Detrending Research Articles

P-320. National Estimates of Rates and Resistance Trends of Enterococci Cases in the United States, 2012-2022

Abstract Background Enterococci are common causes of both community-onset (CO) and hospital-onset (HO) infections; vancomycin-resistant enterococci (VRE) are a serious threat. We sought to describe national estimates of Enterococcus faecalis and E. faecium rates and the proportion of cases that were vancomycin-resistant from 2012-2022 Methods We identified a cohort of patients from the PINC AI and BD Research Insights databases with a clinical culture yielding an E. faecalis or E. faecium isolate with accompanying antimicrobial susceptibility testing (AST). Cultures obtained from rectal, perirectal or nasal swabs were excluded. E. faecalis or faecium isolates resistant to vancomycin were considered VRE. Isolates from patients with no culture yielding the same species and vancomycin AST phenotype in the previous 14 days were counted as an incident case. CO cultures were obtained ≤ day 3 of hospitalization; HO cultures were obtained ≥ day 4. We used a raking-procedure to determine weights for extrapolating the number of discharges included in our sample to match the national distribution of discharges, stratified by bed size, U.S. census division, urban/rural designation, and teaching status, for U.S. hospitals included in the American Hospital Association survey. Weighted rates were calculated over time as cases per 10,000 hospitalizations. The proportion of VRE (%R) was calculated as the rate of VRE cases divided by the rate of all cases of that species with an AST result. Results There were 810 unique acute care hospitals from 2012-2022. Weighted estimates resulted in 124,570 E. faecalis and 516,724 E. faecium incident cases nationally. Rates of CO and HO E. faecalis and E. faecium declined from 2012-2018 and increased from 2018-2021 (Figures 1-4). E. faecalis %R for vancomycin remained stable over the study period (CO: 4-5%, HO: 4-6%), while E. faecium %R declined over the study period from 67% to 57% among CO cases, and from 78% to 70% among HO cases. Conclusion Enterococci and VRE rates have increased recently while the proportion of resistant isolates has been stable for E. faecalis and falling for E. faecium. This suggests rate increases may be due to factors affecting all enterococci (e.g., host susceptibility or pathogen factors) rather than VRE specific factors (e.g., vancomycin exposure). Disclosures All Authors: No reported disclosures

Evolving threats in an unforgiving climate: impact of non-optimal temperatures on life expectancy

This study examines how climate conditions influence life expectancy in Europe based on the impact of year-to-year temperature variability adjusted for economic changes. By focusing on a narrow range of ambient temperatures, we made the model sensitive to both extreme weather and moderately adverse conditions, such as low-temperature modulation of the winter infection season. Regional trend estimates from 1990 to 2019 were pooled to evaluate the overall relationship. We also assessed climate change impacts from 1979–1982 to 2019–2022 and 1981–2010 to 2041–2070 under the RCP 4.5 scenario. Colder-than-optimal temperatures had a greater effect on mortality than warmer ones. In Europe’s coldest regions, harsh winters reduced life expectancy by up to 24 months, while warmer areas like the Azores saw reductions of just 2 months. Overall, climate change has had a small impact in Northern Europe: life expectancy increased by about 1 month due to milder winters but decreased by half a month due to hotter summers. In Southern Europe, the effects were mixed: Atlantic regions gained up to 3 months, while Mediterranean-bordering areas lost up to 3 months. Economic growth maintained a modest effect on life expectancy, even in highly developed regions. Assuming unchanged vulnerability, due to warmer winters life expectancy should be increased by a month, but regions south of the Alps this would be offset by higher heatwave-related deaths.

