Explore the vast range of titles available.

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.

Multivariate climate change presents an urgent need to understand how species adapt to complex environments. Population genetic theory predicts that loci under selection will form monotonic allele frequency clines with their selective environment, which has led to the wide use of genotype–environment associations (GEAs). This study used a set of simulations to elucidate the conditions under which allele frequency clines are more or less likely to evolve as multiple quantitative traits adapt to multivariate environments. Phenotypic clines evolved with nonmonotonic (i.e., nonclinal) patterns in allele frequencies under conditions that promoted unique combinations of mutations to achieve the multivariate optimum in different parts of the landscape. Such conditions resulted from interactions among landscape, demography, pleiotropy, and genetic architecture. GEA methods failed to accurately infer the genetic basis of adaptation under a range of scenarios due to first principles (clinal patterns did not evolve) or statistical issues (clinal patterns evolved but were not detected due to overcorrection for structure). Despite the limitations of GEAs, this study shows that a back-transformation of multivariate ordination can accurately predict individual multivariate traits from genotype and environmental data regardless of whether inference from GEAs was accurate. In addition, frameworks are introduced that can be used by empiricists to quantify the importance of clinal alleles in adaptation. This research highlights that multivariate trait prediction from genotype and environmental data can lead to accurate inference regardless of whether the underlying loci display clinal or nonmonotonic patterns.

Restoration of degraded ecosystems may take decades or even centuries. Accordingly, information about the current direction and speed of recovery provided by methods for predicting time to recovery may give important feedback to restoration schemes. While predictions of time to recovery have so far been based mostly upon change in species richness and other univariate predictors, the novel ordination‐regression based approach (ORBA) affords a multivariate approach based upon species compositional change. We used species composition data from four alpine spoil heaps in western Norway, recorded at three time points, to predict time to recovery using ORBA. This approach uses distances between restored plots and reference plots along a successional gradient, represented by a vector in ordination space, to model linear or asymptotic relationships of compositional change as a function of time. Results from ORBA were compared with results from models of more generic univariate attributes, that is total cover, species richness and properties of the physical environment as functions of time. ORBA predictions of time to species compositional recovery varied from less than 60 years with linear models to 115–212 years with asymptotic models. The long estimated time to recovery suggests that the restoration schemes adopted for these spoil heaps are likely to be suboptimal. Much shorter time to recovery was predicted from some of the more generic univariate attributes, that is species richness and total cover, than from species composition. Given the current rates of recovery, most spoil heaps will reach reference levels for total cover and species richness within 50 years, whereas predictions indicate that 67–111 years are needed to restore levels of soil organic matter and pH. Synthesis and applications. Species composition and soil conditions provide information of generally higher relevance for evaluation of ecosystem recovery processes than the most commonly used metric to assess restoration success, species richness. Species richness is decoupled from species compositional recovery, and likely to be a generally poor measure of restoration success. We therefore encourage further improvement of methods like the ordination‐regression based approach that use species compositional data to predict time to recovery. In alpine areas, where restoration takes decades or even centuries, we need proper methods that can predict time to recovery. Many different metrics are used for such predictions but species composition and soil conditions are argued to be better measures than, for example species richness. Photo by Knut Rydgren.

In community ecology, unconstrained ordination can be used to indirectly explore drivers of community composition, while constrained ordination can be used to directly relate predictors to an ecological community. However, existing constrained ordination methods do not explicitly account for community composition that cannot be explained by the predictors, so that they have the potential to misrepresent community composition if not all predictors are available in the data. We propose and develop a set of new methods for ordination and joint species distribution modelling (JSDM) as part of the generalized linear latent variable model (GLLVM) framework, that incorporate predictors directly into an ordination. This includes a new ordination method that we refer to as concurrent ordination, as it simultaneously constructs unconstrained and constrained latent variables. Both unmeasured residual covariation and predictors are incorporated into the ordination by simultaneously imposing reduced rank structures on the residual covariance matrix and on fixed‐effects. We evaluate the method with a simulation study, and show that the proposed developments outperform canonical correspondence analysis (CCA) for Poisson and Bernoulli responses, and perform similar to redundancy analysis (RDA) for normally distributed responses, the two most popular methods for constrained ordination in community ecology. Two examples with real data further demonstrate the benefits of concurrent ordination, and the need to account for residual covariation in the analysis of multivariate data. This article contextualizes the role of constrained ordination in the GLLVM and JSDM frameworks, while developing a new ordination method that incorporates the best of unconstrained and constrained ordination, and which overcomes some of the deficiencies of existing classical ordination methods.

