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Robinson S, Falinski K, Johnson D, VanWormer E, Shapiro K, Amlin A, Barbieri M. Evaluating the Risk Landscape of Hawaiian Monk Seal Exposure to Toxoplasma gondii. ECOHEALTH 2024:10.1007/s10393-024-01678-7. [PMID: 38850367 DOI: 10.1007/s10393-024-01678-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 03/02/2024] [Accepted: 03/06/2024] [Indexed: 06/10/2024]
Abstract
Toxoplasmosis is a disease of primary concern for Hawaiian monk seals (Neomonachus schauinslandi), due to its apparently acute lethality and especially heavy impacts on breeding female seals. The disease-causing parasite, Toxoplasma gondii, depends on cats to complete its life cycle; thus, in order to understand how this pathogen infects marine mammals, it is essential to understand aspects of the terrestrial ecosystem and land-to-sea transport. In this study, we constructed a three-tiered model to assess risk of Hawaiian monk seal exposure to T. gondii oocysts: (1) oocyst contamination as a function of cat population characteristics; (2) land-to-sea transport of oocysts as a function of island hydrology, and (3) seal exposure as a function of habitat and space use. We were able to generate risk maps highlighting watersheds contributing the most to oocyst contamination of Hawaiian monk seal habitat. Further, the model showed that free-roaming cats most associated with humans (pets or strays often supplementally fed by people) were able to achieve high densities leading to high levels of oocyst contamination and elevated risk of T. gondii exposure.
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Affiliation(s)
| | - Kim Falinski
- UH, Water Resources Research Center, Honolulu, USA
| | | | | | | | - Angela Amlin
- NOAA, Pacific Islands Regional Office, Honolulu, USA
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2
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Valle D, Attias N, Cullen JA, Hooten MB, Giroux A, Oliveira-Santos LGR, Desbiez ALJ, Fletcher RJ. Bridging the gap between movement data and connectivity analysis using the Time-Explicit Habitat Selection (TEHS) model. MOVEMENT ECOLOGY 2024; 12:19. [PMID: 38429836 PMCID: PMC10908110 DOI: 10.1186/s40462-024-00461-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 02/19/2024] [Indexed: 03/03/2024]
Abstract
BACKGROUND Understanding how to connect habitat remnants to facilitate the movement of species is a critical task in an increasingly fragmented world impacted by human activities. The identification of dispersal routes and corridors through connectivity analysis requires measures of landscape resistance but there has been no consensus on how to calculate resistance from habitat characteristics, potentially leading to very different connectivity outcomes. METHODS We propose a new model, called the Time-Explicit Habitat Selection (TEHS) model, that can be directly used for connectivity analysis. The TEHS model decomposes the movement process in a principled approach into a time and a selection component, providing complementary information regarding space use by separately assessing the drivers of time to traverse the landscape and the drivers of habitat selection. These models are illustrated using GPS-tracking data from giant anteaters (Myrmecophaga tridactyla) in the Pantanal wetlands of Brazil. RESULTS The time model revealed that the fastest movements tended to occur between 8 p.m. and 5 a.m., suggesting a crepuscular/nocturnal behavior. Giant anteaters moved faster over wetlands while moving much slower over forests and savannas, in comparison to grasslands. We also found that wetlands were consistently avoided whereas forest and savannas tended to be selected. Importantly, this model revealed that selection for forest increased with temperature, suggesting that forests may act as important thermal shelters when temperatures are high. Finally, using the spatial absorbing Markov chain framework, we show that the TEHS model results can be used to simulate movement and connectivity within a fragmented landscape, revealing that giant anteaters will often not use the shortest-distance path to the destination patch due to avoidance of certain habitats. CONCLUSIONS The proposed approach can be used to characterize how landscape features are perceived by individuals through the decomposition of movement patterns into a time and a habitat selection component. Additionally, this framework can help bridge the gap between movement-based models and connectivity analysis, enabling the generation of time-explicit connectivity results.
