1
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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] [What about the content of this article? (0)] [Affiliation(s)] [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'.
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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
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2
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Li Y, Devenish C, Tosa MI, Luo M, Bell DM, Lesmeister DB, Greenfield P, Pichler M, Levi T, Yu DW. Combining environmental DNA and remote sensing for efficient, fine-scale mapping of arthropod biodiversity. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230123. [PMID: 38705177 PMCID: PMC11070265 DOI: 10.1098/rstb.2023.0123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 01/31/2024] [Indexed: 05/07/2024] Open
Abstract
Arthropods contribute importantly to ecosystem functioning but remain understudied. This undermines the validity of conservation decisions. Modern methods are now making arthropods easier to study, since arthropods can be mass-trapped, mass-identified, and semi-mass-quantified into 'many-row (observation), many-column (species)' datasets, with homogeneous error, high resolution, and copious environmental-covariate information. These 'novel community datasets' let us efficiently generate information on arthropod species distributions, conservation values, uncertainty, and the magnitude and direction of human impacts. We use a DNA-based method (barcode mapping) to produce an arthropod-community dataset from 121 Malaise-trap samples, and combine it with 29 remote-imagery layers using a deep neural net in a joint species distribution model. With this approach, we generate distribution maps for 76 arthropod species across a 225 km2 temperate-zone forested landscape. We combine the maps to visualize the fine-scale spatial distributions of species richness, community composition, and site irreplaceability. Old-growth forests show distinct community composition and higher species richness, and stream courses have the highest site-irreplaceability values. With this 'sideways biodiversity modelling' method, we demonstrate the feasibility of biodiversity mapping at sufficient spatial resolution to inform local management choices, while also being efficient enough to scale up to thousands of square kilometres. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.
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Affiliation(s)
- Yuanheng Li
- Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain, State Key Laboratory of Genetic Resources and Evolution, Chinese Academy of Sciences, Kunming, Yunnan 650223, People’s Republic of China
- Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, People’s Republic of China
- Faculty of Biology, University of Duisburg-Essen, Essen 45141, Germany
| | - Christian Devenish
- School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, Norfolk NR47TJ, UK
| | - Marie I. Tosa
- Department of Fisheries, Wildlife, and Conservation Sciences, Oregon State University, Corvallis, OR 97331, USA
| | - Mingjie Luo
- Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain, State Key Laboratory of Genetic Resources and Evolution, Chinese Academy of Sciences, Kunming, Yunnan 650223, People’s Republic of China
- Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, People’s Republic of China
- Kunming College of Life Sciences, University of Chinese Academy of Sciences, Kunming, People’s Republic of China
| | - David M. Bell
- Pacific Northwest Research Station, U.S. Department of Agriculture Forest Service, Corvallis, OR 97331, USA
| | - Damon B. Lesmeister
- Department of Fisheries, Wildlife, and Conservation Sciences, Oregon State University, Corvallis, OR 97331, USA
- Pacific Northwest Research Station, U.S. Department of Agriculture Forest Service, Corvallis, OR 97331, USA
| | - Paul Greenfield
- CSIRO Energy, Lindfield, New South Wales, Australia
- School of Biological Sciences, Macquarie University, Sydney, Australia
| | | | - Taal Levi
- Department of Fisheries, Wildlife, and Conservation Sciences, Oregon State University, Corvallis, OR 97331, USA
| | - Douglas W. Yu
- Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain, State Key Laboratory of Genetic Resources and Evolution, Chinese Academy of Sciences, Kunming, Yunnan 650223, People’s Republic of China
- Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, People’s Republic of China
- School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, Norfolk NR47TJ, UK
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming Yunnan 650223, People’s Republic of China
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Sittinger M, Uhler J, Pink M, Herz A. Insect detect: An open-source DIY camera trap for automated insect monitoring. PLoS One 2024; 19:e0295474. [PMID: 38568922 PMCID: PMC10990185 DOI: 10.1371/journal.pone.0295474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 02/28/2024] [Indexed: 04/05/2024] Open
Abstract
Insect monitoring is essential to design effective conservation strategies, which are indispensable to mitigate worldwide declines and biodiversity loss. For this purpose, traditional monitoring methods are widely established and can provide data with a high taxonomic resolution. However, processing of captured insect samples is often time-consuming and expensive, which limits the number of potential replicates. Automated monitoring methods can facilitate data collection at a higher spatiotemporal resolution with a comparatively lower effort and cost. Here, we present the Insect Detect DIY (do-it-yourself) camera trap for non-invasive automated monitoring of flower-visiting insects, which is based on low-cost off-the-shelf hardware components combined with open-source software. Custom trained deep learning models detect and track insects landing on an artificial flower platform in real time on-device and subsequently classify the cropped detections on a local computer. Field deployment of the solar-powered camera trap confirmed its resistance to high temperatures and humidity, which enables autonomous deployment during a whole season. On-device detection and tracking can estimate insect activity/abundance after metadata post-processing. Our insect classification model achieved a high top-1 accuracy on the test dataset and generalized well on a real-world dataset with captured insect images. The camera trap design and open-source software are highly customizable and can be adapted to different use cases. With custom trained detection and classification models, as well as accessible software programming, many possible applications surpassing our proposed deployment method can be realized.
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Affiliation(s)
- Maximilian Sittinger
- Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, Institute for Biological Control, Dossenheim, Germany
| | - Johannes Uhler
- Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, Institute for Biological Control, Dossenheim, Germany
| | - Maximilian Pink
- Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, Institute for Biological Control, Dossenheim, Germany
| | - Annette Herz
- Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, Institute for Biological Control, Dossenheim, Germany
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4
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Candolin U. Coping with light pollution in urban environments: Patterns and challenges. iScience 2024; 27:109244. [PMID: 38433890 PMCID: PMC10904992 DOI: 10.1016/j.isci.2024.109244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024] Open
Abstract
Artificial light at night is a growing environmental problem that is especially pronounced in urban environments. Yet, impacts on urban wildlife have received scant attention and patterns and consequences are largely unknown. Here, I present a conceptual framework outlining the challenges species encounter when exposed to urban light pollution and how they may respond through plastic adjustments and genetic adaptation. Light pollution interferes with biological rhythms, influences behaviors, fragments habitats, and alters predation risk and resource abundance, which changes the diversity and spatiotemporal distribution of species and, hence, the structure and function of urban ecosystems. Furthermore, light pollution interacts with other urban disturbances, which can exacerbate negative effects on species. Given the rapid growth of urban areas and light pollution and the importance of healthy urban ecosystems for human wellbeing, more research is needed on the impacts of light pollution on species and the consequences for urban ecosystems.
