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Spatial conservation prioritisation in data-poor countries: a quantitative sensitivity analysis using multiple taxa.

Ahmed El-Gabbas1, Francis Gilbert2, Carsten F Dormann3

  • 1Department of Biometry and Environmental System Analysis, University of Freiburg, 79106, Freiburg, Germany. elgabbas@outlook.com.

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|June 28, 2020
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Summary

Spatial conservation prioritisation (SCP) is sensitive to data quality issues like focal taxon, sampling bias, and connectivity. Improving data quality is crucial for effective conservation planning, especially in data-poor regions.

Keywords:
Data-poor countriesProtected areasSampling biasSpatial conservation prioritisationSpecies distribution modelsZonation

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Area of Science:

  • Conservation Science
  • Computational Ecology
  • Biodiversity Informatics

Background:

  • Spatial conservation prioritisation (SCP) uses computational tools for efficient allocation of conservation areas.
  • SCP applications are susceptible to uncertainties, necessitating their consideration during the prioritisation process.
  • This study assessed the sensitivity of SCP in data-poor scenarios to various uncertainty sources.

Purpose of the Study:

  • To quantify the sensitivity of a spatial conservation prioritisation (SCP) application to data-poor situations.
  • To evaluate the effectiveness of Egypt's protected areas for fauna conservation.
  • To identify priority sites for protected areas expansion.

Main Methods:

  • Utilised the Zonation software for spatial conservation prioritisation (SCP).
  • Assessed sensitivity to surrogate options, sampling bias correction, connectivity integration, species distribution modelling (SDM) algorithms, cell removal rules, and species weighting methods.
  • Evaluated Egyptian protected areas and spatially allocated priority sites.

Main Results:

  • Focal taxon, sampling bias, connectivity, and SDM algorithm choice were the most sensitive parameters, indicating data quality issues.
  • Cell removal rules and species weights had minimal impact on overall variability.
  • Egypt's current protected areas demonstrated low effectiveness in conserving fauna based on available data.

Conclusions:

  • Minimum data quality thresholds exist for SCP; data-poor countries need improved sampling strategies.
  • Conservation planners should routinely conduct sensitivity analyses with diverse settings and consider bias correction.
  • Understanding species' responses to habitat loss is vital due to SCP's sensitivity to connectivity parameters.