Biology subjects

Santini, L.

Publications and source records attributed to Santini, L..

At least 19 recordsLinked to original sources

bioRxiv2026

Mean environmental associations obscure drivers of zooplankton community dynamics

Zooplankton communities are influenced by multiple environmental factors, including temperature, nutrient and resource availability, which fluctuate seasonally and across years. While long-term average effects can identify overall drivers, they may overlook dynamic, context-dependent effects that govern short-term changes in diversity and abundance. Understanding and disentangling both perspectives is crucial for identifying and estimating the drivers that shape community structure under varying environmental states. Here, we applied Empirical Dynamic Modeling (CCM, SMap) to a 12-year weekly zooplankton time series to identify causal environmental drivers of taxonomic and morphological diversity and quantify how the influence of each driver shifts over time. We contrast these results with static long-term average effects inferred from Generalized Linear Models which included predictor sets identified using covariate adjustment and accounting for temporal autocorrelation. Drivers linked to long-term average associations differed from those regulating short-term zooplankton dynamics, revealing a decoupling between mean environmental effects and the drivers of temporal variability. Temperature emerged as a persistent regulator of zooplankton dynamics across multiple diversity dimensions, while variables commonly associated with background trophic conditions (e.g. particulate organic matter) were primarily associated with long-term patterns and showed limited dynamical relevance. Importantly, we find evidence for morphological homogenisation in response to short-term fluctuations in chlorophyll a, which was not detectable in long-term average relationships. This contrast highlights that mean environmental associations do not necessarily reflect the mechanisms governing community dynamics. Impacts might be underestimated if average effects appear weak, or misinterpreted if arising mainly from shared trends or seasonality rather than direct mechanisms Integrating both perspectives clarifies the identity and role of environmental drivers, improving inference and prediction of zooplankton community change through time.

ecology↗

bioRxiv2025

SANE: an Index of Anthropogenic Noise Levels for Wildlife Research in Terrestrial Ecosystems

O_LIUnderstanding the impact of noise pollution on wildlife is challenging due to the complexity of isolating the anthropogenic components from the soundscape. Soundscape studies usually employ acoustic indices that apply arbitrary thresholds to isolate anthrophony from natural sounds. However, natural and anthropogenic sounds do not always conform to these thresholds, hindering both accuracy and comparability of the resulting indices. While significant progress has been made in automated identification of acoustic events through artificial intelligence-based classifiers, an effective method to quantify anthropogenic acoustic pressure is still lacking. C_LIO_LIWe propose a novel index, the Selective Anthropogenic Noise Exposure (SANE), which leverages BirdNET deep neural network to isolate human-related sounds from recordings. The index consists in the sum of the Median Amplitude Index of all human noise categories, thereby capturing both the intensity and cumulative impact of multiple disturbance events, while also enabling the decomposition of noise level across different categories of disturbance (e.g., traffic, human voices). C_LIO_LIWe test SANEs performance in a real urban setting and through soundscape simulations and compare it with two frequency-based indices and another artificial intelligence-based acoustic index. SANE was effective in describing human noises within areas characterized by different levels of urbanization, improving upon other indices shortcomings. Additionally, SANE was robust at very fine temporal scales, precisely quantifying anthrophony levels for single recordings. Among the indices considered, SANE was the only one that remained insensitive to low-frequency biophony, which confounded both frequency and artificial intelligence-based metrics. C_LIO_LIBy leveraging artificial intelligence-based classifiers ability to detect multiple human-made sound classes, SANE index captures the intensity of anthropogenic noise while being insensitive to non-conforming natural sounds to the traditionally identified anthrophony range. Furthermore, SANE has the potential to assess the relative contribution of different noise types to the overall acoustic pollution, opening new research avenues on the acoustic pollution effects on wildlife in high disturbance contexts. C_LI

ecology↗

bioRxiv2025

Insectivores exhibit superior microbial transmission efficiency and elevated zoonotic risk by 2035 relative to rodents and bats

