393
E alua ing he po en ial o DNA me aba coding o ecological
s a us assessmen unde he Wa e F amewo k Di ec i e: A case
s udy on ben hic in e eb a es om Wes e n Ca pa hian s eams
Michaela Šamulko á1,2 , Pa el Be acko2, Pa ik Macko2, Ond ej Va go čík1,2 ,
Ko nélia Tuh ino á1,2 , Zuzana Čiampo o á-Zaťo ičo á1,2 , Ma gi a Lešťáko á3,
Emília Mišíko á Elexo á4, Fedo Čiampo J 1
1 Depa men o Biodi e si y and Ecology, Plan Science and Biodi e si y Cen e, Slo ak Academy o Sciences, Dúb a ská ces a 9, B a isla a 845 23, Slo akia
2 Depa men o Ecology,Facul yo Na u alSciences,ComeniusUni e si yinB a isla a,Ilko ičo a6,B a isla a84215,Slo akia
3 Slo akNa ionalWa e Re e enceLabo a o y,Depa men o Hyd obiologyandMic obiology,Wa e Resea chIns i u e,Náb ežiea m.Gen.L.S obodu4297/5,
B a isla a81249,Slo akia
4 Slo akNa ionalWa e Re e enceLabo a o y,Depa men o Assessmen andAqua icEcosys emsResea ch,Wa e Resea chIns i u e,Náb ežiea m.Gen.L.
S obodu4297/5,B a isla a81249,Slo akia
Co esponding au ho : Pa ik Macko ([email p o ec ed])
Copy igh : © Michaela Šamulko á e al.
This is an open access a icle dis ibu ed unde
e ms o he C ea i e Commons A ibu ion
License (A ibu ion 4.0 In e na ional – CC BY 4.0).
Resea ch A icle
Abs ac
Rou ine biomoni o ing unde he Wa e F amewo k Di ec i e (WFD) ollows a con en-
ional me hodology p ima ily based on he mo phological iden i ica ion o axa wi hin he
i e biological quali y elemen s (phy oplank on, phy oben hos, mac ozooben hos, mac-
ophy es and ish). This iden i ica ion is pa icula ly challenging o mac ozooben hos
due o hei high axonomic di e si y. Mo eo e , his app oach is ime-consuming and
esou ce in ensi e. Ou cu en s udy aims o: (i) e alua e he implemen a ion o DNA
me aba coding in long- e m moni o ed si es o assess he e ec i eness o h ee sam-
ple ypes (bulk samples o mac ozooben hos, and eDNA om wa e and sedimen s), (ii)
e alua e he ecological s a us o wa e bodies using me aba coding da a, and (iii) com-
pa e ecological me ics alues using con en ional and molecula app oaches. Sampling
was conduc ed a 17 locali ies in Slo akia, co e ing eigh s eam ypes. DNA me aba -
coding de ec ed 30% mo e species han con en ional moni o ing pe o med o e 15
yea s wi hou needing ime-in ensi e sample p ocessing and mo phological iden i ica-
ion. A he same ime, he bulk samples cap u ed mo e mac ozooben hos axa han he
o he sample ypes. On a e age, he bulk samples de ec ed 85% o he axa cap u ed by
DNA me aba coding. The di e ences in alues o ecological me ics ob ained wi hin he
con en ional app oach and be ween he con en ional and me aba coding app oaches
we e ela i ely small, ypically co esponding o di e ences wi hin one ecological quali y
class. Howe e , mo e p onounced di e ences we e obse ed in non-abundance-based
me ics, such as EPT and BMWP, indica ing highe sensi i i y o species de ec ion wi h
DNA me aba coding. This s udy demons a es he e ec i eness o DNA me aba coding
ac oss mul iple me ics and highligh s i s po en ial o enhance ou ine biomoni o ing.
Key wo ds: Biomoni o ing, bulk samples, eDNA, mo phological app oach, mac ozoo-
ben hos, Slo akia
Academic edi o : Till-Hend ik Mache
Recei ed:
30 June 2025
Accep ed:
20 Augus 2025
Published:
1 Oc obe 2025
Ci a ion: Šamulko á M, Be acko P,
Macko P, Va go čík O, Tuh ino á
K, Čiampo o á-Zaťo ičo á Z,
Lešťáko á M, Mišíko á Elexo á E,
Čiampo J F (2025) E alua ing he
po en ial o DNA me aba coding o
ecological s a us assessmen unde
he Wa e F amewo k Di ec i e: A
case s udy on ben hic in e eb a es
om Wes e n Ca pa hian s eams.
Me aba coding and Me agenomics
9: e163640. h ps://doi.o g/10.3897/
mbmg.9.163640
Me aba coding and Me agenomics 9: 393–419 (2025)
DOI: 10.3897/mbmg.9.163640
394
Me aba coding and Me agenomics 9: 393–419 (2025), DOI: 10.3897/mbmg.9.163640
Michaela Šamulko á e al.: DNA me aba coding s con en ional me hods in ecological s a us assessmen
In oduc ion
The ea lies p o ocols o assessing aqua ic ecosys ems we e de eloped in he
mid-19 h cen u y (Cohn 1853), bu hei p ac ical applica ion only began abou
a hal -cen u y la e (Kolkwi z and Ma sson 1902, 1908, 1909). In he second
hal o he 20 h cen u y, many indices using ben hic mac oin e eb a es we e
in oduced o assess he s uc u al and unc ional in eg i y o su ace wa-
e s (Wiede holm 1980; Sládeček e al. 1981; Sládečko á and Sládeček 1994;
Rosenbe g and Resh 1993; Hawkes 1998), many o which ha e become in e-
g al o na ional s anda ds and legisla ion o assessing wa e quali y ac oss
se e al coun ies (e.g., Ba bou e al. 1992; He ing e al. 2004a, b; O enböck e
al. 2004). Rega ding wa e -quali y assessmen in Eu ope, i culmina ed wi h he
adop ion o he EU Wa e F amewo k Di ec i e (WFD) in 2000, which es ablished
a ha monised amewo k o assess and p e en he de e io a ion o Eu ope’s
i e s, lakes and g oundwa e (Eu opean Commission 2000). This signi ican ly
changed he app oach o Membe S a es and some non-EU coun ies o wa e
esou ces policy by p io i ising ecosys em in eg i y in decision-making.
