1
|
van Dam-Bates P, Papathomas M, Stevenson BC, Fewster RM, Turek D, Stewart FEC, Borchers DL. A flexible framework for spatial capture-recapture with unknown identities. Biometrics 2024; 80:ujad019. [PMID: 38372400 DOI: 10.1093/biomtc/ujad019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 10/30/2023] [Accepted: 11/22/2023] [Indexed: 02/20/2024]
Abstract
Camera traps or acoustic recorders are often used to sample wildlife populations. When animals can be individually identified, these data can be used with spatial capture-recapture (SCR) methods to assess populations. However, obtaining animal identities is often labor-intensive and not always possible for all detected animals. To address this problem, we formulate SCR, including acoustic SCR, as a marked Poisson process, comprising a single counting process for the detections of all animals and a mark distribution for what is observed (eg, animal identity, detector location). The counting process applies equally when it is animals appearing in front of camera traps and when vocalizations are captured by microphones, although the definition of a mark changes. When animals cannot be uniquely identified, the observed marks arise from a mixture of mark distributions defined by the animal activity centers and additional characteristics. Our method generalizes existing latent identity SCR models and provides an integrated framework that includes acoustic SCR. We apply our method to estimate density from a camera trap study of fisher (Pekania pennanti) and an acoustic survey of Cape Peninsula moss frog (Arthroleptella lightfooti). We also test it through simulation. We find latent identity SCR with additional marks such as sex or time of arrival to be a reliable method for estimating animal density.
Collapse
Affiliation(s)
- Paul van Dam-Bates
- School of Mathematics and Statistics, University of St Andrews, St Andrews, Fife, KY16 9LZ, United Kingdom
| | - Michail Papathomas
- School of Mathematics and Statistics, University of St Andrews, St Andrews, Fife, KY16 9LZ, United Kingdom
| | - Ben C Stevenson
- Department of Statistics, University of Auckland, Auckland, 1010, New Zealand
| | - Rachel M Fewster
- Department of Statistics, University of Auckland, Auckland, 1010, New Zealand
| | - Daniel Turek
- Department of Mathematics and Statistics, Williams College, Williamstown, 01267, United States
| | - Frances E C Stewart
- Department of Biology, Wilfrid Laurier University, Waterloo, N2L 3C5, Canada
| | - David L Borchers
- School of Mathematics and Statistics, University of St Andrews, St Andrews, Fife, KY16 9LZ, United Kingdom
- Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences, University of Cape Town, Private Bag 7700, Rondebosch, South Africa
| |
Collapse
|
2
|
Cove MV, Herrmann V, Herrera DJ, Augustine BC, Flockhart DTT, McShea WJ. Counting the Capital's cats: Estimating drivers of abundance of free-roaming cats with a novel hierarchical model. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2023; 33:e2790. [PMID: 36482050 DOI: 10.1002/eap.2790] [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: 02/09/2022] [Revised: 07/11/2022] [Accepted: 08/23/2022] [Indexed: 06/17/2023]
Abstract
Free-roaming cats are a conservation concern in many areas but identifying their impacts and developing mitigation strategies requires a robust understanding of their distribution and density patterns. Urban and residential areas may be especially relevant in this process because free-roaming cats are abundant in these anthropogenic landscapes. Here, we estimate the occupancy and density of free-roaming cats in Washington D.C. and relate these metrics to known landscape and social factors. We conducted an extended camera trap survey of public and private spaces across D.C. and analyzed data collected from 1483 camera deployments from 2018 to 2020. We estimated citywide cat distribution by fitting hierarchical occupancy models and further estimated cat abundance using a novel random thinning spatial capture-recapture model that allows for the use of photos that can and cannot be identified to individual. Within this model, we utilized individual covariates that provided identity exclusions between photos of unidentifiable cats with inconsistent coat patterns, thus increasing the precision of abundance estimates. This combined model also allowed for unbiased estimation of density when animals cannot be identified to individual at the same rate as for free-roaming cats whose identifiability depended on their coat characteristics. Cat occupancy and abundance declined with increasing distance from residential areas, an effect that was more pronounced in wealthier neighborhoods. There was noteworthy absence of cats detected in larger public spaces and forests. Realized densities ranged from 0.02 to 1.75 cats/ha in sampled areas, resulting in a district-wide estimate of ~7296 free-roaming cats. Ninety percent of cat detections lacked collars and nearly 35% of known individuals were ear-tipped, indicative of district Trap-Neuter-Return (TNR) programs. These results suggest that we mainly sampled and estimated the unowned cat subpopulation, such that indoor/outdoor housecats were not well represented. The precise estimation of cat population densities is difficult due to the varied behavior of subpopulations within free-roaming cat populations (housecats, stray and feral cats), but our methods provide a first step in establishing citywide baselines to inform data-driven management plans for free-roaming cats in urban environments.
