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La redazione di una flora richiede enormi sforzi da parte del florista, spesso con risultati incompleti. Le flore sono generalmente redatte conducendo le attività di campo senza alcun protocollo quantitativo standardizzato, ma secondo criteri soggettivi, che scaturiscono da una combinazione di abilità, esperienza e intuito del florista (il cosìddetto “algoritmo del botanico” sensu Palmer et al. 2002). Se da un lato questo approccio aiuta a massimizzare il numero di taxa rilevati sul campo, dall’altro le flore così prodotte possono differire sensibilmente in quantità e qualità dei dati raccolti. Il passaggio verso un tipo di campionamento probabilistico – anziché opportunistico – è dunque auspicabile per poter (i) applicare analisi statistiche rigorose e comparabili, (ii) confrontare flore di regioni e periodi diversi (Chiarucci, Palmer 2006) e (iii) facilitare la riproducibilità e verificabilità di metodi e risultati. Lo scopo di questo lavoro è quindi quello di sperimentare l’introduzione di approcci probabilistici nella ricerca floristica e misurarne l’efficacia.

Spatio-ecological heterogeneity is strongly linked to many ecological processes and functions such as plant species diversity patterns and change, metapopulation dynamics, and gene flow. Remote sensing is particularly useful for measuring spatial heterogeneity of ecosystems over wide regions with repeated measurements in space and time. Besides, developing free and open source algorithms for ecological modelling from space is vital to allow to prove workflows of analysis reproducible. From this point of view, NASA developed programs like the Surface Biology and Geology (SBG) to support the development of algorithms for exploiting spaceborne remotely sensed data to provide a relatively fast but accurate estimate of ecological properties in vast areas over time. Most of the indices to measure heterogeneity from space are point descriptors : they catch only part of the whole heterogeneity spectrum. Under the SBG umbrella, in this paper we provide a new R function part of therasterdivR package which allows to calculate spatio-ecological heterogeneity and its variation over time by considering all its possible facets. The new function was tested on two different case studies, on multi- and hyperspectral images, proving to be an effective tool to measure heterogeneity and detect its changes over time.

Ecosystem heterogeneity has been widely recognized as a key ecological feature, influencing several ecological functions, since it is strictly related to several ecological functions like diversity patterns and change, metapopulation dynamics, population connectivity, or gene flow.In this paper, we present a new R package - rasterdiv - to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns.The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms.

Predicting the occurrence probability of species is intrinsically dependent on the quality of the training dataset and, in particular, on the sample prevalence (i.e., the ratio between presences and absences). Whenever the number of presences and absences is not equal within the training dataset, the predictions deviate towards higher values as the sample prevalence increases and vice versa. As a result, probability models of species occurrence with different sample prevalence cannot be directly compared. The favourability concept was introduced to amend this limitation. Indeed, the favourability - i.e., the variation in the probability of occurrence regardless the sample prevalence - could reduce the degree of uncertainty when comparing species distributions despite different sample prevalences. To test this hypothesis, we simulated 50 virtual species and compared the predictive performance of four probability-based and favourability-based Species Distribution Models (GLM, GAM, RF, BRT) under a set of different prevalence values and sampling strategies (i.e, random and stratified sampling). Favourability-based models performed slightly better than probability-based models in predicting the species distribution over geographic space, confirming also their capability to reduce the variability of the predictions across different degrees of sample prevalence.

