A Comparison On Integrated Habitat Index For Yellowfin Tuna (Thunnus Albacares) In Waters Near Gilbert Islands Based On QRM，GLM And GAM

With the exploitation of the yellowfin tuna (Thunnus albacares) resources, thestudies on yellowfin tuna are extending, which including growth, feeding, habitat,abundance index and stock assessment. In order to manage and conserve the yellowfintuna resources effectively, it is necessary to use reliable data to determine the optimummethod on the study of yellowfin tuna integrated habitat index. It is necessary to applythe environment variables to the study of yellowfin tuna integrated habitat index andresource abundance index because the environment variables have great relivant withdistribution of yellowfin tuna integrated habitat index and resource abundance.In this study, the data were the actual survey data collected in waters near GilbertIslands from October4,2009through December25,2009. The survey stations were34sites. The profiles of salinity, temperature, dissolved oxygen, chlorophyll-aconcentration, horizontal current and vertical current data were measured. We appliedthe environment variable data and the actual yellowfin tuna catch rate data to theQuantile Regression Method (QRM), the Generalized Linear model (GLM) andGeneralized Additive Model (GAM), and built respective models. These models wereused to predict the corresponding potential catch rate. We applied the wilcoxon test totest if there were significant differences between the predicted catch rate predicted bythe models and the nominal catch rate. The integrated habitat index (IHI) in differentwater strata of yellowfin tuna were estimated by the predicted catch rate predicted bythe models, and got the yellowfin tuna integrated habitat index. Comparing the nominalCPUE and the average integrated habitat index (IHI) in different water strata, thepredicted power of the models built by three methods were evaluated. The correlationcoefficients between the nominal CPUE and the average integrated habitat index (IHI)in different water strata were calculated. This correlation coefficient was used todetermine the environmental variables that influenced the distribution of the yellowfintuna.In addition, the Gilbert Islands survey data in13sites were used to verify theeffectiveness of the three methods. These survey data were collected in waters nearGilbert Islands from20Nov.2010through20Jan.2011. The survey data were inputinto the models buit by three methods for water stratum of40-80m and the whole waterbin (0~240m), and the IHIs of the yellowfin tuna were estimated. The IHI distributionand the observed CPUE were mapped. The Wilcoxon test was used to test if there weredifferences between the predicted CPUE predicted by the models and the observedCPUE. The suitability of the models was evaluated, and the optimum model wasproposed. (1) The results showed that: The IHI distributions of different water strtum (IHIij) weredifferent from three methods. The distribution area of the high IHI obtained from theQRM was the largest among three methods. The next one was from GAM. The leastone was from the GLM;(2) The IHI models of the whole water bin (0～240m) built by three methods werereliable. The distribution area of the high IHI obtained from three methods wasalmost the same. The prediction power of the models was good;(3) QRM was the optimum method to predict the potential abundance of yellowfin tunain waters near Gilbert Islands;(4) GAM is better for analyzing the environmental variables selection of yellowfin tuna,and to show the nonlinear relations between the environmental variables and thecatch rate;(5) GLM can be used to show the relationship between the observed catch rate and theenvironmental variables;(6) the yellowfin tuna mainly distributed in the water stratum of40-120m, and the maindistribution areas were1°20’S~2°20’ N,169°50’E~170°30’ E, and2°40’N~5°N,175°30’ E~176°55’E, the distribution of yellowfin tuna weredifferent from the different water layer;(7) the critical environmental variables influencing the distribution of yellowfin tunawere temperature, salinity, dissolved oxygen and ocean currents, it needs to bestudied further whether chlorophyll-a was the influencing variable, and theenvironmental variables which influenced the distribution of yellowfin tuna weredifferent from the different water layers;(8) It can be used to study the spatial distribution of the pelagic fish by building the IHI model. Theenvironment variables which influenced the vertical distribution and movement behavior ofyellowfin tuna are different from sea areas. The results of this study are only limited to thewaters near Gilbert Islands. In the other sea area, the results still need to be tested in the futurestudy. We should collect more data in a wider area and long time series, such as five to ten yearsdata in the same waters, to verify our results.