Spatio-temporal Datasets Research Articles

Developing a Place–Time-Specific Transmissibility Index to Measure and Examine the Spatiotemporally Varying Transmissibility of COVID-19

The transmission rate of COVID-19 varies by location and time. A proper measure of the transmissibility of an infectious disease should be place- and time-specific, which is currently unavailable. This research aims to better understand the spatiotemporally changing transmissibility of COVID-19. It contributes to COVID-19 research in three ways. First, it presents a generally applicable modeling framework to estimate the transmissibility of COVID-19 in a specific place and time based on daily reported case data, called space-time effective reproduction number, denoted as Then, the developed model is used to create a spatiotemporal data set of values at the county level in the United States. Second, it investigates relationships between and dynamically changing context factors with multiple machine learning and spatial modeling techniques. The research examines the relationships from a cross-sectional perspective and a longitudinal perspective separately. The longitudinal view allows us to understand how local human dynamics and policy factors influence changes in over time in the place, whereas the cross-sectional view sheds light on the demographic, socioeconomic, and environmental factors behind spatial variations of at a specific time slice. Some general trends of the relationships are found, but the level of impact by each context factor varies geographically. Third, the best performing local longitudinal models have promising potential to simulate or forecast future transmissibility. The random forest and the exponential regression models based on time-series data gave the best performances. These models were further evaluated against ground truth data of county-level reported cases. Their good prediction accuracies in the case study prove that these machine learning models are promising in their ability to predict transmissibility in hypothetical or foreseeable scenarios.

In Situ Spatiotemporal SERS Measurements and Multivariate Analysis of Virally Infected Bacterial Biofilms Using Nanolaminated Plasmonic Crystals

In situ spatiotemporal biochemical characterization of the activity of living multicellular biofilms under external stimuli remains a significant challenge. Surface-enhanced Raman spectroscopy (SERS), combining the molecular fingerprint specificity of vibrational spectroscopy with the hotspot sensitivity of plasmonic nanostructures, has emerged as a promising noninvasive bioanalysis technique for living systems. However, most SERS devices do not allow reliable long-term spatiotemporal SERS measurements of multicellular systems because of challenges in producing spatially uniform and mechanically stable SERS hotspot arrays to interface with large cellular networks. Furthermore, very few studies have been conducted for multivariable analysis of spatiotemporal SERS datasets to extract spatially and temporally correlated biological information from multicellular systems. Here, we demonstrate in situ label-free spatiotemporal SERS measurements and multivariate analysis of Pseudomonas syringae biofilms during development and upon infection by bacteriophage virus Phi6 by employing nanolaminate plasmonic crystal SERS devices to interface mechanically stable, uniform, and spatially dense hotspot arrays with the P. syringae biofilms. We exploited unsupervised multivariate machine learning methods, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), to resolve the spatiotemporal evolution and Phi6 dose-dependent changes of major Raman peaks originating from biochemical components in P. syringae biofilms, including cellular components, extracellular polymeric substances (EPS), metabolite molecules, and cell lysate-enriched extracellular media. We then employed supervised multivariate analysis using linear discriminant analysis (LDA) for the multiclass classification of Phi6 dose-dependent biofilm responses, demonstrating the potential for viral infection diagnosis. We envision extending the in situ spatiotemporal SERS method to monitor dynamic, heterogeneous interactions between viruses and bacterial networks for applications such as phage-based anti-biofilm therapy development and continuous pathogenic virus detection.

Exploring Associations between the Built Environment and Cycling Behaviour around Urban Greenways from a Human-Scale Perspective

