The influence of multiple stressors on the spatial distribution of corals
Data files
Jan 14, 2026 version files 361.58 MB
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barangay_demographics_20131127_FINAL.csv
4.22 KB
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barangay_distance_market_20131107.csv
10.10 KB
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barangay_peoples_organizations.csv
706 B
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barangays_villages.zip
47.68 KB
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barangyay_population_getafe_stats_approx2011.csv
764 B
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depth_tides_notGIS.zip
53.38 MB
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depth.zip
16.28 MB
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digitizing2010.zip
14.33 KB
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ecological_zones.zip
675.69 KB
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fishing_effort.zip
52.18 MB
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fishing_gear_use.csv
818.58 KB
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fishing_grounds_final.zip
45.49 MB
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focal_area.zip
665.51 KB
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geomorphology.zip
282.15 KB
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gis_variables_2025.csv
3.63 KB
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habitat.zip
45.02 MB
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habitats_in_fishing_grounds_2010.csv
221.35 KB
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longitude_zones.zip
6 KB
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mpa.zip
87.05 KB
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municipal_waters.zip
582.91 KB
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municipalities.zip
42.77 MB
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philippines_fishing_legislation.csv
3.65 KB
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philippines.zip
67.93 MB
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population.zip
137.13 KB
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ports.zip
5.65 KB
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README.md
42.09 KB
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reef_area.zip
2.35 MB
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river_distance.zip
16.08 MB
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town_distance.zip
16.45 MB
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translations.zip
26.58 KB
Abstract
Coral reef ecosystems are widely threatened by global change, yet the cumulative impacts of multiple interacting stressors remain difficult to quantify over space and time. We evaluate how long-term artisanal fishing effort, blast fishing, human population density, and marine protected areas (MPAs) interact with biophysical and seascape variables to influence the spatial distribution of corals in a biodiverse, but heavily impacted ecosystem.
To address these challenges, we combined satellite habitat mapping, indigenous and local knowledge, and generalized linear mixed-effects models to assess how stressors and seascape characteristics shape the occurrence of living coral. This integrative approach allowed us to capture both ecological and social processes at an ecosystem scale.
Coral was the dominant habitat in only 30% of the study area. The strongest predictor of coral distribution was seascape configuration: corals were more likely to be in compact reef patches. Coral presence was also positively associated with MPAs (18% higher probability inside MPAs) and depth (8% increase in probability of corals per 2.25 m). Increasing distance to seagrass was associated with higher coral probability (6% per 100 m), but this effect diminished at greater distances, reflecting a nonlinear quadratic response.
Disturbance effects were complex. Coral probability declined 6% for each 35 days of blast fishing (fishing with explosives), with an antagonistic interaction with human population density: impacts were strongest in low-population areas (−7.6%) and weaker in high-population areas (−2.4%). Total fishing effort (excluding blast fishing) reduced coral probability by 3.2% for every ~530 fishing days at a site, with a notable 10-year lag, highlighting delayed ecosystem responses.
Our findings emphasize the importance of long time series and the need to account for lagged effects of fishing, which may otherwise be underestimated. They also underscore that conservation outcomes depend on both managing harmful stressors and enhancing beneficial seascape features. Key priorities include reducing spatial overlap of destructive activities, protecting reef configurations that support coral persistence, and addressing stressors with delayed impacts. The approach developed here—integrating spatial, ecological, and social data—offers a framework for adaptively managing coral reef ecosystems that are changing under interacting pressures.
Dataset DOI: 10.5061/dryad.z34tmpgpt
Description of the data and file structure
Files and variables
R code used to analyze data are available at https://github.com/jselgrath/stressors_and_coral_reefs or
the archived release of this GitHub repository: https://doi.org/10.5281/zenodo.17459945
Fishing data were collected from participatory mapping interviews with male fishers and methods were approved by University of British Columbia's Human Behavioural Research Ethics Board (H07-00577). All respondents were given anonymous codes which are used throughout this repository.
Distance variables are in kilometers (km) unless otherwise noted.
Terms
Barangay = small government unit (usually a village, but also a neighborhood in a large city).
Scale: Municipality/Local Government Unit (LGU) > Barangay > Purok (local neighborhood)
Usage notes
The following is a list of the different data types by file suffix included in this repository along with brief
descriptions for how to work with them. Many examples for how these data were used can be found in the
"./bin/driver.R" document from the GitHub repository referenced above. The 'driver.R' file has descriptions of all of the code, and the input and output files from the code. Input files include those below.
.xlsx: Data in Excel format that can be viewed and manipulated with programs like LibreOffice Calc, OpenOffice Calc, Microsoft Office, or imported into Google Sheets.
.txt: Plain text files that can be viewed or manipulated with any plain text editor (e.g., Wordpad, Nano, Vim, Text Editor).
.csv: Comma-separated values files, which are plain text files with comma-delimited data fields. These files can be viewed/manipulated with any plain text editor, with Excel, Google Sheets, or read with the R software
using the read_csv command from the Tidyverse library.
.zip: Zip files are compressed folders that contain several files but can be downloaded in one package. Several .zip files contain a set of files that are part of shapefiles. See below.
Shapefiles are a vector-based geographic information system (GIS) data. They can be opened and used in any GIS software (ArcPro, QGIS) and in R or Python. A shapefile consists of multiple file types beyond the .shp (specifically, .cpg, .dbf, .prj, .sbn, and .sbx). The user only interacts directly with the .shp file but the other files need to be in the same directory.
.tif: GeoTIFF files in GIS are raster images that contain embedded geographic metadata (like coordinate systems and projections) that allows GIS software to place the image accurately on a map, making it ideal for aerial photos, satellite imagery, and base maps. .tif files stores pixel data in tags and can handle various color depths, supporting transparency (alpha channels) for detailed mapping. A geoTIFF consists of multiple file types beyond the .tif (specifically, .aux, .jgs, .rrd, and .XML files). The user only interacts directly with the .shp file but the other files need to be in the same directory.
.gpkg: A GeoPackage (GPKG) is an open, standard, single-file format for storing and transferring geospatial data. Contains files similar to shapefiles, but in a non-propriatary format.
