Global Differences in Climate Asymmetrically Shape Temporal Ecological Processes in Riverine Insect Diversity

Global Differences in Climate Asymmetrically Shape Temporal Ecological Processes in Riverine Insect Diversity | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 18 July 2025 V1 Latest version Share on Global Differences in Climate Asymmetrically Shape Temporal Ecological Processes in Riverine Insect Diversity Authors : Xiaowei Lin , Qingyi Luo , Mei-Hwa Kuo , Zihao Wen 0000-0003-3885-5568 , Qinghua Cai , Ming-Chih Chiu 0000-0003-2793-4981 [email protected] , and Vincent Resh Authors Info & Affiliations https://doi.org/10.22541/au.175285842.22861907/v1 303 views 151 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Climatic fluctuations play a crucial role in shaping global patterns of biodiversity and biogeography. This study examines differences in the spatial patterns of temporal ecological processes, such as local biodiversity patterns, that can influence community dynamics of riverine insects. The data analyzed were from 302 sites globally and spanned 10 to 37 years of collections, totaling 4,751 observations of species composition. Sites were chosen to ensure balanced regional representation. Results indicate that community dynamics were characterized by significant spatial differences. Species richness declined at high latitudes, while temporal beta diversity decreased with both increasing latitude and elevation. The relative importance of stochastic and deterministic processes was found to shift spatially, with the contribution of stochastic processes such as ecological drift increasing with latitude and deterministic processes such as temperature decreasing with latitude. Under fluctuating climates, spatial asymmetry may influence aquatic-insect community dynamics and the prioritization of adaptive, conservation strategies. Global Differences in Climate Asymmetrically Shape Temporal Ecological Processes in Riverine Insect Diversity Xiaowei Lin 1,2# , Qingyi Luo 1# , Mei-Hwa Kuo 3 , Zihao Wen 1 , Qinghua Cai 1 * , Ming-Chih Chiu 3* , and Vincent H. Resh 4 1 Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China 2 College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, China 3 Department of Entomology, National Chung Hsing University, Taiwan 4 Department of Environmental Science, Policy & Management, University of California Berkeley, USA * [email protected] 1 Department of Infectious Disease, Imperial College London, Du Cane Rd, London W12 0NN UK. 2 Department of Biotechnology, Hezekiah University Umudi, Imo State, Nigeria. # Contributed equally * Corresponding Author: QC: [email protected] ; MCC: [email protected] Other authors’ emails XL: [email protected] ; QL: [email protected] ; MHK: [email protected] ; ZW: [email protected] ; VHR: [email protected] Conflict of Interest The authors declare no conflict of interest. Author Contributions All authors conceptualized the project. XL, QC, MCC, and VHR drafted the article. XL, ZW, and MCC devised the research methodology. XL, MHK, QC, MCC, and VHR collected the data. XL and QL conducted data processing and analysis. XL synthesized literature and created graphs and charts. All authors reviewed and revised the article. Acknowledgments: We thank the organizations and individuals who facilitated the acquisition of the data utilized in this study. Their altruistic contribution of data is deeply appreciated. Data availability For a comprehensive list of 22 data sources and their respective links, please refer to Table S6. Abstract Climatic fluctuations play a crucial role in shaping global patterns of biodiversity and biogeography. This study examines differences in the spatial patterns of temporal ecological processes, such as local biodiversity patterns, that can influence community dynamics of riverine insects. The data analyzed were from 302 sites globally and spanned 10 to 37 years of collections, totaling 4,751 observations of species composition. Sites were chosen to ensure balanced regional representation. Results indicate that community dynamics were characterized by significant spatial differences. Species richness declined at high latitudes, while temporal beta diversity decreased with both increasing latitude and elevation. The relative importance of stochastic and deterministic processes was found to shift spatially, with the contribution of stochastic processes such as ecological drift increasing with latitude and deterministic processes such as temperature decreasing with latitude. Under fluctuating climates, spatial asymmetry may influence aquatic-insect community dynamics and the prioritization of adaptive, conservation strategies. Keywords: biogeography, community structure, environmental variations, long-term monitoring, structural equation modeling * [email protected] 1 Department of Infectious Disease, Imperial College London, Du Cane Rd, London W12 0NN UK. 2 Department of Biotechnology, Hezekiah University Umudi, Imo State, Nigeria. Introduction