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Role of Exotic Vegetation in Coastal ProtectionAn Investigation into the Ecosystem Services of Casuarina in Odisha

Saudamini Das, Harpinder Sandhu

Casuarina (casuarina equisetifolia L) trees of Australian origin

have been planted along Indian coasts as bioshields

for protection from cyclones, tsunamis, tidal water

damage, and the like. This paper reviews the ecosystem

services provided by casuarina in Australia and finds no

mention that coastal protection has been the primary

role of this species. The data from the “super cyclone”

of October 1999 that devastated 12 districts of Odisha is

compared with the storm protection afforded by other

native species such as mangroves and mixed cashew

nut forests found along the coast. It is seen that while

native vegetation provided storm protection and saved

human lives, casuarina did not have such an impact. The

findings question the policy of planting casuarina as

storm buffers in all cyclone-prone areas irrespective of

the topography.

The data analysed in this paper was collected for the South Asian Network for Development and Environmental Economics (SANDEE)-sponsored project “Evaluation of Storm Protection Services of Mangroves: Analysis of Super Cyclone of October 1999”. The authors thank SANDEE and Kartik Shanker for many of his valuable suggestions and ideas. For collection of human casualty fi gures, they acknowledge the help of Rabinarayan Dash and Rajlakshmi Dash in Puri district and Jatindra Dash in Kendrapara district. They also thank Akalabya Das for GIS data and an anonymous referee for the suggestions received.

Saudamini Das (saudamini@iegindia.org) is at the Institute of Economic Growth, New Delhi and Harpinder Sandhu (harpinder.sandhu@fl inders.edu.au) is at Flinders University, Adelaide, Australia.

1 Introduction

An escalation in natural calamities affecting coastal areas has given rise to many discussions on coastal bio- shields in recent years (Danielsen et al 2005; FAO

2007). Several studies have examined the role of coastal vege-tation during natural disasters (Cochard et al 2008; Das 2011; Das and Vincent 2009; Feagin et al 2009; Iverson and Prasad 2007; Kerr and Baird 2007; Koch et al 2009). Big coasta l plan-tation schemes have been implemented by international organisations such as the Food and Agriculture Organisation (FAO) and the United Nations Development Programme (UNDP) (FAO 2007; Mukherjee et al 2009). There are concerns that the promotion of exotics as coastal buffers may lead to in-appropriate coastal development in some places (Feagin et al 2010a, 2010b). The planting of species alien to local ecosys-tems may reduce biodiversity and may not deliver the expect-ed results in terms of coastal protection.

Casuarina (casuarina equisetifolia L) of Australian origin is the single most dominant exotic species planted in coastal areas as a bioshield in India and many other south Asian countries (Feagin et al 2010b; Mohanty 1992; Orissa District Gazetteer, Puri 1977; Orissa District Gazetteer, Cuttack 1996). Planting casuarina as a coastal buffer is not a recent initiative in India. Many state governments have been establishing such planta-tions under coastal shelterbelt schemes since the 1920s, mainly to prevent erosion, provide wind breaks, stop tidal water from damaging agriculture, and protect lives and property from cyclones and tsunamis (Orissa District Gazetteer, Puri 1977; Orissa District Gazetteer, Cuttack 1996). However, no study has so far examined the protection these trees offer from natu-ral disasters such as tropical storms, which are the most fre-quent natural calamity along the Indian coast. Some studies have examined the role of casuarina during the 2004 Indian Ocean tsunami and found that they afforded little protection against it (Bhalla 2007; Feagin et al 2010b; Danielsen et al 2005; Tanaka et al 2007).

Several questions remain largely unanswered on the ecosys-tem services (Daily 1997) provided by casuarina, whether storm protection constitutes an important ecological service of this species, and how it fares vis-à-vis other native species it has substituted over the years. This paper partially addresses some of these concerns by fi rst reviewing the ecosystem serv-ices of casuarina for evidence of this species providing storm protection, especially in Australia, from where it originates, and then examining its storm protection services empirically.

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Pinyopusarerk and House 1993; Subbarao and Rodríguez- Barreuco 1995), though there is very limited academic research on these services in India. Casuarina provides one of the best fuelwoods in India by fuelwood value index (Puri et al 1994) and is grown extensively for pulp and paper manufacturing (Kulkarni 2005). Casuarina plantations are said to act as wind belts and anti-sea erosion belts along the sea shore in India, but there is evidence that they may have exacerbated erosion (Chaudhuri et al 2009).

Thus, in Australia, casuarina provides various economic benefi ts to society, but there is no evidence of protection from any coastal hazard, either from cyclonic wind, storm surges, or tsunami. The shelterbelt services are mainly for horticul-ture – implying the importance of casuarina as an inland spe-cies, not as littoral species. In India, casuarina species seem to be popular for their provisioning services rather than regulat-ing services and their role in protecting against natural calamities such as cyclones and tsunamis in vulnerable coastal areas is not supported by the literature, either in Australia or in India.

3 Coastal Vegetation and Plantations in Odisha

Odisha has a coastal stretch of 482 km, the Rushikulya river forming its southern boundary and the Subarnarekha river its northern boundary. The Orissa District Gazetteer (1990) describes the coastline to have a narrow belt of sand dunes towards the shore, covered with tidal forests to the north called the “little Sunderbans” (coastal areas of Kendrapara district; Figure 1a) and casuarina forests and plantations to the south (coastal areas of Puri district; Figure 1b, p 44).

3.1 Plantations in Kendrapara District

Tidal swamp forest covered most (around 80%) of the coast of Cuttack district before the 1950s (NF 45-14 Series U502, Cut-tack sheet from archives of the US Army Corps).2 Swamp for-ests are now found only in Kendrapara district (which includes the famous Bhitarkanika mangrove swamps, a Ramsar site), while they have been fully destroyed in the Jagatsinghpur sector (Figure 1a). Most coastal deforestation in the district is reported to have occurred due to anthropogenic pressure after

We examine its storm protection services in the form of preventing loss of human lives during a super cyclone that occurred in October 1999 in Odisha. We also compare the storm protection of casuarina to those by two other types of coastal forest, mangroves and non-mangrove mixed cashew nut forests, found along the same coastline.1 Mangroves and mixed forests are indigenous, whereas casuarina is an exotic species planted under the state government’s coastal shelter-belt plantation scheme. The empirical model is tested with data from Odisha’s two coastal districts of Kendrapara and Puri. These two districts which are cyclone-prone, were devastated by the super cyclone of October 1999, and have a long history of casuarina plantation under coastal shelterbelt plantation schemes.