The incorporation of cycling as a mode of transport has been shown to have a positive impact on reducing traffic congestion, improving mental health outcomes, and contributing to the development of sustainable cities. The proliferation of bike-sharing systems, characterised by their wide availability and high usage rates, has made cycling in urban areas more accessible and convenient for individuals. While the existence of a relationship between cycling behaviour and the built environment has been established, few studies have specifically examined this connection for weekdays and weekends. With the emergence of new data sources, new methodologies have become available for research into this area. For instance, bike-sharing spatio-temporal datasets have made it possible to precisely measure cycling behaviour over time, while street-view images and deep learning techniques now enable researchers to quantify the built environment from a human perspective. In this study, we used 139,018 cycling trips and 14,947 street-view images to examine the connection between the built environment consisting of urban greenways and cycling behaviour. The results indicated that the greenness and enclosure of the level of greenway were positively correlated with increased cycling on both weekdays and weekends. However, the openness of the greenway appears to have opposing effects on cycling behaviour depending on the day of the week, with high levels of openness potentially promoting cycling on weekends but hindering it on weekdays. Based on the findings of this study, policymakers and planners should focus on the cycling environment and prioritise improving its comfort and safety to promote green transportation and bicycle-friendly cities.

Integrating GRASS GIS and Jupyter Notebooks to facilitate advanced geospatial modeling education

AbstractOpen education materials are critical for the advancement of open science and the development of open‐source software. These accessible and transparent materials provide an important pathway for sharing both standard geospatial analysis workflows and advanced research methods. Computational notebooks allow users to share live code with in‐line visualizations and narrative text, making them a powerful interactive teaching tool for geospatial analytics. Specifically, Jupyter Notebooks are quickly becoming a standard format in open education. In this article, we introduce a new GRASS GIS package, grass.jupyter, that enhances the existing GRASS Python API to allow Jupyter Notebook users to easily manage and visualize GRASS data including spatiotemporal datasets. While there are many Python‐based geospatial libraries available for use in Jupyter Notebooks, GRASS GIS has extensive geospatial functionality including support for multi‐temporal analysis and dynamic simulations, making it a powerful teaching tool for advanced geospatial analytics. We discuss the development of grass.jupyter and demonstrate how the package facilitates teaching open‐source geospatial modeling with a collection of Jupyter Notebooks designed for a graduate‐level geospatial modeling course. The open education notebooks feature spatiotemporal data visualizations, hydrologic modeling, and spread simulations such as the spread of invasive species and urban growth.

Spatiotemporal Prediction Method for Vorticity of Three-dimensional Near-wall Cylindrical Flow Based on Convolutional Recurrent Neural Network

Due to the existence of a wall surface, the flow around the near-wall cylinder is a very complex unsteady dynamic behavior. However, this kind of feature extraction and fast prediction method of complex flow is difficult to solve in engineering. Therefore, a data-driven model based on Convolutional Recurrent Neural Network (CRNN) is proposed to solve the feature extraction and fast prediction of complex flow, and to predict the vorticity of the near wake region of a three-dimensional near-wall cylinder with G/D=1. CRNN model can analyze the relationship between Spatiotemporal evolution. The three-dimensional flow around the near-wall cylinder with Reynolds number 1500 meshed, and the vorticity was calculated after grid calculation to establish the Spatiotemporal evolution data set. For machine learning problems with high dimensions and large amounts of data, the Adaptive Moment (Adam) estimation method served as an effectively optimize. According to the Spatiotemporal prediction results of vorticity, the prediction results of the model fit well with the numerical simulation results, and the vorticity of the future time step is successfully predicted on the space and time series, that is, the evolutionary relationship of the Spatiotemporal series of vorticity near the wake of the near-wall cylinder is established. After the above model is established, only the initial multi-time vorticity data need to be obtained. The model can serve as predicting the vorticity field in the near wake region of the near-wall cylinder in the future.

Long-term exposure to air pollution and increased risk of atrial fibrillation prevalence in China

