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36 results for “structural equation model”
Structural equation modeling reveals determinants of fitness in a cooperatively breeding bird
<p>Even in well-studied organisms, it is often challenging to uncover the social and environmental determinants of fitness. Typically, fitness is determined by a variety of factors that act in concert, thus forming complex networks of causal relationships. Moreover, even strong correlations between social and environmental conditions and fitness components may not be indicative of direct causal links, as the measured variables may be driven by unmeasured (or unmeasurable) causal factors. Standard statistical approaches, like multiple regression analyses, are not suited for disentangling such complex causal relationships. Here, we apply structural equation modeling (SEM), a technique that is specifically designed to reveal causal relationships between variables, and which also allows to include hypothetical causal factors. Therefore, SEM seems ideally suited for comparing alternative hypotheses on how fitness differences arise from differences in social and environmental factors. We apply SEM to a rich data set collected in a long-term study on the Seychelles warbler (Acrocephalus seychellensis), a bird species with facultatively cooperative breeding and a high rate of extra-group paternity. Our analysis reveals that the presence of helpers has a positive effect on the reproductive output of both female and male breeders. In contrast, per capita food availability does not affect reproductive output. Our analysis does not confirm earlier suggestions on other species that the presence of helpers has a negative effect on the reproductive output of male breeders. As such, both female and male breeders should tolerate helpers in their territories, irrespective of food availability.</p>
Frontiers in Ecology and Evolution 01 frontiersin.org Why grazing and soil matter for dry grassland diversity: New insights from multigroup structural equation modeling of micro-patterns
<p>Grazing is recognized as a major process driving the composition of plant<br> communities in grasslands, mostly due to the heterogeneous removal of<br> plant species and soil compaction that results in a mosaic of small patches<br> called micro-patterns. To date, no study has investigated the differences in<br> composition and functioning among these micro-patterns in grasslands in<br> relation to grazing and soil environmental variables at the micro-local scale.<br> In this study, we ask (1) To what extent are micro-patterns different from each<br> other in terms of species composition, species richness, vegetation volume,<br> evenness, and functioning? and (2) based on multigroup structural equation<br> modeling, are those differences directly or indirectly driven by grazing and soil<br> characteristics? We focused on three micro-patterns of the Mediterranean dry<br> grassland of the Crau area, a protected area traditionally grazed in the South-<br> East of France. From 70 plant community relevés carried out in three micro-<br> patterns located in four sites with different soil and grazing characteristics,<br> we performed univariate, multivariate analyses and applied structural equation<br> modeling for the first time to this type of data. Our results show evidence<br> of clear differences among micro-pattern patches in terms of species<br> composition, vegetation volume, species richness, evenness, and functioning<br> at the micro-local scale. These differences are maintained not only by direct<br> and indirect effects of grazing but also by several soil variables such as fine<br> granulometry. Biological crusts appeared mostly driven by these soil variables,<br> whereas reference and edge communities are mostly the result of different<br> levels of grazing pressure revealing three distinct functioning specific to each<br> micro-pattern, all of them coexisting at the micro-local scale in the studied<br> Mediterranean dry grassland. This first overview of the multiple effects of<br> grazing and soil characteristics on communities in micro-patterns is discussed<br> within the scope of the conservation of dry grasslands plant diversity.</p>
Structural equation modeling reveals determinants of fitness in a cooperatively breeding bird
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Data from: Characterizing morphological (co)variation using structural equation models: body size, allometric relationships and evolvability in a house sparrow metapopulation
Body size plays a key role in the ecology and evolution of all organisms. Therefore, quantifying the sources of morphological (co)variation, dependent and independent of body size, is of key importance when trying to understand and predict responses to selection. We combine structural equation modeling with quantitative genetics analyses to study morphological (co)variation in a meta-population of house sparrows (Passer domesticus). As expected, we found evidence of a latent variable 'body size', causing genetic and environmental covariation between morphological traits. Estimates of conditional evolvability show that allometric relationships constrain the independent evolution of house sparrow morphology. We also found spatial differences in general body size and its allometric relationships. On islands where birds are more dispersive and mobile, individuals were smaller and had proportionally longer wings for their body size. While in islands where sparrows are more sedentary and nest in dense colonies, individuals were larger and had proportionally longer tarsi for their body size. We corroborated these results using simulations and show that our analyses produce unbiased allometric slope estimates. This study highlights that in the short term allometric relationships may constrain phenotypic evolution, but that in the long term selection pressures can also shape allometric relationships.
