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The emergence of metacommunity theory has provided a framework for studying the spatial structure of biological communities. To unravel the underlying driving mechanisms of macroinvertebrate metacommunity structure and diversity, this paper evaluates macroinvertebrate data from a water body in eastern China using variance decomposition and stratified partitioning. The results suggest that spatial processes dominate macroinvertebrate metacommunity structure, while the combined effects of spatial processes, aquatic environment, and geoclimatic factors also explain part of the macroinvertebrate community structure. Spatial processes, aquatic environment, and geoclimatic factors significantly influenced macroinvertebrate alpha diversity and indirectly influenced beta diversity through alpha diversity. Therefore, the significant effects of spatial processes and geoclimatic variables cannot be ignored in biological assessments, especially biodiversity assessments. Our findings elucidate the metacommunity dynamics in the region and provide crucial information for conservation planning in riverine ecosystems. [ABSTRACT FROM AUTHOR]

The emergence of metacommunity theory has provided a framework for studying the spatial structure of biological communities. To unravel the underlying driving mechanisms of macroinvertebrate metacommunity structure and diversity, this paper evaluates macroinvertebrate data from a water body in eastern China using variance decomposition and stratified partitioning. The results suggest that spatial processes dominate macroinvertebrate metacommunity structure, while the combined effects of spatial processes, aquatic environment, and geoclimatic factors also explain part of the macroinvertebrate community structure. Spatial processes, aquatic environment, and geoclimatic factors significantly influenced macroinvertebrate alpha diversity and indirectly influenced beta diversity through alpha diversity. Therefore, the significant effects of spatial processes and geoclimatic variables cannot be ignored in biological assessments, especially biodiversity assessments. Our findings elucidate the metacommunity dynamics in the region and provide crucial information for conservation planning in riverine ecosystems. [ABSTRACT FROM AUTHOR]

Abstract The aboveground biomass (AGB) of grassland, a crucial indicator of productivity, is anticipated to widespread changes in key ecosystem attributes, functions and dynamics. Variations in grassland AGB have been extensively documented across various spatial and temporal scales. However, a precise method to disentangle long-term effects from short-term effects on grassland AGB and assess the attribution of explanatory factors for AGB change remains elusive. This study aimed to quantify the impact of key climatic factors, soil properties, and grazing intensity on grassland AGB changes, utilizing data spanning the 1980s and the 2000s in Northern China. The Co-regression model was explored to separate the long-term effects and short-term effects of grassland AGB, while the Generalized Linear Model (GLM) was utilized to analyze the contributions of key variables to AGB. This approach effectively avoids issues related to regression to the mean and mathematical coupling. The results revealed that the influence of climatic variables, soil texture and grazing intensity on grassland AGB changes could be decomposed into long-term, short-term and random effects. Long-term effects explained 73.6% of AGB variation, whereas short-term effect only accounted for 5.9% of AGB change. Additionally, the short-term effect was divided into direct and indirect effects, with the direct effect explaining 1.3% of AGB variation, and the indirect effect explained 4.6% of AGB dynamics. The relative importance of key variables in grassland AGB was assessed, identifying soil parameters and precipitation as the main driving factors in the study area. This study introduces a robust methodology to enhance model performance in distinguishing long-term and short-term effects on grassland AGB, contributing to the sustainable development of grassland ecology in similar regions.

Different mechanisms act to structure metacommunities. Among them, the most studied are those related to environmental and spatial drivers. Also, most studies on metacommunities are based on data obtained in snapshot surveys, hindering the test of other potential mechanisms, including historical effects. Here, we quantified the relative importance of different sets of variables that could potentially structure zooplankton communities in 39 small reservoirs in Midwest Brazil. In addition to local environmental, spatial, and land use variables, as a proxy for potential priority effects, we tested the effect of earlier zooplankton composition on (later) zooplankton metacommunity structure. The relative importance of these sets of explanatory variables varied over time. However, earlier zooplankton community data were, in general, the most consistent predictors (especially for microcrustaceans) of zooplankton community structure. This result may indicate the role of historical effects on later zooplankton metacommunity structure. Our results highlight the importance of considering multiple surveys and past community data in spatiotemporal studies aiming to unveil the determinants of zooplankton metacommunity structure. [ABSTRACT FROM AUTHOR]

The aboveground biomass (AGB) of grassland, a crucial indicator of productivity, is anticipated to widespread changes in key ecosystem attributes, functions and dynamics. Variations in grassland AGB have been extensively documented across various spatial and temporal scales. However, a precise method to disentangle long-term effects from short-term effects on grassland AGB and assess the attribution of explanatory factors for AGB change remains elusive. This study aimed to quantify the impact of key climatic factors, soil properties, and grazing intensity on grassland AGB changes, utilizing data spanning the 1980s and the 2000s in Northern China. The Co-regression model was explored to separate the long-term effects and short-term effects of grassland AGB, while the Generalized Linear Model (GLM) was utilized to analyze the contributions of key variables to AGB. This approach effectively avoids issues related to regression to the mean and mathematical coupling. The results revealed that the influence of climatic variables, soil texture and grazing intensity on grassland AGB changes could be decomposed into long-term, short-term and random effects. Long-term effects explained 73.6% of AGB variation, whereas short-term effect only accounted for 5.9% of AGB change. Additionally, the short-term effect was divided into direct and indirect effects, with the direct effect explaining 1.3% of AGB variation, and the indirect effect explained 4.6% of AGB dynamics. The relative importance of key variables in grassland AGB was assessed, identifying soil parameters and precipitation as the main driving factors in the study area. This study introduces a robust methodology to enhance model performance in distinguishing long-term and short-term effects on grassland AGB, contributing to the sustainable development of grassland ecology in similar regions. Highlights: Co-regression model was effective to separate long- and short- terms impact of AGB. The effect of long-term factors on AGB was higher than that of short-term factors. Soil parameter and precipitation were major driving factors to affect AGB change. [ABSTRACT FROM AUTHOR]

The future trajectory of global forests is closely intertwined with tree demography, and a major fundamental goal in ecology is to understand the key mechanisms governing spatio‐temporal patterns in tree population dynamics. While previous research has made substantial progress in identifying the mechanisms individually, their relative importance among forests remains unclear mainly due to practical limitations. One approach to overcome these limitations is to group mechanisms according to their shared effects on the variability of tree vital rates and quantify patterns therein. We developed a conceptual and statistical framework (variance partitioning of Bayesian multilevel models) that attributes the variability in tree growth, mortality, and recruitment to variation in species, space, and time, and their interactions – categories we refer to as organising principles (OPs). We applied the framework to data from 21 forest plots covering more than 2.9 million trees of approximately 6500 species. We found that differences among species, the species OP, proved a major source of variability in tree vital rates, explaining 28–33% of demographic variance alone, and 14–17% in interaction with space, totalling 40–43%. Our results support the hypothesis that the range of vital rates is similar across global forests. However, the average variability among species declined with species richness, indicating that diverse forests featured smaller interspecific differences in vital rates. Moreover, decomposing the variance in vital rates into the proposed OPs showed the importance of unexplained variability, which includes individual variation, in tree demography. A focus on how demographic variance is organized in forests can facilitate the construction of more targeted models with clearer expectations of which covariates might drive a vital rate. This study therefore highlights the most promising avenues for future research, both in terms of understanding the relative contributions of groups of mechanisms to forest demography and diversity, and for improving projections of forest ecosystems. [ABSTRACT FROM AUTHOR]

