Multiscale

Drivers

of

Water

Chemistry

of

Boreal

Lakes

and

Streams

The

variability

in

surface

water

chemistry

within

and

between

aquatic

ecosystems

is

regulated

by

many

factors

operating

at

several

spatial

and

temporal

scales

The

importance

of

geographic

regional

and

local-scale

factors

as

drivers

of

the

natural

variability

of

three

water

chemistry

variables

representing

buffering

capacity

and

the

importance

of

weathering

-LRB-

acid

neutralizing

capacity

ANC

-RRB-

nutrient

concentration

-LRB-

total

phosphorus

TP

-RRB-

and

importance

of

allochthonous

inputs

-LRB-

total

organic

carbon

TOC

-RRB-

were

studied

in

boreal

streams

and

lakes

using

a

method

of

variance

decomposition

Partial

redundancy

analysis

-LRB-

pRDA

-RRB-

of

ANC

TP

and

TOC

and

environmental

variables

in

lakes

and

streams

showed

the

importance

of

the

interaction

between

geographic

position

and

regional-scale

variables

Geographic

position

and

regional-scale

factors

combined

explained

-LRB-

streams

-RRB-

and

-LRB-

lakes

-RRB-

of

the

variation

in

ANC

TP

and

TOC

The

unique

variance

explained

by

geographic

regional

and

local-scale

variables

alone

was

The

largest

amount

of

variance

was

explained

by

the

pure

effect

of

regional-scale

variables

-LRB-

for

streams

and

for

lakes

-RRB-

followed

by

local-scale

variables

-LRB-

and

-RRB-

and

geographic

position

-LRB-

and

-RRB-

The

combined

effect

of

geographic

position

regional

and

local-scale

variables

accounted

for

between

-LRB-

lakes

-RRB-

and

-LRB-

streams

-RRB-

of

the

variance

in

surface

water

chemistry

These

findings

lend

support

to

the

conjecture

that

lakes

and

streams

are

intimately

linked

to

their

catchments

and

have

important

implications

regarding

conservation

and

restoration

-LRB-

management

-RRB-

endeavors

Introduction

Surface

water

chemistry

is

regulated

by

a

complex

suite

of

processes

and

mechanisms

operating

at

varying

spatial

and

temporal

scales

Early

work

by

lake

ecologists

focused

on

the

importance

of

geographic

position

as

a

strong

predictor

of

lake

water

chemistry

For

instance

in

the

early

Thienemann

-LRB-

-RRB-

and

Naumann

-LRB-

-RRB-

developed

lake

trophic

classification

schemes

that

basically

recognized

differences

between

lowland

nutrient

rich

-LRB-

eutrophic

-RRB-

and

alpine

nutrient

poor

-LRB-

oligotrophic

-RRB-

ecosystems

Although

lake

ecologists

were

early

to

appreciate

the

importance

of

adjacent

land

type

on

lake-water

chemistry

stream

ecologists

have

addressed

the

terrestrial

aquatic

linkage

concept

more

formally

with

streams

being

regarded

as

open

systems

that

are

intimately

linked

with

their

surrounding

landscapes

-LRB-

e.g.

Hynes

-RRB-

However

lake

ecologists

have

recently

revisited

the

landscape

position

hypothesis

and

formalized

paradigms

that

recognize

more

explicitly

the

importance

of

landscape

position

and

its

significance

for

describing

among-lake

variance

-LRB-

e.g.

Kratz

and

others

Soranno

and

others

Riera

and

others

-RRB-

The

surrounding

landscape

-LRB-

catchment

-RRB-

with

its

distinct

geology

hydrology

and

climate

clearly

influences

the

physico-chemical

features

of

a

specific

water

body

-LRB-

e.g.

