We followed the methods of Measey et al. (2016) to build an updated species list for established alien amphibians (105 spp), including consulting new compendia on the topic (+ 19 spp; Capinha et al. 2017), giving us a total of 124 species. We searched Web of Science in September 2019 for publications since 2015 (inclusive) using the most recent taxonomic name (or combinations of older names if any taxonomic movement had occurred since 2015; cf Frost 2019) and a composite term for invasive species [e.g. TS = (“Ambystoma tigrinum”) AND TS = (alien OR invasive OR non-native OR exotic OR non-indigenous OR feral)]. We read titles and abstracts of resulting literature from the searches and obtained electronic copies of any studies that appeared to indicate a study on invasive amphibians (see van Wilgen et al. 2018 for details).

After compiling all literature, we followed Hawkins et al. (2015) to assign an EICAT level in the corresponding impact category to fit any content relating to impact: Minimal Concern, Minor, Moderate, Major and Massive (hereafter MC, MN, MO, MR and MV, respectively). If no EICAT impact level could be ascribed, the paper was retained with a score of Data Deficient (DD). In the case that a score was assigned, we also scored a confidence level (low, medium or high), based on the criteria provided by Hawkins et al. (2015). In brief, the confidence score considers the type of data, the spatial scale of the study, and unanimity of evidence. This process was carried out for every paper published since May 2015 on any alien amphibian, and added to existing data on impact scored for studies prior to this (Kumschick et al. 2017; Bacher et al. 2018). Note that one paper could be scored for more than one alien amphibian species, or more than one category, as appropriate to the data it contained. Therefore, the total number of papers is less than total impact scores.

The same procedure was conducted for SEICAT, but following the guidelines set down by Bacher et al. (2018). We remained cognisant that papers which had been scored for EICAT could also carry SEICAT scores, and vice versa.

We reasoned that over any given period of time, if there was no change in EICAT or SEICAT scoring, studies conducted in any period were likely to have equal chances of receiving the same proportions of EICAT or SEICAT scores as those received overall. Alternatively, an effect could be supported if the proportion of higher impacts (MR and MV) increased relative to the proportion of lower impacts (MC, MN and MO). Because the number of studies (see van Wilgen et al. 2018), and those to which EICAT or SEICAT scores could be attributed, have been increasing over time, we found it necessary to use only studies after 2000 when there were consistently more than three scores per year. Thereafter, we conducted a linear regression on the proportion of high to low impacts ((lower-higher)/total) per year in R (v 3.6.3; R Core Team 2020). Similarly, we tested whether the proportion of higher confidence scores (medium and high) have changed through time compared to lower confidence scores.

In each case where EICAT and SEICAT scores were determined for studies published in the last four years, we contacted the corresponding author of the study, to obtain research costs. We did not consider asking authors of earlier studies as we were concerned that accurate records of funding would not be available. We asked for the costs of the entire project in the form of a questionnaire (see Suppl. material 1), approved by Stellenbosch University’s Social, Behavioural and Education Research (SBER) committee (project: 2019-13163). Specifically, we requested information on the amount of time spent by different members of the research team in conducting the study and producing peer-reviewed papers and the costs of their time (i.e., wage rates). We further requested information on costs of travel, accommodation, equipment used in the field or the lab, publication, and any other relevant costs associated with the study. If the equipment used in the study could be used for other research (e.g. computers, cameras, centrifuges) we asked the authors to estimate the percentage of total use of the equipment that was attributable to the study. Finally, we asked the monetary units used to pay these costs and the years in which the costs were incurred.

All study costs were converted to United States Dollars (USD) using the purchasing power parity (PPP) rates of currency conversion for the year(s) in which study costs were incurred, and the consumer price index (CPI) was used to convert these costs to 2018 USD by accounting for inflation since the time of the study. We obtained CPI measures from the United States Bureau of Labor Statistics. PPP measures were obtained from Organisation for Economic Co-operation and Development (OECD) data. The OECD calculates PPP measures based on the relative prices of consumer goods and services, equipment goods, occupations, and construction projects in different countries. PPP measures deviate from standard currency exchange rates because PPP takes relative price levels and costs of living in different countries into account. That is, by converting wages using the PPP we determined what equivalent USD income would have allowed the individual to maintain the same standard of living in terms of goods and services they could purchase if they were living in the USA rather than the country where the study was conducted (i.e. Australia, India, South Africa, etc.). Similarly, by converting non-wage research expenses to USD using the PPP we accounted for different price levels in different countries. This allowed us to compare study costs without the confounding effect of different salaries and price levels for goods and services across different countries of the world, and means that the costs we present can be directly compared.

