A

DE

KHa

b

a

ARRAA

KCDBLH

1

tapBfeB

cdSiloc

h0

ARTICLE IN PRESSG ModelQBOT-2672; No. of Pages 8

Aquatic Botany xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

Aquatic Botany

jou rn al hom ep age: www.elsev ier .com/ locate /aquabot

istribution and predictive occurrence model of charophytes instonian waters

aire Torna,∗, Anastasiia Kovtun-Kantea, Kristjan Herküla, Georg Martina,elle Mäemetsb

Estonian Marine Institute, University of Tartu, Mäealuse 14, Tallinn 12618, EstoniaCentre for Limnology, Rannu, Tartumaa 61117, Estonia

r t i c l e i n f o

rticle history:eceived 1 August 2013eceived in revised form 21 April 2014ccepted 1 May 2014vailable online xxx

eywords:harophytesistribution

a b s t r a c t

Material collected during the years 1995–2011 was used to describe the distribution and environmentalpreferences of charophyte species in Estonian lakes and its coastal Baltic Sea. Altogether 22 speciesof charophytes were found in Estonian waters. Five taxa occurred in less than 10 localities and wereclassified as rare. Chara aspera and Tolypella nidifica were the most frequent and widespread species. Themajority of species preferred shallow water less than 1 m in Estonian lakes and the coastal sea. Mud wasthe prevailing substrate on locations where charophytes were found, sandy substrate was characteristicfor species which tolerate more exposed localities. Most of freshwater species preferred water alkalinityover 80 mg HCO3

− l−1. A model was developed to predict the probability of the occurrence of Chara spp.

altic Seaakesabitat modelling

in the extent of the whole Estonian marine waters based on several environmental variables. Boostedregression trees (BRT) was chosen as the modelling technique. Based on the model prediction, the vastmajority of charophyte habitats are situated in the sea areas of the West Estonian Archipelago. Thatsea area is characterized by favourable conditions for charophytes: high proportion of shallow areasprotected from wave exposure.

© 2014 Elsevier B.V. All rights reserved.

. Introduction

Charophytes occupy several ecological niches in aquatic ecosys-ems. They may inhabit the deepest areas of clear-water lakes butlso form shallow-water pioneer vegetation in recently formedonds and wetlands (Chambers and Kalff, 1985; Casanova androck, 1999). Charophyte communities are an important habitat

or a number of invertebrate species and provide feeding and nurs-ry areas for several species of fish and birds (e.g. Schubert andlindow, 2003; Torn, 2008).

Human impact and consequent environmental changes hasaused a progressive decrease in the abundance, occurrence andiversity of charophyte species in past decades (Romanov, 2009).ome became rare and several species of charophytes are Red Listedn Europe (Blindow et al., 2003). Charophytes are among the species

Please cite this article in press as: Torn, K., et al., Distribution and prediBot. (2014), http://dx.doi.org/10.1016/j.aquabot.2014.05.005

isted in Annex I of the EU Habitat Directive as characteristic speciesf the habitat type no. 1150 “Coastal lagoons” and are used as indi-ators in procedures of assessment of coastal water quality in many

∗ Corresponding author. Tel.: +372 671 8940; fax: +372 6718900.E-mail address: kaire.torn@ut.ee (K. Torn).

ttp://dx.doi.org/10.1016/j.aquabot.2014.05.005304-3770/© 2014 Elsevier B.V. All rights reserved.

countries (e.g. Germany, Sweden) (European Commission, 2007;Steinhardt et al., 2009). Among inland waters charophyte lakes aredistinguished as an EU Habitat Directive Annex I habitat type no.3140 “Hard oligo-mesotrophic waters with benthic vegetation ofChara spp.”.

Studies on the distribution and ecological demands of charo-phytes in several countries display large disproportions in timeand space. The species richness is commonly directly related to thefield sampling effort and the activity of aquatic botanists. Despitethe fact that the Estonian coastal sea is well-studied and data oncharophytes in this area are constantly being updated (Torn et al.,2004; Kovtun et al., 2011), published information about charophytedistribution in the inland waters is old (Pork, 1954). An importantshortcoming is the absence of a common charophyte database forboth coastal sea and inland waters. The lack of a common databasehas (1) hindered development of a holistic understanding of the dis-tribution and ecology of charophytes as several species are presentin both inland and marine waters, and (2) caused misinforma-

ctive occurrence model of charophytes in Estonian waters. Aquat.

tion: e.g. in some publications only data on brackish water specieshave been used or new data have been combined with 60-yearold records (Urbaniak, 2007; Romanov, 2009). Therefore one ofthe aims of this paper is to give a review of the distribution and

ING ModelA

2 Botan

eb

oHciMetfsmpcuteoapid

2

2

1IAidGeSbTa(gtpWtfiigapu

(unf

2

tri

ARTICLEQBOT-2672; No. of Pages 8

K. Torn et al. / Aquatic

nvironmental preferences of charophyte species in Estonianrackish and fresh waters.

