|Institution:||University of Waterloo|
|Keywords:||ecology; forest transition; forest cover; bifurcations; thresholds; deforestation; mathematical model|
|Full text PDF:||http://hdl.handle.net/10012/12894|
A central topic in modeling land use change is to understand the forest transitionfrom deforestation to net reforestation. Agricultural land use change is the main driver ofthis phenomenon; classically, agricultural land expands considerably to feed a growing population,and then declines as efficiency gains are realized, marginal farmland is abandoned,and rural populations move to cities. As a result, existing models have focused on thesocioeconomic and demographic factors that drive agricultural intensification. However, indoing so, these models often neglect the role of ecological feedback effects and thresholds.These ecological thresholds can cause rapid shifts in ecosystems, such as forest collapse,based on small changes in parameters, and are very difficult to predict. The existence ofthese thresholds implies that agricultural expansion carries a risk of forest collapse. Weaim to use realistic models to assess the risk of collapse in forest cover, dependence on keyparameters, and strategies to avoid it.To address the risk of forest collapse, we develop and analyze a differential equationmodel that incorporates both agricultural intensification and ecological thresholds. We useparameter values from the literature to adapt this model to boreal and tropical forests.We analyze the model with bifurcation diagrams, simulations of key resilience metrics, andfitted time series of real-world data for China, Costa Rica, and Vietnam.Our analysis shows that there is a risk of forest collapse, and that the system is particularlysensitive to agricultural parameters. We find that regardless of the mechanism bywhich collapse occurs, there is a critical value of 20-25% forest cover. In scenarios of interest(i.e. forest transitions), initial deforestation would result in collapse if left unchecked.We estimate model parameters at multiple points along historical time series, which allowsus to infer the risk of collapse and identify historical patterns. This shows that forest transitionscan be caused by more varied parameter patterns than classically assumed in theliterature; in particular, rates of land conversion and agricultural abandonment rate mayremain elevated, instead of declining after intensification. The agricultural abandonmentrate is a key advance predictor of collapse at long time horizons, but at the brink of a crisisforest collapse can best be avoided by reducing the forest conversion rate. We argue thatecological threshold effects should be acknowledged in forest transition models not only forecological accuracy but also to ensure prudent forest management, particularly in the faceof emerging risks such as climate change.