For studies for which we obtained costs, we scored the complexity of the study design using the categories provided by Christie et al. (2019). The categories included (from simple to complex) were: After, Before-After, Control-Impact, Before-After-Control-Impact, and Randomised Control Trial. Along with study design complexity, time taken to conduct the study was also scored. We did not include time taken to publish the study or any delays between the finalisation of data collection and writing up of the study.

In order to determine whether costs (USD PPP) are aligned with EICAT impact score or confidence, we conducted generalized linear mixed effects models (GLMM) using the lme4 package (Bates et al. 2015) in R (v 3.6.3; R Core Team 2020). Prior to analyses, model assumptions (e.g. normality, homogeneity and independence) were assessed for all variables. The independence of impact score and confidence was assessed with a Spearman’s Rank Correlation test. The response variable, Cost (USD PPP), was transformed using natural logs to meet the underlying statistical assumptions of normality. Restricted Maximum Likelihood (REML) methods were used to compare models with different fixed effect structures. A full model included two fixed factors, EICAT impact score and confidence as explanatory variables. We did not anticipate the need to assess models with interaction effects between our predictor variables. We used the continent on which the studies had been conducted as a random effect to account for potential autocorrelation within continents (following Evans and Blackburn 2020). Variance of random effects were minimal (s. d. < 2.00). Relative importance of competing models was evaluated using Akaike information criterion (AIC) and log likelihoods. To evaluate the variance of data explained by each model, R²m (marginal: variance explained by the fixed effects) and R²c (conditional: variance explained by the fixed and random effects) was calculated using the package MuMin (Barton and Barton 2019).

The effect of both study design complexity and the time taken to conduct the study (as predictor variables) on EICAT impact scores (response variable) were analysed using GLMMs, as above. Model assumptions (normality, homogeneity and variable independence) were assessed. The residuals from this analysis were not normally distributed and we were unable to normalize them by transformation. A full model included fixed effects, design complexity and time. All models included the random effect continent. Variance of the random effect, continent, was minimal (s. d. < 2.00).

We found 334 publications published since May 2015 that were on alien amphibians included in our updated list. This included two species (Desmognathus monticola; Bufo japonicus) that had not been previously on the list of Measey et al. (2016) or Capinha et al. (2017). Of the publications found, 109 papers could be scored (with 112 scores on 49 species) for impacts using the EICAT framework (see Suppl. material 2). Of all publications found, only eight new papers contained information that could be used to score socio-economic impacts according to SEICAT.

We found that cheaper studies equated with lower EICAT impact scores (p = 0.0060; model 1 Table 1; Figure 1). The model that included confidence (model 3) was not appreciably worse (within 2 delta AICs) but the R² estimates indicate that confidence did not explain more of the data (Table 1). Confidence and impact score were not correlated (S = 8789.3, p = 0.4457). No relationship between the confidence score and the study cost was found (see Table 1).

Figure 1.

Distribution of costs spent per publication and their EICAT impact level scored. Studies providing higher impacts (MR and MV) cost more money, but studies scoring the lowest impact (MC) cost more on average than those that have medium impact (MN and MO). Cost of the study is ln(USD) purchasing price parity (PPP). Impact scores follow Hawkins et al. 2015: Minimal Concern (MC), Minor (MN), Moderate (MO), Major (MR) and Massive (MV).

Our data showed that study design (complexity) and time taken to conduct the study were significantly related to EICAT score (p = 0.0007). The highest EICAT scores (MR and MV) were obtained from Before-After studies which took longer to conduct, and lower scores typically came from shorter studies ‘After’ invasions had taken place (Table 2). Although the model with study design alone (model 1) was within 2 delta AICs, it was not favoured as it explained less observed variance in the data (Table 2).