Forecasting invasion risk under future climate conditions is critical for the effective management of invasive species, and species distribution models (SDMs) are key tools for doing so. However, SDM‐based forecasts are uncertain, especially when correlative statistical models extrapolate to nonanalog environmental domains, such as future climate conditions. Different assumptions about the functional form of the temperature–suitability relationship can impact predicted habitat suitability under novel conditions. Hence, methods to understand the sources of uncertainty are critical when applying SDMs. Here, we use high‐resolution predictions of lake water temperatures to project changes in habitat suitability under future climate conditions for an invasive macrophyte (Myriophyllym spicatum). Future suitability was predicted using five global circulation models and three statistical models that assumed different species–temperature functional responses. The suitability of lakes for M. spicatum was overall predicted to increase under future climate conditions, but the magnitude and direction of change in suitability varied greatly among lakes. Variability was most pronounced for lakes under nonanalog temperature conditions, indicating that predictions for these lakes remained highly uncertain. Integrating predictions from SDMs that differ in their species–environment response function, while explicitly quantifying uncertainty across analog and nonanalog domains, can provide a more robust and useful approach to forecasting invasive species distribution under climate change. [ABSTRACT FROM AUTHOR]

Differences within species (Intraspecific trait variation - ITV) contribute substantially to overall trait variability and environmental harshness can reduce among-species variation. While aboveground traits have received considerable attention, knowledge about ITV in fine-root traits and how it differs from ITV in aboveground traits remains limited. This study examined the partitioning of trait variation aboveground and fine-root traits in 52 European herbaceous species and how such proportions change in response to drought, offering valuable insights for accurate functional species characterization and inter-species comparisons. We studied seven morphological aboveground and fine-root traits under drought and well-watered conditions in a greenhouse experiment. Linear mixed effect models and permutational multivariate analysis of variance (PERMANOVA) were employed to decompose trait variation, ensuring the robustness of our results. We also calculated variance partitioning for the combination of aboveground traits and the combination of fine-root traits, as well as pairs of analogous leaf and fine-root traits (i.e., traits that fulfill similar functions) for each treatment (control and drought). Among-species trait differences explained a greater proportion of overall variance than within-species variation, except for root dry matter content (RDMC). Height and leaf area stood out, with species' identity accounting for 87-90% of total trait variation. Drought had no significant effect on the proportions of variation in any of the traits. However, the combination of fine-root traits exhibited higher intraspecific variability (44-44%) than aboveground traits (19-21%) under both drought and control. Analogous root traits also showed higher ITV (51-50%) than analogous leaf traits (27-31%). Our findings highlight substantial within-species variation and the nuanced responses of fine-root traits, particularly RDMC, suggesting root traits' flexibility to soil heterogeneity that fosters less differentiation among species. Among-species trait differences, especially aboveground, may underscore distinct strategies and competitive abilities for resource acquisition and utilization. This study contributes to elucidate the mechanisms underlying the multifunctionality of the above- and belowground plants compartments. [ABSTRACT FROM AUTHOR]

The contrasting hydraulic traits observed among different plant life forms are shaped by entangled environmental and evolutionary processes. However, we lack understanding of the relative importance of life form, climate and phylogeny in explaining the variance of hydraulic traits.We analysed seven hydraulic traits and eleven climatic variables of 150 Fabaceae species representing three life forms from 62 sites worldwide, using phylogenetic comparative analyses and variance partitioning.The phylogenetic signal found in most traits disappeared after considering life form, indicating that phylogenetic conservatism in traits originated from the divergence among life forms. The trait‐climate relationships were also phylogenetically dependent, implying that trait responses are driven by climate and phylogeny together. Variance partitioning showed that phylogeny and climate explained greater trait variation than life form did.Synthesis. The climate‐driven hydraulic trait responses in Fabaceae still existed with phylogeny being considered, suggesting that this large family may be particularly sensitive to climate change. Our results emphasise the need to include phylogeny in plant hydraulic adaptation studies under future climate change. [ABSTRACT FROM AUTHOR]

The relative importance of abiotic and biotic factors in shaping forest biomass stocks and fluxes remains a controversial issue. Here, using data gathered from 39 1 ha plots located in flooded and terra firme mature tropical lowland forests of the Amazon and Orinoquia regions of Colombia, we evaluated the importance of climate, soil fertility, and flooding, as well as tree taxonomic/phylogenetic diversity and forest structural properties, in determining the aboveground biomass stocks (AGB; Mg ha−1) and aboveground woody productivity (AWP; Mg ha−1 year−1). Using information‐theoretic multimodel inference and variance partitioning we found that forest structural features such as the number of trees with diameter at breast height ≥ 70 cm, and wood density, are the main drivers of variation in AGB. However, taxonomic diversity also contributes to AGB because it is associated with more large trees in these forests. In contrast, the key drivers of AWP in these forests were soil P and Mg concentrations, with no significant effects of diversity indices. These findings emphasize the need to include major soil cations other than N and P (e.g. Mg) in experimental studies to improve our understanding about the extent to which soil fertility can modulate increases in forest AWP due to climate change. Terra firme forests had higher AGB stocks than flooded forests, but both had similar AWP; and we found similar results for the drivers of AGB and AWP between flooded and terra firme forests. Our results provide limited evidence for strong effects of plant diversity on AGB or AWP. Therefore, we call for caution on generalizations of nature‐based initiatives aiming to preserve diversity based on maximizing carbon stocks and productivity, due to the complex nature of the processes controlling carbon accumulation and carbon fluxes in tropical forests. [ABSTRACT FROM AUTHOR]

During ontogeny, the increase in body size forces species to make trade‐offs between their food requirements, the conditions necessary for growth and reproduction as well as the avoidance of predators. Ontogenetic changes are leading species to seek out habitats and food resources that meet their needs. To this end, ontogenetic changes in nocturnal habitat (vertical use of the water column) and in the type of food resources (based on stable isotopes of nitrogen) were investigated in 12 species of deep pelagic fish from the Bay of Biscay in the Northeast Atlantic. Our results revealed the existence of major differences in the ontogenetic strategies employed by deep pelagic fishes. Some species showed ontogenetic changes in both vertical habitat use and food resources (e.g. Jewel lanternfish (Lampanyctus crocodilus) and Atlantic soft pout (Melanostigma atlanticum)). In contrast, other species showed no ontogenetic change (e.g. Koefoed's searsid (Searsia koefoedi) and Lancet fish (Notoscopelus kroyeri)). Some species only changed food resources (e.g. Spotted lanternfish (Myctophum punctatum), Spotted barracudina (Arctozenus risso) and Stout sawpalate (Serrivomer beanii)), while others seemed to be influenced more by depth than by trophic features (e.g. Bluntsnout smooth‐head (Xenodermichthys copei) and Olfer's Hatchetfish (Argyropelecus olfersii)). These results suggest that to meet their increasing energy requirements during ontogeny, some species have adopted a strategy of shifting their food resources (larger prey or prey with a higher trophic level), while others seemed to maintain their food resources but are most likely increasing the quantity of prey ingested. As fish species can have different functional roles during their development within ecosystems, characterising ontogenetic changes in mesopelagic fish species is a crucial step to be considered in future research aimed at understanding and modelling the complexity of deep‐pelagic food webs. [ABSTRACT FROM AUTHOR]

Abstract The demand for low-cost and time-efficient biomonitoring and ecological assessment strategies has increased due to threats to biodiversity. We evaluated the ability of surrogates (genera and biovolume classes) to access information provided by diatom species. Moreover, we investigated whether the relative importance of spatial and environment on diatom communities identified at the genus or classified at biovolume classes reflects the ecological responses at the species. We used biotic, environmental, and spatial data from Pampa streams. We employed Procrustes analysis to detect whether the spatial variation of communities at the species level was represented by the surrogates and variation partitioning to evaluate the relative importance of spatial and environment. We found high congruence between the surrogates and diatom species communities. Furthermore, whilst both spatial and environment were important to explain species composition, only the environment was important for both surrogates. We suggest that the use of genus-level determinations can provide information about the environmental relationships of species, whereas biovolume classes may be useful to detect changes in diatom communities. These results are a first step to seek for strategies aiming to simplify biological monitoring and assessments at pampean streams, especially considering the demand for the identification of organisms at the species.

