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.

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Climate change is one of the main drivers of species extinction in the twentyfirst-century. Here, we  (1) quantify potential changes in species' bioclimatic area of habitat (BAH) of 135 native potential agroforestry species from the Brazilian flora, using two different climate change scenarios (SSP2-4.5 and SSP5-8.5) and dispersal scenarios, where species have no ability to disperse and reach new areas (non-dispersal) and where species can migrate within the estimated BAH (full dispersal) for 2041–2060 and 2061–2080. We then (2) assess the preliminary conservation status of each species based on IUCN criteria. Current and future potential habitats for species were predicted using MaxEnt, a machine-learning algorithm used to estimate species' probability distribution. Future climate is predicted to trigger a mean decline in BAH between 38.5–56.3% under the non-dispersal scenario and between 22.3–41.9% under the full dispersal scenario for 135 native potential agroforestry species. Additionally, we found that only 4.3% of the studied species could be threatened under the IUCN Red List criteria B1 and B2. However, when considering the predicted quantitative habitat loss due to climate change (A3c criterion) the percentages increased between 68.8–84.4% under the non-dispersal scenario and between 40.7–64.4% under the full dispersal scenario. To lessen such threats, we argue that encouraging the use of these species in rural and peri-urban agroecosystems are promising, complementary strategies for their long-term conservation.

22 23 Abstract 24 Romania and Ukraine share the Black Sea coastline, the Danube Delta and associated habitats, 25 which harbor the unique Pontocaspian biodiversity. Pontocaspian biota represents endemic 26 aquatic taxa adapted to the brackish (anomalohaline) conditions, which evolved in the Caspian 27 and Black Sea basins. Currently, this biota is diminishing both in the numbers of species and28 their abundance because of human activities. Consequently, its future persistence strongly 29 depends on the adequacy of conservation measures. Romania and Ukraine have a common 30 responsibility to effectively address the conservation of this biota. The socio-political and legal 31 conservation frameworks, however, differ in the two countries -Romania is a member of the 32 European Union (EU), thus complying with the EU environmental policy, whereas Ukraine is an 33 EU-associated country. This may result in differences in the social network structure of 34 stakeholder institutions with different implications for Pontocaspian biodiversity conservation.35 Here, we study the structure and implications of the social network of stakeholder organizations 36 involved in conservation of Pontocaspian biodiversity in Romania, and compare it to Ukraine. 37 We apply a mix of qualitative and quantitative social network analysis methods to combine the 38 content and context of the interactions with relational measures. We show that the social 39 networks of stakeholder organizations in Romania and Ukraine are very different. Structurally, in 40 Romanian network there is a room for improvement through e.g. more involvement of 3 41 governmental and non-governmental organizations and increased motivation of central 42 stakeholders to initiate conservation action, whereas Ukrainian network is close to optimal. 43 Regardless, both networks translate into sub-optimal conservation action and the road to optimal 44 conservation is different. We end with sketching implications and recommendations for 45 improved national and cross-border conservation efforts. 46 47 Introduction 48 Pontocaspian (PC) biota is a unique, endemic flora and fauna which includes mollusks, 49 crustaceans, planktonic groups (e.g. dinoflagellates and diatoms) and fish species. This 50 biodiversity evolved in brackish (anomalohaline) conditions of the Black Sea and Caspian Sea 51 basins over the past 2.5 million years [1,2] and nowadays PC communities inhabit the Northern 52 Black Sea, Sea of Azov, Caspian Sea and adjacent river and lake systems, stretching across the 53 vast political and administrative boundaries of the surrounding countries [3]. Currently, PC biota 54 is decreasing in numbers of species and abundances as a result of human activities and their 55 future persistence strongly depends on the adequacy of conservation measures [1,4,5]. Romania 56 and Ukraine hold an important part of the PC habitats. PC species in Romania are limited to the 57 Razim-Sinoe-Babadag lake complex [6,7], the area along the Danube River and the Black Sea 58 coastal zone, which t...

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Threats to global biodiversity are increasingly recognised by scientists and the public as a critical challenge. Molecular sequencing technologies offer means to catalogue, explore, and monitor the richness and biogeography of life on Earth. However, exploiting their full potential requires tools that connect biodiversity infrastructures and resources. As a research infrastructure developing services and technical solutions that help integrate and coordinate life science resources across Europe, ELIXIR is a key player. To identify opportunities, highlight priorities, and aid strategic thinking, here we survey approaches by which molecular technologies help inform understanding of biodiversity. We detail example use cases to highlight how DNA sequencing is: resolving taxonomic issues; Increasing knowledge of marine biodiversity; helping understand how agriculture and biodiversity are critically linked; and playing an essential role in ecological studies. Together with examples of national biodiversity programmes, the use cases show where progress is being made but also highlight common challenges and opportunities for future enhancement of underlying technologies and services that connect molecular and wider biodiversity domains. Based on emerging themes, we propose key recommendations to guide future funding for biodiversity research: biodiversity and bioinformatic infrastructures need to collaborate closely and strategically; taxonomic efforts need to be aligned and harmonised across domains; metadata needs to be standardised and common data management approaches widely adopted; current approaches need to be scaled up dramatically to address the anticipated explosion of molecular data; bioinformatics support for biodiversity research needs to be enabled and sustained; training for end users of biodiversity research infrastructures needs to be prioritised; and community initiatives need to be proactive and focused on enabling solutions. For sequencing data to deliver their full potential they must be connected to knowledge: together, molecular sequence data collection initiatives and biodiversity research infrastructures can advance global efforts to prevent further decline of Earth’s biodiversity.

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.

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