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Article

PECA: An Integrated Real-Time Biosensing Platform for Detecting Thermal Stress in Aquatic Environments

by
Yihang Fu
and
Zongming Ren
*
College of Geography and Environment, Shandong Normal University, Jinan 250358, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(16), 2369; https://doi.org/10.3390/w17162369
Submission received: 30 June 2025 / Revised: 29 July 2025 / Accepted: 8 August 2025 / Published: 10 August 2025
(This article belongs to the Section Biodiversity and Functionality of Aquatic Ecosystems)
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Abstract

Thermal stress poses escalating threats to aquatic ecosystems, yet current biomonitoring tools lack real-time integration of multidimensional physiological responses. To address this gap, we developed the Physiological and Ecological Comprehensive Analyzer (PECA), an integrated platform combining non-contact impedance sensors for behavior analysis, dissolved gas probes for metabolic monitoring, and wearable devices for cardiac signal acquisition in freely swimming fish. Using koi carp (Cyprinus carpio var. koi) under controlled thermal regimes (22 °C, 26 °C, 32 °C) with ethical compliance, the PECA calculated a novel Physiological Stress Index (PSI) integrating behavioral strength, the respiratory quotient, and electrocardiographic parameters. The results demonstrated significant PSI reductions under acute thermal stress, correlating with suppressed metabolism and altered cardiac function. This system provides a real-time solution for detecting thermal anomalies in aquatic environments, enabling proactive water resource management in climate-vulnerable ecosystems.

1. Introduction

In contemporary aquatic research, real-time monitoring of physiological stress responses is critical for assessing ecosystem health and advancing sustainable water resource management. Traditional biomonitoring methods, such as visual inspections and enzyme activity assays, face three fundamental limitations: low temporal resolution due to manual operation, inability to capture multi-dimensional physiological responses, and high costs associated with laboratory analyses [1]. These constraints hinder their applicability in dynamic aquatic environments, particularly under climate-induced stressors, like thermal fluctuations.
The emergence of online biological monitoring systems since the 1970s [2,3] has enhanced toxicity assessment through continuous organismal tracking. Current approaches focus on three paradigms: 1. behavioral monitoring using camera tracking [4,5,6] or impedance systems [7,8,9] to quantify locomotory metrics; 2. metabolic monitoring via sensors measuring the oxygen consumption rate (OCR) and carbon dioxide excretion [10,11,12]; and 3. electrophysiological monitoring leveraging electrocardiograms (ECGs) for cardiac assessment [13,14,15].
Among these, ECG-based systems enable sensitive detection of sublethal stress through cardiac waveform analysis (P, QRS, T waves) and interval tracking (QT, PR) [16,17,18]. Miniaturized sensors and noise-filtering algorithms [19] now permit real-time ECG acquisition in freely swimming fish, supporting applications from toxicity screening [20] to arrhythmia studies [21]. However, existing tools remain limited by a single-endpoint focus, failing to integrate behavioral and metabolic dimensions for comprehensive stress evaluation [22]. Thermal stress further complicates monitoring by disrupting metabolic pathways, altering cardiac rhythms, and exacerbating pollutant impacts [23,24,25,26,27,28], yet conventional systems cannot capture these multidimensional interactions [29,30].
To address these gaps, we developed the Physiological and Ecological Comprehensive Analyzer (PECA), integrating real-time tracking of the following: behavior (via non-contact impedance sensors); metabolism (dissolved O2/CO2 probes); and cardiac activity (wearable ECG recorders). This platform enables the calculation of the Physiological Stress Index (PSI), a composite biomarker quantifying stress-induced deviations in three dimensions: locomotor activity, respiratory quotient, and QT interval.
Leveraging the acute environmental sensitivity and physiological tractability of koi carp (Cyprinus carpio var. koi) [31], we employed this model to validate the PSI under controlled thermal gradients (22 °C, 26 °C, 32 °C). The results demonstrate the PSI’s utility as an early-warning tool for aquatic stress management [26,32].

