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Krebs CJ, Boutin S, Boonstra R. Population and community ecology: past progress and future directions. Integr Zool 2024. [PMID: 38956827 DOI: 10.1111/1749-4877.12863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Population and community ecology as a science are about 100 years old, and we discuss here our opinion of what approaches have progressed well and which point to possible future directions. The three major threads within population and community ecology are theoretical ecology, statistical tests and models, and experimental ecology. We suggest that our major objective is to understand what factors determine the distribution and abundance of organisms within populations and communities, and we evaluate these threads against this major objective. Theoretical ecology is elegant and compelling and has laid the groundwork for achieving our overall objectives with useful simple models. Statistics and statistical models have contributed informative methods to analyze quantitatively our understanding of distribution and abundance for future research. Population ecology is difficult to carry out in the field, even though we may have all the statistical methods and models needed to achieve results. Community ecology is growing rapidly with much description but less understanding of why changes occur. Biodiversity science cuts across all these subdivisions but rarely digs into the necessary population and community science that might solve conservation problems. Climate change affects all aspects of ecology but to assume that everything in population and community ecology is driven by climate change is oversimplified. We make recommendations on how to advance the field with advice for present and future generations of population and community ecologists.
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Affiliation(s)
- Charles J Krebs
- Department of Zoology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Stan Boutin
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Rudy Boonstra
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
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2
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Sévêque A, Lonsinger RC, Waits LP, Brzeski KE, Komoroske LM, Ott-Conn CN, Mayhew SL, Norton DC, Petroelje TR, Swenson JD, Morin DJ. Sources of bias in applying close-kin mark-recapture to terrestrial game species with different life histories. Ecology 2024; 105:e4244. [PMID: 38272487 DOI: 10.1002/ecy.4244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/18/2023] [Accepted: 12/05/2023] [Indexed: 01/27/2024]
Abstract
Close-kin mark-recapture (CKMR) is a method analogous to traditional mark-recapture but without requiring recapture of individuals. Instead, multilocus genotypes (genetic marks) are used to identify related individuals in one or more sampling occasions, which enables the opportunistic use of samples from harvested wildlife. To apply the method accurately, it is important to build appropriate CKMR models that do not violate assumptions linked to the species' and population's biology and sampling methods. In this study, we evaluated the implications of fitting overly simplistic CKMR models to populations with complex reproductive success dynamics or selective sampling. We used forward-in-time, individual-based simulations to evaluate the accuracy and precision of CKMR abundance and survival estimates in species with different longevities, mating systems, and sampling strategies. Simulated populations approximated a range of life histories among game species of North America with lethal sampling to evaluate the potential of using harvested samples to estimate population size. Our simulations show that CKMR can yield nontrivial biases in both survival and abundance estimates, unless influential life history traits and selective sampling are explicitly accounted for in the modeling framework. The number of kin pairs observed in the sample, in combination with the type of kinship used in the model (parent-offspring pairs and/or half-sibling pairs), can affect the precision and/or accuracy of the estimates. CKMR is a promising method that will likely see an increasing number of applications in the field as costs of genetic analysis continue to decline. Our work highlights the importance of applying population-specific CKMR models that consider relevant demographic parameters, individual covariates, and the protocol through which individuals were sampled.
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Affiliation(s)
- Anthony Sévêque
- Department of Wildlife, Fisheries and Aquaculture, Forest and Wildlife Research Center, Mississippi State University, Mississippi State, Mississippi, USA
| | - Robert C Lonsinger
- U.S. Geological Survey, Oklahoma Cooperative Fish and Wildlife Research Unit, Oklahoma State University, Stillwater, Oklahoma, USA
| | - Lisette P Waits
- Department of Fish and Wildlife Resources, University of Idaho, Moscow, Idaho, USA
| | - Kristin E Brzeski
- College of Forest Resources and Environment Science, Michigan Technological University, Houghton, Michigan, USA
| | - Lisa M Komoroske
- Department of Environmental Conservation, University of Massachusetts Amherst, Amherst, Massachusetts, USA
| | - Caitlin N Ott-Conn
- Wildlife Division, Michigan Department of Natural Resources, Marquette, Michigan, USA
| | - Sarah L Mayhew
- Wildlife Division, Michigan Department of Natural Resources, Lansing, Michigan, USA
| | - D Cody Norton
- Wildlife Division, Michigan Department of Natural Resources, Marquette, Michigan, USA
| | - Tyler R Petroelje
- Wildlife Division, Michigan Department of Natural Resources, Marquette, Michigan, USA
| | - John D Swenson
- Department of Environmental Conservation, University of Massachusetts Amherst, Amherst, Massachusetts, USA
| | - Dana J Morin
- Department of Wildlife, Fisheries and Aquaculture, Forest and Wildlife Research Center, Mississippi State University, Mississippi State, Mississippi, USA
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Cook B, Mulgan N. Reply to Barron et al. Comment on "Cook, B.; Mulgan, N. Targeted Mop up and Robust Response Tools Can Achieve and Maintain Possum Freedom on the Mainland. Animals 2022, 12, 921". Animals (Basel) 2023; 13:3343. [PMID: 37958098 PMCID: PMC10650834 DOI: 10.3390/ani13213343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 11/15/2023] Open
Abstract
We are pleased that our paper [...].
