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Does ca chmen geodi e si y os e s eam biodi e si y?
© The Au ho s, 2019
Published e sion
Kä nä, Olli-Ma i; Heino, Jani; Laamanen, Tiina; Jy känkallio-Mikkola, Jenny;
Pajunen, Vi pi; Soininen, Janne; Tolonen, Kimmo T.; Tukiainen, Helena; Hjo , Jan
Kä nä, O.-M., Heino, J., Laamanen, T., Jy känkallio-Mikkola, J., Pajunen, V., Soininen, J., Tolonen,
K. T., Tukiainen, H., & Hjo , J. (2019). Does ca chmen geodi e si y os e s eam biodi e si y?.
Landscape Ecology, 34(10), 2469-2485. h ps://doi.o g/10.1007/s10980-019-00901-z
2019
RESEARCH ARTICLE
Does ca chmen geodi e si y os e s eam biodi e si y?
Olli-Ma i Ka
¨ na
¨.Jani Heino .Tiina Laamanen .Jenny Jy ka
¨nkallio-Mikkola .
Vi pi Pajunen .Janne Soininen .Kimmo T. Tolonen .Helena Tukiainen .
Jan Hjo
Recei ed: 14 Decembe 2018 / Accep ed: 5 Sep embe 2019 / Published online: 14 Sep embe 2019
ÓThe Au ho (s) 2019
Abs ac
Con ex One app oach o main ain he esilience o
bio ic communi ies is o p o ec he a iabili y o
abio ic cha ac e is ics o Ea h’s su ace, i.e. geodi-
e si y. In e es ial en i onmen s, he ela ionship
be ween geodi e si y and biodi e si y is well ecog-
nized. In s eams, he abio ic p ope ies o ups eam
ca chmen s in luence s eam communi ies, bu he
ela ionships be ween ca chmen geodi e si y and
aqua ic biodi e si y ha e no been p e iously es ed.
Objec i es The aim was o compa e he e ec s o
local en i onmen al and ca chmen a iables on
s eam biodi e si y. We speci ically explo ed he
use ulness o ca chmen geodi e si y in explaining
he species ichness on s eam mac oin e eb a e,
dia om and bac e ial communi ies.
Me hods We used 3 geodi e si y a iables, 2 land
use a iables and 4 local habi a a iables o examine
species ichness a ia ion ac oss 88 s eam si es in
wes e n Finland. We used boos ed eg ession ees o
explo e he e ec s o geodi e si y and o he a iables
on biodi e si y.
Resul s We de ec ed a clea e ec o ca chmen
geodi e si y on species ichness, al hough he adi-
ional local habi a and land use a iables we e he
s onges p edic o s. Especially soil- ype ichness
appea ed as an impo an ac o o species ichness.
While a iables ela ed o s eam size we e he mos
impo an o mac oin e eb a e ichness and pa ly
o bac e ial ichness, he impo ance o wa e chem-
is y and land use o dia om ichness was no able.
Conclusions In addi ion o adi ional en i onmen al
a iables, geodi e si y may a ec species ichness
a ia ion in s eams, o example h ough changes in
wa e chemis y. Geodi e si y in o ma ion could be
used as a p oxy o p edic ing s eam species ichness
and o e s a supplemen a y ool o conse a ion e o s.
Keywo ds F eshwa e s En i onmen al
he e ogenei y Ca chmen ea u es 
Mac oin e eb a es Dia oms Bac e ia Species
ichness
Elec onic supplemen a y ma e ial The online e sion o
his a icle (h ps://doi.o g/10.1007/s10980-019-00901-z) con-
ains supplemen a y ma e ial, which is a ailable o au ho ized
use s.
