Tropical Tuna Purse Seine Fisheries Research Articles

Modeling bycatch abundance in tropical tuna purse seine fisheries on floating objects using the Δ method

Abstract Bycatch rates are essential to estimating fishery impacts and making management decisions, but data on bycatch are often limited. Tropical tuna purse seine (PS) fisheries catch numerous bycatch species, including vulnerable silky sharks. Even if bycatch proportion is relatively low, impacts on pelagic ecosystems may be important due to the large size of these fisheries. Partial observer coverage of bycatch is a major impediment to assessing impacts. Here we develop a generic Δ modeling approach for predicting catch of four major bycatch species, including silky sharks, in floating object-associated fishing sets of the French Indian Ocean PS fleet from 2011 to 2018 based on logbook and observer data. Cross-validation and variable selection are used to identify optimal models consisting of a random forest model for presence–absence and a negative binomial general-additive model for abundance when present. Though models explain small to moderate amounts of variance (5–15%), they outperform a simpler approach commonly used for reporting, and they allow us to estimate total annual bycatch for the four species with robust estimates of uncertainty. Interestingly, uncertainty relative to mean catch is lower for top predators than forage species, consistent with these species having similar behavior and ecological niches to tunas.

Large floating abandoned, lost or discarded fishing gear (ALDFG) is frequent marine pollution in the Hawaiian Islands and Palmyra Atoll

Abandoned, lost, or discarded fishing gear (ALDFG) is a major source of marine debris with significant ecological and economic consequences. We documented the frequency, types, sizes, and impacts of ALDFG recovered from Hawaiʻi and Palmyra Atoll in the Central North Pacific Ocean (CNPO) from 2009 to 2021. A total of 253 events weighing 15 metric tons were recovered, including 120 drifting fish aggregating device (dFAD) components, 61 conglomerates, fewer distinct nets, lines, buoys, and unique gear. The Hawaiian Islands were dominated by conglomerates and Palmyra Atoll by dFADs. DFADs were connected to the Eastern Pacific tropical tuna purse seine fishery. Windward O'ahu experienced up to seven events or 1800 kg of ALDFG per month. Across Hawaiʻ, ALDFG was present on 55 % of survey days, including hotspots with 100 % occurrence. Coral reef damage, entangled wildlife, navigational and removal costs are reported. The data highlight the large magnitude of ALDFG and associated impacts in the CNPO.

Benefits, concerns, and solutions of fishing for tunas with drifting fish aggregation devices

AbstractDrifting fish aggregating devices (dFADs) are human‐made floating objects widely used by tropical tuna purse seine (PS) fisheries to increase catch of target species. However, dFAD use has several negative impacts, including increased potential for overfishing, higher juvenile tuna catch, higher bycatch compared to other PS fishing modes, ghost‐fishing, and generation of marine litter. Based on these impacts, some stakeholders, especially environmental non‐governmental organizations and other competing fishing industries, suggest that dFADs should be completely banned. We list the pros and cons of dFAD fishing; address how to improve current management; and suggest solutions for the sustainability of dFAD fishing in the long term. A dFAD ban would lead to major changes in the availability and sourcing of tuna for human consumption and decrease the licensing revenue received by many developing states. Most importantly, we argue that tools exist today to manage for, reduce or eliminate most of the negative impacts of dFADs (e.g., bans on discards, limits on active dFADs, biodegradable non‐entangling constructions, time‐area deployment closures, recovery programs, and full data transparency, among others). Management decisions based on sound scientific reasoning are needed to address the legitimate concerns surrounding dFAD use and ensure the sustainability of both pelagic and coastal ecosystems and tropical tuna PS fisheries.

