1
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Weinstein BG, Marconi S, Zare A, Bohlman SA, Singh A, Graves SJ, Magee L, Johnson DJ, Record S, Rubio VE, Swenson NG, Townsend P, Veblen TT, Andrus RA, White EP. Individual canopy tree species maps for the National Ecological Observatory Network. PLoS Biol 2024; 22:e3002700. [PMID: 39013163 PMCID: PMC11251727 DOI: 10.1371/journal.pbio.3002700] [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: 11/03/2023] [Accepted: 06/05/2024] [Indexed: 07/18/2024] Open
Abstract
The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales provides a unique perspective on forest ecosystems, forest restoration, and responses to disturbance. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual canopy tree species through labeled data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees across 24 sites in the National Ecological Observatory Network (NEON). Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1 km2 shapefiles with individual tree species prediction, as well as crown location, crown area, and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of 6 species per site, ranging from 3 to 15 species per site. All predictions are openly archived and have been uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. We outline the potential utility and limitations of these data in ecology and computer vision research, as well as strategies for improving predictions using targeted data sampling.
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Affiliation(s)
- Ben G. Weinstein
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States of America
| | - Sergio Marconi
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States of America
| | - Alina Zare
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Stephanie A. Bohlman
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, United States of America
| | - Aditya Singh
- Department of Agricultural & Biological Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Sarah J. Graves
- Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Lukas Magee
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, United States of America
| | - Daniel J. Johnson
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, United States of America
| | - Sydne Record
- Department of Wildlife, Fisheries, and Conservation Biology, University of Maine, Orono, Maine, United States of America
| | - Vanessa E. Rubio
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Nathan G. Swenson
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Philip Townsend
- Department of Forest & Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Thomas T. Veblen
- Department of Geography, University of Colorado, Boulder, Colorado, United States of America
| | - Robert A. Andrus
- Department of Geography, University of Colorado, Boulder, Colorado, United States of America
- School of Environment, Washington State University, Pullman, Washington, United States of America
| | - Ethan P. White
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States of America
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2
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Vasilita C, Feng V, Hansen AK, Hartop E, Srivathsan A, Struijk R, Meier R. Express barcoding with NextGenPCR and MinION for species-level sorting of ecological samples. Mol Ecol Resour 2024; 24:e13922. [PMID: 38240168 DOI: 10.1111/1755-0998.13922] [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: 05/23/2023] [Revised: 12/15/2023] [Accepted: 12/21/2023] [Indexed: 03/06/2024]
Abstract
The use of DNA barcoding is well established for specimen identification and large-scale biodiversity discovery, but remains underutilized for time-sensitive applications such as rapid species discovery in field stations, identifying pests, citizen science projects, and authenticating food. The main reason is that existing express barcoding workflows are either too expensive or can only be used in very well-equipped laboratories by highly-trained staff. We here show an alternative workflow combining rapid DNA extraction with HotSHOT, amplicon production with NextGenPCR thermocyclers, and sequencing with low-cost MinION sequencers. We demonstrate the power of the approach by generating 250 barcodes for 285 specimens within 6 h including specimen identification through BLAST. The workflow required only the following major equipment that easily fits onto a lab bench: Thermocycler, NextGenPCR, microplate sealer, Qubit, and MinION. Based on our results, we argue that simplified barcoding workflows for species-level sorting are now faster, more accurate, and sufficiently cost-effective to replace traditional morpho-species sorting in many projects.
