Identifying Movement Behavior States of Striped Bass in a Large Regulating Forebay Using Cluster Analysis

Identifying Movement Behavior States of Striped Bass in a Large Regulating Forebay Using Cluster Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identifying Movement Behavior States of Striped Bass in a Large Regulating Forebay Using Cluster Analysis Taylor Spaulding, Andrew Kalmbach, Eric Chapman, Paul Berman, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8724316/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Predation by Striped Bass in Clifton Court Forebay, a water regulating reservoir in California’s Sacramento–San Joaquin Delta, is considered a major contributor to losses of juvenile salmonids. Understanding how individual Striped Bass use the forebay and surrounding channels is important for designing more effective predator management actions aimed at reducing loss. This study used acoustic telemetry and unsupervised clustering to test whether Striped Bass exhibit discrete movement behavior classes that differ in residency, habitat use, detectability, and movement activity. Then tested whether the occurrence of these classes vary with season, hydrologic conditions, fish age, and radial gate operations. Acoustic detections from 543 tagged Striped Bass were summarized into 2,568 six-month behavior states across eight water years. Four behavioral classes were identified: Commuters (high movement and frequent use of both forebay and outside habitats), Inside Residents–Undetected (persistent forebay residency with few detections), Inside Roamers (primarily forebay-associated with active internal movement), and Outside Residents–Undetected (persistent outside residency with few detections). Behavioral class composition differed significantly by season, water year type, age class, and radial gate openness. Movements were concentrated from February through May, with additional peaks in late summer and fall. Striped Bass Predator Behavior Movement Acoustic Telemetry Reservoir Cluster Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 BACKGROUND Striped Bass are native to the East Coast of the United States and were introduced into California’s Sacramento–San Joaquin Delta (Delta) from New Jersey in 1879 (Dill and Cordone 1997 ). In their native range, Striped Bass are highly mobile and anadromous. Similar behaviors have been documented in California (Moyle 2002 ). Striped Bass are gregarious pelagic predators known to consume juvenile Pacific salmonids (Moyle 2002 ; Grossman et al. 2013 ; Grout 2016). Although individual behavior is highly variable, in California, Striped Bass generally migrate through the Delta to upstream rivers to spawn from February to May, then move back downstream to the San Francisco Bay and ocean from June to October; some individuals move back to freshwater in the Delta to overwinter (Calhoun 1952 ; Sabal et al. 2019 ). Striped Bass are present throughout the Delta, including in the Clifton Court Forebay (CCF), a regulating reservoir in the South Delta that is part of the State Water Project (SWP). Operated and maintained by the California Department of Water Resources (CDWR), the SWP conveys water through the CCF to municipal and agricultural users throughout California. Predation on fish species of concern in the CCF has long been an issue for resource managers and multiple attempts have been made to quantify effects on these species (Clark et al. 2009 ; Castillo et al. 2012 ). Loss of juvenile salmonids in the CCF has been estimated to range from 66% to 99% and Striped Bass predation is thought to be the largest contributor to this loss (Gingras 1997 ; Gingras and McGee 1997 ; Clark et al. 2009 ). To address the high predation risk of salmonids, several studies were implemented to understand predation and the potential impacts to imperiled species in the CCF, including this acoustic telemetry study to determine the presence and movement behavior of Striped Bass. Recent improvements in the accessibility of acoustic telemetry data and computer processing have resulted in the increased use of acoustic telemetry to study behaviors of a wide range of highly mobile species, such as tuna and jack (Capello et al. 2015 ), sharks and rays (Jorgensen et al. 2012 ), sturgeon (Kessel et al. 2017 ), and salmon (Arostegui et al. 2017 ) in marine (Finn et al. 2014 ; Capello et al. 2015 ; Brodie et al. 2018 ; Espinoza et al. 2021 ), estuarine (Arostegui et al. 2017 ; Taylor et al. 2021 ), and freshwater (Arostegui et al. 2017 ; Lowe et al. 2020 ) habitats. Recent applications have used a variety of tools (e.g., network, cluster, and sequence analysis) to identify life history strategies and to inform fisheries management by grouping individuals based on behaviors (Arostegui et al. 2017 ; Brodie et al. 2018 ; Lowe et al. 2020 ; Espinoza et al. 2021 ; Taylor et al. 2021 ; Chen et al., no date). The present study applies cluster analysis to acoustic telemetry data from Striped Bass within the CCF to test the hypothesis that individuals exhibit discrete behavioral classes that differ in their habitat residency and activity level. We further hypothesize that these behavior classes correspond to differing vulnerabilities to predator control (i.e. removal) actions, such as active (e.g., electrofishing) or passive (e.g., fyke net) capture techniques. By isolating and characterizing behaviorally distinct groups, this study seeks to provide a basis for optimizing predator control strategies by informing when and where to target removal efforts and which capture methods may be most effective based on predator behaviors. METHODS Study Site Clifton Court Forebay (CCF) is a shallow (~ 900 ha; mean depth ~ 1.5 m) South Delta reservoir near Tracy, California, that provides operational flexibility to manage SWP flows (Fig. 1 a). Inflow from the Delta enters through five radial gates on the southeast corner; water exits westward via an intake canal leading to the Harvey O. Banks Pumping Plant (Fig. 1 b). Along the intake canal, the John E. Skinner Fish Protective Facility “salvages” small/juvenile fishes from diverted water and trucks them to release sites in the Delta. A localized scour hole (~ 20 m) produced by high flows into CFF occurs just northwest of the radial gates. Outside of CCF, the South Delta comprises natural and engineered sloughs/canals. Acoustic Telemetry Array The receiver array (Fig. 1 b) was designed to detect presence/absence of tagged fish and to infer transits across the radial gates. Each site comprised an in-water hydrophone (HTI Model 590) and beacon tag (HTI Model 795) with a shore-based logger (HTI Model 295/395) and power. Array deployment began in 2013 with nine arrays and expanded in 2015 with an additional five (Fig. 2 ); one external site at Curtis Landing was added in 2016 (Fig. 1 a). Detections from 5/20/13 to 12/31/18 were included in analysis, with quality assurance/quality control provided by the vendor, HTI. Fish Capture and Tagging From 03/12/2013 to 12/30/2016 3,969 Striped Bass were captured and released within the CCF. Each fish was measured for fork length and weight, and scanned for PIT tag. If no PIT tag was detected, a subset (n = 590 fish) were surgically implanted with combined acoustic + PIT tags (HTI Model 795), and the rest were implanted with just a PIT tag. If already tagged, the fish was released. Three sizes of HTI tags with different battery life expectancies were used depending on fish size (Table 1 ). Surgical procedures followed Wingate et al. ( 2011 ). Sedation with AQUI-S®20E (AquaTactics; 35 milligrams per liter) was authorized under the U.S. Fish and Wildlife Service’s Investigational New Animal Drug program (USFWS 2011). Fish were released near their capture sites after recovery, defined as regaining equilibrium and free swimming. Table 1 Acoustic tag size, manufacturer’s life expectancy, and number deployed in Striped Bass in Clifton Court Forebay, May 2013–December 2016. Tag Model # Manufacturer’s Tag Life Expectancy (Innovasea 2023 ) a Striped Bass Tagged Minimum Length (cm) Maximum Length (cm) Minimum Weight (kg) Maximum Weight (kg) LG 220–400 days 235 24.5 41.5 0.45 1.5 LY 2.5–4 years 236 28.5 69.5 0.6 7.5 LZ 4–5 years 74 47.5 93.0 3.0 23.3 Not Recorded b Not Applicable 45 27.0 70.7 0.5 11.0 Total 590 a Life expectancy from the manufacturer based on a single-pulse, 1-millisecond pulse width, with a 10-second pulse rate interval at 10°C. b Fish with unrecorded tag types were assigned a tag life expectancy of 1,460 days, the median of LY and LZ tags, which were used exclusively during their tagging years. Scale Aging Scales from 222 of the captured Striped Bass were imaged under microscope (10x/20x) and 10 megapixel digital microscope camera (AmScope™ Model SM-2T-WF) and aged independently by two readers adapting methods from Quist and Isermann ( 2017 ); a third reader aged a random subset from 78 fish. Ages were assigned when ≥ 2 readers agreed. Data Analysis All data analysis was conducted in R (R Core Team 2025); specific packages used in clustering and statistical analyses are mentioned below. Detections were first filtered and processed to develop movement metrics which were used to classify groups of similar behavior. After successful clustering, the resulting groups were compared against each other with respect to the movement metrics to identify biologically meaningful movement patterns. Then the groups were compared using explanatory variables which were hypothesized to be important factors driving behavior patterns. Detection Filtering and Processing Detection Temporal Aggregation To minimize timing errors from receiver clock drift and daylight-saving adjustments, detections were aggregated to a daily time step: one record per fish per receiver per day. This aggregation is the temporal base for subsequent location state assignment. Tag life, Loss, and Mortality The detection period for analysis of each fish ended at the earlier of the median manufacturer-estimated tag life for that tag size or following a screening process to identify tag shedding/mortality. To screen for tag shedding/mortality, “site groups” were delineated, defined as groups of receivers with overlapping maximum detection ranges (Figure 2). The total time a fish was detected within a single site group with no other detections was analyzed, and all events exceeding the 99th-percentile duration of the specific site group were flagged as likely shed tags or mortalities. Detection histories were then checked for any movement after flagged events; histories with no movement were truncated at the flagged event and assumed to be either a shed tag or mortality). This screening process identified 30 fish meeting the tag shedding/mortality criterion and, therefore, their detection histories were truncated. Location States Each day, a fish was assigned one of three location states based on receiver detections: Inside: detections only inside of the radial gates in CCF that day. Outside: detections only outside of the radial gates and CCF that day. Transit: detections both inside and outside of the radial gates and CCF that day. Because detections were aggregated to a daily time step and “Transit” location state is constrained to a single day, movement direction is defined strictly from day-to-day transitions: Exit: an Inside -> Transit -> Outside sequence indicates ≥1 exits through the radial gates during the Transit day. Entry: an Outside -> Transit -> Inside sequence indicates ≥1 entries during the Transit day. Entry and Exit: Outside -> Transit -> Outside sequence indicates ≥1 entries, followed by ≥1 exits during the Transit day Exit and Entry: an Inside -> Transit -> Inside sequence indicates ≥1 exits, followed by ≥1 entries during the Transit day When a detection history ended (due to assumed end of tag life) with a period of non-detection and the last observed location state as “Transit”, remaining undetected time was coded “Unresolved” because direction could not be assigned without post-Transit state confirmation (n = 4 fish). These rules were used solely to derive transit summary metrics (e.g., exit/entry counts, inter-Transit timing) and are intended to provide minimum transit counts, acknowledging that a fish may have crossed the radial gates more than once within a Transit day. Detection Gaps Days without detections were filled using Continuous Presence Events (CPEs) adapted from Capello et al. (2015) without sub-daily time constraints (inapplicable under daily aggregation). CPEs extend the last known location state forward until a day on which a new state is observed. However, the transit of fish across the radial gates was assumed to be a near instantaneous event and, therefore, to avoid inflating time in transit, Transit was constrained to a maximum of one day. Days without detections immediately before/after Transit days were assigned to the corresponding non-Transit state (Inside/Outside). Consecutive Transit days are possible when a fish is detected both Inside and Outside on those days. It is possible that a fish transited the radial gates multiple times over consecutive days without being detected, given that the radial gates are an acoustically “noisy” area. However, constraining undetected Transit time to one day follows our conservative approach to movements. Behavior-State Clustering and Validation To assess patterns of movement behavior and identify unique “behavior states”, an unsupervised cluster analysis using partitioning around medoids (PAM) on a Gower dissimilarity matrix was undertaken, following the general approach of several recent acoustic telemetry studies on behavior grouping (Arostegui et al. 2017; Brodie et al. 2018; Lowe et al. 2020; Taylor et al. 2021; Espinoza et al. 2021). Detection data were aggregated into 6-month behavior states consisting of one state per fish per 6-month period (Period 1: December 18- June 18; Period 2: June 19- June 17). Period 1 represents the time of year when 98.8% of total juvenile salmonid catch occurs at the John E. Skinner Fish Protective Facility based on salvage data (https://filelib.wildlife.ca.gov/Public/salvage/). We hypothesized that the behavior of Striped Bass might change due to the influx of this prey species. Each behavior state contains all detected movements by that fish within that 6-month period, summarized into metrics which describe the fish’s residency, habitat use, detectability, and movement ( Table 2 ). Table 2. Metrics identified for potential inclusion in clustering analyses. Biological Concept Metric Residency Days of Residence Inside CCF Days of Residence Outside CCF Habitat Use Total Number of Inside CCF Receiver Visits Total Number of Outside CCF Receiver Visits Total Number of All Receiver Visits Average Number of Daily Receiver Visits Detectability Days Spent Undetected Inside CCF Days Spent Undetected Outside CCF Movement Daily Minimum Distance Traveled Daily Mean Distance Traveled Daily Maximum Distance Traveled Period Total Distance Traveled Number of Exits Number of Entries Number of CCF Crossings Number of All Movements Days Between Movements The above metrics were selected for potential inclusion in the cluster analysis because they had the correct resolution of data and are further based on factors that are inherent to movement patterns, such as distance, residence time, detection frequency, and timing of movements. The final data set contains a single row of metrics for each fish for each period in each year it was tracked, collectively called a “behavior state.” Clustering and Validation Prior to clustering, the Spearman correlation coefficient was calculated between each of the metrics (`stats::cor`, method = “spearman”). Metrics with an absolute coefficient value greater than 0.8 (`caret::findCorrelation`, cutoff = 0.8, exact = TRUE) were pruned. Then, a Gower dissimilarity (`cluster::daisy`, metric = "gower") matrix was created from the remaining set of predictors. Finally, this matrix was clustered with PAM (`cluster::pam`) with k ranging from 2 to 8 and selecting the k that maximized the mean silhouette width with backward elimination of variables to improve structure. This involved iteratively removing variables and re-computing the Gower dissimilarity matrix and PAM clustering for k = 2 – 8, dropping each remaining variable individually and reevaluating for each candidate drop. The drop was accepted if either the new average silhouette width ≥ 0.50 or it gained ≥ 0.005 over the current solution. The final PAM solution was used to assign each behavior state record to a class. Predictor means and interquartile range were calculated for all variables (including those pruned due to correlation or to improve clustering) to derive class profiles and assign meaningful names to classes post-hoc. Explanatory Covariates and Movement Timing We hypothesized that age-class, period, hydrological conditions as defined by State Water Resources Control Board Revised Water Right Decision 1641 water year (10/1 to 9/30) types (State Water Board 2000), and percent of time