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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]