Microhabitat selection by macroinvertebrates: generality among rivers and functional interpretation

References

• Ahmadi‐Nedushan B, St‐Hilaire A, Bérubé M, Robichaud É, Thiémonge N, Bobée B. 2006. A review of statistical methods for the evaluation of aquatic habitat suitability for instream flow assessment. River Res Applic. 22(5):503–523.
   Web of Science ®Google Scholar
• Bates D, Mächler M, Bolker B, Walker S. 2015. Fitting linear mixed-effects models using lme4. J Stat Softw. 67(1):1–48.
   Web of Science ®Google Scholar
• Bovee KD. 1982. A guide to stream habitat analysis using the instream flow incremental methodology. Instream Flow Information Paper 12, U.S. Fish and Wildlife Service, Fort Collins, CO.
   Google Scholar
• Briggs JC. 1953. The behavior and reproduction of Salmonid fishes in a small coastal stream. State of California, Department of Fish and Game, Marine Fisheries Branch. 94:62.
   Google Scholar
• Capra H, Plichard L, Bergé J, Pella H, Ovidio M, McNeil E, Lamouroux N. 2017. Fish habitat selection in a large hydropeaking river: strong individual and temporal variations revealed by telemetry. Sci Total Environ. 578:109–120.
   Web of Science ®Google Scholar
• Chambert T, Rotella JJ, Higgs MD. 2014. Use of posterior predictive checks as an inferential tool for investigating individual heterogeneity in animal population vital rates. Ecol Evol. 4(8):1389–1397.
   Web of Science ®Google Scholar
• Chen W, Olden JD. 2018. Evaluating transferability of flow–ecology relationships across space, time and taxonomy. Freshw Biol. 63(8):817–830.
   Web of Science ®Google Scholar
• Chevene F, Doleadec S, Chessel D. 1994. A fuzzy coding approach for the analysis of long-term ecological data. Freshwater Biol. 31(3):295–309.
   Web of Science ®Google Scholar
• Damiani M, Lamouroux N, Pella H, Roux P, Loiseau E, Rosenbaum RK. 2019. Spatialized freshwater ecosystem life cycle impact assessment of water consumption based on instream habitat change modeling. Water Res. 163:114884.
   Web of Science ®Google Scholar
• Dewson ZS, James ABW, Death RG. 2007. A review of the consequences of decreased flow for instream habitat and macroinvertebrates. J N Am Benthol Soc. 26(3):401–415.
   Google Scholar
• Dolédec S, Chessel D. 1994. Co-inertia analysis: an alternative method for studying species–environment relationships. Freshwater Biol. 31(3):277–294.
   Web of Science ®Google Scholar
• Dolédec S, Lamouroux N, Fuchs U, Mérigoux S. 2007. Modelling the hydraulic preferences of benthic macroinvertebrates in small European streams. Freshwater Biol. 52(1):145–164.
   Web of Science ®Google Scholar
• Dolédec S, Castella E, Forcellini M, Olivier J-M, Paillex A, Sagnes P. 2015. The generality of changes in the trait composition of fish and invertebrate communities after flow restoration in a large river (French Rhône). Freshw Biol. 60(6):1147–1161.
   Web of Science ®Google Scholar
• Dray S, Chessel D, Thioulouse J. 2003. Co-inertia analysis and the linking of ecological data tables. Ecology. 84(11):3078–3089.
   Web of Science ®Google Scholar
• Ferguson RI, Sharma BP, Hardy RJ, Hodge RA, Warburton J. 2017. Flow resistance and hydraulic geometry in contrasting reaches of a bedrock channel. Water Resour Res. 53(3):2278–2293.
   Web of Science ®Google Scholar
• Fornaroli R, Calabrese S, Marazzi F, Zaupa S, Mezzanotte V. 2019. The influence of multiple controls on structural and functional characteristics of macroinvertebrate community in a regulated Alpine river. Ecohydrology. 12(2):e2069.
   Web of Science ®Google Scholar
• Fournier DA, Skaug HJ, Ancheta J, Ianelli J, Magnusson A, Maunder MN, Nielsen A, Sibert J. 2012. AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optim Methods Softw. 27(2):233–249.
   Web of Science ®Google Scholar
• Frutiger A, Schib J-L. 1993. Limitations of FST hemispheres in lotic benthos research. Freshwater Biol. 30(3):463–474.