Greater sampling effort can certainly improve our knowledgef the distribution of charophytes and identify threatened species.owever, traditional sampling-point field work is not suitable forovering large areas in high detail as it yields data only from vis-ted sampling sites and leaves most of the study area unsampled.

oreover, extensive in situ field work is very time-consuming andxpensive. Predictive modelling enables a general assessment ofhe distribution of species in large spatial extents that cannot beully covered with in situ sampling (Zimmermann et al., 2010). Aeamless map of the probability of occurrence gives a significantlyore relevant view of the distribution of a species than simple

lotting of field localities on a map. This is especially so, whenonsidering that sites of field sampling are commonly spatiallynequally distributed over extensive areas. Additionally, predic-ive modelling provides an opportunity to examine the effects ofnvironmental variables on the distribution of a species at vari-us spatial scales and help to determine appropriate managementctions (Kumar et al., 2009). Accordingly, the second aim of thisaper was to predict the potential distribution for charophytes

n coastal waters based on available georeferenced environmentalata (depth, wave exposure etc.).

. Material and methods

.1. Data collection

The material for the present study was collected during995–2011 and is based on databases of the Estonian Marinenstitute (University of Tartu) and Centre of Limnology (Estoniangricultural University) (Fig. 1). Sampling in brackish water (salin-

ty over 0.5 psu) has been predominantly performed by SCUBAiving from a boat or directly from the shore. For each locality,PS position, depth, sediment type and abiotic water column prop-rties (e.g. salinity, oxygen content, Secchi depth) were recorded.ampling in fresh water (salinity below 0.5 psu) was performedy dredging with a hook from a boat or directly from a shore.he type of water body (lake, pond, ditch), GPS position, depthnd sediment type were fixed for each site. Six types of sedimentmud, sand, clay, gravel, peaty mud and clayey mud) was distin-uished based on content, consistency, grain size and/or colour ofhe soil. Mud was defined as the remains of biota and inorganicarticles, peaty mud mainly consists detritus of Sphagnum spp.ater alkalinity (HCO3

− mg l−1) and dichromate oxygen consump-ion (CODCr mg O l−1) which reflects the organic content were usedor the characterization of freshwater locations. Samples for chem-cal analyses were collected from the surface layer of water columnn midsummer. Alkalinity was titrated with HCl, dichromate oxy-en consumption determined by the oxidation of organic matter by

solution of K2Cr2O7 in H2SO4. Collected charophyte samples wereacked, labelled and frozen or preserved in formaldehyde solutionntil determination in the laboratory.

For species identification, the determination keys of Krause1997), Schubert and Blindow (2003) and Langangen (2007) weresed. Sterile specimens of Nitella flexilis (Linnaeus) C. Agardh couldot be distinguished from Nitella opaca (Bruzelius) C. Agardh, there-

ore these species were treated as a group of N. opaca/flexilis.

.2. Distribution modelling

Please cite this article in press as: Torn, K., et al., Distribution and prediBot. (2014), http://dx.doi.org/10.1016/j.aquabot.2014.05.005

We aimed to build a model that best predicts the spatial dis-ribution of genus Chara in the Estonian coastal waters. Boostedegression trees (BRT) was chosen as the modelling technique asts predictive performance has been shown to be superior to most

PRESSy xxx (2014) xxx–xxx

other modelling methods (Elith et al., 2006; Revermann et al.,2012). BRT is an ensemble method that combines the strength oftwo algorithms: regression trees and boosting (Elith et al., 2008).Regression trees are good at selecting relevant predictor variablesand can model interactions. Boosting enables a building of a largenumber of trees in a way that each successive tree adds small modi-fications to parts of the model space to fit the data better (Friedmanet al., 2000). BRT has no need for prior data transformation orelimination of outliers, can fit complex nonlinear relationships,can handle different types of predictor variables, and can modelinteraction effects among predictors (Elith et al., 2006). Importantparameters in building BRT models are learning rate and tree com-plexity. Learning rate determines the contribution of each tree tothe growing model and tree complexity defines the depth of inter-actions allowed in a model. The BRT modelling was performed inthe statistical software R version 2.15.1 (R Development Core Team,2012) using packages ‘gbm’ (Ridgeway, 2012) and ‘dismo’ (Hijmanset al., 2012).

The predictor variables included different bathymetrical (depth,slope of seabed), hydrodynamic (wave exposure, current speed),geological (seabed substrate), and physico-chemical (temperature,salinity, oxygen content) variables. Altogether 26 abiotic predictorvariables were used (Table 1) that were all available as georefer-enced raster layers. Input data for the dependent variable, i.e. thesampling point-wise presence-absence data of Chara spp., werecompiled from the benthos database of the Estonian Marine Insti-tute. The input dataset on charophytes included 11 149 samplingsites distributed over the Estonian marine area from the period1995–2011 (Fig. 1). Chara spp. were present in 1146 sites corre-sponding to 10.3% of the total number of sampling sites. Tolypellanidifica (O.F. Müller) Leonhardi was excluded because of somewhatdifferent environmental preferences (e.g. wider depth distribution,salinity tolerance) compared to genus Chara species. Due to thelack of good environmental data from freshwater, the spatial pre-diction of the occurrence of charophytes was made only for thecoastal sea.

Two groups of BRT models were built that had tree complex-ity of 1 and 5, respectively. Tree complexity of 1 fits an additivemodel without interactions between predictors while tree com-plexity of 5 fits a model with up to five-way interactions. In bothgroups, models with learning rates of 0.005, 0.01, 0.05 and 0.1 werebuilt and their predictive performance was estimated by calculatingpredictive deviance and Area Under the Receiver Operating Curve(AUROC, generally abbreviated to AUC) (Fielding and Bell, 1997)using 10-fold cross validation. An AUC value of 0.5 indicates thatthe model prediction is not better than random while the value of1 shows a perfect match between the model prediction and realvalue (Fielding and Bell, 1997). The model with the highest cross-validation AUC value was chosen and it was further subjected tosimplification as implemented in the package ‘dismo’: the routineperforms a backwards elimination of variables to drop those thatgive no evidence of improving predictive performance (Hijmanset al., 2012). After simplification, the model was used for mak-ing the spatial prediction of the probability of occurrence of Charaspp. in the Estonian sea area. The prediction was modelled over a200 × 200 m grid covering water depths of 0 to 15 m.