Using causal diagrams and superpopulation models to correct geographic biases in biodiversity monitoring data

Abstract Biodiversity monitoring schemes periodically measure species' abundances and distributions at a sample of sites to understand how they have changed over time. Often, the aim is to infer change in an average sense across some wider landscape. Inference to the wider landscape is simple if the species' abundances and distributions are similar at sampled to non‐sampled locations. Otherwise, the data are geographically biased, and some form of correction is desirable. We combine causal diagrams with ‘superpopulation models’ to correct time‐varying geographic biases in biodiversity monitoring data. For a given time‐period, expert‐derived causal diagrams are used to deduce the set of variables that explain the geographic bias, and superpopulation models adjust for these variables to produce a corrected estimate of a landscape‐wide mean of for example abundance or occupancy. Estimating a time trend in the variable of interest is achieved by fitting models for multiple time‐periods and, if the drivers of bias are suspected to change over time, by constructing per period causal diagrams. We test the approach using simulated data then apply it to real data from the UK Butterfly Monitoring Scheme (UKBMS). If the variables that explain the geographic bias are known and measured without error, our method is unbiased. Introducing measurement error reduces the method's efficacy, but it is still an improvement on using the sample mean. When applied to data from the UKBMS, the approach gives different results to the scheme's current method, which assumes no geographic bias. Where the goal is to estimate change in some variable of interest at the landscape level (e.g. biodiversity indicators), models that do not adjust for geographic bias implicitly assume it does not exist. Our approach makes the weaker assumption that there is no geographic bias conditional on the adjustment variables, so it should yield more accurate estimates of time trends in many circumstances. The method does require assumptions about the drivers of bias, but these are codified explicitly in the causal diagrams. Operationalising our approach should be less costly than full probability sampling, which would be needed to satisfy the assumptions of conventional approaches.

Comparison of long-term trends and interannual variations of the NO₂ content in the atmosphere according to satellite (OMI) and ground-based spectrometric measurements at NDACC stations

Results of analysis of long-term trends and interannual variations of the NO₂ content in the atmosphere according to measurements with the Ozone Monitoring Instrument (OMI) aboard the EOS-Aura satellite in 2004–2020 are compared to the results of a similar analysis of the NO₂ content derived from independent spectrometric twilight NO₂ measurements by zenith-scattered solar radiation at stations of the Network for the Detection of Atmospheric Composition Change (NDACC)). According to both the data, seasonally dependent estimates of linear NO₂ trends and variations of the NO₂ content under the influence of the 11-year cycle of solar activity and large-scale circulation factors such as the Arctic and Antarctic Oscillations, variations in ocean surface temperature in the Niño 3.4 zone, and the quasi-biennial oscillation in zonal wind in the equatorial stratosphere have been obtained. In general, a good qualitative and, in some cases, quantitative correspondence between estimates of interannual variations of NO₂ has been obtained. For interannual variations of stratospheric NO₂, not a bad correspondence between estimates based on satellite and ground-based data has been obtained on average for all stations, but the correspondence between trend estimates is noticeably worse. The best correspondence between the analysis results has been obtained for Zvenigorod station. For stratospheric NO₂, it was noted in 80–90% of cases, and the correspondence for tropospheric NO₂ is practically 100%.

Historical inventories and future scenarios of multiple hazardous air pollutants (HAPs) emissions from the iron and steel production industry in China.

Iron and steel production (ISP) is one of significant atmospheric pollution emission sources in China. With the implementation of ultra-low emission (ULE) standards, a detailed and new updated emission inventory is urgently needed for better understanding of the temporal trends and spatial variation of emission characteristics. In this study, a unit-based comprehensive emission inventory of multiple hazardous air pollutants (HAPs) for the Chinese ISP spanned from 2012 to 2021, including the conventional pollutants, 13 kinds of Trace elements as well as 2 unconventional but toxic pollutants (PCDD/Fs, F), was dedicatedly developed by integrating dynamic localized emission factors with unit-based information of both the detailed activity level and abatement technology application. Scenario analyses were also conducted to forecast future emission trends up to 2050. The results showed that the lower emission factors owing to ULE retrofits had resulted in different spatial-temporal emission characteristics between outputs and HAPs emissions. The atmospheric emissions of SO2, NOX, PM2.5, PM10, TSP, F and PCDD/Fs from Chinese ISP in 2021 were estimated at about 414.4, 450.5, 1044.3, 2001.6, 3958.8, 9.42 kt and 9.35kg-TEQ, respectively. And most HAPs emissions were lower than those in 2012. Regarding the spatial distribution, Hebei, Jiangsu, Shanxi, ranked as the top 3 emission provinces. Moreover, different manufacturing processes (sintering, blast furnace, steel making, etc.) contributed with different magnitude for specific species, and the future trends estimation showed a great reducing potential caused by the implementation of ULE abatement measures and structure adjustment in future.