   Web of Science ®Google Scholar
• Fuchs U. 1994. Ökologische Grundlagen zur wasserwirtschaftlichen Planung von Abfluß und Morphologie kleinerer Fließgewässer [PhD Thesis]. Dissertation an der Fakultät für Biologie und Geowissenschaften, Universität Karlsruhe.
   Google Scholar
• Fukuda S. 2011. Assessing the applicability of fuzzy neural networks for habitat preference evaluation of Japanese medaka (Oryzias latipes). Ecol Inform. 6(5):286–295.
   Web of Science ®Google Scholar
• Gayraud S, Statzner B, Bady P, Haybachp A, Scholl F, Usseglio-Polatera P, Bacchi M. 2003. Invertebrate traits for the biomonitoring of large European rivers: an initial assessment of alternative metrics. Freshw Biol. 48(11):2045–2064.
   Web of Science ®Google Scholar
• Gibbins CN, Vericat D, Batalla RJ, Buendia C. 2016. Which variables should be used to link invertebrate drift to river hydraulic conditions? Fund App Lim. 187(3):191–205.
   Web of Science ®Google Scholar
• Girard V, Monti D, Valade P, Lamouroux N, Mallet J-P, Grondin H. 2014. Hydraulic preferences of shrimps and fishes in tropical insular rivers: hydraulic preferences of diadromous species. River Res Applic. 30(6):766–779.
   Web of Science ®Google Scholar
• Hart DD, Finelli CM. 1999. Physical-biological coupling in streams: the pervasive effects of flow on benthic organisms. Annu Rev Ecol Syst. 30(1):363–395.
   Google Scholar
• Jowett IG. 1993. A method for objectively identifying pool, run, and riffle habitats from physical measurements. N Z J Mar Freshwater Res. 27(2):241–248.
   Web of Science ®Google Scholar
• Jowett IG. 2003. Hydraulic constraints on habitat suitability for benthic invertebrates in gravel-bed rivers. River Res Applic. 19(5-6):495–507.
   Web of Science ®Google Scholar
• Jowett IG, Davey AJH. 2007. A comparison of composite habitat suitability indices and generalized additive models of invertebrate abundance and fish presence–habitat availability. Trans Am Fish Soc. 136(2):428–444.
   Web of Science ®Google Scholar
• Książek L, Woś A, Florek J, Wyrębek M, Młyński D, Wałęga A. 2019. Combined use of the hydraulic and hydrological methods to calculate the environmental flow: Wisloka river, Poland: case study. Environ Monit Assess. 191(4):254.
   Web of Science ®Google Scholar
• Lamouroux N, Capra H, Pouilly M, Souchon Y. 1999. Fish habitat preferences in large streams of southern France. Freshw Biol. 42(4):673–687.
   Web of Science ®Google Scholar
• Lamouroux N, Dolédec S, Gayraud S. 2004. Biological traits of stream macroinvertebrate communities: effects of microhabitat, reach, and basin filters. J N Am Benthol Soc. 23(3):449–466.
   Google Scholar
• Lamouroux N, Gore JA, Lepori F, Statzner B. 2015. The ecological restoration of large rivers needs science-based, predictive tools meeting public expectations: an overview of the Rhône project. Freshw Biol. 60(6):1069–1084.
   Web of Science ®Google Scholar
• Lamouroux N, Hauer C, Stewardson MJ, Poff NL. 2017. Physical habitat modeling and ecohydrological tools. In Horne A, Webb A, Stewardson MJ, Richter B, Acreman M, editors. Water for the environment. Amsterdam: Elsevier; p. 265–285. https://doi.org/10.1016/B978-0-12-803907-6.00013-9
   Google Scholar
• Lamouroux N, Mérigoux S, Capra H, Dolédec S, Jowett IG, Statzner B. 2010. The generality of abundance-environment relationships in microhabitats: a comment on Lancaster and Downes (2009). River Res Applic. 26(7):915–920.
   Web of Science ®Google Scholar
• Lamouroux N, Mérigoux S, Dolédec S, Snelder TH. 2013. Transferability of hydraulic preference models for aquatic macroinvertebrates. River Res Applic. 29(7):933–937.
   Web of Science ®Google Scholar
• Lamouroux N, Statzner B, Fuchs U, Kohmann F, Schmedtje U. 1992. An unconventional approach to modeling spatial and temporal variability of local shear stress in stream segments. Water Resour Res. 28(12):3251–3258.
   Web of Science ®Google Scholar
• Lancaster J, Downes BJ. 2010. Linking the hydraulic world of individual organisms to ecological processes: putting ecology into ecohydraulics. River Res Applic. 26(4):385–403.
