A climate of uncertainty: accounting for error in climate variables for species distribution models

1. Spatial climate variables are routinely used in species distribution models (SDMs) without accounting for the fact that they have been predicted with uncertainty, which can lead to biased estimates, erroneous inference and poor performances when predicting to new settings – for example under climate change scenarios. 2. We show how information on uncertainty associated with spatial climate variables can be obtained from climate data models. We then explain different types of uncertainty (i.e. classical and Berkson error) and use two statistical methods that incorporate uncertainty in climate variables into SDMs by means of (i) hierarchical modelling and (ii) simulation–extrapolation. 3. We used simulation to study the consequences of failure to account for measurement error. When uncertainty in explanatory variables was not accounted for, we found that coefficient estimates were biased and the SDM had a loss of statistical power. Further, this bias led to biased predictions when projecting change in distribution under climate change scenarios. The proposed errors-in-variables methods were less sensitive to these issues. 4. We also fit the proposed models to real data (presence/absence data on the Carolina wren, Thryothorus ludovicianus), as a function of temperature variables. 5. The proposed framework allows for many possible extensions and improvements to SDMs. If information on the uncertainty of spatial climate variables is available to researchers, we recommend the following: (i) first identify the type of uncertainty; (ii) consider whether any spatial autocorrelation or independence assumptions are required; and (iii) attempt to incorporate the uncertainty into the SDM through established statistical methods and their extensions.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by John Wiley & Sons Ltd on behalf of the British Ecological Society. The published article can be found at: http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%292041-210X.Keywords: Measurement error, Errors-in-variables, Hierarchical statistical models, Climate maps, SIMEX, Prediction error, PRIS

Zinc protoporphyrin IX, a heme oxygenase-1 inhibitor, demonstrates potent antitumor effects but is unable to potentiate antitumor effects of chemotherapeutics in mice

<p>Abstract</p> <p>Background</p> <p>HO-1 participates in the degradation of heme. Its products can exert unique cytoprotective effects. Numerous tumors express high levels of HO-1 indicating that this enzyme might be a potential therapeutic target. In this study we decided to evaluate potential cytostatic/cytotoxic effects of zinc protoporphyrin IX (Zn(II)PPIX), a selective HO-1 inhibitor and to evaluate its antitumor activity in combination with chemotherapeutics.</p> <p>Methods</p> <p>Cytostatic/cytotoxic effects of Zn(II)PPIX were evaluated with crystal violet staining and clonogenic assay. Western blotting was used for the evaluation of protein expression. Flow cytometry was used to evaluate the influence of Zn(II)PPIX on the induction of apoptosis and generation of reactive oxygen species. Knock-down of HO-1 expression was achieved with siRNA. Antitumor effects of Zn(II)PPIX alone or in combination with chemotherapeutics were measured in transplantation tumor models.</p> <p>Results</p> <p>Zn(II)PPIX induced significant accumulation of reactive oxygen species in tumor cells. This effect was partly reversed by administration of exogenous bilirubin. Moreover, Zn(II)PPIX exerted potent cytostatic/cytotoxic effects against human and murine tumor cell lines. Despite a significant time and dose-dependent decrease in cyclin D expression in Zn(II)PPIX-treated cells no accumulation of tumor cells in G1 phase of the cell cycle was observed. However, incubation of C-26 cells with Zn(II)PPIX increased the percentage of cells in sub-G1 phase of the cells cycle. Flow cytometry studies with propidium iodide and annexin V staining as well as detection of cleaved caspase 3 by Western blotting revealed that Zn(II)PPIX can induce apoptosis of tumor cells. B16F10 melanoma cells overexpressing HO-1 and transplanted into syngeneic mice were resistant to either Zn(II)PPIX or antitumor effects of cisplatin. Zn(II)PPIX was unable to potentiate antitumor effects of 5-fluorouracil, cisplatin or doxorubicin in three different tumor models, but significantly potentiated toxicity of 5-FU and cisplatin.</p> <p>Conclusion</p> <p>Inhibition of HO-1 exerts antitumor effects but should not be used to potentiate antitumor effects of cancer chemotherapeutics unless procedures of selective tumor targeting of HO-1 inhibitors are developed.</p

Does morphology predict trophic position and habitat use of ant species and assemblages?