Climatic fluctuations largely determine global patterns of biodiversity and biogeography (Thornton et al., 2014; Bestion et al., 2021; MacDonald et al., 2024). For example, changes in natural climate fluctuations have resulted in significant biodiversity losses over recent decades, particularly within freshwater ecosystems (Barbarossa et al., 2021; Maasri et al., 2022; Lin et al., 2024a). These climatic fluctuations affect key climate variables such as temperature and precipitation patterns, and the occurrence of extreme events (e.g., droughts and floods) that contribute to rapid habitat loss and shifts in species distributions (Chan et al., 2016; Chiu et al., 2021; MacDonald et al., 2024). In addition, climatic fluctuations drive substantial alterations in community composition that affect ecosystem functioning (Dornelas et al., 2014; Vasseur et al., 2014). The global patterns of community dynamics and temporal ecological processes (i.e., those that refer to mechanisms that shape local biodiversity over time) should respond differently to those climatic fluctuations that are dependent on their geographical location. For example, deterministic processes such as temporal habitat suitability and environmental variations were generally linked to local conditions that influenced community structure over time (Larson et al., 2016; Leigh et al., 2019; Matthews et al., 2019). In contrast, stochastic processes, such as ecological drift and random population fluctuations, introduced a degree of unpredictability and randomness into the community dynamics (Zeni et al., 2020; Lin et al., 2024a; Lin et al., 2025). However, the current understanding of spatial patterns in the temporal processes (e.g., of aquatic insects) has focused on a few regional studies (Lin et al., 2024a; Lin et al., 2024b) that focused on specific geographic areas and environmental conditions. This limitation in scope hinders generalizations across regions, consequently leaving a gap in our abilities to understand the underlying mechanisms of the processes involved. Aquatic insects provide a valuable opportunity to investigate how global climatic fluctuations shape the temporal dynamics and processes in communities. The broad geographic distribution and environmental sensitivity of aquatic insects can drive regional variation in their responses to climate fluctuations (Nash et al., 2023; MacDonald et al., 2024). Moreover, as a major component of global freshwater biodiversity (Grigoropoulou et al., 2023; Liu et al., 2025), aquatic insects are vital for ecosystem functioning, particularly in linking aquatic and terrestrial systems through energy and resource transfer (Twining et al., 2018; Barmentlo et al., 2021; Resetarits Jr. et al., 2021). This study used long-term data on aquatic insect communities from 302 riverine sites worldwide, with data collections spanning 10 to 37 years, for a total of 4,751 observations of species composition (Fig. 1a). Using an analytical framework (Fig. 1b), this study addressed the following two questions: (1) Do spatial asymmetries exist in community dynamics and temporal ecological processes under different spatial patterns of climate and long-term variability? (2) What mechanisms drive community dynamics and temporal ecological processes following these changes? Fig. 1. (a) Distribution of the sampling sites and (b) conceptual analytic framework in our study. “Total” represents total temporal beta-diversity. Turnover and Nestedness represent species replacement and species loss or gain in the temporal beta diversity, respectively. GDM, GAM, and pSEM represent the Generalized Dissimilarity Model, Generalized Additive Model, and Piecewise Structural Equation Modelling, respectively. Materials and Methods Riverine insect data We collected long-term monitoring data on riverine insects from published publications, data sets publicly available from monitoring programs, and our unpublished long-term data (see Table S6 for data sources). Most of the data sites were located in Europe, accounting for 75.2% of the total. The next largest concentration of data was from North America, with 19.2% of data locations. The remaining data locations were distributed among Asia and Oceania, with 3.6% and 2.0%, respectively. In total, we considered 22 data sets, and we applied various filtering approaches to them. For example, we selected long-term monitoring data that was collected annually, allowing a maximum gap of two years between sampling occasions, and with a minimum of 10 years of actual sampling records. These sites were selected to ensure a balanced site density across regions, which minimized biases that could arise from uneven data distribution. Because of the high number of sampling sites in some datasets (e.g., in Europe), we randomly selected a subset of sites. This approach prevented the explanation of spatial patterns from being overly influenced by a few well-sampled areas. It also allowed for more representative insights into