This article was downloaded by: [McMaster University]On: 05 November 2014, At: 09:04Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

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How Long Should a Long-Term RiverStudy Be?Jill F. Thomas aa National Council for Air and Stream Improvement , P.O. Box 1259,Anacortes, Washington, 98221, USAPublished online: 11 Jan 2011.

To cite this article: Jill F. Thomas (2005) How Long Should a Long-Term River Study Be?, Journal ofFreshwater Ecology, 20:2, 367-379, DOI: 10.1080/02705060.2005.9664977

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How Long Should a Long-Term River Study Be? Jill F. Thomasa

National Council for Air and Stream Improvement P.O. Box 1259

Anacortes, Washington 98221 USA

ABSTRACT I developed a quantitative method to determine appropriate time lengths for long-

term river studies. A literature review identified three critical components for evaluating length of long-term studies - the length of relevant time spans, sufficient statistical power to see an effect, and the variability of the system. The first component (relevant time spans) does not require data and, therefore, can provide an a p r i o r i estimation of the required study length. The remaining components can be used with historical data or with preliminary data to provide a detailed characterization of study length requirements. The method was tested using fish community and water quality data from a long-term multi-river study and successfully characterized the unique time requirements for each river.

INTRODUCTION Temporal ecological studies can range in time from less than a year to open-ended

programs with no set endpoints. In today's world of accountability and limited resources, researchers need to answer the question, "How long is long enough?" in order to balance data requirements with economic and time resources. At what point has the study accumulated adequate observations to answer the relevant questions with sufficient power?

I developed a "weight of evidence" method to determine the minimum time requirement for a long-term river study within the framework of the study question, components, and preliminary data analysis. The method was developed from the results of a literature review that revealed three critical components - relevant time spans, sufficient experimental power, and background variability. The literature review is here summarized and the method is applied to an experimental design and tested with data from an on-going long-term receiving water study.

The parameters for the literature review were that studies had to be specific to rivers, more than one year in length, and cover biotic andlor abiotic components. The search produced 28 papers and included studies of persistence/stability/variability, temporal recovery, cumulative effects, environmental drivers, long-term monitoring programs, and concept papers. The biological parameters included one or more of the following: periphyton, macrophytes, benthic macroinvertebrates, and fish.

Two thirds of the studies (14 of 21) used historical data sources as a determining factor in setting the study length (Bryant 1985, Poff and Ward 1989, Bradt et al. 1999, Onorato et al. 2000, Lowell et al. 2000, Schubauer-Berigan et al. 2000, Haag and Westrich 2002, Jennings and Jamagin 2002, Kovacs et al. 2002, Lowell and Culp 2002, Metzling et al. 2002, Sosiak 2002, Woodward et al. 2002, Papanicolaou et al. 2003). Additionally, nearly half of the studies related the time of the study to specific changes in the watershed or river conditions from point sources, non-point sources, or events (Bryant 1985, Sullivan 1985, Onorato et al. 2000, Klug and Cottingham 2001, Minshall et al. 2001a, Minshall et al. 2001b, Minshall et al. 2001c, Kovacs et al. 2002, Jennings and Jarnagin 2002, Sosiak 2002); in some cases these were before and after designs that essentially covered many years. There were few studies not using historical data and not

'Current address: Weyerhaeuser, Mail Stop WTC 2G2,32901 Weyerhaeuser Way S, Federal Way, WA 98001 USA; Email: jill.thomas2@weyerhaeuser.com

Journal of Freshwater Ecology, Volume 20. Number 2 -June 2005

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correlated to specific changes (Gilliom et al. 1995, Fmget et a]. 2001, Scarsbrook 2002). However, the use of available data and correlation to watershed or river events were not the only considerations cited in the literature as relevant. Several authors discussed the importance of identifying relevant time spans (e.g., life spans of biological endpoints or time spans for recovery of drought effects) and including these when considering the study length (Minshall 1988, Cooper et al. 1998, Philippi et al. 1998, Scarsbrook 2002). Three papers discussed the necessity of designing the study so that there was sufficient experimental power for detecting effects should effects be present (Philippi et. al. 1998, Urquhart et al. 1998, USEPA 2002). Five of the papers addressed the aspect of the dynamic nature of these systems and therefore the need to include the effects of natural or background variability (Poff and Ward 1989, Scarsbrook 2002, USEPA 2002, Kratz et al. 2003, Papanicolaou et al. 2003).

