Environmental Biology of Fishes

, Volume 97, Issue 5, pp 551–574 | Cite as

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

  • Jordan S. Rosenfeld
  • Nicolaas Bouwes
  • C. Eric Wall
  • Sean M. Naman
Article

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.

Keywords

Drift-foraging Bioenergetics Stream habitat capacity Habitat quality Modelling growth 

Notes

Acknowledgments

The authors would like to thank John Piccolo for organizing this special issue and the drift-foraging symposium on which it is based, and for inviting our participation. We also thank Jim Grant, John Hayes, and John Piccolo for insightful comments that greatly improved the manuscript. We also acknowledge support from NSERC and the B.C. Forest Sciences Program for funding that provided the basis for some of the research presented in this paper, as did grants from the Bonneville Power Administration (BPA 2003-010-00 and 2011-006-00).

References

  1. Anderson KE, Paul AJ, McCauley E, Jackson LJ, Post JR, Nisbet RM (2006) Instream flow needs in streams and rivers: the importance of understanding ecological dynamics. Front Ecol Environ 4:309–318Google Scholar
  2. Anderson KE, Harrison LR, Nisbet RM, Kolpas A (2013) Modeling the influence of flow on invertebrate drift across spatial scales using a 2D hydraulic model and a 1D population model. Ecol Model 265:207–220Google Scholar
  3. Arendt J (1997) Adaptive intrinsic growth rates: an integration across taxa. Q Rev Biol 72:149–177Google Scholar
  4. Armstrong JB, Schindler DE (2011) Excess digestive capacity in predators reflects a life of feast and famine. Nature 476:84–88PubMedGoogle Scholar
  5. Arnold GP, Webb PW, Holford BH (1991) The role of the pectoral fins in station-holding of Atlantic salmon parr (Salmo salar l.). J Exp Biol 156:625–629Google Scholar
  6. Baker EA, Coon TG (1997) Development and evaluation of alternative habitat suitability criteria for brook trout. Trans Am Fish Soc 126:65–76Google Scholar
  7. Beecher HA, Caldwell BA, DeMond SB (2002) Evaluation of depth and velocity preferences of juvenile coho salmon in Washington streams. N Am J Fish Manag 22:785–795Google Scholar
  8. Beecher HA, Caldwell BA, DeMond SB, Seiler D, Boessow SN (2010) An empirical assessment of PHABSIM using long-term monitoring of coho salmon smolt production in Bingham Creek, Washington. N Am J Fish Manag 30:1529–1543Google Scholar
  9. Benke AC, Hawkins CP, Lowe-McConnell RH, Stanford JA, Suberkropp K, Ward JV (1988) Bioenergetic considerations in the analysis of stream ecosystems. J N Am Benthol Soc 7:480–502Google Scholar
  10. Billerbeck J, Lankford T, Conover D (2001) Evolution of intrinsic growth and energy acquisition rates. I. Trade-offs with swimming performance in Menidia menidia. Evolution 55:1863–1872PubMedGoogle Scholar
  11. Bisson PA, Sullivan K, Nielsen JL (1988) Channel hydraulics, habitat use, and body form of juvenile coho salmon, steelhead, and cutthroat trout in streams. Trans Am Fish Soc 117:262–273Google Scholar
  12. Boisclair D (2001) Fish habitat modeling: from conceptual framework to functional tools. Can J Fish Aquat Sci 58:1–9Google Scholar
  13. Boisclair D, Tang M (1993) Empirical analysis of the influence of swimming pattern on the net energetic cost of swimming in fishes. J Fish Biol 42:169–183Google Scholar
  14. Bourassa N, Morin A (1995) Relationships between size structure of invertebrate assemblages and trophy and substrate composition in streams. J N Am Benthol Soc 14:393–403Google Scholar
  15. Bouwes N, Weber N (2012) Growth potential models. In: ISEMP lessons learned synthesis report 2003–2011. Bonneville Power Administration, Portland, pp 174–185 https://isemp.egnyte.com/h-s/20130105/14470aa0343e44b0 accessed Sept 1 2013
