Abstract The location and intensity of small-scale fishing is dynamic over time, greatly shaping ecosystems. However, historical information about fishing effort and fishing gear-use are often unavailable. Within a marine biodiversity hotspot in the Philippines, we characterized spatio-temporal dynamics of fishing (1960–2010) using participatory mapping. First, we compared non-spatial and spatial estimates of total fishing effort. Our non-spatial estimate indicated that fishing increased 2.5 fold, reaching 1.3 million fishing days per year in 2010. Yet, spatial estimates showed fishing effort increased >20 fold, with the highest effort in 1990. Second, we evaluated how spatial characteristics of fishing changed over time. We introduced a method to estimate the sample size of fishers needed to accurately map the extent of fishing. By 2000, fishing extent grew 50% and small-scale fisheries affected over 90% of the coastal ocean. The expanded fishing area coincided with a greater spatial overlap among fishing gears and a proliferation of intensive fishing gears (destructive, active, non-selective). The expansion and intensification of fishing shown here emphasize the need for spatial approaches to management that focus on intensive, and often illegal, fishing gears. Such approaches are critical in targeting conservation actions (e.g. gear restrictions) in the most vulnerable areas. Introduction Quantifying fisheries’ impacts and developing sound approaches to fisheries management requires understanding fishing effort in space and time (Walters, 2003; Stewart et al., 2010). Since the mid-20th century, industrial fisheries have seen global increases in fishing effort (Anticamara et al., 2011), as well as a dramatic spatial expansion of fishing into new areas (Swartz et al., 2010), with corresponding impacts on marine life (Pauly and Zeller, 2016). In industrial fisheries, these dynamics have catalysed the uptake of technologies to track fishers through space and time (e.g. vessel monitoring systems) (Lee et al., 2010), benefitting fisheries management. For example, spatial information enables correct interpret of catch-per-unit-effort (CPUE) trends (Walters, 2003). Moreover, managers have leveraged spatial information about fishing to reduce overlap between high impact practices and sensitive marine life (Rosenberg, 2000). Small-scale fisheries also place significant pressure on the ocean, but knowledge of their spatial and temporal dynamics lags behind that of industrial fisheries. Small-scale fisheries are important for livelihoods and food security—particularly in the developing world (Teh and Sumaila, 2013). The world’s 22 million small-scale fishers have large impacts (Teh and Sumaila, 2013), annually removing ∼30% of global catches (22 million tonnes of marine life) (Pauly and Zeller, 2016). Despite their importance, small-scale fisheries have been poorly documented and are often neglected by management (FAO, 2015). Improving our understanding of spatio-temporal dynamics of small-scale fisheries could help inform recent international sustainability commitments (e.g. FAO Voluntary Guidelines for Securing Sustainable-Scale Fisheries) (FAO, 2015). A spatial and long-term perspective can provide a strong foundation for understanding how fisheries and ocean ecosystems interact. Historical decisions influence the structure and function of contemporary ecosystems (Tomscha and Gergel, 2016), as well as the distribution of species and habitats (McKey et al., 2010). Historical information can help managers evaluate fishers’ responses to past regulations (Walters and Martell, 2004, p. 200). Historical perspectives provide context for how current conditions arose and the effectiveness of past governance (Ostrom, 1990), thereby enabling better decision-making (McClenachan et al., 2012). In coral reef systems, catch declines and ecosystem degradation are often attributed to a growing number of fishers, as well as the intensification of fishing (Mangi and Roberts, 2006). Yet, little long-term data exist to contextualize changes seen in small-scale fisheries. The dynamics of small-scale fisheries are challenging to document. Conventional fisheries data (e.g. catch records) are rarely available. Furthermore, many technologies developed for monitoring industrial fisheries are impractical. One major challenge is the sheer number of people involved (25 times more small-scale fishers than industrial). Additionally, small-scale fisheries tend to be decentralized (spread along a coastline) and occur in regions with limited funds, governance, and/or technical expertise to support advanced technologies (FAO, 2015). As a result, less conventional approaches, such as use of local-ecological knowledge (LEK), can support small-scale fisheries management by providing the best estimates of fishing trends in data-poor situations (Hind, 2015). The need to develop effective management is particularly relevant to small-scale fisheries targeting coral reefs in the Philippines; these reefs are a global conservation priority due to their combination of abundant biodiversity and heavy threats (Selig et al., 2014). In this study, we quantify spatial and temporal changes in small-scale fishing over 50 years (1960–2010) and evaluate implications of these changes. Using a coral reef ecosystem in the central Philippines as a case study, we address three questions: First, do estimates of long-term fishing effort vary when using non-spatial and spatial measures? Second, what are the spatial characteristics of fishing and how do they change over time? Third, how can maps quantifying historic fishing effort be used to strengthen conservation and management? Material and methods Study site Our research focused on the central portion of the Danajon Bank ecosystem in the Central Visayas, Philippines (Figure 1; 10°15′0′N, 124°8′0′E). This double barrier reef supports variable conditions (e.g. turbidity, reef zones). The Danajon Bank sits off the coast of Bohol—a province with extreme poverty and minimal infrastructure (PPDO Bohol, 2013). In the Danajon Bank, the period under study (1960–2010) was characterized by rapid human population growth (Philippine National Statisics Office, 2012) and declines in CPUE (Christie et al., 2006). Fishing is a primary human activity influencing the Danajon Bank because of its high population density and lack of alternative livelihoods. The small-scale fisheries here are multi-gear and multi-species (Christie et al., 2006). Figure 1. View largeDownload slide In the Danajon Bank Ecosystem (Philippines), respondents from interviewed villages were asked to map the history of their fishing practices inside of the mapped area. Figure 1. View largeDownload slide In the Danajon Bank Ecosystem (Philippines), respondents from interviewed villages were asked to map the history of their fishing practices inside of the mapped area. Fisheries management in the Philippines emphasizes co-management, gear restrictions, spatial restrictions, and marine protected areas (MPAs). In the Danajon Bank, these tools are implemented with varying degrees of effectiveness. For example, beginning in the 1930s, national gear-based restrictions banned blast and poison fishing with limited success. More recently the 1998 Fisheries Code [Republic Act (RA) 8550] established largely unenforced regulations restricting fishing to an individual’s home municipality and restricting the use of all destructive, and some active and non-selective fishing gears. Overview Based on participatory mapping and interviews, we compared changes in long-term fishing effort obtained using both non-spatial (days per year) and spatial estimates (days per year at location (i)), and then evaluated how spatial characteristics of the fishery had changed over time (1960–2010) (Supplementary Figure S3.1). We consider how fishing effort has changed by comparing fishing effort during 1 in 10-year intervals (e.g. cumulative days fished by all fishers in 1960, 1970, etc.). Participatory mapping and fisher interviews To document spatial and temporal changes in fishing in the central Danajon Bank, we conducted semi-structured, participatory mapping interviews in 2010 and 2011. During participatory mapping a resource user’s historical experience and expert knowledge are used to create maps of local practices or environments (Chambers, 1994). We interviewed 391 randomly selected fishers from 23 randomly selected villages and towns (Supplementary Table S1.1, Figure 1). We sampled 50% of fishing villages within and up to 10 km from the mapped study area (Figure 1). This extended distance ensured our estimates of fishing effort accounted for those who fished in the mapped area, but lived elsewhere (Supplementary Tables S1.1 and S1.2). From these selected villages, we created a list of all fishers (full- and part-time) using village census records. In the Danajon Bank, women comprise 42% of all fishers and catch about one quarter of the catch mass extracted from Danajon Bank (Kleiber et al., 2014). However, census records did not include women, and we did not attempt to correct this bias (see Supplementary Material S1). From the census-based list, we randomly sampled 7% of fishers in each village to interview (n = 391, with 295 of respondents fishing in study area). Since we were interested in a long time series, we stratified the fishers by age prior to random sampling. We focused our interviews on fishers who were born before 1981 because we estimated that most of this age group would have fished for at least 15 years. This method may underrepresent recent trends driven by young fishers, but prioritizes long time-series of fishing. Prior to interviews, we obtained written consent from local mayors and oral consent from village officials and fishers. Methods were approved by The University of British Columbia’s Human Behavioural Research Ethics Board (H07-00577). After obtaining oral consent from a respondent, we used non-technical language to systematically build up information about the spatial history of his fishing practices (Hall and Close, 2007). Interviews included six steps: personal history, fishing history, gear history, orienting fishers to the map, mapping his fishing grounds, and fishing ground history. Timelines went as far back as the respondents declared they could remember. Documenting individual fishing histories First, to improve the accuracy of recall dates in the interview process, we established a personal timeline for each fisher (e.g. year started fishing; year married), superimposed on a timeline of major events in the Philippines (see questions and timeline in Supplementary Material S4). Second, we recorded years that the respondent fished in the Danajon Bank and any monthly/seasonal or migratory patterns in his fishing. From monthly effort patterns we calculated the total days per year a respondent fished in our study area. Several respondents migrated to fish, so we were careful to disentangle local from distant practices. Third, we made timelines for each fishing gear that a respondent used in our study area, recording the years fished with the gear. We used the respondent’s fishing and gear history to confirm consistency with more specific questions about fishing grounds, later in the interview. Such triangulation helped internally validate interviews to ensure consistency (Chambers, 1994; Neis et al., 1999). Mapping fishing grounds Fourth, we oriented respondents to the hardcopy base-map (later used to draw fishing grounds) by discussing and identifying various landmarks. We tested the respondent’s understanding by asking him to locate places and describe map features until we were comfortable that the map made sense to the respondent. The base-map incorporated a high spatial resolution SPOT-5 satellite image (10 × 10 m grid cell) to improve locational accuracy (Hall and Close, 2007) by allowing fishers to identify specific features in the landscape (e.g. the location of the reef crest). Additionally, the base-map identified anthropogenic landmarks, municipal centers, ports, and MPAs. Fifth, we drew polygons around the respondent’s current and past fishing grounds. The respondent directed us in drawing fishing grounds because many fishers seemed uncomfortable with drawing. Moreover, this method provided consistency among maps made by different fishers (e.g. minimum mapping units). Sixth, we made a spatially referenced timeline for each fishing ground mapped in the previous step (see timeline in Supplementary Material S4). At each fishing ground, we recorded: years fished; months per year fished; days per month fished; and details about fishing gears for each year from 1960 to 2010. We recorded changes (often reported to be gradual) at intervals the fishers chose. Fishing gears and engines Fishers reported using 67 fishing gears in the study area (Supplementary Table S2.1). We created a database of fishing gears based on information derived from surveys, key informants, our own knowledge, and published literature. We evaluated changes in the total number of fishing gears used in a year (gear