Although there is a general consensus on the distribution and ecological features of terrestrial biomes, the allocation of alpine ecosystems in the global biogeographic system is still unclear. Here, ...we delineate a global map of alpine areas above the treeline by modelling regional treeline elevation at 30 m resolution, using global forest cover data and quantile regression. We then used global datasets to 1) assess the climatic characteristics of alpine ecosystems using principal component analysis, 2) define bioclimatic groups by an optimized cluster analysis and 3) evaluate patterns of primary productivity based on the normalized difference vegetation index. As defined here, alpine biomes cover 3.56 Mkm2 or 2.64% of land outside Antarctica. Despite temperature differences across latitude, these ecosystems converge below a sharp threshold of 5.9°C and towards the colder end of the global climatic space. Below that temperature threshold, alpine ecosystems are influenced by a latitudinal gradient of mean annual temperature and they are climatically differentiated by seasonality and continentality. This gradient delineates a climatic envelope of global alpine biomes around temperate, boreal and tundra biomes as defined in Whittaker's scheme. Although alpine biomes are similarly dominated by poorly vegetated areas, world ecoregions show strong differences in the productivity of their alpine belt irrespectively of major climate zones. These results suggest that vegetation structure and function of alpine ecosystems are driven by regional and local contingencies in addition to macroclimatic factors.
Levels of plant invasions in different habitat types were assessed in several regional studies, but few of them were from the Mediterranean. Here we compare the levels of vascular plant invasion ...across habitats and plant communities of Sicily. We used a large dataset of plant species presences/absences in vegetation plots to analyze the invasion patterns across habitats considering biogeography, life form and phenology of alien plants. Vegetation plots were classified based on the EUNIS classification of European habitats. The invasiveness of each species was expressed in terms of its absolute and percentage frequency. Representation of different life forms and phenological patterns was compared between alien and native species. The fidelity of alien species to individual habitats was calculated using the
phi
coefficient. Our analysis shows that annual and woody species are the most represented life forms in the alien flora of Sicily and that alien species tend to have a longer flowering period than the native species. The investigated habitats differed strongly in their level of invasion by alien species, ranging from 0 to 15.6% of aliens of all species recorded. Most of the habitats were colonized by very few alien species or completely lacked them, except for sandy coasts, naturally-disturbed riverbeds, and synanthropic habitats. It must be noted, however, that the number of alien species occurring in a given habitat does not relate to the severity of the impact of invasion in that habitat. Some habitats are invaded by few (or single) species, which attain a high cover, transforming the whole ecosystem. The habitat-based approach proved to be suitable for evaluating the habitat specificity and frequency of alien species at a regional scale, improving the capacity for risk assessment in different ecological contexts.
In the light of the “Biological Diversity” concept, habitats are cardinal pieces for biodiversity quantitative estimation at a local and global scale. In Europe EUNIS (European Nature Information ...System) is a system tool for habitat identification and assessment. Earth Observation (EO) data, which are acquired by satellite sensors, offer new opportunities for environmental sciences and they are revolutionizing the methodologies applied. These are providing unprecedented insights for habitat monitoring and for evaluating the Sustainable Development Goals (SDGs) indicators. This paper shows the results of a novel approach for a spatially explicit habitat mapping in Italy at a national scale, using a supervised machine learning model (SMLM), through the combination of vegetation plot database (as response variable), and both spectral and environmental predictors. The procedure integrates forest habitat data in Italy from the European Vegetation Archive (EVA), with Sentinel-2 imagery processing (vegetation indices time series, spectral indices, and single bands spectral signals) and environmental data variables (i.e., climatic and topographic), to parameterize a Random Forests (RF) classifier. The obtained results classify 24 forest habitats according to the EUNIS III level: 12 broadleaved deciduous (T1), 4 broadleaved evergreen (T2) and eight needleleaved forest habitats (T3), and achieved an overall accuracy of 87% at the EUNIS II level classes (T1, T2, T3), and an overall accuracy of 76.14% at the EUNIS III level. The highest overall accuracy value was obtained for the broadleaved evergreen forest equal to 91%, followed by 76% and 68% for needleleaved and broadleaved deciduous habitat forests, respectively. The results of the proposed methodology open the way to increase the EUNIS habitat categories to be mapped together with their geographical extent, and to test different semi-supervised machine learning algorithms and ensemble modelling methods.
