EcoNicheS: enhancing ecological niche modeling, niche overlap and connectivity analysis using shiny dashboard and R Package DOI
Armando Sunny,

Clere Marmolejo,

Rodrigo López-Vidal

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 18, 2024

Abstract EcoNicheS is a novel, user-friendly tool designed to facilitate ecological niche modeling and species distribution modeling, overlap connectivity analysis support biodiversity conservation efforts. This R package offers streamlined workflow for researchers practitioners assess habitat suitability predict distributions in response environmental changes. Leveraging the power of programming Shinydashboard, provides an intuitive interface data input, model parameterization, visualization results. By integrating occurrence with variables, users can generate robust predictions distributions, aiding identification priority areas management actions. incorporates advanced techniques account uncertainty variability species-environment relationships, enhancing accuracy reliability predictions. Through combination features sophisticated analytical capabilities, empowers effectively mitigate threats rapidly changing world.

Language: Английский

EcoNicheS: enhancing ecological niche modeling, niche overlap and connectivity analysis using shiny dashboard and R Package DOI
Armando Sunny,

Clere Marmolejo,

Rodrigo López-Vidal

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 18, 2024

Abstract EcoNicheS is a novel, user-friendly tool designed to facilitate ecological niche modeling and species distribution modeling, overlap connectivity analysis support biodiversity conservation efforts. This R package offers streamlined workflow for researchers practitioners assess habitat suitability predict distributions in response environmental changes. Leveraging the power of programming Shinydashboard, provides an intuitive interface data input, model parameterization, visualization results. By integrating occurrence with variables, users can generate robust predictions distributions, aiding identification priority areas management actions. incorporates advanced techniques account uncertainty variability species-environment relationships, enhancing accuracy reliability predictions. Through combination features sophisticated analytical capabilities, empowers effectively mitigate threats rapidly changing world.

Language: Английский

Citations

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