Biology subjects

Cantor, M.

Publications and source records attributed to Cantor, M..

9 recordsLinked to original sources

bioRxiv2026

Scale-dependent feedback between sociality and space use in a long-lived marine predator

Spatial and social behaviours in animals are intertwined, yet the causal direction of this feedback--and how it varies across scales--remains largely unresolved. Using long-term data from false killer whales (Pseudorca crassidens) in the main Hawaiian Islands, we developed and applied a scale-explicit analytical framework to test how social reliance and resource ephemerality govern top-down versus bottom-up processes linking movement and sociality. Movements, association networks, genetic relatedness, and isotopic niches reveal that strong social bonds drive bottom-up emergence of short-term intra-group movements, while ephemeral and likely island-associated prey landscapes impose top-down constraints on inter-group dynamics across scales. These complementary processes generate persistent, fine-scale fidelity within some groups and relatively well-differentiated feeding niches among them. Our findings highlight a general mechanism by which life-history strategies and environmental stochasticity jointly determine the scale and direction of feedback between space use and sociality--shaping population structure and connectivity in mobile social predators.

ecology↗

bioRxiv2025

Human activities drive novel behaviours and transitions in dolphins

Intensifying human activities are reshaping coastal ecosystems, yet their impact on wildlife behaviour and survival remain poorly understood. We conducted drone-based focal-group follows of endangered Indian Ocean humpback dolphin groups in Goa, India, to quantify how tourism and fisheries jointly impact behavioural states and transitions. By integrating machine learning and georeferencing techniques, we found that both human activities triggered distinct behavioural transitions absent in undisturbed groups. Dolphins expressed escape behaviours exclusively near tourist boats and were more likely to transition into foraging near fishing nets (mostly purse-seines). Smaller groups reacted more strongly to tourist boats and interacted more frequently with fishing nets, especially in seasons when fish landing data showed a decline in their prey. These findings offer new insights into the behavioural mechanisms underlying the co-occurrence of coastal dolphins with humans and provide broader guidelines for managing tourism and fisheries to reduce anthropogenic pressure on threatened delphinids.

ecology↗

bioRxiv2025

Integrated deep learning and geo-referencing for drone-based animal tracking with flexible camera angles

O_LIThe ability of drones to provide detailed information on animals and their surroundings makes them ideal for studying animal behaviour at fine scales. While drones can provide high-resolution images of what animals are doing, they should also, in theory, be able to provide data on where they are. However, reconstructing geo-referenced tracks from drone videos that follow animals is challenging, particularly because current methods require specific drone flight patterns and large computational power. C_LIO_LIHere, we combine deep learning and object tracking methods with a novel geo-referencing algorithm which allows us to track individuals across video frames and reconstruct their geo-referenced trajectories. We used a Region-based Convoluted Neural Network to detect animals and a Hungarian tracking algorithm to link detections across video frames, and then geo-referenced each detection in every frame to reconstruct individual trajectories. C_LIO_LIWe tested our geo-refencing algorithm through multiple drone flights with varying flight parameters over known Ground Control Points. The median (95% CI) geo-referencing error was 2.81 (0.74 - 23.23) meters, which reduced by 50% when the drone camera was positioned between -90{degrees} and -40{degrees}. Error increased with drone height and camera angle (-90{degrees} refers to the camera pointing towards the ground) but was not impacted by drone orientation. C_LIO_LIWe then demonstrate the utility of our framework with empirical examples using consumer-level drones. First, we tracked a volunteer carrying a high-resolution GPS unit and overlayed their GPS tracks on our estimated tracks to quantify tracking error. Next, we used drone videos of two delphinid species (Tursiops truncates gephyreus, and Sousa plumbea) representing varying environmental and flight conditions. We were able to successfully infer individual tracks across all conditions except when individuals formed tight clusters, in which case tracks were assigned a group identifier. C_LIO_LIOur framework demonstrates an easy and robust approach to translate drone videos of moving animals into geo-referenced animal tracks which is applicable in many research contexts. A major advance over previous methods is that our algorithm is robust to different camera angles, and provides tracks with accuracy on-par with, or even exceeding, the accuracy from GPS tracking. C_LI HeadlineAutomated drone-based animal tracking

