PiecewiseSEM: Piecewise structural equation modelling inr for ecology, evolution, and systematics

Criteria for acceptable studies of animal personality and behavioural syndromes

Two methods of causal analysis have become popular: the Simon-Blalock method and path analysis. The Simon-Blalock method is a "weak" form of path analysis, whereas more information can be deduced by using path analysis. The Driver- Massey data, examined earlier by the Simon-Blalock method, were reanalyzed by path analysis. The result shows that a more complicated path analytic model gives a better fit with the data, and also yields additional information for more detailed analysis—making path analysis a powerful tool for examining causal relationships in hologeistic research.

Coral reefs contain a disproportionately high amount of marine biodiversity and support millions of people worldwide by providing food security and livelihoods. This coupled relationship between reefs and people is increasingly threatened globally by climate change and globalization, as well as locally by overfishing, habitat destruction, and nutrient pollution. Biomonitoring the varied ecological outcomes of these threats and estimating how conservation and management interventions mitigate them are crucial scientific endeavors for understanding the socio-ecological complexity of coral reef dynamics. Common ecological indicators of reef health, however, are theoretically and taxonomically limited when used alone, thus a combination of modeling and methodological approaches is needed for comprehensive assessments of coral reef patterns and processes. In addition, there are under-discussed power dynamics and equity implications of biomonitoring, especially when novel technologies are employed. Here, we address these research gaps with field data collected from coral reefs in Indonesia using visual surveys of fishes and corals and environmental DNA (eDNA) surveys of coral reef animals more broadly. We also evaluate potential equity outcomes of eDNA frameworks and implementations using critical discourse analysis. Engaging with multiple disciplinary paradigms, the aims of this dissertation are to understand the biophysical and human-mediated pathways shaping coral reef fish and habitat assemblages, evaluate the utility of eDNA for uncovering spatial patterns in coral reef biodiversity, and critically examine how the discourses and practices of the eDNA field may lead to inequitable outcomes. Chapter 1 contextualizes the results presented in Chapters 2, 3, and 4. It outlines the global magnitude of coral reef biodiversity and its threats, describes the common bioindicators used to evaluate management practices and ecosystem processes, gives rationale for using an eDNA approach, and establishes the importance of scrutinizing eDNA’s social dimensions. Key information about Indonesia’s coral reefs is also summarized. In Chapter 2, we used structural equation models (SEMs) to evaluate the relative influences of biophysical and human-mediated pathways affecting coral reef fishes and habitats. SEMs are a form of path analysis that allows multiple competing hypotheses about a system to be evaluated within the same theoretical and statistical framework. SEMs allows for understanding coral reef processes because they organize field data from reefs in terms of their causal proximity to each other. In our SEMs, we used visual survey data collected on coral reef fishes and habitat conditions across four regions of Indonesia that vary in their management designations. We also incorporated additional socio-environmental

