ALERT: The Automatic LoRa-Enabled Radio Tracker for Wildlife Monitoring in Remote Environments

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Baille, Aymeric Houstin, Kel Weaver, Alexander Winterl, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8744989/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Biologging has advanced wildlife research by providing detailed insights into animal movement and behaviour. However, data recovery in remote areas without cell coverage remains a major challenge, often relying on costly satellite communication or labor-intensive manual recovery of archival tags, typically through Very High Frequency (VHF) tracking. Current automated VHF monitoring approaches often rely on proprietary hardware and software, offer limited wireless data retrieval options, and require substantial power or infrastructure, which prevent their applicability in remote or extreme environments where they are most needed. Hence, manual tracking is still the most commonly used method. To address these limitations, we developed ALERT (Automatic LoRa-Enabled Radio Tracker), a simple radio-telemetry system that integrates custom-built receiving stations with Long Range Wide Area Network (LoRaWAN) communication. ALERT continuously scans for non-coded VHF signals, detects them using an adaptable custom algorithm, and wirelessly transmits presence information to a central gateway for real-time visualization. ALERT can be built with commercial off the shelf parts and does not require specialized electrical engineering knowledge. The system was tested at Atka Bay, Antarctica, from November 2024 to January 2025. During this period, 18 adult emperor penguins ( Aptenodytes forsteri) were equipped with archival data loggers and VHF transmitters. To facilitate recovery of these archival tags, ALERT was deployed to automatically notify the field team when VHF-tagged penguins returned to the colony from foraging trips. Three receiving stations were strategically installed in the vicinity of the colony, at distances of up to 5 km from the LoRa gateway. Each station reliably detected VHF signals within an effective range of approximately 0.8 km and successfully alerted researchers in real time to the return of all VHF-tagged emperor penguins. This study describes the hardware and software architecture of ALERT and analyses system performance as a function of transmitter-receiver distance, weather conditions, and antenna configuration. It also evaluates the reliability of LoRaWAN data transmission under Antarctic conditions, including factors influencing packet loss and transmission delays. Our findings demonstrate that ALERT is a low-cost, low-power, and scalable solution for automated VHF telemetry, capable of supporting wildlife monitoring in remote and logistically challenging environments. LoRaWan Radio-Telemetry VHF Biologging Emperor Penguins Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Background Biologging and tracking The practice of equipping free-living animals with data loggers to study their behavior in-situ dates back to the 1930s ( 1 ). Since then, remarkable advances in sensor miniaturization, power efficiency, data storage, and wireless communication have transformed the field. Today, animal-borne sensors can record a broad array of data, including geolocation ( 2 ), acceleration ( 3 ), magnetic field strength ( 4 ), temperature ( 5 ), air and water pressure ( 4 ), heart rate ( 6 ), ambient light levels ( 7 ), conductivity ( 8 , 9 ), video footage ( 10 ), acoustic signals ( 11 , 12 ) and more. They are widely used in ecology and conservation to collect detailed information on animal movements, behavior, physiology, and the environments they inhabit ( 13 – 16 ). Animal-borne devices can either transmit recorded data wirelessly to receivers (via electromagnetic or acoustics waves to land, underwater, or orbital receivers) or store data, requiring physical retrieval of the biologger for data access ( 17 ). The latter involves either recapturing the tagged animal to remove the device or locating and retrieving the tag once it has detached to access the data. As a result, biologgers that require physical recovery must also include a means of localization. Commonly used localization techniques include acoustic pingers ( 18 ) and Very High Frequency (VHF) radio transmitters ( 10 ). VHF animal tracking has been widely used to monitor a broad range of taxa, including insects ( 19 – 21 ), birds ( 22 ), reptiles ( 23 ), and mammals ( 24 ), providing valuable insights into animal presence, movement, and habitat use. VHF transmitters can be deployed on animals to either determine their precise geographic location (via elaborate synchronized receiver arrays) or simply to detect their presence within a given area (via a single VHF receiver). This receiver can be used to retrieve archival biologgers, which store data onboard and require physical data download. In this latter scenario, VHF scanning remains predominantly performed by researchers who, using a mobile VHF receiver and a single Yagi antenna, must listen for the presence of a VHF transmitter, requiring significant human effort ( 25 , 26 ). There have been a few attempts to automate VHF radio monitoring to scale detection efforts. In 2011, Kays et al. developed the Automated Radio-Telemetry System, using programmable commercial off the shelf VHF receivers to detect VHF signals emitted from radio-collared animals in a tropical forest. This study demonstrated the feasibility and potential of automated VHF tracking, yet the approach was not widely adopted ( 27 ). Subsequent efforts have revisited and expanded upon this approach. For instance, Wallace et al. (2022) used four Orion receivers with integrated software to track VHF-tagged prairie voles ( Microtus ochrogaster ), and Hayward-Brown et al. (2025) developed custom receiving stations to monitor nanotagged Gouldian finches (Chloebia gouldiae ). In both Wallace et al. (2022) and Hayward-Brown et al. (2025), the receiver's data was stored locally and later manually downloaded for geolocation analysis. When real-time or near real-time information on animal presence is necessary, data can be transmitted via satellite, cellular networks, or other wireless communication protocols, depending on local infrastructure availability (e.g., cellular coverage) as well as practical constraints such as cost, power consumption, and data bandwidth. In remote regions lacking cellular connectivity, long-range, low-power radio technologies, such as LoRa, offer a particularly suitable solution. LoRa (LOng RAnge) LoRa (short for Long Range) is a low-power, long-range, low-bitrate wireless communication technology designed for the Internet of Things (IoT). This Low-Power Wide Area Network protocol (LPWAN) operates in license-free sub-GHz frequency bands (433 MHz in Asia, 868 MHz in Europe, 915 MHz in North America and Australia). It utilizes interference-resistant Chirp Spread Spectrum (CSS) modulation, allowing reliable data transmission over several kilometers in line of sight. The payload of each transmission can range from 2 to 255 bytes, and the data rate may reach up to 50 Kbps under optimal configurations using channel aggregation ( 28 ). While the physical layer (LoRa) is a proprietary modulation technology, the LoRaWAN protocol that defines the Medium Access Layer (MAC) and network layers are developed and maintained as open source software ( 28 ). Several modulation parameters can be tuned to optimize range, power consumption, and error correction rate ( 28 ). The low power consumption and long-range capabilities of LoRa make it well-suited for ecological and remote data transmission applications. Recent studies have successfully applied LoRa for autonomous animal tracking and environmental monitoring in remote field conditions ( 29 , 30 ), where traditional telemetry or cellular networks are impractical. In this study, we developed an automated, low-cost VHF receiving array to facilitate biologger recovery from Emperor penguins, a task complicated by several species- and environment-specific constraints. Specifically, VHF transmitters are mounted low on the animal’s body and operate in close proximity to the dielectric sea ice, substantially reducing transmission range and requiring receivers with very high sensitivity, beyond what is typically achievable with Software Defined Radio (SDR)-based systems. In the following paragraph, we provide background on the Emperor penguin as well as the study setup which motivated the development of a case-specific, yet easily adaptable, VHF receiving system that may be useful for similar applications. Emperor penguins ( Aptenodytes forsteri ) The Emperor penguin ( Aptenodytes forsteri ) is a central place forager that typically returns to its natal colony to breed once it reaches sexual maturity at around 4–6 years of age. From that point onward, adults follow a well-defined annual breeding cycle. As the Antarctic winter approaches between late March and early May, penguins return to their breeding colony where courtship, mating, and egg laying take place over a period of 6–10 weeks. Following egg laying, females leave the colony to forage at sea and replenish their body reserves, while males remain behind to incubate the single egg for approximately two months. Chicks hatch typically between July and August, coinciding with the return of females to the colony. At the beginning of the chick-rearing phase, both parents alternate between caring for the chick at the colony and foraging at sea. As chicks become thermally independent, both parents are able to forage simultaneously to meet the chick’s increasing energetic demands ( 31 , 32 ). After fledging, emperor penguins of all age classes leave the colony and disperse into the Southern Ocean, thereby completing the annual cycle before the next breeding season begins again in the following March. Due to their extensive use of the Southern Ocean, emperor penguins serve as key sentinel species for monitoring changes in the austral marine ecosystems ( 33 , 34 ). The Atka Bay emperor penguin colony, located in a deep, sheltered inlet in the southwestern corner of Atka Bay, comprises approximately 8600 breeding pairs ( 35 ) and benefits from enhanced sea-ice stability that provides favourable breeding conditions. The colony lies approximately 9 km from Neumayer Station III, making it accessible for long-term monitoring while remaining mostly unimpacted by human presence. The Atka Bay colony is intensively studied within the MARE (Monitor the health of the Antarctic marine ecosystems using the Emperor penguin as a sentinel) life observatory.MARE consists of continuous surveillance using the SPOT Penguin Observatory (Richter et al. 2018), population dynamics studies using electronic RFID microchip tagging, and biologging based habitat-at-sea monitoring. Habitat-at-sea monitoring of the Atka Bay colony has relied on a combination of satellite telemetry for year-round tracking of both juvenile and adult emperor penguins ( 36 ) and archival biologging of adult emperor penguins during the chick-rearing season ( 37 ). VHF transmitters are deployed alongside the archival tags to allow detection of returning penguins using VHF radios and subsequent biologger recovery. During the chick-rearing period, emperor penguins’ visits to the breeding colony can be as short as four hours, requiring frequent monitoring. To maximise the probability of tag recovery before moulting, the current gold-standard is to conduct manual VHF scans at the colony every four hours for the duration of the deployments (6–8 weeks). This intensive scanning protocol resulted in a tag recovery rate of 83% ( 37 ). Such frequent scanning in a remote and harsh environment like Antarctica comes at a high cost, requiring approximately 63 hours of effort (including preparation, and round-trip travel from the research base to the colony) and 227 litres of fuel per week (~ 2,300USD) (see Additional File 8 for detailed estimates). Beyond the monetary cost, this schedule repeatedly led to sleep deprivation, team frustration because of perceived unproductive effort, and reduced capacity to conduct other scientific activities, while field time in Antarctica remains at a premium. This manuscript introduces a novel automated radio-telemetry system, the Automatic LoRa-Enabled Radio Tracker (ALERT). ALERT consists of custom-built receiving stations that continuously scan for pre-defined VHF transmissions, detect them using a custom algorithm, and wirelessly transmit detection scores via the Long Range Wide Area Network (LoRaWAN) protocol to a central gateway connected to the internet. The system was tested at Atka Bay, Antarctica, from November 2024 to January 2025. During this period, 18 adult emperor penguins were equipped with archival data loggers and VHF transmitters. Three ALERT receiving stations were strategically deployed in the vicinity of the colony and successfully alerted the field team about the return of VHF-tagged penguins from foraging trips, enabling efficient recovery of the archival biologgers. This manuscript describes the hard- and software architecture of ALERT and evaluates the system’s performance. METHODS VHF Transmitters ALERT was designed to detect frequency-modulated (FM) pulsed single tone signals from uncoded VHF transmitters. In this study, the 18 tagged penguins were equipped with Holohil VHF transmitters deployed alongside archival data loggers. Holohil transmitters are widely used in wildlife tracking because of their compact size, low mass, and long battery life, which can last several months to years. We used the Holohil RI-2B model (now called XM1), which operates within the 148–152 MHz narrowband used for wildlife tracking, with 10 kHz channel spacing to minimize overlap. The transmitters emit pulses at fixed pulse intervals. Although pulse intervals are expected to be consistent across transmitters of the same model, our tests revealed small but measurable deviations between units. To account for this variability, the average pulse interval of each transmitter was determined using low-noise baseband recordings. Each transmitter is also characterised by a single tone frequency (Hz). While this frequency is predefined and transmitter-specific, field measurements revealed that the single tone frequency can drift up to 1,000 Hz, likely driven by temperature and/or battery voltage fluctuations ( 38 ). To accommodate this behaviour, the single tone frequency of each transmitter was re-identified during each scan using spectrogram analysis and a custom detection algorithm. Together, the transmitter pulse interval and the characteristic single tone frequency form the core features used by the custom detection algorithm to determine signal presence (see section Detection Algorithm). ALERT INFRASTRUCTURE In this case study, the ALERT system consists of three ALERT receiving stations and one LoRa gateway which aggregates the data locally. The number of receiving stations and gateways can be increased or reduced as needed, though at least one of each is necessary. ALERT Receiving station An ALERT receiving station continuously tunes it into a predefined list of VHF frequencies, evaluates the presence of a known signal, and relays that information back to the nearby LoRa gateway. Physically, each receiving station consists of five main components: a “power box”, a “processing box”, a long-standing pole (a bamboo stick in our field experiment), a 3-element Yagi antenna, and a LoRa antenna as depicted in Fig. 2 . Three distinct ALERT receiving stations were built. For simplicity, they were named Orange, Grey, and Yellow station, according to the colour of the processing box’s pelican case used. Each ALERT receiving station consists of two main boxes: the power box and the processing box . The power box consists of a 12V battery and DC-DC converter (12V-5V, MEAN WELL USA Inc. SD-15) housed in an aluminum box (Zarges K440–40701) and a mounted solar panel on top (20W,12V). The DC-DC converter was mounted inside the aluminum enclosure to reduce electromagnetic interference (Supplementary Fig. 1).. The processing box consists of a Pelican 1300 case mounted on a bamboo pole, housing a Lotek VHF Biotracker radio receiver (hereafter the VHF radio), a Raspberry Pi 4B, a RS232 hat, a