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The insurance effect of biodiversity—that diversity stabilises aggregate ecosystem properties—is mechanistically underlain by inter‐ and intraspecific trait variation in organismal responses to the environment. This variation, termed response diversity, is therefore a potentially critical determinant of ecological stability. However, response diversity has yet to be widely quantified, possibly due to difficulties in its measurement. Even when it has been measured, approaches have varied. Here, we review methods for measuring response diversity and from them distil a methodological framework for quantifying response diversity from experimental and/or observational data, which can be practically applied in laboratory and field settings across a range of taxa. Previous empirical studies on response diversity most commonly invoke response traits as proxies aimed at capturing species' ecological responses to the environment. Our approach, which is based on environment‐dependent ecological responses to any biotic or abiotic environmental variable, is conceptually simple and robust to any form of environmental response, including nonlinear responses. Given its derivation from empirical data on species' ecological responses, this approach should more directly reflect response diversity than the trait‐based approach dominant in the literature. By capturing even subtle inter‐ or intraspecific variation in environmental responses, and environment dependencies in response diversity, we hope this framework will motivate tests of the diversity–stability relationship from a new perspective, and provide an approach for mapping, monitoring and conserving this critical dimension of biodiversity.

The black soil region in Northeast China is an important production base of commodity grain. However, soil erosion is a major threat that has caused a decline in arable land area and productivity and a series of environmental problems in recent years. To understand the current situation of soil erosion and its changes in the whole black soil region, including six treatment regions, we used the spatial-temporal analysis of soil erosion from 2000 to 2015 and the overlay analysis with its drivers; additionally, soil erosion was evaluated qualitatively with the integrated evaluation method, and its change was indicated by the soil erosion change index (SECI). We found that soil erosion that caused soil loss occurred in each treatment region mainly at the light level in 2015. Water erosion, the most widely distributed erosion type, affected the largest area, while most serious erosion at intensive or higher levels stemmed from wind erosion. Although the situation of water erosion was improved in 2015 compared to that in 2000, the overall situation of soil erosion was worse due to the deterioration of wind and freeze-thaw erosion. Grassland, woodland, and cultivated land changes, such as the conversion from grassland to cultivated land, from woodland to sparse woodland and from dry land to paddy land, revealed these changes to a great extent.

Indian Summer Monsoon Rainfall (ISMR) is one of the most well-documented areas of hydrometeorology; however, the processes associated with ISMR are not well understood. This attributes to the complexities associated with ISMR at multiple spatio-temporal scales. This further results in inconsistencies across the literature to assess the impacts of global warming on the monsoon, though this has huge relevance as a huge population of South Asia is dependent on the same. Here, we review and assess the existing literature on the Indian monsoon, its variability, and its trajectory in a warming scenario. We further synthesize the literature on its impacts on the hydrology of major river basins in South Asia. We also identify a few research questions, addressing which will add value to the understanding of the Indian monsoon and the associated water cycle. We have highlighted that there is a significant lack of understanding of how different large-scale and regional factors affect ISMR at different timescales. These impacts, in turn, get translated into hydrology and water sector in India. There is a need to know where we stand to combat the impacts of climate change on ISMR, which can be translated to adaptation by policy-making processes and water management practices in India.

AIM : Patterns of biological diversity are often investigated across space but little work has attempted to explore the consistency of such observations through time. Here, our aim was to understand the patterns of diversity for a functionally critical taxon, the ants (Hymenoptera: Formicidae) through space and time using an extensive dataset collected across an elevational gradient. In addition, we evaluated the importance of two key postulated drivers of elevational diversity patterns: temperature and available area. LOCATION : The Maloti-Drakensberg Mountains of southern Africa. METHODS : We sampled epigaeic ant communities biannually for 7 years (2006–2012) at eight different elevational sites. We then used an information theoretic approach combined with generalized linear mixed models to : (1) describe diversity patterns through space and time; (2) assess the importance of different abiotic drivers ; and (3) understand how much spatio-temporal variation can be explained by these drivers. Simple regression approaches were also used to test for differences in seasonal variation along the elevational gradient. RESULTS : We found clear mid-elevational peaks of species density and evenness measures. Abundance patterns were complex. The spatial distributions of all three metrics changed across seasons and years . Temperature variables had important roles in explaining both species density and abundance patterns, whilst species density was also influenced by available area. In conjunction, we found much greater seasonal variability in species density at low elevations. This variation was independent of differences in species pool size. MAIN CONCLUSIONS : We found patterns of ant diversity that are strongly modulated by temporal change. There was a consistent and strong signature of seasonality on the elevation–diversity patterns of the ants, whilst annual changes throughout the study period had a weaker influence. We conclude that both spatial and temporal patterns are driven primarily by temperature, with only a weak influence of available elevational area. This study is the first to describe the spatiotemporal distribution of a suite of community-level metrics along an elevational gradient and implies that temporal variation should be considered more carefully in studies of invertebrate diversity, particularly with respect to elevation and the mechanisms that may be maintaining diversity patterns.

