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Carroll SL, Schmidt GM, Waller JS, Graves TA. Evaluating density-weighted connectivity of black bears (Ursus americanus) in Glacier National Park with spatial capture-recapture models. MOVEMENT ECOLOGY 2024; 12:8. [PMID: 38263096 DOI: 10.1186/s40462-023-00445-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 12/19/2023] [Indexed: 01/25/2024]
Abstract
BACKGROUND Improved understanding of wildlife population connectivity among protected area networks can support effective planning for the persistence of wildlife populations in the face of land use and climate change. Common approaches to estimating connectivity often rely on small samples of individuals without considering the spatial structure of populations, leading to limited understanding of how individual movement links to demography and population connectivity. Recently developed spatial capture-recapture (SCR) models provide a framework to formally connect inference about individual movement, connectivity, and population density, but few studies have applied this approach to empirical data to support connectivity planning. METHODS We used mark-recapture data collected from 924 genetic detections of 598 American black bears (Ursus americanus) in 2004 with SCR ecological distance models to simultaneously estimate density, landscape resistance to movement, and population connectivity in Glacier National Park northwest Montana, USA. We estimated density and movement parameters separately for males and females and used model estimates to calculate predicted density-weighted connectivity surfaces. RESULTS Model results indicated that landscape structure influences black bear density and space use in Glacier. The mean density estimate was 16.08 bears/100 km2 (95% CI 12.52-20.6) for females and 9.27 bears/100 km2 (95% CI 7.70-11.14) for males. Density increased with forest cover for both sexes. For male black bears, density decreased at higher grizzly bear (Ursus arctos) densities. Drainages, valley bottoms, and riparian vegetation decreased estimates of landscape resistance to movement for male and female bears. For males, forest cover also decreased estimated resistance to movement, but a transportation corridor bisecting the study area strongly increased resistance to movement presenting a barrier to connectivity. CONCLUSIONS Density-weighed connectivity surfaces highlighted areas important for population connectivity that were distinct from areas with high potential connectivity. For black bears in Glacier and surrounding landscapes, consideration of both vegetation and valley topography could inform the placement of underpasses along the transportation corridor in areas characterized by both high population density and potential connectivity. Our study demonstrates that the SCR ecological distance model can provide biologically realistic, spatially explicit predictions to support movement connectivity planning across large landscapes.
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Affiliation(s)
- Sarah L Carroll
- Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, 80523, USA.
| | - Greta M Schmidt
- Department of Biology, San Diego State University, San Diego, CA, 92182, USA
| | - John S Waller
- Glacier National Park, P.O. Box 128, West Glacier, MT, 59936, USA
| | - Tabitha A Graves
- U.S. Geological Survey, Northern Rocky Mountain Science Center, PO Box 169, West Glacier, MT, 59936, USA
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Murphy SM, Hathcock CD, Espinoza TN, Fresquez PR, Berryhill JT, Stanek JE, Sutter BJ, Gaukler SM. Comparative spatially explicit approach for testing effects of soil chemicals on terrestrial wildlife bioindicator demographics. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 316:120541. [PMID: 36336177 DOI: 10.1016/j.envpol.2022.120541] [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: 06/08/2022] [Revised: 10/07/2022] [Accepted: 10/27/2022] [Indexed: 06/16/2023]
Abstract
Wildlife species are often used as bioindicators to evaluate the extent and severity of environmental contamination and the effectiveness of remediation practices. A common approach for investigating population- or community-level impacts on bioindicators compares demographic parameter estimates (e.g., population size or density) between sites that were subjected to different levels of contamination. However, the traditional analytical method used in such studies is nonspatial capture-recapture, which results in conclusions about potential relationships between demographics and contaminants being inferred indirectly. Here, we extend this comparative approach to the spatially explicit framework, allowing direct estimation of said relationships and comparisons between study areas, by applying spatial capture-recapture (SCR) models to bioindicator (deer mice [Peromyscus spp.]) detection data from two study areas that were subjected to different industrial activities and remediation practices. Bioindicator density differed by 178% between the neighboring study areas, and the area with the highest soil concentrations of polychlorinated biphenyls, chromium, and zinc had the highest bioindicator density. Under the traditional nonspatial approach, we might have concluded that soil chemical levels had negligible influences on demographics. However, by modeling density as a spatial function of select chemical concentrations using SCR models, we found strong support for a positive relationship between density and soil chromium concentrations in one study area (β = 0.82), which was not masked by or associated with habitat-related metrics. To obtain reliable inferences about potential effects of environmental contamination on bioindicator demographics, we contend that a comparative spatially explicit approach using SCR ought to become standard.