  16. Bouwes N, Moberg J, Weber N, Bouwes B, Bennett S, Beasley C, Jordan CE, Nelle P, Polino M, Rentmeester S, Semmens B, Volk C, Ward MB, White J (2011) Scientific protocol for salmonid habitat surveys within the Columbia Habitat Monitoring Program. Project #2011-006, The Integrated Status and Effectiveness Monitoring Program.http://www.pnamp.org/sites/default/files/champhabitatprotocol_20110125.pdf
  17. Braaten PJ, Dey PD, Annear TC (1997) Development and evaluation of bioenergetic-based habitat suitability criteria for trout. Regul River 13(4):345–356Google Scholar
  18. Brandt SB, Mason DM, Patrick EV (1992) Spatially explicit models of fish growth rate. Fisheries 17:23–35Google Scholar
  19. Brittain JE, Eikeland TJ (1988) Invertebrate drift—a review. Hydrobiologia 166:77–93Google Scholar
  20. Careau V, Garland T Jr (2012) Performance, personality, and energetics: correlation, causation, and mechanism. Physiol Biochem Zool 85:543–571Google Scholar
  21. Caviedes-Vidal E, McWhorter TJ, Lavin SR, Chediack JG, Tracy CR, Karasov WH (2007) The digestive adaptation of flying vertebrates: high intestinal paracellular absorption compensates for smaller guts. PNAS 104:19132–19137PubMedCentralPubMedGoogle Scholar
  22. CHaMP (2013) The Columbia habitat monitoring program: 2012 second year lessons learned project synthesis report 2011-006. Prepared for the Bonneville Power Administration by CHaMP. Published by Bonneville Power Administration, Portland, OR. 64pages. https://www.champmonitoring.org/Program/Details/1#documents
  23. Ciborowski JJH (1983a) Downstream and lateral transport of nymphs of 2 mayfly species (Ephemeroptera). Can J Fish Aquat Sci 40:2025–2029Google Scholar
  24. Ciborowski JJH (1983b) Influence of current velocity, density, and detritus on drift of 2 mayfly species (Ephemeroptera). Can J Zool 61:119–125Google Scholar
  25. Ciborowski JJH (1987) Dynamics of drift and microdistribution of 2 mayfly populations—a predictive model. Can J Fish Aquat Sci 44:832–845Google Scholar
  26. Dewson ZS, James ABW, Death RG, Dewson S (2007) A review of the consequences of decreased flow for instream habitat and macroinvertebrates. J N Am Benthol Soc 26:401–415Google Scholar
  27. Enders EC, Boisclair D, Roy AG (2003) The effect of turbulence on the cost of swimming for juvenile Atlantic salmon (Salmo salar). Can J Fish Aquat Sci 1160:1149–1160Google Scholar
  28. Enders EC, Buffin-Belanger T, Boisclair D, Roy AG (2005) The feeding behaviour of juvenile Atlantic salmon in relation to turbulent flow. J Fish Biol 66:242–253Google Scholar
  29. Everest FH, Chapman DW (1972) Habitat selection and spatial interaction by juvenile Chinook salmon and steelhead trout in two Idaho streams. J Fish Res Board Can 29:91–100Google Scholar
  30. Fausch KD (1984) Profitable stream positions for salmonids–relating specific growth-rate to net energy gain. Can J Zool 62:441–451Google Scholar
  31. Finstad AG, Forseth T, Jonsson B et al (2011) Competitive exclusion along climate gradients: energy efficiency influences the distribution of two salmonid fishes. Glob Chang Biol 17:1703–1711Google Scholar
  32. Fretwell SD (1972) Populations in a seasonal environment. Princeton University Press, PrincetonGoogle Scholar
  33. Garshelis DL (2000) Delusions in habitat evaluation: measuring use, selection, and importance. In: Boitani L, Fuller TK (eds) Research techniques in animal ecology: controversies and consequences. Columbia University Press, New York, pp 111–164Google Scholar
  34. Grace JB, Anderson TM, Olff H, Scheiner SM (2010) On the specification of structural equation models for ecological systems. Ecol Monogr 80:67–87Google Scholar
  35. Grant JWA, Noakes DLG (1987) Movers and stayers–foraging tactics of young-of-the-year brook charr, Salvelinus fontinalis. J Anim Ecol 56:1001–1013Google Scholar
  36. Grossman GD, Rincon PA, Farr MD, Ratajczak RE (2002) A new optimal foraging model predicts habitat use by drift-feeding stream minnows. Ecol Freshw Fish 11:2–10Google Scholar