Evaluation of the ecological health of rivers requires a focused examination of how biological indicators respond to chemical stressors to offer key insights for effective conservation strategies. We examined the influence of stressors on aquatic ecosystems by analyzing various ecological entities and biotic integrity metrics of fish communities. A nonmetric multidimensional scaling (NMDS) approach was applied to determine scores based on 19 fish ecological entities (FEs) and a fish-based multi-metric index of biotic integrity (mIBI-F). The composition of fish communities in reference clusters differed from the disturbed clusters due to instream chemical stressors. These chemical stressors, including high levels of nutrients, organic matter, and ionic/suspended solids, were linked to variation in the key indicator FEs, whose guild identities were closely associated with instream chemical degradation. The scores of FEs (abundance weighted) and mIBI-F metrics in the first NMDS axis (NMDS1) were significantly linked with chemical health indicators ( p  < 0.001), such as total phosphorus ( R 2  = 0.67 and 0.47), electrical conductivity ( R 2  = 0.59 and 0.49), and chlorophyll- a ( R 2  = 0.48 and 0.25). These NMDS1 scores showed better accuracy than the conventional mIBI-F score in capturing river ecological health linked with chemical health status as determined by a multi-metric index of water pollution. Our study suggests that based on the ordination approach, the biological integrity of these systems reflected the chemical health.

Claiming to fulfill a 2016 decision by the Greek Orthodox Patriarchate of Alexandria to revive the ancient order of deaconesses, Metropolitan Seraphim of Zimbabwe caught the Orthodox world by surprise by ordaining Angelic Molen (a married woman with two children) as a deaconess {St. Phoebe Center for the Deaconess, May 2). While confirming its 2016 \"decision in principle to revive and activate the institution of deaconesses within its pastoral jurisdiction,\" the statement added that the decision had been \"referred for further examination to establish the details concerning the attire, method of ministry delivery, and liturgical role of deaconesses in the contemporary life of the Church.\" The May 2 ordination will certainly boost the discussion on deaconesses in some sectors of the Orthodox Church, but its wider consequences are still unclear and it remains to be seen whether further ordinations will take place along the same lines. The current context is indeed a sensitive one, with the Moscow Patriarchate recently creating its own network of parishes on the African continent-in part by integrating former clergy of the Alexandria Patriarchate-and eager to position itself as the herald of \"traditional\" Orthodoxy in contrast to supposedly modern interpretations of the faith.

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 maximum photosynthetic carboxylation rate (V cmax) is an influential plant trait that has multiple scaling hypotheses, which is a source of uncertainty in predictive understanding of global gross primary production (GPP). Four trait-scaling hypotheses (plant functional type, nutrient limitation, environmental filtering, and plant plasticity) with nine specific implementations were used to predict global V cmax distributions and their impact on global GPP in the Sheffield Dynamic Global Vegetation Model (SDGVM). Global GPP varied from 108.1 to 128.2 PgC yr−1, 65% of the range of a recent model inter-comparison of global GPP. The variation in GPP propagated through to a 27% coefficient of variation in net biome productivity (NBP). All hypotheses produced global GPP that was highly correlated (r = 0.85–0.91) with three proxies of global GPP. Plant functional type-based nutrient limitation, underpinned by a core SDGVM hypothesis that plant nitrogen (N) status is inversely related to increasing costs of N acquisition with increasing soil carbon, adequately reproduced global GPP distributions. Further improvement could be achieved with accurate representation of water sensitivity and agriculture in SDGVM. Mismatch between environmental filtering (the most data-driven hypothesis) and GPP suggested that greater effort is needed understand V cmax variation in the field, particularly in northern latitudes.

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.