An automatic purse-seine set type classification algorithm to inform tropical tuna management

Management of large-scale tuna purse-seine fisheries often involves consideration of set type because the different set types yield different target species and bycatch species compositions, and may therefore impact those populations differently. Using the ensemble method random forests for classification, annual set type classification algorithms were developed based on a suite of operational characteristics, and catch and bycatch information, collected by onboard observers for the tropical tuna purse-seine fishery in the eastern Pacific Ocean (EPO) during 2010–2019. Two types of algorithms were built for each year: 1) a 2-class algorithm for sets on unassociated tuna schools (NOA) and sets on floating-object-associated tuna (OBJ); and, 2) a 3-class algorithm for all three types of sets that occur in the EPO, which includes sets on marine mammal-associated tuna (DEL). Results indicated that such data can be used to reliably distinguish between purse-seine sets types, highlighting inherent operational and ecological differences among them in the EPO. For the 2-class algorithms, the average misclassification error rates were 3.3% for NOA sets and 4.5% for OBJ sets. For the 3-class algorithms, the average misclassification error rates were 2% for DEL sets, 10.6% for NOA sets and 4.6% for OBJ sets. Comparison of results for NOA sets from the 2-class and 3-class algorithms suggests that the higher NOA-set misclassification error rate for the 3-class algorithm is due to a difficulty in distinguishing NOA sets from DEL sets with the predictors used in this analysis. The most useful bycatch information for predicting set type included amounts of dorado, wahoo, small fish species, such as triggerfishes, and silky sharks, illustrating the importance of bycatch data collection by observers, even for species of no commercial value. In the case of the 2-class algorithms, the annual misclassification error rates for NOA and OBJ sets were not only small, but also fairly stable over the 10-year period, even in the presence of the strong 2015 – 2016 El Niño event. This suggests that the methodology is robust and could be used to validate set type determinations based on other criteria, such as distance to a floating object (or a fish aggregating device), and to verify data quality, more generally.

Simulations of drifting fish aggregating device (dFAD) trajectories in the Atlantic and Indian Oceans

Tropical tuna purse-seine fisheries deploy thousands of human-made drifting fish aggregating devices (dFADs) annually, raising a number of concerns regarding ecosystem impacts. In this study, we explored the use of a Lagrangian particle-tracking model to simulate the drift of dFADs in the Atlantic and Indian Oceans. We simulated more than 100,000 dFADs trajectories using the Lagrangian tool Ichthyop forced with velocity fields from an ocean model output (GLORYS12V1) and two satellite-derived ocean currents products (OSCAR and GEKCO). Importantly, through a collaborative agreement with the French frozen tuna producers’ organization we had access to the true locations of all dFADs along their drift and could therefore evaluate the accuracy of our simulations. The accuracy was assessed by comparing the observed and simulated trajectories in terms of spatial distribution, separation distance, and a non-dimensional skill score (an index based on separation distances normalized by net displacements of dFADs). In the two oceans, simulations forced with GLORYS12V1 were more accurate than with OSCAR and GEKCO, probably due to the differences in the spatio-temporal resolution of the forcing products. When we compared multiple depths for GLORYS12V1, the model performed better at 0 m in the Indian Ocean and at 5 m in the Atlantic Ocean, which could be related to the longer vertical structure of dFADs in the Atlantic Ocean. We showed that including a windage factor did not improve the accuracy of modeled dFADs trajectories. We found that mean model-data separation distances were similar in both oceans, exceeding 100 km after 6–8 days of drift. While separation distances between simulated and observed trajectories show that model errors were similar in the two oceans, the generally longer distances traveled by dFADs in the Indian Ocean than in the Atlantic Ocean lead to considerably higher skill scores in the former than in the latter. This explains the relatively good predictive ability of the model to represent mean dFAD densities at the basin scale in both oceans, while at the same time indicates higher prediction skills for the movements of individual dFADs in the Indian Ocean than in the Atlantic Ocean.

