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Systematic marine conservation planning in data-poor regions: Socioeconomic data is essential
ARTICLE IN PRESS Marine Policy 33 (2009) 794–800
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Marine Policy journal homepage: www.elsevier.com/locate/marpol
Systematic marine conservation planning in data-poor regions: Socioeconomic data is essential Natalie C. Ban a,,1, Gretchen J.A. Hansen b,c, Michael Jones b, Amanda C.J. Vincent a a
Project Seahorse, Fisheries Centre, The University of British Columbia, 2202 Main Mall, Vancouver, British Columbia, Canada V6T 1Z4 Michigan State University, Quantitative Fisheries Center and Department of Fisheries and Wildlife, 13 Natural Resources Building, East Lansing, MI 48824, USA c Center for Limnology, University of Wisconsin-Madison, 680 N Park Street, Madison, WI 53706, USA2 b
a r t i c l e in f o
a b s t r a c t
Article history: Received 5 February 2009 Received in revised form 26 February 2009 Accepted 27 February 2009
Systematic planning for conservation is highly regarded but relies on spatially explicit data that are lacking in many areas of conservation concern. The decision support tool Marxan is applied to a reef system in the central Philippines where 30 marine protected areas (MPAs) have been established in communities without much use of biophysical data. The intent was to explore how Marxan might assist with the legally required expansion to protect 15% of marine waters, and how existing MPAs might affect that process. Results show that biophysical information alone did not provide much guidance in identifying patterns of conservation importance in areas where the data are poor. Socioeconomic data were needed to distinguish among possible areas for protection; but here, as elsewhere in marine environments, the availability of such data was very limited. In the final analysis, local knowledge and integrated understanding of socioeconomic realities may offer the best spatially explicit information. The 30 existing MPAs, which encompassed a small proportion of the reef system, did not limit future options in developing a suite of MPAs on a broader scale. Rather, they appeared to generate the support for MPAs that is obligatory for any larger zoning effort. In summary, establishing MPAs based on community-driven criteria has biological and social value, but efforts should be made to collect ecological and socioeconomic data to guide the continued creation of MPAs. & 2009 Elsevier Ltd. All rights reserved.
Keywords: Marine protected areas Marine reserves Socioeconomic data Marxan Cost Conservation planning
1. Introduction In tropical developing countries, the trade off between knowledge acquisition and conservation action—problematic in most marine ecosystems—becomes particularly acute. These nations have rich marine biodiversity, desperate human need, and few resources available for management. Conservation of marine biodiversity in these areas is a grave international concern because tropical marine ecosystems, especially coral reefs, contain some of the greatest species richness in the world [1]. Such marine systems also provide a crucial source of protein and income for coastal communities and for global consumption [2–4]. Within many countries, human desperation has mounted as population growth, coupled with mismanagement of marine
Corresponding author. Tel.: +6174781 6067; fax: +6174781 6722.
E-mail addresses: [email protected] (N.C. Ban), [email protected] (G.J. Hansen), [email protected] (M. Jones), a.vincent@fisheries.ubc.ca (A.C. Vincent). 1 Present address: Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland 4811, Australia. 2 Current contact information. 0308-597X/$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.marpol.2009.02.011
resources and habitat destruction, has led to depleted coastal marine ecosystems [5]. The pressures are about to worsen as more resources are exported globally; one estimate is that 79% of the world’s marine production will come from developing countries [6]. Yet few developing countries have the technical or financial capacity to undertake thorough biological research on their marine environments. Many developing countries are embracing marine protected areas (MPAs) as important management tools [7–10]. MPAs have been shown in some cases to increase sizes, densities, and biomass of exploited fishes [11,12]. Despite uncertainty about their effectiveness in general, particularly with respect to fisheries enhancement [13], many national governments have forged ahead with commitments to establish MPAs. This commitment is driven simultaneously by international pressure, as mutlilateral agreements promote MPAs, and by the engagement of coastal communities, which have come to value MPAs in many countries [14]. Developed nation approaches favour science-based decisionmaking in MPA establishment just as in other conservation actions and resource management situations [15]. Selecting places for protection in a systematic manner, it is argued, ensures that a
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particular set of conservation objectives is achieved at a minimum cost [15]. The most common conservation objective, in its simplest form, is to represent known components of biodiversity [15–18], although MPAs are often promoted as a means to enhance fisheries [12–19]. The most commonly recognized cost of conservation comes in the form of area (i.e., cost is assumed proportional to area) although other inputs can include acquisition, management, transaction, damage and opportunity costs (see [20] for definitions of these costs). Conservation planning tools such as Marxan [17–21] are designed to assist in the selection of potential MPAs that are representative of known biodiversity, or biodiversity surrogates, whilst minimizing the cost. Although the value of systematic conservation approaches has been demonstrated in MPAs located in temperate, developed countries [22–24], relatively few published studies have evaluated this approach in tropical developing countries [25]. Management approaches to conservation are often exported from developed to developing countries—through academic, non-governmental or donor interest—despite the different set of economic, political, and ecological forces operating in these areas. In many developing countries, local managers, municipal leaders, and conservation workers are key players in the placement of MPAs. With limited technical information and relatively few resources for design and management, decisions are usually based on what is politically astute and socioeconomically tolerable, rather than using systematic planning. A key question in conservation planning is how much time and effort should be invested in taking a