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Frantz BH, Sepúlveda M, García‐Reyes M, Vega R, Palacios DM, Bedriñana‐Romano L, Hückstädt LA, Santos‐Carvallo M, Davis JD, Hines E. Combining potential and realized distribution modeling of telemetry data for a bycatch risk assessment. Ecol Evol 2024; 14:e11541. [PMID: 38932966 PMCID: PMC11199131 DOI: 10.1002/ece3.11541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 05/12/2024] [Accepted: 05/20/2024] [Indexed: 06/28/2024] Open
Abstract
Establishing marine species distributions is essential for guiding management and can be estimated by identifying potential favorable habitat at a population level and incorporating individual-level information (e.g., movement constraints) to inform realized space use. In this research, we applied a combined modeling approach to tracking data of adult female and juvenile South American sea lions (Otaria flavescens; n = 9) from July to November 2011 to make habitat predictions for populations in northern Chile. We incorporated topographic and oceanographic predictors with sea lion locations and environmentally based pseudo-absences in a generalized linear model for estimating population-level distribution. For the individual approach, we used a generalized linear mixed-effects model with a negative exponential kernel variable to quantify distance-dependent movement from the colony. Spatial predictions from both approaches were combined in a bivariate color map to identify areas of agreement. We then used a GIS-based risk model to characterize bycatch risk in industrial and artisanal purse-seine fisheries based on fishing set data from scientific observers and artisanal fleet logs (2010-2015), the bivariate sea lion distribution map, and criteria ratings of interaction characteristics. Our results indicate population-level associations with productive, shallow, low slope waters, near to river-mouths, and with high eddy activity. Individual distribution was restricted to shallow slopes and cool waters. Variation between approaches may reflect intrinsic factors restricting use of otherwise favorable habitat; however, sample size was limited, and additional data are needed to establish the full range of individual-level distributions. Our bycatch risk outputs identified highest risk from industrial fisheries operating nearshore (within 5 NM) and risk was lower, overall, for the artisanal fleet. This research demonstrates the potential for integrating potential and realized distribution models within a spatial risk assessment and fills a gap in knowledge on this species' distribution, providing a basis for targeting bycatch mitigation outreach and interventions.
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Affiliation(s)
- Bethany H. Frantz
- School of the EnvironmentSan Francisco State UniversitySan FranciscoCaliforniaUSA
| | - Maritza Sepúlveda
- Centro de Investigación y Gestión de Recursos Naturales (CIGREN), Instituto de Biología, Facultad de CienciasUniversidad de ValparaísoValparaísoChile
- Núcleo Milenio de Salmónidos Invasores (INVASAL)Universidad de ConcepciónConcepciónChile
| | | | - Rodrigo Vega
- Instituto de Fomento Pesquero (IFOP)ValparaísoChile
| | - Daniel M. Palacios
- Marine Mammal InstituteOregon State UniversityNewportOregonUSA
- Department of Fisheries, Wildlife and Conservation SciencesOregon State UniversityNewportOregonUSA
| | - Luis Bedriñana‐Romano
- Instituto de Ciencias Marinas y Limnológicas, Facultad de CienciasUniversidad Austral de ChileCasilla, ValdiviaChile
- NGO Centro Ballena AzulValdiviaChile
- Centro de Investigación Oceanográfica COPAS CoastalUniversidad de ConcepciónConcepciónChile
| | - Luis A. Hückstädt
- Centre for Ecology and ConservationUniversity of ExeterCornwallUK
- Institute of Marine SciencesUniversity of California Santa CruzSanta CruzCaliforniaUSA
| | - Macarena Santos‐Carvallo
- Centro de Investigación y Gestión de Recursos Naturales (CIGREN), Instituto de Biología, Facultad de CienciasUniversidad de ValparaísoValparaísoChile
| | - Jerry D. Davis
- School of the EnvironmentSan Francisco State UniversitySan FranciscoCaliforniaUSA
| | - Ellen Hines
- School of the EnvironmentSan Francisco State UniversitySan FranciscoCaliforniaUSA
- Estuary & Ocean Science CenterSan Francisco State UniversityTiburonCaliforniaUSA
