Development of habitat prediction models to reduce by-catch of ...

CSIRO PUBLISHING

Marine and Freshwater Research http://dx.doi.org/10.1071/MF14062

Development of habitat prediction models to reduce by-catch of sailfish (Istiophorus platypterus) within the purse-seine fishery in the eastern Pacific Ocean Raul O. Martinez-Rincon A,E, Sofia Ortega-Garcia A, Juan G. Vaca-Rodriguez B,D and Shane P. Griffiths C A

Instituto Polite´cnico Nacional – Centro Interdisciplinario de Ciencias Marinas (CICIMAR), Departamento de pesquerı´as, Avenida IPN s/n, La Paz, B.C.S. 23096, Me´xico. B Programa Nacional de Aprovechamiento del Atu´n y de Proteccio´n de Delfines (PNAAPD), Km 107 Carretera Tijuana-Ensenada, campus CICESE, Ensenada, Baja California, Me´xico. C Commonwealth Scientific and Industrial Research Organisation (CSIRO), Oceans and Atmosphere Flagship, GPO Box 2583, Brisbane, Qld 4001, Australia. D Present address: Facultad de Ciencias Marinas, Universidad Auto´noma de Baja California (UABC), Km 103 Carretera Tijuana-Ensenada, campus UABC, Ensenada, Baja California, Me´xico. E Corresponding author. Present address: Centro de Investigaciones Biolo´gicas del Noroeste (CIBNOR), Avenida IPN s/n, La Paz, B.C.S. 23096, Mexico. Email: [email protected]

Abstract. Sailfish (Istiophorus platypterus) is an important apex predator in neritic and oceanic pelagic ecosystems. The species is also a primary target of important catch-and-release sport fisheries that the support local economies of developing countries. However, commercial purse-seine fisheries that target tuna in the eastern Pacific Ocean (EPO) incidentally catch and discard large numbers of sailfish. Sailfish by-catch data recorded by scientific observers in the Mexican tuna purse-seine fleet in the EPO from 1998 to 2007 was used in generalised additive models (GAMs) to predict environmental and spatial preferences of sailfish. GAM predicted the highest sailfish catches to occur in coastal waters during El Nin˜o events during late autumn and winter, with sea surface temperatures .268C, with negative values of deviation in sea surface height (,
10 cm), and low chlorophyll-a (,0.25 mg m
3). GAM predicted that the catch probability for sailfish increased 1.8-fold during El Nin˜o events in coastal waters and 1.5-fold under La Nin˜a. However, the spatial distribution of sailfish remained largely unchanged during El Nin˜o and La Nin˜a events. Our models may be an additional fisheries management tool that may be used to support temporary spatial-temporal throughout the fishing season to reduce sailfish by-catch in the EPO. Additional keywords: environmental predictors, generalised additive model, spatial predictors. Received 5 March 2014, accepted 8 September 2014, published online 19 February 2015

Introduction There is increasing evidence that depletion of the ocean’s top predators by large-scale fisheries are compromising the integrity and functioning of large marine ecosystems (Scheffer et al. 2005; Heithaus et al. 2008). In a subtropical Pacific ecosystem, Polovina and Woodworth-Jefcoats (2013) showed that the commercial pelagic longline fishery was likely responsible for a significant change in the structure of the ecosystem. This change was caused by a significant decline in the abundance and size of target tuna species, which allowed proliferation of unmarketable species, including lancetfish and snake mackerel. In the Gulf of Thailand, fisheries sequentially moved to target species Journal compilation Ó CSIRO 2015

occupying lower trophic levels, as larger and more desirable species were fished to such low abundances that they were no longer economically viable to target, leaving the ecosystem in a highly degraded state (Pauly and Chuenpagdee 2003). Examples of ‘fishing down the food web’ (Pauly et al. 1998) demonstrate the need for fishery managers to take a holistic approach to management that ensures the long-term sustainability of target and by-catch species that play an important role in maintaining the integrity of pelagic ecosystems. The eastern Pacific Ocean (EPO) supports one of the world’s largest and most valuable pelagic fisheries for high trophic level predators, including bigeye, yellowfin, skipjack and albacore www.publish.csiro.au/journals/mfr

