Análisis e interpretación de resultados

This study shows the wealth of parameters that have been used to examine sensitivity to fishing activities. The multitude of parameters reflects, in part, the different

approaches taken, the different purposes for which the studies were undertaken and the availability of information. Natural variability and the range and complexity of responses to impacts have also required a range of parameters to be used for assessment.

Rochet and Trenkel (2003) reviewed the applicability of several parameters to the assessment of the sensitivity of fish communities. They evaluated a variety of parameters based on four important criteria: meaning; expected effect of fishing; exclusiveness; and measurability. They concluded that the most operational descriptors are those that apply to populations and that community and ecosystem descriptors need further development. Although a large variety of indicators have been developed as demonstrated in their review, few have been validated, very few have associated reference points, and still fewer could be delivered to managers for use in decision making.

Body-size distribution (size spectra) has been suggested as a useful descriptor of exploited communities. It is particularly relevant to fishing, which is always size- selective, so it would be expected that fishing should remove larger fish thereby releasing smaller fish from predation (Shin et al. 2005). Size-based descriptors can consider a range of fisheries impacts rather than simply just target species. Rice and Gislason (1996) simulated a multi species virtual population analysis to show that fishing should lead to a decreasing average length of individuals caught. Bianchi et al. (2000) came to the same conclusion. Hiddink et al. (2006) suggested that size-based models can be used to predict large-scale patterns in biomass, production, and species richness of benthic invertebrate communities. However, methodological constraints based on many simplifications and assumptions make it difficult to determine precisely how fishing affects size spectra (Rochet and Trenkel 2003), and as with many other descriptors, size spectra is not strictly specific to fishing impacts (Shin et al., 2005). Community indices such as diversity have been evaluated as indicators of fishing effects in a number of studies (Ball et al., 2000; Collie et al., 1997, 2000; Kenchington et al., 2001; McConnaughey et al., 2000; Schratzberger and Jennings 2002; Thrush et al., 1998; Tuck et al., 1998; Veale et al., 2000). Diversity is a description of species richness and evenness (dominance). The rationale behind using diversity indices to measure the effects of fishing is that ecosystems have emergent properties (e.g., intrinsic diversity) which can be altered through unequal removals of target and non- target species (Rice 2000). Jennings and Kaiser (1998) argued that fishing should reduce diversity by selectively removing species. However, diversity has been

reported to increase under fishing pressure due to the increases in the evenness index when fishing reduced dominance in an assemblage by decreasing the most abundant stocks (Bianchi et al. 2000). Diversity indices are often difficult to interpret or predict though, as they tend to treat all species as equally informative, and given the selectivity of fishing gear, this is unlikely in reality (Rice, 2000). Species richness is also very difficult to be measured in most marine environments (Rochet and Trenkel 2003). Biological traits analysis considers a range of biological taxon characteristics to assess how functioning varies between assemblages (Bremner et al. 2003, Tillin et al. 2006). Tillin et al. (2006) analysed the relationship between life history and functional roles within the ecosystem in response to trawling intensity using multivariate analyses. Traits considered included those used in the MarLIN sensitivity approach (age of sexual maturity, feeding type, food, mobility, longevity, reproduction, size etc.). Approaches such as these (Tillin et al. 2006, de Juan et al. 2009, Tyler-Walters et al.

2009) using ecological function and life history descriptors, are a useful way to

measure changes in ecosystem function in response to anthropogenic impacts such as fishing (Tillin et al., 2006). Jennings et al. (1998) used abundance, fishing mortality and life history data in order to examine the effects of fisheries exploitation. Their results indicated that a suite of biological traits determine the response to exploitation but such responses cannot be compared without first accounting for phylogenetic relationships among taxa. The use of total mortality has also been put forward as a strong descriptor as it has a clear meaning and predictable effects of fishing, including reference points (Rochet and Trenkel 2003).

Trait based parameters may be a promising way forward because the effects of fishing on many traits can be predicted (Rochet and Trenkel 2003). However, such

parameters have a high data demand, which is often unavailable (Jennings et al., 1998) and no single trait can be said to be exclusive to fishing effects (Rochet and Trenkel 2003). Such analyses highlight the need for more accurate biological and ecological information on species in order to inform management decisions (Tillin et al. 2006). In addition, traits alone do not always predict effect or recoverability accurately. Tyler-Walters et al. (2009) found that in most cases fishing sensitivity assessments based on traits alone agreed with assessments based on direct evidence but in some key instances disagreed. For example, while the horse mussel Modiolus modiolus is long lived, has a high fecundity, produces large numbers of pelagic larvae with a high dispersal potential, their recruitment is sporadic and poor. One population in particular was thought to be senescent, experiencing little or no recruitment in decades (Comely 1978).

It has been suggested that ecosystem representations (models) are required in order to describe the biomass flows between the different elements of exploited ecosystems and to provide predictive answers to fishing management questions that cannot be provided through real world studies (Pauly et al. 2000).

Fulton et al. (2005) used the ‘Atlantis’ simulation model to evaluate the performance of a suite of ecological parameters covering species, assemblages, habitats and

ecosystems in response to the effects of fishing gear as well as broad-scale pressures (e.g., increased nutrient loads). Their results suggest that community descriptors such as biomass, diversity, production and size structure are the most reliable in detecting the impacts of fishing. Pauly et al. (2000) used the ‘mass-balance’ model ‘Ecopath’ to simulate the ecosystem impact of fisheries. The model serves to predict changes in biomasses and trophic interactions through time and space. The authors concluded that ‘Ecopath’ offers promise as a tool for evaluating impacts of fishing on ecosystems but it is not fully capable of representing the trophic flows associated with many large aquatic species. Certain draw-backs of model-based approaches include the

suggestion that such models rely on unverified assumptions and require extensive data which can be unreliable (Rochet and Trenkel, 2003). Models do not capture the real world perfectly, and values are based on numerous assumptions and relationships that all have a degree of uncertainty (Hiddink et al., 2006). Further development is needed before such models can be used to evaluate the effects of large marine fisheries (Rice, 2000).

Extensive research looking at a variety of parameters has concluded that no single descriptor or parameter can effectively or reliably explain the impact of fishing on community structure and habitat response (Rice, 2000). It has been suggested however, that instead of using a single parameter to measure habitat response, a carefully selected suite of attributes/descriptors would be more useful to encapsulate the effects of fishing (Fulton et al., 2005). However, it is still difficult to evaluate descriptors with a known level of rigour and to interpret the results with a high degree of scientific objectivity (Rice, 2000).