DAMA: the global Distribution of Alien Mammals database.

We developed the DAMA (Distribution of Alien Mammals) database, a comprehensive source reporting the global distribution of the 230 species of mammals that have established self-sustaining and free-ranging populations outside their native range due to direct or indirect human action. Every alien range is accompanied by information on its invasion stage, pathway, method of introduction, and date of introduction. We collected information from 827 different sources (scientific literature, books, risk assessments, reports, online biodiversity databases and websites), and used it to draw alien range maps for these species following the IUCN mapping framework. DAMA comprises 2,726 range polygons, covering 199 countries, 2,190 level 1 administrative areas, and 11 zoogeographic realms for the period 21500 BC-AD 2017. The most represented orders among introduced mammal species are Rodentia (n = 58, 25.22%), Cetartiodactyla (n = 49 species, 21.30%), Carnivora (n = 30 species, 13.04%), Diprotodontia (n = 28, 12.17%), and Primates (n = 26, 11.30%). Mammal species have been frequently introduced for hunting (n = 100), pet trade (n = 57), conservation (n = 51), and fauna improvement (n = 42). The majority of range polygons are placed on islands (n = 2,196, 80.56%), encompass populations that have moved beyond establishment and into the invasion stage (n = 1,655, 60.71%), and originated from 1500 AD to the present (n = 1,496, 54.88%). Despite inheriting literature biases towards more studied regions (e.g., developed countries), DAMA is the most up-to-date picture of alien mammal global distribution and can be used to investigate their invasion ecology across different biogeographical regions. There are no copyright or proprietary restrictions; IUCN range maps were modified into a derivative work according to the IUCN's terms of service.

Capturing expert uncertainty in spatial cumulative impact assessments.

Understanding the spatial distribution of human impacts on marine environments is necessary for maintaining healthy ecosystems and supporting 'blue economies'. Realistic assessments of impact must consider the cumulative impacts of multiple, coincident threats and the differing vulnerabilities of ecosystems to these threats. Expert knowledge is often used to assess impact in marine ecosystems because empirical data are lacking; however, this introduces uncertainty into the results. As part of a spatial cumulative impact assessment for Spencer Gulf, South Australia, we asked experts to estimate score ranges (best-case, most-likely and worst-case), which accounted for their uncertainty about the effect of 32 threats on eight ecosystems. Expert scores were combined with data on the spatial pattern and intensity of threats to generate cumulative impact maps based on each of the three scoring scenarios, as well as simulations and maps of uncertainty. We compared our method, which explicitly accounts for the experts' knowledge-based uncertainty, with other approaches and found that it provides smaller uncertainty bounds, leading to more constrained assessment results. Collecting these additional data on experts' knowledge-based uncertainty provides transparency and simplifies interpretation of the outputs from spatial cumulative impact assessments, facilitating their application for sustainable resource management and conservation.