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Nabias J, Barbaro L, Fontaine B, Dupuy J, Couzi L, Vallé C, Lorrilliere R. Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability. PeerJ 2024; 12:e17889. [PMID: 39221279 PMCID: PMC11363910 DOI: 10.7717/peerj.17889] [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: 07/10/2024] [Accepted: 07/18/2024] [Indexed: 09/04/2024] Open
Abstract
Higher efficiency in large-scale and long-term biodiversity monitoring can be obtained through the use of Essential Biodiversity Variables, among which species population sizes provide key data for conservation programs. Relevant estimations and assessment of actual population sizes are critical for species conservation, especially in the current context of global biodiversity erosion. However, knowledge on population size varies greatly, depending on species conservation status and ranges. While the most threatened or restricted-range species generally benefit from exhaustive counts and surveys, monitoring common and widespread species population size tends to be neglected or is simply more challenging to achieve. In such a context, citizen science (CS) is a powerful tool for the long-term monitoring of common species through the engagement of various volunteers, permitting data acquisition on the long term and over large spatial scales. Despite this substantially increased sampling effort, detectability issues imply that even common species may remain unnoticed at suitable sites. The use of structured CS schemes, including repeated visits, enables to model the detection process, permitting reliable inferences of population size estimates. Here, we relied on a large French structured CS scheme (EPOC-ODF) comprising 27,156 complete checklists over 3,873 sites collected during the 2021-2023 breeding seasons to estimate the population size of 63 common bird species using hierarchical distance sampling (HDS). These population size estimates were compared to the previous expert-based French breeding bird atlas estimations, which did not account for detectability issues. We found that population size estimates from the former French breeding bird atlas were lower than those estimated using HDS for 65% of species. Such a prevalence of lower estimations is likely due to more conservative estimates inferred from semi-quantitative expert-based assessments used for the previous atlas. We also found that species with long-range songs such as the Common Cuckoo (Cuculus canorus), Eurasian Hoopoe (Upupa epops) or the Eurasian Blackbird (Turdus merula) had, in contrast, higher estimated population sizes in the previous atlas than in our HDS models. Our study highlights the need to rely on sound statistical methodology to ensure reliable ecological inferences with adequate uncertainty estimation and advocates for a higher reliance on structured CS in support of long-term biodiversity monitoring.
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Affiliation(s)
- Jean Nabias
- Muséum National d’Histoire Naturelle, Centre d’Ecologie et des Sciences de la Conservation, Paris, France
- Ligue Pour la Protection des Oiseaux, Rochefort, France
| | - Luc Barbaro
- Muséum National d’Histoire Naturelle, Centre d’Ecologie et des Sciences de la Conservation, Paris, France
- Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Auzeville-Tolosane, France
| | - Benoît Fontaine
- Muséum National d’Histoire Naturelle, Centre d’Ecologie et des Sciences de la Conservation, Paris, France
- Patrimoine Naturel, Office Français de la Biodiversité, Paris, France
| | - Jérémy Dupuy
- Ligue Pour la Protection des Oiseaux, Rochefort, France
| | - Laurent Couzi
- Ligue Pour la Protection des Oiseaux, Rochefort, France
| | - Clément Vallé
- Muséum National d’Histoire Naturelle, Centre d’Ecologie et des Sciences de la Conservation, Paris, France
| | - Romain Lorrilliere
- Muséum National d’Histoire Naturelle, Centre d’Ecologie et des Sciences de la Conservation, Paris, France
- Centre de Recherches sur la Biologie des Populations d’Oiseaux, Paris, France
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Gaya HE, Smith LL, Moore CT. Accounting for spatial heterogeneity in visual obstruction in line‐transect distance sampling of gopher tortoises. J Wildl Manage 2022. [DOI: 10.1002/jwmg.22338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Affiliation(s)
