Calibration of FISK, an Invasiveness Screening Tool for Nonnative Freshwater Fishes

Adapted from the weed risk assessment (WRA) of Pheloung, Williams, and Halloy, the fish invasiveness scoring kit (FISK) was proposed as a screening tool for freshwater fishes. This article describes improvements to FISK, in particular the incorporation of confidence (certainty/uncertainty) ranking of the assessors' responses, and reports on the calibration of the score system, specifically: determination of most appropriate score thresholds for classifying nonnative species into low-, medium-, and high-risk categories, assessment of the patterns of assessors' confidences in their responses in the FISK assessments. Using receiver operating characteristic (ROC) curves, FISK was demonstrated to distinguish accurately (and with statistical confidence) between potentially invasive and noninvasive species of nonnative fishes, with the statistically appropriate threshold score for high-risk species scores being ≥19. Within the group of species classed as high risk using this new threshold, a "higher risk" category could be visually identified, at present consisting of two species (topmouth gudgeon Pseudorasbora parva and gibel carp Carassius gibelio ). FISK represents a useful and viable tool to aid decision- and policymakers in assessing and classifying freshwater fishes according to their potential invasiveness.     

Application of FISK,an Invasiveness Screening Tool for Non‐Native Freshwater Fishes,in the Murray‐Darling Basin (Southeastern Australia)

The Fish Invasiveness Scoring Kit (FISK) is currently one of the most popular pre‐screening tools for freshwater fishes. A recent upgrade has ensured its wider climatic relevance to countries with subtropical regions. This enhancement is of particular importance to Australia, which encompasses tropical, arid, and temperate zones, and where the introduction of non‐native fish species poses a significant risk to biodiversity. In this study, 55 fish species previously evaluated in a U.K.‐based calibration of FISK are reassessed for their potential invasiveness in the Murray‐Darling Basin (MDB; southeastern Australia), the continent's largest catchment encompassing arid and temperate climates. Approximately half of the species were classed as “medium risk” and the other half as “high risk,” and the ≥19 threshold previously identified from the calibration study was confirmed. The three highest scoring species (common carp Cyprinus carpio carpio, goldfish Carassius auratus, and eastern mosquitofish Gambusia holbrooki) were those already present and invasive in the area, whereas nearly half of the tropical and subtropical species had lower scores compared to U.K. assessments, possibly because of climate change predictions of drier conditions across the MDB. There were some discordances between FISK and two Australian‐based assessment protocols, one of which is qualitative and the other represents a simplified version of FISK. Notably, the Australian origins of FISK should provide for an additional reason for further applications of the tool in other RA areas (i.e., drainage basins) of the continent, ultimately encouraging adoption as the country's reference screening tool for management and conservation purposes.     

Revisions of the Fish Invasiveness Screening Kit (FISK) for its Application in Warmer Climatic Zones,with Particular Reference to Peninsular Florida

The initial version (v1) of the Fish Invasiveness Scoring Kit (FISK) was adapted from the Weed Risk Assessment of Pheloung, Williams, and Halloy to assess the potential invasiveness of nonnative freshwater fishes in the United Kingdom. Published applications of FISK v1 have been primarily in temperate‐zone countries (Belgium, Belarus, and Japan), so the specificity of this screening tool to that climatic zone was not noted until attempts were made to apply it in peninsular Florida. To remedy this shortcoming, the questions and guidance notes of FISK v1 were reviewed and revised to improve clarity and extend its applicability to broader climatic regions, resulting in changes to 36 of the 49 questions. In addition, upgrades were made to the software architecture of FISK to improve overall computational speed as well as graphical user interface flexibility and friendliness. We demonstrate the process of screening a fish species using FISK v2 in a realistic management scenario by assessing the Barcoo grunter Scortum barcoo (Terapontidae), a species whose management concerns are related to its potential use for aquaponics in Florida. The FISK v2 screening of Barcoo grunter placed the species into the lower range of medium risk (score = 5), suggesting it is a permissible species for use in Florida under current nonnative species regulations. Screening of the Barcoo grunter illustrates the usefulness of FISK v2 as a proactive tool serving to inform risk management decisions, but the low level of confidence associated with the assessment highlighted a dearth of critical information on this species.     

