Subtle variation in shade avoidance responses may have profound consequences for plant competitiveness

Background and Aims: Although phenotypic plasticity has been shown to be beneficial for plant competitiveness for light, there is limited knowledge on how variation in these plastic responses plays a role in determining competitiveness. Methods: A combination of detailed plant experiments and functional–structural plant (FSP) modelling was used that captures the complex dynamic feedback between the changing plant phenotype and the within-canopy light environment in time and 3-D space. Leaf angle increase (hyponasty) and changes in petiole elongation rates in response to changes in the ratio between red and far-red light, two important shade avoidance responses in Arabidopsis thaliana growing in dense population stands, were chosen as a case study for plant plasticity. Measuring and implementing these responses into an FSP model allowed simulation of plant phenotype as an emergent property of the underlying growth and response mechanisms. Key Results: Both the experimental and model results showed that substantial differences in competitiveness may arise between genotypes with only marginally different hyponasty or petiole elongation responses, due to the amplification of plant growth differences by small changes in plant phenotype. In addition, this study illustrated that strong competitive responses do not necessarily have to result in a tragedy of the commons; success in competition at the expense of community performance. Conclusions: Together, these findings indicate that selection pressure could probably have played a role in fine-tuning the sensitive shade avoidance responses found in plants. The model approach presented here provides a novel tool to analyse further how natural selection could have acted on the evolution of plastic responses

The limited importance of size-asymmetric light competition and growth of pioneer species in early secondary forest succession in Vietnam

It is generally believed that asymmetric competition for light plays a predominant role in determining the course of succession by increasing size inequalities between plants. Size-related growth is the product of size-related light capture and light-use efficiency (LUE). We have used a canopy model to calculate light capture and photosynthetic rates of pioneer species in sequential vegetation stages of a young secondary forest stand. Growth of the same saplings was followed in time as succession proceeded. Photosynthetic rate per unit plant mass (Pmass: mol C g−1 day−1), a proxy for plant growth, was calculated as the product of light capture efficiency [Φmass: mol photosynthetic photon flux density (PPFD) g−1 day−1] and LUE (mol C mol PPFD−1). Species showed different morphologies and photosynthetic characteristics, but their light-capturing and light-use efficiencies, and thus Pmass, did not differ much. This was also observed in the field: plant growth was not size-asymmetric. The size hierarchy that was present from the very early beginning of succession remained for at least the first 5 years. We conclude, therefore, that in slow-growing regenerating vegetation stands, the importance of asymmetric competition for light and growth can be much less than is often assumed

Is analysing the nitrogen use at the plant canopy level a matter of choosing the right optimization criterion?

Optimization theory in combination with canopy modeling is potentially a powerful tool for evaluating the adaptive significance of photosynthesis-related plant traits. Yet its successful application has been hampered by a lack of agreement on the appropriate optimization criterion. Here we review how models based on different types of optimization criteria have been used to analyze traits—particularly N reallocation and leaf area indices—that determine photosynthetic nitrogen-use efficiency at the canopy level. By far the most commonly used approach is static-plant simple optimization (SSO). Static-plant simple optimization makes two assumptions: (1) plant traits are considered to be optimal when they maximize whole-stand daily photosynthesis, ignoring competitive interactions between individuals; (2) it assumes static plants, ignoring canopy dynamics (production and loss of leaves, and the reallocation and uptake of nitrogen) and the respiration of nonphotosynthetic tissue. Recent studies have addressed either the former problem through the application of evolutionary game theory (EGT) or the latter by applying dynamic-plant simple optimization (DSO), and have made considerable progress in our understanding of plant photosynthetic traits. However, we argue that future model studies should focus on combining these two approaches. We also point out that field observations can fit predictions from two models based on very different optimization criteria. In order to enhance our understanding of the adaptive significance of photosynthesis-related plant traits, there is thus an urgent need for experiments that test underlying optimization criteria and competing hypotheses about underlying mechanisms of optimization

Understanding and optimizing species mixtures using functional–structural plant modelling

Plant species mixtures improve productivity over monocultures by exploiting species complementarities for resource capture in time and space. Complementarity results in part from competition avoidance responses that maximize resource capture and growth of individual plants. Individual organs accommodate to local resource levels, e.g. with regard to nitrogen content and photosynthetic capacity or by size (e.g. shade avoidance). As a result, the resource acquisition in time and space is improved and performance of the community as a whole is increased. Modelling is needed to unravel the primary drivers and subsequent dynamics of complementary growth responses in mixtures. Here, we advocate using functional–structural plant (FSP) modelling to analyse the functioning of plant mixtures. In FSP modelling, crop performance is a result of the behaviour of the individual plants interacting through competitive and complementary resource acquisition. FSP models can integrate the interactions between structural and physiological plant responses to the local resource availability and strength of competition, which drive resource capture and growth of individuals in species mixtures. FSP models have the potential to accelerate mixed-species plant research, and thus support the development of knowledge that is needed to promote the use of mixtures towards sustainably increasing crop yields at acceptable input levels

Testing for disconnection and distance effects on physiological self-recognition within clonal fragments of Potentilla reptans

Evidence suggests that belowground self-recognition in clonal plants can be disrupted between sister ramets by the loss of connections or long distances within a genet. However, these results may be confounded by severing connections between ramets in the setups. Using Potentilla reptans, we examined severance effects in a setup that grew ramet pairs with connections either intact or severed. We showed that severance generally reduced new stolon mass but had no effect on root allocation of ramets. However, it did reduce root mass of younger ramets of the pairs. We also explored evidence for physiological self-recognition with another setup that avoided severing connections by manipulating root interactions between closely connected ramets, between remotely connected ramets and between disconnected ramets within one genet. We found that ramets grown with disconnected neighbors had less new stolon mass, similar root mass but higher root allocation as compared to ramets grown with connected neighbors. There was no difference in ramet growth between closely connected- and remotely connected-neighbor treatments. We suggest that severing connections affects ramet interactions by disrupting their physiological integration. Using the second setup, we provide unbiased evidence for physiological self-recognition, while also suggesting that it can persist over long distances

BAAD: a Biomass And Allometry Database for woody plants

Understanding how plants are constructed—i.e., how key size dimensions and the amount of mass invested in different tissues varies among individuals—is essential for modeling plant growth, carbon stocks, and energy fluxes in the terrestrial biosphere. Allocation patterns can differ through ontogeny, but also among coexisting species and among species adapted to different environments. While a variety of models dealing with biomass allocation exist, we lack a synthetic understanding of the underlying processes. This is partly due to the lack of suitable data sets for validating and parameterizing models. To that end, we present the Biomass And Allometry Database (BAAD) for woody plants. The BAAD contains 259 634 measurements collected in 176 different studies, from 21 084 individuals across 678 species. Most of these data come from existing publications. However, raw data were rarely made public at the time of publication. Thus, the BAAD contains data from different studies, transformed into standard units and variable names. The transformations were achieved using a common workflow for all raw data files. Other features that distinguish the BAAD are: (i) measurements were for individual plants rather than stand averages; (ii) individuals spanning a range of sizes were measured; (iii) plants from 0.01–100 m in height were included; and (iv) biomass was estimated directly, i.e., not indirectly via allometric equations (except in very large trees where biomass was estimated from detailed sub‐sampling). We included both wild and artificially grown plants. The data set contains the following size metrics: total leaf area; area of stem cross‐section including sapwood, heartwood, and bark; height of plant and crown base, crown area, and surface area; and the dry mass of leaf, stem, branches, sapwood, heartwood, bark, coarse roots, and fine root tissues. We also report other properties of individuals (age, leaf size, leaf mass per area, wood density, nitrogen content of leaves and wood), as well as information about the growing environment (location, light, experimental treatment, vegetation type) where available. It is our hope that making these data available will improve our ability to understand plant growth, ecosystem dynamics, and carbon cycling in the world\u27s vegetation

Geriatric Screening, Triage Urgency, and 30-Day Mortality in Older Emergency Department Patients

BACKGROUND: Urgency triage in the emergency department (ED) is important for early identification of potentially lethal conditions and extensive resource utilization. However, in older patients, urgency triage systems could be improved by taking geriatric vulnerability into account. We investigated the association of geriatric vulnerability screening in addition to triage urgency levels with 30-day mortality in older ED patients. DESIGN: Secondary analysis of the observational multicenter Acutely Presenting Older Patient (APOP) study. SETTING: EDs within four Dutch hospitals. PARTICIPANTS: Consecutive patients, aged 70 years or older, who were prospectively included. MEASUREMENTS: Patients were triaged using the Manchester Triage System (MTS). In addition, the APOP screener was used as a geriatric screening tool. The primary outcome was 30-day mortality. Comparison was made between mortality within the geriatric high- and low-risk screened patients in every urgency triage category. We calculated the difference in explained variance of mortality by adding the geriatric screener (APOP) to triage urgency (MTS) by calculating Nagelkerke R2. RESULTS: We included 2,608 patients with a median age of 79 (interquartile range = 74-84) years, of whom 521 (20.0%) patients were categorized as high risk accor

Geriatric screening, fall characteristics and 3-and 12 months adverse outcomes in older patients visiting the emergency department with a fall

Background Falls in older Emergency Department (ED) patients may indicate underlying frailty. Geriatric follow-up might help improve outcomes in addition to managing the direct cause and consequence of the fall. We aimed to study whether fall characteristics and the result of geriatric screening in the ED are independently related to adverse outcomes in older patients with fall-related ED visits. Methods This was a secondary analysis of the observational multicenter Acutely Presenting Older Patient (APOP) study, of which a subset of patients aged >= 70 years with fall-related ED visits were prospectively included in EDs of two Dutch hospitals. Fall characteristics (cause and location) were retrospectively collected. The APOP-screener was used as a geriatric screening tool. The outcome was 3- and 12-months functional decline and mortality. We assessed to what extent fall characteristics and the geriatric screening result were independent predictors of the outcome, using multivariable logistic regression analysis. Results We included 393 patients (median age 80 (IQR 76-86) years) of whom 23.0% were high risk according to screening. The cause of the fall was extrinsic (49.6%), intrinsic (29.3%), unexplained (6.4%) or missing (14.8%). A high risk geriatric screening result was related to increased risk of adverse outcomes (3-months adjusted odds ratio (AOR) 2.27 (1.29-3.98), 12-months AOR 2.20 (1.25-3.89)). Independent of geriatric screening result, an intrinsic cause of the fall increased the risk of 3-months adverse outcomes (AOR 1.92 (1.13-3.26)) and a fall indoors increased the risk of 3-months (AOR 2.14 (1.22-3.74)) and 12-months adverse outcomes (AOR 1.78 (1.03-3.10)). Conclusions A high risk geriatric screening result and fall characteristics were both independently associated with adverse outcomes in older ED patients, suggesting that information on both should be evaluated to guide follow-up geriatric assessment and interventions in clinical care.Public Health and primary careGeriatrics in primary car

BAAD: A biomass and allometry database for woody plants.

Understanding how plants are constructed—i.e., how key size dimensions and the amount of mass invested in different tissues varies among individuals—is essential for modeling plant growth, carbon stocks, and energy fluxes in the terrestrial biosphere. Allocation patterns can differ through ontogeny, but also among coexisting species and among species adapted to different environments. While a variety of models dealing with biomass allocation exist, we lack a synthetic understanding of the underlying processes. This is partly due to the lack of suitable data sets for validating and parameterizing models. To that end, we present the Biomass And Allometry Database (BAAD) for woody plants. The BAAD contains 259 634 measurements collected in 176 different studies, from 21 084 individuals across 678 species. Most of these data come from existing publications. However, raw data were rarely made public at the time of publication. Thus, the BAAD contains data from different studies, transformed into standard units and variable names. The transformations were achieved using a common workflow for all raw data files. Other features that distinguish the BAAD are: (i) measurements were for individual plants rather than stand averages; (ii) individuals spanning a range of sizes were measured; (iii) plants from 0.01– 100 m in height were included; and (iv) biomass was estimated directly, i.e., not indirectly via allometric equations (except in very large trees where biomass was estimated from detailed sub-sampling). We included both wild and artificially grown plants. The data set contains the following size metrics: total leaf area; area of stem cross-section including sapwood, heartwood, and bark; height of plant and crown base, crown area, and surface area; and the dry mass of leaf, stem, branches, sapwood, heartwood, bark, coarse roots, and fine root tissues. We also report other properties of individuals (age, leaf size, leaf mass per area, wood density, nitrogen content of leaves and wood), as well as information about the growing environment (location, light, experimental treatment, vegetation type) where available. It is our hope that making these data available will improve our ability to understand plant growth, ecosystem dynamics, and carbon cycling in the world’s vegetation.EEA Santa CruzFil: Falster, Daniel S. Macquarie University. Biological Sciences; Australia.Fil: Duursma, Remko A. University of Western Sydney. Hawkesbury Insitute for the Environment; Australia.Fil: Ishihara, Masae I. Hiroshima University. Graduate School for International Development and Cooperation; Japón.Fil: Barneche, Diego R. Macquarie University. Biological Sciences; Australia.Fil: FitzJohn, Richard G. Macquarie University. Biological Sciences; Australia.Fil: Vårhammar, Angelica. University of Western Sydney. Hawkesbury Insitute for the Environment; Australia.Fil: Aiba, Masahiro. Tohoku University. Graduate School of Life Sciences; Japón.Fil: Ando, Makoto. Kyoto University. Field Science Education and Research Center; JapónFil: Anten, Niels. Centre for Crop Systems Analysis; Países BajosFil: Aspinwall, Michael J. University of Western Sydney. Hawkesbury Insitute for the Environment; Australia.Fil: Gargaglione Verónica Beatriz. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Gargaglione Verónica Beatriz. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: York, Robert A. University of California Berkeley. Center for Forestry; Estados Unido