Socioeconomic factors associated with fishing pressure in small-scale fisheries along the West Philippine Sea biogeographic region

Ocean & Coastal Management 82 (2013) 27e33

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Socioeconomic factors associated with fishing pressure in small-scale fisheries along the West Philippine Sea biogeographic region Richard N. Muallil a, b, c, *, Deborah Cleland d, Porfirio M. Aliño a, b, * a

Marine Science Institute, University of the Philippines Diliman, 1101 Quezon City, Philippines Marine Environment and Resources Foundation, Inc., Marine Science Institute, University of the Philippines Diliman, 1101 Quezon City, Philippines c Mindanao State University, Tawi-Tawi College of Technology and Oceanography, 7500 Bongao, Tawi-Tawi, Philippines d Fenner School of Environment and Society, College of Medicine, Biology & Environment, Australian National University, Canberra, ACT 0200, Australia b

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 7 June 2013

Small-scale fishers in the Philippines are highly diverse in their fishing behavior and demographic characteristics. Understanding this heterogeneity will provide valuable insights for fisheries management efforts that could result in winewin outcomes, both for (i) improving sustainable incomes for fishers, and (ii) facilitating recovery and resilience of depleted fisheries. We determined how different socioeconomic factors were associated with fishing effort, measured as the number of fishing trips per month, in six neighboring coastal towns along the West Philippines Sea biogeographic region of the Philippines. We found that types of alternative livelihoods and fisher age were the most important factors influencing fishing effort. Employed fishers (e.g. drivers, boat operators, construction workers, carpenters, etc.) had lower fishing effort than both those without alternative livelihoods and selfemployed ones (e.g. subsistence farmers/livestock raisers and small business operators). Younger fishers fished more frequently than older ones. Our study provides valuable insights for management interventions that can effectively foster transitions into alternative livelihoods to alleviate fishing pressure while providing fishers with sustainable source of income. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction In an archipelagic tropical country like the Philippines, fishing is a significant source of livelihood as well as an important way of life for the majority of the coastal population (Pollnac et al., 2001). Fishing used to be a very lucrative livelihood but due to its “openaccess” nature, uncontrolled fish extraction by the burgeoning population resulted in a drastic decline of stocks in many fishing grounds over the past few decades (Alcala and Russ, 2002; Green et al., 2003; Aliño et al., 2004; Lavides et al., 2010). Overfishing has also been considered the major cause of recent ecological extinctions in many heavily fished areas (Jackson et al., 2001; Lavides et al., 2010; Nañola et al., 2011). In effect, overfishing resulted in widespread poverty in fishing communities, which further aggravated Malthusian overfishing as competition for the declining stocks increases (Hardin, 1968; Lim et al., 1995; Bene, 2003;

* Corresponding authors. Marine Science Institute, University of the Philippines Diliman, 1101 Quezon City, Philippines. Tel./fax: þ63 2 433 1806. E-mail addresses: [email protected], [email protected] (R.N. Muallil), [email protected] (P.M. Aliño). 0964-5691/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ocecoaman.2013.04.013

Hamilton, 2003). Worm et al. (2009) estimated that 63% of the world’s oceans are already overfished. A similar 64% of Philippine coastal fisheries are also overfished, although this is a conservative estimate since the impacts of destructive fishing practices, such as blast and poison fishing, and the intrusion of the highly efficient commercial fishers to coastal fishing grounds were not accounted for in the study (Muallil et al., 2012). Green et al. (2003) estimated that fish stocks in major fishing grounds in the Philippines have already been reduced to less than 10% of the 1950s levels. Worm et al. (2006) even made a very bold prediction that global fisheries will collapse by 2048, although this has been strongly criticized by other fisheries scientists (Hilborn, 2006). Some believe that even when destructive fishing methods such as blast and poison fishing are effectively controlled, widespread poverty and the rapidly growing fishing population will eventually deplete the resource to the point where recovery will no longer be feasible (Jackson et al., 2001; Pauly et al., 2002; Worm et al., 2006; Newton et al., 2007; Muallil et al., 2012; Cabral et al., 2013,b). Newton et al. (2007) estimated the maximum sustainable yield for coral reefs to be at 5 mt/km2/yr. For the Philippines with a total of 27,000 km2 coral reef area (Burke et al., 2011), and with more than 1 million small scale fishers alone (BFAR, 2009), a ceiling of less than