systematic approach to marine conservation, in light of limited biophysical data. Most published studies using a systematic approach to marine planning come from developed countries that have better data (e.g., [22,23,26,27]). Such approaches may be less informative in areas that have few data, particularly with an appropriate scale of resolution, and where resources might limit use of even such data as may be available. It may for example, be possible to extract habitat features from satellite imagery (coral reefs, mangroves, seagrass) but the acquisition of such information can be costly and technically challenging. In many regions, available data would be limited to depth contours from basic nautical charts, broad biogeographic regions or a range of latitudes/longitudes. It is unlikely that even local knowledge of species distributions will have been collected and compiled systematically, particularly for non-commercial species. Although the value of including biophysical data in protected area design has long been recognized, the importance of including socioeconomic data has only recently been highlighted [20,23,28–31]. To maximize the effectiveness of future MPAs, it is increasingly acknowledged that they should be designed to incorporate the important biophysical features while minimizing societal costs. In the marine environment, spatially explicit societal costs include measures of fishing opportunity costs (e.g., catches, effort, CPUE, density of fishing boats) [23,27,32–34], and human population density [35]. In theory, a systematic approach to MPA design should be undertaken before protection is implemented, but this may not be feasible in data-poor regions. One often-raised question is whether existing MPAs—which were not selected using the systematic approach—contribute to, or hinder, the efficient selection of new sites [26,27–34]. Previous studies have found that the forced inclusion of existing MPAs results in a more costly (i.e., less efficient) set of MPAs than starting from a blank slate [26,27–34]. In developing countries, however, a local and national determination to implement MPAs often means MPAs have previously been established in places and manners that are acceptable to stakeholders. The question of how existing MPAs
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affect later systematic planning becomes rather important in such circumstances. The Philippines is a tropical developing country that offers particularly good opportunities for research and action in marine conservation. It lies at the centre of global marine biodiversity [36], has huge human dependency on the ocean, and is very minded to implement MPAs [37,38]. National legislation decreed in 1998 mandates all municipalities to protect 15% of their waters (Fisheries Code: RA 8550); marine management within 15 km of the coast is devolved to municipalities in the Philippines. Such national policy is compatible with a strong inclination of local communities to implement MPAs. Despite such convergence of national legislation and societal opinion, MPAs still cover only a tiny percentage of the country’s marine environment [37]. Momentum for MPAs continues to grow and there is considerable scope for the use of systematic approaches to marine protection. The purpose of this paper is to two-fold. First, the utility of the most widely used systematic conservation planning tool, Marxan, in identifying areas of conservation priority in the data-poor region of the Danajon Bank, Philippines, is assessed. Available biophysical data are used, datasets that may approximate the socioeconomic cost of establishing MPAs are incorporated, and the ability of Marxan to direct conservation planning efforts is assessed. Second, the effect of existing small no-take MPAs— selected through a people-oriented approach—on the outputs that arise from this systematic planning is assessed.
2. Methods 2.1. Study area The Danajon Bank in the central Philippines is used as the case study (Fig. 1). The study area includes the entire double reef and all islands within Bohol located on or around the reef up to the mainland of Bohol. Islands under the jurisdiction of other provinces located on the outskirts of Danajon Bank are excluded. The Danajon Bank double barrier reef lies at the global centre of marine biological diversity but overexploitation and poor management have both depleted marine life and degraded marine environments [39]. Over the past 10 years, however, many community-based no-take MPAs have been established with assistance of conservation organizations. Lists and anecdotes suggest that there may be as many as 100 MPAs in the Danajon Bank region, but the nature of protection, level of management, and conservation status of many remain unclear. The analysis is focused on 30 MPAs that the authors know are being managed and enforced by local people; these were catalysed by the Project Seahorse Foundation and its original partner organizations. Together, these small MPAs comprise 753 ha, or 0.32% of the Danajon Bank. Such small MPAs are the norm in the Philippines. (For example, Apo Island, one of the best known coral reef MPAs in the world, is only 22 ha in area [40]). These no-take MPAs were developed in villages according to local interest in marine conservation and receptiveness to a reserve, and not because of any biological precept. Within a village, candidate MPAs were selected by community members—based on a suite of variables including fishing activity levels, enforcement capacity and previous biological productivity—at which point biologists would help in finalizing the precise location. 2.2. Systematic conservation planning The systematic conservation planning tool Marxan [17–21] was used to identify areas of conservation importance. The study area
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governmental conservation agencies on the scale necessary for regional conservation planning in Danajon Bank. Four scenarios that used area as the cost were explored. For each scenario the conservation objective was to include 15% of biodiversity features in the smallest possible area. The first two scenarios selected planning units using only the biophysical data, either with (scenario 1) or without (scenario 2) the existing MPA being necessarily included in the solution. The third and fourth scenarios reproduced the first two, respectively, but with the additional constraint that 15% of each municipality’s marine waters would be protected, not simply 15% of the overall area. The efficiency of the solutions was then compared, to see if there was a difference between the cost of the scenarios. Pressey and Nicholls’ [42] definition of efficiency (E) was used: E ¼ 1 X=T,
Fig. 1. Study area location (black rectangle) in the Philippines.
