Key HPI axis receptors facilitate light adaptive behavior in larval zebrafish.

The vertebrate stress response (SR) is mediated by the hypothalamic-pituitary-adrenal (HPA) axis and contributes to generating context appropriate physiological and behavioral changes. Although the HPA axis plays vital roles both in stressful and basal conditions, research has focused on the response under stress. To understand broader roles of the HPA axis in a changing environment, we characterized an adaptive behavior of larval zebrafish during ambient illumination changes. Genetic abrogation of glucocorticoid receptor (nr3c1) decreased basal locomotor activity in light and darkness. Some key HPI axis receptors (mc2r [ACTH receptor], nr3c1), but not nr3c2 (mineralocorticoid receptor), were required to adapt to light more efficiently but became dispensable when longer illumination was provided. Such light adaptation was more efficient in dimmer light. Our findings show that the HPI axis contributes to the SR, facilitating the phasic response and maintaining an adapted basal state, and that certain adaptations occur without HPI axis activity.

Multiple Prenatal Exposures and Acute Care Clinical Encounters for Asthma among Children Born to Mothers Living near a Superfund Site.

Prenatal exposures are associated with childhood asthma, and risk may increase with simultaneous exposures. Pregnant women living in lower-income communities tend to have elevated exposures to a range of potential asthma risk factors, which may interact in complex ways. We examined the association between prenatal exposures and the risk of childhood asthma acute care clinical encounters (hospitalization, emergency department visit, observational stay) using conditional logistic regression with a multivariable smooth to model the interaction between continuous variables, adjusted for maternal characteristics, and stratified by sex. All births near the New Bedford Harbor (NBH) Superfund site (2000-2006) were followed through 2011 using the Massachusetts Pregnancy to Early Life Longitudinal data system to identify children ages 5-11 with asthma acute care clinical encounters (265 cases among 7,787 with follow-up). Hazard ratios (HRs) were higher for children living closer to the NBH with higher cord blood Pb levels than children living further away from the NBH with lower Pb levels (P<0.001). HRs were highest for girls (HR=4.17, 95% CI: 3.60, 4.82) compared to boys (HR=1.72, 95% CI: 1.46, 2.02). Our results suggest that prenatal Pb exposure in combination with residential proximity to the NBH is associated with childhood asthma acute care clinical encounters.

Community-science reveals delayed fall migration of waterfowl and spatiotemporal effects of a changing climate.

Climate change has well-documented, yet variable, influences on the annual movements of migratory birds. The effects of climate change on fall migration remains understudied compared with spring but appears to be less consistent among species, regions and years. Changes in the pattern and timing of waterfowl migration in particular may result in cascading effects on ecosystem function, and socio-economic and cultural outcomes. We investigated changes in the migration of 15 waterfowl species along a major flyway corridor of continental importance in northeastern North America using 43 years of community-science data. We built spatially- and temporally explicit hierarchical generative additive models for each species and demonstrated that climate, specifically the interaction between minimum temperature and precipitation, significantly influences migration phenology for most species. Certain species' migratory movements responded to specific temperature thresholds (climate migrants) and others reacted more to the interaction of temperature and precipitation (extreme event migrants). There are already significant changes in the fall migration phenology of common waterfowl species with high ecological and economic importance, which may simply increase in the context of a changing climate. If not addressed, climate change could induce mismatches in management, regulations and population surveys which would negatively impact the hunting industry. Our findings highlight the importance of considering species-specific spatiotemporal scales of effect on climate on migration and our methods can be widely adapted to quantify and forecast climate-driven changes in wildlife migration.