USB sound card, a LoRa board (RAK811 or 3172), a power relay hat, and a power port for connecting a 1m cable from the external power box (Fig. 3 ). The VHF radio is a compact, robust receiver designed for wildlife tracking. It offers precise frequency tuning from 138.000 to 174.000 MHz in 0.1 kHz increments and remote programmability through a serial connection. It connects to a Lotek 3-element folding Yagi antenna (145–173 MHz), mounted vertically atop a long vertical pole to maximize detection range ( 39 , 40 ). As metal structures can distort or block VHF transmissions, other materials should be preferred and tested. We used bamboo in our experiment as it is abundantly available as flag poles at the research station. To simplify the power setup and only need to provide one voltage (5V) to the processing box, the 12V VHF radio was modified to bypass its internal 12V-to-5V converter, allowing it to run directly on 5V. The VHF radio includes a 3.5 mm headphone output and an RS232 serial communication port. An RS232 HAT on the Raspberry Pi sends serial commands to the VHF radio to control parameters such as frequency and gain. Audio from the radio's headphone output is routed to the Raspberry Pi via the microphone port of the USB sound card. The Raspberry Pi cycles the VHF radio through a predefined set of frequencies, demodulates the VHF signal, and stores the audible baseband as a 10-second audio sample (WAV format). Each recorded audio file is analysed by a custom detection algorithm (details below) that assigns a score from 0 to 100, reflecting the confidence that the expected VHF signal is present in the recording. Each score, along with its corresponding frequency, is then wirelessly transmitted to a LoRa gateway using a LoRa module (RAK 811: yellow and grey stations, or RAK3172: orange station, 915 MHz, Class C) connected to the Raspberry Pi and a 5 dBi 900 MHz antenna (EM Wave, Inc) mounted on the processing box. Finally, the Raspberry Pi is equipped with a power relay HAT that can power-cycle key components (Raspberry Pi, VHF radio) if a component of the system gets hung up during operation. Transmissions from the stations to the gateway included detection scores from VHF-tagged penguins, as well as periodic signals from fixed VHF transmitters used to verify detection reliability, and heartbeat messages from the onboard Raspberry Pi, used to monitor station health. ALERT Gateway An elevated LoRaWAN gateway receives transmissions from the ALERT receiving stations and stores the data locally. Housed in a compact, weatherproof Pelican 1120 case, the gateway includes a Raspberry Pi 4B equipped with an 8-channel LoRa Gateway HAT (RAK2245), a PoE injector (Alfa APOE03, 12V), a linear voltage regulator (12V to 5V, Texas Instruments LM150), two heatsinks for thermal management, and an external 1-m long 900 MHz antenna as depicted in Fig. 4 . The gateway runs ChirpStack version 4, an open-source LoRaWAN Network and Application Server that manages communication between the gateway and the transmitting nodes (i.e. ALERT receiving stations) and routes data to a local InfluxDB database. Within ChirpStack, an application accepts incoming base64-encoded packets from the transmitting ALERT receiving stations and stores them onto a local “raw” database. A wrapper script then continuously decodes each packet, reformats the data, and writes it to a database in our desired format (see Fig. 5 for a simplified overview of the information flow and Supplementary Fig. 2 for a comprehensive representation). A Grafana interface then feeds on this database to display real-time detection scores across frequencies and ALERT receiving stations (Fig. 6 ). Detection algorithm For each pre-defined frequency, an ALERT station records a 10-second WAV file, long enough to clearly identify the signal, which is locally processed by a detection algorithm. The algorithm assigns a score from 0 to 100, with higher values indicating a greater confidence of detection. A detection is defined as the presence of a known VHF signal in the detection range of the receiving station. Figure 7 shows the algorithm's logic. The detection algorithm begins by trimming the first second of each audio file to remove transient amplitude spikes caused by frequency switching, which can bias detection. The audio is then downsampled from 44 kHz to 10 kHz to reduce computational load of the Fast Fourier Transform (FFT). Next, noise reduction is performed using spectral gating via the noisereduce Python package ( 41 ), which implements a non-stationary forward-backward Infinite Impulse Response (IIR) filter. A spectrogram is generated from the denoised signal, and further smoothed with a two-dimensional Gaussian filter. The Power Spectral Density (PSD) is then computed by averaging over 20 Hz windows. From the PSD, the most prominent frequency is extracted, provided it spans at least 30 Hz in bandwidth to filter spikes. Using this frequency, along with the 10 kHz sample rate and a known pulse duration of 25 ms (per manufacturer), a sine wave template representing the expected signal is then generated. This template is then convolved with the denoised signal in a matched filter approach, to enhance repetitive pulse-like features. Peak detection is performed on the matched filter output across amplitude percentiles from 80 to 99.75. For each percentile, a score, ranging from 0 to 100, is calculated based on the number of detected peaks and their temporal spacing. After evaluating all percentiles, the algorithm selects the highest score among those that detected between 4 and 6 peaks, which is the expected number of pulses in a 9-second recording. This process is summarized as: \(\:{S}_{p}=\:100\times\:\left(max\right(\text{0,1}-\frac{\left|{d}_{O}-{d}_{E}\right|}{{d}_{E}}\) )) $$\:{S}_{final}=max\left({S}_{p}\right|\:4\le\:{N}_{p}\le\:6)$$ where dO=Observed time interval between peaks dE= Expected time interval between peaks, determined from controlled, noise-limited tests for each transmitter. Np= Number of peaks Refer to Additional File. 3 to visualize the multi-step analysis of a single audio file that resulted in a score of 97. The score, along with the corresponding frequency ID, is then transmitted via LoRaWAN to the LoRa gateway. To minimize LoRaWAN payload size, we used simplified frequency IDs (e.g., ‘A1’ for 151.120 MHz) instead of full frequency values, and rely on the ChirpStack gateway’s reception timestamp to date each detection, as the delay between audio recording and transmission is typically only a few seconds. It is important to note that the scoring method is inherently non-linear. When the algorithm correctly identifies the single tone frequency, it assigns a high score, typically between 90 and 99. Conversely, if it selects an incorrect single tone frequency, the resulting score is significantly lower. In practice, the highest score obtainable is 99. Achieving a perfect score of 100 would require the observed peak intervals to align with the expected ones within a tolerance of a few ten-thousandths of a second, an unrealistic level of accuracy under field conditions. LoRa Configuration The yellow and grey stations used the RAK811 LoRa Pi HAT, while the orange station used a RAK3172 module connected to a Raspberry Pi via serial communication (baudrate = 9600, 8 data bits, no parity, 1 stop bit, timeout = 1s) due to module availability. All stations were configured for LoRaWAN using Over-The-Air Activation (OTAA) on the US915 band, operating in Class C mode with adaptive data rate enabled. They were each configured with their respective Application Key provided in their respective configuration file to securely connect to the network server. The yellow and grey stations were configured to attempt to join the network up to five times and, upon success, transmit their packets up to three times. The orange station had additional settings, including RX1/RX2 delays of 1s and 2s, join accept delays of 5s and 6s. It would attempt to join the network up to ten times and to send a payload up to five times with 2-second intervals. If transmission repeatedly fails, the Pi reboots itself and restarts the process while discarding unsent packets. Experiment Layout To evaluate the performance of ALERT and to reduce the efforts required for manual scanning at the Akta Bay emperor penguin colony, we developed and tested the ALERT system during the chick-rearing season from November 2024 to January 2025. During this experiment, three ALERT receiving stations, yellow, orange, and grey, were deployed in the vicinity of the colony: the orange station on sea ice (70°34′02″S, 08°06′23″W), the grey (70°37′06″S, 08°09′36″W ) and yellow (70°36′13″S, 08°08′26″W) ones on the ice shelf (Fig. 8 ). The yellow station, located 0.9 km from the gateway, was initially oriented toward subcolony A and was later repositioned to face north toward the ocean to improve detection of penguins returning from foraging trips. The grey station, situated 0.91 km from the gateway and south of both subcolonies, was oriented to cover both subcolonies simultaneously, as they fall within its direct line of sight. The orange station was deployed approximately 5.1 km from the gateway on the sea ice and oriented westward toward the ice shelf prior to a snowstorm, based on field observations indicating that penguins preferentially follow the ice shelf edge when navigating back to the colony. The gateway was mounted atop a 6-meter mast at the SPOT penguin observatory (70°36′38″S, 08°09′12″W) located approximately 9 km from the research base Neumayer Station III (70°39′32″S, 08°17′05″W). SPOT operates on solar and wind power and maintains a continuous direct WiFi link to the research base enabling seamless data transfer. The ALERT system was programmed to continuously monitor a predefined set of up to 18 frequencies. It was operational from November 15, 2024, shortly after the first round of bio-logger deployments, until January 1, 2025, when sea ice conditions required the system to be removed. Table 01 summarizes the deployment details of all ALERT components. Occasional battery outages caused stations to temporarily go offline until solar recharging or manual battery replacement restored functionality. Table 01 Summary of ALERT component deployments. Start and end dates indicate the deployment period of each component. VHF antenna bearings were measured relative to geographic north (0° = North), from the perspective of an observer standing behind the antenna and facing its orientation. Uptime was calculated for each component; variation reflects differences in deployment duration and power interruptions. During the 2024–2025 season, 18 emperor penguins were each equipped with various biologgers, as well as a Holohil RI-2B VHF transmitter (Fig. 10 ). Of these, seven individuals were fitted with GPS-capable biologgers (TechnoSmart Axy-Trek Marine GPS; individuals A07, A08, A09, A10, A11, A13 and A14; see Additional File 9), recording geolocation (latitude, longitude) every 30-minutes. After deployment, penguin presence at the colony was monitored using the ALERT system’s real-time display, supplemented by opportunistic manual VHF scans for verification. When a penguin returned early in the season and was carrying a high-endurance biologger, the device was not retrieved, allowing the penguin to depart on additional foraging trips before recapture and logger retrieval. Each station required an average of ~ 7 minutes to scan all predefined VHF frequencies (up to 18). Dataset To assess the performance of the ALERT system, we compiled LoRa transmissions from each receiving station, including timestamp, detection score, and scanned VHF frequency. Periods of interference at the yellow unit, caused by concurrent LoRa testing, were excluded from the analysis. Six of the seven GPS-capable biologgers were recovered (adult A07 never returned) and their recovered GPS tracks were incorporated into the analysis. To evaluate the influence of environmental conditions on detection performance, timestamped weather data variables, including solar radiation, humidity, wind speed, and wind direction, were obtained from Neumayer Station III meteorological observatory ( 42 ). VHF- vs. GPS- derived presence at the colony When available, a penguin was considered present at the colony when its GPS-enabled biologger indicated a location within 10 km of SPOT, the midpoint between subcolonies A and B (Fig. 8 ). Entry into and exit from this 10 km radius were used to determine the GPS-derived presence periods. The 10 km radius was selected to account for colony movement and ALERT unit detection range. VHF-derived presence at the colony was determined by manually reviewing VHF detections in the Grafana interface and cross-referencing field logs to confirm penguin returns. A day was marked as a presence day if the ALERT system recorded at least one detection, with detection times rounded to the full calendar date. For example, if a penguin was detected between 11:03 a.m. and 1:34 p.m. on December 3rd, the entire day was counted as present. Comparison of GPS- and VHF-derived presence at the colony showed that all GPS-derived presence periods were also detected by VHF. In addition, for several individuals (e.g. A08, A11 and A14), VHF detected colony returns that were not recorded by GPS due to early battery failure Performance metrics: To assess the performance of each ALERT receiving station in identifying returning penguins, we compared ALERT detections to the actual presence or absence of tagged individuals at the colony. Ground truth presence was determined using either GPS-derived presence (from GPS tracks) or VHF-derived presence (from manual review of VHF detections and field logs). When the tagged penguin was present at the colony, a detection by the ALERT station or system was classified as a true positive (TP) , while a missed detection was considered a false negative (FN) . Conversely, during periods when the tagged penguin was not near the colony, the absence of a detection was classified as a true negative (TN) , and any detection during that time was considered a false positive (FP). Detection A high score (HS) refers to a VHF detection score (ranging from 0 to 100) that exceeds a designated threshold value. A detection is defined as any instance in which an ALERT receiving station identified a penguin’s presence based on one of the following criteria: ( 1 ) a single high-score reading (“single HS”), ( 2 ) at least two HS readings within a 24-minute window (≥ 2 HS < 24 min), or ( 3 ) at least three HS readings within the same window (≥ 3 HS < 24 min). The 24-minute window is based on the average time required for an ALERT station to complete a full scan of all predefined VHF frequencies (~ 7 minutes per scan), allowing for up to three full scan cycles during that interval. Impact of Transmitter-Receiver Distance on Detection Score To assess the effect of transmitter-receiver distance on VHF detection scores, we used data from GPS-tagged penguins. Each VHF detection was matched to the nearest GPS fix within ± 2 minutes to ensure spatial accuracy (~ 86 m, based on an average tobogganing speed of 2.59 km/h; ( 43 )). Detections were excluded if the penguin was > 3 km from the receiving station, beyond which VHF signals are unlikely to be detected reliably ( 22 ). RESULTS Between November 2024 and January 2025, the ALERT system deployed in Atka Bay, Antarctica, successfully detected the return of 17 out of 18 VHF-tagged emperor penguins (25 returns in total due to penguins returning more than once per deployment), enabling the recovery of their biologgers. One individual was never detected after tagging, neither by the ALERT system nor through dedicated manual scans (for that specific animal), suggesting it did not return to the colony this year. System performance ALERT’s performance was evaluated using two datasets: (A) the full dataset including all tagged penguins (n = 17), and (B) a subset of this dataset comprising only GPS-tagged penguins (n = 6). Analyzing the entire dataset for VHF-derived presence, which comprises 25 returns of emperor penguins to the colony, we find that the ALERT system did not record any missed detections (FN = 0) at detection thresholds below 98 when detections were defined by a single high score (HS) (Fig. 11 A). Under