Collapse
Affiliation(s)
- Michael V Cove
- North Carolina Museum of Natural Sciences, Raleigh, North Carolina, USA
- Smithsonian Conservation Biology Institute, Front Royal, Virginia, USA
| | | | - Daniel J Herrera
- Department of Environmental Science and Policy, College of Science, George Mason University, Fairfax, Virginia, USA
| | - Ben C Augustine
- Department of Natural Resources, Cornell University, Ithaca, New York, USA
| | - D T Tyler Flockhart
- Appalachian Laboratory - University of Maryland Center for Environmental Science, Frostburg, Maryland, USA
| | - William J McShea
- Smithsonian Conservation Biology Institute, Front Royal, Virginia, USA
| |
Collapse
|
3
|
Clink DJ, Kier I, Ahmad AH, Klinck H. A workflow for the automated detection and classification of female gibbon calls from long-term acoustic recordings. Front Ecol Evol 2023. [DOI: 10.3389/fevo.2023.1071640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023] Open
Abstract
Passive acoustic monitoring (PAM) allows for the study of vocal animals on temporal and spatial scales difficult to achieve using only human observers. Recent improvements in recording technology, data storage, and battery capacity have led to increased use of PAM. One of the main obstacles in implementing wide-scale PAM programs is the lack of open-source programs that efficiently process terabytes of sound recordings and do not require large amounts of training data. Here we describe a workflow for detecting, classifying, and visualizing female Northern grey gibbon calls in Sabah, Malaysia. Our approach detects sound events using band-limited energy summation and does binary classification of these events (gibbon female or not) using machine learning algorithms (support vector machine and random forest). We then applied an unsupervised approach (affinity propagation clustering) to see if we could further differentiate between true and false positives or the number of gibbon females in our dataset. We used this workflow to address three questions: (1) does this automated approach provide reliable estimates of temporal patterns of gibbon calling activity; (2) can unsupervised approaches be applied as a post-processing step to improve the performance of the system; and (3) can unsupervised approaches be used to estimate how many female individuals (or clusters) there are in our study area? We found that performance plateaued with >160 clips of training data for each of our two classes. Using optimized settings, our automated approach achieved a satisfactory performance (F1 score ~ 80%). The unsupervised approach did not effectively differentiate between true and false positives or return clusters that appear to correspond to the number of females in our study area. Our results indicate that more work needs to be done before unsupervised approaches can be reliably used to estimate the number of individual animals occupying an area from PAM data. Future work applying these methods across sites and different gibbon species and comparisons to deep learning approaches will be crucial for future gibbon conservation initiatives across Southeast Asia.
Collapse
|
4
|
Chaudhuri S, Rajaraman R, Kalyanasundaram S, Sathyakumar S, Krishnamurthy R. N-mixture model-based estimate of relative abundance of sloth bear ( Melursus ursinus) in response to biotic and abiotic factors in a human-dominated landscape of central India. PeerJ 2022; 10:e13649. [PMID: 36523470 PMCID: PMC9745790 DOI: 10.7717/peerj.13649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 06/07/2022] [Indexed: 12/12/2022] Open
Abstract
Reliable estimation of abundance is a prerequisite for a species' conservation planning in human-dominated landscapes, especially if the species is elusive and involved in conflicts. As a means of population estimation, the importance of camera traps has been recognized globally, although estimating the abundance of unmarked, cryptic species has always been a challenge to conservation biologists. This study explores the use of the N-mixture model with three probability distributions, i.e., Poisson, negative binomial (NB) and zero-inflated Poisson (ZIP), to estimate the relative abundance of sloth bears (Melursus ursinus) based on a camera trapping exercise in Sanjay Tiger Reserve, Madhya Pradesh from December 2016 to April 2017. We used environmental and anthropogenic covariates to model the variation in the abundance of sloth bears. We also compared null model estimates (mean site abundance) obtained from the N-mixture model to those of the Royle-Nichols abundance-induced heterogeneity model (RN model) to assess the application of similar site-structured models. Models with Poisson distributions produced ecologically realistic and more precise estimates of mean site abundance (λ = 2.60 ± 0.64) compared with other distributions, despite the relatively high Akaike Information Criterion value. Area of mixed and sal forest, the photographic capture rate of humans and distance to the nearest village predicted a higher relative abundance of sloth bears. Mean site abundance estimates of sloth bears obtained from the N-mixture model (Poisson distribution) and the RN model were comparable, indicating the overall utility of these models in this field. However, density estimates of sloth bears based on spatially explicit methods are essential for evaluating the efficacy of the relatively more cost-effective N-mixture model. Compared to commonly used index/encounter-based methods, the N-mixture model equipped with knowledge on governing biotic and abiotic factors provides better relative abundance estimates for a species like the sloth bear. In the absence of absolute abundance estimates, the present study could be insightful for the long-term conservation and management of sloth bears.
Collapse
Affiliation(s)
- Sankarshan Chaudhuri
- Department of Landscape Level Planning and Management, Wildlife Institute of India, Dehradun, Uttarakhand, India
| | - Rajasekar Rajaraman
- Department of Landscape Level Planning and Management, Wildlife Institute of India, Dehradun, Uttarakhand, India
| | | | - Sambandam Sathyakumar
- Department of Endangered Species Management, Wildlife Institute of India, Dehradun, Uttarakhand, India
| | - Ramesh Krishnamurthy
- Department of Landscape Level Planning and Management, Wildlife Institute of India, Dehradun, Uttarakhand, India
- Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, Canada
| |
Collapse
|
5
|
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
| |
Collapse
|
6
|
A cautionary tale comparing spatial count and partial identity models for estimating densities of threatened and unmarked populations. Glob Ecol Conserv 2022. [DOI: 10.1016/j.gecco.2022.e02268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
|
7
|
Jiménez J, Díaz‐Ruiz F, Monterroso P, Tobajas J, Ferreras P. Occupancy data improves parameter precision in spatial capture-recapture models. Ecol Evol 2022; 12:e9250. [PMID: 36052294 PMCID: PMC9412271 DOI: 10.1002/ece3.9250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 07/22/2022] [Accepted: 08/05/2022] [Indexed: 11/17/2022] Open
Abstract
Population size is one of the basic demographic parameters for species management and conservation. Among different estimation methods, spatially explicit capture-recapture (SCR) models allow the estimation of population density in a framework that has been greatly developed in recent years. The use of automated detection devices, such as camera traps, has impressively extended SCR studies for individually identifiable species. However, its application to unmarked/partially marked species remains challenging, and no specific method has been widely used. We fitted an SCR-integrated model (SCR-IM) to stone marten Martes foina data, a species for which only some individuals are individually recognizable by natural marks, and estimate population size based on integration of three submodels: (1) individual capture histories from live capture and transponder tagging; (2) detection/nondetection or "occupancy" data using camera traps in a bigger area to extend the geographic scope of capture-recapture data; and (3) telemetry data from a set of tagged individuals. We estimated a stone marten density of 0.352 (SD: 0.081) individuals/km2. We simulated four dilution scenarios of occupancy data to study the variation in the coefficient of variation in population size estimates. We also used simulations with similar characteristics as the stone marten case study, comparing the accuracy and precision obtained from SCR-IM and SCR, to understand how submodels' integration affects the posterior distributions of estimated parameters. Based on our simulations, we found that population size estimates using SCR-IM are more accurate and precise. In our stone marten case study, the SCR-IM density estimation increased the precision by 37% when compared to the standard SCR model as regards to the coefficient of variation. This model has high potential to be used for species in which individual recognition by natural markings is not possible, therefore limiting the need to rely on invasive sampling procedures.
