Evaluating the effect of input variables on quantifying the spatial distribution of croaker<italic style="font-style: italic">Johnius belangerii</italic>in Haizhou Bay, China

Yunlei ZHANG; Ying XUE; Binduo XU; Chongliang ZHANG; Xiaoxiao ZAN

doi:10.1007/s00343-020-0193-4

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Evaluating the effect of input variables on quantifying the spatial distribution of croakerJohnius belangeriiin Haizhou Bay, China

Aquaculture and Fisheries | Updated：2023-05-20

- Evaluating the effect of input variables on quantifying the spatial distribution of croakerJohnius belangeriiin Haizhou Bay, China
- Journal of Oceanology and Limnology Vol. 39, Issue 4, Pages: 1570-1583(2021)
- Affiliations：
  
  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
- Author bio：
  
  Xiaoxiao ZAN, E-mail:zanxiaoxiao@email.sdu.edu.cn
- Funds：
- DOI：10.1007/s00343-020-0193-4
  CLC：
- Received：14 May 2020，
  
  Accepted：28 June 2020，
  
  Online First：23 September 2020，
  
  Published：2021-07
- Accepted：
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Yunlei ZHANG, Ying XUE, Binduo XU, et al. Evaluating the effect of input variables on quantifying the spatial distribution of croakerJohnius belangeriiin Haizhou Bay, China[J]. Journal of Oceanology and Limnology, 2021, 39(4): 1570-1583.
DOI：

Yunlei ZHANG, Ying XUE, Binduo XU, et al. Evaluating the effect of input variables on quantifying the spatial distribution of croakerJohnius belangeriiin Haizhou Bay, China[J]. Journal of Oceanology and Limnology, 2021, 39(4): 1570-1583. DOI： 10.1007/s00343-020-0193-4.

摘要

Abstract

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.

关键词

Keywords

references

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⁰

Evaluating the effect of input variables on quantifying the spatial distribution of croakerJohnius belangeriiin Haizhou Bay, China

DOI：10.1007/s00343-020-0193-4

摘要

Abstract

关键词

Keywords

references