatch, pollution and maritime development, continue to impact many marine mammal species, and at least 25% are classified as threatened (Critically Endangered, Endangered or Vulnerable) on the IUCN Red List. Conversely, some species have experienced population increases/recoveries in recent decades, reflecting management interventions, and are heralded as conservation successes. To continue these successes and reverse the downward trajectories of at-risk species, it is necessary to evaluate the threats faced by marine mammals and the conservation mechanisms available to address them. Additionally, there is a need to identify evidence-based priorities of both research and conservation needs across a range of settings and taxa. To that effect we: (1) outline the key threats to marine mammals and their impacts, identify the associated knowledge gaps and recommend actions needed; (2) discuss the merits and downfalls of established and emerging conservation mechanisms; (3) outline the application of research and monitoring techniques; and (4) highlight particular taxa/populations that are in urgent need of focus.
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Affiliation(s)
- SE Nelms
- Centre for Ecology and Conservation, University of Exeter, Cornwall, TR10 9EZ, UK
| | - J Alfaro-Shigueto
- ProDelphinus, Jose Galvez 780e, Miraflores, Perú
- Facultad de Biologia Marina, Universidad Cientifica del Sur, Lima, Perú
| | - JPY Arnould
- School of Life and Environmental Sciences, Deakin University, Burwood, VIC 3125, Australia
| | - IC Avila
- Grupo de Ecología Animal, Departamento de Biología, Facultad de Ciencias Naturales y Exactas, Universidad del Valle, Cali, Colombia
| | - S Bengtson Nash
- Environmental Futures Research Institute (EFRI), Griffith University, Nathan Campus, 170 Kessels Road, Nathan, QLD 4111, Australia
| | - E Campbell
- Centre for Ecology and Conservation, University of Exeter, Cornwall, TR10 9EZ, UK
- ProDelphinus, Jose Galvez 780e, Miraflores, Perú
| | - MID Carter
- Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews, Fife, KY16 8LB, UK
| | - T Collins
- Wildlife Conservation Society, 2300 Southern Blvd., Bronx, NY 10460, USA
| | - RJC Currey
- Marine Stewardship Council, 1 Snow Hill, London, EC1A 2DH, UK
| | - C Domit
- Laboratory of Ecology and Conservation, Marine Study Center, Universidade Federal do Paraná, Brazil
| | - V Franco-Trecu
- Departamento de Ecología y Evolución, Facultad de Ciencias, Universidad de la República, Uruguay
| | - MMPB Fuentes
- Marine Turtle Research, Ecology and Conservation Group, Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA
| | - E Gilman
- Pelagic Ecosystems Research Group, Honolulu, HI 96822, USA
| | - RG Harcourt
- Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - EM Hines
- Estuary & Ocean Science Center, San Francisco State University, 3150 Paradise Dr. Tiburon, CA 94920, USA
| | - AR Hoelzel
- Department of Biosciences, Durham University, South Road, Durham, DH1 3LE, UK
| | - SK Hooker
- Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews, Fife, KY16 8LB, UK
| | - DW Johnston
- Duke Marine Lab, 135 Duke Marine Lab Road, Beaufort, NC 28516, USA
| | - N Kelkar
- Ashoka Trust for Research in Ecology and the Environment (ATREE), Royal Enclave, Srirampura, Jakkur PO, Bangalore 560064, Karnataka, India
| | - JJ Kiszka
- Department of Biological Sciences, Coastlines and Oceans Division, Institute of Environment, Florida International University, Miami, FL 33199, USA
| | - KL Laidre
- Polar Science Center, APL, University of Washington, 1013 NE 40th Street, Seattle, WA 98105, USA
| | - JC Mangel
- Centre for Ecology and Conservation, University of Exeter, Cornwall, TR10 9EZ, UK
- ProDelphinus, Jose Galvez 780e, Miraflores, Perú
| | - H Marsh
- James Cook University, Townsville, QLD 48111, Australia
| | - SM Maxwell
- School of Interdisciplinary Arts and Sciences, University of Washington Bothell, Bothell WA 98011, USA
| | - AB Onoufriou
- School of Biology, University of St Andrews, Fife, KY16 8LB, UK
- Universidad de La Laguna, San Cristóbal de La Laguna, Spain
| | - DM Palacios
- Marine Mammal Institute, Hatfield Marine Science Center, Oregon State University, Newport, OR, 97365, USA
- Department of Fisheries and Wildlife, Oregon State University, Corvallis, OR 97330, USA
| | - GJ Pierce
- Centre for Ecology and Conservation, University of Exeter, Cornwall, TR10 9EZ, UK
- Instituto de Investigaciones Marinas, Consejo Superior de Investigaciones Cientificas, Eduardo Cabello 6, 36208 Vigo, Pontevedra, Spain
| | - LS Ponnampalam
- The MareCet Research Organization, 40460 Shah Alam, Malaysia
| | - LJ Porter
- SMRU Hong Kong, University of St. Andrews, Hong Kong
