Biology subjects

Abrego, N.

Publications and source records attributed to Abrego, N..

7 recordsLinked to original sources

bioRxiv2026

The hidden multiverse of ecological roles in Fungi

Ecological roles are treated as discrete, bounded units of biological organization, even as evidence shows that many taxa are versatile, performing more than one of them. Each record of versatility represents a boundary crossing, yet current categorical paradigms prevent this cumulative evidence from reshaping the roles themselves. We solve this by introducing the ecological multiverse, a self-correcting framework that turns boundary crossings into a network of role connectivity. Applied to Fungi, the multiverse recasts the major form of plant pathogenicity from an isolated disease role into a central hub linking much of fungal functional space, especially benign plant symbioses and decomposition. By exposing this topology, the multiverse transforms categorical views of ecological function into a dynamic map, revealing pathways of role change as organisms and knowledge evolve.

ecology↗

bioRxiv2025

Experimental community ecology in decline: A call to embrace technology

Community dynamics are complex and thus challenging to infer from observational data alone. Experiments, with their ability to control variables and isolate mechanisms, are a powerful tool for uncovering the causal processes that drive community dynamics. They therefore allow us to move beyond correlations and to directly test theoretical predictions. Yet, because experiments are often logistically demanding and resource-intensive, they are less frequently employed than observational approaches in community ecology. Here, we trace the past three decades of experimental research in community ecology through a systematic literature review. We focus on the motivation behind experiments, their links to ecological theory, the types of questions they address, their scale, and the methods used to do this. Our results corroborate the historically tight relationship between experiments and ecological theory and document a gradual increase in experimental complexity --particularly related to the use of molecular methods. However, persistent gaps remain in the taxa and ecosystems studied, with aquatic ecosystems, fungi, and microbes still underrepresented compared to terrestrial plants and animals. Moreover, experiments are still limited in their spatial and temporal scale; they are typically short-term, local, and reliant on manual methods. The integration of high-throughput technologies with experimental workflows is still in its infancy, even though they are increasingly common in biomonitoring. To illustrate the potential of such tools in experimental research, we present a proof-of-concept study. It shows how automated technologies can be incorporated at different stages of the experimental workflow to expand the scale of experiments while reducing the reliance on human labor and potentially lowering financial costs. We conclude that many of the long-lasting biases and challenges in experimental community ecology could be addressed by combining technological innovations with broader collaboration among research groups. Coordinated networks, standardized protocols, and the integration of long-term and large-scale experimental designs can substantially improve in situ replication as well as cross-site comparability. Such efforts are essential for developing a more comprehensive mechanistic understanding of community dynamics across diverse ecosystems.

ecology↗

bioRxiv2024

Common to rare transfer learning (CORAL) enables inference and prediction for a quarter million rare Malagasy arthropods

Modern DNA-based biodiversity surveys result in massive-scale data, including up to millions of species - of which most are rare. Making the most of such data for inference and prediction requires modelling approaches that can relate species occurrences to environmental and spatial predictors, while incorporating information about their taxonomic or phylogenetic placement. Even if the scalability of joint species distribution models to large communities has greatly advanced, incorporating hundreds of thousands of species has not been feasible to date, leading to compromised analyses. Here we present a novel "common to rare transfer learning" approach (CORAL), based on borrowing information from the common species to enable statistically and computationally efficient modelling of both common and rare species. We illustrate that CORAL leads to much improved prediction and inference in the context of DNA metabarcoding data from Madagascar, comprising 255,188 arthropod species detected in 2874 samples.

ecology↗

bioRxiv2024

A large-scale and long-term experiment to identify effectiveness of ecosystem restoration

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.

ecology↗

bioRxiv2021

Distance decay 2.0 - a global synthesis of taxonomic and functional turnover in ecological communities.

Understanding the variation in community composition and species abundances, i.e., {beta}-diversity, is at the heart of community ecology. A common approach to examine {beta}-diversity is to evaluate directional turnover in community composition by measuring the decay in the similarity among pairs of communities along spatial or environmental distances. We provide the first global synthesis of taxonomic and functional distance decay along spatial and environmental distance by analysing 149 datasets comprising different types of organisms and environments. We modelled an exponential distance decay for each dataset using generalized linear models and extracted r2 and slope to analyse the strength and the rate of the decay. We studied whether taxonomic or functional similarity has stronger decay across the spatial and environmental distances. We also unveiled the factors driving the rate of decay across the datasets, including latitude, spatial extent, realm, and organismal features. Taxonomic distance decay was stronger along spatial and environmental distances compared with functional distance decay. The rate of taxonomic spatial distance decay was the fastest in the datasets from mid-latitudes while the rate of functional decay increased with latitude. Overall, datasets covering larger spatial extents showed a lower rate of decay along spatial distances but a higher rate of decay along environmental distances. Marine ecosystems had the slowest rate of decay. This synthesis is an important step towards a more holistic understanding of patterns and drivers of taxonomic and functional {beta}-diversity.

ecology↗

bioRxiv2019

Joint species distribution modelling with HMSC-R

O_LIJoint Species Distribution Modelling (JSDM) is becoming an increasingly popular statistical method for analyzing data in community ecology. JSDM allow the integration of community ecology data with data on environmental covariates, species traits, phylogenetic relationships, and the spatio-temporal context of the study, providing predictive insights into community assembly processes from non-manipulative observational data of species communities. Hierarchical Modelling of Species Communities (HMSC) is a general and flexible framework for fitting JSDMs, yet its full range of functionality has remained restricted to Matlab users only.\nC_LIO_LITo make HMSC accessible to the wider community of ecologists, we introduce HMSC-R 3.0, a user-friendly R implementation of the framework described in Ovaskainen et al (Ecology Letters, 20 (5), 561-576, 2017) and further extended in several later publications.\nC_LIO_LIWe illustrate the use of the package by providing a series of five vignettes that apply HMSC-R 3.0 to simulated and real data. HMSC-R applications to simulated data involve single-species models, models of small communities, and models of large species communities. They demonstrate the estimation of species responses to environmental covariates and how these depend on species traits, as well as the estimation of residual species associations. They further demonstrate how HMSC-R can be applied to normally distributed data, count data, and presence-absence data. The real data consist of bird counts in a spatio-temporally structured dataset, environmental covariates, species traits and phylogenetic relationships. The vignettes demonstrate how to construct and fit many kinds of models, how to examine MCMC convergence, how to examine the explanatory and predictive powers of the models, how to assess parameter estimates, and how to make predictions.\nC_LIO_LIThe package, along with the extended vignettes, makes JSDM fitting and post-processing easily accessible to ecologists familiar with R.\nC_LI

ecology↗

bioRxiv2018

Habitat fragmentation and species diversity in competitive species communities

Abstract.Habitat loss is one of the key drivers of the ongoing decline of biodiversity. However, ecologists still argue about how fragmentation of habitat (independent of habitat loss) affects species richness. The recently proposed habitat amount hypothesis posits that species richness only depends on the total amount of habitat in a local landscape. On the other hand, different empirical studies report contrasting patterns: some find positive and others negative effects of fragmentation per se on species richness.\n\nTo explain this apparent disparity, we devise a stochastic, spatially-explicit model of competitive species communities in heterogeneous habitats. The model shows that habitat loss and fragmentation have a non-monotone and non-linear effect on the species diversity in competitive communities. When the total amount of habitat is large, fragmentation per se tends to increase species diversity, but if the total amount of habitat is small, the situation is reversed: fragmentation per se decreases species diversity.

ecology↗