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Within the field of species distribution modelling an apparent dichotomy exists between process-based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlativeprocess spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the processcorrelation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process-based approaches to species distribution modelling lags far behind more correlative (process-implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process-explicit species distribution models and how they may complement current approaches to study species distributions.
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.
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.
How to understand species' niches and range dynamics: a demographic research agenda for biogeography
(2012)
Range dynamics causes mismatches between a species geographical distribution and the set of suitable environments in which population growth is positive (the Hutchinsonian niche). This is because sourcesink population dynamics cause species to occupy unsuitable environments, and because environmental change creates non-equilibrium situations in which species may be absent from suitable environments (due to migration limitation) or present in unsuitable environments that were previously suitable (due to time-delayed extinction). Because correlative species distribution models do not account for these processes, they are likely to produce biased niche estimates and biased forecasts of future range dynamics. Recently developed dynamic range models (DRMs) overcome this problem: they statistically estimate both range dynamics and the underlying environmental response of demographic rates from species distribution data. This process-based statistical approach qualitatively advances biogeographical analyses. Yet, the application of DRMs to a broad range of species and study systems requires substantial research efforts in statistical modelling, empirical data collection and ecological theory. Here we review current and potential contributions of these fields to a demographic understanding of niches and range dynamics. Our review serves to formulate a demographic research agenda that entails: (1) advances in incorporating process-based models of demographic responses and range dynamics into a statistical framework, (2) systematic collection of data on temporal changes in distribution and abundance and on the response of demographic rates to environmental variation, and (3) improved theoretical understanding of the scaling of demographic rates and the dynamics of spatially coupled populations. This demographic research agenda is challenging but necessary for improved comprehension and quantification of niches and range dynamics. It also forms the basis for understanding how niches and range dynamics are shaped by evolutionary dynamics and biotic interactions. Ultimately, the demographic research agenda should lead to deeper integration of biogeography with empirical and theoretical ecology.
Movement of organisms is one of the key mechanisms shaping biodiversity, e.g. the distribution of genes, individuals and species in space and time. Recent technological and conceptual advances have improved our ability to assess the causes and consequences of individual movement, and led to the emergence of the new field of ‘movement ecology’. Here, we outline how movement ecology can contribute to the broad field of biodiversity research, i.e. the study of processes and patterns of life among and across different scales, from genes to ecosystems, and we propose a conceptual framework linking these hitherto largely separated fields of research. Our framework builds on the concept of movement ecology for individuals, and demonstrates its importance for linking individual organismal movement with biodiversity. First, organismal movements can provide ‘mobile links’ between habitats or ecosystems, thereby connecting resources, genes, and processes among otherwise separate locations. Understanding these mobile links and their impact on biodiversity will be facilitated by movement ecology, because mobile links can be created by different modes of movement (i.e., foraging, dispersal, migration) that relate to different spatiotemporal scales and have differential effects on biodiversity. Second, organismal movements can also mediate coexistence in communities, through ‘equalizing’ and ‘stabilizing’ mechanisms. This novel integrated framework provides a conceptual starting point for a better understanding of biodiversity dynamics in light of individual movement and space-use behavior across spatiotemporal scales. By illustrating this framework with examples, we argue that the integration of movement ecology and biodiversity research will also enhance our ability to conserve diversity at the genetic, species, and ecosystem levels.
The NFX1-LIKE1 (NFXL1) and NFXL2 genes were identified as regulators of salt stress responses. The NFXL1 protein is a nuclear factor that positively affects adaptation to salt stress. The nfxl1-1 loss-of-function mutant displayed reduced survival rates under salt and high light stress. In contrast, the nfxl2-1 mutant, defective in the NFXL2 gene, and NFXL2-antisense plants exhibited enhanced survival under these conditions. We show here that the loss of NFXL2 function results in abscisic acid (ABA) overaccumulation, reduced stomatal conductance, and enhanced survival under drought stress. The nfxl2-1 mutant displayed reduced stomatal aperture under all conditions tested. Fusicoccin treatment, exposition to increasing light intensities, and supply of decreasing CO2 concentrations demonstrated full opening capacity of nfxl2-1 stomata. Reduced stomatal opening presumably is a consequence of elevated ABA levels. Furthermore, seedling growth, root growth, and stomatal closure were hypersensitive to exogenous ABA. The enhanced ABA responses may contribute to the improved drought stress resistance of the mutant. Three NFXL2 splice variants were cloned and named NFXL2-78, NFXL2-97, and NFXL2-100 according to the molecular weight of the putative proteins. Translational fusions to the green fluorescent protein suggest nuclear localisation of the NFXL2 proteins. Stable expression of the NFXL2-78 splice variant in nfxl2-1 plants largely complemented the mutant phenotype. Our data show that NFXL2 controls ABA levels and suppresses ABA responses. NFXL2 may prevent unnecessary and costly stress adaptation under favourable conditions.
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.