  37. Guensch GR, Hardy TB, Addley RC (2001) Examining feeding strategies and position choice of drift-feeding salmonids using an individual-based, mechanistic foraging model. Can J Fish Aquat Sci 58(3):446–457Google Scholar
  38. Hansen EA, Closs GP (2007) Temporal consistency in the long-term spatial distribution of macroinvertebrate drift along a stream reach. Hydrobiologia 575:361–371Google Scholar
  39. Hansen EA, Closs GP (2009) Long-term growth and movement in relation to food supply and social status in a stream fish. Behav Ecol 20:616–623Google Scholar
  40. Hanson P, Johnson T, Kitchell J, Schindler DE (1997) Fish bioenergetics 3.0. University of Wisconsin Sea Grant Institute, MadisonGoogle Scholar
  41. Harvey BC, Railsback SF (2009) Exploring the persistence of stream-dwelling trout populations under alternative real-world turbidity regimes with an individual-based model. Trans Am Fish Soc 138:348–360Google Scholar
  42. Harvey BC, White JL, Nakamoto RJ (2009) The effect of deposited fine sediment on summer survival and growth of rainbow trout in riffles of a small stream. N Am J Fish Manag 29:434–440Google Scholar
  43. Hayes JW, Stark JD, Shearer KA (2000) Development and test of a whole-lifetime foraging and bioenergetics growth model for drift-feeding brown trout. Trans Am Fish Soc 129:315–332Google Scholar
  44. Hayes JW, Hughes NF, Kelly LH (2007) Process-based modelling of invertebrate drift transport, net energy intake and reach carrying capacity for drift-feeding salmonids. Ecol Model 207:171–188Google Scholar
  45. Heritage GL, Milan DJ, Large ARG, Fuller IC (2009) Influence of survey strategy and interpolation model on DEM quality. Geomorphology 112:334–344Google Scholar
  46. Hill J, Grossman GD (1993) An energetic model of microhabitat use for rainbow-trout and rosyside dace. Ecology 74:685–698Google Scholar
  47. Hughes NF (1992) Selection of positions by drift-feeding salmonids in dominance hierarchies: model and test for Arctic grayling (Thymallus arcticus) in subarctic mountain streams, interior Alaska. Can J Fish Aquat Sci 49:1999–2008Google Scholar
  48. Hughes NF, Dill LM (1990) Position choice by drift-feeding salmonids: Model and test for Arctic Grayling (Thymallus arcticus) in subarctic mountain streams, interior Alaska. Can J Fish Aquat Sci 47:2039–2048Google Scholar
  49. Hughes NF, Grand TC (2000) Physiological ecology meets the ideal-free distribution: predicting the distribution of size-structured fish populations across temperature gradients. Environ Biol Fish 59:285–298Google Scholar
  50. Hughes NF, Kelly LH (1996) A hydrodynamic model for estimating the energetic cost of swimming maneuvers from a description of their geometry and dynamics. Can J Fish Aquat Sci 53:2484–2493Google Scholar
  51. Hughes NF, Reynolds JB (1994) Why do Arctic Grayling (Thymallus arcticus) get bigger as you go upstream? Can J Fish Aquat Sci 51:2154–2163Google Scholar
  52. Hughes NF, Hayes JW, Shearer KA, Young RG (2003) Testing a model of drift-feeding using three-dimensional videography of wild Brown Trout, Salmo trutta, in a New Zealand river. Can J Fish Aquat Sci 60:1462–1476Google Scholar
  53. Imre I, Grant JWA, Cunjak RA (2005) Density-dependent growth of young-of-the-year Atlantic salmon Salmo salar in Catamaran Brook, New Brunswick. J Anim Ecol 74:508–516Google Scholar
  54. Imre I, Grant JWA, Cunjak RA (2010) Density-dependent growth of young-of-the-year Atlantic salmon (Salmo salar) revisited. Ecol Freshw Fish 19:1–6Google Scholar
  55. Jenkins AR, Keeley ER (2010) Bioenergetic assessment of habitat quality for stream-dwelling cutthroat trout (Oncorhynchus clarkii bouvieri) with implications for climate change and nutrient supplementation. Can J Fish Aquat Sci 67:371–385Google Scholar
  56. Jenkins TM, Diehl S, Kratz KW, Cooper SD (1999) Effects of population density on individual growth of brown trout in streams. Ecology 80:941–956Google Scholar
