perceptions of fishers to sea turtle bycatch, illegal capture and consumption in the san...

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Integrative Zoology 2014; 9: 70–84 doi: 10.1111/1749-4877.12024

ORIGINAL ARTICLE

Perceptions of fishers to sea turtle bycatch, illegal capture and consumption in the San Ignacio-Navachiste-Macapule lagoon complex, Gulf of California, Mexico

Myrna E. AGUILAR-GONZÁLEZ,1 Antonio LUNA-GONZÁLEZ,1 Alonso AGUIRRE,2 Alan A. ZAVALA-NORZAGARAY,1 Manuel MUNDO-OCAMPO1 and Héctor A. GONZÁLEZ-OCAMPO1

1National Polytechnic Institute (IPN), CIIDIR-IPN, UNIDAD SINALOA, Juan de Dios Batiz Paredes, Guasave, Sinaloa, Mexico and 2Department of Environmental Science and Policy, George Mason University, Front Royal, VA, USA

Abstract In this study, 10% of all registered fishermen in the coastal towns of Navachiste in Sinaloa, in northwestern Mexico, answered a survey designed to collect data on their perceptions of the following topics: the impact of turtle meat consumption; human health; bycatch; illegal turtle fishing; the illegal sea turtle market; the lo-cal economy; pollution; environmental education; the success of protective legislation; and sea turtle-based ec-otourism. Perceptions were analyzed using the fuzzy logic method through classification into 5 fuzzy member-ship sets: VL, very low; L, low; M, moderate; H, high; VH, very high. The 9 topics generated decision areas upon applying fuzzy inference that revealed the membership level of the answers in each fuzzy set. The eco-nomic potential of sea turtle-based ecotourism and the economic profitability of the illegal turtle meat market were perceived as VL. Conservation legislation was perceived as H, although inefficiently applied due to cor-ruption. Ecotourism and impacts on sea turtles were perceived as VL, because they were deemed unprofitable activities at the individual and community levels. Environmental education was perceived as L, because it cen-ters on nesting, hatching and releasing turtles and is directed at elementary and middle-school students. While fishers perceive a serious negative impact of fishing activities on sea turtles in the San Ignacio-Navachiste-Macapule area, they do not see themselves individually as part of the problem. Achieving sea turtle conserva-tion in this region requires: suitable ecotourism infrastructure, government investments in promotion, and stud-ies to estimate the minimum number of tourists needed to assure profitability.

Key words: environmental impact assessment, fishers’ perceptions of impact, impact on coastal fisheries, sea turtle conservation

Correspondence: Héctor A. González-Ocampo, Instituto Politecnico Nacional, CIIDIR-IPN, UNIDAD SINALOA, Juan de Dios Bátiz Paredes No. 250, Col Centro, C.P. 81000, Guasave, Sinaloa, México. Email: [email protected]

INTRODUCTION Fishing bycatch is a primary factor that impacts sea

turtle populations (Hall et al. 2000; Koch et al. 2006; Peckham et al. 2007; Bartram et al. 2010) and the use

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Perceptions of sea turtle bycatch

© 2012 Wiley Publishing Asia Pty Ltd, ISZS and IOZ/CAS

of different types of fishing gear by artisanal fisher-men (Peckham et al. 2007; Bugoni et al. 2008; Alfaro-Shigueto et al. 2010), especially trawling techniques (Casale et al. 2004; Haas et al. 2008) and pelagic long-lines (Swimmer et al. 2005; Carranza et al. 2006; Prad-han & Leung 2006), have worsened its negative effects. Those methods and fishing equipment have reduced the populations of some species of sea turtle over the past 20 years (Broderick et al. 2006), and converted them into subjects of conservation efforts. In fact, all 7 spe-cies of sea turtles distributed around the world are clas-sified in 1 of the categories of the International Union for Conservation of Nature Red List (IUCN 2012): Che-lonia mydas Linnaeus, 1758 and Caretta caretta Linnae-us, 1758 are classified as ‘endangered’; Lepidochelys ol-ivacea Eschscholtz, 1829 and Lepidochelys oplivacea Eschscholtz, 1829 as ‘vulnerable’; Eretmochelys im-bricada Linnaeus, 1766, Lepidochelys kempii Garman, 1880 and Dermochelys coriacea Vandelli, 1761 as ‘crit-ically endangered’; and Natator depresus Garman, 1880 is classified as ‘vulnerable’ or as being ‘data deficient’.

