On a Rare Visit to Texas

<p>Species' sensitivity scores are calculated as their niche breadth*reliance, with higher values indicating species less sensitive to changes in resource abundance or availability. Equivalent, PECBMS-only <i>BREAKPOINT</i> sets for the forest type and region indicators are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097217#pone.0097217.s014" target="_blank">Table S7</a>. ‘0/1’ identifies species that were interchangeable in any given breakpoint set due to equal sensitivity scores – see specific note for each indicator for further details.</p><p><i>*Species also included in current pan-European forest bird indicator (for full list see</i><a href="http://www.ebcc.info/index.php?ID=459" target="_blank">http://www.ebcc.info/index.php?ID=459</a>).</p>a<p>Either species could be included.</p>b<p>Any one of three could be included.</p>c<p>Any two of three could be included.</p

Quantifying the Detrimental Impacts of Land-Use and Management Change on European Forest Bird Populations

The ecological impacts of changing forest management practices in Europe are poorly understood despite European forests being highly managed. Furthermore, the effects of potential drivers of forest biodiversity decline are rarely considered in concert, thus limiting effective conservation or sustainable forest management. We present a trait-based framework that we use to assess the detrimental impact of multiple land-use and management changes in forests on bird populations across Europe. Major changes to forest habitats occurring in recent decades, and their impact on resource availability for birds were identified. Risk associated with these changes for 52 species of forest birds, defined as the proportion of each species' key resources detrimentally affected through changes in abundance and/or availability, was quantified and compared to their pan-European population growth rates between 1980 and 2009. Relationships between risk and population growth were found to be significantly negative, indicating that resource loss in European forests is an important driver of decline for both resident and migrant birds. Our results demonstrate that coarse quantification of resource use and ecological change can be valuable in understanding causes of biodiversity decline, and thus in informing conservation strategy and policy. Such an approach has good potential to be extended for predictive use in assessing the impact of possible future changes to forest management and to develop more precise indicators of forest health

A Niche-Based Framework to Assess Current Monitoring of European Forest Birds and Guide Indicator Species' Selection

Concern that European forest biodiversity is depleted and declining has provoked widespread efforts to improve management practices. To gauge the success of these actions, appropriate monitoring of forest ecosystems is paramount. Multi-species indicators are frequently used to assess the state of biodiversity and its response to implemented management, but generally applicable and objective methodologies for species' selection are lacking. Here we use a niche-based approach, underpinned by coarse quantification of species' resource use, to objectively select species for inclusion in a pan-European forest bird indicator. We identify both the minimum number of species required to deliver full resource coverage and the most sensitive species' combination, and explore the trade-off between two key characteristics, sensitivity and redundancy, associated with indicators comprising different numbers of species. We compare our indicator to an existing forest bird indicator selected on the basis of expert opinion and show it is more representative of the wider community. We also present alternative indicators for regional and forest type specific monitoring and show that species' choice can have a significant impact on the indicator and consequent projections about the state of the biodiversity it represents. Furthermore, by comparing indicator sets drawn from currently monitored species and the full forest bird community, we identify gaps in the coverage of the current monitoring scheme. We believe that adopting this niche-based framework for species' selection supports the objective development of multi-species indicators and that it has good potential to be extended to a range of habitats and taxa

Biodiversity of Bulgaria: Characteristics, protection and trends

Bulgaria is a medium-sized country located in the eastern part of the Balkan Peninsula. It hosts a rich mycota, flora and fauna, and quite well preserved natural and semi-natural ecosystems. This is mostly due to the country's geographic position between the temperate and subtropical zones, the complex geological history, and the big topographic variety. The high species diversity and endemism determine the high conservation value of Bulgarian biodiversity.The Bulgarian flora consist of more than 4,100 species, including more than 45 species of ferns, 250 species of mosses, and 2,800 higher plant species. The animals established in the country belong to 28 phyla and 75 classes. Vertebrates (858 species) comprise 2.7% of the Bulgarian fauna: 242 fishes and fish-like taxa; 24 amphibians; 40 reptiles; 452 birds; and 101 mammal species. Invertebrates account for more than 31,000 species. Over the past 25 years the number of known animals in the country has increased by over 4,500 species: from 29,000 in 1996 to 33,545 species in 2020. The total number of endemic animals is about 1,400 (4.2%). In some groups, the percentage of endemism is very high (95.5% of snails from the family Hydrobiidae and 71% of Clausiliidae; 53.6% of Diplopoda; 50.0% of terrestrial Isopoda). The richest endemic areas in Bulgaria are mostly in the mountains: Rila Mt.- 268, Pirin Mt. - 220, Western Stara Planina Mt. - 184, Western Rhodopes Mts - 183, and the Central Stara Planina Mt. – 181. Molecular data for Bulgarian animals is still insufficient, although Bulgaria ranks among the top 10 European countries in the proportion of the DNA-barcoded animal taxa; sequence coverage of animal specimens in Barcode of Life data System (BOLD) amounts to approximately 36,000 sequences from more than 7,100 Barcode index numbers (BINs).Bulgaria is part of large-scale initiatives of the European research infrastructure such as the Distributed System of Scientific Collections (DiSSCo) and the MOBILISE COST Action, with mass digitization of museum collections currently underway.Legislation to protect nature in Bulgaria dates back to the end of the 19th century and covers forestry (1890), the protection of certain species (1890's) and hunting (1897). Organized civil movements resulted in the establishment of the Union of Nature Protection (1928), the designation of several nature reserves (1933), and the first National Park (1934). More specific regulation followed with Ordinance for the Protection of Nature (1936). The Red Data Book of Bulgaria was published as early as 1984 (vol. 1, Plants) and 1985 (vol. 2, Animals), with a second updated edition in 2011. Bulgaria is also among the first countries to prepare a National Strategy for Biodiversity Protection (1993, adopted in 1998) following the Convention on Biological Diversity (CBD) process. Since then, several national plans for protection of biodiversity have been adopted including assessments of the threats, objectives, and measures for their achievement. According to recent references, such as the Red Data Book (Beshkov 2011) and the Article 17 reports of 2014 and 2020, the main threats to biodiversity in Bulgaria at the beginning of the 21st century have been human induced degradation: fragmentation and loss of habitats; industrial, agricultural and household waste pollution; direct exploitation of biological resources; genetic ingression and invasive alien species; and global climate change effects. A set of drivers for the loss of biodiversity is related to agriculture and land management, including the whole spectrum from intensification to the abandonment of traditional land, and wetland management practices