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Affiliation(s)
- Denis Valle
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, USA.
| | - Nina Attias
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, USA
- Instituto de Conservação de Animais Silvestres, Campo Grande, Mato Grosso do Sul, Brazil
| | - Joshua A Cullen
- Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, USA
| | - Mevin B Hooten
- Department of Statistics and Data Sciences, University of Texas at Austin, Austin, TX, USA
| | - Aline Giroux
- Ecology Department, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil
| | | | - Arnaud L J Desbiez
- Instituto de Conservação de Animais Silvestres, Campo Grande, Mato Grosso do Sul, Brazil
- Royal Zoological Society of Scotland, Murrayfield, Edinburgh, UK
- Instituto de Pesquisas Ecologicas, Nazare Paulista, Sao Paulo, Brazil
| | - Robert J Fletcher
- Department of Wildlife Ecology and Conservation, University of Florida, P.O. Box 110410, Gainesville, FL, USA
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3
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You J, Ellis JL, Adams S, Sahar M, Jacobs M, Tulpan D. Comparison of imputation methods for missing production data of dairy cattle. Animal 2023; 17 Suppl 5:100921. [PMID: 37659911 DOI: 10.1016/j.animal.2023.100921] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 07/14/2023] [Accepted: 07/18/2023] [Indexed: 09/04/2023] Open
Abstract
Nowadays, vast amounts of data representing feed intake, growth, and environmental impact of individual animals are being recorded in on-farm settings. Despite their apparent use, data collected in real-world applications often have missing values in one or several variables, due to reasons including human error, machine error, or sampling frequency misalignment across multiple variables. Since incomplete datasets are less valuable for downstream data analysis, it is important to address the missing value problem properly. One option may be to reduce the dataset to a subset that contains only complete data, but considerable data may be lost via this process. The current study aimed to compare imputation methods for the estimation of missing values in a raw dataset of dairy cattle including 454 553 records collected from 629 cows between 2009 and 2020. The dataset was subjected to a cleaning process that reduced its size to 437 075 observations corresponding to 512 cows. Missing values were present in four variables: concentrate DM intake (CDMI, missing percentage = 2.30%), forage DM intake (FDMI, 8.05%), milk yield (MY, 15.12%), and BW (64.33%). After removing all missing values, the resulting dataset (n = 129 353) was randomly sampled five times to create five independent subsets that exhibit the same missing data percentages as the cleaned dataset. Four univariate and nine multivariate imputation methods (eight machine learning methods and the MissForest method) were applied and evaluated on the five repeats, and average imputation performance was reported for each repeat. The results showed that Random Forest was overall the best imputation method for this type of data and had a lower mean squared prediction error and higher concordance correlation coefficient than the other imputation methods for all imputed variables. Random Forest performed particularly well for imputing CDMI, MY, and BW, compared to imputing FDMI.
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Affiliation(s)
- J You
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - J L Ellis
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada.
| | - S Adams
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - M Sahar
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - M Jacobs
- Trouw Nutrition Innovation Department, Amersfoort, Netherlands
| | - D Tulpan
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
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4
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Lu X, Hooten MB, Raiho AM, Swanson DK, Roland CA, Stehn SE. Latent trajectory models for spatio-temporal dynamics in Alaskan ecosystems. Biometrics 2023; 79:3664-3675. [PMID: 36715694 DOI: 10.1111/biom.13832] [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: 03/25/2022] [Accepted: 01/13/2023] [Indexed: 01/31/2023]
Abstract
The Alaskan landscape has undergone substantial changes in recent decades, most notably the expansion of shrubs and trees across the Arctic. We developed a Bayesian hierarchical model to quantify the impact of climate change on the structural transformation of ecosystems using remotely sensed imagery. We used latent trajectory processes to model dynamic state probabilities that evolve annually, from which we derived transition probabilities between ecotypes. Our latent trajectory model accommodates temporal irregularity in survey intervals and uses spatio-temporally heterogeneous climate drivers to infer rates of land cover transitions. We characterized multi-scale spatial correlation induced by plot and subplot arrangements in our study system. We also developed a Pólya-Gamma sampling strategy to improve computation. Our model facilitates inference on the response of ecosystems to shifts in the climate and can be used to predict future land cover transitions under various climate scenarios.