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Nater CR. Redefining 'state-of-the-art' for integrated population models with immigration. J Anim Ecol 2024; 93:520-524. [PMID: 38634153 DOI: 10.1111/1365-2656.14087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 03/22/2024] [Indexed: 04/19/2024]
Abstract
Research Highlight: Christian, M., Oosthuizen, W. C., Bester, M. N., & de Bruyn, P. N. (2024). Robustly estimating the demographic contribution of immigration: Simulation, sensitivity analysis and seals. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.14053. Immigration can have profound consequences for local population dynamics and demography, but collecting data to accurately quantifying it is challenging. The recent rise of integrated population models (IPMs) offers an alternative by making it possible to estimate immigration without the need for explicit data, and to quantify its contribution to population dynamics through transient Life Table Response Experiments (tLTREs). Simulation studies have, however, highlighted that this approach can be prone to bias and overestimation. In their new study, Christian et al. address one of the root causes of this issue by improving the estimation of time variation in vital rates and immigration using Gaussian processes in lieu of traditionally used temporal random effects. They demonstrate that IPM-tLTRE frameworks with Gaussian processes produce more accurate and less biased estimates of immigration and its contribution to population dynamics and illustrate the applicability of this approach using a long-term data set on elephant seals (Mirounga leonida). Results are validated with a simulation study and suggest that immigration of breeding females has been central for population recovery of elephant seals despite the species' high female site fidelity. Christian et al. thus present new insights into population regulation of long-lived marine mammals and highlight the potential for using Gaussian process priors in IPMs. They also illustrate a suite of 'best practices' for state-of-the-art IPM-tLTRE analyses and provide an inspirational example for the kind of ecological modelling workflow that can be invaluable not just as a starting point for fellow ecologists picking up or improving their own IPM-tLTRE analyses, but also for teaching and in contexts where model estimates are used for informing management and conservation decision-making.
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Affiliation(s)
- Chloé R Nater
- Norwegian Institute of Nature Research (NINA), Trondheim, Norway
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van Dam-Bates P, Papathomas M, Stevenson BC, Fewster RM, Turek D, Stewart FEC, Borchers DL. A flexible framework for spatial capture-recapture with unknown identities. Biometrics 2024; 80:ujad019. [PMID: 38372400 DOI: 10.1093/biomtc/ujad019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 10/30/2023] [Accepted: 11/22/2023] [Indexed: 02/20/2024]
Abstract
Camera traps or acoustic recorders are often used to sample wildlife populations. When animals can be individually identified, these data can be used with spatial capture-recapture (SCR) methods to assess populations. However, obtaining animal identities is often labor-intensive and not always possible for all detected animals. To address this problem, we formulate SCR, including acoustic SCR, as a marked Poisson process, comprising a single counting process for the detections of all animals and a mark distribution for what is observed (eg, animal identity, detector location). The counting process applies equally when it is animals appearing in front of camera traps and when vocalizations are captured by microphones, although the definition of a mark changes. When animals cannot be uniquely identified, the observed marks arise from a mixture of mark distributions defined by the animal activity centers and additional characteristics. Our method generalizes existing latent identity SCR models and provides an integrated framework that includes acoustic SCR. We apply our method to estimate density from a camera trap study of fisher (Pekania pennanti) and an acoustic survey of Cape Peninsula moss frog (Arthroleptella lightfooti). We also test it through simulation. We find latent identity SCR with additional marks such as sex or time of arrival to be a reliable method for estimating animal density.
