Spatially Explicit Models for Freshwater Fish for Conservation Planning

Part of the Ecological Research Monographs book series (ECOLOGICAL)


In conservation biology, spatially explicit models, sometimes known as predictive habitat distribution models (Guisan and Zimmermann 2000), have been widely used to predict distributions of plants (Leathwick 1998), insects (Lobo and Martin-Piera 2002; Gutiérrez et al. 2005), amphibians (Ray et al. 2002), reptiles (Fisher et al. 2002), fishes (Joy and Death 2001; Leathwick et al. 2005), birds (Peterson and Robins 2003), and mammals (Carroll et al. 1999). This modeling technique has been applied to Japan’s freshwater ecosystems: for example, to reconstruct historical global distribution of an endangered fish species with an already diminished distribution range (Fukushima et al. 2011) and to predict potential areas susceptible to invasion by exotic fish species (Iguchi et al. 2004). Because spatially explicit modeling is a correlative statistical technique, it basically requires only two sets of data: response and predictor variables. What makes this modeling technique different from classic techniques is that the variables (1) are spatial in nature, (2) are most often observational rather than designed, and (3) are not repetitive in space because the earth is the only unit (Cressie 1993). In a field-based ecological study, it practically means that the data are geo-referenced in a given landscape (e.g., by a global positioning system, or GPS).


Species Richness Fish Species Geographic Information System Occurrence Probability Masu Salmon 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



I thank T. Iwadate, D. Kishi, I. Koizumi, H. Takayama, and Y. Miyake for their assistance in the field. K. Nakao, M. Kaneko, M. Takada, R. Kitagawa, K. Shimoda, and N. Takamura contributed to the compilation of the fish database used in this study. S. Kameyama, H. Shimazaki, M. Amemiya, and M. Han provided technical assistance for GIS analyses. I also thank E.A. Steel for her constructive comments on earlier versions of this chapter. Fish data were provided by the Biodiversity Center of Japan, Foundation for Riverfront Improvement and Restoration, Hokkaido Fish Hatchery, and Hokkaido Aquaculture Promotion Corporation.


