Marine Policy 64 (2016) 46-54 Contents lists available at ScienceDirect
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Table 3
selectivity is also affected by towing speed and duration as well as fish sustained and burst swimming capacity, and fish condition [63,64]. By contrast with the species-specific factors, such as mor-phology and behaviour, and the environmental conditions during fishing operations, all factors related to the gear and the fishing tactic are modifiable to some degree, thus manageable (Table 2). Many technical management tools are implemented to regulate fishing selectivity, many of which relate to gear characteristics (Table 3). Because fishers are the ones who ultimately decide about fishing selectivity, though under constraints of regulations and markets (Fig. 2), changes are more likely to be implemented when fishers are committed to the management measure [38]. Maintaining a high catch of commercial-sized individuals of the target species is, for example, an important condition to have good acceptance and use of novel or modified gears by professional fishers [60,7]. 4.2. Exploitation patterns further depend on large spatio-temporal dynamics Exploitation pattern is affected by all factors that modify fishing selectivity, added to factors of population availability to the dif-ferent gears deployed in the fishing area (Table 2). Exploitation pattern depends on the interaction between population, or com-munity, spatio-temporal dynamics and the fleet spatio-temporal dynamics. Fishing areas and seasons are key factors of exploitation patterns. In a given area at a given season, the targeted catch de-termines the combination of fishing gears deployed, and the fishing fleets' strategies, i.e. the combinations of fisher tactics over the year. In a fish population, the spatio-temporal distribution of size and/or age groups depends, among others, on environmental conditions including tide, currents and water temperature, and fish ecology, e.g. reproductive and migratory behaviour (Fig. 3). The local aggregations of fish that are available to the gears may have length distributions that differ from the entire population [43]. At the community level, the same factors apply, but the combination of species in a given area at a given season and their interactions have to be considered (Table 2). The factors related to natural population dynamics and com-munities cannot be managed. In contrast, fleet dynamics, espe-cially fishing areas, seasons and the combination of gears deployed in the fishing ground are factors that can be managed. E.g. spatial and seasonal closures were shown to be effective management tools to maintain resources biodiversity and productivity [10]. Regulations can also limit fishing capacity and effort (Table 3), but few technical tools aim at managing the combination of fishing activities. Fishing licences can be set to manage the number of vessels undertaking each metier, i.e. combination of gear and tar-get species, but to date, little consideration has been given to the combination of metiers deployed on a given fishing ground. 4.3. Catch utilisation further depends on markets and regulations Catch utilisation depends on all factors influencing the sorting process (Fig. 3, Table 2). Economic incentives have been identified through interviews of fishers to be the main reason for discarding, especially for non-commercial species and species of low value [3,44,45]. In many cases, regulations such as quotas and minimum landing sizes can also be important reasons for discarding [14,48]. These factors are not independent, e.g. economic incentives oper-ate under constraints of regulations. Quotas, for example, are a strong constraint in the economic strategy implemented by fishers to optimise their landings [40,48]. In the North Sea trawl fishery for instance, the small legal-sized individuals of plaice can be discarded in large quantities in order to save quotas for larger individuals with higher commercial value [48]. The volume and diversity of the catch, resulting from the gear selectivity, affect the duration and complexity of sorting (Fig. 3, Table 2). In purse seiners for example, if the catch is too diverse the entire haul is slipped, that is, released before being brought on-board. Time constraints and hold space limitations can also gen-erate a significant part of the discards [41]. The sorting behaviour of the crew also depends upon market constraints on quality, species-specific spoiling rates [41], meteorological conditions, crew size and experience, and sorting habits [17]. Local social norms, social preferences and opinions about discarding practices were also reported to significantly affect the sorting process [17]. Catch utilisation can be managed through selectivity, and/or regulations and markets. Full retention or banned retention of certain species and sizes, through controls on catch utilisation, can create incentives for fishers to modify fishing selectivity. For ex-ample, a requirement that all tuna species caught by purse seine vessels were fully retained has been proposed to create an in-centive for the vessels to avoid catching small sizes [25]. On the other hand, retention of at-risk shark species, such as thresher sharks, has been banned in several tuna fisheries worldwide with the aim to reduce the fishing mortality of these species [25]. Regulations can be directed at the landed part of the catch or at the total catch (Table 3). In many cases, landed-oriented regula-tions create obligations or incentives to discard ; while control and enforcement are more difficult and/or expensive for catch-or-iented regulations [57,58]. Fisheries certifications and ecolabels are another tool to influence catch utilisation through the market constraints (Fig. 2, [22]). 5. Understanding and monitoring Managing anything requires a minimum knowledge – under-standing how management decisions are likely to affect the sys-tem and have the desired outcome; and monitoring progress to-wards the management objective(s). Knowledge availability greatly differs between fishing selectivity, exploitation patterns, and catch utilisation. 52 L. Fauconnet, M.-J. Rochet / Marine Policy 64 (2016) 46–54 
 
