1
|
Gilbert NA, Blommel CM, Farr MT, Green DS, Holekamp KE, Zipkin EF. A multispecies hierarchical model to integrate count and distance-sampling data. Ecology 2024; 105:e4326. [PMID: 38845219 DOI: 10.1002/ecy.4326] [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: 05/01/2023] [Revised: 03/11/2024] [Accepted: 04/12/2024] [Indexed: 07/02/2024]
Abstract
Integrated community models-an emerging framework in which multiple data sources for multiple species are analyzed simultaneously-offer opportunities to expand inferences beyond the single-species and single-data-source approaches common in ecology. We developed a novel integrated community model that combines distance sampling and single-visit count data; within the model, information is shared among data sources (via a joint likelihood) and species (via a random-effects structure) to estimate abundance patterns across a community. Parameters relating to abundance are shared between data sources, and the model can specify either shared or separate observation processes for each data source. Simulations demonstrated that the model provided unbiased estimates of abundance and detection parameters even when detection probabilities varied between the data types. The integrated community model also provided more accurate and more precise parameter estimates than alternative single-species and single-data-source models in many instances. We applied the model to a community of 11 herbivore species in the Masai Mara National Reserve, Kenya, and found considerable interspecific variation in response to local wildlife management practices: Five species showed higher abundances in a region with passive conservation enforcement (median across species: 4.5× higher), three species showed higher abundances in a region with active conservation enforcement (median: 3.9× higher), and the remaining three species showed no abundance differences between the two regions. Furthermore, the community average of abundance was slightly higher in the region with active conservation enforcement but not definitively so (posterior mean: higher by 0.20 animals; 95% credible interval: 1.43 fewer animals, 1.86 more animals). Our integrated community modeling framework has the potential to expand the scope of inference over space, time, and levels of biological organization, but practitioners should carefully evaluate whether model assumptions are met in their systems and whether data integration is valuable for their applications.
Collapse
Affiliation(s)
- Neil A Gilbert
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
| | - Caroline M Blommel
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA
| | - Matthew T Farr
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
- Washington Cooperative Fish and Wildlife Research Unit, School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, USA
| | - David S Green
- Institute for Natural Resources, Portland State University, Portland, Oregon, USA
| | - Kay E Holekamp
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
| | - Elise F Zipkin
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
| |
Collapse
|
2
|
Roy DB, Alison J, August TA, Bélisle M, Bjerge K, Bowden JJ, Bunsen MJ, Cunha F, Geissmann Q, Goldmann K, Gomez-Segura A, Jain A, Huijbers C, Larrivée M, Lawson JL, Mann HM, Mazerolle MJ, McFarland KP, Pasi L, Peters S, Pinoy N, Rolnick D, Skinner GL, Strickson OT, Svenning A, Teagle S, Høye TT. Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230108. [PMID: 38705190 PMCID: PMC11070254 DOI: 10.1098/rstb.2023.0108] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 01/17/2024] [Indexed: 05/07/2024] Open
Abstract
Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects-from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.
Collapse
Affiliation(s)
- D. B. Roy
- UK Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Wallingford OX10 8BB, UK
- Centre for Ecology and Conservation, University of Exeter, Penryn TR10 9EZ, UK
| | - J. Alison
- Department of Ecoscience and Arctic Research Centre, Aarhus University, C.F Møllers Alle 3, Aarhus, Denmark
| | - T. A. August
- UK Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Wallingford OX10 8BB, UK
| | - M. Bélisle
- Centre d'étude de la forêt (CEF) et Département de biologie, Université de Sherbrooke, 2500 Boulevard de l'Université, Sherbrooke, Québec, Canada J1K 2R1
| | - K. Bjerge
- Department of Electrical and Computer Engineering, Aarhus University, C.F Møllers Alle 3, Aarhus, Denmark
| | - J. J. Bowden
- Natural Resources Canada, Canadian Forest Service – Atlantic Forestry Centre, 26 University Drive, PO Box 960, Corner Brook, Newfoundland, Canada A2H 6J3
| | - M. J. Bunsen
- Mila – Québec AI Institute, Montréal, Québec, Canada H3A 0E9
| | - F. Cunha
- Mila – Québec AI Institute, Montréal, Québec, Canada H3A 0E9
- Federal University of Amazonas, Manaus, 69080–900, Brazil
| | - Q. Geissmann
- Center For Quantitative Genetics and Genomics, Aarhus University, C.F Møllers Alle 3, Aarhus, Denmark
| | - K. Goldmann
- The Alan Turing Institute, 96 Euston Road, London NW1 2DB, UK
| | - A. Gomez-Segura
- UK Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Wallingford OX10 8BB, UK
| | - A. Jain
- Mila – Québec AI Institute, Montréal, Québec, Canada H3A 0E9
| | - C. Huijbers
- Naturalis Biodiversity Centre, Darwinweg 2, 2333 CR Leiden, The Netherlands
| | - M. Larrivée
- Insectarium de Montreal, 4581 Sherbrooke Rue E, Montreal, Québec, Canada H1X 2B2
| | - J. L. Lawson
- UK Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Wallingford OX10 8BB, UK
| | - H. M. Mann
- Department of Ecoscience and Arctic Research Centre, Aarhus University, C.F Møllers Alle 3, Aarhus, Denmark
| | - M. J. Mazerolle
- Centre d'étude de la forêt, Département des sciences du bois et de la forêt, Faculté de foresterie, de géographie et de géomatique, Université Laval, Québec, Canada G1V 0A6
| | - K. P. McFarland
- Vermont Centre for Ecostudies, 20 Palmer Court, White River Junction, VT 05001, USA
| | - L. Pasi
- Mila – Québec AI Institute, Montréal, Québec, Canada H3A 0E9
- Ecole Polytechnique, Federale de Lausanne, Station 21, 1015 Lausanne, Switzerland
| | - S. Peters
- Faunabit, Strijkviertel 26 achter, 3454 Pm De Meern, The Netherlands
| | - N. Pinoy
- Department of Ecoscience and Arctic Research Centre, Aarhus University, C.F Møllers Alle 3, Aarhus, Denmark
| | - D. Rolnick
- Mila – Québec AI Institute, Montréal, Québec, Canada H3A 0E9
- School of Computer Science, McGill University, Montreal, Canada H3A 0E99
| | - G. L. Skinner
- UK Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Wallingford OX10 8BB, UK
| | - O. T. Strickson
- The Alan Turing Institute, 96 Euston Road, London NW1 2DB, UK
| | - A. Svenning
- Department of Ecoscience and Arctic Research Centre, Aarhus University, C.F Møllers Alle 3, Aarhus, Denmark
| | - S. Teagle
- UK Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Wallingford OX10 8BB, UK
| | - T. T. Høye
- Department of Ecoscience and Arctic Research Centre, Aarhus University, C.F Møllers Alle 3, Aarhus, Denmark
| |
Collapse
|
3
|
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'.
Collapse
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
| |
Collapse
|
4
|
Kohlmann B, Salomão RP, Solís Á. New World dung beetle (Coleoptera: Scarabaeidae: Scarabaeinae) colonization of a recent Miocene insular territory: The case of Costa Rica. Ecol Evol 2024; 14:e11436. [PMID: 38826175 PMCID: PMC11139973 DOI: 10.1002/ece3.11436] [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: 03/14/2024] [Revised: 04/25/2024] [Accepted: 05/02/2024] [Indexed: 06/04/2024] Open
Abstract
Costa Rica emerged from the seas as a new geological territory during the Miocene as an insular archipelago. It later became part of a continental area once it became a segment of Central America. Two dung beetle genera that colonized this new territory from South and North America, Canthidium and Onthophagus (Coleoptera: Scarabaeidae: Scarabaeinae), are here studied, in the first analysis of a volcanic paleo-archipelago, colonized from its emergence, and then later becoming terra firma. To assess their biodiversity distribution patterns, we analyzed the effect of biogeography, ecosystem origins, and body size on their altitudinal distribution patterns in three geographic basins of Costa Rica. Based on 32 years of collecting representing more than 158,000 specimens from 1017 localities, we undertook Generalized Linear Models of the two dung beetle genera to assess the effects of biodiversity and biogeographical distribution patterns. Canthidium and Onthophagus species ranged from 0 to 3000 m a.s.l., with an abrupt diversity decline at altitudes above 1500 m. Endemic species tended to show a higher altitudinal mean with a narrow altitudinal band distribution than non-endemic dung beetle species. Although there was a trend of decreasing species body size with the increase in altitude, such a trend depended on the distribution pattern of the species group. This possible insular-mediated endemicity mechanism has generated baffling biodiversity levels, considered the highest worldwide per unit area. Costa Rica is an expanse represented by a geographic overlap of two or more temporally disjunct areas and is not part of a natural transition zone. The effect of the insular Miocene origin of Costa Rica still pervades today, reflected by different insular syndromes shown by the dung beetle fauna. The importance of geological origins in generating biodiversity seems to have been an underrated criterion for conservation biology practices and should be considered ex officio.
