Hierarchical Modeling Approach Research Articles

A hierarchical threshold modeling approach for understanding phenological responses to climate change: when did North American lilacs start to bloom earlier?

Prior to the recent upward climb, global average temperatures were relatively stable. This trend was described by Mann et al. [23] using a hockey-stick model consisting of two line segments (with the x-axis as time and temperature as the y-axis) meeting at a single changepoint. The line segment prior to the changepoint is flat (indicating a stable temperature), and the line after the changepoint has a positive slope (indicating increasing temperatures). Because the long-term average temperature change is a defining characteristic of climate change, researchers have shown that changes in many phenological variables over time can also be described by a hockey-stick model. For phenological variables, the changepoint and the slope of the line after the changepoint represent the timing of the onset and the effect of climate change. However, large annual variation often obscures the pattern when analyzed using data from a single location, whereas regional differences due to spatial variability of climate and weather patterns render pooling data from different locations impractical. We demonstrate that the Bayesian hierarchical modeling approach is effective in separating these two sources of variability by partially pooling data from multiple sites. Using the North American lilac first bloom dates, we show that the Bayesian approach can adequately separate the temporal and spatial variations, thereby quantify site-specific patterns of change as well as national/regional average trends. Our analysis, using the Bayesian hierarchical hockey-stick model, showed that the effects of climate change started as early as the 1970s and the lilacs in North America have been blooming on average one day earlier every three years since.

Toward a supply chain risk identification and filtering framework using systems theory

PurposeThe purpose of the paper is to develop a methodological framework for supply chain risk management using the hierarchical holographic modeling approach. It analyses supply chain risks in a systematic manner and develops a hierarchical methodology for identifying, prioritizing and managing the potential supply chain risks.Design/methodology/approachThis research reviews supply chain risk management literature and develops a conceptual framework, which outlines general principles and guidelines for managing risks in a systematic manner. Through decomposition, the complexity of supply chain risk can be identified by analyzing smaller subsystems.FindingsThe paper provides a conceptual framework to identify supply chain risks from multiple overlapping perspectives. The structured filtering and ranking procedure enables decision-makers to focus on the most critical risks. The research shows that the supply chain risks associated with the sub-systems within the hierarchical structure contribute to and ultimately determine the risks of the overall supply chain system.Research limitations/implicationsThe risks associated with each sub system within the hierarchical structure can contribute to and determine the risks of the overall supply chain system. Further applications in various companies and industry sectors would benefit supply chain managers on a case-by-case basis.Practical implicationsThe hierarchical risk identification framework can serve as guidance for applications to specific supply chain systems and processes. The framework from a holistic overlapping perspective can efficiently and effectively help supply chain managers identify supply chain risks and facilitate the evaluation of the subsystem risks.Originality/valueThe paper applies system thinking in supply chain management and presents an efficient and practical framework for supply chain risk identification and evaluation.

Conspecific and heterospecific crowding facilitate tree survival in a tropical karst seasonal rainforest

Tree mortality plays a vital role in forest dynamics and contributes to species coexistence and community assembly. However, the mechanisms that control tree mortality are poorly understood, particularly in species-rich tropical forests. Size-dependent abiotic constraints and biotic interactions act simultaneously to cause tree mortality in natural forests. However, these drivers are often studied independently, which can limit our understanding of how they interact to affect tree mortality in natural forests. We employed a hierarchical Bayesian logistic regression modeling approach to quantify the simultaneous effects of tree size, topography, neighborhoods, and their interactions on tree mortality in a 15-ha fully mapped tropical karst seasonal rainforest in Southern China. Of the variables tested, tree size exhibited the most consistent and strongest negative effect on tree mortality, while topography and neighborhood competition were of secondary importance. Neighbors, particularly heterospecific neighbors, had a facilitative effect on lowering mortality, while only several species showed conspecific negative density dependent on mortality. The topographical wetness index had a significant positive effect on tree mortality. Size-dependent and neighborhood competition-induced tree mortality was stronger for shade-intolerant than for shade-tolerant species, and the size-dependent mortality was the strongest for shrub species among the three growth forms. Our results indicated that the size-dependent effect was the dominant cause of tree mortality; however, neighbors, particularly heterospecific neighbors, had a facilitative effect on lowering tree mortality. Furthermore, the relative importance of these mechanisms for tree mortality differed within life-history strategies guilds. Our study highlighted the value of simultaneously considering the individual and interactive effects of multiple mechanisms toward understanding the dynamics and coexistence of highly diverse metacommunities in harsh environments.

