Consequences of architecture and resource allocation for growth dynamics of bunchgrass clones.

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2005.In order to understand how bunchgrasses achieve dominance over other plant growth forms and how they achieve dominance over one another in different environments, it is first necessary to develop a detailed understanding of how their growth strategy interacts with the resource limits of their environment. Two properties which have been studied separately in limited detail are architecture and disproportionate resource allocation. Architecture is the structural layout of organs and objects at different hierarchical levels. Disproportionate resource allocation is the manner in which resources are allocated across objects at each level of hierarchy. Clonal architecture and disproportionate resource allocation may interact significantly to determine the growth ability of clonal plants. These interactions have not been researched in bunchgrasses. This thesis employs a novel simulation technique, functional-structural plant modelling, to investigate how bunchgrasses interact with the resource constraints imposed in humid grasslands. An appropriate functional-structural plant model, the TILLERTREE model, is developed that integrates the architectural growth of bunchgrasses with environmental resource capture and disproportionate resource allocation. Simulations are conducted using a chosen model species Themeda triandra, and the environment is parameterised using characteristics of the Southern Tall Grassveld, a humid grassland type found in South Africa. Behaviour is considered at two levels, namely growth of single ramets and growth of multiple ramets on single bunchgrass clones. In environments with distinct growing and non-growing seasons, bunchgrasses are subjected to severe light depletion during regrowth at the start of each growing season because of the accumulation of dead material in canopy caused by the upright, densely packed manner in which they grow. Simulations conducted here indicate that bunchgrass tillers overcome this resource bottleneck through structural adaptations (etiolation, nonlinear blade mass accretion, residual live photosynthetic surface) and disproportionate resource allocation between roots and shoots of individual ramets that together increase the temporal resource efficiency of ramets by directing more resources to shoot growth and promoting extension of new leaves through the overlying dead canopy. The architectural arrangement of bunchgrasses as collections of tillers and ramets directly leads to consideration of a critical property of clonal bunchgrasses: tiller recruitment. Tiller recruitment is a fundamental discrete process limiting the vegetative growth of bunchgrass clones. Tiller recruitment occurs when lateral buds on parent tillers are activated to grow. The mechanism that controls bud outgrowth has not been elucidated. Based on a literature review, it is here proposed that lateral bud outgrowth requires suitable signals for both carbohydrate and nitrogen sufficiency. Subsequent simulations with the model provide corroborative evidence, in that greatest clonal productivity is achieved when both signals are present. Resource allocation between live structures on clones may be distributed proportionately in response to sink demand or disproportionately in response to relative photosynthetic productivity. Model simulations indicate that there is a trade-off between total clonal growth and individual tiller growth as the level of disproportionate allocation between ramets on ramet groups and between tillers on ramets increases, because disproportionate allocation reduces tiller population size and clonal biomass, but increases individual tiller performance. Consequently it is proposed that different life strategies employed by bunchgrasses, especially annual versus perennial life strategies, may follow more proportionate and less proportionate allocation strategies respectively, because the former favours maximal resource capture and seed production while the latter favours individual competitive ability. Structural disintegration of clones into smaller physiologically integrated units (here termed ramet groups) that compete with one another for resources is a documented property of bunchgrasses. Model simulations in which complete clonal integration is enforced are unable to survive for long periods because resource bottlenecks compromise all structures equally, preventing them from effectively overcoming resource deficits during periods when light is restrictive to growth. Productivity during the period of survival is also reduced on bunchgrass clones with full integration relative to clones that disintegrate because of the inefficient allocation of resources that arises from clonal integration. This evidence indicates that clonal disintegration allows bunchgrass clones both to increase growth efficiency and pre-empt potential death, by promoting the survival of larger ramet groups and removing smaller ramet groups from the system. The discrete nature of growth in bunchgrasses and the complex population dynamics that arise from the architectural growth and the temporal resource dynamics of the environment, may explain why different bunchgrass species dominate under different environments. In the final section this idea is explored by manipulating two species tiller traits that have been shown to be associated with species distributions across non-selective in defoliation regimes, namely leaf organ growth rate and tiller size (mass or height). Simulations with these properties indicate that organ growth rate affects daily nutrient demands and therefore the rate at which tillers are terminated, but had only a small effect on seasonal resource capture. Tiller mass size affects the size of the live tiller population where smaller tiller clones maintain greater numbers of live tillers, which allows them to them to sustain greater biomass over winter and therefore to store more reserves for spring regrowth, suggesting that size may affect seasonal nitrogen capture. The greatest differences in clonal behaviour are caused by tiller height, where clones with shorter tillers accumulate substantially more resources than clones with taller tillers. This provides strong evidence there is trade-off for bunchgrasses between the ability to compete for light and the ability to compete for nitrogen, which arises from their growth architecture. Using this evidence it is proposed that bunchgrass species will be distributed across environments in response to the nitrogen productivity. Shorter species will dominate at low nitrogen productivity, while taller species dominate at high nitrogen productivity. Empirical evidence is provided in support of this proposal

Compositional patterns of overstorey and understorey woody communities in a forest–savanna boundary in Ghana

