The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling

Abstract

Northern Eurasia is extensive and includes terrestrial and aquatic ecosystems that cover several natural zones and access to the seas of three oceans. As a result, it has been invaded by numerous invasive alien species (IAS) over large temporal and spatial scales. The purpose of this research was to assess invasion trends and construct species distribution models for the Russian TOP-100 most dangerous IAS. Environmentally suitable regions for IAS were established based on alien species attribute databases, datasets of 169,709 species occurrence records (SOR) and raster layers of environmental variables using species distribution modelling (MaxEnt). The objectives of this research were to (1) create databases of SOR for the TOP-100 IAS in Russia; 2) determine pathways, residence time, donor regions and trends of invasions; (3) determine the main types of spatial distributions of invasive species and their relation to residence time; and (4) distinguish regions with the highest richness of IAS that have a strong impact on the terrestrial and aquatic ecosystems of Russia. We found that although species invasions date back over 400 years, the number of naturalized IAS has increased non-linearly over the past 76 years. The TOP-100 list is mainly represented by unintentionally introduced species (62%) which are characterized by different introduction pathways. Species occurrence records revealed that 56 IAS are distributed locally, 26 are distributed regionally and 18 are widespread in Russia. Species with local, regional or widespread distributions were characterized by residence times of 55, 126 or 190 years, respectively. We found that IAS with local distribution can expand their range into suitable regions more extensively (expected increase by 32%) than widespread species (expected increase by only 7%). The procedure of identifying hot/cold spots locations based on SOR allowed us to identify the Russian regions with the highest richness of IAS. Our results and the integrated database that we created provide a framework for studying IAS over large temporal and spatial scales that can be used in the development of management plans for dangerous IAS.