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Sheard JK, Adriaens T, Bowler DE, Büermann A, Callaghan CT, Camprasse ECM, Chowdhury S, Engel T, Finch EA, von Gönner J, Hsing PY, Mikula P, Rachel Oh RY, Peters B, Phartyal SS, Pocock MJO, Wäldchen J, Bonn A. Emerging technologies in citizen science and potential for insect monitoring. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230106. [PMID: 38705194 PMCID: PMC11070260 DOI: 10.1098/rstb.2023.0106] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 03/29/2024] [Indexed: 05/07/2024] Open
Abstract
Emerging technologies are increasingly employed in environmental citizen science projects. This integration offers benefits and opportunities for scientists and participants alike. Citizen science can support large-scale, long-term monitoring of species occurrences, behaviour and interactions. At the same time, technologies can foster participant engagement, regardless of pre-existing taxonomic expertise or experience, and permit new types of data to be collected. Yet, technologies may also create challenges by potentially increasing financial costs, necessitating technological expertise or demanding training of participants. Technology could also reduce people's direct involvement and engagement with nature. In this perspective, we discuss how current technologies have spurred an increase in citizen science projects and how the implementation of emerging technologies in citizen science may enhance scientific impact and public engagement. We show how technology can act as (i) a facilitator of current citizen science and monitoring efforts, (ii) an enabler of new research opportunities, and (iii) a transformer of science, policy and public participation, but could also become (iv) an inhibitor of participation, equity and scientific rigour. Technology is developing fast and promises to provide many exciting opportunities for citizen science and insect monitoring, but while we seize these opportunities, we must remain vigilant against potential risks. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.
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Affiliation(s)
- Julie Koch Sheard
- Department of Ecosystem Services, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318 Leipzig, Germany
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger Straße 159, 07743 Jena, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany
| | - Tim Adriaens
- Research Institute for Nature and Forest (INBO), Havenlaan 88 bus 73, 1000 Brussels, Belgium
| | - Diana E. Bowler
- UK Centre for Ecology & Hydrology, Wallingford, Oxfordshire, OX10 8BB, UK
| | - Andrea Büermann
- Department of Ecosystem Services, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318 Leipzig, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany
| | - Corey T. Callaghan
- Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, University of Florida, FL 33314, USA
| | - Elodie C. M. Camprasse
- School of Life and Environmental Sciences, Deakin University, Melbourne Burwood Campus, 221 Burwood Highway, Burwood, Victoria 3125, Australia
| | - Shawan Chowdhury
- Department of Ecosystem Services, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318 Leipzig, Germany
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger Straße 159, 07743 Jena, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany
| | - Thore Engel
- Department of Ecosystem Services, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318 Leipzig, Germany
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger Straße 159, 07743 Jena, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany
| | - Elizabeth A. Finch
- Department of Ecosystem Services, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318 Leipzig, Germany
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger Straße 159, 07743 Jena, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany
| | - Julia von Gönner
- Department of Ecosystem Services, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318 Leipzig, Germany
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger Straße 159, 07743 Jena, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany
| | - Pen-Yuan Hsing
- Faculty of Life Sciences, University of Bristol, 12a Priory Road, Bristol BS8 1TU, UK
| | - Peter Mikula
- TUM School of Life Sciences, Ecoclimatology, Technical University of Munich, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany
- Institute for Advanced Study, Technical University of Munich, Lichtenbergstraße 2a, 85748 Garching, Germany
- Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 16500 Prague, Czech Republic
| | - Rui Ying Rachel Oh
- Department of Ecosystem Services, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318 Leipzig, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany
| | - Birte Peters
- Department of Ecosystem Services, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318 Leipzig, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany
| | - Shyam S. Phartyal
- School of Ecology and Environment Studies, Nalanda University, Rajgir 803116, India
| | | | - Jana Wäldchen
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany
- Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Hans-Knöll-Straße 10, 07745 Jena, Germany
| | - Aletta Bonn
- Department of Ecosystem Services, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318 Leipzig, Germany
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger Straße 159, 07743 Jena, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany
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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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Jacobson OT, Barrett BJ, Perry SE, Finerty GE, Tiedeman KM, Crofoot MC. A new approach to geostatistical synthesis of historical records reveals capuchin spatial responses to climate and demographic change. Ecol Lett 2024; 27:e14443. [PMID: 38803140 DOI: 10.1111/ele.14443] [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: 02/15/2024] [Revised: 04/24/2024] [Accepted: 04/29/2024] [Indexed: 05/29/2024]
Abstract
Recent proliferation of GPS technology has transformed animal movement research. Yet, time-series data from this recent technology rarely span beyond a decade, constraining longitudinal research. Long-term field sites hold valuable historic animal location records, including hand-drawn maps and semantic descriptions. Here, we introduce a generalised workflow for converting such records into reliable location data to estimate home ranges, using 30 years of sleep-site data from 11 white-faced capuchin (Cebus imitator) groups in Costa Rica. Our findings illustrate that historic sleep locations can reliably recover home range size and geometry. We showcase the opportunity our approach presents to resolve open questions that can only be addressed with very long-term data, examining how home ranges are affected by climate cycles and demographic change. We urge researchers to translate historical records into usable movement data before this knowledge is lost; it is essential to understanding how animals are responding to our changing world.
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Affiliation(s)
- Odd T Jacobson
- Department for the Ecology of Animal Societies, Max Planck Institute for Animal Behavior, Constance, Germany
- Department of Biology, University of Konstanz, Constance, Germany
- International Max Planck Research School for Quantitative Behavioral Ecology and Evolution, Max Planck Institute for Animal Behavior, University of Konstanz, Constance, Germany
| | - Brendan J Barrett
- Department for the Ecology of Animal Societies, Max Planck Institute for Animal Behavior, Constance, Germany
- Department of Biology, University of Konstanz, Constance, Germany
- Center for the Advanced Study of Collective Behavior, University of Konstanz, Constance, Germany
- Department of Human Behavior, Ecology, and Culture, Max Planck Institute of Evolutionary Anthropology, Leipzig, Germany
| | - Susan E Perry
- Department of Anthropology, University of California-Los Angeles, Los Angeles, California, USA
| | - Genevieve E Finerty
- Department for the Ecology of Animal Societies, Max Planck Institute for Animal Behavior, Constance, Germany
- Department of Biology, University of Konstanz, Constance, Germany
- Center for the Advanced Study of Collective Behavior, University of Konstanz, Constance, Germany
| | - Kate M Tiedeman
- Department of Biology, University of Konstanz, Constance, Germany
| | - Margaret C Crofoot
- Department for the Ecology of Animal Societies, Max Planck Institute for Animal Behavior, Constance, Germany
- Department of Biology, University of Konstanz, Constance, Germany
- Center for the Advanced Study of Collective Behavior, University of Konstanz, Constance, Germany
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4
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Valle D, Mintz J, Brack IV. Estimation and interpretation problems and solutions when using proportion covariates in linear regression models. Ecology 2024; 105:e4256. [PMID: 38361276 DOI: 10.1002/ecy.4256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/28/2023] [Accepted: 01/18/2024] [Indexed: 02/17/2024]
Abstract
Proportion variables, also known as compositional data, are very common in ecology. Unfortunately, few scientists are aware of how compositional data, when used as covariates, can adversely impact statistical analysis. We describe here how proportion covariates result in multicollinearity and parameter identifiability problems. Using simulated data on bird species richness as a function of land use, we show how these problems manifest when fitting a wide range of models in R, both in a frequentist and Bayesian framework. In particular, we show that similar models can often generate substantially different parameter estimates, leading to very different conclusions. Dropping a covariate or the intercept from the model can solve the multicollinearity and parameter identifiability problems. Unfortunately, these solutions do not fix the inherent challenges associated with interpreting parameter estimates. To this end, we propose focusing the interpretation on the difference of slope parameters to avoid the inherent unidentifiability of individual parameters. We also propose conditional plots with two x-axes and marginal plots as visualization techniques that can help users better interpret their modeling results. We illustrate these problems and proposed solutions using empirical data from the North American Breeding Bird Survey. The practical and straightforward approaches suggested in this article will help the fitting of linear models and interpretation of its results when some of the covariates are proportions.
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Affiliation(s)
- Denis Valle
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, USA
| | - Jeffrey Mintz
- School of Natural Resources and Environment, University of Florida, Gainesville, Florida, USA
| | - Ismael Verrastro Brack
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, USA
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Zhang X, Feng Y, Li F, Ding J, Tahseen D, Hinojosa E, Chen Y, Tao C. Evaluating MedDRA-to-ICD terminology mappings. BMC Med Inform Decis Mak 2024; 23:299. [PMID: 38326827 PMCID: PMC10851449 DOI: 10.1186/s12911-023-02375-1] [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: 04/05/2022] [Accepted: 11/14/2023] [Indexed: 02/09/2024] Open
Abstract
BACKGROUND In this era of big data, data harmonization is an important step to ensure reproducible, scalable, and collaborative research. Thus, terminology mapping is a necessary step to harmonize heterogeneous data. Take the Medical Dictionary for Regulatory Activities (MedDRA) and International Classification of Diseases (ICD) for example, the mapping between them is essential for drug safety and pharmacovigilance research. Our main objective is to provide a quantitative and qualitative analysis of the mapping status between MedDRA and ICD. We focus on evaluating the current mapping status between MedDRA and ICD through the Unified Medical Language System (UMLS) and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). We summarized the current mapping statistics and evaluated the quality of the current MedDRA-ICD mapping; for unmapped terms, we used our self-developed algorithm to rank the best possible mapping candidates for additional mapping coverage. RESULTS The identified MedDRA-ICD mapped pairs cover 27.23% of the overall MedDRA preferred terms (PT). The systematic quality analysis demonstrated that, among the mapped pairs provided by UMLS, only 51.44% are considered an exact match. For the 2400 sampled unmapped terms, 56 of the 2400 MedDRA Preferred Terms (PT) could have exact match terms from ICD. CONCLUSION Some of the mapped pairs between MedDRA and ICD are not exact matches due to differences in granularity and focus. For 72% of the unmapped PT terms, the identified exact match pairs illustrate the possibility of identifying additional mapped pairs. Referring to its own mapping standard, some of the unmapped terms should qualify for the expansion of MedDRA to ICD mapping in UMLS.