3. Results

3.1. Distribution of charophytes

ctive occurrence model of charophytes in Estonian waters. Aquat.

Charophytes were found from 1365 locations in coastal areaand from 176 lakes or ponds. Altogether 22 species of charophyteswere found in Estonian waters (Fig. 2). In brackish waters, sevenspecies of stoneworts were found, representing the genera Chara

ARTICLE IN PRESSG ModelAQBOT-2672; No. of Pages 8

K. Torn et al. / Aquatic Botany xxx (2014) xxx–xxx 3

Fig. 1. Distribution of sampling locations (1995–2011) in Estonia. The grey area represents the Estonian marine area up to the outer border of the exclusive economic zone.D

aWJeTT

TPp

Sc

ata from the locations inside the grey area were used for distribution modelling.

nd Tolypella. The most frequent of them were Chara aspera C.L.illdenow and T. nidifica. Chara baltica A. Bruzelius, Chara canescens

Please cite this article in press as: Torn, K., et al., Distribution and prediBot. (2014), http://dx.doi.org/10.1016/j.aquabot.2014.05.005

.L.A. Loiseleur-Deslongschamps and Chara connivens P. Salzmannx A. Braun were also widely distributed in the investigation area.he rarest species was Chara horrida L.J. Wahlstedt. In contrast to. nidifica, C. aspera and C. canescens that were spread along the

able 1redictor variables in the BRT models and the relative influence of variables in the final modrocedure are indicated.

Predictor variable Source Drop

Depth 1

Average depth in 500 m radius 1

Average depth in 2000 m radius 1

Slope of seabed 1

Slope of seabed in 500 m radius 1

Slope of seabed in 2000 m radius 1

Geological type of seabed (large-scale data) 2 xProportion of soft sediment (modelled) 2

Wave exposure 3

Oxygen content, average over 2002–2008 4

Oxygen content, maximum over 2002–2008 4

Oxygen content, minimum over 2002–2008 4

Oxygen content, variance over 2002–2008 4

Salinity of sea surface 2 xSalinity, average over 2002–2008 4

Salinity, maximum over 2002–2008 4 xSalinity, minimum over 2002–2008 4

Salinity, variance over 2002–2008 4 xTemperature, average over 2002–2008 4

Temperature, maximum over 2002–2008 4

Temperature, minimum over 2002–2008 4 xTemperature, variance over 2002–2008 4

Current velocity, average over 2002–2008 4 xCurrent velocity, maximum over 2002–2008 4

Current velocity, minimum over 2002–2008 4 xCurrent velocity, variance over 2002–2008 4

ources:1—Bathymetric raster, developed in the Estonian Marine Institute. 2—Databasesoast (Nikolopoulos and Isæus, 2008). 4—Hydrological model of the Baltic Sea; modelled

coastline, C. baltica, C. connivens and C. tomentosa Linnaeus weremainly restricted to western Estonia.

ctive occurrence model of charophytes in Estonian waters. Aquat.

Three genera of charophytes were found in fresh waters Chara,Nitellopsis and Nitella. The most widely distributed species wereChara globularis J.L. Thuiller, Chara intermedia A. Braun and Characontraria A. Braun ex Kützing occurring in over 40 localities. Nitella

el. Variables that were dropped from the final model during the model simplification

ped from the final model Relative influence in the final model (%)

12.520.017.3

1.81.62.1

4.54.74.43.34.12.0

2.1

2.0

2.73.4

5.4

3.0

3.0

of the Estonian Marine Institute. 3—Wave exposure calculations for the Estonianfor the period of 2002–2008 (Bendtsen et al., 2009).

Please cite this article in press as: Torn, K., et al., Distribution and predictive occurrence model of charophytes in Estonian waters. Aquat.Bot. (2014), http://dx.doi.org/10.1016/j.aquabot.2014.05.005

ARTICLE IN PRESSG ModelAQBOT-2672; No. of Pages 8

4 K. Torn et al. / Aquatic Botany xxx (2014) xxx–xxx

Fig. 2. Geographic distribution of the Characeae species in Estonia collected during 1995–2011.

IN PRESSG ModelA

Botany xxx (2014) xxx–xxx 5

gatcI

rbrlAdgtSsHsNHoaufCitocVfl

3

bwtoiccl

1fist(s

>fmbmCtieGos(

quir

emen

ts

of

char

oph

ytes

in

Esto

nia

. Loc

atio

n

no.

:

sam

pli

ng

site

s

wit

h

dis

tan

ce

mor

e

than

100

m

wer

e

con

sid

ered

as

sep

arat

e

loca

tion

s;

HC

O3

−co

des

:

1—<8

0,

2—80

–240

, 3—

>240

;

CO

DC

rco

des

:

1—<3

5,

2—35

–60,

trat

e

cod

es:

M—

mu

d, S

—sa

nd

, C—

clay

, CM

—cl

ayey

mu

d, G

—gr

avel

, PM

—p

eaty

mu

d;

mai

n

hab

itat

typ

e

is

mar

ked

in

bold

.

Loca

tion

no.