B

Marine and Freshwater Research

tunas, and swordfish. The fishery targets these species using pelagic longline, pole-and-line, and purse-seine gears, the latter of which includes sets either unassociated or in association with floating objects or schools of marine mammals. Over the past decade, there has been a dramatic increase in purse-seine effort, particularly on floating objects and dolphin schools. This has resulted in a steady increase in the catch of large non-target species such as sharks, billfish, and dolphinfish. Such by-catch species were generally discarded either dead or alive, but in the face of declining catch rates of target species, fishing fleets have begun to develop new markets for some by-catch species (e.g. dolphinfish and sharks). As a result, the discard rate of these by-catch species has dramatically declined in the EPO. Sailfish (Istiophorus platypterus; family Istiophoridae) in particular are a vulnerable purse-seine by-catch species, commonly caught in large numbers (4–15 fish) in a single set (Viera 2007). In unassociated sets in the EPO between 1996 and 2008, the discard rate of sailfish declined from 55 to 7% (Hall and Roman 2013), indicating an increase in the fishing pressure on this species. Sailfish is a large predatory species that is widely distributed in tropical and subtropical neritic and oceanic waters of all major ocean basins (Chiang et al. 2011; Hoolihan et al. 2011). It is a fast-growing species, reaching 127 cm by age 3 (Ramı´rez-Pe´rez et al. 2011) and has a lifespan of up to 11 years (CerdenaresLadro´n De Guevara et al. 2011). Despite being an incidental catch of commercial fisheries, sailfish is a key target species of sport fisheries that support the local economies in many Latin American countries bordering the EPO. A recent stock assessment of sailfish in the EPO (Hinton and Maunder 2013) showed a significant decline in annual catches from 1993 to 2007, but lacked sufficient reliable data to determine the status of the stock. In such data-limited situations, precautionary management strategies are required to minimise the fishing mortality on a fished species until more reliable information can be gathered to support a robust sustainability assessment. In the case of the Mexican jurisdiction, mitigating sailfish by-catch in commercial fisheries would reduce conflict with the sport fishery, which is mainly catch-and-release. One strategy is a flexible spatial management approach developed by Hobday et al. (2010) to mitigate by-catch of the highly depleted southern bluefin tuna (SBT) (Thunnus maccoyii) in a multi-species pelagic longline fishery off eastern Australia. The approach combines a habitat model for SBT combined with an oceanographic model to produce near real-time habitat predictions and capture probabilities that can be monitored by fishery managers during the fishing season. Temporary dynamic spatial closures can then be imposed to reduce the catch of SBT until the habitat becomes less favourable for SBT, after which time the fishery in the area is reopened. Distribution models have been widely used to explore species–environment relationships and to predict utilised or potential habitat preferences (Austin 2007; Elith and Leathwick 2009). In this paper, we used generalised additive models (GAMs) to identify habitat preferences of sailfish in the EPO. This technique has been successfully used to describe habitat preferences of target and non-target species in several commercial fisheries that use longline, purse-seine and trawl gear, and often used to make predictions of species distributions

R. O. Martinez-Rincon et al.

(Leathwick et al. 2006; Carvalho et al. 2011; Martı´nez-Rinco´n et al. 2012). Our study builds on this approach by using GAMs to develop a habitat preference model for sailfish in the EPO. By using sailfish by-catch data recorded by the Mexican tuna purse-seine fleet, we determined whether spatial management strategies can be developed in the EPO to reduce the incidental catch of sailfish. If successful, our approach could be extended to other by-catch species to build a more holistic by-catch mitigation approach for pelagic fisheries. Furthermore, habitat modelling may greatly improve our knowledge of the ecology and behaviour of sailfish across a large spatial and temporal scale, a level of coverage that is difficult and expensive to achieve with other methods such as conventional or electronic tagging. Materials and methods Mexican purse-seine by-catch dataset Sailfish by-catch data from 1998 to 2007, collected by observers aboard the Mexican tuna purse-seine fleet, was used to determine habitat preferences of sailfish in the EPO. Tuna purseseine effort and by-catch data, disaggregated by year, month, set type, and 18  18 grid cells were obtained from Mexico’s National Program for Exploitation of Tuna and Protection of Dolphins (Programa Nacional de Aprovechamiento del Atu´n y Proteccio´n de Delfines (PNAAPD)). These data represent ,50% of all reported fishing trips by the Mexican tuna purseseine fleet. Set type describes the way fishers locate and capture target species. In the EPO, fishers often target tuna associated with marine mammals (‘dolphin sets’) or floating objects (‘floating object sets’), or as free-swimming unassociated schools (‘unassociated sets’) (IATTC 2010). Environmental dataset We used remotely sensed data for oceanographic environmental variables including sea surface temperature (SST), sea surface height deviation (SSHD), and sea surface chlorophyll-a concentration (SSCC), derived from the advanced very high resolution radiometer (AVHRR), and the sea-viewing wide field-of-view sensor (SeaWiFS). We used monthly SST, SSHD and SSCC datasets, extending from 208S to 408N at a resolution of 0.18, 0.258 and 0.058 latitude and longitude. These data were obtained from the Environmental Research Division’s Data Access Program (http://coastwatch.pfel.noaa.gov/erddap, accessed December 2012). Spatial resolution of all oceanographic data was transformed to 18 longitude  18 latitude. The Oceanic Nin˜o Index (ONI) (3-month running mean of ERSST. v3b SST anomalies in El Nin˜o 3.4 region (58N–58S, 1208– 1708W)) was obtained from NOAA (2013) and was also used as an environmental variable. Generalised additive models (GAMs) Relationships between sailfish and environmental variables were analysed using GAMs. GAMs are a form of generalised linear model, where the linear predictor is replaced by the sum of smooth functions (Wood 2006). Smoothing functions fit curves through the data, using some statistical methods (e.g. cross correlation) that automatically determines the optimal amount of smoothing (Zuur et al. 2009). According to Guisan et al. (2002),

