scholarly journals Potential range expansion of the invasive Red Shiner, Cyprinella lutrensis (Teleostei: Cyprinidae), under future climatic change

2014 ◽  
Author(s):  
Helen M Poulos ◽  
Barry Chernoff
Keyword(s):  
North America ◽  
Climatic Change ◽  
Range Expansion ◽  
Climate Models ◽  
Characteristic Curve ◽  
Model Performance ◽  
Potential Range ◽  
Cyprinella Lutrensis ◽  
Future Climatic Change ◽  
Red Shiner

We built climate envelope models under contemporary and future climates to explore potential range shifts of the invasive Red Shiner-Cyprinella lutrensis. Our objective was to estimate aquatic habitat vulnerability to Red Shiner invasion in North America under future climatic change. We used presence records from within the species’ native and invaded distributions, a suite of bioclimatic predictor variables from three climate models (CCCma, CSIRO, and HadCM3), and maximum entropy modeling to generate potential distribution maps for the year 2080. Our model predicted major range expansion by Red Shiner under both low and high carbon emissions scenarios. The models exceeded average area under the receiver operator characteristic curve values of 0.92, indicating good overall model performance. The model predictions fell largely outside of areas of climatic extrapolation (i.e. regions predicted into environments different from training region) indicating good model performance. The results from this study highlight the large potential range expansion across North America of Red Shiner under future warmer climates.

scholarly journals Potential range expansion of the invasive Red Shiner, Cyprinella lutrensis (Teleostei: Cyprinidae), under future climatic change

2014 ◽  
Author(s):  
Helen M Poulos ◽  
Barry Chernoff
Keyword(s):  
North America ◽  
Climatic Change ◽  
Range Expansion ◽  
Climate Models ◽  
Characteristic Curve ◽  
Model Performance ◽  
Potential Range ◽  
Cyprinella Lutrensis ◽  
Future Climatic Change ◽  
Red Shiner

We built climate envelope models under contemporary and future climates to explore potential range shifts of the invasive Red Shiner-Cyprinella lutrensis. Our objective was to estimate aquatic habitat vulnerability to Red Shiner invasion in North America under future climatic change. We used presence records from within the species’ native and invaded distributions, a suite of bioclimatic predictor variables from three climate models (CCCma, CSIRO, and HadCM3), and maximum entropy modeling to generate potential distribution maps for the year 2080. Our model predicted major range expansion by Red Shiner under both low and high carbon emissions scenarios. The models exceeded average area under the receiver operator characteristic curve values of 0.92, indicating good overall model performance. The model predictions fell largely outside of areas of climatic extrapolation (i.e. regions predicted into environments different from training region) indicating good model performance. The results from this study highlight the large potential range expansion across North America of Red Shiner under future warmer climates.

scholarly journals Deep Learning Versus Logistic Regression to Predict Opioid Dose Reduction After Spinal Cord Stimulation

Neurosurgery ◽  
2020 ◽  
Vol 67 (Supplement_1) ◽  
Author(s):  
Syed M Adil ◽  
Lefko T Charalambous ◽  
Kelly R Murphy ◽  
Shervin Rahimpour ◽  
Stephen C Harward ◽  
...  
Keyword(s):  
Neural Network ◽  
Spinal Cord ◽  
Deep Learning ◽  
Dose Reduction ◽  
Spinal Cord Stimulation ◽  
Network Architecture ◽  
Characteristic Curve ◽  
Model Performance ◽  
Health Crisis ◽  
Opioid Dose

