Dopamine neurons are thought to encode novelty in addition to reward prediction error . In this study, we compared dopamine activity across the striatum using fiber fluorometry in mice. During classical conditioning, we observed opposite dynamics in dopamine axon signals in the ventral striatum (‘VS dopamine’) and the posterior tail of the striatum (‘TS dopamine’). TS dopamine showed strong excitation to novel cues, whereas VS dopamine showed no responses to novel cues until they had been paired with a reward. TS dopamine cue responses decreased over time, depending on what the cue predicted. Additionally, TS dopamine showed excitation to several types of stimuli including rewarding, aversive, and neutral stimuli whereas VS dopamine showed excitation only to reward or reward-predicting cues. Together, these results demonstrate that dopamine novelty signals are localized in TS along with general salience signals, while VS dopamine reliably encodes reward prediction error.DOI:http://dx.doi.org/10.7554/eLife.21886.001 New experiences trigger a variety of responses in animals. We are surprised by, move towards, and often explore new objects. But how does the brain control these responses?Dopamine is a molecule that controls many processes in the brain and plays critical roles in various mental disorders, diseases that affect movement, and addiction. Rewarding experiences (like a glass of cold water on a hot day) can trigger dopamine neurons and studies have also shown that dopamine neurons respond to new experiences. This suggested that novelty may be rewarding in itself, or that novelty may signal the potential for future reward. On the other hand, it may be that different groups of dopamine neurons play different roles in responding to new or rewarding experiences.In 2015, it was reported that dopamine neurons connected to the rear part of an area in the brain called the striatum receive signals from different parts of the brain than most other dopamine neurons. The dopamine neurons connected to this “tail” of the striatum preferentially received inputs from regions involved in arousal rather than reward, suggesting that they may have a unique role and transmit a different type of information.Now, Menegas et al. have shown that dopamine signals in different areas of the striatum separate reward from novelty and other signals in mice. The results demonstrate that new odors activate dopamine neurons projecting to the tail of the striatum, but that this activity fades as the novelty wears off (as the mice learn to associate the odor with a particular outcome). By contrast, dopamine neurons projecting to the front of the striatum do not respond to novelty, but rather become more active as mice learn which odors accompany rewards (only responding to odors that predict reward). The experiments also show that dopamine neurons in the tail of the striatum encode information about the importance of a stimulus.Together, these findings indicate that some of the roles dopamine plays in the brain may not be related to reward, but are instead linked to the novelty and importance of the stimulus. The next challenge will be to find out how the separate reward and novelty signals in dopamine neurons relate to the animals’ behavior. This may help us to better understand dopamine-related psychiatric conditions, such as depression and addiction.DOI:http://dx.doi.org/10.7554/eLife.21886.002 Animals respond to new stimuli in a characteristic way across species, historically characterized as an ‘orienting reflex’ or a ‘what is it reflex’ . DetectiPhysiologically, it is widely accepted that novelty responses are distributed over a network of many brain areas . Among tAs animals experience the repeated association of a stimulus and a reward, they learn to expect the reward when the stimulus is presented . DopaminNovelty responses in dopamine neurons  were iniOne explanation for why some dopamine neurons respond to novel stimuli could be that some subpopulations of dopamine neurons are not strictly related to reward prediction error coding. Recent studies have shown that there is substantial diversity among dopamine neurons at the molecular level  as well Recent anatomical studies have revealed that dopamine neurons with different projection targets are embedded in separate circuits. The entire set of inputs to dopaminergic nuclei includes a large number of regions . Neural Based on our previous anatomical findings, in this study, we compared the dopamine axon activity in VS and TS while mice learned new odor-outcome associations . Our results revealed opposite dynamics for learning new cue-outcome associations in VS dopamine and TS dopamine. We observed a large response to novel cues in TS dopamine which subsequently decreased over the course of associative learning. On the other hand, we saw no response to novel cues in VS dopamine. Instead, VS dopamine gradually developed responses to reward-predicting cues during learning. These findings revealed that dopamine novelty coding is localized to the posterior part of the striatum, while VS dopamine faithfully encodes reward prediction error. Thus, novelty responses in dopamine may be better formalized separately from the reward prediction error (RPE) framework, rather than being included in the RPE framework.We used optical fiber fluorometry (fiber photometry)  to recorWe chronically implanted optic fibers into the striatum of these mice to deliver 473 nm and 561 nm light and collect GCaMP and tdTomato signals  Figure . For theMice were presented with odors paired with water delivery or no outcome . In someWe identified the fiber implant sites by clearing brains using CLARITY , imagingIn order to examine dopamine activity in VS and TS during associative learning, we recorded both during the initial learning of new odor-outcome associations . After this initial habituation, animals were presented with four randomly interleaved trial types: (1) unpredicted water, (2) odor predicting water followed by water delivery, (3) odor predicting water followed by no outcome (omission), or (4) odor predicting no outcome.−6, t-test, n = 12 animals, We first compared VS dopamine and TS dopamine responses to an odor predicting no outcome over the course of the session . At the −11, repeated measures ANOVA, n = 12 animals, −4, paired t-test, n = 12 animals). To determine whether this decrease in signal was caused by a decrease in effective odor concentration within sessions, we compared responses to fast-decaying and slow-decaying odors . The responses to water-predicting odors in TS dopamine were significantly smaller on the seventh day of training than on the first day  . The aniWe examined the temporal dynamics of the responses to novel odors in TS dopamine. The median onset latency of responses to novel odors in TS dopamine was 140 ms  and the In summary, dopamine axon signals in VS and TS showed opposite initialization while learning stimulus-outcome relationships. Dopamine axon signals in TS showed strong excitation to novel cues that gradually decreased, whereas dopamine axon signals in VS did not respond to cues with an unknown outcome and instead gradually developed cue responses to odors reliably predicting reward. Further, TS dopamine quickly discriminated cues, resulting in differential decrease rates of responses to novel cues depending on the predicted outcome .In order to understand the relationship between novelty responses and value coding, we next examined responses to rewarding and non-rewarding stimuli in VS dopamine and TS dopamine. Mice were trained to associate odors with water or with no outcome. After several weeks of this training, in some sessions, trials with odors predicting either a mild tone (55 dB) or an air puff were interleaved in addition to trials with water and trials with no outcome. We chose a very mild tone with a similar intensity to the background noise in the room to try to minimize the aversiveness of this stimulus. To estimate the aversiveness of auditory stimuli, we measured the behavioral responses to tones of different volumes in a different set of mice . We foun−10, TS: p = 6.5x10−6, t-test, −11, TS: p = 1.0x10−4, t-test, −7, t-test, −8, t-test, We next examined reward prediction error coding, which consists of three key characteristics: (1) larger responses to reward-predicting cues than unrewarded cues, (2) smaller responses to predicted rewards than unpredicted rewards, and (3) a decrease in activity following reward omission. With respect to reward prediction, both VS and TS dopamine had a larger excitatory response to reward-predicting cues than cues predicting nothing   were rec−9, n = 40 animals, Pearson’s correlation). We next examined responses to cues predicting rewarding, neutral or aversive outcomes, and responses to the omission of reward. Responses for all these factors were correlated with the location along anterior-posterior axis to various degrees . Finally, novelty responses were positively correlated with responses to nothing, air puff-predicting cues, and reward omission . By contrast, water responses were observed in both VS and TS, although the amplitudes of water responses were slightly anti-correlated with novelty responses . We observed that water responses were found in all parts of the striatum   and dopamine axon signals in the tail of the striatum (TS) . Over the course of associative learning, TS dopamine and VS dopamine exhibited opposite initialization to novel cues. Large TS dopamine responses to novel cues gradually decayed, whereas VS dopamine had no response to novel cues and gradually developed responses to cues that predicted reward. Our previous study showed that dopamine neurons that project to the TS are anatomical outliers compared to many other dopamine neurons including dopamine neurons that project to the VS . These nThe novelty responses we observed in TS dopamine, and especially the lack of novelty responses in VS dopamine, stand in stark contrast to popular theories explaining how novelty responses could fit into the dopamine RPE framework . These tOver the course of classical conditioning, dopamine axon signals in VS and TS showed different coding principles. For a cue with an unknown outcome, VS dopamine initialized with no value prediction and gradually accumulated evidence for value, whereas TS dopamine began with a large excitation to novel cues and gradually decreased its responses. These dynamics can be conceptualized such that CS-value in VS dopamine was initialized with no value, while CS-salience in TS dopamine was initialized with very high salience.US determines how well the animal can learn the association. In these models, US associability is determined by the prediction error of the US . Once the prediction error decreases, the US loses its effectiveness for creating an association with a CS. By contrast, other models  models , emphasir models  emphasizDopamine dynamics in VS are well suited to serve as US associability, the teaching signal of the Rescorla-Wagner model . TS dopaNovel stimuli may excite TS dopamine because of the unpredictability of the novel stimulus itself (i.e. it occurs with no cue or context predicting it) or because the outcome of these novel stimuli is unknown . These two phenomena have been implemented in the framework of CS associability. For example, Wagner  proposedImportantly, previous learning models incorporated teaching signals in the Rescorla-Wagner model and attention signals