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保全科学における情報のギャップと3つのアプローチ (Information gaps in conservation science and three potential approaches)

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Version 2 2018-03-16, 10:33
Version 1 2016-10-14, 09:47
journal contribution
posted on 2016-10-14, 09:47 authored by Tatsuya AmanoTatsuya Amano
本文書は、日本生態学会第63回大会(仙台)で開催されたシンポジウムS02 「保全科学が挑む情報のギャップ」 での発表「保全科学における情報のギャップと3つのアプローチ」に基づいて執筆されたものです。


This manuscript was written based on a presentation given for the symposium "Challenging information gaps in conservation science" at the 63rd annual meeting of the Ecological Society of Japan.

This manuscript is a pre-print, currently under review for publication. Thus please note that the information provided in this manuscript has yet to be peer-reviewed. Please cite the published version, not this manuscript, once it is accepted and published.


How science can contribute to halting ongoing biodiversity loss is a crucial question in conservation. Conservation science usually collects data, derives scientific knowledge from them and applies the derived knowledge to conservation practices and policies. There exists, however, a number of “gaps” in this process of information use, representing barriers between science and conservation. This paper first reviews the detail of these gaps in conservation science. For example, the amount of existing data varies greatly over space, time, taxa and data types. This is mainly because survey efforts are determined by, not only demands in conservation, but also a variety of other factors, such as the ease of data collection, scientific interests, and other constraints. The research-implementation gap, i.e., science not being used in conservation practices and policies, is another well-known type of gaps in conservation science. This gap results from, for example, a difference between knowledge provided by scientists and that required by practitioners and policy makers, and inaccessibility to scientific information for those decision makers. Finally, this paper lists three types of potential solutions: increasing the amount of scientific data, using statistical modelling to make the best use of imperfect data, and transcending barriers between science and practices.


European Commission Marie Curie International Incoming Fellowship Programme (PIIF-GA-2011-303221) and Isaac Newton Trust