The next section reviews the key ecosystem services provid-ed by casuarina based on studies in Australia and India. In Section 3, we present some details of the coastal vegetation and the history of casuarina plantations in our study area. Sec-tion 4 discusses the study sites and data analysis methods. In Section 5, we present the results of the empirical analysis, which is followed by a discussion and policy arguments for un-derstanding coastal protection better. We conclude with our fi ndings, which point to the need for reviewing the current policy of planting casuarina as storm buffers in all cyclone-prone areas.

2 Ecosystem Services Associated with Casuarina

We follow the Millennium Ecosystem Assessment (MEA) typo-logy to discuss the ecosystem services associated with casua-rina (MEA 2005). Studies based on the Australian experience report casuarina forests to be providing a range of regulating and other services. They regulate atmospheric carbon by stor-ing it as wood (AGO 2005; MIG 2008). They also retain soil within ecosystems and help regulate the hydrological fl ow of water (Docker and Hubble 2009). This vegetation helps to im-prove nutrient cycling and nitrogen fi xation (Russo 2005). They provide excellent shelterbelts to crops, especially horti-culture crops (Bird et al 2002). They are a salt-tolerant species (Niknam and McComb 2000; Sun and Dickinson 1995) and provide soil-borne disease suppression (Mele et al 2003), and a habitat for birds (Chapman and Paton 2007). Providing seeds for bird species (Cameron 2005) seems to be the only provi-sioning service by casuarina in Australia. The remnant vegeta-tion in Australia comprising casuarina woodlands is more than 43,000 years old, and it supports a wide range of verte-brate and invertebrate species (Black et al 2006).

Although casuarina has a long history of plantation in India and is being promoted commercially, it is little researched. It is reported to provide a key regulating service by fi xing atmos-pheric nitrogen (it forms root nodules in symbiotic association with soil Actinomycetesfrankia, which fi xes atmospheric nitro-gen) and regulating the nutrient content of the soil (NABARD 2011). It also seems to have the potential to provide a range of provisioning services such as timber, fuelwood, raw material for manufacturing charcoal and paper pulp, and so on (National Research Council 1984; NABARD 2011; Panda 2010;

Figure 1a: Map of Kendrapara District (Study Area A) with Mangrove and Casuarina Vegetation

MangrovesCasuarinaK boundaryVillages

Kendrapara district

Bay of Bengal

Dhamra river mouth

Mahanadi river mouth

North

20.40N

20.60N

20.79N

20.20NKilometre87.0E86.75E 0 2.5 5 10 20

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1948, particularly after the merger of princely states with the Indian union, which transferred forest ownership from feudal landowners to the state government. Further, the leasing of extensive forest areas to people from outside the state in-creased anthropogenic pressure on forests (Chadha and Kar 1999; Das 2007a; Orissa District Gazetteer, Cuttack 1996: 27). The existing Bhitarkanika mangrove region of Kendrapara has a total of 42 true mangroves, 40 mangrove associates and nine

obligate mangrove species (Thatoi and Biswal 2008). Some of the commonly found species are Heritierafomes, Heritieralit-toralis, Rhizophoramucronata, Sonneratiaapetala, Avicenni-aoffi cinalis, A Alba, Kandeliacandel, Phoenix paludosa and Tamarixindica. The occurrence of three species each of Avi-cennia, Heritiera, Sonneratia, Rhizophora and Xylocarpus and four species of Bruguira are signifi cant in the Bhitarkanika forest of Odisha (Mukherjee and Mukherjee 1978). The only dominant species of grass is Oryzacoarctata that occurs in fresh deposits of silt and helps in holding fl oating seeds that germinate and grow (Chaudhary 1990; Orissa District Gazet-teer, Cuttack 1996). This mangrove forest is largely natural (though plantations of mangroves have taken place recently in a few areas) and all tree species are native.

The state government introduced Casuarina equisetifolia to Kendrapara in 1974 under its coastal shelterbelt scheme after a severe cyclonic storm killed more than 10,000 people in 1971 (Orissa District Gazetteer, Cuttack 1996). The gazetteer states,

To protect coastal areas from the ravages of cyclonic storms which are quite frequent and consequent tidal waves and spreading of coastal sand dunes, the State Government took up afforestation within one kilometre belt of the sea coast as an anti-cyclonic measure from the year 1974 onwards under the Coastal Shelter Belt Afforestation Division with headquarters at Cuttack. Under the scheme, Casuarina, cashew and coconut plantations were raised all along the sea coast of the district and on the estuaries of rivers like Devi, Mahanadi, Brah-mani and their branches.

As the coastal topography of this area is swampy and unsuitable for casuarina, it survived only in a limited area (Mohanty 1992). Though the Kendrapara coast is largely cov-ered with mangroves, there are some coastal areas with casua-rina (Figure 1a).

3.2 Plantations in Puri DistrictPuri district has a sandy coast except at the Devi river mouth area at the north-east adjoining the present Jagatsinghpur dis-trict. The coastal fl ora of this district was described in the 1929 district Gazetteer and quoted in the Orissa District Gazetteer, Puri (1977: 27),

The sand hills stretching between the fertile rich plains and the sea constitute the only really distinctive feature of Puri from a botani-cal point of view. The summit of these ridges are for the most part covered with stiff thorny plants; and in some places, especially the black pagoda (Konark), the surface of the sand is covered by a thick network formed by the interlaced stalks of creeping convolvulus. To the north-east, where Devi (river) fi nds an outlet, there are numerous tidal creeks fringed with jungle; and the banks of the sluggish rivers and creeks, which wind through the swampy low-lying country near the sea, exhibit the vegetation of a mangrove variety.

Mangroves are missing from the mouth of the Devi river now, and much of the sand dunes are covered with casuarina introduced on this coastline in the 1920s. According to the gazetteer,

The original idea for planting Casuarinas to supply fuel wood to the town of Puri was conceived in 1913 by the then conservator of forest, Carter. In between 1915 and 1919, 300 hectares were planted. Subse-quently, it was felt necessary to raise a coastal belt plantation along the entire coast of the district to prevent sand dunes, cyclones and tidal water damaging agricultural fi eld and villages and 2,023 hec-tares were brought under it (Puri 1977: 37).

The casuarina plantations appear to have been established originally for fuelwood, but their objective and location seem to have changed over time. Today, casuarina is the most domi-nant species and in many places forms monocultures along the Puri coast (Figure 1b). The other littoral species found in this area are Pongamiapinnata, Pandanustectorius, Calophyllumi-nophyllum, and Calotropisgigantea (Orissa District Gazetteer 1990), which are native.

