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Quantifying Abrupt Biotic Responses to Palaeoclimate Change (ABRUPT)
Date du début: 11 avr. 2016, Date de fin: 10 avr. 2018 PROJET  TERMINÉ 

Climate change in the 21st century is predicted to push ecosystems across ecological thresholds, potentially resulting in abrupt ecosystem change into new and irreversible states. Ecological theory proposes that non-linear biotic responses are the result of a complex interplay of feedbacks, thresholds and interactions that operate over decades to thousands of years. As a result, standard ecological research methods are generally unable to quantify key ecological dynamics that are relevant for forecasting abrupt ecological change and there is a critical need to integrate long-term ecological data with process-based models. This will result in improved forecasting of climate-change impacts on ecosystems at both local- to global-scales. Such studies will play a critical role in understanding the ‘intrinsic’ factors (e.g. climate-vegetation feedbacks) that can result in non-linear biotic responses to climate change.Sediments are natural data-loggers that preserve the remains of plants and animals over thousands of years. They provide a unique resource for answering current high priority questions related to predicting future ecosystem change because they are the only way to obtain empirical information relevant for understanding long-term ecological dynamics and functioning. In this project I will develop an interdisciplinary framework that integrates state-of-the-art process-based modelling with new high-quality palaeoecological information to quantify the factors that result in non-linear responses to climate change. I will apply the framework to a major vegetation transition in the past: the sclerophyll-rainforest transition in north-east Australia that occurred between 10 and 7 thousand years ago. I will develop this case study for proof-of-concept of a new interdisciplinary framework. This will result in a greater understanding of non-linear biotic responses to climate change.

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