Effects of yearling, juvenile and adult survival on reef manta ray (Manta alfredi) demography

Abstract

BackgroundThe trade in manta ray gill plates has considerably increased over the last two decades. The resulting increases in ray mortality, in addition to mortality caused by by-catch, has caused many ray populations to decrease in size. The aim of this study was to ascertain how yearling and juvenile growth and survival, and adult survival and reproduction affect reef manta ray (Manta alfredi) population change, to increase our understanding of manta ray demography and thereby improve conservation research and measures for these fish.MethodsWe developed a population projection model for reef manta rays, and used published life history data on yearling and juvenile growth and adult reproduction to parameterise the model. Because little is known about reef manta ray yearling and juvenile survival, we conducted our analyses using a range of plausible survival rate values for yearlings, juveniles and adults.ResultsThe model accurately captured observed variation in population growth rate, lifetime reproductive success and cohort generation time in different reef manta ray populations. Our demographic analyses revealed a range of population consequences in response to variation in demographic rates. For example, an increase in yearling or adult survival rates always elicited greater responses in population growth rate, lifetime reproductive success and cohort generation time than the same increase in juvenile survival rate. The population growth rate increased linearly, but lifetime reproductive success and cohort generation time increased at an accelerating rate with increasing yearling or adult survival rates. Hence, even a small increase in survival rate could increase lifetime reproductive success by one pup, and cohort generation time by several years. Elasticity analyses revealed that, depending on survival rate values of all life stages, the population growth rate is either most sensitive to changes in the rate with which juveniles survive but stay juveniles (i.e., do not mature into adults) or to changes in adult survival rate. However, when assessing these results against estimates on population growth and adult survival rates for populations off the coasts of Mozambique and Japan, we found that the population growth rate is predicted to be always most sensitive to changes in the adult survival rate.DiscussionIt is important to gain an in-depth understanding of reef manta ray life histories, particularly of yearling and adult survival rates, as these can influence reef manta ray population dynamics in a variety of ways. For declining populations in particular, it is crucial to know which life stage should be targeted for their conservation. For one such declining population off the coast of Mozambique, adult annual survival rate has the greatest effect on population growth, and by increasing adult survival by protecting adult aggregation sites, this population’s decline could be halted or even reversed.

Use of underwater contactless ultrasonography to elucidate the internal anatomy and reproductive activity of manta and devil rays (family: Mobulidae)

“…These life‐history characteristics are essential parameters needed to design reliable population models. Such models ultimately provide important information on the trend of a population and can predict population vulnerability to natural and anthropogenic stressors to help define appropriate conservation management strategies, necessary to guarantee the long‐term survival of a population or a species (Dulvy et al ., 2014b; Smallegange et al ., 2016). An example of the application of such information is the use of a variant of the classic Euler–Lotka demographic model to show that M. alfredi has one of the lowest maximum intrinsic rates of population growth ( r max ) of any known elasmobranch (Dulvy et al ., 2014a).…”

“…Among the most widespread uses of anatomical investigations in fish are the determination of sex and maturity status (Montealegre‐Quijano et al ., 2014; Stehmann, 2002) and the study of reproductive cyclicity (Pierce et al ., 2009; Sulikowski et al ., 2007; Waltrick et al ., 2012). Gaining accurate knowledge of manta and devil rays' reproductive activity is a crucial factor in designing reliable population vulnerability models (Dulvy et al ., 2014a), which are the fundamental tools upon which conservation management strategies are designed and improved (Brault & Caswell, 1993; Cortés, 2002; Heppell et al ., 1996; Smallegange et al ., 2016). In addition, improved understanding of manta and devil rays' reproductive potential and output is critical to determining the factors limiting population growth (Smallegange et al ., 2016) and to quantifying and effectively mitigating the impact of natural and anthropogenic pressures on these species (Powles et al ., 2000).…”

Opportunistic sightings of manta rays on Brazil's Amazon Coast

“…Most individuals sighted in the BAC were relatively small‐sized manta rays (Table 1), most likely juveniles, a life stage in which survival is key for population maintenance (Smallegange et al, 2016). Juvenile mantas, along with juvenile elasmobranchs in general, demonstrate site fidelity (Bucair, Venables, et al, 2021; Stewart, Nuttall, et al, 2018), suggesting that area‐based measures of conservation may be an effective strategy for population maintenance (Flowers et al, 2016; Werner, 2020).…”

Unusually Paced Life History Strategies of Marine Megafauna Drive Atypical Sensitivities to Environmental Variability

“…In case of M. alfredi, we calculated juvenile mortality rate µ i from its yearling survival rate (P y = 0.63 yr −1 ), its juvenile survival rate (P j = 0.95 yr −1 ) and its average age at maturity (α = 10; Kashiwagi, 2014;Smallegange et al, 2016): Table 2), assuming P = e −µ (Caswell, 2001). In case of M. birostris, we were unable to find estimates for µ i .…”

“…In case of M. birostris, we were unable to find estimates for µ i . We instead took M. alfredi yearling survival rate (P y = 0.63 yr −1 ) and juvenile survival rate (P j = 0.95 yr −1 ; Kashiwagi, 2014;Smallegange et al, 2016), and, given the fact that M. birostris matures on average at 9 years of age (Dulvy et al, 2014;Rambahiniarison et al, 2018), µ i = −log 9 P y × P 8 j ( Table 2). We also could not find an estimate for M. birostris R m and therefore assumed it equaled that of M. alfredi (Table 2).…”