Spatial conservation prioritisation in data-poor countries: a quantitative sensitivity analysis using multiple taxa
Spatial conservation prioritisation in data-poor countries: a quantitative sensitivity analysis using multiple taxa Running title: User decisions affect conservation planning Authors: Ahmed El-Gabbas1*, Francis Gilbert2, Carsten F. Dormann1 1 Department of Biometry and Environmental System Analysis, University of Freiburg, D-79106 Freiburg, Germany 2 School of Life Sciences, University of Nottingham, Nottingham, United Kingdom * Corresponding author – E-Mail: elgabbas@outlook.
Jun 21, 2020
Wrong, but useful: regional species distribution models may not be improved by range-wide data under biased sampling
Wrong, but useful: regional species distribution models may not be improved by range-wide data under biased sampling Ahmed El-Gabbas Carsten F. Dormann https://doi.org/10.1002/ece3.3834 10.1002/ece3.3834 Species distribution modeling (SDM) is an essential method in ecology and conservation.
Jan 1, 2018
Reliability of species distribution modelling for wildlife conservation in developing countries
Main Supervisor:Prof. Carsten F. Dormann, Department of Biometry and Environmental System Analysis, University of Freiburg, Germany. Second Supervisor:Prof. Francis Gilbert, School of Life Sciences, Nottingham University, Nottingham, UK.
Jan 1, 2018
Improved species-occurrence predictions in data-poor regions: using large-scale data and bias correction with down-weighted Poisson regression and Maxent
El-Gabbas A. & Dormann C. F.: Improved species-occurrence predictions in data-poor regions: using large-scale data and bias correction with down-weighted Poisson regression and Maxent. Ecography DOI: 10.1111/ecog.03149. Appendix 1: Supplementary figures and tables Table A1: The estimated optimum combination of Maxent’s feature classes (FC) and Regularization Multiplier (RM) for each species and bias model type.
Jan 1, 2018