Impacts of invasive alien species (IAS) affect different receptor environments, and are often divided into environmental and socio-economic impacts. Some of these impacts can result in substantial monetary costs and/or alterations to entire ecosystems and social systems (O’Dowd et al. 2003, Pimentel et al. 2005, Reaser et al. 2007, Vilà et al. 2010). At an international level the ecological impacts of IAS are addressed by the Convention on Biological Diversity (CBD 1992). Through this convention the need for prioritization and management of priority species has been highlighted in the Strategic Plan for Biodiversity 2011-2020 (COP 10 2010), although no guidance on how to achieve this ideal is provided.

There has been recognition that societies need to mitigate negative impacts of IAS, i.e. find appropriate means to manage IAS in a way that their impacts are at least minimized, e.g. by eradication, reduction below a specific threshold, or containment. However, resources to manage IAS are limited and with increasing globalization, the influx of potentially harmful organisms will likely continue to increase (Perrings et al. 2010, Essl et al. 2011). There are two approaches to this problem. Firstly, to limit new alien species from entering an area/country, for which purpose border control risk assessments have been developed (e.g., Pheloung et al. 1999, Bomford 2008, see Hulme 2012 and Leung et al. 2012 for reviews). Secondly, alien species that escape border controls (Bacon et al. 2012) or have never been subjected to border control risk assessment, and which cause high impact must be managed in their new ranges (see above). Hence, a system is needed that facilitates the optimal allocation of limited resources to manage those IAS that are most harmful in a given area. Such a system would ideally integrate the severity of effects on the environment, as well as on the economy and the society in question, allowing decision makers to prioritize certain high impact species for management. However, also cost-effectiveness of management needs to be taken into account.

Alien species, however, do not have only negative effects. The majority of the alien plants in Europe were deliberately introduced, e.g. as ornamental, horticultural, restoration, agricultural or forestry species (Hopper 2007, Lambdon et al. 2008, Pyšek et al. 2009) with their respective social, economic and environmental benefits. Management of such species, which are detrimental in some aspects (e.g. for biodiversity) and beneficial in others (e.g. forestry), can result in conflicts among involved stakeholders. For example, the introduction to South Africa of alien Acacia species which subsequently became invasive had differential effects on local communities. On the one hand, communities suffered from water scarcity due to increased evapotranspiration by the acacias. On the other hand, they benefitted from fuel wood and building timber (De Wit et al. 2001). Such situations have led to the recognition of a need for a framework to document the different consequences of an IAS for different groups of stakeholders (Stoll-Kleemann and Welp 2006, Binimelis et al. 2007, Kapler et al. 2012) and recently a few attempts have been made to develop applications which incorporate stakeholders (Cook and Proctor 2007, Hurley et al. 2010, Liu et al. 2011, De Lange et al. 2012, Forsyth et al. 2012).

Parker et al. (1999) developed a framework to assess the ecological effect of IAS, arguing that the total effect of an invader includes three fundamental dimensions: range, abundance, and the per-capita or per-biomass effect of the invader, i.e. the magnitude of ecological change it causes. Since then progress has been made in scoring the overall negative effects of alien species (e.g., Nentwig et al. 2010, Kumschick and Nentwig 2010, Pluess 2011). A very detailed assessment scheme for impacts of genetically modified organisms was presented by Kowarik et al. (2008) which can easily be adapted for invasive species. However, few of these impact scoring approaches explicitly addressed potentially competing interests of stakeholders (i.e. various ecological, economic or social interests) within the context of management of IAS. Furthermore, these studies ignore potential positive effects of IAS, which might be crucial for some species and stakeholders (see e.g., Schlaepfer et al. 2011 for a review). These points are important because biological invasions represent a complex societal issue for two reasons. First, the scientific knowledge about biological invasions and the outcome of different management options are highly uncertain and second, both conflicts of interests and values are prominent in a problem-solving context such as the management of IAS (Kueffer and Hadorn 2008). Thus, as in any environmental decision making process, IAS management has to minimize conflicts characterized by ecological, economic and social value judgments of different stakeholders (Liuet al. 2010, 2011). Furthermore, if all types of impacts of IAS are to be assessed (i.e. ecological and socio-economic impacts), then the different scientific disciplines and associated value systems require a common currency with which to measure impact. Another layer of complexity is added by the fact that different sections of administration and stakeholder groups with differing agendas need to be integrated, since plants, animals and human health are in the responsibility of different agencies and/or ministries (e.g., IPPC - International Plant Protection Convention, OIE - World Organization for Animal Health, WHO - World Health Organization).

