Biology subjects

Smith, M. L.

Publications and source records attributed to Smith, M. L..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Resolving haplotype variation and complex genetic architecture in the human immunoglobulin kappa chain locus in individuals of diverse ancestry

Immunoglobulins (IGs), critical components of the human immune system, are composed of heavy and light protein chains encoded at three genomic loci. The IG Kappa (IGK) chain locus consists of two large, inverted segmental duplications. The complexity of IG loci has hindered effective use of standard high- throughput methods for characterizing genetic variation within these regions. To overcome these limitations, we leverage long-read sequencing to create haplotype-resolved IGK assemblies in an ancestrally diverse cohort (n=36), representing the first comprehensive description of IGK haplotype variation at population-scale. We identify extensive locus polymorphism, including novel single nucleotide variants (SNVs) and a common novel [~]24.7 Kbp structural variant harboring a functional IGKV gene. Among 47 functional IGKV genes, we identify 141 alleles, 64 (45.4%) of which were not previously curated. We report inter-population differences in allele frequencies for 14 of the IGKV genes, including alleles unique to specific populations within this dataset. Finally, we identify haplotypes carrying signatures of gene conversion that associate with enrichment of SNVs in the IGK distal region. These data provide a critical resource of curated genomic reference information from diverse ancestries, laying a foundation for advancing our understanding of population-level genetic variation in the IGK locus.

immunology↗

bioRxiv2023

Randomness impacts specific priors building, visual exploration, and perception in object recognition

1.Recognising objects is a vital skill on which humans heavily rely to respond quickly and adaptively to their environment. Yet, we lack understanding on the role visual information sampling plays in this process, and its relation to the individuals priors. To bridge this gap, the eye-movements of 18 adult participants were recorded during a free-viewing object-recognition task using Dots stimuli (Moca et al., 2011). Participants viewed the stimuli in one of three orders: from most visible to least (Descending), least visible to most (Ascending) or in a randomised order (Random). This dictated the strength of their priors along the experiment. Adding to Moca et al.s original finding that visibility order influenced participants recognition performance and visual exploration, we found that while orders allowing for stronger priors generally led participants to visually sample more informative locations, this was not the case of Random participants. Indeed, they appeared to behave naively, and their use of specific object-related priors seemed fully impaired, while they seemed to maintain the ability to use general task-related priors to guide their exploration. These findings have important implications for our understanding of perception, which appears markedly subjective even at the basic level of visual sampling and object perception.

neuroscience↗

bioRxiv2023

Identification of Genetic and Genomic Influences on Progressive Ethanol Consumption in Diversity Outbred Mice

Genetic factors play a significant role in the risk for development of alcohol use disorder (AUD). Using 3-bottle choice intermittent access ethanol (IEA), we have employed the Diversity Outbred (DO) mouse panel as a model of alcohol use disorder in a genetically diverse population. Through use of gene expression network analysis techniques, in combination with expression quantitative trait loci (eQTL) mapping, we have completed an extensive analysis of the influence of genetic background on gene expression changes in the prefrontal cortex (PFC). This approach revealed that, in DO mice, genes whose expression was significantly disrupted by intermittent ethanol in the PFC also tended to be those whose expression correlated to intake. This finding is in contrast to previous studies of both mice and nonhuman primates. Importantly, these analyses identified genes involved in myelination in the PFC as significantly disrupted by IEA, correlated to ethanol intake, and having significant eQTLs. Genes that code for canonical components of the myelin sheath, such as Mbp, also emerged as key drivers of the gene expression response to intermittent ethanol drinking. Several regulators of myelination were also key drivers of gene expression, and had significant QTLs, indicating that genetic background may play an important role in regulation of brain myelination. These findings underscore the importance of disruption of normal myelination in the PFC in response to prolonged ethanol exposure, that genetic variation plays an important role in this response, and that this interaction between genetics and myelin disruption in the presence of ethanol may underlie previously observed behavioral changes under intermittent access ethanol drinking such as escalation of consumption.

genomics↗

bioRxiv2023

Honey bees perform fine-scale detailing that continuously reduces comb area after nest expansion

