Assessing vulnerability of bycatch species in the tuna purse-seine fisheries of the eastern Pacific Ocean

Fisheries Research 219 (2019) 105316

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Assessing vulnerability of bycatch species in the tuna purse-seine fisheries of the eastern Pacific Ocean

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Leanne M. Duffy , Cleridy E. Lennert-Cody, Robert J. Olson, Carolina V. Minte-Vera, Shane P. Griffiths Inter-American Tropical Tuna Commission, 8901 La Jolla Shores Dr., La Jolla, CA, 92037-1509, USA

A R T I C LE I N FO

A B S T R A C T

Handled by Bent Herrmann

Ecological risk assessment (ERA), including Productivity-Susceptibility Analysis (PSA), is becoming increasingly used to assess the relative vulnerability of data-limited non-target species to the impacts by fishing. PSA was developed for the eastern Pacific Ocean (EPO) tuna purse-seine fishery to assess the vulnerability of incidentallycaught species for three set types, “dolphin sets”, “unassociated sets” and “floating-object sets”, during 2005–2013. Because of operational differences between these set types, susceptibility values were combined for each species across the three set types to produce an overall fleet-wide susceptibility estimate. Vulnerability was highest for elasmobranchs, namely the giant manta ray, bigeye and pelagic thresher sharks, smooth and scalloped hammerhead sharks, and silky shark. Billfishes, dolphins, other rays, ocean sunfish, and yellowfin and bigeye tunas were classified as moderately vulnerable while the remaining species, all teleosts, had the lowest vulnerability scores. This purse-seine fleet-wide PSA identified potentially vulnerable species that can be compared with PSAs for other fisheries operating in the EPO, once detailed catch information becomes available for those fisheries. Such information can assist managers with prioritising fishery- and species-specific research programs and/or mitigation measures.

Keywords: Ecological risk assessment Productivity-Susceptibility Analysis Eastern Pacific Ocean Tuna fisheries

1. Introduction Attaining long-term ecological sustainability is the ultimate goal of emerging ecosystem-based fisheries management strategies that are gradually complementing or replacing traditional single-species focused management in many fisheries worldwide (Pikitch et al., 2004). The development and adoption of this management approach has been a result of the increasing evidence of the potential negative impacts that fishing has on habitats and the populations of not only target species, but incidentally-caught species, and the trophic relationships between these species that ultimately determine the structure of the supporting ecosystem (Baum and Worm, 2009; Polovina et al., 2009; Roux et al., 2013). Several tuna Regional Fisheries Management Organisations now have mandates and responsibilities pertaining to the sustainability of non-target species and supporting ecosystems. For example, the InterAmerican Tropical Tuna Commission (IATTC)—the management body for tuna fisheries operating in the eastern Pacific Ocean (EPO)—has adopted the conditions under the Antigua Convention that state, “adopt,

as necessary, conservation and management measures and recommendations for species belonging to the same ecosystem and that are affected by fishing…” and “adopt appropriate measures to avoid, reduce and minimize waste, discards, catch by lost or discarded gear, catch of non-target species and impacts on associated or dependent species, in particular endangered species”. However, such management organisations face significant challenges complying with these mandates, especially in tropical ecosystems where the number of species interactions across the different fisheries can be high (Juan-Jordá et al., 2018). The catches by many tuna fisheries are not monitored by a high-level of onboard observer coverage due to high costs to the management organisation and each Member or Cooperating Non-Member, and as a result, detailed reports of non-target species are often not required. The lack of reporting is also due to non-target species generally having little economic value, are caught infrequently or are taxonomically ambiguous. In some instances where catch data for non-target species is available, it is often provided in a highly-summarised format (e.g. “large fishes”) (Griffiths and Duffy, 2017; Juan-Jordá et al., 2018; Olson and Watters, 2003). Furthermore, these species have generally not been the subject of detailed scientific

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Corresponding author. E-mail addresses: lduff[email protected] (L.M. Duffy), [email protected] (C.E. Lennert-Cody), [email protected] (R.J. Olson), [email protected] (C.V. Minte-Vera), sgriffi[email protected] (S.P. Griffiths). https://doi.org/10.1016/j.fishres.2019.105316 Received 19 September 2018; Received in revised form 14 June 2019; Accepted 17 June 2019 0165-7836/ © 2019 Published by Elsevier B.V.

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purse-seine fishery comprised of vessels with a carrying capacity > 363 mt. The results were then used as a first step to identify potentially vulnerable bycatch species and prioritise those that would require either specific research to improve the confidence in their vulnerability status, or the implementation of mitigation measures to reduce specific threats from fishing.

