Student or educator? Sign up with your school email and get 6 months of Pro free.

The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.

A comparative framework to infer landscape effects on population genetic structure: are habitat suitability models effective in explaining gene flow?

Open access

Abstract

ContextMost current methods to assess connectivity begin with landscape resistance maps. The prevailing resistance models are commonly based on expert opinion and, more recently, on a direct transformation of habitat suitability. However, habitat associations are not necessarily accurate indicators of dispersal, and thus may fail as a surrogate of resistance to movement. Genetic data can provide valuable insights in this respect.ObjectivesWe aim at directly comparing the utility of habitat suitability models for estimating landscape resistance versus other approaches based on actual connectivity data.MethodsWe develop a framework to compare landscape resistance models based on (1) a genetic-based multi model optimization and (2) a direct conversion of habitat suitability into landscape resistance. We applied this framework to the endangered brown bear in the Cantabrian Range (NW Spain).ResultsWe found that the genetic-based optimization produced a resistance model that was more related to species movement than were models produced by direct conversion of habitat suitability. Certain land cover types and transport infrastructures were restrictive factors for species occurrence, but did not appear to impede the brown bear movements that determined observed genetic structure.ConclusionsIn this study case, habitat suitability is not synonymous with permeability for dispersal, and does not seem to provide the best way to estimate actual landscape resistance. We highlight the general utility of this comparative approach to provide a comprehensive and practical assessment of factors involved in species movements, with the final aim of improving the initiatives to enhance landscape connectivity in conservation planning.

Overview

Integrity signals

Registry

Crossref · 10.1007/s10980-015-0194-4

Cites

60 of 60 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Citations

Cites

Show all 60 references

Research works printed but not linked: 1

  1. Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27(2 Part 1):209–220

Cited by

See all 189

Metadata

Authors
Mateo-Sánchez, M. C., Balkenhol, N., Cushman, S., Pérez, T., Domínguez, A., & Saura, S.
Affiliations
  • Universidad Politécnica de Madrid (ES)
  • University of Göttingen (DE)
  • US Forest Service (US)
  • Universidad de Oviedo (ES)
Corresponding author
Mateo-Sánchez, María C.
Venue
Landscape Ecology
Published
2015-04-02
Volume
30
Issue
8
Pages
1405-1420
ISSN
  • 0921-2973 (ISSN-L)
  • 1572-9761
OpenAlex
W2086499388
Cited by
189
References
60
Citations by year
2026: 12 · 2025: 14 · 2024: 16 · 2023: 11 · 2022: 18 · 2021: 18 · 2020: 50 · 2019: 15 · 2018: 11 · 2017: 11 · 2016: 10 · 2015: 3
FWCI
8.33
Type
journal article
Language
en
Open access
Licence
  • cc-by-nc-nd
  • www.springer.com/tdm (tdm)
Locations
  • Landscape Ecology (publishedVersion)
  • Consultation of the Doctoral Thesis Database (TESEO) (Ministerio de Educación, Cultura y Deporte) (submittedVersion, cc-by-nc-nd)
  • GRO (submittedVersion)
Funders
Universidad Politécnica de Madrid
Topics
  • Physical Sciences › Environmental Science › Ecology › Wildlife-Road Interactions and Conservation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
  • Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
Keywords
landscape resistance, habitat suitability models, gene flow, population structure, landscape connectivity, brown bear, dispersal, transport infrastructure, land cover, multi-model optimization, conservation planning, Cantabrian Mountains

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 ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A comparative framework to infer landscape effects on population genetic structure: are habitat suitability models effective in explaining gene flow? https://app.cochat.ai/papers/10.1007/s10980-015-0194-4
The paper
Mateo-Sánchez, M. C., Balkenhol, N., Cushman, S., Pérez, T., Domínguez, A., & Saura, S. (2015). A comparative framework to infer landscape effects on population genetic structure: are habitat suitability models effective in explaining gene flow? Landscape Ecology. https://doi.org/10.1007/s10980-015-0194-4

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.