Contrasting water quality trends are occurring within and across North America, with waterbodies experiencing increasing phytoplankton blooms, increasing dissolved organic matter, or both, while others are becoming clearer and bluer; dramatically changing water color. To assess the spatial and temporal variability in water color, we quantified trends in satellite-derived dominant wavelength (λd) from 1984 to 2020 for 484 reservoirs across the state of Missouri using the LimnoSat-US dataset. Currently, the vast majority of Missouri reservoirs are classified as green and within a range (538–555 nm) that lies closer to the brown color endmember. Nearly one-third of reservoirs (n = 159) experienced significant temporal shifts in water color, with more (n = 91) negative (e.g., bluer) than positive (n = 68) λd trends. Linear mixed-effect models indicate that periods of extreme wetness and drought are associated with browner and bluer waters, respectively, and boosted regression trees further reveal that waterbody and watershed characteristics are important predictors for water color trends. We also analyzed trends in summer water quality (WQ) parameters from two long-term monitoring programs to evaluate independent and synchronous changes with λd. We provide analyses showing that particulate inorganic matter and Secchi depth most strongly correlated with λd, and total nitrogen and total phosphorus concentrations that are not typically associated with satellite-derived data have greater co-variance with λd than chlorophyll a. Although bluer waters often reflect reductions in inorganic turbidity, our findings show that they can at times coincide with increases in chlorophyll a. We further demonstrate that while λd trends broadly align with changes in water quality, co-occurring water quality and color trends in Missouri reservoirs at times defy a simplistic canonical interpretation, particularly in eutrophic waterbodies where changes in nutrient concentrations, chlorophyll a and water color can occur independent of each other. Our results help explain some of the previously observed heterogeneous controls on water color and emphasize the importance of integrating water quality data alongside commonly used landscape and morphological features.

Optical water typing has been widely used in aquatic research to classify water bodies based on their inherent optical properties as perceived through satellite-based measures of water color. While optical water type (OWT) classifications have primarily been used to better understand water color dynamics and improve satellite-based estimates of water clarity, chlorophyll a, and other optically active constituents, its potential for broader water quality assessment has received less attention. In this study, we examine the relationships between a suite of water quality parameters, including nutrient concentrations, and OWTs in Chesapeake Bay, an optically complex temperate estuary with an extensive water quality monitoring program. Using machine learning, we grouped Rrs data into ten dominant OWTs; the optimum number of clusters identified from a statistical within-cluster dispersion test. These OWTs ranged from brown to blue/green estuarine waters and emerged with high spatial contiguity. By analyzing synchronously measured discrete water quality variables grouped by corresponding OWTs, unexpected patterns became evident. Notably, total nitrogen concentration emerged as having statistically significant differences between OWTs, suggesting our approach can enhance understanding of nutrient pollution at the scale of a large optically complex estuary, especially in times of reduced fixed sampling routines (e.g., winter). This study aids in the interpretation of Bay-wide water quality trends, can assist in the dynamic selection of water quality retrieval algorithms, and provides high resolution data to identify regions of water quality impairment.

The majority of heat associated with climate change has been absorbed in the sunlit surface ocean where phytoplankton carry out half of biospheric net primary production (NPP). The physical entrainment of nutrients from depth into the surface constrains NPP across most of the ocean, therefore it has been widely hypothesized that a warmer and more thermally stratified ocean will diminish NPP. As phytoplankton are the dominant driver of ocean color, the satellite remote sensing record is the best approach to assess global NPP trends. Here we show that statistically significant decreases in NPP have occurred in almost half of the ocean and these changes are dominated by declines in the tropical and subtropical stratified ocean. A deeper analysis confirms that strengthening nutrient limitation is largely driving declining NPP. Climate-mediated shifts in NPP represent a fundamental perturbation to biogeochemical cycles that can further weaken global fisheries.

