Biology subjects

Carboni, M.

Publications and source records attributed to Carboni, M..

10 recordsLinked to original sources

bioRxiv2025

Native community resistance modulates the spread of non-native species along Mediterranean mountain roads under global change

AimClimate warming, human modification, and global connectivity are eroding the abiotic barriers (i.e. climatic filtering and low propagule pressure) that have traditionally limited the spread of non-native species in mountain ecosystems. Meanwhile, these global changes are reshaping native plant communities, potentially weakening their biotic resistance to invasion. Yet, the role of native community resistance in modulating the upslope spread of non-native species remains overlooked, potentially masking indirect effects of global change in mountain ecosystems. LocationCentral Apennines Mountain range, Italy MethodsFollowing the Mountain Invasion Research Network (MIREN) protocol, we surveyed vegetation close and far from roads at 60 sites along three mountain roads in the Central Apennines. Using current and historical climatic data and aerial images from the 1950s to today, we quantified climate and land-use changes. Dominant plant traits and functional diversity were used to capture the biotic resistance of native communities. We then applied structural equation modelling to investigate how native biotic resistance regulates the effect of global change drivers, i.e. road disturbance and changes in land-use and climate, on the occurrence and cover of non-native species. ResultsVariance partitioning suggests that, beside climatic filtering, the biotic resistance of native communities is among the strongest drivers of non-native species occurrence and abundance. Although climate and land-use changes have had little direct influence on non-natives, land-use changes occurred over the past 70 years indirectly influenced invasions by altering native community resistance. Agricultural abandonment at low- to middle elevations, favoured short-statured native communities, offering low resistance. In contrast, forest expansion strengthened resistance through dominance of conservative native species. Main conclusionNative community resistance, alongside climate and disturbance, is a key determinant of invasion patterns along elevational gradients in Mediterranean mountains. Despite global changes facilitate non-native plant upward shifts, native communities can modulate this spread and may still maintain a certain resistance to invasion.

ecology↗

bioRxiv2025

Using revisitation data for predicting changes in ecosystem functioning across protected and non-protected coastal dunes

Protected areas are generally designed to conserve biodiversity. However, how well they also contribute to maintaining ecosystem functions that plant diversity supports has rarely been explicitly tested, often due to the lack of historical ecosystem function data. Here, we used a trait-based approach to reconstruct past ecosystem functioning and examine its change over the last 15 years in protected and unprotected coastal dune ecosystems, checking where functions remain stable over time. First, we resurveyed vegetation in quasi-permanent plots and measured in the present several ecosystem functions related to biomass production, carbon, water, nutrient cycling, erosion control, and invasion resistance across six coastal dune sites in Central Italy. Second, using these data, we quantified Biodiversity-Ecosystem Function (BEF) relationships and employed them to hindcast past ecosystem functions based on historical vegetation surveys. Finally, as a case study, we applied this method to assess temporal changes in ecosystem functioning under three protection regimes: national protected areas (i.e. strict protection), Natura 2000 sites (loose protection), and non-protected areas. Biomass production, carbon, and water regulation increased over time in non-protected areas, likely due to an expansion of ruderal and non-native species, that are usually more productive. Within Natura 2000 sites, communities showed a decrease in erosion control and invasion resistance, due to the loss of important dune-building species and the spread of non-natives. Only within national protected areas, ecosystem functions did not undergo significant temporal changes, and invasion resistance even increased. Our results suggest that ecosystem functioning remained stable over time only in areas under strict protection. More broadly, our study demonstrates the potential for using revisitation data in combination with locally estimated BEF relationships to hindcast past ecosystem functioning, providing a valuable tool for monitoring long-term functional changes in response to conservation measures.

ecology↗

bioRxiv2024

Exploring the accuracy of ab initio prediction methods for viral pseudoknotted RNA structures

The prediction of tertiary RNA structures is significant to the field of medicine (e.g. mRNA vaccines, genome editing), and the exploration of viral transcripts. Though many RNA folding software exist, few studies have condensed their locus of attention solely to viral pseudoknotted RNA. These regulatory pseudoknots play a role in genome replication, gene expression, and protein synthesis. This study explores five RNA folding engines that compute either the minimum free energy (MFE) or the maximum expected accuracy (MEA). These folding engines were tested against 26 experimentally derived short pseudoknotted sequences (20-150nt) using metrics that are commonly applied to software prediction accuracy (e.g. F1 scoring, PPV). This paper reports higher accuracy RNA prediction engines, such as pKiss, when compared to previous iterations of the software, and when compared to older folding engines. They show that MEA folding software does not always outperform MFE folding software in prediction accuracy when assessed with metrics such as percent error, sensitivity, PPV, and F1 scoring when applied to viral pseudoknotted RNA. Moreover, the results suggest that thermodynamic model parameters will not ensure accuracy if auxiliary parameters such as Mg2+ binding, dangling end options, and H-type penalties are not applied. The observations reported in this paper highlight the quality between different ab initio prediction methods while enforcing the idea that a better understanding of intracellular thermodynamics is necessary for a more efficacious screening of RNAs. ImportanceThe importance of accurately predicting RNA structures cannot be overstated, particularly in the context of viral biology and the development of therapeutic interventions such as mRNA vaccines and genome editing. Our study addresses the gap in the existing literature by concentrating solely on viral pseudoknotted RNA, which plays a crucial role in viral replication, gene expression, and protein synthesis. Our study sheds light on the debate surrounding minimum free energy (MFE) versus maximum expected accuracy (MEA) models in RNA folding predictions. Contrary to existing beliefs, we found that MEA models do not consistently outperform MFE models, especially in the context of viral pseudoknotted RNAs. Our research contributes to advancing the field of computational biology by providing insights into the efficacy of different prediction methods and emphasizing the need for a deeper understanding of intracellular thermodynamics to improve RNA structure predictions.

