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Aims: Developing a hierarchical classification system for classes, orders and alliances of the diverse dry grasslands of the Central and Eastern Balkan Peninsula and translating this into an electronic expert system (ES) for the automatic assignment of plots. Location: Serbia, Kosovo, North Macedonia, Bulgaria and northern Greece. Methods: We extracted 5734 plots from the Balkan Dry Grassland Database corresponding to eight classes of dry grasslands reported from the region, using the EuroVegChecklist ES. This data set and later the plots within each derived subunit were subjected to a new numerical approach: starting with an initial partitioning (expert‐interpreted TWINSPAN classification), diagnostic species were determined based on their phi‐values for the target vegetation type and the differences in phi‐values to the next similar types. These diagnostic species were fed into an ES to create a new partitioning, a procedure which was iterated until diagnostic species and species of the ES converged. Then the same approach was applied within each of the derived units to define the units of the next‐lower level. Results: The iterative cluster optimisation (ICO) converged in all cases. The resulting hierarchical expert system (HES) classified 95% of all plots to alliances. We distinguished four classes with eight orders and 12 alliances: (1) Tuberarietea guttatae (Romuleion); (2) Stipo‐Brachypodietea distachyi (Clinopodio alpini‐Thymion striati); (3) Festuco‐Brometea with Brachypodietalia pinnati (Chrysopogono‐Danthonion calycinae and Cirsio‐Brachypodion pinnati), Festucetalia valesiacae (Festucion valesiacae), an unnamed order of rocky steppes (with Pimpinello‐Thymion zygioidis) and Koelerietalia splendentis (Centaureo‐Bromion fibrosi, Saturejion montanae and Diantho haematocalycis‐Festucion hirtovaginatae); (four) Koelerio‐Corynephoretea with Sedo acris‐Festucetalia (Festucion vaginatae) and Trifolio arvensis‐Festucetalia ovinae (Armerio rumelicae‐Potentillion and Minuartio montanae‐Poion molinerii all. nov.). Conclusions: We created a unified hierarchical classification with an electronic ES using diagnostic species defined by phi‐values. Our new approach (ICO‐HES: iterative cluster optimisation for hierarchical expert systems) allows dividing large data sets into meaningful units at several hierarchical levels, and thus has high potential for complex classifications. Importantly, it overcomes the divide between ES species and diagnostic species and re‐unites them into one concept. [ABSTRACT FROM AUTHOR]

Vegetation classifications are a valuable tool for guiding management decisions. Vegetation can be classified at different levels of resolution for different purposes. Aggregating finer‐resolution classification units to simplify planning and decision‐making is becoming more common in land management. However, little is known about which resolution or scale of aggregation is appropriate for specific land management practices and the factors to consider when aggregating vegetation classification units. There is a risk that aggregated classification units may overlook vital floristic and structural differences between communities and not accurately represent the patterns of variation in vegetation. This may lead to unanticipated adverse outcomes from management actions. We compared three classification systems to test how well each represented patterns of variation in the vegetation of a semi‐arid region in Victoria, Australia: (a) a finer‐level classification system; (b) a coarser‐resolution system that aggregated finer compositional resolution units for fire management purposes; and (c) a coarser‐resolution classification based on Climate Zones. We conducted floristic and vegetation structure surveys at 249 sites stratified across a climate gradient. Sites were assigned to vegetation classifications based on mapped data layers and checked by ground truthing prior to inclusion. The classification based on a finer compositional resolution best represented variation in both the floristic and structural patterns of the vegetation across this region. This was not unexpected, because accuracy is traded‐off against simplicity when aggregating vegetation classification units. However, the aggregated classification also performed poorly in representing patterns of vegetation when compared with a simple scheme based on Climate Zones. Assessing the adequacy of aggregated vegetation classification units is important, yet often overlooked. To be effective, aggregated vegetation classes must reflect the underlying factors that drive patterns of variation in the vegetation to ensure they support intended management outcomes. [ABSTRACT FROM AUTHOR]

The first comprehensive phytosociological classification of all vegetation types in Europe (EuroVegChecklist; Applied Vegetation Science, 2016, 19, 3–264) contained brief descriptions of each type. However, these descriptions were not standardized and mentioned only the most distinct features of each vegetation type. The practical application of the vegetation classification system could be enhanced if users had the option to select sets of vegetation types based on various combinations of structural, ecological, and biogeographical attributes. Based on a literature review and expert knowledge, we created a new database that assigns standardized categorical attributes of 12 variables to each of the 1106 alliances dominated by vascular plants defined in EuroVegChecklist. These variables include dominant life form, phenological optimum, substrate moisture, substrate reaction, salinity, nutrient status, soil organic matter, vegetation region, elevational vegetation belt, azonality, successional status and naturalness. The new database has the potential to enhance the usefulness of phytosociological classification for researchers and practitioners and to help understand this classification to non‐specialists. [ABSTRACT FROM AUTHOR]

Questions: The classification of plant communities is a well‐established practice in ecology. However, the factors that determine the distribution of common species and the extent of their dominance and overlap are still being debated. Large‐scale studies based on field data are of great interest in the face of biodiversity changes and climate change impacts. This research was designed to answer the following two questions: What are the dominant plant communities in the Patagonian steppes and how do they relate to environmental features, as measured by latitude, longitude, altitude, mean annual precipitation and mean temperature? What have been the recent changes in plant communities in terms of expansion and contraction of different types of communities? Location: Patagonian steppes (South America). Methods: We used a large database of species‐by‐species vegetation cover from 426 monitoring plots in the South American Patagonian steppes, surveyed between 2007 and 2019, with two different assessment dates at least 5 years apart. We applied archetypal analysis to identify different vegetation compositions at a regional scale (called floristic archetypes) and to track their changes over time. In addition, we analyzed the relationship between floristic archetypes and spatial and meteorological variables to assess the association between floristic composition and the environmental context. Results: Seven floristic archetypes were identified in the Patagonian steppes. The dominant and most widespread floristic archetype showed an expanding pattern in floristic trajectories and was positively associated with mean annual temperature. This floristic archetype was characterized by both drought‐ and grazing‐tolerant species. Two floristic archetypes located at the longitudinal extremes showed decreasing relative participation in the vegetation configurations, and the archetype located towards the west showed a positive relationship with annual precipitation, suggesting a threatened scenario in the face of drier conditions. Southern floristic archetypes either showed a slight decrease or remained stable. Conclusions: Patagonian steppes are dominated by both drought‐ and grazing‐tolerant species communities and their spatial distribution is expanding across different biomes, whereas the more forage‐preferred species were associated with a decreasing floristic archetype. A projected drier and hotter climate scenario may potentiate this pattern. The floristic archetypes framework has potential as a monitoring tool for tracking changes in vegetation composition at a mesoscale and for identifying hotspots of vulnerable vegetation in the face of environmental changes. [ABSTRACT FROM AUTHOR]

Clustering is indispensable in the quest for robust vegetation classification schemes that aim to partition, summarise and communicate patterns. However, clustering solutions are sensitive to methods and data and are therefore unstable, a feature that is usually attributed to noise. Viewed through a central‐tendency lens, noise is defined as the degree of departure from type, which is problematic since vegetation types are abstractions of continua, and so noise can only be quantified relative to the particular solution at hand. Graph theory models the structure of vegetation data based on the interconnectivity of samples. Through a graph‐theoretic lens, the causes of instability can be quantified in absolute terms via the degree of connectivity among objects. We simulated incremental increases in sampling intensity in a dataset over five iterations and assessed classification stability across successive solutions derived using algorithms implementing, respectively, models of central‐tendency and interconnectivity. We used logistic regression to model the likelihood of a sample changing groups between iterations as a function of distance to the centroid and degree of interconnectivity. Our results show that the degree to which samples are interconnected is a more powerful predictor of instability than the degree to which they deviate from their nearest centroid. The removal of weakly interconnected samples resulted in more stable classifications, although solutions with many clusters were apparently inherently less stable than those with few clusters, and improvements in stability flowing from the removal of outliers declined as the number of clusters increased. Our results reinforce the fact that clusters abstracted from continuous data are inherently unstable and that the quest for stable, fine‐scale classifications from large regional datasets is illusory. Nevertheless, our results show that using models better suited to the analysis of continuous data may yield more stable classifications of the available data. [ABSTRACT FROM AUTHOR]

Summary: The distribution of roots throughout the soil drives depth‐dependent plant–soil interactions and ecosystem processes, particularly in arctic tundra where plant biomass, is predominantly belowground. Vegetation is usually classified from aboveground, but it is unclear whether such classifications are suitable to estimate belowground attributes and their consequences, such as rooting depth distribution and its influence on carbon cycling.We performed a meta‐analysis of 55 published arctic rooting depth profiles, testing for differences both between distributions based on aboveground vegetation types (Graminoid, Wetland, Erect‐shrub, and Prostrate‐shrub tundra) and between 'Root Profile Types' for which we defined three representative and contrasting clusters. We further analyzed potential impacts of these different rooting depth distributions on rhizosphere priming‐induced carbon losses from tundra soils.Rooting depth distribution hardly differed between aboveground vegetation types but varied between Root Profile Types. Accordingly, modelled priming‐induced carbon emissions were similar between aboveground vegetation types when they were applied to the entire tundra, but ranged from 7.2 to 17.6 Pg C cumulative emissions until 2100 between individual Root Profile Types.Variations in rooting depth distribution are important for the circumpolar tundra carbon‐climate feedback but can currently not be inferred adequately from aboveground vegetation type classifications. See also the Commentary on this article by Weber & Iversen, 240: 457–460. [ABSTRACT FROM AUTHOR]

