Can species distribution models using remotely sensed variables inform reintroductions? Trialling methods with Carterocephalus palaemon the Chequered Skipper Butterfly

• Published in: Journal of insect conservation 2024-10, Vol.28 (5), p.909-921
• Main Authors: Halford, Georgina, Bulman, Caroline R, Bourn, Nigel, Maes, Dirk, Harpke, Alexander, Hodgson, Jenny A.
• Format: Article
• Language: English
• Subjects: Animal Ecology; Biodiversity; Biomedical and Life Sciences; Butterflies & moths; Carterocephalus palaemon; Conservation Biology/Ecology; Decision making; Ecology; Entomology; Geographical distribution; Habitats; Insect ecology; Insects; Land use; Life Sciences; Machine learning; Original Paper; Population studies; Regression analysis; Regression models; Reintroduction; Remote sensing; Species; Species extinction; Vertebrates; Wildlife conservation
• ISSN: 1366-638X; 1572-9753
• DOI: 10.1007/s10841-024-00555-6

Despite large numbers of reintroduction projects taking place and the high cost involved, there is a generally low success rate. Insects in particular are understudied within reintroduction ecology, with guidelines focusing on more iconic vertebrate taxa. Species distribution models (SDMs) examine the associations between species observations and environmental variables to find the conditions in which populations could survive. This study utilises two frequently used SDM approaches, a regression model (general linear model (GLM)) and a machine learning method (MaxEnt) to model habitat suitability for Chequered Skipper,  Carterocephalus palaemon, butterflies, which are being reintroduced to Northamptonshire following extinction in England. We look at how SDMs using widespread remotely sensed variables could be used to inform the reintroduction process by finding areas of suitable habitat that were previously overlooked. These remotely sensed variables have the potential to inform reintroductions without extensive on the ground research as they cover huge areas and are widely available. We found that both models are successful in discriminating between presences and absences, using only a limited number of explanatory variables. We conclude that these wide-scale SDMs are useful as a first step in the decision-making process in determining appropriate sites for reintroductions, but that they are less accurate when establishing precisely where species should be placed. Implications for insect conservation The rates of failure among species reintroductions are very high despite the large costs involved in these projects. By better utilising SDMs and remote sensing variables that cover huge areas, an increased rate of success and cost efficiency in insect reintroductions could be established.