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Knight E, Rhinehart T, de Zwaan DR, Weldy MJ, Cartwright M, Hawley SH, Larkin JL, Lesmeister D, Bayne E, Kitzes J. Individual identification in acoustic recordings. Trends Ecol Evol 2024:S0169-5347(24)00118-6. [PMID: 38862357 DOI: 10.1016/j.tree.2024.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 05/08/2024] [Accepted: 05/15/2024] [Indexed: 06/13/2024]
Abstract
Recent advances in bioacoustics combined with acoustic individual identification (AIID) could open frontiers for ecological and evolutionary research because traditional methods of identifying individuals are invasive, expensive, labor-intensive, and potentially biased. Despite overwhelming evidence that most taxa have individual acoustic signatures, the application of AIID remains challenging and uncommon. Furthermore, the methods most commonly used for AIID are not compatible with many potential AIID applications. Deep learning in adjacent disciplines suggests opportunities to advance AIID, but such progress is limited by training data. We suggest that broadscale implementation of AIID is achievable, but researchers should prioritize methods that maximize the potential applications of AIID, and develop case studies with easy taxa at smaller spatiotemporal scales before progressing to more difficult scenarios.
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Affiliation(s)
- Elly Knight
- Department of Biological Sciences, Alberta Biodiversity Monitoring Institute, University of Alberta, Edmonton, Alberta, T6G 2E6, Canada.
| | - Tessa Rhinehart
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
| | - Devin R de Zwaan
- Department of Biology, Mount Allison University, Sackville, NB, E4L 1E4, Canada; Acadia University, Wolfville, NS, B4P 2R6, Canada
| | - Matthew J Weldy
- Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR, 97331-5704, USA
| | - Mark Cartwright
- Department of Informatics, New Jersey Institute of Technology, Newark, NJ, 07102, USA
| | - Scott H Hawley
- Chemistry and Physics Department, Belmont University, Nashville, TN, 37212, USA
| | - Jeffery L Larkin
- Department of Biology, Indiana University of Pennsylvania, Indiana, PA, 15705-1081, USA; American Bird Conservancy, The Plains, VA, 20198, USA
| | - Damon Lesmeister
- USDA Forest Service, Pacific Northwest Research Station, Corvallis Forestry Science Laboratory, Oregon State University, Corvallis, OR, 97330, USA
| | - Erin Bayne
- Department of Biological Sciences, Alberta Biodiversity Monitoring Institute, University of Alberta, Edmonton, Alberta, T6G 2E6, Canada
| | - Justin Kitzes
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, 15260, USA
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2
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Kéry M, Royle JA, Hallman T, Robinson WD, Strebel N, Kellner KF. Integrated distance sampling models for simple point counts. Ecology 2024; 105:e4292. [PMID: 38538534 DOI: 10.1002/ecy.4292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 01/12/2024] [Indexed: 05/03/2024]
Abstract
Point counts (PCs) are widely used in biodiversity surveys but, despite numerous advantages, simple PCs suffer from several problems: detectability, and therefore abundance, is unknown; systematic spatiotemporal variation in detectability yields biased inferences, and unknown survey area prevents formal density estimation and scaling-up to the landscape level. We introduce integrated distance sampling (IDS) models that combine distance sampling (DS) with simple PC or detection/nondetection (DND) data to capitalize on the strengths and mitigate the weaknesses of each data type. Key to IDS models is the view of simple PC and DND data as aggregations of latent DS surveys that observe the same underlying density process. This enables the estimation of separate detection functions, along with distinct covariate effects, for all data types. Additional information from repeat or time-removal surveys, or variable survey duration, enables the separate estimation of the availability and perceptibility components of detectability with DS and PC data. IDS models reconcile spatial and temporal mismatches among data sets and solve the above-mentioned problems of simple PC and DND data. To fit IDS models, we provide JAGS code and the new "IDS()" function in the R package unmarked. Extant citizen-science data generally lack the information necessary to adjust for detection biases, but IDS models address this shortcoming, thus greatly extending the utility and reach of these data. In addition, they enable formal density estimation in hybrid designs, which efficiently combine DS with distance-free, point-based PC or DND surveys. We believe that IDS models have considerable scope in ecology, management, and monitoring.
