Sex and size influence the spatiotemporal distribution of white sharks, with implications for interactions with fisheries and spatial management in the southwest Indian Ocean

The study was made possible through generous funding by Fischer Productions for fieldwork and equipment costs. TP was supported by a postdoctoral fellowship funded by the Nelson Mandela University Research Career Development Office (2016-2018) and funding from the South African Research Chairs Initiative awarded to Prof AT Lombard by the National Research Foundation, and by a Royal Society Newton International Fellowship (2018-2020, NF170682).Human activities in the oceans increase the extinction risk of marine megafauna. Interventions require an understanding of movement patterns and the spatiotemporal overlap with threats. We analysed the movement patterns of 33 white sharks (Carcharodon carcharias) satellite-tagged in South Africa between 2012 and 2014 to investigate the influence of size, sex and season on movement patterns and the spatial and temporal overlap with longline and gillnet fisheries and marine protected areas (MPAs). We used a hidden Markov model to identify ‘resident’ and ‘transient’ movement states and investigate the effect of covariates on the transition probabilities between states. A model with sex, total length and season had the most support. Tagged sharks were more likely to be in a resident state near the coast and a transient state away from the coast, while the probability of finding a shark in the transient state increased with size. White sharks moved across vast areas of the southwest Indian Ocean, emphasising the need for a regional management plan. White sharks overlapped with longline and gillnet fisheries within 25% of South Africa’s Exclusive Economic Zone and spent 15% of their time exposed to these fisheries during the study period. The demersal shark longline fishery had the highest relative spatial and temporal overlap, followed by the pelagic longline fishery and the KwaZulu-Natal (KZN) shark nets and drumlines. However, the KZN shark nets and drumlines reported the highest white shark catches, emphasising the need to combine shark movement and fishing effort with reliable catch records to assess risks to shark populations accurately. White shark exposure to shark nets and drumlines, by movement state, sex and maturity status, corresponded with the catch composition of the fishery, providing support for a meaningful exposure risk estimate. White sharks spent significantly more time in MPAs than expected by chance, likely due to increased prey abundance or less disturbance, suggesting that MPAs can benefit large, mobile marine megafauna. Conservation of white sharks in Southern Africa can be improved by implementing non-lethal solutions to beach safety, increasing the observer coverage in fisheries, and continued monitoring of movement patterns and existing and emerging threats.Publisher PDFPeer reviewe

Pessimistic assessment of white shark population status in South Africa: comment on Andreotti et al.(2016)

Andreotti et al. (2016; Mar Ecol Prog Ser 552:241−253) estimate an abundance (N) of 438 white sharks Carcharodon carcharias and a contemporary effective population size (CNe) of 333 individuals along the South African coast. N was estimated by using a mark-recapture analysis of photographic identification records from a single aggregation site (Gansbaai). CNe was calculated based on the levels of pairwise linkage disequilibrium of genetic material collected from 4 aggregation sites across approximately 965 km of South African coastline. However, due to the complex stock structure of white sharks and the model assumptions made by Andreotti et al. (2016), the conclusions drawn cannot be supported by their methods and data

How continuing mortality affects recovery potential for prohibited sharks: The case of white sharks in South Africa

It can be difficult to determine whether a prohibition to exploitation ensures effective conservation or recovery for species that remain exposed to fishing effort and other sources of mortality throughout their range. Here we used simulation modeling of four life history scenarios (different productivity and population size) to contextualize potential population response to multiple levels of mortality, using white sharks (Carcharodon carcharias) in South Africa as a case study. The species has been protected since 1991, yet substantial uncertainty about population dynamics persists and recent declines at two aggregation sites have renewed conservation concern. All scenarios indicated that annual removals in the 10s of individuals would substantially limit the potential for and magnitude of any abundance increase following prohibition. Because average known removals from the KwaZulu-Natal Sharks Board’s Bather Protection Program have typically remained higher than these thresholds, they likely eliminated much of the conservation benefit derived from prohibition. The only life history scenario to achieve appreciable increase when simulated removals were similar to published averages assumed maturation occurred at a much younger age than currently understood. Our results demonstrate why general application of life history-based simulations can provide a useful mechanism to evaluate the biological plausibility of life history information and abundance trends, and to explore the scope for population response to recovery actions. For South Africa, our results suggest that even known levels of white shark removals, which likely underestimate total removals within their range, may be sufficient to drive abundance decline and new mitigation measures may be required to ensure population recovery