Although widely distributed (Barton & Roth 2008), sea turtles are especially abundant in the eastern Pa-cific (Seminoff et al. 2004; Seminoff & Dutton 2007) and the Gulf of California in Mexico (Barton & Roth

2008; IUCN 2012), due to favorable geographical lo-cations and the characteristics of diverse host ecosys-tems (Lluch-Cota et al. 2007). A total of 3 species of sea turtle are reported in this area: olive ridleys (L. oli-vacea) (Arzola-Gonzalez 2007; Seminoff et al. 2002), green turtles (C. mydas) (Chassin-Noria et al. 2004) and hawksbills (E. imbricada) (Seminoff et al. 2003). Other authors include the loggerhead (Ca. caretta) and leath-erback turtles (D. coriacea) because all 5 of these spe-cies use the Gulf of California for nesting or feeding. In this region, olive ridleys congregate around the larger islands for feeding, and in the coastal lagoons and bays of Mexico’s Gulf States for nesting. The loggerhead tur-tle, known locally as caguama, the hawksbill or carey, and the green turtle, all feed in the waters of the Gulf of California (Lluch-Cota et al. 2007).

The reduction of biodiversity around the world has made it necessary to create protected areas with the aim of preserving endangered species. The Gulf of Califor-nia was established as a Marine Protected Area (MPA) in 1993 to preserve biodiversity and remediate envi-ronmental damage (Espinoza-Tenorio et al. 2010). Later, in 2008, the Sistema Lagunar San Ignacio-Navachiste–Macapule (SINAMA), located in the south-ern Gulf of California (Fig. 1), was established as a

Figure 1 Fishery field (dark dots) analyzed within the San Ignacio-Navichsite-Macapule Lagoon complex, Sinaloa, Mexico.

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Marine & Coastal Wetlands Area through the Ramsar Convention on Wetlands (RAMSAR 2011). These con-servation projects emerged because the Gulf of Califor-nia and SINAMA are considered ecologically valuable areas. Despite these efforts to protect biodiversity, how-ever, sea turtles are impacted directly and indirectly by small-scale fishing (Seminoff et al. 2002; Lluch-Cota et al. 2007; Seminoff & Dutton 2007), and their migratory movements expose them at every site they visit in the Gulf of California (Garcia-Martinez & Nich-ols 2000; Koch et al. 2006; Mancini & Koch 2009) and beyond (Saba et al. 2008). On a positive note, some MPA in the Gulf of California show preliminary moni-toring results that are encouraging for conservation and fisheries management (Cudney-Bueno et al. 2009), al-though the worldwide distribution of some species of sea turtles also leaves them vulnerable to anthropogenic impacts. In 2011, sea turtle populations worldwide were still in a precarious state of preservation with bycatch and climate change emerging as the most significant risk factors (Wallace et al. 2011).

Fishing activities injure sea turtles in various ways: abrasions, lacerations, broken carapaces, deep cuts, ex-posed tissue, bleeding, buoyancy problems, body discol-oration and, in many cases, coma and even death (Haas et al. 2008). This variety of injuries has been observed in turtles stranded on beaches by shrimp trawlers (Hud-son et al. 2003; Bartram et al. 2010), and in animals that wash up on beaches in the SINAMA lagoon system, es-pecially during the shrimp fishing season (Sep–Oct).

Despite the fact that all species of sea turtles are pro-tected, it is difficult to record the impact of fishing ac-tivities and then relate them to multidimensional eco-systems like the SINAMA. Data are often unavailable and some of the ecological information is qualitative or based on different categories. In addition, classifying ecological impacts is often difficult to address through standard mathematical and statistical approaches. Fi-nally, the vagueness of human thinking (Li et al. 2006) means that fishermen’s perceptions of environmental impact are inherently indefinite, often inaccurate, and plagued by polyvalent concepts. Thus, only decision-making processes that resemble human rea-soning in their use of approximate information and un-certainty can be applied. Fishers certainly fall into this category, so to deal with the uncertainty involved in their decision-making processes one alternative is to use fuzzy logic (FL), a mathematical system designed to an-alyze imprecise, uncertain environments and partially-true concepts. FL has been used to assess environmental

impacts (Bojorquez-Tapia et al. 2002; Gagliardi et al. 2006; Gonzalez-Ocampo et al. 2006; Li et al. 2008) and ecological risks (Adriaenssens et al. 2004; Salihoglu & Karaer 2004; Gevrey et al. 2006) and in conservation planning assessments (Ruíz-López et al. 2012), based on integrating linguistic expressions with input and out-put variables instead of numerical, probabilistic, statisti-cal or perturbation variables.

Given that fishers’ opinions are imprecise, inaccu-rate and marked by polyvalent concepts, this study used FL to evaluate their perceptions of the impact of arti-sanal bycatch, illegal turtle fishing and turtle meat con-sumption, the economic effects of the illegal turtle mar-ket, the pollution of sea turtle feeding areas, the effects of sea turtle meat consumption on human health, envi-ronmental education, the success of protective legisla-tion, and sea turtle-based ecotourism.

MATERIALS AND METHODSA survey with 9 questions (Q1 = Question 1; Qn =

Question ‘n’) was applied to fishers to obtain their per-ceptions of the following topics: the impact of turtle meat consumption; human health; bycatch; illegal turtle fishing; the illegal sea turtle market; the local economy; pollution; environmental education; the success of pro-tective legislation; and sea turtle-based ecotourism. FL analysis was used to establish 5 membership functions: VL, very low; L, low; M, moderate; H, high; VH, very high. Individual fishers were asked to rate each question using 6 criteria (Table 1), designed in accordance with the standards for environmental impact assessments in Mexico.

