Drosophila mushroom body, whose principal neurons, Kenyon cells, receive feedback inhibition from a non-spiking interneuron called the anterior paired lateral (APL) neuron. We used local stimulation and volumetric calcium imaging to show that APL inhibits Kenyon cells’ dendrites and axons, and that both activity in APL and APL’s inhibitory effect on Kenyon cells are spatially localized (the latter somewhat less so), allowing APL to differentially inhibit different mushroom body compartments. Applying these results to the Drosophila hemibrain connectome predicts that individual Kenyon cells inhibit themselves via APL more strongly than they inhibit other individual Kenyon cells. These findings reveal how cellular physiology and detailed network anatomy can combine to influence circuit function.Many neurons show compartmentalized activity, in which activity does not spread readily across the cell, allowing input and output to occur locally. However, the functional implications of compartmentalized activity for the wider neural circuit are often unclear. We addressed this problem in the  A textbook neuron integrates input from dendrites to generate action potentials that spread throughout the neuron, making it a single functional unit. However, many neurons integrate inputs and generate outputs locally so that a single neuron effectively functions as multiple independent units  neuron . The APLIndeed, there are hints that APL may have compartmentalized function. Like its locust homolog, the giant GABAergic neuron (GGN), APL is non-spiking . Unlike We addressed these questions by volumetric calcium imaging in APL. By quantifying the spatial attenuation of the effects of locally stimulating APL, we found that both activity in APL and APL’s inhibitory effect on Kenyon cells are spatially restricted (though the latter somewhat less so), allowing APL to differentially inhibit different compartments of the mushroom body. Combining these physiological findings with recent connectomic data predicts that individual Kenyon cells inhibit themselves via APL more strongly than they do other individual Kenyon cells. Our findings establish APL as a model system for studying local computations and their role in learning and memory.We first asked whether physiological responses to sensory stimuli are spatially localized within APL. At very low odor concentrations, odor responses in APL can be restricted to the β′ lobe of the horizontal lobe , but it APL responds to electric shock , but it 2+ influx. However, the odor isoamyl acetate at 10−2 dilution evoked a different pattern of activity, with somewhat stronger responses in the S region and weaker in γ  neuron, and APL .+ and Ca2+ channels are expressed at lower levels in APL than in all other types of mushroom body neurons  in APL than in all other 20 mushroom body neuron types. Gene Ontology analysis revealed that the set of genes with consistently higher expression in APL is strongly enriched for genes involved in protein synthesis and mitochondrial respiration, perhaps reflecting the metabolic demands of maintaining such a large neuron. APL also expresses higher levels of the biosynthetic enzyme and vesicular transporter of GABA than any other neuron in the dataset . In cont neurons . In contre mixed . These rre mixed , unlike re mixed .2+ channel mRNA, APL must have a significant voltage-gated Ca2+ conductance. When the histamine-gated Cl- channel Ort (2+ influx (and hence GABA release) in APL were voltage-independent , because Ort inhibits neurons by opening a Cl- conductance  may not necessarily translate into absolute protein abundance or ion conductance. Indeed, despite the relatively low expression of voltage-gated Cannel Ort  is ectopnnel Ort . Such a ductance , not regx in APL , indicatBased on the spatially restricted sensory-evoked activity and the low  expression of voltage-gated ion channel mRNA, we hypothesized that intrinsic factors limit the spatial spread of activity within APL. We first tested this by imaging APL while activating Kenyon cells with the heat-activated cation channel dTRPA1. We previously showed that activating all Kenyon cells with dTRPA1 activates APL . We now Drosophila does not natively express P2X ATP receptors  would spread to the calyx (where Kenyon cell dendrites reside), and vice versa . To do teceptors . We locaeceptors , indicatTo rule out the possibility that all P2X2 driven excitation is always spatially restricted, we repeated the experiment but expressed P2X2 in Kenyon cells instead of APL, using mb247-LexA, which drives expression in all Kenyon cells (unlike mb247-GAL4) . We stilLocally induced activity in APL declines to undetectable levels at the other end of the neuron, but how quickly does the activity decay along its journey? In particular, is activity in APL restricted enough for APL to signal differentially in different compartments of the MB lobes (i.e. the compartments defined by dopaminergic and output neuron innervation)? We examined this question by more systematically quantifying spread of activity throughout APL. We used volumetric imaging to record activity in the entire 3D extent of APL (~100 × 100 x 150 µm). We analyzed spatial activity in APL by creating a 3D ‘backbone skeleton’ of the mushroom body. This ‘backbone’ consisted of three lines, each one following the long axis of the vertical lobe, horizontal lobe, or peduncle/calyx. We used evenly spaced points every 20 µm along this backbone to divide the 3D volume of APL into segments (Voronoi cells), and quantified fluorescence signals in APL in each segment-volume . Most neTo avoid the stochastic labeling of APL associated with the intersectional NP2631/GH146 driver, we developed another APL driver, VT43924-GAL4.2-SV40 (henceforth simply VT43924-GAL4.2), based on the original VT43924-GAL4 , as GAL4We locally activated APL by applying ATP to three zones – the tip of the vertical lobe, the calyx, and the horizontal lobe – and quantified the GCaMP6f response at evenly spaced intervals along the backbone skeleton. ATP stimulation of APL evoked a strong local response that gradually decayed as it propagated away from the stimulus site , with liStrikingly, activity evoked by stimulation at the tip of the vertical lobe decayed to an undetectable level by the branching point between the two lobes . StimulaGiven that activity within APL is localized, we next asked whether APL’s inhibitory effect on Kenyon cells is also localized. APL and Kenyon cells form reciprocal synapses throughout the entire extent of the mushroom body neuropil . Although Kenyon cells are mostly silent in the absence of odor, they do exhibit some spontaneous activity . Local scal lobe . For comcal lobe . These r2+ influx throughout Kenyon cell dendrites and axons). However, in APL>P2X2 flies, the simultaneous activation of APL by ATP is strong enough to suppress Kenyon cell spikes despite the local excitation from leaky P2X2. The fact that there is still local Ca2+ influx in the calyx despite local inhibition by APL can