According to the competitive exclusion principle, species with low competitive abilities should be excluded by more efficient competitors, and yet they generally remain as rare species. Here, we describe the positive and negative spatial association networks of 326 disparate assemblages, showing a general organization pattern that simultaneously supports the primacy of competition and the persistence of rare species. Abundant species monopolize negative associations in about 90% of the assemblages. Contrarily, rare species are mostly involved in positive associations, forming small network modules. Simulations suggest that positive interactions among rare species and microhabitat preferences are the most likely mechanisms underpinning this pattern and rare species persistence. The

Aim Phylogenetic niche conservatism (PNC), a pattern of closely related species retaining ancestral niche‐related traits over evolutionary time, is well documented for abiotic (Grinellian) dimensions of the ecological niche. However, it remains unclear whether biotic niche (Eltonian) axes are also phylogenetically conserved, even though knowledge of biotic niches is essential to an understanding of the spatiotemporal dynamics of ecological communities. We conduct the first analysis of biotic PNC by evaluating dietary specialization in a vertebrate class. Location Global. Methods We analysed two global compilations of diets of living mammals and a more detailed database for large carnivores together with a species‐level phylogeny to evaluate trophic PNC. We searched for evidence of PNC by estimating the phylogenetic signal in distinct descriptors of dietary niche. Results Trophic niches were generally similar among related species but not strongly conserved under a niche‐drift macroevolutionary model (Brownian motion). The degree of similarity in trophic niche varied among different taxonomic groups and was, importantly, even within the same group, contingent on the metric of dietary preferences used and the quality of information on the database. Main conclusions Overall, our results showed limited support for PNC in the trophic niche of mammals. However, different data sources and metrics of dietary preferences sometimes offered different conclusions, highlighting the importance of gathering high‐quality quantitative data and considering multiple metrics to describe dietary niche breadth and to assess PNC. The fully quantitative database for large carnivores provided some interesting evidence of PNC that could not be detected with semi‐quantitative or presence/absence descriptors. Subsequent assessments of phylogenetic imprints on dietary specialization would benefit from considering different metrics and using well‐resolved phylogenies jointly with detailed quantitative diet information. While Eltonian trophic niches did not show the same high levels of evolutionary conservatism often displayed by Grinnellian niches, both niche components should be considered to understand range limits of species and clades at biogeographic scales.

Zoogeographical regions, or zooregions, are areas of the Earth defined by species pools that reflect ecological, historical and evolutionary processes acting over millions of years. Consequently, researchers have assumed that zooregions are robust and unlikely to change on a human timescale. However, the increasing number of human‐mediated introductions and extinctions can challenge this assumption. By delineating zooregions with a network‐based algorithm, here we show that introductions and extinctions are altering the zooregions we know today. Introductions are homogenising the Eurasian and African mammal zooregions and also triggering less intuitive effects in birds and amphibians, such as dividing and redefining zooregions representing the Old and New World. Furthermore, these Old and New World amphibian zooregions are no longer detected when considering introductions plus extinctions of the most threatened species. Our findings highlight the profound and far‐reaching impact of human activity and call for identifying and protecting the uniqueness of biotic assemblages.

Social networks are the result of interactions between individuals at different temporal scales. Thus, sporadic intergroup encounters and individual forays play a central role in defining the dynamics of populations in social species. We assessed the rate of intergroup encounters for three western lowland gorilla ( Gorilla gorilla gorilla ) groups with daily observations over 5 years, and non-invasively genotyped a larger population over four months. Both approaches revealed a social system much more dynamic than anticipated, with non-aggressive intergroup encounters that involved social play by immature individuals, exchanges of members between groups likely modulated by kinship, and absence of infanticide evidenced by infants not fathered by the silverback of the group where they were found. This resulted in a community composed of groups that interacted frequently and not-aggressively, contrasting with the more fragmented and aggressive mountain gorilla ( G. beringei beringei ) societies. Such extended sociality can promote the sharing of behavioural and cultural traits, but might also increase the susceptibility of western lowland gorillas to infectious diseases that have decimated their populations in recent times.

To understand how a complex system is organized and functions, researchers often identify communities in the system's network of interactions. Because it is practically impossible to explore all solutions to guarantee the best one, many community-detection algorithms rely on multiple stochastic searches. But for a given combination of network and stochastic algorithm, how many searches are sufficient to find a solution that is good enough? The standard approach is to pick a reasonably large number of searches and select the network partition with the highest quality or derive a consensus solution based on all network partitions. However, if different partitions have similar qualities such that the solution landscape is degenerate, the single best partition may miss relevant information, and a consensus solution may blur complementary communities. Here we address this degeneracy problem with coarse-grained descriptions of the solution landscape. We cluster network partitions based on their similarity and suggest an approach to determine the minimum number of searches required to describe the solution landscape adequately. To make good use of all partitions, we also propose different ways to explore the solution landscape, including a significance clustering procedure. We test these approaches on synthetic networks and a real-world network using two contrasting community-detection algorithms: The algorithm that can identify more general structures requires more searches and networks with clearer community structures require fewer searches. We also find that exploring the coarse-grained solution landscape can reveal complementary solutions and enable more reliable community detection.