O.-M. Ka
¨ na
¨(&)H. Tukiainen J. Hjo
Geog aphy Resea ch Uni , Uni e si y o Oulu,
P. O. Box 8000, 90014 Oulu, Finland
e-mail: [email p o ec ed]
J. Heino T. Laamanen
Finnish En i onmen Ins i u e, F eshwa e Cen e, Paa o
Ha aksen ie, 90570 Oulu, Finland
J. Jy ka
¨nkallio-Mikkola V. Pajunen J. Soininen
Depa men o Geosciences and Geog aphy, Uni e si y o
Helsinki, P. O. Box 33, 00014 Helsinki, Finland
K. T. Tolonen
Depa men o Biological and En i onmen al Science,
Uni e si y o Jy a
¨skyla
¨, P.O. Box 35, 40014 Jy a
¨skyla
¨,
Finland
123
Landscape Ecol (2019) 34:2469–2485
h ps://doi.o g/10.1007/s10980-019-00901-z(0123456789().,- olV)(0123456789().,- olV)
In oduc ion
Global change can be seen as deg ada ion o na u al
ecosys ems, which in u n is he mos impo an eason
unde lying biodi e si y change (Fulle e al. 2007).
Clima e and land use changes modi y bio ic communi-
ies in all kinds o en i onmen s (Sala e al. 2000;Vilmi
e al. 2017), including eshwa e ecosys ems (Donohue
e al. 2009; Heino e al. 2009). Recen ly, i has been
es ima ed ha dec ease o biodi e si y due o an h o-
pogenic s esso s is clea ly highe in eshwa e ecosys-
ems han in e es ial ecosys ems (Abell 2002;Wiens
2016). The undesi able end o biodi e si y loss is
associa ed wi h key s esso s, such as pollu ion, in asi e
species, dams, and modi ica ion o in-s eam habi a s
(Vo
¨ o
¨sma y e al. 2010). The e ec s o hese s esso s
a e highly ala ming because eshwa e ecosys ems
co e only a small ac ion o he Ea h’s su ace a ea
(0.8%), bu ha bo a conside able (6%) p opo ion o all
known species on Ea h (Dudgeon e al. 2006).
S eam ecosys ems a e dependen on ca chmen -
scale p ope ies and p ocesses (Hynes 1970; Allan and
Cas illo 2007). Thus, one could assume ha a use ul
solu ion o main ain biodi e si y is o es ablish
p o ec ed a eas by conside ing he in e -connec ed
ea u es o he ca chmen and s eam en i onmen s
(Wa d e al. 2002; Moilanen e al. 2008; Soininen e al.
2015). T adi ionally, p o ec ed a eas ha e been
designed o he p o ec ion and main enance o biodi-
e si y in land and ma ine ecosys ems (IUCN 1994),
al hough hey a e seldom designed o conse ing
eshwa e biodi e si y only. In some cases, i e s a e
used as he bo de s o p o ec ed a eas (Abell e al.
2007). The wide use o he ca chmen -based conse -
a ion p obably su e s om a lack o posi i e
empi ical examples, he unique posi ion o eshwa e s
(e.g. s eam co ido s) in he landscape (Abell e al.
2007), and he absence o comp ehensi e and s an-
da dized knowledge abou he spa ial dis ibu ion o
he mos i al a eas o biodi e si y (Ca izo e al.
2017). Howe e , i is gene ally unde s ood ha inclu-
si e conse a ion o eshwa e ecosys ems equi es a
whole-ca chmen app oach (Dudgeon e al. 2006).
Conse ing na u e’s s age (CNS) is known as a
s a egy o p o ec biodi e si y by main aining geodi-
e si y (Beie e al. 2015). In CNS, he ocus is on he
abio ic ‘s age’ upon which ecological p ocesses ake
place ins ead o using popula ions, species o com-
muni ies as he uni o conse a ion planning (Beie
and B os 2010). Geodi e si y e e s o he a iabili y
o abio ic cha ac e is ics o Ea h’s su ace and
subsu ace, including ma e ials such as soils, p o-
cesses like e osion, and land o ms such as alleys,
which a e ela i ely s able o e long ime pe iods
(Ande son and Fe ee 2010; G ay 2013). In unning
wa e en i onmen s, geodi e si y could be conside ed
as a d i e o species dis ibu ions and ecological
p ocesses a di e en scales (Lawle e al. 2015). In
addi ion, he ole o geodi e si y has been acknowl-
edged o i s posi i e associa ion wi h biodi e si y in
e es ial ecosys ems (Ande son and Fe ee 2010;
S ein e al. 2014; Bailey e al. 2017; Tukiainen e al.