Motivation: The goal of the Russian Arctic Vegetation Archive (AVA‐RU) is to unite and harmonize data of plot‐based plant species and their abundance, vegetation structure and environmental variables from the Russian Arctic. This database can be used to assess the status of the Russian Arctic vegetation and as a baseline to document biodiversity changes in the future. The archive can be used for scientific studies as well as to inform nature protection and restoration efforts. Main types of variables contained: The archive contains 2873 open‐access geobotanical plots. The data include the full species. Most plots include information on the horizontal (cover per species and morphological group) and vertical (average height per morphological group) structure of vegetation, site and soil descriptions and data quality estimations. In addition to the open‐access data, the AVA‐RU website contains 1912 restricted‐access plots. Spatial location and grain: The plots of 1–100 m2 size were sampled in Arctic Russia and Scandinavia. Plots in Russia covered areas from the West to the East, including the European Russian Arctic (Kola Peninsula, Nenets Autonomous district), Western Siberia (Northern Urals, Yamal, Taza and Gydan peninsulas), Central Siberia (Taymyr peninsula, Bolshevik island), Eastern Siberia (Indigirka basin) and the Far East (Wrangel island). About 72% of the samples are georeferenced. Time period and grain: The data were collected once at each location between 1927 and 2022. Major taxa and level of measurement: Plots include observations of >1770 vascular plant and cryptogam species and subspecies. Software format: CSV files (1 file with species list and abundance, 1 file with environmental variables and vegetation structure) are stored at the AVA‐RU website (https://avarus.space/), and are continuously updated with new datasets. The open‐access data are available on Dryad and all the datasets have a backup on the server of the University of Zurich. The data processing R script is available on Dryad. [ABSTRACT FROM AUTHOR]

Aims: Oak–hornbeam forests and related vegetation types (phytosociological order Carpinetalia betuli) are widespread in temperate western Eurasia. However, their national classification systems are poorly compatible, and a broad‐scale classification based on numerical analyses is lacking. Therefore, we aimed to establish a unified formalized classification system based on a large data set of vegetation plots covering the entire range of these forests. Location: Europe, Anatolia, Caucasus and northern Iran. Methods: We compiled a data set of 15,817 vegetation plots from the European Vegetation Archive and the Hyrcanian Forest Vegetation Database, using the formal definition of the EUNIS habitat type T1E Carpinus and Quercus mesic deciduous forest. We classified the data set using TWINSPAN. Biogeographically and ecologically similar plot clusters were merged into oak–hornbeam forest types, which were interpreted as alliances. We also developed expert systems for automatically classifying vegetation at the alliance level for both the EuroVegChecklist (EVC) system and the revised classification. In addition, we calculated ordinations to show the major gradients in the species composition of the data set. Results: We present a revised classification system of the order Carpinetalia betuli with nine alliances, including basic descriptions of their species composition, distribution, ecology and syntaxonomy. The analyses largely supported the biogeographic concept of classification, analogous to EVC. Compared to EVC, we recognized an additional alliance Physospermo verticillati‐Quercion cerridis (southern Italy) but found no support for the alliances Astrantio‐Carpinion, Erythronio‐Carpinion and Scillo‐Quercion. The greatest difference in species composition was found between the southern and northern‐northeastern Carpinetalia types. Expert systems for the revised classification system (~89% of plots classified) and the EVC system (~72%) are also included. Conclusions: We provide the first comprehensive overview of alliances of the order Carpinetalia betuli across its whole distribution range. The associated expert systems allow consistent application of the classification of these forests in nature conservation, habitat monitoring, and biodiversity and ecological research.We provide the first formal classification for oak–hornbeam forests and related vegetation types (phytosociological order Carpinetalia betuli) across Europe and adjacent areas. It includes both traditional and revised classifications of this vegetation. Furthermore, we developed expert systems for vegetation‐plot assignment to vegetation types, which can be applied in research and biodiversity conservation, monitoring and management. [ABSTRACT FROM AUTHOR]

The updating and rethinking of vegetation classifications is important for ecosystem monitoring in a rapidly changing world, where the distribution of vegetation is changing. The general assumption that discrete and persistent plant communities exist that can be monitored efficiently, is rarely tested before undertaking a classification. Marion Island (MI) is comprised of species‐poor vegetation undergoing rapid environmental change. It presents a unique opportunity to test the ability to discretely classify species‐poor vegetation with recently developed objective classification techniques and relate it to previous classifications. We classified vascular species data of 476 plots sampled across MI, using Ward hierarchical clustering, divisive analysis clustering, non‐hierarchical kmeans and partitioning around medoids. Internal cluster validation was performed using silhouette widths, Dunn index, connectivity of clusters and gap statistic. Indicator species analyses were also conducted on the best performing clustering methods. We evaluated the outputs against previously classified units. Ward clustering performed the best, with the highest average silhouette width and Dunn index, as well as the lowest connectivity. The number of clusters differed amongst the clustering methods, but most validation measures, including for Ward clustering, indicated that two and three clusters are the best fit for the data. However, all classification methods produced weakly separated, highly connected clusters with low compactness and low fidelity and specificity to clusters. There was no particularly robust and effective classification outcome that could group plots into previously suggested vegetation units based on species composition alone. The relatively recent age (c. 450,000 years B.P.), glaciation history (last glacial maximum 34,500 years B.P.) and isolation of the sub‐Antarctic islands may have hindered the development of strong vascular plant species assemblages with discrete boundaries. Discrete classification at the community‐level using species composition may not be suitable in such species‐poor environments. Species‐level, rather than community‐level, monitoring may thus be more appropriate in species‐poor environments, aligning with continuum theory rather than community theory. [ABSTRACT FROM AUTHOR]

Questions: (1) What can be learned by extending a national classification into unsampled forest types? (2) Are both remotely sensed and environmental predictors needed to model and map associations? (3) For mapping, are LiDAR-generated canopy structure parameters or reflectance from spectral imagery more useful? (4) How can we assess uncertainty of a final map? Location: Warawara Forest, Northland, New Zealand. Methods: We sampled 205 vegetation plots and assigned them to an existing national classification using the fuzzy classification framework of noise clustering. Plots too distinct to be assigned were used to define new associations. We produced spatial models of each association using boosted regression trees. Predictors included 11 environmental, 11 canopy reflectance, and 17 canopy structure variables. We created a composite map by assigning each map pixel to the association with the highest occurrence probability. We evaluated uncertainty by examining locations where no class was predicted with probability above 0.2 and by creating a confusion map based on entropy. Results: Forty-five plots were assigned to six of 79 existing national associations and 147 plots were used to define two new forest associations. Three shrubland types are widespread nationally, whereas two young forest types are northern. Three mature forest types are narrowly distributed nationally, with the new "High-elevation hardwood forest" largely restricted to Warawara Forest. Three associations were mapped using remotely sensed predictors alone, whereas two also required environmental predictors. Overall, canopy reflectance predictors explained more deviance than canopy structure. Examining locations where no association was predicted well and where multiple associations were predicted equally showed areas mapped as younger forests to have greatest uncertainty. Conclusions: In answering our questions, we present a vegetation classification and map for Warawara Forest that provides a framework to guide the indigenous people's management responses to threats to valued communities and their species. [ABSTRACT FROM AUTHOR]

Aims: In northwestern North America, montane meadows fed by vernal ground-flow seepage occur scattered throughout an otherwise forested landscape on shallow soils over bedrock. Although their significance for biodiversity has been known, they have never been subject to systematic scientific research and thus, are not included in regional or national vegetation classification schemes. We provide the first vegetation survey of bedrock meadows and compare them to other major non-forest vegetation types in the region. Study area: Northwestern North America (United States, Canada). Methods: We surveyed 110 plots of bedrock meadow vegetation in Montana, Idaho, and British Columbia and compared them with data from 1052 plots from six studies of other open vegetation in the region: prairie and foothill grasslands, maritime mountain, alpine, and timberline meadows. We used cluster analysis to identify groups based on vascular plant composition, related abiotic and structural characteristics to clusters using non-metric multidimensional scaling (NMDS), and compared proportions of plant growth forms and life spans. Results: Cluster analysis identified five groups, with bedrock meadows forming a distinct community. According to the NMDS, bedrock meadows had a high cover of vascular plants, bryophytes, and lichens and a low cover of litter. Their climate was intermediate between maritime mountain and interior timberline meadows. A distinct functional feature of bedrock meadows was their high proportions of annual plants (therophytes) and plants regenerating from bulbs and corms (geophytes)--a functional composition they shared with maritime mountain meadows. Conclusion: Bedrock meadows have distinct floristic, functional, and environmental features, are important hotspots for biodiversity, and host several rare plant species. They are an important habitat for conservation and environmental impact assessment and should be incorporated into regional and national classification schemes. Future research priority should be given to understanding their spatial extent, ecological functioning, and drivers of non-vascular and vascular species richness. [ABSTRACT FROM AUTHOR]

