Methods to account for spatial autocorrelation in the analysis of species distributional data: a review

Species distributional or trait data based on range map (extent‐of‐occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species’ distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing spatial autocorrelation in the errors. However, we found that for presence/absence data the results and conclusions were very variable between the different methods. This is likely due to the low information content of binary maps. Also, in contrast with previous studies, we found that autocovariate methods consistently underestimated the effects of environmental controls of species distributions. Given their widespread use, in particular for the modelling of species presence/absence data (e.g. climate envelope models), we argue that this warrants further study and caution in their use. To aid other ecologists in making use of the methods described, code to implement them in freely available software is provided in an electronic appendix.     

Variations of the candidate SEZ6L2 gene on Chromosome 16p11.2 in patients with autism spectrum disorders and in human populations

Background

Autism spectrum disorders (ASD) are a group of severe childhood neurodevelopmental disorders with still unknown etiology. One of the most frequently reported associations is the presence of recurrent de novo or inherited microdeletions and microduplications on chromosome 16p11.2. The analysis of rare variations of 8 candidate genes among the 27 genes located in this region suggested SEZ6L2 as a compelling candidate.

Methodology/Principal Findings

We further explored the role of SEZ6L2 variations by screening its coding part in a group of 452 individuals, including 170 patients with ASD and 282 individuals from different ethnic backgrounds of the Human Genome Diversity Panel (HGDP), complementing the previously reported screening. We detected 7 previously unidentified non-synonymous variations of SEZ6L2 in ASD patients. We also identified 6 non-synonymous variations present only in HGDP. When we merged our results with the previously published, no enrichment of non-synonymous variation in SEZ6L2 was observed in the ASD group compared with controls.

Conclusions/Significance

Our results provide an extensive ascertainment of the genetic variability of SEZ6L2 in human populations and do not support a major role for SEZ6L2 sequence variations in the susceptibility to ASD.     

Determination of carbohydrates in medicinal plants--comparison between TLC, mf-MELDI-MS and GC-MS

Introduction – Quality control in the pharmaceutical and phytopharmaceutical industries requires fast and reliable methods for the analysis of raw materials and final products. Objective – This study evaluates different analytical approaches in order to recognise the most suitable technique for the analysis of carbohydrates in herbal drug preparations. Methodology – The specific focus of the study is on thin‐layer chromatography (TLC), gas chromatography (GC), and a newly developed mass spectrometric method, i.e. matrix free material enhanced laser desorption/ionisation time of flight mass spectrometry (mf‐MELDI‐MS). Samples employed in the study were standards and microwave‐assisted water extracts from Quercus. Results – TLC analysis proved the presence of mono‐, di‐ and trisaccharides within the biological sample and hinted at the existence of an unknown carbohydrate of higher oligomerisation degree. After evaluation of different derivatisation techniques, GC‐MS confirmed data obtained via TLC for mono‐ to trisaccharides, delivering additionally quantified values under a considerable amount of time. A carbohydrate of higher oligomerisation degree could not be found. The application of mf‐MELDI‐MS further confirmed the presence of carbohydrates up to trisaccharides, also hinting at the presence of a form of tetrasaccharide. Besides this information, mf‐MELDI‐MS delivered further data about other substances present in the extract. Quantitative determination resulted in 1.750, 1.736 and 0.336 mg/mL for glucose, sucrose and raffinose respectively. Conclusion – Evaluation of all three techniques employed, clearly proved the heightened performance of mf‐MELDI‐MS for the qualitative analysis of complex mixtures, as targets do not need modification and analysis requires only a few minutes. In addition, GC‐MS is suitable for quantitative analysis. Copyright © 2011 John Wiley & Sons, Ltd.     

Hormonal and ion regulatory response in three freshwater fish species following waterborne copper exposure

We evaluated effects of sublethal copper exposure in 3 different freshwater fish: rainbow trout (Oncorhynchus mykiss), common carp (Cyprinus carpio) and gibel carp (Carassius auratus gibelio). In a first experiment we exposed these fishes to an equally toxic Cu dose, a Cu level 10 times lower than their 96 h LC₅₀ value: 20, 65, and 150 µg/L Cu. In a second series we exposed them to the same Cu concentration (50 µg/L). Na⁺/K⁺-ATPase activity in gill tissue was disturbed differently in rainbow trout then in common and gibel carp. Rainbow trout showed a thorough disruption of plasma ion levels at the beginning of both exposures, whereas common carp and gibel carp displayed effects only after 3 days. Rainbow trout and common carp thyroid hormones experienced adverse effects in the beginning of the exposure. The involvement of prolactin in handling metal stress was reflected in changes of mRNA prolactin receptor concentrations in gill tissue, with an up regulation of this mRNA in rainbow trout and a down regulation in gibel carp, which was more pronounced in the latter. Overall, rainbow trout appeared more sensitive in the beginning of the exposure, however, when it overcame this first challenge, it handled copper exposure in a better manner then common and gibel carp as they showed more long term impacts of Cu exposure.     

