The elevational range where montane species live is a key factor of spatial niche partitioning, because the limits of such ranges are influenced by interspecies interaction, abiotic stress, and dispersal barriers. At the regional scale, unimodal distributions of single species along the elevation gradient have often been reported, while discontinuous patterns, such as bimodal distributions, and potential ecological implications have been rarely discussed. Here, we used extensive camera trap records to reveal the elevation distribution of Himalaya blue sheep (Pseudois nayaur) and its co-existence with other ground animal communities along a slope of Baima Snow Mountain, southwest China. The results show that Himalaya blue sheep exhibited a distinctive bimodal distribution along the elevation gradient contrasting the unimodal distributions found for the other ungulates in Baima snow mountain. A first distributional peak was represented by a population habituating in scree habitat around 4100 m, and a second peak was found in the dry-hot valley around 2600 m. The two distinct populations co-existed with disparate animal communities and these assemblages were similar both in the dry and rainy seasons. The extremely low abundance of blue sheep observed in the densely forested belt at mid-elevation indicates that vegetation rather than temperature is responsible for such segregation. The low-elevation population relied highly on Opuntia ficus-indica, an invasive cactus species that colonized the region six hundred years ago, as food resource. Being the only animal that developed a strategy to feed on this spiky plant, we suggest invasive species may have formed new foraging niche to support blue sheep population in lower elevation hot-dry river valleys, resulting in the geographic separation from the original population and a potential morphological differentiation, as recorded. These findings emphasize the important conservation values of role of ecological functions to identify different taxa, and conservation values of apparent similar species of different ecological functions.

The mechanisms that underpin the species–area relationship (SAR) are crucial for both the development of biogeographic theory and the application of biodiversity conservation. Since its origin, the resource hypothesis, which proposes that rich resources in vast ecosystems will lower extinction rates and shape the SAR, has not been tested. The impossibility to quantify resources and extinction rates using plants and animals as research subjects, as well as the inability to rule out the influences of the area per se, habitat diversity, dispersal, and the historical background of biodiversity, make testing this hypothesis problematic. To address these challenges and test this hypothesis, two sets of microbial microcosm experimental systems with positive and negative correlated resources and volumes were created in this work. The results of 157 high-throughput sequencing monitoring sessions at 11 time points over 30 consecutive days showed that neither of the experimental groups with positive or negative correlations between total resources and microcosm volume had a significant SAR, and there were no negative correlations between extinction rates and resources. Therefore, in our microcosmic system, resources do not influence extinction rates or shape the SAR. Dispersal should be the principal mode of action if the resource theory is correct.

Located on the southwest slope and plain areas of the Altai Mountains in China, this study aims to explore bird composition variation (beta diversity) in mountain landscape (metacommunity M), riparian landscape (metacommunity R), desert landscape (metacommunity D) and across the three landscapes (metacommunity A), and to assess how patch connectivity with environmental and spatial factors influence species distributional patterns across multiple metacommunities. In 78 transect lines over the study area, 9724 detections of 139 bird species were detected. We calculated the beta diversity, its turnover and nestedness components in four metacommunities. We used the variation partitioning method to investigate the relative importance between the environment, spatial variation and locality connectivity in driving bird community composition variation. We found high beta diversities with a small contribution of nestedness components in all four metacommunities. When only a single set of predictors is contained in the model, the predictor that best explains the variation of bird community composition is connectivity in metacommunity M, R and D and spatial predictor in metacommunity A. In all three sets of predictors, 73.8~85.4% of variations of community composition can be explained in the four metacommunities, and connectivity always contributed the most. High beta diversity and a high turnover component imply that regional-scale conservation efforts should be thought of as preserving overall biodiversity. A conservation strategy is to keep stepping-stone habitats with good connectivity in the middle of the riparian landscape. Along with the Altai-Sayan biodiversity ecoregion, the desert and riparian environments are essential for birds residing in the mountainous terrain. Furthermore, they should be regarded as integral parts of the ecoregion.

Given the widespread conflicts between humans and birds in contexts such as agricultural production, airports, and new energy installations, the development and application of bird deterrence technologies hold significant importance for both safeguarding human interests and advancing the cause of wildlife conservation. Artificial sounds are one of the most widely used methods for bird deterrence; however, there is a lack of systematic research on the effects of sound frequency and their combination. The black-necked crane (Grus nigricollis), a first-class nationally protected wild animal of China, shares some of its habitats with human agricultural activities. This study designed sounds of various frequencies and combinations and conducted deterrence experiments on black-necked cranes at their wintering sites in northeastern Yunnan Province, exploring how sound frequency and combinations affect the behavioral responses, escape distances, and escape speeds of the cranes. Our results show that: (1) in terms of behavioral response intensity, escape distances, and escape speed, sounds with high frequency and high variability significantly outperformed other sounds (n = 479, p < 0.001); (2) there were no significant differences in the responses of black-necked cranes of different flock types and age combinations to sounds. The study recommends using high-frequency, highly variable sounds for short-term control of black-necked cranes. Additionally, this research demonstrates that using either high-frequency or high variable sound combinations can achieve efficient bird deterrence in the short term, and provides a scientific basis for developing and refining bird deterrence strategies for other bird species.

In the study of Species–Area Relationships (SARs) in microorganisms, outcome discrepancies primarily stem from divergent high-throughput sequencing data processing algorithms and their combinations with different fitting models. This paper investigates the impacts and underlying causes of using diverse sequence data processing algorithms in microbial SAR studies, as well as compatibility issues that arise between different algorithms and fitting models. The findings indicate that the balancing strategies employed by different algorithms can result in variations in the calculations of alpha and beta diversity, thereby influencing the SARs of microorganisms. Crucially, incompatibilities exist between algorithms and models, with no consistently optimal combination identified. Based on these insights, we recommend prioritizing the use of the DADA2 algorithm in conjunction with a power model, which demonstrates greater compatibility. This study serves as a comprehensive comparison and reference for fundamental methods in microbial SAR research. Future microbial SAR studies should carefully select the most appropriate algorithms and models based on specific research objectives and data structures.

In recent years, public bicycles have achieved rapid development around the world. However, public bicycle travel is very sensitive to environmental factors. Weather and climate play a crucial role in users’ choice of bicycle travel. This paper mainly studies the influence of weather and climate factors on the use of public bicycles. Taking Suzhou City as an example, the key factors such as rainfall, temperature, wind scale and Air Quality Index(AQI) level were selected, and the hierarchical linear regression and optimal scale regression were used for comparative analysis: The goodness of fit of optimal scale regression is higher than that of hierarchical regression, so the optimal scale regression result is selected as the final result of this paper. The results showed that rainfall, temperature, wind scale and $A Q I$ level all had significant effects on the number of public bicycle rides in Suzhou. Linear regression model was established, and stepwise regression analysis was used to study the leading and lagging effect of weather factors on urban public bicycle travel volume. The results showed that rainfall variable has leading and lagging effect on the travel volume of public bicycles. The results of this study can provide assistance in alleviating the negative effects of various weather factors on public bicycle travel in various ways.