Sali

nit

y

Dep

th

(m)

HC

O3

(mg

l−1)

CO

DC

r(m

gO

1−1)

Subs

trat

e

Bra

ckis

h

Fres

h

0–1

1–2

2–3

3–4

>4

a

Wil

lden

ow

472

x

x

x

x

x x

x

1,

2,

3

1,

2,

3

M, S

, CM

, Ca

Bru

z.

158

x

x

x

x

x

x

M, S

, Gsc

ens

Lois

.-D

eslo

ngs

cham

ps

175

x

x

x x

x

x

M, S

iven

s

Salz

m.e

x

A.B

rau

n

163

x

x

x

x x

x

2,

3

1,

2

M, S

, Gar

ia

A.B

rau

n

ex

Kü

tz.

59

x

x

x 2,

3

1,

2,

3

M, S

, C, C

M, G

, PM

lari

s

Thu

ille

r54

x

x

x

x

x

1,

2,

3

1,

2,

3

M, S

, CM

da

L.

14

x

x x

x

2,

3

1,

2

M, S

, CM

, Gda

Wah

lste

dt

21

x

x x

x

M, S

med

ia

A.B

rau

n

40

x

x x

x

2,

3

1,

2,

3

M, S

cant

ha

A.B

rau

n

10

x

x

1,

2

1,

2

M, S

(A.B

rau

n)

Leon

h.

40

x

x

x

x

x

1,

2,

3

1,

2,

3

M, S

sa

A.B

rau

n

16

x x

x

x

1,

2,

3

1,

2,

3

M, S

, PM

ntos

a

L.

105

x

x x

x

x

2,

3

1,

2,

3

M, S

ta

Kü

tz.

35

x

x

x

x

x

x

1,

2,

3

1,

2,

3

M, S

, PM

ris

L.

9

x

x

2,

3

1,

2,

3

M, P

Mlis

/opa

ca

35

x

x

x

x

x

1,

2,

3

1,

2,

3

M, S

ilis

(Sm

ith

)

Aga

rdh

4

x

x

2,

3

1,

3ro

nata

(A.B

rau

n)

Miq

uel

6

x

x

2,

3

1,

2

M, C

arpa

Thu

ille

r

4

x

x

2

2,

3

S,

PMbt

usa

(Des

vau

x)

Gro

ves

26

x

x

x

x

x

2,

3

1,

2

M, C

Mdi

fica

(Mü

ller

)

Leon

h.

337

x

x

x

x

x

x

S

ARTICLEQBOT-2672; No. of Pages 8

K. Torn et al. / Aquatic

racilis (J.E. Smith) C. Agardh, Nitella mucronata (A. Braun) F. Miquelnd Nitella syncarpa (J.L. Thuillier) Kützing were recorded in upo 5 localities and were therefore defined as rare. In general 1-3harophyte species were present in each investigated waterbody.n three lakes 7 species of charophytes were found.

The distribution pattern of inland charophytes was closelyelated to geological and geomorphological conditions. Limestoneedrock and limestone-rich moraine provided conditions for highichness of charophytes in northern and western Estonia, in drum-in areas of eastern Estonia and in moraine uplands of SE Estonia.mongst the latter, only lakes in the highest areas of uplands wereistinguishable by soft-water and association of N. flexilis—C. vir-ata Kützing. The most unfavourable area for charophytes washe zone of peat bogs that stretches over the central Estonia inW–NE direction. This zone coincides with the maximal transgres-ion limit of the Baltic Sea, bordering the West-Estonian Lowland.igh species richness of charophytes was found in lakes fed by

pring water originating from limestone bedrock of the upland inE Estonia. The group of C. hispida Linnaeus, C. rudis (A. Braun). von Leonhardi and C. intermedia A. Braun was characteristicf spring-fed lakes also in the other districts of eastern Estonia,nd these species were accompanied by C. tomentosa and C. glob-laris in many lakes. The quite rare C. polyacantha A. Braun wasound only in coastal lagoons and coastal lakes in western Estonia.. contraria exhibited contrasting habitats: the species occurred

n small springs and in ultra-alkaline mining ponds but also inhe largest lakes. C. contraria dominated in lake Peipsi (3555 km2),ccurring mainly in the shallow zone of its northern part. C.ontraria had extensive distribution in the other large lake, lakeõrtsjärv (270 km2), in the 1960s, but now is found only in a

ew locations in the shallow alkaline north-western area of theake.

.2. Environmental preferences

Fifteen of the 22 species occurred generally only in salinityelow 0.5 psu (Table 2). The border between brackish and fresh-ater species seemed to be not very clear, probably due to the

ransitional character of the habitats. Due to the post-glacial upliftf land, lagoons have been formed in Western Estonia. The brack-sh water species C. canescens and C. horrida were found in a fewoastal lagoons at very low water salinity −0.1 to 0.3 psu. Also C.ontraria, typically found in freshwater, was found in three coastalagoons with salinity 0.5–0.7 psu.

The majority of the species preferred shallow water less than m; larger species were also common to 2 m depth (Table 2). Sixtyve percent of charophyte occurrences in coastal water were foundhallower than 1 m depth. Mud was the prevailing substrate in loca-ions where charophytes were found. A few brackish water speciesC. baltica, C. cansecens, T. nidifica) were found commonly on a sandyubstrate rather than on a muddy substrate.

