Predicting the effects of climate change on freshwater cyanobacterial blooms requires consideration of the complete cyanobacterial life cycle

To date, most research on cyanobacterial blooms in freshwater lakes has focused on the pelagic life stage. However, examining the complete cyanobacterial life cycle—including benthic life stages—may be needed to accurately predict future bloom dynamics. The current expectation, derived from the pelagic life stage, is that blooms will continue to increase due to the warmer temperatures and stronger stratification associated with climate change. However, stratification and mixing have contrasting effects on different life stages: while pelagic cyanobacteria benefit from strong stratification and are adversely affected by mixing, benthic stages can benefit from increased mixing. The net effects of these potentially counteracting processes are not yet known, since most aquatic ecosystem models do not incorporate benthic stages and few empirical studies have tracked the complete life cycle over multiple years. Moreover, for many regions, climate models project both stronger stratification and increased storm-induced mixing in the coming decades; the net effects of those physical processes, even on the pelagic life stage, are not yet understood. We therefore recommend an integrated research agenda to study the dual effects of stratification and mixing on the complete cyanobacterial life cycle—both benthic and pelagic stages—using models, field observations and experiments

Spatial and Temporal Variability in Recruitment of the Cyanobacterium Gloeotrichia echinulata in an Oligotrophic Lake

Recruitment from dormant stages in the benthos can provide a critically important inoculum for surface populations of phytoplankton, including bloom-forming cyanobacteria. For example, water-column populations of the large (1–3-mm diameter) colonial cyanobacterium Gloeotrichia echinulata (Smith) P. Richter can be strongly subsidized by benthic recruitment. Therefore, understanding controls on recruitment is essential to an investigation of the factors controlling Gloeotrichiablooms, which are increasing in low-nutrient lakes across northeastern North America. We quantified surface abundances and recruitment from littoral sediments at multiple near-shore sampling sites in oligotrophic Lake Sunapee, New Hampshire, USA, during the summers of 2005–2012 and used this data set—the longest known record of cyanobacterial recruitment—to investigate potential drivers of interannual differences in Gloeotrichia recruitment. We found extensive spatiotemporal variability in recruitment. Recruitment was higher at some sites than others, and within seasons, recruitment into replicate traps at the same site was generally more similar than recruitment at different sites. These data suggest that local factors, such as substrate quality or the size of the seed bank, may be important controls on recruitment. Benthic recruitment probably accounted forGloeotrichia recruitment may be related to regional climatic variability

Cyanobacteria as biological drivers of Lake Nitrogen and Phosphorus Cycling

Here we draw attention to the potential for pelagic bloom‐forming cyanobacteria to have substantial effects on nutrient cycling and ecosystem resilience across a wide range of lakes. Specifically, we hypothesize that cyanobacterial blooms can influence lake nutrient cycling, resilience, and regime shifts by tapping into pools of nitrogen (N) and phosphorus (P) not usually accessible to phytoplankton. The ability of many cyanobacterial taxa to fix dissolved N2 gas is a well‐known potential source of N, but some taxa can also access pools of P in sediments and bottom waters. Both of these nutrients can be released to the water column via leakage or mortality, thereby increasing nutrient availability for other phytoplankton and microbes. Moreover, cyanobacterial blooms are not restricted to high nutrient (eutrophic) lakes: blooms also occur in lakes with low nutrient concentrations, suggesting that changes in nutrient cycling and ecosystem resilience mediated by cyanobacteria could affect lakes across a gradient of nutrient concentrations. We used a simple model of coupled N and P cycles to explore the effects of cyanobacteria on nutrient dynamics and resilience. Consistent with our hypothesis, parameters reflecting cyanobacterial modification of N and P cycling alter the number, location, and/or stability of model equilibria. In particular, the model demonstrates that blooms of cyanobacteria in low‐nutrient conditions can facilitate a shift to the high‐nutrient state by reducing the resilience of the low‐nutrient state. This suggests that cyanobacterial blooms warrant attention as potential drivers of the transition from a low‐nutrient, clear‐water regime to a high‐nutrient, turbid‐water regime, a prediction of particular concern given that such blooms are reported to be increasing in many regions of the world due in part to global climate change

