What Is the Impact of Accidentally Transporting Terrestrial Alien Species? A New Life Cycle Impact Assessment Model.

Alien species form one of the main threats to global biodiversity. Although Life Cycle Assessment attempts to holistically assess environmental impacts of products and services across value chains, ecological impacts of the introduction of alien species are so far not assessed in Life Cycle Impact Assessment. Here, we developed country-to-country-specific characterization factors, expressed as the time-integrated potentially disappeared fraction (PDF; regional and global) of native terrestrial species due to alien species introductions per unit of goods transported [kg] between two countries. The characterization factors were generated by analyzing global data on first records of alien species, native species distributions, and their threat status, as well as bilateral trade partnerships from 1870-2019. The resulting characterization factors vary over several orders of magnitude, indicating that impact greatly varies per transportation route and trading partner. We showcase the applicability and relevance of the characterization factors for transporting 1 metric ton of freight to France from China, South Africa, and Madagascar. The results suggest that the introduction of alien species can be more damaging for terrestrial biodiversity as climate change impacts during the international transport of commodities.

Native range estimates for red-listed vascular plants.

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.

More than half of data deficient species predicted to be threatened by extinction.

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.