Kumschick S

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    Options for reducing uncertainty in impact classification for alien species
    (2021) Clarke, D.A.; Palmer, D.J.; McGrannachan, C.; Burgess, T.I.; Chown, S.L.; Clarke, R.H.; Kumschick, S.; Lach, L.; Liebhold, A.M.; Roy, H.E.; Saunders, M.E.; Yeates, D.K.; Zalucki, M.P.; McGeoch, M.A.
    Abstract Impact assessment is an important and cost-effective tool for assisting in the identification and prioritization of invasive alien species. With the number of alien and invasive alien species expected to increase, reliance on impact assessment tools for the identification of species that pose the greatest threats will continue to grow. Given the importance of such assessments for management and resource allocation, it is critical to understand the uncertainty involved and what effect this may have on the outcome. Using an uncertainty typology and insects as a model taxon, we identified and classified the causes and types of uncertainty when performing impact assessments on alien species. We assessed 100 alien insect species across two rounds of assessments with each species independently assessed by two assessors. Agreement between assessors was relatively low for all three impact classification components (mechanism, severity, and confidence) after the first round of assessments. For the second round, we revised guidelines and gave assessors access to each other’s assessments which improved agreement by between 20% and 30% for impact mechanism, severity, and confidence. Of the 12 potential reasons for assessment discrepancies identified a priori, 11 were found to occur. The most frequent causes (and types) of uncertainty (i.e., differences between assessment outcomes for the same species) were as follows: incomplete information searches (systematic error), unclear mechanism and/or extent of impact (subjective judgment due to a lack of knowledge), and limitations of the assessment framework (context dependence). In response to these findings, we identify actions that may reduce uncertainty in the impact assessment process, particularly for assessing speciose taxa with diverse life histories such as Insects. Evidence of environmental impact was available for most insect species, and (of the non-random original subset of species assessed) 14 of those with evidence were identified as high impact species (with either major or massive impact). Although uncertainty in risk assessment, including impact assessments, can never be eliminated, identifying, and communicating its cause and variety is a first step toward its reduction and a more reliable assessment outcome, regardless of the taxa being assessed.
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    A conceptual map of invasion biology: integrating hypotheses into a consensus network
    (2020) Enders, M.; Havemann, F.; Ruland, F.; Bernard-Verdier, M.; Catford, J.A.; Gómez-Aparicio, L.; Haider, S.; Heger, T.; Kueffer, C.; Kühn, I.; Meyerson, L.A.; Musseau, C.; Novoa, A.; Ricciardi, A.; Sagouis, A.; Schittko, C.; Strayer, D.L.; Vilà, M.; Essl, F.; Hulme, P.E.; van Kleunen, M.; Kumschick, S.; Lockwood, J.L.; Mabey, A.L.; McGeoch, M.A.; Palma, E.; Pyšek, P.; Saul, W.-C.; Yannelli, F.A.; Jeschke, J.M.
    Background and aims: Since its emergence in the mid-20th century, invasion biol-ogy has matured into a productive research field addressing questions of fundamen-tal and applied importance. Not only has the number of empirical studies increased through time, but also has the number of competing, overlapping and, in some cases, contradictory hypotheses about biological invasions. To make these contradictions and redundancies explicit, and to gain insight into the field’s current theoretical struc-ture, we developed and applied a Delphi approach to create a consensus network of 39 existing invasion hypotheses.Results: The resulting network was analysed with a link-clustering algorithm that re-vealed five concept clusters (resource availability, biotic interaction, propagule, trait and Darwin’s clusters) representing complementary areas in the theory of invasion biology. The network also displays hypotheses that link two or more clusters, called connecting hypotheses, which are important in determining network structure. The network indicates hypotheses that are logically linked either positively (77 connec-tions of support) or negatively (that is, they contradict each other; 6 connections).Significance: The network visually synthesizes how invasion biology’s predominant hypotheses are conceptually related to each other, and thus, reveals an emergent structure – a conceptual map – that can serve as a navigation tool for scholars, prac-titioners and students, both inside and outside of the field of invasion biology, and guide the development of a more coherent foundation of theory. Additionally, the outlined approach can be more widely applied to create a conceptual map for the larger fields of ecology and biogeography.
