Decay of ecosystem differences and decoupling of tree community soil environment relationships at ecotones

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1 Ecological Monographs, 83(3), 2013, pp Ó 2013 by the Ecological Society of America Decay of ecosystem differences and decoupling of tree community soil environment relationships at ecotones CHRISTOPHER B. BLACKWOOD, 1,3 KURT A. SMEMO, 1,2 MARK W. KERSHNER, 1 LARRY M. FEINSTEIN, 1 AND OSCAR J. VALVERDE-BARRANTES 1 1 Department of Biological Sciences, Kent State University, Kent, Ohio USA 2 The Holden Arboretum, 9500 Sperry Road, Kirtland, Ohio USA Abstract. Ecotones are important landscape features where there is a transition between adjoining ecosystems. However, there are few generalized hypotheses about the response of communities to ecotones, except for a proposed increase in species richness that receives varying empirical support. Based on the assumption that transport of abiotic material and dispersal of organism propagules across ecotones are independent processes, we propose the new hypothesis that ecotones decouple community environment relationships, increasing the importance of spatial structure that is independent of the environment. We tested this hypothesis by examining the effects of ecotones on relationships between trees and soil properties in a temperate deciduous forest. The study area included different landforms defined by topography, hydrology, and geomorphology, which we designated upland, bottomland, and riparian forests. The site also included a mowed herbaceous corridor. We found that soil properties and tree community composition significantly differed among landforms, and thus they could be treated as differing ecosystem types. However, inclusion of plots near ecotones significantly reduced the variance explained by landform due to introduction of increased noise, increased similarity of ecotone plots in different landforms, or both. To examine tree community soil environment relationships, factorial kriging analysis was used to decompose variation in soil properties into structures associated with differing spatial scales, which were then used as predictors of tree composition using redundancy analysis. In agreement with the ecotone-decoupling hypothesis, we found that ecotones introduced significant unexplained variation into correlations between tree community composition and soil properties. In addition, spatial variation in tree community composition that was independent of soil properties was only detected when ecotones were included in the analysis, and little variation in tree community composition was attributed to small-scale soil property structures. Together, these results indicate that dispersal limitation and mass effects in the tree community take on increased importance near ecotones. We found no consistent changes in tree species richness associated with ecotones, and we suggest that the ecotonedecoupling hypothesis may correspond with a more general community-level pattern that warrants further testing. Decoupling of community environment relationships near ecotones also has important implications for accuracy of models predicting community distributions from abiotic information. Key words: Allegheny Plateau, Ohio, USA; community environment relationships; ecosystem edges; ecotones; mass effects; soil properties; spatial structure; tree community composition; tree species richness. INTRODUCTION Manuscript received 3 September 2012; revised 12 March 2013; accepted 12 March Corresponding Editor: D. A. Wardle. 3 cblackwo@kent.edu 403 Ecotones are transitional areas between adjacent patches of differing ecosystem types, and are sometimes referred to as ecosystem edges or boundaries (Gosz 1993, Hufkens et al. 2009). They are defined by biological or environmental gradients that are steeper than gradients within the adjacent ecosystem patches (Cadenasso et al. 2003). Ecosystem dynamics at ecotones are related to the encroachment of one ecosystem patch onto another, which has been associated with a variety of biotic and abiotic drivers at differing spatial scales (Gosz 1993, Malanson et al. 2001, Danz et al. 2011). The properties of ecotones are also widely recognized as critical to understanding effects of habitat fragmentation and shifting species ranges (Debinski and Holt 2000, Fonseca and Joner 2007). However, generalizations about population and community responses to ecotones have proven elusive due to the often idiosyncratic response of individual species to habitat and environmental variation (Murcia 1995, Ries et al. 2004). To deal with varying population patterns in response to ecotones, Ries et al. (2004) developed a conceptual framework for population dynamics at ecotones based on underlying ecological mechanisms, and Strayer et al. (2003) developed a descriptive

2 404 CHRISTOPHER B. BLACKWOOD ET AL. Ecological Monographs Vol. 83, No. 3 classification system for ecotones. However, neither of these advances in ecotone theory dealt explicitly with community-level responses to ecotones, and there has since been a call for greater emphasis on incorporating the multivariate nature of ecological drivers and responses into the definition of ecotones (Hufkens et al. 2009). Hypotheses about community responses to ecotones have been limited to the prediction of Odum (1953) that species richness is higher at ecotones because of mixing of distinct communities from different ecosystems, as well as the presence of ecotones specialists. Evidence for this hypothesis, however, is mixed (Ries et al. 2004, Kark and van Rensberg 2006), raising the question of whether there are other, more general community patterns present at ecotones. The reasoning behind Odum s hypothesis can be extended to predict that communities and abiotic conditions in differing ecosystem types become less distinct the closer they are to an ecotone (Dangerfield et al. 2003). Furthermore, we propose the new hypothesis that ecotones decouple community environment relationships that are otherwise important in explaining variation both among and within ecosystems. Ecosystem types are defined by differing plant communities and abiotic factors. Hence, in a comparison of ecosystems, variation in plant community composition can often be predicted from soil properties (Whittaker 1960, Austin et al. 1972, Host et al. 1988). However, near an ecotone, organisms may disperse from an ecosystem with optimal environmental conditions into a different ecosystem that does not match the species niche. This can result in maintenance of a population in the suboptimal ecosystem due to immigration subsidies, a phenomenon called mass effects (Shmida and Wilson 1985, Logue et al. 2011). Abiotic material such as soil particles, plant detritus, water, and dissolved nutrients may also move across