Today, mo e han 110,000 su ace wa e bodies a e assessed a egula six-
yea in e als in Eu ope, wi h hei quali y exp essed by ecological s a us o po-
en ial, using physico-chemical and hyd o-mo phological pa ame e s and bio-
logical indica o s (Fueyo e al. 2024a; Kaa i and Palonii y 2024). Wi hin hese,
he WFD equi es he es ablishmen o e e ence condi ions o each ype ha
ep esen a s a e wi h minimal o no an h opogenic dis u bance, om which
he assessmen o o he moni o ed wa e bodies is hen de i ed (Jupke e
al. 2022). Among biological indica o s, o biological quali y elemen s (BQEs),
mac ozooben hos is he mos used g oup (Bi k e al. 2012). This is also sup-
po ed by ecen da a om he Eu opean En i onmen Agency’s Wa e base
dashboa d, which shows ha mac ozooben hos is consis en ly moni o ed
ac oss Eu opean su ace wa e bodies (Eu opean En i onmen Agency 2024;
o mo e de ails, see: h ps://wa e .eu opa.eu/ eshwa e / esou ces/me ada-
a/w d-dashboa ds/su ace-wa e s-ecological-s a us). Following mo pholog-
ical iden i ica ion, he species composi ion is used o calcula e indices ha
a e subsequen ly compa ed o e e ence alues de ined o each wa e body
ype. The ecological quali y a io (EQR), de i ed om his p ocess, is e lec -
ed in he ecological s a us class (EQC) 1–5 (Fueyo e al. 2024a). Howe e ,
sampling me hods and he se o me ics used o ecological assessmen
a y conside ably ac oss coun ies, and he choice o me ics can also di e
be ween egions and s eam ypes wi hin he same coun y (Bi k e al. 2012;
Buss e al. 2015; Mako inská e al. 2015, 2021). Fo example, in Slo akia, he
ecological s a us o wa e bodies based on mac ozooben hos is assessed
using a mul ime ic index (MMI) composed o a se o me ics, whose numbe
and selec ion depend on he s eam ype and ca ego ies di ided acco ding
o eco egion (Ca pa hian, Pannonian), ele a ion (up o 200 m, 200–500 m,
500–800 m, abo e 800 m a.s.l.), and ca chmen a ea size (small, medium,
la ge). In his con ex , Slo akia de ines 24 s eam ypes, wi h he se o selec -
ed me ics included in he MMI anging om 6 o 11 me ics (Mako inská e
al. 2015). Despi e i s use, he cu en me hodology is bo h ime- and cos -in-
ensi e, wi h se e al po en ial sho comings, o en ela ed o he p ecise mo -
phological iden i ica ion o o ganisms, which can be a ec ed by pheno ypic
395
Me aba coding and Me agenomics 9: 393–419 (2025), DOI: 10.3897/mbmg.9.163640
Michaela Šamulko á e al.: DNA me aba coding s con en ional me hods in ecological s a us assessmen
plas ici y a di e en de elopmen al s ages o he loss o key mo phological
ea u es in damaged specimens, as well as he inc easingly limi ed a ailabil-
i y o axonomic expe s (Bush e al. 2019; Je de, 2021; Van den Bulcke e
al. 2024; Páll-Ge gely e al. 2024; Elb ech and Leese 2015; Pinna e al. 2024;
Zhang e al. 2023). Mo eo e , in Slo akia, ano he key issue is sub-sampling, in
which only a ac ion o he collec ed sample, ypically a ound 20% (depending
on o ganism densi y) is analysed and subjec o mo phological iden i ica ion
(Mako inská e al. 2015), which can lead o he omission o a e, low-abun-
dance species and unde es ima e he p esen biodi e si y.
Recen ad ancemen s in mac ozooben hos analysis ha e been d i en by
he adop ion o molecula echniques, pa icula ly bulk sample DNA and en-
i onmen al DNA (eDNA) me aba coding, which ha e helped o o e come he
limi a ions o con en ional me hods (Tza es a e al. 2021; Pinna e al. 2024).
These echniques ha e he po en ial o educe cos s and p ocessing ime
(Sepul eda e al. 2020; Zhang e al. 2023), while also acili a ing he iden i i-
ca ion o ju eniles, ea ly ins a s, mechanically damaged indi iduals, o c yp ic
species (Thomsen e al. 2012; Bohmann e al. 2014; Se ana e al. 2019).
Some s udies ha e also highligh ed he po en ial o sample ixa i e om p e-
se ed indi iduals o subsequen iden i ica ion (Zizka e al. 2019; Va go čík
e al. 2024). Me aba coding echniques can con ibu e o he disco e y o new
species (Na di e al. 2020) and may assis in he de ec ion o non-na i e, in a-
si e (Nes e e al. 2020; Sepul eda e al. 2020) and p o ec ed species (Fueyo
e al. 2024b). In pa icula , eDNA me aba coding can enable b oade ecologi-
cal moni o ing ac oss la ge geog aphic a eas, which enhances he e ec i e-
ness o na ional moni o ing p og ams (Poyn z-W igh e al. 2024). Al hough
such moni o ing o e s a mo e comp ehensi e pe spec i e on he ecological
s a us o wa e bodies, i also is no wi hou limi a ions. The pe sis ence o
DNA eleased by o ganisms plays a c ucial ole, while i a ies depending on
he axa and en i onmen al condi ions (Tsu i e al. 2021; Mau isseau e al.
2022). Mo eo e , he empo al and spa ial scale o he eDNA signal emains
di icul o clea ly de ine due o he complex ecology o eDNA, which is in lu-
enced by nume ous biological and en i onmen al ac o s (Ba nes and Tu ne
2016; Mau isseau e al. 2022). eDNA analysis can some imes e eal axa ha
a e no longe p esen a he sampling si e o hose o igina ing om ups eam
loca ions (Fonseca e al. 2023). One o he mos signi ican and ecu ing
limi a ions o using me aba coding app oaches in he moni o ing o wa e s
is he de e mina ion o he species abundance, which is used o calcula -
ing abundance-dependen me ics (e.g., sap obic index, bioecological a ea
index). Some s udies ha e add essed his challenge by ans o ming abun-
dance da a in o p esence/absence da a (Buchne e al. 2019) o using he
numbe o sequences eads as a p oxy o abundance, hough his app oach
emains qui e dispu able (Elb ech e al. 2021; Mache e al. 2021a).
