The IUCN Red List of Threatened Species is essential for practical and theoretical efforts to protect biodiversity. However, species classified as “Data Deficient” (DD) regularly mislead practitioners due to their uncertain extinction risk. Here we present machine learning-derived probabilities of being threatened by extinction for 7699 DD species, comprising 17% of the entire IUCN spatial datasets. Our predictions suggest that DD species as a group may in fact be more threatened than data-sufficient species. We found that 85% of DD amphibians are likely to be threatened by extinction, as well as more than half of DD species in many other taxonomic groups, such as mammals and reptiles. Consequently, our predictions indicate that, amongst others, the conservation relevance of biodiversity hotspots in South America may be boosted by up to 20% if DD species were acknowledged. The predicted probabilities for DD species are highly variable across taxa and regions, implying current Red List-derived indices and priorities may be biased.

Citizen Scientists together with an increasing access to technology provide large datasets that can be used to study e.g. ecology and biodiversity. Unknown and varying sampling effort is a major issue when making inference based on citizen science data. In this paper we propose a modeling approach for accounting for variation in sampling effort due to accessibility. The paper is based on a illustrative case study using citizen science data of moose occurrence in Hedmark, Norway. The aim is to make inference about the importance of two geographical properties known to influence moose occurrence; terrain ruggedness index and solar radiation. Explanatory analysis show that moose occurrences are overrepresented close to roads, and we use distance to roads as a proxy for accessibility. We propose a model based on a Bayesian Log-Gaussian Cox Process specification for occurrence. The model accounts for accessibility through a distance sampling approach. This approach can be seen as a thinning process where probability of thinning, i.e. not observing, increases with increasing distances. For the moose case study distance to roads are used. Computationally efficient full Bayesian inference is performed using the Integrated Nested Laplace Approximation and the Stochastic Partial Differential Equation approach for spatial modeling. The proposed model as well as the consequences of not accounting for varying sampling effort due to accessibility are studied through a simulation study based on the case study. Considerable biases are found in estimates for the effect of radiation on moose occurrence when accessibility is not considered in the model.

Aim: To test the long-expected connection between the native climatic conditions of alien plant species and their preference for urban versus rural conditions, a hypothesis driven by the presence of warmer and drier microclimates in cities due to the urban heat island effect.Location: Western Europe.

Besides being central for understanding both global biodiversity patterns and associated anthropogenic impacts, species range maps are currently only available for a small subset of global biodiversity. Here, we provide a set of assembled spatial data for terrestrial vascular plants listed at the global IUCN red list. The dataset consists of pre-defined native regions for 47,675 species, density of available native occurrence records for 30,906 species, and standardized, large-scale Maxent predictions for 27,208 species, highlighting environmentally suitable areas within species’ native regions. The data was generated in an automated approach consisting of data scraping and filtering, variable selection, model calibration and model selection. Generated Maxent predictions were validated by comparing a subset to available expert-drawn range maps from IUCN (n = 4,257), as well as by qualitatively inspecting predictions for randomly selected species. We expect this data to serve as a substitute whenever expert-drawn species range maps are not available for conducting large-scale analyses on biodiversity patterns and associated anthropogenic impacts.

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