scholarly journals Revealing the functional traits that are linked to hidden environmental factors in community assembly

2020 ◽  
Author(s):  
Valério D. Pillar ◽  
Francesco Maria Sabatini ◽  
Ute Jandt ◽  
Sergio Camiz ◽  
Helge Bruelheide
Keyword(s):  
Environmental Factors ◽  
Functional Traits ◽  
Real World ◽  
Community Assembly ◽  
Type I Error ◽  
Environmental Filtering ◽  
Limiting Factors ◽  
Calcareous Grassland ◽  
Type I ◽  
Leaf Economics Spectrum

AbstractAimTo identify functional traits that best predict community assembly without knowing the driving environmental factors.MethodsWe propose a new method that is based on the correlation r(XY) between two matrices of potential community composition: matrix X is fuzzy-weighted by trait similarities of species, and matrix Y is derived by Beals smoothing using the probabilities of species co-occurrences. Since matrix X is based on one or more traits, r(XY) measures how well the traits used for fuzzy-weighting reflect the observed co-occurrence patterns. We developed an optimization algorithm that identifies those traits that maximize this correlation, together with an appropriate permutational test for significance. Using metacommunity data generated by a stochastic, individual-based, spatially explicit model, we assessed the type I error and the power of our method across different simulation scenarios, varying environmental filtering parameters, number of traits and trait correlation structures. We then applied the method to real-world community and trait data of dry calcareous grassland communities across Germany to identify, out of 49 traits, the combination of traits that maximizes r(XY).ResultsThe method correctly identified the relevant traits involved in the community assembly mechanisms specified in simulations. It had high power and accurate type I error and was robust against confounding aspects related to interactions between environmental factors, strength of limiting factors, and correlation among traits. In the grassland dataset, the method identified five traits that best explained community assembly. These traits reflected the size and the leaf economics spectrum, which are related to succession and resource supply, factors that may not be always measured in real-world situations.ConclusionsOur method successfully identified the relevant traits mediating community assembly driven by environmental factors which may be hidden for not being measured or accessible at the spatial or temporal scale of the study.

scholarly journals Functional traits and community composition: multilevel models outperform community-weighted means

2017 ◽  
Author(s):  
Jesse E D Miller ◽  
Anthony Ives ◽  
Ellen Damschen
Keyword(s):  
Functional Traits ◽  
Simulation Study ◽  
Community Assembly ◽  
Fixed Effects ◽  
Multilevel Models ◽  
Type I Error ◽  
Type I ◽  
Type I Errors ◽  
Weighted Means ◽  
Inflated Type

1. Plant functional traits are increasingly being used to infer mechanisms about community assembly and predict global change impacts. Of the several approaches that are used to analyze trait-environment relationships, one of the most popular is community-weighted means (CWM), in which species trait values are averaged at the site level. Other approaches that do not require averaging are being developed, including multilevel models (MLM, also called generalized linear mixed models). However, relative strengths and weaknesses of these methods have not been extensively compared. 2. We investigated three statistical models for trait-environment associations: CWM, a MLM in which traits were not included as fixed effects (MLM1), and a MLM with traits as fixed effects (MLM2). We analyzed a real plant community dataset to investigate associations between two traits and one environmental variable. We then analyzed permutations of the dataset to investigate sources of type I errors, and performed a simulation study to compare the statistical power of the methods. 3. In the analysis of real data, CWM gave highly significant associations for both traits, while MLM1 and MLM2 did not. Using P-values derived by simulating the data using the fitted MLM2, none of the models gave significant associations, showing that CWM had inflated type I errors (false positives). In the permutation tests, MLM2 performed the best of the three approaches. MLM2 still had inflated type I error rates in some situations, but this could be corrected using bootstrapping. The simulation study showed that MLM2 always had as good or better power than CWM. These simulations also confirmed the causes of type I errors from the permutation study. 4. The MLM that includes main effects of traits (MLM2) is the best method for identifying trait-environmental association in community assembly, with better type I error control and greater power. Analyses that regress CWMs on continuous environmental variables are not reliable because they are likely to produce type I errors.

scholarly journals Revealing the functional traits linked to hidden environmental factors in community assembly

2021 ◽  
Author(s):  
Valério D. Pillar ◽  
Francesco Maria Sabatini ◽  
Ute Jandt ◽  
Sergio Camiz ◽  
Helge Bruelheide
Keyword(s):  
Environmental Factors ◽  
Functional Traits ◽  
Community Assembly

Functional traits and environmental filtering drive community assembly in a species-rich tropical system

Ecology ◽  
2010 ◽  
Vol 91 (2) ◽  
pp. 386-398 ◽  
Author(s):  
Edwin Lebrija-Trejos ◽  
Eduardo A. Pérez-García ◽  
Jorge A. Meave ◽  
Frans Bongers ◽  
Lourens Poorter
Keyword(s):  
Functional Traits ◽  
Community Assembly ◽  
Environmental Filtering ◽  
Tropical System

scholarly journals Extending the CWM approach to intraspecific trait variation: how to deal with overly optimistic standard tests?