radial gates were open might play a role in the behaviors of individual fish and, therefore, may explain the results of behavior state clustering. Behavior states are repeated measures within fish. Therefore, to test class-covariate associations, design-based inference (`survey::svydesign`) grouped by tag ID was applied to the cluster classes. Categorical associations (i.e., period, water year type, and age-class) were evaluated using Rao-Scott adjusted Wald χ² tests (`survey::svychisq`, statistic = “adjWald”) while gate openness (percent of time open each day averaged over the 6-month period) was evaluated using a design-based linear model (`survey::svyglm`) and a joint Wald test (`survey::regTermTest`). Given that gate openness has the potential to violate assumptions due to heteroskedasticity, the suitability of modeling raw percentages was assessed by (i) examining the empirical distribution of gate openness to confirm values were not concentrated near 0% or 100%, (ii) estimating class-specific variances using survey-weighted variance estimates, which were comparable across classes, and (iii) inspecting design-based residuals from the fitted model, which did not indicate strong heteroskedasticity. Age Analysis To assign age-classes, an age-length key was developed following Ogle (2016) using ages from scales and fork length with lengths binned to 1-cm categories to form the key. Unaged fish were assigned ages semi-randomly based on key proportions. For analyses, fish were grouped into three age-classes: 1-2 years, 3-5 years, and 6+ years. Estimated age-class at each detection was derived from days elapsed since capture. We also hypothesized that behavior classes might move differently at different times of the year given the known migration patterns of Striped Bass. Therefore, a brief qualitative investigation was conducted into the timing of Exit, Entry, and CCF Crossing events by plotting the distribution of movements throughout the year. Results Tagged Fish The final analysis set comprised 543 tagged individuals contributing 2,568 behavior states across 8 water years (median records per fish = 3, IQR = 5). There were 1,340 behavior states in Period 1 (December 18 – June 18) and 1,228 states in Period 2 (June 19 – December 17). There were 211 states comprised of Age 1–2 fish, 1,063 states of Age 3–5 fish, 1,286 states of Age 6 + fish, and 8 states that were un-aged due to missing length data for the corresponding fish (n = 2 fish). There were 1,268 states in Critical Dry water years, 700 states in Dry water years, 119 states in Below Normal water years, and 481 states in Wet water years. Clustering The pruning based on metric correlation dropped six variables: outside residency, the total number of receivers visited at all locations, the mean number of receivers visited per day, the mean distance travelled per day during the period, the total distance traveled during the period, and the total number of all movements during the period. The backward elimination procedure retained the remaining 17 predictors and identified 4 classes with a mean silhouette = 0.59. Class sizes were: “Commuters” = 334 behavior states, “Inside Residents – Undetected" = 1,103 behavior states, “Inside Roamers” = 622 behavior states, “Outside Residents – Undetected" = 509 behavior states (Table 3 ). Mean per-class silhouette widths were 0.59 (“Commuters”), 0.57 (“Inside Residents – Undetected"), 0.60 (“Inside Roamers”), and 0.61 (“Outside Residents – Undetected"), indicating well-structured clusters with moderate within-class cohesion and among-class separation (Table 3 ). To assess whether extreme movement values (e.g., long-distance movements and long residence or non-detection intervals) disproportionately influenced class assignment cluster was repeated under increasing levels of winsorization (Wilcox 2012 ). Right-skewed movement variables were capped at the 99th, 95th, 90th, 80th, and 70th percentiles, and for each capped dataset Gower dissimilarities were computed, the PAM clustering was refit, the final number of classes was selected using maximum average silhouette width. Light winsorization (99%) lowered average silhouette width (0.59 to 0.53) without changing the four-class solution, indicating that high-movement individuals contribute biologically meaningful separation rather than acting as statistical outliers. Very strong winsorization (≤ 70%) increased silhouette width (to 0.67) only after altering the solution (5 + classes) and suppressing the long-distance “commuter” behavior. Therefore the uncapped four-class solution was retained for inference. Table 3 Final clustering result statistics and mean (interquartile values) of the movement metrics defining each behavior classification. All data are presented per period. Commuters Inside Residents – Undetected Inside Roamers Outside Residents – Undetected Class Summary Statistics # of Behavior States in Cluster 334 1,103 622 509 % of All Behavior States 13% 43% 24% 20% Mean Silhouette Width 0.59 0.57 0.60 0.61 Days of Residence (days, maximum possible = 183) Inside 75 (44–98) 136 (91–182) 92 (37–157) 0 (0–0) Outside 75 (44–99) 0 (0–0) 1 (0–1) 144 (103–183) Receiver Visits (# of visits) Inside Total 52 (19–75) 2.7 (0–0)* 52 (12–73) 0.1 (0–0)* Outside Total 17 (4–21) 0 (0–0) 2.1 (0–0)* 1.5 (0–0)* All Sites Total 69 (30–92.8) 2.7 (0–0)* 54.1 (13–76) 1.6 (0–0)* Daily Mean 1.6 (1–2) 0.2 (0–0)* 1.3 (1–2) 0.3 (0–0)* Days Spent Undetected (days, maximum possible = 183) Inside 50 (25–75) 94 (100–100) 56 (30–84) 0 (0–0) Outside 91 (88–99) 0 (0–0) 3 (0–0)* 100 (100–100) Distance (km) Daily Minimum 0 (0–0) 0.1 (0–0)* 0 (0–0) 0.3 (0–0)* Daily Mean 2.8 (0.8–3.4) 0.1 (0–0)* 0.9 (0–1.1) 1 (0–0)* Daily Maximum 12.2 (2.4–19.1) 0.1 (0–0)* 3.4 (0–2.4) 2.1 (0–0)* Period Total 103.1 (24.7–129.8) 0.5 (0–0)* 36.1 (0–42.6) 5.7 (0–0)* Movements per Period (# of movements) Exits 0.8 (0–1) 0 (0–0) 0.2 (0–0)* 0.4 (0–1) Entries 0.6 (0–1) 0.1 (0–0)* 0.1 (0–0)* 0 (0–0) CCF Crossing 3 (0–4.8) 0 (0–0) 0.6 (0–0)* 0 (0–0) All Movements 1.4 (1–2) 0.1 (0–0)* 0.3 (0–0)* 0.4 (0–1) Average time Between Movements (days, maximum possible = 183) Days Between Movements 12 (4–17) 183 (183–183) 12 (0–16) 182 (183–183) *Note: metrics with means falling outside the interquartile range indicate right-skewed data, all such metrics were evaluated for appropriateness and clustering impact Class Profiles Classes differed across detectability and residency (Fig. 2 ), receiver visits (Figs. 3 ), distance (Fig. 4 ), and movement frequency (Fig. 5 ) providing the ability to qualitatively describe the behavioral traits of each class. "Commuters" This class of behavior states had the highest movement throughout the array and was the only class that was frequently observed at receivers both inside and outside the CCF with a relatively high frequency of crossing CCF. "Inside Residents - Unobserved" This class of behavior states stayed inside the CCF almost the entire period, with little movement. When detected, they revisited a small set of inside receivers and rarely ventured outside of CCF. "Inside Roamers" This class of behavior states were also mostly detected inside the CCF like “Inside Residents – Undetected”, but actively moved within the Inside network, with only occasional forays across/out of the CCF. "Outside Residents - Unobserved" This class of behavior states is defined by largely staying outside the CCF with only a handful of detections by receivers outside the CCF and generally one or fewer entries inside. Explanatory Covariates Results of the design based test for each of the explanatory variables are provided in Table 4 . All explanatory variables were significantly different among classes ( p < 0.001). Table 4 Association results for design-based tests accounting for repeated measures on each fish. Each of the tested covariates is presented along with the test used, the degrees of freedom for each test, the test statistic, and the significance of the statistic. Covariate Test DF (num, denom) Statistic p-value Sig Period of Year Rao-Scott χ² 3, 541 15.600 < 0.001 *** Water-year Type Rao-Scott χ² 9, 535 8.816 < 0.001 *** Age-class Rao-Scott χ² 6, 538 16.434 < 0.001 *** Gate Openness Design-based ANOVA (Wald) 3 17.941 < 0.001 *** Period Period composition was similar across three of the classes, but the “Commuters” class had the lowest proportion of behavior states in Period 2 (June 19 – December 17) with a larger proportion occurring during Period 1 (December 18 – June 18) (Fig. 6 a). Water Year Type Behavior states during Critical Dry water years made up a larger proportion of the states in the “Commuters” and “Inside Roamers” classes, while making up relatively small proportions of the other two classes (Fig. 6 b). Behavior states occurring during Dry water years were moderately represented in all four classes, but were least represented in the “Commuters” class. Behavior states occurring during Below Normal water years made up a larger proportion of the “Outside Residents – Undetected” class than any other class, with a smaller proportion in the “Inside Residents – Undetected” class, and the smallest proportions in the other two classes. Behavior states during Wet water years were moderately represented in all four classes, but had the lowest proportion in the “Inside Roamers” class. Age-class Behavior states from fish in the 1–2 year age-class were the most common age-class assigned to the “Inside Roamers” class, and were the least common in the “Commuters” and “Outside Residents – Undetected” classes (Fig. 6 c). Behavior states from fish in the 3–5 year age-class were the most common in the “Commuters” class while being moderately represented in the other three classes. Behavior states from fish in the 6 + year age-class were the most common in the “Outside Residents – Undetected” class, but also had significant contributions to the “Commuter” and “Inside Residents – Undetected” classes. Gate Openness Gate openness during behavior states showed a large difference between classes (Fig. 6 d). Gates were open a higher percent of time during behavior states classified as either “Inside Resident – Undetected” or “Outside Resident – Undetected” and open a lower percent of time during behavior states classified as “Commuters” or “Inside Roamers”. Movement Timing Timing of exits, entries, and detected CCF crossings (when a fish was detected at both the Intake Canal receivers and Radial Gate receivers on a single day) was qualitatively assessed based on the distribution of movement events using violin plots across seasons. Only “Inside Roamers” and “Commuters” had > 1 exit, which primarily occurred from February to April (Fig. 7 a) These were also the only classes to have any entries, which were more spread out through the year than exits but did show increased movement from during the same February to April period as well as peaks from August to November (Fig. 7 b). Finally, these classes were also the only two classes detected making movements across the CCF, coincident with Exit and Entry timing (Fig. 7 c). Combining movements across all classes, a strong trend is evident, with exits occurring primarily from February to May, entries also occurring primarily during the same period with additional peaks from August to September and from November to December, a pattern which is repeated for CCF crossings (Fig. 7 d). Combined, movement is highest throughout February through May with a dip from May to Jul and another increase in movement from August to November (Fig. 6 d). DISCUSSION Cluster analysis of movement and residence metrics identified four distinct behavioral classes of Striped Bass within and near CCF. These classes captured consistent differences in space use and activity, reflecting variation in habitat association and mobility. The presence of multiple behavior types within this population indicates behavioral plasticity and spatial heterogeneity in this non-native estuarine species. By applying unsupervised clustering, this study extends movement-classification frameworks developed for other taxa (Brodie et al. 2018 ) and for Striped Bass in other estuarine systems (Taylor et al. 2021 ), yielding biologically interpretable behavior classes relevant to predator-management applications. Behavior patterns observed in this study are consistent with behavior previously observed within the Delta. Clark et al. ( 2009 ) documented extended residency near the intake canal and radial gates and repeated movements between CCF and Delta channels, patterns similar to those of our “Inside Roamers” and “Commuters.” Seasonal movement timing in our dataset with peak movements in late winter and spring and secondary increases in autumn matches long-recognized patterns for Striped Bass in the Delta (Calhoun 1952 ; Chadwick 1967 ; Stevens et al. 1987 ; Gingras and McGee 1997 ). The persistence of these seasonal patterns, despite substantial changes in Delta operations and climate since early studies, suggests they are stable features of Striped Bass behavior. Age was associated with differences in movement behavior, though no age class was restricted to a single behavioral type. Older individuals generally exhibited greater mobility, consistent with observations from the Roanoke River (Patrick et al. 2006 ), yet also appeared more frequently in “resident” classifications. This pattern likely reflects limitations in the spatial extent of the receiver array, as fish remaining within the forebay are more detectable than those that leave monitored regions. Overall, the presence of all behavior classes across all age groups demonstrates that age alone does not determine movement behavior. Seasonal and environmental conditions also influenced movement patterns. Peak activity occurred in late winter and spring, with secondary increases in late summer and fall, corresponding to established migration periods in the Delta (Calhoun 1952 ; Chadwick 1967 ; Stevens et al. 1987 ; Sabal et al. 2019 ). The 2013–2015 drought produced elevated temperatures and reduced flows, conditions known to alter hydrodynamics and prey availability. Observed mobility was higher in dry years, consistent with earlier downstream movements documented during drought periods (Goertler et al. 2021 ). If similar conditions occur more frequently under future climate scenarios, shifts in movement timing may become more common, potentially increasing temporal overlap between Striped Bass and native fishes (Goertler et al. 2021 ). Local habitat characteristics may further influence behavior. CCF is shallow, hydrodynamically simple, and partially isolated from adjacent channels (MacWilliams and Gross 2013 ; Shu and Ateljevich 2017 ). These features, coupled with periodic radial-gate operations, can constrain emigration and promote repeated use of particular forebay areas (Bolster 1986 ; Clark et al. 2009 ). Intrinsic factors, environmental conditions, and structural features therefore interact to produce the behavioral diversity observed among Striped Bass at CCF. Patterns observed at CCF are consistent with broader descriptions of Striped Bass movement ecology in other systems. Striped Bass populations along the Atlantic coast exhibit divergent spatial strategies, ranging from highly mobile individuals to residents with small home ranges (Secor and Piccoli 1996 ; Ng et al. 2007 ; Gahagan et al. 2015 ; Secor et al. 2020b ) and comparable distributional groups have been documented in the Plum Island Estuary (Taylor et al. 2021 ). In these systems, older individuals often occupy larger spatial ranges, although all behavioral types may occur across age classes, consistent with patterns observed in the present study. Taken together, these comparisons indicate that the behavior classes observed at CCF align with broader, species-wide movement strategies and provide context for evaluating the factors contributing to behavioral variation within the forebay. Furthermore, movement classifications like those identified here have been reported across diverse aquatic species. Brodie et al. ( 2018 ) found four functional movement classes across 92 species, demonstrating that multimodal space-use strategies are common among aquatic organisms. Several considerations should be taken into account when interpreting these results. Cluster analysis can produce statistically distinct groups that may not always correspond to biologically meaningful differences; however, the behavior classes identified here exhibited consistent patterns in space use and movement, and silhouette-width validation supported their interpretability. Class assignments were also stable across alternative model configurations. The daily time step used to summarize detections limited the resolution of fine-scale movements and likely underestimated mobility for some individuals, but this constraint was applied uniformly and therefore does not affect relative comparisons among fish. Additional data gaps may influence interpretation. The sex of tagged individuals was not determined, despite known sex-specific migration patterns (Kohlenstein 1981 ; Secor and Piccoli 1996 ; Secor et al. 2020a,b). Most tagged fish were within age classes in which both sexes are known to express a wide range of movement behaviors, and all age classes were represented across all behavior classes, reducing the likelihood that sex-specific differences alone explain the observed behavioral structure. The spatial extent of the receiver array also limited detection of fish that moved outside monitored regions. While this likely reduced observations of highly mobile individuals, the array captured key access points and movement corridors within CCF and reliably distinguished relative differences in movement behavior among detected individuals. Expanded receiver coverage across the Delta would allow further evaluation of whether these behavior classes persist beyond the forebay. Conclusions and Management Implications Predator removal has long been explored as a tool for reducing predation pressure on native fishes in the Delta, yet broad-scale removal programs have yielded mixed results. Previous efforts have often produced limited improvements in survival of native prey species (Beamesderfer et al. 1996 ; Mueller 2009 ; Michel et al. 2020 ), and compensatory increases in consumption by remaining predators can offset removal benefits (Beamesderfer et al. 1996 ). These challenges highlight the need for more targeted approaches. Behavioral heterogeneity has direct implications for the effectiveness of predator removal. Removal efforts that selectively capture lower-mobility behavior types may impose selective pressures favoring more mobile individuals (Eldridge 1988 ; Secor and Piccoli 1996 ). Repeatedly targeting predictable behavioral types could shift the behavioral composition of the Striped Bass population and influence long-term predator dynamics. Incorporating information on behavioral classes can therefore improve the design of removal strategies. The behavior classifications developed in this study provide a framework for refining the timing, location, and methods used for predator removal. Seasonal peaks in residency and movement highlight periods when particular behavior classes may be more vulnerable, allowing for more focused removal windows. Telemetry data can also identify areas where resident or “Inside Roamer” classes consistently concentrate, enabling targeted removal, trapping, or habitat-modification efforts intended to reduce predation risk. Vulnerability to removal differs among behavior classes. Individuals remaining near forebay structures are more susceptible to localized or passive removal methods such as electrofishing or trapping, whereas mobile groups are less likely to encounter these gears. Although management of highly mobile predators remains difficult, long-term programs targeting Northern Pikeminnow in the Columbia River Basin demonstrate that directed angler harvest can reduce predation risk under certain circumstances, though applicability to other species remains uncertain (Friesen and Ward 1999 ). Angler-based programs in the Delta are constrained by legal size limits, as most Striped Bass encountered during removal efforts are below the harvest threshold (Cane 2017; Wilder et al. 2018). Adjustments to size or bag limits, applied seasonally or spatially and informed by telemetry-derived movement patterns, could increase removal feasibility where consistent with regulatory frameworks (Beamesderfer 2020). Cluster analysis captured consistent differences in space use and mobility and illustrated substantial behavioral heterogeneity within this population among the four classes. Accounting for this variation has important implications for predator control. Behavior-based information can guide the timing and placement of removal efforts, clarify differential vulnerability among behavioral types, and highlight potential selective outcomes of repeated removal. Integrating behavioral heterogeneity into predator-management planning may enhance the effectiveness and efficiency of actions aimed at reducing predation pressure on native fishes in the Delta. Abbreviations CCF Clifton Court Forebay CDWR California Department of Water Resources cm centimeter(s) cfs cubic feet per second CPE Continuous Presence Event Delta Sacramento—San Joaquin Delta DF degrees of freedom ha hectare(s) HTI Hydroacoustic Technology, Incorporated IQR interquartile range kg kilogram(s) km kilometer(s) m meter(s) mg/L milligrams per liter mm millimeter(s) PAM Partitioning Around Medoids PIT Passive Integrated Transponder SWP State Water Project USFWS United States Fish and Wildlife Service Declarations Ethics Statement All work with fish was reviewed and approved by the California Department of Fish and Wildlife who issued Scientific Collecting Permit SC-10286 and Consistency Determination 2080-2009-011-00 to the California Department of Water Resources, and was performed in compliance with the requirements issued by NOAA Fisheries in their 2009 Section 7 Biological and Conference Opinion on the Long-term Operation of the Central Valley Project and State Water Project issued to the California Department of Water Resources and the U.S. Bureau of Reclamation. Consent for Publication Not Applicable Availability of Data and Materials Data and findings from this study are available from the California Department of Water Resources upon request. Competing Interests None Declared Funding Funding for this project was provided by the California Department of Water Resources (https://water.ca.gov/) Authors’ Contributions TS contributed the analysis and interpretation of data and substantial portions of the text. EC and AK provided technical expertise for the analysis, review of analytical code and text. RW, SB, and PB contributed substantial portions of the text. PH provided manuscript formatting oversight and coordination. All authors read and approved the final manuscript. Acknowledgements The authors thank Javier Miranda, Kevin Clark, Matt Silva, Veronica Wunderlich, and Matthew Reeve for their contributions during the study, and the many staff members of the California Department of Water Resources, ICF, and Environmental Science Associates who collected data in support of this study. The authors would especially like to thank Dr. Cameron Turner (1979–2019), who made significant contributions to the original analysis of these data and provided his expertise and guidance. References Arostegui MC, Smith JM, Kagley AN, Spilsbury-Pucci D, Fresh KL, Quinn TP. Spatially clustered movement patterns and segregation of subadult Chinook salmon within the Salish Sea. Mar Coastal Fisheries. 2017;9(1):1–12. Beamesderfer R. Managing predators and competitors: deciding when intervention is effective and appropriate. Fisheries. 2000;25(6):18–23. Beamesderfer R, Ward D, Nigro A. Evaluation of the biological basis for a predator control program on northern squawfish ( Ptychocheilus oregonensis ) in the Columbia and Snake rivers. Can J Fish Aquat Sci. 1996;53:2898–908. Bolster BC. 1986. Movement patterns of striped bass ( Morone saxatilis ) in Clifton Court Forebay, Contra Costa County, California. Master’s thesis. California State University, Sacramento. Brodie S, Lédée EJI, Heupel MR, Babcock RC, Campbell HA, Gledhill DC, Hoenner X, Huveneers C, Jaine FRA, Simpfendorfer CA, Taylor MD, Udyawer V, Harcourt RG. Continental-scale animal tracking reveals functional movement classes across marine taxa. Sci Rep. 2018;8(1):3717. Calhoun AJ. Annual migrations of California striped bass. Calif Fish Game. 1952;38(3):391–403. Capello M, Robert M, Soria M, Potin G, Itano D, Holland K, Deneubourg J-L, Dagorn L. A methodological framework to estimate the site fidelity of tagged animals using passive acoustic telemetry. PLoS ONE. 2015;10(8):e0134002. Castillo G, Morinaka J, Lindberg J, Fujimura R, Baskerville-Bridges B, Hobbs J, Tigan G, Ellison L. 2012. Pre-screen loss and fish facility efficiency for Delta Smelt at the South Delta’s State Water Project, California. San Francisco Estuary Watershed Sci 10(4). Chadwick HK. Recent migrations of the Sacramento–San Joaquin River Striped Bass population. Trans Am Fish Soc. 1967;96(3):327–42. Clark K, Bowen M, Mayfield R, Zehfuss K, Taplin J, Hanson C. Quantification of pre-screen loss in juvenile steelhead in Clifton Court Forebay. California Department of Water Resources, Bay-Delta Office; 2009. Dill WA, Cordone AJ. History and status of introduced fishes. California, 1871–1996. Volume 178. California Department of Fish and Game Fish Bulletin; 1997. Dunker KJ, Sepulveda AJ, Massengill RL, Olsen JB, Russ OL, Wenburg JK, Antonovich A. Potential of environmental DNA to evaluate Northern Pike ( Esox lucius ) eradication efforts: an experimental test and case study. PLoS ONE. 2016;11(9):e0162277. DWR (California Department of Water Resouces). 2016. Clifton Court Forebay Predator Reduction Electrofishing Study annual report 2016. California Department of Water Resources, Sacramento. DWR (California Department of Water Resouces). 2017. Clifton Court Forebay Predator Reduction Electrofishing Study annual report 2017. California Department of Water Resources, Sacramento. DWR (California Department of Water Resouces). 2018. Clifton Court Forebay Predator Reduction Electrofishing Study annual report 2018. California Department of Water Resources, Sacramento. DWR (California Department of Water Resources). Clifton Court Forebay predation studies. Final report. Bay-Delta Office; 2020. DWR (California Department of Water Resources). No date. Water year hydrologic classification indices. Available: https://cdec.water.ca.gov/reportapp/javareports?name=WSIHIST Eldridge MB. 1988. Life history strategies and tactics of Striped Bass ( Morone saxatilis ) Walbaum. Thesis, Oregon State University, Corvallis. Espinoza M, Lédée EJI, Smoothey AF, Heupel MR, Peddemors VM, Tobin AJ, Simpfendorfer CA. Intra-specific variation in movement and habitat connectivity of a mobile predator revealed by acoustic telemetry and network analyses. Mar Biol. 2021;168(6):80. Feyrer F, Nobriga ML, Sommer TR. Multidecadal trends for three declining fish species: habitat patterns and mechanisms in the San Francisco Estuary, California, USA. Can J Fish Aquat Sci. 2007;64(4):723–34. Finn JT, Brownscombe JW, Haak CR, Cooke SJ, Cormier R, Gagne T, Danylchuk AJ. Applying network methods to acoustic telemetry data: modeling the movements of tropical marine fishes. Ecol Model. 2014;293(C):139–49. Friesen TA, Ward DL. Management of Northern Pikeminnow and implications for juvenile salmonid survival in the lower Columbia and Snake rivers. North Am J Fish Manag. 1999;19(2):406–20. Gahagan B, Fox D, Secor D. Partial migration of striped bass: revisiting the contingent hypothesis. Mar Ecol Prog Ser. 2015;525:185–97. Gingras M. 1997. Mark/recapture experiments at Clifton Court Forebay to estimate pre-screening loss to juvenile fishes: 1976–1993. Interagency Ecological Program. Gingras M, McGee M. 1997. A telemetry study of Striped Bass emigration from Clifton Court Forebay: implications for predator enumeration and control. Technical Report 54. Interagency Ecological Program for the San Francisco Bay/Delta Estuary. Goertler P, Mahardja B, Sommer T. Striped bass ( Morone saxatilis ) migration timing driven by estuary outflow and sea surface temperature in the San Francisco Bay-Delta, California. Sci Rep. 2021;11(1):1510. Grossman GD, Essington T, Johnson B, Miller J, Monsen NE, Pearsons TN. Effects of fish predation on salmonids in the Sacramento River–San Joaquin Delta and associated ecosystems. Sacramento: California Department of Fish and Wildlife; 2013. Grothues TM, Able KW, Carter J, Arienti TW. 2009. Migration patterns of Striped Bass through nonnatal estuaries of the U.S. Atlantic coast. American Fisheries Society Symposium 69:135–150. Grout DE. Interactions between striped bass (Morone saxatilis) rebuilding programmes and the conservation of Atlantic salmon (Salmo salar) and other anadromous fish species in the USA. ICES J Mar Sci. 2006;63:1346e1352. Hennig C. Cluster-wise assessment of cluster stability. Comput Stat Data Anal. 2007;52(1):258–71. Hollema HM, Kneebone J, McCormick SD, Skomal GB, Danylchuk AJ. Movement patterns of Striped Bass ( Morone saxatilis ) in a tidal coastal embayment in New England. Fish Res. 2017;187:168–77. Innovasea. 2023. HTI Coded Transmitters – 307 kHz. Available: https://www.innovasea.com/wp-content/uploads/2022/01/Innovasea-307khz_tags-ds-0122.pdf Jassby AD, Kimmerer WJ, Monismith SG, Armor C, Cloern JE, Powell TM, Schubel JR, Vendlinski TJ. Isohaline position as a habitat indicator for estuarine populations. Ecol Appl. 1995;5(1):272–89. Jorgensen SJ, Arnoldi NS, Estess EE, Chapple TK, Rückert M, Anderson SD, Block BA. Eating or meeting? Cluster analysis reveals intricacies of white shark ( Carcharodon carcharias ) migration and offshore behavior. PLoS ONE. 2012;7(10):e47819. Kessel S, Hondorp D, Holbrook C, Boase J, Chiotti J, Thomas M, Wills T, Roseman E, Drouin R, Krueger C. Divergent migration within Lake Sturgeon ( Acipenser fulvescens ) populations: multiple distinct patterns exist across an unrestricted migration corridor. J Anim Ecol. 2017;87(1):259–73. Kimmerer WJ, Gross ES, MacWilliams ML. Is the response of estuarine nekton to freshwater flow in the San Francisco Estuary explained by variation in habitat volume? Estuaries Coasts. 2009;32:375–89. Kimmerer WJ, MacWilliams ML, Gross ES. 2013. Variation of fish habitat and extent of the low-salinity zone with freshwater flow in the San Francisco Estuary. San Francisco Estuary Watershed Sci, 11(4). Kohlenstein LC. On the proportion of the Chesapeake Bay stock of striped bass that migrates into the coastal fishery. Trans Am Fish Soc. 1981;110(1):168–79. Lowe MR, Holbrook CM, Hondorp DW. Detecting commonality in multidimensional fish movement histories using sequence analysis. Anim Biotelem. 2020;8(1):10. MacWilliams ML, Gross ES. 2013. Hydrodynamic simulation of circulation and residence time in Clifton Court Forebay. San Francisco Estuary Watershed Sci, 11(2). Michel CJ, Smith JM, Lehman BM, Demetras NJ, Huff DD, Brandes PL, Israel JA, Quinn TP, Hayes SA. Limitations of active removal to manage predatory fish populations. North Am J Fish Manag. 2020;40(1):3–16. Moyle PB. Inland fishes of California: revised and expanded. University of California Press; 2002. Mueller G. Predatory fish removal and native fish recovery in the Colorado River mainstem: What have we learned? Fisheries. 2009;30(9):10–9. Ng CL, Able KW, Grothues TM. Habitat use, site fidelity, and movement of adult Striped Bass in a southern New Jersey estuary based on mobile acoustic telemetry. Trans Am Fish Soc. 2007;136(5):1344–55. NMFS (National Marine Fisheries Service). 2009. Biological opinion and conference opinion on the long-term operations of the Central Valley Project and State Water Project. June 4, 2009. Ogle DH. Introductory fisheries analyses with R. Boca Raton, Florida: Chapman & Hall/CRC; 2016. Orsi JJ. The 1965–1967 migrations of the Sacramento–San Joaquin estuary Striped Bass population. Calif Department Fish Game. 1971;57(4):257–67. Patrick WS, Rulifson RA, Stellwag EJ. 2006. An investigation of Roanoke River striped bass migratory behavior using genetic and PIXE analysis. Final report to NC Sea Grant. FRG 04–EP. Quist MC, Isermann DA, editors. Age and growth of fishes: principles and techniques. Bethesda, Maryland: American Fisheries Society; 2017. Sabal MC, Michel CJ, Smith JM, Hampton A, Hayes SA. Seasonal movement patterns of Striped Bass ( Morone saxatilis ) in their nonnative range. Estuaries Coasts. 2019;42(2):567–79. Secor DH, O’Brien MHP, Gahagan BI, Fox DA, Higgs AL. and J. E. Best. 2020a. Multiple spawning run contingents and population consequences in migratory Striped Bass Morone saxatilis . PLoS ONE 15(11):e0242797. Secor DH, O’Brien MHP, Gahagan BI, Watterson JC, Fox DA. Differential migration in Chesapeake Bay Striped Bass. PLoS ONE. 2020b;15(5):e0233103. Secor DH, Piccoli PM. Age- and sex-dependent migrations of Striped Bass in the Hudson River as determined by chemical microanalysis of otoliths. Estuaries. 1996;19(4):778–93. Shu Q, Ateljevich E. 2017. Clifton Court Forebay Transit Time Modeling Analysis. California Department of Water Resources, Bay-Delta Office, Delta Modeling Section Sacramento, CA. State Water Board (State Water Resources Control Board). 2000. Revised Water Right Decision 1641. In the matter of: implementation of water quality objectives for the San Francisco Bay/Sacramento–San Joaquin Estuary; a petition to change points of diversion of the Central Valley Project and the State Water Project in the southern Delta; and a petition to change places of use and purposes of use of the Central Valley Project. December 29, 1999; revised in accordance with Order WR 2000-02, March 15, 2000. Stevens DE, Chadwick HK, Painter RE. 1987. American Shad and Striped Bass in California’s Sacramento–San Joaquin River system. American Fisheries Society Symposium 1:66–78. Taylor RB, Mather ME, Smith JM, Boles KM. 2021. Can identifying discrete behavioral groups with individual-based acoustic telemetry advance the understanding of fish distribution patterns? Front Mar Sci 8. USFWS (U.S. Fish and Wildlife Service). 