Appendix S1
SUPPORTING INFORMATION
Europe’s freshwater biodiversity under climate change: distribution shifts and
conservation needs
Danijela Markovic, Savrina Carrizo, Jörg Freyhof, Nuria Cid, Lengyel Szabolcs,
Mathias Scholz, Hans Kasperidus, William Darwall
a)
b)
Figure S1: a) A comparison of the different resolutions of the HydroBasins dataset (level 4, level 6 and level 8)
for the Elbe River Basin (green); b) distribution of species occurrence frequencies (defined as the total number of
positive species occurrences (presence in a catchment=1, absence=0) across the studied HydroBasin level 8
catchments (18 783 European catchments).
Remarks:
a) The average catchment area at the HydroBasins level 8 resolution is 536.3 km2. Due to the small size of the
catchments involved (on average about 25 km x 20 km), the parameters such as the mean catchment temperature
and the altitudinal range hold too little variability to be meaningful for modelling distributions of freshwater
species.
b) Notice that the proportion of species occurring in 30-40, 40-50, 50-60 and 60-70 catchments is between 1%
and 2% of the total species data sample. Due to a small number of species having between 20 and 50 and
between 50 and 70 occurrences, using slightly higher or lower values than 50 as a threshold for the SDM
application would not change our results as only 1-2% of species would be affected.
1
Figure S2: Parameter correlation matrix
Remarks: Given that the analyses in our current manuscript involved calculating spatial statistics for about 19
000 catchments, we restricted the extraction of the catchment related worldclim predictor dataset to Altitude,
AnnTMean (mean annual temperature), AnnTMax (maximum temperature of warmest month) and AnnPMean
(mean annual precipitation), plus the mean winter temperature (WinterT), mean summer temperature
(SummerT), temperature seasonality (TSeason), precipitation of the driest quarter (DryQP), temperature of the
driest quarter (DryQT) as well as latitude and GDP (the latter representing anthropogenic pressure). Since our
analysis units were catchments and not sites, we realised that the use of mean temperatures (AnnTMean,
WinterT, SummerT, DryQT) cannot reflect the temperatures that species may experience when dispersing though
stream networks of the catchments. Therefore, we focussed here on AnnTMax. As illustrated in the predictor
correlation matrix, the number of statistically applicable factors (not violating the multicollinearity condition,
i.e. correlation <0.7) was therefore limited to exactly those we used (with GDP and Lat excluded to avoid
overfitting).
2
Figure S3: Illustration of the monthly thermal niche approach. Min/max are the min and max monthly
temperatures across a current distribution range of a hypothetical species; the green and grey squares denote the
predicted mean monthly temperatures for the catchments A and B, respectively according to a single climate
model. Following the monthly thermal niche approach the catchment A would be considered “suitable” (for that
particular climate model) while the catchment B would be considered “not-suitable”. If the same conclusion
(“suitable”) is obtained for all climate models, then a catchment is considered “suitable, otherwise, “notsuitable”.
3
SDMs
monthly thermal
niche approach
a) Common species, no dispersal scenario
SDMs
monthly thermal
niche approach
b) Common species, free-dispersal scenario
SDM- based
monthly thermal
niche approach
c) Rare species, no-dispersal scenario
monthly thermal
niche approach
SDM- based
d) Rare species, free-dispersal scenario
Figure S4. A comparison of the species richness of 2050s distribution patterns for common (a-b) and rare (c-d)
freshwater species across European catchments for the no-dispersal scenario and the free-dispersal scenario
using alternative approaches: SDMs vs. monthly thermal niche approach (a-b) and SDM-parameters based vs.
monthly thermal niche approach (c-d).
Within the SDM- parameters based approach (c-d, left panel), a proxy for the current species’ environmental
niche was estimated by calculating the minimum and the maximum across current species ranges of the
parameters used in SDM-based modelling of common species distributions (maximum air temperature of the
warmest month- AnnTMax, seasonal temperature variability-TSeason, the mean annual precipitation-AnnPMean
and altitude). Within the SDM- parameters based approach a catchment is considered “suitable” for a particular
species in the future only if, for all three climate models considered, the future predicted values of AnnTMax,
TSeason and AnnPMean and the average catchment altitude are between the minimum and the maximum
detected across current species ranges, otherwise it is classified as “not suitable”.
