Predicting species distribution: offering more than simple habitat models

n the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.

Maladies auto-immunes neuromusculaires: diagnostic et prise en charge [Dysimmune neuromuscular disorders: diagnostic challenges and new ways of management].

This review describes some dysimmune neuromuscular disorders and their recent management: syndrome of peripheral nerve hyperexcitability (treatment of cramps, immunosuppressors); Guillain-Barré syndrome (new mechanisms and consensus treatment); chronic inflammatory demyelinating polyradiculoneuropathy (new indication for the use of pulse dexamethasone, new scores of activity); importance of subcutaneous immunoglobulin in multifocal motor neuropathy and of infusions of rituximab in myasthenia gravis; new entities in myositis and their treatment.

Unfolding nature "in silico" : overcoming practical and metodological limitations of species distribution models

Abstract : The existence of a causal relationship between the spatial distribution of living organisms and their environment, in particular climate, has been long recognized and is the central principle of biogeography. In turn, this recognition has led scientists to the idea of using the climatic, topographic, edaphic and biotic characteristics of the environment to predict its potential suitability for a given species or biological community. In this thesis, my objective is to contribute to the development of methodological improvements in the field of species distribution modeling. More precisely, the objectives are to propose solutions to overcome limitations of species distribution models when applied to conservation biology issues, or when .used as an assessment tool of the potential impacts of global change. The first objective of my thesis is to contribute to evidence the potential of species distribution models for conservation-related applications. I present a methodology to generate pseudo-absences in order to overcome the frequent lack of reliable absence data. I also demonstrate, both theoretically (simulation-based) and practically (field-based), how species distribution models can be successfully used to model and sample rare species. Overall, the results of this first part of the thesis demonstrate the strong potential of species distribution models as a tool for practical applications in conservation biology. The second objective this thesis is to contribute to improve .projections of potential climate change impacts on species distributions, and in particular for mountain flora. I develop and a dynamic model, MIGCLIM, that allows the implementation of dispersal limitations into classic species distribution models and present an application of this model to two virtual species. Given that accounting for dispersal limitations requires information on seed dispersal, distances, a general methodology to classify species into broad dispersal types is also developed. Finally, the M~GCLIM model is applied to a large number of species in a study area of the western Swiss Alps. Overall, the results indicate that while dispersal limitations can have an important impact on the outcome of future projections of species distributions under climate change scenarios, estimating species threat levels (e.g. species extinction rates) for a mountainous areas of limited size (i.e. regional scale) can also be successfully achieved when considering dispersal as unlimited (i.e. ignoring dispersal limitations, which is easier from a practical point of view). Finally, I present the largest fine scale assessment of potential climate change impacts on mountain vegetation that has been carried-out to date. This assessment involves vegetation from 12 study areas distributed across all major western and central European mountain ranges. The results highlight that some mountain ranges (the Pyrenees and the Austrian Alps) are expected to be more affected by climate change than others (Norway and the Scottish Highlands). The results I obtain in this study also indicate that the threat levels projected by fine scale models are less severe than those derived from coarse scale models. This result suggests that some species could persist in small refugias that are not detected by coarse scale models. Résumé : L'existence d'une relation causale entre la répartition des espèces animales et végétales et leur environnement, en particulier le climat, a été mis en évidence depuis longtemps et est un des principes centraux en biogéographie. Ce lien a naturellement conduit à l'idée d'utiliser les caractéristiques climatiques, topographiques, édaphiques et biotiques de l'environnement afin d'en prédire la qualité pour une espèce ou une communauté. Dans ce travail de thèse, mon objectif est de contribuer au développement d'améliorations méthodologiques dans le domaine de la modélisation de la distribution d'espèces dans le paysage. Plus précisément, les objectifs sont de proposer des solutions afin de surmonter certaines limitations des modèles de distribution d'espèces dans des applications pratiques de biologie de la conservation ou dans leur utilisation pour évaluer l'impact potentiel des changements climatiques sur l'environnement. Le premier objectif majeur de mon travail est de contribuer à démontrer le potentiel des modèles de distribution d'espèces pour des applications pratiques en biologie de la conservation. Je propose une méthode pour générer des pseudo-absences qui permet de surmonter le problème récurent du manque de données d'absences fiables. Je démontre aussi, de manière théorique (par simulation) et pratique (par échantillonnage de terrain), comment les modèles de distribution d'espèces peuvent être utilisés pour modéliser et améliorer l'échantillonnage des espèces rares. Ces résultats démontrent le potentiel des modèles de distribution d'espèces comme outils pour des applications de biologie de la conservation. Le deuxième objectif majeur de ce travail est de contribuer à améliorer les projections d'impacts potentiels des changements climatiques sur la flore, en particulier dans les zones de montagnes. Je développe un modèle dynamique de distribution appelé MigClim qui permet de tenir compte des limitations de dispersion dans les projections futures de distribution potentielle d'espèces, et teste son application sur deux espèces virtuelles. Vu que le fait de prendre en compte les limitations dues à la dispersion demande des données supplémentaires importantes (p.ex. la distance de dispersion des graines), ce travail propose aussi une méthode de classification simplifiée des espèces végétales dans de grands "types de disperseurs", ce qui permet ainsi de d'obtenir de bonnes approximations de distances de dispersions pour un grand nombre d'espèces. Finalement, j'applique aussi le modèle MIGCLIM à un grand nombre d'espèces de plantes dans une zone d'études des pré-Alpes vaudoises. Les résultats montrent que les limitations de dispersion peuvent avoir un impact considérable sur la distribution potentielle d'espèces prédites sous des scénarios de changements climatiques. Cependant, quand les modèles sont utilisés pour évaluer les taux d'extinction d'espèces dans des zones de montages de taille limitée (évaluation régionale), il est aussi possible d'obtenir de bonnes approximations en considérant la dispersion des espèces comme illimitée, ce qui est nettement plus simple d'un point dé vue pratique. Pour terminer je présente la plus grande évaluation à fine échelle d'impact potentiel des changements climatiques sur la flore des montagnes conduite à ce jour. Cette évaluation englobe 12 zones d'études réparties sur toutes les chaines de montages principales d'Europe occidentale et centrale. Les résultats montrent que certaines chaines de montagnes (les Pyrénées et les Alpes Autrichiennes) sont projetées comme plus sensibles aux changements climatiques que d'autres (les Alpes Scandinaves et les Highlands d'Ecosse). Les résultats obtenus montrent aussi que les modèles à échelle fine projettent des impacts de changement climatiques (p. ex. taux d'extinction d'espèces) moins sévères que les modèles à échelle large. Cela laisse supposer que les modèles a échelle fine sont capables de modéliser des micro-niches climatiques non-détectées par les modèles à échelle large.

