Species Distribution Modelling of Family Sapindaceae in West Africa
Members of the family Sapindaceae provides various benefits in combating increasing malnutrition and poverty in West Africa. Due to their multiple uses, their high nutritional and medicinal value, members of Sapindaceae have been identified as one of the most important forest species to be conserved and valued in Africa. Therefore, it is important to study the potential future distribution of this species and determine strategies for conservation. In order to achieve these, suitability of sites in Africa was evaluated for potential conservation using spatial environmental data in MAXENT modelling framework. A total of 153 geo-referenced records of Sapindaceae were assembled from herbarium records and fieldwork and climatic data were acquired from the Worldclim Database. The main variables that contributed towards predicting the species distribution were annual precipitation and temperature seasonality. Results suggest that the distribution model was excellent with training AUC value of 0.974 and test AUC value of 0.960 confirming the wide distribution of Sapindaceae in West Africa. Also, the environmental variables that affected the model the most are minimum temperature of the coldest month (35.4%), precipitation of the coldest quarter (16.9%) and precipitation of wettest month (14.9%). Recommendations for different conservation strategies include in situ conservation in Protected Areas; ex situ conservation in seed banks and conservation through sustainable utilization.
Received: January 03, 2012;
Accepted: January 30, 2012;
Published: April 25, 2012
Climate change is an increasing concern worldwide; issues such as crop failure
and food security are progressively becoming more worrisome. The fruit pulp,
leaves and seeds of the several genera in the family Sapindaceae are a good
source of vitamins and minerals (Metcalfe and Chalk, 1957;
Chapman, 1984) and are common ingredients in traditional
herbal remedies in rural and urban Africa (Odugbemi and Akinsulire,
2006). Apart from food, they also serve as shade to local people (Watson
and Dallwitz, 2008). Owing to the combination of health claims (e.g. anti-inflammatory
(Sofidiya et al., 2007), anti-fungal (Adesegun
et al., 2008), anti-diarrhoeal, (Antwi et
al., 2009), aphrodisiac (Muanya and Odukoya, 2008),
pre-biotic and antioxidant functions (Ripa et al.,
2010), for eye infections (Pendota et al., 2008),
high calcium content) and food technological functions (high pectin and fiber
content), fruit pulp of several genera has been identified as a suitable candidate
for a new generation of functional foods and drinks (Chapman,
1984). Some yield valuable most widely used is Tulipwood (Harpullia pendula).
Many other members of the Sapindaceae are important nuts, or sources of beverages,
or oils. Saponins are present in the fruits, seeds, barks, twigs, leaves, pericarp
and other tissues of several species; the bark of Jagera pseudorhus Blume
contains large amounts and is used as a foaming agent and as a fish poison.
Some types such as Sapindus saponaria are used as soap substitutes in
the tropics. The fruit of Dodonaea viscosa (hop bush) has been used
as a substitute for hops in the brewing of beer.
Also, due to formation of residential, industrial and other layouts, the natural
slopes, vegetation and land cover gets disturbed however, species composition
can be predicted by local environmental conditions (Ali
and Kauser, 2006). Although members of Sapindaceae are not commonly cultivated,
they can be successfully propagated. Investigating potential sites for cultivation
is therefore an important first step towards the conservation of the representative
species. In this study, geo-referenced Sapindaceae locality data and spatial
environmental data were used in MAXENT, the species distribution modelling framework
to predict potential sites for cultivation in Western Africa.
MATERIALS AND METHODS
Species data: A total of 153 Sapindaceae growing localities (without
duplicates) were assembled from diverse sources. About 32% of the localities
came from recent fieldwork while 68% were herbarium records. In order to reduce
the effect of temporal bias and to match the species data with the environmental
climatic data (produced over the period 1950-2000), herbarium records collected
before 1950 were eliminated.
Environmental data: Growing potential was assessed by characterising
the climate at the presence locations. Twenty spatial datasets from the WorldClim
database (at 5' resolution) (Hijmans et al., 2005)
were used. The Worldclim dataset included altitude and 19 bioclimatic variables
derived from temperature and rainfall. Although 21 spatial datasets were used
in the beginning, some were eliminated during modelling due to their low contribution
to the final model. In agricultural systems, abiotic factors are more limiting
than biotic factors (such as competition or dispersal); therefore no biotic
factors were included in this study. Also, it was assumed that at a continental
scale, climatic variables were much more limiting than human population.
Species distribution modelling: For each plant sample, GPS coordinates
of the collection sites were recorded and subjected to modelling using the Maximum
Entropy Programme (MAXENT) (Phillips and Dudik, 2008).
Maxent predicts the potential species distribution by estimating the probability
distribution of maximum entropy across a specified region, subject to a set
of constraints that represent the missing information (lack of absence data)
about the target distribution (Phillips et al., 2006).