Wild small mammals represent critical sources of zoonotic infections due to their high diversity, global distribution, and proximity to humans. Nevertheless, significant knowledge gaps persist in characterizing pan-taxonomic microbial richness and sharing dynamics, particularly regarding ecologically critical yet understudied Eulipotyphla (true insectivores). Here we take a macroecological approach to compare how microbial hosting and transmission differ across insectivores, rodents, and bats, and what ecological factors drive such disparities. We find that insectivores host comparable microbial richness to rodents and bats while exhibiting superior intra- and cross-order transmission efficiency. Urban adaptation, geographic range area, and longevity are shared drivers of microbial richness and transmission across these host orders, while greater body mass and shorter gestation time specifically are positive predictors of these outcomes within insectivores. Climate change projections identify insectivores as primary transmission hosts in new high-latitude hotspots by 2035, including parts of the US, Canada, and Russia, posing greater zoonotic threats than rodents or bats. Our findings challenge the prevailing paradigm that prioritizes rodents and bats as special zoonotic reservoirs, establishing insectivores as critical but overlooked players in disease ecology. Collective proactive surveillance of insectivores, rodents, and bats is imperative for forecasting emerging zoonotic threats and informing global risk assessment frameworks.

ecology↗

bioRxiv2025

Two forms, two functions: functional strategies of parasitoid bristle flies and their larvae

Species with complete metamorphosis undergo substantial changes in ecological functions, especially parasitoid insects with parasitic larvae and free-living adults. While parasitoids play essential ecosystem roles, limited knowledge is available about their shifting functional strategies. Here we focus on parasitoid bristle flies (Diptera: Tachinidae) to investigate the relationship between larval vs adult functional strategies. We retrieved trait data for 767 European species and defined functional trait spaces for larvae and adults. We then measured both functional distinctiveness (rarity in functional trait combination) and specialisation (variety of resources consumed). We found little correspondence in the functional distinctiveness of adults and larvae, with highly distinct larvae generating either functionally distinct or functionally common adults. In contrast, only specialised larvae (attacking a limited number of hosts) give origin to specialised adults (feeding on a limited number of flowers). This suggests selective pressure towards specialisation might act synergistically across life stages, if trophic resources are restricted in space for both the larva (e.g. caterpillar host) and adult (e.g. flowers). Global change can generate complex patterns of functional homogenisation in parasitoids, which can occur at different (or both) life stages and lead to ecosystem-wide consequences: from the outbreak of herbivore insects to the loss of pollination capacity.

ecology↗

bioRxiv2024

Towards an automated protocol for wildlife density estimation using camera-traps

Camera-traps are valuable tools for estimating wildlife population density, and recently developed models enable density estimation without the need for individual recognition. Still, processing and analysis of camera-trap data are extremely time-consuming. While algorithms for automated species classification are becoming more common, they have only served as supporting tools, limiting their true potential in being implemented in ecological analyses without human supervision. Here, we assessed the capability of two camera-trap based models to provide robust density estimates when image classification is carried out by machine learning algorithms. We simulated density estimation with Camera-Traps Distance Sampling (CT-DS) and Random Encounter Model (REM) under different scenarios of automated image classification. We then applied the two models to obtain density estimates of three focal species (roe deer Capreolus capreolus, red fox Vulpes vulpes, and Eurasian badger Meles meles) in a reserve in central Italy. Species detection and classification was carried out both by the user and machine learning algorithms (respectively, MegaDetector and Wildlife Insights), and all outputs were used to estimate density and ultimately compared. Simulation results suggested that the CT-DS model could provide robust density estimates even at poor algorithm performances (down to 50% of correctly classified images), while the REM model is more unpredictable and depends on multiple factors. Density estimates obtained from the MegaDetector output were highly consistent for both models with the manually labelled images. While Wildlife Insights performance differed greatly between species (recall: badger = 0.15; roe deer = 0.56; fox = 0.75), CT-DS estimates did not vary significantly; on the contrary, REM systematically overestimated density, with little overlap in standard errors. We conclude that CT-DS and REM models can be robust to the loss of images when machine learning algorithms are used to identify animals, with the CT-DS being an ideal candidate for applications in a fully unsupervised framework. We propose guidelines to evaluate when and how to integrate machine learning in the analysis of camera-trap data for density estimation, further strengthening the applicability of camera traps as a cost-effective method for density estimation in (spatially and temporally) extensive multi-species monitoring programs.

ecology↗

bioRxiv2024

A pluripotent stem cell platform for in vitro systems genetics studies of mouse development

The directed differentiation of pluripotent stem cells (PSCs) from panels of genetically diverse individuals is emerging as a powerful experimental system for characterizing the impact of natural genetic variation on developing cell types and tissues. Here, we establish new PSC lines and experimental approaches for modeling embryonic development in a genetically diverse, outbred mouse stock (Diversity Outbred mice). We show that a range of inbred and outbred PSC lines can be stably maintained in the primed pluripotent state (epiblast stem cells -- EpiSCs) and establish the contribution of genetic variation to phenotypic differences in gene regulation and directed differentiation. Using pooled in vitro fertilization, we generate and characterize a genetic reference panel of Diversity Outbred PSCs (n = 230). Finally, we demonstrate the feasibility of pooled culture of Diversity Outbred EpiSCs as "cell villages", which can facilitate the differentiation of large numbers of EpiSC lines for forward genetic screens. These data can complement and inform similar efforts within the stem cell biology and human genetics communities to model the impact of natural genetic variation on phenotypic variation and disease-risk.

developmental biology↗

bioRxiv2024

Generation length of the world's amphibians and reptiles

Variation in life histories influences demographic processes from adaptive changes to population declines leading to extinction. Among life history traits, generation length offers a critical feature to forecast species demographic trajectories such as population declines (widely used by the IUCN Red List of Threatened Species) and adaptability to environmental change over time. Therefore, estimates of generation length are crucial to monitor demographic stability or future change in highly threatened organisms, particularly ectothermic tetrapods (amphibians and reptiles) - which rank among the most threatened groups - but for which uncertainty in future impacts remains high. Despite its importance, generation length for amphibians and reptiles is largely missing. Here, we aimed to fill-in this gap by modeling generation lengths for amphibians, squamates and testudines as a function of species size, climate, life history, and phylogeny using generalized additive models and phylogenetic generalized least squares. We obtained estimates of generation lengths for 4,543 (52%) amphibians, 8,464 (72%) squamates and 118 (32%) testudines. Our models performed well for most families, for example Bufonidae in amphibians, Lacertidae and Colubridae in squamates and Geoemydidae in testudines, while we found high uncertainty around the prediction of a few families, notably Chamaeleonidae. Species body size and mean temperature were the main predictors of generation length in all groups. Although our estimates are not meant to substitute robust and validated measurements from field studies or natural history museums, they can help reduce existing biases in conservation assessments until field data will be comprehensively available.

zoology↗

bioRxiv2024

FoxO transcription factors actuate the formative pluripotency specific gene expression programme

Naive pluripotency is sustained by a self-reinforcing gene regulatory network (GRN) comprising core and naive pluripotency-specific transcription factors (TFs). Upon exiting naive pluripotency, ES cells transition through a formative post-implantation-like pluripotent state, where they acquire competence for lineage-choice. However, the mechanisms underlying disengagement from the naive GRN and initiation of the formative GRN are unclear. Here, we demonstrate that phosphorylated AKT acts as a gatekeeper that prevents nuclear localization of FoxO TFs in naive ESCs. PTEN-mediated reduction of AKT activity upon exit from naive pluripotency allows nuclear entry of FoxO TFs, enforcing a cell fate transition by binding and activating formative pluripotency-specific enhancers. Indeed, FoxO TFs are necessary and sufficient for transition from the naive to the formative pluripotent state. Our work uncovers a pivotal role for FoxO TFs and AKT signalling in mechanisms establishing formative post-implantation pluripotency, a critical early embryonic cell fate transition.

developmental biology↗

bioRxiv2023

Human monoclonal antibodies reveal subdominant gonococcal and meningococcal cross-protective antigens