Honey bees are renowned architects. The workers use expensive wax secretions to build their nests, which reach a mature, seemingly steady state, relatively quickly. After nest expansion is complete, workers do not tear down combs completely and begin anew, but there is the possibility they may make subtle changes like adding, removing, and repositioning existing wax. Previous work has focused on nest initiation and nest expansion, but here we focus on mature nests that have reached a steady-state. To investigate subtle changes to comb shape over time, we tracked six colonies from nest initiation through maturity (211 days), photographing their combs every 1-2 weeks. By aligning comb images over time, we show that workers continuously remove wax from the comb edges, thereby reducing total nest area over time. All six colonies trimmed comb edges, and 98.3% of combs were reduced (n = 59). Comb reduction began once workers stopped expanding their nests and continued throughout the experiment. The extent to which a comb was reduced did not correlate with its position within the nest, comb perimeter, or comb area. It is possible that workers use this removed wax as a reserve wax source, though this remains untested. These results show that the superorganism nest is not static; workers are constantly interacting with their nest, and altering it, even after nest expansion is complete.

animal behavior and cognition↗

bioRxiv2023

Virally induced lipid droplets are a platform for innate immune signalling complexes

Lipid droplets (LDs) are upregulated by host cells in the face of pathogen infection, however, the reason for this phenomenon remains largely unknown. Here, we demonstrate that virally induced LDs house a distinct and dynamic proteome containing key antiviral signalling pathway members, including the essential pattern recognition receptor; RIG-I, key adaptor proteins; STAT1 and STAT2 and prominent interferon inducible proteins; viperin and MX1. Changes in the LD proteome were underpinned by specific key changes in the lipidome of virally driven LDs, particularity in the phospholipid membrane. Following virus infection, key antiviral proteins formed complex protein-protein interactions on the LD surface, positioning this organelle as a key antiviral signalling platform for the first time. It is clear that dynamic regulation of both the proteome and the lipidome of LDs occurs rapidly following viral infection towards the initiation of a successful innate immune response.

immunology↗

bioRxiv2023

Honey bee drones are synchronously hyperactive inside the nest

1Eusocial insects operate as an integrated collective with tasks allocated among individuals. This applies also to reproduction, through coordinated mating flights between male and female reproductives. While in some species male sexuals take only a single mating flight and never return, in the Western honey bee, Apis mellifera, the male sexuals (drones) live in the colony throughout their lives. Prior research has focused almost exclusively on drone behavior outside of the nest (mating flights), while ignoring the majority of their life, which is spent inside the nest. To understand the in-nest behavior of drones across their lives, we used the BeesBook tracking system to track 192 individually-marked drones continuously for over 20 days, to examine how drones moved and spent time in the nest. In agreement with previous work, we find that drones spend most of their time immobile at the nest periphery. However, we also observe that drones have periods of in-nest hyperactivity, during which they become the most active individuals in the entire colony. This in-nest hyperactivity develops in drones after age 7 days, occurs daily in the afternoon, and coincides with drones taking outside trips. We find strong synchronization across the drones in the start/end of activity, such that the drones in the colony exhibit a "shared activation period". The duration of the shared activation period depends on the weather; when conditions are suitable for mating flights, the activation period is extended. At the individual-level, we see that the activation order changes from day to day, suggesting that both the external influence of weather conditions, as well as exchange of social information, influences individual activation. Using an accumulation-to-threshold model of drone activation, we show that simulations using social information match experimental observations. These results provide new insight into the in-nest behavior of drones, and how their behavior reflects their role as the male gametes of the colony.

animal behavior and cognition↗

bioRxiv2022

Phylogenetic inference using Generative Adversarial Networks

MotivationThe application of machine learning approaches in phylogenetics has been impeded by the vast model space associated with inference. Supervised machine learning approaches require data from across this space to train models. Because of this, previous approaches have typically been limited to inferring relationships among unrooted quartets of taxa, where there are only three possible topologies. Here, we explore the potential of generative adversarial networks (GANs) to address this limitation. GANs consist of a generator and a discriminator: at each step, the generator aims to create data that is similar to real data, while the discriminator attempts to distinguish generated and real data. By using an evolutionary model as the generator, we use GANs to make evolutionary inferences. Since a new model can be considered at each iteration, heuristic searches of complex model spaces are possible. Thus, GANs offer a potential solution to the challenges of applying machine learning in phylogenetics. ResultsWe developed phyloGAN, a GAN that infers phylogenetic relationships among species. phy-loGAN takes as input a concatenated alignment, or a set of gene alignments, and infers a phylogenetic tree either considering or ignoring gene tree heterogeneity. We explored the performance of phyloGAN for up to fifteen taxa in the concatenation case and six taxa when considering gene tree heterogeneity. Error rates are relatively low in these simple cases. However, run times are slow and performance metrics suggest issues during training. Future work should explore novel architectures that may result in more stable and efficient GANs for phylogenetics. AvailabilityphyloGAN is available on github: https://github.com/meganlsmith/phyloGAN/. Contactmls16@indiana.edu

evolutionary biology↗

bioRxiv2022

Genetic Mapping of Progressive Ethanol Consumption in the Diversity Outbred Mouse