0 10 20 30 40 50 60 Length (cm) Fig. 4. Schematic selection curves for (a) gears with maximum retention for the largest lengths, such as trawls and pots and (b) gears with retention restricted to intermediate lengths, such as gillnets and trammel nets. Three levels of sharpness or width of fishing selectivity are displayed, with sharp/narrow selectivity (black), medium (dark grey), and smooth/wide selectivity (light grey). 5.1. Best available knowledge for fishing selectivity To understand fishing selectivity, thorough knowledge on fish behaviour and on how gears catch fish is required. For estimating the available selectivity, comparison between individuals that were available to the gear and those that avoided it is necessary. The behaviour of fish in front of the gear can be observed through technologies ranging from simple camera systems to advanced acoustic systems, or a combination of both [27]. For estimating the contact selectivity, comparison between the individuals that came in contact with the gear and those that escaped is necessary. For towed gears, it can be carried out using comparative or direct experiments, either by hauling simultaneously or alternatively a test and a control cod-ends (the latter retains “all” fish entering the cod-end), or by adding a small-mesh cover over the cod-end, which catches the individuals escaping through the meshes [43,67]. The probability that a fish of a given length will be re-tained by the gear is often estimated through mathematical models [67] and displayed as selection curves. The shape and width or slope of the selection curve inform on the gear selectivity (Fig. 4). The relatively small, operational scale, and the long experience in gear technology explain the good knowledge on fishing se-lectivity, especially for contact selectivity for which direct ob-servations can be carried out. This knowledge is not equal among species and gears though. For example, passive gear selectivity is less well known than trawl selectivity, and for most gears, there is no consensus on the most suitable shape of the selection curves. The knowledge on available selectivity is more limited, in parti-cular for non-target species, because it relies on complex and in-direct measurement methods. 5.2. High complexity for exploitation pattern To estimate population exploitation patterns, knowledge of the selectivity of all gears which target this population is necessary, but not sufficient. Spatio-temporal information on all catches and the population are also required. Such information is generally known for commercially important species that are analytically assessed [31], but not for non-target species [37], even though several methods including length-cohort analysis and the swept-area method [49] and methods for Sustainability Assessment for Fishing Effects (SAFE; [71]) were developed. At the community level, the complexity of dealing with several species and large scales makes some simplification necessary. Different approaches to aggregation have been used: by trophic level [6], by functional group [54] or by length or weight [11,52]. Characterizing exploitation patterns requires to describe the distribution of fishing mortality across components (whatever they be). To account for the whole fishing mortality, the indirect part, i.e. death, injuries or increased predation resulting from es-capment or avoidance of the gear and ghost fishing, should also be estimated and added to catch mortality [5]. Only describing the catch, i.e. the direct part of fishing mortality, might not be an easy task, since estimating catch mortality requires to measure what was extracted with respect to what was present in the area. However, every observation method provides a different picture of community components, e.g. survey trawls have different catch-abilities for each species [21]. Since no observation method is able to equally sample the whole community, the exact composition (in species, size, age, etc.) of marine communities remains unknown. So even if the catch composition can be integrated at the com-munity level, it proves difficult to standardise with respect to what it was extracted from [20]. 5.3. Knowledge is improving but still partial on catch utilisation Knowledge of discarding has been developing only over the last two decades, in particular thanks to the development of onboard observer programmes, after the significance of the issue was highlighted [1,30]. Onboard observer programmes provide species and size composition of catches separated into landings and dis-cards [30,8]. Observer data also include information on the con-ditions of the trips and fishing operations, gear and targeted spe-cies, etc. [13,15,66]. They provide a valuable source of data to characterize and quantify discards and thus to better estimate catch utilisation. Landed or discarded fractions are common measures of catch utilisation considered from different view-points. The landed fraction (landings divided by catches) describes the adequacy between what was caught and what was profitable to fishers or/and satisfied some consumer demand. The discarded fraction measures to what extent the catch was not used, or usable, for human purposes. These estimates are often associated with high uncertainty though because of high variability in discard
practices, which is exacerbated by the limited fraction of the fishing activity that can be observed in many fisheries due to budget and human constraints [4,61]. Further, to better under-stand and manage catch utilisation, discarded or landed fractions are not sufficient, a thorough investigation of the reasons for dis-carding is also necessary. Reasons for discarding have been only sporadically examined through interviews during [44] or after the sorting process [3,41,45], or by inference from the catch compo-sition [9]. Such knowledge is often lacking, while it is essential to determine to which extent fishing selectivity can be adjusted to reduce discards. 6. Conclusions
References
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