“One hundred worst” lists of alien species of the greatest concern proved useful for raising awareness of the risks and impacts of biological invasions amongst the general public, politicians and ...stakeholders. All lists so far have been based on expert opinion and primarily aimed at representativeness of the taxonomic and habitat diversity rather than at quantifying the harm the alien species cause. We used the generic impact scoring system (GISS) to rank 486 alien species established in Europe from a wide range of taxonomic groups to identify those with the highest environmental and socioeconomic impact. GISS assigns 12 categories of impact, each quantified on a scale from 0 (no impact detectable) to 5 (the highest impact possible). We ranked species by their total sum of scores and by the number of the highest impact scores. We also compared the listing based on GISS with other expert-based lists of the “worst” invaders. We propose a list of 149 alien species, comprising 54 plants, 49 invertebrates, 40 vertebrates and 6 fungi. Among the highest ranking species are one bird (
Branta canadensis
), four mammals (
Rattus norvegicus, Ondatra zibethicus, Cervus nippon, Muntiacus reevesi
), one crayfish (
Procambarus clarkii
), one mite (
Varroa destructor
), and four plants (
Acacia dealbata, Lantana camara, Pueraria lobata, Eichhornia crassipes
). In contrast to other existing expert-based “worst” lists, the GISS-based list given here highlights some alien species with high impacts that are not represented on any other list. The GISS provides an objective and transparent method to aid prioritization of alien species for management according to their impacts, applicable across taxa and habitats. Our ranking can also be used for justifying inclusion on lists such as the alien species of Union concern of the European Commission, and to fulfill Aichi target 9.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Scientists' warning on invasive alien species Pyšek, Petr; Hulme, Philip E.; Simberloff, Dan ...
Biological reviews of the Cambridge Philosophical Society,
December 2020, Volume:
95, Issue:
6
Journal Article
Peer reviewed
Open access
ABSTRACT
Biological invasions are a global consequence of an increasingly connected world and the rise in human population size. The numbers of invasive alien species – the subset of alien species ...that spread widely in areas where they are not native, affecting the environment or human livelihoods – are increasing. Synergies with other global changes are exacerbating current invasions and facilitating new ones, thereby escalating the extent and impacts of invaders. Invasions have complex and often immense long‐term direct and indirect impacts. In many cases, such impacts become apparent or problematic only when invaders are well established and have large ranges. Invasive alien species break down biogeographic realms, affect native species richness and abundance, increase the risk of native species extinction, affect the genetic composition of native populations, change native animal behaviour, alter phylogenetic diversity across communities, and modify trophic networks. Many invasive alien species also change ecosystem functioning and the delivery of ecosystem services by altering nutrient and contaminant cycling, hydrology, habitat structure, and disturbance regimes. These biodiversity and ecosystem impacts are accelerating and will increase further in the future. Scientific evidence has identified policy strategies to reduce future invasions, but these strategies are often insufficiently implemented. For some nations, notably Australia and New Zealand, biosecurity has become a national priority. There have been long‐term successes, such as eradication of rats and cats on increasingly large islands and biological control of weeds across continental areas. However, in many countries, invasions receive little attention. Improved international cooperation is crucial to reduce the impacts of invasive alien species on biodiversity, ecosystem services, and human livelihoods. Countries can strengthen their biosecurity regulations to implement and enforce more effective management strategies that should also address other global changes that interact with invasions.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
One key hypothesis explaining the fate of exotic species introductions posits that the establishment of a self-sustaining population in the invaded range can only succeed within conditions matching ...the native climatic niche. Yet, this hypothesis remains untested for individual release events. Using a dataset of 979 introductions of 173 mammal species worldwide, we show that climate-matching to the realized native climatic niche, measured by a new Niche Margin Index (NMI), is a stronger predictor of establishment success than most previously tested life-history attributes and historical factors. Contrary to traditional climatic suitability metrics derived from species distribution models, NMI is based on niche margins and provides a measure of how distant a site is inside or, importantly, outside the niche. Besides many applications in research in ecology and evolution, NMI as a measure of native climatic niche-matching in risk assessments could improve efforts to prevent invasions and avoid costly eradications.
