Limnology and Oceanography Letters最新文献

Lakes play a significant role in the global carbon cycle, acting as sources and sinks of carbon dioxide (CO₂). In situ measurements of CO₂ flux (FCO₂) from lakes have generally been collected during daylight, despite indications of significant diel variability. This introduces bias when scaling up to whole-lake annual aquatic carbon budgets. We conducted an international sampling program to ascertain the extent of diel variation in FCO₂ across lakes. We sampled 21 lakes over 41 campaigns and measured FCO₂ at 4-h intervals over a full diel cycle. Rates of FCO₂ ranged from −3.16 to 4.39 mmol m⁻² h⁻¹. Integrated over a day, FCO₂ ranged from −381.68 to 878.49 mg C m⁻² d⁻¹ (mean = 76.54) across campaigns. We identified three characteristic diel patterns in FCO₂ related to trophic status and show that for half of the campaigns, daily flux estimates were biased by > 50% if based on a single (daytime) measurement.

Photosynthesis–irradiance (PI) relationships are important for phytoplankton ecology and quantifying carbon fixation rates in the environment. However, the parameters of PI relationships are typically unknown across space and time. Here we use machine learning, satellite remote-sensing, and a database of in situ PI relationships to build models that predict the seasonal cycle of PI parameters as a function of satellite-observed variables. Using only surface light, temperature, and chlorophyll, we achieve an R² of 58% for predicting photosynthesis rates at saturating light ($��P_{\max }^B�$) and an R² of 78% for predicting the light saturation parameter ($��E_k�$). Predictability is maximized when averaging environmental covariates over 30-d ($��P_{\max }^B�$) and 25-d ($��E_k�$) timescales, indicating that environmental history and community turnover timescales are important for predicting in situ PI relationships. These results will help improve the parameterization of satellite-based primary production models and quantify emergent environmental integration timescales in photosynthetic communities.

China ranks third worldwide in terms of marsh areas. Marshes in China play an important role in regional aquatic ecosystem security and the carbon cycle. Understanding the impacts of extreme climatic changes on marsh vegetation is crucial for predicting the regional carbon cycle. Using the NDVI and meteorological data, we analyzed the spatiotemporal variation in fractional vegetation cover (FVC) and the impacts of extreme temperature changes on the marsh FVC in China. We observed that the marsh FVC during the growing season increased significantly (p < 0.01) by 2.6%/decade in China from 2001 to 2021. The reduction of extreme high-temperature and extreme low-temperature events can both increase marsh FVC in China, but the reduction of extreme low-temperature events has a more significant promoting effect on the increase of marsh FVC. This study highlights the asymmetric effect of extreme high and low temperature changes on the marsh FVC in China.

Ocean color-based estimates of Antarctic net primary productivity (NPP) have indicated low nearshore productivity in ice-adjacent waters, contrasting with coupled physical–biogeochemical models. To understand this discrepancy, we assessed satellite records of polynya NPP by comparing field data with two satellite imagery datasets derived using different processing schemes. Our results indicate historical underestimation of chlorophyll a for imagery obtained using default atmospheric correction processing within approximately 100 km of ice-covered coastlines due to adjacency effects. Using radiative transfer modeling, we find that biases in ocean color polynya observations due to adjacency effects correspond to the high albedo of ice and snow. When applying an atmospheric correction processing scheme more robust to adjacency contamination, estimates of NPP more than doubled in 65% of polynyas, especially smaller eastern Antarctic polynyas. Adjacency effects should therefore be accounted for when analyzing spatial and temporal trends in Antarctic coastal primary productivity.

Aquatic metabolism is reflected in the dynamics of dissolved oxygen (O₂) and carbon dioxide (CO₂) concentrations. Thus, paired measurements of CO₂ and O₂ concentrations can capture the metabolic characteristics of an ecosystem, with promising results in lakes. Yet, for rivers, hydrological, chemical, and biological processes all influence CO₂ and O₂ concentrations, complicating how paired measurements can be used to infer ecosystem processes. Here we combine a data synthesis with a simple mechanistic model of river metabolism, gas exchange, groundwater inputs and carbonate equilibrium to assess how each imprints upon CO₂ : O₂ patterns. Among the physicochemical processes considered, groundwater inputs substantially influenced CO₂ : O₂ relationships. Regardless, analysis of paired CO₂ : O₂ data resolved predictable differences in ecosystem function across rivers with variable productivity and disturbance, as well as along the river continuum. Results indicate that paired CO₂ : O₂ data can aid in assessments of river metabolism, provided that we account for the dynamic physical environment.