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Affiliation(s)
- Xinyuan Zhang
- McWilliam School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yixue Feng
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Fang Li
- McWilliam School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jin Ding
- McWilliam School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Danyal Tahseen
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ezekiel Hinojosa
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yong Chen
- The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Cui Tao
- McWilliam School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.
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Ferrer-Ferrando D, Fernández-López J, Triguero-Ocaña R, Palencia P, Vicente J, Acevedo P. The method matters. A comparative study of biologging and camera traps as data sources with which to describe wildlife habitat selection. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 902:166053. [PMID: 37543342 DOI: 10.1016/j.scitotenv.2023.166053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/10/2023] [Accepted: 08/02/2023] [Indexed: 08/07/2023]
Abstract
Habitat use is a virtually universal activity among animals and is highly relevant as regards designing wildlife management and conservation actions. This has led to the development of a great variety of methods to study it, of which resource selection functions combined with biologging-derived data (RSF) is the most widely used for this purpose. However this approach has some constraints, such as its invasiveness and high costs. Analytical approaches taking into consideration imperfect detection coupled with camera trap data (IDM) have, therefore, emerged as a non-invasive cost-effective alternative. However, despite the fact that both approaches (RSF and IDM) have been used in habitat selection studies, they should also be comparatively assessed. The objective of this work is consequently to assess them from two perspectives: explanatory and predictive. This has been done by analyzing data obtained from camera traps (60 sampling sites) and biologging (17 animals monitored: 7 red deer Cervus elaphus, 6 fallow deer Dama dama and 4 wild boar Sus scrofa) in the same periods using IDM and RSF, respectively, in Doñana National Park (southern Spain) in order to explain and predict habitat use patterns for three studied species. Our results showed discrepancies between the two approaches, as they identified different predictors as being the most relevant to determine species intensity of use, and they predicted spatial patterns of habitat use with a contrasted level of concordance, depending on species and scale. Given these results and the characteristics of each approach, we suggested that although partly comparable interpretations can be obtained with both approaches, they are not equivalent but rather complementary. The combination of data from biologging and camera traps would, therefore, appear to be suitable for the development of an analytical framework with which to describe and characterise the habitat use processes of wildlife.
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Affiliation(s)
- David Ferrer-Ferrando
- Instituto de Investigación en Recursos Cinegéticos (IREC), CSIC-UCLM-JCCM, Ciudad Real, Spain.
| | - Javier Fernández-López
- Université Montpellier, CNRS, EPHE, IRD, Montpellier, France; Universidad Complutense de Madrid, Madrid, Spain.
| | - Roxana Triguero-Ocaña
- Instituto de Investigación en Recursos Cinegéticos (IREC), CSIC-UCLM-JCCM, Ciudad Real, Spain
| | - Pablo Palencia
- Instituto de Investigación en Recursos Cinegéticos (IREC), CSIC-UCLM-JCCM, Ciudad Real, Spain; Università Degli Studi di Torino, Dipartamiento di Scienze Veterinarie, Largo Paolo Braccini, 2, 10095 Grugliasco, Torino, Italy
| | - Joaquín Vicente
- Instituto de Investigación en Recursos Cinegéticos (IREC), CSIC-UCLM-JCCM, Ciudad Real, Spain.
| | - Pelayo Acevedo
- Instituto de Investigación en Recursos Cinegéticos (IREC), CSIC-UCLM-JCCM, Ciudad Real, Spain.
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Cembrowska-Lech D, Krzemińska A, Miller T, Nowakowska A, Adamski C, Radaczyńska M, Mikiciuk G, Mikiciuk M. An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture. BIOLOGY 2023; 12:1298. [PMID: 37887008 PMCID: PMC10603917 DOI: 10.3390/biology12101298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023]
Abstract
This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.
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Affiliation(s)
- Danuta Cembrowska-Lech
- Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland;
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
| | - Adrianna Krzemińska
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
- Institute of Biology, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland;
| | - Tymoteusz Miller
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
- Institute of Marine and Environmental Sciences, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland
| | - Anna Nowakowska
- Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland;
| | - Cezary Adamski
- Institute of Biology, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland;
| | | | - Grzegorz Mikiciuk
- Department of Horticulture, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland;
| | - Małgorzata Mikiciuk
- Department of Bioengineering, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland;
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Sigourney DB, DeAngelis A, Cholewiak D, Palka D. Combining passive acoustic data from a towed hydrophone array with visual line transect data to estimate abundance and availability bias of sperm whales ( Physeter macrocephalus). PeerJ 2023; 11:e15850. [PMID: 37750078 PMCID: PMC10518167 DOI: 10.7717/peerj.15850] [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: 02/02/2023] [Accepted: 07/16/2023] [Indexed: 09/27/2023] Open
Abstract
Visual line transect (VLT) surveys are central to the monitoring and study of marine mammals. However, for cryptic species such as deep diving cetaceans VLT surveys alone suffer from problems of low sample sizes and availability bias where animals below the surface are not available to be detected. The advent of passive acoustic monitoring (PAM) technology offers important opportunities to observe deep diving cetaceans but statistical challenges remain particularly when trying to integrate VLT and PAM data. Herein, we present a general framework to combine these data streams to estimate abundance when both surveys are conducted simultaneously. Secondarily, our approach can also be used to derive an estimate of availability bias. We outline three methods that vary in complexity and data requirements which are (1) a simple distance sampling (DS) method that treats the two datasets independently (DS-DS Method), (2) a fully integrated approach that applies a capture-mark recapture (CMR) analysis to the PAM data (CMR-DS Method) and (3) a hybrid approach that requires only a subset of the PAM CMR data (Hybrid Method). To evaluate their performance, we use simulations based on known diving and vocalizing behavior of sperm whales (Physeter macrocephalus). As a case study, we applied the Hybrid Method to data from a shipboard survey of sperm whales and compared estimates to a VLT only analysis. Simulation results demonstrated that the CMR-DS Method and Hybrid Method reduced bias by >90% for both abundance and availability bias in comparison to the simpler DS -DS Method. Overall, the CMR-DS Method was the least biased and most precise. For the case study, our application of the Hybrid Method to the sperm whale dataset produced estimates of abundance and availability bias that were comparable to estimates from the VLT only analysis but with considerably higher precision. Integrating multiple sources of data is an important goal with clear benefits. As a step towards that goal we have developed a novel framework. Results from this study are promising although challenges still remain. Future work may focus on applying this method to other deep-diving species and comparing the proposed method to other statistical approaches that aim to combine information from multiple data sources.
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Affiliation(s)
| | - Annamaria DeAngelis
- NOAA Northeast Fisheries Science Center, Woods Hole, Massachusetts, United States
| | - Danielle Cholewiak
- NOAA Northeast Fisheries Science Center, Woods Hole, Massachusetts, United States
| | - Debra Palka
- NOAA Northeast Fisheries Science Center, Woods Hole, Massachusetts, United States
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Carraro L, Blackman RC, Altermatt F. Modelling environmental DNA transport in rivers reveals highly resolved spatio-temporal biodiversity patterns. Sci Rep 2023; 13:8854. [PMID: 37258598 DOI: 10.1038/s41598-023-35614-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/21/2023] [Indexed: 06/02/2023] Open
Abstract
The ever-increasing threats to riverine ecosystems call for novel approaches for highly resolved biodiversity assessments across taxonomic groups and spatio-temporal scales. Recent advances in the joint use of environmental DNA (eDNA) data and eDNA transport models in rivers (e.g., eDITH) allow uncovering the full structure of riverine biodiversity, hence elucidating ecosystem processes and supporting conservation measures. We applied eDITH to a metabarcoding dataset covering three taxonomic groups (fish, invertebrates, bacteria) and three seasons for a catchment sampled for eDNA at 73 sites. We upscaled eDNA-based biodiversity predictions to approximately 1900 reaches, and assessed α- and β-diversity patterns across seasons and taxonomic groups over the whole network. Genus richness predicted by eDITH was generally higher than values from direct eDNA analysis. Both predicted α- and β-diversity varied depending on season and taxonomic group. Predicted fish α-diversity increased downstream in all seasons, while invertebrate and bacteria α-diversity either decreased downstream or were unrelated to network position. Spatial β-diversity mostly decreased downstream, especially for bacteria. The eDITH model yielded a more refined assessment of freshwater biodiversity as compared to raw eDNA data, both in terms of spatial coverage, diversity patterns and effect of covariates, thus providing a more complete picture of freshwater biodiversity.
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Affiliation(s)
- Luca Carraro
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, 8057, Zürich, Switzerland.
