  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.