studies of their biology and ecology. Thus, there are often insufficient species-specific catch and biological data available to develop traditional stock assessment models for determining the status of their stocks and to provide reliable information to managers to guide the development of appropriate conservation and management measures. Therefore, alternative methods are required to rapidly and cost-effectively provide fishery managers with sufficient information to prioritise potential species of concern, mitigate specific threats to the sustainability of their populations, or collect detailed species-specific information that can be used in more traditional population assessment approaches. Ecological Risk Assessment (ERA) is one such alternative approach that can be used to assess the vulnerability to unsustainable fishing of highly diverse, data-limited bycatch assemblages impacted by fisheries. ERA approaches differ from traditional stock assessments in that robust estimates of population status are often not provided or assessed against traditional biological reference points (e.g. FMSY, F0.1) because extensive and accurate datasets are not available for the vast majority of bycatch species. ERAs range from qualitative consequence-likelihood methods driven by information derived from expert opinion (Fletcher, 2005), to more data-intensive quantitative spatially-explicit population dynamics models (Zhou and Griffiths, 2006). Semi-quantitative attribute-based ERA methods are being increasingly used, in particular ProductivitySusceptibility Analysis (PSA), which was originally developed to assess the vulnerability of hundreds of data-poor bycatch species caught in tropical demersal prawn trawl fisheries (Stobutzki et al., 2001). PSA is regarded as a “Level 2” analysis of the 3-level hierarchical Ecological Risk Assessment for the Effects of Fishing (ERAEF) framework proposed by Hobday et al. (2011), whereby species having low vulnerability are filtered out and the remaining species are subjected to a management response of either mitigating specific threats or improving data collection and re-assessing in the next quantitative Level 3 analysis. The flexibility and minimal data requirements of PSA has led to the approach being used in a wide range of fisheries that interact with diverse bycatch assemblages. Since the initial development of PSA (Milton, 2001; Stobutzki et al., 2001, 2002), it has been probably the most widely applied ERA method in fisheries worldwide for nearly two decades (Appendix Table A1). For example, PSA has been used to assess non-target species caught in the otter trawl fishery in southeastern Australia (Hobday et al., 2007, 2011), multi-gear, multi-species fisheries in Alaska (Ormseth and Spencer, 2011), and small-scale artisanal fisheries in Baja California, Mexico (Micheli et al., 2014). The application of PSA is ongoing to assess the ecological sustainability of some of the world’s largest fishery management agencies including: the Commission for the Conservation of Atlantic Tunas (ICCAT), the Western and Central Pacific Fisheries Commission (WCPFC), and the Indian Ocean Tuna Commission (IOTC). PSA has been applied to tuna purseseine and longline fisheries in the Atlantic (Arrizabalaba et al., 2011; Cortés et al., 2010; Lucena Frédou et al., 2017; Simpfendorfer et al., 2008), Pacific (Griffiths and Duffy, 2017; Kirby, 2006), and Indian (Lucena Frédou et al., 2017; Murua et al., 2018; Nel et al., 2013; Williams et al., 2018) oceans. PSA has been used in the U.S. by the National Marine Fisheries Service of the National Oceanographic and Atmospheric Administration to assess the vulnerability of species caught by six different federal fisheries (Patrick et al., 2010). Among tuna fisheries, ICCAT implemented conservation and management measures for sharks based on the results of a PSA by Cortés et al. (2010, 2015). PSA is also the recommended method of the Marine Stewardship Council (MSC) to assess ecological sustainability within its assessment framework for fishery certifications, particularly for data-limited fisheries. The vulnerability of many of the species incidentally caught in EPO tuna fisheries to overfishing is unknown, and biological and fisheries data are limited for most of those species (e.g. see Griffiths and Duffy, 2017; Olson and Watters, 2003). In this paper, the PSA approach—as adapted to U.S. fisheries by Patrick et al. (2009)—was modified to provide an overall fleet-wide vulnerability measure for the EPO tuna

2. Materials and methods 2.1. Species interactions As a result of 100% onboard observer coverage for large tuna purseseine vessels fishing in the EPO under the Agreement on the International Dolphin Conservation Program (AIDCP) of the IATTC, detailed catch information is available for a wide range of taxa. The onboard observer program was initially established in late 1979 to monitor the incidental catch of dolphins (Joseph, 1994). However, since the early 1990s, data collection has been continually expanding to provide information for a wide range of compliance monitoring and conservation concerns related to the incidental catch of species such as turtles, sharks and rays (e.g. IATTC Resolutions: C-04-05, C-05-03, C07-03, C-16-05 and C-16-06), and the use of fish-aggregating devices (C-16-01 and C-17-02). Full description of IATTC Resolutions mentioned in this paper can be found at https://www.iattc.org/ ResolutionsActiveENG.htm. Catch data collected by observers (2005–2013) were used to develop a list of species with which the fishery interacted, and to exclude infrequently-caught species. These exclusions were necessary to minimise the potential for incurring false positives, which can occur because the number of individuals caught is independent of susceptibility indices, and thus, a species could be scored as highly susceptible even if only a single individual was caught (Arrizabalaba et al., 2011). Species were excluded from the PSA if the catch of a species constituted < 0.05% of the total purse-seine bycatch in any year, and < 5% of the catch for any set type. Three types of sets are made by purse-seine vessels and for the purposes of PSA, each set type was considered a separate fishery due to spatial and operational differences. These three fisheries are (1) “dolphin sets” whereby the net is deployed around a school of tuna associated with a dolphin aggregation (sets occurred primarily north of the equator), (2) “unassociated sets” whereby the net is deployed around a free-swimming school of tunas (sets were predominantly inshore), and (3) “floating-object sets” whereby the net is deployed around a floating object; floating objects tend to aggregate tunas and other pelagic species. These objects are primarily artificial drifting fish-aggregating devices (FADs) (IATTC, 2018). Objects can also be natural logs, marine debris (e.g. shipping containers), or mats of algae (Hall and Roman, 2013) (sets occurred throughout the EPO) (Fig. 1). 2.2. Description of the Productivity-Susceptibility Analysis methodology The PSA methodology is briefly described here. For further details, the reader is referred to Stobutzki et al. (2001), Hobday et al. (2007, 2011), and Patrick et al. (2009, 2010). PSA estimates the relative vulnerability to overfishing of the populations of individual species by ranking them based on several attributes related to their susceptibility to being captured by a particular fishery or gear type, and the capacity of the population to recover from depletion. The calculated vulnerability score for each species within the PSA is not an absolute measure but rather a relative measure or potential indication of vulnerability based on precise attribute values that are reduced to a categorical ranking. For each species, susceptibility (e.g. geographic distribution, vertical depth occupied by a species relative to fishing gear depth) and productivity (e.g. reproductive strategy, fecundity) attributes are assigned a rank of 1, 2 or 3, that relate to pre-defined value range classes that cover the total value range for all species assessed. For example, if the age-at-maturity ranged between 2 and 16 years, the data range 2

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Fig. 1. Point density estimates with a 1° search radius and a 0.25° output cell size for purse-seine sets on (a) dolphins (n = 14,580), (b) unassociated tuna schools (n = 9874), and (c) floating-objects (n = 53,632) in the eastern Pacific Ocean during 2005–2013. Warmer colours represent higher densities. (For interpretation of the references to colour in this figure, the reader is referred to the web version of this article.)