Files
File: barangay_demographics_20131127_FINAL.csv
Description: detailed information about demographics in the barangay/village where interviews took place
- bgy_id - id for barangay/village/town
- barangay - name
- po_b - does the barangay have a people's organization (binary)
- mpa_b - does the barangay have a MPA (binary)
- mpa_started - year MPA started
- area_ha_baranagay - size of barangay in HA
- mangroves - does barangay have mangroves (binary)
- avg_age_bgy - average age of fishers in barangay (or bgy pop? that seems unlikely)
- distance_market_pasil - distance to the Pasil market (the large fish market in Cebu City) in kilometers (km)
- dist_lapulapu - distance to Lapu Lapu (a specific part of Cebu City) in kilometers (km)
- dist_cebu - distance to cebu city in kilometers (km)
- bdist - distance band from focal area. 0 = inside of focal area. 1 = up to 5km from focal area. 2 = up to 10km from study area.
- fishers_sampled - number of respondents
- fishers_pct- percentage sampled of total fishers in barangay
- households_n - number of households
- bhousearea - household area
- pop_house_u - average number of people living in a household
- bhousepop - unsure
- avg_kids - average # kids for fishermen in that barangay
- lgu - local gov. unit
- location - location of village
- fishers_married_n - number of respondents married
- fishers_married_p - percent of respondents married
- fishers_moved_n - number of fishers who moved to village
- population2010 - village population in 2010
- population_area - population area
- province - province
- fishers_barangay - # fishers in barangay
- proportion_rural_pop - proportion of Philippines population that was rural in ~ 2010
- avg_edu - average education of fishers
- barangay_water_area - water area of barangay
- market_closest_important - most important market for village
- dist_impt_market_km - distance to most important market in kilometers (km). used near tool in ArcGIS to calculate the dist between the barangays and all of the markets in their LGUs. Final distance based on the closest market on the LGU and known patterns. For example, Caubian goes more frequently to the Getafe market
barangyay_population_getafe_stats_approx2011.csv
Description: .csv with basic population stats for villages in Getafe. Data from approximately 2010-2011
- lgu - local government unit
- Barangay_islet - name of barangay or islet
- coastal_area - location (coast or inland)
- households - number of households
- males - number of people identifying as male
- people - number of people
File: barangay_distance_market_20131107.csv
Description: CSV file of distance between villages and local markets (where fish are sold).
- LGU - name of municipality (local government unit)
- Zone - ecological zone of village. not final. (see eco zone shapefiles for final zones)
- Location - location of village related to study focal area
- BarangayName - name of village
- NumbFisher - number of male fishermen in village.
- Interviews - binary. were interviews done?
- NumbInterv - number of interviews
- Notes -notes about distance
- BgyID - ID for village
- Market - name of market used most often by barangay
- Dist_km - distance to market in kilometers (km)
File: barangays_villages.zip
Description: Contains shapefiles of barangays. In the Philippines, the barangay is a small administrative unit. In our study region barangays were villages and towns. These are point files that have metadata about surveys and fishermen. The folder also contains place names and province name files for mapping.
Files in barangays_villages.zip folder:
-readme.txt - text file containing information about files in the folder and the metadata about the datasets
- barangays_household_survey_data_20141020.shp - shapefile of census data for villages collected by Selgrath from barangay health centers. Includes data for all villages in study area.
Columns:
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data. (point)
- BgyID - code for each barangay where interviews were conducted, N/A if no interviews. code used for fisherID
- BgyName - name of the barangay
- LGU - Municipality/Local Government Unit
- Location - ecological zone
- DistToGeta - Distance to Getafe (representing the focal area) in kilometers (km) - category used for assigning sampling of barangays (categories center = within focal area; 0-5 km = 0-5km from center; 5-10km = 5-10km from center.
- fishers_n - number of fishers in village based on village health/census records
- interviews - were interviews conducted in this village?
- intervie_1 - number of interviews conducted
- survey - did Selgrath collect health data from the barangay
- barangays_interviewed_20141020.shp - metadata about interviews in barangays. Includes data only for villages with interviews.
Columns:
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data. (point)
- BgyID - code for each barangay where interviews were conducted, N/A if no interviews. code used for fisherID
- BgyName - name of the barangay
- LGU - Municipality/Local Government Unit
- Location - ecological zone
- DistToGeta - Distance to Getafe (representing the focal area) in kilometers (km) - category used for assigning sampling of barangays (categories center = within focal area; 0-5 km = 0-5km from center; 5-10km = 5-10km from center.
- fishers_n - number of fishers in village based on village health/census records
- interviews -number of interviews conducted
- Notes - notes about barangay
- place_names_db_20141020.shp - place names near Danajon Bank. used for making maps. Also information about islands that are not inhabited. Columns:
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data. (point)
- BgyName - name of the barangay or location (not all are barangays!)
- LGU - Municipality/Local Government Unit
- type - notes about type of place (not complete)
- location - ecological zone
- Inhabited - binary,is this location inhabited?
- barangay - binary, is this location a barangay?
- fishers_n - number of fishers in village based on village health/census records
- interviews - were interviews conducted here?
- Interview- notes on pilot studies
- Notes - notes about location
- province_names_db.shp - local province names. used for making maps. Columns:
- Name - province name. file only used for making maps
File: barangay_peoples_organizations.csv
Description: .csv file containing information about the presence or absence of people's fishing organizations in study areas.
- bgy - barangay
- barangay - barangay name
- LGU - LGU barangay is located in
- PO - does barangay have a people's organization about fishing?
- Note - notes
File: depth.zip
Description: Contains shapefiles of depth data in meters created from a spline interpolation of NAMRIA bathymetry charts (based on manual digitizing) and supplemented with field-based depth readings collected by Jennifer Selgrath in 2007. **Note the names of these files may be different in the R code found in the Zenodo Repository.
Files in depth.zip folder:
- readme.txt - file explaining depth files
- depth_danajon_bank.tif > band 1 - gridcell value represent depth in meters (m)
- depth_danajon_bank.shp (shapefile)
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data.
