Integrating chytrid fungal parasites into plankton ecology: research gaps and needs

Chytridiomycota, often referred to as chytrids, can be virulent parasites with the potential to inflict mass mortalities on hosts, causing e.g. changes in phytoplankton size distributions and succession, and the delay or suppression of bloom events. Molecular environmental surveys have revealed an unexpectedly large diversity of chytrids across a wide range of aquatic ecosystems worldwide. As a result, scientific interest towards fungal parasites of phytoplankton has been gaining momentum in the past few years. Yet, we still know little about the ecology of chytrids, their life cycles, phylogeny, host specificity and range. Information on the contribution of chytrids to trophic interactions, as well as co‐evolutionary feedbacks of fungal parasitism on host populations is also limited. This paper synthesizes ideas stressing the multifaceted biological relevance of phytoplankton chytridiomycosis, resulting from discussions among an international team of chytrid researchers. It presents our view on the most pressing research needs for promoting the integration of chytrid fungi into aquatic ecology

Drivers of phytoplankton responses to summer wind events in a stratified lake: A modeling study

Extreme wind events affect lake phytoplankton by deepening the mixed layer and increasing internal nutrient loading. Both increases and decreases in phytoplankton concentration after strong wind events have been observed, but the precise mechanisms driving these responses remain poorly understood or quantified. We coupled a one-dimensional physical model to a biogeochemical model to investigate the factors regulating short-term phytoplankton responses to summer wind events, now and under expected warmer future conditions. We simulated physical, chemical, and biological dynamics in Lake Erken, Sweden, and found that strong wind could increase or decrease the phytoplankton concentration in the euphotic zone 1 week after the event, depending on antecedent lake physical and chemical conditions. Wind had little effect on phytoplankton concentration if the mixed layer was deep prior to wind exposure. Higher incoming shortwave radiation and hypolimnetic nutrient concentration boosted phytoplankton concentration, whereas higher surface water temperatures decreased concentrations after wind events. Medium-intensity wind events resulted in more phytoplankton than high-intensity wind. Simulations under a future climate scenario did not show marked differences in the way wind events affect phytoplankton concentration. These findings help to better understand how wind impacts vary as a function of local environmental conditions and how climate warming and changing extreme weather dynamics will affect lake ecosystems

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

Schindler’s legacy : from eutrophic lakes to the phosphorus utilization strategies of cyanobacteria

David Schindler and his colleagues pioneered studies in the 1970s on the role of phosphorus in stimulating cyanobacterial blooms in North American lakes. Our understanding of the nuances of phosphorus utilization by cyanobacteria has evolved since that time. We review the phosphorus utilization strategies used by cyanobacteria, such as use of organic forms, alternation between passive and active uptake, and luxury storage. While many aspects of physiological responses to phosphorus of cyanobacteria have been measured, our understanding of the critical processes that drive species diversity, adaptation and competition remains limited. We identify persistent critical knowledge gaps, particularly on the adaptation of cyanobacteria to low nutrient concentrations. We propose that traditional discipline-specific studies be adapted and expanded to encompass innovative new methodologies and take advantage of interdisciplinary opportunities among physiologists, molecular biologists, and modellers, to advance our understanding and prediction of toxic cyanobacteria, and ultimately to mitigate the occurrence of blooms