3

< 4.5 4.5–5.1 > 5.1

Age at maturity (Amat years) (50% if available, otherwise age at first maturity) Mean trophic level (TL)

Fecundity (measured) Breeding strategy

Natural mortality (M)

The position of a stock within the larger fish community can be used to infer stock productivity; lower-trophic-level stocks generally are more productive than higher-trophic-level stocks.

2.7–7.0 ≥ 7.0

< 2.7

10–200,000 1–3 < 10 ≥4

> 0.48 0.25–0.48 < 0.25

> 0.21

> 1.3 ≤ 11 ≤ 200 0.1–1.3 11–20 200–350

0.095–0.21 < 0.095 von Bertalanffy growth coefficient (K)

Table 1 Productivity attributes and scoring thresholds. Attribute definitions taken from Table 1 in Patrick et al. (2010). 4

≤ 0.1 ≥ 20 > 350

The intrinsic rate of population growth or maximum population growth that would occur in the absence of fishing at the lowest population size. Maximum age is a direct indication of the natural mortality rate (M), where low levels of M are negatively correlated with high maximum ages. Maximum size is correlated with productivity, with large fish tending to have lower levels of productivity, although this relationship tends to degrade at higher taxonomic levels. The von Bertalanffy growth coefficient measures how rapidly a fish reaches its maximum size, where long-lived, low productivity stocks tend to have low values of K. Natural mortality rate directly reflects population productivity; stocks with high rates of natural mortality will require high levels of production in order to maintain population levels. Fecundity (i.e., the number of eggs produced by a female for a given spawning event or period) is measured here at the age of first maturity. The breeding strategy of a stock provides an indication of the level of mortality that may be expected for the offspring in the first stages of life. Estimated offspring mortality using Winemiller’s (1989) index of parental investment as described in Patrick et al. (2010). Age at maturity tends to be positively related with maximum age (tmax); long-lived, lower productivity stocks will have higher ages at maturity than shortlived stocks. Intrinsic rate of population growth (r) Maximum age (Amax years) Maximum size (Lmax cm)

2.3. Productivity and susceptibility attributes Nine productivity attributes describing each species’ inertia to fishing mortality and seven susceptibility attributes describing each species’ susceptibility to incurring fishing mortality were defined for the EPO tuna purse-seine fisheries in consultation with scientific experts at the IATTC. For individual species, a rank of 1 (least productive, least susceptible), 2 (moderate) or 3 (most productive, most susceptible) was assigned to each productivity and susceptibility attribute. These parameterised attributes were then analysed with the U.S. National Marine Fisheries Service “PSA” analysis package in http://nft.nefsc.noaa.gov/ PSA.html. Definitions of productivity and susceptibility attributes were taken from Patrick et al. (2009, 2010) and are described in Tables 1 and 2. Information used to score the productivity and susceptibility attributes for each species was compiled from published and unpublished literature, EPO fisheries data, web-based sources (e.g. FishBase) and reliable anecdotal information from experienced scientists and fishers (Appendix Tables A2-A5). Scoring thresholds for productivity (p) attributes were derived by dividing the attribute information into 1/3 percentiles, which correspond to ranking scores of 1, 2 or 3 described above. Scoring criteria for the susceptibility (s) attributes were taken from Patrick et al. (2009) and for some attributes, modified to be better suited to the EPO purse-seine fisheries. For example, conservation resolutions have been adopted for some bycatch species (e.g. sharks and rays) caught in the EPO purse-seine fisheries, while for others (e.g. rainbow runner and yellowtail) no management measures are in place and thus reflected in the scoring of the “Management strategy” attribute (Table 2, Appendix Tables A3-A5). The areal overlap attribute, described below, was modified for this study. Fishing mortality (F) relative to natural mortality (M) and biomass of spawners are attributes used in stock assessments and are unavailable for unassessed stocks therefore, they were removed as in Cope et al. (2011). All attributes were weighted equally and assigned the default weight of 2 (see Patrick et al., 2009). The productivity and susceptibility attributes were treated additively, and their overall scores were calculated as the weighted average of the individual attribute scores (Patrick et al., 2009, 2010). For this study, the susceptibility attribute “areal overlap” is described as the proportion overlap of the distribution of effort for each purse-seine fishery (Gk) with the horizontal overlap of a species’

Low (1)

Definition Productivity attribute

Ranking

Moderate (2)

High (3)

classes may be defined as < 5 yrs (rank = 1, low vulnerability), 5–10 yrs (2, moderate vulnerability), and > 10 yrs (3, high vulnerability). The species having the lowest productivity and highest susceptibility ranks across all attributes and exceed a pre-defined vulnerability threshold value (e.g. v = 2.0) are then deemed to be most vulnerable to becoming unsustainable under current fishing levels (e.g. see Cope et al., 2011). PSA results are graphically displayed as an x-y scatterplot, and the species in the upper right-hand corner of the plot are considered to be most vulnerable (Patrick et al., 2009). These results can be used by managers and fishery stakeholders in the decision-making process for prioritising research or mitigating threats to a species imposed by a specific fishery. Because PSA was developed to assess the vulnerability of bycatch species that often lack detailed studies on life history or susceptibility to capture, it is common to have one or more attributes with either missing data for an individual species or to use information from a related species (Patrick et al., 2010; Stobutzki et al., 2001). For example, information for half of the attributes was unavailable at the species level in the PSA by Stobutzki et al. (2001). Oftentimes attribute information is required to be obtained from grey literature or webbased sources (e.g. FishBase), and even anecdotal accounts or expert opinion of scientists or fishers as a last resort when peer-reviewed scientific studies are unavailable (Arrizabalaba et al., 2011; Cortés et al., 2015; Lucena Frédou et al., 2016, 2017; Micheli et al., 2014; Patrick et al., 2010). However, this is reflected in a data quality score to describe the reliability of the data source (Patrick et al., 2010).