Traditional genetic mapping studies using inbred crosses are a powerful tool for identifying chromosomal regions associated with ethanol-related traits, but typically have very large confidence intervals which make identification of specific and potentially causal candidate genes difficult. Diversity Outbred (DO) mice offer the ability to map quantitative trait loci (QTLs) associated with ethanol-drinking behaviors at a high resolution that allows for easier identification of candidate genes. Here, we exposed a population of 636 male DO mice to four weeks of intermittent ethanol access via a three-bottle choice paradigm, identifying 3 significant (Chrs 3, 4, and 12) and 12 suggestive loci for ethanol-drinking behaviors. The confidence intervals for these loci were narrow (1-4 Mbp for significant QTLs). We then further analyzed positional candidate genes using transcriptomics data from prefrontal cortex samples taken from 220 of these animals, as well as human GWAS data and prior gene set data for ethanol or other drugs of abuse. These results represent the highest-resolution genetic mapping of ethanol consumption behaviors in mice to date, providing for the identification of novel loci and candidate genes for progressive ethanol consumption, including Car8 --the lone gene with a significant cis-eQTL in strong linkage disequilibrium with our QTL for last week ethanol consumption on Chr 4.

genetics↗

bioRxiv2022

Deep learning for histopathological subtyping and grading of lung adenocarcinoma

The histopathological distinction of lung adenocarcinoma (LADC) subtypes is subject to high inter-observer variability, which can compromise the optimal assessment of the patient prognosis. Therefore, this study developed convolutional neural networks (CNNs) capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the LADC tumour grades established recently by the International Association for the Study of Lung Cancer pathology committee. Consensus LADC ground truth histopathological images were obtained from seventeen expert pulmonary pathologists and one pathologist in training. Two deep learning models (AI-1 and AI-2) were trained with EfficientNet b3 architecture to predict eight different LADC classes (lepidic, acinar, papillary, micropapillary, solid, invasive mucinous adenocarcinoma, other carcinoma types, and no carcinoma cells). Furthermore, the trained models were tested on an independent cohort of 133 patients. The models achieved high precision, recall, and F1-scores exceeding 0.90 for most of the LADC classes. Clear stratification of the three LADC grades was reached in predicting the disease-specific survival by the two models. Moreover, the grading prediction of one of the trained models was more accurate than those of 14 out of 15 pulmonary pathologists involved in the study (p=0.0003). Both trained models showed high stability in the segmentation of each pair of predicted grades with low variation in the hazard ratio across 200 bootstrapped samples. These findings indicate that the trained CNNs improve the diagnostic accuracy of the pathologist, standardise LADC subtype recognition, and refine LADC grade assessment. Thus, the trained models are promising tools that may assist in the routine evaluation of LADC subtypes and grades in clinical practice.

pathology↗

bioRxiv2022

FLAIRR-seq: A novel method for single molecule resolution of near full-length immunoglobulin heavy chain repertoires