Metacommunity studies have demonstrated that local macroinvertebrate communities are structured not only by local environmental conditions but also by spatial processes. Effective bioassessment tools should account for spatial processes while doing so with the least amount of cost. In this study, we applied variance partition techniques based on redundancy analysis to assess the performance of three sets of benthic invertebrate metrics in detecting agricultural land-use effects in a SE Brazil rainforest watershed. Macroinvertebrate data were analyzed separately regarding their taxonomic, functional structure and bioindicator metrics developed for the study region. We stipulated that groups of metrics most sensitive to land-use effects should have the highest amount of variance explained by the joint effects of land use and environmental variation, independently of spatial structuring. Statistical analyses were repeated removing rare taxa in order to assess the effects of their inclusion in the responsiveness of each group of metrics. Traditional bioindicator metrics were more responsive to environmental variation associated with agriculture than taxa abundances and functional attributes. Furthermore, a few common taxa drove a high proportion of the variation observed in invertebrate communities, regardless of how invertebrate data were organized. Similar analytic approaches have the potential to be useful in curtailing sorting and identification efforts when developing macroinvertebrate-based biomonitoring protocols, especially in areas where information regarding the taxonomy of benthic communities is still poorly described. [ABSTRACT FROM AUTHOR]

glmm.hp is an R package designed to evaluate the relative importance of collinear predictors within generalized linear mixed models (GLMMs). Since its initial release in January 2022, it has been rapidly gained recognition and popularity among ecologists. However, the previous glmm.hp package was limited to work GLMMs derived exclusively from the lme4 and nlme packages. The latest glmm.hp package has extended its functions. It has integrated results obtained from the glmmTMB package, thus enabling it to handle zero-inflated generalized linear mixed models (ZIGLMMs) effectively. Furthermore, it has introduced the new functionalities of commonality analysis and hierarchical partitioning for multiple linear regression models by considering both unadjusted R 2 and adjusted R 2. This paper will serve as a demonstration for the applications of these new functionalities, making them more accessible to users. [ABSTRACT FROM AUTHOR]

Abstract During ontogeny, the increase in body size forces species to make trade‐offs between their food requirements, the conditions necessary for growth and reproduction as well as the avoidance of predators. Ontogenetic changes are leading species to seek out habitats and food resources that meet their needs. To this end, ontogenetic changes in nocturnal habitat (vertical use of the water column) and in the type of food resources (based on stable isotopes of nitrogen) were investigated in 12 species of deep pelagic fish from the Bay of Biscay in the Northeast Atlantic. Our results revealed the existence of major differences in the ontogenetic strategies employed by deep pelagic fishes. Some species showed ontogenetic changes in both vertical habitat use and food resources (e.g. Jewel lanternfish (Lampanyctus crocodilus) and Atlantic soft pout (Melanostigma atlanticum)). In contrast, other species showed no ontogenetic change (e.g. Koefoed's searsid (Searsia koefoedi) and Lancet fish (Notoscopelus kroyeri)). Some species only changed food resources (e.g. Spotted lanternfish (Myctophum punctatum), Spotted barracudina (Arctozenus risso) and Stout sawpalate (Serrivomer beanii)), while others seemed to be influenced more by depth than by trophic features (e.g. Bluntsnout smooth‐head (Xenodermichthys copei) and Olfer's Hatchetfish (Argyropelecus olfersii)). These results suggest that to meet their increasing energy requirements during ontogeny, some species have adopted a strategy of shifting their food resources (larger prey or prey with a higher trophic level), while others seemed to maintain their food resources but are most likely increasing the quantity of prey ingested. As fish species can have different functional roles during their development within ecosystems, characterising ontogenetic changes in mesopelagic fish species is a crucial step to be considered in future research aimed at understanding and modelling the complexity of deep‐pelagic food webs.

The landscape scale response of ecological communities to environmental drivers can be challenging to efficiently summarise and differentiate from expected background turnover through time. Metacommunity structure can be encapsulated by fitting joint species distribution models (JSDMs) and partitioning the variance explained into environmental, spatial and species-codistribution components. Here we identify how these components respond through time with directed environmental change and propose these changes in metacommunity structure as an indicator of sustained directional pressure. Through simulations, we identify how declines in the variation explained by species codistribution could diagnose ecological disintegration, while increases in the explanatory power of environmental and spatial predictors may indicate losses in peripheral areas and dispersal limitations. We then test these results in two well-studied systems. Butterflies are known to be strongly responding to climate change, and we show that over 21 years the codistribution component declines for butterfly communities in southern England. By contrast, birds in the same region are thought to be responding less strongly to climatic pressure and, despite high occupancy turnover, do not show clear changes in metacommunity structure as measured by this approach. Our results suggest that these approaches could have a high potential to summarise and compare the impacts of external drivers on whole communities. [ABSTRACT FROM AUTHOR]

Premise: Although changes in plant phenology are largely attributed to changes in climate, the roles of other factors such as genetic constraints, competition, and self‐compatibility are underexplored. Methods: We compiled >900 herbarium records spanning 117 years for all eight nominal species of the winter‐annual genus Leavenworthia (Brassicaceae). We used linear regression to determine the rate of phenological change across years and phenological sensitivity to climate. Using a variance partitioning analysis, we assessed the relative influence of climatic and nonclimatic factors (self‐compatibility, range overlap, latitude, and year) on Leavenworthia reproductive phenology. Results: Flowering advanced by ~2.0 days and fruiting by ~1.3 days per decade. For every 1°C increase in spring temperature, flowering advanced ~2.3 days and fruiting ~3.3 days. For every 100 mm decrease in spring precipitation, each advanced ~6–7 days. The best models explained 35.4% of flowering variance and 33.9% of fruiting. Spring precipitation accounted for 51.3% of explained variance in flowering date and 44.6% in fruiting. Mean spring temperature accounted for 10.6% and 19.3%, respectively. Year accounted for 16.6% of flowering variance and 5.4% of fruiting, and latitude for 2.3% and 15.1%, respectively. Nonclimatic variables combined accounted for <11% of the variance across phenophases. Conclusions: Spring precipitation and other climate‐related factors were dominant predictors of phenological variance. Our results emphasize the strong effect of precipitation on phenology, especially in the moisture‐limited habitats preferred by Leavenworthia. Among the many factors that determine phenology, climate is the dominant influence, indicating that the effects of climate change on phenology are expected to increase. [ABSTRACT FROM AUTHOR]

Traits of organisms vary both at inter- and intraspecific levels. For macro-detritivores inhabiting lands and waters, there is only scattered information on the relative contribution of the intraspecific level for traits like litter consumption rates. This basic knowledge is nevertheless required to know how much the intraspecific level could matter to the study of macro-detritivore communities and ecosystem processes like leaf litter decomposition. We performed a laboratory experiment, where thirty individuals each of five abundant macro-detritivore species from a stream and a meadow ecosystem fed ash (Fraxinus excelsior) leaf litter in microcosms, twice at a 1-week interval. This nested and repeated design (individuals nested within species, species within ecosystems) was setup in controlled conditions and then analysed following a variance partitioning approach with linear mixed-effect models, to assess the variance attributable to different levels of biological organisation (i.e., inter- and intraspecific level). Then, we performed the same analysis, but separately on aquatic and terrestrial datasets. From the whole dataset, we demonstrated that half of the trait variation occurred at the interspecific level and a third at the intraspecific level. The variance found at the residual level, accounting for both measurement errors and for the variation of the same individuals between the two times (i.e., intra-individual variation), was significantly lower than what was observed at the other levels of biological organisation. Variance partitioning on separate aquatic and terrestrial datasets were consistent altogether, confirming the global pattern. With this study, we stress the relevance of the intraspecific level for future trait-based approaches applied to macro-detritivores.

