Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-23T22:09:18.539Z Has data issue: true hasContentIssue false

ENSO effect on trophic interactions of three top predators in adjacent waters to Cabo San Lucas, Baja California Sur

Published online by Cambridge University Press:  20 September 2024

Amairani Hernández-Aparicio
Affiliation:
Instituto Politécnico Nacional. Centro Interdisciplinario de Ciencias Marinas (CICIMAR-IPN). Av. IPN s/n, Col Playa Palo de Santa Rita, C.P. 23096, La Paz, Baja California Sur, México
Sofía Ortega-García*
Affiliation:
Instituto Politécnico Nacional. Centro Interdisciplinario de Ciencias Marinas (CICIMAR-IPN). Av. IPN s/n, Col Playa Palo de Santa Rita, C.P. 23096, La Paz, Baja California Sur, México
Arturo Tripp-Valdez
Affiliation:
Instituto Politécnico Nacional. Centro Interdisciplinario de Ciencias Marinas (CICIMAR-IPN). Av. IPN s/n, Col Playa Palo de Santa Rita, C.P. 23096, La Paz, Baja California Sur, México
Ulianov Jakes-Cota
Affiliation:
Instituto Politécnico Nacional. Centro Interdisciplinario de Ciencias Marinas (CICIMAR-IPN). Av. IPN s/n, Col Playa Palo de Santa Rita, C.P. 23096, La Paz, Baja California Sur, México
Rodrigo Moncayo-Estrada
Affiliation:
Instituto Politécnico Nacional. Centro Interdisciplinario de Ciencias Marinas (CICIMAR-IPN). Av. IPN s/n, Col Playa Palo de Santa Rita, C.P. 23096, La Paz, Baja California Sur, México
Sergio A. Hernández-Briones
Affiliation:
Instituto Politécnico Nacional. Centro Interdisciplinario de Ciencias Marinas (CICIMAR-IPN). Av. IPN s/n, Col Playa Palo de Santa Rita, C.P. 23096, La Paz, Baja California Sur, México
*
Corresponding author: Sofía Ortega-García; Email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Trophic competition among top predators is also influenced by environmental variability. However, the magnitude of the changes in contrasting events such as El Niño Southern Oscillation (ENSO) is poorly studied. The stomach contents of striped marlin (Kajikia audax), blue marlin (Makaira nigricans), and dolphinfish (Coryphaena hippurus) were analysed. We included the ENSO effect on the diet because we analysed organisms captured during 2010–2011 and 2014–2015, periods that, according to the Ocean Niño Index, were defined as the cold phase (CP) and warm phase (WP), respectively. Trophic diversity, feeding habits and strategy, trophic position (TP), trophic niche amplitude, and diet overlap were calculated. It was found that, despite a wide trophic spectrum, all three species were specialist predators in both phases. The most important prey species during both phases for striped marlin was Dosidicus gigas, while Auxis spp. was the most important prey of blue marlin. Dolphinfish fed mainly on Oxyporhamphus micropterus during the CP and Pleuroncodes planipes during the WP. Our results indicated that during both ENSO phases, all species maintained a trophic position similar to previous reports for the study area. However, for striped marlin, these differences were significant. Greater trophic competition was found during the CP (seven prey taxa shared) than in the WP (three prey taxa shared). These species often share the same environment, but their preference for feeding on different prey makes them occupy different trophic spaces, an aspect that allows their coexistence in time and space.

Type
Research Article
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press on behalf of Marine Biological Association of the United Kingdom

Introduction

Trophic interactions inferred from feeding habits can contribute to a better understanding of the effects of fishing and climate change on marine resources, although evidence of direct effects of climate change on the local abundance of marine species is limited (Sinclair et al., Reference Sinclair, Arnason, Csirke, Karnicki, Sigurjonsson, Skjoldal and Valdimarsson2002; Hobday and Evans, Reference Hobday and Evans2013). Currently, research has shown that climate change can impact ocean ecosystems through increased water temperature and a consequent decrease in primary productivity (Petrik et al., Reference Petrik, Stock, Andersen, Van Denderen and Watson2020). Considering that these impacts are propagated throughout the trophic web, the diets of top predator species can be considered as a good indicator of changes in the ecosystem due to environmental variability.

Striped marlin (Kajikia audax), blue marlin (Makaira nigricans), and dolphinfish (Coryphaena hippurus) are amongst the most important species for the recreational fishery of Cabo San Lucas (CSL). Striped marlin is an oceanic species that periodically enters coastal waters and is distributed in tropical, subtropical, and temperate waters of the Pacific and Indian Oceans, usually in waters with a sea surface temperature (SST) of 20–25 °C. In the Eastern Pacific Ocean (EPO), its distribution is continuous from southern California (USA) to Chile, and its greatest abundance is recorded off the Mexican Pacific coast (Nakamura, Reference Nakamura1985). Blue marlin is a cosmopolitan, highly migratory species distributed in the tropical and temperate waters of the Pacific and Indian Oceans, generally in waters with SSTs above 24 °C. It spends 90% of the time at depths where the water temperature is 1–2 °C less that SST (Nakamura, Reference Nakamura1985; Su et al., Reference Su, Sun, Punt and Yeh2008). Dolphinfish is a highly migratory epipelagic species inhabiting tropical and subtropical waters worldwide, with its distribution limited by the 20 °C isotherm (Palko et al., Reference Palko, Beardsley and Richards1982; Moltó et al., Reference Moltó, Hernández, Sinopoli, Besbes-Benseddik, Besbes, Mariani, Gambin, Alemany, Morales-Nin, María-Grau, Camiñas, Baez, Vasconcellos, Ceriola and Catalán2020). These species are all distributed in the Pacific Ocean, where the oceanographic event El Niño Southern Oscillation (ENSO) occurs.

The ENSO is an oceanographic event consisting of a warm phase (El Niño), characterized by abnormal warming of the EPO waters, and a cold phase (La Niña) characterized by unusual cooling of the Pacific and the American coast. These events have been found to cause changes in the migratory processes and relative abundance of large pelagic species, since changing environmental conditions, particularly SST, affect the life cycle of many species (Lluch-Belda et al., Reference Lluch-Belda, Lluch-Cota and Lluch-Cota2005). It is now recognized that climate change is decreasing ecosystem productivity in most tropical and subtropical oceans, seas, and lakes, while increasing at high latitudes. Consequently, the presence of ENSO events are predicted to be more frequent and of higher intensity (e.g., Timmermann et al., Reference Timmermann, Oberhuber, Bacher, Esch, Latif and Roeckner1999; Hansen et al., Reference Hansen, Sato, Ruedy, Lo, Lea and Medina-Elizade2006). That is why studies that investigate the behaviour of trophic ecology and its competencies during the cold and warm phases of ENSO are of great importance in predicting species’ behaviour in the presence of these events.