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Affiliation(s)
- Briar Cook
- Tasman District Council, Richmond 7020, New Zealand;
| | - Nick Mulgan
- Zero Invasive Predators, Wellington 6012, New Zealand
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4
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Russo BM, Jones AS, Clement MJ, Fyffe N, Mesler JI, Rubin ES. Camera trapping as a method for estimating abundance of Mexican wolves. WILDLIFE SOC B 2022. [DOI: 10.1002/wsb.1416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Brianna M. Russo
- Arizona Game and Fish Department, Research Branch 5000 W. Carefree Highway Phoenix AZ 85086 USA
| | - Andrew S. Jones
- Arizona Game and Fish Department, Research Branch 5000 W. Carefree Highway Phoenix AZ 85086 USA
| | - Matthew J. Clement
- Arizona Game and Fish Department, Research Branch 5000 W. Carefree Highway Phoenix AZ 85086 USA
| | - Nathan Fyffe
- Arizona Game and Fish Department, Research Branch 5000 W. Carefree Highway Phoenix AZ 85086 USA
| | - Jacob I. Mesler
- Arizona Game and Fish Department, Research Branch 5000 W. Carefree Highway Phoenix AZ 85086 USA
| | - Esther S. Rubin
- Arizona Game and Fish Department, Research Branch 5000 W. Carefree Highway Phoenix AZ 85086 USA
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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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Twining JP, McFarlane C, O'Meara D, O'Reilly C, Reyne M, Montgomery WI, Helyar S, Tosh DG, Augustine BC. A comparison of density estimation methods for monitoring marked and unmarked animal populations. Ecosphere 2022. [DOI: 10.1002/ecs2.4165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Joshua P. Twining
- Department of Natural Resources Cornell University Ithaca New York USA
| | - Claire McFarlane
- School of Biological Sciences Queen's University of Belfast Belfast UK
| | - Denise O'Meara
- Molecular Ecology Research Group, Eco‐innovation Research Centre School of Science and Computing, South East Technological University Waterford UK
| | - Catherine O'Reilly
- Molecular Ecology Research Group, Eco‐innovation Research Centre School of Science and Computing, South East Technological University Waterford UK
| | - Marina Reyne
- School of Biological Sciences Queen's University of Belfast Belfast UK
| | - W. Ian Montgomery
- School of Biological Sciences Queen's University of Belfast Belfast UK
| | - Sarah Helyar
- School of Biological Sciences Queen's University of Belfast Belfast UK
| | - David G. Tosh
- Raithlin LIFE Project The Royal Society for Protection of Birds, Belvoir Park Forest Belfast UK
| | - Ben C. Augustine
- Department of Natural Resources Cornell University Ithaca New York USA
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7
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Using spot pattern recognition to examine population biology, evolutionary ecology, sociality, and movements of giraffes: a 70-year retrospective. Mamm Biol 2022. [DOI: 10.1007/s42991-022-00261-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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8
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Hinojo A, Christe P, Moreno I, Hofmeister RJ, Dandliker G, Zimmermann F. Estimating roe deer density using motion‐sensitive cameras in Switzerland. J Wildl Manage 2022. [DOI: 10.1002/jwmg.22307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Amael Hinojo
- University of Lausanne, Department of Ecology and Evolution, Biophore Quartier Sorge Lausanne CH‐1015 Switzerland
| | - Philippe Christe
- University of Lausanne, Department of Ecology and Evolution, Biophore Quartier Sorge Lausanne CH‐1015 Switzerland
| | - Inès Moreno
- University of Lausanne, Department of Ecology and Evolution, Biophore Quartier Sorge Lausanne CH‐1015 Switzerland
- Carnivore Ecology and Wildlife Management, KORA Talgut Zentrum 5, CH‐3063 Ittigen Switzerland
| | - Robin J. Hofmeister
- University of Lausanne, Department of Computational Biology, Genopode Quartier Sorge Lausanne CH‐1015 Switzerland
| | - Gottlieb Dandliker
- Cantonal Office for Agriculture and Nature Republic and canton of Geneva Rue des Battoirs 7 1205 Geneva Switzerland
| | - Fridolin Zimmermann
- University of Lausanne, Department of Ecology and Evolution, Biophore Quartier Sorge Lausanne CH‐1015 Switzerland
- Carnivore Ecology and Wildlife Management, KORA Talgut Zentrum 5, CH‐3063 Ittigen Switzerland
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Schmidt GM, Graves TA, Pederson JC, Carroll SL. Precision and bias of spatial capture-recapture estimates: A multi-site, multi-year Utah black bear case study. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e2618. [PMID: 35368131 PMCID: PMC9287071 DOI: 10.1002/eap.2618] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 01/13/2022] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
Spatial capture-recapture (SCR) models are powerful analytical tools that have become the standard for estimating abundance and density of wild animal populations. When sampling populations to implement SCR, the number of unique individuals detected, total recaptures, and unique spatial relocations can be highly variable. These sample sizes influence the precision and accuracy of model parameter estimates. Testing the performance of SCR models with sparse empirical data sets typical of low-density, wide-ranging species can inform the threshold at which a more integrated modeling approach with additional data sources or additional years of monitoring may be required to achieve reliable, precise parameter estimates. Using a multi-site, multi-year Utah black bear (Ursus americanus) capture-recapture data set, we evaluated factors influencing the uncertainty of SCR structural parameter estimates, specifically density, detection, and the spatial scale parameter, sigma. We also provided some of the first SCR density estimates for Utah black bear populations, which ranged from 3.85 to 74.33 bears/100 km2 . Increasing total detections decreased the uncertainty of density estimates, whereas an increasing number of total recaptures and individuals with recaptures decreased the uncertainty of detection and sigma estimates, respectively. In most cases, multiple years of data were required for precise density estimates (<0.2 coefficient of variation [CV]). Across study areas there was an average decline in CV of 0.07 with the addition of another year of data. One sampled population with very high estimated bear density had an atypically low number of spatial recaptures relative to total recaptures, apparently inflating density estimates. A complementary simulation study used to assess estimate bias suggested that when <30% of recaptured individuals were spatially recaptured, density estimates were unreliable and ranged widely, in some cases to >3 times the simulated density. Additional research could evaluate these requirements for other density scenarios. Large numbers of individuals detected, numbers of spatial recaptures, and precision alone may not be sufficient indicators of parameter estimate reliability. We provide an evaluation of simple summary statistics of capture-recapture data sets that can provide an early signal of the need to alter sampling design or collect auxiliary data before model implementation to improve estimate precision and accuracy.
