Hierarchical communities in the larval Drosophila connectome: Links to cellular annotations and network topology.

One of the longstanding aims of network neuroscience is to link a connectome's topological properties-i.e., features defined from connectivity alone-with an organism's neurobiology. One approach for doing so is to compare connectome properties with annotational maps. This type of analysis is popular at the meso-/macroscale, but is less common at the nano-scale, owing to a paucity of neuron-level connectome data. However, recent methodological advances have made possible the reconstruction of whole-brain connectomes at single-neuron resolution for a select set of organisms. These include the fruit fly, Drosophila melanogaster, and its developing larvae. In addition to fine-scale descriptions of connectivity, these datasets are accompanied by rich annotations. Here, we use a variant of the stochastic blockmodel to detect multilevel communities in the larval Drosophila connectome. We find that communities partition neurons based on function and cell type and that most interact assortatively, reflecting the principle of functional segregation. However, a small number of communities interact nonassortatively, forming form a "rich-club" of interneurons that receive sensory/ascending inputs and deliver outputs along descending pathways. Next, we investigate the role of community structure in shaping communication patterns. We find that polysynaptic signaling follows specific trajectories across modular hierarchies, with interneurons playing a key role in mediating communication routes between modules and hierarchical scales. Our work suggests a relationship between system-level architecture and the biological function and classification of individual neurons. We envision our study as an important step toward bridging the gap between complex systems and neurobiological lines of investigation in brain sciences.

Long-Horizon Associative Learning Explains Human Sensitivity to Statistical and Network Structures in Auditory Sequences.

Networks are a useful mathematical tool for capturing the complexity of the world. In a previous behavioral study, we showed that human adults were sensitive to the high-level network structure underlying auditory sequences, even when presented with incomplete information. Their performance was best explained by a mathematical model compatible with associative learning principles, based on the integration of the transition probabilities between adjacent and nonadjacent elements with a memory decay. In the present study, we explored the neural correlates of this hypothesis via magnetoencephalography (MEG). Participants (N = 23, 16 females) passively listened to sequences of tones organized in a sparse community network structure comprising two communities. An early difference (∼150 ms) was observed in the brain responses to tone transitions with similar transition probability but occurring either within or between communities. This result implies a rapid and automatic encoding of the sequence structure. Using time-resolved decoding, we estimated the duration and overlap of the representation of each tone. The decoding performance exhibited exponential decay, resulting in a significant overlap between the representations of successive tones. Based on this extended decay profile, we estimated a long-horizon associative learning novelty index for each transition and found a correlation of this measure with the MEG signal. Overall, our study sheds light on the neural mechanisms underlying human sensitivity to network structures and highlights the potential role of Hebbian-like mechanisms in supporting learning at various temporal scales.

A social perspective on perceived distances reveals deep community structure.

Community structure, including relationships between and within groups, is foundational to our understanding of the world around us. For dissimilarity-based data, leveraging social concepts of conflict and alignment, we provide an approach for capturing meaningful structural information resulting from induced local comparisons. In particular, a measure of local (community) depth is introduced that leads directly to a probabilistic partitioning conveying locally interpreted closeness (or cohesion). A universal choice of threshold for distinguishing strongly and weakly cohesive pairs permits consideration of both local and global structure. Cases in which one might benefit from use of the approach include data with varying density such as that arising as snapshots of complex processes in which differing mechanisms drive evolution locally. The inherent recalibrating in response to density allows one to sidestep the need for localizing parameters, common to many existing methods. Mathematical results together with applications in linguistics, cultural psychology, and genetics, as well as to benchmark clustering data have been included. Together, these demonstrate how meaningful community structure can be identified without additional inputs (e.g., number of clusters or neighborhood size), optimization criteria, iterative procedures, or distributional assumptions.

The role of fish predators and their foraging traits in shaping zooplankton community structure.

Differentiation of foraging traits among predator populations may help explain observed variation in the structure of prey communities. However, few studies have investigated the phenotypic effects of predators on their prey in natural communities. Here, we use a comparative analysis of 78 Greenlandic lakes to examine how foraging trait variation among threespine stickleback populations can help explain variation in zooplankton community composition among lakes. We find that landscape-scale variation in zooplankton composition was jointly explained by lake properties, such as size and water chemistry, and the presence and absence of both stickleback and arctic char. Additional variation in zooplankton community structure can be explained by stickleback jaw protrusion, a trait with known utility for foraging on zooplankton, but only in lakes where stickleback co-occur with arctic char. Overall, our results illustrate how trait variation of predators, alongside other ecosystem properties, can influence the composition of prey communities in nature.

Phylogeny structures species' interactions in experimental ecological communities.

Species' traits and interactions are products of evolutionary history. Despite the long-standing hypothesis that closely related species possess similar traits, and thus experience stronger competition, measuring the effect of evolutionary history on the ecology of natural communities remains challenging. We propose a novel framework to test whether phylogeny influences patterns of coexistence and abundance of species assemblages. In our approach, phylogenetic trees are used to parameterize species' interactions, which in turn determine the abundance of species in a given assemblage. We use likelihoods to score models parameterized with a given phylogeny, and contrast them with models built using random trees, allowing us to test whether phylogenetic information helps to predict species' abundances. Our statistical framework reveals that interactions are indeed structured by phylogeny in a large set of experimental plant communities. Our results confirm that evolutionary history can help predict, and potentially manage or conserve, the structure and function of complex ecological communities.

Patterns of genetic connectedness between modern and medieval Estonian genomes reveal the origins of a major ancestry component of the Finnish population.

