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Study system and species
This study was performed in Welgevonden Game Reserve (WGR), a privately owned game reserve in the Limpopo province, South Africa (24° 10′ S; 27° 45′ E to 24° 25′ S; 27° 56′ E). The reserve is located in the mountainous Waterberg region. WGR was established on former agricultural lands in the early 1980s and the main occurring vegetation types are Waterberg Mountain Bushveld and Sour Bushveld. The Waterberg region has a temperate climate, with two distinct seasons, characterized by the rainfall regime: a dry season ranging from April to September and a wet season ranging from October to March, with an average annual precipitation in WGR of 634 mm. Our study area is an enclosed breeding camp within WGR, with a size of approximately 1200 ha. Main predator species such as lion, cheetah and spotted hyena were excluded from this study area, as well as elephant and rhino.
WGR equipped 35 impala (Aepyceros melampus), 34 blue wildebeest (Connochaetes taurinus), 35 plains zebra (Equus burchellii) and 34 common eland (Taurotragus oryx) with a GPS and accelerometer sensor equipped custom made collar; an estimated 23% of the individual impalas present in the area, 48% of the eland, 40% of the wildebeest and 40% of the zebra. However, due to malfunctioning and errors made in the sensor development process, only 83 of the sensors yielded data at any point in time, thus lowering the effective density of sentinel animals. During the experimental intrusions (see below), the median number of data-yielding sensors was 47, and minimally 30. The animal movement data were recorded day and night and transmitted wirelessly in near real-time to five long-range low-power LoRa radiocommunication gateways in the study area, from where data packages were routed to an on-line data warehouse via a 3G/4G backhaul. The deployment of these sentinel animals were approved by the board and CEO of WGR as a management action and was performed in accordance with relevant guidelines and regulations (see Supplementary GPS Collaring letter).
Experimental intrusions
Between September 2017 and March 2018, WGR employees performed experimental intrusions (lasting ca. 2 h) on foot and by car through the study area, at varying locations and movement routes through the study area, independent from the locations of the sentinel animals. The movement of the intrusions were tracked by GPS, and the relevant metadata for each intrusion recorded (mode of transport, group size, start time, end time). The intrusions were distributed in a stratified way over the mornings, middays and afternoons (with time slots relative to specific solar positions: sunrise, solar noon and sunset). Furthermore, the intrusions were temporally spread in such a way to avoid a disturbance overflow for the sentinel animals, by performing a maximum of five experiments per week and a maximum of two experiments per day (and then only with one intrusion in the morning and one in the afternoon).
Data gathering
The animal sensors gathered location data via GPS and overall dynamic body accelerations47 (ODBA) via a tri-axial accelerometer (range ± 2 g; sampling frequency 100 Hz, down-sampled to 10 Hz prior to analysis). The GPS was scheduled to record spatial position at irregular intervals depending on the level of activity as gauged by ODBA. All sensors were scheduled to record locations every 15 min in the absence of sufficient activity (given that successive fixes were further than 5 m apart, else a geofence was applied and the new coordinate was omitted to save bandwidth and battery power, thereby assuming that the animal still was at its previous location). The GPS fix rate was increased up to 2- or 10-min intervals (depending on two different sensor settings) when ODBA indicated sufficient activity (after checking for the geofence). ODBA data were sampled continuously and summarized per 15 s window in a mean, maximum and variance value.
The experimentally intruding groups were outfitted with handheld GPS devices that recorded their location every 5 s and these groups logged and timestamped all their pre-defined activities and metadata on a tablet using CyberTracker48 during their intrusion. Most cars traveling through the study area were tracked by GPS as well to filter the animal data for disturbances by cars unrelated to the experimental intrusions.
Weather data (temperature, radiation, precipitation and wind) in the study area were recorded on a 3-min resolution with a weather station in the north of the study area. We assumed the 1200 ha study area to be sufficiently small to assume the weather station data to be representable for the prevailing weather conditions throughout the study area. GIS data of the study area (summarized in Supplementary Table S1) consisted of information on topography, infrastructure (e.g., fences, roads, powerlines, etc.) and vegetation cover (supervised classification of 25 cm resolution aerial imagery into four classes: trees, herbaceous/grass, sand/soil and other/built-up area).
All further data processing and algorithm development was done in the software R3.5.049.
Data pre-processing
To link the animal location data with the intrusion location data, as well as to correct for the substantial level of positional noise present in the animal location data, we modelled the animal location data to regular 1-min resolution trajectories using the following five steps. First, we filtered out large obvious errors (e.g., obvious outliers and irregularities such as locations far outside the study area) from the data. Second, we corrected systematic medium-scale outliers: ‘spikes’ that occurred due to positional outliers. Such spike-like outliers were visible during sensor testing while following known straight-line trajectories along an airstrip, thereby confirming that these spike-like geometries most likely resulted from positional error rather than true animal movement. Points were classified as anomalous spike points when (a) the displacement to and away from this point was high ( > 500 m), (b) when the distance between the locations before and after this point was small, and (c) when the turning angle at this point approached 180 degrees. Therefore, we corrected the locations that were classified as spike-like anomalies by shifting them closer to the straight line between the neighboring points. The extent of this shift was set relative to the degree of spikiness of the points (the spikier the pattern, the larger the shift towards the midpoint of the adjacent coordinates). Third, after filtering and correcting the original locations we smoothed the timeseries of x/y coordinates at each original timepoint with a Kalman smoother using a dynamic linear model. Fourth, we linearly interpolated the locations to a 10 s resolution based on ODBA, where we considered the animal to be stationary between multiple timepoints if the accelerometer signal suggested the animal was not moving. Fifth, we fitted an X-spline through the data, where we gave the linearly ODBA-interpolated locations a smaller weight, and sampled the fitted spline on a regular 1-min resolution. These pre-processing steps resulted in the modelled animal trajectory data, composed of spatial locations every minute, and averaged ODBA statistics per step (i.e., the segments between consecutive coordinates). These data were used as input for the next steps in the analyses. In contrast to the animal data, the raw intrusion data were of a high temporal resolution and spatial accuracy so that we only needed to subset the data in order to acquire 1-min resolution time-synchronized intrusion trajectories.