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Affiliation(s)
- Greta M. Schmidt
- Department of BiologySan Diego State UniversitySan DiegoCaliforniaUSA
| | - Tabitha A. Graves
- U.S. Geological Survey, Northern Rocky Mountain Science CenterWest GlacierMontanaUSA
| | | | - Sarah L. Carroll
- Graduate Degree Program in EcologyColorado State UniversityFort CollinsColoradoUSA
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10
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Marrotte RR, Howe EJ, Beauclerc KB, Potter D, Northrup JM. Explaining detection heterogeneity with finite mixture and non-Euclidean movement in spatially explicit capture-recapture models. PeerJ 2022; 10:e13490. [PMID: 35694380 PMCID: PMC9186326 DOI: 10.7717/peerj.13490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 05/03/2022] [Indexed: 01/17/2023] Open
Abstract
Landscape structure affects animal movement. Differences between landscapes may induce heterogeneity in home range size and movement rates among individuals within a population. These types of heterogeneity can cause bias when estimating population size or density and are seldom considered during analyses. Individual heterogeneity, attributable to unknown or unobserved covariates, is often modelled using latent mixture distributions, but these are demanding of data, and abundance estimates are sensitive to the parameters of the mixture distribution. A recent extension of spatially explicit capture-recapture models allows landscape structure to be modelled explicitly by incorporating landscape connectivity using non-Euclidean least-cost paths, improving inference, especially in highly structured (riparian & mountainous) landscapes. Our objective was to investigate whether these novel models could improve inference about black bear (Ursus americanus) density. We fit spatially explicit capture-recapture models with standard and complex structures to black bear data from 51 separate study areas. We found that non-Euclidean models were supported in over half of our study areas. Associated density estimates were higher and less precise than those from simple models and only slightly more precise than those from finite mixture models. Estimates were sensitive to the scale (pixel resolution) at which least-cost paths were calculated, but there was no consistent pattern across covariates or resolutions. Our results indicate that negative bias associated with ignoring heterogeneity is potentially severe. However, the most popular method for dealing with this heterogeneity (finite mixtures) yielded potentially unreliable point estimates of abundance that may not be comparable across surveys, even in data sets with 136-350 total detections, 3-5 detections per individual, 97-283 recaptures, and 80-254 spatial recaptures. In these same study areas with high sample sizes, we expected that landscape features would not severely constrain animal movements and modelling non-Euclidian distance would not consistently improve inference. Our results suggest caution in applying non-Euclidean SCR models when there is no clear landscape covariate that is known to strongly influence the movement of the focal species, and in applying finite mixture models except when abundant data are available.
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Affiliation(s)
- Robby R. Marrotte
- Wildlife Research & Monitoring Section, Ministry of Northern Development, Mines, Natural Resources and Forestry, Peterborough, Ontario, Canada
| | - Eric J. Howe
- Wildlife Research & Monitoring Section, Ministry of Northern Development, Mines, Natural Resources and Forestry, Peterborough, Ontario, Canada
| | - Kaela B. Beauclerc
- Wildlife Research & Monitoring Section, Ministry of Northern Development, Mines, Natural Resources and Forestry, Peterborough, Ontario, Canada
| | - Derek Potter
- Wildlife Research & Monitoring Section, Ministry of Northern Development, Mines, Natural Resources and Forestry, Peterborough, Ontario, Canada
| | - Joseph M. Northrup
- Wildlife Research & Monitoring Section, Ministry of Northern Development, Mines, Natural Resources and Forestry, Peterborough, Ontario, Canada,Environmental and Life Sciences Graduate Program, Trent University, Peterborough, Ontario, Canada
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11
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Le Pla MN, Birnbaum EK, Rees MW, Hradsky BA, Weeks AR, Van Rooyen A, Pascoe JH. Genetic sampling and an activity index indicate contrasting outcomes of lethal control for an invasive predator. AUSTRAL ECOL 2022. [DOI: 10.1111/aec.13182] [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)
- Mark N. Le Pla
- Conservation Ecology Centre 635 Lighthouse Road Cape Otway Victoria Australia
| | - Emma K. Birnbaum
- Conservation Ecology Centre 635 Lighthouse Road Cape Otway Victoria Australia
| | - Matthew W. Rees
- Quantitative & Applied Ecology Group, Ecosystem and Forest Sciences University of Melbourne Parkville Victoria Australia
| | - Bronwyn A. Hradsky
- Quantitative & Applied Ecology Group, Ecosystem and Forest Sciences University of Melbourne Parkville Victoria Australia
| | - Andrew R. Weeks
- University of Melbourne Parkville Victoria Australia
- Cesar Australia Pty Ltd Brunswick Victoria Australia
| | | | - Jack H. Pascoe
- Conservation Ecology Centre 635 Lighthouse Road Cape Otway Victoria Australia
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12
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Bengsen AJ, Forsyth DM, Ramsey DSL, Amos M, Brennan M, Pople AR, Comte S, Crittle T. Estimating deer density and abundance using spatial mark-resight models with camera trap data. J Mammal 2022; 103:711-722. [PMID: 35707678 PMCID: PMC9189690 DOI: 10.1093/jmammal/gyac016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 01/28/2022] [Indexed: 11/14/2022] Open
Abstract
Globally, many wild deer populations are actively studied or managed for conservation, hunting, or damage mitigation purposes. These studies require reliable estimates of population state parameters, such as density or abundance, with a level of precision that is fit for purpose. Such estimates can be difficult to attain for many populations that occur in situations that are poorly suited to common survey methods. We evaluated the utility of combining camera trap survey data, in which a small proportion of the sample is individually recognizable using natural markings, with spatial mark-resight (SMR) models to estimate deer density in a variety of situations. We surveyed 13 deer populations comprising four deer species (Cervus unicolor, C. timorensis, C. elaphus, Dama dama) at nine widely separated sites, and used Bayesian SMR models to estimate population densities and abundances. Twelve surveys provided sufficient data for analysis and seven produced density estimates with coefficients of variation (CVs) ≤ 0.25. Estimated densities ranged from 0.3 to 24.6 deer km-2. Camera trap surveys and SMR models provided a powerful and flexible approach for estimating deer densities in populations in which many detections were not individually identifiable, and they should provide useful density estimates under a wide range of conditions that are not amenable to more widely used methods. In the absence of specific local information on deer detectability and movement patterns, we recommend that at least 30 cameras be spaced at 500-1,000 m and set for 90 days. This approach could also be applied to large mammals other than deer.