stricter detection criteria (HS ≥ 2 or HS ≥ 3), we find that one false negative was observed below thresholds of 97 and 98, respectively (Fig. 11 A). In both cases, the missed detection was attributable to a single particularly elusive individual (see Supplementary Fig. 4). At higher detection thresholds (98 and 99), the number of missed detections increased to 5 and 6, respectively. For the same dataset, the number of false positives (FP) decreased steadily with increasing detection thresholds, reaching zero at a threshold of 96 across all detection definitions (HS ≥ 1,2,3) (Fig. 11 B). For the GPS-tagged penguin subset, we find that all three detection criteria produced identical false negative patterns. The system successfully detected all colony returns (FN = 0) at thresholds below 98. At thresholds of 98 and 99, the number of missed penguins (false negatives) increased to 3 and 4 out of 9, respectively (Fig. 11 C and Supplementary Fig. 6D). Figure 11 D shows that false positives again declined with increasing thresholds, reaching zero at a threshold of 92 for all detection criteria. Refer to Additional File. 5 and 6 for detailed plots of FN, TN, FP, and TP across detection scores [60–99], evaluated under all three detection criteria, for both the full dataset of tagged penguins and the GPS-tagged subset. Effect of Transmitter-Receiver Distance on Detection Score A total of 132, 96 and 11 VHF detections on the yellow, grey and orange ALERT receiving stations respectively could be matched to GPS locations of transmitters within ± 2 minutes (Fig. 12 ). Note that while many more detection scores have been recorded across all stations, those are not shown here because those penguins were not equipped with GPS-enabled biologgers. Furthermore, the dataset includes substantially more detections at the yellow and grey stations than at the orange station, owing both to their longer deployment duration (47 days for yellow and grey stations versus 10 days for orange station) and to the fact that the yellow and grey stations recorded penguins during their stay at the colony, whereas the orange station detected only transiting individuals en route to the colony. As expected, within the score range of 0-100, we observe a decrease in detection score as the transmitter-receiver distance increases, a pattern most evident for the yellow and particularly the grey station. For both stations, detections formed two spatial clusters corresponding to prolonged penguin presence at different subcolonies (Fig. 08 ). For the yellow station, detections clustered at 0.20–0.31 km and 1.50–1.66 km correspond to subcolonies A and B, respectively (Fig. 12 A). For the grey station, clusters occurred at 0.16–0.19 km and 1.45–1.55 km, aligning with subcolonies B and A based on station placement (Fig. 12 B) LoRa performance The grey station recorded 831 hours of uptime and transmitted approximately 130,000 LoRa messages (i.e 15,650 messages /100 hours), while the yellow station operated for 939 hours (17,040 messages/100 hours) and sent around 160,000 messages (. In contrast, the orange station ran for only 185 hours and transmitted approximately 19,000 messages (10,270 messages/100 hours). LoRa transmission failure rates varied among stations, reaching 10% for the grey station, 8% for the yellow station, and 21% for the orange station. The average transmission interval was approximately 21 seconds for both the grey and yellow stations, whereas the orange station exhibited a longer average interval of about 34 seconds. High detection scores (> 90) accounted for 0.77%, 0.65%, and 0.08% of detections recorded by the grey, yellow, and orange stations, respectively. Table 02 summarizes key LoRa transmission metrics for each component of the ALERT system over the course of the experiment. Table 02 summarizes key LoRa transmission metrics for each component of the ALERT system over the course of the experiment. Table 02 Summary of LoRa transmission metrics for each ALERT component. Start and end dates reflect each component's deployment period. The Average TX Interval (s), in seconds, refers to the average time between successful LoRa transmissions. The total number of LoRa transmissions from all sources (i.e beacons pings, Raspberry Pi heartbeats, detection scores) is reported (# LoRa TX (All Sources)), along with tag-related transmissions only (i.e detection scores; # LoRa TX (Tags Only)). The number and percentage of tag-related transmissions with detection scores >90% are also shown. Failed transmissions (# and %) are defined as transmission intervals exceeding the median plus 1.5 times the interquartile range, following the outlier detection approach proposed by Tukey (1977) for non-normal distributions. The frequency of failed LoRa transmissions during periods of system uptime is reported as failures per hour (# Failed LoRa TX/h). Differences in reported uptime reflect variation in deployment duration and intermittent power interruptions across components. DISCUSSION Score threshold To make a useful alerting system for the researchers in the field, the most important challenge is to find a detection score threshold and pattern (e.g. repeated detections) that reliably indicate an animal’s presence while minimising false alerts. In this experiment, we find that a single detection score of ≥ 96 reliably indicates tag presence, ensuring no missed detections (FN = 0) and no false alerts (FP = 0). Stricter detection criteria, such as requiring ≥ 2 or ≥ 3 high scores within 24 minutes, reduced false alerts at lower score thresholds but increased missed detections (Fig. 11 ). We consider ourselves fortunate that in this case there was a single threshold that yielded to perfect performance, which is not expected. Hence, we strongly advocate conducting site-specific testing by having a human carry the VHF transmitter at the start of each deployment to optimize threshold selection, regardless of environmental conditions or system configuration. Effect of transmitter-receiver distance on detection score GPS tracks matched to VHF detections show a clear decrease in detection score with increasing distance between the transmitter and receiving station (Fig. 12 ) when the entire range of scores (0-100) is considered. However, when examining true positive relevant scores ≥ 96, the detection score does not correlate with distance anymore (Fig. 13 ). We explain this by the specificity of the detection algorithm to finding the correct dominant frequency. The repetitive pattern of the transmitter is easily picked up by the matched filter if the right frequency is determined, leading to very high detection scores. If the wrong frequency is determined, the matched filter is correlated against noise, hence rarely creating a high detection score. Despite using identical hardware, the two stations exhibited markedly different effective detection ranges. The yellow station detected VHF transmitters up to approximately 0.4 km, whereas the grey station achieved a range of about 0.79 km. This discrepancy is attributed to antenna orientation rather than hardware performance. For most of the season, the yellow station’s Yagi antenna was oriented toward subcolony A, leaving subcolony B largely outside its detection cone. In contrast, the grey station was positioned south of both subcolonies, allowing its antenna to maintain coverage of both sites within its detection cone (Fig. 8 and Additional File 7). Effect of environmental conditions on detection score When examining the relationship between detection score and distance, we observed substantial variability in detection scores even when penguins were well within the expected detection range (Fig. 14 ). In several cases, detection scores varied widely while an individual remained at an approximately constant distance from an ALERT receiving station (Fig. 14 , A & B purple boxes), whereas consistently high scores would be expected under stable conditions. We propose two hypotheses for this pattern: ( 1 ) increased wind speeds may alter antenna orientation, reducing receiver sensitivity and leading to lower detection scores; and ( 2 ) during cold weather conditions, penguins may huddle closely, increasing the amount of biological material (e.g. water-rich tissue) between the transmitter and receiver and thereby attenuating the signal. The influence of wind speed is illustrated by observations at the grey and yellow stations on 27 February 2024, when multiple detections occurred at nearly constant distances from the stations (~ 0.15 km and ~ 0.3 km, respectively), yet detection scores varied widely within the same day (Fig. 14 , A & B purple boxes). This day was characterized by particularly strong winds (mean = 17 m s⁻¹, maximum = 26 m s⁻¹), substantially higher than the seasonal average of 8.5 m s⁻¹ (Fig. 14 D), suggesting that wind conditions negatively affected detection performance. Field observations confirmed that strong winds caused the Yagi antenna to rotate around its bamboo mount, intermittently shifting its orientation away from the intended target area. As a result, detection scores fluctuated with antenna alignment, decreasing when the antenna drifted off target and increasing when alignment was restored. To study our second hypothesis, that penguin huddling behavior has an impact on the detection score, we used the apparent temperature (or perceived penguin temperature) as defined in Richter et al. (2018) with updated coefficients from Winterl et al., 2024. Richter et al. showed that colder apparent temperatures, the temperature as perceived by penguins, correlate with dense penguin huddling, while warmer conditions lead to a more dispersed colony state. We expect to observe lower detection scores on days of lower apparent temperature leading to tighter huddling. The apparent temperatures during the deployments ranged from − 22.3°C to 4.3°C (mean: -3.9°C, STD: 4.2°C). and from − 15.6°C to -0.02°C (mean: -5.7°C, STD: 2.8°C) for the times the penguins were within 1 km of an ALERT receiving station (Fig. 15 ). We find that for temperatures below − 14°C, 10 out of 11 detections recorded within approximately 1 km of a receiving station had low detection scores (≤ 54), with a mean score of 41.00 ± 18.38. In contrast, for apparent temperatures above − 14°C, detection scores were generally higher, with 44.6% of detections exceeding a score of 90. Under these warmer conditions, the mean detection score was 64.58 ± 31.72 (Fig. 15 ). The remaining 55.4% of detections with scores below 90, despite penguins being within 1 km of a receiving station, may be explained by factors such as penguins being outside the antenna’s effective detection cone, signal attenuation due to snow accumulation on the transmitter, or wind-related interference affecting antenna performance. LoRa Performance In this experiment, all three ALERT receiving stations used LoRa to transmit data to the central gateway. This included detections of VHF-tagged penguins, fixed beacon pings, and Raspberry Pi heartbeat messages. Observed transmission failure rates ranged from 7% to 21%, which is consistent with reported values for LoRa-based systems ( 28 ). In a non-testing setting, LoRa traffic could be reduced by transmitting only high-scoring detections, thereby lowering transmission frequency and packet collisions. The grey and yellow stations performed particularly reliably, likely due to their use of the RAK811 Pi HAT LoRa module, which benefited from a well-tested and actively developed open-source python library ( https://github.com/AmedeeBulle/pyrak811 ) , enabling mostly stable and consistent communication. This module has since been discontinued. By contrast, the orange station used the newer RAK3172 chip, which was operated directly via AT commands. This added implementation complexity, together with its greater distance from the gateway, likely contributed to its poorer performance, as reflected by a higher number of failed network joins and transmission attempts. Adaptive data rate (ADR) was enabled, and transmissions at high output power occasionally produced brief, audible interference bursts on the VHF channel during LoRa transmissions. These interference events degraded the analysed audio file, and subsequently, reduced detection scores. In a previous experiment, we used a WiFi link (Ubiquiti NS5) to connect the ALERT station to the SPOT observatory. However, the WiFi transmitter generated substantial electromagnetic interference with the VHF receiver, preventing detection of VHF transmitters even at close range. Replacing WiFi with LoRa proved to be an effective and reliable solution, producing little to no interference while also offering substantially lower power consumption. CONCLUSIONS In this paper, we introduce a novel Automatic Radio-Telemetry System (ALERT) that combines VHF signal detection with LoRa communication to wirelessly relay transmitter detections to a central gateway. The system was successfully tested in Antarctica, where it detected 100% of returning VHF-tagged emperor penguins using three receiving stations deployed up to 5 km from the LoRa gateway. Each station reliably scanned for VHF signals within up to ~ 0.8 km range. This automation significantly improved the efficiency of the tag-recapture process by eliminating the need for 4-hourly manual scans. The time and effort saved could then be redirected toward other scientific efforts such as tagging additional animals and collecting more data, ultimately enhancing research capacity. Beyond emperor penguin detection, ALERT was also tested for monitoring VHF-tagged Weddell seals ( Leptonychotes weddellii ) at a nearby haul-out site. The goal of this test was to assess whether the system could detect seal surfacing events and relay alerts to researchers in near real time. To do so, the orange station was repositioned mid-season approximately 9 km from the gateway and configured to monitor VHF signals from seals occupying a known breathing hole beneath the ice shelf edge. While VHF detections of surfacing seals were successfully recorded at the station, LoRa communication with the gateway proved challenging. The seal hole was located adjacent to the ice shelf edge, which obstructed line-of-sight and significantly attenuated the LoRa signal. As a result, reliable communication required careful receiving station placement to balance two competing constraints: maintaining sufficient proximity and orientation for VHF detection of seals, while also preserving line-of-sight to the gateway for LoRa transmission. This test demonstrated both the versatility of ALERT across species and applications, and a key limitation of LoRa communication in complex Antarctic terrain. During this deployment, ALERT stations operated primarily with one-way communication, receiving only basic acknowledgments such as “Join” confirmations and packet receipts. Looking ahead, incorporating two-way communication through Class C LoRa would allow the gateway to remotely adjust station parameters, such as enabling or disabling specific VHF frequencies, eliminating the need for manual on-site updates and further increasing system flexibility. Abbreviations ADR Adaptive Data Rate (LoRaWAN feature) ALERT Automatic LoRa–Enabled Radio Tracker CSS Chirp Spread Spectrum DC DC –Direct Current to Direct Current dB Decibels dBm Decibels relative to 1 milliwatt dE Expected time interval between VHF signal peaks dO Observed time interval between detected VHF signal peaks FFT Fast Fourier Transform FM Frequency Modulation FN False Negative FP False Positive GPS Global Positioning System HS High Score (detection score threshold) Hz Hertz IIR Infinite Impulse Response IoT Internet of Things kHz Kilohertz LoRa Long Range (wireless modulation technique) LoRaWAN Long Range Wide Area Network LPWAN Low–Power Wide Area Network MAC Medium Access Control MHz Megahertz Np Number of detected peaks OTAA Over–The–Air Activation Pi Raspberry Pi (single–board computer) PoE Power over Ethernet PSD Power Spectral Density RAK RAKwireless (manufacturer of LoRa modules and gateways) RS232 Recommended Standard 232 (serial communication protocol) SDR Software–Defined Radio Sp Intermediate signal detection score Sfinal Final signal detection score SPOT Single Penguin Observation Tower STD Standard Deviation Ta Apparent temperature TN True Negative TP True Positive TX Transmission USB Universal Serial Bus VHF Very High Frequency VHF radio Lotek VHF Biotracker radio receiver WAV Waveform Audio File Format WiFi Wireless Fidelity Declarations Ethics approval and consent to participate Institutional Animal Care and Use Committee (IACUC) of Woods Hole Oceanographic Institution approval for the use of animals and all experimental protocols of this study. Consent for publication Not applicable Funding This work was supported by the Deutsche Forschungsgemeinschaft (DFG) in the framework of the priority programme 1158 “Antarctic Research with comparative investigations in Arctic ice areas” by grant ( ZI1527/7 − 1), by the Alfred-Wegener-Institut Helmholtz-Zentrum für Polar-und Meeresforschung (AWI) within the framework of the Projects SPOT (AWI_ANT_13) and MARE (AWI_ANT_14)47, by the Centre Scientifique de Monaco with additional support from the LIA-647 and RTPI-NUTRESS (CSM/CNRS-UNISTRA), and by the Woods Hole Oceanographic Institution's Grassle Fund. Author Contribution LMRB, DPZ conceived and designed the study; LMRB, KW, AW developed the algorithm; LMRB, AH, AW, GB, DPZ performed the fieldwork; LMRB analyzed the data; LMRB wrote the original draft; LMRB, CB, DPZ secured the funding. All authors reviewed the manuscript. 