Gonococcus (Gc), a bacterium resistant to most antibiotics causing more than 80 million cases of gonorrhea annually, is a WHO high priority pathogen. Recently, vaccine development prospects were boosted by reports that licensed meningococcus serogroup B (MenB) vaccines provided partial protection against Gc infection. To determine antigens responsible for cross-protection, memory B cells from 4CMenB vaccinated volunteers were single-cell sorted to identify antibodies that kill Gc in a bactericidal assay. Nine different antibodies, all deriving from the IGHV4-34 germline carrying unusually long HCDR3s, recognized the PorB protein, four recognized the lipooligosaccharide (LOS), and four unknown antigens. One of the PorB antibodies, tested in vivo, provided protection from Gc infection. The identification of PorB and LOS as key antigens of gonococcal and meningococcal immunity provides a mechanistic explanation of the cross-protection observed in the clinic and shows that isolating human monoclonal antibodies from vaccinees can be instrumental for bacterial antigen discovery.

immunology↗

bioRxiv2023

Optogenetic confirmation of transverse-tubular membrane excitability in intact cardiac myocytes

T-tubules (TT) form a complex network of sarcolemmal membrane invaginations, essential for well-coordinated excitation-contraction coupling (ECC) and, thus, homogeneous mechanical activation of cardiomyocytes. ECC is initiated by rapid depolarization of the sarcolemmal membrane. Whether TT membrane depolarisation is active (local generation of action potentials; AP) or passive (following depolarisation of the outer cell surface sarcolemma; SS) has not been experimentally assessed in cardiomyocytes. Based on the assessment of ion flux pathways needed for AP generation, we hypothesise that TT are excitable. We therefore explored TT excitability experimentally, using an all-optical approach to stimulate and record trans-membrane potential changes in TT that were electrically insulated from the SS membrane by transient osmotic shock. Our results establish that cardiomyocyte TT can generate AP. These AP show electrical features that differ substantially from those observed in SS, consistent with differences in the density of ion channels and transporters in the two different membrane domains. We propose that TT-generated AP represent a safety mechanism for TT AP propagation and ECC, which may be particularly relevant in pathophysiological settings where morpho-functional changes reduce the electrical connectivity between SS and TT membranes. KEY POINTSO_LICardiomyocytes are characterized by a complex network of membrane invaginations (the T-tubular system) that propagate action potentials to the core of the cell, ensuring synchronous and uniform cell contraction. C_LIO_LIIn this study, we investigate whether the T-tubular system is able to generate action potentials autonomously, rather than following depolarization of the outer cell surface sarcolemma. C_LIO_LIFor this purpose, we developed a fully optical platform to probe and manipulate the electrical dynamics of sub-cellular membrane domains. C_LIO_LIOur findings demonstrate that T-tubules are intrinsically excitable, revealing distinct characteristics of self-generated T-tubular action potentials. C_LIO_LIThis active electrical capability may serve as a protective mechanism against voltage drops occurring within the T-tubular network. C_LI

physiology↗

bioRxiv2023

Modelling the probability of meeting IUCN Red List criteria to support reassessments.

Comparative extinction risk analysis - which predicts species extinction risk from correlation with traits or geographical characteristics - has gained research attention as a promising tool to support extinction risk assessment in the IUCN Red List of Threatened Species. However, its uptake has been very limited so far, possibly because these models only predict a species Red List category, without indicating which Red List criteria may be triggered by which such approaches cannot easily be used in Red List assessments. We overcome this implementation gap by developing models that predict the probability of species meeting individual Red List criteria. Using data on the worlds birds, we evaluated the predictive performance of our criterion-specific models and compared it with the typical criterion-blind modelling approach. We compiled data on biological traits (e.g., range size, clutch size) and external drivers (e.g., change in canopy cover) often associated with extinction risk. For each specific criterion, we modelled the relationship between extinction risk predictors and species Red List category under that criterion using ordinal regression models. We found criterion-specific models were better at predicting threatened species compared to a criterion-blind model (higher sensitivity), but less good at predicting not threatened species (lower specificity). As expected, different covariates were important for predicting threat status under different criteria, for example change in annual temperature was important to predict criteria related to population trends, while clutch size was important for criteria related to restricted area of occupancy or small population size. Our criteria-specific method can support Red List assessors by producing outputs that identify species likely to meet specific criteria, and which are the most important predictors: these species can be prioritised for re-evaluation. We expect this new approach to increase the uptake of extinction risk models in Red List assessments, bridging a long-standing research-implementation gap.