The first three parts of the data pre-processing were only needed because of firmware issues in our custom-made sensors. Without these issues, a simple denoising technique like a Kalman filter will suffice.
Feature engineering and processing
We computed a plethora of human-engineered features from the animal trajectories, ODBA data, weather data and several GIS layers with environmental data from the study area (summarized in Supplementary Table S1). All features were computed such that they could not directly be linked to specific points in space or time (by computing movement features relative to the environmental variables), so that only behavioral patterns and abnormalities therein could be linked to intrusion presence. After engineering these base features, we transformed certain features (after visual inspection of the histograms) to approximately symmetric distributions using logarithms. Then we truncated the distributions to the lower and upper 0.001 percentile to correct possible outliers. After that, we standardized all computed features to zero mean and unit variance per species. We also computed scaled versions of selected features by subtracting the mean and dividing by the variance of the selected features per reference set to capture deviations from normal behavior: (1) per area (characterized by a 30 by 30 m neighborhood around each grid cell), (2) per time of day (morning, midday, afternoon) in a period of 5 weeks around each intrusion or control, (3) per area per time of day per 5 weeks, and (4) per individual sentinel per time of day per 5 weeks (Supplementary Table S1). Furthermore, after computing and standardizing the features, we computed more features by applying moving window computations (5 min centered, 10 and 20 min lagging, and the difference between these: 5 min centered minus 10 and 20 min lagging) on the standardized features to capture (the change in) the recent history of animal movement descriptors (mean and standard deviation of all features, fitted Mean Squared Displacement exponential function parameters, net-gross distance ratio and variance of log First Passage Times). Finally, we discretized all features to ordinal values to avoid odd-, fat- and heavy-tailed distributions. In total we computed 2117 features describing different aspects of movement geometry of individual trajectories, herd topology and the interactions with landscape variation.
Subsetting and dimensionality reduction
Before analyzing the computed animal movement features, we applied some filtering on the data. We removed all periods with an experimental intrusion during which there were less than 30 active animal sensors in total. We also removed data of both animals and intrusion when they were close to the reserve’s main gate in order to avoid dilution of the data with other known disturbances. This resulted in 57 intrusions that were selected for further analyses. For every intrusion we selected control data of the same period one or 2 days earlier or later during which no intrusion took place, resulting in an approximately balanced intrusion-control dataset. Furthermore, we removed data from animals that were located within 250 m and within 20 min of a vehicle moving through the area that was not part of our experiment.
For each feature, we computed 4 importance metrics based on binary labelled data: records associated to locations within 1 km from the intrusion (subscript 1) versus an equally-sized random selection of data points during control periods (subscript 0): Mahalanobis distance, marginality (computed as (frac{{mu }_{1}-{mu }_{0}}{{sigma }_{0}}), for sample mean (mu) and sample standard deviation (sigma)), specialization (computed as (frac{{sigma }_{1}}{{sigma }_{0}})) and the Mean Decrease Accuracy of a Random Forest classifier (with default hyperparameters). We then ranked the features according to their importance and selected a feature for further analyses if it occurred in the top 125 features for any of the 4 importance measures described above (resulting in a total of 361 selected features). Subsequently, we converted the selected features per main feature class (Supplementary Table S1) to principal components, keeping those principal components that capture the most variation (in total 95%), which resulted in 99 selected components in total. Finally, we transformed these components again via a second principal component analysis, now across all the selected 99 components. In subsequent training of the animal behavior classifier, we optimized the total number of included components as a hyperparameter, which resulted in the first 8 principal components in the best performing classifier.
Labelling
We labelled the sentinel movement data through visual inspection of the animal and intruder trajectories, where we considered the animals’ behavior to be undisturbed when the animal was not near an intrusion, or when the animal was close to an intrusion yet did not visually display a change in behavior. However, when the animal was near the intrusion and displayed a sudden or gradual behavioral change in response to intrusion proximity, we labelled the data as ‘flight’ (changing the movement direction away from the intrusion, possibly with increased speed) or ‘regroup’ (when individuals clustered together). In total, only ca. 1% of the animal data were associated to either flight or regroup behavior (which we will refer to as ‘response’ behavior). A few animals also appeared to exhibit behavior we could label as ‘freeze’, i.e., halting movement in the proximity of the intrusion, yet this class was too underrepresented to be accurately predicted and hence dropped from the final dataset. Furthermore, we assigned a qualitative measure of intensity to each labelled behavioral response (‘low’, ‘medium’, ‘high’) to describe how visually pronounced this response was. Besides the supervised labelling based on visual inspection of behavioral responses via video animations of the trajectories, we also labelled data using an unsupervised k-means nearest neighbor classifier, where we clustered the feature space consisting of the 99 features selected as described above into 25 clusters per species.
Animal behavior classification
We trained an RBF kernel C-classification Support Vector Machine (SVM) with a subsequent moving window over the outputted probabilities to distinguish undisturbed versus response behavior. In the training datasets we only included the data separated by more than 1 km from the intrusion and labelled as ‘undisturbed’, and removed 90% thereof to train algorithms with a more balanced dataset. Furthermore, we only trained and validated on data with intrusions present in the area. We trained another SVM to distinguish the flight response from the regroup response. All computations were done in R 3.5.0 with the e1071 package on the Linux High Performance Cluster of Wageningen University and Research. We optimized the following hyperparameters and model settings during the training phase for the Average Precision via a grid search (with the selected values between brackets):

