Assessing Accuracy Methods of Species Distribution Models: AUC, Specificity, Sensitivity and the True Skill Statistic

We aimed to assess different methods for evaluating performance accuracy in species distribution models based on the application of five types of bioclimatic models under three threshold selections to predict the distributions of eight different species in Australia treated as an independent area Five discriminatory correlative species distribution models SDMs were used to predict the species distributions of eight different plants A global training data set excluding the Australian locations was used for model fitting Four accuracy measurement methods were compared under three threshold selections of i maximum sensitivity specificity ii sensitivity specificity and iii predicted probability of 0 5 default Results showed that the choice of modeling methods had an impact on potential distribution predictions for an independent area Examination of the four accuracy methods underexamined threshold selections demonstrated that TSS is a more realistic and practical method in comparison with AUC Sensitivity and Specificity Accurate projection of the distribution of a species is extremely complex As models provided slight variances in projections of the same group of species it may be more expedient to use TSS as an intuitive method for measuring the performances of the SDMs in comparison to AUC Sensitivity and Specificit

The effect of audit firm size and age on the quality of audit work

This study aimed to investigate the effect of audit firm size and age on the quality of audit work. The sample of the study consists of 201 firms listed in Tehran Security Exchange whose data has been analyzed during 2006 to 2010. The results of regression tests showed that an increase in age and size of audit firms causes a reduction in the use of Accruals items, consequently, increases audit quality. Results suggest that two factors of establishing audit institutions and the number of auditing staffs to separate effects of each factor have significant effect on audit quality

The effect of audit firm size and age on the quality of audit work

This study aimed to investigate the effect of audit firm size and age on the quality of audit work. The sample of the study consists of 201 firms listed in Tehran Security Exchange whose data has been analyzed during 2006 to 2010. The results of regression tests showed that an increase in age and size of audit firms causes a reduction in the use of Accruals items, consequently, increases audit quality. Results suggest that two factors of establishing audit institutions and the number of auditing staffs to separate effects of each factor have significant effect on audit quality

Climate change impacts on the future distribution of date palms: a modeling exercise using CLIMEX

Climate is changing and, as a consequence, some areas that are climatically suitable for date palm (Phoenix dactylifera L.) cultivation at the present time will become unsuitable in the future. In contrast, some areas that are unsuitable under the current climate will become suitable in the future. Consequently, countries that are dependent on date fruit export will experience economic decline, while other countries’ economies could improve. Knowledge of the likely potential distribution of this economically important crop under current and future climate scenarios will be useful in planning better strategies to manage such issues. This study used CLIMEX to estimate potential date palm distribution under current and future climate models by using one emission scenario (A2) with two different global climate models (GCMs), CSIRO-Mk3.0 (CS) and MIROC-H (MR). The results indicate that in North Africa, many areas with a suitable climate for this species are projected to become climatically unsuitable by 2100. In North and South America, locations such as south-eastern Bolivia and northern Venezuela will become climatically more suitable. By 2070, Saudi Arabia, Iraq and western Iran are projected to have a reduction in climate suitability. The results indicate that cold and dry stresses will play an important role in date palm distribution in the future. These results can inform strategic planning by government and agricultural organizations by identifying new areas in which to cultivate this economically important crop in the future and those areas that will need greater attention due to becoming marginal regions for continued date palm cultivation

A novel GIS-based ensemble technique for flood susceptibility mapping using evidential belief function and support vector machine: Brisbane, Australia

In this study, we propose and test a novel ensemble method for improving the accuracy of each method in flood susceptibility mapping using evidential belief function (EBF) and support vector machine (SVM). The outcome of the proposed method was compared with the results of each method. The proposed method was implemented four times using different SVM kernels. Hence, the efficiency of each SVM kernel was also assessed. First, a bivariate statistical analysis using EBF was performed to assess the correlations among the classes of each flood conditioning factor with flooding. Subsequently, the outcome of the first stage was used in a multivariate statistical analysis performed by SVM. A highest prediction accuracy of 92.11% was achieved by an ensemble EBF-SVM-radial basis function method; the achieved accuracy was 7% and 3% higher than that offered by the individual EBF method and the individual SVM method, respectively. Among all the applied methods, both the individual EBF and SVM methods achieved the lowest accuracies. The reason for the improved accuracy offered by the ensemble methods is that by integrating the methods, a more detailed assessment of the flooding and conditioning factors can be performed, thereby increasing the accuracy of the final map

Impacts of climate change on infestations of Dubas bug (Ommatissus lybicus Bergevin) on date palms in Oman

Climate change has determined shifts in distributions of species and is likely to affect species in the future. Our study aimed to (i) demonstrate the linkage between spatial climatic variability and the current and historical Dubas bug (Ommatissus lybicus Bergevin) distribution in Oman and (ii) model areas becoming highly suitable for the pest in the future. The Dubas bug is a pest of date palm trees that can reduce the crop yield by 50% under future climate scenarios in Oman. Projections were made in three species distribution models; generalized linear model, maximum entropy, boosted regression tree using of four global circulation models (GCMs) (a) HadGEM2, (b) CCSM4, (c) MIROC5 and (d) HadGEM2-AO, under four representative concentration pathways (2.6, 4.5, 6.0 and 8.5) for the years 2050 and 2070. We utilized the most commonly used threshold of maximum sensitivity + specificity for classifying outputs. Results indicated that northern Oman is currently at great risk of Dubas bug infestations (highly suitable climatically) and the infestations level will remain high in 2050 and 2070. Other non-climatic integrated pest management methods may be greater value than climatic parameters for monitoring infestation levels, and may provide more effective strategies to manage Dubas bug infestations in Oman. This would ensure the continuing competitiveness of Oman in the global date fruit market and preserve national yields

