Detrending Research Articles

Intelligent identification on cotton verticillium wilt based on spectral and image feature fusion

BackgroundVerticillium wilt is the major disease of cotton, which would cause serious yield reduction and economic losses, and the identification of cotton verticillium wilt is of great significance to cotton research. However, the traditional method is still manual, which is subjective, inefficient, and labor-intensive, and therefore, this study has proposed a novel method for cotton verticillium wilt identification based on spectral and image feature fusion. The cotton hyper-spectral images have been collected, while the regions of interest (ROI) have been extracted as samples including 499 healthy leaves and 498 diseased leaves, and the average spectral information and RGB image of each sample were obtained. In spectral feature processing, the preprocessing methods including Savitzky-Golay smoothing (SG), multiplicative scatter correction (MSC), de-trending (DT) and mean normalization (MN) algorithms have been adopted, while the feature band extraction methods have adopted principal component analysis (PCA) and successive projections algorithm (SPA). In RGB image feature processing, the EfficientNet was applied to build classification model and 16 image features have been extracted from the last convolutional layer. And then, the obtained spectral and image features were fused, while the classification model was established by support vector machine (SVM) and back propagation neural network (BPNN). Additionally, the spectral full bands and feature bands were used as comparison for SVM and BPNN classification respectively.ResultThe results showed that the average accuracy of EfficientNet for cotton verticillium wilt identification was 93.00%. By spectral full bands, SG-MSC-BPNN model obtained the better performance with classification accuracy of 93.78%. By feature bands, SG-MN-SPA-BPNN model obtained the better performance with classification accuracy of 93.78%. By spectral and image fused features, SG-MN-SPA-FF-BPNN model obtained the best performance with classification accuracy of 98.99%.ConclusionsThe study demonstrated that it was feasible and effective to use fused spectral and image features based on hyper-spectral imaging to improve identification accuracy of cotton verticillium wilt. The study provided theoretical basis and methods for non-destructive and accurate identification of cotton verticillium wilt.

Influence of non-urban reference delineation on trend estimate of surface urban heat island intensity: A comparison of seven methods

Increasing concern over urban heat mitigation creates an emerging necessity to track the evolution of urban heat island intensity (UHII) to assess the effectiveness of existing plans in combating urban heat. However, complex human-environment interactions make it difficult to accurately isolate urban effects in UHII evolutions due to unstable non-urban references. This study provided the first test on the sensitivity of surface UHII (SUHII) trend estimate to seven methods of non-urban reference delineation across 281 Chinese cities. The corresponding seven non-urban references include administrative non-urban areas, administrative non-urban areas excluding extreme elevations, administrative low plant areas, suburb, outer suburb, outer suburbs progressively excluding unstable areas interfered by evident greening/desertification trends and the SUHI footprint of neighboring cities, respectively. Results revealed that the selection of different non-urban references significantly altered SUHII and the nature of observed surface thermal island (heat or cold) in 74% and 8% of cities, respectively. Besides, due to diverse and inconsistent land use and land cover changes across and nearby non-urban areas, the way of non-urban reference delineation also affected the amplitude and even direction of statistically significant SUHII trends in 41% and 21% of cities, respectively. Among the seven methods, the use of suburb which was collectively interfered by urban and non-urban changing factors, produced a large number of misestimated significant downtrends (2.7-4.5 times more cities than other methods) and was therefore in general not recommended. Administrative non-urban areas were not suitable to cities with evident greening/desertification trends, yet the applicability can be largely enhanced by removing areas with extreme elevations. Low plant areas were in general feasible, except for the cities with suburban low plants additionally warmed up by urban areas. Outer suburb was relatively the optimal, particularly the areas with all concerned interferences excluded. Overall, this study provided empirical evidence supporting the need to use stable non-urban references for reliable trend estimate of urban-induced SUHII variations. The results may benefit more accurate assessments of the causes, progress, and consequences of SUHI evolution, thereby minimizing the chances of policy failure in SUHI mitigation.