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Affiliation(s)
- Sean M Murphy
- Department of Forestry and Natural Resources, University of Kentucky, Lexington, KY, USA.
| | - Charles D Hathcock
- Environmental Stewardship Group, Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Tatiana N Espinoza
- Environmental Stewardship Group, Los Alamos National Laboratory, Los Alamos, NM, USA; Space Science and Applications Group, Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Philip R Fresquez
- Environmental Stewardship Group, Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Jesse T Berryhill
- Environmental Stewardship Group, Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Jenna E Stanek
- Environmental Stewardship Group, Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Benjamin J Sutter
- Infrastructure Program Office, Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Shannon M Gaukler
- Environmental Stewardship Group, Los Alamos National Laboratory, Los Alamos, NM, USA.
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3
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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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Robinson SK, McChesney HM. Nesting success of red-winged blackbirds ( Agelaius phoeniceus) in marshes in an anthropogenic landscape. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220266. [PMID: 35911204 PMCID: PMC9326275 DOI: 10.1098/rsos.220266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
Recent analyses show significant population declines in many abundant avian species, especially marsh-nesting species including the red-winged blackbird (RWBL). Hypothesized causes include reduced nesting success resulting from changing land-use patterns and exposure to contaminants. Our goal was to test the hypothesis that landscape and nest characteristics as well as exposure to polychlorinated biphenyls (PCBs) correlate with nesting success. From 2008 to 2014, we measured clutch size, egg and nestling mass, hatching and fledging success and daily survival of 1293 RWBL nests from 32 marshes in the Hudson River valley of New York. Using generalized linear effect and survival models, we found that: (i) Julian date was negatively related to hatching success and clutch size but positively related to egg mass; (ii) nest height was negatively related to hatching success; (iii) nestling mass decreased with increased nest density and distance to edges; (iv) fledging success was significantly lower in nests closer to the ground that were far from water; and (v) clutch size and daily survival were higher in nests farther from water. Results showed that nesting success was correlated with variables associated with flooding, population density and predation and provided no support for the predicted negative effects of PCB exposure.
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Affiliation(s)
- Scott K. Robinson
- Florida Museum of Natural History, University of Florida, Gainesville, FL 32611, USA
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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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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.