At present, several studies have proposed prioritization methods for the management of weeds (e.g., Skinner et al. 2000, Virtue et al. 2001, Robertson et al. 2003, Tassin et al. 2006, Randall et al. 2008, Brunel et al. 2010). However, only few schemes considered potential conflicts of interest when evaluating which weed species to prioritize for control first, mainly in South Africa (Robertson et al. 2003, Forsyth et al. 2012, De Lange et al. 2012). Other studies that specifically tackled the complexities of conflicting interests and values in IAS management have usually focused on particular outbreak situations of a single species (Maguire 2004, Liu et al. 2010, 2012). Based on a participatory approach in Western Australia, the prioritization by an assessment committee differed from current resource allocations in Western Australia (Cook and Proctor 2007). A similar approach was taken by Roura-Pascual et al. (2010) for South Africa. However, such a potentially time-consuming and resource-demanding approach might not always be possible. Hence, decision makers and especially governmental bodies are still in need of a generic decision making aid that facilitates the identification of priority species whilst minimizing or reconciling potential conflicts of interest.

The basic problem with many of the systems to date is a fundamental one. Decisions made for IAS management are heavily influenced by judgements which are predominantly based on inputs from scientists (Wilhere 2008). In general, the role undertaken by scientists in decision making falls along a gradient (Lach et al. 2003). At one end of the continuum, scientists may simply report results that others use to make decisions. Alternatively, they may interpret these results and work with decision makers to integrate these results into the decision making process. At the other end of the continuum, scientists may be actively involved in decision making by advocating for a specific decision or in the extreme, making the decision themselves. While traditionally scientists largely shied away from active involvement in decision making (Lach et al. 2003), recent years have seen increasing advocacy (Marris 2006, Scott et al. 2007). This has resulted in a call for science inputs into decision making to be supportive of the process but dispassionate towards policy outcomes (Lackey 2007), recognising that science is but one vital element that needs to be considered in decision making. Against this background we argue that the process of objectively describing with scientific methods changes associated with an IAS has to be explicitly separated from the proximate subjective societal evaluation of impact, which is based on values. For the actual decision making for management, however, facts (effects on environment and socio-economy) need to be connected to values (judgement of involved stakeholders).

Here, we propose a comprehensive framework which aims to explicitly separate the scientific description of changes caused by IAS from the value systems of affected stakeholders who may have differing interests. Furthermore, it addresses both negative and positive effects of IAS, since positive effects are often neglected in purely ecological impact studies (Goodenough 2010, Davis et al. 2011, but see Pyšek et al. 2012 for a global review).

The framework suggested here is divided into five steps. The different steps are shortly introduced in this section. We then present more details about the scorings and values in the following sections and in Figures 1 and 2.

Step 1: Stakeholder selection and weighting of stakeholder importance. A call for stakeholder participation is launched and they are encouraged to claim their interest. The stakeholder group is formed such that it is sufficiently representative and appropriate for the task at hand. The participating stakeholders are then categorized according to their importance in relation to the issue that is being evaluated. This process produces Stakeholder Weights (SW), and should be conducted in a transparent and logical way.

Step 2: Description and scoring of changes due to IAS. For this step we propose a scoring system, based on two main impact classes (ecological, socio-economic) each with several categories (e.g., agriculture, health, infrastructure, herbivory, hybridization). Negative and positive changes are separately evaluated for each IAS. The outcome of this step is hereafter referred to as Change Assessment Score (CAS).

Step 3: Valuing the relative importance of impact categories by stakeholders. After identifying affected stakeholders in step 1, each stakeholder values the relative importance of all impact categories. Negative and positive categories are valued separately. When valuing the categories, the stakeholders do not know the species assessed in step 2 and their change assessment scores. This is called Stakeholder Value Assessment (SVA).

After selection of stakeholders (step 1), the description of changes (step 2) and the assessment of stakeholder weights (step 3) can be conducted at the same time as one does not depend on the outcome of the other. The following steps in turn can only be performed if the outcomes of the former steps are known.

Step 4: Calculating weighted impact categories. This is done by combining the outcomes of step 1 and step 3, which produces Weighted Impact Categories (WIC).