- Heather E. Gaya
- Georgia Cooperative Fish and Wildlife Research Unit, Warnell School of Forestry and Natural Resources University of Georgia 180 E. Green Street Athens GA 30602 USA
| | - Lora L. Smith
- Jones Center at Ichauway 3988 Jones Center Drive Newton GA 39870 USA
| | - Clinton T. Moore
- U.S. Geological Survey, Georgia Cooperative Fish and Wildlife Research Unit, Warnell School of Forestry and Natural Resources University of Georgia 180 E. Green Street Athens GA 30605 USA
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3
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Semel BP, Karpanty SM, Semel MA, Stauffer DF, Quéméré E, Walters JR, Andrianiaina AF, Rakotonanahary AN, Ranaivoson T, Rasolonirina DV, Vololonirina FF. Highly Variable Densities and a Decline in Critically Endangered Golden-Crowned Sifaka (Propithecus tattersalli) Abundance from 2008–2018. INT J PRIMATOL 2022. [DOI: 10.1007/s10764-022-00314-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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4
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Clement MJ, Hervert JJ, Bright JL. Telemetry‐based aerial surveys for estimating abundance of sparse populations. WILDLIFE SOC B 2022. [DOI: 10.1002/wsb.1253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Matthew J. Clement
- Arizona Game and Fish Department 5000 W Carefree Highway Phoenix 85086 AZ USA
| | - John J. Hervert
- Arizona Game and Fish Department 9140 E 28th Street Yuma 85365 AZ USA
| | - Jill L. Bright
- Arizona Game and Fish Department 9140 E 28th Street Yuma 85365 AZ USA
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Davis KL, Silverman ED, Sussman AL, Wilson RR, Zipkin EF. Errors in aerial survey count data: Identifying pitfalls and solutions. Ecol Evol 2022; 12:e8733. [PMID: 35342571 PMCID: PMC8931709 DOI: 10.1002/ece3.8733] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 02/18/2022] [Accepted: 02/24/2022] [Indexed: 11/08/2022] Open
Abstract
Accurate estimates of animal abundance are essential for guiding effective management, and poor survey data can produce misleading inferences. Aerial surveys are an efficient survey platform, capable of collecting wildlife data across large spatial extents in short timeframes. However, these surveys can yield unreliable data if not carefully executed. Despite a long history of aerial survey use in ecological research, problems common to aerial surveys have not yet been adequately resolved. Through an extensive review of the aerial survey literature over the last 50 years, we evaluated how common problems encountered in the data (including nondetection, counting error, and species misidentification) can manifest, the potential difficulties conferred, and the history of how these challenges have been addressed. Additionally, we used a double-observer case study focused on waterbird data collected via aerial surveys and an online group (flock) counting quiz to explore the potential extent of each challenge and possible resolutions. We found that nearly three quarters of the aerial survey methodology literature focused on accounting for nondetection errors, while issues of counting error and misidentification were less commonly addressed. Through our case study, we demonstrated how these challenges can prove problematic by detailing the extent and magnitude of potential errors. Using our online quiz, we showed that aerial observers typically undercount group size and that the magnitude of counting errors increases with group size. Our results illustrate how each issue can act to bias inferences, highlighting the importance of considering individual methods for mitigating potential problems separately during survey design and analysis. We synthesized the information gained from our analyses to evaluate strategies for overcoming the challenges of using aerial survey data to estimate wildlife abundance, such as digital data collection methods, pooling species records by family, and ordinal modeling using binned data. Recognizing conditions that can lead to data collection errors and having reasonable solutions for addressing errors can allow researchers to allocate resources effectively to mitigate the most significant challenges for obtaining reliable aerial survey data.
