How well do ecosystem indicators communicate the effects of anthropogenic eutrophication?

Anthropogenic eutrophication affects the Mediterranean, Black, North and Baltic Seas to various extents. Responses to nutrient loading and methods of monitoring relevant indicators vary regionally, hindering interpretation of ecosystem state changes and preventing a straightforward pan-European assessment of eutrophication symptoms. Here we summarize responses to nutrient enrichment in Europe's seas, comparing existing time-series of selected pelagic (phytoplankton biomass and community composition, turbidity, N:P ratio) and benthic (macro flora and faunal communities, bottom oxygen condition) indicators based on their effectiveness in assessing eutrophication effects. Our results suggest that the Black Sea and Northern Adriatic appear to be recovering from eutrophication due to economic reorganization in the Black Sea catchment and nutrient abatement measures in the case of the Northern Adriatic. The Baltic is most strongly impacted by eutrophication due to its limited exchange and the prevalence of nutrient recycling. Eutrophication in the North Sea is primarily a coastal problem, but may be exacerbated by climatic changes. Indicator interpretation is strongly dependent on sea-specific knowledge of ecosystem characteristics, and no single indicator can be employed to adequately compare eutrophication state between European seas. Communicating eutrophication-related information to policy-makers could be facilitated through the use of consistent indicator selection and monitoring methodologies across European seas. This work is discussed in the context of the European Commission's recently published Marine Strategy Directive.

Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product

Satellite-based remote sensing of active fires is the only practical way to consistently and continuously monitor diurnal fluctuations in biomass burning from regional, to continental, to global scales. Failure to understand, quantify, and communicate the performance of an active fire detection algorithm, however, can lead to improper interpretations of the spatiotemporal distribution of biomass burning, and flawed estimates of fuel consumption and trace gas and aerosol emissions. This work evaluates the performance of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Fire Thermal Anomaly (FTA) detection algorithm using seven months of active fire pixels detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) across the Central African Republic (CAR). Results indicate that the omission rate of the SEVIRI FTA detection algorithm relative to MODIS varies spatially across the CAR, ranging from 25% in the south to 74% in the east. In the absence of confounding artifacts such as sunglint, uncertainties in the background thermal characterization, and cloud cover, the regional variation in SEVIRI's omission rate can be attributed to a coupling between SEVIRI's low spatial resolution detection bias (i.e., the inability to detect fires below a certain size and intensity) and a strong geographic gradient in active fire characteristics across the CAR. SEVIRI's commission rate relative to MODIS increases from 9% when evaluated near MODIS nadir to 53% near the MODIS scene edges, indicating that SEVIRI errors of commission at the MODIS scene edges may not be false alarms but rather true fires that MODIS failed to detect as a result of larger pixel sizes at extreme MODIS scan angles. Results from this work are expected to facilitate (i) future improvements to the SEVIRI FTA detection algorithm; (ii) the assimilation of the SEVIRI and MODIS active fire products; and (iii) the potential inclusion of SEVIRI into a network of geostationary sensors designed to achieve global diurnal active fire monitoring.

A general framework for aquatic biogeochemical models

One of the most of challenging steps in the development of coupled hydrodynamic-biogeochemical models is the combination of multiple, often incompatible computer codes that describe individual physical, chemical, biological and geological processes. This “coupling” is time-consuming, error-prone, and demanding in terms of scientific and programming expertise. The open source, Fortran-based Framework for Aquatic Biogeochemical Models addresses these problems by providing a consistent set of programming interfaces through which hydrodynamic and biogeochemical models communicate. Models are coded once to connect to FABM, after which arbitrary combinations of hydrodynamic and biogeochemical models can be made. Thus, a biogeochemical model code works unmodified within models of a chemostat, a vertically structured water column, and a three-dimensional basin. Moreover, complex biogeochemistry can be distributed over many compact, self-contained modules, coupled at run-time. By enabling distributed development and user-controlled coupling of biogeochemical models, FABM enables optimal use of the expertise of scientists, programmers and end-users.

Beyond long-term averages: making biological sense of a rapidly changing world

Biological responses to climate change are typically communicated in generalized terms such as poleward and altitudinal range shifts, but adaptation efforts relevant to management decisions often require forecasts that incorporate the interaction of multiple climatic and nonclimatic stressors at far smaller spatiotemporal scales. We argue that the desire for generalizations has, ironically, contributed to the frequent conflation of weather with climate, even within the scientific community. As a result, current predictions of ecological responses to climate change, and the design of experiments to understand underlying mechanisms, are too often based on broad-scale trends and averages that at a proximate level may have very little to do with the vulnerability of organisms and ecosystems. The creation of biologically relevant metrics of environmental change that incorporate the physical mechanisms by which climate trains patterns of weather, coupled with knowledge of how organisms and ecosystems respond to these changes, can offer insight into which aspects of climate change may be most important to monitor and predict. This approach also has the potential to enhance our ability to communicate impacts of climate change to nonscientists and especially to stakeholders attempting to enact climate change adaptation policies.