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<p>EP23A-0621</p> <p>Multiscale impacts of the fragmentation and spatial structure of habitats on freshwater fish distribution: Integrating riverscape and landscape ecologyCline Le Pichon, Jrme Belliard, Evelyne Tals, Guillaume Gorges and Fabienne ClmentHydro-ecology, Cemagref, Parc de Tourvoie BP44, 92163 Antony, France. celine.le-pichon@cemagref.fr</p> <p>1- Background.The European Water Framework Directive (2000) has goals of preservation and restoration of ecological connectivity of river networks which is a key element for fish populations. These goals require the identification of natural and anthropological factors which influence the spatial distribution of species. The spatially continuous analysis of fishhabitat relationships becomes a key element for successful rehabilitations of degraded rivers.</p> <p>4- Application on a rural watershed in FranceRiver is an homogeneous patch in the landscape River is connected to landscape by flux through the ecotones</p> <p>What is your view on rivers and that of fish ?</p> <p>4.1-Study siteWe test the relative roles of spatial arrangement of fish habitats and the presence of physical barriers in explaining fish spatial distributions in a small rural watershed (106 km, Fig.6). We have recorded about 100 physical barriers, on average one every 330 meters; most artificial barriers were road pipe culverts, falls associated with ponds and sluice gates. Contrasted fish communities and densities were observed in the different areas of the watershed, related to various land use (riparian forest or agriculture).</p> <p>Seine River basin</p> <p>FRANCE</p> <p>River is a dynamic landscape itself, the riverscape</p> <p>Environmental variables GIS-based habitat mapping on large extent with high resolution Spatial analysis of habitat patterns and relationships using metrics and methods</p> <p>River is an underwater riverscape for fish</p> <p>4.2-MethodsFigure 1 Different perceptions of rivers, from that of terrestrial observer to that of freshwater fish</p> <p>GIS-based habitaRivers Rognon river</p> <p>0 100 200 300 400 km</p> <p>Orgeval catchment</p> <p>N</p> <p>Figure 6 - The Seine River basin and the Orgeval catchment</p> <p>Natural land use</p> <p>0</p> <p>2</p> <p>4</p> <p>km</p> <p>Agriculture and urban land use Physical barriers</p> <p>2- The riverscape approach.</p> <p>Figure 2 Integrating concepts from the fields of fish ecology, stream ecology and landscape ecology</p> <p>Using the organism point of view adapted to particularities of rivers: linear, irregularly shaped and dominated by (Pringle et al., 1988), we have developed a water flow riverscape (Ward et al. 2002) approach for Figure 3 Flowchart of the riverscape approach with process steps from fishes (Fig. 1), based on the integration environmental variables mapping to spatial analysismetrics and methods of concepts from different disciplines (Fig. 2). It aims at assessing the multiscale relationships between the spatial pattern of fish habitats and processes depending on fish movements. The river is conceptualized as a 2-D spatially continuous mosac of dynamic underwater environments; fish habitats are represented using a GIS-based habitat mapping. Metrics and spatial analysis methods have been adapted to the particularities of rivers: linear and irregularly shaped and dominated by unidirectional water flow. They were chosen for their relevance to quantify fragmentation, spatial relationships and connectivity of fish vital habitats (Fig.3)</p> <p>Local variables are collected in the field, spatial variables are computed on GIS-based maps (Fig.7). We have selected a set of conceptually meaningful spatial variables, such as fragmentation and spatial organisation metrics. We used a spatially continuous sampling scheme based on a large number of small sampling units (SU)(Fig.8). The extent and resolution provide the opportunity to evaluate species-habitat relationships at both small and large scale, from meters to kilometers. We used generalized linear modelling (GLMs) to explore the contribution and role of the environmental variables and spatial metrics in explaining fish presence and abundance. At the scale of SU, we modelled the most probable abundance of bullhead and stone loach using a negative binomial distribution of the data and logistic regression model for trout.</p> <p>((( ( ( ( ( (( !!! ! ! ! ! !!</p> <p>(( !! ( ! ( ! (( !! ( ! ( ! ( ! (( !!</p> <p>(( !! ((( !!! (( !! ((( !!!</p> <p>(( ( (( ( (( ( ( !! ! !! ! !! ! !</p> <p>( ( (( ! ! !! ( !</p> <p>(( !! ((( !!! ( ! ( ! ( ! ( ! (( !! (( !! ( !</p> <p>( ! ( ! ((( !!! ( ! ( ! ( ! (( (( !! !! (( !! (( ( !! !</p> <p>(( !!</p> <p>(( !!</p> <p>( ! ( ! (( !! ( ! ( ! ( !</p> <p>( ! (( !! ( ! (( !! ( (( (( ! !! !! (( !!</p> <p>(( ( !! !</p> <p>( (((( ( (( ! !!!! ! !!</p> <p>(( ( ( ( (( (( ( (( ( ( !! ! ! ! !! !! ! !! ! ! ( ! (( !! (( !! (( !! (( !! ( ! (( !! (( !! ((( ( (( !!! ! !! (( (( ( ( !! !! ! ! (( !! (( !! ( ! (( ( !! ! ( ! ( ! ( ( ( (( ! ! ! !! ( ! (( !! (( !!