be explained by noting that both P2X2 channels and nicotinic acetylcholine receptors (nAChRs) are Ca2+-permeable (2+ entering directly through P2X2 or nAChRs in Kenyon cell dendrites (as opposed to through voltage-gated Ca2+ channels), but inhibition from APL shunts away the resulting depolarization and stops the Kenyon cells from spiking, explaining how local APL stimulation at Kenyon cell dendrites blocks both spontaneous and odor-evoked activity in Kenyon cell axons. These results show that sufficiently strong local inhibition in Kenyon cell dendrites can silence Kenyon cells throughout their axons.Surprisingly, puffing ATP on the calyx induced calcium influx in Kenyon cells in the calyx but inhibited calcium influx in the lobes  and 7C. ermeable . Thus, wThe inhibitory effect was sometimes less localized than APL activity itself. For example, when ATP was ejected at the tip of the vertical lobe, APL activity decreased to zero by 100 µm from the ejection site and there was no activity in the horizontal lobe  and 7B1,2+ influx, so even if APL is depolarized without Ca2+ influx, it should not release GABA where there is no Ca2+ influx. Still, GCaMP6f might not be sensitive enough to detect very low levels of Ca2+ influx. Therefore, to test whether APL releases GABA over a wider area than its area of GCaMP6f increase, we replaced APL stimulation with direct application of GABA . We reasoned that if local ATP stimulation causes APL to release GABA over a wider region than APL’s own GCaMP6f signal, then directly applying GABA should cause a more spatially restricted inhibitory effect than applying ATP (which would evoke wider release of GABA from APL).How could APL inhibit Kenyon cells in areas where it itself is not active? One explanation could be that because we are measuring GCaMP6f signals and not voltage, APL might be depolarized even where GCaMP6f signal is not increased. However, synaptic release still presumably requires CaIn fact, directly applying GABA caused the same inhibitory effect on Kenyon cells as locally stimulating APL with ATP: strongest at the site of stimulation but still affecting them far from the site of stimulation, to a lesser extent . In part2+ influx in APL accurately reflects the localization of GABA release.In other respects as well, puffing GABA elicited the same effects above noted for locally activating APL with ATP. Locally inhibiting Kenyon cells in the calyx inhibited them throughout the lobes . The cloWe next asked how local inhibition by APL affects lateral inhibition between Kenyon cells. APL is thought to act through all-to-all feedback inhibition to sparsen and decorrelate population odor responses in Kenyon cells . Such a To test whether KC-APL synapses are spatially arranged to allow uneven lateral inhibition, we analyzed the hemibrain connectome dataset released by Janelia FlyEM and Google (v. 1.1) , which cTo determine what space constant to use in our analysis, we mapped our recordings onto the reconstructed APL by aligning the connectome neurite skeleton to the average backbone skeleton in Drosophila neurons  . Note th = 9 Ωm)  use Ra = neurons , they no1.02 Ωm) , correspSurprisingly, with both of these values, activity spread too far in APL, to areas where  too far . Similar too far . The latp and α′β′ Kenyon cells inhibit other Kenyon cells of the same subtype more than Kenyon cells of other subtypes.  . A compaDrosophila neurons also show unusual electrical features — Kenyon cells have input resistance >10 GΩ (+ channels) might decrease What may explain this apparent difference? The GGN model used specific membrane and axial resistances with an to >6 m) , other De >10 GΩ , and thee >10 GΩ  — and APDrosophila’s larval APL, which has separate innervations of the lobes and calyx joined by a single process  or might have different ion channel composition from adult APL.In addition, although GGN and APL are both widefield inhibitory interneurons in the mushroom body, they have striking morphological differences. For example, GGN separately innervates the lobes and calyx with numerous thin, branching processes; these tangled innervations are joined by relatively thick processes  . In cont process . The lar process . It is tWe measured local activity and local effects of APL by locally puffing ATP or GABA on the mushroom body. This technique requires two caveats but we argue that neither substantially affects our conclusion that APL acts mostly locally. First, how spatially precise was ATP/GABA application? The co-ejected red dye indicator spread tens of microns from the ejection site . Neurons activated by leaky P2X2 expression could excite or inhibit APL (directly or indirectly). If they excite APL , the ectopic activation should make us overestimate, rather than underestimate, the spread of APL activity (as in the previous paragraph). On the other hand, if ectopically activated neurons inhibit APL, restricted activity spread in APL might be explained by inhibition rather than APL’s intrinsic properties. However, if this scenario were true, puffing ATP on UAS-P2X2 only flies would inhibit APL and thereby increase the activity of Kenyon cells. Contrary to this prediction, when we puffed ATP on the horizontal or vertical lobe of KC>GCaMP6f, UAS-P2X2 control flies, Kenyon cell activity was unaffected  than fa in both . Therefo GABABR2  and APL eceptors , althougLocal inhibition of Kenyon cell output predicts that activity of MBONs near the site of APL activation would be more strongly inhibited than MBONs far away. This prediction may be tested in future experiments, for example locally stimulating APL in the tip of the vertical lobe and comparing the inhibitory effect on MBONs in nearby compartments like α3 and α′3 vs. on MBONs in distant compartments like γ5 and β2.2+ influx in the calyx. Although the calyx was not activated when stimulating Kenyon cells in the vertical lobe tip in While APL inhibits Kenyon cells locally, the inhibitory effect spreads somewhat more widely than APL’s own activity, though weakly . Local Gpreferentially inhibits itself . We ignored certain complicating factors in our model for simplicity – some due to lack of data , others as simply beyond the scope of this study . Some of these neglected factors (e.g. active conducances) could explain why an unusually low ratio of Our findings show that localized activity within APL has two broader implications for mushroom body function. First, local activity in APL leads to stronger inhibition of Kenyon cells nearby than far away, to the point that different compartments of the mushroom body lobes defined by dopaminergic and output neuron innervation can receive different inhibition. This local inhibition would allow the single APL neuron to function effectively as multiple inhibitory interneurons, much like mammalian and fly amacrine cells . Each ‘sSecond, the finding of disproportionate self-inhibition compared to other-inhibition provides a new perspective on APL’s function. Inhibition from APL sparsens and decorrelates