Spatiotemporal factors affecting fish harvest and their use in estimating the monthly yield of single otter trawls in Putuo district of Zhoushan, China

Yingbin Wang; Ji Zheng; Yang Wang; Xianzhi Zheng

doi:10.1007/s00343-012-1215-7

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Spatiotemporal factors affecting fish harvest and their use in estimating the monthly yield of single otter trawls in Putuo district of Zhoushan, China

Updated：2023-05-16

- Spatiotemporal factors affecting fish harvest and their use in estimating the monthly yield of single otter trawls in Putuo district of Zhoushan, China
- Journal of Oceanology and Limnology Vol. 30, Issue 4, Pages: 580-586(2012)
- Affiliations：
  
  School of Fisheries of Zhejiang Ocean University
- Author bio：
- Funds：
- DOI：10.1007/s00343-012-1215-7
  CLC：
- Online First：02 July 2012，
  
  Published：2012
- Accepted：
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Wang, Y., Zheng, J., Wang, Y. et al. Spatiotemporal factors affecting fish harvest and their use in estimating the monthly yield of single otter trawls in Putuo district of Zhoushan, China., Chin. J. Ocean. Limnol. 30, 580–586 (2012). https://doi.org/10.1007/s00343-012-1215-7
DOI：

Wang, Y., Zheng, J., Wang, Y. et al. Spatiotemporal factors affecting fish harvest and their use in estimating the monthly yield of single otter trawls in Putuo district of Zhoushan, China., Chin. J. Ocean. Limnol. 30, 580–586 (2012). https://doi.org/10.1007/s00343-012-1215-7 DOI：

摘要

Abstract

We used generalized additive models (GAM) to analyze the relationship between spatiotemporal factors and catch

and to estimate the monthly marine fishery yield of single otter trawls in Putuo district of Zhoushan

China. We used logbooks from five commercial fishing boats and data in government’s monthly statistical reports. We developed two GAM models: one included temporal variables (month and hauling time) and spatial variables (longitude and latitude)

and another included just two variables

month and the number of fishing boats. Our results suggest that temporal factors explained more of the variability in catch than spatial factors. Furthermore

month explained the majority of variation in catch. Change in spatial distribution of fleet had a temporal component as the boats fished within a relatively small area within the same month

but the area varied among months. The number of boats fishing in each month also explained a large proportion of the variation in catch. Engine power had no effect on catch. The pseudo-coefficients (PCf) of the two GAMs were 0.13 and 0.29 respectively

indicating the both had good fits. The model yielded estimates that were very similar to those in the governmental reports between January to September

with relative estimate errors (REE) of <18%. However

the yields in October and November were significantly underestimated

with REEs of 36% and 27%

respectively.

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Keywords

references

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