Of the five identified components, relevant time spans, sufficient statistical power, and background variability are broadly important to all classes of long-term studies. Additionally, historical data or pilot study information is needed to calculate effect size and sample size. Thus, these components were incorporated into a "weight of evidence" approach to determine how long a study should continue (Table 1). Analysis of each of these components would provide a minimum required time in which to see an effect if an effect existed for each endpoint, resulting in a range of times within which the study length should fall. Determination of the final time frame would rely on investigators interpreting the time ranges in terms of their study question, experimental design, and practical issues.

Table 1. Critical components for determining study length with steps for calculations.

Critical component Element(s) Method for determination

I. Relevant time spans Time spans for 1. List study components and their relevant biotic relevant time spans (e.g., study and abiotic component = large-bodied fish; time components of span = average lifespan for the large- the study bodied fish).

2. Determine length of all relevant time spans (from reference or literature search).

11. Statistical power Power, a, 1. Preset power and a to levels effect size, and acceptable for the study. sample size 2. Calculate effect size from historic or

pilot study data. 3. Use power, a and effect size to

determine necessary sample sizelsite.

4. Sample sizelsampling schedule determines minimum required time.

111. Background variability River 1. Calculate signa1:noise ratio discharge as a

a. Signal = mean change in surrogate for variability discharge

b. Noise = standard deviation.

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METHODS AND MATERIALS In 1998, a 10 to 20 year long-term receiving water study was begun at four

effluent receiving waters in the United States - Codorus Creek in Pennsylvania, the Leaf River in Mississippi, the McKenzie River in Oregon and the Willamette River in Oregon (Hall et al. 1999). Codorus Creek is a wadeable stream flowing northeast through a heavily agricultural and urban area in the Northern Piedmont ecoregion in south central Pennsylvania. There are six sites sampled in the study area covering 44.4 km along the West Branch and mainstem of Codorus Creek. The Leaf River is a moderate size, unregulated fast flowing river that is located on a broad undeveloped flat plain in the Southeastern Plains ecoregion of southern Mississippi. There are six sampling sites covering 5 1.2 km of the lower Leaf River. The McKenzie River is a large, fast flowing river moving in the forested valleys of the western Cascades and the Willarnette Valley ecoregions in west central Oregon. There are five sampling locations in the study area covering 25.6 km along the lower McKenzie River. The Willamette River is a large, fast river flowing north through a wide agricultural valley in the Willamette Valley ecoregion of western Oregon. There are six sampling locations covering 42.4 krn extending along the upper Willamette River. Complete descriptions of the rivers and their individual sampling locations are in Thomas and Hall (2004).

Fish samples were collected quarterly from September 1998 to June 2001 and semi-annually from September 2001 through September 2003 at Codorus Creek, the McKenzie River, and the Willamette River. The Leaf River was sampled annually in October from 1999 through 2003. Fish in Codorus Creek were captured using backpack electrofishing with three runs of approximately 600 s in riffle, run and pool areas. The McKenzie River and the Willamette River were sampled using nearshore boat electrofishing, with two runs of approximately 250 m in length, and nearshore backpack electrofishing of three runs of approximately 600 s each. The Leaf River was sampled using nearshore boat electrofishing of one run of approximately 30 min and nearshore backpack electrofishing of three runs of approximately 600 s each. All captured fish were identified to the lowest practicable taxonomic level (Table 2). Runs were normalized to CPUE (catch per unit effort) (600 s average for the backpack, 500 m total for the Oregon boat electrofishing, and 30 min for the Leaf boat electrofishing).