The Finnish population is a unique example of a genetic isolate affected by a recent founder event. Previous studies have suggested that the ancestors of Finnic-speaking Finns and Estonians reached the circum-Baltic region by the 1st millennium BC. However, high linguistic similarity points to a more recent split of their languages. To study genetic connectedness between Finns and Estonians directly, we first assessed the efficacy of imputation of low-coverage ancient genomes by sequencing a medieval Estonian genome to high depth (23×) and evaluated the performance of its down-sampled replicas. We find that ancient genomes imputed from >0.1× coverage can be reliably used in principal-component analyses without projection. By searching for long shared allele intervals (LSAIs; similar to identity-by-descent segments) in unphased data for >143,000 present-day Estonians, 99 Finns, and 14 imputed ancient genomes from Estonia, we find unexpectedly high levels of individual connectedness between Estonians and Finns for the last eight centuries in contrast to their clear differentiation by allele frequencies. High levels of sharing of these segments between Estonians and Finns predate the demographic expansion and late settlement process of Finland. One plausible source of this extensive sharing is the 8th-10th centuries AD migration event from North Estonia to Finland that has been proposed to explain uniquely shared linguistic features between the Finnish language and the northern dialect of Estonian and shared Christianity-related loanwords from Slavic. These results suggest that LSAI detection provides a computationally tractable way to detect fine-scale structure in large cohorts.

Cortical timescales and the modular organization of structural and functional brain networks.

Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.

Differentiating spillover: an examination of cross-habitat movement in ecology spillover in ecology.

Organisms that immigrate into a recipient habitat generate a movement pattern that affects local population dynamics and the environment. Spillover is the pattern of unidirectional movement from a donor habitat to a different, adjacent recipient habitat. However, ecological definitions are often generalized to include any cross-habitat movement, which limits within- and cross-discipline collaboration. To assess spillover nomenclature, we reviewed 337 studies within the agriculture, disease, fisheries and habitat fragmentation disciplines. Each study's definition of spillover and the methods used were analysed. We identified four descriptors (movement, habitat type and arrangement, and effect) used that differentiate spillover from other cross-habitat movement patterns (dispersal, foray loops and edge movement). Studies often define spillover as movement (45%) but rarely measure it as such (4%), particularly in disease and habitat fragmentation disciplines. Consequently, 98% of studies could not distinguish linear from returning movement out of a donor habitat, which can overestimate movement distance. Overall, few studies (12%) included methods that matched their own definition, revealing a distinct mismatch. Because theory shows that long-term impacts of the different movement patterns can vary, differentiating spillover from other movement patterns is necessary for effective long-term and inter-disciplinary management of organisms that use heterogeneous landscapes.

Consequences of kelp forest ecosystem shifts and predictors of persistence through multiple stressors.

Ecological communities can be stable over multiple generations, or rapidly shift into structurally and functionally different configurations. In kelp forest ecosystems, overgrazing by sea urchins can abruptly shift forests into alternative states that are void of macroalgae and primarily dominated by actively grazing sea urchins. Beginning in 2014, a sea urchin outbreak along the central coast of California resulted in a patchy mosaic of remnant forests interspersed with sea urchin barrens. In this study, we used a 14-year subtidal monitoring dataset of invertebrates, algae, and fishes to explore changes in community structure associated with the loss of forests. We found that the spatial mosaic of barrens and forests resulted in a region-wide shift in community structure. However, the magnitude of kelp forest loss and taxonomic-level consequences were spatially heterogeneous. Taxonomic diversity declined across the region, but there were no declines in richness for any group, suggesting compositional redistribution. Baseline ecological and environmental conditions, and sea urchin behaviour, explained the persistence of forests through multiple stressors. These results indicate that spatial heterogeneity in preexisting ecological and environmental conditions can explain patterns of community change.

Continental scale comparison of mycobiomes in Parmelia and Peltigera lichens from Turkey and South Korea.

BACKGROUND: Lichens, traditionally considered as a simple partnership primarily between mycobiont and photobiont, are, in reality, complex holobionts comprised of a multitude of microorganisms. Lichen mycobiome represents fungal community residing within lichen thalli. While it is acknowledged that factors like the host lichen species and environmental conditions influence the structure of the lichen mycobiome, the existing research remains insufficient. To investigate which factor, host genus or location, has a greater impact on the lichen mycobiome, we conducted a comparative analysis of mycobiomes within Parmelia and Peltigera collected from both Turkey and South Korea, using high-throughput sequencing based on internal transcribed spacer region amplification. RESULTS: Overall, the lichen mycobiome was dominated by Capnodiales (Dothideomycetes), regardless of host or location. At the order level, the taxonomic composition was not significantly different according to lichen genus host or geographical distance. Hierarchical clustering of the top 100 abundant ASVs did not clearly indicate whether the lichen mycobiome was more influenced by host genus or location. Analyses of community similarity and partitioning variables revealed that the structure of the lichen mycobiome is more significantly influenced by location than by host genus. When analyzing the core mycobiome by host genus, the Peltigera mycobiome contained more ASV members than the Parmelia mycobiome. These two core mycobiomes also share common fungal strains, including basidiomycete yeast. Additionally, we used chi-squared tests to identify host genus-specialists and location-specialists. CONCLUSIONS: By comparing lichen mycobiomes of the same genera across different countries, our study advances our comprehension of these microbial communities. Our study elucidates that, although host species play a contributory role, geographic distance exerts a more pronounced impact on the structure of lichen mycobiome. We have made foundational contributions to understanding the lichen mycobiome occupying ecologically crucial niches. We anticipate that broader global-scale investigations into the fungal community structures will provide more detailed insights into fungal residents within lichens.