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Biological invasions are a global challenge that has received insufficient attention. Recently available cost syntheses have provided policy- and decision makers with reliable and up-to-date information on the economic impacts of biological invasions, aiming to motivate effective management. The resultant InvaCost database is now publicly and freely accessible and enables rapid extraction of monetary cost information. This has facilitated knowledge sharing, developed a more integrated and multidisciplinary network of researchers, and forged multidisciplinary collaborations among diverse organizations and stakeholders. Over 50 scientific publications so far have used the database and have provided detailed assessments of invasion costs across geographic, taxonomic, and spatiotemporal scales. These studies have provided important information that can guide future policy and legislative decisions on the management of biological invasions while simultaneously attracting public and media attention. We provide an overview of the improved availability, reliability, standardization, and defragmentation of monetary costs; discuss how this has enhanced invasion science as a discipline; and outline directions for future development. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) have been widely applied to predict geographic ranges of species across space and time under the assumption of niche conservatism (i.e., species niches change very slowly). However, an increasing number of studies have reported evidence of rapid niche changes across space and time, which has sparked a widespread debate on whether SDMs can be transferred to new areas or time periods. Understanding how niche changes affect SDM transferability is thus crucial for the future application and improvement of SDMs. Biological invasions provide an opportunity to address this question due to the geographically independent distributions and diverse patterns of niche changes between species' native and introduced ranges. Here, we synthesized findings on 217 species from 50 studies to elucidate the effects of niche change on the spatial transferability of SDMs. When niche change was considered as a categorical classification (conserved vs. shifted niches) in tests of the niche conservatism hypothesis, SDM transferability was markedly lower for species with a shifted niche in their introduced range. When niche change was measured as numerical dynamics between native and introduced niches, SDM transferability was high for species occupying similar environmental conditions in both ranges and low for species with more environmental space remaining unoccupied in the introduced range. Surprisingly, the number of presence points used for developing SDMs turned out to have an even stronger effect on transferability. Our results thus reveal detrimental effects of both niche change and lack of presence points on SDM transferability. It is necessary to consider both niche change and data quality for improving the transferability of SDMs, so that they can better support conservation management and policy decisions. [ABSTRACT FROM AUTHOR]

The reliability of transferring species distribution models (SDMs) to new ranges and future climates has been widely debated. Biological invasions offer the unique opportunity to evaluate model transferability, as distribution data between species' native and introduced ranges are geographically independent of each other. Here, we performed the first global quantitative synthesis of the spatial transferability of SDMs for 235 invasive species and assessed the association of model transferability with the focal invader, model choice and parameterisation. We found that SDMs had limited spatial transferability overall. However, model transferability was higher for terrestrial endotherms, species introduced from or to the Southern Hemisphere, and species introduced more recently. Model transferability was also positively associated with the number of presences for model calibration and evaluation, respectively, but negatively with the number of predictors. These findings highlight the importance of considering the characteristics of the focal invader, environment and modelling in the application and assessment of SDMs. [ABSTRACT FROM AUTHOR]