A model intercomparison project to study the role of plant functional diversity in the response of tropical forests to drought

Uncertainty in how the land carbon (C) sink will change over time contributes to uncertainty in Earth system model (ESM) projections of climate change. Much of the land sink is thought to reside in old-growth tropical forests, but recent analyses suggest a diminishing C sink in these forests due to rising temperatures and drought. Thus, there is an urgent need to better understand tropical forest responses to drought and to incorporate this understanding into ESMs. Previous work with vegetation demographic models (VDMs) – which represent the dynamics of individuals or cohorts, along with hydrology and biogeochemistry − suggest that functional diversity can enhance tropical forest resilience to climate change. However, there is little understanding of how different approaches to representing trait diversity and demography affect model outcomes. To explore the potential for trait diversity to moderate tropical forest responses to drought, we explored the behavior of nine VDMs, ranging from models with detailed site-level parameterizations to more generalized land models designed as ESM components. The behavior of each model was studied using soil and meteorological data collected at each of two tropical forest sites: Paracou Research Station, French Guiana, and Tapajos National Forest, Brazil. Low and high trait-diversity scenarios were simulated for each model using historical meteorology, as well as reduced rainfall scenarios. Few models showed strong effects of trait diversity on drought resistance (short-term response of forest biomass to rainfall reduction), but most models showed positive effects of diversity on resilience (long-term recovery of forest biomass following the initial biomass loss due to rainfall reduction). Long-term recovery was always associated with shifts in community composition towards greater drought-tolerance. However, there were large differences among models in the degree and time-scale of recovery. These differences were unrelated to the goodness-of-fit of model predictions to observations of biomass, productivity, and soil moisture, suggesting that site-level calibration of model parameters is unlikely to strongly affect biodiversity-ecosystem functioning relationships in VDMs. Rather, the degree to which diversity moderated drought responses depended on which axes of trait variation were represented in the model, as well as model assumptions that affect the time-scale over which community composition shifts in response to environmental change. Our study suggests that incorporating trait diversity and demography into ESMs would likely lead to altered climate projections, but additional empirical and modeling work is needed to provide the ESM community with clear guidance on model development

A Machine Learning Algorithm to Identify Patients at Risk of Unplanned Subsequent Surgery After Intramedullary Nailing for Tibial Shaft Fractures

Objectives: In the SPRINT trial, 18% of patients with a tibial shaft fracture (TSF) treated with intramedullary nailing (IMN) had one or more unplanned subsequent surgical procedures. It is clinically relevant for surgeon and patient to anticipate unplanned secondary procedures, other than operations that can be readily expected such as reconstructive procedures for soft tissue defects. Therefore, the objective of this study was to develop a machine learning (ML) prediction model using the SPRINT data that can give individual patients and their care team an estimate of their particular probability of an unplanned second surgery. Methods: Patients from the SPRINT trial with unilateral TSFs were randomly divided into a training set (80%) and test set (20%). Five ML algorithms were trained in recognizing patterns associated with subsequent surgery in the training set based on a subset of variables identified by random forest algorithms. Performance of each ML algorithm was evaluated and compared based on (1) area under the ROC curve, (2) calibration slope and intercept, and (3) the Brier score. Results: Total data set comprised 1198 patients, of whom 214 patients (18%) underwent subsequent surgery. Seven variables were used to train ML algorithms: (1) Gustilo-Anderson classification, (2) Tscherne classification, (3) fracture location, (4) fracture gap, (5) polytrauma, (6) injury mechanism, and (7) OTA/AO classification. The best-performing ML algorithm had an area under the ROC curve, calibration slope, calibration intercept, and the Brier score of 0.766, 0.954, -0.002, and 0.120 in the training set and 0.773, 0.922, 0, and 0.119 in the test set, respectively. Conclusions: An ML algorithm was developed to predict the probability of subsequent surgery after IMN for TSFs. This ML algorithm may assist surgeons to inform patients about the probability of subsequent surgery and might help to identify patients who need a different perioperative plan or a more intensive approach.Orthopaedics, Trauma Surgery and Rehabilitatio