richness). We grouped gears into eight classes: blast fishing; fish corrals; gleaning; hook-and-line; nets; poison fishing; skin diving; and traps. We defined intensive fishing gears as those with a high magnitude of force, a notable efficiency, and/or a relatively low selectivity (Supplementary Figure S3.2), recognizing their likely higher catchability (particularly for juveniles) and greater impacts (such as habitat damage). We further categorized intensive gears and their non-intensive counterparts into non-exclusive categories as follows: destructive/non-destructive (to reef habitats); active/passive; non-selective/selective; and illegal/legal (relative to when the gear was used). Fishing gears could belong to more than one category (e.g. trawls are destructive, active, and non-selective). Since the availability of boat engines is one factor influencing the distance that fishers travel, we also evaluated the percent of fishers who used engines in each year. Long-term changes in fishing effort Next, we explored different metrics to characterize long-term fishing effort in non-spatial and spatial ways for individual fishers, as well as cumulative measures for all fishers. We focused on fishing effort within in the study area. Since we sampled approximately 50% of fishing communities in the study area, our fishing effort metrics represent approximately half of the total fishing effort by men who fished in the study area. Non-spatial fishing effort Based on interview data, we quantified non-spatial fishing effort using two metrics: (i) individual fishing effort and (ii) cumulative fishing effort by all fishers. This allowed us to evaluate two scales (individual vs. aggregate) over which effort may have changed. First, we calculated mean individual fishing effort where e-ft is the mean of individual effort (days) (e) that respondents (f) fished during 1 year (t): e-ft=Σ1f(e1t+e2t…+eft)ft (1) Second, we estimated the cumulative annual non-spatial fishing effort where Et is the total fishing effort (days) of all fishers from participating communities (F) during one year (t): Et=e-ft×Ft (2) Spatial fishing effort To evaluate how fishing effort changed over time as well as space, we combined information from the fishing timelines and maps using five primary steps. (i) We digitized fishing grounds using heads up digitizing in ArcGIS 10.1 (Environmental Systems Research Institute, Redlands, California). (ii) We linked each respondent’s fishing ground polygons with their reported fishing effort and gear history (based on interviews) to create maps of their total effort as well as gear-specific effort each year. We converted maps to raster format (20 × 20 m grid cell), resulting in one fishing effort raster for each year a respondent fished, as well as additional maps specific to each gear in use that year. (iii) For each year, we summed maps of individual respondents’ to calculate cumulative fishing effort at each location where SpEfti is the cumulative effort reported by all respondents (f) in year (t) in grid cell ( i) and efti is the spatially explicit annual fishing effort for each respondent (f) in year (t) in grid cell ( i): SpEfti=Σ1fe1ti+e2ti…+efti (3) Last, two additional metrics were created to enable more appropriate map comparisons over time. Maps of relative (proportional) fishing effort controlled for the different sample sizes in each year. Maps of cumulative fishing effort for all fishers (from participating communities) were extrapolated from maps of relative fishing effort using demographic information from the region (see Supplementary Material S1 for details on demographic information). Cumulative fishing effort maps accounted for changes in sample size over time and allowed us to consider aggregate changes in effort by all fishers. (iv) For the relative effort map we converted the maps of absolute effort of respondents to maps of proportional effort, where relative SpEfti is the proportional distribution of fishing effort by all respondents (f) in year (t) in grid cell ( i), and Eft is the cumulative number of days ( E) that respondents (f) reported fishing in the study area in year (t): relative SpEfti= SpEftiEft (4) (v) We estimated the cumulative spatial fishing effort from communities where cumulative SpEFti is the cumulative number of fishing days ( E) by all fishers from study communities ( F) in year (t) in grid cell ( i). In this analysis, e-ft is the mean number of fishing days ( e-) that respondents (f) fished in the study area in year (t) in grid cell ( i): cumulative SpEFti= relative SpEftie-ftFt (5) We based the estimated number of fishers in year ( F t) on estimates of demographics changes (Supplementary Table S1.2). Additional details on demographics can be found in Supplementary Material S1. Resulting maps were cropped to a uniform 19 × 22 km area to remove inconsistencies at map edges (total area = 418.0 km2; ocean area = 354.1 km2). Spatial characteristics of fishing To understand how the spatial characteristics of fishing activities changed within the region, we considered five dimensions of fishing: extent, effort, concentration, gear intensity, and location. Where necessary, we also controlled for sample size differences among years. Extent is simply the area (ha) fished within the map. To evaluate changes in effort, we assessed how cumulative fishing effort ( cumulative SpEFti) changed in individual grid cells, and across the study area. The concentration of fishing was characterized by its level of spatial auto-correlation. Intensity of fishing gear was evaluated by analysing maps from three subsets of gears: (i) all fishing gears; (ii) fishing gear categories (e.g. hook-and-line, nets); and (iii) four classifications of intensive fishing gears and their non-intensive counterpart: destructive/non-destructive; active/passive; non-selective/selective; and illegal/legal. Finally, we visually identified ecological and geomorphic characteristics of locations most targeted by fishing. (See Supplementary Material S1 for descriptions). Focusing on relative effort ( proportional SpEfti) and total spatial fishing effort ( cumulative SpEFti) allowed us to compare maps between years, despite different sample sizes in each year (Supplementary Table S1.2). Analyses were performed using the programmes ArcGIS 10.3 and R 3.2.4 (www.r-project.org). Understanding spatial extent using area accumulation curves For each year we quantified the area (km2) which was fished. Additionally, we accounted for how differing sample size of fishers among years