Most freshwater species preferred water alkalinity80 mg HCO3

− l−1. Exceptionally, C. virgata and N. flexilis pre-erred soft-water lakes. The latter species occurred also in the

ost soft-water oligotrophic and semi-dystrophic lakes inhabitedy Lobelia dortmanna Linnaeus and Isoëtes lacustris Linnaeus, andore rarely in lakes of medium alkalinity (80–240 mg HCO3

− l−1).. virgata appeared mainly in soft-water lakes with slightly higherrophic level and alkalinity (>30 mg HCO3

− l−1), rarely occurringn more alkaline waters. The habitats of C. strigosa A. Braunxtended from soft water to the highest alkalinity (Table 2).

Please cite this article in press as: Torn, K., et al., Distribution and predictive occurrence model of charophytes in Estonian waters. Aquat.Bot. (2014), http://dx.doi.org/10.1016/j.aquabot.2014.05.005

enerally charophytes were found in lakes with low to high waterrganic matter content (CODCr < 60 mg O l−1). None of the studiedpecies preferred lakes with very high organic matter contentCODCr > 60 mg O l−1) (Table 2). Ta

ble

2Ec

olog

ical

re3—

>60;

subs

Spec

ies

Char

a

aspe

rCh

ara

balt

icCh

ara

cane

Char

a

conn

Char

a

cont

rCh

ara

glob

uCh

ara

hisp

iCh

ara

horr

iCh

ara

inte

rCh

ara

poly

aCh

ara

rudi

sCh

ara

stri

goCh

ara

tom

eCh

ara

virg

aCh

ara

vulg

aN

itel

la

flexi

Nit

ella

grac

Nit

ella

muc

Nit

ella

sync

Nit

ello

psis

oTo

lype

lla

ni

ARTICLE IN PRESSG ModelAQBOT-2672; No. of Pages 8

6 K. Torn et al. / Aquatic Botany xxx (2014) xxx–xxx

Fig. 3. Probability of occurrence of charophytes as predicted by the BRT model. The full spatial extent of the modelled prediction is not shown as the zoom level for the fulld highei

3

hsditopdm5pcd

aAFt

4

Tttdrre

iBew

isplay would render the map hard to read. Instead of the full extent, three areas ofn the online appendix.

.3. Modelled distribution

A model with tree complexity of 5 and learning rate of 0.01ad the highest cross-validation AUC value and that model wasubjected to the model simplification. The simplification routineropped seven predictors from the model (see Table 1). The result-

ng final model, which was further used for making predictions ofhe occurrence of Chara spp., had 2850 trees, the AUC values basedn model training data was 0.974 and cross-validation 0.954. Theroportions of explained deviance of the model based on trainingata and cross-validation were 80.7% and 70.7%, respectively. Theost influential predictor variables were depth, average depth in

00 m radius, average depth in 2000 m radius, variability of tem-erature, wave exposure, and proportion of soft sediment thatumulatively contributed 64.5% of the total influence of all pre-ictors (see Table 1 for more details).

Based on the results of modelling, larger areas of higher prob-bility of Chara species were situated in the western Estonianrchipelago (Fig. 3, online appendix). Contrastingly, the Gulf ofinland hosted only very limited areas with a high probability forhe occurrence of charophytes.

. Discussion

The Estonian Characeae were represented by four genera (Chara,olypella, Nitella, Nitellopsis) and 22 species. As shown in the dis-ribution maps (Fig. 1) most freshwater species were widespreadhroughout the country with no strong geographical pattern in theistribution of the species. Brackish water species were mostlyestricted to the shallow, sheltered, soft-bottom archipelago envi-onment found especially in western Estonia which provides anxcellent habitat.

A similar number of species of charophytes has been recorded

Please cite this article in press as: Torn, K., et al., Distribution and prediBot. (2014), http://dx.doi.org/10.1016/j.aquabot.2014.05.005

n neighbouring countries: 23 species in Latvia (Schubert andlindow, 2003; Zviedre, 2008) and 21 species in Finland (Langangent al., 2002; Langangen, 2007). Compared to Estonian data thereere 18 and 16 overlapping species with Latvia and Finland,

r probability of Chara spp. are shown. The full extent of the prediction can be found

respectively. Differences were caused by different bedrock type(especially with Finland) and temperature regime (Langangen et al.,2002).

The earliest published information about charophytes in Estoniawas compiled by Pork in 1954. Unfortunately, this overview is alsothe latest published information concerning species from freshwa-ter. According to Pork (1954), there were 15 recorded charophytespecies and additionally 4 species were assumed to be found inEstonia. Among these 4 species C. canescens and C. rudis are quitewidespread in Estonia and N. gracilis was found from 4 lakes basedon our data (Fig. 2). The fourth species, C. filiformis H. Hertzsch, hasnever been found in Estonia. As the northernmost recorded occur-rences of C. filiformis are from southern Sweden and south-easternLatvia, a latitude around 56◦ can be considered the northern dis-tribution limit of that species (Langangen, 2007; Zviedre, 2008). C.intermedia and C. globularis, which were formerly mentioned onlyfrom one location, are common species based on the current data(Fig. 2).

The distribution data for brackish water charophytes in Estoniahave been updated during the last decade (Torn and Martin, 2003,2004a, 2004b; Torn et al., 2004; Kovtun et al., 2011). Comparedto the previous knowledge there has been an increase in the dis-tribution area of C. horrida and C. connivens. C. horrida has beenpreviously found in the coastal water of Estonia in the beginning of20th century (Hasslow, 1939; Pork, 1954). Despite extensive phyto-benthos sampling of coastal waters of western Estonia, the specieswas not found again until 2002 (Torn and Martin, 2004b). Based oncomments in field diaries from 1970 to 1980 (unpublished data byT. Trei) we assume that the species was misidentified and occurredat least in one area where it is most abundant nowadays (Fig. 2).During the last few years several new locations of C. horrida havebeen found in Estonia whereas the distribution range of the speciesin the whole Baltic Sea is restricted and declining and the species is

ctive occurrence model of charophytes in Estonian waters. Aquat.

categorized as near-threatened in the HELCOM Red List (HELCOM,2012).

