Improved Detection Probability Research Articles

Improved YOLOv5 Network for Aviation Plug Defect Detection

Ensuring the integrity of aviation plug components is crucial for maintaining the safety and functionality of the aerospace industry. Traditional methods for detecting surface defects often show low detection probabilities, highlighting the need for more advanced automated detection systems. This paper enhances the YOLOv5 model by integrating the Generalized Efficient Layer Aggregation Network (GELAN), which optimizes feature aggregation and boosts model robustness, replacing the conventional Convolutional Block Attention Module (CBAM). The upgraded YOLOv5 architecture, incorporating GELAN, effectively aggregates multi-scale and multi-layer features, thus preserving essential information across the network’s depth. This capability is vital for maintaining high-fidelity feature representations, critical for detecting minute and complex defects. Additionally, the Focal EIOU loss function effectively tackles class imbalance and concentrates the model’s attention on difficult detection areas, thus significantly improving its sensitivity and overall accuracy in identifying defects. Replacing the traditional coupled head with a lightweight decoupled head improves the separation of localization and classification tasks, enhancing both accuracy and convergence speed. The lightweight decoupled head also reduces computational load without compromising detection efficiency. Experimental results demonstrate that the enhanced YOLOv5 architecture significantly improves detection probability, achieving a detection rate of 78.5%. This improvement occurs with only a minor increase in inference time per image, underscoring the efficiency of the proposed model. The optimized YOLOv5 model with GELAN proves highly effective, offering significant benefits for the precision and reliability required in aviation component inspections.

DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing

AbstractIn the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric refraction, equipment malfunctions, and other factors, resulting in diminished data quality. Traditional radar blockage correction methods, such as employing approximate radial information interpolation and supplementing missing data, often fail to effectively exploit potential patterns in massive radar data, for the large volume of data precludes a thorough analysis and understanding of the inherent complex patterns and dependencies through simple interpolation or supplementation techniques. Fortunately, edge computing possesses certain data processing capabilities and cloud center boasts substantial computational power, which together can collaboratively offer timely computation and storage for the correction of radar beam blockage. To this end, an edge-cloud collaborative driven deep learning model named DenMerD is proposed in this paper, which includes dense connection module and merge distribution (MD) unit. Compared to existing models such as RC-FCN, DenseNet, and VGG, this model greatly improves key performance metrics, with 30.7$$\%$$ % improvement in Critical Success Index (CSI), 30.1$$\%$$ % improvement in Probability of Detection (POD), and 3.1$$\%$$ % improvement in False Alarm Rate (FAR). It also performs well in the Structure Similarity Index Measure (SSIM) metrics compared to its counterparts. These findings underscore the efficacy of the design in improving feature propagation and beam blockage accuracy, and also highlights the potential and value of mobile edge computing in processing large-scale meteorological data.

ED based spectrum sensing over IRS-assisted Rayleigh-FTR fading channels

A recent study has revealed that incorporating intelligent reflecting surfaces (IRS) into wireless systems improves their performance within various signal propagation conditions. This research paper focuses on evaluating the spectrum-sensing abilities of cognitive radio in wireless environments enhanced by IRS technology. To achieve this, the paper develops a statistical model to describe the connections between the primary user (PU) and IRS, as well as between IRS and the secondary user (SU). This model calculates a precise approximation of the probability density function (PDF) of the end-to-end instantaneous signal-to-noise ratio (SNR). The SNR considers both the PU-to-IRS link, which follows a Rayleigh fading distribution, and the IRS-to-SU link, which follows an FTR fading distribution. By utilizing this PDF distribution, the paper derives analytical expressions for the average probability of detection (APD) for single and cooperative secondary users. These APD expressions are then employed to establish the exact closed-form expression for the average area under the receiver operating characteristics (AUC), system throughput, and transmission probability of the SU. To validate these theoretical findings, the paper conducts Monte Carlo simulations. The results from these simulations indicate an improved detection probability when operating in an IRS-enhanced wireless propagation environment. Consequently, the study suggests that incorporating IRS technology can significantly enhance the reliability of communication systems based on cognitive radio principles.

Lifting Wavelet-Assisted EM Joint Estimation and Detection in Cooperative Spectrum Sensing.