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Affiliation(s)
- Andrew J Bengsen
- NSW Department of Primary Industries, Vertebrate Pest Research Unit, 1447 Forest Road, Orange, NSW 2800, Australia
| | - David M Forsyth
- NSW Department of Primary Industries, Vertebrate Pest Research Unit, 1447 Forest Road, Orange, NSW 2800, Australia
| | - Dave S L Ramsey
- Arthur Rylah Institute for Environmental Research, Department of Environment, Land, Water and Planning, 123 Brown Street, Heidelberg, VIC 3084, Australia
| | - Matt Amos
- Queensland Department of Agriculture and Fisheries, 41 Boggo Road, Dutton Park, QLD 4102, Australia
| | - Michael Brennan
- Queensland Department of Agriculture and Fisheries, 41 Boggo Road, Dutton Park, QLD 4102, Australia
| | - Anthony R Pople
- Queensland Department of Agriculture and Fisheries, 41 Boggo Road, Dutton Park, QLD 4102, Australia
| | - Sebastien Comte
- NSW Department of Primary Industries, Vertebrate Pest Research Unit, 1447 Forest Road, Orange, NSW 2800, Australia
| | - Troy Crittle
- NSW Department of Primary Industries, Biosecurity and Food Safety, 4 Marsden Park Road, Calala, NSW 2340, Australia
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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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Margenau LLS, Cherry MJ, Miller KV, Garrison EP, Chandler RB. Monitoring partially marked populations using camera and telemetry data. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e2553. [PMID: 35112750 DOI: 10.1002/eap.2553] [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: 09/20/2021] [Accepted: 10/26/2021] [Indexed: 06/14/2023]
Abstract
Long-term monitoring is an important component of effective wildlife conservation. However, many methods for estimating density are too costly or difficult to implement over large spatial and temporal extents. Recently developed spatial mark-resight (SMR) models are increasingly being applied as a cost-effective method to estimate density when data include detections of both marked and unmarked individuals. We developed a generalized SMR model that can accommodate long-term camera data and auxiliary telemetry data for improved spatiotemporal inference in monitoring efforts. The model can be applied in two stages, with detection parameters estimated in the first stage using telemetry data and camera detections of instrumented individuals. Density is estimated in the second stage using camera data, with all individuals treated as unmarked. Serial correlation in detection and density parameters is accounted for using time-series models. The two-stage approach reduces computational demands and facilitates the application to large data sets from long-term monitoring initiatives. We applied the model to 3 years (2015-2017) of white-tailed deer (Odocoileus virginianus) data collected in three study areas of the Big Cypress Basin, Florida, USA. In total, 59 females marked with ear tags and fitted with GPS-telemetry collars were detected along with unmarked females on 180 remote cameras. Most of the temporal variation in density was driven by seasonal fluctuations, but one study area exhibited a slight population decline during the monitoring period. Modern technologies such as camera traps provide novel possibilities for long-term monitoring, but the resulting massive data sets, which are subject to unique sources of observation error, have posed analytical challenges. The two-stage spatial mark-resight framework provides a solution with lower computational demands than joint SMR models, allowing for easier implementation in practice. In addition, after detection parameters have been estimated, the model may be used to estimate density even if no synchronous auxiliary information on marked individuals is available, which is often the case in long-term monitoring.
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Affiliation(s)
- Lydia L S Margenau
- Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia, USA
| | - Michael J Cherry
- Caesar Kleberg Wildlife Research Institute, Texas A&M University-Kingsville, Kingsville, Texas, USA
| | - Karl V Miller
- Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia, USA
| | - Elina P Garrison
- Florida Fish and Wildlife Conservation Commission, Gainesville, Florida, USA
| | - Richard B Chandler
- Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia, USA
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15
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Review of puma density estimates reveals sources of bias and variation, and the need for standardization. Glob Ecol Conserv 2022. [DOI: 10.1016/j.gecco.2022.e02109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
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16
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Wang Z, Li Y, Jain A, Pierce NE. Agent-based models reveal limits of mark-release-recapture estimates for the rare butterfly, Bhutanitis thaidina (Lepidoptera: Papilionidae). INSECT SCIENCE 2022; 29:550-566. [PMID: 34263543 DOI: 10.1111/1744-7917.12949] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 06/15/2021] [Accepted: 06/16/2021] [Indexed: 06/13/2023]
Abstract
Insect diversity and abundance are in drastic decline worldwide, but quantifying insect populations to better conserve them is a difficult task. Mark-release-recapture (MRR) is widely used as an ecological indicator for insect populations, but the accuracy of MRR estimates can vary with factors such as spatial scale, sampling effort and models of inference. We conducted a 3-year MRR study of B. thaidina in Yanzigou valley, Mt. Gongga but failed to obtain sufficient data for a robust population estimate. This prompted us to integrate B. thaidina life history information to parameterize agent-based models and evaluate the conditions under which successful MRR studies could be conducted. We evaluated: (1) the performance of MRR models under different landscape types, and (2) the influence of experimental design on the accuracy and variance of MRR-based estimates. Our simulations revealed systematic underestimates of true population parameters by MRR models when sampling effort was insufficient. In a total of 2772 simulations, subjective decisions in sampling protocol (e.g., frequency, number of sampling locations, use of spatially explicit models, type of estimands) accounted for nearly half of the variation in estimates. We conclude that MRR-based estimates could be improved with the addition of more field-specific parameters.