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Affiliation(s)
- Paul van Dam-Bates
- School of Mathematics and Statistics, University of St Andrews, St Andrews, Fife, KY16 9LZ, United Kingdom
| | - Michail Papathomas
- School of Mathematics and Statistics, University of St Andrews, St Andrews, Fife, KY16 9LZ, United Kingdom
| | - Ben C Stevenson
- Department of Statistics, University of Auckland, Auckland, 1010, New Zealand
| | - Rachel M Fewster
- Department of Statistics, University of Auckland, Auckland, 1010, New Zealand
| | - Daniel Turek
- Department of Mathematics and Statistics, Williams College, Williamstown, 01267, United States
| | - Frances E C Stewart
- Department of Biology, Wilfrid Laurier University, Waterloo, N2L 3C5, Canada
| | - David L Borchers
- School of Mathematics and Statistics, University of St Andrews, St Andrews, Fife, KY16 9LZ, United Kingdom
- Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences, University of Cape Town, Private Bag 7700, Rondebosch, South Africa
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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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Hewitt DE, Johnson DD, Suthers IM, Taylor MD. Crabs ride the tide: incoming tides promote foraging of Giant Mud Crab (Scylla serrata). MOVEMENT ECOLOGY 2023; 11:21. [PMID: 37069648 PMCID: PMC10108527 DOI: 10.1186/s40462-023-00384-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 04/06/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND Effective fisheries management of mobile species relies on robust knowledge of animal behaviour and habitat-use. Indices of behaviour can be useful for interpreting catch-per-unit-effort data which acts as a proxy for relative abundance. Information about habitat-use can inform stocking release strategies or the design of marine protected areas. The Giant Mud Crab (Scylla serrata; Family: Portunidae) is a swimming estuarine crab that supports significant fisheries harvest throughout the Indo-West Pacific, but little is known about the fine-scale movement and behaviour of this species. METHODS We tagged 18 adult Giant Mud Crab with accelerometer-equipped acoustic tags to track their fine-scale movement using a hyperbolic positioning system, alongside high temporal resolution environmental data (e.g., water temperature), in a temperate south-east Australian estuary. A hidden Markov model was used to classify movement (i.e., step length, turning angle) and acceleration data into discrete behaviours, while also considering the possibility of individual variation in behavioural dynamics. We then investigated the influence of environmental covariates on these behaviours based on previously published observations. RESULTS We fitted a model with two well-distinguished behavioural states describing periods of inactivity and foraging, and found no evidence of individual variation in behavioural dynamics. Inactive periods were most common (79% of time), and foraging was most likely during low, incoming tides; while inactivity was more likely as the high tide receded. Model selection removed time (hour) of day and water temperature (°C) as covariates, suggesting that they do not influence Giant Mud Crab behavioural dynamics at the temporal scale investigated. CONCLUSIONS Our study is the first to quantitatively link fine-scale movement and behaviour of Giant Mud Crab to environmental variation. Our results suggest Giant Mud Crab are a predominantly sessile species, and support their status as an opportunistic scavenger. We demonstrate a relationship between the tidal cycle and foraging that is likely to minimize predation risk while maximizing energetic efficiency. These results may explain why tidal covariates influence catch rates in swimming crabs, and provide a foundation for standardisation and interpretation of catch-per-unit-effort data-a commonly used metric in fisheries science.
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Affiliation(s)
- Daniel E Hewitt
- Fisheries and Marine Environmental Research Lab, Centre for Marine Science and Innovation, School of Biological, Earth and Environmental Science, University of New South Wales, NSW, Sydney, 2052, Australia.
- New South Wales Department of Primary Industries, Port Stephens Fisheries Institute, NSW, Locked Bag 1, Nelson Bay, 2315, Australia.