ecology↗

bioRxiv2023

Drivers of species knowledge across the Tree of Life

Knowledge of biodiversity is unevenly distributed across the Tree of Life. In the long run, such disparity in awareness unbalances our understanding of life on Earth, influencing policy decisions and the allocation of research and conservation funding. We investigated how humans accumulate knowledge of biodiversity by searching for consistent relationships between scientific (number of publications) and societal (number of views in Wikipedia) interest, and species-level morphological, ecological and socio-cultural factors. Across a random selection of 3,019 species spanning 29 Phyla/Divisions, we show that socio-cultural factors are the most important correlates of scientific and societal interest in biodiversity, including the fact that a species is useful or harmful to humans, has a common name and is listed in the IUCN Red List. Furthermore, large-bodied, broadly distributed and taxonomically unique species receive more scientific and societal attention, whereas colorfulness and phylogenetic proximity to humans correlates exclusively with societal attention. These results highlight a favoritism towards limited branches of the Tree of Life, and that scientific and societal priorities in biodiversity research broadly align. This suggests that we may be missing out on key species in our research and conservation agenda simply because they are not on our cultural radar.

ecology↗

bioRxiv2023

Testing the predictive performance of comparative extinction risk models to support the global amphibian assessment

Assessing the extinction risk of species through the IUCN Red List is key to guiding conservation policies and reducing biodiversity loss. This process is resource-demanding, however, and requires a continuous update which becomes increasingly difficult as new species are added to the IUCN Red List. The use of automatic methods, such as comparative analyses to predict species extinction risk, can be an efficient alternative to maintaining up to date assessments. Using amphibians as a study group, we predict which species were more likely to change status, in order to suggest species that should be prioritized for reassessment. We used species traits, environmental variables, and proxies of climate and land-use change as predictors of the IUCN Red List category of species. We produced an ensemble prediction of IUCN Red List categories by combining four different model algorithms: Cumulative Link Models (CLM), phylogenetic Generalized Least Squares (PGLS), Random Forests (RF), Neural Networks (NN). By comparing IUCN Red List categories with the ensemble prediction, and accounting for uncertainty among model algorithms, we identified species that should be prioritized for future reassessments due to high prediction versus observation mismatch. We found that CLM and RF performed better than PGLS and NN, but there was not a clear best algorithm. The most important predicting variables across models were species range size, climate change, and landuse change. We propose ensemble modelling of extinction risk as a promising tool for prioritizing species for reassessment while accounting for inherent models uncertainty.

ecology↗

bioRxiv2022

Global environmental drivers of local abundance-mass scaling in soil animal communities

The relationship between species body masses and densities is strongly conserved around a three-quarter power law when pooling data across communities. However, studies of local within-community relationships have revealed major deviations from this general pattern, which has profound implications for their stability and functioning. Despite multiple contributions of soil communities to people, there is limited knowledge on the drivers of body mass-abundance relationship in these communities. We compiled a dataset comprising 155 soil-animal communities across four countries (Canada, Germany, Indonesia, USA), all sampled using the same methodology. We tested if variation in local climatic and edaphic conditions drives differences in local body mass-abundance scaling relationships. We found substantial variation in the slopes of this power-law relationship across local communities. Structural equation modeling showed that soil temperature and water content have a positive and negative net effect, respectively, on soil communities. These effects are mediated by changes in local edaphic conditions (soil pH and carbon content) and the body-mass range of the communities. These results highlight ways in which alterations of soil climatic and edaphic conditions interactively impact the distribution of abundance, and thus energy, between populations of small and large animals. These quantitative mechanistic relationships facilitate our understanding of how global changes in environmental conditions, such as temperature and precipitation, will affect community-abundance distributions and thus the stability and functioning of soil-animal communities.

ecology↗

bioRxiv2020

Rapid Anthropocene realignment of allometric scaling rules

The negative relationship between body size and population density (SDR) in mammals is often interpreted as resulting from energetic constraints. In a global change scenario, however, this relationship might be expected to change, given the size-dependent nature of anthropogenic pressures and vulnerability to extinction. Here we test whether the SDR in mammals has changed over the last 50 years. We show that the relationship has shifted down and became shallower, corresponding to a decline in population density of 32-72%, for the largest and smallest mammals, respectively. However, the SDRs become steeper in some groups (e.g. carnivores) and shallower in others (e.g. herbivores). The Anthropocene reorganization of biotic systems is apparent in macroecological relationships that were previously believed to be immutable, reinforcing the notion that biodiversity pattens are contingent upon conditions at the time of investigation. We call for an increased attention on the role of global change on macroecological inferences.