Collapse
Affiliation(s)
- José Jiménez
- Instituto de Investigación en Recursos Cinegéticos (IREC, CSIC‐UCLM‐JCCM)Ciudad RealSpain
| | - Francisco Díaz‐Ruiz
- Departamento de Biología Animal, Facultad de CienciasUniversidad de MálagaMálagaSpain
| | - Pedro Monterroso
- CIBIO, Centro de Investigacão em Biodiversidade e Recursos Genéticos, InBIO Laboratório AssociadoUniversidade do PortoVairãoPortugal
- BIOPOLIS Program in Genomics, Biodiversity and Land PlanningCIBIOVairãoPortugal
| | - Jorge Tobajas
- Instituto de Investigación en Recursos Cinegéticos (IREC, CSIC‐UCLM‐JCCM)Ciudad RealSpain
- Departamento de Botánica, Ecología y Fisiología VegetalUniversidad de CórdobaCórdobaSpain
| | - Pablo Ferreras
- Instituto de Investigación en Recursos Cinegéticos (IREC, CSIC‐UCLM‐JCCM)Ciudad RealSpain
| |
Collapse
|
8
|
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
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
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
|
11
|
Brownlee MB, Warbington CH, Boyce MS. Monitoring sitatunga (
Tragelaphus spekii
) populations using camera traps. Afr J Ecol 2022. [DOI: 10.1111/aje.12972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Megan B. Brownlee
- Department of Biological Sciences University of Alberta Edmonton Alberta Canada
| | | | - Mark S. Boyce
- Department of Biological Sciences University of Alberta Edmonton Alberta Canada
| |
Collapse
|
12
|
Murphy SM, Adams JR, Waits LP, Cox JJ. Evaluating otter reintroduction outcomes using genetic spatial capture-recapture modified for dendritic networks. Ecol Evol 2021; 11:15047-15061. [PMID: 34765159 PMCID: PMC8571598 DOI: 10.1002/ece3.8187] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 09/06/2021] [Accepted: 09/10/2021] [Indexed: 11/23/2022] Open
Abstract
Monitoring the demographics and genetics of reintroduced populations is critical to evaluating reintroduction success, but species ecology and the landscapes that they inhabit often present challenges for accurate assessments. If suitable habitats are restricted to hierarchical dendritic networks, such as river systems, animal movements are typically constrained and may violate assumptions of methods commonly used to estimate demographic parameters. Using genetic detection data collected via fecal sampling at latrines, we demonstrate applicability of the spatial capture-recapture (SCR) network distance function for estimating the size and density of a recently reintroduced North American river otter (Lontra canadensis) population in the Upper Rio Grande River dendritic network in the southwestern United States, and we also evaluated the genetic outcomes of using a small founder group (n = 33 otters) for reintroduction. Estimated population density was 0.23-0.28 otter/km, or 1 otter/3.57-4.35 km, with weak evidence of density increasing with northerly latitude (β = 0.33). Estimated population size was 83-104 total otters in 359 km of riverine dendritic network, which corresponded to average annual exponential population growth of 1.12-1.15/year since reintroduction. Growth was ≥40% lower than most reintroduced river otter populations and strong evidence of a founder effect existed 8-10 years post-reintroduction, including 13-21% genetic diversity loss, 84%-87% genetic effective population size decline, and rapid divergence from the source population (F ST accumulation = 0.06/generation). Consequently, genetic restoration via translocation of additional otters from other populations may be necessary to mitigate deleterious genetic effects in this small, isolated population. Combined with non-invasive genetic sampling, the SCR network distance approach is likely widely applicable to demogenetic assessments of both reintroduced and established populations of multiple mustelid species that inhabit aquatic dendritic networks, many of which are regionally or globally imperiled and may warrant reintroduction or augmentation efforts.
Collapse
Affiliation(s)
- Sean M. Murphy
- Wildlife Management DivisionNew Mexico Department of Game & FishSanta FeNew MexicoUSA
| | - Jennifer R. Adams
- Department of Fish and Wildlife SciencesUniversity of IdahoMoscowIdahoUSA
| | - Lisette P. Waits
- Department of Fish and Wildlife SciencesUniversity of IdahoMoscowIdahoUSA
| | - John J. Cox
- Department of Forestry and Natural ResourcesUniversity of KentuckyLexingtonKentuckyUSA
| |
Collapse
|
13
|
Vidal M, Wolf N, Rosenberg B, Harris BP, Mathis A. Perspectives on Individual Animal Identification from Biology and Computer Vision. Integr Comp Biol 2021; 61:900-916. [PMID: 34050741 PMCID: PMC8490693 DOI: 10.1093/icb/icab107] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Identifying individual animals is crucial for many biological investigations. In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. Here, we review current advances of computer vision identification techniques to provide both computer scientists and biologists with an overview of the available tools and discuss their applications. We conclude by offering recommendations for starting an animal identification project, illustrate current limitations, and propose how they might be addressed in the future.