2. Materials and Methods

2.1. System Design and Hardware Configuration

The Physiological and Ecological Comprehensive Analysis System for Aquatic Animals (PECA) was modeled using SolidWorks 2022 (Figure 1), comprising three core modules.
Behavioral Monitoring Module: A quadripole impedance biosensor (stainless steel electrodes, 3 mm thickness) detects disturbances in a high-frequency AC electric field (0.5 to 5 V, 100 Hz) generated by fish movement. Signals are transmitted to a C8051F060-based circuit board for Fast Fourier Transform (FFT) analysis [33], quantifying behavioral strength on a normalized scale (0: immobility; 1: maximum activity) [34].
Metabolic Monitoring Module: Dissolved oxygen (Y504-A fluorescence sensor, manufactured by Yosemitech (Suzhou Yosemitech Technology Co., Ltd.), China) and CO2 (AMT-CO2300 NDIR sensor) are measured via a four-channel flow system. Data acquisition is managed by an STM32F103C8T6 microcontroller (STM32F103C8T6 microcontroller is manufactured by STMicroelectronics, headquartered in Switzerland). with RS-485 communication, sampling at 1 Hz [35,36,37,38].
ECG Monitoring Module: A wearable device (OCFAS) [39] with 0.25 mm Ag needle electrodes captures ECG signals (ADS1293 ADC, 0.3~3 kHz bandpass). Signals are denoised via coif5 wavelet decomposition (10-level) and transmitted wirelessly (TFDU4100 IR module) to a PClab-530C acquisition system [40,41,42].
The main body of the PECA is an oval semi-open pool (L = 323 cm, D = 121 cm), made of 304-grade stainless steel and acrylic. It has PID-regulated water heaters and air-cooled shell-type water chillers (accuracy ±0.3 °C), propeller-driven flow controlled by a handheld flowmeter (0.06 m/s), and PWM-controlled sliding doors for intermittent water sampling [43,44].

2.2. Test Organisms

Koi carp were procured from a licensed aquaculture facility in Jinan, China. After transport, fish were acclimated for 14 days in aerated tanks (22 ± 0.5 °C, dissolved oxygen >6 mg/L) under a 14L:10D photoperiod. Daily feeding with commercial pellets ensured nutritional adequacy. Health screenings excluded individuals showing stress symptoms or anomalies.
Stepwise thermal acclimation was implemented using the PECA’s environmental module, gradually exposing fish to target temperatures with continuous oxygenation. This protocol maintained fish welfare while establishing physiological adaptation for subsequent multidimensional monitoring. Postoperative monitoring (24 h) confirmed the absence of sustained stress responses in all subjects.

2.3. Experimental Design

The experiment was conducted at three temperatures: control group (22 °C, optimal temperature), low-stress group (26 °C, mild thermal stress), and high-stress group (32 °C, approaching the upper thermal tolerance limit). Three koi carp per temperature group were tested as biological replicates (n = 3). Each temperature had three parallel experiments. Koi carp with similar body length and weight were randomly selected. Initial body weight and volume were measured. The experiment lasted for 48 h with a light/dark cycle of 14 h light and 10 h dark; no koi carp died during the experiment.

2.4. Data Acquisition and Processing

The electrical field signals generated by the movement of koi carp were captured by a quadripole impedance biosensor. After FFT processing, the signals were digitally quantified on a scale of 0–1 to represent behavioral strength. Metabolic data were collected from water samples in the respiration chamber using dissolved oxygen and carbon dioxide sensors. The data were then used to calculate VO2 and VCO2, from which the respiratory quotient (RQ) was derived. The calculation formulas are as follows:
V O 2 mg kg / h = D O S l o p e i × V r − V a × 3600 m
V C O 2 mg kg / h = D C O 2 S l o p e i × V r − V a m
R Q = V C O 2 / V O 2
where Vr = sensor volume (L), Va = fish volume (L), and m = fish mass (kg).
The electrocardiogram signals of koi carp were collected through OCFAS and wirelessly transmitted to the signal receiver board. Then, the QT interval was extracted using the Pclab-530C software(version 4.6.10.0). Denoising was performed using Stein’s unbiased risk estimation.

