Enhancing the spatio-temporal connectivity of dynamic landscapes is crucial for species to adapt to climate change.However,the spatio-temporal connectivity network approach considering climate change and species movem...Enhancing the spatio-temporal connectivity of dynamic landscapes is crucial for species to adapt to climate change.However,the spatio-temporal connectivity network approach considering climate change and species movement is often overlooked.Taking Tibetan wild ass on the Qinghai-Xizang Plateau as an example,we simulated species distribution under current(2019)and future scenarios(2100),constructed spatio-temporal connectivity networks,and assessed the spatio-temporal connectivity.The results show that under the current,SSP2–4.5 and SSP3–7.0 scenarios,suitable habitats for the Tibetan wild ass account for 21.11%,21.34%,and 20.95%of the total area,respectively,with increased fragmentation projected by 2100.78.35%of the habitats which are predicted to be suitable under current conditions will remain suitable in the future,which can be regarded as stable climate refuges.With the increase in future emission intensity,the percentage of auxiliary connectivity corridors increases from 27.65%to 33.57%.This indicates that more patches will function as temporary refuges and the auxiliary connectivity corridors will gradually weaken the dominance of direct connectivity corridors.Under different SSP-RCP scenarios,the internal spatio-temporal connectivity is always higher than direct connectivity and auxiliary connectivity,accounting for 42%–43%.Compared with the spatio-temporal perspective,the purely spatial perspective overestimates network connectivity by about 28%considering all current and future patches,and underestimates network connectivity by 16%–21%when only considering all current or future patches.In this study,a new approach of spatio-temporal connectivity network is proposed to bridge climate refuges,which contributes to the long-term effectiveness of conservation networks for species’adaptation to climate change.展开更多
Geotechnical parameters derived from an intrusive cone penetration test(CPT)are used to asses mechanical properties to inform the design phase of infrastructure projects.However,local,in situ 1D measurements can fail ...Geotechnical parameters derived from an intrusive cone penetration test(CPT)are used to asses mechanical properties to inform the design phase of infrastructure projects.However,local,in situ 1D measurements can fail to capture 3D subsurface variations,which could mean less than optimal design decisions for foundation engineering.By coupling the localised measurements from CPTs with more global 3D measurements derived from geophysical methods,a higher fidelity 3D overview of the subsurface can be obtained.Machine Learning(ML)may offer an effective means to capture all types of geophysical information associated with CPT data at a site scale to build a 2D or 3D ground model.In this paper,we present an ML approach to build a 3D ground model of cone resistance and sleeve friction by combining several CPT measurements with Multichannel Analysis of Surface Waves(MASW)and Electrical Resistivity Tomography(ERT)data on a land site characterisation project in the United Arab Emirates(UAE).To avoid a potential overfitting problem inherent to the use of machine learning and a lack of data at certain locations,we explore the possibility of using a prior Geo-Statistical(GS)approach that attempts to constrain the overfitting process by“artificially”increasing the amount of input data.A sensitivity study is also performed on input features used to train the ML algorithm to better define the optimal combination of input features for the prediction.Our results showed that ERT data were not useful in capturing 3D variations of geotechnical properties compared to Vs due to the geographical location of the site(200 m east from the Oman Gulf)and the possible effect of saline water intrusion.Additionally,we demonstrate that the use of a prior GS phase could be a promising and interesting means to make the prediction of ground properties more robust,especially for this specific case study described in this paper.Looking ahead,better representation of the subsurface can lead to a number of benefits for stakeholders involved in developing assets.Better ground/geotechnical models mean better site calibration of design methods and fewer design assumptions for reliability-based design,creating an opportunity for value engineering in the form of lighter construction without compromising safety,shorter construction timelines,and reduced resource requirements.展开更多
基金financially supported by the National Key Research and Development Program of China(Grant No.2022YFF1303201).
文摘Enhancing the spatio-temporal connectivity of dynamic landscapes is crucial for species to adapt to climate change.However,the spatio-temporal connectivity network approach considering climate change and species movement is often overlooked.Taking Tibetan wild ass on the Qinghai-Xizang Plateau as an example,we simulated species distribution under current(2019)and future scenarios(2100),constructed spatio-temporal connectivity networks,and assessed the spatio-temporal connectivity.The results show that under the current,SSP2–4.5 and SSP3–7.0 scenarios,suitable habitats for the Tibetan wild ass account for 21.11%,21.34%,and 20.95%of the total area,respectively,with increased fragmentation projected by 2100.78.35%of the habitats which are predicted to be suitable under current conditions will remain suitable in the future,which can be regarded as stable climate refuges.With the increase in future emission intensity,the percentage of auxiliary connectivity corridors increases from 27.65%to 33.57%.This indicates that more patches will function as temporary refuges and the auxiliary connectivity corridors will gradually weaken the dominance of direct connectivity corridors.Under different SSP-RCP scenarios,the internal spatio-temporal connectivity is always higher than direct connectivity and auxiliary connectivity,accounting for 42%–43%.Compared with the spatio-temporal perspective,the purely spatial perspective overestimates network connectivity by about 28%considering all current and future patches,and underestimates network connectivity by 16%–21%when only considering all current or future patches.In this study,a new approach of spatio-temporal connectivity network is proposed to bridge climate refuges,which contributes to the long-term effectiveness of conservation networks for species’adaptation to climate change.
文摘Geotechnical parameters derived from an intrusive cone penetration test(CPT)are used to asses mechanical properties to inform the design phase of infrastructure projects.However,local,in situ 1D measurements can fail to capture 3D subsurface variations,which could mean less than optimal design decisions for foundation engineering.By coupling the localised measurements from CPTs with more global 3D measurements derived from geophysical methods,a higher fidelity 3D overview of the subsurface can be obtained.Machine Learning(ML)may offer an effective means to capture all types of geophysical information associated with CPT data at a site scale to build a 2D or 3D ground model.In this paper,we present an ML approach to build a 3D ground model of cone resistance and sleeve friction by combining several CPT measurements with Multichannel Analysis of Surface Waves(MASW)and Electrical Resistivity Tomography(ERT)data on a land site characterisation project in the United Arab Emirates(UAE).To avoid a potential overfitting problem inherent to the use of machine learning and a lack of data at certain locations,we explore the possibility of using a prior Geo-Statistical(GS)approach that attempts to constrain the overfitting process by“artificially”increasing the amount of input data.A sensitivity study is also performed on input features used to train the ML algorithm to better define the optimal combination of input features for the prediction.Our results showed that ERT data were not useful in capturing 3D variations of geotechnical properties compared to Vs due to the geographical location of the site(200 m east from the Oman Gulf)and the possible effect of saline water intrusion.Additionally,we demonstrate that the use of a prior GS phase could be a promising and interesting means to make the prediction of ground properties more robust,especially for this specific case study described in this paper.Looking ahead,better representation of the subsurface can lead to a number of benefits for stakeholders involved in developing assets.Better ground/geotechnical models mean better site calibration of design methods and fewer design assumptions for reliability-based design,creating an opportunity for value engineering in the form of lighter construction without compromising safety,shorter construction timelines,and reduced resource requirements.
