From protists to whales: predicting the future of biological systems

Complex biological systems are notoriously unpredictable, but forecasting their fate has arguably never been more important. In a recent seminar in the School of Biological Sciences, Dr. Chris Clements describes his latest research in this emerging field at the interface of ecology and conservation science.

The world is facing an unprecedented biodiversity crisis. As mankind’s ecological footprint grows ever larger, the rate of environmental change continues to accelerate. Identifying at-risk populations or ecosystems before they are irretrievably lost or damaged is becoming an increasingly important goal for conservationists, but predicting how complex biological systems will respond to evolving pressures is challenging.

One way of forecasting the future trajectory of biological systems is to use system-specific models founded on a detailed understanding of the underlying ecological processes. In practice however, scientists’ ability to do this is constrained by a scarcity of in-depth knowledge for the vast majority of ecosystems. An alternative strategy is to concentrate on inferring changes in the underlying state of the system from trends in more readily available data, such as estimates of population abundance. This approach is based on detecting statistical patterns or ‘early warning signals’, which can potentially be used to alert conservationists to the imminent danger of a sudden and catastrophic shift within an ecosystem, or the impending collapse of a population. A large part of Dr. Clements’ current research is focused on testing and extending these techniques.

“Under sustained pressure, the system will eventually reach a tipping point where it is so unstable that even tiny disruptions can trigger an abrupt change”

Dramatic shifts within ecosystems can occur when a change in conditions overwhelms the capacity of the system to return to its original state. Under sustained pressure, the system will eventually reach a tipping point where it is so unstable that even tiny disruptions can trigger an abrupt change. A classic example is the rapid transformation of pristine coral reefs due to declines in the abundance of algae-grazing marine life. While transitions to so-called ‘alternative stable states’ are often difficult to reverse, in theory, it should be possible to detect them in advance: as tipping points approach, predictable changes in statistical signals should become apparent.

Despite the potential usefulness of abundance-based early warning signals, the inherently noisy nature of population estimates can sometimes lead to unreliable predictions. Animals living in complex and inaccessible landscapes are usually elusive, and it can be tricky to estimate population sizes with confidence. One possible solution to this problem is to combine or replace abundance-based early warning signals with information on trends in key individual traits, such as body size, which can be estimated more reliably. Crucially, shifts in the distribution of body sizes within the population at-risk can be indicative of deteriorating environmental conditions, and of a population under strain.

“Dramatic shifts in the variability of body size also predicted plummeting worldwide populations of blue, fin, sei and sperm whales during the historical period of commercial whaling”

By describing his recent experiments on microcosm populations of the predatory protist Didinium nasutum, Chris showed that the collapse of stressed populations was preceded by a sharp decline in mean body size. Switching focus to an analysis of whale populations during the 20th century, Chris went on demonstrate how dramatic shifts in the variability of body size also predicted plummeting worldwide populations of blue, fin, sei and sperm whales during the historical period of commercial whaling. In both cases, trait-based early warning signals produced more accurate predictions about timing of population collapses, compared to those based on measures of abundance.

While our understanding of trait-based early warning signals is progressing rapidly, there is still much to learn about how these techniques can be applied to identify at-risk biological systems in the real world, where populations differ markedly in the rate of environmental change they are exposed to. Using both mathematical models and experimental microcosms, Chris’s research group is currently focused on tackling a range of unresolved questions in this area.

Written by Andrew Szopa-Comley, PhD student in Biological Sciences



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