influenced the spatial extent of fishing. To do so, we adapted the rarefaction curve concept (Gotelli and Colwell, 2001) to estimate the fisher sample size needed to map 90% of mean fishing extent in 2000 and 2010 (the years with the largest sample size in terms of fishers and area fished). In ArcGIS 10.3 we used bootstrapping to estimate the extent of fishing grounds mapped for each year with different samples sizes of respondents (range: 1 respondent—the maximum number of respondents in a year) (Payton et al., 2003). We used 10 iterations per sample size and removed two outlier fishing grounds. Spatial patterns of fishing effort We evaluated changes in the spatial patterns of fishing effort in two ways: via point pattern analysis and grid-based analyses. We used point pattern analysis to assess how fishing effort changed over time in individual grid cells ( i). We sampled 1000 randomly distributed points on the time series of maps. From these, we compared changes in fishing effort at site (i) between successive years (e.g. 1960 vs. 1970) using one-tailed paired sample t tests. Based on the statistical distribution of effort from all grid cells, we used two-sample Kolmogorov-Smirnov (K-S) tests (Mitchell, 2005, p. 84) to evaluate changes in how much of the ocean was fished frequently or rarely. We also used the distribution of effort in all grid cells to assess the spatial autocorrelation of fishing effort, using Moran’s I. Results Of the 391 respondents, 75% fished in the study area during some or all of their fishing careers. The mean percentage of fishers whose boats had engines was fairly steady from 1960 to 1990, then increased sharply in 2000 and 2010 (range: 17–47% boats with engines). See Supplementary Material S1 for further discussion of demographic changes. Long-term changes in fishing effort Individual fishing effort was consistent over time (mean: 218–254 days per year; Table 1: Individual fishing effort). There was, however, a large amount of variance in the number of days that individual respondents reported fishing in the study area. Some fishers fished year-round (full-time or part-time) while others fished in distant provinces, typically for eight months a year. Non-spatial fishing effort—the total number of days that people fished inside the study area—increased 2.5-fold from 1960 to 2010. By 2010, people from the study communities cumulatively spent over 1.3 million days fishing in the study area (Table 1: non-spatial fishing effort). If we assume that this estimate represents half of the cumulative effort (based on our random sample of 50% of fishing communities), we extrapolate that the study area supports 2.6 million fishing days per year by male fishers. Table 1. Changes in the extent and effort (days per year) of small-scale fishing in the entire study area (non-spatial) and at specific locations (grid cells; spatial) in the Danajon Bank Ecosystem (Philippines) (1960–2010). Individual fishing effort Cumulative fishing effort: non-spatiala Cumulative fishing effort: spatiala Year % Ocean fished Individual effort [mean (SD)] Days per year Increase since 1960 (cumulative effort) Days per year (max)a Days per year [mean (SD)] Increase since 1960 (mean effort) 1960 59%b 241 (112) 529 959 1 876 51 (126) 1 1970 71%b 254 (88) 727 202 1.4 960 151 (205) 3 1980 79% 222 (107) 777 444 1.5 2361 544 (543) 10.7 1990 85% 223 (109) 810 382 1.5 5546 1104 (1102) 21.6 2000 92% 218 (111) 1 009 994 1.9 5178 1049 (1057) 20.6 2010 92% 244 (94) 1 343 708 2.5 4213 924 (790) 18.1 Individual fishing effort Cumulative fishing effort: non-spatiala Cumulative fishing effort: spatiala Year % Ocean fished Individual effort [mean (SD)] Days per year Increase since 1960 (cumulative effort) Days per year (max)a Days per year [mean (SD)] Increase since 1960 (mean effort) 1960 59%b 241 (112) 529 959 1 876 51 (126) 1 1970 71%b 254 (88) 727 202 1.4 960 151 (205) 3 1980 79% 222 (107) 777 444 1.5 2361 544 (543) 10.7 1990 85% 223 (109) 810 382 1.5 5546 1104 (1102) 21.6 2000 92% 218 (111) 1 009 994 1.9 5178 1049 (1057) 20.6 2010 92% 244 (94) 1 343 708 2.5 4213 924 (790) 18.1 a Fishing effort estimations based on estimated number of fishers from study villages and the mean number of days fished by respondents in that year. b Fishing extent estimated based on modelling. Mean total spatial fishing effort—the total number of days that people fished at specific sites ( i)—peaked in 1990 at levels that were 21.6 times higher than 1960 levels. After 1990, mean spatial fishing effort slightly declined (Table 1: Spatial fishing effort; Figure 2b and c). Changes in both non-spatial and spatial fishing effort were consistent with a substantial increase in the estimated number of fishers (Supplementary Table S1.2: Estimated fisher population). Figure 2. View largeDownload slide Spatial fishing effort (days per year) in the Danajon Bank Ecosystem (Philippines) from 1960–2010. (a) Relative maps show the percent of the total fishing effort and (b) estimated maps show the cumulative fishing effort by fishers from interviewed villages. Fishing effort is comparable among years. (c) Histograms shows the area (ha) affected by varying levels of fishing effort. Land areas were excluded from the analysis. Figure 2. View largeDownload slide Spatial fishing effort (days per year) in the Danajon Bank Ecosystem (Philippines) from 1960–2010. (a) Relative maps show the percent of the total fishing effort and (b) estimated maps show the cumulative fishing effort by fishers from interviewed villages. Fishing effort is comparable among years. (c) Histograms shows the area (ha) affected by varying levels of fishing effort. Land areas were excluded from the analysis. Spatial characteristics of fishing Understanding spatial extent using area accumulation curves During the 50 years under study, the spatial extent of fishing—within the study area—expanded by a factor of 1.6 (Table 1: Extent; Figure 2). The majority of the increase happened between 1960 and 2000, after which the extent of fishing remained steady until 2010 (Table 1: Extent; Figure 2). The fishing area rarefaction curves (Supplementary Figure S3.3) demonstrated that mapping 90% of the maximum fishing extent required a sample size of 125 fishers (rounded from n = 126). From 1980 onward there were adequate sample sizes (n > 125 fishers). As fisher sample sizes in 1960 and 1970 were too small to accurately estimate the area fished (Supplementary Table S1.2), these two decades required estimates of fishing extent be based on modelling. We compared the fit of linear