Structural features of fragmented woodland communities affect leaf litter decomposition rates

Basic and Applied Ecology 14 (2013) 298–308

Structural features of fragmented woodland communities affect leaf litter decomposition rates Graeme T. Hastwella,∗ , E. Charles Morrisb a b

Ecology & Environment Research Group, University of Western Sydney, Locked Bag 1797, Penrith, NSW 2751, Australia School of Science and Health, University of Western Sydney, Locked Bag 1797, Penrith, NSW 2751, Australia

Received 1 May 2012; accepted 25 March 2013 Available online 2 May 2013

Abstract Disruption to the physical structure of plant communities by habitat fragmentation can change microclimates, so leaf litter decomposition rates, being dependent on temperature and moisture, may also be affected. Similarly, smaller-scale structural features of plant communities can modify microclimates, and so may produce distinctive spatial patterns in decomposition rates. We investigated the effects of three types of structural feature having the potential to alter litter layer microclimates: fragmentationinduced modification that diminishes with distance from remnant edges (edge-core); concentric zones of locally modified conditions imposed by individual trees (Belsky–Canham); and highly localised abiotic modification collectively imposed by herbaceous plants (ground cover). We conducted a litter bag experiment in woodland remnants, testing whether the observed spatial variability in litter decomposition was attributable to one or more of these three structural features. The data provided the strongest support for the Belsky–Canham hypothesis, and the least support for the ground cover hypothesis. However, the hypotheses were not mutually exclusive, for each explained a component of the observed variability not explained by either of the other two. Proximity to remnant edge, proximity to trees, canopy light penetration, and ground cover density each explained part of the observed variability between plots. Decomposition rates did not differ with remnant area per se, for the effects of fragmentation were weak, and differed with cardinal direction. In contrast, the effects of individual trees were much stronger, and accounted for most of the between-plot variability. We found that litter decomposition rates in small remnants are only weakly affected by fragmentation, and we consider that the contributions of small remnants to landscape-scale functioning warrant closer attention.

Zusammenfassung Die Unterbrechung der physikalischen Struktur von Pflanzengemeinschaften durch Habitatfragmentierung wird häufig von Änderungen des Mikroklimas begleitet. Damit können die Streuzersetzungsraten, die von Temperatur und Feuchtigkeit abhängen, ebenfalls betroffen sein. In ähnlicher Weise können kleinräumige Strukturmerkmale von Pflanzengemeinschaften das Mikroklima beeinflussen und so markante räumliche Muster der Zersetzungsraten bewirken. Wir untersuchten die Effekte von drei Typen struktureller Merkmale, die potentiell das Mikroklima in der Streuschicht verändern können: durch Fragmentierung induzierte Modifikationen, die mit der Entfernung vom Rand des Habitats abnimmt (Rand-Kern), konzentrische Zonen mit lokal modifizierten Bedingungen, die von einzelnen Bäumen hervorgerufen werden (Belsky-Canham), und örtlich stark begrenzte Wechsel, die kollektiv von den Pflanzen der Krautschicht bewirkt werden. In Waldfragmenten führten wir ein Streubeutel-Experiment durch und untersuchten, ob die beobachtete räumliche Variabilität der Streuzersetzung auf ein oder

∗ Corresponding

author. Tel.: +61 2 4570 1635; fax: +61 2 4570 1383. E-mail address: [email protected] (G.T. Hastwell).

1439-1791/$ – see front matter © 2013 Gesellschaft für Ökologie. Published by Elsevier GmbH. All rights reserved. http://dx.doi.org/10.1016/j.baae.2013.03.002

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mehrere strukturelle Merkmale zurückgeführt werden konnte. Die Daten erbrachten die stärkste Unterstützung für die BelskyCanham-Hypothese und die geringste für die Krautschicht-Hypothese. Indessen schlossen sich die Hypothesen nicht gegenseitig aus, denn jede erklärte einen Teil der beobachteten Variabilität, die nicht von den anderen beiden Hypothesen erklärt worden war. Die Nähe zum Waldrand, Nähe zu Bäumen, Lichtdurchlässigkeit der Kronenschicht und Dichte der Krautschicht erklärten jeweils einen Teil der Variation zwischen den Probestellen. Die Streuzersetzungsraten variierten nicht mit der Größe der Waldfragmente selbst, denn die Effekte der Fragmentierung waren schwach, aber sie variierten mit der Lage (Himmelsrichtung) der Probestellen im Waldstück. Dagegen waren die Effekte von einzelnen Bäumen viel stärker und erklärten den Hauptteil der Variabilität zwischen den Probestellen. Wir fanden, dass die Streuabbauraten in kleinen Waldstücken nur schwach von der Fragmentierung beeinflusst werden, und wir meinen, dass die Beiträge kleiner Waldfragmente zur Funktionalität auf der Landschaftsebene größere Aufmerksamkeit verdienen. © 2013 Gesellschaft für Ökologie. Published by Elsevier GmbH. All rights reserved. Keywords: Belsky–Canham model; Cumberland Plains Woodland; Ecosystem services; Edge effects; Habitat fragmentation; Microclimate; Nutrient cycle