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Affiliation(s)
- Xinyi Lu
- Department of Statistics, Colorado State University, Fort Collins, Colorado, USA
| | - Mevin B Hooten
- Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, Texas, USA
| | - Ann M Raiho
- The National Aeronautics and Space Administration (NASA) Goddard Space Flight Center, Greenbelt, Maryland, USA
- Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
| | | | - Carl A Roland
- Denali National Park and Preserve, Denali Park, Alaska, USA
- Central Alaska Network Inventory and Monitoring Program, Fairbanks, Alaska, USA
| | - Sarah E Stehn
- Denali National Park and Preserve, Denali Park, Alaska, USA
- Central Alaska Network Inventory and Monitoring Program, Fairbanks, Alaska, USA
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5
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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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6
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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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7
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Wilson RR, Martin MS, Regehr EV, Rode KD. Intrapopulation differences in polar bear movement and step selection patterns. MOVEMENT ECOLOGY 2022; 10:25. [PMID: 35606849 PMCID: PMC9128121 DOI: 10.1186/s40462-022-00326-5] [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: 12/06/2021] [Accepted: 05/14/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND The spatial ecology of individuals often varies within a population or species. Identifying how individuals in different classes interact with their environment can lead to a better understanding of population responses to human activities and environmental change and improve population estimates. Most inferences about polar bear (Ursus maritimus) spatial ecology are based on data from adult females due to morphological constraints on applying satellite radio collars to other classes of bears. Recent studies, however, have provided limited movement data for adult males and sub-adults of both sexes using ear-mounted and glue-on tags. We evaluated class-specific movements and step selection patterns for polar bears in the Chukchi Sea subpopulation during spring. METHODS We developed hierarchical Bayesian models to evaluate polar bear movement (i.e., step length and directional persistence) and step selection at the scale of 4-day step lengths. We assessed differences in movement and step selection parameters among the three classes of polar bears (i.e., adult males, sub-adults, and adult females without cubs-of-the-year). RESULTS Adult males had larger step lengths and less directed movements than adult females. Sub-adult movement parameters did not differ from the other classes but point estimates were most similar to adult females. We did not detect differences among polar bear classes in step selection parameters and parameter estimates were consistent with previous studies. CONCLUSIONS Our findings support the use of estimated step selection patterns from adult females as a proxy for other classes of polar bears during spring. Conversely, movement analyses indicated that using data from adult females as a proxy for the movements of adult males is likely inappropriate. We recommend that researchers consider whether it is valid to extend inference derived from adult female movements to other classes, based on the questions being asked and the spatial and temporal scope of the data. Because our data were specific to spring, these findings highlight the need to evaluate differences in movement and step selection during other periods of the year, for which data from ear-mounted and glue-on tags are currently lacking.
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Affiliation(s)
- Ryan R Wilson
- U.S. Fish and Wildlife Service, Marine Mammals Management, Anchorage, AK, USA.
| | - Michelle St Martin
- U.S. Fish and Wildlife Service, Marine Mammals Management, Anchorage, AK, USA
- U.S. Fish and Wildlife Service, Portland, OR, 97266, USA
| | - Eric V Regehr
- Polar Science Center, University of Washington, Seattle, WA, USA
| | - Karyn D Rode
- U.S. Geological Survey, Alaska Science Center, Anchorage, AK, USA
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8
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Jones‐Todd CM, Pirotta E, Durban JW, Claridge DE, Baird RW, Falcone EA, Schorr GS, Watwood S, Thomas L. Discrete-space continuous-time models of marine mammal exposure to Navy sonar. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e02475. [PMID: 34653299 PMCID: PMC9786920 DOI: 10.1002/eap.2475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 02/01/2021] [Accepted: 05/19/2021] [Indexed: 06/13/2023]
Abstract
Assessing the patterns of wildlife attendance to specific areas is relevant across many fundamental and applied ecological studies, particularly when animals are at risk of being exposed to stressors within or outside the boundaries of those areas. Marine mammals are increasingly being exposed to human activities that may cause behavioral and physiological changes, including military exercises using active sonars. Assessment of the population-level consequences of anthropogenic disturbance requires robust and efficient tools to quantify the levels of aggregate exposure for individuals in a population over biologically relevant time frames. We propose a discrete-space, continuous-time approach to estimate individual transition rates across the boundaries of an area of interest, informed by telemetry data collected with uncertainty. The approach allows inferring the effect of stressors on transition rates, the progressive return to baseline movement patterns, and any difference among individuals. We apply the modeling framework to telemetry data from Blainville's beaked whale (Mesoplodon densirostris) tagged in the Bahamas at the Atlantic Undersea Test and Evaluation Center (AUTEC), an area used by the U.S. Navy for fleet readiness training. We show that transition rates changed as a result of exposure to sonar exercises in the area, reflecting an avoidance response. Our approach supports the assessment of the aggregate exposure of individuals to sonar and the resulting population-level consequences. The approach has potential applications across many applied and fundamental problems where telemetry data are used to characterize animal occurrence within specific areas.