by novelty responses in one dopamine RPE system, with novelty responses as an exception or a bonus to the system . Here, wThus, during the learning of an association between a stimulus and a reward, dopamine signals in VS and TS may cooperate. Salience prediction error in TS dopamine may serve as the CS associability of the stimulus, whereas value prediction error in VS dopamine may serve as the US associability of the reward. Alternatively, similar to value prediction error in VS dopamine during stimulus-reward association, salience prediction error in TS dopamine may reinforce stimulus-stimulus associations.In contrast to TS dopamine, VS dopamine activity appeared to faithfully signal RPE. Several theories have arisen to explain how the reward prediction error could be computed in the brain. Of these, popular models such as Houk’s model  and tempThe development of CS responses in VS dopamine was also much slower than the associative learning observed at the behavioral level. By contrast, US responses decreased as anticipatory licking increased. These results demonstrate that US responses in VS dopamine are well suited as prediction error signals in the Rescorla-Wagner learning model, whereas CS responses are not time-locked to this learning.The time course of CS responses we observed in VS dopamine is not easily explained by simple TD models. In these models, during learning, RPE signals gradually transfer from the timing of the reward to the timing of the preceding stimulus . AlthougThere are different types of novelty . One exaIn our previous study, dopamine neurons that project to TS were mainly observed in the lateral SNc of mice . A previA recent study found that putative dopamine neurons which project to the tail of the caudate (part of posterior striatum) in monkey formed another group of dopamine neurons. These dopamine neurons did not respond to water reward but encoded ‘sustained values’ of visual cues, whereas putative dopamine neurons projecting to the anterior caudate encoded ‘updating values’, when cue-outcome contingency was frequently changed . HoweverWhere do salience signals come from? How are salience signals regulated by novelty and experiences? A map of monosynaptic inputs to TS-projecting dopamine neurons should provide critical information . PreviouOptical fiber fluorometry (fiber photometry) was developed by Kudo et al.  and has Dopamine axons passing through and below the ventral striatum to the cortex  may haveWe found that dopamine responses to novel stimuli are more localized than previously believed. We propose to revise current RPE models so that novelty-driven and salience-driven attention is attributed to TS dopamine, rather than added to the RPE framework as a bonus . Thus, TSlc6a3)-Cre mice  (B6.SJL-tm1.1(cre)BkmnSlc6a3/J, Jackson Laboratory; RRID:IMSR_JAX:006660) (tm9(CAGtdTomato)HzeGt(ROSA)26Sor, Jackson Laboratory). Animals were housed on a 12 hr dark/12 hr light cycle (dark from 07:00 to 19:00), one to a cage, and performed the task at the same time each day. All procedures were performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and approved by the Harvard Animal Care and Use Committee.85 male adult mice were used. These mice were the result of a cross between DAT (:006660)  and tdToTo prepare animals for recording, we performed a single surgery with three key components: (1) AAV-FLEX-GCaMP virus infection into the midbrain, (2) head-plate installation, and (3) one or more optic fiber implants into the striatum. At the time of surgery, all mice were 2–3 months old. All surgeries were performed under aseptic conditions with animals anesthetized with isoflurane (1–2% at 0.5–1.0 l/min). Analgesia  was administered for 3 days following each surgery.12 particles/ml, Penn Vector Core) into both the VTA and SNc . To target the VTA, we injected virus at Bregma −3.0, Lateral 0.6, at all depths between 4.5 and 4.0 mm. To target SNc, we injected virus at Bregma −3.3, Lateral 1.6, at all depths between 4.0 and 3.5 mm. Virus injection lasted several minutes, and then the injection pipette was slowly removed over the course of several minutes to prevent damage to the tissue.To express GCaMP specifically in dopamine neurons, we unilaterally injected 250 nl of AAV5-CAG-FLEX-GCaMP6m . To do this, we first slowly lowered optical fibers  into the striatum. The coordinates we used for targeting were as follows: (VS) Bregma 1.0, Lateral 1.25, Depth 4.1, (DS) Bregma 0.0, Lateral 1.5, Depth 2.25, (DLS) Bregma −0.5, Lateral 2.75, Depth 2.5, (TS) Bregma −2.0, Lateral 3.25, Depth 2.5. Once fibers were lowered, we first attached them to the skull with UV-curing epoxy , and then a layer of black Ortho-Jet dental adhesive . After waiting fifteen minutes for this glue to dry, we applied a very small amount of rapid-curing epoxy  to attach the fiber cannulas even more firmly to the underlying glue and head-plate. After waiting fifteen minutes for the epoxy to dry, the surgery was complete.2+ indicator, GCaMP6m (Fiber fluorometry (photometry)  allows f GCaMP6m  and chro GCaMP6m .2+ transients from dopamine terminals, we injected a Cre-dependent adeno-associated virus (AAV) carrying the GCaMP6m gene into the VTA and SNc of transgenic DAT-Cre mice and implanted 200 µm or 400 µm diameter optic fibers in the striatum.Activity-dependent fluorescence emitted by cells in the vicinity of the implanted fiber’s tip was spectrally separated from the excitation light using a dichroic, passed through a single band filter, and focused onto a photodetector connected to a current preamplifier . To record CaDuring recording, optic fibers were connected (1–2 per recording session) to patch cables which delivered excitation light (473 nm and 561 nm) and collected all emitted light. The emitted light was subsequently split and filtered see  and collAfter surgery, mice were given three weeks to recover and become habituated to the installed head-plate and implanted optic fibers. Additionally, this allowed time for viral expression. After this recovery period, mice were handled for 2–3 days and water deprived. Weight was maintained above 90% of baseline body weight.In the first 2–3 sessions, mice were head-fixed and given unexpected water at random intervals . This allowed mice to become habituated to being head-fixed and allowed us to determine the appropriate laser power  to record >5% dF/F free water responses . These sessions were important, so that recordings during odor-water association could begin from the very first odor presentation on the first day of data collection .The volume of water was constant for all reward trials (predicted or unpredicted) in all conditions. Similarly, the same mild tone  was used in all tone trials and the same intensity air puff was used in all air puff trials. Each behavioral trial began with an odor cue (a puff of odor lasting 1 s), followed by a 1 s delay, and then an outcome . Odors were delivered using a custom olfactometer . Each odOn the first day of classical conditioning, odors were presented to mice for the first time, and either predicted no outcome or reward. We quantified the ‘novelty response’ as the response to the first odor presentation that the mouse experienced, which was associated with no outcome (for n = 13 VS-implanted mice and n = 12 TS-implanted mice). For comparison, the response to the first unpredicted water presentation in those sessions was quantified as well. These ‘novelty responses’ were the first trials of the first day of classical conditioning , while tDuring classical conditioning, odor cues  were associated with either reward or no outcome. In the case of reward trials, water (the ‘unconditioned stimulus’ or ‘US’) would follow odor presentation after 2 s, 90% of the time (i.e. 10% omission). In ~10% of trials, unpredicted water was delivered without odor presentation. During training, GCaMP responses were recorded at time points  rather tAt this point in training, mice were also presented with odor-tone associations  or odor-air puff associations , in addition to the two familiar odors associated with water and with no outcome, allowing us to multiplex data from learning onto this data regarding value or salience coding and prediction error coding. Unpredicted tone or air puff was also delivered in ~5% of trials. Data from these sessions was used in Finally, a subset of these mice (n = 11) were trained with two new odors each day (one associated with water and one associated with no outcome), every day for a week, until mice began to discriminate between odors behaviorally within a few trials see  rather t1’) with the signal at any given point during the trial (‘F2’). The calculation for each point in the trial  was then simply dF/F = (F2 – F1) / F1. We used this measurement because it readily normalized signals . The average responses to a stimulus type within a session  were averaged, and these session averages were used as the data in each figure  Traces were averaged and plotted  along with the standard error  as in To compute the latency of responses to novel cues, each trial was tested for difference from baseline in the first five novel odor trials using time bins of 50 ms. We called the ‘latency’ of the response  the center of the first time bin where five consecutive time bins all showed significant difference from baseline. To compute the latency of novelty discrimination, the sessions were tested for significant difference between familiar and novel odors in the first five trials using time bin of 50 ms. We called the ‘latency’ of discrimination the center of the first time bin where five consecutive time bins all showed significance.While recording GCaMP signals, we also recorded licking. To measure licking, we used a detector that output a voltage based on the disruption of its infra-red light path. We set a threshold for signal corresponding to a ‘lick’ and then made the signal binary by finding each time point where the signal crossed the threshold so that it could easily be quantified. Our main quantification for licking was counting the number of ‘anticipatory licks’, the licks following an odor (CS) and preceding the arrival of the outcome (US).GCaMP responses and licking responses were collected through Labview during the training for offline analysis. Statistical analyses  were run using Matlab (Mathworks). All analyses considered a value of p≤0.05 significant, and exact p-values are reported in the text. To quantify body movement, we used a video camera to capture images of the mice while they performed the task and made a rough estimate of total body movement by subtracting each frame of the video from the last frame, using the ‘imabsdiff’ function in Matlab. We reported these values, which we took to be a proxy for body movement, as ‘arbitrary units’ or ‘a.u.’ because they were measured in pixels.Brains were cleared as previously described  at 37°C Images were acquired with the Zeiss Z.1 Light-sheet microscope . Brains were glued to the tip of a 1 ml syringe (without needle) such that the posterior tip of the cerebellum was in contact with the syringe. A 488 nm laser was used to excite GFP and a 647 nm laser was used to produce autofluorescence. Images were collected through a 5× objective with PCO-Edge scMOS 16 bit cameras  with 1920 × 1920 pixels. Each frame was 2000 × 2000 μm, so each pixel was roughly 1.04 μm. The step size between images was set to 5.25 μm, so the voxels were not isotropic. Brains were imaged horizontally from the dorsal side, and then rotated 180° for horizontal imaging from the ventral side. Each view was tiled with 7 × 6 tiles  and the two views were combined to create a continuous image. Autofluorescence images were subsequently downsized to 1400 × 1200 × 700 pixels for alignment to the reference space. In these downsized images, voxels have 10 μm spacing in all three dimensions. Brains were aligned to a previously described reference space comprised of the average of 25 brains . AlignmeAfter clearing and imaging each brain as a whole volume and aligning these images to determine the exact location of each fiber, we classified each fiber as either (1) an implant into VS, (2) an implant into DS, (3) an implant into TS, or (4) an incorrectly targeted fiber. Our analysis in the current paper focuses on comparing VS-implanted fibers to TS-implanted fibers. Most implants were successfully targeted to VS or TS. We classified implants as successful based on the following criteria. For VS: any fiber within the nucleus accumbens core or shell, between Bregma 2.0 and Bregma 0. For DS: any fiber within the striatum anterior to Bregma 1.5. For TS: any fiber within the striatum posterior to Bregma −1.5. We discarded data from eight animals which had fibers incorrectly targeted. The fibers in these animals were often in areas of cortex directly adjacent to the intended recording site. We observed very little or no signal (compared to our other recordings) in these cases, likely due to the relatively sparse dopaminergic innervation of cortex relative to striatum in mouse. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by two peer reviewers, and the evaluation was overseen by Gary Westbrook as the Senior Editor. The following individual involved in review of your submission has agreed to reveal her identity: Kay M Tye (Reviewer #2).Thank you for submitting your article "Opposite initialization to novel cues in dopamine signaling in ventral and posterior striatum" for consideration by Both reviewers were very positive about your work. The reviewers have discussed the reviews with one another and the Senior Editor has drafted a consolidated summary of the reviews to help you prepare a revised submission.Summary:Menegas and colleagues studied dopamine neuron responses during classical conditioning and compare the responses across different striatal subregions. They used fiber photometry to record calcium responses from DA axons and tracked these signals across learning. The major result is a dramatic difference between dopamine signals in the ventral striatum (VS) compared to the tail of the striatum (TS). While VS projecting DA neurons followed the classical reward prediction error patterns and did not respond to novel cues, TS projecting DA neurons responded strongly to novel cues and also to the overall salience of cues and outcomes. This is a beautiful manuscript reporting on a very timely and important topic. The design of the experiments is elegant and the observed difference between DA responses is striking. The manuscript is very well written and describes an important finding with attention to the points listed below.Essential revisions:1) It would be important to measure the timecourse of the odor signals to assess whether cue responses in the TS are concordant with what is expected based on adaptation in the olfactory bulb or piriform cortex. Please use PID measurements of olfactometer output. This would also inform the issue of whether TS implements a novelty filter or rather it inherits novelty signals.2) Why did the authors use TdTomato as the control instead of the 405 emission? Did the authors also collect this? Do the authors have some evidence that different emission wavelengths produce identical mechanical deflections, or might there be chromatic aberrations that make this different? At this point, the appropriate controls for fiber photometry are still up for discussion, so it would be useful to have the authors directly discuss this issue.3) The description of TS signals beyond novelty and their relationship to RPE is confusing. Would it be correct to simplify the responses as the absolute value of RPE plus a novelty bonus? Either way, please clarify.4) It was not convincing that the neutral tone stimuli  are perc5) The authors should not discuss the signals as "dopamine signals" as they are only looking at the calcium transients within dopamine neurons that may co-release glutamate. They should instead refer to these signals as "signals from VTA DAT+ neurons in the VS or TS" or something similar, and separately discuss what this can reflect.6) It would be important to see histology showing representative expression of GCaMP-expressing fibers in each of the regions sampled7) How much of the photometry signal is due to terminals versus axons of passage?8) Regarding 9) It would be nice to see the anatomy: if the authors retrogradely label VTA DA neurons that project to VS versus TS, where do these TS neurons reside? What is the distribution? This would be helpful information in terms of joining this information with the work by Stephan Lammel on the arrangement of VTA DA neurons projecting to mPFC or NAc m shell or NAc l shell.10) Did the authors control for the order of all of the manipulations? Were the order of manipulations randomized for both the trial types within A, B, C and the epochs  It would be important to measure the timecourse of the odor signals to assess whether cue responses in the TS are concordant with what is expected based on adaptation in the olfactory bulb or piriform cortex. Please use PID measurements of olfactometer output. This would also inform the issue of whether TS implements a novelty filter or rather it inherits novelty signals.These are very important points, and we appreciate this advice. During odor-association learning, we observed that novel-odor-cue-evoked calcium signals in TS-projecting dopamine axons decreased within a session .One potential explanation for the within-session decrease in signal could be that the effective concentration of the odorants decreased over time within a session, and that the TS dopamine axon signals encode stimulus intensity and merely reflect the concentration change. To test this idea, we quantified the change in the effective concentration of the odorants used in our study over trials within a session, as well as the latency of odor delivery within single trials, using a photoionization detector (PID)  . We foun“To determine whether this decrease in signal was caused by a decrease in effective odor concentration within sessions, we compared responses to fast-decaying and slow-decaying odors . TS dopaWhether the decrease of TS dopamine axon responses to novel cues is due to sensory adaptation is also a fundamental question meriting some discussion. First, studies using anesthetized animals have shown that neurons in the piriform cortex, but not in the olfactory bulb, quickly adapt to repeated odor presentations, and recover in a relatively short time period, normally within tens of seconds . Our observation that dopamine novelty response decreases over days  cannot bA recent study suggested that neurons in the olfactory bulb of awake mice  modulated their responses to the repetitive exposure of odors following more complicated principles than the simple adaptation observed in olfactory sensory neurons . Kato et al. showed that neurons in the olfactory bulb quickly reduced their responses to odors following repetitive exposure (within a session) and that the effects of exposure lasted for over a month. Although we cannot directly compare the time-course because of the difference of tasks , the dynamics in olfactory bulb are comparable to our observation in TS dopamine axon signals.It is also important to discuss the latency of novelty detection within a trial. Behaviorally, novelty detection occurs within 200 ms; immediately after a single sniff . In order to understand which brain area could function as a novelty filter, as the reviewers mentioned, it is very important to examine the temporal dynamics of neuronal signals in response to novelty. We found that the median latency of TS dopamine axon responses to novel odors in naïve mice was 140 ms  and the In order to understand the precise temporal relationship between these signals, functional analysis and more precise measurements of timing (single unit and simultaneous awake recording), as well as consideration of temporal coding in the olfactory bulb  is necessary. In short, our results demonstrate that TS dopamine axons signal novelty quickly (within the first sniff), but future studies will examine which brain area or areas function as novelty filters. We added a brief paragraph to the Discussion (subsection “Relation to previous studies”) to address this issue.“Where do salience signals come from? How are salience signals regulated by novelty and experiences? A map of monosynaptic inputs to TS-projecting dopamine neurons should provide critical information . Previous studies showed that various brain areas including olfactory and visual systems are modulated by experience . Whether dopamine neurons receive this processed information from sensory systems or whether more abstract information about salience and novelty is passed to dopamine neurons and sensory systems in parallel is an open question. Of note, behavioral responses to novel odors are very quick, within one sniff in rats . The responses to novel odors in TS dopamine that we observed began within 200 ms, most likely within one sniff, suggesting a potential contribution at the early stages of novelty.”2) Why did the authors use TdTomato as the control instead of the 405 emission? Did the authors also collect this? Do the authors have some evidence that different emission wavelengths produce identical mechanical deflections, or might there be chromatic aberrations that make this different? At this point, the appropriate controls for fiber photometry are still up for discussion, so it would be useful to have the authors directly discuss this issue.Optical fiber fluorometry has long been used in various preparations . Signals collected from excitation with different wavelengths are sometimes used to normalize signals with the goal of removing the effects of autofluorescence, bleaching, and movement . This is an especially important consideration in freely moving animals, where the interface between the implanted fiber and the patch cord used to collect signal can cause additional artifacts. In this paper, we used a head-fixed preparation and reported the signal from GCaMP. We did not normalize by the tdTomato signal because we were not confident that the ratio of GCaMP: tdTomato would be constant between mice. Specifically, TdTomato levels were likely to be similar , but the virally-delivered GCaMP was likely to be present at slightly different levels in each animal. This problem would not likely be solved by using 405 nm excitation to separately collect a baseline as previously outlined  because this autofluorescence signal would also not scale with GCaMP infection size. We added sentences in the Discussion .