Spatio-Temporal Modeling for Forecasting High-Risk Freshwater Cyanobacterial Harmful Algal Blooms in Florida

Due to the occurrence of more frequent and widespread toxic cyanobacteria events, the ability to predict freshwater cyanobacteria harmful algal blooms (cyanoHAB) is of critical importance for the management of drinking and recreational waters. Lake system specific geographic variation of cyanoHABs has been reported, but regional and state level variation is infrequently examined. A spatio-temporal modeling approach can be applied, via the computationally efficient Integrated Nested Laplace Approximation (INLA), to high-risk cyanoHAB exceedance rates to explore spatio-temporal variations across statewide geographic scales. We explore the potential for using satellite-derived data and environmental determinants to develop a short-term forecasting tool for cyanobacteria presence at varying space-time domains for the state of Florida. Weekly cyanobacteria abundance data were obtained using Sentinel-3 Ocean Land Color Imagery (OLCI), for a period of May 2016-June 2019. Time and space varying covariates include surface water temperature, ambient temperature, precipitation, and lake geomorphology. The hierarchical Bayesian spatio-temporal modeling approach in R-INLA represents a potential forecasting tool useful for water managers and associated public health applications for predicting near future high-risk cyanoHAB occurrence given the spatio-temporal characteristics of these events in the recent past. This method is robust to missing data and unbalanced sampling between waterbodies, both common issues in water quality datasets.

Neighborhood context, police legitimacy and willingness to help the police in Shanghai, China

PurposeFollowing social disorganization theory and the process-based model of policing, the present study aims to examine how characteristics of communities are meaningful in shaping citizens' willingness to work with the police in urban China.Design/methodology/approachBased on survey data from Shanghai, China, the study adopts a generalized hierarchical linear modeling (GHLM) approach to examine the effects of both individual- and neighborhood-level predictors on the outcome, while taking into consideration the unobserved additional neighborhood-level variations.FindingsResults suggest potential need of the process-based model to be modified in a Chinese context in that while police presence matters, measures on legitimacy are nonsignificant in shaping willingness to help the police, after controlling for neighborhood characteristics. More importantly, collective efficacy at the neighborhood level is related positively to residents' willingness to work with the police. Constant attention should be paid to the promotion of a collaborative and collectively caring environment, which can contribute to better coordination with the police, and greater willingness to be part of the larger cause of public safety.Originality/valueThe paper advances the scholarship with multi-level modeling and the role of communities in building stronger relationship with the police.

Life-History Modeling Reveals the Ecological and Evolutionary Significance of Autotomy.

AbstractAutotomy, the self-amputation of body parts, serves as an antipredator defense in many taxonomic groups of animals. However, its adaptive value has seldom been quantified. Here, we propose a novel modeling approach for measuring the fitness advantage conferred by the capability for autotomy in the wild. Using a predator-prey system where a land snail autotomizes and regenerates its foot specifically in response to snake bites, we conducted a laboratory behavioral experiment and a 3-year multievent capture-mark-recapture study. Combining these empirical data, we developed a hierarchical model and estimated the basic life-history parameters of the snail. Using samples from the posterior distribution, we constructed the snail's life table as well as that of a snail variant incapable of foot autotomy. As a result of our analyses, we estimated the monthly encounter rate with snake predators at 3.3% (95% credible interval: 1.6%-4.9%), the contribution of snake predation to total mortality until maturity at 43.3% (15.0%-95.3%), and the fitness advantage conferred by foot autotomy at 6.5% (2.7%-11.5%). This study demonstrated the utility of the multimethod hierarchical-modeling approach for the quantitative understanding of the ecological and evolutionary processes of antipredator defenses in the wild.

Modeling interconnected ICT and power systems for resilience analysis

Increasing interdependencies between power and ICT systems amplify the possibility of cascading failures. Resilience against such failures is an essential property of modern and sustainable power systems and networks. To assess the resilience and predict the behaviour of a system consisting of interdependent subsystems, the interconnection requires adequate modeling. This work presents an approach to model and determine the state of these so-called interconnectors in future cyber-physical energy systems with strongly coupled ICT and power systems for a resilience analysis. The approach can be used to capture the impact of various parameters on system performance upon suitable modification. An hierarchical modeling approach is developed with atomic models that demonstrate the interdependencies between a power and ICT system. The modeling approach using stochastic activity nets is applied to an exemplary redispatch process in a cyber-physical energy system. The performance of an interconnector when facing limited performance from the ICT subsystem and its subsequent impact on the power system is analysed using the models. The state of the interconnector, as well as the service level are mapped to a resilience state-space diagram. The representation of system state on the resilience state-space diagram allows interpretation of system performance and quantification of resilience metrics.

