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Application of deterministic and stochastic geo-statistical tools for analysing spatial patterns of fish density in a tropical monsoonal estuary

  • G. B. SreekanthEmail author
  • S. K. Chakraborty
  • A. K. Jaiswar
  • Bappa Das
  • E. B. Chakurkar
Article

Abstract

In this paper, we compared the efficiency of advanced deterministic and stochastic geo-statistical techniques to predict spatial patterns of fish density in the tropical monsoonal estuary, Zuari, using the following environmental descriptors: temperature, salinity, dissolved oxygen, transparency and geographic coordinates. The methods applied in this study were multiple linear regression, Cubist, support vector regression, random forest regression, universal kriging and regression kriging. Fish abundance and environmental data were collected from September, 2013 to August, 2016 in 48 sampling stations distributed along the estuarine gradient. Ranking procedure of various regression methods showed that the Cubist model was the best performing model based on prediction accuracy in the development phase and prediction consistency in the validation phase. Latitude, temperature, salinity and dissolved oxygen had positive influence on fish abundance, while longitude and transparency showed negative impacts. This study offers scope for refining the employed currently models to predict spatial densities of fish populations using a wide range of available biotic and abiotic variables, which will enable to develop an efficient management framework for tropical monsoonal estuaries.

Keywords

Tropical monsoonal estuary Zuari Machine learning tools Geo-statistics Multiple linear regression Cubist Support vector regression Random forest regression Universal kriging Regression kriging 

