Lumpy species coexistence arises robustly in fluctuating resource environments

The effect of life-history traits on resource competition outcomes is well understood in the context of a constant resource supply. However, almost all natural systems are subject to fluctuations of resources driven by cyclical processes such as seasonality and tidal hydrology. To understand community composition, it is therefore imperative to study the impact of resource fluctuations on interspecies competition. We adapted a well-established resource-competition model to show that fluctuations in inflow concentrations of two limiting resources lead to the survival of species in clumps along the trait axis, consistent with observations of “lumpy coexistence” [Scheffer M, van Nes EH (2006) Proc Natl Acad Sci USA 103:6230–6235]. A complex dynamic pattern in the available ambient resources arose very early in the self-organization process and dictated the locations of clumps along the trait axis by creating niches that promoted the growth of species with specific traits. This dynamic pattern emerged as the combined result of fluctuations in the inflow of resources and their consumption by the most competitive species that accumulated the bulk of biomass early in assemblage organization. Clumps emerged robustly across a range of periodicities, phase differences, and amplitudes. Given the ubiquity in the real world of asynchronous fluctuations of limiting resources, our findings imply that assemblage organization in clumps should be a common feature in nature

Species extinctions strengthen the relationship between biodiversity and resource use efficiency

Evidence from terrestrial ecosystems indicates that biodiversity relates to ecosystem functions (BEF), but this relationship varies in its strength, in part, as a function of habitat connectivity and fragmentation. In primary producers, common proxies of ecosystem function include productivity and resource use efficiency. In aquatic primary producers, macroecological studies have observed BEF variance, where ecosystems with lower richness show stronger BEF relationships. However, aquatic ecosystems are less affected by habitat fragmentation than terrestrial systems and the mechanism underlying this BEF variance has been largely overlooked. Here, we provide a mechanistic explanation of BEF variance using a trait-based, numerical model parameterized for phytoplankton. Resource supply in our model fluctuates recurrently, similar to many coastal systems. Our findings show that following an extinction event, the BEF relationship can be driven by the species that are the most efficient resource users. Specifically, in species-rich assemblages, increased redundancy of efficient resource users minimizes the risk of losing function following an extinction event. On the other hand, in species-poor assemblages, low redundancy of efficient resource users increases the risk of losing ecosystem function following extinctions. Furthermore, we corroborate our findings with what has been observed from large-scale field studies on phytoplankton

Optimized classification predictions with a new index combining machine learning algorithms

Voting is a commonly used ensemble method aiming to optimize classification predictions by combining results from individual base classifiers. However, the selection of appropriate classifiers to participate in voting algorithm is currently an open issue. In this study we developed a novel Dissimilarity-Performance (DP) index which incorporates two important criteria for the selection of base classifiers to participate in voting: their differential response in classification (dissimilarity) when combined in triads and their individual performance. To develop this empirical index we firstly used a range of different datasets to evaluate the relationship between voting results and measures of dissimilarity among classifiers of different types (rules, trees, lazy classifiers, functions and Bayes). Secondly, we computed the combined effect on voting performance of classifiers with different individual performance and/or diverse results in the voting performance. Our DP index was able to rank the classifier combinations according to their voting performance and thus to suggest the optimal combination. The proposed index is recommended for individual machine learning users as a preliminary tool to identify which classifiers to combine in order to achieve more accurate classification predictions avoiding computer intensive and time-consuming search

Everything is not everywhere: can marine compartments shape phytoplankton assemblages?

The idea that ‘everything is everywhere, but the environment selects' has been seminal in microbial biogeography, and marine phytoplankton is one of the prototypical groups used to illustrate this. The typical argument has been that phytoplankton is ubiquitous, but that distinct assemblages form under environmental selection. It is well established that phytoplankton assemblages vary considerably between coastal ecosystems. However, the relative roles of compartmentalization of regional seas and site-specific environmental conditions in shaping assemblage structures have not been specifically examined. We collected data from coastal embayments that fall within two different water compartments within the same regional sea and are characterized by highly localized environmental pressures. We used principal coordinates of neighbour matrices (PCNM) and asymmetric eigenvector maps (AEM) models to partition the effects that spatial structures, environmental conditions and their overlap had on the variation in assemblage composition. Our models explained a high percentage of variation in assemblage composition (59–65%) and showed that spatial structure consistent with marine compartmentalization played a more important role than local environmental conditions. At least during the study period, surface currents connecting sites within the two compartments failed to generate sufficient dispersal to offset the impact of differences due to compartmentalization. In other words, our findings suggest that, even for a prototypical cosmopolitan group, everything is not everywhere

Nitrogen as the main driver of benthic diatom composition and diversity in oligotrophic coastal systems

Phytoplankton is the main indicator group for eutrophication in coastal ecosystems, however its high dispersal potential does not enable the assessment of localized effects of coastal nutrient enrichment. Benthic diatoms are sessile microalgae associated with sandy substrates and have the potential to reflect more localized pollution impacts. Although benthic diatoms are widely used bioindicators in freshwater systems, they have rarely been used for assessing the eutrophication status of oligotrophic environments such as the eastern Mediterranean Sea. In the present study, we assess the efficiency of benthic diatoms as bioindicators of nutrient enrichment in oligotrophic coastal systems, by investigating the effect of different physicochemical conditions and nutrient concentrations on the assemblage composition, diversity and individual species populations. To do this, we sampled along a eutrophication gradient formed by anthropogenic nutrient inputs from a metropolitan area. The main driver of assemblage composition, diversity and biomass of diatoms was nitrogen concentration and its temporal and spatial changes. Nitrogen loadings were positively correlated with increased biomass of Cocconeis spp. and negatively correlated with Mastogloia spp. Our findings suggest that in coastal ecosystems of oligotrophic marine ecoregions, benthic diatom assemblage structure and specific taxonomic groups can be reliable predictors of coastal eutrophication offering higher spatial resolution compared to phytoplankton