Collapse
Affiliation(s)
- Maxime Vidal
- School of Life Sciences, Brain Mind Institute, Swiss Federal Institute of Technology (EPFL), Chemin des Mines 9, 1202 Geneva, Switzerland
- Center for Neuroprosthetics, Center for Intelligent Systems, Swiss Federal Institute of Technology (EPFL), Chemin des Mines 9, 1202 Geneva, Switzerland
| | - Nathan Wolf
- Fisheries, Aquatic Science, and Technology Laboratory, Alaska Pacific University, 4101 University Drive, Anchorage, Alaska 99508, USA
| | - Beth Rosenberg
- Fisheries, Aquatic Science, and Technology Laboratory, Alaska Pacific University, 4101 University Drive, Anchorage, Alaska 99508, USA
| | - Bradley P Harris
- Fisheries, Aquatic Science, and Technology Laboratory, Alaska Pacific University, 4101 University Drive, Anchorage, Alaska 99508, USA
| | - Alexander Mathis
- School of Life Sciences, Brain Mind Institute, Swiss Federal Institute of Technology (EPFL), Chemin des Mines 9, 1202 Geneva, Switzerland
- Center for Neuroprosthetics, Center for Intelligent Systems, Swiss Federal Institute of Technology (EPFL), Chemin des Mines 9, 1202 Geneva, Switzerland
| |
Collapse
|
14
|
Ruprecht JS, Eriksson CE, Forrester TD, Clark DA, Wisdom MJ, Rowland MM, Johnson BK, Levi T. Evaluating and integrating spatial capture-recapture models with data of variable individual identifiability. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02405. [PMID: 34245619 PMCID: PMC9286611 DOI: 10.1002/eap.2405] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 01/17/2021] [Accepted: 02/22/2021] [Indexed: 05/05/2023]
Abstract
Spatial capture-recapture (SCR) models have become the preferred tool for estimating densities of carnivores. Within this family of models are variants requiring identification of all individuals in each encounter (SCR), a subset of individuals only (generalized spatial mark-resight, gSMR), or no individual identification (spatial count or spatial presence-absence). Although each technique has been shown through simulation to yield unbiased results, the consistency and relative precision of estimates across methods in real-world settings are seldom considered. We tested a suite of models ranging from those only requiring detections of unmarked individuals to others that integrate remote camera, physical capture, genetic, and global positioning system (GPS) data into a hybrid model, to estimate population densities of black bears, bobcats, cougars, and coyotes. For each species, we genotyped fecal DNA collected with detection dogs during a 20-d period. A subset of individuals from each species was affixed with GPS collars bearing unique markings and resighted by remote cameras over 140 d contemporaneous with scat collection. Camera-based gSMR models produced density estimates that differed by <10% from genetic SCR for bears, cougars, and coyotes once important sources of variation (sex or behavioral status) were controlled for. For bobcats, SCR estimates were 33% higher than gSMR. The cause of the discrepancies in estimates was likely attributable to challenges designing a study compatible for species with disparate home range sizes and the difficulty of collecting sufficient data in a timeframe in which demographic closure could be assumed. Unmarked models estimated densities that varied greatly from SCR, but estimates became more consistent in models wherein more individuals were identifiable. Hybrid models containing all data sources exhibited the most precise estimates for all species. For studies in which only sparse data can be obtained and the strictest model assumptions are unlikely to be met, we suggest researchers use caution making inference from models lacking individual identity. For best results, we further recommend the use of methods requiring at least a subset of the population is marked and that multiple data sets are incorporated when possible.
Collapse
Affiliation(s)
- Joel S. Ruprecht
- Department of Fisheries and WildlifeOregon State University104 Nash HallCorvallisOregon97331USA
| | - Charlotte E. Eriksson
- Department of Fisheries and WildlifeOregon State University104 Nash HallCorvallisOregon97331USA
| | - Tavis D. Forrester
- Oregon Department of Fish and Wildlife1401 Gekeler LaneLa GrandeOregon97850USA
| | - Darren A. Clark
- Oregon Department of Fish and Wildlife1401 Gekeler LaneLa GrandeOregon97850USA
| | - Michael J. Wisdom
- Pacific Northwest Research StationUSDA Forest Service1401 Gekeler LaneLa GrandeOregon97850USA
| | - Mary M. Rowland
- Pacific Northwest Research StationUSDA Forest Service1401 Gekeler LaneLa GrandeOregon97850USA
| | - Bruce K. Johnson
- Oregon Department of Fish and Wildlife1401 Gekeler LaneLa GrandeOregon97850USA
| | - Taal Levi
- Department of Fisheries and WildlifeOregon State University104 Nash HallCorvallisOregon97331USA
| |
Collapse
|
15
|
Brommer JE, Poutanen J, Pusenius J, Wikström M. Estimating preharvest density, adult sex ratio, and fecundity of white-tailed deer using noninvasive sampling techniques. Ecol Evol 2021; 11:14312-14326. [PMID: 34707857 PMCID: PMC8525134 DOI: 10.1002/ece3.8149] [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: 04/06/2021] [Revised: 09/03/2021] [Accepted: 09/06/2021] [Indexed: 11/08/2022] Open
Abstract
Adult sex ratio and fecundity (juveniles per female) are key population parameters in sustainable wildlife management, but inferring these requires abundance estimates of at least three age/sex classes of the population (male and female adults and juveniles). Prior to harvest, we used an array of 36 wildlife camera traps during 2 and 3 weeks in the early autumn of 2016 and 2017, respectively. We recorded white-tailed deer adult males, adult females, and fawns from the pictures. Simultaneously, we collected fecal DNA (fDNA) from 92 20 m × 20 m plots placed in 23 clusters of four plots between the camera traps. We identified individuals from fDNA samples with microsatellite markers and estimated the total sex ratio and population density using spatial capture-recapture (SCR). The fDNA-SCR analysis concluded equal sex ratio in the first year and female bias in the second year, and no difference in space use between sexes (fawns and adults combined). Camera information was analyzed in a spatial capture (SC) framework assuming an informative prior for animals' space use, either (a) as estimated by fDNA-SCR (same for all age/sex classes), (b) as assumed from the literature (space use of adult males larger than adult females and fawns), or (c) by inferring adult male space use from individually identified males from the camera pictures. These various SC approaches produced plausible inferences on fecundity, but also inferred total density to be lower than the estimate provided by fDNA-SCR in one of the study years. SC approaches where adult male and female were allowed to differ in their space use suggested the population had a female-biased adult sex ratio. In conclusion, SC approaches allowed estimating the preharvest population parameters of interest and provided conservative density estimates.
