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Keywords:

  • Caribbean;
  • coral reefs;
  • Marine Protected Areas (MPA);
  • management, social–ecological systems (SES)

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

In view of current worldwide coral reef decline, and the shortcomings of traditional top-down management schemes of Marine Protected Areas (MPAs), decision makers and scientists face the important challenge of developing new approaches to generate effective conservation strategies. This study evaluates MPAs as linked social–ecological systems (SES) to inform better management by calculating indices for ecological health, social adaptive capacity, and the impact intensity of overfishing, pollution, and tourism. A series of ecological and socioeconomic indicators are used to estimate these indices and determine relevant conservation strategies in two protected areas in the Colombian Caribbean. Results reveal a precarious situation of high impact intensity combined with low ecological health and adaptive capacity. This study provides further evidence supporting the need for reconciliation of SES and a framework by which decision makers can assess priorities to increase MPA effectiveness. We highlight the need for system reorganization and recommend bottom-up comanagement schemes as a priority strategy to strengthen adaptive capacity.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

Most Marine Protected Areas (MPAs) worldwide are underresourced, lack evidence-based management plans, and rarely achieve their conservation goals (Mora et al. 2006; Burke et al. 2011). Low efficacy of protection is a global concern given the high rates of marine environment degradation and declines in fisheries resources (Jackson et al. 2001; Hughes et al. 2003; Halpern et al. 2008). Marine resource management has migrated away from “optimal” harvest models based on command-and-control approaches, static environment assumptions, and separation of social and ecological issues (Berkes et al. 2003; Hughes et al. 2005; ResilienceAlliance 2010), because management relying on these approaches does not prepare the systems for dealing with changes, making them more vulnerable to anthropogenic pressures and natural disturbances (ResilienceAlliance 2010). Current management approaches have evolved to recognize the dynamics and different dimensions of communities (physical, socioeconomic, biological, institutional), to challenge maximum sustainable yield-based models, and to encompass social and ecological components that interact closely, forming coupled and integrated systems known as social–ecological systems (SES) (Mangel & Levin 2005; Liu et al. 2007; Levin et al. 2009). Investigating SES is complicated by reciprocal interactions generating feedback loops, exhibiting complex dynamics affected by several factors (e.g., markets, institutions, environmental changes), and nonlinear relationships with thresholds, inherent uncertainties, and varying degrees of resilience (Costanza et al. 1993; Hughes et al. 2005; Liu et al. 2007).

Since SES are dynamic, they can change and transform into multiple alternative states (Folke 2006). Regime shifts involve a change in community structure that alters the generation of ecosystem services, causing subsequent impacts on human society (Elmqvist et al. 2003; Folke et al. 2004). For instance, coral reefs exhibit several alternative stable states such as coral, macroalgal and sea urchin dominated as a result of stressors that make them vulnerable to change, such as reduced herbivory, water pollution, reduced fish stocks, high-erosion level, and coral disease (Nyström et al. 2000; Bellwood et al. 2004; Norström et al. 2009).

In the study of SES, difficulties arise when trying to consolidate and interpret findings coming from diverse sources and several data collection methods (Ostrom 2007). Moreover, the inherent differences of environmental and social sciences impose challenges for scientists seeking to draw connections to holistic analyses (Ostrom 2009). Hence, recent multidisciplinary approaches and frameworks have been developed aiming to investigate marine SES, and have provided important progress toward their understanding (Cinner et al. 2009; McClanahan et al. 2009; Pollnac et al. 2010; Halpern et al. 2012)

Worldwide coral reefs have suffered dramatic declines despite the existence of MPAs and associated management plans (Camargo et al. 2009; De'ath et al. 2012). This highlights the need to perform comprehensive evaluations of MPA effectiveness while incorporating approaches based on SES, as the success of marine reserves highly depend on the interactions between their ecological and social dimensions (Pollnac et al. 2010). The aim of this study is to examine two MPAs in the Colombian Caribbean as linked SES to recommend conservation strategies for enhancing MPA management effectiveness (the extent to which management is protecting natural resources and assuring local community welfare), by identifying factors that may be enhancing or eroding it.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

