Sampling Design Research Articles

The Bahadur representations of quantile estimators in general unequal probability sampling

In this study, we establish the Bahadur representations of quantile estimators based on the Hájek and the Horvitz–Thompson estimators for the population distribution function in unequal probability sampling designs under general conditions. We also present the asymptotic normality of the above quantile estimators. The simulation studies using real data confirm the validity of the asymptotic normality.

Socioeconomic determinants of male contraceptive use in South Africa: a secondary analysis of the 2016 SADHS data

BackgroundHistorically, family planning discussions have predominantly centred on women, overlooking the crucial role men play in contraception. This is particularly concerning in regions like Sub-Saharan Africa (SSA), where reproductive health challenges are pronounced. South Africa, a key country within SSA, continues to grapple with issues such as high unintended pregnancy rates and increasing pregnancy-related mortality. Understanding the factors that deter male contraceptive use is essential for achieving universal access to sexual and reproductive health (SRH) services. This study investigates the socioeconomic disparities influencing male contraceptive use in South Africa, drawing on data from the 2016 South Africa Demographic Health Survey.MethodsThe study utilised data from the 2016 South African Demographic and Health Survey, which employed a stratified, two-stage sampling design across all provinces based on the 2011 Census. The survey focused on men aged 15–59 using the “Man’s Questionnaire.” Various socio-demographic and attitudinal variables were analysed using STATA 17. The analysis included multinomial logistic regression to identify determinants of male contraceptive use and the Erreygers Normalized Concentration Index to assess socioeconomic inequalities in contraceptive uptake.ResultsAmong the surveyed men, 47% reported not using any contraception, 40% used male methods, and 13% relied on their partners’ contraceptive use. A majority began sexual activity between the ages of 14–17 (54%), resided in urban areas (56%), were unmarried (67%), and had attained secondary education (70%). Notably, 78% disagreed that contraception is solely a woman’s responsibility, and 64% expressed positive attitudes toward contraception. Age, marital status, and education level emerged as significant factors influencing contraceptive use. The study revealed a slight pro-rich inequality in male contraception use, with socioeconomic status (SES) being the most significant contributor to this disparity (223.22%). This suggests that higher SES is associated with increased inequality in the adoption of modern male contraceptive methods.ConclusionThese findings highlight the impact of socioeconomic disparities and entrenched cultural beliefs on male contraceptive use in South Africa. To address these challenges, comprehensive strategies that prioritise education, expand family planning media campaigns and ensure equitable access to contraception are urgently needed.

Advancing Holistic Human Capital Development: A Perspective on the Presidential Amnesty Programme

The Presidential Amnesty Programme (PAP) was a policy thrust to tackle the intractable agitations and youth restiveness in the Niger Delta region due to utter neglect of the development of the region despite the enormous contributions to the national treasury from its rich-oil deposits and gas resources. Accordingly, this study uncovers human capital development in the Niger Delta states from the lens of the PAP. The study applied a quantitative research strategy with a structured questionnaire. A sample of 574 respondents was drawn through a multi-stage sampling design from 13,254 chiefs, community leaders, youths and the staff of the amnesty office, which served as the target population of the study. From the primary and secondary data collected and analysed, the findings demonstrated that the Presidential Amnesty Programme had a positive significant effect on vocational skills acquisition, there was a positive relationship between PAP and university educational advancement, and there was a positive relationship between budgetary provision and human capital development among others. We concluded that the PAP has contributed to the human capital development of youths in the region. We recommended inter alia that the amnesty programme of the federal government of Nigeria should be committed to exposing ex-militants to vocational skill acquisition scheme to ensure self-reliance, and also should improve on the university educational advancement for ex-militants to acquire professional certification in the Niger Delta States.

