Polysaccharide determination and habitat classification for fresh Dendrobiums with hyperspectral imagery and modified RBFNN

This research aimed to study the visual and nondestructive detection of mannose (MN) and Dendrobium polysaccharides (DP) in Dendrobiums by using hyperspectral imaging technology.

Relevant habitats neglected by the Directive 92/43 EEC: the contribution of Vegetation Science for their reappraisal in Sicily

Field investigation carried out by the Sicilian botanists in the last 20 years enabled them to identify eight habitat types of high biogeographic and conservation interest, neglected by the Directive 92/43, which deserve ad hoc conservation measures. For each of these habitats, a syntaxonomic interpretation of the corresponding plant communities, their main ecological, physiognomic and syndynamic traits and a list of diagnostic species are provided. Their classification into the macrotypes listed in the Annex I of the Directive 92/43 and the respective correspondence in EUNIS habitat classification are proposed. The habitats here described integrate those already proposed by the Italian Botanical Society, with the hope of an adequate recognition at national at supranational level.

Benthic habitat mapping using Object-Based Image Analysis (OBIA) on Tidung Island, Kepulauan Seribu, DKI Jakarta

Tidung Island is one of the islands in Kepulauan Seribu, DKI Jakarta, Indonesia. This island has various benthic that live on the coastal areas, and benthic habitat has various functions both ecologically and economically. Nowadays, remote sensing technology is one way to detect benthic habitats in coastal areas. Mapping benthic habitat is essential for sustainable coastal resource management and to predict the distribution of benthic organisms. This study aims to map the benthic habitats using the object-based image analysis (OBIA) and calculate the accuracy of benthic habitat classification results in Tidung Island, Kepulauan Seribu, DKI Jakarta. The field data were collected on June 2021, and the image data used is satellite Sentinel-2 imagery acquired in June 2021. The result shows that the benthic habitat classification was produced in 4 classes: seagrass, rubble, sand, and live coral. The accuracy test result obtained an overall accuracy (OA) of 74.29% at the optimum value of the MRS segmentation scale 15;0,1;0.7 with the SVM algorithm. The results of benthic habitat classification show that the Seagrass class dominates the shallow water area at the research site with an area of 118.77 ha followed by Life Coral 104.809 ha, Sand 43.352 ha, and the smallest area is the Rubble class of 42.28 Ha.

Land typology by L. G. Ramenskiy and eunis habitat classification (retrospective view)

The rapid rate of decline in the Earth’s biodiversity under the influence of direct and indirect anthropogenic pressure makes it necessary to develop the scientific foundations for its conservation at all levels of life. Ecologists have come to understand that the best way to ensure the conservation of populations of organisms and their communities is to preserve the environment in which they live. The countries of the European Community, where special programs have been developed since mid 1980s, have shown the greatest activity in preserving environmental conditions. Currently, the «European Union Nature Information System» (EUNIS) has become the most popular among such programs. Habitat is a central concept in EUNIS. For the purposes of EUNIS, habitat is defined asa place where plants or animals normally live, characterized primarily by its physical features (topography, plant or animal physiognomy, soil characteristics, climate, water quality etc.) and secondarily by the species of plants and animals that live there (Davies et al., 2004). Most often, habitat is considered to be synonym of the term biotope. The EUNIS biotope classification would correspond to the ecosystem classification if heterotrophic components were largely present in it. However, at present, these organisms, are not used for classification of terrestrial ecosystems. The latter (especially benthos) are important in the characterization of marine habitat types. The author does not deny the extreme importance of the EUNIS habitat classification for ecological science and solving problems of nature conservation. He is only sure that the concept of habitat classification began to be developed in the Soviet Union as early as 1920–1930th in the papers by L. G. Ramenskiy who in 1927 published the definition of habitat type: The type of habitat or natural area is determined by a combination of climate conditions, relief, irrigation, and the nature of the soil and subsoil. The same type can be covered by a meadow, or a forest, or plowed up, etc.: these are its transitional states (in virgin untouched nature, each type is inhabited by a completely definite combination of plants - steppe, forest, meadow, etc.). Afterwards L. G. Ramenskiy began to use the term land type instead of habitat type. In the 1930s, by the land type he meant an ecosystem unit in which plant community would exist without human influence. The land type in nature is represented by a set of various modifications that arise, as a rule, under man pressure. Modifications can transform into each other and revert to the original state of the type. Later, such plant community was called potential vegetation (Tüxen, 1956). In 1932–1935, L. G. Ramenskiy supervised the inventory of natural forage lands in the USSR, which used this concept of land type (Golub, 2015). The inventory of natural forage lands in the USSR resulted in their hierarchical classification: 19 classes and 43 subclasses were established. The exact number of distinguished types was not calculated, according to L. G. Ramenskiy rough assessment, there were more than thousand. In most cases, the potential vegetation of the types could not be identified. Proceedings of this inventory were not published. However, the L. G. Ramenskiy former post-graduate student N. V. Kuksin, who took part in the inventory in Ukraine, wrote the book about the forage type lands in this republic of the USSR (Kuksin, 1935). The typology of hayfields and pastures presented in that book is very similar to the habitat classification developed on the principles of the EUNIS system (Kuzemko et al., 2018). By the late 1940s, L. G. Ramenskiy had concluded that modern science was unable to establish potential vegetation for many habitat types. Therefore, he recommended calling the land type what he previously attributed to modifications. For practical reasons and for the sake of brevity, it is advisable to also call types the main groups of modifications of land types (forest, meadow, arable) (Ramenskiy, 1950, p. 489). As a result, his understanding of land type became the same as later habitat was interpreted in the EUNIS system. The typology by L. G. Ramenskiy lands and the classification of EUNIS habitats have the same essence and basis, but different groups of human society proposed them: the first exploits land resources, the second tries to protect them. Based on L. G. Ramenskiy typology, recommendations are made on the use of biotopes with the purpose to obtain sustainable maximum economic production. Based on the classification of the EUNIS system, recommendations are drawn up for the protection of plant and animal populations, as well as their community’s characteristic of a given biotope. The land typology by L. G. Ramenskiy could well be deployed towards the protection of biotopes, if there was a demand from society for such use. So keen interest in nature conservation, as now, did not exist in the course of the L. G. Ramenskiy lifetime. At present, the EUNIS biotope classification has begun to be used on the territory of the former USSR, while the land typology by L. G. Ramenskiy has been forgotten. There are two reasons for this phenomenon: 1) isolationism of Soviet science, which separated domestic scientists from their colleagues in the West; 2) L. G. Ramenskiy ideas were too ahead of time, their depth, essence and importance became understandable to biologists only few decades later. The paper shows that the formation of L. G. Ramenskiy views concerning the typology of habitats could been influenced by the ideas of the Russian forest scientist A. A. Krudener.

Reef Larval Recruitment in Response to Seascape Dynamics in the SW Atlantic

Advances in satellite observation have improved our capacity to track changes in the ocean and seascapes with numerous ecological and conservation applications, but yet under explored for coastal ecology. In this study, we assessed dynamics in the Seascape Pelagic Habitat Classification, a satellite remote-sensing product developed by NOAA to monitor biodiversity globally, and invertebrate larval recruitment in order to identify and predict changes in coastal benthic assemblages at tropical reefs in the SW Atlantic. Our results revealed that pelagic Seascapes correlated with monthly and seasonal variations in recruitment rates and assemblage composition. Recruitment was strongly influenced by subtropical Seascapes and was reduced during warm, blooms, and high-nutrient waters, likely to affect reef communities in the long term. Modeling indicate that Seascapes may be more efficient than temperature in predicting benthic larval dynamics. Based on historical Seascape patterns, we identified seven events that may have impacted benthic recruitment in this region in the last decades, which not surprisingly, coincided with consistent global heatwaves. These findings provide new insights into the application of novel satellite remote-sensing Seascape categorizations in benthic ecology and evidenced how reef larval supply in the SW Atlantic could be impacted by recent and future ocean changes.

Mapping gravel beds combining multibeam bathymetry, underwater video, and grab samples with continuous spatial modeling by random forest

<p>In this study, high-resolution (1 m) multibeam echosounder system (MBES) bathymetry data and derivatives, optical images by underwater video drop-frame, and Hamon grab sediment samples, all acquired within 170 km2 of seafloor in offshore Belgian Waters, were integrated to produce a random forest spatial model targeting the prediction of the continuous surficial distribution of gravel %, i.e., a substrate category whose known detailed distribution is central to the environmental stewardship of natural gravel bed habitat. MBES bathymetry reveals explicit details of the seafloor topography, allowing the derivation of geomorphometric variables that are important in the classification process. Underwater video and grab samples provide the means to directly observe the nature and distribution of the response variable. The model output is presented along with a protocol of error and uncertainty estimation, providing detailed information of the gravel spatial distribution that would otherwise remain undetected by categorical-type classifications, focused on predefined habitat classification schemes. Targeting the methodological improvement of this mapping approach, an overview of the limitations identified at the various steps of the acoustic seafloor classification (ASC) pipeline is presented.</p>