2011. Fact sheet: AQUI-S®E & AQUI-S®20E (sedative/anesthetic) Investigational New Animal Drug (INAD) 11–741 and study protocol. Available: https://www.fws.gov/guidance/sites/guidance/files/documents/AQUISE-study-protocol_0.pdf Wagner RW, Stacey M, Brown LR, Dettinger M. Statistical models of temperature in the Sacramento–San Joaquin Delta under climate-change scenarios and ecological implications. Estuaries Coasts. 2011;34:544–56. Wilcox RR. Introduction to Robust Estimation and Hypothesis Testing. 3rd ed. Academic; 2012. Wingate RL, Secor DH, Kraus RT. Seasonal patterns of movement and residency by Striped Bass within a subestuary of the Chesapeake Bay. Trans Am Fish Soc. 2011;140(6):1441–50. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviews received at journal 02 Mar, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers invited by journal 11 Feb, 2026 Editor assigned by journal 02 Feb, 2026 Submission checks completed at journal 30 Jan, 2026 First submitted to journal 28 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8724316","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591127744,"identity":"adbd2c72-16ef-433b-81db-49d838698e39","order_by":0,"name":"Taylor Spaulding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYNCCAmYeBgbmA8Qp5gGTBiAtbAmkaQExDYjTYs9/9uBnHgNrGXP+NR8f/qips+dnP8D44WMOHlsk8pKleQzSeSxnvN1szHPscOLMngRmyZnb8GnhMQBqOcxjcOPsNmkGtgMJBjcY2Jh58WnhP2P8G6LlzPOfP/7V2RPWwpBjBrHlfA8bA28bM+MGglpu5KVZzgH6xeAGm7E0bx/IL4nNeP3C3n/28I03Fdb2BucPP/z44xsoxA4f/PARjxZYzDAwSCTARBgb8KlH0sJ/gIDCUTAKRsEoGLEAACTbSuyAb36ZAAAAAElFTkSuQmCC","orcid":"","institution":"Environmental Science Associates","correspondingAuthor":true,"prefix":"","firstName":"Taylor","middleName":"","lastName":"Spaulding","suffix":""},{"id":591127746,"identity":"a4ef1efb-4fbd-491e-b55a-493a56567531","order_by":1,"name":"Andrew Kalmbach","email":"","orcid":"","institution":"ICF","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Kalmbach","suffix":""},{"id":591127748,"identity":"0a7999e9-a353-4636-97f5-704841a6d654","order_by":2,"name":"Eric Chapman","email":"","orcid":"","institution":"ICF","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Chapman","suffix":""},{"id":591127751,"identity":"d446d32e-1da5-492a-9294-1609f5879581","order_by":3,"name":"Paul Berman","email":"","orcid":"","institution":"Environmental Science Associates","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Berman","suffix":""},{"id":591127753,"identity":"9ca3423b-9732-46a3-8bd8-41da38dd1d83","order_by":4,"name":"Richard Wilder","email":"","orcid":"","institution":"ICF","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Wilder","suffix":""},{"id":591127755,"identity":"dbc927bd-3bfa-4cd4-be20-a7ceed85e979","order_by":5,"name":"Parisa Hurley","email":"","orcid":"","institution":"California Natural Resources Agency","correspondingAuthor":false,"prefix":"","firstName":"Parisa","middleName":"","lastName":"Hurley","suffix":""},{"id":591127763,"identity":"82ca4d95-940a-4836-8ac6-c5b73d2d9457","order_by":6,"name":"Steve Brumbaugh","email":"","orcid":"","institution":"California Natural Resources Agency","correspondingAuthor":false,"prefix":"","firstName":"Steve","middleName":"","lastName":"Brumbaugh","suffix":""}],"badges":[],"createdAt":"2026-01-28 18:09:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8724316/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8724316/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102842558,"identity":"353cfde4-d560-4421-b267-d65df1f48212","added_by":"auto","created_at":"2026-02-17 12:31:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129781,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea) Map of the Sacramento–San Joaquin Delta showing the location of the Curtis Landing acoustic array (star), the Sacramento and San Joaquin rivers and associated Delta sloughs and channels (gray), and the location of Clifton Court Forebay with additional detail in b) the map of Clifton Court Forebay (outlined in dark gray) and surrounding waterways (outlined in light gray). Acoustic receiver locations are displayed based on year of installation: 2013 (triangles), 2015 (circle). Receiver “site groups” with overlapping receiver detection ranges are circled with dashed lines. Points of interest indicated within the text are indicated with arrows.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8724316/v1/0f9c996ef0ae514aaac2be07.png"},{"id":102842553,"identity":"0218fc87-6088-4f89-9946-aade4c23a242","added_by":"auto","created_at":"2026-02-17 12:31:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41977,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea) The total duration of residency (days) a tag spent inside or outside Clifton Court Forebay for each behavior state class. Residency is defined as spending \u0026gt;= 1 day without being detected at the opposite location. Residency duration was summed for each location over the period duration (183 days) for each behavior state b) The proportion of the residency duration that an individual went undetected while assumed to still be in residency.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8724316/v1/b709ddb4c7732ce7e5691f66.png"},{"id":103049216,"identity":"44820d92-97ea-4186-9999-d0c69c3d4884","added_by":"auto","created_at":"2026-02-20 07:38:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":35517,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea) The total number of receiver visits in each location (Inside CCF or Outside CCF) per period for each behavior state class. Receiver visits in each location were summed separately over the period duration (183 days) for each behavior state. b) The average number of unique receiver visits per day for each behavior state class. Receiver visits per day were averaged over the period duration (183 days) for each behavior state.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8724316/v1/03b253bc0633c855a729471a.png"},{"id":102962825,"identity":"b4242354-ca7f-424c-a6f6-c0ebe8dad95a","added_by":"auto","created_at":"2026-02-19 04:11:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":124100,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMinimum daily distance metrics (min, mean, max) and total Period distance for each behavior state class. Daily distance metrics were aggregated over the entire period, while total period distance represents the summed daily distance over the entire period. Horizontal dashed lines show distances between key locations within the study area (the radial gates, the canal intersection due east of the radial gates, and the nearest array edge). The “Inside Resident (Undetected)” class has no distance metrics as they have few if any receiver visits, and thus show a distance of 0 for all distances.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8724316/v1/f9b7df560d2d7d101508e377.png"},{"id":102842555,"identity":"fb67eed6-03dd-405b-b570-ea32a86303f4","added_by":"auto","created_at":"2026-02-17 12:31:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":36932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea) The total number of transits (Crossing CCF, Exiting CCF, Entering CCF) for each behavior state class. Median values for “Inside Resident – Undetected”, “Inside Roamer”, and “Outside Resident – Undetected” were 0, though some fish, especially in the “Inside Roamer” class had a handful of exits and entries observed. b) The average number of days between exits or entries for each class. If fish did not have at least 1 exit or entry in a period this value was set to the period duration (183 days).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8724316/v1/ad21ea92cf71d0d029b2c6fc.png"},{"id":102963664,"identity":"d757b06f-dd89-4f1b-a4cc-2d3b237742fd","added_by":"auto","created_at":"2026-02-19 04:19:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":156665,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClass compositions or means for each of the explanatory covariates. a) The proportion of each 6-month period represented within each behavior state class. b) The proportion of water year types represented within each behavior state class. Water year types were weighted by the proportion of occurrence to account for biases in the frequency of water year type occurrence. c) The proportion of age-classes represented within each behavior state class. Age-classes were weighted by the proportion of each age-class caught to account for biases in the initial age-class distribution d) The mean percent of time the radial gates were open during the 6-month period for each behavior state class.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8724316/v1/98b71d1992447990180ba156.png"},{"id":102842559,"identity":"a5fe3a72-811e-4a84-a119-733a316739fb","added_by":"auto","created_at":"2026-02-17 12:31:58","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":310610,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTiming distributions of various transits for each behavior state class including a) exits, b) entries, and c) crossings, and d) the overall timing distribution across all behavior state classes for each of the transits and overall transits. The total number of movements contributing to the distribution is shown above each density plot. “Inside Resident – Undetected and “Outside Resident – Undetected” classes do not have enough movements to create a density plot.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8724316/v1/23e7e9480a195c12087a21e1.png"},{"id":103056460,"identity":"10185613-015f-42d9-9365-67417412a401","added_by":"auto","created_at":"2026-02-20 09:10:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3461303,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8724316/v1/291a88fd-e847-4dd0-bc34-63fb81d29fb5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identifying Movement Behavior States of Striped Bass in a Large Regulating Forebay Using Cluster Analysis","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eStriped Bass are native to the East Coast of the United States and were introduced into California\u0026rsquo;s Sacramento\u0026ndash;San Joaquin Delta (Delta) from New Jersey in 1879 (Dill and Cordone \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In their native range, Striped Bass are highly mobile and anadromous. Similar behaviors have been documented in California (Moyle \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Striped Bass are gregarious pelagic predators known to consume juvenile Pacific salmonids (Moyle \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Grossman et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Grout 2016). Although individual behavior is highly variable, in California, Striped Bass generally migrate through the Delta to upstream rivers to spawn from February to May, then move back downstream to the San Francisco Bay and ocean from June to October; some individuals move back to freshwater in the Delta to overwinter (Calhoun \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1952\u003c/span\u003e; Sabal et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStriped Bass are present throughout the Delta, including in the Clifton Court Forebay (CCF), a regulating reservoir in the South Delta that is part of the State Water Project (SWP). Operated and maintained by the California Department of Water Resources (CDWR), the SWP conveys water through the CCF to municipal and agricultural users throughout California. Predation on fish species of concern in the CCF has long been an issue for resource managers and multiple attempts have been made to quantify effects on these species (Clark et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Castillo et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Loss of juvenile salmonids in the CCF has been estimated to range from 66% to 99% and Striped Bass predation is thought to be the largest contributor to this loss (Gingras \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Gingras and McGee \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Clark et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). To address the high predation risk of salmonids, several studies were implemented to understand predation and the potential impacts to imperiled species in the CCF, including this acoustic telemetry study to determine the presence and movement behavior of Striped Bass.\u003c/p\u003e \u003cp\u003eRecent improvements in the accessibility of acoustic telemetry data and computer processing have resulted in the increased use of acoustic telemetry to study behaviors of a wide range of highly mobile species, such as tuna and jack (Capello et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), sharks and rays (Jorgensen et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), sturgeon (Kessel et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and salmon (Arostegui et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in marine (Finn et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Capello et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Brodie et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Espinoza et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), estuarine (Arostegui et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Taylor et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and freshwater (Arostegui et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lowe et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) habitats. Recent applications have used a variety of tools (e.g., network, cluster, and sequence analysis) to identify life history strategies and to inform fisheries management by grouping individuals based on behaviors (Arostegui et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Brodie et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lowe et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Espinoza et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Taylor et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chen et al., no date).\u003c/p\u003e \u003cp\u003eThe present study applies cluster analysis to acoustic telemetry data from Striped Bass within the CCF to test the hypothesis that individuals exhibit discrete behavioral classes that differ in their habitat residency and activity level. We further hypothesize that these behavior classes correspond to differing vulnerabilities to predator control (i.e. removal) actions, such as active (e.g., electrofishing) or passive (e.g., fyke net) capture techniques. By isolating and characterizing behaviorally distinct groups, this study seeks to provide a basis for optimizing predator control strategies by informing when and where to target removal efforts and which capture methods may be most effective based on predator behaviors.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eStudy Site\u003c/span\u003e \u003c/p\u003e \u003cp\u003eClifton Court Forebay (CCF) is a shallow (~\u0026thinsp;900 ha; mean depth\u0026thinsp;~\u0026thinsp;1.5 m) South Delta reservoir near Tracy, California, that provides operational flexibility to manage SWP flows (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Inflow from the Delta enters through five radial gates on the southeast corner; water exits westward via an intake canal leading to the Harvey O. Banks Pumping Plant (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Along the intake canal, the John E. Skinner Fish Protective Facility \u0026ldquo;salvages\u0026rdquo; small/juvenile fishes from diverted water and trucks them to release sites in the Delta. A localized scour hole (~\u0026thinsp;20 m) produced by high flows into CFF occurs just northwest of the radial gates. Outside of CCF, the South Delta comprises natural and engineered sloughs/canals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAcoustic Telemetry\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eArray\u003c/span\u003e \u003c/p\u003e \u003cp\u003eThe receiver array (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) was designed to detect presence/absence of tagged fish and to infer transits across the radial gates. Each site comprised an in-water hydrophone (HTI Model 590) and beacon tag (HTI Model 795) with a shore-based logger (HTI Model 295/395) and power. Array deployment began in 2013 with nine arrays and expanded in 2015 with an additional five (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e); one external site at Curtis Landing was added in 2016 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Detections from 5/20/13 to 12/31/18 were included in analysis, with quality assurance/quality control provided by the vendor, HTI.