Beaver dams in Mediterranean ecosystems: trait‐specific effects on macroinvertebrates

AbstractAfter centuries of extinction due to human persecution, Eurasian beavers Castor fiber L. have been released to Southern Europe in the last decades. Being ecosystem engineers, beavers have attracted great attention regarding restoration of aquatic and terrestrial ecosystems. Nonetheless, the effects of the species on aquatic invertebrates known to date are not univocal and mostly refer to central European riverine systems. Here, we evaluated the effects of beaver presence on aquatic macroinvertebrates for the first time in a Mediterranean riverine ecosystem, by applying a sound control‐impact sampling design and controlling for seasonal variation in macroinvertebrate assemblage composition. A significant variation in response to season was evident for macroinvertebrate communities, revealing distinct assemblages during spring and summer. Furthermore, the presence of beavers was also identified as a significant driver of species composition, as samples near the beaver dam showed significant variation from control sites. Macroinvertebrate community traits changed according to the relative position to the beaver dam, as control sites featured on average higher abundances of taxa with higher values of bioindication score, larger size, lower adaptation to drag, and were less frequently of introduced origins. Yet, these differences were strongly taxon‐ and season‐specific in their intensity and direction. Differences across sites were mainly driven by the relative abundances of few taxa—including both alien species and high environmental quality indicators—such as those from genera Potamopyrgus, Baetis, Habrophlebia, Ephemerella, Leuctra, and Radix, which explained about 70% of the observed divergence among conditions. Our results indicate that beavers and their engineering activity may induce highly variable species‐specific responses in macroinvertebrates, thus possibly representing a driver of environmental heterogeneity along Mediterranean rivers, and that both bioindicators and alien species may exploit such heterogeneity.

Differences in Exposures to Adverse Childhood Experiences by Primary Source of Health Care, Behavioral Risk Factor Surveillance System 2019-2020.

To estimate the prevalence of adverse childhood experiences (ACEs) among a population-based sample of adults in the United States by their primary source of health care. Debate continues around the effectiveness and implementation of health care-based screening of ACEs. However, it is unclear how the burden of ACEs would be distributed across different sources of health care (ie, what a health system might expect should it implement ACEs screening). Data are from 8 U.S. states that include optional modules for ACEs and health care utilization in their 2019 or 2020 Behavioral Risk Factor Surveillance System survey. The analytic sample includes respondents with completed interviews (n = 45,820). ACEs were categorized into ordinal categories of 0, 1, 2, 3, or ≥4, and the prevalence of ACEs was summarized across 5 sources of health care: (1) employer-based or purchased plan, (2) Medicare, Medicaid, or other state programs, (3) TRICARE, Veterans Affairs, or military (ie, military-related health care), (4) Indian Health Service, or (5) some other source. All estimates were weighted to account for the complex sampling design. Across all health insurance types, at least 60% of individuals reported at least one ACE. The greatest prevalence of patients reporting ≥4 ACEs occurred for military-related health care (21.6%, 95% CI = 18.2-25.5) and Indian Health Service (45.4%, 95% CI = 22.6-70.3). ACEs are extremely common across sources of health care, but some health systems have greater proportions of patients with high ACE exposures. The unique strengths and challenges of specific health care systems need to be integrated into the debate about clinical ACEs screening.

Ranked Set Sampling Based Regression Estimators in Two-Stage Sampling Design

Ranked Set Sampling Based Regression Estimators in Two-Stage Sampling Design

Characterizing Juvenile Common Snook and Tarpon Habitat to Guide Conservation and Restoration of Coastal Wetlands

To conserve or restore juvenile fish habitat, resource managers, restoration practitioners, and engineers need fine-scale information to understand what conditions they need to preserve or what specifications are needed to create new habitat. The objective of this study was to develop statistical models using a dataset of 18 coastal ponds in Southwest Florida, USA, to predict the abundance of juvenile common snook Centropomus undecimalis (hereafter snook) and presence of juvenile tarpon Megalops atlanticus, both of which are flagship species used for conservation and restoration of subtropical and tropical wetlands. Model predictors included water conditions and characteristics of vegetation, sediment, and geographic position. Modeling results indicated that juvenile snook used coastal ponds that were directly connected to tidal creeks. In contrast, juvenile tarpon were more likely to be present in coastal ponds that were separated from tidal creeks by dense mangrove forest (e.g., elevation > 0.48 m above Mean Low Water) characterized by highly organic sediment and low dissolved oxygen, to which tarpon are well adapted. Overlap between snook abundance and tarpon presence was greatest where connections between ponds and the nearest tidal creek were at a relatively low elevation (e.g., -0.12 m relative to Mean Low Water). Although these findings are most applicable to management and conservation of populations in areas along the Gulf coast of Florida (i.e., in areas of similar tidal range and coastal geomorphology), incorporating more detailed habitat surveys to better capture landscape context, geomorphology, and connectivity into fish sampling designs should be broadly applicable to estuarine fish ecology.

