Large Spatiotemporal Scales Research Articles

Combining large-scale investigation and quantum chemical calculation of pharmaceuticals: Spatiotemporal patterns of occurrence and structural insights into removal

This study investigated the spatiotemporal distribution of 17 pharmaceuticals in wastewater treatment plants (WWTPs) from 17 provinces across China, and explored structural insights into their removal in full-scale wastewater treatment processes by quantum chemistry. Briefly, 10 pharmaceuticals were detected in above 85 % of samples, of which ibuprofen and sulfamethoxazole dominated with concentrations up to the μg/L level. Seasonally, concentrations of psychoactive drugs (PDs) were 1.3–2.6 times higher in summer than in other seasons. Spatially, higher average concentrations were detected in northern WWTPs, and regions with similar economic levels exhibited similar contamination patterns. Pharmaceutical removal in WWTPs ranged from 41.4 % (carbamazepine) to 87.2 % (sulfamethizole), with the secondary treatment segment, especially aerobic treatment units, maintaining an important position. Molecular structural mechanisms behind these removal performances were further revealed. Firstly, we demonstrated a significant association of pharmaceutical overall removal with electrophilicity index (ωcubic) as well as the lowest unoccupied molecular orbital energy (ELUMO). Highly electrophilic pharmaceuticals may persist in WWTPs and their sensitivity to electron exchange reactions accounted for the discrepant removal. In terms of treatment segments, pharmaceuticals with reaction sites masked in molecular structure, such as ibuprofen and venlafaxine, showed a propensity for tertiary treatment suitability. Furthermore, enzymes of aerobic units exhibited excellent docking affinity to pharmaceutical molecules with an average affinity of −7.2 kcal/mol, and hydrogen-bond interactions played an important factor in promoting biodegradation. Our results emphasize the necessity of assessing pharmaceutical contamination on a larger spatiotemporal scale. Moreover, the structural insights into removal phenomena offer scientific molecular-level justification for the design and optimization of pharmaceutical treatment technologies in WWTPs.

Upscaling solute transport in rough single-fractured media with matrix diffusion using a time fractional advection-dispersion equation

Complex structures in fractures and the rock matrix, which are difficult to map exhaustively at the local scale, can dominate contaminant transport in fractured aquifers. An upscaled model is therefore needed to effectively characterize solute transport in a heterogeneous fracture-matrix system on a large spatiotemporal scale. Most existing upscaling methods, however, do not specifically quantify the influence of matrix diffusion and fracture surface roughness on solute transport, and their upscaling parameters are usually difficult to obtain. To fill this knowledge gap, first, a time fractional advection–dispersion equation (t-FADE) model, which can accurately describe solute transport in straight fractured media, is proposed. Next, the influence of local roughness and tortuosity of fractures on transport is comprehensively considered by introducing trend lines. The t-FADE is used to characterize solute transport in each segment of rough fractures, and an upscaled model describing solute transport in rough fractures is established by averaging the governing equations of each segment. Finally, a time-dependent attenuation function is introduced into the convolutional function of the upscaled model to overcome the error caused by overlapping diffusion regions. Model comparison and analysis reveal that the rough wall of fractures strengthens matrix retention capacity, leading to a delayed peak of the breakthrough curve (BTC) with a lower peak concentration, which can be quantitatively characterized by the ratio of the actual fracture length to its longitudinal length; however, the fracture structure does not affect the solute BTC’s trailing concentration at the constant fracture mean flow velocity without considering mechanical dispersion, so the relatively simple straight fracture model can be used to describe the trailing stage of transport in rough-walled fracture media.

Loss and replacement of macroalgal standing stock and productivity after a major environmental disturbance

