RT Journal Article
SR Electronic
T1 Inferring critical points of ecosystem transitions from spatial data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 187799
DO 10.1101/187799
A1 Sabiha Majumder
A1 Krishnapriya Tamma
A1 Sriram Ramaswamy
A1 Vishwesha Guttal
YR 2017
UL http://biorxiv.org/content/early/2017/09/12/187799.abstract
AB Ecosystems can undergo abrupt transitions from one state to an alternative stable state when the driver crosses a threshold or a critical point. Dynamical systems theory suggests that systems take long to recover from perturbations near such transitions. This leads to characteristic changes in the dynamics of the system, which can be used as early warning signals of imminent transitions. However, these signals are qualitative and cannot quantify the critical points. Here, we propose a method to estimate critical points quantitatively from spatial data. We employ a spatial model of vegetation that shows a transition from vegetated to bare state. We show that the critical point can be estimated as the ecosystem state and the driver values at which spatial variance and autocorrelation are maximum. We demonstrate the validity of this method by analysing spatial data from regions of Africa and Australia that exhibit alternative vegetation biomes.