  57. Jowett I, Hayes J, Duncan M (2008) A guide to instream habitat survey methods and analysis. NIWA Science and Technology Series No. 5Google Scholar
  58. Keeley ER, Grant JWA (1997) Allometry of diet selectivity in juvenile Atlantic salmon (Salmo salar). Can J Fish Aquat Sci 54:1894–1902Google Scholar
  59. Kennedy BP, Nislow KH, Folt CL (2008) Habitat-mediated foraging limitations drive survival bottlenecks for juvenile salmon. Ecology 89:2529–2541PubMedGoogle Scholar
  60. Killen SS, Atkinson D, Glazier DS (2010) The intraspecific scaling of metabolic rate with body mass in fishes depends on lifestyle and temperature. Ecol Lett 13:184–193PubMedGoogle Scholar
  61. Kondolf GM, Larsen EW, Williams JG (2000) Measuring and modelling the hydraulic environment for assessing instream flows. N Am J Fish Manag 20:1016–1028Google Scholar
  62. Kramer DL, Rangely RW, Chapman LJ (1997) Habitat selection: patterns of spatial distribution from behavioural decisions. In: Godin JJ (ed) Behavioural ecology of teleost fishes. Oxford University Press, New York, pp 37–80Google Scholar
  63. Leung ES, Rosenfeld JS, Bernhardt J (2009) Habitat effects on invertebrate drift in a small trout stream: implications for prey availability to drift-feeding fish. Hydrobiologia 623:113–125Google Scholar
  64. Lobón-Cerviá J (2008) Habitat quality enhances spatial variation in the self-thinning patterns of stream-resident brown trout (Salmo trutta). Can J Fish Aquat Sci 65:2006–2015Google Scholar
  65. Marcus WA (2012) Remote sensing of the hydraulic environment in gravel-bed rivers gravel-bed rivers. In: Church M, Biron PM, Roy AG (eds) Gravel-bed rivers: processes, tools, environments. Wiley, Chichister, pp 259–285Google Scholar
  66. Mathur D, Bason WH, Purdy EJ, Silver CA (1985) A critique of the instream flow incremental methodology. Can J Fish Aquat Sci 42:825–831Google Scholar
  67. McCarthy SG, Duda JJ, Emlen JM, Hodgson GR, Beauchamp DA (2009) Linking habitat quality with trophic performance of steelhead along forest gradients in the South Fork Trinity River watershed, California. Trans Am Fish Soc 138:506–521Google Scholar
  68. Milan DJ, Heritage GL, Large ARG, Fuller IC (2011) Filtering spatial error from DEMs: implications for morphological change estimation. Geomorphology 125:160–171Google Scholar
  69. Morin A, Stephenson J, Strike J, Solimini AG (2004) Sieve retention probabilities of stream benthic invertebrates. J N Am Benthol Soc 23:383–391Google Scholar
  70. Nakano S (1995) Competitive interactions for foraging microhabitats in a size-structured interspecific dominance hierarchy of two sympatric stream salmonids in a natural habitat. Can J Zool 73:1845–1854Google Scholar
  71. Nakano S, Fausch KD, Kitano S (1999) Flexible niche partitioning via a foraging mode shift: a proposed mechanism for coexistence in stream-dwelling charrs. J Anim Ecol 68:1079–1092Google Scholar
  72. Nielsen JL (1992) Microhabitat-specific foraging behavior, diet, and growth of juvenile coho salmon. Trans Am Fish Soc 121:617–634Google Scholar
  73. Nislow KH, Folt CL, Parrish DL (1999) Favorable foraging locations for young atlantic salmon: application to habitat population restoration. Ecol Appl 9:1085–1099Google Scholar
  74. Nislow KH, Folt CL, Parrish DL (2000) Spatially explicit bioenergetic analysis of habitat quality for age-0 Atlantic salmon. Trans Am Fish Soc 129:1067–1081Google Scholar
  75. Nislow KH, Armstrong JD, Grant JWA (2011) The role of competition in the ecology of juvenile Atlantic salmon. In: Aas O, Einum S, Klemetsen A, Skurdal J (eds) Atlantic salmon ecology. Blackwell Publishing Ltd., Oxford, pp 171–198Google Scholar
  76. Nolte U (1990) Chironomid biomass determination from larval shape. Freshwat Biol 24:443–451Google Scholar
  77. Piccolo JJ, Hughes NF, Bryant MD (2008) Water velocity influences prey detection and capture by drift-feeding juvenile coho salmon (Oncorhynchus kisutch) and steelhead (Oncorhynchus mykiss irideus). Can J Fish Aquat Sci 275:266–275Google Scholar