| | - Daniel D Johnson
- New South Wales Department of Primary Industries, Port Stephens Fisheries Institute, NSW, Locked Bag 1, Nelson Bay, 2315, Australia
| | - Iain M Suthers
- Fisheries and Marine Environmental Research Lab, Centre for Marine Science and Innovation, School of Biological, Earth and Environmental Science, University of New South Wales, NSW, Sydney, 2052, Australia
- Sydney Institute of Marine Science, Mosman, NSW, Australia
| | - Matthew D Taylor
- Fisheries and Marine Environmental Research Lab, Centre for Marine Science and Innovation, School of Biological, Earth and Environmental Science, University of New South Wales, NSW, Sydney, 2052, Australia
- New South Wales Department of Primary Industries, Port Stephens Fisheries Institute, NSW, Locked Bag 1, Nelson Bay, 2315, Australia
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6
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Gilbert NA, McGinn KA, Nunes LA, Shipley AA, Bernath-Plaisted J, Clare JDJ, Murphy PW, Keyser SR, Thompson KL, Maresh Nelson SB, Cohen JM, Widick IV, Bartel SL, Orrock JL, Zuckerberg B. Daily activity timing in the Anthropocene. Trends Ecol Evol 2023; 38:324-336. [PMID: 36402653 DOI: 10.1016/j.tree.2022.10.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 10/12/2022] [Accepted: 10/27/2022] [Indexed: 11/17/2022]
Abstract
Animals are facing novel 'timescapes' in which the stimuli entraining their daily activity patterns no longer match historical conditions due to anthropogenic disturbance. However, the ecological effects (e.g., altered physiology, species interactions) of novel activity timing are virtually unknown. We reviewed 1328 studies and found relatively few focusing on anthropogenic effects on activity timing. We suggest three hypotheses to stimulate future research: (i) activity-timing mismatches determine ecological effects, (ii) duration and timing of timescape modification influence effects, and (iii) consequences of altered activity timing vary biogeographically due to broad-scale variation in factors compressing timescapes. The continued growth of sampling technologies promises to facilitate the study of the consequences of altered activity timing, with emerging applications for biodiversity conservation.
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Affiliation(s)
- Neil A Gilbert
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Kate A McGinn
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Laura A Nunes
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Amy A Shipley
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA; School of Natural Resources, University of Missouri, Columbia, MO 65211, USA
| | - Jacy Bernath-Plaisted
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - John D J Clare
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA; Museum of Vertebrate Zoology, University of California, Berkeley, CA 94720, USA
| | - Penelope W Murphy
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Spencer R Keyser
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Kimberly L Thompson
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA; German Centre for Integrative Biodiversity Research (iDiv), 04103 Halle-Jena-Leipzig, Germany
| | - Scott B Maresh Nelson
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Jeremy M Cohen
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
| | - Ivy V Widick
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Savannah L Bartel
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - John L Orrock
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Benjamin Zuckerberg
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA.
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7
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Affiliation(s)
- Sarah J Converse
- U.S. Geological Survey, Washington Cooperative Fish and Wildlife Research Unit, School of Environmental and Forest Sciences & School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, USA
| | - Brett T McClintock
- Marine Mammal Laboratory, NOAA-NMFS Alaska Fisheries Science Center, Seattle, Washington, USA
| | - Paul B Conn
- Marine Mammal Laboratory, NOAA-NMFS Alaska Fisheries Science Center, Seattle, Washington, USA
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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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9
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Hostetter NJ, Regehr EV, Wilson RR, Royle JA, Converse SJ. Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture-recapture movement model. Ecology 2022; 103:e3772. [PMID: 35633152 PMCID: PMC9787655 DOI: 10.1002/ecy.3772] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 12/21/2021] [Accepted: 01/21/2022] [Indexed: 12/30/2022]
Abstract
Animal movement is a fundamental ecological process affecting the survival and reproduction of individuals, the structure of populations, and the dynamics of communities. Methods to quantify animal movement and spatiotemporal abundances, however, are generally separate and therefore omit linkages between individual-level and population-level processes. We describe an integrated spatial capture-recapture (SCR) movement model to jointly estimate (1) the number and distribution of individuals in a defined spatial region and (2) movement of those individuals through time. We applied our model to a study of polar bears (Ursus maritimus) in a 28,125 km2 survey area of the eastern Chukchi Sea, USA in 2015 that incorporated capture-recapture and telemetry data. In simulation studies, the model provided unbiased estimates of movement, abundance, and detection parameters using a bivariate normal random walk and correlated random walk movement process. Our case study provided detailed evidence of directional movement persistence for both male and female bears, where individuals regularly traversed areas larger than the survey area during the 36-day study period. Scaling from individual- to population-level inferences, we found that densities varied from <0.75 bears/625 km2 grid cell/day in nearshore cells to 1.6-2.5 bears/grid cell/day for cells surrounded by sea ice. Daily abundance estimates ranged from 53 to 69 bears, with no trend across days. The cumulative number of unique bears that used the survey area increased through time due to movements into and out of the area, resulting in an estimated 171 individuals using the survey area during the study (95% credible interval 124-250). Abundance estimates were similar to a previous multiyear integrated population model using capture-recapture and telemetry data (2008-2016; Regehr et al., Scientific Reports 8:16780, 2018). Overall, the SCR-movement model successfully quantified both individual- and population-level space use, including the effects of landscape characteristics on movement, abundance, and detection, while linking the movement and abundance processes to directly estimate density within a prescribed spatial region and temporal period. Integrated SCR-movement models provide a generalizable approach to incorporate greater movement realism into population dynamics and link movement to emergent properties including spatiotemporal densities and abundances.
