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Section: Ecology ; Topics: Biology of interactions, Ecology, Statistics

Trait matching without traits: using correspondence analysis to investigate the latent structure of interaction networks

Nicvert, Lisa; Fritz, Hervé; Dray, Stéphane

10.24072/pcjournal.580 - Peer Community Journal, Volume 5 (2025), article no. e73.
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Species interactions in ecological communities are often represented as networks, the structure of which is thought to be linked to species' interaction niches (or Eltonian niches). Interaction niches are intimately related to the notion of trait matching, which posits that a species interacts preferentially with partners whose traits are complementary to their own. Multivariate methods are commonly used to quantify species environmental niches (or Grinnellian niches). More recently, some of these methods have also been used to study the interaction niche, but they consider only the niche optimum and require trait data. In this article, we use the correspondence analysis (CA) framework to study interaction networks and investigate trait matching without requiring trait data, using the notion of latent traits. We use reciprocal scaling, a method related to CA, to estimate niche optima and breadths, defined respectively as the mean and standard deviation of the latent traits of species' interacting partners. We present the method, test its performance using a simulation model we designed, and analyze a real frugivory network between birds and plants. The simulation study shows that the method is able to recover niche breadths and optima for data generated with parameters typical of ecological networks. The birds-plants network analysis shows strong relationships between species latent traits and niche breadths: a posteriori correlation with measured traits suggests that birds and plants of intermediate size tend to have the broadest niches. Additionally, birds preferentially foraging in the understory have broader niches than birds preferentially foraging in the canopy. CA and reciprocal scaling are described as fruitful exploratory methods to characterize species interaction profiles, provide an ecologically meaningful graphical representation of interaction niches, and explore the effect of latent traits on network structure.
Section: Mathematical & Computational Biology ; Topics: Evolution, Genetics/genomics, Microbiology

A systematic assessment of phylogenomic approaches for microbial species tree reconstruction

Weiner, Samson; Feng, Yutian; Gogarten, J. Peter; Bansal, Mukul S.

10.24072/pcjournal.579 - Peer Community Journal, Volume 5 (2025), article no. e72.
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A key challenge in microbial phylogenomics is that microbial gene families are often affected by extensive horizontal gene transfer (HGT). As a result, most existing methods for microbial phylogenomics can only make use of a small subset of the gene families present in the microbial genomes under consideration, potentially biasing their results and affecting their accuracy. To address this challenge, several methods have recently been developed for inferring microbial species trees from genome-scale datasets of gene families affected by evolutionary events such as HGT, gene duplication, and gene loss. In this work, we use extensive simulated and real biological datasets to systematically assess the accuracies of four recently developed methods for microbial phylogenomics, SpeciesRax, ASTRAL-Pro 2, PhyloGTP, and AleRax, under a range of different conditions. Our analysis reveals important insights into the relative performance of these methods on datasets with different characteristics, identifies shared weaknesses when analyzing complex biological datasets, and demonstrates the importance of accounting for gene tree inference error/uncertainty for improved species tree reconstruction. Among other results, we find that (i) AleRax, the only method that explicitly accounts for gene tree inference error/uncertainty, shows the best species tree reconstruction accuracy among all tested methods, (ii) PhyloGTP (developed previously by the authors of this paper) shows the best overall accuracy among methods that do not account for gene tree error and uncertainty, (iii) ASTRAL-Pro 2 is less accurate than the other methods across nearly all tested conditions, and (iv) explicitly accounting for gene tree inference error/uncertainty can lead to substantial improvements in species tree reconstruction accuracy. Importantly, we also find that all methods, including AleRax and PhyloGTP, are susceptible to biases present in complex real biological datasets and can sometimes yield misleading phylogenies.
Section: Infections ; Topics: Agricultural sciences, Applied biological sciences, Genetics/genomics

Implications of high throughput sequencing of plant viruses in biosecurity – a decade of progress?

Fox, Adrian; Botermans, Marleen; Ziebell, Heiko; Fowkes, Aimee R.; Fontdevila Pareta, Nuria; Massart, Sebastien; Rodoni, Brendan ; Chooi , Kar Mun; Kreuze, Jan; Kumar, P. Lava; Cuellar, Wilmer J.; Carvajal-Yepes, Monica; MacDiarmid , Robin M.

10.24072/pcjournal.572 - Peer Community Journal, Volume 5 (2025), article no. e71.
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In the 15 years since High Throughput Sequencing (HTS) was first used for the detection and identification of plant viruses, the technology has matured and is now being used in frontline plant biosecurity applications. Anticipating the challenges this new approach was starting to reveal, recommendations were made a decade ago to streamline the application of these technologies. The recommendations were (1) for countries to increase baseline surveillance, (2) to address nomenclature for “data inferred” new viral sequence to differentiate from characterised viruses, and (3) to increase the focus on fundamental biological research to deal with the deluge of new discoveries. Here, we review the progress made on these recommendations in the intervening decade and discuss the anticipated future challenges posed by the broader adoption of HTS in routine biosecurity applications, especially as we move towards a potential asymptote in the rate of virus discovery. The three initial recommendations are still relevant, however, the decade of discovery and development has led to a change in approaches and ways of thinking. A fourth recommendation is made here, to reduce the biosecurity risks through equal inclusion and access to research and technology, locally and globally. This equality will create increased consonance between community members, researchers, risk analysts, biosecurity authorities, and policy makers at national and international levels and a step change reduction of biosecurity incursions of phytopathogenic viruses.
Section: Ecology ; Topics: Ecology, Statistics ; Conference: Euring 2023

Effect of spatial overdispersion on confidence intervals for population density estimated by spatial capture–recapture

Efford, Murray G.; Fletcher, David

10.24072/pcjournal.578 - Peer Community Journal, Volume 5 (2025), article no. e70.
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Spatially explicit capture–recapture models are used widely to estimate the density of animal populations. The population is represented by an inhomogeneous Poisson point process, where each point is the activity centre of an individual and density corresponds to the intensity surface. Estimates of density that assume a homogeneous model (`average density') are robust to unmodelled inhomogeneity, and the coverage of confidence intervals is good when the intensity surface does not change, even if it is quite uneven. However, coverage is poor when the intensity surface differs among realisations. Practical examples include populations with dynamic social aggregation, and the population in a region sampled using small detector arrays. Poor coverage results from overdispersion of the number of detected individuals; the number is Poisson when the intensity surface is static, but stochasticity leads to extra-Poisson variation.
We investigated overdispersion from three point processes with a stochastic intensity surface (Thomas cluster process, random habitat mosaic and log-Gaussian Cox process). A previously proposed correction for overdispersion performed poorly. The problem is lessened by assuming population size to be fixed, but this assumption cannot be justified for common study designs. Rigorous correction for spatial overdispersion requires either prior knowledge of the generating process or replicated and representative sampling. When the generating process is known, variation in a new scalar measure of local density predicts overdispersion. Otherwise, overdispersion may be estimated empirically from the numbers detected on independent detector arrays.

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Image Credits

The network image was drawn by Martin Grandjean: A force-based network visualization CC BY-SA