Continent-scale global change attribution in European birds - combining annual and decadal time scales.

Species attributes are commonly used to infer impacts of environmental change on multiyear species trends, e.g. decadal changes in population size. However, by themselves attributes are of limited value in global change attribution since they do not measure the changing environment. A broader foundation for attributing species responses to global change may be achieved by complementing an attributes-based approach by one estimating the relationship between repeated measures of organismal and environmental changes over short time scales. To assess the benefit of this multiscale perspective, we investigate the recent impact of multiple environmental changes on European farmland birds, here focusing on climate change and land use change. We analyze more than 800 time series from 18 countries spanning the past two decades. Analysis of long-term population growth rates documents simultaneous responses that can be attributed to both climate change and land-use change, including long-term increases in populations of hot-dwelling species and declines in long-distance migrants and farmland specialists. In contrast, analysis of annual growth rates yield novel insights into the potential mechanisms driving long-term climate induced change. In particular, we find that birds are affected by winter, spring, and summer conditions depending on the distinct breeding phenology that corresponds to their migratory strategy. Birds in general benefit from higher temperatures or higher primary productivity early on or in the peak of the breeding season with the largest effect sizes observed in cooler parts of species' climatic ranges. Our results document the potential of combining time scales and integrating both species attributes and environmental variables for global change attribution. We suggest such an approach will be of general use when high-resolution time series are available in large-scale biodiversity surveys.

Vegetation canopy structure mediates the response of gross primary production to environmental drivers across multiple temporal scales.

Gross primary production (GPP) is a critical component of the global carbon cycle and plays a significant role in the terrestrial carbon budget. The impact of environmental factors on GPP can occur through both direct (by influencing photosynthetic efficiency) and indirect (through the modulation of vegetation structure) pathways, but the extent to which these mechanisms contribute has been seldom quantified. In this study, we used structural equation modeling and observations from the FLUXNET network to investigate the direct and indirect effects of environmental factors on terrestrial ecosystem GPP at multiple temporal scales. We found that canopy structure, represented by leaf area index (LAI), is a crucial intermediate factor in the GPP response to environmental drivers. Environmental factors affect GPP indirectly by altering canopy structure, and the relative proportion of indirect effects decreased with increasing LAI. The study also identified different effects of environmental factors on GPP across time scales. At the half-hourly time scale, radiation was the primary driver of GPP. In contrast, the influences of temperature and vapor pressure deficit took on greater prominence at longer time scales. About half of the total effect of temperature on GPP was indirect through the regulation of canopy structure, and the indirect effect increased with increasing time scale (GPPNT-based models: 0.135 (half-hourly) vs. 0.171 (daily) vs. 0.189 (weekly) vs. 0.217 (monthly); GPPDT-based models: 0.139 vs. 0.170 vs. 0.187 vs. 0.215; all values were reported in gC m-2 d-1 °C-1, P < 0.001); while the indirect effect of radiation on GPP was comparatively lower, accounting for less than a quarter of the total effect. Furthermore, we observed a direct, negative-to-positive impact of precipitation on GPP across timescales. These findings provide crucial information on the interplay between environmental factors and LAI on GPP and enable a deeper understanding of the driving mechanisms of GPP.

[Research of Parameter Uncertainty for the HSPF Model Under Different Temporal Scales].

Various hydrological models have been applied to the management of water resources and water quality. However, parameter uncertainty is of perpetual interest in the application of hydrological models. In this context, the HSPF model was constructed and calibrated using monthly observed stream data from 1998 to 2010 in the Chaohe River watershed, northeast of Beijing. Specifically, the sensitivity and uncertainty of the model parameters were investigated by the GLUE algorithm with the PEST platform. The major results were illustrated as follows:① the hydrological simulation shows good performance with Nash-Sutcliffe efficiency of 0.84 and 0.55 in the period of calibration and validation, respectively; ② the parameters were divided into three categories:global sensitive parameters (LZSN, INFILT, IRC, and AGWRC), regional sensitive parameters (UZSN), and non-sensitive parameters (DEEPFR, BASETP, AGWEPT, INTFW, and CEPSC); ③ strong correlations were detected within the sensitive parameters, which further involved significant negative correlations (LZSN~INFILT, INFILT~UZSN, and UZSN~AGWRC) and a positive correlation (LZSN~UZSN) and (UZSN~AGWRC); ④ the equifinality for different parameters was found in the HSPF model, indicating that parameter sets determine the simulation performance rather than individual parameters; ⑤ among various external factors, precipitation was identified as the most important condition for simulation uncertainty; and ⑥ the temporal difference in simulation performance was considered using annual, seasonal, and monthly scales with simulation precisions of 81.80%, 78.70%, and 80.56%, implying that the annual scale might be the optimal simulation period with higher accuracy. This research result is useful for the application and localization of the HSPF model.

Neurophysiological basis of creativity in healthy elderly people: a multiscale entropy approach.

OBJECTIVES: Creativity, which presumably involves various connections within and across different neural networks, reportedly underpins the mental well-being of older adults. Multiscale entropy (MSE) can characterize the complexity inherent in EEG dynamics with multiple temporal scales. It can therefore provide useful insight into neural networks. Given that background, we sought to clarify the neurophysiological bases of creativity in healthy elderly subjects by assessing EEG complexity with MSE, with emphasis on assessment of neural networks. METHODS: We recorded resting state EEG of 20 healthy elderly subjects. MSE was calculated for each subject for continuous 20-s epochs. Their relevance to individual creativity was examined concurrently with intellectual function. RESULTS: Higher individual creativity was linked closely to increased EEG complexity across higher temporal scales, but no significant relation was found with intellectual function (IQ score). CONCLUSIONS: Considering the general "loss of complexity" theory of aging, our finding of increased EEG complexity in elderly people with heightened creativity supports the idea that creativity is associated with activated neural networks. SIGNIFICANCE: Results reported here underscore the potential usefulness of MSE analysis for characterizing the neurophysiological bases of elderly people with heightened creativity.

Complexity of spontaneous brain activity in mental disorders.

Recent reports of functional and anatomical studies have provided evidence that aberrant neural connectivity lies at the heart of many mental disorders. Information related to neural networks has elucidated the nonlinear dynamical complexity in brain signals over a range of temporal scales. The recent advent of nonlinear analytic methods, which have served for the quantitative description of the brain signal complexity, has provided new insights into aberrant neural connectivity in many mental disorders. Although many studies have underpinned aberrant neural connectivity, findings related to complexity behavior are still inconsistent. This inconsistency might result from (i) heterogeneity in mental disorders, (ii) analytical issues, (iii) interference of typical development and aging. First, most mental disorders are heterogeneous in their clinical feature or intrinsic pathological mechanisms. Second, neurophysiologic output signals from complex brain connectivity might be characterized with multiple time scales or frequencies. Finally, age-related brain complexity changes must be considered when investigating pathological brain because typical brain complexity is not constant across generations. Future systematic studies addressing these issues will greatly expand our knowledge of neural connections and dynamics related to mental disorders.