The paper presents a method for the scale-dependent validation of the spatio-temporal variability in global weather or climate models and for their bias quantification in relation to dynamics. The method provides a relationship between the bias and simulated spatial and temporal variance by a model in comparison with verifying reanalysis data. For the low resolution (T30L8) subset of ERA-20C data, it was found that 80–90% (depending on season) of the global interannual variance is at planetary scales (zonal wavenumbers k = 0−3), and only about 1% of the variance is at scales with k>7. The reanalysis is used to validate a T30L8 GCM in two configurations, one with the prescribed sea-surface temperature (SST) and another using a slab ocean model. Although the model with the prescribed SST represents the average properties of surface fields well, the interannual variability is underestimated at all scales. Similar to variability, model bias is strongly scale dependent. Biases found in the experiment with the prescribed SST are largely increased in the experiment using a slab ocean, especially in k=0, in scales with missing variability and in seasons with poorly simulated energy distribution. The perfect model scenario (a comparison between the GCM coupled to a slab ocean vs. the same model with prescribed SSTs) shows that the representation of the ocean is not critical for synoptic to subsynoptic variability, but essential for capturing the planetary scales.

Tree stems from wetland, floodplain and upland forests can emit CH4. This emerging field of research has revealed a high spatial and temporal variability on CH4 stem emissions between trees and species, and within and across ecosystems, which is not completely understood. Additionally, there is no consensus on the biophysical mechanisms that could support stem CH4 emissions, including the origin of these emissions. This hinders our understanding of spatial and temporal patterns and hamper the identification of biophysical drivers. Here, we summarize up to 30 opportunities and challenges on stem CH4 emissions research in order to improve estimates of magnitudes, patterns, drivers and trace the potential origin of CH4 emissions. We propose two main challenges: the need for long-term high frequency measurements of stem CH4 emissions, and the need for a mechanistic model including passive and active transport of CH4 from the soil-tree-atmosphere continuum. The first challenge would allow to constrain magnitudes and patterns of CH4 emissions at different temporal scales, and the second would require discovery and integration of pathways and mechanisms of CH4 production and emissions to be integrated into process-base models. Addressing these challenges might improve upscaling of CH4 emissions from trees to the ecosystem scale and the quantification of the role of stem CH4 emissions for the local-to-global CH4 budget.

Tree stems from wetland, floodplain and upland forests can emit CH4. This emerging field of research has revealed a high spatial and temporal variability on CH4 stem emissions between trees and species, and within and across ecosystems, which is not completely understood. Additionally, there is no consensus on the biophysical mechanisms that could support stem CH4 emissions, including the origin of these emissions. This hinders our understanding of spatial and temporal patterns and hamper the identification of biophysical drivers. Here, we summarize up to 30 opportunities and challenges on stem CH4 emissions research in order to improve estimates of magnitudes, patterns, drivers and trace the potential origin of CH4 emissions. We propose two main challenges: the need for long-term high frequency measurements of stem CH4 emissions, and the need for a mechanistic model including passive and active transport of CH4 from the soil-tree-atmosphere continuum. The first challenge would allow to constrain magnitudes and patterns of CH4 emissions at different temporal scales, and the second would require discovery and integration of pathways and mechanisms of CH4 production and emissions to be integrated into process-base models. Addressing these challenges might improve upscaling of CH4 emissions from trees to the ecosystem scale and the quantification of the role of stem CH4 emissions for the local-to-global CH4 budget.

