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Liu H, Julian JT. N-mixture models for population estimation: Application in spotted lanternfly egg mass survey. CURRENT RESEARCH IN INSECT SCIENCE 2024; 5:100078. [PMID: 38576775 PMCID: PMC10992689 DOI: 10.1016/j.cris.2024.100078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/06/2024]
Abstract
Population density and structure are critical to nature conservation and pest management. Traditional sampling methods such as capture-mark-recapture and catch-effort can't be used in situations where catching, marking, or removing individuals are not feasible. N-mixture models use repeated count data to estimate population abundance based on detection probability. They are widely adopted in wildlife surveys in recent years to account for imperfect detection. However, its application in entomology is relatively new. In this paper, we describe the general procedures of N-mixture models in population studies from data collection to model fitting and evaluation. Using Lycorma delicatula egg mass survey data at 28 plots in seven sites from the field, we found that detection probability (p) was negatively correlated with tree diameter at breast height (DBH), ranged from 0.516 [95 % CI: 0.470-0.561] to 0.614 [95 % CI: 0.566-0.660] between the 1st and the 3rd sample period. Furthermore, egg mass abundance (λ) was positively associated with basal area (BA) for the sample unit (single tree), with more egg masses on tree of heaven (TOH) trees. More egg masses were also expected on trees of other species in TOH plots. Predicted egg mass density (masses/100 m2) ranged from 5.0 (95 % CI: 3.0-16.0) (Gordon) to 276.9 (95 % CI: 255.0-303.0) (Susquehannock) for TOH plots, and 11.0 (95 % CI: 9.00-15.33) (Gordon) to 228.3 (95 % CI: 209.7-248.3) (Burlington) for nonTOH plots. Site-specific abundance estimates from N-mixture models were generally higher compared to observed maximum counts. N-mixture models could have great potential in insect population surveys in agriculture and forestry in the future.
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Affiliation(s)
- Houping Liu
- Pennsylvania Department of Conservation and Natural Resources, 400 Market Street, Harrisburg, PA 17105, United States
| | - James T. Julian
- Pennsylvania Department of Conservation and Natural Resources, 137 Penn Nursery Rd, Spring Mills, PA 16875, United States
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Rahman DA, Herliansyah R, Subhan B, Hutasoit D, Imron MA, Kurniawan DB, Sriyanto T, Wijayanto RD, Fikriansyah MH, Siregar AF, Santoso N. The first use of a photogrammetry drone to estimate population abundance and predict age structure of threatened Sumatran elephants. Sci Rep 2023; 13:21311. [PMID: 38042901 PMCID: PMC10693614 DOI: 10.1038/s41598-023-48635-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/28/2023] [Indexed: 12/04/2023] Open
Abstract
Wildlife monitoring in tropical rainforests poses additional challenges due to species often being elusive, cryptic, faintly colored, and preferring concealable, or difficult to access habitats. Unmanned aerial vehicles (UAVs) prove promising for wildlife surveys in different ecosystems in tropical forests and can be crucial in conserving inaccessible biodiverse areas and their associated species. Traditional surveys that involve infiltrating animal habitats could adversely affect the habits and behavior of elusive and cryptic species in response to human presence. Moreover, collecting data through traditional surveys to simultaneously estimate the abundance and demographic rates of communities of species is often prohibitively time-intensive and expensive. This study assesses the scope of drones to non-invasively access the Bukit Tigapuluh Landscape (BTL) in Riau-Jambi, Indonesia, and detect individual elephants of interest. A rotary-wing quadcopter with a vision-based sensor was tested to estimate the elephant population size and age structure. We developed hierarchical modeling and deep learning CNN to estimate elephant abundance and age structure. Drones successfully observed 96 distinct individuals at 8 locations out of 11 sampling areas. We obtained an estimate of the elephant population of 151 individuals (95% CI [124, 179]) within the study area and predicted more adult animals than subadults and juvenile individuals in the population. Our calculations may serve as a vital spark for innovation for future UAV survey designs in large areas with complex topographies while reducing operational effort.
