Harrison, Cheryl S., and Jessica Y Luo, et al., February 2021: Identifying global favourable habitat for early juvenile loggerhead sea turtles. Journal of the Royal Society Interface, 18(175), DOI:10.1098/rsif.2020.0799. Abstract
Loggerhead sea turtles (Caretta caretta) nest globally on sandy beaches, with hatchlings dispersing into the open ocean. Where these juveniles go and what habitat they rely on remains a critical research question for informing conservation priorities. Here a high-resolution Earth system model is used to determine the biophysical geography of favourable ocean habitat for loggerhead sea turtles globally during their first year of life on the basis of ocean current transport, thermal constraints and food availability (defined here as the summed lower trophic level carbon biomass). Dispersal is simulated from eight major nesting sites distributed across the globe in four representative years using particle tracking. Dispersal densities are identified for all turtles, and for the top 15% ‘best-fed’ turtles that have not encountered metabolically unfavourable temperatures. We find that, globally, rookeries are positioned to disperse to regions where the lower trophic biomass is greatest within loggerheads' thermal range. Six out of the eight nesting sites are associated with strong coastal boundary currents that rapidly transport hatchlings to subtropical–subpolar gyre boundaries; narrow spatial migratory corridors exist for ‘best-fed’ turtles associated with these sites. Two other rookeries are located in exceptionally high-biomass tropical regions fuelled by natural iron fertilization. ‘Best-fed’ turtles tend to be associated with lower temperatures, highlighting the inverse relationship between temperature and lower trophic biomass. The annual mean isotherms between 20°C and the thermal tolerance of juvenile loggerheads are a rough proxy for favourable habitat for loggerheads from rookeries associated with boundary currents. Our results can be used to constrain regions for conservation efforts for each subpopulation, and better identify foraging habitat for this critical early life stage.
Krumhardt, Kristen M., Nicole S Lovenduski, Matthew Long, and Jessica Y Luo, et al., June 2020: Potential Predictability of Net Primary Production in the Ocean. Global Biogeochemical Cycles, 34(6), DOI:10.1029/2020GB006531. Abstract
Interannual variations in marine net primary production (NPP) contribute to the variability of available living marine resources, as well as influence critical carbon cycle processes. Here we provide a global overview of near‐term (1 to 10 years) potential predictability of marine NPP using a novel set of initialized retrospective decadal forecasts from an Earth System Model. Interannual variations in marine NPP are potentially predictable in many areas of the ocean 1 to 3 years in advance, from temperate waters to the tropics, showing a substantial improvement over a simple persistence forecast. However, some regions, such as the subpolar Southern Ocean, show low potential predictability. We analyze how bottom‐up drivers of marine NPP (nutrients, light, and temperature) contribute to its predictability. Regions where NPP is primarily driven by the physical supply of nutrients (e.g., subtropics) retain higher potential predictability than high‐latitude regions where NPP is controlled by light and/or temperature (e.g., the Southern Ocean). We further examine NPP predictability in the world's Large Marine Ecosystems. With a few exceptions, we show that initialized forecasts improve potential predictability of NPP in Large Marine Ecosystems over a persistence forecast and may aid to manage living marine resources.
Luo, Jessica Y., Robert H Condon, and Charles A Stock, et al., September 2020: Gelatinous zooplankton-mediated carbon flows in the global oceans: A data-driven modeling study. Global Biogeochemical Cycles, 34(9), DOI:10.1029/2020GB006704. Abstract
Among marine organisms, gelatinous zooplankton (GZ; cnidarians, ctenophores, and pelagic tunicates) are unique in their energetic efficiency, as the gelatinous body plan allows them to process and assimilate high proportions of oceanic carbon. Upon death, their body shape facilitates rapid sinking through the water column, resulting in carcass depositions on the seafloor (“jelly‐falls”). GZ are thought to be important components of the biological pump, but their overall contribution to global carbon fluxes remains unknown. Using a data‐driven, three‐dimensional, carbon cycle model resolved to a 1° global grid, with a Monte Carlo uncertainty analysis, we estimate that GZ consumed 7.9–13 Pg C y−1 in phytoplankton and zooplankton, resulting in a net production of 3.9–5.8 Pg C y−1 in the upper ocean (top 200 m), with the largest fluxes from pelagic tunicates. Non‐predation mortality (carcasses) comprised 25% of GZ production, and combined with the much greater fecal matter flux, total GZ particulate organic carbon (POC) export at 100 m was 1.6–5.2 Pg C y−1, equivalent to 32–40% of the global POC export. The fast sinking GZ export resulted in a high transfer efficiency (Teff) of 38–62% to 1,000 m and 25–40% to the seafloor. Finally, jelly‐falls at depths >50 m are likely unaccounted for in current POC flux estimates and could increase benthic POC flux by 8–35%. The significant magnitude of and distinct sinking properties of GZ fluxes support a critical yet underrecognized role of GZ carcasses and fecal matter to the biological pump and air‐sea carbon balance.
