Biology subjects

Poisot, T.

Publications and source records attributed to Poisot, T..

18 recordsLinked to original sources

bioRxiv2026

Specialists drive biodiversity scaling in symbiotic relationships

The stunning diversity of symbiotic life forms, and their unique vulnerability to extinction, emerge from the close relationship between host and symbiont species richness. The general form of this relationship should be linear, but simulation studies have shown that it becomes sub-linear (and even power law-like) when sampling within a host-symbiont network. Here, we resolve this paradox with a new mathematical model of scaling in bipartite graphs, based on the independent behavior of specialist (single-host) and generalist (multi-host) symbionts. Using this model, we show that specialists constrain the architecture of ecological networks, and at global scales, drive the accumulation of symbiont biodiversity. By definition, specialists also face the highest risk of coextinction with their hosts and -- despite substantial uncertainty about their true richness -- we show that they could easily account for the majority of threatened species on Earth. Our study reveals that symbiosis remains one of the most poorly-understood building blocks of ecosystem function and evolutionary diversification, and serves as a reminder that foundational macroecological principles are still waiting to be discovered from first principles.

ecology↗

bioRxiv2025

Machine Learning Enables Viral Genome-Agnostic Classification of RNA Virus Infections from Host Transcriptomes

Targeted PCR diagnosis of RNA viruses is sequence dependent, meaning that the accuracy of the assay depends on the identity of the viral sequence. However, sequence-targeted assays can miss novel or divergent viruses. We test whether the host transcriptome alone can classify RNA virus infections without using viral sequences. Using publicly available data on Huh7 and Calu-3 cells experimentally infected with diverse negative-sense RNA, we evaluate two host-derived feature sets (differential expression dataset of identified genes and alignment-free nucleotide k-mer spectra) and compare hierarchical clustering with Random Forests. Across datasets and timepoints (12 to 24 hpi), Random forests accurately distinguished cells infected with different viruses. Controls with label permutation and read shuffling across experimental conditions established non-random performance. Influenza A infections exhibited the strongest, most distinct signatures, whereas Ebola and Lassa virus responses were subtler yet still classifiable. These results show that host-only transcriptomics encodes virus-specific, complex signals that machine learning can exploit, enabling genome-agnostic classification of infection statuses. This approach could aid early outbreak triage when primer based detection methods fail or viral genomes are unknown and complements sequence-based discovery.

microbiology↗

bioRxiv2025

Deciphering the dynamics of Cyanobacteria-Phage in a natural lake: Insights from a decade-long investigation

Phages - viruses that infect bacteria - are often seen as key players in bacterial community dynamics and the ecosystem services those communities support. However, much of our understanding of phage-bacteria interactions comes from in vitro studies, which provide limited insights into how these interactions occur in natural environments. In this study, we used cyanobacterial blooms as a model microbial community to evaluate the potential role of phages in driving cyanobacterial populations. These blooms are characterized by massive and usually rapid accumulation of cyanobacterial biomass and occur worldwide, threatening aquatic systems. The frequency and intensity of cyanobacterial blooms are increasing over time, likely due to human activities, and, until now, the role of predators like phages has remained unclear. We used a deep amplicon sequencing approach targeting the g20 capsid gene to profile the cyanophage community in eutrophic Lake Champlain over time, comparing their dynamics and diversity with bacterial communities and examining their associations. We evaluated whether phages simply followed bacterial dynamics, with limited impact on bacterial composition, or instead whether they played an active role in drving bacterial community structure. We found that phages exhibited similar dynamics to their potential bacterial hosts and shared environmental niches. However, we also observed strong differences specific to the phage community, such as inter-annual variation and an increase in Shannon diversity over time. Phage-bacteria and phage-cyanobacteria co-variance uncovered potential interactions that resulted in strongly modular networks. However, while the network structure remained modular, the composition of the modules differed significantly between environmental and temporal conditions. Lastly, viral phylogeny partially explained phage-bacteria co-variance, but only for a small proportion of bacterial ASVs. Our results indicate that phage-bacterial interactions are partly genetically structured and vary within modules across environmental conditions.

