Unconstrained Ordination Research Articles

Linear Morphometry of Male Genitalia Distinguishes the Ant Genera Monomorium and Syllophopsis (Hymenoptera: Formicidae) in Madagascar.

Morphometric analyses of male genitalia are routinely used to distinguish genera and species in beetles, butterflies, and flies, but are rarely used in ants, where most morphometric analyses focus on the external morphology of the worker caste. In this work, we performed linear morphometric analysis of the male genitalia to distinguish Monomorium and Syllophopsis in Madagascar. For 80 specimens, we measured 10 morphometric characters, especially on the paramere, volsella, and penisvalvae. Three datasets were made from linear measurements: mean (raw data), the ratios of characters (ratio data), and the Removal of Allometric Variance (RAV data). The following quantitative methods were applied to these datasets: hierarchical clustering (Ward's method), unconstrained ordination methods including Principal Component Analysis (PCA), Non-Metric Multidimensional Scaling analyses (NMDS), Linear Discriminant Analysis (LDA), and Conditional Inference Trees (CITs). The results from statistical analysis show that the ratios proved to be the most effective approach for genus-level differentiation. However, the RAV method exhibited overlap between the genera. Meanwhile, the raw data facilitated more nuanced distinctions at the species level compared with the ratios and RAV approaches. The CITs revealed that the ratios of denticle length of the valviceps (SeL) to the paramere height (PaH) effectively distinguished between genera and identified key variables for species-level differentiation. Overall, this study shows that linear morphometric analysis of male genitalia is a useful data source for taxonomic delimitation.

Clustering and unconstrained ordination with Dirichlet process mixture models

Abstract Assessment of similarity in species composition or abundance across sampled locations is a common goal in multi‐species monitoring programs. Existing ordination techniques provide a framework for clustering sample locations based on species composition by projecting high‐dimensional community data into a low‐dimensional, latent ecological gradient representing species composition. However, these techniques require specification of the number of distinct ecological communities present in the latent space, which can be difficult to determine in advance. We develop an ordination model capable of simultaneous clustering and ordination that allows for estimation of the number of clusters present in the latent ecological gradient. This model draws latent coordinates for each sample location from a Dirichlet process mixture model, affording researchers with probabilistic statements about the number of clusters present in the latent ecological gradient. The model is compared to existing methods for simultaneous clustering and ordination via simulation and applied to two empirical datasets; JAGS code to fit the proposed model is provided in an appendix. The first dataset concerns presence‐absence records of fish in the Doubs river in eastern France and the second dataset describes presence‐absence records of plant species in Craters of the Moon National Monument and Preserve (CRMO) in Idaho, USA. Results from both analyses align with existing ecological gradients at each location. Development of the Dirichlet process ordination model provides wildlife managers with data‐driven inferences about the number of distinct communities present across monitored locations, allowing for more cost‐effective monitoring and reliable decision‐making for conservation management.

Ecological and statistical models to configure flow regime for environment benefit in highly engineered rivers: a case study in the MacKenzie River, Southeast Australia.

Ecological and statistical models were developed using freshwater algal assemblages to assess water quality and ecological health of a regulated river. These models were used to inform configuration of flows to maintain or improve environmental conditions of the waterway whilst meeting consumptive water supply commitments. The flow regime of the MacKenzie River, western Victoria, Australia, has been substantially modified since the construction of a water supply reservoir on its upper reach in 1887. Water is withdrawn at several locations downstream of the reservoir, creating a substantially modified flow regime, impacting key environmental values of the river. To assess the impact of the different flow regimes on river health and ecosystem function, ten sites were repeatedly sampled along the river between February 2012 and April 2014. Physical and chemical characteristics of water, including pH, temperature, turbidity, electrical conductivity, dissolved oxygen, total nitrogen, total phosphorous, cations, and anions, were measured. Biological properties of the algal periphyton communities, including dry mass, ash-free dry mass, chlorophyll-a concentration, and species composition, were also measured. Exploration of the algal assemblage and water chemistry data using the computationally unconstrained ordination technique such as principal component analysis principal component analysis (PCA), correspondence analysis (CA), detrended correspondence analysis (DCA), and canonical correspondence analysis (CCA) indicated two strong gradients in the data sets. Furthermore, the quantitative ecosystem response models have been developed as the prototype tool to assist in the future configuration of flows in this river. The empirical data and models showed the lower reaches of the river to be in poor condition under low flows, but this condition improved under flows of 35 ML/day, as indicated by the reduction in green algae and cyanobacteria and improvement. Finally, the results are presented to tailor discharge and duration of water volume by amalgamation of consumptive and environmental flows to improve the condition of the stream thereby supplementing the flows dedicated to environmental outcomes.

