A spatial modeling framework for monitoring surveys with different sampling protocols with a case study for bird populations in mid-Scandinavia

Quantifying species abundance is the basis for spatial ecology and biodiversity conservation. Abundance data are mostly collected through professional surveys as part of monitoring programs, often at a national level. These surveys rarely follow the same sampling protocol in different countries, which represents a challenge for producing abundance maps based on the information available for more than one country. We here present a novel solution for this methodological challenge with a case study concerning bird abundance in mid-Scandinavia. We use data from bird monitoring programs in Norway and Sweden. Each census collects abundance data following two different sampling protocols that each contain two different sampling methods. We propose a modeling framework that assumes a common Gaussian Random Field driving both the observed and true abundance with either a linear or a relaxed linear association between them. Thus, the models in this framework can integrate all sources of information involving count of organisms to produce one estimate for the expected abundance, its uncertainty and the covariate effects. Bayesian inference is performed using INLA and the SPDE approach for spatial modeling. We also present the results of a simulation study based on the census data from mid-Scandinavia to assess the performance of the models under misspecification. Finally, maps of the total expected abundance of the bird species present in our study region in mid-Scandinavia were produced. We found that the framework allows for consistent integration of data from surveys with different sampling protocols. Further, the simulation study showed that models with a relaxed linear specification are less sensitive to misspecification, compared to the model that assumes linear association between counts. Relaxed linear specifications improved goodness-of-fit, but not the predictive power of the models.