The Maxent method is currently considered to be the most accurate approach to
modelling presence-only data (Elith et al., 2006;
Pearson et al., 2007).
Model validation: Model performance was evaluated using several methods.
First, model performance was determined by assigning a subset of the presence
records for training and using the remaining records to test the resulting model.
A good model should predict correctly the presence of Sapindaceae in the test
locations. As performance can vary depending upon the particular set of data
selected for building the model and for testing it, 10 random partitions of
the presence records were made to assess the average behaviour of Maxent, following
Phillips et al. (2006). Each partition was created
by randomly selecting 75% of the total presence records to build the model and
the remaining 25% of presence records were used for testing. However, the full
set of presence records were used to build the final model to obtain the best
estimate of the species distribution (for all records model, West Africa model).
Second, Receiver Operating Characteristic (ROC) analysis was used to evaluate
how well the Maxent model compared to a random prediction. The area under the
ROC curve (AUC) serves as a measure of model performance in terms of sensitivity
versus specificity. The sensitivity for a particular threshold is the fraction
of all positive instances that are classified as present and specificity is
the fraction of all negative instances that are classified as not present. The
value of the AUC is typically between 0.5 (random) and 1.0. The closer the AUC
value to 1, the better the model performance. Moreover, the success of the model
was also evaluated by visually examining how well the mapped probability values
matched the presence records. A good model should produce regions of high probability
that cover the majority of presence records and areas of low probability should
contain few or no presence points.
Potential distribution of Sapindaceae in West Africa: All records model,
the resulting Maxent cultivation potential map for Africa predicted members
of Sapindaceae to occur in most of West Africa. For the West Africa model, the
cultivation potential map predicted Sapindaceae to grow in most of the Sahel
from Senegal to south-west Cameroon and in southern Ghana-Nigeria (Fig.
Model performance: All the ten generated training/testing models showed
a high level of performance when compared to random (where the AUC would be
0.5). Training gained in this model ranged from 2.139 - 2.668 (Fig.
2) while the AUC value was 0.974 (Fig. 3). Test AUC values
was lower but close to training AUC i.e., 0.960 (Fig. 3) while
the test gained was 2.117 (Fig. 4). The training/testing models
correctly predicted most of the test locations in all models and standard deviation
Variable contribution: The model indicated that the presence of members
of Sapindaceae was mainly associated with minimum temperature of the coldest
month (35.4%), precipitation of the coldest quarter (16.9%) and precipitation
of wettest month (14.9%) (Table 1).
||Species distribution: Training vs. test locations, Green:
The typical condition of where species are found, Blue: Low probability
of suitable condition, Red: High probability of suitable condition, White
dots: The presence locations used for training, while violet dots show test
||Jack-knife of regularized training gain for Sapindaceae
|| Sensitivity vs. specificity test on Sapindaceae
Maxents jackknife test of variable importance also suggested that the
variables which contributed the most to the model were good predictors as they
had the most information not contained in other variables and they could describe
the distribution of Sapindaceae on their own (without the other variables).
|| Variable contributions to the MAXENT model
|| Jack-knife of regularized test gain for Sapindaceae
Although members of the family Sapindaceae have been recorded to be widely
distributed in Africa, their occurrence is being threatened by high rate of
deforestation and agricultural practices leading to loss of forest and threatened
status of the family as recorded in the IUCN R.L. (2008). MAXENT, a machine-learning
method based on the principle of maximum entropy, are used to predict distribution
for each species of butterfly under current and future climates (Sofy
et al., 2012). Data from this model is used to test the efficiency
of the modeling program through the evaluation of the area under the Receiving
Operator Curve (AUC) (Elith et al., 2006; Phillips
et al., 2006).
Species distribution modelling with climatological data analysis using nineteen
(19) standard environmental variables from the WorldClim database revealed that
the species distribution is most affected by the minimum temperature of the
coldest month (bio 6) with a variable contribution of 35.4%. This is followed
by precipitation of coldest quarter (bio 19) and precipitation of the wettest
month (bio 13) with 16.9 and 14.9% contribution, respectively. These results
support the location of Sapindaceae mostly in the tropics rather than the temperate
Also, from the AUC curve generated, it can be concluded that this present model
is more reliable as it predicts the species distribution better than a typical
random model as shown in Fig. 3. This makes it useful for
future projections. Recommendations for different conservation strategies include
in situ conservation in Protected Areas; ex situ conservation
in seed banks; and conservation through sustainable utilization.
Special thanks to the Head of Molecular Systematics Laboratory, Department
of Botany, University of Lagos for providing enabling environment for this study.
Also, we are grateful to the Explorers Club USA for providing the exploration
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