“Most importantly, calcium transients may not enhance spike counts, because of autofluorescence, bleaching, motion artifacts and inevitable normalization. Although we only applied baseline normalization in this study, additional methods using activity-independent wavelength of excitation  or examination of emission spectrum  may improve fidelity, especially in freely moving animals.”In order to avoid drift in signals due to bleaching, we minimized laser power for each mouse  and ensured that our recording was limited to approximately 45 min per day. Additionally, although we trained mice every day, we recorded only at time points during training . We foun“To determine whether changes in signal intensity observed within a session were likely to have been related to bleaching, we compared responses to free water and familiar odors over the course of sessions . We founTo determine whether our signals were likely to have been contaminated by movement-related artifacts, we collected video of mice (n=6) performing the task and analyzed body movement . We quan“To determine whether the signals we observed could have been caused by a movement-related artifact, we used video analysis to quantify the total body movements of mice performing the task . We foun3) The description of TS signals beyond novelty and their relationship to RPE is confusing. Would it be correct to simplify the responses as the absolute value of RPE plus a novelty bonus? Either way, please clarify.There is a controversy about the excitation of dopamine neurons in response to aversive stimuli, and we therefore tried to avoid oversimplifying our results.Hikosaka and colleagues  found that some dopamine neurons are activated by both aversive and appetitive stimuli. They interpreted this result as dopamine neurons encoding “motivational salience” . In this definition, it is the “aversiveness” or “negative value” of the stimulus that makes these stimuli motivationally salient. However, Fiorillo  acquired a very similar dataset, but proposed that the excitation of dopamine neurons was better explained by stimulus intensity. He argued that this excitation was not correlated with aversion, which he estimated based on choice preferences. Unfortunately, our task was not designed to distinguish between these possibilities. Nonetheless, our data do not perfectly match with either of the ideas. First, we observed similar amplitudes of excitation in response to water and a very quiet tone in thirsty mice. For comparison to other studies, a 90 dB tone did not affect the utility function in thirsty monkeys , and we used a 55 dB tone with our thirsty mice. Second, we also observed large responses to novel cues. We think that it is important to distinguish these TS dopamine axon signals from those proposed in previous studies. We therefore propose that signals in TS are general salience prediction error signals  rather than conceptualizing them as the absolute value of salience plus a separate bonus for novelty.We cannot formally exclude other possibilities regarding TS dopamine axon signals. For example, if novel odors and mild tones are both aversive, TS dopamine axons could be signaling the absolute value of RPE. Or, as a reviewer suggested, responses to novel cues might be differentially conceptualized as a “bonus” added to the signal rather than an aspect of salience. We rephrased parts of our Results and Discussion to try to make our proposals clearer (Discussion “Relation to previous studies”).“A previous study proposed that putative dopamine neurons in the lateral SNc of monkeys encode ‘motivational salience’, which is the absolute value of positive or negative ‘value’ . On the other hand, another study proposed that excitation of dopamine neurons in response to non-rewarding stimuli encodes stimulus intensity, regardless of value . In the present study, the excitation of TS dopamine elicited by various neutral stimuli suggested that the responses in TS could be related to more general salience rather than motivational salience, although we cannot rule out the possibility that the tone and odor predicting nothing had positive or negative motivational values. On the other hand, the fact that signals encoded by TS dopamine are modulated by prediction suggests that they are not encoding pure physical salience (i.e. stimulus intensity). Instead, TS dopamine appears to encode general stimulus salience prediction error, which includes prediction-dependent suppression and prediction. The novelty responses we observed may be the extreme case of salience prediction error, causing large excitation because of minimum prediction, rather than an exception.”4) It was not convincing that the neutral tone stimuli  are percTo address this concern, we have performed behavioral measurements of responses to tones of various sizes. We found that there was not a large behavioral response to the size of tone that we used. However, we cannot say with certainty that the tone was not slightly aversive, so we changed our language in the text from “neutral tone” to “mild tone” or “quiet tone”.To try to minimize the aversiveness of the tone, we used a very small 55 dB tone for this experiment. For reference, ambient noise in the room where recordings were performed was approximately 50 dB. Startle responses have been observed in response to very loud tones, in experiments with background tones that were louder than the tone that we chose . It should also be noted that startle experiments could also actually have a component of perceptual salience , rather than pure aversion.Because we used a tone that was not much louder than the background noise in the room, and because mice became familiar with this tone, we do not think that it was perceived as aversive. To test this, we measured the behavioral responses of mice when presented with tones of different volumes: 55 dB to 105 dB . We founFiorillo examined the effect of a louder tone (90 dB) on the utility function in a water-reward-choice tasks, and showed that their tone did not cause aversion in monkeys . This means that the sound did not bias the monkey’s choice such that it would prefer an equally valuable option less often to prevent the sound from being played. Based on these (admittedly indirect) pieces of evidence, we suggest that signals in TS are likely to encode general salience rather than aversion or the absolute value of “value”. However, we cannot formally exclude the possibility that the 55 dB tone we chose was aversive to mice. Therefore, we rephrased the related sections of the paper. Specifically, we replaced “neutral” to “non-rewarding” in the Results and added a discussion on the behavioral responses we observed to this tone (Results section). Please also see Essential Revision #3.“Responses to rewarding and non-rewarding stimuli in VS and TS dopamineAfter several weeks of this training, in some sessions, trials with odors predicting either a mild tone (55 dB) or an air puff were interleaved in addition to trials with water and trials with no outcome. We chose a very mild tone with a similar intensity to the background noise in the room to try to minimize the aversiveness of this stimulus. To estimate the aversiveness of auditory stimuli, we measured the behavioral responses to tones of different volumes in a different set of mice . We founThe question of why we observed decreases in VS dopamine in response to tone is interesting, and likely to be related. Our tasks include a high probability of reward . In such high reward contexts, neutral stimuli might be interpreted as a signal for “no reward” because they predict that reward will not come in the very near future . Because of this, we have to be careful when interpreting inhibition in VS caused by tone-predicting odor cues or by tones themselves. Notably, familiar odors predicting no outcome also caused a negative inflection in the RPE-type signal that we observed in VS that was similar in size .5) The authors should not discuss the signals as "dopamine signals" as they are only looking at the calcium transients within dopamine neurons that may co-release glutamate. They should instead refer to these signals as "signals from VTA DAT+ neurons in the VS or TS" or something similar, and separately discuss what this can reflect.We agree with the reviewer. We changed our wording in many parts of the paper  to make it clear that the signals we observed are due to increases in calcium in the axons of midbrain dopamine neurons (DAT+) found in either VS or TS. We added a detailed explanation of our nomenclature in the Introduction and at the beginning of the Results section and added some discussion of the meaning of these axon signals in the Discussion section .“We will call the bulk calcium signal that we observed from the axons of DAT+ midbrain dopamine neurons in the striatum “VS dopamine” and “TS dopamine” in the following sections”.“We used optical fiber fluorometry (fiber photometry) to record bulk calcium signals from the axons of midbrain dopamine neurons projecting to several regions of the striatum .”“In this study, we recorded the population activity of dopamine axons in the striatum using GCaMP6m .”6) It would be important to see histology showing representative expression of GCaMP-expressing fibers in each of the regions sampledWe added images of GCAMP-expressing fibers throughout the striatum in 7) How much of the photometry signal is due to terminals versus axons of passage?It is difficult to exclude the possibility of having collected signals from passing axons, we estimated that most of the signals we observed come from axon terminals based on four arguments to this effect.First, the striatum is the main target of dopamine neurons and projections to other areas are much weaker than projection to the striatum . Second,“Dopamine axons passing through and below the ventral striatum to the cortex  may have contributed to the signals in VS dopamine, although calcium transients in passing axons are smaller than in axon terminals and boutons .”8) Regarding We sorted our data based on anticipatory licking (rather than time) and plotted responses to new water-predicting odors and water delivery in VS and TS. We added a figure . Particu9) It would be nice to see the anatomy: if the authors retrogradely label VTA DA neurons that project to VS versus TS, where do these TS neurons reside? What is the distribution? This would be helpful information in terms of joining this information with the work by Stephan Lammel on the arrangement of VTA DA neurons projecting to mPFC or NAc m shell or NAc l shell.We compared 8 subpopulations of dopamine neurons, including ones projecting to mPFC, and published this data in our previous paper . VS-projecting DA neurons are mostly found in the VTA and TS-projecting DA neurons are mostly found in the lateral part of the SNC. This segregation along the medial-lateral axis could roughly coincide with the segregation of axons in the striatum along the anterior-posterior axis. We referenced this in the Discussion (“Relation to previous studies”).“In our previous study, dopamine neurons that project to TS were mainly observed in the lateral SNc of mice . A previous study proposed that putative dopamine neurons in the lateral SNc of monkeys encode ‘motivational salience’, which is the absolute value of positive or negative ‘value’ .”10) Did the authors control for the order of all of the manipulations? Were the order of manipulations randomized for both the trial types within A, B, C and the epochs  or an air puff were interleaved in addition to trials with water and trials with no outcome”.“All trial types were randomized in all of the sessions”.“After introduction of tone and air puff, reward sessions, reward and air puff sessions, and reward and tone sessions were randomized.”