- Department of Aquatic Ecology, Swiss Federal Institute of Aquatic Science and Technology, Eawag, 8600, Dübendorf, Switzerland.
| | - Rosetta C Blackman
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, 8057, Zürich, Switzerland
- Department of Aquatic Ecology, Swiss Federal Institute of Aquatic Science and Technology, Eawag, 8600, Dübendorf, Switzerland
| | - Florian Altermatt
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, 8057, Zürich, Switzerland
- Department of Aquatic Ecology, Swiss Federal Institute of Aquatic Science and Technology, Eawag, 8600, Dübendorf, Switzerland
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10
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Ellis KS, Anteau MJ, MacDonald GJ, Swift RJ, Ring MM, Toy DL, Sherfy MH, Post van der Burg M. Data integration reveals dynamic and systematic patterns of breeding habitat use by a threatened shorebird. Sci Rep 2023; 13:6087. [PMID: 37055434 PMCID: PMC10102276 DOI: 10.1038/s41598-023-32886-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 04/04/2023] [Indexed: 04/15/2023] Open
Abstract
Incorporating species distributions into conservation planning has traditionally involved long-term representations of habitat use where temporal variation is averaged to reveal habitats that are most suitable across time. Advances in remote sensing and analytical tools have allowed for the integration of dynamic processes into species distribution modeling. Our objective was to develop a spatiotemporal model of breeding habitat use for a federally threatened shorebird (piping plover, Charadrius melodus). Piping plovers are an ideal candidate species for dynamic habitat models because they depend on habitat created and maintained by variable hydrological processes and disturbance. We integrated a 20-year (2000-2019) nesting dataset with volunteer-collected sightings (eBird) using point process modeling. Our analysis incorporated spatiotemporal autocorrelation, differential observation processes within data streams, and dynamic environmental covariates. We evaluated the transferability of this model in space and time and the contribution of the eBird dataset. eBird data provided more complete spatial coverage in our study system than nest monitoring data. Patterns of observed breeding density depended on both dynamic (e.g., surface water levels) and long-term (e.g., proximity to permanent wetland basins) environmental processes. Our study provides a framework for quantifying dynamic spatiotemporal patterns of breeding density. This assessment can be iteratively updated with additional data to improve conservation and management efforts, because reducing temporal variability to average patterns of use may cause a loss in precision for such actions.
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Affiliation(s)
- Kristen S Ellis
- U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th St SE, Jamestown, ND, 58401, USA.
| | - Michael J Anteau
- U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th St SE, Jamestown, ND, 58401, USA
| | - Garrett J MacDonald
- U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th St SE, Jamestown, ND, 58401, USA
| | - Rose J Swift
- U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th St SE, Jamestown, ND, 58401, USA
| | - Megan M Ring
- U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th St SE, Jamestown, ND, 58401, USA
| | - Dustin L Toy
- U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th St SE, Jamestown, ND, 58401, USA
| | - Mark H Sherfy
- U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th St SE, Jamestown, ND, 58401, USA
| | - Max Post van der Burg
- U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th St SE, Jamestown, ND, 58401, USA
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11
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Chenery ES, Harms NJ, Fenton H, Mandrak NE, Molnár PK. Revealing large‐scale parasite ranges: An integrated spatiotemporal database and multisource analysis of the winter tick. Ecosphere 2023. [DOI: 10.1002/ecs2.4376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Affiliation(s)
- Emily S. Chenery
- Department of Physical and Environmental Sciences University of Toronto Scarborough Scarborough Ontario Canada
| | - N. Jane Harms
- Animal Health Unit Department of Environment Whitehorse Yukon Canada
| | - Heather Fenton
- Department of Environment and Natural Resources Government of Northwest Territories Yellowknife Northwest Territories Canada
| | - Nicholas E. Mandrak
- Department of Physical and Environmental Sciences University of Toronto Scarborough Scarborough Ontario Canada
- Department of Biological Sciences University of Toronto Scarborough Scarborough Ontario Canada
- Department of Ecology and Evolutionary Biology University of Toronto Toronto Ontario Canada
| | - Péter K. Molnár
- Department of Physical and Environmental Sciences University of Toronto Scarborough Scarborough Ontario Canada
- Department of Biological Sciences University of Toronto Scarborough Scarborough Ontario Canada
- Department of Ecology and Evolutionary Biology University of Toronto Toronto Ontario Canada
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12
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Bassing SB, DeVivo M, Ganz TR, Kertson BN, Prugh LR, Roussin T, Satterfield L, Windell RM, Wirsing AJ, Gardner B. Are we telling the same story? Comparing inferences made from camera trap and telemetry data for wildlife monitoring. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2023; 33:e2745. [PMID: 36107138 DOI: 10.1002/eap.2745] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 07/05/2022] [Accepted: 07/22/2022] [Indexed: 06/15/2023]
Abstract
Estimating habitat and spatial associations for wildlife is common across ecological studies and it is well known that individual traits can drive population dynamics and vice versa. Thus, it is commonly assumed that individual- and population-level data should represent the same underlying processes, but few studies have directly compared contemporaneous data representing these different perspectives. We evaluated the circumstances under which data collected from Lagrangian (individual-level) and Eulerian (population-level) perspectives could yield comparable inference to understand how scalable information is from the individual to the population. We used Global Positioning System (GPS) collar (Lagrangian) and camera trap (Eulerian) data for seven species collected simultaneously in eastern Washington (2018-2020) to compare inferences made from different survey perspectives. We fit the respective data streams to resource selection functions (RSFs) and occupancy models and compared estimated habitat- and space-use patterns for each species. Although previous studies have considered whether individual- and population-level data generated comparable information, ours is the first to make this comparison for multiple species simultaneously and to specifically ask whether inferences from the two perspectives differed depending on the focal species. We found general agreement between the predicted spatial distributions for most paired analyses, although specific habitat relationships differed. We hypothesize the discrepancies arose due to differences in statistical power associated with camera and GPS-collar sampling, as well as spatial mismatches in the data. Our research suggests data collected from individual-based sampling methods can capture coarse population-wide patterns for a diversity of species, but results differ when interpreting specific wildlife-habitat relationships.
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Affiliation(s)
- Sarah B Bassing
- School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA
| | - Melia DeVivo
- Washington Department of Fish and Wildlife, Spokane Valley, Washington, USA
| | - Taylor R Ganz
- School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA
| | - Brian N Kertson
- Washington Department of Fish and Wildlife, Snoqualmie, Washington, USA
| | - Laura R Prugh
- School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA
| | - Trent Roussin
- School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA
- Washington Department of Fish and Wildlife, Colville, Washington, USA
| | - Lauren Satterfield
- School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA
| | - Rebecca M Windell
- School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA
| | - Aaron J Wirsing
- School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA
| | - Beth Gardner
- School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA
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13
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von Hirschheydt G, Stofer S, Kéry M. “Mixed” occupancy designs: When do additional single-visit data improve the inferences from standard multi-visit models? Basic Appl Ecol 2023. [DOI: 10.1016/j.baae.2023.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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14
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Liang D, Bailey H, Hoover AL, Eckert S, Zarate P, Alfaro‐Shigueto J, Mangel JC, de Paz Campos N, Davila JQ, Barturen DS, Rguez‐Baron JM, Fahy C, Rocafuerte A, Veelenturf C, Abrego M, Shillinger GL. Integrating telemetry and point observations to inform management and conservation of migratory marine species. Ecosphere 2023. [DOI: 10.1002/ecs2.4375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Affiliation(s)
- Dong Liang
- Chesapeake Biological Laboratory University of Maryland Center for Environmental Science Solomons Maryland USA
| | - Helen Bailey
- Chesapeake Biological Laboratory University of Maryland Center for Environmental Science Solomons Maryland USA
| | | | - Scott Eckert
- Department of Biology and Natural Resources Principia College Elsah Illinois USA
- Wider Caribbean Sea Turtle Conservation Network (WIDECAST) Godfrey Illinois USA
| | - Patricia Zarate
- Instituto de Fomento Pesquero Valparaíso Chile
- MigraMar Bodega Bay California USA
| | - Joanna Alfaro‐Shigueto
- ProDelphinus Lima Peru
- Carrera de Biologia Marina, Universidad Cientifica del Sur Lima Peru
- Marine Turtle Research Group, Centre for Ecology and Conservation University of Exeter Penryn UK
| | - Jeffrey C. Mangel
- ProDelphinus Lima Peru
- Marine Turtle Research Group, Centre for Ecology and Conservation University of Exeter Penryn UK
| | | | - Javier Quinones Davila
- Oficina de Investigaciones en Depredadores Superiores Instituto del Mar del Perú, Chucuito Callao Peru
| | | | - Juan M. Rguez‐Baron
- JUSTSEA Foundation Bogotá Colombia
- Department of Biology and Marine Biology University of North Carolina Wilmington Wilmington North Carolina USA
| | - Christina Fahy
- Protected Resources Division West Coast Regional Office, National Marine Fisheries Service Long Beach California USA
| | | | | | - Marino Abrego
- Ministerio de Ambiente de Panamá Universidad Marítima Internacional de Panamá Panama City Panama
| | - George L. Shillinger
- Upwell, Heritage Harbor Complex Monterey California USA
- MigraMar Bodega Bay California USA
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15
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Koerich G, Fraser CI, Lee CK, Morgan FJ, Tonkin JD. Forecasting the future of life in Antarctica. Trends Ecol Evol 2023; 38:24-34. [PMID: 35934551 DOI: 10.1016/j.tree.2022.07.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/12/2022] [Accepted: 07/15/2022] [Indexed: 12/24/2022]
Abstract
Antarctic ecosystems are under increasing anthropogenic pressure, but efforts to predict the responses of Antarctic biodiversity to environmental change are hindered by considerable data challenges. Here, we illustrate how novel data capture technologies provide exciting opportunities to sample Antarctic biodiversity at wider spatiotemporal scales. Data integration frameworks, such as point process and hierarchical models, can mitigate weaknesses in individual data sets, improving confidence in their predictions. Increasing process knowledge in models is imperative to achieving improved forecasts of Antarctic biodiversity, which can be attained for data-limited species using hybrid modelling frameworks. Leveraging these state-of-the-art tools will help to overcome many of the data scarcity challenges presented by the remoteness of Antarctica, enabling more robust forecasts both near- and long-term.
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Affiliation(s)
- Gabrielle Koerich
- School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
| | - Ceridwen I Fraser
- Department of Marine Science, University of Otago, PO Box 56, Dunedin 9054, New Zealand
| | - Charles K Lee
- International Centre for Terrestrial Antarctic Research, School of Science, University of Waikato, Private Bag 3105, Hamilton 3240, New Zealand
| | - Fraser J Morgan
- Manaaki Whenua - Landcare Research, Auckland 1072, New Zealand; Te Pūnaha Matatini, Centre of Research Excellence in Complex Systems, Auckland, New Zealand
| | - Jonathan D Tonkin
- School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand; Te Pūnaha Matatini, Centre of Research Excellence in Complex Systems, Auckland, New Zealand; Bioprotection Aotearoa, Centre of Research Excellence, Canterbury, New Zealand.
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16
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Zhang Q, Han J, Xia C, Møller AP. A dataset of bird distributions in zoogeographical regions of China. Biodivers Data J 2022; 10:e93606. [PMID: 36761618 PMCID: PMC9836623 DOI: 10.3897/bdj.10.e93606] [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: 08/18/2022] [Accepted: 11/22/2022] [Indexed: 12/30/2022] Open
Abstract
Background China, the largest country in Asia, has a land area of approximately 9.6 million square kilometres. There are 1481 bird species (following the taxonomy of IOC World Bird List version 12.1) recorded in two zoogeographical realms, seven regions and 19 subregions in the country. From 1955 to 2017, six authoritative monographs were published, which recorded the distribution area for all bird species in China and were widely quoted by research papers and field guides. This massive amount of data could be used to address many hot topics in ornithology, biogeography and ecology. However, rapid changes in the taxonomic status and different schemes of zoogeographical regionalisation in these six monographs provided limits to the utilisation of these valuable data. New information By integrating the data from the six monographs, we presented an open-access dataset on the occurrences and residence types of all Chinese bird species in zoogeographical regions over the past 60 years. The taxonomic statuses for these species were determined following the IOC World Bird List version 12.1 and the zoogeographical regions were based on the updated scheme. These data provide valuable information for the research in bird ecology and conservation biology.
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Affiliation(s)
- Qianyi Zhang
- Ministry of Education Key Laboratory for Biodiversity and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, ChinaMinistry of Education Key Laboratory for Biodiversity and Ecological Engineering, College of Life Sciences, Beijing Normal UniversityBeijingChina
| | - Jingru Han
- Ministry of Education Key Laboratory for Ecology of Tropical Islands, College of Life Sciences, Hainan Normal University, Haikou, ChinaMinistry of Education Key Laboratory for Ecology of Tropical Islands, College of Life Sciences, Hainan Normal UniversityHaikouChina
| | - Canwei Xia
- Ministry of Education Key Laboratory for Biodiversity and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, ChinaMinistry of Education Key Laboratory for Biodiversity and Ecological Engineering, College of Life Sciences, Beijing Normal UniversityBeijingChina
| | - Anders Pape Møller
- Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique et Evolution, Gif-sur-Yvette, FranceUniversité Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique et EvolutionGif-sur-YvetteFrance
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17
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Joint analysis of structured and semi-structured community science data improves precision of relative abundance but not trends in birds. Sci Rep 2022; 12:20289. [PMID: 36433999 PMCID: PMC9700822 DOI: 10.1038/s41598-022-23603-0] [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: 11/08/2021] [Accepted: 11/02/2022] [Indexed: 11/27/2022] Open
Abstract
Estimating absolute and relative abundance of wildlife populations is critical to addressing ecological questions and conservation needs, yet obtaining reliable estimates can be challenging because surveys are often limited spatially or temporally. Community science (i.e., citizen science) provides opportunities for semi-structured data collected by the public (e.g., eBird) to improve capacity of relative abundance estimation by complementing structured survey data collected by trained observers (e.g., North American breeding bird survey [BBS]). We developed two state-space models to estimate relative abundance and population trends: one using BBS data and the other jointly analyzing BBS and eBird data. We applied these models to seven bird species with diverse life history characteristics. Joint analysis of eBird and BBS data improved precision of mean and year-specific relative abundance estimates for all species, but the BBS-only model produced more precise trend estimates compared to the joint model for most species. The relative abundance estimates of the joint model were particularly more precise than the BBS-only estimates in areas where species detectability was low resulting from either low BBS survey effort or low abundance. These results suggest that community science data can be a valuable resource for cost-effective improvement in wildlife abundance estimation.
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18
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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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19
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Meehan TD, Saunders SP, DeLuca WV, Michel NL, Grand J, Deppe JL, Jimenez MF, Knight EJ, Seavy NE, Smith MA, Taylor L, Witko C, Akresh ME, Barber DR, Bayne EM, Beasley JC, Belant JL, Bierregaard RO, Bildstein KL, Boves TJ, Brzorad JN, Campbell SP, Celis‐Murillo A, Cooke HA, Domenech R, Goodrich L, Gow EA, Haines A, Hallworth MT, Hill JM, Holland AE, Jennings S, Kays R, King DT, Mackenzie SA, Marra PP, McCabe RA, McFarland KP, McGrady MJ, Melcer R, Norris DR, Norvell RE, Rhodes OE, Rimmer CC, Scarpignato AL, Shreading A, Watson JL, Wilsey CB. Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e2679. [PMID: 35588285 PMCID: PMC9787853 DOI: 10.1002/eap.2679] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/25/2022] [Accepted: 02/02/2022] [Indexed: 06/15/2023]
Abstract
For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter data sets versus pseudo-absence locations during migratory periods and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre- and postbreeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird-only model for 22 of 24 species-season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings and, thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach to integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds.
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20
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Lauret V, Labach H, Turek D, Laran S, Gimenez O. Integrated spatial models foster complementarity between monitoring programmes in producing large‐scale bottlenose dolphin indicators. Anim Conserv 2022. [DOI: 10.1111/acv.12815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- V. Lauret
- CEFE, Université Montpellier, CNRS, EPHE, IRD Montpellier France
| | - H. Labach
- CEFE, Université Montpellier, CNRS, EPHE, IRD Montpellier France
- MIRACETI, Connaissance et conservation des cétacés Place des traceurs de pierres La Couronne France
| | - D. Turek
- Department of Mathematics and Statistics Williams College Williamstown MA USA
| | - S. Laran
- Observatoire PELAGIS UMS 3462 CNRS‐La Rochelle Université La Rochelle France
| | - O. Gimenez
- CEFE, Université Montpellier, CNRS, EPHE, IRD Montpellier France
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21
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Kasada M, Nakashima Y, Fukasawa K, Yajima G, Yokomizo H, Miyashita T. State‐space model combining local camera data and regional administration data reveals population dynamics of wild boar. POPUL ECOL 2022. [DOI: 10.1002/1438-390x.12138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Minoru Kasada
- Graduate School of Life Sciences Tohoku University Sendai Japan
- Department of Experimental Limnology Leibniz‐Institute of Freshwater Ecology and Inland Fisheries Stechlin Germany
| | | | - Keita Fukasawa
- Biodiversity Division National Institute for Environmental Studies Tsukuba Ibaraki Japan
| | - Gota Yajima
- College of Bioresource Science Nihon University Fujisawa Kanagawa Japan
| | - Hiroyuki Yokomizo
- Health and Environmental Risk Division National Institute for Environmental Studies Tsukuba Ibaraki Japan
| | - Tadashi Miyashita
- Graduate School of Agriculture and Life Sciences The University of Tokyo Tokyo Japan
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22
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Botella C, Bonnet P, Hui C, Joly A, Richardson DM. Dynamic Species Distribution Modeling Reveals the Pivotal Role of Human-Mediated Long-Distance Dispersal in Plant Invasion. BIOLOGY 2022; 11:biology11091293. [PMID: 36138772 PMCID: PMC9495778 DOI: 10.3390/biology11091293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/19/2022] [Accepted: 08/23/2022] [Indexed: 11/16/2022]
Abstract
Plant invasions generate massive ecological and economic costs worldwide. Predicting their spatial dynamics is crucial to the design of effective management strategies and the prevention of invasions. Earlier studies highlighted the crucial role of long-distance dispersal in explaining the speed of many invasions. In addition, invasion speed depends highly on the duration of its lag phase, which may depend on the scaling of fecundity with age, especially for woody plants, even though empirical proof is still rare. Bayesian dynamic species distribution models enable the fitting of process-based models to partial and heterogeneous observations using a state-space modeling approach, thus offering a tool to test such hypotheses on past invasions over large spatial scales. We use such a model to explore the roles of long-distance dispersal and age-structured fecundity in the transient invasion dynamics of Plectranthus barbatus, a woody plant invader in South Africa. Our lattice-based model accounts for both short and human-mediated long-distance dispersal, as well as age-structured fecundity. We fitted our model on opportunistic occurrences, accounting for the spatio-temporal variations of the sampling effort and the variable detection rates across datasets. The Bayesian framework enables us to integrate a priori knowledge on demographic parameters and control identifiability issues. The model revealed a massive wave of spatial spread driven by human-mediated long-distance dispersal during the first decade and a subsequent drastic population growth, leading to a global equilibrium in the mid-1990s. Without long-distance dispersal, the maximum population would have been equivalent to 30% of the current equilibrium population. We further identified the reproductive maturity at three years old, which contributed to the lag phase before the final wave of population growth. Our results highlighted the importance of the early eradication of weedy horticultural alien plants around urban areas to hamper and delay the invasive spread.
