Reviews in Fisheries Science & Aquaculture
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Review of the Fisheries Indicators for Monitoring the Impacts of Fishing on Fish Communities Justin Kantoussan, Raymond Laë & Mbaye Tine To cite this article: Justin Kantoussan, Raymond Laë & Mbaye Tine (2018): Review of the Fisheries Indicators for Monitoring the Impacts of Fishing on Fish Communities, Reviews in Fisheries Science & Aquaculture, DOI: 10.1080/23308249.2018.1458282 To link to this article: https://doi.org/10.1080/23308249.2018.1458282
Published online: 19 Apr 2018.
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REVIEWS IN FISHERIES SCIENCE & AQUACULTURE https://doi.org/10.1080/23308249.2018.1458282
Review of the Fisheries Indicators for Monitoring the Impacts of Fishing on Fish Communities Justin Kantoussana, Raymond La€eb, and Mbaye Tinea a UFR des Sciences Agronomiques de l’Aquaculture et des Technologies Alimentaires (UFR S2ATA), Section Aquaculture, Universite Gaston Berger (UGB), Saint-Louis, Senegal; bIRD UMR LEMAR (CNRS/UBO/IRD/Ifremer), IUEM Rue Dumont d’Urville, Plouzane, France
ABSTRACT
The high demand for fish products has led to important economic issues including the emergence of new markets, especially in developing countries where fishing represents an important economic sector and the main source of animal protein for local communities. The worldwide fisheries have overall passed their peak and many stocks are overfished despite the increasing efforts towards the developpement of fisheries assessment and management tools using global and analytical models. These complex models used for fisheries management, especially in many developing countries, require large amounts of data whose collection and analysis are very costly and expensive. The current studies of the effects of overexploitation on fish communities use an indicator-based approach as an alternative to improve the assessment and management of the fisheries resources. This latter approach is cheaper, simpler and more efficient to establish a link between disruption and its effect on a biotic component. The currently most commonly used indicators have provided successfully results in many studies cases carried out on fish communities. Thus, management approaches based on indicators in the recent years has improved our understanding of the structure and functioning of ecosystems, and therefore, facilitated the interpretation of biological phenomena.
Introduction Fishes are amongst the most important food sources for human population, particularly in developing countries where they represent the main source of animal proteins. In addition of providing a considerable proportion of animal protein, fish and fisheries activities are economically important because they provide job and other investment opportunities, particularly for the poor rural communities who essentially depend on fisheries income for survival (Allison et al., 2009; FAO, 2014). With the growing human population, the demand of food resources has considerably increased, leading to an overexploitation of wild fish populations. Such a situation has resulted to resource fisheries collapses and altered the genetic diversity of many exploited fish populations, therefore calling for an urgent need of their renewal (FAO, 2014; Worm et al., 2009). The worldwide decline of fisheries stocks raise several questions regarding the monitoring, management, sustainable use and conservation of these resources. Although national governments and international
KEYWORDS
Fish communities; fishing impacts; indicators; metric index
organisms are increasingly making a lot of efforts to develop sustainable and durable management plans, the problem is not yet completely solved. The fisheries management means managing fisheries stocks, fishermen but also fisheries related infrastructures such as fishing vessels including rafts, dugout, canoes and boats. All these aspects have to be fully considered when establishing fisheries management plans. The monitoring and management of fisheries resources were traditionally performed using empirical models based on the analysis of fishery statistics such as stockrecruitment estimations or fish productions from the productivity of the ecosystems. It is commonly agreed that fisheries management requires minimising the direct effects of fishing but also defining a more global approach that take into account various impacts and several different components of an ecosystem. For this raison, fisheries managements are developing more comprehensive approaches, which are gradually switching from a single stock-based approach to a global ecosystem approach (Gislason et al., 2000; Sainsbury et al., 2000;
CONTACT Justin Kantoussan
[email protected] UFR des Sciences Agronomiques de l’Aquaculture et des Technologies Alimentaires (UFR S2ATA), Universite Gaston Berger (UGB), Route de Ngallele BP 234, Saint-Louis, Senegal. ORCID: 0000-0001-5522-9767 © 2018 Taylor & Francis