BackgroundAtrial fibrillation (AF) is the most common type of treated heart arrhythmia contributing to adverse cardiovascular events. The association between short-term air pollution exposure and AF episodes has been recognized. But the evidence of the association between long-term air pollution exposure and AF was limited, especially in developing countries. MethodsWe performed a nationwide cross-sectional study among 1,374,423 individuals aged ≥35 years from 13 health check-up centers. Using logistic regression models, we assessed the association between long-term exposure to single air pollution and AF prevalence, including particulate matter (PM2.5 and PM10), ozone (O3) and PM2.5 compositions, which were estimated by high-resolution and high-quality spatiotemporal datasets of ground-level air pollutants for China. The quantile g-computation model was used to explore the joint effect of all exposures to air pollution and the contribution of an individual component to the mixture. ResultsIn single-pollutant models, an increase of 10 μg/m3 in PM2.5 (OR 1.031[95%CI 1.010,1.053]) and PM10 (OR = 1.021 [95%CI 1.009,1.033]) was positively associated with AF prevalence. The stratified analyses revealed that these associations were significantly stronger in females, people <65 years old, and those with hypertension and diabetes. In the further exploration of the joint effect of PM2.5 compositions (OR 1.060 [95%CI 1.022,1.101]) per quintile increase in all five PM2.5 components), we found that PM2.5 sulfate contributed the most. ConclusionsThese findings provide important evidence for the positive relationship between long-term exposure to air pollution and AF prevalence in China and identify sulfate particles of PM2.5 as having the highest contribution to the overall mixture effects among all PM2.5 chemical constituents.

Two-Step Approach toward Alignment of Spatiotemporal Wide-Area Unmanned Aerial Vehicle Imageries

Recently, analysis and decision-making based on spatiotemporal unmanned aerial vehicle (UAV) high-resolution imagery are gaining significant attention in smart agriculture. Constructing a spatiotemporal dataset requires multiple UAV image mosaics taken at different times. Because the weather or a UAV flight trajectory is subject to change when the images are taken, the mosaics are typically unaligned. This paper proposes a two-step approach, composed of global and local alignments, for spatiotemporal alignment of two wide-area UAV mosaics of high resolution. The first step, global alignment, finds a projection matrix that initially maps keypoints in the source mosaic onto matched counterparts in the target mosaic. The next step, local alignment, refines the result of the global alignment. The proposed method splits input mosaics into patches and applies individual transformations to each patch to enhance the remaining local misalignments at patch level. Such independent local alignments may result in new artifacts at patch boundaries. The proposed method uses a simple yet effective technique to suppress those artifacts without harming the benefit of the local alignment. Extensive experiments validate the proposed method by using several datasets for highland fields and plains in South Korea. Compared with a recent work, the proposed method improves the accuracy of alignment by up to 13.21% over the datasets.

Разработка интегрированной пространственно-временной базы данных, характеризующей последствия изменения климата

Currently, at the global and regional (national) levels, the expert community, as well as statesmen, have prioritized the task of practical implementation of the Sustainable Development Goals (characterizing the dynamics of a development of various parameters of socio-ecological and economic systems). Achieving these goals requires serious analytical work based on a deep and comprehensive analysis of a processes taking place in various spheres, as well as the formation of an appropriate information platform, including a set of databases that adequately describe changes taking place in various spheres of state activity. However, today, in Russian practice, this tool is practically absent, which significantly complicates the qualitative and quantitative analysis, assessment and forecasting of the processes of adaptation of Russian regions (including the population living in them) to consequences of global climate change. The purpose of this study is to form a database of indicators characterizing process of adaptation of socio-ecological and economic systems of a northern regions of Western Siberia (The Khanty-Mansiysk Autonomous Okrug and Yamalo-Nenets Autonomous Okrug) to global climate change. The projected database should become the basis for creating an open information resource for a wide range of users when they solve analytical and predictive tasks related to a socio-economic assessment of an impact of global climate warming on permafrost. To achieve the objectives of the study, a set of methods was used: theoretical generalization and comparison, formalization, algorithmization, structuring and grouping, economic and statistical methods (including correlation and regression analysis), cartographic method, graphical modeling method. As a result, we have formed a database representing a single, periodically updated repository containing spatio-temporal data sets characterizing processes and phenomena, on the one hand, socio-ecological and economic systems, on the other – climate change. Further work with the database assumes, on the basis of available quantitative data and methods of mathematical statistics, the identification of elements of the ecological, socio-economic systems of the KhMAO and YaNAO vulnerable to global climate change, as well as forecasting the dynamics of their (systems) transformation.