Kenyon cell odor responses to enhance learned odor discrimination  but in gFlies were kept at 25 °C  or 18 °C (long-term stock maintenance) on a 12 hr/12 hr light/dark cycle in vials containing 80 g medium cornmeal, 18 g dried yeast, 10 g soya flour, 80 g malt extract, 40 g molasses, 8 g agar, 25 ml 10% nipagin in ethanol, and 4 ml propionic acid per 1 L water. Details of fly strains are given in the Key Resources Table.Drosophila and the hsp70 terminator was replaced with the SV40 terminator  via solenoid valves (The Lee Company) controlled by LabVIEW 2015 software . Air flow was 0.5 L/min at the exit of the odor tube, which was positioned ~1 cm from the fly’s head. Odor pulses lasted 5 s. At the end of each recording where odor pulses were given, the tubes were cleaned by passing pure air through. A tube connected to a vacuum pump was positioned behind the fly to deplete the air of odors remaining after the odor pulse.Odors at 10ATP or GABA at the concentrations indicated in the figure legends, together with a red dye to visualize fluid ejection , were aA rectangle of stacked copper plates was brought into contact with the fly’s abdomen and legs using a manually movable stage . The design of the copper plates was based on 2, 5% CO2) external solution . The cuticle overlying the brain was carefully removed using forceps, followed by removal of fat tissue and trachea. During experiments, the brain was continuously perfused with carbogenated external solution (1.96 ml/min) using a Watson-Marlow pump (120S DM2).Flies were cold-anesthetized and mounted using wax and dental floss in a hole in a piece of aluminum foil fixed to a perfusion chamber, such that the fly’s dorsal and ventral sides were kept on opposite sides of the foil. The dorsal part was immersed in carbogenated  and a xenon-arc lamp . Functional imaging was carried out using two-photon laser-scanning microscopy . FluoresResults in 2PO4, 0.3% Triton-X-100, pH 7.2), washed in PBT  and mounted in Vectashield. Confocal stacks were taken on a Nikon A1 confocal microscope in the Wolfson Light Microscopy Facility. Unfixed brains were mounted in PBS using two stacked reinforcement rings (Avery 5722) as spacers and imaged on the two-photon.Dissected VT43924-GAL4.2>GFP brains were fixed in 4% (wt/vol) paraformaldehyde in PBT /F0. ∆F/F data were smoothed by a boxcar filter of five frames . This volume mask was converted to a ‘backbone skeleton’ passing through the center of the major branches of the mushroom body , by manually defining key points  and connecting them into an undirected graph.To compare backbones across different flies, a ‘standard’ APL backbone was generated by taking the average length of each branch across all flies . Each in∆F/F at each node was calculated as both the time-average over the stimulus period , and as the average across flies within a group, to show the time course. In the latter case, time courses were linearly interpolated to a frame time of 0.2 s. ∆F/F vs. distance was plotted according to the normalized distance of each node from the dorsal calyx on the ‘standard’ backbone. Individual nodes were excluded if the standard deviation of ∆F/F during the pre-stimulus period was greater than 1.0 (green channel), or 0.7 (red channel), as such high noise indicated poor signal in that segment. Normalized inhibitory effect (of ATP or GABA) was calculated as  divided by the peak ∆F/F for odor alone. We divided by peak rather than average odor response because this was more robust for recordings where the odor response was low, where normalizing to the average caused extreme magnification of small changes.We reasoned that the strength of lateral inhibition of one Kenyon cell (KC1) onto another (KC2) could be modeled ascylinder . For a t7 pixels; 1 pixel = 8 nm) while the total ‘electrotonic’ length (1/2 (2.0019 x 106 pixels1/2), so −4 m1/2 (5.006 pixels1/2). Note that this value is close, but not equal, to the square root of the average radius . Thus, for example, when we report that we tested 1/2 (1249 pixels1/2).To relate Drosophila neurons range from 0.0907 m , we calculated the distance from each APL-KC synapse to its nearest neighbor APL-KC synapses, as measured along the skeleton of APL neurites. We used Dijkstra’s algorithm on the resulting undirected graph to calculate the distance from each APL-KC synapse to every other APL-KC synapse. We then calculated the distance from each KC-APL synapse to its nearest neighbor APL-KC synapses, and combined this with the distances between APL-KC synapses to find the distance from every KC-APL synapse to every APL-KC synapse. To limit analysis to APL-KC synapses in the calyx, we used APL-KC synapses whose pixel location had a y-value <20000 (160 µm) (i.e. posterior to the peduncle) and that were not annotated as being in the PED(R) ROI , because10 of each resulting value was taken. To prevent negative infinities, we set the log10 of zero values to be −2 (i.e. TPM = 0.01), because the minimum TPM value in the dataset was 0.1. The arithmetic mean of log10(TPM) was taken across biological replicates for each cell type (i.e. geometric mean of TPM). Gene Ontology (Transcripts per million (TPM) values  were sumOntology  was usedOntology ; genes whttp://github.com/aclinlab. Hemibrain connectome data (v1.1) was downloaded using neuprint-python (https://github.com/connectome-neuprint/neuprint-python). No explicit power analysis was used to pre-determine sample sizes; we used sample sizes comparable to those used in similar studies . Parametric  or non-parametric tests  were used depending on whether data passed the D’Agostino-Pearson  normality test. Traces of manual ROIs were analysed in Igor Pro 7 (WaveMetrics). 3D-skeletonization and analysis of connectome and RNA-seq data was carried out with custom software written in Matlab (MathWorks), which is available at ies e.g. . The exp In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:Drosophila, but also point to the potential importance of segregated inhibition by interneurons in neuronal networks in general.The present study addresses for the first time the presence of spatially segregated GABAergic inhibition in the mushroom body, the main learning and memory center in the fly brain. Based on observations of calcium fluxes restricted to distinct subdomains of a widely arborizing, non-spiking, GABAergic neuron (APL), the authors propose that localised/compartmentalized inhibition can arise through local excitation and passive electrical responses in neuronal processes. Although the contribution of this localised inhibition to behaviourally relevant computations or hypothesised functions of the APL in learning, memory or odor coding, remain to be addressed, the observations made here are not only relevant for understanding sensory representation and learning in Decision letter after peer review:Drosophila mushroom body" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Mani Ramaswami as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Ronald Calabrese as the Senior