Emergent research and priorities for shark and ray conservation

Over the past 4 decades there has been a growing concern for the conservation status of elasmobranchs (sharks and rays). In 2002, the first elasmobranch species were added to Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Less than 20 yr later, there were 39 species on Appendix II and 5 on Appendix I. Despite growing concern, effective conservation and management remain challenged by a lack of data on population status for many species, human−wildlife interactions, threats to population viability, and the efficacy of conservation approaches. We surveyed 100 of the most frequently published and cited experts on elasmobranchs and, based on ranked responses, prioritized 20 research questions on elasmobranch conservation. To address these questions, we then convened a group of 47 experts from 35 institutions and 12 countries. The 20 questions were organized into the following broad categories: (1) status and threats, (2) population and ecology, and (3) conservation and management. For each section, we sought to synthesize existing knowledge, describe consensus or diverging views, identify gaps, and suggest promising future directions and research priorities. The resulting synthesis aggregates an array of perspectives on emergent research and priority directions for elasmobranch conservation

Reverse diel vertical movements of oceanic manta rays off the northern coast of Peru and implications for conservation

Abstract 1. An understanding of the vertical movements of elasmobranchs across their range is crucial to defining critical habitat use, its overlap with anthropogenic activities and subsequently managing such interactions. 2. In this study, satellite telemetry was used to investigate the vertical habitat use of three oceanic manta rays (Mobula birostris) tagged on the northern coast of Peru. 3. All three oceanic mantas exhibited patterns of reverse diel vertical migration, where vertical movements were significantly deeper at night than the day, as well as an overall preference for surface habitats (< 2 m). High‐resolution archival data (3–5 s) from two recovered tags revealed fine‐scale behaviours, where individuals predominately remained in coastal surface waters throughout the day, and oscillated up and down through a highly stratified water column at night. 4. Our results suggest that coastal vertical movements were motivated by a combined foraging and thermal recovery strategy, whereby oceanic mantas dived to forage on vertically migrating zooplankton at night and returned to surface waters to rewarm between dives, indicating that the coast of northern Peru may be a foraging habitat for these animals. 5. High use of surface waters here, however, may put oceanic mantas at high risk from several anthropogenic impacts such as entanglement with fishing gear and vessel strikes. 6. Increased sample size and the use of other techniques, such as animal‐borne cameras and tri‐axial sensors, are required to validate our foraging and thermal recovery hypothesis and confirm this region as a foraging habitat for oceanic mantas

The fraction of time each month that male (A) and female (B) white sharks engaged in ‘ROD’ (black) and ‘DVM’ (grey) behavior.

<p>The bars represent median values, while the error bars show the upper inter-quartile range among individuals.</p

Daily median white shark position estimates from 53 tracks, Each position estimate is colored according to behavioral cluster; cluster 1 (yellow; ‘ROD’), cluster 2 (purple; ‘Cluster 2’), cluster 3 (green; ‘Travel’), cluster 4 (magenta; ‘DVM’), cluster 5 (orange; ‘Coastal’).

<p>The distinct diving behaviors, distinguished by each cluster, generally differed in the locations where they most commonly occurred. The ‘Coastal’ behavior occurred primarily along the North American coast, ‘ROD’ primarily at the ‘white shark Café’, while ‘DVM’ occurred throughout the offshore area (the Café, Hawaii, and in between) and ‘Travel’ connected North America and the offshore core areas (the Café and Hawaii).</p

Results from linear regression of fraction of days white sharks engaged in diving behaviors over distance (degrees of longitude) from the center of the Café.

*<p>_All refers to male, female and unknown sex.</p

Dendrogram of white shark behavior, determined from clustering analysis of differences in diving patterns.

<p>Each column represents a 24-hour depth histogram (n = 5571 days from 53 sharks) and is colored by fraction of time. Distinct vertical distribution patterning is evident in the grouping of days with similar depth distributions. The size of each cluster, is indicated by the number of days (n), and percent of total days (in parentheses). The density variable is expressed as a fraction of each day spent in depth bins defined along the y-axis.</p

Spatial dependence of white shark ‘ROD’ (A) and ‘DVM’ (B) behavior in the Café.

<p>The regression shows that for ‘ROD’ the fraction of time (days) white sharks were engaged in ‘ROD’ declined steadily and linearly as a function of distance from the center of the Café region. In contrast no clear spatial relationship was evident for ‘DVM’.</p

White shark seasonal and spatial patterns corresponding to each behavioral mode for males and females.

<p>The dotted lines represent the coast of California (red; near 122°W), the Café (green; near 135°W) and Hawaii (blue; near 156°W) respectively. All longitude estimates for the entire male (left panels) and female (right panels) dataset are shown in grey in the background with only the relevant data for each cluster and sex highlighted in black.</p

Differences in white shark behavior categorized as ‘ROD’ (rapid oscillatory diving) in the Café (a) and in Hawaii (b).

<p>High-resolution time and depth data were corrected for local time and aggregated by cluster over a 24-hour period. The relative density of data (log scale) is shown on a gridded surface. Clustering analysis placed most ROD in the Café, but some occurred in Hawaii. However, the high vertical swimming speed characteristic of ROD in the Café was not present in Hawaii. While the overall depth distribution was similar there were clear differences apparent at time-scales below the cluster data bin size (24 hrs) including a strong daytime density band around 50 m in Hawaii. These differences illustrate that the ROD behavior in the Café is unique.</p