Understanding the interplay of ocean physics and biology, particularly at the submesoscale and below (<30km), is an ongoing challenge in oceanography. While poorly constrained, these scales may be of critical importance for understanding how changing ocean dynamics will impact marine ecosystems. Fronts in the ocean, regions where two disparate water masses meet and isopycnals become tilted towards vertical, are considered hotspots for biophysical interaction, but there is limited observational evidence at the appropriate scales to assess their importance. Western boundary currents like the Gulf Stream are of particular interest as these dynamic physical regions are thought to influence both productivity and composition of primary producers; however, how exactly this plays out, and at what scales, is not well known. Using satellite data and two years of detailed in situ observations across the Gulf Stream front near Cape Hatteras, North Carolina, U.S.A., we investigate how submesoscale frontal dynamics could affect biological communities associated with frontal regions and generate hotspots of productivity and export. In this analysis we assess the seasonality and phenology of the region, generalize the kilometer-scale structure of the front, and analyze 69 transects to assess two physical processes of potential biogeochemical importance: cold shelf filament subduction and high salinity Sargasso Sea obduction. We link these processes observationally to the meander phase of the Gulf Stream and discuss how cold filament subduction could be exporting carbon and how obduction of high salinity water from depth often leads to high chlorophyll-a. Finally, we report on phytoplankton community composition in each of these features and integrate these new observations into our understanding of frontal submesoscale dynamics. Plain Language Summary Phytoplankton move with large currents and are stirred by eddies with diameters ranging from 100s of kilometers down to the meter scale. Their growth is impacted by physical factors like light and temperature and also chemical and biological factors like nutrient availability, and their accumulation is also impacted by top down controls (zooplankton grazing, viral lysis) and competition with other phytoplankton. This interplay of physics and biology in determining the biomass and composition of phytoplankton communities is poorly understood and is key to understanding marine ecosystem resilience and structure in a changing ocean. In this work we investigated the impact of physics and biology on phytoplankton across scales focusing on the Gulf Stream front. Fronts in the ocean are where lines of equal density go from being horizontal to having a vertical tile, and because of this can enable nutrients and plankton to move from depth to the surface and vice versa. The objective of this work is to understand how physics might drive important changes in phytoplankton biomass and composition in the Gulf Stream front, which is amongst the sharpest gradients in temperature, density, and current speed in the global ocean. We find two frequent processes at the front, the apparent subduction of cold filaments down along the edge of the Gulf Stream, associated with meander troughs, and obduction of high salinity Sargasso Sea water into the front linked to meander crests. While ephemeral, these processes are frequent and could have a large impact on local phytoplankton biomass, phytoplankton composition, and the export of organic matter to depth. Key Points The frontal zone between the Gulf Stream and the shelf has an interface water mass which appears to have different origins with a range of biogeochemical impacts. Meanders appear to largely control the frontal interface: troughs lead to subduction of shelf filaments and crests lead to obduction of high salinity water. These two processes are common at the front and could lead to ephemeral kilometer- scale export and productivity.

The impact of whiting events on global inorganic carbon cycle and climate change is debatable at the current ocean pH. This work engineered whiting events in a high pH, high alkalinity microalgal culture for carbon dioxide capture and storage. When growing the marine microalga Nannochloropsis oceanica IMET1 in photobioreactors, culture alkalinity more than doubled, increasing from 72.5 mg L-1 to a maximum level of 159.6 mg L-1. At the same time, culture pH increased from 7.9 to 10.1 with concomitant calcium carbonate production. X-ray diffraction analysis revealed the precipitated calcium carbonate was primarily monohydrocalcite. The lab culture was scaled up to a 340-L bioreactor, in which Nannochloropsis ash-free dry weight productivities ranged from 25.1 to 51.4 g m-2 d-1 and a maximum monohydrocalcite productivity of 133.4 g m2 d-1 was recorded. In this system, Nannochloropsis biomass contained about 23.9% lipids and the eicosapentaenoic acid content was about 1.8%. Together, these results suggest the microalgae-driven calcium carbonate and biomass production process efficiently captures and stores atmospheric carbon dioxide in the form of calcium carbonate while producing valuable bioproducts. Study of the bacterial communities associated with the Nannochloropsis culture identified four dominant species Maricaulis maris, Marinisubtilis pacificus, Gracilimonas sp., and an uncultured bacterium in the OD1 phylum with interesting features that warrant further investigation.

