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Wang Z, Wang T, Zhang X, Wang J, Yang Y, Sun Y, Guo X, Wu Q, Nepovimova E, Watson AE, Kuca K. Biodiversity conservation in the context of climate change: Facing challenges and management strategies. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 937:173377. [PMID: 38796025 DOI: 10.1016/j.scitotenv.2024.173377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 05/16/2024] [Accepted: 05/18/2024] [Indexed: 05/28/2024]
Abstract
Biodiversity conservation amidst the uncertainty of climate change presents unique challenges that necessitate precise management strategies. The study reported here was aimed at refining understanding of these challenges and to propose specific, actionable management strategies. Employing a quantitative literature analysis, we meticulously examined 1268 research articles from the Web of Science database between 2005 and 2023. Through Cite Spaces and VOS viewer software, we conducted a bibliometric analysis and thematic synthesis to pinpoint emerging trends, key themes, and the geographical distribution of research efforts. Our methodology involved identifying patterns within the data, such as frequency of keywords, co-authorship networks, and citation analysis, to discern the primary focus areas within the field. This approach allowed us to distinguish between research concentration areas, specifically highlighting a predominant interest in Environmental Sciences Ecology (67.59 %) and Biodiversity Conservation (22.63 %). The identification of adaptive management practices and ecosystem services maintenance are central themes in the research from 2005 to 2023. Moreover, challenges such as understanding phenological shifts, invasive species dynamics, and anthropogenic pressures critically impact biodiversity conservation efforts. Our findings underscore the urgent need for precise, data-driven decision-making processes in the face of these challenges. Addressing the gaps identified, our study proposes targeted solutions, including the establishment of germplasm banks for at-risk species, the development of advanced genomic and microclimate models, and scenario analysis to predict and mitigate future conservation challenges. These strategies are aimed at enhancing the resilience of biodiversity against the backdrop of climate change through integrated, evidence-based approaches. By leveraging the compiled and analyzed data, this study offers a foundational framework for future research and practical action in biodiversity conservation strategies, demonstrating a path forward through detailed analysis and specified solutions.
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Affiliation(s)
- Zhirong Wang
- College of Horticulture and Gardening, Yangtze University, Jingzhou 434025, China
| | - Tongxin Wang
- College of Horticulture and Gardening, Yangtze University, Jingzhou 434025, China
| | - Xiujuan Zhang
- College of Horticulture and Gardening, Yangtze University, Jingzhou 434025, China.
| | - Junbang Wang
- National Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
| | - Yongsheng Yang
- The Key Laboratory of Restoration Ecology in Cold Region of Qinghai Province, Northwest Institute of Plateau Biology, Chinese Academy of Science, Xining 810001, China
| | - Yu Sun
- College of Horticulture and Gardening, Yangtze University, Jingzhou 434025, China
| | - Xiaohua Guo
- College of Horticulture and Gardening, Yangtze University, Jingzhou 434025, China
| | - Qinghua Wu
- College Life Science, Yangtze University, Jingzhou 434025, China; Department of Chemistry, Faculty of Science, University of Hradec Kralove, Hradec Kralove 500 03, Czech Republic
| | - Eugenie Nepovimova
- Department of Chemistry, Faculty of Science, University of Hradec Kralove, Hradec Kralove 500 03, Czech Republic
| | - Alan E Watson
- National Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Kamil Kuca
- Department of Chemistry, Faculty of Science, University of Hradec Kralove, Hradec Kralove 500 03, Czech Republic.
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Spear D, van Wilgen NJ, Rebelo AG, Botha JM. Collating biodiversity occurrence data for conservation. Front Ecol Evol 2023. [DOI: 10.3389/fevo.2023.1037282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
Abstract
Plant and animal checklists, with conservation status information, are fundamental for conservation management. Historical field data, more recent data of digital origin and data-sharing platforms provide useful sources for collating species locality data. However, different biodiversity datasets have different formats and inconsistent naming systems. Additionally, most digital data sources do not provide an easy option for download by protected area. Further, data-entry-ready software is not readily available for conservation organization staff with limited technical skills to collate these heterogeneous data and create distribution maps and checklists for protected areas. The insights presented here are the outcome of conceptualizing a biodiversity information system for South African National Parks. We recognize that a fundamental requirement for achieving better standardization, sharing and use of biodiversity data for conservation is capacity building, internet connectivity, national institutional data management support and collaboration. We focus on some of the issues that need to be considered for capacity building, data standardization and data support. We outline the need for using taxonomic backbones and standardizing biodiversity data and the utility of data from the Global Biodiversity Information Facility and other available sources in this process. Additionally, we make recommendations for the fields needed in relational databases for collating species data that can be used to inform conservation decisions and outline steps that can be taken to enable easier collation of biodiversity data, using South Africa as a case study.
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Nilsson RH, Andersson AF, Bissett A, Finstad AG, Fossøy F, Grosjean M, Hope M, Jeppesen TS, Kõljalg U, Lundin D, Prager M, Suominen S, Svenningsen CS, Schigel D. Introducing guidelines for publishing DNA-derived occurrence data through biodiversity data platforms. METABARCODING AND METAGENOMICS 2022. [DOI: 10.3897/mbmg.6.84960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
DNA sequencing efforts of environmental and other biological samples disclose unprecedented and largely untapped opportunities for advances in the taxonomy, ecology, and geographical distributions of our living world. To realise this potential, DNA-derived occurrence data (notably sequences with dates and coordinates) – much like traditional specimens and observations – need to be discoverable and interpretable through biodiversity data platforms. The Global Biodiversity Information Facility (GBIF) recently headed a community effort to assemble a set of guidelines for publishing DNA-derived data. These guidelines target the principles and approaches of exposing DNA-derived occurrence data in the context of broader biodiversity data. They cover a choice of terms using a controlled vocabulary, common pitfalls, and good practices, without going into platform-specific details. Our hope is that they will benefit anyone interested in better exposure of DNA-derived occurrence data through general biodiversity data platforms, including national biodiversity portals. This paper provides a brief rationale and an overview of the guidelines, an up-to-date version of which is maintained at https://doi.org/10.35035/doc-vf1a-nr22. User feedback and interaction are encouraged as new techniques and best practices emerge.
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