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Keywords:

  • abundance estimation;
  • Chambal River;
  • closed-population models;
  • detection;
  • Gavialis gangeticus ;
  • individual identification;
  • noninvasive;
  • photographic capture–recapture;
  • program mark

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  1. India's Chambal River hosts the largest population of the critically endangered gharial. Boat-based daylight surveys to date only provide indices of relative abundance, without measures of survey bias or error. No attempt to quantify detection probabilities in these surveys has yet been made, and thus, absolute density estimates of this population remain unknown.
  2. We surveyed 75 km of the River Chambal and photographed individual gharials for capture–recapture analysis. The total sampling effort yielded 400 captures. Population closure was supported (z = −1·48, = 0·069), and closed-population models were used to estimate abundances.
  3. Models were selected using the Akaike Information Criterion (AIC) index of model fit. The best model estimated 231 ± 32 adult, 83 ± 23 subadult and 89 ± 19 juvenile gharials (Mean ± SE), respectively, while the model-averaged estimate was 220 ± 28 adult, 76 ± 16 subadults and 93 ± 16 juvenile gharials, respectively.
  4. The best model estimated absolute densities of 3·08 ± 0·43, 1·11 ± 0·3 and 1·19 ± 0·25 adult, subadult and juvenile gharials km−1, respectively, while the model-averaged estimate was 2·93 ± 0·37, 1·01 ± 0·21 and 1·24 ± 0·21 adult, subadult and juvenile gharials km−1, respectively, compared with relative densities of 0·94, 0·45 and 0·30 adult, subadult and juvenile gharials km−1, respectively, from boat-based daylight surveys. On the basis of our best model, we suggest a detection probability based correction factor of 3·27, 2·47 and 3·97 to boat-based daylight survey estimates of adult, subadult and juvenile gharials, respectively.
  5. Synthesis and applications. Used within the framework of capture–recapture analysis, photoidentification provides a reliable and noninvasive method of estimating population size and structure in crocodilians. We also opine that without determining the current status of gharials, highly intensive strategies, such as the egg-collection and rear-and-release programmes being implemented currently, initiated on the basis of underestimates of population sizes, are unwarranted and divert valuable conservation resources away from field-based protection measures, which are essential in the face of threats like hydrologic diversions, sand mining, fishing and bankside cultivation.

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The Gharial Gavialis gangeticus Gmelin (1789) is endemic to the Indian subcontinent and historically occurred in the Indus, Ganges, Mahanadi, Brahmaputra and Irrawaddy River systems (Smith 1939). In the 1970s, the gharial population was estimated at <200 (Whitaker et al. 1974), following which a conservation initiative involving creation of protected areas and rear-and-release programmes was established. The subsequent recovery of the population was short-lived with the cessation of central government funding. By 2006, the gharial population reportedly experienced a 58% drop in numbers across its range; the total breeding population was estimated to be about 200 individuals, largely restricted to scattered locations in India and Nepal, and the global population, as a single unit, met the International Union for Conservation of Nature criteria for Critically Endangered (Choudhury et al. 2007). The Chambal River in India has the single largest contiguous population, reportedly between 48% and 85% of the global population (Choudhury et al. 2007; Hussain 2009). The gharial is threatened by riverbank land-use changes, reduction in river flows, modification of river morphology, loss of nesting and basking sites, increased mortality in fishing nets and egg-collection for consumption (Whitaker & Members of the GMTF 2007; Hussain 2009); and is especially at risk from flow regulation because it prefers fast-flowing river habitats, which are prime sites for dams (Dudgeon 2000).

Boat-based daylight gharial surveys, to date, only provide indices of relative abundance, without measures of survey bias or error. No attempt to quantify detection probabilities in these surveys has yet been made, and thus, absolute density estimates of this population remain unknown. Despite the release of over 5000 gharials into various Indian rivers over the past few decades, as part of the rear-and-release programmes, only about 200 breeding adults reportedly still survive (Choudhury et al. 2007), and this poses serious questions about age- and size-specific survival rates. These programmes lacked monitoring of survival and dispersal of released animals and hence the efficacy of this programme could not be evaluated. In this scenario, the estimation of absolute abundances can help assess the restocking programme, provide information on the current status of gharials and inform future conservation strategies.