Enhanced Flood Monitoring in the Pearl River Basin via GAIN-Reconstructed GRACE Terrestrial Water Storage Anomalies

Floods are a significant and pervasive threat globally, exacerbated by climate change and increasing extreme weather events. The Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) provide crucial insights into terrestrial water storage anomalies (TWSA), which are vital for understanding flood dynamics. However, the observational gap between these missions presents challenges for flood monitoring, affecting the estimation of long-term trends and limiting the analysis of interannual variability, thereby impacting overall analysis accuracy. Reconstructing the missing data between GRACE and GRACE-FO is essential for systematically understanding the spatiotemporal distribution characteristics and driving mechanisms of interannual changes in regional water reserves. In this study, the Generative Adversarial Imputation Network (GAIN) is applied to improve the monitoring capability for flood events in the Pearl River Basin (PRB). First, the GRACE/GRACE-FO TWSA data gap is imputed with GAIN and compared with long short-term memory (LSTM) and k-Nearest Neighbors (KNN) methods. Using the reconstructed data, we develop the Flood Potential Index (FPI) by integrating GRACE-based TWSA with precipitation data and analyze key characteristics of FPI variability against actual flood events. The results indicate that GAIN effectively predicts the GRACE/GRACE-FO TWSA gap, with an average improvement of approximately 50.94% over LSTM and 68.27% over KNN. The reconstructed FPI proves effective in monitoring flood events in the PRB, validating the reliability of the reconstructed TWSA. Additionally, the FPI achieves a predictive accuracy of 79.7% for real flood events, indicating that short-term flood characteristics are better captured using TWSA. This study demonstrates the effectiveness of GAIN in enhancing data continuity, providing a reliable framework for large-scale flood risk assessment and offering valuable insights for flood management in vulnerable regions.

A Multidecadal Assessment of Haze Development and Fluctuations in Visibility for Arabian Peninsula

AbstractDeteriorating visibility (VR) is an exacerbating problem in the Arabian Peninsula (AP) under the prevailing natural and anthropogenic causes. The main objective of this paper is to analyze the long-term (1975–2023) trends of visibility fluctuations and development of haze for six countries including Kingdom of Saudi Arabia (KSA), Iraq (IRQ), Qatar (QTR), Kuwait (KWT), United Arab Emirates (UAE), and Oman (OMN). To comprehend the time-based patterns of visibility across the ME, we utilized the Mann Kendall (MK) and Modified Mann Kendall (MMK) trend estimators in both space and time. The daily visibility of QTR exhibits significant variations from 1975 to 2023, with the lowest and greatest values representing the dynamic character of visibility in the region. Oman’s daily visibility have revealed complex pattern with frequent occurrence of close to zero visibility starting from 1975. Visibility in KWT was 0.5 km in 1994, indicating a period of exceptionally poor visibility whereas the daily mean visibility for KSA was approximately 0.6 km. The UAE data shows strong seasonal spikes in visibility, with peaks of about 2 km. IRQ air quality was severely misty on days when visibility was approximately 0.4 km. Among the six countries, visibility in KWT is improving at 0.06 km/year, while visibility in IRQ is decreasing rapidly at the rate of -0.26 km/year. Keeping in account the overall trends over the decades to assess current visibility conditions, Visibility in KWT and QTR is improving at the fastest trend of + 0.15 and + 0.05 km/year respectively as compared to OMN, IRQ, UAE and KSA. Policymakers in the region can work on regional environmental policies to further improve the regional atmospheric visibility.