Les changements climatiques ont des influences bien documentées, mais variables, sur les mouvements annuels des oiseaux migrateurs. Les effets des changements climatiques sur les migrations automnales demeurent peu étudiés par rapport aux migrations printanières, mais il semble qu'ils soient moins constants d'une espèce, d'une région et d'une année à l'autre. Les changements dans le patron et le calendrier de la migration de la sauvagine en particulier peuvent avoir des effets en chaîne sur la fonction des écosystèmes et des impacts socio­économiques et culturels. Nous avons étudié les changements dans la migration de 15 espèces de sauvagine le long d'un corridor de migration d'importance continentale dans le nord­est de l'Amérique du Nord, en utilisant 43 ans de données scientifiques communautaires. Nous avons construit des modèles additifs généralisés hiérarchiques spatialement et temporellement explicites pour chaque espèce et avons démontré que le climat, en particulier l'interaction entre la température minimale et les précipitations, influence de manière significative la phénologie de la migration pour la plupart des espèces. Les mouvements migratoires de certaines espèces répondent à des seuils de température spécifiques (migrateurs climatiques) et d'autres réagissent davantage à l'interaction entre la température et les précipitations (migrateurs d'événements extrêmes). La phénologie des migrations automnales d'espèces de sauvagine commune qui ont une grande importance écologique et économique connaît déjà des changements importants, qui pourraient simplement s'accentuer dans le cadre des changements climatiques. S'ils ne sont pas pris en compte, les changements climatiques pourraient induire des décalages dans la gestion, les réglementations et les enquêtes de population, ce qui aurait un impact négatif sur l'industrie de la chasse. Nos résultats soulignent l'importance de prendre en compte les échelles spatio­temporelles spécifiques sur la migration et nos méthodes peuvent être largement adaptées pour quantifier et prévoir les changements induits par le climat dans la migration de la faune.

Massachusetts companion program bolsters COVID-19 vaccine rates among seniors.

In parts of the United States, COVID-19 vaccination rates remained low until late in Fall 2021 owing to both limited vaccine access and hesitancy. With colliding epidemics of RSV, flu, and COVID-19 in the winter, the retrospective evaluation of vaccine incentive policies is needed to inform future routine immunization campaigns. The Massachusetts companion program is one example of a policy that could boost vaccine uptake among older populations. Our regression discontinuity analysis suggests that the program was associated with an increase of up to 22 percentage points in the proportion of individuals aged 75 and older who have been fully vaccinated. Going forward, similar intervention strategies could be invaluable in scenarios where household contacts pose the greatest risk of transmission or where social ties can strongly influence individual decision-making.

Recruitment Patterns and Environmental Sensitivity of Glass Eels of Anguilla japonica in the Yangtze Estuary, China.

The decline of Japanese eel (Anguilla japonica) populations in the Yangtze River estuary represents a critical conservation concern. Eleven-years of daily catch data during recruitment periods (i.e., January-April, 2012-2022) indicate that annual catch averaged from 153 to 1108 eels, and show a bimodal pattern in glass eel arrivals. Utilizing seasonal-trend decomposition and generalized additive models, we demonstrated a strong correlation between catch abundance, optimal water temperatures, and lunar cycles. An auto-regressive integrated moving average (ARIMA) model predicts an increase in glass eel numbers for 2023-2024 but also points to a concerning trend of delayed recruitment timing since 2016, attributable to the 0.48 °C per decade rise in sea surface temperatures. This delay correlates with a significant decrease in the average body weight of glass eels, suggesting potential energy deficits that may hinder successful upstream migration. This study not only furthers our understanding of glass eel recruitment dynamics but also underscores the urgent need for targeted conservation measures. Additionally, it highlights the importance of sustained, detailed monitoring to mitigate the detrimental effects of climate change on these eels, vital for preserving the Yangtze River's ecological integrity.

Amplification of annual and diurnal cycles of alpine lightning.