Remarks: Compared to the SDMs, the monthly thermal niche approach provides conservative predictions of
common freshwater species richness in southern Europe, and vice versa in central and northern Europe (a-b,
right panel). Only for the no-dispersal scenario (a), there is a good agreement between the two approaches. The
latter suggests that the no-dispersal scenario predictions of rare species distributions (see main text) is most
likely comparable to the result that could be obtained by the SDMs if there would be enough positive species
occurrences to run the models and are more reliable than the free-dispersal scenario predictions. Furthermore,
compared to the monthly thermal niche approach (c-d), the SDM-parameters based approach is generally more
conservative. Given the similarity of the patters obtained using SDMs and monthly niche approach for the nodispersal scenario (a), we conclude appropriateness of the latter approach to analyse rare species distributions.
4
Figure S5. Current and SDM- based future patterns of common freshwater species across European catchments:
(a-c) fish, (d-f) molluscs, (g-i) plants, (j-l) odonates and (m-o) amphibians. The first figure in each row shows the
current species richness, the second shows the projection for the 2050s climate assuming no dispersal, and the
third shows the projection for the 2050s climate assuming species dispersal to suitable catchments, constrained
by the hydrological catchment connectivity for fish, crayfish and molluscs.
5
Figure S6: Distribution of rare freshwater species across major European river basins: the bar plot shows the
current proportion of freshwater species that occur in each basin, while the filled and empty squares indicate the
proportion of species predicted to occur under 2050s climate based on the A1b emission scenario for the nodispersal and the free-dispersal scenario, respectively. Abbreviations for major European river basins are as
follows: Danu-Danube, Daug-Daugava, Dnie-Dnieper, Dnis-Dnister Nistru, Don-Don, Duer-Duero, Ebro-Ebro,
Elbe-Elbe, Evro-Evros/Maritsa, Garo-Garonne, Guaq-Guadalquivir, Guad-Guadiana, Loir-Loire, Meze-Mezen,
Narv-Narva, Nemu-Nemunas, Neva-Neva, Nort-Northern Dvina, Oder-Oder, Pech-Pechora, Po-Po, Rhin-Rhine,
Rhon-Rhone, Sein-Seine, Sout-Southern Bug, Tajo-Tajo, Tere-Terek, Ural-Ural, Volg-Volga, Wisl-Wisla.
.
6
Figure S7: Distribution of rare freshwater species across European freshwater ecoregions: the bar plot shows the
current proportion of freshwater species that occur in each ecoregion, while the filled and empty squares indicate
the proportion of species predicted to occur under 2050s climate based on the A1b emission scenario for the nodispersal and the free-dispersal scenario, respectively. Abbreviations for freshwater ecoregions are as follows:
AD-Aegean Drainages, BSD-Barents Sea Drainages, CC-Cantabric Coast - Languedoc, CM-Caspian Marine,
CP-Crimea Peninsula, CWE-Central & Western Europe, DAL-Dalmatia, DLD-Dniester - Lower Danube, DONDon, DSB-Dnieper - South Bug, DU-Upper Danube, EI-Eastern Iberia, GVD-Gulf of Venice Drainages, ICIceland - Jan Mayen, ID-Ionian Drainages, IPI-Italian Peninsula & Islands, IT-Irgyz -Turgai, KSCD-Kura South Caspian Drainages, KUB-Kuban, LOL-Lake Onega - Lake Ladoga, NBD-Northern Baltic Drainages,
NBI-Northern British Isles, NSD-Norwegian Sea Drainages, OB-Ob, SAD-Southeastern Adriatic Drainages,
SBL-Southern Baltic Lowlands, SI-Southern Iberia, THR-Thrace, VAR-Vardar, VD-Volga Delta - Northern
Caspian Drainages, VU-Volga - Ural, WA-Western Anatolia, WCD-Western Caspian Drainages, WI-Western
Iberia, WT-Western Transcaucasia.