ecology↗

bioRxiv2023

A social-ecological geography of southern Canadian Lakes

Anthropogenic pressures, including urban and agricultural expansion, can negatively influence a lakes capacity to provide aquatic ecosystem services (ES). However, identifying lakes most at risk of losing their ES requires integrating information on lake ecological state, global change threats, and ES demand. Here, we provide a social-ecological framework that combines these features within a regional context based on an ecological evaluation of the state of 659 lakes across Canada. From deviation of impacted lakes to reference ones, we identified much higher concentrations of total nitrogen and chloride as the main indicators of altered lake ecological state in all regions identified. Lake ecological state was mapped using an additive colour model along with regional scores of threat levels and recreational ES demand. Population density and agriculture were linked to high lake vulnerability. Lakes in Southern Ontario were most concerning, being highly altered, under threat, and heavily used. Lakes near urban centers along coasts were altered and used, but less threatened, whereas those in the Prairies were altered and threatened, but less used. Our novel framework provides the first social-ecological geography of Canadian lakes, and, is a promising tool to assess lake state and vulnerability at scales relevant for management. Plan language summaryPlain language title: Assessing overall lake health across Canada to identify sites for restoration and conservation Canadians love to swim, fish, and navigate in and on the countless lakes across the country. But Canadian lakes are under a considerable amount of pressure from human activities in their watershed. The expansion of cities, intensive farming, wetland loss, and industrial development all results in the transfer of pollutants to aquatic habitats, threatening the health of lakes and the ecosystem services they provide. Where are lakes being used across Canada? What condition are they in and is their use under threat from different pressures? To answer these questions, we combined information from many different sources, including a national scale lake assessment, through the NSERC Strategic Network Cluster Lake Pulse to create the first social-ecological geography of southern Canadian lakes. Regionally specific baseline conditions were established from lakes considered healthy due to limited human activities in their watershed. When lakes with impacted watershed were compared to healthy ones within their specific region, two early warning signals of human pressure, pollution from nitrogen found in fertilizers and sewage, and chloride found in road salt, determined whether a lake was altered. We combined these two health indicators, with information on future potential lake threats and use by the population for recreational purposes. Using a colour-coded mapping technique, we were able to identify regions where lakes were altered, threatened, and used. These regions occurred primarily around dense urban areas, of southern Ontario and Quebec, and major cities on the east and west coast. Lakes were altered and threatened, but seemingly less used in the Prairie Provinces. The novel approach is very adaptable, easy to understand, and can be used at more regional levels for management to determine priority sites for conservation and restoration, as well as in science communication to describe overall lake health.

ecology↗

bioRxiv2021

The Global Virome in One Network (VIRION): an atlas of vertebrate-virus associations

Data cataloguing viral diversity on Earth have been fragmented across sources, disciplines, formats, and various degrees of open collation, posing challenges for research on macroecology, evolution, and public health. Here, we solve this problem by establishing a dynamically-maintained database of vertebrate-virus associations, called The Global Virome in One Network (VIRION). The VIRION database has been assembled through both reconciliation of static datasets and integration of dynamically-updated databases. These data sources are all harmonized against one taxonomic backbone, including metadata on host and virus taxonomic validity and higher classification; additional metadata on sampling methodology and evidence strength are also available in a harmonized format. In total, the VIRION database is the largest open-source, open-access database of its kind, with roughly half a million unique records that include 9,521 resolved virus "species" (of which 1,661 are ICTV ratified), 3,692 resolved vertebrate host species, and 23,147 unique interactions between taxonomically-valid organisms. Together, these data cover roughly a quarter of mammal diversity, a tenth of bird diversity, and ~6% of the estimated total diversity of vertebrates, and a much larger proportion of their virome than any previous database. We show how these data can be used to test hypotheses about microbiology, ecology, and evolution, and make suggestions for best practices that address the unique mix of evidence that coexists in these data.