Using Algal Indices to Assess the Ecological Condition of the Aras River, Northwestern Iran

This work is the first in a series, and its purpose is the comprehensive assessment of the ecological state of the Aras River using biological indicators of water quality by diatoms based on species’ ecological preferences, pollution indices, statistics, and ecological mapping. Samples of diatoms and soft algae and measurements of water quality were analyzed at sixteen sampling sites (between 2020 and 2022) along the Aras River. The impact of anthropological activity on the river was monitored concerning water quality, river health, and ecosystem function. The physical and chemical characteristics of the water were measured. The biological properties of the algal periphyton communities, including species composition, were also measured. Based on the studies conducted in this research, 280 species were identified. The most prosperous species were Diatoma vulgaris, Amphora ovalis, Cocconeis placentula, Rhoicosphenia abbre-viatae, Cymbella helvetica, Brevisira arentii, Navicula tripunctata, Nitzschia linearis, Microcystis botrys, Microcystis aeruginosa, Pseudanabaena limnetica, Scenedesmus obliquus, and Pleurosira laevis (a pollution-resistant and salinity-resistant species first found in aquatic habitats in the Aras River). As a result, the empirical data and algal indices showed the river’s lower reaches to be in poor condition. Exploration of the algal assemblage and water chemistry data using computationally unconstrained ordination techniques such as principal component analysis (PCA) and canonical correspondence analysis (CCA) indicated two strong gradients in the data sets. The results support that water body classification is a function of water chemistry and biological and hydrological characteristics, as it is necessary to include pollutant effects on biota since the nature of the receiving waters influences the river’s water quality.

Organization of the macroinvertebrate community in a tropical annual agroecosystem into modules.

The structure of macroinvertebrate communities in agroecosystems has been assumed to be modular and organized around key herbivore pests. We characterized the macroinvertebrate community in the annual organic brassica agroecosystem in tropical central Brazil to determine if the community was a random assemblage of independent populations or was organized into repeatable multi-species components. We sampled 36 macroinvertebrate taxa associated with six organic brassica farms at biweekly intervals during the dry season during two years in the Distrito Federal, Brazil. We used an unconstrained ordination based on latent variable modeling (boral) with negative binomial population counts to analyze community composition independent of variation in sample abundance. We evaluated observed community structure by comparing it with randomized alternatives. We found that the community was not a random assemblage and consistently organized itself into two modules based around the major herbivores; one with lepidoptera and whiteflies and their associated natural enemies which was gradually replaced during the season by one with brassica aphids, aphid parasitoids and coccinellids. This analysis suggests that the historical and present-day focus on pest herbivores and their associated species in agroecosystems may be justified based on community structure.

Redundancy analysis includes analysis of variance-simultaneous component analysis (ASCA) and outperforms its extensions

Chemometrics and statistical ecology share interest in the analysis of multivariate data. In ecology, unconstrained and constrained ordination are popular methods to analyze and visualize multivariate data, with principal component analysis (PCA) and redundancy analysis (RDA) as prototype methods. Constraints give more insight and power by focusing on the response of the variables to particular external predictors or experimental factors, after optional adjustment for covariates. In chemometrics, analysis of variance - simultaneous component analysis (ASCA) was proposed decades later, with particular emphasis on the multivariate main and interaction effects in factorial experiments. This paper shows the similarities and differences between ASCA, its extensions, and (partial) RDA, alias reduced-rank regression. ASCA and RDA (understood as a sequence of partial RDAs, just as ASCA uses a sequence of PCAs) are shown to be mathematically identical for equireplicated designed experiments. Differences appear with unequal replication. As a corollary we show that, with equal replication, a particularly attractive form of ASCA, which displays a main effect together with an interaction, is a special case of principal response curve analysis. RDA is a least-squares method and uses the optimal weights in the dimension reduction of the treatment effects, whereas ASCA extensions for unbalanced data use alternative, sub-optimal weights.

