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Video bio-logger hardware
Supplementary Fig. 1a shows an example of the video bio-loggers used during this study. It measures 85 mm length × 35 mm width × 15 mm height. The bio-loggers were attached to either the bird’s back or abdomen by taping them to the bird’s feathers using waterproof tape. When attaching the bio-logger to a bird’s abdomen, a harness made of Teflon ribbon (Bally Ribbon Mills, USA) was also used. When working with streaked shearwaters, the bio-loggers used a 3.7 V 600 mAh battery and weighed approximately 26–27 g. When working with black-tailed gulls, the bio-loggers used a 3.7 V 720 mAh battery and weighed approximately 30 g.
The bio-loggers are controlled by an ATmega328 MCU (32 KB programme memory, 2 KB RAM) and have an integrated video camera (640 × 480, 15 FPS) that can be controlled by the MCU, with the video data streamed directly to its own dedicated storage. Note that digital cameras such as the one used in this bio-logger have a delay of several seconds from powering on to when they can begin recording, which in the case of our bio-logger resulted in a 2- to 3-s delay between when the MCU signals the start of recording and the actual start of recording when attempting to save energy by powering off the camera when not in use (see also Yoshino et al.13 for another example of this camera delay). Our bio-loggers also include several low-cost sensors that are controlled by the MCU (see Supplementary Fig. 1b). Each of these sensors can be used by the MCU as input for AIoA applications (e.g., camera control) and can be archived to long-term storage for analysis upon device retrieval. The bio-loggers had an average battery life of approximately 2 h when continuously recording video and approximately 20 h when recording from all other (i.e., only low-cost) sensors.
Activity recognition method
We achieve AIoA by employing machine learning to conduct activity (behaviour) recognition on board the bio-logging devices. We do this by training an activity recognition model in advance using low-cost sensor data that has been labelled by an ecologist to identify the behaviours that he/she wants to capture. In the case of the black-tailed gulls, we use accelerometer-based features since they can be used to detect the body movements of the animals with only a small (e.g., 1-s) delay between when data collection begins and when behaviours can first be detected. Such features are often used when detecting body movements in human activity recognition studies in order to recognise activities such as running and eating37. For animal-based AI, such body movements can be useful to detect similar types of behaviours, such as flying and foraging38. See Fig. 3 for an example of how such accelerometer-based features can be extracted from raw data and used to train a decision tree classifier model. The features were extracted from 1-s windows of 25 Hz acceleration data, with the raw 3-axis acceleration data converted to net magnitude of acceleration data prior to feature extraction. The activity recognition processes were run once per second on the 1-s windows of data, allowing us to detect target behaviours within about 1 s of their start. See Supplementary Table 1 for descriptions and estimated sizes for all the features used in this study. In addition, Supplementary Fig. 5a shows the acceleration-based features ranked by their Normalised Gini Importance when used to classify behaviours for black-tailed gulls.
Fig. 3: Generating decision trees from acceleration data.

a We start by converting the raw three-axis data (row one) into magnitude of acceleration values (row two) and segmenting the data into 1-s windows. We then extract the ACC features listed in Supplementary Table 1 from each window. Rows three and four show examples of the features extracted, which can be used to differentiate between the behaviours. For example, Crest can be used to identify Flying behaviour, since its values are higher for Flying than for the other two behaviours. b An example decision tree generated from the feature values shown in the lower two rows of (a), with each leaf (grey) node representing a final predicted class for a 1-s segment of data. Supplementary Data 1 provides source data of this figure.

