Home ranges, activity patterns, and habitat preferences in and around no-take marine reserves (NTMRs) were evaluated for 5 exploited snapper-grouper species in diverse coral reef habitats in the Dry Tortugas, Florida. Movements of ultrasonic tagged reef fish were determined using a calibrated array of omnidirectional hydroacoustic receivers. Average home range sizes were 2.09 ± 0.39 km 2 (n = 28; total length, TL = 45 to 66 cm) for red grouper Epinephelus morio, 4.17 ± 1.75 km 2 (n = 5, TL = 48 to 55 cm) for yellowtail snapper Ocyurus chrysurus, 1.44 ± 1.04 km 2 (n = 2, TL = 57 to 75 cm) for black grouper Mycteroperca bonaci, and 7.64 km 2 (n = 1, TL = 70 cm) for mutton snapper Lutjanus analis. Red grouper and yellowtail snapper moved moderate distances (from 700 to 900 m) with moderate frequency. Observed movements for black groupers were relatively small and infrequent. Mutton snappers appeared to make short, frequent movements. A tracked gray snapper L. griseus made long-distance nocturnal migrations. Several exploited-phase groupers and snappers crossed into and out of reserve boundaries. They were most likely to do so in locations where boundaries were positioned over contiguous coral reef and close to home-range centers. We found that home ranges for red grouper, black grouper, and yellowtail snapper were relatively small in comparison to NTMR area. Our observations suggest that the Dry Tortugas NTMRs may reduce exposure to exploitation for these and other species with limited home ranges, especially where NTMR boundaries do not overlie contiguous reef. KEY WORDS: Acoustic tracking · Snapper-grouper complex · Movement patterns · Home range · Marine reserves · Coral reef fishesResale or republication not permitted without written consent of the publisher reef habitats and helping to sustain the region's worldclass fisheries resources. Evidence has already emerged that they are reaching their intended goals (Ault et al. 2006b).Many marine fishes repeatedly use and move throughout particular areas, or home ranges (Burt 1943), for certain periods of the year or for particular life stages (Goeden 1978, Shapiro et al. 1994, Rooij et al. 1996, Zeller 1997, Kramer & Chapman 1999, Bell & Kramer 2000, Bolden 2001, Eristhee & Oxenford 2001, Baras et al. 2002, Lembo et al. 2002, Parsons et al. 2003. Occupation by marine fishes of a particular home range within a spatially heterogeneous landscape -given increased familiarity with key habitat features -may facilitate evasion of predators and increase foraging efficiency (review in Harris et al. 1990).Quantifying short-and longer-term fish movement patterns, home ranges, and habitat use is critical for advancing understanding of the dynamics of reef-fish community ecology and for informing intelligent NTMR design (Russ & Alcala 1996, Palumbi 2001, Meester et al. 2004, O'Dor et al. 2004. Unfortunately, data of this type are extremely limited (Kramer & Chapman 1999, Meyer et al. 2007. As a result, most reserves have been implemented with little quantitative desig...

Understanding large‐scale migratory behaviours, local movement patterns and population connectivity are critical to determining the natural processes and anthropogenic stressors that influence population dynamics and for developing effective conservation plans. Atlantic tarpon occur over a broad geographic range in the Atlantic Ocean where they support valuable subsistence, commercial and recreational fisheries. From 2001 through 2018, we deployed 292 satellite telemetry tags on Atlantic tarpon in coastal waters off three continents to document: (a) seasonal migrations and regional population connectivity; (b) freshwater and estuarine habitat utilization; (c) spawning locations; and (d) shark predation across the south‐eastern United States, Gulf of Mexico and northern Caribbean Sea. These results showed that some mature tarpon make long seasonal migrations over thousands of kilometres crossing state and national jurisdictional borders. Others showed more local movements and habitat use. The tag data also revealed potential spawning locations consistent with those inferred in other studies from observations of early life stage tarpon leptocephalus larvae. Our analyses indicated that shark predation mortality on released tarpon is higher than previously estimated, especially at ocean passes, river mouths and inlets to bays. To date, there has been no formal stock assessment of Atlantic tarpon, and regional fishery management plans do not exist. Our findings will provide critical input to these important efforts and assist the multinational community in the development of a stock‐wide management information system to support informed decision‐making for sustaining Atlantic tarpon fisheries.

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.

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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%.

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