2017a), ma ine ecosys ems (Kaskela e al. 2017) and
ecen ly in s eams a a local-scale (Ka
¨ na
¨e al. 2018).
Running wa e s a e hie a chically s uc u ed
ecosys ems whe e he dis ibu ions o species on a
ce ain loca ion depend on il e ing p ocesses based on
clima e, geology, dispe sal p ocesses, channel mo -
phology and physical–chemical p ope ies o local
habi a s (Po 1997; Fig. 1). In s eams, habi a - and
each-scale il e s comp ise nu ien s, ligh , pH, s eam
wid h and cu en eloci y (F issel e al. 1986). In
addi ion, abio ic d i e s ope a ing a ca chmen scales
(e.g. land-use, soil- ype and geology) a e also consid-
e ed impo an o species dis ibu ions. Fo example,
geology, geomo phological ea u es and land use a
he ca chmen -scale cons ain local habi a condi ions
(F issel e al. 1986; Richa ds e al. 1996; Da ies e al.
2000; Pajunen e al. 2017). The signi icance o abio ic
a iables in s uc u ing species dis ibu ion a ies wi h
he scale and o ganism g oup. Fo ins ance, wa e
chemical p ope ies shape he s uc u e o mic oalgal
communi ies (e.g. dia oms) (Soininen 2007; Jy ka
¨n-
kallio-Mikkola e al. 2016) and bac e ial communi ies
(Heino e al. 2014; Wang e al. 2017). Local
mac oin e eb a e di e si y o en esponds o local
s eam ea u es like s eam wid h and cu en eloci y,
subs a e p ope ies and wa e chemis y (Malmq is
and Ma
¨ki 1994; Heino e al. 2013) and se e al o
ca chmen p ope ies, such as geology and land use
(Richa ds e al. 1996; Sandin and Johnson 2004).
In his s udy, we explo ed he possibili y o explain
species ichness a ia ion in s eams using geodi e -
si y in o ma ion. In addi ion, we had land use da a and
adi ionally used local-scale en i onmen al a iables
as explana o y a iables in ou s a is ical models. We
compiled geodi e si y in o ma ion o a numbe o
bo eal ca chmen s in wes e n Finland a 1-km
2
scale
123
2470 Landscape Ecol (2019) 34:2469–2485
and examined biodi e si y a ia ion along geodi e -
si y and land use g adien s using dia oms, bac e ia and
mac oin e eb a e as ocal o ganismal g oups. A
local-scale, we expec ed o ind a ela ionship be ween
mac oin e eb a e ichness and s eam si e a iables
especially ela ed o s eam size and wa e chemis y
(Heino e al. 2003). Fo dia om and bac e ial ichness,
we expec ed o ind a clea ela ionship o wa e
chemis y a iables (Soininen 2007; Jy ka
¨nkallio-
Mikkola e al. 2016). O land use a iables, we
expec ed o ind e ec s o ag icul u al and a i icial
a eas on dia om, bac e ial (Leland and Po e 2000;
Lea and Lewis 2009), and mac oin e eb a e ichness
(Lena and C aw o d 1994; Sponselle e al. 2001).
Mic obes may be e y sensi i e o en i onmen al
changes due o hei small size (Zeglin 2015), which
could lead o changes in species ichness in ca ch-
men s in luenced by ag icul u e wi h associa ed
inc ease in nu ien le els (Allan and Cas illo 2007).
We also expec ed o ind an indi ec e ec o
geodi e si y on species ichness, because geological
ea u es, besides o he ca chmen p ope ies, con ol
many each-scale cha ac e is ics, such as ege a ion in
he ipa ian co ido , low a iabili y and wa e
chemis y (Leland and Po e 2000; Soininen 2015).
