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Spatial patterns of macroinvertebrate functional feeding groups in streams in relation to physical variables and land-cover in Southwestern France

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Abstract

Artificial neural networks were used to quantify the distribution of macroinvertebrate functional feeding groups (FFGs) in relation to physical variables and to land-cover in the Adour–Garonne stream system (SW France; 116,000 km2). The relative abundances of 5 FFGs were calculated from macroinvertebrate data recorded at 165 sampling sites. Each site was characterized using 5 physical variables (elevation, stream order, stream width, distance from the source, slope) and 3 land-cover variables (% forested, % urban areas, % agricultural areas). The sites were first classified using the Self-Organizing Map algorithm (SOM), according to the physical and land-cover variables. Two major clusters of sites corresponded to anthropogenically modified and natural areas, respectively. Anthropogenically modified areas were clearly divided into agricultural and urban landscapes. Each major cluster was divided into 3–4 subsets of sites according to a topographic gradient of physical variables. To examine the variability of the communities, FFG proportions at the 165 sites were examined on the SOM trained with physical and land-cover variables. When the riverine landscape was natural, FFG patterns responded to the upstream–downstream gradient in physical variables. When the landscape was altered by agriculture or urbanization, the effects of land-cover on FFGs overcame the influence of the physical variables. The categorization of the landscape into forested, agricultural, and urban areas was relevant to detect changes in FFG patterns. In light of increasing development along riparian zones, the use of SOMs to detect responses of FFGs to landscape alterations at regional scales exemplifies an effective technique for assessing river health based on ecological indicator groups.

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References

  • A.F.N.O.R. (1992) Essais des eaux. Détermination de l’Indice Biologique Global Normalisé (I.B.G.N.) Norme française T 90–350:1–8

    Google Scholar 

  • Allan JD (2004) Landscapes and riverscapes: the influence of land-use on stream ecosystems. Annu Rev Ecol Evol Syst 35:257–284

    Article  Google Scholar 

  • Bailey RC, Norris RH, Reynoldson TB (2003) Bioassessment of freshwater ecosystems using the reference condition approach. Kluwer Academic Publishers, New York, USA

    Google Scholar 

  • Basnyat P, Teeter LD, Flynn KM, Lockaby BG (1999) Relationships between landscape characteristics and nonpoint source pollution inputs to coastal estuaries. Environe Manage 23:539–549

    Article  Google Scholar 

  • Bij de Vaate A, Pavluk TI (2004) Practicability of the Index of Trophic Completeness for running waters. Hydrobiologia 519:49–60

    Article  Google Scholar 

  • Blasius BJ, Merritt RW (2002) Field and laboratory investigations on the effects of road salt (NaCl) on stream macroinvertebrate communities. Environ Pollut 120:219–231

    Article  PubMed  CAS  Google Scholar 

  • Blayo F, Demartines P (1991) Data analysis: how to compare Kohonen neural networks to other techniques?. In: Prieto A (ed) Artificial neural networks. International Workshop IWANN ’91. Springer-Verlag, Berlin, Germany, pp 469–476

    Chapter  Google Scholar 

  • Céréghino R, Park YS, Compin A, Lek S (2003) Predicting the species richness of aquatic insects in streams using a limited number of environmental variables. J N Am Benthol Soc 22:442–456

    Article  Google Scholar 

  • Cruickshank MM, Tomlison RW (1996) Application of CORINE land cover methodology to the UK. Some issues raised from Northern Ireland. Global Ecol Biogeogr 4/5:235–248

    Google Scholar 

  • Cummins KW (1974) Structure and function of stream ecosystems. Bioscience 24:631–641

    Article  Google Scholar 

  • Cummins KW, Klug MJ (1979) Feeding ecology of stream invertebrates. Annu Rev Ecol Syst 10:147–172

    Article  Google Scholar 

  • Davies PE, Cook LSJ, McIntosh PD, Munks SA (2005) Changes in stream biota along a gradient of logging disturbance, 15 years after logging at Ben Nevis, Tasmania Forest. Ecol Manage 219:132–148

    Article  Google Scholar 

  • Delong MD, Brusven MA (1998) Macroinvertebrate community structure along the longitudinal gradient of an agriculturally impacted stream. Environ Manage 22:445–457

    Article  PubMed  Google Scholar 

  • Dolédec S, Phillips N, Scarsbrook M, Riley RH, Townsend CR (2006) Comparison of structural and functional approaches to determining landuse effects on grassland stream invertebrate communities. J N Am Benthol Soc 25:44–60

    Article  Google Scholar 

  • Elliott SR, Naiman RJ, Bisson PA (2004) Riparian influences on the biophysical characteristics of seston in headwater streams. Northwest Sci 78:150–157

    Google Scholar 

  • Gevrey M, Dimopoulos L, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol Model 160:249–264

    Article  Google Scholar 

  • Giraudel JL, Lek S (2001) A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination. Ecol Model 146(1–3):329–339