AbstractHabitat‐forming macroalgae are widely recognized as one of the most productive ecosystems on earth. However, relatively little is known about the variation in productivity over large spatiotemporal scales and the relative importance of environmental and biotic factors in influencing the quantity of carbon fixed. Here we leverage a large tectonic event to understand how fluctuations in the biomass of habitat‐forming macroalgae and changes in light availability influence primary productivity along 130 km of coastline over 5 years. We also quantify the effects of changes to species composition on primary productivity. This study revealed significant implications of a compromised light environment for the biomass of macroalgae and productivity rates. Immediately post‐earthquake, there was a substantial loss of biomass (an average of −83%) of a previously dominant large brown alga, Durvillaea spp., and very limited recovery of biomass over the next 5 years. Reductions in biomass coupled with changes in light attenuation resulted in average net primary production (NPP) from Durvillaea decreasing from 1130 g C m−2 year−1 before the earthquake to 192 g C m−2 year−1 after 5 years. Partial compensation of reduced NPP was facilitated by increases in the biomass of a smaller fucoid Carpophyllum maschalocarpum, but its productivity was considerably less on average, resulting in a reduction in carbon fixation. Overall, reductions in biomass resulted in a 2‐fold to 10‐fold drop in carbon fixation by canopy‐forming macroalgae over at least 5 years, while a degradation of the light environment further compromised primary productivity, and most likely detrital subsidies of organic matter to the nearshore ecosystem.

Mapping cover crop species in southeastern Michigan using Sentinel-2 satellite data and Google Earth Engine.

Cover crops are a critical agricultural practice that can improve soil quality, enhance crop yields, and reduce nitrogen and phosphorus losses from farms. Yet there is limited understanding of the extent to which cover crops have been adopted across large spatial and temporal scales. Remote sensing offers a low-cost way to monitor cover crop adoption at the field scale and at large spatio-temporal scales. To date, most studies using satellite data have mapped the presence of cover crops, but have not identified specific cover crop species, which is important because cover crops of different plant functional types (e.g., legumes, grasses) perform different ecosystem functions. Here we use Sentinel-2 satellite data and a random forest classifier to map the cover crop species cereal rye and red clover, which represent grass and legume functional types, in the River Raisin watershed in southeastern Michigan. Our maps of agricultural landcover across this region, including the two cover crop species, had moderate to high accuracies, with an overall accuracy of 83%. Red clover and cereal rye achieved F1 scores that ranged from 0.7 to 0.77, and user's and producer's accuracies that ranged from 63.3% to 86.2%. The most common misclassification of cover crops was fallow fields with remaining crop stubble, which often looked similar because these cover crop species are typically planted within existing crop stubble, or interseeded into a grain crop. We found that red-edge bands and images from the end of April and early July were the most important for classification accuracy. Our results demonstrate the potential to map individual cover crop species using Sentinel-2 imagery, which is critical for understanding the environmental outcomes of increasing crop diversity on farms.

Using microsatellite data to estimate the persistence of field-level yield gaps and their drivers in smallholder systems

One way to meet growing food demand is to increase yields in regions that have large yield gaps, including smallholder systems. To do this, it is important to quantify yield gaps, their persistence, and their drivers at large spatio-temporal scales. Here we use microsatellite data to map field-level yields from 2014 to 2018 in Bihar, India and use these data to assess the magnitude, persistence, and drivers of yield gaps at the landscape scale. We find that overall yield gaps are large (33% of mean yields), but only 17% of yields are persistent across time. We find that sowing date, plot area, and weather are the factors that most explain variation in yield gaps across our study region, with earlier sowing associated with significantly higher yield values. Simulations suggest that if all farmers were able to adopt ideal management strategies, including earlier sowing and more irrigation use, yield gaps could be closed by up to 42%. These results highlight the ability of micro-satellite data to understand yield gaps and their drivers, and can be used to help identify ways to increase production in smallholder systems across the globe.

A Model Integrating Satellite‐Derived Shoreline Observations for Predicting Fine‐Scale Shoreline Response to Waves and Sea‐Level Rise Across Large Coastal Regions

