Anterior cingulate cortex mediates the flexible updating of an animal’s choice responses upon rule changes in the environment. However, how anterior cingulate cortex entrains motor cortex to reorganize rule representations and generate required motor outputs remains unclear. Here, we demonstrate that chemogenetic silencing of the terminal projections of cingulate cortical neurons in secondary motor cortex in the rat disrupts choice performance in trials immediately following rule switches, suggesting that these inputs are necessary to update rule representations for choice decisions stored in the motor cortex. Indeed, the silencing of cingulate cortex decreases rule selectivity of secondary motor cortical neurons. Furthermore, optogenetic silencing of cingulate cortical neurons that is temporally targeted to error trials immediately after rule switches exacerbates errors in the following trials. These results suggest that cingulate cortex monitors behavioral errors and updates rule representations in motor cortex, revealing a critical role for cingulate-motor circuits in adaptive choice behaviors. The anterior cingulate cortex allows an animal to update its behaviour when the environment changes. In this work, the authors identify a pathway from cingulate to secondary motor cortex, critical for updating motor rules following behavioural errors. Previous studies in primates suggested that premotor cortex and supplementary motor areas underlie the planning and execution of sequential movements7. More recently, it has been demonstrated that neurons in the rodent secondary motor cortex (M2) similarly code initiation of sequential movements, motor planning, memory, and values for upcoming choice actions, suggesting that they maintain representations of sensorimotor associations for adaptive choice behavior14. These findings raise a question of what circuit mechanisms enable M2 neurons to process context-dependent information such as task rules. More specifically, how is this information provided from input brain regions and how are they processed by M2 circuits?A central feature of animal intelligence is the hierarchical organization of behaviors and the capacity to adopt flexible strategies that allow complex sequential actions20. However, despite abundant anatomical evidence of cingulate projections to motor cortices24, there are several fundamental issues that remain poorly understood. (1) Dimensions of task rule representations in ACC: what aspects of task rules ACC circuits represent? We addressed this issue by using a behavioral task in which rats were required to adapt their sequential choice behaviors between single-step and two-step choice responses. The rule switches in this task paradigm are asymmetric in their design  and this feature allowed us to distinguish whether ACC circuits are recruited in rule switches that demand an increment of response steps , a decrement of response steps  or in both types of rule switches. (2) Relevance of ACC to motor output pathways: how does ACC entrain motor cortex to update neural representations for rules upon sudden task rule changes? To address this issue, we perturbed the projections from ACC to M2 using chemogenetic and optogenetic methods and examined what aspects of choice behaviors were mediated by the ACC→M2 circuit and how ACC circuits modulate neural activity in M2 after rule switches in which mapping of choice actions to rewards are suddenly changed. Combining these pathway-specific and task period-specific manipulations with animals’ behaviors in a choice decision task and with neural activity measurements from M2, this study revealed a specific functional role of ACC→M2 pathway in flexible updating of rule representations upon rule switches during animals’ sequential choice responses.Studies in humans and other animals have shown that medial prefrontal areas including anterior cingulate cortex (ACC) mediate flexible decisions in the face of rule changes  or under uncertain conditionsWe devised a behavioral task in which an animal updates its sequential choice response for rewards upon abrupt rule changes . Briefly, in the CAS task, animals are required to choose left or right ports based on an auditory tone cue stimulus. Under the 1 step rule condition, animals received a reward after correctly poking a left or right port instructed by one of two tone cues, and could start the next trial after an inter-trial interval (ITI) 26 received projections from ACC (area 24a’/24b’)  Fig. 27,28. Weses Fig. .Fig. 3AnWe then asked what aspects of choice behaviors are mediated by the ACC→M2 circuit that we identified. More specifically, we wanted to examine the potential roles of projections from ACC to M2 in the animal’s ability to update sequential choices upon rule switches. Using bilateral infusion cannulae targeting M2 in rats that had been injected with the inhibitory DREADD virus in ACC, we infused CNO solution in M2 and examined its effect on task performance  Fig. . We obta29, which measures the degree of overlap between two response distributions31. For each M2 single-unit the preferred and non-preferred rule conditions were compared, given two distributions of neuronal activity (see Methods). An ROC curve was then generated by taking the observed firing rate of a neuron and then the area under the ROC curve was calculated. A value of 0.5 indicates that the two distributions were completely overlapped, and thus the neuron is not selective to the rules. A value of 1.0, on the other hand, indicates that the two distributions were completely separated and so the neuron is very selective. Time course of rule selectivity of the representative M2 single-unit showed an increase in rule selectivity in the pre-choice period in the ipsilateral choice condition but not in the contralateral condition  analysision Fig. . We repeion Fig. . Such anion Fig. . We spliTo test if a perturbation of ACC inputs to M2 neurons during pre-choice period could affect an animal’s sequential choice performance in trials following rule switches, we optogenetically excited ACC terminals in M2 specifically during pre-choice period. Indeed, such perturbation affected 2nd choice performance  . Therefore, animals were expected to adjust their 2nd choice responses based on the outcome feedback information. To address whether and how ACC circuits provide M2 neurons with outcome feedback information upon rule switches, we examined M2 activity during the outcome feedback period  (see Methods). We first tested if an M2 neuron showed a greater or smaller mean firing rate during the outcome feedback period than that during the baseline period and conducted several analyses using the database of such neurons. A chemogenetic silencing of ACC neurons decreased the proportions of neurons that were activated either during the positive outcome feedback period  or during the negative outcome feedback period   and 2nd/3rd epochs Fig. . We founTo test if suppression of ACC circuits during the outcome period could affect animals’ sequential choice performance as was suggested by the chemogenetic modulation of activities of outcome-related neurons in M2 Fig. , we optoWe delivered the light for 4 s immediately following the animals’ incorrect 2nd choices Fig.  or the aWe repeated similar experiments with 4 s light applied immediately after the animals’ correct 2nd choices Fig. . Light dFinally, we tested if an optogenetic excitation of ACC inputs to M2 neurons during the outcome period could affect an animal’s sequential choice performance , suggesting that the role of ACC-M2 projection is not limited to performing an extra step but rather is linked to flexibly adjusting the performance of an additional step in an action sequence upon rule switches.What is the functional role of ACC-M2 pathway in an animal’s sequential decision making and rule switching? Do ACC inputs just increase the excitability/activity of M2 neurons in a way that enables them to function normally or do they actually convey task-related information? If the latter is the case, what information do ACC-M2 projections convey? A previous rat study showed that inhibiting M2 neurons caused decrease of left/right binary and single-step choice performance, suggesting that modulating M2 activity per se affects single-step choice performance5. Given that a chemogenetic silencing of ACC-affected rule selectivity of M2 neurons during pre-choice period in trials that immediately followed rule switches  might be necessary to extinguish such non-rewarded movements upon rule switches.We observed an effect of chemogenetic silencing of ACC circuits in updating rules from 1 step to 2 steps but not in the opposite direction  as measured by non-rewarded second actions. This asymmetric effect of chemogenetic silencing may indicate that ACC circuits are recruited in rule switches that demands an increment but not a decrement of response actions. Given that making a 2 steps choice would demand a greater cost on the part of the animals compared to making a single-step response, the asymmetric effect  raises a possibility that ACC circuits are recruited specifically in task settings that demand an increased effort/cost for making choice responsesChemogenetic silencing of ACC neurons increased 2nd choice omission errors in the first epoch of 2 steps condition and, on average, this effect emerged toward the end of the first epoch Figs.  and 4f. 39. It is an open question whether and how such processes could be recruited in distinct ways for flexibly updating sequential choice responses upon changes of rules in the environment.A chemogenetic silencing of ACC neurons or their terminals in M2 decreased 2nd choice performance by around 20% on average but did not completely abolish animals’ ability to update their choice responses upon rule switches Figs. . It migh40. In this study, we minimized the within-trial working memory load while requiring animals to hold rule representations  across trials in specific rule blocks. The cognitive loads of different time scales  might affect how an update of behavioral response would rely on DLPFC vs. ACC circuits32. Alternatively, the reason might be due to the difference in the characters of the task rule that we focused on in this study: in previous rodent studies that showed the involvement of prelimbic/infralimbic cortices in rule switching, these were related to choices in space  while, in this study, we tested animals’ ability of updating internal representations of rules related to response sequences 42. It is an open question what distinct roles ACC and prelimbic/infralimbic circuits play in flexibly adjusting responses upon rule switches.Interestingly, we found no effect of chemogenetic silencing of prelimbic/infralimbic cortex  while previous studies have shown that DLPFC also plays a critical role in updating rule representations in the brain43. Given these considerations, while we found no behavioral effect in ChR2 excitation experiments during the outcome period, a ChR2 excitation with a higher light pulse frequency can possibly affect animals’ task performance. Including this issue, precise circuit mechanisms regarding how ACC-M2 circuits work in rule switching remain unexplored and it is an open question whether and how ACC neurons could modulate downstream M2 neurons possibly in cooperation with local inhibitory neuronal networks.An optogenetic