Collapse
|
16
|
Tosa MI, Dziedzic EH, Appel CL, Urbina J, Massey A, Ruprecht J, Eriksson CE, Dolliver JE, Lesmeister DB, Betts MG, Peres CA, Levi T. The Rapid Rise of Next-Generation Natural History. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.698131] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Many ecologists have lamented the demise of natural history and have attributed this decline to a misguided view that natural history is outdated and unscientific. Although there is a perception that the focus in ecology and conservation have shifted away from descriptive natural history research and training toward hypothetico-deductive research, we argue that natural history has entered a new phase that we call “next-generation natural history.” This renaissance of natural history is characterized by technological and statistical advances that aid in collecting detailed observations systematically over broad spatial and temporal extents. The technological advances that have increased exponentially in the last decade include electronic sensors such as camera-traps and acoustic recorders, aircraft- and satellite-based remote sensing, animal-borne biologgers, genetics and genomics methods, and community science programs. Advances in statistics and computation have aided in analyzing a growing quantity of observations to reveal patterns in nature. These robust next-generation natural history datasets have transformed the anecdotal perception of natural history observations into systematically collected observations that collectively constitute the foundation for hypothetico-deductive research and can be leveraged and applied to conservation and management. These advances are encouraging scientists to conduct and embrace detailed descriptions of nature that remain a critically important component of the scientific endeavor. Finally, these next-generation natural history observations are engaging scientists and non-scientists alike with new documentations of the wonders of nature. Thus, we celebrate next-generation natural history for encouraging people to experience nature directly.
Collapse
|
17
|
Kukielka EA, MartÍnez‐lÓpez B, Ballweber LR, Buttke D, Patrick K, Wold EB, Mazur R. Spatial‐Mark‐Resight Model to Estimate Raccoon Abundance in Yosemite Valley, California. WILDLIFE SOC B 2021. [DOI: 10.1002/wsb.1182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Esther A. Kukielka
- Center for Animal Disease Modeling and Surveillance, Dept. Medicine & Epidemiology, School of Veterinary Medicine University of California Davis, One Shields Avenue, 2108 Tupper Hall Davis CA 95616 USA
| | - Beatriz MartÍnez‐lÓpez
- Center for Animal Disease Modeling and Surveillance, Dept. Medicine & Epidemiology, School of Veterinary Medicine University of California Davis, One Shields Avenue, 2108 Tupper Hall Davis CA 95616 USA
| | - Lora R. Ballweber
- College of Veterinary Medicine and Biomedical Sciences Colorado State University Fort Collins 80523‐1682
| | - Danielle Buttke
- Biological Resources Division/Wildlife Health Branch and Office of Public Health National Park Service 1201 Oakridge Drive, Suite 200 Fort Collins CO 80525
| | - Katie Patrick
- Division of Resources Management and Science Yosemite National Park 9039 Village Drive Yosemite CA 95389 USA
| | - E. Binta Wold
- Division of Resources Management and Science Yosemite National Park 9039 Village Drive Yosemite CA 95389 USA
| | - Rachel Mazur
- Division of Resources Management and Science Yosemite National Park 5083 Foresta Road El Portal CA 95318 USA
| |
Collapse
|
18
|
Phosri K, Tantipisanuh N, Chutipong W, Gore ML, Giordano AJ, Ngoprasert D. Fishing cats in an anthropogenic landscape: A multi-method assessment of local population status and threats. Glob Ecol Conserv 2021. [DOI: 10.1016/j.gecco.2021.e01615] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
|
19
|
Delisle ZJ, Flaherty EA, Nobbe MR, Wzientek CM, Swihart RK. Next-Generation Camera Trapping: Systematic Review of Historic Trends Suggests Keys to Expanded Research Applications in Ecology and Conservation. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.617996] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Camera trapping is an effective non-invasive method for collecting data on wildlife species to address questions of ecological and conservation interest. We reviewed 2,167 camera trap (CT) articles from 1994 to 2020. Through the lens of technological diffusion, we assessed trends in: (1) CT adoption measured by published research output, (2) topic, taxonomic, and geographic diversification and composition of CT applications, and (3) sampling effort, spatial extent, and temporal duration of CT studies. Annual publications of CT articles have grown 81-fold since 1994, increasing at a rate of 1.26 (SE = 0.068) per year since 2005, but with decelerating growth since 2017. Topic, taxonomic, and geographic richness of CT studies increased to encompass 100% of topics, 59.4% of ecoregions, and 6.4% of terrestrial vertebrates. However, declines in per article rates of accretion and plateaus in Shannon's H for topics and major taxa studied suggest upper limits to further diversification of CT research as currently practiced. Notable compositional changes of topics included a decrease in capture-recapture, recent decrease in spatial-capture-recapture, and increases in occupancy, interspecific interactions, and automated image classification. Mammals were the dominant taxon studied; within mammalian orders carnivores exhibited a unimodal peak whereas primates, rodents and lagomorphs steadily increased. Among biogeographic realms we observed decreases in Oceania and Nearctic, increases in Afrotropic and Palearctic, and unimodal peaks for Indomalayan and Neotropic. Camera days, temporal extent, and area sampled increased, with much greater rates for the 0.90 quantile of CT studies compared to the median. Next-generation CT studies are poised to expand knowledge valuable to wildlife ecology and conservation by posing previously infeasible questions at unprecedented spatiotemporal scales, on a greater array of species, and in a wider variety of environments. Converting potential into broad-based application will require transferable models of automated image classification, and data sharing among users across multiple platforms in a coordinated manner. Further taxonomic diversification likely will require technological modifications that permit more efficient sampling of smaller species and adoption of recent improvements in modeling of unmarked populations. Environmental diversification can benefit from engineering solutions that expand ease of CT sampling in traditionally challenging sites.
