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Background and Aims Clonality is a key life-history strategy promoting on-spot persistence, space occupancy, resprouting after disturbance, and resource storage, sharing and foraging. These functions provided by clonality can be advantageous under different environmental conditions, including resource-paucity and fire-proneness, which define most mediterranean-type open ecosystems, such as southwest Australian shrublands. Studying clonality–environment links in underexplored mediterranean shrublands could therefore deepen our understanding of the role played by this essential strategy in open ecosystems globally. Methods We created a new dataset including 463 species, six traits related to clonal growth organs (CGOs; lignotubers, herbaceous and woody rhizomes, stolons, tubers, stem fragments), and edaphic predictors of soil water availability, nitrogen (N) and phosphorus (P) from 138 plots. Within two shrubland communities, we explored multivariate clonal patterns and how the diversity of CGOs, and abundance-weighted and unweighted proportions .of clonality in plots changed along with the edaphic gradients. Key Results We found clonality in 65 % of species; the most frequent were those with lignotubers (28 %) and herbaceous rhizomes (26 %). In multivariate space, plots clustered into two groups, one distinguished by sandy plots and plants with CGOs, the other by clayey plots and non-clonal species. CGO diversity did not vary along the edaphic gradients (only marginally with water availability). The abundance-weighted proportion of clonal species increased with N and decreased with P and water availability, yet these results were CGO-specific. We revealed almost no relationships for unweighted clonality. Conclusions Clonality is more widespread in shrublands than previously thought, and distinct plant communities are distinguished by specific suites (or lack) of CGOs. We show that weighting belowground traits by aboveground abundance affects the results, with implications for trait-based ecologists using abundance-weighting. We suggest unweighted approaches for belowground organs in open ecosystems until belowground abundance is quantifiable.

The nature of community patterns and environmental drivers in kwongan mediterranean‐type shrubland on nutrient‐poor soils occurring in Western Australia remain poorly examined. We aimed to determine whether (i) classification of the kwongan vegetation of the northern Swan Coastal Plain would be ecologically informative and (ii) which environmental drivers underpin the plant community patterns. The study area was positioned on the northern Swan Coastal Plain, locality of Cooljarloo (30°39′ S, 115°22′ E), situated 170 km north of Perth, Western Australia. Compositional (518 species × 337 releves) and environmental data set (29 variables × 87 releves) describing time since last fire, soil chemical and physical properties, and terrain characteristics were analysed using classification and ordination techniques. OptimClass assisted in the selection of a robust data transformation, resemblance function and clustering algorithm to identify the vegetation patterns. Major ecological drivers of the vegetation patterns were detected using distance‐based redundancy analysis (db‐RDA). Classification revealed major groupings of Wet Heath and Banksia Woodland distinguishable by the high prevalence of myrtyoid and proteoid taxa, respectively. On floristic‐sociological grounds, we recognised four Wet Heath and two Banksia Woodland communities. The Wet Heath was constrained to areas of higher litter depth (db‐RDA axis 1: 9%). Soil chemical and physical properties explained the highest proportion (17%) of the compositional variance, while the terrain‐ and fire‐related variables explained 2% and

Long‐term studies of vegetation recovery following post‐mining restoration in low‐productivity, high‐stress areas are limited, but essential for understanding underlying ecological processes and evaluating management practices. This study's goal was to describe temporal patterns of recovery (up to 37 years) in vegetation structure, floristic diversity, and composition following post‐mining restoration at two sites, and identify potential drivers of restoration outcomes, in the nutrient‐poor, seasonally dry, species‐rich, fire‐prone kwongan vegetation of southwest Western Australia. Vegetation development is progressing and restoration measures are within range of native reference values, but there is large variation in both patterns observed and restoration outcomes. Several patterns described share similarities with post‐fire recovery of kwongan, and post‐disturbance recovery of other low‐productivity, high‐stress, fire‐prone systems. However, differences in some patterns between sites indicate differences in the underlying mechanisms of recovery. Many management and environmental variables emerge as significant drivers of restoration outcomes but age, fire, and the planting of seedlings account for the largest amount of variation. Adaptive management at both sites appears to be facilitating improved restoration outcomes over time. Using a combination of space‐for‐time, plot level, and linear mixed effects modeling perspectives to examine patterns provides greater insights into restoration recovery than a chronosequence perspective alone. This study will inform restoration practices and outcomes not only in kwongan but in other comparable systems also.