Grab water samples were collected monthly from 1997 to 2003 for Codorus Creek, the McKenzie River, and the Willamette River, and from 1999 through 2003 for the Leaf River. Analysis included temperature and pH (recorded in the field) and color (Pt-Co, determined on unpreserved samples by HACH Method 10068). Water quality data is summarized in Table 3.

Component I : Relevant tinre spans. Time spans were chosen to reflect the biotic endpoints of the study and the abiotic regional factors that occurred within the 10 to 20 year temporal framework of the study question. The biotic endpoints were fish community and condition; the relevant time spans chosen were the lifespans of representative species of large-bodied and small-bodied fish for each river (www.fishbase.org, Ross 2001, Steiner 2000). The abiotic factors included flow cycles, drought, and flood; the time spans for these were cycle time or recovery time. Potential human disturbances (e.g., newly permitted discharges or accidental events or disturbances) were not considered sufficiently predictable to be included.

Component 2: Sufficient statisticalpower. To determine the length of study based on sufficient statistical power, four components were needed: power, a (type I error), effect size, and sample size. Two of the components, acceptable power and a levels, were preset for this study at 80% and 0.10, respectively. These levels were chosen as

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Tab

le 2

. Sum

mar

y of

fis

h ca

tch

data

fro

m C

odor

us C

reek

(C),

Lea

f Riv

er (

L),

McK

enzi

e R

iver

(M

), a

nd W

illam

ette

Riv

er (

W).

C

aptu

re m

etho

ds w

ere

back

pack

ele

ctro

fish

ing

(1) a

nd b

oat e

lect

rofi

shin

g (2

).

Tax

on

Com

mon

nam

e R

iver

M

etho

d T

axon

C

omm

on n

ame

Riv

er

Met

hod

Aci

pens

er t

rans

mon

tanu

s w

hite

stu

rgeo

n W

2

C. g

ulos

us

riff

le s

culp

in

M, W

I

Acr

oche

ilus

alu

tace

us

chis

elm

outh

W

1,

2

C. p

erpl

exus

re

ticul

ate

scul

pin

M, W

1

,2

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sa a

laba

mae

al

abam

a sh

ad

L 2

C. r

hoth

eus

torr

ent

scul

pin

M, W

1

,2

A.

