Biology subjects

Azaele, S.

Publications and source records attributed to Azaele, S..

8 recordsLinked to original sources

bioRxiv2025

A minimal model of Norway spruce - bark beetle outbreak dynamics

Bark beetles pose a growing threat to coniferous forests worldwide, with outbreaks increasingly linked to environmental stress and host vulnerability. Most mechanistic models focus on equilibrium states, neglecting the dynamical nature of outbreaks triggered by environmental perturbations. Using Ips typographus as focal species, we present a minimal model capturing key processes such as threshold-driven mass attacks, with parameters adaptable to different environmental conditions. Under current conditions, the model predicts a stable endemic state that is reactive, meaning environmental shocks trigger isolated, transient outbreaks. Under altered conditions, reflecting potential climate change scenarios, the system undergoes a regime shift, producing recurrent outbreaks that arise spontaneously from insect-host feedbacks. Finally, we examine repeated environmental disturbances and forest management, showing that the effectiveness of outbreak mitigation depends on the interplay between management frequency and intensity. Overall, we provide a mechanistic framework for understanding outbreak dynamics and guiding management strategies under changing environmental conditions.

ecology↗

bioRxiv2025

Unraveling the temporal dependence of ecological interaction measures

Species interactions--ranging from direct predator-prey relationships to indirect effects mediated by the environment--are central to ecosystem balance and biodiversity. While empirical methods for measuring these interactions exist, their interpretability and limitations remain unclear. Here we examine the empirical matrix of pairwise interactions, a widely used tool, and analyze its temporal variability. We show that apparent fluctuations in interaction strength--and even shifts in interaction signs, often interpreted as transitions between competition and facilitation--can arise intrinsically from population dynamics with fixed ecological roles. Experimental protocols further shape these estimates: the duration of observation and the type of setup in microbial growth studies (e.g., chemostats, batch cultures, or resource conditions) systematically affect measured interactions. Considering interactions across timescales enhances interpretability: short-term measurements primarily capture direct species couplings, whereas long-term observations increasingly reflect indirect community feedback. Taken together, these results establish short-duration inferences, obtained either directly or extrapolated, as a principled way to disentangle direct from indirect interactions. Building on this insight, we propose a model-inference approach that leverages multiple short time series rather than extended longitudinal datasets.

ecology↗

bioRxiv2024

Modelling post-disturbance empirical patterns in a forest ecosystem

We investigate the impact of disturbances on forest ecosystems by examining transient population dynamics in a controlled experiment carried out in a tropical rainforest. We first model the mean species abundance with a simple consumer-resource model, which is then extended into two multi-species frameworks for community dynamics: a neutral model, which emphasises the ability of any species to recover independently of the others; and a non-neutral one, where species interactions play an important role in reconstructing ecosystems structure and patterns. The results indicate that, while both frameworks accurately describe correlation functions and mean-variance relations, the non-neutral model more effectively captures community structure as revealed by an evenness indicator. This suggests that interspecific interactions significantly influence the ecosystems ability to respond to disturbances, providing deeper insights into the recovery dynamics of forest ecosystems.

ecology↗

bioRxiv2024

Unveiling Gene Perturbation Effects through Gene Regulatory Networks Inference from single-cell transcriptomic data

1Physiological and pathological processes are governed by a network of genes called gene regulatory networks (GRNs). By reconstructing GRNs, we can accurately model how cells behave in their natural state and predict how genetic changes will affect them. Transcriptomic data of single cells are now available for a wide range of cellular processes in multiple species. Thus, a method building predictive GRNs from single-cell RNA sequencing (scRNA-seq) data, without any additional prior knowledge, could have a great impact on our understanding of biological processes and the genes playing a key role in them. To this aim, we developed IGNITE (Inference of Gene Networks using Inverse kinetic Theory and Experiments), an unsupervised machine learning framework designed to infer directed, weighted, and signed GRNs directly from unperturbed single-cell RNA sequencing data. IGNITE uses the GRNs to generate gene expression data upon single and multiple genetic perturbations. IGNITE is based on the inverse problem for a kinetic Ising model, a model from statistical physics that has been successfully applied to biological networks. We tested IGNITE on murine pluripotent stem cells (PSCs) transitioning from the naive to formative states. Using as input only scRNA-seq data of unperturbed PSCs, IGNITE simulated single and triple gene knockouts. Comparison with experimental data revealed high accuracy, up to 74%, outperforming currently available methods. In sum, IGNITE identifies predictive GRNs from scRNA-seq data without additional prior knowledge and faithfully simulates single and multiple gene perturbations. Applications of IGNITE range from studying cell differentiation to identifying genes specifically active under pathological conditions.