Species richness (SR) and phylogenetic diversity (PD) are highly correlated measures of plant diversity. Each, by itself, is significantly associated with plant community biomass in biodiversity experiments. As presented by Cadotte (2015) and as we present below, reasonable but alternative analyses that attempt to control for this correlation in different ways provide contradictory or inconclusive support for the hypothesis that PD is superior to SR as a predictor of community biomass. In Venail et al. (2015), we re-analysed data from 16 experimental manipulations of grassland SR to look at how SR and PD influence variation in plant community biomass through time. Using four types of analyses, we showed that, after statistically controlling for variation in SR, PD was not related to community biomass or to the temporal stability of biomass. We did, however, find that SR tends to increase the biomass production of plant communities after controlling for PD. In his comment, Cadotte expressed two concerns about our analyses. One is that we used non-random subsets of experiments, rather than the full data set, for some of our analyses (types 2, 3). We were clear in stating these analyses were based on non-random subsets that were specifically chosen to minimize the SR–PD correlation and avoid problems associated with multicollinearity. We acknowledge that our tests are conservative, a cost of which is that they sacrifice statistical power while, at the same time, minimizing the chance of drawing an incorrect conclusion. But we disagree with Cadotte's suggestion that our use of non-random data subsets led to 'biased' conclusions, and demonstrate later in this response that his claim of bias is unsubstantiated. Cadotte's second concern was that our analyses did not account for differences in biomass across studies. This is an important criticism to consider; we made a mistake by not controlling for variation in biomass. To address this issue, Cadotte used mixed models where study was included as a random effect, and ran analyses that standardized biomass among sites. Collectively, these led Cadotte to conclude 'All analyses strongly support previous literature claims about the value of PD and I further show that: (i) PD provides a more powerful explanation of variation in biomass production than species richness; (ii) PD explains variation in biomass production after controlling for richness; and (iii) the use of data subsets inadvertently biased the conclusions'. We have two concerns with Cadotte's re-analysis. First, Cadotte's approach largely ignores the concerns we raised about multicollinearity. When two or more predictors exhibit a high degree of correlation, each predictor contains little unique information. As a result, it is difficult (if not impossible) to estimate their independent effects using statistical methods like multivariate or partial regression (Dormann et al. 2013). The consequences of multicollinearity include inflated error estimates that can alter conclusions about what predictors are significant or not, as well as unstable parameter estimates that can change in sign and magnitude with minor alterations to analyses (Graham 2003; Zuur, Ieno & Elphick 2010). Multicollinearity is a concern for the data set of Venail et al. (2015) because PD and SR are correlated with r = 0·90. We were concerned about drawing inferences from predictors that have little unique information, which is why we performed analyses that all attempted to hold one of the two predictors constant while examining the impact of the other. In contrast, Cadotte performed model selection using the full data set where the SR–PD correlation was r = 0·90. We remain sceptical of this approach because of the difficulties generating reliable estimates for strongly correlated predictors. A second issue with Cadotte's analyses, which we are guilty of for some analyses in our study, is the assumption that the relationship between biodiversity (PD or SR) and community biomass is linear. Most studies included in the Venail et al. data set have shown that the effect of biodiversity on community biomass is positive, but nonlinear and decelerating. For example, Cardinale et al. (2011) summarized the form of diversity–biomass relationships for 433 experimental manipulations of primary producer richness and concluded 'Of the studies that have shown a positive effect of producer diversity on producer biomass, 79% were best fit by some form of a positive but decelerating curve (log, power, or M-M functions, Fig. 5A)'. In contrast, only 13% of studies to date are best fit by linear relationships. We reran Cadotte's analyses after accounting for nonlinear relationships and found that most of his conclusions did not hold. Our modified analyses (provided in accompanying R-code) rerun the same analyses of Cadotte, which account for variation in biomass among studies, but using ln-transformed predictors to also account for positive, decelerating relationships. Cadotte's first set of analyses modelled biomass in experimental plots as linear functions of SR and/or PD with study included as a random effect to account for differences in biomass among sites. These produced an AIC of 10 216 and 10 194 for SR and PD, respectively, and an AIC of 10 196 for a model including both SR and PD as predictors. In contrast, the best model in our modified analyses included both ln-transformed SR and PD with an AIC of 10 184. This represents an improved fit to data compared to Cadotte's analyses, and confirms that failure to account