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Functional connectivity in sympatric spiny rats reflects different dimensions of Amazonian forest‐association
Open accessAbstract
Aim: Understanding 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. Location: Western Amazon, South America. Taxon: Proechimys simonsi (non‐flooded forests, terra‐firme) and Proechimys steerei (seasonal floodplain forests, várzea). Methods: We 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. Results: There 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 conclusion: Interpreting 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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Metadata
- Authors
- Dalapicolla, J., do Prado, J. R., Percequillo, A. R., & Knowles, L. L.
- Venue
- Journal of Biogeography
- Publisher
- Wiley
- Published
- 2021-11-08
- Volume
- 48
- Issue
- 12
- Pages
- 3196-3209
- ISSN
- 0305-0270
- 1365-2699
- OpenAlex
- W2112348586
- Cited by
- 11,461
- References
- 3
- FWCI
- 5.55
- Type
- journal article
- Language
- en
- Open access
- green
- Funders
- American Museum of Natural History
- Fundação de Amparo à Pesquisa do Estado de São Paulo (09/16009‐1, 15/02853‐6, 16/24464‐4, 16/20055‐2)
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.