Exploring the solution landscape enables more reliable network community detection

Parallel median consensus clustering in complex networks

We develop an algorithm that finds the consensus among many different clustering solutions of a graph. We formulate the problem as a median set partitioning problem and propose a greedy optimization technique. Unlike other approaches that find median set partitions, our algorithm takes graph structure into account and finds a comparable quality solution much faster than the other approaches. For graphs with known communities, our consensus partition captures the actual community structure more accurately than alternative approaches. To make it applicable to large graphs, we remove sequential dependencies from our algorithm and design a parallel algorithm. Our parallel algorithm achieves 35x speedup when utilizing 64 processing cores for large real-world graphs representing mass cytometry data from single-cell experiments.

Assessing spatial structure in marine populations using network theory: A case study of Atlantic sea scallop (Placopecten magellanicus) connectivity

Knowledge of the geographic distribution and connectivity of marine populations is essential for ecological understanding and informing management. Previous works have assessed spatial structure by quantifying exchange using Lagrangian particle-tracking simulations, but their scope of analysis is limited by their use of predefined subpopulations. To instead delineate subpopulations emerging naturally from marine population connectivity, we interpret this connectivity as a network, enabling the use of powerful analytic tools from the field of network theory. The modelling approach presented here uses particle-tracking to construct a transport network, and then applies the community detection algorithm Infomap to identify subpopulations that exhibit high internal connectivity and sparse connectivity with other subpopulations. An established quality metric, the coherence ratio, and a new metric we introduce indicating self-recruitment to subpopulations, dubbed the fortress ratio, are used to interpret community-level exchange. We use the Atlantic sea scallop (Placopecten magellanicus) in the northwest Atlantic as a case study. Results suggest that genetic lineages of P. magellanicus demonstrate spatial substructure that depends on horizontal transport, vertical motility, and suitable habitat. Our results support connectivity previously characterized on Georges Bank and Mid-Atlantic Bight. The Gulf of St. Lawrence genetic lineage is found to consist of five subpopulations that are classified as being a sink, source, permeable, or impermeable using quality metrics. This approach may be applied to other planktonic dispersers and prove useful to management.

Insect diversity estimation in polarimetric lidar

Identifying flying insects is a significant challenge for biologists. Entomological lidar offers a unique solution, enabling rapid identification and classification in field settings. No other method can match its speed and efficiency in identifying insects in flight. This non-intrusive tool is invaluable for assessing insect biodiversity, informing conservation planning, and evaluating efforts to address declining insect populations. Although the species richness of co-existing insects can reach tens of thousands, current photonic sensors and lidars can differentiate roughly one hundred signal types. While the retrieved number of clusters correlate with Malaise trap diversity estimates, this taxonomic specificity, the number of discernible signal types is currently limited by instrumentation and algorithm sophistication. In this study, we report 32,533 observations of wild flying insects along a 500-meter transect. We report the benefits of lidar polarization bands for differentiating species and compare the performance of two unsupervised clustering algorithms, namely Hierarchical Cluster Analysis and Gaussian Mixture Model. Our analysis shows that polarimetric properties could be partially predicted even with unpolarized light, thus polarimetric lidar bands provide only a minor improvement in specificity. Finally, we use the physical properties of the clustered observations, such as wing beat frequency, daily activity patterns, and spatial distribution, to establish a lower bound for the number of species represented by the differentiated signal types.

Mapping robust multiscale communities in chromosome contact networks

To better understand DNA's 3D folding in cell nuclei, researchers developed chromosome capture methods such as Hi-C that measure the contact frequencies between all DNA segment pairs across the genome. As Hi-C data sets often are massive, it is common to use bioinformatics methods to group DNA segments into 3D regions with correlated contact patterns, such as Topologically associated domains and A/B compartments. Recently, another research direction emerged that treats the Hi-C data as a network of 3D contacts. In this representation, one can use community detection algorithms from complex network theory that group nodes into tightly connected mesoscale communities. However, because Hi-C networks are so densely connected, several node partitions may represent feasible solutions to the community detection problem but are indistinguishable unless including other data. Because this limitation is a fundamental property of the network, this problem persists regardless of the community-finding or data-clustering method. To help remedy this problem, we developed a method that charts the solution landscape of network partitions in Hi-C data from human cells. Our approach allows us to scan seamlessly through the scales of the network and determine regimes where we can expect reliable community structures. We find that some scales are more robust than others and that strong clusters may differ significantly. Our work highlights that finding a robust community structure hinges on thoughtful algorithm design or method cross-evaluation.

