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Raudino HC, Bouchet PJ, Douglas C, Douglas R, Waples K. Aerial abundance estimates for two sympatric dolphin species at a regional scale using distance sampling and density surface modeling. Front Ecol Evol 2023. [DOI: 10.3389/fevo.2022.1086686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Monitoring wildlife populations over scales relevant to management is critical to supporting conservation decision-making in the face of data deficiency, particularly for rare species occurring across large geographic ranges. The Pilbara region of Western Australia is home to two sympatric and morphologically similar species of coastal dolphins—the Indo-pacific bottlenose dolphin (Tursiops aduncus) and Australian humpback dolphin (Sousa sahulensis)—both of which are believed to be declining in numbers and facing increasing pressures from the combined impacts of environmental change and extensive industrial activities. The aim of this study was to develop spatially explicit models of bottlenose and humpback dolphin abundance in Pilbara waters that could inform decisions about coastal development at a regional scale. Aerial line transect surveys were flown from a fixed-wing aircraft in the austral winters of 2015, 2016, and 2017 across a total area of 33,420 km2. Spatio-temporal patterns in dolphin density were quantified using a density surface modeling (DSM) approach, accounting for imperfect detection as well as both perception and availability bias. We estimated the abundance of bottlenose dolphins at 3,713 (95% CI = 2,679–5,146; average density of 0.189 ± 0.046 SD individuals per km2) in 2015, 2,638 (95% CI = 1,670–4,168; 0.159 ± 0.135 individuals per km2) in 2016 and 1,635 (95% CI = 1,031–2,593; 0.101 ± 0.103 individuals per km2) in 2017. Too few humpback dolphins were detected in 2015 to model abundance, but their estimated abundance was 1,546 (95% CI = 942–2,537; 0.097 ± 0.03 individuals per km2) and 2,690 (95% CI = 1,792–4,038; 0.169 ± 0.064 individuals per km2) in 2016 and 2017, respectively. Dolphin densities were greatest in nearshore waters, with hotspots in Exmouth Gulf, the Dampier Archipelago, and Great Sandy Islands. Our results provide a benchmark on which future risk assessments can be based to better understand the overlap between pressures and important dolphin habitats in tropical northwestern Australia.
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Hankinson E, Korstjens AH, Hill RA, Wich SA, Slater HD, Abdullah A, Supradi S, Marsh CD, Nijman V. Effects of anthropogenic disturbance on group densities of Thomas' langurs (
Presbytis thomasi
) within a lowland tropical forest, north Sumatra. Ecol Res 2022. [DOI: 10.1111/1440-1703.12373] [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]
Affiliation(s)
- Emma Hankinson
- School of Social Sciences Oxford Brookes University Headington Campus, Oxford UK
- Department of Life and Environmental Sciences Bournemouth University, Christchurch House Talbot Campus, Poole UK
| | - Amanda H. Korstjens
- Department of Life and Environmental Sciences Bournemouth University, Christchurch House Talbot Campus, Poole UK
| | - Ross A. Hill
- Department of Life and Environmental Sciences Bournemouth University, Christchurch House Talbot Campus, Poole UK
| | - Serge A. Wich
- School of Biological and Environmental Sciences Liverpool John Moores University Liverpool UK
| | - Helen D. Slater
- School of Social Sciences Oxford Brookes University Headington Campus, Oxford UK
- School of Natural and Environmental Sciences Newcastle University Newcastle Upon Tyne UK
| | - Abdullah Abdullah
- Fakultas Biologi Universitas Syiah Kuala Darussalam, Banda Aceh Indonesia
| | - Supradi Supradi
- Fakultas Biologi Universitas Syiah Kuala Darussalam, Banda Aceh Indonesia