excitation of ACC-M2 projections during the outcome period did not affect animals’ choice performance while an optogenetic suppression of ACC neurons during the same task period could affect it Fig. . This diWe confirm that our research complies with U.S. National Institutes of Health guidelines and the Massachusetts Institute of Technology Department of Comparative Medicine and Committee of Animal Care .Wild-type male Long-Evans rats (>300 g) were used (Charles River). Rats were pair-housed during initial behavioral training and then single-housed after being injected with viruses or being implanted with electrodes, fiberoptic implants, or cannulae. Rats were kept on a reverse 12 h light/dark cycle, and trained and tested in their dark cycle. Food was available ad libitum, and rats had scheduled access to water for motivating them to work for water reward while monitoring their body weight to ensure they were over 85% of their initial weight.Behavior took place in a custom-made chamber  inside a sound-attenuating cubicle (MED Associates). The cubicle was electromagnetically shielded by a copper mesh sheet or nickel/silver fabric in electrophysiology recording experiments. The behavioral setup consisted of a stainless-steel lever at the center (MED Associates) and two ports equipped with infrared photodiodes on the left and right sides of the lever, arranged side-by-side with a center-to-center distance of 65 mm on a stainless-steel wall. An interruption of the infrared beam signaled port entry. A sipper tube was installed on the front wall 25 mm above the center lever and was connected to a water supply that was controlled by a computer-controlled solenoid. In addition, there were two speakers mounted on the side walls (about 150 mm away from the center lever). Timing of presentations of sounds from the speakers  and delivery of water rewards were controlled using a multifunction digital input/output board  with custom programs written in C++  and LabVIEW  on a computer running a Windows 10 operating system. Behavioral events were timestamped with a precision of <1 ms.Rats self-initiated each trial with a push on the center lever to receive the tone cue stimulus. After a delay of 10 ms, a tone of either 8 or 12 kHz with a sound pressure level of 75 dB was presented in pseudorandom order in each session. The tone was kept on until an animal entered one of the side ports (left or right) as the 1st choice. Choices were rewarded with ~25 μl water if they poked the correct side port. An error feedback buzzer sound was delivered as a penalty if they poked the incorrect side port followed by an elongated period of ITI. A feedback buzzer sound accompanied by an elongated ITI was also delivered if animals made a 2nd choice commission error, that is, entered the opposite side port after making a correct 1st choice and before initiating the next trial (pushing the center lever after an ITI). This completed a trial and, after an ITI period , an LED turned on, signaling the animals to initiate the next trial by pushing the center lever.In the 2 steps condition, in addition to making the first-choice response to one of the side ports (depending on the tone cue), animals were required to make a 2nd response by entering the side port opposite the one that animals chose as the 1st response, instead of pushing the center lever. A correct entry to the side port as a 2nd response was rewarded with ~25 μl water. If animals pushed the center lever before making a correct 2nd response , an error feedback buzzer sound was delivered. In this way, switches from 1 step to 2 steps rules were directly signaled to the rats: when the rule was switched from 1 step to 2 steps conditions and if the rats completes the 1st response , rats would push the center lever to initiate the next trial, causing an error buzzer feedback as an explicit feedback signal.1 step and 2 steps conditions were switched in every 55 trials  in a block-wise manner. In some sessions, behavior experiments started from the 2 steps condition (1st block) and then switched to the 1 step condition (2nd block) and so forth , rats were first trained to either push the lever or enter a side port (left or right) to receive a water reward. Next, rats were trained to perform a contingency task in which a tone sound (4 kHz) was delivered when the animal pushed the center lever, and then the animal was required to poke either the left- or right-side port to receive a reward . Then rats were trained on auditory discrimination in which they were required to choose left- or right-side ports depending on the tone cue stimulus (either 8 or 12 kHz). Once they reached over 70% correct performance for two consecutive days, they were trained with the CAS task without a rule change between the 1 step and 2 steps conditions, requiring animals to make 2 step responses throughout the session. Once they reached over 70% correct performance for 2 consecutive days, they were trained on the CAS task with rule changes between the 1 step and 2 steps conditions once every 70–100 trials per block. The length of a block was gradually shortened to 55 trials to complete the task training phase. In many training sessions and in some testing sessions, we adjusted the proportion of two trial types  to abate animals’ choice bias to specific side ports. Such adjustments were typically limited to a ratio of less than 2.−1) and xylazine (8 mg kg−1), rats were placed in an isoflurane induction chamber for 5–10 min. Then rats were moved to a stereotactic frame and their nose was placed in a cone, which provided 1.5–2% continuous isoflurane flow. After verifying surgical levels of anesthesia with pinch tests and eye blink tests, rats were secured in non-rupture ear bars (Kopf Instruments). The concentration of isoflurane was maintained at 0.75–1.5% throughout the surgery. Slow release buprenorphine (1 mg kg−1) and Ringer’s solution were administered in the middle and at the end of the surgery, respectively. Rats remained on a heating pad until they made a full recovery from anesthesia after surgery. Supplemental nutrient gels were supplied to rats after they were returned to their home cages. Rats were monitored during their recovery from surgery for at least 4 days before restarting experiments.All surgeries were performed under isoflurane anesthesia (1.0–2.0%) using the standard stereotactic technique. Following an IP administration of a cocktail solution of ketamine . For optogenetic silencing of ACC neurons  ml−1 for AAV5-CamKIIa-hM4Di-mCherry, 1.9 × 1013 GC ml−1 for AAV5-CamKIIa-mCherry, 1.3 × 1013 GC ml−1 for AAV9-CamKIIa-eNpHR3.0-eYFP, and 1.1 × 1013 GC ml−1 for AAV9-hSyn-hChR2(H134R)-eYFP viruses. For viruses used in viral tracing experiments . The micropipette was slowly lowered to the target site and remained for 5 min before starting injections . After injections, the micropipette stayed for 10 min before it was withdrawn.Each animal underwent bilateral craniotomies using a 1/4 size drill bit. The virus solutions with a volume of 1 μl were injected using a mineral oil-filled glass micropipette joined by a microelectrode holder to a 10 µl Hamilton micro syringe. A micro syringe pump was used to control the speed of virus injections  and AAVrh8-synP-DIO-sTpEpB-WPRE-bGH with a mixed ration of 1:1 at M2 44. One to two weeks later, RVΔG-4mCherry (EnvA) (1 μl) was injected at the same coordinates45. Viral titers for the injected solution were 1.05 × 1013 GC ml−1 and 1.15 × 1012 GC ml−1 for pENN.AAV.CaMKII.0.4.Cre.SV40 and AAVrh8-synP-DIO-sTpEpB-WPRE-bGH, respectively, and 1.7 × 1010 GC ml−1 for RVΔG-4mCherry (EnvA). One to two weeks  after the injection of the rabies virus, the animal was perfused, brain extracted, sectioned, immunostained, and imaged.For exploring brain regions projecting to M2 in the rat, we used a genetically engineered rabies virus13 GC ml−1 for AAVretro-pmSyn1-EBFP-Cre and 4.7 × 1012 GC ml−1 for AAV5-hSyn-DIO-hM4Di-mCherry.To validate projections from ACC to M2, we injected AAVretro-pmSyn1-EBFP-Cre (Addgene#51507-AAVrg) at M2  and AAV5-hSyn-DIO-hM4Di-mCherry (Addgene#44362) at ACC . The viral titers were 1.1 × 10−1 in PBS), incubated for 30 min at room temperature, and then mounted on microscope slides. Antibodies used for staining were as follows: to stain for hM4Di-mCherry or mCherry alone, slices were incubated with primary rabbit anti-RFP  and visualized using anti-rabbit Alexa 555 or Alexa 568 (1:200). To stain for eNpHR3.0-eYFP and hChR2-eYFP, slices were incubated with primary chicken anti-GFP  and visualized using anti-rabbit Alexa 488 (1:200). Immuno-stained slices were imaged using an epifluorescence (Zeiss Imager.Z2) or a confocal microscope (Zeiss LSM700) with ×5 or ×10 objective lenses. Intensity of each fluorescence channel in imaging data was adjusted using ZEN software .Rats were deeply anesthetized using sodium pentobarbital and then transcardially perfused with saline and 4% paraformaldehyde (PFA). Brains were extracted and incubated in 4% PFA at room temperature overnight. Brains were transferred to PBS, and 50 μm coronal slices were prepared using a vibratome. For immunostaining, each slice was placed in PBS + 0.1% Triton X-100 (PBS-T), with 10% normal goat serum for 1 h and then incubated with primary antibody at 4 °C for 12 h. Slices then underwent three wash steps for 10 min each in PBS-T, followed by 2 h incubation with secondary antibody. After three more wash steps of 10 min each in PBS-T, slices were transferred to DAPI solution  was prepared by first dissolving CNO in dimethyl sulfoxide  followed by adding saline solution . CNO solutions were intraperitoneally injected 35–40 min before starting behavioral testing. In some sessions, we started behavior experiments 60 min after the IP injection of CNO solution to examine whether the duration from the CNO administration to the start time of behavioral testing could affect the animal’s performance. In saline control conditions, a vehicle solution  was intraperitoneally injected 35–40 min before behavioral sessions.Rats were bilaterally injected with AAV5-CaMKIIa-hM4Di-mCherry virus in ACC (area 24a’/24b’) Fig. 27,28. At−1) was bilaterally infused through a 33-gauge dual internal cannula (Plastics One) at a speed of 0.2 μl min−1. After waiting for 4 min after infusions, the infusion cannula was removed. Rats were kept in their home cage for 30 min until we started behavioral experiments.For locally infusing CNO solution or its control solution in M2 in rats infected with inhibitory DREADD virus in ACC, we implanted 26-gauge dual guide cannula (Plastics One) targeting bilateral M2   under light anesthesia, we resumed neural activity measurements. Continuous voltage signals were recorded in the hard disc for offline data analysis. To obtain multiunit spike timestamps in offline analysis, the continuous voltage signals were high-pass filtered (400 Hz) digitally, and multiunit spikes were detected by thresholding the continuous signals at 4 SDs above the baseline level46.For testing if an IP administration of CNO could suppress neural activity in ACC, we measured multiunit spiking activity in ACC in two rats infected with the inhibitory DREADD virus and implanted with silicon probes (Neuronexus)  at around this coordinate elicited contractions of the shoulders or limbs of the rats.The neural activity data were obtained from six rats in which Utah