gðmi Þ ¼ a þ f1 ðX1i Þ þ f2 ðX2i Þ þ f3 ðX3i Þ . . . þ fn ðXni Þ where g is the link function (logit for PA model and log for Catch model), mi is the expected number of sailfish in the catch or the probability of capture, a is the intercept, fn are smooth functions (thin plate regression splines), and Xn are the covariates. The best-fit models were built by fitting the full model (all predictor variables) and then systematically removing each predictor variable in a backward stepwise procedure. This procedure is often used in statistical modelling to identify the most parsimonious model (Zuur et al. 2009). We assessed the change in deviance for both models, using an analysis of deviance with a chi-square test. We also used the Akaike’s Information Criterion (AIC) and the deviance explained (DE) to select the best-fit PA and Catch models. The best-fit models were selected by the lowest AIC value (Dick 2004) and the highest DE. The relative contribution of each predictor variable on sailfish catch was assessed using partial dependence (or effects) plots. These plots show the effect of each predictor variable on sailfish catch relative to the effects of other predictor variables. For plots of each one-dimensional (1-D) smooth term, the x-axis is labelled with the covariate name, whereas the y-axis is labelled s (covariate, effective degrees of freedom), where s is a smooth function (thin plate regression splines). For 2-D plots, the longitude and latitude interaction term, the x- and y-axes are labelled with covariate names and the response is represented as a heat map with overlaid contours. Values of these plots are on the scale of the linear predictor (Wood 2006). Spatial predictions of sailfish by-catch Using the best-fit models and environmental conditions present during the 10-year period, sailfish catch predictions for the EPO were mapped with a spatial resolution of 18 latitude  18 longitude, for both probability of capture and number of fish. To test the null hypothesis that the spatial distribution of sailfish does not change during ENSO events, we computed the mean values of the environmental variables present during the cold and warm phase of ENSO and used the best-fit models to predict and map sailfish catch for these scenarios. We used NOAA’s criterion to define El Nin˜o and La Nin˜a conditions, where El Nin˜o has an ONI value greater than 0.5, whereas La Nin˜a has an ONI less than
0.5.

10N 20N 30°N 40N

C

761

182 113 79

0

51 28

Number of sets

288

10S

12 1

20S

the only underlying statistical assumption is that the functions are additive and that the components are smooth. Two models were used to measure the habitat preferences of sailfish. First, a binomial response model was used to analyse the catch data as present–absent (1 or 0) for sailfish (herein termed the ‘PA model’) to determine the probability of catching sailfish as a function of the covariates. Second, a Poissonresponse model was used to analyse the catch data of sailfish represented as numbers of fish (herein termed the ‘Catch model’). The predictor variables were environmental (SST, SSHD, SSCC, and ONI), spatial (longitude and latitude as an interaction term), and temporal (month). All GAMs were fitted in R 3.0.1 (R Core Team 2013), using the mgcv. package v.1.7– 22 (Wood 2011). Thin plate regression splines were used to adjust non-linear effects of the models (Wood 2003). GAMs were fitted as follows:

Marine and Freshwater Research

Latitude

Habitat prediction models to reduce by-catch

140°W

120°W

100°W

80°W

Longitude Fig. 1. Spatial distribution of the fishing effort of the Mexican purse-seine tuna fleet in the eastern Pacific Ocean from 1998 to 2007.

Results Fishing effort and sailfish by-catch From 1998 to 2007, Mexican purse-seine tuna vessels made 33 549 sets in the EPO composed of 67% dolphin-associated sets, 28% unassociated sets and 5% sets on floating objects (Fig. 1). Fishing effort was mainly restricted to coastal waters of Mexico, primarily off the Baja California Peninsula and near the Gulf of Tehuantepec. Another important fishing area was the offshore waters from 10 to 208N, where most dolphin sets were made (Fig. 1). Of the 1857 sailfish recorded by observers caught by the Mexican purse-seine tuna fleet from 1998–2007, 56.6, 41.9 and 1.5% were respectively caught from dolphin, unassociated and floating object sets. The mean catch rate of sailfish (number of fish per set) by set type was respectively 0.03, 0.01 and 0.07 for dolphin, floating objects and unassociated sets. The average catch rate was respectively two and seven times higher in unassociated sets than dolphin and floating object sets. Mean monthly catch rates show a reasonably constant catch of sailfish during the study period, with the exception of high catch rates in 2006 and 2007 (Fig. 2a). A one-way ANOVA indicate no significant difference in the mean monthly catch rate between years (F9,9178 ¼ 3, P . 0.05). Fig. 2b–e illustrates a pattern of increasing catch rate when SST was .258C, and a similar linear increase in catch rate with increasing ONI. Catch rates decreased when SSCC was higher than 1 mg m
3, with the highest catch rates occurring when SSHD was zero. These results suggest that most of the environmental factors have non-linear effects on sailfish catch rates. Therefore, we used GAMs to identify multidimensional relationships between environmental factors and sailfish catch. Statistical modelling sailfish by-catch Model building Tables 1 and 2 show that AIC increased and DE decreased when a predictor variable was removed from the full model, with the exception of SSCC in the PA model. These results suggest

D

Marine and Freshwater Research

R. O. Martinez-Rincon et al.

(a) 0.30

(b) 0.4

0.20

0.2

0.10

0

0 1998

2000

2002

2004

20

2006

25

Time period (c) SBR (number of fish/sets)

30

SST (°C) (d)

0.30

0.15

0.20

0.10

0.10

0.05 0

0 0

2

4

6

10

8

SSCC (mg m3)

0

10

20

30

SSHD (cm)

(e) 0.12

0.08

0.04

0 1

0

1

2

ONI Fig. 2. Variability of the sailfish by-catch rate (SBR) in the eastern Pacific Ocean from 1998 to 2007 and its relationship with environmental variables: (a) Monthly mean SBR, (b) Sea surface temperature (SST) v. SBR, (c) Sea surface chlorophyll-a concentration (SSCC) v. SBR, (d ) Sea surface height deviation (SSHD) v. SBR, and (e) Oceanic Nin˜o Index (ONI) v. SBR. Bold lines represent local weighted polynomial regressions.

Table 1. Analysis of deviance for the Presence/Absence (PA) model The best-fit model (in bold) includes all predictor variables except SSHD. AIC, Akaike’s Information Criterion; DDev., change in residual deviance; DE, deviance explained; P-value, level of significance; Res. Dev., residual deviance; Res. d.f., residual degree of freedom Model 1 2 3 4 5 6 7

PA ,s(SSCC) þ s(SST) þ s(ONI) þ s(SSHD) þ s(Month) þ s(Longitude, Latitude) –s(SSCC) –s(SSHD) –s(ONI) –s(Month) –s(SST) –s(Longitude, Latitude) [null model]

Res. d.f.