AbstractSatellite‐derived shoreline observations combined with dynamic shoreline models enable fine‐scale predictions of coastal change across large spatiotemporal scales. Here, we present a satellite‐data‐assimilated, “littoral‐cell”‐based, ensemble Kalman‐filter shoreline model to predict coastal change and uncertainty due to waves, sea‐level rise (SLR), and other natural and anthropogenic processes. We apply the developed ensemble model to the entire California coastline (approximately 1,760 km), much of which is sparsely monitored with traditional survey methods (e.g., Lidar/GPS). Water‐level‐corrected, satellite‐derived shoreline observations (obtained from the CoastSat toolbox) offer a nearly unbiased representation of in situ surveyed shorelines (e.g., mean sea‐level elevation contours) at Ocean Beach, San Francisco. We demonstrate that model calibration with satellite observations during a 20‐year hindcast period (1995–2015) provides nearly equivalent model forecast accuracy during a validation period (2015–2020) compared to model calibration with monthly in situ observations at Ocean Beach. When comparing model‐predicted shoreline positions to satellite‐derived observations, the model achieves an accuracy of <10 m RMSE for nearly half of the entire California coastline for the validation period. The calibrated/validated model is then applied for multi‐decadal simulations of shoreline change due to projected wave and sea‐level conditions, while holding the model parameters fixed. By 2100, the model estimates that 24%–75% of California's beaches may become completely eroded due to SLR scenarios of 1.0–3.0 m, respectively. The satellite‐data‐assimilated modeling system presented here is generally applicable to a variety of coastal settings around the world owing to the global coverage of satellite imagery.

Temperature moderates eDNA–biomass relationships in northern pike

AbstractSupport for eDNA as a quantitative monitoring tool is growing worldwide. Despite advances, there are still uncertainties regarding the representability of the eDNA signal over varying spatiotemporal scales, the influence of abiotic forcing, and phenological changes affecting the behavior of the study organism, particularly in open environments. To assess the spatiotemporal variability and predictive power of quantitative eDNA analysis, we applied species‐specific real‐time quantitative PCR on water filtrates during two visits to 22 coastal bays in the Baltic Sea. Within bays, we collected water along four transects across each bay and compared the pooled eDNA concentration to temporally matched catches from standardized angling targeting the northern pike (Esox lucius), a species for which reliable monitoring data is lacking. We found the variability in eDNA concentrations between transects to be moderate (21%) but still considerably lower than across bays and visits (52%), suggesting small‐scale spatial differences are of less importance during spring when pike spawn. Standardized angling catches, bay area, and water temperature together explained 48% of the variance in eDNA concentrations. DNA concentrations decreased with the increasing bay area, likely indicating a dilution effect. Notably, the relationship between eDNA and standardized catches was positive but varied with temperature and the eDNA‐abundance relationship was only significant at higher temperatures, which also coincided with a higher proportion of spawning/spent fish. We conclude that temperature is a key moderating factor driving changes in pike behavior and spring DNA‐dynamics. We recommend that future surveys focus on larger spatiotemporal scales during times when the influence of changing temperatures is minimized.

An autonomous underwater glider serving large spatiotemporal monitoring in hadal zones

Hadal zones are immensely difficult to explore due to their incredibly harsh environments. In this study, an autonomous underwater glider called Petrel-XPLUS was developed for monitoring hadal zones at large spatiotemporal scales. This glider is equipped with a bionic gradient functional housing, the multi-material pressure housing, to cope with the coupled effects of extremely high pressure and density variations, endowing it with light weight, near-neutral buoyancy, and sufficient onboard energy. Furthermore, an innovative dual-eccentric attitude-regulating mechanism is proposed to increase the pitch angle range from [−45°, 45°] to [−90°, 90°] and enrich four types of glider motion modes. This mechanism and a depth-averaged flow-based path planning method enable the glider to handle the superimposed effects of unknown currents and complex topography for effective access to the hadal zone floor. The developed glider has an operating range of 5000 km or 80 dives to a depth of 11,000 m in a single mission lasting 200 d. Petrel-XPLUS successfully completed three dives exceeding 10,000 m with a maximum depth of 10,619 m and an average station-keeping accuracy of 2.018 km during a sea trial in the Mariana Trench. Our study provides a novel technical reference for facilitating hadal exploration.