array electrodes with 4 × 8 matrix shanks  (Blackrock Microsystems) were implanted in M2 targeting an area covering the Bregma coordinate of ML 1.3 mm, AP + 2.0 mm in either left or right hemispheres . This location was chosen because it was the center of the distribution of stimulation sites that resulted in contralateral orienting movement and neurons related to orienting responses were recorded in previous studiesFor implant surgery, animals were anesthetized in the isoflurane chamber and placed in the stereotaxic frame. After applying eye ointment and washing the incision sites with betadine and ethanol, an incision was created over the scalp and connective tissue was removed. The skull was drilled for attaching bone screws and for implanting the array electrodes in M2. After installing bone screws, we performed a durotomy and slowly inserted the array electrodes at M2 using a manipulator and a mounting probe by applying a vacuum to steady the array. Dental cement was applied to secure the cable to the screws and the craniotomy was filled with a surgical silicone adhesive (Kwik-sil). The vacuum was turned off and the array was further secured using dental cement.All recordings were conducted after the rats were fully recovered (at least seven days after surgery). The ground was taken from one of the skull screws typically above the cerebellum. The reference channel was chosen from one of the skull screws or one of the recording channels in which no clear spiking activity was observed. Data were recorded using a unity gain amplifier forwarded, filtered , and stored in the Digital Lynx SX System  for offline data analysis using Cheetah 5 software .Dual fiberoptic cannulae  were implanted in the ACC in task-trained rats under isoflurane anesthesia . A yellow-green laser  with a fiberoptic patch cable  was installed in the behavioral chamber. A TTL pulse was delivered from the behavior system through an interface board  that determined the timing of laser light delivery for optogenetic intervention. Light was continuously delivered for 4 s during the outcome feedback period following the animals’ 2nd choices  were implanted in M2 cortex in task-trained rats under isoflurane anesthesia . A blue laser  with a fiberoptic patch cable  was installed in the behavioral chamber. A TTL pulse was delivered from the behavior system through an interface board  that determined the laser timing for optogenetic intervention. 473 nm light with 5 ms pulses was delivered at 10 or 20 Hz for 1 s during the pre-choice period  attached to an optical fiber  with the tip of the fiber extending beyond the tip of the electrode by 200–300 μm were used for simultaneous optical stimulation and extracellular recordings in anesthetized condition. The optrode was slowly lowered to cingulate cortex where the eNpHR3.0 virus or hChR2 virus was injected. The optical fiber was connected to a yellow-green (561 nm) laser (for eNpHR3.0 virus) or to a blue (473 nm) laser (for hChR2 virus) and was controlled by a beam shutter and a shutter controller. The power intensity of light emitted from the optrode was calibrated to 13–16 mW (for yellow-green laser) and 5–6 mW (for blue laser), respectively, as measured with an optical power/energy meter, which is consistent with the power intensity used in behavior experiments. Then, 561 nm light pulses were delivered at each depth of the optrode (pulses with 1 s duration at 5 Hz for eNpHR3.0 virus and with 5 ms duration at 10 or 20 Hz for hChR2 virus) while neuronal activity in ACC was collected for 10–20 sweeps. Continuous voltage data were monitored online using an oscilloscope and a sound speaker, fed into a preamplifier, transferred to an interface board, and saved in the hard disc. Continuous voltage traces were high-pass filtered with a Butterworth filter and then thresholded typically at around −50 to −80 μV using Offline Sorter software (Plexon). Spike rasters and peri-event time histograms (PETHs) of spiking activities of isolated single-units were plotted using Neuro Explorer software . Rats were sacrificed, and brains were collected and sectioned for histological confirmation of recorded sites.Spike sorting was conducted using Offline Sorter software (Plexon). Spike rastergram Fig.  and someAnimals’ task performance of the first choices in 1 step rule or 2 steps rule blocks  was quantified using a percent error rate in their 1st choice responses . Note that, in 1 step condition, animals were required to make a single choice response  and thus there is no correct 2nd choice although, for convenience, we referred to the correct choice responses in 1 step condition as correct 1st choice responses; indeed, in 1 step condition, committing a 2nd response  was not rewarded and, instead, an error feedback tone was provided. In group analysis, the percent 1st choice error rate for two trial types  were separately calculated and then averaged.In addition, in 2 steps rule blocks, after an animal makes a correct 1st choice response, animals can make two kinds of choice responses. (1) Choose a side port opposite to the one that the animal chose in its 1st response (correct 2nd choice response). (2) Alternatively, animals may omit poking to the side port and, instead, may push the center lever for initiating the next trial (incorrect 2nd choice response). Such a response was classified as an incorrect 2nd choice (or a 2nd choice omission error) and was used as a behavioral measure to quantify the animal’s ability to adapt to switches from 1 step to 2 steps conditions  was calculated as the difference in the timing of center lever entry and first-choice port entry (Choice 1). Similarly, response time for the 2nd choice response (RT2) was calculated as the difference in the timing of second choice port entry (Choice 2) and first-choice port entry (Choice 1).To compare animals’ task performance between the 1st block in the session and the following Rule Switch-blocks (either 1 step or 2 steps rule block), sessions were not included in the group analysis if they do not have Rule Switch-blocks . Also, to compare animals’ task performance across three epochs within a block (either 1 step or 2 steps rule -block), sessions were not included in the group analysis if they have less than 10 trials in the 3rd epoch of at least one Rule Switch-block. To compare the task performance in the first, middle, and third sections in a block, a block was subdivided into three epochs: trial no. 1–18 (1st), 19–36 (2nd), and 37–55 (3rd) for sessions with rule switches in every 55 trials. Similarly, in two sessions in which the rule was switched in every 40 trials, a block was subdivided into three epochs: trial no. 1–13 (1st), 14–26 (2nd), and 27–40 (3rd).In some sessions, behavior experiments started from the 2 steps condition (1st block) and then switched to the 1 step condition (2nd block) and so forth  while 5 rats were used for experiments locally infusing CNO solutions with a dose of 1 μg μl−1 (one rat was used for both IP injection experiment and cannula infusion experiment). Two animals (three sessions) were also tested with an IP injection of CNO with a dose of 40 mg kg−1 but the data were not included in the group analysis. Also, one rat was pilot-tested with a local infusion of CNO solutions with a dose of 0.4 and 4 μg μl−1 but these data were not included in the group analysis.We used a total of 31 rats for chemogenetics experiments; 15 rats were injected with the inhibitory DREADD virus (AAV5-CaMKIIa-hM4Di-mCherry) in ACC, 5 rats were injected with the mCherry control virus (AAV5-CaMKIIa-mCherry) in ACC, 6 and 5 rats were injected with the inhibitory DREADD virus in prelimbic/infralimbic cortex and ventral thalamic nuclei, respectively. Among the 15 rats that were injected with the inhibitory DREADD virus in ACC, 11 rats were used for experiments intraperitoneally administering CNO solutions  were removed from electrophysiology data analysis .48. We analyzed neural data collected in 43 sessions from 5 rats: 29 sessions with saline control , 14 sessions with an IP injection of CNO solution with a dose of 20 mg kg−1 . From these session data, a total of 900 single-units were isolated: 594 units and 306 units for saline and CNO conditions, respectively. All these units were included in the group analysis of mean firing rate . Only a unit with a refractory period (>2 ms) in the auto-correlogram was accepted as single-units−1 in the 1 s window immediately before the animals made the choice in either cue stimulus condition in 1 step condition or before animal’s making the 1st choice in either cue stimulus condition in 2 steps condition .A single-unit was included in the group analysis of rule selectivity Fig.  if it sh31. An ROC analysis measures the degree of overlap between two response distributions. For each M2 single-unit the preferred and non-preferred rule conditions were compared, given two distributions, P and N respectively, of neuronal activity. For example, for some neurons . We then calculated the area under ROC curve (auROC) using these firing rates. To examine how rule selectivity changes throughout the Rule Switch-blocks, a block was subdivided into three epochs: trial nos. 1–18 (1st epoch), 19–36 (2nd epoch), and 37–55 (3rd epoch) for sessions with rule switches in every 55 trials. Similarly, in two sessions in which the rule was switched in every 40 trials, a block was subdivided into three epochs: trial nos. 1–13 (1st epoch), 14–26 (2nd epoch), and 27–40 (3rd epoch).Then ROC analysis was conducted for each epoch separately. For example, to calculate rule selectivity in the 1st epoch of a block following rule switches from 1→2 steps conditions, ROC curves were calculated using distributions of mean firing rates during the pre-choice period of trials in the 2nd and 3rd epochs in the preceding 1 step rule block and of mean firing rates during the pre-choice period of trials in the 1st epoch of subsequent 2 steps rule block. Similarly, rule selectivity in the 2nd (or 3rd) epoch of a block following rule switches from 1→2 steps conditions were calculated using distributions of mean firing rates during the pre-choice period of trials in the 2nd and 3rd epochs in the preceding 1 step rule block and of mean firing rates during pre-choice period of trials in the 2nd (or 3rd) epoch of subsequent 2 steps rule block. Similarly, rule selectivity in each of three epochs of a block following rule switches from 2→1 steps conditions was calculated using distributions of mean firing rates during the pre-choice period of trials in the 2nd and 3rd epochs in the preceding 2 steps rule block and of mean firing rates during the pre-choice period of trials in each of three epochs of subsequent 1 step rule block.30. To test the statistical significance of rule selectivity, we used a bootstrap analysis and repeated the ROC analysis 200 times to obtain 95% percentile threshold in which we assigned the rule condition  at random to each trial and calculated the auROC. In group analysis, trial conditions were grouped in ipsilateral and contralateral -conditions depending on the hemisphere implanted with the electrode array in each animal: in ipsilateral trial conditions, animals were required to first choose the ipsilateral side port and then the contralateral side port as its 2nd choice  while, in “contralateral” trial conditions, they were required to select the