Data Quality Influences the Predicted Distribution and Habitat of Four Southern-Hemisphere Albatross Species

Kimberly T. Goetz*, Fabrice Stephenson, Andrew Hoskins, Aidan D. Bindoff, Rachael A. Orben, Paul M. Sagar, Leigh G. Torres, Caitlin E. Kroeger, Lisa A. Sztukowski, Richard A. Phillips, Stephen C. Votier, Stuart Bearhop, Graeme A. Taylor, David R. Thompson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
15 Downloads (Pure)

Abstract

Few studies have assessed the influence of data quality on the predicted probability of occurrence and preferred habitat of marine predators. We compared results from four species distribution models (SDMs) for four southern-hemisphere albatross species, Buller’s (Thalassarche bulleri), Campbell (T. impavida), grey-headed (T. chrysostoma), and white-capped (T. steadi), based on datasets of differing quality, ranging from no location data to twice-daily locations of individual birds collected by geolocation devices. Two relative environmental suitability (RES) models were fit using minimum and maximum preferred and absolute values for each environmental variable based on (1) monthly 50% kernel density contours and background environmental data, and (2) primary literature or expert opinion. Additionally, two boosted regression tree (BRT) models were fit using (1) opportunistic sightings data, and (2) geolocation data from bird-borne electronic tags. Using model-specific threshold values, habitat was quantified for each species and model. Model variables included distance from land, bathymetry, sea surface temperature, and chlorophyll-a concentration. Results from both RES models and the BRT model fit with opportunistic sightings were compared to those from the BRT model fit using geolocation data to assess the influence of data quality on predicted occupancy and habitat. For all species, BRT models outperformed RES models. BRT models offer a predictive advantage over RES models by being able to identify relevant variables, incorporate environmental interactions, and provide spatially explicit estimates of model uncertainty. RES models resulted in larger, less refined areas of predicted habitat for all species. Our study highlights the importance of data quality in predicting the distribution and habitat of albatrosses and emphasises the need to consider the pros and cons associated with different levels of data quality when using SDMs to inform management decisions. Furthermore, we examine the overlap in preferred habitat predicted by each SDM with fishing effort. We discuss the influence of data quality on predicting the wide-scale distributions of pelagic seabirds and how these impacts could result in different protection measures.

Original languageEnglish
Article number782923
JournalFrontiers in Marine Science
Volume9
DOIs
Publication statusPublished - 18 May 2022

Keywords

  • albatross
  • biologging
  • boosted regression tree
  • geolocation
  • habitat suitability
  • relative environmental suitability
  • seabird conservation
  • species distribution models

ASJC Scopus subject areas

  • Oceanography
  • Global and Planetary Change
  • Aquatic Science
  • Water Science and Technology
  • Environmental Science (miscellaneous)
  • Ocean Engineering

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