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ToolsLi, Yuanheng, Devenish, Christian, Tosa, Marie I., Luo, Mingjie, Bell, David M., Lesmeister, Damon B., Greenfield, Paul, Pichler, Maximilian, Levi, Taal and Yu, Douglas W. (2024) Combining environmental DNA and remote sensing for efficient, fine-scale mapping of arthropod biodiversity. Philosophical Transactions of the Royal Society B: Biological Sciences, 379 (1904). ISSN 0962-8436
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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 km^2 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’.
| Item Type: | Article |
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| Additional Information: | Data accessibility statement: Raw sequence data are archived at NCBI Short Read Archive BioProject PRJNA869351. All scripts and data tables (from bioinformatic processing to statistical analysis to figure generation) are available from the GitHub respository: https://github.com/chnpenny/HJA_analyses_Kelpie_clean/releases/tag/v1.1.0 and archived at https://zenodo.org/records/8303158. Supplementary material is available online. Funding information: D.W.Y. and M.L. were supported by the Key Research Program of Frontier Sciences, CAS (QYZDY-SSW-SMC024), the Strategic Priority Research Program of Chinese Academy of Sciences, grant no. XDA20050202, the State Key Laboratory of Genetic Resources and Evolution (GREKF19-01, GREKF20-01 and GREKF21-01) at the Kunming Institute of Zoology, the Yunnan Revitalization Talent Support Program: High-end Foreign Expert Project, and the University of Chinese Academy of Sciences. D.W.Y. was also supported by the University of East Anglia and a Leverhulme Trust Research Fellowship (RF-2017-342), and benefited from the sCom Working Group at iDiv.de. M.I.T. was supported by the National Science Foundation-funded H.J. Andrews Long-Term Ecological Research (LTER) program (no. DEB-1440409), Oregon State University, the ARCS Oregon Chapter and the US Department of Agriculture Forest Service. Field data collection was funded by Oregon State University, the Pacific Northwest Research Station and the US Department of Agriculture Forest Service. LiDAR data processing was supported by the National Science Foundation-funded H.J. Andrews LTER program (nos. DEB-2025755, DEB-1440409) and the Pacific Northwest Research Station. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official US Department of Agriculture or US Government determination or policy. The use of trade or firm names in this publication is for reader information and does not imply endorsement by the US Government of any product or service. |
| Uncontrolled Keywords: | earth observation,biodiversity indices,environmental dna,forestry,machine learning,systematic conservation planning,biochemistry, genetics and molecular biology(all),agricultural and biological sciences(all) ,/dk/atira/pure/subjectarea/asjc/1300 |
| Faculty \ School: | Faculty of Science > School of Biological Sciences |
| UEA Research Groups: | Faculty of Science > Research Centres > Centre for Ecology, Evolution and Conservation Faculty of Science > Research Groups > Organisms and the Environment |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 30 Sep 2024 15:30 |
| Last Modified: | 17 Oct 2024 00:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/96835 |
| DOI: | 10.1098/rstb.2023.0123 |
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