Nature ecosystems are always complex, full of uncertainties and nonlinear changes. These changes are sometimes catastrophic, and many ecosystems have already been altered from their natural state as a result of human activities. Therefore, abrupt changes are likely to happen, the consequences of which can be irreversible. It becomes urgent to (i) further understand the features of complex ecological systems, and (ii) to identify yearly warning signals (EWS) to allow prediction of catastrophic transitions. This thesis aims for an understanding of one such example of a complex ecological system, i.e. Erhai Lake, Yunnan Province, China, and to determine the EWS in this ecosystem. This thesis focuses on the process of eutrophication in Erhai Lake, using two cores from the lake and a training set from Yunnan province, SW China. The study employed multiple techniques including monitoring, palaeolimnological proxies and modelling. The ideas of feedbacks, resilience and thresholds from complex ecological system theory are used to interpret the lake’s eutrophication process. Fossil diatom data is mainly employed to calculate the EWS for the lake’s ecosystem transition. The conclusions have been supported with a minimal model which is written with STELLA software. The main findings include: 1. The alternative stable states in the training set may affect the accuracy of diatom-based transfer functions. 2. The resilience of the lake’s ecosystem decreased due to the intensification of human activities, and the lake crossed a threshold at around 2001 due to a new positive feedback mechanism. 3. The lake was in a ‘flickering’ state between 1980-2000. Rising variance could be considered as an indicator of EWS but it was most likely caused by flickering rather than ‘critical slowing down’ in these noise-induced critical transitions. 4. The minimal model shows that flickering states can be simulated, and the rising variance due to flickering is also likely to predict the critical transitions in the simulated system. The mutual authentication between palaeo- data and the minimal model can deeply improve the understanding of a complex system, and explanation of complex theories. This work firstly considered the alternative stable states in a training set and presented EWS in a real natural ecosystem. Our findings suggest that rising variance can be seen as a warning signal in a system; therefore, it can be applied for intervention purposes in critical transitions in real ecosystems.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:577261 |
| Date | January 2013 |
| Creators | Wang, Rong |
| Contributors | Dearing, John |
| Publisher | University of Southampton |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://eprints.soton.ac.uk/350660/ |
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