Notes

Acknowledgements

The authors acknowledge the guidance, support and encouragement from the Director and staff of Central Institute of Fisheries Education and Central Coastal Agricultural Research Institute [research institutes under Indian Council of Agricultural Research (ICAR)] for this study. The authors also express heartfelt thanks to the fishermen of Zuari estuary for their kind cooperation with the fishing experiments and collection of data, in particular, the members of Shree Shantadurga Fishermen’s Association, Goa.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Addis P, Secci M, Angioni A, Cau A (2012) Spatial distribution patterns and population structure of the sea urchin Paracentrotus lividus (Echinodermata: Echinoidea), in the coastal fishery of western Sardinia: a geostatistical analysis. Sci Mar 76(4):733–740Google Scholar
  2. Agenbag JJ, Richardson AJ, Demarcq H, Fréon P, Weeks S, Shillington FA (2003) Relating local abundance of South African pelagic fish species to environmental variables using generalized additive and linear models. Prog Oceanogr 59:275–300CrossRefGoogle Scholar
  3. Ansari ZA, Chatterji A, Ingole BS, Sreepada RA, Rivonkar CU (1995) Community structure and seasonal variation of an Inshore Demersal Fish Community at Goa, West Coast of India. Estuar Coast Shelf Sci 41:593–610CrossRefGoogle Scholar
  4. APHA (2005) Standard methods for the examination of water and wastewater, 21st edn. American Public Health Association, Washington, DCGoogle Scholar
  5. Biron M, Wade E, Sabean C, Vienneau R (2007) Estimating the abundance and distribution of snow crab (Chionoecetes opilio) off Cape Breton Island using video camera transects: a complementary technique to the bottom trawl survey. Can Tech Rep Fish Aquat Sci 2748:16Google Scholar
  6. Blaber SJM, Blaber TG (1980) Factors affecting the distribution of juvenile estuarine and inshore fish. J Fish Biol 17:143–162CrossRefGoogle Scholar
  7. Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefGoogle Scholar
  8. Cao Y, Hinz L, Metzke B, Stein J, Holtrop A (2015) Modeling and mapping fish abundance across wadeable streams of Illinois, USA, based on landscape-level environmental variables. Can J Fish Aquat Sci 73(7):1031–1046CrossRefGoogle Scholar
  9. Cheng L, Lek S, Lek-Ang S, Li Z (2012) Predicting fish assemblages and diversity in shallow lakes in the Yangtze River basin. Limnologica 42(2):127–136CrossRefGoogle Scholar
  10. Chiles J, Delfiner P (1999) Geostatistics: modeling spatial uncertainty. Wiley, New YorkCrossRefGoogle Scholar
  11. Darboe FS, Oddsson G (2002) Fish species abundance and distribution in the Gambia estuary. UNU-Fisheries Training Programme Final ProjectGoogle Scholar
  12. De Carvalho LL, Villaça RC (2017) Use of interpolation to describe the spatial distribution of benthic organisms in coastal areas. Caderno de Estudos Geoambientais-CADEGEO 8(01)Google Scholar
  13. Dedecker AP, Goethals PLM, Gabriels W, De Pauw N (2004) Optimization of artificial neural network (ANN) model design for prediction of macroinvertebrates in the Zwalm river basin (Flanders Belgium). Ecol Model 174:161–173CrossRefGoogle Scholar
  14. Deutsch CV, Journel AG (1998) GSLIB: geostatistical software library and user’s guide. Oxford University Press, New YorkGoogle Scholar
  15. Feyrer F, Nobriga ML, Sommer TR (2007) Multidecadal trends for three declining fish species: habitat patterns and mechanisms in the San Francisco Estuary, California, USA. Can J Fish Aquat Sci 64(4):723–734CrossRefGoogle Scholar
  16. Froeschke J, Stunz GW, Wildhaber ML (2010) Environmental influences on the occurrence of coastal sharks in estuarine waters. Mar Ecol Prog Ser 407:279–292CrossRefGoogle Scholar
  17. Fukuda S, De Baets B, Waegeman W, Verwaeren J, Mouton AM (2013) Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models. Environ Modell Softw 47:1–6CrossRefGoogle Scholar
  18. Georgakarakos S, Koutsoubas D, Valavanis V (2006) Time series analysis and forecasting techniques applied on loliginid and ommastrephid landings in Greek waters. Fish Res 78:55–71CrossRefGoogle Scholar
  19. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186CrossRefGoogle Scholar
  20. Haedrich RL (1983) Estuarine fishes. In: Ketchum BH (ed) Ecosystems of the world, vol 26. Estuaries & Enclosed Sea. Elsevier Scientific, Oxford, pp 183–207Google Scholar
  21. He Y, Wang J, Lek-Ang S, Lek S (2010) Predicting assemblages and species richness of endemic fish in the upper Yangtze River. Sci Total Environ 408(19):4211–4220CrossRefGoogle Scholar
  22. Hengl T, Heuvelink GB, Stein A (2004) A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma 120(1):75–93CrossRefGoogle Scholar
  23. Hernández-Flores A, Condal A, Poot-Salazar A, Espinoza-Mendez JC (2015) Geostatistical analysis and spatial modeling of population density for the sea cucumbers Isostichopus badionotus and Holothuria floridana on the Yucatan Peninsula, Mexico. Fish Res 172:114–124CrossRefGoogle Scholar
  24. Horne JK, Smith PE, Schneider DC (1999) Comparative examination of scale-explicit biological and physical processes: recruitment of Pacific hake. Can J Fish Aquat Sci 56:1–10CrossRefGoogle Scholar
  25. Hyndes GA, Platell ME, Potter IC, Lenanton RCJ (1999) Does the composition of the demersal fish assemblages in temperate coastal waters change with depth and undergo consistent seasonal changes? Mar Biol 134:335–352CrossRefGoogle Scholar