Recovery of upland acid grasslands after successful Pteridium aquilinum control: Long-term effectiveness of cutting, repeated herbicide treatment and bruising

There is a clear need for the development of management strategies to control dominant, perennial weeds and restore semi-natural communities and an important part of this is to know how long control treatments take to be effective and how long they last after treatments stop. Here, we report the results from a 17-year long experiment where we compared the effects of five control treatments on dense Pteridium aquilinum (L. Kuhn) relative to an untreated experimental-control in Derbyshire, UK. The experiment was run in two phases. In Phase 1 (2005–2012) we controlled the P. aquilinum by cutting and bruising, both twice and thrice annually, and a herbicide treatment (asulam in year 1, followed by annual spot-re-treatment of all emergent fronds). In Phase 2 (2012–2021) all treatments were stopped, and the vegetation was allowed to develop naturally. Between 2005 and 2021 we monitored P. aquilinum performance annually and full plant species composition at intervals. Here, we concentrate on analysing the Phase 2 data where we used regression approaches to model individual species responses through time and unconstrained ordination to compare treatment effects on the entire species composition over both Phases. Remote sensing was also used to assess edge invasion in 2018.At the end of Phase 1, a good reduction of P. aquilinum and restoration of acid-grassland was achieved for the asulam and cutting treatments, but not for bruising. In Phase 2, P. aquilinum increased through time in all treated plots but the asulam and cutting ones maintained a much lower P. aquilinum performance for nine years on all measures assessed. There was a reduction in species richness and richness fluctuations, especially in graminoid species. However, multivariate analysis showed that the asulam and cutting treatments were stationed some distance from the untreated and bruising treatments with no apparent sign of reversions suggesting an Alternative Stable State had been created, at least over this nine-year period. P. aquilinum reinvasion was mainly from plot edges.The use of repeated P. aquilinum control treatments, either through an initial asulam spray with annual follow-up spot-spraying or cutting twice or thrice annually for eight years gave good P. aquilinum control and helped restore an acid-grassland community. Edge reinvasion was detected, and it is recommended that either whole-patch control be implemented or treatments should be continued around patch edges.

Methods of species pool determination as predictors of survival in seeding and transplanting experiments

Abstract Community composition is limited by a species' ability to reach, establish and survive on a site. Establishment and survival are constrained by both abiotic conditions and biotic interactions that operate together on local scales. They decide which species from the pool will form the community. For this reason, it is very important to clearly define the species pool, against which the community composition is compared. The effect of biotic and abiotic factors can be assessed experimentally, and the species pool by using estimation methods based on broad‐scale observational data. We compared success of five species pool estimation methods in predicting establishment and survival in a seed/transplant addition experiment. In four different locations, we added resident and non‐resident species to plots with and without competition and tested the ability of the species to thrive in both competition‐free gaps (constrained mainly by abiotic conditions) and in intact vegetation (complete community filter). In these treatments, we studied the seedling recruitment and survival, and the establishment and survival of pregrown transplants. The ability of species pool assessment methods to predict species performance in individual treatments was compared. The comparison of results from individual treatments indicates the importance of individual components of the community filter. Species pool assessment methods, based on species co‐occurrence patterns (Beals index, favourability and unconstrained ordination), were the best predictors of species performance in the intact vegetation, but were less successful in the competition‐free environment. Methods based on co‐occurrence patterns were the most effective for predicting seedling establishment, while seed germination alone and transplant survival were poorly predictable. The biotic filter was the principal factor defining our community composition, especially for the process of seedling establishment. The roles of biotic and abiotic filters are very difficult to distinguish without an experimental approach and the ratio of their importance changes during plant ontogenesis. Read the free Plain Language Summary for this article on the Journal blog.

Concurrent ordination: Simultaneous unconstrained and constrained latent variable modelling

Abstract 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.