was divided into planning units—4 ha—that were populated with conservation features. Marxan was used to select a subset of these planning units that meet user-defined conservation objectives at the lowest possible cost using a simulated annealing algorithm [21]. Marxan can produce scenarios with different sizes of reserves; this was achieved by altering the boundary length modifier. A boundary length modifier that gave a range of relatively small MPAs was used. Of these, the smallest MPAs reflect the approximate current size of MPAs, which were known to be politically acceptable. Marxan provides two kinds of outputs. First, it provides the number of times each planning unit is selected out of the total number of runs (500 runs in this case). This output, termed the selection frequency, is a measure of the conservation importance of each planning unit—more frequent selection means more importance for conservation [41]. Second, Marxan provides the reserve system that meets the conservation objectives at the lowest cost out of the 500 runs. This is called the ‘‘best’’ solution. There are usually many possible configurations of reserves that have a very similar cost as the ‘‘best’’ scenario. The conservation objective was to ensure that 15% of biodiversity features were represented in the set of MPAs in the Danajon Bank, based on the Fisheries Code requirement that 15% of marine waters in each municipality be protected. Biodiversity features were used that were available for the data-poor Danajon Bank study area. These features were selected to represent an ecosystem approach, to include all ecosystem types, rather than focusing just on coral reefs. The following data were available: depth classes obtained and interpolated from nautical charts, reef categories digitized from satellite images, proximity to rivers from satellite images, substrate type from nautical charts, longitudinal gradients, and mangroves. These data represent all available biophysical data from various governmental and non-
where X is the number of planning units contained in the best solution and T is the total number of possible planning units. Efficiency can range from 0 to 1, 1 being the most efficient solution. Calculating this metric allowed the further evaluation whether or not including the existing MPAs in future conservation planning would result in a less efficient set of MPAs. The next six scenarios explored the effect of using three different spatially explicit measures of costs (Fig. 2). Here the aim was to provide guidance for meeting the 15% protection mandated for municipal waters. Therefore, the 15% target was used for these waters, and the effect of including and excluding existing MPAs was tested. Three plausible cost scenarios were used (described below). The first two are proxies of fishing effort to represent the opportunity cost of MPAs to fishers, and the third an estimate of enforceability. In all cases a base cost of one was added to all areas that appeared as having no cost, because establishing MPAs is never cost-free. First, the relative artisanal catches was used as contained in a global model [43]—they modeled global artisanal catches using available small-scale catch data, then employed a multivariate regression model to identify geographic, demographic, and socioeconomic variables that best predict the artisanal catch rates. The results of the multivariate model were used to predict artisanal catches in areas where data were not available. The values were rescaled for the Danajon Bank to range from 0 to 100 for the planning units. The cost was therefore considered to be directly proportional to catch. The global map uses a coarser coastline, and hence the marine areas do not line up correctly and some gaps appear. The values contained within the nearest planning units were used to fill in these gaps. Marxan was run to identify areas of conservation priority using this cost measure both while forcing the inclusion of existing MPAs and while treating the existing MPAs the same as any other planning unit (not forcing the inclusion of existing MPAs). As a second measure of cost, population numbers of the village areas—barangays as they are known in the Philippines—were used as another proxy for fishing effort. The assumption is that fishing effort was greater in areas with higher populations. Another assumption was that, village-based fishers with the usual outrigger boat, travel at most 10 km from land to fish—this is an estimate, and will vary with paddle and/or engine efficiency. Linear distance decay from land out up to 10 km was used; this means that more fishing is assumed to happen closer to land than further away, and hence areas closer to land are more costly to protect. Again a scenario was run that forced the inclusion of existing MPAs, and one that did not. Third, feasibility of enforcement as a cost was considered. Villages have often chosen to locate MPAs close to population centers so that the protection measures can be enforced, and guardhouses are commonly built near villages to facilitate
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Fig. 2. Costs used: (a) area; (b) global model of artisanal catches; (c) population density extrapolated onto the water to a distance of 10 km using density decay, to represent a proxy for fishing pressure; (d) the inverse of (c) to represent greater likelihood of enforcement close to populated areas.
Fig. 3. Marxan results of the scenarios using area as the cost: (a) existing MPAs included, municipalities not targeted; (b) existing MPAs included, municipalities targeted; (c) existing MPAs excluded, municipalities not targeted; existing MPAs excluded, municipalities targeted.
enforcement. Therefore, MPAs located close to populated areas would be more likely to be enforceable—and hence less costly—than areas further away. To calculate this cost, the inverse of the population density cost outlined above was used; areas close to populated areas are considered less costly to protect than areas further away. As with the other cost scenarios, a scenario was run that included existing MPAs, and one that did not.
3. Results None of the planning units appeared to be substantially more important than the others, given the data available. Using Marxan with area as the cost did not show patterns of conservation importance when targeting biodiversity features available for this data-poor study area (Fig. 3). The most, any planning unit was selected was 131 times out of 500, or about 26%. There was, therefore, a lot of flexibility in the potential locations of MPAs to represent 15% of the biodiversity features. The ‘‘best’’ result from Marxan was just one of the many possible options that would protect biodiversity features with a similar area.
When using area as the cost, neither existing MPAs nor municipal level planning made any difference to the outcome (Table 1). All the conservation features were included at or just above 15%, whether existing MPAs were included or excluded, and whether municipal waters were included or excluded. Planning units containing the existing MPAs had the same mean selection frequency as other planning units, and therefore, the conservation importance of existing MPAs was no different from other areas. The efficiency of all scenarios was the same, further demonstrating that including the existing MPAs had no impact on the final outcome. When spatially explicit data were included as the costs, patterns of conservation importance emerged (Fig. 4). Each of the three spatial datasets used as costs resulted in different areas being highlighted for their conservation importance. Predictably, planning units that had a lower cost were selected more frequently than those with higher costs. For the same cost function, including or excluding current MPAs did not change the overall efficiency of the solution (Fig. 4). When forcing the inclusion of existing MPAs, the results show higher selection of areas adjacent to the MPAs (Figs. 3 and 4). This
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Table 1 Summary of scenarios with area as the cost. Scenarios
No target for municipalities Target for municipalities No target for municipalities, existing MPAs locked in Target for municipalities, existing MPAs locked in
Mean selection frequency of planning units
Existing MPAs
Non-MPAs
All
74.5 79.1 500 500
75.0 76.0 73.0 75.0
75.0 76.0 76.2 77.5
Planning units selected in ‘‘best’’ run
‘‘Efficiency’’ (1—selected planning units/ total planning units)
9112 9156 9148 9226
0.85 0.85 0.85 0.85
Total number of runs=500.