Res. Dev.

DDev.

P-value (x2 test)

DE (%)

AIC

9179

2837

–

–

19.9

2888

9152 9151 9148 9142 9139 9154

2798 2808 2824 2851 2878 3543

139.4
10.1
16.2
26.4
26.9
665.8

,0.05 ,0.05 ,0.05 ,0.05 ,0.05 ,0.05

21.0 20.7 20.3 19.5 18.8 0

2879 2883 2887 2908 2933 3545

Habitat prediction models to reduce by-catch

Marine and Freshwater Research

E

Table 2. Analysis of deviance for the Catch model The best-fit model (in bold) includes all predictor variables. AIC, Akaike’s Information Criterion; DDev., change in residual deviance; DE, deviance explained; P-value, level of significance; Res. Dev., residual deviance. Res. d.f., residual degree of freedom Model 1 2 3 4 5 6 7

Catch ,s(SSCC) 1 s(SST) 1 s(ONI) 1 s(SSHD) 1 s(Month) 1 s(Longitude, Latitude) –s(SSCC) –s(SSHD) –s(ONI) –s(Month) –s(SST) –s(Longitude, Latitude) [null model]

Res. d.f.

Res. Dev.

9116

6694

9117 9126 9134 9144 9150 9179

6738 6825 6982 7214 7363 10 735

that SSCC must be removed from the PA model. The analysis of deviance tables confirmed that when a predictor variable was removed from the previous model, model performance decreased significantly (Tables 1, 2). The PA model explained 21% of total deviance, whereas the Catch model explained 37.6% of total deviance. The full PA (1) and Catch models (2) can be expressed as: (1) logit(sailfishi) ¼ a þ f1(SSTi) þ f2(ONIi) þ f3(SSHDi) þ f4(Month) þ f5(latitudei, longitudei) (2) log(sailfishi) ¼ a þ f1(SSCCi) þ f2(SSTi) þ f3(ONIi) þ f4(SSHDi) þ f5(Monthi) þ f6(latitudei, longitudei) where logit(sailfishi) is the probability of catching sailfish, log(sailfishi) is the expected number of sailfish, a are the intercepts, and fn are the smooth functions of the covariates. Effects of predictor variables on sailfish catch Environmental, temporal, and spatial preferences of sailfish were assessed using GAMs. Partial dependence plots of the PA model (Fig. 3) show that the highest probability of capturing sailfish is expected when warm temperatures (.268C), positive ONI values (.1 strong El Nin˜o events), and negative values of SSHD (,
10 cm) occur in the EPO during autumn and winter. Additionally, the spatial predictor (interaction between latitude and longitude) showed the highest probability of sailfish catch occurs in coastal waters within ,60 miles (,96.5 km) from the coast. Partial dependence plots of the Catch model (Fig. 4) showed that the highest catch values of sailfish is expected when low values of SSCC (,1 mg m
3), warm temperatures (.268C), strong El Nin˜o (ONI .1), and moderate La Nin˜a events (ONI from
1 to
0.5), and negative values of SSHD (,10 cm) occur in the EPO during autumn and winter. The spatial predictor shows the highest catch in coastal waters and in an offshore area located at 88N, 968W. Predicted spatial distribution of sailfish Figures 4 and 5 show the spatial distribution of the catch probabilities and catch in number of sailfish predicted by the best-fit models (under average El Nin˜o and La Nin˜a conditions in the EPO). These figures show that sailfish have a strong preference for coastal waters, particularly between latitudes 5 and 288N (white dashed line). Interestingly, our models did not predict any

DDev.

P-value (x2 test)

DE (%)

–

–

37.6

7997


44.6
86.5
157.2
232.3
149.1
3371

,0.05 ,0.05 ,0.05 ,0.05 ,0.05 ,0.05

37.2 36.4 35.0 32.8 31.4 0

8040 8109 8249 8463 8600 11 913

AIC

change in spatial distribution during El Nin˜o or La Nin˜a conditions in the EPO. The Catch model shows an important oceanic area at 88N, 968W. The PA model predictions suggest that, under average conditions, the catch probability ranged from 0 to 35.5%, but ranged from 0 to 53% during El Nin˜o events and from 0 to 40.8% during La Nin˜a events. On average, the catch probability of sailfish increased by ,1.7-fold during El Nin˜o events and ,1.1-fold during La Nin˜a events. Similar results were produced for the Catch model, with mean catches expected to be higher during El Nin˜o events, compared with La Nin˜a events and average conditions. Discussion In the EPO the highest by-catch in the tuna purse-seine fishery, in terms of number of fish and species, occurs in sets made on floating objects, followed by unassociated, and dolphin sets (Hall et al. 2000; IATTC 2010). This is a result of both natural and artificial floating objects attracting tropical tunas (e.g. skipjack, yellowfin) and ecologically similar species (e.g. dolphinfish, rainbow runner) for shelter and feeding. Their tendency to aggregate around floating objects therefore increases their vulnerability of being captured by the purse-seine tuna fleet. Species diversity and composition of by-catch in dolphin sets is similar to other set types, but the overall number of individuals is often significantly lower, presumably because migrating dolphin pods do not provide an equivalent level of resources as passive floating objects (Hall 1998; Gerrodette et al. 2012). The logbook data of the Mexican purse-seine tuna fleet revealed that sailfish by-catch was highest in dolphin sets, followed by unassociated sets and floating object sets. These results are consistent with reports of international purse-seine fleets operating in the EPO (IATTC 2012); however, this catch pattern is due to higher fishing effort by dolphin sets compared with others set types. In the Atlantic Ocean, Gaertner et al. (2002) described a similar pattern for sailfish by-catch, where the catch rate was up to five-fold higher in unassociated school sets compared with sets made on floating objects. Our results suggest no significant differences in the monthly sailfish catch rates from 1998 to 2007. However, contrasting results have been described in a recent sailfish stock assessment in the eastern Pacific by Hinton and Maunder (2013). They found that indices of the annual sailfish catch of the Japanese

