Generalized AIC and chi‐squared statistics for path models consistent with directed acyclic graphs

Intraspecific leaf trait variation mediates edge effects on litter decomposition rate in fragmented forests

There is strong trait dependence in species-level responses to environmental change and their cascading effects on ecosystem functioning. However, there is little understanding of whether intraspecific trait variation (ITV) can also be an important mechanism mediating environmental effects on ecosystem functioning. This is surprising, given that global change processes such as habitat fragmentation and the creation of forest edges drive strong trait shifts within species. On 20 islands in the Thousand Island Lake, China, we quantified intraspecific leaf trait shifts of a widely distributed shrub species, Vaccinium carlesii, in response to habitat fragmentation. Using a reciprocal transplant decomposition experiment between forest edge and interior on 11 islands with varying areas, we disentangled the relative effects of intraspecific leaf trait variation versus altered environmental conditions on leaf decomposition rates in forest fragments. We found strong intraspecific variation in leaf traits in response to edge effects, with a shift toward recalcitrant leaves with low specific leaf area and high leaf dry matter content from forest interior to the edge. Using structural equation modeling, we showed that such intraspecific leaf trait response to habitat fragmentation had translated into significant plant afterlife effects on leaf decomposition, leading to decreased leaf decomposition rates from the forest interior to the edge. Importantly, the effects of intraspecific leaf trait variation were additive to and stronger than the effects from local environmental changes due to edge effects and habitat loss. Our experiment provides the first quantitative study showing that intraspecific leaf trait response to edge effects is an important driver of the decrease in leaf decomposition rate in fragmented forests. By extending the trait-based response-effect framework toward the individual level, intraspecific variation in leaf economics traits can provide the missing functional link between environmental change and ecological processes. These findings suggest an important area for future research on incorporating ITV to understand and predict changes in ecosystem functioning in the context of global change.

Stressor fluctuations alter mechanisms of seagrass community responses relative to static stressors

Ecosystems are increasingly affected by multiple anthropogenic stressors that contribute to habitat degradation and loss. Natural ecosystems are highly dynamic, yet multiple stressor experiments often ignore variability in stressor intensity and do not consider how effects could be mediated across trophic levels, with implications for models that underpin stressor management. Here, we investigated the in situ effects of changes in stressor intensity (i.e., fluctuations) and synchronicity (i.e., timing of fluctuations) on a seagrass community, applying the stressors reduced light and physical disturbance to the sediment. We used structural equation models (SEMs) to identify causal effects of dynamic multiple stressors on seagrass shoot density and leaf surface area, and abundance of associated crustaceans. Responses depended on whether stressor intensities fluctuated or remained static. Relative to static stressor exposure at the end of the experiment, shoot density, leaf surface area, and crustacean abundance all declined under in-phase (synchronous; 17, 33, and 30 % less, respectively) and out-of-phase (asynchronous; 11, 28, and 39 % less, respectively) fluctuating treatments. Static treatment increased seagrass leaf surface area and crustacean abundance relative to the control group. We hypothesised that crustacean responses are mediated by changes in seagrass; however, causal analysis found only weak evidence for a mediation effect via leaf surface area. Changes in crustacean abundance, therefore, were primarily a direct response to stressors. Our results suggest that the mechanisms underpinning stress responses change when stressors fluctuate. For instance, increased leaf surface area under static stress could be caused by seagrass acclimating to low light, whereas no response under fluctuating stressors suggests an acclimation response was not triggered. The SEMs also revealed that community responses to the stressors can be independent of one another. Therefore, models based on static experiments may be representing ecological mechanisms not observed in natural ecosystems, and underestimating the impacts of stressors on ecosystems.

Workplace Spirituality as an Alternative Model for Promoting Commitment to Change and Change-Oriented Organisational Citizenship Behaviour

Considering that the relationship between workplace spirituality and organisational change has only recently sparked scholarly interest, the connection could be more evident, as research has only recently begun. Furthermore, there is a growing tendency to add spirituality as the fourth dimension to sustainability in addition to the environment, social responsibility, and the economy. This study contributes to the academic literature by examining the influence of workplace spirituality on lecturers’ responses to change in their commitment to change and change-oriented organizational citizenship behaviour (C-OCB). Private universities in Banjarmasin, Indonesia, were selected to research these links. To collect data from 1050 instructors, self-administered questionnaires were used, and structural equation modelling (SEM) was performed. The findings demonstrated that the workplace spirituality level, exemplified by a solid connection to a higher power, humanity, and nature, affects the lecturers’ commitment to change leading to change-oriented organizational citizenship behaviour (C-OCB), a dimension of OCB less researched. The findings offer a novel perspective on the relationship between workplace spirituality, lecturers’ attitudes towards change, and change-oriented organizational citizenship behaviour (C-OCB) in higher education. Since this result has not been examined in the context of change in higher education, several consequences and suggestions for future research emerge. More studies could be used in future research, given that the literature in this area is still in its early stages. The current work is expected to spark future research on this intriguing and fertile issue.

