Diatoms are microscopic marine algae that are critical for global primary production, carbon sequestration, and fisheries productivity. However, select diatoms may form harmful algal blooms, which threaten marine ecosystems and the fisheries they sustain. Rapidly identifying harmful blooms is necessary to effectively manage marine resources, yet current identification methods are limited by expensive and labor-intensive in-situ point sampling.Hyperspectral remote sensing enables scalable monitoring, but its ability to resolve taxonomic shifts within phytoplankton groups (e.g. diatoms) is largely unknown. To investigate this uncertainty, we cultured four dominant diatom genera from the California Current upwelling system, including this system’s most abundant harmful algae, Pseudo-nitzschia. The hyperspectral absorption and backscatter of these taxa were measured and used to model spectral reflectances that remote sensing platforms (satellites/drones) might detect. Differences between fingerprints of these taxa were quantified using vector-based and statistical analyses.Mean spectral differences of 48% were observed between the most dominant diatom, Thalassiosira, and the most toxic diatom, Pseudo-nitzschia. Differences of approximately 30% were found between Pseudo-nitzschia and the second and third most abundant diatoms, Chaetoceros and Asterionellopsis. Successful identification of Pseudo-nitzschia’s reflectance fingerprint was driven by the presence of a unique feature around 560 nm. The distinct spectral fingerprint of Pseudo-nitzschia indicates that it can be distinguished from other diatom blooms using hyperspectral remote sensing platforms.

Recent studies highlight the application of deriving the attenuation coefficient from spaceborne photon-counting lidar ATLAS/ICESat-2 over open oceans on global scales. However, its performance in the more optically complex and variable environments of marginal seas, which are more susceptible to human activity, has not been validated yet. In this study, we present an in-depth analysis of the consistency between diffuse attenuation coefficient (Kd) detection from MODIS and ICESat-2 in China's Marginal Seas. Findings demonstrate that ICESat-2 possesses strong capabilities for the retrieval of the attenuation coefficient across differing aquatic environments. However, discrepancies exist between the lidar system attenuation coefficient obtained from ICESat-2 and the diffuse attenuation coefficient determined by MODIS, influenced by factors such as multiple scattering. Implementation of a novel multiple scattering correction model demonstrates a notable ability in significantly reducing the inconsistency. Validation with in-situ Biogeochemical Argo float measurements reveals an enhancement in the accuracy of lidar-derived diffuse attenuation coefficients upon correction, with the mean absolute percent difference between lidar-derived Kd and Argo-Kd decreasing from 26 % to 15.7 %. The multiple scattering model developed can bridge the gap between the passive and active remote sensing detection and improve the reliability of lidar-derived attenuation coefficients. Fusing these two missions will greatly improve ocean observation capabilities, providing unprecedented opportunities for precise and comprehensive assessment of marine light environments. This approach has broad implications for ocean science and the application of satellite remote sensing in environmental studies.

Competition for resources has long been viewed as a dominant mechanism for species exclusion in nature. Laboratory phytoplankton competition experiments have clearly demonstrated the principles of resource-based competitive exclusion, yielding the decades-old 'R* rule' stating that the species able to maintain steady-state biomass at the lowest resource level (R*) will outcompete all other species and that the number of stably coexisting species will equal the number of different limiting resources. However, the notion of resource-based competitive exclusion is clearly violated by natural phytoplankton assemblages that consistently exhibit coexisting species with vastly different resource acquisition potentials. Here, we explain why natural phytoplankton communities do not obey the 'R* rule', why cell-to-cell distancing and predator-prey dynamics prevent resource-based competitive exclusion, and why phytoplankton diversity is theoretically unconstrained within size-specific ecological niches created by predator-prey sets. We conclude this manuscript with an appeal that a more holistic, ecological explanation of competitive exclusion and biodiversity be adopted and taught that nurtures a more thorough understanding and modeling of natural phytoplankton and other microbial communities.