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Affiliation(s)
- Marc Kéry
- Swiss Ornithological Institute, Sempach, Switzerland
| | - J Andrew Royle
- USGS Eastern Ecological Science Center, Laurel, Maryland, USA
| | - Tyler Hallman
- Swiss Ornithological Institute, Sempach, Switzerland
- Department of Biology and Chemistry, Queens University of Charlotte, Charlotte, North Carolina, USA
- School of Environmental and Natural Sciences, Bangor University, Bangor, UK
| | - W Douglas Robinson
- Oak Creek Laboratory of Biology, Department of Fisheries, Wildlife, and Conservation Sciences, Oregon State University, Corvallis, Oregon, USA
| | | | - Kenneth F Kellner
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, USA
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3
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Adjei KP, Finstad AG, Koch W, O'Hara RB. Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance. Ecol Evol 2024; 14:e11092. [PMID: 38455149 PMCID: PMC10918728 DOI: 10.1002/ece3.11092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/12/2024] [Accepted: 02/18/2024] [Indexed: 03/09/2024] Open
Abstract
Species distribution models and maps from large-scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multi-species distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to biased prediction of species distributions. Here, we present a general multi-species distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalised linear model for the classification confusion matrix. We compare the performance of the heterogeneous classification model to that of the homogeneous classification model by assessing how well they estimate the parameters in the model and their predictive performance on hold-out samples. We applied the model to gull data from Norway, Denmark and Finland, obtained from the Global Biodiversity Information Facility. Our simulation study showed that accounting for heterogeneity in the classification process increased the precision of true species' identity predictions by 30% and accuracy and recall by 6%. Since all the models in this study accounted for misclassification of some sort, there was no significant effect of accounting for heterogeneity in the classification process on the inference about the ecological process. Applying the model framework to the gull dataset did not improve the predictive performance between the homogeneous and heterogeneous models (with parametric distributions) due to the smaller misclassified sample sizes. However, when machine learning predictive scores were used as weights to inform the species distribution models about the classification process, the precision increased by 70%. We recommend multiple multinomial regression to be used to model the variation in the classification process when the data contains relatively larger misclassified samples. Machine learning prediction scores should be used when the data contains relatively smaller misclassified samples.
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Affiliation(s)
- Kwaku Peprah Adjei
- Department of Mathematical SciencesNorwegian University of Science and TechnologyTrondheimNorway
- Center for Biodiversity DynamicsNorwegian University of Science and TechnologyTrondheimNorway
- Norwegian Institute for Nature ResearchTrondheimNorway
| | - Anders Gravbrøt Finstad
- Center for Biodiversity DynamicsNorwegian University of Science and TechnologyTrondheimNorway
- Department of Natural HistoryNorwegian University of Science and TechnologyTrondheimNorway
| | - Wouter Koch
- Center for Biodiversity DynamicsNorwegian University of Science and TechnologyTrondheimNorway
- Norwegian Biodiversity Information CentreTrondheimNorway
| | - Robert Brian O'Hara
- Department of Mathematical SciencesNorwegian University of Science and TechnologyTrondheimNorway
- Center for Biodiversity DynamicsNorwegian University of Science and TechnologyTrondheimNorway
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4
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Kovalenko V, Doser JW, Bate LJ, Six DL. Paired acoustic recordings and point count surveys reveal Clark's nutcracker and whitebark pine associations across Glacier National Park. Ecol Evol 2024; 14:e10867. [PMID: 38274862 PMCID: PMC10808773 DOI: 10.1002/ece3.10867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 12/28/2023] [Accepted: 01/05/2024] [Indexed: 01/27/2024] Open
Abstract
Global declines in tree populations have led to dramatic shifts in forest ecosystem composition, biodiversity, and functioning. These changes have consequences for both forest plant and wildlife communities, particularly when declining species are involved in coevolved mutualisms. Whitebark pine (Pinus albicaulis) is a declining keystone species in western North American high-elevation ecosystems and an obligate mutualist of Clark's nutcracker (Nucifraga columbiana), an avian seed predator and disperser. By leveraging traditional point count surveys and passive acoustic monitoring, we investigated how stand characteristics of whitebark pine in a protected area (Glacier National Park, Montana, USA) influenced occupancy and vocal activity patterns in Clark's nutcracker. Using Bayesian spatial occupancy models and generalized linear mixed models, we found that habitat use of Clark's nutcracker was primarily supported by greater cone density and increasing diameter of live whitebark pine. Additionally, we demonstrated the value of performing parallel analyses with traditional point count surveys and passive acoustic monitoring to provide multiple lines of evidence for relationships between Clark's nutcracker and whitebark pine forest characteristics. Our findings allow managers to gauge the whitebark pine conditions important for retaining high nutcracker visitation and prioritize management efforts in whitebark pine ecosystems with low nutcracker visitation.