| | - Christopher D. Marsh
- School of Social Sciences Oxford Brookes University Headington Campus, Oxford UK
- Department of Biology University of New Mexico Albuquerque New Mexico USA
| | - Vincent Nijman
- School of Social Sciences Oxford Brookes University Headington Campus, Oxford UK
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Camp RJ, Asing CK, Banko PC, Berry L, Brinck KW, Farmer C, Genz AS. Density surface and excursion sets modeling as an approach to estimating population densities. J Wildl Manage 2022. [DOI: 10.1002/jwmg.22332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Richard J. Camp
- U.S. Geological Survey, Pacific Island Ecosystems Research Center P.O. Box 44 Hawai'i National Park HI 96718 USA
| | - Chauncey K. Asing
- University of Hawai'i‐Mānoa Pacific Cooperative Studies Unit, 3190 Maile Way, St John 408 Honolulu HI 96822 USA
| | - Paul C. Banko
- U.S. Geological Survey, Pacific Island Ecosystems Research Center P.O. Box 44 Hawai'i National Park HI 96718 USA
| | - Lainie Berry
- State of Hawai'i, Department of Land and Natural Resources‐Division of Forestry and Wildlife 1151 Punchbowl St Rm 325 Honolulu HI 96813 USA
| | - Kevin W. Brinck
- University of Hawai'i‐Hilo Hawai'i Cooperative Studies Unit, P.O. Box 44 Hawai'i National Park HI 96718 USA
| | - Chris Farmer
- American Bird Conservancy P.O. Box 1167 Volcano HI 96785 USA
| | - Ayesha S. Genz
- University of Hawai'i‐Hilo Hawai'i Cooperative Studies Unit, P.O. Box 44 Hawai'i National Park HI 96718 USA
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Hinton JW, Hurst JE, Kramer DW, Stickles JH, Frair JL. A model-based estimate of winter distribution and abundance of white-tailed deer in the Adirondack Park. PLoS One 2022; 17:e0273707. [PMID: 36040913 PMCID: PMC9426880 DOI: 10.1371/journal.pone.0273707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 08/11/2022] [Indexed: 11/19/2022] Open
Abstract
In the Adirondack Park region of northern New York, USA, white-tailed deer (Odocoileus virginianus) and moose (Alces alces) co-occur along a temperate-boreal forest ecotone. In this region, moose exist as a small and vulnerable low-density population and over-browsing by white-tailed deer is known to reduce regeneration, sustainability, and health of forests. Here, we assess the distribution and abundance of white-tailed deer at a broad spatial scale relevant for deer and moose management in northern New York. We used density surface modeling (DSM) under a conventional distance sampling framework, tied to a winter aerial survey, to create a spatially explicit estimate of white-tailed deer abundance and density across a vast, northern forest region. We estimated 16,352 white-tailed deer (95% CI 11,762–22,734) throughout the Adirondack Park with local density ranging between 0.00–5.73 deer/km2. Most of the Adirondack Park (91.2%) supported white-tailed deer densities of ≤2 individuals/km2. White-tailed deer density increased with increasing proximity to anthropogenic land cover such as timber cuts, roads, and agriculture and decreased in areas with increasing elevation and days with snow cover. We conclude that climate change will be more favorable for white-tailed deer than for moose because milder winters and increased growing seasons will likely have a pronounced influence on deer abundance and distribution across the Adirondack Park. Therefore, identifying specific environmental conditions facilitating the expansion of white-tailed deer into areas with low-density moose populations can assist managers in anticipating potential changes in ungulate distribution and abundance and to develop appropriate management actions to mitigate negative consequences such as disease spread and increased competition for limiting resources.
