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Computer simulation reveals long-term effects of climate change on lake plankton communities


Nowadays, we hear a lot about the impacts of climate change in general; however, we still lack in-depth knowledge about how climate change might modify the processes determining the ecological status of lakes and the structure and functioning of aquatic communities. This is largely because these processes are intertwined in a complex manner, making any estimation regarding these changes challenging. In their latest study, researchers from the Institute of Aquatic Ecology at the HUN-REN Centre for Ecological Research used model simulations to analyse the effects of warming on phytoplankton dynamics based on field and experimental observations.

Although numerous lakes around the world have been showing an increase in annual mean temperature over the last few decades, assessing long-term warming-related impacts in water bodies with various physical and chemical properties and diverse communities remains difficult. Exploring these impacts is crucial not only for fish, macroinvertebrates, or aquatic macrophytes but also for planktonic organisms, which form the basis of the aquatic food web and substantially influence biogeochemical cycles. Despite the broad range of sophisticated techniques developed to study this important group, elucidating how interrelated environmental factors drive plankton functioning remains challenging due to the typically rapid dynamics of these communities. Monitoring based on regular fieldwork is a crucial part of research on aquatic systems, but it is also time-consuming and labour-intensive, limiting any sampling effort in both space and time. In a sense, this is like following a streaming series with several seasons by only looking at a few snapshots from each episode, trying to guess the actual story.

We need complementary approaches to improve our ability to assess, estimate, or forecast the ecological effects of climate change. Numerical models are promising candidates for this role, gradually gaining importance in ecological research. Generally speaking, such models describe fundamental relationships in the form of mathematical equations based on current data and scientific knowledge. Such relationships include e.g. species growth as a function of food source availability, or the dependence of plant photosynthetic activity on light intensity. The strength of modelling lies in the possibility to create computer-generated simulations of changes in a population, community, or ecosystem and their environment through space and/or time, helping to uncover the causality behind natural phenomena. Thus, while field and experimental observations provide data about a series of temporary states and conditions, modelling focuses on the processes that induce temporal changes in those states and conditions.

numerikus modellek

Field sampling is an essential part of ecological research, although it is generally not sufficient for a detailed exploration of the biogeochemical processes related to the functioning of plankton communities.
(Photo: HUN-REN CER)

In a Hungarian-Greek collaboration, Károly Pálffy, a researcher of the Institute’s Plankton Ecology Group, studied the dynamics of planktonic algae (phytoplankton, major primary producers of aquatic habitats) using an ecological modelling approach. While analysing a data series on Lake Balaton, Hungary in his previous study, he found that the long-term rise in annual mean water temperature was accompanied by increasing seasonal fluctuations in phytoplankton composition (increasing seasonal variability), which might suggest a decline in ecosystem stability. He and his colleagues also managed to demonstrate something highly similar in a mesocosm experiment, raising the question of whether there is a more general connection between warming and the dynamics of planktonic algae.

The newly developed model enabled the simulation of changes in phytoplankton on the species level under various temperature scenarios. The output of the simulations was in line with previous observations: elevated mean temperature caused more pronounced seasonal changes in phytoplankton composition, but the extent of this impact was also highly dependent on how the communities received inorganic nutrients essential for their growth. Accordingly, the ratio of the two most important ones, nitrogen and phosphorus, as well as the temporal fluctuations in nutrient supply, had a significant influence on the effect of warming. This is in close agreement with recent studies that suggest the importance of considering nutrient load conditions (the so-called trophic state of a water body) when assessing the effect of climate change on aquatic ecosystems. Besides nutrients, initial species richness of the simulated communities also affected their response to warming. From a methodological point of view, this is an important finding, as it suggests that choosing an adequate number of species can be crucial in planning community-scale climate change experiments.

The recent paper published in Limnology and Oceanography also sheds light on what long-term consequences an increase in the seasonal variability of phytoplankton can have in terms of stability. At higher mean temperatures, seasonal extremes in community composition became more prominent, shifting the communities towards lower overall evenness. On a longer timescale, elevated temperatures also increased the probability of species loss, providing a mathematical explanation for the role of warming in reducing plankton community stability and thus modifying aquatic ecosystem functioning. The research group has plans for further extending the model, facilitating the simulation of climate change impacts in a spatial context as well as at the level of the planktonic food web.

numeric model

A typical graphical output of a model simulation showing one year run under different seasonal temperature scenarios, with daily temperature values characteristic of the present and increased by 1, 2, or 3°C. Curves with different colours represent seasonal changes in the abundance of different species of algae. The modelling of temporal dynamics in multiple randomly assembled phytoplankton communities under different nutrient load and temperature combinations added up to more than 100,000 simulations. The study focussed on both short-term (one year) and long-term (30 years) changes and impacts.