Sofie Spatharis

Assessing the efficiency of clustering algorithms and goodness-of-fit measures using phytoplankton field data

Investigation of patterns in beta diversity has received increased attention over the last years particularly in light of new ecological theories such as the metapopulation paradigm and metacommunity theory. Traditionally, beta diversity patterns can be described by cluster analysis (i.e. dendrograms) that enables the classification of samples. Clustering algorithms define the structure of dendrograms, consequently assessing their performance is crucial. A common, although not always appropriate approach for assessing algorithm suitability is the cophenetic correlation coefficient c. Alternatively the 2-norm has been recently proposed as an increasingly informative method for evaluating the distortion engendered by clustering algorithms. In the present work, the 2-norm is applied for the first time on field data and is compared with the cophenetic correlation coefficient using a set of 105 pairwise combinations of 7 clustering methods (e.g. UPGMA) and 15 (dis)similarity/distance indices (e.g. Jaccard index). In contrast to the 2-norm, cophenetic correlation coefficient does not provide a clear indication on the efficiency of the clustering algorithms for all combinations. The two approaches were not always in agreement in the choice of the most faithful algorithm. Additionally, the 2-norm revealed that UPGMA is the most efficient clustering algorithm and Wards the least. The present results suggest that goodness-of-fit measures such as the 2-norm should be applied prior to clustering analyses for reliable beta diversity measures.

Phytoplankton Succession in Recurrently Fluctuating Environments

Coastal marine systems are affected by seasonal variations in biogeochemical and physical processes, sometimes leading to alternating periods of reproductive growth limitation within an annual cycle. Transitions between these periods can be sudden or gradual. Human activities, such as reservoir construction and interbasin water transfers, influence these processes and can affect the type of transition between resource loading conditions. How such human activities might influence phytoplankton succession is largely unknown. Here, we employ a multispecies, multi-nutrient model to explore how nutrient loading switching mode might affect phytoplankton succession. The model is based on the Monod-relationship, predicting an instantaneous reproductive growth rate from ambient inorganic nutrient concentrations whereas the limiting nutrient at any given time was determined by Liebig’s Law of the Minimum. When these relationships are combined with population loss factors, such as hydraulic displacement of cells associated with inflows, a characterization of a species’ niche can be achieved through application of the R* conceptual model, thus enabling an ecological interpretation of modeling results. We found that the mode of reversal in resource supply concentrations had a profound effect. When resource supply reversals were sudden, as expected in systems influenced by pulsed inflows or wind-driven mixing events, phytoplankton were characterized by alternating succession dynamics, a phenomenon documented in inland water bodies of temperate latitudes. When resource supply reversals were gradual, as expected in systems influenced by seasonally developing wet and dry seasons, or annually occurring periods of upwelling, phytoplankton dynamics were characterized by mirror-image succession patterns. This phenomenon has not been reported previously in plankton systems but has been observed in some terrestrial plant systems. These findings suggest that a transition from alternating to “mirror-image” succession patterns might arise with continued coastal zone development, with crucial implications for ecosystems dependent on time-sensitive processes, e.g., spawning events and migration patterns.

Application of the lognormal equation to assess phytoplankton community structural changes induced by marine eutrophication

A methodological approach was developed for the quantification of the structural changes of phytoplankton communities induced by marine eutrophication. The lognormal equation assigning species abundance to doubling intervals (octaves) of individuals formed the basis of the proposed methodology and the field validation process was based on phytoplankton enumeration and classification data characteristic of eutrophic, mesotrophic and oligotrophic waters. Five octave sets with different sizes were tested for goodness of fit against field data and the set with the smallest size of doubling intervals was selected for further consideration. The application of the lognormal equation was evaluated statistically with field data and it was considered satisfactory at the 87% level. The changes in the shape of the lognormal equation induced by eutrophication were expressed by three characteristic parameters of the equation: the number of the modal octave, the number of species in the modal octave, and the shaping factor. Significant differences were observed for the three parameters among eutrophic, mesotrophic, and oligotrophic waters; the number of the modal octave was high in eutrophic and mesotrophic waters, the number of species in the modal octave has shown a trend of low values under mesotrophic conditions and the shaping factor has shown a considerable increase from eutrophic to oligotrophic waters.