Sample size

This study used a stratified probability sample based on the presence of sub-populations within the total pop-ulation. The populations of fishermen distributed in the major fishing communities of El Huitussi (548), El Cer-ro Cabezón (353), El Tortugo (145) and Boca del Rio (27) were found to contain the largest number of coop-eratives and highest fishing production in the area, with a total of 1073 fishermen. Population data were obtained from the Counting of Population and Housing 2005 (INEGI 2006).

The sample size was calculated by stratified sampling (Hernández et al. 1998), with α = 0.1 and ρ = 0.9 (Equa-tions 1 and 2). We established a 10% error factor based on 2 assumptions: that the proportions of population registered per fishing town vary significantly; and that

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although we guaranteed the anonymity of all interview-ees, we estimated that 10% would not answer truthful-ly due to mistrust of the questionnaire or the presence of the researchers. The sample size represents 10% of the total population.

The standard error at 0.01 suggests that this average fluctuation of our estimation and with respect that actu-al the population values of > 0.01; that is, in 100 cas-es, 99 times our prediction is correct and the value of ỹ would be placed in a confidence interval that includes the value of ‘Y.’

The formula for determining the size of n for a simple probability sample is:

, (1)

where n′ is the provisional sample size; S2 is the sam-ple variance (sample variance equals population vari-ance divided by sample size); and V2 is the population variance (sum of the differences of the individual values minus the mean population divided by total population).

Considering a standard error (SE) of not more than 0.15 and a probability p of occurrence of 97.5%:

;; and

Table 1 Questions provided to fishermen in the protected area of the San Ignacio-Navachiste-Macapule lagoon complex system to obtain their perception impact on marine turtles. M = magnitude; e = extension; d = duration; s = synergy; c = cumulative; cs = con-troversy; MI = Mitigation index; BI = basic index and SI = supplementary index.

Criteria MI IB IS

0–9 scale 1–9 scalem e d se ce cs

Q1 How do you perceive turtle meat consumption within your community?

2.11 2.56 4.25 2.11 2.56 4.25 0.61 0.20 0.34

Q2 How do you perceive the health impact of marine turtle meat consumption within your community?

2.51 5.49 4.25 2.51 5.49 4.25 0.48 0.22 0.12

Q3 How do you perceive the bycatch impact of marine turtles in the San Ignacio-Navachiste-Macapule lagoon complex system?

3.42 4.33 2.95 3.42 4.33 2.95 0.72 0.28 0.16

Q4 How do you perceive the marine turtle fishery overall?

3.47 5.87 4.22 3.47 5.87 4.22 0.49 0.25 0.15

Q5 How do you perceive the economic impact of the marine turtle meat black market within your community?

2.89 4.53 2.89 2.89 4.53 2.89 0.54 0.21 0.16

Q6 How do you perceive the pollution impact within marine turtle areas?

4.49 5.64 5.58 4.49 5.64 5.58 0.68 0.40 0.35

Q7 How do you perceive the environmental education impact concerning marine turtles within your community?

2.73 4.89 4.13 2.73 4.89 4.13 0.62 0.27 0.24

Q8 How do you perceive the legislative impact concerning marine turtle conservation?

6.55 7.64 6.95 6.55 7.64 6.95 0.34 0.27 0.18

Q9 How do you perceive the marine turtle-based ecotourism impact?

3.27 4.95 3.93 3.27 4.95 3.93 0.62 0.28 0.27

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.

As we know the population size, N = 1073, we ad-justed the formula to:

. (2)

With the population size, N = 1073 fishermen regis-tered in all fishing towns, we calculated the number of fishermen, n, to be interviewed and obtain a standard er-ror (SE) < 0.015. N is the total population of registered fishermen (= 1073); y is the variable average value (= 1); and SE = 0.015. V is the population variance; (SE)2 is the square of the standard error; and S2 = (Se)2 is the sample variance expressed as the occurrence probability of ỹ.

For statistical analysis:

.

The number of samples per stratum (kSh) was calcu-lated per fishing village (Table 2):

, (3)

where fh is the stratified probabilistic sample; Nh is the stratum sample (fishing village sample number χ); and nh is the stratum population (fishing population in vil-lage χ).

Data substitution:

.

This constant was multiplied by the subpopulation to obtain the sample size per stratum:

, (4)

where Nh is the total fishers’ population (village χ; fh = constant kSh; nh = number of fishers to be inter-viewed in village χ). The sample size per fishing com-munity for El Huitussi, El Cerro Cabezón, El Tortugo and Boca del Río were 50, 32, 13 and 2, respectively.

Fishers’ impact survey

The questions on the surveys applied in the field were structured and organized to feed the fuzzy membership function. In addition, to reduce possible biases in the in-formation gathered from the fishermen, a local third par-ty was present at the beginning of each interview to re-assure interviewees of the strict confidentiality (Mancini & Koch 2009).

Fuzzy logic and perceptions of impact

In classic set theory, an object is either a member of a given set (membership = 1) or not (membership = 0). In contrast, the central idea of fuzzy set theory is that an object may have partial membership in a set that, as a result, may possess all possible values between 0 and 1. The closer the membership of an element is to 1, the more it belongs to the set, whereas the closer the mem-bership of an element is to 0, the less it belongs to that set (Ochoa-Gaona et al. 2010).

A 5 fuzzy membership setup was created to facil-itate analysis of fishers’ perceptions as: VL (0–0.19), L (0.20–0.39), M (0.40–0.59), H (0.60–0.79) and VH (0.80–1.0). In addition, 7 quantitative criteria were used: magnitude (m), spatial extension (s), duration (d), syn-ergic effects (se), cumulative effects (ce), controversy among stakeholders (cs) and mitigation efficiency (me). As these criteria are not homogeneous, it is possible to assign a weight to each one to allow for possible aggre-gation. The criteria were explained to each fisherman interviewed to avoid confusion. In each case, we read

Table 2 Stratified sample (fh = 0.1276) calculating the sample size (Nh) for each fishing village

Name of fishing village Registered fishermen (Nh) Sample size (Nh × fh = nh)Huitussi 548 29Cerro Cabezón 353 45Tortugo 145 2Boca del Río 27 12Total 1073 88

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Figure 2 Bojórquez-Tapia (2002) fuzzy sets modified to de-termine gradual ranging (moderate = M) between low (L) and high (H) sets.

a translated definition (Merriam-Webster 2012) of the terms and showed a visual example to make them un-derstandable. For instance, regarding synergic impact, we showed that clearing mangrove areas has a far great-er effect on turtles than destroying birds’ nests or crab shelters has for those species and that, in fact, turtles are more severely affected than any other single mangrove biota. (See Box 1 for the definitions of the criteria ap-plied.)

When consensus could not be reached in roundtable discussions concerning the ratings for the different cri-teria, and insufficient scientific evidence existed to elim-inate uncertainty, the precautionary principle was ap-plied to implement preventive measures (Immordino 2003) that favor the conservation and preservation of natural resources. In our case, if 2 or more answers were discussed and lacked agreement among the experts, the lower fuzzy set was selected; that is, VL instead of L, M, H or VH; L instead of M, H or VH, and so on.

Following Bojórquez-Tapia et al. (2002), 3 indices were constructed to transform crisp values into grades of membership for the linguistic terms used in the fuzzy

sets (called: fuzzification): the basic (BI), supplementary (SI) and mitigation efficiency (MI) indices. The m, e and d criteria were grouped into BI to measure interaction in-tensity using a 1-to-9 ordinal scale. The se, ce and cs cri-teria were grouped into SI and MI to grade environmen-tal vulnerability on a 0-to-9 ordinal scale, assuming that if there is no magnitude, spatial extension or duration, then perceptions will fail to provide material for syner-gic, cumulative, controversy or mitigation procedures.

Fuzzy logic uses 3 steps to compute the model output for any observed value of the primary indicators, whose corresponding membership value in the fuzzy set do-main is then calculated (fuzzification) (Ochoa-Gaona et al. 2010). The first step consists in qualifying each ques-tion using the criteria established previously to deter-mine the degree of truth for the questions applied: this by means of 3 operators, Bij, SIij and MIij (Fig. 2). All op-erators were calculated according to Bojórquez-Tapia et al. (2002). In the second step, following Ochoa-Gao-na (2010), the memberships of each operator were com-bined to produce new fuzzy sets (inferencing) (Table 3). This forms fuzzy relationships between inputs and out-puts by constructing IF-THEN rules based on expert knowledge and/or experimental results (Erdik 2009). Here, the fuzzy sets presented by Bojórquez-Tapia et al. (2002) and modified by Ruiz-Lopez et al. (2012) were established as VL, L, M, H and VH (Fig. 2). These com-binations resulted in the following composition conjunc-tion:

Box 1Magnitude: a numerical quantitative measure expressed

usually as a multiple of a standard unit; example: intensity of an earthquake

Spatial extension: a property whereby something occu-pies space; example: an extra telephone connected to the principal line

Duration: the time during which something exists or lasts; example: a life

Synergic effects: broadly combined action or opera-tion; example: the impact of clearing of mangrove areas are much higher than the destruction of the nest of birds or crab refugees in the mangrove or any other single impact to mangrove biota

Cumulative effects: increasing by successive additions; example: yearly bycatch cumulates its impact to sea turtle populations

Controversy: discussion marked especially by the ex-pression of opposing views; example: the use of the death penalty

Mitigation efficiency: the action of lessening in severity or intensity; example: restoration of polluted areas

Mem

bers

hip

μBij

and

μSIij

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.

When the appropriate fuzzy operator is unknown, IF-THEN rules, or equivalent constructs, are used, includ-ing fuzzy intersection associative matrices, as in classic logic.

The fuzzy intersection operator (fuzzy AND con-nective) applied to 2 fuzzy sets with the membership functions serving to connect simple statements in or-der to produce more complex compound statements. The ‘AND’ operator is used to connect the anteced-ents and ‘min activation’ is chosen for the ‘AND’ oper-ator. This procedure is called ‘min inferencing.’ If there are any consequents in the same subset, then the maxi-mum MD (minimum distance) of these consequents is taken for that subset, a process known as ‘max inferenc-ing.’ Hence, a ‘min–max inferencing’ procedure is per-formed. By taking the point-wise maximum over all of the fuzzy subsets assigned, each of the 5 triggered rules results in a fuzzy subset (Fig. 3a–c) that is then combined by ‘max inferencing’ (Fig. 3d). Once the inference step is completed, the fuzzy results are converted into a discrete numerical output (defuzzification) (Ochoa-Gaona 2010). The method used in this study to arrive at defuzzifica-tion is the most commonly-used approach, and is called

‘center of gravity’ (COG) (Fig. 3d). The result is a con-sequent, or fuzzy set that arrives at a singleton value ex-pressed as a discrete choice through this formula:

COG ,

where COG is the defuzzified output value; MIi is the the output value in the ith subset; and µ(MIi) is the MI of the output value in the ith subset. Summations in the equation above are to be replaced by integrals (Erdik 2009), such that each decision area obtained (Fig. 4) af-ter ‘inferencing’ reveals the membership level of the an-swers in each fuzzy set.

An illustrative example is presented to show the ap-plication of the fuzzy inference system, where 2 input variables (Kij and SIij) (Fig. 3a,b) and an output variable (MIij) are depicted (Fig. 3c).

RESULTSA total of 137 direct surveys representing 10% of

all registered fishermen (n = 1073) in Navachiste were completed for this study to record and then analyze fish-ers’ perceptions of the impact of sea turtle bycatch, tur-tle meat consumption, legislation and alternative activi-ties related to this endangered marine species.

According to Mexican law, it is illegal to kill or cap-ture sea turtles, or to consume sea turtle meat, and 82% of fishers agreed that sea turtle consumption is wrong. The community consumption impact revealed that they have a VL overall perception of this issue and, in fact, cover up what is actually a widespread illegal activity.

The first question regarding perceptions of sea tur-tle meat consumption obtained a decision area in which 19% of the answers were grouped in the VL and L sets. After defuzzing with COG, the value obtained was 0.205, which lies in the L set (Fig. 4a).

The second question addressed sea turtle meat con-sumption and its risks for human health. Perceptions fell within a decision area between the VL and M sets. After defuzzification by COG, the value obtained was 0.155, which corresponds to the VL set (Fig. 4b) and is consis-tent with findings in other studies of cultural demands (van de Merwe et al. 2009b) or ignorance (Senko et al. 2009) associated with the risks of consuming sea turtle meat.

The third question about the perception of bycatch impact obtained a decision area from the VL to H fuzzy

Table 3 Induced decision table (if . . . and . . . then) rules used in the computation of the fuzzy interaction intensity index (VL, very low; L, low; M, moderate; H, high; VH, very high)

Index k VL L M H VH

Index b VL VL L M H VHL L L M H VHM M M M H VHH H H H H VHVH VH VH VH VH VH

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Figure 3 Schematic representation of fuzzy sets: (a) input sets for variable Bij; (b) schematic representation of inputs for both Bij and ISij fuzzy subsets; (c) schematic representation of combined fuzzy subsets (d).

sets. Upon applying defuzzification, a value of 0.334 was obtained, which fell into the L set and integrat-ed 40% of all perceptions (Fig. 4c). This shows that this impact is considered important in terms of sea tur-tle mortality (Peckham et al. 2008; Ning et al. 2009). In addition, 79% of fishers admitted that they sell or con-sume trapped turtles, most of which die by asphyxia in their nets; and 52% recognized the benefit of inciden-tal bycatch. Fishers characterized their perception of im-pact as L, although 58 and 43%, respectively, consider that direct and artisanal fishing activity cause impacts in the lagoon system.

The fourth question assessed the marine turtle fish-ery overall and obtained a decision area from the VL to M fuzzy sets after grouping 98% of total perceptions. Upon applying COG, the value obtained was 0.295, which corresponds to the L set (Fig. 4d). This result re-inforces the finding that this fishing practice is, indeed,

present in some fishing communities in Mexico (Senko et al. 2009).