The po en ial o using DNA-based me hods in he assessmen o biologi-
cal quali y elemen s o wa e is conside able. We aimed o e alua e he po-
en ial o DNA me aba coding unde he eal condi ions o long- e m moni-
o ed si es in Slo akia. Speci ically, we se ou o (i) assess he e ec i eness
o DNA me aba coding in de ec ing axonomic di e si y ac oss h ee sam-
ple ypes (bulk samples o mac ozooben hos, and eDNA om wa e and
sedimen s), (ii) compa e he axonomic composi ion ob ained h ough
396
Me aba coding and Me agenomics 9: 393–419 (2025), DOI: 10.3897/mbmg.9.163640
Michaela Šamulko á e al.: DNA me aba coding s con en ional me hods in ecological s a us assessmen
con en ional and molecula me hods, and (iii) e alua e selec ed ecological
me ics and he o e all ecological s a us de i ed om me aba coding da a,
using ead coun s as a p oxy o abundance. We hypo hesized ha DNA me-
aba coding would e eal highe species di e si y han adi ional me hods,
a ec ing some, bu especially abundance-dependen me ics.
Ma e ial and me hods
Da a collec ion
The ma e ial was collec ed a 17 WFD sampling si es in Slo akia in 2022
and 2023 a ele a ions anging om 147 o 905 m a.s.l. (Fig. 1). These lo-
cali ies ep esen 8 di e en s eam ypes de ined in Špo ka e al. (2009).
Me aba coding analyses included samples om wa e , sedimen s, and bulk
samples. Wa e samples we e collec ed along a ans e se ansec co -
e ing he en i e s eam wid h using a s e ile one-li e con aine un il a o al
olume o 10 L was ob ained in a s e ile essel. Wa e was usually aken
om he wa e column (in smalle s eams only om he su ace laye ),
while a oiding dis u bance o bo om sedimen s. F om he collec ed ol-
ume, one-li e o wa e was il e ed using disposable s e ile 50 mL sy inges
h ough a il e holde (Swinnex Fil e Holde , 47 mm) con aining a cellulose
il e wi h a po e size o 0.45 μm. This p ocedu e was epea ed wice a
each si e ( wo eplica es o eDNA wa e samples). Fil e s we e immedia ely
placed in 96% e hanol in he ield and s o ed in a cooling box un il anspo
o he labo a o y, whe e hey we e kep a −25 °C un il u he p ocessing.
Sedimen s we e also collec ed in wo eplica es using s e ile 50 ml Falcon
ubes. Each eplica e was aken om a di e en mic ohabi a ype on he
i e bed: one om a i le (shallow, as - lowing sec ion) and he o he om
a pool (deepe , slowe - lowing sec ion) o he s eam. App oxima ely 25 ml
o sedimen was collec ed pe eplica e, and a e sedimen se ling, excess
wa e was emo ed. The sample was hen supplemen ed wi h 25 ml o 96%
e hanol, ho oughly mixed, and s o ed in a cooling con aine du ing ans-
po o he labo a o y, whe e samples we e kep a -25 °C. Mac ozooben hos
we e collec ed using he kicking me hod by F os e al. (1971), wi h each col-
lec ion las ing abou 15 minu es and encompassing all p esen mic ohab-
i a s. Mac ozooben hos samples we e subjec ed o mul iple decan a ions
o emo e as much ino ganic ma e ial and la ge non- a ge o ganic deb is
(such as lea es, wigs, and b anches) as possible. The cleaned ma e ial,
ee o excess wa e , was placed in o s e ile 1 L con aine s and p ese ed
wi h 96% e hanol. Samples we e kep in a cooling con aine du ing anspo
o he labo a o y and subsequen ly s o ed a −25 °C. All sample ypes we e
p ocessed as soon as possible a e collec ion, wi h he p ocessing ime
ne e exceeding 3 mon hs. All si es we e moni o ed pe iodically using con-
en ional me hods by he Wa e Resea ch Ins i u e (WRI), which implemen s
he Wa e F amewo k Di ec i e in Slo akia (de ails in suppl. ma e ial 1).
The Wa e Resea ch Ins i u e p o ided da a om con en ional me hods
om 2007 o 2022 (de ails in suppl. ma e ial 2). The ma e ial included in-
o ma ion on species composi ion, hei espec i e abundances, calcula -
ed indi idual indices, and an e alua ion o he o e all ecological s a us.
397
Me aba coding and Me agenomics 9: 393–419 (2025), DOI: 10.3897/mbmg.9.163640
Michaela Šamulko á e al.: DNA me aba coding s con en ional me hods in ecological s a us assessmen
These da a we e used o compa e con en ional (CA – con en ional ap-
p oach) and molecula (MA – me aba coding app oach) me hods. Fo de-
ailed in o ma ion on he sampling and p ocessing o con en ional samples,
please e e o He ing e al. (2004b) and Mako inská e al. (2015).
DNA ex ac ion, PCR ampli ica ion and NGS sequencing
Fo eDNA wa e samples, DNA was ex ac ed om he e hanol ixed il e s
ha we e o n and d ied a 50 °C o app oxima ely 3 hou s un il all he
e hanol had comple ely e apo a ed. The ex ac ion and pu i ica ion we e
pe o med using he ReliaP epTM gDNA Tissue Minip ep Sys em (P omega),
ollowing he manu ac u e ’s p o ocols.
Figu e 1. Geog aphical ep esen a ion o p ocessed sampling si es ac oss Slo akia, wi h s eam ypes indica ed by
colou ed ci cles. The map was gene a ed using QGIS e sion 2.18.15. Explana ion o he s eam ypes: P – Pannonian
eco egion, K – Ca pa hians eco egion; 1 - < 200 m a.s.l, 2 - 201–500 m a.s.l, 3 - 501–800 m a.s.l, 4 - > 800 m a.s.l.; M -
small s eams wi h a ea < 100 km2, S – medium wi h a ea 101–1000 km2, V - la ge wi h a ea > 1000 km2.
