Nomenclature adjustments and new syntaxa of the arctic, alpine and oro-Mediterranean vegetation

Proposte sintassonomiche e nomenclaturali per la vegetazione della Tundra alpina. Il capitolo riguardante l'alta quota appenninica è il più corposo e propone il nuovo syntaxon Leontopodio-Elynion a livello di alleanza. La grande novità sta nel fatto che questa alleanza è da considerarsi anfi-Adriatica e unsice Appennino e balcani, differenziandosi dall'alleanza già definita da altri per le Alpi. i Pirenei e i Carpazi.During preparation of the European checklist of vegetation units (EuroVegChecklist), it became clear that some earlier described syntaxa need to be typified in order to stabilize nomenclature and some new syntaxa need to be described. Here we propose nomenclature adjustments and formal description of four new alliances for the Arctic, alpine and oro-Mediterranean vegetation of Europe, Greenland and Anatolia. First, we typify the class Juncetea trifidi. Second, we describe four new alliances, such as the Puccinellion nuttallianae (Low-Arctic salt steppes of Greenland; class Saxifrago tricuspidatae-Calamagrostietea purpurascentis), Dryado octopetalae- Caricion arctisibiricae (Arctic tundra vegetation of north-eastern European Russia; class Carici rupestris-Kobresietea bellardii), Leontopodio nivalis-Elynion myosuroidis (southern European alpine tundra vegetation; class Carici rupestris-Kobresietea bellardii) and Lagotido uralensis-Caricion ensifoliae (alpine tundra vegetation of the Southern Ural Mountains; class Juncetea trifidi). Two new associations are described within the first two of these alliances. Finally, we present an interpretation of the alliance Muscario-Scillion nivalis

Ecology, biodiversity and mining: Science and solving the challenges

Mining has an impact on the ecology and biodiversity of an area, and this impact is required to be mitigated under regulatory requirements. The mitigation is achieved by applying measures, deeply rooted in scientific knowledge, of the functioning of the impacted (and rehabilitated) ecosystems and the biota supported by these ecosystems. This paper focusses on selected issues of the pre-mining ecology and biodiversity surveys and the scientific basis of regulatory requirements. Using Western Australian experience and drawing on comparisons from other countries, this paper addresses two challenges. The first is the classification of vegetation of mining tenements to inform on the variability of vegetation using a series of data-analytical experiments. The paper reveals flaws in the current practise aimed at recognition and description of plant communities in vegetation surveys. Concrete steps serving and enhancing the clarity and plausibility of regulatory tools (guidance manuals) are identified. It also advises on what we should do if we find discrepancies between the regulatory expectations and the level they reflect current scientific knowledge. The second is the comparative and spatial aspects of the identification of communities of conservation interest. The paper further identifies the missing vital information of the vegetation mapping procedures and briefly analyses the need for tools assisting assessment of conservation value of the vegetation classifications, including the building of a centralised vegetation database, formulation of vegetation and habitat classification systems, and construction of scientifically sound and ecologically informative vegetation maps at various scales of complexity

Gazania lanata and G. splendidissima: Two new species of Asteraceae (tribe Arctotideae) from the Greater Capensis, with an updated key for the genus

Two new species of Gazania Gaert. (Asteraceae, tribe Arctotideae) from the Greater Cape Floristic Region of South Africa are described. Gazania lanata Magee & Boatwr., a local endemic of dolomite lenses near Robertson, is distinguished by the spatulate to sublyrate, simple or weakly pinnatilobed leaves, the bristle-like hairs along the inflorescence and the densely lanate vestiture of the young growth and involucre. Gazania splendidissima Mucina, Magee & Boatwr. from the arid Namaqualand coast between Port Nolloth and Hondeklipbaai, is distinguished by the suffrutescent habit, semi-succulent leaves with a densely mealy uppermost surface, and prominently maculate involucre. An updated key to the now 18 recognised Gazania species is provided to facilitate identification of these and existing taxa in the genus

Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification

Vegetation maps are essential tools for the conservation and management of landscapes as they contain essential information for informing conservation decisions. Traditionally, maps have been created using field-based approaches which, due to limitations in costs and time, restrict the size of the area for which they can be created and frequency at which they can be updated. With the increasing availability of satellite sensors providing multi-spectral imagery with high temporal frequency, new methods for efficient and accurate vegetation mapping have been developed. The objective of this study was to investigate to what extent multi-seasonal Sentinel-2 imagery can assist in mapping complex compositional classifications at fine spatial scales. We deliberately chose a challenging case study, namely a visually and structurally homogenous scrub vegetation (known as kwongan) of Western Australia. The classification scheme consists of 24 target classes and a random 60/40 split was used for model building and validation. We compared several multi-temporal (seasonal) feature sets, consisting of numerous combinations of spectral bands, vegetation indices as well as principal component and tasselled cap transformations, as input to four machine learning classifiers (Support Vector Machines; SVM, Nearest Neighbour; NN, Random Forests; RF, and Classification Trees; CT) to separate target classes. The results show that a multi-temporal feature set combining autumn and spring images sufficiently captured the phenological differences between the classes and produced the best results, with SVM (74%) and NN (72%) classifiers returning statistically superior results compared to RF (65%) and CT (50%). The SWIR spectral bands captured during spring, the greenness indices captured during spring and the tasselled cap transformations derived from the autumn image emerged as most informative, which suggests that ecological factors (e.g. shared species, patch dynamics) occurring at a sub-pixel level likely had the biggest impact on class confusion. However, despite these challenges, the results are auspicious and suggest that seasonal Sentinel-2 imagery has the potential to predict compositional vegetation classes with high accuracy. Further work is needed to determine whether these results are replicable in other vegetation types and regions

