European Nature Information System Research Articles

The application of GIS technology in building a multivariate taphonomic profile for improving PMI estimations in Greece.

Environmental conditions highly affect decomposition rates and therefore a forensic practitioner should consider context-specific information when estimating the post mortem interval (PMI). Traditional methods of collecting environmental data, however, are time-consuming and often impractical for large-scale studies or routine forensic investigations. This study developed an automated computer method by employing the technology of geographic information systems (GIS) and Python programming language to provide contextual information for bodies found outdoors in Greece. The generated coding script underwent testing on 95 bodies in various stages of decomposition, which were examined between the years 1999 and 2022 at the National and Kapodistrian University of Athens and the Forensic Medical Service of Thessaloniki. Using ArcGIS Pro software and publicly available online data, a multilayer map was developed. Individual layers included high-resolution aerial images and data on the European Nature Information System ecosystem type, the Köppen-Geiger climatic type, the population density, the elevation, and the slope. Additionally, 99 national weather stations and their corresponding meteorological data were integrated. By leveraging the geographical coordinates of the recovery site of each case and information about the decedent's disappearance and recovery dates, this script automatically generates details from each of the above layers. Additionally, it calculates the accumulated degree days (ADD) and accumulated humidity days (AHD) values by extracting data from the nearest weather station. The GIS-based approach enables rapid, objective, and reproducible taphonomic profile construction, which can greatly improve the reliability of PMI estimations. By utilizing this method, forensic practitioners can accurately evaluate environmental effects on decomposition, thus standardizing taphonomic profiling globally.

Predictive GAM seabed maps can account for defined and fuzzy boundaries to improve accuracy in a Scottish sea loch seascape

Marine seabed mapping is an important element in marine spatial and conservation planning. Recent large scale mapping programmes have greatly increased our knowledge of the seafloor, yet at finer resolutions, large gaps remain. Loch Eriboll, Scotland, is an area of conservation interest with a diverse marine environment supporting habitats and species of conservation importance. Here we test and present strategies for a predictive seabed substrata map for Loch Eriboll using drop down Stereo Baited Remote Underwater Video (SBRUV) imagery collected as part of systematic underwater survey of the Loch. A total of 216 SBRUV deployments were made across the study site in depths of 3 m–117 m, with six seabed classes identified using an adaptation of the EUNIS (European Nature Information System) hierarchical habitat classification scheme. Four statistical learning approaches were tested, we found, Generalised Additive Models (GAMs) provided the optimal balance between over- and underfitted predictions. We demonstrate the creation of a predictive substratum habitat map covering 63 km2 of seabed which predicts the probability of presence and relative proportion of substratum types. Our method enables naturally occurring edges between habitat patches to be described well, increasing the accuracy of mapping habitat boundaries when compared to categorical approaches. The predictions allow for both defined boundaries such as those between sand and rock and fuzzy boundaries seen among fine mixed sediments to exist in the same model structure. We demonstrate that SBRUV imagery can be used to generate cost effective, fine scale predictive substrata maps that can inform marine planning. The modelling procedure presented has the potential for a wide adoption by marine stakeholders and could be used to establish baselines for long term monitoring of benthic habitats and further research such as animal distribution and movement work which require detailed benthic maps.

Typical earthworm assemblages of European ecosystem types

AbstractFor nature conservation and planning, terrestrial ecosystems are commonly classified based on their plant communities. Although soils are fundamental to ecosystem functioning, ecosystem classifications based on soil organisms are rare, and it is poorly understood whether their assemblage compositions follow existing classification schemes. We examined whether commonly used ecosystem types capture variation in earthworm (Lumbricidae) assemblages—a crucial biotic component of soil ecosystems. To this end, we created four ecosystem classifications by combining large‐scale climatic classifications (Biogeographic Regions [BGR] and Holdridge Life Zones [HLZ]) with small‐scale land cover classifications (CORINE Land Cover [CLC] and European Nature Information System [EUNIS]). European earthworm assemblage data from the sWORM and Edaphobase databases were analysed for variation in composition within and among ecosystem types, using Permutational Analysis of Variance and Analysis of Similarities. Additionally, we used Typical Species Analysis to establish typical earthworm assemblages (TAs) for each ecosystem type. Ecosystem classifications using the BGR explained more variance than HLZ, but HLZ showed a higher separation of assemblages between ecosystem types. The differentiation between Atlantic and Continental climates in the BGR could explain the superiority over the HLZ, which had only one category for the cool temperate zone of our study region. The typical assemblages contained on average six species, with some habitat generalists present in most. This study shows that combinations of ecosystem properties from different spatial scales can be used to distinguish between earthworm assemblages at the European level. However, earthworm assemblages across Europe were highly similar due to low species richness and the dominance of a few widespread species. This limits the possibility of applying TAs on large spatial scales, for example, for environmental monitoring. We suggest that future studies should explore the use of more species‐rich groups of soil organisms to characterize ecosystem types.

Remote Sensing for Mapping Natura 2000 Habitats in the Brière Marshes: Setting Up a Long-Term Monitoring Strategy to Understand Changes

On a global scale, wetlands are suffering from a steady decline in surface area and environmental quality. Protecting them is essential and requires a careful spatialisation of their natural habitats. Traditionally, in our study area, species discrimination for floristic mapping has been achieved through on-site field inventories, but this approach is very time-consuming in these difficult-to-access environments. Usually, the resulting maps are also not spatially exhaustive and are not frequently updated. In this paper, we propose to establish a complete map of the study area using remote sensors and set up a long-term and regular observatory of environmental changes to monitor the evolution of a major French wetland. This methodology combines three dataset acquisition technologies, airborne hyperspectral and WorldView-3 multispectral images, supplemented by LiDAR images, which we compared to evaluate the difference in performances. To do so, we applied the Random Forest supervised classification methods using ground reference areas and compared the out-of-bag score (OOB score) as well as the matrix of confusion resulting from each dataset. Thirteen habitats were discriminated at level 4 of the European Nature Information System (EUNIS) typology, at a spatial resolution of around 1.2 m. We first show that a multispectral image with 19 variables produces results which are almost as good as those produced by a hyperspectral image with 58 variables. The experiment with different features also demonstrates that the use of four bands derived from LiDAR datasets can improve the quality of the classification. Invasive alien species Ludwigia grandiflora and Crassula helmsii were also detected without error which is very interesting when applied to these endangered environments. Therefore, since WV-3 images provide very good results and are easier to acquire than airborne hyperspectral data, we propose to use them going forward for the regular observation of the Brière marshes habitat we initiated.

A deep‐learning framework for enhancing habitat identification based on species composition

AbstractAimsThe accurate classification of habitats is essential for effective biodiversity conservation. The goal of this study was to harness the potential of deep learning to advance habitat identification in Europe. We aimed to develop and evaluate models capable of assigning vegetation‐plot records to the habitats of the European Nature Information System (EUNIS), a widely used reference framework for European habitat types.LocationThe framework was designed for use in Europe and adjacent areas (e.g., Anatolia, Caucasus).MethodsWe leveraged deep‐learning techniques, such as transformers (i.e., models with attention components able to learn contextual relations between categorical and numerical features) that we trained using spatial k‐fold cross‐validation (CV) on vegetation plots sourced from the European Vegetation Archive (EVA), to show that they have great potential for classifying vegetation‐plot records. We tested different network architectures, feature encodings, hyperparameter tuning and noise addition strategies to identify the optimal model. We used an independent test set from the National Plant Monitoring Scheme (NPMS) to evaluate its performance and compare its results against the traditional expert systems.ResultsExploration of the use of deep learning applied to species composition and plot‐location criteria for habitat classification led to the development of a framework containing a wide range of models. Our selected algorithm, applied to European habitat types, significantly improved habitat classification accuracy, achieving a more than twofold improvement compared to the previous state‐of‐the‐art (SOTA) method on an external data set, clearly outperforming expert systems. The framework is shared and maintained through a GitHub repository.ConclusionsOur results demonstrate the potential benefits of the adoption of deep learning for improving the accuracy of vegetation classification. They highlight the importance of incorporating advanced technologies into habitat monitoring. These algorithms have shown to be better suited for habitat type prediction than expert systems. They push the accuracy score on a database containing hundreds of thousands of standardized presence/absence European surveys to 88.74%, as assessed by expert judgment. Finally, our results showcase that species dominance is a strong marker of ecosystems and that the exact cover abundance of the flora is not required to train neural networks with predictive performances. The framework we developed can be used by researchers and practitioners to accurately classify habitats.

Habitat Diversity, Environmental Conditions, and Distribution of Endangered Fungus Sarcosoma globosum (Ascomycota) in Lithuania.

Sarcosoma globosum (Pezizales, Ascomycota) is a rare and endangered fungus, and it is believed to be extinct in most central European countries. Known records of S. globosum in Lithuania reveal that it is situated on the south-western edge of a shrinking geographical distribution range in Europe. An assessment of the species' current habitat conditions and threats could enhance and provide new knowledge and guidelines to facilitate the efficient conservation of this threatened fungus and its habitats. The main aim of this study was to analyse the habitats and environmental conditions of S. globosum in Lithuania. We examined the diversity of habitats, various soil and tree stand characteristics, forest management activities, and natural disturbances in all 28 known fungus localities. S. globosum habitats in Lithuania are restricted to coniferous forests with the presence of Picea abies; the species was observed in boreo-nemoral bilberry western spruce taiga (the European Nature Information System habitat type T3F14), continental tall-herb western spruce taiga (T3F44), and native fir, spruce, larch, and cedar plantations (T3N1). An analysis of forest stand age structures in Lithuanian S. globosum localities revealed a rather large proportion of young Norway spruce stands of cultural origin (25.6% of study plots were assigned to age classes from 21 to 50 years); nevertheless, the majority of fungus growth sites were situated in older forests. Various natural and anthropogenic disturbances that threaten S. globosum habitats were assessed.

Mobilising Long-Term Natural Environment and Biodiversity Data and Exposing it for Federated, Semantic Queries

Biodiversity and ecosystems cannot be studied without assessing the impacts of changing environmental conditions. Since the 1980s, the U.S. National Science Foundation's Long Term Ecological Research (LTER) Network has been a major force in the field of ecology to better understand ecosystems. In Europe, the LTER developments are led by the the Integrated European Long-Term Ecosystem, critical zone and socio-ecological system Research Infrastructure (eLTER RI), a currently project-based infrastructure initiative with the aim to facilitate high impact research and catalyse new insights about the compounded impacts of climate change, biodiversity loss, soil degradation, pollution, and unsustainable resource use on a range of European ecosystems and socio-ecological systems. The European LTER network, which forms the basis for the up-coming eLTER RI, is active in 26 countries and has 500 registered sites that provide legacy data e.g., historical time-series data about the environment (not only biodiversity). Its site information and dataset metadata with the measured variables are available to be searched at the Dynamic Ecological Information Management System - Site and dataset registry (DEIMS-SDR, Wohner et al. 2019). While DEIMS-SDR data models utilize parts of the Ecological Metadata Language (EML) schema 2.0.0, location information follows the European INSPIRE specification. The future eLTER data is planned to consist of site-based, long-term time-series of ecological data. The eLTER projects have defined eLTER Standard Observations (SO), which will include the minimum set of variables as well as the associated method protocols that can characterise adequately the state and future trends of the Earth's systems. (Masó et al. 2020, Reyers et al. 2017). The current eLTER network consists of sites that differ in terms of infrastructure maturity or environment type and may focus on one or several of the future SOs or they are not yet executing any holistic monitoring scheme. The main objective is to convert the eLTER site network into a distributed research infrastructure that incorporates a clearly outlined mandatory monitoring program. Essential to this effort are the suggested variables for eLTER SOs and the corresponding methods and protocols for relevant habitat types according to the European Nature Information System (EUNIS) in each domain. eLTER variables are described by using the eLTER thesaurus "EnvThes". These descriptions are currently enhanced by the use of the InteroperAble Descriptions of Observable Property Terminology (I-ADOPT, Magagna et al. 2022) framework to provide the necessary level of detail required for seamless data discovery and integration. Variables and their associated methods and protocols will be formalised to enable automatic site classifications, by building on existing observation representations such as the Extensible Observation Ontology (OBOE), Open Geospatial Consortium's Observation and Measurement, and the future eLTER Standard Observation ontology. DEIMS-SDR will continue to be used as a core service with an RDF representation of its assets (sites, sensors, activities, people) currently being implemented. This action is synced with the Biodiversity Digital Twin (BioDT) project to ensure maximum findability, accessibility, interoperability and re-usability (FAIRness; Wilkinson et al. 2016) of data through FAIR Digital Objects (FDO). Other (digital) assets such as datasets, models and analytical workflows will be documented in the Digital Asset Register (DAR) alongside semantic mapping and crosswalk techniques, to provide machine-actionable metadata (Schultes and Wittenburg 2019, Schwardmann 2020). The Biodiversity Digital Twin (BioDT) project is bringing together biodiversity and natural environment data from seven thematic use cases for modeling. BioDT prototypes rely on openly available data that comes from multiple heterogeneous sources using a multitude of standards and formats. In the pilot phase, merging data requires "hand picking" from selected sources, and automation of workflows would still require many additional steps. There are ongoing efforts in both the BioDT and eLTER projects to find best ways and practices to bring the raw data together by using suitable standards but also to harmonise the other environment variables by referring to vocabularies and possibly express the data as FDOs. Currently both the EML schema and Darwin Core standard (Darwin Core Task Group 2009; with registered extensions) allow referring to external schemas and vocabularies, which give flexibility but may still prove to be too narrow for the multitude of data types and formats the natural environment data requires. We welcome discussion about how to create good practices for enriching and harmonising natural environment data and species occurrence data in a meaningful way. GBIF's new data model and enriching the raw data with semantic artefacts may prove to be the way to provide thematic data products that combine data from multiple sources.