We noticed three general trends in EICAT scores over time. Firstly, that the amount of literature in the last four years that generated impact scores (not including DD) was equivalent to nearly a third of total scores (112 out of 362). Second, we found a significant negative trend (F_1,17 = 7.451, p = 0.014) with increasing proportions of lower impact studies over time: i.e. studies that scored MR or MV are reduced compared to the total number of scores since 2000. Lastly, we found that the proportion of studies scored with low confidence has a significant negative trend since 2000 (F_1,17 = 11.48, p = 0.003). Our overview of all data (Figure 2) shows that impact scores are higher since the 1990s than in the decades preceding this.

Figure 2.

The change in the proportion of amphibian EICAT impact scores over time (decadal scores starting from the 1930s). Solid shapes represent the percentage of scores (left scale) with totals for the entire period and score codes on the right (total = 424). Black line shows number of papers scored in the same decadal time slots, with scale to the right.

When compared with previous scores (Kumschick et al. 2017), we found that 25% (11 of 45 species) of all amphibians had increased their EICAT impact or confidence in the four years since the original score was determined (Table 3a). This included a growth in the assessed list by 12% (five species), which were previously not assessed (NA) due to a lack of literature. Notably, scores changed for the cane toad, Rhinella marina, from Major (MR) with high confidence to Massive (MV) with medium confidence (see below). Additionally, the confidence score on Major impacts of the African clawed frog, Xenopus laevis, changed from medium to high (see below).

Of the eight new papers for SEICAT scoring, one species (out of the previously scored 6 species) increased its SEICAT impact score, and two studies provided data on species for which there was no previous score (Table 3b). In the four years since the original study, there has been a 25% (three species) increase in species scored for SEICAT since the search made by Measey et al. (2016), and subsequently scored for SEICAT by Bacher et al. (2018).

For EICAT, we found that the number of papers decreased for each increasing impact score, except for the category MC, which is similar in size to MO (Figure 3). However, as the impact score increased, the proportion of studies with low confidence decreased and the proportion of high confidence studies increased.

Figure 3.

Changes in the frequency (number at bottom of column), and proportion of confidence (high – grey, medium – orange, and low – blue) in each of the five impact Environmental Impact Classification for Alien Taxa (EICAT) categories scored for amphibians. Impact scores follow Hawkins et al. 2015: Minimal Concern (MC), Minor (MN), Moderate (MO), Major (MR) and Massive (MV).

Both the data collected from costs of studies (n = 35), and the number of changes in scores (Table 2) were of limited inferential value on their own. We therefore provide four case studies that illustrate the diversity of studies that we found.

Indian bullfrogs – The Indian bullfrog, Hoplobatrachus tigerinus, native to the Indian subcontinent, has invasive populations on the Andaman archipelago and Madagascar (Mohanty et al. in press). The use of this large-bodied species for human consumption has been the primary pathway for its introduction and secondary transfers. Despite its high invasion potential, H. tigerinus invasion dynamics have only been studied in detail on the Andaman archipelago (Mohanty et al. in press). Kumschick et al. (2017) assigned H. tigerinus an EICAT score of Minor with low confidence; the species was considered ‘Data Deficient’ under SEICAT (Bacher et al. 2018). The EICAT score was based on observations of extremely high densities of H. tigerinus at a few sites in Madagascar (Griffin 2010), leading to inference of competition. Mohanty et al. (in press) revised both EICAT and SEICAT assessments of the species on the basis of three recent publications, (Mohanty and Measey 2018, 2019a, b). The revised ‘maximum recorded impact’ was Moderate with medium confidence for EICAT (drastic reduction in survival of endemic frogs Microhyla chakrapanii and Kaloula ghoshi), costing 2018 USD 11 736. This revised assessment resulted from a mesocosm experiment, carried out over three months, that aimed to assess impact (Mohanty and Measey 2019b), equivalent to a Control Impact study (moderate complexity). A new SEICAT score of Moderate with medium confidence (ceasing of poultry-keeping) was assigned at a cost of 2018 USD 22 626, based on a year-long questionnaire-based survey of 91 villages on the Andaman archipelago. However, this study’s bigger aim was to reconstruct the invasion history of the species (Mohanty and Measey 2019a). The invasive population (on the Andaman archipelago) is unlikely to attain a higher EICAT or SEICAT score at this time, given its relatively recent invasion (Mohanty and Measey 2019a). However, the associated confidence scores could be improved by conducting large-scale, before-after-control-impact experiments and focussed socio-economic surveys, for EICAT and SEICAT scores respectively.

