Biology subjects

Hui, F. K. C.

Publications and source records attributed to Hui, F. K. C..

7 recordsLinked to original sources

bioRxiv2026

Reduced-Rank Generalised Dissimilarity Modelling (RRGDM): Model-Based Ordination of Dissimilarity Data

(1) Dissimilarity indices offer an effective way to represent multivariate variability in community composition and to quantify beta diversity and its drivers. However, models that regress dissimilarities against environmental predictors face the challenge of linking site-level predictors to a pairwise response. (2) This mismatch is typically circumvented by regressing dissimilarities against distances in predictor values. But this approach does not reflect how environmental variables act on individual samples, complicates the inclusion of standard modelling features such as random effects, and conflates environmental effects on average composition with effects on compositional dispersion. (3) We propose Reduced-Rank Generalised Dissimilarity Modelling (RRGDM), a framework for model-based ordination that resolves the pair-to-site mismatch by reframing dissimilarity modelling as an ordination problem. RRGDM represents dissimilarities as the outcome of sample positions in a shared, low-dimensional latent space that captures community composition. These positions are in turn modelled as a linear function of predictors and error terms, with an associated dispersion sub-model. (4) Through a simulation study and two empirical examples, we show that RRGDM recovers known environmental effects and represents them as ecologically interpretable outputs. RRGDM enables inference on the relative importance of environmental gradients driving beta diversity and disentangles effects on composition versus dispersion. Although motivated by beta diversity, the approach is applicable to other pairwise dissimilarity data.

ecology↗

bioRxiv2026

On Motivating the Choice of Link Function in Binomial Regression Through A Latent Abundance Process

Ecologists commonly apply binomial regression to study binary data. Often, the link function in binomial regression is not purposefully chosen, but instead selected on the grounds of interpretation or computation efficiency. However, if the binary response is the result of a latent abundance process, then the link function implies assumptions for this underlying process. The most well-known example of this is the complementary log-log (cloglog) link, which has direct connections with the probability of a positive count under a latent Poisson distribution. If the binary response can be assumed to represent a truncated latent count, the cloglog link can be a better choice than the logit link. On the other hand, counts in ecology are often overdispersed, meaning the assumption of an underlying Poisson abundance process is unrealistic. In this article, we provide a framework for consideration of the link choice in binomial regression, in terms of the latent abundance process that binary responses can represent. Aside from the Poisson case mentioned above, we show that the cloglog link is the right choice if the latent abundance process represents the biomass of individuals, or if the latent abundance process comes from an overdispersed count distribution with a linear mean-variance relationship. For the case where the latent count follows a negative binomial distribution with a quadratic mean-variance relationship, we derive a new generalized complementary log-log (gcloglog) link function, which includes the case of the cloglog link as well as that of the logit link, where the latter implies assuming moderate overdispersion in the form of a geometric distribution. Two simulation studies demonstrate that cloglog and logit link binomial regression can exhibit bias and undercoverage when the latent count is more overdispersed than assumed, while gcloglog link models overcome this issue but in practice require replication in the binomial response for reliable estimation. Two real ecological datasets further illustrate how to use the gcloglog link binomial regression in practice including as a means of sensitivity analysis. We encourage ecologists to give more thought to the choice of link function in binomial regression, based on considering the latent abundance process and its properties.

ecology↗

bioRxiv2021

Model-based ordination with constrained latent variables

O_LIIn community ecology, unconstrained ordination can be used to indirectly explore drivers of community composition, while constrained ordination can be used to directly relate predictors to an ecological community. However, existing constrained ordination methods do not explicitly account for community composition that cannot be explained by the predictors, so that they have the potential to misrepresent community composition if not all predictors are available in the data. C_LIO_LIWe propose and develop a set of new methods for ordination and Joint Species Distribution Modelling (JSDM) as part of the Generalized Linear Latent Variable Model (GLLVM) framework, that incorporate predictors directly into an ordination. This includes a new ordination method that we refer to as concurrent ordination, as it simultaneously constructs unconstrained and constrained latent variables. Both unmeasured residual covariation and predictors are incorporated into the ordination by simultaneously imposing reduced rank structures on the residual covariance matrix and on fixed-effects. C_LIO_LIWe evaluate the method with a simulation study, and show that the proposed developments outperform Canonical Correspondence Analysis (CCA) for Poisson and Bernoulli responses, and perform similar to Redundancy Analysis (RDA) for normally distributed responses, the two most popular methods for constrained ordination in community ecology. Two examples with real data further demonstrate the benefits of concurrent ordination, and the need to account for residual covariation in the analysis of multivariate data. C_LIO_LIThis article contextualizes the role of constrained ordination in the GLLVM and JSDM frameworks, while developing a new ordination method that incorporates the best of unconstrained and constrained ordination, and which overcomes some of the deficiencies of existing classical ordination methods. C_LI