Spind: a package for computing spatially corrected accuracy measures

Using an appropriate accuracy measure is essential for assessing prediction accuracy in species distribution modelling. Therefore, model evaluation as an analytical uncertainty is a challenging problem. Although a variety of accuracy measures for the assessment of prediction errors in presence/absence models is available, there is a lack of spatial accuracy measures, i.e. measures that are sensitive to the spatial arrangement of the predictions. We present ‘spind’, a new software package (based on the R software program) that provides spatial performance measures for grid‐based models. These accuracy measures are generalized, spatially corrected versions of the classical ones, thus enabling comparisons between them. Our method for evaluation consists of the following steps: 1) incorporate additional autocorrelation until spatial autocorrelation in predictions and actuals is balanced, 2) cross‐classify predictions and adjusted actuals in a 4 × 4 contingency table, 3) use a refined weighting pattern for errors, and 4) calculate weighted Kappa, sensitivity, specificity and subsequently ROC, AUC, TSS to get spatially corrected indices. To illustrate the impact of our spatial method we present an example of simulated data as well as an example of presence/absence data of the plant species Dianthus carthusianorum across Germany. Our analysis includes a statistic for the comparison of spatial and classical (non‐spatial) indices. We find that our spatial indices tend to result in higher values than classical ones. These differences are statistically significant at medium and high autocorrelation levels. We conclude that these spatial accuracy measures may contribute to evaluate prediction errors in presence/absence models, especially in case of medium or high degree of similarity of adjacent data, i.e. aggregated (clumped) or continuous species distributions.     

Subunit composition and functional properties of G-protein heterotrimers on rat chromaffin granules

Heterotrimeric G-proteins at the plasma membrane serve as switches between heptahelical receptors and intracellular signal cascades. Likewise endomembrane associated G-proteins may transduce signals from intracellular compartments provided they consist of a functional trimer. Using quantitative immunoelectron microscopy we found heterotrimeric G-protein subunits Galpha2, Galpha(q/11), Gbeta2 and Gbeta5 to reside on secretory granules in chromaffin cells of rat adrenal glands.Thus rat chromaffin granules are equipped with functional G-proteins that consist of a specific alpha-, beta- and probably gamma-subunit combination. Serotonin uptake into a crude rat chromaffin granule preparation was inhibited by activated Galphao2 (10 nM) to nearly the same extent as by GMppNp (50 microM) whereas GDPbetaS was ineffective. The data support the idea that vesicular G-proteins directly regulate the transmitter content of secretory vesicles. In this respect Galphao2 appears to be the main regulator of vesicular momoamine transporter activity.     

In vivo differentiated cytokine-producing CD4(+) T cells express functional CCR7

Chemokines and their receptors fulfill specialized roles in inflammation and under homeostatic conditions. CCR7 and its ligands, CCL19 and CCL21, are involved in lymphocyte recirculation through secondary lymphoid organs and additionally navigate lymphocytes into distinct tissue compartments. The role of CCR7 in the migration of polarized T effector/memory cell subsets in vivo is still poorly understood. We therefore analyzed murine and human CD4(+) cytokine-producing cells developed in vivo for their chemotactic reactivity to CCR7 ligands. The responses of cells producing cytokines, such as IFN-gamma, IL-4, and IL-10, as well as of subsets defined by memory or activation markers were comparable to that of naive CD4(+) cells, with slightly lower reactivity in cells expressing IL-10 or CD69. This indicates that CCR7 ligands are able to attract naive as well as the vast majority of activated and effector/memory T cell stages. Chemotactic reactivity of these cells toward CCL21 was absent in CCR7-deficient cells, proving that effector cells do not use alternative receptors for this chemokine. Th1 cells generated from CCR7(-/-) mice failed to enter lymph nodes and Peyer's patches, but did enter a site of inflammation. These findings indicate that CD4(+) cells producing effector cytokines upon stimulation retain the capacity to recirculate through lymphoid tissues via CCR7.     

Relationship Between Structural Properties and Electrochemical Characteristics of Monolithic Carbon Xerogel‐Based Electrochemical Double‐Layer Electrodes in Aqueous and Organic Electrolytes

The impact of the micropore width, external surface area, and meso‐/macropore size on the charging performance of electrochemical double‐layer capacitor (EDLC) electrodes is systematically investigated. Nonactivated carbon xerogels are used as model electrodes in aqueous and organic electrolytes. Monolithic porous model carbons with different structural parameters are prepared using a resorcinol‐formaldehyde‐based sol–gel process and subsequent pyrolysis of the organic precursors. Electrochemical properties are characterized by utilizing them as EDLC half‐cells operated in aqueous and organic electrolytes, respectively. Experimental data derived for organic electrolytes reveals that the respective ions cannot enter the micropores within the skeleton of the meso‐ and macroporous carbons. Therefore the total capacitance is limited by the external surface formed by the interface between the meso‐/macropores and the microporous carbon particles forming the xerogel skeleton. In contrast, for aqueous electrolytes the total capacitance solely depends on the total surface area, including interfaces at the micropore scale. For both types of electrolytes the charging rate of the electrodes is systematically enhanced when increasing the diameter of the carbon xerogel particles from 10 to 75 nm and the meso‐/macropore size from 10 to 121 nm.