Most problematic fishing methods based on ALDFG relative risks
This study presents the first quantitative assessment of gear-specific relative risks from ALDFG. Findings accounted for the: (a) derelict gear leakage rate; (b) fishing gear quantity indicators of catch and area of fishing grounds; and (c) adverse consequences from ALDFG. Maximum global conservation gains can be achieved through focusing ALDFG mitigation efforts on the fishing gears with the highest overall relative risk. Set and fixed gillnets and trammel nets, drift gillnets, gears using drifting and anchored FADs (tuna purse seines and pole-and-lines), and bottom trawls were the five most problematic gears on a global scale. This was followed by traps (fyke nets, pots, barriers, fences, weirs, corrals and pound nets).
The overall RR score indicates a fishing gear’s relative degree of total adverse effects from ALDFG, accounting for the quantity of ALDFG produced by that gear (estimated from the ALDFG leakage rate and indices of fishing gear quantity of catch and area of fishing grounds), and the adverse consequences that result from ALDFG from that gear type relative to other gears. Globally, gillnets have the highest risks from ALDFG, while hand dredges and harpoons were least problematic.
The focus of local management interventions to address problematic derelict fishing gear will be dictated by the specific context. Locally, adopting ALDFG controls following a sequential mitigation hierarchy and implementing effective monitoring, surveillance and enforcement systems are needed to curb derelict gear from these most problematic fisheries. This includes accounting for which fishing gears are predominant and the existing fisheries management framework. For example, a site may have pot and tuna purse seine anchored FAD fisheries. The purse seine fishery has a higher relative risk globally. However, a fisheries management system may have effective ALDFG preventive methods in place for this fishery, such as a high rate of detection and recovery of anchored FADs when they break from moorings, and minimization methods, such as prescribing the use of only non-entangling and biodegradable FAD designs to minimize adverse effects from derelict FADs35, 36. But there may be minimal measures in place to monitor and manage ALDFG from pots. In this hypothetical example, it would be a higher priority locally to improve ALDFG management for the pot fishery.
Priority data quality improvements
There are several priorities for data quality improvement to increase the certainty of future assessments. Given substantial deficits both in estimates of gear-specific quantity/effort and ALDFG rates, it is not yet possible to produce a robust contemporary estimate to replace the ca. five decades-old crude estimate of the magnitude of the annual quantity of leaked ALDFG4, 30. More robust estimates of ALDFG rates are needed for all gear types. Gear-specific estimates have low certainty due to small numbers of studies and sample sizes. Many compiled records estimate only one ALDFG component, typically only loss rates, and therefore may substantially underestimate total ALDFG rates. Most records are dated and may not accurately characterize contemporary rates. There is geographical sampling bias with estimates being primarily derived from the northern hemisphere. Furthermore, many estimates were derived from expert surveys (Supplementary Material Table S1), which have a higher risk of error and bias than approaches higher on the evidence hierarchy37. Substantially more primary studies with robust designs are needed.
An expanded meta-analysis on gear-specific ALDFG rates is an additional priority, once sufficient sample sizes of robust studies accumulate. The statistical modeling approach used by Richardson et al.34 could be readily improved by using (1) a random-effects instead of a fixed effects structure to account for study-specific heterogeneity, and (2) a more appropriate model likelihood, such as zero-inflated Beta likelihood, to account for the zero values in the dataset38. Due to larger sample sizes and the number of independent studies, meta-analyses can produce estimates with increased accuracy, with increased statistical power to detect real effects. By synthesizing estimates from an assortment of independent, small and context-specific studies, pooled estimates from random-effects meta-analyses are generalizable and therefore relevant over diverse settings39. The strength of conclusions of hypotheses based on a single study can vary. This is because a single study can be context-specific, where true results may be affected by conditions specific to that single study, such as the species involved and environmental conditions, that cause the results from the single study to not be applicable under different conditions. A single study may also fail to find a meaningful result due to small sample sizes and low power. However, robust synthesis research, including meta-analysis, is more precise and powerful once a sufficient number of similar studies have accumulated, and therefore investing in more primary ALDFG studies is a high priority.