Arthropod food webs in the foreland of a retreating glacier: Gut content analysis and structural equation modeling (SEM)
<p>Below- and above-ground arthropod communities were explored at a glacier foreland area in low Arctic Southwest Greenland aiming for a better understanding of the mechanisms behind the arthropod succession driven by increasing temperatures in the context of an Arctic climate change scenario. Arthropods were sampled in 2015 and 2016 along a downslope transect where the microclimate became warmer downhill a chronosequence towards a climax vegetation. The arthropod data sets were analyzed in relation to an environmental data set. Bottom-up controlled population developments were important in the early phase of the vegetation development while top-down prevailed in the later phase of the vegetation development. The shift from bottom-up to top-down cascades between arthropod predators and their potential prey populations was mainly driven by increasing temperatures away from the glacier. Structural equation modeling (SEM) shows bottom-up and top-down controlled food chains as bottom-up control was important for spider and harvestman populations while top-down control was important for ground beetle populations. These mechanisms are closely related to the hunting strategies of the predators as bottom-up mechanisms are connected to a sit-and-wait behavior while top-down mechanisms are related to active-search behavior. The SEM analyzes were supported by DNA metabarcoding as well as by the literature. A consequence of the strong top-down cascades in the later phase of the succession is high rates of intra-guild predation (IGP) among all arthropod predators. Particularly in the guts of the linyphiid spider, <em>Collinsia holmgreni </em>Thorell 1871, trophic linkages to other linyphiid and lycosid spiders were detected. The IGP ratio of <em>C. holmgreni</em> was negatively correlated with the activity density of available ground-living prey. Probably as a consequence of the high IGP among the linyphiid spiders, cold-adapted linyphiid species like <em>C. holmgreni</em> decreased in numbers downhill and became extinct in the warmer climax vegetation, where lycosid spiders dominated. SEM shows that the declining activity densities of the soil fauna, such as collembolans and mites, due to predation, are responsible for the increase in organic matter content in the topsoil.</p>
Data from: Nature versus nurture: Structural equation modeling indicates that parental care does not mitigate consequences of poor environmental conditions in Eastern bluebirds (Sialia sialis)
<p>1. How organisms respond to variation in environmental conditions and whether behavioral responses can mitigate negative consequences on growth, condition and other fitness measures are critical to our ability to conserve populations in changing environments. Offspring development is affected by environmental conditions and parental care behavior. When adverse environmental conditions are present, parents may alter behaviors to mitigate the impacts of poor environmental conditions on offspring.</p> <p>2. We determined if parental behavior (provisioning rates, attentiveness, nest temperature) varied in relation to environmental conditions (e.g., food availability, ectoparasites) and if parental behavior mitigated negative consequences of the environment on their offspring in Eastern bluebirds (<i>Sialia sialis</i>).</p> <p>3. We found that offspring on territories with lower food availability had higher hematocrit, and when bird blow flies (<i>Protocalliphora</i> spp.) were present growth rates were reduced. Parents increased provisioning and nest attendance in response to increased food availability but did not alter behavior in response to parasitism by blow flies. While parents altered behavior in response to resource availability, parents were unable to override the direct effects of negative environmental conditions on offspring growth and hematocrit.</p> <p>4. Our work highlights the importance of the environment on offspring development and suggests that parents may not be able to sufficiently alter behavior to ameliorate challenging environmental conditions.</p>
Data from: Nature versus nurture: Structural equation modeling indicates that parental care does not mitigate consequences of poor environmental conditions in Eastern bluebirds (Sialia sialis)
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Arthropod food webs in the foreland of a retreating Greenland glacier: Integrating molecular gut content analysis with Structural Equation Modelling