Study site

We selected two MPAs located in the Colombian Caribbean as the focal systems of the study (Figure 1): Rosario and San Bernardo Coral National Natural Park (CRSB) established in 1977 is 1,200 km2 in size and composed of two archipelagos including coral reef (67.6 km2), mangrove and tropical dry forest habitats. Tayrona National Natural Park (PNNT), established in 1969, is a coastal MPA of 150 km2, also representing coral reef (6.54 km2), mangroves, and dry and rain forest ecosystems. Historically, coral reefs in these areas have been affected by pollution, sedimentation, overfishing, dynamite fishing, and coral mining during the 1980s; minor bleaching events in 1987, 1990, 1995, and 1998; and the severe bleaching in 2005 that left extensive patches of dead Acropora, however, most affected reefs have been shown to recover after 6 months (Wilkinson & Souter 2008). During the first 25 years of the MPAs designation, extraction of marine resources were prohibited even within traditional fishing grounds of afro-descendants and indigenous communities who had been inhabiting these areas for centuries. However, in the late 1990s a new policy of Social Participation for Conservation was introduced in the national parks of Colombia, increasingly involving local communities with environmental education programs and capacity building. These efforts have subsequently weakened however, as conflicts involving coastal development, tourism, and land tenure continue to create tension between the communities and authorities (Durán 2009). Inhabitants of both MPAs have been dependent on natural resources (fishing and tourism) for livelihoods since long before the declaration of protection schemes, so social–ecological dynamics are highly determined by management decisions. These communities are characterized by low income, lack of access to fresh water, sanitation, health and education, and scarce access to other capitals beyond natural capital (Camargo et al. 2009), despite their proximity to neighboring cities (Cartagena and Santa Marta).

image

Figure 1. Location of the studied MPAs on the Caribbean coast of Colombia. Solid stars represent biophysical survey sites inside MPAs, open stars represent sites outside MPAs. Local communities are shown as solid circles. Dotted lines indicate the limits of the MPAs.

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Conceptual model of the MPAs

We conducted a workshop in Santa Marta in April 2008, with the objective of evaluating and selecting multidisciplinary indicators of ecological health, adaptive capacity and impact intensity. Fifty stakeholders participated, representing local environmental organizations (10), MPA staff (15), conservationists and researchers (18), and local fishermen (7). Workshop participants provided their opinion of indicators after listening to talks and open discussions. Systematization of different opinions expressed was possible through Régnier's Abacus method (Lafourcade & Chapuy 2000): Participants voted in a scale of colors that codified the level of agreement or disagreement. Accordingly only the most voted indicators were included in this study (see supporting information Table S1). Following the workshop, results were integrated with existing models (Berkes et al. 2003; Chapin et al. 2006, 2009), to generate a conceptual model (Figure 2) to form the framework of our assessment and a set of indicators based on existent methodologies (Walker et al. 2006; Obura & Grimsditch 2009; ResilienceAlliance 2010)

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Figure 2. Diagram showing the conceptual model of the studied SES constructed around the local impacts (fishing, water pollution, and tourism) that were perceived as the most relevant in the workshop. At a regional scale, external controls such as climate change, regional economy, and governance affect the MPA. Among slow changing ecological variables are benthic cover and biodiversity. Social capital and human capital are included as slow changing social variables. Slow changing variables influence fast changing variables such as fish biomass and density, water quality, and financial capital. The fluctuations in these variables alter the provision of ecosystem services, affecting the well-being of human actors, who modify the subsystems through institutional responses such as management and governance. In this study slow and fast variables were treated as indicators, however we are aware of the difficulty of capturing the dynamics of the system, so the ecological indicators summarize the ecosystem condition instead (adapted from Chapin (2006).

