The objective of our study was to investigate sexual reproduction of Daphnia magna associated with mating behaviors and hatching rates, according to different algal food sources. Since a diatom is known to contain more abundant long‐chain poly unsaturated fatty acids (PUFAs), we hypothesized that the diatom‐consuming D. magna would exhibit more successful reproduction rates. Upon the hypothesis, we designed three experiments using two algal species, a green alga (Chlorella vulgaris) and a diatom (Stephanodiscus hantzschii). From the results, we found that the mating frequency and copulation duration increased in the treatment with S. hantzschii, resulting in a significant increase of hatching rates of resting eggs. In the other two repetitive mating strategies (e.g., one female vs. multiple males, and one male vs. multiple females), we found that the hatching rates of resting eggs were greater in the S. hantzschii treatment. In addition to the mating strategy, male body size significantly increased in the diatom treatment, hence average diameter of penis was also statistically different among the treatments (greater diameter in the S. hantzschii treatment). To examine the effect of algal food quality, we estimated quantity of fatty acids in the two algal species. Our result showed that S. hantzschii had a higher proportion of long‐chain PUFAs than C. vulgaris. Furthermore, a stable isotope analysis revealed that carbon and nitrogen originated from S. hantzschii were more assimilated to D. magna. In summary, our study manifested that diatom consumption of D. magna leads to more successful sexual reproduction. We then discussed how the diatom consumption of zooplankton influences food web dynamics in a freshwater ecosystem.
Screening drug candidates rapidly is the first step for developing new pharmaceutical drugs. One of the most promising ways to reduce the number of screening steps and cost is to directly use living cells for screening instead of using purified target proteins. Compounds screened using living cells will have increased biological activity compared to those screened with in vitro assays. Here, we report a robust method for screening drug candidates in living cells based on single‐protein imaging. We employed single‐protein tracking to observe the variation in the diffusion coefficient of membrane proteins treated with the candidate compounds. The diffusion coefficient shift was introduced as a criterion for selecting the potential candidate compounds. We tested three different membrane proteins, epidermal growth factor receptor, ErbB2, and ErbB3, and found effective natural compounds for each protein. The screening method we introduce will be widely used for screening potential drug candidates using living cells.
The degradation and loss of ecologically important wetlands has been a topical issue in the Great Lakes region, where 60–80% of the coastal wetlands have been lost since the 1800s. The present modeling study aims to guide the restoration efforts in Cootes Paradise marsh, one of the most degraded shallow wetlands in Southern Ontario. We use a process‐based eutrophication model designed to reproduce the biotic competition among multiple phytoplankton and macrophyte functional groups. Our primary focus is to offer guidelines for wetland restoration by characterizing the ecophysiological processes of the autotrophic assemblage, such as the nutrient uptake from the water column and/or the sediment pore waters, the relative ability to harvest light and fuel photosynthesis, and temperature control of the algal/macrophyte growth and basal metabolism. We predict that the additional reduction of external phosphorus loading in Cootes Paradise could induce an abrupt, non‐linear shift from the current turbid phytoplankton‐dominated state to a desirable clear macrophyte‐dominated state. The emergence of this critical (or tipping) point, where the shift to another ecological state may occur, can be accelerated by the presence of a thriving macrophyte community with an enhanced ability to sequester phosphorus. However, it may also be delayed by the presence of a suite of biogeochemical mechanisms (often referred to as “feedback loops”), such as the remobilization of legacy P due to sediment diagenetic processes, wind resuspension, bioturbation, hydraulic loading from local tributaries, water‐level fluctuations, and the leachable P pool of dead plant material that can be returned into the water column through senescence. Our study identifies the restoration actions required to minimize the likelihood of prolonged hysteresis and to facilitate a shift to a desirable ecological state in the foreseeable future. The areal expansion of aquatic vegetation will not only lead to the establishment of a thriving meadow and emergent vegetation community, but may also pave the way for submerged macrophytes through a suite of synergistic mechanisms. Additional point‐source loading reductions will facilitate the transition to an alternative stable clear macrophyte‐dominated state, but could also consolidate the future resilience of the marsh.
In the field of biological conservation, mathematical modeling has been an indispensable tool to advance our understanding of population dynamics. Modeling rare and endangered species with complex ecophysiological tools can be challenging due to the constraints imposed by data availability. One strategy to overcome the mismatch between what we are trying to learn from a modeling exercise and the available empirical knowledge is to develop statistical models that tend to be more parsimonious. In the present study, we introduce a spatially explicit modeling framework to examine the strength and nature of the relationships of snow density and vegetation abundance with Peary caribou (Rangifer tarandus pearyi) populations. Peary caribou are vital to the livelihood and culture of High Arctic Inuit communities, but changing climatic conditions and anthropogenic disturbances may affect the integrity of this endemic species population. Owing to an estimated decline of over 35% during the last three generations, a recent assessment by the Committee on the Status of Endangered Wildlife in Canada assigned a Threatened status to Peary caribou in 2015. Recognizing the uncertainty typically associated with the selection of the best subset of explanatory variables and their optimal functional relationship with the response variable, we examined four models across six island complexes (Banks, Axel Heiberg, Melville, Bathurst, Mackenzie King, and Boothia) of the Arctic Archipelago and formulated two ensembles to synthesize their predictions into averaged Peary caribou population distributions. Our analysis showed that an ensemble strategy with region‐specific weights displayed the highest performance and most balanced error across the six island complexes. The causal linkages between snow, vegetation abundance, and Peary caribou did manifest themselves with the models examined, but the noise‐to‐signal ratios of the corresponding regression coefficients were generally high and there were instances where they were not discernible from zero. We also present a sensitivity analysis exercise that elucidates the influence of the observation/imputation errors on the model‐training phase, thereby highlighting the importance of assigning realistic error estimates that will not hamper the identification of important cause–effect relationships. Our study identifies critical augmentations of the available scientific knowledge that necessitate to design the optimal management actions of Peary caribou populations across the Canadian Arctic Archipelago.
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