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Data from: Characterizing morphological (co)variation using structural equation models: body size, allometric relationships and evolvability in a house sparrow metapopulation
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Supplementary material 1 from: Grace JB (2020) A 'Weight of Evidence' approach to evaluating structural equation models. One Ecosystem 5: e50452. https://doi.org/10.3897/oneeco.5.e50452
This text file contains the R code used to develop the demonstrations included in Grace JB (2020) A 'weight of evidence' approach to evaluating structural equation models. One Ecosystem
Data from: Phylogenetic structural equation modelling reveals no need for an 'origin' of the leaf economics spectrum
The leaf economics spectrum (LES) is a prominent ecophysiological paradigm that describes global variation in leaf physiology across plant ecological strategies using a handful of key traits. Nearly a decade ago, Shipley et al. (2006) used structural equation modelling to explore the causal functional relationships among LES traits that give rise to their strong global covariation. They concluded that an unmeasured trait drives LES covariation, sparking efforts to identify the latent physiological trait underlying the 'origin' of the LES. Here, we use newly developed phylogenetic structural equation modelling approaches to reassess these conclusions using both global LES data as well as data collected across scales in the genus Helianthus. For global LES data, accounting for phylogenetic non-independence indicates that no additional unmeasured traits are required to explain LES covariation. Across datasets in Helianthus, trait relationships are highly variable, indicating that global-scale models may poorly describe LES covariation at non-global scales.
Data from: Identifying drivers of breeding success in a long-distance migrant using structural equation modelling
In migrant animals, conditions encountered at various times and places throughout their annual cycle may affect breeding success. Yet, most studies so far have only investigated the effect of specific parts of the annual cycle, despite the importance to understand how different stages can interact and how these stages compare to intrinsic quality to properly modulate breeding success. Using a structural equation modelling approach, we investigated drivers of breeding success (migration cycle, individual quality, breeding conditions) in hoopoes (Upupa epops), a long-distant migrant. Our causal framework explained 75% of the variation in breeding success. The effect of the migration schedule was negligible, whereas the previous breeding attempt strongly influenced current breeding success. We suggest that the interplay of individual quality and environmental conditions during both previous and current breeding season may be more important drivers of breeding success than migration schedules, even in a long-distance migrant. We conclude that structural equation modeling is a promising tool to investigate causal relationships. Applied to hoopoes, we demonstrated that current breeding success is strongly linked to previous breeding success. Complementary analysis integrating weather and climate conditions during migration and the breeding season may provide a deeper and wider overview of the annual cycle of hoopoes and additional insights into the existence of carry-over effects in breeding success.
Linking socioeconomic inequalities and type 2 diabetes through obesity and lifestyle factors among Mexican adults: a structural equations modeling approach
<p><strong>Objective. </strong>To assess the association between type 2 diabetes (DM2) and socioeconomic inequalities, mediated by the contribution of body mass index (BMI), physical activity (PA), and diet (diet-DII). <strong>Materials and methods</strong>. We conducted a cross-sectional analysis using data of adults participating in the Diabetes Mellitus Survey of Mexico City. Socioeconomic and demographic characteristics as well as height and weight, dietary intake, leisure time activity and the presence of DM2 were measured. We fitted a structural equation model (SEM) with DM2 as the main outcome, and BMI, diet-DII and PA served as mediator variables between socioeconomic inequalities index (SII) and DM2. <strong>Results. </strong>The prevalence of DM2 was 13.6%. From the fitted SEM, each standard deviation increases in the SII was associated with increased scores of DM2 (β=0.174, <em>P</em><0.001). <strong>Conclusion. </strong>The results in the present study show how high scores in the index of SII may influence the presence of DM2.</p>
