Winter conditions are rapidly changing in temperate ecosystems, particularly for those that experience periods of snow and ice cover. Relatively little is known of winter ecology in these systems, due to a historical research focus on summer 'growing seasons'. We executed the first global quantitative synthesis on under-ice lake ecology, including 36 abiotic and biotic variables from 42 research groups and 101 lakes, examining seasonal differences and connections as well as how seasonal differences vary with geophysical factors. Plankton were more abundant under ice than expected; mean winter values were 43.2% of summer values for chlorophyll a, 15.8% of summer phytoplankton biovolume and 25.3% of summer zooplankton density. Dissolved nitrogen concentrations were typically higher during winter, and these differences were exaggerated in smaller lakes. Lake size also influenced winter-summer patterns for dissolved organic carbon (DOC), with higher winter DOC in smaller lakes. At coarse levels of taxonomic aggregation, phytoplankton and zooplankton community composition showed few systematic differences between seasons, although literature suggests that seasonal differences are frequently lake-specific, species-specific, or occur at the level of functional group. Within the subset of lakes that had longer time series, winter influenced the subsequent summer for some nutrient variables and zooplankton biomass.

Boreal lakes are biogeochemical hotspots that alter carbon fluxes by sequestering particulate organic carbon in sediments and by oxidizing terrestrial dissolved organic matter to carbon dioxide (CO2) or methane through microbial processes. At present, such dilute lakes release ∼1.4 petagrams of carbon annually to the atmosphere, and this carbon efflux may increase in the future in response to elevated temperatures and increased hydrological delivery of mineralizable dissolved organic matter to lakes. Much less is known about the potential effects of climate changes on carbon fluxes from carbonate-rich hardwater and saline lakes that account for about 20 per cent of inland water surface area. Here we show that atmospheric warming may reduce CO2 emissions from hardwater lakes. We analyse decadal records of meteorological variability, CO2 fluxes and water chemistry to investigate the processes affecting variations in pH and carbon exchange in hydrologically diverse lakes of central North America. We find that the lakes have shifted progressively from being substantial CO2 sources in the mid-1990s to sequestering CO2 by 2010, with a steady increase in annual mean pH. We attribute the observed changes in pH and CO2 uptake to an atmospheric-warming-induced decline in ice cover in spring that decreases CO2 accumulation under ice, increases spring and summer pH, and enhances the chemical uptake of CO2 in hardwater lakes. Our study suggests that rising temperatures do not invariably increase CO2 emissions from aquatic ecosystems.

Electronic beam steering enables multimission phased array radar to rapidly and adaptively survey the atmosphere while detecting the tracking aircraft.

Interannual variation of 45 annually resolved time series of environmental, limnological, and biotic parameters was quantified (1994-2009) in six lakes within 52,000 km2 to test the hypothesis that influx of energy (E; as irradiance, heat, wind) varies synchronously among sites and induces temporal coherence in lakes and their food webs, whereas influx of mass (m; as water, solutes, particles) reduces synchrony because local catchments uniquely modify hydrologic inputs. Overall, 82% of parameters exhibited significant (P < 0.05) synchrony (S) estimated as mean pair-wise correlation of Z-transformed time series. Influx of E as atmospheric heat and irradiance was both more highly synchronous and less temporally variable (months-to-decades) than influx of m as summer precipitation, snow, or river discharge. Similarly, S of limnological parameters varied from 0.08 to 0.85, with variables known to be regulated by E influx (ice melt, gas solubility) up to twofold more coherent than those regulated by m inputs (organic solutes). Pairs of variables linked by simple direct mechanisms exhibited similar S values (air temperature and ice melt, nutrients and algae), whereas the coherence of other parameters (water temperature, mixing) was intermediate to that of multiple regulatory agents. Overall, aggregate measures of plankton density varied more coherently among lakes than did constituent taxa. These findings suggest that environmental variability is transmitted to most levels of aquatic ecosystems, but that the precise effects depend on whether E or m fluxes predominate, the coherence of each forcing mechanism, and the strength of linkages between exogenous forcing and lake response.

Functional traits are morphological, biochemical, physiological, structural, phenological or behavioural characteristics of organisms that influence performance or fitness. Grouping species by functional characteristics is a long‐standing idea, but there has more recently been rapid development in the application of trait‐based approaches to diverse topics in ecology. Two common applications of functional traits are to characterise community responses to changes in the environment, including community assembly processes, and to quantify the influence of community shifts on ecosystem processes. Practical decisions include: What types of traits should be considered? How can traits be measured or inferred? Are traits correlated or traded‐off? Which, and how many, traits should be assessed? How should trait data be analysed? Functional trait approaches enhance ecological understanding by focusing on the mechanisms that govern interactions between organisms and their environments. Measuring and understanding traits increases our understanding of ecological processes, thus also informing conservation and restoration. Key Concepts Functional traits are morphological, biochemical, physiological, structural, phenological or behavioural characteristics that influence organism performance or fitness. Traits can be broadly classified either as having an effect on ecosystem properties and the services that human societies derive from them, or as characterising a response to environmental change or with respect to processes affecting community assembly. Common data types for traits include continuous, categorical, ordinal and binary variable formats. The data type has repercussions for subsequent data analyses. Methods for measuring traits vary from time‐consuming (hard traits) to rapid (soft traits), and in turn the information content of the resulting data also varies. Trait syndromes describe patterns of inter‐trait correlation that define differences and trade‐offs in ecological strategies. When choosing traits for calculating functional diversity it is important to consider which, and how many, traits are included, as well as what insights they will provide into the ecosystem processes, community structure or assembly processes under consideration. Functional traits are at the forefront of efforts to develop a mechanistic understanding of how species diversity influences ecosystem functioning, and the current ecological literature presents many indices by which functional diversity can be computed.