Fo example, subsu ace p ope ies (e.g. soil ype)
a ec he p ecipi a ion– uno p ocesses in a wa e -
shed (e.g. wa e in il a ion capabili ies), esul ing in
changes in wa e chemis y and consequen a ia ion
in species ichness in headwa e s eams (Fig. 1).
Ma e ials and me hods
Cha ac e is ics o he s udy a ea
We sampled al oge he 88 bo eal s eams om 21
main i e basins in he coas al a eas o wes e n
Fig. 1 Rep esen a ion o he en i onmen al ea u es om
egional-scale and ca chmen -scale o local-scale habi a con-
di ions. En i onmen al ea u es and p ocesses measu ed a
di e en scales can be conside ed as il e s impo an o s eam
biodi e si y pa e ns (F issel e al. 1986; Po 1997). Geodi e -
si y a ca chmen -scale (soil- ype ichness in his example) can
a ec he wa e in il a ion p ocesses and hus wa e chemis y
impo an o s eam o ganisms (Leland and Po e 2000). The
isualized clima e a iable is mean annual empe a u e ac oss
Finland. Pho o c edi J. Jy ka
¨nkallio-Mikkola
123
Landscape Ecol (2019) 34:2469–2485 2471
Finland, which all d ain o he Bal ic Sea (Fig. 2). The
loca ion o su eyed s eam si es (1 si e pe s eam)
s e ched app oxima ely 520 km in no h–sou h di ec-
ion and mo e han 300 km in eas –wes di ec ion.
The landscapes in he sou he n pa s o he s udy
a ea comp ise i e alleys wi h sligh ly undula ing
opog aphy, la ge coas al plains and some lakes. The
sou he n pa s o he s udy a ea a e cha ac e ized by
ill-co e ed bed ock hills and s uc u ally con olled
alleys (Fogelbe g and Seppa
¨la
¨1986). No he n a eas
a e mainly la e ain, wi h ew lakes and ine-
sedimen and ill deposi s. Topog aphy a ies mo e in
he no he nmos pa s o he s udy a ea. Al i ude
anges om sea le el up o 200 m a.s.l. in he eas e n
pa s. Geomo phology o he s udy a ea is cha ac e -
ized by glacial and glacio lu ial elie . Fo example,
eske s cause a ia ion in o he wise qui e la coas al
landscapes. The bed ock o he s udy a ea is p ima ily
Fig. 2 Loca ions o he 88 sampling si es in 21 majo i e basins in Finland
123
2472 Landscape Ecol (2019) 34:2469–2485

composed o c ys alline ocks, and he soils in he a ea
a e mos ly g ound mo aine (A o e al. 1990). In i e
alleys and coas al egions, he e a e also sand
o ma ions (e.g. dunes) and glacio lu ial deposi s wi h
so ed ma e ials.
Biogeog aphically, he s udy a ea anges om
hemibo eal (whe e mixed o es a e dominan ) o
middle bo eal ege a ion zones (whe e bo h coni e -
ous and mixed o es s occu commonly) (Ah i e al.
1968). We lands wi h di e en pea deposi s a e
ela i ely a e in he sou h bu a e inc easingly
common no hwa ds (Ha
¨me -Ah i e al. 1988). Mean
annual ai empe a u e ypically a ies om o e 5 °C
in he sou hwes o 2 °C in he no h (Pi inen e al.
2012). Mean annual p ecipi a ion in he s udy a ea
ypically anges om 500 mm in he no hwes coas
o o e 700 mm in he sou he nmos a ea (Pi inen
e al. 2012).
The land use o he s udy a ea a ies subs an ially.
The sou he nmos s eams a e gene ally si ua ed in
human-domina ed landscapes (e.g. ag icul u al a eas),
whe eas he no he n s eams a e ypically loca ed in
o es -domina ed landscapes. In addi ion, local en i-
onmen al condi ions o s eams a y om nea -
p is ine o es ed headwa e s eams o mo e modi ied
s eams in he ca chmen s o in ensi e ag icul u e
(Jy ka
¨nkallio-Mikkola e al. 2017; Heino e al. 2018).
