Successes, failures, and opportunities in the practical application of drift-foraging models

Authored by Nicolaas Bouwes, Jordan S Rosenfeld, C Eric Wall, Sean M Naman

Date Published: 2014

DOI: 10.1007/s10641-013-0195-6

Sponsors: National Science and Engineering Research Council of Canada (NSERC) Bonneville Power Administration

Platforms: Delft3D

Model Documentation: Other Narrative Flow charts

Model Code URLs: Model code not found

Abstract

Accurately measuring productive capacity in streams is challenging, and field methods have generally focused on the limiting role of physical habitat attributes (e.g. channel gradient, depth, velocity, substrate). Because drift-foraging models uniquely integrate the effects of both physical habitat (velocity and depth) and prey abundance (invertebrate drift) on energy intake for drift-feeding fishes, they provide a coherent and transferable framework for modelling individual growth that includes the effects of both physical habitat and biological production. Despite this, drift-foraging models have been slow to realize their potential in an applied context. Practical applications have been hampered by difficulties in predicting growth (rather than habitat choice), and scaling predictions of individual growth to reach scale habitat capacity, which requires modelling the partitioning of resources among individuals and depletion of drift through predation. There has also been a general failure of stream ecologists to adequately characterize spatial and temporal variation in invertebrate drift within and among streams, so that sources of variation in this key component of drift-foraging models remain poorly understood. Validation of predictions of habitat capacity have been patchy or lacking, until recent studies demonstrating strong relationships between drift flux, modeled Net Energy Intake, and fish biomass. Further advances in the practical application of drift-foraging models will require i) a better understanding of the factors that cause variation in drift, better approaches for modelling drift, and more standardized methods for characterizing it; ii) identification of simple diagnostic metrics that correlate strongly with more precise but time-consuming bioenergetic assessments of habitat quality; and iii) a better understanding of how variation in drift-foraging strategies are associated with other suites of co-evolved traits that ecologically differentiate taxa of drift-feeding salmonids.

Tags

Atlantic salmon Individual-based model Juvenile coho salmon Rainbow-trout Habitat suitability criteria Grayling thymallus-arcticus Mayfly species ephemeroptera Density-dependent growth Invertebrate drift Feeding salmonids