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Affiliation(s)
- Zhengyang Wang
- Department of Organismic and Evolutionary Biology and Museum of Comparative Zoology, Harvard University, Cambridge, MA 02138, USA
| | - Yuanheng Li
- Department of Organismic and Evolutionary Biology and Museum of Comparative Zoology, Harvard University, Cambridge, MA 02138, USA
| | - Anuj Jain
- Nature Society (Singapore), Singapore, Singapore
| | - Naomi E Pierce
- Department of Organismic and Evolutionary Biology and Museum of Comparative Zoology, Harvard University, Cambridge, MA 02138, USA
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17
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The occupancy-abundance relationship and sampling designs using occupancy to monitor populations of Asian bears. Glob Ecol Conserv 2022. [DOI: 10.1016/j.gecco.2022.e02075] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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18
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19
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John Power R, Rogan MS, Naude VN. Mountain refugia limit anthropogenic suppression in a re-established felid population: the case of the Magaliesberg leopard population in South Africa. AFRICAN ZOOLOGY 2021. [DOI: 10.1080/15627020.2021.2011411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- R John Power
- Directorate of Biodiversity Management, Department of Economic Development, Environment, Conservation and Tourism, North West Provincial Government, Mmabatho, South Africa
| | - Matt S Rogan
- Institute for Communities and Wildlife in Africa, University of Cape Town, Cape Town, South Africa
- Centre for Statistics in Ecology, the Environment, and Conservation, Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa
| | - Vincent N Naude
- Institute for Communities and Wildlife in Africa, University of Cape Town, Cape Town, South Africa
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20
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Fonda F, Bacaro G, Battistella S, Chiatante G, Pecorella S, Pavanello M. Population density of European wildcats in a pre-alpine area (northeast Italy) and an assessment of estimate robustness. MAMMAL RES 2021. [DOI: 10.1007/s13364-021-00609-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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21
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Fleming J, Grant EHC, Sterrett SC, Sutherland C. Experimental evaluation of spatial capture-recapture study design. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02419. [PMID: 34278637 DOI: 10.1002/eap.2419] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 01/13/2021] [Accepted: 03/03/2021] [Indexed: 06/13/2023]
Abstract
A principal challenge impeding strong inference in analyses of wild populations is the lack of robust and long-term data sets. Recent advancements in analytical tools used in wildlife science may increase our ability to integrate smaller data sets and enhance the statistical power of population estimates. One such advancement, the development of spatial capture-recapture (SCR) methods, explicitly accounts for differences in spatial study designs, making it possible to equate multiple study designs in one analysis. SCR has been shown to be robust to variation in design as long as minimal sampling guidance is adhered to. However, these expectations are based on simulation and have yet to be evaluated in wild populations. Here we conduct a rigorously designed field experiment by manipulating the arrangement of artificial cover objects (ACOs) used to collect data on red-backed salamanders (Plethodon cinereus) to empirically evaluate the effects of design configuration on inference made using SCR. Our results suggest that, using SCR, estimates of space use and detectability are sensitive to study design configuration, namely the spacing and extent of the array, and that caution is warranted when assigning biological interpretation to these parameters. However, estimates of population density remain robust to design except when the configuration of detectors grossly violates existing recommendations.
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Affiliation(s)
- Jill Fleming
- USGS Eastern Ecological Science Center, SO Conte Anadromous Fish Laboratory, 1 Migratory Way, Turners Falls, Massachusetts, 01376, USA
- Department of Environmental Conservation, University of Massachusetts, 160 Holdsworth Way, Amherst, Massachusetts, 01003, USA
| | - Evan H Campbell Grant
- USGS Eastern Ecological Science Center, SO Conte Anadromous Fish Laboratory, 1 Migratory Way, Turners Falls, Massachusetts, 01376, USA
| | - Sean C Sterrett
- Department of Biology, Monmouth University, 400 Cedar Avenue, West Long Branch, New Jersey, 07764, USA
| | - Chris Sutherland
- Department of Environmental Conservation, University of Massachusetts, 160 Holdsworth Way, Amherst, Massachusetts, 01003, USA
- Centre for Research into Ecological & Environmental Modelling, The Observatory, Buchanan Gardens, University of St Andrews, St Andrews, Fife, KY16 9LZ, United Kingdom
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22
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Howell PE, Wilhite NG, Gardner R, Mohlman JL, Chandler RB, Parnell IB, Martin JA. The Effects of Landscape Characteristics on Northern Bobwhite Density. J Wildl Manage 2021. [DOI: 10.1002/jwmg.22057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Paige E. Howell
- Warnell School of Forestry & Natural Resources University of Georgia 180 East Green Street Athens GA 30602 USA
| | - Nathan G. Wilhite
- Warnell School of Forestry & Natural Resources University of Georgia 180 East Green Street Athens GA 30602 USA
| | - Rachel Gardner
- Warnell School of Forestry & Natural Resources University of Georgia 180 East Green Street Athens GA 30602 USA
| | - Jessica L. Mohlman
- Warnell School of Forestry & Natural Resources University of Georgia 180 East Green Street Athens GA 30602 USA
| | - Richard B. Chandler
- Warnell School of Forestry & Natural Resources University of Georgia 180 East Green Street Athens GA 30602 USA
| | - Ira B. Parnell
- Georgia Department of Natural Resources Wildlife Resources Division 142 Bob Kirk Road Thomson GA 30824 USA
| | - James A. Martin
- Warnell School of Forestry & Natural Resources University of Georgia 180 East Green Street Athens GA 30602 USA
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23
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Nawaz MA, Khan BU, Mahmood A, Younas M, Din JU, Sutherland C. An empirical demonstration of the effect of study design on density estimations. Sci Rep 2021; 11:13104. [PMID: 34162926 PMCID: PMC8222225 DOI: 10.1038/s41598-021-92361-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 06/07/2021] [Indexed: 11/09/2022] Open
Abstract
The simultaneous development of technology (e.g. camera traps) and statistical methods, particularly spatially capture-recapture (SCR), has improved monitoring of large mammals in recent years. SCR estimates are known to be sensitive to sampling design, yet existing recommendations about trap spacing and coverage are often not achieved, particularly for sampling wide-ranging and rare species in landscapes that allow for limited accessibility. Consequently, most camera trap studies on large wide-ranging carnivores relies on convenience or judgmental sampling, and often yields compromised results. This study attempts to highlight the importance of carefully considered sampling design for large carnivores that, because of low densities and elusive behavior, are challenging to monitor. As a motivating example, we use two years of snow leopard camera trapping data from the same areas in the high mountains of Pakistan but with vastly different camera configurations, to demonstrate that estimates of density and space use are indeed sensitive to the trapping array. A compact design, one in which cameras were placed much closer together than generally recommended and therefore have lower spatial coverage, resulted in fewer individuals observed, but more recaptures, and estimates of density and space use were inconsistent with expectations for the region. In contrast, a diffuse design, one with larger spacing and spatial coverage and more consistent with general recommendations, detected more individuals, had fewer recaptures, but generated estimates of density and space use that were in line with expectations. Researchers often opt for compact camera configurations while monitoring wide-ranging and rare species, in an attempt to maximize the encounter probabilities. We empirically demonstrate the potential for biases when sampling a small area approximately the size of a single home range-this arises from exposing fewer individuals than deemed sufficient for estimation. The smaller trapping array may also underestimate density by significantly inflating [Formula: see text]. On the other hand, larger trapping array with fewer detectors and poor design induces uncertainties in the estimates. We conclude that existing design recommendations have limited utility on practical grounds for devising feasible sampling designs for large ranging species, and more research on SCR designs is required that allows for integrating biological and habitat traits of large carnivores in sampling framework. We also suggest that caution should be exercised when there is a reliance on convenience sampling.
