Functional Responses of Phytoplankton Assemblages to Watershed Land Use and Environmental Gradients

Abstract

Watershed land-use changes have been identified as major threats to lake fauna, subsequently affecting ecosystem functioning. In this study, the functional-based approach was used to examine the effects of land use and environmental changes on phytoplankton communities in four selected lakes in Northeast China. We also identified the sensitive functional traits as indicators of environmental stressors. The integration of RLQ analysis with the fourth-corner approach significantly identified five of 18 functional trait categories, including flagella, filamentous, unicellular, mixotrophic, and chlorophyll c, as potential indicators to changes in watershed land-use intensity and environmental gradients. Significant relationships between traits and land use and water quality highlighted the consequential indirect impact of extensive agricultural and urban development on phytoplankton via allochthonous nutrient inputs and various contaminants. In addition, the functional richness of phytoplankton assemblages generally increased along with surface area and forests, but decreased along with intensive agricultural and urban land use, implying that functional homogenization may cause a reduction in ecosystem productivity and reliability to land-use intensity. Given the superior performance of the functional-based approach, our findings also highlighted the importance of the application of both the biological traits and functional diversity index in monitoring programs for lake ecosystems.

The combined effects of filter-feeding bivalves (Cristaria plicata) and submerged macrophytes (Hydrilla verticillate) on phytoplankton assemblages in nutrient-enriched freshwater mesocosms

“…The mesocosms had a diameter of 1 m and a constant water depth of 1.2 m; they contained 0.2 m sediment and 780 L of unfiltered water collected from Chagan Lake ( Figure 1 ). Chagan Lake is a shallow eutrophic freshwater lake (mean depth: 2.5 m) in a catchment area dominant by agricultural lands and grasslands and has relatively high allochthonous inputs of nutrients (especially nitrogen and phosphorus) through precipitation and surface run-off ( Liu et al., 2019 ; Du et al., 2022 ). Nitrogen and phosphorus, as dissolved mixtures of sodium nitrate (NaNO 3 ) and potassium dihydrogen phosphate (KH 2 PO 4 ), respectively, were added daily to each mesocosm to equate to a nutrient load of 36 μg/L and 5 μg/L, which adhered to the Redfield ratio ( Redfield, 1958 ).…”

Analysis of water quality and the response of phytoplankton in the low-temperature environment of Majiagou Urban River, China

“…Qu et al conducted a study on the functional groups of phytoplankton in northern rivers in Germany using redundancy analysis, which revealed that land use and physicochemical indicators were coupled and jointly influenced phytoplankton community composition, they demonstrated that physical and chemical factors, along with land use indicators, accounted for 68.4% of the variance, and Huang et al emphasized that land use patterns are crucial elements in phytoplankton biodiversity [ 7 ]. Du et al investigated the relationship between β-diversity and environmental factors, revealing a significant correlation between phytoplankton and nutrients such as TN, they also found that β-diversity decreased with increasing nutrient concentrations, explaining the variations in phytoplankton diversity, and Sun et al researched the relationship between phytoplankton and nutrients in southeastern China and discovered that, apart from diatoms, other algal groups exhibited a significant negative correlation with nutrients [ 8 ]. Bao et al analyzed the impact of artificial structures on phytoplankton and identified the construction of urban rubber dams as a contributor to increased phytoplankton biomass during the reservoir period, and they attributed phosphorus emissions as the primary cause of changes in the phytoplankton community [ 9 ].…”

Combining functional diversity of lotic fish communities with river health assessment based on multi-metric chemical pollution and biological integrity index models

“…Trait-based studies have covered various aquatic taxa, such as phytoplankton (Pálffy et al, 2013;Du et al, 2022), periphyton (Bichoff et al, 2018;Wang et al, 2022), zooplankton (Kuczyńska-Kippen et al, 2020;Shen et al, 2022), macroinvertebrates (Pallottini et al, 2017;Belmar et al, 2019), and fish (Brind'Amour et al, 2011;Toussaint et al, 2016). They have also examined diverse geographic regions, including North America (Boucek and Rehage, 2014;Sinclair et al, 2021), Europe (Belmar et al, 2019;Wu et al, 2019), Australia (Galego de Oliveira et al, 2019;Lam-Gordillo et al, 2020), and Asia (Matsuzaki et al, 2016;Wang et al, 2022).…”