Terrestrial plant and soil organic carbon stocks are critical for regulating climate change, enhancing soil fertility, and supporting biodiversity. While a global-scale decoupling between plant and soil organic carbon has been documented, the hotspots and interconnections between these two carbon compartments across Africa, the second-largest continent on the planet, have been significantly overlooked. Here, we have compiled over 10,000 existing soil organic carbon observations to generate a high-resolution map, illustrating the distribution pattern of soil organic carbon in Africa. We then showed that above- and below-ground plant carbon are significantly and positively correlated with soil organic carbon across Africa. Both soil and plant carbon compartments shared major hotspots in the tropical regions. Our study provides critical insights into the spatial distribution of carbon hotspots across Africa, essential for soil conservation and safeguarding terrestrial carbon stocks amidst the challenges of climate change.

Revealing interactions between ticks and wild animals is vital for gaining insights into the dynamics of tick-borne pathogens in the natural environment. We aimed to elucidate the factors that determine tick infestation in wild animals by investigating ticks on invasive raccoons (Procyon lotor) in Hokkaido, Japan. We first examined the composition, intensity, and seasonal variation of ticks infesting raccoons in six study areas in Hokkaido from March 2022 to August 2023. In one study area, ticks infesting tanukis (raccoon dog, Nyctereutes procyonoides albus) were collected in May to July in both 2022 and 2023, and questing ticks were collected from the vegetation by flagging every other week in the same period. Next, we screened 17 environmental and host variables to determine factors that affect the number of ticks infesting raccoons using generalized linear (mixed) models. From 245 raccoons, we identified a total of 3,917 ticks belonging to eight species of two genera: the most prominent species were Ixodes ovatus (52.9 %), followed by Haemaphysalis megaspinosa (14.4 %), Ixodes tanuki (10.6 %), and Ixodes persulcatus (9.5 %). Ixodes ovatus was also predominant among questing ticks and ticks infesting tanukis. Although I. tanuki was frequently collected from raccoons and tanukis, it was rarely collected in the field. The variables that significantly affected the infestation on raccoons differed by genus, species and developmental stage of the tick. For instance, the infestation of adult I. ovatus was significantly affected by four variables: night-time temperature during nine days before capturing the raccoon, the size of forest area around the capture site, sex of the raccoon, and sampling season. The first two variables were also responsible for the infestation on raccoons of almost all species and stages of ticks. Our study revealed that the number and composition of ticks infesting raccoons can be affected not only by landscape of their habitats but also by weather conditions in several days before capturing.

Landscape genomic analyses associating genetic variation with environmental variables are powerful tools for studying molecular signatures of species\' local adaptation and for detecting candidate genes under selection. The development of landscape genomics over the past decade has been spurred by improvements in resolutions of genomic and environmental datasets, allegedly increasing the power to identify putative genes underlying local adaptation in non-model organisms. Although these associations have been successfully applied to numerous species across a diverse array of taxa, the spatial scale of environmental predictor variables has been largely overlooked, potentially limiting conclusions to be reached with these methods. To address this knowledge gap, we systematically evaluated performances of genotype-environment association (GEA) models using predictor variables at multiple spatial resolutions. Specifically, we used multivariate redundancy analyses to associate whole-genome sequence data from the plant Arabis alpina L. collected across four neighboring valleys in the western Swiss Alps, with very high-resolution topographic variables derived from digital elevation models of grain sizes between 0.5 m and 16 m. These comparisons highlight the sensitivity of landscape genomic models to spatial resolution, where the optimal grain sizes were specific to variable type, terrain characteristics, and study extent. To assist in selecting variables at appropriate spatial resolutions, we demonstrate a practical approach to produce, select, and integrate multiscale variables into GEA models. After generalizing fine-grained variables to multiple spatial resolutions, a forward selection procedure is applied to retain only the most relevant variables for a particular context. Depending on the spatial resolution, the relevance for topographic variables in GEA studies calls for integrating multiple spatial scales into landscape genomic models. By carefully considering spatial resolutions, candidate genes under selection by a more realistic range of pressures can be detected for downstream analyses, with important applied implications for experimental research and conservation management of natural populations.

Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin-destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales-10-100 m and 100-3000 m-to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems.

