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

Your Location：

Home >

Browse articles >

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
- Evaluating the effect of input variables on quantifying the spatial distribution of croakerJohnius belangeriiin Haizhou Bay, China
- 海洋湖沼学报（英文） 2021年39卷第4期页码：1570-1583
- 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：
  
  the Key Project of Research and Development Plan of China(2017YFE0104400);the National Natural Science Foundation of China(31772852);the National Natural Science Foundation of China(31802301);the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao)(2018SDKJ0501-2);© Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021
- DOI：10.1007/s00343-020-0193-4
  中图分类号：
- 收稿：2020-05-14，
  
  录用：2020-6-28，
  
  网络首发：2020-09-23，
  
  纸质出版：2021-07
- Accepted：
Scan QR Code
Evaluating the effect of input variables on quantifying the spatial distribution of croakerJohnius belangeriiin Haizhou Bay, China[J]. 海洋湖沼学报（英文）, 2021,39(4):1570-1583.

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.
Evaluating the effect of input variables on quantifying the spatial distribution of croakerJohnius belangeriiin Haizhou Bay, China[J]. 海洋湖沼学报（英文）, 2021,39(4):1570-1583. DOI： 10.1007/s00343-020-0193-4.

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

Ahmadi-Nedushan B, St-Hilaire A, Bérubé M, Robichaud É, Thiémonge N, Bobée B. 2006. A review of statistical methods for the evaluation of aquatic habitat suitability for instream flow assessment. River Research and Applications , 22 (5): 503–523, https://doi.org/10.1002/rra.918.

Akaike H. 1998. Information theory and an extension of the maximum likelihood principle. In: Parzen E, Tanabe K, Kitagawa G eds. Selected Papers of Hirotugu Akaike. Springer, New York, https://doi.org/10.1007/978-1-4612-1694-0_15 https://doi.org/10.1007/978-1-4612-1694-0_15 .

Arrizabalaga H, Dufour F, Kell L, Merino G, Ibaibarriaga L, Chust G, Irigoien X, Santiago J, Murua H, Fraile I, Chifflet M, Goikoetxea N, Sagarminaga Y, Aumont O, Bopp L, Herrera M, Fromentin J M, Bonhomeau S. 2015. Global habitat preferences of commercially valuable tuna. Deep Sea Research Part II: Topical Studies in Oceanography , 113 : 102–112, https://doi.org/10.1016/j.dsr2.2014.07.001.

Bailey H, Thompson P M. 2009. Using marine mammal habitat modelling to identify priority conservation zones within a marine protected area. Marine Ecology Progress Series , 378 : 279–287, https://doi.org/10.3354/meps07887.

Bates A E, Helmuth B, Burrows M T, Duncan M I, Garrabou J, Guy-Haim T, Lima F, Queiros A M, Seabra R, Marsh R, Belmaker J, Bensoussan N, Dong Y W, Mazaris A D, Smale D, Wahl M, Rilov G. 2018. Biologists ignore ocean weather at their peril. Nature , 560 (7718): 299–301, https://doi.org/10.1038/d41586-018-05869-5.

Bi H S, Peterson W T, Lamb J, Casillas E. 2011. Copepods and salmon: characterizing the spatial distribution of juvenile salmon along the Washington and Oregon coast, USA. Fisheries Oceanography , 20 (2): 125–138, https://doi.org/10.1111/j.1365-2419.2011.00573.x.

Brennan C E, Maps F, Gentleman W C, Plourde S, Lavoie D, Chassé J, Lehoux C, Krumhansl K A, Johnson C L. 2019. How transport shapes copepod distributions in relation to whale feeding habitat: demonstration of a new modelling framework. Progress in Oceanography , 171 : 1–21, https://doi.org/10.1016/j.pocean.2018.12.005.

Buisson L, Blanc L, Grenouillet G. 2008. Modelling stream fish species distribution in a river network: the relative effects of temperature versus physical factors. Ecology of Freshwater Fish , 17 (2): 244–257, https://doi.org/10.1111/j.1600-0633.2007.00276.x.

