Time is an affliction: Why ecology cannot be as predictive as physics and why it needs time series

Ecological systems depend on both constraints and historical contingencies, both of which shape their present observable system state. In contrast to ahistorical systems, which are governed solely by constraints (i.e. laws), historical systems and their dynamics can be understood only if properly described, in the course of time. Describing these dynamics and understanding long-termvariability can be seen as themission of long time series measuring not only simple abiotic features but also complex biological variables, such as species diversity and abundances, allowing deep insights in the functioning of food webs and ecosystems in general. Long timeseries are irreplaceable for understanding change, and crucially inherent system variability and thus envisaging future scenarios. This notwithstanding current policies in funding and evaluating scientific research discourage the maintenance of long term series, despite a clear need for long-term strategies to cope with climate change. Time series are crucial for a pursuit of the much invoked Ecosystem Approach and to the passage from simple monitoring programs of large-scale and long-termEarth observatories — thus promoting a better understanding of the causes and effects of change in ecosystems. The few ongoing long time series in European waters must be integrated and networked so as to facilitate the formation of nodes of a series of observatories which, together, should allowthe long-termmanagement of the features and characteristics of European waters. Human capacity building in this region of expertise and a stronger societal involvement are also urgently needed, since the expertise in recognizing and describing species and therefore recording them reliably in the context of time series is rapidly vanishing from the European Scientific community

Regime shifts in the marine environment: The scientific basis and political context

Regime shifts in the marine environment have recently received much attention. To date, however, few large-scale meta-analyses have been carried out due to insufficient data coverage and integration between sustained observational datasets because of diverse methodologies used in data collection, recording and archival. Here we review the available data on regime shifts globally, followed by a review of current and planned policies with relevance to regime shifts. We then focus on the North and Baltic Seas, providing examples of existing efforts for data integration in the MarBEF Network of Excellence. Existing gaps in data coverage are identified, and the added value from meta-analyses of multiple datasets demonstrated using examples from the MarBEF integrated data project LargeNet. We discuss whether these efforts are addressing current policy needs and close with recommendations for future integrated data networks to increase our ability to understand, identify and predict recent and future regime shifts