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Global warming may force montane species to shift upward to keep pace with their shifting climate niche. How species differences in such distribution shifts depend on their elevational positions, elevation-dependent warming rates, and other environmental constraints, or plant functional traits is poorly understood. Here, we analyzed for 137 Himalayan tree species how distribution shifts vary with elevational niche positions, environmental constraints, and their functional traits. We developed ecological niche models using MaxEnt by combining species survey and botanical collections data with 19 environmental predictors. Species distributions were projected to 1985 and 2050 conditions, and elevational range parameters and distribution areas were derived. Under the worst-case RCP 8.5 scenario, species are predicted to shift, on average, 3 m/year in optimum elevation, and have 33% increase in distribution area. Highland species showed faster predicted elevational shifts than lowland species. Lowland and highland species are predicted to expand in distribution area in contrast to mid-elevation species. Tree species for which species distribution models are driven by responses to temperature, aridity, or soil clay content showed the strongest predicted upslope shifts. Tree species with conservative trait values that enable them to survive resource poor conditions (i.e., narrow conduits) showed larger predicted upslope shifts than species with wide conduits. The predicted average upslope shift in maximum elevation (8 m/year) is >2 times faster than the current observations indicating that many species will not be able to track climate change and potentially go extinct, unless they are supported by active conservation measures, such as assisted migration. Abstract in Nepali is available with online material.

The Caspian Sea is renowned for its endemic mollusk biodiversity. However, over the past decades, increasing anthropogenic pressures have caused decreases in abundances and even extinction of species. Both key pressures and endemic taxa are distributed spatially unevenly across the Caspian Sea, suggesting that ecologically different taxa such as gastropods and bivalves are also affected differentially. In addition, hotspot and non-hotspot areas for these taxa might differ quantitatively in pressure scores and qualitatively in key individual anthropogenic pressures. To test this working hypothesis, hotspot areas for endemic bivalve and gastropod species were identified using stacked species ranges. Cumulative and individual pressure scores were estimated for hotspot and non-hotspot areas of bivalves and gastropods. Differences in cumulative and individual pressure scores were tested for significance using non-parametric MANOVA and Wilcoxon rank sum tests, respectively. We identified various mollusk biodiversity hotspots across locations and depths, which are differentially affected both in terms of cumulative pressure scores and in the composition of the contributing individual pressures. Similarly, hotspot and non-hotspot areas for both bivalves and gastropods are differentially affected by anthropogenic pressures. By defining endemic hotspot areas and the respective anthropogenic pressures, this study provides an important baseline for mollusk-specific conservation strategies.

Tropical montane systems are characterized by a high plant species diversity and complex environmental gradients. Climate warming may force species to track suitable climatic conditions and shift their distribution upward, which may be particularly problematic for species with narrow elevational ranges. To better understand the fate of montane plant species in the face of climate change, we evaluated a) which environmental factors best predict the distribution of 277 plant species along the Himalayan elevational gradient in Nepal, and b) whether species elevational ranges increase with increasing elevation. To this end, we developed ecological niche models using MaxEnt by combining species survey and presence data with 19 environmental predictors. Key environmental factors that best predicted the distribution of Himalayan plant species were mean annual temperature (for 54.5% of the species) followed by soil clay content (10.2%) and slope (9.4%). Although temperature is the best predictor, it is associated with many other covariates that may explain species distribution, such as irradiance and potential evapotranspiration. Species at both ends of the Himalayan elevational gradient had narrower elevational ranges than species in the middle. Our results suggest that with further global warming, most Himalayan plant species have to migrate upward, which is especially critical for upland species with narrow distribution ranges.

The dataset includes georeferenced occurrences of species listed in Annex I of Resolution 6 of the Bern Convention and, partly, in the Red Data Book of Ukraine. The dataset was compiled within the work of NGO "Ukrainian Nature Conservation Group" aimed to prepare a Shadow list of Emerald Network (European network Areas of Special Conservation Interest) in Ukraine - newly proposed territories aimed at conservation of particular species and habitats mentioned in Resolution 4 and 6 of the Bern Convention. The list was prepared in 2017-2020 for expanding the already existing Emerald Network of Ukraine. Based on actual registrations of flora and fauna collected and gathered by scientists and naturalists in a form of dataset, which is described in the following paper. This dataset provides information about 29,938 occurrences of species from the territory of Ukraine listed in Annex I of Resolution 6 of the Bern Convention, as well as in the Red Data Book of Ukraine. This is the largest public dataset on occurrences of rare and endangered species from Ukraine till now. Data presented here laid the foundations for the proposal of 106 approved Emerald Network sites (2019), as well as for 148 Emerald Network sites that were nominated in 2020. New insights on the endemic species Centaurea pseudoleucolepis Kleopow is provided, which was previously considered to be extinct, according to the IUCN Red List.

The unique aquatic Pontocaspian (PC) biota of the Black Sea Basin (BSB) is in decline. The lack of detailed knowledge on the status and trends of species, populations, and communities hampers a thorough risk assessment and precludes effective conservation. This paper reviews PC biodiversity trends in the BSB (Bulgaria, Romania, Moldova, Ukraine, and Russia) using endemic mollusks as a model group. We aim to assess changes in PC habitats, community structure, and species distribution over the past century and to identify direct anthropogenic threats. The presence/absence data of target mollusk species were assembled from literature, reports, and personal observations. Pontocaspian biodiversity trends in the northwestern BSB coastal regions were established by comparing 20th‐ and 21st‐century occurrences. The direct drivers of habitat and biodiversity change were identified and documented. We found that a pronounced decline of PC species and communities is driven by (a) damming of rivers, (b) habitat modifications that disturbed previous natural salinity gradients and settings in the studied area, (c) pollution and eutrophication, (d) invasive alien species, and (e) climate change. Four out of the 10 studied regions, namely, the Danube Delta–Razim Lake system, Dniester Liman, Dnieper–Bug estuary, and Taganrog Bay–Don Delta, contain favorable ecological conditions for PC communities and still host threatened endemic PC mollusk species. Distribution data are incomplete, but the scale of deterioration of PC species and communities is evident from the assembled data, as are major direct threats. Pontocaspian biodiversity in the BSB is profoundly affected by human activities. Standardized observation and collection data as well as precise definition of PC biota and habitats are necessary for targeted conservation actions. This study will help to set the research and policy agenda required to improve data collection to accommodate effective conservation of the unique PC biota., Distribution data of unique, endemic Pontocaspian (PC) biodiversity of the Black Sea Basin is incomplete, but the scale of deterioration of PC species and communities is evident from the assembled data, as are major direct threats. This review will help to set the research and policy agenda required to improve data collection to accommodate effective conservation of the unique PC biota.