contralateral side port as its 1st choice and then to the ipsilateral side port as its 2nd choice   all the single-units included in the single-unit database were sorted in descending order according to their mean firing rates. (2) The firing rates of single-units measured in the same percentiles were paired between CNO and saline conditions. (3) Then, the spikes of single-units in saline condition were randomly removed so that the mean firing rates across trials were matched between the two single-units in the same percentiles of the sorted lists of neurons measured in CNO and saline conditions. (4) Once the firing rates were matched between CNO and saline conditions, an auROC was estimated in the same manner as in Fig. t-test, one-sided, P = 0.025). Positive outcome-activated neurons were defined as single-units that showed significantly greater firing rates in the positive outcome feedback period  than those in the baseline period. Similarly, positive outcome-suppressed neurons were defined as single-units that showed significantly smaller firing rates in the positive outcome feedback period  than those in the baseline period. Negative outcome-activated neurons and negative outcome-suppressed neurons were defined in similar manners except that, for these neurons, the outcome feedback period was a 3 s period following an error feedback tone instead of a reward delivery (negative outcome feedback period). In Fig. All the isolated M2 single-units were included in the group analysis . They were classified according to their activity in the outcome feedback period. Each single-unit was tested if the mean firing rates in the outcome feedback period were significantly greater or smaller than those in the baseline period   and R (R Studio) were used for data analysis. No statistical analysis was conducted to pre-determine the sample sizes of each experiment. Statistics were run two-sided except mentioned otherwise. In testing the statistical significance of the effect of CNO dose or epoch in Rule Switch-blocks or their interaction in task performance Figs.  and 4d–gFurther information on research design is available in the Supplementary InformationPeer Review FileDescription to Additional Supplementary InformationReporting Summary


Probiotics also caused an enhanced cell cycle by regulating the expression of genes involved in Retinoic acid  and Wnt/β-catenin  signaling pathways, and also modulated phosphate homeostasis by increasing the entpd5a levels. These findings provide new outlooks for the use of probiotics as a prophylactic treatment in accelerating bone regeneration and improving skeletal health in both aquaculture and biomedical fields.Probiotics are live microorganisms that confer several beneficial effects to the host, including enhancement of bone mineralization. However, probiotic action on bone regeneration is not well studied and therefore we analysed various effects of probiotic treatment on the caudal fin regeneration of zebrafish. Morphological analysis revealed an increased regenerated area with shorter and thicker lepidotrichia segments after probiotic treatment. Fourier transform infrared spectroscopy imaging analysis highlighted the distribution of phosphate groups in the regenerated fins and probiotic group showed higher amounts of well-crystallized hydroxyapatite. At the midpoint (5 days post amputation) of regeneration, probiotics were able to modulate various stages of osteoblast differentiation as confirmed by the upregulation of some key marker genes such as  In addition, in the zebrafish model, few studies have provided clear evidence for the role of probiotics in accelerating skeletogenesis and mineralization5. Probiotic administration basically modulates the host-microbiome6 which is proven to affect bone metabolism through many possible ways including metabolite production7, hormonal interactive pathways, osteo-immunological responses8 or via synthesis of vitamins9. Many probiotic species such as Bacillus subtilis are known to produce vitamins with osteogenic properties such as vitamin K210.Probiotics are beneficial microbes that can exert numerous health benefits to the host including effects on bone metabolism as reported in various animal models such as poultry and rodents11. Zebrafish, as a genetic model for bone regeneration, offers the additional worth to translate the findings into bio-medicinal perspective in human regenerative medicines13. Expression of orthologues for important mammalian osteogenic molecular players like β-catenin has been detected in the regenerating fin in addition to the orthologues for its downstream targets16. Zebrafish regenerate amputated caudal fins by creating lineage-restricted blastemal cells18. Following partial amputation, the fin with bony rays and soft inter-ray tissue regenerates very robustly through establishment of blastema, which are populations of lineage-restricted mesenchymal progenitor cells formed via de-differentiation of mature stump cells20. The pool of de-differentiated osteoblasts in the blastema proliferates, re-differentiates exclusively into non-proliferating osteoblasts and deposit bone matrix during the progression of regeneration19. These differentiation steps are distinguished by various osteoblast stage markers such as RUNX family transcription factor 2b (runx2b) for osteoblast progenitors towards the distal part of proximal blastema, followed by sp7 transcription factor (sp7 or osterix) positive osteoblasts and finally bone gamma-carboxyglutamate (gla) protein  positive mature osteoblasts to the proximal end21. Re-expression of sp7 and the decrease in bglap are found to be the indicators of de-differentiation of cells in blastema during regeneration22. Several other signaling pathways including RA (Retinoic Acid)23 and Wnt/β-catenin25 have been identified to be essential for fin regeneration.The zebrafish caudal fin has emerged as a highly successful model system for studying the basic mechanisms of tissue regeneration. Under lab conditions, it has several study advantages such as easy live tracking, fin accessibility and the absence of major amputation-causing detrimental effects on the fish26. Using a multidisciplinary approach, ranging from analysis of morphological parameters related to fin growth, to evaluation of expression of representative genes involved in ossification followed by quantification of phosphates and other macromolecules, we aimed at gaining evidence on the role of probiotic bacteria in bone regeneration which could aid in the development of regenerative medicine protocols as well as improve aquaculture practices. In this regard, the possible probiotic action on bone through either increased osteoblast activity or decreased osteoclast activity are both explored in the current study using specific marker genes of osteoblasts and osteoclasts, such as secreted protein, acidic, cysteine-rich (sparc or osteonectin) and cathepsin K (ctsk), respectively27.Based on the evidence from literature, the aim of the present study was to investigate the potential effects of the administration of probiotics on the caudal fin regeneration process. Fish were subject to pre-treatment with probiotics for two weeks before amputation in order to favor colonization of beneficial bacteria in the gut of treated fish and generate desired positive outcomes as previously confirmed with other probiotics species28. The modified Eq.  and probiotic-treated (P) experimental groups was evaluated at 5 days post amputation (DPA) and 10 DPA by analyzing the fin ray width (RAY), calculated by averaging the width of the second bifurcated fin ray in the dorsal lobe at the first formed segment joint after amputation; segment length (SEG), calculated by averaging the length of the first formed segment after amputation in the second bifurcated fin ray in the dorsal lobe; REG/STU, which is the ratio between regenerated area (REG) and stump width (STU), and lastly REG/PED, which is the ratio between REG and peduncle width (PED). The complete course of regeneration was tracked for both C (n = 7) and P (n = 7) fish every day at the same time. In Fig. This parameter was 8–9% higher in P fins than in C group at 10 DPA  and 10 DPA (p = 0.036) whereas no significant difference was observed between the groups for REG/PED ratio even though it displayed the same pattern as that of REG/STU ; Fig. 1655/A1024 and A1240/A1024 ratios are usually related to the relative amount of the organic component (proteins and collagens) with respect to the inorganic/mineral one (bone HA)31. These two ratios showed a decrease in the proximal area with respect to distal one. Moreover, at 5 DPA, in the distal region, statistically significant lower values were found for these ratios in P group respect to C (p < 0.05), indicative of a higher amount of the mineral component in the P fins. Conversely, no statistically significant difference was observed for these two ratios at 10 DPA between C and P groups in both analyzed regions (p > 0.05)  to a well-crystallized stoichiometric one (represented by the peak at 1024 cm−1)32. In the distal region, statistically significant higher values were found in P samples at both 5 DPA and 10 DPA with respect to C. In the proximal region, the ratio remained same between C and P samples (p > 0.05)  increased during the regeneration at 5 DPA with respected to 0 DPA in both C and P. Both runx2b and col10a1a mRNA showed a significantly higher expression in P group with respect to C at 5 DPA and col10a1a was also highly expressed in the P fins at 0 DPA. The late marker bglap was significantly increased in both C and P when nearing the completion of the regeneration process (10 DPA) but a significant difference between C and P was observed particularly at 5 DPA. The expression of mature osteoblast specific marker, secreted phosphoprotein 1 (spp1 or osteopontin), mRNA was significantly higher in treated fins at 5 DPA  mRNA, was significantly higher in P group and was associated to a decreased expression of retinoic acid receptor gamma-a (rarga) with respect to C. Rarga mRNA expression was also found to be higher in the initial amputated fins (0 DPA) of P treated group than in C. An increase in the expression of ectonucleoside triphosphate diphosphohydrolase 5a (entpd5a), which is involved in the homeostasis of phosphates, was also observed from 0 to 5 DPA in both C and P groups. At 10 DPA, the entpd5a gene expression declined and only at this time point, its mRNA level in P group was significantly higher with respect to the control , beta 1(ctnnb1) was significantly higher in P group at both 5 DPA and 10 DPA whereas its universal transcriptional target gene axin2, which is also a negative feedback regulator in the canonical Wnt signaling, was significantly higher in P fins at 10 DPA. Sparc, representing the gene encoding for sparc protein, which are known to prevent the degradation of β-catenin, was also significantly higher at 5 DPA in both groups with respect to 0 DPA but without any significant difference between C and P groups. The negative regulator of Wnt signaling, sclerostin (sost), and the osteoclast marker gene ctsk were significantly higher in C at 5 DPA whereas at 10 DPA, sost was higher in P group than in C. Another downstream transcriptional target gene of β-catenin is cyclin D1(ccnd1), which is also a marker of cell proliferation, and its mRNA was also