Collapse
|
20
|
Harihar A, Lahkar D, Singh A, Das SK, Ahmed MF, Begum RH. Population density modelling of mixed polymorphic phenotypes: an application of spatial mark‐resight models. Anim Conserv 2021. [DOI: 10.1111/acv.12677] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- A. Harihar
- Panthera New York NY USA
- Nature Conservation Foundation Mysore Karnataka India
| | - D. Lahkar
- Aaranyak Guwahati Assam India
- Department of Life Science and Bioinformatics Assam University (Diphu Campus) Diphu Assam India
| | | | | | | | - R. H. Begum
- Department of Life Science and Bioinformatics Assam University (Diphu Campus) Diphu Assam India
| |
Collapse
|
21
|
Beausoleil RA, Welfelt LS, Keren IN, Kertson BN, Maletzke BT, Koehler GM. Long‐Term Evaluation of Cougar Density and Application of Risk Analysis for Harvest Management. J Wildl Manage 2021. [DOI: 10.1002/jwmg.22007] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Richard A. Beausoleil
- Washington Department of Fish and Wildlife 3515 State Highway 97A Wenatchee WA 98801 USA
| | - Lindsay S. Welfelt
- Washington Department of Fish and Wildlife 3860 State Highway 97A Wenatchee WA 98801 USA
| | - Ilai N. Keren
- Washington Department of Fish and Wildlife 600 Capitol Way N Olympia WA 98801 USA
| | - Brian N. Kertson
- Washington Department of Fish and Wildlife 7007 Curtis Drive SE Snoqualmie WA 98065 USA
| | - Benjamin T. Maletzke
- Washington Department of Fish and Wildlife 1130 W. University Way Ellensburg WA 98943 USA
| | - Gary M. Koehler
- Washington Department of Fish and Wildlife 2218 Stephanie Brooke Wenatchee WA 98801 USA
| |
Collapse
|
22
|
Jiménez J, C. Augustine B, Linden DW, B. Chandler R, Royle JA. Spatial capture-recapture with random thinning for unidentified encounters. Ecol Evol 2021; 11:1187-1198. [PMID: 33598123 PMCID: PMC7863675 DOI: 10.1002/ece3.7091] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 11/16/2020] [Accepted: 11/18/2020] [Indexed: 11/08/2022] Open
Abstract
Spatial capture-recapture (SCR) models have increasingly been used as a basis for combining capture-recapture data types with variable levels of individual identity information to estimate population density and other demographic parameters. Recent examples are the unmarked SCR (or spatial count model), where no individual identities are available and spatial mark-resight (SMR) where individual identities are available for only a marked subset of the population. Currently lacking, though, is a model that allows unidentified samples to be combined with identified samples when there are no separate classes of "marked" and "unmarked" individuals and when the two sample types cannot be considered as arising from two independent observation models. This is a common scenario when using noninvasive sampling methods, for example, when analyzing data on identified and unidentified photographs or scats from the same sites.Here we describe a "random thinning" SCR model that utilizes encounters of both known and unknown identity samples using a natural mechanistic dependence between samples arising from a single observation model. Our model was fitted in a Bayesian framework using NIMBLE.We investigate the improvement in parameter estimates by including the unknown identity samples, which was notable (up to 79% more precise) in low-density populations with a low rate of identified encounters. We then applied the random thinning SCR model to a noninvasive genetic sampling study of brown bear (Ursus arctos) density in Oriental Cantabrian Mountains (North Spain).Our model can improve density estimation for noninvasive sampling studies for low-density populations with low rates of individual identification, by making use of available data that might otherwise be discarded.
Collapse
Affiliation(s)
- José Jiménez
- Instituto de Investigación en Recursos Cinegéticos (IREC, CSIC‐UCLM‐JCCM)Ronda de Toledo, 12Ciudad Real13071Spain
| | - Ben C. Augustine
- U.S. Geological Survey John Wesley Powell CenterCornell Department of Natural ResourcesIthacaNew York14853USA
| | - Daniel W. Linden
- Greater Atlantic Regional Fisheries OfficeNOAA National Marine Fisheries Service55 Great Republic DriveGloucesterMassachusetts01922USA
| | - Richard B. Chandler
- Warnell School of Forestry and Natural ResourcesUniversity of Georgia180 E. Green StreetAthensGeorgia30602USA
| | - J. Andrew Royle
- U.S. Geological SurveyPatuxent Wildlife Research Center12100 Beech Forest RoadLaurelMaryland20708USA
| |
Collapse
|
23
|
Gilbert NA, Clare JDJ, Stenglein JL, Zuckerberg B. Abundance estimation of unmarked animals based on camera-trap data. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2021; 35:88-100. [PMID: 32297655 DOI: 10.1111/cobi.13517] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 04/02/2020] [Accepted: 04/10/2020] [Indexed: 06/11/2023]
Abstract
The rapid improvement of camera traps in recent decades has revolutionized biodiversity monitoring. Despite clear applications in conservation science, camera traps have seldom been used to model the abundance of unmarked animal populations. We sought to summarize the challenges facing abundance estimation of unmarked animals, compile an overview of existing analytical frameworks, and provide guidance for practitioners seeking a suitable method. When a camera records multiple detections of an unmarked animal, one cannot determine whether the images represent multiple mobile individuals or a single individual repeatedly entering the camera viewshed. Furthermore, animal movement obfuscates a clear definition of the sampling area and, as a result, the area to which an abundance estimate corresponds. Recognizing these challenges, we identified 6 analytical approaches and reviewed 927 camera-trap studies published from 2014 to 2019 to assess the use and prevalence of each method. Only about 5% of the studies used any of the abundance-estimation methods we identified. Most of these studies estimated local abundance or covariate relationships rather than predicting abundance or density over broader areas. Next, for each analytical approach, we compiled the data requirements, assumptions, advantages, and disadvantages to help practitioners navigate the landscape of abundance estimation methods. When seeking an appropriate method, practitioners should evaluate the life history of the focal taxa, carefully define the area of the sampling frame, and consider what types of data collection are possible. The challenge of estimating abundance of unmarked animal populations persists; although multiple methods exist, no one method is optimal for camera-trap data under all circumstances. As analytical frameworks continue to evolve and abundance estimation of unmarked animals becomes increasingly common, camera traps will become even more important for informing conservation decision-making.