> 200,000 0

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Probability of survival < 33%

Stock is highly valued or desired by the fishery (> 66% retention) Stock is moderately valued or desired by the fishery (33–66% retention)

Behavioural responses decrease catchability of the gear

Probability of survival > 67%

Stock is not highly valued or desired by the fishery (< 33% retention)

Behavioural responses of both individual fish and the stock in response to fishing.

Fish survival after capture and release varies by species, region, and gear type or even market conditions, and thus can affect the susceptibility of the stock. The assumption that highly valued fish stocks are more susceptible to overfishing or to becoming overfished by recreational or commercial fishermen owing to increased effort.

Schooling/Aggregation and other behavioural responses to gear

Potential survival after capture and release under current fishing practices Desirability/value of catch (percent retention)

Seasonal migrations

Behavioural responses increase the catchability of the gear

< 25% of stock occurs at the depths fished Seasonal migrations decrease overlap with the fishery Vertical overlap with gear

> 50% of the stock occurs in the depths fished Seasonal migrations increase overlap with the fishery

Between 25% and 50% of the stock occurs at the depths fished Seasonal migrations do not substantially affect the overlap with the fishery Behavioural responses do not substantially affect the catchability of the gear 33% < probability of survival ≤ 67%

Proportion of species-occupied grids (Gk/G) fished < 0.33

The horizontal overlap of a species’ catch distribution and the distribution of effort for each purse-seine fishery calculated as: Gk/G; where G is the total number of grid cells occupied by a species, and Gk is the number of grid cells containing at least one unit of fishing effort by fishery k during 2005–2013 The position of the stock within the water column (i.e., whether is demersal or pelagic) in relation to the fishing gear. Seasonal migrations (i.e. spawning or feeding migrations) either to or from the fishery area could affect the overlap between the stock and the fishery. Areal overlap

Proportion of species-occupied grids (Gk/G) fished ≥0.67

No management measures; stocks closely monitored

Stocks specifically named in conservation resolutions; closely monitored Proportion of species-occupied grids (Gk/G) fished between 0.33 and 0.67 Management and proactive accountability measures in place The susceptibility of a stock to overfishing may largely depend on the effectiveness of fishery management procedures used to control catch. Management strategy

High (3) Moderate (2) Low (1)

Ranking Definition Susceptibility attribute

Table 2 Susceptibility attributes and scoring thresholds. Attribute definitions taken from Table 2 in Patrick et al. (2010) apart from areal overlap taken from Griffiths et al. (2018).

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distribution (G), expressed as:

Gk G where G is the total number of 0.5° × 0.5° grid cells occupied by a species, and Gk is the number of grid cells containing at least one set by fishery k during 2005–2013 (Griffiths et al., 2018). G was estimated from relative environmental suitability models using presence-only data developed for each species at 0.5° × 0.5° resolution in AquaMaps (https://www.aquamaps.org/search.php), following the method of Kaschner et al. (2006). A probability-of-occupation threshold of 0.6 for each grid cell was used to define the boundaries of each species distribution (Griffiths et al., 2018). 2.4. Data quality A data quality (DQ) score was assigned to each productivity and susceptibility attribute for each species to provide an estimate of uncertainty. The scoring scale ranged from 1 to 5, where 1 represents high data reliability (i.e. recent data specific to the stock and area of interest) and 5 indicates no information was available. Scoring definitions followed Patrick et al. (2009, 2010) and are defined in Table 3. When a DQ score was 5, the attribute score was precautionarily set to 1 (for productivity) or 3 (for susceptibility). The mean data quality index was computed as the weighted average of the individual scores for productivity and susceptibility (Patrick et al., 2009, 2010) and scores reported as DQ of s by fishery (k) and DQ p scores in Table 4. DQ scores were categorised as: high (< 2), moderate (≥2 and < 3) and low (≥3) (Ormseth and Spencer, 2011). 2.5. Purse-seine fleet-wide calculation of susceptibility For each species, susceptibility values were combined across the three fisheries to produce an overall fleet-wide susceptibility score (s). This was calculated as the weighted sum of each fishery susceptibility values (Table 4), with weights equal to the proportion of sets in each fishery:

s=

∑ sk wk k

where sk is the susceptibility of a species in fishery k, computed using the susceptibility attributes in Table 2, wk =

( ) and N is the total Nk ∑k Nk

k

number of sets by fishery k in 2013, the most-recent year in the dataset used in the analysis. Here, sk ranges from 1 (lowest) to 3 (highest). For a species constituting < 5% of the total annual bycatch in set type k, the lowest susceptibility value was assumed, sk ≡1. 2.6. Vulnerability Relative vulnerability (v) of a species caught by the large-vessel purse-seine fishery in the EPO—defined as the potential for the productivity of a stock to be diminished by direct and indirect fishing pressure (Patrick et al., 2009)—is a combination of a stock’s productivity and susceptibility to a fishery and was calculated, for the three fisheries combined, as:

v=

(p − 3)2 + (s − 1)2

Relative vulnerability was determined by v scores and thresholds were defined as “low” (v ≤ 1.0), “moderate” 1 < v > 2), and “high” (v ≥ 2) and identified by vulnerability isopleths starting from the origin of the PSA plot (productivity score = 3, high; susceptibility score = 1, low) (Patrick et al., 2009). The “high” threshold followed Cope et al.’s (2011) recommendation to identify species at potential “high risk” of becoming unsustainable under current fishing levels. Because our goal of PSA was to assist managers with prioritising either species-specific 5

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Table 3 Scoring definitions of data quality for each productivity and susceptibility attribute used in the Productivity-Susceptibility Analysis of the eastern Pacific Ocean purseseine fisheries. Descriptions and examples from Table 3 in Patrick et al. (2010). Data quality

Description

Example

1

Best data. Information is based on collected data for the stock and area of interest that is established and substantial Adequate data. Information is based on limited coverage and corroboration, or for some other reason is deemed not as reliable as tier-1 data Limited data. Estimates with high variation and limited confidence and may be based on studies of similar taxa or life history strategies Very limited data. Information based on expert opinion or general literature reviews from a wide range of species, or outside of region. No data.

