Occupancy‐based diversity profiles: capturing biodiversity complexities while accounting for imperfect detection.

Abstract

Measuring the multidimensional diversity properties of a community is of great importance for ecologists, conservationists and stakeholders. Diversity profiles, a plotted series of Hill numbers, simultaneously capture the common diversity indices. However, diversity metrics require information on species abundance, often relying on raw count data without accounting for imperfect and varying detection. Hierarchical occupancy models account for variation in detectability, and Hill numbers have been expanded to allow estimation based on occupancy probability. But the ability of occupancy‐based diversity profiles to reproduce patterns in abundance‐based diversity has not been investigated. Here, we fit community occupancy models to simulated animal communities to explore how well occupancy‐based diversity profiles reflect patterns in true abundance‐based diversity. Because we expect occupancy‐based diversity to be overestimated, we further tested a occupancy thresholding approach to reduce potential biases in the estimated diversity profiles. Finally, we use empirical bird community data to present how the framework can be extended to consider species similarity. The simulation study showed that occupancy‐based diversity profiles produced among‐community patterns in diversity similar to true abundance diversity profiles, although within‐community diversity was generally overestimated. Applying an occupancy threshold reduced positive bias, but resulted in negative bias in richness estimates and slightly reduced the ability to reproduce true differences among the simulated communities; thus, we do not recommend application of this threshold. Application of our approach to a large bird dataset indicated differential species diversity patterns in communities of different habitat types. Accounting for phylogenetic and ecological similarities between species reduced variability in diversity among habitats. Our framework allows investigating the complexity of diversity from species detection data, while accounting for imperfect and varying detection probabilities, as well as species similarities. Visualizing results in the form of diversity profiles facilitates comparison of diversity between sites or across time. The approach offers opportunities for further development, for example by using local abundances estimated using the Royle–Nichols or N‐mixture models and further exploration of thresholding methods. In spite of some challenges, occupancy‐based diversity profiles are useful for studying and monitoring patterns in biodiversity.