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Affiliation(s)
- Dede Aulia Rahman
- Department of Forest Resources Conservation and Ecotourism, Faculty of Forestry and Environment, IPB University, Bogor, 16680, Indonesia.
- Primate Research Center, Institute of Research and Community Service, IPB University, Bogor, 16151, Indonesia.
| | - Riki Herliansyah
- School of Statistics, Kalimantan Institute of Technology, Balikpapan, 76127, Indonesia
- School of Mathematics and Maxwell Institute for Mathematical Sciences, University of Edinburgh, Edinburgh, EH9 3FD, UK
| | - Beginer Subhan
- Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University, Bogor, 16680, Indonesia
| | - Donal Hutasoit
- Jambi Natural Resources Conservation Agency, Jambi, 36361, Indonesia
| | | | | | - Teguh Sriyanto
- Jambi Natural Resources Conservation Agency, Jambi, 36361, Indonesia
| | - Raden Danang Wijayanto
- Tropical Biodiversity Conservation Program, Faculty of Forestry and Environment, IPB University, Bogor, 16680, Indonesia
- Yogyakarta Natural Resources Conservation Agency, D.I. Yogyakarta, 55514, Indonesia
| | | | - Ahmad Faisal Siregar
- Tropical Biodiversity Conservation Program, Faculty of Forestry and Environment, IPB University, Bogor, 16680, Indonesia
| | - Nyoto Santoso
- Department of Forest Resources Conservation and Ecotourism, Faculty of Forestry and Environment, IPB University, Bogor, 16680, Indonesia
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Chaudhuri S, Rajaraman R, Kalyanasundaram S, Sathyakumar S, Krishnamurthy R. N-mixture model-based estimate of relative abundance of sloth bear ( Melursus ursinus) in response to biotic and abiotic factors in a human-dominated landscape of central India. PeerJ 2022; 10:e13649. [PMID: 36523470 PMCID: PMC9745790 DOI: 10.7717/peerj.13649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 06/07/2022] [Indexed: 12/12/2022] Open
Abstract
Reliable estimation of abundance is a prerequisite for a species' conservation planning in human-dominated landscapes, especially if the species is elusive and involved in conflicts. As a means of population estimation, the importance of camera traps has been recognized globally, although estimating the abundance of unmarked, cryptic species has always been a challenge to conservation biologists. This study explores the use of the N-mixture model with three probability distributions, i.e., Poisson, negative binomial (NB) and zero-inflated Poisson (ZIP), to estimate the relative abundance of sloth bears (Melursus ursinus) based on a camera trapping exercise in Sanjay Tiger Reserve, Madhya Pradesh from December 2016 to April 2017. We used environmental and anthropogenic covariates to model the variation in the abundance of sloth bears. We also compared null model estimates (mean site abundance) obtained from the N-mixture model to those of the Royle-Nichols abundance-induced heterogeneity model (RN model) to assess the application of similar site-structured models. Models with Poisson distributions produced ecologically realistic and more precise estimates of mean site abundance (λ = 2.60 ± 0.64) compared with other distributions, despite the relatively high Akaike Information Criterion value. Area of mixed and sal forest, the photographic capture rate of humans and distance to the nearest village predicted a higher relative abundance of sloth bears. Mean site abundance estimates of sloth bears obtained from the N-mixture model (Poisson distribution) and the RN model were comparable, indicating the overall utility of these models in this field. However, density estimates of sloth bears based on spatially explicit methods are essential for evaluating the efficacy of the relatively more cost-effective N-mixture model. Compared to commonly used index/encounter-based methods, the N-mixture model equipped with knowledge on governing biotic and abiotic factors provides better relative abundance estimates for a species like the sloth bear. In the absence of absolute abundance estimates, the present study could be insightful for the long-term conservation and management of sloth bears.
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Affiliation(s)
- Sankarshan Chaudhuri
- Department of Landscape Level Planning and Management, Wildlife Institute of India, Dehradun, Uttarakhand, India
| | - Rajasekar Rajaraman
- Department of Landscape Level Planning and Management, Wildlife Institute of India, Dehradun, Uttarakhand, India
| | | | - Sambandam Sathyakumar
- Department of Endangered Species Management, Wildlife Institute of India, Dehradun, Uttarakhand, India
| | - Ramesh Krishnamurthy
- Department of Landscape Level Planning and Management, Wildlife Institute of India, Dehradun, Uttarakhand, India,Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, Canada
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Zhao Q, Fuller AK, Royle JA. Spatial dynamic N‐mixture models with interspecific interactions. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Qing Zhao
- Bird Conservancy of the Rockies Fort Collins CO USA
| | - Angela K. Fuller
- U.S. Geological Survey, New York Cooperative Fish and Wildlife Research Unit, Department of Natural Resources and the Environment, Cornell University Ithaca NY USA
| | - J. Andrew Royle
- U.S. Geological Survey, Eastern Ecological Science Center Laurel MD USA
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Lemos Barão-Nóbrega JA, González-Jaurégui M, Jehle R. N-mixture models provide informative crocodile ( Crocodylus moreletii) abundance estimates in dynamic environments. PeerJ 2022; 10:e12906. [PMID: 35341055 PMCID: PMC8944345 DOI: 10.7717/peerj.12906] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 01/17/2022] [Indexed: 01/11/2023] Open
Abstract
Estimates of animal abundance provide essential information for population ecological studies. However, the recording of individuals in the field can be challenging, and accurate estimates require analytical techniques which account for imperfect detection. Here, we quantify local abundances and overall population size of Morelet's crocodiles (Crocodylus moreletii) in the region of Calakmul (Campeche, Mexico), comparing traditional approaches for crocodylians (Minimum Population Size-MPS; King's Visible Fraction Method-VFM) with binomial N-mixture models based on Poisson, zero-inflated Poisson (ZIP) and negative binomial (NB) distributions. A total of 191 nocturnal spotlight surveys were conducted across 40 representative locations (hydrologically highly dynamic aquatic sites locally known as aguadas) over a period of 3 years (2017-2019). Local abundance estimates revealed a median of 1 both through MPS (min-max: 0-89; first and third quartiles, Q1-Q3: 0-7) and VFM (0-112; Q1-Q3: 0-9) non-hatchling C. moreletii for each aguada, respectively. The ZIP based N-mixture approach shown overall superior confidence over Poisson and NB, and revealed a median of 6 ± 3 individuals (min = 0; max = 120 ± 18; Q1 = 0; Q3 = 18 ± 4) jointly with higher detectabilities in drying aguadas with low and intermediate vegetation cover. Extrapolating these inferences across all waterbodies in the study area yielded an estimated ~10,000 (7,000-11,000) C. moreletii present, highlighting Calakmul as an important region for this species. Because covariates enable insights into population responses to local environmental conditions, N-mixture models applied to spotlight count data result in particularly insightful estimates of crocodylian detection and abundance.
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Affiliation(s)
- José António Lemos Barão-Nóbrega
- Operation Wallacea, Spilsby, Lincolnshire, United Kingdom,School of Science, Engineering and Environment, University of Salford, Salford, Greater Manchester, United Kingdom
| | - Mauricio González-Jaurégui
- Universidad Autónoma de Campeche, Centro de Estudios de Desarrollo Sustentable y Aprovechamiento de la Vida Silvestre, Campeche, Campeche, Mexico
| | - Robert Jehle
- School of Science, Engineering and Environment, University of Salford, Salford, Greater Manchester, United Kingdom
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Pulliam JP, Somershoe S, Sather M, McNew LB. Nest density drives productivity in chestnut-collared longspurs: Implications for grassland bird conservation. PLoS One 2021; 16:e0256346. [PMID: 34428226 PMCID: PMC8384174 DOI: 10.1371/journal.pone.0256346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 08/04/2021] [Indexed: 11/18/2022] Open
Abstract
Grassland birds are declining faster than any other avian guild in North America and are increasingly a focus of conservation concern. Adaptive, outcome-based management of rangelands could do much to mitigate declines. However, this approach relies on quantitative, generalizable habitat targets that have been difficult to extrapolate from the literature. Past work relies heavily on individual versus population response, and direct response to management (e.g. grazing) versus response to outcomes. We compared individual and population-level responses to vegetation conditions across scales to identify quantitative targets of habitat quality for an imperiled grassland songbird, the chestnut-collared longspur (Calcarius ornatus) in northern Montana, USA during 2017-2018. We estimated nest density and nest survival within 9-ha survey plots using open N-mixture and nest survival models, respectively, and evaluated relationships with plot- and nest-site vegetation conditions. Plot-scale conditions influenced nest density, whereas nest survival was unaffected by any measured condition. Nest-site and plot-scale vegetation measurements were only weakly correlated, suggesting that management targets based on nest sites only would be incomplete. While nest survival is often assumed to be the key driver of bird productivity, our results suggest that nest density and plot-scale conditions are more important for productivity of longspurs at the core of the breeding distribution. Habitat outcomes for grassland birds should incorporate nest density and average conditions at scale(s) relevant to management (e.g. paddock or pasture).