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1 kg daily catch per fisher for 15 days per month fishing must be implemented to stay within the limit of this maximum sustainable extraction rate. Such catch rates could hardly provide for the basic daily needs of fishers’ households (Muallil et al., 2011). Moreover, with the patchy distribution of human populations, inaccessibility of some offshore reefs to artisanal fishers and the deteriorated condition of most coral reefs in the Philippines, the impact of fishing pressure is much higher and more detrimental in coral reefs near the centers of population (Jackson et al., 2001; Nañola et al., 2011). Furthermore, the common illegal incursion of highly efficient commercial fishers to coastal waters and the prevalence of destructive fishing practices such as blast and poison fishing will further accelerate overfishing and habitat deterioration (White and Vogt, 2000; Hanna, 2001; Muallil and Geronimo, 2010). Management measures to effectively address the declining fishery must involve reducing fishing effort or simply catching fewer fish. Livelihood approaches have become popular in providing economic assistance to fishers while alleviating fishing pressure as fishers reduce their dependency on the fishery (Allison and Ellis, 2001; Matiya et al., 2005). However, we previously showed that some of the fishers cannot easily exit the fishery even when offered relatively high monthly monetary incentives e.g. higher than the minimum wage in the Philippines (Muallil et al., 2011). Fishing is an important way of life and job satisfaction in the fishery is high in many areas, so fishers do not readily leave the fishery for any other occupation even in the face of a continually declining catch (Pollnac et al., 2001; Cinner et al., 2009; Bavinck et al., 2012; Muallil et al., 2011). Moreover, with over a million highly resource-dependent fishers in the Philippines, the reduction of fishing pressure that can potentially result from livelihood programs may not be enough to avert fishery collapse in some heavily fished areas (SuPFA, 2006; Licuanan et al., 2008; Muallil et al., 2012). Quantifying fishing effort is crucial for effective fisheries management (Piet et al., 2007; Anticamara et al., 2011) but is a very challenging task given the high variability in fishing practices (gear, target fish, seasonality, sizes and types of fishing vessels, fishing time, etc.) (Le Pape and Vigneau, 2001; McCluskey and Lewison, 2008; Cabral et al., 2010). Most published studies have investigated fishing effort as the total effort (e.g. number of vessels, size of vessels,

number of fishing trips, etc.) per unit area of fishing grounds or time (McCluskey and Lewison, 2008; cited in Anticamara et al., 2011). Similarly in our study, we measured fishing effort as the frequency of fishers normally go out to fish in a month. We wanted to determine how fishing effort differs among individual fishers with different socioeconomic and demographic attributes. Similar studies show fishers engaged in alternative non-fishing livelihoods exert lower fishing effort than fulltime fishers (Matiya et al., 2005). In our study we looked further into how different types of alternative livelihoods affect fishing effort to provide empirically-tested insights for future livelihood programs that would effectively reduce fishing pressure, even without fishers exiting completely out of the fishery. This is important for a developing country like the Philippines where government funding for livelihood programs is limited and where opportunities outside the fishery are lacking. Moreover, our study sites lie along the Verde Island Passage (VIP), considered the center of the center of marine shore fish diversity (Carpenter and Springer, 2005) so intervention measures that effectively alleviate fishing pressure and biodiversity loss in this highly vulnerable area is imperative (Caddy and Seijo, 2005; Cabral et al., 2013a). 2. Materials and methods 2.1. Study sites We surveyed six coastal towns in four provinces in the West Philippines Sea biogeographic region of the Philippines (Fig. 1). The sites have varying levels of socioeconomic development and fishery dependency. Three sites (Lubang, Looc and El Nido) are more dependent on the fishery, as a variety of non-fishing livelihood options, such as in industry and tourism are available (Muallil et al., 2011). In addition, the three less fishery-dependent towns (Batangas City, Puerto Galera and Mabini) are more heavily populated and are closer to urban centers of development (Table 1). 2.2. Method One-on-one semi-structured questionnaire-based interviews were conducted from April 2009 to January 2010 with 662

Fig. 1. The geographic location of the 6 study sites showing the West Philippine Sea biogeographic region and the Verde Island Passage (VIP).