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Affiliation(s)
- Nathan J. Hostetter
- Washington Cooperative Fish and Wildlife Research Unit, School of Aquatic and Fishery SciencesUniversity of WashingtonSeattleWashingtonUSA,Present address:
United States Geological Survey, North Carolina Cooperative Fish and Wildlife Research Unit, Department of Applied EcologyNorth Carolina State UniversityRaleighNorth CarolinaUSA
| | - Eric V. Regehr
- Applied Physics LaboratoryPolar Science Center, University of WashingtonSeattleWashingtonUSA
| | - Ryan R. Wilson
- Marine Mammals ManagementUnited States Fish and Wildlife ServiceAnchorageAlaskaUSA
| | - J. Andrew Royle
- United States Geological SurveyEastern Ecological Science CenterLaurelMarylandUSA
| | - Sarah J. Converse
- United States Geological Survey, Washington Cooperative Fish and Wildlife Research Unit, School of Environmental and Forest Sciences and School of Aquatic and Fishery SciencesUniversity of WashingtonSeattleWashingtonUSA
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10
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Chandler RB, Crawford DA, Garrison EP, Miller KV, Cherry MJ. Modeling abundance, distribution, movement and space use with camera and telemetry data. Ecology 2022; 103:e3583. [PMID: 34767254 DOI: 10.1002/ecy.3583] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 08/09/2021] [Accepted: 09/03/2021] [Indexed: 12/13/2022]
Abstract
Studies of animal abundance and distribution are often conducted independently of research on movement, despite the important links between processes. Movement can cause rapid changes in spatial variation in density, and movement influences detection probability and therefore estimates of abundance from inferential methods such as spatial capture-recapture (SCR). Technological developments including camera traps and GPS telemetry have opened new opportunities for studying animal demography and movement, yet statistical models for these two data types have largely developed along parallel tracks. We present a hierarchical model in which both datasets are conditioned on a movement process for a clearly defined population. We fitted the model to data from 60 camera traps and 23,572 GPS telemetry locations collected on 17 male white-tailed deer in the Big Cypress National Preserve, Florida, USA during July 2015. Telemetry data were collected on a 3-4 h acquisition schedule, and we modeled the movement paths of all individuals in the region with a Ornstein-Uhlenbeck process that included individual-specific random effects. Two of the 17 deer with GPS collars were detected on cameras. An additional 20 male deer without collars were detected on cameras and individually identified based on their unique antler characteristics. Abundance was 126 (95% CI: 88-177) in the 228 km2 region, only slightly higher than estimated using a standard SCR model: 119 (84-168). The standard SCR model, however, was unable to describe individual heterogeneity in movement rates and space use as revealed by the joint model. Joint modeling allowed the telemetry data to inform the movement model and the SCR encounter model, while leveraging information in the camera data to inform abundance, distribution and movement. Unlike most existing methods for population-level inference on movement, the joint SCR-movement model can yield unbiased inferences even if non-uniform sampling is used to deploy transmitters. Potential extensions of the model include the addition of resource selection parameters, and relaxation of the closure assumption when interest lies in survival and recruitment. These developments would contribute to the emerging holistic framework for the study of animal ecology, one that uses modern technology and spatio-temporal statistics to learn about interactions between behavior and demography.