398
Me aba coding and Me agenomics 9: 393–419 (2025), DOI: 10.3897/mbmg.9.163640
Michaela Šamulko á e al.: DNA me aba coding s con en ional me hods in ecological s a us assessmen
Fo sedimen samples, a combina ion o phospha e bu e and he DNeasy
Blood and Tissue Ki (Qiagen) was used (Tabe le e al. 2012). B ie ly, he sedimen
samples we e d ied om he 96% e hanol ixa i e a 55 °C o 3 hou s. Phospha e
bu e was added o he d ied samples a a 1:1 a io. The samples we e mixed us-
ing a Mul i-Ro a o Mul i Bio RS-24 s i e (Biosan) and cen i uged a 4,000 RPM
o 30 minu es. The esul ing supe na an was il e ed h ough cellulose sy inge
il e s wi h a po e size o 0.45 μm. The il e s we e also d ied a 50 °C o app oxi-
ma ely 1 hou un il all he phospha e bu e had comple ely e apo a ed, and DNA
ex ac ion was pe o med using he Qiagen manu ac u e ´s p o ocol. .
DNA ex ac ion om bulk samples was pe o med on he whole mixed sam-
ples, which included e hanol and pa ially emo ed non- a ge o ganic and in-
o ganic ma e ial ( o 90 minu es), o a oid he ime-consuming indi idual se-
lec ion p ocess. The whole sample was homogenised using a ki chen blende
(Elec olux ESB2500, 3 min). Th ee eplica es o 1 ml o homogenised ma e ial
we e aken om each sample and d ied a 50 °C o app oxima ely 6 hou s un il
all he e hanol had e apo a ed. DNA was hen ex ac ed and pu i ied using he
ReliaP ep gDNA Tissue Minip ep Sys em (P omega) acco ding o he manu ac-
u e ’s p o ocol. The genomic DNA om all samples was s o ed a -25 °C.
Ampli ica ion o he a ge agmen (~420 bp) wi hin he s anda d ba coding
COI ma ke was ca ied ou using a wo-s ep PCR ( o de ails, see Elb ech and
S einke 2019). Fo each isola e, wo PCR eplica es we e always pe o med,
esul ing in a o al o ou PCR eplica es o wa e and sedimen samples,
and six PCR eplica es o bulk samples. The i s ampli ica ion used BF3 and
BR2 p ime s (Elb ech e al. 2019), ollowed by a second ampli ica ion wi h he
same p ime s con aining adap e s and indexes necessa y o sequencing on
he Illumina MiSeq pla o m. A he same ime, each 96-well pla e con ained 10
nega i e con ols andomly dis ibu ed. Fo he de ailed composi ion o eac-
ion mix u es and PCR p og ams, see suppl. ma e ial 3.
The success o he ampli ica ion was es ed using ho izon al elec opho e-
sis on a 1% aga ose gel. The concen a ion o he PCR p oduc was e alua -
ed using he Ca es eam MI Applica ion p og am and compa ed wi h a DNA
s anda d. The de ec ed concen a ions we e used o p epa e pooled lib a ies
con aining app oxima ely equal amoun s o DNA om each sample. These li-
b a ies we e es ed again on a ho izon al 1% gel elec opho esis, and he de-
si ed p oduc s we e excised and pu i ied om he gel using he Wiza d SV Gel
ki and he PCR Clean-Up Sys em (P omega). Based on he concen a ions o
he pu i ied pa ial lib a ies, a inal lib a y o 20 pM, including 5% PhiX, was
p epa ed and analysed on he Illumina MiSeq wi h Reagen Ki 3 (2 × 300 bp)
a he Ins i u e o Chemis y, Slo ak Academy o Sciences, B a isla a, Slo akia.
Da a p ocessing
Bioin o ma ic sequence p ocessing ollowed he pipeline desc ibed in Va go čík
e al. (2024). B ie ly, he aw sequencing eads unde wen demul iplexing, p im-
e imming (Cu adap 4.1, Ma in 2011), me ging (PEAR 0.9.11, Zhang e
al. 2013), de eplica ion, denoising, leng h il e ing, chime a emo al and g eedy
clus e ing o OTUs a a 97% iden i y h eshold (Vsea ch 2.22, Rognes e al.
2016). Besides ha , ansla ion il e ing a e he denoising s ep (me aMATE,
Andúja e al. 2021) and de aul pos -clus e ing cu a ion (LULU 0.1.0, F øsle
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Michaela Šamulko á e al.: DNA me aba coding s con en ional me hods in ecological s a us assessmen
e al. 2017) we e pe o med. Taxonomy was assigned o he esul ing OTUs
ia BOLD (Ra nasingham and Hebe 2007) using BOLDigge 2.1.0 (Buchne
and Leese 2020). Only OTUs wi h > 85% simila i y o hei bes hi we e hen e-
ained, ocusing on he ollowing animal phyla: Annelida, A h opoda, B yozoa,
Cho da a, Cnida ia, Mollusca, Nema omo pha, Po i e a, and Ro i e a.
Da a analyses
Two da ase s we e included in he s a is ical analysis: he i s comp ised he
species composi ion o 57 samples ob ained h ough CA ( he con en ional da a-
se , suppl. ma e ial 4), while he second con ained he species composi ion o
17 samples ob ained ia DNA me aba coding ( he DNA da ase , suppl. ma e i-
al 5). Bo h da ase s we e con e ed in o TaXon ables o u he analysis using
TaxonTableTools 1.5.1 (TTT, Mache e al. 2021b). Wi hin he DNA da ase , PCR
and ex ac ion eplica es we e me ged in TTT. A e me ging he eplica es, OTUs
wi h ewe han 10 eads pe indi idual sample ype we e emo ed. We also com-
pa ed he e ec i eness o a ious me aba coding app oaches (bulk samples,
eDNA om wa e , and sedimen s) using TTT while Venn diag ams, pa allel ca -
ego y analyses, and ead p opo ions we e gene a ed. Venn diag ams we e c e-
a ed using TTT (each con en ional collec ion compa ed wi h a bulk sample) o
compa e he species spec um o he con en ional and DNA da ase s. The DNA
da ase , including only da a om bulk samples, was adjus ed acco ding o he e-
qui emen s o ASTERICS 4.04 (Fu se e al. 2006), whe e a ious me ics we e cal-
cula ed. Abundance da a we e eplaced by ead coun s ob ained h ough Illumina
sequencing. The ead coun s we e summed o species ep esen ed by mul iple
ope a ional axonomic uni s (OTUs) o c yp ic axa, wi h each species ep esen -
ed by a single Linnaean axon. The me ics used o calcula e Ecological Quali y
Ra ios (EQRs) and he o e all ecological s a us we e selec ed based on s eam
ypology, as de ined by Špo ka e al. (2009). Speci ically, eigh s eam ypes and
wel e di e en me ics we e used (Table 1). Howe e , hese me ics a e sco ed
as con inuous (e.g., Numbe o Families, BMWP sco e) o pe cen age sco es (e.g.,
Oligo [%] sco ed axa = 100%; Ga he e s / Collec o s [%] sco ed axa = 100%), and
he e o e hei alues we e i s no malised, scaling hem o a ange om 0 ( ep e-
sen ing bad s a us) o 1 ( ep esen ing he e e ence condi ion). This no malisa ion
acili a ed he in eg a ion o me ic ou comes (pa ial EQR alues) and he com-
pu a ion o a comp ehensi e mul ime ic index (o e all EQR alue; Špo ka e al.