7
Figure S8: Distribution of common freshwater species across European countries (countries having
area>2000km2 are considered): the bar plot shows the current proportion of common freshwater species that
occur in each individual country, while the filled and empty squares indicate the proportion of species predicted
to occur under 2050s climate based on the A1b emission scenario for the no-dispersal and the free-dispersal
scenario, respectively. Country abbreviations are as follows: AL-Albania, AU-Austria, BE-Belgium, BK-Bosnia
and Herzegovina, BO-Belarus, BU-Bulgaria, DA-Denmark, EI-Ireland, EN-Estonia, EZ-Czech Republic, FIFinland, FR-France, GM-Germany, GR-Greece, HR-Croatia, HU-Hungary, IC-Iceland, IT-Italy, LG-Latvia, LHLithuania, LO-Slovakia, LU-Luxembourg, MD-Moldova, MK-The Former Yugoslav Republic of Macedonia,
MW-Montenegro, NL-Netherlands, NO-Norway, PL-Poland, PO-Portugal, RO-Romania, RS-Russia, SISlovenia, SP-Spain, SR-Serbia, SV-Svalbard, SW-Sweden, SZ-Switzerland, UK-United Kingdom, UP-Ukraine.
8
Figure S9: Distribution of rare freshwater species across European countries (countries having area>2000km2 are
considered): the bar plot shows the current proportion of freshwater species that occur in each individual
country, while the filled and empty squares indicate the proportion of species predicted to occur under 2050s
climate based on the A1b emission scenario for the no-dispersal and the free-dispersal scenario, respectively.
Country abbreviations are as follows: AL-Albania, AU-Austria, BE-Belgium, BK-Bosnia and Herzegovina, BOBelarus, BU-Bulgaria, DA-Denmark, EI-Ireland, EN-Estonia, EZ-Czech Republic, FI-Finland, FR-France, GMGermany, GR-Greece, HR-Croatia, HU-Hungary, IC-Iceland, IT-Italy, LG-Latvia, LH-Lithuania, LO-Slovakia,
LU-Luxembourg, MD-Moldova, MK-The Former Yugoslav Republic of Macedonia, MW-Montenegro, NLNetherlands, NO-Norway, PL-Poland, PO-Portugal, RO-Romania, RS-Russia, SI-Slovenia, SP-Spain, SRSerbia, SV-Svalbard, SW-Sweden, SZ-Switzerland, UK-United Kingdom, UP-Ukraine.
9
Figure S10: Catchments with non-analogue climates by 2050s (red). A catchment is considered to have a nonanalogue climate by 2050s, if at least one of the used global circulation models (ECHAM5, IPSL and HadCM3)
predicted higher values for any of the climatic variables used to calibrate species distribution models (maximum
temperature of the warmest month-AnnTMax; seasonal variability in temperature-TSeason; mean annual
precipitation- AnnPMean) than the overall climate extremes for the 20 th century climate across the study area
(AnnTMaxbaseline,max= 43.7 °C; TSeasonbaseline,max=1.4 °C; AnnPMeanbaseline,max=2306 mm).
a)
b)
Figure S11: (a) The European protected area network (green, Natura 2000 and WDPAs with IUCN categories IIV) and (b) the areas that are currently suitable and predicted to be suitable by the 2050s (blue) for the “gap”
species retaining parts of their current range in the future (see Table S4-S5 for species list) and the urgent priority
catchments for these species (red). We considered a catchment to be an “urgent priority catchment” (UPC) for
conservation if it is currently unprotected, but suitable for a “gap species” under current and future (2050s) climate,
and, if protected, would thus ensure that at least 100km2 of current species range area would be protected.
Remarks: Given the greater density of protected areas in western Europe, it is not surprising that the urgent
priority catchments are mainly located in the eastern parts of Europe (mainly Russia) and Balkan- countries
(Croatia and Montenegro. Note, the UPCs only indicate the catchments that would ensure that a minimum of
100km2 of the range area suitable under the current- and 2050s climate under the no-dispersal scenario would be
protected for the identified “gap” species. As the UPCs do not consider species range shifts, they are a suitable
conservation strategy for only a minority of the gap-species (34 species, see Table S4 and S5). An analysis of the
co-occurrence of these 34 species within the areas identified as currently suitable and suitable in the future, has
revealed that protection measures across only 12 catchments (UPCs area =7 783km2, red area in b) would assure
species removal from the “gap list”.