Environmental factors act at multiple spatial scales in a hierarchical manner to shape the organization of biota. However, the relative influence of different scale-related factors is poorly known, especially in Atlantic Forest Blackwater streams. Therefore, we herein aimed to evaluate local, landscape and spatial factors that shape fish assemblages in 14 blackwater restinga coastal Atlantic Forest streams under natural conditions and verify species occurrence patterns among four sub-basins during the low-precipitation season. When we combined local, landscape and spatial factors, variance partitioning explained a high proportion of variation in species matrix. Local variables pH and Total Dissolved Solids explained most of the variability, and these were the most important factors in determining fish community structure. Significant differences in fish assemblage structure among the four sub-basins were observed, and Mimagoniates microlepis, Phalloceros harpagos, and Hollandichthys multifasciatus were the species that most contributed to this dissimilarity. The important contribution of local predictors, the high number of endemic species herein recorded, the presence of an endangered species (Spintherobolus broccae), and near pristine conditions, may be used as baseline conditions for the assessment of similar environments. [ABSTRACT FROM AUTHOR]

Repeated social interactions with conspecifics and/or heterospecifics during early development may drive the differentiation of behavior among individuals. Competition is a major form of social interaction and its impacts can depend on whether interactions occur between conspecifics or heterospecifics and the directionality of a response could be specific to the ecological context that they are measured in. To test this, we reared tungara frog tadpoles (Engystomops pustulosus) either in isolation, with a conspecific tadpole or with an aggressive heterospecific tadpole, the whistling frog tadpole (Leptodactylus fuscus). In each treatment, we measured the body size and distance focal E. pustulosus tadpoles swam in familiar, novel and predator risk contexts six times during development. We used univariate and multivariate hierarchical mixed effect models to investigate the effect of treatment on mean behavior, variance among and within individuals, behavioral repeatability and covariance among individuals in their behavior between contexts. There was a strong effect of competition on behavior, with different population and individual level responses across social treatments. Within a familiar context, the variance in the distance swam within individuals decreased under conspecific competition but heterospecific competition caused more variance in the average distance swam among individuals. Behavioral responses were also context specific as conspecific competition caused an increase in the distance swam within individuals in novel and predator risk contexts. The results highlight that the impact of competition on among and within individual variance in behavior is dependent on both competitor species identity and context. [ABSTRACT FROM AUTHOR]

Summary: Background: Although the infant gut microbiota has been extensively studied, comprehensive assessment on the microbiota determinants including technical variables has not been performed in large infant cohorts. Methods: We studied the effect of 109 variables on the 16S rRNA gene amplicon-based gut microbiota profiles of infants sampled longitudinally from three weeks to two years of life in the Finnish HELMi birth cohort. Spot faecal samples from both parents were included for intra-family analyses, totalling to 7657 samples from 985 families that were evaluated for beta-diversity patterns using permutational multivariate analysis on Bray–Curtis distances, and differential abundance testing and alpha-diversity for variables of interest. We also assessed the effect of different taxonomic levels and distance methods. Findings: In time point-specific models, the largest share of variation explained, up to 2–6%, were seen in decreasing order for the DNA extraction batch, delivery mode and related perinatal exposures, defecation frequency and parity/siblings. Variables describing the infant gastrointestinal function were continuously important during the first two years, reflecting changes in e.g., feeding habits. The effect of parity/siblings on infant microbiota was modified by birth mode and exposure to intrapartum antibiotics, exemplifying the tight interlinkage of perinatal factors relevant for infant microbiota research. In total, up to 19% of the biological microbiota variation in the infant gut could be explained. Our results highlight the need to interpret variance partitioning results in the context of each cohort's characteristics and microbiota processing. Interpretation: Our study provides a comprehensive report of key factors associated with infant gut microbiota composition across the two first years of life in a homogenous cohort. The study highlights possible important future research areas and confounding factors to be considered. Funding: This research was supported by Business Finland, Academy of Finland, Foundation for Nutrition Research and the Doctoral Program in Microbiology and Biotechnology, University of Helsinki, Finland.

Abstract Environmental factors act at multiple spatial scales in a hierarchical manner to shape the organization of biota. However, the relative influence of different scale-related factors is poorly known, especially in Atlantic Forest Blackwater streams. Therefore, we herein aimed to evaluate local, landscape and spatial factors that shape fish assemblages in 14 blackwater restinga coastal Atlantic Forest streams under natural conditions and verify species occurrence patterns among four sub-basins during the low-precipitation season. When we combined local, landscape and spatial factors, variance partitioning explained a high proportion of variation in species matrix. Local variables pH and Total Dissolved Solids explained most of the variability, and these were the most important factors in determining fish community structure. Significant differences in fish assemblage structure among the four sub-basins were observed, and Mimagoniates microlepis, Phalloceros harpagos, and Hollandichthys multifasciatus were the species that most contributed to this dissimilarity. The important contribution of local predictors, the high number of endemic species herein recorded, the presence of an endangered species (Spintherobolus broccae), and near pristine conditions, may be used as baseline conditions for the assessment of similar environments.

Modern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The usefulness of endophenotypes for delineating the regulatory landscape of the genome and genetic dissection of complex trait variation remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299-genotype and seven-tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation acts through altered transcript abundance for maize kernel traits, including 30 grain carotenoid abundance traits, 20 grain tocochromanol abundance traits, and 22 field-measured agronomic traits. Comparing the efficacy of TWAS with genome-wide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the largely independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits than the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. This not only improves the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants.

Contemporary biological assemblage composition and biodiversity are often shaped by a range of natural environmental factors, human disturbances, and their interactions. It is critical to disentangle the effects of individual natural variables and human stressors in data analysis to support management decision-making. Many statistical approaches have been proposed and used to estimate the biological effects of individual predictors, which often correlated and interacted with one another. In this article, we review nine of those approaches in terms of their strengths, limitations, and related r packages. Among those are hierarchical partitioning, propensity score, the sum of AIC weights, structural equation modeling, and tree-based machine learning algorithms. As no approach is perfect, we offer two suggestions: (1) reducing the number of predictors as low as possible by carefully screening all candidate predictors based on biological and statistical considerations; (2) selecting two or more approaches based on the characteristics of the given dataset and specific research goals of a study, and using them in parallel or sequence. Our review could help ecologists to navigate through this challenging process. [ABSTRACT FROM AUTHOR]

A better understanding of responses to water limitation in woody species can help us to cope with the consequences of the progressing climate change. We focused on the putative transgenerational effects of water limitation in the maternal environment during reproduction. Water was restricted for cuttings of Frangula alnus Mill. in a common garden setting, with a Belgian (local), Italian and Swedish provenance, during the growing season of 2020 and mature berries were collected during the whole reproductive phase. Stones that were extracted from the berries were given a cold stratification. In the next spring, the germination percentage of the stones from the water restricted maternal environment was significantly higher than that of the stones from the normal maternal environment, for the three provenances, notwithstanding the fact that stone weight was not different. The timing of seedling emergence was advanced for the water-limited maternal environment, but only for the stones harvested when mature berry production was the highest (9th and 16th of July 2020). Population differentiation was observed for the timing of seedling emergence, which reflected population differentiation for bud burst of the mother plants in the common garden, including a counter-gradient effect for the Swedish provenance, and corroborating the suggestion that the timing of seedling emergence and leaf phenology may have a common genetic basis. In addition, the Swedish provenance displayed a somewhat more stable germination percentage over the whole berry collection period when the stones were harvested. A partitioning of variance analysis suggested that germination percentage is more genetically determined than timing of seedling emergence, probably reflecting the more important need to sense the environment for an adequate timing of emergence. [ABSTRACT FROM AUTHOR]

