Stand Structure and Abiotic Factors Modulate Karst Forest Biomass in Southwest China

Understanding the driving factors of forest biomass are critical for further understanding the forest carbon cycle and carbon storage management in karst forests. This study aimed to investigate the distribution of forest aboveground biomass (AGB) and the effects of stand structural and abiotic factors on AGB in karst forests in Southwest China. We established a 25 ha plot and sampled all trees (≥1 cm diameter) in a subtropical mixed evergreen–deciduous broadleaf forest. We mapped the forest biomass distribution and applied a variation of partitioning analysis to examine the topographic, stand structural, and spatial factors. Furthermore, we used structural equation models (SEM) to test how these variables directly and/or indirectly affect AGB. The average AGB of the 25 ha plot was 73.92 Mg/ha, but that varied from 3.22 to 198.11 Mg/ha in the 20 m × 20 m quadrats. Topographic, stand structural, and spatial factors together explained 67.7% of the variation in AGB distribution. The structural variables (including tree density and the diameter at breast height (DBH) diversity) and topographic factors (including elevation, VDCN (vertical distance to channel network), convexity, and slope) were the most crucial driving factors of AGB in the karst forests. Structural equation models indicated that elevation, tree density, and DBH diversity directly affected AGB, and elevation also indirectly affected AGB through tree density and DBH diversity. Meanwhile, AGB was indirectly influenced by VDCN, convexity, and slope. The evaluation of stand structural and abiotic drivers of AGB provides better insights into the mechanisms that play a role in carbon storage in karst forests, which may assist in improving forest carbon management.

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Effect analysis of the driving factors of super-gentrification using structural equation modeling

PLoS ONE ◽

10.1371/journal.pone.0248265 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0248265

Author(s):

Jiangang Shi ◽

Kaifeng Duan ◽

Quanwei Xu ◽

Jiajia Li

Keyword(s):

Social Factors ◽

Structural Equation ◽

Driving Factors ◽

Sustainable Urban Development ◽

Practical Significance ◽

Equation Modeling ◽

Path Coefficient ◽

Economic Factors ◽

Political Factors ◽

Spatial Factors

The study of super-gentrification has important practical significance for maintaining social fairness, spatial justice and achieving sustainable urban development. In this article, 23 driving factors influencing super-gentrification are identified by literature research and Delphi method. Then, the 23 driving factors affecting super-gentrification are divided into four dimensions: political, economic, social and spatial dimension. On this basis, hypotheses are proposed and a structural equation model is established. Then, SPSS 25.0 and AMOS 24.0 software are used to test the reliability and validity of the questionnaire data, and the model results are fitted and modified. Finally, the optimization model and path coefficient of super-gentrification driving factors are calculated. The results of the study show that political factors, economic factors, social factors, and spatial factors, all play a positive role in the development of super-gentrification. Social factors are the fundamental factors to promote super-gentrification, political factors, economic factors, and spatial factors also play a key role in the super-gentrification process.

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Spatiotemporal Distribution and Driving Factors of Forest Biomass Carbon Storage in China: 1977–2013

Forests ◽

10.3390/f8070263 ◽

2017 ◽

Vol 8 (7) ◽

pp. 263 ◽

Cited By ~ 5

Author(s):

Jiameng Yang ◽

Xiaoxia Ji ◽

David Deane ◽

Linyu Wu ◽

Shulin Chen

Keyword(s):

Carbon Storage ◽

Forest Biomass ◽

Driving Factors ◽

Spatiotemporal Distribution ◽

Biomass Carbon

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Biogeographic, climatic and spatial drivers differentially affect α -, β - and γ -diversities on oceanic archipelagos

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2013.3246 ◽

2014 ◽

Vol 281 (1784) ◽

pp. 20133246 ◽

Cited By ~ 40

Author(s):

Juliano Sarmento Cabral ◽

Patrick Weigelt ◽

W. Daniel Kissling ◽

Holger Kreft

Keyword(s):

Vascular Plants ◽

Structural Equation ◽

Structural Equation Models ◽

Spatial Arrangement ◽

Relative Importance ◽

Methodological Framework ◽

Spatial Factors ◽

Units Of Analysis ◽

Precipitation Seasonality ◽

Oceanic Archipelagos

Island biogeographic studies traditionally treat single islands as units of analysis. This ignores the fact that most islands are spatially nested within archipelagos. Here, we took a fundamentally different approach and focused on entire archipelagos using species richness of vascular plants on 23 archipelagos worldwide and their 174 constituent islands. We assessed differential effects of biogeographic factors (area, isolation, age, elevation), current and past climate (temperature, precipitation, seasonality, climate change velocity) and intra-archipelagic spatial structure (archipelago area, number of islands, area range, connectivity, environmental volume, inter-island distance) on plant diversity. Species diversity of each archipelago ( γ ) was additively partitioned into α , β , nestedness and replacement β -components to investigate the relative importance of environmental and spatial drivers. Multiple regressions revealed strong effects of biogeography and climate on α and γ , whereas spatial factors, particularly number of islands, inter-island distance and area range, were key to explain β . Structural equation models additionally suggested that γ is predominantly determined by indirect abiotic effects via its components, particularly β . This highlights that β and the spatial arrangement of islands are essential to understand insular ecology and evolution. Our methodological framework can be applied more widely to other taxa and archipelago-like systems, allowing new insights into biodiversity origin and maintenance.