The various survey techniques used to ascertain crocodilian populations world-wide, reviewed by Magnuson (1982) and Bayliss (1987), vary greatly in terms of applicability, cost-effectiveness, the species involved, and the socio–political and administrative environment. Conventional mark–recapture techniques, besides being affected by the above, also suffer from tag loss and unequal catchability (Bayliss 1987); altered natural behaviour (Gauthier-Clerc et al. 2004) and ethical and welfare issues arising from the application of tags or marks (Wilson & McMahon 2006).

The use of natural markings to distinguish between individuals has been used for the identification of chimpanzees from facial characteristics (van Lawick-Goodall 1971); dolphins from dorsal fin cuts and nicks (Mazzoil et al. 2004); Nile crocodiles (Swanepoel 1996); African wild dogs from coat markings (Creel & Creel 1995); tigers from stripe patterns (Karanth 1995) and the use of pattern recognition software to identify cheetahs (Kelly 2001) and whale sharks (Arzoumanian, Holmberg & Norman 2005) from spot patterns. Individual identification, used within the framework of capture–recapture analysis, provides a statistical framework for estimating p (detection or capture probability) and quantities of biological interest such as population size (Nichols 1992).

Our objectives were to determine whether individual gharials were identifiable in the wild; and if individual identification could be used to estimate populations of wild crocodilians. Our approach fills an important gap in gharial monitoring and population estimation by combining photo-identification and capture–recapture techniques. This is the first such attempt for estimating crocodilian abundance in the wild. Our results shed new light on the gharials population status in the Chambal River and have major conservation implications in view of current management objectives and species recovery efforts.

Materials and methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Study area

The 960-km long River Chambal originates in the northern slopes of the Vindhyan escarpment and its basin is characterized by an undulating floodplain, gullies and ravines (Gopal & Srivastava 2008). The Chambal is a typical anterior-drainage pattern river (Mani 1974) and joins River Yamuna to form a part of the greater Gangetic drainage system. The area lies within the semi-arid zone of north-western India (Hussain 1999) and the vegetation consists of ravine thorn forest (Champion & Seth 1968). Much of the landscape has been influenced by a long history of human occupation (Kaul 1962). Evergreen riparian vegetation is completely absent, with only sparse ground cover along the severely eroded riverbanks and adjacent ravine lands (Hussain 1999). Lying between 24°55′ to 26°50′N and 75°34′ to 79°18′E, the 600 km National Chambal Sanctuary (NCS), was established in 1978 to conserve the gharial and the unique Chambal ecosystem.

The study area (Fig. 1) comprises a 75-km stretch of River Chambal, within the NCS, between 26°32′22″N, 77°45′30″E and 26°48′37″N, 78°10′18″E (Daburpur Ghat & Sukhdyan Pura Ghat, Madhya Pradesh, India), and includes the river mainstream, mid-channel islands, sand-bars, rocky outcrops and adjacent banks. The study area exhibits straight and meandering channels with a sinuosity index (meander ratio) of 1·47 and passes through the flat terrain of the Malwa Plateau with an average gradient of 0·21 m km−1 (Jain, Pushpendra & Singh 2007).

image

Figure 1. (a, b) Location of the study area, in north-central India, along the Rajasthan – Madhya Pradesh border. (c) Enlarged map of the study area, showing the 75 km extent of the Chambal River.

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In the dry season during the study (February to May 2010), river depth ranged from 0·02 to 18·6 m, while channel width ranged from 44 to 400 m. River discharge levels varied from 75 (February) to 23·9 (May) m3 s−1. The presence of rapids immediately upriver of the study area was a likely barrier to the movement of gharial during the dry season/low-flow conditions. For gharials, these conditions also resulted in the river being reduced to a series of functionally isolated pools with limited connectivity between them. Sand occupied 29·7% of the shoreline substratum, while gravel, clay–loam and sandstone–rock occupied 16·6%, 20·5% and 33·2% of this stretch, respectively. Anthropogenic influences observed during the study period were chiefly in the form of sand mining, bankside cultivation, domestic activities like bathing, defecation and water collection, gill net and hook-line fishing, livestock herding, grass-soaking, river crossing and temple fairs.

