Inter-American Tropical Tuna Commission Special Reporthttp://hdl.handle.net/1834/176462024-03-28T11:25:41Z2024-03-28T11:25:41ZA review of IATTC research on the early life history and reproductive biology of scombrids conducted at the Achotines Laboratory from 1985 to 2005Margulies, DanielScholey, Venon P.Wexler, Jeanne B.Olson, R.A.Suter, Jenny M.Hunt , Sharon L.http://hdl.handle.net/1834/239502021-06-27T02:33:24Z2007-01-01T00:00:00ZA review of IATTC research on the early life history and reproductive biology of scombrids conducted at the Achotines Laboratory from 1985 to 2005
Margulies, Daniel; Scholey, Venon P.; Wexler, Jeanne B.; Olson, R.A.; Suter, Jenny M.; Hunt , Sharon L.
English:For nearly a century, fisheries scientists have studied marine fish stocks in an effort to understand how theabundances of fish populations are determined. During the early lives of marine fishes, survival isvariable, and the numbers of individuals surviving to transitional stages or recruitment are difficult topredict.The egg, larval, and juvenile stages of marine fishes are characterized by high rates of mortality andgrowth. Most marine fishes, particularly pelagic species, are highly fecund, produce small eggs andlarvae, and feed and grow in complex aquatic ecosystems. The identification of environmental orbiological factors that are most important in controlling survival during the early life stages of marinefishes is a potentially powerful tool in stock assessment.Because vital rates (mortality and growth) during the early life stages of marine fishes are high andvariable, small changes in those rates can have profound effects on the properties of survivors andrecruitment potential (Houde 1989). Understanding and predicting the factors that most stronglyinfluence pre-recruit survival are key goals of fisheries research programs.Spanish:Desde hace casi un siglo, los científicos pesqueros han estudiado las poblaciones de peces marinos en unintento por entender cómo se determina la abundancia de las mismas. Durante la vida temprana de lospeces marinos, la supervivencia es variable, y el número de individuos que sobrevive hasta las etapastransicionales o el reclutamiento es difícil de predecir.Las etapas de huevo, larval, y juvenil de los peces marinos son caracterizadas por tasas altas demortalidad y crecimiento. La mayoría de los peces marinos, particularmente las especies pelágicas, sonmuy fecundos, producen huevos y larvas pequeños, y se alimentan y crecen en ecosistemas acuáticos complejos. La identificación los factores ambientales o biológicos más importantes en el control de lasupervivencia durante las etapas tempranas de vida de los peces marinos es una herramientapotencialmente potente en la evaluación de las poblaciones.Ya que las tasas vitales (mortalidad y crecimiento) durante las etapas tempranas de vida de los pecesmarinos son altas y variables, cambios pequeños en esas tasas pueden ejercer efectos importantes sobrelas propiedades de los supervivientes y el potencial de reclutamiento (Houde 1989). Comprender ypredecir los factores que más afectan la supervivencia antes del reclutamiento son objetivos clave de losprogramas de investigación pesquera.
2007-01-01T00:00:00ZWorkshop on turtle bycatch mitigation for longline fisheries: experimental design and data analysis, 7-8 November 2007, San Ramón de Alajuela, Costa Ricahttp://hdl.handle.net/1834/239512021-06-27T02:34:23Z2008-01-01T00:00:00ZWorkshop on turtle bycatch mitigation for longline fisheries: experimental design and data analysis, 7-8 November 2007, San Ramón de Alajuela, Costa Rica
Large numbers of fishing vessels operating from ports in Latin America participate in surfacelongline fisheries in the eastern Pacific Ocean (EPO), and several species of sea turtles inhabitthe grounds where these fleets operate. The endangered status of several sea turtle species, andthe success of circle hooks (‘treatment’ hooks) in reducing turtle hookings in other ocean areas,as compared to J-hooks and Japanese-style tuna hooks (‘control’ hooks), prompted the initiationof a hook exchange program on the west coast of Latin America, the Eastern Pacific RegionalSea Turtle Program (EPRSTP)1. One of the goals of the EPRSTP is to determine if circle hookswould be effective at reducing turtle bycatch in artisanal fisheries of the EPO withoutsignificantly reducing the catch of marketable fish species. Participating fishers were providedwith circle hooks at no cost and asked to replace the J/Japanese-style tuna hooks on theirlonglines with circle hooks in an alternating manner. Data collected by the EPRSTP showdifferences in longline gear and operational characteristics within and among countries. Theseaspects of the data, in addition to difficulties encountered with implementation of the alternating-hookdesign, pose challenges for analysis of these data.