</p> <p>! (</p> <p>( !</p> <p>! (</p> <p>! (</p> <p>(( !!(!!( ( !</p> <p>!(( (!! !!! ((( !! ((</p> <p>(( ( ( !! ! ! ( ( (( (( ( ( ! ! !! !! ! ! ( !</p> <p>! (</p> <p>(( ( !! ! ((( !!! ( ! ( (( ! !! ( ! ( ! ( ! ( ! (( !! ( ! (( ( !! ! ( ! ((( !!! (( ( !! ! ( ! (( ( !! ! ( (( ! !! (( ( !! ! (( !! ( ! ( ! ( ! ( ! ( ! ( ! ( ! (( !! ( ! ( ! ( ! ( ! ( ! (( !! ( (( ! !!</p> <p>! ( ! (</p> <p>! (</p> <p>3-Spatial analysis methods: examples.Spatial metrics were proposed to quantify the composition and fragmentation of fish habitats. Global map analysis were also used to provide information of the biological connectivity of networks, entire segments or reaches. For large rivers with connected waterbodies and for riverine fishes moving longitudinally and laterally, computing 2-D oriented hydrographic distances seems more appropriate to evaluate hydraulic connectedness and biological connectivity (Fig.4). Moving window analysis was chosen as a global map analysis, useful to evaluate both area and distance based metrics such as heterogeneity (Fig.5)</p> <p>geomorphic channel unitOne map analysis: Habitat proportion Heterogeneity Two maps analysis: Complementary habitatspool riffle chute physical obstacle lotic channel lentic channelwindow size 11m</p> <p>Figure 8 Fish sampling on the Rognon river and longitudinal trout abundance (Salmo trutta fario).</p> <p>na represents the number of possible combinations for couples of habitats Pq is the proportion of the qth couple of habitat</p> <p>Heterogeneity High: 1.88 Low:0</p> <p>4.3-Results</p> <p>0 5 10 20 metres</p> <p>d hydro = RCM(A, B) = min R(x)dx possible ways</p> <p>riparian coverHabitats map</p> <p>Figure 5 Moving window analysis computed using Chloe 3.1 software (Baudry et al. 2006)</p> <p>d biol = RCM(A, B) = min R(x)dx possible waysIntegrating riverscape composition between patches using minimal cumulative resistance (MCR) from Knaapen et al. (1992).</p> <p>Baudry J., Schermann N., Boussard H.(2006) 'Chloe 3.1 : freeware of multi-scales analysis'. INRA, SAD-Paysage." Knaapen, J. P., M. Scheffer and B. Harms. 1992. Estimating habitat isolation in landscape planning. Landscape and Urban Planning 23: 1-16. Le Pichon, C., Gorges, G., Bot, P., Baudry, J., Goreaud, F., and Faure, T. 2006. A spatially explicit resource-based approach for managing stream fishes in riverscapes. Environmental management 37(3): 322 - 335. Pringle, C. M., R. J. Naiman, G. Bretschko, J. R. Karr, M. W. Oswood, J. R. Webster, R. L. Welcomme and M. J. Winterbourn. 1988. Patch dynamics in lotic systems : the stream as a mosaic. Journal of North American Benthological Society 7: 503-524. Ward, J. V., F. Malard and K. Tockner. 2002. Landscape ecology: a framework for integrating pattern and process in river corridors. Landscape Ecology 17: 35-45.</p> <p>AGU Fall Meeting 2009, 14-18 december, San Francisco.</p> <p>Conclusion. The spatially continuous analysis of fishhabitat relationships with the integration of spatial variables provides a more accurate longitudinal view of fish centres of abundance, the potential impact of barriers, riverscape habitats and landuse. It also reveal the importance of habitat spatial relationships such as the proximity to different habitats (complementary habitats) measured with nearest hydrographic distances or the heterogeneity map.</p> <p>Local</p> <p>3</p> <p>Figure 4 Estimation of hydraulic connectedness and biological connectivity with the calculation of hydrographic and biological distances computed using Anaqualand 2.0 (Le Pichon et al. 2006)</p> <p>Hydraulicshelters connectedness</p> <p>5-Conclusion and perspectives We emphasized the usefulness of GIS-based habitat mapping associated with a functional analysis of riverscape/landscape composition and configuration to understand fish spatial distributions. We also pointed out the importance of the spatial context to explain fish presence and abundance. In particular, the role of localized elements such physical barriers and that of spatial habitat relationships in the riverscape. The riverscape approach allows the identification of fish habitat configurations with great value that contributes to setting preservation and restoration priorities. All the spatial analysis methods could be used to simulate different scenarios of restoration. The consequences of the addition of a habitat patch at a specific location could be quantified and visualised using the proposed indexes and maps.</p> <p>fish number</p> <p>About spatial variables we could noticed the positive influence of heterogeneity which integrates, for trout and stone loach the proximity (nearest Hydrographic distances to pool and also to riffle or chute. Globally nearest hydrographic distances to an habitat are more relevant than area percentage of the habitat around the SU. Natural landuses also Influence all three species.</p> <p>Table 1- Results of the AIC-based selection of explanatory variables</p> <p>0</p> <p>AIC selection of monovariable models (AIC</p>