Can incomplete knowledge of species’ physiology facilitate ecological niche modelling? A case study with virtual species

Abstract

Aim: Ecological niche modelling (ENM) is widely used in biogeography and conservation studies. The performance of ENM is influenced by the quality of species’ presence and absence datasets. Presences may include marginal localities, and absences are usually difficult to collect. We evaluated the use of species’ physiological limits to improve selection of presences and absences for ENM in a virtual species framework with defined response functions as surrogates for physiological knowledge. Location: The lower 48 states in USA. Methods: We generated physiologically informed absences based on either complete or incomplete knowledge of species’ physiology. With the same physiological knowledge, we reduced noise (incorrect or marginal locations) from presence datasets, completely or incompletely. We compared (i) models based on physiologically informed absences and random background points, (ii) models based on presences with and without noise and (iii) models obtained with and without incorporating physiological knowledge in absence and presence datasets. Results: Only absences based on complete physiological information produced better performing models than random background points. Model improvement was positively correlated with the percentage of noise being removed from the presence data, and best-performing models were obtained with true presences (all noise removed). Manipulating both absences and presences led to better models than manipulating only presences when all or majority of physiological limits were known. Main conclusions: The benefit of incorporating physiological information into ENM datasets largely depends on completeness of physiological knowledge, but in reality incomplete understanding of species’ physiology is the norm. We found that applying incomplete physiological knowledge to absences may bias ENM, and thus, use of random background points is recommended; on the other hand, removing noise from species’ presence datasets based on incomplete physiological limits increases model performance, thus this approach could potentially improve the effectiveness of ENM applications in conservation planning and invasive species management.