SURENDRA, ANSHUMALI & SINGH 103

Tropical Ecology 50(1): 103-109, 2009 ISSN 0564-3295 © International Society for Tropical Ecology www.tropecol.com

Prediction of plant species diversity by log-linear and power function

models along the east coast of India

A.T. SURENDRA, ANSHUMALI*& GURDEEP SINGH

Department of Environmental Science and Engineering, Indian School of Mines University,

Dhanbad, Jharkhand 826004

Abstract: We examined the fitness and predictability of two non-asymptotic models viz., Log-Linear and Power Function for ‘species-area curve’, and the effect of sample location and scale on their regression derived coefficients (c and z) for estimating the tree species diversity in a dry tropical forest of India. The study area is located in Angul-Talcher-Meramundali region along the east coast of Orissa state. Among all the tree species, Shorea robusta was most abundant. This species had high values of frequency, density, basal area and Important Value Index (IVI). The results revealed that for six out of eight sites, which were examined, the Power Function Model relatively better fits the data set and yields better prediction of number of species (based on 1 ha data). The suitability of the model to fit the data was strongly influenced by the site, plot size and degree of disturbances.

Resumen: Examinamos la adecuación y la capacidad predictiva de dos modelos no asintóticos, el log-lineal y la función potencia, para una ‘curva especies-área’, y el efecto de la localización y la escala de la muestra sobre sus coeficientes de regresión derivados (c y z) para la estimación de la diversidad de especies arbóreas en un bosque tropical seco de la India. El área de estudio se localiza en la región Angul-Talcher-Meramundali a lo largo de la costa oriental del estado de Orissa. Entre todas las especies de árboles, Shorea robusta fue la más abundante. Esta especie tuvo valores altos de frecuencia, densidad, área basal e Índice de Valor de Importancia (IVI). Los resultados mostraron que para seis de los ocho sitios examinados, el modelo de función potencia permitió ajustar el conjunto de datos relativamente mejor e hizo una mejor predicción del número de especies (con base en datos de 1 ha). Lo adecuado del modelo para el ajuste de los datos estuvo fuertemente influenciado por el sitio, el tamaño de la parcela y el grado de disturbio.

Resumo: Examinou-se o ajustamento e o nível de predição de dois modelos não assintóticos viz., Log-linear e função de potência para a “curva de área-espécies”, e o efeito da localização da amostra e escala nos coeficientes de regressão derivados (c e z) para estima da diversidade das espécies arbóreas numa floresta tropical na Índia. A área estudada localizou-se na região de Angul-Talcher-Meramundali ao longo da costa oriental do Estado de Orissa. Entre todas as espécies, a Shorea robusta era a mais abundante. Esta espécie apresentava um elevado valor de frequência, densidade, área basal e Valor do Índice de Importância (IVI). Os resultados revelaram que para 6 estações em oito, que foram examinadas, o Modelo de Função de Potência é relativamente o que melhor se ajusta aos dados e gera a melhor predição do número de espécies (com base nos dados de 1 ha). A adequação do modelo para se ajustar aos dados estava fortemente influenciada pela estação, dimensão da parcela e graus de perturbação.

* Corresponding Author; e-mail: nshmali@yahoo.com

104 PREDICTION OF PLANT SPECIES DIVERSITY

Key words: Disturbance, east coast, Log-Linear, Power Function, species richness.

Introduction

The tropical forests cover only 7% of the earth’s land surface, yet they harbour more than half of the world species known so far (Wilson 1988). With the increase in human population and intensity of land use these forests are undergoing rapid fragmentation and degradation leading to loss of habitat and erosion of biodiversity (Laurence 1999; Pimm 1998). It has been estimated that these forests are disappearing at an alarming rate of 0.8 to 2% per year (May & Stumpf 2000). Hence quantification and documentation of biodiversity for extant tropical forests becomes a priority activity of the conservation agencies and ecologists as outlined in Rio Convention 1991. A number of authors have worked on the patterns of plant species diversity in the tropical forests of India (Jha et al. 2005; Parthasarathy 1999, 2001; Roy et al. 2002; Sagar et al. 2003). Parthasarathy (1999, 2001) studied the tree species diversity and distribution in tropical evergreen forests of the Western Ghats in South India and identified anthropogenic disturbance, seed predation and competitive interaction among trees as major factors influencing the diversity. Roy et al. (2002) and Jha et al. (2005) have reviewed the application of remote sensing and GIS for the assessment and monitoring of tropical forest resources.