10
a)
b)
Figure S12: Boxplots of the Area Under the receiver operating Curve (AUC) a) and of the True Skill Statistic
(TSS) b) per taxa group studied (the least represented taxa, crayfish and turtles, have been excluded from the
analysis). An AUC of 0.5 indicates that a model has no discriminatory power, while an AUC of 1 indicates that
“suitable” and “not suitable” catchments are perfectly discriminated. A TSS of 0 indicates that a model has no
discriminatory power, while a TSS of 1 indicates that “suitable” and “not suitable” catchments are perfectly
discriminated. The bottom and top of the box denote the first and third quartiles, the line inside the box denote
the median, while whiskers denote the extreme values.
11
Table S1: The number of European freshwater species predicted to lose more than 90% of their current range
Plants
Fish
Molluscs Odonates Amphibians
common
species
5
23
19
3
0
rare species
15
178
381
24
5
totals [%]
6%
39%
61%
20%
9%
Table S2: Rare freshwater species predicted to lose suitability across the entire current range area by 2050s,
paired with no suitable catchments in Europe by 2050s. CR=Critically Endangered, EN=Endangered,
VU=Vulnerable, NT=Near Threatened, LC=Least Concern, DD=Data Deficient, NA=Not Assessed.
Scientific name
Alburnus macedonicus
Alosa killarnensis
Alosa macedonica
Alosa sp nov Skadar
Alosa vistonica
Benthophilus mahmudbejovi
Coregonus atterensis
Coregonus baerii
Coregonus bavaricus
Coregonus danneri
Coregonus fontanae
Coregonus hoferi
Coregonus ladogae
Coregonus lucinensis
Coregonus lutokka
Coregonus pollan
Economidichthys trichonis
Gymnocephalus ambriaelacus
Hypomesus olidus
Knipowitschia cameliae
Ponticola eurycephalus
Rhynchocypris czekanowskii
Rutilus meidingeri
Salaria economidisi
Salvelinus evasus
Salvelinus gracillimus
Salvelinus murta
Salvelinus obtusus
Salvelinus struanensis
Salvelinus thingvallensis
Squalius albus
Coenagrion ecornutum
Elatine gussonei
Zannichellia melitensis
Isoetes boryana
Callitriche transvolgensis
Apium bermejoi
Rorippa valdes bermejoi
Taxa
group
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
fish
odonates
plants
plants
plants
plants
plants
plants
Red List
Status
(European)
CR
CR
VU
VU
CR
LC
VU
DD
CR
VU
LC
CR
LC
VU
LC
EN
EN
CR
NA
CR
LC
NA
EN
CR
VU
VU
LC
CR
VU
LC
LC
NA
LC
LC
EN
NA
CR
CR
12
Potamogeton epihydrus
Belgrandia alcoaensis
Belgrandia conoidea
Belgrandia latina
Bithynia hambergerae
Bithynia skadarskii
Bithynia zeta
Bracenica spiridoni
Bythinella carinulata
Bythinella jourdei
Bythinella micherdzinskii
Bythinella viridis
Bythinella zyvionteki
Bythiospeum acicula
Bythiospeum haessleini
Gyraulus meierbrooki
Heleobia dobrogica
Horatia lucidulus
Islamia lagari
Marstoniopsis armoricana
Mercuria sarahae
Mercuria vindilica
Paladilhia pontmartiniana
Plagigeyeria deformata
Pseudamnicola bacescui
Pseudamnicola leontina
Pseudobithynia ambrakis
Pyrgula annulata
Radix skutaris
Radomaniola elongata
Radomaniola lacustris
Salenthydrobia ferrerii
Spiralix collieri
Stagnicola montenegrinus
Theodoxus baeticus
Turricaspia chersonica
Turricaspia ismailensis
Unio gibbus
plants
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
molluscs
NT
CR
EN
VU
DD
EN
EN
EN
EN
VU
VU
EN
EN
VU
VU
EN
CR
CR
VU
CR
CR
EN
DD
EN
VU
DD
VU
LC
EN
CR
CR
EN
DD
NT
CR
NA
VU
CR
13
Table S3: Current species richness and the 2050s perspective for common freshwater species across European
countries. Projections for 2050s are given in terms of proportion (%) of species that may occur in each individual
country for the no-dispersal and the free-dispersal scenario.
Plants
Albania
Austria
Belarus
Belgium
Bosnia and Herz.
