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Data from: The costs of ignoring species detectability on functional diversity estimation

Cite this dataset

Palacio, Facundo; Maragliano, René; Montalti, Diego (2021). Data from: The costs of ignoring species detectability on functional diversity estimation [Dataset]. Dryad.


Functional diversity (FD) approaches have been increasingly used to understand ecosystem functioning in bird communities. These typically rely on the assumption that species are perfectly detected in the field, despite the fact that imperfect detection represents a ubiquitous source of bias in biodiversity studies. This may be notably important in FD studies, because detection may depend on the functional traits used to compute FD metrics. However, little effort has been devoted to account for imperfect detection in FD studies, and therefore the degree to which species traits and detectability affects FD remains poorly understood. We predict that observed FD metrics may either under- or overestimate detection-corrected FD, because FD has multiple independent dimensions with different data properties. We assessed whether detection was related to bird traits (body mass, diet, and foraging stratum), accounting for habitat type, season, and phylogeny. We then used a multi-species occupancy model to obtain detection-corrected FD metrics (functional richness FRic, functional evenness FEve, and functional divergence FDiv), and compared observed and detection-corrected FD estimates in bird communities from east-central Argentina. Some functional types of birds (raptors and insectivores) were more easily overlooked, whereas others (seed and leave eaters) were more easily detected. Some observed FD metrics underestimated detection-corrected FD (FRic and FDiv), whereas some others (FEve) overestimated detection-corrected FD. Both observed and detection-corrected FRic revealed differences between seasons, but not between habitat types. However, detection-corrected FEve and FDiv showed differences between seasons, contrary to observed estimates. Our results indicate that failure to account for unequal ease of detecting species can lead to erroneous estimates of FD because some functional types of birds are more easily overlooked. We outline some guidelines to help ornithologists identifying under which circumstances detection may be a concern and warn against the indiscriminate use of FD metrics without accounting for species detection.


Datasets used to estimate detection-corrected occurrence probabilites (Bayesian multi-species multi-season occupancy model) and compute bird functional diversity metrics.

Usage notes

Multi-species multi-season occupancy model

This dataset serves as input for the Script S1. R code to fit the multi-species multi-season occupancy model. To compute functional diversity metrics, this data frame should be transposed. See README file for a description of the content of all datafiles. multispecies_occupancy_model.csv

Bird functional trait data

See README file for a description of the content of all datafiles. bird_functional_traits.csv