Fig. 4. Marxan results of the scenarios using spatially explicit costs. E denotes efficiency.
pattern is an artifact of the program rather than due to ecological significance: it becomes cheaper to select areas adjacent to existing MPAs, because there is no boundary cost between included or selected area.
4. Discussion With limited ecological data, the utility of a systematic planning tool lies not in spatially directing conservation efforts, but rather in proposing potential solutions and encouraging discussion amongst the communities. In this study, the available ecological data alone were insufficient to provide good guidance on identifying areas of conservation importance in the data-poor study area; rather, much flexibility existed as to where to place them to protect known biodiversity features efficiently. This finding contrasts with published studies showing that patterns of conservation importance do indeed emerge clearly [24,27,34–44], probably because of the greater and better data available in the
latter scenarios. In this data-poor study area, it was the use of spatially explicit costs that substantially changed the output of the systematic selection tool. Other research has similarly emphasized the importance of including socioeconomic data in conservation planning [20–28]. Protecting ecological features using the limited data available without considering societal costs explicitly did not provide informative results in terms of priority areas for conservation. It will usually be difficult to obtain enough biophysical data for systematic conservation planning in developing countries, and in areas of developed nations, too. The Philippines might be expected to have more biophysical knowledge on marine resources than a great many other developing countries: it is very literate (92.6% [45]), exhibits high fluency in English (the usual language for scientific literature), has a well-established university and college system that includes many forms of marine studies, and has attracted tremendous interest and support from international researchers, conservation agencies and donors. Nonetheless, more than a decade of conservation work and coastal management planning in the Danajon Bank region by international aid agencies, universities, and non-governmental organizations had not produced enough biophysical data for areas of conservation importance to emerge when using a site selection tool on this broad regional scale. It was found that the siting of existing MPAs, implemented by communities for sociopolitical reasons, did not compromise conservation options. The existing suite of reserves was compatible with future scenarios, with no influence on the efficiency of the outputs for any of the scenarios in the study area. This contrasts to other studies, where locking in existing MPAs did result in a less efficient solution [27–34], and is presumably a consequence of (a) limited biophysical data and (b) the small proportion of total area protected currently, relative to the 15% target that was explored in this study. The findings do speak to the advantages of supporting an MPA that is politically and socially acceptable where these are small relative to the overall goal of protection, as most are. Even if they are not ideally placed in ecological terms, they add to the total protected area and may engender future support for MPAs, without eliminating options for later ecological influence. The observation that socioeconomic data are vital for systematic planning in areas that lack biophysical data may raise new challenges [20–28]. In areas where ecological data are poor, spatially explicit socioeconomic datasets related to marine environments (those needed for MPA planning) are likely also to be sparse. Proxies for socioeconomic costs can be used, such as the ones employed here (artisanal fisheries from global model, population density, enforcement capacity) but they are certainly neither ideal nor unambiguous. Moreover, the three cost measures produced different results, and it is not known which (if any) of the costs employed most accurately captures the variables in community decision-making. The enforcement cost that was
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applied most closely represents the cost used to establish the existing MPAs, which are all located in proximity to villages. However, as protection is extended to 15% of municipal waters (as required under the Fisheries Code), communities may prefer to locate some of the MPAs farther from their villages, rather than losing all local fishing. Hence, it is possible that the perception of cost may change as the proportion protected increases. Similarly, the perception of cost is closely tied to the perception of benefit. If fishers perceive a spill-over benefit, then they may want MPAs closer. In the long run, the best inputs may well come from local stakeholders’ integrated understanding of socioeconomic needs and pressures, once tempered by meaningful consultation with political leaders, agencies and contributing NGOs. While only a small proportion of the study area is protected, the small community-based MPAs in this study—selected using socio-political-economic imperatives—have generated community buy-in and support for protection. The success of early notake MPAs in the Philippines has set the stage for other communities to want to protect part of their own waters [37]. The importance of the existing MPAs in generating buy-in and support should not be disregarded. Given that the small proportion of protected areas does not influence the efficiency of achieving the 15% target, jurisdictions without MPAs may be advised to follow suit: establish community-based MPAs first to build support and momentum and then build on that protection. This approach is supported by research showing that the locations of reserves established early on in a multi-year process of reserve development may not be of much importance, especially when degradation rates are high in the ecosystem [46]. Early on, it is more important to generate some protection than to protect exactly the right places [47]. This is likely especially true in this study area because community buy-in is crucial for successful MPAs in areas with limited enforcement capacity. Given the importance of social acceptance in MPA success, and the often slow process of gaining community/social acceptance of MPAs, the pragmatic approach in data-poor regions may be to use a mixed method—start with MPAs acceptable to stakeholders, and then combine a systematic approach with engagement of people who have already become familiar with MPAs. Handumon, one of the barangays in the study area, is a good example how small MPAs can engender broader community support for protection. The community began protecting an ecologically degraded strip of reef and adjacent waters in 1995, although it was not formally protected at the municipal level until 1998. This MPA catalysed the creation of many of the others in this study, by hosting familiarization visits from neighbouring villages and by taking lead roles at regional gatherings on marine resource management. In 2007, the Handumon sanctuary was declared the best MPA in the Philippines by a national body. Such spatial protection for reefs has, in many villages including Handumon, now led to a new interest in doing the same for mangroves. It may be difficult to assess the full extent of the benefits that villages accrue from MPAs—the major benefit may lie in the development of social capital as people organize for action—but they are certainly attracting support.