chry

soch

lori

s sk

ipja

ck h

erri

ng

L 2

Cyc

lept

us e

long

atus

bl

ue s

ucke

r L

2 A

mbl

opli

tes

ario

mm

us

shad

ow b

ass

L 2

Cyp

rine

lla

anal

osta

na

satin

fin

shin

er

C

1 A

. ru

pest

ris

rock

bas

s C

1

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pilo

pter

a sp

otfi

n sh

iner

C

1

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eiur

us n

atal

is

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w b

ullh

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c, w

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,2

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enus

ta

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ktai

l sh

iner

L

1,2

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mia

cal

va

bow

fin

L 2

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rini

dae

unid

entif

ied

min

now

C

, L, M

, 1

,2

W

Am

moc

rypt

a be

ani

nake

d sa

nd d

arte

r L

1 C

ypri

nida

e un

iden

tifie

d da

ce

M, W

1

,2

Ang

uill

a ro

stra

ta

amer

ican

eel

L

2 C

ypri

nus

carp

io

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mon

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p c

, w

1,2

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phre

dode

rus

saya

nus

pira

te p

erch

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oros

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dian

um

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A

plod

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us g

runn

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fr

eshw

ater

dru

m

0

L 2

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cym

ba b

lrcc

a~a

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w

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post

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ntra

l st

oner

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r C

1

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x ni

ger

chai

n pi

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el

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piod

es c

ypri

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ker

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eost

oma

blen

nioi

des

gree

nsid

e da

rter

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1

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elif

er

high

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carp

suck

er

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2

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istr

io

harl

equi

n da

rter

L

1 C

atos

tom

us c

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ni

whi

te s

ucke

r C

1

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ted

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er

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roch

eilu

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ker

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2

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aeum

sp

eckl

ed d

arte

r L

2 C

. pla

tyrh

ynch

us

mou

ntai

n su

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, W

1, 2

E

. zon

ale

band

ed d

arte

r C

1

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trar

chid

ae

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entif

ied

sunf

ish

c, L

1, 2

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xogl

ossu

m m

axil

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now

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I

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nost

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uloi

des

rosy

side

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e C

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,2

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per

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kly

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ambu

sia

afi

nis

m

osqu

itofi

sh

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aird

i m

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d sc

ulpi

n M

, W

1, 2

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aste

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eus

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eatu

s th

rees

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stic

kleb

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1 C

. bel

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i pa

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scu

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M

, W

1,2

H

iodo

n te

rgis

us

moo

neye

L

2 C

. cog

natu

s sl

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y m

inno

w

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no

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og s

ucke

r L

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(con

tinue

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Tab

le 2

(co

ntin

ued)

Ic

talu

rus f

urca

tus

blue

cat

fish

L

2 N

. vol

ucel

lus

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ic s

hine

r L

1,2

I.

pun

ctat

us

chan

nel

catf

ish

L 1,

2

N.

win

chel

li

clea

r ch

ub

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Icti

obus

bub

alus

sm

allm

outh

buf

falo

L

2 O

ncor

hync

hus

clar

ki

cutth

roat

trou

t M, W

2

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petr

a ri

char

dson

i w

este

rn b

rook

lam

prey

M

, W

1,2

0

. kis

utch

co

ho s

alm

on

M, W

2

Lep

isos

teus

ocu

latu

s sp

otte

d ga

r L

2

0. m

ykis

s ra

inbo

w tr

out

M, W

1

,2

L. o

sseu

s lo

ngno

se g

ar

L

2 0

. myk

iss

stee

lhea

d W

2

Lep

omis

aur

itus

re

dbre

ast

sunf

ish

C

1 0

. tsh

awyt

scha

ch

inoo

k sa

lmon

M

, W

1,2

L

. cya

nell

us

gree

n su

nfis

h C

1

Per

cina

len

ticu

la

frec

kled

dar

ter

L 2

L. g

ibbo

sus

pum

pkin

seed

c,

w

1,2

P

. ni

grof

asci

ata

blac

kban

ded

dart

er

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,2

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ulos

us

war

mou

th

L 2

P. p

elta

ta

shie

ld d

arte

r C

1

L, m

acro

chir

us

blue

gill

c, L

, w

1, 2

P

. sci

era

dusk

y da

rter

L

1,2

L.

meg

alot

is

long

ear

sunf

ish

L 1,

2

P.

vigi

l sa

ddle

back

dar

ter

L

2 L

. mic

rolo

phus

re

dear

sun

fish

c

, L

1, 2

P

erco

psis

tra

nsm

onta

na

sand

rol

ler

M, W

2

Lux

ilus

cor

nutu

s co

mm

on s

hine

r C

1

Petr

omyz

ontid

ae

unid

entif

ied

lam

prey

M

, W

l,2

o

Mac

rhyb

opsi

s st

orer

iana

si

lver

chu

b -4

L 2

Pim

epha

les

nota

tus

blun

tnos

e m

inno

w

C

1 M

icro

pter

us d

olom

ieu

smal

lmou

th b

ass

c, w

1,

2

P.

