Lael Parrott

Viewing forests through the lens of complex systems science

Complex systems science provides a transdisciplinary framework to study systems characterized by (1) heterogeneity, (2) hierarchy, (3) self-organization, (4) openness, (5) adaptation, (6) memory, (7) non-linearity, and (8) uncertainty. Complex systems thinking has inspired both theory and applied strategies for improving ecosystem resilience and adaptability, but applications in forest ecology and management are just beginning to emerge. We review the properties of complex systems using four well-studied forest biomes (temperate, boreal, tropical and Mediterranean) as examples. The lens of complex systems science yields insights into facets of forest structure and dynamics that facilitate comparisons among ecosystems. These biomes share the main properties of complex systems but differ in specific ecological properties, disturbance regimes, and human uses. We show how this approach can help forest scientists and managers to conceptualize forests as integrated social-ecological systems and provide concrete examples of how to manage forests as complex adaptive systems.

Future landscapes: managing within complexity

A regional landscape is a complex social–ecological system comprising a dynamic mosaic of land uses. Management at this scale requires an understanding of the myriad interacting human and natural processes operating on the landscape over a continuum of spatial and temporal scales. Complexity science, which is not part of traditional management approaches, provides a valuable conceptual framework and quantitative tools for dealing with cross-scale interactions and non-linear dynamics in social–ecological systems. Here, we identify concepts and actions arising from complexity science that can be learned and applied by ecosystem managers and discuss how they might be implemented to achieve sustainable future landscapes.

Spatio‐temporal dynamics in the response of woodland caribou and moose to the passage of grey wolf

Predators impact prey populations not only by consuming individuals, but also by altering their behaviours. These nonlethal effects can influence food web properties as much as lethal effects. The mechanisms of nonlethal effects include chronic and temporary anti-predator behaviours, the nature of which depends on the spatial dynamics of predators and the range over which prey perceive risk. The relation between chronic and ephemeral responses to risk determines predator-prey interactions, with consequences that can ripple across the food web. Nonetheless, few studies have quantified the spatio-temporal scales over which prey respond to predation threat, and how this response varies with habitat features. We evaluated the reaction of radio-collared caribou and moose to the passage of radio-collared wolves, by considering changes in movement characteristics during winter and summer. We used an optimization algorithm to identify the rate at which the impact of prior passage of wolves decreases over time and with the predators distance. The spatial and temporal scales of anti-predator responses varied with prey species and season. Caribou and moose displayed four types of behaviour following the passage of wolves: lack of response, increased selection of safe land cover types, decreased selection of risky cover types and increased selection of food-rich forest stands. For example, moose increased their avoidance of open conifer stands with lichen in summer, which are selected by wolves in this season. Also in winter, caribou increased their selection of conifer stands with lichen for nearly 10 days following a wolfs passage. This stronger selection for food-rich patches could indicate that the recent passage of wolves informs caribou on the current predator distribution and reveals the rate at which this information become less reliable over time. Caribou and moose used anti-predator responses that combine both long- and short-term behavioural adjustments. The spatial game between wolves and their prey involves complex and nonlinear mechanisms that vary between species and seasons. A comprehensive assessment of risk effects on ecosystem dynamics thus requires the characterization of chronic and temporary anti-predator behaviours.

Conceptualization and implementation of a multi-agent model to simulate whale-watching tours in the St. Lawrence Estuary in Quebec, Canada

The Saguenay St. Lawrence Marine Park (SSLMP) and the adjacent Marine Protected Area (MPA) in the St. Lawrence Estuary, in Quebec, cover a territory of exceptional biodiversity including 12 species of marine mammals, nearly half of which are considered to be endangered species. Whale-watching trips and other human activities related to commercial shipping, tourism, and recreation generate very intensive traffic in the area, which pose cumulative threats to the marine wildlife. This study has been undertaken in collaboration with the Marine Park and the MPA managers to develop a multi-agent system (MAS) to investigate the interactions between the traffic and the marine mammals in the estuary. This paper describes the first prototype version of the proposed MAS model where the focus is on the whale-watching boats. It discusses the conceptual model with its principal components: the physical environment and the boat agents and whale entities, and the implementation of the model with the behavior rules of the agents. In this version of the MAS, the whale-watching boats are represented as cognitive agents while the whales are simple reactive entities. The prototype model was implemented in the agent-based modeling platform RePast. An index, the happiness factor (i.e., the ratio of whale observation time over the trip duration) was designed to measure how successful the boat agents are in achieving their goal. Simulations were run to assess different decision strategies of the boat agents and their impacts on the whales. Results show that cooperative behavior that involves a combination of innovator and imitator strategies yields a higher average happiness factor over non-cooperative, purely innovators, behavior. However, this cooperative behavior creates increased risk for the whale populations in the estuary.