Deliverable D12.9 Data Management Plan

The  main goal of the BiCIKL project is to improve, for the first time, seamless access, linking and usage tracking of data within a network of Research Infrastructures managing different data classes (literature, specimens, samples, occurrences, sequences, taxon names and Operational Taxonomic Units (OTU)), ultimately represented also in a biodiversity knowledge graph. To achieve this, the consortium members will operate with huge amount of data during and after the end of the project.As a Horizon 2020 project, BiCIKL conforms to the Open Research Data Pilot (ORDP)1 and   Article 29.3 of the H2020 Model Grant Agreement by default, hence the consortium aims to improve and maximise access, sharing, linking and reuse of FAIR Open Research Data (ORD), generated or managed by the project. A detailed Data Management Plan is a critical part of   the ORDP. The DMP described in the present document is developed in BiCIKL within the first  six months of the project and it will evolve as a “living document” during the lifetime of the project and beyond in order to present the status of the project's reflections on data management.The BiCIKL DMP outlines the handling of research data and provides the basis of the project consortium’s data management life cycle for the data collected, generated and processed by the participants in the project. The DMP also covers the methodologies and standards previously developed for data sharing and open access, curation  and  preservation.  The subject of the DMP is the management of research data. Personal data management  is covered by deliverable D9.1 Protection of Personal Data.The BiCIKL DMP was developed in close collaboration with all project partners and involved Research Infrastructures (RI) who provided information on their data management practices and policies in a questionnaire and planned generation, collection, and processing of data for the purposes of building a resilient data management strategy of the project which meets all criteria for open research.This DMP aims to adhere to the FAIR (Findable, Accessible, Interoperable, Reusable) data management criteria of Horizon 20202

Deliverable D3.1 Project logo, marketing pack and website design and development

This document presents BiCIKL’s recognizable visual identity, including the project logo, visual identity guide, brochure, poster, document, presentation templates and website design and functionality developed in the first three months. These materials will ensure that BiCIKL is communicated effectively and professionally with the aim to raise awareness and build a community from the start of the project.The modern and user-friendly public website (bicikl-project.eu) provides an easy-to-navigate, continuously updated platform allowing fast access to general information about BiCIKL and its activities, operating on several levels. It also prominently features the participating project partners and Research Infrastructures and their extensive service portfolio

Summary of comparisons between the temporal dynamics of alternative index sets (<i>MINIMAL</i>, <i>BREAKPOINT</i>, <i>SENSITIVE</i> and, for the pan-European and regional indicators, existing indicator sets <i>CURRENT</i>) for each indicator type and that of an index based on the population dynamics of all species in the candidate pool from which the sets had been drawn (<i>COMMUNITY</i>).

<p>Data presented are the slope (95% confidence interval) and correlation coefficient <i>r</i> of the relationship between the inter-annual changes of <i>COMMUNITY</i> and each alternative indicator set, derived using Type II major axis regression. Slope values less than one reflect greater inter-annual changes in the specific indicator relative to that of <i>COMMUNITY</i>. The 2011 index value for each alternative indicator is also shown. *P<0.05; **P<0.01; ***P<0.001; <i>ns</i> – not significant.</p>a<p>Calculated from the geometric mean of constituent species' population change between 1980 (1982 for East and 1989 for South) and 2011 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097217#pone.0097217-Gregory3" target="_blank">[13]</a>.</p

Temporal dynamics of pan-European woodland bird indicator, drawn from species currently covered by PECBMS, between 1980 and 2011.

<p>Lines show index values, based on the geometric mean of constituent species' population trends, for <i>MINIMAL</i>, <i>BREAKPOINT</i>, <i>SENSITIVE</i>, the existing pan-European woodland bird index (<i>CURRENT</i>) and <i>COMMUNITY</i> sets. Equivalent figures for the regional and woodland type indicators are provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097217#pone.0097217.s002" target="_blank">Figures S2</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097217#pone.0097217.s003" target="_blank">S3</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097217#pone.0097217.s004" target="_blank">S4</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097217#pone.0097217.s005" target="_blank">S5</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097217#pone.0097217.s006" target="_blank">S6</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097217#pone.0097217.s007" target="_blank">S7</a>.</p

Overview structure of SpecSel, the species' selection algorithm, outlining the process to identify the optimal indicator set for each set size.

<p>SpecSel has been implemented in Java and the program, including detailed coding for the search tree component, can be freely downloaded from <a href="https://www.uea.ac.uk/computing/specsel" target="_blank">https://www.uea.ac.uk/computing/specsel</a>.</p

Relationship between the number of species in the indicator and the average sensitivity score of constituent species in the most sensitive combination for that set size for the pan-European and alternative indicators drawn from all possible species.

<p>Average sensitivity scores calculated as average of niche breadth*reliance across constituent species, with higher scores associated with less sensitive indicators. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097217#pone.0097217.s001" target="_blank">Figure S1</a> for the equivalent figure for pan-European and alternative indicators drawn only from species currently covered by PECBMS.</p