White Shark Research Articles

Research of a novel combined deterministic and probabilistic forecasting system for air pollutant concentration

Hazy weather seriously threatens human health, providing accurate and reliable air pollutant concentration forecast will be of great scientific value and benefit to residents' livelihood and natural environment. However, existing forecasting models lack outlier detection, feature mapping and uncertainty forecast, which results in unreliable forecasting results. Thus, in this paper, a novel combined deterministic and probabilistic forecasting system based on meta-heuristic algorithm is proposed, which contains multiple data pre-processing, combined deterministic and probabilistic forecast. Firstly, the raw data is pre-processed and analyzed from multiple perspectives to fully extract the chaotic features. Then multi-objective white shark optimizer is utilized to combine the three deep learning models to enhance the forecasting accuracy and stability. Furthermore, the Pin-ball loss function is employed to yield prediction intervals at different confidence levels, and a novel interval pseudo-code is constructed to optimize the prediction intervals thus promoting the probabilistic forecasting reliability. The simulations indicated that the developed system enhances the forecasting accuracy by maximum 60.6772%, 54.9793% and 38.6876% in the three steps of PM2.5 prediction and up to 64.9001%, 68.4637% in the interval sharpness at significance level 0.05 and 0.1 than the tested models.

Hybrid forecasting models for wind-PV systems in diverse geographical locations: Performance and power potential analysis

In order to combat the global warming, much stress is put on integrating non-conventional energy resources, such as wind power plants and solar energy systems, into standard electricity generation systems. The stochastic nature of wind energy at a large scale requires precise forecasting in short and medium terms to manage electricity demands for regional grids because the safety and control of electricity systems are highly sensitive to voltage drop. Unfortunately, the existing forecasting models achieve less desirable performance in short term power forecasting. To resolve this issue, based on hybrid stochastic algorithm based intelligent models, which are proven to be a highly effective predictive tool, this paper proposes two new hybrid models using modified white shark optimization (MWSO) algorithm based GRNN and RBFN. To enhance the forecasting performance, the parameter mv is projected as a function of time to enforce the convergence by accelerating computation as well for effective exploration and exploitation to reinforce features of data. The supervised control and data acquisition (SCADA) data provide the input metrics to the system. A hybrid decomposition model is used to prep data for feature enhancement. Six case studies are formulated from Turkey and Malaysia to validate the forecasting performance of our technically advanced models. Recommended modifications allow MWSO for enhanced training. The results are compared with four hybrid ANN topologies in combinations with PSO and BMO stochastic optimization algorithms. Seasonal results are compared graphically and statistically through 15 min ahead forecasting. The results show that the proposed MWSO-RBFNN model outperforms the classic models in all cases for point forecasting and interval forecasting with higher convergence rate and lower stochastic error. The point and interval forecasting accuracy is extremely important to ensure power systems’ stability, efficiency, and safety. Our model achieves Nash-Sutcliffe constant score of 0.99 and exhibits superior performance with the least RMSE, RE, R2, and MAE.

An improved DC link voltage control with double frequency second‐order generalized integrator for enhanced power quality of grid‐interfaced DFIG system

SummaryIn this study, an optimal double frequency second order generalized integrator (DFSOGI) based grid side converter control (GSC) is used for monitoring active power injection from the DFIG wind energy system as well as improving power quality at utility grid terminals. The reference DC voltage is adapted to variations in voltage at the point of common coupling (PCC) that reduces the burden on the converter switches and switching losses. In addition, an intelligent control technique based on white shark optimization algorithm (WSO) is endorsed, where an objective problem is formulated to obtain the desired parameters of the controller. Finally, time‐domain simulations are conducted in MATLAB environment that demonstrate the validity of the implemented control over various operating conditions. To affirm the adequacy of the proposed control strategy, the response of DC link voltage obtained through optimal tuning is compared to the conventional one. The proposed WSO tuning outperforms traditional manual tuning pertaining to enhancing the overall performance of the DFIG system. The designed WSO gains exhibit better steady‐state and transient performance including settling time, steady‐state error, and peak of over amplitude and harmonic distortion. The grid currents' total harmonic distortion (THD) is within 5% that meets specified IEEE‐519 standard.