Understanding contributions of different environmental drivers to the structure and function of river biota is critical for biodiversity and environmental conservation. Yet, there is no consensus on which drivers have the greatest impact on fish diversity, especially in watersheds disturbed by multiple human activities. Fish taxonomic, phylogenetic, and functional diversity at both alpha and beta dimensions and their environmental drivers were studied in a total of 84 river sites from three watersheds, Huai River watershed (HRW), Sui River watershed (SRW), and Yishusi River watershed (YRW) in the Eastern Huai River Basin of China. Results showed that rivers in the three watersheds had no significant difference in both taxonomic and functional alpha diversity. Rivers in the HRW had significantly higher phylogenetic alpha diversity. For taxonomic and phylogenetic beta diversity, the overall beta diversity and turnover component were significant higher in the HRW. For functional beta diversity, the overall beta diversity was significantly lower in the HRW. Multiple linear regression (MLR) and redundancy analysis (RDA) showed that local (e.g., nutrients, dissolved oxygen, river width, transparency), regional (e.g., wetland), climate (e.g., temperature), and spatial variables structured alpha and beta fish diversity. Variation partitioning analysis indicated that the pure contributions of local and spatial variables were more important than that of climate and regional variables, which suggested that spatial effects and local environmental filtering were the main factors driving the beta diversity of fish assemblages in these rivers. The current study highlights the importance of considering multidimensional diversity and multiple environmental factors for conserving river fishes. [ABSTRACT FROM AUTHOR]

Generalized linear mixed models (GLMMs) have been widely used in contemporary ecology studies. However, determination of the relative importance of collinear predictors (i.e. fixed effects) to response variables is one of the challenges in GLMMs. Here, we developed a novel R package, glmm.hp, to decompose marginal R 2 explained by fixed effects in GLMMs. The algorithm of glmm.hp is based on the recently proposed approach 'average shared variance' i.e. used for multivariate analysis. We explained the principle and demonstrated the use of this package by simulated dataset. The output of glmm.hp shows individual marginal R 2s that can be used to evaluate the relative importance of predictors, which sums up to the overall marginal R 2. Overall, we believe the glmm.hp package will be helpful in the interpretation of GLMM outcomes. [ABSTRACT FROM AUTHOR]

Knowledge concerning the relative importance of biotic and abiotic factors associated with the long-term settlement of large carnivores in human-dominated landscapes is important for effective species conservation. However, identifying the major drivers of large carnivore occurrences at a continental scale is challenging as several factors can act in concert to affect large carnivore distributions. Thus, we investigated the relationship between the permanent and sporadic occurrences of large carnivores (brown bear, Eurasian lynx, grey wolf and wolverine) and three groups of predictor variables (i.e., landscape attributes, human disturbance and prey availability) on the continental scale in Europe. Specifically, we used generalized linear models (GLMs) and variation partitioning to estimate the independent and cumulative effects of the three predictor groups on large carnivore occurrences. The explained variance for permanent vs. sporadic occurrence was highest for the wolverine (42.8% and 29.7%), followed by the brown bear (20.5% and 16.4%), Eurasian lynx (15.2% and 11.6%) and grey wolf (15.8% and 6.1%). Landscape attributes, such as forest cover, shrub-land cover, altitude and slope, were positively related to the occurrence of grey wolf, brown bear and Eurasian lynx, whereas human disturbance (human population density, distance to roads and to human settlements) was negatively related to wolverine occurrence. For all species, shared effects between landscape attributes and human disturbance accounted for a considerable portion of the explained variation in both permanent and sporadic occurrence, and landscape attributes were generally more important for explaining permanent than sporadic occurrence, except for the wolverine. Prey availability was marginally associated with the permanent occurrence of the grey wolf but we found no statistical effect of prey availability on the occurrence of the other large carnivores. In conclusion, the sporadic occurrence of large carnivores in Europe is more stochastic and less predictable than their permanent occurrence. Landscape attributes and their joint effects with human disturbance are the most important factors related to the recolonization dynamics of large carnivores, although wolverine distributions appear strongly limited by human disturbance itself. Domestic prey availability seems to play a subordinate role in driving the recent distribution dynamics of large carnivores at the continental scale. Thus, our results are relevant also in light of the ongoing recolonization of large carnivores which, in many rural areas, caused concerns among the locals, especially in relation to conflicts with human activities. For these reasons, we stress the need to continue and improve large carnivore monitoring across Europe. [ABSTRACT FROM AUTHOR]

Summary: The analysis of plant elemental composition and the underlying factors affecting its variation are a current hot topic in ecology. Ecological adaptation to atypical soils may shift plant elemental composition. However, no previous studies have evaluated its relevance against other factors such as phylogeny, climate or individual soil conditions.We evaluated the effect of the phylogeny, environment (climate, soil), and affinity to gypsum soils on the elemental composition of 83 taxa typical of Iberian gypsum ecosystems. We used a new statistical procedure (multiple phylogenetic variance decomposition, MPVD) to decompose total explained variance by different factors across all nodes in the phylogenetic tree of target species (covering 120 million years of Angiosperm evolution).Our results highlight the relevance of phylogeny on the elemental composition of plants both at early (with the development of key preadaptive traits) and recent divergence times (diversification of the Iberian gypsum flora concurrent with Iberian gypsum deposit accumulation). Despite the predominant phylogenetic effect, plant adaptation to gypsum soils had a strong impact on the elemental composition of plants, particularly on sulphur concentrations, while climate and soil effects were smaller.Accordingly, we detected a convergent evolution of gypsum specialists from different lineages on increased sulphur and magnesium foliar concentrations. [ABSTRACT FROM AUTHOR]

Understanding the mechanisms of ecological community dynamics and how they could be affected by environmental changes is important. Population dynamic models have well known ecological parameters that describe key characteristics of species such as the effect of environmental noise and demographic variance on the dynamics, the long‐term growth rate, and strength of density regulation. These parameters are also central for detecting and understanding changes in communities of species; however, incorporating such vital parameters into models of community dynamics is challenging. In this paper, we demonstrate how generalized linear mixed models specified as intercept‐only models with different random effects can be used to fit dynamic species abundance distributions. Each random effect has an ecologically meaningful interpretation either describing general and species‐specific responses to environmental stochasticity in time or space, or variation in growth rate and carrying capacity among species. We use simulations to show that the accuracy of the estimation depends on the strength of density regulation in discrete population dynamics. The estimation of different covariance and population dynamic parameters, with corresponding statistical uncertainties, is demonstrated for case studies of fish and bat communities. We find that species heterogeneity is the main factor of spatial and temporal community similarity for both case studies. [ABSTRACT FROM AUTHOR]

• We assessed interspecific and intraspecific variation in litter consumption rate of aquatic and terrestrial macro-detritivores. • We observed a substantial amount of intraspecific variation (34%) relative to the interspecific variation (53%). • Intraspecific variation was repeatable among individuals, reflecting phenotypic features of individuals rather than inconsistent variation (13%). • Our study highlights the importance of the intraspecific level in trait-based ecological studies on macro-detritivores. Traits of organisms vary both at inter- and intraspecific levels. For macro-detritivores inhabiting lands and waters, there is only scattered information on the relative contribution of the intraspecific level for traits like litter consumption rates. This basic knowledge is nevertheless required to know how much the intraspecific level could matter to the study of macro-detritivore communities and ecosystem processes like leaf litter decomposition. We performed a laboratory experiment, where thirty individuals each of five abundant macro-detritivore species from a stream and a meadow ecosystem fed ash (Fraxinus excelsior) leaf litter in microcosms, twice at a 1-week interval. This nested and repeated design (individuals nested within species, species within ecosystems) was setup in controlled conditions and then analysed following a variance partitioning approach with linear mixed-effect models, to assess the variance attributable to different levels of biological organisation (i.e., inter- and intraspecific level). Then, we performed the same analysis, but separately on aquatic and terrestrial datasets. From the whole dataset, we demonstrated that half of the trait variation occurred at the interspecific level and a third at the intraspecific level. The variance found at the residual level, accounting for both measurement errors and for the variation of the same individuals between the two times (i.e., intra-individual variation), was significantly lower than what was observed at the other levels of biological organisation. Variance partitioning on separate aquatic and terrestrial datasets were consistent altogether, confirming the global pattern. With this study, we stress the relevance of the intraspecific level for future trait-based approaches applied to macro-detritivores. [ABSTRACT FROM AUTHOR]