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Affiliation(s)
- Vladimir Kovalenko
- Department of Ecosystem and Conservation SciencesUniversity of MontanaMissoulaMontanaUSA
- Science CenterGlacier National ParkWest GlacierMontanaUSA
| | - Jeffrey W. Doser
- Department of Integrative Biology, Ecology, Evolution and Behavior ProgramMichigan State UniversityEast LansingMichiganUSA
| | - Lisa J. Bate
- Science CenterGlacier National ParkWest GlacierMontanaUSA
| | - Diana L. Six
- Department of Ecosystem and Conservation SciencesUniversity of MontanaMissoulaMontanaUSA
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5
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Feist F, Terranova F, Petersen GS, Tourtigues E, Friard O, Gamba M, Ludynia K, Gridley T, Pichegru L, Mathevon N, Reby D, Favaro L. Effect of Environmental Variables on African Penguin Vocal Activity: Implications for Acoustic Censusing. BIOLOGY 2023; 12:1191. [PMID: 37759590 PMCID: PMC10525562 DOI: 10.3390/biology12091191] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 08/26/2023] [Accepted: 08/29/2023] [Indexed: 09/29/2023]
Abstract
Global biodiversity is in rapid decline, and many seabird species have disproportionally poorer conservation statuses than terrestrial birds. A good understanding of population dynamics is necessary for successful conservation efforts, making noninvasive, cost-effective monitoring tools essential. Here, we set out to investigate whether passive acoustic monitoring (PAM) could be used to estimate the number of animals within a set area of an African penguin (Spheniscus demersus) colony in South Africa. We were able to automate the detection of ecstatic display songs (EDSs) in our recordings, thus facilitating the handling of large datasets. This allowed us to show that calling rate increased with wind speed and humidity but decreased with temperature, and to highlight apparent abundance variations between nesting habitat types. We then showed that the number of EDSs in our recordings positively correlated with the number of callers counted during visual observations, indicating that the density could be estimated based on calling rate. Our observations suggest that increasing temperatures may adversely impact penguin calling behaviour, with potential negative consequences for population dynamics, suggesting the importance of effective conservation measures. Crucially, this study shows that PAM could be successfully used to monitor this endangered species' populations with minimal disturbance.
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Affiliation(s)
- Franziska Feist
- ENES Bioacoustics Research Team, University of Saint-Etienne, 42100 Saint-Etienne, France
| | - Francesca Terranova
- Department of Life Sciences and Systems Biology, University of Turin, 10124 Turin, Italy
| | - Gavin Sean Petersen
- Southern African Foundation for the Conservation of Coastal Birds (SANCCOB), Cape Town 7441, South Africa
| | - Emma Tourtigues
- ENES Bioacoustics Research Team, University of Saint-Etienne, 42100 Saint-Etienne, France
| | - Olivier Friard
- Department of Life Sciences and Systems Biology, University of Turin, 10124 Turin, Italy
| | - Marco Gamba
- Department of Life Sciences and Systems Biology, University of Turin, 10124 Turin, Italy
| | - Katrin Ludynia
- Southern African Foundation for the Conservation of Coastal Birds (SANCCOB), Cape Town 7441, South Africa
- Department of Biodiversity and Conservation Biology, University of the Western Cape, Robert Sobukwe Road, Bellville 7535, South Africa
| | - Tess Gridley
- Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences, University of Cape Town, Rondebosch, Cape Town 7701, South Africa
| | - Lorien Pichegru
- Institute for Coastal and Marine Research, Nelson Mandela Metropolitan University, Port Elisabeth 6031, South Africa
| | - Nicolas Mathevon
- ENES Bioacoustics Research Team, University of Saint-Etienne, 42100 Saint-Etienne, France
- Institut Universitaire de France, Ministry of Higher Education, Research and Innovation, 1 rue Descartes, CEDEX 05, 75231 Paris, France
| | - David Reby
- ENES Bioacoustics Research Team, University of Saint-Etienne, 42100 Saint-Etienne, France
- Institut Universitaire de France, Ministry of Higher Education, Research and Innovation, 1 rue Descartes, CEDEX 05, 75231 Paris, France
| | - Livio Favaro
- Department of Life Sciences and Systems Biology, University of Turin, 10124 Turin, Italy
- CAPE Department, Stazione Zoologica Anton Dohrn, 80121 Naples, Italy
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6
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Johnson E, Campos-Cerqueira M, Jumail A, Yusni ASA, Salgado-Lynn M, Fornace K. Applications and advances in acoustic monitoring for infectious disease epidemiology. Trends Parasitol 2023; 39:386-399. [PMID: 36842917 DOI: 10.1016/j.pt.2023.01.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/19/2023] [Accepted: 01/19/2023] [Indexed: 02/28/2023]
Abstract
Emerging infectious diseases continue to pose a significant burden on global public health, and there is a critical need to better understand transmission dynamics arising at the interface of human activity and wildlife habitats. Passive acoustic monitoring (PAM), more typically applied to questions of biodiversity and conservation, provides an opportunity to collect and analyse audio data in relative real time and at low cost. Acoustic methods are increasingly accessible, with the expansion of cloud-based computing, low-cost hardware, and machine learning approaches. Paired with purposeful experimental design, acoustic data can complement existing surveillance methods and provide a novel toolkit to investigate the key biological parameters and ecological interactions that underpin infectious disease epidemiology.