A niche-based modeling approach to phytoplankton community assembly rules.

Six niche-based models proposed by Tokeshi, based on different assumptions of resource allocation by species, were fitted on phytoplankton relative abundance distributions, and potential environmental and biotic factors supporting the applicability of the fitted models were discussed. Overall 16 assemblages corresponding to different sampling times, various environmental conditions, and resource regimes within a year were fitted to the models. Phytoplankton biovolume was used as a measure of abundance, and a randomization test was applied to compare the model fit to the field data. The majority of the phytoplankton assemblages (11 of 16) were successfully described by the Random Fraction model, which is based on the theoretical assumption that resource is apportioned by the species in a random way. Only a few assemblages (three of 16), characterized by extremes in resource availability or disturbance, were not fitted by any of the models. The Random Fraction model in particular was rejected due to a steep slope during the first ranks, while the rest of the distribution remained relatively even, providing further evidence of resilience in phytoplankton communities. Although larger cells seem to have the potential to develop higher biomass, it seems that other factors, including the surface-to-volume ratio, counterbalance this advantage, resulting in a random-like behaviour in resource acquisition by phytoplankton, irrespective of cell size or species identity.

Phytoplankton Assemblage Characteristics in Recurrently Fluctuating Environments

Annual variations in biogeochemical and physical processes can lead to nutrient variability and seasonal patterns in phytoplankton productivity and assemblage structure. In many coastal systems river inflow and water exchange with the ocean varies seasonally, and alternating periods can arise where the nutrient most limiting to phytoplankton growth switches. Transitions between these alternating periods can be sudden or gradual and this depends on human activities, such as reservoir construction and interbasin water transfers. How such activities might influence phytoplankton assemblages is largely unknown. Here, we employed a multispecies, multi-nutrient model to explore how nutrient loading switching mode might affect characteristics of phytoplankton assemblages. The model is based on the Monod-relationship, predicting an instantaneous growth rate from ambient inorganic nutrient concentrations whereas the limiting nutrient at any given time was determined by Liebig’s Law of the Minimum. Our simulated phytoplankton assemblages self-organized from species rich pools over a 15-year period, and only the surviving species were considered as assemblage members. Using the model, we explored the interactive effects of complementarity level in trait trade-offs within phytoplankton assemblages and the amount of noise in the resource supply concentrations. We found that the effect of shift from a sudden resource supply transition to a gradual one, as observed in systems impacted by watershed development, was dependent on the level of complementarity. In the extremes, phytoplankton species richness and relative overyielding increased when complementarity was lowest, and phytoplankton biomass increased greatly when complementarity was highest. For low-complementarity simulations, the persistence of poorer-performing phytoplankton species of intermediate R*s led to higher richness and relative overyielding. For high-complementarity simulations, the formation of phytoplankton species clusters and niche compression enabled higher biomass accumulation. Our findings suggest that an understanding of factors influencing the emergence of life history traits important to complementarity is necessary to predict the impact of watershed development on phytoplankton productivity and assemblage structure.

Lumpy species coexistence arises robustly in fluctuating resource environments

Significance Explaining why there are more species than limiting resources in natural systems constitutes a long-standing challenge among ecologists. Recently, this apparent paradox was resolved theoretically by showing that species can coexist in clumps along niche gradients. However, models demonstrating this effect have failed to account for a ubiquitous feature of nature, namely variability in environmental conditions. This leaves open the question of whether the proposed mechanisms underpinning “lumpy coexistence” apply in nature or arise as a coincidence of modeling frameworks. Here, we demonstrate the emergence of lumpy coexistence in assemblages self-organizing under fluctuating resource supplies. We show that clumps form predictably as the result of the dynamic pattern in ambient resources driven by the most competitive species in the assemblage. 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.