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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
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7
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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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8
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La Guardia MJ, Richards NL, Hale RC. A noninvasive environmental monitoring tool for brominated flame-retardants (BFRs) assisted by conservation detection dogs. CHEMOSPHERE 2020; 260:127401. [PMID: 32682128 DOI: 10.1016/j.chemosphere.2020.127401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 06/04/2020] [Accepted: 06/10/2020] [Indexed: 06/11/2023]
Abstract
Fecal matter is a useful noninvasive/nondestructive media for evaluating contaminants in wildlife, as residues therein have been observed to correlate with body burdens. Conservation detection dog-handler teams can be used to optimize the acquisition of fecal samples. To build on previous work, sentinel-species' (i.e. mink (Mustela vison) and otter (Lontra canadensis)) fecal matter was opportunistically located by a detection dog team along the tri-river system of Missoula, Montana, USA. Sediments were also collected. Samples were used to develop an analytical method from fecal matter to determine habitat exposure to the brominated flame-retardants (BFRs): polybrominated diphenyl ethers (PBDEs), hexabromocyclododecane (HBCDD), 2-ethylhexyl 2, 3, 4, 5-tetrabromobenzoate (EH-TBB), di (2-ethylhexyl)-2, 3, 4, 5-tetrabromophthalate (BEH-TEBP) and decabromodiphenyl ethane (DBDPE). Sediments contained PBDEs (BDE-99 and BDE-209) and EH-TBB at detection rates of 67%, 33% and 67%, respectively. BDE-99, -209 and EH-TBB were also detected in mink and otter feces, at rates of 81%, 25% and 81%, respectively; plus BEH-TEBP at 13%. BFR levels correlated positively with human population density except along the lower Bitterroot River, where BDE-209 sediment and feces levels exceeded other sites by several orders of magnitude. Fecal matter body burden estimates indicated marginal PBDE exposure. However, exposure to their replacements, EH-TBB and BEH-TEBP, were at levels that may adversely affect healthy Mustelidae populations. Proof-of-concept was achieved; validation results were within established standards for the development of analytical methods. The established application of conservation dog-handler teams to facilitate the collection of fecal matter for BFR analysis represents a valuable, but currently underutilized environmental monitoring tool.
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Affiliation(s)
- Mark J La Guardia
- Virginia Institute of Marine Science, William & Mary, Gloucester Point, VA, USA, 23062.
| | - Ngaio L Richards
- Working Dogs for Conservation, 10971 Rustic Rd., Missoula, MT, USA, 59802; William R. Maples Center for Forensic Medicine University of Florida, 4800 SW 35th Drive, Gainesville, FL, 32608, USA
| | - Robert C Hale
- Virginia Institute of Marine Science, William & Mary, Gloucester Point, VA, USA, 23062
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Gaukler SM, Murphy SM, Berryhill JT, Thompson BE, Sutter BJ, Hathcock CD. Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models. PLoS One 2020; 15:e0238870. [PMID: 32941472 PMCID: PMC7498087 DOI: 10.1371/journal.pone.0238870] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 08/25/2020] [Indexed: 11/18/2022] Open
Abstract
Monitoring the ecological impacts of environmental pollution and the effectiveness of remediation efforts requires identifying relationships between contaminants and the disruption of biological processes in populations, communities, or ecosystems. Wildlife are useful bioindicators, but traditional comparative experimental approaches rely on a staunch and typically unverifiable assumption that, in the absence of contaminants, reference and contaminated sites would support the same densities of bioindicators, thereby inferring direct causation from indirect data. We demonstrate the utility of spatial capture-recapture (SCR) models for overcoming these issues, testing if community density of common small mammal bioindicators was directly influenced by soil chemical concentrations. By modeling density as an inhomogeneous Poisson point process, we found evidence for an inverse spatial relationship between Peromyscus density and soil mercury concentrations, but not other chemicals, such as polychlorinated biphenyls, at a site formerly occupied by a nuclear reactor. Although the coefficient point estimate supported Peromyscus density being lower where mercury concentrations were higher (β = –0.44), the 95% confidence interval overlapped zero, suggesting no effect was also compatible with our data. Estimated density from the most parsimonious model (2.88 mice/ha; 95% CI = 1.63–5.08), which did not support a density-chemical relationship, was within the range of reported densities for Peromyscus that did not inhabit contaminated sites elsewhere. Environmental pollution remains a global threat to biodiversity and ecosystem and human health, and our study provides an illustrative example of the utility of SCR models for investigating the effects that chemicals may have on wildlife bioindicator populations and communities.
