Anthropogenic climate change (ACC) is anticipated to significantly alter the future distribution of plant species in tropical regions. Predictions about these impacts are primarily based on species distribution models (SDMs) that use coarse-scale climate data. Recent studies have suggested that models relying on macroclimate variables can produce inaccurate predictions, overestimating or underestimating future range changes. In contrast, models incorporating microclimate variables have been shown to deliver more robust and reliable predictions. These findings raise concerns about the reliability and accuracy of predicted ACC impacts, especially in tropical regions.

This PhD project will investigate the role of microclimate variability in driving climate-induced range shifts in Madagascar.

Prospective students should possess strong skills in R or Python, GIS and remote sensing software, experience with geospatial data analysis, climate modelling frameworks and species distribution models.

This PhD project will involve collaboration with an international network of scientists and guidance by a supervisory team of ecologists and GIS specialists.

Interested candidates are encouraged to contact the first supervisor prior to applying.

Anthropogenic climate change (ACC) is anticipated to significantly alter the future distribution of plant species in tropical regions. Predictions about these impacts are primarily based on species distribution models (SDMs) that use coarse-scale climate data. Recent studies have suggested that models relying on macroclimate variables can produce inaccurate predictions, overestimating or underestimating future range changes. In contrast, models incorporating microclimate variables have been shown to deliver more robust and reliable predictions. These findings raise concerns about the reliability and accuracy of predicted ACC impacts, especially in tropical regions.

This PhD project will investigate the role of microclimate variability in driving climate-induced range shifts in Madagascar.

Prospective students should possess strong skills in R or Python, GIS and remote sensing software, experience with geospatial data analysis, climate modelling frameworks and species distribution models.

This PhD project will involve collaboration with an international network of scientists and guidance by a supervisory team of ecologists and GIS specialists.

Interested candidates are encouraged to contact the first supervisor prior to applying.