Proposal for a new customization process for a data-based water quality index using a random forest approach

As the water quality index (WQI) represents water quality, it is crucial to customize the WQI for a specific purpose. In this study, to better represent water quality data using WQI, a random forest (RF) approach was used to derive the parameter weight and calculate the WQI according to the watershed and its use. Eight parameters (water temperature, dissolved oxygen, pH, electrical conductivity, suspended solids, total nitrogen, total phosphorus, and total organic carbon) were evaluated using a total of 220,103 data points collected from 900 monitoring sites throughout South Korea between 2011 and 2020. The estimation of parameter weights, key elements in developing the WQI model, was performed through the variable importance estimation method that can be derived from the RF model. The parameter weights were derived based on various spatiotemporal datasets, and it was confirmed that the spatiotemporal differences in weights according to data characteristics represented the regional and seasonal water quality characteristics. Consequently, a customized WQI representing water quality characteristics could be calculated using data-based weights, and it is expected that a data-based customized WQI could be developed to better match the previous WQI to the purpose and target source.

Spatio-temporal generalized complex covariance models based on convolution

Modeling covariance functions, with values on a complex domain, is essential for geostatistical interpolation or stochastic simulation of complex-valued random fields in space or space-time. However, little has been done for complex spatio-temporal modeling. For this aim, the construction of new classes of spatio-temporal complex-valued covariance models, based on convolution, is provided. Indeed, starting from the Lajaunie and Béjaoui models extended to a space-time domain, generalized families of complex models are obtained through the integration with respect to a positive measure. A procedure for fitting the two parts of the spatio-temporal complex models and for defining the density function considered for the integration is also illustrated. The computational details of this procedure are discussed through a case study on a spatio-temporal dataset of sea currents and the performance of these classes of models is assessed.

Object-based continuous monitoring of land disturbances from dense Landsat time series

An accurate mapping of land disturbances with regard to their timing and locations is the prerequisite for the success of downstream disturbance characterization. This study introduces and tests a novel approach that integrates a spatial perspective into the dense Landsat time series analysis, called “Object-Based COntinuous monitoring of Land Disturbance” (OB-COLD). The new algorithm is based on the recognition that the pixels under effects of the same disturbance event often present similar spectral change concurrently in a short time; such pixels experiencing concurrent change could be grouped as an analytic spatial unit, namely “change object”. OB-COLD first generates a change-magnitude snapshot every 60 days by applying per-pixel time series analysis to measure spectral change history. Then, an object-based change analysis is applied for each time-stamped snapshot: two levels of change objects are generated through over-segmentation and region merging; the changing area is determined by examining three object-level properties derived at different scales: the average change magnitude, the pre-change cover type, and the object size. Lastly, OB-COLD reconstructs model coefficients for each temporal segment based on the temporal breaks indicated by new snapshot-based change maps. We tested the new algorithm using 3000 randomly selected reference sample plots and eight Landsat ARD tiles across the continental United States. The accuracy assessment suggests that OB-COLD achieved 76.9% producer's accuracy, which is significantly higher than the per-pixel time series algorithm (i.e., COLD) (16.3 percentage increase) while keeping a comparable user's accuracies (58.7% vs. 57.8%). The quantitative and qualitative evaluations both suggest that OB-COLD could significantly reduce omission errors, particularly for stress disturbances. Most commission errors (73%) are attributed to agricultural practices and climate variability. The proposed algorithm is computationally scalable to large-scale spatiotemporal datasets with advanced cyberinfrastructure resources, holding great potential as the base detection algorithm for the next generation of land disturbance products.

Study on Regional Eco-Environmental Quality Evaluation Considering Land Surface and Season Differences: A Case Study of Zhaotong City

