Application of a Generalized Additive Model to Reveal the Relations between the Density of Zooplankton with Other Variables in the West Daya Bay, China

Authors: Weiwen Li, Hao Huang, Chengmao You, Jianji Liao, Lei Wang, Lina An

Abstract:

Zooplankton are a central issue in the ecology which makes a great contribution to maintaining the balance of an ecosystem. It is critical in promoting the material cycle and energy flow within the ecosystems. A generalized additive model (GAM) was applied to analyze the relationships between the density (individuals per m³) of zooplankton and other variables in West Daya Bay. All data used in this analysis (the survey month, survey station (longitude and latitude), the depth of the water column, the superficial concentration of chlorophyll a, the benthonic concentration of chlorophyll a, the number of zooplankton species and the number of zooplankton species) were collected through monthly scientific surveys during January to December 2016. GLM model (generalized linear model) was used to choose the significant variables’ impact on the density of zooplankton, and the GAM was employed to analyze the relationship between the density of zooplankton and the significant variables. The results showed that the density of zooplankton increased with an increase of the benthonic concentration of chlorophyll a, but decreased with a decrease in the depth of the water column. Both high numbers of zooplankton species and the overall total number of zooplankton individuals led to a higher density of zooplankton.

Keywords: Density, generalized linear model, generalized additive model, the West Daya Bay, zooplankton.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2643812

References:

[1] Vinobaba P. (2012). Impact of water quality on species composition and seasonal fluctuation of planktons of Batticaloa lagoon, Sri Lanka. Journal of Ecosystem &Ecography, 02(4)doi:10.4172/2157-7625.1000117.
[2] Yongo E, Outa N. (2017).Spatial distribution and abundance of zooplankton communities in Lake Victoria, Kenya. International Journal of Fisheries and Aquatic Research, 2(1):33-35.
[3] Pintar M R,Resetarits W J. (2017). Prey-driven control of predator assemblages: zooplankton abundance drives aquatic beetle colonization. Ecology (2017). doi:10.1002/ecy.1914
[4] Smith W. O, Delizo L M, Herbolsheimer C, et al. (2017). Distribution and abundance of mesozooplankton in the Ross Sea, Antarctica. Polar Biology: Doi: 10.1007/s00300-017-2149-5
[5] Suárez E, Pérez C, M Rivera, R, et al. (2017). 9. Generalized Linear Models. Applications of Regression Models in Epidemiology. John Wiley & Sons, Inc.
[6] Dreano D, Tsiaras K, Triantafyllou G, et al. (2017). Efficient ensemble forecasting of marine ecology with clustered 1d models and statistical lateral exchange: application to the red sea. Ocean Dynamics, 67(7), 935-947.
[7] Solanki H U, Bhatpuria D, Chauhan P. (2015). Applications of generalized additive model (gam) to satellite-derived variables and fishery data for prediction of fishery resources distributions in the Arabian Sea. Geocarto International, 32(1), 1-29.
[8] Chen X J, Tian S Q, Chen Y, Liu B L. （2010）. A modeling approach to identify optimal habitat and suitable fishing grounds forneon flying squid (Ommastrephesbartramii) in the Northwest Pacific Ocean. Fishery Bulletin. 108(1):1–14
[9] Saad S A, Wade C M. (2017). Seasonal and Spatial Variations of Saltmarsh Benthic Foraminiferal Communities from North Norfolk, England. Microbial Ecology, 73(3):539-555.
[10] China Standard Committee. 2008. China state standard GB/T 12763.6- 2007. The specification for oceanographic survey, the sixth part: marine biological research. Beijing: China Standard Press. 159 pp.
[11] Ostrowska M, Matorin D N, Ficek D. (2000). Variability of the specific fluorescence of chlorophyll in the ocean. Part 2. Fluorometric method of chlorophyll a determination. Oceanologia, 42(2):221-229.
[12] McCullagh P, Nelder JA. (1989). Generalized Linear Models, 2nd ed. Chapman and Hall, London
[13] Wood SN. (2004). Stable and efficient multiple smoothing parameter estimation for generalized additive models. Journal of the American Statistical Association, 99(467):673-686.
[14] Hastie T, Tibshirani R. (1990). Generalized Additive Models.London:Chapman& Hall
[15] Wood SN. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(1): 3-36.
[16] Wu J, Yan B, Feng Z, et al. (2011). Zooplankton ecology near the Tianwan Nuclear Power Station. Acta Ecologica Sinica, 31(22):6902-6911.
[17] Candes E, Fan Y, Janson L, et al. (2017). Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection. arXiv: 1610.02351v3
[18] Yamashita T, Yamashita K, Kamimura R. (2007). A stepwise aic method for variable selection in linear regression. Communications in Statistics - Theory and Methods, 36(13), 2395-2403.
[19] Tokuda T, Van Mechelen I, Claeskens G, et al. (2012). Bic selection of the number of classes in latent class models with background variables. International Statistical Institute/international Association for Statistical Computing.
[20] Nizami N, Prasad N. (2017). Factor Analysis and PCA Analysis on Decent Work Indicators. Decent Work: Concept, Theory and Measurement. Springer Singapore.
[21] Raftery, A. E, Dean, N. (2017). Variable selection for model-based clustering. Journal of the American Statistical Association, 101(473), 168-178.
[22] Variyath A M, Chen J, Abraham B. (2010). Empirical likelihood based variable selection. Journal of Statistical Planning & Inference, 140(4), 971-981.
[23] Mosomtai G, Evander M, Sandström P, et al. (2016). Association of ecological factors with rift valley fever occurrence and mapping of risk zones in kenya. International Journal of Infectious Diseases Ijid Official Publication of the International Society for Infectious Diseases, 46(4), 49-55.
[24] Voutilainen A, Jurvelius J, Lilja J, et al. (2016). Associating spatial patterns of zooplankton abundance with water temperature, depth, planktivorous fish and chlorophyll. Boreal Environment Research, 21(1-2), 101-114.
[25] Loiselle S A, Azza N, Cózar A, et al. (2008). Variability in factors causing light attenuation in Lake Victoria. Freshwater Biology, 53(3):535–545.
[26] Moreau S, Mostajir B, Almandoz G O, et al. (2017). Effects of enhanced temperature and ultraviolet b radiation on a natural plankton community of the beagle channel (southern argentina): a mesocosm study. European Journal of Immunology, 10(8), 577-82.
[27] Fazeli N, Savari A, Nabavi S M B, et al. (2013). Seasonal variation of zooplankton abundance, composition and biomass in the chabahar bay, oman sea. International Journal of Aquatic Biology, 1(6), 1411-1419.
[28] Golmarvi D, Kapourchali M F, Moradi A M, et al. (2017). Influence of physico-chemical factors, zooplankton species biodiversity and seasonal abundance in anzali international wetland, iran. Open Journal of Marine Science, 07(1), 91-99.
[29] Harley S J, Myers R A, Dunn A. (2001). Is catch-per-unit-effort proportional to abundance?. Canadian Journal of Fisheries and Aquatic Sciences, 58(9): 1760-1772.
[30] Maunder M N, Punt A E. (2004). Standardizing catch and effort data: a review of recent approaches. Fisheries research, 70(2): 141-159.