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- Materials and methods
Demographic data are useful for determining the effects of stochastic processes on abundance (Sibly & Hone 2002), the type and strength of regulation operating on a population (Sibly et al. 2005; Brook & Bradshaw 2006) and extinction risk faced by populations under various environmental scenarios (Fagan & Holmes 2006). However, demographic data alone cannot always divulge the mechanisms responsible for population trajectories, which is especially inconvenient when management actions are required to mitigate decline (McMahon et al. 2005). Population viability analyses (PVA) have provided a means to examine the relative contributions of competing factors on rates of population change (Cochran & Ellner 1992; Caswell 2001), and have given useful heuristic direction in managing the processes threatening species of conservation concern (Brook et al. 2002). Despite this advance, most PVA models rely on detailed life history data (Ellner et al. 2002) and researchers are forced to make profligate assumptions when such data are missing or based on small samples. As such, the estimation of high-precision demographic parameters such as age- or stage-specific survival and fertility rates should be a major aim of any study attempting to elucidate the mechanisms driving population decline and persistence.
The world's largest fish, the whale shark (Rhincodon typus Smith 1828), is also one of the least-studied and poorly understood shark species. No data on survival rates are available, and the reproductive data that do exist are based on extremely small sample sizes (Joung et al. 1996; Colman 1997). Even basic parameters such as growth, age at first reproduction, longevity and population size are unknown for the majority of populations. However, some data exist for growth rates of captive juveniles (Chang, Leu & Fang 1997), size and age at first reproduction (Pai, Nandakumar & Telang 1983; Satyanarayana Rao 1986; Wintner 2000), size distributions (Pravin 2000; Meekan et al. 2006) and abundance estimates for particular aggregations (Heyman et al. 2001; Meekan et al. 2006).
The predictable aggregation of whale sharks that occurs each year from March to June at Ningaloo Reef, Western Australia (Taylor 1996; Wilson, Taylor & Pearce 2001) has been the site of a large and lucrative eco-tourism industry where extensive photo-identification has been carried out over the last 15 years (Meekan et al. 2006). Recent studies have examined the potential to identify individuals over time using automated (Arzoumanian, Holmberg & Norman 2005) or manual (Meekan et al. 2006) approaches, with the mark–resight data used to predict the size of the super-population participating in the Ningaloo aggregation at 300–500 individuals (Meekan et al. 2006). The photo-identification data set can also be used within a capture–mark–recapture (CMR) modelling framework to estimate demographic parameters such as survival and capture probability.
Good estimates of whale shark demographic rates are essential components for assessing their conservation status. The species is listed as vulnerable according to World Conservation Union criteria (IUCN 2005) based on its rarity and reduction in catch rates in the regions where they are fished to supply meat throughout Asia (CITES 2002; IUCN 2005). Satellite tagging studies have verified that whale sharks attending the Ningaloo aggregation migrate regularly into South-east Asian waters (Wilson et al. 2006; J. Polovina et al. unpublished data), with anecdotal evidence suggesting that some tagged animals have fallen victim to fishing in this region (J. Polovina et al. unpublished data). Additionally, Meekan et al. (2006) reported a decline in the proportion of large whale sharks seen between 1992 and 2004, which may indicate human-mediated changes in the age-class distribution of this population.
In this study we use the photo-identification database described in Meekan et al. (2006) to estimate apparent survival and capture probabilities for the Ningaloo Reef aggregation. We assess variation in survival over time, between the sexes and as a function of an individual's total length. These survival estimates and other available demographic data reported in the literature are then incorporated into a series of age-classified Leslie matrix population models to assess the long-term persistence probability of the aggregation. Our overall aim is to provide a heuristic assessment of the possible population trajectory given our mark–recapture estimates of survival probability for this aggregation. This general template can be used to derive information on population assessments when demographic, abundance and other key data are missing for species of conservation concern.
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- Materials and methods
The paucity of data describing the variation in vital rates in species of conservation concern is a common problem for ecological modellers (Boyce 1992; Morris & Doak 2002). Indeed, obtaining estimates of vital rates and their corresponding variances may be difficult or impossible for many species, especially for long-lived marine vertebrates (Caughley 1994; Heppell, Caswell & Crowder 2000). As such, generalizations for predicting population persistence derived from few data or based on allometric or species-specific ecological characteristics are often sought (Beissinger & Westphal 1998; Belovsky et al. 2004; Brook, Traill & Bradshaw 2006). Although heuristically useful (Brook et al. 2002), matrix population models lacking quantitatively derived vital rates are subject to many assumptions that are difficult to test or validate. In the case of the relatively poorly studied whale shark, we have provided the first estimates of survival rates based on mark–recapture data. These estimates, combined within a series of deterministic Leslie matrix models have permitted the first quantitative appraisal of the projected long-term trends of this vulnerable population.