Geology Can Drive the Diversity–Ecosystem Functioning Relationship in River Benthic Diatoms by Selecting for Species Functional Traits

The biodiversity–ecosystem functioning (BEF) relationship has been studied extensively for the past 30 years, mainly in terrestrial plant ecosystems using experimental approaches. Field studies in aquatic systems are scarce, and considering primary producers, they mainly focus on phytoplankton assemblages, whereas benthic diatoms in rivers are considerably understudied in this regard. We performed a field study across nine rivers in Greece, and we coupled the observed field results with model simulations. We tested the hypothesis that the diversity–biomass (as a surrogate of ecosystem functioning) relationship in benthic diatoms would be affected by abiotic factors and would be time-dependent due to the highly dynamic nature of rivers. Indeed, geology played an important role in the form of the BEF relationship that was positive in siliceous and absent in calcareous substrates. Geology was responsible for nutrient concentrations, which, in turn, were responsible for the dominance of specific functional traits. Furthermore, model simulations showed the time dependence of the BEF form, as less mature assemblages tend to present a positive BEF. This was the first large-scale field study on the BEF relationship of benthic diatom assemblages, offering useful insights into the function and diversity of these overlooked ecosystems and assemblages

Phytoplankton diversity and bloom dynamics in coastal ecosystems under the influence of terrestrial runoff

The research objective of the current work was the quantitative study of the ecological processes regulating the diversity, structure, and dynamics of phytoplankton communities in coastal ecosystems, influenced by terrestrial nutrient loading, mainly of anthropogenic origin. The thesis was based on the analysis of physical, chemical, and biological data sampled during the course of one year in the Gulf of Kalloni, Lesvos Island, Greece, and on an existing database compiled from five coastal areas in the Aegean Sea. The data were analysed with the application of novel methodologies rarely used in marine ecology. According to the results of the study, the primary productivity, biodiversity, and species composition of phytoplankton communities in coastal ecosystems is strongly affected by the nutrient loading from the watershed particularly during the winter months. It has been found that the import of nitrogen into the system was more critical than phosphorus for the development of eutrophication crises and blooms of potentially toxic species. During these blooms, the biodiversity, and community structure and composition change radically since a maximum of two species dominate the community almost entirely. The application of biological models on bloom and non-bloom assemblages has shown that resources rather than self-organisation processes drive the structure of phytoplankton assemblages. The diversity-productivity relationship in phytoplankton was a humpback, when samples from bloom assemblages were included in the analysis. Investigation on the factors affecting the shape of this relationship has demonstrated a possible double abiotic stress mechanism, which regulates phytoplankton species richness in coastal ecosystemsCe travail a pour objet l'analyse quantitative des effets des changements environnementaux et notamment des apports en nutriments d'origine terrestre, sur la biodiversité, la structure et la dynamique des communautés phytoplanctoniques côtières. Elle s'appuie sur des données physico-chimiques et biologiques nouvelles collectées pendant une année dans le Golfe de Kalloni (île de Lesvos, Grèce) et sur une base de données existantes sur cinq régions côtières de l'Égée. Des méthodologies modernes rarement utilisées en écologie marine pour le traitement de l'information ont été appliquées (PERMANOVA, modèles statistiques et biologiques). La productivité primaire, la biodiversité et la composition des communautés phytoplanctoniques dans les écosystèmes marins côtiers sont fortement influencées par les apports du bassin versant et principalement durant la période hivernale. Il a été montré que les apports en azote inorganique étaient plus critiques que les apports en phosphore, provoquant le développement des crises dystrophiques et des floraisons d'espèces potentiellement toxiques comme Pseudo-nitzschia calliantha et Alexandrium insuetum. Durant ces épisodes, la biodiversité, la composition et la structure des communautés phytoplanctoniques changent radicalement vu que dans un bref laps de temps, seulement deux espèces deviennent prédominantes. L'application des modèles biologiques de Tokeshi a permis de démontrer que les communautés phytoplanctoniques sont structurées davantage par la disponibilité des ressources que par les processus d'autorégulation. La relation entre la biodiversité et la productivité du phytoplancton est représentée par une courbe convexe, dont la partie droite est caractérisée par la manifestation des efflorescences. L'analyse des facteurs qui contrôlent cette relation montre que le stress abiotique limite la richesse du phytoplancton lorsque la productivité des écosystèmes côtiers est minimale ou maximaleMONTPELLIER-BU Sciences (341722106) / SudocSudocFranceGreeceFRG

Optimizing biodiversity prediction from abiotic parameters

An integrated methodology is proposed for the effective prediction of biodiversity exclusively from abiotic parameters. Phytoplankton biodiversity was expressed as richness, evenness and dominance indices and abiotic parameters included temperature, salinity, dissolved inorganic nitrogen and phosphates. Prediction was based on three machine learning techniques: model trees, multilayer perceptron and instance based learning. To optimize diversity prediction, indices were calculated on a large number of phytoplankton field assemblages, but also on corresponding noise-free simulated assemblages. Biodiversity was most accurately predicted by the instance based learning algorithm and the efficiency was doubled with simulated assemblages. Based on the optimal algorithm, indices, and dataset, a software package was developed for phytoplankton diversity prediction for Eastern Mediterranean waters. The proposed methodology can be adapted to any group of organisms in marine and terrestrial ecosystems whereas important applications are the integration of community structure in ecological models and in assessments of global change scenarios