2009) wi h he ollowing ecological classes: high (1) ≥ 0.8, good (2) ≥ 0.6 o < 0.8,
mode a e (3) ≥ 0.4 o < 0.6, poo (4) ≥ 0.2 o < 0.4, and bad (5) < 0.2. These da a
(pa ial and o e all EQR alues) we e hen compa ed o esul s ob ained using CA
( e e ence alues o each s eam ype a e p o ided in suppl. ma e ial 6).
Fo each si e, di e ences in me ic alues we e exp essed wi hin he CA and
CA s. MA compa isons. Fo each me ic, hese di e ences we e compa ed
using he Ma ginal eg ession model (gene alised leas -squa es - GLS es ima-
ion p ocedu e) wi h wo explana o y a iables ( ype o app oach, in e ac ion
be ween ype o app oach and s eam ype) and he se ing o an exchangeable
co ela ion s uc u e wi hin he si e. Due o he e oscedas ici y, all models we e
imp o ed by speci ying uni o m a iance wi hin each s eam ype and di e en
a iances be ween s eam ypes. The ma ginal eg ession models we e made
in R 4.2.1 (R Co e Team 2022), using he “nlme” package (Pinhei o e al. 2024).
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Michaela Šamulko á e al.: DNA me aba coding s con en ional me hods in ecological s a us assessmen
The conco dance in classes o ecological s a us e ealed by MA and CA was
isualised using a hea map plo c ea ed in “plo .ma ix” package (Klinke 2022);
he Spea man ank co ela ion was also calcula ed, bo h in R 4.2.1. When e-
pea ed measu emen s o ecological s a us we e conduc ed a a si e, he mos
equen ly obse ed ecological s a us class (ESC) was used o assess he
coincidence be ween he wo app oaches.
Resul s
Compa a i e species de ec ion using di e en DNA me aba coding
samples
Sequencing o he eDNA om wa e , sedimen s and bulk samples esul ed in
11,713,827 demul iplexing eads om 17 sampling si es. A e quali y il e ing,
he eads we e clus e ed in o 10,447 OTUs. Non a ge and low-simila i y se-
quences (< 85%) accoun ed o 9,615 OTUs. By emo ing nega i e con ols and
non a ge axa (e.g., algae, dia oms, cho da es, and e es ial in e eb a es),
he inal DNA da ase consis ed o 625 OTUs o he eshwa e in e eb a es.
Species names we e assigned o 589 OTUs, ep esen ing 463 species mainly in
classes Insec a (343 spp.) and Cli ella a (58 spp.). The mos species- ich o de s
wi hin Insec a we e Dip e a (151 spp.), T ichop e a (60 spp.), Epheme op e a
(56 spp.), Plecop e a (35 spp.) and Coleop e a (28 spp.). C yp ic di e si-
y, indica ed by assigning mo e han one OTU o a single Linnaean species,
Table 1. The lis o me ics used o calcula e he Mul ime ic Index o eigh di e en s eam ypes ( o an explana ion o
he s eam ypes, see Fig. 1).
Me ic S eam ype
K2M K2S K3M K3S K3V (P2) K4M P1S P1V (M1)
Sap obic indices
Sap obic Index (Zelinka and Ma an) ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔
Oligo [%] (sco ed axa = 100%) ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔
Bio ic indices
BMWP Sco e ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔
Zona ion me ics
Me a hi h al [%] (sco ed axa = 100%) ✘ ✔ ✘ ✔ ✔ ✘ ✔ ✘
Rhi h on Typie Index ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔
Index o Biocoeno ic Region ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔
P e e ence o low eloci y
Rheoindex (Banning, wi h abundance classes) ✔ ✘ ✔ ✘ ✘ ✔ ✘ ✘
P e e ence o mic ohabi a
Type Aka + Li + Psa [%] (sco ed axa = 100%) ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔
Di e si y me ics
Di e si y (Ma gale index) ✘ ✔ ✘ ✔ ✘ ✘ ✔ ✘
EPT axa ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔
Numbe o amillies ✘ ✔ ✘ ✔ ✘ ✘ ✔ ✘
Feeding unc ional g oups
Ga he e s/ Collec o s [%] (sco ed axa = 100%) ✘ ✔ ✘ ✔ ✘ ✘ ✔ ✘
Numbe o locali ies 2 3 3 4 1 1 2 1
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Michaela Šamulko á e al.: DNA me aba coding s con en ional me hods in ecological s a us assessmen
was iden i ied in 87 species, wi h he highes numbe eco ded in he amily
Chi onomidae (23 spp. = 51 OTUs). The species wi h he mos OTUs assigned
o hem we e Limnod ilus ho meis e i Clapa ède, 1862 (11 OTUs), Tubi ex u-
bi ex (Mülle , 1774) (9 OTUs), and Gamma us ossa um Koch, 1836 (5 OTUs).
Ac oss all sample ypes (bulk samples, eDNA om wa e and sedimen s),
we iden i ied an a e age o 86 species pe sampling si e, wi h he ewes num-
be o species cap u ed a si e he N033 si e (61 spp.) and he highes a si e
he R017 si e (111 spp.). Bulk samples eme ged as he mos e ec i e me hod
o species de ec ion, as hey cap u ed 66 o 96% (∅ 85.03%) o all species
p esen a he si es (45–99 spp.; Fig. 2). In con as , eDNA om wa e cap u ed
on a e age 61 ewe species (∅ 25 spp. o 29.87%) pe si e ( anging om 6 o
53 species, o 6–55%), while eDNA om sedimen s a e aged 75 ewe species
(∅ 11 spp. o 13.68%) pe si e ( anging om 3 o 23 species, o 3–32%) han
he bulk sampling s a egy. The highes numbe o species eco ded om sedi-
men s was a si e he V011 si e (11 spp.). No ably, he mos signi ican o e lap
was de ec ed be ween bulk samples and eDNA om wa e , a e aging 17 o e -
lapping species ( anging om 1 o 33 spp.; suppl. ma e ial 7).