microbiology↗

bioRxiv2021

Data proliferation, reconciliation, and synthesis in viral ecology

The fields of viral ecology and evolution have rapidly expanded in the last two decades, driven by technological improvements, and motivated by efforts to discover potentially zoonotic wildlife viruses under the rubric of pandemic prevention. One consequence has been a massive proliferation of host-virus association data, which comprise the backbone of research in viral macroecology and zoonotic risk prediction. These data remain fragmented across numerous data portals and projects, each with their own scope, structure, and reporting standards. Here, we propose that synthesis of host-virus association data is a central challenge to improve our understanding of the global virome and develop foundational theory in viral ecology. To illustrate this, we build an open reconciled mammal-virus database from four key published datasets, applying a standardized taxonomy and metadata. We show that reconciling these datasets provides a substantially richer view of the mammal virome than that offered by any one individual database. We argue for a shift in best practice towards the incremental development and use of synthetic datasets in viral ecology research, both to improve comparability and replicability across studies, and to facilitate future efforts to use machine learning to predict the structure and dynamics of the global virome.

ecology↗

bioRxiv2020

Food web structure alters ecological communities top-heaviness with little effect on the biodiversity-functioning relationship.

In a rapidly changing world, the composition, diversity and structure of ecological communities face many threats. Biodiversity-Ecosystem Functioning (BEF) and community food-chain analyses have focused on investigating the consequences of these changes on ecosystem processes and the resulting functions. These different and diverging conceptual frameworks have each produced important results and identified a set of important mechanisms, that shape ecosystem functions. But the disconnection between these frameworks, and the various simplifications of the study systems are not representative of the complexity of real-world communities. Here we use food webs as a more realistic depiction of communities, and use a bioenergetic model to simulate their biomass dynamics and quantify the resulting flows and stocks of biomass. We use tools from food web analysis to investigate how the predictions from BEF and food-chain analyses fit together, how they correlate to food-web structure and how it might help us understand the interplay between various drivers of ecosystem functioning. We show that food web structure is correlated to the communitys efficiency in storing the captured biomass, which may explain the distribution of biomass (top heaviness) across the different trophic compartments (producers, primary and secondary consumers). While we know that ecological network structure is important in shaping ecosystem dynamics, identifying structural attributes important in shaping ecosystem processes and synthesizing how it affects various underpinning mechanisms may help prioritize key conservation targets to protect not only biodiversity but also its structure and the resulting services.

ecology↗

bioRxiv2020

Predicting wildlife hosts of betacoronaviruses for SARS-CoV-2 sampling prioritization

Despite global investment in One Health disease surveillance, it remains difficult--and often very costly--to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can be used to guide sampling prioritization, but predictions from any given model may be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of likely reservoir hosts. In the first quarter of 2020, we generated an ensemble of eight statistical models that predict host-virus associations and developed priority sampling recommendations for potential bat reservoirs and potential bridge hosts for SARS-CoV-2. Over more than a year, we tracked the discovery of 40 new bat hosts of betacoronaviruses, validated initial predictions, and dynamically updated our analytic pipeline. We find that ecological trait-based models perform extremely well at predicting these novel hosts, whereas network methods consistently perform roughly as well or worse than expected at random. These findings illustrate the importance of ensembling as a buffer against variation in model quality and highlight the value of including host ecology in predictive models. Our revised models show improved performance and predict over 400 bat species globally that could be undetected hosts of betacoronaviruses. Although 20 species of horseshoe bats (Rhinolophus spp.) are known to be the primary reservoir of SARS-like viruses, we find at least three-fourths of plausible betacoronavirus reservoirs in this bat genus might still be undetected. Our study is the first to demonstrate through systematic validation that machine learning models can help optimize wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.