ecology↗

bioRxiv2020

Novel imprints in mouse blastocysts are predominantly DNA methylation independent

In mammals, chromatin marks at imprinted genes are asymmetrically inherited to control parentally-biased gene expression. This control is thought predominantly to involve parent-specific differentially methylated regions (DMR) in genomic DNA. However, neither parent-of-origin-specific transcription nor DMRs have been comprehensively mapped. We here address this by integrating transcriptomic and epigenomic approaches in mouse preimplantation embryos (blastocysts). Transcriptome-analysis identified 71 genes expressed with previously unknown parent-of-origin-specific expression in blastocysts (nBiX: novel blastocyst-imprinted expression). Uniparental expression of nBiX genes disappeared soon after implantation. Micro-whole-genome bisulfite sequencing (WGBS) of individual uniparental blastocysts detected 859 DMRs. Only 18% of nBiXs were associated with a DMR, whereas 60% were associated with parentally-biased H3K27me3. This suggests a major role for Polycomb-mediated imprinting in blastocysts. Five nBiX-clusters contained at least one known imprinted gene, and five novel clusters contained exclusively nBiX-genes. These data suggest a complex program of stage-specific imprinting involving different tiers of regulation.

genomics↗

bioRxiv2020

NMD is required for timely cell fate transitions by fine-tuning gene expression and controlling translation

Cell fate transitions depend on balanced rewiring of transcription and translation programmes to mediate ordered developmental progression. Here we identify a feedback loop between nonsense-mediated mRNA decay (NMD) and translation initiation. We show that NMD controls the translation initiation factor Eif4a2 and its premature termination codon encoding isoform (Eif4a2PTC). NMD deficiency leads to translation of a specific truncated Eif4a2 protein, which elicits increased translation rates and is causative for significant delays in mouse embryonic stem cell (ESC) differentiation. Our results show identical mRNA targets for Smg5, Smg6 and Smg7, but illustrate a clear hierarchy between KOs in amplitude of target deregulation and differentiation phenotype (Smg5 > Smg6 > Smg7). This hierarchy highlights heterodimer independent functions for Smg5 and Smg7. Together, our findings expose an intricate link between mRNA stability and translation initiation control, that must be maintained for normal dynamics of cell state transitions.Competing Interest StatementThe authors have declared no competing interest.View Full Text

cell biology↗

bioRxiv2020

Assessing the reliability of species distribution projections in climate change research

AimForecasting changes in species distribution under future scenarios is one of the most prolific areas of application for species distribution models (SDMs). However, no consensus yet exists on the reliability of such models for drawing conclusions on species distribution response to changing climate. In this study we provide an overview of common modelling practices in the field and assess model predictions reliability using a virtual species approach. LocationGlobal MethodsWe first provide an overview of common modelling practices in the field by reviewing the papers published in the last 5 years. Then, we use a virtual species approach and three commonly applied SDM algorithms (GLM, MaxEnt and Random Forest) to assess the estimated (cross-validated) and actual predictive performance of models parameterized with different modelling settings and violations of modelling assumptions. ResultsOur literature review shows that most papers that model species distribution under climate change rely on single models (65%) and small samples (< 50 presence points, 62%), use presence-only data (85%), and binarize models output to estimate range shift, contraction or expansion (74%). Our virtual species approach reveals that the estimated predictive performance tends to be over-optimistic compared to the real predictive performance. Further, the binarization of predicted probabilities of presence reduces models predictive ability considerably. Sample size is one of the main predictors of real accuracy, but has little influence on estimated accuracy. Finally, the inclusion of irrelevant predictors and the violation of modelling assumptions increases estimated accuracy but decreases real accuracy of model projections, leading to biased estimates of range contraction and expansion. Main conclusionsOur study calls for extreme caution in the application and interpretation of SDMs in the context of biodiversity conservation and climate change research, especially when modelling a large number of species where species-specific model settings become impracticable.

ecology↗