Multi‐species hotspots detection using self‐organizing maps: Simulation and application to purse seine tuna fisheries management

Abstract To fulfil fisheries management objectives that often include implementing the precautionary and ecosystem‐based approaches, multispecies fisheries data need to be analysed. Amongst the different methods dealing with these multidimensional data, self‐organizing maps (SOMs) remain rarely used, although they are highly flexible in data input and offer visualization possibilities particularly suited to summarize complex datasets. Here, we propose to combine SOMs with a clustering approach to break down data complexity and produce simple geographic maps showing catch hotspots, which can indicate sensitive zones in terms of fishery management. To promote this approach, we tested it first on simulated datasets and then on the open‐access ICCAT commercial catch database of the tropical tuna fisheries of the Atlantic Ocean. We aimed to detect drifting fish aggregating devices (dFADs) catch hotspots of juveniles of two tuna species, bigeye and yellowfin tunas and of the silky shark, a commonly bycaught vulnerable shark species, in tropical tuna purse seine fisheries. Simulations on datasets increasing in complexity (in number, geographic and duration extent of the hotspots and number of species in the analysis) informed us about the method's sensitivity and limits. Our findings showed that, in the context of multi‐specific fisheries, the detection of the hotspot is dependent on a certain level of catch within the hotspot and that adding species to the analysis tended to mask small and short‐duration hotspots. Applied to tropical tuna fisheries' data, the method confirmed the empirical knowledge on which first time‐area closures were based and provided scientifical support. All in all, the visual support provided by the method, its interpretability and its potential transferability to other fisheries' systems constitute its main strengths and imply a possible implementation in management decisions; specifically, as a tool to reach agreement between stakeholders in the definition of regulated areas for protecting juveniles of tunas and vulnerable associated species to dFAD practices.

Tunabio: biological traits of tropical tuna and bycatch species caught by purse seine fisheries in the Western Indian and Eastern Central Atlantic Oceans.

Along with the development of the tropical tuna purse-seine fishery from the 1960s in the Atlantic Ocean and from the 1980s in the Indian Ocean, many projects and studies have been conducted to improve knowledge about the biology, migrations and dynamics of the stocks of target and non-target (i.e. bycatch) species taken in these fisheries. Since the 2000s, the European Union (EU) has been supporting Member States in the collection of biological data on species caught by their purse seine and pole and line fisheries, thus making it possible to have a long-term series of data. Biological data have never been saved by the different tuna commissions, unlike the catches by species and sizes by areas and periods. However, these data are essential to monitor the status of the fisheries and fuel the assessment models used by the tuna Regional Fisheries Management Organisations (tRFMOs) for the sustainable management and conservation of the fish stocks under their mandate. We combined historical (1974-1999) and current (2003-2020) datasets on the biology of tropical tunas and bycatch fish caught by large-scale purse seiners in the Eastern Atlantic Ocean (EAO) and Western Indian Ocean (WIO). The resulting Tunabio database is presented in the present data paper and contains all available morphometric and biological data collected on more than 80,000 fish individuals.

Record of three specimens of megamouth sharks – Megachasma pelagios – in tropical tuna purse seine fisheries in the Atlantic and Indian Oceans

AbstractThe observer programme onboard the Spanish tropical tuna purse seine fleet recorded the incidental catch of one adult megamouth shark Megachasma pelagios in December 2005 and two juvenile megamouth sharks in the eastern Atlantic in July 2016 and August 2018, respectively. The same fleet also bycaught an adult individual in December 2005 in the western Indian Ocean. The juveniles were caught relatively near to the coast, while the adult was caught in oceanic waters. The companion species in the fishing sets were elasmobranchs, tuna and billfish.

Identification and measurement of tropical tuna species in purse seiner catches using computer vision and deep learning

Fishery monitoring programs are essential for effective management of marine resources, as they provide scientists and managers with the necessary data for both the preparation of scientific advice and fisheries control and surveillance. The monitoring is generally done by human observers, both in port and onboard, with a high cost involved. Consequently, some Regional Fisheries Management Organizations (RFMO) are opting for electronic monitoring (EM) as an alternative or complement to human observers in certain fisheries. This is the case of the tropical tuna purse seine fishery operating in the Indian and Atlantic oceans, which started an EM program on a voluntary basis in 2017. However, even when the monitoring is conducted though EM, the image analysis is a tedious task manually performed by experts. In this paper, we propose a cost-effective methodology for the automatic processing of the images already being collected by cameras onboard tropical tuna purse seiners. Firstly, the images are preprocessed to homogenize them across all vessels and facilitate subsequent steps. Secondly, the fish are individually segmented using a deep neural network (Mask R-CNN). Then, all segments are passed through other deep neural network (ResNet50V2) to classify them by species and estimate their size distribution. For the classification of fish, we achieved an accuracy for all species of over 70%, i.e., about 3 out of 4 individuals are correctly classified to their corresponding species. The size distribution estimates are aligned with official port measurements but calculated using a larger number of individuals. Finally, we also propose improvements to the current image capture systems which can facilitate the work of the proposed automation methodology.