Field methods

The 75-km stretch was divided into 30 segments, each measuring 2·5 km, and a rowboat was used to cover the study area in a south-west to north-east (downstream) direction. Four sampling occasions were undertaken between February and May. Each segment was sampled once in February, March, April and May, that is, once in each sampling occasion. As virtually nothing is known about size-class-specific and seasonal home ranges of gharials, the basis for selecting segment lengths, conservatively, at 2·5 km were: (i) to allow segments to be grouped together in the future, in the light of new information on home-range sizes, (ii) to ensure that there were no ‘holes’ where an individual could have zero capture probability, that is, to cover potential spaces within the study area which may allow animals to escape detection and (iii) based on dry season (low-water level) conditions which restricted gharial movement along the length of the river. Boat survey and stationary bank observations at basking sites were used to collect data. The probability of basking gharial encounters was maximized by stationary bank counts at sites considered favourable based on the depth profile and basking site substrate. The segments were sampled during periods of maximum basking activity (between 1000 and 1700 h during winter; and between 0630–1030 h and 1500–1900 h during summer). At each of these basking sites, all basking individuals were photographed, their location and size-class noted and basking site characteristics measured.

Digiscoping was employed to observe, record and identify individual basking gharials. This was achieved using a 20–60 × 80 mm Spotting Scope (Bushnell Excursion FLP), coupled with a 6 megapixel digital camera (Casio EX-Z110, Casio, Tokyo, Japan) with 3× optical zoom. This was supported by a 9.1 megapixel digital camera with 20× optical zoom (Sony Cybershot DSC-HX1, Sony Corporation, Tokyo, Japan) and a 16 × 50 Porro-Prism Binoculars.

Other important considerations were to ensure that the sampling protocol met the basic assumptions of capture–recapture models. We used powerful optics to overcome any biases against animals in smaller size-classes, and hence, all individuals had an equal probability of being captured. Additionally, with little vegetation on the sand banks, there was no size-class-related visibility bias. Our study did not involve physical capture and restraint of gharial; and data collection through stationary bank observations were being carried out from concealed locations to minimize disturbance and observer influence. Therefore, capture did not affect subsequent recapture. Individual identification of gharial were made, primarily, by comparing natural permanent blotches and markings on the lateral scutes on the tail and by using additional cues like size classes, injuries and scars. These markings are not known to change and are individually unique. Marked animals were accorded no exclusive treatment, because captures and subsequent recaptures through photo-identification is a noninvasive technique. Hence, marked and unmarked individuals have the same probability of survival. As the timing of this study preceded the gharial hatching season, the likelihood of the demographic closure assumption being violated was low. Similarly, as the study coincided with the dry season (low-water level), the likelihood of animals moving in and out of the study area was low, supporting the assumption of geographic closure.

Individual identification

Individual identification of gharials were made, primarily, by comparing the shapes and positions of natural blotches and markings on the lateral scutes of the tail (Singh & Bustard 1976) and also by using additional cues like injuries and scars [Fig. 2; see Nair (2010) for more details]. Photographs lacking clarity were not used for identification.

image

Figure 2. Photoidentification of individual gharials by comparing the shapes and positions of natural blotches and markings on the lateral scutes of the tail.

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All basking individuals were photographed, given a unique identity number and geo-tagged using a Global Positioning System (Garmin GPS 72, Garmin, Olathe, Kansas, USA). Gharial size-classes were determined by calibrating natural objects or landscape features beforehand, or by setting up measured reference markers at basking sites and then estimating gharial lengths from photographs using the software ‘ImageJ’ (available at http://rsb.info.nih.gov/ij; developed by Wayne Rasband 2007; National Institutes of Health, Bethesda, MD, USA). Individuals <90 cm long were considered to be yearlings, those 90–180 cm as juveniles, those 180–300 cm as subadults and those >300 cm as adults.

Estimating population size

To estimate abundance using capture–recapture methods, a capture history matrix for each identified individual was constructed, in the standard ‘X’ matrix format (Otis et al. 1978) such that each entry takes the value ‘1’ for being captured in that particular sampling occasion, or the value ‘0’ for not being captured in the same sampling occasion. Thus, a capture history of 1001 means that a gharial was captured (photographed) on the first- and fourth-sampling occasions but not on the second and third occasions.

