New spatial models for integrating standardized detection-nondetection and opportunistic presence-only data: application to estimating risk factors associated to powerline-induced death of birds

TL;DR

This paper develops Bayesian spatial models to integrate professional surveys and citizen science data for estimating bird mortality risk from powerlines, accounting for sampling biases and data quality differences.

Contribution

It introduces novel latent Gaussian models that fuse multiple data sources and explicitly model preferential sampling and data quality factors.

Findings

Fused data improves parameter estimation accuracy.

Powerline density correlates with bird mortality risk.

Model choice significantly affects risk estimates.

Abstract

The constant increase in energy consumption has created the necessity of extending the energy transmission and distribution network. Placement of powerlines represent a risk for bird population. Hence, better understanding of deaths induced by powerlines, and the factors behind them are of paramount importance to reduce the impact of powerlines. To address this concern, professional surveys and citizen science data are available. While the former data type is observed in small portions of the space by experts through expensive standardized sampling protocols, the latter is opportunistically collected by citizen scientists. We set up full Bayesian spatial models that 1) fusion both professional surveys and citizen science data and 2) explicitly account for preferential sampling that affects professional surveys data and for factors that affect the quality of citizen science data. The proposed models are part of the family of latent Gaussian models as both data types are interpreted as thinned spatial point patterns and modeled as log-Gaussian Cox processes. The specification of these models assume the existence of a common latent spatial process underlying the observation of both data types. The proposed models are used both on simulated data and on real-data of powerline-induced death of birds in the Trondelag in Norway. The simulation studies clearly show increased accuracy in parameter estimates when both data types are fusioned and factors that bias their collection processes are properly accounted for. The study of powerline-induced deaths shows a clear association between the density of the powerline network and the risk that powerlines represent for bird populations. The choice of model is relevant for the conclusions from this case study as different models estimated the association between risk of powerline-induced deaths and the amount of exposed birds differently.