Corresponding author: Lorenzo Vilizzi (
Academic editor: Grzegorz Zięba
Aquatic invasions are one of the major threats for freshwater ecosystems. However, in developing countries, knowledge of biological invasions, essential for the implementation of appropriate legislation, is often limited if not entirely lacking. In this regard, the identification of potentially invasive non-native species by risk screening, followed by a full risk assessment of the species ranked as higher risk, enables decision-makers to be informed about the extent of the threats posed to the recipient (risk assessment) area. In this study, 32 non-native extant and horizon fish species were screened for their risk of invasiveness under current and predicted climate conditions for the South Caucasus – a biodiversity and geopolitical hotspot that includes the countries of Armenia, Azerbaijan and Georgia. Overall, the number of very high-risk species increased from four (12.5%) under current climate conditions to 12 (37.5%) under predicted climate conditions. The highest-risk species under both conditions included the already established gibel carp
Mumladze L, Kuljanishvili T, Japoshvili B, Epitashvili G, Kalous L, Vilizzi L, Piria M (2022) Risk of invasiveness of non-native fishes in the South Caucasus biodiversity and geopolitical hotspot. In: Giannetto D, Piria M, Tarkan AS, Zięba G (Eds) Recent advancements in the risk screening of freshwater and terrestrial non-native species. NeoBiota 76: 109–133.
Biological invasions are a major threat to global biodiversity and pose a considerable challenge for human well-being (
One of the main reasons hindering effective national and cross-national strategic plans against invasive non-native species is the absence of quality biological data for several countries (
The South Caucasus is widely recognised as a biodiversity hotspot characterised by a great diversity of landscapes and climate zones that shelter a highly diverse plant and animal biota. Freshwater biodiversity is the most understudied ecological aspect of the South Caucasus (
To understand the potential risk of invasiveness posed by non-native fishes in the South Caucasus, the aims of the present study were to: (i) screen both extant and horizon species and (ii) discuss the resulting species-specific risk ranks of invasiveness also within the current geopolitical situation affecting the study area with a view to implementing future legislation. Notably, this study represents the first risk screening for the South Caucasus and Georgia in particular. It is anticipated that the outcomes of this study will provide for an important step forward in the understanding of the impacts and related risks of environmental/economic losses caused by invasive non-native fishes in this biodiversity and geopolitical hotspot.
The South Caucasus (hereafter, also the ‘risk assessment area’) is located south of the Great Caucasus mountain range and stretches across the Black and Caspian seas with 80% of its area belonging to the Kura-Aras drainage basin (Caspian Sea Basin) shared with Turkey and Iran and the remaining 20% (western part) to the Black Sea Basin (Fig.
Map of the South Caucasus (Armenia, Azerbaijan, Georgia), representing the risk assessment area, and neighbouring countries.
The climate of the South Caucasus is continental-mesophilic with strong local variation due to its complex topography. According to the updated Köppen-Geiger climate map (
Currently, there are 121 freshwater and anadromous fish species known from the South Caucasus (
In total, 32 freshwater fish taxa (hereafter, for simplicity ‘species’) were selected for risk screening in the South Caucasus (Table