A novel artificial neural network methodology to produce high-resolution bioclimatic maps using Earth Observation data: A case study for Cyprus

The aim of this research is to propose a novel methodology that exploits Earth Observation (EO) data to accurately produce high-resolution bioclimatic maps at large spatiotemporal scales. This method directly links EO products (i.e., land surface temperature - LST and Normalized Difference Vegetation Index - NDVI) to air temperature (Tair) and such thermal indices as the Universal Thermal Climate Index (UTCI), and the Physiologically Equivalent Temperature (PET) to produce large-scale high-quality bioclimatic maps at a spatial resolution of 100 m. The proposed methodology is based on Artificial Neural Networks (ANNs), and the bioclimatic maps are developed with the use of Geographical Information Systems. High-resolution LST maps are produced from the spatial downscaling of EO images and the application of the methodology in the case of the island of Cyprus highlights the ability of EO parameters to estimate accurately Tair as well as the above mentioned thermal indices. The results are validated for different conditions and the overall Mean Absolute Error for each case ranges from 1.9 °C for Tair to 2.8 °C for PET and UTCI. The trained ANNs could be used in near real-time for estimating the spatial distribution of outdoor thermal conditions and for assessing the relationship between human health and the outdoor thermal environment. On the basis of the developed bioclimatic maps, high-risk areas were identified. Furthermore, the study examines the relationship between land cover and Tair, UTCI, and PET, and the results provide evidence of the suitability of the method to monitor the dynamics of the urban environment and the effectiveness of urban nature-based solutions. Studies on bioclimate analysis monitor thermal environment, raise awareness and enhance the capacity of national public health systems to respond to thermally-induced health risks.

Global estimation of phytoplankton pigment concentrations from satellite data using a deep-learning-based model

Based on a global matchup between satellite observations and high performance liquid chromatography (HPLC) measurements, we developed a deep-learning-based model (DL-PPCE model) for globally estimating concentrations of 17 different phytoplankton pigments. The model adopted a fusion architecture of residual and pyramid networks to achieve robust estimation performance. The model inputs include three different data types: essential ocean color parameters, satellite-derived environmental parameters, and the slope of above-surface remote-sensing reflectance (Rrs). We compared the model performances with various input parameters to determine the most effective inputs. The results showed that Rrs in the essential ocean color parameters and sea surface temperature (SST) in the environmental parameters were the most critical input parameters. The estimation of phytoplankton pigment concentrations was validated against HPLC data using the leave-one-out cross-validation method. Except for three pigments, 19′-butanoyloxy-fucoxanthin, prasinoxanthin, and lutein, the estimated pigment concentrations and in-situ observations were strongly correlated for all other pigments (an average relative root-mean-square error of 0.59, R2≥0.60, and regression slopes close to 1). In addition, a time series analysis was performed on the MODIS retrieved global pigment concentrations during 2003–2021 using the established DL-PPCE model to explore the relationship between the distribution of phytoplankton groups and El Niño in the western equatorial Pacific. Our findings revealed that the prokaryotes-dominated area extended eastward from180°E to 150°W during the 2015/2016 El Niño event. From 2003 to 2021, prokaryotic abundance was positively correlated with El Niño intensity (R=0.65,P≪0.01) but negatively correlated with the abundance of the entire phytoplankton community (R=−0.53,P≪0.01). These results demonstrate that the DL-PPCE model presents a novel approach for estimating the concentration of 17 pigments worldwide, and the estimated pigment concentrations are advantageous for analyzing the phytoplankton community dynamics on a large spatiotemporal scale.

Long-term trends in abundances of non-native species across biomes, realms, and taxonomic groups in Europe

Rates of biological invasion have increased over recent centuries and are expected to increase in the future. Whereas increasing rates of non-native species incursions across realms, taxonomic groups, and regions are well-reported, trends in abundances within these contexts have lacked analysis due to a paucity of long-term data at large spatiotemporal scales. These knowledge gaps impede prioritisation of realms, regions, and taxonomic groups for management. We analysed 180 biological time series (median 15 ± 12.8 sampling years) mainly from Long-Term Ecological Research (LTER) sites comprising abundances of marine, freshwater, and terrestrial non-native species in Europe. A high number (150; 83,3 %) of these time series were invaded by at least one non-native species. We tested whether (i) local long-term abundance trends of non-native species are consistent among environmental realms, taxonomic groups, and regions, and (ii) if any detected trend can be explained by climatic conditions. Our results indicate that abundance trends at local scales are highly variable, with evidence of declines in marine and freshwater long-term monitoring sites, despite non-native species reports increasing rapidly since the late 1970s. These declines were driven mostly by abundance trends in non-native fish, birds, and invertebrate species in three biogeographic regions (Continental, Atlantic, and the North Sea). Temperature and precipitation were important predictors of observed abundance trends across Europe. Yet, the response was larger for species with already declining trends and differed among taxa. Our results indicate that trends in biological invasions, especially across different taxonomic groups, are context-dependent and require robust local data to understand long-term trends across contexts at large scales. While the process of biological invasion is spatiotemporally broad, economic or ecological impacts are generally realised on the local level. Accordingly, we urge proactive and coordinated management actions from local to large scales, as invasion impacts are substantial and dynamics are prone to change.

