Application of consensus theory to formalize expert evaluations of plant species distribution models

AIM: Application of environmental envelope modelling (EEM) for conservation planning requires careful validation. Opinions of experts who have worked with species of interest in the field can be a valuable and independent information source to validate EEM because of their first‐hand experience with species occurrence and absence. However, their use in model validation is limited because of the subjectivity of their feedback. In this study, we present a method on the basis of cultural consensus theory to formalize expert model evaluations. METHODS: We developed, for five tree species, distribution models with nine different variable combinations and Maxent EEM software. Species specialists validated the generated distribution maps through an online Google Earth interface with the scores from Invalid to Excellent. Experts were also asked about the commission and omission errors of the distribution models they evaluated. We weighted expert scores according to consensus theory. These values were used to obtain a final average expert score for each of the produced distribution models. The consensus‐weighted expert scores were compared with un‐weighted scores and correlated to four conventional model performance parameters after cross‐validation with test data: Area Under Curve (AUC), maximum Kappa, commission error and omission error. RESULTS: The median consensus‐weighted expert score of all species–variable combinations was close to Fair. In general, experts that reached more consensus with peers were more positive about the EEM outcomes, compared to those that had more opposite judgements. Both consensus‐weighted and un‐weighted scores were significantly correlated to corresponding AUC, maximum Kappa and commission error values, but not to omission errors. More than half of the experts indicated that the distribution model they considered best included areas where the species is known to be absent. One third also indicated areas of species presence that were omitted by the model. CONCLUSIONS: Our results indicate that experts are fairly positive about EEM outcomes. This is encouraging, but EEM application for conservation actions remains limited according to them. Methods to formalize expert knowledge allow a wider use of this information in model validation and improvement, and they complement conventional validation methods of presence‐only modelling. Online GIS and survey applications facilitate the consultation of experts.