First Application of FISK,the Freshwater Fish Invasiveness Screening Kit,in Northern Europe: Example of Southern Finland

The climatic conditions of north temperate countries pose unique influences on the rates of invasion and the potential adverse impacts of non‐native species. Methods are needed to evaluate these risks, beginning with the pre‐screening of non‐native species for potential invasives. Recent improvements to the Fish Invasiveness Scoring Kit (FISK) have provided a means (i.e., FISK v2) of identifying potentially invasive non‐native freshwater fishes in virtually all climate zones. In this study, FISK is applied for the first time in a north temperate country, southern Finland, and calibrated to determine the appropriate threshold score for fish species that are likely to pose a high risk of being invasive in this risk assessment area. The threshold between “medium” and “high” risk was determined to be 22.5, which is slightly higher than the original threshold for the United Kingdom (i.e., 19) and that determined for a FISK application in southern Japan (19.8). This underlines the need to calibrate such decision‐support tools for the different areas where they are employed. The results are evaluated in the context of current management strategies in Finland regarding non‐native fishes.     

Effectiveness of FISK,an Invasiveness Screening Tool for Non‐Native Freshwater Fishes,to Perform Risk Identification Assessments in the Iberian Peninsula

Risk assessments are crucial for identifying and mitigating impacts from biological invasions. The Fish Invasiveness Scoring Kit (FISK) is a risk identification (screening) tool for freshwater fishes consisting of two subject areas: biogeography/history and biology/ecology. According to the outcomes, species can be classified under particular risk categories. The aim of this study was to apply FISK to the Iberian Peninsula, a Mediterranean climate region highly important for freshwater fish conservation due to a high level of endemism. In total, 89 fish species were assessed by three independent assessors. Results from receiver operating characteristic analysis showed that FISK can discriminate reliably between noninvasive and invasive fishes for Iberia, with a threshold of 20.25, similar to those obtained in several regions around the world. Based on mean scores, no species was categorized as “low risk,” 50 species as “medium risk,” 17 as “moderately high risk,” 11 as “high risk,” and 11 as “very high risk.” The highest scoring species was goldfish Carassius auratus. Mean certainty in response was above the category “mostly certain,” ranging from tinfoil barb Barbonymus schwanenfeldii with the lowest certainty to eastern mosquitofish Gambusia holbrooki with the highest level. Pair‐wise comparison showed significant differences between one assessor and the other two on mean certainty, with these two assessors showing a high coincidence rate for the species categorization. Overall, the results suggest that FISK is a useful and viable tool for assessing risks posed by non‐native fish in the Iberian Peninsula and contributes to a “watch list” in this region.     

Quantitative Uncertainty Analysis for a Weed Risk Assessment System

Weed risk assessments (WRA) are used to identify plant invaders before introduction. Unfortunately, very few incorporate uncertainty ratings or evaluate the effects of uncertainty, a fundamental risk component. We developed a probabilistic model to quantitatively evaluate the effects of uncertainty on the outcomes of a question‐based WRA tool for the United States. In our tool, the uncertainty of each response is rated as Negligible, Low, Moderate, or High. We developed the model by specifying the likelihood of a response changing for each uncertainty rating. The simulations determine if responses change, select new responses, and sum the scores to determine the risk rating. The simulated scores reveal potential variation in WRA risk ratings. In testing with 204 species assessments, the ranges of simulated risk scores increased with greater uncertainty, and analyses for most species produced simulated risk ratings that differed from the baseline WRA rating. Still, the most frequent simulated rating matched the baseline rating for every High Risk species, and for 87% of all tested species. The remaining 13% primarily involved ambiguous Low Risk results. Changing final ratings based on the uncertainty analysis results was not justified here because accuracy (match between WRA tool and known risk rating) did not improve. Detailed analyses of three species assessments indicate that assessment uncertainty may be best reduced by obtaining evidence for unanswered questions, rather than obtaining additional evidence for questions with responses. This analysis represents an advance in interpreting WRA results, and has enhanced our regulation and management of potential weed species.     

