Département Eaux de surface

An array of lake sediment proxies including paleobotanic, geochemical, and historical records has been used to determine former environments of Bugbee Pond, a small, mesotrophic pond in northeastern Minnesota. Much research has been produced on the history of climate and vegetation change of the region, yet we have little information on the impact of human settlement. This well-dated, high resolution, multi-proxy record is important for its length and concentration on the historic period. The lake itself became established by ~7000 years ago. Pollen evidence suggests a transition between the regional Prairie Period to the Great Lakes mixed conifer – hardwood forest was established in the region at this time. XRF data suggest dry basin accumulation early in the record after ~7000 cal yr BP, but lake levels substantially increased by ~5600 cal yr BP, during a regionwide climatic transition to more humid conditions. Birch and boreal conifers increased after about 3800 cal yr BP; further increases in boreal conifers occurred by ~2000 cal yr BP. Anthropogenic vegetation changes during the Historic period, beginning in the late 19th century, is well represented by forest clearance of white pine (Pinus strobus), followed by increases in early successional species and an increased sediment accumulation rate due to land clearance. Establishment of farming communities locally are shown by occurrence of corn (Zea mays) and oat (Avena sativa) pollen, and pasturing and grazing are documented by Rumex, Fabaceae and Poaceae pollen, as well as coprophilous fungi, such as Sordaria. The increase and subsequent decline in Pb and S concentrations in the uppermost sediments are mirrored by historically documented, nearby industrial activities.

The legacy of historic anthropogenic disturbance can significantly affect the structure and function of contemporary freshwater ecosystems. Environmental research and management that neglect anthropogenic legacy are likely to lead to a biased interpretation of present and future ecosystem dynamics. Yet, anthropogenic legacy remains poorly considered, mainly because of the challenges associated with its identification. Synthesizing past progress in legacy research, we present a conceptual framework for the systematic identification of anthropogenic legacy. We focus on the dynamic processes occurring during legacy formation (e.g., disturbance regime, ecosystem trajectories). Based on the review of relevant case studies, we discuss the historical and contemporary sources of information (e.g., communication, cartographic, paleoenvironmental sources) that can be employed for legacy identification. Finally, we provide practical examples of anthropogenic legacy identification in real-world freshwater ecosystems. Produced in multidisciplinary collaboration, this review presents a comprehensive approach to anthropogenic legacy to foster its informed and systematic consideration in freshwater research and management.

As the Earth system is exposed to large anthropogenic interferences, it becomes ever more important to assess the resilience of natural systems, i.e., their ability to recover from natural and human-induced perturbations. Several, often related, measures of resilience have been proposed and applied to modeled and observed data, often by different scientific communities. Focusing on terrestrial ecosystems as a key component of the Earth system, we review methods that can detect large perturbations (temporary excursions from a reference state as well as abrupt shifts to a new reference state) in spatio-temporal datasets, estimate the recovery rate after such perturbations, or assess resilience changes indirectly from stationary time series via indicators of critical slowing down. We present here a sequence of ideal methodological steps in the field of resilience science, and argue how to obtain a consistent and multi-faceted view on ecosystem or climate resilience from Earth observation (EO) data. While EO data offers unique potential to study ecosystem resilience globally at high spatial and temporal scale, we emphasize some important limitations, which are associated with the theoretical assumptions behind diagnostic methods and with the measurement process and pre-processing steps of EO data. The latter class of limitations include gaps in time series, the disparity of scales, and issues arising from aggregating time series from multiple sensors. Based on this assessment, we formulate specific recommendations to the EO community in order to improve the observational basis for ecosystem resilience research.