Evaluation of pollutant exposure using virtual walkers and large-eddy simulation: Application to an idealised urban neighbourhood

Urban air quality studies have primarily focused on pollutant dispersion; however, spatial or temporal concentrations collected at discretely distributed grid points (or fixed receptors) do not reflect the actual pollutant exposure of pedestrians. Using large-eddy simulation (LES) with virtual walkers implemented, this study investigates pollutant exposure of walking agents (or moving receptors) in an urban turbulent boundary-layer flow developed over an aligned building array under the influence of different wind directions. The spatial variability of the exposure risks are found to be better captured by the moving receptors than the fixed receptors along the same agent walking tracks. We demonstrate that the actual exposure can differ significantly from results interpreted from data recorded by the fixed receptors (corresponding to Eulerian estimates) and show that large discrepancies occur in avenues near the source, wherein dispersion of the point release has not occurred on larger spatiotemporal scales. In most scenarios, optimal evacuation routes are shown to be ones that deviate as much as possible from the dominant wind direction; however, one needs to decide the priority of moving to further avenues first or immediately adjusting the walking direction. The results should serve as a useful baseline reference for environmental health impact assessment and evacuation route planning against hazardous releases of air pollutants in more complex urban environments.

Sea turtle strandings along the Northwestern Moroccan coast: Spatio-temporal distribution and main threats

We present the first research carried out on a large spatio-temporal scale (324 km) on sea turtle strandings during a 24-year period (1998–2022) in northwestern Morocco. A total of 208 stranded turtles were recorded. Among these were 184 (88.47%) loggerheads, (Caretta caretta), 21 (10.09%) leatherbacks, (Dermochelys coriacea), and 3 (1.44%) unidentified sea turtles. Strandings were most numerous in the summer and early spring, which coincides with the largest trawler, purse seine and longline fishing effort near the Moroccan coast. The majority of loggerhead turtles measured were subadult individuals (81%), while the stranded leatherback turtles included subadults and adults. Our data demonstrate that Moroccan coastal waters provides important development, foraging and/or migration habitats for loggerhead and leatherback turtles, and fisheries bycatch and boat strikes may be the major threats to sea turtles in Moroccan waters. This study serves as an important baseline for the development of sea turtle conservation efforts in the near-shore waters of the Moroccan Mediterranean coast.

A preliminary assessment of Sentinel-3 ocean and land color instrument data for the estimation of chlorophyll-a concentration using bio-optical methods in Annaba Bay and El Kala’s coast (Algerian Basin)

The use of bio-optical methods and high spatial and spectral resolution satellite sensors has enabled the estimation of chlorophyll-a (Chl-a) on a large spatiotemporal scale. However, their application on a regional scale necessitates validation and, in certain cases, calibration, especially when it comes to coastal waters. This study was carried out in the eastern Algerian coastal waters to evaluate the relevance of Sentinel-3 satellite data in Annaba Bay and El Kala’s coast. Sentinel-3 OLCI (Ocean and Land Color Instrument) Level-2 products (OC4Me and Chl-a NN) and the Chl-a generated by applying the C2RCC processor on Level-1B (Chl-a C2RCC) were compared to the measured Chl-a at fixed locations. In addition, nine other Chl-a algorithms have been tested. Finally, six of those later were locally calibrated. The results demonstrate the good performance of the initially available products (Chl-a C2RCC and Chl-a NN), followed by the blue–green algorithms (OC4, OC5, OC6, MedOC4, and ADOC4), whereas the near-infrared (NIR) algorithms (2B-OLCI, 3B-OLCI, and G2B) show underwhelming performance and very poor prediction of the Chl-a concentration. Even though the OC4Me has the best correlation (r = 0.9820), it also has the highest MAE, RMSD, and BIAS (0.628, 1.264, and 0.5696, respectively). The fit of the NIR methods has been significantly refined. Nevertheless, their performance is still inadequate for the estimation of the Chl-a in our region. The performance of the calibrated OCx was improved, with the OC6-AW at the top of the list (r = 0.9881, MAE = 0.08). The latter outperforms the existing ones, which improves the estimation of Chl-a based on the Sentinel-3 OLCI data in our study area.