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Affiliation(s)
| | - Enrico Pirotta
- Department of Mathematics and StatisticsWashington State University14204 NE Salmon Creek AvenueVancouverWashington98686USA
- School of Biological, Earth and Environmental SciencesUniversity College CorkNorth MallDistillery FieldsCorkT23 N73KIreland
- Centre for Research into Ecological and Environmental ModellingThe ObservatoryUniversity of St AndrewsSt AndrewsKY16 9LZUK
| | - John W. Durban
- Southall Environmental Associates Inc.9099 Soquel Drive, Suite 8AptosCalifornia95003USA
| | - Diane E. Claridge
- Bahamas Marine Mammal Research OrganizationMarsh HarbourAbacoBahamas
| | - Robin W. Baird
- Cascadia Research Collective218 ½ W. 4th AvenueOlympiaWashington98501USA
| | - Erin A. Falcone
- Marine Ecology and Telemetry Research2420 Nellita Road NWSeabeckWashington98380USA
| | - Gregory S. Schorr
- Marine Ecology and Telemetry Research2420 Nellita Road NWSeabeckWashington98380USA
| | - Stephanie Watwood
- Naval Undersea Warfare Center DivisionCode 70TNewportRhode Island02841USA
| | - Len Thomas
- Centre for Research into Ecological and Environmental ModellingThe ObservatoryUniversity of St AndrewsSt AndrewsKY16 9LZUK
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9
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Hu C, Elbroch M, Meyer T, Pozdnyakov V, Yan J. Moving‐resting process with measurement error in animal movement modeling. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Chaoran Hu
- Department of Statistics University of Connecticut Storrs CT USA
| | | | - Thomas Meyer
- Department of Natural Resources & the Environment University of Connecticut Storrs CT USA
| | | | - Jun Yan
- Department of Statistics University of Connecticut Storrs CT USA
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10
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Leach CB, Williams PJ, Eisaguirre JM, Womble JN, Bower MR, Hooten MB. Recursive Bayesian computation facilitates adaptive optimal design in ecological studies. Ecology 2021; 103:e03573. [PMID: 34710235 DOI: 10.1002/ecy.3573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 07/07/2021] [Accepted: 08/03/2021] [Indexed: 11/11/2022]
Abstract
Optimal design procedures provide a framework to leverage the learning generated by ecological models to flexibly and efficiently deploy future monitoring efforts. At the same time, Bayesian hierarchical models have become widespread in ecology and offer a rich set of tools for ecological learning and inference. However, coupling these methods with an optimal design framework can become computationally intractable. Recursive Bayesian computation offers a way to substantially reduce this computational burden, making optimal design accessible for modern Bayesian ecological models. We demonstrate the application of so-called prior-proposal recursive Bayes to optimal design using a simulated data binary regression and the real-world example of monitoring and modeling sea otters in Glacier Bay, Alaska. These examples highlight the computational gains offered by recursive Bayesian methods and the tighter fusion of monitoring and science that those computational gains enable.
