Predictive ecology: systems approaches

Abstract

The world is experiencing significant, largely anthropogenically induced, environmental change. This will impact on the biological world and we need to be able to forecast its effects. In order to produce such forecasts, ecology needs to become more predictive-to develop the ability to understand how ecological systems will behave in future, changed, conditions. Further development of process-based models is required to allow such predictions to be made. Critical to the development of such models will be achieving a balance between the brute-force approach that naively attempts to include everything, and over simplification that throws out important heterogeneities at various levels. Central to this will be the recognition that individuals are the elementary particles of all ecological systems. As such it will be necessary to understand the effect of evolution on ecological systems, particularly when exposed to environmental change. However, insights from evolutionary biology will help the development of models even when data may be sparse. Process-based models are more common, and are used for forecasting, in other disciplines, e.g. climatology and molecular systems biology. Tools and techniques developed in these endeavours can be appropriated into ecological modelling, but it will also be necessary to develop the science of ecoinformatics along with approaches specific to ecological problems. The impetus for this effort should come from the demand coming from society to understand the effects of environmental change on the world and what might be performed to mitigate or adapt to them.Keywords: ecological modelling; prediction; climate change; evolution; systems biology; global circulation model 'We appeal to the notorious fact that ZOOLOGY, soon after the commencement of the latter half of the last century, was falling abroad, weighed down and crushed, as it were, by the inordinate number and manifoldness of facts and phenomena apparently separate, without evincing the least promise of systematizing itself by any inward combination, any vital interdependence of its parts'. Samuel Taylor Coleridge [1] The ability to predict, to forecast how a system might behave in the future, is a key feature of any science. Prediction is, according to, the Nobel laureate, immunologist and philosopher of science, Peter Medawar, a 'property that sets the genuine sciences apart from those that arrogate to themselves the title without really earning it' [2]. Some subjects, e.g. economics, engineering, climatology, routinely make predictions that others can use; and people are prepared to rely on them as a guide to the future, despite them sometimes, in the light of experience, being inaccurate. It was a premise of the Royal Society discussion meeting, upon which this volume is based, that ecology needs to become more predictive, especially in the context of creating an understanding of the way in which biological systems might respond to environmental change. It is well understood that making predictions about how any system wi...

Outstanding Challenges in the Transferability of Ecological Models

“…Emphasis should thus be placed on more direct, functional predictors to foster improved model transfers. Understanding the relationship between niche types can help determine when transfers are more likely to succeed or fail (Box 3), and might be facilitated by jointly modeling target species with their competitors, predators, or facilitators [41]; by coupling distribution and population dynamics models; or by incorporating complex eco-evolutionary factors into model formulations [49] (but likely at the expense of higher data requirements [86]). While mechanistic models (Box 3) are well suited to delimiting species' fundamental niches [87], to date their application remains limited to a few, well-studied taxa for which physiological parameters are documented in detail [4].…”

“…Several tools are available to visualize regions whose characteristics depart from the initial covariate range (e.g., [42,48]), and these can help assess the potential impacts of non-analog conditions on predictive performance. However, these tools cannot predict species' responses to novel conditions, which can be particularly unexpected if environmental change imposes selection pressures that disrupt biotic interactions and cause communities to evolve [49]. Further development of these tools for future transfers, and their application in examining of the outcomes of historical transfers, will improve our understanding on how non-analog conditions can be accounted for when transferring models.…”

Ecosystem services—current challenges and opportunities for ecological research

“…Statistical models can be used to identify driving forces of changes in service provision and to predict system shifts and fluctuations in service provision as a consequence of environmental change and anthropogenic intervention (Evans et al, 2012). Simple statistical models (e.g., regression) are based on interpolations along existing gradients and cannot provide predictions about levels of ecosystem services under future conditions outside of these gradients.…”

Beyond correlation: integrating environmentally and behaviourally mediated processes in models of marine mammal distributions

“…Jakeman et al 2006, Evans et al 2011, Grimm & Railsback 2011, Robinson et al 2011, Dormann et al 2012, Cuddington et al 2013. One possible approach, so-called hybrid modeling, takes the output from mechanistic models as input for correlative models to predict spatial distributions (e.g.…”