Although caution must be exercised in interpreting our population matrices (see below), the variants of the age-classified Leslie matrix models using different estimates of non-reproductive female and reproductive female survival and stage duration demonstrate the importance of considering biologically plausible covariates in survival analyses, especially for long-lived and slow-growing species. For example, ignoring the important effect of total length (size) on estimates of survival led to the conclusion of population increase (i.e. λ > 1), regardless of changes to age at first reproduction and frequency of reproduction. However, when we used the more parsimonious information-theoretical model predictions of length-varying survival, the importance of stage duration became much more apparent. With the shorter stage duration and age-specific survival estimates, most scenarios predicted a declining population (λ < 1), and doubling the interval between reproductive events resulted in an increased rate of decline.
Many elasmobranchs have a reproductive cycle of two years (Cortés 2002) and a few species breed more infrequently, every 3 years (Mollet et al. 2000; Cortés 2002). Although the reproduction interval of whale sharks is currently unknown, the precautionary principle for fisheries management (Caddy & Mahon 1995) suggests that assuming annual reproduction would be inappropriate for whale sharks. Reducing the breeding frequency further to once every 3 years, the estimates of λ under the most realistic scenario 4 (length-based survival to age 25) are further depressed to 0·9325 (age at first breeding = 13) and 0·9077 (age at first breeding = 25). Despite the severe lack of demographic data for this species (especially with respect to its reproductive capacity), the models that incorporated the most biologically realistic parameter estimates and assumptions support the conclusion of a declining population visiting Ningaloo Reef each year. However, this conclusion depends on some as yet untested assumptions. The duration of the non-reproductive stage and life span of the species are important determinants in the projections using length-varying estimates of survival. Of these two parameters, perhaps it is more tractable to collect information on growth rates that would verify the onset of reproduction.
The super-population of whale sharks participating in the Ningaloo Reef aggregation has been estimated at 300–500 individuals, of which approximately 16% were identified as female (74% male and 10% indeterminate gender) (Meekan et al. 2006). It should also be noted that pups and yearlings have never been observed at Ningaloo Reef, so pup production is likely to occur elsewhere. It is unknown whether the female component of the Ningaloo aggregation represents a small proportion of females that normally participate in a larger, sexually segregated female population that has yet to be identified. If there is an important sexual segregation of whale sharks, as has been documented for other elasmobranch species (Springer 1967; Klimley 1987; Sims, Nash & Morritt 2001; Sims 2006), then the small number of females observed at Ningaloo might not necessarily comprise the majority of the reproductively active females contributing new individuals to the aggregation. The embryo and juvenile sex ratio of many shark species does not depart from unity (Joung & Chen 1995; Chen, Liu & Chang 1997; Liu et al. 1999; Smale & Goosen 1999; Joung et al. 2005; Hazin et al. 2006), and Beckley et al. (1997) reported an equal sex ratio for stranded, immature whale sharks in South Africa. As such, we expect the low percentage (16%) of females at Ningaloo to be the result of sexual segregation, perhaps with many females within the super-population instead spending their time further north in Southeast Asian waters (Theberge & Dearden 2006), around the Indian coastline (Satyanarayana Rao 1986) or even in the vicinity of the Galápagos Islands (Stewart & Wilson 2005).
Our analyses also revealed some important aspects of the contribution of length- (and age-) specific survival rates to population rates of change. Elasticities from a mean matrix cannot by themselves predict accurately how λ fluctuates with variation in vital rates because of non-equality of change in these parameters, non-linearities in their relationships to λ and differences in the coefficients of variation among matrix elements (Mills, Doak & Wisdom 1999). Additionally, the reported elasticities were derived from deterministic matrices, which can be poor predictors of stochastic elasticities when the environment is extremely variable or includes catastrophic mortality events (Benton & Grant 1996). Although it has been shown previously that whale shark numbers at Ningaloo Reef fluctuate in response to environmental events such as El Niño–Southern Oscillation (ENSO; Wilson et al. 2001), we deliberately avoided using stochastic projections given the uncertainty associated with mean values of reproductive output, reproduction frequency and age at first reproduction.
