Landscape resistance index aiming at functional forest connectivity

Resistance models may quantify the ability of the landscape to impede species’ movement and represent suitable habitats. Moreover, the performance of resistance models parameterized by land-use/land cover attributes evidence that the integrity of the environments subject to urban sprawl is poorly un...

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Published inEnvironmental monitoring and assessment Vol. 195; no. 10; p. 1224
Main Authors Vanderley-Silva, Ivan, Valente, Roberta Averna
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.10.2023
Springer Nature B.V
Subjects
Anthropogenic factors
Approximation
Atmospheric Protection/Air Quality Control/Air Pollution
Biodiversity
Biodiversity hot spots
biomass
Biosphere
Brazil
class
conservation areas
Criteria
Earth and Environmental Science
Ecology
Ecotoxicology
Environment
Environmental Management
Environmental monitoring
equations
Forests
Green belts
Heat
Land cover
Land use
Landscape
Landscape design
landscapes
Mathematical models
Modelling
Monitoring/Environmental Analysis
Multivariate statistical analysis
Remote sensing
species
Structural equation modeling
Urban areas
Urban sprawl
Urbanization
Vegetation
Brazil
Landscape structure analysis
Land use and land cover
Structural equation model
Environmental criteria
Online AccessGet full text
ISSN0167-6369
1573-2959
1573-2959
DOI10.1007/s10661-023-11749-x

Cover

Abstract Resistance models may quantify the ability of the landscape to impede species’ movement and represent suitable habitats. Moreover, the performance of resistance models parameterized by land-use/land cover attributes evidence that the integrity of the environments subject to urban sprawl is poorly understood. In this sense, the study assumed we could identify the forest functional connectivity in a landscape considering the disparity in the landscape mosaic. In this context, we sought to develop a landscape resistance index through structural equation modeling (SEM), supported by the criteria of heat emission, biomass, and anthropogenic barriers, obtained by remote sensing, called observed variables. The landscape studied in the Green Belt Biosphere Reserve of São Paulo has significant remnants of the Atlantic Forest, a biodiversity hotspot. However, our results indicated criteria variability in the landscape modeled through the SEM, obtaining a significant adjustment of the landscape resistance index, with comparative fit index (CFI) of 1.00 and root mean square error of approximation (RMSEA) of 0.00. The index reflects the resistance levels of the land use/land cover, expressed by the class interval, ranging from 0% (1.73) to 100% (493.88), with the highest values associated with the anthropized uses and forest isolation. Thus, our index based on environmental attributes reflects the structure of functional forest connectivity and offers a framework to design forest corridors across landscapes.
AbstractList Resistance models may quantify the ability of the landscape to impede species’ movement and represent suitable habitats. Moreover, the performance of resistance models parameterized by land-use/land cover attributes evidence that the integrity of the environments subject to urban sprawl is poorly understood. In this sense, the study assumed we could identify the forest functional connectivity in a landscape considering the disparity in the landscape mosaic. In this context, we sought to develop a landscape resistance index through structural equation modeling (SEM), supported by the criteria of heat emission, biomass, and anthropogenic barriers, obtained by remote sensing, called observed variables. The landscape studied in the Green Belt Biosphere Reserve of São Paulo has significant remnants of the Atlantic Forest, a biodiversity hotspot. However, our results indicated criteria variability in the landscape modeled through the SEM, obtaining a significant adjustment of the landscape resistance index, with comparative fit index (CFI) of 1.00 and root mean square error of approximation (RMSEA) of 0.00. The index reflects the resistance levels of the land use/land cover, expressed by the class interval, ranging from 0% (1.73) to 100% (493.88), with the highest values associated with the anthropized uses and forest isolation. Thus, our index based on environmental attributes reflects the structure of functional forest connectivity and offers a framework to design forest corridors across landscapes.
Resistance models may quantify the ability of the landscape to impede species' movement and represent suitable habitats. Moreover, the performance of resistance models parameterized by land-use/land cover attributes evidence that the integrity of the environments subject to urban sprawl is poorly understood. In this sense, the study assumed we could identify the forest functional connectivity in a landscape considering the disparity in the landscape mosaic. In this context, we sought to develop a landscape resistance index through structural equation modeling (SEM), supported by the criteria of heat emission, biomass, and anthropogenic barriers, obtained by remote sensing, called observed variables. The landscape studied in the Green Belt Biosphere Reserve of São Paulo has significant remnants of the Atlantic Forest, a biodiversity hotspot. However, our results indicated criteria variability in the landscape modeled through the SEM, obtaining a significant adjustment of the landscape resistance index, with comparative fit index (CFI) of 1.00 and root mean square error of approximation (RMSEA) of 0.00. The index reflects the resistance levels of the land use/land cover, expressed by the class interval, ranging from 0% (1.73) to 100% (493.88), with the highest values associated with the anthropized uses and forest isolation. Thus, our index based on environmental attributes reflects the structure of functional forest connectivity and offers a framework to design forest corridors across landscapes.Resistance models may quantify the ability of the landscape to impede species' movement and represent suitable habitats. Moreover, the performance of resistance models parameterized by land-use/land cover attributes evidence that the integrity of the environments subject to urban sprawl is poorly understood. In this sense, the study assumed we could identify the forest functional connectivity in a landscape considering the disparity in the landscape mosaic. In this context, we sought to develop a landscape resistance index through structural equation modeling (SEM), supported by the criteria of heat emission, biomass, and anthropogenic barriers, obtained by remote sensing, called observed variables. The landscape studied in the Green Belt Biosphere Reserve of São Paulo has significant remnants of the Atlantic Forest, a biodiversity hotspot. However, our results indicated criteria variability in the landscape modeled through the SEM, obtaining a significant adjustment of the landscape resistance index, with comparative fit index (CFI) of 1.00 and root mean square error of approximation (RMSEA) of 0.00. The index reflects the resistance levels of the land use/land cover, expressed by the class interval, ranging from 0% (1.73) to 100% (493.88), with the highest values associated with the anthropized uses and forest isolation. Thus, our index based on environmental attributes reflects the structure of functional forest connectivity and offers a framework to design forest corridors across landscapes.
ArticleNumber 1224
Author Vanderley-Silva, Ivan
Valente, Roberta Averna
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  givenname: Roberta Averna
  surname: Valente
  fullname: Valente, Roberta Averna
  organization: Environmental Sciences Department, Federal University of São Carlos (UFSCAR-Sorocaba)
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CitedBy_id crossref_primary_10_1016_j_jenvman_2025_124412
crossref_primary_10_1016_j_ecolind_2024_113051
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The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.
2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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Keywords Landscape structure analysis
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Environmental criteria
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PublicationSubtitle An International Journal Devoted to Progress in the Use of Monitoring Data in Assessing Environmental Risks to Humans and the Environment
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SubjectTerms Anthropogenic factors
Approximation
Atmospheric Protection/Air Quality Control/Air Pollution
Biodiversity
Biodiversity hot spots
biomass
Biosphere
Brazil
class
conservation areas
Criteria
Earth and Environmental Science
Ecology
Ecotoxicology
Environment
Environmental Management
Environmental monitoring
equations
Forests
Green belts
Heat
Land cover
Land use
Landscape
Landscape design
landscapes
Mathematical models
Modelling
Monitoring/Environmental Analysis
Multivariate statistical analysis
Remote sensing
species
Structural equation modeling
Urban areas
Urban sprawl
Urbanization
Vegetation
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Title Landscape resistance index aiming at functional forest connectivity
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