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Affiliation(s)
- Joseph W. Hinton
- Department of Environmental and Forest Biology, State University of New York College of Environmental Science and Forestry, Syracuse, New York, United States of America
- * E-mail:
| | - Jeremy E. Hurst
- Division of Fish and Wildlife, New York State Department of Environmental Conservation, Albany, New York, United States of America
| | - David W. Kramer
- Division of Fish and Wildlife, New York State Department of Environmental Conservation, Albany, New York, United States of America
| | - James H. Stickles
- Division of Fish and Wildlife, New York State Department of Environmental Conservation, Albany, New York, United States of America
| | - Jacqueline L. Frair
- Roosevelt Wild Life Station, State University of New York College of Environmental Science and Forestry, Syracuse, New York, United States of America
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Miller DL, Becker EA, Forney KA, Roberts JJ, Cañadas A, Schick RS. Estimating uncertainty in density surface models. PeerJ 2022; 10:e13950. [PMID: 36032955 PMCID: PMC9415456 DOI: 10.7717/peerj.13950] [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: 06/30/2022] [Accepted: 08/05/2022] [Indexed: 01/19/2023] Open
Abstract
Providing uncertainty estimates for predictions derived from species distribution models is essential for management but there is little guidance on potential sources of uncertainty in predictions and how best to combine these. Here we show where uncertainty can arise in density surface models (a multi-stage spatial modelling approach for distance sampling data), focussing on cetacean density modelling. We propose an extensible, modular, hybrid analytical-simulation approach to encapsulate these sources. We provide example analyses of fin whales Balaenoptera physalus in the California Current Ecosystem.
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Affiliation(s)
- David L. Miller
- Centre for Research into Ecological & Environmental Modelling and School of Mathematics & Statistics, University of St Andrews, St Andrews, Fife, Scotland
| | - Elizabeth A. Becker
- Ocean Associates, Inc. under contract to Marine Mammal and Turtle Division, Southwest Fisheries Science Center National Marine Fisheries Service, National Oceanic and Atmospheric Administration, La Jolla, CA, United States of America
| | - Karin A. Forney
- Marine Mammal and Turtle Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Moss Landing, CA, United States of America,Moss Landing Marine Laboratories, San Jose State University, Moss Landing, CA, United States of America
| | - Jason J. Roberts
- Marine Geospatial Ecology Laboratory, Nicholas School of the Environment, Duke University, Durham, NC, United States of America
| | - Ana Cañadas
- Marine Geospatial Ecology Laboratory, Nicholas School of the Environment, Duke University, Durham, NC, United States of America
| | - Robert S. Schick
- Marine Geospatial Ecology Laboratory, Nicholas School of the Environment, Duke University, Durham, NC, United States of America
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Miller DL, Fifield D, Wakefield E, Sigourney DB. Extending density surface models to include multiple and double-observer survey data. PeerJ 2021; 9:e12113. [PMID: 34557355 PMCID: PMC8418794 DOI: 10.7717/peerj.12113] [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: 04/23/2021] [Accepted: 08/14/2021] [Indexed: 11/30/2022] Open
Abstract
Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected via different observation processes. Distance sampling is an efficient and widely used survey and analysis technique. Within this framework, observation processes are modelled via detection functions. We seek to take multiple data sources and fit them in a single spatial model. Density surface models (DSMs) are a two-stage approach: first accounting for detectability via distance sampling methods, then modelling distribution via a generalized additive model. However, current software and theory does not address the issue of multiple data sources. We extend the DSM approach to accommodate data from multiple surveys, collected via conventional distance sampling, double-observer distance sampling (used to account for incomplete detection at zero distance) and strip transects. Variance propagation ensures that uncertainty is correctly accounted for in final estimates of abundance. Methods described here are implemented in the dsm R package. We briefly analyse two datasets to illustrate these new developments. Our new methodology enables data from multiple distance sampling surveys of different types to be treated in a single spatial model, enabling more robust abundance estimation, potentially over wider geographical or temporal domains.
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Affiliation(s)
- David L Miller
- Centre for Research into Ecological and Environmental Modelling and School of Mathematics and Statistics, University of St Andrews, St Andrews, Scotland
| | - David Fifield
- Wildlife Research Division, Science and Technology Branch, Environment and Climate Change Canada, Mount Pearl, NL, Canada
| | - Ewan Wakefield
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, Scotland
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