Spectrum sensing in Cognitive radio (CR) is a way to improve spectrum utilization by detecting spectral holes to achieve a dynamic allocation of spectrum resources. As it is often difficult to obtain accurate wireless environment information in real-world scenarios, the detection performance is limited. Signal-to-noise ratio (SNR), noise variance, and channel prior occupancy rate are critical parameters in wireless spectrum sensing. However, obtaining these parameter values in advance is challenging in practical scenarios. A lifting wavelet-assisted Expectation-Maximization (EM) joint estimation and detection method is proposed to estimate multiple parameters and achieve full-blind detection, which uses lifting wavelet in noise variance estimation to improve detection probability and convergence speed. Moreover, a stream learning strategy is used in estimating SNR and channel prior occupancy rate to fit the scenario where the SU has mobility. The simulation results demonstrate that the proposed method can achieve comparable detection performance to the semi-blind EM method.

Alternating Direction Method of Multipliers-Based Constant Modulus Waveform Design for Dual-Function Radar-Communication Systems.

In this paper, we design constant modulus waveforms for dual-function radar-communication (DFRC) systems based on a multi-input multi-output (MIMO) configuration of sensors for a far-field scenario. At first, we formulate a non-convex optimization problem subject to waveform synthesis for minimizing the interference power while maintaining a constant modulus constraint. Next, we solve this non-convex problem, iteratively, using the alternating direction method of multipliers (ADMM) algorithm. Importantly, the designed waveforms approximate a desired beampattern in terms of a high-gain radar beam and a slightly high gain communication beam while maintaining a desired low sidelobe level. The designed waveforms ensure an improved detection probability and an improved bit error rate (BER) for radar and communications parts, respectively. Finally, we demonstrate the effectiveness of the proposed method through simulation results.

Performance evaluation of high‐resolution IMDAA and IMERG for detecting cloudburst events over the Northwest Himalayas

AbstractThe main goal of this research is to assess the Indian Monsoon Data Assimilation and Analysis (IMDAA), a recently established high‐resolution (0.12° × 0.12°) reanalysis dataset, for observing cloudburst events over the Northwest Himalaya (NWH). In addition, a high‐resolution (0.1° × 0.1°) satellite estimate, the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (GPM) version 6 (IMERG‐V06B) (Final run), is validated against the IMDAA. The following is a summary of our significant findings. (1) Reanalysis data from the IMDAA detects 11 out of 16 cloudburst incidences. In addition, 10 events captured in the IMERG‐V06B data are a subset of those captured by IMDAA. According to contingency measures, the probability of detection (POD) of IMERG‐V06B at 99.99th percentile is 33.33%–63.39% along the geographical extent of NWH. When utilizing the improved probability of detection (IPOD) to account for time, the possibility of IMERG‐V06B detecting cloudburst occurrences ranges from 41.24% to 68.25%. (2) According to the finding, the IMDAA accurately detects cloudburst events in Jammu and Kashmir (J&K), where data from the India Metrological Department (IMD) at a resolution of 0.25° × 0.25° have underperformed. As a result, we can deduce that IMDAA can be used to observe extreme events in the Himalayas. IMERG‐V06B is also relevant satellite data for monitoring cloudburst events and validating climate models.

Design of pedestrian radiation portal monitor with NaIL dual-particle detector

Radiation portal monitors (RPMs) are operated in various places such as airports, ports, and nuclear facilities to detect radioactive materials illegally possessed by pedestrians. However, most commercial RPMs can measure only gamma-rays; hence, separate neutron detectors need to be applied when neutron measurement is required. Radioactive materials including special nuclear materials (SNMs) have a high gamma-ray emission rate, but they can be concealed because gamma-rays are easily shielded. By contrast, although SNMs have a low neutron emission rate, it is difficult to shield them due to secondary radiation emission when neutrons are captured. Additionally, neutron-emitting materials can be easily detected due to the low background level of neutrons. So, measurement of both gamma-rays and neutrons is beneficial for improved detection probability of radioactive materials (i.e., SNMs). In the present study, by Monte Carlo (MC) simulations, we designed a dual-particle detection system incorporating NaIL scintillators for application as a pedestrian RPM that can detect neutrons and gamma-rays simultaneously. The simulation model was verified by experiments with a 2 inch × 2 inch cylindrical NaIL detector using gamma-ray and neutron calibration sources. The designed system is expected to offer improved detection performance over that of a commercial system, even with its smaller dimensions.