    Article  Google Scholar 

  • Hawkins CP, Norris RH, Gerritsen J, Hughes RM, Jackson SK, Johnson RK, Stevenson RJ (2000) Evaluation of the use of landscape classifications for the prediction of freshwater biota: synthesis and recommendations. J N Am Benthol Soc 19:541–556

    Article  Google Scholar 

  • Hynes HBN (1975) The stream and its valley. Verh Int Ver Theor Ang Limnol 19:1–15

    Google Scholar 

  • Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cyber 43:59–69

    Article  Google Scholar 

  • Kohonen T (2001) Self-Organizing Maps, 3rd edn. Springer-Verlag, Berlin, Germany

    Google Scholar 

  • Larranaga A, Larranaga S, Basaguren A, Elosegi A, Pozo J (2006) Assessing impact of eucalyptus plantations on benthic macroinvertebrate communities by a litter exclusion experiment. Ann Limnol–Int J Lim 42:1–8

    Google Scholar 

  • Lecerf A, Dobson M, Dang CK, Chauvet E (2005) Riparian plant species loss alters trophic dynamics in detritus-based stream ecosystems. Oecologia 146:432–442

    Article  PubMed  Google Scholar 

  • Lek S, Guégan JF (2000). Artificial Neuronal Networks: application to ecology and evolution. Springer-Verlag, Berlin, Germany

    Google Scholar 

  • Levine ER, Kimes DS, Sigillito VG (1996) Classifying soil structure using neural networks. Ecol Model 92:101–108

    Article  Google Scholar 

  • Maridet L, Wasson JG, Philippe M, Amoros C, Naiman RJ (1998) Trophic structure of three streams with contrasting riparian vegetation and geomorphology. Arch Hydrobiol 144:61–85

    Google Scholar 

  • Merritt RW, Cummins KW (1996) An introduction to the aquatic insects of North America, 3rd edn. Kendall/Hunt, Dubuque, USA

    Google Scholar 

  • Merritt RW, Cummins KW, Berg MB, Novak JA, Higgins MJ, Wessell KJ, Lessard JL (2002) Development and application of a macroinvertebrate functional-group approach in the bioassessment of remanant river oxbows in southwest Florida. J N Am Benthol Soc 21:290–310

    Article  Google Scholar 

  • Moore AA, Palmer MA (2005) Invertebrate biodiversity in agricultural and urban headwater streams: Implications for conservation and management. Ecol Appl 15:1169–1177

    Article  Google Scholar 

  • Oertli B (1993) Leaf litter processing and energy flow through macroinvertebrates in a woodland pond (Switzerland). Oecologia 96:466–477

    Article  Google Scholar 

  • Özesmi S, Özesmi U (1999) An artificial neural network approach to spatial habitat modelling with interspecific interaction. Ecol Model 116:15–31

    Article  Google Scholar 

  • Park YS, Céréghino R, Compin A, Lek S (2003) Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecol Model 160:265–280

    Article  Google Scholar 

  • Pavluk TI, Bij de Vaate A, Leslie HA (2000) Development of an Index of Trophic Completeness for benthic macroinvertebrate communities in flowing waters. Hydrobiologia 427:135–141

    Article  Google Scholar 

  • Raivio K (2006) Analysis of soft handover measurements in 3G network. In: Proceedings of the 9th ACM international Symposium on Modeling Analysis and Simulation of Wireless and Mobile Systems (Terromolinos, Spain, October 02–06, 2006), MSWiM ‘06. ACM Press, New York, pp 330–337

  • Recknagel F, French M, Harkonen P, Yabunaka KI (1997) Artificial neural network approach for modelling and prediction of algal blooms. Ecol Model 96:11–28

    Article  CAS  Google Scholar 

  • Rios SL, Bailey RC (2006) Relationship between riparian vegetation and stream benthic communities at three spatial scales. Hydrobiologia 553:153–160

    Article  Google Scholar 

  • Sachon G, Wasson JG (2002) La directive Eau de l’Union européenne. Conséquences pour la recherche. Nat Sci Soc 10:93–95

    Google Scholar 

  • Santoul F, Cayrou J, Mastrorillo S, Céréghino R (2005) Spatial patterns of the biological traits of freshwater fish communities in S.W. France. J Fish Biol 66:301–314

    Article  Google Scholar 

  • Sirola M, Lampi G, Parviainen J (2004) Using self-organizing map in a computerized decision support system. In: Pal NR, Kasabov N, Mudi RK, Pal S, Parui SK (eds) Neural information processing, 11th International Conference, ICONIP 2004, Calcutta, India, November 22–25, 2004. Springer-Verlag, Berlin, Germany, pp 136–141

  • Slavik K, Peterson BJ, Deegan LA, Bowden WB, Hershey AE, Hobbie JE (2004) Long-term responses of the Kuparuk River ecosystem to phosphorus fertilization. Ecology 85:938–954

    Article  Google Scholar 

  • Sliva L, Williams DD (2001) Buffer zone versus whole catchment approaches to studying land use impact on river water quality. Water Res 35:3462–3472