In post-mining rehabilitation, successful mine closure planning requires specific, measurable, achievable, relevant and time-bound (SMART) completion criteria, such as returning ecological communities to match a target level of similarity to reference sites. Soil microbiota are fundamentally linked to the restoration of degraded ecosystems, helping to underpin ecological functions and plant communities. High-throughput sequencing of soil eDNA to characterise these communities offers promise to help monitor and predict ecological progress towards reference states. Here we demonstrate a novel methodology for monitoring and evaluating ecological restoration using three long-term (>25 year) case study post-mining rehabilitation soil eDNA-based bacterial community datasets. Specifically, we developed rehabilitation trajectory assessments based on similarity to reference data from restoration chronosequence datasets. Recognising that numerous alternative options for microbiota data processing have potential to influence these assessments, we comprehensively examined the influence of standard versus compositional data analyses, different ecological distance measures, sequence grouping approaches, eliminating rare taxa, and the potential for excessive spatial autocorrelation to impact on results. Our approach reduces the complexity of information that often overwhelms ecologically-relevant patterns in microbiota studies, and enables prediction of recovery time, with explicit inclusion of uncertainty in assessments. We offer a step change in the development of quantitative microbiota-based SMART metrics for measuring rehabilitation success. Our approach may also have wider applications where restorative processes facilitate the shift of microbiota towards reference states.

QUESTIONS: The community patterns in kwongan, a mediterranean‐type scrub on nutrient‐poor soils occurring in Western Australia, are poorly understood due to only few, focused studies using disparate sampling designs. We aimed to determine whether (a) classification of the kwongan vegetation of the Eneabba Sandplains leads to an ecologically informative classification scheme, and (b) we could identify environmental drivers underpinning the plant community patterns. LOCATION: Township of Eneabba (29°82′S, 115°27′E), approximately 250 km north of Perth, Western Australia, covering 1,210 km². METHODS: We used a data set consisting of 512 releves, collected following the standard field methodology of the Braun‐Blanquet approach, and accompanied by an extensive set of environmental data consisting of 94 variables representing climate, fire, soil and terrain properties across 189 releves. The data were classified and ordinated by a series of multivariate analyses. OptimClass assisted in the selection of the most robust classification procedure. Distance‐based redundancy analysis (db‐RDA) inferred the major ecological drivers of the vegetation patterns. RESULTS: Numerical classification, nonmetric multidimensional scaling, and syntaxonomic tabular analysis revealed two major community groups (MCG A and B), eight community groups, and 17 communities in the kwongan vegetation of the study area. All vegetation units are characterised in terms of floristic composition and position along major ecological gradients in the studied area. The MCGs separated along a composite gradient of soil‐texture and exchangeable cations. The first two db‐RDA axes explained 21% of the total variance which is very low, considering the high number of environmental variables used. CONCLUSIONS: Our study provides a comprehensive insight into the high variability of vegetation types in hyper‐diverse kwongan scrub at a landscape spatial scale; it is the first syntaxonomic account of the Western Australian kwongan vegetation, presenting a complete tabular comparative analysis. The studied major community groups segregate along a soil‐texture and an exchangeable‐cation content gradient. At a community scale, environmental filtering explained a small fraction of the vegetation–environment relationship. We suggest that the unexplained portion of the vegetation‐environment relationship might be a product of slow‐acting neutral processes in this hyper‐diverse system; this assertion is amenable to rigorous future testing.

Vegetation maps are models of the real vegetation patterns and are considered important tools in conservation and management planning. Maps created through traditional methods can be expensive and time‐consuming, thus, new more efficient approaches are needed. The prediction of vegetation patterns using machine learning shows promise, but many factors may impact on its performance. One important factor is the nature of the vegetation–environment relationship assessed and ecological redundancy. We used two datasets with known ecological redundancy levels (strength of the vegetation–environment relationship) to evaluate the performance of four machine learning (ML) classifiers (classification trees, random forests, support vector machines, and nearest neighbor). These models used climatic and soil variables as environmental predictors with pretreatment of the datasets (principal component analysis and feature selection) and involved three spatial scales. We show that the ML classifiers produced more reliable results in regions where the vegetation–environment relationship is stronger as opposed to regions characterized by redundant vegetation patterns. The pretreatment of datasets and reduction in prediction scale had a substantial influence on the predictive performance of the classifiers. The use of ML classifiers to create potential vegetation maps shows promise as a more efficient way of vegetation modeling. The difference in performance between areas with poorly versus well‐structured vegetation–environment relationships shows that some level of understanding of the ecology of the target region is required prior to their application. Even in areas with poorly structured vegetation–environment relationships, it is possible to improve classifier performance by either pretreating the dataset or reducing the spatial scale of the predictions.