gamma (undisturbed-response: 10–3.2; flight-regroup: 10–2.0);

cost (undisturbed-response: 10–2.2; flight-regroup: 10–1.5);

number of principal components to include as features (undisturbed-response: 8; flight-regroup: 12);

species-specific models versus one model with species dummy variables included in the features (species-specific models);

specific models for the different times of day versus one model with time of day dummy variables (one model);

response intensities to include in the training data (only medium and high intensities);

weights to assign to the classes (equal weights);

the quantile to be computed of the SVM probabilities by the moving window (100%, i.e., maximum value);

the alignment of the moving window (centered);

the size of the moving window (15 min on both sides).

The best model was selected via a leave-one-intrusion-out cross-validation approach. We summarized the predictive performance by computing the Average Precision of the least occurring class (i.e., ‘response’ for the undisturbed-response model: 46%, Supplementary Fig. S2; and ‘regroup’ for the flight-regroup model: 80%, Supplementary Fig. S3). After having computed these probabilities with an SVM and a temporal window smoother, we tried to improve the predicted performance by including the predicted animal response probabilities of nearby animals. However, this spatial explicit approach hardly improved the predictive performance, indicating that the spatial contextualization of behavioral response was sufficiently captured by the computed features. We therefore did not include this spatial contagion effect of predicted animal response probabilities in the final analysis.
System classification—detection
Based on the predicted SVM response probabilities and feature cluster analysis, we computed summary features per 15 min of each intrusion and control period. These summary features related to the odds ratios of the probability of association of unsupervised clusters with intrusions versus controls, the SVM predicted probabilities of behavioral response, and several features describing the values (and its spatial structure, e.g., clustering or autocorrelation) of these SVM predicted response probabilities. After computing summary features per 15 min, we summarized them even further for the intrusions versus controls using the following eight statistics: mean, standard deviation, minimum, maximum, mean of the lagged differences, standard deviation of the lagged differences, minimum of the lagged differences and maximum of the lagged differences.
After computing the summary features, we build a logistic regression classifier to distinguish intrusions from controls. To create a parsimonious model, we iteratively added features to the model and evaluated its performance after each iteration. We evaluated the performance based on the model accuracy and performed validation through 25 times twofold cross-validation in a stratified way (by 25 times choosing a balanced random sample of intrusions and controls). We determined the sequence of adding features to the model by performing an independent two-sample t-test for each feature between the intrusions and controls. The feature with the largest t-value was then added to the model. After each feature addition, we removed its correlation with the remaining features using linear regressions with the added feature as independent variable and the remaining features as dependent variables, from which we extracted the residuals, standardized them to zero mean and unit variance, and applied the t-tests again. The (original) feature with the largest t-value was then added to the model again. This procedure was repeated until all features were ordered corresponding to their “importance”. We then performed logistic regressions without interactions between the features for an increasing number of features (Supplementary Fig. S4). The model already performed quite accurately with only 7 features (86.1% accuracy ± SD 3.3%, precision 82.6% ± SD 6.9%, recall 89.2% ± SD 5.1%). However, with 20 features and 2-way interactions the model achieved the maximum accuracy (90.9%).
System classification—localization
The data gathered during intrusions that were correctly predicted as such by the detection classifier were used to train the intrusion localization algorithm. The probability surface of the location of the intrusion was fitted relative to that of the sentinel animals using:

$${O}_{i,j} sim frac{{p}_{j}left({f}_{wn}left({theta }_{i,j},{mu }_{j},{rho }_{1}right) {f}_{ln}left({gamma }_{i,j},{mu }_{1},{sigma }_{1}right)right) left(1-{p}_{j}right) left({f}_{wn}left({theta }_{i,j},{mu }_{j},{sigma }_{0}right) {f}_{ln}left({gamma }_{i,j},{mu }_{0},{sigma }_{0}right)right)}{{f}_{wn}left({theta }_{i,j},{mu }_{j},{rho }_{0}right) {f}_{ln}({gamma }_{i,j},{mu }_{0},{sigma }_{0})}$$

