space versus place in complex human- natural systems: spatial and multi-level models of tropical...
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Space versus Place in Complex Human-Natural Systems: Spatial and Multi-level Models of Tropical Land Use and Cover Change (LUCC) in Guatemala
David López-Carra Jason Davisa, Marta Jankowskab, Laura Grantc, Anna Carla López-Carrb, T Mitchell Aided, Matthew Clarkee
a Department of Geography, University of California, Santa Barbara, Human-Environment Dynamics Lab, 4836 Ellison Hall UC Santa Barbara (UCSB)Santa Barbara, CA 93106-4060 b Department of Geography, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182c Department of Economics and Bren School of the Environment, UC Santa Barbara (UCSB)Santa Barbara, CA d Department of Biology, University of Puerto Rico, San Juan, PR 00931-3360e Department of Geography, Sonoma State University, Stevenson Hall 3066, 1801 E. Cotati Avenue, Rohnert Park, CA 94928
American Association for the Advancement of Science (AAAS) Annual Meeting: Mapping and Disentangling Human Decisions In Complex Human-Nature Systems Friday, February 18, 2011: 8:30 AM-11:30 AM140B (Washington Convention Center)
World Population Dynamics - 2 Big Trends: Urbanization & Aging
World Population: Average Annual Increase For Each Decade, 1750-2100 (projected) Source: "World
Population in Transition", Population Bulletin, by Thomas W. Merrick and PRB
0
20
40
60
80
100
1750 1800 1850 1900 1950 2000 2050 2100
Iin
cre
as
e (
mil
lio
ns
) Developing Regions
Developed Regions
1994 1996 1998 2000 2002 2004 2006 2008 2010Glo
ba
l p
ig a
nd
po
ultr
y p
rod
uctio
n
(to
nn
es x
106)
130
140
150
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190
Year
1994 1996 1998 2000 2002 2004 2006 2008 2010
So
uth
Am
eri
ca
n s
oyb
ea
n
pro
du
ctio
n (
ha
x 1
06)
15
20
25
30
35
40
45
2001-2009163,326 km2
Aide, Clarke, Lopez-Carr, et al. (2010)..
Increasingly the driver is global urban consumption
NSF CHH Project: Latin America & Caribbean Demographic dynamics and LUCC
Total population change (1990 – 2000)
1990 – 426,465,0212000 – 503,388,073
diff 76,923,052 Municipality level change# of municipalities: 16,052# with negative growth : 4,200% with negative growth: 26.1%Year
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Pop
ula
tion
x 1
06
100
200
300
400
500
Rural Urban
Data from FAOSTATS Our analyses
Aide, Clarke, Lopez-Carr,, Levy, Grau, et al. (2010). Under review at Science. Global Land Project Open Science Meeting. Arizona State University. 17-19 October.
Tro
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For
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Shr
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Per
cent
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oody
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(20
01 -
200
9)
-4
-3
-2
-1
0
1
2
3
4
Pantanal
Pampas
Mangrove
What municipalities are gaining and losing woody forest cover?
Aide, Clarke, Grau, Levy, Lopez-Carr, et al. (2010)..
What is the relationship between population change and woody vegetation change? Nada at the municipal level!
Population change (1990-2000)
-20000 0 20000 40000 60000 80000 100000
Wo
od
y ve
ge
tatio
n c
ha
ng
e 2
00
1-2
00
9
(r -
co
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n c
oe
ffici
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t)
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
ns
p <0.1
p <0.1
n = 16,052, F = 0.21, P = 0.64, r2 = 0.000
Aide, Clarke, Lopez-Carr, et al. (2010).
So what is driving forest conversion?...
Two Latin Americas: 78% Urban but…• Argentina/Chile/Uruguay – 90% Urban• Guatemala, Ecuador, Bolivia – 50% Urban• And within these countries there is VAST variation
• These two Latin Americas are associated with two distinct deforestation Pathways:
• Pathway 1: High population growth, rural-rural migration, low technology, low yields, poverty, subsistence.
• Pathway 2: Falling population growth, urbanization, increased meat consumption, high yield, increasing affluence, high technology, export agriculture.
Macro-Scale demographic, political-economic, social, and ecological dynamics
Urban or Internation
al Destination
s
Rural Destinati
on
Agricultural Extensificati
on
Agricultural Intensificatio
n
Return to Top of Chart
Migration
Fertility regulation
Off-farm Labor
Household Responses
Local Variation
Land Management
Proximate and Underlying CausesWhy poverty-driven deforestation WITH rapid urbanization?
Disproportionate Scale Problem
Other response??
cropscropsforestforest
FARMFARM UNOCCUPIED UNOCCUPIED FORESTFOREST
SURROUNDING FARMSSURROUNDING FARMS
Poverty-driven forest conversion tends to target unoccupied forestland, the external frontier. Commercial agriculture often follows land consolidation and thus may or may not be converting old growth forest (internal frontier)
Internal (place) versus external (space) forest frontiers.