Editor The following individuals involved in review of your submission have agreed to reveal their identity: Tamara Boto (Reviewer #2).Thank you for submitting your article "Localized inhibition in the The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as Summary:Drosophila mushroom body", Lin and colleagues seek to make a systematic description of how widely neuronal activity propagates in APL, a giant, non-spiking inhibitory neuron in the fly olfactory circuit. In order to address this question the authors employ single cell mRNA sequencing as well as a battery of tests ranging from sensory stimulation via olfactory and electric shock pathways, genetically driven stimulation via TRP and PTX channels, and modeling work to demonstrate that neural activity is greatly reduced during propagation throughout the neuron, likely because of a deficit in ion channels required for active propagation of electrical potentials. The authors demonstrate how this decrement in signal amplitude and the reciprocal connectivity with the Kenyon cell population of the mushroom body results in the modulatory effects of the APL neuron on KCs becoming substantially localized. Modeling results suggest that, through the GABA-mediated feedback circuit this leads to KC activity from single neurons preferentially inhibiting similar KCs to a greater extent than dissimilar KCs, and to KCs preferentially inhibiting themselves more than any other KCs. This prediction has implications for mechanisms of sensory coding and learning in Drosophila, as well as for general appreciation of inhibitory mechanisms used by interneurons in neuronal networks. The manuscript is well-written and planned, makes appropriate use of the tools available in the model organism, and presents an interesting, novel result. However, the authors should address several concerns before the manuscript is ready for publication.In their manuscript "Localized inhibition in the Essential revisions:1) The most significant issue that needs to be addressed is the extent to which the APL neuron's response is localized and pertain to data in Figure 6 and Figure 7 and their interpretation. Based on Figure 6 and Figure 7, the authors argue that locally activated APL preferentially inhibits local KCs. Consistent with this, the inhibition effect appeared more pronounced in local KCs than distal KCs when APL was locally activated in the horizontal lobe and vertical lobe. However:(1a) In Figure 6, local activation of APL neuron leads to hyperpolarisation of KCs in other regions as well, in addition to the respective region where APL neuron was activated. This should be explained and discussed.(1b) In Figure 6C , local activation of APL by ATP injection into the calyx leads to depolarisation, rather than inhibition of proximal KCs in calyx region, which could potentially be due to leaky expression of P2X2 channel in KCs . However, this seems unlikely: considering that ATP-evoked activity in KCs is comparable throughout the mushroom body in the negative control flies , the leaky expression cannot explain the greater inhibition in distal KCs than proximal KCs. Assuming that the excitatory responses of KCs we see from the ATP injection on the calyx (APL) is due to leaky expression of P2X2, it still does not explain the inhibition at the vertical and horizontal lobes, which are away further away from the APL activation site. The inhibition at the vertical and horizontal lobes contradicts panels A and B. This discrepancy appears to contradict one of their main findings that spatially restricted APL activity mostly inhibits KCs locally and needs to be addressed.(1c) This apparent discrepancy is considered in the Discussion section, speculating that it could be caused by GABA receptors. Again, the observed discrepancy contradicts their main finding that differential spatial inhibition in KCs arises from local activity in APL and the spatial arrangement of KC-APL and APL-KC synapses. The authors need to revisit their explanation for why APL, when locally activated in the calyx, preferentially inhibits distal KCs rather than proximal ones.(1d) Subsection “Local GABA application mimics the inhibitory effect of local APL stimulation”: "strongest at the site of stimulation but still affecting Kenyon cells far from the site of stimulation, to a lesser extent." This statement appears to contradict the assertion that APL provides mainly local inhibition to KCs. The authors should moderate their claims throughout the text to make clear that APL provides both local and more global inhibition.(1e) If additional experiments are needed to better establish the author's conclusions, then these should be described in the discussion .(2) The authors perform a thorough computational analysis of the connectomics data and suggest that each Kenyon cell inhibits itself more than others. This model would support the idea of a spatially restricted APL-mediated inhibition of Kenyon cells, and the authors concluded that "…our physiological measurements of localized activity of APL, combined with the spatial arrangements of KC-APL and APL-KC synapses, predict that Kenyon cells disproportionately inhibit themselves compared to other individual Kenyon cells" (Discussion section). This conclusion contradicts previous results by the same authors. Lin et al., (2014) published not only that the feedback inhibition is from all Kenyon cells to all Kenyon cells, but taken the specific case of the γ lobes of the mushroom bodies, Lin et al. showed the opposite result than the one expected in the present study: blocking synaptic release in γ lobes results in a decrease of the calcium signal of those same γ Kenyon cells, which contradicts the idea of γ KC strongly inhibiting γ Kenyon cells through APL. The manuscript should be revised to include a more thorough discussion regarding these contradictory results. The APL inhibition might vary depending on specific situations; all-to-all feedback might be essential for odor discrimination, whereas a more compartmentalized inhibition might be crucial during reinforcement: APL receives direct spatially-segregated input from dopaminergic neurons  and these connections are not included in the connectomic analysis.m / Ra). Thus, it should be one order of magnitude larger, and hence the electrotonic lengths smaller by the same factor.(3) Further, regarding Figure 8F-L and connectome analysis in methods, the authors appear to make an error in their choice of length constant. The authors chose length constant = square_root(radius) , leading them to a conclusion consistent with their experimental observation. However, published physiological data suggest that membrane resistivity should really be about two to three orders of magnitude higher than cytoplasmic  resistivity. By definition, length constant = square_root ((radius/2) * RFurther, because the length constant is a multiplicative constant in the exponential, summation followed by division in their formula does not cancel out the effect of changing its scale. ; these authors fit their parameters in a compartmental model to reproduce their patch recording data.) In the present model, does the focality of inhibition remain robust when the value of the length constant is made more realistic as suggested here? If not, can the authors reconcile this discrepancy with their experimental findings?Drosophila mushroom body" for consideration by eLife. Your revised article has been reviewed Ronald Calabrese as the Senior Editor, Mani Ramaswami as the Reviewing Editor, and two reviewers. The following individuals involved in review of your submission have agreed to reveal their identity: Tamara Boto (Reviewer #2).Thank you for submitting your article "Localized inhibition in the The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as Summary:Drosophila, but also point the potential importance of segregated inhibition by interneurons in neuronal networks in general.The present study addresses for the first time the presence of spatially segregated GABAergic inhibition in the mushroom body, the main learning and memory center in the fly brain. Amin et al. perform a well needed analysis of the spatial dynamics of APL activity. The authors observe that, calcium fluxes can be observed in distinct subdomains of a widely arborizing, non-spiking, GABAergic neuron, and propose that such localised/compartmentalized inhibition can arise through local excitation and passive electrical responses in APL processes. Although the contribution of localised inhibition to behaviourally relevant computations or hypothesised functions of the APL in memory suppression, reversal learning or sparse odor coding, remain to be addressed, the observations made here are relevant not only for understanding sensory representation and learning in Essential revisionsAlthough these comments can be addressed through relatively minor changes to the text, we particularly request that the final conceptual point – on the relative roles of lateral and self inhibition be constructively addressed with appropriate, clarifying edits through the manuscript.1) Please note in Figure 3, when referring to α prime and β prime neurons, there is a problem of compatibility and the symbols are not displayed properly.2) Calcium influx: In their rebuttal (point 1b) the authors write: "Dendritic calcium in fly neurons can arise in large part from direct calcium influx through nAChRs, not necessarily from voltage-gated calcium channels, and thus need not reflect depolarization ." This potentially explains attenuated proximal inhibition in the calyx in Figure 6C and Figure 7C. But why, then, does such calcium influx not reflect depolarization triggered by a puff of ATP?3) Extent of inhibition by APL: In their rebuttal and revision, the authors argue that leaky expression of P2X2 in non-APL neurons results in overestimation of inhibition of KCs by APL. Additionally, they argue, inhibition of KCs by APL in the calyx will result in broader inhibition since the calyx houses KC dendrites. Inhibition of KC dendrites will also result in decreased activity in the axons in the horizontal and vertical lobes. These arguments are offered to explain the broader KC inhibition that they observed. However, even if we accept this explanation for results shown in Figure 7A2-C2 and 7A4-C4, it is still puzzling why KC inhibition by puffing GABA also was not as localized as expected . The authors discuss this result , speculating that the broad inhibition may be caused by wider network activity. Although this is possible, no evidence provided here supports this claim. Taken together, the evidence provided by the authors falls short of proving that inhibition from APL is compartmentalized in the MB.4) Minimizing the role of lateral inhibition: In their rebuttal (point 2) the authors write: "…we do not claim that there is no, or insignificant, lateral inhibition. In fact, given lateral inhibition from 2000 KCs, the sum total of lateral inhibition onto any given KC is still stronger than its own self-inhibition." This is an important and clear statement that does not, but should, appear in the text, too. Instead, throughout the text the authors repeatedly emphasize the impact of local inhibition at the expense of lateral inhibition . Throughout the manuscript, the authors should present a more balanced view of local and lateral inhibition, which both appear to substantially affect the responses of KCs. Essential revisions:(1) The most significant issue that needs to be addressed is the extent to which the APL neuron's response is localized and pertain to data in Figure 6 and Figure 7 and their interpretation. Based on Figure 6 and Figure 7, the authors argue that locally activated APL preferentially inhibits local KCs. Consistent with this, the inhibition effect appeared more pronounced in local KCs than distal KCs when APL was locally activated in the horizontal lobe and vertical lobe. However:(1a) In Figure 6, local activation of APL neuron leads to hyperpolarisation of KCs in other regions as well, in addition to the respective region where APL neuron was activated. This should be explained and discussed.We discuss this issue in the Discussion section.(1b) In Figure 6C , local activation of APL by ATP injection into the calyx leads to depolarisation, rather than inhibition of proximal KCs in calyx region, which could potentially be due to leaky expression of P2X2 channel in KCs . However, this seems unlikely: considering that ATP-evoked activity in KCs is comparable throughout the mushroom body in the negative control flies , the leaky expression cannot explain the greater inhibition in distal KCs than proximal KCs. Assuming that the excitatory responses of KCs we see from the ATP injection on the calyx (APL) is due to leaky expression of P2X2, it still does not explain the inhibition at the vertical and horizontal lobes, which are away further away from the APL activation site. The inhibition at the vertical and horizontal lobes contradicts panels A and B. This discrepancy appears to contradict one of their main findings that spatially restricted APL activity mostly inhibits KCs locally and needs to be addressed.We apologize for being unclear. First, to be clear, the KC responses recorded in the calyx and the lobes are not from distinct “distal KCs” and “proximal KCs”. These responses are from the same KCs, whose dendrites comprise the calyx, and axons comprise the lobes, respectively. As such, their processes are continuous from the calyx to the lobes .Second, as the reviewers have noted, calcium does not necessarily directly correspond to depolarization. Dendritic calcium in fly neurons can arise in large part from direct calcium influx through nAChRs, not necessarily from voltage-gated calcium channels, and thus need not reflect depolarization . Both P2X2 and nicotinic acetylcholine receptors (nAChRs) are calcium-permeable , so we argue that the leaky expression of P2X2 (whether in KCs or PNs) caused calcium influx in KC dendrites directly through P2X2 channels (if leaky P2X2 is in KCs) or nAChRs (if leaky P2X2 is in PNs). In UAS-P2X2-only negative controls, the calcium influx  through these channels depolarizes the KCs enough to spike . However, in APL>P2X2 flies, this depolarization is counteracted by GABA locally released from APL evoked by ATP stimulation, which opens GABAA receptors and thereby opens a chloride conductance, which shunts away the depolarization and pushes the Kenyon cell’s membrane potential toward the reversal potential of chloride, which prevents the Kenyon cell from spiking. No spikes means no calcium influx in KC axons. Thus local APL stimulation in the calyx blocks spontaneous and odor-evoked calcium influx in Kenyon cell axons, even though it doesn’t block the local calcium influx in the calyx caused by leaky P2X2 expression. We have revised the text to explain this more clearly .