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Baille","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACPgkGhgMPChjkGEAMBgZmwlrYQCoTDBiMSdPCANSS2CAB5hOjRbr5IdAWm/QNt5sPHmCosE5sIKhF5pgBUEta7oY7xxIOMJxJJ0KLRAJIy+HcbTdyDA4wth0mRkv6B6CW/+lmYC3/iNKSA7LlQAJESwMxWmTOFAC1JBvuv5GWcCDhWLoxQS380u2bP3yosJOXnJF8+MOHGmtZglpQQQJpykfBKBgFo2AU4AIAZPdECUGfAekAAAAASUVORK5CYII=","orcid":"","institution":"Massachusetts Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Loïcka","middleName":"M.R.","lastName":"Baille","suffix":""},{"id":590549508,"identity":"283bd410-80bd-4045-bfcd-9b3cd86845bc","order_by":1,"name":"Aymeric Houstin","email":"","orcid":"","institution":"Woods Hole Oceanographic Institution","correspondingAuthor":false,"prefix":"","firstName":"Aymeric","middleName":"","lastName":"Houstin","suffix":""},{"id":590549511,"identity":"9b76cc97-f242-4037-842d-ff13e6ce8def","order_by":2,"name":"Kel Weaver","email":"","orcid":"","institution":"Woods Hole Oceanographic Institution","correspondingAuthor":false,"prefix":"","firstName":"Kel","middleName":"","lastName":"Weaver","suffix":""},{"id":590549516,"identity":"3387be2d-b59a-4a4a-9727-6d349cc8fce6","order_by":3,"name":"Alexander Winterl","email":"","orcid":"","institution":"University of Erlangen-Nuremberg","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Winterl","suffix":""},{"id":590549520,"identity":"e65d4655-cbae-4d53-9ca2-26bce3e29f34","order_by":4,"name":"Gaël Bardon","email":"","orcid":"","institution":"Scientific Centre of Monaco","correspondingAuthor":false,"prefix":"","firstName":"Gaël","middleName":"","lastName":"Bardon","suffix":""},{"id":590549530,"identity":"ab6a46c8-dd90-48f5-b76f-64b0344de720","order_by":5,"name":"Céline Le Bohec","email":"","orcid":"","institution":"Scientific Centre of Monaco","correspondingAuthor":false,"prefix":"","firstName":"Céline","middleName":"Le","lastName":"Bohec","suffix":""},{"id":590549535,"identity":"9eaefc99-e29c-43f7-acfc-cec3d4c1c2a4","order_by":6,"name":"Daniel P. Zitterbart","email":"","orcid":"","institution":"Woods Hole Oceanographic Institution","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"P.","lastName":"Zitterbart","suffix":""}],"badges":[],"createdAt":"2026-01-30 21:38:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8744989/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8744989/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102760398,"identity":"d3a52c8c-a2c5-4df7-b548-bec4c9fbdb34","added_by":"auto","created_at":"2026-02-16 10:26:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":16157257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eqqq\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/6e774ef70bd740155f9e8228.png"},{"id":102760375,"identity":"db9ac01f-ba25-4c18-9baf-00f0ba17cd97","added_by":"auto","created_at":"2026-02-16 10:26:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2117458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e122212121\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/8cf1a0bfdacfba1bcf096d20.png"},{"id":102760379,"identity":"1b2c78bb-1886-4604-953e-1ed1a9fd332b","added_by":"auto","created_at":"2026-02-16 10:26:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1210478,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eqwqwqwwwqwq\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/f3af08ddf9a170d36134aa75.png"},{"id":102760387,"identity":"4cd86db6-3400-45f0-8c17-1e1aba718ac0","added_by":"auto","created_at":"2026-02-16 10:26:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1469465,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLoRaWAN gateway mounted 6 m above ground level on the mast of the SPOT Penguin Observatory. The gateway is housed in a yellow, weather-resistant enclosure and is equipped with an external 1m-long LoRa antenna operating at 915 MHz.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/75afbfe32c67e87593bec372.png"},{"id":102760400,"identity":"9eee7591-41b4-4888-aa44-a96b866f38c0","added_by":"auto","created_at":"2026-02-16 10:27:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":226650,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSimplified overview of ALERT’s information flow. Detection data are generated at the ALERT receiving stations (orange, grey, and yellow) and transmitted via LoRaWAN to a local gateway. Using an internet connection and WiFi, the gateway uploads the data to an InfluxDB database which is queried by a Grafana interface for real-time visualization of animal presence at each station.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/9417d516f41e235a6aebc4f9.png"},{"id":102760457,"identity":"38ea4800-0556-4911-8e13-1a5078adb940","added_by":"auto","created_at":"2026-02-16 10:27:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2374990,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGrafana interface showing real-time detection scores from each of the three ALERT receiving stations (yellow, grey and orange). Detection data are visualised as a time series (horizontal axis), with each row corresponding to an individual VHF transmitter and each block representing a scan event. Block’s colour reflects the signal’s presence score, as defined in the value mappings legend. Periods with no received data are shown in black. “SPOT ICE: 1690,” “CATHEDRAL: 1801,” and “FINGER ICE: 1610” refer to fixed VHF transmitters that emit regular pings (‘beacon pings’) used to verify detection reliability. “zPi_YELLOW” and “zPi_GREY” denote heartbeat messages (score=999) transmitted by the receiving station’s onboard Raspberry Pi and are used to monitor station operational status.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/5277aa1fa22d722894545101.png"},{"id":102760384,"identity":"378f2fe7-0fa1-4a29-8ddd-5cd4739345a2","added_by":"auto","created_at":"2026-02-16 10:26:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":217123,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFlowchart of the detection algorithm. The flowchart illustrates the processing steps applied to a raw audio file, including trimming, downsampling, noise reduction, and template signal extraction and match filtering, which are used to evaluate the presence of a detection signal by assigning a detection score.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/c6030d85d82facfffc9fcb62.png"},{"id":102760309,"identity":"4bd751cc-f49c-49fc-abda-ac85fa2ec762","added_by":"auto","created_at":"2026-02-16 10:26:42","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":8304260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eOverview of the ALERT system field-testing layout shown at a wide scale (A) and zoomed in around the system (B). The system comprises three ALERT receiving stations (grey, orange, and yellow) and a LoRaWAN gateway distributed across the sea ice and ice shelf. During that season, the colony split into two main subgroups: subcolony A (green) and subcolony B (purple). Station placement and antenna orientations were informed by the 2024 colony configuration and observed penguin movement patterns to maximise detection coverage.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/0b04da4ba9410f896b0c94b2.png"},{"id":102760390,"identity":"6b9f3b7b-20f9-4b11-90ee-70153e25b09b","added_by":"auto","created_at":"2026-02-16 10:26:56","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1213693,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe SPOT penguin observatory at Akta Bay with a mounted LoRaWAN gateway and antenna. SPOT is energetically self-sufficient using solar generators and wind turbines, and is connected to the internet through a WiFi link to the research base Neumayer Station III.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/7ccd5ecefabcface401774ca.png"},{"id":102760415,"identity":"897f99f2-a321-4402-abff-4e7a22033cfa","added_by":"auto","created_at":"2026-02-16 10:27:16","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":3949186,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAdult Emperor penguin equipped with biologging devices and a VHF transmitter (Holohil RI-2B). The penguin is shown inside a corral and is fitted with a hood, following established handling protocols \u003c/em\u003e\u003ca href=\"https://www.zotero.org/google-docs/?7wgfdp\"\u003e(37)\u003c/a\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/d9c5fe5fe3af81abcff6dc0a.png"},{"id":102760391,"identity":"984690b6-f0e4-4a59-af60-f9fa7f1d8c88","added_by":"auto","created_at":"2026-02-16 10:26:56","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":502380,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eALERT system performance metrics across detection score thresholds from 90 to 99. Panels A and B show results for the full dataset, based on VHF-based presence. Panels C and D show GPS-tagged individuals, using GPS-based presence.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/f660f5f924fb04fa09aa33b7.png"},{"id":102760407,"identity":"b8c31a40-af37-4192-87f0-4df378eccbdd","added_by":"auto","created_at":"2026-02-16 10:27:11","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":9952121,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of VHF detection scores as a function of transmitter-receiver distance, shown separately for each ALERT station and for all stations combined. Distances were calculated by matching each VHF detection to the nearest GPS fix within ±2 minutes, corresponding to a spatial resolution of ~86m (based on a tobogganing speed of 2.59 km/h; Kooyman \u0026amp; Kooyman, 1995). The x-axis is truncated at 3 km, beyond which reliable detection is unlikely.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig12.png","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/34d77505a5434e139b860566.png"},{"id":102760396,"identity":"3339c11f-44c0-4c6d-97cc-111d1fe2144e","added_by":"auto","created_at":"2026-02-16 10:26:58","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":1466626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDetection scores [95-100] as a function of transmitter distance to the respective ALERT receiving stations (km). Distances were calculated by matching each VHF detection to the nearest GPS fix within ±2 minutes. The x-axis is truncated at 3 km, beyond which reliable detection is unlikely.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig13.png","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/9bc002fa700903182a818a5c.png"},{"id":102760366,"identity":"b794112a-86d7-45e8-8b9f-235dc4576428","added_by":"auto","created_at":"2026-02-16 10:26:46","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":7740191,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVHF detection scores as a function of transmitter-receiver distance for each ALERT receiving station (A-C), with daily wind conditions shown in panel D. VHF detections were matched to GPS locations within ±2 minute, yielding distance estimates with ~86 m spatial accuracy. Panel D presents daily wind speed statistics (minimum, maximum, and mean) relative to the seasonal average (8.5 m/s). Purple boxes highlight periods during which the penguin remained at a constant distance from the receiver, yet detection scores varied.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig14.png","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/3ad0dd7dbd8e125da381fba1.png"},{"id":102760393,"identity":"fe4937fc-e77f-40f7-8b9c-4c61a9f47a80","added_by":"auto","created_at":"2026-02-16 10:26:57","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":3691276,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVHF detection scores as a function of apparent temperature (°C). Each VHF detection was matched to the nearest weather observation within ±1 hour. To ensure detection scores reflect true detections, only detections located within 1 km (±647 m) of a receiving station were included, based on GPS positions matched within ±15 minutes of each VHF timestamp. The data is colour-coded by day.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig15.png","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/719e91f6b7d375578afdde53.png"},{"id":102760157,"identity":"cb6f1e49-c1cb-4f8e-8859-5ade26000992","added_by":"auto","created_at":"2026-02-16 10:26:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":748728,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/74dfd603-a26e-4d28-bd90-a2795564d9e1.pdf"},{"id":102760458,"identity":"31003e34-815d-4561-8db4-5eca9f2b1080","added_by":"auto","created_at":"2026-02-16 10:27:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9295521,"visible":true,"origin":"","legend":"","description":"","filename":"v1submittedsuppmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8744989/v1/1e3a88ce477c07267bf25678.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":" ALERT: The Automatic LoRa-Enabled Radio Tracker for Wildlife Monitoring in Remote Environments","fulltext":[{"header":"Background","content":"\u003cp\u003eBiologging and tracking\u003c/p\u003e \u003cp\u003eThe practice of equipping free-living animals with data loggers to study their behavior in-situ dates back to the 1930s (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Since then, remarkable advances in sensor miniaturization, power efficiency, data storage, and wireless communication have transformed the field. Today, animal-borne sensors can record a broad array of data, including geolocation (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), acceleration (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), magnetic field strength (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), temperature (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), air and water pressure (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), heart rate (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), ambient light levels (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), conductivity (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), video footage (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), acoustic signals (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and more. They are widely used in ecology and conservation to collect detailed information on animal movements, behavior, physiology, and the environments they inhabit (\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnimal-borne devices can either transmit recorded data wirelessly to receivers (via electromagnetic or acoustics waves to land, underwater, or orbital receivers) or store data, requiring physical retrieval of the biologger for data access (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The latter involves either recapturing the tagged animal to remove the device or locating and retrieving the tag once it has detached to access the data. As a result, biologgers that require physical recovery must also include a means of localization. Commonly used localization techniques include acoustic pingers (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and Very High Frequency (VHF) radio transmitters (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVHF animal tracking has been widely used to monitor a broad range of taxa, including insects (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), birds (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), reptiles (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), and mammals (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), providing valuable insights into animal presence, movement, and habitat use.