Freshwater fish taxa (for simplicity, ‘species’) screened for their potential risk of invasiveness in the South Caucasus – the risk assessment area. For each species, the following information is provided: criterion (Crit.) for selection (1 = translocated species; 2 = non-native species already present in the risk assessment area; 3 = non-native ‘horizon’ species established in neighbouring countries or countries of similar climate to the risk assessment area; 4 = non-native species recorded in the risk assessment area, but in the wild); a priori categorisation outcome into Non-invasive or Invasive. For the a priori categorisation, the results of the related protocol (after
A priori categorisation | ||||||||
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Species name | Common name | Crit. | FishBase |
|
|
|
Google Scholar | Outcome |
|
black bullhead | 3 | Y | Y | – | – | n.a. | Invasive |
|
European eel | 4 | N | Y | – | – | n.a. | Invasive |
|
gibel carp | 2 | Y | Y | – | – | n.a. | Invasive |
|
golden grey mullet | 1 | N | – | – | – | N | Non-invasive |
|
leaping mullet | 1 | N | – | – | – | N | Non-invasive |
|
North African catfish | 3 | Y | Y | Y | – | n.a. | Invasive |
|
vendace | 2 | N | Y | – | – | n.a. | Invasive |
– | 2 | N | – | – | – | N | Non-invasive | |
|
grass carp | 2 | Y | Y | Y | Y | n.a. | Invasive |
|
eastern mosquitofish | 2 | Y | Y | Y | – | n.a. | Invasive |
|
three-spined stickleback | 1 | N | – | – | – | N | Non-invasive |
|
Artvin gudgeon | 1 | N | – | – | – | N | Non-invasive |
|
ruffe | 2 | – | Y | – | – | n.a. | Invasive |
|
sharpbelly | 2 | Y | N | – | – | n.a. | Invasive |
|
silver carp | 2 | Y | Y | Y | Y | n.a. | Invasive |
|
bighead carp | 2 | Y | Y | Y | Y | n.a. | Invasive |
|
channel catfish | 4 | Y | Y | – | – | n.a. | Invasive |
|
pumpkinseed | 3 | Y | N | – | – | n.a. | Invasive |
|
largemouth bass | 3 | Y | Y | Y | – | n.a. | Invasive |
|
flathead grey mullet | 4 | N | – | – | – | N | Non-invasive |
|
black carp | 4 | Y | Y | – | Y | n.a. | Invasive |
|
coho salmon | 4 | N | – | – | Y | n.a. | Invasive |
|
rainbow trout | 2 | Y | Y | Y | Y | n.a. | Invasive |
|
Nile tilapia | 2 | Y | Y | Y | Y | n.a. | Invasive |
|
Eurasian perch | 1 | Y | Y | Y | – | n.a. | Invasive |
|
topmouth gudgeon | 2 | Y | Y | – | – | n.a. | Invasive |
|
Lin’s goby | 2 | N | – | – | – | N | Non-invasive |
|
Sevan trout | 1 | – | – | – | – | n.a. | Non-invasive |
|
brown trout | 2 | Y | Y | Y | Y | n.a. | Invasive |
|
brook trout | 3 | Y | Y | Y | – | n.a. | Invasive |
|
pikeperch | 1 | Y | Y | – | – | n.a. | Invasive |
|
black-striped pipefish | 1 | N | N | – | – | N | Non-invasive |
* Reference species for the a priori categorisation: European whitefish
Translocated species (
Non-native species already present in the risk assessment area (
Non-native ‘horizon’ species established in neighbouring countries or countries of similar climate to the risk assessment area (
Non-native species recorded in the risk assessment area, but in the wild (
Selection of species based on the first three criteria was according to the most recent non-native species list published by
Risk screening was undertaken using the Aquatic Species Invasiveness Screening Kit (
To achieve a valid screening, the assessor must provide for each question a response, a level of confidence for the response (see below) and a justification based on literature sources. The outcomes are a
For the
Implementation of the
There were no differences between the AUCs resulting from the three assessor-specific
Based on the
21 (65.6%) species were ranked as high risk and eleven (34.4%) as medium risk;
Amongst the nine species categorised a priori as non-invasive, one was a false positive (three-spined stickleback
Amongst the 23 species categorised a priori as invasive, 20 were true positives;
Of the 11 medium-risk species, eight were a priori non-invasive and three invasive.
Based on the BRA+CCA outcome scores, hence after accounting for climate change predictions (Table
23 (71.9%) species were ranked as high risk, eight (25.0%) as medium risk and one (3.1%) as low risk (
Amongst the a priori non-invasive species, four were false positives (
Amongst the a priori invasive species, 19 were true positives;
Of the nine medium-risk species, four were a priori non-invasive and four invasive.