Methods: Data Sources
• 2000 Guatemalan Living Standards Measurement Survey– Independent variables: household fertilizer and tractor use
• 2003 Guatemala National Agriculture Census– Independent variables: 2003 percent land area in fallow and
crop yields for coffee, sugar, white corn, yellow corn
• 2001-2009 Forest Cover Change database for all of Latin America (funded by NSF)– Dependent Variables: percent woody cover in 2009 and percent
change in woody cover from 2001 to 2009– Independent variables: 1990 and 2000 population density
Methods: Variables
Dependent Variables = woody vegetation in 2009 in model 1, and the percentage change in municipal woody vegetation from year 2001 to 2009
Guatemala LUCC Multilevel Model
• i represent municipalities within jth departments • β1 is the intercept along with its independent error term
• β2 through βp are regression coefficients with corresponding explanatory variables x2ij through xpij
• εij represent an independent error term for x2ij through xpij
j
Guatemala LUCC GW Model
• Y i = woody vegetation in 2009 in model 1, and the percentage change in municipal woody vegetation from year 2000 to 2009 in model 2.
• β1 through βk are regression parameter estimates with
• corresponding explanatory variables xi1 through xik with independent error term
• The weighting function is based on distance, resulting in locations closer to the estimated point having more influence on the projected value than locations farther away.
i
iikk
ikii xy 1
0
Guatemala LUCC Multilevel Model Structure
(Municipalities within Departments)
Forest Cover Population
Results: OLS for Woody Vegetation Change 2001-2009: Intensive: Fertilizer vs
Extensive TractorsVariables Coefficient Standard Error
Population Density 2000 (persons/km^2)
-0.000 0.000
Percentage Population Density Change from 1990 to 2000
0.055 0.191
Percentage of Households Using Fertilizer
0.569* 0.182
Percentage of Households Owning a Tractor
-1.508+ 0.808
Café (kg/ha) 0.000 0.000
Sugar (kg/ha) -0.000 0.000
White Corn (kg/ha) -0.000 0.001
Yellow Corn (kg/ha) 0.000 0.001
Percentage of Land in Fallow 0.521 0.584
Model R2 = .043, Adjusted R2 = .016
Modeling Results: OLS for Woody Vegetation Cover 2009:
Frontiervs. Settled and UrbanVariables Coefficient Standard Error
Population Density 2000 (persons/km^2)
-0.000* 0.000
Percentage Population Density Change from 1990 to 2000
0.388*** 0.115
Percentage Population Density Change from 1990 to 2000^2
-0.089** 0.032
Percentage of Households Using Fertilizer
-0.064* 0.032
Percentage of Households Owning a Tractor
-0.449** 0.143
Café (kg/ha) 0.000*** 0.000Sugar (kg/ha) -0.000* 0.000White Corn (kg/ha) -0.000** 0.000Yellow Corn (kg/ha) 0.000 0.000Percentage of Land in Fallow -0.538*** 0.104
Model R2 = .274, Adjusted R2 = .251
Results: LUCC Multilevel Model. Woody Cover in 2009 is near PAs and remote areas. Less forest in settled rural areas
Spatial Modeling Results: Moran’s I for Woody Vegetation Change
2001-2009Variable Moran’s I Z Score P Value Autocorrelation
Change in Woody Vegetation 2001 - 2009
.024 3.772 < .0001 Strongly Clustered
Woody Vegetation 2009 .094 12.586 < .0001 Strongly Clustered
OLS Change 2000 – 2009 Standardized Residuals
.010 1.780 .072 Clustered
OLS 2009 Standardized Residuals
.070 9.421 < .0001 Strongly Clustered
GWR Change 2000 – 2009 Standardized Residuals
.005 1.159 .246 Random
GWR 2009 Standardized Residuals
.003 .715 .474 Random
Figure 1. Getis-Ord Gi* maps for woody vegetation in 2009 and change in woody vegetation 2001-2009 with hot spots in red and cold spots in blue.
Figure 2. Coefficient estimates for percent households using fertilizer for the percent woody cover in 2009 model (left) and percent change in woody cover from 2001 to 2009 model (right).
Figure 4. Coefficient estimates for percent population density change from 1990 to 2000 (left), and percent of land in fallow (right) for the percent woody cover in 2009 model.
Figure 3. Coefficient estimates for percent households owning tractors for the percent woody cover in 2009 model (left) and percent change in woody cover from 20001to 2009 model (right).
Conclusionss
• More variation was found internally within model types than between multi-level and spatial models.
• However, the interpretation and utility of the results may be notably distinct in a Geographically Weighted Regression (GWR) versus a multi-level model.
Conclusions• Ordinary Least Squares (OLS) regression
suggests that population increase and density, agricultural intensification in the form of fertilizers and tractors, and higher crop production for sugar cane and white corn are negatively associated with forest cover in 2009 while coffee production is associated with higher forest cover.
Conclusion• Examining forest change during the first decade
of the 2000s for Guatemala, we observe that areas that increasingly relied upon mechanized equipment and/or fertilizers have more thoroughly captured and put into production available agricultural land.
Conclusions• What did GWR and Mlevel regressions tell us
beyond these findings?
• The multi-level model suggests significant differences exist at the municipal and departmental levels and indicates maintains a positive relationship between coffee production and forest cover at both levels of analysis.
• The GWR indicates where these association of changes are most salient. A clear trend emerges: The southwest to northeast gradient of decreasing population density, higher but decreasing forest cover, and lower but increasing technological inputs is particularly illuminated by the GWR.
Conclusions• Why do we care?
• Space is important and WHERE things happen is crucial for policy and management.
• Coupled human-natural systems take home message: The debate in geography and cognate sciences over the importance of space vs. place is often framed by qualitative vs. quantitative research. It need not be so. Place in coupled human-natural systems can be quantified and measured.
El fin. Gracias!
Sunset or Sunrise over Guatemala’s Forests?
Which way is LUCC heading?Which way is LUCC heading?