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    Improving the Environmental Impact Classification for Alien Taxa (EICAT): a summary of revisions to the framework and guidelines
    (2020) Volery, L.; Blackburn, T.M.; Bertolino, S.; Evans, T.; Genovesi, P.; Kumschick, S.; Roy, H.E.; Smith, K.G.; Bacher, S.
    The Environmental Impact Classification for Alien Taxa (EICAT) classifies the impacts caused by alien species in their introduced range in standardised terms across taxa and recipient environments. Impacts are classified into one of five levels of severity, from Minimal Concern to Massive, via one of 12 impact mechanisms. Here, we explain revisions based on an IUCN-wide consultation process to the previously-published EICAT framework and guidelines, to clarify why these changes were necessary. These changes mainly concern: the distinction between the two highest levels of impact severity (Major and Massive impacts), the scenarios of the five levels of severity for the hybridisation and disease transmission mechanisms, the broadening of existing impact mechanisms to capture overlooked mechanisms, the Current (Maximum) Impact, and the way uncertainty of individual impact assessments is evaluated. Our aim in explaining this revision process is to ensure consistency of EICAT assessments, by improving the understanding of the framework.
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    Appropriate uses of EICAT protocol, data and classifications
    (2020) Kumschick, S.; Bacher, S.; Bertolino, S.; Blackburn, T.M.; Evans, T.; Roy, H.E.; Smith, K.
    The Environmental Impact Classification for Alien Taxa (EICAT) can be used to classify alien taxa according to the magnitude and type of their environmental impacts. The EICAT protocol, classifications of alien taxa using the protocol (EICAT classification) and the data underpinning classifications (EICAT data) are increasingly used by scientists and practitioners such as governments, NGOs and civil society for a variety of purposes. However, the properties of the EICAT protocol and the data it generates are not suitable for certain uses. Therefore, we present guidelines designed to clarify and facilitate the appropriate use of EICAT to tackle a broad range of conservation issues related to biological invasions, as well as to guide research and communication more generally. Here we address common misconceptions and give a brief overview of some key issues that all EICAT users need to be aware of to take maximal advantage of this resource. Furthermore, we give examples of the wide variety of ways in which the EICAT protocol, classifications and data can be and have been utilised and outline common errors and pitfalls to avoid.
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    Understanding uncertainty in the Impact Classification for Alien Taxa (ICAT) assessments
    (2020) Probert, A.F.; Volery, L.; Kumschick, S.; Vimercati, G.; Bacher, S.
    The Environmental Impact Classification for Alien Taxa (EICAT) and the Socio-Economic Impact Classification of Alien Taxa (SEICAT) have been proposed to provide unified methods for classifying alien species according to their magnitude of impacts. EICAT and SEICAT (herein “ICAT” when refered together) were designed to facilitate the comparison between taxa and invasion contexts by using a standardised, semi-quantitative scoring scheme. The ICAT scores are assigned after conducting a literature review to evaluate all impact observations against the protocols’ criteria. EICAT classifies impacts on the native biota of the recipient environments, whereas SEICAT classifies impacts on human activities. A key component of the process is to assign a level of confidence (high, medium or low) to account for uncertainty. Assessors assign confidence scores to each impact record depending on how confident they are that the assigned impact magnitude reflects the true situation. All possible sources of epistemic uncertainty are expected to be captured by one overall confidence score, neglecting linguistic uncertainties that assessors should be aware of. The current way of handling uncertainty is prone to subjectivity and therefore might lead to inconsistencies amongst assessors. This paper identifies the major sources of uncertainty for impacts classified under the ICAT frameworks, where they emerge in the assessment process and how they are likely to be contributing to biases and inconsistency in assessments. In addition, as the current procedures only capture uncertainty at the individual impact report, interspecific comparisons may be limited by various factors, including data availability. Therefore, ranking species, based on impact magnitude under the present systems, does not account for such uncertainty. We identify three types of biases occurring beyond the individual impact report level (and not captured by the confidence score): biases in the existing data, data collection and data assessment. These biases should be recognised when comparing alien species based on their impacts. Clarifying uncertainty concepts relevant to the ICAT frameworks will lead to more consistent impact assessments and more robust intra- and inter-specific comparisons of impact magnitudes.