The distribution of C. connivens has been limited in the Baltic Sea(Schubert and Blindow, 2003). The species is believed to be invasive

ING ModelA

Botan

taataecbdm

tFLG

cTmhaMwwDdetgrbl

(wsnfJsbssMw

si(vwtaldsl

dBwi

cwb

ARTICLEQBOT-2672; No. of Pages 8

K. Torn et al. / Aquatic

o the Baltic Sea from Western Europe (Luther, 1979; Leppäkoskind Olenin, 2000). C. connivens has disappeared from the southernreas of the Baltic (Schubert and Blindow, 2003). Beyond Estonia,he species nowadays occurs in the Öregrund archipelago, Swedennd northern Åland archipelago, Finland (Torn, 2008; Appelgrent al., 2004). The distribution area and number of locations of C.onnivens has been continuously increasing in Estonia. The num-er of occurrences has increased from 9 to more than 100 and theistribution area has been expanded from western Estonia to theiddle of the Gulf of Finland (Fig. 2, Torn et al., 2004).C. baltica is a widespread species in the Baltic Sea. Although

he species was recorded from the northern coast of the Gulf ofinland in the beginning of the last century (Langangen et al., 2002;angangen, 2007), the first record from the southern coast of theulf of Finland came as recently as 2001 (Fig. 2).

The distribution patterns of different charophyte species arelosely linked to their requirements for environmental conditions.he spatial distribution of charophytes in coastal waters dependsostly on light conditions (via depth and substrate properties),

ydrodynamic conditions (via wave exposure, depth and slope),nd bottom substrate (Schubert and Blindow, 2003; Torn andartin, 2004a; Torn et al., 2004; Kovtun et al., 2011). In coastalaters, charophytes are most frequent and abundant in shallowater (Blindow, 2000; Munsterhjelm, 2005; Kovtun et al., 2011).epth was also the most influential predictor variable in the pre-ictive model of the occurrence of Chara species. Coastal lakes arextremely shallow, mostly in a range of 0.5–1.5 m. The distribu-ion of charophytes in the inland lakes is obviously limited by theenerally low water transparency (SD). At the highest SD values,ecorded at 8 m in Estonia, large Chara species may occur at 5.5 m,ut such extraordinary conditions exist only in some spring-fed

akes (unpublished data).Chara-dominated lakes are typically calcium-rich hard water

Moore, 1986). The majority of Estonian freshwater charophytesere found in hard or moderately hard water. Surprisingly, C.

trigosa was also found in several soft-water lakes (Table 2). Inorthern Europe and Switzerland this species has been reported

rom lime-rich hard waters only (Langangen, 2007; Audersetoye and Rey-Boissezon, 2014). However, soft-water lakes with C.trigosa in Estonia are located in sandy areas located on limestoneedrock (3 lakes) or in the vicinity of the boundary of sand-tone/limestone outcrop areas (2 lakes). The role of groundwater inoft-water lakes is generally modest but according to the studies ofagnusson et al. (2006) in seepage lakes the inflow of calcium-richater takes place mainly in the littoral zone.

It is generally known that charophytes inhabit waterbodies withoft, sandy and muddy bottoms. However, some species show vary-ng preferences also for different types and quality of soft substratesSchubert and Blindow, 2003; Selig et al., 2007). Mud was the pre-ailing substrate type in locations where the Estonian charophytesere found. The characteristics of muddy sediments, marked as

he most common bottom substrate of charophyte habitats, maylso differ markedly. In smaller stratified lakes an anoxic black mudayer may cover most of the lake bottom, starting at 3–4 m waterepth and being obviously unfavourable for charophytes. A sandyubstrate was characteristic for species that tolerate more exposedocalities (Table 2, Torn and Martin, 2004a).

Salinity is one of the major factors limiting the geographicalistribution of charophyte species in the Baltic Sea (Schubert andlindow, 2003). Salinity does not limit the distribution of brackishater species over the whole Estonian coastline as surface salinity

n the Estonian coastal sea is usually below 7 psu.

Please cite this article in press as: Torn, K., et al., Distribution and prediBot. (2014), http://dx.doi.org/10.1016/j.aquabot.2014.05.005

The very high predictive power of the distribution model indi-ated that the application of distribution modelling of Chara spp.as well justified. The high prediction accuracy can be explained

y several reasons: (1) charophytes exhibit easily distinguishable

PRESSy xxx (2014) xxx–xxx 7

habitat preferences in the coastal sea as they inhabit only very shal-low, soft–sediment areas that are well protected from waves; thisspecific habitat preference provides a very strong signal in modelfitting; (2) the input dataset of presences and absences of Charaspp. was very representative including thousands of records andcovering all important environmental gradients; (3) the modellingalgorithm BRT has been proved to produce highly accurate pre-dictions (e.g. Elith et al., 2006). The modelled distribution of Charaspp. was in good accordance with the general knowledge of thedistribution of charophytes in the Estonian coastal sea. Based onthe model prediction, the vast majority of charophyte habitats aresituated in the sea areas of the West Estonian Archipelago. Thatsea area is characterized by favourable conditions for charophytes:a high proportion of shallow areas that are protected from waveexposure. The modelled distribution map (Fig. 3) clearly improvedthe understanding of the distribution of Chara spp. in the Estoniancoastal sea. Unlike the simple plotting of species occurrences on amap (like in Fig. 2), the modelled distribution maps enable assess-ment of (1) surface area of habitats, (2) distribution of species inthe areas that were not sampled or sampled sparsely.