Is It Just a Game?

Abstract: Building on the long common history of board games and intelligence research, we developed a new deductive reasoning test based on the popular game Mastermind. The research questions of this registered report were: (a) Is a psychometrically sound measurement of the ability to solve Mastermind items possible (i.e., a reliable, uni-dimensional measurement with a good coverage of difficulty)? (b) Is the ability to solve Mastermind items substantially related to other measures of cognitive ability (i.e., matrix test, knowledge test) and need for cognition? (c) Can item difficulty be predicted by the number of colors, positions, premises, and a newly proposed entropy-based index? Based on the results of a pilot study, we developed 30 items and administered them to 351 participants in the preregistered main study using a multiple matrix sampling design. The deductive Mastermind test proved to be (a) a reliable and efficient measure of reasoning across a wide ability range, and (b) showed expectation-consistent patterns for the convergent and divergent measures. (c) The entropy-based index allowed for the prediction of item difficulty to a considerable degree ( R2 = .47). We discuss the ideas of information theory, including entropy, as constructing principles for the rational test development of reasoning tests.

Soil sampling design matters - Enhancing the efficiency of digital soil mapping at the field scale

Optimisation of sampling design (methods chosen to select the samples) and sample size (number of samples) remains a key challenge in digital soil mapping, especially in the area of precision farming with the expected economic benefits from the introduction of new technologies. As the existing information is available in the form of relevant environmental covariates, its combination with non-parametric machine learning techniques requires careful planning from the initial field sampling to the final production of digital soil maps. The aim of this study is to compare widely used covariate-wise sampling designs combined with variable sample sizes for supervised prediction of common soil drivers of agricultural productivity (pH, soil organic carbon, soil macronutrients) in a real case study of a field (35 ha) with heterogeneous soil properties. From a total of 200 samples, we evaluated different sample sets where 10, 30 and 60 field samples were selected by conditioned Latin Hypercube Sampling (cLHS) and Feature Space Coverage Sampling (FSCS) to calibrate random forest (RF) models. The evaluation was performed on independently in-situ sampled test points. In addition to these datasets, we also compared the investigated methods with Simple Random Sampling (SRS) in a numerical benchmark experiment with increasing sample size, comparing the global accuracies of the predicted maps on the test points, but using interpolated maps as the artificial true population for each soil characteristic. The results of the study in both the field experiment and the numerical experiment showed slightly better results for the FSCS method, especially when the number of samples was small. At smaller training sample sizes, the risk of insufficiently accurate prediction models was slightly lower for FSCS and the difference decreased as the sample size increased. Nevertheless, sample size proved to be the most important factor in the accuracy of RF models, regardless of the sampling technique. The results suggest that a sample size between 18 and 30 training samples (0.6 to 1 sample ha−1) seems plausible for covariate-wise predictions using RF at field scale in our case study. The relative importance of each auxiliary variable for each RF calibration was also assessed for the field experiment. The results showed that the additional introduction of spatial proxies overshadowed the importance of other covariates, but only significantly improved the model calibration at larger sample sizes. The calibrated models without spatial proxies showed the strongest effect of remotely sensed surface characteristics.

8156 Exploring Gender-Specific Characteristics Of Diabetes Among Young Adults In Korea: The Korea National Health And Nutrition Examination Survey 2011-2012 And 2019-2020