ecology↗

bioRxiv2020

Revisiting the links-species scaling relationship in food webs

Predicting the number of interactions that species in a food web will establish is an important task. These trophic interactions underlie many ecological and evolutionary processes, ranging from biomass fluxes, ecosystem stability, resilience to extinction, and resistance against novel species. We investigate and compare several ways to predict the number of interactions in food webs. We conclude that a simple beta-binomial model outperforms other models, with the added desirable property of respecting biological constraints. We show how this simple relationship gives rise to a predicted distribution of several quantities related to link number in food webs, including the scaling of network structure with space, and the probability that a network will be stable.

ecology↗

bioRxiv2020

Omnidirectional and omnifunctional connectivity analyses with a diverse species pool

Connectivity among habitat patches in both natural and disturbed landscapes needs to be accounted for in conservation planning for biodiversity maintenance. Yet methods to assess connectivity are often limited, because simulating the dispersal of many species is computationally prohibitive, and current simulations make simplifying assumptions about movement that are potentially erroneous. Here we show how these limits can be circumvented and propose a novel framework for the assessment of omnifunctional and omnidirectional connectivity in a 28000 km2 area in the Laurentian region of Quebec, Canada. Our approach relies on (i) the use of Omniscape, an improved version of Circuitscape which allows omnidirectional simulations that better emulate animal movement and (ii) the synthesis of large volume of species-level dispersal simulations through a posteriori clustering of the current intensity. Our analysis reveals that the movement of 93 species evaluated can be clustered into three functional dispersal guilds, corresponding to mostly aquatic species, terrestrial species able to use aquatic environments, and strictly terrestrial species. These functional guilds do not share connectivity hotspots, suggesting that corridor planning would need to account for the multiplicity of dispersal strategies. Although this approach requires a large volume of computing resources, it provides richer information on which landscape features are critical to maintain or need to be regenerated for broader biodiversity maintenance goals.

ecology↗

bioRxiv2020

Environmental biases in the study of ecological networks at the planetary scale

Ecological networks are increasingly studied at large spatial scales, expanding their focus from a conceptual tool for community ecology into one that also adresses questions in biogeography and macroecology. This effort is supported by increased access to standardized information on ecological networks, in the form of openly accessible databases. Yet, there has been no systematic evaluation of the fitness for purpose of these data to explore synthesis questions at very large spatial scales. In particular, because the sampling of ecological networks is a difficult task, they are likely to not have a good representation of the diversity of Earths bioclimatic conditions, likely to be spatially aggregated, and therefore unlikely to achieve broad representativeness. In this paper, we analyze over 1300 ecological networks in the mangal.io database, and discuss their coverage of biomes, and the geographic areas in which there is a deficit of data on ecological networks. Taken together, our results suggest that while some information about the global structure of ecological networks is available, it remains fragmented over space, with further differences by types of eco-logical interactions. This causes great concerns both for our ability to transfer knowledge from one region to the next, but also to forecast the structural change in networks under climate change.

ecology↗

bioRxiv2018

Leveraging pathogen community distributions to understand outbreak and emergence potential

Understanding pathogen outbreak and emergence events has important implications to the management of infectious disease. Apart from preempting infectious disease events, there is considerable interest in determining why certain pathogens are consistently found in some regions, and why others spontaneously emerge or reemerge over time. Here, we use a trait-free approach which leverages information on the global community of human infectious diseases to estimate the potential for pathogen outbreak, emergence, and re-emergence events over time. Our approach uses pairwise dissimilarities among pathogen distributions between countries and country-level pathogen composition to quantify pathogen outbreak, emergence, and re-emergence potential as a function of time (e.g., number of years between training and prediction), pathogen type (e.g., virus), and transmission mode (e.g., vector-borne). We find that while outbreak and re-emergence potential are well captured by our simple model, prediction of emergence events remains elusive, and sudden global emergences like an influenza pandemic seem beyond the predictive capacity of the model. While our approach allows for dynamic predictability of outbreak and re-emergence events, data deficiencies and the stochastic nature of emergence events may preclude accurate prediction. Together, our results make a compelling case for incorporating a community ecological perspective into existing disease forecasting efforts.