Regulation of PIDD auto-proteolysis and activity by the molecular chaperone Hsp90.

In response to DNA damage, p53-induced protein with a death domain (PIDD) forms a complex called the PIDDosome, which either consists of PIDD, RIP-associated protein with a death domain and caspase-2, forming a platform for the activation of caspase-2, or contains PIDD, RIP1 and NEMO, important for NF-κB activation. PIDDosome activation is dependent on auto-processing of PIDD at two different sites, generating the fragments PIDD-C and PIDD-CC. Despite constitutive cleavage, endogenous PIDD remains inactive. In this study, we screened for novel PIDD regulators and identified heat shock protein 90 (Hsp90) as a major effector in both PIDD protein maturation and activation. Hsp90, together with p23, binds PIDD and inhibition of Hsp90 activity with geldanamycin efficiently disrupts this association and impairs PIDD auto-processing. Consequently, both PIDD-mediated NF-κB and caspase-2 activation are abrogated. Interestingly, PIDDosome formation itself is associated with Hsp90 release. Characterisation of cytoplasmic and nuclear pools of PIDD showed that active PIDD accumulates in the nucleus and that only cytoplasmic PIDD is bound to Hsp90. Finally, heat shock induces Hsp90 release from PIDD and PIDD nuclear translocation. Thus, Hsp90 has a major role in controlling PIDD functional activity.

MALT1 auto-proteolysis is essential for NF-κB-dependent gene transcription in activated lymphocytes.

Mucosa-associated lymphoid tissue 1 (MALT1) controls antigen receptor-mediated signalling to nuclear factor κB (NF-κB) through both its adaptor and protease function. Upon antigen stimulation, MALT1 forms a complex with BCL10 and CARMA1, which is essential for initial IκBα phosphorylation and NF-κB nuclear translocation. Parallel induction of MALT1 protease activity serves to inactivate negative regulators of NF-κB signalling, such as A20 and RELB. Here we demonstrate a key role for auto-proteolytic MALT1 cleavage in B- and T-cell receptor signalling. MALT1 cleavage occurred after Arginine 149, between the N-terminal death domain and the first immunoglobulin-like region, and did not affect its proteolytic activity. Jurkat T cells expressing an un-cleavable MALT1-R149A mutant showed unaltered initial IκBα phosphorylation and normal nuclear accumulation of NF-κB subunits. Nevertheless, MALT1 cleavage was required for optimal activation of NF-κB reporter genes and expression of the NF-κB targets IL-2 and CSF2. Transcriptome analysis confirmed that MALT1 cleavage after R149 was required to induce NF-κB transcriptional activity in Jurkat T cells. Collectively, these data demonstrate that auto-proteolytic MALT1 cleavage controls antigen receptor-induced expression of NF-κB target genes downstream of nuclear NF-κB accumulation.