For some gear types and fisheries, estimated ALDFG rates may overestimate adverse effects when gear that is abandoned, lost or even discarded does not become derelict because another fishing vessel continues to use the gear. For example, gear that is lost by theft remains in use. Macfadyen et al.4 explained that theft was likely a minor contribution to ALDFG, occurring, for instance, in inshore fishing grounds where static commercial fishing gear and recreational marine activities conflict. However, fishing gear theft may be prevalent in some developing country fisheries (e.g., Cambodian crab traps40). And, there is one gear type where theft has become a globally prevalent, routine and largely accepted practice: Tuna purse seine vessels routinely exchange satellite buoys attached to drifting FADs that they encounter at sea. The stolen FAD, lost by the previous vessel that had been tracking its position, remains in-use and not derelict, although it may eventually become derelict41, 42. Furthermore, because ALDFG leakage rates may be highest in illegal and unregulated fisheries4, if only legal fisheries are sampled, then this may produce underestimates. Thus, accounting for theft and illegal and unregulated fishing would increase the certainty of estimates of ALDFG leakage rates for some fishing gear types.
The 20% ALDFG global production rate value used for anchored FADs by pole-and-line fisheries was likely an underestimate. We relied on a single value from the contemporary Maldives pole-and-line fishery’s government-owned and -managed network of anchored FADs. This fishery underwent a substantial reduction in anchored FAD loss rate, from 82 to 20%, by improving designs and a government incentive program that pays fishers to retrieve FADs when they break from their moorings35, 43. For comparison, describing Indonesia’s pole-and-line fishery’s anchored FADs, Widodo et al.44 stated: “Inaccuracy of number and position of FADs in the fishing ground are the outstanding issue facing by fisheries manager…This was largely the result of the current lack of effective systems of FAD registration and monitoring, and also because of the desire of fishing companies and vessel skippers to keep FADs position information confidential. [sic]”.
Proctor et al.45, who estimated that between 5000 and 10,000 anchored FADs are used in Indonesian tuna fisheries, also reported a lack of accurate estimates of the numbers and locations of anchored FADs due to ineffective implementation of the government registration system and to high loss rates, including from storms, strong currents, vandalism, vessel collisions and wear and degradation of the FADs. Using the estimated rates of (1) Shainee and Leira43 that 82% of anchored FADs were lost per year prior to the Maldivian government’s incentives program, which might accurately characterize the Indonesian and other anchored FAD networks used by pole-and-line fisheries, and (2) the 20% loss rate value from Adam et al.35, the posterior mean = 0.506 (95% HDI: 0.15–0.84). Thus, 51% might have been a more appropriate estimate for a global ALDFG production rate for pole-and-line anchored FADs. The Maldivian and Indonesian pole-and-line fisheries, which combined supply over half of global pole-and-line catch, rely heavily on anchored FADs, as do several other smaller pole-and-line fisheries (e.g., Solomon Islands, segments of the Japanese pole-and-line fleet)35, 45,46,47,48.
Units for ALDFG rates are highly variable. Records using different rates cannot be pooled for synthesis research29, 34. For example, some records reported rates of the percent of number of panels (sheets) or fleets (strings) of gillnets that were lost, while others reported the percent of the length or area of gillnets that were lost29. Similarly, for longline gear, some studies reported the percent of the length of the mainline, while others reported the percent of the number of branchlines/snoods that were lost34. Employment of agreed harmonized units for ALDFG rates are needed.
Future assessments could use a ratio of ALDFG risk-to-seafood production to assess gear-specific relative risks locally and globally, similar to assessments of vulnerable fisheries bycatch by using bycatch-to-target catch ratios49. This would enable the assessment of risk from ALDFG to be balanced against meeting objectives of food security and nutritional health.
Relationship between alternative indices with the quantity of fishing gear
We used gear-specific annual catch and area of fishing grounds as indicators of the relative global amount of each gear that is used annually as two terms in the model to assess gear-specific relative risks from ALDFG. However, the assumption of a linear relationship between these indices and gear quantity is questionable for similar reasons that have been raised with the relationship between various indices of effort (number of fishing hours, number of vessels, engine power, vessel length, gross tonnage, gear size, hold capacity, as well as kWh) and catch. For example, the ratio of catch from one set by an anchoveta purse seiner to the volume or weight of the gear is likely substantially different than for pots or driftnets. Not only is the relationship between catch and amount of gear variable by gear type and target species, there is also high variability within gear types—by fishery and within fisheries—due to the broad range of factors that significantly explain fishing efficiency per unit of nominal effort50, 51. Similarly, the relationship between catch weight and number of fishing operations varies substantially across gear types. For example, an industrial tropical tuna purse seine vessel might have a total catch of about 37 t per set on a drifting FAD27 while a tuna pole-and-line vessel catches about 1 t per fishing day52.
