scholarly journals Modeling the potential climate change- induced impacts on future genus Rhipicephalus (Acari: Ixodidae) tick distribution in semi-arid areas of Raya Azebo district, Northern Ethiopia

2019 ◽  
Vol 43 (1) ◽  
Author(s):  
Meseret Hadgu ◽  
Habtamu Taddele Menghistu ◽  
Atkilt Girma ◽  
Haftu Abrha ◽  
Haftom Hagos
Keyword(s):  
Climate Change ◽  
Potential Distribution ◽  
Goodness Of Fit ◽  
Predictive Accuracy ◽  
Climatic Factors ◽  
Model Performance ◽  
Northern Ethiopia ◽  
Distribution Maps ◽  
Tick Distribution ◽  
The Future

Abstract Background Climate change is believed to be continuously affecting ticks by influencing their habitat suitability. However, we attempted to model the climate change-induced impacts on future genus Rhipicephalus distribution considering the major environmental factors that would influence the tick. Therefore, 50 tick occuance points were taken to model the potential distribution using maximum entropy (MaxEnt) software and 19 climatic variables, taking into account the ability for future climatic change under representative concentration pathways (RCPs) 4.5 and 8.5, were used. Results MaxEnt model performance was tested and found with the AUC value of 0.99 which indicates excellent goodness-of-fit and predictive accuracy. Current models predict increased temperatures, both in the mid and end terms together with possible changes of other climatic factors like precipitation which may lead to higher tick-borne disease risks associated with expansion of the range of the targeted tick distribution. Distribution maps were constructed for the current, 2050, and 2070 for the two greenhouse gas scenarios and the most dramatic scenario; RCP 8.5 produced the highest increase probable distribution range. Conclusions The future potential distribution of the genus Rhipicephalus show potential expansion to the new areas due to the future climatic suitability increase. These results indicate that the genus population of the targeted tick could emerge in areas in which they are currently lacking; increased incidence of tick-borne diseases poses further risk which can affect cattle production and productivity, thereby affecting the livelihood of smallholding farmers. Therefore, it is recommended to implement climate change adaptation practices to minimize the impacts.

scholarly journals Impact Assessment of Climate Change on the Near and the Far Future Streamflow in the Bocheongcheon Basin of Geumgang River, South Korea

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2516
Author(s):  
Yoonji Kim ◽  
Jieun Yu ◽  
Kyungil Lee ◽  
Hye In Chung ◽  
Hyun Chan Sung ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Water Resources Management ◽  
Model Performance ◽  
Hydrologic Model ◽  
Management Policy ◽  
Resources Management ◽  
The Future ◽  
Management Plans ◽  
The Impact

Highly concentrated precipitation during the rainy season poses challenges to the South Korean water resources management in efficiently storing and redistributing water resources. Under the new climate regime, water resources management is likely to become more challenging with regards to water-related disaster risk and deterioration of water quality. To alleviate such issues by adjusting management plans, this study examined the impact of climate change on the streamflow in the Bocheongcheon basin of the Geumgang river. A globally accepted hydrologic model, the HEC-HMS model, was chosen for the simulation. By the calibration and the validation processes, the model performance was evaluated to range between “satisfactory” and “very good”. The calibrated model was then used to simulate the future streamflow over six decades from 2041 to 2100 under RCP4.5 and RCP8.5. The results indicated significant increase in the future streamflow of the study site in all months and seasons over the simulation period. Intensification of seasonal differences and fluctuations was projected under RCP 8.5, implying a challenge for water resources managers to secure stable sources of clean water and to prevent water-related disasters. The analysis of the simulation results was applied to suggest possible local adaptive water resources management policy.

Uncertainty in Extrapolations of Predictive Land-Change Models

2005 ◽  
Vol 32 (2) ◽  
pp. 211-230 ◽  
Author(s):  
Robert Gilmore Pontius ◽  
Joseph Spencer
Keyword(s):  
Land Use ◽  
Predictive Accuracy ◽  
Model Performance ◽  
Objective Measure ◽  
Percentage Error ◽  
Change Models ◽  
The Future ◽  
Multiple Resolutions ◽  
Land Use Change Model ◽  
Performance Results

