Predicting the distributions of under-recorded Odonata using species distribution models

2014-03-24T18:44:47Z (GMT) by Christopher Hassall

1. Absences in distributional data may result either from the true absence of a species or from a false absence due to lack of recording effort. I use general linear models (GLMs) and species distribution models (SDMs) to investigate this problem in North American Odonata and present a potential solution.

2. I use multi-model selection methods based on Akaike’s information criterion to evaluate the ability of water–energy variables, human population density, and recording effort to explain patterns of odonate diversity in the USA and Canada using GLMs. Water–energy variables explain a large proportion of the variance in odonate diversity, but the residuals of these models are significantly related to recorder effort.

3. I then create SDMs for 176 species that are found solely in the USA and Canada using model averaging of eight different methods. These give predictions of hypothetical true distributions of each of the 176 species based on climate variables, which I compare with observed distributions to identify areas where potential under-recording may occur.

4. Under-recording appears to be highest in northern Canada, Alaska, and Quebec, as well as the interior of the USA. The proportion of predicted species that have been observed is related to recorder effort and population density. Maps for individual species have been made available online ( to facilitate recording in the future.

5. This analysis has illustrated a problem with current odonate recording in the form of unbalanced recorder effort. However, the SDM approach also provides the solution, targeting recorder effort in such a way as to maximise returns from limited resources.