Impact of biological and environmental variabilities on biological monitoring: an approach using toxicokinetic models

Biological monitoring of occupational exposure is characterized by important variability, due both to variability in the environment and to biological differences between workers. A quantitative description and understanding of this variability is important for a dependable application of biological monitoring. This work describes this variability,using a toxicokinetic model, for a large range of chemicals for which reference biological reference values exist. A toxicokinetic compartmental model describing both the parent compound and its metabolites was used. For each chemical, compartments were given physiological meaning. Models were elaborated based on physiological, physicochemical, and biochemical data when available, and on half-lives and central compartment concentrations when not available. Fourteen chemicals were studied (arsenic, cadmium, carbon monoxide, chromium, cobalt, ethylbenzene, ethyleneglycol monomethylether, fluorides, lead, mercury, methyl isobutyl ketone, penthachlorophenol, phenol, and toluene), representing 20 biological indicators. Occupational exposures were simulated using Monte Carlo techniques with realistic distributions of both individual physiological parameters and exposure conditions. Resulting biological indicator levels were then analyzed to identify the contribution of environmental and biological variability to total variability. Comparison of predicted biological indicator levels with biological exposure limits showed a high correlation with the model for 19 out of 20 indicators. Variability associated with changes in exposure levels (GSD of 1.5 and 2.0) is shown to be mainly influenced by the kinetics of the biological indicator. Thus, with regard to variability, we can conclude that, for the 14 chemicals modeled, biological monitoring would be preferable to air monitoring. For short half-lives (less than 7 hr), this is very similar to the environmental variability. However, for longer half-lives, estimated variability decreased. [Supplementary materials are available for this article. Go to the publisher's online edition of Journal of Occupational and Environmental Hygiene for the following free supplemental resource: tables detailing the CBTK models for all 14 chemicals and the symbol nomenclature that was used.] [Authors]

Integrating detection probabilities in species distribution models of amphibians breeding in Mediterranean temporary ponds

Aim  The imperfect detection of species may lead to erroneous conclusions about species-environment relationships. Accuracy in species detection usually requires temporal replication at sampling sites, a time-consuming and costly monitoring scheme. Here, we applied a lower-cost alternative based on a double-sampling approach to incorporate the reliability of species detection into regression-based species distribution modelling.Location  Doñana National Park (south-western Spain).Methods  Using species-specific monthly detection probabilities, we estimated the detection reliability as the probability of having detected the species given the species-specific survey time. Such reliability estimates were used to account explicitly for data uncertainty by weighting each absence. We illustrated how this novel framework can be used to evaluate four competing hypotheses as to what constitutes primary environmental control of amphibian distribution: breeding habitat, aestivating habitat, spatial distribution of surrounding habitats and/or major ecosystems zonation. The study was conducted on six pond-breeding amphibian species during a 4-year period.Results  Non-detections should not be considered equivalent to real absences, as their reliability varied considerably. The occurrence of Hyla meridionalis and Triturus pygmaeus was related to a particular major ecosystem of the study area, where suitable habitat for these species seemed to be widely available. Characteristics of the breeding habitat (area and hydroperiod) were of high importance for the occurrence of Pelobates cultripes and Pleurodeles waltl. Terrestrial characteristics were the most important predictors of the occurrence of Discoglossus galganoi and Lissotriton boscai, along with spatial distribution of breeding habitats for the last species.Main conclusions  We did not find a single best supported hypothesis valid for all species, which stresses the importance of multiscale and multifactor approaches. More importantly, this study shows that estimating the reliability of non-detection records, an exercise that had been previously seen as a naïve goal in species distribution modelling, is feasible and could be promoted in future studies, at least in comparable systems.

Lipid metabolism in myelinating glial cells: lessons from human inherited disorders and mouse models.

