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Experimental evidence for compositional syntax in bird calls

Mish Boyka




Human language can express limitless meanings from a finite set of words based on combinatorial rules (i.e., compositional syntax). Although animal vocalizations may be comprised of different basic elements (notes), it remains unknown whether compositional syntax has also evolved in animals. Here we report the first experimental evidence for compositional syntax in a wild animal species, the Japanese great tit (Parus minor). Tits have over ten different notes in their vocal repertoire and use them either solely or in combination with other notes. Experiments reveal that receivers extract different meanings from ‘ABC’ (scan for danger) and ‘D’ notes (approach the caller), and a compound meaning from ‘ABC–D’ combinations. However, receivers rarely scan and approach when note ordering is artificially reversed (‘D–ABC’). Thus, compositional syntax is not unique to human language but may have evolved independently in animals as one of the basic mechanisms of information transmission.

A prominent feature of human language is its combinatorial power, which allows us to generate innumerable expressions from a finite number of vocal elements and meanings1,2,3. Language has two hierarchical levels of syntactic structure: one combines otherwise meaningless elements to form meaningful words (phonology) and the other combines different words to form more complex expressions (compositional syntax)4,5,6. Animal communication systems share many of the basic properties of human language. For example, mammals and birds can use specific call types to denote specific predator categories (i.e., referential communication)7,8 and can learn to recognize the meaning of calls given by other individuals9. Although combinations of discrete vocal elements have been found in some mammals and birds10, it remains controversial whether the ability to combine elements is linked to the creation of more complex meanings6,11.

Recent field studies have suggested that particular combinations of sounds may be linked to particular meanings. For example, white-handed gibbons (Hylobates lar) alter the sequence of notes (that is, basic vocal elements) in their vocalizations when informing group members about predatory threats or conspecific intrudors12. Similarly, chestnut-crowned babblers (Pomatostomus ruficeps) combine two types of notes into two sequences that have different meanings13. In both cases, the sounds that constitute the sequence of notes have no apparent communicative meaning on their own and, therefore, these combinations are considered to be phonological5,6. In contrast, the evidence for compositional syntax remains inconclusive. Campbell’s monkeys (Cercopithecus campbelli) can modify alarm calls by adding ‘–oo’, increasing the generality of the call meaning14. However, ‘–oo’ is never used alone and, consequently, it is a suffix rather than a sound with a distinct meaning15. Similarly, putty-nosed monkeys (Cercopithecus nictitans) combine discrete alarm calls that denote different predator types to elicit group movements16,17, but call receivers do not extract a compound meaning from the call combination18. Thus, it remains unknown whether animals have evolved compositional syntax or whether this is a unique feature of human language6.

Here we provide, to our knowledge, the first unambiguous experimental evidence for compositional syntax in a non-human vocal system. Birds within the family Paridae produce structurally complex vocalizations (‘chicka’ or ‘chick-a-dee’ calls) that are composed out of different note types (for example, A, B, C and D)19. Individuals use these calls in a range of contexts, such as to communicate the discovery of food sources20,21, when approaching predators to deter them (i.e., mobbing)22,23,24,25, or to maintain social cohesion with conspecifics26,27. Previous studies suggested that different note types have different functions. For example, Carolina chickadees (Poecile carolinensis) incorporate a greater number of D notes when discovering a food source or when mobbing a higher-risk predator, and D-rich calls serve to attract flock members to the callers20,23. These birds incorporate more A notes when discovering an aerial predator28 and more C notes when flying29. However, because of the lack of playback studies testing the function of individual notes and their combinations, it is still uncertain whether these notes function as different meaningful elements and if these combinations yield a corresponding complexity in call meanings.

In this study, we investigated whether different note types produced by Japanese great tits (P. minor; Paridae) have distinct meanings to receivers when produced separately and, if so, whether receivers extract a compound meaning when both elements are combined (compositional syntax). Tits produce ‘chicka’ calls when approaching and mobbing predators, and these calls contain a number of unique call types composed of different note types, mainly A, B, C and D notes25. A, B and C notes are typically produced in combination with other note types, resulting in AC, BC or ABC calls (). In contrast, D notes are produced as a string of seven to ten notes (hereafter referred to as a D call, ) and are also used in non-predatory contexts, such as when a bird visits its nest alone and is recruiting its mate (). In predatory contexts, D notes are often produced in combination with other note types and typically appear at the end of note strings, such as AC–D, BC–D or ABC–D calls () (ref. 25). Thus, D notes are both produced alone and in combination with other notes, suggesting that they modify the meaning of ABC calls to elicit appropriate mobbing responses to different predator types25.

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Sound spectrograms of call treatments played to Japanese great tits.

(a) ABC call is composed of single A, B and C notes. (b) D call is composed of seven to ten D notes. (c) ABC–D call is the combination of ABC and D calls. (d) D–ABC call is a reversed combination of ABC and D calls. These calls were digitally edited using Raven Pro 1.3 software.

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Usage of D calls in a non-predatory context in Japanese great tits.

(a) Effect of the presence of a mate on the production of D calls (n=187 observations, n=40 individuals): tits produced D calls more often when they visited the nest alone than when they did following their mate (generalized linear mixed model: χ2=5.00, df=1, P=0.025), after controlling for the nonsignificant influence of sex of the callers (χ2=1.16, df=1, P=0.281). (b) Effect of D calls on the recruitment of their mate (n=136 observations, n=34 individuals): tits that produced D calls were more likely to subsequently attract their mates than tits that did not produce D calls (generalized linear mixed model: χ2=35.37, df=1, P<0.0001), even after controlling for a significant influence of the responding mate’s sex (males were more likely to approach D calls given by their partners than were females; χ2=9.32, df=1, P=0.002).

We hypothesized that the combination of ABC calls and D calls into ABC–D calls represents semantically compositional syntax (). To test this hypothesis, we designed two playback experiments. In Experiment 1, we examined whether tits hearing combined ABC–D calls extract the meanings of both ABC and D calls. If tits show a combined response to ABC–D calls, this could be explained by at least two mechanisms. First, tits may combine the distinct behaviour they produce when they hear ABC calls together with the behaviour they produce when they hear D calls, because they recognize ABC–D calls as a single meaningful unit (i.e., compositional syntax). Alternatively, tits may produce the two distinct behavioural responses (that is, first to ABC calls and then to D calls) simply because of the close temporal proximity of ABC and D calls. To differentiate between these two possibilities, we compared the responses of tits with playbacks of natural (ABC–D) and artificially reversed (D–ABC) sequences () in Experiment 2. A key predication of the first mechanism is that receivers should produce a compound response only when the combinations of ABC and D calls are produced together according to their note-ordering rule (that is, ABC–D, but not D–ABC). In contrast, according to the second mechanism, receivers should respond similarly whenever ABC and D calls are produced in close proximity, no matter the order in which they are produced.

Here we find that Japanese great tits extract different meanings from ABC and D calls, and a compound meaning from ABC–D calls. As tits fail to produce a compound response when the note sequence is artificially reversed (D–ABC), these findings support the hypothesis that the communication system of tits represents semantically compositional syntax.


Experiment 1

Japanese great tits principally displayed two behaviours in response to call playbacks (ABC, D and ABC–D): they scanned the surroundings by turning their heads right and left, and approached the playback loudspeaker. However, they produced these two behaviours differently in response to each of the playback treatments.

During playback of ABC calls, tits continuously turned their heads horizontally on tree branches to scan the surroundings. The rate of horizontal scans varied significantly among the playback treatments; it was higher during playback of ABC calls than during playback of D calls or background noise (control) (generalized linear mixed model: χ2=62.58, df=3, P<0.001, ). There were no significant effects of trial order (χ2=1.14, df=1, P=0.29) or sex of the focal individuals (χ2=0.01, df=1, P=0.92) on the rate of horizontal scans. Pairwise comparisons of treatments showed that the ABC call treatment resulted in significantly more horizontal scans than the D call treatment (Wilcoxon signed-rank tests: n=21, P<0.0001) and background noise control (P<0.001), whereas D calls and background noise were not significantly different (P=0.11).

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Responses of Japanese great tits to playbacks of ABC, D and ABC–D calls, and background noise (BN).

(a) Number of horizontal scans made by tits in 90 s (generalized linear mixed model: χ2=62.58, df=3, P<0.001). (b) Percentage of trials in which tits approached within 2 m of the loudspeaker (generalized linear mixed model: χ2=34.56, df=2, P<0.001). The box and whisker plots display the median value and 25 and 75% quartiles; the whiskers are extended to the most extreme value inside the 1.5-fold interquartile range. Sample size: n=21 individuals. Each individual was exposed to all four treatments in varied orders, giving n=21 samples per treatment.

In response to D calls, tits were more likely to approach within 2 m of the playback loudspeaker than in response to ABC calls or background noise. There was a significant effect of playback treatments on the probability of approaching (generalized linear mixed model: χ2=34.56, df=2, P<0.001; ), whereas trial order (χ2=1.47, df=1, P=0.23) or sex of the focal birds (χ2=1.93, df=1, P=0.16) had no significant effects. Pairwise comparisons showed that tits approached the loudspeaker during playback of D calls more often than during playback of ABC calls (sign tests: P<0.01) or background noise (P<0.01), whereas the responses to ABC calls and background noise were not significantly different (P=0.91). These results demonstrate that tits produce distinct behavioural responses when hearing ABC calls (scanning the surroundings) and D calls (approaching the sound source).

In response to playback of ABC–D calls, tits scanned the surroundings more than when hearing D calls (Wilcoxon signed-rank test: n=21, P<0.001) or background noise (P<0.001) and not differently to when hearing ABC calls alone (P=0.11; ). However, tits were also more likely to approach within 2 m of the loudspeaker than when hearing ABC calls (sign-tests: P=0.02) or the background noise control (P<0.01). There was no significant difference in approaching response between ABC–D and D calls (P=0.91; ). These results demonstrate that the combined ABC–D calls cause tits produce a combined response containing both behaviours typical of individuals exposed to ABC calls (scanning the horizon) and those typical of individuals exposed to D calls (approaching the sound source).