The fifth question evaluated the perceptions of the impact of the illegal sea turtle meat market, and gener-ated a decision area between VL and M. After defuzzi-fication, a final value of 0.208 was obtained, which falls in the L set (Fig. 4e). This result supports findings from other studies that have observed that turtle meat con-sumption plays an important role in diverse social ac-tivities, including work meetings, parties, and political or family events (Seminoff et al. 2003; Campbell et al. 2009; Mancini & Koch 2009). Moreover, 67% of fish-ers knew of the existence of an illegal market for tur-tle meat and of established prices for this commodi-ty in their localities. In addition, 70% of fishermen deal in turtle meat exchanges in local markets, where pric-es fluctuate from US$4 to US$7 per kg. While 99% of fishers know that sea turtle consumption is illegal, it

Mem

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hip

μBij

and

μSIij

Mem

bers

hip

μBij

and

μSIij

Mem

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μMij

Mem

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μBij

and

μSIij

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was graded with a VL impact (Fig. 4e), suggesting that it provides only low economic returns. Finally, there is a general perception among fishermen that if the ban were lifted, prices would drop.

The sixth question about pollution impact within ma-rine turtle areas revealed that fishers have a binomial perception. In the first case, it is limited to the VL to L sets, while in the second results varied from L to H. This dual decision area suggests that all fishermen are aware that pollution affects sea turtles and their habitat. After COG analysis, the perception scored 0.283; that is, in the L fuzzy set (Fig. 4f).

In the seventh question, regarding the environmental education impact concerning marine turtles within your community, produced a decision area between the VL and M fuzzy sets. The representative value after defuzz-

ification of those perceptions was 0.223, which again falls into category L (Fig. 4g). On this question, 55% of fishermen responded that most environmental education in the Navachsite region focuses on nesting, hatching and turtle release activities, and is directed to elementa-ry and middle school students (Fig. 4g).

The eighth question about the legislative impact con-cerning marine turtle conservation indicated that legis-lation on biodiversity conservation has been successful, in terms of how fishermen perceive the effectiveness of Mexican legislation in sea turtle conservation. Here, the decision area fell between VL and M. After COG defuzzification, the perception value determined was 0.223; once again, in the L fuzzy set. Of the fishers in-terviewed, 99% admitted or knew that non-compliance with the law is due to shortages of funding and person-

Figure 4 Decision area for the nine survey questions applied to the main fishermen villages within the SIMNS to evaluate the im-pact perception of fishermen on marine turtles. (a) = Question 1; (b) = Question 2; (c) = Question 3; (d) = Question 4; (e) = Question 5; (f) = Question 6; (g) = Question 7; (h) = Question 8; and (i) = Question 9.

a d g

b e h

c f i

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nel (i.e. inadequate enforcement) to combat illegal sea turtle exploitation, with corruption also playing a role (Fig. 4h).

On the final, ninth question, which evaluated the ma-rine turtle-based ecotourism impact, generated a deci-sion area from VL to M. After COG defuzzification, the perception value was 0.163, which corresponds to the VL set (Fig. 4i).

DISCUSSION In general, these results based on fishers’ percep-

tions suggest that fishing activities have a clear and ev-ident impact on sea turtle populations. During our re-search it became clear that fishermen are well aware of the special status of sea turtles, but those laws are not well-enforced in the area. In general, the dimensions of sea turtle meat consumption are very difficult to assess (Hart et al. 2006; Peckham et al. 2007), but even more so if there is no evidence of interaction among different fisheries. As described in other scientific findings, tur-tle meat is typically consumed at social or family events (Mancini & Koch 2009; Senko et al. 2009; Nada & Ca-sale 2011) often attended by the same government of-ficials responsible for monitoring sea turtle trafficking (Seminoff et al. 2003; Peckham et al. 2008; Mancini & Koch 2009). In fact, 27% of the fishers in the SINAMA indicated that much of the demand for sea turtle meat comes precisely from the political sector, and that peo-ple in positions of authority often send subordinates to fishing communities to obtain sea turtle meat.

Sea turtles are caught (by bycatch or incidental-ly) mostly in the summer, precisely when consumption presents the greatest potential hazards to human health (Haswell-Elkins et al. 2007; Senko et al. 2011). Many toxic substances can bioaccumulate in sea turtle tissues (Aguirre et al. 2006; Al-Rawahy et al. 2008; Innis et al. 2008; Garcia-Fernandez et al. 2009; Oros et al. 2009; van de Merwe et al. 2009a), and, as top predators, these animals are exposed to multiple sources of contamina-tion. The fishing communities in our study recognize that pollution affects sea turtle ecosystems and can con-taminate the animals. Moreover, the Navachiste area is sourrounded by industrial and agriculture activities and so receives constant flows of agricultural and industri-al effluents, wastes that pollute the feeding habitats of many wildlife species (Al-Bahry et al. 2009), includ-ing sea turtles (van de Merwe et al. 2009a,b). Although Navachiste fishermen recognize that turtle meat might be contaminated and that eating it could cause health