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Affiliation(s)
- Ulrika Candolin
- Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland
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5
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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Lilkendey J, Barrelet C, Zhang J, Meares M, Larbi H, Subsol G, Chaumont M, Sabetian A. Herbivorous fish feeding dynamics and energy expenditure on a coral reef: Insights from stereo-video and AI-driven 3D tracking. Ecol Evol 2024; 14:e11070. [PMID: 38435013 PMCID: PMC10909578 DOI: 10.1002/ece3.11070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 03/05/2024] Open
Abstract
Unveiling the intricate relationships between animal movement ecology, feeding behavior, and internal energy budgeting is crucial for a comprehensive understanding of ecosystem functioning, especially on coral reefs under significant anthropogenic stress. Here, herbivorous fishes play a vital role as mediators between algae growth and coral recruitment. Our research examines the feeding preferences, bite rates, inter-bite distances, and foraging energy expenditure of the Brown surgeonfish (Acanthurus nigrofuscus) and the Yellowtail tang (Zebrasoma xanthurum) within the fish community on a Red Sea coral reef. To this end, we used advanced methods such as remote underwater stereo-video, AI-driven object recognition, species classification, and 3D tracking. Despite their comparatively low biomass, the two surgeonfish species significantly influence grazing pressure on the studied coral reef. A. nigrofuscus exhibits specialized feeding preferences and Z. xanthurum a more generalist approach, highlighting niche differentiation and their importance in maintaining reef ecosystem balance. Despite these differences in their foraging strategies, on a population level, both species achieve a similar level of energy efficiency. This study highlights the transformative potential of cutting-edge technologies in revealing the functional feeding traits and energy utilization of keystone species. It facilitates the detailed mapping of energy seascapes, guiding targeted conservation efforts to enhance ecosystem health and biodiversity.
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Affiliation(s)
- Julian Lilkendey
- School of ScienceAuckland University of Technology (AUT)AucklandNew Zealand
- Leibniz Centre for Tropical Marine Research (ZMT)BremenGermany
| | - Cyril Barrelet
- Research‐Team ICAR, Laboratoire d'informatique, de robotique et de microélectronique de Montpellier (LIRMM), CNRSUniversity of MontpellierMontpellierFrance
| | - Jingjing Zhang
- School of ScienceAuckland University of Technology (AUT)AucklandNew Zealand
- The New Zealand Institute for Plant and Food Research LimitedAucklandNew Zealand
| | - Michael Meares
- School of ScienceAuckland University of Technology (AUT)AucklandNew Zealand
| | - Houssam Larbi
- Research‐Team ICAR, Laboratoire d'informatique, de robotique et de microélectronique de Montpellier (LIRMM), CNRSUniversity of MontpellierMontpellierFrance
| | - Gérard Subsol
- Research‐Team ICAR, Laboratoire d'informatique, de robotique et de microélectronique de Montpellier (LIRMM), CNRSUniversity of MontpellierMontpellierFrance
| | - Marc Chaumont
- Research‐Team ICAR, Laboratoire d'informatique, de robotique et de microélectronique de Montpellier (LIRMM), CNRSUniversity of MontpellierMontpellierFrance
- University of NîmesNîmesFrance
| | - Armagan Sabetian
- School of ScienceAuckland University of Technology (AUT)AucklandNew Zealand
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7
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Goel N, Liebhold AM, Bertelsmeier C, Hooten MB, Korolev KS, Keitt TH. A mechanistic statistical approach to infer invasion characteristics of human-dispersed species with complex life cycle. bioRxiv 2024:2024.02.09.578762. [PMID: 38405850 PMCID: PMC10888729 DOI: 10.1101/2024.02.09.578762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
The rising introduction of invasive species through trade networks threatens biodiversity and ecosystem services. Yet, we have a limited understanding of how transportation networks determine patterns of range expansion. This is partly because current analytical models fail to integrate the invader's life-history dynamics with heterogeneity in human-mediated dispersal patterns. And partly because classical statistical methods often fail to provide reliable estimates of model parameters due to spatial biases in the presence-only records and lack of informative demographic data. To address these gaps, we first formulate an age-structured metapopulation model that uses a probability matrix to emulate human-mediated dispersal patterns. The model reveals that an invader spreads along the shortest network path, such that the inter-patch network distances decrease with increasing traffic volume and reproductive value of hitchhikers. Next, we propose a Bayesian statistical method to estimate model parameters using presence-only data and prior demographic knowledge. To show the utility of the statistical approach, we analyze zebra mussel (Dreissena polymorpha) expansion in North America through the commercial shipping network. Our analysis underscores the importance of correcting spatial biases and leveraging priors to answer questions, such as where and when the zebra mussels were introduced and what life-history characteristics make these mollusks successful invaders.
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Affiliation(s)
- Nikunj Goel
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, 78712
| | - Andrew M. Liebhold
- USDA Forest Service Northern Research Station, Morgantown, West Virginia, 15349
- Czech University of Life Sciences Prague, Forestry and Wood Sciences, 16500 Prague 6, Czech Republic
| | - Cleo Bertelsmeier
- Department of Ecology and Evolution, Biophore, UNIL-Sorge, University of Lausanne, Lausanne 1015
| | - Mevin B. Hooten
- Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, Texas, 78705
| | - Kirill S. Korolev
- Department of Physics, Graduate Program in Bioinformatics, and Biological Design Center Boston University, Boston, MA, 02215
| | - Timothy H. Keitt
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, 78712
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8
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Martin O, Nguyen C, Sarfati R, Chowdhury M, Iuzzolino ML, Nguyen DMT, Layer RM, Peleg O. Embracing firefly flash pattern variability with data-driven species classification. Sci Rep 2024; 14:3432. [PMID: 38341450 PMCID: PMC10858911 DOI: 10.1038/s41598-024-53671-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 02/03/2024] [Indexed: 02/12/2024] Open
Abstract
Many nocturnally active fireflies use precisely timed bioluminescent patterns to identify mates, making them especially vulnerable to light pollution. As urbanization continues to brighten the night sky, firefly populations are under constant stress, and close to half of the species are now threatened. Ensuring the survival of firefly biodiversity depends on a large-scale conservation effort to monitor and protect thousands of populations. While species can be identified by their flash patterns, current methods require expert measurement and manual classification and are infeasible given the number and geographic distribution of fireflies. Here we present the application of a recurrent neural network (RNN) for accurate automated firefly flash pattern classification. Using recordings from commodity cameras, we can extract flash trajectories of individuals within a swarm and classify their species with an accuracy of approximately seventy percent. In addition to its potential in population monitoring, automated classification provides the means to study firefly behavior at the population level. We employ the classifier to measure and characterize the variability within and between swarms, unlocking a new dimension of their behavior. Our method is open source, and deployment in community science applications could revolutionize our ability to monitor and understand firefly populations.
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Affiliation(s)
- Owen Martin
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA
| | - Chantal Nguyen
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA
| | - Raphael Sarfati
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA
- Department of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA
| | - Murad Chowdhury
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA
| | - Michael L Iuzzolino
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA
| | - Dieu My T Nguyen
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA
| | - Ryan M Layer
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA.
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA.
| | - Orit Peleg
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA.
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
- Department of Physics, University of Colorado, Boulder, CO, USA.
- Department of Applied Math, University of Colorado, Boulder, CO, USA.
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, USA.
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9
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Yu H, Amador GJ, Cribellier A, Klaassen M, de Knegt HJ, Naguib M, Nijland R, Nowak L, Prins HHT, Snijders L, Tyson C, Muijres FT. Edge computing in wildlife behavior and ecology. Trends Ecol Evol 2024; 39:128-130. [PMID: 38142163 DOI: 10.1016/j.tree.2023.11.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 11/16/2023] [Accepted: 11/30/2023] [Indexed: 12/25/2023]
Abstract
Modern sensor technologies increasingly enrich studies in wildlife behavior and ecology. However, constraints on weight, connectivity, energy and memory availability limit their implementation. With the advent of edge computing, there is increasing potential to mitigate these constraints, and drive major advancements in wildlife studies.
