Long‐term change of phytoplankton in Lake Kivu: The rise of the greens

Abstract

Phytoplankton time‐series enable the observation of recurrent seasonal patterns on community composition, but drastic shifts in community structure are rarely observed, unless linked to anthropogenic impacts (e.g. changes in nutrient inputs). Here, we report a unique case of non‐seasonal, multiannual regime shift of phytoplankton community structure in Lake Kivu, East Africa, and explore the possible causes for such changes. We recorded phytoplankton biomass every 2 weeks over a period of 12 years (from 2002 to 2015). Phytoplankton community previously dominated by cyanobacteria and diatoms changed considerably from 2012, with a conspicuous rise in the biomass of green algae (from 3 to 33% lake‐wide), and a decline of cryptophytes and cyanobacteria along with an increase in total chlorophyll a concentration. Concomitantly, we observed significant differences in the mixed layer depth between the two periods, resulting from changes in the thermal gradient. The euphotic zone depth was also reduced in 2012–2015, probably from the higher phytoplankton biomass. Moreover, mean particulate nitrogen and phosphorus increased as well as the particulate N:P ratio. Desmids (Cosmarium laeve) now dominate the mixolimnion, probably due to atelomixis, observed in high‐frequency temperature measurements. We believe that decadal or multidecadal variability of climate on the Indian Ocean may be the reason for this regime shift in thermal stratification, with a strong impact in phytoplankton community structure. In conclusion, our study demonstrates that tropical lakes may be subject to non‐seasonal fluctuations that generate multiannual regime shifts. These regime shifts have probably never been reported previously because of the lack of long phytoplankton time‐series in low latitudes.

Assessment of some key indicators of the ecological status of an African freshwater lagoon (Lagoon Aghien, Ivory Coast)

“…[e.g. 2 , 3 ], there are very limited data available on the water quality and ecological status of most lentic ecosystems in tropical Africa. From these data, it seems that growing eutrophication of these ecosystems [e.g.…”

Fisheries management influences phytoplankton biomass of Amazonian floodplain lakes

“…Vertical mixing in tropical lakes is very dynamic, and the depth of the actively mixing layer can range widely over 24 hr (e.g. Barbosa & Padisák, 2002;Rugema et al, 2019), as a function of water column heating due to solar radiation incidence, causing the actively mix layer depth to be shallower around noon (Amaral et al, 2018;MacIntyre & Melack, 1984). These daily vertical movements were shown to affect nutrients (Melack & Fisher, 1990) and greenhouse gases dynamics (Amaral et al, 2018(Amaral et al, , 2020Barbosa et al, 2020), but the impact to phytoplankton biomass and primary production on Amazon floodplain lakes are yet to be assessed.…”

“…Phytoplankton provides a substantial contribution to primary production in aquatic ecosystems (Zwart et al, 2015), sustaining a network of trophic interactions, with decisive consequences for ecosystem functioning (Abonyi et al, 2018). The role of bottom‐up factors influencing phytoplankton biomass has been studied for decades, but prevailing models developed in temperate zones may be inappropriate for tropical environments due to differences in food web configuration and varying amplitude of seasonal and interannual cycles (Huszar et al, 2006; Melack, 1976; Rugema et al., 2019; Sarmento, 2012). Phytoplankton biomass is regulated by both top‐down and bottom‐up effects in temperate lakes distributed over a wide productivity gradient (Mazumder, 1994).…”

Phytoplankton pigment analysis as a tool for monitoring a tropical great lake, Lake Kivu (East Africa)

“…Since 2002, phytoplankton studies have been conducted in Lake Kivu (Sarmento et al 2012, Rugema et al 2019) using high performance liquid chromatography (HPLC) to determine phytoplankton marker pigments. This technique, which can estimate phytoplankton biomass at the class level, was developed in the marine environment (Wright and Jeffrey 2006) and applies as well to estuarine and fresh waters (Fietz and Nicklisch 2004, Descy et al 2005, Fietz et al 2005, review in Sarmento and Descy 2008.…”

“…This technique, which can estimate phytoplankton biomass at the class level, was developed in the marine environment (Wright and Jeffrey 2006) and applies as well to estuarine and fresh waters (Fietz and Nicklisch 2004, Descy et al 2005, Fietz et al 2005, review in Sarmento and Descy 2008. Monitoring phytoplankton biomass and composition in Lake Kivu, based on the use of specific marker pigments of these classes over 12 years, allowed Rugema et al (2019) to detect dramatic changes in phytoplankton community structure of Lake Kivu, which they attributed to a change in the water column mixing pattern, depending on large-scale climate variability. That study demonstrated the potential of using pigmentbased analysis of phytoplankton composition, complemented by data from microscopy, as a tool to monitor environmental changes in this tropical lake at different time scales.…”