Making predictive ecology more relevant to policy makers and practitioners

Abstract

One of the aims of ecology is to aid policy makers and practitioners through the development of testable predictions of relevance to society. Here, we argue that this capacity can be improved in three ways. Firstly, by thinking more clearly about the priority issues using a range of methods including horizon scanning, identifying policy gaps, identifying priority questions and using evidence-based conservation to identify knowledge gaps. Secondly, by linking ecological models with models of other systems, such as economic and social models. Thirdly, by considering alternative approaches to generate and model data that use, for example, discrete or categorical states to model ecological systems. We particularly highlight that models are essential for making predictions. However, a key to the limitation in their use is the degree to which ecologists are able to communicate results to policy makers in a clear, useful and timely fashion.

Assessment of USDA‐NRCS rangeland conservation programs: recommendation for an evidence‐based conservation platform

“…Knowledge created from monitoring outcomes of conservation practices, especially if collaboratively developed, may contribute to more rapid development of conservation relevant knowledge than more traditional forms of experimental research (Stafford Smith et al 2007, Cundill andFabricius 2009). This provides further justification for greater involvement of agency personnel in identification of priority research questions and establishment of research agendas (Holmes andClark 2008, Sutherland andFreckleton 2012). This provides further justification for greater involvement of agency personnel in identification of priority research questions and establishment of research agendas (Holmes andClark 2008, Sutherland andFreckleton 2012).…”

Integrating Evolution into Ecological Modelling: Accommodating Phenotypic Changes in Agent Based Models

“…Finally, an IBM can be calibrated with data at the level of gene and explicitly account for both ecological and evolutionary dynamics [45]. However, this is highly demanding of data and may be a complication too far [46]. Most environmental stakeholders are not particularly interested in changes in gene frequency; what is observed and the level of organization about which people are typically concerned is that of the individual organism.…”

Modelling farmer decision-making to anticipate tradeoffs between provisioning ecosystem services and biodiversity

“…In general, the current ABMs and IBMs lack transparency in some of the component sub-models that drive simulation outcomes. This can be improved by integration, or coupling, of an ABM of LUCC with an IBM, which offers greater potential to understand processes and feedbacks between human and natural systems (Luus et al, 2011) and to study the indirect effect of policy on ecosystem services through farmer decision making (Milner-Gulland, 2012;Sutherland and Freckleton, 2012). Only a few studies have presented results from such a combination (Bithell and Brasington, 2009;Jepsen et al, 2005;Verburg and Overmars, 2009), but the decision maker agents were not heterogeneous, which limits the relevance of such models since not all land managers react similarly to policies (Beilin et al, 2012).…”