REGION-REFERENCED SPECTRAL POWER DYNAMICS OF EEG SIGNALS: A HIERARCHICAL MODELING APPROACH.

Functional brain imaging through electroencephalography (EEG) relies upon the analysis and interpretation of high-dimensional, spatially organized time series. We propose to represent time-localized frequency domain characterizations of EEG data as region-referenced functional data. This representation is coupled with a hierarchical regression modeling approach to multivariate functional observations. Within this familiar setting we discuss how several prior models relate to structural assumptions about multivariate covariance operators. An overarching modeling framework, based on infinite factorial decompositions, is finally proposed to balance flexibility and efficiency in estimation. The motivating application stems from a study of implicit auditory learning, in which typically developing (TD) children, and children with autism spectrum disorder (ASD) were exposed to a continuous speech stream. Using the proposed model, we examine differential band power dynamics as brain function is interrogated throughout the duration of a computer-controlled experiment. Our work offers a novel look at previous findings in psychiatry and provides further insights into the understanding of ASD. Our approach to inference is fully Bayesian and implemented in a highly optimized Rcpp package.

Multivariate Bayesian hierarchical modeling of the non-stationary traffic conflict extremes for crash estimation

Recent studies have developed univariate and bivariate Bayesian hierarchical extreme value models to improve traffic conflict-based crash estimation through addressing the issues of non-stationarity of conflict extremes and individual conflict indicators representing only partial severity aspects of a traffic event. Although the use of bivariate models showed considerable improvements compared to univariate models, two traffic conflict indicators may not be sufficient to cover all severity aspects of a traffic event. Therefore, a high dimensional multivariate Bayesian hierarchical extreme value model based on more than two conflict indicators merits further research. This study develops a multivariate Bayesian hierarchical extreme value modeling approach, which comprises a multivariate extreme value model and a Bayesian hierarchical structure. The former integrates several traffic conflict indicators in a unified framework, while the latter combines traffic conflicts from different sites, incorporating several covariates and site-specific unobserved heterogeneity. A model estimation approach for the multivariate Bayesian hierarchical extreme value model is proposed and applied to estimate rear-end crashes from four signalized intersections in the city of Surrey, British Columbia. The modified time to collision (MTTC), post encroachment time (PET), and deceleration rate to avoid a crash (DRAC) were employed as conflict indicators. Three covariates including traffic volume, shock wave area, and platoon ratio were considered to account for non-stationarity in conflict extremes. The results show that in terms of crash estimation accuracy, the high dimensional multivariate (e.g., trivariate) Bayesian hierarchical extreme value model outperforms both the low dimensional multivariate (e.g., bivariate) and univariate Bayesian hierarchical extreme value models. This is likely due to the multivariate model having the capability of sharing information across several different conflict indicators.

Capturing Spatiotemporal Patterns in Presence-Absence Data to Inform Monitoring and Sampling Designs for the Threatened Dakota Skipper (Lepidoptera: Hesperiidae) in the Great Plains of the United States.

Declines among species of insect pollinators, especially butterflies, has garnered attention from scientists and managers. Often these declines have spurred governments to declare some species as threatened or endangered. We used existing presence-absence data from surveys for the threatened Dakota skipper Hesperia dacotae (Skinner) to build statistical maps of species presence that could be used to inform future monitoring designs. We developed a hierarchical Bayesian modeling approach to estimate the spatial distribution and temporal trend in Dakota skipper probability of presence. Our model included a spatial random effect and fixed effects for the proportion of two grassland habitat types: those on well-drained soils and those on poorly drained soils; as well as the topographic slope. The results from this model were then used to assess sampling strategies with two different monitoring objectives: locating new Dakota skipper colonies or monitoring the proportion of historically (pre-2000) extant colonies. Our modeling results suggested that the distribution of Dakota skippers followed the distribution of remnant grasslands and that probabilities of presence tended to be higher in topographically diverse grasslands with well-drained soils. Our analysis also showed that the probability of presence declined throughout the northern Great Plains range. Our simulations of the different sampling designs suggested that new detections were expected when sampling where Dakota skippers likely occurred historically, but this may lead to a tradeoff with monitoring existing sites. Prior information about the extant sites may help to ameliorate this tradeoff.