Current Adaptive Immune Receptor Repertoire Sequencing (AIRR-seq) strategies resolve expressed antibody (Ab) transcripts with limited resolution of the constant region. Here we present a novel near full-length AIRR-seq (FLAIRR-Seq) method that utilizes targeted amplification by 5 rapid amplification of cDNA ends (RACE), combined with single molecule, real-time sequencing to generate highly accurate (>Q40, 99.99%) IG heavy chain transcripts. FLAIRR-seq was benchmarked by comparing IG heavy chain variable (IGHV), diversity (IGHD), and joining (IGHJ) gene usage, complementarity-determining region 3 (CDR3) length, and somatic hypermutation to matched datasets generated with standard 5 RACE AIRR-seq and full-length isoform sequencing. Together these data demonstrate robust, unbiased FLAIRR-seq performance using RNA samples derived from peripheral blood mononuclear cells, purified B cells, and whole blood, which recapitulated results generated by commonly used methods, while additionally resolving novel IG heavy chain constant (IGHC) gene features. FLAIRR-seq data provides, for the first time, simultaneous, single-molecule characterization of IGHV, IGHD, IGHJ, and IGHC region genes and alleles, allele-resolved subisotype definition, and high-resolution identification of class-switch recombination within a clonal lineage. In conjunction with genomic sequencing and genotyping of IGHC genes, FLAIRR-seq of the IgM and IgG repertoires from 10 individuals resulted in the identification of 32 unique IGHC alleles, 28 (87%) of which were previously uncharacterized. Together, these data demonstrate the capabilities of FLAIRR-seq to characterize IGHV, IGHD, IGHJ, and IGHC gene diversity for the most comprehensive view of bulk expressed Ab repertoires to date.

immunology↗

bioRxiv2022

Targeted long-read sequencing facilitates phased diploid assembly and genotyping of the human T cell receptor alpha, delta and beta loci

T cell receptors (TCRs) recognize peptide fragments presented by the major histocompatibility complex (MHC) and are critical to T cell mediated immunity. Early studies demonstrated an enrichment of polymorphisms within TCR-encoding (TR) gene loci. However, more recent data indicate that variation in these loci are underexplored, limiting understanding of the impact of TR polymorphism on TCR function in disease, even though: (i) TCR repertoire signatures are heritable and (ii) associate with disease phenotypes. TR variant discovery and curation has been difficult using standard high-throughput methods. To address this, we expanded our published targeted long-read sequencing approach to generate highly accurate haplotype resolved assemblies of the human TR beta (TRB) and alpha/delta (TRA/D) loci, facilitating the detection and genotyping of single nucleotide polymorphisms (SNPs), insertion-deletions (indels), structural variants (SVs) and TR genes. We validate our approach using two mother-father-child trios and 5 unrelated donors representing multiple populations. Comparisons of long-read derived variants to short-read datasets revealed improved genotyping accuracy, and TR gene annotation led to the discovery of 79 previously undocumented V, D, and J alleles. This demonstrates the utility of this framework to resolve the TR loci, and ultimately our understanding of TCR function in disease.

genomics↗

bioRxiv2022

Multistate structures of the MLL1-WRAD complex bound to H2B-ubiquitinated nucleosome

The human Mixed Lineage Leukemia-1 (MLL1) complex orchestrates methylation of histone H3K4 to promote transcription and is stimulated by monoubiquitination of histone H2B. Recent structures of the MLL1-WRAD core complex, which comprises the MLL1 methyltransferase, WDR5, RbBp5, Ash2L, and DPY-30, have revealed variation in the docking of MLL1-WRAD on nucleosomes and left ambiguous portions of Ash2L and the position of DPY30. We used an integrated approach combining cryo-electron microscopy and mass spectrometry-crosslinking to determine structures of the MLL1-WRAD complex bound to ubiquitinated nucleosomes containing the Ash2L intrinsically disordered region (IDR), SPRY insertion region, Sdc1-DPY30 interacting region (SDI-motif), and the DPY30 dimer. We resolved three additional states of MLL1-WRAD lacking one or more subunits, which may reflect different steps in the assembly of MLL1-WRAD. The subunits in all four states are positioned on the nucleosome in manner that is similar to a previous structure of MLL1-WRAD bound to ubiquitinated nucleosome, but that differs from structures with unmodified nucleosomes, suggesting that H2B-ubiquitin favors assembly of the active complex. Our results provide a more complete picture of MLL1-WRAD and the role of ubiquitin in promoting formation of the active methyltransferase complex. SignificanceThe Mixed Lineage Leukemia-1 (MLL1) complex plays a role in activating transcription by methylating lysine 4 in histone H3, a reaction that is stimulated by the presence of ubiquitin conjugated to histone H2B. Recent structures of the core MLL1 complex, termed MLL1-WRAD, have revealed the existence of multiple docking states and have also left ambiguous portions of the structure. Here we combine mass spectrometry-cross linking with cryo-EM to model additional regions of the MLL1-WRAD complex and identify a series of states that light on complex assembly and the role that ubiquitin plays in orienting MLL1-WRAD on nucleosomes.

