Contents 1314I.1315II.1316III.1322IV.1323V.1325VI.1326VII.1326VIII.13271328References1328 Summary Invasions of alien plants are typically studied as invasions of individual species, yet interactions between plants and symbiotic fungi (mutualists and potential pathogens) affect plant survival, physiological traits, and reproduction and hence invasion success. Studies show that plant–fungal associations are frequently key drivers of plant invasion success and impact, but clear conceptual frameworks and integration across studies are needed to move beyond a series of case studies towards a more predictive understanding. Here, we consider linked plant–fungal invasions from the perspective of plant and fungal origin, simplified to the least complex representations or ‘motifs’. By characterizing these interaction motifs, parallels in invasion processes between pathogen and mutualist fungi become clear, although the outcomes are often opposite in effect. These interaction motifs provide hypotheses for fungal‐driven dynamics behind observed plant invasion trajectories. In some situations, the effects of plant–fungal interactions are inconsistent or negligible. Variability in when and where different interaction motifs matter may be driven by specificity in the plant–fungal interaction, the size of the effect of the symbiosis (negative to positive) on plants and the dependence (obligate to facultative) of the plant−fungal interaction. Linked plant–fungal invasions can transform communities and ecosystem function, with potential for persistent legacies preventing ecosystem restoration.

DNA-based techniques are increasingly used for measuring the biodiversity (species presence, identity, abundance and community composition) of terrestrial and aquatic ecosystems. While there are numerous reviews of molecular methods and bioinformatic steps, there has been little consideration of the methods used to collect samples upon which these later steps are based. This represents a critical knowledge gap, as methodologically sound field sampling is the foundation for subsequent analyses. We reviewed field sampling methods used for metabarcoding studies of both terrestrial and freshwater ecosystem biodiversity over a nearly three-year period (n = 75). We found that 95% (n = 71) of these studies used subjective sampling methods and inappropriate field methods and/or failed to provide critical methodological information. It would be possible for researchers to replicate only 5% of the metabarcoding studies in our sample, a poorer level of reproducibility than for ecological studies in general. Our findings suggest greater attention to field sampling methods, and reporting is necessary in eDNA-based studies of biodiversity to ensure robust outcomes and future reproducibility. Methods must be fully and accurately reported, and protocols developed that minimize subjectivity. Standardization of sampling protocols would be one way to help to improve reproducibility and have additional benefits in allowing compilation and comparison of data from across studies.

4.Synthesis. Our findings demonstrate that partial mycoheterotrophy is a trophic continuum between the extreme endpoints of autotrophy and full mycoheterotrophy, Paper previously published as Standard Paper

Classical biomonitoring techniques have focused primarily on measures linked to various biodiversity metrics and indicator species. Next-generation biomonitoring (NGB) describes a suite of tools and approaches that allow the examination of a broader spectrum of organizational levels-from genes to entire ecosystems. Here, we frame 10 key questions that we envisage will drive the field of NGB over the next decade. While Makiola et al. Questions for Next-Generation Biomonitoring not exhaustive, this list covers most of the key challenges facing NGB, and provides the basis of the next steps for research and implementation in this field. These questions have been grouped into current-and outlook-related categories, corresponding to the organization of this paper.

Little is known about the diversity patterns of plant pathogens and how they change with land use at a broad scale. We employed DNA metabarcoding to describe the diversity and composition of putative plant pathogen communities in three substrates (soil, roots, and leaves) across five major land uses at a national scale. Almost all plant pathogen communities (fungi, oomycetes, and bacteria) showed strong responses to land use and substrate type. Land use category could explain up to 24% of the variance in composition between communities. Alpha‐diversity (richness) of plant pathogens was consistently lower in natural forests than in agricultural systems. In planted forests, there was also generally low pathogen alpha‐diversity in soil and roots, but alpha‐diversity in leaves was high compared with most other land uses. In contrast to alpha‐diversity, differences in within‐land use beta‐diversity of plant pathogens (the predictability of plant pathogen communities within land use) were subtle. Our results show that large‐scale patterns and distributions of putative plant pathogens can be determined using metabarcoding, allowing some of the first landscape level insights into these critically important communities.

The effects of land use on soil invertebrates – an important ecosystem component – are poorly understood. We investigated land-use impacts on a comprehensive range of soil invertebrates across New Zealand, measured using DNA metabarcoding and six biodiversity metrics. Rarity and phylogenetic rarity – direct measures of the number of species or the portion of a phylogeny unique to a site – showed stronger, more consistent responses across taxa to land use than widely used metrics of species richness, effective species numbers, and phylogenetic diversity. Overall, phylogenetic rarity explained the highest proportion of land use-related variance. Rarity declined from natural forest to planted forest, grassland, and perennial cropland for most soil invertebrate taxa, demonstrating pervasive impacts of agricultural land use on soil invertebrate communities. Commonly used diversity metrics may underestimate the impacts of land use on soil invertebrates, whereas rarity provides clearer and more consistent evidence of these impacts.

Interactions between individual plant pathogens and their environment have been described many times. However, the relative contribution of different environmental parameters as controls of pathogen communities remains largely unknown.Here we investigate the importance of environmental factors, including geomorphology, climate, land use, soil and plant community composition, for a broad range of aboveground and belowground fungal, oomycete and bacterial plant pathogens.We found that plant community composition is the main driver of the composition and richness of plant pathogens after taking into account all other tested parameters, especially those related to climate and soil.In the face of future changes in climate and land use, our results suggest that changes in plant pathogen community composition and richness will primarily be mediated through changes in plant communities, rather than the direct effects of climate or soils.

Plant pathogens such as rust fungi (Pucciniales) are of global economic and ecological importance. This means there is a critical need to reliably and cost‐effectively detect, identify, and monitor these fungi at large scales. We investigated and analyzed the causes of differences between next‐generation sequencing (NGS) metabarcoding approaches and traditional DNA cloning in the detection and quantification of recognized species of rust fungi from environmental samples. We found significant differences between observed and expected numbers of shared rust fungal operational taxonomic units (OTUs) among different methods. However, there was no significant difference in relative abundance of OTUs that all methods were capable of detecting. Differences among the methods were mainly driven by the method's ability to detect specific OTUs, likely caused by mismatches with the NGS metabarcoding primers to some Puccinia species. Furthermore, detection ability did not seem to be influenced by differences in sequence lengths among methods, the most appropriate bioinformatic pipeline used for each method, or the ability to detect rare species. Our findings are important to future metabarcoding studies, because they highlight the main sources of difference among methods, and rule out several mechanisms that could drive these differences. Furthermore, strong congruity among three fundamentally different and independent methods demonstrates the promising potential of NGS metabarcoding for tracking important taxa such as rust fungi from within larger NGS metabarcoding communities. Our results support the use of NGS metabarcoding for the large‐scale detection and quantification of rust fungi, but not for confirming the absence of species.