Similarly, the relationship between the area of fishing grounds and amount of gear may vary substantially between gear types. A small number of vessels using a relatively small magnitude of active, mobile gear may have a much larger area of fishing grounds than a large number of vessels and shore-based fishers using a large amount of passive and static gears. For example, about 686 large-scale tuna purse seine vessels fish across the tropics53, while gillnets, which may be the most globally prevalent gear type, are used predominantly within 20 nm (37 km) of shore, most intensively in southeast Asia and the northwest Pacific54.
Fishing effort has also been estimated using engine power as well as by using energy expended, such as in kilowatt-hours (kWh), the product of the fishing time and engine power of a fishing vessel, including non-motorized vessels55,56,57. We did not use these metrics for effort because the correlation between rate of production of ALDFG and vessel engine power or kWh, including of non-motorized vessels (1.70 million of the estimated global 4.56 million fishing vessels21), has not been explored. In general, vessel power and power per unit of fishing period largely distinguishes between mobile and passive gears, where the former (e.g., trawls, dredges), use substantially more vessel power per weight of catch than passive gears (traps, gillnets). Also, estimates using these fishing effort metrics used a small number of aggregated gear categories and extrapolated estimates primarily from sampled developed world fisheries (however, see56). These effort indices would also prevent inclusion of shore-based fishing methods.
There have been recent gear specific estimates of effort, in units of time spent fishing and the estimated energy expended (fishing power * fishing time), using Automated Identification Systems (AIS) data, which are available for industrial fishing vessels, primarily using longlines, trawls and pelagic purse seines6, 58. AIS data provide coverage of the majority of large fishing vessels ≥ 24 m in overall length58. However, this accounts for only about 2% of the number of global fishing vessels (of an estimated 4.56 million global fishing vessels, about 67,800 are ≥ 24 m in length21).
ALDFG monitoring, management and performance assessments
A sequential mitigation hierarchy of avoidance, minimization, remediation and offsets can be applied to manage ALDFG29, 59. Referring to the three components of relative risk assessed by this study, avoidance and minimization of risks from ALDFG is achieved by reducing the ALDFG leakage rate, fishing effort, and/or adverse consequences from derelict gear. Remedial methods reduce adverse effects, such as reducing ghost fishing by reducing the duration that ALDFG remains in the marine environment1, 29, 60. In general, preventative methods are more cost effective than remedial methods—it is less expensive to prevent gear abandonment, loss and discarding than it is, for example, to detect and then disable or remove derelict gear61. Methods to prevent ALDFG include, for instance, spatially and temporally separating passive and mobile fishing gears, having bottom trawlers avoid features that could snag the net such as by using high-resolution seabed maps, tracking the real-time position of unattended fishing gears using various electronic technologies, and using gear marking to identify the owner and increase the visibility of passive gears. Furthermore, because some remedial methods, such as using less durable materials for fishing gear components, can reduce economic viability and practicality, preventative methods and remediation through quick recovery of ALDFG may be more effective as well as elicit broader stakeholder support29, 62.
To assess the performance of global ALDFG management interventions against this study’s quantitative benchmark, substantial deficits in monitoring and surveillance of fisheries’ waste management practices must first be addressed1. Of 68 fisheries that catch marine resources managed by regional fisheries management organizations, 47 lack any observer coverage, half do not collect monitoring data on ALDFG, and surveillance and enforcement systems are rudimentary or nonexistent in many fisheries1, 63.
Findings from this quantitative, global assessment of ALDFG risks guide the allocation of resources to achieve the largest improvements from preventing and remediating derelict gear from the world’s 4.6 million fishing vessels. With improved data quality and governance frameworks for fishing vessel waste management, including ALDFG, we can expect reductions in ecological and socioeconomic risks from derelict gear.
Source: Ecology - nature.com
