Analyzing and mapping cultural ecosystem services with multiple approaches in Peru Session T4: Mapping ecosystem services: Comparing methods. October 21, 2016 Bruno Locatelli1,2; Merelyn Valdivia2,3; Améline Vallet1,2,4 1: CIRAD France, 2: CIFOR Peru, 3: UNALM Peru, 4: AgroParisTech France Bruno Locatelli1,2; Raffaele Vignola3; Diego Padilla2 1: CIRAD France, 2: CIFOR Peru, 3: CATIE, Costa Rica

Modeling and mapping multiple ecosystem services Provisioning Regulation Cultural Problems for modeling and mapping: Limited data; Multidimensional (what values?); Subjective (whose values?).

The spirit of the mountain Cultural services I want to climb Recreation (experiential) What a nice view! Scenic beauty (visual) The spirit of the mountain Others Heritage and legacy. Spiritual and religious. Inspiration and creativity. Identity and sense of place. Social relationships. Education and knowledge. (intellectual)

Case study: Peru Questions: What cultural ecosystem services are produced and used? Where? Why there? Who use them? Case study: Peru Apurímac

Multiple methods: Each one provides a piece of the puzzle Big data and maths Local fieldwork and social science

Density of online landscape picture databases Big data and maths Density of online landscape picture databases Method: Automatically download online pictures georeferenced in the region Already filtered by Panoramio for Google Earth No pizzas or parties Mostly landscapes (natural, rural, urban) Analyze and map density

Good representation of where ecosystem services are “used” Results Good representation of where ecosystem services are “used” Do it for all GE pics: Add: ** ROAD C:\Users\BLocatelli\Documents\Bruno\a-Metadata\r. Peru\Peru Red de Vias\RVN\RVN_Eje.shp Geometry Type: Line COD_VIAL = PE-3S ** CITIES C:\Users\BLocatelli\Documents\Bruno\a-Current\Res-Abancay\Data_Maps\ZEE maps\B_CCPP.shp Geometry Type: Point V_CAT_PUEB = Capital* ** NATIONAL PARK C:\Users\BLocatelli\Documents\Bruno\a-Metadata\r. Peru\Peru Areas Protegidas SERNANP\anp_nacional_05_08_2016\anp_nacional_05_08_2016.shp Geometry Type: Polygon FID=1 ** RIVER In ArcGIS: Major rivers in allRios (ESTADO ==2) MERGE WITH B_RED_HID select = "FID" =2283 OR "FID" =2300 OR "FID" =2945 OR "FID" =10937 OR "FID" =10996 OR "FID" =11066 OR "FID" =11147 OR "FID" =11326 OR "FID" =11445 OR "FID" =11458 OR "FID" =11643 OR "FID" =11959 OR "FID" =12034 OR "FID" =12142 OR "FID" =12199 OR "FID" =12601 OR "FID" =12655 OR "FID" =12813 OR "FID" =12902 OR "FID" =13081 OR "FID" =13081 OR "FID" =13271 OR "FID" =13271 OR "FID" =13362 OR "FID" =13457 OR "FID" =13527 OR "FID" =13564 OR "FID" =13620 OR "FID" =13817 OR "FID" =13916 OR "FID" =13922 OR "FID" =14115 OR "FID" =14159 OR "FID" =14291 N= 1904 Area = 50 km x 50 km

Picture content analysis Big data and maths Picture content analysis Method: Observe all online pictures and classify them according to landscape elements and human activities What cultural ecosystem services? CHANGE: ADD MICRO PICTURES

Results Visual Experiential Intellectual Landscape elements: 27% rural areas (mosaics of cropland, grassland, etc.) 18% water (river, lake, waterfall, etc.) 18% mountains and canyons Etc. Experiential Activities: 14% recreation (hiking, relaxing, watching, biking, etc.). Associated with elements (trails, rivers, lakes, etc.) Intellectual Heritage, legacy. Spiritual, religious. Inspiration and creativity. Identity and sense of place. Social relationships. Education and knowledge. ?? This approach captures few services. Focus on landscape beauty. Some info on recreation. Landscape pictures 27.0% rural areas (mosaics of cropland, grassland mosaic, and forest plantations) 18.2% water (river, lake, waterfall, etc.) 17.8% mountains and canyons 13.2% natural area (glacier, forest, grassland, etc.) 11.6% urban and buildings (main square, monument, etc.) 5.0% roads, bridges, tunnels 3.3% plants (flowers, trees, etc.) + Activities 14.3% recreation (hiking, relaxing, watching, biking, etc.)

Identifying who post pictures on Panoramio Big data and maths Identifying who post pictures on Panoramio Method: Automatically download all pictures by all photographers having pictures in the region Determine whether they take pictures locally (region), nationally (Peru) or internationally Compare map density between local, national and international photographers

Results International Local Limited information on actors. More national/international than local. 327 photographers with 95,211 posted pictures. 9% local, 32% national, 25% international. Local International Different locations. But strong effects of population and access.

Explaining picture density Big data and maths Explaining picture density Method: Model the effect of landscape (land use, topography, hydrography, etc.) and human factors (accessibility, population density) on picture density with Random Forest models ?

Results Good model performance (explains 68% of variance) Three best predictors of picture density: For international photographers: Distance to airport (accessibility) Distance to protected area, Distance to wetlands (landscape) For local photographers: Population density, Distance to regional capital city (accessibility) Distance to glacier (landscape)

Analyzing supply and demand of cultural services Big data and maths Analyzing supply and demand of cultural services Method: Density shows service “use” function of supply (by ecosystem) and demand (by people) Supply = model output without human factors Demand = model output without natural factors ? Supply Demand Use

Good approach for decomposing supply and demand Results Demand (by people) Use (service) Supply (by ecosystem) Interna tional photo graphers Supply by ecosystem depends on users: landscape beauty attributes are different for every person Good approach for decomposing supply and demand Local photo graphers

Fieldwork and social science Asking local experts Method: Asking workshop participants to draw maps of where cultural services are used

Do experts know everything? Do they represent all views? Results Maps of location of the use of different services (hiking, “nice” forest landscapes, sacred places, etc.) Do experts know everything? Do they represent all views? No or few repetitions

Interviewing local people Fieldwork and social science Interviewing local people Method: Open interviews with people in the area (n=28) With pictures (selected by interviewees) to prompt discussion Discourse content analysis

This approach captures many services. Most cited are intellectual. Results People cited 40 different cultural ecosystem services and explained why This approach captures many services. Most cited are intellectual.

Fieldwork and social science Survey people Fieldwork and social science Method: 211 people, balanced between: Urban / Rural / Tourists ; Men / Women ; Young (<20), Adults (20-40), Senior 6 pictures presented For each, 8 statements on ecosystem services: Recreation, Beauty, Purity, Identity, Peace, Tradition, Harmony and Production (not cultural) Response on Likert scale “full disagree” to “fully agree” Linear Model: Response as a function of picture, ecosystem service, and people

Results Many significant predictors of responses at p<0.01 > Rural degraded > > > > Rural Lake Grassland Glacier Beauty > Peace, Recreation, Purity > Harmony > Tradition > Identity > Production Urban Also possible to build bundles Non local > Local Urban = Rural Men = Women Newcomer > Long-term resident Old > Young people

Conclusion Different approaches for assessing cultural ecosystem services for different research questions Big data Local fieldwork Number of people studied: + ─ (cost) Details on people: ─ + Number of services studied: ─ + Details on services: ─ + Location of use, supply, demand: + ─ Drivers of use, supply, demand: + (quantitative) + (qualitative)

Thank you! bruno.locatelli@cirad.fr