Potential response to climate change of a semi-arid coastal ecosystem in eastern Mediterranean

Eastern Mediterranean gulfs, adjacent to small semi-arid watersheds are particularly susceptible to climate changes. In this study, an analysis was performed for air temperature and rainfall during 1955–2010 over a coastal ecosystem in NE Aegean, and potential effects of recent changes on the physical setting and ecological status of the marine system were studied. A trend toward drier conditions was revealed, and in order to assess possible effects on the surrounding basin, a watershed model was applied. In addition, the hydrology and ecology of the marine ecosystem were studied using a water budget model along with available field data. Based on local climatological data, dryness may lead to a decrease of one to two orders of magnitude in the amount of runoff during a dry annual cycle, resulting to a fivefold increase in the residence time of the marine system. High residence time associated with terrestrial nutrient inputs and strong stratification result to phytoplankton blooms during winter, including harmful algal blooms. Integrated approaches, modeling both the hydrology and ecology of watersheds and adjacent water bodies, are essential toward forecasting, understanding and management of potential alterations in functioning of coastal ecosystems due to recent climate changes.

Zipf–Mandelbrot model behavior in marine eutrophication: two way fitting on field and simulated phytoplankton assemblages

Zipf–Mandelbrot (ZM) model, linking the evenness and predictability of communities (parameter gamma) to environmental diversification (parameter beta), was fitted on a phytoplankton dataset, representing a wide productivity spectrum characteristic of Eastern Mediterranean. To reveal community characteristics explaining observed patterns in gamma and beta, ZM model was also fitted on simulated assemblages generated by three niche-based models. Parameter gamma showed a decreasing trend with evenness in agreement with theory and related field studies. High gamma values corresponded to phytoplankton assemblages characterized by the presence of a dominant species, the rest being evenly distributed, whereas low gamma was observed for even assemblages. For ZM beta with evenness, a characteristic U-shaped relationship was observed in field and simulated assemblages, implying that high environmental diversification may lead to either high or low evenness. These assemblages are described by MacArthur fraction and dominance decay niche-based models and their only difference is the dominance of a single species, the rest being evenly distributed. At intermediate environmental diversification, more often encountered in the field, phytoplankton assemblages are described by random fraction model resulting to slightly steeper RADs. Two-way fitting approach on field and simulated assemblages provided useful insights on both ZM model behavior and its underlying hypotheses.

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. We propose an integrated framework for optimising diversity prediction.Prediction was based on 3 machine learning algorithms using 4 abiotic parameters.IBk algorithm achieved the best prediction with high predictive power.Prediction was optimized using noise-free simulated phytoplankton assemblages.PREPHYB software is provided for diversity prediction in E. Mediterranean waters.

Interplay between r- and K-strategists leads to phytoplankton underyielding under pulsed resource supply

Fluctuations in nutrient ratios over seasonal scales in aquatic ecosystems can result in overyielding, a condition arising when complementary life-history traits of coexisting phytoplankton species enables more complete use of resources. However, when nutrient concentrations fluctuate under short-period pulsed resource supply, the role of complementarity is less understood. We explore this using the framework of Resource Saturation Limitation Theory (r-strategists vs. K-strategists) to interpret findings from laboratory experiments. For these experiments, we isolated dominant species from a natural assemblage, stabilized to a state of coexistence in the laboratory and determined life-history traits for each species, important to categorize its competition strategy. Then, using monocultures we determined maximum biomass density under pulsed resource supply. These same conditions of resource supply were used with polycultures comprised of combinations of the isolated species. Our focal species were consistent of either r- or K-strategies and the biomass production achieved in monocultures depended on their efficiency to convert resources to biomass. For these species, the K-strategists were less efficient resource users. This affected biomass production in polycultures, which were characteristic of underyielding. In polycultures, K-strategists sequestered more resources than the r-strategists. This likely occurred because the intermittent periods of nutrient limitation that would have occurred just prior to the next nutrient supply pulse would have favored the K-strategists, leading to overall less efficient use of resources by the polyculture. This study provides evidence that fluctuation in resource concentrations resulting from pulsed resource supplies in aquatic ecosystems can result in phytoplankton assemblages’ underyielding.