Cañadas A, Sagarminaga R, De Stephanis R, Urquiola E, Hammond P S. 2005. Habitat preference modelling as a conservation tool: proposals for marine protected areas for cetaceans in southern Spanish waters. Aquatic Conservation: Marine and Freshwater Ecosystems , 15 (5): 495–521, https://doi.org/10.1002/aqc.689.

Chang Y J, Sun C L, Chen Y, Yeh S Z, Dinardo G. 2012. Habitat suitability analysis and identification of potential fishing grounds for swordfish, Xiphias gladius , in the South Atlantic Ocean. International Journal of Remote Sensing , 33 (23): 7 523–7 541, https://doi.org/10.1080/01431161.2012.685980.

Cormon X, Loots C, Vaz S, Vermard Y, Marchal P. 2014. Spatial interactions between saithe ( Pollachius virens ) and hake ( Merluccius merluccius ) in the North Sea. ICES Journal of Marine Science , 71 (6): 1 342–1 355, https://doi.org/10.1093/icesjms/fsu120.

Dambach J, Rödder D. 2011. Applications and future challenges in marine species distribution modeling. Aquatic Conservation: Marine and Freshwater Ecosystems , 21 (1): 92–100, https://doi.org/10.1002/aqc.1160.

Dolan T E, Patrick W S, Link J S. 2016. Delineating the continuum of marine ecosystem-based management: a US fisheries reference point perspective. ICES Journal of Marine Science , 73 (4): 1 042–1 050, https://doi.org/10.1093/icesjms/fsv242.

Giannoulaki M, Iglesias M, Tugores M P, Bonanno A, Patti B, Felice A D, Leonori I, Bigot J L, Tičina V, Pyrounaki M M, Tsagarakis K, Machias A, Somarakis S, Schismenou E, Quinci E, Basilone G, Cuttitta A, Campanella F, Miquel J, Oñate D, Valavanis V. 2013. Characterizing the potential habitat of European anchovy Engraulis encrasicolus in the Mediterranean Sea, at different life stages. Fisheries Oceanography , 22 (2): 69–89, https://doi.org/10.1111/fog.12005.

Gormley A M, Forsyth D M, Griffioen P, Lindeman M, Ramsey D S L, Scroggie M P, Woodford L. 2011. Using presence-only and presence-absence data to estimate the current and potential distributions of established invasive species. Journal of Applied Ecology , 48 (1): 25–34, https://doi.org/10.1111/j.1365-2664.2010.01911.x.

Guedes A P P, Araújo F G, Pessanha A L M, Milagre R R. 2015. Partitioning of the feeding niche along spatial, seasonal and size dimensions by the fish community in a tropical Bay in Southeastern Brazil. Marine Ecology , 36 (1): 38–56, https://doi.org/10.1111/maec.12115.

Guisan A, Edwards Jr T C, Hastie T. 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling , 157 (2–3): 89–100, https://doi.org/10.1016/S0304-3800(02)00204-1.

Guisan A, Thuiller W. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters , 8 (9): 993–1 009, https://doi.org/10.1111/j.1461-0248.2005.00792.x.

Guisan A, Zimmermann N E. 2000. Predictive habitat distribution models in ecology. Ecological Modelling , 135 (2–3): 147–186, https://doi.org/10.1016/s0304-3800(00)00354-9.

Hastie T, Tibshirani R. 1986. Generalized additive models. Statistical Science , 1 (3): 297–310, https://doi.org/10.1214/ss/1177013604.

Jewell K J, Arcese P, Gergel S E. 2007. Robust predictions of species distribution: spatial habitat models for a brood parasite. Biological Conservation , 140 (3–4): 259–272, https://doi.org/10.1016/j.biocon.2007.08.017.

Johnson A F, Jenkins S R, Hiddink J G, Hinz H. 2013. Linking temperate demersal fish species to habitat: scales, patterns and future directions. Fish and Fisheries , 14 (3): 256–280, https://doi.org/10.1111/j.1467-2979.2012.00466.x.

Jordaan A, Chen Y, Townsend D W, Sherman S. 2010. Identification of ecological structure and species relationships along an oceanographic gradient in the Gulf of Maine using multivariate analysis with bootstrapping. Canadian Journal of Fisheries and Aquatic Sciences , 67 (4): 701–719, https://doi.org/10.1139/F10-010.

