## 2 Methods
### Data Analysis  
To examine residency through mark-recapture, the probability of a tiger shark with a dart tag being recaptured was modeled using logistic regression, with sex (categorical) and fork length (continuous) as fixed effects. Two models were fit, one for the probability of being recaptured near Bimini, and one for the probability of being recaptured in the CSTP database throughout the remainder of the region. Data generated from the acoustic array were processed to remove any false detections, defined as a single detection within the array across a one-hour time frame [39], double detections occurring simultaneously on multiple receivers, and any detections within 24 h post tag deployment, to avoid bias of capture for movement behavior [5; 7]. Acoustic detections occurring on the Bimini array from 9 March 2015 through 30 September 2020 were included in the analysis. Acoustic detections received from iTag, FACT and OTN by 1 May 2021 were included in the analysis. To examine local residency near Bimini, a standardized monthly residency index was calculated for each individual as the number of days detected within the Bimini acoustic array divided by the number of days in the month [7]. Drivers of local residency were assessed using a zero-inflated generalized linear mixed-effect model (GLMM) with Poisson distribution, using the 'glmmTMB' package [40] in R (Version 4.0.3; R Core Team, Vienna, Austria). The number of days detected per month was the explanatory variable, with mean monthly water temperature (continuous), FL (continuous), and sex (categorical) included as fixed effects, and the transmitter ID was included as a random effect to account for individual variation in residency behavior [41]. Days in a month, or the number of days remaining in the first month post tagging, was used as an offset to balance the uneven number of days per months. To understand the biological drivers of regional movements, the probabilities of acoustic detections occurring outside The Bahamas were analyzed using a binomial logistic regression model with fork length and sex as predictors. Tiger shark satellite locations were downloaded from the Argos Satellite System via the Wildlife Computers Data Portal. The 'ArgosFilter' package [42] in R was used to prepare satellite detections for movement analyses. Detections were first filtered to exclude any points with location class 'Z' as these indicate the location process failed and the location was not reliable. An estimated maximum sustained swim speed of 3 m s\({}^{-1}\) was used based upon similar telemetry studies of tiger sharks in the region [22]. Subsequently, any detections that would require a greater swim speed from other reliable location points were deemed improbable and were removed. Uplinks from satellite tags were not evenly distributed across days, i.e., some days had several reported locations while others had none. Therefore, a track of the most probable locations was determined with a hierarchical, first-difference, correlated random-walk switching state-based model (hDCRWS) run within a Bayesian framework in the 'BSAM' package [43] in R following methods in [PERSON] et al. [22]. This methodology accounted for location class, mean turning angle, and the estimated mean swim speed of the tiger shark to generate the most probable location points. To balance movements across days, rather than number of location points, a time-step of 12 h was used to produce two location estimates per day for each tiger shark track, even on days with no detections [22]. For instances of gaps in transmissions greater than 20 days, the track was split into sections [22]. To further investigate regional movements and the occupation of tiger sharks within the jurisdiction of The Bahamas Exclusive Economic Zone (EEZ), analyses were conducted at the individual and group levels.