The annual periodicity present in spectral remote sensing reflectances (Rrs(λ)) derived from ocean color satellite data processed by NASA's Ocean Biology Processing Group has been shown to diverge from that observed in situ, with the source of the discrepancies remaining unresolved. Here we investigate such periodicity in reflectances derived from four satellite sensors and seven in situ locations. Periodicity in the time series of Rrs(λ) spectra are examined without prior assumption of the main constituent frequency through application of the floating-mean Lomb-Scargle periodogram. We show that a 1-term sinusoidal function specified at the annual frequency with a constant offset reproduces seasonal trends observed at various regional oceanic sites. Although unique regional features in mean, amplitude, and phase are generally well captured by Rrs(λ) derived from satellite measurements, discrepancies perpetuate the annual frequency into the satellite-to-in situ Rrs(λ) matchup difference. Specifying the origin of the discrepancy is challenging because the annual frequency is significantly contained in time series of many physical measurements, corrections, and ancillary data required for Rrs(λ) retrievals. Until differences in amplitude and phase between satellite and in situ Rrs(λ) time series are reduced, improved uncertainty estimates should be incorporated into downstream product analyses.

Climate-driven warming is projected to intensify wildfires, increasing their frequency and severity globally. Wildfires are an increasingly significant source of atmospheric deposition, delivering nutrients, organic matter, and trace metals to coastal and open ocean waters. These inputs have the potential to fertilize or inhibit microbial growth, yet their ecological impacts remain poorly understood. This study examines how ash leachate, derived from the 2017 Thomas Fire in California and lab-produced ash from Oregon vegetation, affects coastal plankton communities. Shipboard experiments off the California coast examined how pre-existing plankton biomass concentrations mediate responses to ash leachates. We found that ash leachate contained dissolved organic matter (DOM) that significantly increased bacterioplankton specific growth rates and DOM remineralization rates but had a negligible effect on bacterioplankton growth efficiency, suggesting low DOM bioavailability. Furthermore, ash-derived DOM had a higher potential to accumulate in high biomass water, where pre-existing DOM substrates may better support bacterial metabolism. Ash leachate had a neutral to negative effect on phytoplankton division rates and decreased microzooplankton grazing rates, particularly in low biomass water, leading to increased phytoplankton accumulation. Nanoeukaryotes accumulated in low biomass water, whereas picoeukaryotes and Synechococcus accumulated in high biomass water. Our findings suggest that the influence of ash deposition on DOM cycling, phytoplankton accumulation, and broader marine food web dynamics depends on pre-existing biomass levels. Understanding these interactions is critical for predicting the biogeochemical consequences of increasing wildfire activity on marine ecosystems.

Phytoplankton photoacclimation is a well‐documented response to changes in light and nutrient availability, with the Chlorophyll a to phytoplankton Carbon ratio ( = Chl: ) increasing at low light and decreasing under high light to optimize growth rate. Accurate estimation of phytoplankton growth rates and Net Primary Production (NPP) from space requires knowledge of , but cloud cover creates gaps. Current NPP models fill in the gaps by interpolating Chl (and other inputs) from clear‐sky pixels, ignoring the possibility of photoacclimation underneath clouds. Using data from 9,000 matchups between BioGeoChemical‐Argo floats and cloud cover from the Moderate Resolution Imaging Spectroradiometer, we compared the response of to irradiances under cloudy and clear skies. We found that phytoplankton photoacclimate similarly regardless of sky conditions at the global scale. This study highlights an incorrect assumption in current NPP estimates and suggests ways to improve global assessments of both chlorophyll and NPP.

Microorganisms, including bacteria, archaea, viruses, fungi, and protists, are essential to life on Earth and the functioning of the biosphere. Here, we discuss the key roles of microorganisms in achieving the United Nations Sustainable Development Goals (SDGs), highlighting recent and emerging advances in microbial research and technology that can facilitate our transition toward a sustainable future. Given the central role of microorganisms in the biochemical processing of elements, synthesizing new materials, supporting human health, and facilitating life in managed and natural landscapes, microbial research and technologies are directly or indirectly relevant for achieving each of the SDGs. More importantly, the ubiquitous and global role of microbes means that they present new opportunities for synergistically accelerating progress toward multiple sustainability goals. By effectively managing microbial health, we can achieve solutions that address multiple sustainability targets ranging from climate and human health to food and energy production. Emerging international policy frameworks should reflect the vital importance of microorganisms in achieving a sustainable future.

