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Unraveling the drivers and impacts of invaders in a changing world

Biological invasions are a major threat to global biodiversity, economy and public health. Records of alien species continue to increase, suggesting efforts to mitigate invasions have been ineffective in keeping up with increasing globalization. While progress has been made in identifying drivers underlying species’ invasion success, there is an urgent need to explicitly integrate ecological knowledge of the invasion process with the complex environmental, socioeconomic and geopolitical factors steering global change. UNRAVEL aims to fill this gap by investigating different and complementary angles of drivers and impacts of biological invasions to forecast distributions of alien terrestrial vertebrate species under scenarios of change. With an experienced and diverse group of researchers, UNRAVEL thus aims to leverage insights from invasion ecology to support invasive species policy and management under global change, using novel and integrated multidisciplinary approaches for quantifying invasion risk connected to all traded groups of terrestrial vertebrates (i.e., amphibians, reptiles, birds and mammals). The project will be implemented by a team of 17 researchers based in six countries (including three consultants) and seven institutes. The overall project aims will be tackled in six interconnected tasks, each building on one another, providing an integrated and dynamic understanding of the complexity of biological invasions, and produce a more realistic policy framework for invasive alien species prevention and management. We will use distribution modelling to establish a baseline for the potential global distribution of invasive alien species, using the combined effects of global wildlife trade, climate and land-use. Hence, we will first focus solely on currently traded terrestrial vertebrates with established alien populations, mapping a baseline of the current invasion risk based solely on potential propagule pressure (i.e. number of events and individuals introduced into a region over time, often represented by trade data), climate and land-use suitability (T1). Then, we will build the most complete database to date with species traits relevant for invasion success in each stage of the invasion pathway, covering species transport, introduction, establishment and invasive spread. Traits considered will cover attractiveness to sellers and consumers, species ecology and life history (T2). This trait database will include data on all terrestrial vertebrate species, synthesizing multiple published and unpublished sources. This will thus allow testing which species traits most strongly influence invasion success throughout the invasion pathway (T3), and predict species highly likely to become invasive under future environmental, geopolitical and socioeconomic scenarios (T4). These projections will be compared with those built based only on propagule pressure, climate and land-use (T1). Then, in order to quantify ecological impacts of invasive species, we will use clustering and machine learning analyses to evaluate invasion impacts on community trophic structures, and predict future ecological patterns under scenarios of change (T5). Finally, knowledge gained from all previous tasks will be integrated into a comprehensive policy framework considering the views of several stakeholders, to provide policy recommendations and management actions targeting prevention of invasion by likely highly invasive species and in highly vulnerable regions (T6).
Team
Principal Investigator
Luís Reino

Luís Reino

Position: Post-Doc Researcher
Groups:
APPLECOL, INVASIONS
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Researchers
Ana Sofia Vaz

Ana Sofia Vaz

Position: Post-Doc Researcher
Groups:
ECOCHANGE, INVASIONS
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Joana Ribeiro

Joana Ribeiro

Position: Post-Doc Researcher
Groups:
APPLECOL, INVASIONS
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Students
Miguel Monteiro

Miguel Monteiro

Position: PhD Student
Groups:
ECOCHANGE, INVASIONS
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State
Ongoing
Proponent Institution
BIOPOLIS (CIBIO-InBIO)
Funded by
Fundação para a Ciência e a Tecnologia (FCT)
Dates
2021
Participant Institutions
Universidade de Évora
Reference
PTDC/BIA-ECO/0207/2020
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