Constructing stability landscapes to identify alternative states in coupled social-ecological agent-based models

Abstract

ABSTRACT. The resilience of a social-ecological system is measured by its ability to retain core functionality when subjected to perturbation. Resilience is contextually dependent on the state of system components, the complex interactions among these components, and the timing, location, and magnitude of perturbations. The stability landscape concept provides a useful framework for considering resilience within the specified context of a particular social-ecological system but has proven difficult to operationalize. This difficulty stems largely from the complex, multidimensional nature of the systems of interest and uncertainty in system response. Agent-based models are an effective methodology for understanding how cross-scale processes within and across social and ecological domains contribute to overall system resilience. We present the results of a stylized model of agricultural land use in a small watershed that is typical of the Midwestern United States. The spatially explicit model couples land use, biophysical models, and economic drivers with an agent-based model to explore the effects of perturbations and policy adaptations on system outcomes. By applying the coupled modeling approach within the resilience and stability landscape frameworks, we (1) estimate the sensitivity of the system to contextspecific perturbations, (2) determine potential outcomes of those perturbations, (3) identify possible alternative states within state space, (4) evaluate the resilience of system states, and (5) characterize changes in system-scale resilience brought on by changes in individual land use decisions.

Eight grand challenges in socio-environmental systems modeling

“…Alternatively, adaptive network models (Sayama et al, 2013) could bring the structural focus of network modelling into models of SES dynamics. Better methods for identifying critical transitions or regime shifts in model output, including an automatic creation of "stability landscapes" (Bitterman and Bennett, 2016) and statistical methods of regime shift identification (Filatova et al, 2016) are needed. Very often sparse data needs to be used simultaneously to calibrate and validate the model.…”

Resilience in social-ecological systems: identifying stable and unstable equilibria with agent-based models

“…A stability landscape is a multidimensional state space in which stable equilibria are portrayed as valley bottoms and unstable equilibria as hill ridges. Over the past two decades, stability landscapes have frequently been used in resilience theory to exemplify the effects of changing equilibria and regime shifts (e. g., Peterson et al 1998, Scheffer et al 2001, Walker et al 2004, Bitterman and Bennett 2016. In this study, we reconstructed the stability landscapes from the results of the ALUAM-AB runs.…”

“…In our study, we chose to run ALUAM-AB for 10 time-steps (i.e., 10-year periods), so that we could perform enough simulations within an acceptable time. An alternative approach would be to run fewer simulations for longer time periods until stable equilibria have been reached, as was, for instance, done by Bitterman and Bennett (2016). However, in some models it can take many time-steps until equilibria states are reached and a single simulation would become too time consuming.…”

“…1C), which is particularly the case in systems with positive feedbacks (Angeli et al 2004). Stable equilibria have also been referred to as "attractors" (Scheffer et al 2012, Bitterman andBennett 2016). The state space in which system change is in the direction of a stable equilibrium is commonly termed a "basin of attraction" (Holling 1973) or a "valley" in a stability landscape (Scheffer et al 2001).…”

“…The analysis performed with agent-based SES models is generally rather limited: usually only few scenarios are tested ) and proper sensitivity analyses are rarely performed (Schulze et al 2017). A notable exception is the study by Bitterman and Bennett (2016), who successfully identified stable equilibria with an agent-based land-use model. These authors repeatedly ran their model for many combinations of input parameters until, after approximately 20 time steps, an equilibrium was reached.…”