Omernik

and

others

Osborne

and

Wiley

Allan

Kratz

and

others

Soranno

and

others

Riera

and

others

-RRB-

and

several

studies

have

highlighted

the

links

between

surface

water

chemistry

and

catchment

characteristics

particularly

in

relation

to

sensitivity

to

nutrient

enrichment

and

acidification

-LRB-

Vollenweider

Sverdrup

and

others

Hornung

and

others

-RRB-

Indeed

water

chemistry

both

within

and

among

lakes

or

streams

is

considered

to

be

driven

by

factors

acting

on

both

regional

and

local

scales

Regional

factors

such

as

climate

geology

and

weathering

are

interrelated

with

other

factors

such

as

soil

type

and

land

cover/use

whereas

local

factors

like

the

input

and

retention

of

organic

matter

are

related

to

the

vegetation

type

and

topographical

relief

Hence

a

priori

a

close

linkage

is

expected

between

regional

and

local-scale

factors

Geographic

proximity

alone

is

however

often

not

sufficient

to

predict

the

physical

and

chemical

characteristics

of

individual

streams

or

lakes

as

differences

in

external

processes

such

as

stream

hydrology

or

lake

morphometry

and

water

retention

time

as

well

as

internal

processes

such

as

nutrient

cycling

and

strengths

of

interactions

with

the

surrounding

landscape

may

singly

or

in

concert

confound

the

importance

of

regional-scale

factors

Although

a

number

of

studies

have

addressed

the

importance

of

land

use/type

on

surface

water

chemistry

few

studies

have

simultaneously

focused

on

the

importance

of

local

and

regional

factors

as

determinants

of

surface

water

chemistry

and

fewer

still

have

addressed

the

similarities

and

differences

of

lake

and

stream

ecosystems

To

our

knowledge

only

one

study

-LRB-

Essington

and

Carpenter

-RRB-

has

simultaneously

studied

the

response

of

stream

and

lake

ecosystems

These

authors

showed

that

streams

and

lakes

were

surprisingly

similar

in

nutrient

cycling

in

particular

when

adjustments

were

made

for

water

residence

time

By

concurrently

studying

stream

and

lake

ecosystems

we

hope

to

better

our

understanding

of

the

processes

and

mechanisms

that

drive

surface

water

chemistry

in

these

different

but

certainly

not

ecologically

isolated

ecosystems

We

hypothesize

that

both

streams

and

lakes

are

strongly

linked

to

the

surrounding

landscape

and

that

spatial

variation

in

surface

water

chemistry

is

regulated

by

non-mutually

exclusive

factors

acting

on

various

hierarchical

scales

depending

on

landscape

type

and/or

geographic

position

Here

we

study

the

effect

of

regional

and

local-scale

factors

on

three

commonly

measured

water

chemical

variables

Acid

neutralizing

capacity

-LRB-

ANC

-RRB-

was

selected

to

indicate

the

effect

that

catchment

geology

and

weathering

might

have

on

buffering

capacity

Total

phosphorus

-LRB-

TP

-RRB-

was

selected

for

its

key

role

in

driving

ecosystem

productivity

and

because

it

is

biologically

active

-LRB-

i.e.

is

expected

to

decrease

along

e.g.

lake

chains

-RRB-

Finally

total

organic

carbon

-LRB-

TOC

-RRB-

was

used

as

a

surrogate

measure

of

the

importance

of

allochthonous

input

from

the

boreal

catchments

The

sites

used

in

this

study

are

often

natural

brown-water

systems

with

high

contents

of

humic

substances

We

attempted

to

-LRB-

-RRB-

identify

and

quantify

possible

sources

of

variation

in

surface

water

chemistry

of

boreal

streams

and

lakes

-LRB-

-RRB-

determine

which

environmental

factors

and

which

spatial

scales

are

most

important

in

determining

the

surface

water

chemistry

of

boreal

streams

and

lakes

and

-LRB-

-RRB-

determine

similarities/differences

in

the

factors

driving

stream/lake

water

chemistry

Methods

Study

Site

The

data

set

used

in

this

study

consists

of

streams

and

lakes

sampled

as

part

of

the

Swedish

national

stream

and

lake

survey

in

autumn

-LRB-

Johnson

and

Goedkoop

Wilander

and

others

-RRB-

-LRB-

Fig.

-RRB-

A

number

of

factors

suggested

that

this

dataset

was

sufficiently

robust

for

examining

among-site

similarities/dissimilarities

in

surface

water

chemistry

of

boreal

streams

and

lakes

Firstly

streams

and

lakes

were

selected

randomly

thus

the

samples

should

be

representative

of

the

population

of

streams

and

lakes

sampled

In

selecting

lakes

only

lakes

with

surface

areas

ha

were

included

and

two

size

classes

were

used

for

stratifying

stream

sites

-LRB-

catchment

area

classes

of

to

and

to

-RRB-

Because

we

were

interested

in

obtaining

a

depth-integrated

measure

of

surface

water

chemistry

lakes

were

sampled

during

autumn

turnover

Hence

sampling

started

in

the

northernmost

parts

of

the

country

and

progressed

southwards

A

more

detailed

description

of

stream

and

lake

selection

is

given

in

Wilander

and

others

-LRB-

-RRB-

In

this

study

we

were

interested

in

understanding

the

effects

of

local

and

regional-scale

variables

on

the

expected

natural

variability

of

selected

water

chemistry

variables

Thus

sites

deemed

to

be

affected

by

liming

acidification

-LRB-

lakes

critical

load

exceedence

of

S

and

N

Rapp

and

others

-LRB-

-RRB-

-RRB-

and

agriculture/silviculture

-LRB-

catchments

with

more

than

defined

as

arable

and

affected

by

clear-cutting

respectively

-RRB-

were

not

included

in

this

dataset

Fig.

of

the

lakes

and

streams

used

to

assess

the

influence

of

geographic

position

and

regional

and

local

scale

factors

on

surface

water

chemistry

The

streams

and

lakes

can

be

classified

as

relatively

small

-LRB-

mean

stream

width

m

mean

lake

area

-RRB-

nutrient

poor

ranging

from

clear

to

brownwater

ecosystems

-LRB-

mean

stream

abs

nm

mean

lake

abs

nm

-RRB-

The

streams

and

lakes

were

distributed

fairly

evenly

across

the

country

Streams

were

generally

situated

at

a

somewhat

lower

altitude

than

lakes

-LRB-

m

a.s.l.

for

streams

and

mean

altitude

m

a.s.l.

for

lakes

-RRB-

The

catchment

area

of

streams

was

also

smaller

-LRB-

mean

catchment

area

-RRB-

than

that

of

lakes

-LRB-

-RRB-

because

streams

with

catchments

were

not

included

in

the

national

stream

survey

Water

Chemistry

A

single

midstream

or

midlake

-LRB-

approximately

m

depth

-RRB-

water

sample

was

collected

in

autumn

All

water

chemistry

analyses

were

done

by

the

SWEDAC

-LRB-

Swedish

Board

for

Accreditation

and

Conformity

Assessment

-RRB-

certified

laboratory

at

the

Department

of

Environmental

Assessment

Swedish

University

of

Agricultural

Sciences

following

international

-LRB-

ISO

-RRB-

or

European

-LRB-

EN

-RRB-

standards

when

available

ANC

is

a

measure

of

the

buffering

ability

of

lakes

and

streams

against

strong

acid

inputs

This

metric

was

chosen

because

it

includes

humic

substances

and

compensates

for

their

natural

variation

i.e.