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We designed measurements and indicators to calculate three indices: (1) an index of ecological health, (2) an index of social adaptive capacity, and (3) an index of local impact intensity. To calculate the indices, we designed a categorical scale of ecological health, social adaptive capacity, and impact intensity, where each measurement was scored according to thresholds selected by validating with local experts and consulting available literature (Table S1). Once determined, each measurement was classified into four levels ranging from very low to high with an associated numeric value (very low = 0, low = 1/3, moderate = 2/3, and high = 1). Mean measurements scores were used to represent an overall score per indicator and impact, which were classified again (very low = 0–0.25, low = 0.26–0.5, moderate = 0.51–0.75, and high = 0.76–1). Values of indices were calculated using a consistently weighted average numeric score of all indicators and normalizing the result to the unit (Tables 1, 2, and 3). Ecological and social indicators with mean scores of very low and low were considered as potential degraders of effectiveness, and moderate and high considered as enhancers.

Table 1. Indicators and measurements used to determine the ecological health index for two MPAs in the Colombian Caribbean. Results for each measurement and score according to the classification scales are shown by MPA. Please see supporting information Table S2 for the classification values of results on the scale. Indicators with mean scores of very low and low were considered as potential degraders of effectiveness, and moderate and high considered as enhancers
IndicatorResultScorea 
MeasurementPNNTCRSBPNNTCRSBSourceb
  1. a

    Numeric scores for the indicators in parenthesis.

  2. b

    Sources: PT = phototransects, UVS = underwater visual surveys, FA = Foraminifer's analysis.

Benthic cover  Low (0.33)Very low (1.17) 
Coral cover percentage22%23.2%LowLowPT
Algae cover percentage48%66.8%LowVery lowPT
Fish density and biomass  Very low (0)Very low (0) 
Mann–Whitney test significance (P) for differences between fish densities inside and outside MPA0.250.89Very lowVery lowUVS
ANOVA test significance (P) for differences between herbivore fish biomass inside and outside MPA0.60.13Very lowVery lowUVS
ANOVA test significance (P) for differences between predator fish biomass inside and outside MPA0.640.54Very lowVery lowUVS
Species diversity  Low (0.33)Low (0.33) 
Fish beta diversity. Shannon Index (H)2.712.56ModerateLowUVS
Coral beta diversity. Shannon Index (H)1.62.31Very lowLowUVS
Water quality  Low (0.33)Low (0.33) 
Foram Index (FI)3.423.75LowLowFA
Table 2. Indicators and measurements used to determine the social adaptive capacity index for two MPAs in the Colombian Caribbean. Results for each measurement and score according to the classification scales are shown by MPA. Please see supporting information Table S3 for the classification values of results on the scale. Indicators with mean scores of very low and low were considered as potential degraders of effectiveness, and moderate and high considered as enhancers
IndicatorResultScorea 
MeasurementPNNTCRSBPNNTCRSBSourceb
  1. a

    Numeric scores for the indicators in parenthesis.

  2. b

    Sources: PRS = park rangers survey, LCS = local community survey, TS = tourists survey, EEG = economic experimental games.