The Distributed System of Scientific Collections (DiSSCo) Research Infrastructure (RI) is presently in its preparatory phase. DiSSCo is developing a new distributed RI to operate as a one-stop-shop for the envisaged European Natural Science Collection (NSC) and all its derived information. Through mass digitisation, DiSSCo will transform the fragmented landscape of NSCs, including an estimated 1.5 billion specimens, into an integrated knowledge base that will provide interconnected evidence of the natural world. Data derived from European NSCs underpin countless discoveries and innovations, including tens of thousands of scholarly publications and official reports annually (supporting legislative and regulatory processes on sustainability, environmental change, land use, societal infrastructure, health, food, security, etc.); base-line biodiversity data; inventions and products essential to bio-economy; databases, maps and descriptions of scientific observations; educational material for students; and instructive and informative resources for the public. To expand the user community, DiSSCo will strengthen capacity building across Europe for maximum engagement of stakeholders in the biodiversity-related field and beyond, including industry and the private sector, but also policy-driving entities. Hence, it is opportune to reach out to relevant stakeholders in the European environmental policy domain represented by the European Environment Agency (EEA). The EEA aims to support sustainable development by helping to achieve significant and measurable improvement in Europe's environment, through the provision of timely, targeted, relevant and reliable information to policy-making agents and the public. The EEA provides information through the European Environment Information and Observation System (Eionet). The aim of this white paper is to open the discussion between DiSSCo and the EEA and identify the common service interests that are relevant for the European environmental policy domain. The first section describes the significance of (digital) Natural Science Collections (NHCs). Section two describes the DiSSCo programme with all DiSSCo aligned projects. Section three provides background information on the EEA and the biodiversity infrastructures that are developed and maintained by the EEA. The fourth section illustrates a number of use cases where the DiSSCo consortium sees opportunities for interaction between the DiSSCo RI and the Eionet portal of the EEA. Opening the discussion with the EEA in this phase of maturity of DiSSCo will ensure that the infrastructural design of DiSSCo and the development of e-Services accommodate the present and future needs of the EEA and assure data interoperability between the two infrastructures. The aim of this white paper is to present benefits from identifying the common service interests of DiSSCo and the EEA. A brief introduction to natural science collections as well as the two actors is given to facilitate the understanding of the needs and possibilities in the alignment of DiSSCo with the EEA.

Digitisation and publication of museum specimen data is happening worldwide, but far from complete. Museums can start by sharing what they know about their holdings at a higher level, long before each object has its own record. Information about what is held in collections worldwide is needed by many stakeholders including collections managers, funders, researchers, policy-makers, industry, and educators. To aggregate this information from collections, the data need to be standardised (Johnston and Robinson 2002). So, the Biodiversity Information Standards (TDWG) Collection Descriptions (CD) Task Group is developing a data standard for describing collections, which gives the ability to provide: automated metrics, using standardised collection descriptions and/or data derived from specimen datasets (e.g., counts of specimens) and a global registry of physical collections (i.e., digitised or non-digitised). automated metrics, using standardised collection descriptions and/or data derived from specimen datasets (e.g., counts of specimens) and a global registry of physical collections (i.e., digitised or non-digitised). Outputs will include a data model to underpin the new standard, and guidance and reference implementations for the practical use of the standard in institutional and collaborative data infrastructures. The Task Group employs a community-driven approach to standard development. With international participation, workshops at the Natural History Museum (London 2019) and the MOBILISE workshop (Warsaw 2020) allowed over 50 people to contribute this work. Our group organized online "barbecues" (BBQs) so that many more could contribute to standard definitions and address data model design challenges. Cloud-based tools (e.g., GitHub, Google Sheets) are used to organise and publish the group's work and make it easy to participate. A Wikibase instance is also used to test and demonstrate the model using real data. There are a range of global, regional, and national initiatives interested in the standard (see Task Group charter). Some, like GRSciColl (now at the Global Biodiversity Information Facility (GBIF)), Index Herbariorum (IH), and the iDigBio US Collections List are existing catalogues. Others, including the Consortium of European Taxonomic Facilities (CETAF) and the Distributed System of Scientific Collections (DiSSCo), include collection descriptions as a key part of their near-term development plans. As part of the EU-funded SYNTHESYS+ project, GBIF organized a virtual workshop: Advancing the Catalogue of the World's Natural History Collections to get international input for such a resource that would use this CD standard. Some major complexities present themselves in designing a standardised approach to represent collection descriptions data. It is not the first time that the natural science collections community has tried to address them (see the TDWG Natural Collections Description standard). Beyond natural sciences, the library community in particular gave thought to this (Heaney 2001, Johnston and Robinson 2002), noting significant difficulties. One hurdle is that collections may be broken down into different degrees of granularity according to different criteria, and may also overlap so that a single object can be represented in more than one collection description. Managing statistics such as numbers of objects is complex due to data gaps and variable degrees of certainty about collection contents. It also takes considerable effort from collections staff to generate structured data about their undigitised holdings. We need to support simple, high-level collection summaries as well as detailed quantitative data, and to be able to update as needed. We need a simple approach, but one that can also handle the complexities of data, scope, and social needs, for digitised and undigitised collections. The data standard itself is a defined set of classes and properties that can be used to represent groups of collection objects and their associated information. These incorporate common characteristics ('dimensions') by which we want to describe, group and break down our collections, metrics for quantifying those collections, and properties such as persistent identifiers for tracking collections and managing their digital counterparts. Existing terms from other standards (e.g. Darwin Core, ABCD) are re-used if possible. The data model (Fig. 1) underpinning the standard defines the relationships between those different classes, and ensures that the structure as well as the content are comparable across different datasets. It centres around the core concept of an 'object group', representing a set of physical objects that is defined by one or more dimensions (e.g., taxonomy and geographic origin), and linked to other entities such as the holding institution. To the object group, quantitative data about its contents are attached (e.g. counts of objects or taxa), along with more qualitative information describing the contents of the group as a whole. In this presentation, we will describe the draft standard and data model with examples of early adoption for real-world and example data. We will also discuss the vision of how the new standard may be adopted and its potential impact on collection discoverability across the collections community.

Limiting climate change to less than 2°C is the focus of international policy under the climate convention (UNFCCC), and is essential to preventing extinctions, a focus of the Convention on Biological Diversity (CBD). The post-2020 biodiversity framework drafted by the CBD proposes conserving 30% of both land and oceans by 2030. However, the combined impact on extinction risk of species from limiting climate change and increasing the extent of protected and conserved areas has not been assessed. Here we create conservation spatial plans to minimize extinction risk in the tropics using data on 289 219 species and modeling two future greenhouse gas concentration pathways (RCP2.6 and 8.5) while varying the extent of terrestrial protected land and conserved areas from

NLBIF Activiteitenverslag 2019

Social networks, defined as sets of relationships between stakeholder organizations, are important determinants of constructive actions for biodiversity conservation. Such actions are achieved through cooperation between various stakeholders, exchange of information, and joint planning and implementation. We used a mix of qualitative and quantitative social network analysis methods to investigate the interorganizational network of stakeholders in Ukraine, and the implications of network properties for the conservation of Pontocaspian biodiversity. Pontocaspian biota contains unique and endemic fauna, which are threatened by anthropogenic impacts; this makes effective conservation measures an urgent priority. We identified a well-connected, centralized network in Ukraine. However, the strong network has not resulted in effective conservation of Pontocaspian biodiversity. Suboptimal conservation action stems from the subordinate role of Pontocaspian species in the interorganizational interactions, likely due to lack of knowledge regarding Pontocaspian taxa. Social variables, such as funding scarcity and legal constraints, further limit the effectiveness of biodiversity conservation actions. We conclude that the current landscape of stakeholders in Ukraine is well placed to rapidly improve conservation actions if they are supplied with improved information and recognition of conservation needs of Pontocaspian taxa, combined with improved financial and legal conditions.

The Caspian Sea hosts unique native and endemic faunas. However, it is also a source and sink of invasive alien species (IAS), with some listed among the worst 100 invasive species by the IUCN. A common approach to study biodiversity and biogeographic patterns or to predict the invasive potential of species is the application of ecological niche models and species distribution models. These are statistical methods using spatially gridded environmental data and species occurrence information. As the Caspian Sea is not connected to the world's oceans, spatially gridded environmental data for the Caspian Sea are not available in the widely used Bio-ORACLE marine data set. To address this issue, we compiled 28 ecologically relevant spatially gridded environmental variables using Kriging interpolation of point data to model minimum, maximum, mean, and range of temperature, salinity, and dissolved oxygen for the surface and benthic zones of the Caspian Sea. Data were retrieved from the World Ocean Database. Additionally, we utilized raster statistics to create surface layers of maximum, mean, minimum, and range of chlorophyll a from remotely sensed data. We developed these environmental variables as they were previously confirmed to be relevant for the biogeographical classification of the Caspian Sea. To allow projections of models across the world's oceans into the Caspian Sea (and vice versa), we matched our raster dimensions with those of the Bio-ORACLE data set. Our extension of the Bio-ORACLE data set with data from the Caspian Sea provides an important basis for the monitoring and evaluation of suitable habitats for native species as well as predicting the invasive potential of Caspian Sea species into world oceans. Please cite this Data Paper and the associated Figshare data set if the data are used in publications.