Modelling seasonal environmental preferences of tropical tuna purse seine fisheries in the Mozambique Channel

The spatial-temporal environmental preferences and biomass aggregation of tropical tuna from purse seine fishery in the Mozambique Channel (MZC) have barely been investigated. In this study, tuna biomass volume from Fish Aggregating Devices (FADs) and Free-Swimming Schools (FSC), collected by Spanish fishing logbooks during 2003–2013, were modelled separately as a function of a set of oceanographic variables (sea surface temperature, sea surface height, geostrophic currents, salinity, and chlorophyll-a) using Generalized Additive Models (GAMs). Temporal variables (natural day, month and year), and spatial variables (latitude and longitude) were included in the models to account for the spatio-temporal structure of dynamic biomass of tropical tuna volume gathering. Oceanographic, temporal and spatial effects on aggregated catches differed between fishing modes, even though some common aspects appeared along the area and the period of study. Fishable patches of tuna biomass accumulation were explained by sea surface temperature, productivity, sea surface height, geostrophic currents, and apart from the spatio-temporal variables interactions. Although the models predicted slight differences for tuna fishing spots preferences, both fishing modes partially overlapped. Goodness of fit for selected variables showed that models were able to predict tuna catches assembled patterns in the MZC reasonably well. These results highlight a connection between the biophysical state of the oceans and purse seine tuna catches in the MZC, and ultimately may contribute to the scientific advice for the appropriate management and conservation of the exploited resources by purse seine fleets in the area of MZC.

Quantifying drifting Fish Aggregating Device use by the world's largest tuna fishery

Abstract Drifting Fish Aggregating Devices (dFADs) are a major fishing mode for tropical tuna purse seine fisheries worldwide. However, the extent of dFAD use remains poorly understood. We present novel approaches for estimating annual dFAD deployments and number of dFADs monitored by individual vessels, using empirical data and robust estimation procedures. We leveraged observer and logbook data, combined with new dFAD tracking data from the Western and Central Pacific Ocean (WCPO) purse seine fishery, the largest tuna fishery in the world, to evaluate trends in dFAD use across the entire WCPO between 2011 and 2019. Average estimates ranged between 20 000 and 40 000 deployments per year, depending on the methodology, with the total number of deployments appearing relatively stable over the last decade. The median number of active buoys monitored per vessel per day ranged from 45 to 75 depending on the year, well below the current management limit of 350. Our results contrast with other oceans, having fewer buoys monitored per vessel, a unique stable trend, but overall number of deployments two times higher than any other ocean. This study provides a basis for improved monitoring and management of dFAD use in the WCPO, with applicability for other regions.

Machine learning to detect bycatch risk: Novel application to echosounder buoys data in tuna purse seine fisheries

The advent of big data and machine learning offers great promise for addressing conservation and management questions in the oceans. Yet, few applications of machine learning exist to mitigate the overexploitation of marine resources. Tropical tuna purse seine fisheries (TTPSF) are distributed worldwide and account for two thirds of the global tuna catch. In these fisheries, the use of Drifting Fish Aggregating Devices (DFADs)— n-made floating objects massively deployed by fishers to increase their tuna catches—results in the incidental catch of non-target species, termed bycatch. We explored the possibility of applying machine learning on echosounder buoys attached to DFADs, representing an unprecedented source of big data, for identifying high bycatch risk at DFADs. We trained random forests algorithms to differentiate between high and low bycatch occurrence based on matched echosounder and onboard observer data for the same DFADs (representing sample sizes of 838 and 2144 in the Atlantic and the Indian Ocean, respectively). Algorithms showed a better performance in the Atlantic Ocean (accuracy of 0.66 versus 0.58 in the Indian Ocean) and were best at detecting the “high bycatch” occurrence class. These results unravel the potential of machine learning applied to fishers' buoys data for bycatch reduction and improved selectivity in one of the largest fisheries worldwide.