2.5. Physiological Stress Index (PSI)

In the system design proposed in this study, a key factor was introduced to quantify cumulative stress responses, termed the Physiological Stress Index (PSI). The PSI serves as a comprehensive evaluation metric derived from three primary components: behavioral strength (B), metabolic indicators (M), and electrocardiographic indicators (E) of the tested fish. M is characterized by the respiratory quotient (RQ), and E is characterized by the QT interval of the fish. The specific reasons are detailed in the Supplementary Material File.
It is hypothesized that under optimal environmental conditions, when fish are in their most suitable and stress-free state, the PSI approaches 1. Conversely, when fish are exposed to environmental stressors or adverse conditions, their physiological state changes, causing the PSI to deviate from the optimal value. The degree of deviation of the PSI from 1 provides a quantifiable measure of stress severity, with greater deviations indicating more intense stress responses. To formalize this assessment, the PSI is represented as a composite function integrating B, M, and E. The proposed expression aims to evaluate the stress status of fish under various environmental conditions, thereby providing an objective and real-time index for monitoring and managing the stress responses of aquatic organisms. The detailed derivation of the PSI will be provided in the Supplementary Material S1 File; the expression for the PSI is as follows:
P S I = e − k D
D is the weighted comprehensive deviation, as follows:
D = ω B Z B 2 + ω M Z M 2 + ω E Z E 2
Weight robustness: The fixed weights (ωB = 0.5, ωM = 0.3, ωE = 0.2) were chosen on physiological grounds and validated by sensitivity analysis: ±20% perturbations did not alter PSI rankings in 95.7% of 1000 Monte-Carlo trials (Supplementary File S1, Section 5).

2.6. Statistical Analysis

Differences among the temperature groups were analyzed using one-way ANOVA followed by Tukey’s HSD test for multiple comparisons (α = 0.05). Time-series data (circadian rhythms) were evaluated via repeated-measures ANOVA. Data processing was conducted using MATLAB R2014b, visualization was performed with Origin2024, and the calculation of H was implemented using Python 3.9.

3. Results

3.1. System Monitoring Technology Upgrade

In the early monitoring of aquatic animal behaviors, the Multi-Species Freshwater Biota Monitor (MFB) can be used to monitor the behavioral activities of fish [45,46]. It automatically records changes in the electrical field caused by biological movement and intuitively displays the collected data through the Fast Fourier Transform (FFT) [47]. However, the application of MFB is mostly modular. The Early Biological Warning System (BEWs) is designed based on Selye H’s Environmental Stress Threshold Model [48]. Yang exposed zebrafish to Brilliant Blue and Atrazine using BEWs, verifying that the diurnal rhythm of zebrafish behavior was significantly affected by pollutants (p < 0.01), demonstrating the potential of the diurnal rhythm of zebrafish behavior as an environmental stress assessment index [49,50]. The PECA has optimized the water and electrical configurations while ensuring real-time online monitoring functions, changing the waterway mode to circulation flushing and closed internal circulation. Additionally, the original small-sized quadripole impedance biosensor, which could only monitor small individuals, has been enlarged proportionally, allowing for the monitoring of larger fish species as well.
Ren has constructed an Online Conventional Metabolic Monitoring System (ORMMS) based on metabolic-related indicators of biota, such as the oxygen consumption rate and carbon dioxide excretion rate. This system consists of three main parts: a freshwater organism oxygen consumption rate determination system, a freshwater organism carbon dioxide excretion determination system, and a data acquisition system [51,52]. The PECA has upgraded the sensor module on this basis, adopting sensors with higher measurement accuracy and expanding the monitoring channel to four parallel channels, capable of simultaneously monitoring various environmental parameters, such as temperature, salinity, atmospheric pressure, etc. The waterway was optimized, with a propeller driving water circulation and a sliding door regulating the internal and external circulation of the respiratory chamber.
Regarding electrocardiogram (ECG) signal monitoring, the current method often involves inserting electrodes into the abdomen after anesthesia to monitor the ECG of fish. However, this static monitoring method after anesthesia affects the effectiveness of ECG signal monitoring. Ren innovatively improved the traditional method by collecting ECG signals while the fish were swimming, enhancing the credibility of the monitored data [53]. Through real-time online ECG monitoring devices, the ECG signals of fish can be monitored. With different ECG waveforms (P, Q, R, S, and T waves) and interval (PR, QRS, ST, and QT intervals) features, the impact of environmental stress on living organisms can be characterized [54,55]. On this basis, the PECA integrated two-dimensional behavioral trajectory tracing based on infrared signals and combined it with behavioral monitoring to obtain more effective data.