Rules legitimacy and compliance  Moderate (0.52)Moderate (0.62) 
Proportion of park rangers that consider rules are designed to achieve conservation objectives58.33%64.28%ModerateModeratePRS
Proportion of park rangers that consider local community follow fishing rules43.75%38.46%LowLowPRS
Proportion of park rangers that consider environmental authority has legitimacy in the community70.83%60.71%ModerateModeratePRS
Proportion of local community surveyed that has a favorable perception regarding MPA existence76.66%93.4%ModerateHighLCS
Proportion of the community that agrees that denouncing the noncompliance of formal rules is useful65.51%48.93%ModerateLowLCS
Proportion of local community surveyed that declares knowing the rules for the MPA59.32%60.85%ModerateModerateLCS
Proportion of tourists surveyed that declares knowing the rules of entering into a MPA23%51.3%Very lowModerateTS
Enforcement and surveillance  Low (0.44)Moderate (0.56) 
Proportion of community that considers the park has capacity to enforce rules49.15%61.27%LowModerateLCS
Proportion of park rangers that consider the sanctions are easy to implement33.33%21.43%LowVery lowPRS
Frequency in surveillance routes and coverage of control in MPA3 days/weekEvery dayModerateHighPRS
Social capital  Moderate (0.58)Very low (0.25) 
Potential internal cooperation within communities32.62%22.85%LowVery lowEEG
Proportion of reduction in extraction as a result of a comanagement rule in EEGs58.86%56.11%ModerateModerateEEG
Proportion of community that consider the natural resources management must be between community and environmental authority66.1%22.87%ModerateVery lowLCS
Percentage of community that participate in meetings and workshops with park authorities66.1%47.23%ModerateLowLCS
Human capital  Moderate (0.67)Very low (0.17) 
Average years of academic studies7.785.28ModerateLowLCS
Frequency of informal training activities for fishermenFortnightlyMonthlyModerateVery lowPRS
Financial capital  Low (0.33)Low (0.33) 
Average household income per monthUS$ 399US$ 226LowLowLCS
Table 3. Indicators used to determine local impact intensity for two MPAs in the Colombian Caribbean. Results for each measurement and score according to the classification scales are shown by MPA. Please see supporting information Table S4 for the classification values of results on the scale
Impact ResultScorea 
MeasurementDescriptionPNNTCRSBPNNTCRSBSourceb
  1. a

    Numeric scores for the impacts and indicators in parenthesis.

  2. b

    Source: LCS = local community survey, PW = participative workshop with community, SI = secondary information, GIS = geographic information system.

Fishing impact   Low (0.44)Moderate (0.56) 
Perception about fishing impactPercentage of fishermen that consider fishing is highly impacting the ecosystem43%38%LowLowLCS
Use of destructive fishing artsUse of destructive gearYesYesHighHighPW
Frequency of herbivores in fishingPercentage of herbivorous fish in harvest3%30.58%Very lowLowSI
Pollution impact   Moderate (0.73)High (0.80) 
Access to sewage systemPercentage of population having access to sewage system2.5%5.38%HighHighSI
Access to sanitary servicePercentage of rural households that have sanitary service connected to a sewage system1.78%3.71%HighHighSI
Adequate solid-waste disposalPercentage of rural households that report municipality is in charge of waste disposal56%14%LowHighSI
Distance to urban settlementsDistance to inhabited location9.3713.18ModerateLowGIS
Distance to pollution sourcesDistance (kilometers) to points of discharge to the sea (waste and sewage) and ports20.4823.42ModerateModerateGIS
Tourism impact   Low (0.33)Moderate (0.67) 
Distance to tourist placesDistance (kilometers) to tourist places11.175.86LowModerateGIS

Ecological data collection

Data was collected during 2008–2009 at 17 sites located inside the two MPAs (PNNT = 7, CRSB = 10) and 10 sites outside (PNNT = 4, CRSB = 6) (Figure 1). We sampled areas in shallow (3–15 m) highly developed reefs, ensuring geomorphological and environmental similarities. We conducted underwater visual fish censuses by swimming along 2 m × 50 m belt transects (100 m2) recording all individuals of families important to local livelihoods (Acanthuridae, Lutjanidae, Serranidae, Scaridae, Haemulidae, Carangidae, Scombridae, Sphyraenidae) and estimating total fish length; between 4 and 10 transects were carried out for each site. Fish density was compared between MPA and non-MPA survey sites in each study area (PNNT and CRSB) using a Mann–Whitney U test. Herbivore and predator biomass were estimated following Friedlander et al., (2003) and transformed (log× + 1) to compare MPA and non-MPA survey sites in each study area using ANOVA. Analyses were run in R statistical software.

