Nat Env Sci 2012 3(2): 7-14 Journal of Natural & Environmental Sciences www.academyjournals.net Original Article Distribution Modeling of Chrysophyllum Albidum G.Don in South-West Nigeria Paul Terwase LYAM1,2, Temitope Olabisi ADEYEMI 2, Oluwatoyin Temitayo OGUNDIPE2 Biotechnology Unit, National Center for Genetic Resources and Biotechnology, (NACGRAB) P.M.B 5382 Moor plantation, Ibadan, Nigeria. Molecular Systematic Laboratory, Department of Botany, University of Lagos, Akoka, Lagos, Nigeria. Received: 08.02.2012 Accepted: 10.05.2012 Published: 10.12.2012 Abstract Modeling studies were carried out on Chrysophyllum albidum, an important economic species in Nigeria. This study attempts to estimate the occurrence, and at the same time investigate the major climatic factors responsible for the geographical distribution of the species in SouthWest part of the country using Maximum Entropy (MaxEnt) program. A total of 43 geo-referenced records of C. albidum were assembled from various locations and climatic data were acquired from the Worldclim Database. The main variables that contributed towards predicting the species distribution were temperature seasonality, minimum temperature of coldest month and mean temperature of driest quarter. Results suggest that the distribution model was excellent with training AUC value of 0.999 and test AUC value of 1.000 confirming the wide distribution of C. albidum in South-West Nigeria. The testing models correctly predicted most of the test locations in all models and standard deviation is 0.000. In addition, the model indicated that the presence of C. albidum was mainly associated with minimum temperature of the coldest month (55.6%) and precipitation of the coldest quarter (18.6%). Recommendations for different conservation strategies include in situ conservation in Protected Areas; ex situ conservation in DNA banks; and conservation through ‘sustainable utilization’. Key words: Environmental Variables, GPS, MaxEnt, Species Distribution. * Corresponding Author: Paul T. Lyam, pauliam003@yahoo.com INTRODUCTION Chrysophyllum albidum G. Don is a dominant canopy tree of lowland mixed rain forest, sometimes riverine belonging to the family Sapotaceae. Chrysophyllum is a genus of about 70-80 species of tropical trees native to tropical regions throughout the world, with the greatest number of species in northern South America and some part of Africa (Arindam et al. 2010). C. albidum commonly known as the white star apple, nkalate’ or ‘mululu’(Luganda) is a tropical or near-tropical species found within elevation of about 1,400 ft (425 m) in the country. It is a tall tree of about 25-37 m in height and 1.5-2 m girth (Orwa et al. 2009). It bears simple elliptic leaves which are dark green on the adaxial surface and silvery on the abaxial surface and up to 12-30 cm long and 3.8-10 cm wide (Orwa et al. 2009). The bark is usually blackish or brownish-green and white gummy latex is produced in the slash. Flowers occur in dense clusters in the leaf axils bearing creamy white 5-lobed calyx of about 3 mm in length. The fruits are yellow when matured and slightly pointed at the tip, about 3.2 cm in diameter and contains 5 brown shiny seeds 1-1.5 x 2 cm. It is widely A©ademy Journals 2012 Nat Env Sci 2012 3(2): 7-14 Lyam et al. distributed from West Africa to Sudan with an eastern limit in Kakamega forest, Kenya and its natural occurrences have been reported in diverse ecozones in Nigeria, Uganda, Niger Republic, Cameroon and Cote d’lvoire. In Nigeria, the plant is distributed within the southern part of the country especially in the South-West and South-East and its cultivation is highly ecologically specific. This could be due to the adaptability of the plant to environmental and climatic conditions common to the ecological zone. The tree is not soil specific as it grows well in deep, rich earth, clayey loam, sand, or limestone, but it requires perfect drainage (Oyebade et al. 2011). In South-western Nigeria, the fruit is called “agbalumo” and popularly referred to as “udara” in Southeastern Nigeria (Adebayo et al. 2010). Generally, C. albidum is regarded as a plant with diverse uses and Oboh et al. 2009, reported that the plant is gradually becoming a crop of commercial value in Nigeria. C. albidum