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Biogeographic multi-species occupancy models for large-scale survey data.

Jacob B Socolar1,2, Simon C Mills3, Torbjørn Haugaasen1

  • 1Faculty of the Environment and Natural Resources Management Norwegian University of Life Sciences Ås Norway.

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|October 7, 2022
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Summary

Ecologists can now improve species occurrence predictions at large scales using biogeographic multi-species occupancy models (bMSOMs). This method integrates range data, enhancing accuracy and reducing computational costs for ecological surveys.

Keywords:
community modelhierarchical modeloccupancy modelpoolingspatial scale

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Area of Science:

  • Ecology
  • Biogeography
  • Computational Biology

Background:

  • Ecologists use hierarchical models like multi-species occupancy models (MSOMs) to infer species occurrence patterns from survey data.
  • MSOMs pool information across species but face challenges at biogeographic scales due to complex spatial variation.
  • Scaling MSOMs to large areas requires accounting for intricate spatial effects influencing occupancy across diverse species.

Purpose of the Study:

  • To introduce a novel framework, the biogeographic multi-species occupancy model (bMSOM), for large-scale ecological inference.
  • To demonstrate how incorporating preexisting range information overcomes limitations of traditional MSOMs at biogeographic scales.
  • To enhance predictive performance and computational efficiency in multi-species occupancy modeling.

Main Methods:

  • Developed the biogeographic multi-species occupancy model (bMSOM) by integrating preexisting range data into MSOMs.
  • Applied the bMSOM to two distinct datasets: Parulid warblers in the US Breeding Bird Survey and avian communities in Colombia's West Andes.
  • Compared the performance of bMSOM against traditional MSOMs in terms of predictive accuracy and computational cost.

Main Results:

  • The bMSOM demonstrated significantly improved predictive performance compared to traditional MSOMs.
  • bMSOMs achieved better results at a lower computational cost than conventional MSOMs.
  • The new model effectively mitigated spatial biases inherent in traditional MSOMs and allowed for principled inference of species-specific occurrences, including for rare or unobserved species.

Conclusions:

  • Incorporating preexisting range data enables effective information pooling across species in large-scale MSOMs.
  • The bMSOM provides a robust biogeographic framework for multi-species modeling applicable to various hierarchical models predicting species occurrences.
  • This approach offers a principled method for ecological inference at large spatial scales, improving upon existing occupancy modeling techniques.