Sara Taskinen

Key message: We evaluated four existing Monte Carlo-based methods for approximating prediction error variances and reliabilities in a multiple-trait genomic prediction model. All four methods yielded consistent approximations of the exact values. Genomic prediction models, such as genomic best linear unbiased prediction (GBLUP), use genomic data to improve the accuracy of estimated genetic values. As the number of genotypes and traits increases, the exact calculation of prediction error variances (PEVs) and reliabilities becomes computationally infeasible due to the need to invert the coefficient matrix of the mixed model equations, whose dimension increases directly with the number of individuals and traits. The objective of this study was to evaluate the applicability of the Monte Carlo (MC) sampling method to approximate PEVs and reliabilities in a multiple-trait GBLUP framework relevant to hybrid breeding. The MC method avoids direct matrix inversion by repeatedly sampling genetic values from their assumed distributions to approximate PEVs. We applied the MC method using four previously published formulas to approximate PEVs and reliabilities. All formulas produced consistent estimates of PEVs and reliabilities, with convergence rates depending on the formula, the level of reliability, and the MC sample size.

In this discussion, we comment on the paper by Leyder et al., which introduces a new framework for robust independent component analysis based on robust distance correlation. Our discussion focuses on the role of whitening, the choice of scatter matrices, and the implications of the independence property in robust ICA. We further argue that the minimum distance index is preferable to the Amari index as a performance measure, as it admits a direct connection to the limiting distribution of ICA estimators. Overall, the paper by Leyder et al. constitutes an important contribution to robust blind source separation and opens several promising avenues for future research.

Introduction: Smoking impairs the outcome of surgery for many reasons. This study aimed to determine the effect of smoking, smoking cessation intervention, and other factors on the incidence of postoperative complications after elective orthopedic and hand surgery directed at bone or cartilage...

Blind source separation (BSS), particularly independent component analysis (ICA), has been widely used in various fields of science such as biomedical signal processing to recover latent source signals from the observed mixture. While ICA is typically applied to individual datasets, many real-world applications share underlying sources across datasets. Independent vector analysis (IVA) extends ICA to jointly analyze multiple datasets by exploiting statistical dependencies across them. While various IVA methods have been presented in signal processing literature, the statistical properties of methods remains largely unexplored. This article introduces the IVA model, numerous density models used in IVA, and various classical IVA methods to statistics community highlighting the need for further theoretical developments.

Model-based ordination of ecological community data has gained recently significant popularity among practitioners, largely due to increased availability and utilization of computational resources. Specifically, generalized linear latent variable models (GLLVMs)–a factor-analytic and rank-reduced form of mixed effect models–have proven to be both accurate and computationally efficient. GLLVMs have been implemented for a wide range of response types common to ecological community data; presence-absence, biomass, overdispersed and/or zero-inflated counts serving as examples. In this paper, we demonstrate how GLLVMs can be applied in the analysis of high-dimensional compositional count data. These methods are useful for example in the analysis of microbiome data, which are typically collected using modern lab-based sampling tools and are inherently compositional due to the finite capacity of sequencing instruments. We use simulation studies to compare the ordination methods based on GLLVMs with algorithmic compositional data analysis methods that rely on log-transformations. Also recently developed fast model-based ordination methods that utilize Gaussian copula models are included in our comparisons. The methods are illustrated with a microbiome data example.

Ecosystem restoration will increase following the ambitious international targets, which calls for a rigorous evaluation of restoration effectiveness. Here, we present results from a long-term before-after control-impact experiment on the restoration of forestry-drained boreal peatland ecosystems. Our data comprise 151 sites, representing six ecosystem types. Species-level vegetation sampling has been conducted before, two, five, and ten years after restoration. With joint species distribution modelling, we show that, on average, not restoring leads to further degradation, but restoration stops and reverses this trend. The variation in restoration outcomes largely arises from ecosystem types: restoration of nutrient-poor ecosystems has a higher probability of failure. Yet, the ten-year study period is insufficient to capture the restoration effects in slow-recovering ecosystems. Altogether, restoration can effectively halt the biodiversity loss of degraded ecosystems, although ecosystem attributes affect the outcome. This variability in outcomes underlies the need for evidence-based prioritization of restoration efforts across ecosystems.

Generalized linear latent variable models (GLLVMs) have become mainstream models in this analysis of correlated, m‐dimensional data. GLLVMs can be seen as a reduced‐rank version of generalized linear mixed models (GLMMs) as the latent variables which are of dimension induce a reduced‐rank covariance structure for the model. Models are flexible and can be used for various purposes, including exploratory analysis, that is, ordination analysis, estimating patterns of residual correlation, multivariate inference about measured predictors, and prediction. Recent advances in computational tools allow the development of efficient, scalable algorithms for fitting GLLMVs for any response distribution. In this article, we discuss the basics of GLLVMs and review some options for model fitting. We focus on methods that are based on likelihood inference. The implementations available in R are compared via simulation studies and an example illustrates how GLLVMs can be applied as an exploratory tool in the analysis of data from community ecology.

Joint species distribution models (JSDMs) have gained considerable traction among ecologists over the past decade, due to their capacity to answer a wide range of questions at both the species‐ and the community‐level. The family of generalised linear latent variable models in particular has proven popular for building JSDMs, being able to handle many response types including presence‐absence data, biomass, overdispersed and/or zero‐inflated counts. We extend latent variable models to handle percent cover response variables, with vegetation, sessile invertebrate and macroalgal cover data representing the prime examples of such data arising in community ecology. Sparsity is a commonly encountered challenge with percent cover data. Responses are typically recorded as percentages covered per plot, though some species may be completely absent or present, that is, have 0% or 100% cover, respectively, rendering the use of beta distribution inadequate. We propose two JSDMs suitable for percent cover data, namely a hurdle beta model and an ordered beta model. We compare the two proposed approaches to a beta distribution for shifted responses, transformed presence‐absence data and an ordinal model for percent cover classes. Results demonstrate the hurdle beta JSDM was generally the most accurate at retrieving the latent variables and predicting ecological percent cover data.

Ecosystem restoration will increase following the ambitious international targets, which calls for a rigorous evaluation of restoration effectiveness. Studies addressing restoration effectiveness across ecosystems have thus far shown varying and unpredictable patterns. A rigorous assessment of the factors influencing restoration effectiveness is best done with large-scale and long-term experimental data. Here, we present results from a well replicated long-term before-after control-impact experiment on restoration of forestry-drained boreal peatland ecosystems. Our data comprise 151 sites, representing six ecosystem types. Vegetation sampling has been conducted to the species level before restoration and two, five and ten years after restoration. We show that, on average, restoration stops and reverses the trend of further degradation. The variation in restoration outcomes largely arises from ecosystem types: restoration of nutrient-poor ecosystems has higher probability of failure. Our experiment provides clear evidence that restoration can be effective in halting the biodiversity loss of degraded ecosystems, although ecosystem attributes can affect the restoration outcome. These findings underlie the need for evidence-based prioritization of restoration efforts across ecosystems.