Assessment of heavy metal levels in an urban river in the Philippines using an unconstrained ordination- and GIS-based approach: evidence of the return of past pollution after the 2013 Typhoon Haiyan (Yolanda)

Abstract Assessment of urban river sediment quality is paramount to understanding the impacts of urbanization on aquatic ecosystems and public health. The study evaluated the health impacts and sources of heavy metal pollutants in the Mangonbangon river, Tacloban City. With the abundance of heavy metal contaminants in the river sediment (Fe>Mn>Zn>Cu>Cr>Ni>Co), Hazard indices (HIs) ranged from 0.04 to 0.10 for adults and 0.31 to 0.90 for children suggesting little or no non-carcinogenic effects to the population. Lifetime cancer risk (LCR) is below the tolerable threshold of 10−4, with Co contributing 61% of the cancer risk. Using unconstrained ordination and the GIS-based method (UOGM), we showed two non-multidimensional scaling groups of pollutants distributed based on dwelling density, presence of informal settlers, and types of activity at the sample sites. Given that sampling was performed three years after the city-wide destruction by Typhoon Haiyan (Yolanda), our analysis indicates the return of anthropogenic activities and pollution-related health problems in Tacloban. Our results reinforce the urgent need for proper river management and economic zoning to help curb the rapidly growing heavy metal pollution problem at its earliest stage.

The application of Uniform Manifold Approximation and Projection (UMAP) for unconstrained ordination and classification of biological indicators in aquatic ecology

The analysis of community structure in studies of freshwater ecology often requires the application of dimensionality reduction to process multivariate data. A high number of dimensions (number of taxa/environmental parameters × number of samples), nonlinear relationships, outliers, and high variability usually hinder the visualization and interpretation of multivariate datasets. Here, we proposed a new statistical design using Uniform Manifold Approximation and Projection (UMAP), and community partitioning using Louvain algorithms, to ordinate and classify the structure of aquatic biota in two-dimensional space. We present this approach with a demonstration of five previously published datasets for diatoms, macrophytes, chironomids (larval and subfossil), and fish. Principal Component Analysis (PCA) and Ward's clustering were also used to assess the comparability of the UMAP approach compared to traditional approaches for ordination and classification. The ordination of sampling sites in 2-dimensional space showed a much denser, and easier to interpret, grouping using the UMAP approach in comparison to PCA. The classification of community structure using the Louvain algorithm in UMAP ordinal space showed a high classification strength for data with a high number of dimensions than the cluster patterns obtained with the use of a Ward's algorithm in PCA. Environmental gradients, presented via heat maps, were overlayed with the ordination patterns of aquatic communities, confirming that the ordinations obtained by UMAP were ecologically meaningful. This is the first study that has applied a UMAP approach with classification using Louvain algorithms on ecological datasets. We show that the performance of local and global structures, as well as the number of clusters determined by the algorithm, make this approach more powerful than traditional approaches.

Geomorphology variables predict fish assemblages for forested and endorheic rivers of two continents.

Stream fishes are restricted to specific environments with appropriate habitats for feeding and reproduction. Interactions between streams and surrounding landscapes influence the availability and type of fish habitat, nutrient concentrations, suspended solids, and substrate composition. Valley width and gradient are geomorphological variables that influence the frequency and intensity that a stream interacts with the surrounding landscape. For example, in constrained valleys, canyon walls are steeply sloped and valleys are narrow, limiting the movement of water into riparian zones. Wide valleys have long, flat floodplains that are inundated with high discharge. We tested for differences in fish assemblages with geomorphology variation among stream sites. We selected rivers in similar forested and endorheic ecoregion types of the United States and Mongolia. Sites where we collected were defined as geomorphologically unique river segments (i.e., functional process zones; FPZs) using an automated ArcGIS‐based tool. This tool extracts geomorphic variables at the valley and catchment scales and uses them to cluster stream segments based on their similarity. We collected a representative fish sample from replicates of FPZs. Then, we used constrained ordinations to determine whether river geomorphology could predict fish assemblage variation. Our constrained ordination approach using geomorphology to predict fish assemblages resulted in significance using fish taxonomy and traits in several watersheds. The watersheds where constrained ordinations were not successful were next analyzed with unconstrained ordinations to examine patterns among fish taxonomy and traits with geomorphology variables. Common geomorphology variables as predictors for taxonomic fish assemblages were river gradient, valley width, and valley slope. Significant geomorphology predictors of functional traits were valley width‐to‐floor width ratio, elevation, gradient, and channel sinuosity. These results provide evidence that fish assemblages respond similarly and strongly to geomorphic variables on two continents.