for nonlinear relationships led to inferior models. After confirming that relationships between PD, SR and community biomass are better described by nonlinear models, we reran Cadotte's partial regression analyses which found that PD explains a significant fraction of the residual variation in biomass after accounting for effects of SR (F = 4·09, P = 0·04), but SR did not explain residual variation after accounting for effects of PD (F = 0·09, P = 0·77). Using ln-transformed predictors where the SR–PD correlation was lower (r = 0·70), we found that ln(PD) explained 0·05% of the variation unaccounted for by ln(SR) (F = 3·79, P = 0·052, R2 = 0·005). Yet, ln(SR) explained 1·4% of the residual variation in community biomass unaccounted for by ln(PD) (F = 12, P < 0·01, R2 = 0·014). Cadotte also reran our structural equation model (SEM), but used the full data set where the PD–SR correlation was r = 0·90. He accounted for variation among studies by scaling biomass to have a mean = 0 and SD = 1. Cadotte's SEM (reproduced in Fig. 1a) shows that PD explains a significant fraction of variation in scaled biomass and SD through time. In contrast, SR did not explain variation in either. We reran the same SEM on the full data set, but using ln-transformed predictors to account for nonlinear relationships. The modified SEM was a significantly improved fit over the linear version (compare χ2, P-values and AIC for Fig. 1a,b) and led to conclusions that were consistent with those from our original paper (Venail et al. 2015) where we found SR impacts community biomass, but PD does not. In contrast, PD affects the SD of biomass through time, but SR does not. In his final analysis, Cadotte tried to assess whether the five experiments included in our SEM were a 'biased' representation of the full set of 16 experiments. He chose 1000 random subsets of five experiments and, for each subset, ran two mixed effects models – one modelling biomass as a function of PD and one modelling biomass as a function of SR. He then calculated the difference in AIC for the two models. If ΔAIC was <0 (>0), this indicated PD (SR) was a better predictor of biomass for that random subset. The frequency distribution of ΔAIC values (Fig. 3 of his comment) is reproduced in Fig. 1c. The mean of this distribution was significantly <0, suggesting PD is a better predictor of biomass than SR in most random subsets of five experiments. In addition, the subset of five experiments used for our SEM was different than the overall distribution, suggesting biased selection. But Cadotte's conclusions about the 'representativeness' of the five experiments are overturned when we repeat the same analyses using ln-transformed predictor variables. Indeed, the balance of evidence favoured ln(SR) as the better model (Fig. 1d) with the distribution of ΔAIC values being significantly >0 (mean = +5·64, t = 12·06, P < 0·01). The value of ΔAIC for the subset of five experiments used in our SEM is near the centre of the distribution, indicating it was not a biased subset. So where do we stand in this exchange? Cadotte, Cardinale & Oakley (2008) found that PD was not only a significant predictor of community biomass in grassland biodiversity experiments, it explained ~2% more variation than SR. We (Venail et al. 2015) suggested that synthesis did not control for multicollinearity among predictors. When we (Venail et al. 2015) controlled for multicollinearity (but failed to account for biomass differences among studies), we found PD was not a significant predictor of community biomass or stability, whereas SR was. Cadotte argued in his comment that our new analyses were incorrect because we did not account for variation in biomass among studies, and were biased by our use of data subsets to control for multicollinearity. Cadotte's re-analyses led him to conclude that PD is not only significant, but is again a better predictor of community biomass than SR. We responded by pointing out that multicollinearity continues to be a concern about Cadotte's analyses, and his conclusions do not hold after accounting for nonlinear relationships between biodiversity and ecosystem functioning. Whether using the statistical approaches from our original paper (Venail et al. 2015) or model selection favoured by Cadotte, we are led to two conclusions: (i) either SR or PD can explain most of the variation in community biomass and stability on their own because they share so much information. However, (ii) when we examine their effects after statistically controlling for the other, there is little evidence that PD is a better predictor of ecological function than SR. SR is usually a significant predictor of community biomass and stability after controlling for variation in PD, whereas PD is often (though not always) non-significant after controlling for variation in SR. We would caution against interpreting these results as evidence that PD does not matter for ecosystem functioning. Cadotte is correct that experiments analysed to date have not been explicitly designed to test hypotheses about PD, and therefore, we will need studies that orthogonally manipulate PD and SR to fully resolve their relative importance. On the other hand, given the existing data and analyses, we think it is important that researchers refrain from claiming that phylogenetic diversity is a 'strong' predictor of ecosystem functioning, or a 'better' predictor than plant richness in grasslands. Such claims are not supported at this time. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

