Nicholas A. Farmer

Methods for assessment of short-term coral reef fish movements within an acoustic array.

BackgroundArrays of passive receivers are a widely used tool for tracking the movements of acoustically-tagged fish in marine ecosystems; however, the spatial and temporal heterogeneity of coral reef environments pose challenges for the interpretation of tag detection data. To improve this situation for reef fishes, we introduced a novel response variable method that treats signal detections as proportions (i.e., percent transmissions detected or “detection rates”) and compared this against prior approaches to examine the influence of array and transmitter performance, signal distance and environmental factors on detection rates. We applied this method to tagged snappers and groupers in the Florida reef ecosystem and controlled range-tests on static targets in Bayboro Harbor, Florida, to provide methodological guidance for the planning and evaluation of passive array studies for coral reef fishes.ResultsLogistic regression analysis indicated detection rates were primarily a non-linear function of tag distance from receiver. A ‘model-weighted’ function was developed to incorporate the non-linear relationship between detection rate and distance to provide robust positioning estimates and allow for easy extension to tags with different ping rates.ConclusionsOptimal acoustic array design requires balancing the interplay between receiver spacing, detection rates, and positioning error. Spacing receivers at twice the distance of the modeled 50% detection rate may be appropriate when quantification of overall space use is a priority, and would provide a minimum of 75% detection rate. However, for research where missing detections within the array is unacceptable or time-at-arrival based fine-scale positioning is needed, tighter receiver spacing may be required to maintain signal detection probability near 100%.

Fishery gear interactions from stranded bottlenose dolphins, Florida manatees and sea turtles in Florida, U.S.A

Documenting the extent of fishery gear interactions is critical to wildlife conservation efforts, especially for reducing entanglements and ingestion. This study summarizes fishery gear interactions involving common bottlenose dolphins (Tursiops truncatus truncatus), Florida manatees (Trichechus manatus latirostris) and sea turtles: loggerhead (Caretta caretta), green turtle (Chelonia mydas), leatherback (Dermochelys coriacea), hawksbill (Eretmochelys imbricata), Kemps ridley (Lepidochelys kempii), and olive ridley (Lepidochelys olivacea) stranding in Florida waters during 1997-2009. Fishery gear interactions for all species combined were 75.3% hook and line, 18.2% trap pot gear, 4.8% fishing nets, and 1.7% in multiple gears. Total reported fishery gear cases increased over time for dolphins (p<0.05), manatees (p<0.01), loggerheads (p<0.05) and green sea turtles (p<0.05). The proportion of net interaction strandings relative to total strandings for loggerhead sea turtles increased (p<0.05). Additionally, life stage and sex patterns were examined, fishery gear interaction hotspots were identified and generalized linear regression modeling was conducted.

Spatial Distribution and Conservation of Speckled Hind and Warsaw Grouper in the Atlantic Ocean off the Southeastern U.S.

There is broad interest in the development of efficient marine protected areas (MPAs) to reduce bycatch and end overfishing of speckled hind (Epinephelus drummondhayi) and warsaw grouper (Hyporthodus nigritus) in the Atlantic Ocean off the southeastern U.S. We assimilated decades of data from many fishery-dependent, fishery-independent, and anecdotal sources to describe the spatial distribution of these data limited stocks. A spatial classification model was developed to categorize depth-grids based on the distribution of speckled hind and warsaw grouper point observations and identified benthic habitats. Logistic regression analysis was used to develop a quantitative model to predict the spatial distribution of speckled hind and warsaw grouper as a function of depth, latitude, and habitat. Models, controlling for sampling gear effects, were selected based on AIC and 10-fold cross validation. The best-fitting model for warsaw grouper included latitude and depth to explain 10.8% of the variability in probability of detection, with a false prediction rate of 28–33%. The best-fitting model for speckled hind, per cross-validation, included latitude and depth to explain 36.8% of the variability in probability of detection, with a false prediction rate of 25–27%. The best-fitting speckled hind model, per AIC, also included habitat, but had false prediction rates up to 36%. Speckled hind and warsaw grouper habitats followed a shelf-edge hardbottom ridge from North Carolina to southeast Florida, with speckled hind more common to the north and warsaw grouper more common to the south. The proportion of habitat classifications and model-estimated stock contained within established and proposed MPAs was computed. Existing MPAs covered 10% of probable shelf-edge habitats for speckled hind and warsaw grouper, protecting 3–8% of speckled hind and 8% of warsaw grouper stocks. Proposed MPAs could add 24% more probable shelf-edge habitat, and protect an additional 14–29% of speckled hind and 20% of warsaw grouper stocks.

Modeling Coral Reef Fish Home Range Movements in Dry Tortugas, Florida

Underestimation of reef fish space use may result in marine reserves that are too small to effectively buffer a portion of the stock from fishing mortality. Commonly used statistical home range models, such as minimum convex polygon (MCP) or 95% kernel density (95% KD) methods, require the exclusion of individuals who move beyond the bounds of the tracking study. Spatially explicit individual-based models of fish home range movements parameterized from multiple years of acoustic tracking data were developed for three exploited coral reef fishes (red grouper Epinephelus morio, black grouper Mycteroperca bonaci, and mutton snapper Lutjanus analis) in Dry Tortugas, Florida. Movements were characterized as a combination of probability of movement, distance moved, and turning angle. Simulations suggested that the limited temporal and geographic scope of most movement studies may underestimate home range size, especially for fish with home range centers near the edges of the array. Simulations provided useful upper bounds for home range size (red grouper: 2.28 ± 0.81 km2 MCP, 3.60 ± 0.89 km2 KD; black grouper: 2.06 ± 0.84 km2 MCP, 3.93 ± 1.22 km2 KD; mutton snapper: 7.72 ± 2.23 km2 MCP, 6.16 ± 1.11 km2 KD). Simulations also suggested that MCP home ranges are more robust to artifacts of passive array acoustic detection patterns than 95% KD methods.

Forecasting for Recreational Fisheries Management: What's the Catch?

AbstractThe Magnuson–Stevens Fishery Conservation and Management Reauthorization Act of 2006 required regional fishery management councils to implement annual catch limits (ACLs) for nearly all stocks under U.S. federal management. Since 2011, the number of stocks requiring ACLs (and monitoring) has increased nearly 10-fold, with strict accountability measures requiring either in-season quota closures or shortening of subsequent seasons to avoid ACL overages. Robust forecasts of landings can also provide a projected baseline for evaluation of proposed management alternatives. We compared generalized linear models (GLMs), generalized additive models (GAMs), and seasonal autoregressive integrated moving average (SARIMA) models in terms of fit, accuracy, and ability to forecast landings of four representative fish stocks that support recreational fisheries in the southeastern United States. All models were useful in developing reliable forecasts to inform management. The GAMs provided the best fit to the obs...

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

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 (Balistes capriscus), White Grunt (Haemulon plumierii), Red Snapper (Lutjanus campechanus), Vermilion Snapper (Rhomboplites aurorubens), Black Sea Bass (Centropristis striata), and Scamp (Mycteroperca phenax). 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.