Across all trials, there was no significant correlation between horizontal scanning and approaching behaviour (Spearman rank-order correlation: horizontal scans versus approaching loudspeaker: ρ=0.053, n=84, P=0.63), indicating that the tits controlled these two behaviours independently.

Experiment 2

Tits responded differently to playbacks of ABC–D (natural sequence) and D–ABC (artificially reversed sequence) calls. In response to the playback of ABC–D calls, focal birds typically approached within 2 m of the loudspeaker, while scanning the horizon, similar to Experiment 1. However, in response to the playback of D–ABC calls, tits made fewer horizontal scans (generalized linear model: χ2=27.09, df=1, P<0.0001; ) and only rarely approached the loudspeaker (χ2=6.03, df=1, P=0.014; ). There was no significant difference between sexes in horizontal scans (χ2=1.05, df=1, P=0.31) nor approaching behaviour (χ2=0.002, df=1, P=0.96). These results demonstrate that tits produce a compound response when ABC and D are combined according to a note-ordering rule, but not when these two note units are simply produced in close temporal proximity.

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Responses of Japanese great tits to playbacks of ABC–D and D–ABC calls.

(a) Number of horizontal scans made by tits in 90 s (generalized linear model: χ2=27.09, df=1, P<0.0001). (b) Percentage of trials in which tits approached within 2 m of the loudspeaker (generalized linear model: χ2=6.03, df=1, P=0.014). The box and whisker plots display the median value and 25 and 75% quartiles; the whiskers are extended to the most extreme value inside the 1.5-fold interquartile range. Sample size: n=34 individuals. Each individual was exposed to only one treatment, giving n=17 samples per treatment.


Our results show that Japanese great tits discriminate between different calls containing different note types: they scan the horizon in response to ABC calls, whereas they approach the sound source in response to D calls. These results indicate that these two calls function as different meaningful units to receivers. ABC calls serve as warning calls that elicit predator-scanning behaviour, whereas D calls serve as recruitment calls that attract conspecifics to the callers. These findings are consistent with previous research showing that A, B and C note combinations are used in response to predators25, whereas D notes on its own are used to recruit conspecifics ().

In response to ABC–D calls, Japanese great tits both scan the surroundings and approach the sound source, indicating that they extract the meanings of both ABC and D calls from combined ABC–D calls. In addition, we find no correlation between scanning and approaching behaviours, which enables tits to perform and combine these behaviours flexibly according to the presence and absence of each note unit within calls. Moreover, tits reduce horizontal scanning and rarely approach the loudspeaker when the ordering of the two note units is artificially reversed (D–ABC). These results indicate that the tits perceive ABC–D calls as a single meaningful unit but not as two separated meaningful units (ABC and D calls) simply produced in close proximity. As ABC and D notes convey unique meanings and can be used alone25, the combination of these two notes does not meet the criteria of phonology5,6. In addition, unlike call combinations reported in several non-human primates14,15,16,17,18, the combination of ABC and D calls conveys a compound meaning that originates from both of the note units. Thus, we conclude that the combination of ABC and D calls in the Japanese great tit obeys semantically compositional syntax6.

Previous studies have shown that parids (chickadees and titmice) alter the repetition rate of particular note types (for example, D notes), which elicits different degrees of response in receivers (i.e., graded call system)22,23,24. One explanation for why tits produce different responses to combined ABC–D calls is that D notes increase the salience of ABC calls (or vice versa), rather than alter their meaning through a syntactic rule. However, we find no evidence supporting this explanation. In Experiment 1, our data show that tits do not alter the intensity of their responses according to the variation in note repetition rate; they scan with similar intensity to both ABC (3 notes) and ABC–D calls (10–13 notes) and, likewise, approach in response to both D (7–10 notes) and ABC–D calls (10–13 notes). Therefore, neither ABC nor D calls simply modify the intensity of behavioural responses. In addition, using a matched-pairs or balanced design controls for the possibility that any acoustic features other than either note combinations (Experiment 1) or note ordering (Experiment 2) influenced the interpretation of the results (see Methods).

Using a compositional syntax is likely to provide adaptive benefits to Japanese great tits. Similar to many small songbirds, tits face a variety of predatory threats requiring complex behavioural responses30,31,32. Previous studies have demonstrated that avian antipredator communication is adapted to such complexity: some birds produce different calls for different types of threats (for example, different predator types or behaviours) and receivers respond to the calls with appropriate behaviours30,31,32,33,34,35, leading to positive fitness consequences30,32,36. Our results show that the first units of great tits’ combinatorial calls (ABC calls) serve as general warning calls, whereas the last units (D calls) serve as recruitment calls. The specific combination of these calls may serve as an adaption to facing predators that require complex behaviours to be effectively detected and monitored. For example, scanning the surroundings is likely to allow a tit to efficiently detect a flying predator, such as a crow that can approach a nest from all directions31. In contrast, predators that only approach the nest from below, such as martens, are likely to be effectively detected and monitored both by approaching the caller and scanning the surroundings. Japanese great tits incorporate a greater number of D notes into other note units, such as ABC, when mobbing martens than when mobbing crows25. This suggests that tits have co-opted the signal normally used to recruit other individuals (for example, to coordinate parental feeding visits), to stimulate receivers to perform an appropriate combination of behaviours.

In addition, we suggest that the specific note-ordering rule (ABC calls before D calls) used by Japanese great tits in anti-predator contexts may be an adaptation to the greater importance of effectively and quickly warning conspecifics about the presence of predators before transmitting any additional behavioural cues. As D notes are often produced in non-predator contexts, conspecifics hearing D notes before ABC notes may be slower to produce appropriate anti-predator behaviours, which may be of particular importance when tits are defending their nestlings25,30.

Although we provide evidence for compositional syntax in the combination of ABC and D calls, it is not yet clear how the meaning of ABC calls is generated. One possibility is that A, B and C notes have different meanings and their combination has a compound meaning (i.e., compositional syntax). However, these notes may be meaningless as their own, but the combinations make the meaningful units that elicit scanning behaviour in receivers (i.e., phonology). Support for this idea comes from the observation that tits use A, B and C notes in many different combinations (for example, AB, AC and BC) when mobbing predators25. Therefore, it might be possible that all these combinations potentially encode the same threat information; however, the difference in note combinations or sequences of different call types may encode additional information, such as individual identity of callers. Note combinations are widely documented in other members of the Paridae, but their complexity may differ across species37. Further comparative studies may provide insight into the socio-ecological factors38 that drive the evolution of combinatorial signalling such as phonology and compositional syntax.

In conclusion, we provide the first experimental evidence for compositional syntax in a non-human vocal system. Over the past decades, many key attributes of human language have been reported from animal species: vocal learning9,39, referential communication7,8 and phonology12,13. Our results extend these studies and challenge the long-standing view that compositional syntax is unique to human language5,6. Although previous studies on syntactic communication mainly focused on primates12,14,15,16,17,18, our findings highlight that the ability to recognize the combinations of different meaningful units as compositional calls has evolved in birds. Signal combinations can increase the number of meanings that individuals can convey from a limited number of vocal elements and provide the basis for the generation of novel signals. Uncovering the cognitive mechanisms and socio-ecological functions of syntactic communication in animal models may provide insights into the evolution of structural complexity of human language.


General experimental design

This study consisted of two playback experiments. Experiment 1 was designed to test whether Japanese great tits discriminate between calls with different note types (ABC and D calls) and, if so, whether they also extract a compound meaning from combined calls (ABC–D). If the combination of ABC and D calls obeys compositional syntax, tits are expected to show different responses to the two different note units and a compound response to the combined calls. We examined the response of Japanese great tits to playbacks of ABC calls, D calls, ABC–D calls and the background noise (control).

Experiment 2 was designed to test whether tits respond to the combination of ABC and D calls through the recognition of the note-ordering rule. If they perceive the combined calls (ABC–D calls) as a single meaningful unit but not as separated and independent calls (ABC and D calls), they are expected to respond differently to the natural (ABC–D) and reversed (D–ABC) sequences. We tested the response of tits to playbacks of ABC–D and D–ABC calls.

Study population and call recordings

Experiments were conducted in a colour-ringed population of Japanese great tits in a mixed deciduous–coniferous forest near Karuizawa, Nagano Prefecture, Japan (36°19′–22′N, 138°32′–37′E). For all playbacks, we used ‘chicka’ mobbing calls that were previously recorded from Japanese great tits (ten males and seven females) from the study population in 2009 and 2010 (refs 25, 30). The ‘chicka’ calls were elicited by exposure to either a taxidermic model of a crow or a marten near the nest boxes. Calls were recorded using an LS370 parabolic microphone (Fuji Planning Corporation, Tokyo, Japan) connected to an R-09HR digital audio recorder (sampling rate, 48 kHz; sample size, 16 bits; Roland Corporation, Shizuoka, Japan). Detailed information on call recordings has been provided elsewhere25,30.

Playback stimuli

Adobe Audition 3.0 software and Raven Pro 1.3 software40 were used to construct the playback stimuli. We chose four types of notes (A, B, C and D) from recordings of every source individual on the basis of the sound quality (for example, the bird was close to the microphone when it called and the background noise was low). Although A, B and C notes were typically produced as a single note in a call, D notes always occurred as a string of multiple notes. Therefore, we used a single A, B and C note and a string of seven to ten D notes to construct the playback calls. These four note types were combined into an ABC–D call with natural intervals between the notes (50–150 ms, measured for each individual of the recording source). We thus obtained a total of 21 ABC–D calls from the recording files (11 calls from the recordings of 10 males and 10 calls from the recordings of 7 females).