Ecological theory predicts close relationships between macroclimate and functional traits. Yet, global climatic gradients correlate only weakly with the trait composition of local plant communities, ...suggesting that important factors have been ignored. Here, we investigate the consistency of climate-trait relationships for plant communities in European habitats. Assuming that local factors are better accounted for in more narrowly defined habitats, we assigned > 300,000 vegetation plots to hierarchically classified habitats and modelled the effects of climate on the community-weighted means of four key functional traits using generalized additive models. We found that the predictive power of climate increased from broadly to narrowly defined habitats for specific leaf area and root length, but not for plant height and seed mass. Although macroclimate generally predicted the distribution of all traits, its effects varied, with habitat-specificity increasing toward more narrowly defined habitats. We conclude that macroclimate is an important determinant of terrestrial plant communities, but future predictions of climatic effects must consider how habitats are defined.
Understanding and explaining the use of green spaces and forests is challenging for sustainable urban planning. In recent years there has been increasing demand for novel approaches to investigate ...urban green infrastructure by capitalizing on large databases from existing citizen science tools. In this study, we analyzed iNaturalist data to perform an assessment of the intentional use of these urban spaces for their value and to understand the main drivers. We retrieved the total number of observations obtained across a set of 672 European cities and focused on reporting from mapped green areas and forests. We used two separate multivariate explanatory models to investigate which factors explained variations in the number of observations for green areas and forests. We found a relatively heterogeneous use of these two urban green spaces. Gross domestic product was important in explaining the number of visits. Availability and accessibility also had positive relationships with the use of green areas and forests in cities, respectively. This study paves the way for better integration of citizen science data in assessing cultural services provided by urban green infrastructure and therefore in supporting the evaluation of spatial planning policies for the sustainable development of urban areas.
Neophyte invasions in European grasslands Axmanová, Irena; Kalusová, Veronika; Danihelka, Jiří ...
Journal of vegetation science,
March/April 2021, 2021-03-00, 20210301, 2021-03, Letnik:
32, Številka:
2
Journal Article
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Questions
The human‐related spread of alien plants has serious environmental and socioeconomic impacts. Therefore, it is important to know which habitats are most threatened by invasion and why. We ...studied a wide range of European grasslands to assess: (a) which alien species are the most successful invaders in grasslands; (b) how invasion levels differ across European regions (countries or their parts) and biogeographical regions; and (c) which habitat types are the most invaded.
Location
Europe.
Methods
We selected 97,411 grassland vegetation plots from the European Vegetation Archive (EVA) and assigned a native or alien status to each of the 8,212 vascular plant species found in these plots. We considered only neophytes (alien species introduced after 1500 AD), which we further divided according to their origin. We compared the levels of invasion using relative neophyte richness in the species pool, relative neophyte richness and cover per plot, and percentages of invaded plots among regions and habitats.
Results
Only 536 species, representing 6.5% of all grassland vascular plant species, were classified as neophytes. These were mostly therophytes or hemicryptophytes with low habitat specificity. Most of them were present in very few plots, while only three species were recorded in more than 1% of all plots (Onobrychis viciifolia, Erigeron annuus and Erigeron canadensis). Although invasion levels were generally low, we found more invaded plots in the Boreal and Continental regions. When considering only non‐European neophytes, the Pannonian region was the most invaded. Among different grassland habitats, sandy grasslands were most invaded, and alpine and oromediterranean grasslands least invaded.
Conclusions
In general, natural and semi‐natural European grasslands have relatively low levels of neophyte invasions compared with human‐made habitats or alluvial forests, as well as with grasslands on other continents. The most typical neophytes invading European grasslands are species with broad ecological niches.
We provide the first overview of neophyte invasion patterns in European grasslands based on the most comprehensive data set of vegetation plots existing to date. In general, natural and semi‐natural European grasslands have relatively low levels of neophyte invasions compared with European man‐made habitats, riparian vegetation or with grasslands on other continents. The most typical neophytes are therophytes with broad niches.