bioinformatics↗

bioRxiv2021

Dwarf-shrubs dynamics in Mediterranean high mountain ecosystems

QuestionVegetation in the alpine and treeline ecotone faces changes in both climate and land use. Shrub encroachment is considered an effect of these changes, but its still unclear how this effect is mediated by environmental heterogeneity. Our goal is to determine which environmental factors shape the fine-scale spatial distribution and temporal trends of alpine dwarf shrub. LocationThree sites in the Central Apennine, Italy. MethodsWe used a comprehensive set of environmental factors across a broad temporal span to model, at a fine-scale, both (1) the current spatial distribution and (2) the change in shrub cover over the past 60 years. ResultsOur results show that dwarf shrubs have generally increased in our study sites over the past 60 years, yet their distribution is strongly shaped by the joint influence of the fine-scale topography, productivity, land use and micro-climate. In particular, shrubs have been locally favored in areas with harsher alpine environmental constraints and stronger resource limitation. Instead, contrary to expectations, at this fine scale, warmer temperatures and the decline in grazing have not favored shrub encroachment. ConclusionDwarf shrubs appear as a stress-tolerant, pioneer vegetation that is currently distributed mainly over areas that are otherwise sparsely vegetated. It appears that shrubs exhibit poor competitive ability to invade grasslands and, though they have increased overall, they remain restricted to the least productive areas. Fine-scale environmental heterogeneity may strongly influence future responses of dwarf shrubs in changing alpine ecosystems.

ecology↗

bioRxiv2021

Distance decay 2.0 - a global synthesis of taxonomic and functional turnover in ecological communities.

Understanding the variation in community composition and species abundances, i.e., {beta}-diversity, is at the heart of community ecology. A common approach to examine {beta}-diversity is to evaluate directional turnover in community composition by measuring the decay in the similarity among pairs of communities along spatial or environmental distances. We provide the first global synthesis of taxonomic and functional distance decay along spatial and environmental distance by analysing 149 datasets comprising different types of organisms and environments. We modelled an exponential distance decay for each dataset using generalized linear models and extracted r2 and slope to analyse the strength and the rate of the decay. We studied whether taxonomic or functional similarity has stronger decay across the spatial and environmental distances. We also unveiled the factors driving the rate of decay across the datasets, including latitude, spatial extent, realm, and organismal features. Taxonomic distance decay was stronger along spatial and environmental distances compared with functional distance decay. The rate of taxonomic spatial distance decay was the fastest in the datasets from mid-latitudes while the rate of functional decay increased with latitude. Overall, datasets covering larger spatial extents showed a lower rate of decay along spatial distances but a higher rate of decay along environmental distances. Marine ecosystems had the slowest rate of decay. This synthesis is an important step towards a more holistic understanding of patterns and drivers of taxonomic and functional {beta}-diversity.

ecology↗

bioRxiv2021

Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes

We describe the multimodal neuroimaging dataset VEPCON (OpenNeuro Dataset ds003505). It includes raw data and derivatives of high-density EEG, structural MRI, diffusion weighted images (DWI) and single-trial behavior (accuracy, reaction time). Visual evoked potentials (VEPs) were recorded while participants (n=20) discriminated briefly presented faces from scrambled faces, or coherently moving stimuli from incoherent ones. EEG and MRI were recorded separately from the same participants. The dataset contains pre-processed EEG of single trials in each condition, behavioral measures, structural MRIs, individual brain parcellations at 5 spatial resolutions (83 to 1015 regions), and the corresponding structural connectomes computed from fiber count, fiber density, average fractional anisotropy and mean diffusivity maps. For source imaging, VEPCON provides EEG inverse solutions based on individual anatomy, with Python and Matlab scripts to derive activity time-series in each brain region, for each parcellation level. The BIDS-compatible dataset can contribute to multimodal methods development, studying structure-function relations, and to unimodal optimization of source imaging and graph analyses, among many other possibilities.