Among the major components of biodiversity in the tropical forests, the tree layer is most significant as it influences the resource availability and habitat structure for almost all other species (Sagar et al. 2003). Several authors have studied the patterns of tree species diversity in the tropical forests of India. One of the important aspects of diversity investigated so far has been the species area relationship (SAR; Connor & McCoy 1979; Leps & Stursa 1989; May & Stumpf 2000). The SAR arises partly from the fact that habitat diversity is a function of area sampled. This relationship drives quite a few principles in Conservation Biology and often used to assess the effects of habitat fragmentation on diversity. It also forms basis for mathematical models that are

used for predicting species richness at various scales and also provide insights into community structure in ecological studies. Extrapolating species diversity using different models of SAR can yield different values for a given area. According to Colwell & Coddington (1995), different models can be more effective for different environments as the shape of a species area curve depends upon the pattern of relative abundances of the sampled species, although Soberon & Llorent (1993) argue for the a priori choice of models for species accumulation curves. Colwell & Coddington (1995) believe that the best approach is to test all reasonable models as rigorously as possible against known standards (complete or nearly complete inventories) for a wide variety of taxa and localities to avoid summary judgment based on a single data set.

In this study we examine the predictability of two non asymptotic models (Log-Linear and Power) taking field data from tropical dry deciduous forests along the east coast of India. Main objective of the study was to generate a baseline data on plant species richness, diversity and test the SAR in the region.

Materials and methods

The study was conducted in Angul, Talcher and Meramundali region of Orissa state (20° 00” 04’N to 24° 46” 26’ N latitudes and 085° 01” 40’ E to 085° 21” 04’ E longitudes) during 2006-2007. The study area is located close to east coast of India and represented by tropical dry deciduous forests (Champion & Seth 1968). A considerable part of the study area is the opencast coal mine, a massive industrial activity for the generation of thermal power that results into various environ-mental consequences including degradation of forest, soil erosion, loss of essential nutrients, metal pollution, noise pollution and air pollution. We selected eight sites for sampling the plant species diversity viz., Hindol, Meramundali, Barnab, Samal, Nandira, Manibandh, Kosala, and Takua (Fig. 1). The sites were selected on the basis

SURENDRA, ANSHUMALI & SINGH 105

of satellite imagery and field observations to represent the entire range of conditions mainly canopy cover, disturbance regime, and topography. The area experiences tropical monsoon climate with a mean annual rainfall of 1421 mm, of which about 70% is received from southwest monsoon during June-August. The altitude varies from 81 to 190 m asl. The soil is sandy loam in texture and reddish to dark grey in colour.

Eight one hectare plots were established one each at sampling sites. Each one hectare plot was grided into 100 contiguous subplots (each 10 m x10 m in size). All individual trees of ≥ 30 cm circumference over bark at breast height (1.37 m) were enumerated by species. Understanding the spatial distribution of plant species, one of the important tools for extrapolation is the species-area relationship (SAR), relating area ‘A’ to the number of species ‘S’ by using Arrhenius equation i.e. S = cAz, where, c and z are regression-derived

coefficients. The Log-Linear Model (S = a + b ln A where, a and b are regression-derived coefficients) was used for comparison. Species-area relationship was analyzed by plotting cumulative number of species as a function of plot size. To overcome the effects of sampling error, sample order was randomized. Random subsampling for quadrat data were used to pool the sequential accumulation and was repeated 100 times to generate species-area curve data. The site and scale differences were analyzed by ANOVA using the SPSS version-10.

Results and discussion

The forests of Angul, Talcher and Meramundali areas were dominated by a single species i.e., Shorea robusta (Table 1). A total of 3044 individual trees were recorded within 8 ha (8 sites of 1 ha each). Its dominance may have an

Fig. 1. Study area showing sampling sites: F1 – Hindol; F2 – Meramundali; F3 – Barnab; F4 – Samal; F5 – Nandira; F6 – Manibandh; F7 – Kosala; and F8 – Takua.