3.2. Signal Transmission and Processing

The monitoring system, from acquiring signals to transmitting them to the terminal display, undergoes various transmission pathways.
Ensuring signal stability and accuracy is a crucial link. Behavioral monitoring primarily collects signals through receiving electrodes fixed on the inner wall of the four-pole impedance biosensors and connects these electrodes to circuit boards via wires. During this process, since the sensor is submerged in water, we have implemented waterproof measures at the connection points by wrapping them with food-grade waterproof adhesive. This prevents corrosion due to prolonged water immersion caused by electrolysis. The metabolic monitoring system acquires data directly through high-precision intelligent sensors and receives signals via the RS-485 communication port on the circuit board. The STM32F103C8T6 microcontroller processes the signals and transmits them to the terminal, selecting different sensor signals based on various COM ports. ECG signals are transmitted wirelessly and must pass through solid, liquid, and gas phases during transmission. Inevitably, they face interference and loss along the way. Therefore, ADS1293 is used for filtering and amplifying the signals to reduce the impact of interference on signal transmission. The figure (Figure 2) shows the signal amplification circuit of ADS1293.

3.3. Analysis of Monitoring Results

3.3.1. Behavioral Analysis

The behavioral responses of koi carp to different temperature conditions were comprehensively analyzed through the behavioral monitoring module of the PECA. As depicted in Figure 3, the real-time behavioral strength of koi carp exhibited distinct patterns across the three temperature groups. At the control temperature of 22 °C, the koi carp maintained a relatively stable and moderate behavioral strength, indicating their optimal physiological state and normal activity levels. The average behavioral strength was 0.1739, with minimal fluctuations during the 48 h monitoring period, reflecting the fish’s adaptation to the stable thermal environment. In contrast, at the low-stress temperature of 26 °C, the behavioral strength of koi carp showed a noticeable increase compared to the control group. The average behavioral strength rose to 0.2562, suggesting that the mild thermal stress stimulated the fish’s activity. This could be attributed to the fact that the slightly elevated temperature may have enhanced the metabolic rate and muscle excitability of the koi carp, leading to more frequent and vigorous swimming movements. However, the increase in behavioral strength was still within a reasonable range, and the fish did not exhibit signs of excessive fatigue or stress. At the high-stress temperature of 32 °C, the behavioral strength of koi carp significantly decreased, with the average value dropping to 0.0191. This drastic reduction in activity levels indicated that the high temperature had a detrimental effect on the fish’s locomotor ability. The koi carp may have experienced thermal fatigue, resulting in decreased muscle contractility and impaired nervous system function.
The autocorrelation analysis of behavioral strength further revealed the temporal patterns of the fish’s activity. At 22 °C, the autocorrelation coefficients showed a slow decay, indicating that the fish’s behavior had a certain degree of persistence and regularity. In the 26 °C group, the autocorrelation coefficients decreased more rapidly, suggesting that the fish’s behavior became more variable and less predictable under mild thermal stress. At 32 °C, the autocorrelation coefficients were close to zero, indicating that the fish’s behavior was highly irregular and random, possibly due to the severe thermal stress disrupting their normal behavioral rhythms.
To evaluate temperature-induced variations in behavioral strength, we conducted one-way ANOVA with Tukey’s HSD post hoc tests. The analysis revealed statistically significant differences across the three temperature groups (p < 0.05), underscoring the impact of thermal stress on locomotor activity. These results demonstrated that the behavioral monitoring module of the PECA could sensitively detect the changes in fish behavior under different temperature conditions, providing valuable information for assessing their health status.

