Functional trait similarity predicts survival in rare plant reintroductions.

Rare species reintroductions are an increasingly common conservation strategy, but often result in poor survival of reintroduced individuals. Reintroduction sites are chosen primarily based on historical occupancy and/or abiotic properties of the site, with much less consideration given to properties of the larger biotic community. However, ecological niche theory suggests that the ability to coexist with other species is determined in part by the degree of functional similarity between species. The degree to which functional similarity affects the survival of reintroduced plants is poorly understood, but has important implications for the allocation of limited conservation resources. We collected a suite of abiotic, biotic, and functional trait variables centered on outplanted individuals from four reintroduced rare plant species and used logistic regression and model selection to assess their influence on individual survival. We show that higher functional similarity between reintroduced individuals and the local community, measured by differences between their multivariate functional traits and the community-weighted mean traits of their immediate neighbors, increases survival and is a stronger predictor of survival than local variation in abiotic factors, suggesting that the functional composition of the biotic community should be incorporated into site selection to improve reintroduction success.

Functional traits of the understory plant community of a pyrogenic longleaf pine forest across environmental gradients.

Understanding and predicting the response of plant communities to environmental changes and disturbances such as fire requires an understanding of the functional traits present in the system, including within and across species variability, and their dynamics over time. These data are difficult to obtain as few studies provide comprehensive information for more than a few traits or species, rarely cover more than a single growing season, and usually present only summary statistics of trait values. As part of a larger study seeking to understand the dynamics of plant communities in response to different prescribed fire regimes, we measured the functional traits of the understory plant communities located in over 140 permanent plots spanning strong gradients in soil moisture in a pyrogenic longleaf pine forest in North Carolina, USA, over a four-year period from 2011 and 2014. We present over 120,000 individual trait measurements from over 130 plant species representing 91 genera from 47 families. We include data on the following 18 traits: specific leaf area, leaf dry matter content, leaf area, leaf length, leaf width, leaf perimeter, plant height, leaf nitrogen, leaf carbon, leaf carbon to nitrogen ratio, water use efficiency, time to ignition, maximum flame height, maximum burn temperature, mass-specific burn time, mass-specific smolder time, branching architecture, and the ratio of leaf matter consumed by fire. We also include information on locations, soil moisture, relative elevation, soil bulk density, and fire histories for each site.

Revisiting the Holy Grail: using plant functional traits to understand ecological processes.

One of ecology's grand challenges is developing general rules to explain and predict highly complex systems. Understanding and predicting ecological processes from species' traits has been considered a 'Holy Grail' in ecology. Plant functional traits are increasingly being used to develop mechanistic models that can predict how ecological communities will respond to abiotic and biotic perturbations and how species will affect ecosystem function and services in a rapidly changing world; however, significant challenges remain. In this review, we highlight recent work and outstanding questions in three areas: (i) selecting relevant traits; (ii) describing intraspecific trait variation and incorporating this variation into models; and (iii) scaling trait data to community- and ecosystem-level processes. Over the past decade, there have been significant advances in the characterization of plant strategies based on traits and trait relationships, and the integration of traits into multivariate indices and models of community and ecosystem function. However, the utility of trait-based approaches in ecology will benefit from efforts that demonstrate how these traits and indices influence organismal, community, and ecosystem processes across vegetation types, which may be achieved through meta-analysis and enhancement of trait databases. Additionally, intraspecific trait variation and species interactions need to be incorporated into predictive models using tools such as Bayesian hierarchical modelling. Finally, existing models linking traits to community and ecosystem processes need to be empirically tested for their applicability to be realized.

Trait space of rare plants in a fire-dependent ecosystem.

