Biology subjects

Cosme, M.

Publications and source records attributed to Cosme, M..

7 recordsLinked to original sources

bioRxiv2026

Prolonged impact of fire on peatland fungi despite rapid recovery of vegetation, prokaryotes, and soil physicochemistry

Climate change is increasing the frequency of wildfires in ecosystems that historically rarely burn, such as wet heaths and peatlands, thereby threatening carbon storage, biodiversity, and ecosystem functioning. We conducted a three-year, multi-level study to assess early post-fire recovery trajectories of soil physicochemical properties, vegetation, and soil microbial communities in a wet peatland-heathland mosaic affected by a flaming wildfire. Using a paired-plot design of burned and adjacent intact plots, we observed immediate spikes in bioavailable nitrogen (NH, NO-) and phosphorus (POlsen) and a reduction in soil moisture in burned plots, yet two years later these parameters had normalized, indicating rapid abiotic recovery. Vegetation was also strongly altered in the year of the fire, quantifiable by a distinct destruction of herb, moss, tree and litter cover. Although initial regrowth was dominated by a relatively fast resprouting of the graminoid Molinia caerulea, its absolute cover in burned plots never exceeded its cover in intact plots, suggesting this species did not expand post-fire. More typical peatland and wet heath species, including ericoid shrubs and Sphagnum mosses, recovered more gradually but largely returned to pre-fire levels within the timespan of our study, highlighting high vegetation resilience. Soil microbial communities showed contrasting responses. Prokaryotic communities shifted immediately after burning but largely recovered within one year. Fungal communities, however, exhibited stronger and more persistent changes and followed a distinct recovery trajectory shaped by succession of immediate and delayed fungal responders. Overall, pyrophilous and fire-tolerant fungi, such as Coniochaeta spp., increased, as did many presumably generalist or opportunistic saprotrophs. Litter and wood-associated saprotrophs as well as many mycorrhizal taxa, however, declined. Ongoing fungal shifts occurred even after soil chemistry and vegetation had largely returned to baseline, reflecting a temporary decoupling between above- and belowground communities that may have cascading effects on ecosystem functioning. In conclusion, our results reveal differential recovery trajectories across the soil-microbiome-vegetation interface and highlight that seemingly rapid abiotic and aboveground biotic recovery can mask prolonged microbial disruptions. We emphasize the importance of multi-level assessments for understanding ecosystem resilience. HighlightsO_LISoil physicochemistry, vegetation and prokaryotes recovered rapidly after a peatland wildfire C_LIO_LIFungal communities lagged behind and followed a slower recovery trajectory C_LIO_LIThe timing and duration of fungal responses to fire varied across taxa and included immediate or delayed as well as short-lived or persistent responders C_LIO_LIThere was a mismatch between vegetation and fungal recovery trajectories, evidenced by a transient post-disturbance decoupling between above- and belowground biotic communities C_LIO_LIPresumed aboveground recovery can mask prolonged belowground disruptions, with potential implications for decomposition, nutrient cycling, and plant-microbe interactions C_LI

ecology↗

bioRxiv2025

Gene-Culture Coevolution Favours the Emergence of Traditions in Mate Choice through Conformist Social Learning

The emergence of cultural traditions has long been considered to depend on sophisticated social learning mechanisms, particularly conformist transmission exhibiting a disproportionate bias towards majority behaviours. We challenge this assumption by demonstrating that gene-culture coevolutionary dynamics can fundamentally alter the conditions required for tradition formation, especially in the context of mate choice. Using simulation models of mate choice where female preferences are socially transmitted and male traits are genetically inherited, we show that the interaction between cultural and genetic transmission creates novel evolutionary dynamics. In a first model (the Cultural Model) representing classical cultural evolution scenarios, we show that traditions emerge only when conformity disproportionately amplifies majority behaviours. In a second model (the Gene-Culture Model), we introduce a distinction between learned behaviours and realized behaviours and show that when mate choice depends on both conformist learning and male trait availability, even simple social learning strategies can establish persistent traditions. In this context, majority exaggeration is no longer a necessary property of social learning for traditions to arise. This occurs because positive frequency-dependent interactions between preference and trait dynamics provide an additional mechanism that reinforces majority behaviours beyond conformist learning. Our findings have broader implications for cultural evolution theory, suggesting that many behaviours that depend on resource availability may exhibit tradition-like patterns under less stringent social learning conditions than previously assumed. By distinguishing between preference and choice, and demonstrating the importance of gene-culture interactions, this study advocates for more integrated approaches to understanding cultural evolution that consider the interplay between multiple inheritance systems.