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Affiliation(s)
- Emilia Johnson
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow G12 8QQ, UK.
| | | | - Amaziasizamoria Jumail
- Danau Girang Field Centre c/o Sabah Wildlife Department, Wisma Muis, Block B, 5th Floor, 88100 Kota Kinabalu, Sabah, Malaysia; Organisms and Environment Division, Cardiff School of Biosciences, Cardiff University, Sir Martin Evans Building, Museum Avenue, Cardiff CF10 3AX, UK
| | - Ashraft Syazwan Ahmady Yusni
- Danau Girang Field Centre c/o Sabah Wildlife Department, Wisma Muis, Block B, 5th Floor, 88100 Kota Kinabalu, Sabah, Malaysia; Institute for Tropical Biology and Conservation, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
| | - Milena Salgado-Lynn
- Danau Girang Field Centre c/o Sabah Wildlife Department, Wisma Muis, Block B, 5th Floor, 88100 Kota Kinabalu, Sabah, Malaysia; Organisms and Environment Division, Cardiff School of Biosciences, Cardiff University, Sir Martin Evans Building, Museum Avenue, Cardiff CF10 3AX, UK; Wildlife Health, Genetic and Forensic Laboratory, c/o Sabah Wildlife Department, Wisma Muis, Block B, 5th Floor, 88100 Kota Kinabalu, Sabah
| | - Kimberly Fornace
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow G12 8QQ, UK; Centre for Climate Change and Planetary Health and Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK; Saw Swee Hock School of Public Health, National University of Singapore, Singapore; National University Health System, Singapore 117549, Singapore
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7
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Jarrett C, Haydon DT, Morales JM, Ferreira DF, Forzi FA, Welch AJ, Powell LL, Matthiopoulos J. Integration of mark-recapture and acoustic detections for unbiased population estimation in animal communities. Ecology 2022; 103:e3769. [PMID: 35620844 PMCID: PMC9787363 DOI: 10.1002/ecy.3769] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 04/15/2022] [Accepted: 04/21/2022] [Indexed: 12/30/2022]
Abstract
Abundance estimation methods that combine several types of data are becoming increasingly common because they yield more accurate and precise parameter estimates and predictions than are possible from a single data source. These beneficial effects result from increasing sample size (through data pooling) and complementarity between different data types. Here, we test whether integrating mark-recapture data with passive acoustic detections into a joint likelihood improves estimates of population size in a multi-guild community. We compared the integrated model to a mark-recapture-only model using simulated data first and then using a data set of mist-net captures and acoustic recordings from an Afrotropical agroforest bird community. The integrated model with simulated data improved accuracy and precision of estimated population size and detection parameters. When applied to field data, the integrated model was able to produce, for each bird guild, ecologically plausible estimates of population size and detection parameters, with more precision compared with the mark-recapture model. Overall, our results show that adding acoustic data to mark-recapture analyses improves estimates of population size. With the increasing availability of acoustic recording devices, this data collection technique could readily be added to routine field protocols, leading to a cost-efficient improvement of traditional mark-recapture population estimation.