Multi-radar data fusion for maritime moving target detection based on three-dimensional sliding window

ABSTRACT With the continuous improvement of radar resolution, distributed characteristics are presented with regard to maritime moving targets, which occupy multi-resolution cells in Synthetic Aperture Radar (SAR) image domain. However, the current constant false alarm rate (CFAR) detection algorithms rarely consider the impact of distributed characteristics on target detection performance, and thus the corresponding performance evaluation methods could not be applicable to this issue. In this paper, a multi-station fusion detection method for maritime moving target (MMT) is presented based on three-dimensional (3D) sliding window. Firstly, the multi-station echoes in Cartesian-Doppler frequency rate (DFR) domain are obtained under the simple-transmitting and multiple-receiving operation configuration, and the 3D sliding window is designed to achieve the optimal matching for a specified target with any moving direction in terms of its prior information, i.e. Radar Cross Section (RCS), radar resolution and target size. Then, the target cells within the sliding window are directly processed by means of the M/N criterion, and thus to avoid target detection performance loss caused by the artificial construction of extended detection statistics. Finally, a novel quantitative evaluation method in regards to a high-resolution radar is designed by mining the relationship between the target detection performance and the occupied cell number, which could greatly improve detection probability of distributed targets on the premise of a constant false alarm rate. The proposed algorithm does not need to design complex extended detection statistics, which provides a feasible way for robust detection and performance evaluation for a high-resolution radar. Simulation results verify the effectiveness of this research.

King Rail ( Rallus elegans ) response to audio playback: implications for population estimation, monitoring methodology, and trapping approach

Conspecific audio can be broadcast to improve detection probability (detectability) of secretive marsh bird species for population monitoring purposes and as a lure to more effectively trap individuals. Our primary objective was to describe King Rail (<em>Rallus elegans</em>) detectability as a function of distance and so determine if the distance sampling assumption of perfect detectability immediately adjacent to survey points was violated during call-broadcast surveys. We also described what factors affected King Rail detectability during audio broadcast surveys, and how and when this species was detected, and compared effectiveness of various King Rail call recordings used during trapping attempts. To accomplish these objectives, we experimentalized trapping efforts, radio-tagged 12 King Rails, and conducted repeated call-broadcast surveys for each radio-tagged individual in Michigan and Ohio, USA during the 2020 and 2021 breeding seasons. We found that King Rail detectability during 81 surveys was 0.39, which violates the aforementioned distance sampling assumption and necessitates a correction factor. Detectability within survey segments varied among survey periods, though overall detectability remained relatively constant, even after call-broadcast surveys in our study area typically end (post-survey period: 15 June to 10 July), which indicates that King Rail monitoring could effectively continue later than currently occurs. We found that grunt calls resulted in more captures than kek calls and that a multi-call track was most effective at trapping King Rails. Our findings could improve King Rail population estimates and increase success during trapping efforts, and may inform similar efforts for other secretive marsh birds.

Design optimization of X-ray security scanner based on dual-energy transmission imaging with variable tube voltage

At airports and ports, X-ray security scanners based on dual-energy transmission imaging have been operated to prevent importation of contraband articles including weapons, narcotics, and explosives. Security scanners currently operated by the Korea Customs Service use a fixed tube voltage (i.e., 160 kVp); hence, it has a limitation in detecting thinly-coated and/or low-density organic contraband articles. Tube voltage lower than 160 kVp affords an advantage in terms of enhanced contrast for organic materials, albeit at the cost of penetration power, and vice versa. Therefore, a security scanner with variable tube voltage that is adjustable according to the physical/chemical properties of the object to be inspected, has the potential to improve detection probability for contraband articles. In the present study, security scanner design optimization specifically in terms of the X-ray generator and the dual-energy detector was performed by Monte Carlo (MC) simulations. Then, the performance of the designed system was evaluated using a simple phantom to demonstrate the advantages of the variable tube voltage application. Additionally, the standard kits were used for performance evaluation in terms of simple penetration and wire display, which are the two parameters specified in Korean law.