ecotones (Wondzell et al. 1996, Polis et al. 1997, Holtmeier and Broll 2010). Because transport processes affecting organisms and abiotic materials are often determined by unique morphological and surface properties, redistribution of different species and materials across ecotones is likely to result in independent patterns. Hence, we expect community environment relationships to degrade near ecotones, resulting in a decrease in the ability to predict plant community composition from either ecosystem type or soil properties. Alternatively, abiotic factors other than soil properties (e.g., light) may take on increased importance in determining community assembly near ecotones, and community environment relationships may be obscured by ecotone specialist species that are adapted to the increased variation in ecosystem properties at ecotones (Ries et al. 2004, McDonald and Urban 2006). Several studies have simultaneously examined shifts in plant communities and soil properties across ecotones (Roovers et al. 2004, Dick and Gilliam 2007, Burley et al. 2010). However, a change in the strength of the relationship between the plant community and soil properties near an ecotone has not been investigated. Plant community composition may also be predicted from soil properties at smaller spatial scales within ecosystems (Schwarz et al. 2003, Davies et al. 2005, Boerner 2006). Individual trees may have species-specific effects on soil properties, or may be most competitive under certain soil conditions (Russo et al. 2008, Gleason et al. 2010, Weber and Bardgett 2011). Near ecotones, however, the influence of organisms and abiotic material from another ecosystem may dilute the effects of individuals on the environment, and may allow species to persist under conditions where they otherwise would not. Hence, we expect that the consistency of community environment relationships derived from smallscale, within-ecosystem processes will also be reduced by ecotones. If community environment relationships are decoupled near ecotones, the importance of spatial patterns derived from other processes may appear to increase. Spatial structure (i.e., spatial autocorrelation) within ecosystems is commonly found in both soil properties and plant community composition (Schwarz et al. 2003, Gilbert and Lechowicz 2004). Spatial structure in soil and plants may be correlated due to community environment relationships in ecosystems as previously discussed, but spatial structure can also develop through independent mechanisms. For example, geomorphological and hydrological processes can create spatial structure in soil properties independent of plant community composition (Kerry and Oliver 2011), and dispersal limitation associated with mass effects can create spatial structure in plant community composition independent of soil properties (Nekola and White 1999, Zuidema et al. 2010). These independent processes will not necessarily be disrupted by ecotones, and the importance of spatial patterns derived from those processes may actually increase at ecotones due to the decoupling of community environment relationships. The goal of this study was to examine the effects of ecotones on soil and vegetation characteristics of adjacent forested ecosystems. The study was performed in a small, complex forest stand in northeast Ohio, USA, with large topographic, hydrological, and geomorphological variation resulting in potentially different ecosystems in close proximity to each other. We began by testing hypothesis H 1, that the landforms investigated differ with respect to both soil properties and tree community composition, and are therefore distinct ecosystems, but these differences are diminished by inclusion of areas near ecotones. After establishing that these landforms could be considered distinct ecosystems, we then tested hypotheses H 2, that ecotones harbor increased species richness compared to sites within ecosystems (Odum s hypothesis); H 3, that there are both large-scale, between-ecosystem processes and smallscale, within-ecosystem processes causing correlation

3 August 2013 ECOTONES DECAY COMMUNITY RELATIONSHIPS 405 between soil properties and tree community composition; and H 4, that ecotones decouple the relationships between soil properties and the tree community, reducing this correlation and increasing the importance of spatial structure that is independent of the environment. METHODS Study site Jennings Woods is a 30-ha mixed-mesophytic hardwood forest located in northeastern Ohio, USA, which is in the western part of the glaciated Allegheny Plateau ( N, W). Elevation at the site ranges from 316 to 335 m above sea level. Mean temperatures are 08C in winter and 288C in summer, with ;100 cm of precipitation annually. The forest has been unmanaged since Kent State University took ownership of the property in 1973, and before this, it experienced only selective timber harvest, resulting in canopy trees typically years old. The tree community is representative of regional beech maple forests (Smith et al. 2001) that are commonly dominated by sugar maple (Acer saccharum), red maple (Acer rubrum), American beech (Fagus grandifolia), and red oak (Quercus rubra). This property was chosen because habitats and soils were observed to transition over relatively short distances, with abundant variation within each habitat type, allowing us to potentially disentangle spatial separation and environmental differences among locations on several spatial scales. Delineation of landforms We divided the site into three landforms based on steep topographic gradients (Boerner 2006), which also roughly agreed with areas of differing hydrological and geomorphological features. A fourth putative ecosystem type was also defined by human disturbance. The eastern side of the woods contains 600 m of the West Branch of the Mahoning River, a fourth-order stream. The area within 80 m of the river is periodically flooded and contains prior flowpaths of the geologically meandering riverbed, and was classified as riparian forest. Soils in the riparian area are officially classified as Holly silt loam (a fine-loamy, mixed, active, nonacid, mesic Fluvaquentic Endoaquept), but actually vary from flood-associated sand deposits to poorly drained oxbows found at the base of steep slopes that form the topographic edges of the landform. We classified the northwestern plateau area that is m higher in elevation than the riparian forest as upland forest. This area is primarily composed of well-drained soils in the Chili loam series (a fine-loamy, mixed, active, mesic Typic Hapludalf ), but is dotted with poorly drained vernal pools that may contain standing water at any time of year, depending on precipitation. The upland also includes soils in the Geeburg-Glenford silt loam complex (both series are