We estimated relative abundance of coral, crustose coralline algae, microalgal turfs, and frondose macroalgae within 1 m2 with digital photographs taken from 2 × 50 m belt-transects (100 pictures per transect), one transect per site (López-Angarita et al. 2011). We used ImageJ software (National Institutes of Health) to determine percentage cover of each group. We used data for the Caribbean (Gardner et al. 2003) to calibrate threshold values for the benthic cover indicator (Table S1 and S2). PRIMER software was used to calculate Shannon diversity index values per site for fish and coral; mean values were compared against thresholds selected according to Ramirez-Gonzalez (2006) for fish, and Porter (1972) for coral. The latter was well before many of the dramatic changes that Caribbean reefs have suffered in recent decades (e.g., Acropora die-off 1982–1983, Diadema die-off 1984, 1998/2005/2010 bleaching events, etc.), hence we believe this serves as an appropriate baseline.

To determine water quality we used data from the FORAM index by Velazquez et al. (2011), where FI > 4 indicates an environment conducive to reef growth; 2 < FI < 4 represents the limit for coral growth and unsuitable for recovery; and FI < 2 indicates unfavorable conditions for coral growth (Hallock et al. 2003; Uthicke et al. 2010)

Social data collection

Various sources of information were used to gather socioeconomic data. The main components of this study were built upon primary information. However, pollution measurements (access to sewage system, sanitary services, and solid waste disposal) were obtained using secondary information from the Colombian 2005 census data (http://www.dane.gov.co/censo/). First, to gauge the perspective of environmental authorities on the indicator of rules legitimacy and compliance, enforcement and surveillance, and human capital, we conducted interviews with 14 officers from CRSB and 12 from PNNT (covering more than half of the staff). We designed and applied a structured survey for tourists visiting the MPAs, totaling 816 for CRSB and 160 in PNNT. Information from tourists was used for rules legitimacy and compliance indicators.

We collected perceptions and behavior patterns of local communities during focus groups using participative rural diagnosis on six groups from local communities living inside or around the MPAs (three in each). A total of 90 persons from PNNT and 66 from CRSB were involved. Workshops were designed to capture particular subsets of information about the communities and their relationship with the ecosystem, as well as to construct an indicator of fishing impact.

Framed Economic Experimental Games (EEGs) with local communities simulated the extraction of common pooled resources, specifically fishing. EEGs mimic the dilemma of resource use, characterized by simultaneously exhibiting nonexclusion and rivalry (Berkes et al. 1989; Feeny et al. 1990). In EEGs, players must decide how much to harvest (e.g., fishing extraction), and they obtain points that are convertible into money at the end of the game, which tests the individual's response to alternative management schemes for natural resources use (Moreno-Sánchez & Maldonado 2010). We performed games with 235 participants from eight communities located inside or around CRSB, and 60 participants from three communities located close to PNNT. In addition, participants of EEGs completed surveys on their socioeconomic and demographic backgrounds.

To establish thresholds for social measurements, presented as percentages and frequencies of activities, we divided the percentage equally among the four categories (Tables S3 and S4). Income thresholds followed the approach by the World Bank to define poverty (<$2 per person per day) and extreme poverty (<$1.25 per person per day). For the indicators of pollution and tourism, we included spatial information using ArcGIS (9.1) to measure distances from sampled coral reefs to stressors. We assumed a distance-based interpretation by which the threat declines with distance to stress sources (Burke & Maidens 2004). Differences in indices between MPAs were analyzed with nonparametric statistics (Mann–Whitney U test).