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Affiliation(s)
- Richard B Chandler
- Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia, 30602, USA
| | - Daniel A Crawford
- Caesar Kleberg Wildlife Research Institute at Texas A&M University-Kingsville, Kingsville, Texas, 78363, USA
| | - Elina P Garrison
- Florida Fish and Wildlife Conservation Commission, Gainesville, Florida, 32601, USA
| | - Karl V Miller
- Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia, 30602, USA
| | - Michael J Cherry
- Caesar Kleberg Wildlife Research Institute at Texas A&M University-Kingsville, Kingsville, Texas, 78363, USA
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11
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Theng M, Milleret C, Bracis C, Cassey P, Delean S. Confronting spatial capture-recapture models with realistic animal movement simulations. Ecology 2022; 103:e3676. [PMID: 35253209 DOI: 10.1002/ecy.3676] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 08/26/2021] [Accepted: 09/14/2021] [Indexed: 12/13/2022]
Abstract
Spatial capture-recapture (SCR) models have emerged as a robust method to estimate the population density of mobile animals. However, model evaluation has generally been based on data simulated from simplified representations of animal space use. Here, we generated data from animal movement simulated from a mechanistic individual-based model, in which movement emerges from the individual's response to a changing environment (i.e., from the bottom-up), driven by key ecological processes (e.g., resource memory and territoriality). We drew individual detection data from simulated movement trajectories and fitted detection data sets to a basic, resource selection and transience SCR model, as well as their variants accounting for resource-driven heterogeneity in density and detectability. Across all SCR models, abundance estimates were robust to multiple, but low-degree violations of the specified movement processes (e.g., resource selection). SCR models also successfully captured the positive effect of resource quality on density. However, covariate models failed to capture the finer scale effect of resource quality on detectability and space use, which may be a consequence of the low temporal resolution of SCR data sets and/or model misspecification. We show that home-range size is challenging to infer from the scale parameter alone, compounded by reliance on conventional measures of "true" home-range size that are highly sensitive to sampling regime. Additionally, we found the transience model challenging to fit, probably due to data sparsity and violation of the assumption of normally distributed inter-occasion movement of activity centers, suggesting that further development of the model is required for general applicability. Our results showed that further integration of complex movement into SCR models may not be necessary for population estimates of abundance when the level of individual heterogeneity induced by the underlying movement process is low, but appears warranted in terms of accurately revealing finer scale patterns of ecological and movement processes. Further investigation into whether this holds true in populations with other types of realistic movement characteristics is merited. Our study provides a framework to generate realistic SCR data sets to develop and evaluate more complex movement processes in SCR models.
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Affiliation(s)
- Meryl Theng
- School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia
| | - Cyril Milleret
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
| | - Chloe Bracis
- TIMC / MAGE, Université Grenoble Alpes, Grenoble, France
| | - Phillip Cassey
- School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia
| | - Steven Delean
- School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia
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12
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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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13
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Efford MG, Schofield MR. A review of movement models in open population capture–recapture. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Murray G. Efford
- Department of Mathematics and Statistics University of Otago Dunedin New Zealand
| | - Matthew R. Schofield
- Department of Mathematics and Statistics University of Otago Dunedin New Zealand
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14
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Zhao Q, Fuller AK, Royle JA. Spatial dynamic N‐mixture models with interspecific interactions. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Qing Zhao
- Bird Conservancy of the Rockies Fort Collins CO USA
| | - Angela K. Fuller
- U.S. Geological Survey, New York Cooperative Fish and Wildlife Research Unit, Department of Natural Resources and the Environment, Cornell University Ithaca NY USA
| | - J. Andrew Royle
- U.S. Geological Survey, Eastern Ecological Science Center Laurel MD USA
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15
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Comparison of methods for estimating density and population trends for low-density Asian bears. Glob Ecol Conserv 2022. [DOI: 10.1016/j.gecco.2022.e02058] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
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16
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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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