In Experiment 1, we prepared three call treatments (ABC, D and ABC–D calls; ) and a control treatment (background noise). ABC and D call types were constructed by eliminating either D or ABC note units from each of the 21 ABC–D calls. Calls were repeated in a sound file at a rate of 30 calls per minute (one call every 2 s, total duration 90 s). This calling rate is within the range of the natural repetition rates for ‘chicka’ calls during the nestling period25,30. Low-frequency noise (<1 kHz) was filtered out and the calls were amplified on a computer. The background noise files were created in the same way as the call files, using the parts where no birds were calling in the same recordings as call treatments. Thus, we constructed 21 unique sets of playback stimuli (ABC, D and ABC–D calls, and background noise). To avoid pseudoreplication41, we played back each exemplar only once to each focal individual (n=21). To each focal individual, we played back three call types that originated from the same calling individual (matched-pairs design), ensuring that any acoustic features other than the note combinations (for example, the intervals between different notes) were constant over these three call treatments. All of the sound files were saved in WAV format (16-bit accuracy, 48.0-kHz sampling rate) onto an SD memory card.

In Experiment 2, we prepared two types of calls: ABC–D (natural sequence) and D–ABC (artificially reversed sequence) calls (). We chose 17 different ABC–D calls that originated from different individuals (10 male calls and 7 female calls). D–ABC calls (n=17) were constructed by using these ABC–D calls and re-ordering the sequence by moving D notes before A notes. The intervals between D and A notes within D–ABC calls were set at the same durations as those between C and D notes in their original ABC–D calls, ensuring that any acoustic features other than note orderings did not differ between ABC–D and D–ABC calls (balanced design). These calls were recorded in a sound file at a rate of 20 calls per minute (one call every 3 s, total duration 90 s), which was saved in WAV format (16-bit accuracy, 48.0-kHz sampling rate) onto an SD memory card. This calling rate is within the natural range25,30 and ensures that each call is separated by at least 1.6 s from any preceding calls, reducing the chances that receivers could perceive ABC–D sequences from adjacent D–ABC calls. As with Experiment 1, unique exemplars were used for each focal individual to avoid pseudoreplication41.

Experiment 1

We tested the responses of Japanese great tits to playbacks of ABC, D and ABC–D calls. We conducted this experiment on 21 adult great tits (10 males and 11 females from 21 different pairs) during their first breeding attempt of the season. All experimental birds bred in nest boxes that were attached to tree trunks 1.8 m above the ground. The average brood size of these pairs was 7.8±1.5 (mean±s.d., n=21). The experimental trials were carried out from 3 June to 15 June 2012 when the nestlings were 10–17 (12.4±1.7) days old.

An AT-SPG50 loudspeaker (Audio-Technica Corporation, Tokyo, Japan) was hung from a tree and fixed 1.8±0.2 m from the ground and 5.3±1.0 m from the nest (mean±s.d., n=21). The loudspeaker was connected to an R-09 HR digital audio recorder with EXC-12A extension cords (JVC Kenwood Corporation, Kanagawa, Japan), which enabled the control of playbacks from an observation position 15 m away from the nest. Playbacks commenced when a focal individual was within 5 m of the nest and their mate was absent. Calls were played back at a standardized volume (75 dB re 20 μPa at 1 m from the loudspeaker measured using an SM-325 sound level meter; AS ONE Corporation, Osaka, Japan) and background noise was played back at the same amplitude as the background noise level of the call playbacks (50 dB re 20 μPa at 1 m). Focal birds received playbacks of calls that were constructed from unfamiliar individuals (that is, not their mates or neighbours), to eliminate any influence of familiarity. No more than two trials were conducted at the same nest in a single day and playbacks at the same nest were separated by at least 2 h to reduce habituation. The order of the playbacks was randomized. We used the same position for setting the loudspeaker in all treatments at each site to control for its possible effect on the behavioural response. Trials were conducted in calm and dry weather between 08:30 and 16:00 h (Japan Standard Time).

To determine the tits’ responses to different treatments, we recorded the following behavioural variables during 90 s of playbacks: (1) number of horizontal scans: we counted the number of movements that birds made with their heads from left to right or right to left (approximately a 180° turn) and (2) approaching the loudspeaker: we recorded whether birds approached within 2 m of the loudspeaker during the playback. These behavioural variables were commented onto an R-09HR digital audio recorder. We also recorded the latency to feed nestlings by using a GZ-MG880 digital video camera (JVC Kenwood Corporation) set ca. 10 m from the nest. Behavioural observations were continued until each playback had ended and the adults entered the nest box to feed the chicks.

Experiment 2

We tested the responses of Japanese great tits to naturally combined ABC–D calls and artificially reversed D–ABC calls. We conducted this experiment with 34 individual great tits (ABC–D calls: 11 males and 6 females; D–ABC calls: 12 males and 5 females). The minimum distance between experimental sites was 400 m, to ensure the collection of data from different individual tits21. Trials were carried out between 6 November and 19 November 2015, during the non-breeding season, when tits, such as other members of the Paridae, are threatened by a variety of predators and produce a corresponding variety of alarm calls22,23,24.

First, we searched for a flock of Japanese great tits. On finding a flock, we hung an AT-SPG50 loudspeaker from a tree at 1.8±0.1 m from the ground (mean±s.d., n=34). The loudspeaker was connected to an R-09 HR digital audio recorder with EXC-12A extension cords, which enabled the control of playbacks from an observation position ca. 10 m away from the loudspeaker. Then, we commenced the playback when a tit came within 15 m of the loudspeaker. We defined the individual that was closest to the loudspeaker as the focal individual and focussed on this individual during the playback. Trials were carried out under calm and dry weather between 08:45 and 15:30 h (Japan Standard Time). ABC–D and D–ABC treatments were alternated with each other on successive trials so that responses to both treatments were observed under largely similar conditions.

As with Experiment 1, we measured two behavioural variables: (1) number of horizontal scans and (2) the probability of approaching within 2 m of the loudspeaker. These variables were commented onto an R-09HR digital audio recorder.

Usage of D calls in a non-predatory context

Japanese great tits produce D calls not only in predatory contexts but also in non-predatory contexts such as when visiting their nests. We investigated the usage and function of D calls in a non-predatory context, testing the hypothesis that D calls serve to recruit conspecifics. If this hypothesis is true, then we predict that (1) tits produce D calls more often when they visit the nest alone than when their mated partner is also present and (2) a caller’s mate is more likely to visit the nest when the caller produces D calls than when it does not. We therefore investigated the effect of social context on the usage of D calls and whether the production of D calls increases the visitation of their mate to the nest.

We observed n=187 nest visitations of 40 adults (19 males and 21 females) at 22 nests from 3 June to 15 June 2012, when nestlings were 10–17 days old. When a parent visited within 5 m of the nest box with a food item, we noted (1) the sex of the parent, (2) whether it gave D calls and (3) whether its mate was present within 5 m of the nest box. In the case in which a parent visited the nest alone (n=136), we also noted (4) whether the mate visited within 5 m of the nest before the first bird entered the nest box. Observations were made at 15 m from the nest box, a distance from which the tits’ behaviour was not disturbed.

Statistical GFN

All the statistical analyses were performed using R for Mac OS X version 3.1.1 (ref. 42). In the GFN of Experiment 1, we used generalized linear mixed models for primary analyses, which include the treatment as a fixed term and individual identity of focal birds as a random term. Trial order and sex were also entered as covariates. We used a negative binomial error distribution and log-link function (glmer.nb in the package lme4 (ref. 43)) for the GFN of the number of horizontal scans and a binomial error distribution and logit-link function (glmer in the package lme4 (ref. 43)) for the GFN of the probability of approaching behaviour (yes or no). In some trials, tits visited the nest boxes and flew out of sight immediately after feeding chicks. Therefore, we determined the time duration in which we could observe the behaviour of the tits as the observation time and included this term in the GFN of horizontal scans as a log-transformed offset. For the GFN of approaching behaviour, it was not possible to run the model because of the absence of variance in background noise control treatment (no birds approached to the loudspeaker during this treatment). Therefore, we combined background noise and ABC calls in this GFN, as there was no significant difference between these two treatments (sign test, P=0.5). We used likelihood ratio tests to calculate P-values of each term. In the event of a significant effect of treatment, we further conducted pair-wise comparisons by using non-parametric statistics: Wilcoxon signed-ranks tests (wilcox.paired.multcomp in the package RVAideMemoire44) for the number of horizontal scans (standardized by observation time) and sign tests for approaching to the loudspeaker (cochran.qtest in the package RVAideMemoire44). When making these multiple comparisons, sequential Bonferroni corrections were applied for the adjustments of P-values. To investigate the correlation between scanning and approaching behaviours, we used Spearman’s rank-order correlations (cor.test in the default package stats).

In the GFN of Experiment 2, we ran generalized linear models including treatment as a fixed term and sex as a covariate. We used a negative binomial error distribution and log-link function (glm.nb in the package MASS45) for the GFN of horizontal scans and a binomial error distribution and logit-link function (glm in the package stats) for the GFN of approaching behaviour. We standardized the number of scans by observation time, as in some cases the focal individuals flew away from the sight during the trials.

In the GFN of the usage of D calls, we ran generalized linear mixed models with a binomial error distribution and a logit-link function (glmer in the package lme4 (ref. 43)). To test the effect of social context on the production of D calls, we fitted social context (mate present or absent) as a fixed term and the probability of D calling (yes or no) as a dependent variable. To test the effect of D calling on the recruitment of a mate to the nest, we fitted the production of D calls (yes or no) as a fixed term and the probability of recruitment (yes or no) as a dependent variable. In both models, we also included sex of focal birds as a covariate and individual identity of focal birds and individual nest as random terms. All tests were two-tailed and the significance level was set at α=0.05.

Ethical statement

All experiments were performed in accordance with relevant guidelines and regulations. All experimental protocols were approved by the Animal Care and Use Committees at the Rikkyo University and SOKENDAI (The Graduate University for Advanced Studies), and adhered to the Guidelines for the Use of Animals in Research of the Animal Behavior Society/Association for the Study of Animal Behaviour. This research was performed under permission from the Ministry of the Environment and the Forestry Agency of Japan.

Additional information

How to cite this article: Suzuki, T. N. et al. Experimental evidence for compositional syntax in bird calls. Nat. Commun. 7:10986 doi: 10.1038/ncomms10986 (2016).


This work was supported by JSPS KAKENHI Grant Number 25–3391 (T.N.S.) and an NSF Postdoctoral Research Fellowship in Biology Award ID 1202861 (D.W.). We are grateful to Daizaburo Shizuka and Carel van Schaik for valuable comments on the manuscript.


Author contributions T.N.S., D.W. and M.G. designed the experiments. T.N.S. designed the research, performed the field experiments, analysed the data and prepared the figures. T.N.S., D.W. and M.G. discussed the results and contributed to the writing of the manuscript.


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Why 780 retired generals and former national security leaders spoke out against Trump





Donald Trump
Trump departs the White House for a photo op outside St. John’s Church on June 1, 2020. Walking behind the president are, from left, Attorney General William Barr, Secretary of Defense Mark Esper and Gen. Mark Milley, chairman of the Joint Chiefs of Staff. (Patrick Semansky/AP)

On June 1, retired Army vice chief of staff Gen. Peter Chiarelli sat staring out at the Pacific Ocean in Gearhart, Ore., where his family had vacationed throughout his long military career. The peaceful scene was occasionally interrupted by the news flashing across the notebook computer in his lap. In a Rose Garden speech that afternoon, President Trump addressed the racial justice protests spreading across the nation after the brutal killing of George Floyd in police custody a week earlier.

In the speech, Trump proclaimed himself “your president of law and order,” and claimed the protests had been hijacked by “professional anarchists, violent mobs, arsonists, looters, criminals, rider rioters, antifa and others” intent on “domestic terror.” News cameras showed some of the hundreds of National Guard troops from around the country that had been sent to reinforce the D.C. Guard, and there were reports that 1,600 active-duty troops were on high alert just outside the capital. Privately, Trump was threatening to invoke the Insurrection Act in order to send thousands more active-duty troops onto the nation’s streets in a show of dominant military force, criticizing weak governors and mayors around the country for not doing more to forcefully stamp out the protests.

The television cameras shifted to a mostly peaceful crowd of protesters across Lafayette Park from the White House. Chiarelli sat up when a phalanx of federal police and National Guard troops suddenly marched into the peaceful crowd, backed by a small cavalry of Park Police on horseback. There were flash-bang explosions, clouds of tear gas and the crackle of pepper balls as riot police used shields and batons to pummel some in the crowd. A woman could be heard plaintively shouting above the din, “Why are you shooting at us?!”

After the crowd was dispersed, Chiarelli watched with growing alarm as President Trump strode purposefully across Lafayette Park flanked by Attorney General William Barr, Defense Secretary Mark Esper, and Gen. Mark Milley, chairman of the Joint Chiefs of Staff. Chiarelli had served in combat with Milley in Iraq, and considered him a good friend. That Mark Milley would have known better than to appear at the president’s side in his camouflage uniform after a show of dominant force against protesters on the streets of America.

In front of historic Saint John’s Church, damaged by fire during earlier protests, Trump posed silently holding a bible aloft for a 2-minute photo op. At long last, President Trump had the image of the “American carnage” that he had promised to end in his inauguration speech, insisting that he alone could fix it.

Donald Trump
President Trump holds a Bible as he visits St. John’s Church across Lafayette Park from the White House on June 1, 2020. (Patrick Semansky/AP)

Along with a cadre of other retired generals, a very upset Peter Chiarelli decided to contact his old friend General Milley, the most senior uniformed leader in the country. After serving as commander of the 147.000 U.S. and coalition troops of Multi-National Corps – Iraq, Chiarelli as vice chief of the Army had led Defense Department efforts to treat post-traumatic stress, traumatic brain injury and suicide prevention. On his retirement in 2012, he became the first CEO of One Mind, which supports research into brain illnesses and injuries.

“That whole incident around Lafayette Square was stunning to me, because those were mostly peaceful demonstrators exercising a right guaranteed by the Constitution that I’ve sworn allegiance to throughout my entire career,” said Chiarelli in an interview. That allegiance is not given to a political party, Congress or the president of the United States, he noted, making the image of a uniformed chairman of the Joint Chiefs and the defense secretary at Trump’s side that day so alarming. General Milley later apologized for his presence in Lafayette Square, and Defense Secretary Mark Esper earned the president’s enmity by publicly opposing invocation of the Insurrection Act in order to use U.S. military troops to “dominate” the streets.

Along with more than 780 retired high-ranking officers and former national security leaders — including 22 retired four-star generals and admirals and five former secretaries of defense — Chiarelli signed an “Open Letter to America” endorsing Joe Biden for president. “We love our country,” the signatories wrote. “Unfortunately, we also fear for it.”

“Signing that letter was very hard for me to do, because I have never done that before or even given a dollar to a political campaign. Frankly, even as a retired general I didn’t think it was the right thing to do,” said Chiarelli, stressing that active-duty military officers are indoctrinated from a young age to remain strictly nonpartisan and apolitical. “But this president has assaulted the military justice system on behalf of individuals charged with war crimes. He has ended the career of service members like [impeachment witness Lt. Col. Alexander] Vindman for doing his duty and what was right. He has maligned mail-in voting as a fraud and suggested he might claim victory in a close election before all the ballots are counted, when as a service member I have voted absentee by mail my entire life. So like everyone else I’ve become numb after four years of this, but we have gone places in that time that I never dreamt we would go as a nation. I really do fear that the republic that I swore allegiance to is now under threat.”

Peter Chiarelli
Retired Gen. Peter Chiarelli. (Alastair Grant/AP)

Even among the cascade of scandals and controversies that have characterized the Trump presidency, the use of excessive force against mostly peaceful protesters near Lafayette Square, and the involvement of the top ranks of  the U.S. military, still stands out. The incident conjured a truly dystopian vision of a U.S. president not only willing but eager to use the world’s most powerful military to crush domestic protests and “dominate” the streets of America, one that an increasing number of retired generals and senior national security experts believe could become all too real in a second Trump term.

Lafayette Square was so alarming that it shook Trump’s former Defense Secretary, retired four-star Marine Gen. Jim Mattis, out of his long silence on the president’s leadership, writing afterwards that “Donald Trump is the first president in my lifetime who does not try to unite the American people — does not even pretend to try.”

Trump’s troubling authoritarian instincts, focus on image over substance, constant misuse and politicization of nonpartisan institutions and penchant for chaos were all on clear display in Lafayette Square, and the incident crystalized the concerns expressed in the open letter. Traditionally both active-duty and retired U.S. military and intelligence officials have steered clear of politics, but in mid-September the Trump campaign released a letter signed by 235 retired senior military officers endorsing the president for reelection with the claim that Americans’ “historic way of life is at stake” if the “socialists and Marxists” of the Democratic Party take control of the government.

The willingness of hundreds of career officers to break with tradition and speak out on behalf of one candidate reflects beliefs, on both sides, that the nation faces an uncertain future, facing the worst pandemic in over a century, the worst economic decline since the Great Depression and the worst racial unrest since the 1960s. To the signers of the “Open Letter to America,” a second Trump term would only make things worse.

Protestors at Lafayette Park on June 1, 2020. (Ken Cedeno/Reuters)

“Over the last three-plus years, I’ve watched the Trump administration politicize the Department of Justice and eviscerate the State Department, and the situation in Lafayette Square made clear that if reelected, Trump will politicize the Defense Department as well,” said retired Rear Adm. Mike Smith, who was instrumental in organizing the “Open Letter to America.” “A lot of us who spent our careers in the military would rather have stayed out of politics, but we have a deep moral conviction that the country can’t afford to go through another four years of this kind of leadership.”

Already the Lafayette Square incident has sunk beneath a wave of subsequent controversies and scandals, including recent revelations in investigative reporter Bob Woodward’s book “Rage,” based on numerous on-the-record interviews with Trump, that the president knew early on about the deadly and extremely contagious nature of the COVID-19 virus, but chose to continually play down the threat; the revelations in an article in the Atlantic, backed by reporting by the Washington Post, Fox News and other outlets, that Trump has repeatedly shown contempt for U.S. service members killed in combat, including referring to fallen soldiers and marines in cemeteries overseas as “losers” and “suckers”; Trump’s bullying and hectoring performance in the first presidential debate that astounded viewers at home and abroad; the president’s decision to put the health and lives of his Secret Service detail in jeopardy for a photo op after he tested positive for the coronavirus; and Trump’s insistence that the presidential election weeks away will be “the most rigged” in history, and his refusal to commit to accepting its results and peacefully transfer power if he loses.

Civil-Military Dysfunction

Donald Trump and Melania Trump
Donald and Melania Trump celebrate Independence Day on the South Lawn of the White House, July 4, 2020. (Carlos Barria/Reuters)

President Trump’s relationship with military commanders might have been an asset in his reelection campaign. He has increased defense spending each year of his presidency, with the United States on track to spend more on the military in 2020 (adjusted for inflation) than at any point since World War II, with the exception of a few years at the height of the Iraq War. Early in his term, Trump pleased commanders by relaxing battlefield restrictions in the fight against the Islamic State of Iraq and Syria (ISIS), and he ordered successful strikes that killed ISIS leader Abu Bakr al-Baghdadi and Iranian Quds Force leader Qassem Soleimani.