Data rich stock assessment; published literature for which multiple methods are used, etc. Limited temporal or spatial data, relatively old information, etc.

2 3 4 5

Similar genus or family, etc. General data not referenced

Table 4 Productivity (p) and susceptibility (s) scores used to compute the overall vulnerability measure (v). Fisheries: Dolphin = DEL, unassociated = NOA, and floatingobject sets = OBJ. Individual susceptibility scores (sk), are shown for each fishery and as a weighted combination of the individual fishery values, s; see text for details. Vulnerability: green = low (v ≤ 1), yellow = moderate (1 < v < 2), red = high (v ≥ 2). Mean data quality (DQ) scores for susceptibility (sk) by fishery and productivity DQ p are categorized as: green = high (DQ < 2), yellow = moderate (3 < DQ ≥ 2) and red = low (DQ ≥ 3). (For interpretation of the references to colour in this table, the reader is referred to the web version of this article.)

3.2. Catch by fishery

research to obtain more data to better assess vulnerability status using more sophisticated population models or to provide management advice for potential conservation and management measures, our boundaries of “low” and “moderate” were less important than the “high” vulnerability category that would be the focus for providing such advice.

The majority of species included in the PSA were caught in sets on floating objects, including “large” and “small” pelagic fishes, three of the four billfishes, two of the three tunas, silky and hammerhead sharks (Fig. 2). Unassociated sets contributed most to the catches of thresher sharks, two of the three rays, ocean sunfish, yellowtail amberjack, and bigeye trevally. Yellowfin tuna, sailfish, and giant manta were predominantly caught in dolphin sets. (By definition, dolphins were almost exclusively caught in dolphin sets.)

3. Results 3.1. Species-fishery interactions Observers reported the purse-seine fisheries to have interacted with a total of 96 taxa from 2005–2013. Of these, 25 were aggregated taxonomic groups (animals identified to family or genera) and were not included in the PSA, while the remaining 69 were identified to species. After excluding infrequently-caught species (Appendix Table A6), a total of 24 non-target species were included in the PSA: 4 billfishes, 3 dolphins, 7 “large fishes”, 3 rays, 5 sharks, and 2 “small fishes” (Table 4).

3.3. Data quality Mean data quality scores for the productivity attributes ranged from 1.78 for the target tunas (yellowfin and bigeye) to 4.00 for ocean triggerfish (Table 4). These tunas, as well as striped marlin and common dolphinfish (DQ p = 1.89), had the best (lowest) data quality scores. Data availability for productivity parameter values was poor for some “large” fishes, all the “small” fishes and rays, and smooth hammerhead 6

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increasingly important with the widespread adoption of the principles of ecosystem-based fisheries management. There is a growing interest by many fisheries to demonstrate that they are employing ecologically sustainable fishing practices through certifications to allow eco-labelling of their seafood products (MSC, 2017). However, demonstrating ecological sustainability quantitatively is a significant challenge for many fisheries, including the EPO tuna fisheries, where the composition of bycatch is diverse and the availability of species-specific biological data required to assess populations using traditional approaches is often limited, if not entirely absent. PSA is an ecological risk assessment approach that is designed to rapidly and cost-effectively prioritise potentially vulnerable species, allowing managers to adopt conservation and management measures to mitigate their incidental capture (see example applications of PSA in Appendix Table A1). Managers might recommend further monitoring and/or biological research in order to assess the status of highly vulnerable species more quantitatively using conventional assessment approaches applied in more data-rich settings. The present assessment of the EPO tuna purse-seine fisheries classified elasmobranchs as the most vulnerable species, while all teleosts (“large fishes” and “small fishes”) were the least vulnerable. These results can largely be attributed to the life history characteristics of many of the assessed elasmobranchs making them less resilient to fishery impacts. For example, the most vulnerable species, the gaint manta, is estimated to live for 20 years (Marshall et al., 2006), reaches maturity at 6–8 years, and produces 1 pup per litter once every 1–3 years (Dulvy et al., 2014). The next most vulnerable species, bigeye thresher shark, may live for 20 years (Dulvy et al., 2008), 50% of the population matures at around 13 years (Liu et al., 2015), and produces 2 young every year (Dulvy et al., 2008). In contrast, the least vulnerable species, including bigeye trevally, rainbow runner, pompano dolphinfish, yellowtail and wahoo are highly productive by comparison, such as wahoo which is short lived (7 years), attains maturity early in life (7 months), and is a broadcast spawner producing batch fecundities of millions of oocytes (Zischke et al., 2013a,b). Several elasmobranchs were classified as “most vulnerable” in the present study due to their high susceptibility to capture, owing to the high horizontal and vertical overlap of the gear used in these tuna fisheries relative to habitat of the species. These were mainly the giant manta ray, bigeye and pelagic thresher sharks, smooth hammerhead shark, silky shark, and the scalloped hammerhead shark. These species are primarily distributed in warm-temperate to tropical waters (Bessudo et al., 2011; Graham et al., 2012; Klimley et al., 1993; Musyl et al., 2011; Santos and Coelho, 2018; Smith et al., 2008), where the majority of purse-seine fishing occurs in the EPO (Fig. 1). Despite the diel variation in the vertical distribution of some of these species, they spend the majority of time in the top 200 m of the water column. For example, silky sharks spend about 60% of their time in waters less than 30 m depth (Musyl et al., 2011), while scalloped hammerhead sharks spend around 50% of their time at depths less than 50 m (Bessudo et al., 2011). Considering the average estimated depth of the gear in all three purse-seine fisheries is around 200 m (Hall and Roman, 2013), the probability of these species encountering the gear is high. Although many of these elasmobranch species are discarded—either due to their low economic value, or mandated under specific IATTC conservation measures—and some may survive the release procedure, in the absence of reliable species- and regionally-specific data on post-release survival, we were precautionary in this analysis and assumed that post-release survival was zero. This is an important consideration for future research as it will help prompt fisheries managers to prioritise research on postrelease mortality and better assess the efficacy of existing, and potentially new, industry release practices, which will help to better predict impacts of purse-seine fishing on elasmobranchs. This will allow managers to develop best practice release procedures for more sensitive species. Our study had similar outcomes to a PSA applied to the European purse-seine fishery in the Atlantic Ocean (Arrizabalaba et al., 2011),

