Climate-based empirical models show biased predictions of butterfly communities along environmental gradients

A better understanding of the factors that mould ecological community structure is required to accurately predict community composition and to anticipate threats to ecosystems due to global changes. We tested how well stacked climate-based species distribution models (S-SDMs) could predict butterfly communities in a mountain region. It has been suggested that climate is the main force driving butterfly distribution and community structure in mountain environments, and that, as a consequence, climate-based S-SDMs should yield unbiased predictions. In contrast to this expectation, at lower altitudes, climate-based S-SDMs overpredicted butterfly species richness at sites with low plant species richness and underpredicted species richness at sites with high plant species richness. According to two indices of composition accuracy, the Sorensen index and a matching coefficient considering both absences and presences, S-SDMs were more accurate in plant-rich grasslands. Butterflies display strong and often specialised trophic interactions with plants. At lower altitudes, where land use is more intense, considering climate alone without accounting for land use influences on grassland plant richness leads to erroneous predictions of butterfly presences and absences. In contrast, at higher altitudes, where climate is the main force filtering communities, there were fewer differences between observed and predicted butterfly richness. At high altitudes, even if stochastic processes decrease the accuracy of predictions of presence, climate-based S-SDMs are able to better filter out butterfly species that are unable to cope with severe climatic conditions, providing more accurate predictions of absences. Our results suggest that predictions should account for plants in disturbed habitats at lower altitudes but that stochastic processes and heterogeneity at high altitudes may limit prediction success of climate-based S-SDMs.

Pale and dark reddish melanic tawny owls differentially regulate the level of blood circulating POMC prohormone in relation to environmental conditions.

Knowledge of the hormonal pathway controlling genotype-specific norms of reaction would shed light on the ecological factors to which each genotype is adapted. Environmentally mediated changes in the sign and magnitude of covariations between heritable melanin-based colouration and fitness components are frequent, revealing that extreme melanin-based phenotypes can display different physiological states depending on the environment. Yet, the hormonal mechanism underlying this phenomenon is poorly understood. One novel hypothesis proposes that these covariations stem from pleiotropic effects of the melanocortin system. Melanocortins are post-translationally modified bioactive peptides derived from the POMC prohormone that are involved in melanogenesis, anti-inflammation, energy homeostasis and stress responses. Thus, differential regulation of fitness components in relation to environmental factors by pale and dark melanic individuals may be due to colour-specific regulation of the POMC prohormone. Accordingly, we found that the degree of reddish melanic colouration was negatively correlated with blood circulating levels of the POMC prohormone in female tawny owls (Strix aluco) rearing a brood for which the size was experimentally reduced, but not when enlarged, and in females located in rich but not in poor territories. Our findings support the hypothesis that the widespread links between melanin-based colouration and fitness components may be mediated, at least in part, by the melanocortin system.

NanoImpactNet - Stakeholders' knowledge needs for research on the health and environmental impact of nanomaterials

NanoImpactNet (NIN) is a multidisciplinary European Commission funded network on the environmental, health and safety (EHS) impact of nanomaterials. The 24 founding scientific institutes are leading European research groups active in the fields of nanosafety, nanorisk assessment and nanotoxicology. This 4−year project is the new focal point for information exchange within the research community. Contact with other stakeholders is vital and their needs are being surveyed. NIN is communicating with 100s of stakeholders: businesses; internet platforms; industry associations; regulators; policy makers; national ministries; international agencies; standard−setting bodies and NGOs concerned by labour rights, EHS or animal welfare. To improve this communication, internet research, a questionnaire distributed via partners and targeted phone calls were used to identify stakeholders' interests and needs. Knowledge gaps and the necessity for further data mentioned by representatives of all stakeholder groups in the targeted phone calls concerned: potential toxic and safety hazards of nanomaterials throughout their lifecycles; fate and persistence of nanoparticles in humans, animals and the environment; risks associated to nanoparticle exposure; participation in the preparation of nomenclature, standards, methodologies, protocols and benchmarks; development of best practice guidelines; voluntary schemes on responsibility; databases of materials, research topics and themes. Findings show that stakeholders and NIN researchers share very similar knowledge needs, and that open communication and free movement of knowledge will benefit both researchers and industry. Consequently NIN will encourage stakeholders to be active members. These survey findings will be used to improve NIN's communication tools to further build on interdisciplinary relationships towards a healthy future with nanotechnology.

Heterogeneity of human monocytes: an optimized four-color flow cytometry protocol for analysis of monocyte subsets.

