Biology subjects

Matias, M. A.

Publications and source records attributed to Matias, M. A..

12 recordsLinked to original sources

bioRxiv2026

A mechanistic framework linking within-host pathogen progression to vector-mediated transmission under climate forcing

Climate-driven disease forecasts typically assess whether environmental conditions favor pathogen growth, yet epidemic spread depends critically on how physiological processes within infected hosts shape transmission over time. This distinction is particularly consequential for vector-borne plant diseases, where vectors acquire infection from hosts whose pathogen load, symptom severity, and recovery are themselves temperature-dependent. Here, we develop a mechanistic epidemic framework that couples temperature-driven within-host pathogen dynamics to vector-mediated transmission. Infected hosts progress through ordered infection stages with stage-specific infectiousness, while transitions among stages--both progression and regression--are governed by thermal effects on pathogen accumulation and decay. We parameterize the model using experimental data for Pierces disease of grapevine, caused by Xylella fastidiosa, and analyze epidemic invasion under constant, seasonal, stochastic, and empirical temperature regimes. We show that temperature affects invasion not only by altering pathogen growth rates but also by reshaping the time hosts spend in transmissible infection stages. This generates a slow-growth paradox: temperatures that maximize within-host pathogen growth need not maximize epidemic spread, because rapid progression shortens the effective transmission window, whereas mildly suboptimal temperatures can prolong infectiousness and sustain larger epidemics. Conversely, cold conditions can suppress invasion by either halting progression or inducing regression and recovery. Analytical expressions for the basic reproduction number under constant and seasonal forcing capture these mechanisms and predict final epidemic size across diverse climatic regimes. Short-term temperature variability has its strongest effects near thermal thresholds, and empirical temperature series from invaded regions generate markedly different epidemic trajectories despite similar invasion suitability. These results show that ignoring the coupling between within-host physiology and transmission can qualitatively mislead predictions of plant disease dynamics under climate change, misidentifying the thermal regimes that pose the greatest epidemic risk.

ecology↗

bioRxiv2026

Inferring seagrass meadow resilience from self-organized spatial patterns

Assessing ecosystem resilience at large spatial scales remains a major challenge in ecology and conservation. While resilience is typically inferred from temporal dynamics or perturbation experiments, ecosystems governed by spatial self-organization are thought to encode resilience-related information directly in their spatial structure. Here, we show that the spatial patterns of seagrass meadows can be used to infer ecological deterioration and resilience-related states from a single cartographic snapshot. Using a mechanistic model of Posidonia oceanica self-organization, we generated thousands of synthetic seascapes spanning a mortality-driven gradient from continuous meadows through fragmented and collapsed states and trained deep convolutional neural networks to classify discrete pattern states and estimate continuous levels of deterioration along this gradient. Applied to habitat cartography across the Balearic Islands, the framework revealed ecologically interpretable regional variation in meadow condition, enabling large-scale assessment of seagrass resilience from spatial snapshots alone. Networks trained exclusively on synthetic data generalized effectively to real meadows, showcasing that mechanistic models can substitute for empirical training labels. More broadly, our results establish a transferable strategy for integrating ecological theory and machine learning to monitor the resilience of self-organized ecosystems when direct temporal observations are sparse or unavailable.

ecology↗

bioRxiv2026

Thermal stress drives seagrass fragmentation in the Mediterranean Sea

Posidonia oceanica meadows, which underpin Mediterranean coastal ecosystems, are undergoing accelerated decline, partly driven by thermal stress. While previous quantitative studies have identified temperature thresholds beyond which seagrass mortality increases sharply, we show the cumulative and sublethal impacts of prolonged warming under fluctuating subthreshold conditions. To capture these effects, we introduce Stress Degree Days (SDD), a physiologically grounded index derived from an experimentally validated mortality rate function. Using sea surface temperature (SST) data, we quantified the cumulative thermal exposure across the Mediterranean Basin from 2000 to 2020. Leveraging high-resolution satellite imagery and deep learning-based habitat mapping, we linked SDD-derived thermal exposure to meadow fragmentation, which is a proxy for seagrass health. Our results show that high thermal stress (> 50%) is concentrated along the southern and eastern Mediterranean, where meadows exhibit more than 40% cover loss and elevated fragmentation, even though maximum SSTs remained below lethal limits (LT50 = 28.9 {degrees}C). This finding highlights the critical role of chronic sublethal thermal stress in driving structural degradation. Future projections under the RCP8.5, business as usual, and the more moderate RCP4.5 climatic scenarios indicate basin-wide regression, with expected cover losses of approximately 80% and 40%, respectively, by 2100, and near-total habitat suitability collapse in the southern regions. Consequently, fragmentation indices are projected to double or triple, further disrupting clonal connectivity, sediment retention, and oxygen export. In summary, by integrating physiological mechanisms, large-scale remote sensing, and climate modeling, the SDD framework identifies thermal hotspots, reveals emergent vulnerability patterns, and offers a predictive tool to guide conservation strategies in warming oceans.