where ({O}_{i,j}) is the odds ratio of intrusion presence at location (i) evaluated for individual (j), ({p}_{j}) is the SVM-predicted probability that individual (j) is exhibiting response behavior. The function ({f}_{wn}) is the wrapped normal probability density function, ({theta }_{i,j}) is the direction from location (i) to the location of the focal animal (j), ({mu }_{j}) is the movement direction of individual (j), ({rho }_{1}) and ({rho }_{0}) are the standard deviations of the unwrapped distributions. The function ({f}_{ln}) is the lognormal probability density function, where ({gamma }_{i,j}) is the distance of location (i) to (j), ({mu }_{1}) and ({mu }_{0}) as well as ({sigma }_{1}) and ({sigma }_{0}) are the log-normal distribution parameters (respectively log-mean and log-sd).
The parameters ({mu }_{1}), ({sigma }_{1}) and ({rho }_{1}) capture the geometry of intrusion-animal topology for animals that exhibited a predicted behavioral response to the intrusion. Similarly, ({mu }_{0}), ({sigma }_{0}) and ({rho }_{0}) are the corresponding parameters for animals that were predicted to be undisturbed. The parameters ({mu }_{1}), (mathrm{log}({sigma }_{1})) and (mathrm{log}({rho }_{1})) were fitted to the data assuming a 3rd order polynomial relationship to ({t}_{s}): the time (in minutes) since the start of the predicted behavioral response (using the maximum F1 classification score). Since the behavioral response signature is lost over time, we truncated ({t}_{s}) to 45 min (thus ({t}_{s} >45) min was set to ({t}_{s}=45)). The parameters ({mu }_{0}), ({sigma }_{0}) and ({rho }_{0}) were estimated using the data of the controls and with randomly generated intrusion locations in the study area, in order to correct for the effects of geometry of the study area on the predicted response surfaces. The probability surface ({P}_{i}) was then calculated as:

$${P}_{i}=alpha sum_{j}{O}_{i,j}$$

where (alpha) is a normalization constant so that ({P}_{i}) integrates to 1 over the area covered by the rectangular axis-aligned bounding box around the study area.
To measure the prediction accuracy of each localization surface, we simplified each surface to a point coordinate located at the location of maximum probability, and computed the Euclidian distance to the known true position of the intrusion. We then summarized each experimental intrusion by selecting the 10 prediction surfaces with the most condense highest probability density, i.e., those in which the top 5% probability density is contained in the smallest, most condense, area. The spatial error of the localization prediction associated with these selected predictions was further summarized by taking the average Euclidian distance over the 10 selected predictions. More

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Tropical cyclone-induced bloom in NIO
We have analysed the tropical cyclones that occurred over the bay from 1997 to 2019 in accordance with the availability of satellite Chl-a measurements. Out of 51 storm events, 30 are identified as the phytoplankton bloom events (i.e. the Chl-a values greater than of 0.2 mg/m3) in BoB and 18 in AS across all seasons35,36,37. In the case of BoB, we have divided our analyses for pre-monsoon and post-monsoon, as the cyclone occurrences are rare in other seasons (e.g. winter and monsoon). Over AS, some cyclones also occur in the beginning of monsoon season. The spatio-temporal variability of cyclones is closely connected to the seasonal changes in the monsoon trough35,38. In pre-monsoon season, the trough passes over the northern BoB, but it passes through the central bay with an east-west orientation in the post-monsoon, and facilitates the formation of more number of TCs during the period39. The big seasonal change in wind shear and relative vorticity are the reasons for the lower number of cyclones in the pre-monsoon season. In general, the upwelling driven nutrient influx to the surface together with sunlight leads to the enhancement of Chl-a or phytoplankton bloom after the passage of cyclones in the open ocean40.
The Bay of Bengal cyclones
During pre-monsoon, the bay is least productive, but the western boundary current helps more production in the coastal regions41. The higher wind speed associated with TCs deepens (about 30 m) Mixed Layer Depth (MLD), and rupture the pycnocline and pumps nutrients to the surface24. In general, TCs occurring during pre-monsoon move northwards and pass north-eastern coast of India or Bangladesh42 (Fig. 1). To estimate the cyclone-induced phytoplankton bloom, we performed spatial analyses for each cyclone during the period 1997–2019 and selected cases are shown in Supplementary Fig. 1 for BOB01 in 2003, BOB01 in 2004 and Mala in 2006. The maps of Chl-a concentrations superimposed with Sea Level Anomaly (SLA) for the same period are shown in Supplementary Fig. 1. The cyclone BOB01 was a category 1 storm with a maximum sustained wind (MSW) of 39 m/s, which occurred during 10–19 May 2003. The Chl-a remained well below 0.2 mg/m3 prior to occurrence of the cyclone, which enhanced to 0.5 mg/m3 with a small area of about 1 mg/m3 at 9°–10° N, 86°–87° E, just after passage of the cyclone. This is consistent with its high Ekman Pumping Velocity (EPV) and small Translational Speed (TS) in that period. In addition, the Chl-a enhancement was higher for the Category 1 cyclone BOB01 that occurred during 14–19 May 2004 and was about 0.5 mg/m3 after passage of the cyclone. The bloom sustained for the next 5 days as also shown in Supplementary Fig. 1. On the other hand, Mala was the strongest cyclone occurred during the pre-monsoon period (24–28 April 2006) in the last two decades over BoB with a MSW speed of 61 m/s. The Chl-a increased from 0.1 to 1.0 mg/m3 after the cyclone passage in five days, with a small area of Chl-a about 1.0 mg/m3 on the immediate left of its track. The Chl-a concentration remained close to 0.5 mg/m3 in the next 5 days. Both EPV and TS were favourable for sustained upwelling and Chl-a bloom in the case of cyclone Mala. In all three cases, the closed contours of Sea surface Height Anomaly (SSHA) is negative, which indicate the presence of cold-core (cyclonic) eddies that triggered turbulent mixing and sustained Chl-a bloom. We have not used any specific eddy detection method but used the composite of SSHA to identify the presence of eddies, as done by Girishkumar et al.43.
Fig. 1: The cyclone tracks and pre-cyclone background Chl-a value.

a The study region North Indian Ocean (NIO) and the tracks for the pre- (green) and post-monsoon (yellow) cyclones for BoB and AS (red), as analysed for all cyclones occurred during the study period (1997–2019).

