The influence of environmental spatial structure on the life history traits and diversity of species in a metacommunity

Several models have been proposed to understand how so many species can coexist in ecosystems. Despite evidence showing that natural habitats are often patchy and fragmented, these models rarely take into account environmental spatial structure. In this study we investigated the influence of spatial structure in habitat and disturbance regime upon species' traits and species' coexistence in a metacommunity. We used a population-based model to simulate competing species in spatially explicit landscapes. The species traits we focused on were dispersal ability, competitiveness, reproductive investment and survival rate. Communities were characterized by their species richness and by the four life-history traits averaged over all the surviving species. Our results show that spatial structure and disturbance have a strong influence on the equilibrium life-history traits within a metacommunity. In the absence of disturbance, spatially structured landscapes favour species investing more in reproduction, but less in dispersal and survival. However, this influence is strongly dependent on the disturbance rate, pointing to an important interaction between spatial structure and disturbance. This interaction also plays a role in species coexistence. While spatial structure tends to reduce diversity in the absence of disturbance, the tendency is reversed when disturbance occurs. In conclusion, the spatial structure of communities is an important determinant of their diversity and characteristic traits. These traits are likely to influence important ecological properties such as resistance to invasion or response to climate change, which in turn will determine the fate of ecosystems facing the current global ecological crisis.

Metal complexation by electroanalytical techniques: hard- and soft-modelling approaches

En la investigació de la complexació de metalls mitjançant eines electroanalítiques són emprades dues aproximacions generals. La primera, anomenada de modelatge dur (hardmodelling), es basa en la formulació d'un model fisicoquímic conjunt per als processos electròdic i de complexació i en la resolució analítica o numèrica del model. Posteriorment, l'ajust dels paràmetres del model a les dades experimentals donarà la informació desitjada sobre el procés de complexació. La segona aproximació, anomenada de modelatge tou (soft-modelling), es basa en la identificació d'un model de complexació a partir de l'anàlisi numèrica i estadística de les dades, sense cap assumpció prèvia d'un model. Aquesta aproximació, que ha estat extensivament emprada amb dades espectroscòpiques, ho ha estat poquíssim amb dades electroquímiques. En aquest article tractem de la formulació d'un model (hard-modelling) per a la complexació de metalls en sistemes amb mescles de lligands, incloent-hi lligands macromoleculars, i de l'aplicació d

Invasion and eradication of a competitively superior species in heterogeneous landscapes

The effect of heterogeneous environments upon the dynamics of invasion and the eradication or control of invasive species is poorly understood, although it is a major challenge for biodiversity conservation. Here, we first investigate how the probability and time for invasion are affected by spatial heterogeneity. Then, we study the effect of control program strategies (e.g. species specificity, spatial scale of action, detection and eradication efficiency) on the success and time of eradication. We find that heterogeneity increases both the invasion probability and the time to invasion. Heterogeneity also reduces the probability of eradication but does not change the time taken for successful eradication. We confirm that early detection of invasive species reduces the time until eradication, but we also demonstrate that this is true only if the local control action is sufficiently efficient. The criterion of removal efficiency is even more important for an eradication program than simply ensuring control effort when the invasive species is not abundant.

Modelling hydrodynamics in the Rio Parana, Argentina : an evaluation and inter-comparison of reduced-complexity and physics based models applied to a large sand-bed river

Depth-averaged velocities and unit discharges within a 30 km reach of one of the world's largest rivers, the Rio Parana, Argentina, were simulated using three hydrodynamic models with different process representations: a reduced complexity (RC) model that neglects most of the physics governing fluid flow, a two-dimensional model based on the shallow water equations, and a three-dimensional model based on the Reynolds-averaged Navier-Stokes equations. Row characteristics simulated using all three models were compared with data obtained by acoustic Doppler current profiler surveys at four cross sections within the study reach. This analysis demonstrates that, surprisingly, the performance of the RC model is generally equal to, and in some instances better than, that of the physics based models in terms of the statistical agreement between simulated and measured flow properties. In addition, in contrast to previous applications of RC models, the present study demonstrates that the RC model can successfully predict measured flow velocities. The strong performance of the RC model reflects, in part, the simplicity of the depth-averaged mean flow patterns within the study reach and the dominant role of channel-scale topographic features in controlling the flow dynamics. Moreover, the very low water surface slopes that typify large sand-bed rivers enable flow depths to be estimated reliably in the RC model using a simple fixed-lid planar water surface approximation. This approach overcomes a major problem encountered in the application of RC models in environments characterised by shallow flows and steep bed gradients. The RC model is four orders of magnitude faster than the physics based models when performing steady-state hydrodynamic calculations. However, the iterative nature of the RC model calculations implies a reduction in computational efficiency relative to some other RC models. A further implication of this is that, if used to simulate channel morphodynamics, the present RC model may offer only a marginal advantage in terms of computational efficiency over approaches based on the shallow water equations. These observations illustrate the trade off between model realism and efficiency that is a key consideration in RC modelling. Moreover, this outcome highlights a need to rethink the use of RC morphodynamic models in fluvial geomorphology and to move away from existing grid-based approaches, such as the popular cellular automata (CA) models, that remain essentially reductionist in nature. In the case of the world's largest sand-bed rivers, this might be achieved by implementing the RC model outlined here as one element within a hierarchical modelling framework that would enable computationally efficient simulation of the morphodynamics of large rivers over millennial time scales. (C) 2012 Elsevier B.V. All rights reserved.