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Affiliation(s)
- John P. Pulliam
- Department of Animal & Range Sciences, Montana State University, Bozeman, Montana, United States of America
| | - Scott Somershoe
- Division of Habitat Conservation, Land Bird Coordinator U.S. Fish and Wildlife Service Migratory Birds Program, Lakewood, Colorado, United States of America
| | - Marisa Sather
- Wildlife Biologist, Partners for Fish and Wildlife Program, U.S. Fish and Wildlife Service, Glasgow, Montana, United States of America
| | - Lance B. McNew
- Department of Animal & Range Sciences, Montana State University, Bozeman, Montana, United States of America
- * E-mail:
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McDonald JL, Hodgson D. Counting Cats: The integration of expert and citizen science data for unbiased inference of population abundance. Ecol Evol 2021; 11:4325-4338. [PMID: 33976813 PMCID: PMC8093703 DOI: 10.1002/ece3.7330] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 01/25/2021] [Accepted: 01/31/2021] [Indexed: 11/07/2022] Open
Abstract
Free-roaming animal populations are hard to count, and professional experts are a limited resource. There is vast untapped potential in the data collected by nonprofessional scientists who volunteer their time to population monitoring, but citizen science (CS) raises concerns around data quality and biases. A particular concern in abundance modeling is the presence of false positives that can occur due to misidentification of nontarget species. Here, we introduce Integrated Abundance Models (IAMs) that integrate citizen and expert data to allow robust inference of population abundance meanwhile accounting for biases caused by misidentification. We used simulation experiments to confirm that IAMs successfully remove the inflation of abundance estimates caused by false-positive detections and can provide accurate estimates of both bias and abundance. We illustrate the approach with a case study on unowned domestic cats, which are commonly confused with owned, and infer their abundance by analyzing a combination of CS data and expert data. Our case study finds that relying on CS data alone, either through simple summation or via traditional modeling approaches, can vastly inflate abundance estimates. IAMs provide an adaptable framework, increasing the opportunity for further development of the approach, tailoring to specific systems and robust use of CS data.