Schmid, Moritz, Robert Cowen, Kelly L Robinson, Jessica Y Luo, Christian Briseño-Avena, and Su Sponaugle, January 2020: Prey and predator overlap at the edge of a mesoscale eddy: fine-scale, in-situ distributions to inform our understanding of oceanographic processes. Scientific Reports, 10, DOI:10.1038/s41598-020-57879-x. Abstract
Eddies can enhance primary as well as secondary production, creating a diverse meso- and sub-mesoscale seascape at the eddy front which can affect the aggregation of plankton and particles. Due to the coarse resolution provided by sampling with plankton nets, our knowledge of plankton distributions at these edges is limited. We used a towed, undulating underwater imaging system to investigate the physical and biological drivers of zoo- and ichthyoplankton aggregations at the edge of a decaying mesoscale eddy (ME) in the Straits of Florida. Using a sparse Convolutional Neural Network we identified 132 million images of plankton. Larval fish and Oithona spp. copepod concentrations were significantly higher in the eddy water mass, compared to the Florida Current water mass, only four days before the ME's dissipation. Larval fish and Oithona distributions were tightly coupled, indicating potential predator-prey interactions. Larval fishes are known predators of Oithona, however, Random Forests models showed that Oithona spp. and larval fish concentrations were primarily driven by variables signifying the physical footprint of the ME, such as current speed and direction. These results suggest that eddy-related advection leads to largely passive overlap between predator and prey, a positive, energy-efficient outcome for predators at the expense of prey.
Séférian, Roland, Sarah Berthet, Andrew Yool, Julien Palmieri, Laurent Bopp, Alessandro Tagliabue, Lester Kwiatkowski, Olivier Aumont, James R Christian, John P Dunne, Marion Gehlen, Tatiana Ilyina, Jasmin G John, Hongmei Li, Matthew Long, Jessica Y Luo, Hideyuki Nakano, Anastasia Romanou, Jörg Schwinger, and Charles A Stock, et al., August 2020: Tracking Improvement in Simulated Marine Biogeochemistry Between CMIP5 and CMIP6. Current Climate Change Reports, 6, DOI:10.1007/s40641-020-00160-095-119. Abstract
Purpose of Review:
The changes or updates in ocean biogeochemistry component have been mapped between CMIP5 and CMIP6 model versions, and an assessment made of how far these have led to improvements in the simulated mean state of marine biogeochemical models within the current generation of Earth system models (ESMs).
Recent Findings:
The representation of marine biogeochemistry has progressed within the current generation of Earth system models. However, it remains difficult to identify which model updates are responsible for a given improvement. In addition, the full potential of marine biogeochemistry in terms of Earth system interactions and climate feedback remains poorly examined in the current generation of Earth system models.
Summary:
Increasing availability of ocean biogeochemical data, as well as an improved understanding of the underlying processes, allows advances in the marine biogeochemical components of the current generation of ESMs. The present study scrutinizes the extent to which marine biogeochemistry components of ESMs have progressed between the 5th and the 6th phases of the Coupled Model Intercomparison Project (CMIP).
