Agents, Individuals, and Networks: Modeling Methods to Inform Natural Resource Management in Regional Landscapes

Abstract

ABSTRACT. Landscapes are complex systems. Landscape dynamics are the result of multiple interacting biophysical and socioeconomic processes that are linked across a broad range of spatial, temporal, and organizational scales. Understanding and describing landscape dynamics poses enormous challenges and demands the use of new multiscale approaches to modeling. In this synthesis article, we present three regional systems-i.e., a forest system, a marine system, and an agricultural systemand describe how hybrid, bottom-up modeling of these systems can be used to represent linkages across scales and between subsystems. Through the use of these three examples, we describe how modeling can be used to simulate emergent system responses to different conservation policy and management scenarios from the bottom up, thereby increasing our understanding of important drivers and feedback loops within a landscape. The first case study involves the use of an individual-based modeling approach to simulate the effects of forest harvesting on the movement patterns of large mammals in Canada's boreal forest and the resulting emergent population dynamics. This model is being used to inform forest harvesting and management guidelines. The second case study combines individual and agent-based approaches to simulate the dynamics of individual boats and whales in a marine park. This model is being used to inform decision-makers on how to mitigate the impacts of maritime traffic on whales in the Saint Lawrence Estuary in eastern Canada. The third example is a case study of biodiversity conservation efforts on the Eyre Peninsula, South Australia. In this example, the social-ecological system is represented as a complex network of interacting components. Methods of network analysis can be used to explore the emergent responses of the system to changes in the network structure or configuration, thus informing managers about the resilience of the system. These three examples illustrate how bottom-up modeling approaches may contribute to a new landscape science based on scenario building, to find solutions that meet the multiple objectives of integrated resource management in social-ecological systems.

Modeling cross-scale relationships between climate, hydrology, and individual animals: generating scenarios for stream salamanders

“…Over the last decade, spatially-explicit individual-based models linked to landscape-scale process models have emerged as powerful tools in conservation, management, and planning (Grimm and Railsback, 2005;Stillman and Goss-Custard, 2010;McClain et al, 2012;Metcalfe et al, 2012;Parrott et al, 2012). By considering individual variations, entire life cycles, interactions among individuals and interactions between individuals and their immediate environment, this approach allows for accurate predictions about local viability of species in response to land use change or modification of landscape-scale environmental processes.…”

NetLogoR: a package to build and run spatially explicit agent‐based models in R

“…These entities have state-dependent behaviors and they can interact with each other and/or their environment. SE-ABMs have been widely used by ecologists to study populations and communities with, for example, research on human-wildlife interactions (Chion et al 2011, Parrott et al 2012, analyses of movement and habitat selection patterns for animals (McIntire et al 2007, Grosman et al 2011, Semeniuk et al 2012, and models of vegetation dynamics (Wallentin et al 2008). When they are 'spatially-explicit', there is an effect of the landscape and the fate of the entities is therefore constrained by their environment (McIntire et al 2007, Grosman et al 2011.…”

Adaptive management in crop pest control in the face of climate variability: an agent-based modeling approach

“…Thus, ABMs make it possible to formalize and test hypotheses about the conditions under which complex patterns in management practices can emerge from a set of observed behavior-response functions (Zellner 2008, Rounsevell et al 2012. ABMs have been applied to a variety of situations over the last 20 to 30 years, such as systems affected by land use and changes in land cover (e.g., Parker et al 2003, Matthews et al 2007) and resource management and conservation (e.g., Boone et al 2011, Parrott et al 2012, Watkins et al 2013. Although a few studies have focused on agricultural systems (e.g., Berger 2001, Happe et al 2006, Here, we assessed the impacts of climatic and economic variability on farmers' adaptive management in agricultural pest control in the Ecuadorian Andes.…”