Twitter sentiment analysis using hybrid gated attention recurrent network

Sentiment analysis is the most trending and ongoing research in the field of data mining. Nowadays, several social media platforms are developed, among that twitter is a significant tool for sharing and acquiring peoples’ opinions, emotions, views, and attitudes towards particular entities. This made sentiment analysis a fascinating process in the natural language processing (NLP) domain. Different techniques are developed for sentiment analysis, whereas there still exists a space for further enhancement in accuracy and system efficacy. An efficient and effective optimization based feature selection and deep learning based sentiment analysis is developed in the proposed architecture to fulfil it. In this work, the sentiment 140 dataset is used for analysing the performance of proposed gated attention recurrent network (GARN) architecture. Initially, the available dataset is pre-processed to clean and filter out the dataset. Then, a term weight-based feature extraction termed Log Term Frequency-based Modified Inverse Class Frequency (LTF-MICF) model is used to extract the sentiment-based features from the pre-processed data. In the third phase, a hybrid mutation-based white shark optimizer (HMWSO) is introduced for feature selection. Using the selected features, the sentiment classes, such as positive, negative, and neutral, are classified using the GARN architecture, which combines recurrent neural networks (RNN) and attention mechanisms. Finally, the performance analysis between the proposed and existing classifiers is performed. The evaluated performance metrics and the gained value for such metrics using the proposed GARN are accuracy 97.86%, precision 96.65%, recall 96.76% and f-measure 96.70%, respectively.

The importance of the appendicular skeleton for the phylogenetic reconstruction of lamniform sharks (Chondrichthyes: Elasmobranchii).

Lamniform sharks are one of the more conspicuous groups of elasmobranchs, including several emblematic taxa as the white shark. Although their monophyly is well supported, the interrelationships of taxa within Lamniformes remains controversial because of the conflict among various previous molecular-based and morphology-based phylogenetic hypotheses. In this study, we use 31 characters related to the appendicular skeleton of lamniforms and demonstrate their ability to resolve the systematic interrelationships within this shark order. In particular, the new additional skeletal characters resolve all polytomies that were present in previous morphology-based phylogenetic analyses of lamniforms. Our study demonstrates the strength of incorporating new morphological data for phylogenetic reconstructions.

Capturing the Shark. White (Eco-)Masculinity and the Pursuit of Science in the Docuseries Expedition Great White

Expedition Great White is a docuseries that follows a crew composed of professional fishermen and scientists who conduct studies about great white sharks. This article explores three interrelated dimensions of the series. First, while the series repeatedly suggests that the actions performed in front of the cameras ultimately aim to study and protect great whites and that for both the fishermen and scientists, the well-being of sharks is of the highest priority, this masculine care is not only subjected to the pursuit of new scientific insights about sharks but also embedded in a discourse of competition. Second, this pursuit of new knowledge is coded in masculine terms, as traditional notions of masculinity (i.e., confronting and catching the dangerous animal as well as making scientific progress) become re-negotiated in view of the animals’ well-being and, ultimately, their protection. Finally, while the first two dimensions bespeak the desire for human control of the natural world, the digital lives of tagged sharks challenge this human control.

A New Hybrid White Shark and Whale Optimization Approach for Estimating the Li-Ion Battery Model Parameters

Constructing a reliable equivalent circuit of Li-Ion batteries using real operating conditions by estimating optimal parameters is mandatory for many engineering applications, as it controls the energy management of the battery in a hybrid system. However, model parameters can vary according to the electrochemical nature of the battery, so improving the accuracy of the battery model parameters is essential to obtain reliable and accurate equivalent circuits. Therefore, this paper proposes a new efficient hybrid optimization approach for determining the proper parameters of Li-ion battery Shepherd model equivalent circuits. The proposed algorithm comprises a white shark optimizer (WSO) and the whale optimization approach (WOA) for modifying the stochastic behavior of the WSO while searching for food sources. Minimizing the root mean square error between the estimated and measured battery voltages is the objective function considered in this work. The hybrid variant of the WSO (HWSO) was examined with two different types of batteries. Moreover, the proposed HWSO was validated versus a set of recent meta-heuristic approaches including the sea horse optimizer (SHO), artificial gorilla troops optimizer (GTO), coyote optimization algorithm (COA), and the basic version of the WSO. Furthermore, statistical analyses, mean convergence, and fitting curves were conducted for the comparisons. The proposed HWSO succeeded in achieving the least fitness values of 2.6172 × 10−4 and 5.6118 × 10−5 with standard deviations of 9.3861 × 10−5 and 3.2854 × 10−4 for battery 1 and battery 2, respectively. On the other hand, the worst fitness values were 6.5230 × 10−2 and 6.6197 × 10−5 via SHO and WSO for both considered batteries. The proposed HWSO results prove the efficiency of the proposed approach in providing highly accurate battery model parameters with high consistency and a unique convergence curve compared to the other methods.