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Affiliation(s)
- Christophe Botella
- Centre for Invasion Biology (CIB), Department of Botany & Zoology, Stellenbosch University, Stellenbosch 7602, South Africa
- Correspondence:
| | - Pierre Bonnet
- Botany and Modeling of Plant Architecture and Vegetation (AMAP), CIRAD, CNRS, INRAE, IRD, University of Montpellier, 34398 Montpellier, France
| | - Cang Hui
- Centre for Invasion Biology, Department of Mathematical Sciences, Stellenbosch University, Stellenbosch 7602, South Africa
- Biodiversity Informatics Unit, African Institute for Mathematical Sciences, Cape Town 7945, South Africa
| | - Alexis Joly
- Inria, LIRMM, University of Montpellier, 34095 Montpellier, France
| | - David M. Richardson
- Centre for Invasion Biology (CIB), Department of Botany & Zoology, Stellenbosch University, Stellenbosch 7602, South Africa
- Department of Invasion Ecology, Institute of Botany, The Czech Academy of Sciences, 252 43 Průhonice, Czech Republic
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23
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Larsen EA, Belitz MW, Guralnick RP, Ries L. Consistent trait-temperature interactions drive butterfly phenology in both incidental and survey data. Sci Rep 2022; 12:13370. [PMID: 35927297 PMCID: PMC9352721 DOI: 10.1038/s41598-022-16104-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 07/05/2022] [Indexed: 11/17/2022] Open
Abstract
Data availability limits phenological research at broad temporal and spatial extents. Butterflies are among the few taxa with broad-scale occurrence data, from both incidental reports and formal surveys. Incidental reports have biases that are challenging to address, but structured surveys are often limited seasonally and may not span full flight phenologies. Thus, how these data source compare in phenological analyses is unclear. We modeled butterfly phenology in relation to traits and climate using parallel analyses of incidental and survey data, to explore their shared utility and potential for analytical integration. One workflow aggregated “Pollard” surveys, where sites are visited multiple times per year; the other aggregated incidental data from online portals: iNaturalist and eButterfly. For 40 species, we estimated early (10%) and mid (50%) flight period metrics, and compared the spatiotemporal patterns and drivers of phenology across species and between datasets. For both datasets, inter-annual variability was best explained by temperature, and seasonal emergence was earlier for resident species overwintering at more advanced stages. Other traits related to habitat, feeding, dispersal, and voltinism had mixed or no impacts. Our results suggest that data integration can improve phenological research, and leveraging traits may predict phenology in poorly studied species.
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Affiliation(s)
- Elise A Larsen
- Department of Biology, Georgetown University, Regents Hall 501, Washington DC, 20057, USA.
| | - Michael W Belitz
- Florida Museum of Natural History, University of Florida, Gainesville, FL, 32611, USA.,University of Florida Biodiversity Institute, Gainesville, FL, 32603, USA
| | - Robert P Guralnick
- Florida Museum of Natural History, University of Florida, Gainesville, FL, 32611, USA
| | - Leslie Ries
- Department of Biology, Georgetown University, Regents Hall 501, Washington DC, 20057, USA
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Chevalier M, Zarzo-Arias A, Guélat J, Mateo RG, Guisan A. Accounting for niche truncation to improve spatial and temporal predictions of species distributions. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.944116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Species Distribution Models (SDMs) are essential tools for predicting climate change impact on species’ distributions and are commonly employed as an informative tool on which to base management and conservation actions. Focusing only on a part of the entire distribution of a species for fitting SDMs is a common approach. Yet, geographically restricting their range can result in considering only a subset of the species’ ecological niche (i.e., niche truncation) which could lead to biased spatial predictions of future climate change effects, particularly if future conditions belong to those parts of the species ecological niche that have been excluded for model fitting. The integration of large-scale distribution data encompassing the whole species range with more regional data can improve future predictions but comes along with challenges owing to the broader scale and/or lower quality usually associated with these data. Here, we compare future predictions obtained from a traditional SDM fitted on a regional dataset (Switzerland) to predictions obtained from data integration methods that combine regional and European datasets for several bird species breeding in Switzerland. Three models were fitted: a traditional SDM based only on regional data and thus not accounting for niche truncation, a data pooling model where the two datasets are merged without considering differences in extent or resolution, and a downscaling hierarchical approach that accounts for differences in extent and resolution. Results show that the traditional model leads to much larger predicted range changes (either positively or negatively) under climate change than both data integration methods. The traditional model also identified different variables as main drivers of species’ distribution compared to data-integration models. Differences between models regarding predicted range changes were larger for species where future conditions were outside the range of conditions existing in the regional dataset (i.e., when future conditions implied extrapolation). In conclusion, we showed that (i) models calibrated on a geographically restricted dataset provide markedly different predictions than data integration models and (ii) that these differences are at least partly explained by niche truncation. This suggests that using data integration methods could lead to more accurate predictions and more nuanced range changes than regional SDMs through a better characterization of species’ entire realized niches.
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25
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Gámez S, Harris NC. Conceptualizing the 3D niche and vertical space use. Trends Ecol Evol 2022; 37:953-962. [PMID: 35872027 DOI: 10.1016/j.tree.2022.06.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/24/2022] [Accepted: 06/27/2022] [Indexed: 11/19/2022]
Abstract
Spatial partitioning in ecological communities has predominantly been described in two dimensions, yet habitat is complex and 3D. Complex space use mediates community structure and interaction strength by expanding spatial, temporal, and dietary dimensions. Vertical stratification of resources provides opportunities for novel specializations, creating a 3D niche. Competition and predation are mediated by 3D space use, as individuals use the vertical axis to access prey, flee predators, or avoid competitors. The 3D niche is important for long-term conservation strategies as species must navigate tradeoffs in habitat use between strata-specific threats and suboptimal habitat patches. Ultimately, elucidating the 3D niche has implications for protected area management and corridor design that directly influence species persistence and ecosystem function in a rapidly changing world.
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Affiliation(s)
- Siria Gámez
- Applied Wildlife Ecology Lab, Yale School of the Environment, Yale University 195 Prospect Street, New Haven, CT 06511, USA.
| | - Nyeema C Harris
- Applied Wildlife Ecology Lab, Yale School of the Environment, Yale University 195 Prospect Street, New Haven, CT 06511, USA
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26
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Pease BS, Pacifici K, Kays R, Reich B. What drives spatially varying ecological relationships in a wide‐ranging species? DIVERS DISTRIB 2022. [DOI: 10.1111/ddi.13594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Affiliation(s)
- Brent S. Pease
- Foresty Program Southern Illinois University Carbondale Illinois USA
| | - Krishna Pacifici
- Department of Forestry and Environmental Resources North Carolina State University Raleigh North Carolina USA
| | - Roland Kays
- Department of Forestry and Environmental Resources North Carolina State University Raleigh North Carolina USA
- North Carolina Museum of Natural Sciences Raleigh North Carolina USA
| | - Brian Reich
- Department of Statistics North Carolina State University Raleigh North Carolina USA
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27
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A data-integration approach to correct sampling bias in species distribution models using multiple datasets of breeding birds in the Swiss Alps. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2021.101501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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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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29
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Wicquart J, Gudka M, Obura D, Logan M, Staub F, Souter D, Planes S. A workflow to integrate ecological monitoring data from different sources. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2021.101543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Schmidt JH, Wilson TL, Thompson WL, Mangipane BA. Integrating distance sampling survey data with population indices to separate trends in abundance and temporary immigration. J Wildl Manage 2022. [DOI: 10.1002/jwmg.22185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Joshua H. Schmidt
- U.S. National Park Service Central Alaska Network 4175 Geist Road Fairbanks 99709 AK USA
| | - Tammy L. Wilson
- U.S. Geological Survey, Massachusetts Cooperative Fish and Wildlife Research Unit, Department of Environmental Conservation University of Massachusetts 160 Holdsworth Way Amherst 01003 MA USA
| | | | - Buck A. Mangipane
- U.S. National Park Service Lake Clark National Park and Preserve, General Delivery, Port Alsworth 99653 AK USA
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31
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Vallecillo D, Guillemain M, Authier M, Bouchard C, Cohez D, Vialet E, Massez G, Vandewalle P, Champagnon J. Accounting for detection probability with overestimation by integrating double monitoring programs over 40 years. PLoS One 2022; 17:e0265730. [PMID: 35333894 PMCID: PMC8956176 DOI: 10.1371/journal.pone.0265730] [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: 05/21/2021] [Accepted: 03/07/2022] [Indexed: 11/29/2022] Open
Abstract
In the context of wildlife population declines, increasing computer power over the last 20 years allowed wildlife managers to apply advanced statistical techniques that has improved population size estimates. However, respecting the assumptions of the models that consider the probability of detection, such as N-mixture models, requires the implementation of a rigorous monitoring protocol with several replicate survey occasions and no double counting that are hardly adaptable to field conditions. When the logistical, economic and ecological constraints are too strong to meet model assumptions, it may be possible to combine data from independent surveys into the modelling framework in order to understand population dynamics more reliably. Here, we present a state-space model with an error process modelled on the log scale to evaluate wintering waterfowl numbers in the Camargue, southern France, while taking a conditional probability of detection into consideration. Conditional probability of detection corresponds to estimation of a detection probability index, which is not a true probability of detection, but rather conditional on the difference to a particular baseline. The large number of sites (wetlands within the Camargue delta) and years monitored (44) provide significant information to combine both terrestrial and aerial surveys (which constituted spatially and temporally replicated counts) to estimate a conditional probability of detection, while accounting for false-positive counting errors and changes in observers over the study period. The model estimates abundance indices of wintering Common Teal, Mallard and Common Coot, all species abundant in the area. We found that raw counts were underestimated compared to the predicted population size. The model-based data integration approach as described here seems like a promising solution that takes advantage of as much as possible of the data collected from several methods when the logistic constraints do not allow the implementation of a permanent monitoring and analysis protocol that takes into account the detectability of individuals.
