Analysis of taiga and tundra lake browning trends from 2002 to 2021 using MODIS data.

Lakes in taiga and tundra regions may be silently undergoing changes due to global warming. One of those changes is browning in lake color. The browning interacts with the carbon cycle, ecosystem dynamics, and water quality in freshwater systems. However, spatiotemporal variabilities of browning in these regions have not been well documented. Using MODIS remote sensing reflectance at near ultraviolet wavelengths from 2002 to 2021 on the Google Earth Engine platform, we quantified long-term browning trends across 7616 lakes (larger than 10 km2) in taiga and tundra biomes. These lakes showed an overall decreased trend in browning (Theil-Sen Slope = 0.00015), with ∼36% of these lakes showing browning trends, and ∼1% of these lakes showing statistically significant (p-value <0.05) browning trends. The browning trends more likely occurred in small lakes in high latitude, low ground ice content regions, where air temperature increased and precipitation decreased. While temperature is projected to increase in response to climate change, our results provide one means to understand how biogeochemical cycles and ecological dynamics respond to climate change.

Molecular physiology of Antarctic diatom natural assemblages and bloom event reveal insights into strategies contributing to their ecological success.

The continental shelf of the Western Antarctic Peninsula (WAP) is a highly variable system characterized by strong cross-shelf gradients, rapid regional change, and large blooms of phytoplankton, notably diatoms. Rapid environmental changes coincide with shifts in plankton community composition and productivity, food web dynamics, and biogeochemistry. Despite the progress in identifying important environmental factors influencing plankton community composition in the WAP, the molecular basis for their survival in this oceanic region, as well as variations in species abundance, metabolism, and distribution, remains largely unresolved. Across a gradient of physicochemical parameters, we analyzed the metabolic profiles of phytoplankton as assessed through metatranscriptomic sequencing. Distinct phytoplankton communities and metabolisms closely mirrored the strong gradients in oceanographic parameters that existed from coastal to offshore regions. Diatoms were abundant in coastal, southern regions, where colder and fresher waters were conducive to a bloom of the centric diatom, Actinocyclus. Members of this genus invested heavily in growth and energy production; carbohydrate, amino acid, and nucleotide biosynthesis pathways; and coping with oxidative stress, resulting in uniquely expressed metabolic profiles compared to other diatoms. We observed strong molecular evidence for iron limitation in shelf and slope regions of the WAP, where diatoms in these regions employed iron-starvation induced proteins, a geranylgeranyl reductase, aquaporins, and urease, among other strategies, while limiting the use of iron-containing proteins. The metatranscriptomic survey performed here reveals functional differences in diatom communities and provides further insight into the environmental factors influencing the growth of diatoms and their predicted response to changes in ocean conditions. IMPORTANCE: In the Southern Ocean, phytoplankton must cope with harsh environmental conditions such as low light and growth-limiting concentrations of the micronutrient iron. Using metratranscriptomics, we assessed the influence of oceanographic variables on the diversity of the phytoplankton community composition and on the metabolic strategies of diatoms along the Western Antarctic Peninsula, a region undergoing rapid climate change. We found that cross-shelf differences in oceanographic parameters such as temperature and variable nutrient concentrations account for most of the differences in phytoplankton community composition and metabolism. We opportunistically characterized the metabolic underpinnings of a large bloom of the centric diatom Actinocyclus in coastal waters of the WAP. Our results indicate that physicochemical differences from onshore to offshore are stronger than between southern and northern regions of the WAP; however, these trends could change in the future, resulting in poleward shifts in functional differences in diatom communities and phytoplankton blooms.

Widespread phytoplankton blooms triggered by 2019-2020 Australian wildfires.