A Double machine learning trend model for citizen science data

Abstract Citizen and community science datasets are typically collected using flexible protocols. These protocols enable large volumes of data to be collected globally every year; however, the consequence is that these protocols typically lack the structure necessary to maintain consistent sampling across years. This can result in complex and pronounced interannual changes in the observation process, which can complicate the estimation of population trends because population changes over time are confounded with changes in the observation process. Here we describe a novel modelling approach designed to estimate spatially explicit species population trends while controlling for the interannual confounding common in citizen science data. The approach is based on Double machine learning, a statistical framework that uses machine learning (ML) methods to estimate population change and the propensity scores used to adjust for confounding discovered in the data. ML makes it possible to use large sets of features to control for confounding and to model spatial heterogeneity in trends. Additionally, we present a simulation method to identify and adjust for residual confounding missed by the propensity scores. To illustrate the approach, we estimated species trends using data from the citizen science project eBird. We used a simulation study to assess the ability of the method to estimate spatially varying trends when faced with realistic confounding and temporal correlation. Results demonstrated the ability to distinguish between spatially constant and spatially varying trends. There were low error rates on the estimated direction of population change (increasing/decreasing) at each location and high correlations on the estimated magnitude of population change. The ability to estimate spatially explicit trends while accounting for confounding inherent in citizen science data has the potential to fill important information gaps, helping to estimate population trends for species and/or regions lacking rigorous monitoring data.

Trend analysis and climatology of hail in Croatia

This study analyzed the hail climatology across Croatia using data from a national network of 199 stations, which recorded a total of 8551 hail cases. The spatio-temporal analyses were made for: (i) several different periods, (ii) five additional 121 year long data series and (iii) investigation of the connection between weather types and occurrence of hail. Due to high complexity of Croatian territory (which includes the northern lowland, the central mountainous area and the long Adriatic coast with numerous islands and the Istrian peninsula), the spatial and temporal variability of hail as well as the limited number of hail data, three types of stations were defined. The types represent sets of stations with similar annual cycles of hail occurrence. Trend analysis using Mann–Kendall trend significance tests with Theil–Sen trend estimates was performed for three periods: from 1900 to 2020, from 1964 to 2019, and from 1995 to 2019. The first two periods showed a negative and significant trend in the number of hail days, while the most recent one, 25-year-long trend showed a change in sign toward a positive but not significant trend.The northernmost parts of the coast (i.e., inner Istria) and lowland of Croatia had greater hail activity during summer. In contrast, along the southern and central Croatian coast, the highest hail activity was present in the colder part of the year. There was also a transitional area located in between, that recorded the most hail in spring and fall. Diurnal cycle of hail showed a shift in the daily maximum from morning to afternoon hours from the coast toward Croatian lowland.The coastal part of Croatia generally recorded higher hail frequencies than the continental part. The climatology of hail duration revealed a log-normal distribution pattern, further suggesting that most hail cases last between 1 and 5 min with peek duration of about 4 min. The most dominant air masses (over 80% of time) responsible for hail come from the SW, W and NW directions, and 83% of hail is associated with cyclonic influence in the region.

Comparing different trend estimation approaches in country means and standard deviations in international large-scale assessment studies

One major aim of international large-scale assessments (ILSA) like PISA is to monitor changes in student performance over time. To accomplish this task, a set of common items (i.e., link items) is repeatedly administered in each assessment. Linking methods based on item response theory (IRT) models are used to align the results from the different assessments on a common scale. This work employs the one-parameter logistic (1PL) and the two-parameter logistic (2PL) IRT models as scaling models for dichotomous item response data. The present article discusses different types of trend estimates in country means and standard deviations for countries in ILSA. These types differ in three aspects. First, the trend can be assessed by an indirect or direct linking approach for linking a country’s performance at an international metric. Second, the linking for the trend estimation can rely on either all items or only the link items. Third, item parameters can be assumed to be invariant or noninvariant across countries. It is shown that the most often employed trend estimation methods of original trends and marginal trends can be conceived as particular cases of indirect and direct linking approaches, respectively. Through a simulation study and analytical derivations, it is demonstrated that trend estimates using a direct linking approach and those that rely on only link items outperformed alternatives for the 1PL model with uniform country differential item functioning (DIF) and the 2PL model with uniform and nonuniform country DIF. We also illustrated the performance of the different scaling models for assessing the PISA trend from PISA 2006 to PISA 2009 in the cognitive domains of reading, mathematics, and science. In this empirical application, linking errors based on jackknifing testlets were utilized that adequately quantify DIF effects in the uncertainty of trend estimates.