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Witherell et al., 2000; FAO, 2003; Roux and Shannon, 2004; Shannon et al., 2004; Swaleh et al., 2015; Lockerbie et al., 2016). This latter approach requires the development of tools that allow improving the diagnosis and evaluating the effects of fishing on aquatic ecosystems. Thus, indicators have been proposed as privileged tools for monitoring and managing fisheries stocks and their development and utilisation are increasingly been being more and more important since 1990s. In fishery assessment or stock management, biological indicator refers to the use of aquatic organism responses as integrators of the environmental impacts and/or anthropogenic stress on ecosystem health (Adams and Greeley, 2000; Troyer, 2002; Whitfield and Elliott, 2002; Iliopoulou-Georgudaki et al., 2003; Harrison and Whitfield, 2004). Biological indicators may refer to single metric indicators such as species number, diversity and evenness, which are highly variable and were therefore successfully used to assess changes in ecological quality (Martinho et al., 2015). They may also refer to multi-metric indicators such as Estuarine Fish Assessment Index including species richness, number of resident species and number of migrant species which are used to develop a measure of total ecological quality (Martinho et al., 2015; Mostafavi et al., 2015). The indicator approach has been successfully used in the assessment of the contaminant impacts and other environmental stressors in aquatic ecosystems (Suter II, 2001; Greeley, 2002; Power, 2002; Troyer, 2002; Iliopoulou-Georgudaki et al., 2003). Its utilisation in fisheries is increasingly growing and many studies have recently focused on the development of indicators as assessment tool of the effects of fishing pressures on fisheries stocks and ecosystems (Gislason and Rice, 1998; Pauly et al., 1998; Rochet and Trenkel, 2003; Rochet et al., 2005; Bourdaud et al., 2016; Coll et al., 2016; Zgliczynski and Sandin, 2017). This review attempt to summarize current information of the impacts of fishing pressure on fish communities, but it also address current knowledge of indicator applications in fisheries diagnostic and management. The first part of the review is devoted to review the main effects of fishing on fish resources while the second part focuses on the utilisation of indicators for monitoring fisheries. In this second part, we review the current knowledge on indicators and provide examples of their application and relevance in fisheries surveys. The indicators provide simple information on the exploitation of fish resources and state of the ecosystems and can be therefore, used to access the impacts of fishing pressure on fisheries resources and ecosystems.
Impacts of fishing on fish communities The progress of tracking and harvesting techniques as well as that of the packaging of global fisheries resources is a potential factor contributing to the overexploitation of fisheries resources (Murawski, 2000). The considerable efforts made over the last years on developing new techniques of fishing and conservation of fisheries products, have greatly affected the aquatic resources, leading to sever reductions of fish populations and diversity (Albaret and La€e, 2003; Worm et al., 2009). Fishing affects fish communities through changes in the total biomass, species composition and size structures (Botsford et al., 1997; Bianchi et al., 2000; Pauly et al., 2001; Ault et al., 2014). The main direct effect of fishing pressure on fisheries resources is obviously the decrease in abundance of target species, which primarily affects the larger individuals that become rare in the catches and landings (Welcomme, 1999; Stevens et al., 2000; Pauly et al., 2002). The works on fishing in developing countries has specially focused on the impacts of high levels of exploitation of the fisheries resources. These studies have reported many examples of overexploitation of fish stocks (La€e, 1997a; Albaret and La€e, 2003; Balirwa et al., 2003; Laurans et al., 2004; Thiao et al., 2012). The heavy fishing and natural mortalities of large fish individuals can have negative consequences on breeding stocks. Parameters such as egg production or quality, sustainability and recruitments are positively correlated with age, size and experience of egg-laying females (Begg and Marteinsdottir, 2003). Thus, the downsizing of large individuals within a stock has implications on the recruitment success, but also on the structure and dynamics of fish stocks. The response of species to the effects of fishing depends on their own biological characteristics. Some species manage to balance adult mortality with compensatory responses including early sexual maturation and increased fertility (Legendre and Ecoutin, 1989; La€e, 1997b; Rochet, 1998, 2000; Begg et al., 1999; Panfili et al., 2004). The reduction of the generational renewal time of fish populations by early sexual maturation increases the reproductive success of survivors and the possibilities of stock recovery (Law, 2000; Gerritsen et al., 2003; Hutchings and Baum, 2005). However, species with low adaptability are less able to deal with high mortalities, and are therefore, more susceptible to overexploitation (Jennings et al., 1998, 1999). In both cases, the risk of stock collapse are even more important when the exploitation begins to target young fish that have not yet reached the age of sexual maturity (La€e, 1997b; Myers and Worm, 2005). The exploitation of fish juvenile limits
