DOI: 10.5061/dryad.z34tmpgpt
title: The influence of multiple stressors on the spatial distribution of corals

description: 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.
description: 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
 <em>Marine habitats</em> 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 <em>Fishing effort.</em> 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
 km<sup>2</sup> 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
 (<em>SpE<sub>Fti</sub></em>) as the estimated
 number of fishing days (<em>E</em>) by all fishers from study
 villages (<em>F</em>) in year (<em>t</em>) in grid
 cell (<em>i</em>). Estimating total effort allowed us to
 account for changes in the sample size of respondents over time. We based
 the estimated number of fishers (<em>F</em>) in year
 (<em>t</em>) 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 (<em>i</em>) = 5,546 days)
 (Halpern et al., 2015) where normalized SpEFti is the spatial fishing
 effort (SpE) for all fishers from study communities
 (<em>F</em>) in year (<em>t</em>) in grid cell
 (<em>i</em>) scaled to the maximum fishing effort by all
 fishers (<em>F</em>) from all cells (<em>I</em>)
 in all years (<em>T</em>) *         
  normalized SpE<sub>Fti
 = SpFti</sub>/MaxSpE<sub>FTI</sub>                     
                                    (1) We considered
 the influence of normalized fishing effort (normalized
 SpE<sub>Fti</sub>) over three time periods:
 (1) <em>Contemporary effort (2010)</em>: cumulative
 fishing days (SpE) by all fishers from study communities (F) in year
 (2010) in grid cell (i) *           Contemporary
 Effort<sub>2010</sub>= SpE<sub>F2010i</sub>       
                                                 (2) (2)
 <em>Cumulative effort (1980-2010)</em>: cumulative number of
 fishing days (SpE) by all fishers from study communities (F) in two to
 four years (<em>t1, …, t2010</em>) in grid cell (i)
 <br> *           Cumulative
 Effort<sub>t1:2010</sub>=
 SpE<sub>Ft1i</sub>+…+SpE<sub>F2010i</sub>         
                           (3) (3) <em>Lag effort
 (1980-2000)</em>: 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
 Effort<sub>1980:2000</sub>=
 SpE<sub>F1980i</sub>+SpE<sub>F1990i</sub>+SpE<sub>F2000i</sub>             (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 <em>Do multiple stressors affecting corals have additive, synergistic, or antagonistic impacts?</em> 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).   <em>MPAs, human population, and markets.</em> 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 <em>Habitat configuration</em>. 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). <em>Bathymetry, rivers, zonation, aragonite saturation, thermal stress.</em> 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).
description: # The influence of multiple stressors on the spatial distribution of
 corals Dataset DOI:
 [10.5061/dryad.z34tmpgpt](https://doi.org/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](https://github.com/jselgrath/stressors_and_coral_reefs) or\ the archived release of this GitHub repository: [https://doi.org/10.5281/zenodo.17459945](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) &gt; Barangay &gt; 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](https://www.r-project.org/) 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 &gt; 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](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) ``` * 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* 
 
 ###### # folder not used in multiple stressors paper
 **./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.* * FID - ArcGIS assigned identifier * Shape - automatic ArcGIS
 column describing type of data * Hab1 - original habitat category * Hab2 =
 simplified version of original habitat category. * HapPaper = reclassified
 version of habitat data * Geomorphic - geomorphology class of location *
 Location - EcoZone (note not same version as analysis). Olango is a large
 islands near cebu included in mapping, but not analysis * Map - RS =
 remote sensing, LEK = participatory mapping (Local Ecological Knowledge),
 edited = fixed manually due to errors * 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 &gt; 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](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](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: *
 [https://doi.pangaea.de/10.1594/PANGAEA.864306](https://doi.pangaea.de/10.1594/PANGAEA.864306) Data was derived from the following sources: * [http://ctatlas.coraltriangleinitiative.org/Dataset](http://ctatlas.coraltriangleinitiative.org/Dataset) * [http://oneocean.org/download/](http://oneocean.org/download/) * [https://www.namria.gov.ph/products.aspx#gsc.tab=0](https://www.namria.gov.ph/products.aspx#gsc.tab=0) * [https://resources.unep-wcmc.org/products/4b411a9709ea4b0da4ad97a6e19a48e0](https://resources.unep-wcmc.org/products/4b411a9709ea4b0da4ad97a6e19a48e0) * [https://psada.psa.gov.ph/catalog/13](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.