Biological sampling
To con ol o seasonal a ia ion in biological com-
muni ies, s eam mac oin e eb a e, dia om and bac-
e ial samples we e collec ed wi hin 2 weeks in
Sep embe 2014. Sep embe is a sui able mon h o
sampling o ganisms dwelling in bo eal s eams
because di e si y is high and na u al dis u bances
(e.g. snowmel -caused loods) a e ypically less
equen han in he sp ing pe iod (Heino e al.
2013). In addi ion, one- ime snapsho sampling, i
done wi hin a na ow ime window, is sui able o
unco e ing spa ial pa e ns and species ichness–
en i onmen ela ionship. Howe e , i emains silen
on empo al a ia ion, which was beyond he ocus o
his s udy.
Fo mac oin e eb a es, a 2-min kick sample (ne
mesh size 0.5 mm) co e ing mos mic ohabi a s in a
i le sec ion o app oxima ely 100 m
2
was aken.
Such samples con ain usually mo e han 70% o
species a a si e in a gi en season (Myk a
¨e al. 2006).
Mac oin e eb a es and associa ed ma e ial we e
immedia ely p ese ed in e hanol in he ield, and
samples we e aken o he labo a o y o u he
p ocessing and iden i ica ion. Mac oin e eb a es
we e iden i ied o he lowes possible axonomic le el,
i.e. species, species g oup o genus.
Dia om and bac e ial samples we e aken simul a-
neously wi h he mac oin e eb a e sampling. A each
si e, 10 andomly chosen cobble-sized s ones we e
collec ed om ca. 20 cm dep h om di e en pa s o
he i le si e om an a ea co e ing a ound 20 m o he
s eam si e leng h. Dia oms we e collec ed om s ones
by b ushing h ough a ubbe empla e (5 95cmin
size) and he composi e sample was immedia ely
p ese ed in a cool and da k box. In he labo a o y,
dia om us ules we e cleaned om o ganic ma e ial
using we combus ion wi h hyd ogen pe oxide (30%,
H
2
O
2
) and moun ed in Naph ax. A leas 500 us ules
pe sample we e coun ed and iden i ied o species le el
wi h a Nikon Op ipho 2 phase con as ligh
mic oscope.
Bac e ial samples we e wiped o om cobble sized
s ones using s e ile pieces o oam plas ics and we e
ozen s aigh away in he ield un il labo a o y
analyses. Supplemen a y de ails o ield sampling
can be ound om Vilmi e al. (2016) and Jy ka
¨nkal-
lio-Mikkola e al. (2017). In he labo a o y samples
we e i s eeze-d ied and hen he DNA was
ex ac ed using a Powe Soil DNA Isola ion Ki
(MoBio, Ca lsbad, USA). PCRs we e pe o med by
Ve i i The mal Cycle (Li e Technologies) The 16S
DNA egion o bac e ia was ampli ied wi h p ime s
519F 50-CAGCMGCCGCGGTAATWC-30and
926 P1 50-CCTCTCTATGGGCAGTCGGT-
GATCCGT CAATTCCTTTRAGTTT-30. Unique,
nine base ba code p ime s we e used o each sample.
This ba coding sys em allows us o iden i y each
sample in pos -sequencing analyses. The amplicons
we e sequenced using ion o en semiconduc o
sequencing, whe e he o al numbe o aw sequences
was 2,708,611. The sequence lib a y was spli by
samples and quali y il e ed based on he quali y sco es
o each sequence. Sequences wi h quali y sco es
below 25, sho e han 200 bp o longe han 1000 bp
we e emo ed. A e quali y con ol, a o al o 549,548
sequences we e e ained, wi h an a e age sequence
leng h o 3414 bp. The sequences we e clus e ed as
ope a ional axonomic uni s (OTUs) using he
Usea ch61 algo i hm (Edga 2010) wi h 97%
123
Landscape Ecol (2019) 34:2469–2485 2473
sequence simila i ies. OTUs (97% simila i y) we e
de e mined using he Quan i a i e Insigh s In o
Mic obial Ecology (QIIME) pipeline e sion 1.8.0
(Capo aso e al. 2010). The OTU da ase was a e ied
o he lowes numbe o sequences de ec ed (1052),
because sequence numbe s a ied among samples.