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Table 1 Demographic and socioeconomic settings of the study sites. n is the number of respondents in each town. Town

Province

n

Major source of income

Population (as of 2007)

No. of fishers (% of total population)

Distance from centers of development

Puerto Galera Batangas City Mabini Looc Lubang El Nido

Oriental Mindoro Batangas Batangas Occidental Mindoro Occidental Mindoro Palawan

88 51 78 101 86 93

Tourism Industry Industry and Tourism Fishery Fishery and Agriculture Tourism and Fishery

28,035 295,231 40,629 11,310 28,267 30,249

1.19% 0.38% 1.90% 7.85% 2.95% 10.70%

27 km 0 km 14 km 61 km 78 km 149 km

Source: Modified from Muallil et al. (2011).

small-scale male fishers aging 17e77 years old. We did not include women fishers in our study since, in all our study sites, fishing is mostly exercised by men while women attend more to household chores. In the Philippines, small-scale or ‘municipal’ fishers, are those who are using boats of less than three gross tons (as defined in the Philippine Fisheries Code of 1998). A total of 497 respondents who provided complete responses in all the parameters asked were included in the analysis. Incomplete responses were excluded, as regression tree analysis (explained below) works better if there are no missing data (De’Ath and Fabricius, 2000). Furthermore, even after culling for incomplete responses, we still had sample sizes ranging from 51 (Batangas City) to 101 (Looc), or an equivalence of about 3.1% (originally 3.8%) to 11.4% (originally 15.0%), respectively, of the total population of male fishers in our study sites. Snowball sampling, or the chain referral method, was used in choosing the respondents. In each site, each interviewer interviewed the first fisher-respondent he/she met. The respondent would then suggest for the next respondent and so on. Normally, a respondent would suggest his neighbor or a nearby fisher he saw after his interview. In each town, interviews were carried out by at least five local interviewers who had previous experiences in doing similar surveys. Moreover, they were briefed well about the questionnaires to ensure standardization in the delivery of questions as much as possible. Four to six dominant fishing villages (barangays), based on fisher population upon consultation with the local government unit (LGU) of the respective towns, were sampled in each town. Respondents were asked about (i) fishing effort, measured by number of fishing days per month, (ii) age, (ii), educational attainment, (iii) boat ownership, (iv) daily income from fishing computed as the total catch (in kg) multiplied by the respondent’s selling price of his major catch, and (v) engagement in non-fishing or alternative livelihood. Fishers in alternative livelihoods were further divided into “self-employed” (those engaged in subsistence farm/livestock and small businesses) and “employed” (those engaged in carpentry, transport (as vehicle drivers), other non-farm labor (construction workers, mechanics, electricians, waiters etc) and government (mostly LGU personnel)). The latter were mostly minimum daily wage earners. Specifically, we defined “employed” as fishers who were working outside the fishery and were paid for their services on a regular basis by employers or “bosses”. On the contrary, “self-employed” fishers had control over their means of production, and did not necessarily have “bosses”. The most common alternative livelihoods across all towns were subsistence farming followed by carpentry, small businesses and transport (mostly as vehicle drivers). Focus group discussions with key informants, mostly older and experienced fishers and some local leaders, were conducted to validate results from one-on-one interviews. Total population data were obtained from the official website of the National Statistics Office of the Philippines. Fisher population and other socioeconomic information, such as major source of income of the study sites, were obtained from the records of the respective LGUs.

A non-parametric regression tree analysis was used to determine how the different socioeconomic factors (predictors) influenced fishing effort. “ANOVA” method with 0.65 and 0.35 splits based on “information” were the specifications used to generate the regression tree. To validate statistical significance for the most important factors determined by regression tree analysis, we performed nested ANOVA, where towns were treated as random (nesting) factor, and subsequent post hoc Tukey’s test, in the multicomp package, for multiple tests that showed significant P values. We used the rank-transformed data in the parametric tests since the dependent variable did not have a normal distribution. All statistical analyses were implemented in R statistical analysis software version 2.15.2 (R Development Core Team, 2012), using the rpart (regression tree) and nlme (nested ANOVA) packages. 3. Results The respondents had a broad range of socioeconomic characteristics (Tables 1, 2a and 2b). Fishers’ daily gross income from fishing was only US$7.6 on average, with 70% of the respondents actually earning less than this average income. Despite very low catches, about 60% of the fishers did not have any other source of livelihood. Fishing effort also varied widely, with some fishers barely going out to sea once a week, and others almost every day, with the average at 18 days per month, or just over four days per week. Regression tree analysis showed that having an alternative livelihood was the most important factor influencing fishing effort. “Employed” fishers had lower fishing effort (mean 16 days) compared to “self-employed” and those who did not have alternative livelihood (mean 19 days). The latter two types of fishers, based on alternative livelihoods, were grouped together in the regression tree (Fig. 2). Nested ANOVA showed significant difference in fishing effort between “employed” fishers and the other two groups (P ¼ 0.029). When “self-employed” fishers and those not engaged in alterative livelihood were treated as two separate Table 2a Description of categorical variables used in regression tree analysis against fishing effort. Number and percentage of respondents are also shown. Variable

