Improving the forecast for biodiversity under climate change
Structured Abstract
BACKGROUND
As global climate change accelerates, one of the most urgent tasks for the coming decades is to develop accurate predictions about biological responses to guide the effective protection of biodiversity. Predictive models in biology provide a means for scientists to project changes to species and ecosystems in response to disturbances such as climate change. Most current predictive models, however, exclude important biological mechanisms such as demography, dispersal, evolution, and species interactions. These biological mechanisms have been shown to be important in mediating past and present responses to climate change. Thus, current modeling efforts do not provide sufficiently accurate predictions. Despite the many complexities involved, biologists are rapidly developing tools that include the key biological processes needed to improve predictive accuracy. The biggest obstacle to applying these more realistic models is that the data needed to inform them are almost always missing. We suggest ways to fill this growing gap between model sophistication and information to predict and prevent the most damaging aspects of climate change for life on Earth.
ADVANCES
On the basis of empirical and theoretical evidence, we identify six biological mechanisms that commonly shape responses to climate change yet are too often missing from current predictive models: physiology; demography, life history, and phenology; species interactions; evolutionary potential and population differentiation; dispersal, colonization, and range dynamics; and responses to environmental variation. We prioritize the types of information needed to inform each of these mechanisms and suggest proxies for data that are missing or difficult to collect. We show that even for well-studied species, we often lack critical information that would be necessary to apply more realistic, mechanistic models. Consequently, data limitations likely override the potential gains in accuracy of more realistic models. Given the enormous challenge of collecting this detailed information on millions of species around the world, we highlight practical methods that promote the greatest gains in predictive accuracy. Trait-based approaches leverage sparse data to make more general inferences about unstudied species. Targeting species with high climate sensitivity and disproportionate ecological impact can yield important insights about future ecosystem change. Adaptive modeling schemes provide a means to target the most important data while simultaneously improving predictive accuracy.
OUTLOOK
Strategic collections of essential biological information will allow us to build generalizable insights that inform our broader ability to anticipate species’ responses to climate change and other human-caused disturbances. By increasing accuracy and making uncertainties explicit, scientists can deliver improved projections for biodiversity under climate change together with characterizations of uncertainty to support more informed decisions by policymakers and land managers. Toward this end, a globally coordinated effort to fill data gaps in advance of the growing climate-fueled biodiversity crisis offers substantial advantages in efficiency, coverage, and accuracy. Biologists can take advantage of the lessons learned from the Intergovernmental Panel on Climate Change’s development, coordination, and integration of climate change projections. Climate and weather projections were greatly improved by incorporating important mechanisms and testing predictions against global weather station data. Biology can do the same. We need to adopt this meteorological approach to predicting biological responses to climate change to enhance our ability to mitigate future changes to global biodiversity and the services it provides to humans.
Emerging models are beginning to incorporate six key biological mechanisms that can improve predictions of biological responses to climate change.
Models that include biological mechanisms have been used to project (clockwise from top) the evolution of disease-harboring mosquitoes, future environments and land use, physiological responses of invasive species such as cane toads, demographic responses of penguins to future climates, climate-dependent dispersal behavior in butterflies, and mismatched interactions between butterflies and their host plants. Despite these modeling advances, we seldom have the detailed data needed to build these models, necessitating new efforts to collect the relevant data to parameterize more biologically realistic predictive models.
Abstract
New biological models are incorporating the realistic processes underlying biological responses to climate change and other human-caused disturbances. However, these more realistic models require detailed information, which is lacking for most species on Earth. Current monitoring efforts mainly document changes in biodiversity, rather than collecting the mechanistic data needed to predict future changes. We describe and prioritize the biological information needed to inform more realistic projections of species’ responses to climate change. We also highlight how trait-based approaches and adaptive modeling can leverage sparse data to make broader predictions. We outline a global effort to collect the data necessary to better understand, anticipate, and reduce the damaging effects of climate change on biodiversity.
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Volume 353 | Issue 6304
9 September 2016
9 September 2016
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Acknowledgments
This paper originates from the “Ecological Interactions and Range Evolution Under Environmental Change” and “RangeShifter” working groups, supported by the Synthesis Centre of the German Centre for Integrative Biodiversity Research (DFG-FZT-118), DIVERSITAS, and its core projects bioDISCOVERY and bioGENESIS. Supported by the Canada Research Chair, Natural Sciences and Engineering Research Council of Canada, and Quebec Centre for Biodiversity Science (A.G.); the University of Florida Foundation (R.D.H.); KU Leuven Research Fund grant PF/2010/07, ERA-Net BiodivERsA TIPPINGPOND, and Belspo IAP SPEEDY (L.D.M.); European Union Biodiversity Observation Network grant EU-BON-FP7-308454 (J.-B.M. and G.P.); KU Leuven Research Fund (J.P.); and NSF grants DEB-1119877 and PLR-1417754 and the McDonnell Foundation (M.C.U.).
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Forecast for biodiversity under climate change are of little use for science
Science seeks a causal understanding of the processes in our world (and beyond). It progresses by iteratively testing and correcting theories. To test our understanding, theory makes "crucial predictions" to situations where predictions are distinct and thus allow clear evaluation.
Predictions of biodiversity changes under climate change hardly fall into this category. By the time we can evaluate these predictions, in 2100 or so, all scientists behind current predictions and models will long be dead: feedback is slow. Such predictions seem not motivated by the scientific method and are currently questionable.
The evidence for ongoing biodiversity loss is plentiful and the causes are known: human overpopulation, resource overexploitation, and trade, leading to loss of natural habitat, atmospheric pollution, and spreading of pathogens and pests, respectively. There is plenty of scientific evidence, if political decision makers would want it. Predicting biodiversity under climate change does not add any useful dimension, as our current understanding of ecosystems yields massively uncertain predictions. Alas, no culture of quantifying the full range of uncertainty in process-based ecological models exists. Without it we are fooling ourselves, and others, into a false sense of competence.
The review makes a valuable point: ecological models need to become more mechanistic. We must learn to see them as carrier of understanding; if we cannot put a process into a model, we have not understood it, and we cannot make predictions about its effect. But only through tests of such predictions we can make mistakes and learn from them. On the other hand, it is striking that for hundreds of existing ecological models, the authors only cite two studies providing "evidence" that process-based models outperform correlative models, and both are simulations. The day of mechanistic models to make trustworthy predictions about future biodiversity will come, but it is not now.