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Affiliation(s)
- Shannon M. Gaukler
- Environmental Stewardship Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- * E-mail: (SMG); (CDH)
| | - Sean M. Murphy
- Department of Forestry and Natural Resources, University of Kentucky, Lexington, Kentucky, United States of America
| | - Jesse T. Berryhill
- Environmental Stewardship Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Brent E. Thompson
- Environmental Stewardship Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Benjamin J. Sutter
- Infrastructure Program Office, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Charles D. Hathcock
- Environmental Stewardship Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- * E-mail: (SMG); (CDH)
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10
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Hostetter NJ, Royle JA. Movement-assisted localization from acoustic telemetry data. MOVEMENT ECOLOGY 2020; 8:15. [PMID: 32617163 PMCID: PMC7327795 DOI: 10.1186/s40462-020-00199-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2019] [Accepted: 03/09/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Acoustic telemetry technologies are being increasingly deployed to study a variety of aquatic taxa including fishes, reptiles, and marine mammals. Large cooperative telemetry networks produce vast quantities of data useful in the study of movement, resource selection and species distribution. Efficient use of acoustic telemetry data requires estimation of acoustic source locations from detections at receivers (i.e., "localization"). Multiple processes provide information for localization estimation including detection/non-detection data at receivers, information on signal rate, and an underlying movement model describing how individuals move and utilize space. Frequently, however, localization methods only integrate a subset of these processes and do not utilize the full spatial encounter history information available from receiver arrays. METHODS In this paper we draw analogies between the challenges of acoustic telemetry localization and newly developed methods of spatial capture-recapture (SCR). We develop a framework for localization that integrates explicit sub-models for movement, signal (or cue) rate, and detection probability, based on acoustic telemetry spatial encounter history data. This method, which we call movement-assisted localization, makes efficient use of the full encounter history data available from acoustic receiver arrays, provides localizations with fewer than three detections, and even allows for predictions to be made of the position of an individual when it was not detected at all. We demonstrate these concepts by developing generalizable Bayesian formulations of the SCR movement-assisted localization model to address study-specific challenges common in acoustic telemetry studies. RESULTS Simulation studies show that movement-assisted localization models improve point-wise RMSE of localization estimates by >50% and greatly increased the precision of estimated trajectories compared to localization using only the detection history of a given signal. Additionally, integrating a signal rate sub-model reduced biases in the estimation of movement, signal rate, and detection parameters observed in independent localization models. CONCLUSIONS Movement-assisted localization provides a flexible framework to maximize the use of acoustic telemetry data. Conceptualizing localization within an SCR framework allows extensions to a variety of data collection protocols, improves the efficiency of studies interested in movement, resource selection, and space-use, and provides a unifying framework for modeling acoustic data.
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Affiliation(s)
- Nathan J. Hostetter
- U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, 20708 MD USA
- Washington Cooperative Fish and Wildlife Research Unit, School of Aquatic and Fishery Sciences, University of Washington, Seattle, 98195 WA USA
| | - J. Andrew Royle
- U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, 20708 MD USA
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11
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Sanders CW, Pacifici K, Hess GR, Olfenbuttel C, DePerno CS. Metal contamination of river otters in North Carolina. ENVIRONMENTAL MONITORING AND ASSESSMENT 2020; 192:146. [PMID: 31993757 DOI: 10.1007/s10661-020-8106-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 01/21/2020] [Indexed: 06/10/2023]
Abstract
Aquatic apex predators are vulnerable to environmental contaminants due to biomagnification. North American river otter (Lontra canadensis) populations should be closely monitored across their range due to point and nonpoint pollution sources. Nonetheless, no information exists on environmental contaminants in the North Carolina otter population. Metals and metalloids occur naturally across the landscape, are essential for cellular function, and become toxic when concentrated unnaturally. We conducted our study across the three Furbearer Management Units (FMU) and 14 river basins of North Carolina. We determined the concentrations of arsenic, cadmium, calcium, cobalt, copper, iron, lead, magnesium, manganese, mercury, molybdenum, selenium, thallium, and zinc in liver and kidney samples from 317 otters harvested from 2009 to 2016. Arsenic, lead, and thallium samples were tested at levels below the limit of detection. With the exception of cadmium, we detected all other elements at higher levels in the liver compared with the kidney. Specifically, cadmium, cobalt, copper, iron, magnesium, manganese, mercury, molybdenum, and zinc levels differed by tissue type analyzed. Most element concentrations remained stable or increased with otter age. We detected higher levels of mercury and selenium in the Lower Pee Dee and Cape Fear river basins. River basins within the Mountain FMU were higher in cadmium, copper, iron, lead, and zinc, whereas the Coastal Plain FMU was lower in cobalt and manganese. None of the elements occurred at toxic levels. Our research establishes baseline concentration levels for North Carolina, which will benefit future monitoring efforts and provide insight into future changes in the otter population.