Estuarine Recruitment of Longfin Smelt (Spirinchus thaleichthys) north of the San Francisco Estuary

Longfin Smelt (Spirinchus thaleichthys) was an important forage fish in the San Francisco Estuary (the SFE) but was listed as threatened under the California Endangered Species Act in 2009. This has inspired research within the estuary at the southern edge of their distribution. However, populations also exist in other estuaries along the coast, which are far less described despite their potential importance in a meta-population. We surveyed Longfin Smelt populations along the northern California coast for larval recruitment. We conducted surveys in 2019 and 2020 to (1) identify estuaries north of the SFE where spawning occurs, and (2) evaluate how habitat features (e.g., salinity, temperature, dissolved oxygen, turbidity) influenced Longfin Smelt larvae abundance. We detected larvae in four of 16 estuaries we surveyed, and all were large estuaries north of Cape Mendocino. No larvae were detected in eight coastal estuaries in closer proximity to the SFE. Larvae catch probability increased with turbidity and decreased with salinity with no significant influence of temperature and dissolved oxygen. In the wet winter of 2019, we observed lower densities of larvae in Humboldt Bay and the Eel River, and detected no Longfin Smelt in the Klamath and Mad Rivers; in the dry winter of 2020, we detected larvae in two additional estuaries. Possibly elevated freshwater outflow in 2019 increased transport rates to the sea, resulting in the observed low larval recruitment. Our results suggest that, while populations of Longfin Smelt exist in large estuaries north of Cape Mendocino, coastal estuaries in proximity to the SFE were either under-sampled or are not permanently inhabited by Longfin Smelt. This suggests that the threatened estuary Longfin Smelt population may lack the resiliency afforded by meta-populations and advocates for increased monitoring over a range of hydrologic conditions and improving detection probabilities for future assessments of gene flow between populations.

Spectrum Sensing Using CNN With Attention on Switch of Channel States

This work addresses the issue of spectrum sensing with random arrival and departure of primary signals. We first design a convolutional neural network (CNN) with outputs as the posterior probabilities of the arrival and departure of primary signals, leading to a CNN-based detector with the ratio of the posterior probabilities (i.e., the outputs of the CNN) as a test statistic. To further enhance the attention of the network on the switch feature of channel states, we design a switch attention module (SAM) that adaptively weights the received signals. Replacing the convolution plus maximum pooling block in the CNN detector with the SAM block leads to an SAM-CNN detector. Simulations show that the proposed CNN detector significantly outperforms existing detectors, and further improvement of detection probability by 19% is achieved by the SAM-CNN detector.

Many-Objective RadarCom Signal Design via NSGA-II Genetic Algorithm Implementation and Simulation Analysis

In this communication, we investigate the performance of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) in many-objective optimization scenarios pertaining to joint radar and communication functionality. We introduce five objectives relevant to sensing and secure communications and develop a cost function where these objectives can be individually prioritized by a user. We consider three scenarios: Radar Priority, Communication Priority, and All (Objectives) Equal; we then demonstrate the optimization results using an orthogonal frequency-division multiplexing (OFDM) radarcom signal. The objectives with selected weights are shown to improve system performance and thereby validate the viability of our approach. The Radar Priority scenario showed the best improvement in probability of detection, PSLR, and PAPR. Compared to the baseline performance values, the improvements were: from 94.05% to 96%, from 11.7 to 13.6 dB, and from 9.46 to 7.09 dB, respectively. The communication scenario saw the best improvement in BER and clutter similarity (measured by NRMSE) from 3.52% to 0.39% and 0.87 to 0.59, respectively.

Development of a wind power ramp forecasting system via meteorological pattern analysis

AbstractRamp phenomena caused by abrupt changes in wind speed may confound the stable operation of correlated electrical power supply systems, yet accurate numerical predictions are challenging, as the wind is affected by complex interactions between large‐scale weather patterns and local geographical conditions. Further, optimal numerical weather prediction (NWP) methods and physics schemes vary as a function of weather patterns. The present study proposed a new real‐time wind power ramp forecast framework based on the flexible selection of optimal NWP models, which were derived via principal component analysis (PCA). The novelty of this analysis lies in that statistical methods were employed for NWP optimization, compared with their more conventional use during an NWP postprocessing. Here, a weather pattern was classified by PCA using outcomes from the global‐scale prediction models, and the optimum regional NWP system settings were acquired according to the weather patterns for further wind field dynamical downscaling. The performance of the developed prediction system was verified with wind power at wind turbine hub‐heights for three areas in eastern Japan, and the Critical Success Index (CSI) indicated an improvement of prediction accuracy over benchmark predictions by ≤0.184 for ramp‐up events and ≤0.127 for ramp‐down events (both observed in Tohoku area). Higher CSI values were consistently seen in three wind farm areas, indicative of the improvement in detection probability for actual ramp events compared with benchmark.

