1 minute explanation in Spanish
Over the past 100 years, Earth’s climate has become warmer and precipitation regimes have changed. Can we predict the effects of these changes on biodiversity? Research in the lab seeks improving understanding of the key mechanisms governing the distribution of life on Earth, with strong focus on species distributions. Basic research in the lab tends to feed into the development of models to forecast species distributional dynamics under climate- and land-use change scenarios. To address these questions, we integrate large climate and species distributions databases with descriptions of behavioural and physiological traits of species, molecular phylogenies, and the fossil record. Most research in the lab involves statistical analyses of ecological data, including data mining, bioclimatic modeling, and mathematical simulations, but large-scale experiments, including microcosm and mesocosm experiments, are now being devised for testing models and theory on species distributions and species coexistence.
Bioclimatic envelope models (BEMs) are usually used to estimate the relationship between the distributions of species and climate thus enabling projections of altered species distributions under climate change scenarios. However, models are based on a number of weak ecological assumptions and studies have shown that projections by alternative models can be so variable as to compromise the simplest assessment of whether species distributions should be expected to contract or expand for any given scenario. A solution for inter-model variability is to fit several models (termed ‘ensembles’) and use appropriate techniques to explore the resulting range of projections. Ensemble forecasting has proved to be successful for characterizing and reducing ‘algorithmic uncertainties’ in models, and the approach is also widely used other branches of science such as climatology. However, unlike climate models, BEMs are not based on well-known bio-geophysical processes but on correlations between species distributions and aspects of climate. Careful choice of variables can make correlative models quasi-mechanistic, but in most case it is difficult to guarantee that projections are driven by the mechanisms governing the species distributions. To address ‘ecological uncertainties’ in the models, researchers have proposed the development of process-based models. In contrast with BEMs, process-based models begin with an analysis of the organism rather than its distribution; they determine the mechanistic interaction between an organism’s environment and its growth or fitness, usually based on theoretical inferences, experimental knowledge, or a combination of both. Process-based models have generally focused on some aspect of the physiology, phenology, or population dynamics of the target species, but they have generally failed to account for complex interdependencies between species. Can uncertainty from biodiversity models be reduced? A current trend involved the development of ever more complex process-based models. Yet, there are ca. 1,9 million known species and many more remain unknown to science. Therefore, methods that require detailed specific information on all species for forecasting overall biodiversity change are not practical. The critical question is what is the minimum level of model complexity that is required for making useful predictions of climate change impacts on biodiversity.
We have pioneered the implementation and testing of ensemble forecasting methodologies in biodiversity sciences. We were the first to independently validate the performance of species distributions models under historical climate change, demonstrating the value of ensemble forecasting for handling and minimising algorithmic uncertainties of the models. With colleagues in Australia, Europe and the United States we were also the first to develop coupled-niche-metapopulation models to examine the effects of climate change on species distributions at population level. We also discovered, or co-discovered, several eco-geographic rules namely that 1) native species richness of several plan and animal groups have a positive relationship with human population density in Europe—a pattern later to be found general across most continents; 2) current native species richness is highly related to historical climate stability since the last glacial maxima, especially for species with limited dispersal abilities; 3) thermal tolerances to heat are conserved (non-adaptive) among terrestrial animals and plants while thermal tolerances to cold are labile (adaptive), and 4) the degree to which the geographical signature of biotic interactions scales-up to coarse resolutions depends on the strength of positive interactions.
Research in the lab has contributed to change the species distributions modelling practices in use until early 2000, helping setting the standards for new studies at the interface between biodiversity and climate change.