Collapse
Affiliation(s)
- Bert Kohlmann
- BioAlfa Barcoding ProjectSanto Domingo de HerediaCosta Rica
| | - Renato Portela Salomão
- Facultad de Estudios Superiores IztacalaUniversidad Nacional Autónoma de MéxicoTlalnepantla de BazMexico
- Programa de Pós‐graduação Em EcologiaInstituto Nacional de Pesquisas da AmazôniaManausBrazil
| | - Ángel Solís
- BioAlfa Barcoding ProjectSanto Domingo de HerediaCosta Rica
| |
Collapse
|
5
|
Conteddu K, English HM, Byrne AW, Amin B, Griffin LL, Kaur P, Morera-Pujol V, Murphy KJ, Salter-Townshend M, Smith AF, Ciuti S. A scoping review on bovine tuberculosis highlights the need for novel data streams and analytical approaches to curb zoonotic diseases. Vet Res 2024; 55:64. [PMID: 38773649 PMCID: PMC11110237 DOI: 10.1186/s13567-024-01314-w] [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: 05/09/2023] [Accepted: 03/20/2024] [Indexed: 05/24/2024] Open
Abstract
Zoonotic diseases represent a significant societal challenge in terms of their health and economic impacts. One Health approaches to managing zoonotic diseases are becoming more prevalent, but require novel thinking, tools and cross-disciplinary collaboration. Bovine tuberculosis (bTB) is one example of a costly One Health challenge with a complex epidemiology involving humans, domestic animals, wildlife and environmental factors, which require sophisticated collaborative approaches. We undertook a scoping review of multi-host bTB epidemiology to identify trends in species publication focus, methodologies, and One Health approaches. We aimed to identify knowledge gaps where novel research could provide insights to inform control policy, for bTB and other zoonoses. The review included 532 articles. We found different levels of research attention across episystems, with a significant proportion of the literature focusing on the badger-cattle-TB episystem, with far less attention given to tropical multi-host episystems. We found a limited number of studies focusing on management solutions and their efficacy, with very few studies looking at modelling exit strategies. Only a small number of studies looked at the effect of human disturbances on the spread of bTB involving wildlife hosts. Most of the studies we reviewed focused on the effect of badger vaccination and culling on bTB dynamics with few looking at how roads, human perturbations and habitat change may affect wildlife movement and disease spread. Finally, we observed a lack of studies considering the effect of weather variables on bTB spread, which is particularly relevant when studying zoonoses under climate change scenarios. Significant technological and methodological advances have been applied to bTB episystems, providing explicit insights into its spread and maintenance across populations. We identified a prominent bias towards certain species and locations. Generating more high-quality empirical data on wildlife host distribution and abundance, high-resolution individual behaviours and greater use of mathematical models and simulations are key areas for future research. Integrating data sources across disciplines, and a "virtuous cycle" of well-designed empirical data collection linked with mathematical and simulation modelling could provide additional gains for policy-makers and managers, enabling optimised bTB management with broader insights for other zoonoses.
Collapse
Affiliation(s)
- Kimberly Conteddu
- Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Science, University College Dublin, Dublin, Ireland.
| | - Holly M English
- Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Andrew W Byrne
- Department of Agriculture, Food and the Marine, One Health Scientific Support Unit, Dublin, Ireland
| | - Bawan Amin
- Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Laura L Griffin
- Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Prabhleen Kaur
- School of Mathematics and Statistics, University College Dublin, Dublin, Ireland
| | - Virginia Morera-Pujol
- Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Kilian J Murphy
- Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | | | - Adam F Smith
- Department of Wildlife Ecology and Management, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany
- The Frankfurt Zoological Society, Frankfurt, Germany
- Department of National Park Monitoring and Animal Management, Bavarian Forest National Park, Grafenau, Germany
| | - Simone Ciuti
- Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| |
Collapse
|
6
|
Nater CR. Redefining 'state-of-the-art' for integrated population models with immigration. J Anim Ecol 2024; 93:520-524. [PMID: 38634153 DOI: 10.1111/1365-2656.14087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 03/22/2024] [Indexed: 04/19/2024]
Abstract
Research Highlight: Christian, M., Oosthuizen, W. C., Bester, M. N., & de Bruyn, P. N. (2024). Robustly estimating the demographic contribution of immigration: Simulation, sensitivity analysis and seals. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.14053. Immigration can have profound consequences for local population dynamics and demography, but collecting data to accurately quantifying it is challenging. The recent rise of integrated population models (IPMs) offers an alternative by making it possible to estimate immigration without the need for explicit data, and to quantify its contribution to population dynamics through transient Life Table Response Experiments (tLTREs). Simulation studies have, however, highlighted that this approach can be prone to bias and overestimation. In their new study, Christian et al. address one of the root causes of this issue by improving the estimation of time variation in vital rates and immigration using Gaussian processes in lieu of traditionally used temporal random effects. They demonstrate that IPM-tLTRE frameworks with Gaussian processes produce more accurate and less biased estimates of immigration and its contribution to population dynamics and illustrate the applicability of this approach using a long-term data set on elephant seals (Mirounga leonida). Results are validated with a simulation study and suggest that immigration of breeding females has been central for population recovery of elephant seals despite the species' high female site fidelity. Christian et al. thus present new insights into population regulation of long-lived marine mammals and highlight the potential for using Gaussian process priors in IPMs. They also illustrate a suite of 'best practices' for state-of-the-art IPM-tLTRE analyses and provide an inspirational example for the kind of ecological modelling workflow that can be invaluable not just as a starting point for fellow ecologists picking up or improving their own IPM-tLTRE analyses, but also for teaching and in contexts where model estimates are used for informing management and conservation decision-making.
Collapse
Affiliation(s)
- Chloé R Nater
- Norwegian Institute of Nature Research (NINA), Trondheim, Norway
| |
Collapse
|
7
|
Simmonds EG, Adjei KP, Cretois B, Dickel L, González-Gil R, Laverick JH, Mandeville CP, Mandeville EG, Ovaskainen O, Sicacha-Parada J, Skarstein ES, O'Hara B. Recommendations for quantitative uncertainty consideration in ecology and evolution. Trends Ecol Evol 2024; 39:328-337. [PMID: 38030538 DOI: 10.1016/j.tree.2023.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 09/13/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023]
Abstract
Ecological and evolutionary studies are currently failing to achieve complete and consistent reporting of model-related uncertainty. We identify three key barriers - a focus on parameter-related uncertainty, obscure uncertainty metrics, and limited recognition of uncertainty propagation - which have led to gaps in uncertainty consideration. However, these gaps can be closed. We propose that uncertainty reporting in ecology and evolution can be improved through wider application of existing statistical solutions and by adopting good practice from other scientific fields. Our recommendations include greater consideration of input data and model structure uncertainties, field-specific uncertainty standards for methods and reporting, and increased uncertainty propagation through the use of hierarchical models.
Collapse
Affiliation(s)
- Emily G Simmonds
- The Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim 7491, Norway; Institute for Biology, Norwegian University of Science and Technology, Trondheim 7491, Norway; Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FL, UK.
| | - Kwaku P Adjei
- The Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim 7491, Norway; Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim 7034, Norway
| | - Benjamin Cretois
- Norwegian Institute for Nature Research, Torgarden, Trondheim, Trøndelag 7485, Norway
| | - Lisa Dickel
- The Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim 7491, Norway; Institute for Biology, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Ricardo González-Gil
- Observatorio Marino de Asturias (OMA), Departamento de Biología de Organismos y Sistemas, University of Oviedo, 33071 Oviedo, Spain; GOAL, Colonia Castaño Sur, Casa 1901, Calle Paseo Virgilio Zelaya Rubí, Tegucigalpa, Honduras, CA, USA
| | - Jack H Laverick
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow G1 1XH, UK
| | - Caitlin P Mandeville
- The Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim 7491, Norway; Department of Natural History, Norwegian University of Science and Technology, Trondheim, Trøndelag 7491, Norway
| | | | - Otso Ovaskainen
- The Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim 7491, Norway; Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki 00014, Finland; Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä 40014, Finland
| | - Jorge Sicacha-Parada
- Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim 7034, Norway
| | - Emma S Skarstein
- Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim 7034, Norway
| | - Bob O'Hara
- The Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim 7491, Norway; Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim 7034, Norway
| |
Collapse
|
8
|
Kass JM, Fukaya K, Thuiller W, Mori AS. Biodiversity modeling advances will improve predictions of nature's contributions to people. Trends Ecol Evol 2024; 39:338-348. [PMID: 37968219 DOI: 10.1016/j.tree.2023.10.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 10/17/2023] [Accepted: 10/17/2023] [Indexed: 11/17/2023]
Abstract
Accurate predictions of ecosystem functions and nature's contributions to people (NCP) are needed to prioritize environmental protection and restoration in the Anthropocene. However, our ability to predict NCP is undermined by approaches that rely on biophysical variables and ignore those describing biodiversity, which have strong links to NCP. To foster predictive mapping of NCP, we should harness the latest methods in biodiversity modeling. This field advances rapidly, and new techniques with promising applications for predicting NCP are still underutilized. Here, we argue that employing recent advances in biodiversity modeling can enhance the accuracy and scope of NCP maps and predictions. This enhancement will contribute significantly to the achievement of global objectives to preserve NCP, for both the present and an unpredictable future.
Collapse
Affiliation(s)
- Jamie M Kass
- Macroecology Laboratory, Graduate School of Life Sciences, Tohoku University, Sendai, Miyagi, Japan; Biodiversity and Biocomplexity Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa, Japan.
| | - Keiichi Fukaya
- Biodiversity Division, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan
| | - Wilfried Thuiller
- Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, LECA, F-38000 Grenoble, France
| | - Akira S Mori
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| |
Collapse
|
9
|
Li X, Naimi B, Gong P, Araújo MB. Data error propagation in stacked bioclimatic envelope models. Integr Zool 2024; 19:262-276. [PMID: 37259699 DOI: 10.1111/1749-4877.12736] [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] [Indexed: 06/02/2023]
Abstract
Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models (BEMs). The approach can be used to investigate patterns and processes of species richness. If data limitations on individual species distributions are inevitable, but how do they affect inferences of patterns and processes of species richness? We investigate the influence of different data sources on estimated species richness gradients in China. We fitted BEMs using species distributions data for 334 bird species obtained from (1) global range maps, (2) regional checklists, (3) museum records and surveys, and (4) citizen science data using presence-only (Mahalanobis distance), presence-background (MAXENT), and presence-absence (GAM and BRT) BEMs. Individual species predictions were stacked to generate species richness gradients. Here, we show that different data sources and BEMs can generate spatially varying gradients of species richness. The environmental predictors that best explained species distributions also differed between data sources. Models using citizen-based data had the highest accuracy, whereas those using range data had the lowest accuracy. Potential richness patterns estimated by GAM and BRT models were robust to data uncertainty. When multiple data sets exist for the same region and taxa, we advise that explicit treatments of uncertainty, such as sensitivity analyses of the input data, should be conducted during the process of modeling.
