Predicting spatio‐temporal distributions of migratory populations using Gaussian process modelling

Abstract Knowledge concerning spatio‐temporal distributions of populations is a prerequisite for successful conservation and management migratory animals. Achieving cost‐effective monitoring large‐scale movements often difficult due to lack effective inexpensive methods. Taiga bean goose Anser fabalis tundra A. f. rossicus offer an excellent example challenging situation with harvested populations. The subspecies have different statuses population trends. However, their distribution overlaps during migration unknown extent, which, together similar appearance, has created conservation–management dilemma. Gaussian process (GP) models are widely adopted in the field statistics machine learning, but seldom been applied ecology so far. We introduce R package gplite GP modelling use it our case study birdwatcher observation data differences between Finland 2011–2019. demonstrate that offers flexible tool analysing heterogeneous collected by citizens. analysis reveals spatial temporal two Finland. migrates through entire country, whereas occurs only small area south‐eastern later than taiga goose. Synthesis applications . Within studied populations, harvest can be targeted at abundant restricting hunting end period. In general, approach combining citizen science various thus help solving situations. introduced not ecological modelling, wide range analyses other fields science.