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Yunlei ZHANG, Ying XUE, Binduo XU, Chongliang ZHANG, Xiaoxiao ZAN. Evaluating the effect of input variables on quantifying the spatial distribution of croaker Johnius belangerii in Haizhou Bay, China[J]. Journal of Oceanology and Limnology, 2021, 39(4): 1570-1583

Evaluating the effect of input variables on quantifying the spatial distribution of croaker Johnius belangerii in Haizhou Bay, China

Yunlei ZHANG1, Ying XUE1,3, Binduo XU1,3, Chongliang ZHANG1,3, Xiaoxiao ZAN2
1 College of Fisheries, Ocean University of China, Qingdao 266003, China;
2 Marine College, Shandong University, Weihai 264209, China;
3 Field Observation and Research Station of Haizhou Bay Fishery Ecosystem, Ministry of Education, Qingdao 266003, China
A habitat model has been widely used to manage marine species and analyze relationship between species distribution and environmental factors. The predictive skill in habitat model depends on whether the models include appropriate explanatory variables. Due to limited habitat range, low density, and low detection rate, the number of zero catches could be very large even in favorable habitats. Excessive zeroes will increase the bias and uncertainty in estimation of habitat. Therefore, appropriate explanatory variables need to be chosen first to prevent underestimate or overestimate species abundance in habitat models. In addition, biotic variables such as prey data and spatial autocovariate (SAC) of target species are often ignored in species distribution models. Therefore, we evaluated the effects of input variables on the performance of generalized additive models (GAMs) under excessive zero catch (>70%). Five types of input variables were selected, i.e., (1) abiotic variables, (2) abiotic and biotic variables, (3) abiotic variables and SAC, (4) abiotic, biotic variables and SAC, and (5) principal component analysis (PCA) based abiotic and biotic variables and SAC. Belanger's croaker Johnius belangerii is one of the dominant demersal fish in Haizhou Bay, with a large number of zero catches, thus was used for the case study. Results show that the PCA-based GAM incorporated with abiotic and biotic variables and SAC was the most appropriate model to quantify the spatial distribution of the croaker. Biotic variables and SAC were important and should be incorporated as one of the drivers to predict species distribution. Our study suggests that the process of input variables is critical to habitat modelling, which could improve the performance of habitat models and enhance our understanding of the habitat suitability of target species.
Key words:    generalized additive model|principal component analysis|biotic variables|spatial autocovariate   
Received: 2020-05-14   Revised: 2020-06-28
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