Biology subjects

Vanalli, C.

Publications and source records attributed to Vanalli, C..

12 recordsLinked to original sources

bioRxiv2025

The spatial and temporal spread of highly pathogenic avian influenza in North America: Newton's Cradle hypothesis

The recent emergence of highly pathogenic H5N1--especially clade 2.3.4.4b has led to widespread mortality in poultry and wild birds and has raised significant concerns for the dairy industry and human health. Migratory waterfowl are considered the main source of infection, and we used publicly available surveillance data and bird observation data from continental North America to show clear seasonal signals correlated with waterfowl movement, both on the continental scale and in three of the four flyways. In early 2024, the virus expanded its host range, and we observed a phase transition with the loss of the seasonal signal coupled with a concomitant increase in the proportion of mammalian cases. We also identified a second harmonic, with a regional east-to-west movement with infections spreading between regional flyways, followed by local viral amplification. We likened this to the movement of balls in a Newtons Cradle with an analogy between potential and viral energy. We used bird data to identify bird species associated with viral cases and identified specific waterfowl species and highlighted the importance of predatory and scavenging birds, specifically raptors and gulls, in local amplification. These findings will help to focus surveillance strategies both at local and regional levels.

biophysics↗

bioRxiv2025

Multi-scale neural networks enhance species distribution modelling across predictors and taxonomic groups

Ecological processes and patterns are scale-dependent, as reflected in how relationships between species occurrence and the environment vary across scales. In species distribution models (SDMs), spatial scale can strongly affect model predictions and performance. Despite this, the choice of scale is often overlooked. The development of SDMs using deep learning models has enabled spatially structured data, such as patches of environmental raster data, to be considered as model inputs, making the question of scale even more crucial. Here, we evaluate convolutional neural network-based multi-species SDMs considering different scales on a dataset from Switzerland, including more than 8 million observations for 2390 plant and 1006 animal species comprising amphibians, butterflies, beetles, and mammals. We investigate how scale affects model performance and compare single- and multi-scale models. Our results reveal stronger scale effects for remote sensing indices than for bioclimatic and edaphic variables. In single-scale models, we find that plant species perform better with smaller spatial scales, while the opposite is true for animal species. Multi-scale models consistently improve predictive performance and reduce sensitivity to arbitrary scale selection across all modalities and taxonomic groups. The effect of scale is nonetheless smaller than the increase in performance achieved by considering multiple predictor groups. To improve the interpretability of such complex models, we use attribution methods to compute the relative contribution of different scales and predictor groups. Our study demonstrates the importance of accounting for scale and the potential of deep learning methods to integrate ecological complexity with spatial data across scales. HighlightsO_LIDeep learning SDMs can use spatial environmental data as model predictors C_LIO_LIThe spatial extent of the predictors affects model performance C_LIO_LIMulti-scale models improve performance across predictors and taxonomic groups C_LIO_LIMulti-scale modelling reduces sensitivity to arbitrary scale selection C_LIO_LIExplainable AI methods provide contribution scores for scales and predictor groups C_LI

ecology↗

bioRxiv2025

Migrating in a warming world: A deep learning approach to predict pan-American seasonal shifts in the monarch butterfly niche

Climate change is driving biodiversity loss, disrupting ecosystem functioning, and altering species distributions. Migratory species, whose range varies across seasons depending on specific climatic conditions, are particularly sensitive to environmental changes and serve as indicators of ecosystem health. However, current species distribution models often fail to capture the temporal dynamics critical for migratory species, limiting their ability to provide accurate future range estimations. In this study, we address this gap by developing a time-aware deep learning species distribution model for the monarch butterfly (Danaus plexippus), an iconic species for biodiversity conservation. Using monarch occurrence records across the Americas gathered from scientific and citizen science sources, we embed the effect of monthly climatic variables in a sequential framework. We compare the performance of our seasonal model to conventional time-static baselines, showing not only better performance in the present, where the models have been trained and validated, but also in the past. Our findings show that climatic factors such as humidity, temperature, precipitation and cloud coverage strongly influence the ecological niche of the monarch butterfly, with notable seasonal and spatial variability. Applying our model under climate change scenarios, we predict a northwestward shift in the monarch range by the end of the XXI century, with expansion in Canada and significant contraction in California and Central America, key sites for overwintering that also host resident monarch populations. These changes could severely impact the species migratory cycle and population stability. Using Shapley values, an explainable AI technique, we identify the decrease in precipitation and humidity as important environmental drivers responsible for the contraction of overwintering sites. By focusing on a species of high ecological relevance through a time-aware modeling approach, this work brings novel insights for the conservation of migratory species in the face of the challenges posed by climate change.