Monocytes are central mediators in the development of atherosclerotic plaques. They circulate in blood and eventually migrate into tissue including the vessel wall where they give rise to macrophages and dendritic cells. The existence of monocyte subsets with distinct roles in homeostasis and inflammation suggests specialization of function. These subsets are identified based on expression of the CD14 and CD16 markers. Routinely applicable protocols remain elusive, however. Here, we present an optimized four-color flow cytometry protocol for analysis of human blood monocyte subsets using a specific PE-Cy5-conjugated monoclonal antibody (mAb) to HLA-DR, a PE-Cy7-conjugated mAb to CD14, a FITC-conjugated mAb to CD16, and PE-conjugated mAbs to additional markers relevant to monocyte function. Classical CD14(+)CD16(-) monocytes (here termed "Mo1" subset) expressed high CCR2, CD36, CD64, and CD62L, but low CX(3)CR1, whereas "nonclassical" CD14(lo)CD16(+) monocytes (Mo3) essentially showed the inverse expression pattern. CD14(+)CD16(+) monocytes (Mo2) expressed high HLA-DR, CD36, and CD64. In patients with stable coronary artery disease (n = 13), classical monocytes were decreased, whereas "nonclassical" monocytes were increased 90% compared with healthy subjects with angiographically normal coronary arteries (n = 14). Classical monocytes from CAD patients expressed higher CX(3)CR1 and CCR2 than controls. Thus, stable CAD is associated with expansion of the nonclassical monocyte subset and increased expression of inflammatory markers on monocytes. Flow cytometric analysis of monocyte subsets and marker expression may provide valuable information on vascular inflammation. This may translate into the identification of monocyte subsets as selective therapeutic targets, thus avoiding adverse events associated with indiscriminate monocyte inhibition.

Host specialist clownfishes are environmental niche generalists.

Why generalist and specialist species coexist in nature is a question that has interested evolutionary biologists for a long time. While the coexistence of specialists and generalists exploiting resources on a single ecological dimension has been theoretically and empirically explored, biological systems with multiple resource dimensions (e.g. trophic, ecological) are less well understood. Yet, such systems may provide an alternative to the classical theory of stable evolutionary coexistence of generalist and specialist species on a single resource dimension. We explore such systems and the potential trade-offs between different resource dimensions in clownfishes. All species of this iconic clade are obligate mutualists with sea anemones yet show interspecific variation in anemone host specificity. Moreover, clownfishes developed variable environmental specialization across their distribution. In this study, we test for the existence of a relationship between host-specificity (number of anemones associated with a clownfish species) and environmental-specificity (expressed as the size of the ecological niche breadth across climatic gradients). We find a negative correlation between host range and environmental specificities in temperature, salinity and pH, probably indicating a trade-off between both types of specialization forcing species to specialize only in a single direction. Trade-offs in a multi-dimensional resource space could be a novel way of explaining the coexistence of generalist and specialists.

Genetic and environmental components of phenotypic variation in immune response and body size of a colonial bird, Delichon urbica (the house martin).

Directional selection for parasite resistance is often intense in highly social host species. Using a partial cross-fostering experiment we studied environmental and genetic variation in immune response and morphology in a highly colonial bird species, the house martin (Delichon urbica). We manipulated intensity of infestation of house martin nests by the haematophagous parasitic house martin bug Oeciacus hirundinis either by spraying nests with a weak pesticide or by inoculating them with 50 bugs. Parasitism significantly affected tarsus length, T cell response, immunoglobulin and leucocyte concentrations. We found evidence of strong environmental effects on nestling body mass, body condition, wing length and tarsus length, and evidence of significant additive genetic variance for wing length and haematocrit. We found significant environmental variance, but no significant additive genetic variance in immune response parameters such as T cell response to the antigenic phytohemagglutinin, immunoglobulins, and relative and absolute numbers of leucocytes. Environmental variances were generally greater than additive genetic variances, and the low heritabilities of phenotypic traits were mainly a consequence of large environmental variances and small additive genetic variances. Hence, highly social bird species such as the house martin, which are subject to intense selection by parasites, have a limited scope for immediate microevolutionary response to selection because of low heritabilities, but also a limited scope for long-term response to selection because evolvability as indicated by small additive genetic coefficients of variation is weak.

Environmental effects of Deccan volcanism across the Cretaceous-Tertiary transition in Meghalaya, India

The Urn Sohryngkew section of Meghalaya, NE India, located 800-1000 km from the Deccan volcanic province, is one of the most complete Cretaceous-Tertiary boundary (KTB) transitions worldwide with all defining and supporting criteria present: mass extinction of planktic foraminifera, first appearance of Danian species, delta(13)C shift, Ir anomaly (12 ppb) and KTB red layer. The geochemical signature of the KTB layer indicates not only an extraterrestrial signal (Ni and all Platinum Group Elements (PGEs)) of a second impact that postdates Chicxulub, but also a significant component resulting from condensed sedimentation (P), redox fluctuations (As, Co, Fe, Pb, Zn, and to a lesser extent Ni and Cu) and volcanism. From the late Maastrichtian C29r into the early Danian, a humid climate prevailed (kaolinite: 40-60%, detrital minerals: 50-80%). During the latest Maastrichtian, periodic acid rains (carbonate dissolution; CIA index: 70-80) associated with pulsed Deccan eruptions and strong continental weathering resulted in mesotrophic waters. The resulting super-stressed environmental conditions led to the demise of nearly all planktic foraminiferal species and blooms (>95%) of the disaster opportunist Guembelitria cretacea. These data reveal that detrimental marine conditions prevailed surrounding the Deccan volcanic province during the main phase of eruptions in C29r below the KTB. Ultimately these environmental conditions led to regionally early extinctions followed by global extinctions at the KTB. (C) 2011 Elsevier B.V. All rights reserved.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.