A functional traits-based theory of organismal communities is critical for understanding the principles underlying community assembly, and predicting responses to environmental change. This is particularly true for terrestrial arthropods, of which only 20 % are described. Using epigaeic ant assemblages, we asked: (1) can we use morphological variation among species to predict trophic position or preferred microhabitat; (2) does the strength of morphological associations suggest recent trait divergence; (3) do environmental variables at site scale predict trait sets for whole assemblages? We pitfall-trapped ants from a revegetation chronosequence and measured their morphology, trophic position [using C:N stoichiometry and stable isotope ratios (δ)] and characteristics of microhabitat and macrohabitat. We found strong associations between high trophic position (low C:N and high δ¹⁵N) in body tissue and morphological traits: predators were larger, had more laterally positioned eyes, more physical protection and tended to be monomorphic. In addition, morphological traits were associated with certain microhabitat features, e.g. smaller heads were associated with the bare ground microhabitat. Trait-microhabitat relationships were more pronounced when phylogenetic adjustments were used, indicating a strong influence of recent trait divergences. At the assemblage level, our fourth corner analysis revealed associations between the prevalence of traits and macrohabitat, although these associations were not the same as those based on microhabitat associations. This study shows direct links between species-level traits and both diet and habitat preference. Trait-based prediction of ecological roles and community structure is thus achievable when integrating stoichiometry, morphology and phylogeny, but scale is an important consideration in such predictions

Statins Impair Antitumor Effects of Rituximab by Inducing Conformational Changes of CD20

Jakub Golab and colleagues found that statins significantly decrease rituximab-mediated complement-dependent cytotoxicity and antibody-dependent cellular cytotoxicity against B cell lymphoma cells

A probabilistic scenario approach for developing improved Reduced Emissions from Deforestation and Degradation (REDD+) baselines

Performance-based payments are widely seen as a promising tool for Reduced Emissions from Deforestation and forest Degradation (REDD+) in tropical forests. Despite great advances in international REDD+ negotiations, there is a lack of consensus around the development of business-as-usual (BAU) reference scenarios or baselines to derive and quantify net carbon emission reductions. In this paper, we explore a novel approach for developing baselines (point forecasts) using exponential smoothing. Further, we introduce the concept of probabilistic BAU scenario ranges developed using this approach. We compare predictive performance with the linear trend and historical averages approaches conventionally used in policy proposals and REDD+ pilots. We empirically test the relative performance of all three approaches by forecasting BAU baselines and scenario ranges in 36 sites (consisting of 20 countries and 8 Amazonian states with and 8 countries without REDD+ schemes ). Based on two predictive performance measures (the root mean squared error and mean absolute percentage error), we find that exponential smoothing outperforms the linear trend and historical average models at predicting forest cover changes. In addition, we show how prediction intervals based on a desired confidence level generated through exponential smoothing can be used in novel ways to determine likely baseline scenario ranges. In this way it is possible to quantify the degree of variability and uncertainty in datasets. Importantly, this also provides a statistical measure of confidence to determine if REDD+ interventions have been effective. By generating robust probabilistic baseline scenarios, exponential smoothing models can facilitate the effectiveness of REDD+ payments, support a more efficient allocation of scarce conservation resources, and improve our understanding of effective forest conservation investments, also beyond REDD+

A Generalized Estimating Equation Approach to Multivariate Adaptive Regression Splines

<p>Multivariate adaptive regression splines (MARS) is a popular nonparametric regression tool often used for prediction and for uncovering important data patterns between the response and predictor variables. The standard MARS algorithm assumes responses are normally distributed and independent, but in this article we relax both of these assumptions by extending MARS to generalized estimating equations. We refer to this MARS-for-GEEs algorithm as “MARGE.” Our algorithm makes use of fast forward selection techniques, such that in the univariate case, MARGE has similar computation speed to a standard MARS implementation. Through simulation we show that the proposed algorithm has improved predictive performance than the original MARS algorithm when using correlated and/or nonnormal response data. MARGE is also competitive with alternatives in the literature, especially for problems with multiple interacting predictors. We apply MARGE to various ecological examples with different data types. Supplementary material for this article is available online.</p

A weighted partial likelihood approach for zero-truncated models

Zero-truncated data arises in various disciplines where counts are observed but the zero count category cannot be observed during sampling. Maximum likelihood estimation can be used to model these data; however, due to its nonstandard form it cannot be easily implemented using well-known software packages, and additional programming is often required. Motivated by the Rao-Blackwell theorem, we develop a weighted partial likelihood approach to estimate model parameters for zero-truncated binomial and Poisson data. The resulting estimating function is equivalent to a weighted score function for standard count data models, and allows for applying readily available software. We evaluate the efficiency for this new approach and show that it performs almost as well as maximum likelihood estimation. The weighted partial likelihood approach is then extended to regression modelling and variable selection. We examine the performance of the proposed methods through simulation and present two case studies using real data