the

effect

of

acid

deposition

is

more

pronounced

than

in

other

acidification

indicators

such

as

pH

or

sulfate

concentration

Independent

Variables

During

sampling

sites

were

classified

according

to

-LRB-

aquatic

-RRB-

substratum

particle

size

and

vegetation

six

substrate

classes

-LRB-

ranging

from

silt/clay

to

block

-RRB-

two

classes

of

detritus

-LRB-

coarse

and

fine

-RRB-

and

classes

of

riparian

land

use

and

vegetation

were

classified

using

four

categories

as

and

coverage

-LRB-

Table

-RRB-

For

streams

reaches

-LRB-

sampling

site

-RRB-

of

relatively

homogeneous

substratum

were

chosen

and

the

riparian

vegetation

designated

at

a

zone

on

both

sides

of

the

sampling

site

was

classified

as

above

For

lakes

long

and

wide

littoral

areas

of

relatively

homogeneous

substratum

were

chosen

and

riparian

vegetation

designated

at

a

long

and

wide

shoreline

zone

was

classified

as

above

Table

and

independent

variables

used

in

RDAVariableUnitLakes

-LRB-

N

-RRB-

Streams

-LRB-

N

-RRB-

a

-RRB-

DependentChemistryAcid

neutralizing

capacity

-LRB-

ANC

-RRB-

meq

l

-LRB-

-RRB-

-LRB-

-RRB-

Total

phosphorus

-LRB-

TP

-RRB-

μg

l

-LRB-

-RRB-

-LRB-

-RRB-

Total

organic

carbon

-LRB-

TOC

-RRB-

mg

l

-LRB-

-RRB-

-LRB-

-RRB-

b

-RRB-

IndependentExplained

variabilityGeographic

positionLakesStreamsLatitudeDecimal

degreesAltitudem

a.s.l.