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Affiliation(s)
- David Vallecillo
- Tour du Valat, Research Institute for the Conservation of Mediterranean Wetlands, Le Sambuc, Arles, France
- OFB, Unité Avifaune migratrice, La Tour du Valat, Le Sambuc, Arles, France
- * E-mail:
| | | | - Matthieu Authier
- Observatoire Pelagis, UMS 3462 CNRS-LRUniv ADERA, La Rochelle, France
| | - Colin Bouchard
- UMR Ecobiop, e2S, Université de Pau et Pays de l’Adour, INRAE, Saint-Pée sur Nivelle, France
| | - Damien Cohez
- Tour du Valat, Research Institute for the Conservation of Mediterranean Wetlands, Le Sambuc, Arles, France
| | - Emmanuel Vialet
- Parc Naturel Régional de Camargue, Mas du Pont de Rousty, Arles, France
| | - Grégoire Massez
- Les Amis des Marais du Vigueirat, Chemin de l’Etourneau, Mas-Thibert, France
| | | | - Jocelyn Champagnon
- Tour du Valat, Research Institute for the Conservation of Mediterranean Wetlands, Le Sambuc, Arles, France
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32
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Tirozzi P, Orioli V, Dondina O, Kataoka L, Bani L. Population trends from count data: Handling environmental bias, overdispersion and excess of zeroes. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Becker FS, Slingsby JA, Measey J, Tolley KA, Altwegg R. Finding rare species and estimating the probability that all occupied sites have been found. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e2502. [PMID: 34873777 DOI: 10.1002/eap.2502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 04/16/2021] [Indexed: 06/13/2023]
Abstract
Detecting occupied sites of rare species, and estimating the probability that all occupied sites are known within a given area, are desired outcomes for many ecological or conservation projects. Examples include managing all occupied sites of a threatened species or eradicating an emerging invader. Occupied sites may remain undetected because (1) sites where the species potentially occurs had not been searched, and (2) the species could have been overlooked in the searched sites. For rare species, available data are typically scant, making it difficult to predict sites where the species probably occurs or to estimate detection probability in the searched sites. Using the critically endangered Rose's mountain toadlet (Capensibufo rosei), known from only two localities, we outline an iterative process aimed at estimating the probability that any unknown occupied sites remain and maximizing the chance of finding them. This includes fitting a species distribution model to guide sampling effort, testing model accuracy and sampling efficacy using the occurrence of more common proxy species, and estimating detection probability using sites of known presence. The final estimate of the probability that all occupied sites were found incorporates the uncertainties of uneven distribution, relative area searched, and detection probability. Our results show that very few occupied sites of C. rosei are likely to remain undetected. We also show that the probability of an undetected occupied site remaining will always be high for large unsearched areas of potential occurrence, but can be low for smaller areas intended for targeted management interventions. Our approach is especially useful for assessing uncertainty in species occurrences, planning the required search effort needed to reduce probability of unknown occurrence to desired levels, and identifying priority areas for further searches or management interventions.
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Affiliation(s)
- Francois S Becker
- South African National Biodiversity Institute, Kirstenbosch Research Centre, Cape Town, South Africa
- Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa
| | - Jasper A Slingsby
- Department of Biological Sciences and Centre for Statistics in Ecology, Environment and Conservation, University of Cape Town, Cape Town, South Africa
- Fynbos Node, South African Environmental Observation Network, Centre for Biodiversity Conservation, Cape Town, South Africa
| | - John Measey
- Centre for Invasion Biology, Department of Botany & Zoology, Stellenbosch University, Stellenbosch, South Africa
| | - Krystal A Tolley
- South African National Biodiversity Institute, Kirstenbosch Research Centre, Cape Town, South Africa
- Centre for Ecological Genomics and Wildlife Conservation, Department of Zoology, University of Johannesburg, Johannesburg, South Africa
| | - Res Altwegg
- Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa
- African Climate and Development Initiative, University of Cape Town, Cape Town, South Africa
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34
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Johnston A, Matechou E, Dennis E. Outstanding challenges and future directions for biodiversity monitoring using citizen science data. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13834] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Alison Johnston
- Centre for Research into Ecological and Environmental Modelling, Department of Maths and Statistics University of St Andrews St Andrews UK
- Cornell Lab of Ornithology, 159 Sapsucker Woods Road Ithaca NY USA
| | - Eleni Matechou
- School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury Kent UK
| | - Emily Dennis
- School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury Kent UK
- Butterfly Conservation, Manor Yard, East Lulworth, Wareham Dorset UK
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35
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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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36
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Matthiopoulos J, Wakefield E, Jeglinski JWE, Furness RW, Trinder M, Tyler G, Mccluskie A, Allen S, Braithwaite J, Evans T. Integrated modelling of seabird‐habitat associations from multi‐platform data: A review. J Appl Ecol 2022. [DOI: 10.1111/1365-2664.14114] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jason Matthiopoulos
- Institute of Biodiversity, Animal Health & Comparative Medicine College of Medical, Veterinary & Life Sciences, Graham Kerr Building, University of Glasgow Glasgow Scotland
- MacArthur Green Glasgow Scotland
| | - Ewan Wakefield
- Institute of Biodiversity, Animal Health & Comparative Medicine College of Medical, Veterinary & Life Sciences, Graham Kerr Building, University of Glasgow Glasgow Scotland
| | - Jana W. E. Jeglinski
- Institute of Biodiversity, Animal Health & Comparative Medicine College of Medical, Veterinary & Life Sciences, Graham Kerr Building, University of Glasgow Glasgow Scotland
| | | | | | | | - Aly Mccluskie
- RSPB Centre for Conservation Science RSPB, Etive House, Beechwood Park Inverness Scotland
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37
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Dynamics and predicted distribution of an irrupting ‘sleeper’ population: fallow deer in Tasmania. Biol Invasions 2022. [DOI: 10.1007/s10530-021-02703-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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38
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Supp SR, Bohrer G, Fieberg J, La Sorte FA. Estimating the movements of terrestrial animal populations using broad-scale occurrence data. MOVEMENT ECOLOGY 2021; 9:60. [PMID: 34895345 PMCID: PMC8665594 DOI: 10.1186/s40462-021-00294-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 11/11/2021] [Indexed: 06/14/2023]
Abstract
As human and automated sensor networks collect increasingly massive volumes of animal observations, new opportunities have arisen to use these data to infer or track species movements. Sources of broad scale occurrence datasets include crowdsourced databases, such as eBird and iNaturalist, weather surveillance radars, and passive automated sensors including acoustic monitoring units and camera trap networks. Such data resources represent static observations, typically at the species level, at a given location. Nonetheless, by combining multiple observations across many locations and times it is possible to infer spatially continuous population-level movements. Population-level movement characterizes the aggregated movement of individuals comprising a population, such as range contractions, expansions, climate tracking, or migration, that can result from physical, behavioral, or demographic processes. A desire to model population movements from such forms of occurrence data has led to an evolving field that has created new analytical and statistical approaches that can account for spatial and temporal sampling bias in the observations. The insights generated from the growth of population-level movement research can complement the insights from focal tracking studies, and elucidate mechanisms driving changes in population distributions at potentially larger spatial and temporal scales. This review will summarize current broad-scale occurrence datasets, discuss the latest approaches for utilizing them in population-level movement analyses, and highlight studies where such analyses have provided ecological insights. We outline the conceptual approaches and common methodological steps to infer movements from spatially distributed occurrence data that currently exist for terrestrial animals, though similar approaches may be applicable to plants, freshwater, or marine organisms.