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Affiliation(s)
| | - Vivian Feng
- Center for Integrative Biodiversity Discovery, Leibniz Institute for Evolution and Biodiversity Science, Museum für Naturkunde, Berlin, Germany
- Humboldt-Universität zu Berlin, Institut für Biologie, Berlin, Germany
| | - Aslak Kappel Hansen
- Center for Integrative Biodiversity Discovery, Leibniz Institute for Evolution and Biodiversity Science, Museum für Naturkunde, Berlin, Germany
| | - Emily Hartop
- Center for Integrative Biodiversity Discovery, Leibniz Institute for Evolution and Biodiversity Science, Museum für Naturkunde, Berlin, Germany
| | - Amrita Srivathsan
- Center for Integrative Biodiversity Discovery, Leibniz Institute for Evolution and Biodiversity Science, Museum für Naturkunde, Berlin, Germany
| | - Robin Struijk
- Molecular Biology Systems B.V., Goes, The Netherlands
| | - Rudolf Meier
- Center for Integrative Biodiversity Discovery, Leibniz Institute for Evolution and Biodiversity Science, Museum für Naturkunde, Berlin, Germany
- Humboldt-Universität zu Berlin, Institut für Biologie, Berlin, Germany
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3
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Doherty S, Saltré F, Llewelyn J, Strona G, Williams SE, Bradshaw CJA. Estimating co-extinction threats in terrestrial ecosystems. GLOBAL CHANGE BIOLOGY 2023; 29:5122-5138. [PMID: 37386726 DOI: 10.1111/gcb.16836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 05/27/2023] [Indexed: 07/01/2023]
Abstract
The biosphere is changing rapidly due to human endeavour. Because ecological communities underlie networks of interacting species, changes that directly affect some species can have indirect effects on others. Accurate tools to predict these direct and indirect effects are therefore required to guide conservation strategies. However, most extinction-risk studies only consider the direct effects of global change-such as predicting which species will breach their thermal limits under different warming scenarios-with predictions of trophic cascades and co-extinction risks remaining mostly speculative. To predict the potential indirect effects of primary extinctions, data describing community interactions and network modelling can estimate how extinctions cascade through communities. While theoretical studies have demonstrated the usefulness of models in predicting how communities react to threats like climate change, few have applied such methods to real-world communities. This gap partly reflects challenges in constructing trophic network models of real-world food webs, highlighting the need to develop approaches for quantifying co-extinction risk more accurately. We propose a framework for constructing ecological network models representing real-world food webs in terrestrial ecosystems and subjecting these models to co-extinction scenarios triggered by probable future environmental perturbations. Adopting our framework will improve estimates of how environmental perturbations affect whole ecological communities. Identifying species at risk of co-extinction (or those that might trigger co-extinctions) will also guide conservation interventions aiming to reduce the probability of co-extinction cascades and additional species losses.
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Affiliation(s)
- Seamus Doherty
- Global Ecology | Partuyarta Ngadluku Wardli Kuu, College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia
- Australian Research Council Centre of Excellence for Australian Biodiversity and Heritage, Wollongong, New South Wales, Australia
| | - Frédérik Saltré
- Global Ecology | Partuyarta Ngadluku Wardli Kuu, College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia
- Australian Research Council Centre of Excellence for Australian Biodiversity and Heritage, Wollongong, New South Wales, Australia
| | - John Llewelyn
- Global Ecology | Partuyarta Ngadluku Wardli Kuu, College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia
- Australian Research Council Centre of Excellence for Australian Biodiversity and Heritage, Wollongong, New South Wales, Australia
| | - Giovanni Strona
- European Commission, Joint Research Centre, Ispra, Italy
- Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - Stephen E Williams
- Centre for Tropical Environmental and Sustainability Science, College of Science and Engineering, James Cook University, Townsville, Queensland, Australia
| | - Corey J A Bradshaw
- Global Ecology | Partuyarta Ngadluku Wardli Kuu, College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia
- Australian Research Council Centre of Excellence for Australian Biodiversity and Heritage, Wollongong, New South Wales, Australia
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4
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Li D, Record S, Sokol ER, Bitters ME, Chen MY, Chung YA, Helmus MR, Jaimes R, Jansen L, Jarzyna MA, Just MG, LaMontagne JM, Melbourne BA, Moss W, Norman KEA, Parker SM, Robinson N, Seyednasrollah B, Smith C, Spaulding S, Surasinghe TD, Thomsen SK, Zarnetske PL. Standardized
NEON
organismal data for biodiversity research. Ecosphere 2022. [DOI: 10.1002/ecs2.4141] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Affiliation(s)
- Daijiang Li
- Department of Biological Sciences Louisiana State University Baton Rouge Louisiana USA
- Center for Computation and Technology Louisiana State University Baton Rouge Louisiana USA
| | - Sydne Record
- Department of Biology Bryn Mawr College Bryn Mawr Pennsylvania USA
- Department of Wildlife, Fisheries, and Conservation Biology University of Maine Orono Maine USA
| | - Eric R. Sokol
- National Ecological Observatory Network (NEON), Battelle Boulder Colorado USA