(1c) This apparent discrepancy is considered in the Discussion section, speculating that it could be caused by GABA receptors. Again, the observed discrepancy contradicts their main finding that differential spatial inhibition in KCs arises from local activity in APL and the spatial arrangement of KC-APL and APL-KC synapses. The authors need to revisit their explanation for why APL, when locally activated in the calyx, preferentially inhibits distal KCs rather than proximal ones.The fourth paragraph of the Discussion section is unrelated to this issue and rather discuss how can it be that in some cases KCs are more strongly inhibited closer to the axon initial segment than far away from it, given that action potentials travel from the axon initial segment to the distal tip. Our answer on the point raised here is given above to reviewer comment 1b.(1d) Subsection “Local GABA application mimics the inhibitory effect of local APL stimulation”: "strongest at the site of stimulation but still affecting Kenyon cells far from the site of stimulation, to a lesser extent." This statement appears to contradict the assertion that APL provides mainly local inhibition to KCs. The authors should moderate their claims throughout the text to make clear that APL provides both local and more global inhibition.We have moderated our claims to better reflect that APL inhibits KCs strongest locally, but also has a weaker widespread effect:“…activity in APL and APL’s inhibitory effect on Kenyon cells are spatially localized (the latter somewhat less so)…” (Abstract)“…activity in APL and APL’s inhibitory effect on Kenyon cells are spatially restricted (though the latter somewhat less so)…” (Introduction)“Local stimulation of the APL neuron in the lobes strongly reduced the baseline GCaMP6f signal in Kenyon cells locally, but also had a smaller, widespread effect.” “This local activity in APL translates into a spatially non-uniform inhibitory effect on Kenyon cells that is strongest locally and becomes weaker farther from the site of APL stimulation.” (Discussion section)“…local activity in APL leads to stronger inhibition of Kenyon cells nearby than far away…” (Discussion section)(1e) If additional experiments are needed to better establish the author's conclusions, then these should be described in the discussion .We thank the reviewers for their suggestion. Accordingly, we have added a paragraph that discusses the implication of the APL neuron’s mainly local inhibitory effect on MBON odor responses (Discussion section).2) The authors perform a thorough computational analysis of the connectomics data and suggest that each Kenyon cell inhibits itself more than others. This model would support the idea of a spatially restricted APL-mediated inhibition of Kenyon cells, and the authors concluded that ".our physiological measurements of localized activity of APL, combined with the spatial arrangements of KC-APL and APL-KC synapses, predict that Kenyon cells disproportionately inhibit themselves compared to other individual Kenyon cells" (Discussion section). This conclusion contradicts previous results by the same authors. Lin et al., (2014) published not only that the feedback inhibition is from all Kenyon cells to all Kenyon cells, but taken the specific case of the γ lobes of the mushroom bodies, Lin et al. showed the opposite result than the one expected in the present study: blocking synaptic release in γ lobes results in a decrease of the calcium signal of those same γ Kenyon cells, which contradicts the idea of γ KC strongly inhibiting γ Kenyon cells through APL.2+ influx in the γ lobe. However, this result should be attributed to the temperature increase alone, rather than shibire blocking synaptic vesicle release, because even in control flies that expressed no shibire, raising the temperature also reduced odor-evoked Ca2+ influx in the γ lobe . Thus, those results actually suggest that blocking γ KC output has no effect on odor responses in the γ lobe.We respectfully submit that the reviewer may have misinterpreted the Lin et al., 2014 results in the γ lobe. True, in flies expressing shibire in γ KCs, raising the temperature reduced odor-evoked CaThese earlier results are perfectly consistent with our present results. While it is true that our model predicts that KCs disproportionately inhibit themselves more than other KCs, we do not claim that there is no, or insignificant, lateral inhibition. In fact, given lateral inhibition from 2000 KCs, the sum total of lateral inhibition onto any given KC is still stronger than its own self-inhibition. Thus, because γ KCs make up only ~1/3 of KCs, when output from γ KCs is blocked, the lateral inhibition from the remaining KCs should be sufficient to suppress odor responses in the γ lobe. Therefore, our present results do not contradict the finding in Lin et al., 2014 that feedback inhibition from the APL neuron onto KCs can operate in an all-to-all manner. We added a new paragraph in the Discussion section to address this point.The manuscript should be revised to include a more thorough discussion regarding these contradictory results. The APL inhibition might vary depending on specific situations; all-to-all feedback might be essential for odor discrimination, whereas a more compartmentalized inhibition might be crucial during reinforcement: APL receives direct spatially-segregated input from dopaminergic neurons  and these connections are not included in the connectomic analysis.We completely agree, and discuss in the penultimate paragraph of the Discussion section how compartmentalized inhibition might contribute to learning. We added a specific mention of local dopaminergic input: “Thus, APL could locally modulate different compartment-specific aspects of olfactory learning, especially given that different regions of APL respond differently to dopamine  and electric shock punishment ”. We may add dopaminergic inputs to our connectomic analysis in future studies.m / Ra). Thus, it should be one order of magnitude larger, and hence the electrotonic lengths smaller by the same factor.