Abstract INTRODUCTION Opioid misuse persists as a public health crisis affecting approximately one in four Americans.1 Spinal cord stimulation (SCS) is a neuromodulation strategy to treat chronic pain, with one goal being decreased opioid consumption. Accurate prognostication about SCS success is key in optimizing surgical decision making for both physicians and patients. Deep learning, using neural network models such as the multilayer perceptron (MLP), enables accurate prediction of non-linear patterns and has widespread applications in healthcare. METHODS The IBM MarketScan® (IBM) database was queried for all patients ≥ 18 years old undergoing SCS from January 2010 to December 2015. Patients were categorized into opioid dose groups as follows: No Use, ≤ 20 morphine milligram equivalents (MME), 20–50 MME, 50–90 MME, and >90 MME. We defined “opiate weaning” as moving into a lower opioid dose group (or remaining in the No Use group) during the 12 months following permanent SCS implantation. After pre-processing, there were 62 predictors spanning demographics, comorbidities, and pain medication history. We compared an MLP with four hidden layers to the LR model with L1 regularization. Model performance was assessed using area under the receiver operating characteristic curve (AUC) with 5-fold nested cross-validation. RESULTS Ultimately, 6,124 patients were included, of which 77% had used opioids for >90 days within the 1-year pre-SCS and 72% had used >5 types of medications during the 90 days prior to SCS. The mean age was 56 ± 13 years old. Collectively, 2,037 (33%) patients experienced opiate weaning. The AUC was 0.74 for the MLP and 0.73 for the LR model. CONCLUSION To our knowledge, we present the first use of deep learning to predict opioid weaning after SCS. Model performance was slightly better than regularized LR. Future efforts should focus on optimization of neural network architecture and hyperparameters to further improve model performance. Models should also be calibrated and externally validated on an independent dataset. Ultimately, such tools may assist both physicians and patients in predicting opioid dose reduction after SCS.

scholarly journals An evaluation of the performance of chemistry transport models by comparison with research aircraft observations. Part 1: Concepts and overall model performance

2003 ◽  
Vol 3 (5) ◽  
pp. 1609-1631 ◽  
Author(s):  
D. Brunner ◽  
J. Staehelin ◽  
H. L. Rogers ◽  
M. O. Köhler ◽  
J. A. Pyle ◽  
...  
Keyword(s):  
Quantitative Methods ◽  
Climate Models ◽  
Model Performance ◽  
Average Concentration ◽  
Convective Transport ◽  
Trace Gas ◽  
Atmospheric Conditions ◽  
Time Step ◽  
Research Aircraft ◽  
Gas Measurements

Abstract. A rigorous evaluation of five global Chemistry-Transport and two Chemistry-Climate Models operated by several different groups in Europe, was performed. Comparisons were made of the models with trace gas observations from a number of research aircraft measurement campaigns during the four-year period 1995-1998. Whenever possible the models were run over the same four-year period and at each simulation time step the instantaneous tracer fields were interpolated to all coinciding observation points. This approach allows for a very close comparison with observations and fully accounts for the specific meteorological conditions during the measurement flights. This is important considering the often limited availability and representativity of such trace gas measurements. A new extensive database including all major research and commercial aircraft measurements between 1995 and 1998, as well as ozone soundings, was established specifically to support this type of direct comparison. Quantitative methods were applied to judge model performance including the calculation of average concentration biases and the visualization of correlations and RMS errors in the form of so-called Taylor diagrams. We present the general concepts applied, the structure and content of the database, and an overall analysis of model skills over four distinct regions. These regions were selected to represent various atmospheric conditions and to cover large geographical domains such that sufficient observations are available for comparison. The comparison of model results with the observations revealed specific problems for each individual model. This study suggests the further improvements needed and serves as a benchmark for re-evaluations of such improvements. In general all models show deficiencies with respect to both mean concentrations and vertical gradients of important trace gases. These include ozone, CO and NOx at the tropopause. Too strong two-way mixing across the tropopause is suggested to be the main reason for differences between simulated and observed CO and ozone values. The generally poor correlations between simulated and measured NOx values suggest that in particular the NOx input by lightning and the convective transport from the polluted boundary layer are still not well described by current parameterizations, which may lead to significant differences in the spatial and seasonal distribution of NOx in the models. Simulated OH concentrations, on the other hand, were found to be in surprisingly good agreement with measured values.

scholarly journals Tendency Bias Correction in Coupled and Uncoupled Global Climate Models with a Focus on Impacts over North America

2019 ◽  
Vol 32 (2) ◽  
pp. 639-661 ◽  
Author(s):  
Y. Chang ◽  
S. D. Schubert ◽  
R. D. Koster ◽  
A. M. Molod ◽  
H. Wang
Keyword(s):  
North America ◽  
Bias Correction ◽  
Climate Models ◽  
Global Climate ◽  
Storm Track ◽  
Coupled Model ◽  
Global Climate Models ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Transient Wave