global, riverine-insect community dynamics. In addition, if some of the sources had more than one sampling period per year, we chose only one data set with similar sampling times. All taxonomic data were converted to the family level for consistent analysis. Following this procedure, we ended up with 302 data sets spanning the years 1969 - 2021 and a duration of 10 - 37 years (Fig. 1). The latitudinal distribution of sample sites ranged from 36°S to 67°N, with the vast majority of sites located in the Northern Hemisphere and only six in the Southern Hemisphere. The elevation of the sample sites ranged from 0 to 2200 meters asl. * [email protected] 1 Department of Infectious Disease, Imperial College London, Du Cane Rd, London W12 0NN UK. 2 Department of Biotechnology, Hezekiah University Umudi, Imo State, Nigeria. Collection of climate data We obtained elevation, total monthly precipitation, average monthly minimum temperature, and average monthly maximum temperature for the study period at each of the 302 sample sites from the WorldClim 2 database (Fick & Hijmans, 2017). Where elevation information was explicitly available at the river source, we used that information. To describe temporal processes, we derived 19 bioclimatic variables using the ”biovars” function in the R package “dismo” (Hijmans, 2023). These variables were calculated from total monthly precipitation, average monthly minimum temperature, and average monthly maximum temperature, which included: annual mean temperature, mean diurnal temperature range, Isothermality, temperature seasonality, maximum temperature of warmest month, minimum temperature of coldest month, annual temperature range, mean temperature of wettest quarter, mean temperature of driest quarter, mean temperature of warmest quarter, mean temperature of coldest quarter, annual precipitation, precipitation of wettest month, precipitation of driest month, precipitation seasonality, precipitation of wettest quarter, precipitation of driest quarter, precipitation of warmest quarter, and precipitation of coldest quarter. Construction of time variables We used the Asymmetric Eigenvector Map (AEM) method to generate time variables that were used for analyses related to temporal stochastic processes (see subsequent section). AEM was a feature function-based filtering approach used to study directional processes (Blanchet et al., 2011). The AEM method accounted for the asymmetric nature of time that logically preserved temporal autocorrelation where past states influenced subsequent states, which makes it an effective tool for studying time series dynamics (Baho et al., 2015). Because the sampling times of our sites were different, we first compiled and ordered the sampling years of all sites in the same framework, and then generated AEMs. We then selected the appropriate AEM for each site based on its sampling year. In calculating the AEMs, the integrated sampling years were continuous and uninterrupted, with equal time intervals (1 year), and thus were given equal weight in the analysis. AEM typically produced n-1 positive eigenvalues and no negative eigenvalues. The feature functions were classified into two groups: those that were positively correlated with time and those that were negatively correlated with time (Blanchet et al., 2008). Moran’s I test was used to select the significant AEMs that were positively correlated with time (Blanchet et al., 2011). AEMs were obtained using the ”aem.time” function from the ”adespatial” package (Dray, 2022). * [email protected] 1 Department of Infectious Disease, Imperial College London, Du Cane Rd, London W12 0NN UK. 2 Department of Biotechnology, Hezekiah University Umudi, Imo State, Nigeria. Data analyses To test the spatial distribution pattern of climate and its variability, we calculated total monthly precipitation, mean monthly minimum temperature, and mean monthly maximum temperature to obtain total annual precipitation, mean annual temperature. We also calculated the coefficient of variation (CV) of total annual precipitation, mean annual temperature for each sample site over the study period to indicate the degree of variability. Because some sample sites had mean annual temperatures below 0 degrees, we calculated their coefficients of variation by first converting them to absolute temperatures. Because there were only six sample points in the southern hemisphere, we took the absolute values of their latitudes to mitigate potential biases caused by the small-sample distribution imbalance there. This variable was subsequently referred to as ”latitude” rather than ”absolute latitude” in the latter analyses. We then used the Generalized Additive Models (GAMs) to examine the relationships of climatic averages and variations (i.e., in total annual precipitation, mean annual temperature) with elevation and latitude (Wood et al., 2016). To account for hemispheric differences in the climate model, we treated the northern and southern hemispheres as random effects to mitigate