Luca Malatesta

Sapienza University of Rome

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Andrea Catorci

Università di Camerino

Fabio Attorre

Sapienza University of Rome

Federico Maria Tardella

Università di Camerino

Michele De Sanctis

Sapienza University of Rome

Horacio Zeballos

Catholic University of Santa María

S. Cesaretti

Università di Camerino

Karina Piermarteri

Università di Camerino

J.L. Velasquez

Universitat Politècnica de Catalunya

Tullia Riccardi

Sapienza University of Rome

Marco Tavoloni

University of Urbino

Cooperative Institutions

Sapienza University of Rome

Eduardo Mondlane University

University of Bologna

Università di Camerino

Instituto de Investigação Agrária de Moçambique

University of Rome Tor Vergata

University of Belgrade

University of Tirana

Mendel University in Brno

California Institute of Technology

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CARTA DELLE MODIFICHE DI PAESAGGIO DEL PARCO NAZIONALE DEI MONTI SIBILLINI (1988-2015)

Andrea Catorci Marco Tavoloni Luca Malatesta

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Estimation of missing Ellenberg Indicator Values for tree species in South-eastern Europe: a comparison of methods

Ecological Indicators (2024)

Letizia Leccese Giuliano Fanelli Vito Emanuele Cambria Marco Massimi Fabio Attorre

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.

Indicator value

Imputation (statistics)

Collinearity

Environmental indicator

10.1016/j.ecolind.2024.111851

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Analisi diacronica dei cambiamenti del paesaggio pastorale d’alta quota dei Monti Sibillini (1988-2015) e loro possibili impatti sulla biodiversità e sugli habitat di interesse comunitario

Federico Maria Tardella Luca Malatesta Marco Tavoloni Andrea Catorci

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Le immagini satellitari ad alta risoluzione e la classificazione delle comunità vegetali per la creazione della carta della Vegetazione dell'isola di Socotra (Yemen)

DOAJ (DOAJ: Directory of Open Access Journals) (2012)

Luca Malatesta Fabio Attorre Michele De Sanctis Marcello Vitale Alfredo Altobelli

L'arcipelago di Socotra (Yemen), ospita una elevata diversita biologica, con caratteristiche uniche tali da renderlo un territorio rilevante per la conservazione della biodiversita a livello globale: l’alto livello di endemismo, soprattutto per le specie vegetali, lo colloca tra le isole piu importanti del mondo. Vegetation map by satellite imagery in Socotra (Yemen) The present study has produced a high resolution vegetation map of Socotra Island (Yemen) by combining vegetation classification with remote sensing analysis. The satellite data source was represented by two multi–temporal sets of RapidEye™ satellite images with a pixel resolution of 5 m and 5 spectral bands. More than 370 vegetation surveys, carried out with the phytosociological method and used to identify the main vegetation types, were used to obtain the training and evaluation sets. To produce the vegetation map, spectral signatures of the vegetation classes were obtained through a Gaussian mixture distribution model. A Sequential Maximum “a Posteriori” classification method was applied to take into account the heterogeneities in the signatures of some classes and the spatial pattern of the vegetation types. Post–classification sorting was performed to adjust the classification through various rule–based operations. A total of 28 classes were mapped with an accuracy greater than 80%. The resulting map and data will represent a fundamental tool for the elaboration of conservation strategies and the sustainable use of natural resources.

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Effects of long-term mowing on coenological composition and recovery routes of a Brachypodium rupestre-invaded community: insight into the restoration of sub-Mediterranean productive grasslands

Rendiconti lincei. Scienze fisiche e naturali (2018)

Federico Maria Tardella Luca Malatesta Irina Goia Andrea Catorci

Litter

Restoration Ecology

10.1007/s12210-018-0711-x

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Seasonal and annual variations of mountain glaciers surface velocity, implications for ice dynamic. Case study: Karakorum and Pamir.

EGU General Assembly Conference Abstracts (2017)

Ugo Nanni Dirk Scherler F. Ayoub Luca Malatesta Frédéric Herman

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BioNoMo: the Biodiversity Network of Mozambique

Rendiconti lincei. Scienze fisiche e naturali (2023)

Luca Malatesta Tereza Alves Fabio Attorre Denise Von Dolinger de Brito Silvio Cianciullo

Abstract Mozambique biodiversity richness plays a pivotal role to achieve the sustainable development of the country. However, Mozambique’s flora and fauna diversity still remains broadly unknown and poorly documented. To properly address this issue, one of the strategic needs expressed by the Mozambican institutions was the development of a national biodiversity data repository to aggregate, manage and make data available online. Thus, a sustainable infrastructure for the standardisation, aggregation, organisation and sharing of primary biodiversity data was developed. Named the “Biodiversity Network of Mozambique” (BioNoMo), such a tool serves as a national repository of biodiversity data and aggregates occurrence records of plants and animals in the country obtained from floristic and faunistic observations and from specimens of biological collections. In this paper, the authors present the structure and data of BioNoMO, including software details, the process of data gathering and aggregation, the taxonomic coverage and the WebGIS development. Currently, aggregating a total of 273,172 records, including 85,092 occurrence records of plants and 188,080 occurrence records of animals (41.2% terrestrial, 58,8% aquatic), BioNoMo represents the largest aggregator of primary biodiversity data in Mozambique and it is planned to grow further by aggregating new datasets.

