Aw

BMa

b

c

d

a

ARR1AH

KSCUCM

1

f

icmvm

h0

Fisheries Research 164 (2015) 254–265

Contents lists available at ScienceDirect

Fisheries Research

j ourna l ho me pa ge: www.elsev ier .com/ locate / f i shres

comparison of fishing activities between two coastal communitiesithin a biosphere reserve in the Upper Gulf of California

rad Erismana,∗, Ismael Mascarenas-Osoriob, Catalina López-Sagásteguic,arcia Moreno-Báezd, Victoria Jiménez-Esquivelb, Octavio Aburto-Oropezad

University of Texas at Austin, Marine Science Institute, Port Aransas, TX 78373-1015, USACentro para la Biodiversidad Marina y la Conservación A.C., La Paz, BCS 23090, MéxicoUC MEXUS, University of California Riverside, CA 92521, USAMarine Biology Research Division, Scripps Institution of Oceanography, University of California at San Diego, La Jolla, CA 92093-0202, USA

r t i c l e i n f o

rticle history:eceived 31 July 2014eceived in revised form9 December 2014ccepted 20 December 2014andling Editor A.E. Punt

eywords:mall-scale fisheriesollaborative fisheries researchpper Gulf of Californiaolorado River Deltaarine protected areas

a b s t r a c t

We engaged in collaborative research with two small-scale fishing communities inside the Upper Gulf ofCalifornia Biosphere Reserve in Mexico, San Felipe (SF) and El Golfo de Santa Clara (GSC), to test how wellthe geographic heterogeneity of fishing activities within the reserve coincided with current regulations.We compared the two communities in terms of catch composition, fishing effort, ex-vessel prices and rev-enues, seasonal patterns in fishing activities in relation to the reproductive seasons of target species, andspatial patterns of fishing in relation to managed zones within the reserve. The top four species (Cynoscionothonopterus, Micropogonias megalops, Scomberomorus concolor, Litopenaeus stylirostris) in terms of rel-ative effort, catch, and revenues were the same for both communities but overall fisheries production,effort, and revenues were higher in GSC than SF for these species. Fishing activities in GSC followed apredictable annual cycle that began with L. stylirostris and were followed sequentially by the harvestingof C. othonopterus, M. megalops, and S. concolor during their respective spawning seasons, which wereassociated with seasonal variations in ex-vessel prices. Conversely, catch and revenues in SF were morediversified, less dependent on those four species, less seasonal, and did not show seasonal variations inprices. Interactions between fisheries and managed zones also differed such that SF interacted mainlywith the southwest portion of the vaquita (Phocoena sinus) refuge, whereas GSC fished over a larger areaand interacted mainly with the northeast portion of the vaquita refuge and the no-take zone. Our resultsindicate the two communities differ markedly in their socio-economic dependence on fisheries, theirspatio-temporal patterns of fishing, their use of and impacts on species, coastal ecosystems and man-aged areas, and how different regulations may affect livelihoods. Regional management and conservation

efforts should account for these differences to ensure the protection of endangered species and to sustainecosystem services that maintain livelihoods and healthy coastal ecosystems. This study provides fur-ther evidence of the ability of collaborative research between scientists and fishers to produce robust andfine-scale fisheries and biological information that improves the collective knowledge and managementof small-scale fisheries within marine protected areas.

. Introduction

Commercial fisheries are essential to the livelihood, welfare, andood security of coastal communities, and more than 90% of the

∗ Corresponding author. Tel.: +1 361 749 6833; fax: +1 361 749 6777.E-mail addresses: berisman@utexas.edu (B. Erisman),

smael.mascarenas@gocmarineprogram.org (I. Mascarenas-Osorio),atalina@ucr.edu (C. López-Sagástegui),morenobaez@ucsd.edu (M. Moreno-Báez),

ictoria.jimenez@gocmarineprogram.org (V. Jiménez-Esquivel),aburto@ucsd.edu (O. Aburto-Oropeza).

ttp://dx.doi.org/10.1016/j.fishres.2014.12.011165-7836/© 2014 Elsevier B.V. All rights reserved.

© 2014 Elsevier B.V. All rights reserved.

world’s fishers are employed in small-scale fisheries (Chuenpagdeeet al., 2006; FAO, 2012; Teh and Sumaila, 2013). However, small-scale fisheries can be more difficult to manage than industrial(large-scale) fisheries, because they often lack sufficient or reli-able data related to effort, catch, discard rates, the biology of targetspecies, and other information necessary to assess stocks and setregulations (Johannes, 1998; Salas et al., 2007). Moreover, fisheriesinformation is usually available only on coarse spatial and tempo-

ral scales that do not always correspond to the demographics orlife history characteristics of fish stocks or the dynamics of small-scale fisheries that target them (Tzanatos et al., 2005; Erisman et al.,2011; Wilson et al., 2012). Understanding spatial and temporal

B. Erisman et al. / Fisheries Research 164 (2015) 254–265 255

F erve, mo within

paatstcErca(tt

MfiiTrnncmrQ

ig. 1. Map of the Upper Gulf of California and Colorado River Delta Biosphere Resf El Golfo de Santa Clara and San Felipe, and the location of the Biosphere Reserve

atterns of small-scale fisheries and how they align with fisheriesnd conservation regulations is particularly important for the man-gement of marine protected areas. Such information is crucialo assess possible impacts of fishing on endangered or protectedpecies, coastal ecosystems, and managed areas as well as to iden-ify and mitigate socio-economic impacts of regulations on fishingommunities (Gunderson et al., 2008; Abbott and Haynie, 2012;risman et al., 2012; Horta e Costa et al., 2013). Likewise, incorpo-ating spatio-temporal interactions within and among ecosystemomponents and human activities into management decisions isn essential component of ecosystem-based fisheries managementLeslie and McLeod, 2007), which aims to simultaneously protecthe structure and function of marine ecosystems and the serviceshey provide to mankind (FAO, 2005).

The Gulf of California is the most important fishing region inexico, as it contributes more than half of the country’s total annual

sheries production, and small-scale fisheries generate the major-ty of this production (Cisneros-Mata, 2010; Erisman et al., 2011).he Upper Gulf of California (Fig. 1) is arguably the most importantegion in Mexico in terms of small-scale fisheries production, whereearly 1000 small boats use gill nets to harvest blue shrimp (Litope-aeus stylirostris), Gulf corvina (Cynoscion othonopterus), bigeye

roaker (Micropogonias megalops), Spanish mackerel (Scombero-orus concolor), and small volumes of other groups such as sharks,

ays, crustaceans, and bivalves (Cudney and Turk, 1998; Rodríguez-uiroz et al., 2010). Fishing activities in this region have long

anaged areas within the reserve, the locations of the coastal fishing communities the Gulf of California (inset).

interacted with the conservation of two endangered species ofnational and international concern: the vaquita porpoise (Pho-coena sinus) and the totoaba (Totoaba macdonaldi) (Aragón-Noriegaet al., 2010; Bobadilla et al., 2011; Ávila-Forcada et al., 2012). Con-sequently, the region has a storied history of management andconservation efforts that are now most visible through the imple-mentation of the Upper Gulf of California and Colorado River DeltaBiosphere Reserve, which includes a no-take zone in the estuary ofthe Colorado River to protect the spawning grounds of totoaba andGulf corvina and another no-take zone that serves as a refuge forvaquita (Fig. 1).

Successful management of small-scale fisheries in the Bio-sphere Reserve is hindered by a paucity of fisheries informationand ongoing conflicts between fishing communities that operateinside the reserve and the region’s conservation agenda (Bobadillaet al., 2011). The coastal fishing communities of San Felipe, BajaCalifornia, and El Golfo de Santa Clara, Sonora, (henceforth referredto as “Santa Clara” and “San Felipe”) lie within the boundaries ofthe reserve, utilize the buffer zones within the reserve as theirprincipal fishing grounds, and thus are effected greatly by strict reg-ulations implemented within the reserve for conservation purposes(Bobadilla et al., 2011; Pérez Valencia et al., 2011; Ávila-Forcada

et al., 2012). Small-scale fisheries are central socioeconomic com-ponents in both communities. However, they differ in terms ofcapacity and fisheries production. There are 457 boats in SantaClara that collectively operate a total of 925 fishing permits for 20

256 B. Erisman et al. / Fisheries Research 164 (2015) 254–265

Table 1Number of fishing trips monitored with GPS data loggers from February 2012 to October 2013 in San Felipe and Golfo de Santa Clara organized by community, species, andyear.