106 PREDICTION OF PLANT SPECIES DIVERSITY

impact on the occurrence of other species in the study area. The values of total number of species, total number of individuals and Shannon-Wiener Index are given in Table 1. The coefficients calculated for each of the one hectare plots are given in Table 2.

In the species area curves, the order in which samples are added affects the shape of the curve.

Sampling errors as well as heterogeneity among the sampling units are the two important reasons for variation in the shape of the species area curve (Crawley & Harral 2001; Leps & Stursa 1989; Weiher 1999). To remove these kinds of sampling errors the sampling order is recommended to be randomized. The total number of species at each site was divided by the total number of individuals

Table 1. Number of individuals of tree species at eight sites of dry tropical forest.

Species Name Site-1

(Hindol) Site-2

(Meramundali) Site-3

(Barnab) Site-4 (Samal)

Site-5 (Nandira)

Site-6 (Manibandh)

Site-7 (Kosala)

Site-8 (Takua)

Shorea robusta 447 432 568 506 517 574

Cassia fistula 55 48 51 62 210 131 64 76

Madhuca indica 38 26 59 78 24 71

Buchanania lanzan 81 19 143 117 51 267 187

Anacardium occidentale 157 4 43

Boswelia serrata 116 31 47 61 44 43 95

Beuta monosperma 92 29 27 41 34

Soymida fabrifuga 48 23 24 21 48 73

Tectona grandis 197 37 21

Terminalia chebula 71 24 17 27 38

Terminalia belarica 94 59 75 53

Terminalia arjuna 31 21

Diospyros melanoxylon 72 18 23 57

Terminalia tomentosa 8 4

Lagerstroemia parviflora 19

Others 43(30+13) 3

Total No. of Individuals 621 504 682 742 983 975 1194 1313

Total No of Species 4 7 8 10 6 11 13 13

Shannon Index 0.8879 1.7219 1.2438 2.0609 1.1806 1.6734 1.8442 1.7609

Table 2. Species area relationship of tree species (≥ 30 cm cbh) at different scales on eight sites in the study area.

Log-Linear Model Power Function Model

S=a+b lnA S=c AZ Sites

A B R2 S C Z R2 S

Site-1 -1.751 0.515 0.532 2.989 0.14 0.3599 0.5216 3.969

Site-2 -2.932 0.743 0.598 3.886 0.08 0.4626 0.6349 6.1577

Site-3 -2.816 0.738 0.641 3.981 0.08 0.4769 0.7211 6.9114

Site-4 -5.496 1.275 0.776 5.998 0.03 0.6683 0.8394 16.1949

Site-5 -3.558 0.860 0.584 4.361 0.06 0.5202 0.6459 7.6713

Site-6 -6.405 1.522 0.865 7.610 0.03 0.6458 0.9060 12.84

Site-7 -8.813 2.079 0.775 10.335 0.09 0.5403 0.8296 14.23

Site-8 -17.020 3.601 0.893 16.146

0.11 0.5655 0.9079 20.96

SURENDRA, ANSHUMALI & SINGH 107

at the same time to represent the number of species per individual. Species area relationship up to 1 ha has been shown to follow the Log-Linear Model which may be the result of a random placement of species throughout the area (Williams 1943). In our study both the equations were found to be useful in estimating the number of species for all the eight sites.

The results obtained from 1 ha equations indicate that sites Kosala and Takua are governed by the Log Linear equation. The R2 values obtained for these sites further support the utility of Log Linear Model. According to species pool hypothesis (Connor & McCoy 1979) a positive relationship between the coefficients c and z suggests that the small scale patterns are predictive of large scale patterns. Unlike the other sites, Kosala and Takua also favor the ecological significance of Power Function Model, because coefficients c and z are positively related. However, disturbed communities are more nearer to the Power Function Model, because coefficient z is regulated by the disturbances mainly selective felling of plant species. This indicates that in high disturbance regime, it is impossible to apply Log Linear Model due to poor species richness. The formation of new microclimate due to disturbances may be responsible for suitability of Power Function Model to predict the number of species irrespective of the area under study. The discrepancy percentage in some cases exceeds 45% due to the poor species richness in response to high disturbance.