Materials and methods

Sampling for each ENSO phase

The specimens were collected for three consecutive days each month from 2010 to 2011 for the cold phase and from 2014 to 2015 for the warm phase from the landings of the sport fishing fleet that operated in CSL (Figure 1). The organisms were caught by rod and line with either live bait (Mugil cephalus or Decapterus macrosoma) or artificial bait (trolling) between 06:00 and 15:00.

Figure 1. Study area: Cabo San Lucas (CSL), the one where the sport fishing fleet operates.

Environmental information

The Oceanic Niño Index (ONI) is the official ENSO indicator at the National Oceanic and Atmospheric Administration (NOAA). It is based on SSTs in the Niño 3.4 region (5°N-5°S, 120°-170°W) in the east-central tropical Pacific Ocean (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php). ONI values greater than or equal to 0.5 were considered indicative of an El Niño event; values lower than or equal to −0.5 indicated a La Niña event. According to SST anomalies in the study area and the ONI values, the ENSO phases were established as cold phase during 2010 and 2011 and a warm phase during 2014 and 2015. Considering that SST and chlorophyll-a (Chl-a) concentration are two variables closely related to the ENSO (López-Martínez et al., Reference López-Martínez, Farach-Espinoza, Herrera-Cervantes and García-Morales2023), both were used to relate them to prey diversity. SST and Chl-a concentration were inferred from monthly satellite image composites of the AVHRR (advanced very high-resolution radiometer) sensor with a resolution of 1.1 km, taking into account the area of operation of the sport fishing fleet.

Stomach analysis

The lower jaw fork length (LJFL ± 1 cm) for billfishes and curved fork length (FL cm) for the dolphinfish were recorded. A total of 351 stomachs during the cold phase and 448 stomachs during the warm phase were sampled. The stomachs were transported to the laboratory, where their contents were placed in a strainer and washed with water to remove excess gastric fluids and to separate the contents. Prey items were counted and weighed with a precision of ±0.01 g on an analytical balance. When bait was found in the stomach content (identified by its minimal digestion and the evident insertion point of the hook), it was discarded. Likewise, if bait was the only content in the stomach, it was considered empty.

Prey were identified at the lowest possible taxonomic level. For highly digested fish, the identification was based on the axial skeleton, mainly vertebrae, following the criteria of Clothier (Reference Clothier1950), Monod (Reference Monod1968), Miller and Jorgensen (Reference Miller and Jorgensen1973), Barrera-García (Reference Barrera-García2008), Tercerie et al. (Reference Tercerie, Bearez, Pruvost, Bailly and Vignes-Lebbe2022), and McEwan et al. (Reference McEwan, Emery, Quitmyer, Wing and Scudder2022). Slightly digested fish were identified based on the keys by Allen and Robertson (Reference Allen and Robertson1994) and Fischer et al. (Reference Fischer, Krupp, Schneider, Sommer, Carpenter and Niem1995). Hard structures of cephalopods and crustaceans were identified by consulting the keys of Wolff (Reference Wolff1984), Brusca (Reference Brusca1980), and Fischer et al. (Reference Fischer, Krupp, Schneider, Sommer, Carpenter and Niem1995). In the case of squid beaks, identification guides were used based on the studies by Clarke (Reference Clarke1962, Reference Clarke1986), Wolff (Reference Wolff1984), and Acuña-Perales et al. (Reference Acuña-Perales, Córdova-Zavaleta, Arrese-Dávila, Torrejón-Zegarra, Rodríguez-Salazar, González-Pestana and Mangel2020).

The quantification of prey items was based on the number of eye pairs, heads, mouthparts (jaws), telsons, otolith pairs, or other anatomical structures that could serve as a reference for whole specimens (Ortega-García et al., Reference Ortega-García, Arizmendi-Rodríguez and Zúñiga-Flores2017).

Trophic analysis

Using the methodology of Hsieh et al. (Reference Hsieh, Ma and Chao2016), which is a modification to the one proposed by Chao et al. (Reference Chao, Gotelli, Hsieh, Sander, Ma, Colwell and Ellison2014), trophic diversity to infer sample sufficiency was determined from interpolation and extrapolation curves, which allowed us to identify the percentage of the diet calculated from the Hill numbers estimates with 95% confidence intervals.

The contribution of each food taxa to the diet was assessed using the prey-specific index of relative importance (%PSIRI) proposed by Brown et al. (Reference Brown, Bizzarro, Cailliet and Ebert2012) using the formula:

$$\% PSIRI_i = \displaystyle{{\% FO_i\cdot ( {\% PN_i + \% PW_i} ) } \over 2}$$

where %FO i is the number of stomachs containing the prey i divided by the total number of stomachs (%FO i = ni/n), %PN i is the specific abundance per prey, and %PW i is the weight of each prey.

The breadth of the diet (Bi) was estimated with the Levin's index (Krebs, Reference Krebs1989). This method allows inferences on the diet breadth by considering the proportion of each abundance of prey present and how they are distributed to the total. The values obtained from Levin's index range from 0 to 1, which derives if the predator is a specialist (Bi ≤ 0.6) or a generalist (Bi ≥ 0.6).

For the determination of the trophic position (TP K), the equation proposed by Christensen and Pauly (Reference Christensen and Pauly1992) was applied:

$$TP_K = 1 + ( \mathop \sum \limits_{\,j = 1}^n DC_{ij}) ( {PT_j} ) $$

where DCij is the prey proportion (j) in the diet of predators (i), PTj is the trophic position of the prey (j), and n is the total number of groups.

The trophic position of the prey at the species level was obtained from the FishBase database (https://www.fishbase.se/search.php). For those prey that could not be identified at the species level, the position values reported by Cortés (Reference Cortés1999) were used.

The average trophic position was estimated for each species in both phases and compared them applying a Wilcoxon test considering the normality and homoscedasticity test results.

For the analysis of the trophic interactions between the three species during both phases, the prey they shared according to the % Frequency of occurrence and % Specific abundance of the prey were compared using the methodology proposed by Costello (Reference Costello1990) and modified by Amundsen et al. (Reference Amundsen, Gabler and Staldvik1996).

A permutational analysis of variance (PERMANOVA) was used to assess the trophic overlap in the diets entered by the species. A non-metric multidimensional scaling ordination (nMDS) (Field et al., Reference Field, Clarke and Warwick1982) was also applied to better visualize the overall pattern. A similarity of percentages analysis (SIMPER) was conducted to identify which prey taxa were important in discriminating between species (Clarke, Reference Clarke1993). The studies were performed using the package vegan in R statistical software.

The ONI value was used to define the months in which the cold and warm phases of ENSO were present. The relationship between SST and of Chl-a concentration in the feeding and their trophic competition was determined by applying canonical discriminant analysis (CDA) and a MANOVA statistical test.

Results

During the cold phase, the average SST was 25.1 °C (±2.9 SD), and the average Chl-a concentration was 0.51 mg/m3 (±0.4 SD). During the warm phase, the average SST was 26.6 °C (±2.7 SD), and the Chl-a concentration was 0.52 mg/m3 (±0.1 SD). Total number of organisms sampled per species (N), length range (lower jaw fork length for billfish and fork length for dolphinfish), number and percentage of stomachs with food and empty stomachs are shown in Table 1.