Effectiveness of a commercial lure to attract red fox

In camera trap studies, attractants may be used to increase detection probabilities of wildlife, which may help to improve estimates of abundance and occupancy. Using a semi-experimental approach, we investigated if a commercial, strawberry scented lure increased detection probability and visiting time duration in red fox Vulpes vulpes, and the potential reasons for variation in these parameters. In September 2020, within the Stelvio National Park, central Italian Alps, 32 camera sites were randomly assigned to 4 different treatments: 8 to commercial lure, the target of our investigation; 8 to orange aroma, to test for the “curiosity” effect; 8 to cat kibble, to test for the “likability” effect; 8 to camera trap only, the control test. Detection probability and duration of visiting time were estimated using hurdle negative binomial regression models. Daily detection probability was significantly higher with lure (0.078), orange aroma (0.086), kibble (0.075) than with camera trap only (0.031); in the first day after treatment, the time an animal spent in front of the cameras significantly increased with orange aroma (16.61 s) and kibble (33.78 s) compared to lure (9.97 s) and camera trap only (0.38 s). Our results support the use of lures to improve detection probability and visit duration in red fox, but we could not disentangle the drivers of increased parameter estimates. When consumable costs are considered, the use of the commercial strawberry scented lure does not appear justified for both detection probability and visit duration, and cheaper alternatives may be preferable.

Analysis of the Impact of Detection Threshold Adjustments and Noise Uncertainty on Energy Detection Performance in MIMO-OFDM Cognitive Radio Systems.

Due to the capability of the effective usage of the radio frequency spectrum, a concept known as cognitive radio has undergone a broad exploitation in real implementations. Spectrum sensing as a core function of the cognitive radio enables secondary users to monitor the frequency band of primary users and its exploitation in periods of availability. In this work, the efficiency of spectrum sensing performed with the energy detection method realized through the square-law combining of the received signals at secondary users has been analyzed. Performance evaluation of the energy detection method was done for the wireless system in which signal transmission is based on Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing. Although such transmission brings different advantages to wireless communication systems, the impact of noise variations known as noise uncertainty and the inability of selecting an optimal signal level threshold for deciding upon the presence of the primary user signal can compromise the sensing precision of the energy detection method. Since the energy detection may be enhanced by dynamic detection threshold adjustments, this manuscript analyses the influence of detection threshold adjustments and noise uncertainty on the performance of the energy detection spectrum sensing method in single-cell cognitive radio systems. For the evaluation of an energy detection method based on the square-law combining technique, the mathematical expressions of the main performance parameters used for the assessment of spectrum sensing efficiency have been derived. The developed expressions were further assessed by executing the algorithm that enabled the simulation of the energy detection method based on the square-law combining technique in Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing cognitive radio systems. The obtained simulation results provide insights into how different levels of detection threshold adjustments and noise uncertainty affect the probability of detection of primary user signals. It is shown that higher signal-to-noise-ratios, the transmitting powers of primary user, the number of primary user transmitting and the secondary user receiving antennas, the number of sampling points and the false alarm probabilities improve detection probability. The presented analyses establish the basis for understanding the energy detection operation through the possibility of exploiting the different combinations of operating parameters which can contribute to the improvement of spectrum sensing efficiency of the energy detection method.

Location-aware spectrum sensing for cognitive visible light communications over multipath channels

Accurate spectrum sensing is crucial for cognitive visible light communication (CVLC). However, due to multiple light emitting diodes (LEDs) and indoor reflections, a CVLC channel often shows a multipath characteristic which is difficult for a secondary user (SU) to estimate before spectrum sensing, thereby leading to a degraded sensing accuracy. To tackle this issue, we propose a location-aware spectrum sensing scheme for the CVLC system, where its multipath channel can be effectively estimated based on the location information of an SU. By considering different a priori information, we propose three primary user (PU) detection algorithms. Specifically, we first propose a location-aware likelihood-ratio-test detection (LLD) algorithm to achieve an optimal sensing performance by using a priori knowledge of signal and noise variances. To reduce the computational complexity of LLD, we propose a more efficient location-aware semi-blind detection (LSD) algorithm, which requires no a priori knowledge of signal variance. Further, to tackle noise uncertainty, we propose a location-aware near-blind detection (LND) algorithm, which does not require either signal or noise variance. To evaluate the performance of the proposed algorithms, we also develop their respective analytical models based on the multipath channel. It is found that the analytical models can accurately match the results obtained by simulations, and LLD and LSD can improve detection probability by 10% compared with the conventional energy detection (ED) scheme. Moreover, by incorporating LSD with cyclic-prefix-based detection (CD), the detection probability can be further improved by ∼20% and 8% compared with ED and CD, respectively. Finally, LND outperforms all the other algorithms when the noise uncertainty is over 0.5 dB, and it is found that both LSD and LND are robust to receiver tilt and LSD is robust to location error.

