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Interspecific differences in determinants of plant species distribution and the relationships with functional traitsJournal of Ecology 2012, 100, 950–957 Interspeciﬁc differences in determinants of plantspecies distribution and the relationships withfunctional traits Masahiro Aiba*†, Hino Takafumi and Tsutom Hiura Tomakomai Research Station, Field Science Center for Northern Biosphere, Hokkaido University, Takaoka,Tomakomai 053-0035, Japan 1. Environmental control and dispersal limitation are both essential processes in plant communityassembly and species distribution. Although numerous studies in the past decade have examinedtheir importance as determinants of community composition, remarkably little is known aboutinterspeciﬁc differences in the importance of these two processes.
2. To quantify these interspeciﬁc differences, we compared the importance of environmental fac-tors and space as correlates of species distribution among 24 understorey plant species in a Japanesecool–temperate forest by performing variation partitioning at the species level. Speciﬁcally, wehypothesized that the importance of environment and space differs among species, and these differ-ences can be partly predicted from the functional traits and ⁄ or phylogenetic identity of each species.
3. The unique contributions of both environment and space were signiﬁcant in the community-levelanalysis. However, at the species level, the relative and absolute sizes of the unique contributions ofenvironment and space differed considerably among the 24 species. Environment and space werenot necessarily signiﬁcant variables explaining the distribution of many species.
4. No signiﬁcant relationships were found between the unique contribution of environment and thefour functional traits tested, that is, dispersal mode, seed mass, plant height and speciﬁc leaf areaamong the 24 species. In contrast, the unique contribution of space was signiﬁcantly larger in specieswith no dispersal mechanisms than in animal-dispersed species. No signiﬁcant phylogenetic signalwas detected for the unique contribution of environment or space, suggesting that importance ofenvironmental control and dispersal limitation as determinants of species distribution is evolution-arily labile.
5. Synthesis. Our results suggest that the relative and absolute importance of different processes ofcommunity assembly (i.e. environmental control and dispersal limitation) differs remarkablyamong species even within a single community. These interspeciﬁc differences may be explained inpart by interspeciﬁc differences in dispersal mode.
Key-words: community assembly, determinants of plant community diversity and structure,dispersal limitation, forest herbs, functional traits, metacommunity, phylogenetic signal, spa-tial structure, variation partitioning Myers & Harms 2009). Biotic and abiotic environmental factors control the abundance of species via their effects on Understanding the assembly processes of a community is one demographic traits, that is, survival, growth and reproduction of the central themes of ecology. Recent studies have demon- (Hutchinson 1957; Grubb 1977; Nathan & Muller-Landau strated that both environmental control and dispersal limita- 2000; Pulliam 2000). If environmental control dominates tion play essential roles in assembly processes (Cottenie 2005; assembly processes, community composition and speciesdistribution are expected to be rather deterministically predict-able by understanding species' environmental preferences and *Correspondence author. E-mail: [email protected] competitive abilities, as well as the environmental heterogene- †Present address: Graduate School of Life Sciences, TohokuUniversity, Aoba 6-3, Aramaki, Aoba-ku, Sendai 980-8578, Japan.
ity of the site. On the other hand, the spatio-temporal shortage 2012 The Authors. Journal of Ecology 2012 British Ecological Society Community assembly of forest herbs of dispersal units excludes species from their potential habitat, such differences consequently lead to interspeciﬁc differences regardless of the environment (Nee & May 1992; Tilman 1994; in the extent of dispersal limitation remains ambiguous (Flinn Pulliam 2000; Hubbell 2001; Calcagno et al. 2006). As a result, et al. 2010).
if dispersal limitation is a dominant assembly process, commu- Once interspeciﬁc differences in the contribution of environ- nity composition and species distribution would be spatially ment and space to species distribution are detected, seeking structured independent of environment. The relative impor- links between these contributions and functional traits of a spe- tance of these two processes (i.e. environmental control and cies would be an interesting next step. Recently, Flinn et al.