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eFish Capture and Tagging\u003c/span\u003e \u003c/p\u003e \u003cp\u003eFrom 03/12/2013 to 12/30/2016 3,969 Striped Bass were captured and released within the CCF. Each fish was measured for fork length and weight, and scanned for PIT tag. If no PIT tag was detected, a subset (n\u0026thinsp;=\u0026thinsp;590 fish) were surgically implanted with combined acoustic\u0026thinsp;+\u0026thinsp;PIT tags (HTI Model 795), and the rest were implanted with just a PIT tag. If already tagged, the fish was released. Three sizes of HTI tags with different battery life expectancies were used depending on fish size (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Surgical procedures followed Wingate et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Sedation with AQUI-S\u0026reg;20E (AquaTactics; 35 milligrams per liter) was authorized under the U.S. Fish and Wildlife Service\u0026rsquo;s Investigational New Animal Drug program (USFWS 2011). Fish were released near their capture sites after recovery, defined as regaining equilibrium and free swimming.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAcoustic tag size, manufacturer\u0026rsquo;s life expectancy, and number deployed in Striped Bass in Clifton Court Forebay, May 2013\u0026ndash;December 2016.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTag Model #\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManufacturer\u0026rsquo;s\u003c/p\u003e \u003cp\u003eTag Life Expectancy (Innovasea \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStriped Bass Tagged\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimum Length (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaximum Length (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMinimum Weight (kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eMaximum Weight (kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220\u0026ndash;400 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u0026ndash;4 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Recorded\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eLife expectancy from the manufacturer based on a single-pulse, 1-millisecond pulse width, with a 10-second pulse rate interval at 10\u0026deg;C.\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eFish with unrecorded tag types were assigned a tag life expectancy of 1,460 days, the median of LY and LZ tags, which were used exclusively during their tagging years.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eScale Aging\u003c/span\u003e \u003c/p\u003e \u003cp\u003eScales from 222 of the captured Striped Bass were imaged under microscope (10x/20x) and 10 megapixel digital microscope camera (AmScope\u0026trade; Model SM-2T-WF) and aged independently by two readers adapting methods from Quist and Isermann (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); a third reader aged a random subset from 78 fish. Ages were assigned when \u0026ge;\u0026thinsp;2 readers agreed.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eData Analysis\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eAll data analysis was conducted in R (R Core Team 2025); specific packages used in clustering and statistical analyses are mentioned below. Detections were first filtered and processed to develop movement metrics which were used to classify groups of similar behavior. After successful clustering, the resulting groups were compared against each other with respect to the movement metrics to identify biologically meaningful movement patterns. Then the groups were compared using explanatory variables which were hypothesized to be important factors driving behavior patterns.\u003c/p\u003e \u003ch3\u003eDetection Filtering and Processing\u003c/h3\u003e\n\u003ch4\u003eDetection Temporal Aggregation\u003c/h4\u003e\n\u003cp\u003eTo minimize timing errors from receiver clock drift and daylight-saving adjustments, detections were aggregated to a daily time step: one record per fish per receiver per day. This aggregation is the temporal base for subsequent location state assignment.\u003c/p\u003e\n\u003ch4\u003eTag life, Loss, and Mortality\u003c/h4\u003e\n\u003cp\u003eThe detection period for analysis of each fish ended at the earlier of the median manufacturer-estimated tag life for that tag size or following a screening process to identify tag shedding/mortality. To screen for tag shedding/mortality, \u0026ldquo;site groups\u0026rdquo; were delineated, defined as groups of receivers with overlapping maximum detection ranges (Figure 2). The total time a fish was detected within a single site group with no other detections was analyzed, and all events exceeding the 99th-percentile duration of the specific site group were flagged as likely shed tags or mortalities. Detection histories were then checked for any movement after flagged events; histories with no movement were truncated at the flagged event and assumed to be either a shed tag or mortality). This screening process identified 30 fish meeting the tag shedding/mortality criterion and, therefore, their detection histories were truncated.\u003c/p\u003e\n\u003ch4\u003eLocation States\u003c/h4\u003e\n\u003cp\u003eEach day, a fish was assigned one of three location states based on receiver detections:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eInside: detections only inside of the radial gates in CCF that day.\u003c/li\u003e\n \u003cli\u003eOutside: detections only outside of the radial gates and CCF that day.\u003c/li\u003e\n \u003cli\u003eTransit: detections both inside and outside of the radial gates and CCF that day.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBecause detections were aggregated to a daily time step and \u0026ldquo;Transit\u0026rdquo; location state is constrained to a single day, movement direction is defined strictly from day-to-day transitions:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eExit: an Inside -\u0026gt; Transit -\u0026gt; Outside sequence indicates \u0026ge;1 exits through the radial gates during the Transit day.\u003c/li\u003e\n \u003cli\u003eEntry: an Outside -\u0026gt; Transit -\u0026gt; Inside sequence indicates \u0026ge;1 entries during the Transit day.\u003c/li\u003e\n \u003cli\u003eEntry and Exit: Outside -\u0026gt; Transit -\u0026gt; Outside sequence indicates \u0026ge;1 entries, followed by \u0026ge;1 exits during the Transit day\u003c/li\u003e\n \u003cli\u003eExit and Entry: an Inside -\u0026gt; Transit -\u0026gt; Inside sequence indicates \u0026ge;1 exits, followed by \u0026ge;1 entries during the Transit day\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhen a detection history ended (due to assumed end of tag life) with a period of non-detection and the last observed location state as \u0026ldquo;Transit\u0026rdquo;, remaining undetected time was coded \u0026ldquo;Unresolved\u0026rdquo; because direction could not be assigned without post-Transit state confirmation (n = 4 fish).\u003c/p\u003e\n\u003cp\u003eThese rules were used solely to derive transit summary metrics (e.g., exit/entry counts, inter-Transit timing) and are intended to provide minimum transit counts, acknowledging that a fish may have crossed the radial gates more than once within a Transit day.\u003c/p\u003e\n\u003ch4\u003eDetection Gaps\u003c/h4\u003e\n\u003cp\u003eDays without detections were filled using Continuous Presence Events (CPEs) adapted from Capello et al. (2015) without sub-daily time constraints (inapplicable under daily aggregation). CPEs extend the last known location state forward until a day on which a new state is observed. However, the transit of fish across the radial gates was assumed to be a near instantaneous event and, therefore, to avoid inflating time in transit, Transit was constrained to a maximum of one day. Days without detections immediately before/after Transit days were assigned to the corresponding non-Transit state (Inside/Outside). Consecutive Transit days are possible when a fish is detected both Inside and Outside on those days. It is possible that a fish transited the radial gates multiple times over consecutive days without being detected, given that the radial gates are an acoustically \u0026ldquo;noisy\u0026rdquo; area. However, constraining undetected Transit time to one day follows our conservative approach to movements.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eBehavior-State Clustering and Validation\u003c/h3\u003e\n\u003cp\u003eTo assess patterns of movement behavior and identify unique \u0026ldquo;behavior states\u0026rdquo;, an unsupervised cluster analysis using partitioning around medoids (PAM) on a Gower dissimilarity matrix was undertaken, following the general approach of several recent acoustic telemetry studies on behavior grouping\u0026nbsp;(Arostegui et al. 2017; Brodie et al. 2018; Lowe et al. 2020; Taylor et al. 2021; Espinoza et al. 2021). Detection data were aggregated into 6-month behavior states consisting of one state per fish per 6-month period (Period 1: December 18- June 18; Period 2: June 19- June 17). Period 1 represents the time of year when 98.8% of total juvenile salmonid catch occurs at the\u0026nbsp;John E. Skinner Fish Protective Facility based on salvage data (https://filelib.wildlife.ca.gov/Public/salvage/). We hypothesized that the behavior of Striped Bass might change due to the influx of this prey species. Each behavior state contains all detected movements by that fish within that 6-month period, summarized into metrics which describe the fish\u0026rsquo;s residency, habitat use, detectability, and movement (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Metrics identified for potential inclusion in clustering analyses.\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"62%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBiological Concept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eResidency\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDays of Residence Inside CCF\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDays of Residence Outside CCF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eHabitat Use\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal Number of Inside CCF Receiver Visits\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal Number of Outside CCF Receiver Visits\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal Number of All Receiver Visits\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAverage Number of Daily Receiver Visits\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eDetectability\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDays Spent Undetected Inside CCF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDays Spent Undetected Outside CCF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"9\"\u003e\n \u003cp\u003e\u003cem\u003eMovement\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDaily Minimum Distance Traveled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDaily Mean Distance Traveled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDaily Maximum Distance Traveled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePeriod Total Distance Traveled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNumber of Exits\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNumber of Entries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNumber of CCF Crossings\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNumber of All Movements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDays Between Movements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe above metrics were selected for potential inclusion in the cluster analysis because they had the correct resolution of data and are further based on factors that are inherent to movement patterns, such as distance, residence time, detection frequency, and timing of movements. The final data set contains a single row of metrics for each fish for each period in each year it was tracked, collectively called a \u0026ldquo;behavior state.\u0026rdquo;\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eClustering and Validation\u003c/h4\u003e\n\u003cp\u003ePrior to clustering, the Spearman correlation coefficient was calculated between each of the metrics (`stats::cor`, method = \u0026ldquo;spearman\u0026rdquo;). Metrics with an absolute coefficient value greater than 0.8 (`caret::findCorrelation`, cutoff = 0.8, exact = TRUE) were pruned. \u0026nbsp;Then, a Gower dissimilarity (`cluster::daisy`, metric = \u0026quot;gower\u0026quot;) matrix was created from the remaining set of predictors. Finally, this matrix was clustered with PAM (`cluster::pam`) with k ranging from 2 to 8 and selecting the k that maximized the mean silhouette width with backward elimination of variables to improve structure. This involved iteratively removing variables and re-computing the Gower dissimilarity matrix and PAM clustering for k = 2 \u0026ndash; 8, dropping each remaining variable individually and reevaluating for each candidate drop. The drop was accepted if either the new average silhouette width \u0026ge; 0.50 or it gained \u0026ge; 0.005 over the current solution. The final PAM solution was used to assign each behavior state record to a class. Predictor means and interquartile range were calculated for all variables (including those pruned due to correlation or to improve clustering) to derive class profiles and assign meaningful names to classes \u003cem\u003epost-hoc.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003eExplanatory Covariates and Movement Timing\u003c/h3\u003e\n\u003cp\u003eWe hypothesized that age-class, period, hydrological conditions as defined by State Water Resources Control Board Revised Water Right Decision 1641 water year (10/1 to 9/30) types (State Water Board 2000), and percent of time radial gates were open might play a role in the behaviors of individual fish and, therefore, may explain the results of behavior state clustering. Behavior states are repeated measures within fish. Therefore, to test class-covariate associations, design-based inference (`survey::svydesign`) grouped by tag ID was applied to the cluster classes. Categorical associations (i.e., period, water year type, and age-class) were evaluated using Rao-Scott adjusted Wald \u0026chi;\u0026sup2; tests (`survey::svychisq`, statistic = \u0026ldquo;adjWald\u0026rdquo;) while gate openness (percent of time open each day averaged over the 6-month period) was evaluated using a design-based linear model (`survey::svyglm`) and a joint Wald test (`survey::regTermTest`). Given that gate openness has the potential to violate assumptions due to heteroskedasticity, the suitability of modeling raw percentages was assessed by (i) examining the empirical distribution of gate openness to confirm values were not concentrated near 0% or 100%, (ii) estimating class-specific variances using survey-weighted variance estimates, which were comparable across classes, and (iii) inspecting design-based residuals from the fitted model, which did not indicate strong heteroskedasticity.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eAge Analysis\u003c/h4\u003e\n\u003cp\u003eTo assign age-classes, an age-length key was developed following Ogle (2016) using ages from scales and fork length with lengths binned to 1-cm categories to form the key. Unaged fish were assigned ages semi-randomly based on key proportions. For analyses, fish were grouped into three age-classes: 1-2 years, 3-5 years, and 6+ years. Estimated age-class at each detection was derived from days elapsed since capture.\u003c/p\u003e\n\u003cp\u003eWe also hypothesized that behavior classes might move differently at different times of the year given the known migration patterns of Striped Bass. Therefore, a brief qualitative investigation was conducted into the timing of Exit, Entry, and CCF Crossing events by plotting the distribution of movements throughout the year.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTagged Fish\u003c/span\u003e \u003c/p\u003e \u003cp\u003eThe final analysis set comprised 543 tagged individuals contributing 2,568 behavior states across 8 water years (median records per fish\u0026thinsp;=\u0026thinsp;3, IQR\u0026thinsp;=\u0026thinsp;5). There were 1,340 behavior states in Period 1 (December 18 \u0026ndash; June 18) and 1,228 states in Period 2 (June 19 \u0026ndash; December 17). There were 211 states comprised of Age 1\u0026ndash;2 fish, 1,063 states of Age 3\u0026ndash;5 fish, 1,286 states of Age 6\u0026thinsp;+\u0026thinsp;fish, and 8 states that were un-aged due to missing length data for the corresponding fish (n\u0026thinsp;=\u0026thinsp;2 fish). There were 1,268 states in Critical Dry water years, 700 states in Dry water years, 119 states in Below Normal water years, and 481 states in Wet water years.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eClustering\u003c/span\u003e \u003c/p\u003e \u003cp\u003eThe pruning based on metric correlation dropped six variables: outside residency, the total number of receivers visited at all locations, the mean number of receivers visited per day, the mean distance travelled per day during the period, the total distance traveled during the period, and the total number of all movements during the period. The backward elimination procedure retained the remaining 17 predictors and identified 4 classes with a mean silhouette\u0026thinsp;=\u0026thinsp;0.59. Class sizes were: \u0026ldquo;Commuters\u0026rdquo; = 334 behavior states, \u0026ldquo;Inside Residents \u0026ndash; Undetected\" = 1,103 behavior states, \u0026ldquo;Inside Roamers\u0026rdquo; = 622 behavior states, \u0026ldquo;Outside Residents \u0026ndash; Undetected\" = 509 behavior states (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Mean per-class silhouette widths were 0.59 (\u0026ldquo;Commuters\u0026rdquo;), 0.57 (\u0026ldquo;Inside Residents \u0026ndash; Undetected\"), 0.60 (\u0026ldquo;Inside Roamers\u0026rdquo;), and 0.61 (\u0026ldquo;Outside Residents \u0026ndash; Undetected\"), indicating well-structured clusters with moderate within-class cohesion and among-class separation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo assess whether extreme movement values (e.g., long-distance movements and long residence or non-detection intervals) disproportionately influenced class assignment cluster was repeated under increasing levels of winsorization (Wilcox \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Right-skewed movement variables were capped at the 99th, 95th, 90th, 80th, and 70th percentiles, and for each capped dataset Gower dissimilarities were computed, the PAM clustering was refit, the final number of classes was selected using maximum average silhouette width. Light winsorization (99%) lowered average silhouette width (0.59 to 0.53) without changing the four-class solution, indicating that high-movement individuals contribute biologically meaningful separation rather than acting as statistical outliers. Very strong winsorization (\u0026le;\u0026thinsp;70%) increased silhouette width (to 0.67) only after altering the solution (5\u0026thinsp;+\u0026thinsp;classes) and suppressing the long-distance \u0026ldquo;commuter\u0026rdquo; behavior. Therefore the uncapped four-class solution was retained for inference.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFinal clustering result statistics and mean (interquartile values) of the movement metrics defining each behavior classification. All data are presented per period.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommuters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInside Residents \u0026ndash; Undetected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInside Roamers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOutside Residents \u0026ndash; Undetected\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eClass Summary Statistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e# of Behavior States in Cluster\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e% of All Behavior States\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMean Silhouette Width\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDays of Residence (days, maximum possible\u0026thinsp;=\u0026thinsp;183)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eInside\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u0026nbsp;(44\u0026ndash;98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136\u0026nbsp;(91\u0026ndash;182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92\u0026nbsp;(37\u0026ndash;157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026nbsp;(0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOutside\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u0026nbsp;(44\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026nbsp;(0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026nbsp;(0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144\u0026nbsp;(103\u0026ndash;183)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReceiver Visits (# of visits)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eInside Total\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u0026nbsp;(19\u0026ndash;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52\u0026nbsp;(12\u0026ndash;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOutside Total\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u0026nbsp;(4\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026nbsp;(0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAll Sites Total\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69\u0026nbsp;(30\u0026ndash;92.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.1\u0026nbsp;(13\u0026ndash;76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDaily Mean\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u0026nbsp;(1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u0026nbsp;(1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDays Spent Undetected (days, maximum possible\u0026thinsp;=\u0026thinsp;183)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eInside\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026nbsp;(25\u0026ndash;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94\u0026nbsp;(100\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u0026nbsp;(30\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026nbsp;(0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOutside\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91\u0026nbsp;(88\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026nbsp;(0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u0026nbsp;(100\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistance (km)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDaily Minimum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026nbsp;(0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026nbsp;(0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDaily Mean\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8\u0026nbsp;(0.8\u0026ndash;3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u0026nbsp;(0\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDaily Maximum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.2\u0026nbsp;(2.4\u0026ndash;19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.4\u0026nbsp;(0\u0026ndash;2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePeriod Total\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103.1\u0026nbsp;(24.7\u0026ndash;129.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.1\u0026nbsp;(0\u0026ndash;42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.7\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMovements per Period (# of movements)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eExits\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u0026nbsp;(0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026nbsp;(0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u0026nbsp;(0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEntries\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u0026nbsp;(0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026nbsp;(0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCCF Crossing\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026nbsp;(0\u0026ndash;4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026nbsp;(0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026nbsp;(0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAll Movements\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u0026nbsp;(1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u0026nbsp;(0\u0026ndash;0)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u0026nbsp;(0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage time Between Movements (days, maximum possible\u0026thinsp;=\u0026thinsp;183)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDays Between Movements\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u0026nbsp;(4\u0026ndash;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183\u0026nbsp;(183\u0026ndash;183)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (0\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e182\u0026nbsp;(183\u0026ndash;183)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Note: metrics with means falling outside the interquartile range indicate right-skewed data, all such metrics were evaluated for appropriateness and clustering impact\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eClass Profiles\u003c/span\u003e \u003c/p\u003e \u003cp\u003eClasses differed across detectability and residency (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), receiver visits (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), distance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), and movement frequency (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) providing the ability to qualitatively describe the behavioral traits of each class.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003e\"Commuters\"\u003c/span\u003e \u003c/p\u003e \u003cp\u003eThis class of behavior states had the highest movement throughout the array and was the only class that was frequently observed at receivers both inside and outside the CCF with a relatively high frequency of crossing CCF.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003e\"Inside Residents - Unobserved\"\u003c/span\u003e \u003c/p\u003e \u003cp\u003eThis class of behavior states stayed inside the CCF almost the entire period, with little movement. When detected, they revisited a small set of inside receivers and rarely ventured outside of CCF.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003e\"Inside Roamers\"\u003c/span\u003e \u003c/p\u003e \u003cp\u003eThis class of behavior states were also mostly detected inside the CCF like \u0026ldquo;Inside Residents \u0026ndash; Undetected\u0026rdquo;, but actively moved within the Inside network, with only occasional forays across/out of the CCF.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003e\"Outside Residents - Unobserved\"\u003c/span\u003e \u003c/p\u003e \u003cp\u003eThis class of behavior states is defined by largely staying outside the CCF with only a handful of detections by receivers outside the CCF and generally one or fewer entries inside.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eExplanatory Covariates\u003c/span\u003e \u003c/p\u003e \u003cp\u003eResults of the design based test for each of the explanatory variables are provided in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. All explanatory variables were significantly different among classes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation results for design-based tests accounting for repeated measures on each fish. Each of the tested covariates is presented along with the test used, the degrees of freedom for each test, the test statistic, and the significance of the statistic.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003cp\u003e(num, denom)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod of Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRao-Scott χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3, 541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater-year Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRao-Scott χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9, 535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge-class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRao-Scott χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6, 538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGate Openness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDesign-based ANOVA (Wald)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ePeriod\u003c/span\u003e\u003c/h2\u003e \u003cp\u003ePeriod composition was similar across three of the classes, but the \u0026ldquo;Commuters\u0026rdquo; class had the lowest proportion of behavior states in Period 2 (June 19 \u0026ndash; December 17) with a larger proportion occurring during Period 1 (December 18 \u0026ndash; June 18) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eWater Year Type\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eBehavior states during Critical Dry water years made up a larger proportion of the states in the \u0026ldquo;Commuters\u0026rdquo; and \u0026ldquo;Inside Roamers\u0026rdquo; classes, while making up relatively small proportions of the other two classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Behavior states occurring during Dry water years were moderately represented in all four classes, but were least represented in the \u0026ldquo;Commuters\u0026rdquo; class. Behavior states occurring during Below Normal water years made up a larger proportion of the \u0026ldquo;Outside Residents \u0026ndash; Undetected\u0026rdquo; class than any other class, with a smaller proportion in the \u0026ldquo;Inside Residents \u0026ndash; Undetected\u0026rdquo; class, and the smallest proportions in the other two classes. Behavior states during Wet water years were moderately represented in all four classes, but had the lowest proportion in the \u0026ldquo;Inside Roamers\u0026rdquo; class.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAge-class\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eBehavior states from fish in the 1\u0026ndash;2 year age-class were the most common age-class assigned to the \u0026ldquo;Inside Roamers\u0026rdquo; class, and were the least common in the \u0026ldquo;Commuters\u0026rdquo; and \u0026ldquo;Outside Residents \u0026ndash; Undetected\u0026rdquo; classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Behavior states from fish in the 3\u0026ndash;5 year age-class were the most common in the \u0026ldquo;Commuters\u0026rdquo; class while being moderately represented in the other three classes. Behavior states from fish in the 6\u0026thinsp;+\u0026thinsp;year age-class were the most common in the \u0026ldquo;Outside Residents \u0026ndash; Undetected\u0026rdquo; class, but also had significant contributions to the \u0026ldquo;Commuter\u0026rdquo; and \u0026ldquo;Inside Residents \u0026ndash; Undetected\u0026rdquo; classes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eGate Openness\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eGate openness during behavior states showed a large difference between classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). Gates were open a higher percent of time during behavior states classified as either \u0026ldquo;Inside Resident \u0026ndash; Undetected\u0026rdquo; or \u0026ldquo;Outside Resident \u0026ndash; Undetected\u0026rdquo; and open a lower percent of time during behavior states classified as \u0026ldquo;Commuters\u0026rdquo; or \u0026ldquo;Inside Roamers\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMovement Timing\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eTiming of exits, entries, and detected CCF crossings (when a fish was detected at both the Intake Canal receivers and Radial Gate receivers on a single day) was qualitatively assessed based on the distribution of movement events using violin plots across seasons. Only \u0026ldquo;Inside Roamers\u0026rdquo; and \u0026ldquo;Commuters\u0026rdquo; had\u0026thinsp;\u0026gt;\u0026thinsp;1 exit, which primarily occurred from February to April (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea) These were also the only classes to have any entries, which were more spread out through the year than exits but did show increased movement from during the same February to April period as well as peaks from August to November (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). Finally, these classes were also the only two classes detected making movements across the CCF, coincident with Exit and Entry timing (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). Combining movements across all classes, a strong trend is evident, with exits occurring primarily from February to May, entries also occurring primarily during the same period with additional peaks from August to September and from November to December, a pattern which is repeated for CCF crossings (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed). Combined, movement is highest throughout February through May with a dip from May to Jul and another increase in movement from August to November (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eCluster analysis of movement and residence metrics identified four distinct behavioral classes of Striped Bass within and near CCF. These classes captured consistent differences in space use and activity, reflecting variation in habitat association and mobility. The presence of multiple behavior types within this population indicates behavioral plasticity and spatial heterogeneity in this non-native estuarine species. By applying unsupervised clustering, this study extends movement-classification frameworks developed for other taxa (Brodie et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and for Striped Bass in other estuarine systems (Taylor et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), yielding biologically interpretable behavior classes relevant to predator-management applications.