The integrity of central and peripheral nervous system myelin is affected in numerous lipid metabolism disorders. This vulnerability was so far mostly attributed to the extraordinarily high level of lipid synthesis that is required for the formation of myelin, and to the relative autonomy in lipid synthesis of myelinating glial cells because of blood barriers shielding the nervous system from circulating lipids. Recent insights from analysis of inherited lipid disorders, especially those with prevailing lipid depletion and from mouse models with glia-specific disruption of lipid metabolism, shed new light on this issue. The particular lipid composition of myelin, the transport of lipid-associated myelin proteins, and the necessity for timely assembly of the myelin sheath all contribute to the observed vulnerability of myelin to perturbed lipid metabolism. Furthermore, the uptake of external lipids may also play a role in the formation of myelin membranes. In addition to an improved understanding of basic myelin biology, these data provide a foundation for future therapeutic interventions aiming at preserving glial cell integrity in metabolic disorders.

Uncertainty Quantification with Support Vector Regression Prediction Models

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. The paper considers a data driven approach in modelling uncertainty in spatial predictions. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic features and describe stochastic variability and non-uniqueness of spatial properties. It is able to capture and preserve key spatial dependencies such as connectivity, which is often difficult to achieve with two-point geostatistical models. Semi-supervised SVR is designed to integrate various kinds of conditioning data and learn dependences from them. A stochastic semi-supervised SVR model is integrated into a Bayesian framework to quantify uncertainty with multiple models fitted to dynamic observations. The developed approach is illustrated with a reservoir case study. The resulting probabilistic production forecasts are described by uncertainty envelopes.

The effects of physiologically plausible connectivity structure on local and global dynamics in large scale brain models.

Functionally relevant large scale brain dynamics operates within the framework imposed by anatomical connectivity and time delays due to finite transmission speeds. To gain insight on the reliability and comparability of large scale brain network simulations, we investigate the effects of variations in the anatomical connectivity. Two different sets of detailed global connectivity structures are explored, the first extracted from the CoCoMac database and rescaled to the spatial extent of the human brain, the second derived from white-matter tractography applied to diffusion spectrum imaging (DSI) for a human subject. We use the combination of graph theoretical measures of the connection matrices and numerical simulations to explicate the importance of both connectivity strength and delays in shaping dynamic behaviour. Our results demonstrate that the brain dynamics derived from the CoCoMac database are more complex and biologically more realistic than the one based on the DSI database. We propose that the reason for this difference is the absence of directed weights in the DSI connectivity matrix.

Native homing endonucleases can target conserved genes in humans and in animal models.

In recent years, both homing endonucleases (HEases) and zinc-finger nucleases (ZFNs) have been engineered and selected for the targeting of desired human loci for gene therapy. However, enzyme engineering is lengthy and expensive and the off-target effect of the manufactured endonucleases is difficult to predict. Moreover, enzymes selected to cleave a human DNA locus may not cleave the homologous locus in the genome of animal models because of sequence divergence, thus hampering attempts to assess the in vivo efficacy and safety of any engineered enzyme prior to its application in human trials. Here, we show that naturally occurring HEases can be found, that cleave desirable human targets. Some of these enzymes are also shown to cleave the homologous sequence in the genome of animal models. In addition, the distribution of off-target effects may be more predictable for native HEases. Based on our experimental observations, we present the HomeBase algorithm, database and web server that allow a high-throughput computational search and assignment of HEases for the targeting of specific loci in the human and other genomes. We validate experimentally the predicted target specificity of candidate fungal, bacterial and archaeal HEases using cell free, yeast and archaeal assays.

The application of trend surface models to the analysis of time factors in Swiss cancer mortality.

To study different temporal components on cancer mortality (age, period and cohort) methods of graphic representation were applied to Swiss mortality data from 1950 to 1984. Maps using continuous slopes ("contour maps") and based on eight tones of grey according to the absolute distribution of rates were used to represent the surfaces defined by the matrix of various age-specific rates. Further, progressively more complex regression surface equations were defined, on the basis of two independent variables (age/cohort) and a dependent one (each age-specific mortality rate). General patterns of trends in cancer mortality were thus identified, permitting definition of important cohort (e.g., upwards for lung and other tobacco-related neoplasms, or downwards for stomach) or period (e.g., downwards for intestines or thyroid cancers) effects, besides the major underlying age component. For most cancer sites, even the lower order (1st to 3rd) models utilised provided excellent fitting, allowing immediate identification of the residuals (e.g., high or low mortality points) as well as estimates of first-order interactions between the three factors, although the parameters of the main effects remained still undetermined. Thus, the method should be essentially used as summary guide to illustrate and understand the general patterns of age, period and cohort effects in (cancer) mortality, although they cannot conceptually solve the inherent problem of identifiability of the three components.