Comparing simulation methods for modelling the errors of stand inventory data

Abstract

Modelling of vegetative filter strips in catchment scale erosin control

Selostus: Valuma-aluetason mallisovellus suojakaistojen käytöstä eroosion torjunnassa

Interactions between cancer stem cells and their niche govern metastatic colonization.

Metastatic growth in distant organs is the major cause of cancer mortality. The development of metastasis is a multistage process with several rate-limiting steps. Although dissemination of tumour cells seems to be an early and frequent event, the successful initiation of metastatic growth, a process termed 'metastatic colonization', is inefficient for many cancer types and is accomplished only by a minority of cancer cells that reach distant sites. Prevalent target sites are characteristic of many tumour entities, suggesting that inadequate support by distant tissues contributes to the inefficiency of the metastatic process. Here we show that a small population of cancer stem cells is critical for metastatic colonization, that is, the initial expansion of cancer cells at the secondary site, and that stromal niche signals are crucial to this expansion process. We find that periostin (POSTN), a component of the extracellular matrix, is expressed by fibroblasts in the normal tissue and in the stroma of the primary tumour. Infiltrating tumour cells need to induce stromal POSTN expression in the secondary target organ (in this case lung) to initiate colonization. POSTN is required to allow cancer stem cell maintenance, and blocking its function prevents metastasis. POSTN recruits Wnt ligands and thereby increases Wnt signalling in cancer stem cells. We suggest that the education of stromal cells by infiltrating tumour cells is an important step in metastatic colonization and that preventing de novo niche formation may be a novel strategy for the treatment of metastatic disease.

Predicting present and future intra-specific genetic structure through niche hindcasting across 24 millennia.

Paleoclimatic reconstructions coupled with species distribution models and identification of extant spatial genetic structure have the potential to provide insights into the demographic events that shape the distribution of intra-specific genetic variation across time. Using the globeflower Trollius europaeus as a case-study, we combined (1) Amplified Fragment Length Polymorphisms, (2) suites of 1000-years stepwise hindcasted species distributions and (3) a model of diffusion through time over the last 24,000 years, to trace the spatial dynamics that most likely fits the species' current genetic structure. We show that the globeflower comprises four gene pools in Europe which, from the dry period preceding the Last Glacial Maximum, dispersed while tracking the conditions fitting its climatic niche. Among these four gene pools, two are predicted to experience drastic range retraction in the near future. Our interdisciplinary approach, applicable to virtually any taxon, is an advance in inferring how climate change impacts species' genetic structures.

Ensemble methods for environmental data modelling with support vector regression

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computationally efficient than training a single SVR model while reducing error. Boosting, however, does not improve results on this specific problem.

Nanoparticles in SiH4-Ar plasma: Modelling and comparison with experimental data

Experimental and theoretical investigations for growth of silicon nanoparticles (4 to 14 nm) in radio frequency discharge were carried out. Growth processes were performed with gas mixtures of SiH4 and Ar in a plasma chemical reactor at low pressure. A distinctive feature of presented kinetic model of generation and growth of nanoparticles (compared to our earlier model) is its ability to investigate small"critical" dimensions of clusters, determining the rate of particle production and taking into account the influence of SiH2 and Si2Hm dimer radicals. The experiments in the present study were extended to high pressure (≥20 Pa) and discharge power (≥40 W). Model calculations were compared to experimental measurements, investigating the dimension of silicon nanoparticles as a function of time, discharge power, gas mixture, total pressure, and gas flow.

Variation in the proportion of flower visitors of Arum maculatum along its distributional range in relation with community‐based climatic niche analyses

Here, we investigate the geographical constancy in the specificity level of the specialized lure-and-trap pollination antagonism involving the widespread European Arum maculatum and its associated Psychodid pollinators. Until now, studies concurred in demonstrating that one single insect species, Psychoda phalaenoides, efficiently cross-pollinated plants; researches were, however, performed locally in western Europe. In this study we characterize for the first time the flower visitors' composition at the scale of the distribution range of A. maculatum by intensively collecting plants and insects throughout the European continent. We further correlate local climatic characteristics with the community composition of visiting arthropods.Our results show that flowers are generally visited by P. phalaenoides females, but not over the whole distribution range of the plant. In some regions this fly species is less frequent or even absent and another species, Psycha grisescens, becomes the prevailing visitor. This variability is geographically structured and can be explained by climatic factors: the proportion of P. grisescens increases with higher annual precipitations and lower precipitations in the warmest trimester, two characteristics typical of the Mediterranean zone. Climate thus seems driving the specificity of this interaction, by potentially affecting the phenology of one or both interacting species, or even of volatile and heat production in the plant. This result therefore challenges the specificity of other presumably one-to-one interactions covering wide distribution ranges, and provides an example of the direct effect that the abiotic environment can have on the fate of plant-insect interactions.