Ephx1 (encoding for mEH protein) was increased 1.35-fold in sEH KO, while expression of Ephx2 (encoding for sEH protein) was increased 1.4-fold in mEH KO liver; (b) Cyp2c genes, encoding for the predominant epoxygenases in mouse liver, were mostly dysregulated in the same manner in both sEH and mEH KO mice, showing that loss of either EH has a similar impact. Taken together, mEH appears to play a leading role in the hydrolysis of 8,9-EET and 9,10-EpOME and also contributes to the hydrolysis of other FA epoxides. It probably profits from its high affinity for FA epoxides under non-saturating conditions and its close physical proximity to CYP epoxygenases, and compensates its lower abundance by a more widespread expression, being the only EH present in several sEH-lacking cell types.Microsomal and soluble epoxide hydrolase (mEH and sEH) fulfill apparently distinct roles: Whereas mEH detoxifies xenobiotics, sEH hydrolyzes fatty acid (FA) signaling molecules and is thus implicated in a variety of physiological functions. These epoxy FAs comprise epoxyeicosatrienoic acids (EETs) and epoxy-octadecenoic acids (EpOMEs), which are formed by CYP epoxygenases from arachidonic acid (AA) and linoleic acid, respectively, and then are hydrolyzed to their respective diols, the so-called DHETs and DiHOMEs. Although EETs and EpOMEs are also substrates for mEH, its role in lipid signaling is considered minor due to lower abundance and activity relative to sEH. Surprisingly, we found that in plasma from mEH KO mice, hydrolysis rates for 8,9-EET and 9,10-EpOME were reduced by 50% compared to WT plasma. This strongly suggests that mEH contributes substantially to the turnover of these FA epoxides—despite kinetic parameters being in favor of sEH. Given the crucial role of liver in controlling plasma diol levels, we next studied the capacity of sEH and mEH KO liver microsomes to synthesize DHETs with varying concentrations of AA (1–30 μM) and NADPH. mEH-generated DHET levels were similar to the ones generated by sEH, when AA concentrations were low (1 μM) or epoxygenase activity was curbed by modulating NADPH. With increasing AA concentrations sEH became more dominant and with 30 μM AA produced twice the level of DHETs compared to mEH. Immunohistochemistry of C57BL/6 liver slices further revealed that mEH expression was more widespread than sEH expression. mEH immunoreactivity was detected in hepatocytes, Kupffer cells, endothelial cells, and bile duct epithelial cells, while sEH immunoreactivity was confined to hepatocytes and bile duct epithelial cells. Finally, transcriptome analysis of WT, mEH KO, and sEH KO liver was carried out to discern transcriptional changes associated with the loss of EH genes along the CYP-epoxygenase–EH axis. We found several prominent dysregulations occurring in a parallel manner in both KO livers: (a) gene expression of The online version of this article (doi:10.1007/s00204-017-2060-4) contains supplementary material, which is available to authorized users. EPHX1, have been associated with an increased risk to develop preeclampsia, a severe pregnancy complication, characterized by hypertension  and soluble epoxide hydrolase (sEH) belong to the epoxide hydrolase (EH) family, which catalyzes the hydrolysis of epoxides. mEH and sEH were first characterized as detoxifying enzymes, because they can convert genotoxic epoxides to less harmful dihydrodiols Oesch . In partBy contrast, a large body of evidence demonstrates the involvement of sEH in various physiological processes, which is due to the ability of sEH to efficiently hydrolyze endogenous FA epoxides such as the AA-derived epoxyeicosatrienoic acids (EETs) , using specific primer pairs. Genomic DNA was extracted from mouse ear biopsies and the following primers were used to identify homozygous WT and mEH KO mice: mKO forward 5′-CCCGGGACAAGGAGGAGACC-3′ and mKO reverse 5′-AAGGATCACAGGGTGAAAGGAA-3′. The PCR product for homozygous WT had a size of 800 bp and that for homozygous mEH KO mice a size of 1400 bp. For genotyping sEH KO mice, the primers sKO forward 5′-GCGAGGGGCGGTGCTGAGATTGG-3′ and sKO reverse 5′-CTGGAAAGCAATTTGAAACCTGGG-3′ were used. The PCR product for homozygous WT had a size of 305 bp, for homozygous sEH KO a size of 1444 bp.g in a benchtop centrifuge (Eppendorf 5417R); plasma was removed and frozen at −20 °C after adding the antioxidant butylated hydroxytoluene. For oxylipin extraction, 150 μl of plasma was added to 150 μl acetonitrile/methanol (1:1), including the deuterated standards for 8,9-, 11,12-, 14,15-EETs and DHETs; 9,10-, and 12,13-EpOMEs; and 5-, 8-, and 12-HETEs. After addition of 700 μl phosphate-buffered saline , the samples were centrifuged  and subjected to solid-phase extraction, using ISOLUTE® C18 solid-phase extraction columns . The columns were primed with 2 × 1 ml acetonitrile and 2 × 1 ml PBS containing 5% methanol (pH 7.4). After loading the samples, columns were washed with 1 ml PBS containing 5% methanol, dried, and eluted with 3 × 600 μl ethyl acetate. The eluate was dried under nitrogen, and extracted lipids were re-dissolved in 100 μl methanol/20 mM Tris, pH 7.4 (1:1), and subjected to liquid chromatography-tandem mass spectrometry (LC–MS/MS). Incubation of liver homogenates with 50 μM AA was carried out as described previously . Blood was immediately taken from V. cava, using a syringe that was rinsed before with 10% EDTA (pH 8). The collected blood was centrifuged for 10 min at 1700×g; 20 min at 9000×g); the supernatant (S9 fraction) was removed and further centrifuged for 20 min at 100,000×g in an ultracentrifuge, using a TLA-100.3 rotor , which yielded the cytosolic fraction as supernatant. The pellet, containing the microsomal fraction, was washed in 500 µl Tris buffer , ultra-centrifuged again for 20 min at 100,000×g, and resuspended in Tris buffer. The final microsomes were homogenized and shock-frozen in liquid nitrogen. The total protein content of microsomal and cytosolic fractions was determined using a standard Bradford protein assay.100–200 mg frozen livers of all three genotypes  were homogenized on ice in 1 ml buffer containing 125 mM KCl, 250 mM sucrose, 100 mM potassium phosphate, and 1 mM EDTA (pH 7.4). The samples were centrifuged  or microsomal fractions  were added to a final volume of 50 μl of Tris buffer  and preincubated on ice for 10 min with and without the selective sEH inhibitor trans-4-[4-(3-adamantan-1-y1-ureido)-cyclohexyloxy]-benzoic acid  or a combination of the mEH inhibitors 1,1,1-trichloropropene oxide , and 2-nonylsulfanyl-propanamide  . The reaction was started by adding 2 mM NADPH, 0.6 U/mL isocitrate dehydrogenase Type IV, and AA. Samples were incubated at 37 °C for 10–30 min. The reactions were stopped with 100 μl ethyl acetate containing deuterated standards. The organic phase was removed and samples were extracted a second time with 100 μl ethyl acetate. Organic phases were combined and dried under nitrogen. Lipids were re-dissolved in Tris  and analyzed by LC–MS/MS.Microsomes (containing 50 μg protein) were added to a final vol of 50 μl buffer  were separated on a 12.5% SDS-PAGE and transferred onto PVDF membranes  using the semi-dry Trans-Blot®Turbo™ Transfer Starter System . The primary antibodies used were raised in rabbits against rat sEH  was assumed to reflect epoxygenase activity, which increased steadily over time and with increasing AA concentrations. Epoxygenase activity was similar for mEH and sEH KO microsomes and neither genotype showed any indication for epoxygenase saturation, as concentration curves were linear with a constant slope Fig. a, b. ForTo investigate in detail the distribution pattern of mEH and sEH in the mouse liver, IHC was applied to formalin-fixed paraffin-embedded liver sections obtained from both genders from C57BL/6 WT control, mEH KO, and sEH KO animals. In accordance with earlier studies  was 1:2.6  in WT, mEH KO, and sEH KO liver (Suppl. Table 5). Cyp2e1 was by far the most abundant, irrespective of the genotype , only 3 Cyp2j isoenzymes were present . Overall, effects on Cyp2c and Cyp2j isoforms were disparate with certain isoforms either reduced, upregulated, or unchanged in KO relative to WT livers . Loss of mEH led to more inconsistent effects, causing upregulation of Cyp4f13 and Cyp4f15 and suppression of Cyp4a31 .Finally, the complete liver transcriptomes from C57BL/6 WT control, sEH KO, and mEH KO were compared to gain further insight on transcriptional effects caused by the deletion of EH gene transcripts (see Suppl. Table 3 and 4 for a list of the 50 most dysregulated genes in mEH KO and sEH KO livers). In WT liver, the gene expression ratio for pe Table , membersRegarding the quantitative contribution to hepatic FA epoxide hydrolysis, the odds are clearly in favor of sEH: Based on the transcriptome data presented in this study, sEH expression is approximately 2.5 times higher than mEH expression in mouse liver and under saturating conditions, purified mouse sEH is between 24 times  and 800 times  faster in hydrolyzing the respective epoxides compared to mEH. Nevertheless, we found the plasma levels for two FA epoxides substantially decreased in mEH KO relative to WT animals, providing strong evidence for a substantial role of mEH in the metabolism of epoxy FAs in vivo. Moreover, the strong correlation between FA plasma levels and hepatic EH hydrolysis rates  geInterestingly, a recent report corroborated our results that sEH KO liver exhibits high EH activity. In the study by Zhu and colleagues  for 8,9-EET, but a particularly high one for 9,10-EpOME , which is indicative of the notion that they share the same substrates. However, with regard to xenobiotic substrates, mEH and sEH display complementarity: Only sEH can hydrolyze trans-substituted compounds, but is restricted to “slim” substrates, while mEH is capable of hydrolyzing bulky hydrophobic substrates as long as they are not trans-substituted. This suggests that their shared substrates are rather of endogenous origin, e.g., FA epoxides.Cyp in C57BL/6 WT liver (Cyp2e1) was increased in mEH KO (1.8-fold) and sEH KO (1.6-fold) relative to WT (Suppl. Table 5). According to our transcriptome analysis, Cyp2c70, Cyp2c29, Cyp2c50, Cyp2j5, and Cyp2c68 constitute the five most abundant epoxygenases in C57BL/6 WT liver, a result largely in line with previous studies  compared to WT. The AA capacity assay further revealed that the hepatic formation rate for 20-HETE was similar across genotypes, which is consistent with earlier studies  and commonly considered to play an opposing role to the vasodilatory EETs. sEH deficiency leads to transcriptional upregulation of 20-HETE generating CYPs exclusively in kidney, but not in liver  displayed by mEH with all EETs compared to sEH Supplementary material 2 (DOCX 22 kb)Supplementary material 3 (DOCX 23 kb)Supplementary material 4 (PPTX 73 kb)Supplementary material 5 (PPTX 70 kb)Below is the link to the electronic supplementary material.