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Affiliation(s)
- Hui Yu
- Experimental Zoology Group, Wageningen University & Research, Wageningen, the Netherlands.
| | - Guillermo J Amador
- Experimental Zoology Group, Wageningen University & Research, Wageningen, the Netherlands
| | - Antoine Cribellier
- Experimental Zoology Group, Wageningen University & Research, Wageningen, the Netherlands
| | - Marcel Klaassen
- Centre for Integrative Ecology, School of Life and Environmental Science, Deakin University, Geelong, Australia
| | - Henrik J de Knegt
- Wildlife Ecology and Conservation Group, Wageningen University & Research, Wageningen, the Netherlands
| | - Marc Naguib
- Behavioral Ecology Group, Wageningen University & Research, Wageningen, the Netherlands
| | - Reindert Nijland
- Marine Animal Ecology Group, Wageningen University & Research, Wageningen, the Netherlands
| | - Lukasz Nowak
- Experimental Zoology Group, Wageningen University & Research, Wageningen, the Netherlands
| | - Herbert H T Prins
- Department of Animal Sciences, Wageningen University & Research, Wageningen, the Netherlands
| | - Lysanne Snijders
- Behavioral Ecology Group, Wageningen University & Research, Wageningen, the Netherlands
| | - Chris Tyson
- Behavioral Ecology Group, Wageningen University & Research, Wageningen, the Netherlands
| | - Florian T Muijres
- Experimental Zoology Group, Wageningen University & Research, Wageningen, the Netherlands
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10
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Wang Z, Li F, Wu F, Guo F, Gao W, Zhang Y, Yang Z. Environmental DNA and remote sensing datasets reveal the spatial distribution of aquatic insects in a disturbed subtropical river system. J Environ Manage 2024; 351:119972. [PMID: 38159308 DOI: 10.1016/j.jenvman.2023.119972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 12/04/2023] [Accepted: 12/25/2023] [Indexed: 01/03/2024]
Abstract
Biodiversity datasets with high spatial resolution are critical prerequisites for river protection and management decision-making. However, traditional morphological biomonitoring is inefficient and only provides several site estimates, and there is an urgent need for new approaches to predict biodiversity on fine spatial scales throughout the entire river systems. Here, we combined the environmental DNA (eDNA) and remote sensing (RS) technologies to develop a novel approach for predicting the spatial distribution of aquatic insects with high spatial resolution in a disturbed subtropical Dongjiang River system of southeast China. First, we screened thirteen RS-based vegetation indices that significantly correlated with the eDNA-inferred richness of aquatic insects. In particular, the green normalized difference vegetation index (GNDVI) and normalized difference red-edge2 (NDRE2) were closely related to eDNA-inferred richness. Second, using the gradient boosting decision tree, our data showed that the spatial pattern of eDNA-inferred richness could achieve a high spatial resolution to 500 m reach and accurate prediction of more than 80%, and the prediction efficiency of the headwater streams (Strahler stream order = 1) was slightly higher than the downstream (Strahler stream order >1). Third, using the random forest algorithm, the spatial distribution of aquatic insects could reach a prediction rate of over 70% for the presence or absence of specific genera. Overall, this study provides a new approach to achieving high spatial resolution prediction of the distribution of aquatic insects, which supports decision-making on river diversity protection under climate changes and human impacts.
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Affiliation(s)
- Zongyang Wang
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China
| | - Feilong Li
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China.
| | - Feifei Wu
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China
| | - Fen Guo
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China
| | - Wei Gao
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China
| | - Yuan Zhang
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China.
| | - Zhifeng Yang
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China
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11
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Cantwell-Jones A, Tylianakis JM, Larson K, Gill RJ. Using individual-based trait frequency distributions to forecast plant-pollinator network responses to environmental change. Ecol Lett 2024; 27:e14368. [PMID: 38247047 DOI: 10.1111/ele.14368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 01/02/2024] [Accepted: 01/04/2024] [Indexed: 01/23/2024]
Abstract
Determining how and why organisms interact is fundamental to understanding ecosystem responses to future environmental change. To assess the impact on plant-pollinator interactions, recent studies have examined how the effects of environmental change on individual interactions accumulate to generate species-level responses. Here, we review recent developments in using plant-pollinator networks of interacting individuals along with their functional traits, where individuals are nested within species nodes. We highlight how these individual-level, trait-based networks connect intraspecific trait variation (as frequency distributions of multiple traits) with dynamic responses within plant-pollinator communities. This approach can better explain interaction plasticity, and changes to interaction probabilities and network structure over spatiotemporal or other environmental gradients. We argue that only through appreciating such trait-based interaction plasticity can we accurately forecast the potential vulnerability of interactions to future environmental change. We follow this with general guidance on how future studies can collect and analyse high-resolution interaction and trait data, with the hope of improving predictions of future plant-pollinator network responses for targeted and effective conservation.
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Affiliation(s)
- Aoife Cantwell-Jones
- Georgina Mace Centre for The Living Planet, Department of Life Sciences, Silwood Park, Imperial College London, Ascot, UK
| | - Jason M Tylianakis
- Georgina Mace Centre for The Living Planet, Department of Life Sciences, Silwood Park, Imperial College London, Ascot, UK
- Bioprotection Aotearoa, School of Biological Sciences, Private Bag 4800, University of Canterbury, Christchurch, New Zealand
| | - Keith Larson
- Climate Impacts Research Centre, Department of Ecology and Environmental Sciences, Umeå University, Umeå, Sweden
| | - Richard J Gill
- Georgina Mace Centre for The Living Planet, Department of Life Sciences, Silwood Park, Imperial College London, Ascot, UK
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12
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Zeuss D, Bald L, Gottwald J, Becker M, Bellafkir H, Bendix J, Bengel P, Beumer LT, Brandl R, Brändle M, Dahlke S, Farwig N, Freisleben B, Friess N, Heidrich L, Heuer S, Höchst J, Holzmann H, Lampe P, Leberecht M, Lindner K, Masello JF, Mielke Möglich J, Mühling M, Müller T, Noskov A, Opgenoorth L, Peter C, Quillfeldt P, Rösner S, Royauté R, Mestre-Runge C, Schabo D, Schneider D, Seeger B, Shayle E, Steinmetz R, Tafo P, Vogelbacher M, Wöllauer S, Younis S, Zobel J, Nauss T. Nature 4.0: A networked sensor system for integrated biodiversity monitoring. Glob Chang Biol 2024; 30:e17056. [PMID: 38273542 DOI: 10.1111/gcb.17056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 10/13/2023] [Accepted: 10/26/2023] [Indexed: 01/27/2024]
Abstract
Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade-off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real-world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low-cost, and open-source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area-wide ecosystem mapping tasks, thereby providing an exemplary cost-efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services.