Acknowledgements

The work was supported by Institutional research fundingIUT02-20 of the Estonian Research Council and Estonian ScienceFoundation grants no. 8980 and 9439. The authors are grateful to Dr.Mariusz Pełechaty, Dr. Andrzej Pukacz and Dr. Irmgard Blindow forhelp with charophyte determination. Dr. Allan Chivas is acknowl-edged for the language revision of the paper. The data for 16 coastallagoons were obtained from the results of Interreg IV A ProgramNatureship, supported by the European Union investigations wereled by Prof. Ingmar Ott.

Appendix A. Supplementary data

Supplementary data associated with this article can befound, in the online version, at http://dx.doi.org/10.1016/j.aquabot.2014.05.005.

References

Appelgren, K., Snickars, M., Mattila, J., 2004. Chara connivens Saltzm Ex. A. Braun 1835found in the Åland archipelago—a new species to Finland. Mem. Soc. Fauna FloraFenn. 80, 11–13.

Auderset Joye, D., Rey-Boissezon, A., 2014. Will Charophyte Species Regress orIncrease their Distribution in a Changing Environment?, http://dx.doi.org/10.1016/j.aquabot.2014.05.003.

Bendtsen, J., Gustafsson, K.E., Söderkvist, J., Hansen, J.L.S., 2009. Ventilation ofbottom water in the North Sea–Baltic Sea transition zone. J. Mar. Syst. 75,138–149.

Blindow, I., 2000. Distribution of charophytes along the Swedish coast in relation tosalinity and eutrophication. Int. Rev. Hydrobiol. 85, 707–717.

Blindow, I., Garniel, A., Munsterhjelm, R., Nielsen, R., 2003. Conservation andthreats. Proposal of a Red Data Book for charophytes in the Baltic Sea. In:Schubert, H., Blindow, I. (Eds.), Charophytes of the Baltic Sea. The BalticMarine Biologists Publication No.19. Koltz Scientific Books, Köningstein/Taunus,pp. 251–260.

Casanova, M.T., Brock, M.A., 1999. Charophyte occurrence, seed banks and estab-lishment in farm dams in New South Wales. Aust. J. Bot. 47, 437–444.

Chambers, P.A., Kalff, J., 1985. Depth distribution and biomass of submersed aquaticmacrophyte communities in relation to Secchi depth. Can. J. Fish. Aquat. Sci. 42,701–709.

Elith, J., Graham, C.H., Anderson, R.P., Dudik, M., Ferrier, S., Guisan, A., Hijmans,R.J., Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle,B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton J.McC. Peter-son, A.T., Phillips, S.J., Richardson, K.S., Scachetti-Pereira, R., Schapire, R.E.,Soberón, J., Williams, S., Wisz, M.S., Zimmermann, N.E., 2006. Novel methodsimprove prediction of species’ distributions from occurrence data. Ecography 29,

ctive occurrence model of charophytes in Estonian waters. Aquat.

129–151.Elith, J., Leathwick, J.R., Hastie, T., 2008. A working guide to boosted regression trees.

J. Anim. Ecol. 77, 802–813.European Commission, 2007. Guidelines for the Establishment of the Natura 2000

Network in the Marine Environment. Application of the Habitats and Birds

ING ModelA

8 Botan

F

F

HH

H

K

K

K

LL

L

L

M

M

M

N

P

ARTICLEQBOT-2672; No. of Pages 8

K. Torn et al. / Aquatic

Directives. Appendix I: Marine Habitat types Definitions. Update of Interpre-tation Manual of European Union Habitats. European Commission, Availableat http://ec.europa.eu/environment/nature/natura2000/marine/index en.htm(verified October 2013).

ielding, A.H., Bell, J.F., 1997. A review of methods for the assessment of predictionerrors in conservation presence/absence models. Environ. Conserv. 24, 38–49.

riedman, J.H., Hastie, T., Tibshirani, R., 2000. Additive logistic regression: a statisticalview of boosting. Ann. Stat. 28, 337–407.

asslow, O.J., 1939. Einige Characeenbestimmungen. Bot. Not. 1939, 295–301.ELCOM, 2012. Checklist of Baltic Sea Macro-species. Baltic Sea Environment

Proceedings No. 130. HELCOM, Available at http://helcom.fi/Lists/Publications/BSEP130.pdf (verified October 2013).

ijmans, R.J., Phillips, S., Leathwick, J., Elith, J., 2012. dismo: Species Dis-tribution Modeling. R Package Version 0. 7-23, Available at http://CRAN.R-project.org/package=dismo (verified October 2013).

ovtun, A., Torn, K., Martin, G., Kullas, T., Kotta, J., Suursaar, Ü., 2011. Influence ofabiotic environmental conditions on spatial distribution of charophytes in thecoastal waters of West Estonian Archipelago, Baltic Sea. J. Coastal Res. SI64-1,412–416.

rause, W., 1997. Charales (Charophyceae). In: Ettl, H., Gärtner, G., Heynig, H., Mol-lenhauer, D. (Eds.), Freshwater Flora of Central Europe, 18. Gustav Fischer, Jena.

umar, S., Spaulding, S.A., Stohlgren, T.J., Hermann, K.A., Schmidt, T.S., Bahls, L.L.,2009. Potential habitat distribution for the freshwater diatom Didymospheniageminata in the continental US. Front. Ecol. Environ. 7 (8), 415–420.

angangen, A., 2007. Charophytes of the Nordic Countries. Saeculum ANS, Oslo.angangen, A., Koistinen, M., Blindow, I., 2002. The charophytes of Finland. Mem.

