This dataset contains raw spatial prioritisation outputs from the decision-support software tool Marxan. These include the two main outputs from prioritisations: 1) individual solutions created for each conservation scenario run (100 solutions each), and 2) selection frequency data of each of the 20 scenarios, detailing the relative importance of each planning unit in the spatial prioritisation. The allssolnfreq.csv file consists of selection frequency data for each planning unit for each of the 20 conservation prioritisation scenarios run. Selection frequency values reflect the number of times each planning unit was selected as part of a good solution (100 total per scenario). The allsols.csv files consists of each individual solution produced for each scenario (100 solutions per scenario, 20 scenarios total). Binary data, 0 representing unselected planning units; 1 representing selected planning units. Scenario code IDs reflect those used in publication to which these datasets relate.
There are several datasets associated with the study described in the related publication. The study quantifies the individual and interacting effects of three factors - planning-unit size, thematic resolution of habitat maps, and spatial variability of socioeconomic costs - on spatial priorities for conservation, by creating 20 unique prioritisation scenarios involving different levels of each factor. Prioritisations were run using the reserve selection tool Marxan. Because output data from these scenarios are analogous to ecological data, ecological statistics were applied to determine spatial similarities between reserve designs. The other datasets for this study can be found at the Related Data links below.
This dataset is available as 2 comma-separated values (.csv) files.
Coral-reef habitats in Fiji and Micronesia (consisting of the Mariana Islands, Marshall Islands, Palau, Guam, and the Federated States of Micronesia) were used as case studies. Although analyses are grounded in real data, they are demonstration exercises and not intended to inform real-world conservation action in the study regions.
Data time period: 07 04 2015 to 07 05 2016
Data time period:
180,-19.39105 180,-18.74114 180,-17.79815 180,-16.77705 180,-15.77767 180,-14.89923 180,-14.22993 179.50789,-13.83756 178.43846,-13.76203 177.39734,-14.01104 176.48643,-14.55922 175.79492,-15.35092 175.39047,-16.30631 175.3127,-17.32998 175.5692,-18.32111 176.13487,-19.18365 176.95434,-19.83546 177.9474,-20.21545 179.01683,-20.28842 180,-20.04765 180,-19.39105
144.4146,18.32628 138.26225,18.82615 132.81303,9.63641 127.5396,10.67455 129.29741,4.75482 137.03178,7.20218 164.62944,6.6787 168.32085,4.40438 176.05522,5.28014 174.47319,11.19232 159.00444,12.22505 153.55522,15.12825 153.37944,18.65969 144.4146,18.32628
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- Local : f9314a23f787c42ed8b687d3932a1c55
- Local : https://research.jcu.edu.au/data/published/7aef4e2766e0576c6eefe4fcee95c1d7
- DOI : 10.4225/28/579AB69280EAF