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What spatial data do we need to develop global mammal conservation strategies?
Author(s) -
Luigi Boitani,
Luigi Maiorano,
Daniele Baisero,
Alessandra Falcucci,
Piero Visconti,
Carlo Rondinini
Publication year - 2011
Publication title -
philosophical transactions of the royal society b biological sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.753
H-Index - 272
eISSN - 1471-2970
pISSN - 0962-8436
DOI - 10.1098/rstb.2011.0117
Subject(s) - computer science , scale (ratio) , spatial analysis , variety (cybernetics) , credibility , data mining , geography , variable (mathematics) , heuristic , sample (material) , representation (politics) , cartography , mathematics , artificial intelligence , remote sensing , mathematical analysis , chemistry , chromatography , politics , political science , law
Spatial data on species distributions are available in two main forms, point locations and distribution maps (polygon ranges and grids). The first are often temporally and spatially biased, and too discontinuous, to be useful (untransformed) in spatial analyses. A variety of modelling approaches are used to transform point locations into maps. We discuss the attributes that point location data and distribution maps must satisfy in order to be useful in conservation planning. We recommend that before point location data are used to produce and/or evaluate distribution models, the dataset should be assessed under a set of criteria, including sample size, age of data, environmental/geographical coverage, independence, accuracy, time relevance and (often forgotten) representation of areas of permanent and natural presence of the species. Distribution maps must satisfy additional attributes if used for conservation analyses and strategies, including minimizing commission and omission errors, credibility of the source/assessors and availability for public screening. We review currently available databases for mammals globally and show that they are highly variable in complying with these attributes. The heterogeneity and weakness of spatial data seriously constrain their utility to global and also sub-global scale conservation analyses.

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