3. Results

A part of the selected papers used methods that were not explicitly searched for, such as Apriori, multivariate adaptive regression splines (MARS), discriminatory analysis (DA), clustering models (such ask -means clustering, hierarchical clustering analysis), forecasting models (such as conditional autoregressive models – CAR), and kernel density estimation (KDE). Other papers used methods that are more traditionally associated with traditional statistics, despite commonalities with machine learning methods, such as linear regression (LR), generalised linear models (GLM), generalised additive models (GAM) and mixed models, both linear and additive (GLMM, GAMM). None were included in our analyses.

3.1 Threats

The methods most used for threat analysis were MaxEnt, Bayesian and Boosting (Fig. 1, Annex D). The most studied threat according to the CBD classification was Habitat loss & fragmentation. Within this large category, Residential & commercial development and Agriculture & aquaculture were widely studied using ML methods. Overall, most studies on most threats used a common set of methodologies, except for Pollution, for which the most used ML methods were ensembles, DTs and ANNs.

3.2 Conservation measures

Similarly to threats, the most popular methods used to study conservation measures were MaxEnt, Bayesian and ensembles (Fig. 2, Annex E). These were mainly used in studies focusing on the effects of land protection (Annex C), followed by species and land management.
The trends in the number of accumulated publications show the consistent growing popularity of Bayesian methods, MaxEnt and ensemble methods (Fig. 3). Other methods show a plateau in their popularity over the years, which is most clear in evolutionary algorithms.