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Affiliation(s)
- Muhammad Ali Nawaz
- Department of Biological and Environmental Sciences, Qatar University, Doha, Qatar.
| | - Barkat Ullah Khan
- Department of Zoology, Quaid-i-Azam University, Islamabad, Pakistan
- Snow Leopard Foundation, Islamabad, Pakistan
| | - Amer Mahmood
- Department of Zoology, Quaid-i-Azam University, Islamabad, Pakistan
| | | | - Jaffar Ud Din
- Snow Leopard Foundation, Islamabad, Pakistan
- Snow Leopard Trust, Seattle, USA
| | - Chris Sutherland
- Department of Environmental Conservation, University of Massachusetts, Amherst, MA, 01002, USA
- Centre for Research Into Ecological & Environmental Modelling, University of St Andrews, St Andrews, Scotland, UK
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24
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Blount JD, Chynoweth MW, Green AM, Şekercioğlu ÇH. Review: COVID-19 highlights the importance of camera traps for wildlife conservation research and management. BIOLOGICAL CONSERVATION 2021; 256:108984. [PMID: 36531528 PMCID: PMC9746925 DOI: 10.1016/j.biocon.2021.108984] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 01/11/2021] [Accepted: 01/16/2021] [Indexed: 05/26/2023]
Abstract
COVID-19 has altered many aspects of everyday life. For the scientific community, the pandemic has called upon investigators to continue work in novel ways, curtailing field and lab research. However, this unprecedented situation also offers an opportunity for researchers to optimize and further develop available field methods. Camera traps are one example of a tool used in science to answer questions about wildlife ecology, conservation, and management. Camera traps have long battery lives, lasting more than a year in certain cases, and photo storage capacity, with some models capable of wirelessly transmitting images from the field. This allows researchers to deploy cameras without having to check them for up to a year or more, making them an ideal field research tool during restrictions on in-person research activities such as COVID-19 lockdowns. As technological advances allow cameras to collect increasingly greater numbers of photos and videos, the analysis techniques for large amounts of data are evolving. Here, we describe the most common research questions suitable for camera trap studies and their importance for biodiversity conservation. As COVID-19 continues to affect how people interact with the natural environment, we discuss novel questions for which camera traps can provide insights on. We conclude by summarizing the results of a systematic review of camera trap studies, providing data on target taxa, geographic distribution, publication rate, and publication venues to help researchers planning to use camera traps in response to the current changes in human activity.
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Affiliation(s)
- J David Blount
- School of Biological Sciences, University of Utah, 257 South 1400 East, Salt Lake City, UT 84112-0840, USA
| | - Mark W Chynoweth
- Department of Wildland Resources, Utah State University, Uintah Basin, 320 North Aggie Blvd., Vernal, UT 84078, USA
| | - Austin M Green
- School of Biological Sciences, University of Utah, 257 South 1400 East, Salt Lake City, UT 84112-0840, USA
| | - Çağan H Şekercioğlu
- School of Biological Sciences, University of Utah, 257 South 1400 East, Salt Lake City, UT 84112-0840, USA
- College of Sciences, Koç University, Rumelifeneri, İstanbul, Sarıyer, Turkey
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25
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Hardouin M, Searle CE, Strampelli P, Smit J, Dickman A, Lobora AL, Rowcliffe JM. Density responses of lesser-studied carnivores to habitat and management strategies in southern Tanzania's Ruaha-Rungwa landscape. PLoS One 2021; 16:e0242293. [PMID: 33784297 PMCID: PMC8009394 DOI: 10.1371/journal.pone.0242293] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 03/16/2021] [Indexed: 11/19/2022] Open
Abstract
Compared to emblematic large carnivores, most species of the order Carnivora receive little conservation attention despite increasing anthropogenic pressure and poor understanding of their status across much of their range. We employed systematic camera trapping and spatially explicit capture-recapture modelling to estimate variation in population density of serval, striped hyaena and aardwolf across the mixed-use Ruaha-Rungwa landscape in southern Tanzania. We selected three sites representative of different habitat types, management strategies, and levels of anthropogenic pressure: Ruaha National Park’s core tourist area, dominated by Acacia-Commiphora bushlands and thickets; the Park’s miombo woodland; and the neighbouring community-run MBOMIPA Wildlife Management Area, also covered in Acacia-Commiphora. The Park’s miombo woodlands supported a higher serval density (5.56 [Standard Error = ±2.45] individuals per 100 km2) than either the core tourist area (3.45 [±1.04] individuals per 100 km2) or the Wildlife Management Area (2.08 [±0.74] individuals per 100 km2). Taken together, precipitation, the abundance of apex predators, and the level of anthropogenic pressure likely drive such variation. Striped hyaena were detected only in the Wildlife Management Area and at low density (1.36 [±0.50] individuals per 100 km2), potentially due to the location of the surveyed sites at the edge of the species’ global range, high densities of sympatric competitors, and anthropogenic edge effects. Finally, aardwolf were captured in both the Park’s core tourist area and the Wildlife Management Area, with a higher density in the Wildlife Management Area (13.25 [±2.48] versus 9.19 [±1.66] individuals per 100 km2), possibly as a result of lower intraguild predation and late fire outbreaks in the area surveyed. By shedding light on three understudied African carnivore species, this study highlights the importance of miombo woodland conservation and community-managed conservation, as well as the value of by-catch camera trap data to improve ecological knowledge of lesser-studied carnivores.