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We applied the same method to identify the phytoplankton bloom that occurred for each cyclone event in BoB after 1996 and estimated the corresponding EPV and TS for diagnosing the physical mechanisms that made different scales of blooms. The results are presented in Table 1 and Fig. 2. The analyses show that the bloom was comparatively larger for the cyclone BOB01 in 2004, about 3.28 mg/m3. The increase in Chl-a is negatively correlated with TS and is in agreement with the intensity of cyclone with a statistically significant correlation value of −0.30 (at the 95% confidence interval as per the P-test44). The TS is lower and bloom is larger for BOB01 in 2003, as the faster moving storms tend to intensify rapidly when compared to slower moving storms (i.e. wind speed 14 m/s). In contrast, the slow-moving storms expend more time over the ocean and thereby, increases the magnitude of upwelling to enhance the Chl-a over the region45. The estimated EPV is about 1.8 × 10−4 m/s for BOB01 in 2003 and is consistent with the observed Chl-a concentrations, whereas the EPV is about 1 × 10−4 m/s and Chl-a concentration is about 1.87 mg/m3 for the cyclone Mala. It suggests that TS has a prominent role in cyclone-induced upwelling and associated phytoplankton bloom.
Table 1 The cyclone-induced Chl-a blooms in the pre-monsoon seasons since 1997 in BoB.
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Fig. 2: The cyclone-induced Chl-a in BoB.

The observed enhancement in Chl-a (mg/m3) following the cyclone passage (5-day average) in the post and pre-monsoon seasons in BoB. The translational speed of the closest track points where the bloom occurred in (m/s), the ratio of wind speed to translational speed (WS/TS), and EPV (Ekman Pumping Velocity) as the cyclones reach their maximum intensity (m/s) are also shown in the lower panels.

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Post-monsoon is the active storm season over BoB, and about 25 cyclones with significant enhancement in Chl-a concentration are identified during the 1997–2019 period. As the haline stratification is stronger in BoB due to the monsoon rain and river water influx, the presence of BL increases SST, which fuel the storms over the bay46. Presence of BL weakens the impact of cooling in the mixed layer driven by cyclones and favours the intensification of post-monsoon cyclones2. We have also analysed the variability in BL, MLD, isothermal layer depth (ILD) and Chl-a for selected storms passed over the Argo Floats (see next section). The cyclones either form or develop further over the southeast BoB, but some move west northwest and cross the peninsular coast. Some cyclones recurve towards the west central bay and pass the central and northeast coast of India, but some hit Bangladesh and upper Burma coast47, as illustrated in Fig. 1.
Figure 3 presents a closer look at the bloom and its spatial distribution for selected cyclones during the post-monsoon season; e.g. the cyclones Sidr, Madi and Vardah overlaid with SSHA contours. Sidr, a category 4 cyclone occurred during 11–16 November 2007 with a MSW of about 44 m/s. The Chl-a is about 0.5 mg/m3 during the cyclone period at the right side of the track, but the bloom has spread to a wider area with values close to 0.5 mg/m3 just after the passage of cyclone. The analyses of SSHA further provide evidence for the eddy-mediated phytoplankton bloom. The phytoplankton bloom also sustained for another 5 days. This is also in agreement with that reported in other analyses, although bloom values were estimated for 19 November in the other studies48,49. The cyclone Madi, occurred during 6–13 December 2013, showed an enhancement of about 0.5 mg/m3 during the cyclone period with a region of 1 mg/m3 in the left side of the track. Some regions with 2–3 mg/m3 are also observed at the right and left sides of cyclone track, and the bloom sustained for the next 5 days with values of about 1 mg/m3 in the adjacent areas. The closed contours of negative SSHA suggest the presence of cyclonic eddies there. The phytoplankton bloom during this particular period is also contributed by the cyclone Lehar that occurred a week before, in 23–28 November; demonstrating the impact of occurrences of consecutive storms over the same oceanic region. Nevertheless, the cyclone Vardah showed an enhancement of about 1.92 mg/m3, which is higher than that of Sidr due to the higher EPV of the former. As for Lehar and Madi, there was another cyclone Nada that appeared during the period 29 November–2 December 2016, just before the appearance of Vardah, and that storm might have also contributed to the Chl-a bloom during the period of Vardah.
Fig. 3: The spatial extension of Chl-a bloom for selected post-monsoon cyclones in BoB.

The Chl-a averaged, overlaid with SSHA contours (solid—positive and dashed—negative), for 5 days before cyclone, during the entire cyclone period, five days just after the passage of cyclone and next 5 days for a Sidr (2007), b Madi (2013), and c Vardah (2016). The tracks of the respective cyclones are also shown.

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Figure 4 shows the time evolution of physical and biological observations during the period of TCs Phailin, Hudhud and Vardah. The biogeochemical Argo float WMO ID 2902086 was closer to the track of TC Phailin, and the float WMO ID 2902114 was near the tracks of Hudhud and Vardah. Supplementary Table 1 shows the name of cyclones, Argo IDs, and distance between the float and nearest track point of respective cyclones. Figure 4 (right) represents the subsurface temperature up to 200 m with MLD, ILD, BLT and D23 (23° isotherm) for selected cyclones occurred over BoB. Figure 4 (left) represents the subsurface Chl-a concentration up to 200 m driven by the same cyclones. Prior to the passage of cyclones, the profiles represent typical hydrographic state of the oceans with warm waters near the surface and cold waters in the subsurface. The MLD was shallower about 20 m and the ocean was warm from September to December in 2013 during the passage of cyclone Phailin. The other cyclones occurred in 2013 were Helen, Lehar and Madi. Similar situation was observed in September–December of 2014 for Hudhud, but a colder and deeper MLD is observed in September–December of 2016 for Vardah. These are consistent with the climatological oceanic characteristics observed in BoB during the post-monsoon seasons. The time-depth cross-section of Chl-a reveals that the Chl-a concentration remains small in the surface, but about 0.8–1.0 mg/m3 at 40–60 m for the cyclone Phailin. The Chl-a concentration is about 3 mg/m3 for the cyclone Hudhud and about 1.5 mg/m3 for Vardah.
Fig. 4: Bio-Argo measurements.