Impact of coefficient z on species richness is more than that of coefficient c due to its exponential position in the Power equation. Moreover, z value is influenced by combined effects of the area sampled and the cumulative occurrence of new species in the study area. This study showed that z is a site as well as scale dependant coefficient and therefore, location of the sample (i.e., site) as well as the scale used need to be considered in extrapolation of species diversity from species area curve parameters. Similar observations are also made by Sagar et al. (2003). The poor relationship between coefficient c and number of species ha-1 (Fig. 2) indicates high degree of disturbance and the Power Function Model could explain prediction of species from small scale to large scale. This observation is further supported by the poor relationship between

Fig. 2. The relation between Number of species ha-1

(S1) and Coefficient c follows a line equation C= -0.323S1 + 11.241 with R2 = 0.0728 and P<0.05.

Fig. 3. The relation between Coefficient c and Shannon Index follows a line equation Shannon Index = -0.0522C + 1.9818 with R2 = 0.2686 and P<0.05.

Fig. 4. The relation between Number of species/ individual (S2) and Coefficient z follows a line equation Z=14.396S2 + 0.4316 with R2 = 0.0043 and P<0.05.

108 PREDICTION OF PLANT SPECIES DIVERSITY

Shannon-Wiener Index and coefficient c (Fig. 3) as well as coefficient z and number of species per individual (Fig. 4). This makes the Power Function Model more suitable for the prediction of number of species at six disturbed sites viz., Hindol, Meramundali, Barnab, Samal, Nandira and Manibandh.

Utility of a model generally depends on its ability to predict the number of species for a given area. If the area involved ranges from 0.01 to 107 km2 the species area relationship best fits the Power Function Model due to the addition of new ecological conditions and new habitats (Connor & McCoy 1979). In this study, the prediction of the number of species for 3 ha and 15 ha area, by Power Function Model, gave good results. These findings are further supported by other ecologists (Sagar et al. 2003). The Power equation over-

estimated the number of species of 15 ha area for six sites (Meramundali, Barnab, Nandira, Manibandh, Kosala, Takua) and also overestimated species number of 3 ha for four sites (Samal, Manibandh, Kosala, Takua) (Table 3). The Log Linear Model underestimated the species number of 15 ha area for all eight sites. In case of 3 ha area except for one site (Takua) this equation underestimated other sites. Though Log Linear Model predicted underestimation for 3 ha area, these predicted values were nearer to the actual ones. Even in the case of Power Function Model, five out of eight sites gave a nearer prediction to the 3 ha values.

In summary, species area relationship at Kosala and Takua favor the prediction by both Log Linear as well as Power Function Models due to trade off between number of species and the plot

Table 3. Predicted number of species and percentage discrepancy in the prediction by Power Function and Log Linear species area equations.

Percentage Discrepancy in Prediction Sites

Area (ha)

Projected Species Number (Log-Linear Model)

Projected Species Number (Power Function Model) Log-Linear Model Power Function Model

1 3 4 -25 0.00

3 4 6 -66 -50

Site 1

15 4 10 -78 -47.3

1 4 6 -42.8 -14

3 5 10 -54.54 -9.1

Site 2

15 6 22 -64.71 +29.4

1 4 7 -50 -12.5

3 5 12 -64.29 -14.3

Site 3

15 6 25 -68.42 +31.58

1 6 16 -40 +60

3 7 34 -75 +17.24

Site 4

15 9 47 -76 -8.5

1 4 8 -33 +33

3 5 14 -67 -6.7

Site 5

15 7 31 -70.8 +29.2

1 8 13 -27.27 +18.18

3 9 22 -52.63 +15.79

Site 6

15 12 41 -65.71 +14.63

1 10 14 -23.07 +7.69

3 13 28 -31.57 +47.36

Site 7

15 16 37 -33.3 +54.16

1 16 21 +23.07 +61.53

3 20 26 +11.11 +44.44

Site 8

15 26 44 -10.34 +51.72

SURENDRA, ANSHUMALI & SINGH 109

size of the sampled area. At the remaining six sites (i.e. Hindol, Meramundali, Barnab, Samal, Nandira and Manibandh) Power Function Model shows better prediction. This may be due to changes in microclimates in response to high disturbances and poor species richness.

Acknowledgement

We are grateful to Department of Environmental Science and Engineering, ISM University, Dhanbad, for providing the financial and logistic support to conduct the study in Angul-Talcher-Meramundali region, Orissa.

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