Ocean physics and biology can interact in myriad and complex ways. Eddies, features found at many scales in the ocean, can drive substantial changes in physical and biogeochemical fields with major implications for marine ecosystems. Mesoscale eddies are challenging to model and difficult to observe synoptically at sea due to their fine-scale variability yet broad extent. In this work we observed a frontal eddy just north of Cape Hatteras via an intensive hydrographic, biogeochemical, and optical sampling campaign. Frontal eddies occur in western boundary currents around the globe and there are major gaps in our understanding of their ecosystem impacts. In the Gulf Stream, frontal eddies have been studied in the South Atlantic Bight, where they are generally assumed to shear apart passing Cape Hatteras. However, we found that the observed frontal eddy had different physical properties and phytoplankton community composition from adjacent water masses, in addition to continued cyclonic rotation. In this work we first synthesize the overall ecological impacts of frontal eddies in a simple conceptual model. This conceptual model led to the hypothesis that frontal eddies could be well timed to supply zooplankton to secondary consumers off Cape Hatteras where there is a notably high concentration and diversity of top predators. Towards testing this hypothesis and our conceptual model we report on the biogeochemical state of this particular eddy connecting physical and biological dynamics, analyze how it differs from Gulf Stream and shelf waters even in “death”, and refine our initial model with this new data. Key Points In-depth investigation of a frontal eddy in the Gulf Stream off Cape Hatteras, North Carolina Continued physical and biogeochemical differences are observed between the eddy and adjacent water masses even as it begins to shear apart We share a conceptual model of the ecological impact of frontal eddies with a hypothesis that they supply zooplankton to secondary consumers Plain Language Summary Frontal eddies are spinning masses of water (~30km in diameter) that move along western boundary currents like the Gulf Stream. When they form they carry productive coastal water into the Gulf Stream and drive upwelling within their cores. Together this leads to an increase in the amount of phytoplankton within them - much higher compared to surrounding nutrient-limited Gulf Stream water. On the east coast of the United States one common area of frontal eddy formation is just off Charleston, SC. Eddies then travel up the coast and dissipate near Cape Hatteras, NC. In this work we measured a wide range of physical and biological properties of a frontal eddy just north of Cape Hatteras. We compared these properties within the eddy to the coastal water on one side and the Gulf Stream water on the other, finding clear differences in phytoplankton community composition and other physical and chemical properties. Using the results of these observations together with previous studies we share a simple model for how frontal eddies may impact phytoplankton, zooplankton, and fish – hypothesizing that they may contribute to the high diversity and density of top predators off Cape Hatteras.