Community C. othonopterus (Gulf corvina) M. megalops (Bigeye croaker) S. concolor (Spanish mackerel) L. stylirostris (Blue shrimp) Total

2012 2013 2012 2013 2012 2013 2012 2013

fimFtrtt

ltrbmttpmeircgopB

shachipriRrowcfi

2

2r

NiJcFmas

GSC 50 86 198 5

SF 22 – 265 79

Total 72 86 463 84

sheries, and San Felipe has a fleet of 204 boats with 547 fishing per-its covering 27 fisheries. Only about 15% of the workforce in San

elipe is involved in primary production (i.e. fishing), whereas 50%o 80% of the people in Santa Clara actively fish seasonally or yearound (Ávila-Forcada et al., 2012; Vázquez León et al., 2012). Theourism industry supports 64% of San Felipe’s workforce comparedo only 30% in Santa Clara.

Fisheries regulations in the Upper Gulf primarily focus onimiting the total capture on commercial species and the restric-ion of fishing inside specific zones, seasonal restrictions, or gearestrictions (Bobadilla et al., 2011; Pérez Valencia et al., 2011)ut do not consider the possibility that communities may differarkedly in their fishing activities with respect to catch composi-

ion, spatio-temporal patterns in effort, catch, and revenues, andhe overall contribution of fisheries production. Investigations ofotential variations among communities are warranted, since theyay coincide with differential effects on marine fish populations,

cosystems, and managed areas, or how regulations impact fish-ng activities and production. Moreover, adjusting managementegulations to account for spatio-temporal heterogeneity amongommunities would match current support by scientists, non-overnment agencies, and government agencies for the adoptionf ecosystem-based fisheries management approaches for marinerotected areas in the region (OECD, 2006; Cudney-Bueno andasurto, 2009; Ezcurra et al., 2009).

The goal of this research was to test the hypothesis thatmall-scale fisheries in the Biosphere Reserve exhibit geographiceterogeneity at a finer scale than accounted by current man-gement regulations. To accomplish this goal, we engaged inollaborative fisheries research with commercial fishers and stake-olders from Santa Clara and San Felipe to compare their fisheries

n terms of catch composition, fishing effort, revenues, seasonalatterns in fishing activities and ex-vessel prices in relation to theeproductive seasons of target species, and spatial patterns of fish-ng in relation to managed zones within the Biosphere Reserve.esults of this study provide a baseline of fisheries informationelated to the Upper Gulf of California and the Biosphere Reserve,ffer practical insights for the management of small-scale fisheriesithin marine protected areas, and demonstrate the benefits of

ollaborative fisheries research for the management of small-scalesheries.

. Materials and methods

.1. Species composition of catch; annual effort, landings, andevenues

We acquired commercial fisheries data from the Mexicanational Commission for Fisheries and Aquaculture (CONAPESCA)

n Mazatlan, México, for Santa Clara and San Felipe for the periodanuary 2001 to December 2011. These data included 35,902atch reports from Santa Clara and 31,861 catch reports from San

elipe. Data were organized into a fisheries database comprised ofonthly records of landings (kg) and ex-vessel prices (pesos/kg)

rranged by the common name of the species or species group (e.g.nappers, sharks) for all months and years. Ex-vessel revenues were

55 – 611 197 120240 31 63 16 485

95 31 674 213 1687

calculated by multiplying landings by ex-vessel prices, and theresulting values in Mexican pesos were then converted to US dol-lars using published monetary exchange rates. Next, we calculatedmean annual landings (tons) and revenues (USD) for each speciesby community using absolute and relative measures (%). Directcalculations of fishing effort or CPUE were not possible using thefisheries database, because commercial fishers are not required tosubmit detailed daily logs of fishing activities. Therefore, we cal-culated mean annual CPUE for each species by community fromthe fisheries database by dividing the mean annual landings by thenumber of fishing permits distributed. This methodology has itslimitations, as illegal fishing, misreported catch, and unreportedcatch and effort are pervasive in Mexico (Cisneros-Montemayoret al., 2013). However, these represent the only available data onfisheries effort for the region. We also compared CPUE patterns(qualitatively) from the fisheries database with CPUE estimatedfrom our collaborative research with fishers (see below) to deter-mine whether the results were similar, and as a means of assessingthe reliability of official fisheries statistics in this regard. A one-wayANOVA was used to test for significant differences in mean annuallandings, ex-vessel prices, and ex-vessel revenues between the twocommunities for the four most important fisheries: L. stylirostris, C.othonopterus, M. megalops, and S. concolor. Where significant differ-ences were found, post hoc multiple comparisons were performedusing a Tukey test.

We engaged in collaborative fisheries research with commercialfishers of Santa Clara and San Felipe from January 2012 throughDecember 2013 to acquire fine-scale information on catch compo-sition, revenues, and spatial patterns (see Section 2.3) of fishing forL. stylirostris, C. othonopterus, M. megalops, and S. concolor. Fishersrecorded the landings (kg) for each species by trip. We also inter-viewed several fishers (n = 35 for Santa Clara; n = 31 for San Felipe)to estimate the average number of days each year they fished foreach of these four fishes to obtain a semi-quantitative estimate ofmean annual fishing effort by species. In Santa Clara, we obtainedinformation for 1202 fishing trips (Table 1) from 74 different pan-gas and representing 14 cooperatives. In San Felipe, we obtainedinformation for 485 fishing trips from 53 pangas, representing bothfishing federations (i.e. organizations comprised of numerous fish-ing cooperatives) that operate in the community. Sampling wasopportunistic, such that we worked with all fishers and fishingcooperatives willing to participate in the study. Considerable effortwas made to engage and involve as many fishers as possible inboth communities throughout the study to minimize sampling bias.Fisher participation was lower in 2013 due to conflicts betweenfishers, local NGOs, and management agencies that arose after sev-eral fisheries regulations were implemented in the region relatedto the Gulf corvina and shrimp fisheries. We used these data to cal-culate a second estimate of mean annual CPUE for each species bycommunity by dividing landings per fishing trip by the number offishing trips.

2.2. Reproductive seasonality

We conducted biological surveys on a monthly basis at SantaClara and San Felipe from January to December 2012 to determine

s Resea

ttfFofiet

fivpCPihipirtfbspbsft

2

Mrtrfyecataemf

imeuetrmmc

2

Sgto

B. Erisman et al. / Fisherie

he reproductive seasons of the three most common fishes cap-ured by commercial fisheries in the region (C. othonopterus, n = 281emales; M. megalops, n = 253 females; S. concolor, n = 198 females).ish were obtained opportunistically at either local fishing marketsr directly from fishers upon their return from fishing trips. For eachsh collected, we recorded information on total length to the near-st mm, total body weight to the nearest g, and gonad weight tohe nearest 0.1 g.

The sex and reproductive condition of each fish sampled wasrst determined by macroscopic inspections of gonads and latererified by microscopic evaluations of gonad tissue sections pre-ared using standard histological procedures (Erisman et al., 2012).lassification of gonadal development stages followed Brown-eterson et al. (2011). Emphasis was placed on identifying femalesn the actively spawning stage with ovaries containing batches ofydrated or ovulated oocytes to specify months in which spawn-

ng had occurred (Erisman et al., 2012). However, the monthlyroportion of females with ovaries containing batches of oocytes

n advanced stages of vitellogenesis (spawning capable) was alsoecorded to provide additional information on seasonal reproduc-ive activity. The mean monthly gonadosomatic index (GSI) ofemales was calculated as follows: GSI = 100 × (gonad weight/totalody weight). Mean monthly GSI of females was plotted to identifyeasonal peaks that correspond with spawning activity. Monthlyatterns in the percentage of actively spawning and spawning capa-le females were combined with GSI data to define the spawningeason for each species. Our estimates of reproductive seasonalityor each species were then compared to published and unpublishedechnical reports to corroborate our conclusions.