potential bias. To examine the spatial pattern of community dynamics, we first calculated species richness at each sample site over the study period. Using Sørensen pairwise dissimilarity, we then generated three pairwise dissimilarity matrices with presence-absence data using the ’betapart’ package in R (Baselga & Orme, 2012). These matrices represented the total beta diversity (total), the species turnover component (i.e., species replacement), and the nestedness component (i.e., species loss or gain) for each sample site. We then analyzed species richness in relation to total temporal beta diversity (total) and its components, with respect to elevation and latitude, using the GAM model. To account for the effects of different sources, sampling methods, sampling periods, sampling months, and others on the model, we set up random variables to eliminate bias and applied the same treatment in subsequent operations. To validate the spatial asymmetry of temporal ecological processes, we used climate variables to examine the influence of deterministic processes while temporal variables were used for stochastic processes. We applied the Generalized Dissimilarity Models (GDMs) to identify valid predictors of temporal beta diversity and its components at each sample site (Ferrier et al., 2002; Ferrier et al., 2007). Using the “gdm.partition.deviance” function from the “gdm” package in R (Fitzpatrick, 2022), we decomposed the variance in temporal beta diversity attributable to climatic and temporal variables. Finally, we used the GAM models to examine how various ecological processes were related to elevation and latitude. In the GDM framework, the ecological distance (i.e., temporal beta diversity) between sampling sites serves as the response variable, while predictors are represented as paired distances between sites. Each predictor’s effect is smoothed using an I-spline basis function, allowing for a flexible variation of the response across its range. The transformed values for site pairs are then compared, with the absolute differences across all predictors being summed. The model is fitted through non-negative least-squares regression. The number of I-splines constrains the GDM model fit, with a default of three I-splines (Borcard et al., 1992; Jones et al., 2013). In cases where the model did not converge, we adjusted this parameter, increasing the number of I-splines to a maximum of four and setting the iteration limit to 1,000. The GDM assumes a positive relationship between community variance and predicted ecological distance, incorporating a link function to model a nonlinear connection between the predicted ecological distance and biome variance (Mokany et al., 2022). To test how climatic variables affected aquatic insect community dynamics and temporal ecological processes, we performed the Piecewise Structural Equation Modelling (pSEM) using the R package “piecewiseSEM”. pSEM tests multiple hypotheses in a causal network and accommodates various model structures, distributions, and assumptions (Lefcheck, 2016). A full model was structured by defining external environmental variables (i.e., average and variability of precipitation, average and variability of temperature) and internal biotic variables (i.e., species richness, community dynamics, and temporal ecological processes). The full model was constructed based on ecological theory and knowledge. Given the potential nonlinear relationships between variables, all paths were fitted using the GAMs to capture complex environmental-biological associations. Standardized coefficient estimation is not applicable to and cannot be estimated for nonlinear pathways. To evaluate the structural integrity and causal pathways of our model, we conducted a series of statistical tests. We performed a directional-separation test (d-separation test) to systematically identify any missing pathways. The overall test was conducted using Fisher’s C statistic for the entire model, which was calculated after excluding paths that lacked explicit directional assumptions and retaining only those with theory-driven causality (Lefcheck, 2016). We evaluated the final model fit using the C statistic to assess no potentially missing, causal pathways and also the consistency between the observed data and our hypothesized model structure (p > 0.05). Additionally, we assessed the goodness of fit for the model structure using chi-square testing, which can confirm no significant deviation and further support the consistency of the model with the observed data (p > 0.05). Spatial patterns of climate and variability Not surprisingly, environmental variables, including precipitation and temperature, exhibited distinct patterns and variations across latitudinal and elevational gradients (Table S1). The results indicated a general trend of decreasing mean annual precipitation with increasing latitude. Furthermore, as altitude increased, the mean annual precipitation demonstrated a pattern of increasing, then decreasing, and