No effect of model distribution on long-term trends, even with underdispersion

While many studies have illustrated the decline of animal populations—particularly of farmland birds—the statistical analyses, design, and protocols used have raised some concerns and criticism. Using a 27-year dataset (1996–2022) based on recording the number of skylarks (Alauda arvensis) at 160 longitudinal count points, our study confronts two approaches commonly used to model long-term trends. The first uses a single model (based on a priori ecological knowledge), while the second is an a posteriori approach that relies on a multi-model selection among candidate models that account for probability distributions to describe the error structure. Here we investigate whether the statistical distribution of modelled variables and the method of including covariates in the model affect trend estimates. With a large amount of data and in the case of underdispersion, we found that the model distribution used had no impact on the estimation of the long-term trend. Moreover, adding confounding covariates did not change or improve the trend estimation, at least when data were obtained from a well-designed protocol (our case). In contrast to other studies reporting an effect of the model's distribution on long-term trends, especially in the presence of overdispersion, our results offer a new perspective on the presence of underdispersion, where simple models perform equally well as complex ones. Further research is now needed on multiple species data or on smaller data sets to check the generality of our findings.

U.S. shale oil production and trend estimation: Forecasting a Hubbert model

AbstractScarcity and growth analyses about energy include forecasting extraction rates of non‐renewable resources while taking into account technological progression. One mechanism, Moore's (1965) Law of technological improvement, encompasses experience enhanced steady cost reductions. Alternatively, synthesis technological change refers to unforeseen innovative breakthroughs. These trends relate to the Hubbert (1956, 1962) Curve of U.S shale oil extraction in the U.S. Lower 48 contiguous states. Price effects, drilling rigs and the price‐to‐drilling rig relationship are analyzed. Results show we are at peak U.S. shale oil, and could experience comprehensive decline. This implies Catton's (1982) “Age of Exuberance,” and “Carrying Capacity” drawdown.

Making virtual species less virtual by reverse engineering of spatiotemporal ecological models

AbstractThe virtual species (VS) and virtual ecologist (VE) approaches are useful tools that allow testing different methodological aspects of species distribution modelling. However, methods used to generate VS so far lack solutions that can ensure a high degree of biological realism, taking into account spatial and temporal variability of population densities.We have developed a method for generating dynamic VS that can reconstruct their living prototypes in a realistic way. The framework consists of fitting a spatiotemporal model to real abundance data, generating a VS population from that model over the entire study area and spanning the whole study period, calibrating the VS, and obtaining the VE data by sampling from the VS. The effectiveness of the developed approach has been illustrated by data from large‐scale and long‐term bird abundance monitoring, using the whinchatSaxicola rubetraas a study system. We evaluated how well the spatiotemporal model can reconstruct the ‘true’ system by comparing response curves and population trends between those used to generate the VS (i.e. what constitutes the ‘truth’) and those estimated from the replicated instances of VE data. In addition, we performed a sensitivity analysis to test how the varying sampling effort affects the accuracy of trend estimation.The synthetic VE data thoroughly reconstructed the real monitoring data. Response curves from generalized additive mixed models (GAMMs), fitted to these two types of data, showed high concordance, as indicated by the 95% confidence intervals of coverage probability of 87.7%–99.8% (mean 96.9%). The population trend estimated from the VE data accurately reconstructed the ‘true’ trend calculated from VS (coverage probability: 82.3%).The proposed method for generating VS and VE data by reverse engineering of the spatiotemporal ecological model reproduces well the properties of the original system, substantially increasing the ecological realism of simulation results. The method may have further applications in evaluating various modelling techniques used to study species range dynamics, where real‐world properties are of particular importance, like conservation and invasion biology or climate change impact assessment.