and quadratic models with observed extents from 1980 to 2010. The quadratic model of fishing area had the best fit (quadratic r2 = 0.98; linear r2 = 0.91). Thus, we used fishing extent estimates for 1960 and 1970 derived from the quadratic model (Supplementary Figure S3.3). Fishing effort: all fishing gears In our study area, the diversity (richness) of fishing gears used in a year increased over time from 14 gears in 1960 to 60 gears in 2010 (Supplementary Table S1.2). When we compared spatial fishing effort at randomly distributed points through time, we found that effort increased significantly in individual grid cells ( i) over the first three time-steps (t test: p < 0.001; Supplementary Table S1.3). After the year 2000, spatial fishing effort at individual grid cells ( i) did not change significantly (Supplementary Table S1.3). When we assessed how much of the ocean was fished frequently or rarely, there were significant changes in each time step (Figure 2c; K-S test: p < 0.001; Supplementary Table S1.3). From 1960 to 2000 there was a shift from (i) no sites with high levels of fishing to (ii) several sites with high levels of fishing. In the year 2010, this pattern began to reverse as the spatial distribution of fishing effort became somewhat less concentrated. Over the 50-year period under study, fishing remained highly concentrated (Moran’s I > 0.96 for all years and all gear categories). Fishing extent and effort: categories of fishing gear The extent of the four most commonly used fishing gear categories (hook-and-line, nets, diving, traps) expanded over time (Figure 3; Supplementary Table S1.2) and the spatial distribution of fishing effort for these four gears changed significantly in each decadal period (K-S test: p < 0.001; Supplementary Table S1.1). In 1960, hook-and-line fishing was the predominant fishing gear and was used in ∼10% of the study area. At the same time nets, diving, and traps were used in <5% of the study area (Figure 3). From 1970 to 2010, nets’ extent doubled, which was the greatest areal increase for any fishing method. In 2010 hook-and-line fishing and nets were the two most widely used fishing gears (maximum extent = 71%, Figure 3). From 1990 to 2010, blast fishing was used in ∼20% of the ocean, while poison fishing was more limited in extent (area = 2%; Supplementary Table S1.3 and Supplementary Figure S3.4). During the same time, the area gleaned by fishers more than doubled from 3 to 7%, while fish corrals remained scarce (<0.05% of the ocean; Supplementary Table S1.3 and Supplementary Figure S3.4). When comparing fishing effort at individual sites, cumulative fishing days for hook-and-line and nets grew over time (t test: p < 0.001), peaking in 1990 (Supplementary Table S1.3). Dive fishing was the only category of fishing gear in which the number of fishing days at individual sites increased significantly during every decade (t test: p < 0.001; Supplementary Table S1.3). Figure 3. View largeDownload slide Spatial fishing effort (days per year) in the Danajon Bank Ecosystem (Philippines) from 1960 to 2010 for the four most commonly used classes of fishing gears. Fishing effort is comparable among years and among gears. Figure 3. View largeDownload slide Spatial fishing effort (days per year) in the Danajon Bank Ecosystem (Philippines) from 1960 to 2010 for the four most commonly used classes of fishing gears. Fishing effort is comparable among years and among gears. Fishing extent and effort: intensive fishing gears We classified 18% of gears as destructive, 49% of gears as active, and 68% of gears as non-selective. Many gears were illegal (20%), with 15% of all gears becoming illegal after changes in fisheries regulations (1998 Fisheries Code). During the five decades under study, intensive fishing gears were widely used throughout the study area (Supplementary Table S1.3; Figure 4). At individual sites, the cumulative number of days fished for all four categories of intensive gears increased from 1970 to 2000, but did not change significantly from 2000 to 2010 (Supplementary Table S1.1). Figure 4. View largeDownload slide Spatial fishing effort (days per year) in the Danajon Bank Ecosystem (Philippines) from 1960 to 2010 for intensive and non-intensive categories of fishing gears. Fishing effort is comparable among years and among gears. Figure 4. View largeDownload slide Spatial fishing effort (days per year) in the Danajon Bank Ecosystem (Philippines) from 1960 to 2010 for intensive and non-intensive categories of fishing gears. Fishing effort is comparable among years and among gears. From 1960 to 2010 there was a 5.3-fold increase in the spatial extent of destructive gears (Supplementary Table S1.3; Figure 4a) and mean fishing effort by destructive gears grew by a factor of 39 (Table S1.3). Over time, the spatial extent of active gears grew 4.4-fold to target 88% of the mapped ocean (Supplementary Table S1.3; Figure 4c). Non-selective gears consistently were used in a broader area than selective gears (Supplementary Table S1.3; Figure 4e). Over time, mean cumulative days per year of active and non-selective fishing effort increased by a factor of 14. There was a 3-fold increase in the extent of illegal fishing from 1990 to 2000 (Supplementary Table S1.3; Figure 4g). Mean total fishing effort of illegal fishing increased by a factor of 48. The major increase in the extent and effort of illegal fishing gears occurred when all destructive gears, many active gears, and most small-meshed nets became illegal under the 1998 Fisheries Code. Locations of fishing effort Overall, the most heavily fished areas were located in channels, with the highest concentration of fishing—including destructive fishing – located in the Northwest Pass (Figures 2 and 4; see Figure 1 for site names). Through time there was a gradual increase in cumulative spatial fishing effort for all fishing at the northern slope of the Caubian Reef (Figure 2b). This location is isolated from most villages (Figure 1). The trend of growing fishing effort on the northern Caubian reef became more pronounced in 2010 (Figure 2b). This trend was associated with an increase in destructive gears on some reef slopes (Figure 4a), as more fishers began using destructive diving methods to catch invertebrates. Various fishing gears initially were used to target distinct areas in the ecosystem (e.g. reef flats; deep channels). As the use of fishing gears spatially expanded, however, so did their overlapping distribution (Figure 3). Through 1980, hook-and-line fishers predominantly