The spatial heterogeneity on a regional scale of forest biomass is caused by multiple biotic and abiotic factors. However, the contributions of biotic and abiotic factors to the spatial heterogeneity of forest biomass remain unclear. Based on the data of the National Forest Continuous Inventory (NFCI), digital elevation model (DEM), and meteorological data of Guizhou Province in 2015, we studied the spatial heterogeneity of the aboveground forest biomass in Guizhou province and evaluated the contribution rates of its influencing factors using Moran's I, semivariogram, distance-based Moran's eigenvector maps (dbMEMs), and variance partitioning. The results showed that the forest biomass in Guizhou province had strong spatial heterogeneity. Biotic and abiotic factors explained 34.4% and 19.2% of the spatial variation in forest biomass, respectively. Among the biotic factors, the average height of the stand had the greatest influence on forest biomass, while annual precipitation had the greatest influence on forest biomass among abiotic factors. Spatial factors only explained 0.7% of the spatial variation of forest biomass, indicating that the contribution of spatial factors can be explained by some measured abiotic factors. This study provided an effective approach to understand the underlying mechanisms of spatial allocation of forest biomass. [ABSTRACT FROM AUTHOR]

In the context of urban warming associated with rapid urbanization, the relationship between urban landscape patterns and land surface temperature (LST) has been paid much attention. However, few studies have comprehensively explored the effects of two/three-dimensional (2D/3D) building patterns on LST, particularly by comparing their relative contribution to the spatial variety of LST. This study adopted the ordinary least squares regression, spatial autoregression and variance partitioning methods to investigate the relationship between 2D/3D building patterns and summertime LST across 2016–2017 in Shanghai. The 2D and 3D building patterns in this study were quantified by four 2D and six 3D metrics. The results showed that: (1) During the daytime, 2D/3D building metrics had significant correlation with LST. However, 3D building patterns played a significant role in predicting LST. They explained 51.0% and 10.2% of the variance in LST, respectively. (2) The building coverage ratio, building density, mean building projection area, the standard deviation of building height, and mean building height highly correlated with LST. Specifically, the building coverage ratio was the main predictor, which was obviously positively correlated with LST. The correlation of building density and average projected area with LST was positive and significant, while the correlation of building height standard deviation and average building height with LST was negative. The increase in average height and standard deviation of buildings and the decrease in building coverage ratio, average projected area, and density of buildings, can effectively improve the urban thermal environment at the census tract level. (3) Spatial autocorrelation analysis can elaborate the spatial relationship between building patterns and LST. The findings from our research will provide important insights for urban planners and decision makers to mitigate urban heat island problems through urban planning and building design. [ABSTRACT FROM AUTHOR]

Determining the patterns and drivers of the small‐scale species–area relationship (SAR) is crucial for improving our understanding of community assembly and biodiversity patterns. Niche‐based and stochastic processes are two principal categories of mechanisms potentially driving SARs. However, their relative importance has rarely been quantified rigorously owing to scale dependence and the simplified niche volumes often used.In a fully mapped, 24‐hm2 plot of a typical subtropical forest, we built the SARs and well‐defined niche hyper‐volumes of a broad range of environmental variables at scales of 10–70 m (cell sizes). We then simulated passive sampling and partitioned the variances of the SAR slopes to disentangle these two contrasting mechanisms.We found that the small‐scale SAR best followed a power‐law relationship, consistent with large‐scale SARs. The SAR slope declined with increasing scale; it was lower than expected under passive sampling at scales below 30 m and higher at larger scales. Environmental niches explained more (39%–64%) of the slope at larger scales, exceeding 50% at scales >30 m, and these niches always captured the majority of the structured slopes. Environmental position (environmental mean values) effects were steady in absolute strength across scales and explained most (98%–68%) of the niche effect, but this proportion decreased with increasing scale. The effect of environmental heterogeneity increased with spatial scales, starting to rise at the 30 m scale after controlling for environmental position. Excluding soil properties from analyses strongly reduced these niche effects, highlighting the importance of soils for structuring the small‐scale SAR. There was also substantial stochasticity in the SAR slopes, which was only partially explained by passive sampling.Synthesis. Our results show that the small‐scale SAR in the studied subtropical forest follows a power law, exhibits a scale shift in SAR slope at 30 m, and is strongly shaped by niche effects that are dominated by environmental position relative to heterogeneity. However, soil heterogeneity controls the increase in niche effect and the shift in the SAR slope with increasing spatial scales. Hence, edaphic factors can be responsible for scale dependence in small‐scale SARs, thereby linking small‐scale and large‐scale SARs. [ABSTRACT FROM AUTHOR]

Near‐term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state‐space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin‐producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near‐term (1‐ to 4‐week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4‐week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1‐week‐ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long‐term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems. [ABSTRACT FROM AUTHOR]

Abstract Chromosomal inversions have long been recognized for their role in local adaptation. By suppressing recombination in heterozygous individuals, they can maintain coadapted gene complexes and protect them from homogenizing effects of gene flow. However, to fully understand their importance for local adaptation we need to know their influence on phenotypes under divergent selection. For this, the marine snail Littorina saxatilis provides an ideal study system. Divergent ecotypes adapted to wave action and crab predation occur in close proximity on intertidal shores with gene flow between them. Here, we used F2 individuals obtained from crosses between the ecotypes to test for associations between genomic regions and traits distinguishing the Crab‐/Wave‐adapted ecotypes including size, shape, shell thickness, and behavior. We show that most of these traits are influenced by two previously detected inversion regions that are divergent between ecotypes. We thus gain a better understanding of one important underlying mechanism responsible for the rapid and repeated formation of ecotypes: divergent selection acting on inversions. We also found that some inversions contributed to more than one trait suggesting that they may contain several loci involved in adaptation, consistent with the hypothesis that suppression of recombination within inversions facilitates differentiation in the presence of gene flow.

Regional‐scale estimations of sorghum biomass production allow identification of optimum genotype×environment×management (G×E×M) combinations for bioenergy generation. The objective of this study was to determine the degree of contributions of G, E, and M toward variability in sorghum biomass in the United States. Using the Agricultural Production Systems sIMulator in a grid computing platform, biomass was simulated for irrigated and rainfed conditions for 30 years across the United States for four sorghum varietal types (grain—GS, sudangrass—SS, photosensitive—PS, and photo‐insensitive—PI). Simulated biomass was assessed by environments clustered using the sum of intercepted solar radiation (ir), mean of temperature stress factor (tp) and water stress factor (sw). Simulated biomass ranged from 5.8 t ha−1 (GS‐rainfed) to 27.5 t ha−1 (PI‐irrigated). Under high‐temperature environments (mean annual temperature = 25°C), rainfed biomass between 40 and 80 days after planting (DAP) was strongly correlated with sw (r = 0.64–0.86) and irrigated biomass with ir (r = 0.68–0.81). Under low‐temperature environments (mean annual temperature = 18°C) after 40 DAP, tp and ir had greater effects than sw (r = 0.55–0.82). Biomass variance was mainly explained by varietal type (50%–76%) in all environments×irrigation combinations, except in the high‐ and mid‐temperature environments under rainfed conditions where rainfall had the major effect (25%–45%). However, when mean temperature during the growing season decreased from 25°C (high environments) to 18°C (low environments), the contribution of mean temperature to biomass variance increased from 7% to 34% (rainfed) and from 4% to 36% (irrigated). Varietal type had the larger interactions with other factors independently of the environment and irrigation. We demonstrated a need to quantify (i) the main G×E×M drivers of biomass variability based on environmental stress factors and (ii) the variance contribution of these drivers on sorghum biomass. Our regional‐scale estimations are key inputs for future robust biomass projections of energy sorghum genotypes integrating G×E×M under climate change scenarios. [ABSTRACT FROM AUTHOR]