problems, they continue to consume it. It is possible that if more complete and reliable information on the risks of eating sea turtle meat were available locally (Aguirre et al. 2006; Senko et al. 2009), consumption might de-crease or disappear. If consumption continues unabated and people do not implement precautionary measures in light of the potential illnesses caused by eating sea turtle meat (van de Merwe et al. 2009b), those diseases may turn into ongoing, frequent and chronic health problems (Senko et al. 2009). However, additional research into the illnesses caused by eating sea turtle meat in Nava-chiste must be conducted to determine, for example, the presence of heavy metals in sea turtle tissues (through analyses of hair and fat) caused by incidental intake through sea turtle meat. The present study also revealed a serious lack of environmental programs focused on the risk factors involved in consuming sea turtle meat, reflected in the VL perception in local fishing commu-nities. The SINAMA lagoon system receives runoff and sewage from agriculture, shrimp farms and urban sourc-es (Orduña-Rojas & Longoria-Espinoza 2006; Mar-tínez-López et al. 2007; Montes et al. 2011) that con-tribute to chemical and plastic-based pollution (Bugoni et al. 2003; Mrosovsky et al. 2009) of sea turtle habitats and turtle meat.

Sea turtle meat in Navachiste is obtained by 2 means, bycatch and illegal capture, and turtle meat consump-tion in the SINAMA system is a reality despite existing bycatch regulations. Bycatch and the associated illegal turtle trade affect sea turtle populations (Nada & Casale 2011), but this situation is made worse by the fact that, as previous studies have shown, fishers who do strive to protect turtles receive no financial inducement to do so, and although they may protect turtles by releasing them back to the sea, those animals are often caught by oth-er fishermen (Ning et al. 2009). Sea turtle bycatch in the SINAMA system is poorly documented and, as in other studies (Bartram et al. 2010), only preliminary estimates are possible. Moreover, care must be taken when mak-ing comparisons due to the low level of observer cov-erage and poor species identification of animals that are captured incidentally. If 79% of fishers admit, at least indirectly, that they catch sea turtles incidentally, then it is clear that this activity requires special attention, as it affects entire populations. This percentage suggests that current turtle population densities may be comparable to those from other important areas around the world (Ca-sale et al. 2004). However, population density in the SINAMA system may also be affected by the fact that it is one of the most important shrimp fishing zones in

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the Mexican Pacific: an exploitative activity that uses primarily bottom trawlers. It is a well-documented fact that many specimens of sea turtle are caught in those nets (Casale et al. 2004), which means that bycatch rep-resents a significant threat to the sea turtle populations that feed, transit or nest in the areas around the SINA-MA system.

Furthermore, fishermen do not only capture sea tur-tles by accident. We detected that they sometimes do so intentionally in the SINAMA system. As in other places, fishers may catch sea turtles for several reasons (Gar-cia-Martinez & Nichols 2000; Aguirre et al. 2006; Has-well-Elkins et al. 2007; Campbell et al. 2009; Senko et al. 2009), despite their environmental value and the fact that volumes are economically unimportant compared to the returns from legal fishing (Campbell et al. 2009). However, in this case, taxation and the tax status of this commodity affect the quality and attributes (Thorn-ton 1991) of sea turtle meat differently. The statutes that prohibit sea turtle exploitation and their enforce-ment (Moore et al. 2009) determine the composition, the quality, the output and the price of this commodi-ty (Thornton 1991) and, thus, influence illegal sea tur-tle meat markets in the region such that it is likely that prices will remain relatively high as long as the ban is in effect. As a point of comparison, during the prohibition of alcohol (The Noble Experiment), in the USA in the 1920s, the price of a large beer increased by 600%, from US$0.10 to US$0.80, by the time the ban was repealed in 1930 (Thornton 1991). These cases suggest that regu-latory policies can produce an implicit price for sea tur-tles, and that as long as the law of ‘supply and demand’ governs this illegal market, trafficking will continue, in-tentionally or incidentally.

Law enforcement is insufficient to halt the current level of mortality associated with bycatch and consump-tion, so additional protective measures are necessary (Coelho Dias da Silva et al. 2010; Nada & Casale 2011). Two norms, NOM-EM-007-PESC-2004 (DOF 2004) and PROY-NOM-162-SEMARNAT-2011 (DOF 2012), have been decreed to protect sea turtles. The first estab-lishes new specifications for the turtle-excluding equip-ment that must be used by all commercial fishing opera-tions, and educational initiatives for shrimp trawlers that operate in federal waters, to help protect sea turtle pop-ulations and reduce incidental capture. The second out-lines specifications for the protection, recovery and han-dling of sea turtle populations in their nesting habitat. In addition, Mexican law stipulates that the authorities can seize the equipment of violators and can impose fines as

high as US$14 000 or imprisonment for up to 9 years. In this sense, the use of turtle excluders and full en-

forcement of the legislation governing shark fishing and related activities (DOF 2007) must be implemented to prevent, or at least reduce, bycatch.

