Selecting traits that explain species–environment relationships: a generalized linear mixed model approach

Jamil, Tahira; Ozinga, Wim A.; Kleyer, Michael; Braak, Cajo J. F. ter

doi:10.1111/j.1654-1103.2012.12036.x

Cited by 158 publications

(241 citation statements)

References 66 publications

Supporting

Mentioning

239

Contrasting

Order By: Relevance

“…Bernhardt-Römermann et al, 2008;Dray et al, 2014). Using the same type of data, Jamil et al (2013) developed a generalized linear mixed model (GLMM) approach to identify more directly the links between traits, environmental variables, and abundances.…”

Section: (C) Trait Selection: Future Directionsmentioning

confidence: 99%

Revisiting the Holy Grail: using plant functional traits to understand ecological processes

et al. 2016

View full text Add to dashboard Cite

One of ecology's grand challenges is developing general rules to explain and predict highly complex systems. Understanding and predicting ecological processes from species' traits has been considered a 'Holy Grail' in ecology. Plant functional traits are increasingly being used to develop mechanistic models that can predict how ecological communities will respond to abiotic and biotic perturbations and how species will affect ecosystem function and services in a rapidly changing world; however, significant challenges remain. In this review, we highlight recent work and outstanding questions in three areas: (i) selecting relevant traits; (ii) describing intraspecific trait variation and incorporating this variation into models; and (iii) scaling trait data to community- and ecosystem-level processes. Over the past decade, there have been significant advances in the characterization of plant strategies based on traits and trait relationships, and the integration of traits into multivariate indices and models of community and ecosystem function. However, the utility of trait-based approaches in ecology will benefit from efforts that demonstrate how these traits and indices influence organismal, community, and ecosystem processes across vegetation types, which may be achieved through meta-analysis and enhancement of trait databases. Additionally, intraspecific trait variation and species interactions need to be incorporated into predictive models using tools such as Bayesian hierarchical modelling. Finally, existing models linking traits to community and ecosystem processes need to be empirically tested for their applicability to be realized.

show abstract

Section: (C) Trait Selection: Future Directionsmentioning

confidence: 99%

Revisiting the Holy Grail: using plant functional traits to understand ecological processes

et al. 2016

View full text Add to dashboard Cite

show abstract

“…More recently, regression-based methods have been proposed for studying trait-environment relations (Brown et al, 2014; Cormont et al, 2011; Jamil et al, 2013; Pollock, Morris & Vesk, 2012; Warton et al, 2015; Warton, Shipley & Hastie, 2015). These methods model the abundance (or presence–absence) of multiple species across sites (communities) as a function (linear or non-linear) of species traits and environmental variables.…”

Section: Introductionmentioning

confidence: 99%

“…For example, traits might be used to predict the distribution of species in an ‘average’ environment; by adding the environmental effects and their interaction with the traits, the distribution of a given species in a specific environment may be predicted. Also, by setting main effects to be polynomial terms of quantitative trait and environment variables (or, simply, to factors for species and site) the model includes the simplest model for ecological niches, which shows Gaussian species response to the environmental variable and has equal niche breadths (Jamil et al, 2013; Jamil & ter Braak, 2013; Ter Braak & Looman, 1986). If the environmental optima of species in this model are related to their traits, the trait-environment relationships are exactly represented by the interaction terms, all being a product of a given environmental variable and a given trait.…”

Section: Introductionmentioning

confidence: 99%

A critical issue in model-based inference for studying trait-based community assembly and a solution

Braak

Peres‐Neto

Dray

2017

PeerJ

Self Cite

View full text Add to dashboard Cite

Statistical testing of trait-environment association from data is a challenge as there is no common unit of observation: the trait is observed on species, the environment on sites and the mediating abundance on species-site combinations. A number of correlation-based methods, such as the community weighted trait means method (CWM), the fourth-corner correlation method and the multivariate method RLQ, have been proposed to estimate such trait-environment associations. In these methods, valid statistical testing proceeds by performing two separate resampling tests, one site-based and the other species-based and by assessing significance by the largest of the two p-values (the pmax test). Recently, regression-based methods using generalized linear models (GLM) have been proposed as a promising alternative with statistical inference via site-based resampling. We investigated the performance of this new approach along with approaches that mimicked the pmax test using GLM instead of fourth-corner. By simulation using models with additional random variation in the species response to the environment, the site-based resampling tests using GLM are shown to have severely inflated type I error, of up to 90%, when the nominal level is set as 5%. In addition, predictive modelling of such data using site-based cross-validation very often identified trait-environment interactions that had no predictive value. The problem that we identify is not an “omitted variable bias” problem as it occurs even when the additional random variation is independent of the observed trait and environment data. Instead, it is a problem of ignoring a random effect. In the same simulations, the GLM-based pmax test controlled the type I error in all models proposed so far in this context, but still gave slightly inflated error in more complex models that included both missing (but important) traits and missing (but important) environmental variables. For screening the importance of single trait-environment combinations, the fourth-corner test is shown to give almost the same results as the GLM-based tests in far less computing time.

show abstract

“…Community ecologists have sought to insert traits into models to explain the distribution of species along environmental gradients (Bernhardt-R€ omermann et al 2008;Dray & Legendre 2008;Shipley 2010;Kleyer et al 2012;Pollock, Morris & Vesk 2012;Jamil et al 2013), to predict shifts in species distributions in a changing environment Frenette-Dussault et al 2013) and to test the theories of environmental filtering and limiting similarity (Kraft, Valencia & Ackerly 2008).…”

Section: Introductionmentioning

confidence: 99%

The intrinsic dimensionality of plant traits and its relevance to community assembly

2013

View full text Add to dashboard Cite

Summary1. Plants are multifaceted organisms that have evolved numerous solutions to the problem of establishing, growing and reproducing with limited resources. The intrinsic dimensionality of plant traits is the minimum number of independent axes of variation that adequately describes the functional variation among plants and is therefore a fundamental quantity in comparative plant ecology. Given the large number of functional traits that are measured on plants, the dimensionality of plant form and function is potentially vast. 2. A variety of linear and nonlinear methods were used to estimate the intrinsic dimensionality of three large trait data sets. The results of these analyses indicate that while the dimensionality of plant traits is generally larger than we have admitted in the past, it does not exceed six in the most comprehensive data set. 3. The dimensionality of plant form and function is a blessing, not a curse. The higher the intrinsic dimension of traits in an analysis, the more easily our models will be able to accurately discriminate species in trait space and therefore be able to predict species distributions and abundances. Recent analyses indicate that the ability to predict community composition increases rapidly with additional traits, but reaches a plateau after four to eight traits. 4. Synthesis. There appears to be a tractable upper limit to the dimensionality of plant traits. To optimize research efficiency for advancing our understanding of trait-based community assembly, ecologists should minimize the number of traits while maximizing the number of dimensions, because including multiple correlated traits does not yield dividends and including more than eight traits leads to diminishing returns. It is recommended to measure traits from multiple organs whenever possible, especially leaf, stem, root and flowering traits, given their consistent performance in explaining community assembly across different ecosystems.

show abstract

Selecting traits that explain species–environment relationships: a generalized linear mixed model approach

Cited by 158 publications

References 66 publications

Revisiting the Holy Grail: using plant functional traits to understand ecological processes

Revisiting the Holy Grail: using plant functional traits to understand ecological processes

A critical issue in model-based inference for studying trait-based community assembly and a solution

The intrinsic dimensionality of plant traits and its relevance to community assembly

Contact Info

Product

Resources

About