  1. Allan JD, Flecker AS (1993) Biodiversity conservation in running waters. Bioscience 43:32–43CrossRefGoogle Scholar
  2. Arai T, Kotake A, Morita K (2004) Evidence of downstream migration of Sakhalin taimen, Hucho perryi, as revealed by Sr:Ca ratios of otolith. Ichthyol Res 51:377–380CrossRefGoogle Scholar
  3. Bunn SE, Arthington AH (2002) Basic principles and ecological consequences of altered flow regimes for aquatic biodiversity. Environ Manage 30:492–507PubMedCrossRefGoogle Scholar
  4. Carroll C, Zielinski WJ, Noss RF (1999) Using presence-absence data to build and test spatial habitat models for the fisher in the Klamath region, USA. Conserv Biol 13:1344–1359CrossRefGoogle Scholar
  5. Cressie NAC (1993) Statistics for spatial data. Wiley series in probability and mathematical statistics. John Wiley & Sons Inc, New YorkGoogle Scholar
  6. Fisher RN, Suarez AV, Case TJ (2002) Spatial patterns in the abundance of the coastal horned lizard. Conserv Biol 16:205–215CrossRefGoogle Scholar
  7. Fukushima M (1994) Spawning migration and redd construction of Sakhalin taimen, Hucho perryi (Salmonidae) on northern Hokkaido Island, Japan. J Fish Biol 44:877–888CrossRefGoogle Scholar
  8. Fukushima M (2001) Salmonid habitat–geomorphology relationships in low-gradient streams. Ecology 82:1238–1246Google Scholar
  9. Fukushima M (2010) Loss of fish diversity due to damming. In: Tanida K, Murakami T (eds) The ecosystems and their management of dam reservoirs and rivers. The University of Nagoya Press, NagoyaGoogle Scholar
  10. Fukushima M, Kameyama S (2006) The effects of damming on masu salmon and the Sakhalin taimen and the assessment of their conservation areas based on predictive habitat models. Ecol Civ Eng 8:233–244CrossRefGoogle Scholar
  11. Fukushima M, Kameyama S, Kaneko M, Nakao K, Steel EA (2007) Modelling the effects of dams on freshwater fish distributions in Hokkaido, Japan. Freshw Biol 52:1511–1524CrossRefGoogle Scholar
  12. Fukushima M, Kaeriyama M, Goto A (2008) Sakhalin taimen (Hucho perryi): challenges of saving giant freshwater fish species. Jpn J Ichthyol 55:49–53Google Scholar
  13. Fukushima M, Shimazaki H, Rand PS, Kaeriyama M (2011) Reconstructing Sakhalin taimen Parahucho perryi historical distribution and identifying causes for local extinctions. Trans Am Fish Soc 140:1–13Google Scholar
  14. Goto A (1980) Geographic distribution and variations of two types of Cottus nozawae in Hokkaido, and morphological characteristics of C. amblystomopsis from Sakhalin. Jpn J Ichthyol 27:97–105Google Scholar
  15. Goto A (1994) Fishes in rivers and lakes: its origins and adaptive strategies. In: Ishigaki K, Fukuda M (eds) The Nature of Hokkaido. Hokkaido University Publishing, SapporoGoogle Scholar
  16. Goto A, Nakanishi T, Utoh H, Hamada K (1978) A preliminary study of the freshwater fish fauna of rivers in southern Hokkaido. Bull Fac Fish Hokkaido Univ 29:118–130Google Scholar
  17. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186CrossRefGoogle Scholar
  18. Gutiérrez D, Fernández P, Seymour AS, Jordano D (2005) Habitat distribution models: are mutualist distributions good predictors of their associates? Ecol Appl 15:3–18CrossRefGoogle Scholar
  19. Hastie TJ (1992) Generalized additive models. In: Statistical Models, S. Wadsworth & Brooks/Cole computer science series. S. Wadsworth & Brooks/Cole Advanced Books & Software, CaliforniaGoogle Scholar
  20. Heinz Center (2002) Dam removal: science and decision making. H.J. Heinz Center for Science Economics and the Environment, Washington DCGoogle Scholar
  21. Iguchi K, Matsuura K, McNyset KM, Peterson AT, Scachetti-Pereira R, Powers KA, Vieglais DA, Wiley EO, Yodo T (2004) Predicting invasions of North American basses in Japan using native range data and a genetic algorithm. Trans Am Fish Soc 133:845–854CrossRefGoogle Scholar
  22. Joy MK, Death RG (2001) Control of freshwater fish and crayfish community structure in Taranaki, New Zealand: dams, diadromy or habitat structure? Freshw Biol 46:417–429CrossRefGoogle Scholar
  23. Kato F (1991) Life history of masu and amago salmon (Oncorhynchus masou and Oncorhynchus rhodurus). In: Groot C, Margolis L (eds) Pacific salmon life histories. UBC Press, VancouverGoogle Scholar
  24. Kawanabe H, Mizuno N (1989) Freshwater fishes of Japan. Yama-Kei Publishers Co. Ltd, TokyoGoogle Scholar
  25. Kishi D, Takayama H, Kato H, Fukushima M (2003) Riverine fish fauna in the Hidaka region, Hokkaido. Res Bull Hokkaido Univ Forests 60:1–18Google Scholar