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Contents lists available at ScienceDirect
Marine Policy journal homepage: www.elsevier.com/locate/marpol
Systematic marine conservation planning in data-poor regions: Socioeconomic data is essential Natalie C. Ban a,,1, Gretchen J.A. Hansen b,c, Michael Jones b, Amanda C.J. Vincent a a
Project Seahorse, Fisheries Centre, The University of British Columbia, 2202 Main Mall, Vancouver, British Columbia, Canada V6T 1Z4 Michigan State University, Quantitative Fisheries Center and Department of Fisheries and Wildlife, 13 Natural Resources Building, East Lansing, MI 48824, USA c Center for Limnology, University of Wisconsin-Madison, 680 N Park Street, Madison, WI 53706, USA2 b
a r t i c l e in f o
a b s t r a c t
Article history: Received 5 February 2009 Received in revised form 26 February 2009 Accepted 27 February 2009
Systematic planning for conservation is highly regarded but relies on spatially explicit data that are lacking in many areas of conservation concern. The decision support tool Marxan is applied to a reef system in the central Philippines where 30 marine protected areas (MPAs) have been established in communities without much use of biophysical data. The intent was to explore how Marxan might assist with the legally required expansion to protect 15% of marine waters, and how existing MPAs might affect that process. Results show that biophysical information alone did not provide much guidance in identifying patterns of conservation importance in areas where the data are poor. Socioeconomic data were needed to distinguish among possible areas for protection; but here, as elsewhere in marine environments, the availability of such data was very limited. In the final analysis, local knowledge and integrated understanding of socioeconomic realities may offer the best spatially explicit information. The 30 existing MPAs, which encompassed a small proportion of the reef system, did not limit future options in developing a suite of MPAs on a broader scale. Rather, they appeared to generate the support for MPAs that is obligatory for any larger zoning effort. In summary, establishing MPAs based on community-driven criteria has biological and social value, but efforts should be made to collect ecological and socioeconomic data to guide the continued creation of MPAs. & 2009 Elsevier Ltd. All rights reserved.
Keywords: Marine protected areas Marine reserves Socioeconomic data Marxan Cost Conservation planning
1. Introduction In tropical developing countries, the trade off between knowledge acquisition and conservation action—problematic in most marine ecosystems—becomes particularly acute. These nations have rich marine biodiversity, desperate human need, and few resources available for management. Conservation of marine biodiversity in these areas is a grave international concern because tropical marine ecosystems, especially coral reefs, contain some of the greatest species richness in the world [1]. Such marine systems also provide a crucial source of protein and income for coastal communities and for global consumption [2–4]. Within many countries, human desperation has mounted as population growth, coupled with mismanagement of marine
Corresponding author. Tel.: +6174781 6067; fax: +6174781 6722.
E-mail addresses: [email protected] (N.C. Ban), [email protected] (G.J. Hansen), [email protected] (M. Jones), a.vincent@fisheries.ubc.ca (A.C. Vincent). 1 Present address: Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland 4811, Australia. 2 Current contact information. 0308-597X/$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.marpol.2009.02.011
resources and habitat destruction, has led to depleted coastal marine ecosystems [5]. The pressures are about to worsen as more resources are exported globally; one estimate is that 79% of the world’s marine production will come from developing countries [6]. Yet few developing countries have the technical or financial capacity to undertake thorough biological research on their marine environments. Many developing countries are embracing marine protected areas (MPAs) as important management tools [7–10]. MPAs have been shown in some cases to increase sizes, densities, and biomass of exploited fishes [11,12]. Despite uncertainty about their effectiveness in general, particularly with respect to fisheries enhancement [13], many national governments have forged ahead with commitments to establish MPAs. This commitment is driven simultaneously by international pressure, as mutlilateral agreements promote MPAs, and by the engagement of coastal communities, which have come to value MPAs in many countries [14]. Developed nation approaches favour science-based decisionmaking in MPA establishment just as in other conservation actions and resource management situations [15]. Selecting places for protection in a systematic manner, it is argued, ensures that a
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particular set of conservation objectives is achieved at a minimum cost [15]. The most common conservation objective, in its simplest form, is to represent known components of biodiversity [15–18], although MPAs are often promoted as a means to enhance fisheries [12–19]. The most commonly recognized cost of conservation comes in the form of area (i.e., cost is assumed proportional to area) although other inputs can include acquisition, management, transaction, damage and opportunity costs (see [20] for definitions of these costs). Conservation planning tools such as Marxan [17–21] are designed to assist in the selection of potential MPAs that are representative of known biodiversity, or biodiversity surrogates, whilst minimizing the cost. Although the value of systematic conservation approaches has been demonstrated in MPAs located in temperate, developed countries [22–24], relatively few published studies have evaluated this approach in tropical developing countries [25]. Management approaches to conservation are often exported from developed to developing countries—through academic, non-governmental or donor interest—despite the different set of economic, political, and ecological forces operating in these areas. In many developing countries, local managers, municipal leaders, and conservation workers are key players in the placement of MPAs. With limited technical information and relatively few resources for design and management, decisions are usually based on what is politically astute and socioeconomically tolerable, rather than using systematic planning. A key question in conservation planning is how much time and effort should be invested in taking a systematic approach to marine conservation, in light of limited biophysical