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Affiliation(s)
- Dirk Zeuss
- Department of Geography, Environmental Informatics, Philipps-Universität Marburg, Marburg, Germany
| | - Lisa Bald
- Department of Geography, Environmental Informatics, Philipps-Universität Marburg, Marburg, Germany
| | - Jannis Gottwald
- Department of Geography, Environmental Informatics, Philipps-Universität Marburg, Marburg, Germany
| | - Marcel Becker
- Department of Biology, Conservation Ecology, Philipps-Universität Marburg, Marburg, Germany
| | - Hicham Bellafkir
- Department of Mathematics and Computer Science, Distributed Systems and Intelligent Computing, Philipps-Universität Marburg, Marburg, Germany
| | - Jörg Bendix
- Department of Geography, Climatology and Environmental Modelling, Philipps-Universität Marburg, Marburg, Germany
| | - Phillip Bengel
- Department of Geography, Didactics and Education, Philipps-Universität Marburg, Marburg, Germany
| | - Larissa T Beumer
- Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany
| | - Roland Brandl
- Department of Biology, Animal Ecology, Philipps-Universität Marburg, Marburg, Germany
| | - Martin Brändle
- Department of Biology, Animal Ecology, Philipps-Universität Marburg, Marburg, Germany
| | - Stephan Dahlke
- Department of Mathematics and Computer Science, Numerics, Philipps-Universität Marburg, Marburg, Germany
| | - Nina Farwig
- Department of Biology, Conservation Ecology, Philipps-Universität Marburg, Marburg, Germany
| | - Bernd Freisleben
- Department of Mathematics and Computer Science, Distributed Systems and Intelligent Computing, Philipps-Universität Marburg, Marburg, Germany
| | - Nicolas Friess
- Department of Geography, Environmental Informatics, Philipps-Universität Marburg, Marburg, Germany
| | - Lea Heidrich
- Department of Geography, Environmental Informatics, Philipps-Universität Marburg, Marburg, Germany
| | - Sven Heuer
- Department of Mathematics and Computer Science, Numerics, Philipps-Universität Marburg, Marburg, Germany
| | - Jonas Höchst
- Department of Mathematics and Computer Science, Distributed Systems and Intelligent Computing, Philipps-Universität Marburg, Marburg, Germany
| | - Hajo Holzmann
- Department of Mathematics and Computer Science, Stochastics, Philipps-Universität Marburg, Marburg, Germany
| | - Patrick Lampe
- Department of Mathematics and Computer Science, Distributed Systems and Intelligent Computing, Philipps-Universität Marburg, Marburg, Germany
| | - Martin Leberecht
- Department of Biology, Plant Ecology and Geobotany, Philipps-Universität Marburg, Marburg, Germany
| | - Kim Lindner
- Department of Biology, Conservation Ecology, Philipps-Universität Marburg, Marburg, Germany
| | - Juan F Masello
- Department of Animal Ecology & Systematics, Justus Liebig University Gießen, Gießen, Germany
| | - Jonas Mielke Möglich
- Department of Biology, Animal Ecology, Philipps-Universität Marburg, Marburg, Germany
| | - Markus Mühling
- Department of Mathematics and Computer Science, Distributed Systems and Intelligent Computing, Philipps-Universität Marburg, Marburg, Germany
| | - Thomas Müller
- Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany
- Department of Biological Sciences, Goethe University Frankfurt am Main, Frankfurt am Main, Germany
| | - Alexey Noskov
- Department of Geography, Climatology and Environmental Modelling, Philipps-Universität Marburg, Marburg, Germany
| | - Lars Opgenoorth
- Department of Biology, Plant Ecology and Geobotany, Philipps-Universität Marburg, Marburg, Germany
| | - Carina Peter
- Department of Geography, Didactics and Education, Philipps-Universität Marburg, Marburg, Germany
| | - Petra Quillfeldt
- Department of Animal Ecology & Systematics, Justus Liebig University Gießen, Gießen, Germany
| | - Sascha Rösner
- Department of Biology, Conservation Ecology, Philipps-Universität Marburg, Marburg, Germany
| | - Raphaël Royauté
- Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany
- Université Paris-Saclay, INRAE, AgroParisTech, UMR EcoSys, Palaiseau, France
| | - Christian Mestre-Runge
- Department of Geography, Environmental Informatics, Philipps-Universität Marburg, Marburg, Germany
- Department of Biology, Plant Ecology and Geobotany, Philipps-Universität Marburg, Marburg, Germany
| | - Dana Schabo
- Department of Biology, Conservation Ecology, Philipps-Universität Marburg, Marburg, Germany
| | - Daniel Schneider
- Department of Mathematics and Computer Science, Distributed Systems and Intelligent Computing, Philipps-Universität Marburg, Marburg, Germany
| | - Bernhard Seeger
- Department of Mathematics and Computer Science, Database Systems, Philipps-Universität Marburg, Marburg, Germany
| | - Elliot Shayle
- Department of Geography, Environmental Informatics, Philipps-Universität Marburg, Marburg, Germany
| | - Ralf Steinmetz
- Department of Electrical Engineering and Information Technology, Multimedia Communications Lab (KOM), Technical University of Darmstadt, Darmstadt, Germany
| | - Pavel Tafo
- Department of Mathematics and Computer Science, Stochastics, Philipps-Universität Marburg, Marburg, Germany
| | - Markus Vogelbacher
- Department of Mathematics and Computer Science, Distributed Systems and Intelligent Computing, Philipps-Universität Marburg, Marburg, Germany
| | - Stephan Wöllauer
- Department of Geography, Environmental Informatics, Philipps-Universität Marburg, Marburg, Germany
| | - Sohaib Younis
- Department of Mathematics and Computer Science, Database Systems, Philipps-Universität Marburg, Marburg, Germany
| | - Julian Zobel
- Department of Electrical Engineering and Information Technology, Multimedia Communications Lab (KOM), Technical University of Darmstadt, Darmstadt, Germany
| | - Thomas Nauss
- Department of Geography, Environmental Informatics, Philipps-Universität Marburg, Marburg, Germany
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13
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Alison J, Payne S, Alexander JM, Bjorkman AD, Clark VR, Gwate O, Huntsaar M, Iseli E, Lenoir J, Mann HMR, Steenhuisen SL, Høye TT. Deep learning to extract the meteorological by-catch of wildlife cameras. Glob Chang Biol 2024; 30:e17078. [PMID: 38273582 DOI: 10.1111/gcb.17078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/09/2023] [Accepted: 11/12/2023] [Indexed: 01/27/2024]
Abstract
Microclimate-proximal climatic variation at scales of metres and minutes-can exacerbate or mitigate the impacts of climate change on biodiversity. However, most microclimate studies are temperature centric, and do not consider meteorological factors such as sunshine, hail and snow. Meanwhile, remote cameras have become a primary tool to monitor wild plants and animals, even at micro-scales, and deep learning tools rapidly convert images into ecological data. However, deep learning applications for wildlife imagery have focused exclusively on living subjects. Here, we identify an overlooked opportunity to extract latent, ecologically relevant meteorological information. We produce an annotated image dataset of micrometeorological conditions across 49 wildlife cameras in South Africa's Maloti-Drakensberg and the Swiss Alps. We train ensemble deep learning models to classify conditions as overcast, sunshine, hail or snow. We achieve 91.7% accuracy on test cameras not seen during training. Furthermore, we show how effective accuracy is raised to 96% by disregarding 14.1% of classifications where ensemble member models did not reach a consensus. For two-class weather classification (overcast vs. sunshine) in a novel location in Svalbard, Norway, we achieve 79.3% accuracy (93.9% consensus accuracy), outperforming a benchmark model from the computer vision literature (75.5% accuracy). Our model rapidly classifies sunshine, snow and hail in almost 2 million unlabelled images. Resulting micrometeorological data illustrated common seasonal patterns of summer hailstorms and autumn snowfalls across mountains in the northern and southern hemispheres. However, daily patterns of sunshine and shade diverged between sites, impacting daily temperature cycles. Crucially, we leverage micrometeorological data to demonstrate that (1) experimental warming using open-top chambers shortens early snow events in autumn, and (2) image-derived sunshine marginally outperforms sensor-derived temperature when predicting bumblebee foraging. These methods generate novel micrometeorological variables in synchrony with biological recordings, enabling new insights from an increasingly global network of wildlife cameras.
