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Shark Fishing in the Indian Seas: a Quantitative Risk Assessment of the Impacts of Longline Fishing on the Sustainability of Regional Shark Populations

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Shark Fishing in the Indian Seas: a Quantitative Risk Assessment of the Impacts of Longline Fishing on the Sustainability of Regional Shark Populations Shark Fishing in the Indian Seas: A Quantitative Risk Assessment of the Impacts of Longline Fishing on the Sustainability of Regional Shark Populations The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Nagle, Christopher H. 2019. Shark Fishing in the Indian Seas: A Quantitative Risk Assessment of the Impacts of Longline Fishing on the Sustainability of Regional Shark Populations. Master's thesis, Harvard Extension School. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:42004165 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Shark Fishing in the Indian Seas: A Quantitative Risk Assessment of the Impacts of Longline Fishing on the Sustainability of Regional Shark Populations Christopher H. Nagle A Thesis in the Field of Sustainability and Environmental Management for the Degree of Master of Liberal Arts in Extension Studies Harvard University May 2018 Copyright 2018 Christopher H. Nagle Abstract This project endeavored to provide a formative, contemporaneously applicable, and fully quantitative baseline of a significant component of the shark harvest produced by the nation of India, namely, the longline bycatch mortality for sharks generated from the commercial-scale fishing activity in the extensive oceanic region of India’s Exclusive Economic Zone (EEZ), the nature and scope of which is little understood. Worldwide, shark populations have experienced marked declines due to the advent of modern industrial fishing. Additionally, the life-history and reproductive characteristics of these species, which include longer lifespans, slower growth, fewer offspring, and generally reduced fecundity, constrain group level reproduction and replacement rate compared to that of many commercially targeted and managed teleosts. Although generally true at a global level, shark population depletion has varied in nature and/or rate around the world, with significant unknowns persisting in relation to the waters and seas surrounding developing and recently developed economies; in many cases these waters have not been subject to comparable levels of oversight, active management, and/or assessment as those found in other more established global fisheries. India, over the last decade and a half approximately, has been positioned as the second largest contributor to the global shark harvest in terms of overall tonnage which in turn raises the following question: Is this country mirroring similar global trends of shark population decline due to its fishing activity? Addressing this question in a formatively useful way defines the focal pursuit of this research venture. A relevant, significant, though sparsely assessed source of shark fishing mortality experienced within the broader Indian EEZ—non-target bycatch of sharks, specifically that which is generated from the oceanic longline (LL) fishery—was ultimately analyzed to address the primary hypothesis: Although only one among many marine sub-fisheries, the unassessed status of the oceanic LL fishery presently obscures a reality of unsustainable harvest, such that the community of shark species/stocks extant within the oceanic sector of the Indian EEZ are incurring unsustainable levels of fishing mortality (F) through bycatch in longline gear. To examine the impacts of longline fishing on shark populations in the Indian EEZ, this study utilized advanced statistical methods that focus on the rapid assessment of data limited stocks. In particular, a data-limited status assessment methodology devised by Shijie Zhou and various colleagues—known as the Sustainability Assessment for Fishing Effects (SAFE) method—and Bayesian Hierarchical Analysis were applied. The use of the former afforded a tractable method for longline bycatch mortality estimation (F) through the relationship among target species mortality, a number of bycatch species’ catchability parameters, and spatial overlap of fleet fishing effort with bycatch species habitat. Key outputs were defined as parameters or logical functions thereof within said Bayesian hierarchal models and mean posterior estimates were derived. Two separate Bayesian Models where constructed and run using the OpenBUGS statistical software (3.2.2, OpenBUGS Foundation, UK). The first was designed to derive a marginal posterior distribution of fishing mortality (F), and the second was used to produce marginal posterior distributions of the following biological reference points: Fmsm, Flim, and Fcrash. Fmsm