Table 1. Summary of the total number of organisms per species (N), length range and stomachs with food and empty stomachs during the cold and warm phases of ENSO in the adjacent waters to Cabo San Lucas, Baja California Sur

Trophic diversity

From the interpolation and extrapolation curves, 26 prey families were identified in striped marlin trophic diversity during the cold phase (96.1% of their diet) and 28 prey families (98.7% of their diet) during the warm phase. For blue marlin, ten prey families were identified during the cold phase (88.4% of their diet) and three prey families (50% of their diet) during the warm phase. Finally, for dolphinfish, 19 prey families were identified during the cold phase (81% of their diet) and 25 prey families (94.9% of their diet) during the warm phase (Figure 2).

Figure 2. Interpolation and extrapolation curves to determine trophic diversity and represented diet percentage of striped marlin Kajikia audax (A), blue marlin Makaira nigricans (B) and dolphinfish Coryphaena hippurus (C) during cold (1) and warm (2) phase of ENSO in adjacent waters to Cabo San Lucas, Baja California Sur.

Stomach content analysis (SCA)

During the cold phase, the diet of striped marlin consisted of 37 prey taxa, which were identified and from three higher taxonomic groups: 27 fish taxa (%PSIRI = 53.5), nine cephalopod taxa (%PSIRI = 46.1) and one crustacean (%PSIRI = 0.4). For the warm phase, 45 prey taxa were identified, comprising 32 fish taxa (%PSIRI = 62.1), 11 cephalopod taxa (%PSIRI = 36.2), and two crustaceans (%PSIRI = 1.7). The most important prey species for striped marlin during both phases was Dosidicus gigas (%PSIRI CP = 30.4 and WP = 18.5%PSIRI). Other important prey taxa during the cold phase included the cephalopod Argonauta spp. (%PSIRI = 12.5), the fish Caranx caballus (%PSIRI = 19.3) and Auxis spp. (%PSIRI = 6.6) (Table 2). Important prey during the warm phase (Table 3) also included the fish Lagocephalus lagocephalus (%PSIRI = 15.5), Balistes polylepis (%PSIRI = 11.2), as well as the cephalopod Argonauta spp. (%PSIRI = 11.3).

Table 2. Summary of the diet composition of striped marlin Kajikia audax, blue marlin Makaira nigricans and dolphinfish Coryphaena hippurus during cold phase of ENSO in adjacent waters to Cabo San Lucas, Baja California Sur, expressed by percentages frequency of occurrence (%FO), percent prey-specific number (%PN), percent number (%N), percent prey-specific weight (%PW), percent weight (%W) and prey-specific index of relative importance (%PSIRI)

Table 3. Summary of the diet composition of striped marlin Kajikia audax, blue marlin Makaira nigricans and dolphinfish Coryphaena hippurus during warm phase of ENSO in adjacent waters to Cabo San Lucas, Baja California Sur, expressed by percentages frequency of occurrence (%FO), percent prey-specific number (%PN), percent number (%N), percent prey-specific weight (%PW), percent weight (%W) and prey-specific index of relative importance (%PSIRI)

The diet of blue marlin during the cold phase included 20 prey taxa, which were from two higher taxonomic groups: 17 fish taxa (%PSIRI = 82.7) and three cephalopod taxa (%PSIRI = 17.3). During the warm phase, the diet consisted of five prey taxa which were all fish (%PSIRI = 100). The most important prey species during both phases was the fish Auxis spp. (%PSIRI CP = 46.3 and %PSIRI WP = 50). Other important prey during the cold phase was the cephalopod Dosidicus gigas (%PSIRI = 14.8), and the fish Katsuwonus pelamis (%PSIRI = 8.5) and Caranx caballus (%PSIRI = 4.4) (Table 2). During the warm phase, the fish Selar crumenophthalmus (%PSIRI = 20.4), Ablennes hians (%PSIRI = 10), Trachurus symmetricus (%PSIRI = 10), and Auxis thazard (%PSIRI = 9.6) were also important (Table 3).

During the cold phase, the diet of dolphinfish was represented by 28 prey taxa, which were from three higher taxonomic groups: 21 fish taxa (%PSIRI = 70.5), four cephalopod taxa (%PSIRI = 21.9) and three crustaceans (%PSIRI = 7.6). For the warm phase, 36 prey taxa and three higher taxonomic groups were identified: 29 fish taxa (%PSIRI = 72.9), four cephalopod taxa (%PSIRI = 9.9), and three crustaceans (%PSIRI = 17.2). The most important prey species during the cold phase were the fish Oxyporhamphus micropterus (%PSIRI = 20.1), Exocoetus volitans (%PSIRI = 7.7) and Auxis spp. (%PSIRI = 5.7), and the cephalopods Dosidicus gigas (%PSIRI = 12.8) and Argonauta spp. (%PSIRI = 7.1; Table 2). During the warm phase, the most important prey species were red crab Pleuroncodes planipes (%PSIRI = 15.6) and the oceanic pufferfish Lagocephalus lagocephalus (%PSIRI = 14.6), followed by Balistes polylepis (%PSIRI = 9.2), Selar crumenophthalmus (%PSIRI = 9.2) and Hemiramphus saltator (%PSIRI = 5.6; Table 3).

MANOVA results show statistically significant differences between environmental and prey variabilities on which the three species fed during the cold phase (F (3,85) = 5.17, p < 0.0001) and the warm phase (F (3,86) = 9.08, p < 0.0001).

Based on the niche breadth values, striped marlin (Bi = 0.14 in both cold and warm phase), blue marlin (Bi = 0.22 cold phase and 0.38 warm phase), and dolphinfish (Bi = 0.19 cold phase and 0.09 warm phase) were characterized as being specialist predators during both phases of the ENSO.

The average trophic position, standard error and median values are shown in Table 4. Wilcoxon test indicate no significant differences in trophic position during both phases, neither blue marlin (p = 0.4929) nor dolphinfish (p = 0.05994), but significant differences were found in striped marlin (p = 0.0021).

Table 4. Mean trophic position, standard error, median and sample size for each phase

Trophic competition

During the cold phase, striped marlin, blue marlin, and dolphinfish shared seven prey taxa, including Auxis spp., B. polylepis, C. caballus, Etrumeus acuminatus, H. saltator, Scomber japonicus, and D. gigas. During the warm phase, they only shared three prey: the fish Auxis spp., A. thazard, and S. crumenophthalmus (Figure 3).

Figure 3. Trophic interactions among prey shared by striped marlin (Kajikia audax), blue marlin (Makaira nigricans) and dolphinfish (Coryphaena hippurus) during the (A) cold phase and (B) warm phase of ENSO.