subsequently increasing (Fig. 2a). In contrast, both the mean annual temperature showed consistent decreases with increasing latitude and altitude (Fig. 2b). Regarding variability trends, the coefficient of variation for mean annual precipitation first increased, then decreased, and subsequently increased again with latitude, while along elevational gradients it displayed the same sequence (Fig. 2c). The variations in mean annual temperature, however, exhibited overall increasing trends with both increasing latitude and altitude (Figs. 2d). Fig. 2. Latitudinal and elevational patterns of (a and b) averages and (c and d) variabilities of climatic conditions during the study period. Pre and Temp present the annual precipitation (mm) and mean annual temperature (°C), respectively. The y-axis is the partial effect, and the points are the prediction of x plus the residuals from the full model. Latitude values are expressed in their absolute form. Spatial patterns of community dynamics The GAM models for different latitudinal and elevational gradients revealed distinct diversity patterns (Table S2). For instance, richness initially decreased, then increased, but subsequently decreased again with increasing latitude. Higher latitudes generally exhibited lower overall richness. Along elevational gradients, richness followed a more complex trajectory: it first increased, then decreased, and was followed by another increase before ultimately declining at the highest elevations (Fig. 3a). Both total beta diversity and its turnover component (i.e., species replacement) gradually declined with increasing latitude. A similar decreasing trend was observed across elevational gradients for these metrics; however, the nestedness component (i.e., species loss or gain) of beta diversity decreased with latitude but did not exhibit a clear elevational pattern (Fig. 3a). Fig. 3. Latitudinal and elevational patterns of (a) community dynamics and (b) temporal ecological processes. “Total” represents temporal beta diversity. Turnover and Nestedness represent species replacement and species loss or gain in temporal beta diversity, respectively. Stochastic/Deterministic represents the relative importance of stochastic versus deterministic processes. The y-axis is the partial effect, and the points are the prediction of x plus the residuals from the full model. Latitude values are expressed in their absolute form. Spatial patterns of temporal ecological processes The results showed a clear latitudinal pattern in temporal ecological processes for total beta diversity, but no clear elevational pattern (Tables S3 to S5 and Fig. 3b). The relative importance of stochastic versus deterministic processes, as well as the importance of stochastic processes, increased with latitude (Fig. 3b). Conversely, the importance of deterministic processes decreased with increasing latitude (Fig. 3b). However, for the components of temporal diversity (turnover and nestedness), only stochastic processes exhibited increasing importance with latitude, and no significant elevational trends were observed. Other temporal ecological processes showed no distinct spatial patterns (Figs. S1 and S2). Mechanisms underlying the spatial patterns of temporal ecological processes In examining community dynamics, all variables together (i.e., average and variability of precipitation, average and variability of temperature, and richness) accounted for 72% of the total temporal beta diversity, 63% of the turnover component, and 71% of the nestedness component through Piecewise Structural Equation Modelling. The study further revealed that species richness exhibited a direct response to the average and variability of precipitation. The total temporal beta diversity and its turnover component were found to be directly influenced by the average and variability of temperature and species richness. The nestedness component was found to be directly influenced by the average of temperature and species richness (Fig. 4b). The relative importance of deterministic processes is directly influenced by average of precipitation and variability of temperature (not significant), while the ratio of stochastic to deterministic processes are directly affected by average of temperature (Fig. 4d). The model identified variables that explained the relative importance of stochastic and deterministic processes, as well as the ratio of stochastic to deterministic processes. All variables together (i.e., average and variability of precipitation, average and variability of temperature, and richness) explained 17%, 2%, and 6% of the variation in these processes, respectively. This study indicated that species richness is directly influenced by the average and variability of precipitation, and the average of temperature. Fig. 4. Analysis using Piecewise Structural Equation Modelling illustrating how environmental factors influence community dynamics or temporal ecological processes, presented as (a or b) in the full, theoretical