- Depth1 - depth categories (based on meter data)
- Depth_m - depth in meters
File: depth_tides_notGIS.zip
Description: This zip folder contains files about depth and tides in Danajon bank.
depth_metadata_2012.csv - metadata about depth information collected in habitat surveys
- FILE - name of file
- DEPTH - metadata about the files depth data
- NOTE - notes
readme.txt - description of files
SUBFOLDER: nautical_chart_NAMRIA_UTM - NAMRIA is the Philippines mapping and charting agency
Files:
- eastern_bohol.jpg - shapefile of nautical chart of eastern Bohol
- western_bohol.jpg - shapefile of nautical chart of western Bohol
SUBFOLDER: tide_tables_2007. This folder contains tide tables for the port of Cebu photographed from a book in their office (courtesy of Raffy Martinez with the FISH PROJECT). On average, the NAMRIA version differs (is lesser in magnitude) from a range of -1cm to -9cm from the values calculated in the wxtide software.
Files:
- cebu_tides_2007_jan_april.jpg - photo of tide tables Jan-Apr
- cebu_tides_2007may_aug.jpg - photo of tide tables May-Aug
- cebu_tides_2007_sept_dec.jpg - photo of tide tables Sept - Dec
File: digitizing2010.zip
Description: This zip folder contains empty files for digitizing participatory mapping paper maps from interviews.
readme.txt - readme text file that provides descriptions of shapefiles.
FOLDERS
interviewMapCrosses/ -
InterviewMapCrosses.shp - shapefile with coordinates of line intersections for maps from participatory mapping. Used to georectify paper maps.
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data
- Map - north or south
- Location - location of cross on map
- Number - unique ID
- Geographic - general location
- Type - description of cross on map
2010_digitizingTemplates/
BoatArea_TEMPLATE.shp - shapefile template for digitizing where people keep their boats (point)
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data
- Barangay - village respondent is from
- FisherID - unique ID for respondent
FishingGround_TEMPLATE.shp - shapefile for digiziting where people fished. (polygon)
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data.
- Map - north or south
- FG_Number - fishing ground number
- FG_color - color fishing ground was drawn on map. See interview protocol in Selgrath's PhD thesis.
- FG_name - name of fishing ground
- Letters - letters of associated activity. see interview protocol in Selgrath's PhD thesis.
- Gears - gears respondent used in fishing ground.
File: ecological_zones.zip
Description: Contains shapefiles of ecological zones. Ecological zones. Adapted from Hansen, G. J. A., Ban, N. C., Jones, M. L., Kaufman, L., Panes, H. M., Yasué, M., & Vincent, A. C. J. (2011). Hindsight in marine protected area selection: A comparison of ecological representation arising from opportunistic and systematic approaches. Biological Conservation, 144(6), 1866–1875. https://doi.org/10.1016/j.biocon.2011.04.002
Files contain various options for diving reef into zones. This project used ecological zone 2.
-readme.txt - readme file explaining files in folder
- EcoZones1_DB.shp - original EZ files. 6 zones: coastal, inner reef, land, mangroves, outer reef, terrestrial islands
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data.
- eco_zone1 - ecological zone
- eco_zn1_id - id for each category
- EcoZones2_DB.shp - splits some zones into 10 categories with additional zones. added: inner reef - coastal, outer reef - channel, coastal.
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data.
- eco_zone2 - ecological zone
- eco_zn2_id - id for each category
File: fishing_effort.zip
Description: Contains geoTIFF files of summarized maps of fishing effort in specific gridcells for six years - 1960, 1979, 1980, 1990, 2000, 2010.
Each sub-folder contains .tif files and associated files for the fishing effort of specific groups of fishing gears (see list below - names in () are used in file name).
Each file has grid cells that show the total fishing effort (days fished in that year) by all respondents from participatory mapping interviews.
Types of fishing in each sub-folder
a: all fishing effort
b: gear types: blast fishing (blast), trap, poison, net, hook and line (hook), gleaning (glean), skin diving (dive), fish corrals (corral),
c: gear categories: non-selective (1) and selective (0), illegal (1) and non-illegal (0), destructive(1) and non-destructive (0), active (1) and non-active(0)
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in all subfiles the file name structure provides information about that specific geotiff file.
est=estimate dayYr = days per year 2cln = 2nd version, cleaned -
est_dayYr_[GEAR][CATEGORY INFORMATION - SEE BELOW][YEAR]_2cln.tif
- CATEGORY INFORMATION: in the file name structure for categories (eg destructive)
0 = [NOT CATEGORY] and 1= [CATEGORY]
SUB-FOLDERS - containing .tif and associated files
.gis2/fishing/effort_fa - all fishing files - one file per year and gear combination (raw version)
.gis2/fishing/effort_fa_cumulative - cumulative fishing effort across multiple years
.gis2/fishing/effort_fa_lag - lag fishing effort across multiple years
.gis2/fishing/effort_fa_normalized - normalized fishing effort - normalized by the max for all years of that group of fishing gears
./fishing/effort_fa_cumulative_g1 - cumulative fishing effort across multiple years for additional gear categories (e.g., non-selective gears, active, blast, kaykay, non-selective gears)
File: fishing_grounds_final.zip
Description: - this zip folder contains information about the fishing grounds used by respondents in the central Danajon Bank, Philippines collected through interviews in 2010-2011. These are the raw fishing grounds that are the basis of calculations about effort, etc.
readme.txt - text file containing information about files in this subfolder
fishing_grounds_20130128.shp - this shapefile contains all of the fishing grounds used by fishers in the central Danajon Bank that were identified during participatory mapping interviews (2009-2011). These fishing grounds were the basis of effort calculations. fg = fishing ground. fisher = respondent.
Columns:
- FID - automated ID created by ArcGIS or ArcPro
- Shape - Polygon
- fisher_id - unique code for each fisher. codes are [BARANGAY]-[RA]-[RESPONDENT NUMBER]
- fg_id - fishing ground ID. Codes for fishing grounds are:
- [BARANGAY]-[INTERVIEWER]-[RESPONDENT NUMBER]-[FISHING GROUND NUMBER]
- fg_map - north or south section of basemap
- fg_color - color fishing ground was drawn in. Green = current. Red = no longer used. Black = observed other fishing here, do not use personally. Can have more than one color.
- fg_name1 - original fishing ground name provided by respondent
- fg_name2 - cleaned fishing ground name provided by respondent
- fg_depth - deep or shallow fishing ground (approximately - for example shallow fishing grounds are on the reef flat or reef slope, Deep fishing grounds were in the channel.)