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Affiliation(s)
- Jamie Alison
- Department of Ecoscience, Aarhus University, Aarhus, Denmark
| | - Stephanie Payne
- Afromontane Research Unit and Department of Plant Sciences, University of the Free State, Bloemfontein, South Africa
| | - Jake M Alexander
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| | - Anne D Bjorkman
- Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden
- Gothenburg Global Biodiversity Centre, Gothenburg, Sweden
| | - Vincent Ralph Clark
- Afromontane Research Unit and Department of Geography, University of the Free State, Bloemfontein, South Africa
| | - Onalenna Gwate
- Afromontane Research Unit and Department of Geography, University of the Free State, Bloemfontein, South Africa
| | - Maria Huntsaar
- Arctic Biology Department, The University Centre in Svalbard (UNIS), Longyearbyen, Norway
- Department of Arctic and Marine Biology, The Arctic University of Norway (UiT), Tromsø, Norway
| | - Evelin Iseli
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| | - Jonathan Lenoir
- UMR CNRS 7058 "Ecologie et Dynamique des Systèmes Anthropisés" (EDYSAN), Université de Picardie Jules Verne, Amiens, France
| | - Hjalte Mads Rosenstand Mann
- Department of Ecoscience, Aarhus University, Aarhus, Denmark
- Arctic Research Centre, Aarhus University, Aarhus, Denmark
| | - Sandy-Lynn Steenhuisen
- Afromontane Research Unit and Department of Plant Sciences, University of the Free State, Bloemfontein, South Africa
| | - Toke Thomas Høye
- Department of Ecoscience, Aarhus University, Aarhus, Denmark
- Arctic Research Centre, Aarhus University, Aarhus, Denmark
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14
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Lilkendey J. Conserve the open media ecosystem! Legal and ethical considerations when using online repositories for AI training in ecological research. Ecol Lett 2023; 26:2023-2028. [PMID: 37787081 DOI: 10.1111/ele.14311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 09/07/2023] [Accepted: 09/18/2023] [Indexed: 10/04/2023]
Abstract
Ecological researchers who train Artificial Intelligence models using digital media have to be cognizant of legal and ethical implications when sourcing such content from online repositories. The way forward? Complying with Creative Commons licensing requirements, obtaining consent from media creators and adhering to FAIR data principles. Collective action from researchers, repositories, licence providers, and legislators is needed to conserve this complex open media ecosystem. This way, we can continue to develop innovative applications to address pressing ecological issues while maintaining the trust of content creators and respecting the legal and ethical framework of online media use.
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Affiliation(s)
- Julian Lilkendey
- School of Science, Auckland University of Technology (AUT), Auckland, New Zealand
- Data Science Center (DSC), University of Bremen, Bremen, Germany
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15
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O'Brien DA, Deb S, Gal G, Thackeray SJ, Dutta PS, Matsuzaki SIS, May L, Clements CF. Early warning signals have limited applicability to empirical lake data. Nat Commun 2023; 14:7942. [PMID: 38040724 PMCID: PMC10692136 DOI: 10.1038/s41467-023-43744-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 11/17/2023] [Indexed: 12/03/2023] Open
Abstract
Research aimed at identifying indicators of persistent abrupt shifts in ecological communities, a.k.a regime shifts, has led to the development of a suite of early warning signals (EWSs). As these often perform inaccurately when applied to real-world observational data, it remains unclear whether critical transitions are the dominant mechanism of regime shifts and, if so, which EWS methods can predict them. Here, using multi-trophic planktonic data on multiple lakes from around the world, we classify both lake dynamics and the reliability of classic and second generation EWSs methods to predict whole-ecosystem change. We find few instances of critical transitions, with different trophic levels often expressing different forms of abrupt change. The ability to predict this change is highly processing dependant, with most indicators not performing better than chance, multivariate EWSs being weakly superior to univariate, and a recent machine learning model performing poorly. Our results suggest that predictive ecology should start to move away from the concept of critical transitions, developing methods suitable for predicting resilience loss not limited to the strict bounds of bifurcation theory.
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Affiliation(s)
- Duncan A O'Brien
- School of Biological Sciences, University of Bristol, Bristol, BS8 1TQ, UK.
| | - Smita Deb
- Department of Mathematics, Indian Institute of Technology Ropar, Rupnagar, Punjab, 140001, India
| | - Gideon Gal
- Kinneret Limnological Laboratory, Israel Oceanographic & Limnological Research, PO Box 447, Migdal, Israel
| | - Stephen J Thackeray
- Lake Ecosystems Group, UK Centre for Ecology & Hydrology, Bailrigg, Lancaster, UK
| | - Partha S Dutta
- Department of Mathematics, Indian Institute of Technology Ropar, Rupnagar, Punjab, 140001, India
| | - Shin-Ichiro S Matsuzaki
- Biodiversity Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan
| | - Linda May
- UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian, EH26 OQB, UK
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16
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Green Ii DA. Tracking technologies: advances driving new insights into monarch migration. Curr Opin Insect Sci 2023; 60:101111. [PMID: 37678709 DOI: 10.1016/j.cois.2023.101111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/01/2023] [Accepted: 09/02/2023] [Indexed: 09/09/2023]
Abstract
Understanding the rules of how monarch butterflies complete their annual North American migration will be clarified by studying them within a movement ecology framework. Insect movement ecology is growing at a rapid pace due to the development of novel monitoring systems that allow ever-smaller animals to be tracked at higher spatiotemporal resolution for longer periods of time. New innovations in tracking hardware and associated software, including miniaturization, energy autonomy, data management, and wireless communication, are reducing the size and increasing the capability of next-generation tracking technologies, bringing the goal of tracking monarchs over their entire migration closer within reach. These tools are beginning to be leveraged to provide insight into different aspects of monarch biology and ecology, and to contribute to a growing capacity to understand insect movement ecology more broadly and its impact on human life.
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Affiliation(s)
- Delbert A Green Ii
- Department of Ecology and Evolutionary Biology, University of Michigan, 1105 N University Ave, Ann Arbor, MI 48109, USA.