Conditional moment methods for polydisperse cavitating flows

The dynamics of cavitation bubbles are important in many flows, but their small sizes and high number densities often preclude direct numerical simulation. We present a computational model that averages their effect on the flow over larger spatiotemporal scales. The model is based on solving a generalized population balance equation (PBE) for nonlinear bubble dynamics and explicitly represents the evolving probability density of bubble radii and radial velocities. Conditional quadrature-based moment methods (QBMMs) are adapted to solve this PBE. A one-way-coupled bubble dynamics problem demonstrates the efficacy of different QBMMs for the evolving bubble statistics. Results show that enforcing hyperbolicity during moment inversion (CHyQMOM) provides comparable model-form accuracy to the traditional conditional method of moments and decreases computational costs by about ten times for a broad range of test cases. The CHyQMOM-based computational model is implemented in MFC, an open-source multi-phase and high-order-accurate flow solver. We assess the effect of the model and its parameters on a two-way coupled bubble screen flow problem.

Alien species and climate change drive shifts in a riverine fish community and trait compositions over 35 years

Alien fish substantially impact aquatic communities. However, their effects on trait composition remain poorly understood, especially at large spatiotemporal scales. Here, we used long-term biomonitoring data (1984–2018) from 31 fish communities of the Rhine river in Germany to investigate compositional and functional changes over time. Average total community richness increased by 49 %: it was stable until 2004, then declined until 2010, before increasing until 2018. Average abundance decreased by 9 %. Starting from 198 individuals/m2 in 1984 abundance largely declined to 23 individuals/m2 in 2010 (−88 %), and then consequently increased by 678 % up to 180 individuals/m2 until 2018. Increases in abundance and richness starting around 2010 were mainly driven by the establishment of alien species: while alien species represented 5 % of all species and 0.1 % of total individuals in 1993, it increased to 30 % (7 species) and 32 % of individuals in 2018. Concomitant to the increase in alien species, average native species richness and abundance declined by 26 % and 50 % respectively. We identified increases in temperature, precipitation, abundance and richness of alien fish driving compositional changes after 2010. To get more insights on the impacts of alien species on fish communities, we used 12 biological and 13 ecological traits to compute four trait metrics each. Ecological trait dispersion increased before 2010, probably due to diminishing ecologically similar native species. No changes in trait metrics were measured after 2010, albeit relative shares of expressed trait modalities significantly changing. The observed shift in trait modalities suggested the introduction of new species carrying similar and novel trait modalities. Our results revealed significant changes in taxonomic and trait compositions following alien fish introductions and climatic change. To conclude, our analyses show taxonomic and functional changes in the Rhine river over 35 years, likely indicative of future changes in ecosystem services.

Accounting for among-sampler variability improves confidence in fuel moisture content field measurements

Background Direct fuel moisture content measurements are critical for characterising spatio-temporal variations in fuel flammability and for informing fire danger assessments. However, among-sampler variability (systematic differences in measurements between samplers) likely contributes to fuel moisture measurement variability in most field campaigns. Aims We assessed the magnitude of among-sampler variability in plot-scale Calluna vulgaris fuel moisture measurements. Methods Seventeen individuals collected samples from six fuel layers hourly from 10:00 hours to 18:00 hours. We developed mixed effects models to estimate the among-sampler variability. Key results Fuel moisture measurements were highly variable between individuals sampling within the same plot, fuel layer, and time of day. The importance of among-sampler variability in explaining total measured fuel moisture variance was fuel layer dependent. Among-sampler variability explained the greatest amount of measurement variation in litter (58%) and moss (45%) and was more important for live (19%) than dead (4%) Calluna. Conclusions Both consideration of samplers within the experimental design and incorporation of sampler metadata during statistical analysis will improve understanding of spatio-temporal fuel moisture dynamics obtained from field-based studies. Implications Accounting for among-sampler variability in fuel moisture campaigns opens opportunities to utilise sampling teams and citizen science research to examine fuel moisture dynamics over large spatio-temporal scales.

