Using plant traits to compare sward structure and composition of grass species across environmental gradients

2004 ◽  
Vol 7 (1) ◽  
pp. 11-18 ◽  
Author(s):  
M. Duru ◽  
P. Cruz ◽  
D. Magda
CATENA ◽  
2016 ◽  
Vol 147 ◽  
pp. 755-763 ◽  
Author(s):  
Mulatie Mekonnen ◽  
Saskia D. Keesstra ◽  
Coen J. Ritsema ◽  
Leo Stroosnijder ◽  
Jantiene E.M. Baartman

2021 ◽  
Author(s):  
Emily K. Bechtold ◽  
Klaus Nüsslein

Benefits leaf bacterial communities provide to plant hosts are reduced by external stress. Understanding how plant hosts impact phyllosphere community assembly, how microbes influence plant traits, and how this interaction changes under stress will advance our insight into the evolutionary relationship between plants and their microbial communities. We investigated phyllosphere community assembly change over time, between host species, and under drought stress on three native temperate grasses and three non-native tropical grasses. By growing them together, effects of host geography and differences in environmental variables were eliminated allowing us to test evolutionary history on community assembly. We found evidence of phylosymbiosis which increased significantly under drought stress, indicating phyllosphere communities and their response to stress relate to grass species phylogeny. We also show native temperate grasses displayed stronger cophylogenetic relationships between grass hosts and their microbial communities and had increased selection by host species over time compared to non-native tropical hosts. Interestingly, the functional marker gene nifH, though differentially present on all host species was not susceptible to drought. The evidence of shared evolutionary history, presence of functionally important bacteria, and responses to drought suggest that microbial communities are important plant traits that coevolve alongside their plant hosts.


Author(s):  
T.J. Gilliland ◽  
T. Ball ◽  
D. Hennessy

This review addresses key factors and impediments that govern the efficient transfer of nutrient energy from primary producing grassland to ruminant milk and meat. The review focuses on permanent improved grasslands, defined as “swards maintained at a high production potential by grass-to-grass renewal”, frequently of a 5- to 10-yr longevity. Breeding progress to date is examined as are the primary objectives for the next generation of cultivars. This involves aligning grass productivity to ruminant demand in three primary aspects, namely intake potential, nutritional value and productivity profile. The opportunity to selectively improve plant traits affecting sward structure, chemical composition, seasonality and ability to persist and perform under farm conditions is evaluated. The EU context involves appraising the impact of variables such as grass species and cultivar, regional abiotic stresses (water, temperature, nutrients, soil type, etc.), biotic stresses from disease and pests, regional diversity in sward management strategies, and the opportunity to minimise the environmental footprint of ruminant farming.


2001 ◽  
Vol 31 (1) ◽  
pp. 159-168 ◽  
Author(s):  
Luís Sangoi

Maize is the agronomic grass species that is most sensitive to variations in plant density. For each production system, there is a population that maximizes grain yield. This article presents an overview of the factors that affect optimum plant population, emphasizingthe effects of dense stands on ear development and discussing important changes in plant traits that have contributed to increase the tolerance of modern hybrids to high plant densities. Population for maize maximum economic grain yield varies from 30,000 to over 90,000pl.ha-1, depending on water availability, soil fertility, maturity rating, planting date and row spacing. When the number of individuals per area is increased beyond the optimum plant density, there is a series of consequences that are detrimental to ear ontogeny and result in barrenness. First, ear differentiation is delayed in relation to tassel differentiation. Later-initiated earshoots have a reduced growth rate, resulting in fewer spikelet primordia transformed into functional florets by the time of flowering. Functional florets extrude silks slowly, decreasing the number of fertilized spikelets due to the lack of synchrony between anthesis and silking. Limitations in carbon and nitrogen supply to the ear stimulate young kernel abortion immediately after fertilization. Availability of earlier hybrids, with shorter plant height, lower leaf number, upright leaves, smaller tassels and better synchrony between male and female flowering time has enhanced the ability of maize to face high plant populations without showing excessive barrenness. Improved endurance in high stands has allowed maize to intercept and use solar radiation more efficiently, contributing to the remarkable increase in grain yield potential experienced by this crop.


2019 ◽  
Author(s):  
Chenyong Miao ◽  
Thomas P. Hoban ◽  
Alejandro Pages ◽  
Zheng Xu ◽  
Eric Rodene ◽  
...  