The composition of the marine phytoplankton community has been shown to impact many biogeochemical processes and marine ecosystem services. A variety of methods exist to characterize phytoplankton community composition (PCC), with varying degrees of taxonomic resolution. Accordingly, the resulting PCC determinations are dependent on the method used. Here, we use surface ocean samples collected in the North Atlantic and North Pacific Oceans to compare high performance liquid chromatography (HPLC) pigment-based PCC to four other methods: quantitative cell imaging, flow cytometry, and 16S and 18S rRNA amplicon sequencing. These methods allow characterization of both prokaryotic and eukaryotic PCC across a wide range of size classes. PCC estimates of many taxa resolved at the class level (e.g., diatoms) show strong positive correlations across methods, while other groups (e.g., dinoflagellates) are not well captured by one or more methods. Since variations in phytoplankton pigment concentrations are related to changes in optical properties, this combined dataset expands the potential scope of ocean color remote sensing by associating PCC at the genus- and species-level with group- or class-level PCC from pigments. Quantifying the strengths and limitations of pigment-based PCC methods compared to PCC assessments from amplicon sequencing, imaging, and cytometry methods is the first step toward the robust validation of remote sensing approaches to quantify PCC from space.

The gravitational settling of organic particles from the surface to the deep ocean is an important export pathway and one of the largest components of the marine biological carbon pump (BCP). The strength and efficiency of the gravitational pump is often measured using metrics reliant on reference depths and empirical formulations that parameterize the relationship between depth and flux or concentration. Here, BGC-Argo profiles were used to identify the isolume where POC concentration starts to decline, revealing attenuation trends below this isolume that are remarkably consistent across the global ocean. We developed a semi-mechanistic approach that uses observations from the first optical depth to predict POC concentration from the surface ocean to the base of the mesopelagic (1000 m), allowing assessments of spatial and temporal variability in BCP efficiencies. We find that rates of POC attenuation are high in areas of high biomass and low in areas of low biomass, supporting the view that bloom events sometimes result in a relatively weak deep biological pump characterized by low transfer efficiency to the base of the mesopelagic. Our isolume-based attenuation model was applied to satellite data to yield the first remote sensing-based estimate of integrated global POC stock of 3.02 Pg C for the upper 1000 m, with 1.27 Pg C of this global carbon stock located above the reference isolume where POC begins to attenuate.

Airborne high spectral resolution lidar (HSRL) measurements of ocean particulate backscatter (bbp) offer dramatic improvements in spatiotemporal coverage over in situ techniques, filling observational “blind spots” that limit our ability to study ocean processes. However, the technique has been assessed in only a few limited cases, and uncertainties remain regarding its applicability across a diversity of optical domains. In this study, we present the first comprehensive comparison of bbp derived from airborne HSRL, satellite ocean color, and in situ measurements to increase confidence in HSRL bbp retrievals and to demonstrate the value of airborne HSRL for assessing/improving satellite ocean color algorithms. Retrievals of bbp performed using the NASA Langley HSRL-1 instrument agreed with in situ measurements performed across a diversity of optical and ecological domains. Comparisons across multiple campaigns revealed regional and seasonal dependencies in the ocean color retrievals that likely resulted from applying a single configuration of the ocean color retrieval across multiple distinct domains. In two case studies, atmospheric measurements from HSRL-1 and systematic differences in bbp between HSRL and the 2018 NASA ocean color distribution provided evidence of insufficient atmospheric correction of the ocean color retrieval. These differences in bbp were absent from comparisons against the 2022 ocean color distribution, suggesting that changes made to the algorithm resulted in improved retrievals. These cases highlight the advantages of airborne HSRL for assessing and improving ocean color retrievals, namely its ability to provide simultaneous, independent profiles of atmosphere and ocean optical properties, and improvements in the spatiotemporal coverage of satellite matchups.