Trophic overlap

The PERMANOVA showed that there was no statistically significant trophic overlap in diet between the three species during either the cold phase (F (3221) = 10.77, p = 0.001) or the warm phase (F (3264) = 4.96, p = 0.001), due to the preference of these species to feed on different prey and the low number of prey they shared in their diets. The nMDS showed a stress of 0.06 during the cold phase and 0.01 during the warm phase of ENSO (Figure 4). According to the SIMPER analysis, the prey taxa that contributed most to the similarity between the diets of the three species were from the families Echeneidae, Engraulidae, Enteroctopodidae, Exocoetidae, Hemiramphidae, Hemisquillidae, Munididae, Scombridae, and Syngnathidae (cold phase) and the families Belonidae, Scombridae, Mullidae, Munididae, Trachipteridae, Phosichthyidae, Pholidotheuthidae, Tysanoteuthidae, and Ommastrephidae (warm phase).

Figure 4. Non-metric multidimensional scaling (nMDS) to represent trophic overlap in the cold phase (A) and the warm phase (B) of ENSO.

Trophic competitions and environmental variability

The first two axes of the CDA accounted for 100% of the variance between groups during the cold and warm ENSO phases (cold phase: 93.1% and 6.9% for axes 1 and 2, respectively; warm phase: 98.5% and 1.5% for axes 1 and 2, respectively). For the warm phase, only one canonical axis was plotted because the number of blue marlin data was very small and insufficient to construct the confidence ellipse around canonical mean (Figure 5). During the cold phase, significant statistically differences (p < 0.001) were observed in predator trophic interactions between the three species, which were associates with Chl-a concentration on canonical axis 1 and with SST on canonical axis 2 (Table S1). During the warm phase, significant statistical differences (p < 0.001) were observed in the interactions of blue marlin with striped marlin and dolphinfish, which were related to their feeding pattern (PSIRI) on canonical axis 1 and with SST on canonical axis 2. The results indicate that changes in environmental variability (SST and Chl-a) affect the presence of prey species and their interactions.

Figure 5. Canonical discriminant analysis (CDA) showing the relationship between trophic variability and environmental variability in the cold and the warm phase of ENSO.

Discussion

Previous research indicates that among billfish species, the sport fishing fleet in CSL primarily captures striped marlin, with a rate of 0.6 fish per trip (Ortega-García et al., Reference Ortega-García, Klett-Traulsen and Ponce-Díaz2003), followed by blue marlin at 0.06 fish per trip (Ortega-García et al., Reference Ortega-García, Klett-Traulsen and Rodríguez-Sánchez2006). Outside of billfish, dolphinfish are the most frequently caught species in the CSL area, averaging 1.33 fish per trip (Zúñiga-Flores et al., Reference Zúñiga-Flores, Ortega-Garcia and Klett-Traulsen2008). The present study similarly observed a higher presence of striped marlin and dolphinfish compared to blue marlin. This might suggest these species have a greater tolerance to changes in SST and greater adaptability to changes in the environment compared to other large pelagic species (Shimose et al., Reference Shimose, Yokawa and Saito2010). The low number of samples obtained for blue marlin might be related to the marked seasonality in their abundance (Ortega-García et al., Reference Ortega-García, Klett-Traulsen and Rodríguez-Sánchez2006). However, the stomach samples in this study allowed us to identify trophic diversity and at least 85% of the diet of these three predators.

In both phases of the ENSO, most of the stomachs presented some food, which agrees with what was reported by Ortega-García et al. (Reference Ortega-García, Arizmendi-Rodríguez and Zúñiga-Flores2017), who describe that these organisms feed at dawn, coinciding with the time in which this fishery is carried out. Therefore, it is possible to find some prey in different states of digestion in the stomach since this is a continuous process (Abitia-Cárdenas et al., Reference Abitia-Cárdenas, Galván-Magaña and Muhlia-Melo1998).

Fish dominated the diet of striped marlin in both phases; however, at species level, jumbo squid Dosidicus gigas was the main prey in both periods. The abundance of jumbo squid in the diet of striped marlin suggests that this squid is relatively constant in the study area over time and that their contribution to the diet of this species varies according to their availability (Abitia-Cárdenas, Reference Abitia-Cárdenas1992; Abitia-Cárdenas et al., Reference Abitia-Cárdenas, Galván-Magaña and Muhlia-Melo1998, Reference Abitia-Cárdenas, Muhlia-Melo, Cruz-Escalona and Galván-Magaña2002; Shimose et al., Reference Shimose, Yokawa and Saito2010; Torres-Rojas et al., Reference Torres-Rojas, Hernandez Herrera, Ortega-García and Domeier2013 and Ortega-García et al., Reference Ortega-García, Arizmendi-Rodríguez and Zúñiga-Flores2017). Similarities were observed with previous studies, as striped marlin continued to feed over the same species through time.

Jumbo squid is an epipelagic species that occurs in areas where upwelling, convergence, or frontal events are common, such as the waters adjacent to Cabo San Lucas, where these predators are found. It can withstand a wide range of SST (16–30 °C), but prefers waters where SST ranges from 17 to 25 °C (Ehrhardt et al., Reference Ehrhardt, Jacquemin, González, Ulloa, García, Ortiz and Solís1982; Ortega-García et al., Reference Ortega-García, Arizmendi-Rodríguez and Zúñiga-Flores2017). The change in the abundance of jumbo squid during both phases could be because it is a species that presents tropical and subtropical affinity (Fischer et al., Reference Fischer, Krupp, Schneider, Sommer, Carpenter and Niem1995), which performs extensive seasonal migrations concerning temperature changes, so during the warm phase due to the lower primary productivity present in the study area its abundance decreased (Nevárez-Martínez et al., Reference Nevárez-Martínez, Hernández-Herrera, Morales-Bojórquez, Balmori-Ramírez, Cisneros-Mata and Morales-Azpeitia2000, Chávez et al., Reference Chavez, Pennington, Castro, Ryan, Michisaki, Schlining and Collins2002; González-Máynez et al., Reference González-Máynez, Nevárez-Martínez, Gallegos-García, Márquez-García and Rodríguez-Sobreyra2013).

The most common prey species for blue marlin throughout the years of study was Auxis spp. Tunas from this genus are mostly tropical species and their peak abundance in the study areas occurs in the boreal summer, when blue marlin is also more abundant. Further, Auxis spp. form large schools, which can facilitate its capture for the blue marlin and, consequently, is part of this species’ trophic spectrum during both ENSO phases (Abitia-Cárdenas, Reference Abitia-Cárdenas1992; Abitia-Cárdenas et al., Reference Abitia-Cárdenas, Galván-Magaña, Gutiérrez-Sánchez, Rodríguez-Romero, Aguilar-Palomino and Moehl-Hitz1999).

Oxyporhamphus micropterus was the main prey of dolphinfish during the cold phase. It is distributed between 1 and 6 m deep in the pelagic zone (Fischer et al., Reference Fischer, Krupp, Schneider, Sommer, Carpenter and Niem1995), where dolphinfish is also distributed. During the cold phase, the sea temperature (25.1 °C) may have limited the vertical distribution of the dolphinfish due to the isotherm limiting their distribution (Palko et al., Reference Palko, Beardsley and Richards1982), therefore relying more on available prey close to the surface. Although this species had been reported in the diet of dolphinfish in the study area, its contribution to the dolphinfish diet was usually less than 5% (Aguilar-Palomino et al., Reference Aguilar-Palomino, Galván-Magaña, Abitia-Cárdenas, Muhlia-Melo and Rodríguez-Romero1998 and Tripp-Valdez et al., Reference Tripp-Valdez, Galván-Magaña and Ortega-García2010), while it accounted for 20%PSIRI in our dataset.