REVIEWS IN FISHERIES SCIENCE & AQUACULTURE
the potential for stock renewal and has negative impacts on fisheries productivity (Blaber et al., 1999; Fromentin and Fonteneau, 2001). Overexploitation occurs when individuals of a fish population are caught so that it cannot replace them by natural reproduction. Overfishing can lead to a decrease of the biological diversity by reducing the species richness and, in some cases, may result in the extinction of a species or groups of species (Myers and Worm, 2005). This specially occurs when the biological capacity of particular species do not allow them to face intense fishing pressure and to ensure population renewal (Rijnsdorp et al., 1996; Greenstreet et al., 1999; Dulvy et al., 2003, 2004). In such a situation, only few species are able to adapt to fishing pressure and eventually dominate the ecosystem. Therefore, information on genetic composition and structure of population is very important for fisheries management. For a good fisheries management, it is necessary to know if the limits of the management area coincide with that of the population. The genetic diversity of a population is determined by its size and all selective forces affecting it. Consequently, isolated populations are more vulnerable to genetic degradation compared to marine populations with large size and dispersive capacities, which allow make them to be less vulnerable to the extinctions. Isolated populations are also more sensitive to inbreeding, which is commonly associated with reduced genetic variability and reproduction, and with the high occurrence of other negative effects such as diseases, predation and other defects in the vital biological functions such as growth and reproduction. Selective fishing consisting of removing large individuals may slow down growth rate and cause early fish maturation, and therefore, lead to alterations of genetic diversity within populations. Fishing has also potential effects on the genetic diversity through its action on the alteration of sex-ratio and sex-specific size frequency (Kenchington and Heino, 2002). Indeed, fisheries captures can be in favour of one sex over another and, therefore, affect the breeding of populations, which may lead degradation of genetic diversity (Enberg et al., 2012). It has been longtime believed that the loss of genetic diversity of marine fish populations induced by intense fishing pressure was limited, essentially due to the large size of exploited populations (Hauser et al., 2002; Kenchington et al., 2003). However, it has been demonstrated that the selection pressure imposed on a fish stock by intense fishing operations can result in a loss of genetic diversity (Law, 2000; Hauser et al., 2002; Consuegra et al., 2005). The conservation of genetic diversity is important for several reasons including the adaptability maintenance of natural populations and the future use of genetic resources for medical purposes
3
(Kenchington et al., 2003). The genetic diversity gives the populations more ability to adapt to changing environments and maintaining high genetic variation is therefore very important to fish stocks. Nowadays, the conservation of biological diversity in the marine environment requires taking into consideration both target species and those indirectly affected by the fishery, including by catches or incidental catches. These categories include all species that are not subject to direct targets by fishing gear but that are still captured. It is widely accepted that accessory and discarded catches are common in the fisheries but difficult to assess and consequently, their extent remains unclear (Chopin et al., 1996; Hall et al., 2000; Pope et al., 2000; Catchpole et al., 2005; Bellman and Heery, 2013; Lescrauwaet et al., 2013). This ignorance may be explained by the fact that the discarded species are of little or no economic value, and are therefore, not considered as priority (Pope et al., 2000; Stevens et al., 2000). The overall average amounts of commercial fisheries catches discharged was estimated to 27 million tons per year (Alverson et al., 1994). An important part of the catches is discarded at sea because it is either comprised of small species or individuals with low economic value or constituted of species whose capture is prohibited (Cook, 2001). According Machias et al. (2001), the average discharge rate in commercial fisheries of the Northeast Mediterranean area (mainly composed of fish) is estimated to be 44% of the total catch. These discharges constitute an additional source of food for scavengers species (Stevens et al., 2000; Gislason, 2001), food intake that could promote the development of opportunistic species.