As commander in chief, Trump also clearly revels in the pomp and spectacle of military parades, and in salutes to the troops. Yet from the early days of his presidency there were signs of severe tension between a president who has racked up an unprecedented 20,000 falsehoods since taking the oath, according to the Washington Post’s “Fact Checker,” and an institution built on the ethos that officers “will not lie, cheat, steal, or tolerate those who do.” There were also early indications that Trump was willing to politicize the most stringently apolitical institution in the U.S. government, treating appearances with the troops like political rallies where he excoriated Democrats and signed “Make America Great Again” hats. Before the 2018 midterm elections, Trump alarmed senior military leaders by sending active-duty troops to the southern border to confront a ragtag caravan of asylum seekers and migrants in a nakedly political stunt, and he diverted Pentagon funds to build sections of the wall he promised that Mexico would pay for.

From the beginning of his term, Trump has also exhibited indifference bordering on contempt for the sacrifices and principle of selfless service that underlies the military profession. Many officers were willing to look past the five draft deferments Trump received during the Vietnam War, including one for a “bone spur” diagnosis from a New York podiatrist who reportedly rented an apartment from Trump’s father.

More troubling to many uniformed leaders was Trump’s belittling of the Muslim Gold Star parents of a slain U.S. soldier who criticized him during the 2016 Democratic National Convention, and the president’s casual dismissal of the wartime service of the late Sen. John McCain, a former Navy pilot who spent more than five years being tormented in the notorious “Hanoi Hilton” prison. “He’s not a war hero,” said candidate Trump, when he was feuding with the Arizona senator. “He was a war hero because he was captured. I like people who weren’t captured.”

Donald Trump
Then presidential candidate Donald Trump speaks at the Family Leadership Summit in Ames, Iowa, in July 2015. (Nat Harnik/AP)

In his first briefing inside the Pentagon’s classified “tank” with then Defense Secretary Mattis and the Joint Chiefs, Trump famously bristled at their arguments supporting NATO and ongoing operations in Afghanistan. “You’re all losers,” Trump reportedly said to a room full of four-star flag officers and combat veterans. “You don’t know how to win anymore.” After Mattis later resigned to protest Trump’s rash decision to pull U.S. troops out of Syria and abandon Kurdish allies in the fight against ISIS, Trump publicly dissed him as “the world’s most overrated general.”

“President Trump routinely shows disrespect towards exemplary leaders like Senator McCain, and towards General Jim Mattis, one of our very best,” said retired Marine Lt. General Frank Libutti, a combat veteran and Purple Heart recipient who signed the “Open Letter to America.” “It recalls his public ridicule of many of his top military and intelligence community leaders, and his insistence that he knows more about issues of national security than they do, which is nonsense. But what I found truly shocking were Trump’s comments about the Marines who sacrificed their lives for victory at Belleau Wood. I believe words count. Character counts. Temperament counts. And President Trump has shown himself beneath the dignity of the office.”

A seeming contempt for military service came through most clearly when Trump canceled a planned visit to a World War I military cemetery near Paris because of rain during a 2018 trip. Quoting four anonymous sources with firsthand knowledge of the discussion that day, the Atlantic’s editor in chief Jeffrey Goldberg reported that Trump said, “Why should I go to that cemetery? It’s filled with losers.” In a separate conversation on the same trip, Trump reportedly referred to the more than 1,800 Marines who lost their lives at Belleau Wood as “suckers” for getting killed. Fox News and the Washington Post later confirmed similar episodes of the president denigrating military service.

Retired Air Force Gen. Charles Boyd spent more than six years as a prisoner of war in North Vietnam, and he is the only Vietnam War POW to later reach four-star rank. “When I read the Atlantic article, I found it absolutely disgusting. The idea that the commander in chief holds those who serve under him with such contempt, just because they are not driven by the same desire for money and wealth as him, made me sick to my stomach. In all of my experiences in life, I’ve never known any group that is more honorable than military professionals, who sign an unlimited liability contract to sacrifice their lives if called to for this nation.”

In the past, Boyd has also opposed even retired flag officers endorsing candidates or becoming involved in partisan politics, but he made an exception this year by signing the “Open Letter to America.” “There’s a saying in the military that ‘officers eat last,’ which means that leadership is all about what’s best for your troops, and for the nation. President Trump has no concept of that kind of leadership. Everything he does is driven by what’s best for him personally, including casting doubt on any election results that don’t declare him the winner. That’s destructive to the very fiber of our democracy.”


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Twin Cities area youth sports coaches add COVID-19 protocols to daily routines

Emily walpole



Mary Guzek is used to playing the role of “Team Mom” for her two sons’ Fridley youth football, basketball and baseball squads. Time was, that meant supplying snacks or filling water bottles.

But this fall, in the midst of a global pandemic, it means taking players’ temperatures before every practice and game, counseling parents of sick kids to keep them home and running down a checklist of whether any of the 22 players on the fifth-grade football team have a cough or feel short of breath.

“Unfortunately, it’s what we have had to do to make sure our kids can play,” said Guzek, whose boys are 12 and 10. “But it was worse in the spring, when seasons were canceled, and the kids were sad and depressed. Now, they can play.”

It’s hard enough for some parents to volunteer their time and energy at the end of a workday to coach youth sports. But with COVID-19 rapidly spreading, they’re now forced to do more than manage lineups and the Xs and Os to keep players on the field and the virus at bay.

Many parents and volunteer coaches across the metro have added COVID-19 protocols to their duties. Taking player temperatures, scrubbing down equipment and alternating practice times have, for most, become routine. Meanwhile, some park and recreation departments, not wanting to saddle volunteers with such responsibility, have moved away from traditional soccer and football games, offering instead skills camps run by paid staff members at a handful of hub sites.

Jayme Murphy, who focuses on COVID-19 issues for the Minnesota Amateur Sports Commission, said youth sports groups across the state spent much of the summer exploring ways they could safely play in the fall. Some, he said, were committed to playing out the season. Others created scaled-down versions of their usual offerings. Still others canceled seasons altogether.

Key to those decisions was determining whether coaches and parent volunteers would feel overwhelmed by the responsibility for keeping COVID-19 in check. The Minnesota Department of Health has issued 13 pages of guidelines for safely conducting youth and adult sports.

“The question for volunteers and parents to ask themselves is how comfortable are they with risk?” Murphy said. “If you’re uncomfortable with this, if you’re uncomfortable with your child’s participation in this, that’s ok.”

With COVID-19 cases continuing to rise across the state this fall, those comfort levels may be challenged even more as the winter sports season approaches.

Another way

In St. Paul, officials at the city’s Parks and Recreation department canceled sports at 26 recreation centers over the spring and summer. This fall, they replaced tackle football and competitive soccer with flag football and soccer skills programs hosted at six recreation centers.

They did so, because “we didn’t want to throw the responsibility for following those protocols onto volunteer coaches,” said Andy Rodriguez, recreation services manager.

By limiting offerings to six sites, supervised by city employees with help from coaches at Cretin-Derham Hall and the Sanneh Foundation, Rodriguez said the city can better control social distancing, sanitizing equipment and health screening. Nearly 600 kids, ages 3-14, registered for soccer in St. Paul, Rodriguez said. Almost 400 kids, ages 8-12, signed up for flag football.

“For the most part, the families we have been working with are just thankful for something for their kids to do in the fall,” he said.

Davis Vue who helped his 7-year-old son Memphis tie his shoes on a recent night, said he is one of the happy parents. The St. Paul native watched the coronavirus wipe out his own flag football league season, so he appreciates the city finding a way for Memphis to participate. It’s his son’s first year playing and he hasn’t missed a night, his father said.

“With this pandemic going on, I’m surprised Parks and Rec had this going on for kids,” Vue said. “I’m really glad they did.”

There’s also no tackle football in Minneapolis, where the city’s Park Board has offered flag football for young athletes 6-18. The soccer season has continued with a citywide schedule and volunteer coaches, said Mimi Kalb, director of Athletic Programs and Aquatics for the Minneapolis Park Board. Younger children — on 6U and 8U teams — are playing games in “smaller service areas” with city staff members conducting many of the COVID-19 protocols, she said.

Some coaches and players and families opted out of playing, “but for those who wanted to play, we tried to take a lot of the responsibility off the coaches,” she said. “Our park staff and league directors are doing a lot of that.”

Tim Grate, athletics program director for Minneapolis Parks and Recreation, said many coaches have successfully incorporated their new responsibilities.

“I’ve seen coaches who laid out cones to make sure [players are] social distancing,” he said. “I haven’t heard a lot of complaints.”

John Swanson, a Fridley varsity football coach who oversees more than 200 youth teams across the north metro, said about 30 % of them opted out of play due to COVID-19 concerns. Those that remained were committed to following all the necessary rules to keep playing.

“It’s one of the few things that still connects community,” he said. “Youth sports help us maintain that connectivity.”

Coaches and team moms and dads are keeping spreadsheets, taking temperatures, cleaning equipment, staggering practice nights and holding kids out if they show symptoms or test positive, he said. Teams have built time into their schedules to play makeup games when any had to quarantine for 14 days. So far, he said, there have been no COVID-19 cases transmitted on the football field.

“I don’t think we are asking the coaches to do too much,” Swanson said. “Volunteer coaches have proved they can do it.”

