Does an uneven sample size distribution across settings matter in cross-classified multilevel modeling? Results of a simulation study

Evaluating carbon stocks in soils of fragmented Brazilian Atlantic Forests (BAF) based on soil features and different methodologies

Brazil's Atlantic Forest (BAF) is a highly fragmented, strategic environmental and socio-economic region that represents the fourth biodiversity hotspot while also producing many commodities that are exported globally. Human disturbance plays a pivotal role as a driver of BAF's soil dynamics and behaviors. The soils under Late Primary and Secondary Semideciduous Seasonal Forests (LPSF and LSSF) were characterized by high to moderate resilience, with improved chemical properties as human disturbance decreased. The Transitional Forest to Cerrado (TFC) had the worst soil conditions. Disturbed Primary and Secondary Semideciduous Seasonal Forests (DPSF and DSSF) represent a transitional stage between LPSF/LSSF and TFC. Accordingly, SOCs stocks increased from TFC << DPSF, DSSF < LPSF, LSSF. In BAF soils, to avoid unreliable data, SOCs measurements should be (i) conducted to at least 1 m soil depth and (ii) quantified with a CHN analyzer. Human disturbance strongly affected the positive feedback between vegetation succession, SOCs, and soil nutrition. Soil development decreased as human disturbance increased, thus negatively affecting SOCs. Soils in the BAF require a long time to recover after the end of human disturbance, thus suggesting that preservation strategies should be prioritized in remnant BAF fragments.

Methodological insights into multilevel analysis of individual heterogeneity and discriminatory accuracy: An empirical examination of the effects of strata configurations on between-stratum variance and of fixed effects across hierarchical levels

This study aims to advance the Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) approach by addressing two key questions. First, it investigates the impact of using increasingly complex combinations of variables to create intersectional strata on between-stratum variance, measured by the variance partitioning coefficients (VPCs). Second, it examines the stability of coefficients for fixed effects across models with an increasing number of hierarchical levels. The analysis is performed using data from a survey of over 42,000 respondents on the prevalence of gender-based violence in European research organisations conducted in 2022. Results indicate that the number of intersectional strata is not significantly related to the proportion of the total variance attributable to the variance between intersectional strata in the MAIHDA approach. Moreover, the coefficients remain relatively stable and consistent across models with increasing complexity, where levels about organisations and countries are added. The analysis concludes that the MAIHDA approach can be flexibly applied for different research purposes, either to better account for structures of power and inequality; or to provide intersectionality-sensitive estimates. The findings underscore the need for researchers to clarify the specific aims of using MAIHDA, whether descriptive or inferential, and highlight the approach's versatility in addressing intersectionality within quantitative research. The study contributes to the literature by offering empirical evidence on the methodological considerations in applying the MAIHDA approach, thereby aiding in its more effective use for intersectional research.

Maximizing valid eye-tracking data in human and macaque infants by optimizing calibration and adjusting areas of interest

Remote eye tracking with automated corneal reflection provides insights into the emergence and development of cognitive, social, and emotional functions in human infants and non-human primates. However, because most eye-tracking systems were designed for use in human adults, the accuracy of eye-tracking data collected in other populations is unclear, as are potential approaches to minimize measurement error. For instance, data quality may differ across species or ages, which are necessary considerations for comparative and developmental studies. Here we examined how the calibration method and adjustments to areas of interest (AOIs) of the Tobii TX300 changed the mapping of fixations to AOIs in a cross-species longitudinal study. We tested humans (N = 119) at 2, 4, 6, 8, and 14 months of age and macaques (Macaca mulatta; N = 21) at 2 weeks, 3 weeks, and 6 months of age. In all groups, we found improvement in the proportion of AOI hits detected as the number of successful calibration points increased, suggesting calibration approaches with more points may be advantageous. Spatially enlarging and temporally prolonging AOIs increased the number of fixation-AOI mappings, suggesting improvements in capturing infants' gaze behaviors; however, these benefits varied across age groups and species, suggesting different parameters may be ideal, depending on the population studied. In sum, to maximize usable sessions and minimize measurement error, eye-tracking data collection and extraction approaches may need adjustments for the age groups and species studied. Doing so may make it easier to standardize and replicate eye-tracking research findings.

