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67 results for “adaptive heterogeneity”
Data from: Genetic and epigenetic variations associated with adaptation to heterogeneous habitat conditions in a deciduous shrub
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Modeling multipartite virus evolution: the genome formula facilitates rapid adaptation to heterogeneous environments
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Data for variation of magnesium drives plant adaption to heterogeneous environments by regulating efficiency in photosynthesis on a large scale
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Complementary genomic and epigenomic adaptation to environmental heterogeneity
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Genomic insights into adaptation to heterogeneous environments for the ancient relictual Circaeaster agrestis (Circaeasteraceae, Ranunculales)
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Data from: Heterogeneous genomic differentiation in marine threespine sticklebacks: adaptation along an environmental gradient
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Data from: Genetic heterogeneity underlying variation in a locally adaptive clinal trait in Pinus sylvestris revealed by a Bayesian multipopulation analysis
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Data from: Local and regional scale habitat heterogeneity contribute to genetic adaptation in a commercially important marine mollusc (Haliotis rubra) from southeastern Australia
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Data from: Does genetic variation maintained by environmental heterogeneity facilitate adaptation to novel selection?
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Data from: Landscape heterogeneity and local adaptation define the spatial genetic structure of Pacific salmon in a pristine environment
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Data from: Adaptation to local climate in a multi-trait space: evidence from silver fir (Abies alba Mill.) populations across a heterogeneous environment
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Data from: Quantifying the effects of migration and mutation on adaptation and demography in spatially heterogeneous environments
How do mutation and gene flow influence population persistence, niche expansion, and local adaptation in spatially heterogeneous environments? In this article, we analyse a demographic and evolutionary model of adaptation to an environment containing two habitats in equal frequencies, and we bridge the gap between different theoretical frameworks. Qualitatively, our model yields four qualitative types of outcomes: (i) global extinction of the population (ii) adaptation to one habitat only, but also adaptation to both habitats with (iii) specialized phenotypes, or (iv) with generalized phenotypes; and we determine the conditions under which each equilibrium is reached. We derive new analytical approximations for the local densities and the distributions of traits in each habitat under a migration--selection--mutation balance, compute the equilibrium values of the means, variances and asymmetries of the local distributions of phenotypes, and contrast the effects of migration and mutation on the evolutionary outcome. We then check our analytical results by solving our model numerically, and also assess their robustness in the presence of demographic stochasticity. While increased migration results in a decrease in local adaptation, mutation in our model does not influence the values of the local mean traits. Yet, both migration and mutation can have dramatic effects on population size and even lead to metapopulation extinction when selection is strong. Niche expansion, the ability for the population to adapt to both habitats, can also be prevented by small migration rates and a reduced evolutionary potential characterised by rare mutation events of small effects; but niche expansion is otherwise the most likely outcome. Although our results are derived under the assumption of clonal reproduction, we finally show and discuss the links between our model and previous quantitative genetics models.
Data from: Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes
The spatial structure of the environment (e.g., the configuration of habitat patches) may play an important role in determining the strength of local adaptation. However, previous studies of habitat heterogeneity and local adaptation have largely been limited to simple landscapes, which poorly represent the multi-scale habitat structure common in nature. Here, we use simulations to pursue two goals: (1) we explore how landscape heterogeneity, dispersal ability, and selection affect the strength of local adaptation, and (2) we evaluate the performance of several genotype-environment association (GEA) methods for detecting loci involved in local adaptation. We found that the strength of local adaptation increased in spatially aggregated selection regimes, but remained strong in patchy landscapes when selection was moderate to strong. Weak selection resulted in weak local adaptation that was relatively unaffected by landscape heterogeneity. In general, the power of detection methods closely reflected levels of local adaptation. False positive rates (FPRs), however, showed distinct differences across GEA methods based on levels of population structure. The univariate GEA approach had high FPRs (up to 55%) under limited dispersal scenarios, due to strong isolation by distance. By contrast, multivariate, ordination-based methods had uniformly low FPRs (0-2%), suggesting these approaches can effectively control for population structure. Specifically, constrained ordinations had the best balance of high detection and low FPRs, and will be a useful addition to the GEA toolkit. Our results provide both theoretical and practical insights into the conditions that shape local adaptation and how these conditions impact our ability to detect selection.