This paper gives a technique to extrapolate the anticipated accuracy of a prediction of land-use and land-cover change (LUCC) to any point in the future. The method calibrates a LUCC model with information from the past in order to simulate a map of the present, so that it can compute an objective measure of validation with empirical data. Then it uses that observed measurement of predictive accuracy to anticipate how accurately the model will predict a future landscape. The technique assumes that the accuracy of the model will decay to randomness as the model predicts farther into the future and estimates how fast the decay in accuracy will occur based on prior model performance. Results are presented graphically in terms of percentage of pixels classified correctly so that nonexperts can interpret the accuracy visually. The percentage correct is budgeted by three components: agreement due to chance, agreement due to the predicted quantity of each land category, and agreement due to the predicted location of each land category. The percentage error is budgeted by two components: disagreement due to the predicted location of each land category and disagreement due to the predicted quantity of each land category. Therefore, model users can see the sources of the accuracy and error of the model. The entire analysis is computable for multiple resolutions, so users can see how the results are sensitive to changes in scale. We illustrate the method with an application of the land-use change model Geomod to Central Massachusetts, where the predictive accuracy of the model decays to 90% over fourteen years and to near complete randomness over 200 years.

scholarly journals Potential distribution of invasive boxwood blight pathogen (Calonectria pseudonaviculata) as predicted by process-based and correlative models

2021 ◽  
Author(s):  
Brittany Barker ◽  
Leonard Coop ◽  
Chuanxue Hong
Keyword(s):  
Potential Distribution ◽  
Climatic Factors ◽  
Model Performance ◽  
Model Fit ◽  
Model Predictions ◽  
Correlative Models ◽  
Response Measures ◽  
Ascomycete Fungi ◽  
Reliability And Robustness ◽  
Insight Into

Boxwood blight, caused by the ascomycete fungi Calonectria pseudonaviculata and C. henricotiae, is an emerging plant disease of boxwood (Buxus spp.) that has had devastating impacts on the health and productivity of boxwood in both the horticultural sector and native ecosystems. In this study, we predicted the potential distribution of C. pseudonaviculata at regional and global scales and explored how climatic factors shape its known range limits. Our workflow combined multiple modeling algorithms to enhance the reliability and robustness of predictions. We produced a process-based climatic suitability model in the CLIMEX program and combined outputs of six different correlative modeling algorithms to generate an ensemble correlative model. All models were fit and validated using an occurrence record dataset (N = 292 records from 24 countries) comprised of positive detections of C. pseudonaviculata from across its entire known invaded range. Evaluations of model performance provided validation of good model fit for all models. A consensus map of CLIMEX and ensemble correlative model predictions indicated that not-yet-invaded areas in eastern and southern Europe and in the southeastern, midwestern, and Pacific coast regions of North America are climatically suitable for establishment. Most regions of the world where Buxus and its congeners are native are also at risk of establishment, which suggests that C. pseudonaviculata should be able to significantly expand its range globally if susceptible hosts exist. Our findings provide the first insight into the global invasion threat of boxwood blight, and are valuable to stakeholders who need to know where to focus surveillance efforts for early detection and rapid response measures to prevent or slow the spread of the disease.

scholarly journals Suicide disparities across metropolitan areas in the US: A comparative assessment of socio-environmental factors using a data-driven predictive approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258824
Author(s):  
Sayanti Mukherjee ◽  
Zhiyuan Wei
Keyword(s):  
Environmental Factors ◽  
Goodness Of Fit ◽  
Metropolitan Areas ◽  
Predictive Accuracy ◽  
Climatic Factors ◽  
Data Driven ◽  
Us States ◽  
Suicide Rates ◽  
Significant Difference ◽  
The Us

Disparity in suicide rates across various metropolitan areas in the US is growing. Besides personal genomics and pre-existing mental health conditions affecting individual-level suicidal behaviors, contextual factors are also instrumental in determining region-/community-level suicide risk. However, there is a lack of quantitative approach to model the complex associations and interplays of the socio-environmental factors with the regional suicide rates. In this paper, we propose a holistic data-driven framework to model the associations of socio-environmental factors (demographic, socio-economic, and climate) with the suicide rates, and compare the key socio-environmental determinants of suicides across the large and medium/small metros of the vulnerable US states, leveraging a suite of advanced statistical learning algorithms. We found that random forest outperforms all the other models in terms of both in-sample goodness-of-fit and out-of-sample predictive accuracy, which is then used for statistical inferencing. Overall, our findings show that there is a significant difference in the relationships of socio-environmental factors with the suicide rates across the large and medium/small metropolitan areas of the vulnerable US states. Particularly, suicides in medium/small metros are more sensitive to socio-economic and demographic factors, while that in large metros are more sensitive to climatic factors. Our results also indicate that non-Hispanics, native Hawaiian or Pacific islanders, and adolescents aged 15-29 years, residing in the large metropolitan areas, are more vulnerable to suicides compared to those living in the medium/small metropolitan areas. We also observe that higher temperatures are positively associated with higher suicide rates, with large metros being more sensitive to such association compared to that of the medium/small metros. Our proposed data-driven framework underscores the future opportunities of using big data analytics in analyzing the complex associations of socio-environmental factors and inform policy actions accordingly.

