Inferring community assembly processes from functional seed trait variation along elevation gradient

Assembly of plant communities has long been scrutinized through the lens of trait-based ecology. Studies generally analyse functional traits related to the vegetative growth, survival and resource acquisition and thus ignore how assembly rules may affect plants at other stages of their life cycle, particularly when seeds disperse, persist in soil and germinate. Here, we analysed an extensive dataset of 16 traits for 167 species measured in-situ in 36 grasslands located along an elevation gradient and studied the impact of abiotic filtering, biotic interactions and dispersal on traits reflecting different trait categories: plant vegetative growth, germination, dispersal and seed morphology. For each community, we quantified community-weighted means (CWMs) and functional diversity (FD) for all traits and established their relationships to mean annual temperature. The seed traits were weakly correlated with vegetative traits. Therefore, these traits constituted independent axes of plant phenotypical variation that could be affected differently by community assembly rules. Abiotic filtering impacted mostly vegetative traits and to a lesser extent seed germination and morphological traits. Increasing low-temperature stress in upland sites selected for short-stature, slow-growing and frost-tolerant species that produce small quantities of small seeds with high degree of dormancy, high temperature requirements for germination and low germination speed. Biotic interactions, specifically competition in the lowlands and facilitation in uplands, also filtered some functional traits in the studied communities. The benign climate in lowlands seems to promote plant with competitive strategies that include fast growth and resource acquisition (vegetative growth traits) and early and fast germination (germination traits), whereas the effects of facilitation on the vegetative and germination traits were cancelled out by the strong abiotic filtering. The changes in the main dispersal vector from zoochory to anemochory along the elevation gradient strongly affected the dispersal and the seed morphological trait structure of the communities. This may be explained by stronger vertical turbulence and moderate warm upwinds and low grazing intensity in the uplands that select for light and non-round shaped seeds with lower terminal velocity and endozoochorous potential. Synthesis. We demonstrate that, in addition to vegetative traits, seed traits can substantially contribute to functional structuring of plant communities along environmental gradients. Thus, the ‘hard’ seed traits related to germination and dispersal are critical to detect multiple, complex community assembly rules. Consequently, such traits should be included in core lists of plant traits and, when applicable, be incorporated into the analysis of community assembly

Generating species assemblages for restoration and experimentation: A new method that can simultaneously converge on average trait values and maximize functional diversity

1. Restoring resilient ecosystems in an era of rapid environmental change requires a flexible framework for selecting assemblages of species based on functional traits. However, current trait‐based models have been limited to algorithms that select species assemblages that only converge on specified average trait values, and could not accommodate the common desire among restoration ecologists to generate functionally diverse assemblages. 2. We have solved this problem by applying a nonlinear optimization algorithm to solve for the species relative abundances that maximize Rao's quadratic entropy (Q) subject to other linear constraints. Rao's Q is a closed‐form algebraic expression of functional diversity that is maximized when the most abundant species are functionally dissimilar. 3. Previous models have maximized species evenness subject to the linear constraints by maximizing the entropy function (H’). Maximizing Q alone produces an undesirable species abundance distribution because species that exhibit extreme trait values have the highest abundances. We demonstrate that the maximization of an objective function that additively combines Q and H’ produces a more even relative abundance distribution across the trait dimension. 4. Some ecological restoration projects aim to restore communities that converge on one set of traits while diverging across another. The selectSpecies r function can derive assemblages for any size species pool that maximizes the diversity of any set of traits, while simultaneously converging on average values of any other set of traits. We demonstrate how the function works through examples using uniformly spaced trait distributions and data with a known structure. We also demonstrate the utility of the function using real trait data collected on dozens of species from three separate ecosystems: serpentine grasslands, ponderosa pine forests, and subtropical rainforests. 5. The quantitative selection of species based on their functional traits for ecological restoration and experimentation must be both rigorous and accessible to practitioners. The selectSpecies function provides ecologists with an easy‐to‐use open‐source solution to objectively derive species assemblages based on their functional traits

Predictions of biodiversity are improved by integrating trait-based competition with abiotic filtering.

Funder: University of Canterbury; Id: http://dx.doi.org/10.13039/100008414Funder: University of Waikato; Id: http://dx.doi.org/10.13039/100010061Funder: University of Wyoming; Id: http://dx.doi.org/10.13039/100008106Funder: Manaaki Whenua ‐ Landcare ResearchAll organisms must simultaneously tolerate the environment and access limiting resources if they are to persist. Approaches to understanding abiotic filtering and competitive interactions have generally been developed independently. Consequently, integrating those factors to predict species abundances and community structure remains an unresolved challenge. We introduce a new synthetic framework that models both abiotic filtering and competition by using functional traits. First, our framework estimates species carrying capacities along abiotic gradients. Second, it estimates pairwise competitive interactions as a function of species trait differences. Applied to the study of a complex wetland community, our combined approach more than doubles the explained variance of species abundances compared to a model of abiotic tolerances alone. Trait-based integration of competitive interactions and abiotic filtering improves our ability to predict species abundances, bringing us closer to more accurate predictions of biodiversity structure in a changing world