Concept of indicators Population dynamics models (Ricker, 1954; Beverton and Holt, 1957) such as Schaefer and Fox models (Schaefer, 1967; Fox, 1970) were traditonnaly used to preserve the long-term sustainability of the exploited stocks. Although having a considerable importance, these models have a weakness which is evidenced by the fact that the majority of fisheries throughout the world have passed their peak of sustainable exploitation. Moreover, the complexity of fishing effects on the ecosystem has led to the situation that management based on a single stock is increasingly questioned in favor of a more holistic approach (Degnbol and Jarre, 2004; Walters et al., 2005; Lockerbie et al., 2016). Therefore, it was necessary to develop new and more effective approachs to improve the assessment of exploited stocks. The current situation of exploited stocks and the need to ensure sustainable development of fisheries activities resulted in a growing effort of monitoring and evaluation
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of exploited ecosystems. However, the development of indicators remains a challenge for the scientific community because it is necessary to define useful indicators and test their properties and suitability as an assessment tool of ecosystem status. Indicators are developed in many continents including in Africa (Whitfield and Elliott, 2002; Harrison and Whitfield, 2004), America (USEPA, 2000), Europe (Borja et al., 2000) or in more global frameworks such as the FAO (1995) and OCDE (2004). Also, a international scientific cooperation within IndiSeas Working Group (www.indiseas.org) was etablished for an international evaluation and comparison of the state of marine ecosystems under fishing pressure and environmental drivers, but also for communicating the obtained results to large public audiences including scientifics, decision makers and managers (Blanchard et al., 2010; Shin et al., 2010a, 2010b, Shin et al., 2012; Shin and Shannon, 2010). The term indicator in this study correspond to the one proposed by Jackson et al. (2000), definition that was taken up by Kurtz et al. (2001). These authors define the indicators as signs or signals that relay a complex message, potentially resulting from many sources in a simple and useful way. Biological indicators are indicators based on a living part of an ecosystem whose fluctuations are considered to reflect the state of the environment and ecological impacts (Linton and Warner, 2003). The indice provides a single number or value that summarises the complexity of the environment. It can be related statistically to a wide range of physicocal, chimical and biological mesures (Pinto et al., 2009). In fisheries, the use of indicators as a tool for monitoring and assessing the effects of fishing is still subject of much debate. The relevance, sensitivity and reaction time of indicators are indeed the subject of many questions. The reflection also involves the identification of the most appropriate indicators to assist fisheries management (Caddy et al., 1998; Gislason and Rice, 1998; Pauly et al., 1998; Rochet and Trenkel, 2003; Trenkel and Rochet, 2003). Many criteria are proposed to assess the relevance of an indicator in a fishery context. For some authors like Linton and Warner (2003), the choice of an indicator depends on the objectives of the assessment and management methods, knowing that they can be very variable. Despite the inevitable differences, the authors who have worked on this issue agree on a minimum set of additional criteria for the selection of indicators. According to several authors (Khlebovich, 1997; Duquesne et al., 2000; Camacho-Sandoval and Duque, 2001; Dale and Beyeler, 2001; Rice, 2003; Rochet and Trenkel, 2003; Rice and Rochet, 2005) indicators, particularly biological indicators must be: (1) easily measurable; (2) abundant, easy to sample and identifiable; (3)
