scholarly journals On the importance of predictor choice, modelling technique, and number of pseudo‐absences for bioclimatic envelope model performance

2020 ◽  
Vol 10 (21) ◽  
pp. 12307-12317
Author(s):  
Mirza Čengić ◽  
Jasmijn Rost ◽  
Daniela Remenska ◽  
Jan H. Janse ◽  
Mark A. J. Huijbregts ◽  
...  
Keyword(s):  
Model Performance ◽  
Modelling Technique ◽  
Choice Modelling ◽  
Bioclimatic Envelope Model ◽  
Envelope Model ◽  
Bioclimatic Envelope

scholarly journals Bioclimatic envelope model of climate change impacts on blanket peatland distribution in Great Britain

2010 ◽  
Vol 45 ◽  
pp. 151-162 ◽  
Author(s):  
AV Gallego-Sala ◽  
JM Clark ◽  
JI House ◽  
HG Orr ◽  
IC Prentice ◽  
...  
Keyword(s):  
Climate Change ◽  
Great Britain ◽  
Climate Change Impacts ◽  
Bioclimatic Envelope Model ◽  
Envelope Model ◽  
Bioclimatic Envelope

scholarly journals Potential climate-induced distributions of <i>Lophodermium</i> needle cast across central Siberia in the 21 century

Web Ecology ◽  
2016 ◽  
Vol 16 (1) ◽  
pp. 37-39 ◽  
Author(s):  
N. M. Tchebakova ◽  
N. A. Kuzmina ◽  
E. I. Parfenova ◽  
V. A. Senashova ◽  
S. R. Kuzmin
Keyword(s):  
Common Disease ◽  
Regression Analyses ◽  
Needle Cast ◽  
Warming Climate ◽  
Bioclimatic Envelope Model ◽  
Pine Trees ◽  
Potential Pathogen ◽  
Outbreak Areas ◽  
Envelope Model ◽  
Bioclimatic Envelope

Abstract. Needle cast caused by fungi of the genus Lophodermium Chevall. is a common disease in pine trees in Siberia. Regression analyses relating needle cast events to climatic variables in 1997–2010 showed that the disease depended most on precipitation of two successive years. Temperature conditions were important to trigger the disease in wetter years. We used our regional bioclimatic envelope model and IPCC scenarios to model the needle cast distribution and its outbreaks in the 21st century. In a warming climate, the needle cast range would shift northwards. By 2020, needle cast outbreaks would already have damaged the largest forest areas. But outbreak areas would decrease by 2080 because the ranges of modeled pathogen and Scots pine, the disease host, would separate: the host tree progression would be halted by the slower permafrost retreat, which would in turn halt the potential pathogen progression.

Potential effects of climate change on ecosystem distribution in Alberta

2009 ◽  
Vol 39 (5) ◽  
pp. 1001-1010 ◽  
Author(s):  
Richard R. Schneider ◽  
Andreas Hamann ◽  
Dan Farr ◽  
Xianli Wang ◽  
Stan Boutin
Keyword(s):  
Climate Change ◽  
Land Use Planning ◽  
Vegetation Change ◽  
Future Research ◽  
Simple Modification ◽  
Bioclimatic Envelope Model ◽  
Envelope Model ◽  
Disturbance Model ◽  
Ecosystem Changes ◽  
Bioclimatic Envelope

We propose a new and relatively simple modification to extend the utility of bioclimatic envelope models for land-use planning and adaptation under climate change. In our approach, the trajectory of vegetation change is set by a bioclimatic envelope model, but the rate of transition is determined by a disturbance model. We used this new approach to explore potential changes in the distribution of ecosystems in Alberta, Canada, under alternative climate and disturbance scenarios. The disturbance model slowed the rate of ecosystem transition, relative to the raw projections of the bioclimatic envelope model. But even with these transition lags in place, a northward shift of grasslands into much of the existing parkland occurred over the 50 years of our simulation. There was also a conversion of 12%–21% of Alberta’s boreal region to parkland. In addition to aspatial projections, our simulations provide testable predictions about where ecosystem changes as a result of climate change are most likely to be initially observed. We also conducted an investigation of model uncertainty that provides an indication of the robustness of our findings and identifies fruitful avenues for future research.

scholarly journals The benefits of segmentation: Evidence from a South African bank and other studies

2017 ◽  
Vol 113 (9/10) ◽  
Author(s):  
Douw G. Breed ◽  
Tanja Verster
Keyword(s):  
South African ◽  
Direct Marketing ◽  
Model Performance ◽  
Predictive Modelling ◽  
Modelling Technique ◽  
Gradient Boosting ◽  
Data Sets ◽  
Data Set ◽  
Linear Modelling ◽  
Modelling Techniques

