Diverse Management Strategies Produce Similar Ecological Outcomes on Ranches in Western Great Plains: Social-Ecological Assessment

Experiments investigating grazing systems have often excluded ranch-scale decision making, which has limited our understanding of the processes and consequences of adaptive management. We conducted interviews and vegetation monitoring on 17 ranches in eastern Colorado and eastern Wyoming to investigate rancher decision-making processes and the associated ecological consequences. Management variables investigated were grazing strategy, grazing intensity, planning style, and operation type. Ecological attributes included the relative abundance of plant functional groups and categories of ground cover. We examined the environmental and management correlates of plant species and functional group composition using nonmetric multidimensional scaling and linear mixed models. After accounting for environmental variation across the study region, species composition did not differ between grazing management strategy and planning style. Operation type was significantly correlated with plant community composition. Integrated cow-calf plus yearling operations had greater annual and less key perennial cool-season grass species cover relative to cow-calf − only operations. Integrated cow-calf plus yearling ranches were able to more rapidly restock following drought compared with cow-calf operations. Differences in types of livestock operations contributed to variability in plant species composition across the landscape that may support diverse native faunal species in these rangeland ecosystems. Three broad themes emerged from the interviews: 1) long-term goals, 2) flexibility, and 3) adaptive learning. Stocking-rate decisions appear to be slow, path-dependent choices that are shaped by broader social, economic, and political dynamics. Ranchers described having greater flexibility in altering grazing strategies than ranch-level, long-term, annual stocking rates. These results reflect the complexity of the social-ecological systems ranchers navigate in their adaptive decision-making processes. Ranch decision-making process diversity within these environments precludes development of a single “best” strategy to manage livestock grazing.The Rangeland Ecology & Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of similar to 3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.Karl-Oskar Lindgren ingår i gruppen Social Science Genetic Association Consortium</p

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