Regional Analysis of Seafloor Characteristics at Reef Fish Spawning Aggregation Sites in the Caribbean

Overfishing of stock and decreasing biodiversity are grave concerns for the U.S. and the rest of the world. Understanding and applying spatial and temporal information of marine species’ reproductive ecology and critical life habitat is vital to the development of effective strategies for marine resource management. In the Caribbean, one of the critical science gaps hindering effective management is the lack of information on how environmental factors may make fish spawning aggregation (FSA) sites optimal for spawning. Understanding the patterns of seafloor characteristics of spawning aggregation sites is of great interest to managers who need a means to efficiently design marine protected areas to help rebuild regional fish stocks. The specific goals of the study were: (1) to map the seafloor at historically known grouper and snapper spawning aggregation sites in three different countries, and (2) to characterize quantitatively the geomorphology of the sites including horizontal and vertical curvature profiles of the reefs, bottom depth at spawning sites, distance between spawning sites and shelf-edges/reef promontory tips, and the shortest distance between the spawning sites and 100 m water depth. These data were field-collected with a GPS and single-beam eco-sounder that provided latitude/longitude and depth. The point data were interpolated to surfaces in GIS to determine slope, aspect, curvature, and distance from spawning sites and three-dimensional reef structures. This study revealed that all 12 known Nassau grouper spawning aggregation sites in Belize and 5 known sites in the Cayman Islands were located at convex-shaped seaward extending reefs (reef promontories) jutting into deep water, within 1 km of reef promontory tips. However, spawning aggregations did not always occur at the tips of reef promontories, though all were found along the shelf edges within 1 km of promontory tips. Sixteen sites were multi-species spawning sites. These general characteristics were used to predict an undiscovered multi-species spawning aggregation in Belize. A successful prediction in Belize, together with the compiled data from multiple sites indicate: 1) reef promontories are vital locations for transient reef fish spawning aggregations, and 2) this study provides a potential tool for prediction of unknown spawning sites in the Caribbean

Citizen-Science for the Future: Advisory Case Studies From Around the Globe

The democratization of ocean observation has the potential to add millions of observations every day. Though not a solution for all ocean monitoring needs, citizen scientists offer compelling examples showcasing their ability to augment and enhance traditional research and monitoring. Information they are providing is increasing the spatial and temporal frequency and duration of sampling, reducing time and labor costs for academic and government monitoring programs, providing hands-on STEM learning related to real-world issues and increasing public awareness and support for the scientific process. Examples provided here demonstrate the wide range of people who are already dramatically reducing gaps in our global observing network while at the same time providing unique opportunities to meaningfully engage in ocean observing and the research and conservation it supports. While there are still challenges to overcome before widespread inclusion in projects requiring scientific rigor, the growing organization of international citizen science associations is helping to reduce barriers. The case studies described support the idea that citizen scientists should be part of an effective global strategy for a sustained, multidisciplinary and integrated observing system

Timing and locations of reef fish spawning off the southeastern United States

<div><p>Managed reef fish in the Atlantic Ocean of the southeastern United States (SEUS) support a multi-billion dollar industry. There is a broad interest in locating and protecting spawning fish from harvest, to enhance productivity and reduce the potential for overfishing. We assessed spatiotemporal cues for spawning for six species from four reef fish families, using data on individual spawning condition collected by over three decades of regional fishery-independent reef fish surveys, combined with a series of predictors derived from bathymetric features. We quantified the size of spawning areas used by reef fish across many years and identified several multispecies spawning locations. We quantitatively identified cues for peak spawning and generated predictive maps for Gray Triggerfish (<i>Balistes capriscus</i>), White Grunt (<i>Haemulon plumierii</i>), Red Snapper (<i>Lutjanus campechanus</i>), Vermilion Snapper (<i>Rhomboplites aurorubens</i>), Black Sea Bass (<i>Centropristis striata</i>), and Scamp (<i>Mycteroperca phenax</i>). For example, Red Snapper peak spawning was predicted in 24.7–29.0°C water prior to the new moon at locations with high curvature in the 24–30 m depth range off northeast Florida during June and July. External validation using scientific and fishery-dependent data collections strongly supported the predictive utility of our models. We identified locations where reconfiguration or expansion of existing marine protected areas would protect spawning reef fish. We recommend increased sampling off southern Florida (south of 27° N), during winter months, and in high-relief, high current habitats to improve our understanding of timing and location of reef fish spawning off the southeastern United States.</p></div

Logistic regression model fit statistics for probability of encountering a fish within 48 hours of spawning, with percent deviance explained (i.e. percent variability explained by inclusion of additional variable); cross-validation results for Area Under the Curve (AUC), False Positive Rate (FPR) and False Negative Rate (FNR); and percentage of 500 runs where random variable inclusion in model outperformed model-selected bathymetric variable (‘Random test’).

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172968#sec002" target="_blank">Methods</a> for additional details.</p

Probability of encountering a spawning condition female Vermilion Snapper.

<p>Predicted mean (left) and standard error (right) probabilities of observing spawning condition female Vermilion Snapper at time and conditions of peak spawning, relative to external validation observations (+). Raster color-coding based on 2.5 standard deviations from the mean. Green boxes denote no-take marine protected areas and SMZs. Basemap courtesy ESRI Ocean Basemap and partners.</p

Number of gear deployments with multispecies observations of spawning females from SERFS.

<p>Number of gear deployments with multispecies observations of spawning females from SERFS.</p

Spawning condition females and valid sets by month.

<p>Percentage of SERFS samples of females within 48 hours of spawning (<i>left</i>) and number of sets (<i>right</i>) where a histological sample was taken, by species and sampling month.</p

Vermilion Snapper spawning.

<p>Maps of Edisto MPA (green) square and surrounding shelf edge showing <b>A)</b> Depth from multibeam bathymetry and SERFS samples with spawning condition (stars) and non-spawning condition (Xs) female vermilion snapper, <b>B)</b> BPI from Benthic Terrain Modeler and squares denoting habitat type (HB: hardbottom, NH: not hardbottom, PH: potential hardbottom) from SEAMAP-SA, <b>C)</b> Model predictions of spawning locations at month and lunar phase of peak spawning and MARMAP fishery-dependent samples of spawning condition female Vermilion Snapper (crosses), and <b>D)</b> standard error in model predictions of peak spawning.</p

Summary statistics for water depth (m), salinity (ppt), and temperature (°C) during SERFS observations of spawning condition females.

<p>Summary statistics for water depth (m), salinity (ppt), and temperature (°C) during SERFS observations of spawning condition females.</p

Summary statistics (mean ± standard deviation) for apparent use of multi-year spawning locations.

<p>Multi-year spawning location size computed as minimum convex polygon containing all collections within a site.</p