.3. Seasonal patterns in effort, landings, revenues, and prices

We compared the monthly patterns in catch for C. othonopterus,. megalops, and S. concolor from the fisheries database with their

eproductive seasons to estimate the contribution of the reproduc-ive season to fisheries production in each community. First, catcheports (Santa Clara = 33,029 reports; San Felipe = 13,422 reports)or each species from each community were pooled by month andear. Next, we calculated mean monthly landings by species forach community to determine whether seasonal peaks in catchoincided with the reproductive season. We then estimated the rel-tive importance of the reproductive season to fisheries for each ofhe four species by summing the landings recorded during monthsnd dividing these values by the total annual landings (Erismant al., 2010). We multiplied landings recorded during reproductiveonths by monthly ex-vessel prices to estimate ex-vessel revenues

rom reproductive seasons.A one-way ANOVA tested for significant differences in land-

ngs, ex-vessel prices, and ex-vessel revenues by community and byonth separately for both communities. Where significant differ-

nces were found, post hoc multiple comparisons were performedsing a Tukey test to identify peak months in catch, price, and rev-nue. We used the same method to test for differences betweenhe communities with respect to the relative contribution of theeproductive season to total annual landings of each species. Spear-an’s rank correlation was used to test for a relationship betweenonthly landings and ex-vessel price for each species within each

ommunity.

.4. Spatial patterns of fishing activities

Fishing areas for L. stylirostris, C. othonopterus, M. megalops, and

. concolor were identified for both communities using GPS data log-ers (AMOD AGL 3080) that use a Global Positioning System (GPS)o record a boat’s position at regular intervals of 1–30 s dependingn the fishery (Erisman et al., 2012). Data loggers were given to

rch 164 (2015) 254–265 257

fishers as they departed the beach to embark on a fishing trip andwere retrieved upon their return to shore. We implemented a sim-ple protocol that minimized requirements for fishers to operate thedata loggers to avoid disrupting their normal fishing activities andmaximize incentives and willingness of fishers to use data loggers.Briefly, fishers were only requested to turn the loggers on and off atthe beginning and end of fishing trips, respectively, and were notasked to mark any points during trips. Sampling was opportunistic,such that we worked with all fishers willing to participate, whichusually included a large number of fishers representing a large frac-tion of the fishing cooperatives operating in both communities (seeSection 2.1).

We uploaded the data from each fishing trip to a computerand displayed it using GPSBabel (see http://www.gpsbabel.org) toensure files with aberrant data were excluded from the analysis.GPS data were then transformed to ASCII code outputs, importedinto a Geographic Information System (GIS) format using ArcGISTM

10.2 software. Spatial data from each fishing trip were analyzed toidentify the fishing grounds using the object oriented model buildertools in ArcGISTM 10.2 and visual techniques. The fishing groundwas represented as a point, identified as the centroid of a line thatrepresented the spatial pattern of the deployment and retrieval ofa net (i.e. the distance over which the net drifted between deploy-ment and retrieval). Fine-scale patterns of movement and boatspeed facilitated the identification of locations where nets weredeployed and retrieved (Erisman et al., 2012). We validated ourmethod on several occasions by working with fishers after fish-ing trips to confirm our estimations were consistent with theirknowledge of where fishing occurred.

We estimated the fishing area used by each community for eachfishery by simultaneously plotting all marked points on a regionalmap and using the directional distribution tool in ArcGISTM 10.2to create an elliptical polygon centered on the mean for all fish-ing observations. We utilized two standard deviations in order tocover a 95% confidence interval for each fishery, and we createda polygon around the outer boundaries using the aggregate pointstool to calculate the area of the polygon for each fishery. We calcu-lated the percentage of marked points located within these areas toestimate the general distribution of fishing grounds in relation tothe two restricted zones within the Biosphere Reserve (the vaquitarefuge and the no-take zone) for each fishery and each community.For each of the four fisheries within each community, we calculatedthe average distance traveled per boat trip as a simple metric forestimating fishing costs.

All four fisheries use gill nets as the principal method of cap-ture (Rodríguez-Quiroz et al., 2010), and three of these fisheries(those for M. megalops, S. concolor, L. stylirostris) capture their tar-gets species by deploying gill nets and allowing them to soak anddrift in strong currents for 30 min to several hours at a time. Thesenets vary considerably in length from 290 to 1485 m (Rodríguez-Quiroz et al., 2010; Erisman et al., 2012), and some nets may capturefish at locations up to 1–2 km away from the site of deployment.Consequently, these points provide only a general idea of wherefishing activities occur and should not be used to calculate exactlocations of fishing or capture in relation to restricted zones (seeSection 4).

3. Results

3.1. Species composition of catch; annual patterns of effort,landings, and revenues

Mean total landings and revenues were higher in Santa Clarathan San Felipe (Table 2). Fisheries in Santa Clara recorded land-ings for 20 species, with four species representing more than 90%

258 B. Erisman et al. / Fisheries Research 164 (2015) 254–265

Table 2Catch composition of small-scale fisheries from El Golfo de Santa Clara (GSC) and San Felipe (SF) from 2001 to 2011 organized by species, landings, and ex vessel revenues.

GSC SF

Species Mean annuallandings (tons)

Meanannuallandings (%)

Mean annualrevenue (USD)

pcMeanannualrevenue (%)

Species Mean annuallandings (tons)

Meanannuallandings (%)

Mean annualrevenue (USD)

Meanannualrevenue (%)

Gulf corvina 2566.2 44.5 2,189,047 21.1 Bigeye croaker 1055.1 32.4 670,055 9.4Bigeye croaker 1249.8 21.7 889,329 8.6 Spanish mackerel 687.3 21.1 711,346 10Spanish mackerel 1129.8 19.6 1,157,461 11.1 Blue shrimp 437.1 13.4 4,153,042 58.3Blue shrimp 501.2 8.8 5,553,193 53.4 Gulf corvina 195 6 170,290 2.4Other shrimp 47.8 0.8 381,974 3.7 Geoduck 174.7 5.4 304,386 4.3Smoothhound sharks 28.2 0.5 44,615 0.4 Rays 145.9 4.5 144,240 2Rays 24.6 0.4 83,292 0.8 Guitarfishes 105.4 3.2 100,930 1.4Clams 24.6 0.4 17,956 0.2 Crabs 87.4 2.7 76,511 1.1Guitarfishes 19.4 0.3 20,254 0.1 Angel shark 85.4 2.6 109,543 1.5Crabs 15.1 0.2 18,925 0.1 Other sharks 69.1 2.1 96,116 1.3Other sharks 11 0.2 15,780 0.1 Other shrimp 45.4 1.4 308,139 4.3Other corvinas 10.7 0.1 6215 0.05 Flounders 42.3 1.3 73,927 1Mullets 10 0.1 9096 0.05 Gulf coney 31.8 1 115,578 1.6Flounders 2.2 <0.1 4141 <0.05 Ballusa 26.5 0.8 13,081 0.2Mojarras 2.1 <0.1 1088 <0.05 Smoothhound sharks 12.4 0.4 17,096 0.2King croaker 0.7 <0.1 1484 <0.05 Goldspotted bass 12.3 0.4 14,005 0.2Gulf coney 0.2 <0.1 524 <0.05 Groupers 6.7 0.2 8430 0.1Angel shark 0.2 <0.1 217 <0.05 Triggerfish 5.9 0.2 5050 0.1Triggerfish 0.1 <0.1 54 <0.05 Mullets 5.4 0.2 3471 <0.1Snappers 0.01 <0.1 27 <0.05 Snails 4.3 0.1 1210 <0.1Total 5643.91 10,394,672 Pampano 3.9 0.1 1927 <0.1

Other corvinas 3.7 0.1 7822 0.1Octopus 3.3 0.1 9334 0.1Mojarras 2.4 0.1 1240 <0.1Gulf scallop 1.3 <0.1 4884 0.1Snappers 1.2 <0.1 1770 <0.1White seabass 0.9 <0.1 2643 <0.1Jacks 0.7 <0.1 602 <0.1Huachinango 0.05 <0.1 89 <0.1Tot

S al land

osibtoFtCScvlml

TCd

S

ource: CONAPESCA Mazatlan, Mexico. Dotted line indicates 90% of total commerci

f total landings. Conversely, San Felipe recorded landings of 29pecies, and nine species represented more than 90% of total land-ngs. In both communities, four species (blue shrimp, Gulf corvina,igeye croaker, and Spanish mackerel) ranked highest with respecto annual landings and revenues, but their relative importance toverall fisheries production differed between Santa Clara and Sanelipe. Blue shrimp was the most important fishery resource inerms of revenue in both communities, and it ranked 4th in Santalara and 3rd in San Felipe in terms of mean annual landings. Inanta Clara, the Gulf corvina, Spanish mackerel, and bigeye croakeromprised an average of 94.6% of total landings and 94.2% of ex-

essel revenues. The relative contribution of these fisheries wasower in San Felipe, representing an average 72.9% of total com-

ercial landings and 80.1% of ex vessel revenues. Mean Annualandings and ex-vessel revenues for Gulf corvina in Santa Clara were

able 3omparison of annual values in effort, landings, catch-per unit effort, distance traveled bata in El Golfo de Santa Clara (GSC) and San Felipe (SF) from the official fisheries databas

