Evaluating the prevalence of early warning signals in palaeoecological time series of state change

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Abrupt transition between states has emerged as a common phenomenon in a range of systems. Small changes may push a system past a tipping point resulting in a new mode of behaviour. The propensity to change state is related to resistance to change and resilience in the face of perturbation. A better understanding of the relationships between resistance, resilience and ecosystem change is particularly important considering projections for climate change. Critical transitions can affect the management of ecosystems; ecosystem function or services may be fundamentally altered or even lost following state change and reversal may be prohibitively costly.**Early warning signals (EWSs) of impending transition have been derived through theoretical mathematical models, and include increased autocorrelation and variance of the state variable, increased asymmetry of fluctuations, or flickering. Identifying these system responses prior to threshold changes will allow us to better detect EWSs of future change. Our ability to confirm the ubiquity of EWSs in natural systems is hampered, however, by a general lack of long-term observational data prior to transition.**Lake sediment cores provide a wealth of information on past ecosystem states yet a number of issues with the nature of the data need to be addressed before their potential is fully realized. The main focus of the proposed program is to develop an understanding of ecological state change through the use of lake sediments. In particular I will identify the statistical techniques needed to address difficulties in using sediment time series, which include irregular sampling intervals and compaction of sediments. Work will progress on three fronts through the use of ideal time series from well-studied lakes with annually-laminated fossil records (Baldeggersee, Switzerland; Kassjon, Sweden), new, high-resolution sequences from arctic lakes in Alaska, Greenland, and Russia, and new cores from prairie lakes of the northern Great Plains, Canada.**Different lake types provide a novel apparatus with which to study ecosystem state change. Annually-laminated sequences are an ideal system to work on as they circumvent issues of irregular sampling and compaction. Prairie lakes are strongly impacted by climate variability having been shown to respond rapidly to changes in moisture availability. Vegetation in arctic lake catchments is thought to respond to climate warming with threshold-like behaviour yet how the biogeochemistry and ecology of arctic lakes react to this change is largely unknown. In each case I will examine the evidence for threshold responses and EWSs with analyses targeted at periods of rapid change using a range of biological and geochemical data.**Previously, I developed additive models with residual time series processes for modelling palaeoecological data. These models assume that changes are smooth, which precludes examination of threshold behaviour. The use of adaptive splines offers one potential solution and I also envisage progress via the use of structural time series models. As most biological data are high-dimensional, methods suited to such data are also required. I will develop the combination of redundancy analysis with multi-scale measures of temporal variation to extract orthogonal patterns capable of detecting both rapid change and shifts in modes of variability.**The proposed program will advance the quantitative modelling of long-term ecological and palaeolimnological data. I will apply these approaches to theories of CTs and EWSs. Subsequently, I expect to address the relative importance of stochasticity versus niche partitioning in structuring ecological communities in time and study macroecological patterns from a palaeoecological perspective.