Riparian Forest Indicators of Potential Future Stream

7/30/2019 Riparian Forest Indicators of Potential Future Stream

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Riparian forest indicators of potential future stream

condition

Paul L. Ringold a,*, John Van Sickle a, Mike Bollman b,1, Jeff Welty c, Jerry Barkerb,2

aU.S. Environmental Protection Agency, Office of Research and Development, Western Ecology Division, 200 SW 35th Street, Corvallis,

OR 97330, United Statesb Dynamac Corporation, 200 SW 35th Street, Corvallis, OR 97330, United Statesc Weyerhaeuser Company, PO Box 9777, Federal Way, WA 98063, United States

e c o l o g i c a l i n d i c a t o r s 9 ( 2 0 0 9 ) 4 6 2 4 7 5

a r t i c l e i n f o

Article history:

Received 21 April 2007

Received in revised form

26 June 2008

Accepted 27 June 2008

Keywords:

Riparian forest indicator

Stream wood

Stream assessment

Coarse woody debris

Anticipatory indicator

a b s t r a c t

Large wood in streams can play an extraordinarily important role in influencing the physical

structure of streams and in providing habitat for aquatic organisms. Since wood is continually

lostfromstreams,predictingthefutureinputofwoodtostreamsfromriparianforestsiscrucial

to assessing or managing stream ecosystems. Unfortunately, regional monitoring protocols

haveno establishedcapacity to providethis information. Thegoal of this researchis to propose

one or more methods that could meet this need. This goal is pursued by using stream wood

delivery models to aid in the design of a monitoring method. Two questions are asked. First,

does simpler data change model predictions of future contributions of wood from riparian

ecosystems to the stream? The answers to this first question enable monitoring design to be

tailoredto detailsaffectingestimates of futurestreamcondition.Theseanswers areimportant,

becausemoredetaileddata istypicallymorecostly. Second,whichmetrics,ifany, correlate well

with model predictions? If such metrics can be identified, then these measures can serve as

effective indicators of ecosystem function directly, without using ecosystem models.

Thesequestions wereaddressed by collecting highly detailed field observations of riparian

forests from 109 forested riparian sites in the Coast Range, Willamette Valley, and western

Cascades of northwestern Oregon. Detailed and simplified versions of thesedata were used in

models that forecast the potential of riparian forests to provide wood to the stream. Model

predictions with less detailed data typically provided answers different than did predictions

made with more detailed data. Thus, ecosystem assessments requiring these types of model

predictions would benefit from more detailed data. In contrast, riparian metrics easily

observed in the field (e.g. number of basal area of trees) or derived from remotely sensed

imagery (e.g.numberor height of canopy trees) werewell correlatedwith modelpredictionsof

potential stream wood recruitment. When direct model predictions or model scenario ana-

lyses are not required, these metrics can serve as effective indicators of the potential of

riparian forests to provide wood to the future stream network.

Published by Elsevier Ltd.

The information in this document has been funded wholly (or in part) by the U.S. Environmental Protection Agency. It has beensubjected to review by the National Health and Environmental Effects Research Laboratorys Western Ecology Division and approved forpublication. Approval does not signify that the contents reflect the views of the Agency, nor does mention of trade names or commercialproducts constitute endorsement or recommendation for use.

* Corresponding author.E-mail addresses: [email protected] (P.L. Ringold), [email protected] (J. Van Sickle), [email protected] (M. Bollman),

[email protected] (J. Welty), [email protected] (J. Barker).1 Current address: U.S. Environmental Protection Agency, Office of Research and Development, Western Ecology Division, 200 SW 35th

Street, Corvallis, OR 97330, United States.2 Current address: Walsh Environmental Scientists and Engineers LLC, 4888 Pearl E. Circle, Boulder, CO 80301, United States.

a v a i l a b l e a t w w w . s c i e n c e d i r e c t . c o m

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e c o l i n d

1470-160X/$ see front matter. Published by Elsevier Ltd.

doi:10.1016/j.ecolind.2008.06.009
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]://dx.doi.org/10.1016/j.ecolind.2008.06.009http://dx.doi.org/10.1016/j.ecolind.2008.06.009mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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1. Introduction

Large wood in streams can play an extraordinarily impor-

tant role in creating the physical structure of streams and

providing habitat for aquatic organisms (Gregory et al.,

2003a). Wood currently in streams contributes to stream

complexity by creating pools, trapping sediments, and

affecting bank erosion and bar formation. Stream woodalso alters river flow patterns and affects the relationship

between a river and its floodplain (Montgomery et al.,

2003a). Not surprisingly, larger pieces of wood can play a

more prominent and extensive role in influencing stream

structure than can smaller pieces of wood (Bilby and Ward,

1991). While wood can reside in stream channels for

centuries, the residence time of most wood is typically

much shorter. Hyatt and Naiman (2001) found wood in the

Queets River in northwest Washington that had been in the

stream for 1400 years, but also estimated the half-life of a

piece of stream wood at 20 years. Given that wood is lost

from stream channels by fluvial transport, fragmentation,

and decomposition (Keller and Swanson, 1979; Harmonet al., 1986; Gurnell and Gregory, 1995; Bilby and Bisson,

1998; Hyatt and Naiman, 2001; Van Der Nat et al., 2003), an

understanding of the future input of wood is crucial to

assessing and managing stream ecosystems.

Future stream wood originates in current and future

riparian and upland forests. Trees from these forests become

streamwood by a varietyof episodic andchronic mechanisms.