4 Study Sites and Methods

For this study, we selected a few blocks of two cyclone-prone districts in coastal Odisha – Kendrapara (study area A) and Puri (study area B) – to study the storm protection services of casuarina and other native coastal forests. These two districts have both on their coastline, and have a long history of casua-rina plantation under coastal shelterbelt plantation schemes. They were also both devastated by the super cyclone of October 1999.

Study area A comprised 428 villages in two coastal blocks, Mahakalpada and Rajnagar of Kendrapara district, where cas-uarina coexists with mangroves. Mangroves are native to this region and are the dominant species. Casuarina trees were in-troduced in 1974 and are found over a few patches (Figure 1a).

Study area B comprised 391 villages in three coastal blocks, Astarang, Kakatpur, and Gop of Puri district, where exotic cas-uarina is dominant along with a few patches of non-mangrove mixed forest (Figure 1b), which are natives.

The location of Kendrapara and Puri districts vis-à-vis cyclone landfall points were different as the storm made land-fall over Jagatsinghpur district, which is in between them. The average elevation of both these districts is less than 10 metres

Figure 1b: Map of Puri District (Study Area B) along with Casuarina Dense Forest, Casuarina Open Forest, and Mixed Forest Areas along the Coast

Casuarina denseCasuarina openMixed forestVillages

Puri district

19.91N

0 2.5 5 10 20

Bay of Bengal

Puri TownKilometre

86.25E 86.75E

North

19.78N

Devi river mouth

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variable to be clustered with a large number of zeros. The range of non-zero values varies from 1 to 21 in area A and from 1 to 10 in area B. Thus, a linear specifi cation or the use of ordi-nary least squares (OLS) estimates is not suitable for equation 1. The frequency distributions are also skewed, and indicate the non-suitability of OLS estimates for such non-normally distrib-uted variables. The use of different count models such as Pois-son and negative binomial, or zero-infl ated versions of these models are advised for such distributions (Green 2000). We assumed a Poisson specifi cation for equation 1 and, based on the specifi cation test results described later, settled for zero-infl ated negative binomial (ZINB) estimates of the coeffi -cients of the variables used (see Appendix for the ZINB model, p 50). Like Poisson, the ZINB model provides the vector of coef-fi cient estimates by estimating the mean function (λi) which is exponential in nature (see equation A2 in Appendix). We used STATA software to derive the ZINB estimates.

4.2 Data Sources

Data were collected from multiple sources. Death fi gures are from the offi ces of the emergency offi cers of Kendrapara and Puri districts; spatial information is from geographical infor-mation system (GIS) fi les of Odisha bought from a private source (Digital Cartography and Services, Bhubaneswar); forest cover information is from satellite images of 11 October

across the coast (District Planning Map for Puri, Cuttack, Jajpur, Kendrapara and Jagatsinghpur of Orissa, Reg No 112-NA/DP-1000–1000, National Atlas and Thematic Mapping Or-ganisation, Calcutta 2000), but being a mangrove habitat, the Kendrapara coast is likely to be more low lying. We analyse these two study areas separately and compare the storm pro-tection provided by casuarina in Kendrapara with that provid-ed by the mangroves there, and the storm protection provided by casuarina in Puri with that provided by the mixed cashew nut forests present there.

4.1 Methods

Storm protection by these coastal forests is examined in terms of their role in providing protection to human life during the October 1999 storm. Deaths occur during cyclones due to mul-tiple factors, such as cyclone impact, and are infl uenced by vil-lages’ spatial, geographical, and socio-economic features. Fol-lowing Das (2007b, 2011), we take human casualties due to cy-clones to depend on the following variables.

Di = f (Pi, width_foresti, dcoasti, dcypathi, driveri, droadi, vill_roadi, literatei. SCi, CLi, ALi, WHi, MWi, NWi) (Equation 1)

In equation 1, subscript “i” represents villages. Other varia-bles are explained in Table 1.3 Our dependent variable is death in the villages due to the storm; it is a non-negative count vari-able. The death statistics for the two study areas show this

Table 1: List of Variables, Definition and Expected Role during a CycloneName of Variables Definition and Measurement of Variables (km) Expected Roles in Cyclone Damage (Human Death) Function

D (Death) Number of human casualties in a village due to cyclone. Dependent variable.

P (population_1999) Total population of a village in 1999 Total damage will depend on the total population

width_mangrove Width of existing mangrove forest (in km) in between Captures the impact of mangrove vegetation on storm damage a village and the coast

width_casuarina Dummy variable for the presence of casuarina plantation in Captures the impact of casuarina trees and the topography of casuarina between a village and the coast for area A and width of plantation area on storm damage dense casuarina + 0.5* width of open casuarina (in km) in between the village and the coast for area B

width_mixed_ forest Width of mixed forest (in km) in between the village Captures the impact of mixed forest vegetation on storm damages and coast for area B

dcypath Minimum distance of village from the eye of the This is a proxy for cyclone impact at village level; the intensity of both wind and cyclone or from the cyclone path storm surge at any place in a cyclone depend on this variable

Dcoast Minimum distance of village from the coast Dcoast will capture the intensity or severity of storm surge in an interior village

Driver Minimum distance of village from a river Rivers carry away surge water and help in reducing the surge velocity to flooding; nearness to a river should reduce death

Droad Minimum distance of village from a motorable road Proximity to motorable road increases economic well-being by providing better accessibility, thus good health, good houses and less death

vill_road A dummy variable for the presence of a village The benefit of being close to a motorable road can come if there is a village road road that can connect it to the nearest motorable connecting it to the motorable road; villages with village roads can evacuate road (= 1 if village road exists, = 0 otherwise). easily and quickly during a disaster like a cyclone

Literate Percentage of literate people in a village They are expected to be better informed, take precautions during disasters, and suffer less

SC Percentage of scheduled caste people in a village Economically and socially most backward and expected to suffer more

CL Percentage of cultivators (doing cultivation in Economically well-off people in the study area as they are land owners and own land) in a village more such people should mean less death

AL Percentage of agricultural labourers in a village Economically very backward and more of them means more deaths

WH Percentage of people working in (own) household Economically well off, but have their sources of livelihood at home and may be industries in a village reluctant to evacuate before a cyclone and may die

MW Percentage of marginal workers (no fixed or regular Economically very poor and could be taking risks (more exposed) during storms jobs) in a village to earn some extra money as their jobs and income are uncertain. They are more

susceptible to face death*

NW Percentage of non-workers (dependents, such as Such people being dependents economically on earning family members, more children, non-working women and the elderly) of them mean economic backwardness and more loss during cyclones* We consider three categories of poor people separately in the model (scheduled caste, agricultural labour, and marginal workers) as there are social and psychological differences, as explained above, among these people.

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the primary census abstract of these districts for 1991 and 2001. The GIS data was geo-referenced at a 1:50000 scale to be used at the village level.