Kabacoff R. 2015. R in Action: Data Analysis and Graphics with R. 2nd ed. Manning, New York.

Kitchens L L, Rooker J R. 2014. Habitat associations of dolphinfish larvae in the Gulf of Mexico. Fisheries Oceanography , 23 (6): 460–471, https://doi.org/10.1111/fog.12081.

Lasley-Rasher R S, Brady D C, Smith B E, Jumars P A. 2015. It takes guts to locate elusive crustacean prey. Marine Ecology Progress Series , 538 : 1–12, https://doi.org/10.3354/meps11481.

Lee A H, Wang K, Scott J A, Yau K K W, Mclachlan G J. 2006. Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros. Statistical Methods in Medical Research , 15 (1): 47–61, https://doi.org/10.1191/0962280206sm429oa.

Li B, Cao J, Chang J H, Wilson C, Chen Y. 2015. Evaluation of effectiveness of fixed-station sampling for monitoring American lobster settlement. North American Journal of Fisheries Management , 35 (5): 942–957, https://doi.org/10.1080/02755947.2015.1074961.

Li M, Zhang C L, Xu B D, Xue Y, Ren Y P. 2017. Evaluating the approaches of habitat suitability modelling for whitespotted conger ( Conger myriaster ). Fisheries Research , 195 : 230–237, https://doi.org/10.1016/j.fishres.2017.07.024.

Liu X X, Wang J, Zhang Y L, Yu H M, Xu B D, Zhang C L, Ren Y P, Xue Y. 2019. Comparison between two GAMs in quantifying the spatial distribution of Hexagrammos otakii in Haizhou Bay, China. Fisheries Research , 218 : 209–217, https://doi.org/10.1016/j.fishres.2019.05.019.

Luan J, Zhang C L, Xu B D, Xue Y, Ren Y P. 2018. Modelling the spatial distribution of three Portunidae crabs in Haizhou Bay, China. PLoS One , 13 (11): e0207457, https://doi.org/10.1371/journal.pone.0207457.

Martin T G, Wintle B A, Rhodes J R, Kuhnert P M, Field S A, Low-Choy S J, Tyre A J, Possingham H P. 2005. Zero tolerance ecology: improving ecological inference by modelling the source of zero observations. Ecology Letters , 8 (11): 1 235–1 246, https://doi.org/10.1111/j.1461-0248.2005.00826.x.

Marzloff M P, Melbourne-Thomas J, Hamon K G, Hoshino E, Jennings S, Van Putten I E, Pecl G T. 2016. Modelling marine community responses to climate-driven species redistribution to guide monitoring and adaptive ecosystem-based management. Global Change Biology , 22 (7): 2 462–2 474, https://doi.org/10.1111/gcb.13285.

Mouquet N, Lagadeuc Y, Devictor V, Doyen L, Duputié A, Eveillard D, Faure D, Garnier E, Gimenez O, Huneman P, Jabot F, Jarne P, Joly D, Julliard R, Kéfi S, Kergoat G J, Lavorel S, Le Gall L, Meslin L, Morand S, Morin X, Morlon H, Pinay G, Pradel R, Schurr F M, Thuiller W, Loreau M. 2015. REVIEW: predictive ecology in a changing world. Journal of Applied Ecology , 52 (5): 1 293–1 310, https://doi.org/10.1111/1365-2664.12482.

Muramatsu T, Hayama Y, Kutsukake K, Maeda K, Matsumoto T, Kudo H, Fujiwara K, Usami N. 2018. Application of weighted Voronoi diagrams to analyze nucleation sites of multicrystalline silicon ingots. Journal of Crystal Growth , 499 : 62–66, https://doi.org/10.1016/j.jcrysgro.2018.07.028.

Murase H, Kitakado T, Hakamada T, Matsuoka K, Nishiwaki S, Naganobu M. 2013. Spatial distribution of Antarctic minke whales ( Balaenoptera bonaerensis ) in relation to spatial distributions of krill in the Ross Sea, Antarctica. Fisheries Oceanography , 22 (3): 154–173, https://doi.org/10.1111/fog.12011.