Examining factors affecting sustainable performance of building projects using structural equation modeling
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The success of alien plants in an arid ecosystem: Structural equation modeling reveals hidden effects of soil resources
<p> data</p>
35 Years of the Technology Acceptance Model: Insights from Meta-analytic Structural Equation Modelling
<p>The data files are scripts for R software.</p>
Replication Package: Anomaly detection via runtime monitoring data for structural equation modeling
<p>This replication package contains the following information:</p> <ul> <li><strong>Data extraction from literature & interviews: </strong><em>Generation Structural & Measurement Model via literature and interviews.xlsx</em> - here you can find the mapping of the extracted phrases to inductively summarise information regarding the structural and measurement models.</li> <li><strong>Dataset</strong> of runtime monitoring data extracted from TrainTicket via EvoMaster: <br> <ul> <li><em>TrainTicket faults classification.xlxs:</em> Describes the datasets and their faults, in which microservice the fault is injected for better explainability of the obtained results</li> <li><em>IndicatorDescriptionbasedonAnomalyDetectionToolsInterviews.xlsx:</em> description and mapping of selected indicators to the defined parameters from <a href="https://arxiv.org/abs/2408.07816" target="_blank" rel="noopener">previous work </a></li> <li>Unfortunately, the size of the datasets generated via EvoMaster and their injected faults are too big to upload here, thus, they will be available here: <a href="https://uibkacat-my.sharepoint.com/:f:/g/personal/monika_steidl_uibk_ac_at/EjLMt8SYWwtJtp2YuSaqavcBKJoCQ3b5H_l_OY0ifbVRCA?e=fatKyD" target="_blank" rel="noopener">Datasets with injected anomalies</a><br> <ul> <li>the error description can be found <a href="https://github.com/FudanSELab/train-ticket/wiki/Fault-Description" target="_blank" rel="noopener">here</a></li> <li>the datasets are named ts-error-<em>indicatorOfError</em>-reset.zip because the databases are getting reset so that no anomalies are introduced with wrong database entries</li> </ul> </li> </ul> </li> <li><strong>Code</strong> for handling and transforming data to extract indicators describing the whole system's and microservices' behavior from the collected runtime monitoring data collected from TrainTicket:<br> <ul> <li><a href="https://github.com/moniSt13/ConTest-Parsing" target="_blank" rel="noopener">link to the Github repository</a></li> </ul> </li> <li><strong>reports</strong> regarding the established PLS-SEM model using previously handled and transformed runtime monitoring data. Please be aware that opening the reports can leas to out of memory due to their size: <ul> <li><em>Assessment of Measurement Model: MeasurementModel_TrainTicket_erorcleaned.zip & MeasurementModel_Bootstrap_ALL_TrainTicket_errorcleaned.zip</em> </li> <li><em>Assessment of Structural Model: StructuralModel_TrainTicket_errorcleaned.zip & StructuralModel_Bootstrap_ALL_TrainTicket_errorcleaned.zip</em></li> </ul> </li> </ul> <p><br><br>---------------------------------------</p> <p><em>Future work </em>not elaborated in the associated paper due to space restrictions:</p> <ul> <li><strong>reports regarding F5 error</strong>: PLS-SEM model results without interpretation and further mediating effects between microservices included: F5_error.zip</li> </ul> <p> </p> <p> </p> <p> </p>
Supplementary material 1 from: Grace JB, Steiner M (2021) A protocol for modelling generalised biological responses using latent variables in structural equation models. One Ecosystem 6: e67320. https://doi.org/10.3897/oneeco.6.e67320
A protocol for modelling generalised biological responses using latent variables in structural equation models
Supplementary material 3 from: Grace JB, Steiner M (2021) A protocol for modelling generalised biological responses using latent variables in structural equation models. One Ecosystem 6: e67320. https://doi.org/10.3897/oneeco.6.e67320
A protocol for modelling generalised biological responses using latent variables in structural equation models
Supplementary material 2 from: Grace JB, Steiner M (2021) A protocol for modelling generalised biological responses using latent variables in structural equation models. One Ecosystem 6: e67320. https://doi.org/10.3897/oneeco.6.e67320
A protocol for modelling generalised biological responses using latent variables in structural equation models
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.