The de ec ed species spec um comp ised 15 in e eb a e classes, wi h only
6 (Bi al ia, Cli ella a, Copepoda, Hyd ozoa, Insec a, and Malacos aca) eco ded
ac oss all sample ypes (suppl. ma e ial 8). Bulk samples and eDNA om wa e cap-
u ed 11 classes wi h a ying class ep esen a ions, while eDNA om sedimen s
cap u ed 10. A achnida, Gas opoda, and Go dioida we e iden i ied solely om bulk
samples, Phylac olaema a was ound exclusi ely in wa e samples, and Os acoda
appea ed in bo h bulk samples and sedimen s. Addi ionally, eDNA om wa e
and sedimen s cap u ed exclusi ely h ee classes (Bdelloidea, Demospongiae,
Monogonon a). Insec a domina ed nea ly all samples when examining class ep-
esen a ion ela i e o he numbe o eads (suppl. ma e ial 9). Cli ella a we e he
second mos nume ous class, pa icula ly p e alen in sedimen samples.
Con en ional s. DNA me aba coding app oach in biodi e si y
assessmen
F om 2007 o 2022, 57 samples we e p ocessed using CA a he moni o ed
locali ies. The mos equen sampled si es we e M002 (13 imes), P006 (9
imes), V011 (6 imes), and H001 (4 imes). Du ing his pe iod, 319 species
wi hin eigh a ge classes we e iden i ied using mo phological me hods:
Insec a (238 spp.), Cli ella a (33 spp.), Gas opoda (20 spp.), Bi al ia (12 spp.),
Malacos aca (9 spp.), Tu bella ia (6 spp.), and Go dioida (1 sp.). The a e age
numbe o species pe sample was 17, wi h a ange o 7 o 31. The mos spe-
cies- ich locali ies we e he submon ane s eams V420 (2022; 31 species),
P006 (2014; 27 species), H001 (2019; 27 species), and V090 (2018; 25 species).
Compa ing esul s om CA wi h MA (using only bulk samples) showed ha ,
on a e age, 67 species (∅ 71%, anging om 35 o 84 spp.) we e iden i ied ex-
clusi ely h ough bulk samples a all moni o ed si es. An a e age o 21 species
( anging om 12 o 30) we e iden i ied using he CA a he mos equen ly moni-
o ed locali y he Mo a a Ri e (M002), du ing he 13 sampling da es. In con as ,
82 species we e eco ded du ing a single sampling a ha si e using he MA.
Con e sely, a he V420 si e, 45 species we e iden i ied by he MA, while 37 and
41 species we e eco ded in wo samplings using he CA. The species o e lap
408
Me aba coding and Me agenomics 9: 393–419 (2025), DOI: 10.3897/mbmg.9.163640
Michaela Šamulko á e al.: DNA me aba coding s con en ional me hods in ecological s a us assessmen
(one pe si e) and 57 con en ional samples collec ed o e 15 yea s (2007–
2022). Howe e , i is impo an o acknowledge he po en ial limi a ions o DNA
me aba coding, such as he occu ence o alse posi i es ha can a i icially
in la e he numbe o OTUs and hus he o e all axon lis . These may a ise
om he p esence o nuclea copies o mi ochond ial genes (NUMTs; Schul z
and Hebe 2022), e o s o inconsis encies in e e ence da abases (including
synonymous species names o misiden i ied sequences; Baena-Beja ano e al.
2023; Šamulko á e al. 2025), and echnical issues du ing sequencing o ampli-
ica ion ( o synopsis, see Fueyo 2024c). Many o hese p oblems cu en ly lack
de ini i e solu ions, bu in ou s udy, we a emp ed o mi iga e some o hese
issues by building local e e ence da abases (AquaBOL.SK), ca e ully e i ying
he ob ained species spec um agains eshwa e in e eb a e checklis s o
Slo akia (Špo ka e al. 2003) and il e ing ou all OTUs wi h ewe han 10 eads.
Building on his, when compa ing he species spec um cap u ed in a single
DNA me aba coding sample o a single mo phology-based sample using he
CA, he esul s consis en ly show ha DNA me aba coding de ec s mo e spe-
cies, ega dless o s eam ype o yea o sampling. Fo example, a si e M002
(Mo a a Ri e ), 13 samples we e collec ed using he CA, wi h 12 o 30 spe-
cies iden i ied pe sample, while a single DNA me aba coding sample eco ded
up o 82 species. Howe e , he mos ele an example is si e S014 (Rima a
Ri e ), which was sampled simul aneously, and he di e ence be ween bo h
app oaches was up o 37 species in a ou o DNA me aba coding. I is also im-
po an o no e ha he signi ican inc ease in he numbe o eco ded species
by he MA was likely due o he non-a endance o he “subsampling” app oach
and he analysis o he en i e collec ed ma e ial.
Despi e i s b oade species co e age, DNA me aba coding was less e ec-
i e in iden i ying ce ain g oups, such as Gas opoda and Bi al ia, consis-
en ly wi h o he s udies (e.g., Van den Bulcke e al. 2024). On he o he hand,
DNA me aba coding can mo e clea ly dis inguish ep esen a i es o he o de
Dip e a (Bee man e al. 2018; Mache e al. 2025), which is pa icula ly chal-
lenging o p ecise mo phological iden i ica ion. In his s udy, he numbe o
Dip e a species cap u ed by DNA me aba coding was mo e han double ha o
mo pho- axonomy, and his end was also con i med in o he o de s, such as
Epheme op e a, Coleop e a, Tubi icida, and Enchy aeida.