Longitudinal timing of physical activity and associated cardiometabolic and behavioral health outcomes in young adults.

This is the first study to examine longitudinal associations between self-selected timing of moderate-to-vigorous physical activity (MVPA) and health outcomes in young adults over 18 months. Young adults (N = 434, Mage = 23.9, SDage = 4.6 years) enrolled in a weight management trial recorded 4-7 days of ActiGraph wear time at ≥1 time point (baseline, months 6, 12, and 18). Time-of-day categories were based on quartiles of the temporal distribution of MVPA min/h at baseline: morning (06:00-11:59), afternoon (12:00-15:59), evening (16:00-18:59), and night (19:00-00:59). The proportion of weekly MVPA accumulated during each time category was the predictor in longitudinal linear mixed-effects models predicting body mass index (BMI) and total weekly MVPA. Longitudinal quasibinomial generalized estimating equations models predicted cardiometabolic risk. Interactions were tested, and marginal trend estimates were generated for sex and age subgroups. The analytic sample was 79% female and 49% non-Hispanic White, with a mean (±SD) weekly MVPA of 311 ± 167min at baseline. In adjusted models, there were no associations with BMI. Morning MVPA was inversely associated with cardiometabolic risk (OR [95% CI]: 0.99 [0.98-0.99]) for both sex and age groups. Evening MVPA was inversely associated with cardiometabolic risk for 26-35 year olds (0.98 [0.97-0.99]). Morning MVPA was associated with greater total MVPA across subgroups, and afternoon MVPA was associated with less total MVPA in women. Over 18 months, incremental health benefits may accrue with optimal activity timing in young adults. Activity-based interventions designed to improve cardiometabolic and behavioral healthoutcomes in young adults may be optimized by tailoring timing recommendations to demographic factors.

Spatiotemporal Variation and Influencing Factors of Atmospheric Aerosol in the Middle Region of the Yarlung Zangbo River Basin from 2000 to 2020

Based on the aerosol optical depth （AOD） of the MODIS MCD19A2 dataset from 2000 to 2020, combined with meteorological data, land use/land cover data, and socio-economic data, the spatiotemporal distribution of AOD in the middle region of the Yarlung Zangbo River Basin was analyzed using STL and Theil-Sen Median trend estimation and the influence of natural and anthropogenic factors on AOD was revealed using GeoDetector, XGBoost model and SHAP analysis. The results showed that the spatial heterogeneity of AOD in the middle region of the Yarlung Zangbo River Basin was obvious, which was mainly controlled by topography, with high AOD value concentrated in the valley with the altitude lower than 4 300 m. Owing to the precipitation and surface wind speed, the seasonal difference of AOD was obvious, with the highest in winter. From 2000 to 2020, AOD in the middle region of the Yarlung Zangbo River Basin showed an increasing trend, with the annual average AOD rising from 0.008 to 0.012. This increase was especially strong in the valley area, where the AOD was significantly impacted by enhanced anthropogenic activities and the abundance of sand sources of exposed riverbed. Among the factors influencing AOD in the middle region of the Yarlung Zangbo River Basin, the changes in mean annual temperature, mean wind speed, population density, and GDP were positively correlated with the changes in AOD, whereas the changes in precipitation were negatively correlated. In particular, the increase in temperature was the factor that contributed predominantly （61.1%） to the recent changes in AOD.