Climate-smart strategic expansion of protected area networks is essential to protect and reinforce biocultural traditions., New Guinea is the most biologically and linguistically diverse tropical island on Earth, yet the potential impacts of climate change on its biocultural heritage remain unknown. Analyzing 2353 endemic plant species distributions, we find that 63% of species are expected to have smaller geographic ranges by 2070. As a result, ecoregions may have an average of −70 ± 40 fewer species by 2070. Species with future geographic range contractions include 720 endemic plant species that are used by indigenous people, and we find that these will decrease in 80% of New Guinea’s 1030 language areas, with losses of up to 94 species per language area. To mitigate the threats of climate change on the flora, we identify priority sites for protected area expansion that can jointly maximize biodiversity and useful plant conservation.

Made available in DSpace on 2020-12-10T17:39:01Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-08-05 Royal Society International Exchanges Carlsberg Foundation Danish Research Council National Natural Science Foundation of China Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) US National Science Foundation National Parks Board, Singapore Deutsche Forschungsgemeinschaft Synthesys Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Edinburgh Botanic Garden (Sibbald) Trust M. L. MacIntyre Begonia Trust Scottish Government's Rural and Environmental Science and Analytical Services Division New Guinea is the world's largest tropical island and has fascinated naturalists for centuries(1,2). Home to some of the best-preserved ecosystems on the planet(3)and to intact ecological gradients-from mangroves to tropical alpine grasslands-that are unmatched in the Asia-Pacific region(4,5), it is a globally recognized centre of biological and cultural diversity(6,7). So far, however, there has been no attempt to critically catalogue the entire vascular plant diversity of New Guinea. Here we present the first, to our knowledge, expert-verified checklist of the vascular plants of mainland New Guinea and surrounding islands. Our publicly available checklist includes 13,634 species (68% endemic), 1,742 genera and 264 families-suggesting that New Guinea is the most floristically diverse island in the world. Expert knowledge is essential for building checklists in the digital era: reliance on online taxonomic resources alone would have inflated species counts by 22%. Species discovery shows no sign of levelling off, and we discuss steps to accelerate botanical research in the 'Last Unknown'(8). A catalogue of the vascular flora of New Guinea indicates that this island is the most floristically diverse in the world, and that 68% of the species identified are endemic to New Guinea. Royal Bot Gardens, London, England Nat Biodivers Ctr, Leiden, Netherlands Univ Michigan Herbarium, Ann Arbor, MI USA Univ Gottingen, Dept Systemat Biodivers & Evolut Plants, Albrecht von Haller Inst Plant Sci, Gottingen, Germany Royal Bot Garden Edinburgh, Edinburgh, Midlothian, Scotland Aarhus Univ, Dept Biosci, Aarhus, Denmark Univ Vermont, Dept Plant Biol, Pringle Herbarium, Burlington, VT USA Boise State Univ, Dept Biol Sci, Boise, ID 83725 USA Conservatoire & Jardin Bot Ville Geneve, Chambesy, Switzerland Univ Sydney, Sch Life & Environm Sci, Sydney, NSW, Australia Natl Museum Nat & Sci, Dept Bot, Tsukuba, Ibaraki, Japan Univ Minnesota, Dept Plant & Microbial Biol, St Paul, MN 55108 USA New Guinea Binatang Res Ctr, Madang, Papua N Guinea Guangxi Zhuang Autonomous Reg & Chinese Acad Sci, Guangxi Key Lab Plant Conservat & Restorat Ecol K, Guangxi Inst Bot, Guilin, Peoples R China Papua New Guinea Univ Technol, Lae, Papua N Guinea Herbarium Bogoriense, Bogor, Indonesia Univ Fed Mato Grosso do Sul, Tres Lagoas Campus, Tres Lagoas, Brazil Royal Bot Garden Sydney, Sydney, NSW, Australia Univ Papua, Fak Kehutanan, Manokwari, Indonesia Baden Penelitian & Pengembangan Daerah Prov Papua, Manokwari, Indonesia Missouri Bot Garden, St Louis, MO USA Papua New Guinea Forest Res Inst, Lae, Papua N Guinea Natl Bot Gardens Ireland, Glasnevin, Ireland Univ Zurich, Dept Systemat & Evolutionary Bot, Zurich, Switzerland Forest Res Inst Malaysia, Kepong, Malaysia Nat Hist Museum, Dept Life Sci, London, England Univ Bonn, Nees Inst Biodivers Plants, Bonn, Germany Martin Luther Univ Halle Wittenberg, Inst Biol, Herbarium, Halle, Germany Natl Pk Board, Nat Plants Ctr, Singapore, Singapore Natl Pk Board, Singapore Bot Gardens, Singapore, Singapore Univ Aberdeen, Sch Biol Sci, Aberdeen, Scotland Univ Philippines Manila, Coll Arts & Sci, Dept Biol, Manila, Philippines Kyoto Univ, Kyoto Univ Museum, Kyoto, Japan Sao Paulo State Univ, Inst Biosci, Rio Claro, Brazil Royal Bot Gardens Victoria, South Yarra, Vic, Australia Univ Melbourne, Sch BioSci, Parkville, Vic, Australia Univ Oxford, Dept Plant Sci, Oxford, England Queens Univ Belfast, Marine Lab, Portaferry, North Ireland Fern Res Fdn, Kerikeri, New Zealand Univ N Carolina, Wilmington, NC USA Museum New Zealand Papa Tongarewa, Wellington, New Zealand Western Michigan Univ, Dept Biol Sci, Kalamazoo, MI 49008 USA New Mexico State Univ, Dept Anim & Range Sci, Las Cruces, NM 88003 USA Univ Fed Vicosa, Dept Biol Vegetal, Vicosa, MG, Brazil Colorado State Univ, Dept Biol, Ft Collins, CO 80523 USA Trinity Coll Dublin, Sch Nat Sci, Dept Bot, Dublin, Ireland Univ Florida, Dept Biol, Gainesville, FL USA CSIRO Land & Water, Floreat, WA, Australia Univ Western Australia, Sch Biol Sci, Crawley, WA, Australia Chinese Acad Sci, Inst Bot, Beijing, Peoples R China Univ Minnesota, Bell Museum, St Paul, MN 55108 USA Univ Glasgow, Inst Biodivers Anim Hlth & Comparat Med, Glasgow, Lanark, Scotland Chinese Acad Sci, Chengdu Inst Biol, Chengdu, Peoples R China Leiden Univ, Inst Biol Leiden, Leiden, Netherlands Univ Zurich, Dept Evolutionary Biol & Environm Studies, Zurich, Switzerland Sao Paulo State Univ, Inst Biosci, Rio Claro, Brazil Royal Society International Exchanges: IE 170241 National Natural Science Foundation of China: 31570307 CNPq: 153430/2018-4 US National Science Foundation: DEB-1457366 US National Science Foundation: DEB-1754667 US National Science Foundation: DEB-0515678 US National Science Foundation: DEB-0816749 US National Science Foundation: DEB-0841885 Deutsche Forschungsgemeinschaft: LE 1826/3-1 Synthesys: GB-TAF-6305 FAPESP: 2015/13112-7 FAPESP: 2018/09379-6