Complexity and the Limits of Ecological Engineering

The present–day concept of complexity is reviewed and discussed with respect to its potential implications on the practice of ecological engineering applied to ecosystems. It is argued that ecological engineers must incorporate concepts arising from complex system studies such as emergence, scaling, self–organization, and unpredictability into their conceptual model of an ecosystem in order to effectively design, manage, or restore such systems. These four concepts are introduced with reference to complex systems in general, and then with specific reference to ecosystems. A discussion of how ecological engineering should be approached in the context of complex system studies is then presented. While the article specifically addresses ecological engineers, the content is also applicable to anyone working in ecosystem restoration and natural resource management.

Hybrid modelling of complex ecological systems for decision support: Recent successes and future perspectives

Abstract Hybrid models combine multiple modelling approaches to represent complex, integrated systems of human and biophysical components. These models are highly data driven, and serve to aid in pattern extraction and knowledge synthesis, providing an important link between data sources and decision support. By allowing the simulation of the many emergent plausible futures for complex systems, hybrid models will become increasingly important decision-support tools.

A generic, individual-based approach to modelling higher trophic levels in simulation of terrestrial ecosystems

In this article, a description is given of the manner in which higher trophic levels (animals) are represented in a generally configurable ecosystem model. The animals are modelled using an individual-based approach that is sufficiently generic to allow for the representation of organisms of different types of species via the specification of appropriate sets of parameter values. Animal behaviours and physiological functions are described with simple mechanistic rules that are derived from various assumptions about, for example, growth rates, metabolic requirements, digestion and assimilation of food, or gestation. The animals interact in a detailed, spatially explicit environment that consists of a terrain, an atmosphere, and various species of primary producers. The model has been implemented in simulation to explore population dynamics in multi-species ecosystems configured with two and three trophic levels. Sample simulation results are presented, together with a discussion of the effectiveness of the approach for the representation of animals in ecosystem modelling.

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

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 system— and 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 Canadas 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.

Three-dimensional metrics for the analysis of spatiotemporal data in ecology

A suite of simple metrics that can be used to analyse three-dimensional data sets is presented. We show how these metrics can be applied to raster-based, ecological mosaics sampled over uniform time intervals, such as might be obtained from a series of photographs or from repeated spatial sampling in the field. In these analyses, the concept of a 2D landscape “patch” is replaced by a 3D space–time “blob”. The structure of a dataset can be analysed via the characterisation of blobs, using a number of simple composition and configuration metrics. The use of different metrics, including modified versions of some common landscape metrics such as contagion, that describe the distribution of blobs in space and time, is demonstrated using both model and empirical data. With the increasing availability of spatiotemporal data sets in ecology, such three-dimensional metrics may be indispensable tools for the detection and characterization of landscape change in the context of human and naturally caused disturbances.

Design considerations for the implementation of multi-agent systems in the dairy industry

The objectives of this research were: (a) to perform a survey of current research in the area of multi-agent systems in order to learn more about how they could be designed and implemented; (b) to investigate the feasibility of such an approach for agriculture, based on an integration of currently existing technologies; and, more specifically (c) to assess the potential of a multi-agent approach in the context of decision support for dairy production. The results of this work highlighted a number of key concepts in multi-agent system design, including the importance of selecting an appropriate system architecture for agent coordination (e.g., peer-to-peer, federated, or blackboard-based) and the need for well-defined agent communication methods (language and ontology). Alternative technologies, used in the implementation of multi-agent systems (e.g., communication protocols and distributed computing methods), were also explored. Lastly, a case study was carried out, in which some of the discussed technologies were tested and implemented to create a multi-agent heifer management system. The system consists of two different types of agents and several databases, and was implemented on a PC-based network. The agents work together to synthesize data about heifer development from different sources and to present this to the user in a graphical format. The system demonstrates the feasibility of applying an agent-based approach, using currently available technology, to problems such as dairy herd management in which a distributed decision-support solution is often required. It is concluded that the constraints for the implementation of multi-agent systems do not appear to be of a technological nature; the challenge seems to be more one of defining and accepting a common ontology and communication language by members of a given industry. In addition, large-scale distributed systems will require sophisticated agent-coordination methods to ensure robust and efficient operation.