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

Abstract

1. Knowledge concerning spatio-temporal distributions of populations is a prerequisite for successful conservation and management of migratory animals.Achieving cost-effective monitoring of large-scale movements is often difficult due to lack of effective and inexpensive methods.2. Taiga bean goose Anser fabalis fabalis and tundra bean goose A. f. rossicus offer an excellent example of a challenging management situation with harvested migratory populations. The subspecies have different conservation statuses and population trends. However, their distribution overlaps during migration to an unknown extent, which, together with their similar appearance, has created a conservation-management dilemma.3. Gaussian process (GP) models are widely adopted in the field of statistics and machine learning, but have seldom been applied in ecology so far. We introduce the R package gplite for GP modelling and use it in our case study together with birdwatcher observation data to study spatio-temporal differences between bean goose subspecies during migration in Finland in 2011-2019.4. We demonstrate that GP modelling offers a flexible and effective tool for analysing heterogeneous data collected by citizens. The analysis reveals spatial and temporal distribution differences between the two bean goose subspecies in Finland. Taiga bean goose migrates through the entire country, whereas tundra bean goose occurs only in a small area in south-eastern Finland and migrates later than taiga bean goose. Synthesis and applications.Within the studied bean goose populations, harvest can be targeted at abundant tundra bean goose by restricting hunting to southeastern Finland and to the end of the migration period. In general, our approach combining citizen science data with GP modelling can be applied to study spatiotemporal distributions of various populations and thus help in solving challenging management situations. The introduced R package gplite can be applied | 1147

A gradual migratory divide determines not only the direction of migration but also migration strategy of a social migrant bird

“…We also fitted models with quasi-periodic (see Piironen et al, 2022) and squared-exponential (see Rasmussen & Williams, 2006) covariance functions. We assessed the performance of different models using leave-one-out (LOO) cross-validation, and the model presented here performed best (see Table 1 in supplemental information for model assessment).…”

“…We estimated hyperparameters by optimising them to their marginal maximum a posteriori values. We performed the analysis using packages adehabitatHR (displacement measurements; Calenge, 2006), gplite (fitting the GP model; Piironen, 2021) and related packages in R software version 4.1.1 (R Core Team, 2021). We included the script used in the analysis to the supplemental information.…”

A gradual migratory divide determines not only the direction of migration but also migration strategy of a social migrant bird

“…We estimated hyperparameters by optimizing them to their marginal maximum a posteriori values. We performed the analysis using packages adehabitatHR (displacement measurements; [39]), gplite (fitting the GP model; [40]) and related packages in R software v.4.1.1 [41]. We included the script used in the analysis in the electronic supplementary material.…”

“…[22–26]). This is most likely due to their inherent flexibility (no assumptions on the form of dependence between variables are needed [27]), good predictive accuracy [22,23] and rich covariance structure that makes them an auspicious tool to model complex phenomena such as animal migration [25]. Here, we demonstrate that these benefits of GP models make them a useful tool also for studying animal migration from displacement data.…”

“…GPs have started to gain popularity also in ecology (see e.g. [22][23][24][25][26]). This is most likely due to their inherent flexibility (no assumptions on the form of dependence between variables are needed [27]), good predictive accuracy [22,23] and rich covariance structure that makes them an auspicious tool to model complex phenomena such as animal migration [25].…”