Cordero-Quirós, N, A J Miller, A Subramanian, Jessica Y Luo, and Antonietta Capotondi, October 2019: Composite physical-biological El Niño and La Niña conditions in the California Current System in CESM1-POP2-BEC. Ocean Modelling, 142, DOI:10.1016/j.ocemod.2019.101439. Abstract
El Niño-Southern Oscillation (ENSO) is recognized as one of the potentially predictable drivers of California Current System (CCS) variability. In this study, we analyze a 67-year coarse-resolution (1°) simulation using the ocean model CESM-POP2-BEC forced by NCEP/NCAR reanalysis winds to develop a model composite of the physical-biological response of the CCS during ENSO events. The model results are also compared with available observations. The composite anomalies for sea surface temperature (SST), pycnocline depth, 0m-100m vertically averaged chlorophyll, 0m-100m vertically averaged zooplankton, 25m-100m vertically averaged nitrate, and oxygen at 200m depth exhibit large-scale coherent relationships between physics and the ecosystem, including reduced nutrient and plankton concentrations during El Niño, and increased nutrient and plankton concentrations during La Niña. However, the anomalous model response in temperature, chlorophyll, and zooplankton is generally much weaker than observed and includes a 1-2 month delay compared to observations. We also highlight the asymmetry in the model CCS response, where composite model La Niña events are stronger and more significant than model El Niño events, which is a feature previously identified in observations of CCS SST as well as in tropical Pacific Niño-4 SST where atmospheric teleconnections associated with ENSO are forced. These physical-biological composites provide a view of some of the limitations to the potentially predictable impacts of ENSO teleconnections on the CCS within the modeling framework of CESM-POP2-BEC.
Greer, A T., L M Chiaverano, and Jessica Y Luo, et al., March 2018: Ecology and behaviour of holoplanktonic scyphomedusae and their interactions with larval and juvenile fishes in the northern Gulf of Mexico. ICES Journal of Marine Science, 75(2), DOI:10.1093/icesjms/fsx168. Abstract
Pelagia noctiluca is a venomous, globally distributed holoplanktonic scyphomedusa that periodically forms aggregations in coastal environments, yet little is known about its ecology and behaviour in the northern Gulf of Mexico (nGOM). Using a high resolution plankton imaging system, we describe the patch characteristics of Pelagia medusae in relation to fine-scale biological and physical variables during two summers at shallow (∼25 m, 2016) and deeper (∼45 m, 2011) sampling areas on the nGOM shelf. At the deeper site during the day, average Pelagia medusae concentrations just underneath a surface plume of fresher water (10–25 m) ranged from 0.18 to 0.91 ind. m−3, with a Lloyd’s patchiness index of 13.87, indicating strong aggregation tendencies (peak fine-scale concentration reached 27 ind. m−3). These patches were often associated with horizontal gradients in salinity, and concentrations of several zooplankton taxa (e.g. chaetognaths, hydromedusae, siphonophores, and ctenophores) were significantly negatively correlated with Pelagia medusae abundance (p < 0.0001, Spearman correlations). Although larval fish abundance was not correlated with Pelagia medusae on the 1 m3 scale (19.25 m horizontal distance), larval and juvenile fishes between 0.6 and 2.0 cm aggregated underneath the bell of some Pelagia medusae during the daytime only, even within hypoxic waters. Vertical distributions collected on a diel cycle demonstrated that Pelagia medusae perform a reverse diel vertical migration constrained by low salinity near the surface. These data suggest that salinity changes drive the distribution of Pelagia medusae vertically and horizontally, and when sufficient concentrations are present, medusae are capable of exerting a top-down effect on the abundances of their zooplankton prey. For zooplankton with high visual acuity, such as larval and juvenile fishes, the relationship with Pelagia medusae may change on a diel cycle and depend on the sensory ability of potential prey.