| | - DJF Russell
- Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews, Fife, KY16 8LB, UK
- Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews, Fife, KY16 8LB, UK
| | - KA Stockin
- Animal Welfare Science and Bioethics Centre, School of Veterinary Science, Massey University, Private Bag 11-222, Palmerston North, New Zealand
| | - D Sutaria
- School of Interdisciplinary Arts and Sciences, University of Washington Bothell, Bothell WA 98011, USA
| | - N Wambiji
- Kenya Marine and Fisheries Research Institute, P.O. Box 81651, Mombasa-80100, Kenya
| | - CR Weir
- Ketos Ecology, 4 Compton Road, Kingsbridge, Devon, TQ7 2BP, UK
| | - B Wilson
- Scottish Association for Marine Science, Oban, Argyll, PA37 1QA, UK
| | - BJ Godley
- Centre for Ecology and Conservation, University of Exeter, Cornwall, TR10 9EZ, UK
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Buderman FE, Hooten MB, Alldredge MW, Hanks EM, Ivan JS. Time-varying predatory behavior is primary predictor of fine-scale movement of wildland-urban cougars. MOVEMENT ECOLOGY 2018; 6:22. [PMID: 30410764 PMCID: PMC6214169 DOI: 10.1186/s40462-018-0140-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 09/26/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND While many species have suffered from the detrimental impacts of increasing human population growth, some species, such as cougars (Puma concolor), have been observed using human-modified landscapes. However, human-modified habitat can be a source of both increased risk and increased food availability, particularly for large carnivores. Assessing preferential use of the landscape is important for managing wildlife and can be particularly useful in transitional habitats, such as at the wildland-urban interface. Preferential use is often evaluated using resource selection functions (RSFs), which are focused on quantifying habitat preference using either a temporally static framework or researcher-defined temporal delineations. Many applications of RSFs do not incorporate time-varying landscape availability or temporally-varying behavior, which may mask conflict and avoidance behavior. METHODS Contemporary approaches to incorporate landscape availability into the assessment of habitat selection include spatio-temporal point process models, step selection functions, and continuous-time Markov chain (CTMC) models; in contrast with the other methods, the CTMC model allows for explicit inference on animal movement in continuous-time. We used a hierarchical version of the CTMC framework to model speed and directionality of fine-scale movement by a population of cougars inhabiting the Front Range of Colorado, U.S.A., an area exhibiting rapid population growth and increased recreational use, as a function of individual variation and time-varying responses to landscape covariates. RESULTS We found evidence for individual- and daily temporal-variability in cougar response to landscape characteristics. Distance to nearest kill site emerged as the most important driver of movement at a population-level. We also detected seasonal differences in average response to elevation, heat loading, and distance to roads. Motility was also a function of amount of development, with cougars moving faster in developed areas than in undeveloped areas. CONCLUSIONS The time-varying framework allowed us to detect temporal variability that would be masked in a generalized linear model, and improved the within-sample predictive ability of the model. The high degree of individual variation suggests that, if agencies want to minimize human-wildlife conflict management options should be varied and flexible. However, due to the effect of recursive behavior on cougar movement, likely related to the location and timing of potential kill-sites, kill-site identification tools may be useful for identifying areas of potential conflict.
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Affiliation(s)
- Frances E. Buderman
- Colorado State University, Departments of Fish, Wildlife, and Conservation Biology, 1484 Campus Delivery, Fort Collins, CO 80523 USA
| | - Mevin B Hooten
- U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Departments of Fish, Wildlife, and Conservation Biology and Statistics, Colorado State University, 1484 Campus Delivery, Fort Collins, CO 80523 USA
| | - Mathew W Alldredge
- Colorado Parks and Wildlife, 317 W Prospect Road, Fort Collins, CO 80526 USA
| | - Ephraim M Hanks
- Pennsylvania State University, W-250 Millennium Science Complex, University Park, State College, PA 16802 USA
| | - Jacob S Ivan
- Colorado Parks and Wildlife, 317 W Prospect Road, Fort Collins, CO 80526 USA
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