- fishing_cd - code for other fishing grounds (black fishing grounds) or for fishing activities that the fisher observed but did not personally participate in.
- fisher_bgy - barangay/village the respondent lived in
- fg_number - fishing ground number. Specific to respondent. not unique.
- note_gear - notes on fishing gears used in fishing ground by respondent
- note_other - other notes about fishing ground
SUBFOLDER: fishing_grounds_final/fishing_grounds_keyinformant_Handumon/ -this folder contains local knowledge shared with Selgrath during a key informant interview about local fishing ground names and the names of local landmarks
FishingGroundsGetafe.shp - shapefile of lines showing general area of fishing grounds for men from the village of Getafe (in 2010)
- FID - automated ID created by ArcGIS or ArcPro
- Shape - Polygon
- Code - code to link the shapefile data to the fishing ground name file
fishing_ground_names_key_information_20100714.csv - this .csv file contains the names and other metadata about fishing grounds mapped during the key informant interview described above. Links to the file FishingGroundsGetafe.shp
- Code - code for fishing ground
- FG Name - name of fishing ground
- Island - nearest island or barangay
- municipality - municipality of fishing ground
landmark_names_20100715.csv - this .csv file with names of useful landmarks in the central Danajon Bank. Based on key informant interview with an elder fisher in Handumon, Getafe (Bohol).
- CODE - code for landmark
- Name of Landmark - name
- municipality - municipality of landmark
File: fishing_gear_use.csv
Description: this file that contains information about the use of specific fishing grounds in specific years. Codes for fishing grounds are:
[BARANGAY]-[INTERVIEWER]-[RESPONDENT NUMBER]-[FISHING GROUND NUMBER]
Columns:
- ID.yr - [FisherID].[YEAR]
- FisherID - unique code for each fisher. codes are [BARANGAY]-[RA]-[RESPONDENT NUMBER]
- GearID - number of gear per fisher. G[NUMBER]. unique within respondents. not unique across respondents.
- Gear_ID - unique ID for fisher-gear combinations. [FisherID]_[GearID]
- G.ID1 - unique ID for each gear type based on Gear1 (specific) classification of gears.
- g1.ceb - specific fishing gear name in Cebuano/Visayan
- g1.eng - specific fishing gear name in English
- G.ID5 - unique ID for each gear type based on Gear5 (generalized) classification of gears.
- g5.eng - generalized fishing gear name in English
- year - year
- gear.use - fishing gear use during year. binary (0=not used, 1= used)
- rand - random number used for gear diversity calculations
File: focal_area.zip
Description: Folder contains shapefiles of of the focal area (or study area) of the project in the central Danajon Bank, Philippines.
SHAPEFILES
- focal_area.shp - area of habitat and fishing mapping.
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data. (polygon)
- Value = 99999 (meaningless number)
- focal_area_lg.shp - larger area that covers entire northern Danajon Bank. Used for calculating landscape distance metrics.
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data. (polygon)
- Value = 0 (meaningless number)
- focal_area_lg_land2.shp - larger area that covers entire northern Danajon Bank. Used for calculating landscape distance metrics. With sea and land attributes.
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data. (polygon)
- land= 0 = water areas, 1 = land areas
- water = 1 = water areas, 0 = land areas
- focal_area_sm.shp - smaller area used for analysis in Selgrath et al 2025. clipped to reduce edge effects for fishing and habitat variables.
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data. (polygon)
- FID_1= 0 (meaningless number)
- focal_area_sm_land2.shp - smaller area used for analysis in Selgrath et al 2025. clipped to reduce edge effects for fishing and habitat variables. With sea and land attributes.
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data. (polygon)
- land= 0 = water areas, 1 = land areas
- water = 1 = water areas, 0 = land areas
File: geomorphology.zip
Description: Contains shapefile and geotiff of geomorphology of the Danajon Bank, Philippines derived from satellite images.
geomorphology_19x22_20160523.shp
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data.
- Value - geomorphology category
- 800 = deep water
- 801 = mangrove
- 802 = mangrove planted
- 803 = reef flat
- 804 = reef slope
- 99999 = land
- Count - number of pixels converted from geotiff
geomorphology_19x22_20160523.tif - see above for values
File: habitat.zip
Description: Folder contains habitat maps of the Danajon Bank Philippines from satellite images and participatory mapping with fishers.
SUBFOLDERS:
1) db_full_area (in R code the shape files are in a subfolder called "/all_full_area/")
- habitat_full_area_rs_lek_reclass_20250615_union_with_fa2.shp - base habitat map. updated from original map to include ILK and some manual fixes (eg some mangroves near Olango were classified as algae). ILK mostly used in areas that were too deep or too turbid for RS. this is the basis of habitat analysis. Details described in the appendix of Selgrath et al 2025.
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FID - ArcGIS assigned identifier
-
Shape - automatic ArcGIS column describing type of data
-
Hab1 - original habitat category
-
Hab2 = simplified version of original habitat category.
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HapPaper = reclassified version of habitat data
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Geomorphic - geomorphology class of location
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Location - EcoZone (note not same version as analysis). Olango is a large islands near cebu included in mapping, but not analysis
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Map - RS = remote sensing, LEK = participatory mapping (Local Ecological Knowledge), edited = fixed manually due to errors
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Reclass = binary (0.1) has this polygon been reclassified from original remote sensing habitat map
- habitat_full_area_rs_lek_reclass_20250615_updated_with_fa.shp - same as previous. habitats not split by Focal area. See attribute columns above.
2) habitat_images_2025_reclass - folder contains two maps of the areas that were reclassified in 2025 as part of the confusion matrix. (see Selgrath et al 2025)
In R code for final analysis updated habitats based on confusion matrix with LEK data but those files are calculated from the files included here. This improves the confusion matrix from May 2025 using 2010 LEK data. (see Selgrath et al 2025 for more details)
hab_reclass_flat_co_ag_to_ru.pdf - shows areas reclassified from coral-algae to rubble (in reef flat areas)
hab_reclass_slope_co_ru_to_co.pdf - shows areas reclassified from rubble-coral and terrestrial sand to coral (in reef slope areas).