Aims: Classification of plant communities and identification of their diagnostic species in different types of rocky outcrops in mountains from Central Argentina. We also explored how these plant communities differ in their endemisms, chorotypes and non-natives species composition, as well as in rock chemistry, elevation and climatic variables. Location: Córdoba Province, Central Argentina. Methods: The study was based on 505 phytosociological relevés collected from 2009 to 2019 using the Braun-Blanquet cover-abundance scale. The hierarchical ISOmetric feature mapping and Partition Around Medoids (ISOPAM) analysis was applied. The vegetation matrix was ordinated through isometric feature mapping (ISOMAP), and the geochemical and bioclimatic variables were related to the ISOMAP ordination. Results: We identified 14 plant communities distributed in three main clusters. We recorded 59 endemic species at the national level and 28 narrow endemics, restricted to the study area and nearby mountains. The number of non-native plants was relatively low (26 species). Perennial herbs (221 species), grasses (97 species) and shrubs (68 species) were the most prominent life forms. Plant communities were associated with rock chemistry (carbonate and acidic rocks), and also with elevation and climatic variables. Conclusion: This study highlights the remarkable heterogeneity of outcrop vegetation in Central Argentina. It reveals that elevation and rock type (geochemical composition) were relevant factors associated with the floristic composition of plant communities and also, that outcrop habitats comprise a high level of endemisms, as well as a low number of non-native species, suggesting a high value for the conservation of local and regional biodiversity. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) have been widely used to create maps of expected species incidence, often using citizen science (CS) occurrence data as inputs. Environmental policy is informed by knowledge of community distributions, but there have been fewer attempts to utilise the potential of community distribution models (CDMs) to predict these. Many countries have vegetation community classification systems which include phytosociological information on individual species. Within Great Britain, the National Vegetation Classification (NVC) is the primary standard for vegetation communities, and while maps have been produced at regional scales, cost‐effective techniques are required for national scales.Published NVC occurrence records of 22 upland NVC communities in England and Wales were used as observed occurrences (presence‐only data). Predictors for the CDMs were enhanced vegetation index (EVI), elevation, slope, aspect, temperature and rainfall. Five modelling methods were investigated: generalised linear models (GLMs), support vector machines (SVMs), random forests (RFs), maximum entropy (MaxEnt) and maximum likelihood (MaxLike). Model quality was assessed via bootstrapping via area under the curve (AUC), true skill statistic (TSS) and Kappa index.There were only small differences in the accuracy of the models (median TSS model accuracy 0.742; range 0.280 to 0.873) with RF models the best overall CDM method. Across all NVC communities, summer and winter maximum temperatures and annual rainfall were the most important predictor variables. NVCs with spatially disjunct distributions in both lowlands and uplands, or that responded to localised management or environmental conditions, were poorly predicted.Synthesis and applications. Vegetation communities can be reliably predicted at large spatial scales using CDMs from extant datasets. Management practitioners can use community‐level predictions to design targeted field surveys for individual species typically associated with specific communities. Most existing CS survey schemes focus on species rather than communities. Hence future development of new CS schemes similar to the National Plant Monitoring Scheme (NPMS), that aligns with the NVC, will enable CS data to generate up‐to‐date maps of both communities and species. [ABSTRACT FROM AUTHOR]

The aim of this study is to outline syntaxonomical diversity of yew (Taxus baccata L.) in the eastern of Hyrcanian forests, Jahan Nama Protected Area (JNPA), and to identify their main environmental gradients. Vegetation units were classified using modified TWINSPAN (two‐way indicator species analysis) and were translated into syntaxonomic system. Syntaxa were determined by re‐arrangement of each relevé based on diagnostic species. Syntaxa were finally evaluated by diagnostic species and environmental parameters according to phi‐values and analysis of variance, respectively. Detrended correspondence analysis was used to visualize the dissimilarity of syntaxa and their relationships with the environmental factors. The classification of JNPA yew forests resulted in six vegetation units. These patterns were translated into four associations (Asso.), two sub‐associations (Subasso.) and two variants (Var.). (Asso.1) Fago orientalis‐Taxetum baccatae is found in northern aspects with lower slopes and higher soil depth; (Asso.2) Aceri velutini‐Taxetum baccatae is occurring in the moderate but rocky slopes. Asso.1 and Asso.2 are the same in altitude and involving Carpinus betulus as a co‐dominant. (Asso.3) Carpino betuli‐Carpino orientalis‐Taxetum baccatae developed in the intermediate slopes. (Asso.4) Carpino orientalis‐Taxetum baccatae appeared in the highest slope of northeast and northwest aspects with shallow soil depth. The main factors determining the species composition of the JNPA syntaxa are slope, eastness, elevation, and clay content. Finally, we concluded that yew could be associated by different plant species with different ecological desirability in the eastern of Hyrcanian forests. This study also provided the specific pattern of forest community type between C. betulus and C. orientalis in the JNPA using species combination concept. [ABSTRACT FROM AUTHOR]

There is increasing interestin broad‐scale analysis, modeling, and prediction of the distribution and composition of plant species assemblages under climatic, environmental, and biotic change, particularly for conservation purposes. We devised a method to reliably predict the impact of climate change on large assemblages of plant communities, while also considering competing biotic and environmental factors. To this purpose, we first used multilabel algorithms in order to convert the task of explaining a large assemblage of plant communities into a classification framework able to capture with high cross‐validated accuracy the pattern of species distributions under a composite set of biotic and abiotic factors. We applied our model to a large set of plant communities in the Swiss Alps. Our model explained presences and absences of 175 plant species in 608 plots with >87% cross‐validated accuracy, predicted decreases in α, β, and γ diversity by 2040 under both moderate and extreme climate scenarios, and identified likely advantaged and disadvantaged plant species under climate change. Multilabel variable selection revealed the overriding importance of topography, soils, and temperature extremes (rather than averages) in determining the distribution of plant species in the study area and their response to climate change. Our method addressed a number of challenging research problems, such as scaling to large numbers of species, considering species relationships and rarity, and addressing an overwhelming proportion of absences in presence–absence matrices. By handling hundreds to thousands of plants and plots simultaneously over large areas, our method can inform broad‐scale conservation of plant species under climate change because it allows species that require urgent conservation action (assisted migration, seed conservation, and ex situ conservation) to be detected and prioritized. Our method also increases the practicality of assisted colonization of plant species by helping to prevent ill‐advised introduction of plant species with limited future survival probability. Article impact statement: Broad‐scale analysis and modeling of plant assemblages under climatic‐biotic‐environmental cofiltering identifies species for early conservation. [ABSTRACT FROM AUTHOR]

Aims: Classification of European bog vegetation (Oxycocco‐Sphagnetea class); identification of diagnostic species for the class and vegetation subgroups (orders and alliances); development of an expert system for automatic classification of vegetation plots; and production of distribution maps of the Oxycocco‐Sphagnetea class and its alliances. Location: Europe. Methods: A data set of vegetation‐plot records was compiled to include various bog types over most of the European continent. An unsupervised classification (beta‐flexible linkage method, Sørensen distance measure) and detrended correspondence analysis (DCA) ordination were applied. Formal definitions of syntaxa based on species presence and covers, and respecting the results of the unsupervised classification, were developed and included in a classification expert system. Results: The Oxycocco‐Sphagnetea class, its two orders (Sphagno‐Ericetalia tetralicis and Sphagnetalia medii) and seven compositionally distinct alliances were formally defined. In addition to the syntaxa included in EuroVegChecklist, three new alliances were distinguished: Rubo chamaemori‐Dicranion elongati (subarctic polygon and palsa mires); Erico mackaianae‐Sphagnion papillosi (blanket bogs of the northwestern Iberian Peninsula); and Sphagno baltici‐Trichophorion cespitosi (boreal bog lawns). The latter alliance is newly described in this article. Conclusions: This first pan‐European formalized classification of European bog vegetation partially followed the system presented in EuroVegChecklist, but suggested three additional alliances. One covers palsa and polygon mires, one covers Iberian bogs with endemics and one fills the syntaxonomical gap for lawn microhabitats in boreal bogs. A classification expert system has been developed, which allows assignment of vegetation plots to the types described. [ABSTRACT FROM AUTHOR]

Aims: Inventorying the habitats composing Natura 2000 sites is mandatory in the European Union and is necessary to implement relevant conservation measures. Vegetation plots, recording the presence or abundance of all plant species co‐occurring within a plot, are currently used to identify terrestrial Natura 2000 habitat types, whose descriptions are mainly based on phytosociological units. However, vegetation plots are time‐consuming and frequently restricted to the growing season. Moreover, no vegetation plots can be regarded as exhaustive, and significant inter‐observer variation has been highlighted. We studied whether reducing the number of recorded species and the time spent carrying out a vegetation plot had an impact on vegetation unit assignment using species presence. We also studied if vegetation plots recorded in winter could be used for vegetation unit assignment. Location: Mainland France. Methods: We used 273 vegetation plots covering French temperate and mountainous forests. The time at which species were sighted was recorded. We also estimated whether a species was recognisable in winter. We used a classification program to compare assignments based on complete and incomplete vegetation plots. Results: Ten species and five minutes were sufficient to assign a plot to an association, and to an alliance, seven species and four minutes. Vegetation unit assignment proved feasible in winter, especially at the alliance level. Conclusions: We confirmed that a limited number of species is sufficient to assign vegetation plots to vegetation units. However, mapping habitats requires habitat identification and delimitation. This study confirms current field habits, particularly when creating a habitat map, usually based on a limited number of recorded species. Lastly, it confirms that the use of vegetation plots coming from a great variety of sources is relevant to create habitat time series, crucial tools for monitoring habitats at a national scale. [ABSTRACT FROM AUTHOR]