Urban environments contend with an array of stressors, including salinization by deicing road salts. To advance understanding of how road salt pollution affects aquatic ecosystem functioning, we surveyed primary producers in 50 stormwater ponds in Brampton, Canada. We found that chloride concentrations decreased (benthic) periphytic algal biomass but had no detectable effect on the total biomass of (free-floating) phytoplankton. However, impacts were obscured by underlying compositional shifts, as cyanobacteria generally compensated for declines of other taxa. Varying sensitivities of taxonomic groups (inferred from diagnostic pigments) revealed potential bioindicators, with the proportion of periphytic chromophytes declining most significantly and effects on the relative concentrations of green algae differing between planktonic and benthic communities. As chloride concentrations were a leading predictor of cyanobacterial dominance in our study of impaired, nutrient-rich, urban ponds, findings reveal an emerging risk of potentially harmful organisms from the ongoing salinization of freshwater resources.

Coastal upwelling ecosystems associated with strong physical stirring exhibit significant mesoscale hydrographic and biological patchiness. Though many studies have found broad correlations between hydrographic properties (e.g., temperature and salinity) and phytoplankton biomass, we lack a detailed understanding of the mechanisms underlying these correlations. Here, using observational data from coastal waters in the California Current System, we demonstrate that the maximum observed chlorophyll in a water parcel increases with salinity—a conservative water-mass tracer. This relationship arises from the correlations of vertical salinity and sub-euphotic zone nitrate profiles. This allows us to define the maximum potential chlorophyll as a function of salinity, and thus nitrate. We show that variations in salinity explain patterns in phytoplankton community structure, and discuss how growth, grazing, and light and micronutrient limitation can generate chlorophyll values below the maximum potential. Our mechanistic explanation provides a novel framework for diagnosing biological patchiness solely through salinity observations.

Seaweed farming is increasingly promoted as a carbon sequestration strategy, but its effectiveness relies on carbon burial and export to deep waters. Seaweed farms commonly occupy semi-enclosed bays, causing continuous accumulation of organic carbon (OC) and its degradation products, potentially undermining carbon sequestration and driving hypoxia and acidification. These ecological impacts may be amplified in fish–algae polyculture systems, yet they remain unclear. We investigated carbon cycling in Sansha Bay, China, the world's largest seaweed farm and intensive algae–fish polyculture site. During aquaculture seasons, bottom waters experienced rapid OC decomposition, causing severe oxygen depletion and acidification. Vertical mixing spread these effects throughout the water column, turning surface waters into net CO₂ sources. δ¹³C_DIC carbon isotopic analyses indicated seasonal shifts in dominant OC sources, from fish feed in autumn to macroalgal detritus in spring. These findings underscore the importance of evaluating the sustainability of coastal systems when pursuing seaweed-based carbon sequestration.

Zooplankton-microbial interactions play crucial roles in epipelagic ecosystem functions. The distinct west-to-east gradients and complex circulation patterns in the Mediterranean Sea, combined with the ubiquity of pelagic copepods, provide an ideal model to study the ecological processes driving host-associated microbial spatial distribution. Here, we characterized and compared the copepod-associated microbial metacommunities (CAMC) with those from seawater microbial metacommunities (SMC). Copepod-associated microbial metacommunities displayed spatial dissimilarity between the western and eastern basins, while SMC exhibited similar microbial compositions. The within-basin similarity observed in CAMC was associated with connectivity by the surface currents. Ecological drift explained most of CAMC variability, likely as a response to the restricted co-dispersal of the hosts with their microbes, which presented low prevalence and abundance. Seawater microbial metacommunities displayed higher homogenizing dispersal, with widely distributed generalist taxa. We conclude that CAMC better reflect cross-basin gradients and connectivity patterns than SMC, suggesting that CAMC may serve as a useful proxy for studying microbial biogeography.