\u003c/p\u003e \u003cp\u003eVHF transmitters can be deployed on animals to either determine their precise geographic location (via elaborate synchronized receiver arrays) or simply to detect their presence within a given area (via a single VHF receiver). This receiver can be used to retrieve archival biologgers, which store data onboard and require physical data download.\u003c/p\u003e \u003cp\u003eIn this latter scenario, VHF scanning remains predominantly performed by researchers who, using a mobile VHF receiver and a single Yagi antenna, must listen for the presence of a VHF transmitter, requiring significant human effort (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere have been a few attempts to automate VHF radio monitoring to scale detection efforts. In 2011, Kays et al. developed the Automated Radio-Telemetry System, using programmable commercial off the shelf VHF receivers to detect VHF signals emitted from radio-collared animals in a tropical forest. This study demonstrated the feasibility and potential of automated VHF tracking, yet the approach was not widely adopted (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Subsequent efforts have revisited and expanded upon this approach. For instance, Wallace et al. (2022) used four Orion receivers with integrated software to track VHF-tagged prairie voles (\u003cem\u003eMicrotus ochrogaster\u003c/em\u003e), and Hayward-Brown et al. (2025) developed custom receiving stations to monitor nanotagged Gouldian finches \u003cem\u003e(Chloebia gouldiae\u003c/em\u003e). In both Wallace et al. (2022) and Hayward-Brown et al. (2025), the receiver's data was stored locally and later manually downloaded for geolocation analysis.\u003c/p\u003e \u003cp\u003eWhen real-time or near real-time information on animal presence is necessary, data can be transmitted via satellite, cellular networks, or other wireless communication protocols, depending on local infrastructure availability (e.g., cellular coverage) as well as practical constraints such as cost, power consumption, and data bandwidth. In remote regions lacking cellular connectivity, long-range, low-power radio technologies, such as LoRa, offer a particularly suitable solution.\u003c/p\u003e \u003cp\u003eLoRa (LOng RAnge)\u003c/p\u003e \u003cp\u003eLoRa (short for Long Range) is a low-power, long-range, low-bitrate wireless communication technology designed for the Internet of Things (IoT). This Low-Power Wide Area Network protocol (LPWAN) operates in license-free sub-GHz frequency bands (433 MHz in Asia, 868 MHz in Europe, 915 MHz in North America and Australia). It utilizes interference-resistant Chirp Spread Spectrum (CSS) modulation, allowing reliable data transmission over several kilometers in line of sight. The payload of each transmission can range from 2 to 255 bytes, and the data rate may reach up to 50 Kbps under optimal configurations using channel aggregation (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile the physical layer (LoRa) is a proprietary modulation technology, the LoRaWAN protocol that defines the Medium Access Layer (MAC) and network layers are developed and maintained as open source software (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral modulation parameters can be tuned to optimize range, power consumption, and error correction rate (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe low power consumption and long-range capabilities of LoRa make it well-suited for ecological and remote data transmission applications. Recent studies have successfully applied LoRa for autonomous animal tracking and environmental monitoring in remote field conditions (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), where traditional telemetry or cellular networks are impractical.\u003c/p\u003e \u003cp\u003eIn this study, we developed an automated, low-cost VHF receiving array to facilitate biologger recovery from Emperor penguins, a task complicated by several species- and environment-specific constraints. Specifically, VHF transmitters are mounted low on the animal\u0026rsquo;s body and operate in close proximity to the dielectric sea ice, substantially reducing transmission range and requiring receivers with very high sensitivity, beyond what is typically achievable with Software Defined Radio (SDR)-based systems. In the following paragraph, we provide background on the Emperor penguin as well as the study setup which motivated the development of a case-specific, yet easily adaptable, VHF receiving system that may be useful for similar applications.\u003c/p\u003e \u003cp\u003eEmperor penguins (\u003cem\u003eAptenodytes forsteri\u003c/em\u003e)\u003c/p\u003e \u003cp\u003eThe Emperor penguin (\u003cem\u003eAptenodytes forsteri\u003c/em\u003e) is a central place forager that typically returns to its natal colony to breed once it reaches sexual maturity at around 4\u0026ndash;6 years of age. From that point onward, adults follow a well-defined annual breeding cycle. As the Antarctic winter approaches between late March and early May, penguins return to their breeding colony where courtship, mating, and egg laying take place over a period of 6\u0026ndash;10 weeks. Following egg laying, females leave the colony to forage at sea and replenish their body reserves, while males remain behind to incubate the single egg for approximately two months. Chicks hatch typically between July and August, coinciding with the return of females to the colony. At the beginning of the chick-rearing phase, both parents alternate between caring for the chick at the colony and foraging at sea. As chicks become thermally independent, both parents are able to forage simultaneously to meet the chick\u0026rsquo;s increasing energetic demands (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). After fledging, emperor penguins of all age classes leave the colony and disperse into the Southern Ocean, thereby completing the annual cycle before the next breeding season begins again in the following March.\u003c/p\u003e \u003cp\u003eDue to their extensive use of the Southern Ocean, emperor penguins serve as key sentinel species for monitoring changes in the austral marine ecosystems (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The Atka Bay emperor penguin colony, located in a deep, sheltered inlet in the southwestern corner of Atka Bay, comprises approximately 8600 breeding pairs (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) and benefits from enhanced sea-ice stability that provides favourable breeding conditions. The colony lies approximately 9 km from Neumayer Station III, making it accessible for long-term monitoring while remaining mostly unimpacted by human presence. The Atka Bay colony is intensively studied within the MARE (Monitor the health of the Antarctic marine ecosystems using the Emperor penguin as a sentinel) life observatory.MARE consists of continuous surveillance using the SPOT Penguin Observatory (Richter et al. 2018), population dynamics studies using electronic RFID microchip tagging, and biologging based habitat-at-sea monitoring. Habitat-at-sea monitoring of the Atka Bay colony has relied on a combination of satellite telemetry for year-round tracking of both juvenile and adult emperor penguins (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) and archival biologging of adult emperor penguins during the chick-rearing season (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). VHF transmitters are deployed alongside the archival tags to allow detection of returning penguins using VHF radios and subsequent biologger recovery.\u003c/p\u003e \u003cp\u003eDuring the chick-rearing period, emperor penguins\u0026rsquo; visits to the breeding colony can be as short as four hours, requiring frequent monitoring. To maximise the probability of tag recovery before moulting, the current gold-standard is to conduct manual VHF scans at the colony every four hours for the duration of the deployments (6\u0026ndash;8 weeks). This intensive scanning protocol resulted in a tag recovery rate of 83% (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSuch frequent scanning in a remote and harsh environment like Antarctica comes at a high cost, requiring approximately 63 hours of effort (including preparation, and round-trip travel from the research base to the colony) and 227 litres of fuel per week (~\u0026thinsp;2,300USD) (see Additional File 8 for detailed estimates). Beyond the monetary cost, this schedule repeatedly led to sleep deprivation, team frustration because of perceived unproductive effort, and reduced capacity to conduct other scientific activities, while field time in Antarctica remains at a premium.\u003c/p\u003e \u003cp\u003eThis manuscript introduces a novel automated radio-telemetry system, the Automatic LoRa-Enabled Radio Tracker (ALERT). ALERT consists of custom-built receiving stations that continuously scan for pre-defined VHF transmissions, detect them using a custom algorithm, and wirelessly transmit detection scores via the Long Range Wide Area Network (LoRaWAN) protocol to a central gateway connected to the internet.\u003c/p\u003e \u003cp\u003eThe system was tested at Atka Bay, Antarctica, from November 2024 to January 2025. During this period, 18 adult emperor penguins were equipped with archival data loggers and VHF transmitters. Three ALERT receiving stations were strategically deployed in the vicinity of the colony and successfully alerted the field team about the return of VHF-tagged penguins from foraging trips, enabling efficient recovery of the archival biologgers.\u003c/p\u003e \u003cp\u003eThis manuscript describes the hard- and software architecture of ALERT and evaluates the system\u0026rsquo;s performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eVHF Transmitters\u003c/p\u003e \u003cp\u003eALERT was designed to detect frequency-modulated (FM) pulsed single tone signals from uncoded VHF transmitters. In this study, the 18 tagged penguins were equipped with Holohil VHF transmitters deployed alongside archival data loggers. Holohil transmitters are widely used in wildlife tracking because of their compact size, low mass, and long battery life, which can last several months to years.\u003c/p\u003e \u003cp\u003eWe used the Holohil RI-2B model (now called XM1), which operates within the 148\u0026ndash;152 MHz narrowband used for wildlife tracking, with 10 kHz channel spacing to minimize overlap. The transmitters emit pulses at fixed pulse intervals. Although pulse intervals are expected to be consistent across transmitters of the same model, our tests revealed small but measurable deviations between units. To account for this variability, the average pulse interval of each transmitter was determined using low-noise baseband recordings.\u003c/p\u003e \u003cp\u003eEach transmitter is also characterised by a single tone frequency (Hz). While this frequency is predefined and transmitter-specific, field measurements revealed that the single tone frequency can drift up to 1,000 Hz, likely driven by temperature and/or battery voltage fluctuations (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). To accommodate this behaviour, the single tone frequency of each transmitter was re-identified during each scan using spectrogram analysis and a custom detection algorithm.\u003c/p\u003e \u003cp\u003eTogether, the transmitter pulse interval and the characteristic single tone frequency form the core features used by the custom detection algorithm to determine signal presence (see section Detection Algorithm).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eALERT INFRASTRUCTURE\u003c/h2\u003e \u003cp\u003eIn this case study, the ALERT system consists of three ALERT receiving stations and one LoRa gateway which aggregates the data locally. The number of receiving stations and gateways can be increased or reduced as needed, though at least one of each is necessary.\u003c/p\u003e \u003cp\u003eALERT Receiving station\u003c/p\u003e \u003cp\u003eAn ALERT receiving station continuously tunes it into a predefined list of VHF frequencies, evaluates the presence of a known signal, and relays that information back to the nearby LoRa gateway. Physically, each receiving station consists of five main components: a \u0026ldquo;power box\u0026rdquo;, a \u0026ldquo;processing box\u0026rdquo;, a long-standing pole (a bamboo stick in our field experiment), a 3-element Yagi antenna, and a LoRa antenna as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThree distinct ALERT receiving stations were built. For simplicity, they were named Orange, Grey, and Yellow station, according to the colour of the processing box\u0026rsquo;s pelican case used.\u003c/p\u003e \u003cp\u003eEach ALERT receiving station consists of two main boxes: the \u003cem\u003epower box\u003c/em\u003e and the \u003cem\u003eprocessing box\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003epower box\u003c/em\u003e consists of a 12V battery and DC-DC converter (12V-5V, MEAN WELL USA Inc. SD-15) housed in an aluminum box (Zarges K440\u0026ndash;40701) and a mounted solar panel on top (20W,12V). The DC-DC converter was mounted inside the aluminum enclosure to reduce electromagnetic interference (Supplementary Fig.\u0026nbsp;1)..\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eprocessing box\u003c/em\u003e consists of a Pelican 1300 case mounted on a bamboo pole, housing a Lotek VHF Biotracker radio receiver (hereafter the VHF radio), a Raspberry Pi 4B, a RS232 hat, a USB sound card, a LoRa board (RAK811 or 3172), a power relay hat, and a power port for connecting a 1m cable from the external power box (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe VHF radio is a compact, robust receiver designed for wildlife tracking. It offers precise frequency tuning from 138.000 to 174.000 MHz in 0.1 kHz increments and remote programmability through a serial connection. It connects to a Lotek 3-element folding Yagi antenna (145\u0026ndash;173 MHz), mounted vertically atop a long vertical pole to maximize detection range (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). As metal structures can distort or block VHF transmissions, other materials should be preferred and tested. We used bamboo in our experiment as it is abundantly available as flag poles at the research station. To simplify the power setup and only need to provide one voltage (5V) to the processing box, the 12V VHF radio was modified to bypass its internal 12V-to-5V converter, allowing it to run directly on 5V.\u003c/p\u003e \u003cp\u003eThe VHF radio includes a 3.5 mm headphone output and an RS232 serial communication port. An RS232 HAT on the Raspberry Pi sends serial commands to the VHF radio to control parameters such as frequency and gain. Audio from the radio's headphone output is routed to the Raspberry Pi via the microphone port of the USB sound card. The Raspberry Pi cycles the VHF radio through a predefined set of frequencies, demodulates the VHF signal, and stores the audible baseband as a 10-second audio sample (WAV format). Each recorded audio file is analysed by a custom detection algorithm (details below) that assigns a score from 0 to 100, reflecting the confidence that the expected VHF signal is present in the recording. Each score, along with its corresponding frequency, is then wirelessly transmitted to a LoRa gateway using a LoRa module (RAK 811: yellow and grey stations, or RAK3172: orange station, 915 MHz, Class C) connected to the Raspberry Pi and a 5 dBi 900 MHz antenna (EM Wave, Inc) mounted on the processing box.\u003c/p\u003e \u003cp\u003eFinally, the Raspberry Pi is equipped with a power relay HAT that can power-cycle key components (Raspberry Pi, VHF radio) if a component of the system gets hung up during operation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTransmissions from the stations to the gateway included detection scores from VHF-tagged penguins, as well as periodic signals from fixed VHF transmitters used to verify detection reliability, and heartbeat messages from the onboard Raspberry Pi, used to monitor station health.\u003c/p\u003e \u003cp\u003eALERT Gateway\u003c/p\u003e \u003cp\u003eAn elevated LoRaWAN gateway receives transmissions from the ALERT receiving stations and stores the data locally. Housed in a compact, weatherproof Pelican 1120 case, the gateway includes a Raspberry Pi 4B equipped with an 8-channel LoRa Gateway HAT (RAK2245), a PoE injector (Alfa APOE03, 12V), a linear voltage regulator (12V to 5V, Texas Instruments LM150), two heatsinks for thermal management, and an external 1-m long 900 MHz antenna as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe gateway runs ChirpStack version 4, an open-source LoRaWAN Network and Application Server that manages communication between the gateway and the transmitting nodes (i.e. ALERT receiving stations) and routes data to a local InfluxDB database. Within ChirpStack, an application accepts incoming base64-encoded packets from the transmitting ALERT receiving stations and stores them onto a local \u0026ldquo;raw\u0026rdquo; database. A wrapper script then continuously decodes each packet, reformats the data, and writes it to a database in our desired format (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e for a simplified overview of the information flow and Supplementary Fig.\u0026nbsp;2 for a comprehensive representation). A Grafana interface then feeds on this database to display real-time detection scores across frequencies and ALERT receiving stations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDetection algorithm\u003c/p\u003e \u003cp\u003eFor each pre-defined frequency, an ALERT station records a 10-second WAV file, long enough to clearly identify the signal, which is locally processed by a detection algorithm. The algorithm assigns a score from 0 to 100, with higher values indicating a greater confidence of detection. A detection is defined as the presence of a known VHF signal in the detection range of the receiving station. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the algorithm's logic.\u003c/p\u003e \u003cp\u003eThe detection algorithm begins by trimming the first second of each audio file to remove transient amplitude spikes caused by frequency switching, which can bias detection. The audio is then downsampled from 44 kHz to 10 kHz to reduce computational load of the Fast Fourier Transform (FFT).\u003c/p\u003e \u003cp\u003eNext, noise reduction is performed using spectral gating via the \u003cem\u003enoisereduce\u003c/em\u003e Python package (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), which implements a non-stationary forward-backward Infinite Impulse Response (IIR) filter. A spectrogram is generated from the denoised signal, and further smoothed with a two-dimensional Gaussian filter.\u003c/p\u003e \u003cp\u003eThe Power Spectral Density (PSD) is then computed by averaging over 20 Hz windows. From the PSD, the most prominent frequency is extracted, provided it spans at least 30 Hz in bandwidth to filter spikes. Using this frequency, along with the 10 kHz sample rate and a known pulse duration of 25 ms (per manufacturer), a sine wave template representing the expected signal is then generated.\u003c/p\u003e \u003cp\u003eThis template is then convolved with the denoised signal in a matched filter approach, to enhance repetitive pulse-like features. Peak detection is performed on the matched filter output across amplitude percentiles from 80 to 99.75. For each percentile, a score, ranging from 0 to 100, is calculated based on the number of detected peaks and their temporal spacing. After evaluating all percentiles, the algorithm selects the highest score among those that detected between 4 and 6 peaks, which is the expected number of pulses in a 9-second recording. This process is summarized as:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{p}=\\:100\\times\\:\\left(max\\right(\\text{0,1}-\\frac{\\left|{d}_{O}-{d}_{E}\\right|}{{d}_{E}}\\)\u003c/span\u003e \u003c/span\u003e))\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{S}_{final}=max\\left({S}_{p}\\right|\\:4\\le\\:{N}_{p}\\le\\:6)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003edO=Observed time interval between peaks\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003edE= Expected time interval between peaks, determined from controlled, noise-limited tests for each transmitter.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNp= Number of peaks\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRefer to Additional File. 3 to visualize the multi-step analysis of a single audio file that resulted in a score of 97.\u003c/p\u003e \u003cp\u003eThe score, along with the corresponding frequency ID, is then transmitted via LoRaWAN to the LoRa gateway.\u003c/p\u003e \u003cp\u003eTo minimize LoRaWAN payload size, we used simplified frequency IDs (e.g., \u0026lsquo;A1\u0026rsquo; for 151.120 MHz) instead of full frequency values, and rely on the ChirpStack gateway\u0026rsquo;s reception timestamp to date each detection, as the delay between audio recording and transmission is typically only a few seconds.\u003c/p\u003e \u003cp\u003eIt is important to note that the scoring method is inherently non-linear. When the algorithm correctly identifies the single tone frequency, it assigns a high score, typically between 90 and 99. Conversely, if it selects an incorrect single tone frequency, the resulting score is significantly lower. In practice, the highest score obtainable is 99. Achieving a perfect score of 100 would require the observed peak intervals to align with the expected ones within a tolerance of a few ten-thousandths of a second, an unrealistic level of accuracy under field conditions.\u003c/p\u003e \u003cp\u003eLoRa Configuration\u003c/p\u003e \u003cp\u003eThe yellow and grey stations used the RAK811 LoRa Pi HAT, while the orange station used a RAK3172 module connected to a Raspberry Pi via serial communication (baudrate\u0026thinsp;=\u0026thinsp;9600, 8 data bits, no parity, 1 stop bit, timeout\u0026thinsp;=\u0026thinsp;1s) due to module availability. All stations were configured for LoRaWAN using Over-The-Air Activation (OTAA) on the US915 band, operating in Class C mode with adaptive data rate enabled. They were each configured with their respective Application Key provided in their respective configuration file to securely connect to the network server. The yellow and grey stations were configured to attempt to join the network up to five times and, upon success, transmit their packets up to three times. The orange station had additional settings, including RX1/RX2 delays of 1s and 2s, join accept delays of 5s and 6s. It would attempt to join the network up to ten times and to send a payload up to five times with 2-second intervals. If transmission repeatedly fails, the Pi reboots itself and restarts the process while discarding unsent packets.\u003c/p\u003e \u003cp\u003eExperiment Layout\u003c/p\u003e \u003cp\u003eTo evaluate the performance of ALERT and to reduce the efforts required for manual scanning at the Akta Bay emperor penguin colony, we developed and tested the ALERT system during the chick-rearing season from November 2024 to January 2025. During this experiment, three ALERT receiving stations, yellow, orange, and grey, were deployed in the vicinity of the colony: the orange station on sea ice (70\u0026deg;34\u0026prime;02\u0026Prime;S, 08\u0026deg;06\u0026prime;23\u0026Prime;W), the grey (70\u0026deg;37\u0026prime;06\u0026Prime;S, 08\u0026deg;09\u0026prime;36\u0026Prime;W ) and yellow (70\u0026deg;36\u0026prime;13\u0026Prime;S, 08\u0026deg;08\u0026prime;26\u0026Prime;W) ones on the ice shelf (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe yellow station, located 0.9 km from the gateway, was initially oriented toward subcolony A and was later repositioned to face north toward the ocean to improve detection of penguins returning from foraging trips. The grey station, situated 0.91 km from the gateway and south of both subcolonies, was oriented to cover both subcolonies simultaneously, as they fall within its direct line of sight. The orange station was deployed approximately 5.1 km from the gateway on the sea ice and oriented westward toward the ice shelf prior to a snowstorm, based on field observations indicating that penguins preferentially follow the ice shelf edge when navigating back to the colony.\u003c/p\u003e \u003cp\u003eThe gateway was mounted atop a 6-meter mast at the SPOT penguin observatory (70\u0026deg;36\u0026prime;38\u0026Prime;S, 08\u0026deg;09\u0026prime;12\u0026Prime;W) located approximately 9 km from the research base Neumayer Station III (70\u0026deg;39\u0026prime;32\u0026Prime;S, 08\u0026deg;17\u0026prime;05\u0026Prime;W). SPOT operates on solar and wind power and maintains a continuous direct WiFi link to the research base enabling seamless data transfer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe ALERT system was programmed to continuously monitor a predefined set of up to 18 frequencies. It was operational from November 15, 2024, shortly after the first round of bio-logger deployments, until January 1, 2025, when sea ice conditions required the system to be removed. Table\u0026nbsp;01 summarizes the deployment details of all ALERT components. Occasional battery outages caused stations to temporarily go offline until solar recharging or manual battery replacement restored functionality.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;01\u003c/b\u003e \u003cem\u003eSummary of ALERT component deployments. Start and end dates indicate the deployment period of each component. VHF antenna bearings were measured relative to geographic north (0\u0026deg; = North), from the perspective of an observer standing behind the antenna and facing its orientation. Uptime was calculated for each component; variation reflects differences in deployment duration and power interruptions.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"724\" height=\"158\"\u003e\u003c/p\u003e\u003cp\u003eDuring the 2024\u0026ndash;2025 season, 18 emperor penguins were each equipped with various biologgers, as well as a Holohil RI-2B VHF transmitter (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Of these, seven individuals were fitted with GPS-capable biologgers (TechnoSmart Axy-Trek Marine GPS; individuals A07, A08, A09, A10, A11, A13 and A14; see Additional File 9), recording geolocation (latitude, longitude) every 30-minutes. After deployment, penguin presence at the colony was monitored using the ALERT system\u0026rsquo;s real-time display, supplemented by opportunistic manual VHF scans for verification. When a penguin returned early in the season and was carrying a high-endurance biologger, the device was not retrieved, allowing the penguin to depart on additional foraging trips before recapture and logger retrieval.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEach station required an average of ~\u0026thinsp;7 minutes to scan all predefined VHF frequencies (up to 18).\u003c/p\u003e \u003cp\u003eDataset\u003c/p\u003e \u003cp\u003eTo assess the performance of the ALERT system, we compiled LoRa transmissions from each receiving station, including timestamp, detection score, and scanned VHF frequency. Periods of interference at the yellow unit, caused by concurrent LoRa testing, were excluded from the analysis. Six of the seven GPS-capable biologgers were recovered (adult A07 never returned) and their recovered GPS tracks were incorporated into the analysis.\u003c/p\u003e \u003cp\u003eTo evaluate the influence of environmental conditions on detection performance, timestamped weather data variables, including solar radiation, humidity, wind speed, and wind direction, were obtained from Neumayer Station III meteorological observatory (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVHF- vs. GPS- derived presence at the colony\u003c/h3\u003e\n\u003cp\u003eWhen available, a penguin was considered present at the colony when its GPS-enabled biologger indicated a location within 10 km of SPOT, the midpoint between subcolonies A and B (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Entry into and exit from this 10 km radius were used to determine the GPS-derived presence periods. The 10 km radius was selected to account for colony movement and ALERT unit detection range.\u003c/p\u003e \u003cp\u003eVHF-derived presence at the colony was determined by manually reviewing VHF detections in the Grafana interface and cross-referencing field logs to confirm penguin returns. A day was marked as a presence day if the ALERT system recorded at least one detection, with detection times rounded to the full calendar date. For example, if a penguin was detected between 11:03 a.m. and 1:34 p.m. on December 3rd, the entire day was counted as present.\u003c/p\u003e \u003cp\u003eComparison of GPS- and VHF-derived presence at the colony showed that all GPS-derived presence periods were also detected by VHF. In addition, for several individuals (e.g. A08, A11 and A14), VHF detected colony returns that were not recorded by GPS due to early battery failure\u003c/p\u003e\n\u003ch3\u003ePerformance metrics:\u003c/h3\u003e\n\u003cp\u003eTo assess the performance of each ALERT receiving station in identifying returning penguins, we compared ALERT detections to the actual presence or absence of tagged individuals at the colony. Ground truth presence was determined using either GPS-derived presence (from GPS tracks) or VHF-derived presence (from manual review of VHF detections and field logs).\u003c/p\u003e \u003cp\u003eWhen the tagged penguin was present at the colony, a detection by the ALERT station or system was classified as a \u003cem\u003etrue positive (TP)\u003c/em\u003e, while a missed detection was considered a \u003cem\u003efalse negative (FN)\u003c/em\u003e. Conversely, during periods when the tagged penguin was not near the colony, the absence of a detection was classified as a \u003cem\u003etrue negative (TN)\u003c/em\u003e, and any detection during that time was considered a \u003cem\u003efalse positive (FP).\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003eDetection\u003c/h3\u003e\n\u003cp\u003eA high score (HS) refers to a VHF detection score (ranging from 0 to 100) that exceeds a designated threshold value. A detection is defined as any instance in which an ALERT receiving station identified a penguin\u0026rsquo;s presence based on one of the following criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) a single high-score reading (\u0026ldquo;single HS\u0026rdquo;), (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) at least two HS readings within a 24-minute window (\u0026ge;\u0026thinsp;2 HS\u0026thinsp;\u0026lt;\u0026thinsp;24 min), or (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) at least three HS readings within the same window (\u0026ge;\u0026thinsp;3 HS\u0026thinsp;\u0026lt;\u0026thinsp;24 min). The 24-minute window is based on the average time required for an ALERT station to complete a full scan of all predefined VHF frequencies (~\u0026thinsp;7 minutes per scan), allowing for up to three full scan cycles during that interval.\u003c/p\u003e\n\u003ch3\u003eImpact of Transmitter-Receiver Distance on Detection Score\u003c/h3\u003e\n\u003cp\u003eTo assess the effect of transmitter-receiver distance on VHF detection scores, we used data from GPS-tagged penguins. Each VHF detection was matched to the nearest GPS fix within \u0026plusmn;\u0026thinsp;2 minutes to ensure spatial accuracy (~\u0026thinsp;86 m, based on an average tobogganing speed of 2.59 km/h; (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)). Detections were excluded if the penguin was \u0026gt;\u0026thinsp;3 km from the receiving station, beyond which VHF signals are unlikely to be detected reliably (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eBetween November 2024 and January 2025, the ALERT system deployed in Atka Bay, Antarctica, successfully detected the return of 17 out of 18 VHF-tagged emperor penguins (25 returns in total due to penguins returning more than once per deployment), enabling the recovery of their biologgers. One individual was never detected after tagging, neither by the ALERT system nor through dedicated manual scans (for that specific animal), suggesting it did not return to the colony this year.