Description

Level

Description

Education

Whether the respondent graduated from high school

1

Whether the respondent owned boat

1 2

Non-high school graduate High school graduate Without boat With non-motorized boat only With motorized boat No livelihood Self-employed Employed

Boat

2

3 Alternative livelihood

Whether the respondent is engaged in non-fishing livelihood

1 2 3

Count (%) 356(72%) 141(28%) 133(27%) 78(16%)

286 (57%) 297(60%) 79(16%) 121(25%)

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Table 2b Description of continuous variables used in regression tree analysis against fishing effort. Number and percentage of respondents are also shown. Values of age and income were backtransformed from square root and log þ 1 transformation of the raw data, respectively. Variable

Description

Mean

Fishing effort (dependent variable) Income

Number of fishing days per month Gross income of the respondent per fishing day (US$) Number of children Age of the respondent

18

Children Age

7.6

4 41

s.d.

Min.

Max.

7

4

28

10.3

0

144.4

0 17

12 77

2 11

groups, fishing effort also differed significantly, although only at P ¼ 0.057, among the three groups. Post hoc Tukey’s test revealed that “employed” fishers had significantly lower fishing effort (P ¼ 0.052) although only compared to those who did not have an alternative livelihood. “Self-employed” fishers did not differ significantly with either of the other two groups (Fig. 3). Fig. 4 shows the more detailed classification of alternative occupations where “employed” ones had consistently lower fishing effort than self-employed employed and those who did not have alternative livelihoods (P ¼ 0.098). Regression tree analysis also showed that age was the next important factor influencing fishing effort with fishers who were 32 years old and younger having higher fishing effort than older fishers (Fig. 2). This holds true regardless of whether fishers have alternative livelihoods or the type of alternative livelihoods they have. However, nested ANOVA did not show significant difference between the two age groups. Among employed younger fishers, those with more children were the ones exerting higher fishing effort although this was a less important factor in determining fishing effort. Other factors did not show significant effect on fishing effort based on regression tree analysis. 4. Discussion The Philippines is at the heart of the Coral Triangle, the global center of marine biodiversity, which is alarmingly and unsustainably exploited by tens of millions of highly resource-dependent and poor population (Green et al., 2008; Cabral et al., 2013a). The combined impacts of high dependence on the resource, destructive

Fig. 3. Comparison of fishing effort among “self-employed”, “employed” and those without alternative livelihoods using nested ANOVA where study sites (towns) were treated as random factor. Thick line inside the box represents the median value; lower and upper boundaries of box represent first and third quartiles, respectively. Lower and upper whiskers represent minimum and maximum values, respectively. Different letters indicate significant difference at P ¼ 0.1.

fishing practices, poverty, lack of alternative livelihood options and weak law enforcement put the marine ecosystems in the Coral Triangle, particularly in the Philippines, as the most threatened ecosystems in the world (Cabral et al., 2012; Muallil et al., 2012). While well-enforced MPAs may be able to improve the conditions of the ecosystems, MPAs alone may not be enough to sustain food security in the face of the burgeoning population and the high poverty incidence. Management programs particularly focusing on lessening poverty in the area, while reducing exploitative pressure on its vulnerable ecosystems should be among the priority considerations (Foale et al., 2013). Thus research on the behavior of the highly resource-dependent fishers, that has direct implications on fishing pressure, is crucial in achieving sustainable fisheries goal. We found that the type of alternative livelihood is the most important factor influencing fishing effort. While the relationship between engagement in alternative livelihoods and lower fishing

Fig. 2. Regression tree showing the most important factors determining fishing effort. Higher branches offer greater explanatory power. Average fishing effort and number of respondents are listed at each node. The length of the vertical line of each split is proportional to the variation explained by each variable.

