Confirmation and variability of the Allee effect inDictyostelium discoideumcell populations, possible role of chemical signaling within cell clusters.

In studies of the unicellular eukaryoteDictyostelium discoideum, many have anecdotally observed that cell dilution below a certain 'threshold density' causes cells to undergo a period of slow growth (lag). However, little is documented about the slow growth phase and the reason for different growth dynamics below and above this threshold density. In this paper, we extend and correct our earlier work to report an extensive set of experiments, including the use of new cell counting technology, that set this slow-to-fast growth transition on a much firmer biological basis. We show that dilution below a certain density (around 104cells ml-1) causes cells to grow slower on average and exhibit a large degree of variability: sometimes a sample does not lag at all, while sometimes it takes many moderate density cell cycle times to recover back to fast growth. We perform conditioned media experiments to demonstrate that a chemical signal mediates this endogenous phenomenon. Finally, we argue that while simple models involving fluid transport of signal molecules or cluster-based signaling explain typical behavior, they do not capture the high degree of variability between samples but nevertheless favor an intra-cluster mechanism.

Taking the Pulse of Resilience in Conserving Seagrass Meadows.

Foundational habitats such as seagrasses and coral reefs are at severe risk globally from climate warming. Infectious disease associated with warming events is both a cause of decline and an indicator of stress in both habitats. Since new approaches are needed to detect refugia and design climate-smart networks of marine protected areas, we test the hypothesis that the health of eelgrass (Zostera marina) in temperate ecosystems can serve as a proxy indicative of higher resilience and help pinpoint refugia. Eelgrass meadows worldwide are at risk from environmental stressors, including climate warming and disease. Disease outbreaks of Labyrinthula zosterae are associated with recent, widespread declines in eelgrass meadows throughout the San Juan Islands, Washington, USA. Machine language learning, drone surveys, and molecular diagnostics reveal climate impacts on seagrass wasting disease prevalence (proportion of infected individuals) and severity (proportion of infected leaf area) from San Diego, California, to Alaska. Given that warmer temperatures favor many pathogens such as L. zosterae, we hypothesize that absent or low disease severity in meadows could indicate eelgrass resilience to climate and pathogenic stressors. Regional surveys showed the San Juan Islands as a hotspot for both high disease prevalence and severity, and surveys throughout the Northeast Pacific indicated higher prevalence and severity in intertidal, rather than subtidal, meadows. Further, among sites with eelgrass declines, losses were more pronounced at sites with shallower eelgrass meadows. We suggest that deeper meadows with the lowest disease severity will be refuges from future warming and pathogenic stressors in the Northeast Pacific. Disease monitoring may be a useful conservation approach for marine foundation species, as low or absent disease severity can pinpoint resilient refugia that should be prioritized for future conservation efforts. Even in declining or at-risk habitats, disease surveys can help identify meadows that may contain especially resilient individuals for future restoration efforts. Our approach of using disease as a pulse point for eelgrass resilience to multiple stressors could be applied to other habitats such as coral reefs to inform conservation and management decisions.

Reducing adverse impacts of Amazon hydropower expansion.

Proposed hydropower dams at more than 350 sites throughout the Amazon require strategic evaluation of trade-offs between the numerous ecosystem services provided by Earth's largest and most biodiverse river basin. These services are spatially variable, hence collective impacts of newly built dams depend strongly on their configuration. We use multiobjective optimization to identify portfolios of sites that simultaneously minimize impacts on river flow, river connectivity, sediment transport, fish diversity, and greenhouse gas emissions while achieving energy production goals. We find that uncoordinated, dam-by-dam hydropower expansion has resulted in forgone ecosystem service benefits. Minimizing further damage from hydropower development requires considering diverse environmental impacts across the entire basin, as well as cooperation among Amazonian nations. Our findings offer a transferable model for the evaluation of hydropower expansion in transboundary basins.