Pe haps he mos su p ising inding ac oss se e al s udies is he ela i ely
low axonomic o e lap be ween he wo app oaches (CA s MA). Fo example,
Mache e al. (2025) epo ed only 26.5% o e lap (sha ed axa), Vasselon e
al. (2017) ound 13% o e lap o dia om species, Van den Bulcke e al. (2024)
epo ed 34%, and Dua e e al. (2023) ound 23% o e lap o ma ine ben hic
in e eb a es. We also examined he o e lap be ween hese me hods a h ee
speci ic axonomic le els ( amily, genus, and species), e ealing a clea de-
c ease wi h dec easing axonomic ank (69% o e lap a he amily le el, 52% a
he genus le el, and only 33% a he species le el). Con e sely, he numbe o
axa eco ded exclusi ely h ough DNA me aba coding inc eased wi h dec eas-
ing axonomic ank (10% a he amily le el, 24% a he genus le el, and 40% a
he species le el), e lec ing limi a ions o mo phological iden i ica ion (Haase
e al. 2006; Gleason e al. 2020). Howe e , i is impo an o no e ha he incom-
ple eness o e e ence da abases can also signi ican ly in luence hese esul s
(Vasselon e al. 2017), po en ially leading o changes in he numbe o sha ed
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Michaela Šamulko á e al.: DNA me aba coding s con en ional me hods in ecological s a us assessmen
axa, ei he dec easing o inc easing. Addi ionally, a ia ion in oduced by he
sampling p ocess i sel can a ec hese indings. Due o limi ed eplica es in
ou s udy, we could no di ec ly compa e sampling a iabili y be ween CA and
MA, bu p e ious s udies ha e documen ed conside able a iabili y in adi ion-
al sampling (Ramos-Me chan e and P enda, 2017; Seidel e al. 2022), which
should be aken in o accoun when in e p e ing o e lap pe cen ages.
The pe spec i e o DNA me aba coding da a in Mul ime ic Index
calcula ion and ecological quali y assessmen
Imp o ing biodi e si y assessmen and moni o ing is p obably he key eason
o e o s o apply DNA me hods in hese p ocesses. Howe e , in e alua ing
wa e bodies, one o he signi ican challenges o me aba coding is o p o ide
eliable abundance da a, which is essen ial o abundance-dependen me ics
(Elb ech and Leese 2015; Pinol e al. 2015).
Essen ially, he only solu ion is o use he numbe o eads gene a ed by NGS
sequencing as a p oxy o abundance. Al hough his app oach is o en con-
side ed con o e sial due o biases in oduced by PCR ampli ica ion o p im-
e speci ici y (Doi e al. 2017; K ehenwinkel e al. 2017; Shel on e al. 2023);
howe e , i could s ill p o ide usable in o ma ion. The numbe o eads has i s
limi a ions, e.g., i does no dis inguish he li e s ages o o ganisms, which may
be c ucial in speci ic cases. Ou esul s sugges ha despi e he s ochas ici y
o he p ocesses, ead numbe s may be usable. Mo eo e , such posi i e co -
ela ion be ween he numbe o eads and he species abundance has al eady
been demons a ed in se e al s udies (e.g., Deagle e al. 2019; Di Mu i e al.
2020; Salis e al. 2024). Ano he possible solu ion is o ans o m p esence/
absence da a o abundance (1/0) da a as in Buchne e al. (2019), who showed
ha EQC class emained unchanged in 76.6% o cases, dec eased by one class
in 12%, and imp o ed by one class in 11.2%. Howe e , since his app oach can
signi ican ly dis o he ac ual si ua ion, we decided o gi e a chance o he
numbe o eads and analyse hei abili y o e lec he abundance o he spe-
cies p esen . We hypo hesised ha abundance-dependen me ics would show
signi ican ly di e en alues han hose ob ained using con en ional me hod-
ologies, bu he compa ison a he me hod le el (con en ional s. con en ional,
con en ional s. me aba coding) was encou aging in ha (i) no o low change
in EQC occu ed o abundance-dependen me ics (e.g., Sap obic Index, Index
o Biocoeno ic Region), (ii) signi ican change was obse ed in some cases
by up o 3 EQC o abundance-independen me ics (e.g., EPT axa, Numbe
o Families and BMWP Sco e) and (iii) signi ican di e ences be ween indi id-
ual s eam ypes we e demons a ed only o Di e si y (Ma gale Index) and
Rheoindex. Ou es ing suppo ed he assump ion ha ead coun s can po en-
ially eplace classical abundance da a. S ill, hei use needs o be es ed in
mo e de ail, and he use o some me ics in ecological s a us assessmen will
need o be econside ed. I seems ha me ics based on p esence/absence
da a (e.g., BMWP Sco e, EPT Taxa, Numbe o Families) a e mo e p oblema ic
because hey ha e demons a ed signi ican di e ences be ween CA and MA.
Simila indings we e also epo ed by Mú ia e al. (2024), whe e he applica-
ion o me aba coding da a om bulk samples o he IBMWP index (Ibe ian
Biological Moni o ing Wo king Pa y index) led o an imp o emen in EQC a
410
Me aba coding and Me agenomics 9: 393–419 (2025), DOI: 10.3897/mbmg.9.163640
Michaela Šamulko á e al.: DNA me aba coding s con en ional me hods in ecological s a us assessmen
wo o he i e s udied si es. A he emaining h ee si es, he EQC emained
unchanged, and he index alue de i ed om he MA was lowe han ha om
he CA a only one si e. In ano he s udy om Spain pe o med by Fueyo e al.
(2024b), i was demons a ed ha molecula me hods (bulk and eDNA om
wa e ) showed co ela ions wi h mo phological iden i ica ion o EQR alues o
he IBMWP index. Howe e , di e ences we e obse ed in he es ima ed ecolog-
ical s a us o he i e s, wi h bulk samples ending o indica e a highe s a us.
Addi ionally, a ecen comp ehensi e s udy om Ge many (Mache e al. 2025),
which included a wide ange o indices, con i med ha applying me aba cod-
ing da a imp o ed he EQC a nea ly one- hi d o he su eyed si es. Howe e , in
mos cases, he EQC emained unchanged, u he highligh ing he high po en-
ial o DNA me aba coding o ou ine moni o ing.
We can expec e en mo e signi ican di e ences i e e ence ba code da a-
bases, such as BOLD, con inue o imp o e species co e age. This was suppo -
ed by Fueyo e al. (2024a), who also epo ed an imp o emen in he ecological
quali y class based on he non-abundance me ic (IBMWP) in 16% o samples
a e inco po a ing hei newly ob ained sequences in o he BOLD da abase.