Semi-automated segment generation for geographic novelty detection using edge and area metrics

An approach to generating accurate image segments for land-cover mapping applications is to model the process as an optimisation problem. Area-based empirical discrepancy metrics are used to evaluate instances of generated segments in the search process. An edge metric, called the pixel correspondence metric (PCM), is evaluated in this approach as a fitness function for segmentation algorithm free-parameter tuning. The edge metric is able to converge to user-provided reference segments in an earth observation mapping problem when adequate training data are available. Two common metaheuristic search functions were tested, namely particle swarm optimisation (PSO) and differential evolution (DE). The edge metric is compared with an area-based metric, regarding classification results of the land-cover elements of interests for an arbitrary problem. The results show the potential of using edge metrics, as opposed to area metrics, for evaluating segments in an optimisation-based segmentation algorithm parameter-tuning approach

Classification of estuaries in the Ciskei and Transkei regions based on physical and botanical characteristics

For the first time a comprehensive botanical survey has taken place in the Ciskei and Transkei estuaries, Eastern Cape Province, South Africa. In total 54 plant species were found in the 92 estuaries surveyed. These plants could be divided into the following five habitat complexes; reed and sedge beds (23 species), salt marsh (20 species), mangrove forest (4 species), macroalgal assemblages (3 species) and swamp forest (4 species). Ordination showed that salinity and depth were important in influencing the distribution of species. Salinity separated salt marsh from reed and sedge species and depth separated mangrove and associated species (e.g. Zostera capensis, Halophila ovalis and Acrostichum aureum) from reed and sedge species.Based on the plant species composition the estuaries could be divided into those that were permanently open versus those that were temporarily open/closed. The characteristic habitat complexes for the permanently open estuaries were intertidal salt marsh and mangrove forest. This region is a transition between the warm temperate and subtropical biogeographic zones and both permanently open and temporarily open/closed estuaries showed divisions at the Great Kei and Mngazana estuaries. In permanently open estuaries mangrove forest occurred north of the Great Kei Estuary and swamp forest north of the Mngazana Estuary. In temporarily open/closed estuaries reed and sedge beds occurred north of the Great Kei River and swamp forest, reed and sedge beds occurred north of the Mngazana Estuary where they replaced the salt marsh. The temporarily open/closed estuaries in the Ciskei were characterised by salt marsh and macroalgae. Submerged macrophytes such as Ruppia cirrhosa and Potamogeton pectinatus were also common. Salt marsh occurred because of high water column salinity and wide intertidal and supratidal zones. These estuaries were characterised by seawater washing into the estuary over the berm at the mouth and thus had high salinity. The variation in physical environment, changes in climate and transition between biogeographic regions has resulted in regions of high biodiversity. Mangrove, salt marsh and swamp forest species are found in the same region

Forest edge herbaceous vegetation (Trifolio-Geranietea) of northern Spain

A survey of the vegetation of forest (and hedge) fringes, classified within the Trifolio-Geranietea, in the Basque Country and the western and central Pyrenees (northern Spain) is presented. Three plant associations can be distinguished: the Centaureo nemoralis-Origanetum vulgaris, the Agrimonio-Trifolietum medii (both on limerich substrates) and the Hyperico androsaemi-Teucrietum scorodoniae (typical of siliceous soils). The Centaureo nemoralis-Origanetum vulgaris has already been known from the Atlantic zone of France. The Agrimonio-Trifolietum medii was described for Central Europe and it was also found to be widespread in the Pyrenees. The Hyperico androsaemi-Teucrietum scorodoniae is a new syntaxon (described in this paper) and occurs in coastal regions of the Atlantic Basque Country (Santanderino-Vizcaino Subsector)

Minimum message length clustering: an explication and some applications to vegetation data

In this paper, we examine the application of a particular approach to induction, the minimum message length principle and illustrate some of the problems that can be addressed through its use. The MML principle seeks to identify an optimal model within some specified parameterised class of models and for this paper we have chosen to concentrate on a single model class, that of mixture separation or fuzzy clustering. The first section presents, in outline, an MML methodology for fuzzy clustering. We then present some applications, including the nature of the within-cluster model, examination of the univocality of results for different groups of species and the effectiveness of presence data compared to purely quantitative data. Finally, we examine some possibilities of extending MML methodology to include within-class correlation of species, the existence of dependence between observed samples and the comparison of different classes of models