Abstract We revisit the bias correction problem in current climate models, taking advantage of state-of-the-art atmospheric reanalysis data and new data assimilation tools that simplify the estimation of short-term (6 hourly) atmospheric tendency errors. The focus is on the extent to which correcting biases in atmospheric tendencies improves the model’s climatology, variability, and ultimately forecast skill at subseasonal and seasonal time scales. Results are presented for the NASA GMAO GEOS model in both uncoupled (atmosphere only) and coupled (atmosphere–ocean) modes. For the uncoupled model, the focus is on correcting a stunted North Pacific jet and a dry bias over the central United States during boreal summer—long-standing errors that are indeed common to many current AGCMs. The results show that the tendency bias correction (TBC) eliminates the jet bias and substantially increases the precipitation over the Great Plains. These changes are accompanied by much improved (increased) storm-track activity throughout the northern midlatitudes. For the coupled model, the atmospheric TBCs produce substantial improvements in the simulated mean climate and its variability, including a much reduced SST warm bias, more realistic ENSO-related SST variability and teleconnections, and much improved subtropical jets and related submonthly transient wave activity. Despite these improvements, the improvement in subseasonal and seasonal forecast skill over North America is only modest at best. The reasons for this, which are presumably relevant to any forecast system, involve the competing influences of predictability loss with time and the time it takes for climate drift to first have a significant impact on forecast skill.

scholarly journals Effects of Climatic Change on the Potential Distribution of Lycoriella Species (Diptera: Sciaridae) of Economic Importance

Insects ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 831
Author(s):  
Roberta Marques ◽  
Juliano Lessa Pinto Duarte ◽  
Adriane da Fonseca Duarte ◽  
Rodrigo Ferreira Krüger ◽  
Uemmerson Silva da Cunha ◽  
...  
Keyword(s):  
Climate Change ◽  
Range Expansion ◽  
Cold Climate ◽  
Future Climate Change ◽  
Economic Losses ◽  
Potential Range ◽  
Climate Change Scenarios ◽  
Ecological Niche Models ◽  
Greenhouse Production ◽  
Production Areas

Lycoriella species (Sciaridae) are responsible for significant economic losses in greenhouse production (e.g., mushrooms, strawberries, and nurseries). The current distributions of species in the genus are restricted to cold-climate countries. Three species of Lycoriella are of particular economic concern in view of their ability to invade areas in countries across the Northern Hemisphere. We used ecological niche models to determine the potential for range expansion under future climate change scenarios (RCP 4.5 and RCP 8.5) in the distribution of these three species of Lycoriella. Stable environmental suitability under climate change was a dominant theme in these species; however, potential range increases were noted in key countries (e.g., USA, Brazil, and China). Our results illustrate the potential for range expansion in these species in the Southern Hemisphere, including some of the highest greenhouse production areas in the world.

scholarly journals Salvia reflexa (Lamiaceae): a new record for Pakistan

2019 ◽  
Vol 6 (1) ◽  
pp. 17-21
Author(s):  
Wahid Hussain ◽  
Lal Badshah ◽  
Sayed Afzal Shah ◽  
Farrukh Hussain ◽  
Asghar Ali ◽  
...  
Keyword(s):  
South Africa ◽  
New Zealand ◽  
North America ◽  
South America ◽  
Range Expansion ◽  
New Record ◽  
New World ◽  
Southern South America ◽  
Tribal Areas

Salvia reflexa Hornem., a member of the New World subgenus Calosphace, ranges from North America to southern South America, Australia, New Zealand, South Africa and Afghanistan in Asia, and still continues to expand its range. Here we report further range expansion for S. reflexa into the tribal areas of Pakistan and hypothesize that it has been introduced from Afghanistan. This represents a new record for the flora of Pakistan.

Range expansion by Passer montanus in North America

2016 ◽  
Vol 19 (1) ◽  
pp. 5-9
Author(s):  
J. L. Burnett ◽  
C. P. Roberts ◽  
C. R. Allen ◽  
M. B. Brown ◽  
M. P. Moulton
Keyword(s):  
North America ◽  
Range Expansion ◽  
Passer Montanus
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