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Modern pollen spectra can improve the interpretation of fossil pollen records used to reconstruct past vegetation, climate and human impacts. It is important, therefore, to carefully examine the relationships between modern pollen spectra, vegetation, climate and human activity. Here, we present the results of an analysis of the pollen spectra of 143 surface pollen samples from farmland, wasteland, desert, steppe/meadow, forest and river valley along a transect from Lanzhou to Urumqi, in northwestern China. The modern pollen assemblages are mainly composed of Amaranthaceae, Artemisia, Poaceae, Asteraceae, Ephedra and Nitraria. The results indicate that in general the surface pollen assemblages of different vegetation types reliably represent the modern vegetation in terms of the composition of the main taxa and the dominant types. Farmland is dominated by cereal-type (≥ 15%) and Amaranthaceae (≥ 20%), while the pollen assemblages of wasteland (i.e. the vegetation immediately surrounding farmland) are mainly composed of Amaranthaceae (= 25%), Artemisia (≥ 20%), Poaceae (≥ 10%), Asteraceae (≥ 5%) and Cyperaceae (≥ 5%). Amaranthaceae (≥ 45%) and Ephedra (= 10%) are the most important taxa in desert, and Cyperaceae (≥ 35%) and Thalictrum (≥ 2%) are the dominant pollen types in steppe/meadow. Forest and river valley samples are characterized by high frequencies of Picea (≥ 10%) and Cyperaceae (≥ 20%). Both constrained and partial canonical ordination techniques (RDA and partial RDA) of the main pollen types and environmental variables show that the modern pollen spectra are primarily controlled by mean annual precipitation (MAP). Cyperaceae, Thalictrum and Brassicaceae are positively correlated with MAP and negatively correlated with mean July temperature (T July), while the representation of certain other types, such as Amaranthaceae, Ephedra and Nitraria, is negatively correlated with MAP and positively correlated with TJuly. The Human Influence Index (HII) is significantly correlated with cereal-type pollen, and it can also differentiate human-influenced and natural vegetation. Our results provide a basis for improving the interpretation of fossil pollen records from arid northwestern China and similar regions.

Metacommunity patterns and underlying processes in aquatic organisms have typically been studied within a drainage basin. We examined variation in the composition of six freshwater organismal groups across various drainage basins in Finland. We first modelled spatial structures within each drainage basin using Moran eigenvector maps. Second, we partitioned variation in community structure among three groups of predictors using constrained ordination: (1) local environmental variables, (2) spatial variables, and (3) dummy variable drainage basin identity. Third, we examined turnover and nestedness components of multiple-site beta diversity, and tested the best fit patterns of our datasets using the “elements of metacommunity structure” analysis. Our results showed that basin identity and local environmental variables were significant predictors of community structure, whereas within-basin spatial effects were typically negligible. In half of the organismal groups (diatoms, bryophytes, zooplankton), basin identity was a slightly better predictor of community structure than local environmental variables, whereas the opposite was true for the remaining three organismal groups (insects, macrophytes, fish). Both pure basin and local environmental fractions were, however, significant after accounting for the effects of the other predictor variable sets. All organismal groups exhibited high levels of beta diversity, which was mostly attributable to the turnover component. Our results showed consistent Clementsian-type metacommunity structures, suggesting that subgroups of species responded similarly to environmental factors or drainage basin limits. We conclude that aquatic communities across large scales are mostly determined by environmental and basin effects, which leads to high beta diversity and prevalence of Clementsian community types.

The high tropical Andes host one of the richest alpine floras of the world, with exceptionally high levels of endemism and turnover rates. Yet, little is known about the patterns and processes that structure altitudinal and latitudinal variation in plant community diversity. Herein we present the first continental-scale comparative study of plant community diversity on summits of the tropical Andes. Data were obtained from 792 permanent vegetation plots (1 m2) within 50 summits, distributed along a 4200 km transect; summit elevations ranged between 3220 and 5498 m a.s.l. We analyzed the plant community data to assess: 1) differences in species abundance patterns in summits across the region, 2) the role of geographic distance in explaining floristic similarity and 3) the importance of altitudinal and latitudinal environmental gradients in explaining plant community composition and richness. On the basis of species abundance patterns, our summit communities were separated into two major groups: Puna and Páramo. Floristic similarity declined with increasing geographic distance between study-sites, the correlation being stronger in the more insular Páramo than in the Puna (corresponding to higher species turnover rates within the Páramo). Ordination analysis (CCA) showed that precipitation, maximum temperature and rock cover were the strongest predictors of community similarity across all summits. Generalized linear model (GLM) quasi-Poisson regression indicated that across all summits species richness increased with maximum air temperature and above-ground necromass and decreased on summits where scree was the dominant substrate. Our results point to different environmental variables as key factors for explaining vertical and latitudinal species turnover and species richness patterns on high Andean summits, offering a powerful tool to detect contrasting latitudinal and altitudinal effects of climate change across the tropical Andes.