upregulated in P fins with respect to C at 5 DPA  confirms the role of probiotics in modulating the regeneration process at various stages of osteoblast de-differentiation and proliferation. Runx2b is a pre-osteoblast stage marker, which expresses in the blastema during the de-differentiation of osteoblasts in the early stages of regeneration21 and sp7 acts hierarchically downstream to the runx2b in the differentiation pathway. Runx2b also regulates the expression of other important marker genes of various stages of osteoblast differentiation like col10a1a, spp1, bglap and hence is considered as a key regulator. Col10a1a, which acts further downstream to sp7, marks the intermediate stages of skeletogenesis (bone matrix deposition stages)41. It is expressed in early osteoblasts and chondrocytes during intramembranous and perichondral ossification of fish bone45 and our results showed its strong downregulation towards the final stage of regeneration (10 DPA). The sequential activation of early markers points to the same conclusion that P treatment significantly promotes the early and intermediate stages of osteoblast differentiation processes after fin amputation. Regarding mineralization, established marker genes of later stages of osteoblast differentiation and extracellular matrix mineralization such as bglap  and spp1 (osteopontin), both acting downstream and regulated by sp7 and runx2, were evaluated46. In our study, both were upregulated during the regeneration process with a significantly higher expression in the treated fins at 5 DPA. These results are further supported by the higher crystallized HA levels found in P fins, providing clear evidence of the effect of P on the mineralization stage as well.The significantly higher expression of early and intermediate osteoblast differentiation stage markers such as Ctnnb1 (β-catenin) transcript was significantly higher in P fins during regeneration suggesting an activation of Wnt/β-catenin signaling by P. From the expression of two downstream target genes of β-catenin, cell proliferation marker ccnd1 and negative regulator of β-catenin signaling axin2, we could observe that the increased β-catenin signaling at 5 DPA was suppressed towards 10 DPA in both the groups. This confirms that the measured levels of β-catenin are well related with the different regeneration stages, being higher during proliferative phase and lower during the later stages of differentiation/maturation. Elevated expression of sparc at 5 DPA further confirms the previous reports of the involvement of this pathway in the fin regeneration as sparc codes for a matrix protein which is known to increase osteoblastogenesis through enhancing β-catenin mediated signaling and preventing the degradation of β-catenin47. Sparc was earlier found to be enriched at 4 DPA regenerates27 which further agrees with our observation of significantly higher amount of sparc at 5 DPA, although no differences were observed between groups. Also, the higher expression of sost at the midpoint of regeneration is an additional indicator for the modulation of Wnt/β-catenin signaling during blastema formation, since sclerostin is found to be expressed in the blastema during early fin regeneration in zebrafish16. In our study, P caused the downregulation of sost mRNA levels in P fins and a previous trial on zebrafish larvae also reported regulation of sost levels by probiotic treatment5. Wnt/β-catenin signaling is also involved in the suppression of osteoclast activity49 and this evidence is strongly supported by our results showing P ability to downregulate the osteoclast marker gene ctsk27 during regeneration.2 producers such as Bacillus subtilis and vitamin K2 is a crucial class of vitamins known to partake in the positive regulation of bone development50, further studies can be done focusing on probiotic ability to produce vitamin K2. In brief, the significant impact of probiotic treatment on the regeneration process was revealed using zebrafish caudal fin as a model. This observation could be useful in bone regenerative medicine studies where probiotics can be a potential prophylactic candidate to improve bone health.The overall results suggest that P treatment positively affects the caudal fin regeneration process. At molecular level, P mainly affected the regeneration at midpoint . All efforts were made to minimize animal suffering and the study was carried out in compliance with the ARRIVE guidelines.2 < 0.01 mg/L and NO3 < 10 mg/L, were collected from the fish facility and divided into a control group (C) (n = 24) and a probiotic-treated group (P) (n = 24). The trial was conducted using a commercial probiotic mixture, Bactosafe H (Bernaqua), which consists of a mix of 5 different bacteria- Bacillus subtilis, Bacillus licheniformis, Bacillus coagulans and Lactobacillus acidophilus plus the yeast Saccharomyces cerevisiae. Within the probiotic mix, B.subtilis, the strain of our interest is known to produce vitamin K2 or menaquinones, a group of pro-osteogenic vitamins10. C and P groups were fed a commercial diet  at 3% body weight twice a day and only P group received a dietary supplementation with the probiotics at 106 CFU/ml. A 14-day pre-conditioning treatment with the probiotics was administered to the P group to enhance gut colonization. At the end of pre-conditioning, fish were anesthetized using 0.1 g/l MS-222  and the caudal fins were amputated 1–2 segments anterior to the bifurcation of the second bifurcated lepidotrichia29. Amputated fins (0 DPA) were stored at -80 °C for RNA extraction. After amputation, each fish was maintained separately, to have biological replicates at a density of 1 fish per 200 ml in glass containers filled with water from the rearing system. The glass containers were maintained in a water bath equipped with heaters and air stones to keep the temperature homogenous. Fish were allowed to regenerate at 33.0 (± 1) °C to accelerate regeneration as previously reported51. During the regeneration, fish were fed with the same quantity of commercial feed and administered with the same P concentration, as in the 14 days of pre-conditioning. Individual regeneration process was tracked (n = 7 per group) by taking images of the same fish at pre-amputation, post amputation and 1, 5 and 10 DPA. Regenerated caudal fin samples were collected at 5 DPA, representing the mid time point of regeneration, and at 10 DPA as the last point of regeneration for RNA extraction and FTIRI analysis. Fins for RNA extraction (n = 9 per group per time point) were stored at -80 °C. For FTIRI analysis, the fins (n = 3 per group per time point) were fixed in 4% paraformaldehyde (PFA) for 12 h, washed twice with PBS and then stored in PBS at + 4 °C.Three months old wild-type (AB) zebrafish with a mean weight of 100 ± 7 mg and a mean total length of 20 ± 2 mm, maintained at 28.0 °C, pH 7.0, photoperiod 12:12 light: dark, NO29. Fin images were analyzed using ImageJ  and ImageJ macros used to analyze the images was scripted specifically for the regeneration parameters of caudal fin  at pre-amputation, post amputation and 1, 5 and 10 DPA for tracking the progress of regeneration in C and P fins (n = 7 per group). Fish were anaesthetized before the imaging using MS-222 at 0.6 mM and after the imaging, fish could recover in fresh water with aeration. Morphological studies were performed by measuring some of the previously described parameters: REG (regenerated area), PED (peduncle width), STU (stump width), RAY (fin ray width) and SEG (segment length) and two ratios REG/STU and REG/PED−1 spectral range. Each map was 164 × 164-micron side, and it was the result of 4096 pixel/spectra (256 scans), with a spatial resolution of 2.56 × 2.56 micron. Raw IR maps were corrected for carbon dioxide and water vapour and then vector normalized in the full spectral range . False colour images representing the topographical distribution of phosphate groups were obtained by integrating all IR maps in the 1185–980 cm−1 spectral range (assigned to the stretching vibrations of phosphate groups). An arbitrary color scale was used where white/light pink colours represent the zones with the highest absorption of phosphates whereas black/dark blue denote the zones with the lowest absorption of phosphates.FTIRI analysis was performed by a Bruker INVENIO interferometer coupled with a Hyperion 3000 IR-Vis microscope and equipped with a FPA detector . Fin samples from C and P experimental groups (n = 3 per group per time point) were deposited onto CaF2 optical windows. By using a 15× condenser objective, specific areas were selected on each fin sample. The two regions assayed after amputation in the fourth bifurcated fin ray of the dorsal fin lobe are described as: proximal- corresponding to the first distal bifurcation and distal- corresponding to the last segment joint preceding the actinotrichia  of the underlying bands were identified by second derivative minima analysis and fixed during procedure with Gaussian functions . The integrated areas (A) of the underlying bands were used to calculate the following band area ratios: A1655/A1024 (Protein to Phosphate ratio), A1240/A1024 (Collagen to Phosphate ratio), and A1024/A1090 .On each IR map, a submap (ca. 300 spectra/pixel) was extracted in correspondence with the bone at the two analyzed regions. The average spectrum and average ± standard deviation spectra were calculated, and curve was fitted in the 1800–900 cm52.Pools of 3 fins were made to have 3 replicates per group for each time point. Total RNA was extracted from amputated caudal fin (0 DPA) and regenerated caudal fins (5 and 10 DPA) using RNAeasy Microkit . It was then eluted in 20 µl of molecular grade nuclease free water. Final RNA concentrations were determined using a nanophotometer. Total RNA was treated with DNase . One microgram of total RNA was used for cDNA synthesis using iScript cDNA Synthesis Kit  and stored at -20 °C until further use as described previously53. The thermal profile for all reactions was 3 min at 95 °C followed by 45 cycles of 20 s at 95 °C, 20 s at 60 °C and 20 s at 72 °C. Dissociation curve analysis showed a single peak in all the cases. Ribosomal protein L13 (rpl13) and ribosomal protein, large, P0 (rplp0) were used as the housekeeping genes to standardize the results by eliminating variation in mRNA and cDNA quantity. No amplification product was observed in negative controls and primer-dimer formation was never seen. Data was analyzed using iQ5 Optical System version 2.1 (Bio-Rad) including Genex Macro iQ5 Conversion and Genex Macro iQ5 files. Modification of gene expression between the experimental groups is reported as relative mRNA abundance (Arbitrary Units). Primers were used at a final concentration of 10 pmol/ml. All primer sequences used in the study are listed in Supplementary Table qRT-PCRs were performed in C and P fins  with SYBR green  in a CFX thermal cycler  as described before54 and plots were generated using ggplot2 3.2.1. Statistical significance was set at p < 0.05 for all the tests.T-test was used to analyze the morphological regeneration parameter differences between the groups. Two-way analysis of variance (ANOVA) followed by Tukey’s post hoc tests were applied to both IR and qRT-PCR data, to compare differences among experimental groups. All the tests were performed using R version 3.6.1Supplementary Information.