   Web of Science ®Google Scholar
• Legendre P, Legendre LFJ. 1998. Numerical ecology. Amsterdam: Elsevier. Science BV; p. 853.
   Google Scholar
• Leopold LB, Maddock T. Jr. 1953. The hydraulic geometry of stream channels and some physiographic implications. Washington (DC): U.S. Government Printing Office. Professional Paper Report No.: 252. [accessed 2020 May 27]. http://pubs.er.usgs.gov/publication/pp252.
   Google Scholar
• Lorenz S, Wolter C. 2019. Quantitative response of riverine benthic invertebrates to sediment grain size and shear stress. Hydrobiologia. 834(1):47–61.
   Web of Science ®Google Scholar
• Mathur D, Bason WH, Purdy EJ, Jr., Silver CA. 1985. A critique of the in stream flow incremental methodology. Can J Fish Aquat Sci. 42(4):825–831.
   Web of Science ®Google Scholar
• Mérigoux S, Dolédec S. 2004. Hydraulic requirements of stream communities: a case study on invertebrates. Freshw Biol. 49(5):600–613.
   Web of Science ®Google Scholar
• Mérigoux S, Lamouroux N, Olivier J-M, Dolédec S. 2009. Invertebrate hydraulic preferences and predicted impacts of changes in discharge in a large river. Freshw Biol. 54(6):1343–1356.
   Web of Science ®Google Scholar
• Mérigoux S, Forcellini M, Dessaix J, Fruget J-F, Lamouroux N, Statzner B. 2015. Testing predictions of changes in benthic invertebrate abundance and community structure after flow restoration in a large river (French Rhône). Freshw Biol. 60(6):1104–1117.
   Web of Science ®Google Scholar
• Mondy CP, Villeneuve B, Archaimbault V, Usseglio-Polatera P. 2012. A new macroinvertebrate-based multimetric index (I2M2) to evaluate ecological quality of French wadeable streams fulfilling the WFD demands: a taxonomical and trait approach. Ecol Indic. 18:452–467.
   Web of Science ®Google Scholar
• Morel M, Booker DJ, Gob F, Lamouroux N. 2020. Intercontinental predictions of river hydraulic geometry from catchment physical characteristics. J Hydrol. 582:124292.
   Web of Science ®Google Scholar
• Morris MWL, Hondzo M, Power ME. 2011. Scaling Glossosoma (Trichoptera) density by abiotic variables in mountain streams. J N Am Benthol Soc. 30(2):493–506.
   Google Scholar
• Muñoz-Mas R, Fukuda S, Pórtoles J, Martínez-Capel F. 2018. Revisiting probabilistic neural networks: a comparative study with support vector machines and the microhabitat suitability for the Eastern Iberian chub (Squalius valentinus). Ecol Inform. 43:24–37.
   Web of Science ®Google Scholar
• Nestler JM, Milhous RT, Payne TR, Smith DL. 2019. History and review of the habitat suitability criteria curve in applied aquatic ecology: review of habitat suitability criteria in aquatic ecology. River Res Applic. 35(8):1155–1180.
   Web of Science ®Google Scholar
• O’Hara RB, Kotze DJ. 2010. Do not log-transform count data. Methods Ecol Evol. 1(2):118–122.
   Web of Science ®Google Scholar
• Orlofske JM, Baird DJ. 2014. Incorporating continuous trait variation into biomonitoring assessments by measuring and assigning trait values to individuals or taxa. Freshw Biol. 59(3):477–490.
   Web of Science ®Google Scholar
• Peckarsky BL, Horn SC, Statzner B. 1990. Stonefly predation along a hydraulic gradient: a field test of the harsh-benign hypothesis. Freshwater Biol. 24(1):181–191.
   Web of Science ®Google Scholar
• Plichard L, Forcellini M, Le Coarer Y, Capra H, Carrel G, Ecochard R, Lamouroux N. 2020. Predictive models of fish microhabitat selection in multiple sites accounting for abundance overdispersion. River Res Applic. 36(7):1056–1075.
   Web of Science ®Google Scholar
• Poff NL, Allan JD, Bain MB, Karr JR, Prestegaard KL, Richter BD, Sparks RE, Stromberg J. 1997. The natural flow regime: a paradigm for river conservation and restoration. Bioscience. 47(11):769–784.