5 Results

5.1 Summary Statistics

Villages in study area A lay within 0.3 to 22.5 km from the coastline and within 7.4 to 72.82 km from the path of the eye of the cyclone. Villages in study area B lay within 0.37 and 18 km from the coast and 25 to 45 km from the cyclone’s path (Table 2).4

In area A, the width of forest varies from 0 to 10 km for man-groves and from 0 to 1 for casuarina (noted by use of the dum-my variable, as there was little variation in width of casuarina wherever it existed in study area A). For area B, the widths are 0 to 2.8 km for casuarina and 0 to 2.2 km for mixed forest. Area B seems to be comparatively better developed with a bet-ter road network – the average distance from a village to a road being 2.46 km compared to 3.61 km in area A – and has more educated people. The areas are similar occupation-wise, with most people working as cultivators, marginal workers, or agricultural labourers. Area B has a high percentage of scheduled caste people.

5.2 Overview of Deaths in the Study Areas

The study areas had 231 and 188 human deaths, respectively, the average deaths per village numbering 0.54 and 0.48 (Table 3). We categorised these villages into four groups – un-protected (not having any vegetation between the village and the coast); protected by only native vegetation; protected by only exotics; and protected by both, and compared the average deaths per village. In both areas, the villages in the fi rst cate-gory (unprotected ones) are control villages that refl ect the impact of the cyclone without any protection from coastal veg-etation. In study area A, the average deaths were 0.52 for villages without any type of vegetation, 0.28 for villages with only mangroves, 0.41 for villages with both mangrove and

Table 3: Overview of Deaths in Different Villages of Kendrapara (Study Area A) and Puri (Study Area B) Districts Source of Protection Criteria of Selecting Villages Number of Villages Average Deaths

Study area A 1 No protection Width of mangrove=0, casuarina dummy=0 198 0.52

2 Protection only from mangroves Width of mangrove>0, casuarina dummy=0 166 0.28

3 Protection only from casuarina Width of mangrove=0, casuarina dummy=1 23 2.74

4 Protection from both mangrove and casuarina Width of mangrove>0, casuarina dummy=1 41 0.41

Total number of villages 428 0.54

Study area B 1 No protection Width of casuarina=0, Width of mixed forest=0 118 0.66

2 Protection only from casuarina Width of casuarina>0, Width of mixed forest=0 230 0.4

3 Protection only from mixed forests Width of casuarina =0, Width of mixed forest>0 42 0.43

4 Protection from both mixed forests and casuarina Width of casuarina>0, Width of mixed forest>0 1 0

Total number of villages 391 0.48

Table 4: Model Specification Test Results for Kendrapara (Study Area A) and Puri (Study Area B) Districts to Determine the Suitability of the Zero-inflated Negative Binomial Regression ModelSpecification Tests Study Area A Study Area B

Likelihood-ratio test of alpha = 0 for testing suitability of negative binomial against simple Poisson Chibar2 (01) = 117.86, Chibar2 (01) = 36.35,

Pr> Chibar2 = 0.00 Pr>Chibar2 = 0.00

Vuong test of zero-inflated negative binomial vs standard negative binomial Z = 2.77, Z = 24.93, Pr> z = 0.00

Pr> z = 0.0028

1999 (Indian Remote Sensing Satellite IRS-1D, LISS III Pan cen-sor images with 23.9 metre resolution); cyclone details are from the Cyclone Warning Division, Indian Meteorology Department, New Delhi; and population variables are from

Table 2: Summary Statistics of Variables Used in Equation 1 and Explained in Table 1Variables Mean Standard Deviation Minimum Maximum

Study area A (428 villages)D 0.54 1.72 0.00 21.00

P 745.28 767.28 2.00 6098.00

width_mangrove 1.00 2.14 0.00 10.00

width_Casuarina 0.15 0.36 0.00 1.00

Dcoast 9.38 5.69 0.30 22.49

Dcypath 40.59 17.71 7.40 72.82

Driver 1.90 1.77 0.05 9.21

Droad 3.61 2.76 0.10 18.17

vill_road 0.6 0.49 0 1.00

Literate 0.57 0.13 0.00 1.00

SC 0.12 0.17 0.00 1.00

CL 0.17 0.13 0.00 1.00

AL 0.05 0.08 0.00 1.00

WH 0.00 0.01 0.00 0.08

MW 0.10 0.13 0.00 0.56

NW 0.62 0.18 0 0.99

Study area B (391 villages)D 0.48 1.18 0.00 10.00

P 821.57 859.62 11.00 7270.00

width_Casuarina 0.99 0.95 0.00 2.80

width_mixed forest 0.13 0.45 0.00 2.20

Dcoast 8.70 4.35 0.37 18.00

dcypath 33.89 4.11 25.00 45.00

Driver 1.68 1.33 0.02 7.10

Droad 2.46 2.09 0.06 10.65

vill_road 0.83 0.38 0 1.00

Literate 0.65 0.09 0.00 0.83

SC 0.21 0.20 0.00 1.00

CL 0.14 0.07 0.00 0.44

AL 0.06 0.06 0.00 0.40

WH 0.00 0.01 0.00 0.19

MW 0.07 0.08 0.00 0.41

NW 0.68 0.092 0.034 0.83

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casuarina, and 2.74 for villages with only casuarina. Thus, in this area, villages along barren coastlines without any trees had a high number of deaths, but the ones situated behind the casuarina belt had the highest number of deaths on average and the ones behind mangroves the lowest number. In study area B, deaths per village were 0.66 for unprotected villages and 0.43 for villages with only mixed forest, 0.4 for villages with only casuarina and zero for the only village with both casuarina and mixed forest. More deaths occurred in villages located along barren coasts, but there was no difference between villages protected by only casuarina and only mixed forests.

5.3 Drivers of Human Death Due to the Cyclone

We use the estimated coeffi cients to analyse the role of various factors, including different types of coastal vegetation, and use the marginal effect of this vegetation on deaths to measure their storm protection services.

As mentioned, we used count specifi cations starting with Poisson estimates and checked various specifi cation test results to select the most appropriate estimates. The likeli-hood-ratio test of alpha (= 1/ in model described in appendix) = 0 favoured negative binomial estimates compared to Poisson estimates and the Vuong statistic favoured ZINB to standard negative binomial estimates (Table 4, p 46). Thus,

ZINB estimates are used with clustered standard errors where villages are clustered according to the gram panchayats they belong to.

