Experimentally decomposing phytoplankton community change into ecological and evolutionary contributions

Range of trait variation in prey determines evolutionary contributions to predator growth rates

Evolutionary and ecological dynamics can occur on similar timescales and thus influence each other. While it has been shown that the relative contribution of ecological and evolutionary change to population dynamics can vary, it still remains unknown what influences these differences. Here, we test whether prey populations with increased variation in their defence and competitiveness traits will have a stronger impact on evolution for predator growth rates. We controlled trait variation by pairing distinct clonal lineages of the green alga Chlamydomonas reinhardtii with known traits as prey with the rotifer Brachionus calyciforus as predator and compared those results with a mechanistic model matching the empirical system. We measured the impact of evolution (shift in prey clonal frequency) and ecology (shift in prey population density) for predator growth rate and its dependency on trait variation using an approach based on a 2-way ANOVA. Our experimental results indicated that higher trait variation, i.e., a greater distance in trait space, increased the relative contribution of prey evolution to predator growth rate over 3-4 predator generations, which was also observed in model simulations spanning longer time periods. In our model, we also observed clone-specific results, where a more competitive undefended prey resulted in a higher evolutionary contribution, independent of the trait distance. Our results suggest that trait combinations and total prey trait variation combine to influence the contribution of evolution to predator population dynamics, and that trait variation can be used to identify and better predict the role of eco-evolutionary dynamics in predator-prey systems.

Quantifying interspecific and intraspecific diversity effects on ecosystem functioning

Rapid environmental changes result in massive biodiversity loss, with detrimental consequences for the functioning of ecosystems. Recent studies suggest that intraspecific diversity can contribute to ecosystem functioning to an extent comparable to contributions of interspecific diversity. Knowledge on the relative importance of these two sources of biodiversity is essential for predicting ecosystem consequences of biodiversity loss and will aid in the prioritization of conservation targets and implementation of management measures. However, our quantitative insights into how interspecific and intraspecific biodiversity loss affects ecosystem functioning and how the effects of these two sources of biodiversity loss on ecosystem functioning can be compared are still very limited. To facilitate such quantitative insights, we extend the interspecific Price partitioning method originally introduced by J. Fox in 2006, previously used to quantify species loss and gain effects on ecosystem functioning, to also account for the effects of intraspecific diversity loss and gain on ecosystem function. Using this extended version can yield the quantitative information required for answering research questions addressing correlations between interspecific and intraspecific diversity effects on ecosystem functioning, identifying interspecific and intraspecific groups with large effects, and assessing whether intraspecific diversity can compensate for losses in interspecific diversity. Applying this method to carefully designed experiments will provide additional insights into how biodiversity loss at different ecological levels contributes to and changes ecosystem functioning.

From microbiome composition to functional engineering, one step at a time

SUMMARYCommunities of microorganisms (microbiota) are present in all habitats on Earth and are relevant for agriculture, health, and climate. Deciphering the mechanisms that determine microbiota dynamics and functioning within the context of their respective environments or hosts (the microbiomes) is crucially important. However, the sheer taxonomic, metabolic, functional, and spatial complexity of most microbiomes poses substantial challenges to advancing our knowledge of these mechanisms. While nucleic acid sequencing technologies can chart microbiota composition with high precision, we mostly lack information about the functional roles and interactions of each strain present in a given microbiome. This limits our ability to predict microbiome function in natural habitats and, in the case of dysfunction or dysbiosis, to redirect microbiomes onto stable paths. Here, we will discuss a systematic approach (dubbed the N+1/N-1 concept) to enable step-by-step dissection of microbiome assembly and functioning, as well as intervention procedures to introduce or eliminate one particular microbial strain at a time. The N+1/N-1 concept is informed by natural invasion events and selects culturable, genetically accessible microbes with well-annotated genomes to chart their proliferation or decline within defined synthetic and/or complex natural microbiota. This approach enables harnessing classical microbiological and diversity approaches, as well as omics tools and mathematical modeling to decipher the mechanisms underlying N+1/N-1 microbiota outcomes. Application of this concept further provides stepping stones and benchmarks for microbiome structure and function analyses and more complex microbiome intervention strategies.

Statistical methods to identify mechanisms in studies of eco-evolutionary dynamics

While the reciprocal effects of ecological and evolutionary dynamics are increasingly recognized as an important driver for biodiversity, detection of such eco-evolutionary feedbacks, their underlying mechanisms, and their consequences remains challenging. Eco-evolutionary dynamics occur at different spatial and temporal scales and can leave signatures at different levels of organization (e.g., gene, protein, trait, community) that are often difficult to detect. Recent advances in statistical methods combined with alternative hypothesis testing provides a promising approach to identify potential eco-evolutionary drivers for observed data even in non-model systems that are not amenable to experimental manipulation. We discuss recent advances in eco-evolutionary modeling and statistical methods and discuss challenges for fitting mechanistic models to eco-evolutionary data.

