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Open-source data reveal how collections-based fungal diversity is sensitive to global change.

ABSTRACT: Premise of the Study:Fungal diversity (richness) trends at large scales are in urgent need of investigation, especially through novel situations that combine long-term observational with environmental and remotely sensed open-source data. Methods:We modeled fungal richness, with collections-based records of saprotrophic (decaying) and ectomycorrhizal (plant mutualistic) fungi, using an array of environmental variables across geographical gradients from northern to central Europe. Temporal differences in covariables granted insight into the impacts of the shorter- versus longer-term environment on fungal richness. Results:Fungal richness varied significantly across different land-use types, with highest richness in forests and lowest in urban areas. Latitudinal trends supported a unimodal pattern in diversity across Europe. Temperature, both annual mean and range, was positively correlated with richness, indicating the importance of seasonality in increasing richness amounts. Precipitation seasonality notably affected saprotrophic fungal diversity (a unimodal relationship), as did daily precipitation of the collection day (negatively correlated). Ectomycorrhizal fungal richness differed from that of saprotrophs by being positively associated with tree species richness. Discussion:Our results demonstrate that fungal richness is strongly correlated with land use and climate conditions, especially concerning seasonality, and that ongoing global change processes will affect fungal richness patterns at large scales.

SUBMITTER: Andrew C

PROVIDER: S-EPMC6426159 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Open-source data reveal how collections-based fungal diversity is sensitive to global change.

Andrew Carrie C Büntgen Ulf U Egli Simon S Senn-Irlet Beatrice B Grytnes John-Arvid JA Heilmann-Clausen Jacob J Boddy Lynne L Bässler Claus C Gange Alan C AC Heegaard Einar E Høiland Klaus K Kirk Paul M PM Krisai-Greilhüber Irmgard I Kuyper Thomas W TW Kauserud Håvard H

Applications in plant sciences 20190312 3

<h4>Premise of the study</h4>Fungal diversity (richness) trends at large scales are in urgent need of investigation, especially through novel situations that combine long-term observational with environmental and remotely sensed open-source data.<h4>Methods</h4>We modeled fungal richness, with collections-based records of saprotrophic (decaying) and ectomycorrhizal (plant mutualistic) fungi, using an array of environmental variables across geographical gradients from northern to central Europe. ...[more]

PMID: 30937219

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Project description:ObjectivesTo develop and demonstrate the feasibility of a Global Open Source Severity of Illness Score (GOSSIS)-1 for critical care patients, which generalizes across healthcare systems and countries.DesignA merger of several critical care multicenter cohorts derived from registry and electronic health record data. Data were split into training (70%) and test (30%) sets, using each set exclusively for development and evaluation, respectively. Missing data were imputed when not available.Setting/patientsTwo large multicenter datasets from Australia and New Zealand (Australian and New Zealand Intensive Care Society Adult Patient Database [ANZICS-APD]) and the United States (eICU Collaborative Research Database [eICU-CRD]) representing 249,229 and 131,051 patients, respectively. ANZICS-APD and eICU-CRD contributed data from 162 and 204 hospitals, respectively. The cohort included all ICU admissions discharged in 2014-2015, excluding patients less than 16 years old, admissions less than 6 hours, and those with a previous ICU stay.InterventionsNot applicable.Measurements and main resultsGOSSIS-1 uses data collected during the ICU stay's first 24 hours, including extrema values for vital signs and laboratory results, admission diagnosis, the Glasgow Coma Scale, chronic comorbidities, and admission/demographic variables. The datasets showed significant variation in admission-related variables, case-mix, and average physiologic state. Despite this heterogeneity, test set discrimination of GOSSIS-1 was high (area under the receiver operator characteristic curve [AUROC], 0.918; 95% CI, 0.915-0.921) and calibration was excellent (standardized mortality ratio [SMR], 0.986; 95% CI, 0.966-1.005; Brier score, 0.050). Performance was held within ANZICS-APD (AUROC, 0.925; SMR, 0.982; Brier score, 0.047) and eICU-CRD (AUROC, 0.904; SMR, 0.992; Brier score, 0.055). Compared with GOSSIS-1, Acute Physiology and Chronic Health Evaluation (APACHE)-IIIj (ANZICS-APD) and APACHE-IVa (eICU-CRD), had worse discrimination with AUROCs of 0.904 and 0.869, and poorer calibration with SMRs of 0.594 and 0.770, and Brier scores of 0.059 and 0.063, respectively.ConclusionsGOSSIS-1 is a modern, free, open-source inhospital mortality prediction algorithm for critical care patients, achieving excellent discrimination and calibration across three countries.

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Open-source data reveal how collections-based fungal diversity is sensitive to global change.

Publications

Open-source data reveal how collections-based fungal diversity is sensitive to global change.

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