data. Most published studies using a systematic approach to marine planning come from developed countries that have better data (e.g., [22,23,26,27]). Such approaches may be less informative in areas that have few data, particularly with an appropriate scale of resolution, and where resources might limit use of even such data as may be available. It may for example, be possible to extract habitat features from satellite imagery (coral reefs, mangroves, seagrass) but the acquisition of such information can be costly and technically challenging. In many regions, available data would be limited to depth contours from basic nautical charts, broad biogeographic regions or a range of latitudes/longitudes. It is unlikely that even local knowledge of species distributions will have been collected and compiled systematically, particularly for non-commercial species. Although the value of including biophysical data in protected area design has long been recognized, the importance of including socioeconomic data has only recently been highlighted [20,23,28–31]. To maximize the effectiveness of future MPAs, it is increasingly acknowledged that they should be designed to incorporate the important biophysical features while minimizing societal costs. In the marine environment, spatially explicit societal costs include measures of fishing opportunity costs (e.g., catches, effort, CPUE, density of fishing boats) [23,27,32–34], and human population density [35]. In theory, a systematic approach to MPA design should be undertaken before protection is implemented, but this may not be feasible in data-poor regions. One often-raised question is whether existing MPAs—which were not selected using the systematic approach—contribute to, or hinder, the efficient selection of new sites [26,27–34]. Previous studies have found that the forced inclusion of existing MPAs results in a more costly (i.e., less efficient) set of MPAs than starting from a blank slate [26,27–34]. In developing countries, however, a local and national determination to implement MPAs often means MPAs have previously been established in places and manners that are acceptable to stakeholders. The question of how existing MPAs
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affect later systematic planning becomes rather important in such circumstances. The Philippines is a tropical developing country that offers particularly good opportunities for research and action in marine conservation. It lies at the centre of global marine biodiversity [36], has huge human dependency on the ocean, and is very minded to implement MPAs [37,38]. National legislation decreed in 1998 mandates all municipalities to protect 15% of their waters (Fisheries Code: RA 8550); marine management within 15 km of the coast is devolved to municipalities in the Philippines. Such national policy is compatible with a strong inclination of local communities to implement MPAs. Despite such convergence of national legislation and societal opinion, MPAs still cover only a tiny percentage of the country’s marine environment [37]. Momentum for MPAs continues to grow and there is considerable scope for the use of systematic approaches to marine protection. The purpose of this paper is to two-fold. First, the utility of the most widely used systematic conservation planning tool, Marxan, in identifying areas of conservation priority in the data-poor region of the Danajon Bank, Philippines, is assessed. Available biophysical data are used, datasets that may approximate the socioeconomic cost of establishing MPAs are incorporated, and the ability of Marxan to direct conservation planning efforts is assessed. Second, the effect of existing small no-take MPAs— selected through a people-oriented approach—on the outputs that arise from this systematic planning is assessed.
2. Methods 2.1. Study area The Danajon Bank in the central Philippines is used as the case study (Fig. 1). The study area includes the entire double reef and all islands within Bohol located on or around the reef up to the mainland of Bohol. Islands under the jurisdiction of other provinces located on the outskirts of Danajon Bank are excluded. The Danajon Bank double barrier reef lies at the global centre of marine biological diversity but overexploitation and poor management have both depleted marine life and degraded marine environments [39]. Over the past 10 years, however, many community-based no-take MPAs have been established with assistance of conservation organizations. Lists and anecdotes suggest that there may be as many as 100 MPAs in the Danajon Bank region, but the nature of protection, level of management, and conservation status of many remain unclear. The analysis is focused on 30 MPAs that the authors know are being managed and enforced by local people; these were catalysed by the Project Seahorse Foundation and its original partner organizations. Together, these small MPAs comprise 753 ha, or 0.32% of the Danajon Bank. Such small MPAs are the norm in the Philippines. (For example, Apo Island, one of the best known coral reef MPAs in the world, is only 22 ha in area [40]). These no-take MPAs were developed in villages according to local interest in marine conservation and receptiveness to a reserve, and not because of any biological precept. Within a village, candidate MPAs were selected by community members—based on a suite of variables including fishing activity levels, enforcement capacity and previous biological productivity—at which point biologists would help in finalizing the precise location. 2.2. Systematic conservation planning The systematic conservation planning tool Marxan [17–21] was used to identify areas of conservation importance. The study area
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governmental conservation agencies on the scale necessary for regional conservation planning in Danajon Bank. Four scenarios that used area as the cost were explored. For each scenario the conservation objective was to include 15% of biodiversity features in the smallest possible area. The first two scenarios selected planning units using only the biophysical data, either with (scenario 1) or without (scenario 2) the existing MPA being necessarily included in the solution. The third and fourth scenarios reproduced the first two, respectively, but with the additional constraint that 15% of each municipality’s marine waters would be protected, not simply 15% of the overall area. The efficiency of the solutions was then compared, to see if there was a difference between the cost of the scenarios. Pressey and Nicholls’ [42] definition of efficiency (E) was used: E ¼ 1 X=T,
Fig. 1. Study area location (black rectangle) in the Philippines.