We combined and adapted the analytical frameworks developed by McClanahan et al. (2008, 2009) and Cinner et al. (2012) to interpret the indices, and plotted the social adaptive capacity index and ecological health index against the impact intensity index. Plotting indices creates a space with four possible scenarios where particular management actions should be implemented.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

Ecological health index

Algae dominated the reef benthos (PNNT = 48%, CRSB = 67%) and coral cover was low in both MPAs (PNNT = 22%, CRSB = 23%) (Table 1). Fish density at sites within the MPAs showed no significant differences compared to populations outside MPAs for PNNT (S = 25378, Z = −1.14, P = 0.25) or CRSB (S = 84509, Z = −0.13, P = 0.894). Similarly, no significant reserve effects were seen in biomass of herbivores (PNNT: F = 0.29, P = 0.6; CRSB: F = 2.65, P = 0.13) or predators (PNNT: F = 0.24, P = 0.64; CRSB: F = 0.39, P = 0.54). Fish biomass and density indicators were scored as very low on the health scale. Shannon diversity for fish was higher in PNNT (H = 2.71) than in CRSB (H = 2.56), and conversely, coral species diversity was higher in CRSB (H = 2.318) than in PNNT (H = 1.6). According to the threshold values of Shannon index used in the health scale, fish and coral species diversity in CRSB were low; in PNNT, despite fish being moderate on the scale, coral diversity was very low, making the average overall score low (Table 1). FORAM index had low scores in both MPAs (PNNT = 3.42, CRSB = 3.75) represented by values between 2 and 4. In general, most of the ecological indicators scores were low and very low and potentially degraders of effectiveness. The ecological health index normalized to the unit was 0.25 for PNNT and 0.21 for CRSB.

Social adaptive capacity index

The rules legitimacy and compliance indicator showed a moderate value for both MPAs (score PNNT = 0.52, score CRSB = 0.62) (Table 2). Park rangers perceptions were similar for both MPAs, as more than 50% of them believed rules were well designed in both MPAs, and 60–70% considered the environmental authority had legitimacy in the community, despite around 60% believing that locals did not follow fishing rules. A high proportion of respondents during focus groups with locals showed a favorable perception toward the MPAs (CRSB = 76%, PNNT = 93%) and around 60% declared knowing the rules, but only 65% (CRSB) and 49% (PNNT) said they would denounce rule-breaking by others. A greater proportion of tourists showed awareness of MPA regulation in CRSB (51%) than in PNNT (23%).

The enforcement and surveillance indicator exhibited low values in PNNT (0.44) and moderate in CRSB (0.56). Only 33% of park rangers in PNNT and 21% in CRSB considered sanctions easy to implement, while a greater proportion of communities considered authorities to have enforcement capacity in both parks (60% CRSB vs. 50% PNNT).

Social capital scored moderate in PNNT (0.58) but very low in CRSB (0.25) (Table 2). Both groups scored low for potential internal cooperation between communities (CRSB = 23% vs. PNNT = 33%). Although the proportion of the community in support of comanagement during the EEGs increased to near 60% in both parks (CRSB = 56% vs. PNNT = 59%), communities in CRSB showed more reluctance to accept this type of management scheme (CRSB = 23%, PNNT = 66%). Human capital measured as a frequency of training from authorities and average years of academic studies, was moderate in PNNT, but very low in CRSB. Financial capital was ranked as low in both communities (0.33). When combining these indicators, the social adaptive capacity index normalized to the unit was 0.51 for PNNT and 0.38 for CRSB.

Impact intensity index

The local impact intensity index normalized to the unit was 0.50 for PNNT and 0.67 for CRSB. Fishing impact effect was moderate and slightly higher in CRSB than in PNNT (0.56 vs. 0.44). The use of destructive fishing gears was recognized, and yet less than half of surveyed fishermen accepted that fishing highly impacts the ecosystem (43% PNNT and 38% CRSB). Herbivorous fish represented 31% of the harvest in CRSB and 3% in PNNT (Table 3). The pollution indicator scored moderate and high intensity (PNNT = 0.73, CRSB = 0.80). Reefs were relatively close to urban centers (PNNT = 9.37 km, CRSB = 13.18 km) and points of waste disposal and sewage (PNNT = 20.48 km, CRSB = 23.42 km). In both MPAs, less than 5% of locals had access to sewage system and sanitary service, and collection of solid waste by the municipality was markedly low.

