New research

A new, trait-based model of detectability

Garrard, G. E., McCarthy, M. A., Williams, N. S. G., Bekessy, S. A., Wintle, B. A. (2012), A general model of detectability using species traits. Methods in Ecology and Evolution*. doi: 10.1111/j.2041-210x.2012.00257.x

This post is about a new paper now available online in Methods in Ecology and Evolution.  Please email me at if you’d like a copy.

Imperfect detectability of plants and animals during ecological surveys is now widely recognised.  If unaccounted for, imperfect detectability can lead to biased estimates of abundance or occupancy, impaired ability to detect change or response to management action, poorly informed management decisions, escape of invasive species and increased risk of extinction of endangered species.

A range of methods exist for estimating detectability, including distance sampling, mark-recapture, N-mixture models, zero-inflated occupancy models and time-to-detection models.  Each of these models has its own set of assumptions, data requirements and applications.   Often, the data requirements of these models are heavy; data, including abundance counts, presence-absence observations and times-to-detection, are variously required from multiple sites and multiple observers.  All this means that, to date, estimates of detection probability are available for relatively few species.

In this paper, we ask whether we can learn about the influence of species traits on detectability, and use trait-based models to predict the detectability of species for which no species-specific model exists.

Using a time-to-detection model, we investigate the influence of a range of species traits on the detectability of grassland plant species.  Examples of the traits investigated were local abundance, height, likelihood of flowering at the time of survey, flower colour, leaf area, number of similar grassland species and whether the species grows in clumps.

We found that local abundance has a clear influence on detectability, with species that occur in higher numbers having lower detection times (higher detection rates) than those occurring in small numbers (Figure 1).  Species are also more likely to be detected if they are unique or in their peak flowering month at the time of survey, although these results are less definitive.

Figure 1. The relative size of the influence of traits on detection rate (mean and 95% credible intervals).

Our results also show that flower colour may have a large effect on detectability, with pink and red flowered species potentially more easily detected than those with inconspicuous or yellow flowers.  This makes sense in native grasslands, where there are many yellow flowers and few pink or red flowers.  The influence of flower colour is still very uncertain – it will be interesting to see whether more objective measures of flower colour (we used only coarse categories) can help to resolve this uncertainty.

Using trait-based detectability models, we were able to predict average times-to-detection reasonably well to new species (Figure 2).

Figure 2. Comparison of ln(ave detection time) estimates for eight species withheld from the model-fitting data set, as predicted by the trait-based (x-axis) and single-species (y-axis) models.  Filled diamonds are predictions for expert observers and open circels are predictions for intermediate observers.  Error bars are 95% credible intervals.

I’m probably biased, but I think trait-based detectability models are an exciting development in the field of detectability research.  With more than 1300 nationally-listed threatened plant species and another 400 animal species in Australia alone, it’s impossible to consider constructing a species-specific detectability model for every threatened species.  While they may not perfectly predict individual species’ detection probabilities, trait-based models should provide sensible bounded estimates of detectability on which to base survey design and effort requirements.

Mick McCarthy has made an author-submitted copy of the manuscript available here.

* Please note that there’s a small error in Equation 5.  λ should be indexed with ijk throughout this equation.  MEE know about this, so hopefully it will be fixed before the article appears in an issue.

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