Step 5: Final impact scores.The Final Impact Scores (FIS) for each species are calculated by combining the CAS (step 2) with WIC (step 4).

In the following sections, the five steps are described in detail, and in the final part of the paper the usefulness of the scheme for different potential end-users and advantages and potential shortcomings are discussed.

Stakeholders play a central role in the presented scheme, as their opinions are crucial for evaluating the subjective impact categories through formal and structured analysis. The stakeholder process needs to be carefully planned, structured and conducted, accounting for the aim and needs of the problem at hand (Renn and Schweizer 2009). There are three main things to consider: a) who should participate; b) what is the form of the participatory process; and c) whose opinion counts.

The second step of the decision making process aims at recording all changes an IAS causes in the introduced range. An impact or change in this case is defined as any deviation from the state of a system before the invasion happened. In order to make comparison between species, different locations and different measurements of impact, we suggest the use of a generic scoring system (e.g., Nentwig et al. 2010, Kumschick and Nentwig 2010). Nevertheless, scoring systems and other prioritization tools suggested so far often only focus on unwanted changes and rarely take into account possible welcome changes which might result from species introductions, as e.g. increasing population densities of threatened native species (Schlaepfer et al. 2011) or economic benefits (Leung et al. 2012). For a balanced view, however, positive effects should no longer be neglected or even ignored, as many stakeholders profit from such IAS. When a decision is needed on how to deal with an IAS, all possible stakeholder interests need to be accounted for to ensure wide acceptance and support for the decision (Myers et al. 2000, Gardener et al. 2010; see step 1 for more details). However, whether changes are perceived as “positive” or “negative” depends on the value system of the stakeholders concerned (Simberloff et al. 2012). For example, the invasion of the weed Paterson’s Curse (Echium plantagineum) in Australia was perceived as detrimental by ranchers because the plant is toxic to livestock, but beekeepers profited from its proliferous honey production (Harris 1988). In the following description of impact (changes), we continue to use the terms “positive” and “negative”, but aim to define them in an objective, value-free way by describing the direction of change relative to pre-invasion state of the system.

Based on previously published scoring systems (e.g., Nentwig et al. 2010, Kumschick and Nentwig 2010, Kumschick et al. 2011, Pluess 2011, Kumschick et al. 2012), we determined a wide range of changes IAS could cause in the introduced area (Appendix A). The scoring system consists of two main classes of changes, socio-economic and environmental, and each class has 6 categories. The categories for environmental changes are hybridization, competition, transmission of diseases to wildlife, herbivory/toxicity, predation, and ecosystem effects in general. Changes to the environment can be negative or positive. Changes in the negative direction denote a decrease in an attribute of ecosystem function or native biodiversity compared to the state before the IAS was introduced and can range from no changes to the environment (score 0) to the maximum reduction possible (score 5). Positive effects can occur in systems previously altered by human-induced disturbance, e.g. alien species, land-use change, pollutants, eutrophication etc., but where an invader can fulfil some or many of the functions that previously existed or were fulfilled by species before perturbation. Thus, these scores can also range from very low changes (score +1) to the complete restoration of an expected, pre-invasion state of system functioning (score +5). Furthermore, positive effects can occur if an invasive species enhances a function still provided by other resident species. Please note that “positive” and “negative” do not denote human values, but relate to the direction of environmental change after invasion relative to the pre-invasion state of the system: “positive” indicates changes towards the pre-invasion state, “negative” changes away from the pre-invasion state. Because a species might simultaneously cause positive and negative changes within the same category, but through different mechanisms (see e.g. the Echium example from Australia described above), we score these positive and negative changes separately. Furthermore, it is possible that a stakeholder values positive and negative changes differently, so by keeping them separate, the categories might also be weighted differently.

The socio-economic categories are changes to human health, infrastructure, animal production, agriculture, forestry and human social life. Socio-economic changes can also be negative or positive, depending on whether they decrease or increase human well-being. Negative changes often consist of direct monetary or utility losses and can range from no changes (score 0) to the maximum negative change possible (score 5). Positive effects are also possible, for example, more possibilities for hunting an invasive species whilst alleviating hunting pressure on native mammals, or provision of a nectar source for important pollinators of agricultural crops. These effects can also range from very low (score +1) to the highest positive effects possible (score +5). Again, we have positive and negative changes within the same category, but we score these separately because they might occur through different mechanisms.

