A logging agent builds roads based on the location of high-value hotspots, and cuts trees based on road access. A forest monitor sanctions the logger on observed infractions, reshaping the pattern of road development.

We seek to improve understanding of roles enzyme play in soil food webs. We created an agent-based simulation of a simple food web that includes enzymatic activity. The model was used in a publication, Moore et al. (in press; Biochemistry).

The objective of the model is to evaluate the impact of seasonal forecasts on a farmer’s net agricultural income when their crop choices have different and variable costs and returns.

Biobehavioral interactions between two populations under different movement strategies.

The purpose of the AdaptPumpa model is to analyze the robustness of the Pumpa irrigation system in Nepal to climate change.

This is a simulation model of an intelligent agent that has the objective to learn sustainable management of a renewable resource, such as a fish stock.

MOOvPOP is designed to simulate population dynamics (abundance, sex-age composition and distribution in the landscape) of white-tailed deer (Odocoileus virginianus) for a selected sampling region.

The model analyzes the economic and ecological effects of a provision of livestock drought insurance for dryland pastoralists. More precisely, it yields qualitative insights into how long-term herd and pasture dynamics change through insurance.

This model simulates different spread hypotheses proposed for the introduction of agriculture on the Iberian peninsula. We include three dispersal types: neighborhood, leapfrog, and ideal despotic distribution (IDD).

MOOvPOPsurveillance was developed as a tool for wildlife agencies to guide collection and analysis of disease surveillance data that relies on non-probabilistic methods like harvest-based sampling.