biophysics↗

bioRxiv2022

Quality assurance of hematopoietic stem cells by macrophages determines stem cell clonality

Tissue-specific stem cells persist for a lifetime and can differentiate to maintain homeostasis or transform to initiate cancer. Despite their importance, there are no described quality assurance mechanisms for newly formed stem cells. We observed intimate and specific interactions between macrophages and nascent blood stem cells in zebrafish embryos. Macrophage interactions led to two outcomes -- removal of cytoplasmic material and stem cell division, or complete engulfment and stem cell death. Stressed stem cells were marked by surface Calreticulin, which stimulated macrophage interactions. Using cellular barcoding, we found that calreticulin knock-down or embryonic macrophage depletion reduced the number of stem cell clones that established adult hematopoiesis. Our work supports a model in which embryonic macrophages determine hematopoietic clonality by monitoring stem cell quality.

developmental biology↗

bioRxiv2022

A BALB/c IGHV Reference Set, defined by haplotype analysis of long-read VDJ-C sequences from F1 (BALB/c / C57BL/6) mice

The immunoglobulin genes of inbred mouse strains that are commonly used in models of antibody-mediated human diseases are poorly characterized. This compromises data analysis. To infer the immunoglobulin genes of BALB/c mice, we used long-read SMRT sequencing to amplify VDJ-C sequences from F1 (BALB/c x C57BL/6) hybrid animals. Previously unreported strain variations were identified in the Ighm and Ighg2b genes, and analysis of VDJ rearrangements led to the inference of 278 germline IGHV alleles. 169 alleles are not present in the C57BL/6 genome reference sequence. To establish a set of expressed BALB/c IGHV germline gene sequences, we computationally retrieved IGHV haplotypes from the IgM dataset. Haplotyping led to the confirmation of 162 BALB/c IGHV gene sequences. A musIGHV398 pseudogene variant also appears to be present in the BALB/cByJ substrain, while a functional musIGHV398 gene is highly expressed in the BALB/cJ substrain. Only four of the BALB/c alleles were also observed in the C57BL/6 haplotype. The full set of inferred BALB/c sequences has been used to establish a BALB/c IGHV reference set, hosted at https://ogrdb.airr-community.org. We assessed whether assemblies from the Mouse Genome Project (MGP) are suitable for the determination of the genes of the IGH loci. Only 37 (43.5%) of the 85 confirmed IMGT-named BALB/c IGHV and 33 (42.9%) of the 77 confirmed non-IMGT IGHV were found in a search of the MGP BALB/cJ genome assembly. This suggests that Adaptive Immune Receptor Repertoire sequencing (AIRR-Seq) data, but not currently-available genome assemblies, are suited to the documentation of germline IGHV genes.

immunology↗

bioRxiv2022

Dissociable default-mode subnetworks subserve childhood attention and cognitive flexibility: evidence from deep learning and stereotaxic electroencephalography

BackgroundCognitive flexibility encompasses the ability to efficiently shift focus and forms a critical component of goal-directed attention. The neural substrates of this process are incompletely understood in part due to difficulties in sampling the involved circuitry. MethodsStereotactic intracranial recordings that permit direct resolution of local-field potentials from otherwise inaccessible structures were employed to study moment-to-moment attentional activity in children with epilepsy during the performance of an attentional set-shifting task. A combined deep learning and model-agnostic feature explanation approach was used to analyze these data and decode attentionally-relevant neural features. Connectomic profiling of highly predictive attentional nodes was further employed to examine task-related engagement of large-scale functional networks. ResultsThrough this approach, we show that beta/gamma power within executive control, salience, and default mode networks accurately predicts single-trial attentional performance. Connectomic profiling reveals that key attentional nodes exclusively recruit dorsal default mode subsystems during attentional shifts. ConclusionsThe identification of distinct substreams within the default mode system supports a key role for this network in cognitive flexibility and attention in children. Furthermore, convergence of our results onto consistent functional networks despite significant inter-subject variability in electrode implantations supports a broader role for deep learning applied to intracranial electrodes in the study of human attention. FundingNo funds supported this specific investigation. Awards and grants supporting authors include: Canadian Institutes of Health Research (CIHR) Vanier Scholarship (NMW, HY); CIHR Frederick Banting and Charles Best Canada Graduate Scholarship Doctoral Award (SMW); CIHR Canada Graduate Scholarship Masters Award (ONA); and a CIHR project grant (GMI).

