The influence of riparian vegetation on leaf litterinputs to Boreal Shield streams: implications forpartial-harvest logging in riparian reserves

Elisa A. Muto, David P. Kreutzweiser, and Paul K. Sibley

Abstract: Litter inputs to headwater streams were measured to characterize and describe input patterns to the streams andto quantify the relationships between leaf litter inputs and surrounding riparian characteristics in Boreal Shield forests. Ourgoal was to provide information on litter inputs to streams in the Boreal Shield to determine if partial-harvest logging ofcommercial trees within riparian reserves has the potential to cause significant changes to litter inputs. Total leaf littercomprised 87% deciduous leaves and 13% conifer needles during the June to October periods. Commercial deciduous treespecies accounted for approximately 50% of total deciduous leaf litter inputs. Distance-weighted deciduous basal area wasthe best predictor of overstorey deciduous litter (r2 = 0.597, P = <0.001), indicating that the size, abundance, and proxim-ity of deciduous trees to streams are important to deciduous litter inputs. Leaf litter inputs to Boreal Shield streams arestrongly influenced by surrounding riparian vegetation and can be effectively predicted by stem basal area. A linear regres-sion model based on commercial deciduous tree basal area is presented that can be useful to determine the number of treesto retain within a riparian area to maintain leaf litter inputs at or near preharvest levels.

Resume : Les apports de litiere dans les cours d’eau de tete ont ete mesures pour caracteriser et decrire le profil de cesapports dans les cours d’eau et quantifier les relations entre les apports de litiere de feuilles et les caracteristiques river-aines environnantes dans les forets du bouclier boreal. Notre but etait de fournir de l’information au sujet des apports de li-tiere dans les cours d’eau du bouclier boreal pour determiner si une coupe partielle des tiges marchandes dans les bandesriveraines peut entraıner des changements significatifs dans les apports de litiere. La litiere de feuilles totale etait compo-see de 87 % de feuilles d’essences feuillues et de 13 % d’aiguilles de coniferes durant la periode allant de juin a octobre.Les especes feuillues marchandes representaient approximativement 50 % de toute la litiere de feuilles provenant d’es-sences feuillues. La surface terriere des essences feuillues ponderee par la distance etait le meilleur predicteur de la litieredes essences feuillues de l’etage dominant (r2 = 0,597, P = <0,001), ce qui signifie que la taille et l’abondance des arbresfeuillus et leur proximite des cours d’eau sont des facteurs importants dans les apports de litiere d’essences feuillues. Lesapports de litiere de feuilles dans les cours d’eau du bouclier boreal sont fortement influences par la vegetation riveraineenvironnante et on peut reussir a les predire au moyen de la surface terriere des arbres. Un modele de regression lineairebase sur la surface terriere des essences feuillues marchandes est presente. Ce modele peut etre utile pour determiner com-bien d’arbres il faut conserver dans une zone riveraine pour maintenir les apports de litiere de feuilles au meme niveau oupresque qu’avant la coupe.

[Traduit par la Redaction]

IntroductionRiparian zones are defined as three-dimensional areas that

directly link aquatic and terrestrial ecosystems (Gregory etal. 1991; Naiman et al. 2005). The structure and function ofsmall forested streams is largely dependent on the influencesand inputs from the surrounding riparian plant community(Kaushik and Hynes 1968, 1971; Vannote et al. 1980). Forexample, riparian vegetation can regulate stream microcli-

mate through the modification of light, temperature, and hu-midity (Gregory et al. 1991; Moore et al. 2005), and throughthe amount, timing, and form of allochthonous nutrients andstructural elements that are important for stream productivity(Gregory et al. 1991; Wallace et al. 1997; Meyer et al.1998). Leaf litter is a significant form of organic matter in-put (Kaushik and Hynes 1968; Kreutzweiser et al. 2004) anda critical component within the detrital food web of forestedstreams (Conners and Naiman 1984; Cummins et al. 1989;Abelho 2001). Previous studies have demonstrated or re-viewed the importance of leaf litter decomposition as a crit-ical ecosystem-level process within forested streams(Wallace et al. 1997, 1999; Gessner and Chauvet 2002) andhave demonstrated that alterations to leaf litter inputsthrough logging disturbance can affect detrital-based foodwebs in streams (Bilby and Bisson 1992; Webster et al.1992; Stone and Wallace 1998).

At present, harvesting within riparian reserves alongmapped, permanent streams is uncommon in the Canadianprovince of Ontario and in other jurisdictions. Maintaining

Received 24 April 2008. Accepted 23 January 2009. Publishedon the NRC Research Press Web site at cjfr.nrc.ca on 25 April2009.

E.A. Muto and P.K. Sibley.1 Department of EnvironmentalBiology, Ontario Agricultural College, University of Guelph,Guelph, ON N1G 2W1, Canada.D.P. Kreutzweiser. Canadian Forest Service, Natural ResourcesCanada, 1219 Queen Street East, Sault Ste. Marie, ON P6A 5E2,Canada.

1Corresponding author (e-mail: psibley@uoguelph.ca).

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intact riparian vegetation (riparian buffers) has become apopular practice among forest managers to mitigate loggingimpacts and sustain the ecological integrity of stream com-munities (Lee et al. 2004; Swan and Palmer 2004). A mini-mum 30 m protective riparian buffer along permanentstreams is required as part of the forest management guide-lines within Ontario. However, there is current debatewhether these buffer strips are the best management strategyto ensure healthy aquatic ecosystems (Buttle 2002). Currentforest management guidelines within Ontario promote theemulation of natural disturbance as a means of improvingand maintaining habitat and forest productivity (OntarioMinistry of Natural Resources 2001). Nitschke (2005) hassuggested that partial harvesting within riparian reserves ofheadwater streams may be the best approach to emulate nat-ural disturbance because wildfire would normally burn atlower intensity within areas adjacent to water, leaving por-tions of the overstorey intact. However, partial harvestingwithin riparian areas is a relatively novel approach to ripar-ian forest management, and the effects on stream ecosys-tems have rarely been investigated, particularly the potentialreduction of litter inputs to streams.

Kreutzweiser et al. (2004) showed that selective harvest-ing of up to 42% basal area (BA) removal without protec-tive riparian buffers caused no significant impacts on litterinputs to streams in northern hardwood forests. However,while a few studies provided information on how riparianvegetation can influence the quantity, composition, and tim-ing of litter inputs within boreal forests of North America(France and Peters 1995; France et al. 1998), we could findnone that specifically examined the effects of partial har-vesting in riparian buffers on litter inputs to streams withinboreal mixedwood forests. Some work has been done on leaflitter dynamics in the boreal forests of Sweden, (e.g., Nils-son and Grelsson 1990), but within North America, muchof the research on litter dynamics comes from eastern decid-uous forests (e.g., Fisher and Likens 1973; Merriam et al.1982; Wallace et al. 1995) and from the coniferous-dominated Pacific Northwest (e.g., Richardson 1992;Edmonds and Murray 2002; O’Keefe and Naiman 2006).To develop effective riparian management strategies for bor-eal forest water bodies, regionally relevant information is re-quired to understand and predict the influence of riparianforest characteristics on litter input rates. If a relationship be-tween riparian forest characteristics and leaf litter inputs tostreams can be effectively described, then a model can becreated to predict potential logging impacts on leaf litter in-puts. This information will be useful when consideringpartial-harvest logging in riparian areas as an alternativeriparian management strategy for boreal forest watersheds.

The overall objectives of the study were to (1) character-ize and describe leaf litter input patterns to headwaterstreams in relation to surrounding riparian characteristicswithin boreal mixedwood forests and (2) develop a predic-tive model to estimate the effects of riparian forest harvest-ing on leaf litter inputs to streams. Our approach was (1) tomeasure total and seasonal litter input patterns to low-orderstreams and (2) to describe relationships between litter in-puts and riparian forest composition and structure alongstreams in the Boreal Shield. Our focus was on borealmixedwood watersheds because the implications of logging

in riparian areas on leaf litter inputs could be greater inmixedwood forests with a large deciduous component thanin conifer-dominated boreal forests. Deciduous leaf litter isof greater nutritional value to leaf-shredding macroinverte-brates in streams than conifer leaf litter (Richardson et al.2004). In northern Ontario, boreal mixedwoods comprisethe greatest proportion of productive forest land and areamong the least-studied forest ecosystems (Cameron et al.1999).

Methods

Study areaThe study was conducted within the Domtar Inc. White

River Forest Management Area, which is located about 50–100 km from Lake Superior’s northeast shoreline within theBoreal Shield ecozone of Northeastern Ontario (Fig. 1). Theforests within this area are boreal mixedwoods of black andwhite spruce (Picea mariana (Mill.) BSP and Picea glauca(Moench) Voss), balsam fir (Abies balsamea (L.) Mill.),jack pine (Pinus banksiana Lamb.), white birch (Betula pap-yrifera Marsh.), and trembling aspen (Populus tremuloidesMichx.) (MacDonald 1995). Speckled alder (Alnus rugosa(Du Roi) Spreng.) is a common species along most streams,usually within 1–5 m of the water’s edge. The average ageof riparian forests in the study area is 75 years; BA of coni-fer trees (spruce, fir, and pine combined) ranges between11.9 and 28.9 m2�ha–1, birch range between 5.2 and18.2 m2�ha–1, and aspen range between 0–4.1 m2�ha–1 (D.P.Kreutzweiser, unpublished data). The foundation over muchof the area is Precambrian, granite bedrock, and the soilsmainly consist of Humo-Ferric podzols. The climate in thisregion can be characterized by long, cold winters and short,warm summers (Gunn and Pitblado 2004). The area typi-cally receives 1000 mm of precipitation annually, and theaverage annual air temperature is about 2 8C (Kreutzweiseret al. 2005).

Five stream reaches within low-order watersheds (streamorder 1–3, average bankfull widths of 2.6 to 6.4 m) wereused to measure riparian forest characteristics and litter in-puts over two sampling seasons. The stream reaches wheremoderately incised with riparian slopes ranging between 0%and 75% and an average slope of 18%. The study reachesranged from 200 to 400 m in length. Three of the streamreaches had been partially harvested at up to 50% merchant-able tree removal in all or part of the riparian reserves dur-ing 2004 and 2005. The other two stream reaches wereundisturbed by logging. This study was not an assessmentof the impacts of forest harvesting on litter inputs tostreams, and no comparisons were made between loggedand reference stream reaches. Rather, we used the partiallyharvested riparian areas of logged sites to maximize varia-tion in riparian structure for determining associations be-tween leaf litter inputs and riparian structure. A regressionapproach was used to measure these associations between ri-parian characteristics and leaf litter inputs to litter trapsacross the five stream reaches.

Litter collection and processingDirect, vertical litter inputs were collected in traps con-

structed from plastic bins (61 cm � 40.6 cm). Four 5 cm

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holes were drilled into the bottom of the bins and coveredwith nylon mesh (1 mm) to allow drainage of rainwater.The litter traps were placed at the edges of the stream bank-full width, in an alternating layout, along the stream reachesat approximately 20 m intervals. The placement of the trapswas intended to capture the variation in riparian stand struc-ture along the stream reaches. Ninety-seven traps in totalwere set over the 2 year collection period among the fivestream reaches. In 2006, 48 litter traps were set along theedges of three stream reaches and in 2007, 49 litter trapswere set along the remaining stream reaches. Litter sampleswere collected monthly beginning June 2006 to October2006 and June 2007 to October 2007. The litter was col-lected in separate locations over the 2 years to increase thesample size. Overwinter litter was collected once as an accu-mulation from November 2006 to April 2007. A total of 533litter trap samples were collected over the 2 year collectionperiod. We did not measure lateral litter inputs because ofresource limitations, but we acknowledge that their contribu-tion to total litter inputs in forest streams can be substantial(Benfield 1997). However, we would expect lateral inputs toreflect riparian forest structure similar to vertical litter inputsbecause both litter types originate from riparian trees.

We also collected leaf litter entrained on streambed sub-strates to examine the relationship between leaf litter cap-tured in the streamside litter traps and actual, naturallyentrained leaf litter in streambeds. Grab samples of leaf litteraccumulated against natural obstructions (rock or sticks) inthe streams were taken during fall 2006. The grab sampleswere obtained adjacent to seven randomly selected traps ineach of the three stream reaches sampled during 2006. Onegrab sample was taken from midstream and one was taken

from the wetted channel edge at each of the seven selectedtrap locations, giving a total of 14 samples per stream and42 entrained litter samples for all three streams. These datawere used to determine to what extent the relative propor-tions of naturally entrained overstorey and understorey de-ciduous leaf litter at the stream edges and midstreamreflected the corresponding streamside litter traps.

All litter samples were frozen for storage and then thawedand separated into the following litter types: alder leaves,birch leaves, aspen leaves, jack pine needles, spruce needles,balsam fir needles, understorey litter (shrub or herb litter),wood fragments, and miscellaneous material (seeds, cones,catkins, and unidentifiable fragments). Each litter type wasdried for 48 h at 60 8C and weighed to obtain constant drymass (g�m–2). Each litter type from the trap samples wassubsampled and ashed for 2 h at 500 8C. The ash content ofthe subsamples ranged between 2% and 8% of the dry mass,with an average of 4.27%. Since the proportion of ash con-tent was so small and leaf litter was about 96% organic mat-ter, we did not convert dry mass to ash-free dry mass.

Riparian forest structurePlots were established at each streamside litter trap to

characterize overstorey and understorey tree and shrub com-position and structure. Each plot was established by measur-ing 10 m in each of four directions from the trap to create a20 m � 20 m square plot (400 m2). We used 10 m as thedistance from trap to plot edge because we assumed thattrees greater than 10 m from the traps were unlikely to con-tribute significant amounts of leaf litter in comparison withtrees that were closer. Conners and Naiman (1984) showedthat most litter inputs to traps in a similar forest type within

Fig. 1. A map of Canada showing the Boreal Shield ecozone shaded in grey. The star represents the location of the study area (48821’5@N,85820’46@W).

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the boreal forest of eastern Quebec, Canada, occurred nearthe shoreline (£2 m) and decreased substantially past 6 m.

Commercial trees that were greater than 2.5 cm in diame-ter at breast height (DBH) were considered overstorey trees,and the species, DBH, and distance from the trap were re-corded for those that were in the plots. These data wereused to calculate stem density (stems�ha–1), BA (m2�ha–1),and distance-weighted BA (BA per distance from the trap)for overstorey trees. The understorey tree and shrub charac-teristics were measured within 2 m around each trap. Thenumber and species of all woody vegetation within theunderstorey plot that was greater than 0.5 m in height andless than 2.5 cm in diameter were recorded to provide ameasure of understorey stem density (stems�m–2). The slopeof the riparian plot over the 10 m distance on the banksideof each trap was measured as percent slope using a Suuntoclinometer, and the percent canopy cover directly over eachtrap was measured using a spherical densiometer. The bank-full width of the stream (m) at each trap location was alsomeasured.

Data analysisA forward stepwise linear regression analysis was used to

determine the relationships between various forest ripariancharacteristics and litter input rates from the June–Octobercollection periods in 2006 and 2007. Only data from 87traps were used in the regression analysis because litterfrom overturned traps was not collected. Continuous datawere log transformed and percent data were arcsine/square-root transformed to improve homoscedasticity and normality.The distributions of the dependent and independent varia-bles were standardized by a z-score transformation prior tothe regression analysis. Regressions for each dependentvariable were run separately. The dependent variableswere total litter (g�m–2), overstorey deciduous leaf litter(g�m–2), understorey deciduous leaf litter (g�m–2), and coni-fer litter (g�m–2). The common independent variables forthe regression models were understorey stem density(stems�m–2), overstorey stem density (stems�ha–1), BA(m2�ha–1), distance-weighted BA (BA per distance fromthe trap), canopy cover (%), riparian slope (%), and bank-full width (m) of the stream. However, when developingeach regression model, we included only those independentvariables that corresponded with the dependent variable.For example, when developing the regression model foroverstorey deciduous leaf litter inputs (g�m–2), overstoreydeciduous BA (m2�ha–1) was included but conifer BA(m2�ha–1) was not. We considered the commercial tree litter(white birch and aspen) to be overstorey litter, and allother deciduous leaf litter (mainly alder, beaked hazel(Corylus cornuta), and mountain maple (Acer spicatum))to be understorey litter.

The forward stepwise regression model was applied toidentify the independent variables that were significantly as-sociated with litter inputs after accounting for colinearity.However, to fully describe the relationships between ripariancharacteristics and litter inputs, Pearson product momentcorrelations were applied, prior to the stepwise procedure,to examine the associations between the dependent variableand the significant independent variables that were identifiedin the full regression model (step zero). A two-way analysis

of variance (ANOVA) using litter type and entrained posi-tion was used to determine if there were significant differen-ces in the proportions of overstorey and understoreydeciduous litter among the trap, edge, and midstream en-trained sampling positions. When the ANOVA detected asignificant difference between groups, a Tukey’s test wasused to determine which groups were significantly different.All statistical tests were run using Sigmastat for Windows,version 3.5 (2006, Systat Software, Inc.).

Results

Annual and seasonal litterfallOver the 2006 collection period, total annual litterfall

among traps ranged from 19 to 383 g�m–2�year–1, with anaverage litterfall of 167 g�m–2�year–1. Overwinter litter accu-mulation represented litterfall from November 2006 to April2007 (6 months) and contributed 18% to total annual litterinputs. Overwinter litter comprised miscellaneous material(44%), wood (40%), and conifer needles (13%). The remain-ing 82% of annual litter fell within the June–October periodof 2006. Leaf litter (deciduous leaves and conifer needles)made up approximately 71% of total litter inputs to the trapsduring the June–October period, while the remaining 29%comprised wood fragments and miscellaneous material(seeds, cones, catkins, lichen, etc.). Deciduous leaves (alder,birch, aspen, and understorey) contributed most to leaf litterinputs (87%), and conifer needles made up approximately13% of the remaining leaf litter. Of the total deciduous leaflitter, 53% came from overstorey trees and 47% came fromunderstorey trees and shrubs. Similar trends in leaf litterproportions and input patterns were also shown for theJune–October leaf collection period in 2007 (Table 1).

Average monthly litterfall during 2006, including themonthly overwinter accumulation, is shown in Fig. 2. All lit-ter types showed a peak in litterfall during September andOctober 2006 with the exception of wood, which remainedrelatively consistent throughout the year (between 0.5 and2.6 g�m–2�month–1). As expected, most deciduous material(95%) fell during autumn. Birch litterfallremained <0.4 g�m–2�month–1 during the summer (June–August 2006), peaked at 27 g�m–2�month–1 in September,and declined to 15 g�m–2�month–1 in October. Alder andother understorey litterfall were earlier and more pulsedthan birch through autumn, with a peak in September(18.4 and 10.2 g�m–2�month–1, respectively) and a consider-able decline in both litter types during October (1.4 and0.7 g�m–2�month–1, respectively). Conifer needle input re-mained relatively consistent throughout June to August(*1.6 g�m–2�month–1), followed by a slight increase inSeptember and October (*3.97 g�m–2�month–1). Miscella-neous material also increased in September and October

Table 1. The percentage of total leaf litter inputs during June–October collection periods in 2006 and 2007.

Year

Overstoreydeciduousleaves (%)

Understoreydeciduousleaves (%)

Totaldeciduousleaf litter (%)

Coniferneedles (%)

2006 53 47 87 132007 50 50 85 15

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(*8.3 g�m–2�month–1) and was the largest fraction of over-winter litter (*2.05 g�m–2�month–1). An increase in miscel-laneous material also occurred in June (7.6 g�m–2�month–1),which reflected the reproductive timing of most trees andshrubs in the region and comprised mostly bud caps,cones, catkins, etc.

In-stream entrained leaf litterLitter inputs to traps closely reflected litter entrained in

streams at the wetted channel edges. Entrained leaf litter atthe stream edge comprised 49% overstorey deciduous leaflitter and 43% understorey leaf litter. Trap litter inputs were44% overstorey deciduous leaf litter and 38% understoreyleaf litter. Midstream entrained litter comprised 62% over-storey deciduous leaf litter and 33% understorey leaf litter(Fig. 3). The two-way ANOVA indicated there were no sig-nificant differences among the trap, edge, and midstreamsampling positions (P = 0.641) and there was no significantleaf type and sampling position interaction (P = 0.178).However, there were significant differences between theproportions of overstorey deciduous leaf litter and under-storey leaf litter (ANOVA, P = 0.015), with a higher propor-tion of overstorey than understorey deciduous litter at themidstream position only (Tukey’s test, P = 0.004).

Relationships between litter inputs and ripariancharacteristics

A regression analysis of total litter inputs (g�m–2) onunderstorey stem density, overstorey stem density, BA ofthe plots, percent canopy cover, stream bankfull width, andslope detected significant associations (P < 0.05) betweentotal litter inputs and four of the independent variables atstep 0 of the full regression model. Total litter inputs werepositively correlated with canopy cover (r = 0.724) and BA(r = 0.322), and negatively correlated with slope(r = –0.264) and bankfull width (r = –0.285). When the

stepwise regression model was applied, canopy cover wasthe only variable that was significantly and independentlyassociated with total litter inputs (r2 = 0.525, P £ 0.001).Although canopy cover may be a good predictor of total lit-ter inputs, it was not considered to be a useful independentvariable when attempting to predict the potential effects ofpartial-harvest logging on litter inputs to streams because itdoes not distinguish among riparian species, does not pro-vide information on the composition and structure of the ri-parian vegetation, and may not provide an accurateindication of vegetation removal, as the canopy of nearbyvegetation may overlap. Therefore, litter inputs (g�m–2) werecategorized into overstorey deciduous litter, understorey de-ciduous litter, and conifer needle litter dependent variables,and regressions were run separately for each.

Fig. 2. Mean (+ SE) monthly litter inputs to litter traps in 2006.

Fig. 3. The proportions of overstorey and understorey deciduousleaf litter within streamside litter traps and at the stream edge andmidstream entrained sampling positions. The asterisk indicates asignificant difference between means (± SE).

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A regression analysis was run for overstorey deciduousleaf litter using overstorey deciduous stem density, decidu-ous tree BA, distance-weighted deciduous BA, canopycover, stream bankfull width, and slope as the independentvariables. Overstorey deciduous leaf litter was significantlyassociated (P < 0.05) with four of the six variables at step 0of the full regression model. Overstorey deciduous litter in-puts were positively associated with distance-weighted de-ciduous BA (r = 0.773), deciduous tree BA (r = 0.711),overstorey deciduous density (r = 0.484), and total canopycover (r = 0.379). When the stepwise procedure accountedfor colinearity among the independent variables, thedistance-weighted BA of deciduous trees was the only vari-able that was significantly and independently associatedwith overstorey deciduous litter inputs (r2 = 0.597, P £0.001). These results indicate that not only are the numberand size (BA) of deciduous trees important, but the distanceof deciduous trees to the stream (distance-weighted BA) isalso important for overstorey deciduous leaf litter inputs tostreams. However, we recognized that distance-weightedBA as a dependent variable in a predictive model may notbe an operationally practical measurement when applyingthe model to prescribe logging intensities within riparianareas, and therefore, we reapplied the regression after ex-cluding the distance-weighted BA. The final regressionmodel (Table 2) indicated that overstorey deciduous litter in-puts can be effectively predicted from deciduous tree BAalone (r2 = 0.505, P < 0.001) (Fig. 4).

Understorey deciduous leaf litter (g�m–2) was regressedagainst understorey stem density, total canopy cover, bank-full width and slope. Understorey deciduous leaf litter wassignificantly associated with all of the variables at step zeroof the regression model (P < 0.05) (Table 3). Understoreyleaf litter was positively associated with total canopy cover(r = 0.374) and understorey stem density (r = 0.224) andnegatively associated with bankfull width (r = –0.306) andslope (r = –0.435). The final step in the regression modelrevealed that a combination of understorey stem density,canopy density and slope (r2 = 0.341, P < 0.001) is requiredto predict understorey litter inputs (Table 3).

Conifer needle litter (g�m–2) was regressed against coniferstem density, conifer tree BA, distance-weighted BA, totalcanopy cover, bankfull width and slope. Conifer needle in-

puts were significantly (P < 0.05) and positively associatedwith distance-weighted conifer BA (r = 0.412), conifer treeBA (r = 0.387) and canopy cover (r = 0.227) at step zero ofthe regression model. Although the distance-weighted coni-fer BA was the best measure to predict conifer litter inputsin combination with canopy cover (r2 = 0.240, P = 0.007), itwas not included in the stepwise regression procedure be-cause it is not an operationally practical measurement to ob-tain. The combination of conifer BA and canopy cover (r2 =0.222, P = 0.006) provided the best model to predict needlelitter inputs to streams (Table 4).

Discussion

Litter input patternsTotal annual litter inputs to streams at our sites in boreal

mixedwood forests of northern Ontario, Canada, were simi-lar to litter inputs of riparian forests bordering streams ofsimilar size and latitude in other areas of the world (Ben-

Table 2. Results of the forward stepwise linear regression procedure on overstorey deciduous leaf litter inputs.

Variable r r2 P

Full regression model (with distance-weighted basal area removed)*Overstorey deciduous basal area (m2�ha–1) 0.711 na <0.001Overstorey deciduous density (stems�ha–1) 0.484 na <0.001Canopy cover (%) 0.379 na <0.001

Stepwise regression model{

Overstorey deciduous basal area (m2�ha–1) 0.711 0.505 <0.001

Regression equation{

Overstorey deciduous leaf litter (g�m–2) = 4.753 + 5.525(overstorey deciduous basal area) 0.711 0.505 <0.001

Note: n = 87. r, correlation coefficient; r2, coefficient of determination, and P, probability; na, not applicable.*The full regression model (step 0) indicates the strength and direction of association between the dependent and independent variables.{The stepwise model indicates the variables that are significantly and independently associated with overstorey deciduous leaf litter inputs.{The final prediction equation for the stepwise regression model is shown. In the equation, overstorey deciduous basal area is the

independent variable.

Fig. 4. Regression of overstorey deciduous litter inputs based onoverstorey deciduous basal area of the riparian forest plots. Theprediction equation is overstorey deciduous leaf litter = 4.753 +5.525 (overstorey deciduous basal area) (r2 = 0.505, P = <0.001).The values represent litter collected from June–October in 2006 or2007.

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field 1997). However, the species composition and propor-tions of litter within a given geographical area are reflectiveof local factors that may influence riparian vegetation, suchas the age and composition of the riparian forest, the fre-quency of disturbance, light availability, and site productiv-ity (Xiong and Nilsson 1997; Balian and Naiman 2005;Naiman et al. 2005). At our sites, leaf litter inputs (decid-uous leaves and conifer needles) throughout June to Octoberaccounted for 71% of total litter inputs to our litter traps.These results are consistent with those reported by Xiongand Nilsson (1997) who indicated that riparian-derived leaflitter can account for between 72% and 80% of total litterproduction depending on the forest composition.

Deciduous leaf litter accounted for 87% and conifer nee-dles accounted for approximately 13% of total leaf litter in-puts. The proportion of conifer needle inputs was similar tothat found within streamside traps in the boreal forest of

Quebec (Conners and Naiman 1984). Conifer trees ac-counted for 72% of the stem density within our vegetationplots but contributed very little needle litter in comparisonto deciduous trees, a pattern that was also noted by Connersand Naiman (1984). This trend may be a function of treephysiology (low turnover rate of conifer needles), as sug-gested by Conners and Naiman (1984), but may also be afunction of the successional stage of the forest (Richardson1992).

Overstorey and understorey litter each accounted forroughly 50% of the total deciduous litter inputs. We initiallyassumed that the contribution by understorey species to leaflitter inputs would be larger because of a characteristic rib-bon of shrub species, particularly alder, bordering the streamsin these boreal mixedwood forests. However, it was clear thattotal deciduous litter inputs to streams were equally reliant onboth overstorey and understorey riparian vegetation.

Table 3. Results of the forward stepwise linear regression procedure on understorey litter inputs.

Variable r r2 P

Full regression model*Canopy cover (%) 0.374 na <0.001Understorey stem density (stems�m2) 0.224 na 0.037Bankfull width (m) –0.306 na 0.004Slope (%) –0.435 na <0.001

Stepwise regression model{

Slope (%) 0.435 0.190 <0.001Canopy cover (%) 0.520 0.271 <0.001Understorey stem density (stems�m2) 0.584 0.341 0.004

Regression equation{

Understorey litter (g�m–2) = –1.375 + 1.617(understorey stemdensity) + 41.928(canopy cover) – 33.512(slope)

0.584 0.341 0.004

Note: n = 87. r, correlation coefficient; r2, coefficient of determination; P, probability; na, not applicable.*The full regression model (step 0) indicates the strength and direction of the association between the

dependent and independent variables.{The stepwise model indicates the variables that are significantly and independently associated with

understorey litter inputs.{The final prediction equation for the stepwise regression model is shown. In the equation, understorey stem

density, canopy cover, and slope are the independent variables.

Table 4. Results of the forward stepwise linear regression procedure on conifer litter inputs.

Variable r r2 P

Full regression model*Conifer basal area (m2�ha–1) 0.387 na <0.001Canopy cover (%) 0.227 na 0.034

Stepwise regression model{

Conifer basal area (m2�ha–1) 0.387 0.150 <0.001Canopy cover (%) 0.471 0.222 0.006

Regression equation{

Conifer needle litter (g�m–2) = –15.672 + 1.473(conifer basal area) +18.510(canopy cover)

0.471 0.222 0.006

Note: n = 87. r, correlation coefficient; r2, coefficient of determination; P, probability; na, not applicable.*The full regression model (step 0) indicates the strength and direction of association between the dependent

and independent variables.{The stepwise model indicates the variables that are significantly and independently associated with conifer

litter inputs.{The final prediction equation for the stepwise regression model is shown. In the equation, conifer basal area

and canopy cover are the independent variables.

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At our sites, we observed that speckled alder lost leaf ma-terial slightly earlier than other deciduous species in autumn.A similar trend within the Pacific Northwest was detectedfor red alder (Alnus rubra) (O’Keefe and Naiman 2006).We collected samples at monthly intervals during the fall,but more frequent sampling during this peak leaf-fall periodmay have allowed us to detect more subtle timing differen-ces among species and should be considered for future sam-pling initiatives. Conifer needle inputs showed someincrease in autumn but were relatively consistent throughoutthe summer months, and they were greater than deciduouslitter inputs during that time. Miscellaneous material peakedin June concurrent with the reproductive timing of most treespecies within this region. Differences in the seasonality andtiming of leaf fall from riparian trees and shrubs may pro-vide important nutrient subsidies to streams when allochtho-nous resources are limiting in the spring and summermonths, and may sustain stream communities dependent onlitter inputs that occur during the autumn leaf-fall period.

In-stream entrained litterParticulate organic matter is generally retained relatively

close to the point of entry to the stream (Webster et al.1994; Quinn et al. 2007). Retention is higher in smallerstreams and is related to stream depth and the number of ob-structions within the stream (Webster et al. 1994). We foundno differences in the proportions of leaf litter among trap,edge, and midstream sampling locations, and the proportionsof overstorey and understorey leaf litter in the trap and atthe stream edge were similar indicating that the compositionof leaf litter in streamside traps is more reflective of en-trained litter at the stream edge than at midstream positions.The proportion of overstorey litter at midstream was signifi-cantly larger than the understorey proportion. As under-storey leaf litter enters the stream, it may be immediatelyretained by obstructions, such as root masses and low-growingbranches close to the stream bank. In contrast, the crownsof deciduous overstorey trees are larger and extend overthe stream where leaves are more likely to distributeevenly throughout the stream during litterfall in autumn.However, we are confident that our streamside traps gavea reasonable indication of whole-stream leaf litter inputsbecause we would expect that high water levels associatedwith typical fall runoff would redistribute stream-edge litterinto the mainstream. Our entrained litter samples weretaken during base flow in the fall before a significant risein water levels had occurred.

Predicting litter inputs to streamsTotal litter inputs were significantly correlated with adja-

cent riparian characteristics. The strongest relationship wasbetween total litter and canopy cover. However, canopycover was not considered to be a useful measure to predictlitter inputs because it included both overstorey and under-storey canopy and did not account for the overlap in treecrowns or for the number and species of trees. O’Keefe andNaiman (2006) found that crown volumes of individual treespecies were strongly correlated with corresponding leaf lit-ter deposits, but crown volume is not a practical measure forprescribing harvesting intensities. From a riparian manage-ment perspective, in terms of prescribing a given logging in-

tensity, a more practical predictor variable, or set ofvariables, was required. In this regard, we focused on leaflitter categories as our dependent variables because theycontributed 71% of total litter inputs and because they aredirectly relevant to tree removal or understorey regenerationfrom riparian logging activities. When overstorey leaf litterinputs (deciduous and coniferous) were examined separately,the common significant predictor variable was tree BA. Paststudies have documented the use of BA as an effective pre-dictor of litterfall. Turnbull and Madden (1983) and O’Keefeand Naiman (2006) found significant associations betweenthe BA of individual tree species and their corresponding lit-ter inputs, and Edmonds and Murray (2002) found a rela-tionship between needle litter and western hemlock BA inconifer dominated forests of the Pacific Northwest.

Our final regression model to predict overstorey decidu-ous leaf litter inputs to streams was based on deciduous BAbecause it is a useful, practical measure for setting loggingintensity targets. Deciduous tree BA was significantly andindependently associated with overstorey deciduous litter in-puts, and it accounted for 50% of the variation in overstoreydeciduous leaf litter inputs. The strength of the relationshipsbetween deciduous litter and BA was slightly less than thatreported by O’Keefe and Naiman (2006). However, theylooked at the relationships between litter inputs of individualtree species and corresponding BAs. The distance-weighteddeciduous BA was a slightly better predictor of overstoreydeciduous leaf litter, accounting for nearly 60% of the varia-tion in overstorey deciduous litter inputs, indicating that notonly are the frequency (stem density) and size of the treesimportant to leaf litter inputs but so is the proximity of thosetrees to the stream.

Although we did detect a significant relationship betweenneedle litter and conifer BA, we were unable to effectivelypredict conifer needle inputs based on BA alone. Our siteswere a mixture of conifer and deciduous vegetation of vari-ous sizes. Conifer density within the vegetation plots wasoften high, but most conifers were generally smaller (£10DBH) and occurred in clusters throughout the plots with afew larger dominant overstorey conifers present. It is likelythat conifer needles do not travel as far as deciduous leavesbecause of differences in shape and aerodynamic character-istics. This indicates that conifer needle inputs to streams aremost likely to be derived from immediate streamside coni-fers and were, therefore, unrelated to total conifer BA in theplots.

Management implicationsSelective harvesting in riparian buffers of northern hard-

wood forests at up to 42% BA removal did not affect litterinputs to streams (Kreutzweiser et al. 2004). However, theseresults may not apply to boreal mixedwood forests, whereunderstorey and overstorey riparian structure is considerablydifferent from northern hardwood forests. A study by Gov-erno et al. (2004) conducted in the southern bottomland for-ests of Alabama also found that litter inputs to streams ofpartially harvested riparian areas were similar to litter inputsfrom control forests. They attributed their results to harvest-ing recommendations in the Alabama best managementpractices (BMP), i.e., that trees near stream banks remainuncut, suggesting that trees left at the stream bank provided

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the majority of litter inputs to streams (Governo et al. 2004).In contrast, a study conducted in northern Minnesota showeda decline in litter inputs from partially harvested riparianbuffers compared with litter inputs from uncut riparian buf-fers and showed that one-third of litter inputs can originatefrom vegetation beyond 30 m (Palik et al. 1999).

The removal of overstorey deciduous trees within mixed-wood riparian areas may be important because of the highproportion of leaf litter that these trees contribute (50%).Aspen and white birch can be valuable commercial tree spe-cies in Ontario (OMNR 2006) and can be removed from ri-parian areas under a partial-harvest prescription. Somedirection for retaining overstorey deciduous trees in riparianareas would be required to sustain leaf litter inputs tostreams. Our results showed that distance-weighted BA wasa good predictor of litter inputs to streams. Therefore, wesuggest that larger (mature) deciduous trees should be pref-erentially retained closer to the stream. We can use our pre-dictive model to determine the number of trees of aparticular size to retain within a desired distance from thestream to sustain litter inputs at a given level. For example,the average overstorey deciduous leaf litter inputs at ourplots was 35.95 g�m–2. To determine the number of treesneeded to sustain leaf litter inputs at that level, we calcu-lated the number of mature trees (e.g., >25 cm DBH) perhectare by dividing the average overstorey deciduous BA atour plots (5.65 m2�ha–1) by the BA (0.049 m2) of a 25 cmDBH tree and then dividing the length (2000 m) of a 5 mwide section along the stream by the number of trees perhectare. We determined that at least one mature (>25 cm)tree every 17 m should be retained within 5 m of the streamto sustain litter inputs at 35.95 g�m–2. However, in an areawhere deciduous trees are dominant, overstorey deciduouslitter inputs would be higher and, therefore, more treesshould be retained to sustain those litter inputs. For example,the predicted overstorey deciduous leaf litter inputs for a BAof 12 m2�ha–1 was 71.05 g�m–2 (Fig. 4). Based on our model,one mature tree every 8 m would be needed to sustain over-storey deciduous leaf litter inputs at that level. These are notspecific prescription targets but are provided as examples ofhow this model could be used to develop riparian harvestingprescriptions in various areas, depending on the preharveststanding stock of coniferous and deciduous trees. We used adistance of 5 m from the stream because our results suggestthat deciduous overstorey trees be preferentially retainedwithin a few metres of stream edges, since we showed thattrees closer to the stream accounted for more leaf litter in-puts (by the influence of distance-weighted BA on litter in-puts).

Our regression model for overstorey deciduous leaf litterinputs can also help to determine the extent to which decid-uous leaf litter inputs could be reduced if deciduous treeswithin the riparian area were removed. For example, a 70%reduction in overstorey deciduous BA would reduce over-storey deciduous leaf litter inputs by 61%, from 35.95 to14.11 g�m–2 (See Fig. 4). Considering that understorey treesand shrubs can contribute an additional 50% of leaf litter tostreams, overall deciduous litter would be reduced by 31%,from 69.71 to 47.87 g�m–2. In our study area, white birchwas the dominate overstorey deciduous tree species in ripar-ian areas and accounted for almost 98% of overstorey decid-

uous litter inputs. In boreal mixedwood areas where aspentrees dominate, tree retention requirements along streamsmay be different, but we had insufficient data to predict as-pen litter inputs from a given aspen BA.

Some of our litter traps were in riparian stands where par-tial harvest had occurred 1 or 2 years previously. The litterinputs to those traps reflected the removal of commercialtree species and did not appear to have been influenced bychanges in understorey vegetation in response to gap open-ings. We observed (but did not quantify) that the gaps cre-ated by partial harvest at our trap locations wereinsufficient to cause rapid revegetation or changes in under-storey shrub or tree composition in the 1–2 year postloggingperiod. Litter inputs may change over time as understoreyvegetation responds to the gap openings, and these shouldbe examined in a subsequent study.

It is not clear to what extent reductions in deciduous leaflitter inputs to boreal forest streams can occur without meas-urable impacts on stream invertebrate communities and or-ganic matter dynamics. It is known that the productivity ofstream invertebrates can be limited by food supply (Ri-chardson 1991; Wallace et al. 1999). However, less isknown about the threshold in litter reduction to whichchanges in invertebrate productivity would occur. Melodyand Richardson (2004) found that a 75% reduction in litterinputs to experimental mesocosms resulted in differences ininvertebrate densities, suggesting food limitation. Rubbo etal. (2008) also found that variations in leaf litter inputs canaffect the structure of aquatic food webs in artificial pondsand that increasing the quantity of leaf litter can positivelyinfluence the productivity of amphibian larvae, also provid-ing evidence that suggests food limitation by reduced leaflitter. Further studies would be useful to determine to whatextent a reduction in deciduous leaf litter inputs to streamswould invoke invertebrate community responses as a resultof food limitation and to determine if a threshold of leaf lit-ter loading to boreal forest streams exists. This in turn couldprovide a target leaf litter input rate to which our predictivemodel could be applied for prescribing deciduous tree reten-tion rates in riparian buffers of boreal forest streams.

AcknowledgementsWe greatly appreciate the technical assistance in field

sampling and laboratory processing by Scott Capell, KevinGood, Mandy Roberts, Ben McKay, Michelle Nadeau, andRory McGraw. Partial support for this project was providedby a Natural Sciences and Engineering Research Councilgrant to PKS and an Ontario Enhanced Forest ProductivityScience Program grant No. 010-2-R1 to DPK.

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