Abstract Disclosure: C. Chung: None. Background: The escalating global incidence of diabetes among young adults could be linked to dynamic socio-environmental changes including pandemic and climate shifts. In this investigation, we aimed to discern the distinct features of diabetes among young adults in Korea. Methods: This cross-sectional study included 63,489,047 Korean young adults (32,173,248 male, 31,315,799 female) aged 20-39 years who participated in the Korean National Health and Nutrition Examination Survey 2011-2012 and 2020-2021. Diabetes cases were identified through hospital diagnoses or the prescription of insulin or non-insulinemic anti-diabetic medications. The significant insulin resistance was defined as HOMA-IR above 2.9. Complex sampling design and cross-tabulation analyses were used to examine both socioeconomic and laboratory factors. Results: The prevalence of diabetes was higher in men in both 2011-2012 and 2020-2021 (Men vs. Women, 2011-2012: 2.2 ± 0.5 vs. 1.2 ± 0.3 %, p<0.001; 2020-2021: 3.0 ± 0.5 vs. 1.6 ± 0.3 %, p<0.001). The use of insulin and oral hypoglycemic agent (OHA) was comparable between men and women in 2011-2012 (Insulin: 18.1 ± 8.2 vs. 5.2 ± 5.1 %, p=0.305; OHA, 18.2 ± 6.3 vs. 19.5 ± 6.5 %, p=0.239), but more women used insulin and more men used OHA in 2020-2021 (Insulin: 5.3 ± 3.7 vs. 28.7 ± 9.4 %, p=0.016; OHA: 31.4 ± 7.7 vs. 19.6 ± 7.3 %, p=0.009). Execution of fundus exam (25.7 ± 10.6 vs. 68.5 ± 14.1 %, p=0.021) and diagnosis of diabetic retinopathy (0 vs. 8.9 ± 3.3 %, p=0.001) was higher in women in 2020-2021. HbA1c was higher in men in 2020-2021 (8.1 ± 0.4 vs. 7.3 ± 0.3 %, p <0.001), but was similar between men and women in 2011-2012 (8.4 ± 0.6 vs. 8.1 ± 0.6 %, p=0.697). Obesity, one of the most common comorbidities of diabetes, was higher in women in 2011-2012 (24.8 ± 4.8 vs. 79.4 ± 9.3 %, p<0.001), but was higher in men in 2020-2021 (94.4 ± 2.9 vs. 67.8 ± 11.0 %, p<0.001). The factors associated with increase in HOMA-IR in 2020-2021 was different between men and women. Unexpectedly, men with low mental stress showed positive correlation with HOMA-IR (B = 3.9, 95% CI 0.1-7.7, p=0.047). For women, mental stress did not affect HOMA-IR, but those with regular anaerobic exercise (B = -10.5, 95% CI -18.3 - -2.6, p=0.011) and stable employment status (B = -11.9, 95% CI -19.4 - -4.8, p=0.002) presented negative correlation with HOMA-IR. Conclusion: In comparison to before the COVID-19 pandemic, men diabetic patients showed more obese tendency after COVID-19, and there was also a statistically significant difference in HbA1c disparity with women. The higher prevalence of diabetic retinopathy despite of lower HbA1c in women during pandemic era needs to be further investigated in well-designed cohort study. Presentation: 6/3/2024

Prevalence and factors associated with gestational diabetes mellitus in Malaysia: a population-based study comparing 2016 and 2022

BackgroundGestational diabetes mellitus (GDM) poses substantial health risks to both mothers and infants. Malaysia exhibits a heightened prevalence of GDM.ObjectiveThis study aims to examine the changes in the prevalence of GDM between 2016 and 2022 and its determining factors.MethodsThe data analysed in this study were derived from the National Health and Morbidity Survey (NHMS) 2016 and 2022, a nationwide study employing a two-stage stratified random sampling design in Malaysia. Changes in the prevalence were compared between data from NHMS 2016 and 2022, while factors were evaluated based on data from NHMS 2022. Descriptive statistics and multiple logistic regression analyses were performed using IBM SPSS version 27.ResultsThe prevalence of GDM increased from 12.5% in 2016 to 27.1% in 2022. In both years, the prevalence was highest among those aged 44–49 years, those of Indian ethnicity, those in higher income groups and those with higher education levels. Advanced maternal age, high body mass index (BMI) and hypertension were associated with a greater risk of GDM.ConclusionThe prevalence of GDM among the Malaysian population doubled from 2016 to 2022. The findings underscore the importance of implementing targeted programs for expectant mothers in high-risk groups to mitigate the incidence of GDM and its associated morbidities.