Not all changes are equally relevant for different taxa. For example, the difference in changes between alien plants and animals is likely to be quite marked in some cases. Therefore, we propose not to use the scores of change (“impact” scores) as measurement themselves, but to calculate the percentage score achieved out of the maximum possible for a given species. Hence if plants and animals are to be assessed in the same prioritization round, then for questions which are not relevant for plants or animals respectively (e.g. ecological impact through predation for plants), the overall-score should be adjusted by calculating the percentage score achieved out of the maximum attainable score for the species, and then multiplying by the maximum score attainable among all species considered. The critical point here is that in any round of prioritization, each candidate species should have an equal opportunity of attaining the same maximum score possible.

In many cases, effects of IAS on the recipient environment and economy have not been thoroughly studied. As in the whole invasion process, the uncertainty level of impacts can therefore be high and communicating these uncertainties is crucial in the decision making process (Liu et al. 2011, Leung et al. 2012). We therefore suggest including information about certainty in this step to account for the reliability of the data source used for scoring (low, medium, high). For example, this can be based on the type of data source (e.g. Low: mentioned in paper, no reference, speculation, expert judgement. Medium: evidence in literature, observational. High: demonstrated evidence in peer-reviewed literature, experimental) (Spear and van Wilgen, pers. comm.). This also deals with the fact that an impact score of 0 can be both, “no impact known” and “no impact detectable”. Including a certainty level enables to distinguish these possibilities (e.g. Low: no information. Medium: unlikely based on life history, expert judgement, literature observation or speculation that there is no impact. High: demonstrated evidence in peer-reviewed literature, experimental) (Spear and van Wilgen, pers. comm.). These certainty levels are to be communicated to the decision maker and can potentially influence the final decision making. Furthermore, they can identify research needs (e.g., species with large effects with low certainty).

At this stage, we will leave the domain of objective quantification again and focus on the societal context. Scientific measurements of impact are valued differently by different stakeholders and the valuation may differ in space and time and from case to case (Sagoff 2011). Biological invasions will thus be perceived to have different impacts for different societal sectors and different groups of stakeholders. Several species may be perceived as beneficial e.g. for farming, forestry, hunting or landscape restoration, but as detrimental from a nature conservationists point of view.

The approach we suggest here (as vizualised in Figures 1 and 2) would be to let each stakeholder group give scores to each impact category according to their perceived importance for them. For example, for an impact assessment of alien species in a city, two possible stakeholder groups might be the tourism industry and environmentalists. The tourism industry is likely to assign the highest scores to the positive category on human social life, while environmentalists might give highest priority to the negative categories of change in ecosystems in general and on other species (e.g. through competition). There are a few studies on invasive species that explicitly weight the different assessment criteria (Cook and Proctor 2007, Ou et al. 2008, Hurley et al. 2010, Skurka Darin et al. 2011). The processes by which this is done in these studies are mostly rating or paired comparison (analytical hierarchy process), but also fixed point scoring or the ratio method (Grafakos et al. 2010).

We propose a fixed point scoring method to rank the importance of the categories by giving the stakeholders a fixed amount of points (e.g. 100) to freely distribute among the impact categories. This would reflect their preferences, but it is also good to note that if the stakeholders have extreme agendas, they might also end up with extreme point allocations. As mentioned, there are other mechanisms that could alternatively be used, for instance scoring each category at scale 1-5, and possibly also a combination of these. However, whatever the procedure, the stakeholders need to be given clear instructions on what is expected of them.

As indicated in the previous section, negative and positive categories are valued separately. When valuing the categories, the stakeholders do not know the Change Assessment Scores (CAS) of the alien species assigned by scientists in step 2, neither do they know which species are being assessed in order to avoid biased valuation towards certain species. In the same line of argument, stakeholders begin the valuation scoring without knowing the scores or even the participation of other stakeholder groups. If further deliberation in the stakeholder process is desired, it may follow this initial valuation.