The causes of species rarity are of critical concern because of the high extinction risk associated with rarity. Studies examining individual rare species have limited generality, whereas trait-based approaches offer a means to identify functional causes of rarity that can be applied to communities with disparate species pools. Differences in functional traits between rare and common species may be indicative of the functional causes of species rarity and may therefore be useful in crafting species conservation strategies. However, there is a conspicuous lack of studies comparing the functional traits of rare species and co-occurring common species. We measured 18 important functional traits for 19 rare and 134 common understory plant species from North Carolina's Sandhills region and compared their trait distributions to determine whether there are significant functional differences that may explain species rarity. Flowering, fire, and tissue-chemistry traits differed significantly between rare and common, co-occurring species. Differences in specific traits suggest that fire suppression has driven rarity in this system and that changes to the timing and severity of prescribed fire may improve conservation success. Our method provides a useful tool to prioritize conservation efforts in other systems based on the likelihood that rare species are functionally capable of persisting.

Variation in Plant Response to Herbivory Underscored by Functional Traits.

The effects of herbivory can shape plant communities and evolution. However, the many forms of herbivory costs and the wide variation in herbivory pressure, including across latitudinal gradients, can make predicting the effects of herbivory on different plant species difficult. Functional trait approaches may aid in contextualizing and standardizing the assessment of herbivory impacts. Here we assessed the response of 26 old-field plant species to simulated defoliation in a greenhouse setting by measuring whole plant and leaf level traits in control and treated individuals. Simulated defoliation had no significant effects on any plant traits measured. However, the baseline leaf level traits of healthy plants consistently predicted the log response ratio for these species whole plant response to defoliation. The latitudinal mid-point of species' distributions was also significantly correlated with aboveground biomass and total leaf area responses, with plants with a more northern distribution being more negatively impacted by treatment. These results indicate that even in the absence of significant overall impacts, functional traits may aid in predicting variability in plant responses to defoliation and in identifying the underlying limitations driving those responses.

The more things change, the more they stay the same? When is trait variability important for stability of ecosystem function in a changing environment.

The importance of intraspecific trait variability for community dynamics and ecosystem functioning has been underappreciated. There are theoretical reasons for predicting that species that differ in intraspecific trait variability will also differ in their effects on ecosystem functioning, particularly in variable environments. We discuss whether species with greater trait variability are likely to exhibit greater temporal stability in their population dynamics, and under which conditions this might lead to stability in ecosystem functioning. Resolving this requires us to consider several questions. First, are species with high levels of variation for one trait equally variable in others? In particular, is variability in response and effects traits typically correlated? Second, what is the relative contribution of local adaptation and phenotypic plasticity to trait variability? If local adaptation dominates, then stability in function requires one of two conditions: (i) individuals of appropriate phenotypes present in the environment at high enough frequencies to allow for populations to respond rapidly to the changing environment, and (ii) high levels of dispersal and gene flow. While we currently lack sufficient information on the causes and distribution of variability in functional traits, filling in these key data gaps should increase our ability to predict how changing biodiversity will alter ecosystem functioning.

Using network properties to predict disease dynamics on human contact networks.

Recent studies have increasingly turned to graph theory to model more realistic contact structures that characterize disease spread. Because of the computational demands of these methods, many researchers have sought to use measures of network structure to modify analytically tractable differential equation models. Several of these studies have focused on the degree distribution of the contact network as the basis for their modifications. We show that although degree distribution is sufficient to predict disease behaviour on very sparse or very dense human contact networks, for intermediate density networks we must include information on clustering and path length to accurately predict disease behaviour. Using these three metrics, we were able to explain more than 98 per cent of the variation in endemic disease levels in our stochastic simulations.

A structured and dynamic framework to advance traits-based theory and prediction in ecology.

Predicting changes in community composition and ecosystem function in a rapidly changing world is a major research challenge in ecology. Traits-based approaches have elicited much recent interest, yet individual studies are not advancing a more general, predictive ecology. Significant progress will be facilitated by adopting a coherent theoretical framework comprised of three elements: an underlying trait distribution, a performance filter defining the fitness of traits in different environments, and a dynamic projection of the performance filter along some environmental gradient. This framework allows changes in the trait distribution and associated modifications to community composition or ecosystem function to be predicted across time or space. The structure and dynamics of the performance filter specify two key criteria by which we judge appropriate quantitative methods for testing traits-based hypotheses. Bayesian multilevel models, dynamical systems models and hybrid approaches meet both these criteria and have the potential to meaningfully advance traits-based ecology.