Temporal evolution (right panel) of depth-time section up to 200 m of temperature of some selected cyclones in the BoB. The MLD (cyan), ILD (blue), BLT (green), and D23 (black) are also indicated in the figure. The vertical black lines indicate the cyclone period. The subsurface Chl-a concentration (left panel) up to 200 m for some selected cyclones in BoB. The vertical black lines indicate the cyclone period.

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To identify the differential oceanic response of the cyclones at the Argo float locations, we further examined the presence of eddies that play a major role in regulating the physical and biogeochemical processes. The analysis of 7-day SSHA composite before and during the cyclone period at the location of Argo float shows the presence of cold-core (negative SSHA) eddies before the passage of cyclone Phailin, Madi and Hudhud, whereas a warm-core (positive SSHA) eddy prior to the passage of Vardah (Fig. 5). The lower TS and a cold-core eddy during the cyclone Hudhud, and higher TS and a warm-core eddy during the cyclone Vardah produce contrasting oceanic response43. The temperature measurements during the periods of Hudhud and Vardah exhibit comparable response to cold and warm-core eddies, respectively. Another feature of cold-core eddies is trapping the near inertial oscillations in the mixed layer50, which accelerates the entrainment at the bottom of mixed layer and vertical shear as observed during the period of Hudhud. Conversely, the proximity of warm-core eddies triggers rapid vertical dispersion of near inertial energy, which suppresses the mixing and shear as for Vardah50.
Fig. 5: Eddies and primary productivity.

The 7-day composite of sea level anomaly (m) before and during the cyclones in BoB. The black solid lines represent the cyclone tracks, the stars represent the genesis location of the cyclone and the box represents the Argo float location.

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Vardah was a category 1 cyclone that occurred during 6–13 December 2016. The Chl-a amount before the passage of cyclone was about 0.23 mg/m3, but it escalated to 1.92 mg/m3 in 5 days after the passage of cyclone. The bloom continued to exist for the next 5 days as shown in Fig. 3. Unlike the pre-monsoon cases, for which the Chl-a is restored back to open ocean values in 10 days after the passage of cyclones, the bloom continued to persist even longer periods for the post-monsoon cases. The changes in Chl-a concentrations before and after the passage of cyclones in all three cases are greater than 0.2 mg/m3 and are higher for the lower category tropical storms. These analyses are consistent with the frequent occurrence of cyclones over the south east BoB during this season, as shown in Supplementary Table 2. It is also compelling to note that higher intensity cyclones occur over the north as compared to south BoB, which may be due to the presence of BL in the northern BoB as BL does not exist or insignificantly shallow in the south BoB in any season. The barrier layer in turn favours intensification of tropical cyclones whereas the absence of BL favours the storm-induced upwelling that eventually makes the Chl-a blooms45. Note that the stratification is very strong in northern BoB due to the river water input there51.
Supplementary Table 2 and Fig. 2 also show the results of Chl–bloom events in the post-monsoon seasons in 1997–2019. For instance, the Chl-a increased from 0.53 to 1.13 mg/m3 in the northern and southwestern BoB after the super cyclone of 1999 (25 October–3 November), as also shown by Madhu et al.52. Similar enhancements in Chl-a are estimated for BOB08 (1997), BOB05 (2000) and Madi (2013), about 0.6–3 mg/m3, depending on the cyclones. Although the cyclones BOB06 (1999), Sidr (2007), Giri (2010) and Phailin (2013) were category 4 or 5 cyclones, the high TS and lower EPV did not magnify the Chl-a concentrations to the level of bloom initiated by other cyclones. The enhancement of Chl-a estimated in our study during the cyclone Phailin is in agreement with the reported value of 0.9 mg/m3 for the period 16–24 October 2013 by Vidya et al.21. They also computed the bloom associated with Thane, about 0.7 mg/m3 in the post-cyclone period (1–8 January 2012) at 10°–13° N and 82°–86° E. Nevertheless, we have estimated about 1.8 mg/m3 for the post-cyclone period for Thane. The cyclone Hudhud produced a Chl-a bloom of up to 2.8 mg/m3 in 8–15 October 2014 along the track, as analysed by Chacko27 using the MODIS data, which is very close to our estimate of 2.8 mg/m3. We find similar enhancements in Chl-a that reported by Rao et al.26 for BOB05 in 16–23 November 2000, about 1.2 mg/m3 as deduced from the MODIS data. The other cyclones show moderate bloom values, below 1 mg/m3 as listed in Supplementary Table 2. Nevertheless, the low intensity cyclones such as BOB08 (1997) and Thane (2011) exhibit notable increment in Chl-a following the passage of cyclone, about 1.25–1.8 mg/m3, which is in agreement with their comparatively lower TS and higher EPV during the cyclone period. It also attests the impact and significance of TS in deciding the amplitude of phytoplankton bloom; suggesting sustained low intensity winds trigger strong upwelling to cause intense bloom events.
The Arabian Sea cyclones
In Arabian Sea, about 18 out of 33 cyclones are identified as phytoplankton bloom events (55%) during the study period 1997–2019, in which one occurred in pre-monsoon, three in monsoon and nine in post-monsoon seasons. Since the frequency of occurrences is very small, we have not separated the analyses into seasons or regions of landfall, but a selected case is presented in Supplementary Fig 2. For a better understanding of the behaviour of cyclones, we have selected three cyclones, ARB01 (2001), Mukda (2006) and Megh (2015), one in each season for this discussion. Supplementary Fig 3 illustrates the spatial distribution of Chl-a superimposed with SSHA for the selected cyclones. The ARB01 (2001) was a category 3 cyclone that occurred during 21–28 May 2001 with a MSW of about 60 m/s. The surface Chl-a concentration before the cyclone appearance was below 0.2 mg/m3 due to the profound heating in May53,54. It was one of the strongest cyclones appeared over AS, but measurements were sparse during the cyclone period, and thus, only a small area of about 1 mg/m3 is observed at 16°–17° N, 69°–72° E after passage of the cyclone. Analysis of IRS–P4 measurements by Subramanyam et al.29 found a very large bloom of about 5–8 mg/m3 at 17° N, from 67° E to 71° E. However, our analyses show the bloom of about 2.07 mg/m3 in the region 67°–68° E, 16°–17° N. The difference in bloom values could be due to the difference in datasets, region and period of analyses. As found in the case of BoB, there are closed contours of negative SSHA, which strengthens the observed cyclone-induced and eddy-mediated phytoplankton bloom.
Mukda was a tropical storm that occurred during 21–24 September 2006 with a MSW of 28 m/s. The Chl-a along the right side of the track was above 0.5 mg/m3 even before the cyclone period. The cyclone Megh was considered as the worst to hit Yemen and it occurred just after the passage of another cyclone Chapala over the same region. Megh was a category 3 cyclone with a MSW of 57 m/s. The Chl-a was about 0.7 mg/m3 in the post-cyclone stage, but a small region of about 1.0 mg/m3 was also observed at the right end of the track. Table 2 and Fig. 6 show the analysis of phytoplankton bloom occurrences in AS during the study period (1997–2019). It shows higher Chl-a concentrations in connection with the cyclones ARB01 and ARB02 in 2001, Mukda in 2006 and Megh in 2015, and are consistent with their lower TS. The situation in 2015 was also similar, in which the Category 4 cyclone Chapala showed higher bloom than that of the category 3 cyclone Megh. Similarly, the Category 4 cyclone Kyaar triggered higher bloom than that of the Category 3 cyclone Maha in 2019.
Table 2 The cyclone-induced Chl-a blooms in the post-monsoon seasons since 1997 in AS.
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Fig. 6: The Chl-a blooms associated with cyclones in Arabian Sea.