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Affiliation(s)
- Jenni L. McDonald
- Veterinary Department, Cats ProtectionNational Cat CentreHaywards HeathUK
- Bristol Veterinary SchoolUniversity of BristolBristolUK
| | - Dave Hodgson
- Centre for Ecology and ConservationCollege of Life and Environmental SciencesUniversity of ExeterPenrynUK
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Corcoran E, Denman S, Hamilton G. New technologies in the mix: Assessing N-mixture models for abundance estimation using automated detection data from drone surveys. Ecol Evol 2020; 10:8176-8185. [PMID: 32788970 PMCID: PMC7417234 DOI: 10.1002/ece3.6522] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Revised: 05/16/2020] [Accepted: 06/02/2020] [Indexed: 11/16/2022] Open
Abstract
Reliable estimates of abundance are critical in effectively managing threatened species, but the feasibility of integrating data from wildlife surveys completed using advanced technologies such as remotely piloted aircraft systems (RPAS) and machine learning into abundance estimation methods such as N-mixture modeling is largely unknown due to the unique sources of detection errors associated with these technologies.We evaluated two modeling approaches for estimating the abundance of koalas detected automatically in RPAS imagery: (a) a generalized N-mixture model and (b) a modified Horvitz-Thompson (H-T) estimator method combining generalized linear models and generalized additive models for overall probability of detection, false detection, and duplicate detection. The final estimates from each model were compared to the true number of koalas present as determined by telemetry-assisted ground surveys.The modified H-T estimator approach performed best, with the true count of koalas captured within the 95% confidence intervals around the abundance estimates in all 4 surveys in the testing dataset (n = 138 detected objects), a particularly strong result given the difficulty in attaining accuracy found with previous methods.The results suggested that N-mixture models in their current form may not be the most appropriate approach to estimating the abundance of wildlife detected in RPAS surveys with automated detection, and accurate estimates could be made with approaches that account for spurious detections.
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Affiliation(s)
- Evangeline Corcoran
- School of Earth, Environmental and Biological SciencesQueensland University of Technology (QUT)BrisbaneQLDAustralia
| | - Simon Denman
- School of Electrical Engineering and Computer ScienceQueensland University of Technology (QUT)BrisbaneQLDAustralia
| | - Grant Hamilton
- School of Earth, Environmental and Biological SciencesQueensland University of Technology (QUT)BrisbaneQLDAustralia
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Pant G, Maraseni T, Apan A, Allen BL. Trends and current state of research on greater one-horned rhinoceros (Rhinoceros unicornis): A systematic review of the literature over a period of 33 years (1985-2018). THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 710:136349. [PMID: 32050371 DOI: 10.1016/j.scitotenv.2019.136349] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 12/24/2019] [Accepted: 12/24/2019] [Indexed: 06/10/2023]
Abstract
Greater one-horned rhinoceros (Rhinoceros unicornis) is one of the most iconic wildlife species in the world. Once reduced to fewer than 500 during the 1960s, its global population has been recovering and is now over 3500, thanks to effective conservation programs in India and Nepal, the only two countries in the world where this species is found. It is one of the greatest success stories in biodiversity conservation given that hundreds of other species have disappeared, and thousands of species are on the verge of extinction. However, poaching is not the only threat for the long-term survival of rhinoceros. Loss and degradation of grassland habitat and the drying-up of wetlands are emerging threats predicted to worsen in the future, but the published information on rhinoceros has never been synthesized. In order to better understand the trends and current status of rhinoceros research and identify research gaps inhibiting its long-term conservation, we analyzed the themes discussed in 215 articles covering a period of 33 years between 1985 and 2018. Our findings suggest that studies on both free-ranging and captive rhinoceros are skewed towards biological aspects of the species including morphology, anatomy, physiology, and behaviour. There are no studies addressing the likely effects of climate change on the species, and limited information is available on rhinoceros genetics, diseases, habitat dynamics and the impacts of tourism and other infrastructure development in and around rhinoceros habitat. These issues will need addressing to maintain the conservation success of greater one-horned rhinoceros into the future.
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Affiliation(s)
- Ganesh Pant
- University of Southern Queensland, Institute for Life Sciences and the Environment, West Street, Toowoomba, Queensland 4350, Australia; Ministry of Forests and Environment, Singhadurbar, Kathmandu 44600, Nepal
| | - Tek Maraseni
- University of Southern Queensland, Institute for Life Sciences and the Environment, West Street, Toowoomba, Queensland 4350, Australia.
| | - Armando Apan
- University of Southern Queensland, Institute for Life Sciences and the Environment, West Street, Toowoomba, Queensland 4350, Australia
| | - Benjamin L Allen
- University of Southern Queensland, Institute for Life Sciences and the Environment, West Street, Toowoomba, Queensland 4350, Australia; Centre for African Conservation Ecology, Nelson Mandela University, Port Elizabeth 6034, South Africa
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