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17
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Lamperti L, Sanchez T, Si Moussi S, Mouillot D, Albouy C, Flück B, Bruno M, Valentini A, Pellissier L, Manel S. New deep learning-based methods for visualizing ecosystem properties using environmental DNA metabarcoding data. Mol Ecol Resour 2023; 23:1946-1958. [PMID: 37702270 DOI: 10.1111/1755-0998.13861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/29/2023] [Accepted: 08/14/2023] [Indexed: 09/14/2023]
Abstract
Environmental DNA (eDNA) metabarcoding provides an efficient approach for documenting biodiversity patterns in marine and terrestrial ecosystems. The complexity of these data prevents current methods from extracting and analyzing all the relevant ecological information they contain, and new methods may provide better dimensionality reduction and clustering. Here we present two new deep learning-based methods that combine different types of neural networks (NNs) to ordinate eDNA samples and visualize ecosystem properties in a two-dimensional space: the first is based on variational autoencoders and the second on deep metric learning. The strength of our new methods lies in the combination of two inputs: the number of sequences found for each molecular operational taxonomic unit (MOTU) detected and their corresponding nucleotide sequence. Using three different datasets, we show that our methods accurately represent several biodiversity indicators in a two-dimensional latent space: MOTU richness per sample, sequence α-diversity per sample, Jaccard's and sequence β-diversity between samples. We show that our nonlinear methods are better at extracting features from eDNA datasets while avoiding the major biases associated with eDNA. Our methods outperform traditional dimension reduction methods such as Principal Component Analysis, t-distributed Stochastic Neighbour Embedding, Nonmetric Multidimensional Scaling and Uniform Manifold Approximation and Projection for dimension reduction. Our results suggest that NNs provide a more efficient way of extracting structure from eDNA metabarcoding data, thereby improving their ecological interpretation and thus biodiversity monitoring.
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Affiliation(s)
- Letizia Lamperti
- CEFE, Univ Montpellier, CNRS, EPHE-PSL University, IRD, Montpellier, France
- Ecosystems and Landscape Evolution, Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
- Ecosystems and Landscape Evolution, Land Change Science Research Unit, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Switzerland
| | - Théophile Sanchez
- Ecosystems and Landscape Evolution, Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
- Ecosystems and Landscape Evolution, Land Change Science Research Unit, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Switzerland
| | - Sara Si Moussi
- Laboratoire d'Ecologie Alpine, Univ. Grenoble Alpes, Univ. Savoie MontBlanc, CNRS, Grenoble, France
| | - David Mouillot
- MARBEC, Univ Montpellier, CNRS, IFREMER, IRD, Montpellier, France
- Institut Universitaire de France, Paris, France
| | - Camille Albouy
- Ecosystems and Landscape Evolution, Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
- Ecosystems and Landscape Evolution, Land Change Science Research Unit, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Switzerland
| | - Benjamin Flück
- Ecosystems and Landscape Evolution, Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
- Ecosystems and Landscape Evolution, Land Change Science Research Unit, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Switzerland
| | - Morgane Bruno
- CEFE, Univ Montpellier, CNRS, EPHE-PSL University, IRD, Montpellier, France
| | | | - Loïc Pellissier
- Ecosystems and Landscape Evolution, Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
- Ecosystems and Landscape Evolution, Land Change Science Research Unit, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Switzerland
| | - Stéphanie Manel
- CEFE, Univ Montpellier, CNRS, EPHE-PSL University, IRD, Montpellier, France
- Institut Universitaire de France, Paris, France
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18
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Cerini F, O'Brien D, Wolfe E, Besson M, Clements CF. Phenotypic response to different predator strategies can be mediated by temperature. Ecol Evol 2023; 13:e10474. [PMID: 37664517 PMCID: PMC10468988 DOI: 10.1002/ece3.10474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/21/2023] [Accepted: 08/08/2023] [Indexed: 09/05/2023] Open
Abstract
Temperature change affects biological systems in multifaceted ways, including the alteration of species interaction strengths, with implications for the stability of populations and communities. Temperature-dependent changes to antipredatory responses are an emerging mechanism of destabilization and thus there is a need to understand how prey species respond to predation pressures in the face of changing temperatures. Here, using ciliate protozoans, we assess whether temperature can alter the strength of phenotypic antipredator responses in a prey species and whether this relationship depends on the predator's hunting behavior. We exposed populations of the ciliate Paramecium caudatum to either (i) a sit-and-wait generalist predator (Homalozoon vermiculare) or (ii) a specialized active swimmer predator (Didinium nasutum) across two different temperature regimes (15 and 25°C) to quantify the temperature dependence of antipredator responses over a 24-h period. We utilized a novel high-throughput automated robotic monitoring system to track changes in the behavior (swimming speed) and morphology (cell size) of P. caudatum at frequencies and resolutions previously unachievable by manual sampling. The change in swimming speed through the 24 h differed between the two temperatures but was not altered by the presence of the predators. In contrast, P. caudatum showed a substantial temperature-dependent morphological response to the presence of D. nasutum (but not H. vermiculare), changing cell shape toward a more elongated morph at 15°C (but not at 25°C). Our findings suggest that temperature can have strong effects on prey morphological responses to predator presence, but that this response is potentially dependent on the predator's feeding strategy. This suggests that greater consideration of synergistic antipredator behavioral and physiological responses is required in species and communities subject to environmental changes.
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Affiliation(s)
- Francesco Cerini
- Dipartimento Scienze Ecologiche e BiologicheUniversità della TusciaViterboItaly
- School of Biological SciencesUniversity of BristolBristolUK
| | - Duncan O'Brien
- School of Biological SciencesUniversity of BristolBristolUK
| | - Ellie Wolfe
- School of Biological SciencesUniversity of BristolBristolUK
| | - Marc Besson
- Sorbonne Université CNRS UMR Biologie des Organismes Marins, BIOMBanyuls‐sur‐MerFrance
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Li HD, Holyoak M, Xiao Z. Disentangling spatiotemporal dynamics in metacommunities through a species-patch network approach. Ecol Lett 2023; 26:1261-1276. [PMID: 37493107 DOI: 10.1111/ele.14243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/21/2023] [Accepted: 04/23/2023] [Indexed: 07/27/2023]
Abstract
Colonization and extinction at local and regional scales, and gains and losses of patches are important processes in the spatiotemporal dynamics of metacommunities. However, analytical challenges remain in quantifying such spatiotemporal dynamics when species extinction-colonization and patch gain and loss processes act simultaneously. Recent advances in network analysis show great potential in disentangling the roles of colonization, extinction, and patch dynamics in metacommunities. Here, we developed a species-patch network approach to quantify metacommunity dynamics including (i) temporal changes in network structure, and (ii) temporal beta diversity of species-patch links and its components that reflect species extinction-colonization and patch gain and loss. Application of the methods to simulated datasets demonstrated that the approach was informative about metacommunity assembly processes. Based on three empirical datasets, our species-patch network approach provided additional information about metacommunity dynamics through distinguishing the effects of species colonization and extinction at different scales from patch gains and losses and how specific environmental factors related to species-patch network structure. In conclusion, our species-patch network framework provides effective methods for monitoring and revealing long-term metacommunity dynamics by quantifying gains and losses of both species and patches under local and global environmental change.