sensitive to forcing imposed on the system; (4) they must also respond to a predictable and specific forcing; (5) the response must vary weakly; 6) they must generate an understandable and interpretable response; (7) they must also provide a representative picture of the environmental condition; (8) and finally lead a monitoring cost and affordable assessment. The indicator response is judged in terms of relevance (link with the estimated effect), effectiveness (reliability in terms of precision, accuracy with the risk of a false evaluation) and performance (ability to detect and/or predict trends) (Rochet and Trenkel, 2003; Trenkel and Rochet, 2003; Fulton et al., 2005). Several indicators are currently being used as management tools in different environments with the above mentioned criteria. Organizational levels of biological indicators and their scale response The study of the ecosystem health through its biotic component can be addressed at different levels of biological organization, from the molecular level to the upper levels corresponding to the population or community (Table 1). In general, forcing affects first the lower organization levels such as cells or tissues. The higher levels of organization are affected by increases in stress amplitude. The reaction time at higher levels is longer and can take months or even years (Adams, 2002). The ecological relevance of indicators differs according to the biological levels. The most sensitive indicators, i.e. those that respond first to the stress, are less relevant from an ecological point of view because it is often difficult to differentiate the response of the indicator to stress from the normal biological variation within the tissue or organ. Indicators that have a latest response are less sensitive, but allow an easy identification of the stress that induced the response (Attrill, 2002). Biological indicators developed at low levels of biological organization scale remain very interesting because they serve as a basic and earlywarning mechanism for a better management that could lead to a more effective environment health (Adams, 2002). Interest indicators The fisheries management in many developing countries is made from complex models that require large amounts of data whose costs for collection and analysis are high and the results often complex and difficult to understand (FAO, 1999). Therefore, it is necessary in this context, to address the problem differently, for example by developing cheaper, simpler and more efficient methods to establish a link between disruption and its effect on the
REVIEWS IN FISHERIES SCIENCE & AQUACULTURE
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Table 1. Most representative indicators measured at various levels of biological organisation (adapted from Adams and Greeley 2000; Adams 2002). Biological organisation level Indicators
Sub-cellular or cellular / Biochemical
Indicators
Mixed-function oxidase (MFO) enzymes activities Bite metabolites
Indicators Indicators
DNA integrity Stress proteins
Indicators
Antioxidant enzymes
Response time Relevance (Adams, 2002)
Minutes / Hours Basic mechanisms
Cellular or tissue / Physiological
Organ / Histopathology
Individual / organism
Population
Community
Creatinine
Necrosis
Growth
Abundance
Richness
Transaminase enzymes Triglycerides Steroid hormones
Macrophage agregates Parasitic lesions Functional parenchyma Carcinomas
Total body lipid
Size and age distributions Sex ratio Bioenergic parameters Reproductive integrity
Index of biotic integrity Sensitive species Feeding types
Minutes / Hours Basic mechanisms
biological component (Johnson and Collier, 2002; Hauge et al., 2005; Jennings, 2005). The indicator-based studies can help to identify the influence of the disturbances on different levels of biological organisation as population and community levels. The indicators can serve as a basis for implementing more effective interventions to improve the health of organisms and fisheries (Adams and Greeley, 2000). Indicators are also an important tool for communicating scientific results to policy makers and managers (FAO, 1999; Harrison and Whitfield, 2004; Degnbol, 2005; Shin and Shannon, 2010). According Garcia and Cochrane (2005), one of the recurring problems in the management of fishery systems is related to the lack of communication between their different human components. Indicators are also used for evaluating the effectiveness of management measures. For example, in the case of a marine protected areas (MPAs), they can be used to evaluate the ecological and biological impacts of the protected area (Pelletier et al., 2005; Ecoutin et al., 2014; Sadio et al., 2015). Furthermore, ecosystems have multiple properties whose preservation is difficult (Cury and Christensen, 2005). Therefore, indicators are