vigi

lax

bullh

ead

min

now

L

1,2

M

. pun

ctul

atus

sp

otte

d ba

ss

L

1, 2

P

omox

is a

nnul

aris

w

hite

cra

ppie

L,

w

2 M

. sal

moi

des

larg

emou

th b

ass

c, L

, w

1, 2

P

. ni

grom

acul

atus

bl

ack

crap

pie

L, w

2

Min

ytre

ma

mel

anop

s sp

otte

d su

cker

L

2 P

roso

pium

wil

liam

soni

m

ount

ain

whi

tefi

sh

M, W

2

Mor

one

saxa

tili

s st

ripe

d ba

ss

L 2

Pty

choc

heil

us

nort

hern

pik

e m

inno

w

M, W

1

,2

oreg

onen

sis

Mox

osto

ma

cari

natu

m

rive

r re

dhor

se

L 2

Pyl

odic

tis

oliv

aris

fl

athe

ad c

atfi

sh

L 2

M. p

oeci

luru

m

blac

ktai

l re

dhor

se

L 2

Rhi

nich

thys

atr

atul

us

blac

knos

e da

ce

C, M

, W

1 M

ugil

cep

halu

s st

ripe

d m

ulle

t L

2 R

. cat

arac

tae

long

nose

dac

e C

,M,W

1

,2

Myl

oche

ilus

cau

rinu

s pe

amou

th

M, W

1

,2

R. f

alca

tus

leop

ard

dace

M

, W

l,2

N

otem

igon

us c

tyso

leuc

as

gold

en s

hine

r C

1

R. o

scul

us

spec

kled

dac

e M

, W

1,2

N

otro

pis

athe

rino

ides

em

eral

d sh

iner

L

2 R

icha

rdso

nius

bal

teat

us

reds

ide

shin

er

M, W

1

,2

N.

huds

oniu

s sp

otta

il sh

iner

C

1

Salm

o tr

utta

br

own

trou

t C

1

N. l

ongi

rost

ris

long

nose

shi

ner

L 2

Salm

onid

ae

unid

entif

i ed

salm

onid

M

1

,2

N. p

rocn

e sw

allo

wta

il sh

iner

C

1

Sem

otil

us a

trom

acul

atus

cr

eek

chub

C

1

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exan

us

wee

d Sh

iner

L

1

,2

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nect

es m

acul

atus

ho

gcho

ker

L 1

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sufficient to allow reasonable detection with an acceptable error rate at an economically supportable level of sampling; other studies may find different levels of power and a more appropriate. The two other components needed to be calculated from existing data. This study had data available from the first five years; however, should this not be the case, historic data could be used or pilot studies can be conducted. Effect size is a method to quantify the potential differences between control and experimental conditions; this was calculated for each river for both the fish and water quality parameters. Small effect sizes, although detectable, may not be ecologically relevant. In addition, they require extremely large sample sizes, which are not usually feasible in field studies. Therefore, for the purposes of this study, small effect sizes were defined to be "no detectable differences." Using the calculated effect sizes, the required sample sizes were then determined (a priori ANOVA with six groups, GPOWER, Bonn, Germany). As the sampling schedule was preset in the experimental design phase of this study, the length of study time was then determined by dividing the required sample size by the number of samples scheduled per year.

The relative effect size for the multivariate fish community data was calculated as the maximum difference between the average rank dissimilarities between groups (Philippi et al. 1998), using the ANOSIM R-statistic (Primer, Plymouth, UK). The R- statistic has values between 0 and 1 and is a comparative measure of the degree of separation of groups (Clarke and Wanvick 2001). Effect size categories (small, medium, and large) were then determined using K-means clustering with three groups. To test this method of calculating effect size, the dataset was split into a training set and a test set, with the original calculations for determining required sample sizes performed on the training set and then tested using the test set. Results between the training and test set were compared with an acceptable success rate defined as 2 80% match between the training set and the test set.

The univariate water quality effect sizes were calculated with a formula for effect size [(mean of experimental) - (mean of control)] / standard deviation (Cohen 1988) using the mean and standard deviation for three water quality variables (temperature, pH, and color) that met the assumptions of normality and homogeneity of variance. They were then classified into small (5 0.2), medium (> 0.2 to < OX), and large (2 0.8) effect sizes (Cohen 1988). The study design does not include reference (control) streams but is based on multiple sampling locations in an upstream to downstream gradient. Therefore, two effect sizes were calculated for each water quality parameter to give a range of effects seen between sites. The first represented the sn~allest effect seen between two sites (sites with small or no differences in their means), and the other was for the largest difference between two sites.