During the warm phase, the red crab Pleuroncodes planipes was the main prey of dolphinfish. Its presence in the study area could reflect its high tolerance to changes in water temperature, being a highly abundant species throughout the year in the waters adjacent to the Baja California peninsula (Salinas-Zavala et al., Reference Salinas-Zavala, Bazzino-Ferreri, Camarillo-Coop, Rosas-Luis, Mejía-Rebollo and Ramos-Castillejos2010). It reaches its southernmost distribution during the spring and summer seasons off the coast of Cabo San Lucas; however, in warmer waters, it tends to have more dense aggregations, which facilitates its consumption by its predators (Stevenson, Reference Stevenson1970; Aurioles-Gamboa and Pérez-Flores, Reference Aurioles-Gamboa and Pérez-Flores1997 and De Anda-Montañez et al., Reference De Anda-Montañez, Martínez-Aguilar, Balart, Zenteno-Savín, Méndez-Rodríguez, Amador-Silva and Figueroa-Rodríguez2016). In addition, it has been reported that P. planipes is frequent and abundant in areas of coastal upwelling forced by winds since they are productive areas and can feed on phytoplankton and zooplankton transported by the California current (Vallarta-Zárate et al., Reference Vallarta-Zárate, Vásquez-Ortiz, Payán-Alejo and Rojas-González2023).

De Anda-Montañez et al. (Reference De Anda-Montañez, Martínez-Aguilar, Balart, Zenteno-Savín, Méndez-Rodríguez, Amador-Silva and Figueroa-Rodríguez2016) described that, under El Niño environmental conditions in 2004, the abundance of red crab was lower, and that their distribution was restricted to greater depths, thus being more susceptible to capture during the night. In contrast, under La Niña environmental conditions recorded at the end of 2005 and the first three months of 2006, its abundance was greater, and it had a broader distribution. This means that its distribution and abundance are associated with the oceanographic patterns of the area, which coincides with the results obtained in this study, showing a greater contribution to the trophic spectrum of dolphinfish during the warm phase. Recently, Vallarta-Zarate et al. (Reference Vallarta-Zárate, Vásquez-Ortiz, Payán-Alejo and Rojas-González2023) described the distribution and abundance of P. planipes on the western coast of Baja California. The authors reported that the most favourable conditions for red crab distribution and abundance occurred at temperatures greater than 16 °C.

Taxa such as Caranx caballus, Auxis spp., Lagocephalus lagocephalus, Balistes polylepis, and Selar crumenophthalmus form large schools (Fischer et al., Reference Fischer, Krupp, Schneider, Sommer, Carpenter and Niem1995), thus attracting higher predators. Therefore, they form part of the trophic spectrum of the three species analysed and reduce competition. Furthermore, it has been reported by authors such as Ortega-García et al. (Reference Ortega-García, Arizmendi-Rodríguez and Zúñiga-Flores2017) that these species have a wide range of tolerance to SST (23.4–30.6 °C), so it was possible to find them during both ENSO phases.

The seasonal fluctuations in the trophic positions of this predator reveal how climatic variations influence dietary preferences. During fish and squid dominance periods, the increased consumption of these prey with higher trophic positions also increases the trophic position of the predators. On the other hand, when crustaceans or fish with lower trophic positions are abundant, the predators could also exhibit lower trophic positions. Our results indicate that during both phases of ENSO, blue marlin and dolphinfish maintained a trophic position similar to that reported in other studies in the study area (Olson and Watters, Reference Olson and Watters2003; Torres-Rojas et al., Reference Torres-Rojas, Hernandez Herrera, Ortega-García and Domeier2013; Torres- Rojas et al., Reference Torres-Rojas, Hernandez Herrera, Ortega-García and Soto-Jiménez2014). For striped marlin, significant differences were found, possibly attributable to a broader tropic spectrum observed during both phases and the greater number of samples. With prey with high and low trophic position, a large variation in the observed values was generated. As an example of this, this predator feeds on the fish Mugil cephalus, which has a low trophic position (TP = 2.5) because it is an omnivorous species, while taxa such as Auxis spp which is also important prey taxa for striped marlin has a higher trophic position value (TP = 4.4) (Whitfield et al., Reference Whitfield, Panfili and Durand2012; Froese and Pauly, Reference Froese and Pauly2023). Currently, there is no evidence that these differences have ecological relevance beyond statistical significance; more research is required to understand the ecological effect of a decline in trophic positions on energy flows in pelagic food webs.

Very few studies include the analysis of trophic competition between these three species. Most only include trophic competition between billfishes or between dolphinfishes and other large pelagic predators (Abitia-Cárdenas, Reference Abitia-Cárdenas1992; Rudershausen et al., Reference Rudershausen, Buckel, Edwards, Gannon, Butler and Averett2010; Torres-Rojas et al., Reference Torres-Rojas, Hernandez Herrera, Ortega-García and Domeier2013; Loor-Andrade et al., Reference Loor-Andrade, Pincay-Espinoza, Carrera-Fernández and Rosas-Luis2017). In this study, seven prey taxa represented their trophic competition during the cold phase, and three during the warm phase. These results coincide with those reported by Richert et al. (Reference Richert, Galván-Magaña and Klimley2015) for the Gulf of California, who mentions that some of the prey shared by these predators are D. gigas, B. polylepsis, Auxis spp., and S. japonicus, which were all reported in this study, as well as the fish E. acuminatus, H. saltator, and C. caballus. Trophic competition was greater during the cold phase than during the warm phase, which could indicate that SST and food availability seem to influence the abundance and distribution of the species and their prey; however, the low number of prey shared during the warm phase could be biased by the low number of stomachs analysed for blue marlin, so these results should be taken with caution.

Nakamura (Reference Nakamura1985) and Ruíz-Pérez et al. (Reference Ruíz-Pérez, Cerdenares-Ladron de Guevara, López-Herrera and Altamirano-Ramírez2016) reported that although the large pelagic ichthyofauna usually share the same environment, they share the resources through differences in the feeding area, time of ingestion or the species that they predate, thus reducing competition. Hence, the division of habitats is important to understand the interactions of these species and to study their ecological differences.

Alterations in biological interactions can lead to changes in community structure and ecosystem functioning, as these changes occur throughout the food web (Hobday and Evans, Reference Hobday and Evans2013). Therefore, examining the responses of individual species to individual forcing factors, while essential, provides an incomplete story and highlights the need for more comprehensive, multi-species analyses at the ecosystem level (Doney et al., Reference Doney, Ruckelshaus, Emmett-Duffy, Barry, Chan, English, Galindo, Grebmeier, Hollowed, Knowlton, Polovina, Rabalais, Sydeman and Talley2012).

