Where will species go? Incorporating new advances in climate modelling into projections of species distributions

Bioclimatic models are the primary tools for simulating the impact of climate change on species distributions. Part of the uncertainty in the output of these models results from uncertainty in projections of future climates. To account for this, studies often simulate species responses to climates predicted by more than one climate model and/or emission scenario. One area of uncertainty, however, has remained unexplored: internal climate model variability. By running a single climate model multiple times, but each time perturbing the initial state of the model slightly, different but equally valid realizations of climate will be produced. In this paper, we identify how ongoing improvements in climate models can be used to provide guidance for impacts studies. In doing so we provide the first assessment of the extent to which this internal climate model variability generates uncertainty in projections of future species distributions, compared with variability between climate models. We obtained data on 13 realizations from three climate models (three from CSIRO Mark2 v3.0, four from GISS AOM, and six from MIROC v3.2) for two time periods: current (1985–1995) and future (2025–2035). Initially, we compared the simulated values for each climate variable (P, T_max, T_min, and T_mean) for the current period to observed climate data. This showed that climates simulated by realizations from the same climate model were more similar to each other than to realizations from other models. However, when projected into the future, these realizations followed different trajectories and the values of climate variables differed considerably within and among climate models. These had pronounced effects on the projected distributions of nine Australian butterfly species when modelled using the BIOCLIM component of DIVA-GIS. Our results show that internal climate model variability can lead to substantial differences in the extent to which the future distributions of species are projected to change. These can be greater than differences resulting from between-climate model variability. Further, different conclusions regarding the vulnerability of species to climate change can be reached due to internal model variability. Clearly, several climate models, each represented by multiple realizations, are required if we are to adequately capture the range of uncertainty associated with projecting species distributions in the future.     

Breaking conceptual locks in modelling root absorption of nutrients: reopening the thermodynamic viewpoint of ion transport across the root

BackgroundThe top-down analysis of nitrate influx isotherms through the Enzyme-Substrate interpretation has not withstood recent molecular and histochemical analyses of nitrate transporters. Indeed, at least four families of nitrate transporters operating at both high and/or low external nitrate concentrations, and which are located in series and/or parallel in the different cellular layers of the mature root, are involved in nitrate uptake. Accordingly, the top-down analysis of the root catalytic structure for ion transport from the Enzyme-Substrate interpretation of nitrate influx isotherms is inadequate. Moreover, the use of the Enzyme-Substrate velocity equation as a single reference in agronomic models is not suitable in its formalism to account for variations in N uptake under fluctuating environmental conditions. Therefore, a conceptual paradigm shift is required to improve the mechanistic modelling of N uptake in agronomic models.ScopeAn alternative formalism, the Flow-Force theory, was proposed in the 1970s to describe ion isotherms based upon biophysical ‘flows and forces’ relationships of non-equilibrium thermodynamics. This interpretation describes, with macroscopic parameters, the patterns of N uptake provided by a biological system such as roots. In contrast to the Enzyme-Substrate interpretation, this approach does not claim to represent molecular characteristics. Here it is shown that it is possible to combine the Flow-Force formalism with polynomial responses of nitrate influx rate induced by climatic and in planta factors in relation to nitrate availability.ConclusionsApplication of the Flow-Force formalism allows nitrate uptake to be modelled in a more realistic manner, and allows scaling-up in time and space of the regulation of nitrate uptake across the plant growth cycle.     

Application of satellite data for investigation of dynamic processes in inland water bodies: Lake Shira (Khakasia, Siberia), a case study

This work describes avenues to use satellite information to analyse dynamic processes in aquatic ecosystems. Information for this analysis, was retrieved from AVHRR satellite sensor data. This information consisteds of time series of images of radiation temperature and turbidity. We expect this information will be of great value in analysing inland water bodies. Methods to process satellite information using original software and data processing techniques are proposed. For the investigation of the process and analyses of satellite information Shira Lake (Khakasia, Siberia) was used as a case study. To study the variability of the surface temperature and turbidity of the Lake in summer, the satellite and ground-truth data of the lake was applied. This study represents the first evaluation of the dynamic processes for Lake Shira based on satellite, ground-truth and modelling data. We developed algorithms and software to process satellite images to enable the reconstruction of time dependence of temperature and spectral reflectance of water bodies in the visible range, and to make computer-animated films visualising the spatial and temporal dynamics of the study parameters. The analyses of morphometric, meteorological and hydrological characteristics of Lake Shira have provided a realistic opportunity for processing the satellite information and to develop numerical models of variability of the hydrological regime of the lake. The results obtained demonstrate the feasibility of systematically retrieving the spatial information from the satellite data on the dynamics of the surface water temperature and of the suspended matter in the lake.     

A two population model of prion transport through a tunnelling nanotube

This article develops a two prion population model that simulates prion trafficking between an infected dendritic cell and a neuron. The situation when the two cells are connected by a tunnelling nanotube (TNT) is simulated. Two mechanisms of prion transport are considered: lateral diffusion in the TNT membrane and active actin-dependent transport inside endocytic vesicles that are propelled by myosin Va molecular motors. Analytical solutions describing prion concentrations and fluxes are obtained. Numerical results are compared with those predicted by a single prion population model that relies on a single reaction–diffusion equation and accounts for the two modes of prion transport in an effective way.     

A general model of reaction kinetics in biological systems

Dynamic mathematical models in biotechnology require, besides the information about the stoichiometry of the biological reaction system, knowledge about the reaction kinetics. Modulation phenomena like limitation, inhibition and activation occur in different forms of competition with the key enzymes responsible for the respective metabolic reaction steps. The identification of a priori unknown reaction kinetics is often a critical task due to the non-linearity and (over-) parameterization of the model equations introduced to account for all the possible modulation phenomena. The contribution of this paper is to propose a general formulation of reaction kinetics, as an extension of the Michaelis-Menten kinetics, which allows limitation/activation and inhibition effects to be described with a reduced number of parameters. The versatility of the new model structure is demonstrated with application examples.     

A simple framework to describe the regulation of gene expression in prokaryotes

Based on the bimolecular mass action law and the derived mass conservation laws, we propose a mathematical framework in order to describe the regulation of gene expression in prokaryotes. It is shown that the derived models have all the qualitative properties of the activation and inhibition regulatory mechanisms observed in experiments. The basic construction considers genes as templates for protein production, where regulation processes result from activators or repressors connecting to DNA binding sites. All the parameters in the models have a straightforward biological meaning. After describing the general properties of the basic mechanisms of positive and negative gene regulation, we apply this framework to the self-regulation of the trp operon and to the genetic switch involved in the regulation of the lac operon. One of the consequences of this approach is the existence of conserved quantities depending on the initial conditions that tune bifurcations of fixed points. This leads naturally to a simple explanation of threshold effects as observed in some experiments.     

Modelling the establishment and spread of autotetraploid plants in a spatially heterogeneous environment

The establishment and spread of autotetraploids from an original diploid population in a heterogeneous environment were studied using a stochastic simulation model. Specifically, we investigated the effects of heterogeneous habitats and nonrandom pollen/seed dispersal on the critical value (micro) of unreduced 2n gamete production necessary for the establishment of autotetraploids as predicted by deterministic models. Introduction of a heterogeneous environment with random pollen/seed dispersal had little effect on the micro value. In contrast, incorporating nonrandom pollen/seed dispersal into a homogeneous environment considerably reduced the micro value. Incorporating both heterogeneous habitats and nonrandom pollen/seed dispersal may lead either to an increase or to a decrease in the micro value compared to that with random dispersal, indicating that the two factors interact in a complex way.