Development of accurate high-throughput molecular marker systems such as SNPs permits evaluation and selection of favourable gene variants to accelerate elite varietal production. SNP discovery in perennial ryegrass has been based on PCR amplification and sequencing of multiple amplicons designed to scan all components of the transcriptional unit. Full-length genes (with complete intron-exon structure and promoter information) corresponding to well-defined biochemical functions such as lignin biosynthesis and oligosaccharide metabolism are ideal for complete SNP haplotype determination. Multiple SNPs at regular intervals across the transcriptional unit were detected within and between the heterozygous parents and validated in the progeny of the F (1)(NA(6) x AU(6)) genetic mapping family. Haplotype structures in the parental genotypes were defined and haplotypic abundance, structure and variation were assessed in diverse germplasm sources. Decay of LD to r (2) values of c. 0.2 typically occurs over 500-3,000 bp, comparable with gene length and with little apparent variation between diverse, ecotypic and varietal population sub-groups. Similar patterns were revealed as limited blocks of intragenic LD. The results are compatible with the reproductive biology of perennial ryegrass and the effects of large ancestral population size. This analysis provides crucial information to validate strategies for correlation of haplotypic diversity and phenotypic variation through association mapping.

Asexual fungal endophytes of the genus Epichloë form mutually beneficial associations with cool-season pasture grasses such as ryegrasses (Lolium spp.). Alkaloid production by the fungus confers both beneficial (deterrence of invertebrate herbivory) and detrimental (toxicity to mammalian livestock) attributes. A few novel strains with desirable metabolite profiles have been advanced into commercial production by inoculation of perennial ryegrass cultivars. In the present study, an integrated process for discovery of novel endophytes based on exploitation of genotypic information has been designed and implemented. A survey of genetic diversity was performed on a large-scale, customised germplasm collection (containing 244 accessions) in order to identify previously uncharacterised endophyte genotypes. Preliminary qualitative metabolic profiling in the endogenous genetic background permitted elimination of undesirable combinations, and definition of a subset of priority candidates. A novel method was developed for inoculation of endophytes into meristem-culture-derived callus tissue of single genotypes from multiple perennial ryegrass cultivars, in order to allow isogenic comparisons with respect to both host and endophyte genotype. Beneficial toxin profiles were confirmed for associations formed with the grass genotypic panel, and semi-quantitative metabolite analysis provided evidence for genotype-specific effects of both host and genotype on levels of alkaloid production. Vegetative stability was also assessed over both shorter and longer terms. A final set of three prioritised candidates was obtained, two of which (belonging to the known taxa E. festucae var. lolii and LpTG-2) produce the alkaloids ergovaline and peramine. The third candidate endophyte, belonging to a putative novel taxon, solely produces representatives of an additional alkaloid class, the epoxy-janthitrems.

Due to the complex genetic architecture of perennial ryegrass, based on an obligate outbreeding reproductive habit, association-mapping approaches to genetic dissection offer the potential for effective identification of genetic marker–trait linkages. Associations with genes for agronomic characters, such as components of herbage nutritive quality, may then be utilised for accelerated cultivar improvement using advanced molecular breeding practices. The objective of the present study was to evaluate the presence of such associations for a broad range of candidate genes involved in pathways of cell wall biosynthesis and carbohydrate metabolism. An association-mapping panel composed from a broad range of non-domesticated and varietal sources was assembled and assessed for genome-wide sequence polymorphism. Removal of significant population structure obtained a diverse meta-population (220 genotypes) suitable for association studies. The meta-population was established with replication as a spaced-plant field trial. All plants were genotyped with a cohort of candidate gene-derived single nucleotide polymorphism (SNP) markers. Herbage samples were harvested at both vegetative and reproductive stages and were measured for a range of herbage quality traits using near infrared reflectance spectroscopy. Significant associations were identified for ~50% of the genes, accounting for small but significant components of phenotypic variance. The identities of genes with associated SNPs were largely consistent with detailed knowledge of ryegrass biology, and they are interpreted in terms of known biochemical and physiological processes. Magnitudes of effect of observed marker–trait gene association were small, indicating that future activities should focus on genome-wide association studies in order to identify the majority of causal mutations for complex traits such as forage quality.

Genetic analysis of host resistance to the crown rust pathogen of ryegrasses has traditionally involved the use of composite urediniospore collections, which may contain multiple genotypes and virulence races. Consequently, discrimination between major gene responses to alternative races and quantitative resistance has proved challenging for disease trait-dissection studies.Amethod of propagation on detached leaves has been used to derive genetically homogenised crown rust pathogen lines from bulked spore collections obtained from five distinct Australian locations. Spores from individual pustules were repeatedly reinoculated onto fresh leaves. Amplicon complexity was assessed at the conclusion of each cycle using crown rust pathogen expressed sequence tag (EST)-derived simple sequence repeat (SSR) primer pairs. The mean number of amplification products per isolate declined over successive rounds of reinfection. The number of rounds of sequential infection required for decay to stable levels was dependent on the diversity present within the bulked spore collection. Large-scale propagation of the putative homogenised populations developed in the present study may provide inoculum for infection studies to allow quantitative trait loci mapping of specific gene-for-gene responses. The methodology has significant applications for the pyramiding of resistance genes specific to diverse crown rust pathogen genotypes during cultivar development and for pathotyping of existing elite germplasm. This approach is also applicable to other Puccinia species.