With these caveats in mind, we found that the highest elasticities were for immature (i.e. non-reproductive) survival rates. This result agrees with reassessments of elasticities for most elasmobranch species (Mollet & Cailliet 2002, 2003). Even though others have suggested that elasmobranch population rates of change are more sensitive to adult (reproductive) survival (Colman 1997; Smith, Au & Show 1998; Walker 1998; Frisk, Miller & Fogarty 2001; Cortés 2002), the elasticities for many stage-classified models are calculated inappropriately (see Mollet & Cailliet 2003). When calculated correctly (and more easily) using age-classified Leslie matrix models, we found that immature female survival was a far more important determinant of the potential population rate of change for whale sharks; therefore, estimating this parameter precisely should be a prime area of research.
The limitation of producing robust estimates of the reproductive potential of whale sharks is problematic and may ultimately prevent the construction of reliable population viability analyses. There have been only nine ‘juveniles’ (0·55–0·93 total length) recorded for whale sharks (Colman 1997), some of which have been found in the stomach of other oceanic predators (blue shark Prionace glauca and blue marlin Makaira mazara) (Kukuyev 1996; Colman 1997). Nor have there ever been reports of individuals between 0·93 and 3·00 m total length, suggesting that there are either extremely high predation rates on small individuals or that reproduction occurs in the open ocean and is so dispersed that the probability of detecting young individuals is too low to quantify precisely. Another potential limitation is the probable density-related changes in vital rates used to parameterize the models, especially considering the pervasiveness of density dependence in nature (Brook & Bradshaw 2006). We deliberately avoided constructing hypothetical density-dependent relationships in our simple scenarios given the complete lack of associated data, but we acknowledge that persistence predictions and parameter elasticities are likely to vary with the inclusion of density dependence (Grant & Benton 2000; Drake 2005). However, future work on this aggregation and other whale shark populations should attempt to assess the degree to which vital rates are modified by density fluctuations. This may be achieved initially perhaps by examining the evidence for density dependence in phenomenological time series of relative abundance (e.g. sightings-per-unit-effort data; Brook & Bradshaw 2006).
Our analyses beg the following questions: (1) what is the state of the Ningaloo Reef whale shark population; and (2) can our analyses shed light on its persistence probability? Recent evidence from Ningaloo suggests that the population is comprised of a larger proportion of juveniles compared to previous decades (Meekan et al. 2006). However, severe declines have not been reported, so we believe that the real population trajectory lies somewhere between the extremes of our predictions. Additionally, an aggregation of juvenile whale sharks in nearby Thailand has declined recently by 96% (sightings per unit effort from 1992 to 2001) (Theberge & Dearden 2006). These observations, in combination with our results, lend credence to the hypothesis that the regional (Australasian) population of whale sharks is declining. As such, our results have several conservation implications for this and other large oceanic shark species. The wide dispersal range and sensitivity of population growth rates to minor variation in survival makes this species particularly vulnerable to anthropogenic sources of mortality (customary and commercial fishing). Non-reproductive whale sharks aggregating at Ningaloo travel long distances (1000 s km) to Southeast Asian waters (Wilson et al. 2006), where they are potentially susceptible to fishing pressure (Eckert et al. 2002; Polovina et al. unpublished data). The low population size (300–500 individuals; Meekan et al. 2006), the possibility of limited mixing (Wilson et al. 2006; Polovina et al. unpublished data) and the high elasticity of λ to non-reproductive female survival rates demonstrate the need for concerted conservation efforts to span national boundaries (Wilson et al. 2006).
The collection of mark–recapture databases for whale sharks has provided the first quantitative foundation for testing hypotheses regarding population persistence in one of the largest known aggregations of this species. Continued development of this database will be important for adjusting the predictions of matrix-based models, and will also provide a template for other large, oceanic marine vertebrates for which few demographic data exist. Our combination of standard CJS mark–recapture estimates of apparent survival and age-classified Leslie matrix models allowed us to assess the biological reality of the demographic rate estimates for whale sharks. In so doing, our study has highlighted the demographic processes that conservation practitioners should aim to maximize to increase the persistence probability of this, and other large elasmobranch species.