model and (c or d) in the final, fitted model, respectively. Unidirectional solid lines indicate significant causality while dashed lines indicate a non-significant causality. The color of the lines indicates the effect of environmental variables (precipitation and temperature): the red line represents the average and variability of environmental variables, the green line represents the average of environmental variables, and the blue line represents the variability of environmental variables. Bidirectional solid gray lines indicate the correlation between variables. Pre and Temp present average and variability of annual precipitation, average and variability of mean annual minimum temperature, respectively. Stochastic/Deterministic represents the relative importance of stochastic versus deterministic processes. * [email protected] 1 Department of Infectious Disease, Imperial College London, Du Cane Rd, London W12 0NN UK. 2 Department of Biotechnology, Hezekiah University Umudi, Imo State, Nigeria. Discussion The study revealed that significant spatial and temporal patterns in climate, community dynamics of aquatic insects, and ecological processes occur across global gradients. Global patterns of climate and its variability are characterized by a general decreasing trend in precipitation and temperature with increasing latitude. In addition, temperature exhibited a decreasing trend across the altitudinal gradient, while precipitation demonstrated a different pattern. Similarly, there were spatial asymmetries in community dynamics and temporal ecological processes. Species richness exhibited a general decreasing trend with increasing latitude, although this trend was not observed along the altitudinal gradient. Temporal beta diversity exhibited a decreasing trend with increasing latitude and elevation. The importance of stochastic and deterministic processes exhibited a tendency to increase and then decrease, respectively, with increasing latitude. However, temporal ecological processes exhibited an absence of a discernible pattern of elevation. The subsequent sections will examine the underlying mechanisms that underpin the observed spatial patterns in community dynamics and temporal ecological processes. * [email protected] 1 Department of Infectious Disease, Imperial College London, Du Cane Rd, London W12 0NN UK. 2 Department of Biotechnology, Hezekiah University Umudi, Imo State, Nigeria. Global patterns of community dynamics The richness of aquatic insects present at a site reflects the total species pool accumulated over time within a region (Zobel, 2016; Ron et al., 2018). In this study, the richness of aquatic insects generally decreased with increasing latitude, suggesting that a smaller temporal species pool occurs at higher latitudes. The smaller species pool may reflect the decrease in energy availability as latitude increases, which could limit the biomass a region can support and result in fewer coexisting species (Gaston, 2000). The observed decrease in precipitation and temperature along increasing latitude was indicative of this pattern. In addition to the gradual decrease in temperature with elevation and the non-monotonic trend in precipitation with elevation, elevation also has a direct effect on species richness. Variability in precipitation and temperature across the elevation gradient may form a complex altitudinal pattern of species richness. With increasing latitude and elevation, total temporal beta diversity and its components (i.e., turnover and nestedness) exhibited a decreasing trend, which indicates that community stability increased with increasing latitude and elevation. The SEM analysis showed that temperature and its fluctuations exerted a direct effect on temporal beta diversity, while precipitation exerted an indirect effect through its influence by modulating species richness. For example, lower temperatures may impede the foraging and activity of aquatic insects, thus delaying their developmental cycles and emergence (Hodkinson, 2005; Karl et al., 2011). Studies have shown that temperature fluctuations directly affect the rate at which species turnover occurs within a community (Pinsky et al., 2025). Precipitation that altered hydrologic conditions (flow stability and duration) may affect the stability of aquatic insect habitat and, consequently, aquatic insect growth periods (Patrick et al., 2019). A reduction in precipitation and temperatures could increase environmental filters that require aquatic insects to evolve narrower ecological niches to adapt to specific environmental conditions (Callaway et al., 2002; Brooker et al., 2008; Cavieres & Badano, 2009). At the same time, positive interspecific interactions can increase with abiotic stress (e.g., resource shortage and drought), and therefore enhance temporal community stability to a certain extent (Callaway et al., 2002; Smit et al., 2009). Global asymmetry in temporal ecological processes Our study showed that temporal ecological