The conservation of species should be based on knowledge of habitat requirements, population structure and conservation status. This knowledge is quite important to design conservation areas for species and to promote long-term persistence. In this study, we investigated habitat suitability, population size structure and conservation status of Pinanga arinasae in Bali. Plots with palms and adjacent areas with no palms were sampled to characterize key habitat variables. Habitat suitability was modeled using Artificial Neural Network (ANN) and Random Forest (RF) methods. The population size structure was characterized by counting and measuring the height and reproductive status of the individuals found in plots. Furthermore, we assessed the extinction risk of the species using the IUCN Red List Criteria. The ANN variables that best explained occurrence were litter depth, elevation, canopy openness and slope. The RF variables that best explained the data were elevation, litter depth, slope, and aspect. Both ANN and RF are robust models that can be used to predict the occurrence of P. arinasae. The population size structure included many seedlings, but juvenile and mature individuals were found in relatively small numbers. Based on the findings, we proposed Endangered B1+B2ab(i,ii,iii,v); D as the conservation status of P. arinasae.
Vital rates of two small populations of brown bears in Canada and range‐wide relationship between population size and trend