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Measuring the Big Five in Self-Report and Other Ratings

European Journal of Psychological Assessment ◽

10.1027//1015-5759.16.1.31 ◽

2000 ◽

Vol 16 (1) ◽

pp. 31-43 ◽

Cited By ~ 24

Author(s):

Claudio Barbaranelli ◽

Gian Vittorio Caprara

Keyword(s):

Big Five ◽

Structural Equation ◽

Structural Equation Models ◽

Error Variance ◽

The Self ◽

Self Report ◽

Sources Of Information ◽

Third Person ◽

Fit Indexes ◽

Response Modes

Summary: The aim of the study is to assess the construct validity of two different measures of the Big Five, matching two “response modes” (phrase-questionnaire and list of adjectives) and two sources of information or raters (self-report and other ratings). Two-hundred subjects, equally divided in males and females, were administered the self-report versions of the Big Five Questionnaire (BFQ) and the Big Five Observer (BFO), a list of bipolar pairs of adjectives ( Caprara, Barbaranelli, & Borgogni, 1993 , 1994 ). Every subject was rated by six acquaintances, then aggregated by means of the same instruments used for the self-report, but worded in a third-person format. The multitrait-multimethod matrix derived from these measures was then analyzed via Structural Equation Models according to the criteria proposed by Widaman (1985) , Marsh (1989) , and Bagozzi (1994) . In particular, four different models were compared. While the global fit indexes of the models were only moderate, convergent and discriminant validities were clearly supported, and method and error variance were moderate or low.

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Collective Efficacy and Organizational Commitment in an Italian City Hall

European Psychologist ◽

10.1027/1016-9040.14.4.363 ◽

2009 ◽

Vol 14 (4) ◽

pp. 363-371 ◽

Cited By ~ 14

Author(s):

Laura Borgogni ◽

Silvia Dello Russo ◽

Laura Petitta ◽

Gary P. Latham

Keyword(s):

Organizational Commitment ◽

Collective Efficacy ◽

Structural Equation ◽

Structural Equation Models ◽

Practical Significance ◽

City Hall ◽

Italian City ◽

Public Sector Managers ◽

The Individual ◽

Level Group

Employees (N = 170) of a City Hall in Italy were administered a questionnaire measuring collective efficacy (CE), perceptions of context (PoC), and organizational commitment (OC). Two facets of collective efficacy were identified, namely group and organizational. Structural equation models revealed that perceptions of top management display a stronger relationship with organizational collective efficacy, whereas employees’ perceptions of their colleagues and their direct superior are related to collective efficacy at the group level. Group collective efficacy had a stronger relationship with affective organizational commitment than did organizational collective efficacy. The theoretical significance of this study is in showing that CE is two-dimensional rather than unidimensional. The practical significance of this finding is that the PoC model provides a framework that public sector managers can use to increase the efficacy of the organization as a whole as well as the individual groups that compose it.

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A Cautionary Note on Incremental Fit Indices Reported by LISREL

Methodology ◽

10.1027/1614-1881.1.2.81 ◽

2005 ◽

Vol 1 (2) ◽

pp. 81-85 ◽

Cited By ~ 5

Author(s):

Stefan C. Schmukle ◽

Jochen Hardt

Keyword(s):

Factor Analysis ◽

Maximum Likelihood ◽

Structural Equation ◽

Structural Equation Models ◽

Null Model ◽

Likelihood Estimation ◽

Parameter Estimates ◽

Fit Indices ◽

Cautionary Note ◽

Confirmatory Factor

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.

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Forced Zero Cross-Loading Misspecifications in Measurement Component of Structural Equation Models

Methodology ◽

10.1027/1614-2241/a000084 ◽

2014 ◽

Vol 10 (4) ◽

pp. 138-152 ◽

Cited By ~ 5

Author(s):

Hsien-Yuan Hsu ◽

Susan Troncoso Skidmore ◽

Yan Li ◽

Bruce Thompson

Keyword(s):

Structural Equation ◽

Structural Model ◽

Structural Equation Models ◽

Equation Model ◽

Model Fit ◽

Model Specification ◽

Simulation Design ◽

Measurement Models ◽

Fit Statistics ◽

Path Coefficients

The purpose of the present paper was to evaluate the effect of constraining near-zero parameter cross-loadings to zero in the measurement component of a structural equation model. A Monte Carlo 3 × 5 × 2 simulation design was conducted (i.e., sample sizes of 200, 600, and 1,000; parameter cross-loadings of 0.07, 0.10, 0.13, 0.16, and 0.19 misspecified to be zero; and parameter path coefficients in the structural model of either 0.50 or 0.70). Results indicated that factor pattern coefficients and factor covariances were overestimated in measurement models when near-zero parameter cross-loadings constrained to zero were higher than 0.13 in the population. Moreover, the path coefficients between factors were misestimated when the near-zero parameter cross-loadings constrained to zero were noteworthy. Our results add to the literature detailing the importance of testing individual model specification decisions, and not simply evaluating omnibus model fit statistics.

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