Fast model‐based ordination with copulas

Abstract Visualising data is a key step in data analysis, allowing researchers to find patterns, and assess and communicate the results of statistical modelling. In ecology, visualisation is often challenging when there are many variables (often for different species or other taxonomic groups) and they are not normally distributed (often counts or presence–absence data). Ordination is a common and powerful way to overcome this hurdle by reducing data from many response variables to just two or three, to be easily plotted. Ordination is traditionally done using dissimilarity‐based methods, most commonly non‐metric multidimensional scaling (nMDS). In the last decade, however, model‐based methods for unconstrained ordination have gained popularity. These are primarily based on latent variable models, with latent variables estimating the underlying, unobserved ecological gradients. Despite some major benefits, a drawback of model‐based ordination methods is their speed, as they typically take much longer to return a result than dissimilarity‐based methods, especially for large sample sizes. We introduce copula ordination, a new, scalable model‐based approach to unconstrained ordination. This method has all the desirable properties of model‐based ordination methods, with the added advantage that it is computationally far more efficient. In particular, simulations show copula ordination is an order of magnitude faster than current model‐based methods, and can even be faster than nMDS for large sample sizes, while being able to produce similar ordination plots and trends as these methods.

Seasonal and Regional Chemical Variability of the Wild Population of Lantana camara Leaf Essential Oil From Kenya

Studies examining the variability in wild plant metabolic expression propose that environmental factors significantly influence the essential oil (EO) quality and quantity in a plant. Lantana camara is a widely distributed invasive plant species worldwide. However, its immense metabolites can become a source of novel compounds to produce biopesticides in the agricultural industry. Although, the quality aspect has to be considered due to the environmental influence on the metabolites synthesised. Therefore, this research aimed to understand the influence of environmental factors and how it shapes the plant’s metabolite profile in multiple populations of L. camara. Leaf samples were collected from six different geographic regions of Kenya and the corresponding monthly climatic data and soil samples. GC-MS data from leaf EO were analysed with environmental variables (climate and soil data) using unimodally unconstrained and constrained ordination methods for untargeted metabolomics analysis. Partial Least Squares-Discriminant Analysis (PLS-DA) and Random Forests (RF) were used to confirm the variability further. Seasonal and regional variability was observed for secondary metabolites (SMs) in the leaf EO, which correlated to climatic factors and soil attributes. We highlight the season-al-geographic metabolism relationship for L. camara and the combined analytical approach to obtain data that contributes to understanding the influence of environmental factors on the synthesis and accumulation of SMs. This research will have all-embracing implications for maximising phytochemical uniformity.

Rating of parameters used to assess tree vitality by urban foresters and ecologists in Sweden, using the Delphi method

Management of urban trees is key to sustaining and increasing essential ecosystem services, and most management decisions are made based on urban tree inventories. Vitality is one of the key parameters when conducting tree inventories. However, consensus on how vitality should be assessed is lacking, and there is limited understanding of how visual vitality assessments are affected by aspects of different professional background, growing sites and tree age. In a Delphi study with 19 participants completing the whole study, we asked urban foresters and ecologists to assess the vitality of 21 trees in urban and rural settings and to rate how important 40 different parameters were for their assessment of each tree’s vitality. The data obtained were analysed using Cronbach’s alpha, unconstrained ordination, hierarchal clustering and indicator values. In testing for differences, mixed general linear models and constrained ordination were used. Professionals participating in the study showed good overall agreement in ranking trees as more or less vital, but with large differences in what was considered a fully vital tree. When performing vitality assessments, the parameters used differed between old and young trees, and between urban and rural sites. There was also a systematic difference between urban foresters and ecologists in performing tree vitality assessments, with ecologists consistently rating tree vitality higher and using fewer parameters. Parameters used for assessing vitality comprised aspects relating to sign of decay, external damage, loss/death of biomass, growth performance and site conditions. Vitality should thus be regarded as a complex parameter that needs calibration-based assessment approaches and the person performing the assessment should always be included as an additional variable. Overall, this study clearly showed the need to establish consensus on how tree vitality should be assessed and rated.