Trees die from competitive suppression, pests and pathogens,

gradual erosion of root systems, bank erosion, blowdown, fire,

or massive failure of banks or hillslopes. Whentree death does

not directly place wood in the stream channel, transport

processes such as debris flows, landslides, floods, and

downhill creep can move wood into the stream channel(Harmon et al., 1986; Sedell et al., 1988; Benke and Wallace,

1990; Gregory et al., 2003a). A growing literature is quantifying

these processes, the sources of stream wood, and the patterns

of variability in the delivery of stream wood ( Murphy and

Koski, 1989; Benke and Wallace, 1990; Naiman et al., 2000;

Burnett, 2001; Martin and Benda, 2001; Benda et al., 2002; May

and Gresswell, 2003; Montgomery et al., 2003a; Montgomery

et al., 2003b; Reeves et al., 2003; Allan, 2004; Balian and

Naiman, 2005; Bragg, 2000). The picture that emerges is that

the relative roles of these processes vary across the stream

network and landscape, as does the relative contribution of

wood from upland as opposed to riparian sources.

Consistent with the important roles that wood plays instreams, national and regional stream monitoring protocols

quantify wood in streams. While these data provide informa-

tion on the current status of streams and insight on the

effectiveness of current and past regional management, they

do not provide information on the potential future contribu-

tion of wood to streams. Assessment of the potential for future

contributions of wood to the stream network requires

evaluation of riparian and upland systems. The analysis

reported here focuses only on the riparian portion of the

landscape which is appropriate to provide an accounting for

this function required by Federal, regional and state autho-

rities (USDA/FS and DOI/BLM, 1994; Oregon Department of

Forestry, 1996; Phillips et al., 2000; Young, 2000).

Existing regional monitoring protocols for riparian ecosys-

tems reflect a general recognition that riparian ecosystems

provide wood to the stream network, rather than a specific

measurement explicitly based on a specific understanding of

how riparian forests contribute wood to the stream network.

For example, the Northwest Forest Plans aquatic and riparian

effectiveness monitoring plan (Reeves et al., 2004) notes that

wood delivery is a key process. The plan proposes to acquireinformation on this process by securing estimates of the

proportions of watersheds in various seral stages. This

information would be provided by remotely sensed imagery.

EPAs Environmental Monitoring and Assessment Program

(EMAP) acquires field observations on the presence and

characteristics (size class and deciduous or conifer) of the

vegetation within 10 m of the stream (Kaufmann, 2006). The

USGS National Water Quality Assessment Program (NAWQA)

develops information on stem density of all woody species

using field observationsspecifically the point-centered

quarter (PCQ) method (Fitzpatrick et al., 1998).

As for any ecological indicator, the development of an

indicator of future stream wood contributions should reflectecological processes (e.g. Cairns et al., 1993; Soule, 1995;

Jackson et al., 2000; Dale and Beyeler, 2001). Because ecological

models are an explicit representation of our understanding of

ecological processes, their use in indicator design enables the

linkage of ecological processes with indicator development.

Gregory et al. (2003b) lists 12 models which forecast future

contributions of wood from riparian ecosystems to stream

channels. Each of these models requires a characterization of

the riparian forest on which model representations of

ecosystem processes operate in order to make predictions

of potential future contributions of wood to the stream. The

riparian forest data required to support these models typically

includes information on the taxonomic composition of thetrees, the distance of each tree from the stream bank, the

height of each tree, and the number trees in the stand.

While model predictions provide insight into potential

future contributions of wood to streams, these models can

require large amounts of detailed field data requiring

considerable time to collect. Riparian forest data can be

collected from direct field observation or from extensive data

bases. Direct field observation offers the most flexibility and

certainty for site specific observations and many sources

describe field methods for specific attributes and the levels of

certainty that can be expected with these methods (e.g. Barker

et al., 2002a; Mueller-Dombois and Ellenberg, 2002; Gray and

Azuma, 2005). However, field crews are expensive in terms ofboth direct and indirect or opportunity costs.

Extensive data, typically including remotely sensed infor-

mation, allows for the assessment of large areas, but has less

flexibility than does field data collection in the attributes and

spatial character of the data. The number of options available

to provide riparian characterization from remotely sensed

data is growing rapidly (e.g. Fassnacht et al., 2006). One widely

used source for extensive landcover data is Thematic Mapper

(a multispectral sensor TM). Information from this sensor

has been used to provide landcover classifications for millions

of hectaresin 30 m pixels. Others have used data from TM and

from SPOT (a sensor similar to TM, but with 10 m resolution) to

develop regressions between remotely sensed data and

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attributes (e.g. stand height) of Douglas-Fir/Western Hemlock

stands in the western Oregon Cascades (Cohen and Spies,

1992). Although the information provided by these sensorshas

a specific spatial resolution, developers of its products

typically suggest using it at a coarser level of resolution, e.g.

for a 3 3 block of pixels (Cohen and Spies, 1992; Congalton

and Green, 1999; Ohmann and Gregory, 2002). Spatial

accuracy, in addition to spatial resolution, is a considerationin the use of extensive geographic data. For example, IKONOS

imagery (4 m resolution multispectral imagery) can be

purchased with four different levels of spatial accuracy

ranging from RMSE of 11.8 to 0.9 m (Space Imaging, 2006)

with an acquisition cost inversely proportional to its spatial

accuracy.