-LRB-

-RRB-

-LRB-

-RRB-

Ecoregions

Dummy

variable

Arctic/alpineDummy

variable

Northern

borealDummy

variable

Southern

borealDummy

variable

BoreonemoralDummy

variable

NemoralDummy

variableRegional

factors

Mean

annual

discharge

-LRB-

Q

-RRB-

-LRB-

-RRB-

Wet

Dry

NHx

deposition

Wet

dry

non-seasalt

Mg

depositionCatchment

land

use/cover

Urban

areas

Forested

areas

Alpine

treeless

land

cover

-LRB-

-RRB-

-LRB-

-RRB-

Glacier

Open

freshwater

bodies

Marsh/mires

Arable

land

-LRB-

-RRB-

-LRB-

-RRB-

Pasture

Alpine

forested

areas

Local

factorsPhysical

properties

of

sample

site

Stream

width

-LRB-

-RRB-

Lake

-LRB-

-RRB-

Water

temperature

°C

Aquatic

substrate

Classified

Boulder

-LRB-

mm

-RRB-

Classified

Block

-LRB-

mm

-RRB-

Classified

Cobble

-LRB-

mm

-RRB-

Classified

Pebble

-LRB-

mm

-RRB-

Classified

Silt/clay

-LRB-

mm

-RRB-

Classified

Coarse

detritusClassified

Floating

leaved

vegetationClassified

-LRB-

-RRB-

Fine

leaved

submerged

vegetationClassified

PeriphytonClassified

Fine

dead

woodClassified

land

use/cover

Deciduous

forestClassified

HeathClassified

Arable

landClassified

AlpineClassified

PastureClassified

MireClassified

Canopy

coverClassified

-RRB-

Chemistry

variables

-LRB-

n

-RRB-

with

mean

values

and

and

percentiles

in

parentheses

b

-RRB-

Environmental

variables

-LRB-

n

-RRB-

divided

into

three

subsets

included

in

the

analyses

Also

shown

are

the

first

five

variables

-LRB-

explained

variability

in

-RRB-

that

could

best

explain

the

variability

in

ANC

TP

and

TOC

using

RDA

and

stepwise

forward

selection

with

the

order

of

selection

shown

in

parentheses

Note

the

middle

boreal

ecoregion

was

insignificant

in

the

Monte

Carlo

permutation

test

and

excluded

from

the

analysis

Six

major

ecoregions

according

to

the

Nordic

Council

of

Ministers

-LRB-

-RRB-

Classified

as

percent

coverage

where

no

coverage

very

low

coverage

medium

coverage

high

coverage

Catchments

were

classified

as

percentage

land

use/vegetation

cover

according

to

the

same

land

use

categories

used

for

riparian

zones

Hence

catchment

land

use/cover

ranged

from

-LRB-

all

classes

-RRB-

to

Thereby

maximum

urban

areas

in

catchments

were

-LRB-

lakes

-RRB-

and

-LRB-

streams

-RRB-

forested

areas

covered

in

both

lake

and

stream

catchments

and

alpine

treeless

cover

was

very

high

with

-LRB-

lakes

-RRB-

and

-LRB-

streams

-RRB-

Glacier

areas

comprised

only

of

total

lake

catchment

areas

but

covered

of

stream

catchments

other

open

freshwater

bodies

in

the

catchment

comprised

of

lake

and

of

stream

catchments

Maximum

marsh

or

mire

land

cover

was

for

lake

and

for

stream

catchments

whereas

pasture

comprised

-LRB-

lakes

-RRB-

and

-LRB-

streams

-RRB-

Maximum

alpine

forested

area

cover

was

higher

in

lake

-LRB-

-RRB-

than

in

stream

catchments

-LRB-

-RRB-

and

maximum

arable

land

covered

of

lake

and

of

stream

catchments

Ecoregion

delineation

of

Sweden

was

obtained

from

the

Nordic

Council

of

Ministers

-LRB-

-RRB-

The

ecoregions

range

from

the

nemoral

region

in

the

south

to

the

arctic/alpine

complex

in

the

north

The

nemoral

region

is

characterized

by

deciduous

forest

mean

annual

temperatures

°C

and

a

relatively

long

growth

period

-LRB-

d

-RRB-

In

contrast

the

arctic/alpine

complex

in

the

north

is

characterized

by

relatively

low

mean

annual

temperatures

-LRB-

°C

-RRB-

and

short

growth

periods

-LRB-

d

-RRB-

Geographic

position

descriptors

-LRB-

longitude

latitude

altitude

-RRB-

ecoregion

delineation

discharge

deposition

variables

land

use/vegetation

cover

descriptors

physical

properties

-LRB-

stream

width

lake

area

-RRB-

as

well

as

aquatic

substrate

descriptors

resulted

in

a

dataset

of

environmental

-LRB-

independent

-RRB-

variables

Statistical

Analyses

First

detrended

correspondence

analyses

were

conducted

to

obtain

the

gradient

length

of

both

the

stream

and

lake

chemistry

data

Because

the

gradient

lengths

were

in

both

cases

SD

the

linear

method

redundancy

analysis

-LRB-

RDA

-RRB-

was

used

to

study

the

effects

of

environmental

variables

representing

geographic

position

and

regional

and

local-scale

factors

on

stream

and

lake

water

chemistry

Moreover

preliminary

analyses

of

water

chemistry

-LRB-

total

phosphorus

concentration

-RRB-

and

catchment

land

use

-LRB-

agriculture

-RRB-

did

not

reveal

any

step

changes

between

the

northern

and

southern

regions

RDA

was

performed

on

a

correlation

matrix

and

is

a

form

of

direct

gradient

analysis

-LRB-

like

Principal

Components

Analysis

-RRB-

In

a

first

step

in

RDA

the

entire

set

of

environmental

variables

was

tested

to

determine

the

significance

of

individual

variables

using

a

Monte

Carlo

permutation

test

-LRB-

with

unrestricted

permutations

-RRB-

Variables

that

were

not

significantly

correlated

with

the

three

water

chemistry

variables

or

that

were

found

to

co-vary

with

other

environmental

variables

-LRB-

i.e.

variance

inflation

factors

-RRB-

were

removed

-LRB-

n

-RRB-

from

the

data

set

Variance

Partitioning

The

remaining

explanatory

variables

were

grouped

into

three

subsets

to

yield

ecologically

interpretable

variance

components

as

follows

-LRB-

-RRB-

variables

describing

the

geographic

position

-LRB-

G

-RRB-

of

the

water

body

-LRB-

-RRB-

regional

scale

-LRB-

R

-RRB-

and

-LRB-

-RRB-

local

scale

-LRB-

L

-RRB-

variables

-LRB-

Fig.

Table

-RRB-

The

variation

partitioning

technique

used

has

been

previously

described

by

Borcard

and

others

-LRB-

-RRB-

and

hence

we

will

not

go

into

detail

here

In

brief

the

procedure

allows

for

the

variance

in

the

explanatory

data

set

to

be

partitioned

into

different

variable

components

through

the

use

of

covariables

-LRB-

i.e.

variables

whose

influence

is

partialled

out

of

the

analysis

-RRB-

Initially

this

technique

was

used

to

partition

variation

in

ecological

data

sets

into

environmental

and

spatial

components

-LRB-

e.g.

Økland

and

Eilertsen

-RRB-

and

has

been

extended

by

incorporating

three

sets

of

explanatory

variables

-LRB-

e.g.

Anderson

and

Gribble

-RRB-

Fig.

diagram

-LRB-

hypothetical

model

-RRB-

showing

the

unique

variation

the

partial

common

variation

and

the

common

variation

of

the

three

subsets

G

R

and

L

representing

the

environmental

dataTable

procedure

of

variation

partitioning

of

water

chemistry

-LRB-

n

-RRB-

in

streams

-LRB-

n

-RRB-

and

lakes

-LRB-

n

-RRB-

explained

by

three

sets

of

environmental

variables

geographic

-LRB-

G

-RRB-

regional

-LRB-

R

-RRB-

and

local

-LRB-

L

-RRB-

in

partial

redundancy

analysis

-LRB-

pRDA

-RRB-

RunEnvironmental

variableCovariableλstreamsλlakes1GRLNone0

GeoR

R&LN

R&LG

RegG

G&LN

G&LR

LocalG

G&RN

G&RL

aλ

computed

eigenvalue

in

RDA

These

numbers

are

used

to

calculate

the

explanatory

power

of

each

component

-LRB-

see

Table

-RRB-

Table

of

explanatory

power

of

each

component

in

the

variance

partitioning

modelVariation

explained

by

factorsAbbreviation

-LRB-

see

Figs.

-RRB-

Calculation

-LRB-

no

of

run

Table

-RRB-

Geographic

Geographic

Regional

Geographic

regional

-LRB-

-RRB-

-LRB-

-RRB-

Total

UnexplainedUXTotV

Total

aAbbreviations

refer

to

the

legend

in

Fig.

The

figures

in

the

calculation

column

refer

to

the

runs

in

Table

of

variation

in

lake

and

stream

water

chemistry

respectively

Column

labels

indicate

the

variation

-LRB-

-RRB-

in

acid

neutralizing

capacity

total

phosphorus

and

total

organic

carbon

accounted

for

by

each

subset

and

their

combinations

The

total

variance

explained

and

the

unique

contributions

of

each

subset

and

their

joint

effects

were

obtained

by

the

following

-LRB-

-RRB-

RDA

was

run

with

all

three

subsets

as

environmental

variables

and

no

covariables

to

obtain

a

measure

of

the

total

variance

-LRB-

-RRB-

partial

RDA

was

run

with

one

of

the

three

subsets

as

environmental

variables

and

no

covariables

and

-LRB-

-RRB-

partial

RDA

was

run

with

one

of

the

three

subsets

as

environmental

variables

constrained

by

the

remaining

two

groups

as

covariables

and

reverse

The

third

step

was

repeated

three

times

and

each

subset

was

treated

as

environmental

variables

constrained

by

the

remaining

subsets

as

covariables

This

procedure

resulted

in

four

runs

of

RDA

for

each

subset

combination

or

a

total

of

runs

of

RDA

were

done

for

the

full

set

of

analyses

for

each

ecosystem

-LRB-

Table

-RRB-

With

three

subsets

of

environmental

data

the

total

variation

of

water

chemistry

was

then

partitioned

into

seven

components

including

covariance

terms

-LRB-

Fig.