The response of lightning to a changing climate is not fully understood. Historic trends of proxies known for fostering convective environments suggest an increase of lightning over large parts of Europe. Since lightning results from the interaction of processes on many scales, as many of these processes as possible must be considered for a comprehensive answer. Recent achievements of decade-long seamless lightning measurements and hourly reanalyses of atmospheric conditions including cloud micro-physics combined with flexible regression techniques have made a reliable reconstruction of cloud-to-ground lightning down to its seasonally varying diurnal cycle feasible. The European Eastern Alps and their surroundings are chosen as reconstruction region since this domain includes a large variety of land-cover, topographical and atmospheric circulation conditions. The most intense changes over the four decades from 1980 to 2019 occurred over the high Alps where lightning activity doubled in the 2010 s compared to the 1980 s. There, the lightning season reaches a higher maximum and starts one month earlier. Diurnally, the peak is up to 50% stronger with more lightning strikes in the afternoon and evening hours. Signals along the southern and northern alpine rim are similar but weaker whereas the flatlands surrounding the Alps have no significant trend.

Association between serum globulin and cognitive impairment in older American adults.

Background and aims: Cognitive impairment is on the rise around the world, with profound economic and social consequences. Serum globulin, a marker of liver function, may also play a role in cognitive function. Unfortunately, no consistent conclusion exists regarding the association between serum globulin and cognitive function. Methods: Data from the 2011 to 2014 National Health and Nutrition Examination Survey were used to assess the association between serum globulin and cognitive impairment. Cognitive function was assessed by three tests: Consortium to Establish a Registry for Alzheimer's Disease (CERAD), Animal Fluency (AF), and Digit Symbol Substitution Test (DSST). Furthermore, the breakthrough point of cognitive impairment correlated with CERAD < 5, AF < 14, and DSST < 34. A weighted multiple logistics regression model was used to verify the association between serum globulin and cognitive impairment. Generalized additive models (GAMs) and a smooth curve fit (penalty spline method) were used to determine a non-linear relationship between serum globulin and cognitive impairment. Finally, subgroup analysis and interaction tests were conducted to further verify the association between serum globulin and cognitive impairment. Results: Data from 2,768 participants aged ≥60 (in accordance with the study design) were collected for the final analysis. Data suggested that serum globulin levels were associated with an elevated cognitive impairment based on the AF [full adjustment, OR = 1.05, 95% CI: 1.01-1.08] and DSST [full adjustment, OR = 1.06, 95% CI: 1.02-1.10] tests. Eventually, the GAM and smooth curve fit model was conducted to confirm that the association between serum globulin and cognitive impairment was non-linear. Moreover, the inflection point was 27 g/L serum globulin based on the CERAD test and 35 g/L serum globulin based on the AF test. Finally, the interaction term between serum globulin and cognitive impairment based on the AF test indicated no significant interactions among all variables (all p for interaction >0.05). Conclusion: The association between serum globulin levels and cognitive impairment is non-linear. A threshold effect exists between serum globulin and cognitive impairment. Large-scale prospective clinical trials are needed to validate our findings.

The canonical HPA axis facilitates and maintains light adaptive behavior.

The vertebrate stress response (SR) is mediated by the hypothalamic-pituitary-adrenal (HPA) axis and contributes to generating context appropriate physiological and behavioral changes. Although the HPA axis plays vital roles both in stressful and basal conditions, research has focused on the response under stress. To understand broader roles of the HPA axis in a changing environment, we characterized an adaptive behavior of larval zebrafish during ambient illumination changes. The glucocorticoid receptor (nr3c1) was necessary to maintain basal locomotor activity in light and darkness. The HPA axis was required to adapt to light more efficiently but became dispensable when longer illumination was provided. Light adaptation was more efficient in dimmer light and did not require the mineralocorticoid receptor (nr3c2). Our findings show that the HPA axis contributes to the SR at various stages, facilitating the phasic response and maintaining an adapted basal state, and that certain adaptations occur without HPA axis activity.

Associations between Dengue Incidence, Ecological Factors, and Anthropogenic Factors in Singapore.