10.1007/s12210-023-01144-6

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Finite Mixture Model-based classification of a complex vegetation system

Vegetation Classification and Survey (2020)

Fabio Attorre Vito Emanuele Cambria Emiliano Agrillo Nicola Alessi Marco Alfò

Aim : To propose a Finite Mixture Model (FMM) as an additional approach for classifying large datasets of georeferenced vegetation plots from complex vegetation systems. Study area : The Italian peninsula including the two main islands (Sicily and Sardinia), but excluding the Alps and the Po plain. Methods : We used a database of 5,593 georeferenced plots and 1,586 vascular species of forest vegetation, created in TURBOVEG by storing published and unpublished phytosociological plots collected over the last 30 years. The plots were classified according to species composition and environmental variables using a FMM. Classification results were compared with those obtained by TWINSPAN algorithm. Groups were characterized in terms of ecological parameters, dominant and diagnostic species using the fidelity coefficient. Interpretation of resulting forest vegetation types was supported by a predictive map, produced using discriminant functions on environmental predictors, and by a non‐metric multidimensional scaling ordination. Results : FMM clustering obtained 24 groups that were compared with those from TWINSPAN, and similarities were found only at a higher classification level corresponding to the main orders of the Italian broadleaf forest vegetation: Fagetalia sylvaticae, Carpinetalia betuli, Quercetalia pubescenti-petraeae and Quercetalia ilicis . At lower syntaxonomic level, these 24 groups were referred to alliances and sub-alliances. Conclusions : Despite a greater computational complexity, FMM appears to be an effective alternative to the traditional classification methods through the incorporation of modelling in the classificatory process. This allows classification of both the co-occurrence of species and environmental factors so that groups are identified not only on their species composition, as in the case of TWINSPAN, but also on their specific environmental niche. Taxonomic reference : Conti et al. (2005). Abbreviations : CLM = Community-level models; FMM = Finite Mixture Model; NMDS = non‐metric multidimensional scaling.

Vegetation Classification

Multidimensional scaling

Georeference

10.3897/vcs/2020/48518

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First woody cover vegetation map of Kruger National Park in 1939–1944: Evidence from historical black and white aerial photography

Ecological Informatics (2024)

Tullia Riccardi Benjamin J. Wigley L. Kleyn Corli Coetsee Sandra MacFadyen

Long-term spatial studies are crucial for understanding how the Earth's surface has changed. Before satellite imagery, landscapes were monitored using black and white (B&W) aerial photographs. However, surveys were infrequent and image analysis was a manual process that was both time-consuming and costly. In this study, we created a composite of high spatial resolution (0.5–0.75 m) B&W aerial images from 1939 to 1944, covering about 91% of Kruger National Park (KNP)'s nearly 2 million ha. We used this to produce the first historical woody cover (tall trees and shrubs) map of KNP, which until now was only partially understood through fragmented descriptions in period literature and small-area case studies. We established a supervised learning workflow using Google Earth Engine (GEE) which included performing an Object-based Image Analysis (OBIA) with a Random Forest classifier. This approach, enhanced by integrating texture, shape, neighboring features, and spectral variables into the training/validation dataset, enabled the identification of woody vegetation from B&W landscape objects. To enhance accuracy, we guided our sampling method using vegetation types with comparable woody cover and species composition. Initially, we tested our method on a smaller set of images (25 km2), and after confirming its effectiveness, we then expanded the approach to cover all available historical aerial imagery. Our results show that in 1939–1944, 26% of KNP was covered in woody vegetation (overall accuracy of 89%, producer's accuracy (non-woody = 88%, woody = 90%), and user's accuracy (non-woody = 90%, woody = 87%)). The importance of geological substrate in driving vegetation pattern is reflected in a higher woody cover percentage on granite (28%) than on basalt (21%) soils, with the lowest woody cover on northern basalts (11%) and the highest on north-central granites (32%). This study highlights the potential of GEE and OBIA for analyzing large-area, high spatial resolution B&W aerial photographs in a systematic and efficient manner and the importance of creating large-scale historical land cover baselines to support environmental planning and landscape management.

Aerial photography

White (mutation)

Vegetation Cover

Aerial photos

10.1016/j.ecoinf.2024.102590

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An updated checklist of Mozambique’s vascular plants

PhytoKeys (2022)

Délcio Odorico Enrico Domenico Giovanni Nicosia Castigo Datizua Clayton Langa Raquel Raiva

An updated checklist of Mozambique's vascular plants is presented. It was compiled referring to several information sources such as existing literature, relevant online databases and herbaria collections. The checklist includes 7,099 taxa (5,957 species, 605 subspecies, 537 varieties), belonging to 226 families and 1,746 genera. There are 6,804 angiosperms, 257 pteridophytes, and 38 gymnosperms. A total of 6,171 taxa are native to Mozambique, while 602 are introduced and the remaining 326 taxa were considered as uncertain status. The endemism level for Mozambique's flora was assessed at 9.59%, including 278 strict-endemic taxa and 403 near-endemic. 58.2% of taxa are herbaceous, while shrubs and trees account respectively for 26.5% and 9.2% of the taxa. The checklist also includes ferns (3.6%), lianas (1.7%), subshrubs (0.5%) and cycads (0.3%). Fabaceae, Poaceae and Asteraceae are the three most represented families, with 891, 543 and 428 taxa, respectively. The extinction risk of 1,667 taxa is included, with 158 taxa listed as Vulnerable, 119 as Endangered and as 24 Critically Endangered. The geographical distribution, known vernacular names and plants traditional uses are also recorded.

Herbarium

Subspecies

Endemism

Vascular plant

Pteridophyte

Data deficient

10.3897/phytokeys.189.75321

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Citations (14)