Patterns of ecological response to environmental change as observed in palaeoecological data
Palaeoecological data, developed from lake sediments and similar archives, represents a treasure trove of information on ecological processes operating at annual to millennial scales. Unfortunately, all too often palaeocology is consider a descriptive or at best qualitative science. In part this is due to inherent complexities in the data arising from age uncertainties, irregular sampling in time, etc, which have limited the types of analysis that we have performed and the scope of the scientific questions we're willing to address. This situation is largely the result of a failure of the field, in general, to make use of novel approaches to data analysis and new statistical time series methods, which begin to address these complexities. Worse, we are often guilty of failing to account for the lack of independence in our data, potentially undermining our results and findings.
Here I outline the difficulties associated with modelling statistically palaeoecological series and illustrate a range of approaches that address these difficulties through examples covering i) the effect of climate change on a lake diatom community in a pre-human-settlement period, ii) our ability to detect of recovery from eutrophication in algal communities in European lakes, and iii) the robustness of lake sediment-derived evidence of early warning signals (EWS) of impending critical transitions in lake ecosystems.
Rapid cooling 2.2kyr ago in northern Sweden has previously been shown to have significantly perturbed a lake diatom community. Using wavelets and Monte Carlo tests, this amelioration in climate also fundamentally altered patterns of variability in species composition, suppressing cyclicity patterns that required several hundred years to reestablish.
Despite indications of the onset of recovery from eutrophication in a number of European lakes, analysis combining principal curves and additive modelling indicates that in only four of 13 lakes is a consistent recovery signal discernible from the highly multivariate and strongly autocorrelated data.
The detection of EWS in a shallow lake community was hampered considerably by a combination of the initial use of ad hoc moving window metrics and the irregular sampling of the series and methods applied to correct this. Initial results using more formal statistical models suggest they may be able to detect the presence of EWS of a critical transition.
Developing statistical models for features of the data that we wish to test is a powerful approach to the analysis of palaeoecological data, one which will allow us to tackle more pressing and fundamental ecological questions.