Fig. 2. Productivity-Susceptibility Analysis (PSA) x–y plot for target and bycatch species caught in the purse-seine fisheries of the EPO during 2005–2013. Dashed lines represent vulnerability (v) isopleths starting from the origin and have v values of 0.5, 1.0, 1.5, and 2.0 with categories defined as low (v ≤ 1.0, green), moderate (1 < v < 2, yellow), and high (v ≥ 2.0, red). Proportion of catch by set type displayed in the pie charts. 3-alpha species codes and details of data quality (DQ) scores defined in Table 4. Coloured boxes represent mean DQ scores: green = high (DQ < 2), yellow = moderate (3 < DQ ≥ 2), red = low (DQ ≥ 3). *If categories of a species’ susceptibility and productivity DQ score differed, colour for the lowest quality category was used. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

shark. These included bigeye trevally and ocean sunfish (DQ p=3.56), smoothtail manta (DQ p = 3.44), pompano dolphinfish, rainbow runner, spinetail manta, smooth hammerhead shark, bluestriped chub (DQ p=3.33), and giant manta (DQ p=3.11). Mean data quality for the susceptibility attributes varied by fishery as not all species were caught in each set type (Table 4). The majority of species had moderate DQ sk scores that ranged from 2.00 for some billfishes to 2.71 for bigeye trevally and ocean triggerfish caught in unassociated and floating-object sets, respectively. The tunas and dolphins—for which specific management measures are in place and detailed life history studies have been carried out—had the lowest scores (DQ sk < 2). No DQ scores exceeded 3.00, indicating data quality for the susceptibility attributes was greater than those for the productivity attributes. 3.4. Vulnerability Relative vulnerability by species is presented in Fig. 2. Elasmobranchs had the highest vulnerability values, i.e., the lowest productivity and highest susceptibility scores, specifically the giant manta ray (v = 2.21), bigeye and pelagic thresher sharks (v = 2.19 and 2.18, respectively), smooth and scalloped hammerhead sharks (v = 2.08 and 2.03, respectively), and the silky shark (v = 2.07). In contrast, most “large” and “small” pelagic fishes, and skipjack tuna, were classified least vulnerable (v < 1.00), located in the high productivity/low susceptibility region of the plot. The remaining species, including billfishes, dolphins, smoothtail and spinetail rays, ocean sunfish, and yellowfin and bigeye tuna were all classified as moderately vulnerable (Table 4). 4. Discussion Assessing

ecological

sustainability

in

fisheries

has become 7

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more than one fishery have attempted to be assessed by other authors (Ormseth and Spencer, 2011; Patrick et al., 2009) in separate PSAs for each gear type with results of each PSA combined according to the proportion catch of each species/stock by each gear type (i.e. an average of the productivity and susceptibility scores were weighted by the proportion of catch by gear type to estimate vulnerability). For data-limited fisheries, for example, where only a list of species interactions is available and catch information is not recorded (e.g. the ICCAT bycatch list in the Atlantic Ocean (Arrizabalaba et al., 2011)), such approaches are not possible. A novel feature of the current PSA was the attempt to address the cumulative impacts by the three tuna purse-seine fisheries by weighting the susceptibility scores by the proportion of effort (number of sets) by each fishery. Although we feel that our cumulative index provides a reasonable overall purse-seine fleet-wide susceptibility estimate, we were unable to address the cumulative impacts from other fisheries. During this process, we identified a major shortcoming of the PSA method for assessing cumulative impacts, whereby susceptibility values for individual fisheries are not simply additive, primarily owing to gear types that have differing levels of catchability (i.e. the proportion of a species’ stock that can be caught with one unit of effort). If such catchability information could be quantified, it may be possible to develop a cumulative susceptibility index. However, if such fine-resolution information was available, PSA would be a less desirable method to use over more reliable quantitative assessment methods that could better utilise these data. Few cumulative ERAs have been attempted to evaluate the impacts of co-occuring fisheries and other threats to the ecosystem (Battista et al., 2017; Micheli et al., 2014). Micheli et al. (2014) evaluated two approaches for assessing the cumulative impacts of multiple small-scale fisheries operating along the coast of Baja California, Mexico. They developed an index for (1) fisheries with the greatest impact (FGI) and (2) aggregated susceptibility (AS). Although their efforts to improve upon the PSA have merit, it is unknown whether the cumulative impacts are in fact being measured. In their FGI score, the dominant fishery with the highest susceptibility score overrides those with a lesser susceptibility. Thus, a fishery with a significant impact but not the most significant, is effectively ignored. In contrast, using their AS score allows the addition of multiple fisheries, but is still bounded to a maximum susceptibility of 3, so that the overall impact increases only until the maximum value is met (Micheli et al., 2014). This effectively reduces the proportional impact of each fishery as more fisheries are added to the calculation. Battista et al. (2017) developed the Comprehensive Assessment of Risk to Ecosystems (CARE) method to evaluate relative risk from multiple threats and their interactions (e.g. fishing and climate change) on an ecosystem as a whole to inform management decisions. In their method, categorical attribute risk scores for each threat are summed to produce a cumulative risk score, which effectively is a dimensionless index that has no biological or ecological reference point value to which the assessment value can be compared. Undoubtedly assessing cumulative impacts of multiple and oftentimes data-poor fisheries presents a significant challenge to fisheries scientists. Future work should include developing methods that seek to develop robust biological reference points for assessing vulnerability to facilitate our understanding of such cumulative impacts, which in turn will allow managers to more confidently identify vulnerable species and develop appropriate conservation and management measures as a result.

which, like the tuna fisheries in the EPO, also interacted with groups of varying life history characteristics (e.g. sharks, rays, billfishes, tunas, other teleosts, and turtles). In their study the scalloped and smooth hammerhead sharks and silky shark were also ranked as highly vulnerable. Similarly, a PSA for the Western and Central Pacific Ocean (WCPO) tuna purse-seine fishery identified sharks, including silky, hammerhead and pelagic thresher sharks among those with the highest vulnerability (Kirby, 2006). However, Kirby (2006) suggested the silky shark has a greater potential to be overexploited due to its lower fecundity (< 15 pups) relative to hammerhead sharks (30 pups). In the present study of the EPO, vulnerability was similar for silky, smooth and scalloped hammerhead sharks (v = 2.07, 2.08, 2.03, respectively). Our results are consistent with the results from a PSA conducted for the pelagic longline tuna fishery in the Atlantic Ocean which also identified the bigeye thresher and silky shark as highly vulnerable, while hammerhead sharks were found to be less vulnerable (Cortés et al., 2010). Their study found these species to be most vulnerable primarily because of their low productivity. These slight differences in vulnerability may be due to the method of computation of susceptibility values. Studies with susceptibility calculated as the product of attributes may underestimate susceptibility (Cortés et al., 2010) as opposed to those, like the present study and Patrick et al. (2009), that use additive measures to compute susceptibility. An important outcome of the present study was not only the identification of species that are potentially vulnerable to overexploitation, but the use of data quality scores provided a means by which the reliability of the PSA results could be interpreted (Patrick et al., 2009) and to identify information gaps that may be filled by future research to improve species-specific assessments, whether that be by ERA or more traditional stock assessment approaches. For example, in general data quality was poor for many productivity attributes, specifically for “large fishes” and “small fishes” and rays (Table A2), meaning there may be false positives as precautionary ‘worst case’ values were used in the absence of reliable life history data. Categories of mean data quality scores were colour coded in the PSA plot to provide transparency in reliability of PSA results (Fig. 2). The giant manta ray had the highest vulnerability value, given it’s low fecundity, relatively late age at maturity and long lifespan, although poor to no biological information was available for many of its productivity attributes, namely the intrinsic rate of population growth and natural mortality (Table A2). This resulted in a mean data quality value of 3.11 (Table 4), indicating there is large uncertainty in the attribute values. Therefore, it is possible that the vulnerability of this species may be overestimated, however, a desirable feature of PSAs is to incur false positives rather than false negatives (Hobday et al., 2011). Nonetheless, given the low productivity and high susceptibility—bycatch estimates of mobulid mortality by the purse-seine fishery in the EPO from 1993 to 2013 ranged from 1100 to 6500 individuals year−1— they are inherently vulnerable to overexploitation (Croll et al., 2016), and therefore our vulnerability classification is probably justified. Similarly, data quality for the productivity attributes was poor for the smooth hammerhead shark (3.33) which was classified as “most vulnerable” (v = 2.08). Such results indicate that biological studies are needed for these potentially vulnerable species to improve our confidence in the vulnerability classifications produced by the PSA for the EPO purse-seine fishery and other fisheries impacting these species. Although PSAs have been applied to many regions around the world, they have generally been specific to a particular fishery (Arrizabalaba et al., 2011; Cortés et al., 2010; Kirby, 2006; Lucena Frédou et al., 2017; Patrick et al., 2009; Stobutzki et al., 2001) with little regard for the cumulative effects that multiple fisheries may have on the various impacted species. This is mainly due to the categorical scoring, on a scale of 1–3, of susceptibility and productivity attributes that produce a non-additive susceptibility index, which does not easily allow the cumulative impacts of multiple fisheries operating in a particular region of interest to be assessed. Species or stocks impacted by

4.1. Implications of PSA results for future research and management of EPO fisheries Our assessment for the large-vessel purse-seine fisheries in the EPO identified a range of species, all elasmobranchs, that should be considered for future, or continued, conservation efforts. Fortunately, the IATTC has already made significant progress in recent years by 8

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observers onboard fishing vessels, who are required to complete a number of forms that constitute undoubtedly the richest source of detailed and precise information of the fishing activities and on the species caught, including those caught incidentally. Currently, only the largest purse-seine vessels (> 363 mt) are obliged to carry an observer onboard, while the allocation of observers onboard smaller vessels is either voluntary or limited to special circumstances (e.g. when one or more catch wells in the vessel are sealed under Resolution C-12-08). With regards to longline vessels, the existing limitations are even more important as a consequence of a double restriction: the obligation to carry observers onboard applies only to 5% of longline vessels > 20 m length overall under Resolution C-11-08, in spite of the fact that many IATTC members have concurred with the recommendation of the scientific staff that this percentage be increased at least to 20%. In addition, an important consideration is that many of the developing nations, particularly those in Central America, do not have the resources to adequately monitor all commercial fisheries, let alone the thousands of artisanal vessels that operate from a diversity of diffuse access points along thousands of kilometers of coastline (Aires-da-Silva et al., 2016). Therefore, collaborative efforts are essential for acquiring species-specific data for the various fisheries in the EPO to monitor stocks and conduct vulnerability assessments, which can facilitate the decision-making process to implement appropriate conservation and management measures. There are developing research efforts underway in an attempt to better understand the artisanal fisheries and their catches, especially sharks (Aires-da-Silva et al., 2016; Siu and Aires-daSilva, 2016), but it may be several years before sufficiently reliable data are available to better assess the impacts of these fisheries on vulnerable species in the EPO. Nonetheless, with improved data, the IATTC will continue to make progress toward fullfilling goals under its adopted ecosystem-based fisheries management approach outlined in the Antigua Convention; to ensure long-term ecological sustainability of target and non-target species impacted by its fisheries. Improvements in data collection for the various fisheries that operate in the EPO will certainly enable scientists and managers to better assess the cumulative impacts of fisheries on the vulnerability of species they interact with. However, a key element missing from PSAs is the ability to assess a species status against traditional biological reference points used in stock assessment and familiar to fisheries managers. Because PSA calculates a vulnerability score for each species based on its productivity and susceptibility scores, the vulnerability reference point (i.e. the v score) is only a relative index of vulnerability. Few attempts have been made to validate the results of PSA against results obtained using other benchmark methods (Cope et al., 2011, 2015; Zhou et al., 2016) simply because the PSA attribute scores (1, 2, or 3) have been derived from source data that range from highly precise (e.g. von Bertalanffy “K” parameter from an ageing study using otoliths) to imprecise data (e.g. adopting a K value from a species in the same genus or family). Quantitative evaluation and validation is necessary for managers to confidently implement conservation measures. Therefore, future research on EPO fisheries impacts should focus on developing quantitative fisheries dynamics models to better evaluate vulnerability of bycatch species as in Hordyk and Carruthers (2018). A method that produces estimates of fishing mortality to compare against scientifically-defensible biological reference points (e.g. FMSY, F40%) and incorporates uncertainty for data-poor bycatch species has recently been developed and validated using integrated stock assessment results for data-rich species (Griffiths et al., 2019). However, development of such new methods may take several years to complete and implement, and thus fisheries will likely continue to require the use of methods such as PSA to continually address the ecological sustainability requirements of their respective governing bodies. Such quantitative methods may then be validated by comparing model results to those generated by fully integrated statistical stock assessments.