Evidence of Non-Random Social Interactions between Pairs of Bait-Attracted White Sharks in Gansbaai (South Africa)

Knowledge about the social behavior of sharks is a growing research field, but not many observations are available on the social interactions between pairs of sharks in the presence of passive surface bait and mainly related to aggregations. Between 2009 and 2018, in Gansbaai, South Africa, 415 white sharks were sighted, and 525 surface-generated social interactions were identified, exhibited by 169 different white sharks. The mean sighting rate was 0.91 (range 0.18–1.53) white sharks per hour. Eight patterns of social interaction were exhibited: swim by, parallel swim, follow/give way, follow, give way, stand back, splash fights, and piggyback. Non-random interactions occurred when pairs of specimens approached the passive surface bait, confirming that the white sharks made a real choice, showing a dominance hierarchy during the ten years of data collection. Evidence of non-random social interactions in the surface behavior of bait-attracted white sharks Carcharodon carcharias in Gansbaai’s transient population was the goal of this research.

Observations of Multiple Young-of-the-Year to Juvenile White Sharks (Carcharodon carcharias) within South-West Australian Waters and Its Implications for a Potential Nursery Area(s)

The white shark (Carcharodon carcharias) is a globally distributed top predator. Due to its ecological importance, increasing knowledge through continued research can enhance management measures. One such facet of biological knowledge is the identification of shark nursery areas, as protection of these regions is critical to species survival. Presently, there are two known C. carcharias nursery areas in association with the Eastern Australian subpopulation; however, a nursery area associated with the Southern-Western Australian C. carcharias subpopulation has yet to be identified. Herein, we report opportunistic laser photogrammetry, stereo-photogrammetry, and baited remote underwater video systems (BRUVS) data that resulted in the identification of sixteen young-of-the-year (YOY)-juvenile C. carcharias from two separate regions (i.e., Salisbury Island and Daw Island) in South-West Australia. Additionally, anecdotal bycatch data associated with two YOY C. carcharias (i.e., 1.40 and 1.70 m total length) from another location within the Great Australian Bight are reported. While it is premature to conclude that these sites represent discrete or an expansive interconnected nursery area, the sightings success in this study is indicative that future research may want to consider implementing a study during a similar time period (i.e., February–March) while using a similar attractant methodology (i.e., bottom-set BRUVS baited with squid [Sepioteuthis australis]) to help elucidate the unique life-history characteristics of this C. carcharias subpopulation.

Optimal parameter characterization of an enhanced mathematical model of solar photovoltaic cell/module using an improved white shark optimization algorithm

AbstractIn the modeling and designing of PhotoVoltaic (PV) systems, parameter characterization in PV cell/module models remains a crucial field of research. Diode‐based models, such as single‐diode model (SDM), double‐diode model (DDM), and the three‐diode model, are frequently employed, and SDM and DDM are the most significant models. As a result, the difference between the estimated and experimental current can be minimized by using an objective function to solve the parameter characterization of such models. Metaheuristic optimization algorithms have recently been employed to get around the difficulty of finding accurate and highly reliable outcomes quickly. As a result, this research modifies the fundamental SDM and DDM and considers an objective function based on the modified models. Additionally, an improved version of a novel metaheuristic algorithm called White Shark Optimizer (WSO) is proposed by modifying the force control parameters of the WSO, and a chaotic generator is infused to improve the exploitation ability of WSO. The modified algorithm is named IWSO and applied to extract the PV parameters. This paper uses the new objective function to compare the conventional and the modified PV models. The outcomes of the experiment demonstrated IWSO's dominance over competing algorithms. With an average Freidman's ranking test value of 1.171, the proposed IWSO is superior to all selected algorithms. The average accuracy of modified SDM and DDM is 12% better than the traditional PV models. According to the findings, IWSO's estimated parameter values are the best, with the smallest difference between estimated and experimental current.