F

Marine and Freshwater Research

R. O. Martinez-Rincon et al.

1.5

0

s(ONI,5.9)

s(SST,1)

1

1 2

0.5 0.5 1.5

3 20

25

1

30

0

SST

2

1.0

s(Month,3.38)

1

s(SSHD,2.65)

1

ONI

1 3

0.5

0

0.5

5 10

0

10

20

30

SSHD

2

4

6

8

10

12

Month

Linear predictor 30

0

10 40

Latitude

20

0





5

20

30

5 3

10 140

10

15

2 5

120

40 60

100

3

0

5 0

80

Longitude Fig. 3. Effect plots of six predictor variables in the best-fit model for predicting sailfish catch probability. All values are on the scale of the linear predictor. Dashed lines represent two standard errors above and below the estimate of the smooth curve. Bottom-left panel represents the effect of the interaction between latitude and longitude on sailfish.

longline fishery and the Mexican recreational fishery declined from 1990 to 2005, after which catches stabilised. Hall and Roman (2013) found high interannual variability in sailfish catches reported by the purse-seine tuna fishery from 1993 to 2009. They also noted a precipitous decline in the sailfish catch in unassociated sets. However, these data may just reflect changes in the relative composition of fishing methods (i.e. set type) used to capture target species, rather than depletion of the sailfish stock, because the fishing effort by unassociated sets decreased by at least 25% in recent years (IATTC 2012). With regards to habitat preference of sailfish, our results indicate that SST was the most important environmental predictor variable of the best-fit models, whereby the probability of capture and the catch of sailfish increases when the SST is .268C. Conversely, Mourato et al. (2010) suggested that more adult sailfish may be expected to be caught when the SST is ,268C in the Atlantic Ocean. Several tagging studies have reported that sailfish have a distinct preference for warm water temperatures (Prince and Goodyear 2006; Boyce et al. 2008).

For example, Hoolihan et al. (2011) used data from pop-up satellite tags in the eastern tropical Pacific to determine that sailfish have a strong preference for SST .258C, as predicted by our models. Some authors suggest that when comparing fishery data with tagging data, habitat preferences may be expected to be different (Kerstetter et al. 2011). This is because electronic tags record the actual utilisation of a habitat by a fish, whereas catch data explain the habitat utilised by the fishery, which is more likely to relate to the preferred habitat of the primary target species rather than by-catch species. For example, Kerstetter et al. (2011) found that sailfish in the southern Gulf of Mexico and Florida straits have a clear preference for surface waters but undertake numerous short-duration vertical movements below the mixed layer, suggesting that the majority of sailfish are at depth only for feeding. This dive pattern increases the likelihood of interaction between sailfish and pelagic longline gear operating in deep waters, therefore introducing a misleading interpretation of habitat preferences of the species when analysing catch data alone.

Habitat prediction models to reduce by-catch

Marine and Freshwater Research

G

2

0

s(SST,6.76)

s(SSCC,1)

0 2 4

4

8

6 0

2

4

6

8

20

25

SSCC

30

SST

s(SSHD,8.73)

s(ONI,8.58)

1.5

0.5

0.5

2

6

10

1.5 1

0

1

10

2

0

10

ONI

20

30

SSHD Linear predictor 30

Latitude

s(Month,8.68)

1.0

0

20

0

10 0

50

100

50

10

1.0 2

4

6

8

10

12

Month

200

140

120

100

150

250

80

Longitude

Fig. 4. Effect plots of seven predictor variables in the best-fit model for predicting sailfish catch. All values are on the scale of the linear predictor. Dashed lines represent two standard errors above and below the estimate of the smooth curve. Bottom right panel represents the effect of the interaction between latitude and longitude on sailfish.

According to our Catch model, the catch of sailfish in the purseseine fishery is higher when chlorophyll-a is ,0.25 mg m
3. Similar results were described for other pelagic species, such as blue marlin (Makaira nigricans), albacore (Thunnus alalunga), and wahoo (Acanthocybium solandri) (Su et al. 2008; Zainuddin et al. 2008; Martı´nez-Rinco´n et al. 2012). These large pelagic predators appear to prefer waters of low productivity, possibly because the water column may be clear enough to facilitate visual pursuit of prey. Marine waters with high phytoplankton biomass are usually turbid, which is likely to make prey detection difficult for large visual predators such as billfishes. It was clear from our analyses that SSHD has an important effect on sailfish catch in the purse-seine fishery. Both models predicted higher catches with decreasing sea surface height. This negative relationship with sea surface height is related to large scale La Nin˜a events (Roemmich and Gilson 2011) or cold core eddies (Rooker et al. 2012). We found that the effect of both

processes are captured in both models, because the PA model predicted a higher catch probability of sailfish during La Nin˜a events and the Catch model predicted higher catches in an offshore region located at 88N, 968W (Fig. 6). This area (known as the Costa Rica Dome) has been described as a centre of oceanic upwelling influenced by mesoscale eddies, where the sea level is usually below the mean (Fiedler and Talley 2006; Kessler 2006). With respect to temporal variability, both models predicted sailfish catch to be higher during late autumn and winter (November to March). Ehrhardt and Fitchett (2006) also suggested that sailfish abundance in coastal waters of the central eastern Pacific is higher during the same period. They concluded that habitat contraction during the winter increases the vulnerability of sailfish to being caught by the recreational fishery. We predicted the monthly distribution of sailfish using both models (results not shown), which indicate that sailfish remain in coastal waters throughout the year. Therefore, we find little evidence to