Given that there are divergent methods and a wide rangeof

options for collecting riparian forest data that could be used in

stream wood contribution models, we pose two questions.

First: does simpler data change model predictions? If simpler

data provides the same prediction as more detailed data, then

perhaps model predictions can be made with less effort.

Second, which metrics are well correlated with best modelpredictions? If such metrics can be identified, and if these

measures are simpler to collect than the full set of data

required for model predictions, then these parsimonious

measures, although not allowing for the models to be

operated, can serve as an indicator of the potential of a

riparian stand to provide wood to the stream network.

The approach to addressing these two questions is shown

in Fig.1. To respond to the first question, best and simplified

model input data sets were created and used as input for two

different models (Van Sickle and Gregory, 1990; Welty et al.,

2002). The models forecast the potential of riparian forests to

provide wood to the stream and compare the predictions

arising from different data. Data were simplified by represent-

ing stands with class averages, by reducing spatial resolution,

or by adding spatial error. To respond to the second question,

correlations between simulated metrics, features that could

easily be observed by field crews or in remotely sensed

imagery, and best model predictions were evaluated.

2. Methods

2.1. Detailed field observations

Detailed field observations were made at 109 forested riparian

plots in northwestern Oregon. The description of the sampling

and the sites is described in (Barker et al., 2002a; Barker et al.,

2002b); therefore only an abbreviated description is provided

here. Sample plots were selected on a stratified random basis

from thestream network of this region. Strata were defined by

three ecoregions (Coast Range, Willamette Valley, and West

Cascades) (Clarke and Bryce, 1997) and four vegetation classes

(Broadleaf and other, Small Conifer/Mixed, Medium Conifer/Mixed, andLarge or Very Large Conifer/Mixed). Measurements

in each plot (0.16 ha 40 m 40 m) included the size (in

diameter at breast height or DBH), location (including true

horizontal distance from the streams bankfull edge), and

taxonomic identity of each tree greater than 10 cm DBH. One

to 4 h was required to collect this information for each plot;

this excludes travel time to the plots; more time was required

for plots with more trees.

Sampling on 109 plots identified 6645 trees. The distribu-

tion of trees within 40 m of thestream is consistentwithother

reports (e.g. Gregory et al., 1991; Nierenberg and Hibbs, 2000;

Russell and McBride, 2001; Wimberly and Spies, 2001) noting

Fig. 1 Approach to the analysis. Tables 713 are available in the Appendix.



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that basal area density (m2 per ha) of trees increases with

distance from the stream, while the density of trees (number

per ha) is stable. Deciduous trees (D) were more prevalent (in

numbers and in basal area) closer to the stream; conifers (C)

were more prevalent further from the stream. Large conifer

(C5 those with dbh > 50 cm) were few in number, but evenly

distributed as a function of distance from the stream.

Deciduous trees were, on average, narrower and shorter than

conifers. These observations are summarized in Fig. 2 and

Table 1.

The medianbankfullwidth of the streams at the study sites

was 6.7 m and ranged from 1.7 to 64 m.

These field observations were used to estimate metrics that

could be derived either by field observation or remote sensing,

and to provide model input as shown in Fig. 1.

2.2. Simulated metrics

Two categories of candidate metrics werederivedfrom the field

data. One reflects metrics that could be developed by field

measurements, while the other is constructed around metrics

that could be developed by remotely sensed imagery. These

metrics were developedto support the secondquestion: Which

metrics are well correlated with best model predictions?

Fig. 2 Basal area (A) and tree stem density (B) as a function of distance from stream edge.



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2.2.1. Simulated field metricsMetrics of tree number(N) and basal area(B) were derived from

the detailed field observations. These metrics described all

trees (N or B) or large conifers (those with dbh = > 50 cm and

noted as NC50 or BC50) over the 40 m breadth of the plots for

varying cumulative distances from the stream bank in 5 m

increments, i.e. 05, 010. . . through 040 m from the stream.

Candidate field metrics were derived by simulating two

different field methods one based on a count-plot (or fixed

area plot or FAP) method, and the second based on the PCQ

method (Mueller-Dombois and Ellenberg, 2002). While the FAP

method is considered the most robust method, the PCQ

method is an efficient field method providing results well

correlated with the count-plot method if the trees arerandomly distributed (Cottam and Curtis, 1956; Mueller-

Dombois and Ellenberg, 2002). The PCQ method wassimulated

not only because of its efficiency as a field method, but also

because of its use by the USGS to quantify riparian vegetation

(Fitzpatrick et al., 1998).

To simulate measures that could be derived using the

count-plot method, field observations for each plot were

simply summarized for the appropriate set of trees and

location. To estimate metrics that could be derived if field

crews were to have used the PCQ method, the placement of

PCQ sampling points was simulated in the plots. This

procedure is illustrated in Fig. 3. For example, to develop a

PCQ estimate of riparian forest attributesin the 20 m closestto

the stream, two sample points, each 10 m from the stream,were defined. One was 10 m from the downstream plot edge,

and thesecond was 10 m from the upstream plot edge. At each

simulated PCQ sample point, the surrounding space was

divided into four quadrants (A, B, C, and D). The quadrants

were defined by two lines, one perpendicular to the stream

and a second parallel to the stream. The lines intersected at

the sample point. In each quadrant thedistance to the nearest

tree was calculated based on the xy coordinates in the field

data. Only trees within the plots radius of the sample point

were used. The distances from the sample point to each tree

were used to calculate tree number orbasal area in the defined

area as described by (Mueller-Dombois and Ellenberg, 2002).

