Winter primary production in seasonally ice‐covered lakes historically has not been well studied, but it is increasingly recognized as an important component of lake metabolism. Lake Baikal in Siberia is not only the World's oldest, deepest, and most biologically diverse lake, but also where large under‐ice blooms of the diatom Aulacoseira baicalensis (formerly Melosira) occur in some years. The phenomenon of “Melosira years” is noteworthy both for the intensity of the diatom blooms, in which total under‐ice production can be a majority of total annual production, and for the enigmatic regularity of their occurrence every 3–4 yr. The degree to which these episodic blooms might be controlled by external forcing and endogenous lake processes has been debated for decades. We used a 50‐yr time series of phytoplankton observations to statistically model the occurrence of Aulacoseira blooms as a function of meteorological and climatological predictor variables. The results support the hypothesis that a confluence of meteorological conditions in the preceding fall season, which favor clear ice formation with minimal snow cover, also favor Aulacoseira blooms in the following spring. Further, we observe that this confluence of factors is related to relatively strong states of the Siberian High which, while not strictly periodic, do explain a significant fraction of the interannual bloom pattern. Finally, our analyses show that the timing of the peak abundance of A. baicalensis shifted 1.6 months later across the 50‐yr time series, corresponding with the delay in ice‐on timing that has been associated with climate change.

There is an increasing appreciation that food–energy–water (FEW) nexus problems are approaching criticality in both the developing and developed world. As researchers and managers attempt to address these complex resource management issues, the concept of the FEW nexus has generated a rapidly growing footprint in global sustainability discourse. However, this momentum in the FEW nexus space could be better guided if researchers could more clearly identify what is and is not a FEW problem. Without this conceptual clarity, it can be difficult to defend the position that FEW innovations will produce desired outcomes and avoid unintended consequences. Here we examine the growing FEW nexus scholarship to critically evaluate what features are necessary to define a FEW nexus. This analysis suggests that the FEW nexus differs from sector-focused natural resource or sustainability problems in both complexity and stakes. It also motivates two new foci for research: the identification of low-dimension indexes of FEW system status and approaches for identifying boundaries of specific FEW nexuses.

As an increasing variety and complexity of environmental issues confront scientists and natural resource managers, assembling the most relevant and informative data into accessible data systems becomes critical to timely problem solving. Data interoperability is the key criterion for succeeding in that assembly, and much informatics research is focused on data federation, or synthesis to produce interoperable data. However, when candidate data come from numerous, diverse, and high‐value legacy data sources, the issue of data variety or heterogeneity can be a significant impediment to interoperability. Research in informatics, computer science and philosophy has frequently focused on resolving data heterogeneity with automation, but subject matter expertise still plays a large role. In particular, human expertise is a large component in the development of tools such as data dictionaries, crosswalks, and ontologies. Such representations may not always match from one data system to another, presenting potentially inconsistent results even with the same data. Here, we use a long‐term data set on management actions designed to improve stream habitat for endangered salmon in the Pacific Northwest, to illustrate how different representations can change the underlying information content in the data system. We pass the same data set comprised of 49,619 records through three ontologies, each developed to address a rational management need, and show that the inferences drawn from the data can change with choice of data representation or ontology. One striking example shows that the use of one ontology would suggest water quality improvement projects are the rarest and most expensive restoration actions undertaken, while another will suggest these actions to be the most common and least expensive type of management actions. The discrepancy relates to the origins of the data dictionaries themselves, with one designed to catalog management actions and the other focused on ecological processes. Thus, we argue that in data federation efforts humans are “in the loop” rationally, in the form of the ontologies they have chosen, and diminishing the human component in favor of automation carries risks. Consequently, data federation exercises should be accompanied by validations in order to evaluate and manage those risks.

Aim: The emergence of pattern in the natural world can carry important messages about underlying processes. For example, collections of broadly similar terrestrial ecosystems have historically been categorized as biomes – groupings of systems that sort along energetic and structural process axes. In marine systems however, a similar classification of biomes has not emerged. The aim here is to develop an effective classification scheme for marine biomes and communities.Approach: Candidate predictor variables that could explain pattern and process in differentiating marine communities, such as light, nutrients, depth, etc., were collected from the existing literature with a systematic review. The candidate predictors were then evaluated in an inductive process, allowing community level observations to demonstrate patterns across marine biomes. Marine biomes and communities were identified a priori, and emergent patterns were evaluated quantitatively, via principal component ordination based on the vectors of explanatory predictors, and qualitatively via mapping.Conclusions: Gross primary production and substrate mobility not only effectively sort marine biomes, but also work on finer scales discriminating communities within biomes. As a result, these predictors were more effective in classifying marine communities than other scales, such as available light and nutrients. The richness of this classification is also demonstrated in revealing other patterns, such as the distribution of human impacts. The effectiveness of this mapping provides support for, but is not a test of, the hypothesis that primary production and substrate mobility are important underlying processes that interact to structure marine ecosystems.