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Affiliation(s)
- Hai-Dong Li
- State Key Laboratory of Integrated Management of Pest Insects and Rodents in Agriculture, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Marcel Holyoak
- Department of Environmental Science and Policy, University of California, Davis, California, USA
| | - Zhishu Xiao
- State Key Laboratory of Integrated Management of Pest Insects and Rodents in Agriculture, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
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20
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Pantel JH, Becks L. Statistical methods to identify mechanisms in studies of eco-evolutionary dynamics. Trends Ecol Evol 2023; 38:760-772. [PMID: 37437547 DOI: 10.1016/j.tree.2023.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 03/28/2023] [Accepted: 03/30/2023] [Indexed: 07/14/2023]
Abstract
While the reciprocal effects of ecological and evolutionary dynamics are increasingly recognized as an important driver for biodiversity, detection of such eco-evolutionary feedbacks, their underlying mechanisms, and their consequences remains challenging. Eco-evolutionary dynamics occur at different spatial and temporal scales and can leave signatures at different levels of organization (e.g., gene, protein, trait, community) that are often difficult to detect. Recent advances in statistical methods combined with alternative hypothesis testing provides a promising approach to identify potential eco-evolutionary drivers for observed data even in non-model systems that are not amenable to experimental manipulation. We discuss recent advances in eco-evolutionary modeling and statistical methods and discuss challenges for fitting mechanistic models to eco-evolutionary data.
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Affiliation(s)
- Jelena H Pantel
- Ecological Modelling, Faculty of Biology, University of Duisburg-Essen, Universitätsstraße 2, 45117 Essen, Germany.
| | - Lutz Becks
- University of Konstanz, Aquatic Ecology and Evolution, Limnological Institute University of Konstanz Mainaustraße 252 78464, Konstanz/Egg, Germany
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21
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Zweifel R, Pappas C, Peters RL, Babst F, Balanzategui D, Basler D, Bastos A, Beloiu M, Buchmann N, Bose AK, Braun S, Damm A, D'Odorico P, Eitel JUH, Etzold S, Fonti P, Rouholahnejad Freund E, Gessler A, Haeni M, Hoch G, Kahmen A, Körner C, Krejza J, Krumm F, Leuchner M, Leuschner C, Lukovic M, Martínez-Vilalta J, Matula R, Meesenburg H, Meir P, Plichta R, Poyatos R, Rohner B, Ruehr N, Salomón RL, Scharnweber T, Schaub M, Steger DN, Steppe K, Still C, Stojanović M, Trotsiuk V, Vitasse Y, von Arx G, Wilmking M, Zahnd C, Sterck F. Networking the forest infrastructure towards near real-time monitoring - A white paper. Sci Total Environ 2023; 872:162167. [PMID: 36775147 DOI: 10.1016/j.scitotenv.2023.162167] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
Forests account for nearly 90 % of the world's terrestrial biomass in the form of carbon and they support 80 % of the global biodiversity. To understand the underlying forest dynamics, we need a long-term but also relatively high-frequency, networked monitoring system, as traditionally used in meteorology or hydrology. While there are numerous existing forest monitoring sites, particularly in temperate regions, the resulting data streams are rarely connected and do not provide information promptly, which hampers real-time assessments of forest responses to extreme climate events. The technology to build a better global forest monitoring network now exists. This white paper addresses the key structural components needed to achieve a novel meta-network. We propose to complement - rather than replace or unify - the existing heterogeneous infrastructure with standardized, quality-assured linking methods and interacting data processing centers to create an integrated forest monitoring network. These automated (research topic-dependent) linking methods in atmosphere, biosphere, and pedosphere play a key role in scaling site-specific results and processing them in a timely manner. To ensure broad participation from existing monitoring sites and to establish new sites, these linking methods must be as informative, reliable, affordable, and maintainable as possible, and should be supplemented by near real-time remote sensing data. The proposed novel meta-network will enable the detection of emergent patterns that would not be visible from isolated analyses of individual sites. In addition, the near real-time availability of data will facilitate predictions of current forest conditions (nowcasts), which are urgently needed for research and decision making in the face of rapid climate change. We call for international and interdisciplinary efforts in this direction.
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Affiliation(s)
- Roman Zweifel
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Christoforos Pappas
- Department of Civil Engineering, University of Patras, Rio, Patras 26504, Greece.
| | - Richard L Peters
- Department of Environmental Sciences, Institute of Botany, University of Basel, Schönbeinstrasse 6, 4056 Basel, Switzerland.
| | - Flurin Babst
- School of Natural Resources and the Environment, University of Arizona, 1064 E Lowell St, Tucson, AZ 85721, USA; Laboratory of Tree-Ring Research, University of Arizona, 1215 E Lowell St, Tucson, AZ 85721, USA.
| | - Daniel Balanzategui
- GFZ German Research Centre for Geosciences, Wissenschaftpark "Albert Einstein", Telegrafenberg, Potsdam, Germany; Geography Department, Humboldt University of Berlin, Rudower Ch 16, 12489 Berlin, DE, USA.
| | - David Basler
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland; Department of Environmental Sciences, Institute of Botany, University of Basel, Schönbeinstrasse 6, 4056 Basel, Switzerland.
| | - Ana Bastos
- Max Planck Institute for Biogeochemistry, Dept. of Biogeochemical Integration, Hans Knöll Str. 10, 07745 Jena, Germany.
| | - Mirela Beloiu
- Institute of Terrestrial Ecosystems, ETH Zurich, Zurich, Switzerland.
| | - Nina Buchmann
- Department of Environmental Systems Science, ETH Zurich, Universitätstr. 2, LFW C56, 8092 Zurich, Switzerland.
| | - Arun K Bose
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland; Forestry and Wood Technology Discipline, Khulna University, Khulna 9208, Bangladesh.
| | - Sabine Braun
- Institute for Applied Plant Biology, Benkenstrasse 254A, 4108 Witterswil, Switzerland.
| | - Alexander Damm
- Department of Geography, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland; Eawag, Swiss Federal Institute of Aquatic Science & Technology, Surface Waters - Research and Management, Ueberlandstrasse 133, 8600 Duebendorf, Switzerland.
| | - Petra D'Odorico
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Jan U H Eitel
- Department of Natural Resource and Society, University of Idaho, 1800 University Lane, 83638 McCall, ID, USA.
| | - Sophia Etzold
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Patrick Fonti
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | | | - Arthur Gessler
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Matthias Haeni
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Günter Hoch
- Department of Environmental Sciences, Institute of Botany, University of Basel, Schönbeinstrasse 6, 4056 Basel, Switzerland.
| | - Ansgar Kahmen
- Department of Environmental Sciences, Institute of Botany, University of Basel, Schönbeinstrasse 6, 4056 Basel, Switzerland.
| | - Christian Körner
- Department of Environmental Sciences, Institute of Botany, University of Basel, Schönbeinstrasse 6, 4056 Basel, Switzerland.
| | - Jan Krejza
- Global Change Research Institute of the Czech Academy of Sciences, Bělidla 4a, 603 00 Brno, Czech Republic.
| | - Frank Krumm
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Michael Leuchner
- Department of Physical Geography and Climatology, Institute of Geography, RWTH Aachen University, 52056 Aachen, Germany.
| | - Christoph Leuschner
- Plant Ecology, University of Göttingen, Untere Karspüle 2, 37073 Göttingen, Germany.
| | - Mirko Lukovic
- Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf 8600, Switzerland.
| | - Jordi Martínez-Vilalta
- CREAF, Bellaterra (Cerdanyola del Valles), Catalonia E08193, Spain; Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Valles), Catalonia E08193, Spain.