The observed enhancement in Chl-a (mg/m3) following the cyclone passage (5-day average) over AS. The translational speed of the closest track points where the bloom occurred in (m/s), the ratio of wind speed to translational speed (WS/TS) and EPV when cyclone reached its maximum intensity (m/s) are also shown.

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Supplementary Fig. 3 shows the time evolution of biological and physical observations from the Argo float (WMO ID 2902120) in the period of TCs Nilofar, Chapala and Megh. The distance between float location and nearest track point is provided in Supplementary Table 1. The temperature profiles show warm waters near the surface and cold waters in the subsurface before the cyclone passage, as for a typical oceanic state (Supplementary Fig. 4). The MLD is shallower about 20 m and the ocean is warm throughout September–December 2014 and October–December 2015 during the passage of cyclones Nilofar, Megh and Chapala. The time-depth cross-section of chlorophyll shows about 0.8–1 mg/m3 at 40–60 m. However, the Chl-a values remain about 1 mg/m3 for cyclones Megh and Chapala close to the surface; supporting the satellite measurements. The analysis of 7-day SSHA composite shows a cold-core eddy before the passage of cyclone Nilofar whereas warm-core eddies before the passage of Chapala and Megh at the buoy location (Supplementary Fig. 5). The warm-core eddies before the passage of Chapala and Megh could also be the reason for their rapid intensification.
Tropical storms and category 1 cyclones
Although a number of cyclones occurred over NIO during the 1997–2019 period, the phytoplankton bloom happened mostly for the storms and lower category cyclones. For instance, there were five cyclones that appeared over BoB in the pre-monsoon seasons that triggered phytoplankton bloom, four (80%) of them were either tropical storms or category 1 cyclones (Table 1). Similarly, out of 25 cyclones that made Chl-a blooms in BoB during the post-monsoon seasons, 20 of them were (80%) either tropical storms or category 1 cyclones. An analogues occurrence of cyclone-induced phytoplankton bloom is observed for the lower category cyclones in AS, where eight cyclones out of 18 (44.4%) were either tropical storms or category 1 cyclones. These analyses suggest that the slow-moving storms stay more time over the oceans and impart high momentum to upwell the subsurface nutrient-rich water, leading to the phytoplankton blooms in the open oceans with a time lag of 4–12 days, as illustrated in Fig. 7 (blue coloured bar chart). The bloom is as higher as about 20–500% with respect to the pre-cyclone Chl-a levels, and is even up to 1385% as for the case of BoB01 in 2003 and 3758% for the cyclone Gonu in AS (Supplementary Fig. 6); demonstrating the impact and scale of cyclone-induced primary productivity in the open oceans. This slow-moving cyclone-induced primary productivity is very important in the context of climate change, as there is a global slowdown in the translational speed of tropical cyclones.
Fig. 7: The change in cyclone-induced bloom and time lag.