used deep areas, but over time they began fishing more often in shallow reef areas (Figure 3a). Net fishers targeted both deep and shallow areas, but did not use nets in the deeper and more exposed Camotes Sea (Figure 3b). Respondents who used diving primarily fished on reef slopes at offshore reefs (Figure 3c). This pattern was consistent over time, but from 1980 diver density began increasing in reef flats and reef slopes of the inshore reef. Trap fishers initially targeted shallow areas, but began fishing in deeper areas from 2000 (Figure 3d). Discussion Our 50-year analysis of small-scale fisheries in the Danajon Bank offers a rare in-depth look at the spatial and temporal development of small-scale fishing, one of the major influences on coral reef ecosystems (Johnson et al., 2013). When comparing spatial and non-spatial metrics, we found that ignoring space greatly under-estimated the escalation of fishing effort (non-spatial: 250% vs. spatial: 1800% increase from 1960 to 2010). Here we quantified five mechanisms potentially affecting these fisheries: (i) a rapid increase in cumulative fishing effort (days per year)—driven by significant growth in the number of fishers; (ii) an expansion of fished areas; (iii) a diversification of fishing gears; (iv) a proliferation of intensive fishing gears; and (v) a growing overlap among multiple types of fishing gears. Further, we developed a novel method—area accumulation curves—to estimate the sample size of fishers needed to accurately estimate the entire fished area. Our methods are highly transferrable to other data-poor small-scale fisheries impacted by growing fishing pressures. Mechanisms of change: increasing effort, spatial expansion, evolving fishing gears The sharp increase in fishing effort we illustrated in this case study remain an enduring challenge for small-scale fisheries management in the Philippines and elsewhere (Pauly and Chua, 1988; Muallil et al., 2014). Increasing effort can increase annual catches up to a point, but also creates higher variability and produces lower trophic level catches (McClanahan et al., 2008). Significant tension exists between the recognition that the current number of fishers is unsustainable, and the seemingly insurmountable task of reducing the number of fishers. Impediments to lowering fishing effort include a growing human population (www.nscb.gov.ph/secstat/d_popn.asp), a lack of institutions that might limit entry to the fishery (Ostrom, 1990), few alternatives livelihoods (Hill et al., 2012), and feedbacks between poverty and overexploitation (Muallil et al., 2014). Since the large number of fishers drove the incredibly high levels of fishing effort, it is unlikely that this small-scale fishery—and others like it (Muallil et al., 2014)—can become sustainable unless fewer fishers are involved. We base this conclusion on the fairly stable level of individual-level fishing effort and the moderately steady relative fishing effort we documented. These unwavering trends of individual effort are in sharp contrast to the large increase in the total number of fishers. Spatial expansion of fishing, such as we documented here, can influence the distribution of fishing effort and can occur in response to catch declines or new technologies (Walters, 2003; Daw, 2008). Similar to Brazilian fisheries, we documented that locations of the most heavily fished locations were moderately stable over several decades (Begossi, 2006). Unlike Brazilian fisheries, however, we report a simultaneous expansion into previously unfished areas. Our respondents discussed conditions driving their spatial expansion, including growing competition and the need to use less desirable fishing grounds after preferred spots were degraded. These reasons were consistent with the growing number of fishers documented here and widespread reports of environmental degradation (e.g. Marcus et al., 2007). An additional factor allowing fishing to expand and shift was likely the growing number of engines, particularly from 2000 to 2010. During these decades more engines corresponded to increasing effort on the relatively remote northern reefs and, unexpectedly, to a decrease in the spatial fishing effort at the most concentrated sites. We hypothesize that the growing availability of engines allowed some fishers to transfer their effort from heavily fished locations to areas beyond our study site, as seen in Nicaragua (Daw, 2008). The types of fishing gears used influence the environmental impacts of fishing and intensive gears amplify the impacts of high fishing effort (Mangi and Roberts, 2006). Despite this knowledge, our work represents one of few attempts to quantify changes in small-scale fishing gears over time (Johnson et al., 2013). A small, but growing body of work on small-scale fishing gears has included estimates of how many people use gears, collateral impacts, and catches (e.g. Begossi, 2006; Hicks and McClanahan, 2012). We provide a different perspective by quantifying aggregate changes in the gears used in an ecosystem over five decades. We report three specific developments in the use of fishing gears: the increasing diversity (richness) of fishing gears, the increasing intensity of fishing gears, and the increasing extent—and therefore overlap—of different fishing gears. Having identified such specific changes, we can identify management targets and research gaps. For example, the range expansion of fishing and concurrent use of multiple different gears has unknown, yet likely important, effects on fishers and reef systems. Elsewhere, changes in fishing practices have emerged out of necessity (e.g. species substitutions when original targets decline) and opportunity (e.g. emerging technologies or global market access) (Anderson et al., 2011). However, the ecological effects are less clear. It is unknown how growth in gear overlap will interact with other stressors to influence coral reefs, particularly at high levels of total fishing effort (Ban et al., 2014). Historical participatory mapping can strengthen conservation and management When retrospective participatory mapping is designed to account for biases, as with the design of this study, this approach can provide valuable information about past practices. Historical LEK is influenced by the presence of recall inaccuracies, the tendency for past memories to be influenced by windfall events (e.g. unusually large catches), shifting-baselines, and the under-reporting of illegal practices (Pauly et al., 1998; Gavin et al., 2010; O’Donnell et al., 2012). We