Understanding and predicting harmful algal blooms in a changing climate: A trait‐based framework

Abstract

Harmful algal blooms (HABs) in both freshwater and marine systems present a significant problem for water quality and ecosystem services and may be increasing globally. Our ability to understand and predict HABs in diverse aquatic ecosystems is still limited. Trait-based approaches where the focus is on functional traits provide a mechanistic framework to better understand the environmental drivers of HABs. Characterizing trait differences between HAB-forming and other taxa, as well as across and within different HAB-forming species will help elucidate their ecological niches and adaptive potential under different conditions. Trait-based models should enable better HAB predictions at different temporal and spatial scales.

Combined effect of warming, nutrients, and species pool size on the seasonal variability of phytoplankton composition: A modeling perspective

“…In the absence of more species‐level data, model parameterization could also be refined by taking trait ranges of different taxonomic groups into account. Larger groups of algae tend to have different ranges for temperature optimum and nutrient affinity, which can be used as a guideline or background information for more realistic parameter estimation and community‐level simulation in future modeling efforts (Litchman 2023). In addition to bottom‐up effects, the impact of zooplankton grazing would be another important point to consider here, but this is beyond the focus of this study.…”

Lake browning counteracts cyanobacteria responses to nutrients: Evidence from phytoplankton dynamics in large enclosure experiments and comprehensive observational data

“…Climate‐proofing of lake management requires considering also other aspects than P thresholds, such as hypolimnetic oxygen depletion promoted by high temperatures and prolonged stratification periods, with negative impacts on zooplankton, benthic fauna and fish (Brothers et al., 2014); phosphorus and methane release from sediments as a result of anoxia (Bartosiewicz et al., 2021; Knoll et al., 2018); or the role of N availability in controlling cyanobacteria biomass and cyanotoxin production (Gobler et al., 2016; Hellweger et al., 2022; Litchman, 2023). Furthermore, for brownwater lakes attention is needed to reduce the risk of other harmful algal blooms, such as the rapidly spreading, skin irritating Gonyostomum semen , which tends to be favoured by nutrients and browning (Hagman et al., 2020), especially if high concentrations of iron (and Mn) contribute to the brown color (Lebret, Östman, et al., 2018).…”

“…or the role of N availability in controlling cyanobacteria biomass and cyanotoxin production (Gobler et al, 2016;Hellweger et al, 2022;Litchman, 2023). Furthermore, for brownwater lakes attention is needed to reduce the risk of other harmful algal blooms, such as the rapidly spreading, skin irritating Gonyostomum semen, which tends to be favoured by nutrients and browning (Hagman et al, 2020), especially if high concentrations of iron (and Mn) contribute to the brown color .…”

Mitigating the Impact of Harmful Algal Blooms on Aquaculture Using Technological Interventions: Case Study on a South African Farm

“…David et al [43] conducted a detailed review of the models developed in the past decade and classified the HAB models into processbased, statistical, and hybrid models. Process-based models [44] are more suited to the study of long-term impact and prediction, for example, the impact of climate change. In comparison, machine learning models based on statistical methods [45] can be used to deliver short-term predictions.…”