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Affiliation(s)
- Clinton B Leach
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, 80523, USA
| | - Perry J Williams
- Department of Natural Resources and Environmental Science, University of Nevada, Reno, Nevada, 89557, USA
| | - Joseph M Eisaguirre
- Department of Natural Resources and Environmental Science, University of Nevada, Reno, Nevada, 89557, USA.,U.S. Fish and Wildlife Service, Marine Mammals Management, Anchorage, Alaska, 99503, USA
| | - Jamie N Womble
- Southeast Alaska Inventory and Monitoring Network, National Park Service, Juneau, Alaska, 99801, USA.,Glacier Bay Field Station, National Park Service, Juneau, Alaska, 99801, USA
| | - Michael R Bower
- Southeast Alaska Inventory and Monitoring Network, National Park Service, Juneau, Alaska, 99801, USA
| | - Mevin B Hooten
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, 80523, USA.,U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, Colorado, 80523, USA.,Department of Statistics, Colorado State University, Fort Collins, Colorado, 80523, USA
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11
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Johnson D, Pelland N, Sterling J. A continuous-time semi-Markov model for animal movement in a dynamic environment. Ann Appl Stat 2021. [DOI: 10.1214/20-aoas1408] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Devin Johnson
- Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
| | - Noel Pelland
- Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
| | - Jeremy Sterling
- Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
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12
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Continuous-Time Discrete-State Modeling for Deep Whale Dives. JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2021. [DOI: 10.1007/s13253-020-00422-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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13
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Schafer TLJ, Wikle CK, VonBank JA, Ballard BM, Weegman MD. A Bayesian Markov Model with Pólya-Gamma Sampling for Estimating Individual Behavior Transition Probabilities from Accelerometer Classifications. JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2020. [DOI: 10.1007/s13253-020-00399-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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14
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Brost BM, Hooten MB, Small RJ. Model‐based clustering reveals patterns in central place use of a marine top predator. Ecosphere 2020. [DOI: 10.1002/ecs2.3123] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Affiliation(s)
- Brian M. Brost
- Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado 80523 USA
| | - Mevin B. Hooten
- Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado 80523 USA
- U.S. Geological Survey Colorado Cooperative Fish and Wildlife Research Unit Colorado State University Fort Collins Colorado 80523 USA
- Department of Statistics Colorado State University Fort Collins Colorado 80523 USA
| | - Robert J. Small
- Division of Wildlife Conservation Alaska Department of Fish and Game Juneau Alaska 99801 USA
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15
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Michelot T, Gloaguen P, Blackwell PG, Étienne M. The Langevin diffusion as a continuous‐time model of animal movement and habitat selection. Methods Ecol Evol 2019. [DOI: 10.1111/2041-210x.13275] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Théo Michelot
- School of Mathematics and Statistics University of Sheffield Sheffield UK
- School of Mathematics and Statistics, Centre for Research into Ecological and Environmental Modelling University of St Andrews St Andrews UK
| | | | - Paul G. Blackwell
- School of Mathematics and Statistics University of Sheffield Sheffield UK
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16
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Fleming CH, Noonan MJ, Medici EP, Calabrese JM. Overcoming the challenge of small effective sample sizes in home‐range estimation. Methods Ecol Evol 2019. [DOI: 10.1111/2041-210x.13270] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Christen H. Fleming
- Smithsonian Conservation Biology Institute Front Royal VA USA
- Department of Biology University of Maryland College Park MD USA
| | - Michael J. Noonan
- Smithsonian Conservation Biology Institute Front Royal VA USA
- Department of Biology University of Maryland College Park MD USA
| | - Emilia Patricia Medici
- Lowland Tapir Conservation Initiative, Instituto de Pesquisas Ecologicas Campo Grande Mato Grosso do Sul Brazil
| | - Justin M. Calabrese
- Smithsonian Conservation Biology Institute Front Royal VA USA
- Department of Biology University of Maryland College Park MD USA
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17
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Scharf HR, Hooten MB, Wilson RR, Durner GM, Atwood TC. Accounting for phenology in the analysis of animal movement. Biometrics 2019; 75:810-820. [PMID: 30859552 DOI: 10.1111/biom.13052] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2018] [Accepted: 02/26/2019] [Indexed: 11/29/2022]
Abstract
The analysis of animal tracking data provides important scientific understanding and discovery in ecology. Observations of animal trajectories using telemetry devices provide researchers with information about the way animals interact with their environment and each other. For many species, specific geographical features in the landscape can have a strong effect on behavior. Such features may correspond to a single point (eg, dens or kill sites), or to higher dimensional subspaces (eg, rivers or lakes). Features may be relatively static in time (eg, coastlines or home-range centers), or may be dynamic (eg, sea ice extent or areas of high-quality forage for herbivores). We introduce a novel model for animal movement that incorporates active selection for dynamic features in a landscape. Our approach is motivated by the study of polar bear (Ursus maritimus) movement. During the sea ice melt season, polar bears spend much of their time on sea ice above shallow, biologically productive water where they hunt seals. The changing distribution and characteristics of sea ice throughout the year mean that the location of valuable habitat is constantly shifting. We develop a model for the movement of polar bears that accounts for the effect of this important landscape feature. We introduce a two-stage procedure for approximate Bayesian inference that allows us to analyze over 300 000 observed locations of 186 polar bears from 2012 to 2016. We use our model to estimate a spatial boundary of interest to wildlife managers that separates two subpopulations of polar bears from the Beaufort and Chukchi seas.
