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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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0 30 6015 meters
lentic channel
poolrifflechutephysical obstaclelotic channel
Riverscape habitats
Land usegrasslandforestpoplar plantationfallows
sampling unit
Local environmental
variables
Spatial variables
!(
moving window 11m
nearest distance
100m around SU
Land use variables
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N
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0 30 6015 meters0 30 6015 meters
lentic channel
poolrifflechutephysical obstaclelotic channel
Riverscape habitats
lentic channel
poolrifflechutephysical obstaclelotic channellentic channel
poolrifflechutephysical obstaclelotic channel
Riverscape habitats
Land usegrasslandforestpoplar plantationfallows
Land usegrasslandgrasslandforestforestpoplar plantationpoplar plantationfallowsfallows
sampling unit
Local environmental
variables
Local environmental
variables
Spatial variablesSpatial
variables
!(!(
moving window 11m
nearest distance
100m around SU
Land use variablesLand use variables
!(!(
N
N
0 1000 2000 3000 4000
01
23
45
6
fish
num
ber
Co
nfl
uen
ce
larg
e w
oo
dy
deb
ris
Ch
ute
longitudinal distribution of stone longitudinal distribution of stone loachloach ((barbatulabarbatula barbatulabarbatula))
Distance from first SU (m)dowstream upstream
Ch
ute
Co
nfl
uen
ce
0 1000 2000 3000 4000
01
23
45
6
fish
num
ber
Co
nfl
uen
ce
larg
e w
oo
dy
deb
ris
Ch
ute
longitudinal distribution of stone longitudinal distribution of stone loachloach ((barbatulabarbatula barbatulabarbatula))
Distance from first SU (m)dowstream upstream
Ch
ute
Co
nfl
uen
ce
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Longitudinal electrofishing survey using a point abundance sampling scheme(264 sampling units on a 5 km reach)
Ru des Avenelles
Ru
de C
ourg
y
Rognon
Salmo trutta fario (nb/SU)0
1-2
N
0 300 600150 meters
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Longitudinal electrofishing survey using a point abundance sampling scheme(264 sampling units on a 5 km reach)
Ru des Avenelles
Ru
de C
ourg
y
Rognon
Salmo trutta fario (nb/SU)00
1-21-2
N
N
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
Movingwindowanalysis
Movingwindowanalysis
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
dbiol
R(x)dxminB)RCM(A,d wayspossible
hydro
R(x)dxminB)RCM(A,d wayspossible
biol
Feeding habitat
Channel R=1
spawning habitat
Aquatic habitats withresistance values
R=5R=20R=50R=100R=500 physical obstacle
Feeding habitat
Channel R=1Channel R=1
spawning habitat
Aquatic habitats withresistance values
R=5R=20R=50R=100R=500 physical obstacle
Dam
Hydraulic connectedness
dhydro
Dam
Pollution
Biological connectivity
dbiol
Dam
Pollution
Biological connectivity
dbiol
R(x)dxminB)RCM(A,d wayspossible
hydro
R(x)dxminB)RCM(A,d wayspossible
biol
lentic channel
poolrifflechutephysical obstaclelotic channel
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)
pool and riffle > 5% (6m²)
pool and riffle < 5% (6m²)
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