African clawed frogs – The African clawed frog, Xenopus laevis, is native to southern Africa, but was exported globally from South Africa from the 1930s (van Sittert and Measey 2016) first for pregnancy testing, then as a model amphibian in research laboratories (Gurdon and Hopwood 2000), and lastly in the pet trade (Measey 2017). The first studies on invasive populations were conducted in California and published in the 1980s (McCoid and Fritts 1980), but the discovery of increasing invasive populations around the world (van Sittert and Measey 2016) led to this becoming one of the most well studied alien anurans (van Wilgen et al. 2018). The European Union invested ~$1.1 million USD in a 5 year project (INVAXEN) to understand European invasions of X. laevis. As part of this larger effort, researchers designed a study to measure the impacts of the French population of X. laevis on native amphibians at 76 sites in a transect from the introduction site (~1980) and through the invaded area and into uninvaded areas of the same region (Courant et al. 2018), equivalent to a Control-Impact study (moderate complexity). Researchers found a significant negative relationship between amphibian diversity and increasing X. laevis density, and that amphibian diversity increased as distance from the introduction site increased. Two amphibians (Bufo spinosus and Triturus cristatus) were not detected when X. laevis was present. The extirpation of these native species from within the invaded range in France increased the EICAT impact score to Major, with medium confidence. The study was conducted within a single year and is estimated to have cost 2018 USD 25 777. No literature allows for SEICAT impact scores to be assessed for invasive X. laevis, although fishermen in the region have complained that since the invasion, the quality of leisure fishing has reduced due to fewer fish being caught and interference by unintended capture of X. laevis (J. Courant pers. com.). Given the current scale and locations of invasions, the EICAT score is not expected to increase with further study, but more studies from other invasions may increase the confidence. A targeted study is still required to produce a SEICAT score.

Cane toads – Cane toads, Rhinella marina, are native to the Americas, ranging from the southern United States to central Brazil (Shine 2010). During the early 20^th century, these toads were extensively moved around the world as a biocontrol agent for invertebrate pests. In 1935, 101 R. marina were imported into Australia from Hawaii (Easteal 1981; Shanmuganathan et al. 2010) and by 1947 over 60 000 Australian-born juvenile R. marina had been produced and were released (Easteal 1981; Shanmuganathan et al. 2010). The potential threat these toads posed to Australian ecosystems was identified from the outset of their arrival, with Froggatt (1936) stating that “this great toad, immune from enemies, omnivorous in its habits, and breeding all the year round, may become as great a pest as the rabbit or cactus”; however it took several decades before notable impacts on wildlife became apparent (Breeden 1963; Pockely 1965; Rayward 1975). Owing to these early concerns, the toad’s 80 year long range expansion across northern Australia has been both well-documented and modelled (e.g. Sabath et al. 1981; Freeland and Martin 1985; Urban et al. 2008; Doody et al. 2019), with substantial resources being invested into toad research. For example, between 1986 and 2009 the Australian Government invested over ~$13.8 million USD into R. marina research, management, community groups, and education (R. Shine pers. comm.). The ecological impacts have been far-reaching (Shine 2010), including competition with native species (Greenlees et al. 2006; Bleach et al. 2014; Taylor et al. 2017), multiple native species population declines (Phillips et al. 2009; Fukuda et al. 2016; Taylor et al. 2017), localised extinctions (Oakwood and Foster 2008; Doody et al. 2017), trophic disruption (Greenless et al. 2006; Doody et al. 2015), and community-level reorganisation (Brown et al. 2013; Feit et al. 2018; Brown and Shine 2019).