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Affiliation(s)
- Henry R Scharf
- Department of Statistics, Colorado State University, Fort Collins, Colorado
| | - Mevin B Hooten
- Department of Statistics, Colorado State University, Fort Collins, Colorado.,Department of Fish, Wildlife, and Conservation Biology, Colorado Cooperative Fish and Wildlife Research Unit, U.S. Geological Survey, Fort Collins, Colorado
| | - Ryan R Wilson
- Marine Mammals Management, U.S. Fish and Wildlife Service, Anchorage, Alaska
| | - George M Durner
- Alaska Science Center, U.S. Geological Survey, Anchorage, Alaska
| | - Todd C Atwood
- Alaska Science Center, U.S. Geological Survey, Anchorage, Alaska
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18
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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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19
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Hooten MB, Scharf HR, Hefley TJ, Pearse AT, Weegman MD. Animal movement models for migratory individuals and groups. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.13016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Mevin B. Hooten
- U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research UnitDepartment of Fish, Wildlife, and ConservationDepartment of Fish, Wildlife, and ConservationColorado State University Fort Collins Colorado
- Department of StatisticsColorado State University Fort Collins Colorado
| | - Henry R. Scharf
- Department of StatisticsColorado State University Fort Collins Colorado
| | - Trevor J. Hefley
- Department of StatisticsKansas State University Manhattan Kansas
| | - Aaron T. Pearse
- U.S. Geological SurveyNorthern Prairie Wildlife Research Center Jamestown North Dakota
| | - Mitch D. Weegman
- School of Natural ResourcesUniversity of Missouri Columbia Missouri
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20
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Wilson K, Hanks E, Johnson D. Estimating animal utilization densities using continuous‐time Markov chain models. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.12967] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Kenady Wilson
- Alaska Fisheries Science Center NOAA Fisheries Seattle WA USA
| | - Ephraim Hanks
- Department of Statistics Pennsylvania State University University Park PA USA
| | - Devin Johnson
- Alaska Fisheries Science Center NOAA Fisheries Seattle WA USA
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21
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Williams PJ, Hooten MB, Womble JN, Esslinger GG, Bower MR. Monitoring dynamic spatio-temporal ecological processes optimally. Ecology 2018; 99:524-535. [PMID: 29369341 DOI: 10.1002/ecy.2120] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 10/05/2017] [Accepted: 12/04/2017] [Indexed: 11/08/2022]
Abstract
Population dynamics vary in space and time. Survey designs that ignore these dynamics may be inefficient and fail to capture essential spatio-temporal variability of a process. Alternatively, dynamic survey designs explicitly incorporate knowledge of ecological processes, the associated uncertainty in those processes, and can be optimized with respect to monitoring objectives. We describe a cohesive framework for monitoring a spreading population that explicitly links animal movement models with survey design and monitoring objectives. We apply the framework to develop an optimal survey design for sea otters in Glacier Bay. Sea otters were first detected in Glacier Bay in 1988 and have since increased in both abundance and distribution; abundance estimates increased from 5 otters to >5,000 otters, and they have spread faster than 2.7 km/yr. By explicitly linking animal movement models and survey design, we are able to reduce uncertainty associated with forecasting occupancy, abundance, and distribution compared to other potential random designs. The framework we describe is general, and we outline steps to applying it to novel systems and taxa.
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Affiliation(s)
- Perry J Williams
- Colorado Cooperative Fish and Wildlife Research Unit, Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, 80523, USA.,Department of Statistics, Colorado State University, Fort Collins, Colorado, 80523, USA
| | - Mevin B Hooten
- Department of Statistics, Colorado State University, Fort Collins, Colorado, 80523, USA.,U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, 80523, USA
| | - Jamie N Womble
- National Park Service, Southeast Alaska Inventory and Monitoring Network, 3100 National Park Road, Juneau, Alaska, 99801, USA.,National Park Service, Glacier Bay Field Station, 3100 National Park Road, Juneau, Alaska, 99801, USA
| | - George G Esslinger
- U.S. Geological Survey, Alaska Science Center, 4210 University Drive, Anchorage, Alaska, 99508, USA
| | - Michael R Bower
- National Park Service, Southeast Alaska Inventory and Monitoring Network, 3100 National Park Road, Juneau, Alaska, 99801, USA
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22
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Guest Editor’s Introduction to the Special Issue on “Animal Movement Modeling”. JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2017. [DOI: 10.1007/s13253-017-0299-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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