Evaluating sampling bias correction methods for invasive species distribution modeling in Maxent

Abstract

As globalization continues, the spread of invasive species is accelerating, posing a severe threat to native biodiversity. To manage such species, reduce their negative impact on native biota and utilize management costs efficiently, a profound understanding of their geographical distribution pattern is mandatory. In this study, the species distribution model Maxent was used to predict the potential spatial distribution of U. europaeus. To account for sampling bias, three bias correction methods were applied, including a novel approach to increase the number of presence points by sampling occurrences based on satellite images. Furthermore, a decision structured process was used to evaluate and select optimal Maxent parameterization and account for limitations of single evaluation criteria. The currently suitable area of U. europaeus is primarily distributed in the coastal and central regions of Chilean natural region Zona Sur in south-central Chile. Annual mean temperature (bio1), annual precipitation (bio12), and precipitation seasonality (bio15) were the most important environmental variables that affected the distribution of U. europaeus. The sampling of additional presence points could effectively correct for sampling bias in species occurrence data. The use of a decision structured process for model evaluation proved to be useful in determining optimal model parameterization for decreased model complexity. This study highlights the importance of optimized Maxent calibrations to yield results as accurately as possible. The predicted suitable habitats can inform nature conservation planners and landscape managers to guide and prioritize conservation measures.