Collapse
Affiliation(s)
- Neil A Gilbert
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, 53706, U.S.A
| | - John D J Clare
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, 53706, U.S.A
| | - Jennifer L Stenglein
- Wisconsin Department of Natural Resources, 2901 Progress Drive, Madison, WI, 53716, U.S.A
| | - Benjamin Zuckerberg
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, 53706, U.S.A
| |
Collapse
|
24
|
Warbington CH, Boyce MS. Population density of sitatunga in riverine wetland habitats. Glob Ecol Conserv 2020. [DOI: 10.1016/j.gecco.2020.e01212] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
|
25
|
Stevenson BC, Dam‐Bates P, Young CKY, Measey J. A spatial capture–recapture model to estimate call rate and population density from passive acoustic surveys. Methods Ecol Evol 2020. [DOI: 10.1111/2041-210x.13522] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Ben C. Stevenson
- Department of Statistics University of Auckland Auckland New Zealand
| | - Paul Dam‐Bates
- School of Mathematics and Statistics University of St Andrews St Andrews UK
| | - Callum K. Y. Young
- Department of Statistics University of Auckland Auckland New Zealand
- Ministry of Business, Innovation, and Employment Auckland New Zealand
| | - John Measey
- Centre for Invasion Biology Department of Botany and Zoology Stellenbosch University Stellenbosch South Africa
| |
Collapse
|
26
|
Clink DJ, Klinck H. Unsupervised acoustic classification of individual gibbon females and the implications for passive acoustic monitoring. Methods Ecol Evol 2020. [DOI: 10.1111/2041-210x.13520] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- Dena J. Clink
- Center for Conservation Bioacoustics Cornell Laboratory of Ornithology Cornell University Ithaca NY USA
| | - Holger Klinck
- Center for Conservation Bioacoustics Cornell Laboratory of Ornithology Cornell University Ithaca NY USA
| |
Collapse
|
27
|
Davis RS, Stone EL, Gentle LK, Mgoola WO, Uzal A, Yarnell RW. Spatial partial identity model reveals low densities of leopard and spotted hyaena in a miombo woodland. J Zool (1987) 2020. [DOI: 10.1111/jzo.12838] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- R. S. Davis
- School of Animal, Rural and Environmental Sciences Nottingham Trent University Southwell Nottinghamshire UK
| | - E. L. Stone
- Department of Applied Sciences University of the West of England Bristol UK
- Carnivore Research Malawi & Conservation Research Africa Lilongwe Malawi
| | - L. K. Gentle
- School of Animal, Rural and Environmental Sciences Nottingham Trent University Southwell Nottinghamshire UK
| | - W. O. Mgoola
- Department of National Parks and Wildlife Malawi Lilongwe Malawi
| | - A. Uzal
- School of Animal, Rural and Environmental Sciences Nottingham Trent University Southwell Nottinghamshire UK
| | - R. W. Yarnell
- School of Animal, Rural and Environmental Sciences Nottingham Trent University Southwell Nottinghamshire UK
| |
Collapse
|
28
|
Spatial proximity moderates genotype uncertainty in genetic tagging studies. Proc Natl Acad Sci U S A 2020; 117:17903-17912. [PMID: 32661176 DOI: 10.1073/pnas.2000247117] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Accelerating declines of an increasing number of animal populations worldwide necessitate methods to reliably and efficiently estimate demographic parameters such as population density and trajectory. Standard methods for estimating demographic parameters from noninvasive genetic samples are inefficient because lower-quality samples cannot be used, and they assume individuals are identified without error. We introduce the genotype spatial partial identity model (gSPIM), which integrates a genetic classification model with a spatial population model to combine both spatial and genetic information, thus reducing genotype uncertainty and increasing the precision of demographic parameter estimates. We apply this model to data from a study of fishers (Pekania pennanti) in which 37% of hair samples were originally discarded because of uncertainty in individual identity. The gSPIM density estimate using all collected samples was 25% more precise than the original density estimate, and the model identified and corrected three errors in the original individual identity assignments. A simulation study demonstrated that our model increased the accuracy and precision of density estimates 63 and 42%, respectively, using three replicated assignments (e.g., PCRs for microsatellites) per genetic sample. Further, the simulations showed that the gSPIM model parameters are identifiable with only one replicated assignment per sample and that accuracy and precision are relatively insensitive to the number of replicated assignments for high-quality samples. Current genotyping protocols devote the majority of resources to replicating and confirming high-quality samples, but when using the gSPIM, genotyping protocols could be more efficient by devoting more resources to low-quality samples.
Collapse
|
29
|
Tourani M, Dupont P, Nawaz MA, Bischof R. Multiple observation processes in spatial capture-recapture models: How much do we gain? Ecology 2020; 101:e03030. [PMID: 32112415 DOI: 10.1002/ecy.3030] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 12/27/2019] [Accepted: 01/29/2020] [Indexed: 11/06/2022]
Abstract
Population monitoring data may originate from multiple methods and are often sparse and fraught with incomplete information due to practical and economic constraints. Models that can integrate multiple survey methods and are able to cope with incomplete data may help investigators exploit available information more thoroughly. Here, we developed an integrated spatial capture-recapture (SCR) model to incorporate multiple data sources with imperfect individual identification. We contrast inferences drawn from this model with alternate models incorporating only subsets of the data available. Using extensive simulations and an empirical example of multi-method brown bear (Ursus arctos) monitoring data from northern Pakistan, we quantified the benefits of including multiple sources of information in SCR models in terms of parameter precision and bias. Our multiple observation processes SCR model (MOP) yielded a more complete picture of the underlying processes, reduced bias, and led to more precise parameter estimates. Our results suggest that the greatest gains from integrated SCR models can be expected in situations where detection probability is low, a large proportion of detections is not attributable to individuals, and the degree of overlap between individual home ranges is low.