epidemiology↗

bioRxiv2018

Revealing biases in the sampling of ecological interaction networks

The structure of ecological interactions is commonly understood through analyses of interaction networks. However, these analyses may be sensitive to sampling biases in both the interactors (the nodes of the network) and interactions (the links between nodes), because the detectability of species and their interactions is highly heterogeneous. These issues may affect the accuracy of empirically constructed ecological networks. Yet statistical biases introduced by sampling error are difficult to quantify in the absence of full knowledge of the underlying ecological networks structure. To explore properties of large-scale modular networks, we developed EcoNetGen, which constructs and samples networks with predetermined topologies. These networks may represent a wide variety of communities that vary in size and types of ecological interactions. We sampled these networks with different sampling designs that may be employed in field observations. The observed networks generated by each sampling process were then analyzed with respect to the number of components, size of components and other network metrics. We show that the sampling effort needed to estimate underlying network properties accurately depends both on the sampling design and on the underlying network topology. In particular, networks with random or scale-free modules require more complete sampling to reveal their structure, compared to networks whose modules are nested or bipartite. Overall, the modules with nested structure were the easiest to detect, regardless of sampling design. Sampling according to species degree (number of interactions) was consistently found to be the most accurate strategy to estimate network structure. Conversely, sampling according to module (representing different interaction types or taxa) results in a rather complete view of certain modules, but fails to provide a complete picture of the underlying network. We recommend that these findings be incorporated into field sampling design of projects aiming to characterize large species interactions networks to reduce sampling biases.\n\nAuthor SummaryEcological interactions are commonly modeled as interaction networks. Analyses of such networks may be sensitive to sampling biases and detection issues in both the interactors and interactions (nodes and links). Yet, statistical biases introduced by sampling error are difficult to quantify in the absence of full knowledge of the underlying networks structure. For insight into ecological networks, we developed software EcoNetGen (available in R and Python). These allow the generation and sampling of several types of large-scale modular networks with predetermined topologies, representing a wide variety of communities and types of ecological interactions. Networks can be sampled according to designs employed in field observations. We demonstrate, through first uses of this software, that underlying network topology interacts strongly with empirical sampling design, and that constructing empirical networks by starting with highly connected species may be the give the best representation of the underlying network.

ecology↗

bioRxiv2018

Homogenization of species composition and species association networks are decoupled

Ecological communities are comprised of both species and the biotic relationships among them. Biotic homogenization in species composition (i.e. increased site-to-site similarity) is recognized a common consequence of global change, but less is known about how species relationships change over space and time. Does homogenization of species composition lead to homogenization of species relationships or are the dynamics of species relationships decoupled from changes in species composition? To answer this question, we used long-term resurvey data to analyze changes in plant species association patterns between the 1950s and 2000s at 266 sites distributed among three community types in Wisconsin, USA. We used species associations (quantified via local co-occurrence patterns) as a proxy for species relationships. Species pairs that co-occur more/less than expected by chance have positive/negative associations. Shifts in species associations consistently exceeded the shifts observed in species composition. Less disturbed forests of northern Wisconsin have converged somewhat in species composition but not much in species associations. In contrast, forests in central Wisconsin succeeding from pine barrens to closed-canopy forests have strongly homogenized in both species composition and species associations. More fragmented forests in southern Wisconsin also tended to converge in species composition and in the species negative associations, but their positive associations diverged over the last half century. We conclude that long-term shifts in species relationships may be decoupled from those of species composition despite being affected by similar environmental variables.

