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Spatial analysis of ecosystem services provision in the Colombian Amazon using participatory research and GIS Miroslav Honzák, Ph.D. mhonzak@conservation.org.

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Presentation on theme: "Spatial analysis of ecosystem services provision in the Colombian Amazon using participatory research and GIS Miroslav Honzák, Ph.D. mhonzak@conservation.org."— Presentation transcript:

1 Spatial analysis of ecosystem services provision in the Colombian Amazon using participatory research and GIS Miroslav Honzák, Ph.D.

2 forest-agriculture interface”
ASSETS Project “Managing ecosystem services for food security and the nutritional health of the rural poor at the forest-agriculture interface” Multi-institutional collaboration part of ESPA Initiative. Themes: Linkages between food security, nutritional health and ES Crises and tipping points involving past, current and future interactions between food security and ES Science – policy interface

3 The team UK: University of Southampton (PI Poppy) University of Dundee
USA: Conservation International Spain: Basque Centre for Climate Change Colombia: CIAT Universidad Nacional de Colombia Peru Instituto de Investigación de la Amazonia Peruana - IIAP Malawi: Chancellor College (Co-PI Chiotha) LEAD Southern and Eastern Africa plus Ministry of Forestry, Forest Research Institute of Malawi Rhodes University South Africa

4 The Study Areas on the Forest Transition Curve
Lower Caquetá River Pucallpa River Zomba Plateau High forest cover Low deforestation High forest cover High deforestation Low forest cover Low deforestation Natural land use transition Forest replenishment period

5

6 Indigenous communities
There are more than 15 indigenous ethnic groups, mostly from the middle and lower ríos Caquetá, Mirití and Apaporis Most prominent among them are the Yucuna, Miraña, Tanimuca, Matapí and Macuna. Over the last two decades indigenous peoples in the lower Caquetá River basin in Colombia have reported detrimental changes in the provision of important ecosystem services in ways that have significant implications for the maintenance of their traditional livelihoods.

7 Human dimensions of ecosystem services
Ecosystem services offer benefits to humans: direct and indirect material and non-material monetary and non-monetary These benefits are reflected in human wellbeing Often the poorest are the most dependent on ES to satisfy their basic needs

8 Links Between Ecosystem Services, Benefits, Food Security and Well-being

9 Livelihoods and Land Use
Participatory Rural Appraisal Livelihoods and Land Use Food Security Ecosystem Services

10 Standardising Procedure

11 Participatory Rural Appraisal
Qualitative information Focus group – ES matrix rating Women Comeyafú Focus group – ES matrix rating Men Puerto Cordoba

12 Ecosystem Service Benefit Perceived change Perceived drivers Direct Indirect Fish (particularly catfish) Income generation  Over-exploitation, pollution, giant otters Population growth, change in practices and consumption patterns, expanding trading networks, climate change and seasonality Ornamental resources Dresses and masks for traditional dances Over-exploitation Population growth Bush meat (large mammals) Over-exploitation, unsustainable practices  Population growth, change in practices and consumption patterns Timber Building material for houses, malokas and canoes, income generation Unsuitable logging practices, illegal logging Thatch Building material for houses and malokas Unsustainable harvesting practices Soil fertility Traditional shifting agriculture Over exploitation Population grow, loss or lack of traditional knowledge Material for handicrafts and traditional tools Brooms, baskets, kitchen elements, handicrafts +/- Fruits Resins Glue, sealants and body painting Natural medicines Health Results of this study demonstrate that in the past two decades the demand for food and raw materials have intensified and the stocks of these services in traditional use areas have decreased as a result. We found that these changes correlate with demographic factors such as greater need for income generation, change in livelihood practices and consumption patterns.

13 Area of critical ES flow
Spatial context of ecosystem services Provisioning region Rival use region Sink region Blocked flow Non-rival use region Depleted flows Area of critical ES flow The provisionshed is constituted by all different ecosystem sources where the service is generated. The benefitshed identifies areas where potential recipients or users of benefit are. Precise pathways of flow from their point of origin to beneficiaries are identified. One of the key features of ARIES and what makes it different from other approaches is the flow analysis of ecosystem services. 13

14 Mapping ES in Madroño Community
Participatory GIS Mapping in Bucuri To assess these changes we have combined participatory mapping methods and focus group discussions with indigenous people dedicated to hunting, fishing, harvesting activities as well as indigenous people with local spatial knowledge on environmental resources in the study area. We characterized two types of changes: (1) spatial location of ecosystem services provisioning areas and (2) the stock of ecosystem services. Mapping ES in Madroño Community Transect walk in Comeyafú

