Everyone Needs Data for Conservation, But Few Want to Share It

Duong Thi Minh Phuong

Faculty of Social Sciences and Humanities, Ton Duc Thang University

19-05-2026

© Wix

The global environmental crisis is becoming increasingly urgent. Species are disappearing, ecosystems are degrading, and conservation decisions often need to be made quickly—sometimes immediately. Yet acting fast is not enough. To increase the chances of success, conservation interventions must also be guided by evidence (Sutherland et al., 2004; Christie et al., 2020). This is why evidence syntheses—approaches that combine knowledge from many studies and sources—have become increasingly important in conservation science. By gathering and summarizing available evidence, researchers can reduce bias and provide more reliable guidance for decision-making (Pullin, 2012; Cooke et al., 2023).

However, producing good evidence requires good data, and that is where an unexpected challenge emerges.

Conservation knowledge is scattered across many places. Research projects generate valuable case studies from different regions that can later be combined into large datasets. But biodiversity information also comes from many other sources. Environmental impact assessments for infrastructure projects, government monitoring programs, citizen science initiatives, and field observations can all reveal new species occurrences and ecological patterns (de Oliveira et al., 2024). Together, these scattered pieces of information could form a much richer understanding of nature.

Yet paradoxically, although conservation increasingly depends on large and comprehensive databases, many researchers and practitioners remain reluctant to share their data publicly.

A recent study by Dasoler and colleagues explored this issue using their experience compiling wildlife datasets in Brazil. They identified two broad categories of obstacles: sharing barriers and compilation barriers. Sharing barriers affect those who possess the data and include concerns about losing originality, receiving insufficient authorship credit, or facing contractual limitations. Compilation barriers affect those attempting to assemble datasets and include difficulties in contacting collaborators, incomplete data submissions, and limited funding support.

The researchers proposed several practical strategies to overcome these obstacles. Project coordinators should actively build trust and communicate the value of data-sharing initiatives. Contributors should receive proper recognition and opportunities for co-authorship. User-friendly templates and training can reduce technical difficulties, while applying FAIR principles—making data Findable, Accessible, Interoperable, and Reusable—can improve data quality from the beginning. Funding agencies can also play a critical role by supporting dedicated teams responsible for organizing and curating large datasets.

Yet the challenge may run deeper than technical barriers. Current biodiversity data systems often reflect a reductionist mindset in which information is collected for isolated projects, disciplines, or institutional goals. Nature itself does not function this way. Ecosystems operate through relationships and interactions among species, humans, and environments. This is where the concept of Nature Quotient (NQ) may offer a broader perspective. NQ emphasizes understanding nature as a complex network of interconnected systems rather than a collection of isolated parts (Vuong, 2025). From this perspective, biodiversity data are not merely personal assets or institutional property; they are shared informational resources supporting both human and non-human well-being.

Saving biodiversity may therefore require more than collecting more data. It requires changing how people think about knowledge itself—from something owned individually to something shared collectively (Khuc & Nguyen, 2026). After all, protecting interconnected ecosystems may first require building interconnected ways of understanding and sharing information (Nguyen & Ho, 2026).

References

Christie, A. P., et al. (2020). Poor availability of context-specific evidence hampers decision-making in conservation. Biological Conservation, 248, 108666. https://doi.org/10.1016/j.biocon.2020.108666

Cooke, S. J., et al. (2023). Environmental evidence in action: on the science and practice of evidence synthesis and evidence-based decision-making. Environmental Evidence, 12, 10. https://doi.org/10.1186/s13750-023-00302-5

Dasoler, B. T., et al. (2026). Demanding but not sharing: barriers and counteracting strategies for compilation of biodiversity data from researchers and practitioners. Perspectives in Ecology and Conservation, 24(2), 141-145. https://doi.org/10.1016/j.pecon.2026.02.001

de Oliveira, T. G., et al. (2024). Ecological modeling, biogeography, and phenotypic analyses setting the tiger cats’ hyperdimensional niches reveal a new species. Scientific Reports, 14, 2395. https://doi.org/10.1038/s41598-024-52379-8

Khuc, V. Q. & Nguyen, M. H. (2026). Cultural Additivity Theory. https://books.google.com/books?id=Y4XZEQAAQBAJ

Nguyen, M. H., & Ho, M. T. (2026). The absurdist approach to unveiling possible paradoxical thinking for innovative socio-psychological research. MethodsX, 16, 103910. https://doi.org/10.1016/j.mex.2026.103910

Pullin, A. S. (2012). Realising the potential of environmental data: a call for systematic review and evidence synthesis in environmental management. Environmental Evidence, 1, 2, 10. https://doi.org/10.1186/2047-2382-1-2

Sutherland, W. J., et al. (2004). The need for evidence-based conservation. Trends in Ecology & Evolution, 19, 305-308. https://doi.org/10.1016/j.tree.2004.03.018  

Vuong, Q. H. (2025). Wild Wise Weird. AISDL. https://books.google.com/books?id=C5dDEQAAQBAJ