In the 428 villages of Mahakalpada and Rajnagar tahasils of Kendrapara district that constitute study area A, factors that signifi cantly affected the death toll were the width of man-grove, the presence of casuarina, the distance from the cyclone’s path, the village population, the percentage of liter-ate people, the number of scheduled caste people, and the per-centage of marginal workers (Table 5). Of these, the width of mangroves, the percentage of literates, and the scheduled caste population helped decrease deaths in villages whereas the others seem to have increased the death toll. The count models estimated being exponential, the coeffi cient values reported in Table 5 are not directly interpretable and one has to estimate the marginal effects or the elasticity of factors from these coeffi cients to determine the change in dependent variable for a unit change in each of the independent varia-bles. Here, we present the elasticity measures for comparing the marginal effects. The elasticity (column 3, Table 5) shows that proximity to the eye of the cyclone and percentage of liter-ate population in the villages had the highest impact on the death toll (but the result on literates is signifi cant only at a 10% level of signifi cance). Mangroves emerge as the second impor-tant factor, the results showing a 10% increase in the width of

mangroves reducing deaths in villages by 8%. The presence of casuarina, in contrast, is found to have aggravated the occurrence of deaths. The coeffi -cient is signifi cant with a positive sign, though the elasticity measure is found to be quite low, 1.2%.

In study area B, we fi nd that six fac-tors had a signifi cant effect on deaths, of which the width of mixed forests and the presence of village roads seem to have reduced deaths. Proxim-ity to the coast, proximity to the cy-clone path, village population and, surprisingly, the percentage of liter-ates increased the death toll (column 4, Table 5). The width of casuarina forests, though negative, is not signif-icant. The width of mixed forests seems to have a small effect on reduc-ing deaths as seen from the measure of elasticity, that is, a 10% increase in the width of mixed forest will de-crease average deaths in study area B by only 0.4%. However, this result is signifi cant only at 10% and thus is not so robust. Area B has plenty of casuarina forests, but the analysis did not support the hypothesis that cas-uarina trees provided any signifi cant protection during the super cyclone.

Table 5: Zero-inflated Negative Binomial Estimates of Regression Coefficients of Equation 1 for Kendrapara (Study Area A) and Puri (Study Area B) DistrictsIndependent Variables Study Area A Study Area B

Estimated Coefficients Measure of Elasticity Estimated Coefficients Measure of Elasticity

P 0.0005 *** (0.0001) 0.74*** 0.0002 *** (0.00005) 0.69***

width_mangrove -0.799 *** (0.233) -0.80*** — —

width_Casuarina 0.813 ** (0.359) 0.12** -0.109 (0.126) -0.11

width_mixed_ forest — — -0.302* (0.167) -0.04*

dcypath -0.031 *** (0.0101) -1.25*** -0.226*** (0.038) -7.65***

Dcoast -0.059 (0.038) -0.55 -0.173 *** (0.034) -1.51***

Driver -0.033 (0.100) -0.06 -0.036 (0.070) -0.06

Droad 0.037 (0.045) 0.13 0.018 (0.079) 0.04

vill_road -0.312 (0.243) -0.18 -0.537 ** (0.212) -0.45**

Literate -2.202 * (1.371) -1.25* 3.201 ** (1.581) 2.09**

SC -1.449 ** (0.7002) -0.17** 0.284 (0.441) 0.06

CL 0.130 (1.098) 0.02 3.380 (2.208) 0.46

AL -1.431 (2.227) -0.08 3.033 (2.113) 0.18

WH 5.921 (10.958) 0.02 -4.346 (12.112) -0.02

MW 2.387** (1.035) 0.25** 2.429 (2.371) 0.16

NW 0.623 (0.514) 0.38 4.028 ** (2.021) 2.72**

Constant 1.83 * (0.938) 3.647 (2.411)

Inflation variable

(population 1999) -0.007 *** (0.0019) -0.0012 *** (0.0004)

Constant 2.726 *** (0.646) 1.177 *** (0.336)

Lnalpha 0.034 (0.379) -14.455 (1.267)

Alpha 1.035 (0.393) 5.27e-07 (6.68e-07)

Number of observations 428 391

Non-zero observations 95 96

Zero observations 333 295

Wald Chi2 (15) 126.75 (P > Chi = 0.00) 371.12 , P > Chi2 = 0.00

The dependent variable is village-level human deaths due to the cyclone; ***, **, and * imply a level of significance at 1%, 5%, and 10% respectively; figures in parenthesis are the clustered standard errors.

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6 Discussion

6.1 Storm Protection Services of Casuarina

The restoration of coastal bioshields as buffers against natural calamities has been advocated after the Indian Ocean tsunami and been implemented by many governments in the region. In India, this concept has been promoted by both national and state governments and by donor agencies such as the UNEP and UNDP (Mukherjee et al 2009). Non-native trees such as casuarina have been planted along the eastern coast of India in long stretches to act as bioshields, displacing native vegeta-tion and ecosystems (Feagin et al 2010b). This is in addition to their plantation under the coastal shelterbelt scheme. These trees are also being planted in many parts of the world such as north Africa, west Africa, Yemen, Somalia, west Asia, and south China (National Research Council 1984) as shelterbelts and bioshields.

Although casuarina provides some provisioning services (for example, fuelwood) and regulating services (for example, soil erosion control, nutrient cycling) it does not seem to pro-vide effective storm protection, the purpose for which it has been planted in coastal areas of India. At least, the results from this study that examined the storm protection services of casuarina indicate so. Study area A in Kendrapara district, which is low lying and primarily a mangrove habitat, showed that the presence of mangroves helped reduce human deaths, but the presence of casuarina seemed to have increased mor-tality, though the marginal effect is quite low (Table 5). This may be attributed to casuarina wood hitting people after being carried by high velocity surges. Our fi ndings for area B in Puri district, which has a mainly sandy coast and plenty of casuarina, showed that mixed native forests (but not casuarina) may have saved lives. These mixed forests have some casuarina mixed with other indigenous species of trees. Thus, a mixed species casuarina plantation seems to have provided better storm protection than casuarina monoculture.

Coastal bioshields protect human lives by attenuating storm surge velocity, the biggest killer during tropical storms – coastal forests being permeable structures, water gets transmitted through them with reduced energy. The degree of reduction in water depth and velocity, however, depends on how much water is held back and the energy adsorbed by the coastal forest. Field observations and laboratory research conducted after the Indian Ocean tsunami established that several key parameters of vege-tation determine the magnitude of tsunami mitigation offered by various types of coastal forests (Alongi 2008; Forbes and Broad-head 2007). These parameters are vegetation features such as forest width, tree density, age, tree diameter, tree height, and species composition, and locational factors such as shore slope, bathymetry, spectral characteristics of incident waves and tidal stage on entering the forest, and so on. This study compares the storm protection of casuarina with that of native trees from the same study site, so locational factors affecting surge attenuation are the same for both casuarina and native trees and the differ-ence in storm protection services of different forests should be attributed to features of the vegetation.