\u003c/p\u003e \u003cp\u003eBehavior patterns observed in this study are consistent with behavior previously observed within the Delta. Clark et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) documented extended residency near the intake canal and radial gates and repeated movements between CCF and Delta channels, patterns similar to those of our “Inside Roamers” and “Commuters.” Seasonal movement timing in our dataset with peak movements in late winter and spring and secondary increases in autumn matches long-recognized patterns for Striped Bass in the Delta (Calhoun \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1952\u003c/span\u003e; Chadwick \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Stevens et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Gingras and McGee \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The persistence of these seasonal patterns, despite substantial changes in Delta operations and climate since early studies, suggests they are stable features of Striped Bass behavior.\u003c/p\u003e \u003cp\u003eAge was associated with differences in movement behavior, though no age class was restricted to a single behavioral type. Older individuals generally exhibited greater mobility, consistent with observations from the Roanoke River (Patrick et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), yet also appeared more frequently in “resident” classifications. This pattern likely reflects limitations in the spatial extent of the receiver array, as fish remaining within the forebay are more detectable than those that leave monitored regions. Overall, the presence of all behavior classes across all age groups demonstrates that age alone does not determine movement behavior.\u003c/p\u003e \u003cp\u003eSeasonal and environmental conditions also influenced movement patterns. Peak activity occurred in late winter and spring, with secondary increases in late summer and fall, corresponding to established migration periods in the Delta (Calhoun \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1952\u003c/span\u003e; Chadwick \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Stevens et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Sabal et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The 2013–2015 drought produced elevated temperatures and reduced flows, conditions known to alter hydrodynamics and prey availability. Observed mobility was higher in dry years, consistent with earlier downstream movements documented during drought periods (Goertler et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). If similar conditions occur more frequently under future climate scenarios, shifts in movement timing may become more common, potentially increasing temporal overlap between Striped Bass and native fishes (Goertler et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLocal habitat characteristics may further influence behavior. CCF is shallow, hydrodynamically simple, and partially isolated from adjacent channels (MacWilliams and Gross \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Shu and Ateljevich \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These features, coupled with periodic radial-gate operations, can constrain emigration and promote repeated use of particular forebay areas (Bolster \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Clark et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Intrinsic factors, environmental conditions, and structural features therefore interact to produce the behavioral diversity observed among Striped Bass at CCF.\u003c/p\u003e \u003cp\u003ePatterns observed at CCF are consistent with broader descriptions of Striped Bass movement ecology in other systems. Striped Bass populations along the Atlantic coast exhibit divergent spatial strategies, ranging from highly mobile individuals to residents with small home ranges (Secor and Piccoli \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Ng et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Gahagan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Secor et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e) and comparable distributional groups have been documented in the Plum Island Estuary (Taylor et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In these systems, older individuals often occupy larger spatial ranges, although all behavioral types may occur across age classes, consistent with patterns observed in the present study. Taken together, these comparisons indicate that the behavior classes observed at CCF align with broader, species-wide movement strategies and provide context for evaluating the factors contributing to behavioral variation within the forebay.\u003c/p\u003e \u003cp\u003eFurthermore, movement classifications like those identified here have been reported across diverse aquatic species. Brodie et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found four functional movement classes across 92 species, demonstrating that multimodal space-use strategies are common among aquatic organisms.\u003c/p\u003e \u003cp\u003eSeveral considerations should be taken into account when interpreting these results. Cluster analysis can produce statistically distinct groups that may not always correspond to biologically meaningful differences; however, the behavior classes identified here exhibited consistent patterns in space use and movement, and silhouette-width validation supported their interpretability. Class assignments were also stable across alternative model configurations. The daily time step used to summarize detections limited the resolution of fine-scale movements and likely underestimated mobility for some individuals, but this constraint was applied uniformly and therefore does not affect relative comparisons among fish.\u003c/p\u003e \u003cp\u003eAdditional data gaps may influence interpretation. The sex of tagged individuals was not determined, despite known sex-specific migration patterns (Kohlenstein \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Secor and Piccoli \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Secor et al. 2020a,b). Most tagged fish were within age classes in which both sexes are known to express a wide range of movement behaviors, and all age classes were represented across all behavior classes, reducing the likelihood that sex-specific differences alone explain the observed behavioral structure. The spatial extent of the receiver array also limited detection of fish that moved outside monitored regions. While this likely reduced observations of highly mobile individuals, the array captured key access points and movement corridors within CCF and reliably distinguished relative differences in movement behavior among detected individuals. Expanded receiver coverage across the Delta would allow further evaluation of whether these behavior classes persist beyond the forebay.\u003c/p\u003e "},{"header":"Conclusions and Management Implications","content":"\u003cp\u003ePredator removal has long been explored as a tool for reducing predation pressure on native fishes in the Delta, yet broad-scale removal programs have yielded mixed results. Previous efforts have often produced limited improvements in survival of native prey species (Beamesderfer et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Mueller \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Michel et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and compensatory increases in consumption by remaining predators can offset removal benefits (Beamesderfer et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). These challenges highlight the need for more targeted approaches.\u003c/p\u003e\u003cp\u003eBehavioral heterogeneity has direct implications for the effectiveness of predator removal. Removal efforts that selectively capture lower-mobility behavior types may impose selective pressures favoring more mobile individuals (Eldridge \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Secor and Piccoli \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Repeatedly targeting predictable behavioral types could shift the behavioral composition of the Striped Bass population and influence long-term predator dynamics. Incorporating information on behavioral classes can therefore improve the design of removal strategies.\u003c/p\u003e\u003cp\u003eThe behavior classifications developed in this study provide a framework for refining the timing, location, and methods used for predator removal. Seasonal peaks in residency and movement highlight periods when particular behavior classes may be more vulnerable, allowing for more focused removal windows. Telemetry data can also identify areas where resident or “Inside Roamer” classes consistently concentrate, enabling targeted removal, trapping, or habitat-modification efforts intended to reduce predation risk.\u003c/p\u003e\u003cp\u003eVulnerability to removal differs among behavior classes. Individuals remaining near forebay structures are more susceptible to localized or passive removal methods such as electrofishing or trapping, whereas mobile groups are less likely to encounter these gears. Although management of highly mobile predators remains difficult, long-term programs targeting Northern Pikeminnow in the Columbia River Basin demonstrate that directed angler harvest can reduce predation risk under certain circumstances, though applicability to other species remains uncertain (Friesen and Ward \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Angler-based programs in the Delta are constrained by legal size limits, as most Striped Bass encountered during removal efforts are below the harvest threshold (Cane 2017; Wilder et al. 2018). Adjustments to size or bag limits, applied seasonally or spatially and informed by telemetry-derived movement patterns, could increase removal feasibility where consistent with regulatory frameworks (Beamesderfer 2020).\u003c/p\u003e\u003cp\u003eCluster analysis captured consistent differences in space use and mobility and illustrated substantial behavioral heterogeneity within this population among the four classes. Accounting for this variation has important implications for predator control. Behavior-based information can guide the timing and placement of removal efforts, clarify differential vulnerability among behavioral types, and highlight potential selective outcomes of repeated removal. Integrating behavioral heterogeneity into predator-management planning may enhance the effectiveness and efficiency of actions aimed at reducing predation pressure on native fishes in the Delta.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClifton Court Forebay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDWR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCalifornia Department of Water Resources\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecm\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecentimeter(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecfs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecubic feet per second\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eContinuous Presence Event\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDelta\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSacramento\u0026mdash;San Joaquin Delta\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edegrees of freedom\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eha\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehectare(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHTI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHydroacoustic Technology, Incorporated\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ekg\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ekilogram(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ekm\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ekilometer(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003em\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emeter(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emg/L\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emilligrams per liter\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emm\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emillimeter(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePartitioning Around Medoids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePIT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePassive Integrated Transponder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSWP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eState Water Project\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUSFWS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited States Fish and Wildlife Service\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll work with fish was reviewed and approved by the California Department of Fish and Wildlife who issued Scientific Collecting Permit SC-10286 and Consistency Determination 2080-2009-011-00 to the California Department of Water Resources, and was performed in compliance with the requirements issued by NOAA Fisheries in their 2009 \u0026nbsp;Section 7 Biological and Conference Opinion on the Long-term Operation of the Central Valley Project and State Water Project issued to the California Department of Water Resources and the U.S. Bureau of Reclamation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Consent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData and findings from this study are available from the California Department of Water Resources upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone Declared\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for this project was provided by the California Department of Water Resources (https://water.ca.gov/)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTS contributed the analysis and interpretation of data and substantial portions of the text. EC and AK provided technical expertise for the analysis, review of analytical code and text. RW, SB, and PB contributed substantial portions of the text. PH provided manuscript formatting oversight and coordination. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Javier Miranda, Kevin Clark, Matt Silva, Veronica Wunderlich, and Matthew Reeve for their contributions during the study, and the many staff members of the California Department of Water Resources, ICF, and Environmental Science Associates who collected data in support of this study. The authors would especially like to thank Dr. Cameron Turner (1979\u0026ndash;2019), who made significant contributions to the original analysis of these data and provided his expertise and guidance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArostegui MC, Smith JM, Kagley AN, Spilsbury-Pucci D, Fresh KL, Quinn TP. Spatially clustered movement patterns and segregation of subadult Chinook salmon within the Salish Sea. Mar Coastal Fisheries. 2017;9(1):1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeamesderfer R. Managing predators and competitors: deciding when intervention is effective and appropriate. Fisheries. 2000;25(6):18\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeamesderfer R, Ward D, Nigro A. Evaluation of the biological basis for a predator control program on northern squawfish (\u003cem\u003ePtychocheilus oregonensis\u003c/em\u003e) in the Columbia and Snake rivers. Can J Fish Aquat Sci. 1996;53:2898\u0026ndash;908.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolster BC. 1986. Movement patterns of striped bass (\u003cem\u003eMorone saxatilis\u003c/em\u003e) in Clifton Court Forebay, Contra Costa County, California. Master\u0026rsquo;s thesis. California State University, Sacramento.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrodie S, L\u0026eacute;d\u0026eacute;e EJI, Heupel MR, Babcock RC, Campbell HA, Gledhill DC, Hoenner X, Huveneers C, Jaine FRA, Simpfendorfer CA, Taylor MD, Udyawer V, Harcourt RG. Continental-scale animal tracking reveals functional movement classes across marine taxa. Sci Rep. 2018;8(1):3717.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalhoun AJ. Annual migrations of California striped bass. Calif Fish Game. 1952;38(3):391\u0026ndash;403.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCapello M, Robert M, Soria M, Potin G, Itano D, Holland K, Deneubourg J-L, Dagorn L. A methodological framework to estimate the site fidelity of tagged animals using passive acoustic telemetry. PLoS ONE. 2015;10(8):e0134002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastillo G, Morinaka J, Lindberg J, Fujimura R, Baskerville-Bridges B, Hobbs J, Tigan G, Ellison L. 2012. Pre-screen loss and fish facility efficiency for Delta Smelt at the South Delta\u0026rsquo;s State Water Project, California. San Francisco Estuary Watershed Sci 10(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChadwick HK. Recent migrations of the Sacramento\u0026ndash;San Joaquin River Striped Bass population. Trans Am Fish Soc. 1967;96(3):327\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClark K, Bowen M, Mayfield R, Zehfuss K, Taplin J, Hanson C. Quantification of pre-screen loss in juvenile steelhead in Clifton Court Forebay. California Department of Water Resources, Bay-Delta Office; 2009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDill WA, Cordone AJ. History and status of introduced fishes. California, 1871\u0026ndash;1996. Volume 178. California Department of Fish and Game Fish Bulletin; 1997.