fine mesic Aquic Hapludalfs, but Geeburg is illitic and Glenford has mixed clay mineralogy). The upland forest is also drained by two first-order streams that eventually reach the river. One first-order stream runs directly into the riparian forest. The second stream descends ;10 m into a lower elevation plateau that is very poorly drained; much of this plateau is saturated with water throughout the year. We classified this area as bottomland forest. Soils in this area are officially classified as Holly silt loam or in the Geeburg-Glenford silt loam complex. The fourth putative ecosystem type was a permanent disturbed corridor created by yearly cutting of aboveground vegetation to maintain a gas pipeline installed 90 cm belowground. The disturbed corridor cuts through the upland and bottomland forests and is ;25 m across. Soils in this area are compacted, and vegetation is typical of old-field and wetland herbaceous habitat near forest edges. Study design The goal of our study design was to capture variation at multiple spatial scales within putative ecosystems and ecotones. We initially established soil sampling plots (12.5 m 2 ) using a layout that combined a grid for overall spatial coverage and multiple transects targeting each component of the landscape. This provided good coverage both within ecosystems and across ecotones (Appendix A), with separation distances between plots ranging continuously from 2 to 580 m. The grid consisted of 24 plots separated by ;90 m along the north south axis and ;120 m along the east west axis. Nested within each ecosystem, two 60-m transects were established, each consisting of three pairs of adjacent soil sampling plots as shown in Appendix A. Two similar transects were placed perpendicular to ecotones at each of the riparian upland, upland bottomland, and riparian bottomland interfaces, centered on the area of the steepest topographic gradient. Two transects were also positioned perpendicular to the disturbed corridor to capture the bottomland-disturbed and upland-disturbed ecotones, and two transects were placed perpendicular to the river to increase sampling of the riparian forest (Appendix A). GPS coordinates for each plot were obtained using a Trimble GeoXT GPS receiver (Trimble Navigation Limited, Sunnyvale, California, USA). We consider ecotones to be areas of variable width depending on the ecosystems in question and the ecosystem components being examined (Gosz 1993, Hufkens et al. 2009). To avoid confusion in our terminology, we designated plots at the precise interface between two putative ecosystems as transition plots (i.e., at the point of steepest topographic slope or at the mowing line of the disturbed corridor). Transition plots cannot be categorized as either of the adjacent ecosystem types. Plots in a particular ecosystem but,30 m from a transition to another ecosystem were considered potentially part of an ecotone and were

4 406 CHRISTOPHER B. BLACKWOOD ET AL. Ecological Monographs Vol. 83, No. 3 designated as edge plots. Plots.30 m from another ecosystem were designated as core plots. Tree survey Tree community composition was determined by identifying all woody species within 15 m radius circular plots centered on the soil plots previously described (hereafter referred to as tree measurement rings). Transect locations with two adjacent plots were characterized by a single tree measurement ring (Appendix A). The distance of each tree to the center of the tree measurement ring was recorded, as well as species identity and diameter at breast height (dbh). All trees with dbh.10 cm within a tree measurement ring were counted, as well as trees with dbh.5 cm if they were within 5 m of the center of the tree measurement ring. When tree measurement rings around different plots overlapped, each individual tree was assigned only to the ring in which it was closest to the center (Appendix A). Soil sample collection and analysis In May 2008, five soil cores (10 cm depth, 1.5 cm diameter) were collected from each plot. These samples are predominantly representative of the soil A horizon (;80% of plots have an A horizon over 10 cm deep [C. B. Blackwood, unpublished data]). Soil cores were combined for each plot and sieved (2 mm) to remove roots and coarse material. Soil was dried at 608C to determine percentage moisture and before other physical and chemical analyses. Soil ph was measured in a 1:1 mixture with distilled water. Total carbon (C) and nitrogen (N) were determined on an elemental analyzer (Costech Analytical, Ventura, California, USA). To measure particulate organic matter (POM) C and N, the sand fraction and associated organic matter was isolated following Cambardella and Elliott (1992), and C and N content were determined using the method previously described. Readily available soil inorganic phosphorus (P) and easily mineralizable organic P were extracted from pulverized, oven-dried soil by adding 0.5 mol/l NaH- CO 3 (ph 8.5) and shaking at 100 rpm on an orbital shaker (Lab-Line, Melrose Park, Illinois, USA) for 30 min (Olsen et al. 1954). Inorganic P was determined colorimetrically using the modified ascorbic acid method (Kuo 1996) directly on the NaHCO 3 extracts, whereas organic P was determined by the increase in P detected after NaHCO 3 extract digestion with 1.8 mol/l H 2 SO 4 and (NH 4 ) 2 S 2 O 2 (U.S. EPA 1971). Soil texture was measured after oxidation of organic material using concentrated H 2 O 2 heated to 908C, followed by dispersion overnight in sodium metaphosphate. The size spectra of mineral particles was measured by laser diffraction using a MasterSizer 2000 (Malvern Instruments, Worcestershire, UK), resulting in measurements of relative mass in each of 100 categories ranging from 0.02 to 2000 lm. Size spectra were submitted to principal components analysis (PCA) to identify the dominant axes of variation in soil texture at this site. The first PCA axis accounted for 65% of the variation in soil texture and was highly correlated with percentages of clay and silt (r ¼ 0.91 and 0.97, respectively), and negatively correlated with percentage of sand (r ¼ 0.96). We refer to this as the clayþsilt axis. The second PCA axis accounted for an additional 22% of the variation and contrasted fine sand ( lm, r ¼ 0.82) with mediumþcoarse sand ( lm; r ¼ 0.9). We refer to this as the fine-sand axis. Statistical analysis Statistical analyses were performed in the software R (R Development Core Team 2010) supplemented with R packages Gstat (Pebesma 2004), Packfor (Blanchet et al. 2008), PCNM (Legendre et al. 2010), and vegan (Oksanen et al. 2010). Geographic distances among sampling points were calculated from Universal Transverse Mercator GPS coordinates in meters. Soil properties were scaled to unitless Z scores with zero mean and unit standard deviation for most analyses