evolutionary biology↗

bioRxiv2023

A single changing hypernetwork to represent (social-)ecological dynamics

AO_SCPLOWBSTRACTC_SCPLOWTo understand and manage (social-)ecological systems, we need an intuitive and rigorous way to represent them. Recent ecological studies propose to represent interaction networks into modular graphs, multiplexes and higher-order interactions. Along these lines, we argue here that non-dyadic (non-pairwise) interactions are common in ecology and environmental sciences, necessitating fresh concepts and tools for handling them. In addition, such interaction networks often change sharply, due to appearing and disappearing species and components. We illustrate in a simple example that any ecosystem can be represented by a single hypergraph, here called the ecosystem hypernetwork. Moreover, we highlight that any ecosystem hypernetwork exhibits a changing topology summarizing its long term dynamics (e.g., species extinction/invasion, pollutant or human arrival/migration). Qualitative and discrete-event models developed in computer science appear suitable for modeling hypergraph (topological) dynamics. Hypernetworks thus also provide a conceptual foundation for theoretical as well as more applied studies in ecology (at large), as they form the qualitative backbone of ever-changing ecosystems.

ecology↗

bioRxiv2023

A tripartite bacterial-fungal-plant symbiosis in the mycorrhiza-shaped microbiome drives plant growth and mycorrhization

Plant microbiomes play crucial roles in nutrient cycling and plant growth, and are shaped by a complex interplay between plants, microbes, and the environment. The role of bacteria as mediators of the 400-million-year-old partnership between the majority of land plants and, arbuscular mycorrhizal (AM) fungi is still poorly understood. Here we test whether AM hyphae-associated bacteria influence the success of the AM symbiosis. Using partitioned microcosms containing field soil, we discovered that AM hyphae and roots selectively assemble their own microbiome from the surrounding soil. In two independent experiments, we identified several bacterial genera, including Devosia, that are consistently enriched on AM hyphae. Subsequently, we isolated 144 pure bacterial isolates from a mycorrhiza-rich sample of extraradical hyphae and isolated Devosia sp. ZB163 as root and hyphal colonizer. We show that this AM-associated bacterium synergistically acts with mycorrhiza on the plant root to strongly promote plant growth, nitrogen uptake, and mycorrhization. Our results highlight that AM fungi do not function in isolation and that the plant-mycorrhiza symbiont can recruit beneficial bacteria that support the symbiosis.

microbiology↗

bioRxiv2022

Mycorrhizas shape the evolution of plant adaptation to drought

O_LIPlant adaptation to drought facilitates major ecological transitions, and is likely to play a vital role under looming climate change. Mycorrhizas can influence the physiological capacity of plants to tolerate drought. Here, I show how mycorrhizal strategy and drought adaptation shape one another throughout the course of plant evolution. C_LIO_LITo characterize the evolutions of both plant characters, I applied a phylogenetic comparative method using data of 1,638 extant species globally distributed. C_LIO_LIThe detected correlated evolution unveiled gains and losses of drought tolerance occurring at faster rates in lineages with an ecto- or ericoid mycorrhizal strategy, which were on average about 15 and 300 times quicker than that in lineages with the arbuscular mycorrhizal and naked root (non-mycorrhiza or facultatively arbuscular mycorrhiza) strategy, respectively. Among mycorrhiza shifts, the arbuscular mycorrhiza losses in drought sensitive lineages were more frequent than any symbiont switching or other mutualism breakdown. C_LIO_LIMy study suggests that mycorrhizas play a key facilitator role in the evolutionary process of plant adaptation to critical changes in water availability across global climates. C_LI