The interaction effect coefficient ψ has been a much-discussed, fundamental parameter of indirect genetic effect (IGE) models since its formal mathematical description in 1997. The coefficient simultaneously describes the form of changes in trait expression caused by genes in the social environment and predicts the evolutionary consequences of those IGEs. Here, we report a striking mismatch between theoretical emphasis on ψ and its usage in empirical studies. Surveying all IGE research, we find that the coefficient ψ has not been equivalently conceptualized across studies. Several issues related to its proper empirical measurement have recently been raised, and these may severely distort interpretations about the evolutionary consequences of IGEs. We provide practical advice on avoiding such pitfalls. The majority of empirical IGE studies use an alternative variance-partitioning approach rooted in well-established statistical quantitative genetics, but several hundred estimates of ψ (from 15 studies) have been published. A significant majority are positive. In addition, IGEs with feedback, that is, involving the same trait in both interacting partners, are far more likely to be positive and of greater magnitude. Although potentially challenging to measure without bias, ψ has critically-developed theoretical underpinnings that provide unique advantages for empirical work. We advocate for a shift in perspective for empirical work, from ψ as a description of IGEs, to ψ as a robust predictor of evolutionary change. Approaches that "run evolution forward" can take advantage of ψ to provide falsifiable predictions about specific trait interactions, providing much-needed insight into the evolutionary consequences of IGEs. [ABSTRACT FROM AUTHOR]

Landscape genomics identifies how spatial and environmental factors structure the amount and distribution of genetic variation among populations. Landscape genomic analyses have been applied across diverse taxonomic groups and ecological settings, and are increasingly used to analyse datasets composed of large numbers of genomic markers and multiple environmental predictors.It is in this context that multivariate methods show their strengths. Redundancy analysis (RDA) is a constrained ordination that, in a landscape genomics framework, models linear relationships among environment predictors and genomic variation, effectively identifying covarying allele frequencies associated with the multivariate environment. RDA can be used at both individual and population levels, can include covariates to account for confounding factors and can be used to directly infer genotype–environment associations on the landscape. The modelling of both multivariate response and explanatory variables allows RDA to accommodate the genomic and environmental complexity found in nature, producing a powerful and efficient tool for landscape genomics.In this review, we outline the diverse uses of RDA in landscape genomics, including variable selection, variance partitioning, genotype–environment associations, and the calculation of adaptive indices and genomic offset. To illustrate these applications, we use a published dataset for lodgepole pine that includes genomic, phenotypic and environmental data. We provide an introduction to the statistical basis of RDA, a tutorial on its use and interpretation in landscape genomics applications, discuss limitations and provide guidelines to avoid misuse.This review and associated tutorial provide a comprehensive resource to the landscape genomics community to improve understanding of RDA as a modelling framework, and encourage the appropriate use of RDA across diverse landscape genomics applications. RDA is truly a Swiss Army Knife for landscape genomics: a multipurpose, adaptable and versatile approach to identifying, evaluating and forecasting relationships between genetic and environmental variation. [ABSTRACT FROM AUTHOR]

ABSTRACT Ecological transitions usually represent vulnerable ecosystems and high biodiversity. Investigating their drivers is important from both biogeographic and conservationist perspectives. One of these transitions is situated between the two largest biogeographic domains of South America - the Amazon and the Cerrado. We evaluated variation in tree floristic composition throughout this transition and assessed which factors explain this variation. We used 17,240 records of occurrences of 2,530 tree species from the NeotropTree database. We investigated variation in floristic composition using UPGMA cluster analysis with bootstrap resampling and NMDS ordination, and modeled the distribution of species in relation to environmental and spatial predictors by means of transformation-based Redundancy Canonical Analysis (tb-RDA) and variance partitioning. We found four general floristic groups: 1) flooded forests; 2) white sand dwarf-forests; 3) savannic and dwarf forests; and 4) non-flooded forest types. Floristic variation along the transition was influenced by the environment, especially variables related to fire and soil moisture, and by space, especially variables acting on broader scales. Correspondence between the environmental heterogeneity found throughout the transition and our results demonstrates once again the importance of conserving biogeographical transition zones in general, and the most comprehensive of South America in particular.

A better understanding of responses to water limitation in woody species can help us to cope with the consequences of the progressing climate change. We focused on the putative transgenerational effects of water limitation in the maternal environment during reproduction. Water was restricted for cuttings of Frangula alnus Mill. in a common garden setting, with a Belgian (local), Italian and Swedish provenance, during the growing season of 2020 and mature berries were collected during the whole reproductive phase. Stones that were extracted from the berries were given a cold stratification. In the next spring, the germination percentage of the stones from the water restricted maternal environment was significantly higher than that of the stones from the normal maternal environment, for the three provenances, notwithstanding the fact that stone weight was not different. The timing of seedling emergence was advanced for the water-limited maternal environment, but only for the stones harvested when mature berry production was the highest (9th and 16th of July 2020). Population differentiation was observed for the timing of seedling emergence, which reflected population differentiation for bud burst of the mother plants in the common garden, including a counter-gradient effect for the Swedish provenance, and corroborating the suggestion that the timing of seedling emergence and leaf phenology may have a common genetic basis. In addition, the Swedish provenance displayed a somewhat more stable germination percentage over the whole berry collection period when the stones were harvested. A partitioning of variance analysis suggested that germination percentage is more genetically determined than timing of seedling emergence, probably reflecting the more important need to sense the environment for an adequate timing of emergence.

Contemporary biological assemblage composition and biodiversity are often shaped by a range of natural environmental factors, human disturbances, and their interactions. It is critical to disentangle the effects of individual natural variables and human stressors in data analysis to support management decision-making. Many statistical approaches have been proposed and used to estimate the biological effects of individual predictors, which often correlated and interacted with one another. In this article, we review nine of those approaches in terms of their strengths, limitations, and related r packages. Among those are hierarchical partitioning, propensity score, the sum of AIC weights, structural equation modeling, and tree-based machine learning algorithms. As no approach is perfect, we offer two suggestions: (1) reducing the number of predictors as low as possible by carefully screening all candidate predictors based on biological and statistical considerations; (2) selecting two or more approaches based on the characteristics of the given dataset and specific research goals of a study, and using them in parallel or sequence. Our review could help ecologists to navigate through this challenging process.

Recognizing others’ social interactions is a crucial human ability. Using simple stimuli, previous studies have shown that social interactions are selectively processed in the superior temporal sulcus (STS), but prior work with movies has suggested that social interactions are processed in the medial prefrontal cortex (mPFC), part of the theory of mind network. It remains unknown to what extent social interaction selectivity is observed in real world stimuli when controlling for other covarying perceptual and social information, such as faces, voices, and theory of mind. The current study utilizes a functional magnetic resonance imaging (fMRI) movie paradigm and advanced machine learning methods to uncover the brain mechanisms uniquely underlying naturalistic social interaction perception. We analyzed two publicly available fMRI datasets, collected while both male and female human participants (n = 17 and 18) watched two different commercial movies in the MRI scanner. By performing voxel-wise encoding and variance partitioning analyses, we found that broad social-affective features predict neural responses in social brain regions, including the STS and mPFC. However, only the STS showed robust and unique selectivity specifically to social interactions, independent from other covarying features. This selectivity was observed across two separate fMRI datasets. These findings suggest that naturalistic social interaction perception recruits dedicated neural circuity in the STS, separate from the theory of mind network, and is a critical dimension of human social understanding.