ecology↗

bioRxiv2021

Fast model-based ordination with copulas

O_LIVisualising data is a vital part of analysis, allowing researchers to find patterns, and assess and communicate the results of statistical modeling. In ecology, visualisation is often challenging when there are many variables (often for different species or other taxonomic groups) and they are not normally distributed (often counts or presence-absence data). Ordination is a common and powerful way to overcome this hurdle by reducing data from many response variables to just two or three, to be easily plotted. C_LIO_LIOrdination is traditionally done using dissimilarity-based methods, most commonly non-metric multidimensional scaling (nMDS). In the last decade however, model-based methods for unconstrained ordination have gained popularity. These are primarily based on latent variable models, with latent variables estimating the underlying, unobserved ecological gradients. C_LIO_LIDespite some major benefits, a major drawback of model-based ordination methods is their speed, as they typically taking much longer to return a result than dissimilarity-based methods, especially for large sample sizes. C_LIO_LIWe introduce copula ordination, a new, scalable model-based approach to unconstrained ordination. This method has all the desirable properties of model-based ordination methods, with the added advantage that it is computationally far more efficient. In particular, simulations show copula ordination is an order of magnitude faster than current model-based methods, and can even be faster than nMDS for large sample sizes, while being able to produce similar ordination plots and trends as these methods. C_LI

ecology↗

bioRxiv2020

Model-based ordination for species with unequal niche widths

O_LIIt is common practice for ecologists to examine species niches in the study of community composition. The response curve of a species in the fundamental niche is usually assumed to be quadratic. The center of a quadratic curve represents a species optimal environmental conditions, and the width its ability to tolerate deviations from the optimum. C_LIO_LIMost multivariate methods assume species respond linearly to the environment of the niche, or with a quadratic curve that is of equal width and height for all species. However, it is widely understood that some species are generalists who tolerate deviations from their optimal environment better than others. Rare species often tolerate a smaller range of environments than more common species, corresponding to a narrow niche. C_LIO_LIWe propose a new method, for ordination and fitting Joint Species Distribution Models, based on Generalized Linear Mixed-Effects Models, which relaxes the assumptions of equal tolerances and equal maxima. C_LIO_LIBy explicitly estimating species optima, tolerances, and maxima, per ecological gradient, we can better predict change in species communities, and understand how species relate to each other. C_LI

ecology↗

bioRxiv2020

Model-based ordination of pin-point cover data: effect of management on dry heathland

Recently, there has been an increasing interest in model-based approaches for the statistical modelling of the joint distribution of multi-species abundances. The Dirichlet-multinomial distribution has been proposed as a suitable candidate distribution for the joint species distribution of pin-point plant cover data and is here applied in a model-based ordination framework. Unlike most model-based ordination methods, both fixed and random effects are in our proposed model structured as p-dimensional vectors and added to the latent variables before the inner product with the species-specific coefficients. This changes the interpretation of the parameters, so that the fixed and random effects now measure the relative displacement of the vegetation by the fixed and random factors in the p-dimensional latent variable space. This parameterization allows statistical inference of the effect of fixed and random factors in vector space, and makes it easier for practitioners to perform inferences on species composition in a multivariate setting. The method was applied on plant pin-point cover data from dry heathlands that had received different management treatments (burned, grazed, harvested, unmanaged), and it was found that treatment have a significant effect on heathland vegetation both when considering plant functional groups or when the taxonomic resolution was at the species level.

ecology↗

bioRxiv2017

Uncovering The Drivers Of Animal-Host Microbiotas With Joint Distribution Modeling

In addition to the processes structuring free-living communities, host-associated microbiota are directly or indirectly shaped by the host. Therefore, microbiota data have a hierarchical structure where samples are nested under one or several variables representing host-specific factors, often spanning multiple levels of biological organization. Current statistical methods do not accommodate this hierarchical data structure, and therefore cannot explicitly account for the effect of the host in structuring the microbiota. We introduce a novel extension of joint species distribution models which can straightforwardly accommodate and discern between effects such as host phylogeny and traits, recorded covariates like diet and collection site, among other ecological processes. Our proposed methodology includes powerful yet familiar outputs seen in community ecology overall, including: (i) model-based ordination to visualize and quantify the main patterns in the data; (ii) variance partitioning to asses how influential the included host-specific factors are in structuring the microbiota; and (iii) co-occurrence networks to visualize microbe-to-microbe associations.

microbiology↗