Understanding the variation of soil physical properties in relation to land use and elevation is essential for modeling soil-landscape relationships and sustainable land management. Hence, this study investigates the spatio-temporal variability of soil physical properties in a lower Himalayan watershed, where agriculture, forest, and grasslands are dominant. Samples from 104 sites in a 422 km 2 watershed were collected using a gridded sampling scheme (2 km × 2 km resolution) over 57 weeks. Spatial patterns were analyzed using the Kriging technique, and Spearman rank correlation was employed to identify landform-dependent correlations between soil properties and elevation. The interdependence of the properties was detected using principal component analysis (PCA), while the random forest (RF) approach explored the factors influencing electrical conductivity (EC), organic content (OC), soil temperature (ST), and soil moisture (SM). The results revealed that forest landforms have higher coarser fractions (40%) compared to other landforms, while grasslands have higher soil fines (66%). A positive correlation was observed for elevation with sand content (0.15*), organic content (0.42*), and specific gravity (0.03), while a negative correlation was observed for silt (0.10), clay (0.21*), bulk density (0.52*), electrical conductivity (0.41*), soil moisture (0.28*), and temperature (0.31*). Elevation, soil texture, and specific gravity were identified as critical controls for EC, OC, ST, and SM, emphasizing the importance of soil properties, especially elevation and texture, in shaping spatial distributions. These findings contribute to creating a high-resolution regional inventory for effective land use management, adaptation to climate change, and improved livelihood, specifically for mountain people.

Based on the daily precipitation data from 33 meteorological stations in Yunnan Province from 1959 to 2013, the standardized precipitation index was calculated to analyze the drought frequency, station proportion, and drought intensity. The following conclusions were drawn from the analysis: (1) Drought exhibited a well-defined spatial-temporal variability characteristic. The drought frequency was much higher in the central and western areas of Yunnan, although it was the opposite throughout most of the southwestern and eastern regions. In past decades, centers with a high probability of drought was found: In the 1960s, it was located in the northwestern region of Yunnan. It was found in the northwestern and southeastern areas in the 1970s, the central region in the 1980s, the northwestern area in the 1990s, and central Yunnan and western Yunnan after 2000. (2) The drought frequency and intensity both increased at multiple timescales. The affected areas exhibited a well-defined upward trend in summer, autumn and winter. (3) The drought was normalized in Yunnan. However, in 1973, there was no severe inter-annual drought event. Moreover, there is 7% of the observed months in which no drought occurred. (4) The drought intensity increased at multiple timescales, especially in the summer and autumn. (5) There was a piecewise linear correlation between the drought area and the drought intensity. The enhancement of the intensity coincided with an increase in the affected areas. When the intensity value exceeded 1.5, the drought area decreased immediately, although it exhibited linear positive correlation with intensity.

Trace metals are vital to primary productivity and play an essential role as main components in regulating oceanic biogeochemical cycles. Dissolved and particulate trace metals within the water column may vary due to primary production, temperature, and nutrient changes, factors that may also vary spatially and temporally. Furthermore, assessment of trace metals mainly relies on in situ observation, and so wide-area investigation of trace-metal concentration may be challenging and subject to technical constraints. A specific approach is therefore necessary that combines biogeochemical proxies, satellite data, and trace-metal linear correlation. This study aims to assess the potential spatio-temporal variability of sea surface cadmium (Cd) and copper (Cu) concentrations in Indonesian seas and surrounding areas. The correlations of Cd and Cu concentrations with primary production and nutrient data were used to convert hindcast satellite data into estimates of the metals’ concentrations. The potential variability of trace metals can be determined by overlaying both data. Indonesia’s Fisheries Management Areas (FMAs) were used for data clustering and analysis. The results show that Cd and Cu trace metals have similar distribution patterns throughout the year. However, dissolved Cu has a more diverse coverage area than dissolved Cd, including within the Halmahera, Seram, and Maluku Seas (FMAs 716 and 717), the Makassar Strait (FMA 717), and the Java–Sumatra upwelling area (FMA 573). Both Cd and Cu concentrations in the Java–Sumatra upwelling region follow the periodic upwelling pattern. Overall, both Cd and Cu show a declining trend in concentration from 2012 to 2019. It is estimated that dissolved Cd concentration declined from 1500–2000 pmol/kg in 2012 to 1000–1500 pmol/kg in 2019 for all locations. Dissolved Cu concentration decreased from 30–35 nmol/kg in 2012 to 25–30 nmol/kg in 2019. Estimated dissolved Cd and Cu follow the linear functions of silicate (SiO 4 ), nitrate (NO 3 ), and primary productivity. The fluctuation of anthropogenic activities and global warming are likely to indirectly impact the decline in metal concentrations by affecting nutrients and primary productivity.