Summary: Close coupling between water loss and carbon dioxide uptake requires coordination of plant hydraulics and photosynthesis. However, there is still limited information on the quantitative relationships between hydraulic and photosynthetic traits.We propose a basis for these relationships based on optimality theory, and test its predictions by analysis of measurements on 107 species from 11 sites, distributed along a nearly 3000‐m elevation gradient.Hydraulic and leaf economic traits were less plastic, and more closely associated with phylogeny, than photosynthetic traits. The two sets of traits were linked by the sapwood to leaf area ratio (Huber value, vH). The observed coordination between vH and sapwood hydraulic conductivity (KS) and photosynthetic capacity (Vcmax) conformed to the proposed quantitative theory. Substantial hydraulic diversity was related to the trade‐off between KS and vH. Leaf drought tolerance (inferred from turgor loss point, –Ψtlp) increased with wood density, but the trade‐off between hydraulic efficiency (KS) and –Ψtlp was weak. Plant trait effects on vH were dominated by variation in KS, while effects of environment were dominated by variation in temperature.This research unifies hydraulics, photosynthesis and the leaf economics spectrum in a common theoretical framework, and suggests a route towards the integration of photosynthesis and hydraulics in land‐surface models. [ABSTRACT FROM AUTHOR]

Understanding the within- and among-population distribution of trait variation within seed collections may provide a means to approximate standing genetic variation and inform plant conservation. This study aimed to estimate population- and family-level seed trait variability for existing seed collections of Torrey pine (Pinus torreyana), and to use these data to guide sampling of future collections. We quantified variation in 14 seed morphological traits and seedling emergence within and among Torrey pine populations. Using a simulation-based approach, we used estimates of within-population variance to assess the number of maternal families required to capture 95 % of trait variation within each existing seed collection. Substantial structure was observed both within and among Torrey pine populations, with island and mainland seeds varying in seed size and seed coat thickness. Despite morphological differences, seedling emergence was similar across populations. Simulations revealed that 83 % and 71 % of all maternal families within island and mainland seed collections respectively needed to be resampled to capture 95 % of seed trait variation within existing collections. From a conservation perspective, our results indicate that to optimize genetic diversity captured in Torrey pine seed collections, maximizing the number of maternal families sampled within each population will be necessary. [ABSTRACT FROM AUTHOR]

Large river management is in increasing demand to establish the ecological quality set by sustainability-oriented legislation. However, there is still a lack of research regarding most relevant scales at which pressures should be addressed. In this study, we compared the relative effects of riparian land-cover at 16 different buffer sizes and hydromorphology at four different river lengths on benthic invertebrate (BI) and fish assemblages in large rivers. Environmental data were obtained digitally (applying GIS) on a broad range of abiotic conditions and were related to biological data using direct gradient analyses. Compared to land-cover, hydromorphology showed greater effect on BI and fish, with in-channel habitat quality characteristics explaining most biotic variability. Both assemblages were best explained by hydromorphology inventoried at the largest 5,000 m scale. We found, however, consideration of longer river segments to be more important for explaining fish variability. Riparian land-cover explained similar percentages of biotic variability across most analysed riparian buffers. Nevertheless, a significant effect of riparian length rather than width was identified. Our findings indicate that, in large rivers, hydromorphological and riparian land-cover conditions affect BI and especially fish at generally longer river segments, implying potential ecological benefits of management measures implemented at larger spatial scales. [ABSTRACT FROM AUTHOR]

Recent developments in environmental DNA (eDNA) metabarcoding suggest that eDNA-based representation of ecological communities can be a promising tool in both fundamental ecological research and environmental assessment. However, it is less known, how eDNA performs in characterising ecological communities and community-environment relationships at the regional scale compared with traditional sampling methods. Here, we used electrofishing (EF), gillnetting (GN) and eDNA-based surveys to compare their congruency in characterising the taxonomic and trait-based structure of (oxbow) lake fish communities and their structuring mechanisms. eDNA proved to be more effective in detecting taxa in the total samples and by traits than EF and GN. Principal coordinate analysis and multiple factor analysis showed a moderate separation of communities according to sampling methods for the taxon and the trait-based structures, respectively, but eDNA samples were always located in intermediate position in the ordination plots. Procrustes analyses indicated significant among-method congruency in community structure. However, in general, eDNA-based community patterns always showed higher correlation with either the EF or the GN-based community patterns, than the two traditional methods to each other. Variance partitioning in redundancy analyses indicated large differences among the sampling methods in the importance of environmental and spatial variables in shaping metacommunity structure. These results thus suggest that the sampling method can largely influence the identified mechanisms which govern fish metacommunity organisation. Our results suggest, that eDNA metabarcoding can be the best universal method for understanding the taxonomic and trait-based organisation of lake fish metacommunities.

In recent years, the relative importance of the processes driving metacommunity composition has aroused extensive attention and become a powerful approach to identify community patterns and their regulatory mechanisms. We investigated variations in the composition of benthic community in restored wetlands and natural wetlands in the Yellow River Delta (Shandong Province, China). First, spatial structures within each wetland were modeled with Moran eigenvector maps. Next, the variation in community structure among local environmental and spatial variables was partitioned using constrained ordination, and the “elements of metacommunity structure” analysis was used to determine the patterns of best fit for species distributions within metacommunities. Finally, the null model was used to analyze non-random patterns of species co-occurrence. The community structure of benthic invertebrates in restored wetlands and natural wetlands differed significantly. The benthic invertebrate metacommunity structure showed a nested distribution in restored wetlands and a quasi-Clementsian structure in natural wetlands. Pure environmental fractions and pure spatial fractions were critical in regulating benthic invertebrate metacommunities of restored wetlands. In natural wetlands, pure spatial fractions and the interaction between environmental and spatial factors (shared fractions) played a major role in the metacommunity. A species co-occurrence analysis showed that species co-occurred more frequently than expected by chance, demonstrating that biotic interactions were not the main driver of metacommunity structures in both wetland types. Accordingly, the benthic invertebrate metacommunity in estuarine wetlands following freshwater releases was mostly determined by environmental and spatial effects, which resulted in a metacommunity with nested distribution. These results are important for biodiversity protection and ecosystem management of estuarine wetlands in the Yellow River Delta.

Structural and functional neuroimaging have been widely used to track and predict demographic and clinical variables, including treatment outcomes. However, it is challenging to establish and compare the respective weights and contributions of brain structure and function in prediction studies. The present study aimed to directly investigate the respective roles of brain structural and functional indices, along with their contributions to the prediction of demographic variables (age/sex) and clinical changes in schizophrenia patients. The present study enrolled 492 healthy people from the Southwest University Adult Lifespan Dataset (SALD) for demographic variable analysis and 39 patients with schizophrenia from the West China Hospital for treatment analysis. We conducted a model fit test with two variables (one voxel-based structural metric and another voxel-based functional metric) and then performed variance partitioning on the voxels that could be predicted sufficiently. Permutation tests were applied to compare the difference in contribution between each pair of structural and functional measurements. We found that voxel-based structural indices had stronger predictive value for age and sex, while voxel-based functional metrics showed stronger predictive value for treatment. Therefore, through variance partitioning, we could clearly and directly explore and compare the voxel-based structural and functional indices with respect to particular variables. In sum, for the variables reflecting long-term changes (age) and constant biological features (sex), the voxel-based structural metrics would contribute more than voxel-based functional metrics, but for the variable reflecting short-term changes (schizophrenia treatment), the functional metrics could contribute more.