  26. Kuwata O (1963) On the management of the salmon protection drainages. Sakana To Ran 101:8Google Scholar
  27. Leathwick JR (1998) Are New Zealand’s Nothofagus species in equilibrium with their environment? J Veg Sci 9:719–732CrossRefGoogle Scholar
  28. Leathwick JR, Rowe D, Richardson J, Elith J, Hastie T (2005) Using multivariate adaptive regression splines to predict the distributions of New Zealand’s freshwater diadromous fish. Freshw Biol 50:2034–2052CrossRefGoogle Scholar
  29. Lobo JM, Martin-Piera F (2002) Searching for a predictive model for species richness of Iberian dung beetle based on spatial and environmental variables. Conserv Biol 16:158–173CrossRefGoogle Scholar
  30. March JG, Benstead JP, Pringle CM, Scatena FN (2003) Damming tropical island streams: ­problems, solutions, and alternatives. Bioscience 53:1069–1078CrossRefGoogle Scholar
  31. Marchant R, Hehir G (2002) The use of AUSRIVAS predictive models to assess the response of lotic macroinvertebrates to dams in south-east Australia. Freshw Biol 47:1033–1050CrossRefGoogle Scholar
  32. McDowall RM (1988) Diadromy in fishes: migrations between freshwater and marine environments. Croom Helm, LondonGoogle Scholar
  33. Miyakoshi Y (2006) Evaluation of stock enhancement programs and stock assessment for masu salmon in Hokkaido, northern Japan. Sci Rep Hokkaido Fish Hatchery 60:1–64Google Scholar
  34. MLITT (The Ministry of Land, Infrastructure, Transport and Tourism) (2010) National Regional Planning Bureau, Digital national land information. Accessed Jan 2010.
  35. Montgomery DR, Buffington JM (1998) Channel process, classification, and response. In: Naiman RJ, Bilby RE (eds) River Ecology and Management Lessons from the Pacific Coastal Ecoregion. Springer, New YorkGoogle Scholar
  36. Morita K, Yamamoto S (2002) Effects of habitat fragmentation by damming on the persistence of stream-dwelling charr populations. Conserv Biol 16:1318–1323CrossRefGoogle Scholar
  37. Nagatsu M, Ohbayashi K, Hodoki Y, Ono Y, Murano N (2007) The distribution and habitat of the endangered ‘ezo’ eight-barbell loach, Lefua nikkonis (Jordan and Fowler), on Hokkaido Island, Japan. Jpn J Conserv Ecol 12:60–65Google Scholar
  38. Peterson AT, Robins CR (2003) Using ecological-niche modeling to predict barred owl invasions with implications for spotted owl conservation. Conserv Biol 17:1161–1165CrossRefGoogle Scholar
  39. Poulsen AF, Poeu O, Viravong S, Suntornratana U, Tung NT (2002) Fish migrations of the Lower Mekong Basin: implications for development, planning and environmental management. MRC Technical Paper No. 8. Mekong River Commission, Phnom PenhGoogle Scholar
  40. Prendergast JR, Quinn RM, Lawton JH (1999) The Gaps between theory and practice in selecting nature reserves. Conserv Biol 13:484–492CrossRefGoogle Scholar
  41. Pressey RL, Johnson IR, Wilson PD (1994) Shades of irreplaceability: towards a measure of the contribution of sites to a reservation goal. Biodivers Conserv 3:242–262CrossRefGoogle Scholar
  42. Rand PS (2006) Hucho perryi. In: IUCN 2011. IUCN Red List of Threatened Species. Version 2011.2. Available on <>
  43. Ray N, Lehmann A, Joly P (2002) Modeling spatial distribution of amphibian populations: a GIS approach based on habitat matrix permeability. Biodivers Conserv 11:2143–2165CrossRefGoogle Scholar
  44. Scott JM, Davis F, Csuti B, Noss R, Butterfield B, Groves C, Anderson H, Caicco S, Derchia F, Edwards TC, Ulliman J, Wright RG (1993) Gap analysis: a geographic approach to protection of biological diversity. Wildl Monogr 123:1–41Google Scholar
  45. Stanley EH, Doyle MW (2003) Trading off: the ecological effects of dam removal. Front Ecol Environ 1:15–22CrossRefGoogle Scholar
  46. Suzuki N, Murasawa K, Nansai K, Sakurai T, Moriguchi Y, Tanabe K, Nakasugi O, Morita M (2003) River networking database for geo-referenced fate modeling of Japanese rivers, Research Report from the National Institute for Environmental Studies, Tsukuba, No. 179Google Scholar
  47. Yamashiro S (1965) Age and growth of the Ito (Hucho perryi) in northeastern Hokkaido. Bull Jpn Soc Sci Fish 31:1–7CrossRefGoogle Scholar
  48. Zolotukhin SF, Semenchenko AY, Belyaev VA (2000) Taimen and lenok of Russian Far East. Khabarovsk Branch of TINRO-Center, KhabarovskGoogle Scholar

Copyright information

© Springer 2012

Authors and Affiliations

  1. 1.National Institute for Environmental StudiesTsukubaJapan

Personalised recommendations