For each stakeholder, the values of each impact category are multiplied with the weight score given to this stakeholder by the decision maker. For example, two stakeholders, named A and B here, assign 2 and 5 points, respectively to the category “agriculture”. The decision maker weights the opinion of stakeholder A as 3 times higher than the opinion of stakeholder B, thus the impact scores of A are multiplied by a factor 3 while those of B remain unchanged (see Figure 2 for another example). The overall weighted impact score for the category “agriculture” would in this example receive a score of 2 × 3 + 5 × 1 = 11. This procedure yields weighted impact values for each category, incorporating the value system of all stakeholders and their importance for the decision maker. The highest weighted impact values represent the categories that are valued most across all stakeholders, i.e. those categories in which impacts would have the most serious effects for society.

The final impact scores for each species are calculated by multiplying the Change Assessment Scores (CAS) for each impact category (step 2, Figure 1) by the Weighted Impact Categories (WIC) over all stakeholders (step 4, Figure 1). This procedure essentially combines the objectively measured impact with an overall valuation of this impact by society. To calculate an overall impact score for a species, all final impact scores are summed. It should be noted here that upon calculation of the weighted impact scores, the sign of the CAS should be inverted to negative values, so that the final summed impact score reflects the net perceived impact of a given species. For example, if a high scoring negative-change species has a high positive-change score in one category, and the major stakeholders rank this positive change highly, then the finally obtained impact score will be reduced by the larger positive weighted impact score. The same procedure is applied for the associated certainty scores. We would therefore also learn about the combined certainty attached to the impact scores. Species can then be ranked according to their overall impact scores and/or by the certainty of the scores. Species responsible for large changes in an impact category that is of relatively low value to society are down-weighted, while species scoring low in impact categories that are of high societal value are given more weight (Figure 2). These final overall impact scores are a crucial element in the prioritization of management, amongst others like cost-effectiveness.

Conservation organizations, governments and other interest groups need to prioritize which species to spend limited funds on in order to manage and achieve the best socio-economic and ecological benefit. Here, we have presented a prioritization system which combines objective ecological information on how species change the state of the invaded environment, with stakeholder assessments evaluating impact categories according to their specific interests and perception of value, to create an overall impact score. Species are thus ranked in importance by combining the overall impact score with an a priori stakeholder rank, according to the perceived importance of stakeholders to the decision maker, who is the ultimate funding body of management measures. The system clearly distinguishes science from values in the decision making process, which is crucial for transparent, rational and sustainable policy making (Wilhere et al. 2012).

By combining stakeholder views and scientific information on species impact, this impact prioritization system can ensure that the outcome of action to manage the most problematic species has little bias from opinions of scientists, or from unintended dominance by any one stakeholder with a loud voice. Ultimately, the decision maker can have some influence on the decision of which species to manage, by deciding which stakeholder group’s opinion is the most important. This weighting of importance should be made in a transparent and repeatable way, for example by using the size of the stakeholder group (assuming that larger groups are of greater importance), but other ways of weighting stakeholder importance might be more appropriate, depending on the situation. However, any ranking of stakeholder importance should be done a priori.

For the system to be used in practice, it needs a few more specifications from the side of the user. For example it needs to be specified how to choose and reach the stakeholders, and according to which criteria and by whom they should be weighted. The system is very flexible and easily adaptable in this respect, as well as in relation to the impact scoring scheme that is used. For these and other possible adaptations and specifications, the system should be tested in practice and it should be documented precisely how the steps were performed and which changes were necessary. Generally, the more a system is used in practice, and the higher the awareness of its shortcomings are, the better and more broadly applicable it can become. A good example for this is the Australian weed risk assessment (WRA), which has been tested worldwide and in different ecosystems, and adapted accordingly (Pheloung et al. 1999, Weber et al. 2009). For the WRA, it has been tested whether it accurately rejects invaders and accepts harmless, non-invasive species. In the case of the framework suggested here, there is no “right” or “wrong” species to manage in any case since most (if not all) invasive species affect the newly inhabited environment in one or the other way. Naturally, cost-effectiveness of management makes managing some species a more sensible thing to do than other species. Furthermore, because of the subjective influence of the stakeholders in the decision making process, testing the system will not give a definite answer as to whether it would work in practice.

However, we believe the scheme we have presented here is general enough for different types of decision makers/funding bodies to use at different scales, with minimal modifications required. For instance, relatively local invasive species management projects by conservation organizations may only have a small number of species to assess, with few stakeholders involved. The system could just as easily be used at a regional or national level by government bodies. This process would be facilitated by the lists of problematic IAS that already exist in many countries, e.g. Australia’s ‘Weeds of National Significance’ List (Virtue et al. 2001) and list of harmful alien mammals and birds in Europe (Nentwig et al. 2010, Kumschick and Nentwig 2010). This framework could also be of special significance in guiding actions against IAS in developing countries that may lack the policy tools to give action to their national legislation or international obligations.

