Poster AGU4

EP23A-0621Multiscale impacts of the fragmentation and spatial structure of habitats on

freshwater fish distribution: Integrating riverscape and landscape ecology Céline Le Pichon, Jérôme Belliard, Evelyne Talès, Guillaume Gorges and Fabienne Clément Hydro-ecology, Cemagref, Parc de Tourvoie BP44, 92163 Antony, France. [email protected]

What is your view on rivers and that of fish ?

AGU Fall Meeting 2009, 14-18 december, San Francisco.

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 km², 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).

4.2-Methods

4.3-Results

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lentic channel

poolrifflechutephysical obstaclelotic channel

Riverscape habitats

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Spatial variables

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Land usegrasslandgrasslandforestforestpoplar plantationpoplar plantationfallowsfallows

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variables

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variables

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nearest distance

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longitudinal distribution of stone longitudinal distribution of stone loachloach ((barbatulabarbatula barbatulabarbatula))

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longitudinal distribution of stone longitudinal distribution of stone loachloach ((barbatulabarbatula barbatulabarbatula))

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Longitudinal electrofishing survey using a point abundance sampling scheme(264 sampling units on a 5 km reach)

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Salmo trutta fario (nb/SU)0

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Longitudinal electrofishing survey using a point abundance sampling scheme(264 sampling units on a 5 km reach)

Ru des Avenelles

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Salmo trutta fario (nb/SU)00

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N

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0 300 600150 meters0 300 600150 meters

Figure 6 - The Seine River basin and the Orgeval catchment

River is an homogeneous patch in the landscape

River is a dynamic landscape itself, the riverscape

River is an underwater riverscape for fish

River is connected to landscape by flux through the ecotones

Figure 1 – Different perceptions of rivers, from that of terrestrial observer to that of freshwater fish

Figure 2 – Integrating concepts from the fields of fish ecology, stream ecology and landscape ecology

2- The riverscape approach.Using the organism point of view(Pringle et al., 1988), we have developed ariverscape (Ward et al. 2002) approach for fishes (Fig. 1), based on the integrationof 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 mosaïc 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)

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 fish–habitat relationships becomes a key element for successful rehabilitations of degraded rivers.

habitat proportion,

heterogeneity

bottomsubstrate

shelters

riparian cover

Environmental variables

discharge Y

discharge X

depth

discharge Y

discharge X

current velocity

Dynamic variables

GIS-based habitat mapping

geomorphicchannel unit

vital habitat C map

vital habitat B map

vital habitat A map

Resistance to movement map

variables combination

habitats mosaïc map

Speciespreferences

Composition : area, patch number

Fragmentation : nearest neighbor distance,

proximity index

metricscalculation

area of complementary

habitats

Movingwindowanalysis

Spatial analysis methods

least costmodelling

Probabilitymap to reachthe nearesthabitat C

habitat proportion,

heterogeneity

bottomsubstratebottom

substrate

sheltersshelters

riparian coverriparian cover

Environmental variablesEnvironmental variables

discharge Y

discharge X

depth

discharge Y

discharge X

current velocity

Dynamic variables

GIS-based habitat mappingGIS-based habitat mapping

geomorphicchannel unitgeomorphicchannel unit

vital habitat C map

vital habitat C map

vital habitat B map

vital habitat B map

vital habitat A map

vital habitat A map

Resistance to movement map

variables combination

habitats mosaïc map

Speciespreferences

Speciespreferences

Composition : area, patch number

Fragmentation : nearest neighbor distance,

proximity index

metricscalculation

metricscalculation

area of complementary

habitats



Spatial analysis methodsSpatial analysis methods

least costmodellingleast costmodelling

Probabilitymap to reachthe nearesthabitat C

Figure 3 – Flowchart of the riverscape approach with process steps from environmental variables mapping to spatial analysismetrics and methods

Stream ecology

Fluvial dynamic and hydrological connectivity

Serial discontinuity concept and principle of uniqueness

« Linear » nature of floodplain and directionality

Fish ecologyMobile organism with migratory

processes

Scale dependence of fi sh response to environment

Spatially structured populations in hydrographic networks

Underwater multi-habitat species

Shif ting mosaïc of patches

Spatial habitats patternand relationships

Hierarchical habitat-based model

I ncreasing the scope of riverscape mapping

Spatially explicit riverscape

Landscape ecology

Spatially explicit landscape structure

Hierarchical structure

Landscape view at diff erent scale and resolution

Mosaic of heterogeneous patches

Connectedness and connectivity

Embracing the hierarchical,

heterogeneous, dynamic and

continuous nature of river with its

abrupt transitions and directionality

Stream ecology

Fluvial dynamic and hydrological connectivity

Serial discontinuity concept and principle of uniqueness

« Linear » nature of floodplain and directionality

Fish ecologyMobile organism with migratory

processes

Scale dependence of fi sh response to environment

Spatially structured populations in hydrographic networks

Underwater multi-habitat species

Shif ting mosaïc of patches

Spatial habitats patternand relationships

Hierarchical habitat-based model

I ncreasing the scope of riverscape mapping

Spatially explicit riverscape

Landscape ecology

Spatially explicit landscape structure

Hierarchical structure

Landscape view at diff erent scale and resolution

Mosaic of heterogeneous patches

Connectedness and connectivity

Embracing the hierarchical,

heterogeneous, dynamic and

continuous nature of river with its

abrupt transitions and directionality

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)