Monitoring the resilience of a no-take marine reserve to a range extending species using benthic imagery.

Global climate change is driving the redistribution of marine species and thereby potentially restructuring endemic communities. Understanding how localised conservation measures such as protection from additional human pressures can confer resilience to ecosystems is therefore an important area of research. Here, we examine the resilience of a no-take marine reserve (NTR) to the establishment of urchin barrens habitat. The barrens habitat is created through overgrazing of kelp by an invading urchin species that is expanding its range within a hotspot of rapid climate change. In our study region, a multi-year monitoring program provides a unique time-series of benthic imagery collected by an Autonomous Underwater Vehicle (AUV) within an NTR and nearby reference areas. We use a Bayesian hierarchical spatio-temporal modelling approach to estimate whether the NTR is associated with reduced formation of urchin barrens, and thereby enhances local resilience. Our approach controls for the important environmental covariates of depth and habitat complexity (quantified as rugosity derived from multibeam sonar mapping), as well as spatial and temporal dependence. We find evidence for the NTR conferring resilience with a strong reserve effect that suggests improved resistance to the establishment of barrens. However, we find a concerning and consistent trajectory of increasing barrens cover in both the reference areas and the NTR, with the odds of barrens increasing by approximately 32% per year. Thus, whereas the reserve is demonstrating resilience to the initial establishment of barrens, there is currently no evidence of recovery once barrens are established. We also find that depth and rugosity covariates derived from multibeam mapping provide useful predictors for barrens occurrence. These results have important management implications as they demonstrate: (i) the importance of monitoring programs to inform adaptive management; (ii) that NTRs provide a potential local conservation management tool under climate change impacts, and (iii) that technologies such as AUVs and multibeam mapping can be harnessed to inform regional decision-making. Continuation of the current monitoring program is required to assess whether the NTR can provide long term protection from a phase shift that replaces kelp with urchin barrens.

Impact of HEXACO Personality Factors on Consumer Video Game Engagement: A Study on eSports.

This article aims to uncover novel insights into personality factors and consumer video game engagement modeling. This research empirically validates the role of specific HEXACO personality factors that foster consumer engagement (CE) in electronic sports (eSports) users. Using a survey-based approach, we incorporated the HEXACO 60 items and consumer video game engagement scales for data collection. Data were collected from eSports users, with 250 valid responses. WarpPLS 6.0 was used for partial least squares–structural equation modeling analyses comprising measurement and structural model assessment. The results showed that the reflective measurement model is reliable and sound, whereas the second-order formative measurement model also meets the criteria of indicator weights and collinearity values variance inflation factor (VIF). The results based on the structural model indicate that openness to experience, extraversion, agreeableness, and conscientiousness positively predict CE in eSports. This article is first among others that conceptualizes and validates the HEXACO personality traits as a reflective formative model using the hierarchical component model approach. The research model carries the explanatory capacity for CE in eSports concerning personality dimensions as indicated by the HEXACO model. It highlights the potential benefits of such research especially to marketers who could potentially employ personality modeling to develop tailored strategies to increase CE in video games.

Refinement-Based Hierarchical Modeling and Correctness Verification of Cross-Organization Collaborative Emergency Response Processes

When an emergency occurs, one of the important challenges is how to form an effective and timely response. An emergency disposal plan is usually organized as a series of emergency response processes manipulated by one emergency command center and several subordinate emergency organizations. Moreover, these subordinate organizations are usually geographically dispersed and need to collaborate with each other. In this case, designing and verifying such cross-organizational collaborative emergency response processes are complicated and time-consuming. To address this problem, we propose a hierarchical modeling and correctness verification approach. A general framework for hierarchical modeling and correctness verification of such processes is first introduced. Then, a top-level model and two kinds of bottom-level models (complex and simple bottom-level ones) are proposed to model such processes and collaboration patterns from different abstraction levels. Next, Petri net refinement operation is adopted to refine the top-level model by using its corresponding bottom-level models to obtain the refined model. Finally, the correctness of the refined model is verified based on reachability graph. A typical running case of cross-organization collaborative fire emergency response processes is given to validate our proposed method.