Habitat mapping of Portugal’s north-western continental shelf and its application to implement the European Marine Strategy Framework Directive

Seafloor habitat maps allow to integrate various data layers and compare areas, being valuable tools for assessing environmental quality. Although the European Nature Information System (EUNIS) was created to catalogue habitats throughout Europe, its use in ecological assessment is relatively uncommon. This work proposes a EUNIS, level 5, benthic habitats map for the Portuguese continental shelf North of Nazaré canyon. Seven EUNIS level 5 habitats were identified, of which six were new to EUNIS, as this catalogue is less complete for southern Europe. Hereafter this map was used to assess the environmental status (ES) within the Marine Strategy Framework Directive (MSFD), which was developed to help European Union countries achieve a good environmental status (GES) in their marine ecosystems. In this sense, eleven quality descriptors (and respective criteria) were set to assess the environmental status (ES), which can be performed using diverse indicators. In this study, the ES status was assessed for two of such descriptors, namely D1, Biodiversity, which was assessed using the Marine Biological Value (MBV); and D6, Seafloor Integrity, which was assessed through the AZTI Marine Biotic Index (AMBI) and Multivariate-AMBI (M-AMBI). For both descriptors, an overall GES (or higher) was determined for this study area. These results set a baseline for future ES assessments within the MSFD, while the use of the EUNIS habitat maps can be expanded to assess the ES of other descriptors, namely D2, non-indigenous species, D3, commercially exploited fish and shellfish and D4, food webs.

Modeling approach for coastal dune habitat detection on coastal ecosystems combining very high‐resolution UAV imagery and field survey