  78. Poff NL, Ward JV (1991) Drift responses of benthic invertebrates to experimental streamflow variation in a hydrologically stable stream. Can J Fish Aquat Sci 48:1926–1936Google Scholar
  79. Railsback SF, Harvey BC (2002) Analysis of habitat-selection rules using an individual-based model. Ecology 83:1817–1830Google Scholar
  80. Railsback SF, Stauffer HB, Harvey BC (2003) What can habitat preferences models tell us? Tests using a virtual trout population. Ecol Appl 13:1580–1594Google Scholar
  81. Railsback S, Harvey B, Hayse J, LaGory K (2005) Tests of theory for diel variation in salmonid feeding activity and habitat use. Ecology 86:947–959Google Scholar
  82. Railsback SF, Harvey BC, Jackson SK, Lamberson RH (2009) InSTREAM: the individual-based stream trout research and environmental assessment model. General Technical Report–Pacific Southwest Research Station, USDA Forest Service(PSW-GTR-218):154 pp.-154 ppGoogle Scholar
  83. Railsback SF, Gard M, Harvey BC, White JL, Zimmerman JKH (2013) Contrast of degraded and restored stream habitat using an individual-based salmon model. N Am J Fish Manag 33:384–399Google Scholar
  84. Reeves GH, Everest FH, Sedell JR (1993) Diversity of juvenile anadromous salmonid assemblages in coastal oregon basins with different levels of timber harvest. Trans Am Fish Soc 122:309–317Google Scholar
  85. Rosenberger AE, Dunham JB (2005) Validation of abundance estimates from mark–recapture and removal techniques for rainbow trout captured by electrofishing in small streams. N Am J Fish Manag 25:1395–1410Google Scholar
  86. Rosenfeld JS (2003) Assessing the habitat requirements of stream fishes: an overview and evaluation of different approaches. Trans Am Fish Soc 132:953–968Google Scholar
  87. Rosenfeld JS, Boss SM (2001) Fitness consequences of habitat use for juvenile cutthroat trout: energetic costs and benefits in pools and riffles. Can J Fish Aquat Sci 58:585–593Google Scholar
  88. Rosenfeld JS, Ptolemy R (2012) Modelling available habitat versus available energy flux: do PHABSIM applications that neglect prey abundance underestimate optimal flows for juvenile salmonids? Can J Fish Aquat Sci 69:1920–1934Google Scholar
  89. Rosenfeld JS, Raeburn E (2009) Effects of habitat and internal prey subsidies on juvenile coho salmon growth: implications for stream productive capacity. Ecol Freshw Fish 18:572–584Google Scholar
  90. Rosenfeld JS, Taylor JC (2003) Modelling the effects of changes in turbidity and channel structure on growth of juvenile salmonids. 19. BC Forest Sciences Program Report R2003-183 http://www.for.gov.bc.ca/hfd/library/FIA/2003/R2003-183.pdf
  91. Rosenfeld JS, Taylor J (2009) Prey abundance, channel structure and the allometry of growth rate potential for juvenile trout. Fish Manag Ecol 16:202–218Google Scholar
  92. Rosenfeld JS, Leiter T, Lindner G, Rothman L (2005) Food abundance and fish density alters habitat selection, growth, and habitat suitability curves for juvenile coho salmon (Oncorhynchus kisutch). Can J Fish Aquat Sci 1701:1691–1701Google Scholar
  93. Rosenfeld JS, Campbell K, Leung ES, Bernhardt J, Post J (2011) Habitat effects on depth and velocity frequency distributions: Implications for modeling hydraulic variation and fish habitat suitability in streams. Geomorphology 130:127–135Google Scholar
  94. Roy M (2012) Habitat variability and the individual behaviour of Atlantic salmoin (Salmo salar). Ph.D. thesis, University de Montreal, Montreal, QuebecGoogle Scholar
  95. Sullivan K, Martin DJ, Cardwell RD, Toll JE, Duke S (2000) An analysis of the effects of temperature on Salmonids of the Pacific Northwest with implications for selecting temperature criteria. Sustainable Ecosystems Institute, PortlandGoogle Scholar