Mozambique and Italy share a history of academic cooperation spanning almost half a century. The topical collection “Environment, biodiversity and health in university scientific cooperation in ...Mozambique” stems from the desire to collect the scientific progress achieved through this alliance. Research papers in the collection cover themes including biodiversity conservation for the sustainable use of natural resources, diagnostics and molecular epidemiology of genetic and infectious diseases, and the anthropogenic impact on the environment under the one health principle. The sustainable growth of a country depends, to a large extent, on the establishment of solid research capacity, ensuring the ownership and full involvement of local institutions. The availability of adequate scientific research frameworks is critical to guarantee the integrated conservation of the ecological, socio-economic and cultural value of biodiversity. The works published within this collection emphasize the importance of international cooperation in scientific research.
•Estimating missing Ellenberg Indicator Values (EIV) could help plant ecology studies.•We tested and compared several methods for estimating missing EIV from existing data.•Multiple Linear Regression ...and k-Nearest Neighbour performed better than the others.•Statistical methods are more effective than imputation based on expert knowledge.•This approach would greatly facilitate monitoring species with unknown EIV.
Ellenberg indicator values (EIV) are widely used in vegetation ecology, but the values for many species in Southeastern Europe are not available due to incomplete knowledge of their ecology: it is therefore of paramount importance to estimate missing values in existing databases. The entire EIV set for a single species can be missing or a single EIV can be missing for species for which other indicator values are available. Our aim here is to provide a simple method to impute missing values for species who have missing data in a single or multiple EIV. For this purpose, we adopt a multiple imputation procedure and compare a number of imputation methods on the basis of two datasets: i) “indices”, the set of 9 Ellenberg indicators taken from literature, available for 10,824 species and ii) “vegetation”, a set describing the physical and climatic characteristics (Light, Temperature, Continentality, Soil moisture, Nitrogen, Soil pH, Hemeroby index, Humidity, Organic_matter) of 29,935 relevés from Southeastern Europe where at least one tree species is present. The imputation methods we considered are: k-Nearest Neighbour, multiple linear regression (with or without collinearity correction), Reprediction Algorithm, Weighted Averaging (WA) and Weighted Averaging Partial Least Squares (WAPLS) regression. The different methods of imputation were compared by looking at the output produced and its deviation from the “true” observed values for a set of species with known EIVs. We have considered a set of species with known EIVs and proceeded to multiple imputation using the methods above; as a measure of performance we adopted the mean squared error (MSE) estimate, and expert judgement of ecological consistency. Models based on Regression and k-Nearest Neighbour seem to outperform the others. On the contrary, Reprediction algorithm in its different forms: produced less satisfactory results.
Imputation of missing values is generally based on expert knowledge or on some variant of weighted averaging (also known as Hill’s method). Here we show that other methods may be more effective and should be appropriately considered by vegetation scientists, since those may allow the application of EIVs in other biogeographic regions.
Analysis of land cover dynamics in Mozambique (2001–2016) Cianciullo, Silvio; Attorre, Fabio; Trezza, Francesca Romana ...
Atti della Accademia nazionale dei Lincei. Rendiconti Lincei. Scienze fisiche e naturali,
03/2023, Letnik:
34, Številka:
1
Journal Article
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Land cover change (LCC) is a complex and dynamic process influenced by social, economic, and biophysical factors that can cause significant impacts on ecological processes and biodiversity ...conservation. The assessment of LCC is particularly relevant in a country like Mozambique where livelihood strongly depends on natural resources. In this study, LCC was assessed using a point-based sampling approach through Open Foris Collect Earth (CE), a free and open-source software for land assessment developed by the Food and Agriculture Organization of the United Nations. This study aimed to conduct an LCC assessment using CE for the entire Mozambique, and according to three different land classifications: administrative boundaries (provinces), ecoregions, and protected vs unprotected areas. A set of 23,938 randomly selected plots, with an area of 0.5 hectares, placed on a 4 × 4 km regular grid over the entire country, was assessed using CE. The analysis showed that Mozambique has gone through significant loss of forest (− 1.3 Mha) mainly to the conversion to cropland. Deforestation is not occurring evenly throughout the country with some provinces, such as Nampula and Zambezia, characterized by higher rates than others, such as Gaza and Niassa. This result can be explained considering a combination of ecological and socio-economic factors, as well as the conservative role played by the protected areas. Our study confirmed that LCC is a complex phenomenon, and the augmented visual interpretation methodology can effectively complement and integrate the LCC analyses conducted using the traditional wall-to-wall mapping to support national land assessment and forest inventories and provide training data for environmental modeling.
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