neuroscience↗

bioRxiv2021

The connectome spectrum as a canonical basis for a sparse representation of fast brain activity

The functional organization of neural processes is constrained by the brain s intrinsic structural connectivity. Here, we explore the potential of exploiting this structure in order to improve the signal representation properties of brain activity and its dynamics. Using a multi-modal imaging dataset (electroencephalography, structural MRI and diffusion MRI), we represent electrical brain activity at the cortical surface as a time-varying composition of harmonic modes of structural connectivity. The harmonic modes are termed connectome harmonics, and their representation is known as the connectome spectrum of the signal. We found that: first, the brain activity signal is more compactly represented by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. And last, the connectome spectrum characterises fast brain dynamics with fewer degrees of freedom than area-based signal representations. Specifically, we show that with the connectome spectrum representation, fewer dimensions are needed to capture the differences between low-level and high-level visual processing, and the topological properties of the signal. In summary, this work provides statistical, functional and topological evidence supporting that by accounting for the brain s structural connectivity fosters a more comprehensive understanding of large-scale dynamic neural functioning.

neuroscience↗

bioRxiv2020

Once upon a time in the south: local drivers of plant invasion in the harsh sub-Antarctic islands

AimHere, we aim to: (i) investigate the local effect of environmental and human-related factors on alien plant invasion in sub-Antarctic islands; (ii) explore the relationship between alien species features and their dependence on anthropogenic propagule pressure; and (iii) unravel key traits conferring invasiveness in the sub-Antarctic. LocationPossession Island, Crozet archipelago (French sub-Antarctic islands). TaxonNon-native vascular plants (Poaceae, Caryophyllaceae, Juncaceae). MethodsSingle-species distribution models were used to explore the effect of high-resolution topoclimatic and human-related variables on the occurrence of six of the most aggressive alien plants colonizing French sub-Antarctic islands. Furthermore, the interaction between alien species traits and their response to anthropogenic propagule pressure was analysed by means of a multi-species distribution model. This allowed identifying the features of species that were associated to low dependence on human-assisted introductions, and were thus potentially more invasive. ResultsWe observed two main invasion patterns: low-spread species strongly dependent on anthropogenic propagule pressure and high-spread species limited mainly by harsh climatic conditions. Differences in invasiveness across species mostly related to their residence time, life history and plant height, with older introductions, perennial and low-stature species being more invasive. Main conclusionsThe availability of high-resolution data improved our understanding of the role of environmental and human-related factors in driving alien species distribution on sub-Antarctic islands. At the same time, the identification of alien species features conferring invasiveness may help anticipating future problematic invasions.

ecology↗

bioRxiv2020

Connectome spectral analysis to track EEG task dynamics on a subsecond scale

We present an approach for tracking fast spatiotemporal cortical dynamics in which we combine white matter connectivity data with source-projected electroencephalographic (EEG) data. We employ the mathematical framework of graph signal processing in order to derive the Fourier modes of the brain structural connectivity graph, or "network harmonics". These network harmonics are naturally ordered by smoothness. Smoothness in this context can be understood as the amount of variation along the cortex, leading to a multi-scale representation of brain connectivity. We demonstrate that network harmonics provide a sparse representation of the EEG signal, where, at certain times, the smoothest 15 network harmonics capture 90% of the signal power. This suggests that network harmonics are functionally meaningful, which we demonstrate by using them as a basis for the functional EEG data recorded from a face detection task. There, only 13 network harmonics are sufficient to track the large-scale cortical activity during the processing of the stimuli with a 50 ms resolution, reproducing well-known activity in the fusiform face area as well as revealing co-activation patterns in somatosensory/motor and frontal cortices that an unconstrained ROI-by-ROI analysis fails to capture. The proposed approach is simple and fast, provides a means of integration of multimodal datasets, and is tied to a theoretical framework in mathematics and physics. Thus, network harmonics point towards promising research directions both theoretically - for example in exploring the relationship between structure and function in the brain - and practically - for example for network tracking in different tasks and groups of individuals, such as patients.

neuroscience↗

bioRxiv2019

Using structural connectivity to augment community structure in EEG functional connectivity

Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity (SC) in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between non-connected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG- and volume-conduction-free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions. Author summaryIn this study, we combine high-density EEG recorded during resting state with white matter connectivity obtained from diffusion MRI and fiber tracking. We leverage the additional information contained in the structural connectome towards augmenting the source level EEG functional connectivity. In particular, it is known - and confirmed in this study - that the activity of brain regions that possess a direct anatomical connection is, on average, more strongly correlated than that of regions that have no such direct link. We use the structural connectome to define a graph and smooth the source reconstructed EEG signal in the space spanned by this graph. We compare the resulting "filtered" signal correlation matrices to those obtained from fMRI and find that such "graph filtering" improves the agreement between EEG and fMRI functional connectivity structure. This suggests that structural connectivity can be used to attenuate some of the limitations imposed by volume conduction.

neuroscience↗