Annotated list of the coastal habitats flora of the Southern Crimea and its brief analysis

The aim of this work is to assess the level of taxonomic diversity and to analyze the structure of the vascular flora of the coastal biotopes of the Southern Crimea. The work is based on the results of long standing field research, which was carried out by the traditional route-reconnaissance method, analysis of YALT herbarium materials and data from literary and Internet sources. Arealogical and biomorphological characteristics of the species are given according to "Biological Flora of Crimea" by V.N. Golubev, biotope coding – according to EUNIS habitat classification. The nomenclature of taxa corresponds to the "Spontaneuos flora of the Crimean peninsula" by A.V. Yena and international databases IPNI, Euro+Med PlantBase, The Plant List, Catalog of Life. 17 types of biotopes of the local, regional and European levels were identified in the coastal landscapes of the Southern Crimea according to the EUNIS habitat classification. An annotated list of vascular plants of coastal habitats has been compiled, which includes 334 species and subspecies from 223 genera of 58 families. The "core" of the studied flora has been identified, which includes 94 species most characteristic of it from 74 genera of 29 families. The analysis of the systematic, geographical and biomorphological structures of the flora and its sozological assessment are carried out. It has been established that the diversity of the coastal landscapes of the Southern Crimea is represented by 17 types of habitats, the flora of which includes 334 species and subspecies from 223 genera of 58 families of vascular plants. The most characteristic for the studied biotopes are 94 species from 74 genera of 29 families, which constitute the "core" of their flora. 14 coastal biotopes and 41 plant species have conservation status of different levels.

Using Habitat Classification to Assess Representativity of a Protected Area Network in a Large, Data-Poor Area Targeted for Deep-Sea Mining

Extractive activities in the ocean are expanding into the vast, poorly studied deep sea, with the consequence that environmental management decisions must be made for data-poor seafloor regions. Habitat classification can support marine spatial planning and inform decision-making processes in such areas. We present a regional, top–down, broad-scale, seafloor-habitat classification for the Clarion-Clipperton Fracture Zone (CCZ), an area targeted for future polymetallic nodule mining in abyssal waters in the equatorial Pacific Ocean. Our classification uses non-hierarchical, k-medoids clustering to combine environmental correlates of faunal distributions in the region. The classification uses topographic variables, particulate organic carbon flux to the seafloor, and is the first to use nodule abundance as a habitat variable. Twenty-four habitat classes are identified, with large expanses of abyssal plain and smaller classes with varying topography, food supply, and substrata. We then assess habitat representativity of the current network of protected areas (called Areas of Particular Environmental Interest) in the CCZ. Several habitat classes with high nodule abundance are common in mining exploration claims, but currently receive little to no protection in APEIs. There are several large unmanaged areas containing high nodule abundance on the periphery of the CCZ, as well as smaller unmanaged areas within the central CCZ, that could be considered for protection from mining to improve habitat representativity and safeguard regional biodiversity.

Developing the use of convolutional neural networking in benthic habitat classification and species distribution modelling

Management of the marine environment is increasingly being conducted in accordance with an ecosystem-based approach, which requires an integrated approach to monitoring. Simultaneous acquisition of the different data types needed is often difficult, largely due to specific gear requirements (grabs, trawls, and video and acoustic approaches) and mismatches in their spatial and temporal scales. We present an example to resolve this using a convolutional neural network (CNN), using ad hoc multibeam data collected during multi-disciplinary surveys to predict the distribution of seabed habitats across the western English Channel. We adopted a habitat classification system, based on seabed morphology and sediment dynamics, and trained a CNN to label images generated from the multibeam data. The probability of the correct classification by the CNN varied per habitat, with accuracy above 60% for 85% of habitats in a training dataset. Statistical testing revealed that the spatial distribution of 57 of the 100 demersal fish and shellfish species sampled across the region during the surveys possessed a non-random relationship with the multibeam-derived habitats using CNN. CNNs, therefore, offer the potential to aid habitat mapping and facilitate species distribution modelling at the large spatial scales required under an ecosystem-based management framework.