Alnus acuminata is a keystone tree species in the Yungas forests and host to a wide range of fungal symbionts. While species distribution models (SDMs) are routinely used for plants and animals to study the effects of climate change on montane forest communities, employing SDMs in fungi has been hindered by the lack of data on their geographic distribution. The well-known host specificity and common biogeographic history of A. acuminata and associated ectomycorrhizal (ECM) fungi provide an exceptional opportunity to model the potential habitat for this symbiotic assemblage and to predict possible climate-driven changes in the future. We (1) modeled the present and future distributions of suitable habitats for A. acuminata; (2) characterized fungal communities in different altitudinal zones of the Yungas using DNA metabarcoding of soil and root samples; and (3) selected fungi that were significant indicators of Alnus. Fungal communities were strongly structured according to altitudinal forest types and the presence of Alnus. Fungal indicators of Alnus, particularly ECM and root endophytic fungi, were also detected in Alnus roots. Current and future (year 2050) habitat models developed for A. acuminata predict a 25–50 percent decrease in suitable area and an upslope shift of the suitable habitat by ca. 184–380 m, depending on the climate change scenario. Although A. acuminata is considered to be an effective disperser, recent studies suggest that Andean grasslands are remarkably resistant to forest invasion, and future range contraction for A. acuminata may be even more pronounced than predicted by our models.

Aggregating content of museum and scientific collections worldwide offers us the opportunity to realize a virtual museum of our planet and the life upon it through space and time. By mapping specimen-level data records to standards and publishing this information, an increasing number of collections contribute to a digitally accessible wealth of knowledge. Visualizing these digital records by parameters such as collection type and geographic origin, helps collections and institutions to better understand their digital holdings and compare them to other such collections, as well as enabling researchers to find specimens and specimen data quickly (Singer et al. 2018). At the higher level of collections, related people and their activities, and especially the great majority of material that is yet to be digitised, we know much less. Many collections hold material not yet digitally discoverable in any form. For those that do publish collection-level data, it is commonly text-based data without the Global Unique Identifiers (GUIDs) or the controlled vocabularies that would support quantitative collection metrics and aid discovery of related expertise and publications. To best understand and plan for our world’s bio- and geodiversity represented in collections, we need standardised, quantitative collections-level metadata. Various groups planet-wide are actively developing tools to capture this much-needed metadata, including information about the backlog, and more detailed information about institutions and their activities (e.g. staffing, space, species-level inventories, geographic and taxonomic expertise, and related publications) (Smith et al. 2018). The Biodiversity Information Standards organization (TDWG) Collection Descriptions (CD) Data Standard Task Group aims to provide a data standard for describing natural scientific collections, which enables the ability to provide: automated metrics, using standardised collection descriptions and/or data derived from specimen datasets (e.g., counts of specimens) and a global registry of physical collections (either digitised or non-digitised). automated metrics, using standardised collection descriptions and/or data derived from specimen datasets (e.g., counts of specimens) and a global registry of physical collections (either digitised or non-digitised). The group will also produce a data model to underpin the new standard, and provide guidance and reference implementations for the practical use of the standard in institutional and collaborative data infrastructures. Our task group includes members from a myriad of groups with a stake in mobilizing such data at local, regional, domain-specific and global levels. With such a standard adopted, it will be possible to effectively share data across different community resources. So far, we have carried out landscape analyses of existing collection description frameworks, and amassed a portfolio of use cases from the group as well as from a range of other sources, including the Collection Descriptions Dashboard working group of ICEDIG ("Innovation and consolidation for large scale digitisation of natural heritage"), iDigBio (Integrated Digitized Biocollections), Smithsonian, Index Herbariorum, the Field Museum, GBIF (Global Biodiversity Information Facility), GRBio (Global Registry of Biodiversity Repositories) and fishfindR.net. These were used to develop a draft data model, and between them inform the first iteration of CD draft data standard. A variety of challenges present themselves in developing this standard. Some relate to the standard development process itself, such as identifying (often learning) effective tools and methods for collaborative working and communication across globally distributed volunteers. Others concern the scope and gaining consensus from stakeholders, across a wide range of disciplines, while maintaining achievable goals. Further challenges arise from the requirement to develop a data model and standard that support such a variety of use cases and priorities, while retaining interoperability and manageability of the data. We will present some of these challenges and methods for addressing them, and summarise the progress and draft outputs of the group so far. We will also discuss the vision of how the new standard may be adopted and its potential impact on collections discoverability across the natural science collections community.

With digitisation of natural history collections over the past decades, their traditional roles — for taxonomic studies and public education — have been greatly expanded into the fields of biodiversity assessments, climate change impact studies, trait analyses, sequencing, 3D object analyses etc. (Nelson and Ellis 2019; Watanabe 2019). Initial estimates of the global natural history collection range between 1.2 and 2.1 billion specimens (Ariño 2010), of which 169 million (8-14% - as of April 2019) are available at some level of digitisation through the Global Biodiversity Information Facility (GBIF). With iDigBio (Integrated Digitized Biocollections) established in the United States and with the European DiSSCo (Distributed Systems of Scientific Collections) accepted on the ESFRI roadmap, it has become a priority to digitize natural history collections at an industrialized scale. Both iDigBio and DiSSCo aim at mobilising, unifying and delivering bio- and geo-diversity information at the scale, form and precision required by scientific communities, and thereby transform a fragmented landscape into a coherent and responsive research infrastructure. In order to prioritise digitisation based on scientific demand, and efficiency using industrial digitisation pipelines, it is required to arrive at a uniform and unambiguously accepted collection description standard that would allow comparing, grouping and analysing natural history collections at diverse levels. Several initiatives attempt to unambiguously describe natural history collections using taxonomic and storage classification schemes. These initiatives include One World Collection, Global Registry of Scientific Collections (GRSciColl), TDWG (Taxonomic Databases Working Group) Natural Collection Descriptions (NCD) and CETAF (Consortium of European Taxonomy Facilities) passports, among others. In a collaborative effort of DiSSCo, ICEDIG (Innovation and consolidation for large scale digitisation of natural heritage), iDigBio, TDWG and the Task Group Collection Digitisation Dashboards, the various schemes were compared in a cross-walk analysis to propose a preliminary natural collection description standard that is supported by the wider community. In the process, two main user groups of collection descriptions standards were identified; scientists and collection managers. The classification produced intends to meet requirements from them both, resulting in three classification schemes that exist in parallel to each other (van Egmond et al. 2019). For scientific purposes a ‘Taxonomic’ and ‘Stratigraphic’ classification were defined, and for management purposes a ‘Storage’ classification. The latter is derived from specimen preservation types (e.g. dried, liquid preserved) defining storage requirements and the physical location of specimens in collection holding facilities. The three parallel collection classifications can be cross-sectioned with a ‘Geographic’ classification to assign sub-collections to major terrestrial and marine regions, which allow scientists to identify particular taxonomic or stratigraphic (sub-)collections from major geographical or marine regions of interest. Finally, to measure the level of digitisation of institutional collections and progress of digitisation through time, the number of digitised specimens for each geographically cross-sectioned (sub-)collection can be derived from institutional collection management systems (CMS). As digitisation has different levels of completeness a ‘Digitisation’ scheme has been adopted to quantify the level of digitisation of a collection from Saarenmaa et al. 2019, ranging from ‘not digitised’ to extensively digitised, recorded in a progressive scale of MIDS (Minimal Information for Digital Specimen). The applicability of this preliminary classification will be discussed and visualized in a Collection Digitisation Dashboards (CDD) to demonstrate how the implementation of a collection description standard allows the identification of existing gaps in taxonomic and geographic coverage and levels of digitisation of natural history collections. This set of common classification schemes and dashboard design (van Egmond et al. 2019) will be contributed to the TDWG Collection Description interest group to ultimately arrive at the common goal of a 'World Collection Catalogue'.