S11 parameters show a diode rectification ratio of 12 dB which is linked to the rectification behaviour of the direct current conductance. From the RF measurements, we extrapolate a cut-off frequency of 520 GHz. A comparison with the silicon RF-Schottky diodes, architecture suggests that the RF-molecular diodes are extremely attractive for scaling and high-frequency operation.Molecular electronics originally proposed that small molecules sandwiched between electrodes would accomplish electronic functions and enable ultimate scaling to be reached. However, so far, functional molecular devices have only been demonstrated at low frequency. Here, we demonstrate molecular diodes operating up to 17.8 GHz. Direct current and radio frequency (RF) properties were simultaneously measured on a large array of molecular junctions composed of gold nanocrystal electrodes, ferrocenyl undecanethiol molecules and the tip of an interferometric scanning microwave microscope. The present nanometre-scale molecular diodes offer a current density increase by several orders of magnitude compared with that of micrometre-scale molecular diodes, allowing RF operation. The measured  Molecular electronics holds promise to overcome scaling limits of conventional technologies, but is currently limited to low frequency operation. Here, Trasobares et al. show radio frequencies of up to 17.8 GHz in a molecular diode based on ferrocenyl undecanethiol self-assembled monolayers on gold nanodots. This frequency domain called the THz gap and corresponding to frequencies between the microwaves and far-infrared, is currently attracting a lot of attention. Electronic devices such as Schottky diodes, resonant tunnelling diodes or THz single-photon detectors continue to move into the THz range1In the field of electronics, there is still a tremendous need for increasing devices, speed—more precisely the cut-off frequency In that context, molecular electronics, that is, small molecules connected between electrodes5678910111213141516171819202122232425891011121314151617181921222324131415161718192021222324G/(2πC), with G and C the device dynamic conductance and capacitance, respectively. This limit was recently improved for single-molecule devices by chemical engineering of the molecule/electrode contact using a gold-carbon covalent bond 293031G0=77.5 μS. A second issue, barely adressed in molecular electronics, is the capacitive circuitry33343538394041424344474849505152First, as pointed out in ref. 891011121314G=δI/δV up to 0.36 mS and 110 aF range fringe capacitances. The origin of the large measured conductance is discussed, as well as the limiting speed parameters and the perspectives for getting new insights into molecular electronics transport from RF measurements. Finally, based on experimental data and simple models, we compare the RF-silicon Schottky and the RF-molecular diode architectures. We evidence the interesting perspectives of the molecular electronics approach for scaling and high-frequency operation.Here, we demonstrate RF molecular diodes operated up to 17 GHz with an estimated cut-off frequency of 520 GHz. The combination of a large array of single-crystal gold nanoelectrodes (few tens of nanometre in size corresponding to about 150 molecules in the molecular junction) and an interferometric scanning microwave microscope iSMM were the11SH), and a Pt tip. The tip curvature radius is large compared with the gold nanocrystal that defines the area of the molecular device (545511SH (E=0.34 and 0.37 V (versus Ag/AgCl), in the range of expectation for Fc molecules13152. It is assessed from the total charge measured by cyclic voltammograms at different sweep rates, and with consideration of an uncertainty on the averaged nanocrystal area —see details in Supplementary Information.545556131415r device , leadingvice 5455, and to vice 5455. Chemica545511SH , and the545511SH  illustra545511SH , and a f545511SH  and 723.545511SH , inset, 545511SH . Second,545511SH  shows ontal area . This avRd.c.=|I(V=1 V)/I(V=−1 V)|∼5) is in the range of usually reported values for many other types of molecule diodes11121314151617181920212223G=δI/δV, calculated from the data in 6316171819The d.c. rectification ratio (The dynamic conductance (up to 0.36 mS at 1 V) is suitable for high frequency operation as discussed later in the paper. The capacitance will be discussed together with RF measurements.S11 amplitude (|S11|) and phase (ϕS11) collected simultaneously at 0.8 V and 3.78 GHz. S11 is the ratio of reflected RF signal power over the incident RF signal power. |S11|−V and ϕS11–V two-dimensional (2D) histograms are shown in |S11| at ±1 V, RRF=|S11|V=1 V−|S11|V=−1 V of 12 dB, whereas for a symmetric molecular junction , no rectification was observed –V at 17.8 GHz), although we notice a reduced rectification ratio in |S11| at ±1 V of 4 dB. We do not notice any increase in the d.c. induced by the RF signal (photocurrent). I–V with/without RF applied on the tip (black solid line), confirming that no photocurrent is measured, even at 17.8 GHz. This result is not surprising at room temperature as the RF excitation corresponds to an energy of 73.6 μeV, much smaller than the thermal energy (25 meV). Also, the estimated absorbed power (<1 μW) is typically lower than Joules power (up to few μW). Eventually, we could have expected a RF-modulation of interfacial dipoles5765S11 via a capacitance change due to a polarization effect3551S11 can be related to the d.c. properties. Considering the RC equivalent circuit shown in G must be derived from δI/δV of the d.c. current–voltage curve or from the RF measurements as detailed below, the capacitance is C=Cmol+Cp with Cmol the capacitance of the molecular junction and Cp the fringe capacitance between the tip and substrate), the RF reflection coefficient S11 of the device under test is related to the impedance of the measured device, Y=G+jCω by refs observed . The molA0 takes into account losses, gain and shifts due to cables, passive and active elements and Y0 is the admittance for which fully destructive interference occurs (S11=0), and Zc is the characteristic impedance of 50 Ω. To extract the RF conductance, GRF, from the S11 measurements  at 3.78 GHz and  at 17.8 GHz, G0≈0, C0=300 aF, and C=110 aF. Using these values in GRF–V curves =520 GHz, where Gmax is the maximum dynamic conductance . Nonetheless, we also believe that the molecular packing plays a role as the alkyl chains, that govern the coupling strength between the Fc molecules and the bottom electrode1516The present nanometre-scale molecular diodes offer a current density increased by more than nine orders of magnitude compared with that of micrometre-scale molecular diodes with the same molecule151617181929303132|C−C0|ω as a plateau in |S11| at low bias. Theoretical predictions suggest that an additional inductive contributionS11 due ferrocene spinninget al.S11 parameterIn the present experiments, the change of frequency from 3.78 GHz to 17.8 GHz already points out the appearance of the capacitive contribution fT is typically the figure of merit, not only for the maximum frequency operation, but also for optimized power consumption in practical applications77RS). A thin undoped epitaxial layer (few nms) enables to keep the highly doped substrate as close as possible to the interface while not getting a too large junction capacitance CJ . fT is usually considered as 1/(2πRSCJ)77CJ scales as the junction area A, RS scales as A−1/2 because the substrate thickness is large compared with that of the junction diameter dfT scaling as A−1/2 (green line fT of few THz was demonstrated for a 250 nm diameter diode8 A cm−2)79RS to scale with A, leading to a saturation of fT at d below 50 nm ((2πRS·Cmol), resulting in a gain of CJ/Cmol∼5.6–8.8 when compared with Schottky diodes , such limit is not reached with these 20 nm-diameter gold nanocrystal electrodes . The comparison of the experimental values at 1 V for Rmol=1/G and fT with theoretical optima suggests that the actual limiting factor is not the molecule conductance, but rather fringe capacitances that degrade fT by about two orders of magnitude. Still, improvement of the device conductance  and capacitances. The present configuration was optimized to demonstrate a 17 GHz molecular rectifier with a 520 GHz cut-off frequency: The ∼150 molecules sandwiched between a gold nanocrystal and a Pt tip electrode enables to get a ∼0.36 mS dynamic conductance while keeping a small capacitance in the 100 aF range. The sole reduction of the fringe capacitance to 50 aF by using an ultra-sharp tip84The fabrication of the gold nanocrystal arrays has been described elsewhere54The e-beam acceleration voltage was 100 keV. The dose per dot corresponds to 3–4 fC ref. . Just pr2 atmosphere, we get Single crystal Au nanodots  during typically 24 h in a glovebox in the darkness. The treated substrates were rinsed in ethanol and further cleaned with gentle ultrasonication  in chloroform (99% from Carlo Erba) during 1 min. The gentle ultrasonication was required to avoid pollution of the tip from adsorbed molecules on silica. In the future, ultrasonication may not be necessary if a monolayer of silane molecules is grafted between dots to prevent non-specific adsorption. Similarly, recent studies performed by Jian et al.For the FcCXPS measurements have been performed using a monochromatic Al (Kα) X-ray source (1486.6 eV) and an analyser pass energy of 12 eV. The spectrometer was from Physical Electronics .The analyser acceptance angle, the detection angle and the analysed area were set to 14°, 45° and 400 μm, respectively.a1, that is split into two parts. One part of the signal, used as a reference, is adjusted in magnitude by the variable attenuator in order to adjust the interference; the second part feeds the AFM tip through a coupler and is then reflected back by the device under test (DUT). Both signals are combined inside the second coupler and then the signal, a3, is amplified and analysed the VNA receiver.The interferometric scanning microwave microscope (iSMM), recently developed at IEMNThis iSMM is connected with an amperometer by a bias-T that allows electrical characterization of the d.c. simultaneously . As an i−1 enabled to control force and set it to 18 nN  were 300 μm-long, 60 μm-wide and a shank length of 80 μm with a tip curvature radius of about 20 nm. In practice, we found that the curvature radius for these Pt wire tips was about 80 nm ref , so we h4 (0.1 M in water) as the electrolyte  are measured to confirm that there is no peak due to contamination. Cyclic voltammetry .Supplementary Figures 1-15, Supplementary Tables 1-2, Supplementary Notes 1-4, Supplementary Methods and Supplementary References