2021 ◽  
Author(s):  
David Zelený ◽  
Kenny Helsen ◽  
Yi-Nuo Lee
Keyword(s):  
Error Rate ◽  
Real World ◽  
Type I Error ◽  
Cloud Forest ◽  
Error Rates ◽  
Type I ◽  
Trait Variation ◽  
Site Specific ◽  
Intraspecific Trait Variation ◽  
Type I Error Rate

AbstractCommunity weighted means (CWMs) are widely used to study the relationship between community-level functional traits and environment variation. When relationships between CWM traits and environmental variables are directly assessed using linear regression or ANOVA and tested by standard parametric tests, results are prone to inflated Type I error rates, thus producing overly optimistic results. Previous research has found that this problem can be solved by permutation tests (i.e. the max test). A recent extension of this CWM approach, that allows the inclusion of intraspecific trait variation (ITV) by partitioning information in fixed, site-specific and intraspecific CWMs, has proven popular. However, this raises the question whether the same kind of Type I error rate inflation also exists for site-specific CWM or intraspecific CWM-environment relationships. Using simulated community datasets and a real-world dataset from a subtropical montane cloud forest in Taiwan, we show that site-specific CWM-environment relationships also suffer from Type I error rate inflation, and that the severity of this inflation is negatively related to the relative ITV magnitude. In contrast, for intraspecific CWM-environment relationships, standard parametric tests have the correct Type I error rate, while being somewhat conservative, with reduced statistical power. We introduce an ITV-extended version of the max test for the ITV-extended CWM approach, which can solve the inflation problem for site-specific CWM-environment relationships, and which, without considering ITV, becomes equivalent to the “original” max test used for the CWM approach. On both simulated and real-world data, we show that this new ITV-extended max test works well across the full possible magnitude of ITV. We also provide guidelines and R codes of max test solutions for each CWM type and situation. Finally, we suggest recommendations on how to handle the results of previously published studies using the CWM approach without controlling for Type I error rate inflation.

scholarly journals Understanding Community Assembly Based on Functional Traits, Ontogenetic Stages, Habitat Types and Spatial Scales in a Subtropical Forest

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1055 ◽  
Author(s):  
Yanpeng Li ◽  
Yue Bin ◽  
Han Xu ◽  
Yunlong Ni ◽  
Ruyun Zhang ◽  
...  
Keyword(s):  
Leaf Area ◽  
Functional Traits ◽  
Community Assembly ◽  
Spatial Scales ◽  
Environmental Filtering ◽  
Biotic Interactions ◽  
Habitat Types ◽  
Ontogenetic Stages ◽  
Dispersion Patterns ◽  
Natural Communities

Community assembly in natural communities is commonly explained by stochastic and niche-based processes such as environmental filtering and biotic interactions. Many studies have inferred the importance of these processes using a trait-based approach, however, there are still unknowns around what factors affect the importance of different assembly processes in natural communities. In this study, the trait dispersion patterns of 134 species were examined across different functional traits, habitat types, ontogenetic stages and spatial scales from a 20-ha Dinghushan Forest Dynamic Plot in China. The results showed that (1) functional traits related to productivity such as specific leaf area and leaf area mainly showed functional clustering, indicating these two functional traits were more affected by environmental filtering. However, trait dispersion patterns depended on more than the ecological significances of functional traits. For example, trait dispersions of leaf dry matter content, leaf thickness and maximum height did not show consistent patterns across habitat types and ontogenetic stages, suggesting more complex mechanisms may operate on these traits; (2) the trait dispersion varied with the habitat types and ontogenetic stages. Specifically, we found that habitat types only affected the strength of trait dispersions for all the five traits, but ontogenetic stages influenced both the strength and direction of trait dispersions, which depended on the traits selected; (3) the relative importance of soil, topography and space to trait dispersion varied with ontogenetic stages. Topography and space were more important for trait dispersion of saplings but soil was more important for trait dispersion of adults; (4) biotic interactions dominated community assembly at smaller spatial scales but environmental filtering dominated community assembly at larger spatial scales. Overall, the results highlight the importance of functional traits, habitat types, ontogenetic stages and spatial scales to community assembly in natural communities.

Power prior for borrowing the real-world data in bioequivalence test with a parallel design

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Lei Huang ◽  
Liwen Su ◽  
Yuling Zheng ◽  
Yuanyuan Chen ◽  
Fangrong Yan
Keyword(s):  
Sample Size ◽  
Real World ◽  
Type I Error ◽  
Bioequivalence Study ◽  
Type I ◽  
Real World Data ◽  
Reference Drug ◽  
World Data ◽  
Power Prior ◽  
Trial Efficiency

Abstract Recently, real-world study has attracted wide attention for drug development. In bioequivalence study, the reference drug often has been marketed for many years and accumulated abundant real-world data. It is therefore appealing to incorporate these data in the design to improve trial efficiency. In this paper, we propose a Bayesian method to include real-world data of the reference drug in a current bioequivalence trial, with the aim to increase the power of analysis and reduce sample size for long half-life drugs. We adopt the power prior method for incorporating real-world data and use the average bioequivalence posterior probability to evaluate the bioequivalence between the test drug and the reference drug. Simulations were conducted to investigate the performance of the proposed method in different scenarios. The simulation results show that the proposed design has higher power than the traditional design without borrowing real-world data, while controlling the type I error. Moreover, the proposed method saves sample size and reduces costs for the trial.

