Improving the forecast for biodiversity under climate change

By M. C. Urban, Greta Bocedi, A. Hendry, J. Mihoub, Guy Peer, Alexander Singer, J. Bridle, L. Crozier, L. De Meester, William Godsoe and 12 more. Science, 2016

1,131 citations recorded by the source corpus.

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. 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.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/653f5f850f110dc2eb622c0d7b07f79b44e1118c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (2019, methodology): “In the absence of detailed process knowledge, which is the norm for ecological systems (Urban et al. 2016), statistical models such as those we have explored here are essential tools in many applied ecological arenas.”
Connectivity for species on the move: supporting climate‐driven range shifts (2019, background): “Although large vertebrate species may be capable of moving long distances and traversing landscapes despite unsuitable climatic conditions, plants and smaller animals (including invertebrates) may be considerably more constrained in terms of both how far they can disperse and what terrains they can ”
Outstanding Challenges in the Transferability of Ecological Models. (2018, background): “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].”
Analysing eco‐evolutionary dynamics—The challenging complexity of the real world (2019, background): “…complexity may lead to erroneous judgements on the importance or even the direction of eco-evolutionary dynamics, so that relying on proof-of-principle experiments in highly simplified settings is risky, for instance when making predictions on responses to human-induced change (Urban et al., 2016).”
Insect responses to global change offer signposts for biodiversity and conservation (2020, background): “, 2014), combining a range of approaches is vital for mechanistic understanding of insect biodiversity change and for mitigating negative impacts (Urban et al., 2016).”
Climate change amplifies plant invasion hotspots in Nepal (2019, background): “Shifts in species range involve multiple ecological processes such as dispersal, demography, physiology, species interactions, population interactions and evolution operating at multiple scales (Urban et al., 2016).”
Global scenarios for biodiversity need to better integrate climate and land use change (2017, background): “Biodiversity models and scenarios are developed to explore the possible future impacts of these threats (Thuiller et al., 2013; Urban et al., 2016).”
Complex responses of global insect pests to climate warming (2020, background): “For example, a recent meta- analysis demonstrated that models integrating biological mechanisms from multiple traits greatly improved predictions of climate- change impacts on global biodiversity (Urban et al. 2016).”
Biodiversity change is uncoupled from species richness trends: Consequences for conservation and monitoring (2018, background): “It also provides a first step towards the understanding of mechanisms driving biodiversity change in monitoring (Truchy, Angeler, Sponseller, Johnson, & McKie, 2015; Urban et al., 2016) and thus the type of information needed to make management decisions (Tittensor et al., 2014).”