Coastal wetlands are among the most productive ecosystems in the world and provide important ecosystem services related to improved water quality, carbon sequestration, and biodiversity. In many locations, wetlands are threatened by coastal development and rising sea levels, prompting an era of tidal wetland restoration. The creation and restoration of tidal marshes necessitate the need for ecosystem monitoring. While satellite remote sensing is a valuable monitoring tool; the spatial and temporal resolution of imagery often places operational constraints, especially in small or spatially complex environments. Unoccupied aircraft systems (UAS) are an emerging remote sensing platform that collects data with flexible on-demand capabilities at much greater spatial resolution than sensors on aircraft and satellites, and resultant imagery can be readily rendered in three dimensions through Structure from Motion (SfM) photogrammetric processing. In this study, UAS data at 5 cm resolution was collected at an engineered wetland at Poplar Island, located in Chesapeake Bay, MD United States five times throughout 2019 to 2022. The wetland is dominated by two vegetation species: Spartina alterniflora and Spartina patens that were originally planted in 2005 in low and high marsh elevation zones respectively. During each survey, UAS multispectral reflectance, canopy elevation, and texture were derived and used as input into supervised random forest classification models to classify species-specific marsh vegetation. Overall accuracy ranged from 97% to 99%, with texture and canopy elevation variables being the most important across all datasets. Random forest classifications were also applied to down-sampled UAS data which resulted in a decline in classification accuracy as spatial resolution decreased (pixels became larger), indicating the benefit of using ultra-high resolution imagery to accurately and precisely distinguish between wetland vegetation. High resolution vegetation classification maps were compared to the 2005 as-built planting plans, demonstrating significant changes in vegetation and potential instances of marsh migration. The amount of vegetation change in the high marsh zone positively correlated with interannual variations in local sea level, suggesting a feedback between vegetation and tidal inundation. This study demonstrates that UAS remote sensing has great potential to assist in large-scale estimates of vegetation changes and can improve restoration monitoring success.

As global surface temperatures continue to rise as a result of anthropogenic climate change, effects in temperate lakes are likely to be more pronounced than in other ecosystems. Decreases in snow and ice cover extent and duration and extended periods of summer stratification have been observed in temperate lake systems throughout the Anthropocene. However, the effects of changing snow and ice cover on lacustrine communities remain largely uninvestigated. We examined underwater light climate and associated primary productivity patterns under snow-covered and clear-lake ice in 6 inland lakes in Minnesota, USA, spanning gradients of water column optical properties (blue, green, brown) associated with trophic status and organic material content. In all lakes, snow cover influenced not only the intensity, but also the spectral signature of light penetrating into the water column. Specifically, the wavelength of maximum penetration was shifted towards longer wavelengths under snow cover in green (eutrophic) lakes but was shifted towards shorter wavelengths in blue and brown lakes. Volumetric primary productivity was often higher than anticipated (e.g., ∼1200 mg m−3 d−1; Lake Minnetonka, snow-covered ice). Carbon assimilation rates were lower under snow-covered ice throughout the water column in all lake types except immediately under cleared ice in eutrophic lakes, where phytoplankton were likely photoinhibited because of the penetration of intense, short-wavelength light. These findings suggest that changes to snow and ice cover under ongoing climate change scenarios can affect patterns of phytoplankton primary productivity in sensitive aquatic ecosystems.

Phytoplankton photosynthesis and organic matter production fuel marine ecosystems and provide the source material for trophic transfer and export to the ocean interior. Methods for directly measuring photosynthetic rates (both Gross and Net Primary Production, GPP and NPP) are limited in extent and not suitable for broad regional or global assessments. Satellite-based models for estimating GPP and NPP offer the most practical means to meet this need. A growing suite of models has proliferated over the last 25 years and helped shape our understanding of biological variability in the ocean, its role in elemental cycling, and feedbacks with the Earth climate system. This review documents efforts to model GPP and NPP from remote sensing measurements throughout the satellite ocean color era and highlights scientific findings from application of these approaches. In addition to discussing past milestones, we synthesize current state-of-the-art approaches and discuss similarities and differences in their global distributions of NPP or GPP. This review also covers published validation efforts to assess ensemble and model-specific accuracy and last, we provide a vision for future efforts that identifies remaining barriers to improving satellite estimates of GPP and NPP.