Data from: Decomposing changes in phylogenetic and functional diversity over space and time

1. The α, β, γ diversity decomposition methodology is commonly used to investigate changes in diversity over space or time but rarely conjointly. However, with the ever-increasing availability of large-scale biodiversity monitoring data, there is a need for a sound methodology capable of simultaneously accounting for spatial and temporal changes in diversity. 2. Using the properties of Chao's index, we adapted Rao's framework of diversity decomposition between orthogonal dimensions to a multiplicative α, β, γ decomposition of functional or phylogenetic diversity over space and time, thereby combining their respective properties. We also developed guidelines for interpreting both temporal and spatial β-diversities and their interaction. 3. We characterised the range of β-diversity estimates and their relationship to the nested decomposition of diversity. Using simulations, we empirically demonstrated that temporal and spatial β-diversities are independent from each other and from α and γ-diversities when the study design is balanced, but not otherwise. Furthermore, we showed that the interaction term between the temporal and the spatial β-diversities lacked such properties. 4. We illustrated our methodology with a case study of the spatio-temporal dynamics of functional diversity in bird assemblages in four regions of France. Based on these data, our method makes it possible to discriminate between regions experiencing different diversity changes in time. Our methodology may therefore be valuable for comparing diversity changes over space and time using large-scale datasets of repeated surveys

Decomposing changes in phylogenetic and functional diversity over space and time

International audienc

Linking functional traits and demography to model species-rich communities

It has long been anticipated that relating functional traits to species demography would be a cornerstone for achieving large-scale predictability of ecological systems. If such a relationship existed, species demography could be modeled only by measuring functional traits, transforming our ability to predict states and dynamics of species-rich communities with process-based community models. Here, we introduce a new method that links empirical functional traits with the demographic parameters of a process-based model by calibrating a transfer function through inverse modeling. As a case study, we parameterize a modified Lotka-Volterra model of a high-diversity mountain grassland with static plant community and functional trait data only. The calibrated trait-demography relationships are amenable to ecological interpretation, and lead to species abundances that fit well to the observed community structure. We conclude that our new method offers a general solution to bridge the divide between trait data and process-based models in species-rich ecosystems.ISSN:2041-172

Linking functional traits and demography to model species-rich communities

It has long been anticipated that relating functional traits to species demography would be a cornerstone for achieving large-scale predictability of ecological systems. If such a relationship existed, species demography could be modeled only by measuring functional traits, transforming our ability to predict states and dynamics of species-rich communities with process-based community models. Here, we introduce a new method that links empirical functional traits with the demographic parameters of a process-based model by calibrating a transfer function through inverse modeling. As a case study, we parameterize a modified Lotka-Volterra model of a high-diversity mountain grassland with static plant community and functional trait data only. The calibrated trait-demography relationships are amenable to ecological interpretation, and lead to species abundances that fit well to the observed community structure. We conclude that our new method offers a general solution to bridge the divide between trait data and process-based models in species-rich ecosystems. Advances in process-based community ecology models are hindered by the challenge of linking functional traits to demography in species-rich systems, where a high number of parameters need to be estimated from limited data. Here the authors propose a new Bayesian framework to calibrate community models via functional traits, and validate it in a species-rich plant community

Fucntional traits dataset

functional traits averages for individual species found in two communities at different elevation

Robinson Crusoe

ISSN:0269-8463ISSN:1365-243

A landscape-scale assessment of the relationship between grassland functioning, community diversity, and functional traits

Livestock farmers rely on a high and stable grassland productivity for fodder production to sustain their livelihoods. Future drought events related to climate change, however, threaten grassland functionality in many regions across the globe. The introduction of sustainable grassland management could buffer these negative effects. According to the biodiversity–productivity hypothesis, productivity positively associates with local biodiversity. The biodiversity–insurance hypothesis states that higher biodiversity enhances the temporal stability of productivity. To date, these hypotheses have mostly been tested through experimental studies under restricted environmental conditions, hereby neglecting climatic variations at a landscape-scale. Here, we provide a landscape-scale assessment of the contribution of species richness, functional composition, temperature, and precipitation on grassland productivity. We found that the variation in grassland productivity during the growing season was best explained by functional trait composition. The community mean of plant preference for nutrients explained 24.8% of the variation in productivity and the community mean of specific leaf area explained 18.6%, while species richness explained only 2.4%. Temperature and precipitation explained an additional 22.1% of the variation in productivity. Our results indicate that functional trait composition is an important predictor of landscape-scale grassland productivity