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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.

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.

Assignment or exclusion of an individual to specific populations or cultivars based on molecular genetic markers provides an attractive approach for varietal identification at the individual level in cross-pollinated plant species. The objectives of this study were (i) to explore the molecular diversity and relationships between Australasian perennial ryegrass (Lolium perenne L.) populations; (ii) to investigate accuracy of assignment of individuals to different types of populations including ecotypic, nonrestricted- and restricted-based cultivars; and (iii) to determine the effect of variable number of SSR loci and different statistical analysis methods on assignment accuracy. Eight forage perennial ryegrass populations comprising 48 individual plants per population were genotyped with 29 simple sequence repeat (SSR) marker loci. The number of alleles per locus ranged from 3.72 to 6.76. The mean observed heterozygosity varied from 0.419 to 0.538. Various genetic distance estimates and clustering methods obtained results consistent with breeding history. Genetic variation among (8.7%) and within populations (91.3%) was significant. Accuracy of individual assignment differed by population type, and it was higher (>90%) for restricted-based cultivars than for ecotypic and nonrestricted-based populations. These results indicate that SSR marker profiles can be effectively used to assign individuals for outbreeding populations such as perennial ryegrass. The approach used in this study may be useful for ryegrass germplasm management issues such as cultivar identification at the individual level.

To test the hypothesis that resistance in Phytophthora cinnamomi to control by the fungicide phosphite (phosphonate) would arise in sites with prolonged use of phosphite, 30 P. cinnamomi isolates were collected from a range of sites with different phosphite-use histories, including phosphite-treated and untreated avocado orchards, and phosphite-treated and untreated native vegetation sites. The colonizing ability of these isolates was tested by different inoculation methods against a range of host tissues, treated and untreated with phosphite, including mycelial stem inoculation on clonally propagated Leucadendron sp., mycelial root inoculation of lupin seedlings and zoospore inoculation of Eucalyptus sieberi cotyledons. Isolates from avocado orchards with a long history of phosphite use were, on average, more extensive colonizers of the phosphite-treated Leucadendron sp., lupin seedling roots and Eucalyptus sieberi cotyledons. These isolates did not colonize untreated plant tissue (Leucadendron sp.) more extensively than isolates from sites with no history of phosphite use and no isolates were resistant to control by phosphite. Analysis of all isolates with microsatellite markers revealed the majority were from a single clonal lineage. Selection for decreased sensitivity to phosphite in planta has taken place within asexual clonal lineages of P. cinnamomi in sites with prolonged use of phosphite.

The symbiotic association between perennial ryegrass (Lolium perenne L.) and the fungal endophyte Neotyphodium lolii is associated with host-specific adaptations, particularly in response to abiotic and biotic stresses. Knowledge of the origin of the symbiosis and the contribution of endophyte genotype to host phenotypic variation is currently limited. Simple sequence repeat (SSR) markers were used to assess endophyte genetic diversity in a globally distributed collection of perennial ryegrass accessions. Consistent in planta detection was achieved with 18 of 22 SSR markers (primer pairs). Endophytes representing as many as four different taxa were detected in 42 accessions from 20 different countries, N. lolii being predominant. A total of 33 unique N. lolii genotypes were discriminated, of which 29 clustered into three major groups with limited within-group variation. The three major N. lolii groups were associated with distinct perennial ryegrass chloroplast haplotypes. The alkaloid profiles of accessions were apparently associated with the presence of specific N. lolii genotypes. Genotypic analysis provides a powerful method for genetic dissection of the grass-endophyte interaction and prediction of phenotypic variation based on genotypic variation.