With the aim to identify future challenges and opportunities in vegetation science, we brought together a group of 22 early career vegetation scientists from diverse backgrounds to perform a horizon scan. In this contribution, we present a selection of 15 topics that were ranked by participants as the most emergent and impactful for vegetation science in the face of global change. We highlight methodological tools that we expect will play a critical role in resolving emerging issues by providing ways to unveil new aspects of plant community dynamics and structure. These tools include next generation sequencing, plant spectral imaging, process-based species distribution models, resurveying studies and permanent plots. Further, we stress the need to integrate long-term monitoring, the study of novel ecosystems, below-ground traits, pollination interactions and global networks of near-surface microclimate data at fine spatio-temporal resolutions to fully understand and predict the impacts of climate change on vegetation dynamics. We also emphasize the need to integrate traditional forms of knowledge and a diversity of stakeholders into research, teaching, management and policy-making to advance the field of vegetation science. The conclusions reached by this horizon scan naturally reflect the background, expertise and interests of a representative pool of early career vegetation scientists, which should serve as basis for future developments in the field., We would like to thank the IAVS for their support in organizing the workshop in October. FAY is grateful to J. Jeschke for his suggestions and advice on logistics for the horizon scan. FAY would like to thank the support of the Rising Star Junior Fellowship in the Department of Biology, Chemistry and Pharmacy of the Freie Universität and specially the women’s representative office at the Department of Biology, Chemistry and Pharmacy of the Freie Universität (Frauenbeauftragte BCP) for providing financial support for childcare during Germany’s COVID-19 lockdowns. Without this help, working on this manuscript would have not been possible during these hard times. We acknowledge PhD grants from Fundação de Amparo à Pesquisa do Estado de São Paulo - FAPESP (2018/09054-0) to GD, from Conselho Nacional do Desenvolvimento Científico e Tecnológico- CNPq (141715/2018-9) to JT, from an Indonesia Endowment Fund for Education (LPDP) Scholarship to MJMH, from the Ministry of Education, Science and Technological Development of the Republic of Serbia granted to TŠ, from the Slovenian Research Agency (ARRS) to FK and from Mexico’s CONACyT (CVU: 348078) to CXPV

Aims: i) To disentangle the global patterns of native and alien plant diversity on coastal sand dune ecosystems across habitats and floristic kingdoms, ii) to determine the main drivers of variation in species richness in native and alien species in these endangered ecosystems, and iii) to test for an interaction between spatial scale and native‐alien richness patterns, as predicted by the invasion paradox. Location: Global. Methods: We collated a dataset of 14,841 vegetation plots in coastal sand dune ecosystems from around the world. Generalized Linear Models (GLMs) and Generalized Linear Mixed Models (GLMMs) were used to assess the patterns and main ecological determinants underlying native and alien species richness. Variation partitioning revealed the relative importance of environmental and anthropogenic variables. Results: GLMs revealed strong differences among both habitats and floristic kingdoms in the number of native and alien species. Specifically, native species richness increased along the sea‐inland gradient and was higher in the Cape and Paleotropical kingdoms. In contrast, alien species richness was relatively similar across habitats and kingdoms, though some differences were detected. There were strong differences between the drivers of native and alien richness; anthropogenic factors such as Gross Domestic Product were positively associated with alien richness whereas native richness was more strongly related to environmental factors. Furthermore, we found a weak support for an invasion paradox effect. Conclusions: Our results revealed the complexity of causal processes underpinning coastal sand dune plant biodiversity and highlight the importance of considering native and alien species separately. Recognition of these differences while researching variation in biodiversity patterns and processes at multiple spatial scales will lead to a better mechanistic understanding of the causes of invasion worldwide, and in coastal ecosystems in particular, allowing the development of more focused control and management measures.