Droughts and climate-change-driven warming are leading to more frequent and intense wildfires1-3, arguably contributing to the severe 2019-2020 Australian wildfires4. The environmental and ecological impacts of the fires include loss of habitats and the emission of substantial amounts of atmospheric aerosols5-7. Aerosol emissions from wildfires can lead to the atmospheric transport of macronutrients and bio-essential trace metals such as nitrogen and iron, respectively8-10. It has been suggested that the oceanic deposition of wildfire aerosols can relieve nutrient limitations and, consequently, enhance marine productivity11,12, but direct observations are lacking. Here we use satellite and autonomous biogeochemical Argo float data to evaluate the effect of 2019-2020 Australian wildfire aerosol deposition on phytoplankton productivity. We find anomalously widespread phytoplankton blooms from December 2019 to March 2020 in the Southern Ocean downwind of Australia. Aerosol samples originating from the Australian wildfires contained a high iron content and atmospheric trajectories show that these aerosols were likely to be transported to the bloom regions, suggesting that the blooms resulted from the fertilization of the iron-limited waters of the Southern Ocean. Climate models project more frequent and severe wildfires in many regions1-3. A greater appreciation of the links between wildfires, pyrogenic aerosols13, nutrient cycling and marine photosynthesis could improve our understanding of the contemporary and glacial-interglacial cycling of atmospheric CO2 and the global climate system.

Decline in plankton diversity and carbon flux with reduced sea ice extent along the Western Antarctic Peninsula.

Since the middle of the past century, the Western Antarctic Peninsula has warmed rapidly with a significant loss of sea ice but the impacts on plankton biodiversity and carbon cycling remain an open question. Here, using a 5-year dataset of eukaryotic plankton DNA metabarcoding, we assess changes in biodiversity and net community production in this region. Our results show that sea-ice extent is a dominant factor influencing eukaryotic plankton community composition, biodiversity, and net community production. Species richness and evenness decline with an increase in sea surface temperature (SST). In regions with low SST and shallow mixed layers, the community was dominated by a diverse assemblage of diatoms and dinoflagellates. Conversely, less diverse plankton assemblages were observed in waters with higher SST and/or deep mixed layers when sea ice extent was lower. A genetic programming machine-learning model explained up to 80% of the net community production variability at the Western Antarctic Peninsula. Among the biological explanatory variables, the sea-ice environment associated plankton assemblage is the best predictor of net community production. We conclude that eukaryotic plankton diversity and carbon cycling at the Western Antarctic Peninsula are strongly linked to sea-ice conditions.

Specific eukaryotic plankton are good predictors of net community production in the Western Antarctic Peninsula.

Despite our current realization of the tremendous diversity that exists in plankton communities, we have little understanding of how this biodiversity influences the biological carbon pump other than broad paradigms such as diatoms contributing disproportionally to carbon export. Here we combine high-resolution underway O2/Ar, which provides an estimate of net community production, with high-throughput 18 S ribosomal DNA sequencing to elucidate the relationship between eukaryotic plankton community structure and carbon export potential at the Western Antarctica Peninsula (WAP), a region which has experienced rapid warming and ecosystem changes. Our results show that in a diverse plankton system comprised of ~464 operational taxonomic units (OTUs) with at least 97% 18 S identity, as few as two or three key OTUs, i.e. large diatoms, Phaeocystis, and mixotrophic/phagotrophic dinoflagellates, can explain a large majority of the spatial variability in the carbon export potential (76-92%). Moreover, we find based on a community co-occurrence network analysis that ecosystems with lower export potential have more tightly coupled communities. Our results indicate that defining plankton communities at a deeper taxonomic resolution than by functional groups and accounting for the differences in size and coupling between groups can substantially improve organic carbon flux predictions.

Remote chlorophyll-a estimates for inland waters based on a cluster-based classification.