The highest-scoring (‘top invasive’) species (based on an ad hoc ‘very high risk’ threshold = 40) were gibel carp
Risk outcomes for the freshwater fish species screened with the Aquatic Species Invasiveness Screening Kit (
Species name | A priori |
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|
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Score | Score | ||||||||||||||
Min | Max | Mean | Rank | Class | Min | Max | Mean | Rank | Class | Delta | Total |
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|
||
|
Y | 26.5 | 35.0 | 31.5 | H | TP | 24.5 | 47.0 | 36.8 | H | TP | 5.3 | 0.70 | 0.71 | 0.61 |
|
Y | 1.0 | 15.0 | 9.3 | M | – | −5.0 | 17.0 | 8.0 | M | – | −1.3 | 0.72 | 0.72 | 0.71 |
|
Y | 36.0 | 52.0 | 44.0 |
|
TP | 48.0 | 64.0 | 55.3 |
|
TP | 11.3 | 0.74 | 0.75 | 0.67 |
|
N | 14.0 | 25.0 | 17.7 | M | – | 16.0 | 20.0 | 18.3 | H | FP | 0.7 | 0.70 | 0.72 | 0.46 |
|
N | 13.0 | 23.0 | 16.7 | M | – | 15.0 | 20.0 | 17.3 | M | – | 0.7 | 0.65 | 0.68 | 0.42 |
|
Y | 38.0 | 45.0 | 40.3 |
|
TP | 46.0 | 55.0 | 49.7 |
|
TP | 9.3 | 0.66 | 0.68 | 0.51 |
|
Y | 5.0 | 19.5 | 11.2 | M | – | −3.0 | 7.5 | 1.2 | M | – | −10.0 | 0.71 | 0.72 | 0.63 |
N | 1.0 | 18.0 | 9.0 | M | – | −7.0 | 6.0 | −0.3 | L | TN | −9.3 | 0.70 | 0.71 | 0.61 | |
|
Y | 18.0 | 23.5 | 20.7 | H | TP | 14.5 | 31.5 | 24.7 | H | TP | 4.0 | 0.69 | 0.72 | 0.49 |
|
Y | 31.5 | 38.0 | 34.5 | H | TP | 37.5 | 48.0 | 43.2 |
|
TP | 8.7 | 0.70 | 0.72 | 0.51 |
|
N | 37.0 | 38.0 | 37.7 | H | FP | 38.0 | 44.0 | 41.0 |
|
FP | 3.3 | 0.67 | 0.69 | 0.50 |
|
N | 5.0 | 14.0 | 8.7 | M | – | 7.0 | 15.0 | 12.0 | M | – | 3.3 | 0.59 | 0.61 | 0.44 |
|
Y | 34.0 | 46.0 | 39.3 | H | TP | 46.0 | 58.0 | 50.7 |
|
TP | 11.3 | 0.63 | 0.65 | 0.50 |
|
Y | 32.0 | 35.0 | 33.8 | H | TP | 42.0 | 45.0 | 43.8 |
|
TP | 10.0 | 0.71 | 0.73 | 0.58 |
|
Y | 20.5 | 24.0 | 22.8 | H | TP | 18.5 | 34.0 | 28.8 | H | TP | 6.0 | 0.65 | 0.67 | 0.51 |
|
Y | 25.5 | 28.0 | 26.8 | H | TP | 19.5 | 38.0 | 30.8 | H | TP | 4.0 | 0.67 | 0.68 | 0.61 |
|
Y | 26.0 | 33.0 | 29.0 | H | TP | 32.0 | 45.0 | 39.0 | H | TP | 10.0 | 0.64 | 0.66 | 0.47 |
|
Y | 25.5 | 36.0 | 29.8 | H | TP | 37.5 | 46.0 | 40.5 |
|
TP | 10.7 | 0.71 | 0.72 | 0.63 |
|
Y | 22.0 | 38.5 | 31.2 | H | TP | 34.0 | 50.5 | 41.2 |
|
TP | 10.0 | 0.70 | 0.72 | 0.56 |
|
N | 6.0 | 22.0 | 11.3 | M | – | 12.0 | 18.0 | 14.7 | M | – | 3.3 | 0.63 | 0.66 | 0.42 |
|
Y | 20.0 | 24.0 | 22.0 | H | TP | 28.0 | 34.0 | 32.0 | H | TP | 10.0 | 0.66 | 0.69 | 0.44 |
|
Y | 4.0 | 15.0 | 11.2 | M | – | 8.0 | 17.0 | 11.8 | M | – | 0.7 | 0.64 | 0.66 | 0.50 |
|
Y | 15.0 | 26.5 | 20.2 | H | TP | 15.0 | 18.5 | 16.8 | M | – | −3.3 | 0.63 | 0.66 | 0.35 |
|
Y | 24.0 | 38.0 | 32.7 | H | TP | 34.0 | 48.0 | 42.0 |
|
TP | 9.3 | 0.65 | 0.68 | 0.49 |
|
Y | 17.0 | 51.0 | 32.0 | H | TP | 23.0 | 63.0 | 41.3 |
|
TP | 9.3 | 0.66 | 0.68 | 0.50 |
|
Y | 32.0 | 47.0 | 40.0 |
|
TP | 44.0 | 57.0 | 49.3 |
|
TP | 9.3 | 0.77 | 0.78 | 0.71 |
|
N | 16.0 | 17.5 | 16.5 | M | – | 26.0 | 28.0 | 27.2 | H | FP | 10.7 | 0.54 | 0.53 | 0.63 |
|
N | 5.0 | 25.0 | 16.7 | M | – | −7.0 | 20.0 | 10.0 | M | – | −6.7 | 0.64 | 0.65 | 0.58 |
|
Y | 34.0 | 39.0 | 36.0 | H | TP | 31.0 | 40.0 | 36.7 | H | TP | 0.7 | 0.63 | 0.66 | 0.46 |
|
Y | 17.0 | 33.0 | 23.7 | H | TP | 17.0 | 29.0 | 23.0 | H | TP | −0.7 | 0.70 | 0.75 | 0.31 |
|
Y | 30.0 | 50.0 | 43.0 |
|
TP | 38.0 | 59.0 | 46.3 |
|
TP | 3.3 | 0.69 | 0.70 | 0.58 |
|
N | 9.0 | 27.0 | 16.3 | M | – | 5.0 | 37.0 | 20.3 | H | FP | 4.0 | 0.70 | 0.72 | 0.50 |
Aquatic Species Invasiveness Screening Kit (
In terms of confidence in responses, the mean CLTotal was 2.69 ± 0.03 SE, the mean CL
The present study, which is the first to conduct a risk screening for the South Caucasus, was able to identify with excellent discriminatory power the level of risk of invasiveness of the non-native fish species under evaluation. The calibrated threshold value (
Amongst the screened species, 20 were ranked as carrying a high or very high risk of invasiveness under both current (
Overall, the a priori invasive species found to carry a high or very high risk of invasiveness (Table
A very high risk of invasiveness was also attributed to
The threats posed by other established species ranked as carrying a high risk (
Other species ranked as high (or very high) risk included those that are regularly stocked in the risk assessment area, but have not yet established self-sustaining populations, namely grass carp