Piecewise Structural Equation Modeling of the Quantity Implicature in Child Language

We review an array of experimental methodological factors that either contribute to or detract from the measurement of pragmatic implicatures in child language. We carry out a truth value judgment task to measure children's interpretations of the Spanish existential quantifier algunos in implicature-consistent and implicature-inconsistent contexts. Independently, we take measures of children's inhibition, working memory, attention, approximate number ability, phrasal syntax, and lexicon. We model the interplay of these variables using a piecewise structural equation model (SEM), common in the life sciences, but not in the social and behavioral sciences. By 6 years of age, the children in our sample were not statistically different from adults in their interpretations. Syntax, lexicon, and inhibition significantly predict implicature generation, each accounting for unique variance. The approximate number system and inhibition significantly predict lexical development. The statistical power of the piecewise SEM components, with a sample of 64 children, is high, in comparison to a traditional, globally estimated SEM of the same data.

Human Resources Information System (HRIS) to Enhance Civil Servants’ Innovation Outcomes: Compulsory or Complimentary?

The Internet of things (IoT) has oriented organisations digitally in administrating human resources. In line with this trend, Indonesian public sectors are adopting Human Resources Information Systems (HRIS) to boost employee innovation outcomes. However, coercive pressure to implement HRIS has only resulted in fiascos for the technology, which cannot be fully considered to eliminate long, ineffective, and inefficient practices. This study examines the instrumental adoption factors to adopt HRIS in boosting employee innovation outcomes from technological, organisational, people, and social outlooks. The empirical data consisting of 500 valid datasets were obtained from public servants in Indonesia via web-based questionnaires. structural equation modelling (SEM), which was used to examine the relationship among constructs. Technology fit, organisational resources, knowledge, and social influences have positive impacts on technology adoption. However, when treated as a mediation, the negative path from HRIS to innovation outcomes implied that e-HRM reflected in HRIS implementation would not make employees innovative. The scrutinized under ability, motivation, and opportunity (AMO) framework and empirical insights clearly portray that Indonesian’s ability to fit into this framework is problematical, making technological innovation in the Indonesian public sector only complimentary, not compulsory. This study suggests that HRM reform in public organisations is a top priority if the country wishes to achieve world-class bureaucracy by 2025.

Foundational biodiversity effects propagate through coastal food webs via multiple pathways

Relatively few studies have attempted to resolve the pathways through which the effects of biodiversity on ecosystem functioning cascade from one trophic level to another. Here, we manipulated the richness of habitat-forming seaweeds in a western Atlantic estuary to explore how changes in foundation species diversity affect the structure and functioning of the benthic consumer communities that they support. Structural equation modeling revealed that macroalgal richness enhanced invertebrate abundance, biomass, and diversity, both directly by changing the quality and palatability of the foundational substrate and indirectly by increasing the total biomass of available habitat. Consumer responses were largely driven by a single foundational seaweed, although stronger complementarity among macroalgae was observed for invertebrate richness. These findings with diverse foundational phyla extend earlier inferences from terrestrial grasslands by showing that biodiversity effects can simultaneously propagate through multiple independent pathways to maintain animal foodwebs. Our work also highlights the potential ramifications of human-induced changes in marine ecosystems.

Glioma Grading Prediction Using Multiparametric Magnetic Resonance Imaging-based Radiomics Combined with Proton Magnetic Resonance Spectroscopy and Diffusion Tensor Imaging

PURPOSE

To evaluate the efficacy of three-dimensional (3D) segmentation-based radiomics analysis of multiparametric MRI combined with proton magnetic resonance spectroscopy (¹ H-MRS) and diffusion tensor imaging (DTI) in glioma grading.