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Affiliation(s)
- Charles W Sanders
- Fisheries, Wildlife, & Conservation Biology Program, Department of Forestry and Environmental Resources, College of Natural Resources, North Carolina State University, Raleigh, NC, 27695, USA.
| | - Krishna Pacifici
- Fisheries, Wildlife, & Conservation Biology Program, Department of Forestry and Environmental Resources, College of Natural Resources, North Carolina State University, Raleigh, NC, 27695, USA
| | - George R Hess
- Fisheries, Wildlife, & Conservation Biology Program, Department of Forestry and Environmental Resources, College of Natural Resources, North Carolina State University, Raleigh, NC, 27695, USA
| | - Colleen Olfenbuttel
- Surveys and Research Program, Wildlife Management Division, North Carolina Wildlife Resources Commission, Pittsboro, NC, 27312, USA
| | - Christopher S DePerno
- Fisheries, Wildlife, & Conservation Biology Program, Department of Forestry and Environmental Resources, College of Natural Resources, North Carolina State University, Raleigh, NC, 27695, USA
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Lamb CT, Ford AT, Proctor MF, Royle JA, Mowat G, Boutin S. Genetic tagging in the Anthropocene: scaling ecology from alleles to ecosystems. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2019; 29:e01876. [PMID: 30913353 DOI: 10.1002/eap.1876] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 01/04/2019] [Accepted: 02/01/2019] [Indexed: 06/09/2023]
Abstract
The Anthropocene is an era of marked human impact on the world. Quantifying these impacts has become central to understanding the dynamics of coupled human-natural systems, resource-dependent livelihoods, and biodiversity conservation. Ecologists are facing growing pressure to quantify the size, distribution, and trajectory of wild populations in a cost-effective and socially acceptable manner. Genetic tagging, combined with modern computational and genetic analyses, is an under-utilized tool to meet this demand, especially for wide-ranging, elusive, sensitive, and low-density species. Genetic tagging studies are now revealing unprecedented insight into the mechanisms that control the density, trajectory, connectivity, and patterns of human-wildlife interaction for populations over vast spatial extents. Here, we outline the application of, and ecological inferences from, new analytical techniques applied to genetically tagged individuals, contrast this approach with conventional methods, and describe how genetic tagging can be better applied to address outstanding questions in ecology. We provide example analyses using a long-term genetic tagging dataset of grizzly bears in the Canadian Rockies. The genetic tagging toolbox is a powerful and overlooked ensemble that ecologists and conservation biologists can leverage to generate evidence and meet the challenges of the Anthropocene.
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Affiliation(s)
- Clayton T Lamb
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, T6G 2E9, Canada
| | - Adam T Ford
- Department of Biology, University of British Columbia, Kelowna, British Columbia, V1V 1V7, Canada
| | | | - J Andrew Royle
- Patuxent Wildlife Research Center, U.S. Geological Survey, Laurel, Maryland, 20708, USA
| | - Garth Mowat
- Ministry of Forests, Lands and Natural Resource Operations, Nelson, British Columbia, V1L 4K3, Canada
- Earth and Environmental Sciences, University of British Columbia, Kelowna, British Columbia, V1V 1V7, Canada
| | - Stan Boutin
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, T6G 2E9, Canada
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