leaves are used for the management of infections and ailments since prehistoric times, although scientific evidence for its antimicrobial effect is inadequate. Recently, Duyilemi and Lawal (2009), investigated the antimicrobial and phytochemical screening of C. albidum leaves using water and methanolic extracts against local clinical bacteria isolates. Several authors have reported the nutritional importance of C. albidum, including CENRAD, (1999); Adisa (2000); Egunyomi, et al. (2005) and Akubugwo, (2007). In addition, the medicinal value of this crop has been reported by Adewisi, (1997); Amusa et al. (2003); Ugbogu and Akukwe, (2008); Duyilemi and Lawal (2009); Oboh et al. (2009); Adebayo et al. (2010) and Adewoye et al. (2010) while other economic importance including propagation and management has been reported by Aduradola et al. (2005); Egunyomi et al. (2005); Ogunsumi et al. (2005); Ige et al. (2007); Ehiagbonare et al. (2008); Ugbogu and Akukwe; (2008) and Arindam et al. (2010). Phenological activities in C. albidum are well observed; the plant is usually in leaf bloom at the onset of raining season achieving peak growth from August to October. Flowering is evident between early September and October while fruiting occurs during the months of December and April (Osabohien, et al. 2007). The introduction potential of C. albidum from the centre of diversity remains untapped. In order to unlock this diversity potential, it is paramount to study the geographical distribution as well as factors responsible for the distribution of the species. Species distribution models (SDMs) are used to estimate the relationship between species records at sites and the environmental and/or spatial characteristics of those sites (Franklin 2009). They are widely used for many purposes in biogeography, conservation biology and ecology (Elith and Leathwick 2009). In the last two decades, there have been many developments in the field of species distribution modeling, and multiple methods are now available. A major distinction among methods is the kind of species data they use (Elith et al. 2011). The desire to maximize the utility of such resources has spawned an array of SDM methods for modeling presence-only data. The Maximum Entropy method (MaxEnt) (Phillips et al. 2006; Phillips & Dudı′k 2008) is one such method and was adopted for this study. MaxEnt is a general-purpose machine learning method with a simple and precise mathematical formulation, and it has a number of aspects that make it well-suited for species distribution modeling (Philips et al. 2004 and 2006). MaxEnt has been shown to be a high-performing model-building program that can create useful models using small numbers of known locations (Elith et al. 2006 and Hernandez et al. 2006). It relies on an unbiased sample (as do all species modeling methods), so efforts in collecting a comprehensive set of presence records (cleaned for duplicates and errors) and dealing with biases are critical (Newbold 2010). However, methods are also implemented for dealing with biased species data (Dudı′k et al. 2006; Phillips et al. 2009; Elith et al. 2010). Despite the numerous economic importance of the white star apple, there is no existing literature on this aspect of research and to the actual extent of its distribution in Nigeria. The aim of this work is to carry out an exploration while estimate the occurrence of the white star apple and at the same time investigating major climatic factors responsible for the geographical distribution of the species in South-West Nigeria using Maximum entropy (MaxEnt) program. MATERIALS AND METHODS Species Data Leaf samples of C. albidum were collected from different locations within several states of the Southwest region of Nigeria (Figure 1) and GPS coordinates of collection points were recorded (Table 1). Figure 1 Map showing the southwest region of Nigeria (Area covered in this study). 