An efficient feature selection method for arabic and english speech emotion recognition using Grey Wolf Optimizer

Nowadays, analyzing and interpreting emotions through human speech communication have drawn a great attention in the field of human-computer interaction. Therefore, many speech recognition systems have been suggested to recognize the emotional states of the speaker utilizing the speech recordings of their spoken utterances. Feature extraction is an important step in building an emotion recognition system in which it is used to extract emotional features from speech data. However, not all extracted features are relevant to classify the emotion states of the speaker. The existence of irrelevant and redundant features generates unmeaningful patterns that lead to inaccurate and undesirable emotion classification performance. Therefore, this study proposes an intelligent feature selection method based on a novel bio-inspired optimization algorithm that mimics the hunting mechanism of wolves in the nature, called Grey Wolf Optimizer (GWO) and K-nearest neighbor (KNN) classifier, to find the most relevant subset of features to enhance the classification performance of an emotion recognition systems. The proposed method is called GWO-KNN. Emotion classification is performed on three distinct databases including Arabic Emirati-accented speech database, Ryerson Audio-Visual Database of Emotional Speech and Song dataset (RAVDESS), and Surrey Audio-Visual Expressed Emotion dataset (SAVEE). A combined or single feature extraction method is applied to extract the features from each dataset. The proposed method provides better classification performance for speech emotion recognition system compared to classical methods such as bat algorithm (BAT), cuckoo search (CS), White Shark Optimizer (WSH), and arithmetic optimization algorithm (AOA). Our proposed method also surpasses several state-of-the-art recent approaches that use the same datasets.

An enhanced approach-based grid flexibility analysis for combined heat and power systems with variable renewable energy systems

An enhanced approach-based grid flexibility analysis for combined heat and power (CHP) systems with variable renewable energy systems is proposed in this article. The proposed system is the combined execution of White Shark Optimizer (WSO) and Pelican Optimization Algorithm (POA). The WSO approach updating behavior is enhanced by the POA approach hence named as enhanced WSO technique. The main purpose of the proposed technique is to provide the optimal grid flexibility by utilizing the CHP systems. Utilizing the proposed method, the demand for the sources is predicted in the year 2030. The proposed approach considers the optimization problem as a reduction of cost and analyzes the flexibility in both upward and downward directions. The sources like photovoltaic, wind turbine, hydropower plants, nuclear, and coal demand power are analyzed. Based on the seasons, the proposed approach analyzed the system flexibility. Finally, the performance of the proposed method is simulated in MATLAB/Simulink platform and the performance is compared to the various existing methods.

Advanced Energy Management Strategy of Photovoltaic/PEMFC/Lithium-Ion Batteries/Supercapacitors Hybrid Renewable Power System Using White Shark Optimizer.

The slow dynamic response of a proton exchange membrane fuel cell (PEMFC) to high load change during deficit periods must be considered. Therefore, integrating the hybrid system with energy storage devices like battery storage and/or a supercapacitor is necessary. To reduce the consumed hydrogen, an energy management strategy (EMS) based on the white shark optimizer (WSO) for photovoltaic/PEMFC/lithium-ion batteries/supercapacitors microgrid has been developed. The EMSs distribute the load demand among the photovoltaic, PEMFC, lithium-ion batteries, and supercapacitors. The design of EMSs must be such that it minimizes the use of hydrogen while simultaneously ensuring that each energy source performs inside its own parameters. The recommended EMS-based-WSO was evaluated in regard to other EMSs regarding hydrogen fuel consumption and effectiveness. The considered EMSs are state machine control strategy (SMCS), classical external energy maximization strategy (EEMS), and optimized EEMS-based particle swarm optimization (PSO). Thanks to the proposed EEMS-based WSO, hydrogen utilization has been reduced by 34.17%, 29.47%, and 2.1%, respectively, compared with SMCS, EEMS, and PSO. In addition, the efficiency increased by 6.05%, 9.5%, and 0.33%, respectively, compared with SMCS, EEMS, and PSO.

A historical approach to describing the complex behaviour of a large species of predatory shark — Case study 2: the white shark, Carcharodon carcharias

Abstract A historical example, case study 2, is one of two cases illustrating the complexity of interactions between sharks in their natural environment. Evidence is presented that the white shark is a selective feeder, consuming seals and sea lions with high fat content yet rejecting a pelican, human, and sea otters with low fat content. After biting seals, these sharks carry them in their jaws for a prolonged time underwater to kill them through blood-loss, i.e., exsanguination. Multiple sharks compete to feed on the remaining seal carcass when it floats to the surface. The sharks perform the Tail Slap, consisting of lifting the caudal fin and splashing water toward the accompanying shark. A combatant is permitted to feed further on a seal only if the vigor and frequency of its tail slaps are greater than those of its opponent. Thus, this is an agonistic display, functioning to ward off potential competitors.