ecology↗

bioRxiv2025

Shifting ecological niches of Prunus species under climate change

Climate change is strongly impacting agriculture, reducing crop production and shifting the geographic distribution of suitable areas for crop cultivation. To safeguard future global yield and feed a growing world population, the migration of crop production areas to new suitable sites represents a way to adapt to a changing climate. Here, we aim to identify the ecological niche of Prunus species, namely peach, plum, almond, apricot and sweet cherry, and examine their expected future shifts under climate change. For each Prunus species, we selected from the literature processed-based phenological models for dormancy break, blooming and fruit ripening, whose fulfillment determines whether an area is suitable for crop cultivation. Historically, the ecological niche of Prunus species spans mid-low European latitudes, while higher latitudes fail satisfying the forcing requirements for blooming and fruit ripening. In future, this constraint is expected to become less restrictive with a northwards expansion of the suitable areas. However, this will be contrasted by a contraction of the niche at low latitudes due to dormancy break failures. While bridging established mechanistic knowledge on the climatic effects of plant phenological traits with citizen science observations, our work brings new insights into how fruit crops will respond to global warming.

ecology↗

bioRxiv2024

Modeling the dietary effects on bat viral shedding and potential consequences for pathogen spillover

Changes in the quality and quantity of food resources can affect individuals health, the way they control infections and consequently the likelihood of onward transmission of pathogens. Dietary shifts have been proposed as one of the factors driving spillovers of zoonotic viruses from bats through a bridging host to humans. While there is a general understanding of the relationship between nutrition and infection in model systems, how diet affects pathogen shedding and the risk of spillover from bats is lacking. We used a data-driven mathematical modeling approach to disentangle the relation between diet, immunity, and viral shedding of Jamaican fruit bats infected with H18N11 and fed different dietary regimes. Model selection indicates that the synergistic interaction between the metabolite citrulline and the cytokine TNF controls viral shedding in a diet-dependent manner. Bats on a sub-optimal fat diet are more successful in terminating shedding than bats on an optimal or sub-optimal sugar diet. However, when bat foraging behavior is considered, bats on the optimal diet show a lower spillover hazard, probably because of a feeding behavior less conducive to transmission. This study provides novel insights into the diet-driven mechanisms of viral shedding and how they can potentially contribute to spillover events.

ecology↗

bioRxiv2023

Phenological and epidemiological impacts of climate change on peach production

Agricultural food security is threatened by climate change impacts, which can distress crop growth and favor the spread of infectious diseases. Here, we examined the synergism of the two potentially most disruptive causes of future yield failure in peach production: the effects of global climate change on fruit growth and on the spread of fungal diseases. Coupling a phenological and epidemiological model across the French continental territory, we provided projections of yield losses for four peach cultivars (early, mid-early, mid-late, and late) in the XXI century under different climate change scenarios. Global warming is expected to impair fruit phenology with blooming failure events in the south-western part of the country. This will be less extreme under the more moderate emission scenario, even though sporadic blooming failures will still occur. In contrast, future warmer and drier conditions will decrease brown rot-induced yield loss in the historical locations devoted to peach cultivation. To adapt to these changes, the benefits of shifting peach production sites to new suitable areas are evaluated. Thanks to this strategy, the peach national yield could still be fulfilled even under the most extreme climate change scenario. Comprehensive mathematical frameworks, that concomitantly consider the climatic effects on the plant hosts and on their pathogens, are required to provide reliable future predictions of crop yields and to inform control and adaptation strategies to guarantee food security under global warming.

ecology↗

bioRxiv2023

A metapopulation framework integrating landscape heterogeneity to model an airborne plant pathogen: the case of brown rot of peach in France

Plant disease dynamics are driven by the concurrent interplay of host susceptibility, pathogen presence, and environmental conditions. While host susceptibility and local environmental conditions can readily be characterised, the transmission of an airborne pathogen depends on the biotic and abiotic conditions of the surrounding environment. Here, we propose an original metapopulation framework integrating landscape heterogeneity, in terms of climate and host density, where local populations of plant hosts are connected via air-masses which allow pathogen dispersal. We explicitly account for climatic drivers affecting pathogen release and survival while modelling aerial dispersal using Lagrangian simulations, as well as host phenology and infection. We calibrate the model parameters according to the literature and using Approximate Bayesian Computation against observations of brown rot incidence in French peach orchards from 2001-2020 across an area of 50,000 km2. We used the model to produce maps of risk, distinguishing site dangerousness (risk of causing secondary infection in other sites) and vulnerability (risk of becoming infected) across the our study area. We find that most dangerous and vulnerable sites are located along the Rhone Valley, due to the concurrence of high density of peach cultivation, a suitable climate and persistent airborne connections. Our work represents a first step to integrate metapopulation theory, epidemiology and air-mass movements to inform plant protection strategies, and could be adapted to optimize crop protection under future climate projections.