Luo, Jessica Y., et al., December 2018: Automated plankton image analysis using convolutional neural networks. Limnology and Oceanography: Methods, 16(12), DOI:10.1002/lom3.10285. Abstract
The rise of in situ plankton imaging systems, particularly high‐volume imagers such as the In Situ Ichthyoplankton Imaging System, has increased the need for fast processing and accurate classification tools that can identify a high diversity of organisms and nonliving particles of biological origin. Previous methods for automated classification have yielded moderate results that either can resolve few groups at high accuracy or many groups at relatively low accuracy. However, with the advent of new deep learning tools such as convolutional neural networks (CNNs), the automated identification of plankton images can be vastly improved. Here, we describe an image processing procedure that includes preprocessing, segmentation, classification, and postprocessing for the accurate identification of 108 classes of plankton using spatially sparse CNNs. Following a filtering process to remove images with low classification scores, a fully random evaluation of the classification showed that average precision was 84% and recall was 40% for all groups. Reliably classifying rare biological classes was difficult, so after excluding the 12 rarest taxa, classification accuracy for the remaining biological groups became > 90%. This method provides proof of concept for the effectiveness of an automated classification scheme using deep‐learning methods, which can be applied to a range of plankton or biological imaging systems, with the eventual application in a variety of ecological monitoring and fisheries management contexts.
Durden, J, and Jessica Y Luo, et al., November 2017: Integrating “Big Data” into Aquatic Ecology: Challenges and Opportunities. Limnology and Oceanography Bulletin, 26(4), DOI:10.1002/lob.10213. Abstract
Got “Big Data”? Not sure how best to use it? Big Data is becoming an important facet of aquatic ecology, and researchers must learn to harness it to reap the rewards of using it. The benefits of using Big Data are many, and include advancements in scientific understanding at larger scales and higher resolution, applications to improving environmental management and policy, and public engagement. We aim to demystify the use of Big Data for individual scientists, and provide some food for thought for the aquatic ecology community on how to develop this sphere. To achieve this, we highlight six key challenges: (1) how to recognize if you have Big Data, (2) handling Big Data, (3) issues with classical analytical techniques, (4) verification of Big Data, (5) considerations for data sharing, and (6) community development of knowledge infrastructures. We then present approaches and tools which have been successfully applied to these challenges in aquatic ecology and other scientific fields.
Kelly, P T., Tom Bell, A J Reisinger, T L Spanbauer, L E Bortolotti, J A Brentrup, Christian Briseño-Avena, Xiaoli Dong, A M Flanagan, E M Follett, J Grosse, T Guy-Haim, M A Holgerson, R A Hovel, and Jessica Y Luo, et al., May 2017: Ecological Dissertations in the Aquatic Sciences: An Effective Networking and Professional Development Opportunity for Early Career Aquatic Scientists. Limnology and Oceanography Bulletin, 26(2), DOI:10.1002/lob.10180.
Robinson, Kelly L., and Jessica Y Luo, et al., April 2017: A Tale of Two Crowds: Public Engagement in Plankton Classification. Frontiers in Marine Science, 4, 82, DOI:10.3389/fmars.2017.00082. Abstract
“Big data” are becoming common in biological oceanography with the advent of sampling technologies that can generate multiple, high-frequency data streams. Given the need for “big” data in ocean health assessments and ecosystem management, identifying and implementing robust, and efficient processing approaches is a challenge for marine scientists. Using a large plankton imagery data set, we present two crowd-sourcing approaches applied to the problem of classifying millions of organisms. The first used traditional crowd-sourcing by asking the public to identify plankton through a web-interface. The second challenged the data science community to develop algorithms via an industry partnership. We found traditional crowd-sourcing was an excellent way to engage and educate the public while crowd-sourcing data scientists rapidly generated multiple, effective solutions. As the need to process and visualize large and complex marine data sets is expected to grow over time, effective collaborations between oceanographers and computer and data scientists will become increasingly important.