Factors influencing grazing behavior in species‐rich grasslands have been little studied. Methodologies have mostly had a primary focus on grasslands with lower floristic diversity.We test the hypothesis that grazing behavior is influenced by both animal and plant factors and investigate the relative importance of these factors, using a novel combination of video technology and vegetation classification to analyze bite and step rates.In a semi‐natural, partially wooded grassland in northern Estonia, images of the vegetation being grazed and records of steps and bites were obtained from four video cameras, each mounted on the sternum of a sheep, during 41 animal‐hours of observation over five days. Plant species lists for the immediate field of view were compiled. Images were partnered by direct observation of the nearest‐neighbor relationships of the sheep. TWINSPAN, a standard vegetation classification technique allocating species lists to objectively defined classes by a principal components procedure, was applied to the species lists and 25 vegetation classes (15 open pasture and 10 woodland) were identified from the images.Taking bite and step rates as dependent variables, relative importance of animal factors (sheep identity), relative importance of day, and relative importance of plant factors (vegetation class) were investigated. The strongest effect on bite rates was of vegetation class. Sheep identity was less influential. When the data from woodland were excluded, sheep identity was more important than vegetation class as a source of variability in bite rate on open pasture.The original hypothesis is therefore supported, and we further propose that, at least with sheep in species‐rich open pastures, animal factors will be more important in determining grazing behavior than plant factors. We predict quantifiable within‐breed and between‐breed differences, which could be exploited to optimize conservation grazing practices and contribute to the sustainability of extensive grazing systems. [ABSTRACT FROM AUTHOR]

Aims: To propose a comprehensive classification framework for Japanese forest vegetation, an expert system based on a traditional system was developed, using a large‐scale data set covering the whole of Japan. Location: The entire Japanese archipelago. Methods: A data set of 12,720 vegetation plots from the National Vegetation Survey database was established. Then, an expert system for automatic hierarchical classification was developed, based on the traditional system. The classification effectiveness was verified using fidelity measures, the occurrence of traditional characteristic species, consistency with semi‐supervised K‐means and modified TWINSPAN, and the similarity between each unit. To investigate correspondence to the environmental variables, a detrended correspondence analysis with the "envfit" function was used. Additionally, the occurring taxa were collated and compared with the flora lists of areas surrounding Japan. Results: The Japanese forest data set was classified into 34 alliances by the expert system, and 14 orders and six classes were distinguished. Most of these units had diagnostic taxa including existing characteristic species and the heatmap of the Bray–Curtis index showed the independence of each unit, and the similarity of the plot groups assigned to the same unit. Out of 34 alliances, clustering with semi‐supervised K‐means supported 21 alliances, and modified TWINSPAN supported 16 alliances. The four classes correspond to the thermal conditions and support the existing concept of vegetation zones. The lower‐level vegetation units correspond to a narrower range of environmental variables than higher‐level units, and species composition varied with limited environmental variables. A floristic composition comparison emphasized the complexity of the species composition of Japanese forests. Conclusions: This study proposes the first formal Japanese forest classification at the class, order, and alliance levels, reflecting traditional phytosociology systems and based on large‐scale vegetation plot data. Our classification provides an important foundation for the future revision of formal phytosociological systems throughout East Asia. [ABSTRACT FROM AUTHOR]

Questions: Understanding spatial variation in floristic composition is crucial to quantify the extent, patchiness and connectivity of distinct habitats and their spatial relationships. Broad‐scale variation in floristic composition and the degree of uniqueness of different regions remains poorly mapped and understood in several areas across the globe. We here aim to map vegetation heterogeneity in Amazonia. Location: Middle Juruá river region, Amazonas State, Brazil. Methods: We mapped four plant groups by applying machine learning to scale up locally observed community composition and using environmental and remotely sensed variables as predictors, which were obtained as GIS layers. To quantify how reliable our predictions were, we made an assessment of model transferability and spatial applicability. We also compared our floristic composition map to the official Brazilian national‐level vegetation classification. Results: The overall performance of our floristic models was high for all four plant groups, especially ferns, and the predictions were found to be spatially congruent and highly transferable in space. For some areas, the models were assessed not to be applicable, as the field sampling did not cover the spectral or environmental characteristics of those regions. Our maps show extensive habitat heterogeneity across the region. When compared to the Brazilian vegetation classification, floristic composition was relatively homogeneous within dense forests, while floristic heterogeneity in rainforests classified as open was high. Conclusion: Our maps provide geoecological characterization of the regions and can be used to test biogeographical hypotheses, develop species distribution models and, ultimately, aid science‐based conservation and land‐use planning. [ABSTRACT FROM AUTHOR]

Questions: What is the potential use of maps derived from a merged geographical and phytosociological approach to support the design of public environmental policies? Do these approaches and data sources deliver complementary land‐cover/vegetation maps? Objective: The present article documents a joint phytosociological and geographical approach to improve vegetation cartography in temperate‐tropical transitional ecosystems. Location: The research was conducted at national (Mexico) and state (Michoacán) scales. Mexico and Michoacán have been recognized as regions of high eco‐geographical complexity, where temperate‐tropical conditions intermingle, creating large eco‐socio‐cultural mosaics. Methods: Data from 268 field verification sites and 223 relevés surveyed during the last two decades and recent land cover sources were used as the main inputs. The results were further validated by three workshops with local botanists and field verification during 2021. Results: At the national level, Mexico's forests, shrubs, herbs, and non‐vascular major formation classes were hierarchically split by dominant life forms and prevailing climatic affiliations. At the state level, these major formation classes split into 19 sub‐formations, of which 15 were forest communities. Conclusions: We discuss the scientific challenge of transitioning from land cover into vegetation maps and (dis)similarities of approaches reviewing concepts and analytical (quanti)qualitative instruments. The paper contrasts the present output with the experiences of other countries such as Canada, the United States, Bolivia, and Colombia. Finally, the results are discussed in light of their relevance for constructing public environmental policies, such as land use planning, establishment of protected areas, allocation of incentives for sustainable environmental services, and long‐term conservation practices. [ABSTRACT FROM AUTHOR]

The two most common approaches to target species introduction in European meadow restoration are green‐hay transfer from species‐rich donor sites and the use of diverse seed mixtures reflecting the chosen target community. The potential of both approaches to restore species‐rich grassland has been variously reviewed, but very few studies have experimentally compared them at one and the same site. Moreover, studies involving one or both approaches have rarely taken into account environmental gradients at a site, and measured the impacts of such gradients on restoration outcomes. Such gradients do, for example, exist during grassland restoration on former arable land in river floodplains, where gradients in the occurrence of flooding, and in associated edaphic characteristics such as nutrient availability, might affect restoration outcomes. Using a randomized complete block experimental design, based on five different indicators of restoration progress, we compared the usefulness of green‐hay application and diverse seeding to restore species‐rich grazed meadows of the MG5 grassland type according to the British National Vegetation Classification, and also investigated how restoration outcomes differed after 4 years between areas within experimental plots characterized by high flood risk and areas characterized by low flood risk. Overall, both restoration approaches yielded similar results over the course of the experiment, whereas high flood risk and associated edaphic factors such as high availability of phosphorus negatively affected restoration progress particularly in terms of floristic similarity to restoration targets. These results highlight the need to take into account environmental gradients during meadow restoration. [ABSTRACT FROM AUTHOR]

Green hay transfer from species‐rich donor sites is now commonly used in Europe to restore species‐rich semi‐natural grassland, both on ex‐arable land and on former intensive grassland. However, species transfer rates are usually well below 100%, and due to lack of further colonization by additional target species after initial restoration, continued progress toward the target plant community is often very slow. We used data from a restoration experiment aiming to reestablish species‐rich grazed meadows of the MG5 grassland type according to the British National Vegetation Classification to investigate relationships between species abundance at a donor site, species capture by green hay and its seed content, and success of species establishment on experimental plots in formerly intensively managed species‐poor grassland undergoing restoration. Our results show that species with higher abundance at the donor site were more likely captured as seed in green hay, and were more likely to establish after hay application at the recipient site. Species with low abundance at the donor site that also possessed specific germination requirements that might prevent immediate establishment after green hay transfer were particularly unlikely to get established after transfer. These findings can provide guidance for additional measures aimed at ensuring establishment of a wider range of target species. Such measures could include targeted sowing of species in addition to green hay application, and management of restored grassland swards to extend or reopen an initial window of opportunity for the establishment of green hay species that might not be germinable immediately after hay transfer. [ABSTRACT FROM AUTHOR]

Aims: The machine‐readable formal language of classification expert systems has become a standard for applying plot assignment rules in vegetation classification. Here we present an efficient algorithm implementing the vegetation classification expert system in the statistical programming language R. Methods: The principal idea of the R implementation is to solve the assignments to vegetation types not sequentially plot by plot but to parse the assignment rules into (nested) components that each can be evaluated by simultaneous vector‐based processing of all plots in a database. Results and conclusions: We demonstrate the algorithm taking the EUNIS classification expert system of European habitat types (EUNIS‐ESy) as an example. The R code version of the vegetation classification expert system is particularly useful in large vegetation‐plot databases because it solves all logical operations vector‐wise across all plots, allowing for efficient evaluation of membership expressions and formulas. Another advantage of the R implementation is that membership formulas are not only readable but can also be produced as a machine‐written result, for example as the output of classification algorithms run in R. [ABSTRACT FROM AUTHOR]