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunker KJ, Sepulveda AJ, Massengill RL, Olsen JB, Russ OL, Wenburg JK, Antonovich A. Potential of environmental DNA to evaluate Northern Pike (\u003cem\u003eEsox lucius\u003c/em\u003e) eradication efforts: an experimental test and case study. PLoS ONE. 2016;11(9):e0162277.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDWR (California Department of Water Resouces). 2016. Clifton Court Forebay Predator Reduction Electrofishing Study annual report 2016. California Department of Water Resources, Sacramento.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDWR (California Department of Water Resouces). 2017. Clifton Court Forebay Predator Reduction Electrofishing Study annual report 2017. California Department of Water Resources, Sacramento.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDWR (California Department of Water Resouces). 2018. Clifton Court Forebay Predator Reduction Electrofishing Study annual report 2018. California Department of Water Resources, Sacramento.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDWR (California Department of Water Resources). Clifton Court Forebay predation studies. Final report. Bay-Delta Office; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDWR (California Department of Water Resources). No date. Water year hydrologic classification indices. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cdec.water.ca.gov/reportapp/javareports?name=WSIHIST\u003c/span\u003e\u003cspan address=\"https://cdec.water.ca.gov/reportapp/javareports?name=WSIHIST\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEldridge MB. 1988. Life history strategies and tactics of Striped Bass (\u003cem\u003eMorone saxatilis\u003c/em\u003e) Walbaum. Thesis, Oregon State University, Corvallis.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEspinoza M, L\u0026eacute;d\u0026eacute;e EJI, Smoothey AF, Heupel MR, Peddemors VM, Tobin AJ, Simpfendorfer CA. Intra-specific variation in movement and habitat connectivity of a mobile predator revealed by acoustic telemetry and network analyses. Mar Biol. 2021;168(6):80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeyrer F, Nobriga ML, Sommer TR. Multidecadal trends for three declining fish species: habitat patterns and mechanisms in the San Francisco Estuary, California, USA. Can J Fish Aquat Sci. 2007;64(4):723\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinn JT, Brownscombe JW, Haak CR, Cooke SJ, Cormier R, Gagne T, Danylchuk AJ. Applying network methods to acoustic telemetry data: modeling the movements of tropical marine fishes. Ecol Model. 2014;293(C):139\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriesen TA, Ward DL. Management of Northern Pikeminnow and implications for juvenile salmonid survival in the lower Columbia and Snake rivers. North Am J Fish Manag. 1999;19(2):406\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGahagan B, Fox D, Secor D. Partial migration of striped bass: revisiting the contingent hypothesis. Mar Ecol Prog Ser. 2015;525:185\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGingras M. 1997. Mark/recapture experiments at Clifton Court Forebay to estimate pre-screening loss to juvenile fishes: 1976\u0026ndash;1993. Interagency Ecological Program.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGingras M, McGee M. 1997. A telemetry study of Striped Bass emigration from Clifton Court Forebay: implications for predator enumeration and control. Technical Report 54. Interagency Ecological Program for the San Francisco Bay/Delta Estuary.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoertler P, Mahardja B, Sommer T. Striped bass (\u003cem\u003eMorone saxatilis\u003c/em\u003e) migration timing driven by estuary outflow and sea surface temperature in the San Francisco Bay-Delta, California. Sci Rep. 2021;11(1):1510.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrossman GD, Essington T, Johnson B, Miller J, Monsen NE, Pearsons TN. Effects of fish predation on salmonids in the Sacramento River\u0026ndash;San Joaquin Delta and associated ecosystems. Sacramento: California Department of Fish and Wildlife; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrothues TM, Able KW, Carter J, Arienti TW. 2009. Migration patterns of Striped Bass through nonnatal estuaries of the U.S. Atlantic coast. American Fisheries Society Symposium 69:135\u0026ndash;150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrout DE. Interactions between striped bass (Morone saxatilis) rebuilding programmes and the conservation of Atlantic salmon (Salmo salar) and other anadromous fish species in the USA. ICES J Mar Sci. 2006;63:1346e1352.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHennig C. Cluster-wise assessment of cluster stability. Comput Stat Data Anal. 2007;52(1):258\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHollema HM, Kneebone J, McCormick SD, Skomal GB, Danylchuk AJ. Movement patterns of Striped Bass (\u003cem\u003eMorone saxatilis\u003c/em\u003e) in a tidal coastal embayment in New England. Fish Res. 2017;187:168\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInnovasea. 2023. HTI Coded Transmitters\u0026thinsp;\u0026ndash;\u0026thinsp;307 kHz. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.innovasea.com/wp-content/uploads/2022/01/Innovasea-307khz_tags-ds-0122.pdf\u003c/span\u003e\u003cspan address=\"https://www.innovasea.com/wp-content/uploads/2022/01/Innovasea-307khz_tags-ds-0122.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJassby AD, Kimmerer WJ, Monismith SG, Armor C, Cloern JE, Powell TM, Schubel JR, Vendlinski TJ. Isohaline position as a habitat indicator for estuarine populations. Ecol Appl. 1995;5(1):272\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJorgensen SJ, Arnoldi NS, Estess EE, Chapple TK, R\u0026uuml;ckert M, Anderson SD, Block BA. Eating or meeting? Cluster analysis reveals intricacies of white shark (\u003cem\u003eCarcharodon carcharias\u003c/em\u003e) migration and offshore behavior. PLoS ONE. 2012;7(10):e47819.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKessel S, Hondorp D, Holbrook C, Boase J, Chiotti J, Thomas M, Wills T, Roseman E, Drouin R, Krueger C. Divergent migration within Lake Sturgeon (\u003cem\u003eAcipenser fulvescens\u003c/em\u003e) populations: multiple distinct patterns exist across an unrestricted migration corridor. J Anim Ecol. 2017;87(1):259\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKimmerer WJ, Gross ES, MacWilliams ML. Is the response of estuarine nekton to freshwater flow in the San Francisco Estuary explained by variation in habitat volume? Estuaries Coasts. 2009;32:375\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKimmerer WJ, MacWilliams ML, Gross ES. 2013. Variation of fish habitat and extent of the low-salinity zone with freshwater flow in the San Francisco Estuary. San Francisco Estuary Watershed Sci, 11(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKohlenstein LC. On the proportion of the Chesapeake Bay stock of striped bass that migrates into the coastal fishery. Trans Am Fish Soc. 1981;110(1):168\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLowe MR, Holbrook CM, Hondorp DW. Detecting commonality in multidimensional fish movement histories using sequence analysis. Anim Biotelem. 2020;8(1):10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacWilliams ML, Gross ES. 2013. Hydrodynamic simulation of circulation and residence time in Clifton Court Forebay. San Francisco Estuary Watershed Sci, 11(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichel CJ, Smith JM, Lehman BM, Demetras NJ, Huff DD, Brandes PL, Israel JA, Quinn TP, Hayes SA. Limitations of active removal to manage predatory fish populations. North Am J Fish Manag. 2020;40(1):3\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoyle PB. Inland fishes of California: revised and expanded. University of California Press; 2002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMueller G. Predatory fish removal and native fish recovery in the Colorado River mainstem: What have we learned? Fisheries. 2009;30(9):10\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg CL, Able KW, Grothues TM. Habitat use, site fidelity, and movement of adult Striped Bass in a southern New Jersey estuary based on mobile acoustic telemetry. Trans Am Fish Soc. 2007;136(5):1344\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNMFS (National Marine Fisheries Service). 2009. Biological opinion and conference opinion on the long-term operations of the Central Valley Project and State Water Project. June 4, 2009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgle DH. Introductory fisheries analyses with R. Boca Raton, Florida: Chapman \u0026amp; Hall/CRC; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrsi JJ. The 1965\u0026ndash;1967 migrations of the Sacramento\u0026ndash;San Joaquin estuary Striped Bass population. Calif Department Fish Game. 1971;57(4):257\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatrick WS, Rulifson RA, Stellwag EJ. 2006. An investigation of Roanoke River striped bass migratory behavior using genetic and PIXE analysis. Final report to NC Sea Grant. FRG 04\u0026ndash;EP.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuist MC, Isermann DA, editors. Age and growth of fishes: principles and techniques. Bethesda, Maryland: American Fisheries Society; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSabal MC, Michel CJ, Smith JM, Hampton A, Hayes SA. Seasonal movement patterns of Striped Bass (\u003cem\u003eMorone saxatilis\u003c/em\u003e) in their nonnative range. Estuaries Coasts. 2019;42(2):567\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSecor DH, O\u0026rsquo;Brien MHP, Gahagan BI, Fox DA, Higgs AL. and J. E. Best. 2020a. Multiple spawning run contingents and population consequences in migratory Striped Bass \u003cem\u003eMorone saxatilis\u003c/em\u003e. PLoS ONE 15(11):e0242797.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSecor DH, O\u0026rsquo;Brien MHP, Gahagan BI, Watterson JC, Fox DA. Differential migration in Chesapeake Bay Striped Bass. PLoS ONE. 2020b;15(5):e0233103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSecor DH, Piccoli PM. Age- and sex-dependent migrations of Striped Bass in the Hudson River as determined by chemical microanalysis of otoliths. Estuaries. 1996;19(4):778\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShu Q, Ateljevich E. 2017. Clifton Court Forebay Transit Time Modeling Analysis. California Department of Water Resources, Bay-Delta Office, Delta Modeling Section Sacramento, CA.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eState Water Board (State Water Resources Control Board). 2000. Revised Water Right Decision 1641. In the matter of: implementation of water quality objectives for the San Francisco Bay/Sacramento\u0026ndash;San Joaquin Estuary; a petition to change points of diversion of the Central Valley Project and the State Water Project in the southern Delta; and a petition to change places of use and purposes of use of the Central Valley Project. December 29, 1999; revised in accordance with Order WR 2000-02, March 15, 2000.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens DE, Chadwick HK, Painter RE. 1987. American Shad and Striped Bass in California\u0026rsquo;s Sacramento\u0026ndash;San Joaquin River system. American Fisheries Society Symposium 1:66\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor RB, Mather ME, Smith JM, Boles KM. 2021. Can identifying discrete behavioral groups with individual-based acoustic telemetry advance the understanding of fish distribution patterns? Front Mar Sci 8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUSFWS (U.S. Fish and Wildlife Service). 2011. Fact sheet: AQUI-S\u0026reg;E \u0026amp; AQUI-S\u0026reg;20E (sedative/anesthetic) Investigational New Animal Drug (INAD) 11\u0026ndash;741 and study protocol. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fws.gov/guidance/sites/guidance/files/documents/AQUISE-study-protocol_0.pdf\u003c/span\u003e\u003cspan address=\"https://www.fws.gov/guidance/sites/guidance/files/documents/AQUISE-study-protocol_0.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWagner RW, Stacey M, Brown LR, Dettinger M. Statistical models of temperature in the Sacramento\u0026ndash;San Joaquin Delta under climate-change scenarios and ecological implications. Estuaries Coasts. 2011;34:544\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilcox RR. Introduction to Robust Estimation and Hypothesis Testing. 3rd ed. Academic; 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWingate RL, Secor DH, Kraus RT. Seasonal patterns of movement and residency by Striped Bass within a subestuary of the Chesapeake Bay. Trans Am Fish Soc. 2011;140(6):1441\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"animal-biotelemetry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"abit","sideBox":"Learn more about [Animal Biotelemetry](http://animalbiotelemetry.biomedcentral.com)","snPcode":"40317","submissionUrl":"https://submission.nature.com/new-submission/40317/3","title":"Animal Biotelemetry","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Striped Bass, Predator, Behavior, Movement, Acoustic Telemetry, Reservoir, Cluster Analysis","lastPublishedDoi":"10.21203/rs.3.rs-8724316/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8724316/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePredation by Striped Bass in Clifton Court Forebay, a water regulating reservoir in California\u0026rsquo;s Sacramento\u0026ndash;San Joaquin Delta, is considered a major contributor to losses of juvenile salmonids. Understanding how individual Striped Bass use the forebay and surrounding channels is important for designing more effective predator management actions aimed at reducing loss. This study used acoustic telemetry and unsupervised clustering to test whether Striped Bass exhibit discrete movement behavior classes that differ in residency, habitat use, detectability, and movement activity. Then tested whether the occurrence of these classes vary with season, hydrologic conditions, fish age, and radial gate operations.\u003c/p\u003e \u003cp\u003eAcoustic detections from 543 tagged Striped Bass were summarized into 2,568 six-month behavior states across eight water years. Four behavioral classes were identified: Commuters (high movement and frequent use of both forebay and outside habitats), Inside Residents\u0026ndash;Undetected (persistent forebay residency with few detections), Inside Roamers (primarily forebay-associated with active internal movement), and Outside Residents\u0026ndash;Undetected (persistent outside residency with few detections). Behavioral class composition differed significantly by season, water year type, age class, and radial gate openness. Movements were concentrated from February through May, with additional peaks in late summer and fall.\u003c/p\u003e","manuscriptTitle":"Identifying Movement Behavior States of Striped Bass in a Large Regulating Forebay Using Cluster Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 12:31:46","doi":"10.21203/rs.3.rs-8724316/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-10T17:34:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T15:12:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T11:12:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T19:00:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285259828563606444684813014860268417172","date":"2026-02-17T08:42:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277182302802626133258454763881051799914","date":"2026-02-12T13:48:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27923479326718629822305592892047682124","date":"2026-02-11T21:48:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-11T21:27:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-02T21:14:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-30T05:06:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Animal Biotelemetry","date":"2026-01-28T18:03:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"animal-biotelemetry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"abit","sideBox":"Learn more about [Animal Biotelemetry](http://animalbiotelemetry.biomedcentral.com)","snPcode":"40317","submissionUrl":"https://submission.nature.com/new-submission/40317/3","title":"Animal Biotelemetry","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9a6a9856-aa7b-44b7-89af-3f7792a6e056","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T15:53:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 12:31:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8724316","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8724316","identity":"rs-8724316","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}