Community Species CONAPESCA data

No. Permits Mean annuallandings (tons)

Mean arevenu

GSC

C. othonopterus (Gulf corvina) 405 2566.2 2,189M. megalops (Bigeye croaker) 405 1249.8 889S. concolor (spanish mackerel) 405 1129.8 1,157L. stylirostris (Blue shrimp) 423 501.2 5,553

SF

C. othonopterus (Gulf corvina) 239 195.0 170M. megalops (Bigeye croaker) 239 1055.1 670S. concolor (spanish mackerel) 239 687.3 711L. stylirostris (Blue shrimp) 220 437.1 4,153

ource: CONAPESCA Mazatlan, Mexico) and our GPS trackers program (this study; Februa

al 3252.85 7,126,757

ings and ex-vessel revenues.

approximately 13 times greater than those in San Felipe (Table 2).The same pattern was found for Spanish mackerel, for which rev-enues were nearly 40% higher in Santa Clara than in San Felipe. Incontrast, landings and revenues for L. stylirostris and M. megalopswere similar between the two communities.

Overall, fishing effort was comparable for both communities:Santa Clara issued more fishing permits but was similar to SanFelipe in terms of the number of trips per boat per year for thefour fisheries (Table 3). In both communities, effort was highest forblue shrimp (L. stylirostris) with more than 100 trips per boat perseason, followed by bigeye croaker (M. megalops), Spanish mackerel

(S. concolor), and Gulf corvina (C. othonopterus). The relative CPUEfor each of the four fisheries calculated from the fisheries databaseand the CPUE calculated from our GPS trackers program were com-parable for Santa Clara and San Felipe, such that CPUE was highest

y fishers, and ex-vessel revenues of the four most important small-scale fisheriese (2001–2011.

GPS data

nnuale (USD)

Mean CPUE(tons/permits/year)

Mean effort(trips/season/boat)

MeanDistance(km)

Mean CPUE(kg/trip)

,047 6.33 23 90.8 749.26,329 3.08 45 91.5 300.67,461 2.78 30 97.7 275.33,193 1.1 100 99.6 27.35

,290 0.81 30 70.6 437.83,055 3 50 67.2 328.94,346 2.8 40 72.3 254.81,042 1.9 110 71.9 24.09

ry 2012 – October 2013).

B. Erisman et al. / Fisheries Research 164 (2015) 254–265 259

Fig. 2. Annual trends in landings for the four most important fisheries in El Golfode Santa Clara (solid line) and San Felipe (dashed line) from 2001 to 2011.

Source: CONAPESCA Mazatlan, Mexico.

faabeC

tGt2SpCtcp(

Fig. 3. Spawning seasons of Gulf corvina (Cynoscion othonopterus; top), bigeyecroaker (Micropogonias megalops; middle), and Spanish mackerel (Scomberomorusconcolor; bottom) in the Upper Gulf of California from changes in the monthly per-centage of spawning capable females (%SC) and actively spawning females (%AS),and the mean gonadosomatic index (%GSI) of adult females. Lines represent 95%

or Gulf corvina and followed by bigeye croaker, Spanish mackerel,nd blue shrimp (Table 3). CPUE for blue shrimp, bigeye croaker,nd Spanish mackerel were similar in Santa Clara and San Felipe,ut the CPUE for Gulf corvina was 781% and 171% higher for the fish-ries database and the GPS trackers program collectively, in Santalara Santa.

Variation in annual landings of blue shrimp in both communi-ies were not significant (F264,1 = 0.64, p > 0.05). Annual landings ofulf corvina were relatively constant in Santa Clara over the entire

ime period but were higher in San Felipe during the period of008–2011 than the period of 2001–2007 (F132,1 = 34.5, p < 0.0001).ince 2007, mean annual landings of bigeye croaker (F132,1 = 11.8,

< 0.001) and Spanish mackerel (F132,1 = 7.05, p < 0.01) in Santalara were significantly higher during the period of 2008–2011han during the period of 2001–2007. Annual landings for bigeyeroaker (F132,1 = 8.9, p < 0.01) and Spanish mackerel (F132,1 = 60.3,

< 0.0001) in San Felipe were also higher after 2007 than beforeFig. 2).

confidence intervals.

3.2. Reproductive seasonality

While all three fish species were best categorized as springspawners, the onset and duration of the reproductive season dif-fered among Gulf corvina, bigeye croaker, and Spanish mackerel(Fig. 3). Female Gulf corvina classified as spawning capable, withovaries in advanced stages of vitellogenesis were collected fromlate February through September. Mean female GSI in Gulf corvinaincreased from February through April and then steadily declinedfrom May through September. Female corvina classified as activelyspawning, with clear macroscopic and microscopic evidence ofimminent spawning (via hydrated or ovulated oocytes withinovaries), were collected from February to September. These datasuggested that Gulf corvina reproduce from late February throughearly June, with a peak in activity during March and April. Big-eye croaker females in the spawning capable phase were collectedfrom February through early November, during which time GSIlevels were elevated above basal levels. However, actively spawn-ing female croaker were only collected from March through July.These patterns indicated a protracted reproductive season for big-eye croaker starting in March and running through early October,with a peak in spawning activity from March through July. FemaleSpanish mackerel in the Spawning Capable phase were collectedfrom April through October, and mean female GSI peaked from Aprilthrough June. Actively Spawning female mackerel were collectedfrom April through July. Collectively, these results indicated that

reproductive season for Spanish mackerel began in April, peakedfrom April through July, and ceased by September.

260 B. Erisman et al. / Fisheries Research 164 (2015) 254–265

F portf

S ings i

3

dbpatsipfctfiprapae

(sSpa

ig. 4. Mean monthly landings (bars) and ex-vessel prices (lines) of the four most imrom 2001 to 2011 in relation to their spawning season (hatched box).

ource: CONAPESCA Mazatlan, Mexico. Dark gray bars indicate months of peak land

.3. Seasonal interactions between fishing and spawning

Seasonal interactions between fishing activities and the repro-uctive seasons of three of the four main target species differedetween Santa Clara and San Felipe (Fig. 4). In both communities,eaks in landings and revenues for blue shrimp were not associ-ted with the reproductive season since fishing is closed duringhat period. Fishing in Santa Clara began each year with the bluehrimp season from September through March, and showed a peakn landings (F11,132 = 19.1, p < 0.0001) and revenues (F11,132 = 16.4,

< 0.0001) during October and November. The shrimp season wasollowed sequentially by fishing seasons for Gulf corvina, bigeyeroaker, and Spanish mackerel in coincidence with their respec-ive reproductive seasons (Fig. 4). Monthly landings and revenuesor Gulf corvina in Santa Clara were significantly higher dur-ng March and April than other months (landings: F11,132 = 45.9,

< 0.0001; revenues: F11,132 = 41.1, p < 0.0001). Both landings andevenues for bigeye croaker in Santa Clara were highest in Aprilnd May (landings: F11,132 = 10.2, p < 0.0001; revenues: F11,132 = 3.9,

< 0.0001). Spanish mackerel revenues and landings peaked in Maynd June in Santa Clara (landings: F11,132 = 15.8, p < 0.0001; rev-nues: F11,132 = 12.4, p < 0.0001).

Seasonal patterns of fishing looked different in San FelipeFig. 4). Similar to Santa Clara, fishing in San Felipe for blue

hrimp season took place after the closed spawning season: fromeptember through March with peaks in landings (F11,132 = 37.1,

< 0.0001) and revenues (F11,132 = 39.4, p < 0.0001) during Octobernd November. However, landings and revenues for Gulf corvina

ant species from El Golfo de Santa Clara (right column) and San Felipe (left column)

dentified by Tukey post hoc tests. Lines indicate 95% confidence intervals.

showed no strong seasonal pattern and were relatively consistentacross all months of the year. Both landings (F11,132 = 9.9, p < 0.0001)and revenues (F11,132 = 6.1, p < 0.0001) for bigeye croaker in SanFelipe were highest at the start of their spawning season in Apriland May. Spanish mackerel were harvested steadily throughoutthe year in San Felipe but showed a small yet significant peak inlandings (F11,132 = 3.2, p < 0.001) and revenues (F11,132 = 2.9, p < 0.01)during the peak reproductive month of May.