Singapore experiences endemic dengue. Vector control remains the primary means to reduce transmission due to the lack of available therapeutics. Resource limitations mean that vector-control tools need to be optimized, which can be achieved by studying risk factors related to disease transmission. We developed a statistical modelling framework which can account for a high-resolution and high-dimensional set of covariates to delineate spatio-temporal characteristics that are associated with dengue transmission from 2014 to 2020 in Singapore. We applied the proposed framework to two distinct datasets, stratified based on the primary type of housing within each spatial unit. Generalized additive models reveal non-linear exposure responses between a large range of ecological and anthropogenic factors as well as dengue incidence rates. At values below their mean, lesser mean total daily rainfall (Incidence rate ratio (IRR): 3.75, 95% CI: 1.00-14.05, Mean: 4.40 mm), decreased mean windspeed (IRR: 3.65, 95% CI: 1.87-7.10, Mean: 4.53 km/h), and lower building heights (IRR: 2.62, 95% CI: 1.44-4.77, Mean: 6.5 m) displayed positive associations, while higher than average annual NO2 concentrations (IRR: 0.35, 95% CI: 0.18-0.66, Mean: 13.8 ppb) were estimated to be negatively associated with dengue incidence rates. Our study provides an understanding of associations between ecological and anthropogenic characteristics with dengue transmission. These findings help us understand high-risk areas of dengue transmission, and allows for land-use planning and formulation of vector control policies.

Proliferation of atmospheric datasets can hinder policy making: a data blending technique offers a solution.

The proliferation of atmospheric datasets is a key outcome from the continued development and advancement of our collective scientific understanding. Yet often datasets describing ostensibly identical processes or atmospheric variables provide widely varying results. As an example, we analyze several datasets representing rainfall over Nepal. We show that estimates of extreme rainfall are highly variable depending on which dataset you choose to look at. This leads to confusion and inaction from policy-focused decision makers. Scientifically, we should use datasets that sample a range of creation methodologies and prioritize the use of data science techniques that have the flexibility to incorporate these multiple sources of data. We demonstrate the use of a statistically interpretable data blending technique to help discern and communicate a consensus result, rather than imposing a priori judgment on the choice of dataset, for the benefit of policy decision making.

Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics.

The advent of technological developments is allowing to gather large amounts of data in several research fields. Learning analytics (LA)/educational data mining has access to big observational unstructured data captured from educational settings and relies mostly on unsupervised machine learning (ML) algorithms to make sense of such type of data. Generalized additive models for location, scale, and shape (GAMLSS) are a supervised statistical learning framework that allows modeling all the parameters of the distribution of the response variable with respect to the explanatory variables. This article overviews the power and flexibility of GAMLSS in relation to some ML techniques. Also, GAMLSS' capability to be tailored toward causality via causal regularization is briefly commented. This overview is illustrated via a data set from the field of LA. This article is categorized under:Application Areas > Education and LearningAlgorithmic Development > StatisticsTechnologies > Machine Learning.

Sex differences in the non-linear association between BMI and LDL cholesterol in type 2 diabetes.

Background: A data-based study reported the linear relationship between body mass index (BMI) and low-density lipoprotein cholesterol (LDL-C) in a normal population. However, there were no studies giving the suggestion for diabetes patients limited by sample size. This study aimed to investigate the non-linear dose-response relationship between BMI and LDL-C in type 2 diabetes mellitus (T2DM). Method: The study participants registered at the National Metabolic Management Center (MMC) of Beijing Luhe hospital from June 2017 to June 2021. T2DM was diagnosed according to the 1999 World Organization criteria. The generalized additive models (GAMs) were used to investigate the non-linear association between BMI and LDL-C. The relationship between BMI and LDL-C was visualized via the smooth splines function plot by sex. Segmented regressions were fitted to calculate the slopes with different estimated breakpoints. Results: After data cleaning, a total of 2500 participants with T2DM aged 30 to 70 years were included in this study. Compared with females, the spline between BMI and LDL-C showed an Inverted U shape in males. In males, the slopes below and above the breakpoint (26.08. 95% CI: 24.13 to 28.03) were 2.38 (95%CI: 1.06, 3.70) and -0.36 (95%CI: -1.20, 0.48), respectively. Conclusion: There was an Inverted U shape association between BMI and LDL-C in male participants with T2DM, for which the LDL-C was increased with BMI in the lean population, while LDL-C gradually tended to be flat or even decreased in the obese population. However, the Inverted U-shape between BMI and LDL-C was not found in female patients with T2DM.

