Anticipating species distributions: Handling sampling effort bias under a Bayesian framework

- Invasive species can modify the structure and function of ecosystems.
- Reliable anticipation of species invasions relies on the quality of input data.
- Sampling effort bias leads to an over- or under-estimation of species occurrence.
- We propose methods to consider sampling effort bias in species distribution modeling.
- We demonstrate the power of incorporating uncertainty in species distribution models.

Anticipating species distributions in space and time is necessary for effective biodiversity conservation and for prioritising management interventions. This is especially true when considering invasive species. In such a case, anticipating their spread is important to effectively plan management actions. However, considering uncertainty in the output of species distribution models is critical for correctly interpreting results and avoiding inappropriate decision-making. In particular, when dealing with species inventories, the bias resulting from sampling effort may lead to an over- or under-estimation of the local density of occurrences of a species. In this paper we propose an innovative method to i) map sampling effort bias using cartogram models and ii) explicitly consider such uncertainty in the modeling procedure under a Bayesian framework, which allows the integration of multilevel input data with prior information to improve the anticipation species distributions.

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