scholarly journals Impact of climate change on potential distribution of xero-epiphytic selaginellas (Selaginella involvens and S. repanda) in Southeast Asia

2017 ◽  
Vol 18 (4) ◽  
pp. 1680-1695
Author(s):  
AHMAD DWI SETYAWAN ◽  
JATNA SUPRIATNA ◽  
DEDY DARNAEDI ◽  
ROKHMATULOH ROKHMATULOH ◽  
SUTARNO SUTARNO ◽  
...  
Keyword(s):  
Climate Change ◽  
Southeast Asia ◽  
Potential Distribution ◽  
Climatic Factors ◽  
Future Climate ◽  
Future Climate Change ◽  
Water Medium ◽  
Lower Altitude ◽  
Impact Of Climate Change ◽  
The Impact

Setyawan AD, Supriatna J, Darnaedi D, Rokhmatuloh, Sutarno, Sugiyarto, Nursamsi I, Komala WR, Pradan P. 2017. Impact of climate change on potential distribution of xero-epiphytic selaginellas (Selaginella involvens and S. repanda) in Southeast Asia. Biodiversitas 18: 1680-1695. Climate change is one of the greatest challenges for all life on earth, as it may become the dominant driver of changes in ecosystem services and biodiversity loss at the global level. Selaginella is a group of spike-mosses that seem easily affected by global warming (climate change) due to requiring water medium for fertilization. However, some species have been adapted to dry condition and may grow as epiphytes, such as S. involvens and S. repanda. Both species are commonly found in opposing a range of elevation. S. involvens is often found in high-altitude regions, whereas S. repanda is often found at lower-altitude regions. The difference in this altitudinal distributions is expected to limit redistribution mechanism of each species to adapt the climate change projections. This study model examines the potential geographic distribution of S. involvens and S. repanda under current climatic conditions and models the impact of projected climate change on their potential distribution. Future climate predictions are made with four detailed bioclimatic scenarios (i.e. RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) and three-time intervals (2030, 2050, 2080), which combine various climatic factors. In this modeling, it can be concluded that S. involvens and S. repanda can adapt to future climate change, and continue to be sustainable, although it is strongly influenced and shifting habitat distribution in some areas.

Projecting thermal stratification and hypolimnetic oxygen conditions by coupling paleolimnological and 1D lake model approaches

2021 ◽  
Author(s):  
Jean-Philippe Jenny ◽  
Olivia Itier ◽  
Victor Frossard ◽  
David Etienne ◽  
Jean Guillard
Keyword(s):  
Climate Change ◽  
Thermal Regime ◽  
Thermal Stratification ◽  
Climatic Factors ◽  
Climate Data ◽  
The Past ◽  
Lake Model ◽  
Hypolimnetic Oxygen ◽  
The Future ◽  
Oxygen Conditions

<p>Climate change raises many questions about the future of lakes’ thermal regime and hypolimnetic oxygen conditions. One dimensional models have been widely implemented over that last years <sup>1–3</sup>, but most of these models are calibrated against very few years of limnological records, potentially limiting the robustness in long-term reconstructions and preventing inclusion of future scenarios. To analysis the variability and the effects of climate change on thermal regime and oxygen conditions of deep hard-water lakes, we relayed on paleolimnological records and 1D thermal lake model calibrated against time series of limnological data collected by the French Observatoire des LAcs (OLA). Continuous sediment records on four peri-alpine lakes (Lake Geneva, Lake Annecy, Lake Bourget and Lake Aiguebelette) were analysed using micro-XRF Mn-Fe ratio as proxy to infer near-annual trends of oxygen conditions for the past 300 years<sup>4</sup>. Past hypoxia dynamics were further inferred from varved records preserved in sediment cores<sup>5</sup>. General Lake Model (GLM), i.e. a 1-D modelling tool, has been constrained by climate data derived from meteorological observations and CMIP6 simulations in order to reconstruct and forcast stratification regims for the next century. Our paleolimnological results show that fluctuations in hypoxic volumes since the 1950s were great and that these fluctuations were essentially driven by climatic factors, legitimating the use of thermal model approaches for future projections of hypolimnetic oxygen conditions. In this line, thermal regime simulations based on GLM forecast an intensification in thermal stratification and an increase in volumes of water warmer than 9°C over the period 1850-2100 with potential consequence for hypolimnetic oxygen conditions and ecological habitats. Coupling model and paleolimnological approaches seem a promising way to examine the evolution of lakes in the past, and to realistically anticipate the future of lakes for the next decades.</p>

scholarly journals Non-Pessimistic Predictions of the Distributions and Suitability of Metasequoia glyptostroboides under Climate Change Using a Random Forest Model