The labo a o y and bioin o ma ics me hods a e p e-
sen ed in mo e de ailed in Heino e al. (2015) and
Jy ka
¨nkallio-Mikkola e al. (2017).
In all s a is ical analysis, we used o e all species
ichness (i.e. numbe o species o OTUs) o each
o ganism g oup as a measu e o biodi e si y. We
acknowledge ha by using such a simple measu e o
biodi e si y we could lose in o ma ion ega ding
a ia ion in species ai s and phylogene ic ela edness
(e.g. Heino and Tolonen 2017). Howe e , as he
numbe o species emains he mos commonly
u ilized measu e o biodi e si y in gene al (e.g.
Magu an 2004) and among eshwa e s udies in
pa icula (e.g. Feld e al. 2009), we decided o
concen a e on species ichness as he esponse
a iable.
En i onmen al a iables
In each s eam si e, cu en eloci y and wa e dep h
we e measu ed a 30 loca ions, and s eam wid h was
measu ed om 10 c oss-s eam ansec s co e ing he
s udy si e. Mean alues o he physical a iables we e
used in s a is ical analyses. Wa e samples o de e -
mining o al phospho us, o al ni ogen and wa e
colo we e collec ed. Elec ic conduc i i y and pH
we e measu ed wi h YSI-P o essional Plus wa e
quali y me e (YSI Inco po a ed, Yellow Sp ings,
USA). All en i onmen al a iables we e collec ed
simul aneously wi h biological sampling.
Ca chmen ea u es and geodi e si y
Fo each s udy si e, ups eam ca chmen a ea was
de e mined using a digi al ele a ion model (g id
esolu ion 10 910 m, Na ional Land Su ey o
Finland 2013) in A cGIS 10.5 so wa e. Ca chmen
size, land use ype and geodi e si y we e u he
calcula ed o each ca chmen . Land use was ob ained
om CORINE Land Co e da a (20 920 m, Finnish
En i onmen Ins i u e 2013).
Geodi e si y a iables consis ed o geomo pholog-
ical, soil- and ock- ype ichness a esolu ion o 1-km
2
g id cells (Table 1). Geomo phological, o land o m,
da a we e quan i ied using land o m obse a ions,
GIS-based en i onmen al a iables and gene alized
addi i e modelling (Hjo and Luo o 2012). Land o m
obse a ions we e ob ained by an expe geomo phol-
ogis who sys ema ically examined geomo phological
maps (1:50,000) and ae ial pho og aphs (*30 cm
esolu ion). A e ha , he geomo phological dis i-
bu ion modelling app oach was used o p edic he
numbe o land o ms in each 1-km
2
g id cell co e ing
he whole Finland. The e, he land o m obse a ions
and GIS-based en i onmen al a iables we e used in
gene alized addi i e modelling o gene a e geomo -
phological ichness alues o he g id cells (Hjo and
Luo o 2012,2013; Tukiainen e al. 2017a). Soil and
ock- ype ichness we e calcula ed by summing he
numbe o di e en soil and ock ypes in a 1-km
2
g id
cell. The calcula ions we e based on digi al soil and
bed ock maps, espec i ely (Geological Su ey o
Finland, GSF 2010a,b). Rock- ypes we e classi ied by
an expe in o 16 gene ically and geochemically
dis inc classes (Tukiainen e al. 2017a). All geodi-
e si y measu es we e i s calcula ed o he whole
Finland and a e wa ds educed o ma ch wi h ca ch-
men bounda ies in A cGIS 10.5 en i onmen . A mo e
de ailed desc ip ion o he geodi e si y da a can be
ound om Tukiainen e al. (2017a).