AbstractEarth observation (EO) data, derived from remote sensing and unmanned aerial vehicle (UAV), have been recently demonstrated to be essential tools for the ecosystem monitoring and habitat mapping, combining high technological and methodological procedures for applied ecology. However, research based on EO data analyses often tend to focus on image processing techniques, neglecting the development of a detailed sampling design scheme needed for an exhaustive habitat detection. This paper shows the results of a novel approach for mapping coastal dune habitats at a fine scale, using a supervised machine learning model, through the combination of vegetation plot sampling scheme, synergic use of multi‐sensor spectral imagery (UAV‐VHR) and environmental predictors (e.g., LiDAR), object‐based image analysis, and landscape metrics analysis. Proposed approach was tested in a protected area, established to preserve notable habitats along the Italian Tyrrhenian coast. A detailed sampling scheme was designed and carried out during spring and summer of 2019, combining simultaneously UAV flight acquisition and field vegetation survey data, collected at high precision positioning. The calibrated classification model achieved an overall accuracy of 78.6% (standard error 4.33), allowing us to accurately classify and map five coastal habitats, according to EUNIS (European Nature Information System) classification, which were further verified through a fully independent validation field survey. Results demonstrate that VHR imageries, combined with specific field survey schemes, can be exploited to train classification models used for the detection of plant communities (i.e., meso‐habitat) and plant species at local scale. Our findings demonstrate that UAV‐VHR data is a valid tool to produce high spatial resolution information in sand beach ecosystems, giving ecology research a new way for responsive, timely, and cost‐effective ecosystem monitoring.

ReSurveyGermany: Vegetation-plot time-series over the past hundred years in Germany

Vegetation-plot resurvey data are a main source of information on terrestrial biodiversity change, with records reaching back more than one century. Although more and more data from re-sampled plots have been published, there is not yet a comprehensive open-access dataset available for analysis. Here, we compiled and harmonised vegetation-plot resurvey data from Germany covering almost 100 years. We show the distribution of the plot data in space, time and across habitat types of the European Nature Information System (EUNIS). In addition, we include metadata on geographic location, plot size and vegetation structure. The data allow temporal biodiversity change to be assessed at the community scale, reaching back further into the past than most comparable data yet available. They also enable tracking changes in the incidence and distribution of individual species across Germany. In summary, the data come at a level of detail that holds promise for broadening our understanding of the mechanisms and drivers behind plant diversity change over the last century.

Marine priority habitat mapping in a Mediterranean conservation area (Gyaros, South Aegean) through multi-platform marine remote sensing techniques

The aim of this study is to present the results of the first complete marine habitat mapping through marine remote sensing techniques in Gyaros Island, a remote island in the Cyclades archipelago with a great historical and ecological value. Gyaros Island is of great biological importance and, for this reason, since 2011, is part of the NATURA 2000 network and, by 2019, is characterised as a marine protected area (MPA). More than 80 km2 of seafloor area were inspected through a multi-platform marine remote sensing technique survey through two individual expeditions in 2014 and 2017. The remote sensing survey was conducted utilising side scan sonar, sub bottom profiler and multi- and single-beam echosounders. In addition to the remote sensing survey, an extensive ground truth network was established utilising a underwater towed camera and VanVeen sediment grabber. These resulted in the area being fully covered with high-quality data. Through these, a classification of the area was performed based on three distinct seafloor habitat mapping schemes (Expert, European Nature Information System and NATURA HD). The survey and the classification revealed that over 50% of the seafloor is covered by Posidonia oceanica meadows and coralligenous and other calcareous bio-concretion priority habitats. Based on the results of this work, Gyaros MPA was also established and different protections and conservation zones were set.

A Multidisciplinary Approach for A Better Knowledge of the Benthic Habitat and Community Distribution in the Central and Western English Channel

About 80% of the seabed of the English Channel (EC) is covered by coarse sediment, from coarse sand to pebbles. Quantitative data on the benthic macrofauna in these types of sediment remains are rare due to the difficulty of using grab corers in such hard substrates. The deepest central part of the EC (45–101 m depth) was prospected during two VIDEOCHARM surveys in June 2010 and June 2011 to increase knowledge of such sublittoral coarse sediment benthic habitats. Sampling focussed on a longitudinal transect in the deepest part of the EC (13 boxes), extending from the western approach to the Greenwich meridian. Both indirect (side scan sonar, Remote Operated Vehicule) and direct (grab sampling with benthos determination, and grain-size analyses) approaches were used and combined, permitting description of the benthic habitats and communities using seven methods. Five benthic EUNIS habitats (European Nature Information System) were reported: MC3215, MD3211, MC4, MC3212 and MC4215, of which two extended main habitats (MC3211 and M23212) corresponded to an eastern/western gradient from sandy gravel to sandy gravel and pebbles sediment. Three other spatially discrete habitats were associated with poor coarse sand and gravel habitats as well as sandy gravel and pebbles with the presence of the brittle star Ophiothrix fragilis. Taxonomic richness of both extended habitats was on the same order of magnitude as the coarse sand habitat reported elsewhere in the EC, whilst the abundances were among the lowest in deeper areas with low nutrient input and low primary production. The epifauna appeared relatively homogenous in this type of sediment at the scale of the sampling area and was not determined to assign a EUNIS habitat/class. ROV footage illustrated the presence of large epifauna and provided valuable information to ground truth in other sampling methods such as side scan sonar mosaic. Grab photos showing surface sediment was relevant to determine the sediment type, whilst granulometric analyses gave additional information on fine particles content (typically very low).