Full size image

The energy-saving microcontroller units (MCUs) in bio-loggers tend to have limited memory and low computing capability, which makes it difficult to run the computationally expensive processes needed for the pretrained machine learning models on board the bio-loggers. Therefore, this study proposes a method for generating space-efficient, i.e., programme memory efficient, decision tree classifier models that can be run on such MCUs. Decision trees are well suited for use on MCUs, since the tree itself can be implemented as a simple hierarchy of conditional logic statements, with most of the space needed for the tree being used by the algorithms needed to extract meaningful features from the sensor data, such as the variance or kurtosis of 1-s windows of data. In addition, since each data segment is classified by following a single path through the tree from the root node to the leaf node that represents that data segment’s estimated class, an added benefit of using a tree structure is that the MCU only needs to extract features as they are encountered in the path taken through the tree, allowing the MCU to run only a subset of the feature extraction processes for each data segment. However, the feature extraction algorithms needed by the tree can be prohibitively large, e.g., kurtosis requires 680 bytes (Supplementary Table 1), when implemented on MCUs that typically have memory capacities measured in kilobytes, e.g., 32 KB, which is already largely occupied by the functions needed to log sensor data to storage.
Standard decision tree algorithms, e.g., scikit-learn’s default algorithm, build decision trees that maximise classification accuracy with no option to weight the features used in the tree based on a secondary factor such as memory usage39. The trees are built starting from the root node, with each node in the tree choosing from among all features the one feature that can best split the training data passed to it into subsets that allow it to differentiate well between the different target classes. A new child node is then created for each of the subsets of training data output from that node, with this process repeating recursively until certain stopping conditions are met, e.g., the subsets generated by a node reach a minimum size. Figure 4b shows an example of a decision tree built using scikit-learn’s default decision tree classifier algorithm using the black-tailed gull data, which results in an estimated memory footprint of 1958 bytes (Supplementary Table 1). Note that since the basic system functions needed to record sensor data to long-term storage already occupy as much as 95% of the bio-logger’s flash memory, incorporating a decision tree with this large of a memory footprint can cause the programme to exceed the bio-logger’s memory capacity (see the bio-logger source code distributed as Supplementary Software for more details).
Fig. 4: Generating space-efficient trees.

a Our process for the weighted random selection of features. We start with a list of features along with their required programme memory sizes in bytes (first panel). Each feature is assigned a weight proportional to the inverse of its size, illustrated using a pie chart where each feature has been assigned a slice proportional to its weight (second panel). We then perform weighted random selection to choose the subset of features that will be used when creating a new node in the tree. In this example, we have randomly placed four dots along the circumference of the circle to simulate the selection of four features (second panel). The resulting subset of features will then be compared when making the next node in the decision tree (third panel). b Example decision tree built using scikit-learn’s default decision tree classifier algorithm using the black-tailed gull data described in “Methods”. Each node is coloured based on its corresponding feature’s estimated size in bytes when implemented on board the bio-logger (scale shown in the colour bar). c Several space-efficient decision trees generated using the proposed method from the same data used to create the tree in (b). d Example space-efficient tree selected from the trees shown in (c) that costs much less than the default tree in (b) while maintaining almost the same accuracy.

Full size image

In this study, we propose a method for generating decision tree classifiers that can fit in bio-loggers with limited programme memory (e.g., 32 KB) that is based on the random forests algorithm40, which is a decision tree algorithm that injects randomness into the trees it generates by restricting the features compared when creating the split at each node in a tree to a randomly selected subset of the features, as opposed to the standard decision tree algorithm that compares all possible features at each node, as was described above. Our method modifies the original random forests algorithm by using weighted random selection when choosing the subset of features to compare when creating each node. Figure 4a illustrates the weighted random selection process used by our method. We start by assigning each feature a weight that is proportional to the inverse of its size. We then use these weights to perform weighted random selection when selecting a group of features to consider each time we create a new node in the tree, with the feature used at that node being the best candidate from amongst these randomly selected features.
Using our method for weighted random selection of nodes described above, we are then able to generate randomised trees that tend to use less costly features. When generating these trees, we can estimate the size of each tree based on the sizes of the features used in the tree and limit the overall size by setting a threshold and discarding trees above that threshold. Figure 4c shows an example batch of trees output by our method where we have set a threshold size of 1000 bytes. We can then select a single tree from amongst these trees that gives our desired balance of cost to accuracy. In this example, we have selected the tree shown in Fig. 4d based on its high estimated accuracy. Comparing this tree to Fig. 4b, we can see that our method generated a tree that is 42% the size of the default tree while maintaining close to the same estimated accuracy. We developed our method based on scikit-learn’s (v.0.20.0) RandomForestClassifier.
