Co-creation of individual-based models by practitioners and modellers to inform environmental decision-making

Effective environmental decision-making, in the form of evidence-based management and policy, is a key prerequisite to help balance nature conservation, natural resource management and human socio-economic activities (Sutherland et al. 2004). To aid such decision-making, the need for predictive tools that are accurate, robust and parsimonious has arguably never been greater. The Earth is currently in a time of environmental change unprecedented in human history, due to climate change, growing human population size and resource use, land-use changes and intensification, habitat loss and fragmentation, pollution and invasive species. Thus, the ability to predict how biological systems will change over time is as fundamental to research ecologists as it is to practitioners engaged in environmental decision-making (Evans 2012). As the competition between people and other organisms for space and resources intensifies with continued human population growth, public support for environmental management and policy can only be retained if environmental decision-making is scientifically sound. Yet, it is widely recognized that ecologists need to be better at prediction, as current approaches are inadequate (Evans 2012). The use of empirical relationships between biological properties and explanatory factors, typically measured for a narrow range of environmental conditions, may not hold as conditions change. Hence, predicting beyond the empirical range may not offer a sound basis for environmental management and policy. In contrast, individual-based models (IBMs), also known as agentbased models (ABMs), predict the behaviours of individual organisms and their population-level consequences on the basis of simple decision rules, such as fitness maximization (Stillman & Goss-Custard 2010). Fitness may be a measure of reproductive success or a short-term proxy such as rate of energy gain. The decision rules which form the basis of IBM predictions are not expected to change even if the environment changes. This basis means that IBMs can produce accurate, robust predictions outside of the range of environmental conditions for which the model was parameterized (Grimm & Railsback 2005). Hence, IBMs are key decision support tools to inform environmental management and policy and facilitate evidence-based decision-making (DeAngelis & Mooij 2005; McLane et al. 2011). An ever-growing number of IBMs have been developed by modellers, who aim to aid practitioners and inform a range of issues related to conservation, natural resource management, wildlife management and human socio-economic activities (Grimm & Railsback 2005). Such applications of IBMs include the following: (i) wading bird conservation within commercial fisheries (Stillman et al. 2003), (ii) assessing the impacts of river restoration on fish populations (Railsback et al. 2009), (iii) examining the dynamics of mangrove forests (Berger et al. 2008), (iv) interactions between humans and large carnivores (Ahearn et al. 2001) and (v) managing herbivore grazing (Wood et al. 2014). The range of practitioners using IBMs to inform their decision-making processes include statutory authorities with responsibilities in environmental and natural resource management, non-governmental organizations such as conservation charities and those interested in the sustainable use of natural resources. Thanks to advances in computational power, data availability and ecological theory, increasingly complicated, sophisticated IBMs can be produced. Yet, this does not mean that these models will be more useful in informing environmental decision-making. IBMs typically require specialist computational knowledge to build and refine the model and analyse the model outputs, and so practitioners are unlikely to have the requisite skills to use IBMs directly. *Correspondence author. E-mail: rstillman@bournemouth.ac.uk