thus assume that data from earlier years (1960, 1970) are less precise than later years. We aimed to minimize biases by adopting a technically rigorous approach including randomization of communities and respondents, and internal validation of responses (i.e. triangulation) (Chambers, 1994). Snowball sampling has been promoted in fisheries research, but during a pilot study we found that this method was inappropriate because it led to two significant biases: under-sampling of illegal fishing gears and over-sampling of fishing gears used by our initial respondents. We also aimed to improve accuracy by focusing on fishing activities that often changed gradually (e.g. gear use and effort). These activities can be recalled more precisely than those with large fluctuations, such as catches (Neis et al., 1999). Historical maps can lead to better-informed policies in data-poor systems. Maps of historical fishing can help evaluate the influence of past governance approaches (e.g. 1998 Fisheries Code) and be used to set future conservation targets. For example, one could build on our mapping methods to set conservation priorities in locations where vulnerable species and habitats coincide with mounting fishing pressure (Soykan et al., 2014). Furthermore, because it is appreciated that effective conservation hinges on local support, participatory mapping can enhance effectiveness by creating a space for local engagement and trust (Chambers, 1994). Participatory mapping can also improve local buy-in by providing opportunities to identify options that meet conservation targets and minimize impacts to fishers (Klein et al., 2008). Conclusion Our quantitative assessment sheds light on the significant transformations of small-scale fisheries over the past half century. We demonstrate participatory mapping of long-term fishing can foster a deeper understanding of otherwise poorly documented fisheries and can be used to contextualize the conditions found in today’s oceans (McClenachan et al., 2012). From this approach, we identified five mechanisms through which fishing has changed: effort, extent, diversity, intensity, and overlap. Armed with these mechanisms—and supported by 2015 revision to the Philippine Fisheries Code (RA 10654)—managers can work to scale back fishing activity in this overfished ecosystem. However, outcomes will depend on community support and will depend on complementary strategies to address the poverty and overpopulation underlying fishing transformations (D’Agnes et al., 2010). Where stakeholder buy-in is established, maps of historic fishing effort can allow managers to set achievable targets for fostering sustainability in small-scale fisheries. Supplementary data Supplementary material is available at the ICESJMS online version of the manuscript. Acknowledgements This is a contribution from Project Seahorse. We thank the communities of the Danajon Bank, Project Seahorse Foundation for Marine Conservation (now ZSL Philippines), SeaLifeBase, the John G. Shedd Aquarium, and the International Rice Research Institute for their support. We also thank G. Sucano, B. Calinijan, V. Calinawan, V. Lazo, S. Ravensbergen, I. Eddy, and J. Cristiani for help in the field and lab. S. Foster, P. Molloy, D. Kleiber, L. Aylesworth, T. Loh, A.R.E. Sinclair, C. Walters, and three anonymous reviewers provided insights. Funding This research was funded by Planet Action, the Explorer’s Club, and the Point Defiance Zoo and Aquarium. J.C.S. was supported by Fulbright Scholarship, UBC Graduate Fellowship, UBC Department of Zoology Fellowship, Cordula and Gunter Paetzold Fellowship, and Rick Hansen Man in Motion Fellowship. S.E.G. was supported by the NSERC DG programme. Project Seahorse is supported by Guylian Chocolates (Belgium) and an anonymous donor through partnerships for marine conservation. References Anderson S. C., Flemming J. M., Watson R., Lotze H. K. 2011. Rapid global expansion of invertebrate fisheries: Trends, drivers, and ecosystem effects. PloS One , 6: e14735. Google Scholar CrossRef Search ADS PubMed Anticamara J. A., Watson R., Gelchu A., Pauly D. 2011. Global fishing effort (1950–2010): trends, gaps, and implications. Fisheries Research , 107: 131– 136. Elsevier B.V. Google Scholar CrossRef Search ADS Ban S. S., Graham N. A. J., Connolly S. R. 2014. Evidence for multiple stressor interactions and effects on coral reefs. Global Change Biology , 20: 681– 697. Google Scholar CrossRef Search ADS PubMed Begossi A. 2006. Temporal stability in fishing spots: Conservation and co-management in Brazilian artisanal coastal fisheries. Ecology and Society , 11: 5. [online]. Google Scholar CrossRef Search ADS Chambers R. 1994. Participatory Rural Appraisal (PRA): Analysis of Experience. World Development , 22: 1253– 1268. Google Scholar CrossRef Search ADS Christie P., Armada N. B., White A. T., Gulayan A. M., de Dios H. Y. H. 2006. Coastal environmental and fisheries profile of Danajon Bank, Bohol, Philippines. Cebu City, Philippines. 63 pp. D’Agnes L., D’Agnes H., Schwartz J. B., Amarillo M. L., Castro J. 2010. Integrated management of coastal resources and human health yields added value: a comparative study in Palawan (Philippines). Environmental Conservation , 37: 398– 409. Google Scholar CrossRef Search ADS Daw T. M. 2008. Spatial distribution of effort by artisanal fishers: Exploring economic factors affecting the lobster fisheries of the Corn Islands, Nicaragua. Fisheries Research , 90: 17– 25. Google Scholar CrossRef Search ADS FAO. 2015. Voluntary guidelines for securing sustainable small-scale fisheries in the context of food security and poverty eradication. FAO, Rome. Gavin M. C., Solomon J. N., Blank S. G. 2010. Measuring and monitoring illegal use of natural resources. Conservation Biology , 24: 89– 100. Google Scholar CrossRef Search ADS PubMed Gotelli N. J., Colwell R. K. 2001. Quantifying biodiversity: Procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters , 4: 379– 391. Google Scholar CrossRef Search ADS Hall G. B., Close C. 2007. Local knowledge assessment for a small-scale fishery using geographic information systems. Fisheries Research , 83: 11– 22. Google Scholar CrossRef Search ADS Hicks C. C., McClanahan T. R. 2012. Assessing gear modifications needed to optimize yields in a heavily exploited, multi-species, seagrass and coral reef fishery. PLoS One , 7: e36022. Google Scholar CrossRef Search ADS PubMed Hill N. A. O., Rowcliffe J. M., Koldewey H. J., Milner-Gulland E. J. 2012. The interaction between seaweed farming as an alternative occupation