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Affiliation(s)
- Kayla L. Davis
- Department of Integrative BiologyMichigan State UniversityEast LansingMichiganUSA
- Ecology, Evolution, and Behavior ProgramMichigan State UniversityEast LansingMichiganUSA
| | | | - Allison L. Sussman
- U.S. Geological Survey Eastern Ecological Science Center at the Patuxent Research RefugeLaurelMarylandUSA
| | - R. Randy Wilson
- Migratory Bird ProgramU.S. Fish and Wildlife ServiceJacksonMississippiUSA
| | - Elise F. Zipkin
- Department of Integrative BiologyMichigan State UniversityEast LansingMichiganUSA
- Ecology, Evolution, and Behavior ProgramMichigan State UniversityEast LansingMichiganUSA
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6
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Roy C, Gilliland SG, Reed ET. A hierarchical dependent double‐observer method for estimating waterfowl breeding pairs abundance from helicopters. WILDLIFE BIOLOGY 2021. [DOI: 10.1002/wlb3.01003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Christian Roy
- Canadian Wildlife Service, Environment and Climate Change Canada Gatineau QC Canada
| | - Scott G. Gilliland
- Canadian Wildlife Service, Environment and Climate Change Canada Sackville NB Canada
| | - Eric T. Reed
- Canadian Wildlife Service, Environment and Climate Change Canada Yellowknife NT Canada
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Gowan TA, Crum NJ, Roberts JJ. An open spatial capture-recapture model for estimating density, movement, and population dynamics from line-transect surveys. Ecol Evol 2021; 11:7354-7365. [PMID: 34188818 PMCID: PMC8216936 DOI: 10.1002/ece3.7566] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 03/17/2021] [Accepted: 03/22/2021] [Indexed: 11/26/2022] Open
Abstract
The purpose of many wildlife population studies is to estimate density, movement, or demographic parameters. Linking these parameters to covariates, such as habitat features, provides additional ecological insight and can be used to make predictions for management purposes. Line-transect surveys, combined with distance sampling methods, are often used to estimate density at discrete points in time, whereas capture-recapture methods are used to estimate movement and other demographic parameters. Recently, open population spatial capture-recapture models have been developed, which simultaneously estimate density and demographic parameters, but have been made available only for data collected from a fixed array of detectors and have not incorporated the effects of habitat covariates. We developed a spatial capture-recapture model that can be applied to line-transect survey data by modeling detection probability in a manner analogous to distance sampling. We extend this model to a) estimate demographic parameters using an open population framework and b) model variation in density and space use as a function of habitat covariates. The model is illustrated using simulated data and aerial line-transect survey data for North Atlantic right whales in the southeastern United States, which also demonstrates the ability to integrate data from multiple survey platforms and accommodate differences between strata or demographic groups. When individuals detected from line-transect surveys can be uniquely identified, our model can be used to simultaneously make inference on factors that influence spatial and temporal variation in density, movement, and population dynamics.
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Affiliation(s)
- Timothy A. Gowan
- Fish and Wildlife Research InstituteFlorida Fish and Wildlife Conservation CommissionSt. PetersburgFLUSA
- Department of Wildlife Ecology and ConservationUniversity of FloridaGainesvilleFLUSA
| | - Nathan J. Crum
- Fish and Wildlife Research InstituteFlorida Fish and Wildlife Conservation CommissionSt. PetersburgFLUSA
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Riley IP, Conway CJ. Methods for estimating vital rates of greater sage-grouse broods: a review. WILDLIFE BIOLOGY 2020. [DOI: 10.2981/wlb.00700] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Ian P. Riley
- I. P. Riley ✉ , Idaho Cooperative Fish and Wildlife Research Unit, Dept of Fish & Wildlife Sciences, Univ. of Idaho, 875 Perimeter Drive MS 1136, Moscow, ID 83844-1136, USA
| | - Courtney J. Conway
- C. J. Conway, U. S. Geological Survey, Idaho Cooperative Fish and Wildlife Research Unit, Dept of Fish & Wildlife Sciences, Univ. of Idaho, Moscow, USA
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Bischof R, Dupont P, Milleret C, Chipperfield J, Royle JA. Consequences of ignoring group association in spatial capture–recapture analysis. WILDLIFE BIOLOGY 2020. [DOI: 10.2981/wlb.00649] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Richard Bischof
- R. Bischof ✉ , P. Dupont and C. Milleret, Faculty of Environmental Sciences and Natural Resource Management, Norwegian Univ. of Life Sciences, NO-1432 Aas, Norway