Information on changes in feeding habits due to climatic conditions could be useful for fisheries' management of these species, their prey, and other predators with which they share space and resources. This is because many nations depend on fisheries for food security and public revenue (Sinclair et al., Reference Sinclair, Arnason, Csirke, Karnicki, Sigurjonsson, Skjoldal and Valdimarsson2002), and research is needed to relate variations in the availability and abundance of prey and predator to changes in environmental variables such as SST and Chl-a.

Our results revealed that the three species studied play a very important role in the area and can be good indicators of the changes in the structure of the pelagic ecosystem's food web. The information on the variation in feeding habits due to climatic conditions could be useful for the fisheries management of these species and their prey, as well as other predators with which they share space and resources. It is necessary to carry out research that relates variations in the availability and abundance of prey and predators to changes in environmental variables such as SST and Chl-a concentration that helps us predict possible feeding changes or behaviours in the face of global warming.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S002531542400064X.

Data

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Acknowledgements

Amairani Hernández Aparicio acknowledges the grants by CONACYT (Consejo Nacional de Ciencia y Tecnología) and BEIFI-IPN (Beca de Estimulo Institucional de Formación de Investigadores) fellowship. Sofía Ortega-García and Rodrigo Moncayo-Estrada are granted COFAA (Comisión de Operación y Fomento de Actividades Académicas) fellowships. We want to acknowledge the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions.

Author contributions

AHA analysed the data, interpreted the findings, and wrote the article. SOG designed the study, collected fish samples, interpreted the findings, and wrote the article. ATV designed the study, interpreted the findings, and wrote the article. UJC interpreted the findings and wrote the article, RME interpreted the findings and reviewed the article, and SABH interpreted the findings and reviewed the article.

Financial support

This study was supported by Secretaria de Investigacion y Posgrado del Instituto Politecnico Nacional through different projects: SIP20100384, SIP20110725, SIP141071, and SIP20150861.

Competing interests

The authors declare no conflict of interest.