Capture–recapture data analysis and the estimation of abundance were carried out using program mark (White & Burnham 1999), by modelling for variations in capture (p) and recapture (c) probabilities, using closed-population models. Analysis of the capture–recapture data in program capture (Otis et al. 1978; Rexstad & Burnham 1991) supported population closure (= −1·48, = 0·069). Finite mixture models (Pledger 2000), as incorporated in program mark, employing two mixtures of P-values, were used to investigate the effects of individual heterogeneity, that is, individuals may have independent probabilities of being captured on account of their age, size, social status, etc. This is modelled by letting capture probabilities come from more than one capture probability distribution. There are three parameters with the two-distribution mixture model – the probability that a given capture probability will come from the first distribution (π), the mean capture probability of the first distribution and the mean capture probability of the second distribution (Pledger 2000).

Time was considered an important parameter because of the decreased intensity of basking, with the progress of the dry season, during the study (Nair 2010). Moreover, gharials are ‘thermoconformers’, avoiding extreme temperatures (Lang 1987a,b) and therefore, number of captures of basking animals are expected to vary from winter to summer. Individual heterogeneity (i.e. individuals have independent probabilities of capture) was also considered an important parameter because there are differences in accessibility to basking sites, because of social hierarchies; differences in individual responses to disturbances; and individual thermal behaviour is known to vary, influenced by a range of external (social mileau, climate, etc.) and internal (age, nutritional status, etc.) factors (Lang 1987a). Although size-related wariness is reported in crocodilians (Bayliss et al. 1986), we did not observe any obvious differences in wariness among individuals.

Models were selected using the Akaike Information Criterion (AIC) index of model fit. The model with the lowest Akaike Information Criterion corrected (AICc) score was considered the most parsimonious, thus minimizing estimate bias and optimizing precision (Burnham & Anderson 1998). When the AICc scores of the models were close, their Delta AICc (∆AICc) values were used to evaluate the model fit. Models with ∆AICc < 2 were considered good models (see Table 2), because these models are best supported by the data, while models with ∆AICc between 3 and 7 have moderate support and those >7–10 are relatively poor (Anderson & Burnham 1999; Burnham & Anderson 2002). Estimates of the derived parameters (Burnham & Anderson 2004), from models with good and moderate support (∆AICc < 7), were model averaged in program mark, to produce an estimate which is conditional on the results from the above selected models.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Abundance estimate

The total sampling effort yielded 400 captures (332 unique photographs; 159 only left flank, 134 only right flank and 39 both flanks). Only captures histories of ‘left flank’ and ‘both flank’ individuals were used in the analysis (Table 1). Photographs lacking clarity were not used for identification.

Table 1. Summary statistics for photographic capture–recapture data (left-side only + both sides) of 198 gharials (114 adults, 37 subadults and 47 juveniles) sampled in National Chambal Sanctuary during February–May 2010
 Sampling occasion
1234
Animals caught at occasion52696166
Newly caught at occasion52614639
Recaught at occasion081527
Total caught at end of occasion52113159198

Abundance estimation was carried out using program mark, using closed-population models and three groups (adults, subadults and juveniles). Models were selected using the AIC index of model fit (Table 2).

Table 2. Model selection by program mark for gharial capture–recapture data from the National Chambal Sanctuary during February–May 2010, using AICc, ∆AICc, AICc Weight, Model Likelihood and number of parameters (k)
ModelAICc∆ AICcAICc weightModel likelihood k
  1. Heterogeneity parameter, that is, probability of mixture (pi); varying across mixtures (g); varying over time (t); constant over time (.)

  2. AIC, Akaike Information Criterion; AICc, Akaike Information Criterion corrected; AICc Weight is the relative likelihood of each model.

{pi(g), p(g + t) = c(g + t), N(g)}−482·81650·00000·380511·000015
{pi(g), pa(t) = ca(t), pb(t) = cb(t), N(g)}−482·68980·12670·357150·938614
{pi(g), p(.) = c(.), N(g)}−481·39761·41890·187180·49194
{pi(g), p(t) = c(t), N(g)}−478·51944·29710·044390·11667
{pi(g), p(g) = c(g), N(g)}−477·55355·26300·027390·07206
{pi(g), p(g + t), c(g + t), N(g)}−473·15669·65990·003040·008039
{pi(g), pa(g + t) = ca(g + t), pb(g + t) = cb(g + t), N(g)}−468·852713·96380·000350·001030

Capture probability (p) and recapture probability (c) were modelled as c, because the study design did not modify gharial behaviour across the four occasions, that is, no behavioural effects. Capture probability (p) and recapture probability (c) were modelled either as varying over time (t), constant over time (.), varying across mixtures (g), or varying across both mixtures and time (g + t). The heterogeneity parameter, that is, probability of mixture (pi) and population size (N), was modelled across mixtures (g) to compute independent estimates for adults, subadults and juveniles.