Background: Forest and savanna vegetation in the zone of transition (ZOT) contain distinct woody species due to fire, drought and herbivory barriers that constrain forest species from invading adjacent savannas and vice-versa. Little is known if these barriers cause divergence in species composition between the overstorey and understorey strata in these vegetation types. Aim: We investigated woody species composition across overstorey and understorey strata in the ZOT and explored the relationship between soil fertility and species composition patterns. Methods: We sampled overstorey and understorey woody species and determined soil nutrient concentrations in twenty-five 20 m × 20 m plots in a ZOT in Ghana. Results: Forest and savanna species dominated the overstorey and understorey of their respective environments. However, species composition was decoupled between the overstorey and understorey strata in both forest and savanna vegetations. Few savanna and forest species had individuals co-occurring in both overstorey and understorey such that ~65% of the dominant species was limited to only one stratum. Soil fertility had little effect on these patterns. Conclusion: These patterns indicate that, forest and savanna species face significant recruitment barriers in their respective environments, suggesting that requirements for juvenile establishment may differ from recruitments to the canopy layer.</p

AusTraits: a curated plant trait database for the Australian flora

INTRODUCTION AusTraits is a transformative database, containing measurements on the traits of Australia’s plant taxa, standardised from hundreds of disconnected primary sources. So far, data have been assembled from &gt; 250 distinct sources, describing &gt; 400 plant traits and &gt; 26,000 taxa. To handle the harmonising of diverse data sources, we use a reproducible workflow to implement the various changes required for each source to reformat it suitable for incorporation in AusTraits. Such changes include restructuring datasets, renaming variables, changing variable units, changing taxon names. While this repository contains the harmonised data, the raw data and code used to build the resource are also available on the project’s GitHub repository, http://traitecoevo.github.io/austraits.build/. Further information on the project is available in the associated publication and at the project website austraits.org. Falster, Gallagher et al (2021) AusTraits, a curated plant trait database for the Australian flora. Scientific Data 8: 254, https://doi.org/10.1038/s41597-021-01006-6 CONTRIBUTORS The project is jointly led by Dr Daniel Falster (UNSW Sydney), Dr Rachael Gallagher (Western Sydney University), Dr Elizabeth Wenk (UNSW Sydney), and Dr Hervé Sauquet (Royal Botanic Gardens and Domain Trust Sydney), with input from &gt; 300 contributors from over &gt; 100 institutions (see full list above). The project was initiated by Dr Rachael Gallagher and Prof Ian Wright while at Macquarie University. We are grateful to the following institutions for contributing data Australian National Botanic Garden, Brisbane Rainforest Action and Information Network, Kew Botanic Gardens, National Herbarium of NSW, Northern Territory Herbarium, Queensland Herbarium, Western Australian Herbarium, South Australian Herbarium, State Herbarium of South Australia, Tasmanian Herbarium, Department of Environment, Land, Water and Planning, Victoria. AusTraits has been supported by investment from the Australian Research Data Commons (ARDC), via their “Transformative data collections” (https://doi.org/10.47486/TD044) and “Data Partnerships” (https://doi.org/10.47486/DP720) programs; fellowship grants from Australian Research Council to Falster (FT160100113), Gallagher (DE170100208) and Wright (FT100100910), a grant from Macquarie University to Gallagher. The ARDC is enabled by National Collaborative Research Investment Strategy (NCRIS). ACCESSING AND USE OF DATA The compiled AusTraits database is released under an open source licence (CC-BY), enabling re-use by the community. A requirement of use is that users cite the AusTraits resource paper, which includes all contributors as co-authors: Falster, Gallagher et al (2021) AusTraits, a curated plant trait database for the Australian flora. Scientific Data 8: 254, https://doi.org/10.1038/s41597-021-01006-6 In addition, we encourage users you to cite the original data sources, wherever possible. Note that under the license data may be redistributed, provided the attribution is maintained. The downloads below provide the data in two formats: austraits-3.0.2.zip: data in plain text format (.csv, .bib, .yml files). Suitable for anyone, including those using Python. austraits-3.0.2.rds: data as compressed R object. Suitable for users of R (see below). Both objects contain all the data and relevant meta-data. AUSTRAITS R PACKAGE For R users, access and manipulation of data is assisted with the austraits R package. The package can both download data and provides examples and functions for running queries. STRUCTURE OF AUSTRAITS The compiled AusTraits database has the following main components: austraits ├── traits ├── sites ├── contexts ├── methods ├── excluded_data ├── taxanomic_updates ├── taxa ├── definitions ├── contributors ├── sources └── build_info These elements include all the data and contextual information submitted with each contributed datasets. A schema and definitions for the database are given in the file/component definitions, available within the download. The file dictionary.html provides the same information in textual format. Full details on each of these components and columns are contained within the definition. Similar information is available at http://traitecoevo.github.io/austraits.build/articles/Trait_definitions.html and http://traitecoevo.github.io/austraits.build/articles/austraits_database_structure.html. CONTRIBUTING We envision AusTraits as an on-going collaborative community resource that: Increases our collective understanding the Australian flora; and Facilitates accumulation and sharing of trait data; Builds a sense of community among contributors and users; and Aspires to fully transparent and reproducible research of the highest standard. As a community resource, we are very keen for people to contribute. Assembly of the database is managed on GitHub at traitecoevo/austraits.build. Here are some of the ways you can contribute: Reporting Errors: If you notice a possible error in AusTraits, please post an issue on GitHub. Refining documentation: We welcome additions and edits that make using the existing data or adding new data easier for the community. Contributing new data: We gladly accept new data contributions to AusTraits. See full instructions on how to contribute at http://traitecoevo.github.io/austraits.build/articles/contributing_data.html

AusTraits, a curated plant trait database for the Australian flora

International audienceWe introduce the austraits database-a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual-and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge