Indigenous resistance to colonisation impeded deforestation in the Southern Atlantic Forest for 300+ years
Freg J. Stokes & Barney Harris
Timóteo da Silva Verá Tupã Popyguá
Ará Rete (Sandra Benites)
COMPLEG team
and many more…
About the team
Our co-researchers
Guaraní co-researchers: Timóteo da Silva Verá Tupã Popyguá (Photo: Edu Simões) & Ará Rete (Sandra Benites); Freg Stokes, Phil Riris (PI), Patrick Roberts (Co-PI)
The Atlantic Forest
A biodiversity hotspot
~20,000 species of plants
~300 species of mammals
~700 species of amphibians and reptiles
Indivisible from humans
“Nature is part of us” Karai Djekupe (Thiago), Guarani youth leader
Tekoa: the place where the Guaraní way of life is possible
A history of exploitation
Only ~10% of Atlantic Forest remains
Deforested by Spanish and Portuguese colonists (from 16th C. onwards)
Coffee, cocoa, cattle, gold, silver, soy, wheat
Indigenous resistance
Key Indigenous figures led resistance against the Spanish, such as the Guaraní leader Ñezú (1628).
Major battles included Mbororé (1641)
Spatial distribution of resistance important
Question:
Can we model the relationship between Indigenous resistance and forest survival?
Geospatial modelling
Benefits of formal spatial modelling frameworks
Identify complex spatial relationships
Quantify the impact of resistance
Enable counterfactual simulations
Feed into policy in a way that qualititative history cannot
Data collection
Resistance events
Archival visits
Collated data on Indigenous resistance events
Consultation with Indigenous communities
Data collection
Deforestation
Declassified CIA satellite imagery
Digitisation of surviving forest (~1965)
Difference with RESOLVE Biomes data
Key datasets
Binomial deforestation (response)
Indigenous resistance events (predictor)
Reasonable spatial correlation visible, however…
Least cost path analyses
Colonisers needed routes through the Atlantic Forest
Mobility impacted by topography, river flow strength and direction + rapids
Spatial impact of resistance on colonial routes local and dispersed
Building the model
A reproducible approach using R
Modelling with R
forest survival ~ indig. impact + envir + anthro
{targets} aids reproduciblity + HPC
{spatialRF}explanatory spatial random forest models
Spatial predictors (MEMs) capture spatial structure within data
Built-in diagnostic tools and spatial cross-validation procedures
{tidyverse} + {terra} = (spatial) data wrangling and geoprocessing
Sampling strategy
Forest survival is binomial (1/0 = survived/deforested)
Highly spatially contiguous
Highly spatially autocorrelated
Many observations (~35,000)
Difficult to model!
Bootstrap aggregation (bagging)
242 individual spatial RF models
~100–140 observations each
40km resolution lattice used to enforce min. distance
Oversampling of small fragments = class balance & less sampling redundancy
Results
Mixed in terms of AUCROC (< 0.7)
Some model have spatially correlated residuals (Moran’s I <= 0.05)
Caution when interpreting results
Predictions
Closer to Indigenous resistance => higher chance of forest survival
Simulation
Counterfactual scenario with no Indigenous resistance
20–22% increase in deforestation (minimum)
Concluding thoughts
Ultimately the Atlantic Forest was heavily deforested…
however, a similar story continues to play out in the Amazon today…
Freg will share our research with Guarani communities in Misiones, Argentina
… and work with Brazilian Guaraní researcher Ivanilde Kerexu to assess how research might help support future forest conservations strategy