Component 3: Background variability. River discharge was chosen to represent the stability of background environmental conditions in order to assess the temporal variability of the system. Discharge is correlated with rainfall and so will account for drought or wet seasons. It is also correlated with some water quality variables (e.g., suspended solids and turbidity) and has been shown to be correlated with aquatic

Table 3. Summary of the river water quality data used in this analysis. Temperature pH Color N Mean OC N Mean N Mean

Codorus Creek 359 13.5 359 7.7 348 57.4 Leaf River 240 22.0 238 6.5 235 72.6 McKenzie River 302 10.7 302 6.7 295 13.2 Willamette River 299 11.4 300 6.7 291 21.6

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community composition (Koel and Peterka 2003). Background variability was calculated as the signal to noise ratio - the mean change in discharge per year (signal) to the standard deviation (noise) for the nearest USGS gauging station in each river. A higher signal to noise ratio represents a system with smaller background variability; a lower signal to noise ratio indicates a system with more background variability.

Uncertainties. Contributing to the uncertainty of the outcome for each of these criteria were the assumptions that were made as part of this method. The relevant time span assessment assumed that the contribution of the longer (e.g., > 20 years) abiotic time spans, such as the North Atlantic Oscillation, did not interfere with the interpretation of the results at a less than decadal level. This may not be true, particularly if a cycle is in a period of rapid alteration during the relevant time period. The biotic time spans also assumed that relevant effects would show up within one life cycle. The time span used for drought was from a different ecosystem and may not reflect the outcome at these rivers. Additionally, flood frequency was used for flood recovery time, as the recovery after flood disturbances is typically rapid (Lake 2000); however, if the flood event is severe or out of season, the recovery time may be much longer. Additionally, the flood recovery time used was a national average for flood frequency; specific areas may have much higher or lower flood frequencies. Effect size included the assumption that the calculated effect size was not significantly impacted by the dynamic state of a river or by expected changes between sites due to river continuum changes (Vannote et al. 1980). It also assumed that the effect size was constant over the period of the study. This particular assumption can be evaluated periodically during the study and modified as necessary. Sample collection was also assumed to occur at a rate that represents a reasonable long-term experimental design (e.g., if 30 samples were required they were not collected during 30 consecutive days). The preset type I and type I1 error rates were also assumed to be sufficient to allow reasonable detection of effect with minimal risk of finding an effect where none exists and at an economically supportable level of sampling. Additionally, the use of river flow to represent background variability may not fully represent the level of variability experienced by the aquatic communities or the variable nature of the different aquatic community's responses to flow changes.

RESULTS Contponent results. The relevant time spans found large-bodied fish had the longest life spans, ranging from 12 years (Codorus Creek) to 16 years (Leaf River), and the small- bodied fish had two (Leaf River, McKenzie River and Willamette River) to three year life spans (Codorus Creek). The abiotic component time spans were the same for all rivers, with one year for seasonal flow cycle, supra-seasonal drought recovery time of 10 years for fish (Lake 2003) and one year average for bankfull (flood) stage (Leopold et al. 1964).

The effect size for the fish communities ranged from a low of 0.188 with a required sample size of 280 (McKenzie River boat electrofishing captured population) to a high of 0.836 with a required sample size of 24 (Codorus Creek) (Table 4). The Leaf River had insufficient sample size for this component to be calculated. Testing of the multivariate effect size method was done for Codorus Creek only, which had a success rate of 86.7%, well above the required 80%. There were insufficient sample sizes for the McKenzie River and the Willamette River to test the effect size. The time required to see an effect ran from two years (Codorus Creek) to 8.5 years (Willamette River).

The water quality effect sizes ranged from small (0.1) to large (2.9) (Table 5). The small effect sizes were discarded and samples required determined for the medium to large effect size parameters. The required sample sizes ranged from 21 for large effect

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sizes to 133 for medium effect sizes. The time required for sampling ranged from less than one year to 4.5 years.

Background variability signal to noise ratio ranged from a low of 0.69 to 1.72. The Leaf River had the lowest ratio (0.69) indicating substantial background variability. The two rivers with the lowest background variability were the McKenzie River (1.72) and the Willamette River (1.24). Codorus Creek had a moderate ratio of 0.84.

River study length assessments. Analysis of the four rivers provided a unique combination of time lengths for each. Results are summarized in Table 6.