Overall, the contribution of the reproductive season to totallandings and revenues was significantly higher for all three speciesof fish in Santa Clara than for San Felipe (Table 4), comprising94–99% of total annual landings and revenues in Santa Clara com-pared to 48–92% in San Felipe (Table 5). In Santa Clara, Gulf corvinaand Spanish mackerel were harvested in large volumes exclusivelyduring their spawning season, whereas San Felipe harvested thesespecies consistently, but in lower monthly volumes, throughout theyear. Both communities targeted bigeye croaker during their peakspawning season in April and May.

Seasonal and overall patterns in ex-vessel prices for all fourspecies also differed between Santa Clara and San Felipe. Meanex-vessel price for Gulf corvina was slightly higher in San Felipe($0.88 kg−1) than Santa Clara ($0.71 kg−1) (F212,214 = 18.86, p < 0.01).Prices for corvina did not vary significantly by month in San Felipe(F117,112 = 1.4, p = 0.17), but did vary by month in Santa Clara

(F85,73 = 2.9, p < 0.01), such that prices were highest in February andMarch at the start of the spawning season and declined steadilyafterwards. There was a positive relationship between landingsand price of Gulf corvina in Santa Clara (rs = 0.58; p = 0.04). Mean

B. Erisman et al. / Fisheries Research 164 (2015) 254–265 261

Table 4Summary of the contribution of the spawning season of the three most commercially important fish species in the Upper Gulf of California to landings (tons) and ex-vesselrevenues in El Golfo de Santa Clara (GSC) and San Felipe (SF) from 2001 to 2011.

Community Species References Spawningseason

Avg annuallandings fromaggregations (tons)

% of annuallandings fromaggregations

Avg annualrevenue fromaggregations (USD)

% of annualrevenue fromaggregations

GSCC. othonopterus (Gulf corvina) Erisman et al. (2012);

this studyFeb–Jun 2546.9 99.2 2,178,466 99.5

M. megalops (Bigeye croaker) Román-Rodríguez(2000);Castro-González(2004); this study

Mar–Oct 1190.9 95.2 845,469 95.1

S. concolor (spanish mackerel) Valdovinos-Jacobo(2006); this study

Apr–Sep 1066.7 94.4 1,095,301 94.6

SFC. othonopterus (Gulf corvina) Erisman et al. (2012);

this studyFeb–Jun 94.3 48.3 82,535 48.5

M. megalops (Bigeye croaker) Román-Rodríguez(2000);Castro-González(2004); this study

Mar–Oct 953.2 90.3 616,082 91.9

S. concolor (spanish mackerel) Valdovinos-Jacobo(2006); this study

Apr–Sep 354.6 51.5 358,743 50.4

Source: CONAPESCA Mazatlan, Mexico.

Table 5Results of a 1-way ANOVA comparing the percent contribution of the spawningseason to annual landings and ex-vessel revenues for the three most commerciallyimportant fish species in El Golfo de Santa Clara and San Felipe during the periodof 2001 to 2011. All pairwise comparisons were significant and indicated a highercontribution of the spawning season to fisheries in El Golfo de Santa Clara.

Scientific name Effects F p-value

C. othonopterus % Landings 78.06 <0.0001(Gulf corvina) % Revenues 88.1 <0.0001

M. megalops % Landings 11.83 0.002(Bigeyecroaker) % Revenues 13.61 <0.001

e(p(tapSeawvpp

3

bfiRfcfBtbbc

S. concolor % Landings 30.35 <0.0001(Spanish mackerel) % Revenues 30.94 <0.0001

x-vessel price for blue shrimp was higher in Santa Clara$12.07 kg−1) than San Felipe ($10.60 kg−1) (F153,151 = 12.9,

< 0.01). In both Santa Clara (F76,69 = 4.81, p < 0.01) and San FelipeF77,70 = 6.85, p < 0.01), monthly ex-vessel prices were lowest athe start of the fishing season in September and increased to peakt the end of the season in February and March. Mean ex-vesselrice for bigeye croaker was similar in Santa Clara ($0.57 kg−1) andan Felipe ($0.52 kg−1) and did not vary significantly by month inither community. Mean ex-vessel price for Spanish mackerel waslso similar in Santa Clara ($0.93 kg−1) and San Felipe ($0.90 kg−1),as relatively consistent in San Felipe throughout the year, but

aried significantly in Santa Clara (F100,88 = 2.7, p < 0.01), such thatrices were elevated during the spawning season and showed aositive relationship with landings (rs = 0.03; p = 0.03).

.4. Spatial patterns of fishing

We found noticeable differences in spatial patterns of fishingetween Santa Clara and San Felipe with respect to the total areashed, the average distance traveled, and the use of the Biosphereeserve (Fig. 5). Fishers from Santa Clara used an area of 2307.8 km2

or blue shrimp; 1291.1 km2 for Gulf corvina; 1452.4 km2 for bigeyeroaker; and 1809.7 km2 for Spanish mackerel. Fishing activitiesrom Santa Clara occurred primarily in the northeast portion of theiosphere Reserve, with fishing taking place in the buffer zone,

he no-take zone, and the vaquita refuge. Fishing grounds forlue shrimp, bigeye croaker and Spanish mackerel were locatedetween the northeast boundary of the vaquita refuge and theoastline of Sonora, reaching the boundaries of the no-take zone

to the north and the buffer zone to the south (Fig. 5a, c and d).However, Gulf corvina were fished from the northeast boundary ofthe vaquita refuge to the coastline and within the no-take zone ofthe estuary, inside the two channels surrounding Montague Island(Fig. 5b). For all four species, the average distance fishers from SantaClara traveled during a fishing trip was greater than fishers from SanFelipe (Table 3).

Fishing areas were must smaller for San Felipe compared toSanta Clara fishers for all species except bigeye croaker, such thatSan Felipe fishers covered an area of 597.9 km2 for blue shrimp;938 km2 for Gulf corvina; 1682.4 km2 for bigeye croaker; and1094.8 km2 for Spanish mackerel (Fig. 5a–h). In contrast with SantaClara, fishers from San Felipe focused their effort primarily alongthe southwestern portion of the Biosphere Reserve and to the southalong the coast of Baja California. Their most important fishing areaswere located adjacent to San Felipe, in the corridor between theBaja California coastline and the western boundary of the vaquitarefuge.

Fishing activities of both communities interacted partially withthe vaquita refuge and the no-take zone. For Santa Clara, the per-centages of observations that occurred inside the no-take zonewere low for Spanish mackerel (1.5%), blue shrimp (3.4%) and big-eye croaker (2.9%). However, for Gulf corvina this percentage wasconsiderably higher (61.1%). The percentage of fishing points byfishers of Santa Clara inside the vaquita refuge was highest for theSpanish mackerel fishery (15.5%), followed by blue shrimp (11.9%),bigeye croaker (3.2%) and Gulf corvina (0.4%). For San Felipe,the percentages recorded inside the vaquita refuge were higher(Gulf corvina = 39%; blue shrimp = 30.3%; bigeye croaker = 16.5%and Spanish mackerel = 5.7%) when compared to the no-take zone(Gulf corvina = 2.8%; bigeye croaker = 0.07%; blue shrimp and Span-ish mackerel = not registered).

4. Discussion

Collectively, our results show that blue shrimp, Gulf corvina,bigeye croaker, and Spanish mackerel represent the most impor-tant fisheries in terms of production and revenue for both SantaClara and San Felipe within the Biosphere Reserve in the Upper

Gulf of California. Similar findings were reported in previous stud-ies that examined small-scale fisheries in this region (Cudney andTurk, 1998; Aragón-Noriega et al., 2010; Rodríguez-Quiroz et al.,2010). Likewise, our results support previous technical reports

2 s Rese

tfGC2wtawSft

ssoasacvtcE

Fa

62 B. Erisman et al. / Fisherie

hat the Biosphere Reserve encompasses the breeding groundsor the most important commercial fish species in the Upperulf of California (Arvizu-Martínez, 1987; Román-Rodríguez, 2000;astro-González, 2004; Valdovinos-Jacobo, 2006; Aragón-Noriega,007). However, by engaging in collaborative fisheries researchith commercial fishers to collect novel, fine-scale spatial and

emporal data on effort, catch, revenues, the location of fishingctivities, and the timing of reproduction in these four species,e have identified important differences between the fisheries of

anta Clara and San Felipe that have direct, meaningful implicationsor the management of small-scale fisheries inside and adjacent tohe Biosphere Reserve.