Aims: Traditional quantitative approaches to forest classification are based on differences in species abundance or incidence among communities. In these approaches, all species are regarded as biologically equidistant regardless of the biological heterogeneity. The objective of the study is to evaluate the potential of the "Discriminating Avalanche" approach, which integrates species abundance and biological heterogeneity, as a new basis for forest classification. Location: China, India, Iran, Ukraine, Germany, Italy, Mexico, Peru, and South Africa. Method: We illustrate our approach using a set of 35 large tree‐mapped forest plots from various regions of the world. Our dissimilarity matrices, which integrate species abundance with biological heterogeneity, are compared with the standard Bray–Curtis and Whittaker dissimilarity indices, and provide the quantitative basis for a hierarchical cluster analysis. Results: Four distinct groups of forests were identified using the proposed forest dissimilarity matrix. Afro‐montane forests from South Africa constitute a first group. A second group includes temperate deciduous broad‐leaved forests dominated by oak (Quercus) and beech (Fagus) from Europe and China. Conifer‐dominated forests constitute a third group. The remaining forests constitute the fourth group. Conclusion: Biological heterogeneity provides a practical basis for vegetation classification. The results of this study, based on a variety of temperate and tropical forests, suggest that a measure of biological dissimilarity based on evolutionary and morphological differences among species is more effective than the traditional species abundance‐based approaches to classify an arbitrary set of plant communities. This approach promises greater refinement and consistency in ecological classification. In particular, it has advantage in classifying forests along large geographic scales in situations of high beta diversity and species turnover. [ABSTRACT FROM AUTHOR]

Aim: Vegetation types of Mediterranean thermophilous pine forests dominated by Pinusbrutia, Pinushalepensis, Pinuspinaster and Pinuspinea were studied in various areas. However, a comprehensive formal vegetation classification of these forests based on a detailed data analysis has never been developed. Our aim is to provide the first broad‐scale classification of these pine forests based on a large data set of vegetation plots. Location: Southern Europe, North Africa, Levant, Anatolia, Crimea and the Caucasus. Methods: We prepared a data set of European and Mediterranean pine forest vegetation plots. We selected 7,277 plots dominated by the cold‐sensitive Mediterranean pine species Pinusbrutia, Pinus halepensis, Pinus pinaster and Pinus pinea. We classified these plots using TWINSPAN, interpreted the ecologically and biogeographically homogeneous TWINSPAN clusters as alliances, and developed an expert system for automatic vegetation classification at the class, order and alliance levels. Results: We described Pineteahalepensis as a new class for the Mediterranean lowland to submontane pine forests, included in the existing Pinetaliahalepensis order, and distinguished 12 alliances of native thermophilous pine forests, including four newly described and three informal groups merging supposedly native stands and old‐established plantations. The main gradients in species composition reflect elevational vegetation belts and the west–east, and partly north–south, biogeographical differences. Both temperature and precipitation seasonality co‐vary with these gradients. Conclusions: We provide the first formal classification at the order and alliance levels for all the Mediterranean thermophilous pine forests based on vegetation‐plot data. This classification includes traditional syntaxa, which have been critically revised, and a new class and four new alliances. We also outline a methodological workflow that might be useful for other vegetation classification syntheses. The expert system, which is jointly based on pine dominance and species composition, is a tool for applying this classification in research and nature conservation survey, monitoring and management. [ABSTRACT FROM AUTHOR]

Aims: River gravel bars are endangered habitats in Europe. However, classification schemes of their vegetation and habitat types differ among European countries, and they are even ignored in some national schemes. This causes problems in conservation planning, monitoring and management. Hence we aimed at building the first unified vegetation classification for river gravel‐bar habitats across European mountain systems. Location: Europe. Methods: In total 4,769 vegetation plot records of river gravel‐bar plant communities were collected from national, regional or private databases, digitized from the literature and newly collected in the field. A hierarchical classification expert system with formal definitions of vegetation types was created. The definitions combined the criteria of presence or cover of groups of species with similar ecology or single species narrowly specialized to a particular gravel‐bar habitat. The TWINSPAN classification was applied to early‐successional vegetation types to check whether the classification based on formal definitions was supported by the results of unsupervised classification. Similarity patterns among vegetation types were visualized using the detrended correspondence analysis (DCA) ordination. Results: Early‐successional and scrub gravel‐bar vegetation types were respectively classified into two classes: Thlaspietea rotundifolii and Salicetea purpureae. Eleven associations and four alliances (Calamagrostion pseudophragmitae, Epilobion fleischeri, Salicion cantabricae and Salicion eleagno‐daphnoidis) were defined formally. Based on a critical revision, some associations or alliances defined in the previous literature were merged or discarded. The main gradient in variability within the gravel‐bar vegetation is connected with the altitudinal gradient, biogeographical variation, local hydromorphological processes and various successional changes. Conclusions: The first unified and formalized classification system of the European mountain river gravel‐bar vegetation was created, and species composition, ecology and distribution of these types were characterized. The syntaxonomical nomenclature of these types was checked and revised. This study provides a base for conservation planning of these threatened and rapidly disappearing habitats. [ABSTRACT FROM AUTHOR]

The fourth edition of the International Code of Phytosociological Nomenclature (ICPN) was prepared by the Steering Committee of the IAVS Working Group for Phytosociological Nomenclature (GPN). The edition consists of 14 Definitions, 7 Principles, 53 Articles, and 7 Appendices. When compared with the previous edition, the main amendments are: (a) the acceptance of electronic publications (Art. 1); (b) the introduction of binding decisions (Definition XIV, Principle II, Articles 1, 2b, 3c, 29b, 40, 42, 44, Appendices 6 and 7); (c) the mandatory use of the English or Latin terminology for syntaxonomic novelties (Definition II, Principle II, Articles 3d and 3i); (d) the introduction of autonyms for the main ranks when the corresponding secondary ranks are created (Articles 13b and 24); (e) the automatic correction of the taxon names (name‐giving taxa) used in the names of syntaxa in accordance with the International Code of Nomenclature for algae, fungi, and plants (ICN) (Article 44); (f) the possibility to mutate the name of a syntaxon in using other correct, alternative names for the name‐giving taxa (Article 45); (g) the introduction of inadequate names, a new category of rejected names (Definition V, Articles 43 through 45); and (h) the introduction of a conserved type (Definition XIII, Article 53). The fourth edition of ICPN was approved by the GPN on 25 May 2019 and becomes effectively binding on 1 January 2021. [ABSTRACT FROM AUTHOR]

Aims: Vegetation classification seeks to partition the variability of vegetation into relatively homogeneous but distinct types. There are many ways to evaluate, and potentially improve, such a partitioning. One effective approach involves calculating silhouette widths which measure the goodness‐of‐fit of plots to their cluster. We introduce a new iterative reallocation clustering method — Reallocation of Misclassified Objects based on Silhouette width (REMOS) — and compare its performance with an existing algorithm — OPTimizing SILhouette widths (OPTSIL). REMOS reallocates misclassified objects to their nearest‐neighbour cluster iteratively. Of its two variants, REMOS1 reallocates only the object with the lowest silhouette width, while REMOS2 reallocates all objects with negative silhouette width in each iteration. We test how REMOS1, REMOS2 and OPTSIL perform in terms of: (a) cluster homogeneity and separation; (b) the number of diagnostic species; and (c) runtime. Methods: We classified simulated data with the flexible‐beta algorithm for values of beta from −1 to 0. These classifications were subsequently optimized by REMOS1, REMOS2 and OPTSIL and compared for mean silhouette widths, misclassification rate, and runtime. We classified three vegetation data sets from two to ten clusters, optimized all outcomes with the three reallocation methods, and compared their mean silhouette widths, misclassification rate, and number of diagnostic species. Results: OPTSIL achieved the highest mean silhouette width across the majority of the data sets. REMOS achieved zero or negligible misclassifications, outperforming OPTSIL on this criterion. REMOS algorithms were typically more than an order of magnitude faster to calculate than OPTSIL. There was no clear difference between REMOS and OPTSIL in the number of diagnostic species. Conclusions: REMOS algorithms may be preferable to OPTSIL when: (a) the primary objective is to reduce the number of negative silhouette widths in a classification, as opposed to maximizing mean silhouette width; or (b) when the time efficiency of the algorithm is important. [ABSTRACT FROM AUTHOR]

Aim: Existing phytogeographical frameworks for tropical Africa lack either spatial completeness, unit definitions smaller than the regional scale or a quantitative approach. We investigate whether physical environmental variables can be used to interpolate floristically defined vegetation units, presenting an interpolated, hierarchical, quantitative phytogeographical framework for tropical Africa, which is compared to previously defined regions. Location: Tropical mainland Africa 24°N to 24°S. Taxon: 31,046 vascular plant species and infraspecific taxa. Methods: We calculate a betasim dissimilarity matrix from a comprehensive whole‐flora database of plant species distributions. We investigate environmental correlates of floristic turnover with local non‐metric multidimensional scaling. We derive a hierarchical biogeographical framework by clustering the dissimilarity matrix. The framework is modelled using a classification decision tree method and 12 physical environmental variables to interpolate and increase the resolution of the framework across the study region. Results: Floristic turnover is related strongly to water availability and temperature, with smaller contributions from land cover, topographic ruggedness and lithology. Region can be predicted with 90% accuracy by the model. We define 19 regions and 99 districts. We find a novel arrangement of the arid regions. Regional subdivision within the savanna biome is supported with minor variation to borders. Within the forests of west and central Africa, our whole‐flora gridded regionalization supports the divisions identified by a previous analysis of trees only. Main conclusions: Physical environmental variables can be used to predict floristically defined vegetation units with very high accuracy, and the approach could be pursued for other incompletely sampled taxa and areas outside of tropical Africa. Geographical coherence is higher than in previous quantitative phytoregional definitions. For most tropical African vascular plant species, we provide predictions of which species will occur within each mapped district and region of tropical Africa. The framework should be useful for future studies in ecology, evolution and conservation. [ABSTRACT FROM AUTHOR]