METHOD

A total of 100 patients with histologically confirmed gliomas (grade II-IV) were examined using conventional MRI, ¹ H-MRS, and DTI. Tumor segmentations of T1-weighted imaging (T1WI), contrast-enhanced T1WI (T1WI+C), T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) mapping, and fractional anisotropy (FA) mapping were performed. In total, 396 radiomics features were extracted and reduced using basic tests and least absolute shrinkage and selection operator (LASSO) regression. The selected features of each sequence were combined, and logistic regression with ten-fold cross-validation was applied to develop the grading model. Sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were compared. The model developed from the training set was applied to the test set to measure accuracy. One optimal grading quantitative parameter was selected for each ¹ H-MRS and DTI analysis. A radiomics nomogram model including radiomics signature, quantitative parameters, and clinical features was developed.

RESULTS

T1WI+C exhibited the highest grading efficacy among single sequences (AUC, 0.92; sensitivity, 0.89; specificity, 0.85), but the efficacy of the combined model was higher (AUC, 0.97; sensitivity, 0.94; specificity, 0.91). The AUCs of all models exhibited high accuracy, and no significant differences were observed in AUCs between the training and test sets. The visualized nomogram was developed based on the combined radiomics signature and choline (Cho)/N-acetyl aspartate (NAA) from ¹ H-MRS.

CONCLUSION

Multiparametric MRI can be used to predict the pathological grading of HGG and LGG by combining radiomics features with quantitative parameters. The visualized nomogram may provide an intuitive assessment tool in clinical practice.

CLINICAL TRIAL REGISTRATION

This trial was not registered, as it was a retrospective study and was approved by the local institutional review board. This article is protected by copyright. All rights reserved.

Analysis of genetic and environmental correlation between leisure activities and cognitive function in aging Chinese twins

OBJECTIVE

Leisure activity has been shown to be beneficial to mental health and cognitive aging. The biological basis of the correlation is, however, poorly understood. This study aimed at exploring the genetic and environmental impacts on correlation between leisure activities and cognitive function in the Chinese middle- and old-aged twins.

METHODS

Cognition measured using a screening test (Montreal Cognitive Assessment, MoCA) and leisure activities including intellectual and social activity were investigated on 379 complete twin pairs of middle- and old-aged twins. Univariate and bivariate twin models were fitted to estimate the genetic and environmental components in their variance and covariance.

RESULTS

Moderate heritability was estimated for leisure activities and cognition (0.44-0.53) but insignificant for social activity. Common environmental factors accounted for about 0.36 of the total variance to social activity with no significant contribution to leisure activity, intellectual activity and cognition. Unique environmental factors displayed moderate contributions (0.47-0.64) to leisure activities and cognition. Bivariate analysis showed highly and positively genetic correlations between leisure activities and cognition (r_G=0.80-0.96). Besides, intellectual activity and cognition presented low but significant unique environmental correlation (r_E=0.12).

CONCLUSIONS

Genetic factor had the moderate contribution to leisure activities and cognition. Cognitive function was highly genetically related to leisure activities. Intellectual activity and cognitive function may share some unique environmental basis.

Climate affects the outbreaks of a forest defoliator indirectly through its tree hosts

Although spatial variation in climate can directly affect the survival and reproduction of forest insects and the tree species compositions of forests, little is known about the indirect effects of climate on outbreaks of forest insects through its effects on forest composition. In this study, we use structural equation modeling to examine the direct and indirect effects of climate, water capacity of the soil, host tree density, and non-host density on the spatial extent of Lymantria dispar outbreaks in the Eastern USA over a period of 44 years (1975-2018). Host species were subdivided into four taxonomic and ecologically distinct groups: red oaks (Lobatae), white oaks (Lepidobalanus), other preferred hosts, and intermediate (less preferred) hosts. We found that mean annual temperature had stronger effects than mean annual precipitation on the spatial extent of outbreaks, and that indirect effects of temperature (via its effects on oak density) on defoliation were stronger than direct effects. The density of non-host trees increased with increasing precipitation and, consistent with the 'associational resistance hypothesis', defoliation decreased with increasing density of non-host trees. This study offers quantitative evidence that geographic variation in climate can indirectly affect outbreaks of a forest insect through its effects on tree species composition.

