Biology subjects

Morales-Castilla, I.

Publications and source records attributed to Morales-Castilla, I..

4 recordsLinked to original sources

bioRxiv2025

sabinaHSBM: An R package for link prediction network reconstruction using Hierarchical Stochastic Block Models

O_LINetwork analysis is a powerful framework for investigating complex systems across disciplines, including ecology, where it helps uncover patterns in predator-prey, host- parasite, or plant-pollinator interactions. However, ecological network data are often incomplete or error-prone due to sampling limitations, detection failures, and taxonomic uncertainty--leading to missing (false negative) and spurious (false positive) links that obscure structure and hinder inference. The hierarchical stochastic block model (HSBM), particularly in its degree-corrected form, is among the most effective tools for reconstructing networks under such uncertainty. Despite its robustness, the primary implementation of HSBM in the Python-based graph-tool library has remained largely inaccessible to ecologists. C_LIO_LIHere, we introduce sabinaHSBM, the first R package that makes degree-corrected HSBM broadly available through a user-friendly, flexible workflow. By bridging a gap between advanced network modeling and widely used ecological analysis platforms, sabinaHSBM facilitates network reconstruction and link prediction from binary bipartite data. The workflow involves three main steps: (1) preparing input data, (2) estimating posterior link probabilities, and (3) reconstructing the network. The package supports detection of undocumented and spurious links, exploration of hierarchical structure, and propagation of uncertainty throughout. Key features include cross-validation, flexible thresholding, probabilistic evaluation metrics, and two link prediction modes: estimating all link probabilities or identifying undocumented ones. C_LIO_LIWe illustrate the packages functionality through a case study using a published global dataset of carnivore-parasite associations, showing that inferred groupings are phylogenetically clustered. To assess predictive accuracy, we examined the top 10 highest-probability links identified by the model and found published evidence for 8, despite their absence from the original dataset. This highlights the models ability to recover biologically meaningful but underreported interactions. C_LIO_LIBy integrating all components of HSBM-based reconstruction into an accessible R package, sabinaHSBM empowers researchers to improve relational data quality and uncover overlooked patterns in complex ecological networks and beyond. C_LI

ecology↗

bioRxiv2022

Significant phylogenetic signal is not enough to trust phylogenetic predictions

In a recent study, Cantwell-Jones et al. (2022) proposed a list of 1044 species as promising key sources of B vitamins based primarily on phylogenetic predictions. To identify candidate plants, they fitted lambda models of evolution to edible species with known values in each of six B vitamins (232 to 280 species) and used the estimated parameters to predict B-vitamin profiles of edible plants lacking nutritional data (6460 to 6508 species). The latter species were defined as potential sources of a B vitamin if the predicted vitamin content was [≥]15% towards recommended dietary allowances for active females between 31-50 years per 100 g of fresh edible plant material consumed. Unfortunately, the reliability of the predictions that informed the list of candidate species is questionable due to insufficient phylogenetic signal in the data (Pagels {lambda} between 0.171 and 0.665) and a high incidence of species with missing values (over 95% of all the species analyzed in the study). We found that of the 1044 species proposed as promising B-vitamin sources, 626 to 993 species showed accuracies that were indistinguishable from those obtained under a white noise model of evolution (i.e. random predictions conducted in absence of any phylogenetic structure) in at least one of the vitamins, which proves the weakness of the inference drawn from imputed information in the original study. We hope this commentary serves as a cautionary note for future phylogenetic imputation exercises to carefully assess whether the data meet the requirements for the predictions to be valuable, or at least more accurate than expected by chance.

evolutionary biology↗

bioRxiv2021

A simple explanation for declining temperature sensitivity with warming

Temperature sensitivity--the magnitude of a biological response per {degrees}C--is a fundamental concept across scientific disciplines, especially biology, where temperature determines the rate of many plant, animal and ecosystem processes. Recently, a growing body of literature in global change biology has found temperature sensitivities decline as temperatures rise (Fuet al., 2015; Gusewell et al., 2017; Piao et al., 2017; Chen et al., 2019; Dai et al., 2019). Such observations have been used to suggest climate change is reshaping biological processes, with major implications for forecasts of future change. Here we present a simple alternative explanation for observed declining sensitivities: the use of linear models to estimate non-linear temperature responses. We show how linear estimates of sensitivities will appear to decline with warming for events that occur after a cumulative thermal threshold is met--a common model for many biological events. Corrections for the non-linearity of temperature response in simulated data and long-term phenological data from Europe remove the apparent decline. Our results show that rising temperatures combined with linear estimates based on calendar time produce observations of declining sensitivity--without any shift in the underlying biology. Current methods may thus undermine efforts to identify when and how warming will reshape biological processes. Significance statementRecently a growing body of literature has observed declining temperature sensitivities of plant leafout and other events with higher temperatures. Such results suggest that climate change is already reshaping fundamental biological processes. These temperature sensitivities are often estimated as the magnitude of a biological response per {degrees}C from linear regression. The underlying model for many events--that a critical threshold of warmth must be reached to trigger the event--however, is non-linear. We show that this mismatch between the statistical and biological models can produce the illusion of declining sensitivities with warming using current methods. We suggest simple alternative approaches that can better identify when and how warming will reshape biological processes.

plant biology↗

bioRxiv2019

Mechanistic macroecology: exploring the drivers of latitudinal variation in terrestrial body size in a General Ecosystem Model

Many mechanisms have been hypothesized to explain Bergmanns rule - the correlation of body size with latitude. However, it is not feasible to assess the contribution of hypothesised mechanisms by experimental manipulation or statistical correlation. Here, we evaluate two of the principal hypothesised mechanisms, related to thermoregulation and resource availability, using structured experiments in a mechanistic global ecosystem model. We simulated the broad structure of assemblages and ecosystems using the Madingley model, a mechanistic General Ecosystem Model (GEM). We compared emergent modelled biogeographic patterns in body mass to empirical patterns for mammals and birds. We then explored the relative contribution of thermoregulation and resource availability to body mass clines by manipulating the models environmental gradients. Madingley produces body size gradients that are in broad agreement with empirical estimates. Thermoregulation and resource availability were both important controls on body mass for endotherms, but only temperature for ectotherms. Our results suggest that seasonality explains animal body mass patterns through a complex set of mechanisms. Process-based GEMs generate broadly realistic biogeographic body mass patterns. Ecologists can use them in novel ways: to explore causality, or for generating and testing hypotheses for large-scale, emergent ecological patterns. At the same time, macroecological patterns are useful for evaluating mechanistic models. Iteratively developing GEMs, and evaluating them against macroecological patterns, could generate new insights into the complex causes of such patterns.

ecology↗