Collapse
Affiliation(s)
- Xueyan Li
- Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Science, Guangzhou, China
| | - Babak Naimi
- 'Rui Nabeiro' Biodiversity Chair, CHANGE-MED Institute, University of Évora, Évora, Portugal
| | - Peng Gong
- Department of Geography and Department of Earth Sciences, University of Hong Kong, Hong Kong, China
| | - Miguel B Araújo
- 'Rui Nabeiro' Biodiversity Chair, CHANGE-MED Institute, University of Évora, Évora, Portugal
- Department of Biogeography and Global Change, National Museum of Natural Sciences, CSIC, Madrid, Spain
| |
Collapse
|
10
|
Hartig F, Abrego N, Bush A, Chase JM, Guillera-Arroita G, Leibold MA, Ovaskainen O, Pellissier L, Pichler M, Poggiato G, Pollock L, Si-Moussi S, Thuiller W, Viana DS, Warton DI, Zurell D, Yu DW. Novel community data in ecology-properties and prospects. Trends Ecol Evol 2024; 39:280-293. [PMID: 37949795 DOI: 10.1016/j.tree.2023.09.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 09/28/2023] [Accepted: 09/29/2023] [Indexed: 11/12/2023]
Abstract
New technologies for monitoring biodiversity such as environmental (e)DNA, passive acoustic monitoring, and optical sensors promise to generate automated spatiotemporal community observations at unprecedented scales and resolutions. Here, we introduce 'novel community data' as an umbrella term for these data. We review the emerging field around novel community data, focusing on new ecological questions that could be addressed; the analytical tools available or needed to make best use of these data; and the potential implications of these developments for policy and conservation. We conclude that novel community data offer many opportunities to advance our understanding of fundamental ecological processes, including community assembly, biotic interactions, micro- and macroevolution, and overall ecosystem functioning.
Collapse
Affiliation(s)
- Florian Hartig
- Theoretical Ecology, University of Regensburg, Regensburg, Germany.
| | - Nerea Abrego
- Department of Biological and Environmental Science, University of Jyväskylä, P.O. Box 35 (Survontie 9C), FI-40014 Jyväskylä, Finland
| | - Alex Bush
- Lancaster Environment Centre, Lancaster University, Lancaster, UK
| | - Jonathan M Chase
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | | | | | - Otso Ovaskainen
- Department of Biological and Environmental Science, University of Jyväskylä, P.O. Box 35 (Survontie 9C), FI-40014 Jyväskylä, Finland; Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, P.O. Box 65, Helsinki 00014, Finland
| | - Loïc Pellissier
- Ecosystems and Landscape Evolution, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zürich, 8092 Zurich, Switzerland; Unit of Land Change Science, Swiss Federal Research Institute for Forest, Snow and Landscape Research (WSL), 8903 Birmensdorf, Switzerland
| | | | - Giovanni Poggiato
- Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, F38000, Grenoble, France
| | - Laura Pollock
- Department of Biology, McGill University, Montreal, Quebec, Canada
| | - Sara Si-Moussi
- Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, F38000, Grenoble, France
| | - Wilfried Thuiller
- Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, F38000, Grenoble, France
| | | | | | | | - Douglas W Yu
- Kunming Institute of Zoology; Yunnan, China; University of East Anglia, Norfolk, UK
| |
Collapse
|
11
|
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.
Collapse
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.
| |
Collapse
|
12
|
Cazelles K. Isolating interactions from co-occurrences. Nat Ecol Evol 2024; 8:184-185. [PMID: 38012362 DOI: 10.1038/s41559-023-02245-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Affiliation(s)
- Kevin Cazelles
- College of Biological Science, University of Guelph, Guelph, Ontario, Canada.
| |
Collapse
|
13
|
Pocock MJ, Adriaens T, Bertolino S, Eschen R, Essl F, Hulme PE, Jeschke JM, Roy HE, Teixeira H, de Groot M. Citizen science is a vital partnership for invasive alien species management and research. iScience 2024; 27:108623. [PMID: 38205243 PMCID: PMC10776933 DOI: 10.1016/j.isci.2023.108623] [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] [Indexed: 01/12/2024] Open
Abstract
Invasive alien species (IAS) adversely impact biodiversity, ecosystem functions, and socio-economics. Citizen science can be an effective tool for IAS surveillance, management, and research, providing large datasets over wide spatial extents and long time periods, with public participants generating knowledge that supports action. We demonstrate how citizen science has contributed knowledge across the biological invasion process, especially for early detection and distribution mapping. However, we recommend that citizen science could be used more for assessing impacts and evaluating the success of IAS management. Citizen science does have limitations, and we explore solutions to two key challenges: ensuring data accuracy and dealing with uneven spatial coverage of potential recorders (which limits the dataset's "fit for purpose"). Greater co-development of citizen science with public stakeholders will help us better realize its potential across the biological invasion process and across ecosystems globally while meeting the needs of participants, local communities, scientists, and decision-makers.
Collapse
Affiliation(s)
| | - Tim Adriaens
- Research Institute for Nature and Forest (INBO), Brussels, Belgium
| | - Sandro Bertolino
- Department of Life Sciences and Systems Biology, University of Turin, Turin, Italy
| | | | - Franz Essl
- Division of BioInvasions, Global Change & Macroecology, Department of Botany and Biodiversity Research, University of Vienna, Vienna, Austria
| | - Philip E. Hulme
- Bioprotection Aotearoa, Department of Pest Management and Conservation, Lincoln University, PO Box 84850, Christchurch, Lincoln 7648, New Zealand
| | - Jonathan M. Jeschke
- Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany
- Institute of Biology, Freie Universität Berlin, Berlin, Germany
| | - Helen E. Roy
- UK Centre for Ecology & Hydrology, Wallingford, Oxfordshire, UK
- Centre for Ecology and Conservation, Faculty of Environment, Science and Economy, University of Exeter, Penryn, United Kingdom
| | - Heliana Teixeira
- Centre for Environmental and Marine Studies, Department of Biology, University of Aveiro, Campus de Santiago, Aveiro, Portugal
| | - Maarten de Groot
- Slovenian Forestry Institute, Večna pot 2, 1000 Ljubljana, Slovenia
| |
Collapse
|
14
|
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.
Collapse
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.
| |
Collapse
|
15
|
Van Ee JJ, Hagen CA, Jr DCP, Fricke KA, Koslovsky MD, Hooten MB. Melding wildlife surveys to improve conservation inference. Biometrics 2023; 79:3941-3953. [PMID: 37443410 DOI: 10.1111/biom.13903] [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: 11/19/2022] [Accepted: 06/29/2023] [Indexed: 07/15/2023]
Abstract
Integrated models are a popular tool for analyzing species of conservation concern. Species of conservation concern are often monitored by multiple entities that generate several datasets. Individually, these datasets may be insufficient for guiding management due to low spatio-temporal resolution, biased sampling, or large observational uncertainty. Integrated models provide an approach for assimilating multiple datasets in a coherent framework that can compensate for these deficiencies. While conventional integrated models have been used to assimilate count data with surveys of survival, fecundity, and harvest, they can also assimilate ecological surveys that have differing spatio-temporal regions and observational uncertainties. Motivated by independent aerial and ground surveys of lesser prairie-chicken, we developed an integrated modeling approach that assimilates density estimates derived from surveys with distinct sources of observational error into a joint framework that provides shared inference on spatio-temporal trends. We model these data using a Bayesian Markov melding approach and apply several data augmentation strategies for efficient sampling. In a simulation study, we show that our integrated model improved predictive performance relative to models for analyzing the surveys independently. We use the integrated model to facilitate prediction of lesser prairie-chicken density at unsampled regions and perform a sensitivity analysis to quantify the inferential cost associated with reduced survey effort.