The minimum description length principle for pattern mining: a survey

Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration, the selection of patterns constitutes a major challenge. The Minimum Description Length (MDL) principle, a model selection method grounded in information theory, has been applied to pattern mining with the aim to obtain compact high-quality sets of patterns. After giving an outline of relevant concepts from information theory and coding, we review MDL-based methods for mining different kinds of patterns from various types of data. Finally, we open a discussion on some issues regarding these methods.

Late Holocene anthropogenic landscape change in northwestern Europe impacted insect biodiversity as much as climate change did after the last Ice Age

Since the last Ice Age (ca 115 000-11 700 years ago), the geographical ranges of most plants and animals have shifted, expanded or contracted. Understanding the timing, geographical patterns and drivers of past changes in insect communities is essential for evaluating the biodiversity implications of future climate changes, yet our knowledge of long-term patterns is limited. We applied a network modelling approach to the recent fossil record of northwestern European beetles to investigate how their taxonomic and trait composition changed during the past 16 000 years. We found two major changes in beetle faunas 4000-3500 and 10 000-9500 years ago, coinciding with periods of human population growth in the Late Holocene and climate warming in the Early Holocene. Our results demonstrate that humans have affected insect biodiversity since at least the introduction of agropastoralism, with landscape-scale effects that can be observed at sites away from areas of direct human impact.

PISA data clusters reveal student and school inequality that affects results

The data from the PISA survey show that student performance correlates with socio-economic background, that private schools have higher results and more privileged students, and that this varies between countries. We explore this further and analyze the PISA data using methods from network theory and find clusters of countries whose students have similar performance and socio-economic background. Interestingly, we find a cluster of countries, including China, Spain and Portugal, characterized by less privileged students performing well. When considering private schools only, some countries, such as Portugal and Brazil, are in a cluster with mostly wealthy countries characterized by privileged students. Swedish grades are compared to PISA results, and we see that the higher grades in private schools are in line with the PISA results, suggesting that there is no grade inflation in this case, but differences in socio-economic background suggest that this is due to school segregation.

Punctuated ecological equilibrium in mammal communities over evolutionary time scales

The study of deep-time ecological dynamics has the ability to inform conservation decisions by anticipating the behavior of ecosystems millions of years into the future. Using network analysis and an exceptional fossil dataset spanning the past 21 million years, we show that mammalian ecological assemblages undergo long periods of functional stasis, notwithstanding high taxonomic volatility due to dispersal, speciation, and extinction. Higher functional richness and diversity promoted the persistence of functional faunas despite species extinction risk being indistinguishable among these different faunas. These findings, and the large mismatch between functional and taxonomic successions, indicate that although safeguarding functional diversity may or may not minimize species losses, it would certainly enhance the persistence of ecosystem functioning in the face of future disturbances.

A multiscale view of the Phanerozoic fossil record reveals the three major biotic transitions

The hypothesis of the Great Evolutionary Faunas is a foundational concept of macroevolutionary research postulating that three global mega-assemblages have dominated Phanerozoic oceans following abrupt biotic transitions. Empirical estimates of this large-scale pattern depend on several methodological decisions and are based on approaches unable to capture multiscale dynamics of the underlying Earth-Life System. Combining a multilayer network representation of fossil data with a multilevel clustering that eliminates the subjectivity inherent to distance-based approaches, we demonstrate that Phanerozoic oceans sequentially harbored four global benthic mega-assemblages. Shifts in dominance patterns among these global marine mega-assemblages were abrupt (end-Cambrian 494 Ma; end-Permian 252 Ma) or protracted (mid-Cretaceous 129 Ma), and represent the three major biotic transitions in Earth’s history. Our findings suggest that gradual ecological changes associated with the Mesozoic Marine Revolution triggered a protracted biotic transition comparable in magnitude to the end-Permian transition initiated by the most severe biotic crisis of the past 500 million years. Overall, our study supports the notion that both long-term ecological changes and major geological events have played crucial roles in shaping the mega-assemblages that dominated Phanerozoic oceans.

Rojas et al. present a new multi-scale model that reveals the three major biotic transitions of the Phanerozoic fossil record. This new model supports the hypothesis that both long-term ecological changes and major geological events played crucial roles in shaping ocean mega-assemblages through the Phanerozoic.

A modularity analysis helps improving the structure of the International Code of Zoological Nomenclature

BACKGROUND

In a recent work I transformed a complex and integrated text like the International Code of Zoological Nomenclature into a network of interconnected parts of text. This new approach allowed understanding that a continuous body of text cannot accurately reflect the true structure of the Code, and provided a scientific methodology to identify a priori parts that could be affected by future revisions. In this next step, I investigate further the structure of the Code, seeking to use the network in order to identify the various conceptual communities grouping the various articles and other text items of the Code.

METHODS

Using the first version of the network of the Code, I perform a comprehensive modularity analysis in two rounds: the first round aims to identify the fewest and largest communities or modules for the entire network, whereas the second round identifies the sub-modules within each larger module. The potential conflicts between the current structure of the Code and the module composition are evaluated with a parcellation analysis.

RESULTS

The optimal modularity search identified 10 different modules in the entire network of varying size (ranging from 75 to 200 nodes). Each module can be further divided into smaller modules, that all-together allow describing the 65 conceptual groups of text items in the Code. Parcellation analysis revealed that two-thirds of the current chapters of the Code are in excellent or good accordance with the recovered conceptual modules, whereas the current composition of six chapters is in serious conflict with the conceptual structure of the Code.

DISCUSSION

Judging only the composition and not the order of appearance of the Articles in the Chapters of the Code, I show that in many cases the current structure of the Code is found to correspond quite well to the concepts presented therein. The most important conflict is found on the provisions related to the various groups of names governed by the Code: family-, genus-, and species-group names. Currently, these provisions are spread out in different Articles in different Chapters, along the entire length of the Code. The modularity analysis suggests that re-organizing the Code in chapters that will deal with all aspects related to a given group (e.g., chapters including information on name formation, availability, typification, and validity for a given group), could potentially improve reader experience and, consequently, the applicability of the Code.

Regularities in species' niches reveal the world's climate regions

Climate regions form the basis of many ecological, evolutionary, and conservation studies. However, our understanding of climate regions is limited to how they shape vegetation: they do not account for the distribution of animals. Here, we develop a network-based framework to identify important climates worldwide based on regularities in realized niches of about 26,000 tetrapods. We show that high-energy climates, including deserts, tropical savannas, and steppes, are consistent across animal- and plant-derived classifications, indicating similar underlying climatic determinants. Conversely, temperate climates differ across all groups, suggesting that these climates allow for idiosyncratic adaptations. Finally, we show how the integration of niche classifications with geographical information enables the detection of climatic transition zones and the signal of geographic and historical processes. Our results identify the climates shaping the distribution of tetrapods and call for caution when using general climate classifications to study the ecology, evolution, or conservation of specific taxa.

Community detection in bipartite networks with stochastic block models

In bipartite networks, community structures are restricted to being disassortative, in that nodes of one type are grouped according to common patterns of connection with nodes of the other type. This makes the stochastic block model (SBM), a highly flexible generative model for networks with block structure, an intuitive choice for bipartite community detection. However, typical formulations of the SBM do not make use of the special structure of bipartite networks. Here we introduce a Bayesian nonparametric formulation of the SBM and a corresponding algorithm to efficiently find communities in bipartite networks which parsimoniously chooses the number of communities. The biSBM improves community detection results over general SBMs when data are noisy, improves the model resolution limit by a factor of sqrt[2], and expands our understanding of the complicated optimization landscape associated with community detection tasks. A direct comparison of certain terms of the prior distributions in the biSBM and a related high-resolution hierarchical SBM also reveals a counterintuitive regime of community detection problems, populated by smaller and sparser networks, where nonhierarchical models outperform their more flexible counterpart.

Fingers zipped up or baby mittens? Two main tetrapod strategies to return to the sea

The application of network methodology in anatomical structures offers new insights on the connectivity pattern of skull bones, skeletal elements and their muscles. Anatomical networks helped to improve our understanding of the water-to-land transition and how the pectoral fins were transformed into limbs via their modular disintegration. Here, we apply the same methodology to tetrapods secondarily adapted to the marine environment. We find that these animals achieved their return to the sea with four types of morphological changes, which can be grouped into two different main strategies. In all marine mammals and the majority of the reptiles, the fin is formed by the persistence of superficial and interdigital connective tissues, like a 'baby mitten', whereas the underlying connectivity pattern of the bones does not influence the formation of the forefin. On the contrary, ichthyosaurs 'zipped up' their fingers and transformed their digits into carpal-like elements, forming a homogeneous and better-integrated forefin. These strategies led these vertebrates into three different macroevolutionary paths exploring the possible spectrum of morphological adaptations.

Spatial resolution and location impact group structure in a marine food web

Ecological processes in food webs depend on species interactions. By identifying broad-scaled interaction patterns, important information on species' ecological roles may be revealed. Here, we use the group model to examine how spatial resolution and proximity influence group structure. We examine a data set from the Barents Sea, with food webs described for both the whole region and 25 subregions. We test how the group structure in the networks differ comparing (1) the regional metaweb to subregions and (2) subregion to subregion. We find that more than half the species in the metaweb change groups when compared to subregions. Between subregions, networks with similar group structure are spatially related. Interestingly, although species overlap is important for similarity in group structure, there are notable exceptions. Our results highlight that species ecological roles vary depending on fine-scaled differences in the patterns of interactions, and that local network characteristics are important to consider.