seminar13 mar 2013 - sesion 1 - analysis of forest-livelihoods nexus global data set by sdewi
DESCRIPTION
We explore methodologies that allow conclusions to be drawn from the large Poverty Environment Network (PEN) dataset. First, we characterize the diverse parts of the tropics in terms of factors that influence forest resources, access and livelihoods. Secondly, for the conclusions drawn from the site-based analysis to be useful for roader policy recommendations, we need to know the extrapolation domains. We compared the characteristics of landscapes where PEN studies took place with overall tropical landscapes, and those of PEN villages with 'random' villages. Both methods rely on variables derived from global data sets using spatial analysis. Thirdly, we study the relationships of livelihoods and forests using multilevel regression analysis. Our study suggests that for global comparative analysis, it is necessary to identify the overall variation of the system of interest, to define the extrapolation domain of the samples/study sites, and to address relationships that by nature involve multiple scale processes. Available global data set, advances in spatial techniques and relatively cheap computer storage and computational power allow such analysis to be done, adding value through global comparative analysis of the interesting site-level findings.TRANSCRIPT
Analysis of forest-livelihoods nexus: how can global data set help?
Sonya Dewi, Brian Belcher,
Atie Puntodewo
“Tree cover transitions & investment in multicolored economy” One Day Seminar, March 13 2013, Bogor
Outline
• PEN study and dataset
• Characterization of the diverse parts of the tropics
• Extrapolation domain of large scale, comparative studies
• Multilevel analysis of relationships of livelihoods and forests
ABOUT PEN STUDY
The PEN data set
PEN is a…
• Large (360 villages, 10,000+ households) • pan-tropical (25 countries, 3 regions)
• collection of detailed and (intended) high-quality data by
• 38 PhD student partners on the
• poverty-forest (environment) nexus at the household level,
Aim: produce the most comprehensive (breadth and depth) analysis of poverty-forest links
CHARACTERIZATION OF THE DIVERSE PARTS OF THE TROPICS
Global dataset
Spatial analysis of global maps clipped for the tropics only: • Global land cover: JRC, 2006. The Global Land Cover 2006 • Ecoregion: WWF, 2005. WWF Terrestrial Ecoregions • Population density: CIESIN, 2005. Estimated Population Density
2005 from Gridded population of the World (GPW) version 2 • Settlement locations: World Gazeteer – population figures for cities,
places, regions, countries (http://world-gazeteer.com/) • Roads: DMA, 2006. Digital Chart of the World, Roads • Protected areas: UNEP, 2010. World Database on Protected Areas
(WDPA) • Elevation: GTOPO30 • Watersheds: WWF Conservation Science Program, 2009.
Hydrological basins derived from HydroSHEDS.
Ecosystem
Scale 1:10,000,000
Source: WWF, 2005. WWF Terrestrial Ecoregions
Ecosystem: Area and Population
How much is protected?
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Inside PA
10 -100 km
1 - 10 km
> 100 km
< 1 km
How much is forested?
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800 M
illio
ns
Forest Mosaics
Forest Edge
Forest Core
Non-forest
Forest configuration
EXTRAPOLATION DOMAIN
Sub-basin: typology (FT)
% Medium Broadleaved forest % Open broadleaved forest % Mixed tree cover
Dominant Ecosystem FC 1 FC 2 FC 3 FC 4 FC 5 FC 6 Total
Tropical and subtropical
moist broadleaf forests 83 21 34 5 28 171
Tropical and subtropical
dry broadleaf forests 9 7 16
Tropical and subtropical
grasslands, savannas, and
shrublands 2 27 16 65 5 115
Tropical and subtropical
coniferous forests 10 10
Montane grasslands 6 6
Deserts and xeric
shrublands 2 2
Outside the tropics 13
Total 85 48 60 80 10 37 333
Proportion of Area
Number of PEN villages
Dominant Ecosystem FT1 FT2 FT3 FT4 FT5 FT6 Tropical and subtropical moist broadleaf forests 0.074 0.1077 0.152 0.0025 0.022 0.076
Tropical and subtropical dry broadleaf forests 0.002 0.0051 0.0128 0.0143 0.0443 0.0386
Tropical and subtropical grasslands, savannas, and shrublands 0.0204 0.1095 0.0763 0.0719 0.0086 0.0572
Tropical and subtropical coniferous forests 0.0003 0.0004 0.0009 0.0005 0.0003 0.0004
Montane grasslands 0.0024 0.0006 0.0034 0.0013 0.004 0.0012
Flooded grasslands 0.0001 0.0012 0 0 0 0 Mangroves 0.0001 0.0008 0 0 0 0.0006 Deserts and xeric shrublands 0.0001 0.001 0 0.0038 0.0022 0.0642 Total 0.0994 0.2263 0.2455 0.0943 0.0815 0.2383
• In area under earlier FT stages for moist broadleaf forest
•Livelihoods in tropical and subtropical dry broadleaved forest are not much captured
Of Population
MULTILEVEL ANALYSIS OF RELATIONSHIPS OF LIVELIHOODS AND FORESTS
- Multi-level
o Hh characteristics o Resource base o Access to market o Access to
resources o …
- Policies should
address multiple-level issues
Coeff Signif.
Total income (ln) Intercept 0.805 Household-level variables Members -0.159 ** Age of head -0.003 ** Number of adults eq 0.162 ** Female headed -0.235 ** Percent of forest land managed -0.001 Percent of agricultural land managed -0.04 Total land (ln ha) 0.183 ** Herfindahl index (diversity of source of income) ** Village-level variables Road density 0.443 ** Population density 2.84 ** Road dens x Population dens -0.298 ** Distance to Protected Areas 0.079 ** Sub-montane compared to lowland -0.227 ** Montane compared to lowland -0.018 ** Sub-alpine compared to lowland -0.675 ** Alpine compared to lowland -0.445 **
Coeff Signif.
Watershed-level variables Dry broadleaved forest compared to Moist broadleaved forest -0.356 ** Grassland, savanna, shrubland -0.747 ** Coniferous forest 0.733 ** Montane grassland -0.737 * Desert and xeric shrubland -1.240 ** Distance to core forest 0.154 ** % Core forest 1.125 ** Mean Population dens 0.632 ** FT x dry broadleaved forest -0.077 FT x grassland, savanna, shrubland -0.217 ** FT x coniferous forest -0.275 ** FT x montane grassland -0.474 ** FT x Desert and xeric shrubland -0.133 Village x WS-level Population density -0.197 **
Global dataset can help …
• Providing context to case studies and comparative studies at different scales
• Finding the sampling frame and population
• Analysis of typologies; finding extrapolation domain
• Generating data for multiple and cross-scale analysis, e.g., with multiple level regression analysis
THANK YOU