was divided into planning units—4 ha—that were populated with conservation features. Marxan was used to select a subset of these planning units that meet user-defined conservation objectives at the lowest possible cost using a simulated annealing algorithm [21]. Marxan can produce scenarios with different sizes of reserves; this was achieved by altering the boundary length modifier. A boundary length modifier that gave a range of relatively small MPAs was used. Of these, the smallest MPAs reflect the approximate current size of MPAs, which were known to be politically acceptable. Marxan provides two kinds of outputs. First, it provides the number of times each planning unit is selected out of the total number of runs (500 runs in this case). This output, termed the selection frequency, is a measure of the conservation importance of each planning unit—more frequent selection means more importance for conservation [41]. Second, Marxan provides the reserve system that meets the conservation objectives at the lowest cost out of the 500 runs. This is called the ‘‘best’’ solution. There are usually many possible configurations of reserves that have a very similar cost as the ‘‘best’’ scenario. The conservation objective was to ensure that 15% of biodiversity features were represented in the set of MPAs in the Danajon Bank, based on the Fisheries Code requirement that 15% of marine waters in each municipality be protected. Biodiversity features were used that were available for the data-poor Danajon Bank study area. These features were selected to represent an ecosystem approach, to include all ecosystem types, rather than focusing just on coral reefs. The following data were available: depth classes obtained and interpolated from nautical charts, reef categories digitized from satellite images, proximity to rivers from satellite images, substrate type from nautical charts, longitudinal gradients, and mangroves. These data represent all available biophysical data from various governmental and non-
where X is the number of planning units contained in the best solution and T is the total number of possible planning units. Efficiency can range from 0 to 1, 1 being the most efficient solution. Calculating this metric allowed the further evaluation whether or not including the existing MPAs in future conservation planning would result in a less efficient set of MPAs. The next six scenarios explored the effect of using three different spatially explicit measures of costs (Fig. 2). Here the aim was to provide guidance for meeting the 15% protection mandated for municipal waters. Therefore, the 15% target was used for these waters, and the effect of including and excluding existing MPAs was tested. Three plausible cost scenarios were used (described below). The first two are proxies of fishing effort to represent the opportunity cost of MPAs to fishers, and the third an estimate of enforceability. In all cases a base cost of one was added to all areas that appeared as having no cost, because establishing MPAs is never cost-free. First, the relative artisanal catches was used as contained in a global model [43]—they modeled global artisanal catches using available small-scale catch data, then employed a multivariate regression model to identify geographic, demographic, and socioeconomic variables that best predict the artisanal catch rates. The results of the multivariate model were used to predict artisanal catches in areas where data were not available. The values were rescaled for the Danajon Bank to range from 0 to 100 for the planning units. The cost was therefore considered to be directly proportional to catch. The global map uses a coarser coastline, and hence the marine areas do not line up correctly and some gaps appear. The values contained within the nearest planning units were used to fill in these gaps. Marxan was run to identify areas of conservation priority using this cost measure both while forcing the inclusion of existing MPAs and while treating the existing MPAs the same as any other planning unit (not forcing the inclusion of existing MPAs). As a second measure of cost, population numbers of the village areas—barangays as they are known in the Philippines—were used as another proxy for fishing effort. The assumption is that fishing effort was greater in areas with higher populations. Another assumption was that, village-based fishers with the usual outrigger boat, travel at most 10 km from land to fish—this is an estimate, and will vary with paddle and/or engine efficiency. Linear distance decay from land out up to 10 km was used; this means that more fishing is assumed to happen closer to land than further away, and hence areas closer to land are more costly to protect. Again a scenario was run that forced the inclusion of existing MPAs, and one that did not. Third, feasibility of enforcement as a cost was considered. Villages have often chosen to locate MPAs close to population centers so that the protection measures can be enforced, and guardhouses are commonly built near villages to facilitate
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Fig. 2. Costs used: (a) area; (b) global model of artisanal catches; (c) population density extrapolated onto the water to a distance of 10 km using density decay, to represent a proxy for fishing pressure; (d) the inverse of (c) to represent greater likelihood of enforcement close to populated areas.
Fig. 3. Marxan results of the scenarios using area as the cost: (a) existing MPAs included, municipalities not targeted; (b) existing MPAs included, municipalities targeted; (c) existing MPAs excluded, municipalities not targeted; existing MPAs excluded, municipalities targeted.
enforcement. Therefore, MPAs located close to populated areas would be more likely to be enforceable—and hence less costly—than areas further away. To calculate this cost, the inverse of the population density cost outlined above was used; areas close to populated areas are considered less costly to protect than areas further away. As with the other cost scenarios, a scenario was run that included existing MPAs, and one that did not.
3. Results None of the planning units appeared to be substantially more important than the others, given the data available. Using Marxan with area as the cost did not show patterns of conservation importance when targeting biodiversity features available for this data-poor study area (Fig. 3). The most, any planning unit was selected was 131 times out of 500, or about 26%. There was, therefore, a lot of flexibility in the potential locations of MPAs to represent 15% of the biodiversity features. The ‘‘best’’ result from Marxan was just one of the many possible options that would protect biodiversity features with a similar area.
When using area as the cost, neither existing MPAs nor municipal level planning made any difference to the outcome (Table 1). All the conservation features were included at or just above 15%, whether existing MPAs were included or excluded, and whether municipal waters were included or excluded. Planning units containing the existing MPAs had the same mean selection frequency as other planning units, and therefore, the conservation importance of existing MPAs was no different from other areas. The efficiency of all scenarios was the same, further demonstrating that including the existing MPAs had no impact on the final outcome. When spatially explicit data were included as the costs, patterns of conservation importance emerged (Fig. 4). Each of the three spatial datasets used as costs resulted in different areas being highlighted for their conservation importance. Predictably, planning units that had a lower cost were selected more frequently than those with higher costs. For the same cost function, including or excluding current MPAs did not change the overall efficiency of the solution (Fig. 4). When forcing the inclusion of existing MPAs, the results show higher selection of areas adjacent to the MPAs (Figs. 3 and 4). This
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Table 1 Summary of scenarios with area as the cost. Scenarios
No target for municipalities Target for municipalities No target for municipalities, existing MPAs locked in Target for municipalities, existing MPAs locked in
Mean selection frequency of planning units
Existing MPAs
Non-MPAs
All
74.5 79.1 500 500
75.0 76.0 73.0 75.0
75.0 76.0 76.2 77.5
Planning units selected in ‘‘best’’ run
‘‘Efficiency’’ (1—selected planning units/ total planning units)
9112 9156 9148 9226
0.85 0.85 0.85 0.85
Total number of runs=500.