POC and PIC are indispensable components in the global ocean carbon cycle, their transport and space distribution being driven by the biological carbon pump and the carbonate pump. However, passive ocean color remote sensing, usually employed for POC and PIC research, experiences serious shortcomings in polar winter conditions due to its reliance on sunlight, leading to scarce data coverage in the polar regions. In contrast, CALIOP has shown considerable promise in high-latitude ocean observing. Past approaches to estimate POC from CALIOP data relied on bbp measurements obtained through the application of algorithms that presume an empirical linear correlation between bbp and the backscatter coefficient measured at 180°. This method does not account for any spatiotemporal variability in the conversion coefficient. Furthermore, the potential of CALIOP to provide estimates of PIC has not been explored yet. Here, we developed an innovative Two-Branch-Two-Step (TBTS) model to estimate POC and PIC from CALIOP data, which effectively expands the spatial coverage of the MODIS products. This method exploits the strength of deep learning while encapsulating the generalizability of physical parameters. This method consists of two branches: (1) a deep learning branch based on lidar attenuated backscatter waveform and (2) a branch focusing on physical parameters, including the total column-integrated depolarization ratio and the subsurface cross-polarized component of column-integrated backscatter. The model’s generalizability and accuracy are confirmed through the evaluation of a test dataset and validation using in-situ measurements. Our model outperforms several other prevalent machine learning models. We also dissected the importance of different parts of the input data using SHAP tools, thereby providing insights into the black-box nature of deep learning models. Using CALIOP products, we put forth the inaugural estimation of interannually resolved PIC and POC standing stocks in polar regions. The implementation of CALIOP in polar regions bridges the gap inherent in passive ocean color measurements. Lidar-derived total global PIC standing stock is estimated to be 8% higher than that from MODIS, while the POC standing stock is boosted by 17.2%. The carbon standing stock in polar regions exhibits significant inter-annual variability and apparent seasonal periodicity. Hence, results from this research effort clearly reveal that the exploitation of the CALIOP-derived POC and PIC measurements, in combination with the application of new approaches and algorithms to future space lidar data, will undeniably enhance our comprehension of the polar ocean carbon cycle. However, it is important to acknowledge that the CALIOP products may inherit biases from the MODIS data used for training. Hence, where MODIS data is available, it’s still the “better” product to use, but where there isn’t MODIS data, the CALIOP product is extremely useful, especially in the polar regions.

Marine phytoplankton are a diverse group of photoautotrophic organisms and key mediators in the global carbon cycle. Phytoplankton physiology and biomass accumulation are closely tied to mixed layer depth, but the intracellular metabolic pathways activated in response to changes in mixed layer depth remain less explored. Here, metatranscriptomics was used to characterize the phytoplankton community response to a mixed layer shallowing (from 233 to 5 m) over the course of two days during the late spring in the Northwest Atlantic. Most phytoplankton genera downregulated core photosynthesis, carbon storage, and carbon fixation genes as the system transitioned from a deep to a shallow mixed layer and shifted towards catabolism of stored carbon supportive of rapid cell growth. In contrast, phytoplankton genera exhibited divergent transcriptional patterns for photosystem light harvesting complex genes during this transition. Active virus infection, taken as the ratio of virus to host transcripts, increased in the Bacillariophyta (diatom) phylum and decreased in the Chlorophyta (green algae) phylum upon mixed layer shallowing. A conceptual model is proposed to provide ecophysiological context for our findings, in which integrated light limitation and lower division rates during transient deep mixing are hypothesized to disrupt resource-driven, oscillating transcript levels related to photosynthesis, carbon fixation, and carbon storage. Our findings highlight shared and unique transcriptional response strategies within phytoplankton communities acclimating to the dynamic light environment associated with transient deep mixing and shallowing events during the annual North Atlantic bloom.

Amines were measured by aerosol mass spectrometry (AMS) and Fourier transform infrared (FTIR) spectroscopy during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) cruises. Both AMS non-refractory (NR) amine ion fragments comprising the AMS CxHyNz family and FTIR non-volatile (NV) primary (C–NH2) amine groups typically had greater concentrations in continental air masses than in marine air masses. Secondary continental sources of AMS NR amine fragments were identified by consistent correlations with AMS NR nitrate, AMS NR f44 (the contribution of AMS ion signal at m/z 44 (CO2+) to the total AMS NR organic mass (OM) signal), ion chromatography (IC) non-sea-salt potassium (nssK+), and radon for most air masses. FTIR NV amine group mass concentrations for particles with diameters <1µm showed large contributions from a primary marine source that was identified by significant correlations with measurements of wind speed, chlorophyll a (chl a), seawater dimethylsulfide (DMS), AMS NR chloride, and IC sea salt as well as FTIR NV alcohol groups in both marine and continental air masses. FTIR NV amine group mass concentrations in <0.18 and <0.5 µm particle samples in marine air masses likely have a biogenic secondary source associated with strong correlations with FTIR NV acid groups, which are not present for <1 µm particle samples. The average seasonal contribution of AMS NR amine fragments and FTIR NV amine groups ranged from 27±57 % amine from primary marine sources and 73±152 % secondary continental amine during early spring to 53±76 % amine from primary marine sources and 47±68 % secondary continental amine during winter. These results demonstrate that AMS NR and FTIR NV amine measurements are complementary and can be used together to investigate the variety and sources of amines in the marine environment.