evolutionary biology↗

bioRxiv2021

Model-checking ecological state-transition graphs

Model-checking is a methodology developed in computer science to automatically assess the dynamics of discrete systems, by checking if a system modelled as a state-transition graph satisfies a dynamical property written as a temporal logic formula. The dynamics of ecosystems have been drawn as state-transition graphs for more than a century, from state-and-transition models to assembly graphs. Thus, model-checking can provide insights into both empirical data and theoretical models, as long as they sum up into state-transition graphs. While model-checking proved to be a valuable tool in systems biology, it remains largely underused in ecology. Here we promote the adoption of the model-checking toolbox in ecology through its application to an illustrative example. We assessed the dynamics of a vegetation model inspired from state-and-transition models by model-checking Computation Tree Logic formulas built from a proposed catalogue of patterns. Model-checking encompasses a wide range of concepts and available software, mentioned in discussion, thus its implementation can be fitted to the specific features of the described system. In addition to the automated analysis of ecological state-transition graphs, we believe that defining ecological concepts with temporal logics could help clarifying and comparing them. Author summaryEcologists have drawn state-transition graphs representing the dynamics of ecosystems for more than a century. Model-checking is an automated method for the analysis of such graphs developed in computer science and acknowledged by a Turing award in 2007. Ecologists appear to be mostly unaware of model-checking despite its successes in systems biology to assess the dynamics of biological networks. We promote model-checking of ecological state-transition graphs through its application to an illustrative vegetation model. We exemplify the insights provided by model-checking by assessing management policies aiming to tackle savanna encroachment. We also provide a catalogue of patterns to help ecologists with the difficulty of formally expressing dynamical properties. We also discuss the wide range of model-checking concepts and available software, enabling to fit the specific features of the studied system, such as durations or probabilities. Model-checking can be applied to both empirical data and theoretical models, as long as they sum up into state-transition graphs. It provides automated and accurate answers to complex questions that could barely be analysed through human examination, if not impossible to answer this way. In addition to the automated analysis of ecological state-transition graphs, we believe that formally defining ecological concepts within the model-checking framework could help in clarifying and comparing them.

bioinformatics↗

bioRxiv2021

East-African savanna dynamics: from a knowledge-based model to the possible futures of a social-ecological system

Sub-Saharan savanna ecosystems are undergoing transitions such as bush encroachment, desertification or agricultural expansion. Such shifts and persistence of land cover are increasingly well understood, especially bush encroachment which is of major concern in pastoral systems. Although dominant factors can explain such transformations, they often result from intertwined causes in which human activities play a significant role. Therefore, in this latter case, these issues may require integrated solutions, involving many interacting components. Ecosystem modelling has proved appropriate to support decision-makers in such complex situations. However, ecosystem models often require lots of quantitative information for estimating parameters and the precise functional form of interactions is often unknown. Alternatively, in rangeland management, States-and-Transitions Models (STMs) have been developed to organize knowledge about system transitions and to help decision-makers. However, these conceptual diagrams often lack mathematical analyzing tools, which strongly constrains their complexity. In this paper, we introduce the Ecological Discrete-Event Network (EDEN) modelling approach for representing the qualitative dynamics of an East-African savanna as a set of discrete states and transitions generated from empirical rules. These rules are derived from local knowledge, field observations and scientific literature. In contrast with STMs, EDEN generates automatically every possible states and transitions, thus enabling the prediction of novel ecosystem structures. Our results show that the savanna is potentially resilient to the disturbances considered. Moreover, the model highlights all transitions between vegetation types and socio-economic profiles under various climatic scenarios. The model also suggests that wildlife diversity may increase socio-economic resistance to seasonal drought. Tree-grass coexistence and agropastoralism have the widest ranges of conditions of existence of all vegetation types and socio-economic profiles, respectively. As this is a preliminary use of EDEN for applied purpose, analysis tools should be improved to enable finer investigation of desirable trajectories. By translating local knowledge into ecosystem dynamics, the EDEN approach seems promising to build a new bridge between managers and modellers.

ecology↗