  96. Sweka JA, Hartman KJ (2001) Influence of turbidity on brook trout reactive distance and foraging success. Trans Am Fish Soc 130:138–146Google Scholar
  97. Tucker S, Rasmussen JB (1999) Using 137Cs to measure and compare bioenergetic budgets of juvenile Atlantic salmon (Salmo salar) and brook trout (Salvelinus fontinalis) in the field. Can J Fish Aquat Sci 56:875–887Google Scholar
  98. Tunney TD, Steingrimsson SO (2012) Foraging mode variation in three stream-dwelling salmonid fishes. Ecol Freshw Fish 21:570–580Google Scholar
  99. Urabe H, Nakajima M, Torao M, Aoyama T (2010) Evaluation of habitat quality for stream salmonids based on a bioenergetics model. Trans Am Fish Soc 139:1665–1676Google Scholar
  100. Van Horne B (1983) Density as a misleading indicator of habitat quality. J Wildl Manag 47:893–901Google Scholar
  101. Van Leeuwen TE, Rosenfeld JS, Richards JG (2011a) Failure of physiological metrics to predict dominance in juvenile Pacific salmon (Oncorhynchus spp.): habitat effects on the allometry of growth in dominance hierarchies. Can J Fish Aquat Sci 68:1811–1818Google Scholar
  102. Van Leeuwen TE, Rosenfeld JS, Richards JG (2011b) Adaptive trade-offs in juvenile salmonid metabolism associated with habitat partitioning between coho salmon and steelhead trout in coastal streams. J Anim Ecol 80:1012–1023PubMedGoogle Scholar
  103. Van Winkle W, Jager HI, Railsback SF, Holcomb BD, Studley TK, Baldrige JE (1998) Individual-based model of sympatric populations of brown and rainbow trout for instream flow assessment: model description and calibration. Ecol Model 110:175–207Google Scholar
  104. Van Zwol JA, Neff BD, Wilson CC (2012) The effect of competition among three juvenile salmonids on dominance and growth during the juvenile stage. Ecol Freshw Fish 21:533–540Google Scholar
  105. Waddle TJ (2001) PHABSIM for windows user’s manual and exercises: U.S. Geological Survey Open-File Report 2001-340. p 288Google Scholar
  106. Wall CE (2013) Use of a net energy intake model to examine differences in steelhead abundance and the energetic implications of physical habitat alterations. M.Sc. thesis, Utah State University, Logan, UtahGoogle Scholar
  107. Wankowski JWJ (1979) Morphological limitations, prey size selectivity, and growth response of juvenile Atlantic salmon, Salmo salar. J Fish Biol 14:89–100Google Scholar
  108. Ward DM, Nislow KH, Armstrong JD, Einum S, Folt CL (2007) Is the shape of the density-growth relationship for stream salmonids evidence for exploitative rather than interference competition? J Anim Ecol 76:135–138PubMedGoogle Scholar
  109. Watz J, Piccolo JJ (2011) The role of temperature in the prey capture probability of drift-feeding juvenile brown trout (Salmo trutta). Ecol Freshw Fish 20:393–399Google Scholar
  110. Weber NP (2009) Evaluation of macroinvertebrates as a food resource in the assessment of lotic salmonid habitat. M.Sc. thesis, Utah State University, Logan, UtahGoogle Scholar
  111. Weir LK, Grant JWA (2004) The causes of resource monopolization: interaction between resource dispersion and mode of competition. Ethology 110:63–74Google Scholar
  112. Woodward G, Perkins DM, Brown LE (2010) Climate change and freshwater ecosystems: impacts across multiple levels of organization. Philos Trans R Soc Lond Ser B 365:2093–2106Google Scholar
  113. Young KA (2004) Asymmetric competition, habitat selection, and niche overlap in juvenile salmonids. Ecology 85:134–149Google Scholar

Copyright information

© Crown Copyright 2013

Authors and Affiliations

  • Jordan S. Rosenfeld
    • 1
  • Nicolaas Bouwes
    • 2
  • C. Eric Wall
    • 3
  • Sean M. Naman
    • 4
  1. 1.British Columbia Ministry of the EnvironmentUniversity of British ColumbiaVancouverCanada
  2. 2.Eco Logical Research, Inc.ProvidenceUSA
  3. 3.Department of Watershed SciencesUtah State UniversityLoganUSA
  4. 4.Department of ZoologyUniversity of British ColumbiaVancouverCanada

Personalised recommendations