Table

-RRB-

The

variation

explained

by

these

subsets

is

subtracted

from

the

total

variation

-LRB-

in

case

of

RDA

-RRB-

to

obtain

the

unexplained

variation

Stepwise

RDA

Stepwise

RDA

with

forward

selection

was

performed

with

all

environmental

variables

as

independent

variables

and

the

water

chemical

variables

-LRB-

ANC

TP

and

TOC

-RRB-

as

dependent

variables

to

determine

the

best

predictors

-LRB-

high

values

-RRB-

In

this

procedure

selected

variables

are

run

as

co-variables

and

subsequent

variables

-LRB-

step

and

on

-RRB-

need

to

explain

a

significant

amount

of

the

residual

variance

-LRB-

tested

by

Monte

Carlo

permutation

-RRB-

Redundancy

analyses

and

partial

RDA

were

done

using

CANOCO

for

Windows

Version

-LRB-

Ter

Braak

and

Smilauer

-RRB-

Prior

to

all

statistical

analyses

-LRB-

RDA

-RRB-

chemical

and

deposition

variables

stream

width

lake

area

and

altitude

were

log-transformed

and

proportional

catchment

land

use/vegetation

cover

variables

were

arcsine

square-root

transformed

to

achieve

normal

distribution

-LRB-

SAS

-RRB-

Results

Variance

decomposition

using

redundancy

analysis

showed

that

all

independent

variables

combined

explained

more

than

of

the

total

variation

in

stream

and

lake

surface

water

chemistry

-LRB-

Table

-RRB-

The

amount

of

variation

explained

was

somewhat

higher

for

streams

-LRB-

λstreams

-RRB-

compared

to

lakes

-LRB-

λlakes

-RRB-

The

largest

proportion

of

variance

was

explained

by

the

interaction

between

all

three

scale

factors

-LRB-

Fig.

-RRB-

Both

stream

and

lake

surface

water

chemistry

was

more

influenced

by

regional-scale

factors

than

either

by

geographic

position

or

local-scale

factors

However

the

unique

variance

explained

by

geographic

position

regional

or

local-scale

variables

was

low

-LRB-

-RRB-

-LRB-

Fig.

-RRB-

For

streams

the

unique

variance

explained

by

regional-level

variables

-LRB-

-RRB-

was

substantially

higher

than

that

explained

by

local-scale

variables

-LRB-

-RRB-

or

geographic

position

-LRB-

-RRB-

Similarly

for

lakes

the

unique

variance

explained

by

regional-scale

variables

-LRB-

-RRB-

was

higher

than

that

explained

by

local-scale

variables

-LRB-

-RRB-

and

that

explained

by

geographic

position

-LRB-

-RRB-

Geographic

position

and

regional-scale

factors

-LRB-

G&R

-RRB-

were

better

predictors

of

surface

water

chemistry

than

regional

and

local

-LRB-

R&L

-RRB-

or

geographic

position

and

local

-LRB-

G&L

-RRB-

factors

The

strongest

interaction

was

found

between

geographic

position

and

regional-scale

variables

For

streams

the

interaction

between

geographic

position

and

regional-scale

variables

-LRB-

G&R

-RRB-

explained

of

the

variance

in

stream

chemistry

For

lakes

the

G&R

interaction

explained

of

the

variance

in

lake

chemistry

The

relation

between

geographic

position

and

local-scale

variables

was

much

weaker

in

particular

for

streams

The

G&L

interaction

explained

of

the

variance

in

stream

and

of

the

variance

in

lake

chemistry

The

amount

of

variance

explained

by

the

interaction

between

regional

and

local-scale

variables

was

for

streams

and

for

lakes

Ordination

of

stream

chemistry

and

environmental

variables

showed

that

the

primary

RDA

axis

represented

a

latitudinal

gradient

-LRB-

Fig.

-RRB-

Eigenvalues

for

the

first

and

second

RDA

axes

were

and

respectively

Streams

situated

in

alpine

forested

or

alpine

treeless

catchments

were

placed

on

the

right

side

of

the

ordination

whereas

lowland

streams

situated

in

pasture

and

arable

landscape

in

the

south

-LRB-

e.g.

boreonemoral

ecoregion

-RRB-

with

high

wet

and

dry

deposition

of

NHx

-LRB-

WDNHx

-RRB-

were

placed

to

left

ANC

and

TP

were

strongly

associated

with

pasture

and

arable

land

use

and

high

WDNHx

TOC

concentration

was

positively

correlated

with

forested

catchments

and

habitats

with

high

amounts

of

coarse

detrital

matter

and

negatively

correlated

with

mean

annual

discharge

-LRB-

Q

-RRB-

and

like

lake-TOC

unrelated

to

arctic/mountainous

characteristics

The

second

RDA

axis

was

related

to

glacial

land

cover

and

whether

the

stream

was

located

in

the

southern

boreal

ecoregion

-LRB-

-RRB-

Fig.

biplot

of

environmental

factors

and

ANC

TP

and

TOC

of

-LRB-

A

-RRB-

streams

and

-LRB-

B

-RRB-

lakes

riparian

pasture

cover

floating

leaved

vegetation

riparian

deciduous

forest

cover

riparian

alpine

cover

riparian

heath

cover

boulder

block

pebble

periphyton

cobble

fine

leaved

submerged

vegetation

water

temperature

wet

dry

non

sea

salt

Mg

deposition

riparian

arable

cover

-LRB-

streams

-RRB-

alpine

forest

-LRB-

lakes

-RRB-

arctic/alpine

ecoregion

northern

boreal

ecoregion

southern

boreal

ecoregion

boreonemoral

ecoregion

nemoral

ecoregion

WDNHx

Wet

Dry

NHx

deposition

coarse

detritus

fine

detritus

FWD

fine

wooded

debris

-LRB-

substrate

-RRB-

Q

annual

mean

discharge

All

three

lake

chemistry

variables

were

negatively

correlated

with

the

first

RDA

axis

-LRB-

Fig.