| | - Pierre Dupont
- R. Bischof ✉ , P. Dupont and C. Milleret, Faculty of Environmental Sciences and Natural Resource Management, Norwegian Univ. of Life Sciences, NO-1432 Aas, Norway
| | - Cyril Milleret
- R. Bischof ✉ , P. Dupont and C. Milleret, Faculty of Environmental Sciences and Natural Resource Management, Norwegian Univ. of Life Sciences, NO-1432 Aas, Norway
| | - Joseph Chipperfield
- J. Chipperfield, Norwegian Inst. for Nature, Res., Bergen, Norway. – J. A. Royle, USGS Patuxent Wildlife Research Center, Laurel, MD, USA
| | - J. Andrew Royle
- R. Bischof ✉ , P. Dupont and C. Milleret, Faculty of Environmental Sciences and Natural Resource Management, Norwegian Univ. of Life Sciences, NO-1432 Aas, Norway
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Granjon A, Robbins MM, Arinaitwe J, Cranfield MR, Eckardt W, Mburanumwe I, Musana A, Robbins AM, Roy J, Sollmann R, Vigilant L, Hickey JR. Estimating abundance and growth rates in a wild mountain gorilla population. Anim Conserv 2020. [DOI: 10.1111/acv.12559] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- A.‐C. Granjon
- Department of Primatology Max Planck Institute for Evolutionary Anthropology Leipzig Germany
| | - M. M. Robbins
- Department of Primatology Max Planck Institute for Evolutionary Anthropology Leipzig Germany
| | - J. Arinaitwe
- Bwindi Mgahinga Conservation Area Uganda Wildlife Authority Kampala Uganda
| | - M. R. Cranfield
- Mountain Gorilla Veterinary Project School of Veterinary Medicine University of California Davis Davis CA USA
| | - W. Eckardt
- The Dian Fossey Gorilla Fund International Musanze Rwanda
| | - I. Mburanumwe
- Parc National des Virunga‐sud Institut Congolais pour la Conservation de la Nature Gisenyi Rwanda
| | - A. Musana
- Parc National des Volcans Rwanda Development Board Kigali Rwanda
| | - A. M. Robbins
- Department of Primatology Max Planck Institute for Evolutionary Anthropology Leipzig Germany
| | - J. Roy
- Department of Primatology Max Planck Institute for Evolutionary Anthropology Leipzig Germany
| | - R. Sollmann
- Department of Wildlife, Fish, and Conservation Biology University of California Davis Davis CA USA
| | - L. Vigilant
- Department of Primatology Max Planck Institute for Evolutionary Anthropology Leipzig Germany
| | - J. R. Hickey
- International Gorilla Conservation Programme Kigali Rwanda
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Hickey JR, Sollmann R. A new mark-recapture approach for abundance estimation of social species. PLoS One 2018; 13:e0208726. [PMID: 30571710 PMCID: PMC6301682 DOI: 10.1371/journal.pone.0208726] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 11/22/2018] [Indexed: 11/24/2022] Open
Abstract
Accurate estimates of population abundance are a critical component of species conservation efforts in order to monitor the potential recovery of populations. Capture-mark-recapture (CMR) is a widely used approach to estimate population abundance, yet social species moving in groups violate the assumption of CMR approaches that all individuals in the population are detected independently. We developed a closed CMR model that addresses an important characteristic of group-living species–that individual-detection probability typically is conditional on group detection. Henceforth termed the Two-Step model, this approach first estimates group-detection probability and then–conditional on group detection–estimates individual-detection probability for individuals within detected groups. Overall abundance is estimated assuming that undetected groups have the same average group size as detected groups. We compared the performance of this Two-Step CMR model to a conventional (One-Step) closed CMR model that ignored group structure. We assessed model sensitivity to variation in both group- and individual-detection probability. Both models returned overall unbiased estimates of abundance, but the One-Step model returned deceptively narrow Bayesian confidence intervals (BCI) that failed to encompass the correct population abundance an average 52% of the time. Contrary, under the Two-Step model, CI coverage was on average 96%. Both models had similar root mean squared errors (RMSE), except for scenarios with low group detection probability, where the Two-Step model had much lower RMSE. For illustration with a real data set, we applied the Two-Step and regular model to non-invasive genetic capture-recapture data of mountain gorillas (Gorilla beringei beringei). As with simulations, abundance estimates under both models were similar, but the Two-Step model estimate had a wider confidence interval. Results support using the Two-Step model for species living in constant groups, particularly when group detection probability is low, to reduce risk of bias and adequately portray uncertainty in abundance estimates. Important sources of variation in detection need to be incorporated into the Two-Step model when applying it to field data.