Interactive learning: harnessing technology to alleviate foreign language anxiety in foreign literature education

ABSTRACT This study explores how information technologies can be utilized in foreign literature education (FLE) to alleviate foreign language anxiety (FLA) among Chinese students. FLA, characterized by apprehension and discomfort when engaging with a foreign language, often hampers motivation, confidence, and comprehension, especially in literary contexts. Using a multistage cluster sampling design, data were collected from 455 students through online questionnaires and analyzed with SPSS. The findings reveal that integrating interactive learning technologies, such as virtual reality (VR) and gamification, significantly reduces FLA. These technologies foster a supportive and immersive environment, enhancing oral proficiency, cultural understanding, and student confidence. The study introduces innovative strategies to mitigate psychological barriers, contributing valuable insights into the use of technology to create more conducive learning environments. The research underscores the importance of interactive technology in minimizing anxiety, enriching language acquisition, and promoting a more effective and engaging FLE experience.

Validation of gut transit rate assessment methodology and the mitigation of sampling stress in Atlantic salmon, Salmo salar

Gut transit rate is an important component of the digestive process in fish, particularly in the context of aquaculture, where factors such as rearing temperature and dietary formulation impact on-farm performance. As such, developing and validating research methodologies that explore these critical physiological responses in fish is vital in the context of precision aquaculture practices. However, there are inherent methodological constraints of gut transit rate assessments, particularly when assessing the effect of sampling on the stress response of fish. In light of this, the present study aimed to assess the gut transit rate and plasma cortisol levels of Atlantic salmon (Salmo salar) subjected to the more commonly applied, repeated sampling model that results in multiple tanks disturbances (e.g., 2, 4, 8, 16 and 24 h post-feed) in comparison to one requiring only a singular tank disturbance at one of the designated time points. An inert beaded marker was integrated into diet formulations to determine the transit rate of digesta in three major gastrointestinal tract regions: the stomach, mid-intestine, and distal intestine over a period of 48 h following the provision of food. A significantly higher plasma cortisol concentration was recorded in fish subjected to a repeated disturbance, particularly at 16 h post feed (22.99 ± 6.88 ng mL−1), compared to fish sampled from tanks subjected to a single sampling event (11.35 ± 0.92 ng mL−1). Despite this, gut transit rate was generally unaffected by sampling design over the duration of the study with both groups of fish displaying a similar rate of gut transit. This suggests that repeated tank sampling (i.e. sampling multiple fish from the same tank at multiple timepoints) is suitable to assess gut transit rate in fish as it does not significantly confound gut transit rate assessments. It is envisioned that this data will contribute to the growing understanding of the digestive process of Atlantic salmon and the relationship with stress response in fish and experimental design. Moreover, it should strengthen future studies aimed at assessing key digestive processes in cultured species.

Analysis of data from a national micronutrient survey with a linear mixed model: estimates, predictions and lessons for future surveys.

Because micronutrient deficiencies affect public health, countries monitor population status by national-scale, multi-stage, micronutrient surveys (MNS). In design-based surveys, inclusion probabilities are specified for sample units and the corresponding sample weights allow design-unbiased estimates to be made of population parameters. Corrections may be possible on departures from the design; an alternative is to use linear mixed models (LMM), with an estimated covariance structure reflecting the sampling design, to obtain model-based estimates. The Ethiopia National Micronutrient Survey (2016) specified inclusion probabilities at enumeration area (EA) and household (HH) levels, and sample weights are provided. However, the design was not followed as it would have resulted in insufficient sampling from women of reproductive age. Having found no evidence that sample weights were informative for target serum micronutrient concentrations (Zn), we estimated LMM parameters, with Regions as fixed effects, and the variation of individuals nested within households, households within EA, and EA within regions, random effects. We obtained LMM standard errors, Best Linear Unbiased Estimates (BLUEs) of regional means, and empirical Best Linear Unbiased Predictions for sampled/unsampled EA and HH. The probability that each true regional mean exceeded the sufficiency threshold was evaluated. The variances of BLUEs of regional means, under alternative sampling designs, were bootstrapped from LMM variance components. We demonstrate use of LMM to obtain model-unbiased estimates and predictions when surveys deviate from the original design; and the use of LMM variance components to evaluate alternative designs for further sampling, or for sampling comparable populations.

