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When Do Single-Species Occupancy Models Outperform Multispecies Models?

Gavin G Cotterill1, Douglas A Keinath2, Tabitha A Graves1

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|November 25, 2025
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Summary
This summary is machine-generated.

Multispecies occupancy models (MSOMs) may provide biased results for species-specific effects at low sampling intensities. Single-species occupancy models (SSOMs) offer more accurate estimates for individual species, especially rare ones, under certain conditions.

Keywords:
JAGSeDNAguildshabitat restorationhierarchical community modelingimperfect detectionpollinator conservationpower analysispresence/absence dataspecies exchangeability assumption

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Area of Science:

  • Ecology
  • Conservation Biology
  • Statistical Modeling

Background:

  • Occupancy models are crucial for species monitoring, utilizing detection/non-detection data.
  • Multispecies occupancy models (MSOMs) offer potential advantages over single-species models (SSOMs) due to hierarchical structures, especially with sparse data.
  • Data constraints in species monitoring, such as limited resources or rare species, necessitate careful model selection.

Purpose of the Study:

  • To evaluate the performance of MSOMs versus SSOMs under varying sampling intensities and community-level effects.
  • To determine conditions under which MSOMs outperform SSOMs for ecological studies, particularly those involving habitat treatments.
  • To provide guidance for researchers designing studies and selecting appropriate occupancy models.

Main Methods:

  • Conducted a simulation study using hypothetical pollinator communities.
  • Varied sampling intensities (spatial and temporal replicates) and simulated different community-level effects from habitat treatments.
  • Fitted both MSOMs and SSOMs to simulated datasets and assessed model performance and accuracy of effect estimates.

Main Results:

  • MSOMs exhibited biased community-level treatment effect estimates at low sampling intensities (< 20 spatial, < 4 temporal replicates).
  • SSOMs provided more accurate species-specific effect estimates than MSOMs, even at higher sampling intensities, particularly in scenarios with high variance.
  • MSOMs can inaccurately pull species-specific effects towards the community mean, potentially leading to misinterpretations of treatment impacts on individual species.

Conclusions:

  • SSOMs are more robust for estimating species-specific effects, especially for rare species, when data are sparse or variance is high.
  • The choice between MSOMs and SSOMs depends on data volume, community characteristics, and research objectives (community vs. species-specific effects).
  • Findings inform study design, simulation studies, and the decision-making process regarding the trade-offs between MSOM precision and SSOM accuracy for individual species.