| | - Radim Matula
- Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 129, Praha 6, Suchdol 16521, Czech Republic.
| | - Henning Meesenburg
- Northwest German Forest Research Institute, Grätzelstr. 2, D-37079 Göttingen, Germany.
| | - Patrick Meir
- School of Geosciences, University of Edinburgh, Alexander Crum Brown Road, Edinburgh EH93FF, UK.
| | - Roman Plichta
- Department of Forest Botany, Dendrology and Geobiocoenology, Mendel University in Brno, Zemedelska 1, 61300 Brno, Czech Republic.
| | - Rafael Poyatos
- CREAF, Bellaterra (Cerdanyola del Valles), Catalonia E08193, Spain; Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Valles), Catalonia E08193, Spain.
| | - Brigitte Rohner
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Nadine Ruehr
- Institute of Meteorology and Climate Research - Atmospheric Environmental Research, Karlsruhe Institute of Technology KIT, Garmisch-Partenkirchen 82467, Germany.
| | - Roberto L Salomón
- Departamento de Sistemas y Recursos Naturales, Universidad Politécnica de Madrid, 28040 Madrid, Spain.
| | - Tobias Scharnweber
- DendroGreif, University Greifswald, Soldmannstrasse 15, D-17487 Greifswald, Germany.
| | - Marcus Schaub
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - David N Steger
- Department of Environmental Sciences, Institute of Botany, University of Basel, Schönbeinstrasse 6, 4056 Basel, Switzerland.
| | - Kathy Steppe
- Laboratory of Plant Ecology, Department of Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, 9000 Gent, Belgium.
| | - Christopher Still
- Forest Ecosystems and Society Department, Oregon State University, Corvallis, OR 97331, USA.
| | - Marko Stojanović
- Global Change Research Institute of the Czech Academy of Sciences, Bělidla 4a, 603 00 Brno, Czech Republic.
| | - Volodymyr Trotsiuk
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Yann Vitasse
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Georg von Arx
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland; Oeschger Centre for Climate Change Research, University of Bern, 3012 Bern, Switzerland.
| | - Martin Wilmking
- DendroGreif, University Greifswald, Soldmannstrasse 15, D-17487 Greifswald, Germany.
| | - Cedric Zahnd
- Department of Environmental Sciences, Institute of Botany, University of Basel, Schönbeinstrasse 6, 4056 Basel, Switzerland.
| | - Frank Sterck
- Forest Ecology and Forest Management Group, Wageningen University and Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands.
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22
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Cerini F, Childs DZ, Clements CF. A predictive timeline of wildlife population collapse. Nat Ecol Evol 2023; 7:320-331. [PMID: 36702859 DOI: 10.1038/s41559-023-01985-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 01/06/2023] [Indexed: 01/27/2023]
Abstract
Contemporary rates of biodiversity decline emphasize the need for reliable ecological forecasting, but current methods vary in their ability to predict the declines of real-world populations. Acknowledging that stressor effects start at the individual level, and that it is the sum of these individual-level effects that drives populations to collapse, shifts the focus of predictive ecology away from using predominantly abundance data. Doing so opens new opportunities to develop predictive frameworks that utilize increasingly available multi-dimensional data, which have previously been overlooked for ecological forecasting. Here, we propose that stressed populations will exhibit a predictable sequence of observable changes through time: changes in individuals' behaviour will occur as the first sign of increasing stress, followed by changes in fitness-related morphological traits, shifts in the dynamics (for example, birth rates) of populations and finally abundance declines. We discuss how monitoring the sequential appearance of these signals may allow us to discern whether a population is increasingly at risk of collapse, or is adapting in the face of environmental change, providing a conceptual framework to develop new forecasting methods that combine multi-dimensional (for example, behaviour, morphology, life history and abundance) data.
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Affiliation(s)
- Francesco Cerini
- School of Biological Sciences, University of Bristol, Bristol, UK.
| | - Dylan Z Childs
- School of Biosciences, University of Sheffield, Sheffield, UK
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23
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van Moorsel SJ, Thébault E, Radchuk V, Narwani A, Montoya JM, Dakos V, Holmes M, De Laender F, Pennekamp F. Predicting effects of multiple interacting global change drivers across trophic levels. Glob Chang Biol 2023; 29:1223-1238. [PMID: 36461630 PMCID: PMC7614140 DOI: 10.1111/gcb.16548] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/18/2022] [Accepted: 11/23/2022] [Indexed: 05/26/2023]
Abstract
Global change encompasses many co-occurring anthropogenic drivers, which can act synergistically or antagonistically on ecological systems. Predicting how different global change drivers simultaneously contribute to observed biodiversity change is a key challenge for ecology and conservation. However, we lack the mechanistic understanding of how multiple global change drivers influence the vital rates of multiple interacting species. We propose that reaction norms, the relationships between a driver and vital rates like growth, mortality, and consumption, provide insights to the underlying mechanisms of community responses to multiple drivers. Understanding how multiple drivers interact to affect demographic rates using a reaction-norm perspective can improve our ability to make predictions of interactions at higher levels of organization-that is, community and food web. Building on the framework of consumer-resource interactions and widely studied thermal performance curves, we illustrate how joint driver impacts can be scaled up from the population to the community level. A simple proof-of-concept model demonstrates how reaction norms of vital rates predict the prevalence of driver interactions at the community level. A literature search suggests that our proposed approach is not yet used in multiple driver research. We outline how realistic response surfaces (i.e., multidimensional reaction norms) can be inferred by parametric and nonparametric approaches. Response surfaces have the potential to strengthen our understanding of how multiple drivers affect communities as well as improve our ability to predict when interactive effects emerge, two of the major challenges of ecology today.
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Affiliation(s)
- Sofia J. van Moorsel
- Department of Evolutionary Biology and Environmental StudiesUniversity of ZurichZurichSwitzerland
- Department of GeographyUniversity of ZurichZurichSwitzerland
| | - Elisa Thébault
- Sorbonne Université, CNRS, IRD, INRAE, Université Paris Est Créteil, Université Paris Cité, Institute of Ecology and Environmental Sciences of Paris (iEES‐Paris)ParisFrance
| | - Viktoriia Radchuk
- Department of Ecological DynamicsLeibniz Institute for Zoo and Wildlife ResearchBerlinGermany
| | - Anita Narwani
- Department of Aquatic EcologyEawagDübendorfSwitzerland
| | - José M. Montoya
- Theoretical and Experimental Ecology StationCNRSMoulisFrance
| | - Vasilis Dakos
- Institut des Sciences de l'Evolution de Montpellier (ISEM)Université de Montpellier, IRD, EPHEMontpellierFrance
| | - Mark Holmes
- Namur Institute for Complex Systems (naXys), Institute of Life, Earth, and Environment (ILEE), Research Unit in Environmental and Evolutionary Biology, University of NamurNamurBelgium
| | - Frederik De Laender
- Namur Institute for Complex Systems (naXys), Institute of Life, Earth, and Environment (ILEE), Research Unit in Environmental and Evolutionary Biology, University of NamurNamurBelgium
| | - Frank Pennekamp
- Department of Evolutionary Biology and Environmental StudiesUniversity of ZurichZurichSwitzerland
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