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-Carr a Jason Davis a, Marta Jankowska b, Laura Grant c, Anna Carla López-Carr b, T Mitchell Aide d, Matthew Clarke e a Department of Geography, University of California, Santa Barbara, Human-Environment Dynamics Lab, 4836 Ellison Hall UC Santa Barbara (UCSB)Santa Barbara, CA b Department of Geography, San Diego State University, 5500 Campanile Drive, San Diego, CA c 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 e Department of Geography, Sonoma State University, Stevenson Hall 3066, 1801 E. Cotati Avenue, Rohnert Park, CA 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 AM 140B (Washington Convention Center)

World Population Dynamics - 2 Big Trends: Urbanization & Aging

,326 km 2 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, – 503,388,073 diff 76,923,052 Municipality level change # of municipalities : 16,052 # with negative growth : 4,200 % with negative growth: 26.1% Data from FAOSTATSOur analyses Aide, Clarke, Lopez-Carr,, Levy, Grau, et al. (2010). Under review at Science. Global Land Project Open Science Meeting. Arizona State University October.

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! 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 International Destinations Rural Destination Agricultural Extensification Agricultural Intensification Return to Top of Chart MigrationFertility regulation Off-farm Labor Household Responses Local Variation Land Management Proximate and Underlying Causes Why poverty-driven deforestation WITH rapid urbanization? Disproportionate Scale Problem Other response??

crops forest FARM UNOCCUPIED FOREST SURROUNDING 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 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 j th departments β 1 is the intercept along with its independent error term β 2 through β p are regression coefficients with corresponding explanatory variables x 2ij through x pij ε ij represent an independent error term for x 2ij through x pij

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. β 1 through β k are regression parameter estimates with corresponding explanatory variables x i1 through x ik 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.

Guatemala LUCC Multilevel Model Structure (Municipalities within Departments) Forest Cover Population

Results: OLS for Woody Vegetation Change : Intensive: Fertilizer vs Extensive Tractors VariablesCoefficientStandard Error Population Density 2000 (persons/km^2) Percentage Population Density Change from 1990 to Percentage of Households Using Fertilizer 0.569*0.182 Percentage of Households Owning a Tractor Café (kg/ha)0.000 Sugar (kg/ha) White Corn (kg/ha) Yellow Corn (kg/ha) Percentage of Land in Fallow Model R 2 =.043, Adjusted R 2 =.016

Modeling Results: OLS for Woody Vegetation Cover 2009: Frontiervs. Settled and Urban VariablesCoefficientStandard Error Population Density 2000 (persons/km^2) *0.000 Percentage Population Density Change from 1990 to ***0.115 Percentage Population Density Change from 1990 to 2000^ **0.032 Percentage of Households Using Fertilizer *0.032 Percentage of Households Owning a Tractor **0.143 Café (kg/ha)0.000***0.000 Sugar (kg/ha)-0.000*0.000 White Corn (kg/ha)-0.000**0.000 Yellow Corn (kg/ha)0.000 Percentage of Land in Fallow-0.538***0.104 Model R 2 =.274, Adjusted R 2 =.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 VariableMoran’s IZ ScoreP ValueAutocorrelation Change in Woody Vegetation <.0001Strongly Clustered Woody Vegetation <.0001Strongly Clustered OLS Change 2000 – 2009 Standardized Residuals Clustered OLS 2009 Standardized Residuals <.0001Strongly Clustered GWR Change 2000 – 2009 Standardized Residuals Random GWR 2009 Standardized Residuals Random

Figure 1. Getis-Ord Gi* maps for woody vegetation in 2009 and change in woody vegetation 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).

Conclusions s 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?