8 A©ademy Journals 2012 Nat Env Sci 2012 3(2): 7-14 Lyam et al. Source: Adapted from Ogunsumi et al. 2005. Table 1 Showing sample with collection details for Chrysophyllum albidum Collection code Sample code GPS Coordinates Location PTL/Ca/NGB/001 PTL/Ca/NGB/001 PTL/Ca/YA/002 PTL/Ca/YA/001 N70 23.088; E0030 50.416 N60 30 981; E003 23.122 NACGRAB Admin block, Ibadan. Customs office, Akoka, Yaba, Lagos. PTL/Ca/UL/003 PTL/Ca/UL/001 N60 30.852; E0030 22.940 23/06/10 PTL/Ca/AP/004 PTL/Ca/AP/001 N70 22.971; E0030 50.491 Faculty of Education UNILAG Campus, Lagos state. Apata (In the wild), Ibadan PTL/Ca/NHT/005 PTL/Ca/NHT/001 N7024.292; E 0030 50.887 NIHORT field, Ibadan 18/06/10 PTL/Ca/NHT/006 PTL/Ca/NHT/002 N7024.293; E0030 .50.892 NIHORT field, Ibadan 18/06/10 PTL/Ca/NHT/007 PTL/Ca/NHT/003 N70 24.193; E0030 51. 025 PTL/Ca/NHT/008 PTL/Ca/NHT/009 PTL/Ca/NHT/010 PTL/Ca/NHT/004 PTL/Ca/NHT/005 PTL/Ca/NHT/006 Collection Date 18/06/10 23/06/10 18/06/10 NIHORT field Ibadan 18/06/10 0 0 NIHORT field Ibadan 18/06/10 0 0 NIHORT field Ibadan 18/06/10 0 0 NIHORT field Ibadan 18/06/10 0 0 N7 24.189; E003 51.035 N7 24.185; E003 51.043 N7 24.171; E003 51.051 PTL/Ca/NHT/011 PTL/Ca/NHT/007 N7 24.172; E003 51.055 NIHORT field Ibadan 18/06/10 PTL/Ca/NHT/012 PTL/Ca/NHT/008 N70 24.167; E0030 51.055 NIHORT field Ibadan 18/06/10 PTL/Ca/NHT/013 PTL/Ca/NHT/014 PTL/Ca/NGB/015 PTL/Ca/NGB/016 PTL/Ca/NGB/017 PTL/Ca/NGB/018 PTL/Ca/NGB/019 PTL/Ca/NGB/020 PTL/Ca/NGB/021 PTL/Ca/NGB/022 PTL/Ca/NHT/009 PTL/Ca/NHT/010 PTL/Ca/NGB/002 PTL/Ca/NGB/003 PTL/Ca/NGB/004 PTL/Ca/NGB/005 PTL/Ca/NGB/006 PTL/Ca/NGB/007 PTL/Ca/NGB/008 PTL/Ca/NGB/009 0 0 NIHORT field Ibadan 18/06/10 0 0 N7 24.175; E003 51.060 N7 24.171; E003 51.063 NIHORT field Ibadan 18/06/10 O NACGRAB field Oyo state 16/07/10 0 NACGRAB field Oyo state. 16/07/10 N7 23.19; E0030 50.30 N7 23. 24; E0030 50. 30 0 N7 23.25; E0030 50. 23. NACGRAB field Oyo state. 16/07/10 0 0 NACGRAB field Oyo state. 16/07/10 0 0 NACGRAB field Oyo state. 16/07/10 0 0 NACGRAB field Oyo state. 16/07/10 0 0 NACGRAB field Oyo state. 16/07/10 0 0 NACGRAB field Oyo state. 16/07/10 N7 23. 23; E003 50. 32. N7 23. 22; E003 50. 33. N7 23. 22; E003 50. 32. N7 23. 21; E003 50. 33. N7 23. 22; E003 50. 32. 0 PTL/Ca/NGB/023 PTL/Ca/NGB/010 N7 23 26; E003 50. 33. NACGRAB field Oyo state. 16/07/10 PTL/Ca/AKR/024 PTL/Ca/AKR/001 N7°15' E5°12' Akure F.R, Owena, Ondo state. 17/07/10 PTL/Ca/IFE/025 PTL/Ca/IFE/001 N7°14' 'E4°56' OAU, Ile-Ife, Osun state. 17/07/10 PTL/Ca/IKR/026 PTL/Ca/IKR/001 N07°11.835; E005°14.58′ Ikere, Ekiti state. 7/8/2010 PTL/Ca/JBD/027 PTL/Ca/JBD/001 N06°52′45; E003°56 03.9 PTL/Ca/UI/028 PTL/Ca/UI/029 PTL/Ca/OM/030 PTL/Ca/OM/031 PTL/Ca/UI/001 PTL/Ca/UI/002 PTL/Ca/OM/001 PTL/Ca/OM/002 Ijebu- Ode 7/8/2010 0 o UI campus (1) 25/10/10 0 o UI Campus (2) 25/10/10 0 0 Dan. O farms Omi-Adio, Ibadan. 9/11/2010 Omi- Adio, Ibadan. 9/11/2010 N07 27.456; E003 52.798 N07 27.422; E003 52.766 N07 23. 923; E003 46.584 0 0 N07 23.926; E003 46. 579. 9 A©ademy Journals 2012 Nat Env Sci 2012 3(2): 7-14 Lyam et al. PTL/Ca/OM/032 PTL/Ca/OM/003 N07 23.970; E003 46. 670. Omi-Adio, Ibadan –Abeokuta. 9/11/2010 PTL/Ca/MI/033 PTL/Ca/MI/001 N07030.669; E003053. 907. PTL/Ca/MI/034 PTL/Ca/MI/035 PTL/Ca/II/036 PTL/Ca/II/037 PTL/Ca/II/038 PTL/Ca/II/039 PTL/Ca/MI/002 PTL/Ca/MI/003 PTL/Ca/II/001 PTL/Ca/II/002 PTL/Ca/II/003 PTL/Ca/II/004 0 0 Akingbile, along IITA, Ibadan. 16/11/10 0 0 Shasha office, Ibadan. 16/11/10 0 0 Shasha, Idiori, Ibadan. 16/11/10 0 0 Olufon village, Lagelu LGA. 16/11/10 Abioye village, Osun state. 16/11/10 N07 29.242; E003 54. 843. N07 28.996; E003 54. 884. N07 26.177; E004 00.369. 0 N07 27.128; E004. 02.708. 0 N07 30.417; E004. 04.910. Alfa village, Iwo, Osun state. 16/11/10 0 0 Iwo, Osun state. 16/11/10 0 0 N07 37.474; E004 09. 284 PTL/Ca/II/040 PTL/Ca/II/005 N07 29.112; E004 03.365 Iyana offa, Oyo state. 16/11/10 PTL/Ca/UL/041 PTL/Ca/UL/002 N06031.133; E0030 24 .023 UNILAG botanical garden. 19/11/10 UNILAG guest house, Lagos state. 19/11/10 Moniya cemetery, Oyo state. 16/11/10 PTL/Ca/UL/042 PTL/Ca/MI/043 PTL/Ca/UL/003 PTL/Ca/MI/004 0 0 N06 31.302; E003 23. 967. 0 o N07 30.612; E003 53.879   Key Abbreviation. Full meaning and location. 1. NACGRAB. –National Centre for Genetic Resources and Biotechnology, Ibadan, Oyo state. 2. NIHORT. - National Horticultural Research Institute, Ibadan, Oyo state. 3. OAU. - Obafemi Awolowo University, Ile ife, Osun state. 4. UNILAG - University of lagos, lagos. 5. UI - University of Ibadan.     