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DATABASES AND SCIENCE‐BASED MANAGEMENT IN THE CONTEXT OF WILDLIFE AND HABITAT: TOWARD A CERTIFIED ISO STANDARD FOR OBJECTIVE DECISION‐MAKING FOR THE GLOBAL COMMUNITY BY USING THE INTERNET
Author(s) -
HUETTMANN FALK
Publication year - 2005
Publication title -
the journal of wildlife management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.94
H-Index - 111
eISSN - 1937-2817
pISSN - 0022-541X
DOI - 10.2193/0022-541x(2005)069[0466:dasmit]2.0.co;2
Subject(s) - standardization , wildlife , context (archaeology) , adaptive management , documentation , backup , environmental resource management , certification , computer science , data management , database , business , knowledge management , geography , political science , ecology , environmental science , archaeology , law , biology , programming language , operating system
Adaptive and science‐based management is widely accepted as necessary to safeguard wildlife and their habitats into the future. However, many of the decisions in this field are still based on unsupported ideas that lack validation with real data and which do not make their analysis available for a public review. Decisions based on soft foundations can be harmful to wildlife, habitat and the survival of both. I suggest a new wildlife management approach founded on scientific databases that has become possible, if not imperative, with improved technology and increasing access to freely available data over the Word Wide Web (WWW). This approach is partly a consequence of the effective implementation of the U.S. Freedom of Information Act and the National Spatial Data Infrastructure. In order to justify management decisions relating to wildlife, habitat, and conservation, the listing, use, and full investigation of all available and relevant databases needs to be implemented, voluntarily or legally, as a prerequisite. Second, similar to International Organization for Standardization (ISO) Standards, each major wildlife management decision needs to add a standard management documentation system as a backup that clearly records what data and research were or were not available at that time and what the recommended research still needs to address in order to complete the data situation and to decrease uncertainty.