File: habitats_in_fishing_grounds_2010.csv
Description: A .csv of habitats in fishing grounds in 2010
- FisherID
- fg - fishing ground number - specific to respondent, but not unique
- FG_ID - unique fishing ground ID for each fishing ground [FisherID]_[fg]
- FGID - same as FG_ID but no _ in fisherID
- FisherID2 - same as FG_ID but no '_'
- FGID3 - same as FG_ID but no _ and [fg][FisherID]
- Comments - comments about habitats
- Declining - binary 0 = not declining; 1 = declining
- Hab2010 - habitat codes
- FiHab1 - dominant habtiat
- FiHab2 - secondary habitat
- FiHab3 - tertiary habitat
- Mixed - binary 0 = not mixed; 1 = mixed
- MixedHab - combined habitat code if mixed
- Taganas - deep area. binary
File: gis_variables_2025.csv
Description: A .csv list of files used in GIS map (and available here) and basic metadata and purpose.
- for model - binary (0,1) was this file used in modeling in Selgrath et al 2025
- filename - name of file
- use - purpose of file
File: longitude_zones.zip
Description: Folder contains shapefiles of longitude zones, adapted from Reefs At Risk Revisited.
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data.
- ID - unique ID for each of two longitudinal zones
File: mpa.zip
Description: Contains shapefiles of Marine Protected Areas in the central Danajon Bank. From the USAID FISH Project.
MPA_FA_20160525_2.shp (has more columns than version 3 below)
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data.
- ISO3 - country code
- COUNTRY - country MPA is located in
- NAME - name of MPA
- LON - longitude
- LAT - latitude
- DESIG - designation
- LEGAL - law establishing MPA
- DESIG_TYPE - type of process that led to designation
- DOMAIN - marine
- STATUS - status of MPA
- DATE_ESTAB - date MPA established
- AREA_R_HA - area in hectares
- MGT_PLAN - does the MPA have a management plan?
- IUCN_CAT - IUCN category of MPA
- STATE_PROV - state or province where MPA is located
- ZONES - does the MPA have zones?
- NO_TAKE - is the MPA no take?
- NO_TK_R_HA - area in HA that is no take
- MGT_EFF - management effectiveness (circa the year 2010)
- SOURCE - source of information
- NOTES - notes about MPA
- Brgy - barangay that established the MPA or where the MPA is located
- YearEst - year established. 9999 = not a MPA
- ID - 601 = MPA, 600 - not MPA
- ID_MEAT - MEAT is a method for classifying MPAs
- Id_binary - 601 = MPA, 600 - not MPA
MPA_FA_20160525_3.shp
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data.
- NAME - name of MPA
- DESIG - designation
- LEGAL - law establishing MPA
- STATUS - status of MPA
- DATE_ESTAB - date MPA established
- AREA_R_HA - area in hectares
- MGT_PLAN - does the MPA have a management plan?
- ZONES - does the MPA have zones?
- NO_TAKE - is the MPA no take?
- NO_TK_R_HA - area in HA that is no take
- MGT_EFF - management effectiveness (circa the year 2010)
- SOURCE - source of information
- NOTES - notes about MPA
- Brgy - barangay that established the MPA or where the MPA is located
- YearEst - year established. 9999 = not a MPA
- ID - 601 = MPA, 600 - not MPA
- ID-MEAT - MEAT is a method for classifying MPAs
- Id_binary - 601 = MPA, 600 - not MPA
MPA_FA_20160525_3_50m_buf.shp (this file is the previous file buffered 50m, and only MPAs)
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data.
- NAME - name of MPA
- DESIG - designation
- LEGAL - law establishing MPA
- STATUS - status of MPA
- DATE_ESTAB - date MPA established
- AREA_R_HA - area in hectares
- MGT_PLAN - does the MPA have a management plan?
- ZONES - does the MPA have zones?
- NO_TAKE - is the MPA no take?
- NO_TK_R_HA - area in HA that is no take
- MGT_EFF - management effectiveness (circa the year 2010)
- SOURCE - source of information
- NOTES - notes about MPA
- Brgy - barangay that established the MPA or where the MPA is located
- YearEst - year established. 9999 = not a MPA
- ID - 601 = MPA, 600 - not MPA
- ID-MEAT - MEAT is a method for classifying MPAs
- Id_binary - 601 = MPA, 600 - not MPA
- BUFF_DIST - distance of buffer = 50m
MPA_FA_20160525_4_50m_buf.shp (this file is the previous file with MPAs and non-protected areas)
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data.
- NAME - name of MPA
- DESIG - designation
- LEGAL - law establishing MPA
- STATUS - status of MPA
- DATE_ESTAB - date MPA established
- AREA_R_HA - area in hectares
- MGT_PLAN - does the MPA have a management plan?
- ZONES - does the MPA have zones?
- NO_TAKE - is the MPA no take?
- NO_TK_R_HA - area in HA that is no take
- MGT_EFF - management effectiveness (circa the year 2010)
- SOURCE - source of information
- NOTES - notes about MPA
- Brgy - barangay that established the MPA or where the MPA is located
- YearEst - year established. 9999 = not a MPA
- ID - 601 = MPA, 600 - not MPA
- ID-MEAT - MEAT is a method for classifying MPAs
- Id_binary - 601 = MPA, 600 - not MPA
- BUFF_DIST - distance of buffer = 50m
File: municipalities.zip
Description: Contains shapefiles of municipalities (also called local government units) in central Visayas region of Philippines.
Mun50k_Philippines_UTM.shp - shapefile of municipalities for whole country
-
FID - ArcGIS assigned identifier
-
Shape - automatic ArcGIS column describing type of data.
-
TOWN - name of town/barangay
-
PROVINCE - province town is in
-
REGION - official Philippines region
-
REG_DESC - description of region
-
FeatNo - number of feature
-
Island - binary (0,1) is it a small island
Mun50k_nearBohol.shp - shapefile of municipalities for region surrounding study area
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data.
- TOWN - name of town/barangay
- PROVINCE - province town is in
- ISLAND - binary (0,1) is it a small island
File: municipal_waters.zip
Description: Contains shapefiles of boundaries for municipal waters in the Danajon Bank. Adapted from the USAID FISH Project (source unknown).
municipal_waters.shp
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data
- Id_MunWtr - unique ID for the municipal water
File: philippines.zip
Description: Folder contains two shapefiles of Philippines and its provinces.