Present biodiversity comprises the evolutionary heritage of Earth's epochs. Lineages from particular epochs are often found in particular habitats, but whether current habitat decline threatens the heritage from particular epochs is unknown. We hypothesized that within a given region, humans threaten specifically habitats that harbor lineages from a particular geological epoch. We expect so because humans threaten environments that dominated and lineages that diversified during these epochs. We devised a new approach to quantify, per habitat type, diversification of lineages from different epochs. For Netherlands, one of the floristically and ecologically best‐studied regions, we quantified the decline of habitat types and species in the past century. We defined habitat types based on vegetation classification and used existing ranking of decline of vegetation classes and species. Currently, most declining habitat types and the group of red‐listed species are characterized by increased diversification of lineages dating back to Paleogene, specifically to Paleocene‐Eocene and Oligocene. Among vulnerable habitat types with large representation of lineages from these epochs were sublittoral and eulittoral zones of temperate seas and 2 types of nutrient‐poor, open habitats. These losses of evolutionary heritage would go unnoticed with classical measures of evolutionary diversity. Loss of heritage from Paleocene‐Eocene became unrelated to decline once low competition, shade tolerance, and low proportion of non‐Apiaceae were accounted for, suggesting that these variables explain the loss of heritage from Paleocene‐Eocene. Losses of heritage from Oligocene were partly explained by decline of habitat types occupied by weak competitors and shade‐tolerant species. Our results suggest a so‐far unappreciated human threat to evolutionary heritage: habitat decline threatens descendants from particular epochs. If the trends persist into the future uncontrolled, there may be no habitats within the region for many descendants of evolutionary ancient epochs, such as Paleogene. Article impact statement: A new method shows that a regional flora is losing heritage of oldest geological epochs by losing habitats of low competition intensity. [ABSTRACT FROM AUTHOR]

Aims: Assigning vegetation plots to vegetation units is a key step in biodiversity management projects. Nevertheless, the process of plot assignment to types is usually non‐standardized, and assignment consistency remains poorly explored. To date, the efficiency of automatic classification programs has been assessed by comparing them with a unique expert judgment. Therefore, we investigated the consistency of five phytosociology expert judgments, and the consistency of these judgements with those of automatic classification programs. Location: Mainland France. Methods: We used 273 vegetation plots distributed across France and covering the diversity of the temperate and mountainous forest ecosystems of Western Europe. We asked a representative panel of five French organizations with recognized expertise in phytosociology to assign each plot to vegetation units. We provided a phytosociological classification including 228 associations, 43 alliances and eight classes. The assignments were compared among experts using an agreement ratio. We then compared the assignments suggested by three automatic classification programs with the expert judgments. Results: We observed small differences among the agreement ratios of the expert organizations; a given expert organization agreed with another one on association assignment one time in four on average, and one time in two on alliance assignment. The agreement ratios of the automatic classification programs were globally lower, but close to expert judgments. Conclusions: The results support the current trend toward unifying the existing classifications and specifying the assignment rules by creating guiding tools, which will decrease inter‐observer variation. As compared to a pool of phytosociology experts, programs perform similarly to individual experts in vegetation unit assignment, especially at the alliance level. Although programs still need to be improved, these results pave the way for the creation of habitat time series crucial for the monitoring and conservation of biodiversity. [ABSTRACT FROM AUTHOR]

Aims: Vegetation‐plot sampling usually involves estimating species cover. For classifying plots to vegetation types, covers are often transformed to decrease the effect of dominant species. However, it remains unclear which transformation is optimal. We suggest that for vegetation classification, optimal is such transformation that contributes to creating clusters of plots in an unsupervised classification that are most similar to the widely accepted vegetation types, e.g., phytosociological associations. Here our aim is to find and recommend such optimal transformation by testing a range of transformation options against the national vegetation classifications of three European countries. Location: Czech Republic, The Netherlands, Great Britain. Methods: Three national datasets of vegetation plots with species cover information, classified to associations or community types of the respective national vegetation classification systems, were analysed. From each dataset, multiple subsets of plots were selected randomly, each subset representing a vegetation‐plot table containing several similar associations/community types. Species cover values in these subsets were subjected to various transformations (power transformation, logarithmic transformation and pseudo‐species cut levels). Then each subset was classified by an agglomerative classification method (beta‐flexible clustering with different beta values), and the classification was compared with the units of the national vegetation classification using the adjusted Rand index. Results: Power transformations of percentage covers with an exponent between 0.3 and 0.6 produced the best match between the unsupervised classifications and the national vegetation classifications. This result did not depend on the classification method used. A similar degree of matching was achieved with some cut levels of pseudo‐species and with logarithmic transformation of percentage cover. Conclusions: If an unsupervised classification of vegetation plots aims at defining vegetation types that are close to the phytosociological associations accepted in national vegetation classifications, the best transformation is close to the square‐root of percentage cover (i.e., power transformation with exponent 0.5). [ABSTRACT FROM AUTHOR]

Questions: Which are the main wet and mesic grassland vegetation types along the climatic gradient on the southern margin of the Pannonian basin? Which are the most important environmental gradients that determine their distribution and species composition? Location: The southern margin of the Pannonian plain (N and E Slovenia, N Croatia, N Serbia). Methods: A database of 2,575 vegetation plots originally assigned to the Molinio‐Arrhenatheretea was compiled. Semi‐supervised classification based on the K‐means algorithm was applied to assign plots into 12 a‐priori alliances and to search for new alliances. Non‐metric Multidimensional Scaling (NMDS) ordination with Ellenberg's indicator values and bioclimatic variables were applied to show the influence of ecological conditions on species composition. The plots within each alliance were further classified into associations using the beta flexible method. Results: Classification resulted in 12 alliances recognized in the EuroVegChecklist, and two new groups appeared. The first group occurs in the eastern part of the research area, where many ruderal species appear. This group was treated as a new alliance, Salvio nemorosae‐Arrhenatherion forming a vicariant syntaxon of the alliance Arrhenatherion. The second group occurs in the western part. It is a transitional group between mesic (Arrhenatherion) and wet meadows (Deschampsion) and can be treated as Alopecurion. Besides, three new associations and six new subassociations were described, together with the typification of seven associations and of three subassociations. Numerical analyses revealed floristic, ecological and geographical differentiation among analysed vegetation types. Conclusions: The communities showed typical Central European features in the western part, but their floristic and ecological conditions changed towards the eastern part, where the continental influence was more pronounced. The vegetation structure and plant species composition in the studied area were influenced by soil moisture and soil nutrient availability as well as by strong biogeographical and climatic gradients. [ABSTRACT FROM AUTHOR]

Previous analyses of historical aerial photography and satellite imagery have shown thickening of woody cover in Australian tropical savannas, despite increasing fire frequency. The thickening has been attributed to increasing precipitation and atmospheric CO2 enrichment. These analyses involved labour‐intensive, manual classification of vegetation, and hence were limited in the extent of the areas and the number of measurement times used. Object‐based, semi‐automated classification of historical sequences of aerial photography and satellite imagery has enabled the spatio‐temporal analysis of woody cover over entire landscapes, thus facilitating measurement, monitoring and attribution of drivers of change. Using this approach, we investigated woody cover change in 4000 ha of intact mesic savanna in the Ranger uranium lease and surrounding Kakadu National Park, using imagery acquired on 10 occasions between 1950 and 2016. Unlike previous studies, we detected no overall trend in woody cover through time. Some variation in cover was related to rainfall in the previous 12 months, and there were weak effects of fire in the year of image acquisition and the antecedent 4 years. Our local‐scale study showed a mesic eucalypt savanna in northern Australia has been resilient to short‐term variation in rainfall and fire activity; however, changes in canopy cover could have occurred in other settings. When applying this semi‐automated approach to similar studies of savanna dynamics, we recommend maximising the time depth and number of measurement years, standardising the time of year for image acquisition and using many plots of 1 ha in area, rather than fewer, larger plots. [ABSTRACT FROM AUTHOR]

Questions: To improve our knowledge on how environmental conditions determine the development of high‐value Calluna vulgaris heathland habitats, we studied the floristic and structural characteristics of heathland plant communities across North Germany and how they are influenced by edaphic, climatic and management factors. We ask how heathland development is related to these factors and what are the implications for conservation management and restoration. Location: North German Plain. Methods: We collected 350 relevés in 18 dry Calluna heathland areas. Plant communities were classified using Isopam, and Redundancy Analysis (RDA) determined effects of environmental conditions. Potential pathways of development and the nature conservation status of the communities were identified on a multifactorial basis. Results: We found nine floristically and structurally distinct heathland plant communities. Heathland vegetation showed distinct patterns along Calluna age development stages and environmental conditions. Soil conditions and related effects of long‐term site history and recent management turned out to be the predominant factors influencing species composition and diversity, resulting in three potential heathland succession pathways. Mosaic‐like communities with particularly high taxonomic diversity and conservation value occurred on early‐successional inland dunes or as regeneration stage growing on nutrient‐poor sandy soils without humus accumulation. Conclusions: The study reveals fundamental differences between historically farmed heathland in the oceanic Northwest and former military training areas mainly in northeastern Germany with consequences for restoration ecology. Present nature conservation criteria turned out to be insufficient in predicting habitat quality, as lichens are frequently disregarded. Our findings highlight the need for intense soil disturbance to maintain early‐stage soil conditions and a diverse Calluna growth‐phase composition, as these factors essentially determine species richness in lowland heaths. [ABSTRACT FROM AUTHOR]