African bush mango trees (Irvingiaceae) are priority food trees in West and Central Africa. There are bitter- and sweet-fruited species, which are difficult to distinguish based on morphological characters. This has led to a debate about their correct taxonomic status. Furthermore, it is unclear whether they are native to the Dahomey Gap, the dry and hot area, separating the two West African forest blocks. This study evaluates the ecological differences between bitter- and sweet-fruited species in tropical Africa, and the nature (wild vs. cultivated) of the occurrences in the Dahomey Gap, in order to discuss the current taxonomical opinions. Irvingia gabonensis and I. wombolu occurrence data were combined with climate and soil data in MaxEnt to produce environmental niche models. Environmental niche identity tests were carried out in ENM-Tools. Wild sweet-fruited trees were predicted in the Guinean–Congolian phytogeographical region, while the predicted occurrence of bitter-fruited trees extended to the Guineo–Congolia/Sudania and Lake Victoria transition zones. The related niche difference is significant, supporting the taxonomical opinion that bitter- and sweet-fruited species are two different taxa. We also conclude that bitter-fruited trees occur naturally in the Volta forests (Dahomey Gap). Moreover, our results support that I. gabonensis is not native to the Dahomey Gap. In historical times, they were probably introduced from Nigeria.

DiSSCo – the Distributed System of Scientific Collections – will mobilise, unify and deliver bio- and geo-diversity information at the scale, form and precision required by scientific communities, and thereby transform a fragmented landscape into a coherent and responsive research infrastructure. At present DiSSCo has 115 partners from 21 countries across Europe. The DiSSCo research infrastructure will enable critical new insights from integrated digital data to address some of the world's greatest challenges, such as biodiversity loss, food security and impacts of climate change. A requirement analysis for DiSSCo was conducted to ensure that all of its envisioned future uses are accommodated through a large survey using epic user stories. An epic user story has the following format: As [e.g. scientist] I want to [e.g. map the distribution of a species through time] so that I [e.g. analyse the impact of climate change] for this I need [e.g. all georeferenced specimens records through time] Several consultation rounds within the ICEDIG community resulted in 78 unique user stories that were assigned to one, or more, out of seven recognized stakeholder categories: Research, Collection management, Technical support, Policy, Education, Industry, and External. Research, Collection management, Technical support, Policy, Education, Industry, and External. Each user story was assessed for the level of collection detail it required; four levels of detail were recognised: Collection, Taxonomic, Storage unit, and Specimen level. Furthermore, it was assessed whether the future envisioned use of digitised natural history collections were possible without the DiSSCo research infrastructure. Subsequently 1243 identified stakeholders were invited to review the DiSSCo user stories through a Survey Monkey questionnaire. Additionally, an invitation for review was posted in several Facebook groups and announced on Twitter. A total of 379 stakeholders responded to the invitation, which led to 85 additional user stories for the envisioned use of the DiSSCo research infrastructure. In order to assess which component of the DiSSCo data flow diagram should facilitate the described user story, all user stories were mapped to the five phases of the DiSSCo Data Management Cycle (DMC), including data: acquisition, curation, publishing, processing, and use. acquisition, curation, publishing, processing, and use. At present, the user stories are being analysed and the results will be presented in this symposium.

Natural Science Collections (NSCs) contain specimen-related data from which we extract valuable information for science and policy. Openness of those collections facilitates development of science. Moreover, virtual accessibility to physical containers by means of their digitization will allow an exponential increase in the level of available information. Digitization of collections will allow us to set a comprehensive registry of reliable, accurate, updated, comparable and interconnected information. Equally, the scope of interested potential users will largely expand and so will the different levels of granularity required by researchers, institutions and governmental bodies. Meeting diverse needs entails a special effort in data management and data analysis to extract, digest and present information on a compressed but still precise and objective-oriented format. The Collections Digitisation Dashboard (CDD) underpins such an attempt. The CDD stands as a practical tool that specifically aims to support high-level decisions with a wide coverage of data, by providing a visual, simplified and structured arrangement that will allow discovery of key indicators concerning digitization of bio- and geodiversity collections. The realm of possible approaches to the CDD covers levels of digitization, collection exceptionality, resourceavailability and many others. Still all those different angles need to be aligned and processed at once to provide an overall overview of the status of NSCs in the digitization process and analyse its further development. The CDD is a powerful mechanism to identify priorities, specialisation lines together with regional development, gaps and niches and future capabilities as well, and strengths and weaknesses across collections, institutions, countries and regions. It can perfectly underpin measurable and comparable assessments, with evolution indexes and progress indicators, all under an overarching homogenous approach. The Distributed System of Scientific Collections (DiSSCo) Research Infrastructure, currently in its preparatory phase, is built on top of the largest ever community of collections-related institutions across Europe and anchored on the Consortium of European Taxonomic Facilities (CETAF). It aims to provide a unique virtual access point to NSCs by facilitating a large and massive digitisation effort throughout Europe. Setting up priorities and specialization areas is pivotal to its success. To that end, the DiSSCo CDD will provide a valuation tool to summarize and showcase NSC's digitization status on a first-hand visualization. Different projects and initiatives will contribute, jointly and on a synergetic basis, to the production of the DiSSCo CDD. The ICEDIG project will address its basics features, terms of classification and tiers of information, and will produce a prototype and a set of recommendations on how to better attempt a massive dashboard by collating specific collections-based information and defining global strategic representations. CETAF working groups on collections and digitization will provide the desired homogeneity in describing and capturing the different implementation requirements from the users’ perspectives, which will be complemented by the contributions made under the umbrella of the COST Action MOBILISE. The Action will use networking activities to identify the right standards and policies to enable enlarging the scope of the DiSSCo CDD and its broader implementation by linking to the TDWG criteria and adopted standards. Complementarily, the ELViS platform to be developed under the SYNTHESYS+ project will provide the right virtual environment. Furthermore, SYNTHESYS+ will address the assessment capabilities of the CDD to enable the visual representation becoming a practical assessment mechanism and endow it with a dynamic feature for analysis over the time. The DiSSCo CDD will thus become an instrumental mechanism for decision-taking that will be embedded into the clustering initiative of products and services provided to the EOSCby the ENVRI-FAIR project in the environmental domain.