H

Marine and Freshwater Research

R. O. Martinez-Rincon et al.

1998−2007 (average)

El Niño events

30

30

60

60

40

20

30 20 10

50

Latitude

Latitude

50

40

20

30 20 10

10

10 0

0 0 130

0 120

110

100

90

130

80

120

Longitude

110

100

90

80

Longitude

La Niña events

60

Latitude

50 40

20

30 20 10

10 0

Catch probability (%)

50 30

40 30 20 10

0 130

120

110

100

90

80

Average

El Niño

La Niña

Longitude Fig. 5. Spatial distribution of sailfish catch probability in the eastern Pacific Ocean from 1998 to 2007, predicted by the best-fit model. (a) Predicted under average environmental conditions (ONI .
0.5 and ,0.5), (b) predicted during El Nin˜o conditions (ONI .0.5), (c) predicted during La Nin˜a conditions (ONI ,
0.5), and (d ) box plot of sailfish catch probability in the sailfish ‘hot spot’ area (white dashed line). Bold line, median; box, 1st and 3rd quartile; whiskers, minimum and maximum.

support the notion of seasonal latitudinal shifts of ‘hot spots’ of high sailfish abundance. Predictions show that the probability of sailfish capture is highest in coastal waters of the central eastern Pacific between 5 and 288N. In contrast, fishery data from the IATTC (2012) indicate that annual purse-seine tuna fishery catches from 2006 to 2010 in the EPO were lower than other oceanic areas in the eastern Pacific, especially for skipjack and bigeye tuna, which are usually captured south of the equator. Hobday et al. (2010) suggest that if the preferred horizontal habitats of target and by-catch species are sufficiently separated, a spatial approach for fishery management would be suitable. We suggest that a reduction in fishing effort or even a closure of coastal waters to purse-seine fishing may substantially reduce sailfish by-catch without significantly affecting the purse-seine tuna fishery. A similar approach to reduce by-catch in all purseseine tuna fisheries was proposed by Hall and Roman (2013), suggesting that if high by-catch levels are predictable in space and time, seasonal closures might be suitable to mitigate bycatch. Fishery closures during specific times and areas may significantly reduce the fishing mortality on billfish by-catch and simultaneously reduce the interaction with recreational

fisheries. Our results suggest that sailfish have a seasonal movement pattern; therefore, a seasonal closure may be an effective management option. We detected an important effect of large scale climate cycles such as the ENSO, on the sailfish catch. The PA model predicted the probability of catching sailfish increases by almost 80% in coastal waters when El Nin˜o is present. Therefore, we suggest that implementing closures, with attention to prevailing largescale environmental conditions, may be an acceptable option to mitigate billfish by-catch, without significantly affecting the purse-seine tuna fishery. There is strong evidence that the range of pelagic fish species in tropical and subtropical waters is affected by ENSO. LluchBelda et al. (2005) described changes in the distribution of pelagic fish in the California Current System during El Nin˜o events, where tropical species were reported to migrate northward. Lehodey et al. (1997) showed that latitudinal shifts in skipjack (Katsuwonus pelamis) populations are linked to large zonal displacements of the ‘Warm Pool’ oceanographic province during ENSO events. We expected to see similar changes in the distribution of sailfish associated with ENSO events; however, our model did not predict any changes in the range of sailfish

Habitat prediction models to reduce by-catch

Marine and Freshwater Research

1998−2007 (average)

El Niño events

30

15

10

20

5

Latitude

Latitude

30

10

15

10

20

5

10 0

0

0 130

I

0 120

110

100

90

130

80

120

Longitude

110

100

90

80

Longitude

Latitude

30

15

10

20

5

10 0

0 130

Catch (Number of fish)

La Niña events

15

10

5

0 120

110

100

90

80

Average

El Niño

La Niña

Longitude Fig. 6. Distribution of sailfish catch in number of fish in the eastern Pacific Ocean from 1998 to 2007, predicted by the best-fit model. (a) Predicted under average environmental conditions (ONI .
0.5 and ,0.5), (b) predicted during El Nin˜o conditions (ONI .0.5), (c) predicted during La Nin˜a conditions (ONI ,
0.5), and (d ) box plot of the sailfish catch in number of fish in the ‘hot spot’ area (white dashed line). Bold line, median; box, 1st and 3rd quartile; whiskers, minimum and maximum.

between El Nin˜o or La Nin˜a events. Therefore, we demonstrate that sailfish have a strong preference to inhabit coastal waters even when large scale environmental phenomena are present. Acknowledgements We thank Ira Fogel of CIBNOR for editing and improving English quality of this manuscript. The PNAAPD kindly provided by-catch data. Mitchell Zischke provided help with an early draft. Funding was provided by Consejo Nacional de Ciencia y Tecnologı´a (CONACYT grant 202150). R. O. MartinezRincon was a recipient of a doctoral fellowship (CONACYT grant 202150) and S. Ortega-Garcı´a received a COFAA fellowship.