Winter, historically a largely un-monitored season, is important and changing. There is evidence of the importance of under-ice phytoplankton in temperate lakes, but it is currently unknown if the often high winter phytoplankton biomass translates to high productivity and what influence it has on year-round lake metabolism. Winters are getting shorter, but our ability to forecast change is hindered by our limited understanding of what happens under the ice. Here, we compare under-ice and open-water rates of areal gross production (AGP) and areal respiration (AR) from 3 Canadian reservoirs and one large lake using oxygen (O2) changes in light-dark bottle experiments, δ18O-O2 models, and fluorometry. During the open-water season, AGP was 81× greater than under-ice rates, with AR rates 8× higher than measured during winter. Open-water samples indicated autotrophy (P:R= 1.10). Consistent with current assumptions, the cold under-ice environment is associated with low primary productivity. Our results challenge the assumption that mean water column irradiance is lowest during the winter in dimictic water bodies; we find similar light conditions during the open-water season. Winter mean light is regulated by snow thickness; upon manual snow removal, we observe a 67 % increase in under-ice mean water column irradiance. The first-ever under-ice application of the δ18O2-method indicated that AGP responded to improvements in light. This study reveals further insights into the importance of under-ice metabolism on year-round processes in a changing climate.

To ensure the long-term sustainable use of African Great Lakes (AGL), and to better understand the functioning of these ecosystems, authorities, managers and scientists need regularly collected scientific data and information of key environmental indicators over multi-years to make informed decisions. Monitoring is regularly conducted at some sites across AGL; while at others sites, it is rare or conducted irregularly in response to sporadic funding or short-term projects/studies. Managers and scientists working on the AGL thus often lack critical long-term data to evaluate and gauge ongoing changes. Hence, we propose a multi-lake approach to harmonize data collection modalities for better understanding of regional and global environmental impacts on AGL. Climate variability has had strong impacts on all AGL in the recent past. Although these lakes have specific characteristics, their limnological cycles show many similarities. Because different anthropogenic pressures take place at the different AGL, harmonized multi-lake monitoring will provide comparable data to address the main drivers of concern (climate versus regional anthropogenic impact). To realize harmonized long-term multi-lake monitoring, the approach will need: (1) support of a wide community of researchers and managers; (2) political goodwill towards a common goal for such monitoring; and (3) sufficient capacity (e.g., institutional, financial, human and logistic resources) for its implementation. This paper presents an assessment of the state of monitoring the AGL and possible approaches to realize a long-term, multi-lake harmonized monitoring strategy. Key parameters are proposed. The support of national and regional authorities is necessary as each AGL crosses international boundaries.

Satellite remote sensing permits large-scale monitoring of coastal waters through synoptic measurements of water-leaving radiance that can be scaled to relevant water quality metrics and in turn help inform local and regional responses to a variety of stressors. As both the incident and water-leaving radiance are affected by interactions with the intervening atmosphere, the efficacy of atmospheric correction algorithms is essential to derive accurate water-leaving radiometry. Modern ocean color satellite sensors such as the Ocean and Land Colour Instrument (OLCI) onboard the Copernicus Sentinel-3A and -3B satellites are providing unprecedented operational data at the higher spatial, spectral, and temporal resolution that is necessary to resolve optically complex coastal water quality. Validating these satellite-based radiance measurements with vicarious in situ radiometry, especially in optically complex coastal waters, is a critical step in not only evaluating atmospheric correction algorithm performance but ultimately providing accurate water quality metrics for stakeholders. In this study, a regional in situ dataset from the Chesapeake Bay was used to evaluate the performance of four atmospheric correction algorithms applied to OLCI Level-1 data. Images of the Chesapeake Bay are processed through a neural-net based algorithm (C2RCC), a spectral optimization-based algorithm (POLYMER), an iterative two-band bio-optical-based algorithm (L2gen), and compared to the standard Level-2 OLCI data (BAC). Performance was evaluated through a matchup analysis to in situ remote sensing reflectance data. Statistical metrics demonstrated that C2RCC had the best performance, particularly in the longer wavelengths (>560 nm) and POLYMER contained the most clear day coverage (fewest flagged data). This study provides a framework with associated uncertainties and recommendations to utilize OLCI ocean color data to monitor the water quality and biogeochemical dynamics in Chesapeake Bay.

