Post-disturbance vegetation dynamics during the Late Pleistocene and the Holocene: An example from NW Iberia

Abstract

There is a wealth of studies dealing with the reconstruction of past environmental changes and their effects on vegetation composition in NW Iberia, but none of them have focused specifically on the post-disturbance dynamics (i.e. the type of response) of the vegetation at different space and time scales. To fill this gap, we analysed the record of pollen and non-pollen palynomorphs (NPP) of a 235-cm thick colluvial sequence spanning the last ~ 13,900 years. The aims were to detect the changes in vegetation, identify the responsible drivers and determine the type of responses to disturbance. To extract this information we applied multivariate statistical techniques (constrained cluster analysis and principal components analysis on transposed matrices, PCAtr) to the local (hydro-hygrophytes and NPP) and regional (land pollen) datasets separately. In both cases the cluster analysis resulted in eight local and regional assemblage zones, while five (local types) and four (regional types) principal components were obtained by PCAtr to explain 94.1% and 96.6% of the total variance, respectively. The main drivers identified were climate change, grazing pressure, fire events and cultivation. The vegetation showed gradual, threshold and elastic responses to these drivers, at different space (local vs. regional) and time scales, revealing a complex ecological history. Regional responses to perturbations were sometimes delayed with respect to the local response. The results also showed an ecosystem resilience, such as the persistence of open Betula-dominated vegetation community for ~ 1700 years after the onset of the Holocene, and elastic responses, such as the oak woodland to the 8200 cal yr BP dry/cold event. Our results support the notion that palaeoecological research is a valuable tool to investigate ecosystem history, their responses to perturbations and their ability to buffer them. This knowledge is critical for modelling the impact of future environmental change and to help to manage the landscape more sustainably.