Anaerobic digestion is the most successful bioenergy technology worldwide with, at its core, undefined microbial communities that have poorly understood dynamics. Here, we investigated the relationships of bacterial community structure (>400,000 16S rRNA gene sequences for 112 samples) with function (i.e., bioreactor performance) and environment (i.e., operating conditions) in a yearlong monthly time series of nine full-scale bioreactor facilities treating brewery wastewater (>20,000 measurements). Each of the nine facilities had a unique community structure with an unprecedented level of stability. Using machine learning, we identified a small subset of operational taxonomic units (OTUs; 145 out of 4,962), which predicted the location of the facility of origin for almost every sample (96.4% accuracy). Of these 145 OTUs, syntrophic bacteria were systematically overrepresented, demonstrating that syntrophs rebounded following disturbances. This indicates that resilience, rather than dynamic competition, played an important role in maintaining the necessary syntrophic populations. In addition, we explained the observed phylogenetic differences between all samples on the basis of a subset of environmental gradients (using constrained ordination) and found stronger relationships between community structure and its function rather than its environment. These relationships were strongest for two performance variables— methanogenic activity and substrate removal efficiency— both of which were also affected by microbial ecology because these variables were correlated with community evenness (at any given time) and variability in phylogenetic structure (over time), respectively. Thus, we quantified relationships between community structure and function, which opens the door to engineer communities with superior functions.

Distance-based ordinations have played a critical role in community ecology for more than half a century, but are still under active development. These methods employ a matrix of pairwise distances or dissimilarities between sample units, and map sample units from the high-dimensional distance or dissimilarity space to a low-dimensional representation for analysis. Distance- or dissimilarity-based methods employ continuum or gradient ecological theory and a variety of statistical models to achieve the mapping. Recently, ecologists have developed model-based ordinations based on latent vectors and individual species response models. These methods employ the individualistic perspective of Gleason as the ecological model, and Bayesian or maximum-likelihood methods to estimate the parameters for the low dimensional space represented by the latent vectors. In this research I compared two distance-based methods (NMDS and t-SNE) with two model-based methods (BORAL and REO) on five data sets to determine which methods are superior for (1) extracting meaningful ecological drivers of variability in community composition, and (2) estimating sample unit locations in ordination space that maximize the goodness-of-fit of individual species response models to the estimated sample unit locations. Environmental variables and species were fitted to the ordinations by generalized additive models (GAMs) with Gaussian, negative binomial, or Poisson distribution models as appropriate. Across the five data sets, 22 models of environmental variability and 449 models of species distributions were calculated for each of the ordination methods. To minimize the effects of stochasticity the entire analysis was replicated three times and results averaged across the replicates. Results were evaluated by deviance explained and AIC for environmental variables and species distributions, averaged by ordination method for each data set, and ranked from best to worst. For the four assessments distance-based methods ranked 1 and 2 in three cases, and 1 and 3 in one case, significantly out performing the model-based methods. t-SNE was the top-performing method, out performing NMDS especially on the more complex data sets. In general the gradient-based theoretical basis and data sufficiency of distance-based methods allowed distance-based methods to outperform model-based methods in every assessment.

Amount of semi-natural habitats (permanent grasslands, woodlands and hedgerows) and their level of fragmentation are among the main determinants of wild bee diversity in agricultural landscapes. However, their impact on the distribution of bee ecological traits has received little attention. In this study, we aimed to explore whether changes in the distribution of bee ecological traits along gradients of habitat amount and fragmentation were due to a direct effect of landscape context on multiple traits (‘response traits’) or to a correlation of response traits with other ecological traits not involved in the response of bee species to landscape context. In two study regions in southwest France and southeast Australia, we used a RLQ analysis (three-table ordination method) to link bee traits with habitat amount and fragment isolation measured at the landscape scale. We found that bee ecological traits shifted at the community-level in association with landscape gradients, whereas species-level associations among bee traits and phylogenetic clustering in bee communities were of only minor importance in determining such shifts. We found that traits such as body size and nest location were closely linked to habitat amount and fragmentation. We also observed regionally-specific relationships among ecological traits, suggesting that the regional species pool can play an important role in determining the response of bee communities to habitat amount and fragmentation. Our findings suggest that improved knowledge about how trait-based responses mediate the impact of landscapes on wild bee communities will allow better prediction and understanding of subsequent effects on ecosystem functioning.

A flexible method is needed for constrained ordination on the basis of any distance or dissimilarity measure, which will display a cloud of multivariate points by reference to a specific a priori hypothesis. We suggest the use of principal coordinate analysis (PCO, metric MDS), followed by either a canonical discriminant analysis (CDA, when the hypothesis concerns groups) or a canonical correlation analysis (CCorA, when the hypothesis concerns relationships with environmental or other variables), to provide a flexible and meaningful constrained ordination of ecological species abundance data. Called \"CAP\" for \"Canonical Analysis of Principal coordinates,\" this method will allow a constrained ordination to be done on the basis of any distance or dissimilarity measure. We describe CAP in detail, including how it can uncover patterns that are masked in an unconstrained MDS ordination. Canonical tests using permutations are also given, and we show how the method can be used (1) to place a new observation into the canonical space using only interpoint dissimilarities, (2) to classify observations and obtain misclassification or residual errors, and (3) to correlate the original variables with patterns on canonical plots. Misclassification error or residual error is used to obtain a non-arbitrary decision concerning the appropriate dimensionality of the response data cloud (number of PCO axes) for the ensuing canonical analysis. We suggest that a CAP ordination and an unconstrained ordination, such as MDS, together will provide important information for meaningful multivariate analyses of ecological data by reference to explicit a priori hypotheses.