Another adaptation of the system, should there be a national, multi-species management plan with sufficient funds, would be to split potential species into taxonomic groups (e.g. plants, mammals, birds, invertebrates), or according to habitat/ecosystem (e.g. freshwater or other aquatic habitats, grasslands), which would allow multiple species to be selected for management which are likely to have very different types of impacts in different areas or ecosystems. However, our proposed impact assessment in Appendix A is flexible and broadly applicable enough to allow prioritization for management across a wide range of taxa and ecosystems, if this approach is desired.

Whilst our system can be a useful tool for identifying the highest priority species for management according to society and science, it does not take into account how cost-effective management implementation might be. Ideally, we should try to target those species that are more cost-effective to control or eradicate, at least at local scales. However, the chances of successful control will also depend on other factors than the species itself. Recent studies on the feasibility of eradication found that eradication success mainly depended on the extent of the invaded area (Pluess et al. 2012a) and the habitat type (Pluess et al. 2012b). If the circumstances of the infestation by the top-priority species prohibit effective eradication, then the next species in the list could be chosen. This could also be a useful strategy for picking a single species for management, should several end up with a tied ‘1^st place’ priority score, and if funds do not allow the management of all. Alternatively the management strategy could switch from a focus on eradication to containment and damage limitation of the top species in sensitive areas. Additionally, management actions can also have potential negative effects on the environment, which can possibly be larger than the effect of the species itself. Britton et al. (2011) provide examples on how these risks can be incorporated in management decisions. Furthermore, given legislation and policies cannot be ignored; Governments have certain policy restrictions, for example regarding safety, health issues and nature conservation, which they have to comply with. Therefore, even a species that is not ranked as top priority in the proposed framework might have to be dealt with when it is required by a country’s legislation.

This framework could potentially be useful for decision makers who need to set priorities for optimal resource allocation. Possible end-users of the framework besides governmental environmental agencies could also be nature conservation organizations (e.g., WWF), or any other organization interested in assessing the impact of IAS. The framework allows the end-user to set priorities for the management of problematic species across a wide range of taxa, by combining the actual change as described in step 2 (scientific input) and impact valuation in step 3 (stakeholders’ valuation of impact categories).

A major strength of the approach highlighted here is the integration of scientific (i.e., objectively measurable) and social (i.e., value-based) assessments of invasive species impacts to prioritize species of concern according to impact severity. In addition, the generic nature of the impact assessment in step 2 and the category valuation by stakeholder makes the system flexible for use on different spatial scales and in different regions.

One potential weakness of the procedure proposed rests at the second step scientific impact assessment. In practice, information on impacts generally has a high uncertainty and often is based only on expert judgements (Leung et al. 2012). Moreover, better-quantified impacts may be site-specific in their expression and magnitude, making generalization difficult (Virtue et al. 2001). Our scheme could provide an opportunity for targeting more thorough research and assessment of impacts of greatest concern to society, by communicating weighted impact values back to scientists. However, it should be borne in mind that public opinion is fluid, and may not immediately register the less tangible, but potentially detrimental impacts that invasive species can have on society.

Although the framework suggested is primarily meant to prioritize established and invasive species, it could also be used for border control of species which are invasive elsewhere and already known to cause impact, e.g. quarantine species. However, one should be aware of the problems associated with the prediction of future potentially harmful species, and also that this system does not assess entry or establishment probabilities. Particularly early during an invasion, management of species which are still harmless due to their small distributional range but may have a great potential to be detrimental in the future might be more cost-effective than trying to manage widespread species. Understanding how to predict impact is challenging but not impossible, and management decisions have to be made anyway (Leung et al. 2012).

In summary, we have presented a framework for prioritising invazive species according to impact severity, which involves the integration of scientifically assessed impacts per species, and socio-economic valuation of general impact importance across stakeholder groups. In theory, this framework could be implemented at multiple spatial scales, and for any group of species considered for management. However, the real value of the framework is revealed only once it has been thoroughly applied and tested, and we encourage the use of this framework to test whether or not it can work in practice as a useful prioritization and decision making tool in invasive species management.