Accurate and robust retrieval of ocean color from remote sensing enables critical observations of aquatic natural systems, from open ocean biological oceanography, coastal biodiversity, and water quality for human health. In the last decade, studies have increasingly highlighted the important role of small‐scale processes in coastal and marine ecology and biogeochemistry, but observation and modeling at these scales remains technologically limited. Unoccupied aircraft systems (UAS, aka drones) can rapidly sample large areas with high spatial and temporal resolution; but the challenge of accurately retrieving ocean color, particularly with common wide field‐of‐view multispectral imagers, has limited the adoption of this technology. As UAS endurance, autonomy, and sensor capabilities continue to increase, so does this technology's potential to observe the ocean at fine scales, but only if proper protocols are followed. The present study provides a guide for achieving (1) ideal viewing geometry of UAS‐borne ocean color sensors, (2) techniques for the removal of sun glint and reflected skylight to derive water‐leaving radiances, (3) characterization of uncertainty in these measurements, and (4) converting water‐leaving radiances to remote‐sensing reflectance for analytic end products such as chlorophyll a estimates. Documented open‐source code facilitates replication of this emerging technique. Using this methodology, we briefly describing fine‐scale variability of the Gulf Stream front off North Carolina alongside synoptic satellite data and in situ measurements for comparison. These results demonstrate how UAS‐based ocean color measurements complement and enhance conventional ocean observations and modeling to resolve fine‐scale variability and close the lacuna between satellite and in situ methods.

Phytoplankton photosynthetic physiology can be investigated through single-turnover variable chlorophyll fluorescence (ST-ChlF) approaches, which carry unique potential to autonomously collect data at high spatial and temporal resolution. Over the past decades, significant progress has been made in the development and application of ST-ChlF methods in aquatic ecosystems, and in the interpretation of the resulting observations. At the same time, however, an increasing number of sensor types, sampling protocols, and data processing algorithms have created confusion and uncertainty among potential users, with a growing divergence of practice among different research groups. In this review, we assist the existing and upcoming user community by providing an overview of current approaches and consensus recommendations for the use of ST-ChlF measurements to examine in-situ phytoplankton productivity and photo-physiology. We argue that a consistency of practice and adherence to basic operational and quality control standards is critical to ensuring data inter-comparability. Large datasets of inter-comparable and globally coherent ST-ChlF observations hold the potential to reveal large-scale patterns and trends in phytoplankton photo-physiology, photosynthetic rates and bottom-up controls on primary productivity. As such, they hold great potential to provide invaluable physiological observations on the scales relevant for the development and validation of ecosystem models and remote sensing algorithms.

Despite increasing interest in winter limnology, few studies have examined under-ice zooplankton communities and the factors shaping them in different types of temperate lakes. To better understand drivers of zooplankton community structure in winter and summer, we sampled 13 lakes across a large trophic status gradient for crustacean zooplankton abundance, taxonomic and functional community composition and C/N stable isotopes. Average winter zooplankton densities were one-third of summer densities across the study lakes. Proportionally, cladocerans were more abundant in summer than winter, with the opposite pattern for calanoids and cyclopoids. In green (eutrophic) lakes, zooplankton densities were higher under the ice than in brown (dystrophic) and blue (oligotrophic) lakes, suggesting better conditions for zooplankton in productive lakes during winter. Overall, zooplankton communities were more similar across lakes under the ice than during the open water season. Feeding group classification showed a decrease in herbivore abundance and an increase in predators from summer to winter. C/N stable isotope results suggested higher lipid content in overwintering zooplankton and potentially increased reliance on the microbial loop by winter zooplankton. Our results show substantial variation in the seasonality of zooplankton communities in different lake types and identify some of the factors responsible for this variation.