adopting resolutions pertaining to conservation measures for several species of sharks (e.g. silky and hammerhead sharks) and mobulid rays. For example, persistently high catch rates of silky shark and hammerhead sharks in the purse-seine fishery prompted the adoption of Resolutions C-16-05 and C-16-06, the latter of which prohibits retention of silky sharks. Similar conservation measures were implemented for mobulid rays (Resolution C-15-04) due to their high vulnerability to overfishing as a result of their low biological productivity. These measures were primarily developed in response to increasing purseseine effort, primarily on FADs, that has increased the biomass and diversity of bycatch species caught (IATTC, 2018). These measures have likely afforded some protection to the most vulnerable species identified in this study to any further risk of overexploitation in the largevessel purse-seine fishery. However, as discussed in relation to the limitation of PSA to assess cumulative impacts of multiple fisheries, our assessment did not consider other fisheries that may be a major mortality source for not only the most vulnerable species, but even moderately vulnerable species, where the accumulated mortality across all fisheries may be unsustainable. For example, together, the large-scale tuna ‘industrial’ or ‘Far Seas’ longline fishery (vessels > 20 m length overall), artisanal and multi-gear fisheries operating throughout the EPO account for significantly higher catches of silky sharks than the three purse-seine fisheries combined (IATTC, 2014). Unfortunately, reliable catch estimates for non-target species have not been available for the industrial longline fishery due to observer coverage of only 5% of the fleet and the coarse spatial (5° × 5°) and taxonomic (e.g. “sharks”) resolution of logbook catch and effort data (Griffiths and Duffy, 2017). However, the recent adoption of a requirement for Members and Cooperating Non-Members of the IATTC to supply operational-level observer data may greatly improve our ability to expand catch rates to more reliably estimate fleet-wide catches of non-target species. Another major mortality source that needs to be considered in assessing the sustainability of species evaluated in this study are the small purse-seine, longline, and expanding and increasingly efficient artisanal fleets that operate in the EPO (Aires-da-Silva et al., 2016; Siu and Airesda-Silva, 2016). For example, the artisanal longline fisheries are increasingly resembling industrial-like longlining operations and fish in their Exclusive Economic Zone and in areas beyond national juristictions to primarily target sharks, billfishes and tunas (Martínez-Ortiz et al., 2015). The Ecuadorian artisanal fishery alone reported a catch of > 3000 t of silky shark between 2008 and 2012 (Martínez-Ortiz et al., 2015). Several artisanal fishing camps on the Pacific coast of Baja California, Mexico either primarily or secondarily target elasmobranchs, in particular the smooth hammerhead, which accounted for 4% of the total gillnet catch reported from a single fishing camp (Cartamil et al., 2011) and > 2% and > 4% of the total gillnet and longline catch, respectively (Ramirez-Amaro et al., 2013). Such fisheries can have a large impact particularly on sharks, as they are taken as both targeted catch and bycatch, depending on the fishery (Aires-daSilva et al., 2016; Siu and Aires-da-Silva, 2016). Martínez-Ortiz et al. (2015) provided a detailed spatio-temporal description of the catch composition of the Ecuadorian artisanal fleet by gear type and showed a high impact on large predatory fishes, including a notable impact on sharks. Although we identified a limitation of the PSA method, namely to effectively assess the cumulative impacts by multiple fisheries, there are also several political hurdles to overcome before all EPO fisheries can be included in future ecological or stock assessments. In addition to the fact that the mandate of the IATTC is limited to those species covered by the Antigua Convention, there are also important limitations regarding the data that must be provided on fishing activities. First, the general rules adopted by the IATTC on the submission of catch data and gear contain fewer data requirements for artisanal and recreational fisheries under Resolution C-03-05. This difference in treatment is compounded by a similar discrimination concerning the allocation of 9

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We would like to thank Mark Maunder, Alexandre Aires-da-Silva, Michael Hinton, and Michael Scott for providing their valuable expertise to help score attributes; Rick Deriso for suggesting the weighting scheme defined in Section 2.5; Daniel Fuller for assisting with the point density estimates for Fig. 1 and Christine Patnode for improving the graphics, Jean-Francois Pulvenis de Séligny for assistance with describing the political hurdles associated with EPO fisheries and Alexandre Aires-da-Silva and Guillermo Compean for reviewing this manuscript. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.fishres.2019.105316. 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