These regulatory policies and their incomplete and spotty enforcement in Mexico permit the existence of il-legal sea turtle meat markets in the SINAMA area. The deficiencies of this legal situation have been made clear in earlier studies from the region (Mancini & Koch 2009; Senko et al. 2009). The price of sea turtle meat that, as we have seen, is overvalued by prohibition, has considerable economic value in other regions (van de Merwe et al. 2009b) and the study area may not be an exception, despite fishermen’s claims that it is not a sus-tainable economic option in the SINAMA system. As in other lagoon systems where artisanal fishermen ex-ploit multiple species in response to seasonal variabili-ty (Heyman & Granados-Dieseldorff 2012), commerce in sea turtle meat is a sporadic activity that does provide profits, although it is accompanied by high risks that can include imprisonment, hefty fines and confiscation of fishing gear, including boats. Despite its illegality, when the sea turtle market is compared to other fisheries (such as marine shrimp) its economic weight is fairly small (Heyman & Granados-Dieseldorff 2012), a finding that is also true for the SINAMA region. Illegal sea turtle meat in the region generates up to US$13 000 per year if we estimate an upper limit of 20% of total mortality recorded per stranding (Hart et al. 2006): a small mar-ket, indeed, compared to the shrimp fisheries that pro-duced more than US$500 000 in 2009 alone (SAGARPA-CONAPESCA 2010).

If, indeed, the illegal sea turtle market is not a sus-tainable economic activity in the SINAMA system, then fishers in this area could consider alternatives; name-ly, fostering ecotourism based on the observation of sea turtles and other wildlife. This activity has been very ef-fective in environmental education and sea turtle con-servation in other places (Tisdell & Wilson 2002), and is a demonstrably effective economic alternative for the sustainable utilization of natural resources and devel-opment (Tisdell & Wilson 2002). In the SINAMA fish-ing communities, this concept, if applied efficaciously, might detonate local economic development, as its con-ceptualization is easily understood and appreciated by local towns and stakeholders (Libosada 2009). Instead of fishers being forced to rely for their day-to-day sub-sistence on the existing fishing infraestructure, which is inadequate and limited (Senko et al. 2011), they might

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be persuaded to foster ecotourism as an alternative ac-tivity through initiatives supported by collaboration with government.

Implementing ecotourism begins with environmen-tal education to enhance the desire to conserve wild-life through practical actions performed through spe-cific programs (Tisdell & Wilson 2002; Higham et al. 2008; Libosada 2009). Sea turtle conservation is usual-ly incorporated into the educational programs offered in the SINAMA complex, but, as we found, is insufficient to achieve success. As local people’s knowledge of eco-tourism is limited mainly to protecting nesting sites and releasing trapped turtles, they have not yet come to un-derstand that sea turtle-watching could be a viable eco-nomic activity in the area. Sea turtle-based ecotourism is possible under certain rules and conditions (Landry & Taggart 2009), but fishers in the SINAMA region must be trained and receive financial support to develop the required infrastructure. Thus, marketing studies and funding must be negotiated to make sea turtle-based ec-otourism and other sustainable and ecologically-friendly activities possible in the SINAMA system.

While fishermen in the SINAMA system admit that sea turtles are trapped, both incidentally and intention-ally, as individuals they do not see themselves as part of the problem. In relation to enforcement of existing laws, additional protective measures are urgently required to combat the illegal trade of sea turtle meat through ef-fective application of fines and penalties. From the eco-nomic perspective, although the income obtained from sea turtle fishing may not be very significant and does not enhance the local economy, as long as a certain lev-el of demand–supply persists in the zone, sea turtle traf-ficking will continue, either willfully or incidentally.

Turning to consumption, people (not fishermen or fishing towns’ inhabitants) who eat sea turtle meat are generally unaware that they are ingesting a product that has most likely been exposed to contamination; hence, environmental education programs must be revised to include this topic. Finally, our study suggests that well-planned, appropriately financed and aptly implemented ecotourism programs could help local economies. In ad-dition to improving the conservation measures for sea turtles in the SINAMA lagoon system, we suggest that local scientific researchers reinforce their monitoring of sea turtle populations, implement more conservation projects, and extend the logistics of existing ones. These studies should focus on such topics as fluctuations in sea turtle populations, tourism carrying-capacity, lo-

cal climate-population interaction, socioeconomic stud-ies of potential visitors, and estimates of the educational levels of potential tourists as a means of estimating the minimum number of visitors required to make ecotour-ism sustainable. However, even before that, basic tour-ism infrastructure must be constructed to offer potential tourists suitable facilities. This would require local fish-ing communities to negotiate funding with governments and/or the private sector.

ACKNOWLEDGMENTSWe thank the IPN (SIP 20080127, SIP 20090442),

COECYT-SINALOA (CECyT-SIN 2009) and FOMIX CONACYT-SINALOA (FOMIX-SIN-2008-C01-99712) for financial support.

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