Models with good and moderate support (ΔAICc < 7) were model averaged, to produce an estimate (Table 3) which is conditional on the results of the selected models. Abundance estimates from the most parsimonious models (low ∆AICc) exert most influence to the final estimate. The standard error (SE inline image) of the model-averaged estimate is a function of the SE inline image from each model and the extent of compatibility between model-specific estimates (Conn et al. 2006). The best model estimated 231 ± 32 adult, 83 ± 23 subadult and 89 ± 19 juvenile gharials respectively, while the model-averaged estimate was 220 ± 28 adult, 76 ± 16 subadult and 93 ± 16 juvenile gharials, respectively.

Table 3. Abundance estimates (inline image) and standard errors (SE) for models with good and moderate support (∆AICc < 7) and the model-averaged estimate, from gharial capture–recapture data from the National Chambal Sanctuary during February–May 2010
ModelSize-class inline image SE inline image
  1. AICc, Akaike Information Criterion corrected.

  2. All values rounded off to nearest integer.

{pi(g), p(g + t) = c(g + t), N(g)}Adult23132
Subadult8323
Juvenile8919
{pi(g), pa(t) = ca(t), pb(t) = cb(t), N(g)}Adult19623
Subadult6911
Juvenile9415
{pi(g), p(.) = c(.), N(g)}Adult23728
Subadult7712
Juvenile9714
{pi(g), p(t) = c(t), N(g)}Adult23627
Subadult7612
Juvenile9714
{pi(g), p(g) = c(g), N(g)}Adult23132
Subadult8424
Juvenile9621
Model-averaged estimateAdult22028
Subadult7616
Juvenile9316

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

To obtain life-history parameters of animals and to be able to determine critical resource requirements, it is essential to monitor individuals for extended periods of time. Individuals use habitats differently (e.g. Peake 1997), employ different behavioural strategies (e.g. Rohner 1996) and have different reproductive success (Newton 1995); and therefore, individuals may have different conservation values (McGregor & Peake 1998). Knowledge of the life histories of individual animals used in a new generation of predictive models (Sutherland 1995) may prove vital to the conservation of those species that are especially vulnerable to human disturbance and for which predictive measures may be of more importance than reactive measures (McGregor & Peake 1998).

Information on gharial distribution and abundance, across its range, is either scant or completely lacking and thus are an impediment to effectively understand the conservation needs of the species. Additionally, as gharials, like other large crocodilians (Brandt 1991), are slow-growing and long-lived, long-term studies on various demographic parameters are lacking. Photographic identification of individual gharials offer several advantages employed within the sampling framework of capture–recapture for estimating detection probabilities and population size. It will also enable regular monitoring of their critically endangered populations. Photo-identification has the advantages of being a noninvasive technique, with fewer economic and logistic constraints of capture, handling, capture and postcapture stress, tracking, altered behaviour, and capable of providing large sample sizes.

Conventional crocodilian boat surveys, relying on daylight or eye-shine counts, have been shown to underestimate population sizes because of size-related wariness and visibility biases (Bayliss et al. 1986). This, together with the fact that captive-reared gharial have been released on an ongoing basis in many Indian rivers, has made it difficult to assess the true status of gharials based on existing population counts (Choudhury et al. 2007). Previous surveys (Hussain 1999) have erroneously assumed that ‘during the census all gharials present were basking and that all the basking gharials were seen’. Consequently, protected–area managers have assumed that such survey figures are absolute estimates, and have resorted to restocking programmes to attain an arbitrary ‘desired’ population figure (Murthy 2004). In addition, boat-based daylight surveys and nest counts, both indices of relative abundance, indicated population increases of c. 8% for the period between 2007 and 2009 (R.K. Sharma, unpublished data; S.C. Bhadoria, N. Luikham & R.K. Sharma, unpublished data). However, this period witnessed a winter mass-mortality in 2007–2008, which claimed a significant proportion of adult and subadult gharials in the NCS (Whitaker, Basu & Huchzermeyer 2008), and yet, this decline and other gharial mortality events (R.S. Bhadauria, personal communication) remained undetected in these surveys. While relative density estimates have been used throughout the world to monitor trends in crocodilian populations (Bayliss 1987), they are often limited to the level of resolution they provide. Hence, we believe that absolute estimates at 3–5 year intervals, although time and resource intensive, have a crucial role in monitoring populations and informing conservation strategies, especially that of critically endangered species like the gharial.