Wave attenuation by coastal vegetation is a trade-off between stem diameter and spacing of trees. Young casuarina stands have a high density of small stems that can attenuate wave forces, but the stems break easily. When the stems get bigger, there is more space between the trees, which lowers the ability to attenuate waves (Khazai et al 2007; Tanaka et al 2007). In both our study areas, the casuarina trees are likely to have a bigger stem size with space in between as the planta-tions date to the early 1980s or earlier (the shelterbelt planta-tion scheme ended in 1984). Thus the ability of these trees to attenuate storm surges may have been low. Moreover, unlike the tsunami, the storm surge was accompanied by strong winds and, as reported by residents and others, the casuarina trees shattered into pieces during the cyclone.5 The debris may have been carried by the high velocity surge and hit people. This probably explains our empirical fi nding that mangroves and mixed forests were able to provide storm protection, while the casuarina forests could not.

One of the important ecosystem services of casuarina is reported to be acting as a wind break, but there seems to be a threshold to the wind velocity bearing capacity of these trees. “A wind strong enough to blow hats off can be stripped of its force by a belt of casuarina two or three deep, leaving the lee-ward air calmed” (National Research Council 1984). Being able to withstand a wind force that “blows hats off” is certainly no qualifi cation for being used as a cyclone buffer in places where very severe and super cyclones strike. Study area resi-dents reported that casuarina trees collapsed with the fi rst few gusts of wind during the super cyclone and did not provide any protection from the cyclonic wind. This questions the storm protection services of these trees. Complete destruction of casuarina was also reported in Cuddalur, Pondicherry, after cyclone Thane and there were reports of native trees such as tamarind, mango, neem, and banyan surviving better than fast-growing trees such as casuarina during the recent cyclone Phailin in Odisha.6

6.2 Policy Implications

Natural disasters such as storms, cyclones, and tsunamis cause severe damage to human lives and material assets in coastal areas (Das and Vincent 2009; Day et al 2007). Coast-al areas in India are governed by the revenue and forest departments of the states under the Coastal Regulation Zone (CRZ) notifi cation (Environment Protection Act, 1986). State governments identify, classify, and record all the CRZ areas in their territory and manage them under the umbrella of the Union Ministry of Environment and Forests. Coastal plan-ning activities after the tsunami of 2004 have focused main-ly on building massive coastal belts (shelterbelts) to act as bioshields (Selvam et al 2005). Both native (mangroves) and non-native species (casuarina) are being promoted as lead-ing candidates for bioshields, but casuarina monoculture is more widespread as mangrove habitats are limited. This is despite the fact that non-native species have impacts on coastal ecosystems and biodiversity (Feagin et al 2009; Shanker et al 2008).

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Economic & Political Weekly EPW JANUARY 4, 2014 vol xlIX no 1 49

The main benefi ts of exotic species like casuarina are fuel-wood, and wood for scaffolding, the paper industry, and the like (Kulkarni 2005; NABARD 2011). Previous studies do not provide any evidence of casuarina being used as storm protec-tion or bioshields. This study empirically assessed the effec-tiveness of casuarina in providing storm protection and did not fi nd any evidence of it. Therefore, the policy of using cas-uarina as bioshields should be adopted with caution. However, there is some evidence that it can be effectively used with local species (Narasimhan 2007; Rodrigues et al 2011). Our results for study area B, where mixed forests do have some casuarina, support this observation. Of course, there is a gap in under-standing the total economic value of casuarina, and more research is required to assess the value of its various ecosys-tem services. This will help policymakers design appropriate mechanisms to include casuarina as a source of livelihood and/or coastal bioshields when mixed with other species.

7 Conclusions

The study’s fi ndings question the policy of planting casuarina on vulnerable coastlines, particularly in mangrove habitats, for protection from cyclonic wind or storm surges. However,

casuarina mixed with native species of trees appears to pro-vide some level of protection during storms. It has been sug-gested by local communities and private landowners with commercial plantations that the mixed species approach, using native species along with casuarina as a tsunami buffer, may better serve local needs (Narasimhan 2007; Rodrigues et al 2011). This would certainly appear to be a better bioshield approach than having casuarina monoculture plantations on cyclone vulnerable coastlines, as demonstrated in this study. Casuarina also provides high-quality fuel wood and is a fast-growing tree of high economic value to the pulp industry. The economic potential can be realised in agro forestry areas pro-moted by the pulp industry (Kulkarni 2005) and the Indian Council for Forest Research and Education (Katwal et al 2003). However, such benefi ts are overshadowed by that they increase human deaths in coastal areas where they are being promoted as bioshields. Therefore, effective ecosystem-based management of coastal areas requires a signifi cant policy shift towards using casuarina along with native species or by replacing them with more wind-resistant varieties of trees or by not having them in low-lying coastal areas, especially mangrove habitats.

Notes

1 These mixed forests do have a few casuarina trees, but the dominant species are cashew nut and other local species.

2 The coastal areas of Cuttack district were bi-furcated into Jagatsinghpur and Kendrapara districts in 1993. Map of India and Pakistan 1-250,000 Tile NF 45-14 Cuttack.jpg, http://bit.ly/UuPEUS, viewed on 5 April 2013.

3 Das (2007a and 2011) estimated a similar func-tion, but that had more variables than the present case. This was because the data availa-ble on study area B was limited.

4 The cyclone’s eye is circular, and the radius of its eye was approximately 15 km (IMD 2000). We drew the cyclone path by joining the land-fall point to the centre of the eye area of the storm as it moved inland.

5 “Casuarina Trees Broke like Matchsticks dur-ing Super Cyclone”, available at http://www.livemint.com/Opinion/TwUKgDdliqqVsJctljh-WqO/Cyclone-Phailin-A-blundering-and-un-prepared-Odisha-administ.html

6 See http://www.rediff.com/news/report/odis-ha-lost-26-lakh-trees-after-cyclone-phailin/2 0131020.htm; and http://voices.yahoo.com/tree-preparation-hurricane-season-16604.html

References

AGO (2005): Australian Methodology for the Esti-mation of Greenhouse Gas Emissions and Sinks (Canberra: Australian Greenhouse Offi ce).

Alongi, D M (2008): “Mangrove Forests: Resilience, Protection from Tsunamis, and Responses to Global Climate Change”, Estuarine Coastal and Shelf Sciences, 76: 1-13.

Bhalla, R S (2007): “Do Bioshields Affect Tsunami Inundation?”, Current Science, 93 (6): 831-33.