ecology↗

bioRxiv2026

Intermittent precipitation and spatial Allee effects drive irregular vegetation patterns in semiarid ecosystems

Vegetation in semi-arid ecosystems frequently organizes into spatially heterogeneous mosaics that regulate ecosystem functioning, productivity, and resilience. These patterns arise from local biological interactions, including facilitation among neighboring plants and competition for limiting resources. Classical theoretical approaches have attributed such organization to scale-dependent feedbacks, predicting regular spatial patterns and abrupt transitions to collapse. However, growing empirical and theoretical evidence reveal that environmental variability and demographic stochasticity can fundamentally reshape spatial organization, driving irregular clusters, dynamic mosaics, and gradual rather than catastrophic vegetation declines. In drylands, rainfall variability is a dominant source of environmental forcing: precipitation typically occurs in short, irregular pulses that transiently enhance survival and recruitment before competitive interactions again dominate. Near persistence thresholds, ecosystem dynamics are therefore governed not only by average climatic conditions but also by the timing and spatial coincidence of favorable events. Under these conditions, positive density dependence and local facilitation can critically determine whether vegetation patches persist, expand, or collapse. Here, we develop an individual-based model that integrates intermittent precipitation with local Allee effects to examine how stochastic rainfall shapes spatial organization and persistence. We show that the interaction between pulsed resource availability and density-dependent survival generates irregular cluster structures and strongly modulates extinction risk, with resilience emerging from local spatial covariance and neighborhood density rather than from total biomass alone. These results highlight the importance of individual-level, stochastic processes in determining ecosystem resilience and provide a mechanistic basis for understanding vegetation persistence under increasing climatic variability.

ecology↗

bioRxiv2024

Unravelling the universal spatial properties of coral reefs

Coral reefs are under rapid decline due to human pressures such as climate change. Achieving the Kunming-Montreal Global Biodiversity Framework goals, which include restoring 30% of degraded habitats like coral reefs by 2030, requires a comprehensive understanding of their extent and structure, which has been hitherto lacking. We address this limitation based on the unprecedented canonical inventory of coral reefs extracted from the Allen Coral Atlas of shallow-water tropical reefs. We identified a total of 1,579,772 individual reefs globally, extending over a total of 52,423 km2 of ocean area with mean and median sizes of 3.32 ha and 0.3 ha, respectively. We unravelled three universal laws that are common to all coral reef provinces: the size-frequency distribution, the inter-reef distance distribution and the area-perimeter relation, which follow power laws with an exponent of 1.8, 2.33 and 1.26, respectively. We demonstrate that coral reefs develop universal fractal patterns characterised by a perimeter fractal dimension of DP = 1.3 and a surface fractal dimension of DA = 1.6. Our analysis shows that coral reefs display intricate fractal-like geometries and exhibit universal macroecological patterns, largely independent of their geographical location. The universality of the observed patterns suggests that these features possibly stem from the highly conserved interactions of biological, physical, and chemical processes. Over geological scales, these processes lead to reef landscape patterns common among all provinces, providing new information relevant to reef growth modelling.

ecology↗

bioRxiv2024

Linking intercontinental biogeographic events to decipher how European vineyards escaped Pierce's disease

