Combining energetic profitability and cover effects to evaluate salmonid habitat quality

• Kawai, Hideyuki

• Nagayama, Shigeya

• Urabe, Hirokazu

• Akasaka, Takumi

• Nakamura, Futoshi

抄録

Recent studies have demonstrated that the energetic profitability (net energy intake potential; NEI potential) of a habitat, which is calculated as the gross energy gain from foraging minus the energy expenditure from swimming at a focal point, may be a useful tool for predicting the salmonid biomass. The effectiveness of the NEI potential should be tested in various systems. Even if the NEI potential is validated, its predictive accuracy and transferability could be limited if the cover habitat, which is known to be an important factor for determining salmonid abundance, is not considered. We tested whether the NEI potential is effective for predicting the salmonid biomass even in a stream with abundant cover and whether combining the NEI potential and cover effects can improve the predictability of fish biomass using a generalized linear model. Our results demonstrated that the NEI potential could generally predict the fish biomass (percent deviance explained = 79.9 %), and the model that incorporated both the NEI potential and the cover ratio improved the predictive accuracy (percent deviance explained = 88.5 %). These results suggest that energetic profitability can be an effective indicator for assessing habitat quality and is relatively transferable to other systems. Furthermore, when cover effects are considered, the habitat quality is more accurately represented; thus, combining the energetic profitability and the cover effects might improve the transferability of the assessment across habitats.

収録刊行物

• Environmental Biology of Fishes

  Environmental Biology of Fishes 97 (5), 575-586, 2014-05

  Springer

キーワード

• Drift feeder
• Bioenergetics model
• Cover
• Salmonid biomass
• Habitat assessment

詳細情報

• CRID: 1050282813993472256
• NII論文ID: 120005661425
• HANDLE: 2115/60044
• ISSN: 03781909
• 本文言語コード: en
• 資料種別: journal article
• データソース種別:
  • IRDB
  • CiNii Articles