The ecology of testate amoebae and Cladocera in Hawaiian montane peatlands and development of a hydrological transfer function

Peatland complexes in the humid highlands of Hawai‘i are vital refuges of biodiversity and freshwater resources. Hawaiian peat deposits are also rare repositories of terrestrial ecosystem archives located in an otherwise vast expanse of ocean. We investigated the potential for researching the paleohydrological history of Hawaiian montane peatlands on Kohala, Hawai‘i Island through analyses of testate amoebae and Cladocera. Surface peat was collected from a variety of ecohydrological habitats (from water pools to hummocks) and analyzed for modern testate amoeba and cladoceran species relative abundance. We identified 54 morphotype taxa from 21 genera of testate amoebae, 4 taxa and genera of littoral Cladocera, and the common peat rotifer Habrotrocha angusticollis. Testate amoeba diversity and morphotype occurrence mirrored observations from many high-latitude peatland studies. Constrained and unconstrained ordinations support the hypothesis that surface moisture, measured as water-table depth, is an important control on the distribution of testate amoebae and Cladocera in Hawaiian peatlands. Transfer functions relying on weighted-averaging and modern analogs were developed to predict water-table depths from species relative abundance data, and these perform well under leave-one-site-out cross-validation: RMSEP = 9.75–10.3 cm, R2 = 0.56–0.62. Including cladoceran abundance data in the calibration dataset produced modest model improvement: RMSEP = 1–8% and R2 = 2–13%. A weighted-average partial-least-squares transfer function was applied to microfossil assemblages from a 0.5 m-long peat core with a 210Pb decay chronology anchored by ten existing measurements of 210Pb activity and a Bayesian statistical framework. Microfossils were well-preserved in the peat core. The water-table depth optima of an abundant down-core taxa, Hyalosphenia subflava, is not precisely constrained in the calibration data set, but estimates match those of other tropical studies. A reconstruction of water-table depth indicates dry early nineteenth-century conditions, wet conditions in the late 19th to early twentieth centuries, followed by progressive drying for much of the twentieth-century. Testate amoeba composition appears to have been sensitive to severe drought in recent decades. The results signal that assemblages of testate amoebae and Cladocera are useful proxies of Hawaiian peatland paleohydrology and should be considered alongside other archives of Hawaiian environmental history.

Arctic chironomids of the northwest North Atlantic reflect environmental and biogeographic gradients

AbstractAimWhile we understand broad climate drivers of insect distributions throughout the Arctic, less is known about the role of spatial processes in determining these relationships. As such, there is a need to understand how spatial controls may influence our interpretations of chironomid environment relationships. Here, we evaluated whether the distribution of chironomids followed spatial gradients, or were primarily controlled by environmental factors.LocationEastern Canadian Arctic, Greenland, Iceland.TaxonNon‐biting midges (Chironomidae).MethodsWe examined chironomid assemblages from 239 lakes in the western North Atlantic Arctic region (specifically from the Arctic Archipelago of Canada, two parts of west Greenland (the southwest and central west) and northwest Iceland). We used a combination of unconstrained ordination (Self Organizing Maps); a simple method with only one data matrix (community data), and constrained ordination (Redundancy Analysis); a canonical ordination with two datasets where we extracted structure of community related to environmental data. These methods allowed us to model chironomid assemblages across a large bioregional dimension and identify specific differences between regions that were defined by common taxa represented across all regions in high frequencies, as well as rare taxa distinctive to each region found in low frequencies. We then evaluated the relative importance of spatial processes versus local environmental factors.ResultsWe find that environmental controls explained the largest amount of variation in chironomid assemblages within each region, and that spatial controls are only significant when crossing between regions. Broad‐scale biogeographic effects on chironomid distributions are reflected by the distinct differences among chironomid assemblages of Iceland, central‐west Greenland, and eastern Canada, defined by the presence of certain common and low‐frequency, rare taxa for each region. Environmental gradients, especially temperature, defined species distributions within each region, whereas spatial processes combine with environmental gradients in determining what mix of species characterizes each broad and geographically distinct island region in our study.Main conclusionsWhile biogeographic context is important for defining interpretations of environmental controls on species distributions, the primary control on distributions within regions is environmental. These influences are fundamentally important for reconstructing past environmental change and better understanding historical distributions of these insect indicators.