Made available in DSpace on 2018-12-11T17:17:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-12-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Ministério da Ciência, Tecnologia e Inovação Ministério da Educação Species distribution models (SDMs) are widely used in ecology and conservation. Presence-only SDMs such as MaxEnt frequently use natural history collections (NHCs) as occurrence data, given their huge numbers and accessibility. NHCs are often spatially biased which may generate inaccuracies in SDMs. Here, we test how the distribution of NHCs and MaxEnt predictions relates to a spatial abundance model, based on a large plot dataset for Amazonian tree species, using inverse distance weighting (IDW). We also propose a new pipeline to deal with inconsistencies in NHCs and to limit the area of occupancy of the species. We found a significant but weak positive relationship between the distribution of NHCs and IDW for 66% of the species. The relationship between SDMs and IDW was also significant but weakly positive for 95% of the species, and sensitivity for both analyses was high. Furthermore, the pipeline removed half of the NHCs records. Presence-only SDM applications should consider this limitation, especially for large biodiversity assessments projects, when they are automatically generated without subsequent checking. Our pipeline provides a conservative estimate of a species' area of occupancy, within an area slightly larger than its extent of occurrence, compatible to e.g. IUCN red list assessments. Coordenação de Botânica Museu Paraense Emílio Goeldi, Av. Magalhães Barata 376, C.P. 399 Programa de Pós-Graduação em Ciência Ambientais Universidade Federal Do Pará, Rua Augusto Corrêa 01 Biodiversity Dynamics Naturalis Biodiversity Center, PO Box 9517 Marine and Coastal Management, Deltares, Boussinesqweg 1 Coordenação de Biodiversidade Instituto Nacional de Pesquisas da Amazônia - INPA, Av. André Araújo 2936 EMBRAPA Centro de Pesquisa Agroflorestal de Roraima, BR 174 km 8, Distrito Industrial Herbario Amazónico Colombiano Instituto SINCHI, Calle 20 No 5-44 Department of Integrative Biology University of California Universidad San Francisco de Quito Colegio de Ciencias Biológicas Diego de Robles y Vía Interoceánica Coordenação de Pesquisas em Ecologia Instituto Nacional de Pesquisas da Amazônia - INPA, Av. André Araújo 2936 School of Geography University of Leeds, Woodhouse Lane Department of Wetland Ecology Institute of Geography and Geoecology Karlsruhe Institute of Technology - KIT, Josefstr 1 Biogeochemistry Max Planck Institute for Chemistry, Hahn-Meitner Weg 1 Departamento de Botânica Instituto de Pesquisas Científicas e Tecnológicas Do Amapá - IEPA Campus Do IEPA da Fazendinha, Rodovia JK Km 10 AMAP IRD Cirad CNRS INRA Université de Montpellier, TA A-51/PS2, Bd. de la Lironde Science and Education Field Museum, 1400 S. Lake Shore Drive Coordenação de Dinâmica Ambiental Instituto Nacional de Pesquisas da Amazônia - INPA, Av. André Araújo 2936 Jardín Botánico de Missouri Departamento de Ecologia Universidade Estadual Paulista - UNESP Instituto de Biociências - IB, Av. 24 A 1515 Centro de Biociências Departamento de Ecologia Universidade Federal Do Rio Grande Do Norte, Av. Senador Salgado Filho 3000 Divisao de Sensoriamento Remoto - DSR Instituto Nacional de Pesquisas Espaciais - INPE, Av. dos Astronautas 1758 Herbario Vargas Universidad Nacional de San Antonio Abad Del Cusco, Avenida de la Cultura, Nro 733 Departamento de Geografia Universidade Estadual Paulista - UNESP Instituto de Geociências e Ciências Extas - IGCe Departamento de Biologia Universidade Federal de Rondônia, Rodovia BR 364 s/n Km 9.5 Center for Tropical Conservation Duke University Nicholas School of the Environment Programa de Pós-Graduação em Biodiversidade e Biotecnologia - PPG-Bionorte Universidade Federal de Rondônia Campus Porto Velho, Km 9.5 Instituto Boliviano de Investigacion Forestal Santa Cruz, Av. 6 agosto 28 Km 14,Doble via La Guardia Casilla, 6204 Programa de Pós-Graduação em Ecologia e Conservação Universidade Do Estado de Mato Grosso Instituto de Investigaciones de la Amazonía Peruana - IIAP, Av. A. Quiñones km 2.5 Geography College of Life and Environmental Sciences University of Exeter Departamento de Ciencias Forestales Universidad Nacional de Colombia, Calle 64 x Cra 65 Agteca-Amazonica Facultad de Ciencias Agrícolas Universidad Autónoma Gabriel René Moreno Santa Cruz Prédio da Botânica e Ecologia Embrapa Recursos Genéticos Biotecnologia Parque Estação Biológica, Av. W5 Norte Projeto Dinâmica Biológica de Fragmentos Florestais Instituto Nacional de Pesquisas da Amazônia - INPA, Av. André Araújo 2936 Centre for Tropical Environmental and Sustainability Science College of Science and Engineering James Cook University Laboratório de Ecologia de Doenças Transmissíveis da Amazônia (EDTA) Instituto Leônidas e Maria Deane Fiocruz, Rua Terezina 476 Programa de Pós-graduação em Biodiversidade Saúde Instituto Oswaldo Cruz IOC/FIOCRUZ Pav. Arthur Neiva, Térreo Av. Brasil 4365 Centro de Investigaciones Ecológicas de Guayana Universidad Nacional Experimental de Guayana Calle Chile, urbaniz Chilemex Laboratorio de Ecología de Bosques Tropicales y Primatología Universidad de Los Andes, Carrera 1 # 18a- 10 Programa de Pós-Graduação em Biologia (Botânica) Instituto Nacional de Pesquisas da Amazônia - INPA, Av. André Araújo 2936 Institute of Biodiversity and Ecosystem Dynamics University of Amsterdam, Sciencepark 904 Programa de Ciencias Del Agro y El Mar Herbario Universitario (PORT) UNELLEZ-Guanare Endangered Species Coalition, 8530 Geren Rd MAUA Working Group Instituto Nacional de Pesquisas da Amazônia - INPA, Av. André Araújo 2936 Inventory and Monitoring Program National Park Service, 120 Chatham Lane Center for Conservation Education Sustainability Smithsonian Conservation Biology Institute, 1100 Jefferson Dr. SW, Suite 3123 Biologia Vegetal Universidade Estadual de Campinas, Caixa Postal 6109 Institute of Molecular Plant Sciences University of Edinburgh, Mayfield Rd Royal Botanic Garden Edinburgh, 20a Inverleith Row Programa de Pós-Graduação em Ecologia Instituto Nacional de Pesquisas da Amazônia - INPA, Av. André Araújo 2936 Forest Ecology and Forest Management Group Wageningen University Wageningen University and Research, Droevendaalsesteeg 3, P.O. Box 47 Natural Capital and Plant Health Royal Botanic Gardens Kew Ecosistemas Biodiversidad y Conservación de Especies Universidad Estatal Amazónica, Km. 2 1/2 vía a Tena (Paso Lateral) Museo de Historia Natural Noel Kempff Mercado Universidad Autónoma Gabriel Rene Moreno Santa Cruz, Avenida Irala 565 Casilla Postal 2489 Centro de Biociências Departamento de Botânica e Zoologia Universidade Federal Do Rio Grande Do Norte Campus Universitário Department of Biology University of Miami Coral Fairchild Tropical Botanic Garden Coral Instituto de Biociências Dept. Ecologia Universidade de Sao Paulo - USP, Rua do Matão, Trav. 14, no. 321 Diretoria de Pesquisas Científicas Instituto de Pesquisas Jardim Botânico Do Rio de Janeiro Escuela de Biología Herbario Alfredo Paredes Universidad Central, Ap. Postal 17.01.2177 International Center for Tropical Botany (ICTB) Department of Biological Sciences Florida International University, 11200 SW 8th Street, OE 243 Grupo de Ecología de Ecosistemas Terrestres Tropicales Universidad Nacional de Colombia Sede Amazonía Institute of Biological and Health Sciences Federal University of Alagoas, Av. Lourival Melo Mota, s/n Museu Universitário Centro de Ciências Biológicas e da Natureza Laboratório de Botânica e Ecologia Vegetal Universidade Federal Do Acre Universidad Regional Amazónica IKIAM, Km 7 via Muyuna Cirad UMR Ecofog AgrosParisTech CNRS INRA Univ Guyane Campus Agronomique Iwokrama International Programme for Rainforest Conservation Department of Biological Sciences Humboldt State University, 1 Harpst Street New York Botanical Garden, 2900 Southern Blvd Servicios de Biodiversidad EIRL, Jr. Independencia 405 Herbario Nacional de Bolivia Universitario UMSA, Casilla 10077 Correo Central Missouri Botanical Garden, P.O. Box 299 School of Environmental Sciences University of East Anglia Laboratoire Evolution et Diversité Biologique CNRS Université Paul Sabatier UMR 5174 EDB Department of Forestry Management Universidad Nacional Agraria la Molina, Avenido La Molina, Apdo. 456 School of Geosciences University of Edinburgh, 201 Crew Building, King's Buildings Biology Department Center for Energy Environment and Sustainability Wake Forest University, 1834 Wake Forest Rd Department of Anthropology University of Texas at Austin, SAC 5.150, 2201 Speedway Stop C3200 Andes to Amazon Biodiversity Program Madre de Dios Fundación Puerto Rastrojo, Cra 10 No. 24-76 Oficina 1201 Colegio de Ciencias Biológicas y Ambientales-COCIBA Galapagos Institute for the Arts and Sciences-GAIAS Universidad San Francisco de Quito-USFQ Department of Wildlife Ecology Conservation University of Florida, 110 Newins-Ziegler Hall Fundación Estación de Biología, Cra 10 No. 24-76 Oficina 1201 Embrapa Amazônia Oriental Trav. Dr. Enéas Pinheiro S/no Instituto de Ciencias Naturales Universidad Nacional de Colombia, Apartado, 7945 Amazon Conservation Team, Doekhieweg Oost #24 Facultad de Ciencias Forestales y Medio Ambiente Universidad Nacional de San Antonio Abad Del Cusco, Jirón San Martín 451 Laboratory of Human Ecology Instituto Venezolano de Investigaciones Científicas - IVIC, Ado 20632 Departement EV Muséum National d'Histoire Naturelle de Paris, 16 rue Buffon Instituto de Ciência Agrárias Universidade Federal Rural da Amazônia, Av. Presidente Tancredo Neves 2501 PROTERRA Instituto de Investigaciones de la Amazonía Peruana (IIAP), Av. A. Quiñones km 2 5 ACEER Foundation, Jirón Cusco No 370 Universidad Autónoma Del Beni José Ballivián Campus Universitario Final, Av. Ejercito Regional Norte Amazónico Centro de Investigación y Promoción Del Campesinado, C/Nicanor Gonzalo Salvatierra No 362 Environmental Change Institute Oxford University Centre for the Environment Dyson Perrins Building, South Parks Road School of Agriculture and Food Sciences ARC Centre of Excellence for Environmental Decisions CEED University of Queensland Instituto de Investigaciones para El Desarrollo Forestal (INDEFOR) Universidad de Los Andes, Conjunto Forestal, C.P. 5101 School of Environmental and Forest Sciences University of Washington University of Nottingham, University Park Geography and the Environment University of Texas at Austin, 305 E. 23rd Street, CLA building Laboratório de Ciências Ambientais Universidade Estadual Do Norte Fluminense, Av. Alberto Lamego 2000 Department of Biology University of Maryland GeoIS, El Día 369 y El Telégrafo, 3o Piso Environmental Science and Policy Northern Arizona University FOMABO Manejo Forestal en Las Tierras Tropicales de Bolivia Escuela de Ciencias Forestales (ESFOR) Universidad Mayor de San Simon (UMSS) Agricultural Services Ministry of Agro-Industry and Food Security Agricultural Services Ministry of Agro-Industry and Food Security Mauritius Herbarium Department of Bioscience Aarhus University, Building 1540 Ny Munkegade Ciencias Biológicas Universidad de Los Andes, Carrera 1 # 18a- 10 Medio Ambiente PLUSPRETOL Center for Conservation Education and Sustainability Smithsonian's National Zoo Conservation Biology Institute, National Zoological Park, 3001 Connecticut Ave Tropenbos International, Lawickse Allee 11 PO Box 232 Instituto de Biodiversidade e Floresta Universidade Federal Do Oeste Do Pará Campus Tapajós, Rua Vera Paz Department of Biology University of Missouri Facultad de Biologia Universidad Nacional de la Amazonia Peruana, Pevas 5ta cdra School of Anthropology and Conservation University of Kent, Marlowe Building Herbario Nacional Del Ecuador Universidad Técnica Del Norte Ecology and Biodiversity Group Utrecht University, Padualaan 8 Systems Ecology Free University, De Boelelaan 1087 Departamento de Ecologia Universidade Estadual Paulista - UNESP Instituto de Biociências - IB, Av. 24 A 1515 Departamento de Geografia Universidade Estadual Paulista - UNESP Instituto de Geociências e Ciências Extas - IGCe CAPES: 407232/2013-3-PVE CNPq: 407232/2013-3-PVE Ministério da Ciência, Tecnologia e Inovação: 407232/2013-3-PVE Ministério da Educação: 407232/2013-3-PVE