Unlike most grapevine diseases of American origin, the vector-borne bacterium Xylella fastidiosa (Xf) responsible for Pierces disease (PD) has not yet spread to continental Europe. The reasons for this lack of invasiveness remain unclear. Here, we present phylogenetic, epidemiological and historical evidence to explain how European vineyards escaped Xf. Using Bayesian temporal reconstruction, we show that the export of American grapevines to France as rootstocks to combat phylloxera ([~]1872-1895) preceded the spread of the Xf grapevine lineage in the US. In the dated tree, the time of the most recent common ancestor places the introduction of Xf into California around 1875, which agrees with the emergence of the main PD outbreak and the onset of its expansion into the southeastern US around 1895. We also show that between 1870 and 1990, climatic conditions in continental Europe were mostly below the threshold for PD epidemics. This lack of spatiotemporal concurrence between factors that could facilitate the establishment of the Xf grapevine lineage would explain the historical absence of PD in continental Europe. However, our model indicates that there has been an inadvertent expansion of risk in southern European vineyards since the 1990s, which is accelerating with global warming. Our temporal approach identifies the biogeographic conditions that have so far prevented PD, and gives continuity to predictions of increased risk in important southern European wine-producing areas under a forthcoming scenario of +2 and +3{degrees}C temperature increases.

evolutionary biology↗

bioRxiv2024

Mapping the distribution of seagrass meadows from space with deep convolutional neural networks

Seagrass meadows play a vital role in supporting coastal communities by promoting biodiversity, mitigating coastal erosion and contributing to local economies. These ecosystems face significant threats, including habitat loss and degradation or climate change. United Nations has recognized the urgency of conserving marine ecosystems, highlighting the need for evidence-based conservation strategies and high-quality monitoring. However, traditional monitoring approaches are often time-consuming, labor-intensive, and costly, limiting their scalability and effectiveness. The growing availability of remote sensing data coupled to the rise of machine learning technologies offer an unprecedented opportunity to develop autonomous, efficient and scalable monitoring systems. Despite many efforts, the development of such systems for seagrass meadows remains a challenge, with recent attempts presenting several limitations such as limited satellite imagery, inadequate metrics for evaluating model performance or insufficient ground truth data, leading to simple proof of concepts rather than useful solutions. Here, we overcome these limitations by developing a comprehensive framework to map Posidonia oceanica meadows in the Mediterranean Sea using an extensive georeferenced habitat dataset and diverse satellite imagery for model training. We successfully evaluate the model generalization capability across different regions and provide the trained model for broader application in biodiversity monitoring and management.

ecology↗

bioRxiv2024

High-resolution climate data reveals increased risk of Pierce's Disease for grapevines worldwide

Range shifts in plant disease distributions are sensitive to scaling processes, but few crop case studies have included these predictions under climate change. High-quality wines are increasingly produced in topographically heterogeneous river valleys, whereby disease models that capture steep relief gradients become especially relevant. Here we show how non-linear epidemiological models more accurately reflect the threat of an emerging grapevine pathogen in areas with significant spatial gradients. By comparing the results of simulations using climate data with different spatial resolutions, we identify an increased risk of Pierces disease (PD), caused by the vector-borne bacterium Xylella fastidiosa, in wine regions globally. Over 100,000 vine presence records worldwide were analysed with respect to their closer risk-grid cell, observing an increase from 21.8% to 41.2% of the area at risk in European vineyards, from 5.6% to 47.2% in South Africa and to a lesser extent in other wine-growing regions. This general trend has been preceded by an accelerating rate of increase in risk within wine-growing areas. Our analysis demonstrates the importance of microclimatic conditions, highlighting previously unresolved risk zones in areas close to rivers and valleys, and the insufficiency of lower resolution data sets to capture complex climatic variations.

ecology↗

bioRxiv2023

Contrasting Patterns of Pierce's Disease Risk in European Vineyards Under Global Warming

Pierces Disease (PD) is a vector-borne disease caused by the bacterium Xylella fastidiosa, which poses a significant threat to grapevines worldwide. Despite its importance, the risk of future PD establishment in Europe remains unclear due to previous incomplete methodologies followed by conflicting results. Here we present a comprehensive approach considering the compound effect of climate change on the pathosystem. Within the general trend of progressively increasing PD risk, we identified the +3{o}C scenario as a turning point for potential spreading beyond Mediterranean regions, representing a serious risk for French and Italian viticulture. Our innovative methodology reveals PD risk as a multi-factor multi-scale process, showing contrasting spatial patterns and different risk velocities across regions, as well as a high timing uncertainty. By overcoming previous limitations, our findings contribute to a better understanding of the potential spread of PD in Europe, supporting informed decision-making for disease management and prevention.