Small-scale fisheries in Santa Clara are best characterized byequential changes in catch composition that coincide with sea-onal regulations (shrimp) and seasonal reproductive migrationsf target species into the nearby area, which in turn, drive fluctu-tions in market demands and ex-vessel prices. The fishing cycletarts with the opening of the shrimp fishing season in September,nd nearly all fishers focus their efforts on this species until itloses the following March due to the high prices this fishery pro-ides to fishers. The end of the shrimp season marks the start of

he massive spring spawning migration of the Gulf corvina, whichoincides with a high domestic demand for fish in Mexico duringaster (Erisman et al., 2012), and an initial increase in ex-vessel

ig. 5. Fishing zones of the most important species to small-scale fisheries in El Golfo de

nd d) C. othonopterus; (e and f) M. megalops; (g and h) S. concolor.

arch 164 (2015) 254–265

prices. However, after Easter the supply of corvina greatly exceedsdemand (i.e. a market glut), and prices drop abruptly. Fishing activi-ties for Gulf corvina are quickly replaced by the targeted harvestingof bigeye croaker that start to form spawning aggregations in Apriland the Spanish mackerel aggregations that start to form in May.Fishing effort for croaker and mackerel decrease during the sum-mer months as the availability of these species declines, and thelandings become more diversified with low volumes of other bonyfishes, sharks, rays, and sea jellies until the cycle start again withthe onset of the next season for blue shrimp in the Fall. This sea-sonal fishing cycle is critical to the economic livelihood of SantaClara, as it supports a large fleet of 457 boats that incorporate upto 80% of the available workforce of the community (Ávila-Forcadaet al., 2012; Vázquez León et al., 2012).

In contrast, the small-scale fisheries of San Felipe tend to beless seasonal and less dependent on the reproductive periods offishes. Fisheries are also more diversified in terms of catch com-position, such that they tend to catch lower volumes per speciesbut across a wider range of species, and many species are caughtyear round. Related to this, market demands and ex-vessel pricesremain relatively constant throughout the year. The fishing sector

employs a much smaller proportion of the workforce in San Felipe,where the tourism industry offers a viable alternative source ofemployment (Ávila-Forcada et al., 2012; Vázquez León et al., 2012).

Santa Clara (a–d) and San Felipe (e–h) from 2012 to 2013. (a and b) L. stylirostris; (c

B. Erisman et al. / Fisheries Research 164 (2015) 254–265 263

(Cont

Cfi

iisTabtttcfCabfeoefSmw

Fig. 5.

onsequently, the fishing fleet is smaller than Santa Clara, and totalsheries production and revenues are lower.

Fishers from Santa Clara and San Felipe also show differencesn their spatial patterns of fishing, such that fishing activities occurn close proximity to their respective communities but differ in theize of the area that is fished and the average distance traveled.he large fishing fleet from Santa Clara fishes over a large expansend operates mainly in the areas between the north and northeastoundaries of vaquita refuge and the coastline of Sonora, includinghe two channels surrounding Montague Island. The large size ofhe fishing fleet in Santa Clara, all of which utilize gill nets, meanshat fishers tend to travel farther to avoid net entanglements. Inontrast, the smaller fleet from San Felipe focus their activitiesurther southwest, in a small area in the corridor between Bajaalifornia coastline and western boundary of vaquita refuge, andlong the northern boundary of no-take zone and well to the southeyond the boundary of the reserve. For San Felipe, fishing groundsor three of the top four species (Gulf corvina, blue shrimp and big-ye croaker) overlap with the vaquita refuge. Santa Clara showsnly a weak interaction with the vaquita refuge for Spanish mack-rel and blue shrimp, but a strong interaction with the no-take zone

or the Gulf corvina fishery. The smaller size of the fishing fleet inan Felipe and the large distance of the community from Santa Claraean that fishers do not have to travel as far to avoid entanglementith nets of other fishers.

inued)

Differences in catch composition between Santa Clara and SanFelipe, including the diversity of catch and the relative volumeslanded per species reflect differences in the spatial patterns oftheir fishing activities and the coastal habitats that occur adja-cent to each community. Similar to most small-scale fishers in theGulf of California, fishers here use small boats (pangas) to fish thecoastal habitats adjacent to their community (Erisman et al., 2011;Moreno-Báez et al., 2012). Whereas the nearshore habitats off SantaClara are primarily sandy or mud bottoms, San Felipe also housesrocky reefs along the coastline and nearby islands, which tend tohold a higher diversity of fishes due to increased habitat complex-ity (Hastings and Findley, 2010; Robago Quiroz et al., 2011). Also,Santa Clara’s proximity to the Colorado Delta where several speciessuch as shrimp reproduce and several species of sciaenid fishes (e.g.Gulf corvina, bigeye croaker) form spawning aggregations has madethis community an important commercial fishing port (Flanaganand Hendrickson, 1976; Pérez Valencia et al., 2011). These cleardifferences in size, use and location of the fishing grounds arestrongly related with the seasonal distributions of species (Aragón-Noriega et al., 2010; Erisman et al., 2012) and the distance of fishinggrounds from the community. Both factors have an important effect

on the total costs and the total revenues for the fleet (Aragón-Noriega et al., 2010). In this case, the longer average distancetraveled by fishers from Santa Clara should equate to higher fishingcosts.

2 s Rese

iotnvFpotloliqdtspbooetuiHGitsatrta

fRadmwpdsf2leiwwsmcifbtrtefiwt

64 B. Erisman et al. / Fisherie

A previous study by Rodríguez-Quiroz et al. (2010) used fishernterviews to construct maps that described the size and locationf fishing grounds in relation to various areas and habitats withinhe Biosphere Reserve, and they estimated that nearly 77% of theo-take zone and the 100% vaquita refuge were heavily fished. Con-ersely, by working directly with fishers from Santa Clara and Sanelipe to deploy GPS data loggers on hundreds of fishing trips, weroduced the first fine-scale map showing the spatial distributionf fishing activities in this region. Using this approach, we foundhat the fishing activities inside the two restricted areas were muchower for Santa Clara and San Felipe in comparison with the resultsf the previous study. Differences in the results of these two studiesikely reflect the methodologies applied to generate maps of fish-ng activities and their associated biases. Methodologies that utilizeualitative methods such as interviews, printed maps to localizeelineate or estimate fishing grounds to provide coarse approxima-ions of the use of different areas, including protected areas in con-tant conflict (between fisheries and conservation strategies), canotentially lead to the overestimation of size, use and interactionsetween fishing and no-take zones, due to the “selective mem-ry retention” or the decline of the reliability of fishers memoriesver time (Close and Hall, 2006; Moreno-Báez et al., 2010; Taylort al., 2011). Conversely, the use of GPS data loggers provides spa-ial data that are more reasonable for estimating interactions andse between fishing, coastal ecosystems, and managed areas and

dentifying differences in fishing activities among communities.owever, this methodology also has its limitations, which requiresPS data be used properly and responsibly when extrapolating

nformation for management purposes. In this case, it is importanto note that gill nets used by fishers in the Upper Gulf vary in length,oak times, and spatial orientation depending on the target speciesnd the strength of surface currents at time of deployment. Also,he locations of gear deployment and retrieval do not specificallyeflect the exact locations where fish were captured. Finally, fishersraveling with GPS data loggers may preferentially avoid protectedreas out of concern for penalties (Erisman et al., 2012).