Data from: Ecological specialization in populations adapted to constant versus heterogeneous environments
Populations vary in their degree of ecological specialization. An intuitive, but often untested, hypothesis is that populations evolving under greater environmental heterogeneity will evolve to be less specialized. How important is environmental heterogeneity in explaining among-population variation in specialization? We assessed juvenile viability of 20 Drosophila melanogaster populations evolving under one of four regimes: (i) a salt-enriched environment, (ii) a cadmium-enriched environment, (iii) a temporally varying environment, and (iv) a spatially varying environment. Juvenile viability was tested in both the original selective environments and a set of novel environments. In both the original and novel environments, populations from the constant cadmium regime had the lowest average viability and the highest variance in viability across environments but populations from the other three regimes were similar. Our results suggest that variation in specialization among these populations is most simply explained as a pleiotropic by-product of adaptation to specific environments rather than resulting from a history of exposure to environmental heterogeneity.
Data from: Should I change or should I go? phenotypic plasticity and matching habitat choice in the adaptation to environmental heterogeneity
It can be challenging for organisms to achieve a good match between their phenotypic characteristics and environmental requirements that vary in space and time. The evolution of adaptive phenotypes can result from genetic differentiation at the population level. Individuals, however, could also change their phenotype (adaptive plasticity) or select an environment because it matches with their phenotype (matching habitat choice). It is poorly known under which conditions these different solutions to environmental heterogeneity evolve and whether they operate together. Using an individual-based simulation model, we assessed which solutions evolved depending on degree of temporal variation, costs of multiple underlying traits, and order of dispersal and development. Population genetic divergence was superseded by plasticity or matching habitat choice as temporal variation increased. Plasticity and matching habitat choice were limited by their trait costs, even when this involved only a part of the underlying traits. Independent of the order of dispersal and development, plasticity evolved more commonly than matching habitat choice, in part because the match a phenotype can achieve by matching habitat choice is limited by the types of environments available. Our results explain the apparent relative rarity of matching habitat choice in nature. At the same time, our results can be used to look for matching habitat choice in those biological systems where the conditions for other solutions seem unfavorable.
Data from: Abiotic heterogeneity drives parasite local adaptation in coevolving bacteria and phages
Spatial abiotic heterogenity can result in divergent selection, hence might increase the magnitude of host-parasite local adaptation (the mean difference in fitness of sympatric versus allopatric host-parasite combinations). We explicitly tested this hypothesis by measuring local adapation in experimentally coevolved populations of bacteria and viruses evolved in the same or different nutrient media. Consistent with previous work, we found that mean levels of evolved phage infectivity and bacteria resistance varied with nutrient concentration, with maximal levels at nutrient concentrations that supported the greatest densities of bacteria. Despite this variation in evolved mean infectivity and resistance between treatments, we found that parasite local adaptation was greatly increased when measured between populations evolved in different, compared to the same, media. This pattern is likely to have resulted from different media imposing divergent selection on bacterial hosts, and phages in turn adapting to their local hosts. These results demonstrate that the abiotic environment can play a strong and predictable role in driving patterns of local adaptation.
Data from: The genetics of adaptation to discrete heterogeneous environments: frequent mutation or large-effect alleles can allow range expansion
Range expansions are complex evolutionary and ecological processes. From an evolutionary standpoint, a populations' adaptive capacity can determine the success or failure of expansion. Using individual-based simulations, we model range expansion over a two-dimensional, approximately continuous landscape. We investigate the ability of populations to adapt across patchy environmental gradients and examine how the effect sizes of mutations influence the ability to adapt to novel environments during range expansion. We find that genetic architecture and landscape patchiness both have the ability to change the outcome of adaptation and expansion over the landscape. Adaptation to new environments succeeds via many mutations of small effect or few of large effect, but not via the intermediate between these cases. Higher genetic variance contributes to increased ability to adapt, but an alternative route of successful adaptation can proceed from low genetic variance scenarios with alleles of sufficiently large effect. Steeper environmental gradients can prevent adaptation and range expansion on both linear and patchy landscapes. When the landscape is partitioned into local patches with sharp changes in phenotypic optimum, the local magnitude of change between subsequent patches in the environment determines the success of adaptation to new patches during expansion.
Data from: Adaptation of Drosophila to a novel laboratory environment reveals temporally heterogeneous trajectories of selected alleles
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Data from: Adaptation to abiotic conditions drives local adaptation in bacteria and viruses coevolving in heterogeneous environments
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Data from: An analysis toolbox to explore mesenchymal migration heterogeneity reveals adaptive switching between distinct modes
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.