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Affiliation(s)
- Sarah R. Supp
- Data Analytics Program, Denison University, Granville, OH 43023 USA
| | - Gil Bohrer
- Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210 USA
| | - John Fieberg
- Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, Minneapolis, MN 55455 USA
| | - Frank A. La Sorte
- Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850 USA
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39
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Watson J, Joy R, Tollit D, Thornton SJ, Auger-Méthé M. Estimating animal utilization distributions from multiple data types: A joint spatiotemporal point process framework. Ann Appl Stat 2021. [DOI: 10.1214/21-aoas1472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Joe Watson
- Department of Statistics, University of British Columbia
| | - Ruth Joy
- School of Environmental Science, Simon Fraser University and SMRU Consulting
| | | | | | - Marie Auger-Méthé
- Institute for the Oceans & Fisheries and the Department of Statistics, University of British Columbia
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40
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Rufener MC, Kristensen K, Nielsen JR, Bastardie F. Bridging the gap between commercial fisheries and survey data to model the spatiotemporal dynamics of marine species. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02453. [PMID: 34520094 DOI: 10.1002/eap.2453] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 02/08/2021] [Accepted: 05/17/2021] [Indexed: 06/13/2023]
Abstract
Monitoring and assessment of natural resources often require inputs from multiple data sources. In fisheries science, for example, the inference of a species' abundance distribution relies on two main data sources, namely commercial fisheries and scientific survey data. Despite efforts to combine these data into an integrated statistical model, their coupling is frequently hampered due to differences in their sampling designs, which imposes distinct bias sources in the estimator of the abundance distribution. We developed a flexible species distribution model (SDM) that can integrate both data sources while filtering out their relative bias contributions. We applied the model on three different age groups of the western Baltic cod stock. For each age group, we tested the model on (1) survey data and (2) integrated data (survey + commercial) as a means to compare their differences and investigate how the inclusion of commercial fisheries data improved the spatiotemporal abundance estimator and parameter estimates. Moreover, we proposed a novel validation approach to evaluate whether the inclusion of commercial fisheries data in the integrated model is not in direct contradiction with the survey data. Following our approach, the results indicated that the use of commercial fisheries data is suitable for the integrated model. Across all age groups, our results demonstrated how commercial fisheries supplied additional information on cod's spatiotemporal abundance dynamics, highlighting sometimes abundance hot spots that were not detected by the survey model alone. Additionally, the integrated model provided a reduction of up to 20% and 10% in the uncertainty (SE) of the predicted abundance fields and fixed-effect parameters, respectively. The proposed model represents thus a valuable benchmark for evaluating spatiotemporal dynamics of fish, and strengthens the science-based advice for marine policymakers.
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Affiliation(s)
- Marie-Christine Rufener
- Institute for Aquatic Resources, Section for Ecosystem based Marine Management, Technical University of Denmark, Kemitorvet, Kongens Lyngby, 2800, Denmark
| | - Kasper Kristensen
- Institute for Aquatic Resources, Section for Marine Living Resources, Technical University of Denmark, Kemitorvet, Kongens Lyngby, 2800, Denmark
| | - J Rasmus Nielsen
- Institute for Aquatic Resources, Section for Ecosystem based Marine Management, Technical University of Denmark, Kemitorvet, Kongens Lyngby, 2800, Denmark
| | - Francois Bastardie
- Institute for Aquatic Resources, Section for Ecosystem based Marine Management, Technical University of Denmark, Kemitorvet, Kongens Lyngby, 2800, Denmark
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41
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Mandeville CP, Koch W, Nilsen EB, Finstad AG. Open Data Practices among Users of Primary Biodiversity Data. Bioscience 2021; 71:1128-1147. [PMID: 34733117 PMCID: PMC8560312 DOI: 10.1093/biosci/biab072] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Presence-only biodiversity data are increasingly relied on in biodiversity, ecology, and conservation research, driven by growing digital infrastructures that support open data sharing and reuse. Recent reviews of open biodiversity data have clearly documented the value of data sharing, but the extent to which the biodiversity research community has adopted open data practices remains unclear. We address this question by reviewing applications of presence-only primary biodiversity data, drawn from a variety of sources beyond open databases, in the indexed literature. We characterize how frequently researchers access open data relative to data from other sources, how often they share newly generated or collated data, and trends in metadata documentation and data citation. Our results indicate that biodiversity research commonly relies on presence-only data that are not openly available and neglects to make such data available. Improved data sharing and documentation will increase the value, reusability, and reproducibility of biodiversity research.
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Affiliation(s)
- Caitlin P Mandeville
- Department of Natural History, Norwegian University of Science and Technology, Trondheim, Norway
| | - Wouter Koch
- Department of Natural History, Norwegian University of Science and Technology, Trondheim, Norway
| | - Erlend B Nilsen
- Faculty of Biosciences and Aquaculture, Nord University, Steinkjer, Norway
| | - Anders G Finstad
- Department of Natural History, Norwegian University of Science and Technology, Trondheim, Norway
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42
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Adde A, Casabona i Amat C, Mazerolle MJ, Darveau M, Cumming SG, O'Hara RB. Integrated modeling of waterfowl distribution in western Canada using aerial survey and citizen science (eBird) data. Ecosphere 2021. [DOI: 10.1002/ecs2.3790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- Antoine Adde
- Département des sciences du bois et de la forêt Université Laval Québec Québec Canada
- Boreal Avian Modelling Project University of Alberta Edmonton Alberta Canada
| | - Clara Casabona i Amat
- Département des sciences du bois et de la forêt Université Laval Québec Québec Canada
| | - Marc J. Mazerolle
- Département des sciences du bois et de la forêt Université Laval Québec Québec Canada
| | - Marcel Darveau
- Département des sciences du bois et de la forêt Université Laval Québec Québec Canada
| | - Steven G. Cumming
- Département des sciences du bois et de la forêt Université Laval Québec Québec Canada
- Boreal Avian Modelling Project University of Alberta Edmonton Alberta Canada
| | - Robert B. O'Hara
- Department of Mathematical Sciences Norwegian University of Science and Technology Trondheim Norway
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Hoegh A, Peel AJ, Madden W, Ruiz Aravena M, Morris A, Washburne A, Plowright RK. Estimating viral prevalence with data fusion for adaptive two-phase pooled sampling. Ecol Evol 2021; 11:14012-14023. [PMID: 34707835 PMCID: PMC8525136 DOI: 10.1002/ece3.8107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 06/09/2021] [Accepted: 06/18/2021] [Indexed: 11/16/2022] Open
Abstract
The COVID-19 pandemic has highlighted the importance of efficient sampling strategies and statistical methods for monitoring infection prevalence, both in humans and in reservoir hosts. Pooled testing can be an efficient tool for learning pathogen prevalence in a population. Typically, pooled testing requires a second-phase retesting procedure to identify infected individuals, but when the goal is solely to learn prevalence in a population, such as a reservoir host, there are more efficient methods for allocating the second-phase samples.To estimate pathogen prevalence in a population, this manuscript presents an approach for data fusion with two-phased testing of pooled samples that allows more efficient estimation of prevalence with less samples than traditional methods. The first phase uses pooled samples to estimate the population prevalence and inform efficient strategies for the second phase. To combine information from both phases, we introduce a Bayesian data fusion procedure that combines pooled samples with individual samples for joint inferences about the population prevalence.Data fusion procedures result in more efficient estimation of prevalence than traditional procedures that only use individual samples or a single phase of pooled sampling.The manuscript presents guidance on implementing the first-phase and second-phase sampling plans using data fusion. Such methods can be used to assess the risk of pathogen spillover from reservoir hosts to humans, or to track pathogens such as SARS-CoV-2 in populations.
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Affiliation(s)
- Andrew Hoegh
- Department of Mathematical SciencesMontana State UniversityBozemanMTUSA
| | - Alison J. Peel
- Centre for Planetary Health and Food SecurityGriffith UniversityNathanQLDAustralia
| | - Wyatt Madden
- Department of Microbiology and ImmunologyMontana State UniversityBozemanMTUSA
| | - Manuel Ruiz Aravena
- Department of Microbiology and ImmunologyMontana State UniversityBozemanMTUSA
| | - Aaron Morris
- Department of Veterinary MedicineUniversity of CambridgeCambridgeUK
| | | | - Raina K. Plowright
- Department of Microbiology and ImmunologyMontana State UniversityBozemanMTUSA
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Sheard JK, Rahbek C, Dunn RR, Sanders NJ, Isaac NJB. Long-term trends in the occupancy of ants revealed through use of multi-sourced datasets. Biol Lett 2021; 17:20210240. [PMID: 34665990 PMCID: PMC8526175 DOI: 10.1098/rsbl.2021.0240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 09/29/2021] [Indexed: 11/12/2022] Open
Abstract
We combined participatory science data and museum records to understand long-term changes in occupancy for 29 ant species in Denmark over 119 years. Bayesian occupancy modelling indicated change in occupancy for 15 species: five increased, four declined and six showed fluctuating trends. We consider how trends may have been influenced by life-history and habitat changes. Our results build on an emerging picture that biodiversity change in insects is more complex than implied by the simple insect decline narrative.
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Affiliation(s)
- Julie K. Sheard
- Center for Macroecology, Evolution and Climate, University of Copenhagen, Universitetsparken 15, Copenhagen 2100, Denmark
| | - Carsten Rahbek
- Center for Macroecology, Evolution and Climate, University of Copenhagen, Universitetsparken 15, Copenhagen 2100, Denmark
- Center for Global Mountain Biodiversity, GLOBE Institute, University of Copenhagen, Universitetsparken 15, Copenhagen 2100, Denmark
- Institute of Ecology, Peking University, Beijing 100871, People's Republic of China
- Department of Life Sciences, Imperial College London, Ascot SL5 7PY, UK
- Danish Institute for Advanced Study, University of Southern Denmark, Campusvej 55, Odense M 5230, Denmark
| | - Robert R. Dunn
- Department of Applied Ecology, North Carolina State University, Raleigh, NC, USA
| | - Nathan J. Sanders
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
| | - Nick J. B. Isaac
- UK Centre for Ecology and Hydrology, Crowmarsh Gifford, Maclean Building, Wallingford OX10 8BB, UK
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Chevalier M, Broennimann O, Cornuault J, Guisan A. Data integration methods to account for spatial niche truncation effects in regional projections of species distribution. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02427. [PMID: 34318974 DOI: 10.1002/eap.2427] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 03/12/2021] [Accepted: 03/22/2021] [Indexed: 06/13/2023]
Abstract
Many species distribution models (SDMs) are built with precise but geographically restricted presence-absence data sets (e.g., a country) where only a subset of the environmental conditions experienced by a species across its range is considered (i.e., spatial niche truncation). This type of truncation is worrisome because it can lead to incorrect predictions e.g., when projecting to future climatic conditions belonging to the species niche but unavailable in the calibration area. Data from citizen-science programs, species range maps or atlases covering the full species range can be used to capture those parts of the species' niche that are missing regionally. However, these data usually are too coarse or too biased to support regional management. Here, we aim to (1) demonstrate how varying degrees of spatial niche truncation affect SDMs projections when calibrated with climatically truncated regional data sets and (2) test the performance of different methods to harness information from larger-scale data sets presenting different spatial resolutions to solve the spatial niche truncation problem. We used simulations to compare the performance of the different methods, and applied them to a real data set to predict the future distribution of a plant species (Potentilla aurea) in Switzerland. SDMs calibrated with geographically restricted data sets expectedly provided biased predictions when projected outside the calibration area or time period. Approaches integrating information from larger-scale data sets using hierarchical data integration methods usually reduced this bias. However, their performance varied depending on the level of spatial niche truncation and how data were combined. Interestingly, while some methods (e.g., data pooling, downscaling) performed well on both simulated and real data, others (e.g., those based on a Poisson point process) performed better on real data, indicating a dependency of model performance on the simulation process (e.g., shape of simulated response curves). Based on our results, we recommend to use different data integration methods and, whenever possible, to make a choice depending on model performance. In any case, an ensemble modeling approach can be used to account for uncertainty in how niche truncation is accounted for and identify areas where similarities/dissimilarities exist across methods.
