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

TL;DR

This paper introduces a spatial modeling framework that combines data from different bird survey protocols to estimate total abundance, using Bayesian methods and spatial analysis, demonstrated with a case study in mid-Scandinavia.

Contribution

It develops a novel methodology for merging diverse survey data using Gaussian Random Fields and Bayesian inference, accounting for protocol differences and detectability.

Findings

Effective integration of multiple survey protocols for abundance estimation.

Accurate spatial maps with uncertainty quantification.

Robustness demonstrated through simulation study.

Abstract

We present a new methodology to model total abundance by merging count data information from surveys with different sampling protocols. The proposed methods are used for data from national breeding bird monitoring programs in Norway and Sweden. Each census collects abundance data following two different sampling protocols in each country, i.e. these protocols provides data from four different sampling processes. The modeling framework assumes a common Gaussian Random Field shared by both the observed and true abundance with either a linear or a relaxed linear association between them. The models account for particularities of each sampling protocol by including terms that affect each observation process, i.e. accounting for differences in observation units and detectability. Bayesian inference is performed using the Integrated Nested Laplace Approximation (INLA) and the Stochastic Partial Differential Equation (SPDE) approach for spatial modeling. We also present the results of a simulation study based on the empirical census data from mid-Scandinavia to assess the performance of the models under model misspecification. Finally, maps of the total expected abundance of birds in our study region in mid-Scandinavia are presented with uncertainty estimates.