Raw values of δ2Hf ranged from -107.3 to +5.0‰, with higher values at the Caatinga biome (northeast Brazil) and lower values at the Amazon and Pantanal. A Random Forest (RF) method was used to model the spatial surface, using a range of environmental data as auxiliary variables. The RF model indicated a negative relationship between δ2Hf and Mean Annual Precipitation, Precipitation in the Warmest Quarter, and Annual Temperature Range and positive relationships for amount-weighted February-April precipitation δ2H (δ2Hp(Feb-April)) and Mean Annual Solar Radiation. Modelled δ2Hf values ranged from -85.7 to -13.6‰. Ours is the first δ2Hf isoscape for Brazil that can greatly assist our understanding of both ecological and biogeochemical processes controlling spatial variation in δ2H for this region. This isoscape can be used with caution, due to its poor predictive power  and can benefit from new sample input, new GNIP data, ecological and physiological studies, and keratin standard material better encompassing the range in feather samples from Brazil. So, we encourage new attempts to build more precise feather H isoscapes, as well as isoscapes based on other elements.Spatial patterns of stable isotopes in animal tissues or “isoscapes” can be used to investigate animal origins in a range of ecological and forensic investigations. Here, we developed a feather hydrogen isotope (δ The world is rapidly losing biodiversity  due to h2H and δ18O models from precipitation based primarily on the >60-year dataset from the International Atomic Energy Agency (IAEA), the Global Network of Isotopes in Precipitation (GNIP) [2H is influenced, then, by diet composition, body size, and spatial environmental gradients in food web δ2H [2H values can be linked to them.Hydrogen and oxygen have well established isotopic patterns across continents  as demon web δ2H . Because web δ2H ). The po2H isoscapes are available today, ranging from continental models [Danaus plexippus [Recent development of isotopic techniques to infer origins of birds and other animals have included more refined regional isoscapes –14 and ul models , 18 to cl models , 20. In l models . For sevl models  such as lexippus ), dragonlexippus , and sevlexippus , 25, inclexippus , 26–28.Forensic applications based on stable isotope measurements are numerous –31. In B2H isoscape for Brazil based on existing knowledge of precipitation-based δ2H models, environmental variables, and known-origin feathers from museum collections. Our goal was to derive a useful forensic tool that best fits the precipitation and feather isotope data based on a machine learning approach.Here, we established a feather δThraupidae due to the wide range of distribution of this family in Brazil and its prominent use in the illegal bird trade [Fringillidae (genus Euphonia) and two from family Cardinalidae (genus Cyanoloxia). Taxonomic classification follows the Brazilian Ornithological Records Committee [Feathers were sampled from non-migratory passerine birds held in ten scientific ornithological collections in Brazil, and also from field campaigns between 2016–2018 in different Brazilian National Parks, as part of a Brazilian isotope forensic project . Our dataset comprised 192 samples distributed among 129 locations in Brazil and included 49 species. We chose species belonging to six most representative sub-families of rd trade . We alsoommittee .2Hf values [We prioritized specimens collected since 2013 and the use of flight feathers. Body and tail feathers were included only when flight feathers were not allowed from collections. Wing primaries or secondaries are more associated with a specific molting period, but, if grown simultaneously, body and flight feathers are expected to have similar δf values . Due to Most birds in Brazil molt their remiges and rectrices following reproduction, showing little or no overlap between reproduction and molt, especially in Thraupidae and generally in frugivores , 40. TheThe procedures described here were approved by the Animal Use and Ethics Committee–University of Brasília (55712/2016). Field collection permits were done under the Chico Mendes Institute for Biodiversity Conservation’s license (SISBIO 8745–1). Feathers were transported from Brazil to Canada for stable isotopes analysis under CITES license (144180 and 144541). All Genetic Patrimony Access was also registered under the SISGEN system (#A4C6D05).2 gas was analyzed on a coupled Thermo Delta V Plus  continuous-flow isotope-ratio mass spectrometer at the University of Western Ontario LSIS-AFAR laboratory. Stable-hydrogen isotope results are reported for the non-exchangeable H fraction using the comparative equilibration method of Wassenaar and Hobson [Feathers were steam-dried at 100°C for 20 min to meet requirements for international transportation to Canada. Before isotopic analysis, feathers were soaked in a 2:1 chloroform:methanol solution overnight, drained and air-dried under a fume hood. Distal feather vane was cut, weighed (~ 0.35 mg) into silver capsules, crushed, and placed into a Uni Prep carousel  at 60°C. The carousel was evacuated, flushed, and held under pressurized helium flow and samples were combusted in a Eurovector 3000 elemental analyzer using a glassy carbon reactor held at 1350°C. Resultant Hd Hobson  based on2Hf are affected by year of sample collection, feather type , biome where sample was collected, sub-family to which the species belong, latitude, and longitude . Latitude and longitude were tested through simple linear models. Additionally, year of collection was also tested through a linear model, where model residuals were also explored.2Hf values. We used “dredge” and “model average” functions to summarize the best models, ranking them by increasing Akaike’s Information Criteria (AICc) and considering models within ΔAIC < 2 as competitive. To run this analysis we used the package ‘MuMIn’ [Subsequentially, we also ran a linear model selection (LM), with all variables, to explore variables’ importance to explain the measured δ ‘MuMIn’  availabl ‘MuMIn’ .The R code used and the results for each analysis are available as 2Hf isoscape was modeled using a Random Forest analysis approach. First, using the “Recursive Feature Elimination” method, we selected environmental variables that most contributed to the prediction of δ2Hf values. Then, using the selected variables, we ran the Random Forest analysis to predict the best-fitting isoscape.Our Brazilian δraster’ [sf’ [dismo’ [caret’ [rgdal’ [randomForest’ [ggplot2’ [pdp’ [The R script used here was adapted from Bataille et al.  and Senaraster’ , ‘sf’ [4er’ [sf’ , ‘dismo’ [dismo’ , ‘caret’ [caret’ , 52, ‘rg [rgdal’ , and ‘ramForest’ . Plots aggplot2’  and ‘pdp2’ [pdp’  in R, bu2’ [pdp’  and Qgis2’ [pdp’  free sofWe used data for temperature, precipitation, solar radiation, wind speed, water vapor pressure and altitude from WorldClim database ; and datdismo’[The precipitation and the maximum and minimum temperature were calculated for the period of 2007–2018, and the bioclimatic variables for this period were calculated using package ‘dismo’. The R sdismo’.2Hp) [2Hp and δ2Hf in Brazil (r2 = 0.167) was low when compared to the same correlation in North America ), which gave us the best fit for δ2Hf observed data (r2 = 0.252). The idea of joining the more representative months is consistent with the idea of WorldClim bioclimatic variables, where year-quarters (3 subsequent months) are used as units of analysis. The criteria and R code used to define this timeframe is available as S4 Fig and S4 Table in We also used data from Waterisotopes database , corresp2Hp) . The cor = 0.83; ). In tem2Hf values from our known-origin feathers. This approach reduces the collinearity between variables which would result in overfitting [Before applying a Random Forest analysis, we used a Recursive Feature Elimination (RFE) method to select a reduced number of variables most relevant in predicting our δrfitting . Here, wtry) was defined as mtry = 3, based on out-of-bag error and avoiding mtry = 1 due to overfitting .2Hf values and predicted δ2Hf values, for the following datasets: (A) training dataset after 10-fold cross-validation and (B) testing dataset (20% of data). For each linear regression we compared the Mean Absolute Error , the Root Mean Squared Error (RMSE = the root squared of MAE), and the r-squared (r2).Model performance was evaluated through linear regressions between observed δraster’ package [Spatial predictions were conducted using the function “predict” available in the ‘ package . The fin2Hf ranged from -107.3‰ to +5.0‰ , followed by Atlantic Forest values that were also higher than values found in all other biomes  . The most sampled years (2013–2018) were homogeneous in δ2Hf but 2009 had higher values than years 2013–2019  . Samplesf values , althougues , Mean Annual Precipitation (Bio12), Precipitation at the Warmest Quarter (Bio18), Mean Annual Solar Radiation, and δ2Hp(Feb-April).Through the RFE method, we selected the environmental variables predicting the δ2Hf values, and its removal from the model caused an ~20% increase in the Mean Standard Error (MSE). The δ2Hp(Feb-April) was the second most-important variable (~15%), followed by Precipitation at the Warmest Quarter (~13%), Mean Annual Solar Radiation (~7%), and Annual Temperature Range (~5%).Mean Annual Precipitation was the most important predictor for our known-origin feather δ2Hf values. We observed each of these relationships through the partial plots ) varied from -5.2 to -59.4‰ and our samples were more evenly distributed for this environmental variable. The δ2Hp(Feb-April) was positively related to the observed δ2Hf values  varied from 0.81–3.78 kJ/m2/day and our samples were concentrated in areas with 1–2 kJ/m2/day. The MARS was positively related to δ2Hf  varied from 8–24 ºC and was negatively related to δ2Hf values (The Precipitation at the Warmest Quarter (PWQ) varied from 50.8–762.1 mm and our samples were concentrated in areas with 200–450 mm of rain at the warmest period of the year. The PWQ was negatively related to δ2Hf , as site to δ2Hf , as sitef values .2Hf values explained 25.6%  of the variance in observed values, with a MAE of 231.21 and a RMSE of 15.21 (2Hf and the holdout testing data (n = 36), the predicted values explained 90.5%  of the variation, with a MAE of 34.73 and a RMSE of 5.89  represented a δd -13.6‰ .Here we developed the first feather hydrogen isoscape for Brazil based on an extensive analysis of available climatic data using a machine learning approach. Besides its novelty, this is the first such attempt in the Brazilian territory and we acknowledge the need for further improvements. The isoscape presented here has a low predictive power when compared to isoscapes developed for temperate regions. Possible reasons for such low predictability are (1) the poor modelled precipitation isoscape for Brazil due to low GNIP station coverage, (2) the complex climate and hydrology of Brazil, (3) the relatively low sample sizes of birds when compared to other works in temperate zones, (4) the complexities related to species ecology, migration and molt strategies, and (5) the poor ability to evaluate over which period of integration of H from precipitation supports local food webs.The GNIP coverage in Brazil accounted for 15 to 20 stations between the 1980’s and 1990’s. However, those stations were deactivated and only one or two stations operated between 2008 and 2013 , 67. Sin2 and with a complex climatic and hydrological structure. The main air masses influencing the climate are the Equatorial Atlantic, the Tropical Atlantic, and the Polar Atlantic air masses [Brazil is a continental country, holding a land surface of 8.5 million