ecology↗

bioRxiv2023

A deep catalog of protein-coding variation in 985,830 individuals

Coding variants that have significant impact on function can provide insights into the biology of a gene but are typically rare in the population. Identifying and ascertaining the frequency of such rare variants requires very large sample sizes. Here, we present the largest catalog of human protein-coding variation to date, derived from exome sequencing of 985,830 individuals of diverse ancestry to serve as a rich resource for studying rare coding variants. Individuals of African, Admixed American, East Asian, Middle Eastern, and South Asian ancestry account for 20% of this Exome dataset. Our catalog of variants includes approximately 10.5 million missense (54% novel) and 1.1 million predicted loss-of-function (pLOF) variants (65% novel, 53% observed only once). We identified individuals with rare homozygous pLOF variants in 4,874 genes, and for 1,838 of these this work is the first to document at least one pLOF homozygote. Additional insights from the RGC-ME dataset include 1) improved estimates of selection against heterozygous loss-of-function and identification of 3,459 genes intolerant to loss-of-function, 83 of which were previously assessed as tolerant to loss-of-function and 1,241 that lack disease annotations; 2) identification of regions depleted of missense variation in 457 genes that are tolerant to loss-of-function; 3) functional interpretation for 10,708 variants of unknown or conflicting significance reported in ClinVar as cryptic splice sites using splicing score thresholds based on empirical variant deleteriousness scores derived from RGC-ME; and 4) an observation that approximately 3% of sequenced individuals carry a clinically actionable genetic variant in the ACMG SF 3.1 list of genes. We make this important resource of coding variation available to the public through a variant allele frequency browser. We anticipate that this report and the RGC-ME dataset will serve as a valuable reference for understanding rare coding variation and help advance precision medicine efforts.

genetics↗

bioRxiv2022

Voluntary and forced exposure to ethanol vapor produces similar escalation of alcohol drinking but differential recruitment of brain regions related to stress, habit, and reward in male rats.

A major limitation of the most widely used current animal models of alcohol dependence is that they use forced exposure to ethanol including ethanol-containing liquid diet and chronic intermittent ethanol (CIE) vapor to produce clinically relevant blood alcohol levels (BAL) and addiction-like behaviors. We recently developed a novel animal model of voluntary induction of alcohol dependence using ethanol vapor self-administration (EVSA). In this model, naive outbred rats given intermittent access to alcohol vapor self-administration exhibit BAL in the 150-300 mg% range and develop somatic signs of withdrawal during acute abstinence. However, it is unknown whether EVSA leads to an escalation of alcohol drinking per se, and whether such escalation is associated with neuroadaptations in brain regions related to stress, reward, and habit. To address these issues, we compared the levels of alcohol drinking during withdrawal between rats passively exposed to alcohol (CIE) or voluntarily exposed to EVSA and measured the number of Fos+ neurons during acute withdrawal (16 h) in the central nucleus of the amygdala (CeA), dorsomedial striatum (DMS), dorsolateral striatum (DLS), nucleus accumbens core (Nacc), periaqueducal grey area (PAG), lateral Habenula (HbL), and the paraventricular nucleus of the thalamus (PVT). The rats were first trained to orally self-administer alcohol in standard operant chambers and then divided in 4 groups (CIE, CI-Air, EVSA and Air-SA) and exposed to intermittent ethanol vapor (passive or active) or intermittent air (passive or active) for 8 h/day, 3 days a week. CIE and EVSA rats exhibited similar BAL (150-300 mg% range) and similar escalation of alcohol drinking during withdrawal, while no changes in terms of drinking were observed in the air exposed rats. CIE and EVSA also increased the motivation for alcohol compared to their respective air control groups under a progressive ratio schedule of reinforcement. Acute withdrawal from EVSA and CIE recruited a similar number of Fos+ neurons in the CeA, however, acute withdrawal from EVSA recruited a higher number of Fos+ neurons in every other brain region analyzed compared to acute withdrawal from CIE. Moreover, acute withdrawal from EVSA specifically recruited the DMS and PVT, a pattern not observed in CIE rats. In summary, these results demonstrate that EVSA produces similar escalation of alcohol drinking, motivation to drink, and blood-alcohol levels than the CIE model, while letting animals voluntary initiate alcohol exposure and maintain alcohol dependence. Moreover, while the behavioral measures of alcohol dependence between the voluntary (EVSA) and passive (CIE) model was similar, the recruitment of neuronal ensembles during acute withdrawal was very different with a higher recruitment of Fos+ neurons in key brain regions important for stress, reward and habit-related processes. The EVSA model may be particularly useful to unveil the neuronal networks and pharmacology responsible for the voluntary induction and maintenance of alcohol dependence and may improve translational studies by providing preclinical researchers with an animal model with better face validity for alcohol use disorder.