Satellite tracking data of white sharks in the southwest Indian Ocean (2012-2014)

These data comprise locations and individual metadata from 34 white sharks (Carcharodon carcharias) instrumented March-May 2012 with telemetry devices along the coast of South Africa. These devices were SPOT5 transmitters (SPOT-257, SPOT-258; Wildlife Computers) which transmit locations via ARGOS CLS. All research methods were approved and conducted under the South African Department of Environmental Affairs: Oceans and Coasts permitting authority. This dataset is linked to the manuscript Kock et al. 2021 "Sex and size influence the spatiotemporal distribution of white sharks, with implications for interactions with fisheries and spatial management in the southwest Indian Ocean". The data are structured in long format, so that each row in the dataset represents an observation. The columns in the data are as follows. DeployID: This a factor variable identifying each individual shark. It has 34 levels. SPOT: This is a numeric variable identifying the tag number unique to each shark. Date: This is a date variable (POSIXct) that gives the date and time of a geographic location record in UTC time. Type: This is a character variable identifying the type of location record. Quality: This is a character variable made up of numbers and letters giving the location error associated with each location as provided by ARGOS. Latitude: This is a numeric variable and gives the latitude of the shark at the time of each record. Longitude: This is a numeric variable and gives the longitude of the shark at the time of each record. Area_tagged: This is a character variable that gives the area where the shark was tagged. Sex: This is a character variable identifying the sex of the shark, either "F" or "M" for female and male. TL: This is a numeric variable giving the total length of the shark in centimetres. Maturity: This is a character variable giving the maturity of the shark based on its total length following Malcolm et al. 2001: juveniles (male and female: 175-300 cm TL), sub-adults (male: >300-360 cm TL; females: >300-480 cm TL) and adults (male: >360 cm TL; female: >480 cm TL)

Reply to: Caution over the use of ecological big data for conservation

[Extract] Our global analysis1 estimated the overlap and fishing exposure risk (FEI) using the space use of satellite-tracked sharks and longline fishing effort monitored by the automatic identification system (AIS). In the accompanying Comment, Harry and Braccini2 draw attention to two localized shark–longline vessel overlap hotspots in Australian waters, stating that 47 fishing vessels were misclassified as longline and purse seine vessels in the Global Fishing Watch (GFW)3 2012–2016 AIS fishing effort data product that we used. This, they propose2, results in misidentifications that highlight fishing exposure hotspots that are subject to an unexpected level of sensitivity in the analysis and they suggest that misidentifications could broadly affect the calculations of fishing exposure and the central conclusions of our study1. We acknowledged in our previously published paper1 that gear reclassifications were likely to occur for a small percentage of the more than 70,000 vessels studied, however, here we demonstrate that even using much larger numbers of vessel reclassifications than those proposed by Harry and Braccini2, the central results and conclusions of our paper1 do not change

Reply to: Shark mortality cannot be assessed by fishery overlap alone

[Extract] Our previously published paper1 provided global fine-scale spatiotemporal estimates (1° × 1°; monthly) of overlap and fishing exposure risk (FEI) between satellite-tracked shark space use and automatic identification system (AIS) longline fishing effort. We did not assess shark mortality directly, but in addition to replying to the Comment by Murua et al.2, we confirm—using regression analysis of spatially matched data—that fishing-induced pelagic shark mortality (catch per unit effort (CPUE)) is greater where FEI is higher. We focused on assessing shark horizontal spatiotemporal overlap and exposure risk with fisheries because spatial overlap is a major driver of fishing capture susceptibility and previous shark ecological risk assessments (ERAs) assumed a homogenous shark density within species-range distributions3,4,5 or used coarse-scale modelled occurrence data, rather than more ecologically realistic risk estimates in heterogeneous habitats that were selected by sharks over time. Furthermore, our shark spatial exposure risk implicitly accounts for other susceptibility factors with equal or similar probabilities to those commonly used in shark ERAs3,5