- Institute of Arctic and Alpine Research (INSTAAR) University of Colorado Boulder Boulder Colorado USA
| | - Matthew E. Bitters
- Department of Ecology and Evolutionary Biology University of Colorado Boulder Boulder Colorado USA
| | - Melissa Y. Chen
- Department of Ecology and Evolutionary Biology University of Colorado Boulder Boulder Colorado USA
| | - Y. Anny Chung
- Departments of Plant Biology and Plant Pathology University of Georgia Athens Georgia USA
| | - Matthew R. Helmus
- Integrative Ecology Lab, Center for Biodiversity, Department of Biology Temple University Philadelphia Pennsylvania USA
| | | | - Lara Jansen
- Department of Environmental Science and Management Portland State University Portland Oregon USA
| | - Marta A. Jarzyna
- Department of Evolution, Ecology and Organismal Biology The Ohio State University Columbus Ohio USA
- Translational Data Analytics Institute The Ohio State University Columbus Ohio USA
| | - Michael G. Just
- Ecological Processes Branch U.S. Army ERDC CERL Champaign Illinois USA
| | | | - Brett A. Melbourne
- Department of Ecology and Evolutionary Biology University of Colorado Boulder Boulder Colorado USA
| | - Wynne Moss
- Department of Ecology and Evolutionary Biology University of Colorado Boulder Boulder Colorado USA
| | - Kari E. A. Norman
- Department of Environmental Science, Policy, and Management University of California Berkeley Berkeley California USA
| | - Stephanie M. Parker
- National Ecological Observatory Network (NEON), Battelle Boulder Colorado USA
| | - Natalie Robinson
- National Ecological Observatory Network (NEON), Battelle Boulder Colorado USA
| | - Bijan Seyednasrollah
- School of Informatics, Computing and Cyber Systems Northern Arizona University Flagstaff Arizona USA
| | - Colin Smith
- Environmental Data Initiative University of Wisconsin‐Madison Madison Wisconsin USA
| | - Sarah Spaulding
- Institute of Arctic and Alpine Research (INSTAAR) University of Colorado Boulder Boulder Colorado USA
| | - Thilina D. Surasinghe
- Department of Biological Sciences Bridgewater State University Bridgewater Massachusetts USA
| | - Sarah K. Thomsen
- Department of Integrative Biology Oregon State University Corvallis Oregon USA
| | - Phoebe L. Zarnetske
- Department of Integrative Biology Michigan State University East Lansing Michigan USA
- Ecology, Evolution, and Behavior Program Michigan State University East Lansing Michigan USA
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5
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Spiers AI, Royle JA, Torrens CL, Joseph MB. Estimating species misclassification with occupancy dynamics and encounter rates: a semi‐supervised, individual‐level approach. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Anna I. Spiers
- Earth Lab, Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder CO USA
- Department of Ecology and Evolutionary Biology University of Colorado Boulder CO USA
| | | | - Christa L. Torrens
- Institute of Arctic and Alpine Research University of Colorado Boulder CO USA
| | - Maxwell B. Joseph
- Earth Lab, Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder CO USA
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Stephens K, Alexander GJ, Makhubo BG, Telford NS, Tolley KA. Mistaken identity: challenges with specimen identification for morphologically conservative skinks (Trachylepis) leads to taxonomic error. AFR J HERPETOL 2022. [DOI: 10.1080/21564574.2021.2019838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Kirstin Stephens
- South African National Biodiversity Institute, Kirstenbosch Research Centre, Cape Town, South Africa
| | - Graham J Alexander
- Animal, Plant and Environmental Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Buyisile G Makhubo
- School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Nicolas S Telford
- South African National Biodiversity Institute, Kirstenbosch Research Centre, Cape Town, South Africa
| | - Krystal A Tolley
- South African National Biodiversity Institute, Kirstenbosch Research Centre, Cape Town, South Africa
- Animal, Plant and Environmental Sciences, University of the Witwatersrand, Johannesburg, South Africa
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Pringle RM, Hutchinson MC. Resolving Food-Web Structure. ANNUAL REVIEW OF ECOLOGY, EVOLUTION, AND SYSTEMATICS 2020. [DOI: 10.1146/annurev-ecolsys-110218-024908] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Food webs are a major focus and organizing theme of ecology, but the data used to assemble them are deficient. Early debates over food-web data focused on taxonomic resolution and completeness, lack of which had produced spurious inferences. Recent data are widely believed to be much better and are used extensively in theoretical and meta-analytic research on network ecology. Confidence in these data rests on the assumptions ( a) that empiricists correctly identified consumers and their foods and ( b) that sampling methods were adequate to detect a near-comprehensive fraction of the trophic interactions between species. Abundant evidence indicates that these assumptions are often invalid, suggesting that most topological food-web data may remain unreliable for inferences about network structure and underlying ecological and evolutionary processes. Morphologically cryptic species are ubiquitous across taxa and regions, and many trophic interactions routinely evade detection by conventional methods. Molecular methods have diagnosed the severity of these problems and are a necessary part of the cure.
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Affiliation(s)
- Robert M. Pringle
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey 08544, USA
| | - Matthew C. Hutchinson
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey 08544, USA
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