Climate change and its impacts in extreme events in Morocco (observation, monitoring, and forecasting)

ABSTRACT Rising extreme weather events in Morocco pose a growing threat to various socioeconomic sectors. These events, including heatwaves and cold spells, are exhibiting an alarming increase in intensity, frequency, and duration. To understand this trend and its future implications, a comprehensive study is essential. So, this research investigates the link between climate change and extreme weather events. It specifically focuses on how climate change influences the occurrence and intensity of these extremes. The study employs two main phases: Phase 1 is about the historical analysis (1984–2018). This phase uses robust methods like Sen's slope test for trend estimation and Mann–Kendall test for significance to evaluate observed changes in climatic extremes. Phase 2 is about future projections (2041–2060). This phase utilizes four climate models to project future changes in thermal and rainfall extremes under the RCP 4.5 scenario. Five climate indices (Tmm, Tx90p, WSDI, PRCPTOT, and SPI) are employed for analysis. Historical analysis (1984–2018) reveals a significant increase in hot extremes across Morocco. Rainfall extremes, however, show no significant changes in trends. Future projections (2041–2060) for all four climate models agree on significant warming in Morocco. They also project a decrease in annual precipitation across most of the country.

Impact of different solar extreme ultraviolet (EUV) proxies and Ap index on hmF2 trend analysis

Abstract. Long-term trend estimation in the peak height of the F2 layer, hmF2, needs the previous filtering of much stronger natural variations such as those linked to the diurnal, seasonal, and solar activity cycles. If not filtered, they need to be included in the model used to estimate the trend. The same happens with the maximum ionospheric electron density that occurs in this layer, NmF2, which is usually analyzed through the F2 layer critical frequency, foF2. While diurnal and seasonal variations can be easily managed, filtering the effects of solar activity presents more challenges, as does the influence of geomagnetic activity. However, recent decades have shown that geomagnetic activity may not significantly impact trend assessments. On the other hand, the choice of solar activity proxies for filtering has been shown to influence trend values in foF2, potentially altering even the trend's sign. This study examines the impact of different solar activity proxies on hmF2 trend estimations using data updated to 2022, including the ascending phase of solar cycle 25, and explores the effect of including the Ap index as a filtering factor. The results obtained based on two mid-latitude stations are also comparatively analyzed to those obtained for foF2. The main findings indicate that the squared correlation coefficient, r2, between hmF2 and solar proxies, regardless of the model used or the inclusion of the Ap index, is consistently lower than in the corresponding foF2 cases. This lower r2 value in hmF2 suggests a greater amount of unexplained variance, indicating that there is significant room for improvement in these models. However, in terms of trend values, foF2 shows greater variability depending on the proxy used, whereas the inclusion or exclusion of the Ap index does not significantly affect these trends. This suggests that foF2 trends are more sensitive to the choice of solar activity proxy. In contrast, hmF2 trends, while generally negative, exhibit greater stability than foF2 trends.

A Novel Framework for Heterogeneity Decomposition and Mechanism Inference in Spatiotemporal Evolution of Groundwater Storage: Case Study in the North China Plain

AbstractProperly understanding the evolution mechanisms of groundwater storage anomaly (GWSA) is the basis of making effective groundwater management strategies. However, current analysis methods cannot objectively capture the spatiotemporal evolution characteristics of GWSA, which might lead to erroneous inferences of the evolution mechanisms. Here, we developed a new framework to address the challenge of spatiotemporal heterogeneity in the GWSA evolution analysis. It is achieved by integrating the Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST), the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), and the Optimal Parameters‐based Geographical Detector (OPGD). In the case study of the North China Plain (NCP), the GWSA time series is divided into four stages by three trend change points in BEAST. An increasing trend of GWSA is observed at Stage IV, and the third trend change point occurs before the third seasonal change point. This distinguishes the positive feedback of anthropogenic interventions and the effects of seasonal precipitations for the first time. Moreover, the spatial distribution of GWSA in the NCP is classified into two clusters by BIRCH in each stage. The differences in GWSA trends and responses to environmental changes between Cluster‐1 and Cluster‐2 are significant. Then the driving effects of 16 factors on the evolution of GWSA are identified using OPGD, in which the contributions of topographic and aquifer characteristics are highlighted by quantitative analysis. This framework provides a novel method for examining the spatiotemporal heterogeneity of GWSA, which can be extended to analyze spatiotemporal trends in GWSA at diverse scales.