References

Abitia-Cárdenas, LA (1992) Espectro trófico energético del marlín rayado Tetrapturus audax (PhilippI, 1887) y marlín azul Makaira mazara (Jordan y Snider, 1901), del área de Cabo San Lucas, BCS, México (M.Sc. thesis). Instituto Politécnico Nacional. Centro Interdisciplinario de Ciencias Marinas. La Paz, México, 78 pp.Google Scholar
Abitia-Cárdenas, LA, Galván-Magaña, F and Muhlia-Melo, A (1998) Espectro trófico del marlín rayado Tetrapturus audax (Philippi, 1887) en el área de Cabo San Lucas, Baja California Sur, México. Revista de Biología Marina y Oceanografía 33, 277290.Google Scholar
Abitia-Cárdenas, LA, Galván-Magaña, F, Gutiérrez-Sánchez, FJ, Rodríguez-Romero, J, Aguilar-Palomino, B and Moehl-Hitz, A (1999) Diet of blue marlin Makaira mazara off the coast of Cabo San Lucas, Baja California Sur, Mexico. Fisheries Research 44, 95100.Google Scholar
Abitia-Cárdenas, LA, Muhlia-Melo, A, Cruz-Escalona, V and Galván-Magaña, F (2002) Trophic dynamics and seasonal energetics of striped marlin Tetrapturus audax in the southern Gulf of California, Mexico. Fisheries Research 57, 287295.Google Scholar
Acuña-Perales, N, Córdova-Zavaleta, F, Arrese-Dávila, V, Torrejón-Zegarra, R, Rodríguez-Salazar, J, González-Pestana, A and Mangel, JC (2020) Guía de identificación de picos de algunas especies de cefalópodos obtenidos en el Mar Peruano. Lima, Peru: ProDelphinus, 26.Google Scholar
Aguilar-Palomino, B, Galván-Magaña, F, Abitia-Cárdenas, LA, Muhlia-Melo, A and Rodríguez-Romero, J (1998) Aspectos alimentarios del dorado Coryphaena hippurus Linnaeus, 1758 en Cabo San Lucas, Baja California Sur, México. Ciencias Marinas 24, 253265.Google Scholar
Allen, GR and Robertson, DR (1994) Fishes of the Tropical Eastern Pacific. Honolulu, HI: University of Hawaii Press.Google Scholar
Amundsen, PA, Gabler, HM and Staldvik, FJ (1996) A new approach to graphical analysis of feeding strategy from stomach contents data—modification of the Costello (1990) method. Journal of Fish Biology 48, 607614.Google Scholar
Aurioles-Gamboa, D and Pérez-Flores, R (1997) Seasonal and bathymetric changes in feeding habits of the benthic red crab Pleuroncodes planipes (Decapoda, Anomura, Galatheidae) off the Pacific coast of Baja California Sur, Mexico. Crustaceana 70, 272287.Google Scholar
Barrera-García, AM (2008) Descripción osteológica para la identificación de peces de la familia Carangidae del Pacífico mexicano (D.Sc. thesis). Instituto Politécnico Nacional. Centro Interdisciplinario de Ciencias Marinas. La Paz, México.Google Scholar
Brown, SC, Bizzarro, JJ, Cailliet, GM and Ebert, DA (2012) Breaking with tradition: redefining measures for diet description with a case study of the Aleutian skate Bathyraja aleutica (Gilbert 1896). Environmental Biology of Fishes 95, 320.Google Scholar
Brusca, RC (1980) Common Intertidal Invertebrates of the Gulf of California, Vol. 2. Tucson: University of Arizona Press.Google Scholar
Chao, A, Gotelli, NJ, Hsieh, TC, Sander, EL, Ma, KH, Colwell, RK and Ellison, AM (2014) Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecological Monographs 84, 4567.Google Scholar
Chavez, FP, Pennington, JT, Castro, CG, Ryan, JP, Michisaki, RP, Schlining, B and Collins, CA (2002) Biological and chemical consequences of the 1997–1998 El Niño in central California waters. Progress in Oceanography 54, 205232.Google Scholar
Christensen, V and Pauly, D (1992) Ecopath II – A software for balancing steady-state ecosystem models and calculating network characteristics. Ecological Modelling 61, 169185.Google Scholar
Clarke, M (1962) The identification of cephalopod beaks and their relationship between beak size and total body weight. Bulletin of the British Museum (Natural History), Zoology 8, 422480.Google Scholar
Clarke, MR (1986) A Handbook for the Identification of Cephalopod Beaks. Oxford: Clarendon Press.Google Scholar
Clarke, KR (1993) Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology 18, 117143.Google Scholar
Clothier, CR (1950) A key to some southern California fishes based on vertebral characters. California Department of Fish and Game. Fisheries Bulletin 79, 183.Google Scholar
Cortés, E (1999) Standardized diet compositions and trophic levels of sharks. ICES Journal of Marine Science 56, 707717.Google Scholar
Costello, MJ (1990) Predator feeding strategy and prey importance: a new graphical analysis. Journal of Fish Biology 36, 261263.Google Scholar
De Anda-Montañez, JA, Martínez-Aguilar, S, Balart, EF, Zenteno-Savín, T, Méndez-Rodríguez, L, Amador-Silva, E and Figueroa-Rodríguez, M (2016) Spatio-temporal distribution and abundance patterns of red crab Pleuroncodes planipes related to ocean temperature from the Pacific coast of the Baja California Peninsula. Fisheries Science 82, 115.Google Scholar
Doney, SC, Ruckelshaus, M, Emmett-Duffy, J, Barry, JP, Chan, F, English, CA, Galindo, HM, Grebmeier, JM, Hollowed, AB, Knowlton, N, Polovina, J, Rabalais, NN, Sydeman, NJ and Talley, LD (2012) Climate change impacts on marine ecosystems. Annual Review of Marine Science 4, 1137.Google Scholar
Ehrhardt, NM, Jacquemin, PS, González, G, Ulloa, RP, García, F, Ortiz, CJ and Solís, A (1982) Descripción de la pesquería de calamar gigante Dosidicus gigas durante 1980 en el Golfo de California. Flota y poder de pesca. Ciencia Pesquera 3, 4160.Google Scholar
Field, J, Clarke, KR and Warwick, RM (1982) A practical strategy for analyzing multispecies distribution patterns. Marine Ecology Progress Series 8, 3752.Google Scholar
Fischer, W, Krupp, F, Schneider, W, Sommer, C, Carpenter, K and Niem, VH (1995) Guía FAO para La Identificación De Especies Para Los Fines De La Pesca. Pacífico Centro-Oriental. Volumen II & III, Vertebrados, parte 1 & 2, 8001651.Google Scholar
Froese, R and Pauly, D (2023) FishBase. World Wide Web electronic publication. Available at www.fishbase.org version (10/2023).Google Scholar
González-Máynez, VE, Nevárez-Martínez, MO, Gallegos-García, A, Márquez-García, E and Rodríguez-Sobreyra, R (2013) Abundancia del calamar gigante Dosidicus gigas y su relación con factores ambientales en el Golfo de California, México. Ciencia pesquera 21, 513.Google Scholar
Hansen, J, Sato, M, Ruedy, R, Lo, K, Lea, DW and Medina-Elizade, M (2006) Global temperature change. Proceedings of the National Academy of Sciences 103, 1428814293.Google Scholar
Hobday, AJ and Evans, K (2013) Detecting climate impacts with oceanic fish and fisheries data. Climatic Change 119, 4962.Google Scholar
Hsieh, TC, Ma, HK and Chao, A (2016) A Quick Introduction to iNEXT via Examples. Available at http://chao stat nthu edu tw/blog/software-download/ (accessed 228 2017).Google Scholar
Krebs, CJ (1989) Ecological Methodology. New York, USA: Harper & Row.Google Scholar
Lluch-Belda, D, Lluch-Cota, DB and Lluch-Cota, SE (2005) Changes in marine faunal distributions and ENSO events in the California Current. Fisheries Oceanography 14, 458467.Google Scholar
Loor-Andrade, P, Pincay-Espinoza, J, Carrera-Fernández, M and Rosas-Luis, R (2017) Feeding habits of billfishes (Carangaria: Istiophoriformes) in the Ecuadorian Pacific Ocean. Neotropical Ichthyology 15, e160162[1]e160162[8].Google Scholar
López-Martínez, J, Farach-Espinoza, EB, Herrera-Cervantes, H and García-Morales, R (2023) Long-term variability in sea surface temperature and chlorophyll a concentration in the Gulf of California. Remote Sensing 15, 4088.Google Scholar
McEwan, B, Emery, KF, Quitmyer, IR, Wing, ES and Scudder, S (2022) Pictorial Skeletal Atlas of Fishes. Environmental Archaeology at the Florida Museum of Natural History. Available at https://www.floridamuseum.ufl.edu/fishatlas/. Accessed online July 30, 2023.Google Scholar
Miller, DJ and Jorgensen, SC (1973) Meristic characters of some marine fishes of the western Atlantic Ocean. Fisheries Bulletin 71, 301312.Google Scholar
Moltó, V, Hernández, P, Sinopoli, M, Besbes-Benseddik, A, Besbes, R, Mariani, A, Gambin, M, Alemany, F, Morales-Nin, B, María-Grau, A, Camiñas, JA, Baez, JC, Vasconcellos, M, Ceriola, L and Catalán, IA (2020) A global review on the biology of the dolphinfish (Coryphaena hippurus) and its fishery in the Mediterranean Sea: advances in the last two decades. Reviews in Fisheries Science & Aquaculture 28, 376420.Google Scholar
Monod, T (1968) Le complexe urophore des poissons teleosteens. Memories de L'Intitute Fundamental D’ Affrique Noire, no. 81, 705.Google Scholar
Nakamura, I (1985) Billfishes of the world. An annotated and illustrated catalogue of marlins, sailfishes, spearfishes and swordfishes known to date. FAO Species Catalogue. FAO Fisheries Synopsis 5, 65.Google Scholar
Nevárez-Martínez, MO, Hernández-Herrera, A, Morales-Bojórquez, E, Balmori-Ramírez, A, Cisneros-Mata, MA and Morales-Azpeitia, R (2000) Biomass and distribution of the jumbo squid (Dosidicus gigas; d'Orbigny, 1835) in the Gulf of California, Mexico. Fisheries Research 49, 129140.Google Scholar
Olson, RJ and Watters, GM (2003) A model of the pelagic ecosystem in the eastern tropical Pacific Ocean. Inter-American Tropical Tuna Commission 22, 133218.Google Scholar
Ortega-García, S, Klett-Traulsen, A and Ponce-Díaz, G (2003) Analysis of sportfishing catch rates of striped marlin (Tetrapturus audax) at Cabo San Lucas, Baja California Sur., Mexico, and their relation to sea surface temperature. Marine and Freshwater Research 54, 483488.Google Scholar
Ortega-García, S, Klett-Traulsen, A and Rodríguez-Sánchez, R (2006) Some biological aspects of blue marlin (Makaira nigricans) in the recreational fishery at Cabo San Lucas, Baja California Sur, Mexico. Bulletin Of Marine Science 79, 739746.Google Scholar
Ortega-García, S, Arizmendi-Rodríguez, D and Zúñiga-Flores, MS (2017) Striped marlin (Kajikia audax) diet variability off Cabo San Lucas, B.C.S., México during El Niño–La Niña events. Journal of the Marine Biological Association of the United Kingdom 98, 15131524.Google Scholar
Palko, BJ, Beardsley, GL and Richards, WJ (1982) Synopsis of the biological data on dolphinfishes, Coryphaena hippurus Linnaeus and Coryphaena equiselis Linnaeus. U.S. Department of Commerce, NOAA Technical Memorandum NMFS Circular 443, 128.Google Scholar
Petrik, CM, Stock, CA, Andersen, KH, Van Denderen, PD and Watson, JR (2020) Large pelagic fish are most sensitive to climate change despite pelagification of ocean food webs. Frontiers in Marine Science 7, 1023.Google Scholar
Richert, JE, Galván-Magaña, F and Klimley, AP (2015) Interpreting nitrogen stable isotopes in the study of migratory fishes in marine ecosystems. Marine Biology 162, 10991110.Google Scholar
Rudershausen, PJ, Buckel, JA, Edwards, J, Gannon, DP, Butler, CM and Averett, TW (2010) Feeding ecology of blue marlins, dolphinfish, yellowfin tuna, and wahoos from the North Atlantic Ocean and comparisons with other oceans. Transactions of the American Fisheries Society 139, 13351359.Google Scholar
Ruíz-Pérez, NE, Cerdenares-Ladron de Guevara, G, López-Herrera, DL and Altamirano-Ramírez, IR (2016) Trophic interactions between five pelagic fish species cohabiting in the coast of Oaxaca. Mexico. Hydrobiological 26, 7785.Google Scholar
Salinas-Zavala, CA, Bazzino-Ferreri, G, Camarillo-Coop, S, Rosas-Luis, R, Mejía-Rebollo, A and Ramos-Castillejos, JE (2010) El calamar gigante Dosidicus gigas (DOrbigny 1835). Dinámica del ecosistema Pelágico frente a Baja California 1997, 469486.Google Scholar
Shimose, T, Yokawa, K and Saito, H (2010) Habitat and food partitioning of billfishes (Xiphioidei). Journal of Fish Biology 76, 24182433.Google Scholar
Sinclair, M, Arnason, R, Csirke, J, Karnicki, Z, Sigurjonsson, J, Skjoldal, HR and Valdimarsson, G (2002) Responsible fisheries in the marine ecosystem. Fisheries Research 58, 255265.Google Scholar
Stevenson, MR (1970) On the physical and biological oceanography near the entrance of the Gulf of California, October 1966-August 1967.Google Scholar
Su, NJ, Sun, CL, Punt, AE and Yeh, SZ (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, 432445.Google Scholar
Tercerie, S, Bearez, P, Pruvost, P, Bailly, N and Vignes-Lebbe, R (2022) Osteobase. World Wide Web electronic publication. osteobase.mnhn.fr, version may 2022.Google Scholar
Timmermann, A, Oberhuber, J, Bacher, A, Esch, M, Latif, M and Roeckner, E (1999) Increased El Niño frequency in a climate model forced by future greenhouse warming. Nature 398, 694697.Google Scholar
Torres-Rojas, Y, Hernandez Herrera, A, Ortega-García, S and Domeier, M (2013) Stable isotope differences between blue marlin (Makaira nigricans) and striped marlin (Kajikia audax) in the southern Gulf of California, Mexico. Bulletin of Marine Science 89, 421436.Google Scholar
Torres-Rojas, Y, Hernandez Herrera, A, Ortega-García, S and Soto-Jiménez, MF (2014) Feeding habits variability and trophic position of dolphinfish in waters south of the Baja California Peninsula, Mexico. Transactions of the American Fisheries Society 143, 528542.Google Scholar
Tripp-Valdez, A, Galván-Magaña, F and Ortega-García, S (2010) Feeding habits of dolphinfish (Coryphaena hippurus) in the southeastern Gulf of California, Mexico. Journal of Applied Ichthyology 26, 578582.Google Scholar
Vallarta-Zárate, JRF, Vásquez-Ortiz, M, Payán-Alejo, J and Rojas-González, RI (2023) Patrones de distribución de la langostilla (Pleuroncodes planipes) en la costa occidental de la península de Baja California, México. Revista Mexicana de Biodiversidad 94, e944862[1]e944862[14].Google Scholar
Whitfield, AK, Panfili, J and Durand, JD (2012) A global review of the cosmopolitan flathead mullet Mugil cephalus Linnaeus 1758 (Teleostei: Mugilidae), with emphasis on the biology, genetics, ecology and fisheries aspects of this apparent species complex. Reviews in Fish Biology and Fisheries 22, 641681.Google Scholar
Wolff, CA (1984) Identification and estimation of size from the beaks of eighteen species of cephalopods from the Pacific Ocean. NOAA Technical Report. NMFS 17, 50.Google Scholar
Zúñiga-Flores, MS, Ortega-Garcia, S and Klett-Traulsen, A (2008) Interannual and seasonal variation of dolphinfish (Coryphaena hippurus) catch rates in the southern Gulf of California, Mexico. Fisheries Research 94, 1317.Google Scholar
Figure 0