Collapse
Affiliation(s)
- Justin J Van Ee
- Department of Statistics, Colorado State University, Fort Collins, Colorado, USA
| | - Christian A Hagen
- Department of Fisheries, Wildlife, and Conservation Sciences, Oregon State University, Corvallis, Oregon, USA
| | - David C Pavlacky Jr
- Bird Conservancy of the Rockies, Brighton, Colorado, USA
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA
| | - Kent A Fricke
- Kansas Department of Wildlife and Parks, Emporia, Kansas, USA
| | - Matthew D Koslovsky
- Department of Statistics, Colorado State University, Fort Collins, Colorado, USA
| | - Mevin B Hooten
- Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, Texas, USA
| |
Collapse
|
16
|
Zipkin EF, Doser JW, Davis CL, Leuenberger W, Ayebare S, Davis KL. Integrated community models: A framework combining multispecies data sources to estimate the status, trends and dynamics of biodiversity. J Anim Ecol 2023; 92:2248-2262. [PMID: 37880838 DOI: 10.1111/1365-2656.14012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 09/07/2023] [Indexed: 10/27/2023]
Abstract
Data deficiencies among rare or cryptic species preclude assessment of community-level processes using many existing approaches, limiting our understanding of the trends and stressors for large numbers of species. Yet evaluating the dynamics of whole communities, not just common or charismatic species, is critical to understanding and the responses of biodiversity to ongoing environmental pressures. A recent surge in both public science and government-funded data collection efforts has led to a wealth of biodiversity data. However, these data collection programmes use a wide range of sampling protocols (from unstructured, opportunistic observations of wildlife to well-structured, design-based programmes) and record information at a variety of spatiotemporal scales. As a result, available biodiversity data vary substantially in quantity and information content, which must be carefully reconciled for meaningful ecological analysis. Hierarchical modelling, including single-species integrated models and hierarchical community models, has improved our ability to assess and predict biodiversity trends and processes. Here, we highlight the emerging 'integrated community modelling' framework that combines both data integration and community modelling to improve inferences on species- and community-level dynamics. We illustrate the framework with a series of worked examples. Our three case studies demonstrate how integrated community models can be used to extend the geographic scope when evaluating species distributions and community-level richness patterns; discern population and community trends over time; and estimate demographic rates and population growth for communities of sympatric species. We implemented these worked examples using multiple software methods through the R platform via packages with formula-based interfaces and through development of custom code in JAGS, NIMBLE and Stan. Integrated community models provide an exciting approach to model biological and observational processes for multiple species using multiple data types and sources simultaneously, thus accounting for uncertainty and sampling error within a unified framework. By leveraging the combined benefits of both data integration and community modelling, integrated community models can produce valuable information about both common and rare species as well as community-level dynamics, allowing for holistic evaluation of the effects of global change on biodiversity.
Collapse
Affiliation(s)
- Elise F Zipkin
- Department of Integrative Biology; Ecology, Evolutionary Biology, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
| | - Jeffrey W Doser
- Department of Integrative Biology; Ecology, Evolutionary Biology, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
| | - Courtney L Davis
- Department of Integrative Biology; Ecology, Evolutionary Biology, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
- Cornell Lab of Ornithology, Cornell University, Ithaca, New York, USA
| | - Wendy Leuenberger
- Department of Integrative Biology; Ecology, Evolutionary Biology, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
| | - Samuel Ayebare
- Department of Integrative Biology; Ecology, Evolutionary Biology, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
| | - Kayla L Davis
- Department of Integrative Biology; Ecology, Evolutionary Biology, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
| |
Collapse
|
17
|
Wu R, Qi J, Li W, Wang L, Shen Y, Liu J, Teng Y, Roos C, Li M. Landscape genomics analysis provides insights into future climate change-driven risk in rhesus macaque. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 899:165746. [PMID: 37495138 DOI: 10.1016/j.scitotenv.2023.165746] [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: 02/27/2023] [Revised: 07/01/2023] [Accepted: 07/21/2023] [Indexed: 07/28/2023]
Abstract
Climate change significantly affects the suitability of wildlife habitats. Thus, understanding how animals adapt ecologically and genetically to climate change is important for targeted species protection. Rhesus macaques (Macaca mulatta) are widely distributed and multi-climatically adapted primates. This study explored how rhesus macaques adapt to climate change by integrating ecological and genetic methods and applying species distribution models (SDMs) and a gradient forest (GF) model. The findings suggested that temperature seasonality primarily affects habitat suitability and indicated that climate change will have a dramatic impact on macaque populations in the future. We also applied genotype-environment association (GEA) analyses and selection signature analyses to identify genes associated with climate change and provide possible explanations for the adaptation of rhesus macaques to climatic environments. The population genomics analyses suggested that the Taihang population has the highest genomic vulnerability with inbreeding and low heterozygosity. Combined with the higher ecological vulnerability, additional conservation strategies are required for this population under higher risk of climate change. Our work measured the impact of climate change and enabled the identification of populations that exhibit high vulnerability to severe climate change. Such information is useful for selecting populations of rhesus macaques as subject of long-term monitoring or evolutionary rescue under future climate change.
Collapse
Affiliation(s)
- Ruifeng Wu
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiwei Qi
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Wenbo Li
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Ling Wang
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ying Shen
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiawen Liu
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Teng
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Christian Roos
- Primate Genetics Laboratory, German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
| | - Ming Li
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China.
| |
Collapse
|
18
|
McCleery R, Guralnick R, Beatty M, Belitz M, Campbell CJ, Idec J, Jones M, Kang Y, Potash A, Fletcher RJ. Uniting Experiments and Big Data to advance ecology and conservation. Trends Ecol Evol 2023; 38:970-979. [PMID: 37330409 DOI: 10.1016/j.tree.2023.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 06/19/2023]
Abstract
Many ecologists increasingly advocate for research frameworks centered on the use of 'big data' to address anthropogenic impacts on ecosystems. Yet, experiments are often considered essential for identifying mechanisms and informing conservation interventions. We highlight the complementarity of these research frameworks and expose largely untapped opportunities for combining them to speed advancements in ecology and conservation. With nascent but increasing application of model integration, we argue that there is an urgent need to unite experimental and big data frameworks throughout the scientific process. Such an integrated framework offers potential for capitalizing on the benefits of both frameworks to gain rapid and reliable answers to ecological challenges.
Collapse
Affiliation(s)
- Robert McCleery
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32618, USA.
| | - Robert Guralnick
- Florida Museum of Natural History, University of Florida, Gainesville, FL 32618, USA
| | - Meghan Beatty
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32618, USA
| | - Michael Belitz
- Florida Museum of Natural History, University of Florida, Gainesville, FL 32618, USA
| | - Caitlin J Campbell
- Department of Biology, University of Florida, Gainesville, FL 32618, USA
| | - Jacob Idec
- Florida Museum of Natural History, University of Florida, Gainesville, FL 32618, USA
| | - Maggie Jones
- School of Natural Resources and the Environment, University of Florida, Gainesville, FL 32618, USA
| | - Yiyang Kang
- Department of Environmental Engineering Sciences, University of Florida, Gainesville, FL 32618, USA
| | - Alex Potash
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32618, USA
| | - Robert J Fletcher
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32618, USA
| |
Collapse
|
19
|
Boyd RJ, August TA, Cooke R, Logie M, Mancini F, Powney GD, Roy DB, Turvey K, Isaac NJB. An operational workflow for producing periodic estimates of species occupancy at national scales. Biol Rev Camb Philos Soc 2023; 98:1492-1508. [PMID: 37062709 DOI: 10.1111/brv.12961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 03/31/2023] [Accepted: 04/03/2023] [Indexed: 04/18/2023]
Abstract
Policy makers require high-level summaries of biodiversity change. However, deriving such summaries from raw biodiversity data is a complex process involving several intermediary stages. In this paper, we describe an operational workflow for generating annual estimates of species occupancy at national scales from raw species occurrence data, which can be used to construct a range of policy-relevant biodiversity indicators. We describe the workflow in detail: from data acquisition, data assessment and data manipulation, through modelling, model evaluation, application and dissemination. At each stage, we draw on our experience developing and applying the workflow for almost a decade to outline the challenges that analysts might face. These challenges span many areas of ecology, taxonomy, data science, computing and statistics. In our case, the principal output of the workflow is annual estimates of occupancy, with measures of uncertainty, for over 5000 species in each of several defined 'regions' (e.g. countries, protected areas, etc.) of the UK from 1970 to 2019. This data product corresponds closely to the notion of a species distribution Essential Biodiversity Variable (EBV). Throughout the paper, we highlight methodologies that might not be applicable outside of the UK and suggest alternatives. We also highlight areas where the workflow can be improved; in particular, methods are needed to mitigate and communicate the risk of bias arising from the lack of representativeness that is typical of biodiversity data. Finally, we revisit the 'ideal' and 'minimal' criteria for species distribution EBVs laid out in previous contributions and pose some outstanding questions that should be addressed as a matter of priority. Going forward, we hope that this paper acts as a template for research groups around the world seeking to develop similar data products.
Collapse
Affiliation(s)
- Robin J Boyd
- UK Centre for Ecology and Hydrology, MacLean Building, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK
| | - Thomas A August
- UK Centre for Ecology and Hydrology, MacLean Building, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK
| | - Robert Cooke
- UK Centre for Ecology and Hydrology, MacLean Building, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK
| | - Mark Logie
- UK Centre for Ecology and Hydrology, MacLean Building, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK
| | - Francesca Mancini
- UK Centre for Ecology and Hydrology, MacLean Building, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK
| | - Gary D Powney
- UK Centre for Ecology and Hydrology, MacLean Building, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK
| | - David B Roy
- UK Centre for Ecology and Hydrology, MacLean Building, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK
| | - Katharine Turvey
- UK Centre for Ecology and Hydrology, MacLean Building, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK
| | - Nick J B Isaac
- UK Centre for Ecology and Hydrology, MacLean Building, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK
| |
Collapse
|
20
|
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.
Collapse
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;
| |
Collapse
|
21
|
Braun CD, Arostegui MC, Farchadi N, Alexander M, Afonso P, Allyn A, Bograd SJ, Brodie S, Crear DP, Culhane EF, Curtis TH, Hazen EL, Kerney A, Lezama-Ochoa N, Mills KE, Pugh D, Queiroz N, Scott JD, Skomal GB, Sims DW, Thorrold SR, Welch H, Young-Morse R, Lewison RL. Building use-inspired species distribution models: Using multiple data types to examine and improve model performance. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2023; 33:e2893. [PMID: 37285072 DOI: 10.1002/eap.2893] [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: 02/01/2023] [Accepted: 05/22/2023] [Indexed: 06/08/2023]
Abstract
Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery dependent (conventional mark-recapture tags, fisheries observer records) and two fishery independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage the strengths of individual data types while statistically accounting for limitations, such as sampling biases.