Bayesian feedback in the framework of ecological sciences

In ecological studies, it is not uncommon to encounter scenarios where the same phenomenon (e.g., species occurrence, species abundance) is observed using two different types of samplers. For example, species data can be collected from scientific sampling with a completely random sample pattern, but also from opportunistic sampling (e.g., whale watching from commercial fishing vessels or bird watching from citizen science), where observers tend to look for particular species in areas where they expect to find them.Species Distribution Models (SDMs) are widely used tools for analysing this type of ecological data. In particular, two models are available for the aforementioned data: a geostatistical model (GM) for data collected where the sampling design is not directly related to the observations, and a preferential model (PM) for data obtained from opportunistic sampling.The integration of information from disparate sources can be addressed through the use of expert elicitation and integrated models. This paper focuses on a sequential Bayesian procedure for linking two models by updating prior distributions. The Bayesian paradigm is implemented together with the integrated nested Laplace approximation (INLA) methodology, which is an effective approach for making inference and predictions in spatial models with high performance and low computational cost. This sequential approach has been evaluated through the simulation of various scenarios and the subsequent comparison of the results from sharing information between models using a variety of criteria. The procedure has also been exemplified on a real dataset.The primary findings indicate that, in general, it is preferable to transfer information from the independent (with a completely random sampling) model to the preferential model rather than in the alternative direction. However, this depends on several factors, including the spatial range and the spatial arrangement of the sampling locations.

Comparing Genetic Ne Reconstructions Over Time With Long-Time Wolf Monitoring Data in Two Populations.

Many methods are now available to calculate N e , but their performance varies depending on assumptions. Although simulated data are useful to discover certain types of bias, real empirical data supported by detailed known population histories allow us to discern how well methods perform with actual messy and complex data. Here, we focus on two genomic data sets of grey wolf populations for which population size changes of the past 40-120 years are well documented. We use this background to explore in what detail we can retrieve the known population history from these populations, in the light of pitfalls relating to population history, sampling design and the change in the spatial scale at which N e is estimated as we go further back in time. The Scandinavian wolf population was founded in the early 1980s from a few individuals and has gradually expanded up to 510 wolves. Although the founder event of the Scandinavian population was detected by GONE, the founding effective population size was strongly overestimated when the most recent samples were used, but less so when older samples were considered. Nevertheless, the present-day N e corresponds to theoretical expectations. The western Great Lakes wolf population of Minnesota is the only population in the contiguous United States that persisted throughout the 20th century, surviving intense persecution. We found a good concordance between the estimated N e and trends in census size data, but the reconstruction of N e clearly highlights the difficulty of interpreting results in spatially structured populations that underwent demographic fluctuations.

Optimizing population mean estimation under ranked set sampling with applications to Engineering

This article aims to develop two novel mean estimators of finite populations under ranked set sampling. The suggested estimators result in a high-efficiency gain due to their efficient formulation by taking the linear combination of classical estimators using optimization constants. We also discuss the theoretical properties and derive bias and mean squared error up to the first order of approximation. To theoretically validate our proposed estimators, we derive the conditions under which our estimators prevail over the competing estimators. The findings suggest that when ranked set sampling is implemented effectively, it saves cost and improves precision, serving as a potent instrument for improved decision-making, specifically in engineering applications, such as environmental monitoring, resource management, and quality control. An empirical and simulation study has been conducted using real and synthetic data to validate our methodology empirically. The suggested estimators offer a significant contribution to survey sampling in estimating mean under ranked set sampling. Further, the proposed estimator can be seamlessly adapted in other sampling designs such as simple random sampling, stratified random sampling, cluster sampling, etc, as well as in estimating other parameters such as variance, skewness, coefficient of correlation, etc. in various domains.

