Abundance distributions for tree species in Great Britain: A two‐stage approach to modeling abundance using species distribution modeling and random forest

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 km2 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.

Predicting the Potential Distribution of an Invasive Species, Solenopsis invicta Buren (Hymenoptera: Formicidae), under Climate Change using Species Distribution Models

“…RF performs well among the SDMs because RF avoids overfitting by randomly selecting variables to create a large number of classification trees; then each tree is individually trained using means of out-of-bag (OOB) samples for voting (Jin et al 2016). In addition, these results agree with the findings of previous studies (Hill et al 2017;Mi et al 2017;Wang et al 2015).…”

The Global Potential Distribution of Invasive Plants: Anredera cordifolia under Climate Change and Human Activity Based on Random Forest Models

“…RF model generates a large number of classification regression trees, and then selects subsamples for regression analysis [39], which is one of the most accurate models based on classified regression trees; at the same time, RF is very effective in processing large amounts of data [40], handing missing data well. A previous study used an RF model to predict the abundance of multiple tree species in Great Britain, and the accuracy of predictions reached high levels [41]. The large-scale distribution of Pinus yunnanensis was also predicted by RF, and prediction results were better than for other models [42].…”

Small Woodlands and Trees in Traditional Agricultural Landscapes of Slovakia

“…Similar mapping methodology and analysis framework has been applicable to regional and global scales (Aksoy et al, 2010;Clark & Kilham, 2016;Hill et al, 2017) and could be essential for many ecological applications ranging from species conservation to landscape planning.…”