The previous EICAT assessment identified R. marina as one of the top 10 amphibian species with the highest Maximum Recorded Impacts, receiving the listing of Major impacts in both the Predation and Poisoning/toxicity categories (Kumschick et al. 2017). Our current reassessment has up-listed the R. marina Maximum Recorded Impacts from Major with high confidence to Massive with medium confidence within the category of Poisoning/toxicity. This shift was a result of a study based in the Northern Territory and aimed at quantifying community-level impacts and examining ecological responses post-invasion (Doody et al. 2017); equivalent to a long-term Before-After study (moderate complexity). The impacts of toads in the region resulted in extirpations (e.g., complete removal of two species of predator and the dramatic reduction of a third), community-level changes (e.g., the mesopredator-release of one species), and no indication of ecological recovery 12 years post-invasion (Doody et al. 2017). The study also notes that R. marina are too prolific and widespread to eradicate (Shine and Doody 2011; Doody et al. 2017), resulting in their impacts being functionally irreversible by the Hawkins et al. (2015) definition. The study was conducted intermittently over 12 years and is estimated to have cost 2018 USD 408 737. We note that this cost estimate is conservative because we only included costs that could definitely be attributed to the published research findings. Further research looking for similar findings across a wider geographic area or an example of a complete species extinction would likely serve to increase the EICAT confidence rating for the Massive impact score (currently rated at medium confidence). Additionally, the previous SEICAT assessments of R. marina had listed the maximum impact score as Major with medium confidence (Bacher et al. 2018) and it is likely that further research into the socio-economic impacts of R. marina are required and may yield higher SEICAT impact and confidence scores if the appropriate attention is given to this topic.

Guttural toads – The guttural toad, Sclerophrys gutturalis, is a common African bufonid naturally distributed across central and southern Africa (Telford et al. 2019). Guttural toads were first introduced to Mauritius and Reunion in 1922 and 1927, respectively, as an attempt at biocontrol for invertebrate pests (Cheke and Hume 2008; Kraus 2009; Dervin et al. 2014; Telford et al. 2019). These toads are now widespread across both islands and a recently introduced population in Cape Town, South Africa, became established in 2000 (de Villiers 2006; Vimercati et al. 2018; Telford et al. 2019). Due to their extensive predation on Mauritian land-snails (Griffiths 1996), Kumschick et al. (2017) assigned S. gutturalis an EICAT score of Moderate with low confidence, based on a study that equates to After design (low complexity). Although there are growing concerns regarding the potential impact of S. gutturalis on native species in the Mascarene Islands and Cape Town, South Africa (Griffith 1996; de Villiers 2006), hardly any research has been conducted regarding its impact. A recent publication noted that property owners are more willing to support the removal of toads in Constantia due to their loud reproductive call that can be heard over hundreds of metres (Measey et al. 2017). The objectives of this study were to provide a historical overview of invasive amphibian dispersal pathways in southern Africa, give an overview of legislation in South Africa regarding amphibian invasions and assess the status of three invasive amphibian populations in South Africa, namely Hyperolius marmoratus, Xenopus laevis and Sclerophrys gutturalis. Although Measey et al. (2017) did not aim to describe the socio-economic impact of S. gutturalis, the anecdotal evidence presented in this study allowed us to assign a SEICAT score for S. gutturalis. The change from Data Deficient to Minor for Health/Social, spiritual and cultural relations with a low confidence level came at an estimated cost of 2018 USD 58 110. Given sufficient directed work, particularly on Reunion and Mauritius where local endemism is high, we consider that the EICAT and SEICAT scores and confidence in them will increase.

Here, we provide the first set of estimates of the cost of published studies used when implementing the ICAT schemes. We found evidence that more expensive and complex studies are needed to score the highest levels of impacts for amphibians. This is something that should concern those who wish to implement the use of these scores as we show that the highest scores may require high levels of funding, investment of time and expertise to achieve. If scores inform policy, then this may result in a circularity where poor investment in impact forming research results in true impacts not being revealed.