3.3.2. Metabolic Analysis

The metabolic responses of koi carp to temperature changes were assessed via OCR, CER, and RQ, as illustrated in Figure 4. At 22 °C, koi carp exhibited relatively stable OCR and CER, with average values of 367.25 (mg·kg1·h−1) and 260.01 (mg·kg1·h−1), respectively. At 26 °C, the OCR and CER of koi carp increased compared to the control group, with average values of 414.45 (mg·kg1·h1) and 299.03 (mg·kg1·h1), respectively. The increase in OCR and CER indicates that koi carp consumed more oxygen and produced more carbon dioxide to meet the elevated energy demand associated with higher temperatures. This may be attributed to the acceleration of biochemical reactions and upregulation of metabolic pathways under thermal stress. However, at 32 °C, the OCR and CER of koi carp significantly decreased, with average values of 252.18 (mg·kg−1·h−1) and 262.87 (mg·kg−1·h−1). The reduction in OCR and CER suggests that high temperatures have a negative impact on the respiratory function and metabolic activities of fish. It is possible that the fish experienced hypoxia and metabolic suppression, leading to decreased energy production.
Analysis of the RQ values across the three groups revealed that RQ remained relatively stable at 22 °C and 26 °C, with mean values of 0.74 and 0.72, respectively, displaying a pronounced diurnal pattern. The average RQ during the dark cycle was significantly higher than during the light cycle, suggesting that koi carp preferentially utilized carbohydrates as their primary energy source during the dark period. In contrast, the RQ at 32 °C fluctuated more drastically, with an average RQ of 1.19, showing no evident diurnal pattern. This indicates that the fish’s metabolic function was impaired, possibly due to mitochondrial dysfunction caused by thermal stress.
Repeated measures ANOVA indicated significant differences in OCR, CER, and RQ between the three temperature groups at different time points (p < 0.05). These findings suggest that the PECA metabolic monitoring module is effective in tracking the metabolic changes of koi carp under varying temperature conditions, providing crucial insights into their energy metabolism and physiological adaptations.

3.3.3. ECG Analysis

As illustrated in Figure 5, at 22 °C, the ECG signals of koi carp were regular and stable, with an average QT interval of 0.2538 s, indicating normal cardiac conduction and function. The stable ECG signals demonstrate that the hearts of the fish are beating regularly and efficiently, maintaining adequate blood circulation and oxygen delivery to tissues. At 26 °C, the ECG signals of koi carp exhibited slight changes compared to the control group, with the QT interval shortening to 0.2313 s. This suggests that mild thermal stress may have influenced cardiac ion channels, possibly due to sympathetic nervous activation enhancing potassium channel conduction and accelerating cardiac repolarization. However, the overall ECG waveform remained relatively normal, indicating that the cardiac function of the fish was still operating within physiological ranges. At 32 °C, significant abnormalities were observed in the ECG signals of koi carp, with the QT interval extended to 0.293 s. This indicated that high temperatures had severely affected the cardiac function of the fish, with the prolonged QT interval suggesting delayed repolarization, which may increase the risk of arrhythmias and cardiac dysfunction.
Statistical analysis revealed significant differences in the average QT intervals among the three temperature groups (p < 0.05). These results indicate that the PECA ECG monitoring module can sensitively detect the cardiac responses of koi carp to temperature changes, providing important information for assessing their health status.

3.4. Effectiveness Analysis of PSI Based on Experimental Results

The Physiological Stress Index (PSI) quantifies cumulative stress levels in koi carp based on deviations of B, M, and E. As shown in Figure 6, at 22 °C, the PSI exhibited minimal fluctuations (0.8–1), indicating negligible physiological disturbance. At 26 °C, the PSI ranged moderately (0.4–0.8) with significant temporal variations, reflecting adaptive stress responses. Under acute thermal stress (32 °C), the PSI sharply declined to 0–0.2, demonstrating severe physiological impairment. The box plot confirmed an inverse temperature correlation (median PSI: 22 °C = 0.8 > 26 °C = 0.5 > 32 °C = 0.1), with a reduced interquartile range (IQR) at 32 °C, indicating homogenized stress effects. Outliers at 22 °C and 26 °C suggest individual variability in thermal resilience.
The results demonstrate that the PSI is an effective and sensitive metric for assessing thermal stress levels in koi carp. PSI variations align with individual behavioral, metabolic, and cardiac parameters, confirming the feasibility of composite biomarkers for real-time stress evaluation. This index provides an integrated assessment of physiological status, enabling early detection of sublethal stressors and supporting proactive management of aquatic environments.
Comparative benchmarking against established systems (Supplementary Table S1) reveals the PECA’s superior temporal resolution (1 Hz vs. 0.1–0.017 Hz) and multiparametric capacity. Crucially, the high internal consistency among behavioral, metabolic, and cardiac parameters at 32 °C (Pearson r > 0.92 between deviations) provides non-invasive validation of systemic stress responses, eliminating the need for destructive biomarker sampling.