Question: Filtering vegetation plot records according to sampling size is an essential methodological step in vegetation studies. In fens, the variation of traditionally used plot sizes seems to limit continental‐scale syntheses following the Braun‐Blanquet approach. Which plot sizes harbour the analogous number of habitat specialists (i.e., diagnostic/indicator species) and capture the main compositional gradients identically? Location: Scandinavia, central Europe. Methods: The data set of fen vegetation plot records was compiled using large databases and categorised into four distinct habitats. For each habitat, semi‐log species–area curves of specialists and other species were fitted using generalised additive models (GAM). In addition, we surveyed 72 sites in a series of plot sizes (0.07, 0.25, 1, 4, 16 m2) where we applied, separately for each plot size, Non‐Metric Multi‐Dimensional Scaling (NMDS) and compared the resulting patterns with Procrustes analysis. Results: Consistently across different fen habitats, the species–area curves of specialists increased steeply up to the plot size of 1 m2, while increasing negligibly in the plot size range of 1–25 m2. In contrast, the species–area curves of other species displayed mostly linear to linear‐exponential trends. NMDS ordinations of medium (1 and 4 m2) and large plots (16 m2) were the most congruent, while the patterns captured in the ordination of the smallest plots (0.07 m2) differed most from the others. Conclusions: In fens, plot sizes of at least 1 m2 describe sufficiently the broad‐scale pattern in specialists' diversity as well as the main environmental gradients. The range of plot sizes of 1–25 m2 may be safely merged in broad‐scale analyses of fen vegetation without introducing substantial bias, at least when compared with other possible uncertainty sources. [ABSTRACT FROM AUTHOR]

Questions: Can co‐occurrence and phylogenetic resemblance of taxa in plots be integrated into a single composite numerical framework so that vegetation classification expresses both in a unique system? How should phylogenetic similarity of vicariant taxa, in synvicariant communities, be dealt with for use in vegetation classifications? Is this meaningful to Vegetation Science and Biogeography? Is Macaronesia's woody vegetation an adequate paradigm to test a methodological workflow for phylogenetically informed classification? Location: Macaronesia: the Azores, Madeira, Canaries, and Cape Verde archipelagos. Methods: We used both (a) co‐occurrence‐only and (b) phylogenetic Bray–Curtis similarity indexes in straightforward classification algorithms: Ward's method clustering and isopam divisive classification. Phylogenetic distances for (b) were taken from an ad hoc phylogenetic tree. The tree was obtained by grafting/pruning subtrees from DNA sequence alignments of rcbL, ITS and matK into a subset of an available megaphylogeny. Results: Co‐occurrence‐only similarity is not a reliable predictor of phylogenetic similarity. Two distinct approaches to similarity imply significant differences in classifications. As groups in both classifications may be equated to woody‐vegetation classes, alternative arrangements of such will appear. Vegetation classes are quasi‐specific to each archipelago if accounting for co‐occurrence‐only. Conversely, larger units spanning several archipelagos emerge when phylogeny is considered. Conclusion: We recognise the usefulness of incorporating phylogeny in the definition of formal vegetation units so that the concept of syntaxon may include it. Supra‐class units issuing from phylogenetically informed similarity originate, for the most, from vegetation synvicariance rather than from sharing of species. Therefore, these units correspond preferably to biogeographical concepts (phytochoria), not to phytocoenological units. Regarding "Macaronesia" being a consistent biogeographical unit, if compositional and ecological vegetation patterns are preferred, we support the separation of Macaronesia in several biomes and thus into several Regions within two Kingdoms. Contrariwise, if common ancestry of taxa and synvicariance in syntaxa is to be emphasised, then the one‐Macaronesia Region model re‐appears. [ABSTRACT FROM AUTHOR]

How and why does geography inform the organisational strategies of political parties? Encuentro por la Democracia y la Equidad (Encounter for Democracy and Equity, hereafter EDE) is a centre‐left Argentine political party that, since arriving in Buenos Aires in 2008, quickly became one of the largest oppositional parties in the city in terms of its activist base and electoral representation. This was achieved by explicitly prioritising a territorial organisational strategy: building the party at the scale of the neighbourhood by opening branches, accumulating activists and constructing linkages with civil society. Through a qualitative analysis of EDE's organisational strategy over 10 years (2008–2018), the paper proposes four dimensions for understanding the relationship between grassroots territorial organising and party strategy: party‐building; electoral success; democratic linkages; and alliance building. The findings are of relevance not only to party‐building in Latin America but also to the recent surge of territorial organising in parties in Europe. The paper makes three contributions to geographical literature. First, it adds to limited Anglophone geographical engagements with Latin American electoral and party politics by highlighting the centrality of spatiality to party‐building. Second, it emphasises sub‐national dynamics of party organising, contributing to a dismantling of methodological state centrism that pervades party scholarship. Third, it develops a geographical analytical approach to party organisation that demonstrates how sociospatial relations constitute parties' unfolding relations with the state and civil society. How and why does geography inform the organisational strategies of political parties? The paper proposes a model for analysing the geography of political parties by focusing on the relationship between territory and party organisation. It draws on empirical work in Buenos Aires to outline four dimensions through which parties strategically organise in and through territory in order to pursue their objectives. [ABSTRACT FROM AUTHOR]

Aims: To create a comprehensive, consistent and unequivocal phytosociological classification of European marsh vegetation of the class Phragmito‐Magnocaricetea. Location: Europe. Methods: We applied the Cocktail method to a European data set of 249,800 vegetation plots. We identified the main purposes and attributes on which to base the classification, defined assignment rules for vegetation plots, and prepared formal definitions for all the associations, alliances and orders of the class Phragmito‐Magnocaricetea using formal logic. Each formula consists of the combination of "functional species groups", cover values of individual species, and in the case of high‐rank syntaxa also of "discriminating species groups" created using the Group Improvement (GRIMP) method. Results: The European Phragmito‐Magnocaricetea vegetation was classified into 92 associations grouped in 11 alliances and six orders. New syntaxa (previously invalidly published according to the International Code of Phytosociological Nomenclature) were introduced: Bolboschoeno maritimi‐Schoenoplection tabernaemontani, Glycerio maximae‐Sietum latifolii, Glycerio notatae‐Veronicetum beccabungae, Schoenoplectetum corymbosi and Thelypterido palustris‐Caricetum elongatae. Based on a critical revision, some other syntaxa were rejected or excluded from the class Phragmito‐Magnocaricetea. Conclusions: This work provides the first consistent classification of the class Phragmito‐Magnocaricetea at the European scale, which is an important tool for nature conservation. Our classification largely respects previously existing concepts of syntaxa, but it also proposes modifications to the recently published EuroVegChecklist. This work also provides a protocol that can be used for extending the current classification to new syntaxa and geographical regions. [ABSTRACT FROM AUTHOR]

This paper examines the determinants of the spatial and topological structure of three types of urban networks within the Yangtze River Delta. These networks consist of transport infrastructure links, business interactions in producer services firms, and leisure mobility. The influence of distance, size, administrative borders, landform contiguity, cultural affinities, economic alliances and administrative rank are examined. We position our findings as an empirical elaboration of the formation of multiplex urban networks. Our results show that in spite of significant correlations between all of these explanatory factors and the three urban networks, only some factors affect each of the three networks. More specifically, the business network has a weak dependence on distance and cultural affinities; intercity mobility is closely related to the size of cities' populations and distance; and landform patterns remain a fundamental basis for intercity transport linkages. Our results highlight China's hierarchical‐administrative specifics in the shaping of urban networks. [ABSTRACT FROM AUTHOR]

Questions: What vegetation types can be distinguished on the basis of plant functional traits using numerical classification? How do they match syntaxonomical units? Location: Poland. Methods: Six thousand nine hundred and eighty‐five vegetation plots representing mesic and wet grasslands (Molinio‐Arrhenatheretea, Polygono‐Poetea) were retrieved from the Polish Vegetation Database. Plant functional trait data were assembled from the LEDA and Clo‐Pla databases for most species occurring in the data set. Community‐weighted mean for five traits were calculated for each plot: specific leaf area, canopy height, seed mass, bud bank index and clonality index. Plots were classified using Ward's method and iterative relocation based on silhouette widths. The clusters were interpreted and characterized on the basis of species and trait composition, functional diversity, functional redundancy, Ellenberg indicator values, and geographical distribution. The similarity between the trait‐based classification and the syntaxonomical assignment of plots is evaluated both statistically and by expert knowledge. Results: Twelve clusters were distinguished. The classification mirrored the main gradients structuring grasslands in Poland, although, some vegetation types with the strong dominance of functionally unique species appeared more distinct than they are treated in syntaxonomy. Clusters did not differ significantly in functional diversity and redundancy. The differences of clusters in species and trait composition and environmental background are discussed. Conclusion: The application of trait data and numerical methods is a promising approach for obtaining vegetation classifications. Such classifications can be in closer relationship with the most important ecosystem processes than floristic classifications because communities comprising different species but similar functional trait distribution are not separated. Trait‐based classifications match phytosociological units to a variable degree. Functional uniqueness and variation of abundance determines how individual species influence the delimitation of vegetation types using our approach. [ABSTRACT FROM AUTHOR]