Correction factors developed by Warde and Petranka (1981)were used for sample points with no trees within one or more

sample quadrants.

2.2.2. Simulated remotely sensed metrics

Three forest attributes were derived from our field data to

simulate metrics that could be derived from remotely sensed

imagery: the mean diameter at breast height, mean height (H),

and tree density (N) (Cohen and Spies, 1992). These attributes

were derived for two sets of trees: canopy (c) and canopy and

subcanopy trees (c + s) for a total of six metrics. These

estimates were developed only for the coarsest resolution

allowed by the data sets 40 m 40 m. This dimension is

roughly the size of the useful scale of a 3 3 pixel SPOT

Table 1 Characteristics of trees observed in riparian field plots

Individual taxa Number of individuals

Numberof plots

Mean diameterat breast height (cm)

Mean estimatedheight (m)

VSGgroup

RAISgroup

Douglas Fir (Pseudotsuga menziesii) 2137 88 35 26 3 1

Alder (Alnus spp.) 1812 84 25 21 1 3

Western Hemlock (Tsuga heterophylla) 911 53 30 22 5 2

Maple (Acer macrophyllum) 466 42 30 18 2 4

Red Cedar (Thuja plicata) 327 41 43 23 4 2

California Laurel (Umbellularia californica) 223 6 22 15 2 4

Silver Fir (Abies amabilis) 166 11 21 15 3 1

Ash (Fraxinus latifolia) 105 11 35 20 1 3

Yew (Taxus brevifolia) 75 19 19 8 4 2

Mt. Hemlock (Tsuga mertensiana) 71 3 43 24 5 2

Oak (Quercus garryana) 50 5 23 15 2 4

Grand Fir (Abies grandis) 39 9 44 27 3 1

Cottonwood (Populus balsamifera ssp. trichocarpa) 33 6 24 21 1 3

Incense Cedar (Calocedrus decurrens) 32 3 25 13 4 2

Noble Fir (Abies procera) 28 5 33 23 3 1

Sitka Spruce (Picea sitchensis) 27 10 71 36 6 1

Cherry (Prunus emarginata) 23 5 19 20 1 3

Englemann Spruce (Picea engelmannii) 2 1 60 36 6 1

Other 118 24 17 13

Total 6645 110

Groups of trees Number of individuals

Number ofplots

Mean diameter atbreast height (cm)

Mean estimatedheight (m)

Effective treeheight VSG (m)

All trees 6645 30 22 17

All deciduous trees 2811 26 19 14

All conifer trees 3834 34 24 19

All trees >50 cm dbh 862 77 43 38

Conifers >50 cm dbh 696 82 46 41

The category Other includes 13 smaller species which were not included in the analyses.



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imagery, and smaller than the 3 3 pixel TM imagery. We

simulated these metricsby simplesummariesof the field data.

We calculated tree height from DBH using regression models

(Garman et al., 1995, Wang and Hann, 1988, or Larsen and

Hann, 1987). c + s trees were defined using Kings (1966)

methods. Because these metrics were simulated from field

data rather than from remotely sensed imagery, our analysis

excludes errors that would be associated with the processing

of imagery.

2.3. Modeling methods

2.3.1. The models

The VSG model (Van Sickle and Gregory, 1990) estimates the

delivery of tree boles to the stream from sources of tree fall

such as windthrow, decomposition, and stream bank erosion.

Trees were assumed to fall independently of each other;

multiple-tree input events such as debris flows and landslides

were not considered. Estimates of wood input (bole number

and volume) were modeled as functions of a riparian stands

species mix, effective tree height, stem density, and of the

assumed probabilities that trees fall (the assumption is made

that all trees fall, thus simulating an episode of complete

mortality of the current stand) and upon falling land in a user-specified direction. Tree height was modeled from DBH as

described above. Effective tree height is 5 m less than tree

height to account for small pieces of wood at the tops of trees

that would not function in streams (Robison and Beschta,

1990; Van Sickle and Gregory, 1990). Themodel sums expected

inputs from all trees in the stand to yield a mean and variance

of total input from the stand; here results were presented

based only on mean inputs.

The RAIS model (Riparian Aquatic Interaction Simulator)

(Welty et al., 2002) was chosen to evaluate the implications of

stand growth and future mortality. This model combines the

mechanisms of tree fall incorporated in the VSG model with a

tree growth model, ORGANON (Hann et al., 1995). This

simulator describes the growth of the trees and simulates

rates of mortality over time, particularly as a function of stand

density. Thus, while the two models appear to predict the

same thing, they are predictions that reflect very different

processes and are not directly or simply comparable to one

another. The VSG model is used to describe the potential

stream wood contribution given complete mortality of a

current stand, while RAIS describes the contribution of streamwood that would result from chronic mortality over a specified

period as the trees in the stand grow.

Both models have been widely cited and used. The RAIS

model, for example, is the foundation of much of the analysis

of riparian management in Washington state (Washington

Department of Natural Resources, 2001)

2.3.2. Predictions

The predictions from the VSG model were all wood, or conifer

wood > 50 cm dbh, and were expressed in terms of the

number (per 100 m of riparian forest) or the volume of pieces

(m3 per 100 m of riparian forest) of pieces in each category.

Volume described the wood volume within the bankfullchannel. Predictions for RAIS were the cumulative number of

potentially contributed pieces (per 100 m of riparian forest) of

all wood or functional wood accumulated over 10, 50, and 100

year periods. Functional wood was defined by Bilby and

Wards (1991) formulation which, for an 11 m channel our

best case assumption required that a piece of functional

wood must be at least 50 cm in diameter.

The VSG and RAIS models predict potential stream wood

contributions from riparian forests. The predictions are

potential contributions because they are based on general

assumptions not necessarily true in any specific location or

time. In a reach or watershed assessment, these predictions

could be modified by reach-specific information to providemore realistic estimates of the potential contribution of wood

to the stream.

2.3.3. Model assumptions and variations in model

assumptions

Model stands were assumed to have a width of 40 m, divided

into four equal 10 m wide subplots as illustrated in Fig. 4A.

Streams were assumed to have a bankfull channel width of

11 m with trees falling in any direction with equal probability.

The VSG model tracks seven taxonomic groups of trees. The

RAIS model tracks four taxonomic groups. The reassignments

from observed taxa to these seven or four groups are provided

in Table 1.Sensitivity of the predictions to variations in these model

structural assumptions was evaluated by examining predic-

tions for different channel widths (2 m, 30 m, and observed

width), for a different subplot structure of the models (four

subplots of unequal width5 m, 5 m, 10 m and 20 m width

with the smaller subplots closer to the stream), and for the

assumption that trees fell towards the stream rather than at

random.

2.4. Model input data

Input for the models was developed from the original highly

detailed field data in six versions: the best case, and five

Fig. 3 Illustration of PCQ simulation.



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simplifications of the best case. Comparisons were made

between model predictions based on differentinput datafor six

pairsofthecasesshowninTable2. The specific rules generating

each of these cases from the original highly detailed field data

aredescribed in the following sectionsalong withan illustration

ofthe application oftheserulesprovided forone plot in Fig.4. In

this figure D is a deciduous tree, C is a conifer. The size of

the letter denotes the size of the tree.

Fig. 4 Illustration of the Data Simplification Cases.



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2.4.1. Generating the best input data

In the best, or 10 m case,trees were assigned to 10 m 40 m

model subplots based on their observed presence in those

subplots. The plot is properly registered with regard to the

location of the stream. The best case input data is depicted

in Fig. 4A.

2.4.2. Generating input data with coarser spatial resolution

In the coarser resolution, or 40 m case, the plot average values

for numbers, types, and sizes of trees were assigned to each

10 m 40 m subplot. In the example plot depicted in Fig. 4

there were 40 trees in the plot. The number of trees in thesubplots ranged from 5 to 18. When the information for these

four subplots is simplified to the 40 m case, the average

number of trees per subplot, 10, is allocated to each 10 m

subplot as shown in Fig. 4C.

2.4.3. Generating two cases based on forest stand classes

To provide descriptions of forest classes, each 40 m 40 m plot

and each 40 m 10 m subplot was assigned to one of 13 forest

classes. Twelve classes were defined by the combination of the

size of the dominant and co-dominant trees (small 5075 cm; and very large

>75 cm)andthe standcomposition (conifer>70% coniferbasal

area, deciduous >70% deciduous,and mixed allotherstands).Dominant and co-dominant trees were identified following the

method of King (1966) a 13th class had no trees. Average tree

density, tree size, and stand composition were calculated for

each class in the class cases. Those average attributes were

assigned to each plot or subplot of the class. Descriptions were

generated at two spatial resolutions10 m and 40 m.

Specific examples of these simplifications are shown in

Fig. 4. The stand classification assignment for each subplot is

shown in Fig. 4B. One subplot is classified as very large mixed.

In the full dataset 13 of the109 plots were members of the very

large mixed class; the average plot in this class has 37 trees; 10

were conifers. In creating a 10 m class dataset, that density of

trees is assigned to subplots classifiedas very large mixed.Thesame process is used to transform the 40 m case to the 40 m

class case (Fig. 4D), except that the accounting is done at the

plot level rather than at the subplot level.

2.4.4. Generating cases with simulated errors in geo-

registration

To simulate the effect of a geo-registration error, a data set was

created in which the position of the stream was displaced by

40 m. The result of this simulated displacement was that trees

actually 40 m from the stream were listed as on the stream

bank, and trees that were actually on the stream bank were

listedas 40 m from the stream. This case is illustrated in Fig.4E.

This simulated 40 m displacement is in the direction that

provides the maximum difference from a properly registered

plot.If thedisplacement were parallel to thestream rather than

perpendicular to it, there would be no meaningful error.

2.5. Analytical methods

2.5.1. Linkages between model predictions and simulated

metrics

Spearman correlations, r, were calculated to quantify associa-

tions between simulated metrics and best case model

predictions. To evaluate the generality of the results the

correlation analysis was conducted not only for all sites, butalso forsubsets of the plots associated with specificecoregions

or assigned to different forest stand classes. To be included in

the analysis subsets of the data were required to contain at

least 25 plots. The generality of the results was also assessed

by calculating r for model predictions based on variations in

model structure.

2.5.2. Evaluation of the effects of data simplification

We established three criteria for deciding if predictions from a

simplified data set are equivalent to predictions using the

best case data. Two criteria are established for the reach

scale and one for the network scale. The reach scale is the

individual stream reach adjacent to a riparian stand this isthe scale at which field observations are typically made and

reported. The network scale is an important scale because it

may be the total amount of wood in a stream network that

influences stream function rather, than the wood at any one

point within the stream network.

At the reach scale, less detailed data may be appropriately

used, so long as twoconditions aremet. First, if theprediction

error is less than the error of measuring in-stream wood, and

second, if r, the correlation between the two predictions, is

greater than 0.7 (i.e. if half the variability in predictions from

one set of assumptions is accounted for by predictions using

another set of assumptions).

Prediction error for potential stream wood uses thefollowing formulation:

RMSEp SQRTP

i1 tonlog10Xi log10Yi2

n

!(1)

Xi is the predicted contribution of stream wood from plot i in

thecase when themodel is provided with less information, Yi

is the predicted contribution of stream wood from plot i when

the model is provided with more information, and n is the

number of the field plots. Measurement error for observations

of stream wood is documented in this region (Kaufmann et al.,

1999). These values serve as a benchmark and were designated

as RMSEK. Predictions and measurements values of 0 were

Table 2 Listing of the cases compared and the information loss evaluated in each comparison

Case with less information Case with more information Information loss evaluated

40 m 10 m Spatial resolution

40 m class 10 m Spatial resolution and stand classification

10 m class 10 m Stand classification

40 m class 10 m class Spatial resolution

40 m class 40 m Stand classification

40 m offset 10 m 10 m Geo-registration error



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replaced with a value equal to 0.1 times the smallest nonzero

measurement so that log values could be calculated for all

cases.

For the network scale, the simulation simply summedpredictions across all plots. A bootstrap approach generated a

network scale benchmark using paired visit data from

Kaufmanns (1999) repeat visits, the data underlying RMSEKcalculations. These pairs were sampled with replacement 109

times (matching the number of plots forwhich there were field

observations) at random. This sampling was repeated 1000

times and a benchmark for network relative error, NREK as

defined in Eq. (2), was calculated. For each sample, one

member of the pair of visits was assigned to measurement 1,

M1 and the other to measurement 2, M2. For each set of 109

samples, we calculated the network relative error and

averaged the result over the 1000 sets to give NREk

NREK Average over 1000setsof 109samples of

jSM1 SM2j

AverageSM1;SM2

(2)

Network relative error was calculated for each comparison

of data simplification cases as shown in Eq. (3).

NREP 100% jSYj SXjj

AverageSYj;SXj

!(3)

NREP is the network relative error for a particularcomparison of cases; SYj and SXj were the total amounts

of predicted stream wood summed over all plots given cases

with respectively more and less information; j designates the

case of interest. The analysis is based on a comparison of NREPto NREK. Our evaluation is an approximation of contributions

to a stream network because the riparian data were collected

from multiple ecoregions and watersheds rather than from

the stream network of a single watershed.

3. Results

3.1. Model predictions

Best model predictions for all plots are summarized in

Table 3. The standard deviation describes the variability in the

predictions across the 109 plots. Since best case RAIS

predictions forfunctional wood forvirtuallyall plots were zero

for 10 and 50 years, analysis of RAIS functional wood

Table 3 Model predictions of potential stream wood contributions for the best case

Amount

Mean (per 100 m of stream length) S.D.

VSG predictions

Total wood (number of pieces) 23.3 13.7

Large conifer (number of pieces) 5.1 5.9

Total wood volume (m3) 10.1 9.3

Large conifer volume (m3) 6.9 9.7

RAIS predictions

Total wood (number of pieces) 10 years 14.2 22.3



Functional wood (number of pieces) 10 years 0.00 0.00



Table 4 Summary of model predictions in comparison to three evaluation criteria

(X)a 40 ma 40 m classa 10 m classa 40 m classa 40 m classa 40 m offset 10 ma

(Y)

b

10 m

b

10 m

b

10 m

b

10 m class

b

40 m

b

10 m

b

Model Indicator type Pass or fail

VSG All wood Number Pass 2 2 2 2 l and 2

Volume l and 3 l and 2 l and 3 3 Pass All

Large conifer Number Pass l and 3 l and 3 3 l and 3 Pass

Volume 3 1 l and 3 3 1 3

RAIS Total wood 10 Years l and 3 All All All All All

50 Years l and 3 All All All All All

100 Years 3 2 and 3 All l and 2 l and 2 All

Functional wood 100 Years All All All All All l and 2

The use of simpler data can fail in comparison to any of three criteria: 1 Reach scale prediction error > reach scale measurement error; 2 low

correlation between predictions; and 3 Network scale prediction error > network scale measurement error.a Case with less information.b

Case with more information.



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predictions were restricted to accumulations over 100 years.

Predictions for variations on the best case assumptions are

summarized in Appendix Table 7 for simplified data and

Appendix Table 8 for variations in model structure.

3.1.1. Model prediction sensitivity to the level of detail in theinput data

Only4 of the 48 comparisons passed all three of our evaluation

criteria (Table 4), i.e. network and reach prediction errors were

smaller than our benchmarks, and reach scale predictions

were reasonably well correlated with best case predictions.

Of the 44 cases failing, 27 failed both at reach and network

scales, 11 failed only at the reach scale,and 6 failed only at the

network scale. As at the reach scale the RAIS model was more

sensitive to the loss of information than was the VSG modelwith all RAIS cases failing to meet one or more criteria while

83% of the VSG cases failed to meet the criteria.

At the reach scale, 79% (38 of 48)of thecomparisons did not

meet the criteria, i.e. variations in the level of detail in the

input data led to model predictions that were different from or

poorly correlated with model predictions from the best case

data(Table 4 and Appendix Table 9). Inmost instances(23), the

comparisons failed to meetboth the RMSE and r criteria. In ten

cases, the failure was solely a failure to meet the RMSE

criterion, i.e. RMSEP> RMSEK, and five additional cases failed

because the r was less than 0.7 ranging from 0.42 to 0.50 (see

Appendix Table 10). Overall, information loss with the VSG

model was more likely to result in predictions that met thecriteria than the RAIS model (37% meeting the criteria vs. 4%).

The bestcategory of predictions was VSG Large Conifer, where

half of the comparisons met the criterion. Examination of the

columns ofTable 4 and Appendix Table 9 showed that some

types of information loss led to more frequently acceptable

results than others. For example, half of the 10 m vs. 40 m

resolution cases had acceptable results; none of the 10 m to

10 m class did.

At the network scale, 69% (33 of 48) of the comparisons did

not meet the criteriaset for them (Table 4 and Appendix Table

11), i.e. variations in the level of detail in the input data led to

model predictions that were different from model predictions

from the best case data. Overall, information loss with theVSG model was more likely to result in predictions that met

the criterion than the RAIS model (50% meeting the criteria vs.

13%). The best category of predictions was VSG All Wood

number, where all cases met the criterion.

3.2. Indicators of the potential of a riparian forest to

contribute wood to the stream network

3.2.1. Simulated field measures as indicators

Features easily observed in the field using fixed area plot

methods were well correlated with best-case model

predictions of potential stream wood recruitment (see

Table 5). Three aspects of this result are noteworthy: first,

Table 5 Maximum correlations between field observations of riparian status and model prediction metrics of riparianfunction

Model predictions Simulated field observations Correlation betweenfield metric and model

prediction

Metric Time period Model Metric Cumulative extent(m from stream)

Fixed area plot PCQ

Total wood (number of pieces) 10 RAIS N 10 0.75 0.65



Total wood (number of pieces) VSG N 15 0.89 0.78

Large conifer (number of pieces) VSG NC50 40 0.98 0.87

Total wood volume VSG B 20 0.85 0.66

Large conifer volume VSG BC50 30 0.94 0.80

Fig. 5 Spearman correlation coefficients between best

case model predictions and simulated fixed area plot field

observations for six different subsets of the plots and two

model predictions.



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the specific field metrics that correlated best with the model

predictions, second, the cumulative spatial extents where

these correlations were maximized, and third the robust

nature of these results across subsets of the data and across

varying assumptions of model structure. Observations that

could be collected by using the point-center quarter (PCQ)

method also correlated well with model predictions, but the

magnitude of the correlation was on average 14 percentagepoints less than for fixed area plot methods.

Field metrics with highest r were logical matches to the

model prediction metrics (Table5):e.g.numberofalltreesinthe

riparian plots correlated well with the number of trees

potentially contributed to the stream, and basal area of trees

observed in the riparian plots paired with the volume of wood

potentially contributed to the stream. The cumulative extent at

which thesecorrelations weremaximized correspondedwell to

the effective height of the trees in the group of trees of interest.

For example, the average estimated effective height of all trees

i s 1 7 m (Table 1); r was at a maximum when the model

prediction of all wood ranges was correlated with field

observations that ranged from 0 to 10 through 0 to 20 m fromthestream bank (Figs.5and6). Similarly, the average estimated

effective height of large conifers is 41 m (Table 1); r was at a

maximum when model prediction of potential conifer con-

tributions were correlated with field observations that ranged

from0 to30 through 0 to40 m from the streambank (Figs. 5 and

6). There was no substantial sensible correlation between field

observations and the three functional wood predictions, in part

because so many of the plots were predicted to contribute no

functional wood even at 100 years. As a result, there was little

variability in model predictions to associate with the variability

in field observations.

These results were robust in several ways, as illustrated in

Figs. 5 and 6. First, variations in the cumulative distance ofthese field observations resulted in correlations almostas high

as the maximum correlations and changing the cumulative

distance of the field observation by 10 m usually led to small

changes in r. Second, variations in model assumptions

(different spatial structure, different stream widths, or

different assumptions about tree fall direction) showed the

same patterns in correlations as the best estimates did with

the field metrics see two examples of this in Fig. 6 and

Appendix Table 12. Third, the pattern of maximum correla-

tions was also robust across subsets of the data for ecoregion

and vegetation class subsets of the data see two examples of

this in Fig. 5.

3.2.2. Simulated remotely sensed measures

Features observable in remotely sensed imagery were well

correlated with model prediction metrics of potential stream

wood recruitment (Table 6). Two aspects of this result are

noteworthy: first, the specific metrics best correlated with the

model predictions and second, the robust nature of these

results across subsets of the data and across varying

assumptions of model structure. The remotely sensed metrics

Fig. 6 Spearman correlation coefficients between best

case model predictions and field observations for fivevariations in model structure and two model predictions.

Table 6 Maximum correlations between observations of riparian status derived from simulations of metrics that couldbe derived from remotely sensed data and model prediction metrics

Model prediction Simulated remotelysensed metric

Correlation betweensimulated metric and best

model predictionMetric Time period Model

Number of pieces of wood 10 RAIS Nc + s 0.57



Number of pieces of wood VSG Nc + s 0.82

Number of pieces of large conifer wood VSG Hc + s 0.81

Volume of all pieces of wood VSG Hcan 0.69

Volume of large conifer wood VSG Hc + s 0.77



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that correlated with the best model estimates were either

average tree height or stand densityfor canopyand subcanopy

trees or just canopy trees. Simulated remotely sensed

measures were well correlated with model predictions across

ecoregion and vegetation subsets of the plots (the correlations

for these subsets average 93% of the value of the correlations

shown in Table 6 see Appendix Table 13), as was the case for

the simulated field measures.

4. Discussion and conclusions

In general, model predictions derived from simplified data did

not compare well with the best model predictions (Table 4).

The important exceptions were the VSG model predictions for

the numberof pieces ofwoodandfor numberof pieces oflarge

conifer wood. In these instances,a loss in spatial resolution (to

40 m from 10 m) led to VSG model predictions similar to (at

both the reach and network scale) and well correlated with

model predictions that had finer spatial resolution (see Table 4

and Appendix Tables 911). The implication is that where theprocesses embodied in the VSG model are appropriate, and

when numbers of pieces of wood or of large conifer wood are

an indicatorusefulfor addressingassessmentor management

questions, then information based on less expensive efforts

can provide useful results. For other types of information loss

or simplification, or for other indicators, the resulting model

predictions were different from or not well correlated with

model predictions using more refined data. Of special note is

that forest stand classification always leads to model predic-

tions that were different from or not well correlated with the

model predictions based on the direct enumeration of the

characteristics of the forest stands.

Simple field measures were stronglycorrelated with modelprediction metrics of the potential for riparian forests to

provide wood to the stream network (see Table 5, Appendix

Table 13, and Figs. 5 and 6). The measures were counts of the

cumulative number of trees or basal area within specified

distances of the stream edge.Metrics that can be derived from

remotely sensed imagery were also well correlated with model

predictions of the potential for riparian forests to provide

wood to the stream (see Table 6 and Appendix Table 13). Thus

metrics that can be obtained either from field observations or

from remotely sensed imagery can serve as an effective

indicatorof the potential of a riparian stand to provide wood to

the stream network.

An indicator of the potential of a riparian forest to providewood to the stream network, whether derived from field

measures (and listed in Table 5) or by remotelysensedimagery

(and listed in Table 6), would enable managers to track

changesin thepotential for a specific riparian stand or class of

stands to provide wood to the stream network over time.

Within a class of riparian stands, it would provide the capacity

to rank the potential of sites to provide wood to the stream.

Class definitions or boundaries would needto be delineated by

classes of mortality and transport processes that could be

associated with a stand. For example, in the best case, it is

assumed that trees fall in a random direction. The sensitivity

analysis shows that if all treeswereto fall towards thestream,

the indicator selected could effectively remain the same

(Fig. 6). However,the numberof pieces (or the volume of wood)

would more than double see Appendix Table 8. So, the

indicator would not change, but the assessment of the stand

might. A number of attributes, or context information,

including hillslope, rooting condition, and species influence

tree fall direction (Sobota et al., 2006). As knowledge about the

influence of these factors improves, the usefulness of this

indicator will increase.The use of additional, or context information, to support

the interpretation of a metric is common. One example of this

requirement is in the use of the Observed/Expected index used

to evaluate the health of macroinvertebrate assemblages in

streams (e.g. (Hawkins et al., 2000; Stoddard et al., 2005). The

denominator for this index is developed on the basisof a set of

calculations using candidate variables that describe the

climate and geomorphic settings of stream reaches. In the

case of potential stream wood contributions, the context

variables in the models provide one way to approach the

delineation of this additional information.

These conclusions have some implications for the three

riparian monitoring protocols used within this study region.Clearly, the PCQ method used by the NAWQA program could

be easilyadapted either to provide an indicatorthat would be

correlated with the potential of a site to provide wood to the

stream, orto provide input to a model,such asthe VSGmodel,

which provides predictions of the potential contribution of

wood to the stream network. While remotely sensed imagery

is capable of providing extensive information on forest cover

withknown certainty(Cohen et al., 1995; Stehman et al., 2003)

the seral stage information to be derived from Northwest

Forest Plan monitoring does not appear to be useful for

driving models such the VSG or RAIS models. However,

counts of trees and their size can be derived from remotely

sensed imagery (e.g. Cohen and Spies, 1992; Ohmann andGregory,2002;Fassnachtetal.,2006 )andthisinformationisof

moredirect value in supporting assessmentsof riparianareas

based on the processes reflected in the VSG model. Last, the

EMAP protocol (Peck et al., 2006) does not provide direct

support for developing the field metrics identified in this

analysis.

Acknowledgements

This research would have been impossible without the efforts

of the field crew members who collected the field data upon

which thiseffortis based. In addition to authors (M.B. and J.B.):Brooke Abbruzzese, Mary Barczac, Chris Brugato, Adrien

Elseroad, Andy Herstrom, Christy Larson, Lynn McAllister,

Kouya Nester, Jennifer Sackinger, Greg Verret, and Ann

Versluis. This paper has also benefited by comments from

John Faustini, Phil Kaufmann, Kelly Burnett, and several

anonymous reviewers as well as from the editorial assistance

of Sarah L. Ringold.

Appendix A. Supplementary data

Supplementary data associated with this article can be

found,intheonlineversion,at doi:10.1016/j.ecolind.2008.06.009 .

http://dx.doi.org/10.1016/j.ecolind.2008.06.009http://dx.doi.org/10.1016/j.ecolind.2008.06.009


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Riparian Forest Indicators of Potential Future Stream

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