Land-use legacies affect Norway spruce Picea abies colonization on abandoned marginal agricultural land in Eastern Baltics

Understanding forest regeneration in relation to land-use legacies is an important goal of research on global environmental changes and future ecosystem patterns. Recent findings on spruce regeneration on abandoned agricultural land showed it to be linked to land-use legacies involving increased soil fertility by fertilization. We argue that indirect drivers like land-use changes from arable land to grassland, which affect soil fertility and vegetation density, could also be involved. We selected a study area where changes in land-use involved gradual abandonment of agricultural intervention and created substantial heterogeneity in forest stand age and species composition. Over a 60-year period, the tree colonization pattern changed from deciduous tree species in 1950–1970 to Norway spruce in the later period. Generalized linear logistic regression models were generated to explain the presence of deciduous tree and spruce colonization in relation to soil and land-use factors. The importance of plant community composition on establishment of tree species was studied by Cluster analysis and unconstrained ordination. Deciduous forest development was negatively related to past agricultural use as arable land and positively related to extensively used grasslands (meadows and pastures) and no soil factors entered the explanatory model. Ecological amplitude of deciduous tree species and differing patterns of agricultural land management at landscape and farm scales are discussed as possible explanations. Spruce regeneration was significantly explained by soil moisture and agricultural use as arable land followed by conversion to grassland. We conclude that spruce regeneration is a phenomenon characteristic not only for previously fertilized agricultural land but also for marginal agricultural land if the management ensures development of dense herbaceous layer.

Long-Term Change of Demersal Fish Assemblages on the Inshore Agulhas Bank Between 1904 and 2015

Without baseline data from near pristine assemblages, measures of ecosystem change may be significantly underestimated. A unique historical dataset provided an opportunity to investigate long-term change in demersal fish assemblages of South Africa's inshore trawl grounds. Three sites surveyed over a period from 1903 to 1904 were re-surveyed in 2015 using replicated historical gear and methods. Catch composition was contrasted between historical and modern periods using unconstrained ordination, permutational multivariate analysis of variance, permutational tests of the homogeneity of multivariate group dispersions and similarity percentage analyses. After 111 years, the re-survey revealed a drastically transformed demersal assemblage, with the period effect explaining nearly half of the variance among samples. Historical catches were dominated by kob (Argyrosomus spp.), panga (Pterogymnus laniarius) and east coast sole (Austroglossus pectoralis), jointly contributing 70-84% of the catch. The same taxa made up a minor component (1.5-5.5%) of modern assemblages. Instead, the re-survey catches consisted predominantly of gurnards (Chelidonichthys spp.), Cape horse mackerel (Trachurus capensis), spiny dogfish (Squalus spp.), shallow-water hake (Merluccius capensis) and white sea catfish (Galeichthys feliceps), with their summed contribution rising to 85% from the historical 3%. These results suggest that a century of trawling may have altered benthic habitats, indirectly contributing to changes in the fish community. Historical assemblages included a substantial proportion of taxa that associate with reef habitats, whereas the re-survey assemblages were characterised by species that inhabit unconsolidated sediments or both reef and non-reef habitats. The unique historical context and data, comparable gear and methods and long temporal period revealed striking baseline changes that may be overlooked in most fisheries. Reconstructing this important historical context improves our ability to assess, interpret and manage changing marine ecosystems.

Woody plant subregions of the Amazon forest

Abstract The Amazon forest covers 7.5 million km2 in nine countries, hosts 25% of the global biodiversity and is a major contributor to the biogeochemical and climatic functioning of the Earth system. Despite its global importance, a regionalization of the Amazon tree flora is still lacking. Clear and data‐driven delimitation of subregions is important for macroecological studies, to the identification of metacommunities and is a requisite for conservation planning. We aimed at identifying and mapping plant species subregions and investigated their relationships with environmental, historical and human correlates. We provide the first woody plant regionalization of the entire Amazon forest using a data‐driven approach based on assemblage composition patterns. We compiled data on woody species composition from 301 assemblages based on species occurrences. We then used unconstrained ordination, interpolation and clustering techniques to identify and map discrete woody subregions. Hierarchical clustering analysis was conducted in order to investigate the relationships between the identified subregions. We used multinomial logistic regression model and deviance partitioning to investigate the influence of environmental, historical and human factors on subregions distribution. We identified 13 woody subregions in the entire Amazon forest. The hierarchical subregion classification showed a broad Andean‐Cratonic east–west division. Variation in subregions was explained jointly by human factors and spatial structure followed by environmental factors and spatial structure combined. Synthesis. Our woody plant subregions differed from World Wildlife Fund ecoregions and physiognomic‐based maps, highlighting the importance of basing regionalizations on taxon‐specific groups and confirming that vegetation maps should not be used as proxies to plant diversity subregions. Our findings also confirm the need for multiple and extensive protected areas in the Amazon forest. The relevance of current climate factors in our study alerts to a profound impact that climate change could have on the spatial organization of the Amazon flora.