Plant–flower Visitor Networks In A Serpentine Metacommunity: Assessing Traits Associated With Keystone Plant Species

Consistent topology of plant–pollinator networks across space may be due to substitutability of the plant species most important for community function (keystone species). It is unclear, however, whether keystone species identity varies within a community type and what traits underlie this variation. Using a network biology approach, we assess whether keystone plant species vary across a metacommunity of five serpentine seeps in California and determine the features that predict their identity. We define keystone species as those with high strength, low node specialization index (NSI), and/or low d′ and determine whether these parameters are predicted by floral traits (flower biomass, number of open flowers per plant, symmetry, or stamen number) and/or ecological features (variation in local floral abundance, endemism) within seeps and across the metacommunity. Keystone species identity varied among seeps and was associated with local flower abundance: mean floral abundance correlated positively with strength but negatively with NSI within most seeps as well as across the metacommunity. For the metacommunity, flower biomass correlated negatively with NSI while variation in flower abundance correlated negatively with strength. Across the metacommunity, the d′ metric was associated with flower biomass, whereby plants with smaller flowers interacted with the most abundant pollinators across the metacommunity. Results suggest that connectance and interaction evenness may not be greatly influenced by community composition turnover due to substitution of keystone plant species across space. Keystone species can be predicted by functional traits but which trait (flower abundance or size) depended on the metric used and the level observed

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