Our study demonstrates the feasibility of combining photo-identification within the framework of capture–recapture analysis to estimate the abundance of crocodilians, in the wild. Although natural markings have been used to identify young individual gharials in captivity (Singh & Bustard 1976), this is the first attempt to do so in the wild. While Swanepoel (1996) used natural tail marks to individually identify Nile crocodiles in the wild, this method has never been adapted to estimate populations of crocodilians.

We estimated 231 ± 32 adult, 83 ± 23 subadult and 89 ± 19 juvenile gharials (best model) and 220 ± 28 adults, 76 ± 16 subadults and 93 ± 16 juveniles (model-averaged estimate), for our 75-km study area, while a 2009 survey based on boat-based daylight ‘total’ counts (S.C. Bhadoria, N. Luikham & R.K. Sharma, unpublished data), reported 102 adults, 49 subadults and 33 juveniles for a 109 km stretch of the NCS, within which our 75-km study area falls. Based on these values, we estimate absolute densities of adult, subadult and juvenile gharials at 3·08 ± 0·43, 1·11 ± 0·3 and 1·19 ± 0·25 per km, respectively (best model), and 2·93 ± 0·37, 1·01 ± 0·21 and 1·24 ± 0·21 per km, respectively (model-averaged estimate). On the other hand, Bhadoria, Luikham and Sharma estimate densities at 0·94, 0·45 and 0·30 adult, subadult and juvenile gharials per km, respectively. Although not accurate or precise, we suggest, on the basis of our best model, a detection probability based correction factor of 3·27, 2·47 and 3·97, to relative abundance estimates of adult, subadult and juvenile gharials, respectively, obtained from boat-based daylight surveys, until such time that better correction factors can be derived. Similarly, estimation of correction factors in other gharial habitats will enable a comparison between sites and a prioritization of conservation actions.

Adults (>300 cm), subadults (180–300 cm) and juveniles (90–180 cm) were determined on the basis of size, because demographic and reproductive variables in crocodilians are dependent on size rather than age (Nichols 1987). Yearlings (<90 cm) were not considered in this study because logistical and optical constraints impeded our ability to consistently obtain clear photographs. In robust and stable populations, between 30% and 50% of adult females are considered mature breeding animals (Gharial Species Recovery Plan, unpublished data; J. Lang, personal communication) and 14% of adults are considered mature breeding males (Hussain 1999). The high proportion of adults, as estimated in this study, was previously reported in the 2009 survey for gharial in the NCS (S.C. Bhadoria, N. Luikham & R.K. Sharma, unpublished data). Generally, in crocodile populations, depleted populations have population structures heavily biased towards juveniles, with few adults, and as they recover, this shifts to populations heavily biased towards adults and subadults, with low numbers of juveniles (Webb et al. 2000; Fukuda et al. 2011). These changes in population size structures are usually thought to reflect density-dependent changes in survivorship, which involve complex interactions between predation, cannibalism, social exclusion, and increased rates of both emigration and mortality (Messel et al. 1981; Webb & Manolis 1992; Fukuda et al. 2011). Among gharials, smaller size-classes are more susceptible to drowning in fishing nets (Sharma & Basu 2004) and also prone to being washed out into unsuitable habitats during flooding events because of the sudden discharge of impounded water (Choudhury et al. 2007), resulting in their decreased survival and recruitment. Additionally, juvenile and subadult size-classes have been accorded an upper size limit (180 and 300 cm respectively) and on reaching this, move onto the subsequent class, whereas the adult size-class does not have an upper limit and therefore, individuals in this class accumulate over time. These could be among the factors shaping the observed population structure. This size-class distribution (adults > juveniles > subadults) was previously reported in 2009 in the NCS (S.C. Bhadoria, N. Luikham & R.K. Sharma, unpublished data). However, a different size-class distribution (juveniles > subadults > adults) observed from 1988 to 1992 has been attributed to gharial reintroductions (Hussain 1999) at the height of the rear-and-release programme [juveniles/subadults/adults; 421 : 129 : 100 (1988), 522 : 221 : 97 (1990) and 469 : 296 : 109 (1992), respectively] (Hussain 1999).