Spatial management can significantly reduce dFAD beachings in Indian and Atlantic Ocean tropical tuna purse seine fisheries

Debris from fisheries pose significant threats to coastal marine ecosystems worldwide. Tropical tuna purse seine fisheries contribute to this problem via the construction and deployment of thousands of human-made drifting fish aggregating devices (dFADs) annually, many of which end up beaching in coastal areas. Here, we analyzed approximately 40,000 dFAD trajectories in the Indian Ocean and 12,000 dFAD trajectories in the Atlantic Ocean deployed over the decade 2008–2017 to identify where and when beachings occur. We find that there is tremendous promise for reducing beaching events by prohibiting deployments in areas most likely to lead to a beaching. For example, our results indicate that 21% to 40% (depending on effort redistribution after closure) of beachings can be prevented if deployments are prohibited in areas in the south of 8°S latitude, the Somali zone in winter, and the western Maldives in summer for the Indian Ocean, and in an elongated strip of areas adjacent to the western African coast for the Atlantic Ocean. In both oceans, the riskiest areas for beaching are not coincident with areas of high dFAD deployment activity, suggesting that these closures could be implemented with relatively minimal impact to fisheries. Furthermore, the existence of clear hotspots for beaching likelihood and the high rates of putative recovery of dFAD buoys by small-scale fishers in some areas suggests that early warning systems and dFAD recovery programs may be effective in areas that cannot be protected via closures if appropriate incentives can be provided to local partners for participating in these programs.

Quantifying the increase in fishing efficiency due to the use of drifting FADs equipped with echosounders in tropical tuna purse seine fisheries

Abstract Numerous pelagic species are known to associate with floating objects (FOBs), including tropical tunas. Purse seiners use this behaviour to facilitate the capture of tropical tunas by deploying artificial drifting fish aggregating devices (dFADs). One major recent change has been the integration of echosounders in satellite-tracked GPS buoys attached to FOBs, allowing fishers to remotely estimate fishable biomass. Understanding the effects of this new technology on catch of the three main tuna species (yellowfin tuna, Thunnus albacares; bigeye tuna, Thunnus obesus; and skipjack tuna, Katsuwonus pelamis) is important to accurately correct for this change in catch-per-unit-effort (CPUE) indices used for stock assessments. We analysed catch data from the French purse seine fleet for the period 2010–2017 in the Indian Ocean to assess the impact of this fleet’s switch to echosounder buoys around 2012. Results indicate that echosounders do not increase the probability a set will be succesful, but they have a positive effect on catch per set, with catches on average increasing by ≈2−2.5 tonnes per set (≈10%) when made on the vessel's own dFADs equipped with an echosounder buoy. Increases were due to a decrease in sets below ≈25 tonnes and an increase in those greater than ≈25 tonnes, with a non-linear transition around this threshold. This increase explains the considerable investment of purse seiners in echosounder buoys, but also raises concerns about bias in stock size estimates based on CPUE if we do not correct for this fishing efficiency increase.

Predicting bycatch hotspots in tropical tuna purse seine fisheries at the basin scale

