Biotic invasions as drivers of the grass-woodland transition

LAURA YAHDJIAN

Punta del este

Workshop; International Workshop on Abrupt grass-woodland transitions: Determinants and consequences for ecosystem services.; 2012

SARAS

Understanding the phenomenon of abrupt transitions in a variety of ecological systems has grown dramatically in the last few years as a result of the discovery of new mathematical tools and unique system characteristics that yield early-warning signals (Scheffer et al. 2009). Ecosystems experience a critical slowing down as they approach a tipping point (van Nes and Scheffer 2007). And, this ecosystem property results from a reduction in resilience expressed in the decreased recovery time to stochastic disturbances. The critical slowing down has been quantified through the autocorrelation of data points through time that increases as the system approaches a tipping point (Dakos et al. 2008). This phenomenon has been described for a number of systems ranging from experimental microcosms to natural ecosystems (Drake and Griffen 2010, Veraart et al. 2012). Another property of ecosystems approaching a tipping point or critical transition is flickering, which is an increase in variance of one or more variables as the ecosystem shifts back and forth into two alternative-stable states (Carpenter and Brock 2006). Large-scale experiments involving entire lakes showed this increase in variance prior to shifting into a different domain (Carpenter et al. 2011). Predicting tipping points has interest from the point of view of systems dynamics and ecosystem management. These predictive tools do not require an understanding of the socio-ecological mechanisms that result in this transition. However, coupling recent progress in detecting tipping points with system-specific mechanisms will create enormous progress in understanding functioning socio-ecological systems and improve our ability to avoid undesirable transitions and foster desirable changes. While these new statistical tools help us predicting tipping points, identifying the best actions to avoid or take advantage of the transition requires a mechanistic understanding of the systems. The next step in this fertile area of research is to couple statistical predictive tools, such as those described above, with observations and mechanistic models that link ecological and social drivers.