Search for: All records

Many plant species can exhibit remarkable variation in leaf characteristics, depending on their abiotic and biotic environment. Environmental changes therefore have the potential to alter leaf traits, which in turn scale up to influence ecosystem processes including net primary productivity, susceptibility to fire, and palatability to herbivores. It is not well understood how consistent trait–environment relationships are among species, across sites and over time. This presents a fundamental challenge for functional ecology, because no study can measure all relevant species in all places at all times. Thus, understanding the limits of transferability is critical. We collected leaf trait measurements on 13 species of grass (family: Poaceae) across 11 sites and five years (n = 3091 individuals). Sites were arrayed along a spatial precipitation gradient in coastal northern California (annual precipitation of 590–1350 mm) with substantial interannual precipitation variability (from 60% below the 30‐year average to 100% above average). Temporal and spatial linear relationships between precipitation and specific leaf area (SLA) appear at first idiosyncratic, with each species sometimes displaying positive and sometimes negative responses. However, this variation arises from sampling different portions of an underlying hump‐shaped relationship, which was shared across most species. This hump‐shaped relationship was driven primarily by changes in leaf tissue density. These results suggest the potential for transferability among species, as well as between space and time, as long as the gradients are sufficiently long to capture the nonlinear response. Future work could explore the physiological basis of the nonlinear SLA response, including the possibility that distinct physiological mechanisms are operating at the two extremes of the gradient.

 

Plant traits are useful for predicting how species may respond to environmental change and/or influence ecosystem properties. Understanding the extent to which traits vary within species and across climatic gradients is particularly important for understanding how species may respond to climate change. We explored whether climate drives spatial patterns of intraspecific trait variation for three traits (specific leaf area (SLA), plant height, and leaf nitrogen content (Nmass)) across 122 grass species (family: Poaceae) with a combined distribution across six continents. We tested the hypothesis that the sensitivity (i.e. slope) of intraspecific trait responses to climate across space would be related to the species' typical form and function (e.g. leaf economics, stature and lifespan). We observed both positive and negative intraspecific trait responses to climate with the distribution of slope coefficients across species straddling zero for precipitation, temperature and climate seasonality. As hypothesized, variation in slope coefficients across species was partially explained by leaf economics and lifespan. For example, acquisitive species with nitrogen-rich leaves grew taller and produced leaves with higher SLA in warmer regions compared to species with low Nmass. Compared to perennials, annual grasses invested in leaves with higher SLA yet decreased height and Nmass in regions with high precipitation seasonality (PS). Thus, while the influence of climate on trait expression may at first appear idiosyncratic, variation in trait–climate slope coefficients is at least partially explained by the species' typical form and function. Overall, our results suggest that a species' mean location along one axis of trait variation (e.g. leaf economics) could influence how traits along a separate axis of variation (e.g. plant size) respond to spatial variation in climate. 

Grass species (family Poaceae) are globally distributed, adapted to a wide range of climates and express a diversity of functional strategies. We explored the functional strategies of grass species using the competitor, stress tolerator, ruderal (CSR) system and asked how a species’ strategy relates to its functional traits, climatic distribution and propensity to become naturalized outside its native range. We used a global set of trait data for grass species to classify functional strategies according to the CSR system based on leaf traits. Differences in strategies in relation to lifespan (annual or perennial), photosynthetic type (C3 or C4), or naturalisation (native or introduced) were investigated. In addition, correlations with traits not included in the CSR classification were analyzed, and a model was fitted to predict a species’ average mean annual temperature and annual precipitation across its range as a function of CSR scores. Values for competitiveness were higher in C4 species than in C3 species, values for stress tolerance were higher in perennials than in annuals, and introduced species had more pronounced competitive-ruderal strategies than native species. Relationships between the CSR classification, based on leaf traits, and other functional traits were analyzed. Competitiveness was positively correlated with height, while ruderality was correlated with specific root length, indicating that both above- and belowground traits underlying leaf and root economics contribute to realized CSR strategies. Further, relationships between climate and CSR classification showed that species with competitive strategies were more common in warm climates and at high precipitation, whereas species with stress tolerance strategies were more common in cold climates and at low precipitation. The findings presented here demonstrate that CSR classification of functional strategies based on leaf traits matches expectations for the adaptations of grass species that underlie lifespan, photosynthetic type, naturalization and climate.

 

Abstract Here we provide the ‘Global Spectrum of Plant Form and Function Dataset’, containing species mean values for six vascular plant traits. Together, these traits –plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass – define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date. 