Bio14 (precipitation of driest month), Bio15 (precipitation seasonality (coefficient of variation), Bio16 ((precipitation of wettest quarter), Bio17 ((precipitation of driest quarter), Bio18 (precipitation of warmest quarter), Bio19 (precipitation of coldest quarter) Species Distribution Modelling For each location, GPS coordinates were recorded and subjected to modelling using the MaxEnt Programme as reported by 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). 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 ie.  Bio1 (annual mean temperature).  Bio2 (mean diurnal range (mean of monthly (max temp-min temp),  Bio3 (isothermality (p2/p7) (100),  Bio4 (temperature seasonality (standard deviation 100),  Bio5 (max temp of warmest month).  Bio6 (min temp of coldest month),  Bio7 (temp annual range (P5-P6),  Bio8 (mean temp of wettest quarter)  Bio9 (mean temp of driest quarter)  Bio10 (mean temp of warmest quarter)  Bio11 (mean temp of coldest quarter)  Bio12 (annual precipitation),  Bio13 (precipitation of wettest month), 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 C. albidum 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 10 A©ademy Journals 2012 Nat Env Sci 2012 3(2): 7-14 Lyam et al. 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. 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. the species, green indicating conditions typical of those where the species is found and lighter shades of blue indicating low predicted probability of suitable conditions. For C. albidum, suitable conditions are predicted to be highly probable through most of the forest areas of the southwest. This can be better appreciated on an enlarged portion of the region around the sampled area on the map. Model also shows areas with typical environmental conditions outside the sampled area. This suggests that C. albidum is in existence or may thrive well in some neighbouring countries. This finding is in agreement with the report of Bada (1997). The figure also indicates Omission rates and predicted areas, tested as a function of the cumulative threshold, and on the test records RESULTS Potential distribution of C. albidum in South West Nigeria. Figure 3: Sensitivity Vs specificity. The fit of the model to the testing data (indicated by the blue line) which is the real test of the models predictive power, is further towards the top left of the graph indicates how superior the model is at predicting the presences contained in the test sample of the data. Variable Contribution The model indicated that the presence of C. albidum was mainly associated with minimum temperature of the coldest month (55.6%) and precipitation of the coldest quarter (18.6%) (Table 3). MaxEnt’s 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 C. albidum on their own (without the other variables). Figure 2: A Maxent model of the distribution of C. albidum. This figure indicates the projection of the MaxEnt model for Chrysophyllum albidum onto the environmental variables. Warmer colors show areas with better predicted conditions. White dots show the presence locations used for training, while violet dots show test locations. The image uses colors to indicate predicted probability that conditions are suitable, with red indicating high probability of suitable conditions for 11 A©ademy Journals 2012 Nat Env Sci 2012 3(2): 7-14 Lyam et al. Table 3 Variable contributions to the MaxEnt model Variable % contribution Variable % contribution Bio1_a 0 Bio11_a 0.4 Bio2_a 2.4 Bio12_a 0 Bio3_a 1.1 Bio13_a 0 Bio4_a 0.3 Bio14_a 2.5 Bio5_a 0 Bio15_a 6.1 Bio6_a 55.6 Bio16_a 0 Bio7_a 1.8 Bio17_a 7.2 Bio8_a 1.8 Bio18_a 2.3 Bio9_a 0 Bio19_a 18.6 Bio10_a 0 Figure 4 Jacknife of regularize training gain for Chrysophyllum albidum. Figure 5 Jacknife of regularize test gain for Chrysophyllum albidum. The results of the jackknife test of variable importance revealed that the environmental variable with highest gain when used in isolation is bio6_a, (min temp of coldest month) which therefore appears to have the most useful information by itself. This variable (bio6_a) allows a reasonably good fit to the training data. 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 was 4.130 (Figue 4) while the AUC value was 0.999 (Figure 3). Test AUC values was higher i.e, 1.000 (Figure 3) while the test gained was 5.888 (Figure 5). The training/testing models correctly predicted most of the test locations in all models and standard deviation is 0.000. 