Seasonal Variation Characteristics and the Factors Affecting Plankton Community Structure in the Yitong River, China

To explore how environmental factors affected the plankton structure in the Yitong River, we surveyed the water environmental factors and plankton population in different seasons. The results showed high total nitrogen concentrations in Yitong River throughout the year, while the total phosphorus, water temperature (WT), and chemical oxygen demand in summer were significantly higher than those in other seasons (p < 0.05), and the dissolved oxygen (DO) concentrations and TN/TP ratio were significantly lower (p < 0.01) than those in other seasons. There was no significant seasonal change in other environmental factors. Cyanophyta, Chlorophyta, and Bacillariophyta were the main phytoplankton phylum, while Protozoa and Rotifera were the main zooplankton phylum. The abundance and biomass of zooplankton and phytoplankton in the summer were higher than those in other seasons. Non-Metric Multidimensional scaling methods demonstrated obvious seasonal variation of phytoplankton in summer compared to spring and winter, while the seasonal variation of the zooplankton community was not obvious. The results of the redundancy analysis showed that WT, DO and nitrate nitrogen were the main environmental factors affecting phytoplankton abundance. In contrast to environmental factors, phytoplankton was the main factor driving the seasonal variation of the zooplankton community structure. Cyanophyta were positively correlated with the changes in the plankton community.

Development of harmful algal blooms species responsible for lipophilic and amnesic shellfish poisoning intoxications in southwestern Mediterranean coastal waters

Mediterranean waters have undergone environmental changes during the last decades leading to various modifications of the structure of phytoplankton populations, especially Harmful Algal Blooms (HABs) species. Monitoring of the potentially toxic phytoplankton species was carried out biweekly in the western Mediterranean coast of Morocco from March 2018 to March 2019. Lipophilic Shellfish Toxins (LSTs) using LC-MS/MS and Domoic Acid (DA) using HPLC-UV were measured in the exploited mollusks, the cockle Acanthocardia tuberculata and the smooth clam Callista chione. We also determined the prevailing environmental factors in four surveyed sites (M'diq bay, Martil, Kaa Asras, and Djawn) selected to cover a variety of coastal ecosystems. Results showed that Pseudo-nitzschia spp. a DA producer species, was abundant with a pick of 50 × 10³ cells l^-1 on October 2018 in Djawn. Dinophysis caudata was the dominate Dinophysis species and showed a maximum density of 2200 cells l^-1 on July in Djawn. Prorocentrum lima, an epibenthic dinoflagellate, appeared rarely in the water column with densities <80 cells l^-1. Gonyaulax spinifera and Protoceratium reticulatum were found occasionally with a maximum density of 160 cells l^-1. Karenia selliformis was detected only five times (<80 cells l^-1) throughout the survey period. LC-MS/MS analyses revealed the presence of OA/DTX3, PTX-2, PTX-2 sa, and PTX-2 sa epi in the cockle at concentrations of up to 44.81 (OA/DTX-3+PTXs) ng g^-1 meat. GYM-A was detected in the clam at concentrations of up to 4.22 ng g^-1 meat. For the first time, AZAs and YTXs were detected in the southwestern Mediterranean with maximum values of 2.49 and 10.93 ng g^-1 meat of cockle, respectively. DA was detected in moderate concentrations not exceeding 5.65 μg g^-1 in both mollusks. Results showed that the observed toxic algae in the water column were responsible from the analysed toxins in the mollusks. It is likely that the southwestern Mediterranean waters could see the development of emergent species producing potent toxins (YTXs, AZAs, GYM-A). These dinoflagellates have to be isolated, ribotyped, and their toxin profiles determined.

Evolution of Phytoplankton as Estimated from Genetic Diversity

Phytoplankton are photosynthetic, single-celled organisms producing almost half of all oxygen on Earth and play a central role as prey for higher organisms, making them irreplaceable in the marine food web. As Global Change proceeds, imposing rapidly intensifying selection pressures, phytoplankton are forced to undergo evolution, local extinction, or redistribution, with potentially cascading effects throughout the marine ecosystem. Recent results from the field of population genetics display high levels of standing genetic diversity in natural phytoplankton populations, providing ample ‘evolutionary options’ and implying high adaptive potential to changing conditions. This potential for adaptive evolution is realized in several studies of experimental evolution, even though most of these studies investigate the evolution of only single strains. This, however, shows that phytoplankton not only evolve from standing genetic diversity, but also rely on de novo mutations. Recent global sampling campaigns show that the immense intraspecific diversity of phytoplankton in the marine ecosystem has been significantly underestimated, meaning we are only studying a minor portion of the relevant variability in the context of Global Change and evolution. An increased understanding of genomic diversity is primarily hampered by the low number of ecologically representative reference genomes of eukaryotic phytoplankton and the functional annotation of these. However, emerging technologies relying on metagenome and transcriptome data may offer a more realistic understanding of phytoplankton diversity.