250
Y. Sadovy de Mitcheson and B. Erisman
evaluating the importance of aggregation-exploitation per se on fi sheries. It also
helps to identify the kinds of information needed in the future to address the question in more detail.
8.3.1.1 Approach One – Spawning Aggregation-Fishing
as a Possible Threat Factor Across Taxa
Many aggregating species produce higher catch per unit of effort (CPUE) or higher
percentages of annual landings when taken from aggregations than during other
times of the year. Indeed, many of those species that have suffered serious declines
or are otherwise considered to be of conservation concern, such as those listed as
threatened under International Union for Conservation of Nature (IUCN) criteria, or
depleted by FAO, are mainly or exclusively fi shed during aggregation periods.
The FAO database of world fi sheries for 2004 indicates that of stocks for which
information is available (c. 441 stocks), 52% are fully exploited and a further 24%
overexploited or depleted (FAO 2005 ) . Of the latter group many involve exploitation of spawning aggregations. Examples include European plaice Pleuronectes
platessa, Atlantic cod , Gadus morhua, haddock, Melanogrammus aeglefi nus ,
Atlantic herring , Clupea harengus, whiting , Merlangius merlangus, Argentine hake ,
Merluccius hubbsi, Geelbeck croaker , Atractoscion aequidens, Red steenbras Petrus
rupestris, , icefi sh , Champsocephalus spp. Atlantic bonito , Sarda sarda , Atlantic
halibut, Hippoglossus hippoglossus , and Pacifi c halibut Hippoglossus stenolepis
(King 1985 ; Smale 1988 ; Griffi ths and Hecht 1995 ; Smedbol and Wroblewski 1997 ;
Hutchings et al. 1999 ; Parkes 2000 ; Hoarau et al. 2005 ; Pajaro et al. 2005 ; Zengin
and Cincer 2006 ; Loher and Seitz 2008 ; Tobin et al. 2010 ) . Particularly noteworthy
are species like the Atlantic and southern bluefi n tunas, Thunnus thynnus and T.
maccoyii . These two tunas show aggregating behaviour (Farley and Davis 1998 ;
Fromentin and Powers 2005 ) that appears to be more concentrated than for any
other pelagic fi shes and both have undergone some of the most marked declines among
pelagic species, with heavy fi shing focused on their aggregations. Other examples
are provided below in the sections on groupers, South African sea breams (Sparidae)
and croakers. Among reef fi shes, species of many taxa typically form aggregations,
and 60% of known tropical aggregations have undergone declines (Sadovy de
Mitcheson et al. 2008 ) .
Fuzzy logic (a form of mathematical logic in which truth can assume a continuum of values between 0 and 1) is one approach to explore possible factors associated with intrinsic extinction vulnerability to fi shing. Many aggregating species
have other characteristics, in addition to concentrated spawning, associated with
high vulnerability to fi shing or threat factors, such as large body size and longevity
(e.g., Reynolds et al. 2005 ) . For reef fi shes in Fiji, Cheung et al. ( 2005 ) considered
maximum body size, natural mortality and spawning aggregations, among other
variables, and found, using fuzzy logic, that the incorporation of aggregations in this
novel analysis greatly increased the goodness-of-fi t between the estimated vulnerabilities and the empirical population trends, suggesting that aggregating species
Y. Sadovy de Mitcheson and B. Erisman
evaluating the importance of aggregation-exploitation per se on fi sheries. It also
helps to identify the kinds of information needed in the future to address the question in more detail.
8.3.1.1 Approach One – Spawning Aggregation-Fishing
as a Possible Threat Factor Across Taxa
Many aggregating species produce higher catch per unit of effort (CPUE) or higher
percentages of annual landings when taken from aggregations than during other
times of the year. Indeed, many of those species that have suffered serious declines
or are otherwise considered to be of conservation concern, such as those listed as
threatened under International Union for Conservation of Nature (IUCN) criteria, or
depleted by FAO, are mainly or exclusively fi shed during aggregation periods.
The FAO database of world fi sheries for 2004 indicates that of stocks for which
information is available (c. 441 stocks), 52% are fully exploited and a further 24%
overexploited or depleted (FAO 2005 ) . Of the latter group many involve exploitation of spawning aggregations. Examples include European plaice Pleuronectes
platessa, Atlantic cod , Gadus morhua, haddock, Melanogrammus aeglefi nus ,
Atlantic herring , Clupea harengus, whiting , Merlangius merlangus, Argentine hake ,
Merluccius hubbsi, Geelbeck croaker , Atractoscion aequidens, Red steenbras Petrus
rupestris, , icefi sh , Champsocephalus spp. Atlantic bonito , Sarda sarda , Atlantic
halibut, Hippoglossus hippoglossus , and Pacifi c halibut Hippoglossus stenolepis
(King 1985 ; Smale 1988 ; Griffi ths and Hecht 1995 ; Smedbol and Wroblewski 1997 ;
Hutchings et al. 1999 ; Parkes 2000 ; Hoarau et al. 2005 ; Pajaro et al. 2005 ; Zengin
and Cincer 2006 ; Loher and Seitz 2008 ; Tobin et al. 2010 ) . Particularly noteworthy
are species like the Atlantic and southern bluefi n tunas, Thunnus thynnus and T.
maccoyii . These two tunas show aggregating behaviour (Farley and Davis 1998 ;
Fromentin and Powers 2005 ) that appears to be more concentrated than for any
other pelagic fi shes and both have undergone some of the most marked declines among
pelagic species, with heavy fi shing focused on their aggregations. Other examples
are provided below in the sections on groupers, South African sea breams (Sparidae)
and croakers. Among reef fi shes, species of many taxa typically form aggregations,
and 60% of known tropical aggregations have undergone declines (Sadovy de
Mitcheson et al. 2008 ) .
Fuzzy logic (a form of mathematical logic in which truth can assume a continuum of values between 0 and 1) is one approach to explore possible factors associated with intrinsic extinction vulnerability to fi shing. Many aggregating species
have other characteristics, in addition to concentrated spawning, associated with
high vulnerability to fi shing or threat factors, such as large body size and longevity
(e.g., Reynolds et al. 2005 ) . For reef fi shes in Fiji, Cheung et al. ( 2005 ) considered
maximum body size, natural mortality and spawning aggregations, among other
variables, and found, using fuzzy logic, that the incorporation of aggregations in this
novel analysis greatly increased the goodness-of-fi t between the estimated vulnerabilities and the empirical population trends, suggesting that aggregating species