An optimal and adaptive double threshold-based approach to minimize error probability for spectrum sensing at low SNR regime.

With the recent explosion in the number of wireless communication technologies, the frequency spectrum has become a scarce resource. The need of the hour is an efficient method to utilize the existing spectrum and Cognitive Radio is one such technology that can mitigate the spectrum scarcity. In a cognitive radio system, the unlicensed secondary user accesses the spectrum allotted to licensed primary users when it lies vacant. To implement dynamic or opportunistic access of spectrum, secondary users perform spectrum sensing, which is a quintessential part of a Cognitive radio. From the Cognitive user’s point of view, lesser error probability means an increased likelihood of channel reuse when it is vacant, and a higher detection probability signifies better protection to the licensed users. In both cases the decision threshold plays a pivotal role in determining the fate of the unused spectrum. In this paper, we study the difficulty of selecting an appropriate threshold to minimize the error probability in an uncertain low SNR regime. The sensing failure issue is analyzed, and an optimal threshold is computed that yields minimum error rate. An adaptive double threshold concept has been proposed to make the detection robust and a closed-form equation for optimal threshold has been derived to minimize the error. The novel findings through simulation results exhibit improvement in Probability of detection and reduction in probability of error at low SNR in the presence of noise uncertainty factor.

An Evaluation of the Factors Affecting 'Poacher' Detection with Drones and the Efficacy of Machine-Learning for Detection.

Drones are being increasingly used in conservation to tackle the illegal poaching of animals. An important aspect of using drones for this purpose is establishing the technological and the environmental factors that increase the chances of success when detecting poachers. Recent studies focused on investigating these factors, and this research builds upon this as well as exploring the efficacy of machine-learning for automated detection. In an experimental setting with voluntary test subjects, various factors were tested for their effect on detection probability: camera type (visible spectrum, RGB, and thermal infrared, TIR), time of day, camera angle, canopy density, and walking/stationary test subjects. The drone footage was analysed both manually by volunteers and through automated detection software. A generalised linear model with a logit link function was used to statistically analyse the data for both types of analysis. The findings concluded that using a TIR camera improved detection probability, particularly at dawn and with a 90° camera angle. An oblique angle was more effective during RGB flights, and walking/stationary test subjects did not influence detection with both cameras. Probability of detection decreased with increasing vegetation cover. Machine-learning software had a successful detection probability of 0.558, however, it produced nearly five times more false positives than manual analysis. Manual analysis, however, produced 2.5 times more false negatives than automated detection. Despite manual analysis producing more true positive detections than automated detection in this study, the automated software gives promising, successful results, and the advantages of automated methods over manual analysis make it a promising tool with the potential to be successfully incorporated into anti-poaching strategies.

Maternal Care-Providing Cichlid Neolamprologus furcifer Selectively Focuses on High-Threat Carnivorous Intruders, Limiting Attention to Other Threats

Animals adjust their behaviors based on information from multiple sources; however, the brain can effectively process limited amounts of information. Therefore, attention is restricted to a small portion of environmental stimuli. When animals process multiple information inputs, focusing on information that is deemed important improves detection probability. However, selective focus limits attention to other stimuli and associated behavioral responses. In this study, we examined how Tanganyikan cichlid, Neolamprologus furcifer, mothers selectively attack intruder fishes depending on the threat level and presence or absence of offspring. Species composition is complicated in Lake Tanganyika, and fish density is exceedingly high. Thus, parents must focus on high-threat-level intruders according to their parental care stage. Compared to females without offspring, mothers preferentially attacked carnivorous fishes farther from the nest over closer scale-eating fishes. Moreover, the percentage of females with injuries from scale-eating fish was significantly higher in those caring for offspring than those without offspring, demonstrating the cost of limited attention. Our results show that females focus on the early detection of carnivorous fishes because these predators dart in from long distances to forage eggs, fry, and juveniles, but this selective focus limits the attention placed on low-level threats. This study is the first to document the cost of limited attention in parents guarding offspring.