   Web of Science ®Google Scholar
• Quinn JM, Hickey CW. 1994. Hydraulic parameters and benthic invertebrate distributions in two gravel-bed New Zealand rivers. Freshwater Biol. 32(3):489–500.
   Web of Science ®Google Scholar
• R Development Core Team. 2018. R: a language and environment for statistical computing. Vienna (Austria): R Foundation for Statistical Computing. http://www.R-project.org.
   Google Scholar
• Rempel LL, Richardson JS, Healey MC. 2000. Macroinvertebrate community structure along gradients of hydraulic and sedimentary conditions in a large gravel-bed river. Freshw Biol. 45(1):57–73.
   Web of Science ®Google Scholar
• Richter B, Baumgartner J, Wigington R, Braun D. 1997. How much water does a river need? Freshw Biol. 37(1):231–249.
   Web of Science ®Google Scholar
• Robert P, Escoufier Y. 1976. A unifying tool for linear multivariate statistical methods: the RV-coefficient. J R Stat Soc C-Appl. 25(3):257–265.
   Web of Science ®Google Scholar
• Shearer KA, Hayes JW, Jowett IG, Olsen DA. 2015. Habitat suitability curves for benthic macroinvertebrates from a small New Zealand river. N Z J Mar Freshwater Res. 49(2):178–191.
   Web of Science ®Google Scholar
• Snelder T, Booker D, Lamouroux N. 2011. A method to assess and define environmental flow rules for large jurisdictional regions 1. J Am Water Resour Assoc. 47(4):828–840.
   Web of Science ®Google Scholar
• Spearman C. 1904. The proof and measurement of association between two things. Am J Psychol. 15(1):30.
   Google Scholar
• Statzner B. 1993. Response to Frutiger & Schib (1993) ‘Limitations of FST hemispheres in lotic benthos research. Freshwater Biol. 30(3):475–483.
   Web of Science ®Google Scholar
• Statzner B. 2008. How views about flow adaptations of benthic stream invertebrates changed over the last century. Internat Rev Hydrobiol. 93(4-5):593–605.
   Web of Science ®Google Scholar
• Statzner B, Gore JA, Resh VH. 1988. Hydraulic stream ecology: observed patterns and potential applications. J N Am Benthol Soc. 7(4):307–360.
   Web of Science ®Google Scholar
• Statzner B, Muller R. 1989. Standard hemispheres as indicators of flow characteristics in lotic benthos research. Freshwater Biol. 21(3):445–459.
   Web of Science ®Google Scholar
• Steinmann P. 1907. Die Tierwelt der Gebirgsbäche: eine faunistisch-biologische Studie. Annales de Biologie Lacustre. 2:30–150.
   Google Scholar
• Steuer JJ, Newton TJ, Zigler SJ. 2008. Use of complex hydraulic variables to predict the distribution and density of unionids in a side channel of the Upper Mississippi River. Hydrobiologia. 610(1):67–82.
   Google Scholar
• Tachet H, Richoux P, Bournaud M, Usseglio-Polatera P. 2010. Invertébrés d’eau douce: systématique, biologie, écologie. Paris: CNRS éditions.
   Google Scholar
• Ter Braak CJF. 1987. The analysis of vegetation-environment relationships by canonical correspondence analysis. Vegetatio. 69(1-3):69–77.
   Google Scholar
• Vaudor L, Lamouroux N, Olivier J-M. 2011. Comparing distribution models for small samples of overdispersed counts of freshwater fish. Acta Oecol. 37(3):170–178.
   Web of Science ®Google Scholar
• Vilizzi L, Copp GH, Roussel J-M. 2004. Assessing variation in suitability curves and electivity profiles in temporal studies of fish habitat use. River Res Appl. 20(5):605–618.
   Web of Science ®Google Scholar
• Vimos‐Lojano DJ, Martínez‐Capel F, Hampel H, Vázquez RF. 2019. Hydrological influences on aquatic communities at the mesohabitat scale in high Andean streams of southern Ecuador. Ecohydrology. 12(1):e2033.
   Web of Science ®Google Scholar
• Wellnitz TA, Poff NL, Cosyleon G, Steury B. 2001. Current velocity and spatial scale as determinants of the distribution and abundance of two rheophilic herbivorous insects. Landsc Ecol. 16(2):111–120.
   Web of Science ®Google Scholar
• Young WJ. 1992. Clarification of the criteria used to identify near-bed flow regimes. Freshwater Biol. 28(3):383–391.
   Web of Science ®Google Scholar