Understanding community assembly of living organisms has been a prominent ecological question since the early days of the discipline. The impact of habitat filtering and limiting similarity on plant community structures is well-known, as both processes are influenced by individual responses to environmental changes. However, identifying and quantifying potential abiotic and biotic factors that ultimately influence community structures at a fine scale remains a challenge. Here, we employed different species pool null models to assess the importance of habitat filtering and limiting similarity at two spatial scales. We used 63 natural vegetation plots, each measuring 5m x 5m, with 3 nested subplots measuring 1m x 1m, from the 2021 field survey, to examine the alpha diversity of plots and subplots as well as Beta diversity. We also used linear mixed-effects models (LMEs) to assess how environmental factors affect the assembly process. Our findings indicate that habitat filtering was the dominant assembly process at both the plot and subplot levels, while limiting similarity was stronger at the subplot level. Plot-level limiting similarity was positively correlated with fine-scale partitioning, suggesting that trait divergence resulted from a combination of limiting similarity species and spatial partitioning. Our results also suggest that the assembly process varies more strongly along the mean annual temperature (MAT) gradient than the mean annual precipitation (MAP). Additionally, the community assembly process of different traits varied with these environmental factors, indicating the importance of multi-dimensional traits.This study provides a valuable example of non-random assembly rules from spatial scale and environmental factors in grassland communities in the loess hilly region. These results highlight the essential role of additional constraints with spatial scales and environmental factors for understanding the process of grassland community assembly.
Understanding community assembly of living organisms has been a prominent ecological question since the early days of the discipline. The impact of habitat filtering and limiting similarity on plant community structures is well-known, as both processes are influenced by individual responses to environmental changes. However, identifying and quantifying potential abiotic and biotic factors that ultimately influence community structures at a fine scale remains a challenge. Here, we employed different species pool null models to assess the importance of habitat filtering and limiting similarity at two spatial scales. We used 63 natural vegetation plots, each measuring 5m x 5m, with 3 nested subplots measuring 1m x 1m, from the 2021 field survey, to examine the alpha diversity of plots and subplots as well as Beta diversity. We also used linear mixed-effects models (LMEs) to assess how environmental factors affect the assembly process. Our findings indicate that habitat filtering was the dominant assembly process at both the plot and subplot levels, while limiting similarity was stronger at the subplot level. Plot-level limiting similarity was positively correlated with fine-scale partitioning, suggesting that trait divergence resulted from a combination of limiting similarity species and spatial partitioning. Our results also suggest that the assembly process varies more strongly along the mean annual temperature (MAT) gradient than the mean annual precipitation (MAP). Additionally, the community assembly process of different traits varied with these environmental factors, indicating the importance of multi-dimensional traits. This study provides a valuable example of non-random assembly rules from spatial scale and environmental factors in grassland communities in the loess hilly region. These results highlight the essential role of additional constraints with spatial scales and environmental factors for understanding the process of grassland community assembly.
Light use efficiency (LUE) is a crucial indicator used to reflect the ability of terrestrial ecosystems to transform light energy. Understanding the long-term trends in LUE and its influencing factors are essential for determining the future carbon sink and carbon sequestration potential of terrestrial ecosystems. However, the long-term interannual variability of LUE in grasslands in northern China at the ecosystem scale is poorly understood due to the limitations of the year length and the coverage of the site data. In this study, we assessed the long-term LUE trends in the grasslands of northern China from 1982 to 2018 and then revealed the relationships between interannual variability in LUE and climate factors. Our study showed a substantial rising trend for LUE from 1982 to 2018 in the grasslands of northern China (3.42 × 10−3 g C/MJ/yr). Regarding the different grassland types, alpine meadow had the highest growth rate (4.85 × 10−3 g C/MJ/yr), while temperate steppe had the lowest growth rate (1.58 × 10−3 g C/MJ/yr). The climate factors driving LUE dynamics were spatially heterogeneous in grasslands. Increasing precipitation accelerated the interannual growth rate of LUE in temperate steppe, and increasing temperature accelerated the interannual growth rate of LUE in other types. In addition, the temporal dynamic of LUE showed different trends in relation to time scales, and the growth trend slowed down after 1998. Our results should be considered in developing future grassland management measures and predicting carbon cycle–climate interactions.
The grasslands in high-latitude areas are sensitive to climate warming and drought. However, the drought stress effect on the long-term variability of grassland productivity at the continental scale still hinders our understanding. Based on aboveground net primary production (ANPP) surveys, satellite remote sensing Normalized Difference Vegetation Index (NDVI), and meteorological data, we comprehensively analyzed three Aridity metrics and their effect on ANPP in Eurasian grassland from 1982 to 2020. Our results showed that the ANPP had an overall uptrend from 1982 to 2020, increasing most in the Tibetan Plateau alpine steppe subregion (TPSSR). Among three Aridity indicators, vapor pressure deficit (VPD) had an overall uptrend, while the trend of Aridity and soil moisture (SM) was insignificant from 1982 to 2020. Soil drought had negative effects on ANPP for all Eurasian grassland, while the atmospheric VPD had a positive effect on ANPP for TPSSR and the Mongolian Plateau steppe subregion (MPSSR), but a negative effect for the Black Sea–Kazakhstan steppe subregion (BKSSR) which was the driest subregion. SM had been the predominant driving factor for the interannual variability of ANPP in MPSSR since 1997. The increasing VPD had facilitated grassland productivity in alpine grasslands due to its cascading effect with an increasing temperature after 2000. The cascading effects networks of climate factors—drought factors (VPD, Aridity, and SM)—ANPP (CDA–CENet) indicated that SM was the predominant driving factor of the interannual variability of ANPP in MPSSR and BKSSR, and the dominance of SM had enhanced after the year 1997. The inhibitory effect of VPD on ANPP transformed into a facilitating effect after 1997, and the facilitating effect of SM is weakening in TPSSR.
Abstract How communities of living organisms assemble has long been a central question in ecology. The impact of habitat filtering and limiting similarity on plant community structures is well known, as both processes are influenced by individual responses to environmental fluctuations. Yet, the precise identifications and quantifications of the potential abiotic and biotic factors that shape community structures at a fine scale remains a challenge. Here, we applied null model approaches to assess the importance of habitat filtering and limiting similarity at two spatial scales. We used 63 natural vegetation plots, each measuring 5 × 5 m, with three nested subplots measuring 1 × 1 m, from the 2021 field survey, to examine the alpha diversity as well as beta diversity of plots and subplots. Linear mixed‐effects models were employed to determine the impact of environmental variables on assembly rules. Our results demonstrate that habitat filtering is the dominant assembly rules at both the plot and subplot levels, although limiting similarity assumes stronger at the subplot level. Plot‐level limiting similarity exhibited a positive association with fine‐scale partitioning, suggesting that trait divergence originated from a combination of limiting similarity and spatial partitioning. Our findings also reveal that the community assembly varies more strongly with the mean annual temperature gradient than the mean annual precipitation. This investigation provides a pertinent illustration of non‐random assembly rules from spatial scale and environmental factors in plant communities in the loess hilly region. It underscores the critical influence of spatial and environmental constraints in understanding the assembly of plant communities.
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