\u003c/p\u003e \u003cp\u003eSystem performance\u003c/p\u003e \u003cp\u003eALERT\u0026rsquo;s performance was evaluated using two datasets: (A) the full dataset including all tagged penguins (n\u0026thinsp;=\u0026thinsp;17), and (B) a subset of this dataset comprising only GPS-tagged penguins (n\u0026thinsp;=\u0026thinsp;6).\u003c/p\u003e \u003cp\u003eAnalyzing the entire dataset for VHF-derived presence, which comprises 25 returns of emperor penguins to the colony, we find that the ALERT system did not record any missed detections (FN\u0026thinsp;=\u0026thinsp;0) at detection thresholds below 98 when detections were defined by a single high score (HS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA). Under stricter detection criteria (HS\u0026thinsp;\u0026ge;\u0026thinsp;2 or HS\u0026thinsp;\u0026ge;\u0026thinsp;3), we find that one false negative was observed below thresholds of 97 and 98, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA). In both cases, the missed detection was attributable to a single particularly elusive individual (see Supplementary Fig.\u0026nbsp;4). At higher detection thresholds (98 and 99), the number of missed detections increased to 5 and 6, respectively. For the same dataset, the number of false positives (FP) decreased steadily with increasing detection thresholds, reaching zero at a threshold of 96 across all detection definitions (HS\u0026thinsp;\u0026ge;\u0026thinsp;1,2,3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eFor the GPS-tagged penguin subset, we find that all three detection criteria produced identical false negative patterns. The system successfully detected all colony returns (FN\u0026thinsp;=\u0026thinsp;0) at thresholds below 98. At thresholds of 98 and 99, the number of missed penguins (false negatives) increased to 3 and 4 out of 9, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC and Supplementary Fig.\u0026nbsp;6D). Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eD shows that false positives again declined with increasing thresholds, reaching zero at a threshold of 92 for all detection criteria.\u003c/p\u003e \u003cp\u003eRefer to Additional File. 5 and 6 for detailed plots of FN, TN, FP, and TP across detection scores [60\u0026ndash;99], evaluated under all three detection criteria, for both the full dataset of tagged penguins and the GPS-tagged subset.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEffect of Transmitter-Receiver Distance on Detection Score\u003c/p\u003e \u003cp\u003eA total of 132, 96 and 11 VHF detections on the yellow, grey and orange ALERT receiving stations respectively could be matched to GPS locations of transmitters within \u0026plusmn;\u0026thinsp;2 minutes (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). Note that while many more detection scores have been recorded across all stations, those are not shown here because those penguins were not equipped with GPS-enabled biologgers.\u003c/p\u003e \u003cp\u003eFurthermore, the dataset includes substantially more detections at the yellow and grey stations than at the orange station, owing both to their longer deployment duration (47 days for yellow and grey stations versus 10 days for orange station) and to the fact that the yellow and grey stations recorded penguins during their stay at the colony, whereas the orange station detected only transiting individuals en route to the colony.\u003c/p\u003e \u003cp\u003eAs expected, within the score range of 0-100, we observe a decrease in detection score as the transmitter-receiver distance increases, a pattern most evident for the yellow and particularly the grey station. For both stations, detections formed two spatial clusters corresponding to prolonged penguin presence at different subcolonies (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e08\u003c/span\u003e). For the yellow station, detections clustered at 0.20\u0026ndash;0.31 km and 1.50\u0026ndash;1.66 km correspond to subcolonies A and B, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA). For the grey station, clusters occurred at 0.16\u0026ndash;0.19 km and 1.45\u0026ndash;1.55 km, aligning with subcolonies B and A based on station placement (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLoRa performance\u003c/p\u003e \u003cp\u003eThe grey station recorded 831 hours of uptime and transmitted approximately 130,000 LoRa messages (i.e 15,650 messages /100 hours), while the yellow station operated for 939 hours (17,040 messages/100 hours) and sent around 160,000 messages (. In contrast, the orange station ran for only 185 hours and transmitted approximately 19,000 messages (10,270 messages/100 hours). LoRa transmission failure rates varied among stations, reaching 10% for the grey station, 8% for the yellow station, and 21% for the orange station. The average transmission interval was approximately 21 seconds for both the grey and yellow stations, whereas the orange station exhibited a longer average interval of about 34 seconds. High detection scores (\u0026gt;\u0026thinsp;90) accounted for 0.77%, 0.65%, and 0.08% of detections recorded by the grey, yellow, and orange stations, respectively.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e02\u003c/span\u003e summarizes key LoRa transmission metrics for each component of the ALERT system over the course of the experiment.\u003c/p\u003e \u003cp\u003eTable 02 summarizes key LoRa transmission metrics for each component of the ALERT system over the course of the experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 02\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Summary of LoRa transmission metrics for each ALERT component. Start and end dates reflect each component\u0026apos;s deployment period. The Average TX Interval (s), in seconds, refers to the average time between successful LoRa transmissions. The total number of LoRa transmissions from all sources (i.e beacons pings, Raspberry Pi heartbeats, detection scores) is reported (# LoRa TX (All Sources)), along with tag-related transmissions only (i.e detection scores; # LoRa TX (Tags Only)). The number and percentage of tag-related transmissions with detection scores \u0026gt;90% are also shown. Failed transmissions (# and %) are defined as transmission intervals exceeding the median plus 1.5 times the interquartile range, following the outlier detection approach proposed by Tukey (1977) for non-normal distributions. The frequency of failed LoRa transmissions during periods of system uptime is reported as failures per hour (# Failed LoRa TX/h). Differences in reported uptime reflect variation in deployment duration and intermittent power interruptions across components.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cimg 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\" width=\"724\" height=\"85\"\u003e\u003c/em\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eScore threshold\u003c/p\u003e \u003cp\u003eTo make a useful alerting system for the researchers in the field, the most important challenge is to find a detection score threshold and pattern (e.g. repeated detections) that reliably indicate an animal\u0026rsquo;s presence while minimising false alerts. In this experiment, we find that a single detection score of \u0026ge;\u0026thinsp;96 reliably indicates tag presence, ensuring no missed detections (FN\u0026thinsp;=\u0026thinsp;0) and no false alerts (FP\u0026thinsp;=\u0026thinsp;0). Stricter detection criteria, such as requiring\u0026thinsp;\u0026ge;\u0026thinsp;2 or \u0026ge;\u0026thinsp;3 high scores within 24 minutes, reduced false alerts at lower score thresholds but increased missed detections (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). We consider ourselves fortunate that in this case there was a single threshold that yielded to perfect performance, which is not expected. Hence, we strongly advocate conducting site-specific testing by having a human carry the VHF transmitter at the start of each deployment to optimize threshold selection, regardless of environmental conditions or system configuration.\u003c/p\u003e \u003cp\u003eEffect of transmitter-receiver distance on detection score\u003c/p\u003e \u003cp\u003eGPS tracks matched to VHF detections show a clear decrease in detection score with increasing distance between the transmitter and receiving station (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e) when the entire range of scores (0-100) is considered. However, when examining true positive relevant scores\u0026thinsp;\u0026ge;\u0026thinsp;96, the detection score does not correlate with distance anymore (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). We explain this by the specificity of the detection algorithm to finding the correct dominant frequency. The repetitive pattern of the transmitter is easily picked up by the matched filter if the right frequency is determined, leading to very high detection scores. If the wrong frequency is determined, the matched filter is correlated against noise, hence rarely creating a high detection score.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDespite using identical hardware, the two stations exhibited markedly different effective detection ranges. The yellow station detected VHF transmitters up to approximately 0.4 km, whereas the grey station achieved a range of about 0.79 km. This discrepancy is attributed to antenna orientation rather than hardware performance. For most of the season, the yellow station\u0026rsquo;s Yagi antenna was oriented toward subcolony A, leaving subcolony B largely outside its detection cone. In contrast, the grey station was positioned south of both subcolonies, allowing its antenna to maintain coverage of both sites within its detection cone (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and Additional File 7).\u003c/p\u003e \u003cp\u003eEffect of environmental conditions on detection score\u003c/p\u003e \u003cp\u003eWhen examining the relationship between detection score and distance, we observed substantial variability in detection scores even when penguins were well within the expected detection range (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e). In several cases, detection scores varied widely while an individual remained at an approximately constant distance from an ALERT receiving station (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e, A \u0026amp; B purple boxes), whereas consistently high scores would be expected under stable conditions. We propose two hypotheses for this pattern: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) increased wind speeds may alter antenna orientation, reducing receiver sensitivity and leading to lower detection scores; and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) during cold weather conditions, penguins may huddle closely, increasing the amount of biological material (e.g. water-rich tissue) between the transmitter and receiver and thereby attenuating the signal.\u003c/p\u003e \u003cp\u003eThe influence of wind speed is illustrated by observations at the grey and yellow stations on 27 February 2024, when multiple detections occurred at nearly constant distances from the stations (~\u0026thinsp;0.15 km and ~\u0026thinsp;0.3 km, respectively), yet detection scores varied widely within the same day (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e, A \u0026amp; B purple boxes). This day was characterized by particularly strong winds (mean\u0026thinsp;=\u0026thinsp;17 m s⁻\u0026sup1;, maximum\u0026thinsp;=\u0026thinsp;26 m s⁻\u0026sup1;), substantially higher than the seasonal average of 8.5 m s⁻\u0026sup1; (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003eD), suggesting that wind conditions negatively affected detection performance. Field observations confirmed that strong winds caused the Yagi antenna to rotate around its bamboo mount, intermittently shifting its orientation away from the intended target area. As a result, detection scores fluctuated with antenna alignment, decreasing when the antenna drifted off target and increasing when alignment was restored.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo study our second hypothesis, that penguin huddling behavior has an impact on the detection score, we used the apparent temperature (or perceived penguin temperature) as defined in Richter et al. (2018) with updated coefficients from Winterl et al., 2024.\u003c/p\u003e \u003cp\u003eRichter et al. showed that colder apparent temperatures, the temperature as perceived by penguins, correlate with dense penguin huddling, while warmer conditions lead to a more dispersed colony state. We expect to observe lower detection scores on days of lower apparent temperature leading to tighter huddling.\u003c/p\u003e \u003cp\u003eThe apparent temperatures during the deployments ranged from \u0026minus;\u0026thinsp;22.3\u0026deg;C to 4.3\u0026deg;C (mean: -3.9\u0026deg;C, STD: 4.2\u0026deg;C). and from \u0026minus;\u0026thinsp;15.6\u0026deg;C to -0.02\u0026deg;C (mean: -5.7\u0026deg;C, STD: 2.8\u0026deg;C) for the times the penguins were within 1 km of an ALERT receiving station (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe find that for temperatures below \u0026minus;\u0026thinsp;14\u0026deg;C, 10 out of 11 detections recorded within approximately 1 km of a receiving station had low detection scores (\u0026le;\u0026thinsp;54), with a mean score of 41.00\u0026thinsp;\u0026plusmn;\u0026thinsp;18.38. In contrast, for apparent temperatures above \u0026minus;\u0026thinsp;14\u0026deg;C, detection scores were generally higher, with 44.6% of detections exceeding a score of 90. Under these warmer conditions, the mean detection score was 64.58\u0026thinsp;\u0026plusmn;\u0026thinsp;31.72 (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe remaining 55.4% of detections with scores below 90, despite penguins being within 1 km of a receiving station, may be explained by factors such as penguins being outside the antenna\u0026rsquo;s effective detection cone, signal attenuation due to snow accumulation on the transmitter, or wind-related interference affecting antenna performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLoRa Performance\u003c/p\u003e \u003cp\u003eIn this experiment, all three ALERT receiving stations used LoRa to transmit data to the central gateway. This included detections of VHF-tagged penguins, fixed beacon pings, and Raspberry Pi heartbeat messages. Observed transmission failure rates ranged from 7% to 21%, which is consistent with reported values for LoRa-based systems (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). In a non-testing setting, LoRa traffic could be reduced by transmitting only high-scoring detections, thereby lowering transmission frequency and packet collisions.\u003c/p\u003e \u003cp\u003eThe grey and yellow stations performed particularly reliably, likely due to their use of the RAK811 Pi HAT LoRa module, which benefited from a well-tested and actively developed open-source python library (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/AmedeeBulle/pyrak811\u003c/span\u003e\u003cspan address=\"https://github.com/AmedeeBulle/pyrak811\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, enabling mostly stable and consistent communication. This module has since been discontinued.\u003c/p\u003e \u003cp\u003eBy contrast, the orange station used the newer RAK3172 chip, which was operated directly via AT commands. This added implementation complexity, together with its greater distance from the gateway, likely contributed to its poorer performance, as reflected by a higher number of failed network joins and transmission attempts. Adaptive data rate (ADR) was enabled, and transmissions at high output power occasionally produced brief, audible interference bursts on the VHF channel during LoRa transmissions. These interference events degraded the analysed audio file, and subsequently, reduced detection scores.\u003c/p\u003e \u003cp\u003eIn a previous experiment, we used a WiFi link (Ubiquiti NS5) to connect the ALERT station to the SPOT observatory. However, the WiFi transmitter generated substantial electromagnetic interference with the VHF receiver, preventing detection of VHF transmitters even at close range. Replacing WiFi with LoRa proved to be an effective and reliable solution, producing little to no interference while also offering substantially lower power consumption.