Forests ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 62
Author(s):  
Xiaoyan Zhang ◽  
Haiyan Wei ◽  
Xuhui Zhang ◽  
Jing Liu ◽  
Quanzhong Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Random Forest ◽  
East Asia ◽  
Potential Distribution ◽  
Western Europe ◽  
Metasequoia Glyptostroboides ◽  
The Past ◽  
Bioclimatic Variables ◽  
The Future ◽  
Suitable Habitats

Metasequoia glyptostroboides Hu & W. C. Cheng, which is a remarkable rare relict plant, has gradually been reduced to its current narrow range due to climate change. Understanding the comprehensive distribution of M. glyptostroboides under climate change on a large spatio-temporal scale is of great significance for determining its forest adaptation. In this study, based on 394 occurrence data and 10 bioclimatic variables, the global potential distribution of M. glyptostroboides under eight different climate scenarios (i.e., the past three, the current one, and the next four) from the Quaternary glacial to the future was simulated by a random forest model built with the biomod2 package. The key bioclimatic variables affecting the distribution of M. glyptostroboides are BIO2 (mean diurnal range), BIO1 (annual mean temperature), BIO9 (mean temperature of driest quarter), BIO6 (min temperature of coldest month), and BIO18 (precipitation of warmest quarter). The result indicates that the temperature affects the potential distribution of M. glyptostroboides more than the precipitation. A visualization of the results revealed that the current relatively suitable habitats of M. glyptostroboides are mainly distributed in East Asia and Western Europe, with a total area of approximately 6.857 × 106 km2. With the intensification of global warming in the future, the potential distribution and the suitability of M. glyptostroboides have a relatively non-pessimistic trend. Whether under the mild (RCP4.5) and higher (RCP8.5) emission scenarios, the total area of suitable habitats will be wider than it is now by the 2070s, and the habitat suitability will increase to varying degrees within a wide spatial range. After speculating on the potential distribution of M. glyptostroboides in the past, the glacial refugia of M. glyptostroboides were inferred, and projections regarding the future conditions of these places are expected to be optimistic. In order to better protect the species, the locations of its priority protected areas and key protected areas, mainly in Western Europe and East Asia, were further identified. Our results will provide theoretical reference for the long-term management of M. glyptostroboides, and can be used as background information for the restoration of other endangered species in the future.

scholarly journals Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Vinushi Amaratunga ◽  
Lasini Wickramasinghe ◽  
Anushka Perera ◽  
Jeevani Jayasinghe ◽  
Upaka Rathnayake
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Climatic Factors ◽  
Computational Time ◽  
Training Algorithm ◽  
Climate Data ◽  
Nonlinear Relationships ◽  
The Future ◽  
Artificial Neural ◽  
Paddy Yield

Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has been accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing day-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and paddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually complex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex, nonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple food is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic parameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala, and Puttalam) with available data. Three training algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each other to find the better training algorithm. Correlation coefficient (R) and Mean Squared Error (MSE) were used as the performance indicators to evaluate the performance of the developed ANN models. The results obtained from this study reveal that LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic factors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction process is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the prediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. The ANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data from various climate models.

Future distribution of cotton and wheat in Australia under potential climate change

2015 ◽  
Vol 154 (2) ◽  
pp. 175-185 ◽  
Author(s):  
F. SHABANI ◽  
B. KOTEY
Keyword(s):  
Climate Change ◽  
Triticum Aestivum ◽  
Climate Models ◽  
Emission Scenario ◽  
Climatic Factors ◽  
Global Climate ◽  
Global Climate Models ◽  
Cotton Production ◽  
The Future ◽  
Potential Climate Change

SUMMARYThe present study applies refined and improved scenarios for climate change to quantify the effects of potential alterations in climatic factors on localities for wheat and cotton production, which are two crops important to Australia's economy. The future distributions of Gossypium (cotton) and Triticum aestivum L. (wheat) were modelled using CLIMEX software with the A2 emission scenario generated by CSIRO-Mk3·0 and MIROC-H global climate models. The results were correlated to identify areas suitable for these economically important crops for the years 2030, 2050, 2070 and 2100 in Australia. The analysis shows that the areas where wheat and cotton can be grown in Australia will diminish from 2030 to 2050 and 2070 through to 2100. While cotton can be grown over extensive areas of the country until 2070, the area grown to wheat will decrease significantly over the period.

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