Stomorhina discolor (Fabricius), a species of blow fly that mimics wasps, is distributed worldwide, but detailed information about characteristics of its adult terminalia is incomplete. To help fill this gap in the information, the morphology of adult stages of S. discolor was investigated using light microscopy and scanning electron microscopy. Observations using the light microscope revealed unique characteristics of the male genitalia that are markedly different from other blow fly species. More morphological detail, including observation of several sensilla  along the male terminalia and female ovipositor, was seen under the scanning electron microscope. These details can be taxonomically valuable for identifying males and females of S. discolor and may help address matters concerning copulation in this species. Stomorhina discolor  is a blow fly species in the subfamily Rhiniinae and is known as the wasp-mimicking fly. It has been recorded in many parts of the world [Camponotus angusticollis (Jerdon), among the roots of Hevea brasiliensis Muell. Arg. affected by the fungus Botryodiplodia theobromae Patton, and riddled with small boring beetles. Adults are occasionally attracted by opening an ant nest. Males are known to often exhibit a hovering behavior in small swarms under trees [S. discolor that have been published, only limited terminalia illustrations of male genitalia of the species have been provided [he world ,3,4,5. Bhe world . Senior-S. discolor were studied by using light microscopy (LM) and scanning electron microscopy (SEM).In order to elaborate the biological information of this fly species, morphological features of both male and female terminalia of S. discolor near a blooming Buchanania lanzan Spreng tree at Mahidol University Nakhonsawan Campus, Nakhonsawan Province in the north-central part of Thailand . Individual flies were observed moving over the flowers and were collected using a sweep net. They were anesthetized in bottles with ethyl acetate solution and transported back to the lab for species confirmation under a stereomicroscope  using the taxonomic key of Kurahashi and Bunchu [In December 2012, we discovered numerous blow fly specimens of S. discolor are small-medium size blow flies with a dark colored thorax with dense pollinosity. Wings are transparent with an apical dark spot in both sexes. The abdomen is black or brown posteriorly with a contrasting yellow on tergites 1 + 2. Males are easily distinguished from females by their holoptic eyes; whereas, the eyes of females are dichoptic. After specific identity of specimens had been confirmed, male and female flies of S. discolor were individually selected to further investigate the ultrastructural morphology of their external genitalia.As seen in Fifteen fly specimens of each sex were cut between tergites 4 and 5 using a sharp forceps under a stereomicroscope to separate the terminalia in order to eventually manipulate the genitalia at optimal observational angles. The terminalia were then individually macerated in a mixture of 2 mL of 10% (w/v) potassium hydroxide (KOH) solution and 0.12 mL of 95% ethanol in each well of a 12-well plate for 4 d. Specimens were rinsed once with distilled water to remove the caustic KOH solution and transferred into a 0.85% sodium chloride (NaCl) solution on a dissecting plate so that the genitalia could be dissected from the terminalia. Male genitalia and female ovipositors were gently separated from the macerated tissue using fine needles (No. 23) and sharp forceps under the stereomicroscope (Olympus). Each specimen was photographed using a digital camera  and illustrated by painting with Chinese ink.Three other specimens of each sex were examined by scanning electron microscopy. All specimens were dissected as previously described in the light microscopic portion of this study. The terminalia were cleaned by soaking in phosphate buffer  to remove contamination artifacts. After that, they were primary fixed in 2.5% glutaraldehyde at 4 °C for 24 h. They were then rinsed with PB three times at 15 min intervals and post-fixed with 1% osmium tetroxide at room temperature for 1 h. Postfixation was followed by rinsing again as previously described and dehydrating with increasing alcohol concentrations as follow: 70%, 80%, 90%, and 95%. Specimens were left in each alcohol concentration for 12 h. After the gradual dehydration process in the alcohol series, the specimens were dehydrated with absolute alcohol for two 12 h periods. Finally, they were subjected to critical point drying, attached to aluminum stubs with carbon double-stick tape, and coated with gold in a sputter-coating apparatus. The gold–coated specimens were then examined under a JEOL JSM-6610LV scanning electron microscope  at the Medical Science Research Equipment Center, Faculty of Medicine, Chiang Mai University in Chiang Mai, Thailand. The terminology used for most fly structures in this study follows the descriptions of Bernhard and Haenni [S. discolor were examined by light and scanning electron microscopy. The average body length (head–abdomen) of all specimens was 6.88 mm, varying from 4.80–9.30 mm, but the distinct features were similar. Characteristic features of the terminalia of S. discolor are softly sclerized and formed posterior to the fifth abdominal segment as external reproductive organs. Examination under light microscopy showed that male terminalia were hardened and consisted of epandria, surstyli, and cerci , and the subanal plate (SBAP), all joined together by a softer, less sclerotized membranous exoskeleton  have been previously published [Stomorhina lunata (Fabricius) which are distinctly different from S. discolor  is equipped with both sensilla trichodea and sensilla basiconica, in addition to sensilla placodea and sensilla styloconica [Lucilia sericata (Meigen). Therefore, sensilla on the ovipositor may also have an olfactory function. In addition, sensilla on the female terminalia of the mosquito Aedes aegypti (Linnaeus) have been proposed to play a role in copulation as well as in ovipositional behavior [Drosophila spp. [A more complete description of the external genitalia of both sexes of ublished . Resultsublished ; howeverublished  provideddiscolor . Two typloconica . Sensillloconica . Wang etloconica  examinedbehavior . Howeverila spp. .S. discolor which are not only useful for taxonomic purposes, but also provide important revelations that may be useful in future functional studies of the external genitalia of this fly species.In conclusion, this study provides several distinctive details of the external terminalia of


Vultur gryphus). Andean condors vary largely in weight and show a huge sexual dimorphism that allowed us to evaluate the effect of sex and body size on their daily routines. We found that condors use an intermediate solution strategy between the best times to exploit carcasses and uplifts, with this strategy changing over the year. Bigger males scheduled earlier routines that aligned more closely with uplift availability compared to smaller females, resulting in a partial temporal segregation between sexes. Condors’ routines reflect a sexual-size dependent trade-off that may underpin ecological and sociobiological traits of the studied population.Animals are expected to synchronize activity routines with the temporal patterns at which resources appear in nature. Accordingly, species that depend on resources showing temporally mismatched patterns should be expected to schedule routines that balance the chances of exploiting each of them. Large avian scavengers depend on carcasses which are more likely available early in the morning, but they also depend on wind resources (i.e. uplifts) to subside flight which are stronger in afternoon hours. To understand how these birds deal with this potential trade-off, we studied the daily routines of GPS-tagged individuals of the world’s largest terrestrial soaring scavenger, the Andean condor ( Broadly speaking, performance trade-offs occur when a beneficial change in one trait is necessarily linked to a detrimental change in some other trait3. As a consequence, animal behavior cannot be optimized across the board as excellence in one behavioral trait will come at the cost of performance in another. This is how trade-offs often give rise to compromise behavioral phenotypes; i.e. individuals performing sub-optimally due to counteracting demands7.Performance trade-offs are thought to impose strong constraints on the adaptive evolution of animal behavior8. For instance, to search for food, animals choose times of the day that maximize the energy intake but taking into account potential penalties derived from encounters with predators, competitors and unfavorable weather conditions11. Because of this, when the environment drives an animal’s decisions towards opposite extremes, natural selection should then act on the individual to get the best balance between them12.Daily behavioral routines of animals are shaped by performance trade-offs. This is because, in order to make important decisions (e.g. when to feed), individuals need to evaluate costs and benefits in a multidimensional space in whose axes the variables of interest often show mismatched temporal patterns15 that result in a temporal niche partitioning between sexes17. In the case of size-dimorphic soaring birds, males and females often show differences in flight performances that may have functional significance and lead sexes to exploit scenarios with different weather conditions19. Similarly, trade-off solutions can be adjusted to the seasonal changes in physiology, weather and food availability leading daily routines to vary throughout the year22.Individual attributes can lead animals to find different trade-off solutions that ultimately shape daily routines of individuals. For instance, sexual differences may imply that males and females have different nutritional requirements, roles in offspring rearing and competitive abilities24. In fact, it is early in the morning when the chances of finding a profitable carcass are highest because ungulate mortality commonly peaks during night time due to thermal stress or predation by nocturnal carnivores25. On the other hand, large avian scavengers strongly depend on wind resources in the form of uplifts as their body size makes powered flight energetically costly. Thus, they are expected to match up activities that require long-distance movements to particular weather conditions27. In terrestrial environments the morning hours typically do not present wind speeds and temperatures high enough to create profitable uplift conditions28 and, therefore, there are good reasons to think that these birds face a mismatch between the optimal times to exploit food and wind resources.Large avian scavengers are expected to deal with an important trade-off when scheduling their daily routines. On the one hand, they depend on a sparsely distributed food resource  that forces them to forage early in the morning in order to ensure time to find food and reduce the risk of starvationVultur gryphus) is an excellent model to explore the existence of this type of trade-offs as it is an obligate scavenger, the world’s largest terrestrial soaring bird and a sexually size-dimorphic species in which males are larger than females. In this study, we first hypothesize that the daily routines of GPS-tagged condors inhabiting the Andean Patagonia are shaped by a trade-off between the temporally mismatched needs of obtaining food and subsiding flight with wind resources. On the basis of this trade-off, the birds should decide among: i) foraging as early as possible in the morning to maximize the probability of finding food (‘food-prioritization strategy’), ii) foraging in mid-afternoon to minimize energy expenditure in locomotion by exploiting the most profitable wind resources (‘wind-prioritization strategy’) or iii) to reach a solution that is intermediate by foraging between the best time to exploit food and wind resources (‘intermediate solution strategy’)  require condors to undertake the energetically demanding task of climbing from low steppes to high mountains in the Andes, we expect that larger males schedule their routines especially to align these flights to the most profitable uplift conditions. On the other hand, we expect that condors adjust their routines to track seasonal changes in weather and daylight hours available to commute between nesting and foraging areas. By combining movement data with Bayesian hierarchical modeling, our study evaluates how different life-history constraints can act together to shape animal behavior and ecology.In addition, we hypothesize that the daily routines of condors vary with sex and body size as well as among seasons of the year. The Andean condor represents a very singular case of sexual size dimorphism in which males are up to 50% larger than females, whereas inter-individual differences within each sex can reach up to 25% in weight Fig. . These dCondors scheduled their routines in a regular way over the day Fig. . As expeSex and body size explained a large proportion of variability in daily routines Fig. . In geneDaily routines were scheduled in such a way that foraging activities peaked at intermediate