The altitude distribution patterns of soil microorganisms and their driving mechanisms are crucial for understanding the consequences of climate change on terrestrial ecosystems. There is an obvious altitude difference in Datong River Basin in the Qilian Mountains. Two spatial scale transections were set up along the mountain slope (with altitude spanning 1 000 m) and the mainstream direction (with altitude spanning 300-500 m), respectively. The distribution characteristics of the soil bacterial community structure and diversity along the altitude gradients were examined using high-throughput sequencing technology. Based on the FAPROTAX database, the altitude distribution patterns of nitrogen cycling functional groups were analyzed to investigate the major environmental factors influencing the altitude distribution patterns of soil bacterial communities. The findings revealed that:① Soil physicochemical characteristics varied significantly with altitude. The content of total nitrogen (TN) and nitrate nitrogen (NO3-) were positively correlated with the altitude (P < 0.01), whereas the soil bulk density and pH were negatively connected (P < 0.001). ② The abundance of OTU increased significantly along the altitude (P < 0.01), and the richness and diversity indices increased along the altitude, although the trend was not statistically significant (P > 0.05). ③ The predominant bacterial communities were Acidobacteria, Proteobacteria, and Bacteroidetes, and as altitude climbed, their relative abundances varied between increasing, decreasing, and slightly decreasing, respectively. ④ The nitrogen cycling processes involved 13 functional groups, primarily nitrification, aerobic ammonia oxidation, aerobic nitrite oxidation, etc. As the altitude increased, the response law changed, with an increase in the abundance of nitrobacteria (P < 0.01), a slight increase in the abundance of aerobic ammonia-oxidizing bacteria and nitrite-oxidizing bacteria, and a hump-back tendency in bacteria abundance for nitrogen respiration. ⑤ Redundancy analysis revealed that the key determinants influencing soil bacterial populations at the phylum level were altitude, pH, and the content of NH4+. Mantel analysis showed that the dominant groups of soil bacterial nitrogen cycling were all statistically and significantly driven by altitude (P < 0.01). ⑥ The α-diversity of the bacterial community with increasing altitude were both increased along the mountain slope and the mainstream direction, but the soil properties, the abundance of N-cycling functional groups, and the main environmental factors differed. Therefore, it is of great significance to explore the altitude distribution pattern of soil microorganisms at different spatial scales.

With anthropogenic changes altering the environment and the subsequent decline of natural habitats, it can be challenging to predict essential habitats for elusive and difficult to study taxa. Primary burrowing crayfish are one such group due to the complexity in sampling their semi-terrestrial, subterranean habitat. Sampling burrows usually requires a labor-intensive, time-consuming excavation or trapping process. However, limited information on burrowing crayfish suggests that fine-scale habitat variation may drive burrowing crayfish habitat choice. This project aimed to evaluate the fine-scale habitat characteristics that influence burrowing crayfish presence and abundance at a large, restored-remnant grassland preserve in north-central Illinois. We documented burrow abundance and quadrat-specific habitat variables such as root biomass, canopy cover, apparent seasonal high-water table (water table) depth and dominant vegetation at sites with and without burrowing crayfish populations. Data was recorded at every quadrat and analyzed using generalized linear mixed models. A total of 21 models were created to determine what habitat variables affected burrow presence and abundance. We found that the water table depth was a significant driver of burrow presence and abundance. Root biomass and vegetation cover were not significant drivers, although they did show up in the final models, explaining the data. These findings demonstrate empirical support for previous observations from other burrowing crayfish research and demonstrate the influence of fine-scale habitat when modeling elusive taxa requirements.

One of the fundamental objectives in ecology is to investigate the ecological processes and associated factors governing the abundance and spatial distribution patterns of biodiversity. However, the reaction of biological communities to environmental degradation remains relatively unknown, even for ecologically crucial communities like macroinvertebrates in aquatic ecosystems. Here, we sampled 117 locations to quantify relative contributions of geographical and environmental factors, including water quality, land use, climate, and hydrological factors, to determine the absolute and relative compositions of macroinvertebrate communities and their spatial distribution in the Yellow River Basin (YRB), the sixth-longest river system on Earth. We assessed relative roles of species sorting and dispersal in determining macroinvertebrate community structure along YRB. Our results demonstrated that alpha and beta diversity indices showed an increase from the up- to low-reaches of YRB. The middle and low-reaches exhibited elevated species diversity and both regions exhibited relatively stable community compositions. The biodiversity of macroinvertebrates was influenced by a combination of geographical factors and environmental variables, with environmental factors predominantly serving as the principal determinants. Results of multiple linear regression and variance decomposition showed that the effect of environmental factors was approximately three times greater than that of spatial factors. These findings provide support for the hypothesis that species sorting, driven by environmental gradients, plays a significant role in shaping the community structure of macroinvertebrates in running water ecosystems at the basin scales. Moreover, the factors contributing to substantial shifts in biodiversity across different segments of YRB indicate that distinct river sections have been influenced by varying stressors, with downstream areas being more susceptible to the impacts of water pollution and urbanization resulting from human activities.