Tourism impact was scored as low in PNNT (0.33) and moderate in CRSB (0.67) (distance to city centers: PNNT = 11.17 km, CRSB = 5.86 km).

Wilcoxon Rank Tests showed no significant differences between the MPAs for both social and ecological indices (N = 25, Z = 0.71, P = 0.47) or for impact intensity index results (N = 9, Z = −0.6, P = 0.54).

The plot of the indices showed low ecological health in both MPAs. Low social adaptive capacity and higher impact intensity placed CRSB into the quadrant corresponding to relief and reorganization (Figure 3), following McClanahan et al. (2008). PNNT was placed in the center, without falling into one clear strategy.

image

Figure 3. Plot of the social adaptive capacity index against impact intensity index. The third dimension of the analysis, the ecological health index, is represented by the size of the bubble (larger = healthier). This theoretical model used to establish conservation actions was adapted from McClanahan et al. (2008) and Cinner et al. (2009). Plotting the indices creates a space with four possible scenarios where particular management actions should be implemented. Lighter color represents a more desirable state of the SESs. Both MPAs have low ecological health, however CRSB is in a more vulnerable scenario due to low adaptive capacity and increasing local impact. PNNT has potential to move toward more desirable scenarios.

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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

This study represents a contribution to the growing number of models assessing ocean health, as it includes higher resolution data of an understudied region, necessary to improve the power of these evaluations (Halpern et al. 2012). Our model was successful in providing a better understanding of the MPA performance incorporating SES complexities and identifying factors that may enhance or erode effectiveness. The ecological subsystem in PNNT and CRSB showed signs of damage as all indicators behaved as degraders: (1) algae dominated the benthos and coral cover was low, (2) fish density and biomass suggested no response to protection, (3) fish and coral diversity was low, and (4) poor water quality provided an environment unsuitable for recovery. These results are unexpected given that the MPAs have been operating for almost 40 years, but are consistent with regional declining trends of fish abundances (Paddack et al. 2009) and coral cover (Gardner et al. 2003). Other studies in PNNT and CRSB have recorded low densities and biodiversity of fish (Camargo et al. 2009), coral cover declining while algae increasing (INVEMAR 2012), and high concentrations of nutrients and sediments (Vivas-Aguas et al. 2010) that lead to poorer water quality in areas inside the MPAs than outside (Velásquez et al. 2011). Algae-dominated reefs have poor productivity and diversity, which translates into the loss of response diversity and functional redundancy vital for ecosystem recovery (Bellwood et al. 2004). Additionally, these reefs exhibit high larval retention by coral populations revealing limited connectivity (Foster et al. 2012). Oligotrophic waters and the higher levels of genetic diversity brought by connectivity are essential for maintaining an integral system resilient to disturbances (Hallock et al. 2003; Van Oppen & Gates 2006).

In the social domain legitimacy of rules and compliance behaved as enhancers, given the positive attitude of park rangers, local communities and tourists in valuing MPAs and the need for conserving their services provision. However, in practice, enforcing rules seems difficult because of large MPA size and budget constraints of environmental authorities. Social capital was higher in PNNT since communities supported the need for sharing responsibilities between authorities and local stakeholders, whereas CRSB communities were more reluctant as a result of past experiences where strong, top-down policies have discouraged local involvement (Moreno-Sánchez & Maldonado 2010). However, during the EEGs both communities reduced extraction in a hypothetical, comanagement scenario, showing potential for adaptability. The communities are restricted by factors related to human and financial capitals. Low education levels and high poverty trap local communities in activities such as fishing, in turn affecting the adaptive capacity of SES (Cinner 2011). This situation is more severe in the larger MPA (CRSB) where communities are more isolated and have less access to both formal education and training. Potential degraders of effectiveness found in this study require attention from decision makers, as local stakeholders have traditionally been ignored in the process of creating and maintaining MPAs, participatory factors that have proved crucial in compliance and in MPA success (Pollnac et al. 2001). The impact of pollution was the highest since most communities did not have sewage systems or sanitary services, and untreated waters were discharged directly to the sea (Vivas-Aguas et al. 2010).

The ecological health, adaptive capacity, and impact intensity indices suggested CRSB and PNNT lack institutional and biophysical resources for overcoming current anthropogenic impacts. The interpretation model positioned CRSB in the reorganization and relief area, which according to McClanahan et al. (2009) corresponds to regions that do not have resources or ability to adapt to high levels of external impacts and need to strengthen their social networks, find alternative sources of livelihoods, and reduce the high dependence on local reef resources at risk. PNNT was located in the center suggesting it has more potential to move toward desirable scenarios, however given the low ecological health we suggest the conservation action to be reorganized as a precautionary approach. Part of this reorganization should include (1) increasing community participation in MPA decision-making and management processes by building strong linkages and trust between park managers and local communities and other stakeholders for nurturing self-organization (Walker 2006; Ostrom 2009); (2) building community capacity by reinforcing local ecological knowledge through capacity building on specific issues and knowledge sharing (Folke et al. 2002; Cinner et al. 2009); and (3) promoting ecosystem monitoring of key ecosystem variables through the development of dynamic warning indicators of resilience loss (Folke et al. 2002; Littler & Littler 2007), ideally with active participation of locals. These concrete actions would reduce vulnerability by building a more suitable scenario for the conciliation of social and ecological systems.

Alternative schemes different to the traditional top-down approach, such as comanagement of natural resources in MPAs, have been demonstrated to be a more effective way of dealing with the challenge of conserving marine biodiversity (Moreno-Sánchez & Maldonado 2010). In attempting to quantify dynamic SES, we are aware of the limitations in generating ecological inferences and delivering concluding remarks on MPA management effectiveness with low spatial and temporal replication. However, our approach is shown to be a practical tool for environmental authorities to uncover and understand relationships among social and ecological dimensions of MPAs, and identify those variables that should be prioritized to improve effectiveness. Applying methodologies to evaluate MPAs as SES, along with periodic monitoring of ecological and social indicators, will provide comprehensive information to determine which are the major drivers causing an effect on ecological health, and highlight how indicators change in response to management measures, helping to minimize incurred degradation costs to local communities. This signifies a vital step toward adaptive management, allowing managers to assess the SES over time and make decisions based on the current trajectories of their systems.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

Special thanks to E. Alvarado (Jorge Tadeo Lozano University) and to M.P. Restrepo (Universidad de los Andes) for their collaboration during the early stages of this research. We acknowledge the support of National Natural Parks of Colombia, in particular PNNT staff (R. Franke, C. García, J. Velandia), director Ing. Sánchez and research permit DTC-CR-T-036–03–09 (2009). Also from CRSB, I. Pineda, M. Cabezas, E. Zarza, and Y. Mendoza. Funding was provided by UniAndes, Colciencias (Grant No. 1204-09-17774) and NOAA coral-grants program (Grant number: NA07NOS4630021). We gratefully acknowledge colleagues of the BIOMMAR and GEMAR (Universidad de los Andes), for their help. Special thanks to: A. Tilley, and five anonymous reviewers for their helpful comments on this manuscript. We recognize the participation and support from local communities.

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  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

Disclaimer: Supplementary materials have been peer-reviewed but not copyedited.

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The following supplementary material is available for this article:

Table S1. Rationale of the indicators selected for the study. Relevance in the literature, and theoretical justifications for the values used as thresholds to construct the scales are shown. Indicator importance is presented as the mean score obtained applying Régnier's Abacus method. Workshop participants voted in a scale of importance ranging from 0 (less important) to 4 (very important)

Table S2. Measurements used to determine the ecological health index and values used as reference to classify results on the scale

Table S3. Measurements used to determine the social adaptive capacity index, and values used as reference to classify results on the scale

Table S4. Measurements used to determine local impact intensity and values used as reference to classify results on the scale

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