Calibrating Tropical Forest Coexistence in Ecosystem Demography Models Using Multi-Objective Optimization Through Population-Based Parallel Surrogate Search

Tropical forest diversity governs forest structures, compositions, and influences the ecosystem response to environmental changes. Better representation of forest diversity in ecosystem demography (ED) models within Earth system models is thus necessary to accurately capture and predict how tropical forests affect Earth system dynamics subject to climate changes. However, achieving forest coexistence in ED models is challenging due to their computational expense and limited understanding of the mechanisms governing forest functional diversity. This study applies the advanced Multi-Objective Population-based Parallel Local Surrogate-assisted search (MOPLS) optimization algorithm to simultaneously calibrate ecosystem fluxes and coexistence of two physiologically distinct tropical forest species in a size- and age-structured ED model with realistic representation of wood harvest. MOPLS exhibits satisfactory model performance, capturing hydrological and biogeochemical dynamics observed in Barro Colorado Island, Panama, and robustly achieving coexistence for the two representative forest species. This demonstrates its effectiveness in calibrating tropical forest coexistence. The optimal solution is applied to investigate the recovery trajectories of forest biomass after various intensities of clear-cut deforestation. We find that a 20% selective logging can take approximately 40 years for aboveground biomass to return to the initial level. This is due to the slow recovery rate of late successional trees, which only increases by 4% over the 40-year period. This study lays the foundation to calibrate coexistence in ED models. MOPLS can be an effective tool to help better represent tropical forest diversity in Earth system models and inform forest management practices.publishedVersio

Global biosphere-climate interaction : a causal appraisal of observations and models over multiple temporal scales

Improving the skill of Earth system models (ESMs) in representing climate-vegetation interactions is crucial to enhance our predictions of future climate and ecosystem functioning. Therefore, ESMs need to correctly simulate the impact of climate on vegetation, but likewise feedbacks of vegetation on climate must be adequately represented. However, model predictions at large spatial scales remain subjected to large uncertainties, mostly due to the lack of observational patterns to benchmark them. Here, the bidirectional nature of climate-vegetation interactions is explored across multiple temporal scales by adopting a spectral Granger causality framework that allows identification of potentially co-dependent variables. Results based on global and multi-decadal records of remotely sensed leaf area index (LAI) and observed atmospheric data show that the climate control on vegetation variability increases with longer temporal scales, being higher at inter-annual than multi-month scales. Globally, precipitation is the most dominant driver of vegetation at monthly scales, particularly in (semi-)arid regions. The seasonal LAI variability in energy-driven latitudes is mainly controlled by radiation, while air temperature controls vegetation growth and decay in high northern latitudes at inter-annual scales. These observational results are used as a benchmark to evaluate four ESM simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Findings indicate a tendency of ESMs to over-represent the climate control on LAI dynamics and a particular overestimation of the dominance of precipitation in arid and semi-arid regions at inter-annual scales. Analogously, CMIP5 models overestimate the control of air temperature on seasonal vege-tation variability, especially in forested regions. Overall, climate impacts on LAI are found to be stronger than the feedbacks of LAI on climate in both observations and models; in other words, local climate variability leaves a larger imprint on temporal LAI dynamics than vice versa. Note however that while vegetation reacts directly to its local climate conditions, the spatially collocated character of the analysis does not allow for the identification of remote feedbacks, which might result in an underestimation of the biophysical effects of vegetation on climate. Nonetheless, the widespread effect of LAI variability on radiation, as observed over the northern latitudes due to albedo changes, is overestimated by the CMIP5 models. Overall, our experiments emphasise the potential of benchmarking the representation of particular interactions in online ESMs using causal statistics in combination with observational data, as opposed to the more conventional evaluation of the magnitude and dynamics of individual variables

Tree mortality and Extreme Rainfall in the Amazon

Mesoscale Convective Systems (MCS) are responsible for severe rainfall in the Amazon and can produce strong descending winds that can uprooting or break trees the most dominant mode of tree mortality in the Amazon. Our results show that severe rainfall help to explain the observed residence time of woody biomass and tree mortality in the Amazon

Benchmarking and parameter sensitivity of physiological and vegetation dynamics using the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) at Barro Colorado Island, Panama

Plant functional traits determine vegetation responses to environmental variation, but variation in trait values is large, even within a single site. Likewise, uncertainty in how these traits map to Earth system feedbacks is large. We use a vegetation demographic model (VDM), the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to explore parameter sensitivity of model predictions, and comparison to observations, at a tropical forest site: Barro Colorado Island in Panama. We define a single 12-dimensional distribution of plant trait variation, derived primarily from observations in Panama, and define plant functional types (PFTs) as random draws from this distribution. We compare several model ensembles, where individual ensemble members vary only in the plant traits that define PFTs, and separate ensembles differ from each other based on either model structural assumptions or non-trait, ecosystem-level parameters, which include (a) the number of competing PFTs present in any simulation and (b) parameters that govern disturbance and height-based light competition. While single-PFT simulations are roughly consistent with observations of productivity at Barro Colorado Island, increasing the number of competing PFTs strongly shifts model predictions towards higher productivity and biomass forests. Different ecosystem variables show greater sensitivity than others to the number of competing PFTs, with the predictions that are most dominated by large trees, such as biomass, being the most sensitive. Changing disturbance and height-sorting parameters, i.e., the rules of competitive trait filtering, shifts regimes of dominance or coexistence between early- and late-successional PFTs in the model. Increases to the extent or severity of disturbance, or to the degree of determinism in height-based light competition, all act to shift the community towards early-successional PFTs. In turn, these shifts in competitive outcomes alter predictions of ecosystem states and fluxes, with more early-successional-dominated forests having lower biomass. It is thus crucial to differentiate between plant traits, which are under competitive pressure in VDMs, from those model parameters that are not and to better understand the relationships between these two types of model parameters to quantify sources of uncertainty in VDMs