equals the instantaneous fishing mortality rate that corresponds to the maximum number of fish in the population that can be killed by fishing in the long term; Flim equals instantaneous fishing mortality rate that corresponds to the limit biomass Blim, where Blim is assumed to be half of the biomass that supports a maximum sustainable fishing mortality (0.5Bmsm); Fcrash equals minimum unsustainable instantaneous fishing mortality rate that, in theory, will lead to population extinction in the long term. These reference points were derived via integration with respect to the specific parameter across all model-defined upstream conditional dependencies; the relationships where showcased in formal terms using a series of Directed Acyclic Graphs (DAG) and a defined graphical lexicon. The evaluation of integrals was accomplished via large iteration Monte Carlo Markov Chain simulation using a Gibbs sampling algorithm within the OpenBUGS software suite. Mean values (estimates) of corresponding marginal posteriors as well as their credible intervals acquired via simulation were equivalent to point values for the otherwise unknown parameters of interest. Once acquired, these values were then elevated for usage in a final, species-specific status determination based on a straight forward value relationship, specifically between Instantaneous Fishing Mortality (F) and a corresponding set of biological reference points (Fmsm, Flim, Fcrash). The relationships and corresponding species status determinations were garnered using the Credible Interval (CRI), which is the Bayesian analogue to the frequentist Confidence Interval, as follows: F < Fmsm, = Low-Risk (L; i.e. sustainable mortality); F ≥ min[Fmsm] or F + 95% CRI ≥ Fmsm, = Precautionary medium risk (m); Fmsm ≤ F < Flim,= Medium risk (M); F ≥ min[Flim] or F + 95% CRI ≥ Flim, = Precautionary high risk (h); Flim ≤ F < Fcrash, = High risk (H); F ≥ min[Fcrash] or F +95% CRI ≥ Fcrash, = Precautionary extreme high risk (e); and F ≥ Fcrash, = Extreme high risk (E). Fcrash is the level at which minimum, unsustainable fishing mortality has been achieved, which, when maintained over the long-run, results in local/population extinction. Values in still greater excess thereof further intensify the rate of depletion towards that eventuality. Spanning the time period 2010-2014, both for individual years and as a grand ω mean thereof, Fishing mortality (F) estimates (posterior given as: ̂F ) were generated for (30) species of sharks, all of which, either exclusively or in part, are known to inhabit the oceanic ecosystem of the Indian EEZ. Biological reference points Fmsm, Flim, Fcrash (posteriors: 휃Fmsm, 휃Flim, 휃Fcrash) were produced for 17 (of 30) species. Additionally, point values for other relevant parameters were derived (e.g. natural mortality [M]) for 17 species (posterior: 휃푀), some of which are new within the scientific literature (specifically the Longfin Mako (Isurus paucus) and possibly the Gulper Shark (Centrophorus granulosus for the Indian Ocean Stock). Others include the mean, year- wise longline yellowfin tuna (YFT; Thunnus albacares) catch (C) within the Indian Oceanic EEZ, and the average area of longline fishing impact within the Indian Oceanic 휀 EEZ (posterior: 퐴0 ). The fishing mortality values derived in this study may likely be the first values in the literature for many of the species evaluated, at least in the context of the Indian Ocean and certainly for the India EEZ. Of the 17 shark species for which both F and corresponding biological reference points (Fmsm, Flim, Fcrash) were derived, one species was defined as Low Risk, one as Precautionary Medium Risk, three as Precautionary High Risk, one as High Risk, two as Precautionary Extreme High Risk, and nine species as Extreme High Risk. Many of the species represented in the higher risk categories are currently subjected to trade control and general protection under both the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) and the Convention on Migratory Species Protection Agreement (CMS); India is a ratified member Party to both conventions and thus is theoretically obliged to respect/implement necessary control actions. Dedication To my loving family, both present and departed. To my friends, those joyful shepherds of spirit. To my mentors, both inspired and sage. To India, Bhārata, the southern petal in the shade of Meru. To fisherman, sailors of ships, and all those who venture on unsteady seas. To the future, may it be gilded with the fruits of our enduring efforts, known and unknown, great and small. इति िे ज्ञानमा奍यािं गु या饍गुयिरं मया। विम�ृ यैिदशेषणे यथ楍े छसि िथा कु 셁।।१८-६३।। ۞ ۞ ۞ viii Acknowledgements Many generous and brilliant actors where integral in the conceptualization and execution of this research
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