Challenges and opportunities when studying movement ecology in science and practical conservation

Movement is a key mechanism influencing biodiversity patterns and ecosystem processes. Movement ecology science aims to understand the causal relationships between environmental conditions, animal movements, interactions and coexistence of species, as well as effects of movement patterns on ecosystem processes. In contrast, practical conservation primarily aims to understand organisms' movements to improve species management, protection, legal monitoring or risk assessment, and species-habitat interactions. Despite the many studies of movement ecology in basic and applied sciences as well as in practical conservation in terrestrial ecosystems, knowledge gain and transfer between disciplines are limited. Better integration and linking of both disciplines would result in diverse science-practice synergies, but these are currently constrained by numerous challenges that need to be overcome. From a scientific perspective, knowledge gain from practice is limited by multitude of case studies with limited spatial and temporal resolution. This can be overcome by improving access and combining the diversity of data for a research area that often deals with small sample sizes. From a practical perspective, the movement ecology framework which is often dedicated to basic research, and access and language barriers of scientific publications limit the application of scientific results in practice. Here movement ecologists should be encouraged to consider conservation issues more frequently in addition to basic research. The transfer of scientific results could be improved by scientists providing sufficient details for practitioners to extract relevant information and publish at least an open-access abstract in local language with clear management recommendations. Further, the use of open-access repositories allows both, scientists and practitioners an overview of the multitude of studies and helps to share data in order to derive general conclusions. Challenges impacting science and practice can be conceptual, organisational and technical in nature. Such constrains can be overcome, for example, by providing verified trapping protocols, using recent technological developments and analytical methods combined with trainings on these state-of-the-art tracking and analysing tools. In particular, collaborative project planning between scientists and practitioners can help to improve the sampling design of applied studies and broaden the data base for science in order to significantly advance the movement ecology framework and gain comprehensive knowledge for practical conservation.

Prevalence of bean scab caused by Elsinoë phaseoli and challenges associated with bean cultivation in Kenya.

This study investigated the prevalence of scab caused by Elsinoë phaseoli causing yield losses on beans in Kenya. The research focused on common practices and challenges faced by subsistence farmers with the aim of providing insights into scab prevalence, impact, and potential management challenges. A structured questionnaire was employed in a survey conducted in 2022 and 2023, covering major bean-growing regions using a three-stage sampling design. Data from 128 bean farmers included information on farm size, seed sources, cropping systems, awareness of challenges, and pest/disease management practices. Scab prevalence was determined by scouting for symptoms, with a total of 84 farms surveyed in 2021. The incidence of bean scab was confirmed in all surveyed clusters, indicating its widespread occurrence across various agro-ecological zones. Farmers exhibited common practices such as preference for uniform bean seeds (61%), use of uncertified seeds (83%), intercropping (80%), and limited crop rotation. Challenges included disease and pest infestations, with limited diversity in management options. Confirmation of the presence of bean scab in diverse agro-ecological zones emphasizes its importance and the need for further research on its impact and epidemiology. Challenges with crop rotation were evident due to small farm sizes and subsistence-focused farming. The study recommends further research for a comprehensive understanding of the link between increased scab importance and current bean farming practices such as short rotation periods and the use of susceptible varieties. Training programs are also vital to improve farmers' knowledge on safe agro-chemical use, ensuring sustainable constraint management in common bean cultivation in Kenya.

A nationwide analysis of the spatial patterns and correlates of heart disease and sleep deficiency in the United States

In the United States, heart disease stands as the primary cause of mortality across genders and various racial and ethnic groups. A health-related behavioral factor that could potentially contribute to the regional differences in heart disease is the lack of adequate sleep. We employed hierarchical regression and a complex sample general linear model (CSGLM) statistics to enhance our understanding of the factors influencing heart disease, with a particular emphasis on exploring these factors from a spatial viewpoint. Utilizing the Getis-Ord G* hot-spot analysis, we identified a clustering of contiguous hotspots in the southeast regions for both the death rate associated with heart disease and the prevalence of sleep deficiency, as observed in their geographic distribution. Furthermore, within the hierarchical regression model, a substantial correlation between heart disease and deficient sleep persisted, even after adjusting for confounding factors and addressing multicollinearity. This implies that the mortality risk associated with heart disease tends to rise with an increasing prevalence of deficient sleep (R 2 = 0.548). Within the CSGLM framework, an R 2 value of 0.627 underscored the robust indication that mortality due to heart disease was notably linked to sleep deficiency. This association persisted even after considering complex sample designs and incorporating adjustments for weighting. This geographic relationship has not been previously documented in existing literature. These discoveries suggest a requirement for additional exploration into regional variations in heart disease and sleep deficiency, offering new insights into our comprehension of the origins of heart disease.