Collapse
Affiliation(s)
- Camrin D Braun
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
| | - Martin C Arostegui
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
| | - Nima Farchadi
- Institute for Ecological Monitoring and Management, San Diego State University, San Diego, California, USA
| | | | - Pedro Afonso
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
- Okeanos and Institute of Marine Research, University of the Azores, Horta, Portugal
| | - Andrew Allyn
- Gulf of Maine Research Institute, Portland, Maine, USA
| | - Steven J Bograd
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Monterey, California, USA
| | - Stephanie Brodie
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Monterey, California, USA
- Institute of Marine Sciences, University of California, Santa Cruz, California, USA
| | - Daniel P Crear
- ECS Federal, in Support of National Marine Fisheries Service, Atlantic Highly Migratory Species Management Division, Silver Spring, Maryland, USA
| | - Emmett F Culhane
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
- Massachusetts Institute of Technology-Woods Hole Oceanographic Institution Joint Program in Oceanography-Applied Ocean Science and Engineering, Cambridge, Massachusetts, USA
| | - Tobey H Curtis
- National Marine Fisheries Service, Atlantic Highly Migratory Species Management Division, Gloucester, Massachusetts, USA
| | - Elliott L Hazen
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Monterey, California, USA
- Institute of Marine Sciences, University of California, Santa Cruz, California, USA
| | - Alex Kerney
- Gulf of Maine Research Institute, Portland, Maine, USA
| | - Nerea Lezama-Ochoa
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Monterey, California, USA
- Institute of Marine Sciences, University of California, Santa Cruz, California, USA
| | | | - Dylan Pugh
- Gulf of Maine Research Institute, Portland, Maine, USA
| | - Nuno Queiroz
- Research Network in Biodiversity and Evolutionary Biology, Universidade do Porto, Vairão, Portugal
- Marine Biological Association of the United Kingdom, The Laboratory, Plymouth, UK
| | - James D Scott
- NOAA Earth System Research Laboratory, Boulder, Colorado, USA
- Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, USA
| | - Gregory B Skomal
- Massachusetts Division of Marine Fisheries, New Bedford, Massachusetts, USA
| | - David W Sims
- Marine Biological Association of the United Kingdom, The Laboratory, Plymouth, UK
- Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Southampton, UK
| | - Simon R Thorrold
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
| | - Heather Welch
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Monterey, California, USA
- Institute of Marine Sciences, University of California, Santa Cruz, California, USA
| | | | - Rebecca L Lewison
- Institute for Ecological Monitoring and Management, San Diego State University, San Diego, California, USA
| |
Collapse
|
22
|
Kays R, Wikelski M. The Internet of Animals: what it is, what it could be. Trends Ecol Evol 2023; 38:859-869. [PMID: 37263824 DOI: 10.1016/j.tree.2023.04.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 04/07/2023] [Accepted: 04/14/2023] [Indexed: 06/03/2023]
Abstract
One of the biggest trends in ecology over the past decade has been the creation of standardized databases. Recently, this has included live data, formal linkages between disparate databases, and automated analytics, a synergy that we recognize as the Internet of Animals (IoA). Early IoA systems relate animal locations to remote-sensing data to predict species distributions and detect disease outbreaks, and use live data to inform management of endangered species. However, meeting the future potential of the IoA concept will require solving challenges of taxonomy, data security, and data sharing. By linking data sets, integrating live data, and automating workflows, the IoA has the potential to enable discoveries and predictions relevant to human societies and the conservation of animals.
Collapse
Affiliation(s)
- Roland Kays
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA; North Carolina Museum of Natural Sciences, Raleigh, NC, USA; Smithsonian Tropical Research Institute, Balboa, Republic of Panama.
| | - Martin Wikelski
- Smithsonian Tropical Research Institute, Balboa, Republic of Panama; Department of Animal Migration, Max Planck Institute of Animal Behaviour, Radolfzell, Germany; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
| |
Collapse
|
23
|
Zhou H, Feng L, Fu L, Sharma RP, Zhou X, Zhao X. Modelling the effects of topographic heterogeneity on distribution of Nitraria tangutorum Bobr. species in deserts using LiDAR-data. Sci Rep 2023; 13:13673. [PMID: 37608034 PMCID: PMC10444836 DOI: 10.1038/s41598-023-40678-5] [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: 02/09/2023] [Accepted: 08/16/2023] [Indexed: 08/24/2023] Open
Abstract
Microclimate ecology is attracting renewed attention because of its fundamental importance in understanding how organisms respond to climate change. Many hot issues can be investigated in desert ecosystems, including the relationship between species distribution and environmental gradients (e.g., elevation, slope, topographic convergence index, and solar insolation). Species Distribution Models (SDMs) can be used to understand these relationships. We used data acquired from the important desert plant Nitraria tangutorum Bobr. communities and desert topographic factors extracted from LiDAR (Light Detection and Ranging) data of one square kilometer in the inner Mongolia region of China to develop SDMs. We evaluated the performance of SDMs developed with a variety of both the parametric and nonparametric algorithms (Bioclimatic Modelling (BIOCLIM), Domain, Mahalanobi, Generalized Linear Model, Generalized Additive Model, Random Forest (RF), and Support Vector Machine). The area under the receiver operating characteristic curve was used to evaluate these algorithms. The SDMs developed with RF showed the best performance based on the area under curve (0.7733). We also produced the Nitraria tangutorum Bobr. distribution maps with the best SDM and suitable habitat area of the Domain model. Based on the suitability map, we conclude that Nitraria tangutorum Bobr. is more suited to southern part with 0-20 degree slopes at an elevation of approximately 1010 m. This is the first attempt of modelling the effects of topographic heterogeneity on the desert species distribution on a small scale. The presented SDMs can have important applications for predicting species distribution and will be useful for preparing conservation and management strategies for desert ecosystems on a small scale.
Collapse
Affiliation(s)
- Huoyan Zhou
- School of Ecology and Environment Science, Yunnan University, Kunming, 650031, Yunnan Province, People's Republic of China
- Research Institute of Forestry Policy and Information, Chinese Academy of Forestry, Beijing, 100091, People's Republic of China
| | - Linyan Feng
- Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, People's Republic of China
| | - Liyong Fu
- Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, People's Republic of China
| | - Ram P Sharma
- Institute of Forestry, Tribhuvan University, Kritipur, Kathmandu, 44600, Nepal
| | - Xiao Zhou
- International Center for Bamboo and Rattan, Key Laboratory of National Forestry and Grassland Administration, Beijing, 100091, China
| | - Xiaodi Zhao
- Research Institute of Forestry Policy and Information, Chinese Academy of Forestry, Beijing, 100091, People's Republic of China.
- Faculty of Forestry, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
| |
Collapse
|
24
|
Tran DV, Vu TT, Fukutani K, Nishikawa K. Demographic and ecological niche dynamics of the Vietnam warty newt, Paramesotriton deloustali: Historical climate influences. PLoS One 2023; 18:e0290044. [PMID: 37594998 PMCID: PMC10437943 DOI: 10.1371/journal.pone.0290044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/01/2023] [Indexed: 08/20/2023] Open
Abstract
Quaternary climatic cycles strongly affected the genetic diversification and ranges of organisms, shaping current genetic structures and distribution patterns. Urodeles provide ideal examples for exploring these dynamics over time and across space. In this study, we integrated a phylogeographic approach and ensemble species distribution modeling (eSDM) to infer the historical demography and distribution patterns of the Vietnam warty newt, Paramesotriton deloustali. Mitochondrial data revealed two groups, West and East, which diverged approximately 1.92 million years ago (Mya). Diversification was likely driven by change in the climate during early stages of the Pleistocene, with increasing monsoon and drought intensities. Biogeographic analysis indicated that the newt's current distribution formed as a result of vicariance events. In addition, the two groups occupy distinct ecological niches. Demographic reconstruction showed signs of expansion in the effective population sizes of the two major groups beginning around 0.11 and 0.15 Mya, respectively. However, eSDM showed fluctuating predicted distributions during the last interglacial, last glacial maximum, mid-Holocene, and present. Mountain systems in northern Vietnam are likely to have served as climatic refuges and to have played a crucial role in safeguarding species from the effects of climate change.
Collapse
Affiliation(s)
- Dung Van Tran
- Graduate School of Human and Environmental Studies, Kyoto University, Kyoto, Japan
- Wildlife Department, Vietnam National University of Forestry, Ha Noi, Vietnam
| | - Thinh Tien Vu
- Wildlife Department, Vietnam National University of Forestry, Ha Noi, Vietnam
| | - Kazumi Fukutani
- Graduate School of Human and Environmental Studies, Kyoto University, Kyoto, Japan
| | - Kanto Nishikawa
- Graduate School of Human and Environmental Studies, Kyoto University, Kyoto, Japan
- Graduate School of Global Environmental Studies, Kyoto University, Kyoto, Japan
| |
Collapse
|
25
|
Braun CD, Lezama-Ochoa N, Farchadi N, Arostegui MC, Alexander M, Allyn A, Bograd SJ, Brodie S, Crear DP, Curtis TH, Hazen EL, Kerney A, Mills KE, Pugh D, Scott JD, Welch H, Young-Morse R, Lewison RL. Widespread habitat loss and redistribution of marine top predators in a changing ocean. SCIENCE ADVANCES 2023; 9:eadi2718. [PMID: 37556548 PMCID: PMC10411898 DOI: 10.1126/sciadv.adi2718] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 07/05/2023] [Indexed: 08/11/2023]
Abstract
The Northwest Atlantic Ocean and Gulf of Mexico are among the fastest warming ocean regions, a trend that is expected to continue through this century with far-reaching implications for marine ecosystems. We examine the distribution of 12 highly migratory top predator species using predictive models and project expected habitat changes using downscaled climate models. Our models predict widespread losses of suitable habitat for most species, concurrent with substantial northward displacement of core habitats >500 km. These changes include up to >70% loss of suitable habitat area for some commercially and ecologically important species. We also identify predicted hot spots of multi-species habitat loss focused offshore of the U.S. Southeast and Mid-Atlantic coasts. For several species, the predicted changes are already underway, which are likely to have substantial impacts on the efficacy of static regulatory frameworks used to manage highly migratory species. The ongoing and projected effects of climate change highlight the urgent need to adaptively and proactively manage dynamic marine ecosystems.