Усиление штормовой активности в восточном секторе российской Арк­тики

Currently, the Russian Arctic is actively engaged in economic activity related to exploration, extraction and transportation of minerals, fishing, and shipping. Data on wind wave parameters were obtained based on the WAVEWATCH III spectral model, and on wind and sea ice concentration from the NCEP/CFSR/CFSv2 reanalysis for the period from 1979 to 2021. Data on cyclones were used from a database created on the basis of NCEP reanalysis and includes information on the trajectory of cyclones and their main characteristics from 1979 to 2015. Estimates of the interannual and seasonal variability in the number and duration of storms in the Laptev Sea, East Siberian and Chukchi Seas were obtained. The obtained trend estimates indicate an increase in the number and duration of storms in all the studied seas. A relationship was found between an increase in the number of storms and a decrease in the area of sea ice, as well as an increase in the number of cyclones.

Incorporating Phenology to Estimate Species’ Population Trends from Snapshot Citizen-Science Data

AbstractCitizen-science data are increasingly used to contribute to our understanding of biodiversity change, but analysing such data requires suitable statistical methods, often to deal with forms of bias. We develop a new approach for modelling data from a snapshot, mass-participation citizen-science scheme for UK butterflies, the Big Butterfly Count (BBC). Butterfly abundance varies throughout the year as one or more generations of each species emerge and die off, and the timing (phenology) of emergences varies annually due to weather and climate. Thus, counts from the short 3-week BBC sampling period are susceptible to bias due to this inter-annual variation in phenology. We adapt the Generalised Abundance Index, drawing upon phenology estimates from standardised monitoring scheme data, to account for phenological bias in the estimation of species’ abundance trends from BBC data. The method is demonstrated via application to empirical and simulated data, revealing that not accounting for phenology leads to biased trend estimates, particularly for summer-flying single-generation species. Drawing upon phenology information, the new approach allows for the reporting of abundance trends from a snapshot citizen-science scheme, creating the potential to maximise available data sources to increase our understanding of changes in butterfly populations, particularly in urban environments.

Freshwater fish in mid and northern German rivers – Long-term trends and associated species traits

To understand biodiversity change and support conservation decision-making, estimates of species' long-term population trends at regional and national scales are essential. However, such estimates are missing for many freshwater taxa, despite the diverse range of threats that they face. For this study, we mobilised monitoring data on riverine freshwater fish abundance collected across different regions of Germany. We applied generalized mixed effect models to estimate the population trends for 55 native species and 11 non-native species between 2004 and 2020. In addition, we used boosted regression trees to identify trait-based predictors of species trends and assessed their predictive ability. We found evidence of increasing abundance trends for 14 species and decreasing trends for 15 species; while the remaining species were mostly stable (26 species). Native species were more typically decreasing than increasing (14 vs 10 species); while non-native species were more often increasing (4 vs 1 species). Important traits associated with trends were maximum length, spawning temperature, and water quality tolerance, with small species, those spawning at high temperatures, and those preferring unpolluted waters, being most likely to have positive trends. Despite these associations, overall trait-based models showed limited power to predict trends in terms of direction as well as magnitude. Our results highlight the ongoing change in riverine fish communities and the importance of on-going species-level monitoring. The trait-based associations also indicate climate change and invasive species as important drivers of change in European freshwater rivers.

Declining Trend of Sudden Cardiac Death in Younger Individuals: A 20-Year Nationwide Study.