Faillettaz, R, M Picheral, and Jessica Y Luo, et al., April 2016: Imperfect automatic image classification successfully describes plankton distribution patterns. Methods in Oceanography, 15-16, DOI:10.1016/j.mio.2016.04.003. Abstract
Imaging systems were developed to explore the fine scale distributions of plankton (<10 m), but they generate huge datasets that are still a challenge to handle rapidly and accurately. So far, imaged organisms have been either classified manually or pre-classified by a computer program and later verified by human operators. In this paper, we post-process a computer-generated classification, obtained with the common ZooProcess and PlanktonIdentifier toolchain developed for the ZooScan, and test whether the same ecological conclusions can be reached with this fully automatic dataset and with a reference, manually sorted, dataset. The Random Forest classifier outputs the probabilities that each object belongs in each class and we discard the objects with uncertain predictions, i.e. under a probability threshold defined based on a 1% error rate in a self-prediction of the learning set. Keeping only well-predicted objects enabled considerable improvements in average precision, 84% for biological groups, at the cost of diminishing recall (by 39% on average). Overall, it increased accuracy by 16%. For most groups, the automatically-predicted distributions were comparable to the reference distributions and resulted in the same size-spectra. Automatically-predicted distributions also resolved ecologically-relevant patterns, such as differences in abundance across a mesoscale front or fine-scale vertical shifts between day and night. This post-processing method is tested on the classification of plankton images through Random Forest here, but is based on basic features shared by all machine learning methods and could thus be used in a broad range of applications.
Luo, Jessica Y., et al., September 2014: Environmental drivers of the fine-scale distribution of a gelatinous zooplankton community across a mesoscale front. Marine Ecology Progress Series, 510, DOI:10.3354/meps10908. Abstract
Mesoscale fronts occur frequently in many coastal areas and often are sites of elevated productivity; however, knowledge of the fine-scale distribution of zooplankton at these fronts is lacking, particularly within the mid-trophic levels. Furthermore, small (<13 cm) gelatinous zooplankton are ubiquitous, but are under-studied, and their abundances underestimated due to inadequate sampling technology. Using the In Situ Ichthyoplankton Imaging System (ISIIS), we describe the fine-scale distribution of small gelatinous zooplankton at a sharp salinity-driven front in the Southern California Bight. Between 15 and 17 October 2010, over 129000 hydromedusae, ctenophores, and siphonophores within 44 taxa, and nearly 650000 pelagic tunicates were imaged in 5450 m3 of water. Organisms were separated into 4 major assemblages which were largely associated with depth-related factors. Species distribution modeling using boosted regression trees revealed that hydromedusae and tunicates were primarily associated with temperature and depth, siphonophores with dissolved oxygen (DO) and chlorophyll a fluorescence, and ctenophores with DO. The front was the least influential out of all environmental variables modeled. Additionally, except for 6 taxa, all other taxa were not aggregated at the front. Results provide new insights into the biophysical drivers of gelatinous zooplankton distributions and the varying influence of mesoscale fronts in structuring zooplankton communities.
McClatchie, S, Robert Cowen, K Nieto, A T Greer, and Jessica Y Luo, et al., April 2012: Resolution of fine biological structure including small narcomedusae across a front in the Southern California Bight. Journal of Geophysical Research: Oceans, 117(C4), DOI:10.1029/2011JC007565. Abstract
We sampled a front detected by SST gradient, ocean color imagery, and a Spray glider south of San Nicolas Island in the Southern California Bight between 14 and 18 October 2010. We sampled the front with an unusually extensive array of instrumentation, including the Continuous Underway Fish Egg Sampler (CUFES), the undulating In Situ Ichthyoplankton Imaging System (ISIIS) (fitted with temperature, salinity, oxygen, and fluorescence sensors), multifrequency acoustics, a surface pelagic trawl, a bongo net, and a neuston net. We found higher fluorescence and greater cladoceran, decapod, and euphausiid densities in the front, indicating increased primary and secondary production. Mesopelagic fish were most abundant in oceanic waters to the west of the front, market squid were abundant in the front associated with higher krill and decapod densities, and jack mackerel were most common in the front and on the shoreward side of the front. Egg densities peaked to either side of the front, consistent with both offshore (for oceanic squid and mesopelagic fish) and shelf origins (for white croaker and California halibut). We discovered unusually high concentrations of predatory narcomedusae in the surface layer of the frontal zone. Potential ichthyoplankton predators were more abundant either in the front (decapods, euphausiids, and squid) or shoreward of the front (medusae, chaetognaths, and jack mackerel). For pelagic fish like sardine, which can thrive in less productive waters, the safest place to spawn would be offshore because there are fewer potential predators.