Sediment core records and impact factors of polycyclic aromatic hydrocarbons in Chinese lakes.

Lake sediment is a natural sink for polycyclic aromatic hydrocarbons (PAHs). PAH sedimentation characteristics and their impact factors of Chinese lakes have mainly been qualitative assessed. However, quantitative impacts of PAH sedimentation from different factors have not been well analyzed. To fill this gap, we screened PAH sedimentation records from the literature, for 51 lakes in China and other regions of the world, to identify historical concentration variation and the impact factors of PAHs in different regions, in lake sediment. The results show that PAH concentrations in the sediment core in the selected Chinese lakes (478 ± 812 ng/g dry weight (dw)) were significantly lower than those in North America (5518 ± 6572 ng/g dw) and Europe (3817 ± 4033 ng/g dw). From 1900 to 2015, most of the lakes in China showed an increasing trend of PAH sedimentation concentrations, with the lakes in Southeastern China showed a decreasing trend of PAH concentration in the period of 2001-2015, which was later than the peak times shown in Western countries (1941-1970). The 2-3-ring PAHs were the main components in the sediment core of Chinese lakes, but the proportion to the total PAHs decreased from 72% in 1900-1940 to 55% in 2001-2015. Generalized additive modeling (GAM) was adopted to simulate the associations between PAH sedimentation records and the impact factors. There are large regional variations of economic and industrial development in China. The impact factors of PAH accumulation in the lake sediments differ in different regions. However, population and the consumption of coal, pesticides, and fertilizer were identified to be the most important impact factors influencing PAH sedimentation. The Chinese government needs to strengthen control measures on pollutant discharge to reduce the anthropogenic impact of PAH sedimentation in lakes.

[Expected education deficiencies from Covid-related school-lockdowns in spring 2020-empirical evidence on family-education resources using nonlinear regression]. / Erwartete Schulprobleme als Folge der Corona-Schulschließungen im Frühjahr 2020 ­ Empirische Evidenz zur Bedeutung familialer Ressourcen mittels nichtlinearer Modellierung.

In light of the Covid-19-related school lockdowns in Germany in 2020 schools, families and the students were faced with the major challenge to continue instruction at home. This paper examines the expectations of the parents that their children will experience school-related problems as a result to the lockdown-induced homeschooling within the next six months. For our explorative analysis, we choose a nonlinear regression approach. In the course of this, we introduce nonlinear models and highlight their added value compared to methods commonly used in empirical educational research. For the analysis we combine data from the National Educational Panel Study (NEPS) with additional data sources like the COVID-19-Dashboard of the Robert-Koch-Institut (RKI). Our results show that parental expectations of future school problems were particularly prevalent among those parents whose children had low reading competencies and low diligence as an aspect of school effort. In addition, we find a relationship between a lower occupational status (ISEI) and higher parental expectations of school-related problems. Furthermore, parents' short-term and long-term concerns about Covid-19 show a positive association, making school problems more likely in the eyes of the parents. The purpose of this paper, in addition to applying and explaining nonlinear models for the first time in empirical educational research, is to analyze expectations regarding problems of homeschooling in the first lockdown from a parents' perspective and to explore variables that influence these parental expectations.

A note of feature screening via a rank-based coefficient of correlation.

Feature screening is a useful and popular tool to detect informative predictors for ultrahigh-dimensional data before developing statistical analysis or constructing statistical models. While a large body of feature screening procedures has been developed, most methods are restricted to examine either continuous or discrete responses. Moreover, even though many model-free feature screening methods have been proposed, additional assumptions are imposed in those methods to ensure their theoretical results. To address those difficulties and provide simple implementation, in this paper we extend the rank-based coefficient of correlation to develop a feature screening procedure. We show that this new screening criterion is able to deal with continuous and binary responses. Theoretically, the sure screening property is established to justify the proposed method. Simulation studies demonstrate that the predictors with nonlinear and oscillatory trajectories are successfully retained regardless of the distribution of the response. Finally, the proposed method is implemented to analyze two microarray datasets.