Bulgaria
Croatia
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Iceland
Ireland
Italy
Latvia
Lithuania
Luxembourg
Moldova
Montenegro
Netherlands
Norway
Poland
Portugal
Romania
Russia
Serbia
Slovakia
Slovenia
Spain
Svalbard
Sweden
Switzerland
FYR Macedonia
Ukraine
United Kingdom
c
nd
187
229
216
190
181
202
220
209
176
173
178
254
224
198
221
64
167
244
182
188
184
191
183
188
177
220
194
211
239
206
215
217
232
11
188
214
192
218
188
-43
-13
-13
-10
-32
-38
-33
-12
-5
-14
-3
-13
-9
-45
-37
-5
-7
-23
-12
-11
-11
-39
-37
-12
-6
-11
-18
-19
-3
-44
-17
-34
-22
-27
-2
-14
-52
-12
-5
Fish
fd
c
-35 48
-6
70
-5
55
5
50
-26 73
-30 90
-26 86
-4
68
7
44
14 42
30 49
-6
91
-1
75
-30 54
-29 62
27
7
10 26
-18 90
12 47
3
49
2
36
-31 80
-35 64
5
55
18 47
-4
73
-15 38
-15 88
-1 121
-35 79
-8
68
-26 73
-12 67
104 1
19 52
-4
60
-40 73
-10 102
10 39
Molluscs
nd
fd
-33
-10
-13
-14
-22
-27
-26
-24
-11
-10
-8
-11
-11
-17
-29
0
-8
-23
-6
-14
-25
-18
-25
-20
-6
-10
-8
-10
-3
-29
-21
-26
-27
0
-8
-15
-45
-11
-5
-23
6
2
6
-23
-26
-29
11
-7
12
34
-6
6
-25
-22
0
9
-11
9
-8
-7
-9
-21
-10
2
9
-12
-8
2
-28
1
-16
-22
0
-4
-7
-34
-2
3
c
nd
Odonates
Amphibians
all taxa
fd
c
nd
fd
c
nd
fd
c
nd
fd
68 -26 -29
106 -11 -3
76
-9
-4
83
-8
-2
74 -16 -27
75 -19 -22
101 -28 -25
84
-7
-1
81
-7
4
69
-7
-4
68
-4
17
111 -12 -9
98
-2
4
74 -36 -33
90 -38 -24
9
0
0
55
-5
4
124 -19 -12
72
0
4
75
-4
-1
72 -10 -6
76 -24 -14
68 -10 -23
82
-9
-5
70
-3
1
86
-3
8
58
-5
-5
82 -23 -25
86
-1
1
84 -33 -28
84 -18 -6
101 -30 -26
95 -19 -16
1 -100 0
71
-1
4
93 -12 -4
73 -38 -22
86 -12 -1
77
-1
-1
65
81
70
69
69
70
76
77
62
59
55
96
78
72
73
1
32
97
64
67
67
63
63
68
47
74
67
70
85
69
76
76
86
0
60
78
69
75
59
-9
-12
-9
-13
-14
-13
-14
-14
-6
-8
-4
-8
-4
-19
-23
0
-13
-10
-8
-4
-10
-17
-8
-19
-2
-5
-9
-14
-1
-14
-17
-22
-16
0
-3
-12
-17
-11
-5
-2
4
1
10
-5
-7
-13
1
49
27
33
1
17
-9
-7
0
122
-3
19
12
13
-2
-5
3
27
9
-6
-10
0
-1
-6
-10
-6
200
22
6
-4
-1
19
14
20
13
20
16
18
21
19
13
12
5
29
21
16
17
0
3
27
13
13
17
13
13
19
6
18
18
18
16
18
18
18
25
0
12
22
13
19
19
-7
-15
0
-10
-31
-33
-33
-5
-8
0
0
-10
-5
-31
-35
0
-33
-30
0
0
0
-38
0
-11
0
0
-6
-17
0
-17
-17
-22
-16
0
-8
-18
-8
-5
-11
-17
19
31
32
-12
-24
-21
32
122
50
70
5
35
-18
-9
100
220
-16
80
29
29
-7
4
15
44
39
-11
-9
0
-6
21
-17
-3
0
47
13
-24
26
28
388
513
434
421
420
460
512
464
381
358
359
592
506
419
468
81
286
592
382
396
385
427
398
418
351
478
379
474
553
462
466
492
513
13
386
477
427
506
388
-31
-12
-11
-11
-24
-28
-28
-13
-6
-11
-4
-11
-7
-34
-34
-4
-7
-20
-8
-8
-12
-29
-24
-13
-5
-8
-12
-17
-2
-34
-18
-29
-20
-31
-3
-13
-41
-11
-4
-25
0
-2
6
-21
-24
-24
1
13
13
30
-5
6
-25
-22
23
23
-13
13
3
2
-18
-23
1
16
4
-11
-15
0
-25
-5
-21
-12
103
15
-1
-28
-4
10
The “c”, “nd, and “fd” denote current species richness, proportion (%) of species projected for 2050s following
the no-dispersal scenario and, proportion of species projected for 2050s following the free-dispersal scenario,
respectively. The columns “all taxa” summarize the results across all species groups (plants, fish, molluscs,
odonates, amphibians, crayfish and turtles).