An Ecological Risk Assessment of Nonnative Boas and Pythons as Potentially Invasive Species in the United States

The growing international trade in live wildlife has the potential to result in continuing establishment of nonnative animal populations in the United States. Snakes may pose particularly high risks as potentially invasive species, as exemplified by the decimation of Guam's vertebrate fauna by the accidentally introduced brown tree snake. Herein, ecological and commercial predictors of the likelihood of establishment of invasive populations were used to model risk associated with legal commercial imports of 23 species of boas, pythons, and relatives into the United States during the period 1989-2000. Data on ecological variables were collected from multiple sources, while data on commercial variables were collated from import records maintained by the U.S. Fish and Wildlife Service. Results of the risk-assessment models indicate that species including boa constrictors (Boa constrictor), ball pythons (Python regius), and reticulated pythons (P. reticulatus) may pose particularly high risks as potentially invasive species. Recommendations for reducing risk of establishment of invasive populations of snakes and/or pathogens include temporary quarantine of imports to increase detection rates of nonnative pathogens, increasing research attention to reptile pathogens, reducing the risk that nonnative snakes will reach certain areas with high numbers of federally listed species (such as the Florida Keys), and attempting to better educate individuals purchasing reptiles.     

Ensemble Habitat Mapping of Invasive Plant Species

Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species‐environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species‐environment matching models for risk analysis.     

Evaluating Potential Distribution of High‐Risk Aquatic Invasive Species in the Water Garden and Aquarium Trade at a Global Scale Based on Current Established Populations

Aquatic non‐native invasive species are commonly traded in the worldwide water garden and aquarium markets, and some of these species pose major threats to the economy, the environment, and human health. Understanding the potential suitable habitat for these species at a global scale and at regional scales can inform risk assessments and predict future potential establishment. Typically, global habitat suitability models are fit for freshwater species with only climate variables, which provides little information about suitable terrestrial conditions for aquatic species. Remotely sensed data including topography and land cover data have the potential to improve our understanding of suitable habitat for aquatic species. In this study, we fit species distribution models using five different model algorithms for three non‐native aquatic invasive species with bioclimatic, topographic, and remotely sensed covariates to evaluate potential suitable habitat beyond simple climate matches. The species examined included a frog (Xenopus laevis), toad (Bombina orientalis), and snail (Pomacea spp.). Using a unique modeling approach for each species including background point selection based on known established populations resulted in robust ensemble habitat suitability models. All models for all species had test area under the receiver operating characteristic curve values greater than 0.70 and percent correctly classified values greater than 0.65. Importantly, we employed multivariate environmental similarity surface maps to evaluate potential extrapolation beyond observed conditions when applying models globally. These global models provide necessary forecasts of where these aquatic invasive species have the potential for establishment outside their native range, a key component in risk analyses.     

Risk‐Based Probabilistic Approach to Assess the Impact of False Mussel Invasions on Farmed Hard Clams

The purpose of this article is to provide a risk‐based predictive model to assess the impact of false mussel Mytilopsis sallei invasions on hard clam Meretrix lusoria farms in the southwestern region of Taiwan. The actual spread of invasive false mussel was predicted by using analytical models based on advection‐diffusion and gravity models. The proportion of hard clam colonized and infestation by false mussel were used to characterize risk estimates. A mortality model was parameterized to assess hard clam mortality risk characterized by false mussel density and infestation intensity. The published data were reanalyzed to parameterize a predictive threshold model described by a cumulative Weibull distribution function that can be used to estimate the exceeding thresholds of proportion of hard clam colonized and infestation. Results indicated that the infestation thresholds were 2–17 ind clam^?1 for adult hard clams, whereas 4 ind clam^?1 for nursery hard clams. The average colonization thresholds were estimated to be 81–89% for cultivated and nursery hard clam farms, respectively. Our results indicated that false mussel density and infestation, which caused 50% hard clam mortality, were estimated to be 2,812 ind m^?2 and 31 ind clam^?1, respectively. This study further indicated that hard clam farms that are close to the coastal area have at least 50% probability for 43% mortality caused by infestation. This study highlighted that a probabilistic risk‐based framework characterized by probability distributions and risk curves is an effective representation of scientific assessments for farmed hard clam in response to the nonnative false mussel invasion.     