ecology↗

bioRxiv2022

A compartmental model for Xylella fastidiosa diseases with explicit vector seasonal dynamics

The bacterium Xylella fastidiosa (Xf) is mainly transmitted by the spittlebug, Philaenus spumarius, in Europe, where it has caused significant economic damage to olive and almond trees. Understanding the factors that determine disease dynamics in pathosystems that share similarities can help design control strategies focused on minimizing transmission chains. Here we introduce a compartmental model for Xf-caused diseases in Europe that accounts for the main relevant epidemiological processes, including the seasonal dynamics of P. spumarius. The model was confronted with epidemiological data from the two major outbreaks of Xf in Europe, the olive quick disease syndrome (OQDS) in Apulia, Italy, caused by the subspecies pauca, and the almond leaf scorch disease (ALSD) in Majorca, Spain, caused by subspecies multiplex and fastidiosa. Using a Bayesian inference framework, we show how the model successfully reproduces the general field data in both diseases. In a global sensitivity analysis, the vector-plant and plant-vector transmission rates, together with the vector removal rate, were the most influential parameters in determining the time of the infected host population peak, the incidence peak and the final number of dead hosts. We also used our model to check different vector-based control strategies, showing that a joint strategy focused on increasing the rate of vector removal while lowering the number of annual newborn vectors is optimal for disease control.

plant biology↗

bioRxiv2022

Global predictions for the risk of establishment of Pierce's disease of grapevines

The vector-borne bacterium Xylella fastidiosa is responsible for Pierces disease (PD), a lethal grapevine illness that originated in the Americas. The international plant trade is expanding the geographic range of this pathogen, posing a new threat to viticulture worldwide. To assess the potential incidence of PD, we have built a dynamic epidemiological model based on the response of 36-grapevine varieties to the pathogen in inoculation assays and on the vectors distribution when this information is available. Key temperature-driven epidemiological processes, such as PD symptom development and recovery, are mechanistically modelled. Integrating into the model highresolution spatiotemporal climatic data from 1981 onward and different infectivity (R0) scenarios, we show how the main wine-producing areas thrive mostly in non-risk, transient, or epidemic-risk zones with potentially low growth rates in PD incidence. Epidemic-risk zones with moderate to high growth rates are currently marginal outside the United States. However, a global expansion of epidemic-risk zones coupled with small increments in the disease growth rate is projected for 2050. Our study globally downscales the risk of PD establishment while highlighting the importance of considering climate variability, vector distribution and an invasive criterion in obtaining accurate risk maps to guide policy decision-making in plant health.

plant biology↗

bioRxiv2021

Spatial effects in parasite induced marine diseases of immobile hosts

Marine infectious diseases are more prevalent in recent times due to climate change and other anthropogenic pressures, posing a substantial threat to marine ecosystems and the conservation of their biodiversity. An important subset of marine organisms are sessile, for which the most common mechanism for disease transmission is direct contact with waterborne parasites. Only recently, some deterministic compartmental models have been proposed to describe this kind of epidemics, being these models based on non-spatial descriptions where space is homogenised and parasite mobility is not explicitly accounted for. However, in realistic situations, epidemic transmission is conditioned by the spatial distribution of hosts and the parasites mobility patterns. Thus, the interplay between these factors is expected to have a crucial effect in the evolution of the epidemic, so calling for a explicit description of space. In this work we develop a spatially-explicit individual-based model to study disease transmission by waterborne parasites in sessile marine populations. We investigate the impact of spatial disease transmission, performing extensive numerical simulations and analytical approximations. Specifically, the effects of parasite mobility into the epidemic threshold and the temporal evolution of the epidemic are assessed. We show that larger values of pathogen mobility have two main implications: more severe epidemics, as the number of infections increases, and shorter time-scales to extinction. Moreover, an analytical expression for the basic reproduction number of the spatial model, [Formula], is derived as function of the non-spatial counterpart, R0, which characterises a transition between a disease-free and a propagation phase, in which the disease propagates over a large fraction of the system. This allows to determine a phase diagram for the epidemic model as function of the parasite mobility and the basic reproduction number of the non-spatial model.

ecology↗