@article{paperid:1072718,
author = {Karimi, Azadeh and Ayesha Tulloch and Greg Brown and Marc Hockings},
title = {Understanding the effects of different social data on selecting priority conservation areas},
journal = {Conservation Biology},
year = {2017},
volume = {31},
number = {6},
month = {December},
issn = {0888-8892},
pages = {1439--1449},
			numpages = {10},
keywords = {Conservation success is contingent on assessing social and environmental factors so that cost‐effective implementation of strategies and actions can be placed in a broad social–ecological context. Until now; the focus has been on how to include spatially explicit social data in conservation planning; whereas the value of different kinds of social data has received limited attention. In a regional systematic conservation planning case study in Australia; we examined the spatial concurrence of a range of spatially explicit social values and land‐use preferences collected using a public participation geographic information system and biological data. We used Zonation to integrate the social data with the biological data in a series of spatial‐prioritization scenarios to determine the effect of the different types of social data on spatial prioritization compared with biological data alone. The type of social data -i.e.; conservation opportunities or constraints- significantly affected spatial prioritization outcomes. The integration of social values and land‐use preferences under different scenarios was highly variable and generated spatial prioritizations 1.2–51% different from those based on biological data alone. The inclusion of conservation‐compatible values and preferences added relatively few new areas to conservation priorities; whereas including noncompatible economic values and development preferences as costs significantly changed conservation priority areas -48.2% and 47.4%; respectively-. Based on our results; a multifaceted conservation prioritization approach that combines spatially explicit social data with biological data can help conservation planners identify the type of social data to collect for more effective and feasible conservation actions.},
}