Examining influences of science teachers' practices and beliefs about technology-based assessment on students’ performances: A hierarchical linear modeling approach

This study aimed at investigating how the factors across the student and school levels would influence students' performance on a technology-based assessment (TBA). TBAs not only enable teachers to evaluate their students' complex abilities, but have also been adopted by large-scale and international evaluation programs in recent years. Although some factors such as students' engagement or teachers' beliefs about assessments have been articulated, relatively little is understood about how the factors across levels affect students' performances on TBAs. This study thus collected data of 494 science teachers and 1774 eighth and 11th graders from 32 schools, and conducted a hierarchical linear modeling analysis to provide a more accurate estimation of the effects of the variables in each level on students' performances. The results indicated the importance of students' engagement in related learning activities and their computer experiences at both the individual and school levels. Additionally, although none of the teachers' variables at the school level such as the time teachers spent on the use of various types of TBAs or their intentions to use TBAs had significant main effects on students' performances, the teachers' influence at the school level on students' learning could still be found by a significant moderating effect from teachers' usage of TBAs. Our results provide insight into how to promote students’ performance on TBAs and can contribute in various ways to future research efforts concerning the use of TBAs in classrooms.

Pattern transitions of localized deformation in high-porosity sandstones: Insights from multiscale analysis

We employ a hierarchical multiscale modeling approach to investigate the transitions of localized deformation patterns in high-porosity sandstone subjected to sustained shear to understand their underlying physics. The multiscale approach is based on hierarchical coupling between finite element method (FEM) with discrete element method (DEM) to offer cross-scale predictions for granular rocks without assuming phenomenological constitutive relations. Our simulations show that when a high-porosity sandstone specimen is subjected to continuous deviatoric loading, compaction bands may occur and evolve, featuring a steady movement of the compaction front (i.e. the boundary between the compaction band and the rest uncompacted zone). The specimen reaches a homogeneous state of reduced porosity when the compaction fronts traverse the entire specimen. A re-hardening response is initiated in the specimen under further shear, which is followed by a shearing dominating stage with the emergence of shear bands. The material responses inside the ultimate shear bands approach a “steady state” of constant porosity and stress ratio. Cross-scale analyses reveal that debonding and pore collapse are dominant mechanisms for the compaction stage of the specimen, and debonding and particle rotation dictate the physics for the shear banding stage. The transitions from compaction to shear banding occurs due to the degradation of the cohesive contact network and significant reduction in porosity. There are limited number of interparticle bonds remaining at the “steady state” under sustained shear, with a preferential direction perpendicular to the loading direction, leading to a higher steady void ratio than the critical state void ratio of non-cohesive sand.

Two-Component Bayesian Hierarchical Models for Cost-Benefit Analysis of Traffic Barrier Crash Count

Road departure crashes tend to be hazardous, especially in rural areas like Wyoming. Traffic barriers could be installed to mitigate the severity of those crashes. However, the severity of traffic barriers crashes still persists. Besides various drivers and environmental characteristics, the roadways and barrier geometric characteristics play a critical role in the severity of barrier crashes. The Wyoming department of transportation (WYDOT) has initiated a project to identify and optimize the heights of those barriers that are below the design standard, while prioritizing them based on the monetary benefit. This is to optimize first barriers that need an immediate attention, considering the limited budget, and then all other barriers being under design. In order to account for both aspects of frequency and severity of crashes, equivalent property damage only (EPDO) was considered. The data of this type besides having an over-dispersion, exhibits excess amounts of zeroes. Thus, a two-component model was employed to provide a flexible way of addressing this problem. Beside this technique, one-component hierarchical modeling approach was considered for a comparison purpose. This paper presents an empirical cost-benefit analysis based on Bayesian hierarchical machine learning techniques. After identifying the best model in terms of the performance, deviance information criterion (DIC), the results were converted into an equation, and the equation was used for a purpose of machine learning technique. An automated method generated cost based on barriers’ current conditions, and then based on optimized barrier heights. The empirical analysis showed that cost-sensitive modeling and machine learning technique deployment could be used as an effective way for cost-benefit analysis. That could be achieved through measuring the associated costs of barriers’ enhancements, added benefits over years and consequently, barrier prioritization due to lack of available budget. A comprehensive discussion across the two-component models, zero-inflated and hurdle, is included in the manuscript.