Abundance and associated factors of Amblyomma tigrinum (Acari: Ixodidae) infesting wild foxes in north-central Chile.

The tick Amblyomma tigrinum inhabits areas with diverse climatic conditions, with adult stages parasitizing wild canids, such as chilla (Lycalopex griseus) and culpeo (Lycalopex culpaeus) foxes. We described the infestation loads in wild foxes captured at three sites (periurban, rural and wild) through an anthropization gradient in north-central Chile. We tested whether local-scale environmental and/or individual host factors can predict tick abundance by using negative binomial models. During 2018-2020 (spring and summer), we captured 116 foxes (44 chillas and 72 culpeos), and 102 of them were infested with ticks (87.9%, CI=80.6-93.2%). We collected 996 A. tigrinum adult ticks, estimating a total mean abundance of 8.6±0.8 ticks/host. Periurban and rural foxes harbored greater tick loads than foxes from the wild site (2.34 and 1.71 greater, respectively) while tick abundance in summer decreased by up to 57% compared to spring. Tempered, more humid climate conditions of the periurban site could favor the development and survival of adults A. tigrinum; and ticks may have adopted a quiescent stage or similar survival mechanisms to cope with summer temperature increases related to the ongoing megadrought. Further studies are warranted to understand the underlying factors determining the life cycle of A. tigrinum at larger spatiotemporal scales.

Different roles of concurring climate and regional land-use changes in past 40 years’ insect trends

Climate and land-use changes are main drivers of insect declines, but their combined effects have not yet been quantified over large spatiotemporal scales. We analysed changes in the distribution (mean occupancy of squares) of 390 insect species (butterflies, grasshoppers, dragonflies), using 1.45 million records from across bioclimatic gradients of Switzerland between 1980 and 2020. We found no overall decline, but strong increases and decreases in the distributions of different species. For species that showed strongest increases (25% quantile), the average proportion of occupied squares increased in 40 years by 0.128 (95% credible interval: 0.123–0.132), which equals an average increase in mean occupancy of 71.3% (95% CI: 67.4–75.1%) relative to their 40-year mean occupancy. For species that showed strongest declines (25% quantile), the average proportion decreased by 0.0660 (95% CI: 0.0613–0.0709), equalling an average decrease in mean occupancy of 58.3% (95% CI: 52.2–64.4%). Decreases were strongest for narrow-ranged, specialised, and cold-adapted species. Short-term distribution changes were associated to both climate changes and regional land-use changes. Moreover, interactive effects between climate and regional land-use changes confirm that the various drivers of global change can have even greater impacts on biodiversity in combination than alone. In contrast, 40-year distribution changes were not clearly related to regional land-use changes, potentially reflecting mixed changes in local land use after 1980. Climate warming however was strongly linked to 40-year changes, indicating its key role in driving insect trends of temperate regions in recent decades.

Squishing Skyrmions: Symmetry-Guided Dynamic Transformation of Polar Topologies Under Compression.

Mechanical controllability of recently discovered topological defects (e.g., skyrmions) in ferroelectric materials is of interest for the development of ultralow-power mechano-electronics that are protected against thermal noise. However, fundamental understanding is hindered by the "multiscale quantum challenge" to describe topological switching encompassing large spatiotemporal scales with quantum mechanical accuracy. Here, we overcome this challenge by developing a machine-learning-based multiscale simulation framework─a hybrid neural network quantum molecular dynamics (NNQMD) and molecular mechanics (MM) method. For nanostructures composed of SrTiO3 and PbTiO3, we find how the symmetry of mechanical loading essentially controls polar topological switching. We find under symmetry-breaking uniaxial compression a squishing-to-annihilation pathway versus formation of a topological composite named skyrmionium under symmetry-preserving isotropic compression. The distinct pathways are explained in terms of the underlying materials' elasticity and symmetry, as well as the Landau-Lifshitz-Kittel scaling law. Such rational control of ferroelectric topologies will likely facilitate exploration of the rich ferroelectric "topotronics" design space.