Romania and Ukraine share the Black Sea coastline, the Danube Delta and associated habitats, which harbor the endemic, aquatic Pontocaspian biota. Currently, this biota is diminishing both in numbers of species and their abundance because of human activities, and its future persistence strongly depends on the adequacy of conservation measures. Romania and Ukraine have a common responsibility to address the conservation of Pontocaspian biodiversity. The two countries, however have different socio-political and legal conservation frameworks, which may result in differences in the social network structure of stakeholder institutions with different implications for Pontocaspian biodiversity conservation. Here, we study the social network structure of stakeholder organizations involved in conservation of Pontocaspian biodiversity in Romania and the implications of network structure for conservation outcomes. Then we compare the findings from Romania to an earlier similar study from Ukraine. We apply a mix of qualitative and quantitative social network analysis methods to combine the content and context of the interactions with relational measures. We show that Pontocaspian biodiversity plays a minor and mostly incidental role in the inter-organizational interactions in Romania. Furthermore, there is room for improvement in the network structure through e.g. more involvement of governmental and nongovernmental organizations and increased motivation of central stakeholders to initiate conservation actions. Social variables, such as lack of funding, hierarchical, non-inclusive system of conservation governance and continuous institutional reforms in the public sector are consequential for the network relations and structure. Social network of stakeholders in Ukraine is more connected and central stakeholders utilize their favorable positions. However, neither in Ukraine is the Pontocaspian biodiversity a driver of organizational interactions. Consequently, both networks translate into sub-optimal conservation actions and the roads to optimal conservation are different. We end with sketching out conservation implications and recommendations for improved national and cross-border conservation efforts.

Species distribution models (SDMs) are widely used to predict the occurrence of species. Because SDMs generally use presence-only data, validation of the predicted distribution and assessing model accuracy is challenging. Model performance depends on both sample size and species’ prevalence, being the fraction of the study area occupied by the species. Here, we present a novel method using simulated species to identify the minimum number of records required to generate accurate SDMs for taxa of different pre-defined prevalence classes. We quantified model performance as a function of sample size and prevalence and found model performance to increase with increasing sample size under constant prevalence, and to decrease with increasing prevalence under constant sample size. The area under the curve (AUC) is commonly used as a measure of model performance. However, when applied to presence-only data it is prevalence-dependent and hence not an accurate performance index. Testing the AUC of an SDM for significant deviation from random performance provides a good alternative. We assessed the minimum number of records required to obtain good model performance for species of different prevalence classes in a virtual study area and in a real African study area. The lower limit depends on the species’ prevalence with absolute minimum sample sizes as low as 3 for narrow-ranged and 13 for widespread species for our virtual study area which represents an ideal, balanced, orthogonal world. The lower limit of 3, however, is flawed by statistical artefacts related to modelling species with a prevalence below 0.1. In our African study area lower limits are higher, ranging from 14 for narrow-ranged to 25 for widespread species. We advocate identifying the minimum sample size for any species distribution modelling by applying the novel method presented here, which is applicable to any taxonomic clade or group, study area or climate scenario.