neuroscience↗

bioRxiv2021

Using all gene families vastly expands data available for phylogenomic inference in primates

Traditionally, single-copy orthologs have been the gold standard in phylogenomics. Most phylogenomic studies identify putative single-copy orthologs using clustering approaches and retain families with a single sequence per species. This limits the amount of data available by excluding larger families. Recent advances have suggested several ways to include data from larger families. For instance, tree-based decomposition methods facilitate the extraction of orthologs from large families. Additionally, several methods for species tree inference are robust to the inclusion of paralogs, and could use all of the data from larger families. Here, we explore the effects of using all families for phylogenetic inference by examining relationships among 26 primate species in detail, and by analyzing five additional datasets. We compare single-copy families, orthologs extracted using tree-based decomposition approaches, and all families with all data. We explore several species tree inference methods, finding that identical trees are returned across nearly all subsets of the data and methods for primates. The relationships among Platyrrhini remain contentious; however, the species tree inference method matters more than the subset of data used. Using data from larger gene families drastically increases the number of genes available and leads to consistent estimates of branch lengths, nodal certainty and concordance, and inferences of introgression in primates. For the other datasets, topological inferences are consistent whether single-copy families or orthologs extracted using decomposition approaches are analyzed. Using larger gene families is a promising approach to include more data in phylogenomics without sacrificing accuracy, at least when high-quality genomes are available.

evolutionary biology↗

bioRxiv2021

Learning to embed lifetime social behavior from interaction dynamics

Interactions of individuals in complex social systems give rise to emergent behaviors at the group level. Identifying the functional role that individuals take in the group at a specific time facilitates understanding the dynamics of these emergent processes. An individuals behavior at a given time can be partially inferred by common factors, such as age, but internal and external factors also substantially influence behavior, making it difficult to disentangle common development from individuality. Here we show that such dependencies on common factors can be used as an implicit bias to learn a temporally consistent representation of a functional role from social interaction networks. Using a unique dataset containing lifetime trajectories of multiple generations of individually-marked honey bees in two colonies, we propose a new temporal matrix factorization model that jointly learns the average developmental path and structured variations of individuals in the social network over their entire lives. Our method yields inherently interpretable embeddings that are biologically relevant and consistent over time, allowing one to compare individuals functional roles regardless of when or in which colony they lived. Our method provides a quantitative framework for understanding behavioral heterogeneity in complex social systems, and is applicable to fields such as behavioral biology, social sciences, neuroscience, and information science. Author summaryGroup-level emergent behaviors are the result of interactions between individual group members. To understand these social dynamics, one must objectively measure the function of an individual in their group at any given time. Ideally, one would also like to compare individuals from different groups, for example, to measure how specific environmental conditions or other external factors influence group behavior. Unfortunately, such an objective measure is hard to obtain because the group and its dynamics constantly change, making it challenging to define an individuals role in the group as a function of its actions and interactions. We propose a principled approach to model individuals in complex social systems by considering that function often depends, at least partially, on common factors such as age. The model learns a meaningful and interpretable descriptor for all individuals, and can be used to understand how complex social systems function and the emergence of group behavior.

animal behavior and cognition↗

bioRxiv2021

The dominant axes of lifetime behavioral variation in honey bees

Insect colonies are decentralized systems that employ task allocation, whereby individuals undertake different roles to fulfil colony needs, such as honey bee "nurses", "nest workers", and "foragers". However, the extent to which individuals can be well-classified by discrete "roles", how they change behavior from day-to-day, over entire lifetimes, and with environmental conditions, is poorly understood. Using long-term automated tracking of over 4,200 individually-identified bees Apis mellifera, we use behavioral metrics to quantify and compare behavior. We show that individuals exhibit behavioral variation along two dominant axes that represent nest substrate use and movement within the nest. Across lifetimes, we find that individuals differ in foraging onset, and that certain bees exhibit lifelong consistencies in their movement patterns. Furthermore, we examine a period of sudden nectar availability where the honey stores tripled over 6 days, and see that the colony exhibits a distributed shift in activity that did not require a large-scale colony reorganization. Our quantitative approach shows how collective units differ over days and lifetimes, and how sources of variation and variability contribute to the colonys robust yet flexible response.

animal behavior and cognition↗