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Fig. 4. Average number of fishing days per month of fishers with different types of alternative livelihoods. None, “self-employed” (small business, farming/livestock) and “employed” (carpentry, other non-farm labor, government, transport). Bars represent standard deviation.

effort is intuitive and had been previously demonstrated (see Matiya et al., 2005; Cinner and Bodin, 2010), our study showed that livelihood per se does not necessarily result in reduced fishing effort. Specifically, we showed “employed” fishers exert lower effort than those without alternative livelihood and “selfemployed” ones. “Employed” fishers are engaged in livelihood activities where they need to personally perform their job and therefore, unlike the “self-employed” fishers who can easily delegate their duties to other members of the family, the former do not have the luxury to fish when they want. In addition, “selfemployed” fishers, like small business operators and subsistence farmers/livestock raisers, normally have significant capital outlay but the return benefit is delayed. Therefore, they may need to fish from time to time to be able to provide for their households’ daily needs. In contrast, “employed” fishers in our study are mostly daily wage earners and therefore have a reasonably reliable income. This points to the ongoing critical role of fishing for daily food consumption in households that cannot reliably meet their living expenses through alternative livelihoods alone. Food security is a critical consideration when introducing more traditional fishing controls, such as closed seasons and no-take areas. Therefore, employment in organizational alternative occupations such as those enumerated above (see methods), that can provide for immediate nutrition or cash needs are to be preferred above those that require longer-term investments to realize benefits, such as farming and raising livestock. Similarly, restocking and habitat restoration programs may be a more efficacious means to increase food security and slow ecosystem degradation than the expensive livelihoods projects undertaken by non-government organizations that are often considered failures by observers (Ferraro and Gjertsen, 2009). Our study also revealed that fishers under 33 had higher fishing effort than older ones. It is possible that younger to middle age fishers are the ones more likely to be shouldering the most financial responsibilities (e.g. with growing children), thereby increasing their fishing effort. Our argument that young fishers have more financial responsibilities is supported by our finding that young fishers with more than two children, were the ones associated with higher fishing effort than those with fewer or no children despite having a reliable source of income from alternative livelihoods (Fig. 2). Due to poverty, most fishers cannot afford to send their children to college. Instead, children stay at home to help supplement the family’s income. Thus, fishers with grown up children may not need to go out fishing as often as younger fishers in order to provide for the daily needs of their households. Various studies have demonstrated the role of children in supplementing the meager income of their parents in fishing communities (Kronen, 2004). It is also possible that older and experienced

fishers know where productive fishing grounds are, so that they do not have to fish as often as the younger ones. Our data support this view as indicated by the significantly positive relationship between age of fishers and their daily income from fishing (P ¼ 0.012, R2 ¼ 0.014). Our findings suggest that alternative livelihood programs may not necessarily result in lower fishing effort if not carefully designed. Proper selection of beneficiaries, especially with limited resources as is often the case in the Philippines, must be well implemented. Specifically, younger fishers especially those with growing kids should be the primary target of livelihood programs as they are the ones associated with higher fishing effort. Further, younger or those who were newer in the fishery, had been previously shown as those who are more open into shifting to non-fishing livelihoods (Muallil et al., 2011). Furthermore, capacity building must be geared toward increasing chances of fishers to be absorbed in other sectors as “employed” workers. One way to achieve this is to strengthen education and capacity building programs especially for the youth to minimize the number of new entrants into the fishery. Alternatively, sustainable regional development approaches that look toward integrating good governance and social enterprises to provide employment and foster stewardship would also potentially reduce fishing pressure (Muallil et al., 2011). Many studies showed that where alternative livelihoods exist, fishers can successfully partake in them (Allison and Ellis, 2001; Cinner and Bodin, 2010). This is also consistent with our study where a greater percentage of the fishers in the three less fishery-dependent towns (Puerto Galera, Batangas City and Mabini) are engaged in non-fishing livelihoods (Fig. 5). Fig. 5 further showed that higher proportion of fishers with higher educational attainment were consistently engaged in alternative livelihood as “employed” workers suggesting that education and other capacity training activities could potentially reduce fishing effort as the chance of fishers being absorbed in alternative occupations will be higher. Lastly, policy makers must consider the possibility that reduction in poverty through livelihood programs and other economic assistance provided for fishers may allow them to purchase better fishing technology and increase their catches whilst reducing frequency of fishing trips. Thus some sort of conditionality must be made such that fishers lower their fishing effort and become involved in marine stewardship, such as by becoming fish wardens, in exchange for economic assistance. One potentially effective vehicle for these changes would be through adapting the conditional cash transfer (CCT) scheme of the Philippine government that provides monthly financial incentives to poor households with a condition that the recipients send their children to school and have them regularly checked up at health centers (Chaudhury et al., 2013). Such programs could create the winewin outcomes e improving sustainable incomes for fishers and

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Fig. 5. Proportion of fishers engaged in different types of alternative livelihoods compared by educational attainment in the six study sites. Non-HS Grad are fishers who did not graduate from high school while, HS Grad are those who completed high school education.

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