ABSTRACTAutomatically scoring plant traits using a combination of imaging and deep learning holds promise to accelerate data collection, scientific inquiry, and breeding progress. However, applications of this approach are currently held back by the availability of large and suitably annotated training datasets. Early training datasets targeted arabidopsis or tobacco. The morphology of these plants quite different from that of grass species like maize. Two sets of maize training data, one real-world and one synthetic were generated and annotated for late vegetative stage maize plants using leaf count as a model trait. Convolutional neural networks (CNNs) trained on entirely synthetic data provided predictive power for scoring leaf number in real-world images. This power was less than CNNs trained with equal numbers of real-world images, however, in some cases CNNs trained with larger numbers of synthetic images outperformed CNNs trained with smaller numbers of real-world images. When real-world training images were scarce, augmenting real-world training data with synthetic data provided improved prediction accuracy. Quantifying leaf number over time can provide insight into plant growth rates and stress responses, and can help to parameterize crop growth models. The approaches and annotated training data described here may help future efforts to develop accurate leaf counting algorithms for maize.


2013 ◽  
Vol 10 (8) ◽  
pp. 5497-5515 ◽  
Author(s):  
L. M. Verheijen ◽  
V. Brovkin ◽  
R. Aerts ◽  
G. Bönisch ◽  
J. H. C. Cornelissen ◽  
...  

Abstract. In many current dynamic global vegetation models (DGVMs), including those incorporated into Earth system models (ESMs), terrestrial vegetation is represented by a small number of plant functional types (PFTs), each with fixed properties irrespective of their predicted occurrence. This contrasts with natural vegetation, in which many plant traits vary systematically along geographic and environmental gradients. In the JSBACH DGVM, which is part of the MPI-ESM, we allowed three traits (specific leaf area (SLA), maximum carboxylation rate at 25 °C (Vcmax25) and maximum electron transport rate at 25 °C (Jmax25)) to vary within PFTs via trait–climate relationships based on a large trait database. The R2adjusted of these relationships were up to 0.83 and 0.71 for Vcmax25 and Jmax25, respectively. For SLA, more variance remained unexplained, with a maximum R2adjusted of 0.40. Compared to the default simulation, allowing trait variation within PFTs resulted in gross primary productivity differences of up to 50% in the tropics, in > 35% different dominant vegetation cover, and a closer match with a natural vegetation map. The discrepancy between default trait values and natural trait variation, combined with the substantial changes in simulated vegetation properties, together emphasize that incorporating climate-driven trait variation, calibrated on observational data and based on ecological concepts, allows more variation in vegetation responses in DGVMs and as such is likely to enable more reliable projections in unknown climates.


2011 ◽  
Vol 38 (7) ◽  
pp. 594 ◽  
Author(s):  
Meisha-Marika Holloway-Phillips ◽  
Timothy J. Brodribb

Plant traits that improve crop water use efficiency are highly sought after but difficult to isolate. Here, we examine the integrated function of xylem and stomata in closely related forage grasses to determine whether quantitative differences in water transport properties could be used to predict plant performance under limited water conditions. Cultivars of two forage grass species with different drought tolerance ratings, Lolium multiflorum Lam. and Festuca arundinacea Schreb., were assessed for maximum hydraulic conductivity (Kmax), vulnerability of xylem to hydraulic dysfunction (P50) and stomatal sensitivity to leaf water potential. Species-specific differences were observed in several of these traits, and their effect on whole-plant performance was examined under well-watered and restricted watering conditions. It was shown that although P50 was comparable between species, for F. arundinacea cultivars, there was greater hydraulic risk associated with reduced stomatal sensitivity to leaf hydration. In contrast, L. multiflorum cultivars expressed a higher capacity for water transport, but more conservative stomatal regulation. Despite different susceptibilities to leaf damage observed during acute drought, under the sustained moderate drought treatment, the two strategies were balanced in terms of water conservation and hydraulic utilisation, resulting in similar dry matter production. Characterisation of water use patterns according to the key hydraulic parameters is discussed in terms of implications to yield across different environmental scenarios as well as the applicability of water transport related traits to breeding programs.


2018 ◽  
Author(s):  
Legay Nicolas ◽  
Grassein Fabrice ◽  
Arnoldi Cindy ◽  
Segura Raphaël ◽  
Laîné Philippe ◽  
...  