Modeling Public Opinion Over Time: a Simulation Study of Latent Trend Models

Abstract With the growing availability of multi-wave surveys, social scientists are turning to latent trend models to examine changes in social and political attitudes. Aiming to facilitate this research, we propose a framework for estimating trends in public opinion consisting of three components: the measurement model that links the observed survey responses to the latent attitude, the latent trend model that estimates a trajectory based on aggregated individual latent scores, and representativeness adjustments. We use individual-level item response theory models as the measurement model that is tailored to analyzing public opinion based on pooled data from multi-wave surveys. The main part of our analysis focuses on the second component of our framework, the latent trend models, and compares four approaches: thin-plate splines, Gaussian processes, random walk (RW) models, and autoregressive (AR) models. We examine the ability of these models to recover latent trends with simulated data that vary the shape of the true trend, model complexity, and data availability. Overall, under the conditions of our simulation study, we find that all four latent trend models perform well. We find two main performance differences: the relatively higher squared errors of AR and RW models, and the under-coverage of posterior intervals in high-frequency low-amplitude trends with thin-plate splines. For all models and across all scenarios, performance improves with increased data availability, which emphasizes the need of supplying sufficient data for accurate estimation of latent trends. To further illustrate the differences between the four latent trend models, we present a case study with an analysis of trends in political trust in Hungary, Poland, and Spain between 1995 and 2019. We note the relatively weaker performance of splines compared to other models in this application and conclude by discussing factors to consider when choosing the latent trend model, and further opportunities in this line of research.

Additive autoencoder for dimension estimation

Dimension reduction is one of the key data transformation techniques in machine learning and knowledge discovery. It can be realized by using linear and nonlinear transformation techniques. An additive autoencoder for dimension reduction, which is composed of a serially performed bias estimation, linear trend estimation, and nonlinear residual estimation, is proposed and analyzed. Compared to the classical model, adding an explicit linear operator to the overall transformation and considering the nonlinear residual estimation in the original data dimension significantly improves the data reproduction capabilities of the proposed model. The computational experiments confirm that an autoencoder of this form, with only a shallow network to encapsulate the nonlinear behavior, is able to identify an intrinsic dimension of a dataset with low autoencoding error. This observation leads to an investigation in which shallow and deep network structures, and how they are trained, are compared. We conclude that the deeper network structures obtain lower autoencoding errors during the identification of the intrinsic dimension. However, the detected dimension does not change compared to a shallow network. As far as we know, this is the first experimental result concluding no benefit from a deep architecture compared to its shallow counterpart.

The acceleration of sea-level rise along the coast of the Netherlands started in the 1960s

Abstract. The global acceleration of sea-level rise (SLR) during the 20th century is now established. On the local scale, this is harder to establish as several drivers of SLR play a role, which can mask the acceleration. Here, we study the rate of SLR along the coast of the Netherlands from the average of six tide gauge records covering the period 1890–2021. To isolate the effects of the wind field variations and the nodal tide from the local sea-level trend, we use four generalised additive models (GAMs) which include different predictive variables. From the sea-level trend estimates, we obtain the continuous evolution of the rate of SLR and its uncertainty over the observational period. The standard error in the estimation of the rate of SLR is reduced when we account for nodal-tide effects and is reduced further when we also account for the wind effects, meaning these provide better estimates of the rate of SLR. A part of the long-term SLR is due to wind forcing related to a strengthening and northward shift of the jet stream, but this SLR contribution decelerated over the observational period. Additionally, we detect wind-forced sea-level variability on multidecadal timescales with an amplitude of around 1 cm. Using a coherence analysis, we identify both the North Atlantic Oscillation and the Atlantic Multidecadal Variability as its drivers. Crucially, accounting for the nodal-tide and wind effects changes the estimated rate of SLR, unmasking an SLR acceleration that started in the 1960s. Our best-fitting GAM, which accounts for nodal and wind effects, yields a rate of SLR of about 1.72.21.3 mm yr−1 in 1900–1919 and 1.51.91.2 mm yr−1 in 1940–1959 compared to 2.93.52.4 mm yr−1 in 2000–2019 (where the lower and upper bounds denote the 5th and 95th percentiles). If we discount the nodal tide, wind and fluctuation effects and assume a constant rate of SLR, then the probability (p value) of finding a rate difference between 1940–1959 and 2000–2019 of at least our estimate is smaller than 1 %. Consistent with global observations and the expectations based on the physics of global warming, our results show unequivocally that SLR along the Dutch coast has accelerated since the 1960s.

Proposal for ‘Video Design Study’ to Train the Next Generation of Visual Creators—An Examination of Video Design Study and Its Influence on a Toyota Special Exhibition

Presently, in the video production community, large amounts of content are being created. Accordingly, both Arduino Uno and Raspberry Pi are significantly influencing the progression of media arts. Considering this matter, research on media arts is quite important with regard to the estimation and conception of future trends in video content and artwork. Despite this, all content that is created will eventually be forgotten over time. Therefore, when applying new technology and creating new video content, it is necessary to archive a particular movie creator’s hard work in order to save instructions for later generations on how to “design” video content. This study considers the example of projection mapping and elaborates on the importance of Video Design Study. Notably, “clarification of creating method and process”, which is a very important aspect of Video Design Study, is emphasized in this paper. In addition, a specific example of Video Design Study is introduced via the following special exhibitions: “Dreams of Kiichiro” and “Evolution of Toyota: Dreams of Kiichiro and After That.”

Climate Trend Estimation of the Powell Basin Hydrophysical Characteristics

The trend assessment of the dynamic state of the Powell Basin waters in the Weddell Sea was made on the basis of the hydrological data of the 79th cruise of the R/V AkademikMstislavKeldysh (January 16–February 6, 2020) and the World Ocean Database-2018 data for January-February from 1975 to 2020. At each node of the quarter-degree grid, a linear trend was constructed for the calculated values of the maximum buoyancy frequency and the maximum amplitude of the vertical component of the internal wave velocity. It is shown that the southwestern and northwestern parts of the Powell Basin differ significantly in their hydrophysical characteristics. In the northwest of the basin, the linear trend of the maximum buoyancy frequency is negative, the trends of the depths of the maximum values of the Väisälä-Brent frequency and the amplitude of the vertical velocity component are positive. In the southwestern part of the basin, the opposite is true: the trend of the maximum buoyancy frequency is positive, the trends in the depths of the maximum values of the Väisälä-Brunt frequency and the amplitude of the vertical velocity component are negative.

Trends of Anthropogenic Dissolved Inorganic Carbon in the Northwest Atlantic Ocean Estimated Using a State Space Model

AbstractThe northwest Atlantic Ocean is an important sink for carbon dioxide produced by anthropogenic activities. However the strong seasonal variability in the surface waters paired with the sparse and summer biased observations of ocean carbon makes it difficult to capture a full picture of its temporal variations throughout the water column. We aim to improve the estimation of temporal trends of dissolved inorganic carbon (DIC) due to anthropogenic sources using a new statistical approach: a time series generalization of the extended multiple linear regression (eMLR) method. Anthropogenic increase of northwest Atlantic DIC in the surface waters is hard to quantify due to the strong, natural seasonal variations of DIC. We address this by separating DIC into its seasonal, natural and anthropogenic components. Ocean carbon data is often collected in the summer, creating a summer bias, however using monthly averaged data made our results less susceptible to the strong summer bias in the available data. Variations in waters below 1000m have usually been analyzed on decadal time scales, but our monthly analysis showed the anthropogenic carbon component had a sudden change in 2000 from stationary to an increasing trend at the same rate as the waters above. All depths layers had similar rates of anthropogenic increase of ∼0.57µmol kg−1 year−1, and our uncertainty levels are smaller than with eMLR results. Integration throughout the water column (0–3,500 m) gives an anthropogenic carbon storage rate of 1.37 ± 0.57 mol m−2 year−1, which is consistent with other published estimates.

Combining trends from disparate monitoring programs to inform Red List assessments: The case of the Cape Vulture (Gyps coprotheres)

Conservation decisions are often informed by Red List assessments, which are substantially influenced by estimated population trends. Population trend estimates used by Red List assessments should therefore be as quantitative, comprehensive, and transparent as possible. We combine counts of breeding pairs of the Cape Vulture (Gyps coprotheres) that occur at separate breeding colonies and span differing time periods. The methodological concept is simple: transform time series into interannual rates of change, put those rates into a year-by-series matrix, then average that matrix to estimate the interannual population wide rate of change. Our analysis uses state-space models and basic arithmetic to estimate interannual rates of change per time series. Analyses are performed under a Bayesian framework to ensure that uncertainty is propagated into a composite index that estimates the percent global population reduction over three generations. Our results indicate that the global Cape Vulture population declined by 25 % (95 % CRI = 0.5 %–44 %) from 1977 to 2019. Such a decline suggests the species should be listed as Near Threatened under Criterion A2, instead of Vulnerable, as the species is currently listed. However, we performed a sensitivity analysis that suggested the species might indeed be Vulnerable if unmonitored colonies are in decline. Although the analysis for each species and time series should be customized, we suggest that the general practice of averaging the interannual rates of change for all available time series could improve qualitative estimates of population reduction used in many Red List assessments.