Collapse
Affiliation(s)
- Camrin D. Braun
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
| | - Nerea Lezama-Ochoa
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Monterey, CA 93940, USA
- Institute of Marine Sciences, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Nima Farchadi
- Institute for Ecological Monitoring and Management, San Diego State University, San Diego, CA 92182, USA
| | - Martin C. Arostegui
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
| | | | - Andrew Allyn
- Gulf of Maine Research Institute, Portland, ME 04101, USA
| | - Steven J. Bograd
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Monterey, CA 93940, USA
| | - Stephanie Brodie
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Monterey, CA 93940, USA
- Institute of Marine Sciences, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Daniel P. Crear
- ECS Federal, in Support of National Marine Fisheries Service, Atlantic Highly Migratory Species Management Division, Silver Spring, MD 20910, USA
| | - Tobey H. Curtis
- National Marine Fisheries Service, Atlantic Highly Migratory Species Management Division, Gloucester, MA 01930, USA
| | - Elliott L. Hazen
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Monterey, CA 93940, USA
- Institute of Marine Sciences, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Alex Kerney
- Gulf of Maine Research Institute, Portland, ME 04101, USA
| | | | - Dylan Pugh
- Gulf of Maine Research Institute, Portland, ME 04101, USA
| | - James D. Scott
- NOAA Earth System Research Laboratory, Boulder, CO 80305, USA
- Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Heather Welch
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Monterey, CA 93940, USA
- Institute of Marine Sciences, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | | | - Rebecca L. Lewison
- Institute for Ecological Monitoring and Management, San Diego State University, San Diego, CA 92182, USA
| |
Collapse
|
26
|
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.
Collapse
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
| |
Collapse
|
27
|
Dey S, Moqanaki E, Milleret C, Dupont P, Tourani M, Bischof R. Modelling spatially autocorrelated detection probabilities in spatial capture-recapture using random effects. Ecol Modell 2023. [DOI: 10.1016/j.ecolmodel.2023.110324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
|
28
|
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.
Collapse
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
| |
Collapse
|
29
|
Mostert PS, O'Hara RB. PointedSDMs:
An R package to help facilitate the construction of integrated species distribution models. Methods Ecol Evol 2023. [DOI: 10.1111/2041-210x.14091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Affiliation(s)
- Philip S. Mostert
- Department of Mathematical sciences Norwegian University of Science and Technology Trondheim Norway
- Centre for Biodiversity Dynamics Norwegian University of Science and Technology Trondheim Norway
| | - Robert B. O'Hara
- Department of Mathematical sciences Norwegian University of Science and Technology Trondheim Norway
- Centre for Biodiversity Dynamics Norwegian University of Science and Technology Trondheim Norway
| |
Collapse
|
30
|
Cervantes F, Altwegg R, Strobbe F, Skowno A, Visser V, Brooks M, Stojanov Y, Harebottle DM, Job N. BIRDIE: A data pipeline to inform wetland and waterbird conservation at multiple scales. Front Ecol Evol 2023. [DOI: 10.3389/fevo.2023.1131120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023] Open
Abstract
IntroductionEfforts to collect ecological data have intensified over the last decade. This is especially true for freshwater habitats, which are among the most impacted by human activity and yet lagging behind in terms of data availability. Now, to support conservation programmes and management decisions, these data need to be analyzed and interpreted; a process that can be complex and time consuming. The South African Biodiversity Data Pipeline for Wetlands and Waterbirds (BIRDIE) aims to help fast and efficient information uptake, bridging the gap between raw ecological datasets and the information final users need.MethodsBIRDIE is a full data pipeline that takes up raw data, and estimates indicators related to waterbird populations, while keeping track of their associated uncertainty. At present, we focus on the assessment of species abundance and distribution in South Africa using two citizen-science bird monitoring datasets, namely: the African Bird Atlas Project and the Coordinated Waterbird Counts. These data are analyzed with occupancy and state-space models, respectively. In addition, a suite of environmental layers help contextualize waterbird population indicators, and link these to the ecological condition of the supporting wetlands. Both data and estimated indicators are accessible to end users through an online portal and web services.Results and discussionWe have designed a modular system that includes tasks, such as: data cleaning, statistical analysis, diagnostics, and computation of indicators. Envisioned users of BIRDIE include government officials, conservation managers, researchers and the general public, all of whom have been engaged throughout the project. Acknowledging that conservation programmes run at multiple spatial and temporal scales, we have developed a granular framework in which indicators are estimated at small scales, and then these are aggregated to compute similar indicators at broader scales. Thus, the online portal is designed to provide spatial and temporal visualization of the indicators using maps, time series and pre-compiled reports for species, sites and conservation programmes. In the future, we aim to expand the geographical coverage of the pipeline to other African countries, and develop more indicators specific to the ecological structure and function of wetlands.
Collapse
|
31
|
McNichol BH, Russo SE. Plant Species' Capacity for Range Shifts at the Habitat and Geographic Scales: A Trade-Off-Based Framework. PLANTS (BASEL, SWITZERLAND) 2023; 12:1248. [PMID: 36986935 PMCID: PMC10056461 DOI: 10.3390/plants12061248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/18/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Climate change is causing rapid shifts in the abiotic and biotic environmental conditions experienced by plant populations, but we lack generalizable frameworks for predicting the consequences for species. These changes may cause individuals to become poorly matched to their environments, potentially inducing shifts in the distributions of populations and altering species' habitat and geographic ranges. We present a trade-off-based framework for understanding and predicting whether plant species may undergo range shifts, based on ecological strategies defined by functional trait variation. We define a species' capacity for undergoing range shifts as the product of its colonization ability and the ability to express a phenotype well-suited to the environment across life stages (phenotype-environment matching), which are both strongly influenced by a species' ecological strategy and unavoidable trade-offs in function. While numerous strategies may be successful in an environment, severe phenotype-environment mismatches result in habitat filtering: propagules reach a site but cannot establish there. Operating within individuals and populations, these processes will affect species' habitat ranges at small scales, and aggregated across populations, will determine whether species track climatic changes and undergo geographic range shifts. This trade-off-based framework can provide a conceptual basis for species distribution models that are generalizable across plant species, aiding in the prediction of shifts in plant species' ranges in response to climate change.
Collapse
Affiliation(s)
- Bailey H. McNichol
- School of Biological Sciences, University of Nebraska–Lincoln, 1101 T Street, 402 Manter Hall, Lincoln, NE 68588-0118, USA;
| | - Sabrina E. Russo
- School of Biological Sciences, University of Nebraska–Lincoln, 1101 T Street, 402 Manter Hall, Lincoln, NE 68588-0118, USA;
- Center for Plant Science Innovation, University of Nebraska–Lincoln, 1901 Vine Street, N300 Beadle Center, Lincoln, NE 68588-0118, USA
| |
Collapse
|
32
|
Elliott SA, Deleys N, Beaulaton L, Rivot E, Réveillac E, Acou A. Fisheries-dependent and -independent data used to model the distribution of diadromous fish at-sea. Data Brief 2023; 48:109107. [PMID: 37095755 PMCID: PMC10121382 DOI: 10.1016/j.dib.2023.109107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/26/2023] [Accepted: 03/24/2023] [Indexed: 04/03/2023] Open
Abstract
A database of 168 904 hauls covering the period from 1965 to 2019, from 46 surveys containing both fisheries-dependent (fishing vessels) and -independent data (scientific surveys) were collated from across the eastern Atlantic (Greater North Sea, Celtic Sea, Bay of Biscay and Iberian coast) and Metropolitan French Mediterranean waters. Data on diadromous fish (the European sturgeon (Acipenser sturio), allis shad (Alosa alosa), twait shad (Alosa fallax), Mediterranean twaite shad (Alosa agone), European eel (Anguilla anguilla), thinlip mullet (Chelon ramada), river lamprey (Lampetra fluviatilis), sea lamprey (Petromyzon marinus), smelt (Osmerus eperlanus), European flounder (Platichthys flesus), Atlantic salmon (Salmo salar) and the sea trout (Salmo trutta)) presence-absence was extracted and cleaned. The gear type and gear category which caught these species, their spatial location, and the date of capture (year and month), were also cleaned and standardised. Very little is known about diadromous fish at-sea and modelling data-poor and poorly detectable species such as diadromous fish is challenging for species conservation. Furthermore, databases which contain both scientific surveys and fisheries-dependent data on data-poor species at the temporal and geographical scale of this database are uncommon. This data could therefore be used to improve knowledge of diadromous fish spatial and temporal trends, and modelling techniques for data-poor species.