We applied different modelling techniques to six data sets from different disciplines in the industry, on which predictive models can be developed, to demonstrate the benefit of segmentation in linear predictive modelling. We compared the model performance achieved on the data sets to the performance of popular non-linear modelling techniques, by first segmenting the data (using unsupervised, semi-supervised, as well as supervised methods) and then fitting a linear modelling technique. A total of eight modelling techniques was compared. We show that there is no one single modelling technique that always outperforms on the data sets. Specifically considering the direct marketing data set from a local South African bank, it is observed that gradient boosting performed the best. Depending on the characteristics of the data set, one technique may outperform another. We also show that segmenting the data benefits the performance of the linear modelling technique in the predictive modelling context on all data sets considered. Specifically, of the three segmentation methods considered, the semi-supervised segmentation appears the most promising.

scholarly journals New modelling technique for improving crop model performance - Application to the GLAM model

2019 ◽  
Vol 118 ◽  
pp. 187-200 ◽  
Author(s):  
I. Droutsas ◽  
A.J. Challinor ◽  
M. Swiderski ◽  
M.A. Semenov
Keyword(s):  
Model Performance ◽  
Crop Model ◽  
Modelling Technique

Predictors of Purchasing a Hearing Aid After an Evaluation Period: A Prospective Study in Dutch Older Hearing Aid Candidates

2019 ◽  
Vol 28 (3S) ◽  
pp. 802-805 ◽  
Author(s):  
Marieke Pronk ◽  
Janine F. J. Meijerink ◽  
Sophia E. Kramer ◽  
Martijn W. Heymans ◽  
Jana Besser
Keyword(s):  
Help Seeking ◽  
Hearing Aid ◽  
Model Performance ◽  
Area Under The Curve ◽  
Secondary Data ◽  
Support Program ◽  
Evaluation Period ◽  
A Prospective Study ◽  
Study Participants ◽  
Baseline Covariates

Purpose The current study aimed to identify factors that distinguish between older (50+ years) hearing aid (HA) candidates who do and do not purchase HAs after having gone through an HA evaluation period (HAEP). Method Secondary data analysis of the SUpport PRogram trial was performed ( n = 267 older, 1st-time HA candidates). All SUpport PRogram participants started an HAEP shortly after study enrollment. Decision to purchase an HA by the end of the HAEP was the outcome of interest of the current study. Participants' baseline covariates (22 in total) were included as candidate predictors. Multivariable logistic regression modeling (backward selection and reclassification tables) was used. Results Of all candidate predictors, only pure-tone average (average of 1, 2, and 4 kHz) hearing loss emerged as a significant predictor (odds ratio = 1.03, 95% confidence interval [1.03, 1.17]). Model performance was weak (Nagelkerke R 2 = .04, area under the curve = 0.61). Conclusions These data suggest that, once HA candidates have decided to enter an HAEP, factors measured early in the help-seeking journey do not predict well who will and will not purchase an HA. Instead, factors that act during the HAEP may hold this predictive value. This should be examined.

Constructing and Deconstructing Concepts

2016 ◽  
Vol 63 (5) ◽  
pp. 249-262 ◽  
Author(s):  
Charles A. Doan ◽  
Ronaldo Vigo
Keyword(s):  
Information Theory ◽  
Model Performance ◽  
Unsupervised Classification ◽  
Family Resemblance ◽  
Category Membership ◽  
Formal Models ◽  
Human Beings ◽  
One Dimensional ◽  
Relational Patterns ◽  
Optimal Classification

Abstract. Several empirical investigations have explored whether observers prefer to sort sets of multidimensional stimuli into groups by employing one-dimensional or family-resemblance strategies. Although one-dimensional sorting strategies have been the prevalent finding for these unsupervised classification paradigms, several researchers have provided evidence that the choice of strategy may depend on the particular demands of the task. To account for this disparity, we propose that observers extract relational patterns from stimulus sets that facilitate the development of optimal classification strategies for relegating category membership. We conducted a novel constrained categorization experiment to empirically test this hypothesis by instructing participants to either add or remove objects from presented categorical stimuli. We employed generalized representational information theory (GRIT; Vigo, 2011b , 2013a , 2014 ) and its associated formal models to predict and explain how human beings chose to modify these categorical stimuli. Additionally, we compared model performance to predictions made by a leading prototypicality measure in the literature.

Discrete Choice Modelling (Teil I)

2008 ◽  
Vol 37 (7) ◽  
pp. 356-362 ◽  
Author(s):  
Jochen Gönsch ◽  
Robert Klein ◽  
Claudius Steinhardt
Keyword(s):  
Discrete Choice ◽  
Choice Modelling ◽  
Discrete Choice Modelling
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