The straining regions of the ocean in between mesoscale eddies contain large vertical velocities that may be important in regulating phytoplankton accumulation rates. We analyze time series of variables measured by ocean Bio-Argo floats (mixed layer depths [MLDs], chlorophyll, and carbon concentrations) in conjunction with variables derived from satellite altimetry (strain rates, Lyapunov exponents, vertical velocities) to determine the evolution of mixed layer phytoplankton biomass in response to straining by the mesoscale geostrophic flow. A Lagrangian (water parcel following) framework is justified by restricting the analysis to profiles whose value of a Quasi-Planktonic Index-an index quantifying averaged distance between a float trajectory and a surface geostrophic trajectory over three consecutive time steps-is less than 5 km. Bin-averaged Lagrangian derivatives of phytoplankton biomass and chlorophyll concentration are positive for elevated strain rate and upwelling quasigeostrophic vertical velocities. Lagrangian derivatives of MLD and phytoplankton carbon averaged at straining fronts (in rotated along- and across-front coordinates) have features in common with submesoscale dynamics, including increasing phytoplankton carbon (and chlorophyll) and a shoaling mixed layer over the front. To elucidate a mechanism, we average time derivatives of modeled cell division rates, finding the pattern approximately matches the pattern of phytoplankton accumulation rates and is controlled primarily by the term modulating light stress, suggesting that frontal dynamics cause accelerations of division rates by increasing available light. Regions of increasing chlorophyll are also approximately co-located with upwelling quasigeostrophic velocity, suggesting non-Lagrangian behavior of floats causes some imprint of larger scale, more persistent mesoscale signals.

The biodiversity of the plankton has been interpreted largely through the monocle of competition. The spatial distancing of phytoplankton in nature is so large that cell boundary layers rarely overlap, undermining opportunities for resource-based competitive exclusion. Neutral theory accounts for biodiversity patterns based purely on random birth, death, immigration, and speciation events and has commonly served as a null hypothesis in terrestrial ecology but has received comparatively little attention in aquatic ecology. This review summarizes basic elements of neutral theory and explores its stand-alone utility for understanding phytoplankton diversity. A theoretical framework is described entailing a very nonneutral trophic exclusion principle melded with the concept of ecologically defined neutral niches. This perspective permits all phytoplankton size classes to coexist at any limiting resource level, predicts greater diversity than anticipated from readily identifiable environmental niches but less diversity than expected from pure neutral theory, and functions effectively in populations of distantly spaced individuals.

The year 2023 marked the tenth anniversary of the first published description of global ocean plankton stocks based on measurements from a satellite lidar. Diverse studies have since been conducted to further refine and validate the lidar retrievals and use them to discover new characteristics of plankton seasonal dynamics and marine animal migrations, as well as evaluate geophysical products from traditional passive ocean color sensors. Surprisingly, all of these developments have been achieved with lidar instruments not designed for ocean applications. Over this same decade, we have witnessed unprecedented changes in ocean ecosystems at unexpected rates and driven by a multitude of environmental stressors, with a dominant factor being climate warming. Understanding, predicting, and responding to these ecosystem changes requires a global ocean observing network linking satellite, in situ, and modeling approaches. Inspired by recent successes, we promote here the creation of a lidar global ocean climate record as a key element in this envisioned advanced observing system. Contributing to this record, we announce the development of a new satellite lidar mission with ocean-observing capabilities and then discuss additional technological advances that can be envisioned for subsequent missions. Finally, we discuss how a potential near-term gap in global ocean lidar data might, at least partially, be filled using on-orbit or soon-to-be-launched lidars designed for other disciplinary purposes, and we identify upcoming needs for in situ support systems and science community development.