Time trends in associations between neighbourhood and school characteristics and mental health problems among Dutch adolescents

We investigated trends in associations between physical and social neighbourhood and school characteristics and adolescent mental health problems between 2005 and 2017. Nationally representative, repeated cross-sectional data collected in the Netherlands among primary (N = 5,871) and secondary school students (N = 20,778) were analysed through cross-classified multilevel models. Hardly any evidence was found for over-time changes in associations between neighbourhood and school characteristics and adolescent mental health problems. Findings showed that for both groups of students, only social characteristics within the two contexts were associated with adolescent mental health problems. For secondary school students, school effects were larger than neighbourhood effects, while the opposite was true for primary school students. Specifically, primary school students residing in more socially fragmented or lower socioeconomic status (SES) neighbourhoods, and lower SES schools, reported more conduct problems and peer relationship problems. For secondary school students, only the SES of the neighbourhood and the school was associated with all four aspects of mental health problems. Remarkably, the direction of the associations between neighbourhood/ school SES and adolescent mental health problems varied across the different mental health outcomes. More research is warranted to replicate our findings.

Urbanization, climate and species traits shape mammal communities from local to continental scales

Human-driven environmental changes shape ecological communities from local to global scales. Within cities, landscape-scale patterns and processes and species characteristics generally drive local-scale wildlife diversity. However, cities differ in their structure, species pools, geographies and histories, calling into question the extent to which these drivers of wildlife diversity are predictive at continental scales. In partnership with the Urban Wildlife Information Network, we used occurrence data from 725 sites located across 20 North American cities and a multi-city, multi-species occupancy modelling approach to evaluate the effects of ecoregional characteristics and mammal species traits on the urbanization-diversity relationship. Among 37 native terrestrial mammal species, regional environmental characteristics and species traits influenced within-city effects of urbanization on species occupancy and community composition. Species occupancy and diversity were most negatively related to urbanization in the warmer, less vegetated cities. Additionally, larger-bodied species were most negatively impacted by urbanization across North America. Our results suggest that shifting climate conditions could worsen the effects of urbanization on native wildlife communities, such that conservation strategies should seek to mitigate the combined effects of a warming and urbanizing world.

Adolescent individual, school, and neighborhood influences on young adult diabetes risk

Using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), this study examines the association between adolescent school and neighborhood contexts and the likelihood of diabetes in young adulthood. We apply cross-classified multi-level modeling (CCMM) techniques to examine the simultaneous influence of non-nested school and neighborhood contexts as well as individual, school, and neighborhood-level factors (N = 14,041 participants from 128 schools, 1933 neighborhoods). Our findings suggest that individual-level factors are most associated with young adult diabetes, with small contributions from school and neighborhood factors and a small proportion of the variation explained by school and neighborhood contexts.

Adolescent individual, school, and neighborhood influences on young adult hypertension risk

BACKGROUND

Geographic and contextual socioeconomic risk factors in adolescence may be more strongly associated with young adult hypertension than individual-level risk factors. This study examines the association between individual, neighborhood, and school-level influences during adolescence on young adult blood pressure.

METHODS

Data were analyzed from the National Longitudinal Study of Adolescent to Adult Health (1994-1995 aged 11-18 and 2007-2008 aged 24-32). We categorized hypertension as systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg. Secondary outcomes included continuous systolic and diastolic blood pressure. We fit a series of cross-classified multilevel models to estimate the associations between young adulthood hypertension with individual-level, school-level, and neighborhood-level factors during adolescence (i.e., fixed effects) and variance attributable to each level (i.e., random effects). Models were fit using Bayesian estimation procedures. For linear models, intra-class correlations (ICC) are reported for random effects.

RESULTS

The final sample included 13,911 participants in 128 schools and 1,917 neighborhoods. Approximately 51% (7,111) young adults were hypertensive. Individual-level characteristics-particularly older ages, Non-Hispanic Black race, Asian race, male sex, BMI, and current smoking-were associated with increased hypertension. Non-Hispanic Black (OR = 1.21; 95% CI: 1.03-1.42) and Asian (OR = 1.28; 95% CI: 1.02-1.62) students had higher odds of hypertension compared to non-Hispanic White students. At the school level, hypertension was associated with the percentage of non-Hispanic White students (OR for 10% higher = 1.06; 95% CI: 1.01-1.09). Adjusting for individual, school, and neighborhood predictors attenuated the ICC for both the school (from 1.4 null to 0.9 fully-adjusted) and neighborhood (from 0.4 to 0.3).