Paquin M M, Buckley T W, Hibpshman R E, Canino M F. 2014. DNA-based identification methods of prey fish from stomach contents of 12 species of eastern north pacific groundfish. Deep Sea Research Part I Oceanographic Research Papers , 85 : 110–117, https://doi.org/10.1016/j.dsr.2013.12.002.

Peck M A, Arvanitidis C, Butenschön M, Canu D M, Chatzinikolaou E, Cucco A, Domenici P, Fernandes J A, Gasche L, Huebert K B, Hufnagl M, Jones M C, Kempf A, Keyl F, Maar M, Mahévas S, Marchal P, Nicolas D, Pinnegar J K, Rivot E, Rochette S, Sell A F, Sinerchia M, Solidoro C, Somerfield P J, Teal L R, Travers-Trolet M, van de Wolfshaar K E. 2018. Projecting changes in the distribution and productivity of living marine resources: a critical review of the suite of modelling approaches used in the large European project VECTORS. Estuarine, Coastal and Shelf Science , 201 : 40–55, https://doi.org/10.1016/j.ecss.2016.05.019.

Pikitch E K, Santora C, Babcock E A, Bakun A, Bonfil R, Conover D O, Dayton P, Doukakis P, Fluharty D, Heneman B, Houde E D, Link J, Livingston P A, Mangel M, McAllister M K, Pope J, Sainsbury K J. 2004. Ecosystem-based fishery management. Science , 305 (5682): 346–347, https://doi.org/10.1126/science.1098222.

Richards S A. 2008. Dealing with overdispersed count data in applied ecology. Journal of Applied Ecology , 45 (1): 218–227, https://doi.org/10.1111/j.1365-2664.2007.01377.x.

Sagarese S R, Frisk M G, Cerrato R M, Sosebee K A, Musick J A, Rago P J. 2014. Application of generalized additive models to examine ontogenetic and seasonal distributions of spiny dogfish ( Squalus acanthias ) in the Northeast (US) shelf large marine ecosystem. Canadian Journal of Fisheries and Aquatic Sciences , 71 (6): 847–877, https://doi.org/10.1139/cjfas-2013-0342.

Schmiing M, Afonso P, Tempera F, Santos R S. 2013. Predictive habitat modelling of reef fishes with contrasting trophic ecologies. Marine Ecology Progress Series , 474 : 201–216, https://doi.org/10.3354/meps10099.

Segurado P, Araújo M B. 2004. An evaluation of methods for modelling species distributions. Journal of Biogeography , 31 (10): 1 555–1 568, https://doi.org/10.1111/j.1365-2699.2004.01076.x.

Smith P. 1994. Autocorrelation in the logistic regression modelling of species distributions. Global Ecology and Biogeography Letters , 4 : 47–61.

Vezza P, Muñoz-Mas R, Martinez-Capel F, Mouton A. 2015. Random forests to evaluate biotic interactions in fish distribution models. Environmental Modelling & Software , 67 : 173–183, https://doi.org/10.1016/j.envsoft.2015.01.005.

Vinagre C, Fonseca V, Cabral H, Costa M J. 2006. Habitat suitability index models for the juvenile soles, Solea solea and Solea senegalensis , in the Tagus estuary: defining variables for species management. Fisheries Research , 82 (1–3): 140–149, https://doi.org/10.1016/j.fishres.2006.07.011

Virgili A, Racine M, Authier M, Monestiez P, Ridoux V. 2017. Comparison of habitat models for scarcely detected species. Ecological Modelling , 346 : 88–98, https://doi.org/10.1016/j.ecolmodel.2016.12.013.

Wood S N. 2006. Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC, Boca Raton, Florida.

Wu D Q, Liu J, Zhang G S, Ding W J, Wang W, Wang R Q. 2009. Incorporating spatial autocorrelation into cellular automata model: an application to the dynamics of Chinese tamarisk ( Tamarix chinensis Lour.). Ecological Modelling , 220 (24): 3 490–3 498, https://doi.org/10.1016/j.ecolmodel.2009.03.008.