ecology↗

bioRxiv2023

Helminth ecological requirements shape the impact of climate change on the hazard of infection

Outbreaks and spread of infectious diseases are often associated with seasonality and changes caused by global warming. Free-living stages of soil-transmitted helminths are highly susceptible to environmental drivers, however, how multiple climatic variables affect helminth species, and the long-term consequences of these interactions, is poorly understood. We used experiments on nine trichostrongylid species to develop a temperature- and humidity-dependent model of infection hazard, which was then implemented at the European scale under climate change scenarios. Intestinal and stomach helminths exhibited contrasting climatic responses, with the former group strongly affected by temperature while the latter primarily impacted by humidity. These differences generated seasonal changes in the timing and intensity of the infection hazard and spatial heterogeneities within and between the two groups. A future range expansion of both groups toward northern latitudes is expected to create new opportunities for the co-circulation of the studied helminth species.

ecology↗

bioRxiv2023

Temperature affects viral kinetics and vectorial capacity of Aedes aegypti mosquitoes co-infected with Mayaro and Dengue viruses

Increasing global temperatures and unpredictable climatic extremes have contributed to the spread of vector-borne diseases. The mosquito Aedes aegypti is the main vector of multiple arboviruses that negatively impact human health, mostly in low socioeconomic areas of the world. Co-circulation and co-infection of these viruses in humans have been increasingly reported; however, how vectors contribute to this alarming trend remains unclear. Here, we examine single and co-infection of Mayaro virus (-D strain, Alphavirus) and dengue virus (serotype 2, Flavivirus) in Ae. aegypti adults and cell lines at two constant temperatures, moderate (27{degrees}C) and hot (32{degrees}C), to quantify vector competence and the effect of temperature on infection, dissemination and transmission, including on the degree of interaction between the two viruses. Both viruses were primarily affected by temperature but there was a partial interaction with co-infection. Dengue virus quickly replicates in adult mosquitoes, with a tendency for higher titers in co-infected mosquitoes at both temperatures and mosquito mortality was more severe at higher temperatures in all conditions. For dengue, and to a lesser extent Mayaro, vector competence and vectorial capacity were higher at hotter temperature in co- vs single infections and was more evident at earlier timepoints (7 vs 14 days post infection). The temperature-dependent phenotype was confirmed in vitro by faster cellular infection and initial replication at higher temperatures for dengue but not for Mayaro virus. Our study suggests that contrasting kinetics of the two viruses could be related to their intrinsic thermal requirements, where alphaviruses thrive better at lower temperatures compared to flaviviruses, but further studies are necessary to clarify the role of co-infection at different and variable temperature regimes. Author summaryGlobal warming is having devastating consequences for the environment, and a cause of concern is the increase in local abundance and geographic range of mosquitoes and the associated viruses they transmit. This study explores how temperature affects the mosquitos ability to survive and potentially spread two viruses, Mayaro and dengue, in single or co-infections. We found that Mayaro virus was not clearly affected by temperature or the presence of dengue infection. In contrast, dengue virus showed higher infection and potential for higher transmission in mosquitoes kept at high temperatures, and this trend was stronger in co-infections compared to single infections. Mosquito survival consistently decreased at high temperatures. We hypothesize the differences observed for dengue virus are due to the faster growth and viral activity in the mosquito at hotter temperatures, a pattern not observed for Mayaro virus. More studies under different temperature regimes are needed to clarify the role of co-infection.

microbiology↗

bioRxiv2022

Modeling the contribution of antibodies to the within-host dynamics of single and dual helminth infections in a natural system