Despite these limitations, it is clear the two communities dif-er in their interactions with managed areas of the Biosphereeserve, critical habitats for endangered and protected species,nd reproductive periods of commercially important species. Theseifferences are important to consider within the context of theanagement of regional fisheries and the reserve, particularlyithin the framework of Ecosystem Based Management. For exam-le, San Felipe has a stronger interaction with the vaquita refugeue to its close proximity and because that area historically repre-ented one of the most productive and important fishing groundsor shrimp by fishers from that community (Moreno-Báez et al.,010, 2012). Therefore, the survival and recovery of the vaquita is

ikely to be more impacted by fishing activities from San Felipe, andnforcement and monitoring should be focused accordingly. Sim-larly, fishers from Santa Clara have a much stronger interaction

ith the no-take zone, which lies adjacent to the community andas associated with the most productive fishing grounds for blue

hrimp and Gulf corvina prior to the establishment and enforce-ent of the reserve. Given that the no-take zone represents the

ritical breeding habitat for blue shrimp and Gulf corvina, the mon-toring and enforcement of the no-take zone should necessarilyocus more on Santa Clara to protect the reproductive activity ofoth species and maintain a level of reproductive output necessaryo maintain the fisheries. The successful management of the vaquitaefuge and the no-take zone will also require government agencieso properly account for the losses in fishing revenues incurred to

ach community as a consequence of losing their most productiveshing grounds when devising strategies to incentivize complianceith reserve restrictions and reduce illegal fishing activities inside

heir boundaries.

arch 164 (2015) 254–265

The synchronization between fishing activities in Santa Claraand the timing and locations of the seasonal spawning aggrega-tions of Gulf corvina, bigeye croaker, and Spanish mackerel shouldbe a focal point for managing fisheries in this community. Count-less studies have demonstrated that intense fishing pressure onspawning aggregations, which are predictable in time and space arehighly vulnerable to overfishing, and can result in rapid declines (orcollapses) if not well-managed (reviewed by Sadovy de Mitchesonand Erisman, 2012). This scenario characterizes the history of thetotoaba fishery in the Upper Gulf, in which relentless harvesting ofthousands of tons fish at their spawning aggregation sites in theestuaries of the Colorado River Delta resulted in a sudden fisherycollapse by the 1950s (Cisneros-Mata et al., 1995). Current fishingactivities for the Gulf corvina mimic those observed in the totoabafishery several decades ago, with up to five thousand tons of fishharvested from the only known spawning aggregation site for thespecies, and indeed a suite of regulations have been implemented toavoid the same disastrous result (Erisman et al., 2012, 2014). Theseinclude gear restrictions, a seasonal quota of total harvest, andattempts to enforce the no-take zone. However, such regulationsshould also extend to the management of spawning aggregations ofbigeye croaker, which is currently harvested in larger volumes thanGulf corvina, garners a higher market value, utilizes gill nets thatexceed 1 km in length, and may have a much greater impact on pro-tected species (e.g. vaquita), but is completely unregulated in termsof harvest or gear restrictions. Likewise, the fishery for the Spanishmackerel, which is listed by the IUCN as vulnerable to extinctiondue to its small geographic range in the Upper Gulf of Californiaand its propensity to form spawning aggregations that are heavilyexploited (Collette et al., 2011), would benefit from seasonal regu-lations or catch limits in Santa Clara that protect spawning for thebenefit of a sustainable fishery.

Differences in the ecological impact of fishing between the twocommunities may necessitate specific management regulations foreach community, but it is equally important to consider the socialand economic impacts of such actions. For example, regulationsthat reduce and restrict harvest levels of blue shrimp, Gulf corvina,bigeye croaker, Spanish mackerel may have a disproportionatelylarger negative impact on Santa Clara than San Felipe given itsdependence on these four fisheries, the lower diversity of alter-native species in the area to exploit commercially, the overalldependence of fisheries production as the main source of employ-ment, and the general lack of economic alternatives (e.g. tourism)currently available to recoup lost fisheries revenues. Such impactsare particularly important to consider in relation to the dependenceof Santa Clara’s fisheries production on the seasonal availability andharvest of the four principle species, which are already highly sen-sitive to fluctuations in ex-vessel prices, and thus will need to becountered by policies that help stabilize or increase market values,develop other sources of revenue (e.g. sportfishing) that will helpmaintain the livelihoods of the communities, and provide incen-tives to comply with fisheries and conservation regulations. Ofcourse, harvest restrictions on the four principal fisheries will alsohave a serious effect on the fishing industry in San Felipe, and thesesame principles must therefore be applied. However, the diversityof species exploited and the existence of a tourist industry may helpmitigate the impacts of such regulations on the overall livelihoodof the community.

Acknowledgements

We thank fishers of San Felipe and El Golfo Santa de Clara,Y. Flores, J. Vázquez, J. Montanez, L. Pérez, V. Corrales, H. Ruiz,A. Domínguez, R. Carrillo, C. Tirado, R. Franco, A. García, J. Cota-Nieto, G. Hinojosa, and Alto Golfo Sustentable for their partnership

s Resea

aPaF

R

A

A

A

A

Á

B

B

C

C

C

C

C

C

C

C

C

E

E

E

E

B. Erisman et al. / Fisherie

nd support. This study was funded by the World Wildlife Fund-rograma del Golfo de California, Walton Family Foundation, Davidnd Lucile Packard Foundation, and the Environmental Defenseund, Mexico.

eferences

bbott, J.K., Haynie, A.C., 2012. What are we protecting? Fisher behavior and theunintended consequences of spatial closures as a fishery management tool. Ecol.Appl. 22, 762–777.

ragón-Noriega, E.A., 2007. Coupling the reproductive period of blue shrimpLitopenaeus stylirostris Stimpson, 1874 (Decapoda: Penaeidae) and sea surfacetemperature in the Gulf of California. Rev. Biol. Mar. Oceanogr. 42, 167–175.

ragón-Noriega, E.A., Rodríguez-Quiroz, G., Cisneros-Mata, M.A., Ortega-Rubio, A.,2010. Managing a protected marine area for the conservation of critically endan-gered vaquita (Phocoena sinus Norris, 1958) in the upper Gulf of California. Int.J. Sust. Dev. World Ecol. 17, 410–416.

rvizu-Martínez, J., 1987. Fisheries activities in the Gulf of California. Mex. Calif.Coop. Ocean. Fish. Invest. 28, 32–36.

vila-Forcada, S., Martínez-Cruz, A.L., Munoz-Pina, C., 2012. Conservation of vaquitamarina in the Northern Gulf of California. Mar. Pol. 36, 613–622.

obadilla, M., Álvarez-Borrego, S., Ávila-Foucat, S., Lara-Valencia, F., Espejel, I., 2011.Evolution of environmental policy instruments implemented for the protectionof totoaba and the vaquita porpoise in the Upper Gulf of California. Environ. Sci.Pol. 14, 998–1007.

rown-Peterson, N.J., Wyanski, D.M., Soborido Rey, F., Macewicz, C.J., LowerreBarbieri, S.K., 2011. A standardized terminology for describing reproductivedevelopment in fishes. Mar. Coast. Fish. Dynam. Manag. Ecosyst. Sci. 3, 52–70.

astro-González, J.J., (M.S. thesis) 2004. Estudio Base y Estrategias de Manejo de laPesquería del Chano Micropogonias megalops, Caso de Estudio: Alto Golfo deCalifornia. Universidad Autónoma de Baja California, pp. 99.

huenpagdee, R., Liguori, L., Palomares, M.L.D., Pauly, D., 2006. Bottom-up, GlobalEstimates of Small-Scale Marine Fisheries Catches. Fisheries Centre ResearchReports, vol. 14. Fisheries Centre, University of British Columbia, Vancouver,Canada, pp. 105.

isneros-Mata, M.A., 2010. The importance of fisheries in the Gulf of California andecosystem-based sustainable co-management for conservation. In: Brusca, R.(Ed.), The Gulf of California, Biodiversity and Conservation. University of ArizonaPress, Tucson.

isneros-Mata, M.A., Montemayor-Lopez, G., Román-Rodriguez, M.J., 1995.Life history and conservation of Totoaba macdonaldi. Conserv. Biol. 9,806–841.

isneros-Montemayor, A.M., Cisneros-Mata, M.A., Harper, S., Pauly, D., 2013. Extentand implications of IUU catch in Mexico’s marine fisheries. Mar. Policy 39,283–288.

lose, C., Hall, G.B., 2006. A GIS-based protocol for the collection and use oflocal knowledge in fisheries management planning. J. Environ. Manag. 78,341–352.