Fisheries observer programs represent the most reliable way to collect data on fisheries bycatch. However, their limited coverage leads to important data gaps that preclude bycatch mitigation at the basin scale. Habitat models developed from available fisheries observer programs offer a potential solution to fill these gaps. We focus on tropical tuna purse seine fisheries (TTPSF) that span across the tropics and extensively rely on floating objects (FOBs) for catching tuna schools, leading to the bycatch of other species associated with these objects. Bycatch under floating objects is dominated by five species, including the vulnerable silky shark Carcharhinus falciformis and four bony fishes (oceanic triggerfish Canthidermis maculata, rainbow runner Elagatis bipinnulata, wahoo Acanthocybium solandri, and dolphinfish Coryphaena hippurus). Our objective was to predict possible bycatch hotspots associated with FOBs for these five species across two tropical oceans. We used bycatch data collected from observer programs onboard purse seiners in the Atlantic and Indian oceans. We developed a generalized additive model per species and per ocean relating bycatch to a set of environmental covariates (depth, chlorophyll-a concentration, sea surface temperature, mixed layer depth, surface salinity, total kinetic energy and the density of floating objects) and temporal covariates (year and month). We extrapolated modeled relationships across each ocean within the range of environmental covariates associated with the bycatch data and derived quarterly predictions. We then detected bycatch hotspots as the 90th percentiles of predictions. In the Atlantic Ocean, bycatch hotspots were predicted throughout tropical and subtropical waters with little overlap between species. By contrast in the Indian Ocean, major overlapping hotspots were predicted in the Arabian Sea throughout most of the year for four species, including the silky shark. Our modeling approach provides a new analytical way to fill data gaps in fisheries bycatch. Even with the lack of evaluation inherent to extrapolations, our modeling effort represents the first step to assist bycatch mitigation in TTPSF and is applicable beyond these fisheries.

Using a Bayesian modelling approach (INLA-SPDE) to predict the occurrence of the Spinetail Devil Ray (Mobular mobular)

To protect the most vulnerable marine species it is essential to have an understanding of their spatiotemporal distributions. In recent decades, Bayesian statistics have been successfully used to quantify uncertainty surrounding identified areas of interest for bycatch species. However, conventional simulation-based approaches are often computationally intensive. To address this issue, in this study, an alternative Bayesian approach (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation, INLA-SPDE) is used to predict the occurrence of Mobula mobular species in the eastern Pacific Ocean (EPO). Specifically, a Generalized Additive Model is implemented to analyze data from the Inter-American Tropical Tuna Commission’s (IATTC) tropical tuna purse-seine fishery observer bycatch database (2005–2015). The INLA-SPDE approach had the potential to predict both the areas of importance in the EPO, that are already known for this species, and the more marginal hotspots, such as the Gulf of California and the Equatorial area which are not identified using other habitat models. Some drawbacks were identified with the INLA-SPDE database, including the difficulties of dealing with categorical variables and triangulating effectively to analyze spatial data. Despite these challenges, we conclude that INLA approach method is an useful complementary and/or alternative approach to traditional ones when modeling bycatch data to inform accurately management decisions.

Elasmobranch bycatch distributions and mortality: Insights from the European tropical tuna purse-seine fishery

Despite bycatch of elasmobranch (sharks and rays) being a major concern in most fisheries worldwide, there is a lack of knowledge on their spatio-temporal species distribution, biology (life stage and sex-ratios), as well as their at-haulback mortality rate. Observer data from the French and Spanish tropical purse-seine tuna fisheries operating in the eastern Atlantic and western Indian Oceans between 2005 and 2017 were analysed to investigate elasmobranch bycatch. Data included 24 elasmobranchs species and distribution patterns of catch per unit of effort (CPUE) by species and sex-ratio were found to vary with life stages, areas, seasons and fishing modes. In general, higher catches were found in FAD-associated sets (>40%) than free tuna school sets (<17%) although this can vary depending on the species. For the large majority of species, a high proportion of juveniles were caught (30.7–100%), apparent at-haulback mortality rates was high (24.3–63.9%) and finally sex ratios was unbalanced (13.3–66.7% of females). Areas and seasons identified from these different components should be of interest for the monitoring and management of elasmobranch bycatches.