Tracing Cognitive Processes in Insight Problem Solving: Using GAMs and Change Point Analysis to Uncover Restructuring.

Insight problems are likely to trigger an initial, incorrect mental representation, which needs to be restructured in order to find the solution. Despite the widespread theoretical assumption that this restructuring process happens suddenly, leading to the typical "Aha!" experience, the evidence is inconclusive. Among the reasons for this lack of clarity is that many measures of insight rely solely on the solvers' subjective experience of the solution process. In our previous paper, we used matchstick arithmetic problems to demonstrate that it is possible to objectively trace problem-solving processes by combining eye movements with new analytical and statistical approaches. Specifically, we divided the problem-solving process into ten (relative) temporal phases to better capture possible small changes in problem representation. Here, we go a step further to demonstrate that classical statistical procedures, such as ANOVA, cannot capture sudden representational change processes, which are typical for insight problems. Only nonlinear statistical models, such as generalized additive (mixed) models (GAMs) and change points analysis, correctly identified the abrupt representational change. Additionally, we demonstrate that explicit hints reorient participants' focus in a qualitatively different manner, changing the dynamics of restructuring in insight problem solving. While insight problems may indeed require a sudden restructuring of the initial mental representation, more sophisticated analytical and statistical approaches are necessary to uncover their true nature.

Long-term daily stream temperature record for Scotland reveals spatio-temporal patterns in warming of rivers in the past and further warming in the future.

Stream temperature is directly and indirectly affected by climate change. To be able to project future changes in stream temperature, historic trends and factors influencing these trends need to be understood. There is a demand for daily data to analyse historical trends and future changes in stream temperature. However, long-term daily stream temperature data are rare and observations of coarse temporal resolution (e.g. once-a-month) do not allow for robust trend analyses. Here, we present a methodology to reconstruct a national long-term daily stream temperature record (1960-2080) from 40 years of once-a-month observations (for 45 Scottish catchments). This involved implementing climatic and hydrological variables in generalized additive models. These models were then used in combination with regional climate projections (UKCP18 Strand 3 - RCP8.5) to predict future spatio-temporal temperature patterns. The results indicate that for the Scottish dataset (i) in addition to air temperature, the dominant environmental controls on stream temperature are unique combinations for each catchment; (ii) a general increase of up to 0.06 °C/year in historic stream temperature over all catchments resulted mainly from increases in spring and summer stream temperatures; (iii) future spatial patterns in stream temperature are more homogenous and differ therefore from the past where temperatures in N Scotland were relatively lower; (iv) future changes of up to +4.0 °C in annual stream temperature are strongest in those catchments which show lower stream temperature in the past (NW and W Scotland). These results are important in the context of water quality and stream temperature management. The methodology can be applied to smaller scale sites or to other national/global datasets enabling the analysis of historic trends and future changes at a high temporal resolution.

Association of the components of ambient fine particulate matter (PM2.5) and chronic kidney disease prevalence in China.