developed to address complex environmental problems and to help guide management decision (Kurtz et al., 2001). They are used to characterize the status, predict trends and identify forcing factors (Kurtz et al., 2001; Rochet and Trenkel, 2003; Degnbol and Jarre, 2004; Fulton et al., 2005). The indicators can also be used, in some cases, to compare fisheries or to study their evolution over time (FAO, 1999). Candidates indicators for fisheries management Rochet and Trenkel (2003) distinguish three groups of fisheries indicators classified according to their level in the hierarchy of biological organization. The first group is called Population Indicators and includes the intrinsic
Days Basic mechanisms
Organo-indices Condition factor Gross anomalies
Weeks Ecological relevance
Months Ecological relevance
Diversity indices Taxonomic diversity indices Years Ecological relevance
growth rate (r), total mortality rate (Z), exploitation rate (Z/F) and average size of catches (Lbar). The second group named as Assemblage Indicators describes the characteristics of populations such as diversity, dominance, species composition that are affected by the fishing pressure. Finally, Community Indicators represent the group that focuses on networks of interactions among populations or individuals (e.g., total biomass, trophic composition, trophic transfer rate). The development of fisheries indicators must take into account the spatial dimension of fisheries including the spatial heterogeneity of habitat, distribution of resources, genetically distinct sub-populations, fleet dynamics, behavior and spatiotemporal adaptation of the fishermen (Babcock et al., 2005; Shin et al., 2018). Spatial indicators have been developed and applied to the Benguela ecosystem by Freon et al. (2005). Points, directions, reference trajectories of indicators for fish stock management The question of establishing benchmarks arises both in the stock management framework based on dynamic methods of traditional populations in the context of new approaches such as the use of indicators. The reference points in management can be grouped into two categories. Target reference points (TRPs) define the desired conditions of a stock whereas the limit reference points (LRPs) determine the critical values, that when exceeded, harms fish stocks and fisheries (Sainsbury et al., 2000; Caddy, 2002; Campbell, 2004b; Jennings and Dulvy, 2005). The points of the most common reference generally relate fishing mortality (F) or total mortality coefficient (Z D F C M), maximum sustainable yield (MSY), maximum yield per recruit (YPR) (Die and Caddy, 1997; Caddy, 1999; Witherell et al., 2000; Collie and Gislason, 2001; Caddy, 2002). These benchmarks set resource
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limits and also allow to follow the evolution of the resource or the performance of a management action (Garcia and Cochrane, 2005). While the definition of benchmarks is applicable at the stock level, the problem becomes complex at the assemblage or ecosystem level. Indeed, the assessment of a stock is based on mechanisms such as stock recruitment relationship whereas the ecosystem is entirely empirical (Link et al., 2002). The establishment of benchmarks within the framework of an ecosystem approach is very complex due to poor data quality, selectivity of fishing gears and the need to incorporate environmental parameters and life history traits of species (Link et al., 2002; Degnbol and Jarre, 2004; Jennings and Dulvy, 2005; Rochet et al., 2005). For all these reasons, it is commonly accepted to simply define directions or reference trajectories through an ecosystem approach. Directions or paths in response to forcing factors are well established for most indicators (Kurtz et al., 2001; Rochet and Trenkel, 2003). For example, stress due to organic enrichment in nitrogen or carbon has negative impacts on benthic species diversity, richness, biomass of benthic community except for opportunistic or tolerant species, which will increase in proportion (Kurtz et al., 2001). In such a situation, the specific composition of the community will be changed globally These modifications due to organic enrichment were also observed in fish communities under high fishhing pressure and/or environmental drivers as hypersalinity (Blanchard et al., 2004; Ecoutin et al., 2010).
