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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.

An understanding of genetic diversity and the population genetic processes that impact future population viability is vital for the management and recovery of declining populations of threatened species. Styphelia longissima (Ericaceae) is a critically endangered shrub, restricted to a single fragmented population near Eneabba, 250 km north of Perth, Western Australia. For this population, we sought to characterize population genetic variation and its spatial structure, and aspects of the mating portfolio, from which strategies that optimize the conservation of this diversity are identified. A comprehensive survey was carried out and 220 adults, and 106 seedlings from 14 maternal plants, were genotyped using 13 microsatellite markers. Levels of genetic variation and its spatial structure were assessed, and mating system parameters were estimated. Paternity was assigned to the offspring of a subsection of plants, which allowed for the calculation of realized pollen dispersal. Allelic richness and levels of expected heterozygosity were higher than predicted for a small isolated population. Spatial autocorrelation analysis identified fine-scale genetic structure at a scale of 20 m, but no genetic structure was found at larger scales. Mean outcrossing rate (tm = 0.66) reflects self-compatibility and a mixed-mating system. Multiple paternity was low, where 61 % of maternal siblings shared the same sire. Realized pollen dispersal was highly restricted, with 95 % of outcrossing events occurring at 7 m or less, and a mean pollen dispersal distance of 3.8 m. Nearest-neighbour matings were common (55 % of all outcross events), and 97 % of mating events were between the three nearest-neighbours. This study has provided critical baseline data on genetic diversity, mating system and pollen dispersal for future monitoring of S. longissima. Broadly applicable conservation strategies such as implementing a genetic monitoring plan, diluting spatial genetic structure in the natural population, genetically optimizing ex situ collections and incorporating genetic knowledge into translocations will help to manage the future erosion of the high genetic variation detected., Styphelia longissima (Ericaceae) is a critically endangered shrub restricted to a single fragmented population near Eneabba, 250 km north of Perth, Western Australia. Here, 13 microsatellite markers were used to genotype 220 adults and 106 seedlings to assess genetic diversity and structure, mating system parameters and pollen dispersal. The only population of S. longissima was found to be characterised by surprisingly high levels of genetic diversity, with small groups of related individuals within which the vast majority of mating occurs. This study has provided critical baseline data for future monitoring and to inform conservation strategies.

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.

The concepts of traits, plant functional types (PFT), and functional communities are effective tools for the study of complex phenomena such as plant community assembly. Here, we (1) suggest a procedure formalising the classification of response traits to construct a PFT system; (2) integrate the PFT, and species compositional data to formally define functional communities; and, (3) identify environmental drivers that underpin the functional-community patterns. A species–trait data set featuring species pooled from two study sites (Eneabba and Cooljarloo, Western Australia), both supporting kwongan vegetation (sclerophyllous scrub and woodland communities), was subjected to classification to define PFTs. Species of both study sites were replaced with the newly derived PFTs and projected cover abundance-weighted means calculated for every plot. Functional communities were defined by classifications of the abundance-weighted PFT data in the respective sites. Distance-based redundancy analysis (using the abundance-weighted community and environmental data) was used to infer drivers of the functional community patterns for each site. A classification based on trait data assisted in reducing trait-space complexity in the studied vegetation and revealed 26 PFTs shared across the study sites. In total, seven functional communities were identified. We demonstrate a putative functional-community pattern-driving effect of soil-texture (clay—sand) gradients at Eneabba (42% of the total inertia explained) and that of water repellence at Cooljarloo (36%). Synthesis. This paper presents a procedure formalising the classification of multiple response traits leading to the delineation of PFTs and functional communities. This step captures plant responses to stresses and disturbance characteristic of kwongan vegetation, including low nutrient status, water stress, and fire (a landscape-level disturbance factor). Our study is the first to introduce a formal procedure assisting their formal recognition. Our results support the role of short-term abiotic drivers structuring the formation of fine-scale functional community patterns in a complex, species-rich vegetation of Western Australia.