In addition, in order to achieve robust activity recognition, our method also has the following features: (i) robustness to sensor positioning, (ii) robustness to noise, and (iii) reduction of sporadic false positives. Note that robustness to noise and positioning are extremely important when deploying machine learning models on bio-loggers, as the models will likely be generated using data collected in previous years, possibly using different hardware and methods of attachment. While there is a potential to improve prediction accuracy by removing some of these variables, e.g., by collecting from the same animal multiple times using the same hardware, moving to more animal-dependent models is generally not practical as care must be taken to minimise the handling of each animal along with the amount of time the animals spend with data loggers attached34. See “Robust activity recognition” for more details.
GPS features
Due to the low resolution of GPS data (e.g., metre-level accuracy), GPS-based features cannot detect body movements with the same precision as acceleration-based features, but are useful when analysing patterns in changes in an animal’s location as it traverses its environment. For animal-based AI, these features can be used to differentiate between different large-scale movement patterns, such as transit versus ARS. In this study, we used GPS-based features to detect ARS by streaked shearwaters. These features were extracted once per minute from 1/60th Hz GPS data using 10-min windows. Supplementary Fig. 5b shows these features ranked by their Normalised Gini Importance when used to classify behaviours for streaked shearwaters. Supplementary Fig. 2 shows an example of two such 10-min windows that correspond to target (ARS) and non-target (transit) behaviour, along with several examples of GPS-based features extracted from those windows. Supplementary Table 1 describes all the GPS features used in this study along with their estimated sizes when implemented on board our bio-logger. Note that the variance and mean cross features were extracted after first rotating the GPS positions around the mean latitude and longitude values at angles of 22.5°, 45.0°, 67.5°, and 90.0° in order to find the orientation that maximised the variance in the longitude values (see Supplementary Table 1, feature Y: rotation). This was done to provide some measure of rotational invariance to these values without the need for a costly procedure such as principal component analysis. The primary and secondary qualifiers for these features refer to whether the feature was computed on the axis with maximised variance vs. the perpendicular axis, respectively.
Robust activity recognition
In this study, we also incorporated two methods for improving the robustness of the recognition processes in the field. First, we addressed how loggers can be attached to animals at different positions and orientations, such as on the back to maximise GPS reception or on the abdomen to improve the camera’s field of view during foraging. For example, during our case study involving black-tailed gulls, the AI models were trained using data collected from loggers mounted on the birds’ backs, but in many cases were used to detect target behaviour on board loggers mounted on the birds’ abdomens. We achieved this robustness to positioning by converting the three-axis accelerometer data to net magnitude of acceleration values, thereby removing the orientation information from the data. To test the robustness of the magnitude data, we evaluated the difference in classification accuracy between raw three-axis acceleration data and magnitude of acceleration data when artificial rotations of the collection device were introduced into the test data. In addition, we also evaluated the effectiveness of augmenting the raw three-axis data with artificially rotated data as an alternative to using the magnitude of acceleration data. These results are shown in Supplementary Fig. 6a. Note that the results for the magnitude of acceleration data are constant across all levels of test data rotation, since the magnitudes are unaffected by the rotations. Based on these results, we can see that extracting features from magnitude of acceleration data allows us to create features that are robust to the rotations of the device that can result from differences in how the device is attached to an animal.