transfer, create conditions for the establishment of farmers' behavioral psychological contracts in the process of agricultural land transfers, and guide farmers to establish relationship psychological contracts. The second is to improve the market system, properly cultivate and develop agricultural land transfer intermediaries, reduce transaction costs, and reduce the probability of farmers' psychological contracts being broken. The third is to guide farmers to establish a positive agricultural land transfer psychology based on their resource endowments such as labor force quality and cultural quality, and encourage farmers to make agricultural land transfer decisions such as subcontracting, leasing, reselling, and interchanging.

The aim of the present study was to investigate the mediating role of organizational justice in the relationship between transformational leadership and succession planning in higher education. The research method was descriptive correlational. The statistical population of this study included all administrators (200 people) of Kharazmi University were selected by convenience sampling method. For data collection were used the three Questionnaire of succession planning (Kim, 2006), Bass & Avolio (2004) transformational leadership, and organizational justice (Niehoff, and Moorman1993). Data analysis was done by using structural equation modeling in AMOS And Spss-22 software. The results of structural equation modeling showed that the conceptual model of research with experimental data fits very well and organizational justice plays the role of a complete mediator in the relationship between transformational leadership and succession planning. Accordingly, the variable relationship between transformational leadership and succession planning through organizational justice was confirmed in the form of path analysis. Thus, the variable of organizational justice showed a facilitating role in the relationship between leadership and transformational leadership and succession planning. Also, the leading variable of transformational leadership had a direct and positive effect on the succession planning of administrators. It can be concluded from the results of the study that organizational justice in the university has a facilitating role in the development of succession planning programs. Thus, by adopting a transformational leadership approach at the university, organizational justice will be established at the university, and eventually the process of succession planning will be upgraded and developed.

Background and Aim: School Life Expectancy, enthusiasm for school and academic self-concept can affect the academic progress of students in different ways. Therefore, the aim of the present study was to predict academic progress based on hope for education and enthusiasm for school, with the mediating role of academic self-concept in elementary school. Methods: The research method was correlation and structural equation model. The statistical population of the research was elementary school boys in district 5 of Tehran in the academic year of 1401-1402. Among them, 240 male students were selected by multi-stage cluster sampling method according to Tabakenbek and Fidel (2001) who considered the minimum sample size to be 200 sufficient for modeling structural equations.. Students completed the questionnaires of Kharmai and Kameri's School Life Expectancy (2016), enthusiasm for school, Wang, Wilt and Eccles (2011) and Hisen Chen's School Self-Concept Inventory (2004); And the academic progress was determined based on the students' GPA of the previous semester. Data analysis was done in the form of path analysis with Imus24 software. Results: The results showed that the fit indices of the model are in the appropriate range, which indicates the appropriate fit of the research model to the data. The coefficient of the total path between School Life Expectancy and academic achievement is significant, as well as the coefficient of the total path between enthusiasm for school and academic achievement. School Life Expectancy and academic self-concept explain 25% of the variable of academic achievement, and passion for school and academic self-concept explain 19% of the variance of the variable of academic achievement. Conclusion: In order to improve the academic progress of students, it is possible to strengthen them. School Life Expectancy, enthusiasm for school, and academic self-concept by planning and applying measures and holding educational classes in schoo

In this article, a new class of functional models in which smoothing splines are used to model fixed effects as well as random effects is introduced. The linear mixed effects models are extended to nonparametric mixed effects models by introducing functional random effects, which are modeled as realizations of zero-mean stochastic processes. The fixed functional effects and the random functional effects are modeled in the same functional space, which guarantee the population-average and subject-specific curves have the same smoothness property. These models inherit the flexibility of the linear mixed effects models in handling complex designs and correlation structures, can include continuous covariates as well as dummy factors in both the fixed or random design matrices, and include the nested curves models as special cases. Two estimation procedures are proposed. The first estimation procedure exploits the connection between linear mixed effects models and smoothing splines and can be fitted using existing software. The second procedure is a sequential estimation procedure using Kalman filtering. This algorithm avoids inversion of large dimensional matrices and therefore can be applied to large data sets. A generalized maximum likelihood (GML) ratio test is proposed for inference and model selection. An application to comparison of cortisol profiles is used as an illustration.

Regular Expressions: Mixed Effects Models.

Microclimate variables such as air temperature and relative humidity influence habitat conditions and ecological processes in riparian forests. The increased relative humidity levels within riparian areas are essential for many plant and wildlife species. Information about relative humidity patterns within riparian areas and adjacent uplands are necessary for the prescription of effective buffer widths. Relative humidity monitoring is more expensive than temperature monitoring due to greater sensor costs, and it is primarily conducted for research purposes. To make relative humidity monitoring in riparian areas more cost effective, we explored modeling relative humidity as a function of air temperature and other covariates using linear fixed and linear mixed effects models applied to two case studies. Localizing predictions for stream reaches using a linear mixed effects model or a linear fixed effects model with correction factor improved model predictions, especially when large variability among stream reaches was present. A minimum of three to five relative humidity measurements per stream reach seem sufficient to estimate the random stream reach effect or correction factor for the linear mixed and linear fixed effects models, respectively. Including covariates that describe distance to stream and canopy cover in addition to air temperature improved model performance. Although further model refinement is probably needed to allow detection of small changes in relative humidity associated with changes in stand structure from partial overstory removal, the models developed provide a means towards decreasing the costs of monitoring microclimates of importance to riparian area function.