Collapse
|
33
|
Davis CL, Guralnick RP, Zipkin EF. Challenges and opportunities for using natural history collections to estimate insect population trends. J Anim Ecol 2023; 92:237-249. [PMID: 35716080 DOI: 10.1111/1365-2656.13763] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 06/13/2022] [Indexed: 11/28/2022]
Abstract
Natural history collections (NHC) provide a wealth of information that can be used to understand the impacts of global change on biodiversity. As such, there is growing interest in using NHC data to estimate changes in species' distributions and abundance trends over historic time horizons when contemporary survey data are limited or unavailable. However, museum specimens were not collected with the purpose of estimating population trends and thus can exhibit spatiotemporal and collector-specific biases that can impose severe limitations to using NHC data for evaluating population trajectories. Here we review the challenges associated with using museum records to track long-term insect population trends, including spatiotemporal biases in sampling effort and sparse temporal coverage within and across years. We highlight recent methodological advancements that aim to overcome these challenges and discuss emerging research opportunities. Specifically, we examine the potential of integrating museum records and other contemporary data sources (e.g. collected via structured, designed surveys and opportunistic citizen science programs) in a unified analytical framework that accounts for the sampling biases associated with each data source. The emerging field of integrated modelling provides a promising framework for leveraging the wealth of collections data to accurately estimate long-term trends of insect populations and identify cases where that is not possible using existing data sources.
Collapse
Affiliation(s)
- Courtney L Davis
- Department of Integrative Biology; Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA.,Cornell Lab of Ornithology, Cornell University, Ithaca, New York, USA
| | - Robert P Guralnick
- Florida Museum of Natural History, University of Florida, Gainesville, Florida, USA.,Biodiversity Institute, University of Florida, Gainesville, Florida, USA
| | - Elise F Zipkin
- Department of Integrative Biology; Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
| |
Collapse
|
34
|
Ketz AC, Storm DJ, Barker RE, Apa AD, Oliva‐Aviles C, Walsh DP. Assimilating ecological theory with empiricism: Using constrained generalized additive models to enhance survival analyses. Methods Ecol Evol 2023. [DOI: 10.1111/2041-210x.14057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Alison C. Ketz
- Wisconsin Cooperative Research Unit, Department of Forest and Wildlife Ecology University of Wisconsin Madison Wisconsin USA
| | - Daniel J. Storm
- Wisconsin Department of Natural Resources Rhinelander Wisconsin USA
| | - Rachel E. Barker
- Department of Forest and Wildlife Ecology University of Wisconsin Madison Wisconsin USA
| | | | | | - Daniel P. Walsh
- U.S. Geological Survey Montana Cooperative Wildlife Research Unit Missoula Montana USA
| |
Collapse
|
35
|
von Gönner J, Bowler DE, Gröning J, Klauer AK, Liess M, Neuer L, Bonn A. Citizen science for assessing pesticide impacts in agricultural streams. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159607. [PMID: 36273564 DOI: 10.1016/j.scitotenv.2022.159607] [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: 08/25/2022] [Revised: 10/16/2022] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
The majority of central European streams are in poor ecological condition. Pesticide inputs from terrestrial habitats present a key threat to sensitive insects in streams. Both standardized stream monitoring data and societal support are needed to conserve and restore freshwater habitats. Citizen science (CS) offers potential to complement international freshwater monitoring while it is often viewed critically due to concerns about data accuracy. Here, we developed a CS program based on the Water Framework Directive that enables citizen scientists to provide data on stream hydromorphology, physicochemical status and benthic macroinvertebrates to apply the trait-based bio-indicator SPEARpesticides for pesticide exposure. We compared CS monitoring data with professional data across 28 central German stream sites and could show that both CS and professional monitoring identified a similar average proportion of pesticide-sensitive macroinvertebrate taxa per stream site (20 %). CS data were highly correlated to the professional data for both stream hydromorphology and SPEARpesticides (r = 0.72 and 0.76). To assess the extent to which CS macroinvertebrate data can indicate pesticide exposure, we tested the relationship of CS generated SPEARpesticides values and measured pesticide concentrations at 21 stream sites, and found a fair correlation similar to professional results. We conclude that given appropriate training and support, citizen scientists can generate valid data on the ecological status and pesticide contamination of streams. By complementing official monitoring, data from well-managed CS programs can advance freshwater science and enhance the implementation of freshwater conservation goals.
Collapse
Affiliation(s)
- Julia von Gönner
- Helmholtz Centre for Environmental Research - UFZ, Department Ecosystem Services, Permoserstr. 15, 04318 Leipzig, Germany; Friedrich Schiller University Jena, Institute of Biodiversity, Dornburgerstr.159, 07743 Jena, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstr. 4, 04103 Leipzig, Germany.
| | - Diana E Bowler
- Friedrich Schiller University Jena, Institute of Biodiversity, Dornburgerstr.159, 07743 Jena, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstr. 4, 04103 Leipzig, Germany; UK Centre for Ecology & Hydrology, Benson Lane, Wallingford OX10 8BB, UK
| | - Jonas Gröning
- Helmholtz Centre for Environmental Research - UFZ, Department System-Ecotoxicology, Permoserstr. 15, 04318 Leipzig, Germany
| | - Anna-Katharina Klauer
- Saxony State Foundation for Nature and the Environment (LaNU), Riesaer Str. 7, 01129 Dresden, Germany
| | - Matthias Liess
- Helmholtz Centre for Environmental Research - UFZ, Department System-Ecotoxicology, Permoserstr. 15, 04318 Leipzig, Germany
| | - Lilian Neuer
- Friends of the Earth Germany e.V. (BUND), Kaiserin-Augusta-Allee 5, 10553 Berlin, Germany
| | - Aletta Bonn
- Helmholtz Centre for Environmental Research - UFZ, Department Ecosystem Services, Permoserstr. 15, 04318 Leipzig, Germany; Friedrich Schiller University Jena, Institute of Biodiversity, Dornburgerstr.159, 07743 Jena, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstr. 4, 04103 Leipzig, Germany
| |
Collapse
|
36
|
Slingsby JA, Wilson AM, Maitner B, Moncrieff GR. Regional ecological forecasting across scales: A manifesto for a biodiversity hotspot. Methods Ecol Evol 2023. [DOI: 10.1111/2041-210x.14046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- 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
| | - Adam M. Wilson
- Department of Geography, Department of Environment and Sustainability University at Buffalo Buffalo New York USA
| | - Brian Maitner
- Department of Geography, Department of Environment and Sustainability University at Buffalo Buffalo New York USA
| | - Glenn R. Moncrieff
- Fynbos Node, South African Environmental Observation Network, Centre for Biodiversity Conservation Cape Town South Africa
- Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences University of Cape Town Cape Town South Africa
| |
Collapse
|
37
|
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
| |
Collapse
|
38
|
A new avenue for Bayesian inference with INLA. Comput Stat Data Anal 2023. [DOI: 10.1016/j.csda.2023.107692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
|
39
|
McCrea R, King R, Graham L, Börger L. Realising the promise of large data and complex models. Methods Ecol Evol 2023. [DOI: 10.1111/2041-210x.14050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Rachel McCrea
- Department of Mathematics and Statistics Lancaster University Lancaster UK
| | - Ruth King
- School of Mathematics and Maxwell Institute for Mathematical Sciences University of Edinburgh Edinburgh UK
| | - Laura Graham
- Geography, Earth & Environmental Sciences University of Birmingham Birmingham UK
- Biodiversity, Ecology & Conservation Group International Institute for Applied Systems Analysis Vienna Austria
| | - Luca Börger
- Department of Biosciences Swansea University Swansea UK
- Centre for Biomathematics Swansea University Swansea UK
| |
Collapse
|
40
|
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.
Collapse
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.
| |
Collapse
|
41
|
Nater CR, Burgess MD, Coffey P, Harris B, Lander F, Price D, Reed M, Robinson RA. Spatial consistency in drivers of population dynamics of a declining migratory bird. J Anim Ecol 2023; 92:97-111. [PMID: 36321197 PMCID: PMC10099983 DOI: 10.1111/1365-2656.13834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 10/13/2022] [Indexed: 11/17/2022]
Abstract
Many migratory species are in decline across their geographical ranges. Single-population studies can provide important insights into drivers at a local scale, but effective conservation requires multi-population perspectives. This is challenging because relevant data are often hard to consolidate, and state-of-the-art analytical tools are typically tailored to specific datasets. We capitalized on a recent data harmonization initiative (SPI-Birds) and linked it to a generalized modelling framework to identify the demographic and environmental drivers of large-scale population decline in migratory pied flycatchers (Ficedula hypoleuca) breeding across Britain. We implemented a generalized integrated population model (IPM) to estimate age-specific vital rates, including their dependency on environmental conditions, and total and breeding population size of pied flycatchers using long-term (34-64 years) monitoring data from seven locations representative of the British breeding range. We then quantified the relative contributions of different vital rates and population structure to changes in short- and long-term population growth rate using transient life table response experiments (LTREs). Substantial covariation in population sizes across breeding locations suggested that change was the result of large-scale drivers. This was supported by LTRE analyses, which attributed past changes in short-term population growth rates and long-term population trends primarily to variation in annual survival and dispersal dynamics, which largely act during migration and/or nonbreeding season. Contributions of variation in local reproductive parameters were small in comparison, despite sensitivity to local temperature and rainfall within the breeding period. We show that both short- and long-term population changes of British breeding pied flycatchers are likely linked to factors acting during migration and in nonbreeding areas, where future research should be prioritized. We illustrate the potential of multi-population analyses for informing management at (inter)national scales and highlight the importance of data standardization, generalized and accessible analytical tools, and reproducible workflows to achieve them.
Collapse
Affiliation(s)
- Chloé R Nater
- Norwegian Institute for Nature Research (NINA), Trondheim, Norway.,Centre for Biodiversity Dynamics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Malcolm D Burgess
- RSPB Centre for Conservation Science, Sandy, UK.,PiedFly.Net, Yarner Wood, Devon, UK.,Centre for Research in Animal Behaviour, University of Exeter, Exeter, UK
| | | | - Bob Harris
- Merseyside Ringing Group, Merseyside, UK
| | - Frank Lander
- PiedFly.Net, Yarner Wood, Devon, UK.,Forest of Dean, Gloucestershire, UK
| | | | - Mike Reed
- 143 Daniells Welwyn Garden City, Hertfordshire, UK
| | | |
Collapse
|
42
|
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.