dispersal limitation) in plant community assembly has been (2010) performed variation partitioning for a subset of a wet- examined in numerous studies in the last decade (e.g. Gilbert & land herb community, which was grouped by dispersal mode, Lechowicz 2004; Svenning et al. 2004; Cottenie 2005; Clark to show that spatial variables are more important for a group et al. 2007; Myers & Harms 2009).
of species with limited dispersal ability than for a group of spe- The statistical procedure of variation partitioning has been cies with higher dispersal ability. Several studies on aquatic used as an effective tool in these studies (Borcard, Legendre & organisms have reported similar results (Beisner et al. 2006; Drapeau 1992). Variation partitioning divides the total variance Van De Meutter, De Meester & Stoks 2007; Vanschoenwinkel of a response variable (here, community data) into two or more et al. 2007). However, the methodology of these studies is not subsets, which are respectively explained by suites of explana- appropriate for relating the results of variation partitioning tory variables (here, environmental and spatial variables). Ordi- with non-categorical traits. Many essential functional traits of nation techniques such as redundancy analysis (RDA) or plants, for example seed mass, plant height and speciﬁc leaf canonical correspondence analysis (CCA) have been used to area (SLA) (e.g. Westoby 1998), are continuous variables. In relate the variance of a response variable with explanatory vari- addition to seed traits, plant height is an important determinant ables. For plant communities, studies using variation partition- of dispersal distance (Soons et al. 2004; Thomson et al. 2011).
ing framework have demonstrated that both environment and Vegetative traits such as SLA may also relate to the strength of space are moderately important as determinants of the spatial dispersal limitation by changing the probability of establish- pattern, from highly diverse tropical regions to less species-rich ment after dispersal (Tremlova & Munzbergova 2007). Species areas at higher latitudes (e.g. Gilbert & Lechowicz 2004; Sven- with certain traits may also be more severely controlled by envi- ning et al. 2004; Karst, Gilbert & Lechowicz 2005; Jones et al.
ronment. For example, Cornwell & Ackerly (2009), who analy- 2008; Legendre et al. 2009; Flinn et al. 2010). For example, in sed trait distribution patterns of trees in a California forest, the case of a Canadian wetland herb community, the unique found that trait ranges of wood density and tree height were contributions of environment, space and the spatially struc- positively correlated with soil moisture because the occurrence tured environment (i.e. variance shared by environment and of short-shrub species with dense wood was conﬁned to wetter space) made up 9.7%, 9.1% and 13.4% of the total variance, sites. In this case, the strength of environmental control may be respectively (Flinn et al. 2010). Similarly, for a Costa Rican pte- positively correlated with wood density and negatively corre- ridophyte community, the three components were 17%, 6% lated with plant height within the community. Furthermore, and 9%, respectively (Jones et al. 2008). However, our under- analysis at the species level provides an opportunity to test the standing of interspeciﬁc differences in the importance of envi- strength of phylogenetic signals (Blomberg, Garland & Ives ronment and space as determinants of spatial distribution is still 2003; Losos 2008) in determinants of species distribution. Phy- remarkably limited, as all analyses have been performed at a logenetic signals may be detected even if we fail to ﬁnd correla- community level. This is surprising given that variation parti- tioning at the species level is not technically difﬁcult, as the environmental control or dispersal limitation, which would explained variance at the community level offered by RDA is a provide evidence suggesting the importance of untested traits.
weighted mean of the R2 of multiple regressions for each of the In this study, we comparatively analysed the importance of constituent species (Peres-Neto et al. 2006).
environment and space as correlates of species distribution of An interspeciﬁc comparison of the importance of environ- understorey plants in a Japanese cool–temperate forest. Our ment and space as determinants of spatial distribution is essen- hypotheses were that the importance of environment and space tial for answering several ecologically important questions.
differ among species and that interspeciﬁc differences can be First, two major theories based on dispersal limitation, the predicted from the functional traits and ⁄ or phylogenetic iden- neutral model and the competition–colonization trade-off tity of each species. In particular, we expected a higher contri- hypothesis, operate under contrasting assumptions for inter- bution of space in species with no dispersal mechanisms speciﬁc differences in the extent of dispersal limitation. Basic (gravity-dispersed species), large seeds and ⁄ or shorter heights, neutral models assume that both dispersal ability and the prob- due to the limited dispersal ability of these species.
ability of local extinction are uniform among species (Bell2000; Hubbell 2001). On the other hand, interspeciﬁc differ-ences in dispersal ability are the essence of species coexistence Materials and methods in the competition–colonization trade-off hypothesis (Nee &May 1992; Tilman 1994; Calcagno et al. 2006). Second, despite accumulating evidence of interspeciﬁc differences in plant dis- The study was conducted in the 2715-ha Tomakomai Experimental persal ability (see a review by Vittoz & Engler 2007), whether Forest (TOEF), located in Hokkaido, the northernmost main island 2012 The Authors. Journal of Ecology 2012 British Ecological Society, Journal of Ecology, 100, 950–957 M. Aiba, H. Takafumi & T. Hiura of Japan (4241¢N, 14136¢E). The mean monthly temperature is We used environmental variables measured in 1-m2 permanent 6.7 C, with the highest monthly mean of 19.9 C in August and low- quadrats established for a separate ongoing study adjacent to our est of )6.1 C in January. Mean annual precipitation is 1100 mm, census quadrats. The appearances of measurement points (e.g. vege- and snow cover reaches a depth of 50 cm from December to March.
tation, topography and canopy openness) were similar to those of the A large part of TOEF is ﬂat and the inclination is <5. The forest census quadrats in all cases. Topographic positions of each of these established on 2-m deep regosols accumulated during the eruptions of quadrats (hereafter in this paragraph, the term ‘quadrats' indicates a nearby volcano, Mt. Tarumae, in 1669 and 1739. Approximately 1-m2 permanent quadrats) were classiﬁed into one of the four catego- 350 vascular plants have been recorded in TOEF (Kudo & Yoshimi ries: ﬂat, ridge, slope and valley. Aspect was deﬁned as one of the 1916). The dominant canopy tree species in the natural stands are following categories: ﬂat (slope angle £5), north, east, south and Quercus crispula, Acer mono, Sorbus alnifolia and Tilia japonica west. Topographic position and aspect were coded as dummy vari- (Hiura 2001). The current landscape of TOEF is a mosaic of primary ables. We treated ‘ﬂat' as a baseline category for both topographic forest, secondary forest and plantations of various tree species; each position and aspect. Slope angle was measured in the steepest direc- stand type occupies one-third of TOEF (Hiura 2005).
tion across quadrats using a clinometer. Soil water content was mea-sured at three points for each quadrat using TRIME-FM (IMKOGmbH, Ettlingen, Germany) and then averaged. The A0 layer of soil within a 0.0625-m2 frame was collected after leaf fall in autumn and We randomly located 60 square quadrats of 9 m2 in primary stands.
oven-dried to constant weight as an index of litter accumulation.
We recorded coverage, to the nearest 10%, for each species of non- Four 5-cm deep soil samples were collected at each quadrat. These woody vascular plants, lianas and small shrubs, which were typically four samples were pooled and analysed for pH, NO , P, K, Ca, Mg <1 m in height, in June and July 2010. Coverage was estimated by and humus content at JAHT Co., Ltd (Toyonaka, Japan). We took two independent observers, and mean values were used for analyses.
hemispherical photographs from a height of 1 m at one corner of each Juveniles under 5 cm were excluded because identiﬁcation was often quadrat to measure the light environment in August 2008. Fractions difﬁcult among related species, but adults of very small species that of total transmitted radiation were calculated from the photographs often reach maturity when <5 cm tall were included. Two species of using Gap Light Analyzer software (version 2.0; Frazer, Canham & Trillium were excluded from analyses as reliable identiﬁcation was Lertzman 1999). Current-year shoots of all vascular plants that were difﬁcult without ﬂowers. In total, 96 species were included in the com- <1.5 m tall were harvested in a 0.25-m2 frame at a location adjacent munity-level analysis. For variation partitioning at the species level, to the quadrats in August 2009. This biomass measurement was used we focused on the abundance distribution of 24 relatively frequently as an indicator of productivity. Trees larger than 5 cm in diameter at occurring species that were present in at least 20 quadrats. The 24 spe- breast height (DBH) within a 5 m radius from the centre of the quad- cies are listed in Table 1.
rats were measured to obtain median DBH and basal area. For Table 1. Unique contributions (percentage) of the six selected environmental variables to the spatial abundance distribution of the 24 species Signs for the contribution of environmental variables represent direction of the effects. Values with asterisks were signiﬁcant atP < 0.05 after 999 permutations.
2012 The Authors. Journal of Ecology 2012 British Ecological Society, Journal of Ecology, 100, 950–957 Community assembly of forest herbs continuous variables (excluding pH), we log-transformed the values variables described earlier to community structure and species assuming that unit differences of these values would be more impor- distribution. While variation partitioning is usually performed at the tant when their absolute sizes are small (Jones et al. 2008). We calcu- community level using ordination methods such as RDA or CCA, in lated variance inﬂation factors of the environmental variables; as a this study, we also performed variation partitioning at the species result, a dummy variable that represents ‘slope' topography was level based on multiple regression. This approach is an application of excluded from later analyses to reduce multicollinearity. We gener- variation partitioning based on RDA, as the result of variation parti- ated quadratics for continuous variables by centring and squaring to tioning based on RDA is the weighted means of R2 of multiple regres- model unimodal (or U-shaped) responses of species along environ- sions for each of the constituent species (Peres-Neto et al. 2006). We mental gradients (Flinn et al. 2010; Gilbert & Bennett 2010). Thus, used values of adjusted R2 as explained variance because normal R2 our full model consisted of 37 environmental variables in total.
are strongly affected by the number of samples and explanatory vari- We generated spatial variables using the principal coordinates of ables (Peres-Neto et al. 2006). First, we separately performed forward neighbour matrices (PCNM) method to characterize spatial structure selection for environmental and spatial explanatory variables to at multiple scales (Borcard & Legendre 2002; Dray, Legendre & ensure only signiﬁcant variables were used in the ﬁnal models. To Peres-Neto 2006). The PCNM variables are a suite of orthogonal avoid overestimation of adjusted R2, forward selection of variables variables, which is ordered based on the spatial scales they represent.
was performed using adjusted R2 of the full model as a second requi- To obtain the PCNM variables, we ﬁrst generated a Euclidean dis- site, in addition to the signiﬁcance of each variable, to stop selection tance matrix of the 60 quadrats. This matrix was then truncated using (Blanchet, Legendre & Borcard 2008). This step was only performed a threshold value (the length of the longest edge of the minimum at the community level, and the same suite of variables was used for spanning tree connecting the quadrats), above which all distances all species in later regressions at the species level to minimize the risk were considered very distant. (We actually replaced the real distances of selecting superﬂuous variables through repetition of variable selec- by four times the length of the threshold value.) Finally, we calculated tion. We then performed three multiple regressions for each species to principal coordinates of this matrix. We used 16 variables with posi- obtain the percentage of variance explained by environmental vari- tive Moran's I values whose wavelengths ranged from about 700 to ables only, spatial variables only and both. Finally, total variances of 7000 m as spatial explanatory variables (Dray, Legendre & Peres- species abundances in our quadrats were divided into four fractions, that is, the unique contribution of environment (variance explained We collected values of functional traits of the 24 species using ﬁeld by environment independent of space), the unique contribution of and literature surveys. In August 2009, plant height to the highest liv- space (variance explained by space independent of environment), the ing part including reproductive organs of the largest individual of contribution of spatially structured environment (variance shared by each species was measured (within 20 m from the centre of the 1-m2 environment and space) and residuals by sequential subtractions.
permanent quadrats) in up to three quadrats (depending on their These fractions can be negative, and in such cases, the fractions were availability) where the abundance of the species was highest in a preli- bounded to zero. Additionally, we calculated the unique contribu- minary vegetation census performed in July 2009. Ramets were then tions of each environmental variable using other environmental vari- sampled and stored in a cooler until returned to the ﬁeld station. To ables and spatial variables as covariables. These unique contributions obtain SLA, 1–20 typical-sized, sound and mature leaves, including of single environmental variables occasionally exceeded the total petioles and rachises of compound leaves (analysed leaf number was unique contribution of the environment because some fractions were depending on leaf size and availability), were digitally scanned before negative, as described above. The signiﬁcance of the explained vari- oven-drying (at 60 C to constant weight). We used more than six ances was veriﬁed using permutation tests. We also performed varia- leaves in total for most species, excluding Maianthemum dilatatum, tion partitioning at the community level including all 96 species based whose ramets often consist of a single leaf. For two of the 24 species, on RDA. In this case, a community data matrix was Hellinger-trans- the plant body had already senesced by the sampling period, and thus formed before variation partitioning (Legendre & Gallagher 2001).
these traits were unavailable. Shoot height was also not measured for Later procedures were conducted in the same manner as those of the the four liana species. Height and SLA were averaged for each species for use in later analyses. Thus, these values represent average func- Relationships between the unique contributions of environment tional traits of sound individuals at relatively preferred habitats in this and space and the functional traits were tested using the Kruskal– landscape for each species. Seed mass data were obtained mainly Wallis rank-sum test (for dispersal mode) or Spearman's rank corre- from Nakayama, Inokuchi & Minamitani (2000), and the Seed Infor- lation (for the other traits). For the analysis of phylogenetic signals, mation Database (Royal Botanic Gardens Kew 2008) was referenced we constructed a phylogenetic tree of the 23 species based on the most supplementarily. Seed mass was not available in the literature or the recent phylogenetic supertree of angiosperms (R20100318, available data base for three species. Pteridophytes were excluded from the at http://svn.phylodiversity.net/tot/megatrees/). Lycopodium serra- analysis of seed mass. Dispersal mode was obtained from Asano & tum was excluded from phylogenetic analyses because the supertree Kuwahara (1990) and Asano (2005) and classiﬁed into one of the does not include Lycopodiophyta. The branch lengths were then cal- following categories: spore (pteridophytes), no dispersal mechanisms culated based on known node ages using the BLADJ algorithm, (gravity-dispersed species), animal-dispersed species and wind- which is offered in the software Phylocom (version 4.1; Webb, Ackerly dispersed species. When records of dispersal mode were not available & Kembel 2008). One of the indices that represents the extent of the in the literature, they were complemented by ﬁeld observations.
phylogenetic signal, the K statistic (Blomberg, Garland & Ives 2003), Functional traits of the 24 species are summarized in Table S1 in was calculated based on the phylogeny. A K-value of 1 indicates that the traits evolved under Brownian motion, K < 1 indicates randomor divergent trait evolution more than expected under the Brownianmotion model and K > 1 indicates conserved patterns of trait evolu- tion more than expected under Brownian motion. Signiﬁcance was We performed variation partitioning (Borcard, Legendre & Drapeau assessed by comparing the observed K statistics with the distribution 1992) to quantify the contribution of the environmental and spatial of K statistics obtained by 999 permutations of trait values across tips 2012 The Authors. Journal of Ecology 2012 British Ecological Society, Journal of Ecology, 100, 950–957 M. Aiba, H. Takafumi & T. Hiura of the tree. All statistical analyses were performed in the statistical facing slope, slope angle and soil humus content were non-zero environment r 2.12.1 (R Development Core Team 2010).
for 11, 9, 13, 5, 13 and 9 species, respectively. The directions(positive or negative) of the effects of the six selected environ- mental variables were rather variable among species (Table 1).
The effects of south-facing slope, soil nitrate content, west- In variation partitioning at the community level including all facing slope, slope angle and soil humus content were positive 96 species, the unique contributions of environment and space for 3, 5, 2, 3 and 3 species, respectively. For soil Mg content, and the contribution of spatially structured environment were the responses of 12 species were unimodal, whereas that of one 7.2%, 4.0% and 1.4%, respectively. Selected environmental species was U-shaped.
variables were, in order of unique contribution, south-facing Correlations between the unique contribution of environ- slope (the unique contribution was 1.6%), soil nitrate content ment and functional traits (i.e. dispersal mode, seed mass, plant (1.3%), the quadratic of soil Mg content (0.87%), west-facing height and SLA) were generally weak and non-signiﬁcant slope (0.82%), slope angle (0.73%) and soil humus content (Fig. 2). The unique contribution of spatial variables was sig- (0.14%). The selected spatial variables were the ﬁrst, second, niﬁcantly larger in species with no dispersal mechanisms than third, fourth, ﬁfth and sixth of the 16 PCNM variables, which in animal-dispersed species (Fig. 3a). The sample sizes of the represent relatively broad-scale spatial structure.
other two dispersal modes (i.e. spore and wind-dispersed In variation partitioning at the species level, the unique con- species) were too small to evaluate the results. No signiﬁcant tribution of environment varied substantially among the 24 correlations were found between the unique contribution of species and ranged from 0.0% to 27.7% (mean ± SD, space and the other three functional traits (Fig. 3b–d). No sig- 8.8 ± 9.1%; Fig. 1). The unique contribution of space ranged niﬁcant phylogenetic signal was detected for either the unique from 0.0% to 25.7% (6.3 ± 7.2%), and the contribution of contribution of environment (K = 0.33, P = 0.37) or that of spatially structured environment ranged from 0.0% to 16.5% space (K = 0.18, P = 0.86) among the 23 species.
(5.1 ± 4.8%). The relative unique contributions of environ-ment and space against total explained variance ranged from 0.0% to 93.5% and from 0.0% to 90.1%, respectively. Theunique contribution of environment and space was signiﬁcant The relative and absolute sizes of the unique contributions of for 10 and 9 species, respectively. The abundance distributions environment and space were considerably different among the of only 5 of the 24 species were signiﬁcantly correlated with 24 relatively frequently occurring plant species. For many both environment and space. The contributions of the respec- species, both environment and space were not necessarily tive environmental variables were often zero after adjustment signiﬁcant as explanatory variables of species distribution, at the species level (Table 1). The effects of south-facing slope, whereas both environment and space were signiﬁcant at the soil nitrate content, the quadratic of soil Mg content, west- community level. Although these analyses were the ﬁrst trial ofvariation partitioning of distributions at the species level, sev-eral previous studies have reported results consistent with ours.
Flinn et al. (2010) reported differences in the effects of environ-ment and space on community structure among subsets of a Canadian wetland herb community that were grouped by dis-persal mode. Similarly, seed-sowing experiments quantifying the extent of dispersal limitation in mainly patchy, discretehabitats have demonstrated that the severity of dispersal limi-tation differs considerably among co-occurring species (e.g.
Ehrle´n & Eriksson 2000; Svenning & Wright 2005; Moore & Explained variance (%) Elmendorf 2006). These results suggest that interspeciﬁc differences in the importance of environment and space as determinants of distribution are widespread among plant The unique contribution of the respective environmental variables and the direction of the effects differed widely among Diarrhena j. Sanicula c. Solidago v. Chamaele d. Dryopteris c. Scutellaria i. species. These results demonstrate considerable interspeciﬁc Chloranthus s. Hydrangea p. Lycopodium s. Platanthera u. Schisandra c. Maianthemum d. Maianthemum j. Pachysandra t. differences in environmental preferences among the study spe- Schizophragma h. cies. Gilbert & Lechowicz (2004) showed that the relative importance of environmental variables differed among taxa orgrowth types in a Canadian understorey plant community.
Fig. 1. Variation partitioning at the species level for spatial abun- However, our species-level analysis indicated that similarity dance distribution of the 24 relatively frequently occurring species.
in the unique contribution of environmental variables or in Fractions that were signiﬁcant at P < 0.05 after 999 permutations the direction of those effects was relatively rare among related are marked with asterisks. Note that the signiﬁcance of the correlatedeffects of environment and space is not testable.
species, for example two Carex species, two Galium species and 2012 The Authors. Journal of Ecology 2012 British Ecological Society, Journal of Ecology, 100, 950–957 Community assembly of forest herbs Fig. 2. Correlations between (a) dispersal mode, (b) seed mass, (c) plant height and (d) speciﬁc leaf area (SLA) versus the unique contribution of environment. Results of Unique contribution of Unique contribution of Kruskal–Wallis rank-sum test (for dispersal envrionmental variables (%) environmental variables (%) mode) or Spearman's rank correlation (for the other three traits) are also shown. Note that the K value in this ﬁgure is not the K sta-tistic for phylogenetic signal but is the test statistic of the Kruskal–Wallis test. Boxes and whiskers indicate the interquartile range and maximum and minimum values within 1.5 times the interquartile range, respec-tively. Individual values are also plotted.
Gravity, animal and wind represent no dis- persal mechanisms (gravity dispersal), ani- Unique contribution of Unique contribution of environmental variables (%) environmental variables (%) respectively. X-axes are log-scaled for contin- uous traits.
Fig. 3. Correlations between (a) dispersal mode, (b) seed mass, (c) plant height and (d)speciﬁc leaf area (SLA) versus the unique contribution of space. Results of the Krus- Unique contribution of spatial variables (%) Unique contribution of spatial variables (%) kal–Wallis rank-sum test (for dispersal mode) or Spearman's rank correlation (for the other three traits) are also shown. Note that the K value in the ﬁgure is not the K sta-tistic for the phylogenetic signal but is the test statistic of the Kruskal–Wallis test.
Boxes and whiskers indicate the interquartilerange and maximum and minimum values within 1.5 times the interquartile range, respectively. Individual values are also plot-ted. Gravity, animal and wind represent no dispersal mechanisms (gravity dispersal), Unique contribution of spatial variables (%) Unique contribution of spatial variables (%) animal dispersal and wind dispersal, respec- tively. X-axes are log-scaled for continuous two Maianthemum species. Therefore, phylogenetic signals in tat, especially those newly created from environmental changes environmental preferences appear to be weak in these species, or local extinction. As a result, spatial distances explain a larger in contrast to the results of Gilbert & Lechowicz (2004).
portion of the variance in the spatial abundance distribution of As we hypothesized, the unique contribution of space was species with no dispersal mechanisms than those of animal-dis- signiﬁcantly larger in species with no dispersal mechanisms persed species.
(gravity-dispersed species) compared with animal-dispersed In contrast, interspeciﬁc differences in the unique contribu- species. This result is not surprising, given the considerable tions of environment and space were generally independent of differences in dispersal ability between species with no dis- the other three functional traits, that is, seed mass, plant height persal mechanisms and animal-dispersed species. In their and SLA. The lack of a linkage between the unique contribu- review, Vittoz & Engler (2007) showed that the upper limit of tion of space and seed mass is especially interesting, as this the distance within which 99% of seeds will disperse is only result deviates from the assumption of the competition–coloni- 5 m for species with no dispersal mechanisms, whereas that for zation trade-off hypothesis of species coexistence (Nee & May some animal-dispersed species exceeds 500 m. Due to this lim- 1992; Tilman 1994; Calcagno et al. 2006). According to this ited dispersal ability, species with no dispersal mechanisms hypothesis, small-seeded species should be less dispersal-lim- would fail to reach some distant parts of their potential habi- ited to allow escape from competition with large-seeded, often 2012 The Authors. Journal of Ecology 2012 British Ecological Society, Journal of Ecology, 100, 950–957 M. Aiba, H. Takafumi & T. Hiura competitive species for coexistence at a metacommunity level.
In conclusion, our results suggest that dispersal limitation, The absence of links between the contribution of space and as well as environmental control, is an essential process in the seed mass is possibly due to the rather weak correlation community assembly of understorey plant species at the study between seed mass and dispersal distance (Thomson et al.
site. However, at the same time, we found considerable inter- speciﬁc differences in the relative and absolute importance of The results of the previous studies on the relationship environmental control and dispersal limitation and of essential between functional traits and the extent of dispersal limitation environmental variables that are not explicitly accounted for in have not been consistent. In the case of Flinn et al. (2010), the basic neutral theories. These results support the importance of contribution of space to community assembly was more incorporating interspeciﬁc differences in both environmental important in species whose seeds are dispersed by ants, preferences and the extent of dispersal limitation into stochas- explosion, splash or gravity than in animal-dispersed or wind- tic models of community assembly, which has been attempted dispersed species. Tremlova & Munzbergova (2007) demonst- in several recent studies (e.g. Ruokolainen et al. 2009; rated that patch occupancy correlates positively with wind Salomon, Connolly & Bode 2010). We demonstrated that dispersal, external animal dispersal, seed bank formation and interspeciﬁc differences in the importance of space as correlates above-ground biomass in Bohemian grasslands fragmented of distribution were partially predictable from the dispersal into agricultural ﬁelds. On the other hand, Moore & Elmen- mode of species. However, the results of existing reports on dorf (2006) failed to ﬁnd a correlation between the extent of relationships between functional traits and the contributions seed limitation and seed mass or seed dormancy in a California of environment and space are far from consistent. A re-analy- grassland. In a meta-analysis of numerous seed-sowing experi- sis at the species level of past studies, which were all performed ments, Clark et al. (2007) showed that the extent of seed limita- at the community level, would promote our general under- tion is independent of dispersal mode, whereas they found a standing of the relationship between determinants of spatial signiﬁcant positive correlation between the extent of seed limi- patterns and functional traits.
tation and seed size.
In this study, interspeciﬁc differences in the unique contribu- tions of environment and space were not explained by phyloge-netic identity. In fact, the unique contributions of environment We thank the staff of TOEF for assistance in the ﬁeld. Angela Moles, Roberto and space differed considerably even among congeneric Salguero-Gomez and two anonymous referees provided helpful comments on aprevious version of this manuscript. This study was partly supported by a species. For example, whereas the unique contribution of space Research Fellowship for Young Scientists (to MA) and grants from the Japan was 10.2% for Carex japonica (P < 0.05), no unique Society for the Promotion of Science (No. 21248017 to TH) and the Ministry of contribution of space was detected for Carex rugata. Similarly, Environment (No. D-0909 to TH and No. S-9-3 to MA and TH).
the unique contribution of environment was 11.7% for Maian-themum japonica (P < 0.05), but no unique contribution of environment was detected for M. dilatatum. Given that Asano, S. (2005) Sadao Asano's Biological Flora of Japan. Zenkoku Noson dispersal mode is often conserved among related species Kyoiku Kyokai, Tokyo.
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The dyskinesia impairment scale: a new instrument to measure dystonia and choreoathetosis in dyskinetic cerebral palsy
DEVELOPMENTAL MEDICINE & CHILD NEUROLOGY The Dyskinesia Impairment Scale: a new instrument to measuredystonia and choreoathetosis in dyskinetic cerebral palsy ELEGAST MONBALIU1,2 ELS ORTIBUS3 JOS DE CAT3,4 BERNARD DAN5,6 LIEVE HEYRMAN1 PETER PRINZIE2,7 PAUL DE COCK8,9 HILDE FEYS1 1 Department of Rehabilitation Sciences, Katholieke Universiteit Leuven, Leuven. 2 Dominiek Savio Institute, DC GID(t)S Gits. 3 Department of Paediatric Neurology, UniversityHospitals Leuven, Leuven. 4 Clinical Motion Analysis Laboratory Leuven, University Hospital Pellenberg, Leuven. 5 Department of Neurology, Hpital Universitaire des EnfantsReine Fabiola, Brussels. 6 Faculty of Medicine, Universit libre de Bruxelles, Brussels, Belgium. 7 Department of Child and Adolescent Studies, Utrecht University, Utrecht, theNetherlands. 8 Centre for Developmental Disabilities, University Hospitals Leuven, Leuven. 9 Faculty of Medicine, Katholieke Universiteit Leuven, Leuven, Belgium.