-RRB-

Eigenvalues

for

the

first

and

second

RDA

axes

were

and

respectively

The

first

RDA

axis

represented

gradients

in

latitude

and

catchment/ecoregion

Lakes

situated

in

alpine

treeless

catchments

at

high

latitude

and

altitude

were

situated

to

the

right

whereas

sites

situated

in

forested

catchments

or

catchments

with

pasture

or

arable

land

use

were

placed

to

the

left

in

the

ordination

Both

ANC

and

TP

were

positively

correlated

with

the

amount

of

catchment

classified

as

pasture

and

arable

Moreover

many

of

these

lakes

were

situated

in

the

boreonemoral

ecoregion

-LRB-

-RRB-

with

high

wet

and

dry

deposition

of

NHx

-LRB-

WDNHx

-RRB-

In

contrast

lake

water

total

organic

carbon

-LRB-

TOC

-RRB-

was

associated

with

forested

catchments

with

high

amounts

of

coarse

detrital

matter

-LRB-

-RRB-

The

second

RDA

axis

represented

gradients

in

the

amount

of

catchment

classified

as

mire

-LRB-

or

bog

-RRB-

in

particular

the

importance

of

local

factors

such

as

substrate

type

water

temperature

and

riparian

mire

and

fine

wooded

debris

-LRB-

FWD

-RRB-

Stepwise

RDA

of

stream

and

lake

chemistry

as

dependent

variables

and

the

single

variables

of

geographic

position

and

regional

and

local

environmental

variables

showed

that

all

variables

accounted

for

-LRB-

lakes

-RRB-

and

-LRB-

streams

-RRB-

of

the

total

variance

The

amount

of

alpine

treeless

areas

in

the

catchment

was

the

single

most

important

predictor

of

lake

water

chemistry

-LRB-

explaining

of

the

explained

variance

-RRB-

The

second

variable

selected

was

altitude

-LRB-

i.e.

the

amount

of

residual

variance

explained

after

running

the

first

variable

selected

alpine

treeless

areas

in

the

catchment

as

a

covariable

-RRB-

followed

by

the

amount

of

arable

land

in

the

catchment

-LRB-

-RRB-

percent

coverage

of

floating

leaved

vegetation

in

the

littoral

-LRB-

-RRB-

and

lake

surface

area

-LRB-

-RRB-

For

streams

the

five

best

single

predictors

of

water

chemistry

were

the

amount

of

arable

land

in

the

catchment

-LRB-

-RRB-

followed

by

the

amount

of

alpine

treeless

areas

in

the

catchment

altitude

-LRB-

-RRB-

stream

width

-LRB-

-RRB-

and

mean

annual

discharge

Q

-LRB-

-RRB-

Discussion

Lakes

and

streams

are

often

perceived

as

structurally

and

functionally

different

ecosystems

and

indeed

major

dissimilarities

do

exist

regarding

differences

in

water

movement

For

example

streams

are

characterized

by

unidirectional

turbulent

flow

and

high

flushing

rates

whereas

lake

chemistry

is

more

affected

by

the

timing

and

frequency

of

turnover

events

-LRB-

e.g.

polymictic

to

dimictic

mixing

in

boreal

lakes

-RRB-

Furthermore

obvious

differences

in

nutrient

cycling

and

recycling

are

expected

due

to

the

relative

importance

of

benthic

vs.

pelagic

productivity

-LRB-

Essington

and

Carpenter

-RRB-

The

surface

water

chemistry

of

streams

is

considered

to

be

tightly

linked

to

catchment

characteristics

with

geomorphology

determining

soil

type

and

availability

of

ions

through

weathering

-LRB-

e.g.

Allan

-RRB-

Lakes

on

the

other

hand

have

until

recently

been

perceived

as

separate

entities

more

isolated

than

streams

from

the

surrounding

landscape

-LRB-

e.g.

Kratz

and

others

Soranno

and

others

Riera

and

others

Quinlan

and

others

-RRB-

Clearly

terrestrial

aquatic

linkages

are

important

predictors

of

surface

water

chemistry

for

both

streams

and

lakes

but

the

strength

of

this

interaction

should

vary

with

geologic

and

hydrologic

settings

Thus

the

major

difference

between

the

River

Continuum

Concept

-LRB-

Vannote

and

others

-RRB-

and

the

concept

of

lake

landscape

position

-LRB-

Kratz

and

others

-RRB-

probably

lies

in

large

differences

in

water

residence

times

between

streams

and

lakes

Given

the

differences

in

water

movement

in

particular

flushing

rates

one

might

expect

that

streams

and

lakes

differ

in

the

external

drivers

that

affect

water

chemistry

Surprisingly

our

findings

do

not

support

this

conjecture

the

major

part

of

the

variation

in

water

chemistry

in

both

streams

and

lakes

was

explained

by

all

components

-LRB-

i.e.

geographic

position

as

well

as

regional

and

local-scale

variables

-RRB-

followed

by

the

combination

-LRB-

or

interaction

-RRB-

of

geographic

position

and

regional-scale

factors

These

findings

support

the

premise

that

variability

in

surface

water

chemistry

is

driven

by

interactions

between

geographic

position

and

regional

factors

Our

finding

however

that

regional

factors

alone

accounted

for

a

large

part

of

variation

in

ANC

TP

and

TOC

indicates

the

pivotal

role

that

catchment

land

use/cover

plays

in

determining

surface

water

chemistry

The

finding

that

the

surface

water

chemistry

of

streams

and

lakes

could

be

partly

predicted

by

regional-scale

variables

in

particular

catchment

land

use

-LRB-

e.g.

arable

-RRB-

agrees

with

the

findings

of

several

earlier

studies

-LRB-

e.g.

