Analyses of Catch Data for Oceanic Whitetip and Silky Sharks in Hawaii
Analyses of Catch Data for Oceanic Whitetip and Silky Sharks reported by Fishery
Observers in the Hawaii-based Longline Fishery in 1995–2010
William A. Walsh and Shelley C. Clarke.
This report presents descriptive statistical summaries and generalized linear model (GLM)
analyses of catch data for oceanic whitetip shark Carcharhinus longimanus and silky shark C.
falciformis in the Hawaii-based pelagic longline fishery. This paper is a collaborative effort
begun at the Secretariat of the Pacific Community (SPC) in New Caledonia and completed at the
NOAA Fisheries Pacific Islands Fisheries Science Center (PIFSC) in Hawaii. The data were
collected by fishery observers aboard commercial vessels in 1995–2010. Oceanic whitetip shark
mean annual nominal CPUE decreased significantly from 0.428/1,000 hooks in 1995 to
0.036/1,000 hooks in 2010. This reflected a significant decrease in nominal CPUE on longline
sets with positive catch from 1.690/1,000 hooks to 0.773/1,000 hooks, and a significant increase
in longline sets with zero catches from 74.7% in 1995 to 95.3% in 2010. Oceanic whitetip shark
CPUE was standardized by delta-lognormal and zero-inflated Poisson GLM methods. The latter
method was employed because 90.1% of the longline sets caught zero oceanic whitetip sharks.
Four factors (16 haul years; calendar quarters; deep- and shallow-set fishery sectors; eight
fishing regions) were significant explanatory variables in these analyses. Sea surface
temperature was a significant continuous explanatory variable in a binomial GLM of the
presence or absence of oceanic whitetip shark catches. The haul year effect coefficients from
these models were used to compute indices of relative abundance. These time series were highly
correlated, and each was also highly correlated with the time series of nominal CPUE. The silky
shark catch data differed from the oceanic whitetip shark data in four major respects. The first
was that nearly all silky sharks are caught on deep sets. The second was that most (62.5%) of the
silky shark catch was taken from 0-10⁰N, although only 3.4% of the observed fishing occurred in
those latitudes. The third difference was that sample sizes were very small before 2000. Finally,
although 46.3% of the longline sets from 0-10⁰N caught zero silky sharks, 54.5% of the silky
shark catch in these waters was taken on 11.5% of the longline sets, which caught ≥5 silky
sharks. These differences led to use of the data from 0-10⁰N in the deep sector from 2000–2010
in the GLM analyses, which were fitted by delta-lognormal and quasi-Poisson (i.e.,
overdispersed) methods. These GLM analyses had low explanatory power. Silky shark CPUE
has ranged from 0.034/1,000 hooks to 1.840/1,000 hooks, but with no significant trend.
Therefore, it is concluded that the relative abundance of silky shark in tropical waters exploited
by this fishery, particularly near the Line Islands, has remained fairly stable since 2000. This
was not the case with oceanic whitetip shark, which has apparently undergone a highly
significant decline in relative abundance in this fishery since 1995.
PIFSC Working Paper WP‐11‐010 Issued 28 July 2011