Aim To analyse the underpinnings of historical drivers of diversity and their contributions to current distributions and future roles in a changing climate, we studied the relationship between ecological niche divergence and phylogenetic signal in tropical carnivorous pitcher plants. Location Southeast Asia. Methods Estimates of realized ecological niches were reconstructed and plotted along a newly created multilocus molecular phylogeny. Phylogenetic signal was analysed by comparisons of calculated phylogenetic relatedness with ecological niche divergence. Current and projected future potentially suitable habitats were mapped for several species of plants with variable evolutionary histories and distributions. Results Highland and lowland species had distinct phylogenetic signals. Higher altitude species had significantly lower molecular divergence as compared with the lowland species, yet ecological niches with less overlap. When projected onto a future climate scenario, highland species lose a greater amount of potentially suitable habitat compared to lower altitude species, and the majority of studied higher altitude species will face an overall loss of future suitable habitat. Main conclusion We conclude that distinct phylogenetic signals not only unravel differing evolutionary histories but also show that the implications of species' tolerances to future changing climate vary. Over the past million years, historical climate change shaped the differing evolution and ecological niches of highland and lowland tropical pitcher plant species. Rapid, recent radiations of the higher altitude species are reflected in limited molecular divergence, which is in sharp contrast with the more gradually evolved and genetically distinct lower altitude species in our study. Our projections for future potentially suitable habitats show that on-going climate shifts will have detrimental effects on especially the higher altitude species due to a narrower niche tolerance and dramatic loss of potentially suitable habitat.

When development impacts a broad landscape and causes the loss of multiple ecosystem services, decisions about which of these impacts to offset must be made. We use industrial oil-palm developments in Kalimantan and quantify the potential for restoration to offset oil-palm impacts on carbon storage and biodiversity. We developed a unique backcasting approach combined with a spatial conservation prioritisation framework to identify priority areas for restoration offsetting. We calculated the past impacts of oil-palm development, quantified the future benefits of restoration for carbon storage and biodiversity over one oil-palm planting cycle of 25 years, and prioritised areas for restoration to balance the impacts and benefits for the least cost. We estimate that offsetting the carbon emissions attributable to the existing 4.6 Mha of industrial oil-palm plantation in Kalimantan is most cost-effectively achieved by restoring 0.4–1.6 Mha of degraded peatlands, including failed agricultural projects, at a cost of US$0.7–2.9 billion. On the other hand, offsetting biodiversity losses would require at least 4.7 Mha of degraded areas to be restored (equating to 8.7% of Kalimantan) at a cost of US$7.7 billion. We show that priority areas for offsetting biodiversity losses overlap poorly with those for compensating carbon emissions. Our analysis suggests that reconciling multiple impacts at landscape scales will necessitate difficult choices among contested socio-political preferences. Our findings also clarify the fundamental importance of conserving biodiversity-rich primary forests and peatlands in the tropics and the need to avoid converting these areas in the future.

European badger populations in the Netherlands suffered strong declines in the 1900s, becoming endangered in the 1980s. Despite mitigation actions, recovery of the population has been slow. Here, we use ecological niche modelling, relating 1515 badger sett (burrow) localities to data on topographic, groundwater table, soil type and land-cover variables, to investigate the factors defining badger habitat suitability. Niche modelling of mobile animals such as badgers is challenging, as relevant features that determine habitat suitability surrounding animal sightings or burrow locations are often unaccounted for. In this study, habitat properties of the entire home range of individuals were incorporated via neighbourhood analysis on land-cover variables. The neighbourhood analysis was applied at different spatial scales, to assess maximum model fit at the scale most representative of badger home-range area in the Netherlands, which was approximately 3.6 km2. Our results showed that marine and river clay render highly unsuitable habitat for badgers. Grassland and maize crops presence, typically reported as driving factors, had little effect on badger distribution in the Netherlands. Instead, moderate vegetation cover, remoteness from urban infrastructures and low groundwater tables resulted in optimal conditions. We conclude that food availability is not a limiting factor for badgers in the Netherlands, but rather appropriate soil conditions for sett digging and non-urban landscapes with sufficient cover for hideout determine their distribution. Our predictions indicate suitable areas that are not currently colonized. The results presented have important implications for management and conservation strategies in the Netherlands. Furthermore, we provide a useful general approach for niche modelling of mobile animals.

Aim The aims of this study were (1) to assess the spatial distribution of orchid species richness in New Guinea, and (2) to examine patterns of species turnover in the orchid community through phytogeographical regionalization. We aimed to achieve these goals using botanical collection records, species distribution models (SDMs) and partitioning around medoids (PAM) clustering. Location New Guinea, the Bismarck Archipelago and surrounding islands. Methods We combined 6760 collection records of 532 orchid species with 16 uncorrelated environmental predictor variables, to model species distributions at 5 arc-min resolution, using the presence-only modelling algorithm, Maxent. All SDMs were tested for significant deviation from random expectation using bias-corrected null models. The results from significant SDMs were stacked to create a map of orchid species richness. The same significant SDMs were used to derive phytogeographical regions through a PAM cluster analysis. Results Of the 532 modelled species distributions, 283 showed significantly stronger correlation with environmental conditions than expected by chance alone. It is inferred that the central mountain ranges of eastern New Guinea, including part of the Papuan Peninsula, harbour the highest levels of orchid species richness. The study area was divided into eight phytogeographical regions that maximized internal community similarity among orchids. Two of these regions are found almost exclusively in western New Guinea, while the remainder are more evenly distributed. Main conclusions Our study provides an objective assessment of the distribution of relative orchid species richness in New Guinea. The eight derived phytogeographical regions are largely consistent with existing knowledge of the island's internal biogeography. These findings give insight into one of the global hotspots of orchid diversity, and improve our understanding of the phytogeography of New Guinea.

There are concerns that Reduced Emissions from Deforestation and forest Degradation (REDD+) may fail to deliver potential biodiversity cobenefits if it is focused on high carbon areas. We explored the spatial overlaps between carbon stocks, biodiversity, projected deforestation threats, and the location of REDD+ projects in Indonesia, a tropical country at the forefront of REDD+ development. For biodiversity, we assembled data on the distribution of terrestrial vertebrates (ranges of amphibians, mammals, birds, reptiles) and plants (species distribution models for 8 families). We then investigated congruence between different measures of biodiversity richness and carbon stocks at the national and subnational scales. Finally, we mapped active REDD+ projects and investigated the carbon density and potential biodiversity richness and modeled deforestation pressures within these forests relative to protected areas and unprotected forests. There was little internal overlap among the different hotspots (richest 10% of cells) of species richness. There was also no consistent spatial congruence between carbon stocks and the biodiversity measures: a weak negative correlation at the national scale masked highly variable and nonlinear relationships island by island. Current REDD+ projects were preferentially located in areas with higher total species richness and threatened species richness but lower carbon densities than protected areas and unprotected forests. Although a quarter of the total area of these REDD+ projects is under relatively high deforestation pressure, the majority of the REDD+ area is not. In Indonesia at least, first-generation REDD+ projects are located where they are likely to deliver biodiversity benefits. However, if REDD+ is to deliver additional gains for climate and biodiversity, projects will need to focus on forests with the highest threat to deforestation, which will have cost implications for future REDD+ implementation.

In 2010, Naturalis Biodiversity Center started one of the largest and most diverse programs for natural history collection digitization to date. From a total collection of 37 million specimens and related objects, 7 million relevant objects are to be digitized in a 5-year period. This article provides an overview of the program and discusses the chosen industrial production line approach, the applied method for prioritization of collections that are to be digitized, and some preliminary results.