(3) Further, regarding Figure 8F-L and connectome analysis in methods, the authors appear to make an error in their choice of length constant. The authors chose length constant = square_root(radius) , leading them to a conclusion consistent with their experimental observation. However, published physiological data suggest that membrane resistivity should really be about two to three orders of magnitude higher than cytoplasmic  resistivity. By definition, length constant = square_root ((radius/2) * RFurther, because the length constant is a multiplicative constant in the exponential, summation followed by division in their formula does not cancel out the effect of changing its scale. ; these authors fit their parameters in a compartmental model to reproduce their patch recording data.) In the present model, does the focality of inhibition remain robust when the value of the length constant is made more realistic as suggested here? If not, can the authors reconcile this discrepancy with their experimental findings?proportional to sqrt(radius), and the whole point of our curve fitting in Figure 8E-F  was to figure out the constant of proportionality between the length constant and sqrt(r), which is determined biophysically by the factors the reviewer points out, i.e. the ratio of membrane to axial resistivity.We apologize for not describing our methods more clearly. We did not literally set length constant = sqrt(radius), although we can see how our original Materials and methods section created that impression. Rather, length constant is We have laid out the mathematical derivation of our distance calculations more extensively in the revised Materials and methods section. Note that we now treat skeleton segments not as cylinders but as truncated cones , and we test a wide range of space constants derived from different ratios of membrane to axial resistivity . Notably, the ratio of membrane to axial resistivity is much lower in a non-spiking interneuron in the visual system (HS cells)  compared to olfactory projection neurons . We note that the ratio of membrane to axial resistivity that best fits our data is unusually low compared to the range of reported estimates of this ratio, and discuss this in subsection “Connectomic analysis predicts that each Kenyon cell disproportionately inhibits itself”. For example, if APL has active conductances that lower the membrane resistance when APL is depolarized, but not at rest, then its space constant would be shorter than would be predicted from purely passive properties. We also note caveats for the connectome-based model in Discussion section.We now also report that if we treat all neurite segments as having the same radius , we obtain similar results to those in Figure 8 .Essential revisions:[…]1) Please note in Figure 3, when referring to α prime and β prime neurons, there is a problem of compatibility and the symbols are not displayed properly.Thank you – now fixed.2) Calcium influx: In their rebuttal (point 1b) the authors write: "Dendritic calcium in fly neurons can arise in large part from direct calcium influx through nAChRs, not necessarily from voltage-gated calcium channels, and thus need not reflect depolarization ." This potentially explains attenuated proximal inhibition in the calyx in Figure 6C and Figure 7C. But why, then, does such calcium influx not reflect depolarization triggered by a puff of ATP?We are unsure what the reviewer is referring to here. Calcium influx does reflect depolarization triggered by a puff of ATP in Figure 4 and Figure 5. In Figure 6 and Figure 7, calcium influx does not reflect depolarization triggered by a puff of ATP because the neuron being depolarized by ATP (APL) is not the neuron whose calcium influx is being imaged (KCs). If this is about the leaky expression of P2X2 in Figure 6C and Figure 7C, we surmise that the reviewer is asking why is it that, in KC>GCaMP6f, APL>P2X2 flies, puffing ATP on the calyx increases calcium influx in KCs in the calyx but decreases calcium influx in the lobes. That is, why doesn’t leaky P2X2 expression in KCs cause depolarization  in KC axons? The answer is that the ATP puff locally activates APL, which releases GABA onto the KCs, which counteracts the depolarization in KCs caused by leaky P2X2 expression , and prevents the KCs from firing action potentials, thus blocking depolarization  in KC axons. This explanation is supported by the fact that in negative control (UAS-P2X2 only) flies, in the absence of ATP-induced inhibition from APL, the ATP puff does increase calcium influx throughout KC axons. This reasoning is explained in subsection “APL inhibits Kenyon cells mostly locally”.3) Extent of inhibition by APL: In their rebuttal and revision, the authors argue that leaky expression of P2X2 in non-APL neurons results in overestimation of inhibition of KCs by APL.that excite APL could potentially make us overestimate the spatialextent of spread of activation within APL, and we do not argue that this explains why inhibition by APL appears to spread more widely than activity within APL itself. (Discussion section).This is not the reviewer’s main point, but because this will be published with the main article if the manuscript is accepted, we would like to clarify that we argued that leaky expression of P2X2 in non-APL neurons Additionally, they argue, inhibition of KCs by APL in the calyx will result in broader inhibition since the calyx houses KC dendrites. Inhibition of KC dendrites will also result in decreased activity in the axons in the horizontal and vertical lobes. These arguments are offered to explain the broader KC inhibition that they observed. However, even if we accept this explanation for results shown in Figure 7A2-C2 and 7A4-C4, it is still puzzling why KC inhibition by puffing GABA also was not as localized as expected . The authors discuss this result , speculating that the broad inhibition may be caused by wider network activity. Although this is possible, no evidence provided here supports this claim. Taken together, the evidence provided by the authors falls short of proving that inhibition from APL is compartmentalized in the MB.We also find this result puzzling and agree that our discussions of this result are indeed speculation. We have added “We speculate that…” and “Future experiments may test these possibilities” into the relevant paragraph of the Discussion section.We have shown that inhibition from localized activation of APL in the MB lobes is, indeed, localized. Broader than the localized activation of APL itself, yes, but still localized. That is, when APL is locally activated in the lobes, KCs are more strongly inhibited close to the stimulation site, compared to far away. Thanks to the reviewers’ important comments on this point in the previous round of revision, the text is now appropriately nuanced on this point by stressing that although inhibition from APL is spatially non-uniform, it is somewhat less localized than the activation of APL itself.4) Minimizing the role of lateral inhibition: In their rebuttal (point 2) the authors write: "…we do not claim that there is