Reading the heterogeneity and spatial structuring of benthic habitats in macrophyte wracks

In coastal socio-ecosystems, the European Commission has proposed a set of indicators that could meet the objectives of the Marine Strategy Framework Directive. Most of them refer to the state of benthic habitats, a marine biodiversity compartment under major threat. However, biodiversity monitoring in the marine environment is logistically, technologically, and economically challenging. One possibility may be the monitoring of marine biodiversity “ex situ”, i.e. thanks to the material transported naturally along the sea to land continuum, but the relationship between benthic donor habitats and beach-casted macrophytes remains unclear. Only few studies have previously explored this relationship and they raised two questions: Could some wrack macrophyte species inform on the composition of proximate marine habitats? What is the contribution of local donor habitats in the composition of macrophyte wrack communities from one site to another? Here we provide answers to these questions from the sampling of macrophyte wracks on 131 beaches and the use of benthic habitat maps from the European Nature Information System. From linear regressions, we first show that the α-diversity of non-floating beach casted macrophytes reflects the heterogeneity of benthic habitats lying in straight marine buffers. Second, using non-linear modelling we show that this correlation peaks at 500 m offshore and maintains up to 1000 m far, which corresponds to the infralittoral seabed along our study coastline. Finally, regressions on distance matrices reveal that dissimilarities in wrack communities of non-floating macrophytes are more the result of differences in proximate benthic habitats rather than geographic patterns. While macrophyte wracks are already considered relevant indicators of geomorphological and ecological processes on coastal socio-ecosystem, this study advocates for their further consideration as ex situ indicators of benthic ecosystems.

Central European forest floor bryophytes: Richness, species composition, coexistence and diagnostic significance across environmental gradients of forest habitats

Bryophytes are good indicators of habitat conditions and show associations with different types of forests. In this study we assessed the diversity and distribution of forest floor bryophytes across a wide gradient of Central European forests using vegetation records from the Polish Vegetation Database (PVD). We identified forest types using the Habitat Classification of the European Nature Information System (EUNIS) – the main comprehensive pan-European hierarchical classification of habitats. Based on 9258 plots containing bryophyte species and representing 20 forest types, we assessed the species number and cover, species composition of the bryophyte layer, species tendency for coexistence, and bryophyte diagnostic value for forest types. We explained the observed trends by environmental variables and bryophyte species traits to understand how bryophytes function in forest habitats.Bryophyte species number and cover were high in forest types on moist sites, both with deciduous and coniferous tree stands. The richness and abundance of bryophyte species, as well as the species composition of the bryophyte layer depended mainly on substrate moisture, fertility and pH, as well as on the percentage cover of coniferous and deciduous tree species in the stand. Species traits which responded to environmental factors were, first of all, requirements for substrate moisture, fertility and pH. Bryophytes of closed forests and short-lived shuttle species preferred deciduous stands, while bryophytes occurring in forests as well as on open land and perennial species were associated with coniferous stands.Bryophyte species showed a clear preference for forest types, but their fidelity was usually not high. Based on the fidelity of species, we also identified 10 groups of coexisting species which were also indicators of forest types and were dependent on environmental variables.Forest floor bryophytes respond to environmental gradients by species number, cover and composition, and they play a diagnostic role in forest types. Our research shows the importance of databases in learning about bryophyte ecology. However, the use of vegetation data has some limitations.