How do soil microbial communities respond to fire in the intermediate term? Investigating direct and indirect effects associated with fire occurrence and burn severity

Fires transform soil microbial communities directly via heat-induced mortality and indirectly by altering plant and soil characteristics. Emerging evidence suggests the magnitude of changes to some plant and soil properties increases with burn severity, but the persistence of changes varies among plant and soil characteristics, ranging from months to years post-fire. Thus, which environmental attributes shape microbial communities at intermediate time points during ecosystem recovery, and how these characteristics vary with severity, remains poorly understood. We identified the network of properties that influence microbial communities three years after fire, along a burn severity gradient in Sierra Nevada mixed-conifer forest. We used phospholipid fatty acid (PLFA) analysis and bacterial 16S-rDNA amplicon sequencing to characterize the microbial community in mineral soil. Using structural equation modelling, we applied a systems approach to identifying the interconnected relationships among severity, vegetation, soil, and microbial communities. Dead tree basal area, soil pH, and extractable phosphorus increased with severity, whereas live tree basal area, forest floor mass, and the proportion of the ≥53 μm soil fraction decreased. Forest floor loss was associated with decreased soil moisture across the severity gradient, decreased live tree basal area was associated with increased shrub coverage, and increased dead tree basal area was associated with increases in total and inorganic soil nitrogen. Soil fungal abundance decreased across the severity gradient, despite a slightly positive response of fungi to lower soil moisture in high severity areas. Bacterial phylogenetic diversity was negatively related to severity and was driven by differences in nutrients and soil texture. The abundance of Bacteroidetes increased and the abundance of Acidobacteria decreased across the severity gradient due to differences in soil pH. Overall, we found that the effects of burn severity on vegetation and soil physicochemical characteristics interact to shape microbial communities at an intermediate time point in ecosystem recovery.

Reconstructing regime-dependent causal relationships from observational time series

Inferring causal relations from observational time series data is a key problem across science and engineering whenever experimental interventions are infeasible or unethical. Increasing data availability over the past few decades has spurred the development of a plethora of causal discovery methods, each addressing particular challenges of this difficult task. In this paper, we focus on an important challenge that is at the core of time series causal discovery: regime-dependent causal relations. Often dynamical systems feature transitions depending on some, often persistent, unobserved background regime, and different regimes may exhibit different causal relations. Here, we assume a persistent and discrete regime variable leading to a finite number of regimes within which we may assume stationary causal relations. To detect regime-dependent causal relations, we combine the conditional independence-based PCMCI method [based on a condition-selection step (PC) followed by the momentary conditional independence (MCI) test] with a regime learning optimization approach. PCMCI allows for causal discovery from high-dimensional and highly correlated time series. Our method, Regime-PCMCI, is evaluated on a number of numerical experiments demonstrating that it can distinguish regimes with different causal directions, time lags, and sign of causal links, as well as changes in the variables' autocorrelation. Furthermore, Regime-PCMCI is employed to observations of El Niño Southern Oscillation and Indian rainfall, demonstrating skill also in real-world datasets.

High temperatures drive offspring mortality in a cooperatively breeding bird

An improved understanding of life-history responses to current environmental variability is required to predict species-specific responses to anthopogenic climate change. Previous research has suggested that cooperation in social groups may buffer individuals against some of the negative effects of unpredictable climates. We use a 15-year dataset on a cooperative breeding arid zone bird, the southern pied babbler Turdoides bicolor, to test (i) whether environmental conditions and group size correlate with survival of young during three development stages (egg, nestling, fledgling) and (ii) whether group size mitigates the impacts of adverse environmental conditions on survival of young. Exposure to high mean daily maximum temperatures (mean T_max) during early development was associated with reduced survival probabilities of young in all three development stages. No young survived when mean T_max > 38°C, across all group sizes. Low survival of young at high temperatures has broad implications for recruitment and population persistence in avian communities given the rapid pace of advancing climate change. Impacts of high temperatures on survival of young were not moderated by group size, suggesting that the availability of more helpers in a group is unlikely to buffer against compromised offspring survival as average and maximum temperatures increase with rapid anthropogenic climate change.

A 'Weight of Evidence' approach to evaluating structural equation models

It is possible that model selection has been the most researched and most discussed topic in the history of both statistics and structural equation modeling (SEM). The reason for this is because selecting one model for interpretive use from amongst many possible models is both essential and difficult. The published protocols and advice for model evaluation and selection in SEM studies are complex and difficult to integrate with current approaches used in biology. Opposition to the use of p-values and decision thresholds has been voiced by the statistics community, yet certain phases of model evaluation have been historically tied to reliance on p-values. In this paper, I outline an approach to model evaluation, comparison and selection based on a weight-of-evidence paradigm. The details and proposed sequence of steps are illustrated using a real-world example. At the end of the paper, I briefly discuss the current state of knowledge and a possible direction for future studies.