neuroscience↗

bioRxiv2020

Social network architecture and the tempo of cumulative cultural evolution

The ability to build upon previous knowledge--cumulative cultural evolution--is a hallmark of human societies. While cumulative cultural evolution depends on the interaction between social systems, cognition and the environment, there is increasing evidence that cumulative cultural evolution is facilitated by larger and more structured societies. However, such effects may be interlinked with patterns of social wiring, thus the relative importance of social network architecture as an additional factor shaping cumulative cultural evolution remains unclear. By simulating innovation and diffusion of cultural traits in populations with stereotyped social structures, we disentangle the relative contributions of network architecture from those of population size and connectivity. We demonstrate that while more structured networks, such as those found in multilevel societies, can promote the recombination of cultural traits into high-value products, they also hinder spread and make products more likely to go extinct. We find that transmission mechanisms are therefore critical in determining the outcomes of cumulative cultural evolution. Our results highlight the complex interaction between population size, structure and transmission mechanisms, with important implications for future research.

evolutionary biology↗

bioRxiv2020

The role of habitat configuration in shaping animal population processes: a framework to generate quantitative predictions

By shaping where individuals move, habitat configuration can fundamentally structure animal populations. Yet, we currently lack a framework for generating quantitative predictions about the role of habitat configuration in modulating population outcomes. For example, it is well known that the social structure of animal populations can shape spreading dynamics, but it remains underexplored to what extent such dynamics are determined by the underlying habitat configuration. To address this gap, we propose a framework and model inspired by studies using networks to characterize habitat connectivity. We first define animal habitat networks, explain how they can integrate information about the different configurational features of animals habitats, and highlight the need for a bottom-up generative model that can depict realistic variations in habitat structural connectivity. Second, we describe a model for simulating animal habitat networks (available in the R package AnimalHabitatNetwork), and demonstrate its ability to generate alternative habitat configurations based on empirical data, which forms the basis for exploring the consequences of alternative habitat structures. Finally, we use our framework to demonstrate how transmission properties, such as the spread of a pathogen, can be impacted by both local connectivity and landscape-level characteristics of the habitat. Our study highlights the importance of considering the underlying habitat configuration in studies linking social structure with population-level outcomes.

ecology↗

bioRxiv2019

Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank

The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had [≥]1 predicted loss of function carrier, and most genes (>69%) had [≥]10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.

genomics↗

bioRxiv2018

Interaction pathways promote spliceosome module integration and network-level robustness to cascading effects

The functionality of distinct types of protein networks depends on the patterns of protein-protein interactions. A problem to solve is understanding the fragility of protein networks to predict system malfunctioning due to mutations and other errors. Spectral graph theory provides tools to understand the structural and dynamical properties of a system based on the mathematical properties of matrices associated with the networks. We combined two of such tools to explore the fragility to cascading effects of the network describing protein interactions within a key macromolecular complex, the spliceosome. Using S. cerevisiae as a model system we show that the spliceosome network has more indirect paths connecting proteins than random networks. Such multiplicity of paths may promote routes to cascading effects to propagate across the network. However, the modular network structure concentrates paths within modules, thus constraining the propagation of such cascading effects, as indicated by analytical results from the spectral graph theory and by numerical simulations of a minimal mathematical model parameterized with the spliceosome network. We hypothesize that the concentration of paths within modules favors robustness of the spliceosome against failure, but may lead to a higher vulnerability of functional subunits which may affect the temporal assembly of the spliceosome. Our results illustrate the utility of spectral graph theory for identifying fragile spots in biological systems and predicting their implications.

molecular biology↗