Left atrium MRI image segmentation using efficient Xception stochastic depth based generative adversarial network

ABSTRACT Globally, cardiovascular diseases are an important cause of death. The general underlying mechanism of the key cardiovascular diseases is thrombus formation inside the blood vessels. For these thrombi, the Left Atrial Appendage (LAA) is the significant repository. It is a residual appendix as of the Left Atrium (LA) embryonic development; in addition, thrombus formation in healthy patients is prevented by its high contractility. Hence, LA segmentation is required. Proposing an effectual technique to attain automatic segmentation of the LA of a Magnetic Resonance Imaging (MRI) input image is the goal. By employing a Bitwise Left and Right Shift-centric White Shark Optimizer (BLRSWSO), optimized boundary detection is conducted. To segment LA, the Xception Stochastic Depth-centric Generative Adversarial Network (XSDGAN) is developed. In the end, by deploying the Union Histogram Intersection Box filter (UHIBF), contrast enhancement is carried out. For proving the efficacy, the proposed methodologies’ performance is weighed against the prevailing techniques. The proposed system segments the LA effectively as per the experimentation.

Inferring habitat use of the Pacific White Shark using vertebral chemistry

The conservation and management of highly migratory sharks relies on understanding age-related movements and nursery habitat utilization. We reconstructed the habitat use and migratory history of young White Sharks (Carcharodon carcharias), a highly protected species, by utilizing natural chemical tracers (element:Ca ratios and stable isotope analysis, SIA) in vertebral cartilage growth bands. Two nursery areas in the northeastern Pacific are known, but migration patterns of immature White Sharks within the Gulf of California (GC) and natal philopatry are poorly understood. Vertebrae from coastal Mexican artisanal fisheries off central Baja California in the Pacific (12 neonates and juveniles; 139-280 cm total length) and the GC (3 subadults; 289-355 cm TL) were analyzed to characterize (1) trophic histories from collagen δ13C and δ15N values, and (2) in utero patterns and post-birth environmental histories from element:Ca time-series. Mean δ15N values from vertebral edges of GC sharks, representing the most recent feeding, was +5‰ higher than in the Pacific, reflecting the intense denitrification that permeates the regional food web and supporting SIA as tracers of migration between regions. A subadult from the GC likely resided within the system throughout its life, and two subadults migrated into the GC. Most neonate and juvenile sharks caught in the Pacific had SIA that did not overlap with those of the GC, but a single subadult likely migrated to the GC. Element:Ca ratios displayed ontogenetic trends, with Li:Ca, Zn:Ca, and Ba:Ca significantly higher before the birth mark in sharks captured in the GC. Edge values were significantly higher in Zn:Ca and Ba:Ca in the GC compared to the Pacific, suggesting elemental ratios may serve as tracers of migration between regions. Subadult sharks collected from GC displayed elevated maternal Zn:Ca and Ba:Ca, suggesting mothers may have resided in the GC for an extensive period pre-birth. Some White Sharks may reside within the GC from birth until at least the subadult stage (ca. 3 m TL), and there may be an unidentified nursery. Chemical tracers, coupled with genomic and tagging studies, should improve understanding of the importance of the GC to White Shark populations in the northeast Pacific.

Could convulsive body shuddering of a white shark near a shark cage be an element of a threat display?

Abstract In this short note, we describe the convulsive body shuddering of a white shark as it approached two large metallic shark cages, each with multiple divers standing within them. When animals feel threatened, they experience conflicting instincts — one is to escape and another is to fight. In this situation, they do not always fight but often perform an agonistic, or aggressive display. Having arrived at the source of an olfactory corridor, this white shark was confronted with highly visible cages made with aluminium bars. The divers use hookah air hoses to breathe, and were therefore releasing bubbles, which reflect light and generate sounds as they oscillate toward the surface. The photographers may also have been taking pictures of the shark with their flash-bulb equipped cameras, which produce a sudden disruptive flash of irradiance. The shark’s behaviour is illustrated with a series of video frames as he approaches the cage. The body of the shark shutters convulsively and he opens his mouth, keeping it open for a prolonged period of 2.8 s as he passes close to the cage, while (1) depressing his pectoral fins, (2) hunching his back, (3) keeping his caudal fin held at right angle to the axis of view to increase his apparent size, and (4) shaking his body with spasmodic oscillations. The shark appears frightened, and hence may perform the display to discourage any aggression directed at him by the cage with humans emitting a panoply of frightening stimuli. Alternative explanations of the motivation behind this behaviour are also discussed. We hope that it will lead other scientists to look for this behaviour when observing the behaviour of white sharks from a cage, so they can provide further evidence shedding light upon the shark’s motivation for performing this conspicuous behaviour.

