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Anthropogenic activities are triggering global changes in the environment, causing entire communities of plants, pollinators and their interactions to restructure, and ultimately leading to species declines. To understand the mechanisms behind community shifts and declines, as well as monitoring and managing impacts, a global effort must be made to characterize plant–pollinator communities in detail, across different habitat types, latitudes, elevations, and levels and types of disturbances. Generating data of this scale will only be feasible with rapid, high-throughput methods. Pollen DNA metabarcoding provides advantages in throughput, efficiency and taxonomic resolution over traditional methods, such as microscopic pollen identification and visual observation of plant–pollinator interactions. This makes it ideal for understanding complex ecological networks and their responses to change. Pollen DNA metabarcoding is currently being applied to assess plant–pollinator interactions, survey ecosystem change and model the spatiotemporal distribution of allergenic pollen. Where samples are available from past collections, pollen DNA metabarcoding has been used to compare contemporary and past ecosystems. New avenues of research are possible with the expansion of pollen DNA metabarcoding to intraspecific identification, analysis of DNA in ancient pollen samples, and increased use of museum and herbarium specimens. Ongoing developments in sequencing technologies can accelerate progress towards these goals. Global ecological change is happening rapidly, and we anticipate that high-throughput methods such as pollen DNA metabarcoding are critical for understanding the evolutionary and ecological processes that support biodiversity, and predicting and responding to the impacts of change.

Aim In recent years biogeography has been transformed by the increased availability of large‐scale distributional data, phylogenies, and novel quantitative analysis methods and models. More case studies, however, are needed to test the performance of various approaches, in particular at global scales and in species‐rich groups. In this study, we inferred bioregionalization and estimated ancestral areas for the largest plant family, the Asteraceae. Location Global. Methods We used the Global Compositae Checklist data to infer Asteraceae bioregions with cluster and modularity analysis. We reconstructed a phylogeny of genus‐terminals for the Asteraceae family from a supermatrix of nuclear ribosomal internal transcribed spacer and chloroplast data. Combining areas based on the bioregions from modularity analysis and the phylogeny, we then estimated ancestral ranges across the Asteraceae phylogeny under 12 biogeographic models. Results Cluster analysis resulted in several small bioregions from areas with low taxon numbers and linear and disjunct bioregions between Eurasia and Africa. Modularity analysis produced larger and compact bioregions, and we based downstream analysis on its results. The favoured model for ancestral area estimation was BAYAREALIKE+j+x, demonstrating the importance of long distance dispersal in the biogeographic history of the Asteraceae and a strong distance‐dependence of dispersal. Main conclusions Differences between cluster and modularity analysis suggest that the latter may be more robust to incomplete data and produces less disjunct and thus presumably biologically more realistic bioregions. With few exceptions, results of ancestral area estimation confirmed the results of previous studies, in particular South America as the ancestral area of the family, subsequent dispersal to and a secondary radiation from Africa, and the ancestral areas of individual tribes of the family.

Bioregions are an important concept in biogeography, and are key to our understanding of biodiversity patterns across the world. The use of networks in biogeography to produce bioregions is a relatively novel approach that has been proposed to improve upon current methods. However, it remains unclear if they may be used in place of current methods and/or offer additional biogeographic insights. We compared two network methods to detect bioregions (modularity and map equation) with the conventional distance‐based clustering method. We also explored the relationship between network and biodiversity metrics. For the analysis we used two datasets of iconic Australian plant groups at a continental scale, Acacia and eucalypts, as example groups. The modularity method detected fewer large bioregions produced the most succinct bioregionalisation for both plant groups corresponding to Australian biomes, while map equation detected many small bioregions including interzones at a natural scale of one. The clustering method was less sensitive than network methods in detecting bioregions. The network metric called participation coefficient from both network partition methods identified interzones or transition zones between bioregions. Furthermore, another network metric (betweenness) was highly correlated to richness and endemism. We conclude that the application of networks to biogeography offers a number of advantages and provides novel insights. More specifically, our study showed that these network partition methods were more efficient than the clustering method for bioregionalisation of continental‐scale data in: 1) the identification of bioregions and 2) the quantification of biogeographic transition zones using the participation coefficient metric. The use of network methods and especially the participation coefficient metric adds to bioregionalisation by identifying transition zones which could be useful for conservation purposes and identifying biodiversity hotspots.

Mutualistische interacties komen overal voor in de natuur en zijn cruciaal voor de overleving en reproductie van veel soorten. Sinds kort wordt pas erkend dat mutualistische interaties belangrijk zijn voor de verbindingen tussen soorten, voor de modulatie van ecosysteemfunctionering en voor het behoud van biodiversiteit. Mutualistische gemeenschappen, zoals plant-bestuiver gemeenschappen, kunnen worden beschreven als netwerken van interacties tussen mutualistiche partners (bijv. planten en dieren). Met behulp van deze aanpak hebben vele studies hun licht kunnen schijnen op de structuur van mutualistische gemeenschappen en hun eco-evolutionaire patronen. De vraag is: welke ecologische en evolutionaire processen liggen ten grondslag aan de opbouw van mutualistische gemeenschappen? We hebben wiskundige modellen en simulaties ontwikkeld en deze gecombineerd met data analyse om deze vragen te kunnen beantwoorden voor een scala aan mutualistische systemen, waarbij we vooral gefocust hebben op mutualistische plant-dier interacties. We hebben gevonden dat spatio-temporele variabiliteit de belangrijkste factor is van de mutualistische netwerk structuur. De combinatie van ruimtelijke structuur en de variabiliteit in de timing van interacties tussen soorten (fenologie) is wat het meest de mutualistische netwerkstructuur bepaald. De stabiliteit en diversiteit van deze gemeenschappen hangt echter af van de balans van deze processen met andere typen ecologische interacties en de life-history van de soort. Bovendien blijkt het dat veel voorkomende evolutionaire patronen in mutualistiche netwerken, zoals evolutionaire convergentie en complementariteit, lijken te ontstaan uit simpele ecologische (bijv. dispersielimitatie en ecologische drift) en evolutionaire (bijv. mutatie, recombinatie en genetische drift) processen. We concluderen dat: 1) neutrale eco-evolutionare processen niet moeten worden genegeerd bij het bestuderen van de evolutie van ecologische netwerken en 2) ruimtelijke

The interactions between plants and arbuscular mycorrhizal fungi (AMF) maintain a crucial link between macroscopic organisms and the soil microbial world. These interactions are of extreme importance for the diversity of plant communities and ecosystem functioning. Despite this importance, only recently has the structure of plant-AMF interaction networks been studied. These recent studies, which used genetic data, suggest that these networks are highly structured, very similar to plant-animal mutualistic networks. However, the assembly process of plant- AMF communities is still largely unknown, and an important feature of plant-AMF interactions has not been incorporated: they occur at an extremely localized scale. Studying plant-AMF networks in a spatial context seems therefore a crucial step. This paper studies a plant-AMF spatial co-occurrence network using novel methodology based on information theory and a unique set of spatially explicit species-level data. We apply three null models of which only one accounts for spatial effects. We find that the data show substantial departures from null expectations for the two non-spatial null models. However, for the null model considering spatial effects, there are few significant co-occurrences compared with the other two null models. Thus, plant-AMF spatial co-occurrences seem to be mostly explained by stochasticity, with a small role for other factors related to plant-AMF specialization. Furthermore, we find that the network is not significantly nested or modular. We conclude that this plant-AMF spatial co-occurrence network lacks substantial structure and, therefore, plants and AMF species do not track each other over space. Thus, random encounters seem more important in the first step of the assembly of plant-AMF communities.