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The detection of disease clustering and a generalized regression approach.

Nathan Mantel · PubMed · 1967

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AimUnderstanding how the landscape influences gene flow is important in explaining biodiversity, especially when co‐distributed taxa across heterogeneous landscapes exhibit species‐specific habitat associations. Here, we test predictions about the effects of forest‐type on population connectivity in two sympatric species of spiny rats that differ in their forest associations. Specifically, we evaluate the hypothesis that seasonal floodplain forests (várzea) provide linear connectivity, facilitating gene flow among individuals, while non‐flooded forests (terra‐firme) may diminish the functional connectivity.LocationWestern Amazon, South America.TaxonProechimys simonsi (non‐flooded forests, terra‐firme) and Proechimys steerei (seasonal floodplain forests, várzea).MethodsWe analyse about 13,000 single nucleotide polymorphisms along with characterizations of landscape heterogeneity for two forest types to test for differences in the functional connectivity. Influence of the landscape and environmental variables are quantified using maximum‐likelihood population effect models to identify the relative importance of variables in explaining the gene flow.ResultsThere are significant differences in functional connectivity between species. However, the genomic data does not support the conventional hypotheses of higher connectivity for inhabitants of várzea than those of terra‐firme. Stronger genetic structure in P. steerei than P. simonsi based on isolation by distance models suggests reduced gene flow in species associated with várzea forests. Isolation by resistance reinforces that wetland habitats inhibit and promote the functional connectivity in P. simonsi and P. steerei, respectively, although large distances along the rivers can prevent gene flow in P. steerei.Main conclusionInterpreting differences between connectivity in taxa apparent from genetic analyses through the lens of a single dimension of Amazonian heterogeneity—that is, forest type—may be an oversimplification. Our statistical modelling and fit of the data to different models points to specific environmental and habitat differences between the ecological divergent spiny rat species that may contribute to differences in the genetic structure of these sympatric taxa.

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APA 7

Mantel, N. (1967). The detection of disease clustering and a generalized regression approach. https://doi.org/10.1111/jbi.14281

MLA

Mantel, Nathan. "The detection of disease clustering and a generalized regression approach." 1967. https://doi.org/10.1111/jbi.14281.

Chicago

Mantel, Nathan. 1967. "The detection of disease clustering and a generalized regression approach.". https://doi.org/10.1111/jbi.14281.

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Mantel, N. 1967, The detection of disease clustering and a generalized regression approach, PubMed, available at: https://doi.org/10.1111/jbi.14281 [Accessed 9 Aug. 2026].

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Titolo
The detection of disease clustering and a generalized regression approach.
Autore / collaboratori
Nathan Mantel
Editore
PubMed
Anno di pubblicazione
1967
Lingua
Inglés

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