ecology↗

bioRxiv2017

Scientific Theories and Artificial Intelligence

The grand ambition of theorists studying ecology and evolution is to discover the logical and mathematical rules driving the worlds biodiversity at every level from genetic diversity within species to differences between populations, communities, and ecosystems. This ambition has been difficult to realize in great part because of the complexity of biodiversity. Theoretical work has led to a complex web of theories, each having non-obvious consequences for other theories. Case in point, the recent realization that genetic diversity involves a great deal of temporal and spatial stochasticity forces theoretical population genetics to consider abiotic and biotic factors generally reserved to ecosystem ecology. This interconnectedness may require theoretical scientists to adopt new techniques adapted to reason about large sets of theories. Mathematicians have solved this problem by using formal languages based on logic to manage theorems. However, theories in ecology and evolution are not mathematical theorems, they involve uncertainty. Recent work in Artificial Intelligence in bridging logic and probability theory offers the opportunity to build rich knowledge bases that combine logics ability to represent complex mathematical ideas with probability theorys ability to model uncertainty. We describe these hybrid languages and explore how they could be used to build a unified knowledge base of theories for ecology and evolution.\n\ncase study you explore using the Salix tritrophic system.

scientific communication and education↗

bioRxiv2017

Data-based, synthesis-driven: setting the agenda for computational ecology

Computational thinking is the integration of algorithms, software, and data, to solve general questions in a field. Computation ecology has the potential to transform the way ecologists think about the integration of data and models. As the practice is gaining prominence as a way to conduct ecological research, it is important to reflect on what its agenda could be, and how it fits within the broader landscape of ecological research. In this contribution, we suggest areas in which empirical ecologists, modellers, and the emerging community of computational ecologists could engage in a constructive dialogue to build on one anothers expertise; specifically, about the need to make predictions from models actionable, about the best standards to represent ecological data, and about the proper ways to credit data collection and data reuse. We discuss how training can be amended to improve computational literacy.

ecology↗

bioRxiv2017

Analyzing ecological networks of species interactions

Networks provide one of the best representations for ecological communities, composed of many species with sometimes complex connections between them. Yet the methodological literature allowing one to analyze and extract meaning from ecological networks is dense, fragmented, and unwelcoming. We provide a general overview to the field of using networks in community ecology, outlining both the intent of the different measures, their assumptions, and the contexts in which they can be used. When methodologically justified, we suggest good practices to use in the analysis of ecological networks. We anchor this synopsis with examples from empirical studies, and conclude by highlighting what identified as needed future developments in the field.

ecology↗

bioRxiv2016

Ecological Interactions and the Netflix Problem

0Species interactions are a key component of ecosystems but we generally have an incomplete picture of who-eats-who in a given community. Different techniques have been devised to predict species interactions using theoretical models or abundances. Here, we explore the K nearest neighbour approach, with a special emphasis on recommendation, along with other machine learning techniques. Recommenders are algorithms developed for companies like Netflix to predict if a customer would like a product given the preferences of similar customers. These machine learning techniques are well-suited to study binary ecological interactions since they focus on positive-only data. We also explore how the K nearest neighbour approach can be used with both positive and negative information, in which case the goal of the algorithm is to fill missing entries from a matrix (imputation). By removing a prey from a predator, we find that recommenders can guess the missing prey around 50% of the times on the first try, with up to 881 possibilities. Traits do not improve significantly the results for the K nearest neighbour, although a simple test with a supervised learning approach (random forests) show we can predict interactions with high accuracy using only three traits per species. This result shows that binary interactions can be predicted without regard to the ecological community given only three variables: body mass and two variables for the species phylogeny. These techniques are complementary, as recommenders can predict interactions in the absence of traits, using only information about other species interactions, while supervised learning algorithms such as random forests base their predictions on traits only but do not exploit other species interactions. Further work should focus on developing custom similarity measures specialized to ecology to improve the KNN algorithms and using richer data to capture indirect relationships between species.

ecology↗