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.Peer reviewe

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. Ecological theory predicts that such extreme events should have large impacts on ecosystem structure and function. For lake ecosystems, high winds and rainfall associated with storms are linked by short term runoff events from catchments and physical mixing of the water column. Although we have a well-developed understanding of how such wind and precipitation events alter lake physical processes, our mechanistic understanding of how these short-term disturbances 48 translate from physical forcing to changes in phytoplankton communities is poor. Here, we provide a conceptual model that identifies how key storm features (i.e., the frequency, intensity, and duration of wind and precipitation) interact with attributes of lakes and their watersheds to generate changes in a lake’s physical and chemical environment and subsequently phytoplankton community structure and dynamics. We summarize the current understanding of storm-phytoplankton dynamics, identify knowledge gaps with a systematic review of the literature, and suggest future research directions by generating testable hypotheses across a global gradient of lake types and environmental conditions.Fil: Stockwell, Jason D.. University of Vermont; Estados UnidosFil: Adrian, Rita. Leibniz Institute of Freshwater Ecology and Inland Fisheries; AlemaniaFil: Andersen, Mikkel. Dundalk Institute of Technology; IrlandaFil: Anneville, Orlane. Institut National de la Recherche Agronomique; FranciaFil: Bhattacharya, Ruchi. University of Missouri; Estados UnidosFil: Burns, Wilton G.. University of Vermont; Estados UnidosFil: Carey, Cayelan C.. Virginia Tech University; Estados UnidosFil: Carvalho, Laurence. Freshwater Restoration & Sustainability Group; Reino UnidoFil: Chang, ChunWei. National Taiwan University; República de ChinaFil: De Senerpont Domis, Lisette N.. Netherlands Institute of Ecology; Países BajosFil: Doubek, Jonathan P.. University of Vermont; Estados UnidosFil: Dur, Gaël. Shizuoka University; JapónFil: Frassl, Marieke A.. Griffith University; AustraliaFil: Gessner, Mark O.. Leibniz Institute of Freshwater Ecology and Inland Fisheries; AlemaniaFil: Hejzlar, Josef. Biology Centre of the Czech Academy of Sciences; República ChecaFil: Ibelings, Bas W.. University of Geneva; SuizaFil: Janatian, Nasim. Estonian University of Life Sciences; EstoniaFil: Kpodonu, Alfred T. N. K.. City University of New York; Estados UnidosFil: Lajeunesse, Marc J.. University of South Florida; Estados UnidosFil: Lewandowska, Aleksandra M.. Tvarminne Zoological Station; FinlandiaFil: Llames, Maria Eugenia del Rosario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Matsuzaki, Shin-ichiro S.. National Institute for Environmental Studies; JapónFil: Nodine, Emily R.. Rollins College; Estados UnidosFil: Nõges, Peeter. Estonian University of Life Sciences; EstoniaFil: Park, Ho-Dong. Shinshu University; JapónFil: Patil, Vijay P.. US Geological Survey; Estados UnidosFil: Pomati, Francesco. Swiss Federal Institute of Water Science and Technology; SuizaFil: Rimmer, Alon. Kinneret Limnological Laboratory; IsraelFil: Rinke, Karsten. Helmholtz-Centre for Environmental Research; AlemaniaFil: Rudstam, Lars G.. Cornell University; Estados UnidosFil: Rusak, James A.. Ontario Ministry of the Environment and Climate Change; CanadáFil: Salmaso, Nico. Research and Innovation Centre - Fondazione Mach; ItaliaFil: Schmitt, François. Laboratoire d’Océanologie et de Géosciences; FranciaFil: Seltmann, Christian T.. Dundalk Institute of Technology; IrlandaFil: Souissi, Sami. Universite Lille; FranciaFil: Straile, Dietmar. University of Konstanz; AlemaniaFil: Thackeray, Stephen J.. Lancaster Environment Centre; Reino UnidoFil: Thiery, Wim. Vrije Unviversiteit Brussel; Bélgica. Institute for Atmospheric and Climate Science; SuizaFil: Urrutia Cordero, Pablo. Uppsala University; SueciaFil: Venail, Patrick. Universidad de Ginebra; SuizaFil: Verburg, Piet. 8National Institute of Water and Atmospheric Research; Nueva ZelandaFil: Williamson, Tanner J.. Miami University; Estados UnidosFil: Wilson, Harriet L.. Dundalk Institute of Technology; IrlandaFil: Zohary, Tamar. Israel Oceanographic & Limnological Research; IsraelGLEON 20: All Hands' MeetingRottnest IslandAustraliaUniversity of Western AustraliaUniversity of AdelaideGlobal Lake Ecological Observatory Networ

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 process-based 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

What makes a cyanobacterial bloom disappear? A review of the abiotic and biotic cyanobacterial bloom loss factors

Cyanobacterial blooms present substantial challenges to managers and threaten ecological and public health. Although the majority of cyanobacterial bloom research and management focuses on factors that control bloom initiation, duration, toxicity, and geographical extent, relatively little research focuses on the role of loss processes in blooms and how these processes are regulated. Here, we define a loss process in terms of population dynamics as any process that removes cells from a population, thereby decelerating or reducing the development and extent of blooms. We review abiotic (e.g., hydraulic flushing and oxidative stress/UV light) and biotic factors (e.g., allelopathic compounds, infections, grazing, and resting cells/programmed cell death) known to govern bloom loss. We found that the dominant loss processes depend on several system specific factors including cyanobacterial genera-specific traits, in situ physicochemical conditions, and the microbial, phytoplankton, and consumer community composition. We also address loss processes in the context of bloom management and discuss perspectives and challenges in predicting how a changing climate may directly and indirectly affect loss processes on blooms. A deeper understanding of bloom loss processes and their underlying mechanisms may help to mitigate the negative consequences of cyanobacterial blooms and improve current management strategies

Temperature Effects Explain Continental Scale Distribution of Cyanobacterial Toxins

Insight into how environmental change determines the production and distribution of cyanobacterial toxins is necessary for risk assessment. Management guidelines currently focus on hepatotoxins (microcystins). Increasing attention is given to other classes, such as neurotoxins (e.g., anatoxin-a) and cytotoxins (e.g., cylindrospermopsin) due to their potency. Most studies examine the relationship between individual toxin variants and environmental factors, such as nutrients, temperature and light. In summer 2015, we collected samples across Europe to investigate the effect of nutrient and temperature gradients on the variability of toxin production at a continental scale. Direct and indirect effects of temperature were the main drivers of the spatial distribution in the toxins produced by the cyanobacterial community, the toxin concentrations and toxin quota. Generalized linear models showed that a Toxin Diversity Index (TDI) increased with latitude, while it decreased with water stability. Increases in TDI were explained through a significant increase in toxin variants such as MC-YR, anatoxin and cylindrospermopsin, accompanied by a decreasing presence of MC-LR. While global warming continues, the direct and indirect effects of increased lake temperatures will drive changes in the distribution of cyanobacterial toxins in Europe, potentially promoting selection of a few highly toxic species or strains.Peer reviewe