Chlorophyll–total phosphorus relationships emerge from multiscale interactions from algae to catchments

Abstract

Understanding controls of lake primary productivity is imperative to tackling issues related to water quality. A common way to assess lake water quality across broad spatial scales is the relationship between chlorophyll concentration and total phosphorus (TP). However, despite continuous refinement in our understanding of the chlorophyll-TP relationship, there is still uncertainty regarding the mechanisms underpinning the variation in chlorophyll at a given TP concentration. We used a process-model that primarily focuses on biogeochemical and physiological mechanisms to infer broad shifts in algal limitation status underly the shape of the chlorophyll-TP relationship and these shifts in limitation are a product of interactions among landscape, ecosystem, and algal physiologic drivers. Our work provides integrative insights that place patterns of lake primary productivity in a more general framework.

Geographic Characteristics and Meteorological Factors Dominate the Variation of Chlorophyll‐a in Lakes and Reservoirs With Higher TP Concentrations

“…Primary productivity, the fundamental function of aquatic ecosystems, is influenced by the variation of phytoplankton biomass, making it a crucial topic in limnology (Liang et al., 2020; Oliver et al., 2017; Olson & Jones, 2022; Paltsev & Creed, 2022; Peng et al., 2021; Wu et al., 2022; Xu et al., 2021). Global satellite remote sensing data indicate that phytoplankton blooms in large lakes have increased since the 1980s (Ho et al., 2019; Hou et al., 2022).…”

“…While several studies have explored the empirical relationships between the concentrations of nutrients and chlorophyll-a (Chl-a) and the underlying mechanisms influencing phytoplankton biomass, our knowledge of these factors remains incomplete (Davidson et al, 2023;Olson & Jones, 2022;Paltsev & Creed, 2022;Peng et al, 2021;Quinlan et al, 2020), in part, because of three predominant barriers. First, there is a lack of long-term and large-scale systematical data that can be used to reveal the temporal and spatial patterns in phytoplankton biomass (Ma et al, 2023).…”

“…Third, investigations into the driving mechanisms of phytoplankton biomass have primarily focused on the relationships between nutrients and algae. Empirical models have revealed that the relationship between TP and Chl‐a typically follows a sigmoidal function, and some studies have noted variations in the TP‐Chl‐a relationship with latitude (Olson & Jones, 2022; Quinlan et al., 2020). However, it is crucial to consider the specific ecosystem when examining the relationship between nutrients and Chl‐a.…”

Towards smart nutrient retention networks to improve water quality: perspectives, theories and tools

“…are not yet applied to real world cases which limits assessment of their performance in practice (Table 7.1). Olson and Jones (2022) developed…”

“…another numerical and conceptual model that -at first sight -produces a similar chlorophyll (i.e., for phytoplankton) to TP load-response curve as the GPLake-type models. They calibrated their model with chlorophyll and TP concentration data from the U.S. National Lakes Assessment, whilst lacking hydrologic and elemental load data (Olson and Jones 2022). In the original GPLake model, such data lacks were overcome by using normalized P loading (Vollenweider 1975; for field, lab and model data .…”