Fig. 4. Marxan results of the scenarios using spatially explicit costs. E denotes efficiency.
pattern is an artifact of the program rather than due to ecological significance: it becomes cheaper to select areas adjacent to existing MPAs, because there is no boundary cost between included or selected area.
4. Discussion With limited ecological data, the utility of a systematic planning tool lies not in spatially directing conservation efforts, but rather in proposing potential solutions and encouraging discussion amongst the communities. In this study, the available ecological data alone were insufficient to provide good guidance on identifying areas of conservation importance in the data-poor study area; rather, much flexibility existed as to where to place them to protect known biodiversity features efficiently. This finding contrasts with published studies showing that patterns of conservation importance do indeed emerge clearly [24,27,34–44], probably because of the greater and better data available in the
latter scenarios. In this data-poor study area, it was the use of spatially explicit costs that substantially changed the output of the systematic selection tool. Other research has similarly emphasized the importance of including socioeconomic data in conservation planning [20–28]. Protecting ecological features using the limited data available without considering societal costs explicitly did not provide informative results in terms of priority areas for conservation. It will usually be difficult to obtain enough biophysical data for systematic conservation planning in developing countries, and in areas of developed nations, too. The Philippines might be expected to have more biophysical knowledge on marine resources than a great many other developing countries: it is very literate (92.6% [45]), exhibits high fluency in English (the usual language for scientific literature), has a well-established university and college system that includes many forms of marine studies, and has attracted tremendous interest and support from international researchers, conservation agencies and donors. Nonetheless, more than a decade of conservation work and coastal management planning in the Danajon Bank region by international aid agencies, universities, and non-governmental organizations had not produced enough biophysical data for areas of conservation importance to emerge when using a site selection tool on this broad regional scale. It was found that the siting of existing MPAs, implemented by communities for sociopolitical reasons, did not compromise conservation options. The existing suite of reserves was compatible with future scenarios, with no influence on the efficiency of the outputs for any of the scenarios in the study area. This contrasts to other studies, where locking in existing MPAs did result in a less efficient solution [27–34], and is presumably a consequence of (a) limited biophysical data and (b) the small proportion of total area protected currently, relative to the 15% target that was explored in this study. The findings do speak to the advantages of supporting an MPA that is politically and socially acceptable where these are small relative to the overall goal of protection, as most are. Even if they are not ideally placed in ecological terms, they add to the total protected area and may engender future support for MPAs, without eliminating options for later ecological influence. The observation that socioeconomic data are vital for systematic planning in areas that lack biophysical data may raise new challenges [20–28]. In areas where ecological data are poor, spatially explicit socioeconomic datasets related to marine environments (those needed for MPA planning) are likely also to be sparse. Proxies for socioeconomic costs can be used, such as the ones employed here (artisanal fisheries from global model, population density, enforcement capacity) but they are certainly neither ideal nor unambiguous. Moreover, the three cost measures produced different results, and it is not known which (if any) of the costs employed most accurately captures the variables in community decision-making. The enforcement cost that was
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applied most closely represents the cost used to establish the existing MPAs, which are all located in proximity to villages. However, as protection is extended to 15% of municipal waters (as required under the Fisheries Code), communities may prefer to locate some of the MPAs farther from their villages, rather than losing all local fishing. Hence, it is possible that the perception of cost may change as the proportion protected increases. Similarly, the perception of cost is closely tied to the perception of benefit. If fishers perceive a spill-over benefit, then they may want MPAs closer. In the long run, the best inputs may well come from local stakeholders’ integrated understanding of socioeconomic needs and pressures, once tempered by meaningful consultation with political leaders, agencies and contributing NGOs. While only a small proportion of the study area is protected, the small community-based MPAs in this study—selected using socio-political-economic imperatives—have generated community buy-in and support for protection. The success of early notake MPAs in the Philippines has set the stage for other communities to want to protect part of their own waters [37]. The importance of the existing MPAs in generating buy-in and support should not be disregarded. Given that the small proportion of protected areas does not influence the efficiency of achieving the 15% target, jurisdictions without MPAs may be advised to follow suit: establish community-based MPAs first to build support and momentum and then build on that protection. This approach is supported by research showing that the locations of reserves established early on in a multi-year process of reserve development may not be of much importance, especially when degradation rates are high in the ecosystem [46]. Early on, it is more important to generate some protection than to protect exactly the right places [47]. This is likely especially true in this study area because community buy-in is crucial for successful MPAs in areas with limited enforcement capacity. Given the importance of social acceptance in MPA success, and the often slow process of gaining community/social acceptance of MPAs, the pragmatic approach in data-poor regions may be to use a mixed method—start with MPAs acceptable to stakeholders, and then combine a systematic approach with engagement of people who have already become familiar with MPAs. Handumon, one of the barangays in the study area, is a good example how small MPAs can engender broader community support for protection. The community began protecting an ecologically degraded strip of reef and adjacent waters in 1995, although it was not formally protected at the municipal level until 1998. This MPA catalysed the creation of many of the others in this study, by hosting familiarization visits from neighbouring villages and by taking lead roles at regional gatherings on marine resource management. In 2007, the Handumon sanctuary was declared the best MPA in the Philippines by a national body. Such spatial protection for reefs has, in many villages including Handumon, now led to a new interest in doing the same for mangroves. It may be difficult to assess the full extent of the benefits that villages accrue from MPAs—the major benefit may lie in the development of social capital as people organize for action—but they are certainly attracting support.
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