Declining cardiovascular mortality rates have been well-documented, yet temporal trends of sudden cardiac death (SCD) in young individuals remain unclear. We provide contemporary nationwide estimates of the temporal trends of SCD in young individuals (1-35 years of age) from 2000 through 2019 and correlate these trends to changes in out-of-hospital cardiac arrest (OHCA) patterns, rates of inherited cardiac diseases, and implantations of implantable cardioverter defibrillators (ICD). All individuals between 1 and 35 years of age living in Denmark from 2000 through 2019 were included, with annual re-evaluation of the at-risk population in regard to age. Adjudication of SCD cases relied on multiple sources, including death certificates, medical files, and autopsy reports. Information on OHCA, diagnostic rates, and ICD implantations were captured from nationwide administrative registries. Annual incidence rates of SCD were calculated, and temporal trends in SCD incidence were computed as percentage change annualized. Trends in OHCA survival and characteristics, diagnostic rates of inherited cardiac diseases, and ICD implantations were assessed. During the 20-year study period (47.5 million person-years), 1057 SCDs were identified (median age, 29 years; 69% male). The overall incidence of SCD was 2.2 per 100 000 person-years and declined by 3.31% (95% CI, 2.42-4.20) annually, corresponding to a 49% (95% CI, 38.7-57.6) reduction during the study. Rates of witnessed SCD declined markedly (percentage change annualized -7.03% [95% CI, -8.57 to -5.48]), but we observed no changes in the rate of unwitnessed SCD (percentage change annualized -0.09% [95% CI, -1.48 to 1.31]). Therefore, the proportion of unwitnessed SCD increased by 79% (P<0.001). Survival after OHCA in young individuals (1 to 35 years of age) increased from 3.9% to 28%, mainly because of increased bystander cardiopulmonary resuscitation and defibrillation rates. Diagnostic rates of inherited cardiac diseases increased 10-fold (incidence rate ratio, 10.4 [95% CI, 8.46-12.90]) and the ICD implantation rate increased 2-fold (incidence rate ratio, 1.97 [95% CI, 1.51-2.60]). SCD incidence rates in young individuals declined by 49% over the past 2 decades. The decline was paralleled by improved survival of OHCA, higher diagnostic rates of inherited cardiac diseases, and higher ICD implantation rates. However, rates of unwitnessed SCD were unchanged, which calls for new perspectives in preventive strategies.

Assessing spatial and temporal trends over time in potential species richness using satellite time-series and ecological niche models

AbstractAssessing biodiversity in protected areas is essential for effective conservation management. However, conventional methods for estimating species richness can lead to inaccurate assessments of biodiversity in these regions. To address this, we present a complementary approach to assess potential species richness at a 1 km resolution in the special conservation area of Montesinho/Nogueira (SAC-MN; Portugal), based on a spatial and temporal estimates of species richness trends over time (2001–2021). We compiled 10,190 species occurrence records from four taxonomic groups: amphibians, reptiles, birds, and mammals. We created correlative ecological niche models (MaxEnt) with a time series of satellite-remote sensing variables (SRS-ENMs) using Google Earth Engine (GEE). We calculated annual potential species richness (PSR) maps by stacking the SRS-ENMs and identified positive and negative trends with the Mann–Kendall test. Each species’ ecological niche model revealed unique patterns of habitat suitability within SAC-MN over time. The eastern and western boundaries of SAC-MN revealed an increase in PSR, while the central area indicated PSR declines. Each group displayed different trends in PSR: birds and mammals were more similar between them than amphibians and reptiles. This approach provides ecologists and conservationists with valuable tools to evaluate PSR and trends over time, offering crucial insights for decision-makers and stakeholders to support local biodiversity conservation efforts.

Estimating an Elephant Population Size Through Local Ecological Knowledge.

