Predicting Invasive Species Distribution from Presence Data


This project was developed to improve the accuracy of species distribution predictions when only occurrence (i.e. presence-only) field data are available. Traditionally, statistical models need both presence and absence data to make predictions. However, in the majority of cases, only the geographical location of species occurrence is recorded. In this study, we looked at and compared two advanced statistical models to see which one lead to the most accurate predictions.


  • Predicting the potential habitat of invasive termite species under both current and future climate change scenarios
  • Compare a Bayesian linear logistic regression approach adjusted for presence-only data against the widely used maximum entropy approach (Maxent)
  • Understand species’ response to different environmental conditions

Research Team

Francesco Tonini, Fabio Divino, Giovanna Jona Lasinio, Hartwig H. Hochmair, and Rudolf H. Scheffrahn


Predicting the potential habitat of species under both current and future climate change
scenarios is crucial for monitoring invasive species and understanding a species’ response to different
environmental conditions. Frequently, the only data available on a species is the location of its
occurrence (presence-only data). Using occurrence records only, two models were used to predict
the geographical distribution of two destructive invasive termite species, Coptotermes gestroi (Wasmann)
and Coptotermes formosanus Shiraki. The first model uses a Bayesian linear logistic regression
approach adjusted for presence-only data while the second one is the widely used maximum entropy
approach (Maxent). Results show that the predicted distributions of both C. gestroi and C. formosanus
are strongly linked to urban development. The impact of future scenarios such as climate warming and
population growth on the biotic distribution of both termite species was also assessed. Future climate
warming seems to affect their projected probability of presence to a lesser extent than population
growth. The Bayesian logistic approach outperformed Maxent consistently in all models according to
evaluation criteria such as model sensitivity and ecological realism. The importance of further studies
for an explicit treatment of residual spatial autocorrelation and a more comprehensive comparison
between both statistical approaches is suggested


  • Tonini, F., Divino, F., Jona Lasinio, G., Hochmair, H. H., and Scheffrahn, R. H. (2014). Predicting the geographical distribution of two invasive termite species from occurrence data. Environmental Entomology, 43 (5 ), pp. 1135-1144.