Timely and quantitatively evaluating regional eco-environmental quality (EEQ) is of great significance for realizing regional sustainable development goals. Especially for cloudy areas, it was a great challenge to construct a regional EEQ dataset with high quality and high resolution. However, existing studies failed to consider the influence of land surface and season elements in evaluating regional EEQ. Therefore, this study aimed to promote an accurate EEQ-evaluating framework for cloudy areas. Zhaotong city, a typical karst and cloudy region, was chosen as the study area. First, we integrated multi-source spatiotemporal datasets and constructed a novel eco-environmental comprehensive evaluation index (ECEI) to assess its EEQ from 2000 to 2020. Next, standard deviation ellipse (SDE) and trend analysis methods were applied to investigate regional EEQ’s change trends. Finally, ecological index (EI) values for different years were calculated to validate the effectivity of the ECEI. The main findings were as follows: (1) The EEQ of Zhaotong showed an upward-fluctuating trend (0.0058 a−1), with average ECEI values of 0.729, 0.693, 0.722, 0.749, and 0.730. (2) The spatial distribution pattern of the EEQ showed high values in the north and low values in the south, with Zhaoyang district having the lowest ECEI value. (3) From 2000 to 2020, the standard deviation of the major axis of the ellipse moved northeast of Zhaotong city with θ of SDE changing from 57.06° to 62.90°, thus, indicating the improvement of northeastern regions’ EEQ. (4) The coefficients of the determinant (R2) between the EI and ECEI were 0.84, which was higher than that of EI–RSEI (R2 = 0.56). This indicated that our promoted framework and the ECEI could acquire more accurate EEQ results and provide suggestions for relevant policymakers.

Integrating consumer risk perception and awareness with simulation-based quantitative microbial risk assessment using a coupled systems framework: A case study of private groundwater users in Ontario

Private well users in Ontario are responsible for ensuring the potability of their own private drinking water source through protective actions (i.e., water treatment, well maintenance, and regular water quality testing). In the absence of regulation and limited surveillance, quantitative microbial risk assessment (QMRA) represents the most practical and robust approach to estimating the human health burden attributable to private wells. For an increasingly accurate estimation, QMRA of private well water should be represented by a coupled model, which includes both the socio-cognitive and physical aspects of private well water contamination and microbial exposure. The objective of the current study was to determine levels of waterborne exposure via well water consumption among three sub-groups (i.e., clusters) of private well users in Ontario and quantify the risk of waterborne acute gastrointestinal illness (AGI) attributed to Giardia, shiga-toxin producing E. coli (STEC) and norovirus from private drinking water sources in Ontario. Baseline simulations were utilized to explore the effect of varying socio-cognitive scenarios on model inputs (i.e., increased awareness, protective actions, aging population). The current study uses a large spatio-temporal groundwater quality dataset and cross-sectional province-wide survey to create socio-cognitive-specific QMRA simulations to estimate the risk of waterborne AGI attributed to three enteric pathogens in private drinking waters source in Ontario. Findings suggest significant differences in the level of exposure among sub-groups of private well users. Private well users within Cluster 3 are characterised by higher levels of exposure and annual illness attributable to STEC, Giardia and norovirus than Clusters 1 and 2. Provincial incidence rates of 520.9 (1522 illness per year), 532.1 (2211 illness per year) and 605.5 (5345 illness per year) cases/100,000 private well users per year were predicted for private well users associated with Clusters 1 through 3. Established models will enable development of necessary tools tailored to specific groups of at-risk well users, allowing for preventative public health management of private groundwater sources.

Evaluation of global teleconnections in CMIP6 climate projections using complex networks

Abstract. In climatological research, the evaluation of climate models is one of the central research subjects. As an expression of large-scale dynamical processes, global teleconnections play a major role in interannual to decadal climate variability. Their realistic representation is an indispensable requirement for the simulation of climate change, both natural and anthropogenic. Therefore, the evaluation of global teleconnections is of utmost importance when assessing the physical plausibility of climate projections. We present an application of the graph-theoretical analysis tool δ-MAPS, which constructs complex networks on the basis of spatio-temporal gridded data sets, here sea surface temperature and geopotential height at 500 hPa. Complex networks complement more traditional methods in the analysis of climate variability, like the classification of circulation regimes or empirical orthogonal functions, assuming a new non-linear perspective. While doing so, a number of technical tools and metrics, borrowed from different fields of data science, are implemented into the δ-MAPS framework in order to overcome specific challenges posed by our target problem. Those are trend empirical orthogonal functions (EOFs), distance correlation and distance multicorrelation, and the structural similarity index. δ-MAPS is a two-stage algorithm. In the first place, it assembles grid cells with highly coherent temporal evolution into so-called domains. In a second step, the teleconnections between the domains are inferred by means of the non-linear distance correlation. We construct 2 unipartite and 1 bipartite network for 22 historical CMIP6 climate projections and 2 century-long coupled reanalyses (CERA-20C and 20CRv3). Potential non-stationarity is taken into account by the use of moving time windows. The networks derived from projection data are compared to those from reanalyses. Our results indicate that no single climate projection outperforms all others in every aspect of the evaluation. But there are indeed models which tend to perform better/worse in many aspects. Differences in model performance are generally low within the geopotential height unipartite networks but higher in sea surface temperature and most pronounced in the bipartite network representing the interaction between ocean and atmosphere.