processes did not exhibit a distinct altitudinal pattern, but did exhibit a significant latitudinal pattern. For example, the results of the GAM analysis demonstrated a direct relationship between latitude and the relative importance of stochastic processes; The role of deterministic processes decreased and the influence of stochastic processes intensified with increasing latitude. Likewise, the ratio of stochastic to deterministic processes increased with increasing latitude. This pattern suggests the mechanisms driving community dynamics may change under different spatial patterns of climate and its corresponding long-term variability (Lin et al., 2024a; Lin et al., 2024b). Ecological processes were important in the decrease of total temporal beta diversity that occurred with increasing latitude. This pattern could reflect increases in resource scarcity. For example, ecological niche differentiation fosters species coexistence by ensuring the full utilization of resources that consequently enhances the stability of communities (HilleRisLambers et al., 2012). Lower latitudes are associated with more favorable light and heat conditions along with more abundant resources, which makes ecological niche differentiation more evident and enables different species to use different microhabitats or resources. For example, as latitude increases, resources gradually become limiting, making community structure more vulnerable to stochastic events (e.g., ecological drift and stochastic colonization; Conradi et al., 2017). Temperature exhibited a direct impact on the ratio of stochastic to deterministic processes, and precipitation exhibited a direct impact on deterministic processes. In high-latitude environments, temperature and precipitation gradually become the main limiting factors, and resource scarcity increases (Forsman & Mönkkönen, 2003). Such events significantly affect aquatic insects’ survival and reproduction, allowing for an enhanced influence of stochastic factors on community dynamics (Lin et al., 2024a). Resources are often insufficient to sustain strong interspecific competition, leading to a gradual reduction in the role of deterministic processes (e.g., the interplay of biological interactions and environmental filtering) (Weiher et al., 2011; Conradi et al., 2017; Wu et al., 2024). This result was supported by our results that species richness was higher at lower latitudes, although the study found no notable effect of species richness on temporal ecological processes. This phenomenon may have been related to adaptive strategies developed by riverine macroinvertebrates (i.e., including aquatic insects) in response to resource constraints and biological interactions. In studies of biogeographic distributions, elevation and latitude are often considered somewhat interchangeable (Loewen et al., 2023). However, in this study, the effects of elevation and latitude on community dynamics and ecological processes were not consistently demonstrated. Latitude showed a clear pattern of gradients of temporal ecological processes, while elevation did not. This contradictory pattern may be related to complex trend changes in precipitation and its variability across the altitudinal gradient. These changes may have facilitated shifts in the balance between temporal ecological processes across the altitudinal gradient that masked trends in altitudinal change. Future studies incorporating broader elevational gradients would further clarify how elevational patterns shape temporal ecological processes in aquatic insect communities. * [email protected] 1 Department of Infectious Disease, Imperial College London, Du Cane Rd, London W12 0NN UK. 2 Department of Biotechnology, Hezekiah University Umudi, Imo State, Nigeria. Conclusions and management implications Our results show that significant spatial asymmetries occur in the riverine insect communities globally and also in their underlying ecological processes. 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Keywords biogeography community structure environmental variations long-term monitoring structural equation modeling Authors Affiliations Xiaowei Lin Institute of Hydrobiology Chinese Academy of Sciences View all articles by this author Qingyi Luo Chinese Academy of Sciences View all articles by this author Mei-Hwa Kuo National Chung Hsing University View all articles by this author Zihao Wen 0000-0003-3885-5568 Institute of Hydrobiology Chinese Academy of Sciences View all articles by this author Qinghua Cai Institute of Hydrobiology Chinese Academy of Sciences View all articles by this author Ming-Chih Chiu 0000-0003-2793-4981 [email protected] National Chung Cheng University View all articles by this author Vincent Resh University of California Berkeley View all articles by this author Metrics & Citations Metrics Article Usage 303 views 151 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xiaowei Lin, Qingyi Luo, Mei-Hwa Kuo, et al. 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