4. Discussion

The development of the Physiological and Ecological Comprehensive Analyzer (PECA) platform and its derived Physiological Stress Index (PSI) represents a paradigm shift in real-time biomonitoring of aquatic ecosystems. Our findings demonstrate that the PSI—integrating behavioral strength (B), the respiratory quotient (M), and the cardiac QT interval (E)—provides a multidimensional quantification of thermal stress in Cyprinus carpio, with significant sensitivity to acute temperature changes (32 °C induced ~90% PSI decline). This integrated approach addresses critical limitations in conventional biomonitoring tools, such as the Multispecies Freshwater Biomonitor (MFB), which focuses solely on behavioral endpoints through impedance sensors [45], and metabolic systems, like ORMMS, which monitor oxygen consumption in isolation [51]. The synergy of the PECA’s tri-modal sensing (non-contact impedance [10], dissolved gas probes, and wearable ECG [53]) enables the detection of stress cascades before lethal thresholds are reached. For instance, the 47.3% hyperactivity at 26 °C (Figure 3A) preceded metabolic suppression at 32 °C (31.3% OCR reduction), aligning with stress progression models proposed by Selye’s environmental stress theory [48].
Recent advances in biosensing technology underscore the value of multiplexed detection. While photoelectrochemical (PEC) dual-modal biosensors have improved diagnostic accuracy in medical fields (e.g., PEC-electrochemical sensors for carcinoembryonic antigen detection), aquatic applications lag in integration depth. The PECA platform achieves unprecedented ecological relevance by simultaneously tracking locomotor patterns, respiratory quotients (reflecting energy substrate shifts from 0.74 RQ at 22 °C to 1.19 at 32 °C), and cardiac repolarization abnormalities (15.4% QT prolongation). This holistic approach is conceptually aligned with emerging “organ-on-chip” microphysiological systems (MPS) that integrate microfluidic biosensors for real-time biomarker monitoring, but the PECA extends this philosophy to whole-organism responses in naturalistic aquatic environments. The platform’s 1 Hz sampling frequency and closed-loop water circulation (propeller-driven flow at 0.06 m/s) overcome temporal resolution constraints of manual biomonitoring, capturing circadian disruptions in RQ rhythms (Figure 4) that single-timepoint assays would miss. Future work will validate the PSI under chronic thermal exposure (>30 days) and multi-stressor scenarios (e.g., pollutant–thermal synergy) to assess biomarker stability and ecological relevance.
The PSI’s mathematical formulation provides a transferable framework for stress quantification beyond thermal scenarios. Weighted deviations accommodate species-specific baselines—koi carp’s QT interval sensitivity to thermal stress mirrors zebrafish ECG responses to cadmium exposure, suggesting cardiac parameters may universally weigh heavily in PSI calibration for teleosts. Crucially, the PSI’s threshold behavior (<0.3 indicates acute stress) offers actionable intelligence for aquaculture management. During heatwaves, IoT-enabled PECA networks could trigger aerators or shade systems, potentially mitigating mass mortality events projected to increase 140% by 2050 under climate change scenarios. This real-time intervention capability surpasses conventional biomarker assays (e.g., cortisol or HSP70 measurements) that require destructive sampling and laboratory processing delays.
While the PECA system demonstrates significant advancements in real-time multidimensional monitoring, several limitations warrant consideration. Firstly, the current ECG module relies on invasive needle electrodes, which may induce minor stress responses in fish, despite the validation of signal stability. Employing non-contact electrophysiological sensing methods, such as optical mapping, could further reduce behavioral interference. Additionally, wireless signal transmission in aquatic environments (Figure 2) occasionally experienced intermittent noise during high-activity phases, potentially compromising the resolution of ECG waveforms [56]. This study primarily focused on acute thermal stress through a 48 h exposure, which limits insights into chronic or multigenerational thermal adaptation. Long-term monitoring under fluctuating temperatures, such as those experienced during seasonal cycles, is essential for assessing ecological relevance [57]. Furthermore, all experiments were conducted on koi carp (Cyprinus carpio), a temperate species, raising questions about the generalizability of these findings to tropical or cold-water species, which requires further validation.
The PECA system’s application can be extended beyond koi carp to holistic health monitoring of diverse aquatic organisms, providing a scientific basis for ecological conservation and climate-resilient aquaculture. A critical priority is bridging laboratory innovations to real-world ecological challenges. For instance, in dam-regulated rivers where hydraulic projects disrupt fish migration and ecosystem integrity [58,59,60,61,62,63], the PECA could quantify energy and material flows during fish passage by integrating real-time behavioral, metabolic, and ECG data under controlled hydraulic conditions (e.g., flow velocity, turbulence). This would enable optimization of fishway designs to enhance migratory success—a pressing need for biodiversity conservation. Concurrently, technological refinements are essential: replacing invasive ECG electrodes with non-contact optical sensing would minimize stress artifacts, while edge computing architectures (e.g., Raspberry Pi) could empower field deployability in remote habitats. Replacing needle electrodes with non-contact optical sensing remains a priority to minimize artifacts in free-swimming applications.
To maximize ecological relevance, future work must expand validation across species (e.g., tropical tilapia, cold-water salmon) and stressor regimes. Chronic exposure studies simulating seasonal thermal fluctuations will refine the PSI as a predictor of fitness outcomes (growth, reproduction). Furthermore, the PSI’s utility as an ecosystem-level biomarker should be tested in multipollutant scenarios (e.g., microplastic–thermal stress synergy) to disentangle synergistic stressor impacts. Interdisciplinary integration—coupling the PSI with omics profiling (transcriptomics, proteomics) and AI-driven forecasting models—could unravel molecular mechanisms of thermal adaptation and predict population resilience under climate change. Ultimately, translating the PSI into hydraulic engineering guidelines and pollution early-warning systems will transform aquatic ecosystem management, balancing anthropogenic development with biodiversity preservation [64,65,66,67].

5. Conclusions

In summary, this study presents the development and validation of the Physiological and Ecological Comprehensive Analyzer (PECA), an integrated platform enabling real-time, multidimensional monitoring of aquatic organism health. By simultaneously tracking behavioral activity, metabolic responses (respiratory quotient), and cardiac function (QT interval) in freely swimming fish, the PECA provides a holistic assessment of physiological status. Central to this system is the Physiological Stress Index (PSI), a novel biomarker derived from integrated deviations across these parameters. Validation under controlled thermal regimes demonstrated the PSI’s significant responsiveness to stress gradients, effectively detecting sublethal thermal impacts through correlated alterations in locomotion, metabolism, and electrocardiography. The platform’s capacity to identify stress before overt mortality offers a proactive tool for climate-resilient aquaculture management and ecological risk assessment in thermally vulnerable aquatic ecosystems. Furthermore, the PECA’s multiparametric design establishes a versatile foundation for future applications in monitoring diverse environmental stressors beyond thermal anomalies, including water pollution and toxicant exposure.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w17162369/s1.

Author Contributions

Conceptualization, Y.F. and Z.R.; methodology, Y.F.; software, Y.F.; validation, Y.F.; formal analysis, Y.F.; investigation, Y.F.; resources, Y.F. and Z.R.; data curation, Y.F.; writing—original draft preparation, Y.F.; writing—review and editing, Z.R.; visualization, Y.F.; supervision, Z.R.; project administration, Y.F. and Z.R.; funding acquisition, Z.R. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the National Natural Science Foundation of China (42077224).

Institutional Review Board Statement

To ensure the ethical conduct and compliance of the experiment, it was officially approved by the Animal Ethics Committee of Shandong Normal University. (Approval No. AEECSDNU2024142). Furthermore, all procedures were strictly performed in accordance with current animal welfare policies in China.

Data Availability Statement

The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding authors.

Acknowledgments

During the preparation of this manuscript, the authors used ChatGPT (version 4.0) to suggest improvements to writing clarity, grammar, and sentence structure. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The 3D structural model of the Physiological and Ecological Comprehensive Analysis System for Aquatic Animals (PECA).
Figure 1. The 3D structural model of the Physiological and Ecological Comprehensive Analysis System for Aquatic Animals (PECA).
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Figure 2. Signal amplification circuit of ADS1293. Amplifies weak biopotential signals (ECG) with programmable gain and noise reduction for wearable monitoring.
Figure 2. Signal amplification circuit of ADS1293. Amplifies weak biopotential signals (ECG) with programmable gain and noise reduction for wearable monitoring.
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Figure 3. Behavioral dynamics of koi carp under thermal stress. (A) Real-time behavioral strength (solid line) with standard deviation (dashed shading) and dark periods (vertical bars). (B) Mean activity at 22 °C, 26 °C, and 32 °C (letters indicate statistical significance, p < 0.05; ANOVA). (C) Autocorrelation analysis of activity patterns across temperatures.
Figure 3. Behavioral dynamics of koi carp under thermal stress. (A) Real-time behavioral strength (solid line) with standard deviation (dashed shading) and dark periods (vertical bars). (B) Mean activity at 22 °C, 26 °C, and 32 °C (letters indicate statistical significance, p < 0.05; ANOVA). (C) Autocorrelation analysis of activity patterns across temperatures.
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Figure 4. Respiratory dynamics of koi carp under thermal stress. Upper figures: oxygen consumption rate (OCR) and CO2 excretion rate (CER) trajectories. Solid lines: real-time data; shaded areas: ±1SD; vertical bars: dark periods. Lower panels: respiratory quotient (RQ) rhythms. Dashed boxes highlight disrupted diurnal patterns at 32 °C.
Figure 4. Respiratory dynamics of koi carp under thermal stress. Upper figures: oxygen consumption rate (OCR) and CO2 excretion rate (CER) trajectories. Solid lines: real-time data; shaded areas: ±1SD; vertical bars: dark periods. Lower panels: respiratory quotient (RQ) rhythms. Dashed boxes highlight disrupted diurnal patterns at 32 °C.
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Figure 5. Cardiac responses of koi carp to thermal gradients. Upper figure: representative ECGs at 22 °C (control), 26 °C (mild stress), and 32 °C (severe stress). Left of the below figures: real-time QT intervals. Solid lines: mean values; shading: ±1SD; vertical bars: dark phases. Right of the below figures: mean QT interval comparisons. Letters indicate significant differences (p < 0.05; ANOVA).
Figure 5. Cardiac responses of koi carp to thermal gradients. Upper figure: representative ECGs at 22 °C (control), 26 °C (mild stress), and 32 °C (severe stress). Left of the below figures: real-time QT intervals. Solid lines: mean values; shading: ±1SD; vertical bars: dark phases. Right of the below figures: mean QT interval comparisons. Letters indicate significant differences (p < 0.05; ANOVA).
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Figure 6. Physiological Stress Index (PSI) dynamics during thermal exposure. (A) Temporal PSI variation at 22 °C (control), 26 °C (moderate stress), and 32 °C (acute stress). Solid lines: group means; shading: ±1 SD. A red dashed line at PSI = 0.3 indicates the threshold value. (B) Distribution of PSI values across temperature groups. Boxes: IQR; horizontal lines: medians. Outliers are highlighted with red circles. Statistical differences among groups are denoted by different letters (a, b, c), where each letter represents a significant difference from the others.
Figure 6. Physiological Stress Index (PSI) dynamics during thermal exposure. (A) Temporal PSI variation at 22 °C (control), 26 °C (moderate stress), and 32 °C (acute stress). Solid lines: group means; shading: ±1 SD. A red dashed line at PSI = 0.3 indicates the threshold value. (B) Distribution of PSI values across temperature groups. Boxes: IQR; horizontal lines: medians. Outliers are highlighted with red circles. Statistical differences among groups are denoted by different letters (a, b, c), where each letter represents a significant difference from the others.
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Fu, Y.; Ren, Z. PECA: An Integrated Real-Time Biosensing Platform for Detecting Thermal Stress in Aquatic Environments. Water 2025, 17, 2369. https://doi.org/10.3390/w17162369

AMA Style

Fu Y, Ren Z. PECA: An Integrated Real-Time Biosensing Platform for Detecting Thermal Stress in Aquatic Environments. Water. 2025; 17(16):2369. https://doi.org/10.3390/w17162369

Chicago/Turabian Style

Fu, Yihang, and Zongming Ren. 2025. "PECA: An Integrated Real-Time Biosensing Platform for Detecting Thermal Stress in Aquatic Environments" Water 17, no. 16: 2369. https://doi.org/10.3390/w17162369

APA Style

Fu, Y., & Ren, Z. (2025). PECA: An Integrated Real-Time Biosensing Platform for Detecting Thermal Stress in Aquatic Environments. Water, 17(16), 2369. https://doi.org/10.3390/w17162369

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