Integrating riverscape composition between patches using minimal cumulative resistance (MCR) from Knaapen et al. (1992).

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)

Feeding habitat

Channel R=1

spawning habitat

Aquatic habitats withresistance values

R=5R=20R=50R=100R=500 physical obstacle

Dam

Hydraulic connectedness

dhydro

Dam

Pollution

Biological connectivity

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Channel R=1

spawning habitat



Feeding habitat

Channel R=1Channel R=1

spawning habitat



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Hydraulic connectedness

dhydro

Dam

Pollution


dbiol

Dam

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dbiol


hydro


biol

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0 10 205 metres

HeterogeneityHigh: 1.88

Low:0

HeterogeneityHabitats map

•One map analysis:Habitat proportionHeterogeneity•Two maps analysis:Complementary habitats

Pool proportion Complementary habitats

na represents the number of possible combinations for couples of habitatsPq is the proportion of the qth couple of habitat

Percentage100

0

Percentage100

0

window size 11m

pool and riffle > 5% (6m²)

pool and riffle < 5% (6m²)





Figure 5 – Moving window analysis computed using Chloe 3.1 software (Baudry et al. 2006)

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., Boët, 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.

4- Application on a rural watershed in France

Figure 7 – Multiscale measurement and calculation of variables.

Figure 8 – Fish sampling on the Rognon river and longitudinal trout abundance (Salmo trutta fario).

Seine River basin

0 100 200 300 400 km

FRANCE

Rognon river

Natural land use

Agriculture and urban land use

Rivers

Orgeval catchment

0 2 4 km

N

Physical barriers

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.

Conclusion. The spatially continuous analysis of fish–habitat 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.

Figure 9 – Longitudinal distribution of stone loach (Barbatula barbatula)

The longitudinal densities are discontinuous for trout, stone loach (Fig.8 and Fig.9) and bullhead. Both trout and stone loach densities are impacted by the presence of many barriers associated with ponds. Trout is clearly absent at the upstream part of the reach. Stone loach is absent in the reach sector dominated by barriers composed of woody debris. For this species, the significant variables selected by AIC procedure confirms this effect with the negative effect of riparian cover and distances far from woody debris (Table 1). For these species the negative influence of ponds in the landscape around the SU is also significant. Local environmental variables are more important for bullhead but also the proximity of a chute and the presence of grassland in the landscape.

About spatial variables we could noticed the positive influence of heterogeneity which integrates, for trout and stone loach the proximity (nearestHydrographic distancesto pool and also to riffleor chute. Globally nearest hydrographic distances to an habitat are more relevant than area percentage of thehabitat around the SU.Natural landuses alsoInfluence all three species.

AIC selection of monovariable models (AIC<AIC of nul model -2), significant model are indicated with their influence.Stepwise procedure for significant monovariable models; in yellow, selected significant variables

Cottus gobio Barbatula barbatula Salmo trutta fario

riffle

gravel,pebble and cobbleabsence absence

absencewoody debris far from

pool closed to closed toriffle closed to

chute closed to closed to

physical obstacle pool rifflechute

lentic channel

lotic channel

high high

grassland forests

poplar plantations

fallows ponds

Spatial variable (from SU to

entire riverscape)

Land use in the landscape

(100m around each SU)

Nearest hydrographic distance to:

(both upstream and downstream)

Percentage in a squared window of

11m (120m²)

Percentage of

Heterogeneity in a squared window of 11m (120m²)

Bottom substrateRiparian cover

Shelters

Local environmental

variable (SU scale)

Geomorphic channel unit

Water depth

Current velocity

Table 1- Results of the AIC-based selection of explanatory variables

5-Conclusion and perspectivesWe 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.

• GIS-based habitat mapping on large extent with high resolution GIS-based habitat mapping on large extent with high resolution

• Spatial analysis of habitat patterns and relationships using metrics and methods Spatial analysis of habitat patterns and relationships using metrics and methods

adapted to particularities of rivers: linear, irregularly shaped and dominated by adapted to particularities of rivers: linear, irregularly shaped and dominated by

water flowwater flow

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Poster AGU4