Limnology and Oceanography Letters最新文献

The PEG (Plankton Ecology Group) model predicts differences in phenology between eutrophic and oligotrophic lakes regarding the occurrence, timing and magnitude of annual chlorophyll maxima and minima. While these predictions have been tested between lakes, hardly any tests exist using long-term data. We test these predictions using chlorophyll time-series (1980–2019) from Lake Constance in which trophic status shifted from eutrophic to oligotrophic conditions. We show that oligotrophication subsequently resulted in reduction of the summer and spring blooms, and finally the loss of the clear-water phase. In contrast to the PEG model the spring bloom was not delayed, but advanced with oligotrophication. Warming modified the seasonal patterns via advancing clear-water timing. Oligotrophication did not only influence phenologies, but also the importance of independent variables driving phenologies. Thus, the decline of nutrients was the dominant factor in shaping the seasonal patterns of chlorophyll in Lake Constance during the last four decades.

Microbial biodiversity is fundamental to maintain ecosystem functioning in seasonally variable ecosystems. However, it remains unclear how alterations in water availability caused by episodic drying compromise the ability of stream microbes to maintain multiple functions simultaneously (e.g., primary production and carbon cycling). Using data from 32 streams, we investigated how the phenology of annual drying influences stream sediment microbial biodiversity and their capacity to sustain multifunctionality. Our results showed that stream multifunctionality and most bacteria did not respond to changes in drying phenology. Only two bacterial groups, the drying-resistant Sphingobacteriia and the drying-sensitive Acidobacteria_Gp7, exhibited positive associations with multifunctionality; whereas, bacterial diversity showed a negative correlation with functions. Among these biodiversity aspects, Sphingobacteriia showed the strongest capacity to maintain multifunctionality at low and moderate performance levels. Our findings will help to better understand the mechanisms through which biodiversity sustains the functioning of seasonally variable streams and their responses to global change.

Recent rapid changes in climate and environmental conditions have significantly impacted coastal ecosystem functioning. However, the complex interplay between global and local effects makes it challenging to pinpoint the primary drivers. In a multi-ecosystem study, we analyzed pluri-decadal trends of bivalve-δ¹³C as recorder of global environmental changes. These trends were correlated with large-scale natural and anthropogenic climate proxies to identify whether coastal biota responded to global effects. Our findings revealed decreasing bivalve-δ¹³C trends in all sea regions, mainly linked with increased temperature and atmospheric-CO₂ concentrations, the later generating a decrease in atmospheric-CO₂ δ¹³C values (Suess effect) because of fossil-fuel burning. After removing the Suess effect from bivalve-δ¹³C trends, ongoing global climate variability continues to affect most ecosystems, possibly intensified by combined, interacting regional or local effects. These results highlight the need to consider large-scale effects to fully understand ecosystem and food web responses to the multiple effects of global change.

The implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep-learning-based methods including detection and classification of phytoplankton and zooplankton images, foraging and swimming behavior analysis, and finally ecological modeling. Deep learning has the potential to speed up the analysis and reduce the human experimental bias, thus enabling data acquisition at relevant temporal and spatial scales with improved reproducibility. We also discuss shortcomings and show how deep learning architectures have evolved to mitigate imprecise readouts. Finally, we suggest opportunities where deep learning is particularly likely to catalyze plankton research. The examples are accompanied by detailed tutorials and code samples that allow readers to apply the methods described in this review to their own data.

As cross-shelf gradients of most properties are typically much steeper than those in the alongshore direction, transport across isobaths tends to be inhibited, particularly at oceanic fronts where cross-shelf gradients are markedly pronounced. Consequently, variations in cross-shelf gradients may exert a significant influence on offshore transport; however, this influence is not yet well understood. This study employs reconstructed daily suspended sediment concentration (SSC) data from the Yellow Sea's offshore region to investigate the dynamics of offshore transport. Our analysis on an interannual scale shows that offshore SSC correlates more with temperature gradients at the oceanic front than with winter storms, despite the latter's vital role in causing frontal instability. The observed increase in offshore transport over the past two decades is likely connected to Kuroshio Current warming, which has strengthened the horizontal density gradient at the oceanic front, driving the strengthened offshore transport of coastal sediments during instability episodes.

In 2015, the Indian Ocean exhibits an exceptionally weakened CO₂ uptake, highlighting strong interannual variability of ocean carbon sink. By utilizing multiple ocean CO₂ partial pressure (pCO₂) data and a state-of-the-art ocean biogeochemical model, we show that the 2015 ocean CO₂ anomaly is characterized by a basin-wide amplification of ocean pCO₂, differing from ocean pCO₂ responses to other Indian Ocean Dipole events (e.g., 1997 and 2019). The distinct ocean pCO₂ anomaly is attributed to an amplified warming and an unprecedented weakening Indonesian Throughflow under the influence of co-occurrence of positive IOD and extreme El Niño in 2015. The amplified warming drives higher ocean pCO₂ in the western and central Indian Ocean, while the ITF transports anomalously high ocean pCO₂ water from the Pacific Ocean to the southeastern Indian Ocean. This newly identified ocean carbon response provides deeper insights into the Indian Ocean carbon interannual variability.

The impact of climate warming on coastal benthic fauna is already observed, but forecasting their long-term fate remains challenging. This study uses δ¹⁸O_shell data of specimens of five bivalve species collected at six locations and results from kilometer-scale atmosphere–ocean climate model for the time intervals of 1987–2017 and 2070–2100, to estimate changes in bivalve growth phenology. All species will benefit from climate warming during winter, experiencing a longer growing season than currently. The growth of Aequipecten opercularis, Flexopecten glaber, and Pecten jacobaeus will decrease in summer, resulting in up to 3 months of reduced growth per year. Glycymeris pilosa and Venus verrucosa in the southern Adriatic Sea will be more affected than those in the north, with up to 4 months longer annual growth. These findings can inform adaptation plans for bivalve management in the Adriatic Sea but also in areas where the studied species are present.

Polyphosphate (polyP) is important to phytoplankton ecology, but a unified view of its variability and roles in ecosystem-scale phosphorus (P) cycling is lacking. We study polyP in the world's largest freshwater ecosystem, the Laurentian Great Lakes, covering pelagic to nearshore areas across a wide nutrient gradient. We show that polyP (average 10.99 ± 3.90 nmol L⁻¹) constitutes 3.8–30.2% (average 18.1 ± 7.2%) of total particulate P (TPP). PolyP accumulation is higher in low-P pelagic waters compared with more productive nearshore areas. PolyP is preferentially degraded in the water column of the Great Lakes, enhancing P recycling and relieving the nitrogen (N) : P imbalance. Our data enables a coherent large-scale freshwater-to-oceanic comparison. We show that while different plankton groups accumulate different levels of polyP with smaller plankton accumulating more, P availability is the key driver of polyP variability within and across systems.