    Article  PubMed  CAS  Google Scholar 

  • Southwood TRE (1977) Habitat, the templete for ecological strategies?. J Anim Ecol 46:337–365

    Google Scholar 

  • Sponseller RA, Benfield EF, Valett HM (2001) Relationships between land use, spatial scale and stream macroinvertebrate communities. Freshwater Biol 46:1409–1424

    Article  Google Scholar 

  • Strayer DL, Beighley RE, Thompson LC, Brooks S, Nilsson C, Pinay G, Naiman RJ (2003) Effects of land cover on stream ecosystems: roles of empirical models and scaling issues. Ecosystems 6:407–423

    Article  Google Scholar 

  • Suren AM, McMurtrie S (2005) Assessing the effectiveness of enhancement activities in urban streams: II. Responses of invertebrate communities. River Res Appl 21:439–453

    Article  Google Scholar 

  • Townsend CR, Hildrew AG (1994) Species traits in relation to a habitat templet for river systems. Freshwater Biol 31:265–275

    Article  Google Scholar 

  • Tuma A, Haasis HD, Rentz O (1996) A comparison of fuzzy expert systems, neural networks and neuro-fuzzy approaches controlling energy and material flows. Ecol Model 85:93–98

    Article  Google Scholar 

  • Ultsch A, Siemon HP (1990) Kohonen’s self organizing feature maps for exploratory data analysis. In: Proceeding of the INNC’90 International Neural Network Conference. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp 305–308

  • Van Sickle J, Hughes RM (2000) Classification strengths of ecoregions, catchments, and geographic clusters for aquatic vertebrates in Oregon. J N Am Benthol Soc 19:370–384

    Article  Google Scholar 

  • Vannote RL, Minshall GW, Cummins KW, Sedell JR, Cushing CE (1980) The River Continuum Concept. Can J Fish Aquat Sci 37:130–137

    Article  Google Scholar 

  • Verneaux J, Galmiche P, Janier F, Monot A (1982) Une nouvelle méthode pratique d’évaluation de la qualité des eaux courantes. Un indice biologique de qualité générale. Annu Sci Univ Franche Comté 4:11–21

    Google Scholar 

  • Vesanto J, Hollmen J (2003) An automated report generation tool for the data understanding phase. In: Abraham A, Jain L (eds) Innovations in intelligent systems: design, management and applications, studies in fuzziness and soft computing, chapter 5. Springer-Verlag, Berlin, Germany

  • Vesanto J, Himberg J, Alhoniemi E, Parhankangas J (1999) Self-organising map in Matlab: the SOM Toolbox. In: Proceedings of the Matlab Digital Signal Processing Conference. Espoo, Finland, pp 35–40

  • Vesanto J, Himberg J, Alhoniemi E, Parhankangas J (2000) SOM Toolbox for Matlab 5. Technical Report A57, Neural Networks Research Centre, Helsinki University of Technology, Helsinki, Finland

    Google Scholar 

  • Vondracek B, Blann KL, Cox CB, Nerbonne JF, Mumford KF, Nerbonne BA, Sovell LA, Zimmerman JKH (2005) Land use, spatial scale, and stream systems: lessons from an agricultural region. Environ Manage 36:775–791

    Article  PubMed  Google Scholar 

  • Wallace JB, Webster JR (1996) The role of macroinvertebrates in stream ecosystem function. Annu Rev Entomol 41:115–139

    Article  PubMed  CAS  Google Scholar 

  • Walley WJ, Martin RW, O’Connor MA (2000) Self-organising maps for classification of river quality from biological and environmental data. In: Denzer R, Swayne DA, Purvis M, Schimak G (eds) Environmental software systems: environmental information and decision support. IFIP Conference Series. Kluwer Academic Publishers, Boston, Massachusset, USA, pp 27–41

    Google Scholar 

  • Ward JV (1998) Riverine landscapes: Biodiversity patterns, disturbance regimes, and aquatic conservation. Biol Conserv 83:269–278

    Article  Google Scholar 

  • Ward JV, Stanford JA (1983) The intermediate disturbance hypothesis: an explanation for biotic diversity patterns in lotic systems. In: Fontaine TD, Bartell SM (eds) Dynamics of lotic ecosystems. Ann Arbor Sciences, Ann Arbor, Michigan, USA, pp 347–356

    Google Scholar 

  • Woodiwiss FS (1964) The biological system of stream classification used by the Trent river Board. Chem Ind 11:443–447

    Google Scholar 

  • Wright JF, Sutcliffe DW, Furse MT (2000) Assessing the biological quality of fresh waters: RIVPACS and other techniques. Freshwater Biological Association, Ambleside, UK

    Google Scholar 

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Acknowledgements

We wish to thank Dr. D. Anderson and three anonymous Reviewers for their constructive comments on an earlier version of this paper.

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Correspondence to Arthur Compin.

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Compin, A., Céréghino, R. Spatial patterns of macroinvertebrate functional feeding groups in streams in relation to physical variables and land-cover in Southwestern France. Landscape Ecol 22, 1215–1225 (2007). https://doi.org/10.1007/s10980-007-9101-y

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