The horizon species black bullhead
Of the eleven species found to carry a medium risk of invasiveness (based on the
The remaining species carrying a medium risk of invasiveness are all native to the South Caucasus and translocated, except for
The remaining translocated species ranked as medium risk included migratory European eel
Overall, under predicted climate change, 12 species in total were ranked as very high risk (Table
In this study, the South Caucasus has been treated as a distinct biogeographic unit rather than a politically defined entity at the country level, hence in line with the preferred approach to the definition of a risk assessment area (
In the European Union, policies, legislation and management approaches have been developed to address the issue of non-native species, based on Regulation (EU) no. 1143/2014 of the European Parliament and of the Council on the prevention and management of the introduction and spread of invasive alien species (
Overall, to date, none of the South Caucasus countries has achieved a clear understanding of non-native species management within a national legislation plan (
Full risk assessment of any potentially invasive species should focus on those ranked as high risk (or very high risk, depending on availability of resources). Thus, whenever possible, a comprehensive risk screening, as achieved in this study, should be conducted and species-specific risk-rank outcomes presented to decision-makers. In this study, 12 very high-risk species in total (after accounting for climate change predictions) were identified that should be prioritised for follow-up full risk assessment.
Knowledge gap analysis and improvement of the legal basis for species introductions related to aquaculture/game fisheries and the pet trade. Ideally, this should be jointly agreed upon by the SCR countries to be fully effective.
Early detection and communication of freshwater non-native species is a process already under way, with researchers publishing results about new introductions of potentially invasive non-native species and citizen science platforms regularly receiving data from the general public on the identification of new non-native species. However, data and knowledge developing over time must be standardised in order to be rapidly communicated to stakeholders and decision-makers. In addition, adequate measures should be taken to enhance data collection from all potential sources. For instance, there is currently no information on non-native species available from local markets.
Continuous development of an in-depth monitoring scheme (including infrastructure for data collection based on fieldwork, barcoding/metabarcoding approaches, data management and presentation). This is a critical step to understand the history of non-native species colonisation and the accompanying processes related to community perception, including associated costs for damage/mitigation.
Prevention of introductions (cf. ‘blacklists’ of species). Since there is a large amount of data on freshwater non-native species worldwide, it would be straightforward to develop a list of potentially invasive non-native species for the South Caucasus (see
Impact assessment research, including that related to already established invasive non-native species. Currently, there is no evaluation of the economic/environmental costs related to freshwater non-native species, nor for terrestrial ones. This makes it difficult to assess the effects of non-native species on local communities and to manage in an optimal way available resources to prevent/mitigate non-native species introductions. The results of this study can, therefore, be used to prioritise the list of fish species in the South Caucasus to be evaluated for impact assessment.
LK was partially supported by the Technology Agency of the Czech Republic under project “DivLand” (SS02030018) and IRP MSMT CZU 60460709. MP was supported by the EIFAAC Project “Management/Threat of Aquatic Invasive Species in Europe". LM, BJ and GE were supported, and the publication cost were covered, by the government subsidised grant "Current status and conservation of fauna of Georgia" implemented at the Institute of Zoology of Ilia State University.
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