Studies focusing on patterns of spatial variation in marine soft-bottom assemblages suggest that variability is mainly concentrated at small spatial scale (from tens of centimeters to few meters), but there is still a lack of knowledge about the consistency of this spatial pattern across habitats and seasons. To address this issue, we quantified the variability in the structure of macrozoobenthic assemblages and in the abundance of dominant macroinvertebrate species in the Mellah Lagoon (Algeria) at three spatial scales, i.e., Plot (meters apart), Station (10\'s m apart) and Site (kms apart) scale, in Ruppia maritima (Ruppia) beds and unvegetated sediments (Unvegetated), and in two dates in winter and two dates in summer 2016. Spatial variability of the most dominant bivalve Mytilaster marioni varied significantly between habitats, but consistent across the two seasons, with a more heterogeneous distribution in Ruppia than in Unvegetated at the Station scale. Furthermore, a second-order interaction among the hierarchical nature of spatial variability, season and habitat emerged for the assemblage structure. Spatial variability between habitats varied significantly in winter, with the largest variation at the Plot scale in Unvegetated and more heterogenous assemblages at the Plot and Site scales than at the Station scale in Ruppia, but did not vary in summer when most of the variance was at the Site scale. We demonstrate that the scales of influence of the processes operating in the Mellah Lagoon are contingent on the specific habitat and/or period of the year at which the study was conducted, highlighting the importance of examining all these sources of variation simultaneously to increase the accuracy of explanatory models derived from the observed patterns in sedimentary environments.

Relationships between land use and water quality of rivers and lakes vary spatially and temporally. These variations were analyzed using spatial analysis and mathematical statistical methods for the Suzhou Creek in Shanghai. Based on the data of water quality and land use in 2001， 2005， 2010， 2015， and 2020， five spatial scales （200， 500， 1 000， 2 000， and 5 000 m reach buffer） of the landscape pattern were extracted using correlation and redundancy analysis to explore the impact of land use composition and spatial pattern on water quality at different spatial and temporal scales. The results showed that： ① the water quality of Suzhou Creek has gradually improved in the past 20 years； other indicators were between Class II to Class IV in 2020 except TN， and TN was the main pollutant. ② The main land use type of the buffer zone was construction land， and the proportion of greenland and woodland showed a small growth trend. ③ The water quality was closely related to landscape pattern， showing temporal and spatial scale effects. On the time scale， indicators such as construction land， agricultural land， landscape dominance， aggregation， and diversity had significant correlations with various water quality parameters， and there was an inverse correlation in 2010 compared with that in other years for NH4+-N， TP， and TN. The landscape pattern in 2001 had the greatest explanation for water quality， with an explanation rate of 93.65%. The impact of greenland and woodland on water quality has begun to emerge in the past 10 years. ④ On the spatial scale， there were significant correlations between greenland and woodland， patch number， landscape shape index， diversity index， and water quality. There was a strong positive regulatory effect of greenland and woodland on NH4+-N， TP， and TN at the scale of 2 000 m. The patch number and landscape shape index had relatively strong regulatory effects on water quality on a larger spatial scale， whereas the Shannon diversity index had a better positive regulatory effect on water quality on a small scale. The landscape pattern within a buffer of 2 000 m had the highest interpretation degree for all factors， with an explanation rate of 68.47%. The study showed that rationally planning the proportion of greenland and woodland within the 2 000 m buffer zone and optimizing its landscape configuration is an important measure to purify the surface water quality of Suzhou Creek.

Community assembly is influenced by environmental niche processes as well as stochastic processes that can be spatially dependent (e.g. dispersal limitation) or independent (e.g. priority effects). Here, we sampled senesced tree leaves as unit habitats to investigate fungal community assembly at two spatial scales: (i) small neighborhoods of overlapping leaves from differing tree species and (ii) forest stands of differing ecosystem types. Among forest stands, ecosystem type explained the most variation in community composition. Among adjacent leaves within stands, variability in fungal composition was surprisingly high. Leaf type was more important in stands with high soil fertility and dominated by differing tree mycorrhizal types (sugar maple vs. basswood or red oak), whereas distance decay was more important in oak-dominated forest stands with low soil fertility. Abundance of functional groups was explained by environmental factors, but predictors of taxonomic composition within differing functional groups were highly variable. These results suggest that fungal community assembly processes are clearest for functional group abundances and large spatial scales. Understanding fungal community assembly at smaller spatial scales will benefit from further study focusing on differences in drivers for different ecosystems and functional groups, as well as the importance of spatially independent factors such as priority effects.