Methods S1:
Target species
As model systems, we studied three widespread African vertebrates that in West Africa
occur in peripheral and isolated populations (Fig. 1): 1) the Patas monkey (Erythrocebus
patas Schreber, 1774) ranges from north of the equatorial forests to the southern limit of
the Sahara, and from western Senegal to central Ethiopia, where it occupies plains from
open grassland to wooded savannas and dry woodlands. Marginal populations are found
in Mauritanian mountains, and Aïr (Niger) and Ennedi (Chad) mountains in the Central
Sahara, where the species is restricted to productive environments around rocky pools
(locally known as gueltas), rivers and streams (de Jong et al. 2009; Brito et al. 2010); 2)
the Bull frog (Hoplobatrachus occipitalis Günther, 1858) is a savannah species ranging
from southern Mauritania to Ethiopia, through East Africa to northern Zambia, occupying
many habitats and associated with small to medium-sized temporary bodies of water.
Marginal populations are found in south-western Libya, in Mauritanian mountains, in the
Aïr mountains, and in Adrar des Iforas (Mali), where it is restricted to gueltas and other
temporary water features (Rödel et al. 2006; Padial et al. in press) 3) the Rock hyrax
(Procavia capensis Pallas, 1766) is distributed throughout sub-Saharan, north-east Africa,
and west Arabian Peninsula, occupying a wide range of habitats, from arid deserts to
rainforest, but typically associated with rocky outcrops, cliffs or boulders. Isolated
populations are found in Mauritanian and Algerian mountains, where it appears to be
dependent of permanent water sources (Barry et al. 2008; Brito et al. 2010) (Fig. 1).
Training areas
The West Africa training area is located in a biogeographic transition between Palaearctic
and Afro-tropical regions, where the southern range limit of species with Palearctic affinity
meets with the northern range limit of species with Afro-tropical affinity (Dekeyser and
Villiers 1956; Barry et al. 1987; Brito et al. 2010; Isenmann et al. 2010). There is a cool, dry
season from November to February and a hot, dry season from March to June (Cooper et
al. 2006). Annual average temperature ranges from 20.4 ºC in coastal Southern Morocco
to 30.8 ºC in Western Mauritania (Hijmans et al. 2005). There is a marked north-south
gradient in annual precipitation, from 11 mm in the north-eastern desert areas to 1334 mm
in the extreme southern region (Hijmans et al. 2005). Rain falls in a single wet season from
July to October, with most precipitation in August and September (Cooper et al. 2006).
Most of the study area is covered by sandy, stony and bare deserts (30.0%, 17.9%, 10.0%,
respectively; Bicheron et al. 2008), but croplands and mosaics cropland/vegetation
(17.6%), and close to open shrublands and grasslands (11.8%) are more frequent in
southern regions. The region is characterised by a latitudinal gradient in climate and
habitat (Anyamba &Tucker 2005). This gradient is disrupted by mountains, which are
characterised by a mix of both Mediterranean and Tropical climates (Le Houérou 1997)
and provide suitable habitats for otherwise absent widespread African species.
Species data
For Africa dataset, observations were randomly selected from a cluster of species
occurrences and two datasets were built: 214 observations for training and testing and
another with 134 observations for validating models (Table S1 in Supporting Information).
In both training and validation datasets, clustering of observations was decreased by
randomly removing localities that were clustered according to the Nearest Neighbour Index
(NNI) given by ArcGIS 9.3: 0.88 (p=0.07) and 0.83 (p=0.08) in E. patas, 0.92 (p=0.15) and
0.88 (p=0.05) in H. occipitalis, and 0.88 (p=0.06) and 0.82 (p=0.05) in P. capensis for the
training and validation datasets, respectively.
We followed the same methodology for West African data. Observations were randomly
selected from a cluster of species occurrences and two datasets were built: 101
observations for training models and another with 41 for validating models (Table S1 in
Supporting Information). The clustering of observations was decreased in both datasets by
randomly removing clustered localities, according to NNI: 0.91 (p=0.29) and 2.70 (p=0.00)
for E. patas, 0.92 (p=0.32) and 0.88 (p=0.26) for H. occipitalis, and 0.86 (p=0.22) and 1.75
(p=0.00) for P. capensis for training and validation datasets, respectively.
To quantify prediction biases in ENMs, we randomly generated a pseudo-absence dataset,
with the same number of observations used in the training datasets. Pseudo-absences
(hereafter absences) likely corresponded with true absences because they were selected
from areas outside buffers encompassing the IUCN polygons of species distribution
(IUCN, 2011) and the presence dataset of each species. The buffer sizes were set
according to study areas: 1) 100km around IUCN polygons of species distribution for
Africa; and 2) 10km around the observations of H. occipitalis and P. capensis and 40km
around E. patas observations in West Africa. Buffer size in West Africa was set according
to home range size estimations, which were larger for E. patas (Isbell & Chism, 2007).
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