References Austin, M. (2007). Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecological Modelling 200, 1–19. doi:10.1016/J.ECOLMODEL.2006.07.005 Boyce, D. G., Tittensor, D. P., and Worm, B. (2008). Effects of temperature on global patterns of tuna and billfish richness. Marine Ecology Progress Series 355, 267–276. doi:10.3354/MEPS07237 Carvalho, F. C., Murie, D. J., Hazin, F. H. V., Hazin, H. G., Leite-Mourato, B., and Burges, G. H. (2011). Spatial predictions of blue shark (Prionace glauca) catch rate and catch probability of juveniles in the Southwest Atlantic. Journal of Marine Science 68, 890–900.

Cerdenares-Ladro´n De Guevara, G., Morales-Bojo´rquez, E., and Rodrı´guezSa´nchez, R. (2011). Age and growth of the sailfish Istiophorus platypterus (Istiophoridae) in the Gulf of Tehuantepec, Mexico. Marine Biology Research 7, 488–499. doi:10.1080/17451000.2010. 528201 Chiang, W.-C., Musyl, M. K., Sun, C.-L., Chen, S.-Y., Chen, W.-Y., Liu, D.-C., Su, W.-C., Yeh, S.-Z., Fu, S.-C., and Huang, T.-L. (2011). Vertical and horizontal movements of sailfish (Istiophorus platypterus) near Taiwan determined using pop-up satellite tags. Journal of Experimental Marine Biology and Ecology 397, 129–135. doi:10.1016/ J.JEMBE.2010.11.018 Dick, E. J. (2004). Beyond ‘lognormal versus gamma’: discrimination among error distributions for generalized linear models. Fisheries Research 70, 351–366. doi:10.1016/J.FISHRES.2004.08.013 Ehrhardt, N. M., and Fitchett, M. D. (2006). On the seasonal dynamic characteristics of the sailfish, Istiophorus platypterus, in the Eastern Pacific off central America. Bulletin of Marine Science 79, 589–606. Elith, J., and Leathwick, J. R. (2009). Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology Evolution and Systematics 40, 677–697. doi:10.1146/ ANNUREV.ECOLSYS.110308.120159 Fiedler, P. C., and Talley, L. D. (2006). Hydrography of the eastern tropical Pacific: a review. Progress in Oceanography 69, 143–180. doi:10.1016/ J.POCEAN.2006.03.008

J

Marine and Freshwater Research

R. O. Martinez-Rincon et al.

Gaertner, D., Me´nard, F., and Develter, C. (2002). Bycatch of billfishes by the European tuna purse-seine fishery in the Atlantic Ocean. Fishery Bulletin 100, 683–689. Gerrodette, T., Olson, R., Reilly, S., Watters, G., and Perrin, W. (2012). Ecological metrics of biomass removed by three methods of purse-seine fishing for tunas in the Eastern Tropical Pacific Ocean. Conservation Biology 26, 248–256. doi:10.1111/J.1523-1739.2011.01817.X Guisan, A., Edwards, T. C., and Hastie, T. (2002). Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling 157, 89–100. doi:10.1016/S0304-3800 (02)00204-1 Hall, M. A. (1998). An ecological view of the tuna-dolphin problem: impacts and trade-offs. Reviews in Fish Biology and Fisheries 8, 1–34. doi:10.1023/A:1008854816580 Hall, M. A., and Roman, M. (2013). Bycatch and non-tuna catch in the tropical tuna purse seine fisheries of the world. FAO Fisheries and Aquaculture Technical Paper 568, Food and Agriculture Organization of the United Nations, Rome. Hall, M. A., Alverson, D. L., and Metuzals, K. I. (2000). By-Catch: problems and solutions. Marine Pollution Bulletin 41, 204–219. doi:10.1016/ S0025-326X(00)00111-9 Heithaus, M. R., Frid, A., Wirsing, A. J., and Worm, B. (2008). Predicting ecological consequences of marine top predator declines. Trends in Ecology & Evolution 23, 202–210. doi:10.1016/J.TREE.2008.01.003 Hinton, M. G., and Maunder, M. N. (2013). ‘Status of Sailfish in the Eastern Pacific Ocean in 2011 and Outlook for the Future.’ (Inter-American Tropical Tuna Commission: La Jolla, CA.) Hobday, A. J., Hartog, J. R., Timmiss, T., and Fielding, J. (2010). Dynamic spatial zoning to manage southern bluefin tuna (Thunnus maccoyii) capture in a multi-species longline fishery. Fisheries Oceanography 19, 243–253. doi:10.1111/J.1365-2419.2010.00540.X Hoolihan, J. P., Luo, J., Goodyear, C. P., Orbesen, E. S., and Prince, E. D. (2011). Vertical habitat use of sailfish (Istiophorus platypterus) in the Atlantic and eastern Pacific, derived from pop-up satellite archival tag data. Fisheries Oceanography 20, 192–205. doi:10.1111/J.1365-2419. 2011.00577.X IATTC (2010). ‘Annual report of the Inter-American Tropical Tuna Commission, 2008.’ (Inter-American Tropical Tuna Commission: La Jolla, CA.) IATTC (2012). Fishery status report 10. Tunas and billfishes in the eastern Pacific Ocean in 2011, Inter-American Tropical Tuna Commission, La Jolla, CA. Kerstetter, D. W., Bayse, S. M., Fenton, J. L., and Graves, J. E. (2011). Sailfish habitat utilization and vertical movements in the southern Gulf of Mexico and Florida Straits. Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science 3, 353–365. doi:10.1080/ 19425120.2011.623990 Kessler, W. S. (2006). The circulation of the eastern tropical Pacific: a review. Progress in Oceanography 69, 181–217. doi:10.1016/ J.POCEAN.2006.03.009 Leathwick, J. R., Elith, J., Francis, M. P., Hastie, T., and Taylor, P. (2006). Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees. Marine Ecology Progress Series 321, 267–281. doi:10.3354/MEPS321267 Lehodey, P., Bertignac, M., Hampton, J., Lewis, A., and Picaut, J. (1997). El Nino Southern Oscillation and tuna in the Western Pacific. Nature 389, 715–718. doi:10.1038/39575 Lluch-Belda, D., Lluch-Cota, D. B., and Lluch-Cota, S. E. (2005). Changes in marine faunal distributions and ENSO events in the California Current. Fisheries Oceanography 14, 458–467. doi:10.1111/J.13652419.2005.00347.X Martı´nez-Rinco´n, R. O., Ortega-Garcı´a, S., and Vaca-Rodrı´guez, J. G. (2012). Comparative performance of generalized additive models and boosted regression trees for statistical modeling of incidental catch of wahoo (Acanthocybium solandri) in the Mexican tuna purse-seine