Soc. Fauna Flora Fennica. 78, 17–48.eppäkoski, E., Olenin, S., 2000. Non-native species and rated of spread: lessons from

the brackish Baltic Sea. Biol. Invasions 2, 151–163.uther, H., 1979. Chara connivens in the Baltic Sea area. Ann. Bot. Fenn. 16,

141–150.agnusson, J.J., Kratz, T.K., Benson, B.J., 2006. Long-Term Dynamics of Lakes in the

Landscape. University Press, Oxford.oore, J.A., 1986. Charophytes of Great Britain and Ireland. In: BSBI Handbook No.

5. Botanical Society of the British Isles, London.unsterhjelm, R., 2005. Natural Succession and Human-Induced Changes in the

Soft-Bottom Macrovegetation of Shallow Brackish Bays on the Southern Coast

Please cite this article in press as: Torn, K., et al., Distribution and prediBot. (2014), http://dx.doi.org/10.1016/j.aquabot.2014.05.005

of Finland. Walter & Andrée de Nottbeck Found. Sci. Rep. 26. Helsinki.ikolopoulos, A., Isæus, M., 2008. Wave Exposure Calculations for the Estonian coast.

AquaBiota Water Research, Stockholm.ork, M., 1954. Charophytes (Charophyta) of Estonian SSR. Eesti NSV Teaduste

Akadeemia, Tartu [in Estonian].

PRESSy xxx (2014) xxx–xxx

R Development Core Team, 2012. R: A Language and Environment for StatisticalComputing. The R Foundation for Statistical Computing. R Development CoreTeam, Vienna, Austria, Available at http://www.r-project.org (verified October2013).

Revermann, R., Schmid, H., Zbinden, N., Spaar, R., Schröder, B., 2012. Habitat at themountain tops: how long can Rock Ptarmigan (Lagopus muta helvetica) surviverapid climate change in the Swiss Alps? A multi-scale approach. J. Ornithol. 153,891–905.

Ridgeway, G., 2012. gbm: Generalized Boosted Regression Models. R Package Ver-sion 1. 6-3.2, Available at http://CRAN.R-project.org/package=gbm (verified June2013).

Romanov, R.E., 2009. Charophytes (Charales: Streptophyta) of the South of the WestSiberian Plain. Rastitelnyi mir Aziatskoi Rossii 1, 19–30 [in Russian].

Schubert, H., Blindow, I. (Eds.), 2003. Charophytes of the Baltic Sea. The Baltic MarineBiologists Publication No.19. Koltz Scientific Books, Köningstein/Taunus.

Selig, U., Schubert, M., Eggert, A., Steinhardt, T., Sagert, S., Schubert, H., 2007. Theinfluence of sediments on soft bottom vegetation in inner coastal waters ofMecklenburg-Vorpommern (Germany). Estuarine Coastal Shelf Sci. 71 (1–2),241–249.

Steinhardt, T., Karez, R., Selig, U., Schubert, H., 2009. The German procedure forthe assessment of ecological status in relation to the biological quality elementMacroalgae & Angiosperms pursuant to the European Water Framework Direc-tive (WFD) for inner coastal waters of the Baltic Sea. Rostock. Meeresbiolog.Beitr. 22, 7–42.

Torn, K., 2008. Distribution and ecology of charophytes in the Baltic Sea. In: Ph.D.Thesis. Dissertationes Biologicae Universitatis Tartuensis. University of Tartu,Tartu.

Torn, K., Martin, G., 2003. Changes in the distribution of charophyte species inenclosed sea bays of western Estonia. Proc. Est. Acad. Sci. Biol. Ecol. 52, 134–140.

Torn, K., Martin, G., 2004a. Environmental factors affecting the distribution of charo-phyte species in Estonian coastal waters, Baltic Sea. Proc. Est. Acad. Sci. Biol. Ecol.53, 251–259.

Torn, K., Martin, G., 2004b. Distribution of charophytes (Charophyta) in Estoniancoastal waters. Estonia Maritima 6, 45–51 [in Estonian].

Torn, K., Martin, G., Kukk, H., Trei, T., 2004. Distribution of charophyte species inEstonian coastal waters (NE Baltic Sea). Sci. Mar. 68, 129–136.

Urbaniak, J., 2007. Distribution of Chara braunii Gmellin 1826 (Charophyta) in Poland.

ctive occurrence model of charophytes in Estonian waters. Aquat.

Acta Soc. Bot. Pol. 76 (4), 313–320.Zimmermann, N.E., Edwards Jr., T.C., Graham, C.H., Pearman, P.B., Svenning, J.-C.,

2010. New trends in species distribution modelling. Ecography 33, 985–989.Zviedre, E., 2008. Freshwater Charophyte (Charophyta) Flora and Ecology in Latvia.

In: Summary of the Ph.D. Thesis. University of Latvia, Riga.