Unoccupied aircraft systems (UAS, or drones) equipped with off-the-shelf multispectral sensors originally designed for terrestrial applications can also be used to derive water quality properties in coastal waters. The at-sensor total radiance a UAS measured constitutes the sum of water-leaving radiance (L W ) and incident radiance reflected off the sea surface into the detector’s field of view (L SR ). L W is radiance that emanates from the water and contains a spectral shape and magnitude governed by optically active water constituents interacting with downwelling irradiance while L SR is independent of water constituents and is instead governed by a given sea-state surface reflecting light; a familiar example is sun glint. Failure to accurately account for L SR can significantly influence Rrs, resulting in inaccurate water quality estimates once algorithms are applied. The objective of this paper is to evaluate the efficacy of methods that remove L SR from total UAS radiance measurements in order to derive more accurate remotely sensed retrievals of scientifically valuable in-water constituents. UAS derived radiometric measurements are evaluated against in situ hyperspectral Rrs measurements to determine the best performing method of estimating and removing surface reflected light and derived water quality estimates. It is recommended to use a pixel-based approach that exploits the high absorption of water at NIR wavelengths to estimate and remove L SR . Multiple linear regressions applied to UAS derived Rrs measurements and in situ chlorophyll a and total suspended solid concentrations resulted in 37 and 9% relative error, respectively, which is comparable to coastal water quality algorithms found in the literature. Future research could account for the high resolution and multi-angular aspect of L SR by using a combination of photogrammetry and radiometry techniques. Management implications from this research include improved water quality monitoring of coastal and inland water bodies in order to effectively track trends, identify and mitigate pollution sources, and discern potential human health risks.

We review alternative hypotheses and associated mechanisms to explain Lake Victoria’s Nile perch (Lates niloticus) takeover and concurrent reduction in haplochromines through a (re)analysis of long-term climate, limnological, and stock observations in comparison with size-spectrum model predictions of co-existence, extinction, and demographic change. The empirical observations are in agreement with the outcomes of the model containing two interacting species with life histories matching Nile perch and a generalized haplochromine. The dynamic interactions may have depended on size-related differences in early juvenile mortality: mouth-brooding haplochromines escape predation mortality in early life stages, unlike Nile perch, which have miniscule planktonic eggs and larvae. In our model, predation on the latter by planktivorous haplochromine fry acts as a stabilizing factor for co-existence, but external mortality on the haplochromines would disrupt this balance in favor of Nile perch. To explain the observed switch, mortality on haplochromines would need to be much higher than the fishing mortality that can be realistically reconstructed from observations. Abrupt concomitant changes in algal and zooplankton composition, decreased water column transparency, and widespread hypoxia from increased eutrophication most likely caused haplochromine biomass decline. We hypothesize that the shift to Nile perch was a consequence of an externally caused, climate-triggered decrease in haplochromine biomass and associated recruitment failure rather than a direct cause of the introduction.

The Carbon, Absorption, and Fluorescence Euphotic-resolving (CAFE) net primary production model is an adaptable framework for advancing global ocean productivity assessments by exploiting state-of-the-art satellite ocean color analyses and addressing key physiological and ecological attributes of phytoplankton. Here we present the first implementation of the CAFE model that incorporates inherent optical properties derived from ocean color measurements into a mechanistic and accurate model of phytoplankton growth rates (mu) and net phytoplankton production (NPP). The CAFE model calculates NPP as the product of energy absorption (Q(PAR)), and the efficiency (phi(mu)) by which absorbed energy is converted into carbon biomass (C-Phyto), while mu is calculated as NPP normalized to C-Phyto. The CAFE model performance is evaluated alongside 21 other NPP models against a spatially robust and globally representative set of direct NPP measurements. This analysis demonstrates that the CAFE model explains the greatest amount of variance and has the lowest model bias relative to other NPP models analyzed with this data set. Global oceanic NPP from the CAFE model (52 Pg C m(-2) yr(-1)) and mean division rates (0.34 day(-1)) are derived from climatological satellite data (2002-2014). This manuscript discusses and validates individual CAFE model parameters (e.g., Q(PAR) and phi(mu)), provides detailed sensitivity analyses, and compares the CAFE model results and parameterization to other widely cited models.