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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. GLOBAL CHANGE BIOLOGY 2024; 30:e17056. [PMID: 38273542 DOI: 10.1111/gcb.17056] [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: 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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Robinson WD, Errichetti D, Pollock HS, Martinez A, Stouffer PC, Shen FY, Blake JG. Big Bird Plots: Benchmarking Neotropical Bird Communities to Address Questions in Ecology and Conservation in an Era of Rapid Change. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.697511] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Extensive networks of large plots have the potential to transform knowledge of avian community dynamics through time and across geographical space. In the Neotropics, the global hotspot of avian diversity, only six 100-ha plots, all located in lowland forests of Amazonia, the Guianan shield and Panama, have been inventoried sufficiently. We review the most important lessons learned about Neotropical forest bird communities from those big bird plots and explore opportunities for creating a more extensive network of additional plots to address questions in ecology and conservation, following the model of the existing ForestGEO network of tree plots. Scholarly impact of the big bird plot papers has been extensive, with the papers accumulating nearly 1,500 citations, particularly on topics of tropical ecology, avian conservation, and community organization. Comparisons of results from the plot surveys show no single methodological scheme works effectively for surveying abundances of all bird species at all sites; multiple approaches have been utilized and must be employed in the future. On the existing plots, abundance patterns varied substantially between the South American plots and the Central American one, suggesting different community structuring mechanisms are at work and that additional sampling across geographic space is needed. Total bird abundance in Panama, dominated by small insectivores, was double that of Amazonia and the Guianan plateau, which were dominated by large granivores and frugivores. The most common species in Panama were three times more abundant than those in Amazonia, whereas overall richness was 1.5 times greater in Amazonia. Despite these differences in community structure, other basic information, including uncertainty in population density estimates, has yet to be quantified. Results from existing plots may inform drivers of differences in community structure and create baselines for detection of long-term regional changes in bird abundances, but supplementation of the small number of plots is needed to increase generalizability of results and reveal the texture of geographic variation. We propose fruitful avenues of future research based on our current synthesis of the big bird plots. Collaborating with the large network of ForestGEO tree plots could be one approach to improve understanding of linkages between plant and bird diversity. Careful quantification of bird survey effort, recording of exact locations of survey routes or stations, and archiving detailed metadata will greatly enhance the value of benchmark data for future repeat surveys of the existing plots and initial surveys of newly established plots.
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