Selec ion o inal p edic o a iables
We selec ed al oge he nine p edic o a iables o he
inal analyses, o which wo we e land use, h ee we e
geodi e si y and ou we e local en i onmen al a i-
ables (Table 2). Ha el e al.’s (1996) sh inkage ule
ecommends including no mo e han n/10 p edic o s in
he inal model, which suppo s he s ic selec ion o
nine a iables o he inal analyses. The selec ion o
p edic o s was based on he heo e ical and empi ical
backg ound o impo an a iables o s eam o gan-
isms. In addi ion, p elimina y examina ions o Pea son
co ela ions be ween species ichness and en i on-
men al a iables we e used o de ec he p elimina y
ela ionship be ween he a iables and bio ic ichness,
and o p e en mul icollinea i y. To limi he numbe
o physico-chemical a iables, we selec ed he ones
ha ha e been ound o in luence bio ic communi ies
in bo eal s eams: pH, wid h, dep h and eloci y
(Malmq is and Ma
¨ki 1994; Myk a
¨e al. 2007; Lea
e al. 2009; Heino e al. 2012,2014). Fu he mo e, we
123
2474 Landscape Ecol (2019) 34:2469–2485
excluded o al phospho us, o al ni ogen and wa e
colo om u he analysis because hey had low
Pea son co ela ions wi h bio ic ichness in ou
p elimina y analyses. In addi ion, conduc i i y was
emo ed because o high co ela ion wi h land use
a iables (see Appendix S1). We used wo land use
classes, a i icial and ag icul u al a eas in o de o
desc ibe an h opogenic e ec s in he ca chmen a ea
abo e ou s eam sampling poin (Tonkin e al. 2016;
Jy ka
¨nkallio-Mikkola e al. 2017; Pajunen e al. 2017).
S a is ical me hods
Fi s , all p edic o a iables we e es ed o no mali y
and ans o med when necessa y. Fo en i onmen al
a iables (wid h and dep h) and geodi e si y a i-
ables, loga i hmic ans o ma ions we e used. Fo land
Table 1 In o ma ion on
geomo phological ea u es,
ock ypes and soil ypes
based on which
geodi e si y a iables we e
calcula ed
No e ha geomo phological
ichness included land o ms
om a ious
geomo phological p ocess
g oups (e.g. see Hjo e al.
2012, Supplemen al
Ma e ial 1)
Geodi e si y a iable Fea u es o p ocesses
Geomo phological ichness Aeolian
Biogenic
C yogenic
Flu ial
Glacigenic
Glacio lu ial
Li o al
Ma ine
Mass-was ing polygene ic bed ock
Slope
Wea he ing
Rock- ype ichness Ul ama ic in usi e o olcanic ocks
Ma ic in usi e o olcanic ocks
In e media e, in usi e olcanic ocks
G ani ic o esic ocks
Peli ic sedimen a y ocks
Conglome a es
A kosic sedimen a y ocks
Black schis s
Qua z- ich sedimen a y ocks
Sedimen a y ca bona e ocks o ca bona i es
Gneisses and migma i es
I on o e
High-g ade me amo phic ocks
Me asoma ic ocks
Impac mel ocks
Sulphide o e
Soil- ype ichness Rock (ba e ock o hin soil co e , 1m)
Till (glacigenic deposi s)
S ony a eas and block ields
Sand and g a el
Sil
Clay
Gy ja (lake and sea sedimen s, [6% o ganic ma e ial)
Pea
123
Landscape Ecol (2019) 34:2469–2485 2475
use a iables, a csine-squa e oo - ans o ma ion was
used. Second, Pea son co ela ion was used o exam-
ine cong uence be ween he bio ic ichness and he
p edic o a iables (see also Appendix S1–S2).
We used boos ed eg ession ees (BRTs) o explain
he a ia ion o species ichness and o measu e he
ela i e in luence o he di e en p edic o s on species
ichness using he package ‘gbm’ ( e sion 2.1.1.) in R
3.1.2 (R Co e Team 2017). BRTs is a non-pa ame ic,
machine lea ning me hod ha can be unde s ood as a
p og essi e ype o eg ession modelling (Eli h e al.