Arrhythmic risk stratification in non-ischaemic dilated cardiomyopathy

Dilated cardiomyopathy is a primary disease of the heart muscle, which affects relatively young patients with a low comorbidity profile. It is characterized by structural and/or functional abnormalities leading to systolic dysfunction of the left ventricle or of both ventricles, often associated with dilatation, in the absence of an ischaemic, valvular, or pressure overload cause sufficient to explain the phenotype. Although the prognosis of the disease has greatly improved over the last few decades, prognostic stratification remains a fundamental objective, especially about the prediction of potentially life-threatening arrhythmic events. An accurate diagnostic work-up and an appropriate aetiopathogenetic characterization affect the patients' outcome and represent the essential basis of an adequate prognostic stratification. It is necessary to adopt a multiparametric approach, especially when the aim is the prediction of arrhythmic risk; it includes an integration of medical history and physical examination with cardiac imaging and genetic testing, in order to obtain a personalized diagnosis and therapeutic strategies. Furthermore, the evaluation should be repeated at every clinical check-up, considering the dynamic trend of the pathology and the arrhythmic risk changes over time. This article aims to illustrate how, starting from an exhaustive aetiological and clinical-instrumental characterization, including all diagnostic methods available at present time, it is possible to obtain a tailored diagnostic evaluation and stratification of the arrhythmic risk as accurate as possible

Critical analysis of the 2023 ESC guidelines on cardiomyopathy management

The first European Society of Cardiology (ESC) guidelines on the management of cardiomyopathies (CMPs), published 1 year ago, remain highly relevant. These guidelines provide a comprehensive framework to manage the complexity of CMPs, consolidating previous approaches. All CMPs are now addressed systematically in one document. The ESC recommends a ‘CMP-oriented’ approach, emphasizing thorough clinical assessments and phenotype-first categorization into hypertrophic, dilated, arrhythmogenic, restrictive, and non-dilated left ventricular CMP. Despite the utility of this method, certain classifications, such as arrhythmogenic right ventricular CMP and the novel non-dilated left ventricular CMP, raise controversies. Key advances in the guidelines include the use of genetic testing and cardiac magnetic resonance imaging to refine diagnoses and inform treatment, especially for high-risk genotypes. These guidelines advocate for personalized, multidisciplinary care. Overall, they represent a significant step forward but highlight the evolving nature of CMP management as scientific understanding progresses

A metadata reporting framework (FRAMES) for synthesis of ecohydrological observations

Metadata describe the ancillary information needed for data preservation and independent interpretation, comparison across heterogeneous datasets, and quality assessment and quality control (QA/QC). Environmental observations are vastly diverse in type and structure, can be taken across a wide range of spatiotemporal scales in a variety of measurement settings and approaches, and saved in multiple formats. Thus, well-organized, consistent metadata are required to produce usable data products from diverse environmental observations collected across field sites. However, existing metadata reporting protocols do not support the complex data synthesis and model-data integration needs of interdisciplinary earth system research. We developed a metadata reporting framework (FRAMES) to enable management and synthesis of observational data that are essential in advancing a predictive understanding of earth systems. FRAMES utilizes best practices for data and metadata organization enabling consistent data reporting and compatibility with a variety of standardized data protocols. We used an iterative scientist-centered design process to develop FRAMES, resulting in a data reporting format that incorporates existing field practices to maximize data-entry efficiency. Thus, FRAMES has a modular organization that streamlines metadata reporting and can be expanded to incorporate additional data types. With FRAMES\u27s multi-scale measurement position hierarchy, data can be reported at observed spatial resolutions and then easily aggregated and linked across measurement types to support model-data integration. FRAMES is in early use by both data originators (persons generating data) and consumers (persons using data and metadata). In this paper, we describe FRAMES, identify lessons learned, and discuss areas of future development

Causes and consequences of pronounced variation in the isotope composition of plant xylem water

Stable isotopologues of water are widely used to derive relative root water uptake (RWU) profiles and average RWU depth in lignified plants. Uniform isotope composition of plant xylem water (delta(xyl)) along the stem length of woody plants is a central assumption of the isotope tracing approach which has never been properly evaluated.Here we evaluate whether strong variation in delta(xyl) within woody plants exists using empirical field observations from French Guiana, northwestern China, and Germany. In addition, supported by a mechanistic plant hydraulic model, we test hypotheses on how variation in delta(xyl) can develop through the effects of diurnal variation in RWU, sap flux density, diffusion, and various other soil and plant parameters on the delta(xyl) of woody plants.The hydrogen and oxygen isotope composition of plant xylem water shows strong temporal (i.e., sub-daily) and spatial (i.e., along the stem) variation ranging up to 25.2 parts per thousand and 6.8 parts per thousand for delta H-2 and delta O-18, respectively, greatly exceeding the measurement error range in all evaluated datasets. Model explorations predict that significant delta(xyl) variation could arise from diurnal RWU fluctuations and vertical soil water heterogeneity. Moreover, significant differences in delta(xyl) emerge between individuals that differ only in sap flux densities or are monitored at different times or heights.This work shows a complex pattern of delta(xyl) transport in the soil-root-xylem system which can be related to the dynamics of RWU by plants. These dynamics complicate the assessment of RWU when using stable water isotopologues but also open new opportunities to study drought responses to environmental drivers. We propose including the monitoring of sap flow and soil matric potential for more robust estimates of average RWU depth and expansion of attainable insights in plant drought strategies and responses