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Resilience is a major research focus covering a wide range of topics from biodiversity conservation to ecosystem (service) management. Model simulations can assess the resilience of, for example, plant species, measured as the return time to conditions prior to a disturbance. This requires process-based models (PBM) that implement relevant processes such as regeneration and reproduction and thus successfully reproduce transient dynamics after disturbances. Such models are often complex and thus limited to either short-term or small-scale applications, whereas many research questions require species predictions across larger spatial and temporal scales. We suggest a framework to couple a PBM and a statistical species distribution model (SDM), which transfers the results of a resilience analysis by the PBM to SDM predictions. The resulting hybrid model combines the advantages of both approaches: the convenient applicability of SDMs and the relevant process detail of PBMs in abrupt environmental change situations. First, we simulate dynamic responses of species communities to a disturbance event with a PBM. We aggregate the response behavior in two resilience metrics: return time and amplitude of the response peak. These metrics are then used to complement long-term SDM projections with dynamic short-term responses to disturbance. To illustrate our framework, we investigate the effect of abrupt short-term groundwater level and salinity changes on coastal vegetation at the German Baltic Sea. We found two example species to be largely resilient, and, consequently, modifications of SDM predictions consisted mostly of smoothing out peaks in the occurrence probability that were not confirmed by the PBM. Discrepancies between SDM- and PBM-predicted species responses were caused by community dynamics simulated in the PBM and absent from the SDM. Although demonstrated with boosted regression trees (SDM) and an existing individual-based model, IBC-grass (PBM), our flexible framework can easily be applied to other PBM and SDM types, as well as other definitions of short-term disturbances or long-term trends of environmental change. Thus, our framework allows accounting for biological feedbacks in the response to short- and long-term environmental changes as a major advancement in predictive vegetation modeling.
EXO modifies sucrose and trehalose responses and connects the extracellular carbon status to growth
(2013)
Plants have the capacity to adapt growth to changing environmental conditions. This implies the modulation of metabolism according to the availability of carbon (C). Particular interest in the response to the C availability is based on the increasing atmospheric levels of CO2. Several regulatory pathways that link the C status to growth have emerged. The extracellular EXO protein is essential for cell expansion and promotes shoot and root growth. Homologous proteins were identified in evolutionarily distant green plants. We show here that the EXO protein connects growth with C responses. The exo mutant displayed altered responses to exogenous sucrose supplemented to the growth medium. Impaired growth of the mutant in synthetic medium was associated with the accumulation of starch and anthocyanins, altered expression of sugar-responsive genes, and increased abscisic acid levels. Thus, EXO modulates several responses related to the C availability. Growth retardation on medium supplemented with 2-deoxy-glucose, mannose, and palatinose was similar to the wildtype. Trehalose feeding stimulated root growth and shoot biomass production of exoplants where as it inhibited growth of the wildtype. The phenotypic features of the exo mutant suggest that apoplastic processes coordinate growth and C responses.
SDM performance varied for different range dynamics. Prediction accuracies decreased when abrupt range shifts occurred as species were outpaced by the rate of climate change, and increased again when a new equilibrium situation was realised. When ranges contracted, prediction accuracies increased as the absences were predicted well. Far- dispersing species were faster in tracking climate change, and were predicted more accurately by SDMs than short- dispersing species. BRTs mostly outperformed GLMs. The presence of a predator, and the inclusion of its incidence as an environmental predictor, made BRTs and GLMs perform similarly. Results are discussed in light of other studies dealing with effects of ecological traits and processes on SDM performance. Perspectives are given on further advancements of SDMs and for possible interfaces with more mechanistic approaches in order to improve predictions under environmental change.
Little is known about genes that control growth and development under low carbon (C) availability. The Arabidopsis (Arabidopsis thaliana) EXORDIUM-LIKE1 (EXL1) gene (At1g35140) was identified as a brassinosteroid-regulated gene in a previous study. We show here that the EXL1 protein is required for adaptation to C-and energy-limiting growth conditions. In-depth analysis of EXL1 transcript levels under various environmental conditions indicated that EXL1 expression is controlled by the C and energy status. Sugar starvation, extended night, and anoxia stress induced EXL1 gene expression. The C status also determined EXL1 protein levels. These results suggested that EXL1 is involved in the C-starvation response. Phenotypic changes of an exl1 loss-of-function mutant became evident only under corresponding experimental conditions. The mutant showed diminished biomass production in a short-day/low-light growth regime, impaired survival during extended night, and impaired survival of anoxia stress. Basic metabolic processes and signaling pathways are presumed to be barely impaired in exl1, because the mutant showed wild-type levels of major sugars, and transcript levels of only a few genes such as QUA-QUINE STARCH were altered. Our data suggest that EXL1 is part of a regulatory pathway that controls growth and development when C and energy supply is poor.