Risk Analysis for Invasive Species: General Framework and Research Needs

A joint workshop was convened by the Society for Risk Analysis Ecological Risk Assessment Specialty Group and the Ecological Society of America Theoretical Ecology Section to provide independent scientific input into the formulation of methods and processes for risk assessment of invasive species. In breakout sessions on (1) the effects of invasive species on human health, (2) effects on plants and animals, (3) risk analysis issues and research needs related to entry and establishment of invasive species, and (4) risk analysis issues and research needs related to the spread and impacts of invasive species, workshop participants discussed an overall approach to risk assessment for invasive species. Workshop participants agreed on the need for empirical research on areas in which data are lacking, including potential invasive species, native species and habitats that may be impacted by invasive species, important biological processes and phenomena such as dispersal, and pathways of entry and spread for invasive species. Participants agreed that theoretical ecology can inform the process of risk assessment for invasive species by providing guidelines and conceptual models, and can contribute to improved decision making by providing a firm biological basis for risk assessments.     

Allee Effects and the Risk of Biological Invasion

The Allee effect is a nonlinear phenomenon exhibited in the population dynamics of sparse populations in which the per capita population growth rate increases with increasing population density. In sufficiently sparse populations, the Allee effect may lead to extinction and is known to generate a threshold in the probability of establishment when presented as a function of introduced population size or density. As introduced populations are generally small, Allee effects are probably common in biological invasions and their consideration is necessary for accurately assessing the risk of invasion by many species, including all sexually reproducing species. Bythotrephes longimanus, an invasive, freshwater, cladoceran zooplankter from Europe, is one such species. Here, I review a previously published model of the Allee effect for continuously sexually reproducing species. Then, I develop a new model for seasonally parthenogenetic species such as Bythotrephes, and thereby demonstrate the potential consequences of Allee effects. This result underscores the importance of considering nonlinear phenomena, including thresholds, when conducting risk analysis for biological introductions.     

Induced Earthquakes from Long‐Term Gas Extraction in Groningen,the Netherlands: Statistical Analysis and Prognosis for Acceptable‐Risk Regulation

Recently, growing earthquake activity in the northeastern Netherlands has aroused considerable concern among the 600,000 provincial inhabitants. There, at 3 km deep, the rich Groningen gas field extends over 900 km² and still contains about 600 of the original 2,800 billion cubic meters (bcm). Particularly after 2001, earthquakes have increased in number, magnitude (M, on the logarithmic Richter scale), and damage to numerous buildings. The man‐made nature of extraction‐induced earthquakes challenges static notions of risk, complicates formal risk assessment, and questions familiar conceptions of acceptable risk. Here, a 26‐year set of 294 earthquakes with M ≥ 1.5 is statistically analyzed in relation to increasing cumulative gas extraction since 1963. Extrapolations from a fast‐rising trend over 2001–2013 indicate that—under “business as usual”—around 2021 some 35 earthquakes with M ≥ 1.5 might occur annually, including four with M ≥ 2.5 (ten‐fold stronger), and one with M ≥ 3.5 every 2.5 years. Given this uneasy prospect, annual gas extraction has been reduced from 54 bcm in 2013 to 24 bcm in 2017. This has significantly reduced earthquake activity, so far. However, when extraction is stabilized at 24 bcm per year for 2017–2021 (or 21.6 bcm, as judicially established in Nov. 2017), the annual number of earthquakes would gradually increase again, with an expected all‐time maximum M ≈ 4.5. Further safety management may best follow distinct stages of seismic risk generation, with moderation of gas extraction and massive (but late and slow) building reinforcement as outstanding strategies. Officially, “acceptable risk” is mainly approached by quantification of risk (e.g., of fatal building collapse) for testing against national safety standards, but actual (local) risk estimation remains problematic. Additionally important are societal cost–benefit analysis, equity considerations, and precautionary restraint. Socially and psychologically, deliberate attempts are made to improve risk communication, reduce public anxiety, and restore people's confidence in responsible experts and policymakers.     

Modeling Extreme Risks in Ecology

Extreme risks in ecology are typified by circumstances in which data are sporadic or unavailable, understanding is poor, and decisions are urgently needed. Expert judgments are pervasive and disagreements among experts are commonplace. We outline approaches to evaluating extreme risks in ecology that rely on stochastic simulation, with a particular focus on methods to evaluate the likelihood of extinction and quasi‐extinction of threatened species, and the likelihood of establishment and spread of invasive pests. We evaluate the importance of assumptions in these assessments and the potential of some new approaches to account for these uncertainties, including hierarchical estimation procedures and generalized extreme value distributions. We conclude by examining the treatment of consequences in extreme risk analysis in ecology and how expert judgment may better be harnessed to evaluate extreme risks.     

Risk Assessment Can Be a Game‐Changing Information Technology—But Too Often It Isn't

The printing press was a game‐changing information technology. Risk assessment could be also. At present, risk assessments are commonly used as one‐time decision aids: they provide justification for a particular decision, and afterwards usually sit on a shelf. However, when viewed as information technologies, their potential uses are much broader. Risk assessments: (1) are repositories of structured information and a medium for communication; (2) embody evaluative structures for setting priorities; (3) can preserve information over time and permit asynchronous communication, thus encouraging learning and adaptation; and (4) explicitly address uncertain futures. Moreover, because of their “what‐if” capabilities, risk assessments can serve as a platform for constructive discussion among parties that hold different values. The evolution of risk assessment in the nuclear industry shows how such attributes have been used to lower core‐melt risks substantially through improved templates for maintenance and more effective coordination with regulators (although risk assessment has been less commonly used in improving emergency‐response capabilities). The end result of this evolution in the nuclear industry has been the development of “living” risk assessments that are updated more or less in real time to answer even routine operational questions. Similar but untapped opportunities abound for the use of living risk assessments to reduce risks in small operational decisions as well as large policy decisions in other areas of hazard management. They can also help improve understanding of and communication about risks, and future risk assessment and management. Realization of these opportunities will require significant changes in incentives and active promotion by the risk analytic community.     

A Spatio‐Temporal Exposure‐Hazard Model for Assessing Biological Risk and Impact

We developed a simulation model for quantifying the spatio‐temporal distribution of contaminants (e.g., xenobiotics) and assessing the risk of exposed populations at the landscape level. The model is a spatio‐temporal exposure‐hazard model based on (i) tools of stochastic geometry (marked polygon and point processes) for structuring the landscape and describing the exposed individuals, (ii) a dispersal kernel describing the dissemination of contaminants from polygon sources, and (iii) an (eco)toxicological equation describing the toxicokinetics and dynamics of contaminants in affected individuals. The model was implemented in the briskaR package (b iological risk a ssessment with R ) of the R software. This article presents the model background, the use of the package in an illustrative example, namely, the effect of genetically modified maize pollen on nontarget Lepidoptera, and typical comparisons of landscape configurations that can be carried out with our model (different configurations lead to different mortality rates in the treated example). In real case studies, parameters and parametric functions encountered in the model will have to be precisely specified to obtain realistic measures of risk and impact and accurate comparisons of landscape configurations. Our modeling framework could be applied to study other risks related to agriculture, for instance, pathogen spread in crops or livestock, and could be adapted to cope with other hazards such as toxic emissions from industrial areas having health effects on surrounding populations. Moreover, the R package has the potential to help risk managers in running quantitative risk assessments and testing management strategies.     

What Can Decision Analysis Do for Invasive Species Management?

