Spatial data and supporting information for a Philippines case study on social equity in marine systematic conservation planning

This dataset includes supporting information and spatial data
for a Philippines case study on social equity in marine systematic conservation
planning (SCP). The study focuses on three dimensions of social equity
(recognition, procedure, and distribution) in relation to planning marine
protected areas (MPAs) under a SCP framework. It documents how social equity
dimensions can be addressed within the planning stages of SCP and compares
three planning scenarios to investigate how recognition and procedure equity
can impact MPA plans in terms of distribution equity, ecological
representation, and spatial efficiency. The study region is located in Sogod
Bay in Southern Leyte, Philippines and encompasses the coastal waters of six municipalities:
Pintuyan, San Francisco, Liloan, Malitbog, Padre Burgos, and Limasawa. Biodiversity
and fishery data were collected using remote sensing and participatory mapping
methods, respectively. The data were integrated into the spatial prioritization
tool, Marxan with Zones, to develop and compare three planning scenarios.

The supporting
information file contains supplemental
information on (1) biodiversity data and remote sensing analyses; (2) fishery
data and participatory mapping workshops; (3) existing MPA system in
the study region; and (4) Marxan with Zones analyses including (a) summary
tables of the final Marxan with Zones analysis of each planning scenario, (b)
input and output files of Marxan with Zones analyses by scenario, and (c) maps
of Marxan with Zones outputs under the three scenarios.

The spatial data file
includes SHP and KML files on (1) the administrative boundaries for (a)
barangays/communities, (b) municipalities, and (c) municipal waters in the study region;
(2) (a) the amount of each biodiversity feature in each planning unit, (b) the distribution of coastal habitats derived from a remote sensing
analysis of three WorldView-2 images, (c) areas with cloud cover where habitat
classes could not be derived from the remote sensing analysis; (d) mangrove
data derived from secondary data sources; (3) the amount of each fishery feature in each planning unit at (a) a barangay/community scale and (b) a municipal scale; and (4) (a) the
location and attribute information on existing MPAs in 2015, (b) planning
units selected in the MPA zone of the “best” Marxan with Zones solution (lowest
score of 100 runs) by scenario, and (c) planning unit selection frequencies for
the MPA zone by scenario.

***Note*** Barangay/community names have been replaced with codes to maintain the confidentiality agreement of this study.