Collapse
|
43
|
Haas EK, La Sorte FA, McCaslin HM, Belotti MCTD, Horton KG. The correlation between eBird community science and weather surveillance radar-based estimates of migration phenology. GLOBAL ECOLOGY AND BIOGEOGRAPHY : A JOURNAL OF MACROECOLOGY 2022; 31:2219-2230. [PMID: 36590324 PMCID: PMC9795923 DOI: 10.1111/geb.13567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 06/11/2022] [Accepted: 06/28/2022] [Indexed: 06/17/2023]
Abstract
AIM Measuring avian migration can prove challenging given the spatial scope and the diversity of species involved. No one monitoring technique provides all the pertinent measures needed to capture this macroscale phenomenon - emphasizing the need for data integration. Migration phenology is a key metric characterizing large-scale migration dynamics and has been successfully quantified using weather surveillance radar (WSR) data and community science observations. Separately, both platforms have their limitations and measure different aspects of bird migration. We sought to make a formal comparison of the migration phenology estimates derived from WSR and eBird data - of which we predict a positive correlation. LOCATION Contiguous United States. TIME PERIOD 2002-2018. MAJOR TAXA STUDIED Migratory birds. METHODS We estimated spring and autumn migration phenology at 143 WSR stations aggregated over a 17-year period (2002-2018), which we contrast with eBird-based estimates of spring and autumn migration phenology for 293 nocturnally migrating bird species at the 143 WSR stations. We compared phenology metrics derived from all species and WSR stations combined, for species in three taxonomic orders (Anseriformes, Charadriiformes and Passeriformes), and for WSR stations in three North American migration flyways (western, central and eastern). RESULTS We found positive correlations between WSR and eBird-based estimates of migration phenology and differences in the strength of correlations among taxonomic orders and migration flyways. The correlations were stronger during spring migration, for Passeriformes, and generally for WSR stations in the eastern flyway. Autumn migration showed weaker correlation, and in Anseriformes correlations were weakest overall. Lastly, eBird-based estimates slightly preceded those derived from WSR in the spring, but trailed WSR in the autumn, suggesting that the two data sources measure different components of migration phenology. MAIN CONCLUSIONS We highlight the complementarity of these two approaches, but also reveal strong taxonomic and geographic differences in the relationships between the platforms.
Collapse
Affiliation(s)
- Elaina K. Haas
- Department of Fish, Wildlife, and Conservation BiologyColorado State UniversityFort CollinsColoradoUSA
| | | | - Hanna M. McCaslin
- Department of Fish, Wildlife, and Conservation BiologyColorado State UniversityFort CollinsColoradoUSA
| | - Maria C. T. D. Belotti
- Department of Fish, Wildlife, and Conservation BiologyColorado State UniversityFort CollinsColoradoUSA
| | - Kyle G. Horton
- Department of Fish, Wildlife, and Conservation BiologyColorado State UniversityFort CollinsColoradoUSA
| |
Collapse
|
44
|
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.
Collapse
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
| |
Collapse
|
45
|
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
| |
Collapse
|
46
|
Bellingham PJ, Arnst EA, Clarkson BD, Etherington TR, Forester LJ, Shaw WB, Sprague R, Wiser SK, Peltzer DA. The right tree in the right place? A major economic tree species poses major ecological threats. Biol Invasions 2022. [DOI: 10.1007/s10530-022-02892-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AbstractTree species in the Pinaceae are some of the most widely introduced non-native tree species globally, especially in the southern hemisphere. In New Zealand, plantations of radiata pine (Pinus radiata D. Don) occupy c. 1.6 million ha and form 90% of planted forests. Although radiata pine has naturalized since 1904, there is a general view in New Zealand that this species has not invaded widely. We comprehensively review where radiata pine has invaded throughout New Zealand. We used a combination of observational data and climate niche modelling to reveal that invasion has occurred nationally. Climate niche modelling demonstrates that while current occurrences are patchy, up to 76% of the land area (i.e. 211,388 km2) is climatically capable of supporting populations. Radiata pine has mainly invaded grasslands and shrublands, but also some forests. Notably, it has invaded lower-statured vegetation, including three classes of naturally uncommon ecosystems, primary successions and secondary successions. Overall, our findings demonstrate pervasive and ongoing invasion of radiata pine outside plantations. The relatively high growth rates and per individual effects of radiata pine may result in strong effects on naturally uncommon ecosystems and may alter successional trajectories. Local and central government currently manage radiata pine invasions while propagule pressure from existing and new plantations grows, hence greater emphasis is warranted both on managing current invasions and proactively preventing future radiata pine invasions. We therefore recommend a levy on new non-native conifer plantations to offset costs of managing invasions, and stricter regulations to protect vulnerable ecosystems. A levy on economic uses of invasive species to offset costs of managing invasions alongside stricter regulations to protect vulnerable ecosystems could be a widely adopted measure to avert future negative impacts.
Collapse
|
47
|
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.
Collapse
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
| |
Collapse
|
48
|
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.
Collapse
|
49
|
Grabow M, Louvrier JLP, Planillo A, Kiefer S, Drenske S, Börner K, Stillfried M, Hagen R, Kimmig S, Straka TM, Kramer-Schadt S. Data-integration of opportunistic species observations into hierarchical modeling frameworks improves spatial predictions for urban red squirrels. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.881247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The prevailing trend of increasing urbanization and habitat fragmentation makes knowledge of species’ habitat requirements and distribution a crucial factor in conservation and urban planning. Species distribution models (SDMs) offer powerful toolboxes for discriminating the underlying environmental factors driving habitat suitability. Nevertheless, challenges in SDMs emerge if multiple data sets - often sampled with different intention and therefore sampling scheme – can complement each other and increase predictive accuracy. Here, we investigate the potential of using recent data integration techniques to model potential habitat and movement corridors for Eurasian red squirrels (Sciurus vulgaris), in an urban area. We constructed hierarchical models integrating data sets of different quality stemming from unstructured on one side and semi-structured wildlife observation campaigns on the other side in a combined likelihood approach and compared the results to modeling techniques based on only one data source - wherein all models were fit with the same selection of environmental variables. Our study highlights the increasing importance of considering multiple data sets for SDMs to enhance their predictive performance. We finally used Circuitscape (version 4.0.5) on the most robust SDM to delineate suitable movement corridors for red squirrels as a basis for planning road mortality mitigation measures. Our results indicate that even though red squirrels are common, urban habitats are rather small and partially lack connectivity along natural connectivity corridors in Berlin. Thus, additional fragmentation could bring the species closer to its limit to persist in urban environments, where our results can act as a template for conservation and management implications.
Collapse
|
50
|
Jiménez J, Díaz‐Ruiz F, Monterroso P, Tobajas J, Ferreras P. Occupancy data improves parameter precision in spatial capture–recapture models. Ecol Evol 2022; 12:e9250. [PMID: 36052294 PMCID: PMC9412271 DOI: 10.1002/ece3.9250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 07/22/2022] [Accepted: 08/05/2022] [Indexed: 11/17/2022] Open
Abstract
Population size is one of the basic demographic parameters for species management and conservation. Among different estimation methods, spatially explicit capture–recapture (SCR) models allow the estimation of population density in a framework that has been greatly developed in recent years. The use of automated detection devices, such as camera traps, has impressively extended SCR studies for individually identifiable species. However, its application to unmarked/partially marked species remains challenging, and no specific method has been widely used. We fitted an SCR‐integrated model (SCR‐IM) to stone marten Martes foina data, a species for which only some individuals are individually recognizable by natural marks, and estimate population size based on integration of three submodels: (1) individual capture histories from live capture and transponder tagging; (2) detection/nondetection or “occupancy” data using camera traps in a bigger area to extend the geographic scope of capture–recapture data; and (3) telemetry data from a set of tagged individuals. We estimated a stone marten density of 0.352 (SD: 0.081) individuals/km2. We simulated four dilution scenarios of occupancy data to study the variation in the coefficient of variation in population size estimates. We also used simulations with similar characteristics as the stone marten case study, comparing the accuracy and precision obtained from SCR‐IM and SCR, to understand how submodels' integration affects the posterior distributions of estimated parameters. Based on our simulations, we found that population size estimates using SCR‐IM are more accurate and precise. In our stone marten case study, the SCR‐IM density estimation increased the precision by 37% when compared to the standard SCR model as regards to the coefficient of variation. This model has high potential to be used for species in which individual recognition by natural markings is not possible, therefore limiting the need to rely on invasive sampling procedures.
Collapse
Affiliation(s)
- José Jiménez
- Instituto de Investigación en Recursos Cinegéticos (IREC, CSIC‐UCLM‐JCCM) Ciudad Real Spain
| | - Francisco Díaz‐Ruiz
- Departamento de Biología Animal, Facultad de Ciencias Universidad de Málaga Málaga Spain
| | - Pedro Monterroso
- CIBIO, Centro de Investigacão em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado Universidade do Porto Vairão Portugal
- BIOPOLIS Program in Genomics, Biodiversity and Land Planning CIBIO Vairão Portugal
| | - Jorge Tobajas
- Instituto de Investigación en Recursos Cinegéticos (IREC, CSIC‐UCLM‐JCCM) Ciudad Real Spain
- Departamento de Botánica, Ecología y Fisiología Vegetal Universidad de Córdoba Córdoba Spain
| | - Pablo Ferreras
- Instituto de Investigación en Recursos Cinegéticos (IREC, CSIC‐UCLM‐JCCM) Ciudad Real Spain
| |
Collapse
|