Figure 1. Study area: Cabo San Lucas (CSL), the one where the sport fishing fleet operates.

Figure 1

Table 1. Summary of the total number of organisms per species (N), length range and stomachs with food and empty stomachs during the cold and warm phases of ENSO in the adjacent waters to Cabo San Lucas, Baja California Sur

Figure 2

Figure 2. Interpolation and extrapolation curves to determine trophic diversity and represented diet percentage of striped marlin Kajikia audax (A), blue marlin Makaira nigricans (B) and dolphinfish Coryphaena hippurus (C) during cold (1) and warm (2) phase of ENSO in adjacent waters to Cabo San Lucas, Baja California Sur.

Figure 3

Table 2. Summary of the diet composition of striped marlin Kajikia audax, blue marlin Makaira nigricans and dolphinfish Coryphaena hippurus during cold phase of ENSO in adjacent waters to Cabo San Lucas, Baja California Sur, expressed by percentages frequency of occurrence (%FO), percent prey-specific number (%PN), percent number (%N), percent prey-specific weight (%PW), percent weight (%W) and prey-specific index of relative importance (%PSIRI)

Figure 4

Table 3. Summary of the diet composition of striped marlin Kajikia audax, blue marlin Makaira nigricans and dolphinfish Coryphaena hippurus during warm phase of ENSO in adjacent waters to Cabo San Lucas, Baja California Sur, expressed by percentages frequency of occurrence (%FO), percent prey-specific number (%PN), percent number (%N), percent prey-specific weight (%PW), percent weight (%W) and prey-specific index of relative importance (%PSIRI)

Figure 5

Table 4. Mean trophic position, standard error, median and sample size for each phase

Figure 6

Figure 3. Trophic interactions among prey shared by striped marlin (Kajikia audax), blue marlin (Makaira nigricans) and dolphinfish (Coryphaena hippurus) during the (A) cold phase and (B) warm phase of ENSO.

Figure 7

Figure 4. Non-metric multidimensional scaling (nMDS) to represent trophic overlap in the cold phase (A) and the warm phase (B) of ENSO.

Figure 8

Figure 5. Canonical discriminant analysis (CDA) showing the relationship between trophic variability and environmental variability in the cold and the warm phase of ENSO.

Supplementary material: File

Hernández-Aparicio et al. supplementary material

Hernández-Aparicio et al. supplementary material
Download Hernández-Aparicio et al. supplementary material(File)
File 15.1 KB