Within-host models of infection can provide important insights into the processes that affect parasite spread and persistence in host populations. However, modeling is often limited by the availability of empirical data, a problem commonly encountered in natural systems. Here, we used six years of immune-infection observations of two gastrointestinal helminths (Trichostrongylus retortaeformis and Graphidium strigosum) from a population of European rabbits (Oryctolagus cuniculus) to develop an age-dependent, within-host mathematical model that explicitly included species-specific and cross-reacting antibody (IgA and IgG) responses to each helminth in hosts with single or dual infections. Different models of single infection were formally compared to test alternative mechanisms of parasite regulation. The two models that best described single infections of each helminth species were then coupled through antibody cross-immunity to examine how the presence of one species could alter the host immune response to, and the within-host dynamics of, the other species. For both single infections, model selection suggested that either IgA or IgG responses could equally explain the observed parasite intensities at different host age. However, the strength of this response drastically changed between the two helminths, being stronger against T. retortaeformis than against G. strigosum and causing contrasting age-intensity profiles. When the two helminths coinfect the same host, we found a decrease of the species-specific immune response to both species together with an asymmetric cross-immune response driven by IgG. Changes in the level and affinity of antibodies from single to dual infections contributed to the significant increase of both helminth abundances. By combining mathematical modeling with immuno-infection data, our work provides a model framework for disentangling some of the complexities generated by host-parasite and parasite-parasite interactions from natural systems. Our approach thus offers a tractable general tool to examine immune-infection relationships within hosts. Author summaryThe host immune response often plays a critical role in regulating parasite dynamics and transmission. We developed a mathematical model to evaluate whether and how the observed variation in host antibody responses could explain the relative differences in the abundance of two parasite species in hosts with single and dual infections from a natural rabbit population. Our results indicated that either IgA or IgG could describe the contrasting trends of the two parasites in rabbits with single infections. Specifically, antibodies appeared to control T. retortaeformis, while the effect was less clear for G. strigosum. For dual infections, we found a weaker specific antibody response against both helminths and an asymmetric cross-immunity, which could explain the significantly greater intensities observed for T. retortaeformis and, secondly, for G. strigosum. Our within-host mathematical framework provides a plausible mechanism for the mediated role of antibodies in hosts with single and dual parasite infections, and how the strength of these infection-immune interactions changes with host age. This model framework offers a way forward to our understanding of the within-host processes that generate individual variation in infection and is relatively flexible to be applied to other natural systems.

immunology↗

bioRxiv2022

A climate-driven compartmental model for fungal diseases in fruit orchards: The impacts of climate change on a brown rot-peach system

As a well-known driving force of diseases in crops, climate change is likely to impact future crop yields. In the present work, we account for climate-related influences on the key parameters of a SIR-type epidemiological model for fungal diseases in stone fruit aimed at simulating different observed epidemic patterns, and, eventually, evaluating the possible impacts of climate change on the disease-induced yield loss. Brown rot disease in peach orchards is used here as a study system. We contrasted simulation results with epidemiological measures collected from an experimental orchard in Avignon (southern France) in two consecutive years, characterized by dissimilar brown rot outbreaks. The capacity of our climate-driven model to adequately reproduce the observed disease patterns suggests temperature and precipitation as key drivers of brown rot epidemics. Overall, the model predicts a potential decrease of brown rot severity under warmer and drier climatic conditions. To comprehensively understand the effect of future climate change on peach yield, alterations of crop phenology must also be accounted for. We thus build a model that considers the synergism between the two factors: vulnerability to the pathogen and varying phenology. Using plausible climate change scenarios, we find that the peach yield in the considered Mediterranean region will be considerably impaired: although brown rot-related yield losses are expected to decrease in warmer and drier climatic conditions, climate change will hinder fruit blooming and consequently the yield because milder winters will impede the achievement of dormancy. By deepening our understanding of climatic impacts on crop fungal infections, the present study may serve as a useful tool to plan and implement suitable adaptation strategies for peach cultivation.

plant biology↗

bioRxiv2020

Shifts in the thermal niche of fruit trees under climate change: the case of peach cultivation inFrance.

Climate influences plant phenological traits, thus playing a key role in defining the geographical range of crops. Foreseeing the impact of climate change on fruit trees is essential to inform policy decisions to guide the adaptation to new climatic conditions. To this end, we propose and use a phenological process-based model to assess the impacts of climate change upon the phenology, the suitability and the distribution of economically important cultivars of peach (Prunus persica), across the entire continental France. The model combines temperature dependent sub-models of dormancy, blooming, fruit survival and ripening, using chilling units, forcing units, frost occurrence and growing degree days, respectively. We find that climate change will have divergent impacts upon peach production. On the one hand, blooming will occur earlier, warmer temperatures will decrease spring frost occurrence and fruit ripening will be easily achieved before the start of fall. On the other hand, milder winters will impede the plant buds from breaking endodormancy, with consequent abnormal patterns of fruit development or even blooming failure. This latter impact will dramatically shift the geographic range of sites where peach production will be profitable. This shift will mainly be from the south of France (Languedoc-Roussillon, Rhone-Alpes and Provence-Alpes-Cote dAzur), to northwestern areas where the winter chilling requirement will still be fulfilled. Our study provides novel insights for understanding and forecasting climate change impacts on peach phenology and it is the first framework that maps the ecological thermal niche of peach at national level.

plant biology↗