Reanalysis datasets outperform other gridded climate products in vegetation change analysis in peripheral conservation areas of Central Asia

Global environmental research requires long-term climate data. Yet, meteorological infrastructure is missing in the vast majority of the world’s protected areas. Therefore, gridded products are frequently used as the only available climate data source in peripheral regions. However, associated evaluations are commonly biased towards well observed areas and consequently, station-based datasets. As evaluations on vegetation monitoring abilities are lacking for regions with poor data availability, we analyzed the potential of several state-of-the-art climate datasets (CHIRPS, CRU, ERA5-Land, GPCC-Monitoring-Product, IMERG-GPM, MERRA-2, MODIS-MOD10A1) for assessing NDVI anomalies (MODIS-MOD13Q1) in two particularly suitable remote conservation areas. We calculated anomalies of 156 climate variables and seasonal periods during 2001–2018, correlated these with vegetation anomalies while taking the multiple comparison problem into consideration, and computed their spatial performance to derive suitable parameters. Our results showed that four datasets (MERRA-2, ERA5-Land, MOD10A1, CRU) were suitable for vegetation analysis in both regions, by showing significant correlations controlled at a false discovery rate < 5% and in more than half of the analyzed areas. Cross-validated variable selection and importance assessment based on the Boruta algorithm indicated high importance of the reanalysis datasets ERA5-Land and MERRA-2 in both areas but higher differences and variability between the regions with all other products. CHIRPS, GPCC and the bias-corrected version of MERRA-2 were unsuitable and not important in both regions. We provide evidence that reanalysis datasets are most suitable for spatiotemporally consistent environmental analysis whereas gauge- or satellite-based products and their combinations are highly variable and may not be applicable in peripheral areas.

On seeking souls in brain graphs: fancy neuroimaging analysis gone wrong?

An increasing number of studies is currently focusing on ‘personality neuroscience’, a term labeling the research aimed at neuroimaging correlates of inter-individual temperament and character variability. Among other methods, a graph theoretical analysis of the functional connectivity in resting state functional magnetic resonance imaging data was applied in a recent study by Gao et al. (2013, Frontiers in Human Neuroscience). This paper aims to replicate this study and extends the original statistical methods in order to demonstrate the effect of multiple comparison problem. In contrast to the original study, five personality dimensions were obtained in the revised ‘Big Five’ Personality Inventory. Using a larger sample (84 subjects) and an equivalent data analysis procedure, we obtained widely disagreeing results compared to the original study. While the Gao et al. reported a range of significant correlations between personality dimensions and some of the network metrics, we failed to replicate any significant correlations when FDR testing was applied. These results demonstrate that as with other neuroimaging studies, appropriate control of multiple comparison problem should be meticulously applied in order to prevent such false alarms in research into neurological substrates of personality differences. Of course, we do not attempt to disprove the existence of some link between personality and brain’s intrinsic functional architecture. Nevertheless, this link is very likely much more subtle and elusive than was suggested in previous studies.