An ignored habitat in Türkiye: Sandy steppes

The steppes habitat in Türkiye took bedrock formed of soil as the basis, and was classified by separating into groups, such as calcareous, with gypsum, volcanic, and serpentine. However, a classification was not made that took the soil texture as the basis. In this study, the stable sand dunes’ soil texture observed in Karapınar, Konya, Turkiye erosion region, was evaluated for flora and syntaxonomy. Within the scope of the study, observations were conducted in the sandy habitats found to the south-southwest of the Karapınar county center, the plant species that preferred the habitat were determined, and the data obtained was compared with the steppes’ habitat in the close environs and with the studies made previously. Furthermore, it emphasized the necessity of analyzing syntaxonomically the sandy steppes in Türkiye, which are evaluated within the “E1.A5 - Irano-Anatolian inland dunes” habitat type according to the European Nature Information System (EUNIS).

Mapping the macrofauna communities of Portugal's continental shelf north of Nazaré Canyon using Community Distribution Modelling (CDM)

The conservation and sustainable use of marine ecosystems is a worldwide concern, and to achieve it, managers and decision-makers require detailed environmental and biological information, namely supplied as maps of the seafloor. This work was conducted on Portugal's continental shelf located north of Nazaré Canyon. Sediment data was obtained in 226 grab samples, of which 169 were used to study the macroinvertebrate benthic communities. Acoustic transects were run for more than 2500 Km to obtain depth and maps of environmental variables were produced through spatial interpolation of the point data. A multivariate analysis of the biological data identified seven benthic communities, characterized by different species and environmental conditions (i.e., sediment type and depth). The spatial distribution of each biological community was modelled as the response to environmental variables using generalized linear models with a binomial distribution. Depth was the variable more often significantly related to the distribution of the benthic communities, selected in 5 of the 7 Community Distribution Models (CDM). Using the CDM expressions and maps of the environmental variables, probability maps were produced for the distribution of each community. Their combination allowed to obtain a final map of the most probable benthic communities throughout the study area, showing a high agreement (81%) between the observed and the predicted distributions. The maps produced in this study are valuable tools for the decision-making process involved in the management of the marine environment and their resources, for instance to classify these habitats according to the European nature information system (EUNIS) and in the scope of the Marine Strategy Framework Directive.

Large-Scale Distribution of the European Seahorses (Hippocampus Rafinesque, 1810): A Systematic Review.

Simple SummaryPopulations of many marine species are threatened by a number of interacting factors, including anthropogenic activities, climate change, and biodiversity loss. The assessment of the conservation status of such populations relies heavily on several types of data, such as large-scale geographical and ecological distribution. Seahorses are charismatic fish sensitive to environmental pressures, and according to the IUCN directive 95/2020, they should be considered a model for environmental quality assessment. As in many other areas, the data on seahorse ecological distribution in Europe are scattered, patchy, and mainly focused on small-scale studies. Therefore, we undertook a systematic review using the PRISMA protocol to identify the current knowledge status, detect gaps, and propose future research priorities. We analyzed 32 years of published studies and described the distribution of Hippocampus guttulatus and H. hippocampus across 176 sites in the Atlantic Ocean, Mediterranean Sea, and Black Sea as a function of habitat, depth, and degree of confinement. The applied method evidenced the overall lack of a detailed habitat description in published studies. Seahorse conservation would benefit from an analytical description of habitats, such as data on the depth, nature of the substrate, and associated biological communities, as well as the use of a standardized habitat classification system, such as formally recognized EUNIS habitat codes.Human pressures on marine ecosystems have caused extensive degradation of marine habitats and several local extinctions. Overexploitation and destructive fishing practices are responsible for biodiversity loss in many coastal ecosystems. The definition of conservation programs in marine fish requires comprehensive knowledge on large-scale geographical distribution, while considering distribution/abundance patterns in relation to key environmental variables. Due to their life-cycle traits, the two European seahorses (Hippocampus guttulatus and H. hippocampus), as with other congeneric species, are particularly sensitive to the effects of anthropogenic activities and habitat changes. However, information on the ecological distribution of these two species is scattered, patchy, and mainly focused on small-scale studies. In this paper, we followed an international standard protocol for systematic reviews (the PRISMA protocol) to provide a detailed assessment of the two species’ geographical distribution in relation to the environmental characteristics. According to the 134 analyzed studies, Hippocampus guttulatus is more common in confined areas, while H. hippocampus is found in marine shelf waters. With several interspecific differences, seagrasses were the most used holdfasts of both species. The EUNIS codes (European nature information system) referring to a specific and unique habitat were discussed as a potential tool for defining the ecological distribution of the two species. The obtained results and their future implementation could help plan conservation actions.

