A Multi-Species Approach for Assessing the Ecological Impact of Urban Development Using Landscape Graphs

Transcription

1 A Multi-Species Approach for Assessing the Ecological Impact of Urban Development Using Landscape Graphs, Cécile Tannier Jean-Christophe Foltête, First Congress of the Society for Urban Ecology Progress in urban ecology and ecological challenges in urban development -

2 Issue and objectives Issue and objectives Groups of species Assumptions - Simulating a series of realistic scenarios of urban development - Assessing the simulated patterns regarding the habitat connectivity for various animal species (forest mammals) - Proposing tools to compare the scenarios and support landscape planning decisions - Study area: urban area of Besançon (East of France) 2/22

3 Identification of groups of species Issue and objectives Groups of species Assumptions Selection of some forest mammals in the study area using national and regional databases Creation of different groups of species regarding their functional traits (home range and dispersal distance) Minor, E., & Lookingbill, T. (2010). A multiscale network analysis of protectedarea connectivity for mammals in the United States. Conservation Biology, 24,

4 Identification of groups of species Issue and objectives Groups of species Assumptions 4/22

5 Issue and objectives Groups of species Assumptions Identification of groups of species Dissimilarity 8 6 Selection of species groups using an Ascending Hierarchical Classification Dendrogram Ermine Weasel Squirrel Rabbit Lynx Red deer Wildcat Roe deer Pine martin Wild boar 5/22

6 Selected species Issue and objectives Groups of species Assumptions Boreal lynx Wildcat Red squirrel Lynx lynx Felis silvestris Sciurus vulgaris 6/22

7 Assumptions Issue and objectives Groups of species Assumptions The impact of urban growth is different for each scenario Habitat loss Conway, 2009 Landscape fragmentation Forman, 1995 Barrier effect of urbanization Bierwagen 2005, 2007; Mortberg et al., 2007; Czamanski et al., 2008; Aguilera et al., 2011; Tannier et al., 2012 Barrier effect of traffic Fu et al., 2010; Aguilera et al., 2011; Girardet et al., /22

8 Landscape map of the study area Study area Urbanization scenarios Traffic simulation Cost map Landscape graphs Sources: French cartographic services (IGN, DREAL) 8/22

9 How to create scenarios of urban development 1400 housing/year during 20 years Study area Urbanization scenarios Traffic simulation Cost map Landscape graphs Global urbanization rules (compact, polycentric, diffuse) - Three types of urban patches Biggest patch: the main city Medium-size patches: secondary urban centers Small patches: villages & hamlets - Accessibility rules Access to shops and services Proximity to public transport stations Local urbanization rules - Fractal dimension 1,46 (moderetaly dense) 1,77 (dense) - Accessibility rules Access to shops and services Proximity to public transport stations Access to non built areas Proximity to roads Using MUP-City and Mobisim software Tannier C, Foltête J-C,Girardet X. Assessing the capacity of different urban forms to preserve the connectivity of ecological habitats. Landsc Urban Plan 2012;105(1 2): /22

10 Scenario 1: Compact city Study area Urbanization scenarios Traffic simulation Cost map Landscape graphs Global pattern (proportion of urban growth) Local built pattern Proportion of House /Building Biggest urban patch Medium-size urban patches Small urban patches Fractal dimension Biggest patch Medium-sized patches Small patches 80% 15% 5% 1,77 70% 30% 30% 70% 0% 100% 10/22

11 Scenario 2: Moderately compact city Study area Urbanization scenarios Traffic simulation Cost map Landscape graphs Global pattern (proportion of urban growth) Local built pattern Proportion of House /Building Biggest urban patch Medium-size urban patches Small urban patches Fractal dimension Biggest patch Medium-sized patches Small patches 80% 15% 5% 1,46 70% 30% 30% 70% 0% 100% 11/22

12 Scenario 3: Controlled development of periurban areas Study area Urbanization scenarios Traffic simulation Cost map Landscape graphs Global pattern (proportion of urban growth) Local built pattern Proportion of House /Building Biggest urban patch Medium-size urban patches Small urban patches Fractal dimension Biggest patch Medium-sized patches Small patches 40% 30% 30% 1,77 70% 30% 50% 50% 0% 100% 12/22

13 Scenario 4: Urban sprawl Study area Urbanization scenarios Traffic simulation Cost map Landscape graphs Global pattern (proportion of urban growth) Local built pattern Proportion of House /Building Biggest urban patch Medium-size urban patches Small urban patches 40% 30% 30% Fractal dimension 1,77 (Biggest urban patch) 1,46 (Smallest urban patches) Biggest patch Medium-sized patches Small patches 70% 30% 50% 50% 0% 100% 1

14 Scenario 5: Transit Oriented Development (TOD) Study area Urbanization scenarios Traffic simulation Cost map Landscape graphs Global pattern (proportion of urban growth) Local built pattern Proportion of House /Building 500 m tram stations 1000 m main train stations 500 m small trains stations 40% 30% 30% Fractal dimension 1,77 (Biggest urban patch) 1,46 (Smallest urban patches) Biggest patch Medium-sized patches Small patches 70% 30% 50% 50% 0% 100% 14/22

15 Traffic simulation Study area Urbanization scenarios Traffic simulation Cost map Landscape graphs Simulation of traffic using a LUTI model (Mobisim) before and after urbanization Example for scenario 3: Vehicles/day (average) Variation rate Major roads N % N % D % D % D % Intra-urban roads N % N % Others % 15/22

16 Cost map Study area Urbanization scenarios Traffic simulation Cost map Landscape graphs For each target species: a cost for each land use class The cost of roads depends on the traffic Land use Name Cost class 1 High-speed railway Highway Roads [ ] 4 Bridge 10 5 Lake, Pond, River Artificial area Railway 10 8 Wetland Forest 1 10 Crop Meadow and bare ground Intraurban /22

17 Creation of landscape graphs Study area Urbanization scenarios Traffic simulation Cost map Landscape graphs Boreal lynx (d= m) Red squirrel (d=650 m) One landscape graph for each species, before and after urbanization Global metric: PC (Saura & Pascual-Hortal, 2007) Local metric: PC Flux (Saura & Rubio, 2010) Wildcat (d= m) Software: Graphab (Foltête et al., 2012) 17/22

18 Ecological impacts of urbanization scenarios Global impacts Local impacts Comparison of scenarios 2,00% 1,80% 1,60% 1,40% 1,20% 1,00% 0,80% 0,60% 0,40% 0,20% 0,00% Global impacts of the five urbanization scenarios on landscape connectivity (variation rate of PC index) Red squirrel Wildcat Boreal lynx Target Species Scenario Variation rate of PC index Red squirrel S1-0,73% S2-0,72% S5-0,78% S3-1,23% S4-1,47% Wildcat S2-1,56% S3-1,62% S5-1,66% S1-1,80% S4-1,87% Boreal lynx S1-0,43% S2-0,74% S5-0,75% S3-1,42% S4-1,69% 18/22

19 Location of the impacts Global impacts Local impacts Comparison of scenarios 19/22

20 Comparison of scenarios: an example Global impacts Local impacts Comparison of scenarios 20/22

21 Assessment of the ecological impact of urbanization Amount and location of impacts vary according to the simulated urban form Impacts are different for the three species groups (higher impacts for the wildcat) Compact city: more valuable than the other scenarios for the red squirrel and the lynx But a strong barrier effect for the wildcat Urban sprawl: the worst scenario for all the target species 21/22

22 Limits and Perspectives Only one study area Possible sensitivity of the results to the definition of species groups Going deeper into the comparison of scenarios 22/22

23 Thank you for your attention!