Functional Diversity of Leaf Litter Mixtures Slows Decomposition of Labile but Not Recalcitrant Carbon Over Two Years

Functional diversity of leaf litter mixtures slows decomposition of labile but not recalcitrant carbon over two years

1,2 JAKE J. G ROSSMAN , JEANNINE CAVENDER-BARES , AND SARAH E. HOBBIE Department of Ecology, Evolution, and Behavior, University of Minnesota–Twin Cities, 140 Gortner Laboratory, 1479 Gortner Avenue, Saint Paul, Minnesota 55108 USA

Citation: Grossman, J. J., J. Cavender-Bares, and S. E. Hobbie. 2020. Functional diversity of leaf litter mixtures slows decomposition of labile but not recalcitrant carbon over two years. Ecological Monographs 90(3):e01407. 10.1002/ecm.1407

Abstract. The decomposition of leaf litter constitutes a major pathway of carbon and nutrient cycling in terrestrial ecosystems. Though it is well established that litter decomposition varies among species, most leaf litter decomposes not alone, but in mixture with litter from het- erospecifics. The consequences of this mixing, and of the role of multiple dimensions of plant biodiversity, for litter decomposition are ambiguous, with past research suggesting that mixing diverse litter can speed up, slow down, or have no effect on decomposition. Furthermore, different chemical constituents of litter decompose at different rates, and the consequences of diversity for each of these rates are not completely understood. We created litterbags corresponding to 49 different litter mixtures ranging from one to 12 temperate forest species and allowed them to decompose over 2 yr in a common garden in temperate eastern Minnesota, USA. Following collections at 2, 4, 12, and 24 months, we assessed total mass loss and changes in four classes of litter carbon (soluble cell contents, hemicellulose and bound proteins, cellulose, and lignin/acid unhydrolyzable recalcitrants). Species varied in litter decomposition rate (losing from 8% to 41% of total mass) and they lost soluble cell contents (up to 64% of ash-free mass) and hemicellulose and bound proteins (69%) much more rapidly over 2 yr than they lost cellulose (40%) and acid-unhydrolyzable residues (12%). A variety of macro- and micronutrients supported litter decomposition, with calcium, in particular, promoting it. In mixtures of litter from 2, 5, or 12 species, neither species richness nor phylogenetic diversity was associated with deviations from expected decomposition rates based on monocultures. Yet more functionally diverse litter mixtures lost labile carbon (soluble cell contents and hemicellulose) significantly more slowly than expected. This novel finding of the effect of litter diversity not on total litter decomposition, but on the decomposition of a particular class of litter compounds eluci- dates potential consequences of biodiversity for cycling of nutrients and energy in forest ecosystems. Key words: biodiversity–ecosystem function; cellulose; dimensions of biodiversity; functional diversity; hemicellulose; IDENT; lignin; litterbags; phylogenetic diversity; soluble cell contents; tree diversity experiments.

2004). Yet nonadditive mixing effects have often been INTRODUCTION shown to be idiosyncratic, with most authors reporting In an era of rapidly changing biodiversity (Vellend that mixing more than one species of litter enhanced et al. 2017), it is crucial to understand how the loss of decomposition (synergism) and some suggesting that tree diversity will affect the decomposition of forest lit- mixing retarded decomposition (antagonism). Cardinale ter. Empirical studies do not yet point toward a general- et al. (2011) reported a similar pattern, noting that the ized relationship between biodiversity and leaf litter high degree of variation in decomposition rates weakly decomposition. In a review of the first generation of supported a pattern of synergistic, nonadditive diversity such experiments, mixing effects on decomposition were effects. In their reviews, Hattenschwiler€ et al. (2005) and nonadditive in two-thirds of cases, meaning that mixed Srivastava et al. (2009) argued that such ambiguous evi- litter did not decompose as expected based on rates of dence does not support the existence of a general, non- single-species litter decomposition (Gartner and Cardon additive relationship between litter diversity and decomposition. Manuscript received 25 September 2019; revised 21 Decem- More recent experiments have yielded similarly mixed ber 2019; accepted 28 January 2020. Corresponding Editor: results. In some cases, idiosyncratic effects of mixing Samantha K. Chapman. 1Present address: Arnold Arboretum of Harvard University, have matched additive predictions (Scherer-Lorenzen 1300 Centre Street, Boston, Massachusetts02131USA et al. 2007, Ball et al. 2008, Tardif and Shipley 2013, 2E-mail: [email protected] 2014, Jewell et al. 2016, Setiawan et al. 2016). In such