Aims: To develop forest vegetation classification at the level of alliances and associations across the Hyrcanian ecoregion, Northern Iran, and to explore the effects of main environmental and geographic gradients on their distribution. Location: Hyrcanian ecoregion, Northern Iran. Methods: A database of 1,597 vegetation plots of mostly 400 m2 in size with a total of 802 vascular plant taxa was established, covering the whole geographic range of the Hyrcanian forests at altitudes ranging from −22 to 2,850 m a. s. l. An expert system was developed for automatic classification of vegetation plots into alliances and associations. Detrended correspondence analysis (DCA) and canonical correspondence analysis (CCA) were used to determine the most important environmental and geographic gradients affecting species composition. Results: Twenty‐one associations and seven alliances of these forests, belonging to five orders and four classes, were defined. Among them, eleven associations and five alliances were described as new syntaxa. Alnion glutinosae and Smilaco excelsae‐Alnion barbatae, both distributed in the lowland belt, include swamp and wet forests, respectively. Parrotio persicae‐Carpinion betuli and Alnion subcordatae are respectively mesic and wet forests of the submontane belt. Solano kieseritzkii‐Fagion orientalis is a mesic beech forest in the montane belt, and finally, Quercion macrantherae, an open oak forest, and Centaureo hyrcanicae‐Carpinion orientalis, a dry hornbeam forest, occur in the upper‐montane belt. DCA and CCA analyses showed that the distribution of these alliances and associations is mainly related to altitude and mean annual temperature. Conclusions: Based on our results and comparison between the Hyrcanian and European forests, we propose a new, comprehensive syntaxonomic scheme for the Hyrcanian forests, supported by a classification expert system. Unlike previous studies, we linked the classification system to that of EuroVegChecklist because, though this area is outside of Europe, its vegetation is very similar to that of the European temperate forest vegetation. [ABSTRACT FROM AUTHOR]

Aims: The primary objective of this study is to map the distribution and quantify the cover of vegetation alliances over the entirety of San Clemente Island (SCI). To this end, we develop and evaluate the mapping method of hierarchical object‐based classification with a rule‐based expert system. Location: San Clemente Island, California, USA. Methods: We developed and tested an approach based on hierarchical object‐based classification with a rule‐based expert system to effectively map vegetation communities on SCI following the Manual of California Vegetation classification system. In this mapping approach, the shrub species defining each vegetation community and non‐shrub growth forms were first mapped using aerial imagery and lidar data, then used as input in an automated mapping rule set that incorporates the percent cover rules of a field‐based mapping rule set. Results: The final vegetation map portrays the distribution of 19 vegetation communities across SCI, with the largest areas comprised of California Annual and Perennial Grassland (35%) and three types of coastal sage scrub and maritime succulent scrub, comprising a combined 53% of the area. Map accuracy was assessed to be 79% based on fuzzy methods and 61% with a traditional accuracy assessment. The accuracy of tree identification was assessed to be 81%, but species‐level tree accuracy was 45%. Conclusions: Semi‐automated approaches to vegetation community mapping can produce repeatable maps over large spatial extents that facilitate ecological management efforts. However, some low‐statured shrub community types were difficult to differentiate due to patchy canopies of co‐occurring species including abundant non‐native grasses characteristic of complex disturbance histories. Species‐level tree mapping accuracy was low due to the difficulty of identifying species within poorly illuminated canyons, resulting from sub‐optimal image acquisition timing. [ABSTRACT FROM AUTHOR]

Most plant communities consist of different structural and ecological subsets, ranging from cryptogams to different tree layers. The completeness and approach with which these subsets are sampled have implications for vegetation classification. Non‐vascular plants are often omitted or sometimes treated separately, referring to their assemblages as "synusiae" (e.g. epiphytes on bark, saxicolous species on rocks). The distinction of complete plant communities (phytocoenoses or holocoenoses) from their parts (synusiae or merocoenoses) is crucial to avoid logical problems and inconsistencies in the resulting classification systems. We here describe theoretical differences between the phytocoenosis as a whole and its parts, and outline consequences of this distinction for practice and terminology in vegetation classification. To implement a clearer separation, we call for modifications of the International Code of Phytosociological Nomenclature and the EuroVegChecklist. We believe that these steps will make vegetation classification systems better applicable and raise recognition of the importance of non‐vascular plants in the vegetation as well as their interplay with vascular plants. [ABSTRACT FROM AUTHOR]

Question: Understanding the mechanisms determining species composition is one of the central goals in ecology. The limestone landscape of southwestern China is one of the most widely known landscapes developed on carbonate bedrock in the world. We explored the following question: What are the patterns of species composition and phylogenetic structures of the communities developed on the acid and limestone soils? Location: Southwestern China. Methods: Using 536 plots on acid and limestone soils, we identified distinct vegetation types on both soil types based on hierarchical clustering and ordination. Variation partitioning partitioned the effects of climate, geographic distance and soil on species composition. Generalized linear models determined the effects of climate and soil on phylogenetic diversities. Results: The typical vegetation on acid soils is evergreen broad‐leaved forests characterized by Castanopsis species, while typical vegetation on limestone soils is mixed evergreen and deciduous forests characterized by a high proportion of deciduous trees and calciphytes. Soil type was much more important in driving species composition than climate and spatial distance. The standardized mean pairwise phylogenetic distance (ses.MPD) and mean nearest taxon distance (ses.MNTD) were significantly lower on limestone soils than on acid soils, indicating a more clustered phylogenetic structure. The effect of soil on ses.MPD was stronger than on ses.MNTD, and ses.MNTD increased with increasing annual temperature and precipitation seasonality on acid soils but decreased on limestone soils, suggesting that soil affected phylogenetic structure at relatively deep branches, while climate combined with soil mainly affects the relatively recent branches, as ses.MPD measures phylogenetic structure at deep branches and ses.MNTD that at shallow branches. Conclusions: This study highlights the importance of soil in shaping community structure at a regional scale, and it also reveals the unique features of limestone vegetation and supports the validity of limestone vegetation types in the Chinese vegetation classification system. [ABSTRACT FROM AUTHOR]

Aims: We examined all available literature and some unpublished data on the We examined all available literature and some unpublished data on the grasslands dominated by Lygeum spartum from Southern Europe and North Africa to produce a formalised classification of this vegetation and to identify the main factors determining its plant species composition. Location: Mediterranean Basin and Iberian Peninsula. Methods: We used a dataset of 728 relevés, which were resampled to reduce unbalanced sampling effort, resulting in a dataset of 568 relevés and 846 taxa. We classified the plots by TWINSPAN, interpreted the resulting pools, and used them to develop formal definitions of phytosociological alliances characterised by L. spartum vegetation. The definitions were included in an expert system to assist automatic vegetation classification. We related the alliances to climatic factors and described their biogeographical features and ecological preferences. The floristic relationships between these alliances were analysed and visualised using distance‐based redundancy analysis. Results: We defined eleven alliances of L. spartum vegetation, including the newly described Launaeo laniferae–Lygeion sparti from SW Morocco and the Noaeo mucronatae–Lygeion sparti from the Algerian highlands and NE Morocco. Biogeographical, climatic, and edaphic factors were revealed as putatively driving the differentiation between the alliances. Vegetations of clayey slopes and inland salt basins displayed higher variability in comparison with those of coastal salt marshes. Main conclusions: A comprehensive formal classification, accompanied by an expert system, of the grasslands from Southern Europe and North Africa dominated by Lygeum spartum vegetation was formulated. Eleven phytosociological alliances were recognised, whose plant species composition is influenced by biogeographic, climatic, and edaphic drivers. The expert system, containing formal definitions of the phytosociological alliances, will assist in identifying the syntaxonomic position of new datasets. [ABSTRACT FROM AUTHOR]

Quick identification of vegetation types in the field, based on species composition but not requiring time‐consuming plot sampling, is often needed for vegetation mapping, conservation assessment, teaching and other applications of vegetation classification. Here, we propose a new method that identifies the probability of belonging to the units of an established vegetation classification for vegetation stands encountered in the field. The method is based on calculating the probability that a few species observed in the field would co‐occur in a priori defined vegetation types, using the existing information on species occurrence frequency in these types. The method has been implemented in a freely available Android application called Probabilistic Vegetation Key, which makes it possible to employ it in the field using smartphones or tablets, even in the absence of internet access. [ABSTRACT FROM AUTHOR]

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 relevés) and environmental data set (29 variables × 87 relevés) 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 <0.001%, respectively. While fire explained little compositional variance overall, a separate db‐RDA analysis found that it may play an important pattern‐structuring role within Banksia Woodlands. Fine‐scale compositional patterns correspond only to a small extent to environmental data; the substantial unexplained variance may be due to slow‐acting neutral and stochastic processes. [ABSTRACT FROM AUTHOR]

Question: What are the differences between plant communities recognised using supervised versus un‐supervised methods? Location: Northeastern Australia. Methods: Two classifications of savanna plant communities were formed independently with two different approaches: supervised and un‐supervised (using agglomerative hierarchical clustering). Each approach used the same vegetation datasets and, importantly, classification criteria. The communities occur on two different landscapes, with differing environmental gradients, covering an area of 53,500 km2. We compared the internal characteristics of plant communities between approaches and landscapes using four evaluation criteria: identifiability, distinctiveness, similarity of internal heterogeneity and predictability of species foliage cover. Additionally, we compared the central floristic concepts and compositional boundaries of communities identified by each approach. Results: Supervised and un‐supervised approaches recognised similar floristic community concepts. Compositional boundaries between communities were similar on the landscape with steeper environmental gradients but significantly different on the landscape with gradual environmental gradients. However, communities distinguished using supervised methods were significantly less distinct and identifiable, worse at predicting species foliage cover and significantly more variable in species composition than those identified using un‐supervised methods. Conclusions: Using supervised rather than un‐supervised approaches to distinguish plant communities can result in less recognisable communities, possibly reducing their usefulness for land management planning. Importantly, we found a large disparity between the two approaches in delineating compositional boundaries between communities on landscapes with gradual environmental gradients. This is particularly relevant to communities in biomes such as the savanna which comprises 20% of the Earth's landmass. Ecologists can be more confident using a supervised approach on landscapes with steep environmental gradients but should target landscapes with gradual environmental gradients for un‐supervised classification. [ABSTRACT FROM AUTHOR]