Theories and applications of some indicators in fisheries mangement The basis of any indicator is the existence of a valid theoretical foundation that defines the direction of change and the expected response of the indicator in case of disruption. The indicators described in this section, as examples, are those studied by many authors as Rochet and Trenkel (2003) at assemblage and community levels and the most used in fisheries diagnostic: (i) indicators of fishing effort; (ii) indicators of assemblage structure; (iii) indicators of size structure; (iv) abundance indicators; (v) indicators of trophic dynamics (Table 2). Indicators of fishing effort The exploitation of fish resources requires often various fishing techniques and a wide range of fishing strategies. The range of gears is permanently changing according to the dynamics of the resource at short-scale (seasonal) or in trend in the event of progressive increase in fishing effort (Welcomme, 1999, Kantoussan et al., 2007,
McCluskey and Lewison, 2008). Fishing gears are changed according to the target species and the composition of exploited populations (McClanahan and Mangi, 2004; Salas and Gaertner, 2004). When the exploitation level increases and large fish become scarce, fishermen adapt the fishing strategies by reducing the sizes of mesh and/or changing fishing techniques (Albaret and La€e, 2003; La€e et al., 2004). Such changes in fishing effort structure with the use of fishing gears with larger fishing capacity and small mesh often made of monofilament, more frequent and longer fishing trips reflect a priori a situation of intensive fishing pressure on the stocks directed towards small species and are specially designed to maintain the catch at a satisfactory level for fishermen. Indicators of assemblage structure Assemblage diversity is studied from the beginning of ecology and can be defined by two criteria: the wealth that is the number of categories of elements or taxa (species, genera, families) and regularity that measures how biomass or numbers are distributed among the taxa (Hill, 1973; Rice, 2000; Maignan et al., 2003). Several indicators exist for synthesizing the overall diversity of biota components of the ecosystems (Daget, 1976; Ricotta, 2000; Ricotta, 2002; Hill et al., 2003). The most used traditional index are the Shannon, Simpson diversity and taxonomic diversity index. The decline in diversity threatens ecosystems because the stability of many ecological processes increases with the richness and complexity of specific interactions (Peterson et al., 1998; Carr et al., 2002). In some ecosystems, the decline of diversity is observed and attributed, at least partially, to overexploitation by fishing (Gislason and Rice, 1998; Greenstreet et al., 1999; Lim et al., 1999; Daskalov, 2002; Blanchard et al., 2004). However, these observations cannot be generalized because some studies have shown that diversity index may remain unchanged or even increase after intense exploitation of certain stocks or ecosystems (Rogers and Ellis, 2000; Balirwa et al., 2003; Lobry et al., 2003; La€e et al., 2004; Piet and Jennings, 2005). The use of diversity, including the number of taxa, as indicator of fishing effects on ecosystems poses some difficulties. Diversity is estimated from a sample size whose size depends on the sampling effort. Several studies have established a relationship between the sample size and the estimated biological diversity of an ecosystem (Madrid et al., 1997; Piet and Rijnsdorp, 1998; Clarke and Lidgard, 2000; Kaiser, 2003; Petry et al., 2003; Bady et al., 2005). This relationship implies that the variability of sampling protocols has a considerable impact on the estimated diversity. In case data of commercial fisheries are used to calculate the indicators, diversity index may
Categories of indicators analyzed
Ecological Specific indicators compositions
Abundances and yields
Fisheries Fishing effort indicators
Class of indicators
0
High High High High
Low
Diversity index N2 Hill 1973
Hill evenness index (E) Taxonomic diversity index (D)
Taxonomic distinctness index (D)
K-dominance curves Low abundance, High biomass
High
Diversity index N1 of Hill 1973
ABC curves
High
Pielou evenness index (J )
High
Shannon-Wiener (H0 )
Low
Catches per unit area High
High
Total catch
Richness
Big Big High
High abundance, Low biomass
High
Low
Low Low
Low
Low
Low
Low
Low
High
Low
Small, important catch Small Small Low
High
Low Big, low catch
High
Low
Low exploited or non High exploited or disturbed ecosystem disturbed ecosystem
Mesh sizes Hook size CPUEm
Size of fishing gears
Number of settlement, fishing families, fishing units Number of fishing trips (month¡1 or year¡1)
Indicators
Direction of change expected
1
1
t ud
CPUE:
Total catch : Surface area
½U D
Pi2 :
C
x ðx i i i xx i
