Predicting the habitat usage of African black rhinoceros
(Diceros bicornis) using random forest models
Lucy Lush
1,2
*, Martin Mulama
3
and Martin Jones
2
1
Centre for Environmental and Marine Sciences, University of Hull, Scarborough campus, Filey Road, YO11 3AZ., Scarborough, U.K.,
2
Division of
Biology and Conservation Ecology, Faculty of Science & Engineering, Manchester Metropolitan University, John Dalton Building, Chester Street,
Manchester, M1 5GD, U.K. and
3
Ol Pejeta Conservancy, Private Bag, Nanyuki, 10400, Kenya
Abstract
Species distribution models are often used in ecology to
ascertain relationships between environmental variables
and species presence. Modelling to understand this rela-
tionship can be used to aid conservation management
strategies. In this paper, we applied the random forest
classification method to predict habitat used by black rhino
for browsing. The random forest model was created using
detailed habitat data collected from Ol Pejeta Conservancy
in Kenya. Variables from plots where rhino had been
present were compared to those not used by rhino.
Independent data were used to test the predictive accuracy
of the rules generated. The model performed well with the
independent test data, correctly classifying 69% of the
sampling plots where black rhino were present. Important
habitat features that affected rhino presence were browse
availability and density of vegetation, with Vachellia
drepanolobium (formerly Acacia) and Euclea divinorum being
important components. The analysis also highlighted areas
of potential high browse pressure, which should be the
focus of continued monitoring and management.
Key words: black rhino, browsing, habitat, random forest,
savannah
R esum e
En ecologie, on utilise souvent des mod eles de distribution
des esp eces pour etablir les relations entre des variables
environnementales et la pr esence d’esp eces. Pour com-
prendre cette relation, la mod elisation peut ^ etre utilis ee
pour aider les strat egies de gestion de la conservation.
Dans cet article, nous avons appliqu e la m ethode de
classement par for^ et al eatoire pour pr edire quel habitat
fr equente le rhino noir pour se nourrir. Le mod ele de
classement par for^ et al eatoire a et e cr e e en utilisant des
donn ees d etaill ees sur l’habitat, collect ees dans l’Aire de
conservation d’Ol Pejeta, au Kenya. Les variables de
parcelles o u le rhino etait pr esent furent compar ees a
celles d’autres, non fr equent ees par le rhino. Des donn ees
ind ependantes ont permis de tester la justesse pr edictive
des r egles g en er ees. Le mod ele donnait de bons r esultats
avec les donn ees ind ependantes du test, classant correct-
ement 69 % des parcelles echantillons o u le rhino etait
pr esent. D’importantes caract eristiques de l’habitat qui
affectaient la pr esence du rhino etaient la disponibilit e de la
nourriture ad equate et la densit e de la v eg etation, dont
Vachellia drepanolobium (anc. Acacia) et Euclea divinorum
sont des el ements importants. L’analyse a aussi mis en
lumi ere les zones o u la pression de la consommation des
rhinos risque d’^ etre elev ee, ce qui devrait ^ etre le point focal
de la poursuite du suivi et de la gestion.
Introduction
Modelling to understand the presence of animals based on
habitat data has been utilized extensively by ecologists to
aid conservation management strategies (Manel, Williams
& Ormerod, 2001; Stockwell & Peterson, 2002). However,
errors in the accuracy of predicting species presence have
been found in various studies, resulting in inappropriate
management strategies (Manel, Williams & Ormerod,
2001; Loiselle et al., 2003; Ara ujo & Guisan, 2006). The
ability to accurately predict suitable habitat for endangered
species becomes more important as habitat fragmentation
increases, and suitable habitat for many taxa is dramat-
ically reduced.
The techniques most often used to model species
distribution are artificial neural networks, discriminant *Correspondence: E-mail: llush@hotmail.co.uk
© 2015 John Wiley & Sons Ltd, Afr. J. Ecol. 1