Storm impacts on phytoplankton community dynamics in lakes

Abstract

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.

Keywords: climate change; environmental disturbance; extreme events; functional traits; mixing; nutrients; stability; watershed.

FIGURE 1

Summary of the systematic review linking three types of storm events, wind (a), rain (b), and wind plus rain (c), to six variables related to lake chemical and physical condition (center column) and their consequent links to eight phytoplankton variables (right column). For details see Table 1. The connectors between different variables represent the links described by the authors in the studies or supported by data presented in the publications. The width of the connectors between weather events and lake conditions is proportional to the percent occurrence of each link in the studies which met our criteria. The percent occurrence and the total number of reported links (in parentheses) are located above the connectors. For clarity, only connectors between the lake condition variables and the phytoplankton‐related variables that were reported in at least 9% (lighter connectors) or more than 16% (darker connectors) of the studies were included in the figure. The numbers to the right of the phytoplankton‐related variables represent the percent occurrence and total number of links (in parentheses) in which each phytoplankton‐related variable was found. The table to the right indicates the number of storm events which resulted in (1) a positive (+), negative (−), variable (σ), or no change (0) in phytoplankton‐related variables when the response could be directional (e.g., increase in biomass), or (2) a change (∆) or no change (0) when the response could not be directional (e.g., change in functional composition). “na” indicates not applicable. The number of links may be greater than the number of storm events as a single storm may have multiple physical and chemical pathways (links) to a phytoplankton‐related variable. * indicates biomass or any other quantification of phytoplankton abundance different from chlorophyll. # indicates production or any other rate processes such as nutrient uptake rates

FIGURE 2

Conceptual model of how storm (a), lake (b), and watershed (c) attributes, and antecedent conditions, combine to alter light and nutrient conditions of lakes (d), with examples of phytoplankton (e) and higher trophic level (f) functional traits which likely play important roles in phytoplankton competition for survival and growth after storm‐induced disturbances, and ultimately ecosystem functions and services (g). However, details on the interactions of higher trophic levels and ecosystem functions and services in relation to storm impacts on phytoplankton is beyond the scope of this paper. Superscript¹ indicates the role antecedent conditions may play in mediating the effects of storms on the lake ecosystem. Responses of lake ecosystem components to direct and indirect storm impacts manifest over variable timescales and lags, as indicated by t ₀ to t ₆, and response trajectories may not be linear; t ₀—immediate impact; t ₁ to t ₆—increasing timescales from hours to possibly decades

FIGURE 3

Impacts of wind events on surface water temperature, light availability, and internal nutrient loading are expected to be mediated by lake fetch, antecedent (“pre”) water column stability, and trophic state. As momentum and mechanical energy flux across the lake–air interface, they scale as the wind speed squared and cubed, respectively (Wüest & Lorke, 2003). Thus, even relatively modest increases in wind speed could lead to disproportionately large changes in lake stratification and mixing dynamics. Lake depth also plays a role in mediating the impacts of wind events (see Figure S1). (a) In general, if a lake is stratified, wind will deepen the upper mixed layer, increase the volume of water within the upper layer, and thus reduce surface temperature. Polymictic lakes (lower prestability) still tend to have cooler temperatures at depth and the same processes could be important in altering surface temperatures, albeit to a lesser extent. Strong antecedent stability is characterized by sharp temperature gradients and resistance to mixing, but such conditions also set the stage for the greatest change in surface temperature. For example, if stability and wind speed are high, we expect a seiche to develop with the potential for upwelling of cold, hypolimnetic waters to the lake surface. (b) Wind events on lakes with weaker antecedent water column stability and greater fetch will have larger negative effects on light availability than on lakes with stronger antecedent stability and shorter fetch. (c) Wind events are expected to have the greatest impact on internal nutrient loading in lakes with greater fetch, stronger antecedent stability, and higher productivity. In particular, strong antecedent stability is expected to facilitate the buildup of nutrients in hypolimnetic waters (deeper lakes) and nutrient release through sediment anoxia (shallower lakes; see Figure S1 for more details), although well‐oxygenated hypolimnia likely result in little effect

FIGURE 4

Impacts of precipitation events on light availability, system flushing, and external nutrient loading are expected to be mediated by lake and watershed attributes that include ratio of watershed area to lake surface area (WA:LSA), lake volume, and anthropogenic land use (e.g., urban or agricultural development). (a) Sediment and dissolved organic carbon delivered to lakes by runoff from precipitation will reduce light availability (penetration) in lakes. We expect that light availability to phytoplankton will be more negatively impacted as WA:LSA and anthropogenic land use increase and lake volume decreases (Figure S2). (b) Flushing rates of lake systems as a result of precipitation runoff will be greatest in lakes with large WA:LSA, more anthropogenic land use, and small lake volumes. Lakes with large volumes, relatively small watershed areas, and less developed landscapes will be more buffered from precipitation‐induced flushing. We expect similar patterns for external nutrient loading. In particular, external nutrient loads will be diluted in lakes with larger volumes, and therefore are less impacted by precipitation events, at least in the short term. Long‐term buildup of external nutrient loads can eventually lead to excessive internal nutrient loading (Figures S1 and S2)

FIGURE 5

Seasonal mapping of morpho‐functional traits (see legend and Table 2) and C‐S‐R strategies as a function of environmental conditions susceptible to storm‐induced modification (modified from Madgwick, Jones, Thackeray, Elliott, & Miller, 2006). Dashed arrows represent the range of light/mixing (x‐axis) and nutrient (represented as NO₂ in this case, y‐axis) conditions a functional trait could span. The seasonal plots are derived from temperature‐dependent growth of phytoplankton groups associated with each trait (Paerl & Otten, 2013)