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn this paper, we introduce a novel Automatic Radio-Telemetry System (ALERT) that combines VHF signal detection with LoRa communication to wirelessly relay transmitter detections to a central gateway. The system was successfully tested in Antarctica, where it detected 100% of returning VHF-tagged emperor penguins using three receiving stations deployed up to 5 km from the LoRa gateway. Each station reliably scanned for VHF signals within up to ~\u0026thinsp;0.8 km range. This automation significantly improved the efficiency of the tag-recapture process by eliminating the need for 4-hourly manual scans. The time and effort saved could then be redirected toward other scientific efforts such as tagging additional animals and collecting more data, ultimately enhancing research capacity.\u003c/p\u003e \u003cp\u003eBeyond emperor penguin detection, ALERT was also tested for monitoring VHF-tagged Weddell seals (\u003cem\u003eLeptonychotes weddellii\u003c/em\u003e) at a nearby haul-out site. The goal of this test was to assess whether the system could detect seal surfacing events and relay alerts to researchers in near real time. To do so, the orange station was repositioned mid-season approximately 9 km from the gateway and configured to monitor VHF signals from seals occupying a known breathing hole beneath the ice shelf edge.\u003c/p\u003e \u003cp\u003eWhile VHF detections of surfacing seals were successfully recorded at the station, LoRa communication with the gateway proved challenging. The seal hole was located adjacent to the ice shelf edge, which obstructed line-of-sight and significantly attenuated the LoRa signal. As a result, reliable communication required careful receiving station placement to balance two competing constraints: maintaining sufficient proximity and orientation for VHF detection of seals, while also preserving line-of-sight to the gateway for LoRa transmission. This test demonstrated both the versatility of ALERT across species and applications, and a key limitation of LoRa communication in complex Antarctic terrain.\u003c/p\u003e \u003cp\u003eDuring this deployment, ALERT stations operated primarily with one-way communication, receiving only basic acknowledgments such as \u0026ldquo;Join\u0026rdquo; confirmations and packet receipts. Looking ahead, incorporating two-way communication through Class C LoRa would allow the gateway to remotely adjust station parameters, such as enabling or disabling specific VHF frequencies, eliminating the need for manual on-site updates and further increasing system flexibility.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eADR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdaptive Data Rate (LoRaWAN feature)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eALERT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAutomatic LoRa\u0026ndash;Enabled Radio Tracker\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCSS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChirp Spread Spectrum\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003eDC\u003c/b\u003e\u0026ndash;Direct Current to Direct Current\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003edB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecibels\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003edBm\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecibels relative to 1 milliwatt\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003edE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExpected time interval between VHF signal peaks\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003edO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eObserved time interval between detected VHF signal peaks\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFFT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFast Fourier Transform\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFrequency Modulation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse Negative\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse Positive\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGPS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal Positioning System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh Score (detection score threshold)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHz\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHertz\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIIR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInfinite Impulse Response\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIoT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternet of Things\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ekHz\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKilohertz\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLoRa\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLong Range (wireless modulation technique)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLoRaWAN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLong Range Wide Area Network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLPWAN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow\u0026ndash;Power Wide Area Network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMAC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedium Access Control\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMHz\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMegahertz\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNp\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNumber of detected peaks\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOTAA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOver\u0026ndash;The\u0026ndash;Air Activation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePi\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRaspberry Pi (single\u0026ndash;board computer)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePoE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePower over Ethernet\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePSD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePower Spectral Density\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRAK\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRAKwireless (manufacturer of LoRa modules and gateways)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRS232\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRecommended Standard 232 (serial communication protocol)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSDR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSoftware\u0026ndash;Defined Radio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSp\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntermediate signal detection score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSfinal\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFinal signal detection score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSPOT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle Penguin Observation Tower\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSTD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTa\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eApparent temperature\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTrue Negative\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTrue Positive\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTX\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTransmission\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUSB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUniversal Serial Bus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eVHF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVery High Frequency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eVHF radio\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLotek VHF Biotracker radio receiver\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWAV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaveform Audio File Format\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWiFi\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWireless Fidelity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eInstitutional Animal Care and Use Committee (IACUC) of Woods Hole Oceanographic Institution approval for the use of animals and all experimental protocols of this study.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Deutsche Forschungsgemeinschaft (DFG) in the framework of the priority programme 1158 \u0026ldquo;Antarctic Research with comparative investigations in Arctic ice areas\u0026rdquo; by grant ( ZI1527/7\u0026thinsp;\u0026minus;\u0026thinsp;1), by the Alfred-Wegener-Institut Helmholtz-Zentrum f\u0026uuml;r Polar-und Meeresforschung (AWI) within the framework of the Projects SPOT (AWI_ANT_13) and MARE (AWI_ANT_14)47, by the Centre Scientifique de Monaco with additional support from the LIA-647 and RTPI-NUTRESS (CSM/CNRS-UNISTRA), and by the Woods Hole Oceanographic Institution's Grassle Fund.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLMRB, DPZ conceived and designed the study; LMRB, KW, AW developed the algorithm; LMRB, AH, AW, GB, DPZ performed the fieldwork; LMRB analyzed the data; LMRB wrote the original draft; LMRB, CB, DPZ secured the funding. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the seal team, especially Horst Bornemann, Christoph Held, and Henning Schr\u0026ouml;der, from the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), Germany, for enabling the testing of this technology for seal detection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data and code used in this paper are available at GitHub https://github.com/whoi-mars/ALERT_public\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eScholander PF. Experimental investigations on the respiratory function in diving mammals and birds. 2009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHebblewhite M, Haydon DT. Distinguishing technology from biology: a critical review of the use of GPS telemetry data in ecology. Philos Trans R Soc B Biol Sci 2010 July 27;365(1550):2303\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown DD, Kays R, Wikelski M, Wilson R, Klimley AP. Observing the unwatchable through acceleration logging of animal behavior. Anim Biotelemetry. 2013;1(1):20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams HJ, Holton MD, Shepard ELC, Largey N, Norman B, Ryan PG, et al. Identification of animal movement patterns using tri-axial magnetometry. Mov Ecol. 2017;5(1):6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGodyń D, Herbut P, Angrecka S. Measurements of peripheral and deep body temperature in cattle \u0026ndash; A review. J Therm Biol. 2019;79:42\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldbogen JA, Cade DE, Calambokidis J, Czapanskiy MF, Fahlbusch J, Friedlaender AS, et al. Extreme bradycardia and tachycardia in the world\u0026rsquo;s largest animal. 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Diving Behavior of Emperor Penguins Nurturing. 1995.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"animal-biotelemetry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"abit","sideBox":"Learn more about [Animal Biotelemetry](http://animalbiotelemetry.biomedcentral.com)","snPcode":"40317","submissionUrl":"https://submission.nature.com/new-submission/40317/3","title":"Animal Biotelemetry","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"LoRaWan, Radio-Telemetry, VHF, Biologging, Emperor Penguins","lastPublishedDoi":"10.21203/rs.3.rs-8744989/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8744989/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBiologging has advanced wildlife research by providing detailed insights into animal movement and behaviour. However, data recovery in remote areas without cell coverage remains a major challenge, often relying on costly satellite communication or labor-intensive manual recovery of archival tags, typically through Very High Frequency (VHF) tracking.\u003c/p\u003e \u003cp\u003eCurrent automated VHF monitoring approaches often rely on proprietary hardware and software, offer limited wireless data retrieval options, and require substantial power or infrastructure, which prevent their applicability in remote or extreme environments where they are most needed. Hence, manual tracking is still the most commonly used method.\u003c/p\u003e \u003cp\u003eTo address these limitations, we developed ALERT (Automatic LoRa-Enabled Radio Tracker), a simple radio-telemetry system that integrates custom-built receiving stations with Long Range Wide Area Network (LoRaWAN) communication. ALERT continuously scans for non-coded VHF signals, detects them using an adaptable custom algorithm, and wirelessly transmits presence information to a central gateway for real-time visualization. ALERT can be built with commercial off the shelf parts and does not require specialized electrical engineering knowledge.\u003c/p\u003e \u003cp\u003eThe system was tested at Atka Bay, Antarctica, from November 2024 to January 2025. During this period, 18 adult emperor penguins (\u003cem\u003eAptenodytes forsteri)\u003c/em\u003e were equipped with archival data loggers and VHF transmitters. To facilitate recovery of these archival tags, ALERT was deployed to automatically notify the field team when VHF-tagged penguins returned to the colony from foraging trips. Three receiving stations were strategically installed in the vicinity of the colony, at distances of up to 5 km from the LoRa gateway. Each station reliably detected VHF signals within an effective range of approximately 0.8 km and successfully alerted researchers in real time to the return of all VHF-tagged emperor penguins.\u003c/p\u003e \u003cp\u003eThis study describes the hardware and software architecture of ALERT and analyses system performance as a function of transmitter-receiver distance, weather conditions, and antenna configuration. It also evaluates the reliability of LoRaWAN data transmission under Antarctic conditions, including factors influencing packet loss and transmission delays.\u003c/p\u003e \u003cp\u003eOur findings demonstrate that ALERT is a low-cost, low-power, and scalable solution for automated VHF telemetry, capable of supporting wildlife monitoring in remote and logistically challenging environments.\u003c/p\u003e","manuscriptTitle":" ALERT: The Automatic LoRa-Enabled Radio Tracker for Wildlife Monitoring in Remote Environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 10:24:09","doi":"10.21203/rs.3.rs-8744989/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-18T14:50:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-18T11:35:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T10:16:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-05T02:43:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306572254001208806235782372665980580793","date":"2026-03-04T13:51:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34140008411628713861823879009761221403","date":"2026-02-26T00:12:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-25T18:17:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10175016448329395190349160910517106113","date":"2026-02-12T23:57:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-10T23:47:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-05T21:39:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-05T11:20:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Animal Biotelemetry","date":"2026-01-30T21:32:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"animal-biotelemetry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"abit","sideBox":"Learn more about [Animal Biotelemetry](http://animalbiotelemetry.biomedcentral.com)","snPcode":"40317","submissionUrl":"https://submission.nature.com/new-submission/40317/3","title":"Animal Biotelemetry","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5398e0d5-395d-4c6a-9c56-26d54c9e9b08","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T14:57:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 10:24:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8744989","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8744989","identity":"rs-8744989","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}