Bird, P R, T T Jackson, G A Kearney and K W Wil-liams (2002): “Effect of Two Tree Windbreaks on Adjacent Pastures in South-western Victo-ria, Australia”, Australian Journal of Experi-mental Agriculture, 42: 809-30.

Black, M P, S D Mooney and H A Martin (2006): “A 43000-year Vegetation and Fire History from

Lake Baraba, New South Wales, Australia”, Quaternary Science Review, 25: 3003-16.

Cameron, M (2005): “Group Size and Feeding Rates of Glossy Black-Cockatoos in Central New South Wales”, Emu – Austral Ornithology, 105: 299-304.

Chadha, S and C S Kar (1999): Bhitarkanika, Myth and Reality (Dehradun: Natraj Publisher).

Chapman, T F and D C Paton (2007): “Casuarina Ecology: Factors Limiting Cone Production in the Drooping She-oak, Allo Casuarinaverticil-lata”, Australian Journal of Botany, 55: 171-77.

Chaudhary, B P (1990): “Bhitarkanika: Mangrove Swamps”, Journal of Environmental Science, 3 (1): 1-16.

Chaudhari, S, K V Devi Prasad and K Shanker (2009): Impact of Casuarina Plantations on Ol-ive Ridley Turtle Nesting along the Northern Tamil Nadu Coast, India (Bangalore: ATREE and Mamallapuram: MCBT).

Cochard, R, S L Ranamukhaarachchi, G P Shivakoti, O V Shipin, P J Edwards and K T Seeland (2008): “The 2004 Tsunami in Aceh and South-ern Thailand: A Review on Coastal Ecosys-tems, Wave Hazards and Vulnerability”, Per-spective in Plant Ecology, Evolutions and Sys-tematics, 10: 3-40.

Daily, G C (1997): Nature’s Services: Societal Dependence on Natural Ecosystems (Washing-ton DC: Island Press).

Danielsen, F, M K Sorensen, M F Olwig, V Selvam, F Parish, N D Burgess, T Hiraishi et al (2005): “The Asian Tsunami: A Protective Role for Coastal Vegetation”, Science, 320: 643.

Das, S (2007a): “Economic Valuation of a Selected Ecological Function – Storm Protection: A Case Study of Mangrove Forests of Orissa”, PhD thesis, University of Delhi.

– (2007b): “Storm Protection by Mangroves in Orissa: An Analysis of the 1999 Super Cyclone”, SANDEE Working Paper No 25-07, Kathmandu.

– (2011): “Examining the Storm Protection Serv-ices of Mangroves of Orissa during the 1999 Cyclone”, Economic & Political Weekly, 46 (24): 60-68.

Das, S and J R Vincent (2009): “Mangroves Pro-tected Villages and Reduced Death Toll during

Indian Super Cyclone”, proceedings of the US National Academy of Sciences, 106: 7357-60.

Day, J W, D F Boesch Jr, E J Clairain, G P Kemp, S B Laska, W J Mitsch, K O H Mashriqui et al (2007): “Restoration of the Mississippi Delta: Lessons from Hurricanes Katrina and Rita”, Science, 315: 1679-84.

Docker, B B and T C Hubble (2009): “Modelling the Distribution of Enhanced Soil Shear Strength beneath Riparian Trees of South-eastern Aus-tralia”, Ecological Engineering, 35: 921-34.

Environment Protection Act (1986): Gazette of India No 525, 12 November, Government of India.

FAO (2007): Coastal Protection in the Aftermath of the Indian Ocean Tsunami: What Role for For-ests and Trees? (Bangkok: Food and Agriculture Organisation).

Feagin, R A (2008): “Vegetation’s Role in Coastal Protection”, Science, 320: 176-77.

Feagin, R A, S M Lozada-Bernard, T M Ravens, I Möller, K M Yeager and A H Baird (2009): “Does Vegetation Prevent Wave Erosion of Salt Marsh Edges?” proceedings of the US National Academy of Sciences, 106: 10109-113.

Feagin, R A, W K Smith, N P Psuty, D R Young, M Luisa Martínez, G A Carter, K L Lucas et al (2010a): “Barrier Islands: Coupling Anthropo-genic Stability with Ecological Sustainability”, Journal of Coastal Research, 26: 987-92.

Feagin, R A, N Mukherjee, K Shanker, A H Baird, J Cinner, A M Kerr, N Koedam et al (2010b): “Shelter from Storm? Use and Misuse of Coast-al Vegetation Bioshields for Managing Natural Disasters”, Conservation Letters, 3: 1-11.

Forbes, K and J Broadhead (2007): The Role of Coastal Forests in the Mitigation of Tsunami Im-pacts, Food and Agriculture Organisation, Bangkok.

Green, W H (2000): Econometric Analysis, 4th edi-tion (Delhi: Pearson Education Asia).

IMD (2000): Tracks of Cyclones in Bay of Bengal and Arabian Sea, 1890-1990, New Delhi: Indian Meteorological Department.

Iverson, L R and M Prasad (2007): “Using Land-scape Analysis to Assess and Model Tsunami Damage in Aceh Province, Sumatra”, Land-scape Ecology, 22: 323-31.

SPECIAL ARTICLE

JANUARY 4, 2014 vol xlIX no 1 EPW Economic & Political Weekly50

Katwal, R P S, R K Srivastva, S Kumar and V Jeeva (2003): State of Forest Genetic Resources Con-servation and Management in India (Rome: Food and Agriculture Organisation).

Kerr, A M and A H Baird (2007): “Natural Barriers to Natural Disasters”, Bioscience, 57: 102-03.

Khazai, B, J C Ingram and D S Saah (2007): The Protective Role of Natural and Engineered Defence Systems in Coastal Hazards, Spatial Informatics Group, San Leandro, CA, Litera-ture review report prepared for Kaulanani Ur-ban and Community Forestry Programme of the Department of Land and Natural Resour-ces, US Department of Agriculture.

Koch, E W, E B Barbier, B R Silliman, D J Reed, G M E Perillo, S D Hacker, E E Granek et al (2009): “Non-linearity in Ecosystem Services: Temporal and Spatial Variability in Coastal Protection”, Frontiers in Ecological Environ-ment, 7: 29-37.

Kulkarni, H D (2005): “ITC’s Fuel wood Tree Im-provement Research and Its Role in Improve-ment of Socio-economic Status of Farmers through Social and Farm Forestry – A Case Study”, paper presented at the 17th Common-wealth Forestry Conference, Colombo.

McLachlan, G J and D Peel (2000): Finite Mixture Models (New York: John Wiley).

MEA (2005): Millennium Ecosystem Assessment Synthesis Report (Washington DC: Island Press).