Habitat Classification of Cilo Glaciers and Their Surroundings

This study was carried out in 2018-2019 for the purpose of habitat classification of the Cilo glaciers and their surroundings, which have significant biodiversity within the borders of the National park of Hakkari Province and have been under serious threat especially due to global climate change in recent years. As a result of the studies, 11 habitat types were determined on the area according to habitat types of The European Nature Information System (EUNIS) and observations about these habitats were given. In addition, 4 types of vegetation are determined according to the plant density with satellite images of the area. This research reveals the importance of habitat type determination studies for important nature areas

Two different new Tamarix smyrnensis Bunge associations on the Yeşilırmak Delta Plain (Samsun/Türkiye)

The aim of this paper is to deal with syntaxonomical and ecological characteristics of Tamarix smyrnensis communities developed on both salt marsh and riparian habitats of the Yeşilırmak Delta Plains. The floristic composition of the communities was analysed by Braun-Blanquet method and the effect of edaphic parameters on the communities were determined using ordination techniques (CCA). The habitat types were identified according to the European Nature Information System (EUNIS) habitat coding list. Junco acuti-Tamaricetum smyrnensis ass. nov. developed on salt marshes and belonging to the alliance of Tamaricion tetragynae, and Tamaricetum tetrandrae-smyrnensis ass. nov. developed on meander banks/river island (eyot) and belonging to the alliance of Rubo sancti—Nerion oleandri distributed under influence of electrical conductivity (EC) of soil. Additionally, it was observed that both associations belonged to subunits of Mediterraneo-Macaronesian tamarisk thickets according to EUNIS.

Syntaxonomy of steppe depression vegetation of Ukraine

Aims: To revise the syntaxonomy of the vegetation of steppe depressions (pody), in particular (1) to identify the associations and to reveal their environmental, structural and compositional peculiarities; (2) to assign the associations to higher syntaxa; and (3) to correct nomenclatural aspects according to the ICPN.Study area: Steppe zone of Ukraine, Left-Bank of the Lower Dnieper basin.Methods: 641 relevés were included in the final analysis in the PCOrd program integrated into Juice software. Two expert systems (EVC and EUNIS-ESy) were used to assign relevés to vegetation classes and to EUNIS units.Results: The analysis resulted in nine clusters, which were interpreted asFestuco-Brometea(two units),Molinio-Arrhenatheretea(three units),Isoёto-Nanojuncetea(three units) and one derivate community of theFestuco-Puccinellietea. Detailed characteristics of the species composition, structure, distribution, and environmental conditions are provided for each unit. According to the DCA ordination, the leading factors of the syntaxa differentiation are soil moisture and fluctuating water level.Conclusions: We could clarify the placement of steppe depression vegetation in the system of syntaxonomic units of Europe. The previously described syntaxa of the rank of alliance (Myosuro-Beckmannion eruciformis), suballiance (Galio ruthenici-Caricenion praecocis), and six associations are validated. Two associations and two subassociations are described as a new to science.Taxonomic references: Euro+Med PlantBase (https://www.emplantbase.org), except Mosyakin and Fedoronchuk (1999) forPhlomis scythicaKlokov & Des.-Shost. andTulipa scythicaKlokov & Zoz.Syntaxonomic references: Mucina et al. (2016) for syntaxa from alliance to class level; Dubyna et al. (2019) for associations.Abbreviations: DCA = Detrended Correspondence Analysis; DES = Didukh Ecological Scales; EUNIS = European Nature Information System; EVC = EuroVegChecklist; GIVD = Global Index of Vegetation-Plot Databases; ICPN = International Code of Phytosociological Nomenclature.