fishery. Ecological Modelling 233, 20–25. doi:10.1016/J.ECOL MODEL.2012.03.006 Mourato, B. L., Hazin, H. G., Wor, C., Travassos, P., Arfelli, C. A., Amorim, A. F., and Hazin, F. H. V. (2010). Environmental and spatial effects on size distribution of sailfish in the Atlantic Ocean. Ciencias Marinas 36, 225–236. doi:10.7773/CM.V36I3.1735 NOAA (2013). The Oceanic Nin˜o Index, Climate Prediction Center, National Oceanic and Atmospheric Administration. Available at http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ ensoyears.shtml [Verified 15 November 2013]. Pauly, D., and Chuenpagdee, R. (2003). Development of fisheries in the Gulf of Thailand large marine ecosystem: analysis of an unplanned experiment. In ‘Large Marine Ecosystems of the World: Change and Sustainability’. pp. 337–354. (Elsevier Science: Amsterdam.) Pauly, D., Christensen, V., Dalsgaard, J., Froese, R., and Torres, F., Jr (1998). Fishing down marine food webs. Science 279, 860–863. doi:10.1126/SCIENCE.279.5352.860 Polovina, J. J., and Woodworth-Jefcoats, P. A. (2013). Fishery-induced changes in the subtropical Pacific pelagic ecosystem size structure: observations and theory. PLoS ONE 8, e62341. doi:10.1371/JOURNAL. PONE.0062341 Prince, E. D., and Goodyear, C. P. (2006). Hypoxia-based habitat compression of tropical pelagic fishes. Fisheries Oceanography 15, 451–464. doi:10.1111/J.1365-2419.2005.00393.X R Core Team (2013). ‘R: A Language and Environment for Statistical Computing.’ (R Foundation for Statistical Computing: Vienna, Austria.) Available at http://www.r-project.org/ [Verified February 2013]. Ramı´rez-Pe´rez, J. S., Quin˜o´nez-Vela´zquez, C., Abitia-Cardenas, L. A., and Melo-Barrera, F. N. (2011). Age and growth of sailfish Istiophorus platypterus (Shaw in Shaw and Nodder, 1792) from Mazatlan, Sinaloa, Mexico. Environmental Biology of Fishes 92, 187–196. doi:10.1007/ S10641-011-9832-0 Roemmich, D., and Gilson, J. (2011). The global ocean imprint of ENSO. Geophysical Research Letters 38, L13606. doi:10.1029/2011GL047992 Rooker, J. R., Simms, J. R., Wells, R. J. D., Holt, S. A., Holt, G. J., Graves, J. E., and Furey, N. B. (2012). Distribution and habitat associations of billfish and swordfish larvae across mesoscale features in the Gulf of Mexico. PLoS ONE 7, e34180. doi:10.1371/JOURNAL.PONE.0034180 Scheffer, M., Carpenter, S., and Young, B. (2005). Cascading effects of overfishing marine systems. Trends in Ecology & Evolution 20, 579–581. doi:10.1016/J.TREE.2005.08.018 Su, N.-J., Sun, C.-L., Punt, A. E., and Yeh, S.-Z. (2008). Environmental and spatial effects on the distribution of blue marlin (Makaira nigricans) as inferred from data for longline fisheries in the Pacific Ocean. Fisheries Oceanography 17, 432–445. doi:10.1111/J.1365-2419.2008.00491.X Viera, A. (2007). Note about observations of sailfish during an observer cruise on a French purse-seiner in the Gulf of Guinea. Collected Volumes of Scientific Papers ICCAT 60, 309–313. doi:10.1111/1467-9868.00374 Wood, S. N. (2003). Thin-plate regression splines. Journal of the Royal Statistical Society. Series B. Methodological 65, 95–114. doi:10.1111/ 1467-9868.00374 Wood, S. N. (2006). ‘Generalized Additive Models: An Introduction with R.’ (CRC Press: Boca Raton, FL.) Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society. Series B. Methodological 73, 3–36. doi:10.1111/J.1467-9868.2010.00749.X Zainuddin, M., Saitoh, K., and Saitoh, S.-I. (2008). Albacore (Thunnus alalunga) fishing ground in relation to oceanographic conditions in the western North Pacific Ocean using remotely sensed satellite data. Fisheries Oceanography 17, 61–73. doi:10.1111/J.1365-2419.2008.00461.X Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A., and Smith, G. M. (2009). ‘Mixed effects models and extensions in ecology with R.’ (Springer ScienceþBusiness Media: New York.)

www.publish.csiro.au/journals/mfr