We estimate the correlation coefficient between two variables with repeated observations on each variable, using linear mixed effects (LME) model. The solution to this problem has been studied by many authors. Bland and Altman (1995) considered the problem in many ad hoc methods. Lam, Webb and O'Donnell (1999) solved the problem by considering different correlation structures on the repeated measures. They assumed that the repeated measures are linked over time but their method needs specialized software. However, they never addressed the question of how to choose the correlation structure on the repeated measures for a particular data set. Hamlett et al. (2003) generalized this model and used Proc Mixed of SAS to solve the problem. Unfortunately, their method also cannot implement the correlation structure on the repeated measures that is present in the data. We also assume that the repeated measures are linked over time and generalize all the previous models, and can account for the correlation structure on the repeated measures that is present in the data. We study how the correlation coefficient between the variables gets affected by incorrect assumption of the correlation structure on the repeated measures itself by using Proc Mixed of SAS, and describe how to select the correlation structure on the repeated measures. We also extend the model by including random intercept and random slope over time for each subject. Our model will also be useful when some of the repeated measures are missing at random.

Structural equation modeling is many dimensions are a statistic is the technique of analysis, which is structural Used to analyze relationships. This technique includes factor analysis and multiple regression analysis and Is a combination of measured variables hidden constructions. Structural equations specify how the set of variables are interrelated based on linear equations, cause and effect (cause models) or paths through statistically (path analysis) sorted networks. Structural Equation Modeling (SEM) is a quantitative research technique that integrates standard methods. SEM is often used for research, rather than to explore or explain an event a research study is designed to verify the design. Structural Equation Modeling (SEM) is standard A quantity that integrates methods Is the research technique. Used show causal relationships between SEM variables. The relationships shown in the SEM refer to the researchers' hypotheses. In general, these relationships cannot be statistically tested for diversion. Structural equation modeling is a small number of 'structures' Defined as a class of methods that represent the mechanisms, variations, and hypotheses of data that are inferred on the basis of parameters. 'Configuration' parameters. Path analysis is a special case of SEM. Most models you as seen in the literature, SEM are higher than path analytics. Between the two types of models the main difference is that all variables in the path analysis are measured without error Considers. SEM uses hidden variables to calculate the measurement error. Structural Equation Modeling (SEM) is a multiple regression Factor analysis and various techniques Integration is an advanced technique ANOVA. It evaluates the causal relationship between more than one dependent variable and several independent variables.

Hexachlorobenzene (HCB) deposition in maternal and fetal tissues of rat and mouse: II. Statistical quantification of HCB in tissues

This study examines the influence of Internal Marketing Mix that includes Top Management Support Mix, Bussines Support Mix and Cross Coordination on employee performance. The sample of this research is about 200 education staff of Sebelas Maret University. This research uses analysis method in the form of Path Analysis with SPSS analysis tool 22. The result of research analysis shows that Top Management Support Mix, Bussines Support Mix influence to the performance of education personnel. While Cross Functional Coordination as a component of Internal Marketing Mix does not affect the performance of educational personnel. This research provides advice to university leaders to improve top management support and support on the service process so that the performance of education personnel. As well as suggestions for future similar research to expand the scope of the research to be not limited only to educational personnel in one university.

An organization was formed not only intended to produce the product. The resulting products of an organization can be a goods and service also can distinguish thoseorganizations with other organizations. While the perpetrators who carry out the work referred to the performance. The notion of performance in General can be said asthe magnitude of the contributions or the results of the work accomplished provided employees towards progress and development or organizational goals or organisation where he worked. This type of research is descriptive research and verifikatif with analysis tools in the form of path analysis (Path analysis). This research was conducted in the public works agency Batang Hari Jambi with a number of employees as many as 95 people as research samples. the results of hypothesis testing simultaneously between the variables (X 1), organization culture, leadership style (X 2), supervision (X 3), with a motivational variable (Y) shows that F calculate the significant level 9,669 0000. from the table above shows that the value of t count variable (Y) motivation is significant level 4,488 0000. because 0.000 H0 is rejected then the 0.05 &lt; Hoaccepted (significant). The results show that partially motivated (Y) effect on performance (Z) on public works Districts Batanghari Jambi.Keywords: organizational culture, leadership style