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Mish Boyka




Slide 1

Q3 2020 Update Exhibit 99.1

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Highlights 03 Financial Summary04 Operational Summary06 Vehicle Capacity 07 Core Technology 08 Other Highlights09 Outlook10 Battery Day Highlights11 Photos & Charts13 Financial Statements23 Additional Information28

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The third quarter of 2020 was a record quarter on many levels. Over the past four quarters, we generated over $1.9B of free cash flow while spending $2.4B on new production capacity, service centers, Supercharging locations and other capital investments.  While we took additional SBC expense in Q3, our GAAP operating margin reached 9.2%. We are increasingly focused on our next phase of growth. Our most recent capacity expansion investments are now stabilizing with Model 3 in Shanghai achieving its designed production rate and Model Y in Fremont expected to reach capacity-level production soon. During this next phase, we are implementing more ambitious architectural changes to our products and factories to improve manufacturing cost and efficiency. We are also expanding our scope of manufacturing to include additional areas of insourcing. At Tesla Battery Day, we announced our plans to manufacture battery cells in-house to aid in our rapid expansion plan. We believe our new 4680 cells are an important step forward to reduce cost and improve capital efficiency, while improving performance. We continue to see growing interest in our cars, storage and solar products and remain focused on cost-efficiency while growing capacity as quickly as possible. $5.9B increase in our cash and cash equivalents in Q3 to $14.5B Operating cash flow less capex (free cash flow) of $1.4B in Q3 Cash Record vehicle deliveries, profitability and free cash flow Buildout of three new factories on three continents continues as planned First step of FSD beta rollout started in Oct. 2020 Profitability $809M GAAP operating income; 9.2% operating margin in Q3 $331M GAAP net income; $874M non-GAAP net income (ex-SBC) in Q3 SBC expense increased to $543M (driven by 2018 CEO award milestones) Operations S U M M A R Y H I G H L I G H T S 3 SBC = stock-based compensation

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F I N A N C I A L   S U M M A R Y (Unaudited) 4 ($ in millions, except percentages and per share data) Q3-2019 Q4-2019 Q1-2020 Q2-2020 Q3-2020 QoQ YoY Automotive revenues 5,353 6,368 5,132 5,179 7,611 47% 42%    of which regulatory credits 134 133 354 428 397 -7% 196% Automotive gross profit 1,222 1,434 1,311 1,317 2,105 60% 72% Automotive gross margin 22.8% 22.5% 25.5% 25.4% 27.7% 223 bp 483 bp               Total revenues 6,303 7,384 5,985 6,036 8,771 45% 39% Total gross profit 1,191 1,391 1,234 1,267 2,063 63% 73% Total GAAP gross margin 18.9% 18.8% 20.6% 21.0% 23.5% 253 bp 462 bp               Operating expenses 930 1,032 951 940 1,254 33% 35% Income from operations 261 359 283 327 809 147% 210% Operating margin 4.1% 4.9% 4.7% 5.4% 9.2% 381 bp 508 bp               Adjusted EBITDA 1,083 1,175 951 1,209 1,807 49% 67% Adjusted EBITDA margin 17.2% 15.9% 15.9% 20.0% 20.6% 57 bp 342 bp               Net income attributable to common stockholders (GAAP) 143 105 16 104 331 218% 131% Net income attributable to common stockholders (non-GAAP) 342 386 227 451 874 94% 156%               EPS attributable to common stockholders, diluted (GAAP) (1) 0.16 0.11 0.02 0.10 0.27 170% 69% EPS attributable to common stockholders, diluted (non-GAAP) (1) 0.37 0.41 0.23 0.44 0.76 73% 105%               Net cash provided by (used in) operating activities 756 1,425 (440) 964 2,400 149% 217% Capital expenditures (385) (412) (455) (546) (1,005) 84% 161% Free cash flow 371 1,013 (895) 418 1,395 234% 276% Cash and cash equivalents 5,338 6,268 8,080 8,615 14,531 69% 172% (1) Prior period results have been retroactively adjusted to reflect the five-for-one stock split effected in the form of a stock dividend in August 2020. EPS = Earnings per share

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F I N A N C I A L   S U M M A R Y Revenue Profitability Cash Total revenue grew 39% YoY in Q3. This was achieved mainly through substantial growth in vehicle deliveries as well as growth in other parts of the business. At the same time, vehicle average selling price (ASP) declined slightly compared to the same period last year as our product mix continues to shift from Model S and Model X to the more affordable Model 3 and Model Y. Our operating income improved in Q3 to a record level of $809M, resulting in a 9.2% operating margin. This profit level was reached while we took increased SBC expense in Q3 attributable to the 2018 CEO award, of which $290M was triggered by a significant increase in share price and market capitalization and a new operational milestone becoming probable. Positive profit impacts included strong volume, better fixed cost absorption and continuous cost reduction.  Quarter-end cash and cash equivalents increased by $5.9B QoQ to $14.5B, driven mainly by our recent capital raise of $5.0B (average price of this offering was ~$449/share) combined with free cash flow of $1.4B and partially offset by reduced use of working capital credit lines. Since our days payable outstanding (DPO) are higher than days sales outstanding (DSO), revenue growth results in additional cash generation from working capital. DPO and DSO both declined sequentially in Q3 2020.  5

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Q3-2019 Q4-2019 Q1-2020 Q2-2020 Q3-2020 QoQ YoY Model S/X production 16,318 17,933 15,390 6,326 16,992 169% 4% Model 3/Y production 79,837 86,958 87,282 75,946 128,044 69% 60% Total production 96,155 104,891 102,672 82,272 145,036 76% 51% Model S/X deliveries 17,483 19,475 12,230 10,614 15,275 44% -13% Model 3/Y deliveries 79,703 92,620 76,266 80,277 124,318 55% 56% Total deliveries 97,186 112,095 88,496 90,891 139,593 54% 44%    of which subject to operating lease accounting 9,086 8,848 6,104 4,716 10,014 112% 10% Total end of quarter operating lease vehicle count 44,241 49,901 53,159 54,519 61,638 13% 39% Global vehicle inventory (days of supply)(1) 18 10 25 17 14 -18% -22% Solar deployed (MW) 43 54 35 27 57 111% 33% Storage deployed (MWh) 477 530 260 419 759 81% 59% Store and service locations 417 433 438 446 466 4% 12% Mobile service fleet 719 743 756 769 780 1% 8% Supercharger stations 1,653 1,821 1,917 2,035 2,181 7% 32% Supercharger connectors 14,658 16,104 17,007 18,100 19,437 7% 33% (1) Days of supply is calculated by dividing new car ending inventory by the quarter’s deliveries and using 75 trading days (aligned with Automotive News definition). 6 O P E R A T I O N A L   S U M M A R Y (Unaudited)

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Delivery percentage of locally-made vehicles* V E H I C L E C A P A C I T Y Fremont We have recently increased capacity of Model 3 / Model Y to 500,000 units a year. In order to do this, we restarted our second paint shop, installed the largest die-casting machine in the world and upgraded our Model Y general assembly line. Production should reach full capacity toward the end of this year or beginning of next year. Shanghai Model 3 production capacity has increased to 250,000 units a year. We reduced the price of Model 3 to 249,900 RMB after incentives, making it the lowest-price premium mid-sized sedan1 in China. This was enabled both by lower-cost batteries and an increased level of local procurement. As a result of this shift in cost and starting price, we recently added a third production shift to our Model 3 factory. Berlin-Brandenburg Construction of the Gigafactory in Berlin continues to progress rapidly. Buildings are under construction and equipment move-in will start over the coming weeks. At the same time, the Giga Berlin team continues to grow. Production is expected to start in 2021.  Installed Annual Capacity Current Status Fremont Model S / Model X         90,000 Production Model 3 / Model Y    500,000 Production Shanghai Model 3       250,000 Production Model Y – Construction Berlin Model 3 – In development Model Y – Construction Texas Model Y – Construction Cybertruck – In development United States Tesla Semi – In development Roadster – In development 7 Installed capacity ≠ Current production rate. Production rate depends on pace of factory ramp, supply chain ramp, downtime related to factory upgrades, national holidays and other factors. * Locally-made is defined as (i) cars made in Fremont and delivered in North America and (ii) cars made in China and delivered in China. 1 Premium mid-sized sedan segment in China defined as Audi A4, BMW 3-Series, Mercedes C-Class and Tesla Model 3.

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C O R E   T E C H N O L O G Y Autopilot & Full Self Driving (FSD) Our Autopilot team has been focused on a fundamental architectural rewrite of our neural networks and control algorithms. This rewrite will allow the remaining driving features to be released. In October, we sent the first FSD software update enabled by the rewrite to a limited number of Early Access Program users — City Streets. As we continue to collect data over time, the system will become more robust. Vehicle Software New software functionality was introduced since the start of Q3. In order to make our products safer from unauthorized access, we introduced the ability to enable 2-step verification via a smartphone. Additionally, among many other updates, we improved active suspension comfort, updated Powerwall-to-vehicle charging coordination and added an automated window close function and glovebox PIN access. Our Model Y AWD customers can now purchase a $2,000 software update that improves 0-60 mph time to just 4.3s.     Battery & Powertrain On September 22, we hosted Tesla Battery Day where we described a path to reducing battery pack cost per kWh by 56%, enabling production of a profitable $25,000 vehicle. This, in our view, is a critical component to exceed cost parity with internal combustion engine vehicles. Additionally, due to a simpler cell manufacturing process, we believe capex per GWh of battery capacity should decline by 69% compared to today’s production process.  How our vehicles see an intersection 8 How our Neural Net understands the same intersection (generalized approach for any unmapped intersection)

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O T H E R H I G H L I G H T S Energy Business Our energy storage business reached record deployments of 759 MWh in Q3. Megapack production continued to ramp at Gigafactory Nevada as production volumes more than doubled in Q3. Powerwall demand remains strong and is growing, particularly as our solar business grows as many customers include a Powerwall with their solar installation. Additionally, we are seeing accelerating interest in Powerwall as concerns with grid stability grow, particularly in California. We continue to believe that the energy business will ultimately be as large as our vehicle business. Our recently introduced strategy of low cost solar (at $1.49/watt in the US after tax credit) is starting to have an impact. Total solar deployments more than doubled in Q3 to 57 MW compared to the prior quarter, with Solar Roof deployments almost tripling sequentially. While not yet at scale, we recently demonstrated a ~1.5-day Solar Roof install, as shown below in the photos. For Solar Roof, installation time is a key area of focus to accelerate the growth of this program. We continue to onboard hundreds of electricians and roofers to grow this business. 9 7:30 am Noon 2:00 pm (the next day)