Questions: Despite the increasing scientific interest in distance decay of compositional similarity in ecology, the scale-dependence of geographical vs environmental control on distance decay of biological communities has not been properly addressed so far. The present work highlights the relative importance of niche-based processes vs dispersal limitations on distance decay patterns of epilithic bryophyte assemblages at different spatial scales. Location: Serra de Sintra, central Portugal. Methods: We adopted a nested sampling design with 32 selected sampling sites in each of which two clusters, each with five rocks, were surveyed. Each cluster was characterized by a set of 15 macro-scale variables, which were divided into environmental and anthropogenic. For each rock eight micro-scale variables were recorded. Partial Mantel tests were used to assess the relative importance of geographical and environmental distance on community dissimilarity for each grain size (site, cluster, rock). Quantile regressions were used to describe the decay patterns of community similarity with respect to geographical and environmental distances. Ordination analyses and variation partitioning techniques were applied to assess the pure and shared effects of measured variables on bryophyte community composition. Results: Environmental distance based upon macro-scale predictors was significantly correlated to community similarity, while no significant correlation was found for ecological distance calculated for micro-scale predictors, except at the largest grain size. The decrease of community similarity with geographical and environmental distance was thus consistently strengthened with increasing sample grain. Compositional variation was best explained by anthropogenic variables. Conclusions: The relative importance of environmental vs geographical distance on compositional similarity in epilithic bryophyte communities varies with the spatial scale of the predictors and with the sample grain. The decrease of similarity with increasing distance is related to changes in habitat features, especially those driven by human disturbance, while is weakly affected by variations in substrate features.

Mapping the distribution of invasive species under current and future climate conditions is crucial to implement sustainable and effective conservation strategies. Several studies showed how invasive species may benefit from climate change fostering their invasion rate and, consequently, affecting the native species community. In the Canary Islands and on Tenerife in particular, previous research mostly focused on climate change impacts on the native communities, whereas less attention has been paid on alien species distribution under climate change scenarios. In this study, we modelled the habitat distribution of Pennisetum setaceum, one of the most invasive alien species on Tenerife. In addition, we described the species’ potential distribu- tion shift in the light of two climate change scenarios (RCP2.6, RCP8.5), highlighting the areas that should be prioritized during management and eradication programs. P. setaceum’s suitable areas are located in the coastal area, with higher habitat suitability near cities and below 800 m asl. In both future climate change scenarios, the geographic distribution of P. setaceum suitable areas is characterized by an eleva- tional shift, which is more pronounced in the RCP8.5 scenario. Despite being drought resistant, water supply is crucial for the species’ seed germination, thus supporting future species’ shift to higher elevation and in the north–north–west part of the island, where it could benefit from the combined effect of orographic precipitations and humidity carried by trade winds.

Environmental DNA (eDNA) metabarcoding and metagenomics analyses can improve taxonomic resolution in biodiversity studies. Only recently, these techniques have been applied in aerobiology, to target bacteria, fungi and plants in airborne samples. Here, we present a nine-month aerobiological study applying eDNA metabarcoding in which we analyzed simultaneously airborne diversity and variation of fungi and plants across five locations in North and Central Italy. We correlated species composition with the ecological characteristics of the sites and the seasons. The most abundant taxa among all sites and seasons were the fungal genera Cladosporium, Alternaria, and Epicoccum and the plant genera Brassica, Corylus, Cupressus and Linum, the latter being much more variable among sites. PERMANOVA and indicator species analyses showed that the plant diversity from air samples is significantly correlated with seasons, while that of fungi varied according to the interaction between seasons and sites. The results consolidate the performance of a new eDNA metabarcoding pipeline for the simultaneous amplification and analysis of airborne plant and fungal particles. They also highlight the promising complementarity of this approach with more traditional biomonitoring frameworks and routine reports of air quality provided by environmental agencies.

