scholarly journals High thermal plasticity, and vulnerability, in extreme environments at the warm distributional edge: The case of a tidepool shrimp

2021 ◽  
Vol 545 ◽  
pp. 151641
Author(s):  
Eyal Amsalem ◽  
Gil Rilov
Keyword(s):  
Extreme Environments ◽  
Thermal Plasticity

scholarly journals High thermal plasticity and vulnerability in extreme environments at the warm distributional edge: the case of a tidepool shrimp

2020 ◽  
Author(s):  
Eyal Amsalem ◽  
Gil Rilov
Keyword(s):  
Climate Change ◽  
Prolonged Exposure ◽  
Thermal Environment ◽  
Extreme Environments ◽  
Open Water ◽  
Thermal Fluctuations ◽  
Future Climate Change ◽  
Regional Warming ◽  
Thermal Plasticity ◽  
Wide Range

1.AbstractClimate change threatens the resilience of species, especially at their warm distributional edge in extreme environments. However, not much is known about the thermal vulnerability of marine intertidal species at this edge. We investigated the thermal vulnerability of the tidepool shrimp, Palaemon elegans in the fast-warming southeastern Mediterranean, its warm distributional edge. Tidepool organisms experience strong and fast thermal fluctuations. This might make them more resilient to change, but also bring them closer to their thermal limits during extreme conditions. To test the shrimp’s resilience, we tested three hypotheses: (1) P. elegance in the southeast Mediterranean has higher critical thermal maximum (CTMax) than in cooler regions, (2) the shrimp possess seasonal acclimatization, but (3) long exposure to extreme summer temperatures might erode its thermal performance making it vulnerable to future climate change. We characterized the shrimp’s thermal environment and population dynamics, determined CTMax and tested diverse physiological performance attributes (respiration, digestion, activity, growth) under a wide range of temperatures during winter and summer. P. elegans has a wide optimum performance range between 20-30°C during summer and its CTMax is 38.1°C, higher than its Atlantic counterparts. However, its warming tolerance is only 0.3°C, indicating low capacity for dealing with further warming in pools compared to northeast Atlantic populations that have wider tolerance. Prolonged exposure to current mean summer values in open water (∼ 32°C) would also significantly reduce its performance and increase mortality. This suggests that its population viability may be reduced under continuous regional warming and intensification of extreme events.

scholarly journals The Evolution of Phenotypic Plasticity in Response to Temperature Stress

2020 ◽  
Vol 12 (12) ◽  
pp. 2429-2440
Author(s):  
Francois Mallard ◽  
Viola Nolte ◽  
Christian Schlötterer
Keyword(s):  
Gene Expression ◽  
Phenotypic Plasticity ◽  
Extreme Environments ◽  
Natural Populations ◽  
Ancestral Population ◽  
Thermal Environments ◽  
Cold Environments ◽  
Thermal Plasticity ◽  
Different Temperatures ◽  
Response To Temperature

Abstract Phenotypic plasticity is the ability of a single genotype to produce different phenotypes in response to environmental variation. The importance of phenotypic plasticity in natural populations and its contribution to phenotypic evolution during rapid environmental change is widely debated. Here, we show that thermal plasticity of gene expression in natural populations is a key component of its adaptation: evolution to novel thermal environments increases ancestral plasticity rather than mean genetic expression. We determined the evolution of plasticity in gene expression by conducting laboratory natural selection on a Drosophila simulans population in hot and cold environments. After more than 60 generations in the hot environment, 325 genes evolved a change in plasticity relative to the natural ancestral population. Plasticity increased in 75% of these genes, which were strongly enriched for several well-defined functional categories (e.g., chitin metabolism, glycolysis, and oxidative phosphorylation). Furthermore, we show that plasticity in gene expression of populations exposed to different temperatures is rather similar across species. We conclude that most of the ancestral plasticity can evolve further in more extreme environments.