CONCLUSION

We find that adolescents' schools and individual-level factors influence young adult hypertension, more than neighborhoods. Unequal conditions in school environments for adolescents may increase the risk of hypertension later in life. Our findings merit further research to better understand the mechanisms through which adolescents' school environments contribute to adult hypertension and disparities in hypertension outcomes later in life.

Mammals adjust diel activity across gradients of urbanization

Time is a fundamental component of ecological processes. How animal behavior changes over time has been explored through well-known ecological theories like niche partitioning and predator-prey dynamics. Yet, changes in animal behavior within the shorter 24-hour light-dark cycle have largely gone unstudied. Understanding if an animal can adjust their temporal activity to mitigate or adapt to environmental change has become a recent topic of discussion and is important for effective wildlife management and conservation. While spatial habitat is a fundamental consideration in wildlife management and conservation, temporal habitat is often ignored. We formulated a temporal resource selection model to quantify the diel behavior of eight mammal species across ten U.S. cities. We found high variability in diel activity patterns within and among species and species-specific correlations between diel activity and human population density, impervious land cover, available greenspace, vegetation cover, and mean daily temperature. We also found that some species may modulate temporal behaviors to manage both natural and anthropogenic risks. Our results highlight the complexity with which temporal activity patterns interact with local environmental characteristics, and suggest that urban mammals may use time along the 24-hour cycle to reduce risk, adapt, and therefore persist, and in some cases thrive, in human-dominated ecosystems.

Prediction With Mixed Effects Models: A Monte Carlo Simulation Study

Oftentimes in many fields of the social and natural sciences, data are obtained within a nested structure (e.g., students within schools). To effectively analyze data with such a structure, multilevel models are frequently employed. The present study utilizes a Monte Carlo simulation to compare several novel multilevel classification algorithms across several varied data conditions for the purpose of prediction. Among these models, the panel neural network and Bayesian generalized mixed effects model (multilevel Bayes) consistently yielded the highest prediction accuracy in test data across nearly all data conditions.

Wealth and urbanization shape medium and large terrestrial mammal communities

Urban biodiversity provides critical ecosystem services and is a key component to environmentally and socially sustainable cities. However, biodiversity varies greatly within and among cities, leading to human communities with changing and unequal experiences with nature. The "luxury effect," a hypothesis that predicts a positive correlation between wealth, typically measured by per capita income, and species richness may be one indication of these inequities. While the luxury effect is well studied for some taxa, it has rarely been investigated for mammals, which provide unique ecosystem services (e.g., biological pest control) and exhibit significant potential for negative human-wildlife interactions (e.g., nuisances or conflicts). We analyzed a large dataset of mammal detections across 20 North American cities to test whether the luxury effect is consistent for medium- to large-sized terrestrial mammals across diverse urban contexts. Overall, support for the luxury effect, as indicated by per capita income, was inconsistent; we found evidence of a luxury effect in approximately half of our study cities. Species richness was, however, highly and negatively correlated with urban intensity in most cities. We thus suggest that economic factors play an important role in shaping urban mammal communities for some cities and species, but that the strongest driver of urban mammal diversity is urban intensity. To better understand the complexity of urban ecosystems, ecologists and social scientists must consider the social and political factors that drive inequitable human experiences with nature in cities.

Landscape-scale differences among cities alter common species' responses to urbanization

Understanding how biodiversity responds to urbanization is challenging, due in part to the single-city focus of most urban ecological research. Here, we delineate continent-scale patterns in urban species assemblages by leveraging data from a multi-city camera trap survey and quantify how differences in greenspace availability and average housing density among 10 North American cities relate to the distribution of eight widespread North American mammals. To do so, we deployed camera traps at 569 sites across these ten cities between 18 June and 14 August. Most data came from 2017, though some cities contributed 2016 or 2018 data if it was available. We found that the magnitude and direction of most species' responses to urbanization within a city were associated with landscape-scale differences among cities. For example, eastern gray squirrel (Sciurus carolinensis), fox squirrel (Sciurus niger), and red fox (Vulpes vulpes) responses to urbanization changed from negative to positive once the proportion of green space within a city was >~20%. Likewise, raccoon (Procyon lotor) and Virginia opossum (Didelphis virginiana) responses to urbanization changed from positive to negative once the average housing density of a city exceeded about 700 housing units/km² . We also found that local species richness within cities consistently declined with urbanization in only the more densely developed cities (>~700 housing units/km² ). Given our results, it may therefore be possible to design cities to better support biodiversity and reduce the negative influence of urbanization on wildlife by, for example, increasing the amount of green space within a city. Additionally, it may be most important for densely populated cities to find innovative solutions to bolster wildlife resilience because they were the most likely to observe diversity losses of common urban species.

