Understanding climate change effects on the potential distribution of an important pollinator species, Ceratina moerenhouti (Apidae: Ceratinini), in the Eastern Afromontane biodiversity hotspot, Kenya

Mukundamago, Mukundi; Dube, Timothy; Mudereri, Bester Tawona; Babin, Régis; Lattorff, H. Michael G.; Tonnang, Henri E. Z.

doi:10.1016/j.pce.2023.103387

Cited by 2 publications

(1 citation statement)

References 80 publications

(118 reference statements)

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“…While there is no universal consensus on a specific VIF threshold value to indicate the presence of multicollinearity, some researchers have suggested 10 as a threshold (Mukundamago et al 2023;Abegaz et al 2023;Thompson et al 2017). In this study, we have also chosen 10 as the VIF threshold.…”

Section: Shap Analysismentioning

confidence: 99%

A 1D CNN-Based Emulator of CMAQ: Predicting NO2 Concentration over the Most Populated Urban Regions in Texas

Payami,

Choi,

Salman

et al. 2024

Artificial Intelligence for the Earth Systems

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In this study, we developed an emulator of the Community Multiscale Air Quality (CMAQ) model by employing a 1-dimensional Convolutional Neural Network (CNN) algorithm to predict hourly surface nitrogen dioxide (NO2) concentrations over the most densely populated urban regions in Texas. The inputs for the emulator were the same as those for the CMAQ model, which includes emission, meteorology, and land use land cover data. We trained the model over June, July, and August (JJA) of 2011 and 2014 and then tested it on JJA of 2017, achieving an Index of Agreement (IOA) of 0.95 and a correlation of 0.90. We also employed temporal 3-fold cross-validation to evaluate the model’s performance, ensuring the robustness and generalizability of the results. To gain deeper insights and understand the factors influencing the model’s surface NO2 predictions, we conducted a Shapley Additive Explanations analysis. The results revealed solar radiation reaching the surface, Planetary Boundary Layer height, and NOx (NO + NO2) emissions are key variables driving the model’s predictions. These findings highlight the emulator’s ability to capture the individual impact of each variable on the model’s NO2 predictions. Furthermore, our emulator outperformed the CMAQ model in terms of computational efficiency, being more than 900 times faster in predicting NO2 concentrations, enabling the rapid assessment of various pollution management scenarios. This work offers a valuable resource for air pollution mitigation efforts, not just in Texas, but with appropriate regional data training, its utility could be extended to other regions and pollutants as well.

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Section: Shap Analysismentioning

confidence: 99%

A 1D CNN-Based Emulator of CMAQ: Predicting NO2 Concentration over the Most Populated Urban Regions in Texas

Payami,

Choi,

Salman

et al. 2024

Artificial Intelligence for the Earth Systems

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Ecological niche modelling of Culicoides imicola and future range shifts under climate change scenarios in Italy

Del Lesto,

Magliano,

Casini

et al. 2024

Medical Vet Entomology

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Culicoides imicola is the main vector of viral diseases of livestock in Europe such as bluetongue (BT), African horse sickness and epizootic haemorrhagic disease. Climatic factors are the main drivers of C. imicola occurrence and its distribution might be subject to rapid shifts due to climate change. Entomological data, collected during BT surveillance, and climatic/environmental variables were used to analyse ecological niche and to model C. imicola distribution and possible future range shifts in Italy. An ensemble technique was used to weigh the performance of machine learning, linear and profile methods. Updated future climate projections from the latest phase of the Climate Model Intercomparison Project were used to generate future distributions for the next three 20‐year periods, according to combinations of general circulation models and shared socioeconomic pathways and considering different climate change scenarios. Results indicated the minimum temperature of the coldest month (BIO 6) and precipitation of the driest‐warmest months (BIO 14) as the main limiting climatic factors. Indeed, BIO 6 and BIO 14 reported the two highest values of variable importance, respectively, 9.16% (confidence interval [CI] = 7.99%–10.32%), and 2.01% (CI = 1.57%–2.44%). Under the worst‐case scenario of climate change, C. imicola range is expected to expand northward and shift away from the coasts of central Italy, while in some areas of southern Italy, environmental suitability will decrease. Our results provide predictions of C. imicola distribution according to the most up‐to‐date future climate projections and should be of great use to surveillance management at regional and national scales.

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Understanding climate change effects on the potential distribution of an important pollinator species, Ceratina moerenhouti (Apidae: Ceratinini), in the Eastern Afromontane biodiversity hotspot, Kenya

Cited by 2 publications

References 80 publications

A 1D CNN-Based Emulator of CMAQ: Predicting NO2 Concentration over the Most Populated Urban Regions in Texas

A 1D CNN-Based Emulator of CMAQ: Predicting NO2 Concentration over the Most Populated Urban Regions in Texas

Ecological niche modelling of Culicoides imicola and future range shifts under climate change scenarios in Italy

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