The Willamette River required study time lengths ranging from less than one year to 15 years. The longest biotic component was the relevant temporal scale for the large- bodied fish (15 years). The longest abiotic component, 10 years for drought, was relevant for this river, which had experienced drought conditions during the study period. Power components for the biotic communities required 8.5 years, while the water quality variables required from one to four years. The background variability indicated a relatively minor influence, suggesting that the shorter ranges should be adequate to detect effects. These results indicate a study length of 8.5 years should be sufficient to detect biotic and abiotic influences. However, caution should be used if selecting a shorter time for the study as effects on large-bodied fishes may be missed and confounding effects from drought recovery may make interpretation of the results misleading. If large-bodied fishes were an endpoint of concern, then their relevant time span should supersede the shorter time rages, and the study length should be set at 15 years. Given that the power calculation is based on a minimum sample size to achieve sufficient power, larger sample sizes will only enhance the power.

The McKenzie River had similar results to the Willamette River, with required lengths of time ranging from less than one year to 15 years. The longest biotic component was the relevant temporal scale for the large-bodied fishes. The longest abiotic component, 10 years for drought, was relevant for this river, which had experienced record drought conditions during the study period. Power components for the biotic communities were seven years, while the water quality variables required from one to 4.5 years. The background variability indicated a minor influence, suggesting that the shorter ranges should be adequate to detect effects. These results indicate a minimum study length of seven years would be sufficient to detect effects in the biotic and abiotic communities, although there is a risk of confounding effects from the drought not being isolated. As with the Willamette River, if large-bodied fish were of concern, then their relevant time spans should become the deciding factor over the shorter power calculation, resulting in a minimum study length of 15 years.

The Leaf River had required time lengths ranging from less than one year to non- determined. The Leaf River had a less frequent sampling schedule than the other rivers

Table 4. Summary of the fish community effect determinations for each river.

River Effect size Effect size Required

(R-statistic) category sample size Success rate ("A)

Codorus 0.836 Large 24 86.7 McKenzie (backpack 0.394 Medium 70 Not tested

population) McKenzie (boat population) 0.188 Small 280 Not tested

Willamette (backpack 0.259 Small 162 Not tested population)

Willamette (boat population) 0.328 Medium 102 Not tested

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and did not have the necessary sample size to calculate the biotic community power component. The longest relevant biotic temporal scale was 16 years, for the large-bodied fish. The longest abiotic component, 10 years for drought, was relevant for this river, which had experienced drought conditions during the study period. Power requirements for the water quality components indicate that a study of two years would be sufficient to detect effects in the medium effect size category. However, the background variability was the highest for this river, reflecting the drought and flooding that had occurred during the study period, suggesting that extreme caution be used when determining minimum study length for this river. Using the relevant time span information for this river, a minimum study length to assess biotic components would be estimated to be 10 to 16 years. This would allow for small and large-bodied fish effects to be detected and for effects from drought conditions to be determined.

Codorus Creek had the shortest range of required times ranging from less than one year to 12 years. The longest biotic component was the relevant temporal scale for the large-bodied fish. The longest abiotic component, 10 years for drought, was relevant for this river, which had experienced drought conditions during the study period. Power components for the biotic communities required two years, while the water quality variables required from one year to 4.5 years. The background variability indicated a moderate impact, suggesting that caution be used with the shorter study lengths. These results indicate that a study of two years would be sufficient to assess most water quality issues and small-bodied fish effects. It should be noted that this short time span will not adequately represent effects in the large-bodied fish with their 12 year relevant time span, whereas, a longer study length (e.g., 12 years) would ensure that both large and small- bodied fish effects would be seen, that drought impact would be detected and that long- term variability would be addressed.

Table 5. Summary of the water quality effect sizes for each river. Variable Codorus Creek Leaf River McKenzie Willamette

River River Temperature Medium (0.3) Medium (0.3) Small (0.1) to Small (0.2) to

to large (2.9) to large (2.4) large (2.4) large ( 1.9)

PH Small (0.2) to None (0) to Small (0.2) to Small (0.1) to large (2.4) large (2.2) large (2.3) large (2.6)

Color Small (0.1) to Small (0. I) to None (0) to Small (0.1) to large (2.1) large (2.0) large (2.3) large (2.4)

DISCUSSION Long-term ecological studies increase our understanding of ecosystem dynamics

and recovery (Adams et al. 2002, Kratz et al. 2003). It has been suggested that one of the main purposes of long-term studies is to provide the ability to detect cause and effect relationships through assessment of trends (Kratz et al. 2003). However, how long a long-term study runs is dependent on a number of factors, including the availability of monetary resources (Niemi et al. 1993) as well as physical and time constraints (Oswood and Barber 1982). These factors can potentially result in studies not being able to collect sufficient data. Additionally, analysis of subsets of early data may result in misleading results. The method developed and presented in this study has two points of major significance. It can ensure that the results of long-term studies (whether assessed with complete or truncated datasets) are supported as ecologically and statistically valid; and, it can provide a means of optimizing the use of experimental resources.

Field studies have the advantage over laboratory studies of being more ecologically relevant. The disadvantages are the multiple confounding factors and

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minimal ability to control or manipulate events to show cause and effect, although many studies do attempt to suggest relationships through weight of evidence and correlation between components (Adams 2003). Nonetheless, without demonstrating that the study covered sufficient time for organisms to respond or to account for confounding factors, detected trends may not truly represent the status of the system. Additionally, without substantiating that sufficient data were collected to show effects should effects be present, a finding of "no effects" cannot be considered significant. Results and conclusions from field studies would have added weight if information confirming the relevance of the time length studied, the statistical validity of the sample size, and the relationship of the results to the background variability was provided.

The optimum use of resources is another beneficial outcome of the use of this method. Field studies are labor intensive and time consuming to undertake. Efficient use of resources can be accomplished by early use of this analysis to design studies that include adequate samples to accommodate sufficient power and background variability. Additionally, use of this method will enable early conclusion of a study if the study goals are achieved early or discontinuing a study if the goals are not achievable within the time frame, and resources can then be directed to new study locations.

An additional conclusion from the analysis of the four rivers in this study was the need for river specific study length assessments. This was demonstrated by the differences in the effects sizes for the biotic endpoints. Rivers with large effect sizes in the biotic communities could be assessed in much less time than those with smaller effect sizes. Additionally, this method indicated that sampling the aquatic community of a river or a site once does not provide sufficient information to assess the community status. In fact, assessment of the effect size with two samples was insufficient for all four rivers in this study, and the river with the largest effect size still required a study length of two years for the biotic component. This provides evidence to support the concept that long- term studies provide more accurate analysis of ecosystem structure than short-term studies (Bradt et al. 1999, Kratz et al. 2003). The results also indicated that, for rivers with moderate to low background variability, the coarse estimate made by the first step (identification of relevant time spans) will provide a reasonable short-term assessment of time length with the more detailed analysis following initial data collection. However, for more variable rivers, the coarse estimate of time length may of necessity be the only available option to predict study length, with final study length perhaps not determined until nearing the end of the study. This is supported by the findings of Urquhart et al. (1998) in which they predicted that large variance components could overwhelm other study components.

Table 6. Summary of the analysis of the temporal components for each river.

Component Codorus Leaf McKenzie Willamette Creek River River River

Longest relevant time span 12 years 16 years 15 years 15 years Sufficient power: biotic 2.0 years 7.0 years 8.5 years Sufficient power: abiotic 4.5 years 2.0 years 4.5 years 4.0 years Variability Moderate High Low Low

ACKNOWLEDGEMENTS Thanks to the members of the Long-Term Receiving Water Study Scientific

Advisory Panel for their project oversight and discussion: Thomas Deardorff, Barry Firth, Wayne Landis, Wayne Minshall, Tibor Kovacs, and John Rodgers. Thanks also to the many people who contributed to the research presented in this paper: Joan Ikoma, Bill Arthurs, Dan McGarvey, Renee Ragsdale, and Rick Haley for their field and lab contributions; Tim Hall, Reid Miner, Robert Fisher, and Judy Dudley for their work on originating the experimental design.

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Received: 13 September 2004 Accepted: 27 December 2004

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