Next, we addressed the varying amount of noise that can be introduced into the sensor data stemming from how loggers are often loosely attached to a bird’s feathers. We achieved this noise robustness by augmenting our training dataset with copies of the dataset that were altered with varying levels of random artificial noise, with this noise added by multiplying all magnitude of acceleration values in each window of data by a random factor. We tested the effect of this augmentation by varying the amount of artificial noise added to our training and testing data and observing how the noise levels affected performance (see Supplementary Fig. 6b). Based on these results, the training data used for fieldwork was augmented using the 0.2 level. Note that at higher levels of simulated noise (test noise greater than 0.15) the training noise settings of 0.25 and 0.3 both appear to outperform the 0.2 setting. However, since these results are based solely on laboratory simulations, we chose to use the more conservative setting of 0.2 in the field.
Reduction of sporadic false positives
When activating the camera to capture target behaviour, it is possible to reduce the number of false positives (i.e., increase our confidence in the classifier’s output) by considering multiple consecutive outputs from the classifier before camera activation. We accomplish this using two methods. In the first, we assume that the classifier can reliably detect the target behaviour throughout its duration, allowing us to increase our confidence in the classifier’s output by requiring multiple consecutive detections of the target behaviour before activating the camera. We employed this method when detecting ARS behaviour for streaked shearwaters using GPS data, since the characteristics of the GPS data that allow for detection of the target behaviour were expected to be consistent throughout its duration, with the number of consecutive detections required set to 2.
In the second method, we assume that the classifier cannot reliably detect the target behaviour throughout its duration, since the actions corresponding to the target behaviour that the classifier can reliably detect occur only sporadically throughout its duration. In this case, we can instead consider which behaviours were detected immediately prior to the target behaviour. When controlling the camera for black-tailed gulls, we assume that detection of the target behaviour (foraging) is more likely to be a true positive after detecting flying behaviour, since the birds typically fly when searching for their prey. Therefore, we required that the logger first detect five consecutive windows of flying behaviour to enter a flying state in which it would activate the camera immediately upon detecting foraging. This flying state would time out after ten consecutive windows of stationary behaviour. Note that in this case, while the intervals of detectable target behaviour may be short and sporadic, the overall duration of the target behaviour is still long enough that we can capture video of the behaviour despite the delay between behaviour detection and camera activation (see “Video bio-logger hardware” for details).
Procedures of experiment of black-tailed gulls
We evaluated the effectiveness of our method by using AIoA-based camera control on board ten bio-loggers that were attached to black-tailed gulls (on either the bird’s abdomen or back) from a colony located on Kabushima Island near Hachinohe City, Japan18, with the AI trained to detect possible foraging behaviour based on acceleration data. The possible foraging events were identified based on dips in the acceleration data. The training data used for the AI was collected at the same colony in 2017 using Axy-Trek bio-loggers (TechnoSmArt, Roma, Italy). These Axy-Trek bio-loggers were mounted on the animals’ backs when collecting data. Along with the AIoA-based bio-loggers, three bio-loggers were deployed using a naive method (periodic sampling), with the cameras controlled by simply activating them once every 15 min. All 13 loggers recorded 1-min duration videos.
Sample size was determined by the time available for deployment and the availability of sensor data loggers. The birds were captured alive at their nests by hand prior to logger deployment and subsequent release. Loggers were fitted externally within 10 min to minimise disturbance. Logger deployment was undertaken by the ecologists participating in this study. Loggers that suffered hardware failures (e.g., due to the failure of the waterproofing material used on some loggers) were excluded.
Ethics statement
All experimental procedures were approved by the Animal Experimental Committee of Nagoya University. Black-tailed gulls: the procedures were approved by the Hachinohe city mayor, and the Aomori Prefectural Government. Streaked shearwaters: the study was conducted with permits from the Ministry of the Environment, Japan.