Schonter

and

Novotny

Allan

and

others

-RRB-

Johnson

and

others

-LRB-

-RRB-

showed

for

example

that

urban

land

use

and

rowcrop

agriculture

were

important

factors

in

explaining

variability

in

stream

water

chemistry

Similarly

Hunsaker

and

Levine

-LRB-

-RRB-

were

able

to

explain

more

than

of

the

variance

in

total

nitrogen

using

landscape

metrics

In

our

study

we

were

interested

in

analyzing

natural

variability

so

we

removed

sites

judged

to

be

affected

by

agriculture

-LRB-

i.e.

sites

with

of

their

catchments

classified

as

arable

were

not

included

-RRB-

Hence

our

finding

that

the

amount

of

arable

land

in

a

catchment

explained

nearly

of

the

variability

in

stream

water

chemistry

was

not

expected

using

these

data

and

implies

that

even

a

small-scale

agricultural

land

use

within

a

catchment

may

affect

phosphorus

concentration

The

importance

of

a

riparian

zone

has

been

proposed

to

be

less

important

in

explaining

among-site

differences

in

heavily

managed

catchments

-LRB-

Omernik

and

others

-RRB-

These

authors

suggested

that

the

total

amount

of

agriculture

and

forest

in

a

catchment

are

more

important

predictors

of

water

chemistry

than

the

vegetation

composition

of

the

riparian

zone

Our

finding

that

less

than

of

the

variation

in

surface

water

chemistry

was

explained

by

local

factors

alone

-LRB-

such

as

the

presence

of

a

riparian

zone

-RRB-

supports

this

conclusion

Furthermore

in

contrast

to

regional-scale

factors

only

a

small

amount

of

the

variation

was

hidden

in

joint

effects

or

interaction

terms

between

regional

and

local

-LRB-

-RRB-

and

between

geographic

and

local

-LRB-

-RRB-

variables

Hence

other

factors

not

considered

here

such

as

where

the

land

use

is

located

in

the

catchment

and

in

relation

to

the

water

body

presumably

need

to

be

considered

Indeed

studies

of

small

scale

or

local

factors

have

been

shown

to

be

important

in

modifying

larger

scale

effects

e.g.

several

studies

have

shown

the

ameliorative

influence

of

a

vegetated

riparian

zone

-LRB-

e.g.

Cooper

Osborne

and

Kovacic

-RRB-

Redundancy

analysis

showed

that

the

variability

in

both

stream

and

lake

water

chemistry

was

explained

by

the

similar

regional

and

local-scale

variables

For

example

as

discussed

above

the

proportion

of

arable

land

use

in

the

catchments

was

a

strong

predictor

of

stream

water

chemistry

-LRB-

-RRB-

followed

by

alpine

treeless

land

cover

-LRB-

-RRB-

For

lake

water

chemistry

the

amount

of

alpine

treeless

land

cover

was

a

good

predictor

-LRB-

-RRB-

followed

by

altitude

-LRB-

-RRB-

and

catchment

arable

land

use

-LRB-

-RRB-

Clearly

several

of

the

variables

in

different

local

and

regional

components

covary

For

instance

the

amount

of

alpine

treeless

land

cover

in

the

region/catchment

and

stream

width

are

presumably

correlated

with

altitude

However

as

demonstrated

here

regional

factors

were

better

predictors

of

stream

and

lake

water

chemistry

and

thus

contribute

largely

to

the

explanatory

power

of

the

covariation

components

All

three

water

chemistry

variables

were

strongly

correlated

with

variables

representing

a

latitudinal

gradient

for

example

sites

in

the

south

are

more

well

buffered

and

nutrient

rich

compared

to

sites

in

the

north

This

distinct

north

south

gradient

in

water

chemistry

was

not

unexpected

but

can

be

easily

explained

by

major

landscape-level

differences

between

the

northern

and

southern

parts

of

the

country

For

instance

the

legacy

of

historical

processes

on

present-day

distribution

patterns

of

vegetation

is

clearly

visible

in

Sweden

At

approximately

N

latitude

a

marked

difference

in

vegetation

occurs

and

this

ecotone

-LRB-

limes

norrlandicus

-RRB-

basically

delineates

the

transition

of

broad-leaved

-LRB-

e.g.

English

oak

and

elm

-RRB-

and

coniferous

mixed

-LRB-

e.g.

Scots

pine

and

spruce

-RRB-

forests

in

the

south

from

the

boreal

pine

and

spruce

forests

in

the

north

-LRB-

Nordic

Council

of

Ministers

-RRB-

In

addition

the

limes

norrlandicus

ecotone

is

also

correlated

with

the

highest

postglacial

coastline

or

the

highest

level

the

sea

reached

after

the

last

ice

age

and

below

which

fluvial

sediments

have

been

deposited

Hence

these

two

landscape-scale

discontinuities

in

vegetation

and

soil

type

can

have

profound

importance

for

surface

water

chemistry

Finally

broad-scale

climatic

differences

also

exist

between

the

northern

and

southern

parts

of

the

country

which

are

manifested

in

differences

in

discharge

regimes

For

example

streams

in

the

south

are

dominated

by

autumn

and

winter

rains

whereas

streams

in

the

north

are

dominated

by

snowmelt-driven

peaks

in

runoff

during

spring

-LRB-

Anonymous

-RRB-

Given

the

profound

differences

in

climate

geomorphology

and

vegetation

between

the

northern

and

southern

parts

of

the

country

we

anticipated

discernible

differences

in

the

factors

driving

surface

water

chemistry

Indeed

the

finding

that

landscape

position

is

important

in

explaining

variability

in

surface

water

chemistry

has

been

shown

in

previous

studies

-LRB-

e.g.

