Potential predictability of the spring bloom in the Southern Ocean sea ice zone

This work was supported by the High Meadows Environmental Institute at Princeton University and the NSF's Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Project under the NSF Award PLR-1425989. F.A.H. was supported by NASA Grant 80NSSC19K1115 and by the European Union (ERC, VERTEXSO, 101041743). G.A.M was supported under SOCCOM and UKRI Grant MR/W013835/1. T.L.F was supported by Swiss National Science Foundation (Grant P00P2_198897) and the Swiss National Supercomputing Centre. N.L was supported by the European Union's Horizon 2020 research and innovation program under Grant 820989 (project COMFORT) and no. 862923 (project AtlantECO) as well as the Bretscher Funds.Every austral spring when Antarctic sea ice melts, favorable growing conditions lead to an intense phytoplankton bloom, which supports much of the local marine ecosystem. Recent studies have found that Antarctic sea ice is predictable several years in advance, suggesting that the spring bloom might exhibit similar predictability. Using a suite of perfect model predictability experiments, we find that November net primary production (NPP) is potentially predictable 7 to 10 years in advance in many Southern Ocean regions. Sea ice extent predictability peaks in late winter, followed by absorbed shortwave radiation and NPP with a 2 to 3 months lag. This seasonal progression of predictability supports our hypothesis that sea ice and light limitation control the inherent predictability of the spring bloom. Our results suggest skillful interannual predictions of NPP may be achievable, with implications for managing fisheries and the marine ecosystem, and guiding conservation policy in the Southern Ocean.Publisher PDFPeer reviewe

A Novel 8-Predictors Signature to Predict Complicated Disease Course in Pediatric-onset Crohn’s Disease: A Population-based Study

International audienceBackground The identification of patients at high risk of a disabling disease course would be invaluable in guiding initial therapy in Crohn’s disease (CD). Our objective was to evaluate a combination of clinical, serological, and genetic factors to predict complicated disease course in pediatric-onset CD. Methods Data for pediatric-onset CD patients, diagnosed before 17 years of age between 1988 and 2004 and followed more than 5 years, were extracted from the population-based EPIMAD registry. The main outcome was defined by the occurrence of complicated behavior (stricturing or penetrating) and/or intestinal resection within the 5 years following diagnosis. Lasso logistic regression models were used to build a predictive model based on clinical data at diagnosis, serological data (ASCA, pANCA, anti-OmpC, anti-Cbir1, anti-Fla2, anti-Flax), and 369 candidate single nucleotide polymorphisms. Results In total, 156 children with an inflammatory (B1) disease at diagnosis were included. Among them, 35% (n = 54) progressed to a complicated behavior or an intestinal resection within the 5 years following diagnosis. The best predictive model (PREDICT-EPIMAD) included the location at diagnosis, pANCA, and 6 single nucleotide polymorphisms. This model showed good discrimination and good calibration, with an area under the curve of 0.80 after correction for optimism bias (sensitivity, 79%, specificity, 74%, positive predictive value, 61%, negative predictive value, 87%). Decision curve analysis confirmed the clinical utility of the model. Conclusions A combination of clinical, serotypic, and genotypic variables can predict disease progression in this population-based pediatric-onset CD cohort. Independent validation is needed before it can be used in clinical practice