in order to facilitate comparison of variances and other statistical properties. To reduce the number of soil variables for univariate analyses and use as regression predictors, we selected a parsimonious set of soil variables to represent groups of correlated and theoretically related variables. We chose %C to represent %N and %POM C and N (r ¼ 0.96, 0.88, 0.88, respectively). Proportion POM C represents proportion POM N (r ¼ 0.89). Total P represents inorganic P (r ¼ 0.94). H 1 : Differences among landforms and the effect of ecotones. To visualize differences between landform types and the effects of ecotones, transition graphs (sensu Cadenasso et al. 2003) were constructed to show how soil variables, tree species richness, and tree community composition change from one ecosystem to another. For transition graphs, edge and transition plots were further categorized according to the specific adjoining landfoms (e.g., upland edge plots in the upland bottomland ecotone are only shown in the upland bottomland transition graph). Analysis of individual ecotones was not performed for ecotones involving the disturbed corridor because of low replication; however, these ecotones were included in global tests of a common ecotone effect across all ecotone types. The importance of the ecotone effect on soil properties was tested in a multistep procedure using redundancy analysis (RDA), a multivariate extension of ANOVA and linear regression, using indicator variables for ecosystem type as predictors (Legendre and Anderson 1999). Analyses were performed on the multivariate set of soil variables, as well as on each soil variable individually. First, the amount of variance that was explained by landform type was estimated for core plots only, and the significance of differences between landforms was tested using random permutation (Legendre and Anderson 1999). Then, the significance

5 August 2013 ECOTONES DECAY COMMUNITY RELATIONSHIPS 407 and amount of variance explained by landform were estimated considering core and edge plots together. Comparison of the explanatory power of landform in the core and coreþedge data sets was based on adjusted R 2 to correct for the effect of differing numbers of samples on variance explained (Peres-Neto et al. 2006). Significance of the ecotone effect on RDA results was tested using a restricted randomization test (Fagan et al. 2003) under the null hypothesis that edge plots do not affect the amount of variation explained by landform type. For each of 999 permutations, designation of core and edge plots within each landform was randomly assigned, keeping the number of core and edge plots within each landform constant, and the RDA analyses were repeated. This restricted randomization procedure is analogous to using a mixed model to account for the hierarchical structure in the experimental design, because plots in the same ecosystem type would be expected to be more similar to each other than to plots in different ecosystems. To calculate a P value, the empirical proportional increase in adjusted R 2 due to exclusion of edge plots was compared to a null distribution of values derived from the randomization. The effect of landform type and ecotones on tree community composition was assessed using multivariate transition graphs and RDA as previously described for soil properties. The consistency of results was assessed by testing H 1 using several ecological community distance metrics (Bray-Curtis, Chao, chi-squared, Hellinger, and Jaccard distances), and with relative species abundances calculated using both basal area and number of stems. Subsequent analyses were based on Hellinger distance through analysis of square-root relative abundances for each species (Legendre and Gallagher 2001). The responses of individual tree species to ecotones were examined by inspection of transition graphs and the amount of variance explained for each species by the RDA model. H 2 : Effect of ecotones on tree species richness. To test Odum s ecotone diversity hypothesis, tree species richness was calculated for each plot. Species richness was calculated by rarefaction to adjust for differences in the number of stems among plots. Averages and confidence intervals were then calculated for core, edge, and transition plots for each ecosystem type, as previously described. Decomposition of soil variables into structures associated with differing spatial scales. Before testing H 3 and H 4, we decomposed variation in soil variables into structures associated with differing spatial scales. First, we explored the spatial structure in soil properties by calculating semivariance (a spatially lagged variance measure) as a function of geographic distance to quantify spatial autocorrelation in soil properties. Distance classes were varied in order to adequately capture small-scale autocorrelation (Goovaerts 1997). The maximum distances between pairs in the first and second distance classes were set to 10 and 25 m, respectively. Maximum distance for all other distance classes increased in 25-m increments, except in the final distance class, which included all pairs with m separation distance. Spatial autocorrelation for each soil variable was then modeled using a nugget and single exponential spatial structure (Goovaerts 1997), fitting both semivariance sills and the range of autocorrelation. The exponential model of spatial structure was found to be generally the best-fitting model during comparison to several other common semivariogram models (linear, pure nugget, spherical, Gaussian); the fit was assessed by weighted sums of squares (Goovaerts 1997). To examine how autocorrelation was affected by comparisons across ecosystems instead of within ecosystems, each pair of sampling points was categorized as a within-ecosystem or between-ecosystem comparison. For this analysis, both core and edge plots could be part of a within-ecosystem pair, but any pairs including transition plots were included in the between-ecosystem category. Within- and between-ecosystem semivariances were then calculated using the corresponding sample pairs. Factorial kriging analysis was used to decompose variation in soil properties into structures associated with differing spatial scales (Goovaerts 1997, Goovaerts et al. 2005). The first step of this method involved fitting a common semivariogram model to each soil variable, where the same basic structures and ranges were used for all variables. This provides a common basis for comparison, because, for each soil variable, the fitting procedure finds sills that indicate the relative importance of each of the spatial structures in the model. We chose to use a model with a nugget and two exponential spatial structures with ranges fixed at 10 and 100 m (therefore, with effective ranges of spatial autocorrelation of 30 and 300 m, respectively). This set of ranges was chosen because it covers the range of spatial scales captured by our sampling design, encompasses the spatial structures found in soil variables using single-structure models previously described, and allows us to test H 3 about multiple-scale phenomena within and between ecosystems. After fitting the semivariogram models, we used factorial kriging to separately estimate values associated with each spatial structure at each sampling point. Essentially, this involves estimating a soil variable value at a sampling point using a weighted average of values at neighboring points, with weights calculated from covariances among points derived from one of the spatial structures in the two-structure model. The 30 closest sample points were used to estimate values at a target point. As in Goovaerts et al. (2005), the local mean of the sample neighborhood was incorporated in the estimation of values associated with the largest spatial structure. Hence, factorial kriging analysis allowed us to calculate three new soil variables from each initial variable: a long-range variable that represented variation of the soil variable over m scales, a shortrange variable that represented variation over 5 40 m

6 408 CHRISTOPHER B. BLACKWOOD ET AL. Ecological Monographs Vol. 83, No. 3 scales, and a nugget variable that represented residual variation not associated with spatial structure captured by our sampling design. H 3 : Tree community composition correlation with soil properties. RDA was used to assess the correspondence between tree community composition and soil properties, and how this relationship was affected by ecotones (H 3 and H 4 ). RDA was performed using Hellinger-transformed tree species abundance as response variables and spatially decomposed soil variables obtained from factorial kriging analysis as predictor variables. We selected a parsimonious set of soil variables by forward selection according to Blanchet et al. (2008), with a threshold P value set to This approach allows forward selection to proceed only if an initial global analysis including all potential predictor variables is statistically significant. In addition, the adjusted R 2 of the model during forward selection is not allowed to exceed the adjusted R 2 of the full model. This analysis was performed on data sets composed of core plots, coreþedge plots, and all plots. H 4 : Ecotone effect on tree community correlation with soil properties and spatial structure. The significance of the reduction in variance explained due to inclusion of edge and transition plots was determined using restricted randomization of core/edge positions as described previously. In this case, randomization was performed under the null hypothesis that inclusion of edge plots does not affect the amount of variation in tree community composition explained by soil properties. Again, a P value was calculated by comparing the empirical proportional increase in adjusted R 2 due to exclusion of edge plots to a null distribution of values derived from the randomization. Procrustes analysis was used to determine the similarity of RDA ordinations derived from core and coreþedgeþtransition data sets (Peres-Neto and Jackson 2001). This procedure was performed on ordination scores of core plots and species. Procrustes analysis determines the extent to which the relative positions of these objects in one ordination are similar to their relative positions in another ordination. The significance of the similarity is tested by permutation of sample identities for one of the ordinations. We used principal coordinates analysis of neighbor matrices (PCNM) to examine spatial variation in tree community composition that is not explained by soil variables. This method generates a set of orthogonal vectors directly from spatial coordinates of our plots. These PCNM vectors capture potential spatial variation at progressively decreasing spatial scales, and can be used as regression predictors in RDA to model complex spatial variation (Borcard et al. 2004). To avoid overparameterization of the spatial model, PCNM vectors with Moran s I scores significantly different from zero were subjected to forward selection RDA as previously described. Variance partitioning was then used to quantify the overlap in variance explained by soil variables and PCNM vectors, and the independent (nonoverlap) fractions were tested for significance (Peres-Neto et al. 2006). As an additional way to examine spatial structure in tree community composition, we also calculated pairwise sample Hellinger distances between each pair of samples. These sample pairs were categorized into within- and between-ecosystem comparisons, and were then plotted as a multivariate, community composition semivariance using the distance classes previously described. Semivariograms for individual species abundances were also calculated. The hypothesis that community composition or species abundance was spatially autocorrelated was tested by comparing empirical community semivariances in each distance class to a null distribution derived from 999 random permutations of plot spatial locations. Community semivariance was also fit to the nuggetþsingle exponential component model previously described to determine the proportion of variation accounted for by spatial autocorrelation. RESULTS H 1 : Differences among landforms and the effect of ecotones Riparian, bottomland, and upland forests were delineated based on topographic boundaries that also corresponded with geomorphological and hydrological landscape features, as implied by the forest type names. The disturbed corridor was delineated by disturbance and anthropogenic management. In accordance with this delineation, transition graphs show that plots designated as different forest types differ dramatically in elevation, with smooth transitions across ecotones (Appendix B). Soil properties. Regardless of whether edge plots were included in the data set, landform type had a significant effect on soil variables (P, 0.05 for proportion POM C; P, 0.01 for all other soil variables). However, as expected, exclusion of edge plots from the data set significantly (P, 0.05) increased the amount of variance explained by landform for all variables except proportion POM C and the fine-sand axis (Table 1). For several variables, the amount of variation explained by landform more than doubled due to exclusion of edge plots (Table 1). In a multivariate analysis across all soil variables, the variance explained by landform declined from 44% to 24% due to addition of edge plots. The greatest differences in soil properties were between the core plots of the upland and riparian forests (Fig. 1; first axis of RDA). Upland core plots had the lowest soil ph and highest %C, C:N, organic P, and fine-sand content (Appendix B). In contrast, bottomland forest had the highest percentage moisture, total P, and clayþsilt content. Bottomland plots were more similar to upland than to riparian core plots in most cases (clayþsilt axis, ph, %C, proportion POM C, C:N,

7 August 2013 ECOTONES DECAY COMMUNITY RELATIONSHIPS 409 TABLE 1. type. Variance in soil properties and tree community composition explained by ecosystem Adjusted R 2 Increase in amount of variation explained by excluding edge plots Soil property Core only Coreþedge Fold-change P All soil properties 44** 24** Clayþsilt axis 54** 31** Fine-sand axis 40** 45** ph 39** 15** %C 42** 22** Proportion POM C * C:N 50** 35** Percentage moisture 56** 26** Total P 41** 19** Organic P 46** 20** All soil properties, nugget All soil properties, short 21** 16** All soil properties, long 63** 43** Trees, BA, Hellinger 14** 8** Trees, BA, Bray-Curtis 8.2** 4.4** Trees, BA, Chi-square 9.4** 5.7** Trees, SC, Hellinger 20** 12** Trees, SC, Bray-Curtis 14** 7.7** Trees, SC, chi-square 12** 7.0** Trees, P/A, Jaccard 15** 9.2** Trees, P/A, Chao 4.5** 1.9** Notes: Core plots were.30 m from transitions, whereas edge plots within an ecosystem were,30 m from a transition. POM is particulate organic matter. For trees, BA refers to analysis of basal area, SC refers to stem counts, and P/A refers to presence/absence; results from several ecological community distance metrics are shown. Fold-change is a ratio: (core adj. R 2 /coreþedge adj. R 2 ). Asterisks indicate significance of ecosystem effect on either core or coreþedge data set. * P, 0.05; ** P, percentage moisture, total P), but were separated from upland by the second RDA axis (Fig. 1). Most upland riparian transition graphs show that transition and edge plots have average values between the upland and riparian core plot averages, indicating a predictable, smooth transition due to autocorrelation across ecosystems (Fig. 1; Appendix B). Generally, upland bottomland ecotones either also displayed autocorrelation, or there was little change across the transition because bottomland and upland core plots were similar. Therefore, including edge plots along the upland riparian and upland bottomland ecotones in the calculation of ecosystem averages made these soils appear more similar due to the characteristics of one ecosystem spilling over across ecosystem boundaries. In contrast, bottomland riparian ecotones displayed much less regular patterns, with edge and transition plots having increased variation (Fig. 1; see Appendix B). Relative to both riparian and bottomland core plots, %C, proportion POM C, C:N, and organic P are all elevated at the bottomland riparian ecotone, and ph is reduced, indicating a buildup of organic matter and some similarity to upland plots in this area. Hence, the multivariate paths from bottomland to riparian show little progress until the final step (Fig. 1). Including edge plots along the bottomland riparian ecotone therefore primarily contributed noise to the differences between these ecosystems. Tree community composition. Thirty-three tree species were found (Appendix E). Variation in tree community composition was significantly explained by landform type (Table 1). However, the amount of variation explained by landform was much lower for tree community composition than for soil properties. The amount of variance explained also dropped due to inclusion of edge plots, consistent with H 1 (Table 1). These results were consistent for analyses of relative abundance based on basal area and stem counts, and using different distance metrics (Table 1). The decline in variance explained was significant for the Hellinger, Bray-Curtis, and Chao distances, but not chi-squared and Jaccard distances (Table 1). The latter two distances weight the rarest species more heavily than other distance metrics, indicating that the presence of rare species is too noisy for edge effects to be detected. Transitions of individual tree species abundances from one landform to another were highly variable, with abundances in ecotone areas often equal to zero due to low sample size and the apparently stochastic presence of many tree species. For individual tree species abundances in core ecosystem plots, the amount of variation explained (adjusted R 2 ) by landform type ranged from 8% (Quercus rubra) to 52% (Fraxinus nigra); see Appendix E. Riparian and upland forests had the most consistently different tree community composition, being completely separated on RDA axis 1, and

8 410 CHRISTOPHER B. BLACKWOOD ET AL. Ecological Monographs Vol. 83, No. 3 FIG. 1. Transition graphs showing shifts in soil-characteristics RDA axes across ecotones from the core of one ecosystem to the core of another ecosystem: R, riparian forest; U, upland forest; B, bottomland forest. Single letters represent ecosystem cores.30 m from the ecosystem boundary. Two letters represent edge plots that are in one ecosystem (first letter) but,30 m from the boundary of another ecosystem (second letter). T- indicates the transition between two ecosystems. RDA axes are derived from a single analysis of all core ecosystem plots, with eigenvector loadings for each axis shown on the left. Soil property abbreviations are: %M, percentage moisture; POM C, particulate organic matter carbon; Po, organic P; Ptot, total P. Scores for edge and transition plots were calculated post hoc. Error bars with wide and narrow caps represent estimated population standard deviation and the 95% confidence interval around the mean, respectively. Differences among ecosystem core plots reflected by RDA axis 1 capture 29.7% of the variation in soil properties; RDA axis 2 captures 9.7% (adjusted R 2 ). also were significantly different on RDA axis 2 (see Fig. 2 confidence intervals). This was primarily due to the strong association of Platanus occidentalis and Carya cordiformis with the riparian forest; both of these species were absent from the upland and were much lower in abundance in the bottomland. In addition, although both Acer saccharum and Acer rubrum were found in all landforms, A. saccharum had a tendency to have higher abundance in the riparian forest than the bottomland forest, whereas the reverse was true for A. rubrum. Both Acer species had intermediate abundances in the upland. Bottomland forest was also separated from upland on RDA axis 2 (Fig. 2). This axis captured the exclusive distribution of Quercus palustris and Fraxinus nigra in the bottomland forest, and the higher abundances of Fagus grandifolia and Prunus serotina in the upland forest. H 2 : Effect of ecotones on tree species richness There were 8 33 trees/plot, so species richness for all plots was rarefied to 10 individuals; the two plots (both in ecosystem cores) with fewer than 10 trees were not included in this analysis. Tree species richness did not differ among ecosystems, as indicated by overlapping 95% confidence intervals (Fig. 3). In contrast to Odum s ecotone diversity hypothesis, there were also no significant differences in tree species richness between edge or transition plots and core plots, and no trends indicating increased richness in ecotone plots. Decomposition of soil variables into structures associated with differing spatial scales Semivariance analysis exhibited spatial autocorrelation of all soil properties except proportion of POM C, with semivariance increasing with spatial separation distance (Appendix C). Modeling semivariances with a nugget component and single exponential component indicated that spatial autocorrelation accounted for from 0% (proportion POM C) to 96% (soil moisture) of variation in soil variables (Table 2). Furthermore, both between-ecosystem and within-ecosystem semivariances also exhibited spatial autocorrelation, with decreased semivariances in the first few distance classes (Appendix C). For several variables (%C, C:N, total P, organic P), between-ecosystem semivariance was larger than withinecosystem semivariance in the first few distance classes (,70 m); however, this was only consistent across a larger range for C:N. Given the differences among ecosystems described previously, the large within-ecosystem semivariance in many soil variables was unexpected. However, edge plots were included in the calculation of within-ecosystem semivariance, and the previously described analysis of H 1 that they would have substantially increased within-ecosystem semivariance. Factorial kriging was used to partition each soil variable into components capturing long-range ( m), short-range (10 40 m), and nugget (0 m) variation. No variance in the fine-sand axis was attributed to the short-range structure, and no variance in proportion

9 August 2013 ECOTONES DECAY COMMUNITY RELATIONSHIPS 411 FIG. 2. Transition graphs showing shifts in tree community-composition RDA axes across ecotones from the core of one ecosystem to the core of another ecosystem. See Fig. 1 for a complete description of figure layout. For eigenvector loadings, only tree species with eigenvalues 0.2 on one of the RDA axes are shown. Differences among ecosystem core plots reflected by RDA axis 1 capture 7.8% of the variation in tree community composition; RDA axis 2 captures 4.0% (adjusted R 2 ). Tree species are indicated by the following abbreviations: Acer rubrum (Acerub), Acer saccharum (Acesac), Carya cordiformis (Carcor), Fagus grandifolia (Fagame), Fraxinus nigra (Franig), Platanus occidentalis (Plaocc), Prunus serotina (Pruser), Quercus palustris (Quepal). POM C was attributed to the long-range structure. Multivariate RDA indicated that long and short-range variables were both significantly associated with ecosystem type (Table 1), although the amount of variation explained by ecosystem type was three times greater in long-range variables than short-range variables. In both cases, inclusion of edge plots decreased the proportion of variation explained by ecosystem type, but this decrease was significant only for long-range variables (Table 1). Krige maps of long-range variables showed patterns that closely follow ecosystem types (Appendix D). H 3 : Tree community composition correlation with soil properties and spatial structure A global analysis including all 27 potential predictor variables (nine soil properties decomposed into three spatial components each) was initially used to test whether tree community composition could be significantly related to soil properties. In agreement with H 3,a significant relationship (P, 0.01) was detected in all of the data sets tested (core, coreþedge, and coreedgeþtransition). For each data set, the forward selection process retained five soil variables, including long-range components of ph, total P, and %C (Fig. 4). The shortrange component of proportion POM C was also selected for both the core and coreþedge data sets. The importance of short-range soil variables in explaining variation in tree community composition was generally weak compared to long-range variables that were previously shown to be largely consistent with ecosystem differences. Indeed, re-running the selection procedure FIG. 3. Transition plots showing changes in rarefied species richness across ecotones from the core of one ecosystem to the core of another ecosystem. See Fig. 1 for a complete description of figure layout.

10 412 CHRISTOPHER B. BLACKWOOD ET AL. Ecological Monographs Vol. 83, No. 3 TABLE 2. Size and range of spatial structure indicated by modeling semivariance using an exponential (spatial) component and nugget (unexplained or nonspatial) component of variance. Exponential variance component Proportion Soil property Nugget variance Range, m Semivariance sill spatial variation Clayþsilt axis Fine-sand axis ph %C Proportion POM C 0.85 NA NA 0 C:N Percentage moisture Total P Organic P Trees, BA, Hellinger Notes: Abbreviations are: POM, particulate organic matter; NA, not applicable. The proportion spatial variation is calculated by formula: exponential sill/(nugget þ exponential sill). using tree abundance residuals after correcting for ecosystem effects resulted in selection of no long-range variables and only one short-range variable for both the core and coreþedge data sets, explaining,2% of the residual variation. H 4 : Ecotone effect on tree community correlation with soil properties and spatial structure In the global analysis including all 27 predictor soil variables, the amount of variation explained increased 1.82-fold due to exclusion of edge and transition plots (P, 0.1). Soil characteristics explained 26% of variation in tree community composition in the core plots, but only 15% and 14% in the coreþedge and coreþedgeþtransition plots, respectively. The increase in variation explained by soil properties due to exclusion of edge and transition plots was also present after forward selection of a more parsimonious set of soil variable predictors (1.60-fold due to exclusion of edge plots, 1.66-fold due to exclusion of edge and transition plots), again supporting H 4. This change in variance explained by soil properties is shown in Fig. 4 as the change in size of the soilþoverlap area (see also Appendix E for the ecotone effect on the ability to explain individual species relative abundance using soil variables). Four of the same explanatory variables were retained during forward selection analyses of core and coreþedge data sets (long-range components of ph, total P, and %C, and the short-range component of proportion POM C). When the analysis was limited to these four common variables, there was a 1.62-fold increase in the amount of variation explained due to exclusion of edge plots, and this increase in variation explained was highly significant (P ¼ 0.001). Despite this change in the proportion of variation explained, the core and coreþedge RDA ordinations were actually very similar in terms of core plots and species (Procrustes correlations of 0.99 for core plots and 0.92 for species, P ¼ 0.001). This implies that underlying relationships between soil properties and tree species were not altered by inclusion of edge plots, but edge plots caused significantly increased unexplained variation around these relationships to be introduced. Global analysis of PCNM vectors was also significant for the core, coreþedge, and coreþedgeþtransition data sets (P, 0.01), so forward selection was used to select parsimonious PCNM vectors for variance partitioning. In contrast to the pattern observed with soil properties, the variation explained by PCNM vectors was reduced by exclusion of edge plots, from 18% in coreþedge and coreþedgeþtransition plots to 16% in the core plots (shown in Fig. 4 as the size of the spaceþoverlap box). In core plots, PCNM vectors explained no variation after removing overlap variation that could be jointly explained by both soil properties and PCNM vectors, but soil properties explained a significant portion of variation after removing the effects of PCNM vectors (Fig. 4). In contrast, in the coreþedge and coreþ edgeþtransition data sets, PCNM vectors explained approximately twice as much variation as did soil vectors (Fig. 4). Hence, in agreement with H 4, inclusion of edge and transition plots results in a clear shift away from the importance of soil properties in explaining variation in tree community composition, and toward spatial autocorrelation that is independent of soil properties. Consistent with the PCNM analysis that we have described, tree community composition semivariance showed a small, but significant, increase in variation with increasing separation distance (Appendix C), with the first four distance classes having semivariances significantly lower than expected by chance (P, 0.05). Fitting the nuggetþexponential semivariogram model to community semivariance indicated that spatial autocorrelation accounted for 19% of the variation in tree community composition (Table 2). Interestingly, this is almost identical to the percentage of variance explained by selected PCNM vectors in the full data set (18%), as described previously. Semivariances for within- and between-ecosystem comparisons were essentially the same (Appendix C). The increase in semivariance with distance appeared linear, and the overall Mantel correlation between community composition and geographic distance also indicated significant

11 August 2013 ECOTONES DECAY COMMUNITY RELATIONSHIPS 413 FIG. 4. Change in variance partitioning of tree community composition due to inclusion of ecotones. Variance components include soil properties, PCNM (principal coordinates analysis of neighbor matrices) vectors ( space ), and the overlap between soil characteristics and PCNM vectors. Variance partitioning was performed after selection of parsimonious subsets of all soil characteristics and PCNM vectors. Selected soil variables are shown under each variance partition graph. Explanatory variance components shown are statistically significant (P, 0.01), as described in Results. Long- and short-range variables refer to variable components obtained using factorial kriging analysis, and correspond approximately with and m structures, respectively. autocorrelation (R M ¼ 0.19; P ¼ 0.001). In analyses of individual tree species abundance, there was no consistent trend toward larger or smaller autocorrelation in the between-ecosystems comparisons relative to withinecosystems. DISCUSSION Although plant community composition and soil properties are known to shift across ecotones (Gosz 1993, Cadenasso et al. 2003), the effect of these shifts on community environment relationships has not been previously addressed. The area we studied (Jennings Woods) includes various landforms that differ in terms of soil properties and tree community composition, and thus should be considered distinct ecosystems. However, ecosystem differences were reduced near ecotones, consistent with our first hypothesis (H 1 ). Despite confirming the presence of ecotones in the Jennings landscape, we did not find increased species richness in these regions (H 2 ), even though this has been the primary hypothesis regarding community responses to ecotones since Odum (1953). As expected, tree community composition was found to be correlated with soil properties (H 3 ), particularly due to large-scale structures within the soil properties that correspond with differing ecosystems. However, most importantly, we confirmed our ecotone-decoupling hypothesis (H 4 ), showing that ecotones introduced significant unexplained variation into correlations between tree community composition and soil properties. The increased spatially structured variation in tree community composition near ecotones also indicates that mass effects may be particularly evident in these regions of interaction between very different environments. Further analyses will be necessary to understand the generality of these patterns, but our findings may have important implications for efforts to model distributions of community types or species from abiotic information such as soil maps or climatological data (Hijmans and Graham 2006, Morin and Lechowicz 2008, Douma et al. 2012). If ecotones commonly decouple community environment relationships, predictions are likely to be most accurate in the core of ecosystems, with higher standard error of predicted values near ecotones. Once ecotones are identified, it is possible that statistical methods could be used to reduce the impact of ecotones on predictive power in these regions (Dormann et al. 2007, Ashcroft et al. 2012). On the other hand, explicit incorporation of mass effects at ecotones into species distribution models would be an important step in the effort to link statistical distribution modeling with ecological theory (Elith and Leathwick 2009). Implications of the decoupling of community environment relationships at ecotones The increase in unexplained variation near ecotones implies that the distributions of soil properties and trees are more independent near ecotones, despite the fact that they are both affected by the adjoining ecosystems. Transport of seeds and soil components (mineral particles, water, nutrients, and organic matter) across ecotones is, of course, accomplished by different mechanisms. Soil properties were generally highly spatially structured and affected by ecosystem type and ecotones, whereas tree community composition appeared to be much more stochastic, with high amounts of unexplained variation. Soil components are generally smaller and are transported in greater numbers than tree