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Affiliation(s)
- Crinan Jarrett
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical Veterinary and Life SciencesUniversity of GlasgowGlasgowUK,Biodiversity InitiativeBelmontMassachusettsUSA
| | - Daniel T. Haydon
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical Veterinary and Life SciencesUniversity of GlasgowGlasgowUK
| | - Juan M. Morales
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical Veterinary and Life SciencesUniversity of GlasgowGlasgowUK,Grupo de Ecología Cuantitativa, INIBIOMA‐CONICETUniversidad Nacional del ComahueBarilocheArgentina
| | - Diogo F. Ferreira
- Biodiversity InitiativeBelmontMassachusettsUSA,CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de VairãoUniversidade do PortoVairãoPortugal,BIOPOLIS Program in Genomics, Biodiversity and Land PlanningCIBIOVairãoPortugal
| | | | - Andreanna J. Welch
- Biodiversity InitiativeBelmontMassachusettsUSA,Department of BiosciencesDurham UniversityDurhamUK
| | - Luke L. Powell
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical Veterinary and Life SciencesUniversity of GlasgowGlasgowUK,Biodiversity InitiativeBelmontMassachusettsUSA,CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de VairãoUniversidade do PortoVairãoPortugal,BIOPOLIS Program in Genomics, Biodiversity and Land PlanningCIBIOVairãoPortugal,Department of BiosciencesDurham UniversityDurhamUK
| | - Jason Matthiopoulos
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical Veterinary and Life SciencesUniversity of GlasgowGlasgowUK
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8
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Doser JW, Finley AO, Kéry M, Zipkin EF. spOccupancy
: An R package for single‐species, multi‐species, and integrated spatial occupancy models. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jeffrey W. Doser
- Department of Forestry Michigan State University East Lansing MI USA
- Ecology, Evolution, and Behavior Program Michigan State University East Lansing MI USA
| | - Andrew O. Finley
- Department of Forestry Michigan State University East Lansing MI USA
- Ecology, Evolution, and Behavior Program Michigan State University East Lansing MI USA
| | - Marc Kéry
- Swiss Ornithological Institute Sempach Switzerland
| | - Elise F. Zipkin
- Ecology, Evolution, and Behavior Program Michigan State University East Lansing MI USA
- Department of Integrative Biology Michigan State University East Lansing MI USA
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9
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Wu SH, Chang HW, Lin RS, Tuanmu MN. SILIC: A cross database framework for automatically extracting robust biodiversity information from soundscape recordings based on object detection and a tiny training dataset. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2021.101534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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10
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Doser JW, Leuenberger W, Sillett TS, Hallworth MT, Zipkin EF. Integrated community occupancy models: A framework to assess occurrence and biodiversity dynamics using multiple data sources. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13811] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jeffrey W. Doser
- Department of Forestry Michigan State University East Lansing MI USA
- Ecology, Evolution, and Behavior Program Michigan State University East Lansing MI USA
| | - Wendy Leuenberger
- Ecology, Evolution, and Behavior Program Michigan State University East Lansing MI USA
- Department of Integrative Biology Michigan State University East Lansing MI USA
| | - T. Scott Sillett
- Migratory Bird Center Smithsonian Conservation Biology Institute Washington DC USA
| | | | - Elise F. Zipkin
- Ecology, Evolution, and Behavior Program Michigan State University East Lansing MI USA
- Department of Integrative Biology Michigan State University East Lansing MI USA
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11
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Hale AM, Hein CD, Straw BR. Acoustic and Genetic Data Can Reduce Uncertainty Regarding Populations of Migratory Tree-Roosting Bats Impacted by Wind Energy. Animals (Basel) 2021; 12:81. [PMID: 35011186 PMCID: PMC8749617 DOI: 10.3390/ani12010081] [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: 09/30/2021] [Revised: 11/04/2021] [Accepted: 11/08/2021] [Indexed: 11/17/2022] Open
Abstract
Wind turbine-related mortality may pose a population-level threat for migratory tree-roosting bats, such as the hoary bat (Lasiurus cinereus) in North America. These species are dispersed within their range, making it impractical to estimate census populations size using traditional survey methods. Nonetheless, understanding population size and trends is essential for evaluating and mitigating risk from wind turbine mortality. Using various sampling techniques, including systematic acoustic sampling and genetic analyses, we argue that building a weight of evidence regarding bat population status and trends is possible to (1) assess the sustainability of mortality associated with wind turbines; (2) determine the level of mitigation required; and (3) evaluate the effectiveness of mitigation measures to ensure population viability for these species. Long-term, systematic data collection remains the most viable option for reducing uncertainty regarding population trends for migratory tree-roosting bats. We recommend collecting acoustic data using the statistically robust North American Bat Monitoring Program (NABat) protocols and that genetic diversity is monitored at repeated time intervals to show species trends. There are no short-term actions to resolve these population-level questions; however, we discuss opportunities for relatively short-term investments that will lead to long-term success in reducing uncertainty.
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Affiliation(s)
- Amanda M. Hale
- Department of Biology, Texas Christian University, Fort Worth, TX 76129, USA
| | - Cris D. Hein
- National Renewable Energy Laboratory, Arvada, CO 80007, USA;
| | - Bethany R. Straw
- Fort Collins Science Center, U. S. Geological Survey, Fort Collins, CO 80526, USA;
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