One issue that needs to be addressed is the fact that only one side of the gharial is visible at any one time and that unless both sides are photographed, an individual's identity remains unknown when presented with only the unrecorded side. However, this can be dealt with by identifying basking sites in advance and positioning oneself before these animals emerge to bask. Thereafter, the sequence of events that accompany most basking episodes provide a way to address the issue. Gharials emerge head first onto basking sites, providing an opportunity to ‘capture’ one side and then re-orient themselves so as to face the river. This allows for the other side to be captured too. Other factors that could complicate the implementation of this technique are poor weather conditions, animals lying at odd angles or congregating in a way such that individuals are blocking the observers' view of other individuals, or disturbances that drive basking gharials into the water.

Almost all available population literature on gharials offer only the first level of resolution (relative abundance estimates), which help determine whether these populations are stable, increasing or decreasing (Webb & Smith 1987). However, we believe that our study is a transition to the second level of resolution, which quantifies the basic population parameters of population size, population age and sex structure, rates of reproduction, rates of survival and rates of immigration and emigration (Webb & Smith 1987). We also believe that this level of resolution is necessary in the current scenario – to assess the restocking programme, provide information on the current status of gharials and inform future conservation strategies, such as the recent Government of India initiative (Nair 2011). Multiseason capture–recapture data would additionally enable the estimation of various demographic parameters, vital rates and state variables (e.g. Karanth et al. 2006). We also believe that our approach could be further developed to include the spatial component of such data by using spatially explicit capture–recapture models (Borchers & Efford 2008).

Gharial conservation over the last three decades has centred around strategies like egg-collection and rear-and-release programmes, whose theoretical basis as a conservation tool is based largely on the assumption that natural breeding is incapable of sustaining wild populations and that captive-reared individuals have a better chance of survival and recruitment. Such measures, touted as a panacea to gharial conservation challenges, often attract favourable media attention and hence are popular management interventions. While these strategies hope to address hatchling and juvenile mortality, they do not address current threats to gharials that are primarily related to habitat loss, fishing-related mortality and disturbances from activities such as hydrologic diversions, sand mining, gill net and dynamite fishing and riverside cultivation. Our results demonstrate that present-day population assessments do not provide an accurate account of the gharials population status, and we, therefore, contend that highly intensive strategies like egg-collection and rear-and-release programmes, on the basis of underestimates of population sizes are unwarranted and divert valuable conservation resources away from field-based protection measures, which are essential in the face of the aforementioned threats. Further, the observed population structure (biased towards adults with low numbers of juveniles) indicates a recovering population (Webb et al. 2000; Fukuda et al. 2011), which also suggests that artificial supplementation is unnecessary for the Chambal population. Moreover, gharial reintroductions are poorly monitored, have low success rates (Ballouard et al. 2010) and have never re-established viable breeding populations in areas where they were locally extirpated, for all the currently recognized breeding sites had surviving populations when the restocking programmes were initiated (Choudhury et al. 2007). Future conservation and management efforts should be based on periodic and rigorous monitoring of demographic and reproductive parameters of gharial populations, and we suggest a reassessment of all reintroduction and restocking programmes.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

This study was funded by the Department of Science and Technology, Government of India. We thank National Centre for Biological Sciences, Tata Institute of Fundamental Research, Wildlife Conservation Society-India and Centre for Wildlife Studies for academic and logistic support. We thank the Rajasthan, Madhya Pradesh and Uttar Pradesh Forest Departments for research permissions. H.S. Pabla (IFS), S.C. Badoria, R.K. Sharma, Vijay Singh Tomar, Jyothi, Daulat Ram, Tejpal and Ramveer (all MPFD), Rajeev Tomar, Rakesh Vyas, Rakesh Singh, Ram Awatar, Kaptan Singh, Jagdish, Rajveer and Shyam Singh for field and logistical support; Romulus Whitaker, Jeffery Lang, Ajith Kumar, Devcharan Jathanna, Nachiket Kelkar, Varun Goswami, Umesh Srinivasan, Nishant M.S. and Barry Noon for all the discussions and critical inputs at different stages of the study; Krishnapriya Tamma for generously lending her camera and Madhukar Rao, for demonstrating the fundamentals of digiscoping.

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  6. Discussion
  7. Acknowledgements
  8. References
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