times of the day, when the joint availability of carcasses and wind resources was the best for condors Fig. . At the From summer onward, the length of daily routines contracted in accordance with the reduction in the available daylight hours Fig. . RoutineAndean condors scheduled their daily routines according to an intermediate solution strategy; i.e. they either did not venture out in search of food as early as the first sunlight was available or as late as the best uplift conditions occurred. Instead, they concentrated outbound flights, foraging and inbound flights on three, six and ten hours after twilight respectively, resulting in quite symmetric routines around the day. In addition, individuals of different sex and body size scheduled their activities differently, with routines becoming increasingly more morning-skewed and less-time flexible as body size increased. These differences resulted in a temporal partitioning between males and females due mainly to the pronounced sexual size dimorphism. Interestingly, these differences also led the birds to align inbound flights with the most profitable uplift conditions, particularly in the case of larger birds within each sex. Finally, daily routines were adjusted according to the variation in daylength and weather of each season of the year. As a whole, our results are consistent with the hypothesis that condors in Patagonia are subject to a trade-off between exploiting food and wind resources.Gyps fulvus) typically start to forage around 2 hours after twilight (9.30 a.m.)23 whereas Cinereous vultures (Aegypius monachus) delay foraging between 2 and 3.5 hours after that moment (1.5 and 3 hours after sunrise), depending on the season20. White-backed vultures (Gyps africanus) and Ruppell’s vultures (Gyps rueppelli) in Namibia commonly leave the roost between 3 and 3.30 hours after twilight respectively30, whereas in Kenya, Gyps vultures are more abundant at carcasses 2 hours after that moment (8 a.m.)24. Most of these species feed on supplementary feeding stations that reduce the temporal unpredictability of food resources, conditions which have been found to reduce the urgency of birds to forage early in the morning31. Andean condors may be experiencing similar foraging conditions as, in our study area, they exploit relatively abundant and predictable food sources that may allow them to forage later. Delaying foraging activities may also be favored by the combination of two factors; the high inter-specific competitive ability of condors that allow them to displace other individuals from carcasses and the low densities of their populations that reduce the size of foraging groups34.Previous studies on Old-World vultures support the idea that large avian scavengers are subject to a similar trade-off as their daily routines are also scheduled according to an intermediate solution strategy. For instance, griffon vultures  allowing them to use potential energy to subside, at least in part, the costs of transportation to the feeding areas.The most energetically demanding movement phase for the studied birds is the return to their nesting areas. This is because condors have to climb from low- to high-altitude areas usually carrying full crops, conditions which importantly increases the sinking rate and ultimately the flight costs42. In winter, daily routines not only contracted but also showed an important variability that distorted the typical pattern found in the rest of the year. Distorted routines during winter could be caused by the urgency of obtaining food under almost permanently bad uplift conditions and shorter days, as suggested for other vulture species20.As with many other species living in seasonal environments, the studied condors modified daily routines over the year to take advantages of all daylight hours available in each season33. Therefore, we suggest that differences in the daily routines caused by inter-individual body size variation are revealing different degree of compromise when facing the trade-off between exploiting food and wind resources.An alternative explanation to the general pattern we found in condors’ daily routines could be that the birds are simply exploiting the whole time range with sunlight availability to symmetrically arrange activities around the day. Under this idea, however, it is more difficult to explain why the heavier birds showed markedly different routines compared to the smaller birds. As discussed above, this occurs even when heavier birds  could delay foraging as they dominate intra-specific relationships43. Here we provide evidence supporting the existence of a trade-off in the Andean condor associated with the exploitation of key resources. This trade-off may have far-reaching implications for ecology and sociobiology of the species but also for its conservation. Trade-offs impose limitations on the capacity of organisms to respond to environmental changes44. When variables involved in trade-offs are influenced by human activities the responsiveness may be even lower because changes occur more rapidly45. In the case of Andean condors, both food and wind resources are subject to change processes related to human activities47 making them especially vulnerable to future scenarios as they might destabilize energy budgets and mechanisms of coexistence among individuals.While behavioral trade-offs are a cornerstone in behavioral ecology and evolution, they are among the most challenging situations to empirically identifyThis work was conducted in accordance with relevant national and international guidelines, and conforms to all legal requirements. Captures of the studied birds and all experimental procedures performed on them were duly approved by the Dirección de Fauna de Río Negro, Argentina (File number: 132730-DF-2010).48. Extensive livestock production is one of the most important economic activities on the rangelands located on east of the region, with an average of 3.4 ha/LSU 49. In this context, the steppes regularly provide condors with food resources whereas mountains in the west mainly serve as nesting and roosting sites.This work was carried out in the Northwest Andean Patagonia of Argentina and Chile (38-42°S/69-72°W). The west side of this region is dominated by steep mountains and dense subantartic forests that abruptly change into flat and grassy steppes on the east side48. A strong west-east gradient of precipitation crosses the region partly promoted by the predominance of winds from the west. Westerly winds constitute between 65 and 75% of daily observations in the year and their intensities range from 15 to 22 km/hr48. The area is characterized by marked seasonal patterns in temperature and rain with cold and rainy winters and hotter and drier summers50. The annual distribution of wind speed shows a maximum between spring and summer and a minimum in winter time. Wind speeds and temperatures follow hump-shaped patterns throughout the day, with low values during the early morning, increasing from that time to the afternoon and decreasing again in the evening 26. It is an obligate scavenger bird that mainly feeds upon carcasses of medium-to-large sized mammals. In the Andean Patagonia, the condors’ diet is mainly made up of domestic animals  kept as livestock and secondarily of wild exotic species 46. A large proportion of carcasses of these animals become available for scavenger species through the predation by pumas (Puma concolor) and foxes  which abandon them at their kills53. As these mammal predators show crepuscular and nocturnal hunting habits52, carcass biomass available for diurnal scavenger species peaks during early morning hours and decays throughout the day.The Andean condor is the world’s heaviest soaring bird with individuals reaching up to almost 16 kg in weight  and backpack 100 g Solar GPS–GSM CTT-1070-1100 units from CellTrack Inc. (n = 13)  ‘nest-guarding’, 2) ‘foraging’ and 3) ‘commuting’. The birds were assumed to be ‘nest-guarding’ anytime they were within their nesting areas either carrying out strictly reproductive activities (e.g. incubating) or others (e.g. resting). Nesting areas were defined as the 50% volume contours of Kernel Density Estimators55 and computed in ABODE56 using all available data for each bird. In the study area, condors primarily use the steppes to search for food and perch in large communal roosting sites58. For this reason, the birds were assumed to be ‘foraging’ anytime they visited the steppe except when they occasionally were within a 1 km radius of any communal roosting sites in this environment. Finally, we assumed that the birds were ‘commuting’ anytime they were between the areas above either taking outbound or inbound flights; i.e. flights from or to the nesting areas respectively. Since our main purpose was to evaluate how the birds allocate time to the different behaviors throughout the day, we only used movement data from the days when the individuals commuted between nesting and foraging areas. We conservatively excluded six individuals from statistical analyses as they showed no clear central-place foraging tactics, thus preventing us from reliably assigning GPS locations to the studied behaviors. After applying these criteria, our movement dataset contained a total of 8534 GPS locations from 17 individuals condors covering 250 days of the annual cycle.Most of the GPS-tagged Andean condors behave as central-place foragers making long commuting flights every two or three days between spatially well-defined nesting and foraging areashours.after.twilight). Thus, if pt,k][ represents the probability that a bird is in behavior k as a function of time t, we can then express this function as follow:To study how condors scheduled their daily routines, we first computed the hours passed after the morning civil twilight for every location (i.e. from now on ‘hours after twilight’) as from this moment on there is enough light to allow birds to fly. We then simultaneously modeled the probability that a bird is in anyone of the three behaviors  as a sinusoidal function of the hours after twilight  and p2 (probability of commuting) whilst p3 (probability of nest-guarding) was obtained as the residual probability 1 − p3 − p2. We wrote a system of equation to be sure that the sum of pk reaches one. This model was constructed as a multilevel model with parameters varying at the individual level  as follow:In order to evaluate how the general daily routines were affected by sex and body size, we expanded the model described above by incorporating the variables 3 and γ3) which allowed us to test if the effect of body size changed between sexes. To test the effect of seasonality, we modelled all the three parameters  as linear functions of the observation-level variable season which contained the season of the year for each GPS location according to the Gregorian calendar. All the models were fitted using Bayesian statistics where priors for hyper-parameters were set as t distributions. We run three chains with 10000 iterations each and ruled out 5000 as burn-in. The models were implemented in JAGS via the ‘jagsUI’ package of R61.Eq.  allowed 62, it is still reasonable for our study as the condors mostly commute on an area dominated by a steep relief (Supplementary Fig. After model fitting, we graphically compared the condors’ routines to the daily patterns of availability of food and wind resources. Given the lack of empirical data on the daily variation of carcasses in the study area, we assumed a linear decay model throughout the day. To describe the daily variation of wind resources, we used hourly wind speed data as a proxy of uplift availability. Specifically, we assumed that when wind speeds increased, so did the overall availability of uplifts. While this approach does not consider the fact that strong winds tend to break apart thermal formationThe datasets analysed during the current study are available from the corresponding author on reasonable request.Supplementary Information