Estimating uncertainty in density surface models

PeerJ. 2022 Aug 23:10:e13950. doi: 10.7717/peerj.13950. eCollection 2022.

Abstract

Providing uncertainty estimates for predictions derived from species distribution models is essential for management but there is little guidance on potential sources of uncertainty in predictions and how best to combine these. Here we show where uncertainty can arise in density surface models (a multi-stage spatial modelling approach for distance sampling data), focussing on cetacean density modelling. We propose an extensible, modular, hybrid analytical-simulation approach to encapsulate these sources. We provide example analyses of fin whales Balaenoptera physalus in the California Current Ecosystem.

Keywords: Density surface models; Distance sampling; Environmental uncertainty; Model uncertainty; Spatial modelling; Species distribution modelling; Uncertainty quantification.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Animals
  • Cetacea
  • Computer Simulation
  • Ecosystem*
  • Fin Whale*
  • Uncertainty

Associated data

  • figshare/10.6084/m9.figshare.20132291

Grants and funding

This work was funded by OPNAV N45 and the SURTASS LFA Settlement Agreement, and being managed by the U.S. Navy’s Living Marine Resources program under Contract No. N39430-17-C-1982. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.