Statistics and reproducibility
Fisher’s exact tests were done using the exact2x2 package (v. 1.6.3) of R (v. 3.4.3). The GLMM analysis was conducted using the lmerTest package (v. 2.0–36) of R (v. 3.4.3). In regards to reproducibility, no experiments as such were conducted, rather our data are based on tracked movements of individual birds.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

Ethics statement
All procedures for fieldwork in Pacuare Nature Reserve followed approved protocol under Monash University’s School of Biological Sciences Animal Ethics Committee (Protocol No. BSCI/2016/13), the University of Maryland Center for Environmental Sciences’ Institutional Animal Care and Use Committee (IACUC) (Research Protocol No. S-CBL-16–11), and the Costa Rican Ministerio Del Ambiente y Energia, Sistema Nacional de Áreas de Conservación (SINAC), Área de Conservación La Amistad Caribe (ACLAC) (RESOLUCIÓN SINAC-ACLAC-PIME-VS-R-022-2016; RESOLUCIÓN SINAC-ACLAC-PIME-VS-R-025-2016). The study was performed in accordance with the approved guidelines.
Hatchling tracking
To examine in-situ factors of turtle dispersal into the offshore environment, leatherback hatchlings were tagged with coded acoustic transmitters between 20 August and 3 September 2016 in Pacuare Nature Reserve, Limón Province, Costa Rica (Fig. 1). At Pacuare, hatchlings were obtained from hatchery, incubator-reared, and relocated nests. Hatchery nests consisted of eggs collected as they were laid, transported in plastic bags for less than 5 km, and reburied in two separate protected areas. In these protected, monitored enclosures, the eggs were safeguarded (e.g. from predation) and otherwise developed naturally. Relocated eggs were collected from nests laid the night prior, transported in plastic bags a short distance above the high tide line, reburied on the nesting beach and unmonitored thereafter, until such time as hatchlings emerged. Hatchlings from hatchery and relocated nests were collected as they naturally emerged from the buried nests. Incubator-reared eggs were collected as they were laid, transported up to 1 km in vacuum-sealed bags, and raised under 3 treatments: control, low-oxygen, and high-oxygen in accordance with the Williamson38 protocol. Eggs were incubated for the first 5 days of development in: hypoxia (1% O2) for the low-oxygen treatment and hyperoxia (42% O2) for the high-oxygen treatment. As they did not have to expend time and energy exiting a nest, incubator-hatched turtles were left to absorb their yolk for 2 days38. Turtles held post-emergence from their nest (hatchery and relocated hatchlings) or eggs (incubator-reared hatchlings) were kept in moistened, sand-lined incubators at approximately 30 °C to reduce energy expenditure prior to trial release and prevent potential decreases in swimming performance39. To minimise the influence of genetic relatedness, hatchlings were taken from all available nests (n = 9 in total from hatchery, relocated, and incubator nests) at the time of the study, resulting in parentage by nine females. Turtles were weighed and measured prior to trials using a scale and calliper. To prevent overheating on the boat, turtles were transported in a bucket covered by a wet towel with a moistened cloth inside.
Acoustic tracking was conducted using Vemco V5-180 kHz transmitter tags (0.38 g in-water weight; 0.65 g in-air weight) and tethered to the turtle via a line-float-transmitter assembly (6.85 g in-air weight) and Vetbond based on Gearheart et al.24 and Hoover et al.25 (Fig. S1). The monofilament line in the line-float-transmitter assembly was a total length of 2 m; the first float was suspended 1.5 m behind the hatchling, and the second float was an additional 0.5 m. The brightly coloured orange floats (4.4 cm by 1.9 cm) allowed for visual tracking in the water when acoustic signal was insufficient. Tracking began outside the surf zone, approximately 0.4 km from shore, where turtles were taken via a small 6 m, 150 hp motorboat. The release location was the approximate midpoint of the two hatcheries where hatchlings were collected. Between sunrise and sunset, each turtle was followed at a distance of 10–20 m in the boat using a Vemco VR100 acoustic receiver and VH180-D-10M directional hydrophone22. The V5 tag detection range was approximately 200 m. The VR100 receiver stored the detections, and the data were downloaded to reconstruct hatchling movement paths. The mobile acoustic receiver allowed tracking of the turtles’ movements for a longer period and over a broader area than visual tracking alone because turtles were found acoustically when visual contact was lost.