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Affiliation(s)
- Mathieu Chevalier
- Department of Ecology and Evolution, University of Lausanne, Biophore, Lausanne, CH-1015, Switzerland
| | - Olivier Broennimann
- Department of Ecology and Evolution, University of Lausanne, Biophore, Lausanne, CH-1015, Switzerland
- Institute of Earth Surface Dynamics, University of Lausanne, Géopolis, Lausanne, CH-1015, Switzerland
| | | | - Antoine Guisan
- Department of Ecology and Evolution, University of Lausanne, Biophore, Lausanne, CH-1015, Switzerland
- Institute of Earth Surface Dynamics, University of Lausanne, Géopolis, Lausanne, CH-1015, Switzerland
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Zulian V, Miller DAW, Ferraz G. Integrating citizen‐science and planned‐survey data improves species distribution estimates. DIVERS DISTRIB 2021. [DOI: 10.1111/ddi.13416] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Affiliation(s)
- Viviane Zulian
- Programa de Pós‐Graduação em Ecologia Instituto de Biociências Universidade Federal do Rio Grande do Sul Porto Alegre Brazil
| | - David A. W. Miller
- Department of Ecosystem Science and Management Pennsylvania State University University Park Pennsylvania USA
| | - Gonçalo Ferraz
- Programa de Pós‐Graduação em Ecologia Instituto de Biociências Universidade Federal do Rio Grande do Sul Porto Alegre Brazil
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Lauret V, Labach H, Authier M, Gimenez O. Using single visits into integrated occupancy models to make the most of existing monitoring programs. Ecology 2021; 102:e03535. [PMID: 34514594 DOI: 10.1002/ecy.3535] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 06/21/2021] [Accepted: 07/08/2021] [Indexed: 11/07/2022]
Abstract
A major challenge in statistical ecology consists of integrating knowledge from different data sets to produce robust ecological indicators. To estimate species distribution, occupancy models are a flexible framework that can accommodate several data sets obtained from different sampling methods. However, repeating visits at sampling sites is a prerequisite for using standard occupancy models. Occupancy models were recently developed to analyze detection/non-detection data collected during a single visit. To date, single-visit occupancy models have never been used to integrate several different data sets. Here, we showcase an approach that combines two data sets into an integrated single-visit occupancy model. As a case study, we estimated the distribution of common bottlenose dolphin (Tursiops truncatus) over the northwestern Mediterranean Sea by combining 24,624 km of aerial surveys and 21,464 km of at-sea monitoring. We compared the outputs of single- vs. repeated-visit occupancy models into integrated occupancy models. Integrated models allowed a better sampling coverage of the targeted population, which provided a better precision for occupancy estimates than occupancy models using data sets in isolation. Overall, single- and repeated-visit integrated occupancy models produced similar inference about the distribution of bottlenose dolphins. We suggest that single-visit occupancy models open promising perspectives for the use of existing ecological data sets.
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Affiliation(s)
- Valentin Lauret
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Place Eugène Bataillon, Montpellier, 34000, France
| | - Hélène Labach
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Place Eugène Bataillon, Montpellier, 34000, France
- MIRACETI, Connaissance et conservation des cétacés, Place des traceurs de pierres, La Couronne, 13500, France
| | - Matthieu Authier
- ADERA, 162 avenue Albert Schweitzer, Pessac Cedex, 33608, France
- Observatoire PELAGIS, UMS 3462, CNRS-La Rochelle Université, 5 allée de l'Océan, La Rochelle, 17000, France
| | - Olivier Gimenez
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Place Eugène Bataillon, Montpellier, 34000, France
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Doser JW, Weed AS, Zipkin EF, Miller KM, Finley AO. Trends in bird abundance differ among protected forests but not bird guilds. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02377. [PMID: 33988277 DOI: 10.1002/eap.2377] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Revised: 12/16/2020] [Accepted: 02/04/2021] [Indexed: 06/12/2023]
Abstract
Improved monitoring and associated inferential tools to efficiently identify declining bird populations, particularly of rare or sparsely distributed species, is key to informed conservation and management across large spatiotemporal regions. We assess abundance trends for 106 bird species in a network of eight forested national parks located within the northeast United States from 2006 to 2019 using a novel hierarchical model. We develop a multispecies, multiregion, removal-sampling model that shares information across species and parks to enable inference on rare species and sparsely sampled parks and to evaluate the effects of local forest structure. Trends in bird abundance over time varied widely across parks, but species showed similar trends within parks. Three parks (Acadia National Park and Marsh-Billings-Rockefeller and Morristown National Historical Parks [NHP]) decreased in bird abundance across all species, while three parks (Saratoga NHP and Roosevelt-Vanderbilt and Weir-Farm National Historic Sites) increased in abundance. Bird abundance peaked at medium levels of basal area and high levels of percent forest and forest regeneration, with percent forest having the largest effect. Variation in these effects across parks could be a result of differences in forest structural stage and diversity. By sharing information across both communities and parks, our novel hierarchical model enables uncertainty-quantified estimates of abundance across multiple geographical (i.e., network, park) and taxonomic (i.e., community, guild, species) levels over a large spatiotemporal region. We found large variation in abundance trends across parks but not across bird guilds, suggesting that local forest condition might have a broad and consistent effect on the entire bird community within a given park. Research should target the three parks with overall decreasing trends in bird abundance to further identify what specific factors are driving observed declines across the bird community. Understanding how bird communities respond to local forest structure and other stressors (e.g., pest outbreaks, climate change) is crucial for informed and lasting management.
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Affiliation(s)
- Jeffrey W Doser
- Department of Forestry, Michigan State University, East Lansing, Michigan, 48824, USA
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, 48824, USA
| | - Aaron S Weed
- Northeast Temperate Inventory and Monitoring Network, National Park Service, Woodstock, Vermont, 05091, USA
| | - Elise F Zipkin
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, 48824, USA
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, 48824, USA
| | - Kathryn M Miller
- Northeast Temperate Inventory and Monitoring Network, National Park Service, Bar Harbor, Maine, 04609, USA
| | - Andrew O Finley
- Department of Forestry, Michigan State University, East Lansing, Michigan, 48824, USA
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, 48824, USA
- Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, Michigan, 48824, USA
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Warrier R, Noon BR, Bailey LL. A Framework for Estimating Human-Wildlife Conflict Probabilities Conditional on Species Occupancy. FRONTIERS IN CONSERVATION SCIENCE 2021. [DOI: 10.3389/fcosc.2021.679028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Managing human-wildlife conflicts (HWCs) is an important conservation objective for the many terrestrial landscapes dominated by humans. Forecasting where future conflicts are likely to occur and assessing risks to lives and livelihoods posed by wildlife are central to informing HWC management strategies. Existing assessments of the spatial occurrence patterns of HWC are based on either understanding spatial patterns of past conflicts or patterns of species distribution. In the former case, the absence of conflicts at a site cannot be attributed to the absence of the species. In the latter case, the presence of a species may not be an accurate measure of the probability of conflict occurrence. We present a Bayesian hierarchical modeling framework that integrates conflict reporting data and species distribution data, thus allowing the estimation of the probability that conflicts with a species are reported from a site, conditional on the species being present. In doing so, our model corrects for both false-positive and false-negative conflict reporting errors. We provide study design recommendations using simulations that explore the performance of the model under a range of conflict reporting probabilities. We applied the model to data on wild boar (Sus scrofa) space use and conflicts collected from the Central Terai Landscape (CTL), an important tiger conservation landscape in India. We found that tolerance for wildlife was a predictor of the probability with which farmers report conflict with wild boars from sites not used by the species. We also discuss useful extensions of the model when conflict data are verified for potential false-positive errors and when landscapes are monitored over multiple seasons.
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50
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Bowler DE. Complex causes of insect declines. Nat Ecol Evol 2021; 5:1334-1335. [PMID: 34282316 DOI: 10.1038/s41559-021-01508-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Diana E Bowler
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany. .,Friedrich Schiller University Jena, Institute of Biodiversity, Jena, Germany. .,Helmholtz-Center for Environmental Research - UFZ, Department Ecosystem Services, Leipzig, Germany.
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