2008). Machine lea ning me hods ha e many bene i s
o e adi ional s a is ical models, such as hei
obus ness o missing alues and mul icollinea i y in
he da a (Do mann e al. 2013) and, especially in he
case o ou s udy, hei abili y o handle nonlinea
ela ionships and a iable in e ac ions (Eli h e al.
2008). In ecen yea s, simila me hods o decision
ees ha e been used in many ields, including ecology
(Thuille e al. 2003; Mouche e al. 2015; Jy ka
¨nkal-
lio-Mikkola e al. 2017), land-use change (Mu
¨lle e al.
2013) and geodi e si y–biodi e si y explo a ions in
e es ial en i onmen (Bailey e al. 2017; Tukiainen
e al. 2017a).
BRTs consis o eg ession ees, which explain he
de iance o a dependen a iable by i ing simple
models on pa i ions o he whole da a space. Pa i-
ions a e esul s om spli ing up he da a space in o
assemblages ha a e as simila as possible in e ms o
esponse and ha minimize p edic ion e o s. A e -
wa ds, BRTs combine simple decision ees (i.e.
boos ing) by adding ees in a o wa d and s age-wise
ashion o minimize he loss unc ion o he model
(Eli h e al. 2008). Using he ‘gbm.s ep’ unc ion
allowed us o calib a e models wi h h ee pa ame e s
o speci y. Fi s , ee complexi y ( c) means he model
complexi y in e ms o allowed in e ac ions be ween
independen a iables. Second, bag ac ion (b )
sepa a es he inpu da a o calib a ion and e alua ion
da a. Thi d, lea ning a e (l ), also known as he
sh inkage pa ame e , can be speci ied. A e es ing,
we se c o h ee, b o 0.75, l o 0.001 and used a
Gaussian e o dis ibu ion o all esponse a iables,
excep o bac e ial ichness o which he Poisson
e o dis ibu ion was used.
The e iciency o he models was e alua ed using
he pe cen o explained de iance [(null
de iance - esidual de iance)/null de iance]. To
unde s and he e ec s o indi idual a iables on
species ichness, he ela i e in luence (sum up o
100%) o e e y p edic o a iable was acqui ed om
‘gbm.s ep’. We also c ea ed pa ial dependency plo s
o explo e he ela ionship be ween species ichness
and he p edic o a iables (Mu
¨lle e al. 2013;
Mouche e al. 2015).
Final models we e alida ed using 10- old c oss-
alida ion (CV). This me hod subsamples he da a 10
imes acco ding o de ined b (0.75). This means ha
Table 2 De ails o he en i onmen al a iables used in boos ed eg ession ee analyses o examining he ela ionship be ween he
en i onmen and species ichness o s eam mac oin e eb a es, dia oms and bac e ia in wes e n Finland (n = 88 s eams)
En i onmen al a iable Uni Mean (min o max) Sou ce
Soil- ype ichness Numbe o soil ypes 2.6 (1–6) GSF
Rock- ype ichness Numbe o ock ypes 1.6 (1–6) GSF
Geomo phological ichness Numbe o geomo phological ea u e ypes 4.7 (1–9) GAM
Ag icul u al a eas % 16.8 (0–60.9) CORINE
A i icial a eas % 2.2 (0–9.5) CORINE
Wa e pH 7.2 (5.8–8.1) Field
Wid h m 3.7 (0.72–15.5) Field
Dep h m 0.18 (0.1–0.4) Field
Veloci y m/s 0.24 (0.04–0.5) Field
No e ha mean alues o he geodi e si y (soil-, ock- and geomo phological- ypes) in he able a e based on he mean alues o all
88 ca chmen s a 1 km-scale. Minimum and maximum alues o geodi e si y a iables e e o he 1 km-scale minimum and
maximum alues om all he s udied ca chmen s
GAM gene alized addi i e model, GSF Geological Su ey o Finland, CORINE coo dina ion o in o ma ion on he en i onmen
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