Hatchlings were tracked only during daylight hours over a 3-week period given hatchling and boat availability. Although hatchlings generally emerge during cooler, evening hours of the day in Costa Rica, no effect on the overall innate behaviour of hatchlings was anticipated18,40. The tracking data should still be indicative of the orientation and speed at which hatchlings are likely to swim. Nighttime tracking was logistically infeasible because of the hazards associated with the oceanic entry point. For a track to provide enough data for inclusion in the analysis, a minimum tracking time of 30 min was established. Turtles were tracked individually for approximately 90 min. Track duration was a trade-off between obtaining a large sample of tracks to account for individual variability, while providing robust speed and orientation information. At the end of each track, the turtle was recovered with a small net, the line-float-transmitter attachment was completely removed, and the turtle was released at the recovery location. The Velcro piece easily removed from the carapace, and there were no evident damages, marks, or lesions from this attachment method on the leatherback hatchlings. Handling was kept to a minimum to reduce any unnecessary stress on the turtles.
Surface current trajectories
Two drifters were used to obtain data on local sea surface currents to evaluate the effect of currents on hatchling movements. A Pacific Gyre Microstar drifter was deployed at the beginning of each turtle track (Fig. S2A). The drifter’s surface float was equipped with a GPS unit that used the iridium short burst data service to broadcast location coordinates every 5 min. A flag was attached to the surface float for increased visibility. Sea surface temperature was recorded by the drifter with a Pacific Gyre probe with 0.1 °C accuracy. The position and temperature data of each drifter release were retrieved from the Pacific Gyre website (https://www.pacificgyre.com). One drifter track was removed from analysis because it entered the surf zone and did not represent nearshore surface currents.
A secondary drifter was launched when equipment permitted at the approximate halfway point during tracking of a turtle. This better estimated the immediate currents the hatchling was experiencing and was used to estimate shifts in the nearshore currents as the turtles headed offshore. This second drifter was constructed using a Davis Instruments aluminium radar reflector with 80 cm of parachute cord attached to a 20.3 cm diameter Panther Plast trawl float (Fig. S2B). The centre of the drifter sat 1 m from the water’s surface, similar to the depth of the Microstar drifter. A piece of wood affixed to the top of the float had a Samsung Galaxy Core Prime mobile phone attached in a waterproof bag. A GPS application was started with each drifter release to provide locations at one minute intervals. Foam tubing was zip-tied around the middle of the trawl float to maintain the GPS unit in an upright position. The float had a flag attached for visibility on the water. Positions were stored on the mobile phone and downloaded upon retrieval of the drifter. Both drifters were recovered at the completion of each individual turtle track.
Environmental data
To understand the influence of tidal states and bathymetry on local currents experienced by hatchling leatherbacks, daily tidal currents were obtained at Limón, Costa Rica (10.00° N, 83.03° W; https://tides.mobilegeographics.com). Periods of peak tides (i.e. high and low) were defined as one hour before and after the measured minima or maxima, with ebb and flow tides between those periods of peak tides. Tidal states were categorised as: high, ebb, low, and flow (Fig. S3A). High-resolution, near-shore bathymetry data were obtained at a 0.0011° resolution from the Global Multi-Resolution Topography Synthesis dataset (https://www.gmrt.org). Missing values were filled with data from the General Bathymetric Chart of the Oceans dataset (GEBCO-2014; https://www.gebco.net; 0.0042° resolution). Bathymetric values for each hatchling track were extracted with a ‘bilinear’ interpolation in the R ‘raster’ package41 (Fig. S3B). All analyses were conducted in the R environment42.
Hatchling movement analysis
An acclimation period of five min was applied to each turtle track to provide time for the hatchling to orient and adjust to the water temperature. Intervals greater than five min between recorded hatchling positions were removed to prevent erroneous calculations (0.03% of recorded positions). These time lapses occurred when the boat was actively searching for a hatchling. Despite the combination of surface floats and the directional hydrophone, maintaining visual and acoustic contact with turtles was challenging, even in calm waters. Many hatchlings dove for short periods ( More

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