systems biology↗

bioRxiv2021

Redundancy-selection trade-off in phenotype-structured populations

Realistic fitness landscapes generally display a redundancy-fitness trade-off: highly fit trait configurations are inevitably rare, while less fit trait configurations are expected to be more redundant. The resulting sub-optimal patterns in the fitness distribution are typically described by means of effective formulations. However, the extent to which effective formulations are compatible with explicitly redundant landscapes is yet to be understood, as well as the consequences of a potential miss-match. Here we investigate the effects of such trade-off on the evolution of phenotype-structured populations, characterised by continuous quantitative traits. We consider a typical replication-mutation dynamics, and we model redundancy by means of two dimensional landscapes displaying both selective and neutral traits. We show that asymmetries of the landscapes will generate neutral contributions to the marginalised fitness-level description, that cannot be described by effective formulations, nor disentangled by the full trait distribution. Rather, they appear as effective sources, whose magnitude depends on the geometry of the landscape. Our results highlight new important aspects on the nature of sub-optimality. We discuss practical implications for rapidly mutant populations such as pathogens and cancer cells, where the qualitative knowledge of their trait and fitness distributions can drive disease management and intervention policies.

evolutionary biology↗

bioRxiv2019

Spatial patterns emerging from a stochastic process near criticality

There is mounting empirical evidence that many communities of living organisms display key features which closely resemble those of physical systems at criticality. We here introduce a minimal model framework for the dynamics of a community of individuals which undergoes local birth-death, immigration and local jumps on a regular lattice. We study its properties when the system is close to its critical point. Even if this model violates detailed balance, within a physically relevant regime dominated by fluctuations, it is possible to calculate analytically the probability density function of the number of individuals living in a given volume, which captures the close-to-critical behavior of the community across spatial scales. We find that the resulting distribution satisfies an equation where spatial effects are encoded in appropriate functions of space, which we calculate explicitly. The validity of the analytical formulae is confirmed by simulations in the expected regimes. We finally discuss how this model in the critical-like regime is in agreement with several biodiversity patterns observed in tropical rain forests.

ecology↗

bioRxiv2018

Inferring macro-ecological patterns from local species’ occurrences

O_LIBiodiversity provides support for life, vital provisions, regulating services and has positive cultural impacts. It is therefore important to have accurate methods to measure biodiversity, in order to safeguard it when we discover it to be threatened. For practical reasons, biodiversity is usually measured at fine scales whereas diversity issues (e.g. conservation) interest regional or global scales. Moreover, biodiversity may change across spatial scales. It is therefore a key challenge to be able to translate local information on biodiversity into global patterns.\nC_LIO_LIMany databases give no information about the abundances of a species within an area, but only its occurrence in each of the surveyed plots. In this paper, we introduce an analytical framework to infer species richness and abundances at large spatial scales in biodiversity-rich ecosystems when species presence/absence information is available on various scattered samples (i.e. upscaling).\nC_LIO_LIThis framework is based on the scale-invariance property of the negative binomial. Our approach allows to infer and link within a unique framework important and well-known biodiversity patterns of ecological theory, such as the Species Accumulation Curve (SAC) and the Relative Species Abundance (RSA) as well as a new emergent pattern, which is the Relative Species Occupancy (RSO).\nC_LIO_LIOur estimates are robust and accurate, as confirmed by tests performed on both in silico-generated and real forests. We demonstrate the accuracy of our predictions using data from two well-studied forest stands. Moreover, we compared our results with other popular methods proposed in the literature to infer species richness from presence-absence data and we showed that our framework gives better estimates. It has thus important applications to biodiversity research and conservation practice.\nC_LI

ecology↗

bioRxiv2016

Biodiversity of Tropical Forests

The quantification of tropical tree biodiversity worldwide remains an open and challenging problem. In fact, more than two-fifths of the global tree population can be found either in tropical or sub-tropical forests1, but species identities are known only for {approx} 0.000067% of the individuals in all tropical forests2. For practical reasons, biodiversity is typically measured or monitored at fine spatial scales. However, important drivers of ecological change tend to act at large scales. Conservation issues, for example, apply to diversity at global, national or regional scales. Extrapolating species richness from the local to the global scale is not straightforward. Indeed, a vast number of different biodiversity estimators have been developed under different statistical sampling frameworks3-7, but most of them have been designed for local/regional-scale extrapolations, and they tend to be sensitive to the spatial distribution of trees8, sample coverage and sampling methods9. Here, we introduce an analytical framework that provides robust and accurate estimates of species richness and abundances in biodiversity-rich ecosystems, as confirmed by tests performed on various in silico-generated forests. The new framework quantifies the minimum percentage cover that should be sampled to achieve a given average confidence in the upscaled estimate of biodiversity. Our analysis of 15 empirical forest plots shows that previous methods10,11 have systematically overestimated the total number of species and leads to new estimates of hyper-rarity10 at the global scale11, known as Fishers paradox2. We show that hyper-rarity is a signature of critical-like behavior12 in tropical forests13-15, and it provides a buffer against mass extinctions16. When biotic factors or environmental conditions change, some of these rare species are more able than others to maintain the ecosystems functions, thus underscoring the importance of rare species.

ecology↗