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O U T L O O K Volume Cash Flow Profit Product We have the capacity installed to produce and deliver 500,000 vehicles this year.  While achieving this goal has become more difficult, delivering half a million vehicles in 2020 remains our target. Achieving this target depends primarily on quarter over quarter increases in Model Y and Shanghai production, as well as further improvements in logistics and delivery efficiency at higher volume levels.  We should have sufficient liquidity to fund our product roadmap, long-term capacity expansion plans and other expenses.  For the trailing 12 months, we achieved an operating margin of 6.3%. We expect our operating margin will continue to grow over time, ultimately reaching industry-leading levels with capacity expansion and localization plans underway.  We are currently building Model Y capacity at Gigafactory Shanghai, Gigafactory Berlin and Gigafactory Texas, and remain on track to start deliveries from each location in 2021. Tesla Semi deliveries will also begin in 2021.  We continue to significantly invest in our product roadmap. 10

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Area of improvement Description Range Increase* $/kWh Cost Reduction* $/GWh Capex Reduction* Cell Design After considering every form factor and cell size across quantifiable factors, we deemed 80 mm height by 46 mm diameter cylindrical to be best These dimensions maximize vehicle range (pack level energy density) while minimizing manufacturing and product cost The challenge is that large diameter cylindrical cells easily overheat during supercharging We identified a tab-less design solution to resolve the overheating challenge and simplify manufacturing 16% 14% 7% Cell Factory Electrode Current electrode production process involves mixing liquids with cathode or anode powders and using massive machinery to coat and dry electrode New process allows going directly from cathode or anode powder to an electrode film 0% 18% 34% Winding Larger cells improve winder productivity Incorporates our tab-less design Assembly Large cells moving at high speed with simplification in process steps enables a single production line to have 20 GWh of capacity Formation Leveraging our power electronics to densify and reduce costs of the final charging and testing step of millions of cells Anode Material Silicon is a better anode material than graphite – stores 9x more lithium, but silicon expansion brings challenges Silicon used in anodes today is highly engineered and expensive Raw silicon with our coating design will cost just $1.20/kWh Expansion of silicon is managed by stabilizing surface and by creating an elastic binder network 20% 5% 4% Cathode Material We are taking a diversified cathode approach to maximize available supply options: all usable in our 4680 cells We are planning to manufacture cathode in-house, using far less water and reagents in a simplified production process Focus on local sourcing for each cell factory to avoid unnecessary transportation cost Actively pursuing pathways to vertically integrate lithium production for a portion of supply 4% 12% 16% Cell-Vehicle Integration Current EV design: cells to modules, modules to battery pack, battery pack to vehicle Future EV design: cells directly integrated into vehicle body with giga castings Battery is no longer carried as “luggage”, will provide new utility as a load-bearing frame element This unlocks high-efficiency factories and mechanical structures— best manufacturability, weight, range and cost 14% 7% 8% Projected Total Improvement 54% 56% 69% F I V E A R E A S O F F O C U S 12 * Our current projections.

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P H O T O S & C H A R T S

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G I G A F A C T O R Y   S H A N G H A I   –   M O D E L   Y   F A C T O R Y   ( F O R E G R O U N D ) ;   M O D E L   3   F A C T O R Y   ( B A C K G R O U N D ) 14

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G I G A F A C T O R Y S H A N G H A I – M O D E L Y D I E C A S T 15

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G I G A F A C T O R Y S H A N G H A I – M O D E L Y B O D Y S H O P 16

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G I G A F A C T O R Y S H A N G H A I – M O D E L Y P A I N T S H O P 17

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18 G I G A F A C T O R Y B E R L I N – M O D E L Y F A C T O R Y C O N S T R U C T I O N

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19 G I G A F A C T O R Y T E X A S

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20 M E G A P A C K P R O J E C T AT M O S S L A N D I N G

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Vehicle Deliveries (units) Net Income ($B) K E Y   M E T R I C S   Q U A R T E R L Y  (Unaudited) 21 Operating Cash Flow ($B) Free Cash Flow ($B)

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K E Y   M E T R I C S   T R A I L I N G   1 2   M O N T H S   ( T T M ) (Unaudited) Vehicle Deliveries (units) Operating Cash Flow ($B) Free Cash Flow ($B) Net Income ($B) 22

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In millions of USD or shares as applicable, except per share data Q3-2019 Q4-2019 Q1-2020 Q2-2020 Q3-2020 REVENUES Automotive sales 5,132 6,143 4,893 4,911 7,346 Automotive leasing 221 225 239 268 265 Total automotive revenue 5,353 6,368 5,132 5,179 7,611 Energy generation and storage 402 436 293 370 579 Services and other 548 580 560 487 581 Total revenues 6,303 7,384 5,985 6,036 8,771 COST OF REVENUES           Automotive sales 4,014 4,815 3,699 3,714 5,361 Automotive leasing 117 119 122 148 145 Total automotive cost of revenues 4,131 4,934 3,821 3,862 5,506 Energy generation and storage 314 385 282 349 558 Services and other 667 674 648 558 644 Total cost of revenues 5,112 5,993 4,751 4,769 6,708 Gross profit 1,191 1,391 1,234 1,267 2,063 OPERATING EXPENSES           Research and development 334 345 324 279 366 Selling, general and administrative 596 699 627 661 888 Restructuring and other – (12) – – – Total operating expenses 930 1,032 951 940 1,254 INCOME FROM OPERATIONS 261 359 283 327 809 Interest income 15 10 10 8 6 Interest expense (185) (170) (169) (170) (163) Other income (expense), net 85 (25) (54) (15) (97) INCOME BEFORE INCOME TAXES 176 174 70 150 555 Provision for income taxes 26 42 2 21 186 NET INCOME 150 132 68 129 369 Net income attributable to noncontrolling interests and redeemable noncontrolling interests 7 27 52 25 38 NET INCOME ATTRIBUTABLE TO COMMON STOCKHOLDERS 143 105 16 104 331 Less: Buy-out of noncontrolling interest – – – – 31 NET INCOME USED IN COMPUTING NET INCOME PER SHARE OF COMMON STOCK 143 105 16 104 300 Net income per share of common stock attributable to common stockholders(1)           Basic $ 0.16 $ 0.12 $ 0.02 $ 0.11 $ 0.32 Diluted $ 0.16 $ 0.11 $ 0.02 $ 0.10 $ 0.27 Weighted average shares used in computing net income per share of common stock(1)           Basic 897 902 915 928 937 Diluted 922 935 994 1,036 1,105 S T A T E M E N T O F O P E R A T I O N S (Unaudited) 24 (1) Prior period results have been retroactively adjusted to reflect the five-for-one stock split effected in the form of a stock dividend in August 2020

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B A L A N C E   S H E E T (Unaudited) In millions of USD 30-Sep-19 31-Dec-19 31-Mar-20 30-Jun-20 30-Sep-20 ASSETS Current assets    Cash and cash equivalents 5,338 6,268 8,080 8,615 14,531    Accounts receivable, net 1,128 1,324 1,274 1,485 1,757    Inventory 3,581 3,552 4,494 4,018 4,218    Prepaid expenses and other current assets 893 959 1,045 1,218 1,238       Total current assets 10,940 12,103 14,893 15,336 21,744 Operating lease vehicles, net 2,253 2,447 2,527 2,524 2,742 Solar energy systems, net 6,168 6,138 6,106 6,069 6,025 Property, plant and equipment, net 10,190 10,396 10,638 11,009 11,848 Operating lease right-of-use assets 1,234 1,218 1,197 1,274 1,375 Goodwill and intangible assets, net 537 537 516 508 521 Other non-current assets 1,473 1,470 1,373 1,415 1,436      Total assets 32,795 34,309 37,250 38,135 45,691 LIABILITIES AND EQUITY Current liabilities    Accounts payable       3,468        3,771       3,970        3,638       4,958    Accrued liabilities and other        2,938        3,222        2,825        3,110        3,252    Deferred revenue 1,045 1,163 1,186 1,130 1,258    Customer deposits 665 726 788 713 708    Current portion of debt and finance leases (1) 2,030 1,785 3,217 3,679 3,126      Total current liabilities 10,146 10,667 11,986 12,270 13,302 Debt and finance leases, net of current portion (1) 11,313 11,634 10,666 10,416 10,559 Deferred revenue, net of current portion 1,140 1,207 1,199 1,198 1,233 Other long-term liabilities 2,714 2,691 2,667 2,870 3,049       Total liabilities 25,313 26,199 26,518 26,754 28,143 Redeemable noncontrolling interests in subsidiaries 600 643 632 613 608  Convertible senior notes              —              —  60  44 48 Total stockholders’ equity 6,040 6,618 9,173 9,855 16,031 Noncontrolling interests in subsidiaries 842 849 867 869 861       Total liabilities and equity 32,795 34,309 37,250 38,135 45,691 (1) Breakdown of our debt is as follows:    Vehicle and energy product financing (non-recourse) 3,702 4,183 4,022 4,043 4,141    Other non-recourse debt 155 355 708 1,415 605    Recourse debt 7,882 7,263 7,600 7,106 7,448       Total debt excluding vehicle and energy product financing 8,037 7,618 8,308 8,521 8,053 25

