Big Decisions and Sparse Data: Adapting Scientific Publishing to the Needs of Practical Conservation

Avian Conservation and Ecology

Doug P. Armstrong,¹  Michael A. McCarthy,² 

[1] Massey University;University of Melbourne

关键词: adaptive management;    Bayesian methods;    hypothesis testing;    scientific publishing;    strong inference.;   

DOI  :  10.5751/ACE-00209-020214

学科分类：社会科学、人文和艺术（综合）

来源: Resilience Alliance Publications

【 摘 要 】

The biggest challenge in conservation biology is breaking down the gap between research and practical management. A major obstacle is the fact that many researchers are unwilling to tackle projects likely to produce sparse or messy data because the results would be difficult to publish in refereed journals. The obvious solution to sparse data is to build up results from multiple studies. Consequently, we suggest that there needs to be greater emphasis in conservation biology on publishing papers that can be built on by subsequent research rather than on papers that produce clear results individually. This building approach requires: (1) a stronger theoretical framework, in which researchers attempt to anticipate models that will be relevant in future studies and incorporate expected differences among studies into those models; (2) use of modern methods for model selection and multi-model inference, and publication of parameter estimates under a range of plausible models; (3) explicit incorporation of prior information into each case study; and (4) planning management treatments in an adaptive framework that considers treatments applied in other studies. We encourage journals to publish papers that promote this building approach rather than expecting papers to conform to traditional standards of rigor as stand-alone papers, and believe that this shift in publishing philosophy would better encourage researchers to tackle the most urgent conservation problems.

【 授权许可】

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