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Affiliation(s)
- Marie Hardouin
- Faculty of Natural Sciences, Imperial College London, Ascot, United Kingdom
- * E-mail:
| | - Charlotte E. Searle
- Wildlife Conservation Research Unit, Department of Zoology, The Recanati-Kaplan Centre, Tubney, United Kingdom
| | - Paolo Strampelli
- Wildlife Conservation Research Unit, Department of Zoology, The Recanati-Kaplan Centre, Tubney, United Kingdom
| | - Josephine Smit
- Southern Tanzania Elephant Program, Iringa, Tanzania
- Department of Psychology, University of Stirling, Stirling, United Kingdom
| | - Amy Dickman
- Wildlife Conservation Research Unit, Department of Zoology, The Recanati-Kaplan Centre, Tubney, United Kingdom
| | | | - J. Marcus Rowcliffe
- Faculty of Natural Sciences, Imperial College London, Ascot, United Kingdom
- Institute of Zoology, Zoological Society of London, London, United Kingdom
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26
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Dupont G, Royle JA, Nawaz MA, Sutherland C. Optimal sampling design for spatial capture-recapture. Ecology 2021; 102:e03262. [PMID: 33244753 DOI: 10.1002/ecy.3262] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 09/10/2020] [Accepted: 10/09/2020] [Indexed: 11/06/2022]
Abstract
Spatial capture-recapture (SCR) has emerged as the industry standard for estimating population density by leveraging information from spatial locations of repeat encounters of individuals. The precision of density estimates depends fundamentally on the number and spatial configuration of traps. Despite this knowledge, existing sampling design recommendations are heuristic and their performance remains untested for most practical applications. To address this issue, we propose a genetic algorithm that minimizes any sensible, criteria-based objective function to produce near-optimal sampling designs. To motivate the idea of optimality, we compare the performance of designs optimized using three model-based criteria related to the probability of capture. We use simulation to show that these designs outperform those based on existing recommendations in terms of bias, precision, and accuracy in the estimation of population size. Our approach, available as a function in the R package oSCR, allows conservation practitioners and researchers to generate customized and improved sampling designs for wildlife monitoring.
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Affiliation(s)
- Gates Dupont
- Department of Environmental Conservation, University of Massachusetts, 160 Holdsworth Way, Amherst, Massachusetts, 01003, USA.,Organismic and Evolutionary Biology Graduate Program, University of Massachusetts, 204C French Hall, 230 Stockbridge Road, Amherst, Massachusetts, USA
| | - J Andrew Royle
- U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, Maryland, 20708, USA
| | - Muhammad Ali Nawaz
- Department of Animal Sciences, Quaid-i-Azam University, Islamabad, 44000, Pakistan.,Snow Leopard Trust, 4649 Sunnyside Avenue North, Suite 325, Seattle, Washington, 98103, USA.,Department of Biological and Environmental Sciences, College of Arts and Sciences, University of Qatar, Doha, Qatar
| | - Chris Sutherland
- Department of Environmental Conservation, University of Massachusetts, 160 Holdsworth Way, Amherst, Massachusetts, 01003, USA.,Centre for Research into Ecological and Environmental Modelling, University of St Andrews, Fife, KY16 9LZ, St. Andrews, UK
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27
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Green AM, Chynoweth MW, Şekercioğlu ÇH. Spatially Explicit Capture-Recapture Through Camera Trapping: A Review of Benchmark Analyses for Wildlife Density Estimation. Front Ecol Evol 2020. [DOI: 10.3389/fevo.2020.563477] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Camera traps have become an important research tool for both conservation biologists and wildlife managers. Recent advances in spatially explicit capture-recapture (SECR) methods have increasingly put camera traps at the forefront of population monitoring programs. These methods allow for benchmark analysis of species density without the need for invasive fieldwork techniques. We conducted a review of SECR studies using camera traps to summarize the current focus of these investigations, as well as provide recommendations for future studies and identify areas in need of future investigation. Our analysis shows a strong bias in species preference, with a large proportion of studies focusing on large felids, many of which provide the only baseline estimates of population density for these species. Furthermore, we found that a majority of studies produced density estimates that may not be precise enough for long-term population monitoring. We recommend simulation and power analysis be conducted before initiating any particular study design and provide examples using readily available software. Furthermore, we show that precision can be increased by including a larger study area that will subsequently increase the number of individuals photo-captured. As many current studies lack the resources or manpower to accomplish such an increase in effort, we recommend that researchers incorporate new technologies such as machine-learning, web-based data entry, and online deployment management into their study design. We also cautiously recommend the potential of citizen science to help address these study design concerns. In addition, modifications in SECR model development to include species that have only a subset of individuals available for individual identification (often called mark-resight models), can extend the process of explicit density estimation through camera trapping to species not individually identifiable.