15 Digital map of service provisioning areas
2013 1993 A Google Earth map with a vector layers depicting houses, access routes, sources and sinks of ecosystem services in and around the village of Monilla Amena near Leticia, Colombia (Source: ESPA – ASSETS project). Source data: ESPA – ASSETS (2013) 15

16 An example of a “heat map” of provisioning areas
To obtain a “heat map” of SPAs we calculated the density of overlapping polygons using a customized tool developed in ArcGIS (Martinez, 2012; (Ramírez-Gómez and Martinez 2013). 16

17 Map of service provisioning area hotspots
The resultant SPA hotspots map (above) was derived using a standardized threshold, corresponding to the upper third quantile of the density distribution, similar to what has been done in other studies (Alessa, Kliskey, and Brown 2008; Brown and Pullar 2012). Source data: ESPA – ASSETS (2013) 17

18 Tabular statistics of hotspots
Indigenous reserve Indigenous reserve total Area (ha) Number of SPA hotspots Total SPA hotspot area (ha) Proportion of indigenous reserve occupied by SPA hotspots (%) 1993 2013 Camaritagua 8,456 100 202 324 809 3.8 9.6 Comeyafu 19,023 79 33 2,111 1,932 11.1 10.2 Curare-Los Ingleses 237,643 76 209 662 5,373 0.3 2.3 Puerto Cordoba 46,897 17 78 124 1,169 2.5 Vereda Madroño 20,351 6 7 14 25 0.1 State Forest Reserve 15,417 89 32 1,131 1,063 7.3 6.9 MEAN 57,965 61 94 728 1,729 5.2 To assess these changes we have combined participatory mapping methods and focus group discussions with indigenous people dedicated to hunting, fishing, harvesting activities as well as indigenous people with local spatial knowledge on environmental resources in the study area. We characterized two types of changes: (1) spatial location of ecosystem services provisioning areas and (2) the stock of ecosystem services.

19 Spatial statistics of hotspots
Results of this study demonstrate that in the past two decades the demand for food and raw materials have intensified and the stocks of these services in traditional use areas have decreased as a result. We found that these changes correlate with demographic factors such as greater need for income generation, change in livelihood practices and consumption patterns.

20 Change in the area of hotspots
The ecosystem services analyzed were provisioning services that included food, raw materials and medicinal resources.

21 Conclusions We found that PRA and PGIS can be a useful means of helping indigenous communities to visualize perceived changes in the provisioning areas and overall stocks of ecosystem services over time. Local perceptions can be represented on maps which can more easily convey this local understanding to external decision-makers. The methods used had notable advantages in terms of relatively low cost, efficiency and ability to incorporate local expert knowledge. We believe that the generated information will help strengthen existing ecosystem-based management strategies of indigenous peoples in the Colombian Amazon. We found that the methodology for the analysis of change in ecosystem service provision implemented in this study is particularly useful in marginalized and data-poor regions. We highlight how information regarding change in ecosystem service stocks and spatial change in service provisioning areas strengthens existing ecosystem-based management strategy of indigenous peoples in the Amazon region.

22 Limitation of the analysis
An important limitation of participatory methods, including PGIS, relying on indigenous knowledge, is that their outputs do not automatically fit into “scientific” demands for technical accuracy and statistical estimation (Dunn 2007; Chambers 2008). Therefore, despite the consistency observed in most key findings reported; at present the accuracy and generalizability of La Pedrera indigenous peoples’ perceptions and representations remains uncertain. Data obtained through participatory workshops within initiation phases of ecosystem services assessment should not constitute the endpoint for decision support processes (Brown and Pullar 2012). Therefore, results obtained through this analysis constitutes only the early, exploratory and hypothesis-generating stages of the ASSET project. We found that the methodology for the analysis of change in ecosystem service provision implemented in this study is particularly useful in marginalized and data-poor regions. We highlight how information regarding change in ecosystem service stocks and spatial change in service provisioning areas strengthens existing ecosystem-based management strategy of indigenous peoples in the Amazon region.

23 Thank you for your attention

24 espa ASSETS @espaassets
espa ASSETS @espaassets This presentation was produced by ASSETS (NE-J ), funded with support from the Ecosystem Services for Poverty Alleviation Programme (ESPA). The ESPA programme is funded by the Department for International Development (DFID), the Economic and Social Research Council (ESRC) and the Natural Environment Research Council (NERC), as part of the UK’s Living with Environmental Change Programme (LWEC). The views expressed here are those of the authors and do not necessarily represent those of the funders, the ESPA Programme, the ESPA Directorate, or LWEC.


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