Decomposition integration and error correction method for photovoltaic power forecasting

Photovoltaic power generation has remarkable environmental benefit, and it is one of the effective means to fundamentally solve environmental problem. An accurate photovoltaic power forecasting system is critical for short-term photovoltaic power station scheduling and power generation plan operation. Aiming at the problems of high randomness and low prediction accuracy of photovoltaic power, a multi-factor forecasting model for photovoltaic power based on fuzzy C-means (FCM), improved variational mode decomposition (VMD) by white shark optimizer (WSO), long short-term memory (LSTM) optimized by artificial gorilla troops optimizer (GTO), error correction (EC), named WVMD-GTO-LSTM-EC, is proposed. First, screen out the main meteorological factors affecting photovoltaic power by Spearman correlation coefficient, and use these factors as clustering feature vector of FCM, divide photovoltaic power by FCM, and obtain similar daily samples. Second, improved VMD by WSO, named WVMD, is proposed to decompose similar daily samples of different meteorological models to obtain intrinsic mode functions (IMFs). Third, LSTM optimized by GTO, named GTO-LSTM, is proposed, and the IMFs combined with meteorological factors data are input into GTO-LSTM for training and testing. Final, EC is established to further increase the forecasting precision and the evaluation index, correlation coefficient R and DM test are introduced to comprehensively evaluate the model. In this paper, to demonstrate the superiority of proposed model, two groups of photovoltaic power data gathered by an Australian solar power generation system are used as experimental data, and the prediction performance is compared with that of eight comparison models. The experimental results show that forecasting value of this model accords with true value to the greatest extent, R = 0.99, and MAPE, MAE, RMSE and R2 can reach 0.02, 0.09, 0.12 and 0.99, respectively, which indicates that its forecasting ability is superior to all comparison models.

Detecting Mediterranean White Sharks with Environmental DNA

The white shark (Carcharodon carcharias) is a globally distributed, ecologically important top predator whose biology and population dynamics are challenging to study. Basic biological parameters remain virtually unknown in the Mediterranean Sea due to its historically low population density, dwindling population size, and lack of substantial sightings. White sharks are considered Critically Endangered in the Mediterranean Sea, and recent analyses suggest that the population has declined by 52% to 96% from historical levels in different Mediterranean sectors (Moro et al., 2020). Thus, white shark sightings dating back to 1860 are being used to estimate population trajectories throughout the entire region. Though the population size is unknown, remaining individuals are thought to be primarily restricted to a handful of hotspots deemed important for their reproduction and foraging. One of these hypothesized hotspots is the Sicilian Channel, which accounts for 19% of total historical sightings.

An open spatial capture-recapture framework for estimating the abundance and seasonal dynamics of white sharks at aggregation sites

The present study provides the first estimate of abundance for the white shark at a new aggregation site in the western North Atlantic, which required the development of a novel modeling framework to accommodate the species’ migratory behavior. Estimates of abundance are needed to evaluate the performance of existing conservation measures for white shark populations worldwide but have historically been infeasible to obtain in the region. Following the recent emergence of Cape Cod, Massachusetts, USA, as a seasonal aggregation site, we conducted a photographic capture-recapture survey and identified 393 individual white sharks from 2015-2018. As conventional capture-recapture models do not adequately represent the species’ migratory behavior, we extended an existing open spatial capture-recapture framework to allow for movements into and out of the surveyed area and accommodate variation in residency and habitat use among individuals. Using simulations, we demonstrated that failing to account for these processes resulted in biased estimates of abundance that would be misleading if used as the basis for management advice. We applied the model developed to describe the seasonal dynamics of the Cape Cod aggregation site and estimated a superpopulation size of 800 (393-1286) individuals, which provides an important baseline for this species of conservation concern. Because it directly links changes in abundance over time to the demographic processes underpinning them, the model described provides a more mechanistic understanding of the dynamics of white shark aggregations and improves the applied relevance of the results for the conservation and management of the species.