Abundance distributions for tree species in Great Britain: A two‐stage approach to modeling abundance using species distribution modeling and random forest. Issue 4 (22nd January 2017) - Hill, Louise; Hector, Andy; Hemery, Gabriel; Smart, Simon; Tanadini, Matteo; Brown, Nick
Abstract: High‐quality abundance data are expensive and time‐consuming to collect and often highly limited in availability. Nonetheless, accurate, high‐resolution abundance distributions are essential for many ecological applications ranging from species conservation to epidemiology. Producing models that can predict abundance well, with good resolution over large areas, has therefore been an important aim in ecology, but poses considerable challenges. We present a two‐stage approach to modeling abundance, combining two established techniques. First, we produce ensemble species distribution models (SDMs) of trees in Great Britain at a fine resolution, using much more common presence–absence data and key environmental variables. We then use random forest regression to predict abundance by linking the results of the SDMs to a much smaller amount of abundance data. We show that this method performs well in predicting the abundance of 20 of 25 tested British tree species, a group that is generally considered challenging for modeling distributions due to the strong influence of human activities. Maps of predicted tree abundance for the whole of Great Britain are provided at 1 km 2 resolution. Abundance maps have a far wider variety of applications than presence‐only maps, and these maps should allow improvements to aspects of woodland management and conservation including analysis of habitats and ecosystem functioning, epidemiology, and disease management, providing a useful contribution to the protection of British trees. We also provide complete R scripts to facilitate application of the approach to other scenarios. Abstract : Producing accurate, fine‐resolution abundance distributions has long been an important aim for theoretical ecology and would have a wide range of practical applications. We have developed a straightforward and user‐friendly method for this, first modeling the probability of occupancy of a species and then linking the results of this to a small amount of abundance data, and show that it produces good‐quality maps of predicted abundance for 20 British tree species.