Alien insects are increasingly being dispersed around the world through international trade, causing a multitude of negative environmental impacts and billions of dollars in economic losses annually. ...Border controls form the last line of defense against invasions, whereby inspectors aim to intercept and stop consignments that are contaminated with harmful alien insects. In Europe, member states depend on one another to prevent insect introductions by operating a first point of entry rule--controlling goods only when they initially enter the continent. However, ensuring consistency between border control points is difficult because there exists no optimal inspection strategy. For the first time, we developed a method to quantify the volume of agricultural trade that should be inspected for quarantine insects at border control points in Europe, based on global agricultural trade of over 100 million distinct origin-commodity-species-destination pathways. This metric was then used to evaluate the performance of existing border controls, as measured by border interception results in Europe between 2003 and 2007. Alarmingly, we found significant gaps between the trade pathways that should be inspected and actual number of interceptions. Moreover, many of the most likely introduction pathways yielded none or very few insect interceptions, because regular interceptions are only made on only a narrow range of pathways. European countries with gaps in border controls have been invaded by higher numbers of quarantine alien insect species, indicating the importance of proper inspections to prevent insect invasions. Equipped with an optimal inspection strategy based on the underlying risks of trade, authorities globally will be able to implement more effective and consistent border controls.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for ...parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial scales and through time. Across disciplines, different approaches to addressing collinearity problems have been developed, ranging from clustering of predictors, threshold-based pre-selection, through latent variable methods, to shrinkage and regularisation. Using simulated data with five predictor-response relationships of increasing complexity and eight levels of collinearity we compared ways to address collinearity with standard multiple regression and machine-learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset with strong collinearity by evaluating its performance on a test dataset without any collinearity. We found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold-based pre-selection. Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold-based pre-selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the ‘ folk lore ’ -thresholds of correlation coefficients between predictor variables of |r| > 0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre-analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
There has been a dramatic growth in research on biological invasions over the past 20 years, but a mature understanding of the field has been hampered because invasion biologists concerned with ...different taxa and different environments have largely adopted different model frameworks for the invasion process, resulting in a confusing range of concepts, terms and definitions. In this review, we propose a unified framework for biological invasions that reconciles and integrates the key features of the most commonly used invasion frameworks into a single conceptual model that can be applied to all human-mediated invasions. The unified framework combines previous stage-based and barrier models, and provides a terminology and categorisation for populations at different points in the invasion process.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Species moved by human activities beyond the limits of their native geographic ranges into areas in which they do not naturally occur (termed aliens) can cause a broad range of significant changes to ...recipient ecosystems; however, their impacts vary greatly across species and the ecosystems into which they are introduced. There is therefore a critical need for a standardised method to evaluate, compare, and eventually predict the magnitudes of these different impacts. Here, we propose a straightforward system for classifying alien species according to the magnitude of their environmental impacts, based on the mechanisms of impact used to code species in the International Union for Conservation of Nature (IUCN) Global Invasive Species Database, which are presented here for the first time. The classification system uses five semi-quantitative scenarios describing impacts under each mechanism to assign species to different levels of impact-ranging from Minimal to Massive-with assignment corresponding to the highest level of deleterious impact associated with any of the mechanisms. The scheme also includes categories for species that are Not Evaluated, have No Alien Population, or are Data Deficient, and a method for assigning uncertainty to all the classifications. We show how this classification system is applicable at different levels of ecological complexity and different spatial and temporal scales, and embraces existing impact metrics. In fact, the scheme is analogous to the already widely adopted and accepted Red List approach to categorising extinction risk, and so could conceivably be readily integrated with existing practices and policies in many regions.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Defining the Impact of Non‐Native Species JESCHKE, JONATHAN M; BACHER, SVEN; BLACKBURN, TIM M ...
Conservation biology,
October 2014, Volume:
28, Issue:
5
Journal Article
Peer reviewed
Open access
Non‐native species cause changes in the ecosystems to which they are introduced. These changes, or some of them, are usually termed impacts; they can be manifold and potentially damaging to ...ecosystems and biodiversity. However, the impacts of most non‐native species are poorly understood, and a synthesis of available information is being hindered because authors often do not clearly define impact. We argue that explicitly defining the impact of non‐native species will promote progress toward a better understanding of the implications of changes to biodiversity and ecosystems caused by non‐native species; help disentangle which aspects of scientific debates about non‐native species are due to disparate definitions and which represent true scientific discord; and improve communication between scientists from different research disciplines and between scientists, managers, and policy makers. For these reasons and based on examples from the literature, we devised seven key questions that fall into 4 categories: directionality, classification and measurement, ecological or socio‐economic changes, and scale. These questions should help in formulating clear and practical definitions of impact to suit specific scientific, stakeholder, or legislative contexts.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Extensive literature is available on the diversity and magnitude of impacts that alien species cause on recipient systems. Alien species may decrease or increase attributes of ecosystems (e.g. total ...biomass or species diversity), thus causing negative and positive environmental impacts. Alien species may also negatively or positively impact attributes linked to local human communities (e.g. the number of people involved in a given activity). Ethical and societal values contribute to define these environmental and socio-economic impacts as deleterious or beneficial. Whilst most of the literature focuses on the deleterious effects of alien taxa, some recognise their beneficial impacts on ecosystems and human activities. Impact assessment frameworks show a similar tendency to evaluate mainly deleterious impacts: only relatively few, and not widely applied, frameworks incorporate the beneficial impacts of alien species. Here, we provide a summary of the frameworks assessing beneficial impacts and briefly discuss why they might have been less frequently cited and applied than frameworks assessing exclusively deleterious impacts. Then, we review arguments that invoke a greater consideration of positive and beneficial impacts caused by alien species across the invasion science literature. We collate and describe arguments from a set of 47 papers, grouping them in two categories (value-free and value-laden), which span from a theoretical, basic science perspective to an applied science perspective. We also provide example cases associated with each argument. We advocate that the development of transparent and evidence-based frameworks assessing positive and beneficial impacts might advance our scientific understanding of impact dynamics and better inform management and prioritisation decisions. We also advise that this development should be achieved by recognising the underlying ethical and societal values of the frameworks and their intrinsic limitations. The evaluation of positive and beneficial impacts through impact assessment frameworks should not be seen as an attempt to outweigh or to discount deleterious impacts of alien taxa but rather as an opportunity to provide additional information for scientists, managers and policymakers.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
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