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Affiliation(s)
- Jena R. Hickey
- International Gorilla Conservation Programme, Musanze, Rwanda
- * E-mail:
| | - Rahel Sollmann
- Department of Wildlife, Fish, and Conservation Biology, University of California Davis, Davis, CA, United States of America
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Conroy MJ, Harris G, Stewart DR, Butler MJ. Evaluation of desert bighorn sheep abundance surveys, southwestern Arizona, USA. J Wildl Manage 2018. [DOI: 10.1002/jwmg.21463] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Michael J. Conroy
- Warnell School of Forestry and Natural Resources; University of Georgia; Athens GA 30602 USA
| | - Grant Harris
- U.S. Fish and Wildlife Service; P.O. Box 1306 Albuquerque NM 87103 USA
| | - David R. Stewart
- U.S. Fish and Wildlife Service; P.O. Box 1306 Albuquerque NM 87103 USA
| | - Matthew J. Butler
- U.S. Fish and Wildlife Service; P.O. Box 1306 Albuquerque NM 87103 USA
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13
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Clement MJ, Converse SJ, Royle JA. Accounting for imperfect detection of groups and individuals when estimating abundance. Ecol Evol 2017; 7:7304-7310. [PMID: 28944018 PMCID: PMC5606903 DOI: 10.1002/ece3.3284] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Revised: 06/16/2017] [Accepted: 06/28/2017] [Indexed: 11/29/2022] Open
Abstract
If animals are independently detected during surveys, many methods exist for estimating animal abundance despite detection probabilities <1. Common estimators include double‐observer models, distance sampling models and combined double‐observer and distance sampling models (known as mark‐recapture‐distance‐sampling models; MRDS). When animals reside in groups, however, the assumption of independent detection is violated. In this case, the standard approach is to account for imperfect detection of groups, while assuming that individuals within groups are detected perfectly. However, this assumption is often unsupported. We introduce an abundance estimator for grouped animals when detection of groups is imperfect and group size may be under‐counted, but not over‐counted. The estimator combines an MRDS model with an N‐mixture model to account for imperfect detection of individuals. The new MRDS‐Nmix model requires the same data as an MRDS model (independent detection histories, an estimate of distance to transect, and an estimate of group size), plus a second estimate of group size provided by the second observer. We extend the model to situations in which detection of individuals within groups declines with distance. We simulated 12 data sets and used Bayesian methods to compare the performance of the new MRDS‐Nmix model to an MRDS model. Abundance estimates generated by the MRDS‐Nmix model exhibited minimal bias and nominal coverage levels. In contrast, MRDS abundance estimates were biased low and exhibited poor coverage. Many species of conservation interest reside in groups and could benefit from an estimator that better accounts for imperfect detection. Furthermore, the ability to relax the assumption of perfect detection of individuals within detected groups may allow surveyors to re‐allocate resources toward detection of new groups instead of extensive surveys of known groups. We believe the proposed estimator is feasible because the only additional field data required are a second estimate of group size.
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Affiliation(s)
- Matthew J Clement
- U.S. Geological Survey Patuxent Wildlife Research Center Laurel MD USA.,Arizona Game and Fish Department Phoenix AZ USA
| | - Sarah J Converse
- U.S. Geological Survey Patuxent Wildlife Research Center Laurel MD USA.,U.S. Geological Survey Washington Cooperative Fish and Wildlife Research Unit School of Environmental and Forest Sciences (SEFS) and School of Aquatic and Fishery Sciences (SAFS) University of Washington Seattle WA USA
| | - J Andrew Royle
- U.S. Geological Survey Patuxent Wildlife Research Center Laurel MD USA
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