Johnson

-RRB-

and

supports

the

use

of

ecoregions

to

partition

natural

variability

Ecoregions

have

been

suggested

as

appropriate

ecological

units

for

classification

because

they

are

generally

perceived

as

being

relatively

homogeneous

having

similar

climate

geology

and

other

environmental

characteristics

-LRB-

Wright

and

others

-RRB-

and

hence

are

considered

as

relatively

good

predictors

of

spatial

patterns

of

surface

water

chemistry

-LRB-

e.g.

Landers

and

others

Larsen

and

others

-RRB-

However

to

be

an

appropriate

classification

tool

an

ecoregion

should

minimize

within

and

maximize

among

region

variability

and

ideally

knowledge

of

how

both

natural

and

human-induced

variability

affect

ecosystem

processes

should

be

known

in

order

to

fully

assess

the

adequacy

of

ecoregions

for

partitioning

natural

variability

For

example

it

is

well

known

that

catchment

management

practices

can

have

profound

effects

on

surface

water

quality

For

instance

whether

a

catchment

is

forested

-LRB-

promotes

infiltration

high

transpiration

and

reduces

runoff

-RRB-

clear-cut

-LRB-

results

in

lower

infiltration

and

transpiration

and

increased

runoff

-RRB-

or

reforested

may

singly

or

in

concert

affect

the

water

chemistry

of

aquatic

ecosystems

-LRB-

Foster

and

others

-RRB-

The

results

of

this

study

showed

the

importance

of

interactions

between

variables

acting

on

multiple

spatial

scales

on

among-lake

and

stream

water

chemistry

Somewhat

surprising

was

the

finding

that

the

major

drivers

were

similar

between

lakes

and

streams

despite

the

obvious

differences

in

ecosystem

types

For

instance

in

streams

nutrients

are

spiralling

downstream

whereas

in

lakes

nutrient

retention

is

relatively

high

depending

on

lake

size

and

morphometry

Obviously

the

chemical

composition

of

a

surface

water

body

is

a

product

of

a

series

of

mechanisms

and

processes

acting

along

a

scale

continuum

i.e.

from

broad

-LRB-

geographic

-RRB-

to

small

-LRB-

local

-RRB-

scales

Moreover

the

environmental

characteristics

of

a

specific

habitat

are

not

random

but

are

considered

to

be

controlled

by

macro-scale

geomorphic

patterns

-LRB-

Frissell

and

others

-RRB-

Building

on

this

premise

a

conceptual

framework

has

been

developed

where

the

aquatic

-LRB-

stream

-RRB-

organism

assemblage

at

a

site

can

be

seen

as

a

product

of

a

series

of

filters

-LRB-

e.g.

from

continental

to

microhabitat

-RRB-

with

each

species

occurring

at

a

site

having

passed

through

these

filters

-LRB-

e.g.

Tonn

and

others

Poff

-RRB-

Similarly

surface

water

chemistry

of

a

particular

site

is

also

constrained

to

some

extent

by

a

number

of

environmental

filters

Small-scale

systems

develop

within

the

constraints

set

by

broad-scale

systems

of

which

they

are

part

and

likewise

local-scale

processes

and

conditions

are

generated

by

broad-scale

geographic

patterns

and

conditions

The

idiosyncrasies

of

both

ecosystems

might

be

suppressed

by

the

effects

of

large-scale

factors

Geographic

position

functions

as

a

template

determining

both

regional

and

local

factors

At

the

catchment

level

geology

controls

soil

type

weathering

determines

ion

concentrations

-LRB-

and

buffering

capacity

-RRB-

and

climate

determines

vegetation

type

-LRB-

and

land

use

-RRB-

However

changes

in

land

use

-LRB-

e.g.

afforestation

of

arable

to

urban

-RRB-

and/or

vegetation

cover

-LRB-

e.g.

deforestation

or

afforestation

of

arable

land

-RRB-

are

sources

of

catchment

variation

that

might

generate

high

amounts

of

variability

or

noise

making

it

difficult

to

tease

apart

components

of

natural

variation

from

the

effects

of

anthropogenic

impact

on

surface

water

chemistry

This

inherent

catchment

variation

is

probably

responsible

for

the

large

amount

of

variation

explained

by

regional

factors

which

may

hide

the

effects

of

individual

features

of

lakes

and

streams

appearing

similar

in

their

response

to

environmental

factors

and

influences

However

another

caveat

in

addressing

issues

of

scale-effects

is

that

the

spatial

resolution

at

which

observations

are

made

can

confound

interpretation

of

scale-related

processes

-LRB-

e.g.

Minshall

Manel

and

others

-RRB-

For

example

environmental

variables

such

as

nutrient

concentrations

and

hydrology

are

more

influenced

by

regional-scale

processes

whereas

other

variables

such

as

in-stream

vegetation

cover

are

more

influenced

by

local

control

mechanisms

-LRB-

e.g.

Allan

and

others

-RRB-

Our

findings

of

the

importance

of

interactions

between

geographic

position

and

regional

and

local-scale

variables

support

this

conclusion

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B I O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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anc
total phosphorus
toc
partial rda
variation partitioning
geographic position