  26. Jaureguizar AJ, Menni R, Guerrero R, Lasta C (2004) Environmental factors structuring fish communities of the Rio de la Plata estuary. Fish Res 66:195–211CrossRefGoogle Scholar
  27. Jensen OP, Miller TJ (2005) Geostatistical analysis of the abundance and winter distribution patterns of the blue crab Callinectes sapidus in Chesapeake Bay. Trans Am Fish Soc 134(6):1582–1598CrossRefGoogle Scholar
  28. Knudby A, LeDrew E, Brenning A (2010) Predictive mapping of reef fish species richness, diversity and biomass in Zanzibar using IKONOS imagery and machine-learning techniques. Remote Sens Environ 114(6):1230–1241CrossRefGoogle Scholar
  29. Koutsikopoulos C, Lacroix N (1992) Distribution, abundance of sole (Solea solea L.) eggs, larvae in the Bay of Biscay between 1986 and 1989. Neth J Sea Res 29:81–91CrossRefGoogle Scholar
  30. Lin LI (1989) A concordance correlation-coefficient to evaluate reproducibility. Biometrics 45(1):255–268CrossRefGoogle Scholar
  31. Marais JFK (1982) The effects of river flooding on the fish populations of two eastern Cape estuaries. S Afr J Zool 17:96–104CrossRefGoogle Scholar
  32. Odeh I, McBratney A, Chittleborough D (1994) Spatial prediction of soil properties from landform attributes derived from a digital elevation model. Geoderma 63(3–4):197–214CrossRefGoogle Scholar
  33. Olaya-Marín EJ, Martínez-Capel F, Costa RMS, Alcaraz-Hernández JD (2012) Modelling native fish richness to evaluate the effects of hydro-morphological changes and river restoration (Júcar River Basin, Spain). Sci Total Environ 440:95–105CrossRefGoogle Scholar
  34. Peterson MS, Ross ST (1991) Dynamics of littoral fishes and decapods along a coastal river-estuarine gradient. Estuar Coast Shelf Sci 33:467–483CrossRefGoogle Scholar
  35. Qasim SZ (1973) Productivity of backwaters and estuaries. IBP Ecol Stud 3:143–154CrossRefGoogle Scholar
  36. Qasim SZ, Sen Gupta R (1981) Environmental characteristics of the Mandovi-Zuari estuarine system in Goa. Estuar Coast Shelf Sci 13:557–578CrossRefGoogle Scholar
  37. R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
  38. Santos AMP (2000) Fisheries oceanography using satellite and airborne remote sensing methods: a review. Fish Res 49:1–20CrossRefGoogle Scholar
  39. Santra P, Kumar M, Panwar N (2017) Digital soil mapping of sand content in arid western India through geostatistical approaches. Geoder Reg 9:56–72CrossRefGoogle Scholar
  40. Selleslagh J, Amara R (2008) Environmental factors structuring fish composition and assemblages in a small macrotidal estuary (eastern English Channel). Estuar Coast Shelf Sci 79(3):507–517CrossRefGoogle Scholar
  41. Selleslagh J, Amara R, Laffargue P, Lesourd S, Lepage M, Girardin M (2009) Fish composition and assemblage structure in three Eastern English Channel macrotidal estuaries: a comparison with other French estuaries. Estuar Coast Shelf Sci 81(2):149–159CrossRefGoogle Scholar
  42. Seoane J, Carrascal LM, Alonso CL, Palomino D (2005) Species-specific traits associated to prediction errors in bird habitat suitability modelling. Ecol Model 185:299–308CrossRefGoogle Scholar
  43. Shirodkar PV, Deepthi M, Vethamony P, Mesquita AM, Pradhan UK (2012) Tide dependent seasonal changes in water quality and assimilative capacity of anthropogenically influenced Mormugao harbour water. Indian J Mar Sci 41(4):314–330Google Scholar
  44. Simard Y, Lavoie D, Saucier FJ (2002) Channel head dynamics: Capelin (Mallotus villosus) aggregation in the tidally-driven upwelling system of the Saguenay-St. Lawrence Marine Park’s whale feeding ground. Can J Fish Aquat Sci 59:197–210CrossRefGoogle Scholar
  45. Soares A (2006) Geostatistics for the earth and environment sciences, 2nd edn. Collection Teaching Science and Technology. Higher Technical InstituteGoogle Scholar
  46. Sreekanth GB, Manju Lekshmi N, Chakraborty SK, Jaiswar AK, Zacharia PU, Renjith VR, Singh NP, Pazhayamadom DG (2016) Effect of monsoon on coastal fish diversity of Goa: an example from gill net fishery. Indian J Fish 63(2):8–18CrossRefGoogle Scholar
  47. Sreekanth GB, Lekshmi NM, Singh NP (2017) Temporal patterns in fish community structure: environmental perturbations from a well-mixed tropical estuary. Proc Natl Acad Sci India B 87(1):135–145Google Scholar
  48. Thiel R, Sepulveda A, Kafemann R, Nellen W (1995) Environmental factors as forces structuring the fish community of the Elbe estuary. J Fish Biol 46:47–69CrossRefGoogle Scholar
  49. Vincenzi S, Zucchetta M, Franzoi P, Pellizzato M, Pranovi F, De Leo GA, Torricelli P (2011) Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy. Ecol Model 222(8):1471–1478CrossRefGoogle Scholar
  50. Wackernagel H (1998) Multivariate Geostatistics, 2nd ednGoogle Scholar
  51. Whitfield AK (1999) Ichthyofaunal assemblages in estuaries: a South African case study. Rev Fish Biol Fisher 9:151–186CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • G. B. Sreekanth
    • 1
    Email author
  • S. K. Chakraborty
    • 2
  • A. K. Jaiswar
    • 2
  • Bappa Das
    • 1
  • E. B. Chakurkar
    • 1
  1. 1.ICAR-Central Coastal Agricultural Research InstituteOld GoaIndia
  2. 2.ICAR-Central Institute of Fisheries EducationMumbaiIndia

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