CASTLE: A CONTEXT-AWARE SPATIAL-TEMPORAL LOCATION EMBEDDING PRE-TRAINING MODEL FOR NEXT LOCATION PREDICTION

Abstract. Next location prediction is helpful for service recommendation, public safety, intelligent transportation, and other location-based applications. Existing location prediction methods usually use sparse check-in trajectories and require massive historical data to capture complex spatial-temporal correlations. High spatial-temporal resolution trajectories have rich information. However, obtaining personal trajectories with long time series and high spatio-temporal resolution usually proves challenging. Herein, this paper proposes a two-stage Context-Aware Spatial-Temporal Location Embedding (CASTLE) model, a multi-modal pre-training model for sequence-to- sequence prediction tasks. The method is built in two steps. First, large-scale location datasets, which are sparse but easier to be acquired (i.e., check-in and anomalous navigation data), are used for pre-training location embedding to capture the multi-functional properties under different contexts. After that, the learned contextual embedding is used for downstream location prediction in small-scale but higher spatio-temporal resolution trajectory datasets. Specifically, the CASTLE model combines Bidirectional and Auto-Regressive Transformers to generate contextual embedding vectors rather than a fixed vector for each location. Furthermore, we introduce a location and time-aware encoder to reflect the spatial distances between locations and visit times. Experiments are conducted on two real trajectory datasets. The results show that the CASTLE model can pre-train beneficial location embedding and outperforms the model without pre-training by 4.6–7.1%. The proposed method is expected to improve the next location prediction accuracy without massive historical data, which will greatly drive the use of trajectory data.

Construction of a High Spatiotemporal Resolution Dataset of Satellite-DerivedpCO2and Air–Sea CO2Flux in the South China Sea (2003–2019)

Construction of a High Spatiotemporal Resolution Dataset of Satellite-Derived<i>p</i>CO₂and Air–Sea CO₂Flux in the South China Sea (2003–2019)

Using unstable data from mobile phone applications to examine recent trajectories of retail centre recovery

The COVID-19 pandemic has changed the ways in which we shop, with significant impacts on retail and consumption spaces. Yet, empirical evidence of these impacts, specifically at the national level, or focusing on latter periods of the pandemic remain notably absent. Using a large spatio-temporal mobility dataset, which exhibits significant temporal instability, we explore the recovery of retail centres from summer 2021 to 2022, considering in particular how these responses are determined by the functional and structural characteristics of retail centres and their regional geography. Our findings provide important empirical evidence of the multidimensionality of retail centre recovery, highlighting in particular the importance of composition, e-resilience and catchment deprivation in determining such trajectories, and identifying key retail centre functions and regions that appear to be recovering faster than others. In addition, we present a use case for mobility data that exhibits temporal stability, highlighting the benefits of viewing mobility data as a series of snapshots rather than a complete time series. It is our view that such data, when controlling for temporal stability, can provide a useful way to monitor the economic performance of retail centres over time, providing evidence that can inform policy decisions, and support interventions to both acute and longer-term issues in the retail sector.

Spatio-temporal fluctuations analysis of land surface temperature (LST) using Remote Sensing data (LANDSAT TM5/8) and multifractal technique to characterize the urban heat Islands (UHIs)