14
Table S4: List of common freshwater “gap” species (species in urgent priority catchments are marked with an
asterisk). We considered a catchment to be an “urgent priority catchment” (UPC) for conservation if it is
currently unprotected, but suitable for a “gap species” under current and future (2050s) climate, and, if protected,
would thus ensure that at least 100km2 of current species range area would be protected (see Fig. S7).
Scientific name
Acipenser sturio
Barbus kubanicus
*
Barbus waleckii
Benthophilus durrelli
*
Chondrostoma kubanicum
Clupeonella caspia
Red List Status
(European)
Fish
NA
Fish
LC
Fish
LC
Fish
LC
Fish
LC
*
Fish
LC
*
Fish
NA
Fish
NA
Coregonus muksun
Coregonus nasus
*
Taxa
*
Coregonus pallasii
Fish
LC
*
Fish
LC
*
Fish
LC
Gobio occitaniae
Fish
LC
Gobio sarmaticus
Fish
LC
*
Fish
LC
Fish
LC
Fish
LC
Fish
LC
Fish
LC
Fish
LC
Belgrandia minuscula
Molluscs
DD
Bythinella ferussina
Molluscs
LC
Bythinella simoniana
Molluscs
LC
Fissuria boui
Molluscs
NT
Islamia piristoma
Molluscs
LC
Moitessieria locardi
Molluscs
LC
Moitessieria simoniana
Molluscs
LC
Theodoxus prevostianus
Molluscs
EN
Turricaspia lindholmiana
Molluscs
NA
Odonates
LC
Odonates
NA
Odonates
DD
Plants
NA
Plants
NA
Gobio brevicirris
Gobio kubanicus
Gobio volgensis
Lethenteron reissneri
Leuciscus danilewskii
*
Leuciscus oxyrrhis
Luciobarbus albanicus
Sabanejewia kubanica
Aeshna crenata
*
*
Somatochlora graeseri
*
Somatochlora sahlbergi
Trapa alatyrica
*
Zannichellia clausii
*
*
15
Table S5: List of rare freshwater “gap” species in urgent priority catchments. We considered a catchment to be
an “urgent priority catchment” (UPC) for conservation if it is currently unprotected, but suitable for a “gap
species” under current and future (2050s) climate, and, if protected, would thus ensure that at least 100km 2 of
current species range area would be protected.
Salmo obtusirostris
Red List
Status
(European)
Fish
Alburnoides kubanicus
Fish
LC
Squalius tenellus
Fish
EN
Chondrostoma knerii
Fish
VU
Squalius svallize
Fish
VU
Phoxinellus alepidotus
Fish
EN
Chondrostoma phoxinus
Fish
EN
Scardinius dergle
Fish
NT
Cobitis dalmatina
Fish
VU
Cobitis narentana
Fish
VU
Phoxinellus pseudalepidotus
Fish
VU
Salmo ezenami
Fish
CR
Bithynia mostarensis
Molluscs
DD
Theodoxus subterrelictus
Molluscs
EN
Saxurinator sketi
Molluscs
EN
Paladilhiopsis solida
Molluscs
DD
Plagigeyeria mostarensis
Molluscs
DD
Lanzaia kotlusae
Molluscs
VU
Scientific name
Red List Status
(European)
EN
16