Search for: All records

Plant traits are important for understanding community assembly and ecosystem processes, yet our understanding of intraspecific trait variation (ITV) is limited. This gap in our knowledge is partially because collecting trait data across a species' entire range is impractical, let alone across the ranges of multiple species within a plant family. Using machine learning techniques to predict spatial ITV is an attractive and cost‐effective alternative to sampling across a species range, although this has not been applied beyond regional scales. We compiled a trait database of over 1000 grass species (family: Poaceae), encompassing six key functional traits: specific leaf area (SLA), leaf dry matter content (LDMC), plant height, leaf area, leaf nitrogen (Nmass) and leaf phosphorus content (Pmass). Using a random forest machine learning approach, we predicted local trait values within species' ranges considering climate, soil type, phylogeny, lifespan, and photosynthetic pathway as influential factors. An iterative random forest modeling technique incorporated correlations between traits, resulting in improved model performance (observed versus predicted R range of 0.72–0.91). Our models also highlight the importance of climate in predicting trait variation. For a subset of species (n = 860), we projected trait predictions across their known distribution, informed by expert maps from Royal Botanic Gardens, Kew, to create global maps of ITV for grasses. Such maps have the potential to inform conservation efforts and predictions of grazing and fire dynamics in grasslands worldwide. Overall, our research demonstrates the value and ecological applications of predicting plant traits. 

Abstract Climate change is increasing the frequency and severity of droughts globally, and grasslands are particularly vulnerable to such hydrological extremes. Drought effects at the ecosystem scale have been assessed both experimentally and through the study of naturally occurring drought, with emerging evidence that the magnitude of drought effects may vary depending on the approach used. We took advantage of a decadal study of four grasslands to directly contrast responses of aboveground net primary productivity (ANPP) to simulated vs. natural drought. The grasslands spanned a ~ threefold mean annual precipitation gradient (335–857 mm) and were all subjected to a natural 1-year drought (~ 40% reduction in precipitation from the long-term mean) and a 4 year experimental drought (~ 50% precipitation reduction). We expected that the 4 year drought would reduce ANPP more, and that post-drought recovery would be delayed, compared to the 1-year drought. We found instead that the short-term natural drought reduced ANPP more strongly than the simulated drought in all grasslands (~ 10 to ~ 50%) likely due to the co-occurrence of higher temperatures and vapor pressure deficits with reduced precipitation. Post-drought recovery was site specific and each site differed in their recovery from the natural and experimental droughts. These results align with past analyses that experiments that only manipulate soil moisture likely underestimate the magnitude of natural drought events. However, experiments can provide valuable insight into the relative sensitivity of ecosystems to reduced precipitation and soil moisture, a key aspect of drought. 

ABSTRACT Extreme droughts are intensifying, yet their impact on temporal variability of grassland functioning and its drivers remains poorly understood. We imposed a 6‐year extreme drought in two semiarid grasslands to explore how drought influences the temporal variability of ANPP and identify potential stabilising mechanisms. Drought decreased ANPP while increasing its temporal variability across grasslands. In the absence of drought, ANPP variability was strongly driven by the dominant plant species (i.e., mass‐ratio effects), as captured by community‐weighted traits and species stability. However, drought decreased the dominance of perennial grasses, providing opportunities for subordinate species to alter the stability of productivity through compensatory dynamics. Specifically, under drought, species asynchrony emerged as a more important correlate of ANPP variability than community‐weighted traits or species stability. Our findings suggest that in grasslands, prolonged, extreme droughts may decrease the relative contribution of mass‐ratio effects versus compensatory dynamics to productivity stability by reducing the influence of dominant species. 

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. 

Abstract Plant populations are limited by resource availability and exhibit physiological trade‐offs in resource acquisition strategies. These trade‐offs may constrain the ability of populations to exhibit fast growth rates under water limitation and high cover of neighbours. However, traits that confer drought tolerance may also confer resistance to competition. It remains unclear how fitness responses to these abiotic conditions and biotic interactions combine to structure grassland communities and how this relationship may change along a gradient of water availability.To address these knowledge gaps, we estimated the low‐density growth rates of populations in drought conditions with low neighbour cover and in ambient conditions with average neighbour cover for 82 species in six grassland communities across the Central Plains and Southwestern United States. We assessed the relationship between population tolerance to drought and resistance to competition and determined if this relationship was consistent across a precipitation gradient. We also tested whether population growth rates could be predicted using plant functional traits.Across six sites, we observed a positive correlation between low‐density population growth rates in drought and in the presence of interspecific neighbours. This positive relationship was particularly strong in the grasslands of the northern Great Plains but weak in the most xeric grasslands. High leaf dry matter content and a low (more negative) leaf turgor loss point were associated with high population growth rates in drought and with neighbours in most grassland communities.Synthesis: A better understanding of how both biotic and abiotic factors impact population fitness provides valuable insights into how grasslands will respond to extreme drought. Our results advance plant strategy theory by suggesting that drought tolerance increases population resistance to interspecific competition in grassland communities. However, this relationship is not evident in the driest grasslands, where above‐ground competition is likely less important. Leaf dry matter content and turgor loss point may help predict which populations will establish and persist based on local water availability and neighbour cover, and these predictions can be used to guide the conservation and restoration of biodiversity in grasslands. 

Abstract 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. 

Abstract Plant traits can be helpful for understanding grassland ecosystem responses to climate extremes, such as severe drought. However, intercontinental comparisons of how drought affects plant functional traits and ecosystem functioning are rare. The Extreme Drought in Grasslands experiment (EDGE) was established across the major grassland types in East Asia and North America (six sites on each continent) to measure variability in grassland ecosystem sensitivity to extreme, prolonged drought. At all sites, we quantified community‐weighted mean functional composition and functional diversity of two leaf economic traits, specific leaf area and leaf nitrogen content, in response to drought. We found that experimental drought significantly increased community‐weighted means of specific leaf area and leaf nitrogen content at all North American sites and at the wetter East Asian sites, but drought decreased community‐weighted means of these traits at moderate to dry East Asian sites. Drought significantly decreased functional richness but increased functional evenness and dispersion at most East Asian and North American sites. Ecosystem drought sensitivity (percentage reduction in aboveground net primary productivity) positively correlated with community‐weighted means of specific leaf area and leaf nitrogen content and negatively correlated with functional diversity (i.e., richness) on an intercontinental scale, but results differed within regions. These findings highlight both broad generalities but also unique responses to drought of community‐weighted trait means as well as their functional diversity across grassland ecosystems. 

Abstract Plant traits are useful proxies of plant strategies and can influence community and ecosystem responses to climate extremes, such as severe drought. Few studies, however, have investigated both the immediate and lagged effects of drought on community‐weighted mean (CWM) plant traits, with even less research on the relative roles of interspecific vs. intraspecific trait variability in such responses.We experimentally reduced growing season precipitation by 66% in two cold‐semi‐arid grassland sites in northern China for four consecutive years to explore the drought resistance of CWM traits as well as their recovery 2 years following the drought. In addition, we isolated the effects of both interspecific and intraspecific trait variability on shifts in CWM traits.At both sites, we observed significant effects of drought on interspecific and intraspecific trait variability which, in some cases, led to significant changes in CWM traits. For example, drought led to reduced CWM plant height and leaf phosphorous content, but increased leaf carbon content at both sites, with responses primarily due to intraspecific trait shifts. Surprisingly, these CWM traits recovered completely 2 years after the extreme drought. Intraspecific trait variability influenced CWM traits via both positive and negative covariation with interspecific trait variability during drought and recovery phases.These findings highlight the important role of interspecific and intraspecific trait variability in driving the response and recovery of CWM traits following extreme, prolonged drought. Read the freePlain Language Summaryfor this article on the Journal blog.