Thei s udy highligh s he impo ance o con ibu ing o and de eloping egion-
al e e ence da abases, which can enhance he de ec ion and iden i ica ion o
no el axa, he eby imp o ing he accu acy o ecological s a us assessmen s
de i ed om me aba coding da a. I is wo h men ioning ha , especially in
Cen al Eu ope, ba code e e ence da abases ha e ecen ly been signi ican ly
imp o ed (Mo iniè e e al. 2017; Macko e al. 2024; Vua az e al. 2024), which
may ha e in luenced ou esul s. Con e sely, signi ican gaps in hese da a-
bases s ill emain (e.g. Csabai e al. 2023). Finally, ou knowledge o he ue
di e si y o insec s, e en in a “well-s udied” egion such as Eu ope, emains lim-
i ed, as demons a ed e.g. by he me aba coding s udy o Buchne e al. (2024).
Recen s udies epea edly highligh he need o build egional e e ence da a-
bases, which ul ima ely play he mos c ucial ole in he b oade in eg a ion o
DNA me aba coding in o biomoni o ing. On he o he hand, despi e he gaps in
he da abases, ou esul s, along wi h hose o simila me aba coding s udies
(Mú ia e al. 2024; Mache e al. 2025), demons a e ha he assessmen o
he ecological s a us o eshwa e ecosys ems using he me aba coding ap-
p oach is as e and can be mo e e icien han he con en ional me hodology
used so a .
Rega ding obse ed imp o emen s in ecological s a us wi hin his s udy, his
could be a ma e o he e e ence alues. They a e essen ial o calcula ing
indi idual me ics bu o iginally we e de e mined based on CA. Implemen ing
DNA me aba coding in o ou ine biomoni o ing will likely equi e hei e-e al-
ua ion, which could signi ican ly educe disc epancies in he esul ing assess-
men s be ween he wo app oaches.
Conclusion
Assessing he ecological s a us o aqua ic ecosys ems is essen ial o hei
conse a ion, es o a ion, and sus ainable managemen . Cu en p ac ices un-
de line he u gen need o inno a i e app oaches o make his ime-in ensi e
and cos ly p ocess mo e e icien . Ou s udy con i ms he po en ial and easi-
bili y o using DNA me aba coding o ou ine moni o ing wi hin he con ex
411
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Michaela Šamulko á e al.: DNA me aba coding s con en ional me hods in ecological s a us assessmen
o WFD implemen a ion. Among he h ee sample ypes es ed (eDNA om
wa e , sedimen , and bulk samples), he bulk samples de ec ed he mos com-
p ehensi e species spec um. Howe e , we ecommend inco po a ing eDNA
om wa e and sedimen in o moni o ing p og ams, as hese sample ypes e-
eal unique componen s o species composi ion no cap u ed by bulk samples.
DNA me aba coding demons a es clea ad an ages in e ms o e iciency and
comp ehensi eness, especially in de ec ing axonomically challenging g oups
such as Dip e a and Cli ella a.
The analysis o indi idual me ics shows ha using he numbe o eads as a
p oxy o abundance can yield esul s compa able o con en ional me hods o
abundance-dependen me ics. Howe e , me ics elian on p esence/absence
da a, such as BMWP sco es o EPT axa, e ealed signi ican di e ences due o
DNA me aba coding’s enhanced species de ec ion capabili ies. This highligh s
he need o calib a e such me ics o align wi h he b oade de ec ion ange o
his me hod. Fu he mo e, expanding and e ining e e ence da abases emains
essen ial, as his would likely enhance he de ec ion o e en mo e species and
imp o e ecological assessmen s. Despi e cu en challenges, ou indings sug-
ges ha DNA me aba coding is al eady a iable al e na i e o con en ional ap-
p oaches in ou ine biomoni o ing. Di e ences in he es ima ion o ecological
s a e classes could hope ully be esol ed by e ising he h esholds ha a e
conside ed in he in e p e a ion o he me ic alues bu ha e been se based
on and o da a om con en ional p ocedu es. Wi h inc easing en i onmen al
p essu es on eshwa e ecosys ems, he adop ion o his inno a i e me hod
has he po en ial o signi ican ly imp o e biodi e si y assessmen a es. This,
in u n, could acili a e mo e e ec i e conse a ion and managemen p ac ic-
es, suppo ing he objec i es o he WFD and con ibu ing o he sus ainable
managemen o aqua ic ecosys ems. Howe e , as acknowledged by p e ious
s udies, including ou own, u he esea ch is needed o add ess he known
limi a ions and challenges o hese echniques.
Acknowledgemen s
The au ho s would like o hank D . Má ia Šedi á a he Chemical Ins i u e o
Slo ak Academy o Science o he coope a ion in NGS sequencing.
Addi ional in o ma ion
Con lic o in e es
The au ho s ha e decla ed ha no compe ing in e es s exis .
E hical s a emen
No e hical s a emen was epo ed.
Use o AI
No use o AI was epo ed.
Funding
This wo k was inancially suppo ed by he Slo ak Na ional G an Agency (VEGA),
p ojec no. 2/0084/21 and 1/0170/25.
412
Me aba coding and Me agenomics 9: 393–419 (2025), DOI: 10.3897/mbmg.9.163640
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Au ho con ibu ions
Concep ualiza ion: MŠ, PM, FČ. Da a cu a ion: MŠ. Resou ces: MŠ, PM, EME, ML. Fo mal
analysis: PB. Me hodology: MŠ, PM, OV, KT. P ojec adminis a ion: ZČZ. Supe ision:
FČ. Valida ion: FČ. Visualisa ion: MŠ, PB, PM. W i ing – o iginal d a : MŠ, PB, PM, FČ.
W i ing – e iew and edi ing: OV, KT, ZČZ, ML, EME.
Au ho ORCIDs
Michaela Šamulko á h ps://o cid.o g/0009-0000-9599-5255
Pa el Be acko h ps://o cid.o g/0000-0001-7680-0854
Pa ik Macko h ps://o cid.o g/0009-0008-0714-7490
Ond ej Va go čík h ps://o cid.o g/0009-0007-7728-8566
Ko nélia Tuh ino á h ps://o cid.o g/0009-0007-2315-3826
Zuzana Čiampo o á-Zaťo ičo á h ps://o cid.o g/0000-0003-0506-6212
Ma gi a Lešťáko á h ps://o cid.o g/0009-0000-9415-1245
Emília Mišíko á Elexo á h ps://o cid.o g/0009-0005-5659-0163
Fedo Čiampo J h ps://o cid.o g/0000-0001-6269-3592
Da a a ailabili y
Addi ional da a a e in supplemen a y ma e ials on FigSha e (h ps:// igsha e.com/) un-
de h ps://doi.o g/10.6084/m9. igsha e.29269859 o a ailable on eques .
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