An improved DenseNet model for prediction of stock market using stock technical indicators

Reliable estimation of future cost prices of stocks are a significant and exciting task in both educational and economic research. Current progressions in machine learning (ML), and explicitly, in the area of deep learning (DL) have permitted the research community to utilize the financial information from the social and finance websites to estimate the future prices of products in a viable manner. Besides, the accurate estimation of stock future trends is a complex task due to the unstable nature of the finance data. We have tried to overcome the limitations of existing studies by presenting a more reliable DL-based approach. More clearly, the proposed work is based on estimating the final prices of products by utilizing the 10 years of stock data from the Yahoo Finance website along with the computed Stock Technical Indicators (STIs). The calculated STIs are initially passed as input to the autoencoder to reduce the feature space by eliminating the highly correlated data and providing a more nominative set of STIs. The processed STIs together with the Yahoo finance data are passed as input to the DenseNet-41. The final feature vector computed by the DenseNet-41 is then passed to the SoftMax layer of the network to determine the closing cost prices of products for small, medium, and longtime horizons. Based on the predicted prices of stocks the proposed approach provides three types of signals designated as buy, sell, or hold to assist the investors in their decision-making. Performance evaluation demonstrates that our work outperforms the latest methods by acquiring a minimum MAPE score of 0.32.

Observations in the Spanish Mediterranean Waters: A Review and Update of Results of 30-Year Monitoring

The Instituto Español de Oceanografía (IEO, Spanish Institute of Oceanography) has maintained different monitoring programs in the Spanish Mediterranean waters (Western Mediterranean) since 1992. All these monitoring programs were unified in 2007 under the current program RADMED (series temporales de datos oceanográficos en el Mediterráneo), which is devoted to the in situ multidisciplinary sampling of the water column of coastal and open-sea waters by means of periodic oceanographic campaigns. These campaigns, together with a network of tide-gauges, are part of the IEO Observing system (IEOOS). In some cases, the temperature and salinity time series collected in the frame of these monitoring programs are now more than 30 years long, whereas sea level time series date to the beginning of the 1940s. This information has been complemented with international databases and has been analyzed in numerous works by the Grupo mediterráneo de Cambio Climático (GCC; Mediterranean Climate Change Group) for more than 20 years. These works have been devoted to the detection and quantification of the changes that climate change is producing on the physical, chemical, and biological properties of the Spanish Mediterranean waters. In this work, we review the results obtained by the GCC since 2005 in relation to the changes in the physical properties of the sea: water column temperature, salinity, and density, heat content, mixed layer depth, and sea level. Time series and results are updated from the last works, and the reliability of the existing time series for the detection of climatologies and long-term trends are analyzed. Furthermore, the different sources of uncertainty in the estimation of linear trends are considered in the present work. Besides this review and update of the results obtained from the data collected in the frame of the IEOOS, we conduct a review of the existing monitoring capabilities from other institutions in the Spanish Mediterranean waters and a review of results dealing with climate change in the Spanish Mediterranean obtained by such institutions. In particular, we include a review of the results obtained by SOCIB (Servicio de Observación y Predicción Costero de las Islas Baleares; Balearic Islands costal observing and forecasting system) in relation to the study of marine heat waves and the warming of the sea surface, and the results corresponding to the intense warming of the Catalan continental shelf at L’Estartit oceanographic station. All these results evidence that the surface Spanish Mediterranean waters are warming up at a rate higher than that affecting the global ocean (>2 °C/100 years). This warming and a salinity increase are also observed along the whole water column. Marine heat waves are increasing their intensity, frequency, and duration since 1982, and coastal sea level is increasing at a rate of 2.5 mm/yr. The salinity increase seems to have compensated for the warming, at least at surface and intermediate waters where no significant trends have been detected for the density. This could also be the reason for the lack of significant trends in the evolution of the mixed layer depth. All these results highlight the importance of monitoring the water column and the necessity of maintaining in situ sampling programs, which are essential for the study of changes that are occurring throughout the Spanish Mediterranean waters.