Networks of protected areas are fundamental for biodiversity conservation, but many factors determine their conservation efficiency. In particular, on top of other human-driven disturbances, invasions by non-native species can cause habitat and biodiversity loss. Jointly understanding what drives patterns of plant diversity and of non-native species in protected areas is therefore a priority. We tested whether the richness and composition of native and non-native plant species within a network of protected areas follow similar patterns across spatial scales. Specifically, we addressed three questions: (a) what is the degree of congruence in species richness between native and non-native species? (b) do changes in the composition of non-native species across ecological gradients reflect a similar turnover of native species along the same gradients ? (c) what are the main environmental and human disturbance drivers controlling species richness in these two groups of species? Species richness and composition of native and non-native plant species were compared at two spatial scales: the plot scale (10m × 10m) and the Protected Area scale (PA). In addition, we fit Generalized Linear Models to identify the most important drivers of native and non-native species richness at each scale, focusing on environmental conditions (climate, topography) and on the main sources of human disturbance in the area (land use and roads). We found a significant positive correlation between the turnover of native and non-native species composition at both plot and PA scales, whereas their species richness was only correlated at the larger PA scale. The lack of congruence between the richness of native and non-native species at the plot scale was likely driven by differential responses to fine scale environmental factors, with non-natives favoring drier climates and milder slopes (climate and slope). In addition, more non-native species were found closer to road-ways in the reserve network. In contrast, the congruence in the richness of native and non-native species at the broader PA scale was mainly driven by the common influence of PA area, but also by similar responses of the two groups of species to climatic heterogeneity. Thus, our study highlights the strong spatial dependence of the relationship between native and non-native species richness and of their responses to environmental variation. Taken together, our results suggest that within the study region the introduction and establishment of non-native species would be more likely in warmer and dryer areas, with high native species richness at large spatial scale but intermediate levels of anthropogenic disturbances and mild slope inclinations and elevation at fine scale. Such an exhaustive understanding of the factors that influence the spread of non-native species, especially in networks of protected areas is crucial to inform conservation managers on how to control or curb non-native species.

In temperate climates, vegetation dynamics can dominate the fate of the badlands. In fact, in absence of disturbances such a grazing and burning, plant encroachment can condemn badlands to disappear. Thus, this type of badlands is either formed and developed under more arid conditions or due to some sort of mismanagement and misuse. This study provides an update on the denudation rate on this type of badlands (for a biancana badland in Central Italy, close to the ancient town of Siena). The result shows a denudation rate of 2 cm a−1. Hence, the biancana site resulted to have been formed starting from the XIV century. The climatic conditions were similar to the present one, even if cooler and wetter. Hence, plants could have easily colonized the erosion scars indicating that factors controlling the badland origin should be searched elsewhere. Anthropic action was explored with a deep insight into the medieval period. It resulted that (1) the plague depopulation (which kept peasant population very low for more than 50 years), (2) the change of the social structure in charge of the countryside management and (3) the need for the Siena republic to attract peasants concurred to the creations of favourable circumstances for the badlands formation from previous pastures and croplands. Similar factors can hold for many badlands, following local history.

I recenti sviluppi nel campo dell’informatizzazione e conservazione della biodiversità stanno portando allo sviluppo di nuovi sistemi informatici, che rendono possibile una efficiente raccolta, modifica, analisi e condivisione di dati e metadati. In quest’ottica è stato sviluppato un geodatabase che organizza dati di flora e vegetazione dell’area di Trieste, provenienti da diversi dataset e raccolti nell’ambito di vari progetti del Dipartimento di Scienze della Vita dell’Università di Trieste. A questi sono stati aggiunti i dati sul verde pubblico e sulle specie arboree ottenuti dal Comune di Trieste. I dataset sono stati standardizzati per sistema di riferimento e tassonomia, e successivamente integrati in un database PostgreSQL, abbinato all’estensione spaziale PostGIS. Il geodatabase, interfacciato con un sistema GIS, è risultato un indispensabile strumento per la visualizzazione dei dati e per la loro analisi spaziale testo.

Spatially-explicit dataset of plant species occurrences collected in the Province of Siena (Central Italy) is analysed, with the aim of investigating a) the relative role of environmental factors in shaping spatial patterns of plant species richness, and b) how the spatial scale at which predictors have been sampled determines the explicative power of species richness models. The optimal spatial resolution of analysis was evaluated with respect to the total deviance explained by models, using a set of environmental and remotely sensed derived predictors calculated at different spatial scales. Results confirm the hypothesis that the predictive power of landscape structure is influenced by the spatial scale at which predictor variables have been sampled. Furthermore, the relevance of identifying a proper geographical scale of investigation, hence minimizing the redundancy in the predictor variables and maximising the explanatory power of the single groups of predictor variables, is highlighted as well.