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28
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Durbach I, Borchers D, Sutherland C, Sharma K. Fast, flexible alternatives to regular grid designs for spatial capture–recapture. Methods Ecol Evol 2020. [DOI: 10.1111/2041-210x.13517] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ian Durbach
- Centre for Research into Ecological and Environmental Modelling University of St Andrews St Andrews UK
- Centre for Statistics in Ecology, the Environment, and Conservation University of Cape Town Cape Town South Africa
| | - David Borchers
- Centre for Research into Ecological and Environmental Modelling University of St Andrews St Andrews UK
| | - Chris Sutherland
- Centre for Research into Ecological and Environmental Modelling University of St Andrews St Andrews UK
- Department of Environmental Conservation University of Massachusetts‐Amherst Amherst MA USA
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29
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McFarlane S, Manseau M, Steenweg R, Hervieux D, Hegel T, Slater S, Wilson PJ. An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou. Ecol Evol 2020; 10:11631-11642. [PMID: 33144989 PMCID: PMC7593142 DOI: 10.1002/ece3.6797] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 08/06/2020] [Accepted: 08/20/2020] [Indexed: 11/12/2022] Open
Abstract
Accurately estimating abundance is a critical component of monitoring and recovery of rare and elusive species. Spatial capture-recapture (SCR) models are an increasingly popular method for robust estimation of ecological parameters. We provide an analytical framework to assess results from empirical studies to inform SCR sampling design, using both simulated and empirical data from noninvasive genetic sampling of seven boreal caribou populations (Rangifer tarandus caribou), which varied in range size and estimated population density. We use simulated population data with varying levels of clustered distributions to quantify the impact of nonindependence of detections on density estimates, and empirical datasets to explore the influence of varied sampling intensity on the relative bias and precision of density estimates. Simulations revealed that clustered distributions of detections did not significantly impact relative bias or precision of density estimates. The genotyping success rate of our empirical dataset (n = 7,210 samples) was 95.1%, and 1,755 unique individuals were identified. Analysis of the empirical data indicated that reduced sampling intensity had a greater impact on density estimates in smaller ranges. The number of captures and spatial recaptures was strongly correlated with precision, but not absolute relative bias. The best sampling designs did not differ with estimated population density but differed between large and small ranges. We provide an efficient framework implemented in R to estimate the detection parameters required when designing SCR studies. The framework can be used when designing a monitoring program to minimize effort and cost while maximizing effectiveness, which is critical for informing wildlife management and conservation.
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Affiliation(s)
- Samantha McFarlane
- Environmental and Life Sciences DepartmentTrent UniversityPeterboroughOntarioCanada
- Landscape Science and Technology DivisionEnvironment and Climate Change CanadaOttawaONCanada
| | - Micheline Manseau
- Environmental and Life Sciences DepartmentTrent UniversityPeterboroughOntarioCanada
- Landscape Science and Technology DivisionEnvironment and Climate Change CanadaOttawaONCanada
| | - Robin Steenweg
- Fish and Wildlife Stewardship BranchAlberta Environment and ParksGrande PrairieABCanada
- Canadian Wildlife Service—Pacific RegionEnvironment and Climate Change CanadaKelownaBCCanada
| | - Dave Hervieux
- Fish and Wildlife Stewardship BranchAlberta Environment and ParksGrande PrairieABCanada
| | - Troy Hegel
- Regional Resource ManagementAlberta Environment and ParksEdmontonABCanada
| | - Simon Slater
- Fish and Wildlife Stewardship BranchAlberta Environment and ParksEdmontonABCanada
| | - Paul J. Wilson
- Environmental and Life Sciences DepartmentTrent UniversityPeterboroughOntarioCanada
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30
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Phoebus I, Boulanger J, Eiken HG, Fløystad I, Graham K, Hagen SB, Sorensen A, Stenhouse G. Comparison of grizzly bear hair-snag and scat sampling along roads to inform wildlife population monitoring. WILDLIFE BIOLOGY 2020. [DOI: 10.2981/wlb.00697] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Isobel Phoebus
- I. Phoebus (https://orcid.org/0000-0001-5333-0298) ✉ , K. Graham, A. Sorensen and G. Stenhouse (https://orcid.org/0000-0003-4551-4585), fRI Research Grizzly Bear Program, Hinton, AB, Canada
| | - John Boulanger
- J. Boulanger (https://orcid.org/0000-0001-8222-1445), Integrated Ecological Research, Nelson, BC, Canada
| | - Hans Geir Eiken
- H. G. Eiken (https://orcid.org/0000-0002-5368-3648), I. Fløystad (https://orcid.org/0000-0002-0484-4265) and S. B. Hagen (https://orcid.org/0000-0001-8289-7752), Norwegian Inst. of Bioeconomy Research, Ås, Akershus, Norway
| | - Ida Fløystad
- H. G. Eiken (https://orcid.org/0000-0002-5368-3648), I. Fløystad (https://orcid.org/0000-0002-0484-4265) and S. B. Hagen (https://orcid.org/0000-0001-8289-7752), Norwegian Inst. of Bioeconomy Research, Ås, Akershus, Norway
| | - Karen Graham
- I. Phoebus (https://orcid.org/0000-0001-5333-0298) ✉ , K. Graham, A. Sorensen and G. Stenhouse (https://orcid.org/0000-0003-4551-4585), fRI Research Grizzly Bear Program, Hinton, AB, Canada
| | - Snorre B. Hagen
- H. G. Eiken (https://orcid.org/0000-0002-5368-3648), I. Fløystad (https://orcid.org/0000-0002-0484-4265) and S. B. Hagen (https://orcid.org/0000-0001-8289-7752), Norwegian Inst. of Bioeconomy Research, Ås, Akershus, Norway
| | - Anja Sorensen
- I. Phoebus (https://orcid.org/0000-0001-5333-0298) ✉ , K. Graham, A. Sorensen and G. Stenhouse (https://orcid.org/0000-0003-4551-4585), fRI Research Grizzly Bear Program, Hinton, AB, Canada
| | - Gordon Stenhouse
- I. Phoebus (https://orcid.org/0000-0001-5333-0298) ✉ , K. Graham, A. Sorensen and G. Stenhouse (https://orcid.org/0000-0003-4551-4585), fRI Research Grizzly Bear Program, Hinton, AB, Canada
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31
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Density of wild felids in Sonora, Mexico: a comparison of spatially explicit capture-recapture methods. EUR J WILDLIFE RES 2020. [DOI: 10.1007/s10344-020-01401-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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32
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Bohm T, Hofer H. Population numbers, density and activity patterns of servals in savannah patches of Odzala-Kokoua National Park, Republic of Congo. Afr J Ecol 2018. [DOI: 10.1111/aje.12520] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Torsten Bohm
- Department of Evolutionary Ecology; Leibniz Institute for Zoo and Wildlife Research; Berlin Germany
- African Parks; Brazzaville Congo
| | - Heribert Hofer
- Department of Evolutionary Ecology; Leibniz Institute for Zoo and Wildlife Research; Berlin Germany
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