Disentangling individual, school, and neighborhood effects on screen time among adolescents and young adults in the United States

OBJECTIVES

To examine the association between individual, neighborhood, and school-level influences on individual screen time among adolescents and young adults (AYAs) in the National Longitudinal Study of Adolescent to Adult Health.

METHODS

We classified screen time continuously as self-reported total hours per week of television, videos, and video/computer games at baseline and categorical as extended screen time (≥14 h per week). We fit cross-classified multilevel models (CCMM) to examine to examine the individual-, school- and neighborhood-level demographic and socioeconomic factors associated with screen time. Models were fit using MLwiN with Bayesian estimation procedures.

RESULTS

AYAs reported an average of 22.8 (SD = 19.4) and 21.9 (SD = 20.3) hours of screen time, respectively. At the individual level, younger age, male sex, Black/multiracial race, receipt of public assistance, and lower parental education were associated with higher screen time. At the school level, being out of session (i.e., school and national holidays including summer), having a higher proportion of non-White students, and having a lower proportion of parents with a college education were associated with higher individual screen time.

CONCLUSIONS

We found that individual-level factors most influence youth screen time, with smaller contributions from school factors.

Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices

Recognizing that health outcomes are influenced by and occur within multiple social and physical contexts, researchers have used multilevel modeling techniques for decades to analyze hierarchical or nested data. Cross-Classified Multilevel Models (CCMM) are a statistical technique proposed in the 1990s that extend standard multilevel modeling and enable the simultaneous analysis of non-nested multilevel data. Though use of CCMM in empirical health studies has become increasingly popular, there has not yet been a review summarizing how CCMM are used in the health literature. To address this gap, we performed a scoping review of empirical health studies using CCMM to: (a) evaluate the extent to which this statistical approach has been adopted; (b) assess the rationale and procedures for using CCMM; and (c) provide concrete recommendations for the future use of CCMM. We identified 118 CCMM papers published in English-language literature between 1994 and 2018. Our results reveal a steady growth in empirical health studies using CCMM to address a wide variety of health outcomes in clustered non-hierarchical data. Health researchers use CCMM primarily for five reasons: (1) to statistically account for non-independence in clustered data structures; out of substantive interest in the variance explained by (2) concurrent contexts, (3) contexts over time, and (4) age-period-cohort effects; and (5) to apply CCMM alongside other techniques within a joint model. We conclude by proposing a set of recommendations for use of CCMM with the aim of improved clarity and standardization of reporting in future research using this statistical approach.

Adolescent Sexual Initiation: A Cross-Classified Multilevel Analysis of Peer Group-, School-, and Neighborhood-Level Influences

PURPOSE

This study examines the simultaneous roles of neighborhood, school, and peer group contexts on variation in age of U.S. adolescent sexual initiation (coitarche). All three contexts have been shown to be important determinants of adolescent sexual and reproductive health outcomes but are typically examined separately, leaving a large gap in our understanding of their relative and joint importance. Furthermore, little is known about whether these contexts matter differently for boys and girls.

METHODS

Using sociocentric network data from the National Longitudinal Study of Adolescent to Adult Health, we combine gender-stratified analyses, social network community detection (to identify teens' social cliques), and cross-classified multilevel modeling to simultaneously analyze gender, neighborhood, school, and peer group effects. These results are compared against results from traditional multilevel models (MLMs), which analyze the contexts individually.

RESULTS

Evaluated separately in MLM, peer groups accounted for 6.79% of the total variation in coitarche, schools for 3.56%, and neighborhoods for 4.11%. Under simultaneous cross-classified multilevel modeling analysis, a different story emerges: peer groups and schools accounted for 3.66% and 3.19% of the total variation in coitarche, respectively, whereas neighborhood explained only 1.16% of the total variation. Stratified analyses indicate that gender modifies these associations.

CONCLUSIONS

Results demonstrate that omitting any one of these contexts may lead to an overestimation of the importance of contexts included in models. When modeled simultaneously with neighborhoods, our findings suggest that peer groups and schools are meaningful contributing contexts to the variance in sexual initiation, and that these contexts matter differently for boys and girls.