Coral community composition varies considerably due to both environmental conditions and disturbance histories. However, the extent to which coral composition influences associated fish assemblages remains largely unknown. Here an ecological trait-based ordination analysis was used to compare functional richness (range of unique trait combinations), functional evenness (weighted distribution of fishes with shared traits), and functional divergence (proportion of total abundance supported by species with traits on the periphery of functional space) of fish assemblages among six distinct coral habitats. Despite no significant variation in species richness among habitats, there were differences in the functional richness and functional divergence, but not functional evenness, of fish assemblages among habitats. Structural complexity of coral assemblages was the best predictor of the differences in functional richness and divergence among habitats. Functional richness of fish assemblages was highest in branching Porites habitats, lowest in Pocillopora and soft coral habitats, and intermediate in massive Porites , staghorn Acropora , and mixed coral habitats. Massive and branching Porites habitats displayed greater functional divergence in fish assemblages than the Pocillopora habitat, whilst the remaining habitats were intermediate. Differences in functional richness and divergence were largely driven by the presence of small schooling planktivores in the massive and branching Porites habitats. These results indicate that differential structural complexity among coral communities may act as an environmental filter, affecting the distribution and abundance of associated species traits, particularly those of small-bodied schooling fishes.

The evolution of the Indian Summer Monsoon (ISM) has been widely investigated and traditionally interpreted as consequences of orbital forcing change and the migration of the Intertropical Convergent Zone (ITCZ). The potential influence of sea-surface temperature (SST) on ISM variation at millennial time scales has been progressively proposed in recent studies. Towards a better understanding of Holocene evolution of ISM, especially the potential response to variations in SST, a 643 cm continuous sediment sequence covering the past 11.1 ka was recovered from Beihai Wetland in western Yunnan Plateau. Fossil pollen analysis revealed that evergreen broadleaved forest dominated the regional vegetation in the early Holocene, which represented a warm and humid period associated with strong ISM. Expansions of deciduous broadleaved forest and grassland from 5.2 cal. ka BP to the present, together with the appearance of abundant plant remains, indicate the formation of the wetland system. Ordination analysis of the pollen assemblages further confirmed the abrupt vegetation turnover at ca. 5.2 cal. ka BP, which is consistent with the abrupt decline in reconstructed mean annual precipitation (Pann, from ca. 1400 to 1100 mm), and indicates an abrupt decline in the summer monsoon. We performed a climate regime shift test on various records from the ISM domain and eastern Africa and found this phenomenon as well, which could not be explained by changes in solar insolation or the migration of the ITCZ. A mid-Holocene cooling was detected from the Indian Ocean, especially a decreased SST in the tropical Indian Ocean ca. 5.5 cal. ka BP. Cooling of the tropical Indian Ocean possibly caused the abrupt retreat of the summer monsoon by restraining the duration of the monsoonal season and the convergent process, which was supported by an abrupt monsoon decline based on results from a minimal concept model.

A survey was conducted in regions of North China to better understand the effect of antibiotic residue pollution from swine feedlots to nearby groundwater environment. A total of nine experimental sites located in the regions of Beijing, Hebei, and Tianjin were selected to analyze the presence of residues of 11 most commonly used antibiotics, including tetracyclines (TCs), fluoroquinolones (FQNs), sulfonamides (SAs), macrolides, and fenicols, by using liquid chromatography spectrometry. The three most common antibiotics were TCs, FQNs, and SAs, with mean concentrations of 416.4, 228.8, and 442.4 μg L −1 in wastewater samples; 19.9, 11.8, and 0.3 μg L −1 in groundwater samples from swine feedlots; and 29.7, 14.0, and 0 μg L −1 in groundwater samples from villages. Ordination analysis revealed that the composition and distribution of antibiotics and antibiotic resistance genes (AGRs) were similar in groundwater samples from swine feedlots and villages. FQNs and TCs occurred along the path from wastewater to groundwater at high concentrations and showed correlations with ARGs, with a strong correlation between FQN resistance gene (qnrA) copy number. FQN concentration was also found ( P  < 0.01) in wastewater and groundwater in villages ( P  < 0.01). Therefore, antibiotics discharged from swine feedlots through wastewater could disseminate into surrounding groundwater environments together with ARG occurrence (i.e., qnrA, sulI, sulII, tetG, tetM, and tetO). Overall, this study suggests that the spread of veterinary antibiotics from swine feedlots to groundwater environments should be highly attended and controlled by restricting excess antibiotic usage or improving the technology of manure management.