Files in .zip folder:
- Philippines_Provinces_14Oct20.shp
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data.
- PROVINCE - name of province
Philippines_Outline_2014Oct20.shp
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data.
- Id=0 (value has no meaning)
File: philippines_fishing_legislation.csv
Description: .csv - file containing details about fisheries legislation in the Philippines through 2011.
- Year - year of legislation
- Legislation - name of legislation
- Covered - what legislation covered
- References - references for information
File: population.zip
Description: Contains shapefiles and a geopackage of population density estimates for barangays in the study area through 2011.
population.gpkg > main.barangay_pop2010_area - geopackage of village population data
- fid - unique assigned identifier
- geom - automatic column describing type of data.
- country = country of data
- ID_1 = province ID
- ID_2 = lgu id
- ID_3 = village ID
- ENGTYPE_3 = village
- province = province
- lgu = local government unit
- barangay = barangay
- BgyName = barangay
- pop_2010 - population in 2010, based on most recent village census data
- Area_GEO - area
- fishers_n - number of fishers
- interviews - interviews (binary)
- interviews_n - number of interviews
- area_m2_orig - original area in meters2 (based on full land area of barangay)
- pop_dens_km2_orig - original population density in meters2 (based on full land area of barangay)
- area_m2_inhab -- area in meters2 (# removes mangrove only islands from area calculations)
- area_km2_inhab - area in kilometes 2 (# removes mangrove only islands from area calculations)
- pop_dens_km2_inhab - population density in kilometes 2 (# removes mangrove only islands from area calculations)
BarangayPopulation2010p.shp - shapefile of population in villages
- FID - unique assigned identifier
- Shape - automatic column describing type of data.
- ID_0 - country ID
- ISO - country code
- NAME_0 = country of data
- ID_1 = province ID
- ID_2 = lgu id
- ID_3 = village ID
- ENGTYPE_3 = village
- province = province
- lgu = local government unit
- Barangay = barangay
- pop_2010 - population in 2010, based on most recent village census data
- Area_GEO - area
BarangayPopulation2010p_inhabited_2025.shp - shapefile of population data and data about villages
-
FID - unique assigned identifier
-
Shape - automatic column describing type of data.
-
ID_0 - country ID
-
ISO - country code
-
NAME_0 = country of data
-
ID_1 = province ID
-
ID_2 = lgu id
-
ID_3 = village ID
-
ENGTYPE_3 = village
-
province = province
-
lgu = local government unit
-
Barangay = barangay
-
pop_2010 - population in 2010, based on most recent village census data
-
Area_GEO - area - removes mangrove only islands from area calculations
village_fishing.shp - shapefile of fishing data. location central to village location, but near coast (point data)
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data.
- BgyName = barangay
- fishers_n - number of fishers
- interviews - interviews (details)
- intervie_1 - number of interviews
File: ports.zip
Description: Contains shapefile of location of ports/piers in the Danajon Bank.
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data.
- Port - name of Port, where known
File: reef_area.zip
Description: Contains shapefiles of reef area in the Philippines, including the Danajon Bank. File from the USAID Fish Project.
reef_region_UTM.shp - shapefile of reef regions
- FID - ArcGIS assigned identifier
- Shape - automatic ArcGIS column describing type of data.
- reef_name - blank, except states "Danajon Bank" for the reefs in the Danajon Bank area
File: river_distance.zip
Description: Contains geoTIFF raster of distance to rivers in kilometers (km) from pixels in the Focal Area. River mouths were identified using local knowledge and satellite images.
DistRiver.tif
- Band1 = band of geoTIFF with data. cell values represent km distance from rivers.
File: town_distance.zip
Description: Contains geoTIFF raster of distance from villages to towns from pixels in the Focal Area. cell values represent km distance from nearest towns (municpal centers, usually having the same name as the LGU (local government unit).
DistTown.tif
- Band1 = band of geoTIFF with data. cell values represent km distance from towns.
File: translations.zip
Description: Contains .csv files of translations for various topics related to interviews about fishing, gears and habitats. Note that because these are primarily from interviews with fishermen in Bohol many of these words are the Boholiano dialect if Visayan.
translations_fishing_roles_20160130.csv
- Name - name of role
- Description - description of role
translations_gear_descriptions_20160130.csv
- ID1 - ID for gear category - used to link to namevariants, which is used for Vlookup tables in other files
- Source who the information is from
- Name.Variants - all variations on the name
- Cebu.Name - standard cebuano name - note this was somewhat arbitrary
- Eng.Name1 - standard english name - this name has a fuller description of the gear
- Eng.Name2 - shorter english name, maintaining information on habitat impacts if applicable
- Eng.Name3 - generic english name
- Eng.Name4 - fishing, gleaning, both, swf
- ID2 - ID corresponding to Eng.Name2
- ID3 - ID corresponding to Eng.Name3
- ID4 - ID corresponding to Eng.Name4
- YrIllegal.National - Year Gear was made illegal (National legislation only)
- Legislation - Code that made gear illegal
- Reason - reason gear is illegal
- Variable - is the information on legality variable (0/1)
- # Fishers - estimate # of fishers who use the gear
- GearDescription - general description of the gear
- Time - time that the gear is used (day/night/both)
- Habitat - habitat that the gear generally targets - sometimes this depends on what species is being targeted
- Seagrass - if the gear targets this habitat
- Coral - if the gear targets this habitat
- Sand - if the gear targets this habitat
- Mangrove - if the gear targets this habitat
- mud - if the gear targets this habitat
- Deep.Midwater - if the gear targets this habitat
- active/passive - if the gear is active or passive. Based on local knowledge of research assistants.