Collapse
Affiliation(s)
- Mahdieh Tourani
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432, Ås, Norway
| | - Pierre Dupont
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432, Ås, Norway
| | - Muhammad Ali Nawaz
- Department of Animal Sciences, Quaid-i-Azam University, Islamabad, 44000, Pakistan.,Snow Leopard Trust, Islamabad, 44000, Pakistan
| | - Richard Bischof
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432, Ås, Norway
| |
Collapse
|
30
|
Augustine BC, Kéry M, Olano Marin J, Mollet P, Pasinelli G, Sutherland C. Sex‐specific population dynamics and demography of capercaillie (
Tetrao urogallus
L.) in a patchy environment. POPUL ECOL 2019. [DOI: 10.1002/1438-390x.12031] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Ben C. Augustine
- Department of Natural Resources, Atkinson Center for a Sustainable FutureCornell University Ithaca New York
| | - Marc Kéry
- Swiss Ornithological Institute Sempach Switzerland
| | | | | | | | - Chris Sutherland
- Department of Environmental ConservationUniversity of Massachusetts Amherst Massachusetts
| |
Collapse
|
31
|
Sun CC, Royle JA, Fuller AK. Incorporating citizen science data in spatially explicit integrated population models. Ecology 2019; 100:e02777. [PMID: 31168779 DOI: 10.1002/ecy.2777] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 04/04/2019] [Accepted: 04/16/2019] [Indexed: 11/09/2022]
Abstract
Information about population abundance, distribution, and demographic rates is critical for understanding a species' ecology and for effective conservation and management. To collect data over large spatial and temporal extents for such inferences, especially for species with low densities or wide distributions, citizen science can be an efficient approach. Integrated models have also emerged as an important methodology to estimate population parameters by combining multiple types of data, including citizen science data. We developed a spatially explicit integrated model that combines opportunistically collected presence-absence (PA) data, commonly collected in citizen science efforts, with systematically collected spatial capture-recapture (SCR) data, which are often limited to small spatial and temporal extents. We conducted single and multi-season simulations with parameters informed by North American black bear (Ursus americanus) populations, to evaluate the influence of varying amounts of opportunistic PA data collected at larger spatial and temporal extents on the estimation of population-level parameters. Integrating opportunistic PA data increased the precision and accuracy of posterior estimates of abundance, and survival and recruitment rates. In some cases, adding PA locations improved abundance estimates more than increasing PA detection probability. Posterior estimates were as precise and unbiased as when higher quality, but sparse, SCR data were available. We also applied the integrated model to SCR and citizen science PA data collected on black bears in New York, with results consistent with our simulations. Our findings indicate that citizen science in integrated models can be a cost-efficient way to improve estimates of population parameters and increase the spatiotemporal extent of inference. Continued developments with integrated models and citizen science data will offer additional ways to improve our understanding of population structure and demographics.
Collapse
Affiliation(s)
- Catherine C Sun
- New York Cooperative Fish and Wildlife Research Unit, Department of Natural Resources, Cornell University, 226 Mann Drive, Ithaca, New York, 14853, USA
| | - J Andrew Royle
- U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, Maryland, 20708, USA
| | - Angela K Fuller
- U.S. Geological Survey, New York Cooperative Fish and Wildlife Research Unit, Department of Natural Resources, Cornell University, 226 Mann Drive, Ithaca, NY, 14853, USA
| |
Collapse
|
32
|
Murphy SM, Wilckens DT, Augustine BC, Peyton MA, Harper GC. Improving estimation of puma (Puma concolor) population density: clustered camera-trapping, telemetry data, and generalized spatial mark-resight models. Sci Rep 2019; 9:4590. [PMID: 30872785 PMCID: PMC6418282 DOI: 10.1038/s41598-019-40926-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 02/26/2019] [Indexed: 11/23/2022] Open
Abstract
Obtaining reliable population density estimates for pumas (Puma concolor) and other cryptic, wide-ranging large carnivores is challenging. Recent advancements in spatially explicit capture-recapture models have facilitated development of novel survey approaches, such as clustered sampling designs, which can provide reliable density estimation for expansive areas with reduced effort. We applied clustered sampling to camera-traps to detect marked (collared) and unmarked pumas, and used generalized spatial mark-resight (SMR) models to estimate puma population density across 15,314 km2 in the southwestern USA. Generalized SMR models outperformed conventional SMR models. Integrating telemetry data from collars on marked pumas with detection data from camera-traps substantially improved density estimates by informing cryptic activity (home range) center transiency and improving estimation of the SMR home range parameter. Modeling sex of unmarked pumas as a partially identifying categorical covariate further improved estimates. Our density estimates (0.84–1.65 puma/100 km2) were generally more precise (CV = 0.24–0.31) than spatially explicit estimates produced from other puma sampling methods, including biopsy darting, scat detection dogs, and regular camera-trapping. This study provides an illustrative example of the effectiveness and flexibility of our combined sampling and analytical approach for reliably estimating density of pumas and other wildlife across geographically expansive areas.
Collapse
Affiliation(s)
- Sean M Murphy
- Wildlife Management Division, New Mexico Department of Game & Fish, Santa Fe, 87507, USA. .,Department of Forestry and Natural Resources, University of Kentucky, Lexington, 40546, USA.
| | - David T Wilckens
- Wildlife Management Division, New Mexico Department of Game & Fish, Santa Fe, 87507, USA
| | - Ben C Augustine
- Atkinson Center for a Sustainable Future, Department of Natural Resources, Cornell University, Ithaca, 14853, USA
| | - Mark A Peyton
- Valles Caldera National Preserve, U.S. National Park Service, Jemez Springs, 87025, USA
| | - Glenn C Harper
- Department of Natural Resources, Pueblo of Santa Ana, Santa Ana Pueblo, 87004, USA
| |
Collapse
|