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In recent years, unexplained declines in lake total phosphorus (TP) concentrations have been observed at northern latitudes (> 42°N latitude) where most of the world's lakes are found. We compiled data from 389 lakes in Fennoscandia and eastern North America to investigate the effects of climate on lake TP concentrations. Synchrony in year‐to‐year variability is an indicator of climatic influences on lake TP, because other major influences on nutrients (e.g., land use change) are not likely to affect all lakes in the same year. We identified significant synchrony in lake TP both within and among different geographic regions. Using a bootstrapped random forest analysis, we identified winter temperature as the most important factor controlling annual TP, followed by summer precipitation. In Fennoscandia, TP was negatively correlated with the winter East Atlantic Pattern, which is associated with regionally warmer winters. Our results suggest that, in the absence of other overriding factors, lake TP and productivity may decline with continued winter warming in northern lakes.

 

Abstract Climate change interacts with local processes to threaten biodiversity by disrupting the complex network of ecological interactions. While changes in network interactions drastically affect ecosystems, how ecological networks respond to climate change, in particular warming and nutrient supply fluctuations, is largely unknown. Here, using an equation-free modelling approach on monthly plankton community data in ten Swiss lakes, we show that the number and strength of plankton community interactions fluctuate and respond nonlinearly to water temperature and phosphorus. While lakes show system-specific responses, warming generally reduces network interactions, particularly under high phosphate levels. This network reorganization shifts trophic control of food webs, leading to consumers being controlled by resources. Small grazers and cyanobacteria emerge as sensitive indicators of changes in plankton networks. By exposing the outcomes of a complex interplay between environmental drivers, our results provide tools for studying and advancing our understanding of how climate change impacts entire ecological communities. 

This dataset accompanies a paper submitted for publication to Limnology and Oceanography Letters, expected publication year 2023, by Isles et al., entitled "Widespread synchrony in phosphorus concentrations in northern lakes linked to winter temperature and summer precipitation." This dataset comprises April-November median TP concentrations for 389 lakes in Fennoscandia, the north-central and northeastern USA, and central to eastern Canada, between 1998 and. 2017. The dataset also includes seasonal means for climate variables divided into winter (DJF), spring (MAM), summer (JJA), and fall (SON) means of air temperature, wind speed, and precipitation. The data all originate with publicly collected datasets, and many data source have data from additional time periods or for additional variables collected over longer time periods available from websites or through contact forms. 

Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty.

We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping).

We introduce thetraitstrapR package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow,trait_fill, allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample.

Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling.

Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.

 

In many regions across the globe, extreme weather events such as storms have increased in frequency, intensity, and duration due to climate change. Ecological theory predicts that such extreme events should have large impacts on ecosystem structure and function. High winds and precipitation associated with storms can affect lakes via short‐term runoff events from watersheds and physical mixing of the water column. In addition, lakes connected to rivers and streams will also experience flushing due to high flow rates. Although we have a well‐developed understanding of how wind and precipitation events can alter lake physical processes and some aspects of biogeochemical cycling, our mechanistic understanding of the emergent responses of phytoplankton communities is poor. Here we provide a comprehensive synthesis that identifies how storms interact with lake and watershed attributes and their antecedent conditions to generate changes in lake physical and chemical environments. Such changes can restructure phytoplankton communities and their dynamics, as well as result in altered ecological function (e.g., carbon, nutrient and energy cycling) in the short‐ and long‐term. We summarize the current understanding of storm‐induced phytoplankton dynamics, identify knowledge gaps with a systematic review of the literature, and suggest future research directions across a gradient of lake types and environmental conditions.