A general framework to quantify and compare ecological impacts under temporal dynamics.

Biodiversity is diminishing at alarming rates due to multiple anthropogenic drivers. To mitigate these drivers, their impacts must be quantified accurately and comparably across drivers. To enable that, we present a generally applicable framework introducing fundamental principles of ecological impact quantification, including the quantification of interactions between multiple drivers. The framework contrasts biodiversity variables in impacted against those in unimpacted or other reference situations while accounting for their temporal dynamics through modelling. Properly accounting for temporal dynamics reduces biases in impact quantification and comparison. The framework addresses key questions around ecological impacts in global change science, namely, how to compare impacts under temporal dynamics across stressors, how to account for stressor interactions in such comparisons, and how to compare the success of management actions over time.

Identifying, reducing, and communicating uncertainty in community science: a focus on alien species.

Community science (also often referred to as citizen science) provides a unique opportunity to address questions beyond the scope of other research methods whilst simultaneously engaging communities in the scientific process. This leads to broad educational benefits, empowers people, and can increase public awareness of societally relevant issues such as the biodiversity crisis. As such, community science has become a favourable framework for researching alien species where data on the presence, absence, abundance, phenology, and impact of species is important in informing management decisions. However, uncertainties arising at different stages can limit the interpretation of data and lead to projects failing to achieve their intended outcomes. Focusing on alien species centered community science projects, we identified key research questions and the relevant uncertainties that arise during the process of developing the study design, for example, when collecting the data and during the statistical analyses. Additionally, we assessed uncertainties from a linguistic perspective, and how the communication stages among project coordinators, participants and other stakeholders can alter the way in which information may be interpreted. We discuss existing methods for reducing uncertainty and suggest further solutions to improve data reliability. Further, we make suggestions to reduce the uncertainties that emerge at each project step and provide guidance and recommendations that can be readily applied in practice. Reducing uncertainties is essential and necessary to strengthen the scientific and community outcomes of community science, which is of particular importance to ensure the success of projects aimed at detecting novel alien species and monitoring their dynamics across space and time. Supplementary Information: The online version contains supplementary material available at 10.1007/s10530-022-02858-8.

The EICAT+ framework enables classification of positive impacts of alien taxa on native biodiversity.

Species introduced through human-related activities beyond their native range, termed alien species, have various impacts worldwide. The IUCN Environmental Impact Classification for Alien Taxa (EICAT) is a global standard to assess negative impacts of alien species on native biodiversity. Alien species can also positively affect biodiversity (for instance, through food and habitat provisioning or dispersal facilitation) but there is currently no standardized and evidence-based system to classify positive impacts. We fill this gap by proposing EICAT+, which uses 5 semiquantitative scenarios to categorize the magnitude of positive impacts, and describes underlying mechanisms. EICAT+ can be applied to all alien taxa at different spatial and organizational scales. The application of EICAT+ expands our understanding of the consequences of biological invasions and can inform conservation decisions.

Ranking alien species based on their risks of causing environmental impacts: a global assessment of alien ungulates.

For an efficient allocation of the limited resources to alien species management, the most damaging species should be prioritized. Comparing alien species based on their impacts is not straightforward, as the same species can cause different types and magnitudes of impacts when introduced to different contexts, making it difficult to summarize its overall impact. The Environmental Impact Classification of Alien Taxa (EICAT) systematically summarizes and compares detrimental impacts caused by alien populations to native biota and has been adopted by the International Union for Conservation of Nature (IUCN). For each alien species, all reported impacts to native populations within the introduced range are classified into five levels of severity, from negligible impact to irreversible local extinction. Currently, EICAT only compares alien species based on their highest impact, thereby ignoring variation in impact magnitudes. Here, we used information on the variation in impact magnitudes of alien species to estimate their risks to cause high impacts if introduced to a novel environment. We demonstrate the usefulness of this approach by classifying the global impacts of alien ungulates. We found impact reports for 27 of the 66 alien ungulate species established worldwide, highlighting substantial knowledge gaps in invasion science. We classified a total of 441 impacts to native fauna and flora caused by these 27 species. Twenty-six of the species were found to cause harmful impacts (native population declines or local extinctions). Mouflon (Ovis orientalis, Gmelin, 1774) and dromedary (Camelus dromedarius, Linnaeus, 1758) had a higher risk of causing local extinctions if introduced to a novel environment than sika deer (Cervus nippon, Temminck, 1838) and goats (Capra hircus, Linnaeus, 1758). Including risk of high impacts allows to discriminate among species with the same EICAT classification and improves alien species prioritization for management.