scholarly journals The evolution of phenotypic plasticity in response to temperature stress

2020 ◽  
Author(s):  
Francois Mallard ◽  
Viola Nolte ◽  
Christian Schlötterer
Keyword(s):  
Gene Expression ◽  
Phenotypic Plasticity ◽  
Extreme Environments ◽  
Natural Populations ◽  
Ancestral Population ◽  
Thermal Environments ◽  
Cold Environments ◽  
Thermal Plasticity ◽  
Different Temperatures ◽  
Response To Temperature

AbstractPhenotypic plasticity is the ability of a single genotype to produce different phenotypes in response to environmental variation. The importance of phenotypic plasticity in natural populations and its contribution to phenotypic evolution during rapid environmental change is widely debated. Here, we show that thermal plasticity of gene expression in natural populations is a key component of its adaptation: evolution to novel thermal environments increases ancestral plasticity rather than mean genetic expression. We determined the evolution of plasticity in gene expression by conducting laboratory natural selection on a Drosophila simulans population in hot and cold environments. After more than 60 generations in the hot environment, 325 genes evolved a change in plasticity relative to the natural ancestral population. Plasticity increased in 75% of these genes, which were strongly enriched for several well-defined functional categories (e.g. chitin metabolism, glycolysis and oxidative phosphorylation). Furthermore, we show that plasticity in gene expression of populations exposed to different temperatures is rather similar across species. We conclude that most of the ancestral plasticity can evolve further in more extreme environments.

scholarly journals Thermal reaction norms of key metabolic enzymes reflect divergent physiological and behavioral adaptations of closely related amphipod species

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lena Jakob ◽  
Kseniya P. Vereshchagina ◽  
Anette Tillmann ◽  
Lorena Rivarola-Duarte ◽  
Denis V. Axenov-Gribanov ◽  
...  
Keyword(s):  
Lactate Dehydrogenase ◽  
Lake Baikal ◽  
Enzyme Activities ◽  
Large Scale ◽  
Metabolic Enzymes ◽  
Thermal Reaction ◽  
Reaction Norms ◽  
Ecological Niches ◽  
Amphipod Species ◽  
Thermal Plasticity

AbstractLake Baikal is inhabited by more than 300 endemic amphipod species, which are narrowly adapted to certain thermal niches due to the high interspecific competition. In contrast, the surrounding freshwater fauna is commonly represented by species with large-scale distribution and high phenotypic thermal plasticity. Here, we investigated the thermal plasticity of the energy metabolism in two closely-related endemic amphipod species from Lake Baikal (Eulimnogammarus verrucosus; stenothermal and Eulimnogammarus cyaneus; eurythermal) and the ubiquitous Holarctic amphipod Gammarus lacustris (eurythermal) by exposure to a summer warming scenario (6–23.6 °C; 0.8 °C d−1). In concert with routine metabolic rates, activities of key metabolic enzymes increased strongly with temperature up to 15 °C in E. verrucosus, whereupon they leveled off (except for lactate dehydrogenase). In contrast, exponential increases were seen in E. cyaneus and G. lacustris throughout the thermal trial (Q10-values: 1.6–3.7). Cytochrome-c-oxidase, lactate dehydrogenase, and 3-hydroxyacyl-CoA dehydrogenase activities were found to be higher in G. lacustris than in E. cyaneus, especially at the highest experimental temperature (23.6 °C). Decreasing gene expression levels revealed some thermal compensation in E. cyaneus but not in G. lacustris. In all species, shifts in enzyme activities favored glycolytic energy generation in the warmth. The congruent temperature-dependencies of enzyme activities and routine metabolism in E. verrucosus indicate a strong feedback-regulation of enzymatic activities by whole organism responses. The species-specific thermal reaction norms reflect the different ecological niches, including the spatial distribution, distinct thermal behavior such as temperature-dependent migration, movement activity, and mating season.

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

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