Modelling Species Distributions with Deep Learning to Predict Plant
  Extinction Risk and Assess Climate Change Impacts
Joaquim Estopinan Pierre Bonnet Maximilien Servajean François Munoz Alexis Joly
The post-2020 global biodiversity framework needs ambitious, research-based
targets. Estimating the accelerated extinction risk due to climate change is
critical. The International Union for Conservation of Nature (IUCN) measures
the extinction risk of species. Automatic methods have been developed to
provide information on the IUCN status of under-assessed taxa. However, these
compensatory methods are based on current species characteristics, mainly
geographical, which precludes their use in future projections. Here, we
evaluate a novel method for classifying the IUCN status of species benefiting
from the generalisation power of species distribution models based on deep
learning. Our method matches state-of-the-art classification performance while
relying on flexible SDM-based features that capture species' environmental
preferences. Cross-validation yields average accuracies of 0.61 for status
classification and 0.78 for binary classification. Climate change will reshape
future species distributions. Under the species-environment equilibrium
hypothesis, SDM projections approximate plausible future outcomes. Two extremes
of species dispersal capacity are considered: unlimited or null. The projected
species distributions are translated into features feeding our IUCN
classification method. Finally, trends in threatened species are analysed over
time and i) by continent and as a function of average ii) latitude or iii)
altitude. The proportion of threatened species is increasing globally, with
critical rates in Africa, Asia and South America. Furthermore, the proportion
of threatened species is predicted to peak around the two Tropics, at the
Equator, in the lowlands and at altitudes of 800-1,500 m.