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9 results for “Spatial demography”
Data for fitting spatial capture-recapture (SCR) models to estimate spatially explicit demographics of Mojave desert tortoises on a demography plot in California, USA
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Spatial connectedness imposes local- and metapopulation-level selection on life history through feedbacks on demography
<p>Dispersal evolution impacts the fluxes of individuals and hence, connectivity in metapopulations. Connectivity is therefore decoupled from the structural connectedness of the patches within the spatial network. Because of demographic feedbacks, local selection also drives the evolution of other life history traits.</p> <p>We investigated how different levels of connectedness affect trait evolution in experimental metapopulations of the two-spotted spider mite<i>. </i>We separated local- and metapopulation-level selection and linked trait divergence to population dynamics.</p>
Figure 2 in Historical demography and spatial genetic structure of the subterranean rodent Ctenomys magellanicus in Tierra del Fuego (Argentina)
Figure 2. Bayesian inference trees of Ctenomys genus. A, tree derived from the D-loop marker. B, tree derived from Cyt b. Numbers next to branches are bootstrap support values and Bayesian posterior probabilities, respectively.
Figure 1 in Historical demography and spatial genetic structure of the subterranean rodent Ctenomys magellanicus in Tierra del Fuego (Argentina)
Figure 1. Geographical distribution of Ctenomys magellanicus sampling sites along the study area. Squares show the two regions: north (steppe, chromosome form 2n = 34) and south (ecotone, chromosome form 2n = 36). Each region was subdivided into subpopulations: two for the north (subpopulations A and B) and four for the south (subpopulations C–F).
Spatial connectedness imposes local- and metapopulation-level selection on life history through feedbacks on demography
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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: Quantifying the effects of migration and mutation on adaptation and demography in spatially heterogeneous environments
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Figure 3. A in Historical demography and spatial genetic structure of the subterranean rodent Ctenomys magellanicus in Tierra del Fuego (Argentina)
Figure 3. A, minimum spanning tree of nine mtDNA haplotypes of Ctenomys magellanicus from Tierra del Fuego, Argentina. Areas are proportional to haplotype frequencies, shading indicates populations, and cross hatches represent nucleotide differences between haplotypes. Haplotype numbers correspond to those of Table 1. Abbreviations for populations are given in Figure 1. B, observed and expected mismatch distributions for C. magellanicus (south + north). Dashed line, observed distribution; solid line, theoretical expected distribution under a population expansion model.
Figure 4 in Historical demography and spatial genetic structure of the subterranean rodent Ctenomys magellanicus in Tierra del Fuego (Argentina)
Figure 4. Relationship between pairwise geographical distances and Fst for Ctenomys magellanicus from Tierra del Fuego, based on Fst from mitochondrial control region sequences. The relationship between variables was non-significant (see Results).
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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.