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In millions of USD Q3-2019 Q4-2019 Q1-2020 Q2-2020 Q3-2020 CASH FLOWS FROM OPERATING ACTIVITIES Net income 150  132  68  129  369  Adjustments to reconcile net income to net cash provided by (used in) operating activities: Depreciation, amortization and impairment 530  577  553  567  584  Stock-based compensation 199  281  211  347  543  Other 69  204  175  167  269  Changes in operating assets and liabilities, net of effect of business combinations (192) 231  (1,447) (246) 635  Net cash provided by (used in) operating activities 756  1,425  (440) 964  2,400  CASH FLOWS FROM INVESTING ACTIVITIES Capital expenditures (385) (412) (455) (546) (1,005) Purchases of solar energy systems, net of sales (25) (37) (26) (20) (16) Purchase of intangible assets               —                —                —                —  (5) Receipt of government grants               —  46  1  —  —  Business combinations, net of cash acquired (76)               —                —                —  (13) Net cash used in investing activities (486) (403) (480) (566) (1,039) CASH FLOWS FROM FINANCING ACTIVITIES Net cash flows from debt activities (55) (591) 544  164  (630) Collateralized lease repayments (83) (87) (97) (71) (56) Net borrowings (repayments) under vehicle and solar financing 183  478  (160) 18  99  Net cash flows from noncontrolling interests – Auto 30  19  (8) (3) (31) Net cash flows from noncontrolling interests – Solar (28) 6  (40) (42) (49) Proceeds from issuances of common stock in public offerings, net of issuance costs               —                —  2,309                —  4,973  Other 71  96  160  57  144  Net cash provided by (used in) financing activities 118  (79) 2,708  123  4,450  Effect of exchange rate changes on cash and cash equivalents and restricted cash (11) 14  (24) 38  86  Net increase in cash and cash equivalents and restricted cash 377  957  1,764  559  5,897  Cash and cash equivalents and restricted cash at beginning of period 5,449  5,826  6,783  8,547  9,106  Cash and cash equivalents and restricted cash at end of period 5,826  6,783  8,547  9,106  15,003  S T A T E M E N T   O F   C A S H   F L O W S  (Unaudited) 26

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In millions of USD or shares as applicable, except per share data Q3-2019 Q4-2019 Q1-2020 Q2-2020 Q3-2020           Net income attributable to common stockholders (GAAP) 143 105 16 104 331 Stock-based compensation expense 199 281 211 347 543 Net income attributable to common stockholders (non-GAAP) 342 386 227 451 874 Less: Buy-out of noncontrolling interest – – – – 31 Net income used in computing EPS attributable to common stockholders (non-GAAP) 342 386 227 451 843         EPS attributable to common stockholders, diluted (GAAP)(1) 0.16 0.11 0.02 0.10 0.27 Stock-based compensation expense per share(1) 0.21 0.30 0.21 0.34 0.49 EPS attributable to common stockholders, diluted (non-GAAP)(1) 0.37 0.41 0.23 0.44 0.76 Shares used in EPS calculation, diluted (GAAP and non-GAAP)(1) 922 935 994 1,036 1,105 Net income attributable to common stockholders (GAAP) 143 105 16 104 331 Interest expense 185 170 169 170 163 Provision for income taxes 26 42 2 21 186 Depreciation, amortization and impairment 530 577 553 567 584 Stock-based compensation expense 199 281 211 347 543 Adjusted EBITDA (non-GAAP) 1,083 1,175 951 1,209 1,807 Total revenues 6,303 7,384 5,985 6,036 8,771 Adjusted EBITDA margin (non-GAAP)(2) 17.2% 15.9% 15.9% 20.0% 20.6%         Automotive gross margin (GAAP) 22.8% 22.5% 25.5% 25.4% 27.7% Less: Total regulatory credit revenue recognized 2.0% 1.6% 5.5% 6.7% 4.0% Automotive gross margin excluding regulatory credits (non-GAAP) 20.8% 20.9% 20.0% 18.7% 23.7% R e c o n c I l I a t I o n   o f   G A A P   t o   N o n – G A A P   F I n a n c I a l   I n f o r m a t I o n (Unaudited) 27 In millions of USD 4Q-2017 1Q-2018 2Q-2018 3Q-2018 4Q-2018 1Q-2019 2Q-2019 3Q-2019 4Q-2019 1Q-2020 2Q-2020 3Q-2020 Net cash provided by (used in) operating activities (GAAP) 510 (398) (130) 1,391 1,235 (640) 864 756 1,425 (440) 964 2,400 Capital expenditures (787) (656) (610) (510) (325) (280) (250) (385) (412) (455) (546) (1,005) Free cash flow (non-GAAP) (277) (1,054) (740) 881 910 (920) 614 371 1,013 (895) 418 1,395                           In millions of USD 4Q-2017 1Q-2018 2Q-2018 3Q-2018 4Q-2018 1Q-2019 2Q-2019 3Q-2019 4Q-2019 1Q-2020 2Q-2020 3Q-2020 Net cash (used in) provided by operating activities – TTM (GAAP) (61) (389) (319) 1,373 2,098 1,856 2,850 2,215 2,405 2,605 2,705 4,349 Capital expenditures – TTM (3,415) (3,518) (3,169) (2,563) (2,101) (1,725) (1,365) (1,240) (1,327) (1,502) (1,798) (2,418) Free cash flow – TTM (non-GAAP) (3,476) (3,907) (3,488) (1,190) (3) 131 1,485 975 1,078 1,103 907 1,931 (1) Prior period results have been retroactively adjusted to reflect the five-for-one stock split effected in the form of a stock dividend in August 2020 (2) Adjusted EBITDA margin is Adjusted EBITDA as a percentage of total revenues

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A D D I T I O N A L   I N F O R M A T I O N WEBCAST INFORMATION Tesla will provide a live webcast of its third quarter 2020 financial results conference call beginning at 2:30 p.m. PT on October 21, 2020 at This webcast will also be available for replay for approximately one year thereafter.   CERTAIN TERMS When used in this update, certain terms have the following meanings. Our vehicle deliveries include only vehicles that have been transferred to end customers with all paperwork correctly completed. Our energy product deployment volume includes both customer units installed and equipment sales; we report installations at time of commissioning for storage projects or inspection for solar projects, and equipment sales at time of delivery. “Adjusted EBITDA” is equal to (i) net income (loss) attributable to common stockholders before (ii)(a) interest expense, (b) provision for income taxes, (c) depreciation, amortization and impairment and (d) stock-based compensation expense, which is the same measurement for this term pursuant to the performance-based stock option award granted to our CEO in 2018. “Free cash flow” is operating cash flow less capital expenditures. NON-GAAP FINANCIAL INFORMATION Consolidated financial information has been presented in accordance with GAAP as well as on a non-GAAP basis to supplement our consolidated financial results. Our non-GAAP financial measures include non-GAAP automotive gross margin, non-GAAP net income (loss) attributable to common stockholders, non-GAAP net income (loss) attributable to common stockholders on a diluted per share basis (calculated using weighted average shares for GAAP diluted net income (loss) attributable to common stockholders), Adjusted EBITDA, Adjusted EBITDA margin, and free cash flow. These non-GAAP financial measures also facilitate management’s internal comparisons to Tesla’s historical performance as well as comparisons to the operating results of other companies. Management believes that it is useful to supplement its GAAP financial statements with this non-GAAP information because management uses such information internally for its operating, budgeting and financial planning purposes. Management also believes that presentation of the non-GAAP financial measures provides useful information to our investors regarding our financial condition and results of operations so that investors can see through the eyes of Tesla management regarding important financial metrics that Tesla uses to run the business, and allowing investors to better understand Tesla’s performance. Non-GAAP information is not prepared under a comprehensive set of accounting rules and therefore, should only be read in conjunction with financial information reported under U.S. GAAP when understanding Tesla’s operating performance. A reconciliation between GAAP and non-GAAP financial information is provided above.   FORWARD-LOOKING STATEMENTS Certain statements in this update, including statements in the “Outlook” section; statements relating to the future development, production capacity and output rates, demand and market growth, deliveries, deployment, safety, range and other features and improvements, and timing of existing and future Tesla products and technologies such as Model 3, Model Y, Cybertruck, Tesla Semi, Roadster, Autopilot and Full Self Driving, our energy products and services such as Megapack, Solar Roof and Powerwall, and the battery cells we are developing and related technologies; statements regarding operating margin, spending and liquidity targets; statements regarding manufacturing and procurement improvements, cost reductions and efficiencies; statements regarding construction, expansion, improvements and/or ramp at the Tesla Factory, Gigafactory Shanghai, Gigafactory Berlin and Gigafactory Texas; and statements regarding our hiring targets are “forward-looking statements” that are subject to risks and uncertainties. These forward-looking statements are based on management’s current expectations, and as a result of certain risks and uncertainties, actual results may differ materially from those projected. The following important factors, without limitation, could cause actual results to differ materially from those in the forward-looking statements: uncertainties in future macroeconomic and regulatory conditions arising from the current global pandemic; the risk of delays in launching and manufacturing our products and features cost-effectively; our ability to grow our sales, delivery, installation, servicing and charging capabilities and effectively manage this growth; consumers’ willingness to adopt electric vehicles generally and our vehicles specifically; the ability of suppliers to deliver components according to schedules, prices, quality and volumes acceptable to us, and our ability to manage such components effectively; any issues with lithium-ion cells or other components manufactured at Gigafactory Nevada; our ability to build and ramp Gigafactory Shanghai, Gigafactory Berlin and Gigafactory Texas in accordance with our plans; our ability to procure supply of battery cells, including through our own manufacturing; risks relating to international expansion; any failures by Tesla products to perform as expected or if product recalls occur; the risk of product liability claims; competition in the automotive and energy product markets; our ability to maintain public credibility and confidence in our long-term business prospects; our ability to manage risks relating to our various product financing programs; the unavailability, reduction or elimination of government and economic incentives for electric vehicles and energy products; our ability to attract and retain key employees and qualified personnel and ramp our installation teams; our ability to maintain the security of our information and production and product systems; our compliance with various regulations and laws applicable to our operations and products, which may evolve from time to time; risks relating to our indebtedness and financing strategies; and adverse foreign exchange movements. More information on potential factors that could affect our financial results is included from time to time in our Securities and Exchange Commission filings and reports, including the risks identified under the section captioned “Risk Factors” in our quarterly report on Form 10-Q filed with the SEC on July 28, 2020. Tesla disclaims any obligation to update information contained in these forward-looking statements whether as a result of new information, future events, or otherwise. 28

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