and fisher numbers in the central Philippines. Conservation Biology , 26: 324– 334. Google Scholar CrossRef Search ADS PubMed Hind E. J. 2015. A Review of the past, the present, and the future of fishers’ knowledge reserach: a challenge to established fisheries science. ICES Journal of Marine Science , 72: 341– 358. Google Scholar CrossRef Search ADS Johnson A. E., Cinner J. E., Hardt M. M. J., Jacquet J. L., McClanahan T. R., Sanchirico J. N. 2013. Trends, current understanding and future research priorities for artisanal coral reef fisheries research. Fish and Fisheries , 14: 281– 292. Google Scholar CrossRef Search ADS Kleiber D., Harris L. M., Vincent A. C. J. 2014. Improving fisheries estimates by including women's catch in the Central Philippines. Canadian Journal of Fisheries and Aquatic Sciences , 71: 656– 664. Google Scholar CrossRef Search ADS Klein C. J., Chan A., Kircher L., Cundiff A. J., Gardner N., Hrovat Y., Scholz A. J. et al. 2008. Striking a balance between biodiversity conservation and socioeconomic viability in the design of marine protected areas. Conservation Biology , 22: 691– 700. Google Scholar CrossRef Search ADS PubMed Lee J., South A. B., Jennings S. 2010. Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data. ICES Journal of Marine Science , 67: 1260– 1271. Google Scholar CrossRef Search ADS Mangi S. C., Roberts C. M. 2006. Quantifying the environmental impacts of artisanal fishing gear on Kenya’s coral reef ecosystems. Marine Pollution Bulletin , 52: 1646– 1660. Google Scholar CrossRef Search ADS PubMed Marcus J. E., Samoilys M. A., Meeuwig J. J., Villongco Z. A. D., Vincent A. C. J. 2007. Benthic status of near-shore fishing grounds in the central Philippines and associated seahorse densities. Marine Pollution Bulletin , 54: 1483– 1494. Google Scholar CrossRef Search ADS PubMed McClanahan T. R., Hicks C. C., Darling E. S. 2008. Malthusian overfishing and efforts to overcome it on Kenyan coral reefs. Ecological Applications , 18: 1516– 1529. Google Scholar CrossRef Search ADS PubMed McClenachan L. E., Ferretti F., Baum J. K. 2012. From archives to conservation: why historical data are needed to set baselines for marine animals and ecosystems. Conservation Letters , 5: 349– 359. Google Scholar CrossRef Search ADS McKey D., Rostain S., Iriarte J., Glaser B., Birk J. J., Holst I., Renard D. 2010. Pre-Columbian agricultural landscapes, ecosystem engineers, and self-organized patchiness in Amazonia. Proceedings of the National Academy of Sciences of the United States of America , 107: 7823– 7828. Google Scholar CrossRef Search ADS PubMed Mitchell A. 2005. The ESRI Guide to GIS Analysis, Volume 2: Spatial Measurements and Statistics. ESRI Press, Redlands, CA. 238 pp. Muallil R. N., Mamauag S. S., Cabral R. B., Celeste-Dizon E. O., Aliño P. M. 2014. Status, trends and challenges in the sustainability of small-scale fisheries in the Philippines: Insights from FISHDA (Fishing Industries’ Support in Handling Decisions Application) model. Marine Policy , 44: 212– 221. Google Scholar CrossRef Search ADS Neis B., Schneider D. C., Felt L., Haedrich R. L., Fischer J., Hutchings J. A. 1999. Fisheries assessment: What can be learned from interviewing resource users? Canadian Journal of Fisheries and Aquatic Sciences , 56: 1949– 1963. Google Scholar CrossRef Search ADS O’Donnell K. P., Molloy P. P., Vincent A. C. J. 2012. Comparing fisher interviews, logbooks, and catch landings estimates of extraction rates in a small-scale fishery. Coastal Management , 40: 594– 611. Google Scholar CrossRef Search ADS Ostrom E. 1990. Governing the Commons: The Evolution of institutIons for Collective Action . Cambridge University Press, New York, NY. 280 pp. Google Scholar CrossRef Search ADS Pauly D., Christensen V., Dalsgaard J., Froese R., Torres F. 1998. Fishing down marine food webs. Science , 279: 860– 863. Google Scholar CrossRef Search ADS PubMed Pauly D., Chua T. 1988. The overfishing of marine resources: socioeconomic background in southeast Asia. Ambio , 17: 200– 206. Pauly D., Zeller D. 2016. Catch reconstructions reveal that global marine fisheries catches are higher than reported and declining. Nature Communications , 7: 10244. Google Scholar CrossRef Search ADS PubMed Payton M. E., Greenstone M. H., Schenker N. 2003. Overlapping confidence intervals or standard error intervals: What do they mean in terms of statistical significance? Journal of Insect Science , 3: 34. Google Scholar CrossRef Search ADS PubMed Philippine National Statisics Office. 2012. 2010 Census of Population and Housing. Manila. 125 pp. PPDO Bohol. 2013. Bohol Poverty Profile. http://www.ppdobohol.lgu.ph/profile/bohol-poverty-profile/. Rosenberg A. 2000. Ecosystem approaches to fishery management through essential fish habitat. Bulletin of Marine Science , 66: 535– 542. Selig E. R., Turner W. R., Troëng S., Wallace B. P., Halpern B. S., Kaschner K., Lascelles B. G. et al. 2014. Global priorities for marine biodiversity conservation. PLoS One , 9: 1– 11. Soykan C. U., Eguchi T., Kohin S., Dewar H. 2014. Prediction of fishing effort distributions using boosted regression trees. Ecological Applications , 24: 71– 83. Google Scholar CrossRef Search ADS PubMed Stewart K. R., Lewison R. L., Dunn D. C., Bjorkland R. H., Kelez S., Halpin P. N., Crowder L. B. 2010. Characterizing fishing effort and spatial extent of coastal fisheries. PloS One , 5: e14451. Google Scholar CrossRef Search ADS PubMed Swartz W., Sala E., Tracey S. R., Watson R., Pauly D. 2010. The spatial expansion and ecological footprint of fisheries (1950 to present). PLoS One , 5: e15143. Google Scholar CrossRef Search ADS PubMed Teh L. C. L., Sumaila U. R. 2013. Contribution of marine fisheries to worldwide employment. Fish and Fisheries , 14: 77– 88. Google Scholar CrossRef Search ADS Tomscha S. A., Gergel S. E. 2016. Ecosystem service trade-offs and synergies misunderstood without landscape history. Ecology and Society , 21: 43. Google Scholar CrossRef Search ADS Walters C. J. 2003. Folly and fantasy in the analysis of spatial catch rate data. Canadian Journal of Fisheries and Aquatic Sciences , 60: 1433– 1436. Google Scholar CrossRef Search ADS Walters C. J., Martell S. J. D. 2004. Fisheries Ecology and Management . Princeton University Press, Princeton. 448 pp. © International Council for the Exploration of the Sea 2017. All rights reserved. For Permissions, please email: email@example.com
ICES Journal of Marine Science – Oxford University Press
Published: Jan 1, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera