Comment on “Functional Traits and Niche-Based Tree Community Assembly in an Amazonian Forest”

Abstract

Kraft et al. (Reports, 24 October 2008, p. 580) used a variety of metrics describing the distribution of functional traits within a tropical forest community to demonstrate simultaneous environmental filtering and niche differentiation. We discuss how these results could have arisen from sampling design and statistical assumptions, suggesting alternative approaches that could better resolve these questions.K raft et al.(1) used the distribution of functional traits in a diverse tropical forest community to test hypotheses of community assembly. The study focused on six traits that drive diverse ecological functions. By comparing metrics of observed distributions of each trait with those expected under different null models, the authors showed evidence for both classical niche differentiation and environmental filtering at the scale of 20-by 20-m quadrats. Relative to null expectations, environmental filtering was indicated by shifting trait means, decreased trait range, and variance within quadrats, whereas differentiation was indicated by overdispersion, measured by standard deviation (SD) of nearestneighbor distance in trait space, and kurtosis of the within-quadrat trait distribution. Combined, these tests were used to argue for the important role of stabilizing processes in the maintenance of biodiversity in a hyperdiverse tropical forest. This approach represents movement toward a more comprehensive use of functional traits in testing processes of community assembly. Nevertheless, there are some subtle problems in the Kraft et al. analysis that we expand upon here.First, Kraft et al.(1) inappropriately tested for both environmental filtering and niche differentiation using null models that select randomly from the entire species pool. If environmental filtering limits the range of traits of species that can occur in a given quadrat, an accurate test of niche differentiation within quadrats must sample only from species that fall between the minimum and maximum traits present in a quadrat. The larger the available trait space, the larger the possible range of nearest-neighbor distances in trait space, and hence the bigger the SD of nearest-neighbor distances will be under a random draw from that available trait space. Thus, if limitations on the range of trait values in each quadrat due to habitat filtering are not accounted for in the null trait distribution used in the niche differentiation test, one might artificially conclude that the observed SD of nearest-neighbor distances is smaller than expected by chance and that niche differentiation is occurring. Similar problems might arise in the comparison of kurtosis values.Second, Kraft et al. ignored intraspecific variation. Due to the obvious difficulty of collecting trait data for all individuals, the authors targeted outer canopy leaves of trees in the 1-to 5-cm diameter at breast height (dbh) size class growing under closed canopy that were readily accessed from the ground, leading to a collection of 2 to 5 leaves from 1 to 20 individuals for most of t...

The distribution of below‐ground traits is explained by intrinsic species differences and intraspecific plasticity in response to root neighbours

“…This contradicts previous studies focused on above‐ground trends, where species turnover had a strong effect on leaf trait distribution (Kraft, Valencia & Ackerly ; Cornwell & Ackerly ). This disagreement suggests either that contrasting processes shape the distributions of above‐ and below‐ground community traits (such as greater symmetry of competition below‐ground compared with above‐ground (Cahill & Casper )) or that there is unmeasured variation in above‐ground traits at a scale smaller than what has been considered in previous studies (as suggested by Lake & Ostling ). It also implies that diverse fine root morphologies could play an important role in differentiating resource use below‐ground, complementing the reported above‐ground trait similarity among coexisting species (Kraft, Valencia & Ackerly ).…”

Intraspecific variability and trait‐based community assembly

“…Evidence for the existence of filtering processes has been demonstrated mainly by approaches based on mean trait values (e.g. Stubbs & Wilson 2004; Kraft, Valencia & Ackerly 2008; Schamp, Chau & Aarssen 2008), without considering intraspecific variability (Lake & Ostling 2009). Indeed, a basic assumption of comparative plant ecology (Grime, Hodgson & Hunt 1988; Keddy 1992a) is that the difference in functional trait values between species is larger than within species (Garnier et al.…”

“…Both processes can be detected using a null model approach (Gotelli & Graves 1996), where trait distribution observed in the field is compared to trait distributions in randomly assembled communities. Such random communities should be generated with regard to both inter‐ and intraspecific components of trait variability (Lake & Ostling 2009).…”

Plant functional traits suggest novel ecological strategy for an invasive shrub in an understorey woody plant community

“…2005; McGill et al. 2006; Lake & Ostling 2009). (ii) Is the invasive shrub exhibiting a suite of traits that is novel in the community somehow (beyond taking advantage of unused trait space)?…”