kmr masses . All of r masses . Althougr masses , 70).When avian sample size is compared to studies conducted in North America  for ker2Hf modelled for Brazil varied longitudinally, with values decreasing from east to west resulting in feathers enriched in 2H on the northeastern coast and more depleted westward. This reflects the atmospheric circulation over Brazil that predominantly brings rainfall from the east coast to the interior [While previous global models strongly account for latitudinal variation, the geospatial structure of δinterior .2Hf dataset. Wing feathers  are preferred over body and tail feathers as they are less likely to be lost and replaced over periods that do not represent the annual molt. As expected, body feathers presented higher variability than tail and wing feathers. The most distinguishable subfamily in our sample was Diglossinae, which comprises a highly trafficked species . The observed differences among years were not taken here as a concern, since they can be associated with the region from which those samples came from. The 2008, 2011 and 2012 samples were from Atlantic Forest, while the 2009 samples were from Caatinga . As Caatinga is represented by additional 23 samples, the higher δ2Hf values were likely not associated with a specific year, but to the region of origin.Feather type, avian subfamily, and year of collection introduced variability to our δThe isotopic variability resulting from different subfamilies and feather types should be considered. We recommend that future researches should focus initially on one species or genera and on wing feathers, to reduce the possible associated noise.2Hf values and part of this effect can be explained by the proximity to the coast. This region has the lowest values of Mean Annual Precipitation  and high Mean Annual Solar Radiation . It receives the Equatorial Atlantic air mass, which is mostly hot and humid [Hirundo rustica), where breeding individuals from Mississippi presented mean δ2Hf equal to -36.4‰ and were associated to non-breeding grounds in northeast South-America [The dry northeastern region (Caatinga) presented the highest δnd humid , but the-America . This co-America .2Hf values with an isotopic gradient observed from east to west. The region presents the highest MAP (1500–3500 mm) and a low Annual Temperature Range . This region is affected by the Equatorial Continental air mass, which is hot and humid, and transports humidity to the central part of the country [2H values. However, our findings suggest that this region may not be so homogeneous, but due to the lack of sampling on these remote landings, we suggest that more effort should be dedicated to isotopic investigations in this region. However, our findings suggest the possibility of using a more structured isoscape based on tissue δ2H as a geographical tracer of animals from the region.The northwestern region (Amazon) presented a 20‰ range in δ country , 69. Iso country , 74. Ter country  also obs2Hf values, but we did find a depletion in 2Hf from northeast to southwest. This pattern may occur due to the atmospheric circulation of the rains in the central region of Brazil, being more affected by Equatorial air masses in the northern part, while the southern part is more affected by Tropical (drier) air masses [2H values of precipitation in the west of the Cerrado are more depleted in 2H.The central region (Cerrado) is primarily represented by savanna. It is formed by a very heterogeneous formation, where grasslands and gallery forests occur in juxtaposition. This heterogeneity was not reflected in our δr masses . Additio2/day) than northern Cerrado and Caatinga. This region is more depleted in 2H in its eastern portion, similar to the adjacent Cerrado portion. However, the western portion presented a similar pattern to the δ2Hp(Feb-April) surface, with higher values than the eastern portion.The central western region  is located west of the Cerrado biome. It is a seasonally flooded steppe savanna, affected by the Equatorial Continental air mass coming from the Amazon and by the Polar Atlantic air mass . The MAP2H, with higher values in the northern portion and lower in the southern. The pattern aligns with the MAP values, the δ2Hp(Feb-April) surface, the PWQ and the MARS values, with southern portion holding higher precipitation amounts, lower δ2Hp values and lower solar radiation. This biome is affected by the Tropical Atlantic air mass in the coast and by the Polar Atlantic mass in the south [2H depletion in the region.The Atlantic Forest is present along the Brazilian coast extending to the interior of the country in the south. This region had more latitudinal variation in feather δhe south . The pre2Hf values consistent with the most southern and coldest region in Brazil.The southernmost region (Pampas) is a biome dominated by grasslands. It has the highest ATR (20 ºC) and intermediate values for the other bioclimatic variables. The region is affected by the Polar Atlantic air mass  and pres2 = 0.9) may indicate a strong spatial dependency between the testing and training dataset. That is expected because our data comes from museums and is highly related to bird capture hotspots. To minimize this limitation, we also used the 10-fold cross-validation with five repetitions to access the model performance. We believe it is the most reliable and conservative method to assess the model performance.Our model presented a spatial pattern consistent with the expected result for Brazil. However, users should pay attention to some weaknesses of the model regarding the spatial pattern of the samples and the model’s performance in explaining the variance of observed data. We are aware that the testing dataset should be spatially independent of the training dataset . The hig2Hf variation, significantly departing from the 1:1 correspondence line. As shown in Methods, the initial relationship between δ2Hp from waterisotope database and observed δ2Hf in Brazil explained 16.7% of the variation in δ2Hf, while the relationship found for waterisotope database selected quarter (February to April) explained 25.2% of the variation (2Hp(Feb-April) variable, and that improvements on GNIP network in Brazil can likely bring great benefits for the development of future tissue-specific hydrogen isoscapes.As shown by model validation using the training dataset after 10-fold cross-validation, our model explained 25.6% of δariation . This reThe isoscape presented here can help direct future sampling for new isoscapes and for origin assignments, based on designed sampling strategies. According to Contina et al. (2021) , an end-2Hf values over large regions of Brazil can be compensated for through the use of informed Bayesian priors [13C, δ15N, δ18O, δ87Sr). A multiple-isotopic approach has been successfully used in other regions, improving the origin-assignment of migratory bird species [The isoscape developed and presented here was designed to test the possibility of using isotopic tracing techniques to assist in academic and enforcement applications in Brazil. Although presenting a lower predictability when compared to isoscapes from Northern regions, it is aligned with low predictability found in other tropical regions . The obsn priors , 79 in a species , 80, 81 2Hf values, and the use of a more robust Brazilian surface of water δ2H.We encourage the improvement of this isoscape through the use of a larger sample size focused on a taxon-specific approach and on flight feathers, the use of new keratin standard material for higher δDespite the acknowledged improvements needed and weaknesses discussed, the provided isoscape can be very useful for broader scale questions, such as identifying whether an animal seized in Brazil comes from captivity or from the wild. Other uses are also viable if the isoscape is used with caution and attention to its limited spatial precision.S1 TableIdentification number (ID), year of collection, species name under CBRO , family(PDF)Click here for additional data file.S1 FileR code and results for Analysis of Variance followed by a Tukey’s HSD , Linear Models (variable latitude and longitude), and Model Selection with all variables.(PDF)Click here for additional data file.S2 FileR code to calculate WorldClim bioclimatic variables for a specific timeframe.(PDF)Click here for additional data file.S3 File2Hf and δ2Hp) and S4 Table .R code and results. Including S4 Fig (Linear regressions between δ(PDF)Click here for additional data file.S4 FileFirst step- Prepare data. R code to input raster and observations, and extract values. Including S5 Table in (PDF)Click here for additional data file.


Decomposition of soil organic matter (SOM) can be stimulated by fresh organic matter input, a phenomenon known as the ‘priming effect’. Despite its global importance, the relationship of the priming effect to mineral weathering and nutrient release remains unclear. Here we show close linkages between mineral weathering in the critical zone and primed decomposition of SOM. Intensified mineral weathering and rock-derived nutrient release are generally coupled with primed SOM decomposition resulting from “triggered” microbial activity. Fluxes of organic matter products decomposed via priming are linearly correlated with weathering congruency. Weathering congruency influences the formation of organo-mineral associations, thereby modulating the accessibility of organic matter to microbial decomposers and, thus, the priming effect. Our study links weathering with primed SOM decomposition, which plays a key role in controlling soil C dynamics in space and time. These connections represent fundamental links between long-term lithogenic element cycling (= weathering) and rapid turnover of carbon and nutrients (= priming) in soil. 2 emissions. Here the authors link weathering with primed organic matter decomposition, which plays a key role in controlling soil C dynamics.Mineral weathering and microbial priming are two important processes that regulate soil formation and CO Decomposition of organic compounds in soils involves either the hydrolysis of biopolymers and dispersion of supramolecular aggregates into smaller and simpler components such as those found in dissolved organic matter (DOM), or complete mineralization and release of CO2 and nutrients4. These processes contribute to a large CO2 efflux from the soil that is a major part of the global C cycle. DOM represents a fundamental link between terrestrial and aquatic C and nutrient cycles and plays an important role in soil C dynamics and biogeochemical processes6. Though DOM largely comprises byproducts of microbial decomposition of plant materials, it is a mobile energy source for heterotrophs and can fuel microbial activity7. Fluxes of DOM in terrestrial ecosystems are several-fold larger than those in riverine systems8. To date, studies of DOM concentration and composition have focused mainly on surface waters11 and water-soluble organic matter in soils13 . The vertical dynamics and environmental controls of DOM in mobile soil porewaters have received insufficient attention, limiting our understanding of the transport, cycling and C-budget consequences in such systems and their responses to environmental change.Soils contain more carbon (C) than plant biomass and the atmosphere combined, and the release of this C as CO16 . However, the priming effect has seldom been incorporated into Earth System Models, rendering the robustness of predictions of global soil C stocks and fluxes uncertain19. Prior studies have identified nutrient availability, soil physico-chemical conditions , microbial properties , and climate variables as important factors controlling the priming effect21, yet the underlying mechanisms of