Accurate estimates of chlorophyll-a concentration (Chl-a) from remotely sensed data for inland waters are challenging due to their optical complexity. In this study, a framework of Chl-a estimation is established for optically complex inland waters based on combination of water optical classification and two semi-empirical algorithms. Three spectrally distinct water types (Type I to Type III) are first identified using a clustering method performed on remote sensing reflectance (R(rs)) from datasets containing 231 samples from Lake Taihu, Lake Chaohu, Lake Dianchi, and Three Gorges Reservoir. The classification criteria for each optical water type are subsequently defined for MERIS images based on the spectral characteristics of the three water types. The criteria cluster every R(rs) spectrum into one of the three water types by comparing the values from band 7 (central band: 665 nm), band 8 (central band: 681.25 nm), and band 9 (central band: 708.75 nm) of MERIS images. Based on the water classification, the type-specific three-band algorithms (TBA) and type-specific advanced three-band algorithm (ATBA) are developed for each water type using the same datasets. By pre-classifying, errors are decreased for the two algorithms, with the mean absolute percent error (MAPE) of TBA decreasing from 36.5% to 23% for the calibration datasets, and from 40% to 28% for ATBA. The accuracy of the two algorithms for validation data indicates that optical classification eliminates the need to adjust the optimal locations of the three bands or to re-parameterize to estimate Chl-a for other waters. The classification criteria and the type-specific ATBA are additionally validated by two MERIS images. The framework of first classifying optical water types based on reflectance characteristics and subsequently developing type-specific algorithms for different water types is a valid scheme for reducing errors in Chl-a estimation for optically complex inland waters.

A semi-analytical algorithm for remote estimation of phycocyanin in inland waters.

Phycocyanin (PC) is the unique and important accessory pigment for monitoring toxic cyanobacteria in inland waters. In this study, a semi-analytical algorithm combining both three band indices and a baseline algorithm (TBBA) was developed to estimate PC concentrations and then tested in three eutrophic and turbid reservoirs. TBBA does not need to optimize wavelengths as either the traditional baseline algorithm or three-band algorithms does when it is used across different study sites. TBBA evidently corrects some effects of absorptions due to colored detritus matter and other pigments and backscattering of water column. TBBA accurately estimated PC concentrations with R(2)=0.8573 and rRMSE=31.4% for water samples with the PC range from 1.4 mgm(-3) to 146.1 mgm(-3). Particularly, TBBA outperformed three-band algorithms and a previously proposed semi-empirical algorithm for the prediction of low PC (PC ≤ 50 mgm(-3)) concentration. Further analysis reveals that both the variations of PC:Chl-a and PC:TSM are important factors influencing the performance of all PC algorithms examined in this study and more efforts are required to improve the performance of TBBA on water samples with low PC concentration.

Hyperspectral determination of eutrophication for a water supply source via genetic algorithm-partial least squares (GA-PLS) modeling.

Morse Reservoir (MR), a major source of the water supply for the Indianapolis metropolitan region, is now experiencing nuisance cyanobacterial blooms. These blooms cause water quality degradation, as well as reducing the aesthetic quality of water by producing toxins, scums, and foul odors. Hyperspectral remote sensing data from both in situ and airborne AISA measurements were applied to GA-PLS by relating the spectral signal with measured water eutrophication parameters, e.g., chlorophyll-a (Chl-a), phycocyanin (PC), total suspended matter (TSM), and Secchi disk depth (SDD). Our results indicate that GA-PLS relating field sensor acquired spectral reflectance to the above-mentioned four parameters yielded low root mean square error between measured and estimated Chl-a (RMSE=10.4; Range (R): 1.8-215.8 µg/L), PC (RMSE=18.6; R: 1.4-371.0 µg/L), TSM (RMSE=3.8; R: 3.6-81.4 mg/L), SDD (RMSE=5.8; R: 25-135 cm) for MR. The GA-PLS model also yielded high performance with AISA image spectra, and the RMSEs were 12.1 µg/L, 25.3 µg/L, 5.9 mg/L and 5.7 cm, respectively for Chl-a, PC, TSM, and SDD. Four water quality parameters were mapped with GA-PLS using AISA hyperspectral image. Based on these results, in situ and airborne hyperspectral remote sensors can provide both quantitative and qualitative information on the distribution and concentration of cyanobacteria, suspended matter, and transparency in MR.