A Comparison of the Qualitative Analytic Hierarchy Process and the Quantitative Frequency Ratio Techniques in Predicting Forest Fire-Prone Areas in Bhutan Using GIS

Forest fire is an environmental disaster that poses immense threat to public safety, infrastructure, and biodiversity. Therefore, it is essential to have a rapid and robust method to produce reliable forest fire maps, especially in a data-poor country or region. In this study, the knowledge-based qualitative Analytic Hierarchy Process (AHP) and the statistical-based quantitative Frequency Ratio (FR) techniques were utilized to model forest fire-prone areas in the Himalayan Kingdom of Bhutan. Seven forest fire conditioning factors were used: land-use land cover, distance from human settlement, distance from road, distance from international border, aspect, elevation, and slope. The fire-prone maps generated by both models were validated using the Area Under Curve assessment method. The FR-based model yielded a fire-prone map with higher accuracy (87%
 success rate; 82% prediction rate) than the AHP-based model (71% success rate; 63% prediction rate).
 However, both the models showed almost similar extent of 'very high' prone areas in Bhutan, which corresponded to coniferous-dominated areas, lower elevations, steeper slopes, and areas close to human settlements, roads, and the southern international border. Moderate Resolution Imaging Spectroradiometer (MODIS) fire points were overlaid on the model generated maps to assess their reliability in predicting forest fires. They were found to be not reliable in Bhutan, as most of them overlapped with fire-prone classes, such as 'moderate', 'low', and 'very low'. The fire-prone map derived from the FR model will assist Bhutan's Department of Forests and Park Services to update its current National Forest Fire Management Strategy

Landslide susceptibility mapping: machine and ensemble learning based on remote sensing big data

Predicting landslide occurrences can be difficult. However, failure to do so can be catastrophic, causing unwanted tragedies such as property damage, community displacement, and human casualties. Research into landslide susceptibility mapping (LSM) attempts to alleviate such catastrophes through the identification of landslide prone areas. Computational modelling techniques have been successful in related disaster scenarios, which motivate this work to explore such modelling for LSM. In this research, the potential of supervised machine learning and ensemble learning is investigated. Firstly, the Flexible Discriminant Analysis (FDA) supervised learning algorithm is trained for LSM and compared against other algorithms that have been widely used for the same purpose, namely Generalized Logistic Models (GLM), Boosted Regression Trees (BRT or GBM), and Random Forest (RF). Next, an ensemble model consisting of all four algorithms is implemented to examine possible performance improvements. The dataset used to train and test all the algorithms consists of a landslide inventory map of 227 landslide locations. From these sources, 13 conditioning factors are extracted to be used in the models. Experimental evaluations are made based on True Skill Statistic (TSS), the Receiver Operation characteristic (ROC) curve and kappa index. The results show that the best TSS (0.6986), ROC (0.904) and kappa (0.6915) were obtained by the ensemble model. FDA on its own seems effective at modelling landslide susceptibility from multiple data sources, with performance comparable to GLM. However, it slightly underperforms when compared to GBM (BRT) and RF. RF seems most capable compared to GBM, GLM, and FDA, when dealing with all conditioning factors

Dry stress decreases areas suitable for Neoleucinodes elegantalis (Lepidoptera: Crambidae) and affects its survival under climate predictions in South America

© 2018 Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 24 month embargo from date of publication (June 2018) in accordance with the publisher’s archiving policyProjections of climate change show some regions of the world getting warmer, colder, dryer or wetter. Consequently, the effects of climate change on insect pests can alter the threat to agricultural systems. As a result of changed climate, areas can become more or less suitable for insect pests. Neoleucinodes elegantalis is one of the major pests of solanaceous crops in South America. Host plants for N. elegantalis are widely present in South America, however, N. elegantalis is absent from many regions in South America. Hence, future climate effects on suitability for development and spread of N. elegantalis in South America should be investigated. Due to these reasons, we developed a model of the climate for N. elegantalis using CLIMEX software for South America using A2 Special Report on Emissions Scenarios (SRES) for 2030, 2050, 2070 and 2100 and using two models, CSIRO-Mk3.0 and MIROC-H. The results of both models indicate that areas in South America that are climatically suitable at the present time will become climatically unsuitable for N. elegantalis by 2100 as a consequence of progressive increase of dry stress. This was confirmed using developmental bioassays, where survival was lowest at low relative humidity levels. There are also altering areas that are currently unsuitable that become suitable in the future. These results are helpful in developing future strategies to take advantage of new opportunities in solanaceous crops in regions that may be unsuitable for N. elegantalis and provide important information for anticipated possible risks of infestation of N. elegantalis