Estimates of marine net primary production (NPP) commonly rely on limited in situ 14 C incubations or satellite‐based algorithms mainly constrained to the surface ocean. Here we combine data from biogeochemical Argo floats with a carbon‐based productivity model (CbPM) to compute vertically resolved estimates of NPP. Inferred NPP profiles derived by informing the CbPM with float‐based, depth‐resolved, bio‐optical data are able to qualitatively reproduce the vertical structure in NPP inferred from in situ 14 C incubations at various ocean regions. At station ALOHA, float‐based estimates agree within uncertainty with productivity observations at depth, but underestimate surface NPP. We test the ability of the CbPM to infer the depth‐resolved structure in NPP from bio‐optical properties in the mixed layer, in similar fashion as how remote sensing algorithms of ocean productivity operate. In Southern Ocean waters, the depth‐reconstructing implementation of the CbPM overestimates phytoplankton division rates and Chl:C below the mixed layer, resulting in artificially high subsurface NPP when compared with the fully float‐informed implementation of the model. The CbPM subsurface extrapolation of phytoplankton Chl, Chl:C, division rates, and NPP improves by accounting for deep nutrient (iron) stress impacts on photoacclimation in the Southern Ocean. This improvement is also observed in vertically integrated NPP, where the mean bias between model implementations in depth‐integrated productivity south of 30°S is reduced by 62% when account for deep iron limitation. Our results demonstrate that profiling data from biogeochemical Argo floats can serve to inform regional adjustments that lead to the improvement of marine productivity algorithms.

Phytoplankton respond to changes in their light environment by altering the composition of the photosynthetic electron transport (PET) chain. This process, called photoacclimation, balances light absorption with metabolic energy needs and is necessary for cells to maximize growth rate under changing light conditions. PET chain structure affects the shape of fluorescence curves (i.e. transients), and thus fluorescence may contain information about phytoplankton photoacclimation state. In this study, we examined whether the shape of the fluorescence transient measured in the marine diatoms Thalassiosira weissflogii and Phaeodactylum tricornutum changes in response to photoacclimation to different growth irradiances. In addition, we utilized various inhibitors of photosynthesis (DBMIB, methyl viologen, nigericin, and propyl gallate) to test whether the same PET components were controlling transient shape in both species. The overall shape of the fluorescence transient significantly differed between T. weissflogii and P. tricornutum, but in both species the transient shape changed predictably in response to increasing irradiance. However, the effect of various inhibitors differed between the two species, suggesting significant differences in the composition and activity of PET chain components. We hypothesize that the differences in transient shape and the effects of PET chain inhibitors in T. weissflogii and P. tricornutum reflect differences in photoacclimation strategy due to evolution in different marine environments.

Dissolved organic carbon (DOC) produced by primary production in the sunlit ocean can be physically transported to the mesopelagic zone. The majority of DOC exported to this zone is remineralized by heterotrophic microbes over a range of timescales. Capturing a deep convective mixing event is rare, as is observing how microbes respond in situ to the exported DOC. Here, we report ship and Argo float observations from hydrostation North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) 2 Station 4 (N2S4; 47.46°N, 38.72°W), a retentive anticyclonic eddy in the subtropical region of the western North Atlantic. Changes in biogeochemistry and bacterioplankton responses were tracked as the water column mixed to approximately 230 m and restratified over the subsequent 3 days. Over this period, rapid changes in bacterioplankton production (BP) and cell abundance were observed throughout the water column. BP increased by 91% in the euphotic zone (0–100 m) and 55% in the upper mesopelagic zone (100–200 m), corresponding to 33% and 103% increases in cell abundance, respectively. Within the upper mesopelagic, BP upon the occupation of N2S4 (20 ± 4.7 nmol C L–1 d–1) was significantly greater than the average upper mesopelagic BP rate (2.0 ± 1.6 nmol C L–1 h–1) at other stations that had been stratified for longer periods of time. BP continued to increase to 31 ± 3.0 nmol C L–1 d–1 over the 3-day occupation of N2S4. The rapid changes in BP in the upper mesopelagic did not coincide with rapid changes in community composition, but the taxa that increased in their relative contribution included those typically observed in the epipelagic zone. We interpret the subtle but significant community structure dynamics at N2S4 to reflect how injection of labile organic matter into the upper mesopelagic zone by physical mixing supports continued growth of euphotic zone-associated bacterioplankton lineages on a timescale of days.