- scaring - if the gear uses scaring device
- Boat - if the gear uses swimming, boat, waking, etc
- Target - target species - not exhaustive
- target.other - other target species - not exhaustive
- Action - action used to work the gear
- Habitat Damage - information on the gear's habitat damage
- Bycatch - information on the gear's bycatch
- Mesh size/eye (cm) - mesh size of nets - estimated from Bernie and Gerry
- Notes - notes
- References - references if any, esp for habitat damage
translations_habitat.csv
- Code - code for habitat
- English - English name
- Cebuano - Vijayan/Cebuano name
- Category - habitat or area
translations_misc_20160130.csv
- Visaya - Visayan/Cebuano word
- English - English word
- catch - mostly blank, but some information on targeted species
- translations_name_variants_fishing_gear_20160130.csv - this file is a code to link gear timelines with specific gear names and types
- NameVariant - variant of the gear
- ID - unique ID number for each gear
- translations_target_species_20160130.csv
- Target - Bisaya - targeted species. Visayan/Cebuano word
- English - English word for species
- source - source of translation, not complete
- method - method of fishing used to catch species, not complete
Code/software
Analyses were conducted in R and R Studio.
Code for main analyses are available on
https://github.com/jselgrath/stressors_and_coral_reefs
DOI: 10.5281/zenodo.17459945
Code for accuracy assessment are available on:
https://github.com/jselgrath/stressors_and_coral_reefs_habitat_accuracy
DOI: 10.5281/zenodo.17460032
Other files were created in ArcPro.
Access information
Other publicly accessible locations of the data:
Data was derived from the following sources:
- http://ctatlas.coraltriangleinitiative.org/Dataset
- http://oneocean.org/download/
- https://www.namria.gov.ph/products.aspx#gsc.tab=0
- https://resources.unep-wcmc.org/products/4b411a9709ea4b0da4ad97a6e19a48e0
- https://psada.psa.gov.ph/catalog/13
Human subjects data
We have received permission from our participants to publish de identified data in the public domain. Data are highly summarized and have no information linking information to respondents.
Study site
The Philippines, located in the global center of marine biodiversity, supports 21,983 km2 of coral reefs, and is a global priority for conservation due to the high threats to the region (Halpern et al., 2008; Roberts et al., 2002) and a relatively high resilience to coral bleaching (Sully et al., 2019). The Danajon Bank (10˚15’0’N, 124˚8’0’E) is a double barrier reefs in the central Philippines that sits off the northern edge of Bohol – a province characterized by small-scale farming and high levels of erosion (Provincial Government of Bohol, 2011). Several characteristics of the Danajon Bank potentially enable it to resist anthropogenic stressors. Such characteristics include high biodiversity and low incidence of coral bleaching (excluding the 1998 bleaching event) (Bayley et al., 2019; Philippine Coral Bleaching Watch, 2013; Sully et al., 2019), although data for many stressors are sparse or at coarse scales (Table S1; Data Sources). Located at the southern end of the sheltered Camotes Sea, the Danajon Bank’s oceanography is characterized by a well-mixed water column, alongshore flow, weak tidal circulation, and the absence of large waves (Villanoy et al., 2006).
The Danajon Bank lies adjacent to Cebu City (the second largest metropolitan area in the Philippines, 3.1 million people), but is itself located in a rural region, which struggles with extreme poverty (Provincial Government of Bohol, 2011). Small-scale fisheries are based on the mainland of Bohol and on small cays, and have exclusive fishing rights to coastal areas (Christie et al., 2006). These fishers use many fishing gears and target diverse species of marine life (Kleiber et al., 2014; Selgrath et al., 2017). Since 1998, small-scale fisheries have been co-managed and many communities have established locally-enforced no-take marine protected areas (MPAs), including 12 MPAs in our study area (Pajaro, 2010; Selgrath et al., 2018). MPAs in the region were established by local fishing communities and NGOs, and frequently placed in areas that had been degraded by fishing prior to their protection (Hansen et al., 2011). Despite these management efforts, heavy fishing pressure, destructive fishing, and high population densities have depleted marine life and degraded much of the marine environment (Bayley et al., 2020; Magdaong et al., 2014; Selgrath et al., 2016).
Overview
This spatial analysis focused on a 19km by 22km area in the central Danajon Bank (hereafter ‘Study Area’). In the Study Area, we evaluated the relationship between reef state (presence of living coral vs rubble) and 25 spatially explicit variables (Table S1; Data Sources). Variables described anthropogenic and biophysical attributes of the reef system. These social-ecological attributes were derived from remote sensing imagery, participatory mapping with local fishers, and publicly available data sources (Table S1; Data Sources). Using mixed, logistic models, we considered additive, antagonistic, and synergistic effects of stressors to understand their influence on the system.
For this analysis, we used a binary measurement of reef state, classified as (a) ‘coral-dominated’, or (b) ‘rubble dominated’ (see Marine habitats below for classification details, hereafter ‘coral’ or ‘rubble’). Using a binary variable as a proxy for reef functioning represents the inherent trade-offs between highly detailed surveys of small areas and the coarser mapping of whole ecosystems.
Spatial datasets Marine habitats
We created maps of marine habitats of the Study Area by classifying high spatial resolution (2 m) multi-spectral satellite images (WorldView2, dates: 2010/05/10 and 2012/20/04) using object-based image analysis (OBIA). These two dates were the earliest times with clear WorldView2 images of the Study Area, as the satellite was launched in October 2009. Mapping was restricted to shallow areas (approximately 15 m depth). Geomorphic zones (e.g. reef crest, reef flat) were identified and further classified into 16 benthic (seafloor) habitat types or classes (Table S2).
For this analysis, we simplified coastal habitats into 5 classes: (living) coral, rubble, sand, seagrass, and mangroves (Table S2). The rubble-dominated category included degraded rubble, deal coral, and macroalgae. Since the Danajon Bank contains areas with mixed habitats, the rubble-dominated class included areas containing rubble mixed with other habitats (e.g., sand, seagrass). During map classification, we used an approximately 30% cover threshold from field survey data to classify reefs as ‘living coral’ during the classification process. As the satellite data map did not capture habitats beds in turbid shallow waters or in deeper waters, we supplemented the habitat maps with ILK information about habitats that we documented during participatory mapping interviews with fishers. See Selgrath et al., 2016 for details on habitat mapping. Using independent data from a long-term monitoring program (Project Seahorse Foundation/ZSL Philippines) we created a confusion matrix to evaluate map accuracy and updated three categories to improve classification accuracy (Table S3).
Fishing and other anthropogenic pressures
Fishing effort. Fishing is one of the greatest threats to coral reef ecosystems (Burke et al., 2011), but longitudinal spatial measures of fishing effort on reefs are exceedingly rare (Selgrath, Gergel, et al., 2017). We developed maps of fishing effort and fishing gear use during semi-structured, participatory mapping interviews. We interviewed 391 randomly sampled male fishers from 23 randomly selected villages (approximately 7% of fishers from 50% of villages). Villages were located both within the Focal Area, and up to 10km outside of the Focal Area. Of the total respondents, 295 individuals fished inside of the Focal Area. Before conducting interviews, we obtained written consent from local mayors and/or councils, and oral consent from village officials and fishers. Research methods, including consent procedures, were approved by the University of British Columbia’s Human Behavioural Research Ethics Board (H07-00577). In the mapped area, fishers reported using 88 fishing gears (e.g., hand-line, bottom-set gillnets, skin diving, blast fishing). We estimated that during 2010 approximately 8,000 men fished full- or part-time inside the 418 km2 study area (Selgrath et al., 2017).
To evaluate fishing effort, we digitized fishing grounds from interviews using heads up digitizing in ArcGIS 10.1 (Environmental Systems Research Institute, Redlands, California). Maps depicted fishing effort in 20 m by 20 m grid cells. For each year, we integrated individual respondent’s fishing maps to map cumulative spatial fishing effort (SpEFti) as the estimated number of fishing days (E) by all fishers from study villages (F) in year (t) in grid cell (i). Estimating total effort allowed us to account for changes in the sample size of respondents over time. We based the estimated number of fishers (F) in year (t) on estimates of demographics changes in study villages. We also conducted an area accumulation analyses to ensure that we interviewed a representative number of fishers (see Selgrath & Gergel, 2019 for the methods and detailed results of the area accumulation curve analysis).
To evaluate the long-term influence of fishing effort, we normalized pixel values of the yearly fishing effort maps from 0-1 relative to the maximum effort from all years (maximum fishing days per year in grid cell (i) = 5,546 days) (Halpern et al., 2015) where normalized SpEFti is the spatial fishing effort (SpE) for all fishers from study communities (F) in year (t) in grid cell (i) scaled to the maximum fishing effort by all fishers (F) from all cells (I) in all years (T)
* normalized SpEFti = SpFti/MaxSpEFTI (1)
We considered the influence of normalized fishing effort (normalized SpEFti) over three time periods:
(1) Contemporary effort (2010): cumulative fishing days (SpE) by all fishers from study communities (F) in year (2010) in grid cell (i)
* Contemporary Effort2010= SpEF2010i (2)
(2) Cumulative effort (1980-2010): cumulative number of fishing days (SpE) by all fishers from study communities (F) in two to four years (t1, …, t2010) in grid cell (i)
* Cumulative Effortt1:2010= SpEFt1i+…+SpEF2010i (3)
(3) Lag effort (1980-2000): cumulative number of fishing days (SpE) by all fishers from study communities (F) in three years (1980, 1990, 2000) in grid cell (i)
* Legacy Lag Effort1980:2000= SpEF1980i+SpEF1990i+SpEF2000i (4)
We created fishing pressure maps using the same methods for blast fishing using explosives, which we hypothesized would have a large impact on the probability of an area supporting living coral (see Do multiple stressors affecting corals have additive, synergistic, or antagonistic impacts? below). Blast fishing effort was normalized relative to the maximum blast effort from all years (maximum blast fishing days per year in grid cell (i) = 336 days).
MPAs, human population, and markets. We assessed the influence of protection by considering sites located inside and outside of existing MPAs. We used population information collected from village census data to evaluate the influence of four anthropogenic variables: population density; distance to the regional fish market in Cebu City; distance to towns; and distance to fishing communities (Table S1). Several ocean stressors (e.g. nutrient loading, trampling) are correlated with the population densities of adjacent communities (Mora, 2008; Rangel-Buitrago et al., 2024). We therefore used distance decay with a square root decay to calculate the risk from population density that decreased with distance to the communities (i.e., an environmental risk surface; Table S1) (McPherson et al., 2008).
Biophysical attributes
Habitat configuration. From the habitat map described above, we derived landscape pattern indices describing the arrangement of habitats in the seascape (Table S1)(Wedding et al., 2011). We emphasized class-level indices describing coral and rubble patches as well as their relationships to other habitat classes (Table S1; Fig. 2)(McGarigal & Cushman, 2002). For points within coral and rubble patches, metrics included: nearest neighbor distance between points and neighboring patches; patch area; patch edge length; and patch compactness (incorporating edge:area ratio and patch size - inverse of patch shape index described in McGarigal et al., 2012). Because reefs exhibit zonation patterns (Hoegh-Guldberg et al., 2011), we also determined the distance of points within coral/rubble patches to mangroves and seagrass habitats (i.e., the distance between the coral/rubble patch and the edge of the nearest mangroves and nearest seagrass beds. We divided distance by 100 m to look at changes per 100 m of distance (Gelman & Hill, 2007).
Bathymetry, rivers, zonation, aragonite saturation, thermal stress. We obtained information for biophysical attributes using existing data sources (Table S1; Fig. 2). First, we created a bathymetry layer by interpolating depth soundings from a NAMRIA nautical chart. Spline interpolation creates a continuous raster surface from sampled point values using a two-dimensional minimum curvature spline technique. Second, we identified river mouths using WorldView-2 satellite images and characterized the seascape based on distance to rivers. Third, we classified the study area into six ecological zones modified from Hansen et al., 2011: (1) inshore coastal zone of turbid waters, extensive mangroves, and some larger communities supporting rice farming and mining; (2) terrestrial islands that support extensive mangroves and farming; (3) coastal inner reef zone near terrestrial islands with moderately clear waters and mangroves; (4) an inner reef zone with intermediate characteristics of the coastal and outer reefs; (5) an offshore, outer reef zone with clear waters, extensive seagrass beds, scattered cays adjacent to the channel between the Danajon Bank and Olango Island; and (6) an offshore, outer reef zone with clear waters, extensive seagrass beds that is adjacent to the Camotes Sea. Fourth, we used data from regional models of aragonite saturation and past thermal stress (1998-2007) (Burke et al., 2011). Calculations used R (R Core Team, 2025) and ArcPro 3.5 (Environmental Systems Research Institute, Redlands, USA).
