Applying Metabolic Scaling Laws to Predicting Extinction Risk
By Claire Asher, on 25 September 2014
The Earth is warming. That much were are now certain of. A major challenge for scientists hoping to ameliorate the effect of this on biodiversity is to predict how temperature increases will affect populations. Predicting the responses of species living in complex ecosystems and heterogenous environments is a difficult task, but one starting point is to begin understanding how temperature increases affect small, laboratory populations. These populations can be easily controlled, and it is hoped that the lessons learned from laboratory populations can then begin to be generalised and applied to real populations. Recent research from GEE academics attempted to evaluate the predictive power of a simple metabolic model on the extinction risk of single-celled organisms in the lab. Their results indicate that simple scaling rules for temperature, metabolic rate and body size can be extremely useful in predicting the extinction of populations, at least in laboratory conditions.
Current estimates suggest that over the next 100 years we can expect a global temperature rise of between 1.1°C and 6.4°C. This change will not be uniformly distributed across different regions however, with some areas expected to experience warming at twice the global average rate. Temperature is known to be a crucial component in some of the most basic characteristics of life – metabolism, body size, birth, growth and mortality rates. These characteristics have been shown to scale with temperature in an easily predictable way, formalised in the Arrhenius equation. This equation yields a roughly 3/4 scaling rule, so that as temperature increases, metabolism increases around 75% as fast. This relationship appears to hold true for a variety of taxa with different life histories and positions in the food chain. Models based upon this rule can be designed that are very simple, which makes it easy for scientists to collect the data needed to plug into the model. But are they accurate in predicting extinction?
Recent research conducted by GEE and ZSL academics Dr Ben Collen and Prof. Tim Blackburn, in collaboration with the University of Sheffield and The University of Zurich, investigated the predictive power of simple metabolic models on extinction risk in a single-celled protist Loxocephallus. They first collected data on the population and extinction dynamics of a population held at constant temperature. This data was fed into a model based on scaling laws for metabolic rates and temperature, which in turn attempted to predict extinction risk under different temperature changes. The researchers tested how real protists responded to temperature changes – for 70 days they monitored populations of the protist Loxocephallus under either decreases or increases in temperature. Populations began at 20°C and increased to 26°C or decreased to 14°C at different rates (0.5°C, 0.75°C, 1.5°C or 3°C each week). Most populations eventually went extinct, but these extinctions happened sooner in hotter environments, and mean temperature showed a strong correlation with the date at which the population went extinct. Extinction tended to happen sooner in populations subjected to more rapid warming.
None of this is particularly surprising, but what the researchers found when they ran their models was that, even with relatively minimal data to start out with (population dynamics under constant ‘normal’ conditions), and using only simple scaling laws to predict extinction, their model was able to accurately predict when populations would go extinct under different warming or cooling conditions, with an accuracy of 84%. One important factor was the specifics of the temperature changes that were input into the model – using average temperature across the experiment rather than actual temperature changes produced much less accurate results.
This research is a first step in creating models that may help us predict the future extinction dynamics of wild populations subjected to unevenly distributed climatic warming over coming decades. It is a long way from a simple model of a laboratory population to a model that can accurately predict the future of complex assemblages of wild animals that are also subject to predation, disease and a healthy dose of luck. But the fact that these models can work for simple systems in laboratory conditions is a great first start – if they didn’t work for these populations, we could be fairly sure they wouldn’t generalise to natural populations. This shows that simple phenomenological models based on basic metabolic theory can be useful to understand how climate change will effect populations.
This research was made possible by funding from the Natural Environment Research Council (NERC).