Inferring community assembly processes from trait diversity across environmental gradients

2016 ◽  
Vol 32 (4) ◽  
pp. 290-299 ◽  
Author(s):  
Yong Shen ◽  
Shi-Xiao Yu ◽  
Ju-Yu Lian ◽  
Hao Shen ◽  
Hong-Lin Cao ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Soil Fertility ◽  
Functional Diversity ◽  
Community Assembly ◽  
Environmental Gradients ◽  
Environmental Filtering ◽  
Competitive Interactions ◽  
Dry Matter Content ◽  
Ecological Processes ◽  
Trait Diversity

Abstract:Environmental filtering and competitive interactions are important ecological processes in community assembly. The contribution of the two processes to community assembly can be evaluated by shifts in functional diversity patterns. We examined the correlations between functional diversity of six traits (leaf chlorophyll concentration, dry matter content, size, specific leaf area, thickness and wood density) and environmental gradients (topography and soil) for 92 species in the 20-ha Dinghushan forest plot in China. A partial Mantel test showed that most of the community-weighted mean trait values changed with terrain convexity and soil fertility, which implied that environmental filtering was occurring. Functional diversity of many traits significantly increased with increasing terrain convexity and soil fertility, which was associated with increased light and below-ground resources respectively. These results suggest that co-occurring species are functionally convergent in regions of strong abiotic stress under the environmental filtering, but functionally divergent in more benign environments due to resource partitioning and competitive interactions. Single-trait diversity and multivariate functional diversity had different relationships with environmental factors, indicating that traits were related to different niche axes, and associated with different ecological processes, which demonstrated the importance of focusing niche axes in traits selection. Between 9% and 41% of variation in functional diversity of different traits was explained by environmental factors in stepwise multiple regression models. Terrain convexity and soil fertility were the best predictors of functional diversity, which contributed 30.5% and 29.0% of total R2to the model. These provided essential evidence that different environmental factors had distinguishing impacts on regulating diversity of traits.

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

One Size Fits All?

Methodology ◽  
2012 ◽  
Vol 8 (1) ◽  
pp. 23-38 ◽  
Author(s):  
Manuel C. Voelkle ◽  
Patrick E. McKnight
Keyword(s):  
Repeated Measures ◽  
Structural Equation ◽  
Type I Error ◽  
Equation Modeling ◽  
Type I ◽  
Finite Sample ◽  
Traditional Methods ◽  
Repeated Measures Anova ◽  
Special Cases ◽  
Latent Curve

The use of latent curve models (LCMs) has increased almost exponentially during the last decade. Oftentimes, researchers regard LCM as a “new” method to analyze change with little attention paid to the fact that the technique was originally introduced as an “alternative to standard repeated measures ANOVA and first-order auto-regressive methods” (Meredith & Tisak, 1990, p. 107). In the first part of the paper, this close relationship is reviewed, and it is demonstrated how “traditional” methods, such as the repeated measures ANOVA, and MANOVA, can be formulated as LCMs. Given that latent curve modeling is essentially a large-sample technique, compared to “traditional” finite-sample approaches, the second part of the paper addresses the question to what degree the more flexible LCMs can actually replace some of the older tests by means of a Monte-Carlo simulation. In addition, a structural equation modeling alternative to Mauchly’s (1940) test of sphericity is explored. Although “traditional” methods may be expressed as special cases of more general LCMs, we found the equivalence holds only asymptotically. For practical purposes, however, no approach always outperformed the other alternatives in terms of power and type I error, so the best method to be used depends on the situation. We provide detailed recommendations of when to use which method.

Assessing Person Fit With the Information Matrix Test

Methodology ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Jochen Ranger ◽  
Jörg-Tobias Kuhn
Keyword(s):  
Simulation Study ◽  
Type I Error ◽  
Information Matrix ◽  
Small Samples ◽  
Type I ◽  
Person Fit ◽  
Power Of The Test ◽  
Order Expansion ◽  
Trait Stability ◽  
Information Matrix Test

In this manuscript, a new approach to the analysis of person fit is presented that is based on the information matrix test of White (1982) . This test can be interpreted as a test of trait stability during the measurement situation. The test follows approximately a χ2-distribution. In small samples, the approximation can be improved by a higher-order expansion. The performance of the test is explored in a simulation study. This simulation study suggests that the test adheres to the nominal Type-I error rate well, although it tends to be conservative in very short scales. The power of the test is compared to the power of four alternative tests of person fit. This comparison corroborates that the power of the information matrix test is similar to the power of the alternative tests. Advantages and areas of application of the information matrix test are discussed.

Sign in / Sign up

Export Citation Format

Share Document