2008-01-01T00:00:00ZAn Evaluation of the area stratification used for sampling tunas in the eastern Pacific Ocean and implications for estimating total annual catchesSuter, Jenny M.http://hdl.handle.net/1834/239522021-06-27T02:36:55Z2010-01-01T00:00:00ZAn Evaluation of the area stratification used for sampling tunas in the eastern Pacific Ocean and implications for estimating total annual catches
Suter, Jenny M.
The Inter-American Tropical Tuna Commission (IATTC) staff has been sampling thesize distributions of tunas in the eastern Pacific Ocean (EPO) since 1954, and the species composition of the catches since 2000. The IATTC staff use the data from the species composition samples, in conjunction with observer and/or logbook data, and unloading data from the canneries to estimate the total annual catches of yellowfin (Thunnus albacares),skipjack (Katsuwonus pelamis), and bigeye (Thunnus obesus) tunas. These sample data arecollected based on a stratified sampling design. I propose an update of the stratification of the EPO into more homogenous areas in order to reduce the variance in the estimates of thetotal annual catches and incorporate the geographical shifts resulting from the expansion of the floating-object fishery during the 1990s.The sampling model used by the IATTC is a stratified two-stage (cluster) random sampling design with first stage units varying (unequal) in size. The strata are month, area, and set type. Wells, the first cluster stage, are selected to be sampled only if all of the fish were caught in the same month, same area, and same set type. Fish, the second cluster stage, are sampled for lengths, and independently, for species composition of the catch. The EPO is divided into 13 sampling areas, which were defined in 1968, based on the catch distributions of yellowfin and skipjack tunas. This area stratification does not reflect the multi-species, multi-set-type fishery of today. In order to define more homogenous areas, I used agglomerative cluster analysis to look for groupings of the size data and the catch and effort data for 2000–2006. I plotted the results from both datasets against the IATTC Sampling Areas, and then created new areas. I also used the results of the cluster analysis to update the substitution scheme for strata with catch, but no sample. I then calculated the total annual catch (and variance) by species by stratifying the data into new Proposed Sampling Areas and compared the results to thosereported by the IATTC. Results showed that re-stratifying the areas produced smaller variances of the catch estimates for some species in some years, but the results were not significant.
2010-01-01T00:00:00ZEstimating relative abundance from catch and effort data, using neural networksMaunder, Mark N.Hinton, Michael G.http://hdl.handle.net/1834/239102021-06-26T06:35:38Z2006-01-01T00:00:00ZEstimating relative abundance from catch and effort data, using neural networks
Maunder, Mark N.; Hinton, Michael G.
We develop and test a method to estimate relative abundance from catch and effort data usingneural networks. Most stock assessment models use time series of relative abundance as theirmajor source of information on abundance levels. These time series of relative abundance arefrequently derived from catch-per-unit-of-effort (CPUE) data, using general linearized models(GLMs). GLMs are used to attempt to remove variation in CPUE that is not related to the abundanceof the population. However, GLMs are restricted in the types of relationships between theCPUE and the explanatory variables. An alternative approach is to use structural models basedon scientific understanding to develop complex non-linear relationships between CPUE and theexplanatory variables. Unfortunately, the scientific understanding required to develop thesemodels may not be available. In contrast to structural models, neural networks uses the data toestimate the structure of the non-linear relationship between CPUE and the explanatory variables.Therefore neural networks may provide a better alternative when the structure of the relationshipis uncertain. We use simulated data based on a habitat based-method to test the neuralnetwork approach and to compare it to the GLM approach. Cross validation and simulation testsshow that the neural network performed better than nominal effort and the GLM approach. However,the improvement over GLMs is not substantial. We applied the neural network model toCPUE data for bigeye tuna (Thunnus obesus) in the Pacific Ocean.
2006-01-01T00:00:00Z