In planning and monitoring measures to protect wildlife in an area, it is important to have a reliable baseline estimate of population size and trends. There has been minimal published information on a small population of elephants, a keystone and endangered species, in a large area west of Etosha National Park in Namibia, known locally as the Northern Highlands. It is a highly remote, mountainous area in which it is difficult to count elephants. It is semi-desert, where the protection of wildlife is at increasing risk from climate change events, and research on the elephant population is a priority. We interviewed 34 community game guards in the Northern Highlands, focusing on the number of elephants and distinguishing features in known groups. Based on the collated knowledge, and analysis to reduce double counting of known groups, we estimate that there are between 78 and 212 elephants in the Northern Highlands, with a best estimate of 128. The wide range is an indication of the current uncertainties in the method. However, we conclude that this low-cost method, if adapted based on lessons from this pilot study, would be applicable for longer-term ecological monitoring in areas that have a low population density.

Near Infrared Reflectance Spectroscopy-Driven Chemometric Modeling for Predicting Key Quality Traits in Lablab Bean (Lablab purpureus L.) Germplasm

Lablab bean (Lablab purpureus L.) is a multipurpose crop, commonly used for food, feed, and fodder, and its potential as a plant-based meat alternative. Its nutritional diversity, including high protein, starch, and phenolic content, makes it a suitable candidate for nutritional profiling, which is essential for developing nutritionally enhanced varieties. Traditional methods for analyzing its nutritional parameters are labor-intensive, time-consuming, and expensive. This study employs Near-Infrared Reflectance Spectroscopy (NIRS) as a rapid, non-destructive alternative to evaluate 112 Lablab bean genotypes. We developed prediction models for starch, amylose, protein, fat, and phenols using a Modified Partial Least Squares (MPLS) approach, with spectral pre-processing using Standard Normal Variate (SNV) to remove scatter effects and Detrending (DT) to reduce baseline shifts and noise. The models were optimized for derivatives, gap selection, and smoothing, and evaluated using independent test data and key performance metrics including coefficient of determination (R²), bias, and Residual Prediction Deviation (RPD). The best-performing models were: starch (R² = 0.959, RPD = 4.57), amylose (R² = 0.737, RPD = 1.76), protein (R² = 0.911, RPD = 3.09), fat (R² = 0.894, RPD = 2.92), and phenols (R² = 0.816, RPD = 2.36). Statistical tests, including paired t-tests, correlation, and reliability analysis, confirmed the robustness of these models. This study presents a first report offering rapid, multi-trait assessment method for evaluating Lablab bean germplasm, demonstrating high predictive accuracy for pre-breeding practices. It has broad applications in developing nutritionally enhanced varieties, supporting plant-based protein alternatives, and optimizing food production processes to meet the growing demand for healthier, sustainable foods.

Combining hyperspectral imaging technology and visible-near infrared spectroscopy with a data fusion strategy for the detection of soluble solids content in apples

Soluble solids content (SSC) is an important indicator for evaluating apple quality. This study aimed to assess two spectral techniques (hyperspectral imaging (HSI) and visible-near infrared (Vis-NIR)) combined with low-level and mid-level fusion strategies (LLF and MLF) for the detection of SSC in apples. Firstly, baseline correction (BC), detrending (DT), multiplicative scatter correction (MSC), savitzky-golay (SG), and standard normal variables (SNV) were used for the preprocessing of spectral data. Secondly, genetic algorithm (GA), competitive adaptive reweighted sampling (CARS), and their combinations were used for feature variable extraction. Finally, models were developed using partial least squares regression (PLSR) for spectral data. The results showed that Vis-NIR has an advantage over HSI in predicting apple SSC if only a single spectral technique was considered. The data fusion strategy showed better performance in predicting SSC metrics compared to individual spectral data. Among them, the LLF strategy showed the best performance in predicting SSC, with an Rp2 of 0.927, an RMSEP of 0.529 °Brix, and an Akike information criterion (AIC) of 332.96. In addition, an optimal model based on HSI data was used to achieve visual maps of SSC. It was demonstrated that the fusion of HSI and Vis-NIR data provided a promising method for detecting the SSC in apples.