Previous research has implicated PM2.5 as a potential environmental risk factor for CKD, but little is known about the associations between its components and CKD. We conducted a nationwide cross-sectional study using the updated air pollution data in the nationwide population (N = 2,938,653). Using generalized additive models, we assessed the association between long-term exposure to PM2.5 and its components (i.e., black carbon [BC], organic matter [OM], nitrate [NO3-], ammonium [NH4+], sulfate [SO42-]), and CKD prevalence. The air pollution data was estimated using high-resolution and high-quality spatiotemporal datasets of ground-level air pollutants in China. Besides, we adopted a novel quantile-based g-computation approach to assess the effect of a mixture of PM2.5 constituents on CKD prevalence. The average concentration of PM2.5 was 78.67 ± 22.5 µg/m3, which far exceeded WHO AQG. In the fully adjusted generalized additive model, at a 10 km × 10 km spatial resolution, the ORs per IQR increase in previous 1-year average PM2.5 exposures was 1.380 (95%CI: 1.345-1.415), for NH4+ was 1.094 (95%CI: 1.062-1.126), for BC was 1.604 (95%CI: 1.563-1.646), for NO3- was 1.094 (95%CI: 1.060-1.130), for SO42- was 1.239 (95%CI: 1.208-1.272), and for the OM was 1.387 (95%CI: 1.354-1.421), respectively. Subgroup analysis showed females, younger, and healthier were more vulnerable to this effect. In the further exploration of the joint effect of PM2.5 compositions (OR 1.234 [95%CI 1.222-1.246]) per quartile increase in all 5 PM2.5 components, we found that PM2.5SO42- contributed the most. These findings provide important evidence for the positive relationship between long-term exposure to PM2.5 and its chemical constituents and CKD prevalence in a Chinese health check-up population, and identified PM2.5SO42- has the highest contribution to this relationship. This study provides clinical and public health guidance for reducing specific air particle exposure for those at risk of CKD.

Quantifying phenological landmarks of migration shows nonuniform use of the Caribbean by shorebirds.

Of the boreal- and Arctic-breeding North American shorebirds that migrate south through the Caribbean, most individuals continue farther south. However, for many species, some individuals remain beyond the southbound migration period (i.e., throughout the temperate winter and/or summer). This variation among individuals adds complexity to observation data, obscures migration patterns, and could prevent the examination of the use of different Caribbean regions by various shorebird species during migration and in the nonmigratory seasons. Here, we present a novel method that leverages a well-established statistical approach (generalized additive models) to systematically identify migration phenology even for complex passage migrant species with individuals that remain beyond migration. Our method identifies the active migration period using derivatives of a fitted GAM and then calculates phenology metrics based on quantiles of that migration period. We also developed indices to quantify oversummering and overwintering patterns with respect to migration. We analyzed eBird data for 16 North American shorebird species as they traveled South through the insular Caribbean, identifying separate migratory patterns for Cuba, Puerto Rico, Guadeloupe, Aruba, Bonaire, Curaçao, and Trinidad and Tobago. Our results confirm past reports and provide additional detail on shorebird migration in the Caribbean, and identify several previously unpublished regional patterns. Despite Puerto Rico being farther north and closer to continental North America, most species reached Puerto Rico later than other regions, supporting a long-standing hypothesis that migration strategy (transcontinental vs. transoceanic) leads to geographic differences in migration timing. We also found distinct patterns of migration curves, with some regions and species consistently having either symmetrical or skewed curves; these differences in migration curve shape reflect different migration processes. Our novel method proved reliable and adaptable for most species and serves as a valuable tool for identifying phenological patterns in complex migration data, potentially unlocking previously intractable data.

Boosting multivariate structured additive distributional regression models.

We develop a model-based boosting approach for multivariate distributional regression within the framework of generalized additive models for location, scale, and shape. Our approach enables the simultaneous modeling of all distribution parameters of an arbitrary parametric distribution of a multivariate response conditional on explanatory variables, while being applicable to potentially high-dimensional data. Moreover, the boosting algorithm incorporates data-driven variable selection, taking various different types of effects into account. As a special merit of our approach, it allows for modeling the association between multiple continuous or discrete outcomes through the relevant covariates. After a detailed simulation study investigating estimation and prediction performance, we demonstrate the full flexibility of our approach in three diverse biomedical applications. The first is based on high-dimensional genomic cohort data from the UK Biobank, considering a bivariate binary response (chronic ischemic heart disease and high cholesterol). Here, we are able to identify genetic variants that are informative for the association between cholesterol and heart disease. The second application considers the demand for health care in Australia with the number of consultations and the number of prescribed medications as a bivariate count response. The third application analyses two dimensions of childhood undernutrition in Nigeria as a bivariate response and we find that the correlation between the two undernutrition scores is considerably different depending on the child's age and the region the child lives in.