AbstractThe leaf economics spectrum (LES) is based on a suite of leaf traits related to plant functioning and ranges from resource-conservative to resource-acquisitive strategies. However, the relationships with root traits, and the associated belowground plant functioning such as N uptake, including nitrate (NO3-) and ammonium (NH4+), is still poorly known. Additionally, environmental variations occurring both in time and in space could uncouple LES from root traits. We explored, in subalpine grasslands, the relationships between leaf and root morphological traits for 3 dominant perennial grass species, and to what extent they contribute to the whole-plant economics spectrum. We also investigated the link between this spectrum and NO3- and NH4+ uptake rates, as well as the variations of uptake across four grasslands differing by the land-use history at peak biomass and in autumn. Although poorly correlated with leaf traits, root traits contributed to an economic spectrum at the whole plant level. Higher NH4+ and NO3- uptake abilities were associated with the resource-acquisitive strategy.Nonetheless, NH4+ and NO3- uptake within species varied between land-uses and with sampling time, suggesting that LES and plant traits are good, but still incomplete, descriptors of plant functioning. Although the NH4+: NO3- uptake ratio was different between plant species in our study, they all showed a preference for NH4+, and particularly the most conservative species. Soil environmental variations between grasslands and sampling times may also drive to some extent the NH4+ and NO3- uptake ability of species. Our results support the current efforts to build a more general framework including above- and below-ground processes when studying plant community functioning.


Author(s):  
Meghna Krishnadas

Species traits influence their response to environmental conditions and the match between phenotypes and environment mediates spatial changes in species composition. These trait-environment linkages can be disrupted in human-modified landscapes. Human land-use creates habitat fragments where dispersal limitation or edge effects can exclude species that may otherwise suit a given macro-scale environment. Furthermore, stressful micro-environments in fragments may limit viable trait combinations resulting in stronger trait covariance compared to contiguous forest, especially in harsher macroenvironments. In a wet tropical forest landscape in the Western Ghats Biodiversity Hotspot of peninsular India, I compared fragments with adjacent contiguous forest for signatures of trait-mediated assembly of tree communities along macroenvironmental gradients. Using four key plant traits—seed size, specific leaf area (SLA), wood density, and maximum height—I evaluated changes in trait-mediated abundances and trait covariance across environmental gradients. Trait-mediated abundances primarily changed along the elevation gradient in contiguous forest, smaller-seeded, shorter, thinner-leaved species increased at higher elevations. In fragments, higher SLA species increased in more seasonal climate and decreased with higher precipitation, and larger seeds decreased at warmer sites. However, traits only weakly predicted abundances and only contiguous forests experienced significant compositional change via traits, driven by trait syndromes varying along a composite environmental gradient defined by elevation, water deficit, and soil C:N ratio. Covariance of seed size and maximum height along gradients of precipitation and temperature revealed divergent constraints on viable phenotypes in fragments and contiguous forest. Notably, local biotic conditions (functional diversity) had stronger effects than environment in explaining trait covariance. Overall, the results imply that trait syndromes and trait covariance, rather than single traits, determine the phenotypes best suited to different macroenvironmental conditions and should inform management or restoration goals in fragments.


2012 ◽  
Vol 9 (12) ◽  
pp. 18907-18950 ◽  
Author(s):  
L. M. Verheijen ◽  
V. Brovkin ◽  
R. Aerts ◽  
G. Bönisch ◽  
J. H. C. Cornelissen ◽  
...  

Abstract. In current dynamic global vegetation models (DGVMs), including those incorporated into Earth System Models (ESMs), terrestrial vegetation is represented by a small number of plant functional types (PFTs), each with fixed properties irrespective of their predicted occurrence. This contrasts with natural vegetation, in which many plant traits vary systematically along geographic and environmental gradients. In the JSBACH DGVM, which is part of the MPI-ESM, we allowed three traits (specific leaf area (SLA), maximum carboxylation rate at 25 °C (Vcmax25) and maximum electron transport rate (Jmax25)) to vary within PFTs via trait-climate relationships based on a large trait database. For all three traits, the means of observed natural trait values strongly deviated from values used in the default model, with mean differences of 32.3% for Vcmax25, 26.8% for Jmax25 and 17.3% for SLA. Compared to the default simulation, allowing trait variation within PFTs resulted in GPP differences up to 50% in the tropics, in > 35% different dominant vegetation cover, and a closer match with a natural vegetation map. The discrepancy between default trait values and natural trait variation, combined with the substantial changes in simulated vegetation properties, together emphasize that incorporating observational data based on the ecological concepts of environmental filtering will improve the modeling of vegetation behavior in DGVMs and as such will enable more reliable projections in unknown climates.


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