Individual-based modelling of adaptive physiological traits of cyanobacteria: Responses to light history

Adaptive physiological traits of cyanobacteria allow plasticity of responses to environmental change at multiple time scales. Most conventional phytoplankton models only simulate responses to current conditions without incorporating antecedent environmental history and adaptive physiological traits, thereby potentially missing mechanisms that influence dynamics. We developed an individual-based model (IBM) that incorporates information on light exposure history and cell physiology coupled with a hydrodynamic model that simulates mixing and transport. The combined model successfully simulated cyanobacterial growth and respiration in a whole-lake nutrient enrichment experiment in a temperate lake (Peter Lake, Michigan, USA). The model also incorporates non-photochemical quenching (NPQ) to improve simulations of cyanobacteria biomass based on validation against cyanobacteria cell counts and chlorophyll concentration. The IBM demonstrated that physical processes (stratification and mixing) significantly affect the dynamics of NPQ in cyanobacteria. Cyanobacteria had high fluorescence quenching and long photo-physiological relaxation periods during stratification, and low quenching and rapid relaxation in response to low light exposure history as the mixing layer deepened. This work demonstrates that coupling adaptive physiological trait with physical mixing into models can improve our understanding and enhance predictions of bloom occurrences in response to environmental changes

A Practical Guide for Managing Interdisciplinary Teams: Lessons Learned from Coupled Natural and Human Systems Research

Interdisciplinary team science is essential to address complex socio-environmental questions, but it also presents unique challenges. The scientific literature identifies best practices for high-level processes in team science, e.g., leadership and team building, but provides less guidance about practical, day-to-day strategies to support teamwork, e.g., translating jargon across disciplines, sharing and transforming data, and coordinating diverse and geographically distributed researchers. This article offers a case study of an interdisciplinary socio-environmental research project to derive insight to support team science implementation. We evaluate the project’s inner workings using a framework derived from the growing body of literature for team science best practices, and derive insights into how best to apply team science principles to interdisciplinary research. We find that two of the most useful areas for proactive planning and coordinated leadership are data management and co-authorship. By providing guidance for project implementation focused on these areas, we contribute a pragmatic, detail-oriented perspective on team science in an effort to support similar projects

Storm impacts on phytoplankton community dynamics in lakes

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

The Global Lake Ecological Observatory Network (GLEON): the evolution of grassroots network science

Nine years later, with over 380 members from 40 countries, and 50 publications to its credit, GLEON is growing at a rapid pace and pushing the boundaries of the practice of network science. GLEON is really three networks: a network of lakes, data, and peopl

A Global lake ecological observatory network (GLEON) for synthesising high-frequency sensor data for validation of deterministic ecological models

A Global Lake Ecological Observatory Network (GLEON; www.gleon.org) has formed to provide a coordinated response to the need for scientific understanding of lake processes, utilising technological advances available from autonomous sensors. The organisation embraces a grassroots approach to engage researchers from varying disciplines, sites spanning geographic and ecological gradients, and novel sensor and cyberinfrastructure to synthesise high-frequency lake data at scales ranging from local to global. The high-frequency data provide a platform to rigorously validate processbased ecological models because model simulation time steps are better aligned with sensor measurements than with lower-frequency, manual samples. Two case studies from Trout Bog, Wisconsin, USA, and Lake Rotoehu, North Island, New Zealand, are presented to demonstrate that in the past, ecological model outputs (e.g., temperature, chlorophyll) have been relatively poorly validated based on a limited number of directly comparable measurements, both in time and space. The case studies demonstrate some of the difficulties of mapping sensor measurements directly to model state variable outputs as well as the opportunities to use deviations between sensor measurements and model simulations to better inform process understanding. Well-validated ecological models provide a mechanism to extrapolate high-frequency sensor data in space and time, thereby potentially creating a fully 3-dimensional simulation of key variables of interest