Assessing the risk of disruption of wind turbine operations in Saudi Arabia using Bayesian spatial extremes

Saudi Arabia has been seeking to reduce its dependence on oil by diversifying its energy portfolio, including the largely underused energy potential from wind. However, extreme winds can possibly disrupt the wind turbine operations, thus preventing the stable and continuous production of wind energy. In this study, we assess the risk of disruptions of wind turbine operations, based on return levels with a hierarchical spatial extreme modeling approach for wind speeds in Saudi Arabia. Using a unique Weather Research and Forecasting dataset, we provide the first high-resolution risk assessment of wind extremes under spatial non-stationarity over the country. We account for the spatial dependence with a multivariate intrinsic autoregressive prior at the latent Gaussian process level. The computational efficiency is greatly improved by parallel computing on subregions from spatial clustering, and the maps are smoothed by fitting the model to cluster neighbors. Under the Bayesian hierarchical framework, we measure the uncertainty of return levels from the posterior Markov chain Monto Carlo samples, and produce probability maps of return levels exceeding the cut-out wind speed of wind turbines within their lifetime. The probability maps show that locations in the South of Saudi Arabia and near the Red Sea and the Persian Gulf are at very high risk of disruption of wind turbine operations.

Parallelized Multiscale Off-Road Vehicle Mobility Simulation Algorithm and Full-Scale Vehicle Validation

Abstract In this work, a scalable parallel computing scheme for the hierarchical multiscale off-road vehicle mobility simulation capability is developed with the hybrid message passing interface (MPI)/OpenMP framework, and it is validated against full-scale vehicle test data. While the hierarchical multiscale modeling approach has been introduced to high-fidelity off-road mobility simulations to eliminate limitations of existing single-scale deformable terrain models, computational complexities associated with the large dimensionality of multibody vehicle equations, involving nonlinear finite element tires and multiscale terrain models, need to be addressed for use in full-scale vehicle mobility predictions. To this end, a co-simulation framework for the multiscale off-road vehicle mobility model is proposed by exploiting the moving soil patch technique. This allows for systematically extracting four moving soil patches for four tires in a vehicle model from the deformable terrain domain, and time integrations of the four tire–soil subsystems are performed concurrently to enable computational speedup. Furthermore, an automated updating scheme for multiscale moving soil patches for a full vehicle model is developed, considering the multipass effect in various vehicle maneuvering scenarios. To demonstrate the off-road mobility prediction capability using the proposed parallelized multiscale vehicle–terrain interaction simulation algorithm, full-scale vehicle validation is presented for the vehicle drawbar pull as well as variable grade hill climb tests on soft soil.

Granular measures of agricultural land use influence lake nitrogen and phosphorus differently at macroscales.

Agricultural land use is typically associated with high stream nutrient concentrations and increased nutrient loading to lakes. For lakes, evidence for these associations mostly comes from studies on individual lakes or watersheds that relate concentrations of nitrogen (N) or phosphorus (P) to aggregate measures of agricultural land use, such as the proportion of land used for agriculture in a lake's watershed. However, at macroscales (i.e., in hundreds to thousands of lakes across large spatial extents), there is high variability around such relationships and it is unclear whether considering more granular (or detailed) agricultural data, such as fertilizer application, planting of specific crops, or the extent of near-stream cropping, would improve prediction and inform understanding of lake nutrient drivers. Furthermore, it is unclear whether lake N and P would have different relationships to such measures and whether these relationships would vary by region, since regional variation has been observed in prior studies using aggregate measures of agriculture. To address these knowledge gaps, we examined relationships between granular measures of agricultural activity and lake total phosphorus (TP) and total nitrogen (TN) concentrations in 928 lakes and their watersheds in the Northeastern and Midwest U.S. using a Bayesian hierarchical modeling approach. We found that both lake TN and TP concentrations were related to these measures of agriculture, especially near-stream agriculture. The relationships between measures of agriculture and lake TN concentrations were more regionally variable than those for TP. Conversely, TP concentrations were more strongly related to lake-specific measures like depth and watershed hydrology relative to TN. Our finding that lake TN and TP concentrations have different relationships with granular measures of agricultural activity has implications for the design of effective and efficient policy approaches to maintain and improve water quality.

Investigation of Turkey's PISA 2015 Science Achievement and Associated Variables Using Hierarchical Linear Modeling

The purpose of this study is to investigate the variables related to science achievement of high school students in Turkey portion of PISA 2015, by using hierarchical linear modeling (HLM) approach. The findings revealed that the relationship between the science achievement and demographic characteristics of the students is stronger than the relationship between science achievement and the affective domain as well as the learning environment. Based on these findings, it is thought that improving students’ science achievement is possible by taking into account the variables that affect the success, especially with improvements that can provide equal opportunities in the context of demographic variables and the construction of inclusive school systems.