Xiao Z T, Wu J Y, Xu B D, Zhang C L, Ren Y P, Xue Y. 2019. Uniqueness measure based on the weighted trophic field overlap of species in the food web. Ecological Indicators , 101 : 640–646, https://doi.org/10.1016/j.ecolind.2019.01.042.

Xu B D, Zhang C L, Xue Y, Ren Y P, Chen Y. 2015. Optimization of sampling effort for a fishery-independent survey with multiple goals. Environmental Monitoring and Assessment , 187 (5): 252, https://doi.org/10.1007/s10661-015-4483-9.

Xue Y, Tanaka K, Yu H M, Chen Y, Guan L S, Li Z G, Yu H Q, Xu B D, Ren Y P, Wan R. 2018. Using a new framework of two-phase generalized additive models to incorporate prey abundance in spatial distribution models of juvenile slender lizardfish in Haizhou Bay, China. Marine Biology Research , 14 (5): 508–523, https://doi.org/10.1080/17451000.2018.1447673.

Youcef W A, Lambert Y, Audet C. 2013. Spatial distribution of Greenland halibut Reinhardtius hippoglossoides in relation to abundance and hypoxia in the estuary and Gulf of St. Lawrence. Fisheries Oceanography , 22 (1): 41–60, https://doi.org/10.1111/fog.12004.

Yu W, Guo A, Zhang Y, Chen X J, Qian W G, Li Y S. 2018. Climate-induced habitat suitability variations of chub mackerel Scomber japonicus in the East China Sea. Fisheries Research , 207 : 63–73, https://doi.org/10.1016/j.fishres.2018.06.007.

Zerbini A N, Friday N A, Palacios D M, Waite J M, Ressler P H, Rone B K, Moore S E, Clapham P J. 2016. Baleen whale abundance and distribution in relation to environmental variables and prey density in the Eastern Bering Sea. Deep Sea Research Part II: Topical Studies in Oceanography , 134 : 312–330, https://doi.org/10.1016/j.dsr2.2015.11.002.

Zhang C L, Chen Y, Xu B D, Xue Y, Ren Y P. 2018a. Comparing the prediction of joint species distribution models with respect to characteristics of sampling data. Ecography , 41 (11): 1 876–1 887, https://doi.org/10.1111/ecog.03571.

Zhang Y L, Xue Y, Yu H M, Yu H Q, Zhang C L, Xu B D, Ren Y P. 2018b. Study on the habitat suitability of Johnius belangerii during spring in the Haizhou Bay, China. Acta Oceanologica Sinica , 40 (6): 83–91, https://doi.org/10.3969/j.issn.0253-4193.2018.06.008. (in Chinese with English abstract)

Zhang Y L, Yu H M, Yu H Q, Xu B D, Zhang C L, Ren Y P, Xue Y, Xu L L. 2020. Optimization of environmental variables in habitat suitability modeling for mantis shrimp Oratosquilla oratoria in the Haizhou Bay and adjacent waters. Acta Oceanologica Sinica , 39 (6): 36–47, https://doi.org/10.1007/s13131-020-1546-8.

Zhao J, Cao J, Tian S Q, Chen Y, Zhang S Y, Wang Z H, Zhou X J. 2014. A comparison between two GAM models in quantifying relationships of environmental variables with fish richness and diversity indices. Aquatic Ecology , 48 (3): 297–312, https://doi.org/10.1007/s10452-014-9484-1.

Zuur A, Ieno E N, Smith G M. 2007. Analyzing Ecological Data. Springer, New York.

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Comprehensive quality evaluation and principal component analysis of Crassostrea gigas from North Yellow Sea, China: characterizing nutritional, flavor, and functional profiles

Mantle sources of Cenozoic volcanoes around the South China Sea revealed by geochemical and isotopic data using the principal component analysis

A statistical study on relationship of El Niño events to the hydrography along 137 °E in winter

Standardizing CPUE of Ommastrephes bartramii for Chinese squid-jigging fishery in Northwest Pacific Ocean

基于NSGA II算法分布度改进的ATREX发动机性能优化方法研究

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

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