12 A©ademy Journals 2012 Nat Env Sci 2012 3(2): 7-14 Lyam et al. rate per month, quarter and annually. Of all the variables under consideration, the minimum temperature of the coldest month displayed a higher percentage of contribution to the MaxEnt model having 55.6%. This was followed by the precipitation of the coldest quarter with 18.6% and then precipitation of the driest quarter with 7.2% (table 3). It then follows that the distribution of this species tends to be affected more by these 3 variables (Minimum temperature of the coldest month, precipitation of the coldest quarter and then little with precipitation of the driest quarter). In Nigeria, the coldest month in the year is usually between November and December while October – December is usually the coldest and driest quarter. These months coincides with the flowering period of most C albidum that were found on the field. It implies that these 3 variables favour the growth and distribution of this species than any of the other 16 variables. DISCUSSION Figure 1 indicates the geographical map of the sampled area. For C. albidum, suitable conditions are predicted to be highly probable through most of the forest areas of the southwest. This can be better appreciated on an enlarged portion of the MaxEnt model on figure 2. Model also shows areas with typical environmental conditions outside the sampled area. This suggests that C. albidum is in existence or may thrive well in some neighbouring countries as suggested by Bada (1997). In the analysis of variable contributions, the heuristic estimate of relative contributions of each environmental variables to the MaxEnt model revealed that the environmental layer with the highest contribution is bio6_a while 7 variables (bio1,5,9,10,12,13 and 16) are with no significant contribution (table 3). The results of the jackknife test of variable importance revealed that the environmental variable with highest gain when used in isolation is bio6_a, (min temp of coldest month) which therefore appears to have the most useful information by itself. This variable (bio6_a) allows a reasonably good fit to the training data (Figure 5). The environmental variable that decreases the gain the most when it is omitted is bio19_a, (precipitation of coldest quarter) which therefore appears to have the most information that is not present in the other variables. In order words this variable achieves almost no gain, so it is not (by itself) useful for estimating the distribution of C. albidum. In addition, the response curves (figures not included) displayed how each environmental variable affects the Max-Ent prediction. The curves show how the logistic prediction changes as each environmental variable is varied, keeping all other environmental variables at their average sample value. In other words, the curves show the marginal effect of changing exactly one variable whereas the model may take advantage of sets of variables changing together. The marginal effect of changing exactly one variable revealed that the probability of occurrence of the sample increases with varying bio 4_a, bio6_a and bio 9_a; decreases with varying bio2_a, bio3_a, bio7_a, bio_14, bio15_a, bio17_a and remains constant with varying bio1_a, bio5_a, bio8_a, bio10_a, bio11_a, bio12_a, bio13_a, bio16_a. In contrast to the marginal response curves for effect of changing exactly one environmental variable (figures not included), a MaxEnt model created using only the corresponding variable reflects the dependence of predicted suitability both on the selected variable and on dependencies induced by correlations between the selected variable and other variables. Furthermore, the study has revealed that the distribution of C. albidum in the South/west Nigeria is largely affected by changes in temperature seasonality and mean precipitation CONCLUSION Based on the findings of this study especially regarding the status, importance, relevance and geographical distribution of the white star apple in Nigeria, we recommend that further research and development, targeted at conservation; both in situ, ex-situ and molecular characterization should be exploited for the sustainable utility of the species. 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