Towards reliable estimates of abundance trends using automated non‐lethal moth traps

Abstract Monitoring insect abundance or species richness at high spatial and temporal resolution is difficult due to personnel, maintenance, and post‐processing costs as well as ethical considerations. Non‐invasive automated insect monitoring systems could provide a solution to address these constraints. However, every new insect monitoring design needs to be evaluated with respect to reliability and bias based on comparisons with conventional methods. In this study, we evaluate the effectiveness of an automated moth trap (AMT), built from off‐the‐shelf‐hardware, in capturing declines in moth abundance, by comparing it to a conventional, lethal trap. Both trap types were operated five times on 16 plots from the beginning of July 2021 to the end of August 2021. On average AMTs recorded fewer individuals than conventional traps. However, both trap types depicted the same seasonal decline of approximately 3% per day, which corresponded to a total difference of ~85% over the sampling period. Given our sample size, both trap types had the same limitations in their reliability to detect smaller changes in abundance trends. This first proof of concept demonstrated that AMTs depict large magnitude events such as phenological patterns just as well as conventional, lethal traps. Therefore, AMTs are a promising tool for future autonomous and non‐lethal monitoring, which paves the way for high temporal coverage and resolution in insect monitoring. However, this initial quantitative field test revealed that its long‐term applicability must be preceded by several adjustments to the image quality, power supply and to data transfer.

Travel for medical or dental care by race/ethnicity and rurality in the U.S.: Findings from the 2001, 2009 and 2017 National Household Travel Surveys

The travel burden for medical or dental care is a well-documented barrier to healthcare access, particularly in rural areas. There is limited research providing national estimates of the travel trends for medical/dental care, particularly among racial/ethnic groups, and among rural and urban populations. We analyzed data from the 2001, 2009, and 2017 National Household Travel Surveys. Main outcomes were the average travel distance (in miles), average travel time (in minutes), and travel burden, characterized as the percentage of trips lasting ≥ 30 miles or minutes for medical/dental care. We used ordinary least squares and multivariable logistic regressions to examine trends in the travel time/distance and travel burden, controlling for socio-demographic and travel dynamics. Among rural residents, the average travel distance for medical/dental care increased by 17.8% between 2001 and 2017, while no increase was observed among urban residents. Thirty-six percent of trips among rural residents lasted ≥ 30 minutes in 2001 but increased to 47.4% in 2017. Logistic regression estimates show that though Blacks experienced higher odds of a travel time burden compared to Whites, the burden lessened over time. In 2017, urban Blacks (OR = 0.41, 95% C.I. = 0.26,0.66), and rural Blacks (OR = 0.16, 95% C.I. = 0.05,0.55) were less likely to spend ≥ 30 minutes traveling for medical/dental care compared to Whites, using the year 2001 as the baseline. The travel distance and time for medical/dental care have increased in rural areas. However, the travel burden among rural and urban Black residents has decreased. Continuing to alleviate excess burdens of transportation may be beneficial.

Large uncertainty in observed estimates of tropical width from the meridional stream function

Abstract. Recent Hadley cell expansion rate estimates vary substantially, as a multitude of methods and reanalysis datasets yield conflicting results. Among the many methods of estimating the Hadley cell width, the meridional-stream-function 500 hPa zero crossing is the most widely used, as it is directly related to the poleward edge of the Hadley cell (HC). Other common metrics use atmospheric phenomena associated with the HC as a proxy, for instance the zonal-surface-wind zero crossing. As each of these metrics requires different reanalysis data, each with varying error, the level of data-driven uncertainty differs between each metric. While previous work has analyzed the statistical and dynamical relationships between metrics, to date no study has quantified and compared the uncertainty due to reanalysis data error in different HC metrics. In this study, we use ERA5 ensemble members, which include small perturbations in atmospheric variables based on the data error, to quantify the uncertainty associated with six commonly used HC metrics as well as the range of their trend estimates. In the Northern Hemisphere, the tropical expansion rate calculated by the stream function is roughly 0.05∘ per decade, while the Southern Hemisphere rate is 0.2∘ per decade over the period from 1979–2022. Of the six metrics, only the meridional stream function and precipitation minus evaporation have substantial uncertainties. The stream function errors are large due to uncertainty in the underlying meridional-wind data and the presence of large regions of near-neutral circulation at the poleward edge of the tropics. These errors have decreased in recent decades because of improvements in the assimilated observations. Despite these improvements, metrics which use well-observed and constrained quantities such as the zonal-surface-wind zero crossing have lower uncertainty, particularly in summer and fall in the Northern Hemisphere.