priming remain controversial. Several studies have inferred that root exudates boost microbial activity and enzyme production and serve as ‘co-metabolites’25. Keiluweit et al.22 proposed a further mechanism, i.e., that root exudates liberate organic matter from protective mineral-organic associations and accelerate their decomposition.Release of root exudates or downward movement of labile C in soils can lead to a ‘priming effect’—that is, a short-term increase in the rate of microbial decomposition of soil organic matter (SOM) resulting from fresh organic C input2 in pore spaces, ranking among the most important processes influencing critical zone evolution, CO2 sequestration, and global climate change28. Mineral weathering supplies microorganisms and plants with rock-derived nutrients, influences the saturation status of secondary minerals with high specific surface area (SSA), and affects the surface characteristics of minerals protecting organic matter  into soil solution and consumes COter Fig. . All the30. However, biogeochemical processes and their couplings in deep soil are frequently neglected. To advance our understanding of processes operating in the deep soil, we analyzed time series data for porewater chemistry (including organic and inorganic solutes) and soil gas (CO2 and O2) partial pressures at four depths  in three instrumented pedons in mixed-conifer forest through long-term, continuous, field-based monitoring, taking advantage of Critical Zone Observatory (Jemez River Basin) infrastructure in New Mexico  and HIX values with depth point to reduced aromaticity (linked to plant-derived lignin monomers) and molecular complexity with depth  and DOM biodegradability is often invoked to assert that more condensed aromatic compounds are less biodegradable than compounds with a lower condensation degreepth Fig. , which ipth Fig. . DOM witpth Fig. .2) groups of amino acids from proteins are cleaved to produce HS− and NH4+, followed by oxidation to SO42− and NO3− species4. Similar decomposition pathways for other organic compounds  release these and other anions  to soil porewater34. In general, microbial decomposition of organic compounds is an important source of SO42− in soil porewater . DOC fluxes (fDOC) and fluxes of SO42−  exhibit a close relationship in the midsoil and deep soil, but similar correlations are lacking in the surface soil  and amino  at depth  for mineral weathering intensity generally show consistent temporal trends  is relatively decoupled from SOM decomposition, although fP and decomposition rate are often coupled at higher fP  DOM input, may accelerate SOM decomposition.Weathering intensity shows generally consistent trends with fluxes of SOM decomposition products Table –9. Compa fP Fig. –9. Phosporr Fig. . This reity Fig. –9. We exDOM) can be assessed by comparing rates of SOM decomposition before and after “fresh” DOM input to a soil during periods of steady state weathering   to 2.4 (4/19/2016), resulting in increases of fDIC-corr from ~84 to ~160 mg C m−2 yr-1 and fCO2 from ~1.4 to ~1.6 μmol m−2 s−1. In a few cases, fCO2 and fDIC-corr show inconsistent trends, which may be attributed to CO2 dissolution in the soil porewater.Weathering intensity and congruency are two parameters characterizing the alteration of primary minerals and controlling the formation of secondary minerals Table –9. In thtes Fig. . To excltes Fig. –9. Fluxetes Fig. . When wetes Fig. , DIC andple Fig. , [Si]/, [CO32−], and soil CO2, which would react with porewater to form DIC:2CO3aq represents the hydrated form of dissolved CO2.All soil-porewater and precipitation samples were kept frozen and sent overnight to the University of Arizona Laboratory for Emerging Contaminants to be filtered within 48 h of collection using 0.45 μm nylon filters. Sample aliquots for DOM and DIC were filtered through combusted 0.7 μm glass-fiber filters. Concentrations of DOC ([DOC]) and DIC ([DIC]) were determined by infrared detection of CO2CO3* dominates DIC, with HCO3− dominating at 6.4 < pH < 10.352. Per Henry’s law, the amount of dissolved CO2 in water is proportional to the soil PCO2:KH represents Henry’s law constant for CO2 (34.06 mol m−3 atm−1) at 25 °C. Henry’s law implies that DIC can reflect the PCO2 in soil pore spaces and thus the intensity of microbial decomposition of SOM to DOM via soil heterotrophic respiration.The speciation of Eq.  is media3−, and thus the concentration of dissolved organic nitrogen ([DON]) can be calculated as the difference between [DN] and the concentration of NO3–N. Concentrations of anions  were analyzed by ion chromatography using Dionex ICS-1000 equipped with an AS22 Analytical Column, Sunnyvale, CA. Concentrations of cations and silicon were determined by ICP-MS . Concentration of dissolved nitrogen ([DN]) was measured by nitrate colorimetric analysis after high-temperature combustion of all N species to nitrate. We assume that inorganic N is mainly present in the form of NOΩ is the relative saturation, IAP is the ion activity product, and Ksp is the solubility product constant at a given temperature. Positive SI specifies supersaturation and precipitation of the solid, and negative SI specifies undersaturation and dissolution. DOC concentration and site density (2.4 × 10−6 mol of sites per mg DOC) were used as input parameters to account for metal-ligand complex formation.Soil porewater inorganic chemistry data were used to perform aqueous geochemical modeling with MINTEQ3.1. The modeling involves determination of aqueous phase speciation and calculation of SI values of pore waters with respect to various potentially precipitating secondary phases. The SI was calculated as:x-axis unevenly distributed but improving the visual aspect of the plots. These graphical transformations did not alter any data in the porewater geochemical dataset. In a few cases , mean values were used to better compare information conveyed in different types of plots.Time series of mineralogical and organic and inorganic geochemical parameters were plotted for four depths within the instrumented pedons  using filtered data. For each sampling date, data for the three pedons are spread out slightly on the temporal axis for display purposes—for example, data for 4/19/2016 are plotted as 4/18/2016 for pit 1, 4/19/2016 for pit 2, and 4/20/2016 for pit 3. Because the distribution of sampling dates is uneven, we used a ‘homogenization’ function in Excel to make the plots more uniform, rendering the dates on the 53, which is considered a “conservative” tracer of mineral dissolution, because it does not re-incorporate into secondary solid structures, and it is a weak competitor for cation exchange sites. Here we use the ratio of {[Si]solution/[Na]solution}/{[Si]solid/[Na]solid} (simplified as [Si]/[Na]norm) to provide a normalized measure of weathering stoichiometry (weathering congruency). The priming effect caused by fresh DOM input can be quasi-quantitatively estimated by comparing fDIC-corr and fCO2 before and after the introduction of “fresh” DOM during the period with very minor change in weathering congruency  and fCO2 (μmol m−2 s−1) after and before the input of more bioavailable (“fresh”) DOM, respectively. The criteria for selection of the period of the priming-effect calculation is a notable increase in FI  accompanied by diminishing SUVA254 (increasing bioavailability) with minor variation in weathering congruency, as this kind of period may represent a “fresh” DOM-induced priming process. Thus, fDIC-corr and fCO2 at the beginning of this period can be viewed as a control group for the calculation of the priming effect at different depths.Products of congruent weathering are only dissolved species, whereas products of incongruent weathering also contain solid phases Table . The relnorm values as a “control group” with low weathering congruency. It is worth noting that the priming effect in the control group was not unimpacted by chemical weathering but it had almost lowest weathering congruency. Another precondition for the selection of the “control group” is that corresponding FI values should be relatively low, that is, microbial activity has not been primed by bioavailable DOM. The influence of chemical weathering (congruency) on the priming effect can be assessed through comparing SOM decomposition rate (including fDIC-corr and fCO2) in “experimental” groups with varying degrees of weathering congruency  and control group with low weathering congruency. In particular, we focus on the response of SOM decomposition rate to weathering congruency. In order to exclude the influence of variation of DOM bioavailability on our assessment, we chose the periods with only minor change in SUVA254 (DOM bioavailability) and FI  values for each “experimental group”.After calculating the priming effect at different depths, we quantify the influence of mineral weathering on the priming effect. For a specific soil depth, we view the period with relatively low [Si]/[Na]254) and at 280 nm (SUVA280) were calculated by dividing the UV absorbances measured at 254 nm and 280 nm with the cell path length and DOC concentration. All samples had absorbance reading <0.4 at 254 nm. Because iron also absorbs light at 254 nm, relatively high concentrations of Fe  can cause incorrect SUVA values54. However, we confirmed that the vast majority of data (>98%) had Fe concentrations much lower than 500 μg L−1.Molecular characterization of DOM was conducted using UV-Vis spectroscopy with Shimadzu Scientific Instruments UV-2501PC  and fluorescence excitation-emission matrices (EEMs) . Samples for UV-Vis spectroscopy analysis were placed in 1 cm path-length quartz cuvettes. The SUVAs at 254 nm  range of 200–450 nm and an emission (Em) range of 250–650 nm. Spectra were collected with Em and Ex slits at 2 and 5 nm band widths, respectively, with an integration time of 100 nm. Ultrapure water blank EEMs were subtracted, and fluorescence intensities were normalized to the area under the water Raman peak. An inner-filter correction was performed according to the corresponding UV-Vis scans56:55. Humification index (HIX) was estimated by the ratio of the area of emission spectra (435–480 nm) to that of emission spectra (300–345 nm) using an EX wavelength at 255 nm55:Fluorescence index (FI) was calculated as the ratio of the emission intensity at 450 nm and 500 nm using corrected EEMs57. PARAFAC modeling of fluorescence EEMs was conducted using the DOMFluor toolTable in Matlab following the protocol and quality control procedure described in Stedmon and Markager58.PARAFAC modeling can take overlapping fluorescence spectra and decompose the data into score and loading vectors. An original PARAFAC model was derived from hundreds of fully corrected EEMs from soil porewater, stream water, and precipitation samples from the JRB CZO2O2 to remove organic matter and dispersion with 5% sodium hexametaphosphate. Total C and N measurements were made using a Finnigan Delta Plux XL coupled to an elemental analyzer. Total C and N were assumed to equal to organic C and N because of no evidence for carbonates. Selective extraction was performed on the fine-earth soil fraction using sodium pyrophosphate (PP), acid ammonium oxalate (OX) and dithionite-citrate to determine the contents of Fe and Al phases. The Brunauer–Emmett–Teller SSA of soil fine earth fraction was determined using isotherm and N2 gas adsorption by Micromeritics 160202. The samples were degassed prior to measurement by heating at 130 °C and purged by N2 gas for ~3 h.Soils and soil porewater samples were taken at depths of 5–10 cm (topsoil), 20–30 cm (surface soil) 40–60 cm (midsoil), and 120–140 cm (deep soil) (Supplementary Table Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10