Machine learning for characterizing tropical tuna aggregations under Drifting Fish Aggregating Devices (DFADs) from commercial echosounder buoys data

The use of echosounder buoys deployed in conjunction with Drifting Fish Aggregating Devices (DFADs) has progressively increased in the tropical tuna purse seine fishery since 2010 as a means of improving fishing efficiency. Given the broad distribution of DFADs, the acoustic data provided by echosounder buoys can provide an alternative to the conventional CPUE index for deriving trends on tropical tuna stocks. This study aims to derive reliable indices of presence of tunas (and abundance) using echosounder buoy data. A novel methodology is presented which utilizes random forest classification to translate the acoustic backscatter from the buoys into metrics of tuna presence and abundance. Training datasets were constructed by cross-referencing acoustic data with logbook and observer data which reported activities on DFADs (tuna catches, new deployments and visits of DFADs) in the Atlantic and Indian Oceans from 2013 to 2018. The analysis showed accuracies of 75 and 85 % for the recognition of the presence/absence of tuna aggregations under DFADs in the Atlantic and Indian Oceans, respectively. The acoustic data recorded at ocean-specific depths (6–45 m in the Atlantic and 30–150 m in the Indian Ocean) and periods (4 a.m.–4 p.m.) were identified by the algorithm as the most important explanatory variables for detecting the presence of tuna. The classification of size categories of tuna aggregations showed a global accuracy of nearly 50 % for both oceans. This study constitutes a milestone towards the use of echosounder buoys data for scientific purposes, including the development of promising fisheries-independent indices of abundance for tropical tunas.

Drifting along in the open-ocean: The associative behaviour of oceanic triggerfish and rainbow runner with floating objects

Multispecies aggregations at floating objects are a common feature throughout the world's tropical and subtropical oceans. The evolutionary benefits driving this associative behaviour of pelagic fish remains unclear and information on the associative behaviour of non-tuna species remains scarce. This study investigated the associative behaviour of oceanic triggerfish (Canthidermis maculata) and rainbow runner (Elagatis bipinnulata), two major bycatch species in the tropical tuna purse seine fishery, at floating objects in the western Indian Ocean. A total of 24 rainbow runner and 46 oceanic triggerfish were tagged with acoustic transmitters at nine drifting FADs equipped with satellite linked receivers. Both species remained associated with the same floating object for extended periods; Kaplan-Meier survival estimates (considering the censored residence time due to equipment failure and fishing) suggested that mean residence time by rainbow runner and oceanic triggerfish was of 94 and 65 days, respectively. During daytime, the two species increased their home range as they typically performed short excursions (<2 h) away from the floating objects. Rainbow runner performed more excursions per unit time than oceanic triggerfish; the mean excursion index was 0.86 (±0.8 SD) for oceanic triggerfish and 1.31 (±1.1 SD) for rainbow runner. Ambient light intensity appears to be the stimulus triggering the onset and end of the associative modes. The observed prolonged residency of these two major bycatch species suggests that they are more vulnerable to the tropical tuna purse seine gear than the targeted tuna species.

Environmental characteristics associated with the presence of the Spinetail devil ray (Mobula mobular) in the eastern tropical Pacific.

In the eastern Pacific Ocean, the tropical tuna purse-seine fishery incidentally captures high numbers of five mobulid bycatch species; all of which are classified as mortalities by the Inter-American Tropical Tuna Commission due to uncertainties in post-release mortality rates. To date, the factors (operational or environmental) leading to the capture of these species by the fishery have not been well studied. Here, we developed Generalized Additive Models for fisheries observer data to analyze the relationships between the presence/absence of Mobula mobular bycatch and oceanographic conditions, the spatial and temporal variability in fishing location, and the set type (associated with dolphins, free-swimming tuna schools or floating objects). Our results suggest that chlorophyll concentration and sea surface height are the most important variables to describe the presence of M. mobular in conjunction with geographic location (latitude and longitude) and set type. Presence of the species was predicted in waters with chlorophyll concentrations between 0.5–1 mg·m-3 and with sea surface height values close to 0; which indicates direct relationships with productive upwelling systems. Seasonally, M. mobular was observed more frequently during December-January and August-September. We also found the highest probability of presence observed in School sets, followed by Dolphin sets. Three areas were observed as important hotspots: the area close to the coastal upwelling of northern Peru, the area west to Islands Colon Archipelago (Galapagos) and the area close to the Costa Rica Dome. This information is crucial to identify the mobulids habitat and hotspots that could be managed and protected under dynamic spatial management measures to reduce the mortality of mobulid rays in the eastern Pacific purse-seine fishery and, hence, ensure the sustainability of the populations of these iconic species.