Introduction Although the adverse effect of habitat fragmentation on biodiversity is a frequent theme in the ecological literature (Fahrig 2003), little research has been conducted into the effects of fragmentation on ecosystem processes (Turner 2005). For example, the published literature on litter decomposition in fragmented landscapes comprises four studies, three of which were conducted at the same site with divergent results (Didham 1998; Rubinstein & Vasconcelos 2005; Vasconcelos & Laurance 2005, see also Lindsay & Cunningham 2009). Given litter decomposition’s central role in the nutrient cycle (Vitousek 2004), the effects of fragmentation on decomposer processes should be determined. Further, litter dynamics regulate fuel accumulation and hence affect fire dynamics (Walker 1981), while they also play an important—albeit idiosyncratic—role in regulating plant community composition through litter’s effects on recruitment (Facelli & Pickett 1991; Hastwell & Facelli 2000). Consequently, any effects of fragmentation on litter decomposition are of great relevance to managing fragmented landscapes. Habitat fragmentation involves major changes to the physical structure of vegetation beyond remnant edges, altering the microclimate within remnants (Chen et al. 1999; DaviesColley, Payne, & van Elswijk 2000). This gives a prima facie reason to expect temperature- and moisture-sensitive ecosystem processes, such as primary productivity, nutrient cycling and litter decomposition, to be affected (Aerts 1997; Vitousek 2004; Zhang & Zak 1995). The spatial variability in temperature and moisture arising from fragmentation should produce corresponding spatial patterns in these processes. Structural features of vegetation—such as tree canopies—can also locally modify temperature and moisture through shading, rainfall redirection and reduced air flow (Belsky & Canham 1994; Vetaas 1992), so our focus is to determine whether spatial patterns in litter decomposition within remnants are attributable to fragmentation or to other features. We hypothesized that three types of structural

features, each operating at different spatial scales, have the potential to affect litter decomposition by modifying temperature and moisture.

The edge-core hypothesis Within-remnant microclimatic variability induced by habitat fragmentation (Chen, Franklin, & Spies 1995; Redding, Hope, Fortin, Schmidt, & Bailey 2003; Young & Mitchell 1994) is often dichotomised in the literature into edge and core zones, with conditions being modified according to the zone’s proximity to the ‘matrix’ (e.g. Laurance & Yensen 1991). An edge zone’s microclimate is said to be strongly influenced by the conditions prevailing in the adjacent ‘matrix’, while the core zone is defined as the unmodified interior region. In the simplest representations of the ‘edge-core’ hypothesis, abrupt stepwise thresholds mark the boundaries between the ‘matrix’ and the edge zone, and between the edge zone and the core zone (Fig. 1A). Observed microclimatic patterns are often more complex (Chen et al. 1995; Davies-Colley et al. 2000; Redding et al. 2003), so some ecologists argue that the relationship between abiotic conditions and proximity to the ‘matrix’ is better modeled as a gradient (Fischer & Lindenmayer 2006; Saunders, Chen, Drummer, & Crow 1999, Fig. 1B).

The Belsky–Canham hypothesis Individual tree canopies strongly modulate solar inputs, and may intercept or redirect rainfall (Breshears 2006). Belsky and Canham (1994) characterized these modifications as sets of concentric ‘zones of influence’ around individual trees. These zones diminish in magnitude with distance from the tree, but may extend somewhat beyond the perimeter of the canopy (Fig. 1C). Under the Belsky–Canham hypothesis, individual trees locally modify their environment, altering soil nutrient and moisture levels, reducing temperatures and evaporation rates, and changing light quality and intensity (Belsky, Mwonga, Amundson, Duxbury, & Ali 1993; Facelli

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Microclimate

(A)

(B)

Edge

Core

Edge

(C)

Core

(D)

Microclimate

Core

Edge

Core

Distance

Edge

Distance

Fig. 1. Postulated within-remnant relationship between microclimatic factors and distance from remnant edge, based on the (A) step-wise and (B) gradient versions of the edge-core and (C) Belsky–Canham hypotheses. (D) Represents the combination of the two hypotheses.

& Brock 2000; Vetaas 1992). As the inter-canopy gaps decrease, the zones of influence subtended by each tree begin to overlap, so that microclimatic spatial variability changes with the degree of discontinuity in canopy cover (Breshears 2006; Martens Breshears, & Meyer 2000).

The ground cover hypothesis Guttation from herbaceous vegetation can enhance moisture levels in the litter layer and prolong the duration of leaf wetness (Gilliam 2007; Hughes & Brimblecombe 1994), creating a ground cover microclimate that may accelerate litter decomposition. Conversely, dense ground cover may indicate local edaphic or hydrological features that promote decomposition. We hypothesized that litter decomposition rates were correlated with ground cover density.

Comparing predictions The spatial patterns in microclimate predicted by the edge-core and Belsky–Canham hypotheses differ from each other when remnants have discontinuous canopies (Fig. 1). Further, the patterns in soil temperature and moisture imposed by discontinuous canopies (Belsky & Canham 1994; Breshears 2006) suggest that litter decomposition rates in woodland communities are unlikely to be spatially uniform irrespective of any fragmentation. In contrast, the edge-core

model predicts differences in litter decomposition between, but not within, the two zones. This does not preclude the possibility that canopies may also collectively contribute to spatial variability, giving a hybrid pattern (Fig. 1D). Further, it is apparent that the remnant-level effects of fragmentation will depend on both the width of the edge zone, and the magnitude of fragmentation-induced modification relative to the underlying canopy-driven variability in microclimate.

Cumberland Plains Woodland: a model system The Cumberland Plains Woodland (cpw), a highly fragmented eucalypt-dominated plant community in peri-urban Sydney, Australia, offers a model system for studying the effects of fragmentation on litter decomposition. First, clearing for agriculture has reduced the cpw to less than 11% of its former extent, with most remnants being less than 79 ha (French, Callaghan, & Hill 2000; Tozer 2003). Next, the recalcitrant leaf litter produced by the cpw offers a sensitive test of temperature-induced changes in decomposition rates, for microbial response to temperature increases as litter quality decreases (Fierer, Craine, McLauchlan, & Schimel 2005). In addition, cpw canopy cover varies from sparse to mid-dense, and ground cover from very sparse to very dense, enabling testing across a wide range of levels of local modification.

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Table 1. Site dimensions. Length is the remnant’s longest axis. Topographic relief was interpolated from the 1:25,000 series topographic maps (Kurrajong 9030-4N 3rd edition and Wilberforce 9030-1N 3rd edition).

of mature cpw trees. Girth was measured 1 m above ground. ‘Trunk bearing’—which in part determines whether a tree will shadow a plot—was given as the bearing from the plot to the largest nearby tree with respect to magnetic south, i.e.

Locale and remnant

Area (ha)

Length (m)

Trunk bearing = | 180 − compass bearing |

Richmond A Richmond B Scheyville A Scheyville B

3.5 101.7 62.1 60.3

357 1903 1480 1976

Relief (m) 10 10 >30 >20

We conducted a litter bag experiment to test whether proximity to remnant edges, proximity to individual trees, or ground cover density could explain spatial variability in litter decomposition rates in the (cpw). We further employed the method of multiple working hypotheses (Chamberlin 1897; Elliott & Brook 2007) to test whether each of our three hypotheses accounted for a distinct component of the observed variability in litter loss.

Materials and methods Of the cpw remnants in the study area, we excluded those showing evidence of recent anthropogenic disturbance, or that were extensively invaded by exotic plants. We selected remnants spanning the available range of sizes and shapes, establishing 25 76 cm × 76 cm randomly located plots within each of four remnants (Table 1, see Appendix A: Table 1). Plots were between 2.7 and 280 m from the nearest remnant edge, and were a minimum of 4 m apart. Two remnants were in Scheyville National Park (locale ‘Scheyville’) and the others were on the University of Western Sydney’s Hawkesbury campus (locale ‘Richmond’), 16 km from Scheyville. One plot at Scheyville remnant A was vandalised, so no data from that plot are presented here. The two locales have similar climates (Bureau of Meteorology 1979; Tozer 2003), but differ in soils and topography. The Richmond locale is on flat, poorly drained podsolic sands with heavy clay B horizons, while Scheyville is undulating with clay soils. Mean annual rainfall at a nearby official weather station is 801.4 mm (Bureau of Meteorology, 1881–2008; Richmond, UWS Hawkesbury). Mean monthly minima and maxima for January and July are 16.8 and 29.4 ◦ C, and 3.2 and 17.3 ◦ C respectively (Bureau of Meteorology, 1907–1975).

Characterizing plots and remnants To test our hypotheses, we measured the size and proximity of nearby trees, and ground cover for each plot. We calculated remnant areas and the distances of plots from edges using differentially corrected gps data. We measured the location and girth of the largest tree trunk within 6 m of each plot, which approximates the canopy radii

The sun traverses the north sky south of the equator, so plots with Trunk bearing = 0 are not shaded by the largest nearby tree unless the tree is large and the plot is near its trunk; plots with Trunk bearing = 90 are shaded by the largest nearby tree early or late in the day; and plots with Trunk bearing = 180 are shaded by the largest nearby tree at midday unless the tree is small and the plot far from the tree. It was not feasible to monitor soil temperature and moisture across 100 plots. Instead, we used canopy openness as a proxy, as transmitted solar radiation is a major determinant of microclimate (Geiger 1966). Canopy openness thus provided a measure of the collective effect of all nearby trees. We used GLA v2.0 to process vertical fisheye photographs, and to calculate the cumulative transmitted solar radiation to the end of each retrieval period for each plot, accounting for seasonal differences in day length and solar angle (Frazer, Canham, & Lertzman 1999). Then, for each litter bag, we estimated the mean total transmitted solar radiation (‘Light’) for each retrieval period as Light =

Cumulative transmitted solar radiation Days

where Days are the duration of litter bag exposure. Herbaceous ground cover was visually estimated using a modified Braun–Blanquet scale (see Appendix A: Fig. 5).

Leaf litter and litter bags Litter bags were made from white polyester fabric having 2 mm × 3 mm oval pores in rows 4.5 mm apart. This allowed access by most invertebrate detritivores, while retaining undecomposed leaf material (Bradford, Tordoff, Eggers, Jones, & Newington 2002). The 19 cm × 38 cm bags were labelled with metal tags, and were randomly allocated to plots and to retrievals within the 76 cm × 76 cm plots. Leaves were collected from a dead Eucalyptus moluccana—a common cpw tree—to provide a uniform substrate for decomposers. Twigs, stems and galled leaves were discarded to homogenise the litter. Leaves were oven dried to constant weight at 65 ◦ C, and a weighed amount of dried leaves was added to each bag (4.134 ± 0.021 g, mean ± s.e., n = 792). At each plot, we removed surface litter and clipped any forbs or grass before placing eight litter bags in a 4 × 2 array, affixing the bags to the soil surface with nails, and placing wire mesh above them to deter kangaroos. A total of 800 litter bags were deployed 17 December 2007 to 9 January 2008, of which 792 were later retrieved. Two randomly assigned litter bags were retrieved from each plot on four occasions over an 18-month period; in May 2008, September 2008,

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January 2009 and late May–early June 2009. Bag contents were dried to constant weight at 65 ◦ C, sorted with a 2 mm sieve, hand-picked to remove foreign particles and extraneous plant material, and ashed to account for any remaining inorganic material. The bags’ pore size and uniform litter helped us identify and remove any extraneous material that entered the bags during the experiment, thereby mitigating a source of measurement error. We also ashed 14 leaf samples that had not been exposed to field conditions, and used linear regression to estimate the ash fraction of clean leaves.

Model selection and data analyses We adopted the method of multiple working hypotheses (Chamberlin 1897; Elliott & Brook 2007) to allow the possibility that each of the three hypotheses accounts for a different component of the observed variability in litter loss. We built models—fitting parsimonious statistical models for each hypothesis by identifying models with the lowest Akaike’s Information Criterion (aic)—and then selected models using aic to compare parsimonious models singly and in combination, thus determining whether the models offer competing or complementary explanations (Burnham & Anderson 2004, also see ‘Model building and model selection’ in Appendix A). Model building involved iteratively reducing full models, fitting all models that differed from the current model by a single non-marginal term (interaction terms being nonmarginal to their main effects, and main effects becoming non-marginal when they are no longer in any of the model’s interaction terms). We removed whichever non-marginal term would minimise model aic, and iterated until this procedure could not further reduce model aic (Pinheiro & Bates 2000; Venables & Ripley 1999). We then allowed deletion of any non-marginal terms that would increase model aic by less than 2, e.g. Days × Light × Trunk distance (Appendix A: Table 2), on the grounds that the models did not differ ‘significantly’, and that—given potentially unstable parameter estimates amongst correlated terms—this may lead to other terms being removed and model aic being further reduced. All statistical models included a random-effects term to model the within-plot repeated measures; initial dry weight and the duration of exposure (“Days”) as covariates (Table 2); and final ash-free dry weight as the response variable (collectively, “Shared” terms). We used R 2.7.1 with the nlme package for analyses (Pinheiro & Bates 2000; R Development Core Team 2008). Data met the assumptions of anova, and did not need transformation.

Visualizing relationships with effect displays We used effect displays to visualise the effects of interaction terms in isolation from the effects of other terms in the models (Fox 2003). Effect displays plot fitted values of terms related by marginality, the fitted values being computed by

Table 2. Main effects used in the statistical models. Initial wt: dry weight of litter placed in litter bag; Days: duration of the deployment of the litter bag; Locale of remnant; Ground cover: a categorical term for visual estimates of ground cover; Area of remnant; East: distance of plot from the nearest easterly remnant edge (likewise for North, South and West); Light: mean estimated total transmitted solar radiation to which the litter bag was exposed; Trunk bearing: bearing from the plot to the largest tree trunk within 6 m (likewise for Trunk distance and Trunk girth). Term

Ground cover

Edge-core

Belsky–Canham

Initial wt Days Locale Ground cover Area East North South West Light Trunk bearing Trunk distance Trunk girth

   

  

  

    

   

combining main effects with their interactions and holding other predictors constant.

Results Visual examination of the data suggests litter loss rates tended to be higher at Scheyville than at Richmond. Across both Locales, median mass remaining at the first retrieval was 60% of the initial dry weight (Fig. 2). Some 3% of weight loss was due to the ashing procedure (ash weight = 0.029936 × clean leaf dry weight, s.e. = 0.004663, n = 14, r2 = 0.7745). Despite the close proximity of litter bags to each other, within-plot variability in litter loss was large, and more variable at Scheyville than at Richmond (see Appendix A: Fig. 1).

Data analyses Selecting parsimonious models Several terms in the initial Edge-core and Belsky–Canham models were not supported by the data, and so were not retained in the parsimonious models. Edge-core: Although remnant area varied 30-fold, there was no support for retaining Area as an explanatory factor (see Appendix A: Table 2). Of the edge proximity terms, the North × South and East × West terms were not supported, indicating that while decomposition rates may be affected by proximity to an edge, that effect is not conditional on proximity to the opposite edge. Had edge-effects been strong and extensive, with plots being affected by opposite edges, the

G.T. Hastwell, E.C. Morris / Basic and Applied Ecology 14 (2013) 298–308 Richmond A

Table 4. The effects of ground cover, proximity to remnant edges, and nearby trees on leaf litter loss rates. Mixed effects anova of the tripartite model. D = Days, E = East, GC = Ground cover, L = Light, Loc = Locale, N = North, S = South, TB = Trunk bearing, TD = Trunk distance, TG = Trunk girth, W = West.

Richmond B

60

40

dfnum , dfden Litter remaining (%)

303

F

p

5746.744 623.600 1558.963 76.896

<0.0001 <0.0001 <0.0001 <0.0001

13.406 4.160 4.629

<0.0001 0.0025 0.0021

27.490 1.446 0.058 1.069 9.126 9.635

<0.0001 0.2329 0.8105 0.3044 0.0034 0.0027

8.779 5.741 6.080 0.000 7.546 32.101 0.179 3.931 0.010 0.418 2.019 0.004 7.949 5.436

0.0032 0.0190 0.0159 0.9907 0.0062 <0.0001 0.6726 0.0478 0.9209 0.5199 0.1594 0.9525 0.0050 0.0224

20

0 Scheyville A

Scheyville B

60

40

20

0 1

2

3

4

1

2

3

4

Retrieval

Fig. 2. The percentage of leaf litter (expressed as the ash-free dry weight) remaining in litter bags at each retrieval at four eucalypt woodland remnants at the Richmond and Scheyville locales near Sydney, Australia. Retrieval 1 = May 2008, 2 = September 2008, 3 = January 2009, 4 = May/June 2009. • = median, box = values between 1st and 3rd quartiles, whiskers = values ≤1.5×interquartile range, ◦ = values >1.5×interquartile range.

interaction terms would be supported: their lack of support implies that edge effects are weak, of limited spatial extent, or both. Belsky–Canham: The data show that the effects of tree proximity were not conditional on canopy cover (Light × Trunk distance) and did not change seasonally (Days × Trunk distance, see Appendix A: Table 2). However, Light × Trunk girth × Days, which model temporal changes in the Light × Trunk girth interaction (Table 4), was supported. Ground cover: The data supported the retention of all terms in the model.

Table 3. Comparisons of the individual and combined models in order of increasing informativeness. Model

df

aic

log Likelihood

Shared Ground cover Edge-Core Belsky–Canham Edge-Core + Ground cover Belsky–Canham + Ground cover Belsky–Canham + Edge-Core Tripartite

6 18 12 20 24 32 26 38

779.37 757.62 749.70 739.51 734.78 715.40 710.18 696.35

−383.68 −360.81 −362.85 −349.76 −343.39 −325.70 −329.09 −310.17

Shared terms Intercept 1, 680 Initial wt 1, 680 Days 1, 680 Locale 1, 76 Ground cover terms Ground cover 4, 76 GC × D 4, 680 GC × Loc 4, 76 Edge-core terms East 1, 76 North 1, 76 South 1, 76 West 1, 76 E×S 1, 76 N×W 1, 76 Belsky–Canham terms Light 1, 680 Trunk bearing 1, 76 Trunk distance 1, 76 Trunk girth 1, 76 L×D 1, 680 L × Loc 1, 680 L × TG 1, 680 TB × D 1, 680 TG × D 1, 680 TB × TD 1, 76 TB × TG 1, 76 TD × TG 1, 76 L × TG × D 1, 680 TB × TD × TG 1, 76

Comparing and combining models Although the Belsky–Canham model was more consistent with the data than either the ground cover or edge-core models, the reduction in aic that occurred when models were combined indicates that the models describe different aspects of the observed variability in litter loss. The combination of all three models (the ‘tripartite’ model) provides a large improvement in explanatory ability over any of the individual or pairwise models (Table 3). Analysis of the Tripartite model Edge-core component: Litter loss was affected by proximity to edge, but the effect was small, varied with cardinal directions, and was conditional on proximity to orthogonal edges (Fig. 3 and Table 4). More litter remained in bags from plots near an edge to both the south and the east, or to both the north and the west, than if they were far from one and not the other (Fig. 3A and B). Although the North × West

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(A)

1 2 3 4 5 6

300

East 100−200m

East 200−300m

2.1 2 1.9 1.8 1.7 1.6 1.5 0 50100

200

300

0 50100

200

300

South (m) (B)

0 50100

West 0−100m

Ash free weight (g)

Trunk Girth 0−130 cm Trunk Girth 0−130 cm Trunk Girth 0−130 cm Trunk Bearing 0−60 Trunk Bearing 60−120 Trunk bearing 120−180

200

Ash free weight (g)

Ash free weight (g)

East 0−100m

200

3.5 3 2.5 2 1.5 1 0.5 0

Trunk girth131−265cm Trunk girth131−265cm Trunk girth131−265cm Trunk bearing 0−60 Trunk bearing 60−120 Trunk bearing120−180

3.5 3 2.5 2 1.5 1 0.5 0

300

West 100−200m

West 200−300m

2.1 2 1.9

1 2 3 4 5 6

1.8

1 2 3 4 5 6

Trunk distance (m)

1.7 1.6 1.5 0 50100

200

300

0 50100

200

300

Fig. 4. Effects display of the relationship between litter loss, trunk girth, and plot position with respect to the largest nearby trunk. Dashed lines indicate 95% confidence intervals for the fitted regression lines. See caption of Fig. 3.

North (m)

Fig. 3. Effects displays of the (A) East × South and (B) North × West interactions, showing the influence of proximity to remnant edges on leaf litter remaining. Dashed lines indicate 95% confidence intervals for the fitted regression lines. Note that the fitted estimates are based on mean values for all variables in the tripartite statistical model that are not explicitly plotted e.g. Days = 326 days (approximately the third litter bag retrieval) and Initial wt = 4.134 g.

interaction was stronger than the South × East interaction when plots were close to two edges, proximity to an edge to the north had no effect on litter loss when the western edge was distant (>200 m, Fig. 3B) The East × South and North × West interactions combine two elements of the litter loss-edge proximity relationship: (i) modification of the effect of proximity to an edge by proximity to a second edge (as might occur near a remnant corner); and (ii) proximity to an oblique edge, where the cardinal distance overestimates distance to edge due to the cosine effect, for which the interaction term functions as a ‘correction’. Few plots were near remnant corners, and each remnant had at least one oblique edge, so in this instance the East × South and North × West terms mainly model the ‘correction’ for the cosine effect. Belsky–Canham component: Proximity to trees and exposure to light had complex and conditional effects on litter loss rates (Table 4, see Appendix A: Fig. 2). Temporal changes in the effect of Trunk girth were conditional on the degree of light penetration through the collective canopy (see Appendix A: Figs. 3 and 4). We interpret this as evidence of seasonal changes in the effects of shading by the largest

nearby tree relative to the effects of shading by the collective canopy. The effect of Trunk girth also depended on Trunk distance and Trunk bearing, and their combined effects had a much larger effect on litter loss than proximity to remnant edges (Fig. 4, cf. Fig. 3). Litter loss was highest near larger trunks in plots north of the trunk (Trunk bearing 0–60), where litter was shaded at midday in summer but might be sunlit at midday in winter (Fig. 4). Litter loss was much lower in plots further north of larger trunks, where litter might always be sunlit at midday. In contrast, litter loss in plots north of smaller trunks showed little effect of Trunk distance. Plots shaded by largest nearby trees early or late in the day (Trunk bearing 60–120) had small changes in litter loss as Trunk girth and Trunk distance changed (Fig. 4). The effects of Trunk distance on litter loss at plots shaded near midday (Trunk bearing 120–180) depended on Trunk girth, litter loss being highest near small trunks or far from large trunks. The effect of Trunk bearing also changed through time (Table 4), which is consistent with shade effects. The effect of Light was large but conditional, changing through time and differing between Locales (Table 4). Litter loss decreased with Light at Scheyville, but increased with Light at Richmond (see Appendix A: Fig. 2). The effects of Light also depended on the size (Trunk girth) of the largest nearby tree (see Appendix A: Fig. 4). Whereas litter remaining increased with Light when Trunk girth was less than 130 cm, it decreased sharply in plots near larger trunks late in the experiment. Overall, the effect sizes of interaction terms involving Light were akin to those involving Trunk distance, Trunk

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bearing or Trunk girth, suggesting that the influences of individual trees and collective canopies are comparable in this plant community. However, Trunk distance and Trunk girth are mildly correlated (Kendall’s τ = 0.170, p < 0.05), with larger trunks tending to be further from plots, so the effect of Trunk girth may be underestimated. Groundcover component: Groundcover affected litter loss rates at Scheyville, but not at Richmond (see Appendix A: Fig. 5). Litter loss was greatest at Scheyville plots having the highest level of groundcover, and this effect became more pronounced later in the experiment (see Appendix A: Fig. 5, post hoc contrasts). There were no detectable differences in litter loss rates at lower ground cover levels.

Discussion The extent to which litter decomposition was regulated by the structural features of circumjacent vegetation is perhaps the most striking aspect of our results. Canopy light transmission, the size of the largest nearby tree, and litter bag position with respect to that tree had much greater effect on litter decomposition than the fragmentation-related features of remnant area and litter bag position with respect to remnant edges. Further, the effects of these circumjacent features were temporally dynamic, adding weight to the view that local heterogeneity created by individual canopies modulating solar radiation is ecologically important (Breshears 2006; Hastwell & Facelli 2003). Collectively, our multiple working hypotheses postulate that aspects of the physical structure of plant communities modulate temperature and moisture—thereby affecting litter decomposition rates—and that this occurs at different spatial scales. Ground cover density, and position within a remnant and with respect to tree trunks, ‘explain’ different aspects of the spatial variability in litter decomposition through their effects in modifying microclimates. In turn, moisture and temperature modulate the biochemical activity and population dynamics of the decomposer community (Schimel, Balser, & Wallenstein 2007). While offering complementary explanations of spatial variability in litter decomposition, the hypotheses also provide contrasting perspectives on plant-environment interactions. The Belsky–Canham hypothesis portrays individual perennial plants as active participants in their landscapes, having strong localised effects that contrast with the conditions prevailing immediately beyond their canopies (Belsky & Canham 1994; Vetaas 1992). The ground-cover hypothesis is more equivocal, with particular species or lifeforms being seen as either indicators of local conditions, or as agents of highly localised modification. The edge-core hypothesis has a fundamental paradox: it implies that removed trees had strongly modified their environment, for their absence produces dissimilar abiotic conditions, yet claims of extensive edge effects imply that remnant trees have limited ability to modify conditions.

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Of earlier studies of decomposition in fragmented landscapes, two litter bag experiments found no change in decomposition rates with distance from remnant edge ((Vasconcelos & Laurance 2005), or with remnant area (Rubinstein & Vasconcelos 2005). In contrast, a third study—conducted at the same site as the others— reported lower decomposition rates in the centres of the smallest remnants, and higher decomposition rates near the edges of large remnants (Didham 1998). It also claimed that decomposition rates did not change near the edges of the much larger ‘continuous’ forest (Didham 1998), although the reported P = 0.061 suggests that a difference may have been detectable with a larger sample. Other studies of the effect of remnant area on litter decomposition did not quantify within-remnant variability. One, in temperate Eucalyptus camaldulensis woodland remnants of 0.8–49.5 ha, found little evidence that remnant area affects litter decomposition (Hahs 2006). Another found that litter loss rates increased with area among small islands (0.02–15.0 ha, Wardle 1997). Three quarters of the islands were no more than 1 ha, i.e. smaller than the remnants in other studies (Didham 1998; Hahs 2006; Rubinstein & Vasconcelos 2005; Vasconcelos & Laurance 2005). Wardle (1997) also points out that the effect of island area was confounded with successional stage, for larger islands are more frequently disturbed by lightning-generated fires. Nonetheless, the results provide a valuable comparison with our data. In our experiment, remnant area had no detectable effect on litter loss. This is similar to the results of some studies (Hahs 2006; Rubinstein & Vasconcelos 2005), but not others (Didham 1998; Wardle 1997), and inconsistent with claims that small remnants are ‘dominated’ by edge effects (e.g. Young & Mitchell 1994). Although we found that litter loss rates near edges differ from those further into remnants, the extent to which litter loss rates decreased near edges depended on orientation. This is broadly consistent with microclimate studies in which gradients across remnant edges differed with orientation (Chen et al. 1995; Redding et al. 2003; Young & Mitchell 1994). Overall, the difference in decomposition due to proximity to remnant edges was not large enough to affect mean within-remnant decomposition rates across the range of remnant sizes in our experiment. We found no evidence of large discontinuities in litter loss rates with distance from remnant edge, so the stepwise version of the edge-core hypothesis was not supported (Fig. 1A). No very large remnants remain in the Richmond– Scheyville area, so we were unable to test whether remnant area affects litter loss at larger scales. Given the modest edge effects seen in our experiment, we anticipate no such relation would be observable. Nonetheless, uncertainty remains around this question, and it needs to be confirmed in other woodland ecosystems. Further, litter loss rates within remnants need to be compared with those in the adjacent matrix to evaluate the contribution of remnants to landscape-level functioning.

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The effects of herbaceous ground cover were novel. Guttation, which humidifies microclimates and prolongs the duration of leaf wetness (Hughes & Brimblecombe 1994), is one mechanism by which ground cover can increase litter loss. We observed during the cooler months that forbs, grasses and litter bags in plots with high ground cover remained wet hours after sunrise, but plots with low ground cover dried much sooner. Conversely, high levels of ground cover may indicate high soil moisture levels, which of itself would increase the frequency and magnitude of guttation. This might explain why the effect was detectable at the Scheyville remnants, whose soils have high waterholding capacity, and not at Richmond where soils are lighter. While the three working hypotheses explain differences in decomposition rates between plots, they do not account for the large within-plot variability. One possible explanation lies in the range of functional diversity and organism sizes within the decomposer community, combined with the high abiotic and geochemical heterogeneity within soils (Hinsinger, Bengough, Vetterlein, & Young 2009). Both arthropod and microbial decomposers are affected by microscale abiotic heterogeneity, but the effects vary with organism size (Kaspari & Weiser 2007; Schimel et al. 2007). In our study, it appears that the combined effects of decomposer diversity and micro-scale soil heterogeneity are of similar magnitude to the effects of microclimatic gradients imposed by plant structural features. Although the literature emphasizes the spatial extent of edge effects (Fischer & Lindenmayer 2007), less attention has been given to their magnitude. Many studies show that large microclimatic modifications are limited to narrow peripheral zones (Chen et al. 1995, 1999; DaviesColley et al. 2000; Geiger 1966; Redding et al. 2003, but cf. Young & Mitchell 1994). The degree of abiotic modification further into remnants tends to be small, and arguably only detectable in large datasets. Regrettably, the biological relevance of these smaller changes is rarely tested. We contend that—for communities with a woody perennial component—the effects of fragmentation are likely to be contingent on the degree of canopy continuity, with savannas and tropical rainforests constituting the extremes. This view is strongly supported by our finding that much of the spatial variability in litter decomposition is associated with the proximity to trees, with the size of those trees, and with the amount of light penetrating the local collective canopy. While herbaceous ground cover and proximity to remnant edges both affected decomposition rates, their effects were markedly smaller than those attributable to individual trees. Our finding that herbaceous ground cover and proximity to remnant edges impose additional patterns on litter decomposition demonstrates how the method of multiple working hypotheses can provide a more complete picture of biological phenomena (Chamberlin 1897).

Implications for conserving small remnants The ecological value of small remnants is often challenged when land management conflicts arise. In our view, the contributions of small remnants to landscape-scale functioning, particularly in highly fragmented peri-urban landscapes, warrants greater attention. Our results challenge the view that ecosystem processes in small remnants are inherently compromised or severely degraded by the effects of fragmentation. From a functional perspective, we contend that small remnants are under-valued; that in toto they may prove to be indispensable providers of ecosystem services in fragmented landscapes.

Acknowledgements We thank José M. Facelli for advice on ashing and litter bag methods, and Paul Thomas for assisting setting up the experiment. The study was conducted under a New South Wales National Parks & Wildlife Service Scientific Licence number S12311: we thank Jonathon Sanders of NSW NPWS for his co-operation and encouragement. Several anonymous referees made comments that improved this paper. GTH was supported by a PostDoctoral Fellowship from the University of Western Sydney.

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.baae.2013.03.002.

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