Decisions about management of invasive species are difficult for all the reasons typically addressed by multiattribute decision analysis: uncertain outcomes, multiple and conflicting objectives, and many interested parties with differing views on both facts and values. This article illustrates how the tools of multiattribute analysis can improve management of invasive species, with an emphasis on making explicit the social values and preferences that must inform invasive species management. Risk assessment protocols developed previously for invasive species management typically suffer from two interacting flaws: (1) separating risk assessment from risk management, thus disrupting essential connections between the social values at stake in invasive species decisions and the scientific knowledge necessary to predict the likely impacts of management actions, and (2) relying on expert judgment about risk framed in qualitative and value-laden terms, inadvertently mixing the expert's judgment about what is likely to happen with personal preferences. Using the values structuring and probability-modeling elements of formal decision analysis can remedy these difficulties and make invasive species management responsive to both good science and public values. The management of feral pigs in Hawaiian ecosystems illustrates the need for such an integrated approach.     

Better Negative than Positive? Evidence of a Bias for Negative Information about Possible Health Dangers

Do the results of a scientific study influence confidence in the study's validity and the magnitude of change in the resulting perceived danger of the health risk investigated? Findings from the three investigations reported here indicate that scientific results that confirm a danger (negative results) do affect confidence in a study's validity and resulting risk assessments differently than results indicating low risk (positive results). Findings of Study 1 revealed that research results indicating a health risk were more trusted than results indicating little health risk. This effect was independent of the credibility of the information source. Study 2 demonstrated that confidence in research results increased with an increasing indication of health risk. Study 3 showed that people have more confidence in the results of animal tests on a food additive indicating negative human health effects than in animal tests indicating that a food additive is harmless. The findings have important practical implications. The observed asymmetry between positive and negative research results may be one reason that people are afraid of many of the hazards they are faced with in modern society.     

Using In Vitro High‐Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards

Today there are more than 80,000 chemicals in commerce and the environment. The potential human health risks are unknown for the vast majority of these chemicals as they lack human health risk assessments, toxicity reference values, and risk screening values. We aim to use computational toxicology and quantitative high‐throughput screening (qHTS) technologies to fill these data gaps, and begin to prioritize these chemicals for additional assessment. In this pilot, we demonstrate how we were able to identify that benzo[k]fluoranthene may induce DNA damage and steatosis using qHTS data and two separate adverse outcome pathways (AOPs). We also demonstrate how bootstrap natural spline‐based meta‐regression can be used to integrate data across multiple assay replicates to generate a concentration–response curve. We used this analysis to calculate an in vitro point of departure of 0.751 μM and risk‐specific in vitro concentrations of 0.29 μM and 0.28 μM for 1:1,000 and 1:10,000 risk, respectively, for DNA damage. Based on the available evidence, and considering that only a single HSD17B4 assay is available, we have low overall confidence in the steatosis hazard identification. This case study suggests that coupling qHTS assays with AOPs and ontologies will facilitate hazard identification. Combining this with quantitative evidence integration methods, such as bootstrap meta‐regression, may allow risk assessors to identify points of departure and risk‐specific internal/in vitro concentrations. These results are sufficient to prioritize the chemicals; however, in the longer term we will need to estimate external doses for risk screening purposes, such as through margin of exposure methods.     

Quantifying the Establishment Likelihood of Invasive Alien Species Introductions Through Ports with Application to Honeybees in Australia

The cost of an uncontrolled incursion of invasive alien species (IAS) arising from undetected entry through ports can be substantial, and knowledge of port‐specific risks is needed to help allocate limited surveillance resources. Quantifying the establishment likelihood of such an incursion requires quantifying the ability of a species to enter, establish, and spread. Estimation of the approach rate of IAS into ports provides a measure of likelihood of entry. Data on the approach rate of IAS are typically sparse, and the combinations of risk factors relating to country of origin and port of arrival diverse. This presents challenges to making formal statistical inference on establishment likelihood. Here we demonstrate how these challenges can be overcome with judicious use of mixed‐effects models when estimating the incursion likelihood into Australia of the European (Apis mellifera) and Asian (A. cerana) honeybees, along with the invasive parasites of biosecurity concern they host (e.g., Varroa destructor). Our results demonstrate how skewed the establishment likelihood is, with one‐tenth of the ports accounting for 80% or more of the likelihood for both species. These results have been utilized by biosecurity agencies in the allocation of resources to the surveillance of maritime ports.