ollette, B., Acero, A., Boustany, A., Canales Ramirez, C., Cardenas, G., Carpenter, K.E.,Di Natale, A., Die, D., Fox, W., Graves, J., Hinton, M., Juan Jorda, M., Minte Vera, C.,Miyabe, N., Montano Cruz, R., Nelson, R., Restrepo, V., Schaefer, K., Schratweiser,J., Serra, R., Yanez, E., 2011. Scomberomorus Concolor. The IUCN Red List ofThreatened Species. Version 2014.3., www.iucnredlist.org (Downloaded on 12December 2014).

udney, R., Turk, P.J., 1998. Pescando entre mareas del Alto Golfo de California. Cen-tro Intercultural de Estudio de Desiertos y Océanos, Puerto Penasco, Mexico,pp. 166.

udney-Bueno, R., Basurto, X., 2009. Lack of cross-scale linkages reduces robust-ness of community based fisheries management. PLoS ONE 4 (7), e6253,http://dx.doi.org/10.1371/journal.pone.0006253.

risman, B., Mascarenas, I., Paredes, G., Sadovy de Mitcheson, Y., Aburto-Oropeza,O., Hastings, P.A., 2010. Seasonal, annual and long term trends in commercialfisheries for aggregating reef fishes in the Gulf of California. Mex. Fish. Res. 106,279–288.

risman, B.E., Paredes, G.A., Plomozo Lugo, T., Cota Nieto, J.J., Hastings, P.A., AburtoOropeza, O., 2011. Spatial structure of marine fisheries in Northwest Mexico.ICES J. Mar. Sci. 68, 564–571.

risman, B., Aburto Oropeza, O., González Abraham, C., Mascarenas Osorio, I., Moreno

Báez, M., Hastings, P.A., 2012. Spatio-temporal dynamics of a fish spawningaggregation and its fishery in the Gulf of California. Sci. Rep. 2, 1–11.

risman, B.E., Apel, A., MacCall, A., Román, M.J., Fujita, R., 2014. The influence of gearselectivity and spawning behavior on a data-poor assessment of a spawningaggregation fishery. Fish. Res. 159, 75–87.

rch 164 (2015) 254–265 265

Ezcurra, E., Aburto-Oropeza, O., de los Ángeles Carvajal, M., Cudney-Bueno, R., Torre,J., 2009. Gulf of California, Mexico. In: McLeod, K., Leslie, H. (Eds.), Ecosystem-based Management for the Oceans. Island Press, London, p. 392.

FAO (Food and Agriculture Organization of the United Nations), 2005. Putting intoPractice the Ecosystem Approach to Fisheries. FAO, Rome, pp. 76.

FAO (Food and Agriculture Organization of the United Nations), 2012. The State ofWorld Fisheries and Aquaculture. FAO, Rome.

Flanagan, C.A., Hendrickson, J.R., 1976. Observation on the commercial fishery andreproductive biology of the totoaba Cynoscion macdonaldi in the northern Gulfof California. Fish. Bull. 74, 531–544.

Gunderson, D.R., Parma, A.M., Hilborn, R., Cope, J.M., Fluharty, D.L., Miller, M.L., Vet-ter, R.D., Heppell, S.S., Greene, H.G., 2008. The challenge of managing temperatereef resources. Fish 33, 172–179.

Hastings, P.A., Findley, L.T., 2010. Marine fishes of the Upper Gulf Biosphere Reserve,Northern Gulf of California. In: Felger, S., Broyles, B. (Eds.), Dry Borders: GreatNatural Reserves of the Sonoran Desert. University of Arizona Press, Tucson.

Horta e Costa, B., Batista, M.I., Gonc alves, L., Erzini, K., Caselle, J.E., Cabral, H.N.,Goncalves, E.J., 2013. Fishers’ behaviour in response to the implementation of amarine protected area. PLoS ONE 8, e65057.

Johannes, R.E., 1998. The case for data-less marine resource management: examplesfrom tropical nearshore finfisheries. Trends Ecol. Evol. 13, 243–246.

Leslie, H.M., McLeod, K.L., 2007. Confronting the challenges of implementing marineecosystem-based management. Front. Ecol. Environ. 5, 540–548.

Moreno-Báez, M., Orr, B.J., Cudney-Bueno, R., Shaw, W.W., 2010. Using fishers’ localknowledge to aid management at regional scales: spatial distribution of small-scale fisheries in the northern Gulf of California. Mex. Bull. Mar. Sci. 86, 339–353.

Moreno-Báez, M., Cudney-Bueno, R., Orr, B.J., Shaw, W.W., Pfister, T., Torre-Cosio,J., Loaiza, R., Rojo, M., 2012. Integrating the spatial and temporal dimensions offishing activities for management in the Northern Gulf of California. Mex. OceanCoast. Manag. 55, 111–127.

OECD (Organisation for Economic Co-operation and Development), 2006. ApplyingStrategic Environmental Assessment: Good Practice Guidance for DevelopmentCo-operation. OECD, Paris, pp. 160.

Pérez Valencia, S., Gorostieta Monjaraz, M., Castaneda Fernández de Lara, V., LoaizaVillanueva, R., Turk Boyer, P., et al., 2011. Manifestación de Impacto Ambientalpara la pesca riberena responsable en la Reserva de la Biosfera Alto Golfo deCalifornia y Delta del Río Colorado: Costa Este y Oeste. Centro Intercultural deEstudios de Desiertos y Océanos, AC, Puerto Penasco, Sonora, pp. 221.

Robago Quiroz, C.H., López Martínez, J., Valdez Holguín, J.E., Nevárez Martínez, M.O.,2011. Distribución latitudinal y batimétrica de las especies más abundantesy frecuentes en la fauna de acompanante de camarón del Golfo de California,México. Rev. Biol. Trop. 59, 255–267.

Rodríguez-Quiroz, G., Aragón-Noriega, E.A., Valenzuela-Quinones, W., Esparza-Leal,H.M., 2010. Artisanal fisheries in the conservation zones of the Upper Gulf ofCalifornia. Rev. Biol. Mar. Ocean. 45, 89–98.

Román-Rodríguez M.J., 2000. Estudio poblacional del chano norteno, Micropogo-nias megalops y la curvina golfina Cynoscion othonopterus (Gilbert) (Pisces:Sciaenidae), especies endémicas del alto Golfo California, México. Instituto delMedio Ambiente y Desarrollo Sustentable del Estado de Sonora, México D.F,Informe final SNIB-CONABIO proyecto No. L298.

Sadovy de Mitcheson, Y., Erisman, B.E., 2012. Fishery and biological implicationsof fishing spawning aggregations, and the social and economic importance ofaggregating fishes. In: Sadovy, Y., Colin, P.L. (Eds.), Reef Fish Spawning Aggrega-tions: Biology, Research, and Management. Springer, New York.

Salas, S., Chuenpagdee, R., Seijo, J.C., Charles, A., 2007. Challenges in the assessmentand management of small-scale fisheries in Latin America and the Caribbean.Fish. Res. 87, 5–16.

Taylor, R.B., Morrison, M.A., Shears, N.T., 2011. Establishing baselines for recoveryin a marine reserve (Poor Knights Islands, New Zealand) using a local ecologicalknowledge. Biol. Conserv. 144, 3038–3046.

Teh, L.C.L., Sumaila, U.R., 2013. Contribution of marine fisheries to worldwideemployment. Fish Fish. 14, 77–88.

Tzanatos, E., Dimitriou, E., Katselis, G., Georgiadis, M., Koutsikopoulos, C., 2005. Com-position, temporal dynamics and regional characteristics of small-scale fisheriesin Greece. Fish. Res. 73, 147–158.

Valdovinos-Jacobo, L.A., (M.S. thesis) 2006. Edad, crecimiento y mortalidad de laSierra del Golfo Scomberomorus concolor (Lockington, 1879) en el Golfo deCalifornia. Instituto Politecnico Nacional Centro Interdisciplinario de CienciasMarinas, pp. 60.

Vázquez León, C.I., Ferman Almada, J.L., García Gastelúm, A., Arredondo García, M.C.,

2012. Equidad distributiva del ingreso pesquero en la reserva de la biosfera AltoGolfo de California y delta del Río Colorado en México. Front. Norte 24, 117–143.

Wilson, J.R., Kay, M.C., Colgate, J., Qi, R., Lenihan, H.S., 2012. Small-scale spatial varia-tion in population dynamics and fishermen response in a coastal marine fishery.PLoS ONE 7, e52837.