Human activities currently play a dominant role in shaping and eroding Earth’s biodiversity, but the historical dynamics leading to this situation are poorly understood and contentious. Importantly, these dynamics are often studied and discussed without an emphasis on cultural evolution, despite its potential importance for past and present biodiversity dynamics. Here, we investigate whether cultural filtering, defined as the impact of cultural evolution on species presence, has driven the range dynamics of five historically widespread megafauna taxa (Asiatic elephant, rhinoceroses, tiger, Asiatic black bear, and brown bear) across China over the past 2 millennia. Data on megafauna and sociocultural history were compiled from Chinese administrative records. While faunal dynamics in China are often linked to climate change at these time scales, our results reveal cultural filtering as the dominant driver of range contractions in all five taxa. This finding suggests that the millennia-long spread of agricultural land and agricultural intensification, often accompanied by expansion of the Han culture, has been responsible for the extirpation of these megafauna species from much of China. Our results suggest that cultural filtering is important for understanding society’s role in the assembly of contemporary communities from historical regional species pools. Our study provides direct evidence that cultural evolution since ancient times has overshadowed climate change in shaping broadscale megafauna biodiversity patterns, reflecting the strong and increasing importance of sociocultural processes in the biosphere.

Urban natural surfaces and non-surface human activities are key factors determining the urban heat island (UHI), but their relative importance remains highly controversial and may vary at different spatial scales and focal urban systems. However, systematic studies on the scale-dependency system-specificity remain largely lacking. Here, we selected 32 major Chinese cities as cases and used Landsat 8 images to retrieve land surface temperature (LST) and quantify natural surface variables using point of interest (POI) data as a measure of the human activity variable and using multiple regression and relative weight analysis to study the contribution and relative importance of these factors to LST at a range of grain sizes (0.25–5 km) and spatial extents (20–60 km). We revealed that the contributions and relative importance of natural surfaces and human activities are largely scale-dependent and system-specific. Natural surfaces, especially vegetation cover, are often the most important UHI determinants for a majority of scales, but the importance of non-surface human activities is increasingly pronounced at a coarser spatial scale with respect to both grain and spatial extent. The scaling relations of the UHI determinants and their relative importance were mostly linear-like at the city-collective level, but highly diverse across individual cities, so reducing non-surface heat emissions could be the most effective measure in particular cases, especially at relatively large spatial scales. This study advances the understanding of UHI formation mechanisms and highlights the complexity of the scale issue underpinning the UHI effect.

Presently, China has the largest high-speed rail (HSR) system in the world. However, our understanding of the network structure of the world’s largest HSR system remains largely incomplete due to the limited data available. In this study, a publicly available data source, namely, information from a ticketing website, was used to collect an exhaustive dataset on the stations and routes within the Chinese HSR system. The dataset included all 704 HSR stations that had been built as of June, 2016. A classical set of frequently used metrics based on complex network theory were analyzed, including degree centrality, betweenness centrality, and closeness centrality. The frequency distributions of all three metrics demonstrated highly consistent bimodal-like patterns, suggesting that the Chinese HSR network consists of two distinct regimes. The results indicate that the Chinese HSR system has a hierarchical structure, rather than a scale-free structure as has been commonly observed. To the best of our knowledge, such a network structure has not been found in other railway systems, or in transportation systems in general. Follow-up studies are needed to reveal the formation mechanisms of this hierarchical network structure.

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Aim The aim was to assess whether and to what extent the role of local landscape attributes in shaping macroscopic biodiversity patterns is sensitive to spatial and thematic resolutions of land cover data. Location Sub‐Saharan Africa and continental China. Time period Early 21st century. Taxa studied Terrestrial mammals. Methods We conducted spatial and thematic scaling analyses to generate land cover datasets of different spatial (0.3, 0.5, 1.0 and 9.0 km) and thematic (two, three and five classes) resolutions. We calculated landscape metrics based on the resulting land cover maps and examined the power of landscape metrics for explaining species richness patterns, using non‐spatial (OLS) and spatial (SAR) linear models and random forest (RF) models. We systematically assessed the resolution dependence of explanatory power for different geographical regions, different scaling approaches and different model types. We also compared the explanatory power of landscape attributes with that of macroclimate. Results Collectively, local landscape attributes generally had strong explanatory power for species richness. For the African system, the largest explanatory power was c. 60% based on the OLS models and random forest models and c. 30% based on the non‐spatial components of the SAR models. For the Chinese system, the largest explanatory power was c. 35% based on the OLS models and c. 40% based on the SAR and random forest models. We observed a linear scaling relationship, which is robust to studied systems, scaling approaches and model types. In contrast, the scaling relationship varies substantially among single landscape metrics. At coarse resolutions, the addition of landscape attributes collectively would not improve climate‐envelope models significantly, whereas at finer resolutions, landscape attributes collectively have explanatory power that is close to or even exceeds climate. Main conclusions Local landscape attributes play an important role in shaping macroscopic biodiversity patterns. However, their strength is highly sensitive to both spatial and thematic resolutions of land cover data, with stronger explanatory power detected at finer resolutions. Strong sensitivity to spatial and thematic resolutions makes landscape attributes highly plastic determinants, leading to contrasting conclusions if based on greatly different resolutions of land cover data. Scaling analyses are needed to examine such cross‐scale effects of macroecological determinants systematically.