A key feature of life’s diversity is that some species are common but many more are rare. Nonetheless, at global scales, we do not know what fraction of biodiversity consists of rare species. Here, we present the largest compilation of global plant diversity to quantify the fraction of Earth’s plant biodiversity that are rare. A large fraction, ~36.5% of Earth’s ~435,000 plant species, are exceedingly rare. Sampling biases and prominent models, such as neutral theory and the k-niche model, cannot account for the observed prevalence of rarity. Our results indicate that (i) climatically more stable regions have harbored rare species and hence a large fraction of Earth’s plant species via reduced extinction risk but that (ii) climate change and human land use are now disproportionately impacting rare species. Estimates of global species abundance distributions have important implications for risk assessments and conservation planning in this era of rapid global change. 

Plant species can show considerable morphological and functional variation along environmental gradients. This intraspecific trait variation (ITV) can have important consequences for community assembly, biotic interactions, ecosystem functions and responses to global change. However, directly measuring ITV across many species and wide geographic areas is often infeasible. Thus, a method to predict spatial variation in a species’ functional traits could be valuable.

We measured specific leaf area (SLA), height and leaf area (LA) of grasses across California, covering 59 species at 230 sampling locations. We asked how these traits change along climate gradients within each species and used machine learning to predict local trait values for any species at any location based on phylogenetic position, local climate and that species’ mean traits. We then examined how much these local predictions alter patterns of assemblage‐level trait variation across the state.

Most species exhibited higher SLA and grew taller at higher temperatures and produced larger leaves in drier conditions. The random forests predicted spatial variation in functional traits very accurately, with correlations up to 0.97. Because trait records were spatially biased towards warmer areas, and these areas tend to have higher SLA individuals within each species, species means of SLA were upwardly biased. As a result, using species means over‐estimates SLA in the cooler regions of the state. Our results also suggest that height may be substantially under‐predicted in the warmest areas.

Synthesis. Using only species mean traits to characterize the functional composition of communities risks introducing substantial error into trait‐based estimates of ecosystem properties including decomposition rates or NPP. The high performance of random forests in predicting local trait values provides a way forward for estimating high‐resolution patterns of ITV without a massive data collection effort.

 

Latitudinal and elevational richness gradients have received much attention from ecologists but there is little consensus on underlying causes. One possible proximate cause is increased levels of species turnover, or β diversity, in the tropics compared to temperate regions. Here, we leverage a large botanical dataset to map taxonomic and phylogenetic β diversity, as mean turnover between neighboring 100 × 100 km cells, across the Americas and determine key climatic drivers. We find taxonomic and tip‐weighted phylogenetic β diversity is higher in the tropics, but that basal‐weighted phylogenetic β diversity is highest in temperate regions. Supporting Janzen's ‘mountain passes’ hypothesis, tropical mountainous regions had higher β diversity than temperate regions for taxonomic and tip‐weighted metrics. The strongest climatic predictors of turnover were average temperature and temperature seasonality. Taken together, these results suggest β diversity is coupled to latitudinal richness gradients and that temperature is a major driver of plant community composition and change.

 

Addressing global environmental challenges requires access to biodiversity data across wide spatial, temporal and taxonomic scales. Availability of such data has increased exponentially recently with the proliferation of biodiversity databases. However, heterogeneous coverage, protocols, and standards have hampered integration among these databases. To stimulate the next stage of data integration, here we present a synthesis of major databases, and investigate (a) how the coverage of databases varies across taxonomy, space, and record type; (b) what degree of integration is present among databases; (c) how integration of databases can increase biodiversity knowledge; and (d) the barriers to database integration.

Global.

Contemporary.

Plants and vertebrates.

We reviewed 12 established biodiversity databases that mainly focus on geographic distributions and functional traits at global scale. We synthesized information from these databases to assess the status of their integration and major knowledge gaps and barriers to full integration. We estimated how improved integration can increase the data coverage for terrestrial plants and vertebrates.

Every database reviewed had a unique focus of data coverage. Exchanges of biodiversity information were common among databases, although not always clearly documented. Functional trait databases were more isolated than those pertaining to species distributions. Variation and potential incompatibility of taxonomic systems used by different databases posed a major barrier to data integration. We found that integration of distribution databases could lead to increased taxonomic coverage that corresponds to 23 years’ advancement in data accumulation, and improvement in taxonomic coverage could be as high as 22.4% for trait databases.

Rapid increases in biodiversity knowledge can be achieved through the integration of databases, providing the data necessary to address critical environmental challenges. Full integration across databases will require tackling the major impediments to data integration: taxonomic incompatibility, lags in data exchange, barriers to effective data synchronization, and isolation of individual initiatives.