Mele, P M, I A Yunusa, K B Kingston and M A Rab (2003): “Response of Soil Fertility Indices to a Short Phase of Australian Woody Species, Con-tinuous Annual Crop Rotations or a Permanent Pasture”, Soil and Tillage Research, 72: 21-30.

MIG (2008): “Criterion 5 – Maintenance of Forest Contribution to Global Carbon Cycles”, State of the Forests Report 2008, Montreal Process Im-plementation Group for Australia, Canberra.

Mohanty, N C (1992): Odisara Luna Jangal abauon Banyapranni (Mangroves of Orissa), Project Swarajya Publication (Bhubaneswar: Nayapalli) (in Oriya).

Mukherjee, N, M Balakrishnan and K Shanker (2009): “Bioshields and Ecological Restoration in Tsunami-affected Areas in India” in E Moks-ness, E Dahl and J Støttrup (ed.), Integrated Coastal Zone Management (Oxford: Wiley-Blackwell).

Mukherjee, B B and J Mukherjee (1978): “Man-groves of Sundarbans, India”, Phytomorpholo-gy, 28(2): 177-91.

NABARD (2011): “Model Bankable Projects”, avail-able at http://bit.ly/V3U1of

Narasimhan, D (2007): “Use of Coastal Shelterbelts along the East Coast of India with Special Ref-erence to Tamil Nadu and Andhra Pradesh”, available at http://bit.ly/SasSBr

National Research Council (1984): Casuarinas: Nitrogen-Fixing Trees for Adverse Sites (Wash-ington, DC: National Academy Press).

Niknam, S R and J McComb (2000): “Salt Tolerance Screening of Selected Australian Woody Spe-cies – A Review”, Forest Ecology and Manage-ment, 139: 1-19.

Orissa District Gazetteer (1990): Gazetteers Unit, Department of Revenue, Bhubaneswar, Gov-ernment of Orissa.

Orissa District Gazetteer, Cuttack (1996): Gazet-teers Unit, Department of Revenue, Bhu-baneswar, Government of Orissa.

Orissa District Gazetteer, Puri (1977): Gazetteers Unit, Department of Revenue, Bhubaneswar, Government of Orissa.

Panda, T (2010): “Role of Fungi in Litter Decompo-sition associated with Casuarina equisetifolia L Plantations in Coastal Sand Dunes, Orissa, India”, International Journal of Biodiversity Science, Ecosystems Services and Management, 6: 52-60.

Pinyopusarerk, K and A P N House (1993): Casuari-na: An Annotated Bibliography (Nairobi: Inter-national Centre for Research in Agroforestry).

Puri, S, S Singh and B Bhushan (1994): “Evaluation of Fuel Wood Quality of Indigenous and Exotic Tree Species of India’s Semiarid Region”, Agro-forestry Systems, 26: 123-30.

Rodrigues, R S, A Mascarenhas, T G Jagtap (2011): “An Evaluation of Flora from Coastal Sand Dunes of India: Rationale for Conservation and Management”, Ocean and Coastal Manage-ment, 54: 181-88.

Russo, R O (2005): “Nitrogen Fixing Trees with Actinorhiza in Forestry and Agro-forestry” in D Werner and W E Newton (ed), Nitrogen Fixa-tion in Agriculture, Forestry, Ecology, and the Environment (Netherlands: Springer):143-71.

Selvam, V, T Ravishankar, V M Karunagaran, R Ramasubramanian, P Eganathan and A K Parida (2005): Toolkit for Establishing Coastal Bioshield (Chennai: M S Swaminathan Rese-arch Foundation).

Shanker, K, N Namboothri, S Rodriguez and A Sridhar (2008): “Beyond the Tsunami: Social, Ecological and Policy Analyses of Coastal and Marine Systems on the Mainland Coast of India” in K Shanker, N Namboothri, S Rodriguez and A Sridhar (ed), Post Tsunami Environment Impact Report (Bangalore: ATREE).

Subbarao, N S and C Rodríguez-Barreuco (1995): Casuarinas (New Delhi: Oxford and IBH Pub-lishing).

Sun, D and G R Dickinson (1995): “Survival and Growth Response of a Number of Australian Tree Species Planted on a Saline Site in Tropi-cal North Australia”, Journal of Applied Ecolo-gy, 32: 817-26.

Tanaka N, Y Sogasaki, M I M Mowjood and K B S N Jinadasa (2007): “Coastal Vegetation and Their Functions in Tsunami: Experience of the Re-cent Indian Ocean Tsunami”, Landscape Ecolo-gy and Engineering, 3: 33-45.

Thatoi, H N and A K Biswal (2008): “Mangroves of Orissa Coast: Floral Diversity and Conserva-tion Status”, Special Habitats and Threatened Plants of India, Vol 11 No 1, available at http://bit.ly/VaNjCu

Appendix: The Zero-Inflated Negative Binomial (ZINB) ModelZero-infl ated negative binomial regression is suitable for modelling over-dispersed (vari-ance greater than mean) count variables with excessive zeros. This model assumes the excess zeros and the count values to be generated by two separate processes and models the excess zeros independently using a Logit specifi cation

whereas the count values are modelled by a negative binomial specifi cation (McLachlan and Peel 2000). The probability distribution of a ZINB variable takes the following form.

P(Y = y) =

,2,1,)1()1()(!)()1(

0,)1()1(

yyy

y

y ...

(Eq A1)

In the above expression, the left hand side is the probability of the count variable Y taking the value y. The right hand side divides the probability values into two parts, the upper part being the probability of zero values and the lower part the probability of non-zero val-ues. The parameters are explained below.: The proportion assigned to extra zeros, (): the proportion assigned to other count values which follow a negative binomial distribution and 0 1, : The Poisson mean that is assumed Gamma distributed with the over dispersion parameter in this model,: It quantifi es the amount of over dispersion such that over dispersion is zero if = or if (1/) = 0. If both and (1/) equals zero, then the ZINB distribution reduces to a Poisson dis-tribution.The ZINB regression model estimates the fol-lowing equations related to and :

niZti

X

ii

ii

,....2,1,)(log

)exp(

(Equation A2)

where X and Z are the vectors of variables ex-plaining the count and zero values, and are the vector of regression coeffi cients and i repre-sents the units. Thus, the ZINB model provides the vector of estimates of the coeffi cients of in-terest, β, by estimating the mean function (i) that is exponential in nature.We include all the variables described in equa-tion 1 in X vector, whereas only village popula-tion is used as the infl ation variable (vector Z) to explain the extra zeros. To identify the infl ation variable, we fi rst estimated a Logit function taking zero deaths equal to 1 and the non-zero deaths equal to zero and found village popula-tion the only signifi cant variable explaining the zero deaths.

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