Data‐centric species distribution modeling: Impacts of modeler decisions in a case study of invasive European frog‐bit

Abstract

Abstract Premise Species distribution models (SDMs) are widely utilized to guide conservation decisions. The complexity of available data and SDM methodologies necessitates considerations of how data are chosen and processed for modeling to enhance model accuracy and support biological interpretations and ecological applications. Methods We built SDMs for the invasive aquatic plant European frog‐bit using aggregated and field data that span multiple scales, data sources, and data types. We tested how model results were affected by five modeler decision points: the exclusion of (1) missing and (2) correlated data and the (3) scale (large‐scale aggregated data or systematic field data), (4) source (specimens or observations), and (5) type (presence‐background or presence‐absence) of occurrence data. Results Decisions about the exclusion of missing and correlated data, as well as the scale and type of occurrence data, significantly affected metrics of model performance. The source and type of occurrence data led to differences in the importance of specific explanatory variables as drivers of species distribution and predicted probability of suitable habitat. Discussion Our findings relative to European frog‐bit illustrate how specific data selection and processing decisions can influence the outcomes and interpretation of SDMs. Data‐centric protocols that incorporate data exploration into model building can help ensure models are reproducible and can be accurately interpreted in light of biological questions.

Keywords

• aquatic invasive plants
• data‐centric
• Hydrocharis morsus‐ranae
• machine learning
• natural history collections
• open data