Left: with respect to pre-cyclone Chl-a values (blue), 0.5 mg/m3 (dark blue) and 0.2 mg/m3 (magenta) for the post-monsoon cyclones in Bay of Bengal (BoB). Right: The time lag in days in cyclone-induced Chl-a bloom with respect to the pre-cyclone Chl-a (blue histograms, left) for the post-monsoon cyclones of BoB.

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To test robustness of the estimates of cyclone-induced change in Chl-a (i.e. Fig. 7), we also considered two other background Chl-a values (i.e. 0.2 and 0.5 mg/m3), which were also taken as the background Chl-a of the ocean basins and the Chl-a threshold for bloom detection. Since the value extracted from the 1° × 1° latitude-longitude region at the track (i.e. blue diagrams) is different from the basin average and bloom threshold values, there are significant differences in the amplitude of blooms, as displayed in Fig. 7. It shows that the pre-cyclone Chl-a values are between 0.5 and 0.2 mg/m3. Therefore, the change in Chl-a is higher with the estimates based on 0.2 mg/m3 (magenta histogram) and about 10 cyclones show a change in Chl-a of about 400%. The highest bloom of about 800–850% is found for Madi (2013), Hudhud (2014) and BOB05 (1999). On the other hand, the change in Chl-a with respect to 0.5 mg/m3 (dark blue histogram) is lower than that with the pre-cyclone estimates, and the change is mostly within 250%, although few cyclones show around 400%. The highest bloom is observed for the cyclone Madi (2013), about 300%. In AS, the Chl-a bloom is mostly between 300 and 1000%, except for Gonu in 2007. The change in Chl-a is about 6000% with respect to the basin average of 0.2 mg/m3, and about 3500% based on the bloom threshold for the cyclone Gonu. The assessment confirm that the cyclone-induced bloom (change in percent) in AS is about five times higher than that of BoB.
The impact of ENSO and IOD on Chl-a blooms
Several studies have examined the relationship between El Niño and Southern Oscillation (ENSO) and cyclone activity across different oceanic basins12,55,56. The influence of ENSO on tropical cyclone activity in BoB during the period 1997–2010 is also investigated by Girishkumar et al.19. We have chosen the dates after the cyclone passage, and considered the Niño and IOD indices to classify the cyclones occurred in the El Niño, La Niña, normal, positive IOD (PIOD) and negative IOD (NIOD) years, as listed in Table 1, Table 2 and Supplementary Table 2. In addition, we have prepared the composites of Chl-a and SSHA for 10 days before and after the passage of each cyclone to assess the inter-annual variability in Chl-a and SSHA with respect to the El Niño, La Niña, normal, PIOD and NIOD years (Fig. 8, for BoB). Out of the 25 cyclones, three of them occurred in El Niño, fourteen in La Niña, nine in normal, four in PIOD and five in NIOD years. The cyclones those occurred in PIOD or NIOD years also happened to be in the El Niño/La Niña years and therefore included in both analyses, and are shown in the figure. The number of cyclones are more in the La Niña years, which were mostly followed by the normal, PIOD and NIOD years. The magnitude of phytoplankton bloom is higher in the PIOD years than that in the NIOD years. In the El Niño years, the magnitude of bloom is comparatively smaller and the bloom in normal years is around 0.5 mg/m3.
Fig. 8: The differences in Chl-a during El Niño, La Niña, normal, PIOD and NIOD years.

The SSHA a 10-day before and b during the passage of cyclone) and Chl-a composite maps with respect to El Niño, La Niña, normal, PIOD and NIOD years in the BoB. The respective cyclone tracks are also shown.

Full size image

Supplementary Fig. 9 shows the composite of SSHA and Chl-a with respect to El Niño, La Niña, normal, PIOD and NIOD years in AS. Here, more number of cyclones occurred in the El Niño years as compared to that in the La Niña and normal years. In contrast, there are more number of cyclones in the PIOD years than the NIOD years in AS, but the amplitude of bloom is higher for the NIOD years. These are also the reasons for the differences in phytoplankton bloom in AS and BoB, as the impact of ENSO and IOD events is different in both basins. The response of cyclones in IOD years are similar to those in the La Niña years. Although the spatial extent of bloom is larger in the El Niño years owing to the higher number of cyclone occurrences, the magnitude of bloom is higher for the cyclones occurred in La Niña and NIOD years. The normal years exhibit bloom similar to that of the El Niño years. In BoB, the analysis of SSHA composite for the El Niño, La Niña, IOD and normal years is dominated by negative SSHA (suggesting the presence of cold-core eddies), but the normal years are more influenced by warm-core eddies (e.g. Fig. 8). In AS, on the other hand, the normal, La Niña and IOD years are dominated by cold-core eddies, whereas the El Niño years are overwhelmed by warm-core eddies (e.g. Supplementary Fig. 9). The influence of IOD is higher than that of ENSO, which is one of the reasons for the inter-annual variability of phytoplankton blooms. The characteristics of phytoplankton blooms in AS and BoB are in contrast with the differences in SST in IOD years in both basins, and this feature is also found with the cyclone-induced blooms. There are noticeable difference in Chl-a concentrations among the normal and El Niño, La Niña, PIOD or NIOD years, and are exhibited in Supplementary Figs. 7 and 8. More

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