Article e01407; page 1 Article e01407; page 2 JAKE J. GROSSMAN ET AL. Ecological Monographs Vol. 90, No. 3 cases of additive decomposition, functional trait values predictor of ecological function when functional trait (e.g., litter chemistry; Makkonen et al. 2012), rather than information is not available or when the traits contribut- dissimilarity among species in these traits appeared to ing to a particular function are not known. And though predict patterns of decomposition in mixture (Schindler functional diversity has been shown to be associated and Gessner 2009, Frainer et al. 2015). In other cases, with faster litter decomposition (Scherer-Lorenzen 2008, decomposition of mixtures was nonadditive, with syner- Handa et al. 2014, Garcıa-Palacios et al. 2017), there is gistic rather than antagonistic effects (Vos et al. 2013, still no consensus on the extent to which it affects Barantal et al. 2014, Handa et al. 2014, Trogisch et al. decomposition independently of taxonomic and phylo- 2016). So, the available evidence suggests that decompo- genetic diversity. Biodiversity experiments (Grossman sition of whole community-level litter responds to diver- et al. 2017) and analytical techniques (Cadotte et al. sity (particularly, species richness) inconsistently across 2008, Flynn et al. 2011) designed to disentangle the con- study systems. Taken together, these findings suggest tributions of these dimensions of diversity to other that the strength and direction of diversity–decomposi- ecosystem functions promise to contribute to resolution tion effects may depend on how both biodiversity and of their distinct contributions to litter decomposition. litter decomposition are each defined and measured. Furthermore, consideration of the particular processes Below, we briefly review how a more nuanced perspec- underlying decomposition of litter may help to clarify tive of both biodiversity and litter decomposition may ambivalent findings regarding the potential conse- contribute to a better understanding of the consequences quences of litter diversity for decomposition. Because of the former for the latter. leaf litter consists of numerous chemical compounds Not all dimensions of litter biodiversity (Naeem et al. arranged in complex physical structures, litter decom- 2012) may have a strong effect on decomposition. Recent position represents the sum of many simultaneous pro- ecological scholarship has, in particular, distinguished cesses. These include the rapid leaching or decomposi- among taxonomic diversity (species richness), functional tion of soluble cell contents over days and weeks and the diversity (the degree to which individuals in a commu- slow decay of lignin and other more complex molecules nity vary in their traits), and phylogenetic diversity (the over years (Melillo et al. 1989, Berg 2014, Berg and degree of evolutionary differentiation separating individ- McClaugherty 2014). Particular litter constituents may uals in a community) (Purschke et al. 2013, Cavender- accelerate the decomposition of one class of carbon Bares et al. 2016, Grossman et al. 2018); these dimen- compounds while having no effect on or even decelerat- sions of biodiversity may affect ecological processes in ing the decomposition of another class (Yue et al. 2016). distinct ways. Mechanistic explanations for how taxo- For instance, though nitrogen-rich litter generally nomic diversity might affect decomposition, or other decomposes more quickly than nitrogen-poor litter ini- ecosystem functions, generally hinge on its use as a sur- tially, nitrogen can retard lignin degradation (Melillo rogate for functional diversity, which is harder to mea- et al. 1982, Berg and Ekbohm 1993, Berg 2014). Lignin sure (Flynn et al. 2011, Gravel et al. 2012). For instance, decomposition, on the other hand, may increase with combining litter of different species is assumed to pro- higher litter manganese (Berg et al. 2015). duce litter more diverse in its physical and chemical Furthermore, one class of litter compounds may accel- functional traits, causing it to decompose more quickly erate or decelerate the decomposition of another class of due to nutrient transfer, i.e., a synergistic effect (as in compounds via priming (sensu Kuzyakov et al. 2000, Schimel and Hattenschwiler€ 2007), or to provision of Talbot and Treseder 2012). When positive priming complementary nutrients to soil detritivores and occurs, a fast-decomposing pool of organic matter (e.g., microbes (e.g., Hattenschwiler€ and Gasser 2005). cellulose) enhances the decomposition of a slower-de- Indeed, in several cases, the nonadditive effects of litter composing pool (e.g., lignin) through one or more mech- mixing have resulted in faster decomposition at anisms, many of them mediated by microbial composers. increased functional diversity (Barantal et al. 2014, For instance, an enzymatic byproduct from microbial Handa et al. 2014, but see Chapman and Koch 2007, processing of cellulose might indirectly hasten lignin Frainer et al. 2015). decomposition (co-metabolism) or products from cellu- Because the number of species can fail to capture lose decomposition might directly provide lignin decom- functional biodiversity, by treating, for instance, two posers with the energy required to break down their species of oak as equal in diversity to an oak and a pine, more recalcitrant substrate (mutualism; Guenet et al. phylogenetic metrics have been proposed as more eco- 2010). Mutualistic nitrogen transfer from nitrogen-rich logically informative (Webb et al. 2002, Losos 2008). By to nitrogen-poor litter can also accelerate decomposition measuring the presumed functional divergence concomi- (Schimel and Hattenschwiler€ 2007, but see Lummer tant with evolutionary distance, and thus integrating et al. 2012 for an example of transfer from nitrogen-poor across both measured and unmeasured traits, phyloge- to nitrogen-rich litter). Priming provides one explanation netic diversity may serve as a more unbiased predictor of for nonadditive mixing effects of litter decomposition, ecosystem function than taxonomic or functional diver- especially when it is possible to track the decay of dis- sity (Cadotte et al. 2009). In particular, phylogenetic tinct, slow- and fast-decomposing pools of litter carbon. diversity may outperform functional diversity as a For this reason, assessment of the consequences of litter August 2020 LITTER DIVERSITY SLOWS DECOMPOSITION Article e01407; page 3 diversity for the decomposition of particular chemical oak; Q. rubra, red oak; and Tilia Americana, basswood) classes of litter compounds holds the potential to tell a was collected at Cedar Creek Ecosystem Science Reserve more satisfying story than is possible if only litter mass (CCESR) in East Bethel, Minnesota, USA (45°250 N, loss is measured. 93°100 W). Litter of one additional species was collected To investigate the consequences of different axes of on private property in Hudson, Wisconsin, USA temperate forest leaf litter diversity for decomposition, (44°980 N, 92°660 W; Juniperus virginiana, eastern red we carried out a two-year litterbag study using litter col- cedar). Litter was air-dried and stored at room tempera- lected from 12 tree species. Litter was allowed to decom- ture in darkness. In spring 2015, litter was used to fill pose in single-species “monocultures” and in 37 mixtures 20 9 20 cm square bags constructed of 1-mm fiberglass varying orthogonally in taxonomic, phylogenetic, and mesh. Bags were filled with 2.5 g of air-dried litter and functional diversity. We quantified not only mass loss, heat-sealed. All masses were adjusted to reflect oven- but also changes in four carbon fractions—soluble cell dried (>24 h at 60°C) mass and loss-on-handling as esti- contents, hemicellulose and bound proteins, cellulose mated from one-species litterbags that had been assem- and acid-unhydrolyzable residues (e.g., lignin and similar bled, deployed in the field, and immediately returned compounds; AURs)—over the study period. Our experi- and weighed. ment focused specifically on microbial decomposition Litterbags contained one of 49 mixture types (or through the use of litterbags made of 1-mm2 mesh, compositions): one monoculture for each study species, which excluded large invertebrates. We expected that (1) 28 bicultures, eight 5-species mixtures, and a single 12- The four carbon fractions measured would display dis- species mixture (Table 1; Appendix S1: Table S1). Bag tinct profiles of decomposition over 2 yr given their con- compositions were chosen to align with composition of trasting ease of breakdown and consumption by plots in the Forests and Biodiversity (FAB; Grossman microbes (Melillo et al. 1989, Bray et al. 2012, Berg et al. 2017) tree diversity experiment, a part of the 2014), (2) Microbial decomposition of total litter mass IDENT network (Tobner et al. 2014), towards disentan- and all four fractions would vary by species and be con- gling the effects of species richness, and phylogenetic served within lineages and that more nutrient-rich and and functional diversity. Briefly, 10 of the 2-species mix- AUR-poor litter would decompose more quickly (Hob- tures were selected randomly from all possible two-spe- bie et al. 2006, Cornwell et al. 2008), (3) Particular rela- cies combinations of the 12-species pool. The other 18 tionships between litter chemistry and lignin (here, mixtures were chosen using a stratified sampling AUR) decomposition would include a negative effect of approach to ensure that, to the greatest extent possible, nitrogen and a positive effect of both manganese and the two-species mixtures included in the experiment labile carbon (soluble cell contents, hemicellulose, and covered a range of possible values of both phylogenetic cellulose) on loss of AURs (Melillo et al. 1982, Berg and and functional diversity. As such, 2-species mixtures Ekbohm 1993, Talbot and Treseder 2012, Berg 2014), included in this experiment consisted of species pairs and (4) because of synergistic interactions among nutri- that varied widely and orthogonally in their phyloge- ent and carbon fraction traits of litter, multidimensional netic and functional diversity. Five-species mixtures functional trait diversity would predict deviation from were assembled randomly from the 12 species, and a expected decomposition rates, by capturing information few randomly chosen species were replaced by others related to leaf chemical diversity, better than would spe- because of litter shortages during bag filling. Bags were cies richness (Chapman and Koch 2007, Tardif and filled with litter from 1, 2, 5, or 12 species such that all Shipley 2013, 2014, Barantal et al. 2014, Handa et al. bags were filled with litter of each constituent species in 2014, Jewell et al. 2016). To the extent that phylogenetic equal proportions but the total mass was the same diversity predicts decomposition, it would do so because across mixture types (e.g., a 2-species litterbag con- important functional traits are phylogenetically contained 1.25 g of litter from each species and a 5-species served. litterbag contained 0.5 g of litter per species). Each 1-, 2-, and 5-species mixture was replicated 12 times and the 12-species mixture was replicated 24 times, giving a METHODS total of 600 bags. Sets of four replicate bags were tied together with nylon string (N = 150). One of the four Litterbag construction bags was harvested from each string at four different Litter from 12 temperate woody species native to east- dates, as described below, so replication per harvest ern Minnesota that often co-occur in forests was date was three (unique one-, two-, and five-species mix- included in this study (Fig. 1). In October 2014, we col- tures) or six (the 12-species mixture). lected freshly senesced litter from adult trees of native provenance in two locations. Litter from 11 species (Acer Measuring litter diversity negundo, box elder; A. rubrum, red maple; Betula papyrifera, paper birch; Pinus banksiana, jack pine; P. resinosa, The 12 species included in the experiment span the red pine; P. strobus, white pine; Quercus alba, white oak; seed plant phylogeny, including four gymnosperms and Q. ellipsoidalis, northern pin oak; Q. macrocarpa,bur eight angiosperms (Fig. 1). To compare species richness Article e01407; page 4 JAKE J. GROSSMAN ET AL. Ecological Monographs Vol. 90, No. 3 Speciﬁc leaf Soluble cell Cellulose Nitrogen Manganese Calcium Condened Species area contents Zinc (ppm) (%/mass) (%/mass) (%/mass) (%) tannins (%) (cm2/g) (%) Red pine (Pinus resinosa) 26.2 53.0% 19.5% 0.4% 0.025% 36.33 0.947% 22.10% Jack pine (Pinus banksiana) 35.1 45.0% 20.9% 0.6% 0.033% 32.71 0.913% 18.32% White pine (Pinus strobus) 65.3 32.3% 25.7% 0.7% 0.042% 86.90 1.227% 7.71% Eastern red cedar (Juniperus virginiana) 32.4 49.0% 19.1% 1.7% 0.037% 35.79 3.169% 1.97% Red maple (Acer rubrum) 178.2 67.2% 15.8% 0.7% 0.038% 40.28 1.020% 4.69% Box elder (Acer negundo) 166.6 61.8% 17.7% 1.5% 0.011% 21.15 2.608% 6.99% Basswood (Tilia americana) 368.7 54.0% 20.5% 1.2% 0.027% 23.92 4.525% 12.13% White oak (Quercus alba) 156.7 56.7% 17.9% 0.8% 0.060% 17.88 1.994% 3.86% Bur oak (Quercus macrocarpa) 94.4 48.8% 18.4% 2.0% 0.044% 29.41 1.215% 5.01% Northern pin oak (Quercus ellipsoidalis) 116.7 44.7% 17.3% 1.3% 0.044% 31.02 1.079% 6.83% Red oak (Quercus rubra ) 136.0 42.3% 22.5% 0.9% 0.093% 47.90 1.403% 3.96% Paper birch (Betula papyrifera ) 225.1 57.5% 14.4% 0.9% 0.096% 280.79 1.841% 5.42% 25% 66% 95% Phylogenec Moran's I 0.064 0.166 -0.003 -0.131 0.053 -0.100 -0.095 0.063 Phylogenec similarity signal (P value) (0.056) (0.011) (0.174) (0.622) (0.077) (0.591) (0.489) (0.066)

FIG. 1. Species identity and chemical and physical characteristics of litter used in the study. Litter was collected from 12 species native to eastern Minnesota that span the seed plant phylogeny. Species-level means for eight traits thought to control decomposition are shown here; full trait information for these and 10 other traits is given in Appendix S1: Table S2. Percentages and concentrations are given on a dry mass basis. The phylogenetic tree on the left side of the figure represents evolutionary relationships among the 12 species as in Grossman et al. (2017). Moran’s I, a metric of phylogenetic signal, is given for each trait in the last row of the table with P values from randomization tests against null models in parentheses. P values of traits for which phylogenetic signal is at least marginally significant are shown in boldface type.

litter decomposition. Leaf litter was first ground in a TABLE 1. Litter bags treatment and harvest design. Wiley Mill at 0.425 mm, and then leaf carbon and nitro- No. unique Replicates No. Total gen concentrations were analyzed by dry combustion GC Treatment mixtures per harvest harvests bags analysis on a Costech Analytical ECS 4010 (Valencia, California, USA); carbon to nitrogen (C:N) ratios were Mixture type 1-species 12 3 4 144 calculated from these values. Leaf phosphorus, calcium, 2-species 10 3 4 120 potassium, magnesium, manganese, molybdenum, zinc, (random and iron concentrations were measured through the mul- choice) ti-element, ICP-dry ash method on an iCap 7600 Duo 2-species 18 3 4 216 ICP-OES Analyzer (Thermo Fischer Scientific, Wal- (stratified random tham, Massachusetts, USA). We used an ANKOM 200 choice) fiber analyzer (Macedon, New York, USA; Riggs et al. 5-species 8 3 4 96 2015) to measure the concentration by mass of four oper- 12-species 1 6 4 24 ational “carbon fractions” in ground litter: soluble cell Total bags 600 contents, hemicellulose and bound proteins, cellulose, and acid-unhydrolyzable residues (including lignin and Note: Harvests took place 2, 4, 12, and 24 months after deployment. hereafter referred to as AUR) in dried, ground leaves. All carbon fractions are reported on an ash-free dry mass basis. Finally, we measured condensed tannin concentra- to phylogenetic diversity of mixtures as predictors of tions for all species on freeze-dried, ground samples decomposition, we first calculated phylogenetic species using the butanol-HCl method (Porter et al. 1986) and variability (PSV; Helmus et al. 2007), which increases birch standards purified by the R. Lindroth lab (Madi- from zero to one independently of species richness and, son, Wisconsin, USA; Kopper et al. 2001); we were not as it nears one, reflects greater evolutionary divergence able to measure hydrolyzable tannins, though they likely among species in a community. We calculated PSV using are also important contributors to microbial dynamics in Zanne et al.’s (2014) phylogeny and the picante package decomposing litter. in R (Kembel et al. 2010). We assessed phylogenetic conservation of these traits To assess functional diversity of mixtures, we measured using Keck et al.’s (2016) phylosignal R package and a 18 leaf physical and chemical traits for all study species distance matrix calculated from the Zanne et al. (2014) (Fig. 1; Appendix S1: Fig. S1, Table S2). Trait data were phylogeny and picante. For all traits, we calculated both collected from litter used in this experiment or from sym- Moran’s autocorrelation (I; Gittleman and Kot 1990) patrically growing conspecifics. Fresh leaf area and dry and Blomberg et al.’s (2003) phylogenetic signal (K) met- mass for measurement of two physical traits, relative leaf ric. Values for I indicate autocorrelation of actual trait water content and specific leaf area (SLA), were calcu- values for species of known phylogenetic distance lated through leaf scans with the SIOX ImageJ plug-in whereas K compares trait values observed across the (Wang 2016) and mass measurements. We also measured phylogeny to what would be expected given a Brownian a suite of 16 chemical traits potentially related to leaf motion model of evolution. For both, randomization August 2020 LITTER DIVERSITY SLOWS DECOMPOSITION Article e01407; page 5 tests against null models consisting of the reference phy- demonstrated that the plant community in which logeny with random trait values attached to all tips decomposition occurs can have consequences for its allowed for statistical testing. Results for both metrics absolute rate and litter mixing effects to a greater extent were qualitatively similar (Diniz-Filho et al. 2012), con- than macroclimate (Joly et al. 2017) or litter functional flicting regarding the phylogenetic conservativism of diversity (Barantal et al. 2011, Seidelmann et al. 2016). only one trait, leaf manganese content (for which K does Our particular approach allows us to ask whether, when not indicate conservativism, but I does). We report I,for microclimate and other indirect effects of the prevailing which only four traits showed signs of significant phylo- plant community are controlled for, litter functional genetic signal: SLA and leaf concentrations of soluble diversity can still alter rates of decomposition. The cell contents, manganese, and condensed tannins. Ran- extent to which these diversity effects might be aug- domization tests against null models indicate that these mented or reduced by decomposition in the community traits are particularly conserved in Acer, Betula, Pinus, or origin might be tested in future experimental work. and Tilia (Fig. 1; Appendix S1: Table S2). Each string of four litterbags was stretched to its full For all litter mixture compositions, we estimated func- length so that bags were not touching and staked in tional trait identity and diversity for each of these traits place so that the entire bottom surface of each bag was (Appendix S1: Table S1). We represent community-level in contact with the existing litter layer. Bags were not trait identity as the community-weighted mean (CWM; covered when deployed but became covered with a layer Mokany et al. 2008) for a given trait and community, the of freshly fallen litter from four months post-deployment abundance-weighted mean of the trait value across all onward. Because bags were deployed over an area large constituent species in a community. (In this case, CWM enough to vary in microtopography, overstory vegeta- equaled the mean, since all species were represented in tion, exposure to deer trampling, etc., we divided bags equal abundance.) CWMs were calculated for all traits into three blocks, with 50 strings arranged randomly across all communities using the FD R package (Lalib- within each block. Strings were assigned to blocks so erte and Legendre 2010). The same R package was used that each bag composition was represented across all to calculate functional dispersion, a metric of functional three blocks. Blocks varied in aspect from 0° to 5° but diversity, for all traits across all communities. For exam- did not vary systematically otherwise. ple, whereas the CWM for cellulose content increases in One litterbag from each of the 150 strings was collected a community with species that all have litter that is rela- at62d(2months),124d(4months),363d(1year),and tively rich in cellulose, the functional dispersion for cel- 731 d (2 years) following deployment. On collection, each lulose is highest when species are maximally dissimilar bag was cleaned manually of mineral soil, allochthonous for this trait, given the set of 12 species included in the litter, ingrown plant material, and soil animals (including study. Finally, we calculated multidimensional func- small earthworms). Litter was removed from each bag, tional dispersion for all 18 traits across all communities. cleaned further, oven dried at 60° for >24 h, and weighed. This multidimensional metric of functional diversity is Unfortunately, because it was often impossible to identify highest when species in a community differ widely from litter to species following decomposition, we pooled the each other across a broad suite of traits. mass of all litter in a single bag rather than weighing litter on a per-species basis (as in, for example, Scherer-Loren- zen et al. 2007). Dried litter was then ground and carbon Litterbag deployment and collection fractions were measured as described above. Post-decom- All litterbags were deployed in a common garden at position litter was ashed at 550°C for 4 h and all litter CCESR on 12 June 2015. The common garden was mass estimates and carbon fractions are presented on an located in a secondary, unmanaged stand of trees, con- ash-free dry mass basis. Three bags were not recovered, sisting primarily of Populus grandidentata (bigtooth giving a final sample size of 597 bags across 150 strings. aspen) and Pinus strobus interspersed with Acer spp. Understory growth was minimal and largely consisted of Data analysis the seasonally abundant legume Amphicarpaea bracteata (hog peanut). A thin duff layer covered the mineral soil All foregoing statistical analyses were performed in R horizon in the common garden and was left intact. We version 3.4.3 (R Core Team 2017). note that the applicability of experiments in common gardens to cases of natural decomposition of litter in its Calculation of decomposition rates.—To compare the community of origin is to some extent limited. In real- consequences of litter chemistry for decomposition, we world cases of decomposition, existing vegetation not calculated the decomposition rate (k or ka) for mass, sol- only determines litter physical and chemical qualities, uble cell contents, hemicellulose and bound proteins, cel- but also affects the microclimate of decomposition (e.g., lulose, and AUR for each string of four bags collected through shading) and the diversity and abundance of the over 2 yr (N = 150) and compared these values among decomposer community. All of these factors interact to strings. For whole litter mass decomposition, we also determine the rate of litter in its site of production (Hob- calculated the fraction of initial mass in the nondecom- bie et al. 2006). And previous litterbag experiments have posing pool (A). The pre-decomposition contributions Article e01407; page 6 JAKE J. GROSSMAN ET AL. Ecological Monographs Vol. 90, No. 3 of each carbon fraction to a litterbag’s mass were esti- and the routine employed to assess trait data as mated based on species composition and species-level described above. carbon fraction measurements. Thus, it was possible to estimate the proportion of soluble cell contents, hemicel- A priori assessment of relationships between litter chem- lulose and bound proteins, cellulose, and AUR lost from istry and AUR decomposition.—In addition to our open- each bag over the course of decomposition. ended variable selection approach, we used correlation To determine the best-fitting model to describe data tests to assess whether the proportion of litter mass on proportion of mass or carbon fraction remaining, we remaining in all bags (N = 597) was associated with each first fit models to data from all replicate bags of the bag’s initial chemistry. We assessed the dependence of same mixture composition (ignoring blocking) com- AUR decomposition on initial litter C:N, manganese, bined, yielding a decomposition rate for each mixture and labile carbon (soluble cell contents and hemicellu- type (N = 49). We fit raw data on total mass or carbon lose and bound proteins, and cellulose). fraction content to the three models of decomposition most commonly adopted in the literature to determine Relative mixing effect on decomposition (MER).—To the model that best described the decomposition dynam- determine the extent to which litter diversity affected ics: a (1) single-exponential, (2) double-exponential, and decomposition rate, we compared observed decomposi- (3) asymptotic decomposition model (Weider and Lang tion rates (k or ka) of 2-, 5-, and 12-species mixtures to 1982, Riggs et al. 2015) expected decomposition rates (ke) for these mixtures based on litter decomposition in single-species bags. We ¼ Àkt; X e (1) did so by modeling the consequences of litter diversity in a given mixture for the relative effect of mixing (MER; À À X ¼ Ce k1t þ ðÞ1 À C e k2t; (2) Pretzsch et al. 2010) on decomposition, calculated as k/ ke. Values of MER greater than one indicate faster À X ¼ A þ ðÞ1 À A e kat; (3) decomposition than expected based on monoculture, while MERs less than one suggest that litter in mixture in which X is the proportion of initial mass or of a given decomposed more slowly than expected based on single- carbon fraction remaining at t years after deployment. species bags. These results can be shown graphically in In the single-exponential model (Eq. 1), k is the decom- plots of ke vs. k, in which case points above a 1:1 line À position rate (yr 1). In the double-exponential model indicate a MER > 1 and points below indicate a (Eq. 2), the decomposing litter is assumed to comprise MER < 1. two pools: one pool (C) decomposing at the rate of k1 To identify which of the 37 litter mixtures (2-, 5-, and and a second pool (1 À C) decomposing at the rate of 12-species combinations) deviated from expected decom- k2. In the asymptotic model (Eq. 3), the slow pool (A)is position rates (MER 6¼ 1), we calculated 90% confi- assumed to decompose at a rate of 0 and the fast pool dence intervals around the mean value for each mixture decomposes at a rate of ka. All models were fit using the type (N = 6 for the 12-species mixture and three for all bbmle R package (Bolker 2017). We used the corrected other mixtures) using the given Student’s t distribution. Akaike Information Criterion (AICc) as a measure of When a given mixture’s confidence interval did not model fit. Single-and double-exponential decay models include one, we concluded that the mixture deviated fit most mixtures best, but the latter group frequently from expected, additive predictions of decomposition generated biologically unrealistic decomposition rates. rates. Additionally, the asymptotic model fit mass loss data relatively well and seemed most biologically realistic Predictors of decomposition and mixing effects.—We (Appendix S1: Table S3). Therefore, as described in used mixed-effects regression modeling to explore the Results, we used asymptotic models to represent mass relationship between both decomposition rates (k) and loss and single-exponential models to assess decomposi- diversity-related changes in decomposition rates (MER) tion of carbon fractions. and species richness, phylogenetic diversity, functional After selecting which decomposition models to use, identity, and functional diversity. Models were fit using we refit all models on a per-string (rather than per-mix- the lmer function in the lme4 R package (Bates et al. ture) basis to account for variability among replicate 2015) with a log-likelihood criterion. The structure of all bags decomposing under different conditions. We calcu- models was as follows lated k values specific to mass and the four carbon frac- = b þ = l þ e; tions for each set of four replicate bags (N 150) and ka Y Xi i Block Mixture Type (4) and A values for mass. Rates calculated from monocultures only (N = 36) were compared using one-way where Y is the dependent variable (either k or MER for ANOVA and post-hoc Tukey tests (Agricolae R pack- decomposition rate of mass or one of the four carbon age; Mendiburu 2016) to assess species-level differences fractions measured), Xi is the known vector of values for in decomposition rates. Phylogenetic conservativism of a given fixed predictor i (species richness, phylogenetic decomposition rate was calculated using monocultures diversity, functional identity, or functional diversity), b August 2020 LITTER DIVERSITY SLOWS DECOMPOSITION Article e01407; page 7 is an unknown vector of fixed effects for predictor i, l is RESULTS an unknown vector of random effects corresponding to Changes in mass were generally well-represented by the random effect of mixture type nested in block, and e asymptotic models and changes in soluble cell contents, is an unknown vector of random errors. hemicellulose and bound proteins, and cellulose were To assess the relationship between decomposition generally well-represented by single-exponential models rates (k for total mass loss and k for loss of carbon frac- a (Appendix S1: Table S3). In some cases, double- tions) and the chemical composition of litter, we first exponential decay models generated lower AIC scores or assessed multicollinearity of the full candidate predictor fit decomposition data slightly better than did single- set using correlation tests, and selected a suite of ecologi- exponential decay models. However, in these cases, high cally relevant litter traits whose values were not corre- k values often caused the preferred double-exponential lated with each other (but were correlated with those of 2 models to collapse, functionally, into poorly fitting other litter traits) in our data set: leaf cellulose, man- asymptotic decay models. In other cases, and especially ganese, calcium, nitrogen, and zinc. We then, for each for slower-decomposing carbon fractions, estimated given response variable, used Farrar-Glauber testing (a parameters in double-exponential models were unrealis- suite of multicollinearity measures incorporated into the tic. Since single-exponential decay models were almost imcdiag function of the mctest R package; Ullah and as well-supported in model comparison and are more Aslam 2018) to guide sequential removal of predictors straightforward to interpret, we report the decomposi- until remaining predictors were not collinear. (The Far- tion rate parameters from these models below as k rar-Glauber protocol entails a v2 test to determine (whether k from single-pool decomposition models or k whether there is multicollinearity among a group of pre- a from asymptotic models) or decomposition rates for dictors followed by an F test and multiple t tests to each carbon fraction. And because it was clear that determine which variables contribute to this collinear- some carbon fractions (e.g., AURs) had not substan- ity). Backward variable selection was then used to tially decomposed over the course of the experiment, we remove predictors that did not improve model fit used the asymptotic model decomposition model to esti- (ols_step_backward_p function of the olsrr R package; mate the rate of mass loss of leaf litter on the whole, Hebbali 2018). Remaining predictors were then fit as extracting k rates from these models to approximate fixed effects in mixed-effects linear regression models as a decomposition. in Eq. 4. We followed the same procedure to model the extent to which the MER for mass or a given carbon fraction was predicted by the diversity of particular Species- and fraction-specific patterns of decomposition traits, initiating variable selection with a predictor set consisting of single-trait functional dispersion for the Decomposition of total mass and carbon fractions same five traits whose CWMs we used to predict decom- varied widely among species (Table 2). Mass loss over 2 position rate. We also assessed an a priori interest in the yr ranged from 8% (white pine) to 41% (basswood; effect of manganese on the decomposition of AURs Fig. 2). The decomposition of soluble cell contents (Berg et al. 2015). (losses ranging from 17% to 64%), hemicellulose and As an alternative to prediction of decomposition rates bound proteins (6–69%), and cellulose (8–40%) demon- (k) and diversity effects (MER) by single litter traits or strated qualitatively similar patterns (Fig. 2). Only east- suites of traits, we also used principal components analy- ern red cedar lost appreciable amounts of AUR (12% of sis (PCA) to synthesize the effects of all measured traits initial AUR content lost). Other species either did not on these two responses (Appendix S1: Fig. S2). We first show signs of AUR degradation or were enriched in used PCA to reduce the dimensionality of all trait AURs (Appendix S1: Fig. S3). The apparent increase in CWMs and assessed the dependence of k on principal AUR mass for some species may reflect the translocation component axes following Eq. 4. We then did the same of allochthonous plant lignin into litterbags (Berg and for functional dispersion of each trait and used the resul- McClaugherty 2014, Yue et al. 2016) or the presence of tant axes to predict MER. microbially produced AURs (e.g., cell wall constituents). Finally, we expected that MER for mass loss and loss Fungal biomass, and, in particular, melanin-rich fungal of carbon fractions would vary with the functional trait tissues, may have contributed considerably to the AUR diversity of litter mixtures. To assess these relationships, of leaf litter colonized by fungal decomposers (C. See for each dependent variable (MER of mass loss and loss et al., unpublished data). The three more labile carbon of each carbon fraction) we first fit a global model in fractions (soluble cell contents, hemicellulose, and cellu- which species richness, phylogenetic diversity, and multi- lose) decomposed at rates roughly commensurate with dimensional functional diversity were included in that mass loss, with the former two decomposing slightly fas- order as fixed predictors. We then used v2 tests to com- ter than total mass and cellulose decomposing slightly pare these models to reduced models, sequentially more slowly (Fig. 3). And, in our observations of single- removing each fixed predictor. When there was not a sig- species decomposition, we found an accelerated loss nificant difference in v2 statistics between models, we (higher k) of soluble cell contents (q = 0.74, t = 3.49, chose the more parsimonious one. P = 0.005) and hemicellulose and bound proteins Article e01407; page 8 JAKE J. GROSSMAN ET AL. Ecological Monographs Vol. 90, No. 3

TABLE 2. Species means (and standard errors) for decomposition rates of total mass and carbon fractions.

Decomposition rate, k (yrÀ1) Species Mass Soluble cell contents Hemicellulose and bound proteins Cellulose AURs Acer negundo 6.1 (2.0)a 3.3 (0.13)a 0.93 (0.15)bc 0.27 (0.06)b noneb Acer rubrum 5.1 (0.68)ab 2.0 (0.66)bc 0.16 (0.09)c 0.18 (0.07)b noneb Betula papyrifera 2.9 (0.10)abc 1.8 (0.26)bcd 1.2 (0.17)b 0.43 (0.11)ab 0.07 (0.06)ab Juniperus virginiana 2.7 (1.1)abc 1.5 (0.39)bcde 0.83 (0.29)bc 0.47 (0.18)ab 0.19 (0.04)a Pinus banksiana 0.65 (0.21)bc 0.39 (0.04)ef 0.48 (0.07)bc 0.22 (0.05)b 0.05 (0.03)b Pinus resinosa 0.40 (0.07)bc 0.84 (0.17)cdef 0.67 (0.08)bc 0.20 (0.05)b 0.01 (0.01)b Pinus strobus 1.5 (1.4)abc 0.25 (0.07)f 0.26 (0.04)c 0.30 (0.06)b 0.05 (0.03)ab Quercus alba 2.2 (0.96)abc 0.75 (0.06)def 0.63 (0.07)bc 0.32 (0.05)b noneb Quercus ellipsoidalis 2.5 (1.6)abc 0.34 (0.04)ef 0.42 (0.05)bc 0.23 (0.03)b 0.03 (0.02)b Quercus macrocarpa 0.40 (0.25)bc 0.41 (0.04)ef 0.48 (0.07)bc 0.18 (0.01)b noneb Quercus rubra 0.27 (0.10)c 0.22 (0.02)f 0.43 (0.02)bc 0.39 (0.02)ab 0.03 (0.02)b Tilia americana 1.8 (0.38)abc 2.4 (036)ab 3.1 (0.44)a 0.75 (0.12)a 0.08 (0.05)ab Notes: Within each column, values with the same superscript are equivalent at the 0.10 level via post-hoc Tukey test. AURs, acid- unhydrolyzable residues.

A) Mass decomposition B) Soluble cell contents decomposition C) Hemicellulose and bound proteins decomposition a a a 8.0 3.0 bc ab 3.0 6.0 ab bcd bcde 2.0 2.0 abc abc 4.0 abc ) abc b

-1 abc abc cdef bc bc 1.0 def 1.0 k (yr 2.0 bc bc ef bc bc bc bc ef ef bc bc f c bc c f c 0 0 0

D) Cellulose decomposition a E) AUR decomposition a osition constant, Pinus resinosa (red pine) p

m 0.20 Pinus banksiana (jack pine) o 0.75 ab Pinus strobus (white pine) Dec Juniperus virginiana (eastern red cedar) ab 0.15 ab Acer rubrum (red maple) 0.50 ab ab Acer negundo (box elder) b b 0.10 Tilia Americana (basswood) b ab b Quercus alba (white oak) b b b 0.25 b b b b Quercus macrocarpa (bur oak) 0.05 Quercus ellipsoidalis (pin oak) b Quercus rubra (red oak) b b b b 0 0 Betula papyrifera (paper birch)

À1 FIG. 2. Species-level decomposition rates (k;yr ) for (A) mass and (B–E) all carbon fractions. Letters above columns indicate the results of Tukey post-hoc testing at the 0.10 level; values of k for species that share a letter are not significantly different. Error bars indicate standard error. Note different y-axis scales. Decomposition rates for acid-unhydrolyzable residues (AURs) (lignin and lignin-like compounds; panel E) should be interpreted with caution; enrichment with allochthonous AURs or microbial synthesis of AURs may have contributed to apparent increases in mass rather than mass loss over time.

(q = 0.83, t = 4.66, P < 0.001) in litter of species with Relationships between litter chemistry and decomposition high initial concentrations of soluble cell contents and hemicellulose, respectively. Mass loss (Moran’s I = 0.270, As expected, initial litter chemistry predicted mass loss P = 0.008) and loss of soluble cell contents (Moran’s and the decomposition of litter carbon fractions over 2 yr I = 0.215, P = 0.006), but not decomposition of other (Table 3) with concentrations of specific nutrients associ- carbon fractions, showed evidence of phylogenetic signal. ated with patterns in the decomposition of specific car- Conservation of high mass decomposition rates distin- bon fractions. Variable selection and principal guished the three malvid species in our experiment (Acer components analysis of litter chemistry (Appendix S1: negundo, A. rubrum,andT. americana)fromtheother Fig. S2) showed that five traits—litter cellulose, nitrogen, (rosid) angiosperms and the gymnosperm species. calcium, zinc, and manganese content—captured August 2020 LITTER DIVERSITY SLOWS DECOMPOSITION Article e01407; page 9

200 A) Soluble cell B) Hemicellulose and contents bound proteins

150

HBP = -0.16 + 0.99×Mass 100 SSC = -0.18 + 0.95×Mass

0 r2 = 0.73 r2 = 0.63

200 C) Cellulose D) AURs AUR = 0.42+ 150 0.90×Mass Carbon fraction remaining (%)

CEL = -0.13 + 100 1.2×Mass 2 months

4 months 50 12 months 24 months 2 2 0 r = 0.71 r = 0.29

0 50100 150 0 50 100 150 Initial mass remianing (%)

FIG. 3. Loss of (A) soluble cell contents (SSC), (B) hemicellulose and bound proteins (HBP), and (C) cellulose (CEL) occurred at a rate roughly equivalent to that of total mass. (D) AURs (including lignin) were lost more slowly and their degradation was less tightly coupled to mass loss. Simple linear regressions (equations shown) of the percentage of a given carbon fraction remaining against the percentage of total mass remaining indicate that the most labile fractions were lost more rapidly than total mass, and vice versa for more recalcitrant fractions. All litterbags (N = 597) are represented in each plot, with color coding indicating decomposition time. considerable leaf litter diversity. And because models of positively correlated with the loss of AUR mass decomposition using subsets of these traits explained as (q = 0.13, t = 3.40, P < 0.001), as expected. Litter with much variation in changes in mass and all carbon frac- initially higher soluble cell contents lost AURs more tions as principal components integrating all litter traits, slowly (q = 0.33, t = 8.36, P < 0.001), while higher ini- we present the former set of trait-based models here tial content of hemicellulose and bound proteins (Table 3). Overall, litter rich in nutrients, most notably (q = À0.13, t = À3.17, P = 0.002) and cellulose calcium, and cellulose tended to decompose faster. Litter (q = À0.20, t = À4.94, P < 0.001) was associated with 2 = traits explained loss of soluble cell contents (Rm 0.59), faster AUR decomposition. 2 = hemicellulose and bound proteins (Rm 0.58), and cellu- 2 = 2 = lose (Rm 0.48) better than mass loss (Rm 0.07) or 2 = Consequences of litter mixing for decomposition change in AURs (Rm 0.13; Table 3). Simple regressions of the proportion of litter AUR Generally, litter mixtures decomposed additively, mass remaining against the initial litter concentrations meaning that they decomposed as expected based on sin- of several chemical constituents complemented our gle-litter bags or that our experimental design was above findings regarding the effect of litter chemistry on unable to detect significant deviations from additivity decomposition. AUR in nitrogen-rich litter (low C:N (Table 4; Appendix S1: Table S4). Overall mass loss was ratio) decomposed more slowly than in nitrogen-poor additive for 78% of the 37 mixtures we included in our litter (q = 0.09, t = 2.10, P = 0.038), consistent with study; three mixtures decomposed more slowly than what we expected. Manganese was significantly and expected (antagonism) while five mixtures decomposed Article e01407; page 10 JAKE J. GROSSMAN ET AL. Ecological Monographs Vol. 90, No. 3

TABLE 3. Best models of decomposition rates (ks) based on community-weighted means (CWMs) of trait values.

Random Fixed terms Estimate t terms St. dev. Levels A. Mass—k Cellulose CWM À0.235 À2.96 Number of Obs. NA 150 Manganese CWM À0.075 À0.926 Block/Composition <0.001 147 Calcium CWM 0.052 0.645 Marginal R2 = 0.065 Conditional R2 = 0.065 B. Soluble cell contents—k Cellulose CWM À0.418 À6.99 Number of Obs. NA 150 Manganese CWM À0.331 À4.54 Block/Composition 0.219 147 Calcium CWM 0.542 8.97 Nitrogen CWM À0.132 À2.07 Zinc CWM 0.167 2.20 Marginal R2 = 0.589 Conditional R2 = 0.684 C. Hemicellulose and bound proteins—k Cellulose CWM 0.071 1.18 Number of Obs. NA 150 Manganese CWM À0.084 À1.15 Block/Composition <0.001 147 Calcium CWM 0.757 12.4 Nitrogen CWM À0.32 À0.506 Zinc CWM 0.168 2.20 Marginal R2 = 0.582 Conditional R2 = 0.582 D. Cellulose—k Cellulose CWM 0.189 3.04 Number of Obs. NA 150 Manganese CWM 0.156 2.57 Block/Composition 0.112 147 Calcium CWM 0.711 10.7 Nitrogen CWM À0.032 À0.466 Marginal R2 = 0.478 Conditional R2 = 0.969 E. AURs—k Cellulose CWM 0.310 3.89 Number of Obs. NA 150 Manganese CWM 0.060 0.763 Block/Composition 0.064 147 Calcium CWM 0.224 2.84 Marginal R2 = 0.128 Conditional R2 = 0.746 Notes: Predictors were chosen through selection of CWMs for litter cellulose, manganese, calcium, nitrogen, and zinc, are included for mass (A) and carbon fraction decomposition (B–E) as fixed predictors. Litter composition type, nested within block, is included as a random predictor. All estimates for fixed predictors are standardized. more quickly than expected (synergism). Change in car- TABLE 4. Number of each of 37 litter mixtures included in this bon fractions showed different patterns depending on experiment for which we observed antagonistic (slower than expected based on monoculture), synergistic (faster than the fractions. Cellulose and AUR decomposed roughly expected), or additive (as expected) decomposition of whole as expected in mixture, showing antagonistic and syner- litter mass and carbon fractions. gistic deviations from expectations at roughly the same frequency. Antagonistic effects were more common for Nonadditive soluble cell contents (30% of all mixtures), and to a les- Decomposition type Antagonistic Synergistic Additive ser extent for hemicellulose and bound proteins (16% of Mass 3 5 29 all mixtures). Soluble cell contents 11 1 25 Though more diverse litter did not, overall, decom- Hemicellulose and bound 6229 pose differently than expected based on monoculture proteins (Fig. 4), the two most labile litter carbon fractions Cellulose 3 2 32 decomposed more slowly than expected in functionally AURs 3 2 32 diverse litter mixtures (Table 4). The best models of Note: Decomposition rates are considered nonadditive if they MER for soluble cell contents and hemicellulose and were outside of a 90% confidence interval around the expected bound protein decomposition were those that included rates. August 2020 LITTER DIVERSITY SLOWS DECOMPOSITION Article e01407; page 11

ko = 0.12 + 0.75×ke

DFP = -3.3×FD + 1.55

FIG. 4. Decomposition of total mass and soluble cell contents in mixed litter. Observed vs. expected (subscript o and e, respectively) decomposition rates (k) of (A) total mass and (B) soluble cell contents in mixed litter. Error bars indicate confidence intervals based on 3- (or 6-, for 12-species bags) replicate sets of bags with the same composition. In these plots, points that fall above a 1:1 line indicate a mixture that decomposed faster than expected based on monoculture and points that fall below the line indicate slower-than-expected decomposition. Total mass loss in mixtures did not deviate from expectations (A), but a simple linear regression shows that observed soluble cell contents decomposition rate in mixtures was roughly 75% of what would be expected based on monocultures (B). (C) The relative mixing effect (MER; kobserved/kexpected) on total mass did not vary systematically with multidimensional functional diversity of mixed litter, but (D) more functionally diverse litter lost soluble cell contents more slowly than 2 = would be expected. The regression slope in panel D corresponds to the model presented in Table 5A (RMarginal 0.134). All points are color-coded by species richness of the litter mixture as indicated in panel A; the 12-species mixture is further distinguished with an arrow and label. as a fixed effect only multidimensional functional diver- species decomposition rates. Marginal R2 values for sity, and not species richness or phylogenetic diversity. these models indicate that functional diversity explained As expected, functional diversity better predicted MER 13% of the variation in soluble cell contents MER and than did other dimensions of diversity (v2 = 11.5, 10% of the variation in hemicellulose and bound protein P < 0.001). In particular, species richness was barely MER. We did not find evidence for this pattern in mod- predictive of MER for these fractions, and phylogenetic els of total mass loss or cellulose or AUR decomposition diversity was not. The lack of importance of phyloge- MERs, for which the best-supported models contain netic diversity in these models is not surprising given only an intercept and random effects (Appendix S1: that most measured functional traits were not Table S5). phylogenetically conserved and, of the four that were, Single-trait diversity models of MERs echoed our soluble cell contents, zinc, and manganese content pre- finding that functional trait diversity mediated the rela- dicted decomposition and MER only modestly (Tables 3 tionship between diversity and decomposition in our lit- and 5). terbag experiment (Table 5; Appendix S1: Table S5). In models of MER for both soluble cell contents and And echoing our finding of the negative relationship hemicellulose and bound protein decomposition, a nega- between multidimensional functional diversity and tive coefficient for functional diversity indicates that lit- decomposition, diversity in several litter chemical traits, ter mixtures with more diverse functional traits most notably manganese and calcium, also led to slower decomposed more slowly than expected based on single- decomposition rates. Article e01407; page 12 JAKE J. GROSSMAN ET AL. Ecological Monographs Vol. 90, No. 3

TABLE 5. Best models of relative mixing effects (MERs) on decomposition based on multidimensional functional diversity (A–B) or univariate trait diversity (C–D).

Fixed terms Estimate t Random terms St. dev. Levels A. Soluble cell contents—MER—multidimensional trait diversity Functional dispersion À3.28 À4.18 Number of Obs. NA 114 Block/Composition <0.001 111 Marginal R2 = 0.134 Conditional R2 = 0.134 B. Hemicellulose and bound proteins—MER—multidimensional trait diversity Functional dispersion À2.83 À3.45 Number of Obs. NA 114 Block/Composition <0.001 111 Marginal R2 = 0.095 Conditional R2 = 0.095 C. Soluble cell contents—MER—univariate trait diversity Calcium functional dispersion À0.295 À3.61 Number of Obs. NA 114 Block/Composition 0.208 111 Marginal R2 = 0.104 Conditional R2 = 0.361 D. Hemicellulose and bound proteins—MER—univariate trait diversity Manganese functional dispersion À0.192 À2.16 Number of Obs. NA 114 Calcium functional dispersion À0.245 À2.94 Block/Composition <0.001 111 Marginal R2 = 0.108 Conditional R2 = 0.108 Notes: These models are included for soluble cell contents (A, C) and hemicellulose and bound protein decomposition (B, D) as fixed predictors. Litter composition type, nested within block, is included as a random predictor. In C and D, all estimates for fixed predictors are standardized. Models for mass, cellulose, and AURs are given in Appendix S1: Table S4.

of limited cellulose loss is also consistent with the expec- DISCUSSION tation that, because some cellulose is physically We tracked decomposition of total mass and four car- enmeshed with lignin in leaf litter, unlignified cellulose bon fractions in litter of 12 species decomposing individ- will decompose relatively quickly, but that cellulose loss ually and in 37 polycultural mixtures over 2 yr. Carbon will then level off and keep pace with (slow) lignin fractions showed different decomposition profiles and decomposition (Herman et al. 2008, Berg 2014). Finally, species varied significantly in their decomposition rates, as expected, loss of more labile carbon fractions domi- with more labile fractions and more nutrient-rich litter nated early decomposition (Appendix S1: Fig. S4), while decomposing more quickly. Most notably, labile litter loss of more recalcitrant fractions occurred slowly over 2 carbon decomposed more slowly in mixture than was yr (Fig. 3). We note that these more labile fractions also expected, and this effect was stronger in litter from spe- decomposed more rapidly in the litter of species that cies with diverse chemical traits. were originally enriched in these compounds (e.g., basswood litter had the highest initial levels of hemicellulose and bound proteins of all species and also was depleted Carbon fractions varied in decomposition rate in this carbon fraction more rapidly than any other spe- The decomposition of four measured litter carbon cies). We speculate that, if we had monitored decomposi- fractions varied according to their chemical composition for more than 2 yr, we would have observed this tion. We expected that soluble cell contents and hemicel- relationship between initial carbon fraction content and lulose plus bound proteins would decompose rapidly decomposition of the fraction in question for cellulose and that cellulose and AURs would decompose slowly, if and AURs as well. at all, and would stabilize within the 2-yr study period Indeed, because our experiment took place over only 2 (Adair et al. 2008, Berg 2014, Berg and McClaugherty yr, the loss of soluble cell contents, the most labile car- 2014, Riggs et al. 2015), expectations that were con- bon fraction measured, dominated decomposition. Dur- firmed (Appendix S1: Fig. S3). These patterns suggest ing this 2 yr, 50–80% of total change in mass could be rapid, physically and microbially mediated decomposi- predicted by change in soluble cell contents tion of labile carbon and relatively limited decomposi- (Appendix S1: Fig. S4). This is, in reality, unsurprising tion of more recalcitrant carbon (Berg et al. 2010, Bray given that lignin decomposition occurs over many years, et al. 2012, Chapman et al. 2013, Berg 2014) and/or con- and even decades, rather than over the first 2 yr follow- version of labile litter constituents into more resistant ing litterfall (Berg and McClaugherty 2014). Had we organic matter (Lehmann and Kleber 2015). Our finding tracked loss of cellulose and lignin over a longer time August 2020 LITTER DIVERSITY SLOWS DECOMPOSITION Article e01407; page 13 period, we might have documented mixing effects similar employed litter bags with a larger mesh size, we might to those we observed in soluble cell contents and hemi- have observed an even stronger positive effect of calcium cellulose decomposition. on decomposition due to the effect of, for instance, calcium-hungry millipedes (Cotrufo et al. 2010, Handa et al. 2014). Furthermore, the use of mesh litterbags is Litter chemistry predicted decomposition known to affect colonization and decomposition of Our findings confirm our expectations that leaf litter microbial decomposers (reviewed in Bradford et al. chemistry predicts whole mass and carbon fraction 2002). In fact, St. John (1980) reports that the use of decomposition rates, although some of the particular bags can retard microbial decomposition, and especially chemical traits implicated in decomposition were not the activity of fungal decomposers, relative to decompo- those we expected. sition in open mesocosm by limiting environmental inoc- Total litter decomposed more quickly if it was initially ulation. Yet the same experiment indicated that bags higher in calcium and more slowly if it was high in cellu- made of fine mesh, like ours, can accelerate decomposi- lose or manganese, consistent with the current consensus tion relative to bags of coarse mesh by increasing the that nutrient-rich litter with low lignin content (and thus moisture content of bagged litter. higher proportions of labile fractions such as soluble cell contents) decomposes more rapidly, especially in early Support for a priori predictions of particular compounds’ stages of decomposition (Cornelissen 1996, Cornwell effects on AUR loss et al. 2008, Berg and McClaugherty 2014, Djukic et al. 2018). Interestingly, nitrogen content, generally thought Furthermore, we were interested in the consequences to indicate nutrient-rich, easy-to-degrade litter, was a of initial litter nitrogen, manganese, and soluble cell con- less important predictor of decomposition than were tents for the decomposition rate of the AUR fraction, concentrations of important micronutrients such as cal- which includes lignin. Loss of AURs was positively asso- cium, zinc, and manganese (Makkonen et al. 2012 report ciated with initial litter carbon to nitrogen ratios, consis- a similar finding regarding the role of magnesium and tent with the expectation that nitrogen slows lignin tannins). Lack of a strong effect of nitrogen on decom- decomposition by microbes (Melillo et al. 1982, Berg position, in particular, suggests that litter decomposition and Ekbohm 1993, Berg 2014). We were also curious to may have been limited primarily by micronutrients. assess the extent to which leaf manganese concentration, However, patterns in litter chemistry across nutrients a phylogenetically conserved trait, accelerated AUR were generally collinear; concentrations of most macro- decomposition given that manganese, as an essential and micronutrients tended to be correlated at the species constituent of lignin-degrading manganese peroxidase level (Appendix S1: Fig. S1). Given this, we conclude produced by white-rot fungi, is considered essential to that, though micronutrients were particularly predictive litter lignin decomposition (Berg 2014, Berg et al. 2015). of decomposition in this experiment, our findings gener- Indeed, manganese was included in our model of the ally comport with the literature suggesting that nutrient- AUR decomposition rate via variable selection and, in rich litter decomposes quickly (Hobbie 1992, Barantal bivariate analysis, manganese concentration was signifi- et al. 2014). cantly and positively correlated with AUR decomposi- Litter with higher initial calcium content lost not only tion rates. Finally, past work on priming on organic mass, but also hemicellulose and bound proteins, cellu- matter decomposition in soils has suggested that the lose, and AURs more rapidly than low-calcium litter, input of microbially accessible, labile carbon may pro- echoing past work that this nutrient contributes impor- vide the energy necessary to facilitate, or prime, decom- tantly to the first months and years of litter decomposi- position of slowly decomposing organic matter tion (Table 3; Davey et al. 2007, Berg et al. 2017). (Kuzyakov et al. 2000, de Vries and Caruso 2016). Alter- Whereas other nutrients such as nitrogen, phosphorus, natively, the addition of labile organic matter may cause and magnesium are generally thought to accelerate shifts in the decomposer community, slowing decompo- microbially mediated decomposition, calcium is believed sition of more recalcitrant carbon (de Graaff et al. to make litter more attractive to earthworms (Reich 2010). In limited work assessing priming in leaf litter et al. 2005, Hobbie et al. 2006) and oribatid mites (Gist decomposition, initial litter cellulose content has been and Crossley 1975, Norton and Behan-Pelletier 1991), shown to prime the decomposition of lignin (Talbot and thus facilitating fragmentation and translocation of Treseder 2012). Our findings corroborate this, and also decomposing litter. Indeed, though the 1-mm mesh size suggest a negative role of initial litter soluble cell con- of our litterbags likely prevented consumption of litter tents and hemicellulose on AUR decomposition over 2 by adult earthworms, we found small earthworms inside yr. It may be that, in our study, cellulose-rich litter was litterbags; these may have migrated into the bags as juve- susceptible to co-metabolization of lignin and cellulose niles and preferentially consumed high-calcium litter. by decomposers (Lindahl and Tunlid 2015), while litter Detritivorous oribatid mites, though not directly rich in labile carbon recruited microbial communities observed, would also have easily been able to enter our less able to efficiently process more recalcitrant organic bags, contributing to primary decomposition. If we had matter (Chigineva et al. 2009). Article e01407; page 14 JAKE J. GROSSMAN ET AL. Ecological Monographs Vol. 90, No. 3 diverse litter may decompose more slowly than it would In mixture, decomposition of labile carbon, but not total in the absence of leaf litter mixing. Below, we explore litter, deviated from expectations possible mechanisms explaining this pattern. Based on the balance of current experimental evidence, we expected that species richness would not alter Slower litter decomposition is associated with its the decomposition rate of whole leaf litter or that its functional, and not phylogenetic, diversity effects would be idiosyncratic (Gartner and Cardon 2004, Hattenschwiler€ et al. 2005, Srivastava et al. 2009, Given the strong control of decomposition by litter Cardinale et al. 2011). This expectation was met. Despite chemistry, we expected that deviations from expected idiosyncratic deviations from predicted rates, total litter decomposition rates might be related to litter functional in mixtures did not lose mass faster or slower than diversity (Barantal et al. 2014, Handa et al. 2014, Tardif expected. and Shipley 2014, but see Chapman and Koch 2007). Yet litter decomposition studies, including our own, Indeed, we found that multidimensional functional dis- continue to document measurable, if idiosyncratic or persion of 18 leaf litter traits predicted deviation from system-specific, nonadditive decomposition of litter mix- expected decomposition of labile carbon fractions better tures containing two or more species, as predicted by the than did taxonomic or phylogenetic diversity (Fig. 4). In diversity hypothesis (e.g., Vos et al. 2013, Barantal et al. our consideration of the role of particular traits, litter 2014, Handa et al. 2014, Trogisch et al. 2016). Our find- mixtures that varied most in soluble cell contents and ings suggest a new potential mechanism behind other- calcium content showed the slowest decomposition com- wise ambiguous previous findings of changes in pared to predictions from single-species bags (Table 5). decomposition across species richness gradients. We This antagonistic effect of functional diversity on car- argue that more labile, or quickly decomposing, frac- bon loss does not appear to be due to the leaching of tions of litter may be disproportionately affected by compounds soon after litterbag deployment, but rather functional diversity, and especially diversity in litter to changes in microbial decomposition. We documented micronutrient and soluble cell contents during early monocultural mass loss of up to ~20% of original dry decomposition. Roughly 30% of the 37 litter mixtures mass (although often far less; Appendix S1: Fig. S3) dur- included in our experiment showed significant, antago- ing the first two months of litter decomposition, the per- nistic, nonadditive changes in soluble cell contents, a iod during which leaching of leaf carbon and nutrients clear signal that the labile carbon fraction of mixed litter present at abscission would have occurred. Published tended to decompose more slowly than it would have if research indicates that fast carbon decomposition (dur- separated into constituent species. Litter also lost soluble ing these first weeks of decomposition) is due to both cell contents at a higher rate (17–64% of original mass) microbial decomposition and leaching (Berg and than was the case for any other fraction, and soluble cell McClaugherty 2014), with leaching perhaps playing a contents were the only fraction for which decomposition minor role (as in Tietema and Wessel 1994) and that spe- rates were phylogenetically conserved. (Malvids in Acer cies vary widely in their loss of carbon and nutrients dur- and Tilia had the highest decomposition rates.) The ing this period (Corrigan and Oelbermann 2013). same patterns were exhibited, at smaller magnitude, by Indeed, roughly 80% of litter mass loss during this time hemicellulose and bound proteins, the second most consisted of soluble cell contents (with considerable vari- labile carbon fraction we measured. Thus, as the most ation among litter mixtures; Appendix S1: Fig. S4A). actively decomposing carbon fractions, soluble cell con- Yet plenty of soluble cell contents remained after this ini- tents and hemicellulose may have been most responsive tial period of decomposition. In fact, as shown in panels to diversity over the first 2 yr of litter decomposition. B–D of Appendix S1: Fig. S4, decomposition of soluble Indeed, because loss of soluble cell contents domi- cell contents continued to dominate mass loss for the nated decomposition over the 2 yr of the study period entire 2-yr period of the experiment. At the end of the (Appendix S1: Fig. S4), the effect of litter mixing on the experiment, between 20% and 80% of original SCC decomposition of this carbon fraction is of considerable remained in monocultural bags (Appendix S1: Fig. S3). importance to early decomposition of leaf litter in gen- Given all this, the observed diversity effects on labile car- eral. Cellulose and AURs did not show high rates of bon decomposition are likely the result of changes in the mass loss and, to the extent that they did, did not deviate activity of the microbial decomposer community. systematically from expected values based on monocul- Early decomposition is mediated by rapidly shifting ture. Thus, the nonadditive consequences of diversity for assemblages of bacterial and fungal decomposers (Aneja more labile fractions were masked by insensitivity to et al. 2006, Chapman et al. 2013, Voriskova and Bal- diversity in the decomposition of other fractions. Our drian 2013), which, we argue, appear to have responded findings thus provide one potential explanation for the to functional diversity in mixed litterbags. The composi- past findings of idiosyncratic, nonadditive decomposition and functioning of these microbial decomposer tion of mixed leaf litter (e.g., Scherer-Lorenzen et al. communities are highly dependent on available 2007, Tardif and Shipley 2013, Jewell et al. 2016, Seti- resources, shifting rapidly to take advantage of high- awan et al. 2016): the labile carbon in more functionally quality litter (Strickland et al. 2009, Bray et al. 2012, August 2020 LITTER DIVERSITY SLOWS DECOMPOSITION Article e01407; page 15

Schneider et al. 2012). Thus, the physical proximity of generally not phylogenetically conserved among our nutritionally diverse litter is expected to have one of two study species (Losos 2008); this is not always the case in effects. Diversity might, on one hand, facilitate decom- BEF research (Cadotte et al. 2011). As such, it is unsur- position through transfer of nutrients from rich- to prising that functional diversity, largely independently of poor-nutrient litter (Schimel and Hattenschwiler€ 2007, phylogenetic signal, was the best predictor of decompo- Bonanomi et al. 2014) and/or niche complementarity, sition. And though the conserved trait of leaf manganese whereby activity of detritivores in mixtures is greater did predict some patterns of decomposition, other labile because of availability of diverse nutrients and concomi- leaf traits, such as leaf calcium, were also important pre- tant easing of nutrient limitation (Vos et al. 2013, Handa dictors of changes in mass and carbon fractions. As et al. 2014), leading to synergistic, faster than expected such, it appears that rather than serving as a proxy or decomposition (Chapman and Koch 2007). Alterna- complement to functional diversity (Webb et al. 2002, tively, and consistent with our findings, diverse litter Cadotte et al. 2009, Flynn et al. 2011), phylogenetic might limit the abundance or disrupt the functioning of information did not contribute substantially to our assemblages of the highly efficient, specialized decom- understanding of how litter diversity affects decomposi- posers that might thrive on single-species litter. This tion. This finding highlights the importance of disentan- interpretation draws attention to the need for an addi- gling the ecological consequences of these dimensions of tional focus on the mechanisms behind antagonistic, diversity (Cadotte et al. 2008). nonadditive effects on mixed-litter decomposition to complement past work on synergistic effects (Vos et al. CONCLUSION 2013, Barantal et al. 2014, Handa et al. 2014, Setiawan et al. 2016, Trogisch et al. 2016) and suggests that for Our analysis of data from almost 600 litterbags indicates decomposition as well as for primary productivity, func- that functional traits, more than taxonomic diversity or tional trait diversity may serve as an important predictor evolutionary history, are critical predictors of the decom- of ecosystem functioning (Tilman et al. 1997, Diaz and position of temperate forest litter. Functional trait identity Cabido 2001, Cadotte et al. 2009, Flynn et al. 2011, (community-weighted means) predicted decomposition Eduardo 2016). rates of total litter mass and carbon fractions (Mokany In this experiment, we did not separate litter by spe- et al. 2008, Tardif and Shipley 2013, Jewell et al. 2016). cies and measure species-specific decomposition rates. And functional diversity of leaf traits predicted decompo- This was impossible because mixed litter quickly sition of labile carbon fractions (soluble cell contents and becomes indistinguishable (especially at the subgeneric hemicellulose and bound proteins) in mixed litter due to level). Had we been able to calculate species-specific largely antagonistic, nonadditive interactions. This finding decomposition rates in litter mixtures, we could better contrasts with past work documenting a synergistic effect have described the mechanisms behind mixing effects (as of functional diversity on decomposition (Barantal et al. in Scherer-Lorenzen et al. 2007, Vos et al. 2013). For 2014, Handa et al. 2014); we attribute our novel finding to instance, it is possible that lower rates of labile carbon the particular effects of functional diversity on fast-decom- decomposition in functionally diverse mixtures resulted posing carbon fractions. from leaching of inhibitory compounds (e.g., condensed Leaf litter decomposition contributes through multiple tannins) from one species to another. In this case, mixing processes to the maintenance of soil fertility (Cotrufo would have depressed decomposition across the board. et al. 2013, Hobbie 2015) and to the sequestration of car- Alternately, decomposition of some species’ litter might bon in forest soils (Prescott 2010). Both shorter-term have been especially depressed relative to that of other inhibition of decomposition in functionally diverse mix- species. This might have been the result of the limitation tures (as we found in our study) and long-term stabiliza- of specialized decomposers, which might be more active tion (either through physical means or presumed when preferred litter is present. Finally, different effects resistance to microbial decomposition; Lehmann and of mixing on particular species could also provide evi- Kleber 2015) can lead to the sequestration of carbon, dence of priming: we might have expected to find evi- which would otherwise contribute to atmospheric green- dence of this dynamic if the presence of labile, nutrient- house gas stocks, as soil organic matter (Mueller et al. rich litter accelerated the decomposition of recalcitrant, 2015, Soong et al. 2015). Absent nonadditive effects of lit- nutrient-poor litter (Guenet et al. 2010). Future decom- ter mixing, the rate at which carbon and nutrients are position studies could include separation of visibly dis- sequestered in this way simply depends on species abun- tinct classes of litter over the course of labile carbon loss dances. But given evidence from our work that litter mix- in order to better pinpoint the mechanisms behind the ing can depress labile carbon decomposition rates, it is diversity-mediated mixing effects we observed. possible that more biodiverse forests could lose these The design of our experiment and our use of orthogo- compounds at a slower rate than less diverse forests. Such nal diversity metrics allowed us to determine that func- nonadditive diversity effects should be considered in the tional trait diversity, and not taxonomic or phylogenetic conservation and management of soil fertility in forests diversity, predicted deviations from expected patterns of and plantations and in attempts to model carbon and decomposition. The litter traits we considered were biogeochemical cycles in tree-dominated landscapes. Article e01407; page 16 JAKE J. GROSSMAN ET AL. Ecological Monographs Vol. 90, No. 3 Berg, B., B. Erhagen, M. B. Johansson, M. Nilsson, J. Stendahl, ACKNOWLEDGMENTS F. Trum, and L. Vesterdal. 2015. Manganese in the litter fall- All authors conceived of and designed the experiment and forest floor continuum of boreal and temperate pine and — contributed to data analysis and writing. J. J. Grossman con- spruce forest ecosystems a review. Forest Ecology and Man- – ducted field and laboratory work and wrote the first draft of the agement 358:248 260. manuscript. This work was supported by the U.S. National Berg, B., M. B. Johansson, C. Liu, M. Faituri, P. Sanborn, L. Science Foundation Long-Term Ecological Research (DEB- Vesterdal, X. Ni, K. Hansen, and L. Ukonmaanaho. 2017. — 0620652 and DEB-1234162) and Dimensions of Biodiversity Calcium in decomposing foliar litter a synthesis for boreal (DEB-1342872) Programs. J. J. Grossman was supported by a and temperate coniferous forests. Forest Ecology and Man- – Doctoral Dissertation Fellowship from the University of Min- agement 403:137 144. nesota; fellowships from the Crosby, Rothman, Wilkie, Ander- Blomberg, S. P., T. Garland, and A. R. Ives. 2003. Testing for son, and Dayton Funds and by the department of Ecology, phylogenetic signal in comparative data: behavioral traits are – Evolution, and Behavior; and a visiting fellowship at the Arnold more labile. Evolution 57:717 745. Arboretum of Harvard University. The authors wish to thank Bolker, B.2017. bblme: Tools for general maximum likelihood Charlotte Riggs, Xiaojing Wei, Hanna Dort, Ada Breitenbucher, estimation. https://cran.r-project.org/web/packages/bbmle/in Aspen Ward, Marcela Sofıa Vaca Sanchez, Allen J. 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SUPPORTING INFORMATION Additional supporting information may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/ecm.1407/full

DATA AVAILABILITY Data are available from the LTER Network Data Portal: https://doi.org/10.6073/pasta/ccdc61d8798480fdeb2f10c0720f0dbd