Accurate estimation of the microenvironment and radiative susceptibility, as well as forecasting of climate and weather scenarios in urban contexts, are critical for achieving sustainable development goals (SDGs). Rapid urbanization has a substantial impact on both the local and global atmosphere, resulting in Urban Heat Islands (UHIs). This study is the first to attempt to analyze spatiotemporal space-borne sensor datasets in the Indian Himalayan foothills using multifractal detrended fluctuation analysis (MDFA). The coefficients derived through MDFA such as holder exponent (h), spectral width (α), irregularity, truncation spectra, and spatial distribution are analyzed for various land features to illustrate the dynamic patterns and corroborate the multifractalitybehavior of the land surface temperature (LST). The negative correlation between LST and normalized difference vegetation index (NDVI) suggests that vegetated land can assist to mitigate the effects of UHIs, whereas the positive correlation between LST and normalized difference built-up index(NDBI) suggests that urbanization can aggravate the effects of UHIs. The obtained result encourages to implementation of the suggested framework to address anthropogenic heat and the transportation of surface heat fluxes, as well as empowers to work on non-linear dynamic climate models, which are critical for building real-time resilient infrastructure to meet environmental sustainability.

Characterization of spatio-temporal patterns of grassland utilization intensity in the Selinco watershed of the Qinghai-Tibetan Plateau from 2001 to 2019 based on multisource remote sensing and artificial intelligence algorithms

ABSTRACT Due to the limitations of spatial quantification methods, the spatio-temporal patterns of grassland utilization intensity (GUI) in the Selinco watershed (SLCW), the core region of ecological security on the Qinghai-Tibetan Plateau, is unclear under multiple utilization modes. This paper quantified GUI by constructing the association between the potential and actual Enhanced Vegetation Index (EVI) of grasslands in terms of interannual variability. To obtain an accurate spatio-temporal dataset of potential EVI, the following two components were considered on. Firstly, the temporal lag effects of each raw climate factor were investigated to determine the optimal climate variables affecting vegetation productivity. Secondly, four machine learning (ML) algorithms, including an artificial neural network, random forest, support vector machine, and gradient boosting regression tree combined with the Bayesian model average, were used to construct grassland potential EVI models involving EVI, grassland type, and environmental factors (topography, soil, raw climate, and bioclimatic). Meanwhile, to maximize the performance of ML models, variable selection, variable transformation, and hyperparameter optimization were systematically implemented, where the hyperparameter optimization algorithms employ the grid search algorithm, Bayesian optimization, genetic algorithm, and particle swarm optimization. Then, the spatio-temporal dataset of GUI in the SLCW from 2001 to 2019 was established by using the above quantification method based on multisource remote sensing and artificial intelligence algorithms. The analysis of spatio-temporal variation in GUI showed that the implementation of ecological restoration projects leads to a significant and rapid decline in the overall GUI of the SLCW after 2010 (declining by 4.8%), which is more obvious in the non-nature reserve (declining by 9.3%). In the Qiangtang Nature Reserve within the SLCW, although the GUI shows a declining trend after 2010 because of the implementation of ecological restoration projects, it shows an insignificant increase from 2001 to 2019 due to the recovery increase of wildlife populations in recent decades. Besides, by exploring the effects of elevation and slope on the GUI, it is found that grasslands on higher slopes at lower elevations are at a greater risk of degradation due to more intensive grassland utilization.

Disentangling the genetic consequences of demographic change.

Quantifying the impact of human activity on the capacity of populations to persist is paramount to conservation biology, as numerous species and populations have already been driven to or beyond the brink of extinction. Those populations that persist are often a sobering example of the evolutionary power of human-disturbance, such as the loss of tusks in African elephants resulting from ivory harvesting (Campbell-Staton et al.,2021) and rapid life-history evolution in northern Atlantic cod in response to fisheries (Olsen et al.,2004). These evolutionary responses reflect a delicate interplay between demographic and selective processes (e.g., evolutionary rescue: Bell & Gonzalez,2009; Gomulkiewicz & Holt,1995), both of which can modify genetic variation for fitness. While quantifying fitness remains a difficult challenge, generalizable insights into the evolutionary consequences of population collapse can be provided in systems with independent demographic shifts in response to human activity. Unfortunately, such was the case for sea otter populations across its range in the 18th and 19th centuries, where the fur-trade had catastrophic, range-wide effects on sea otter (Enhydra lutris) populations. In a From the Cover article in this issue of Molecular Ecology, Beichman et al.(2022) combine a population genomic spatiotemporal data set and theoretical simulations not only to quantify past demographic change in response to sea otter exploitation, but also to understand the consequences of population collapse on species persistence.