no, or insignificant, lateral inhibition. In fact, given lateral inhibition from 2000 KCs, the sum total of lateral inhibition onto any given KC is still stronger than its own self-inhibition." This is an important and clear statement that does not, but should, appear in the text, too. Instead, throughout the text the authors repeatedly emphasize the impact of local inhibition at the expense of lateral inhibition . Throughout the manuscript, the authors should present a more balanced view of local and lateral inhibition, which both appear to substantially affect the responses of KCs.We thank the reviewer for this point. We agree and have added this statement into both the Results section and Discussion section. We also replaced ‘strong’ in the phrase mentioned by the reviewer with ‘disproportionate’ (Discussion section).individual Kenyon cells”), we deliberately added the second “individual” (emphasis added) to indicate that we do not mean that self-inhibition in total is stronger than lateral inhibition in total, rather that the inhibition from the median KC1 onto itself is stronger than the average inhibition from KC1 onto an arbitrarily chosen single KC2.Elsewhere in the text, e.g., in the Abstract (“individual Kenyon cells inhibit themselves via APL more strongly than they inhibit other


Individuals with autism spectrum disorder (ASD) often report difficulties with inhibition in everyday life. During inhibition tasks, adults with ASD show reduced activation of and connectivity between brain areas implicated in inhibition, suggesting impairments in inhibitory control at the neural level. Our study further investigated these differences by using magnetoencephalography (MEG) to examine the frequency band(s) in which functional connectivity underlying response inhibition occurs, as brain functions are frequency specific, and whether connectivity in certain frequency bands differs between adults with and without ASD.We analysed MEG data from 40 adults with ASD  and 39 control adults  who performed a Go/No-go task. The task involved two blocks with different proportions of No-go trials: Inhibition (25% No-go) and Vigilance (75% No-go). We compared whole-brain connectivity in the two groups during correct No-go trials in the Inhibition vs. Vigilance blocks between 0 and 400 ms.Despite comparable performance on the Go/No-go task, adults with ASD showed reduced connectivity compared to controls in the alpha band (8–14 Hz) in a network with a main hub in the right inferior frontal gyrus. Decreased connectivity in this network predicted more self-reported difficulties on a measure of inhibition in everyday life.Measures of everyday inhibitory control were not available for all participants, so this relationship between reduced network connectivity and inhibitory control abilities may not be necessarily representative of all adults with ASD or the larger ASD population. Further research with independent samples of adults with ASD, including those with a wider range of cognitive abilities, would be valuable.Our findings demonstrate reduced functional brain connectivity during response inhibition in adults with ASD. As alpha-band synchrony has been linked to top-down control mechanisms, we propose that the lack of alpha synchrony observed in our ASD group may reflect difficulties in suppressing task-irrelevant information, interfering with inhibition in real-life situations. Inhibition is one of the core executive functions that allows an individual to control their attention, thoughts, and behaviour by suppressing processes that hinder or are irrelevant to one’s goals . The extThese behavioural reports of inhibition deficits suggest differences in the functioning of the inhibitory control brain network in ASD. This network consists of several right-lateralized frontoparietal regions –17, inclSeveral studies have demonstrated atypical activation and functional connectivity of the inhibition brain network in the ASD population, even when behavioural differences were not observed. Compared to controls, fMRI and MEG studies have found that many individuals with ASD exhibit reduced activation of regions in this network, such as in the right IFG and insula , ACC 6565–67, anN (11/group) has explored the specificity of connectivity differences in certain frequency bands [The present study examines functional brain connectivity involved in inhibition in adults with ASD to better understand the relationship between these differences in brain function and the inhibitory difficulties experienced by this population. As considerable evidence has suggested that ASD may be characterized by patterns of altered functional connectivity , 75–79, cy bands , even thcy bands . SpecifiWe recruited 45 control adults and 54 adults with ASD between the ages of 18–40 years for this study. We screened for full-scale, two subtest IQ ≥ 70 as measured by the Wechsler Abbreviated Scale of Intelligence (WASI or WASI-II) , 81, no t(77) = 0.26, p = 0.79), sex (X2(1) = 0, p = 1), or IQ (t(65.28) = 1.01, p = 0.32). In the ASD group, we had scores for 37 adults on the Autism Diagnostic Observation Schedule, Generic or Version 2 (ADOS-G or ADOS-2) [Thus, in the final sample, there were 39 control adults and 40 adults with ASD. The two groups did not differ on age ( ADOS-2) , 83. Dem, sex  . The BRIt scores were used as a measure of ASD symptom severity in our sample, wherein higher scores represent more severe ASD symptomatology.Participants were also asked to complete the Social Responsiveness Scale, Second Version (SRS-2) . The SRSParticipants performed a Go/No-go task in the MEG scanner Fig. , adaptedTo maintain a rapid response rate, we adapted the stimulus and interstimulus interval (ISI) durations to participants’ performance, as done previously . StimulThe task was run in two counterbalanced blocks: Inhibition and Vigilance. In the Inhibition condition, 75% of trials were Go and 25% were No-go, to ensure the establishment of a prepotent response that would have to be inhibited during the No-go trials. In the Vigilance condition, 25% of trials were Go and 75% were No-go, so very little inhibitory control was required for No-go trials. The Vigilance condition was run as a control for the Inhibition condition; while much of the existing literature has compared No-go trials to Go trials, the Go trials contain a strong motor response not present in No-go trials, so we instead contrasted No-go trials from a highly demanding situation  to those in a less demanding situation .Presentation 18.1 . Each of the blocks ended when participants successfully completed 80 correct No-go trials or until 10 min had passed.Participants were familiarized with both blocks of the task before the MEG session. In the MEG scanner, the stimuli were back-projected onto a screen that was 80 cm away from the dewar and presented using Participants’ MEG data were acquired while lying supine in a 151-channel CTF MEG system  inside a magnetically shielded room. Head position was monitored in real time using fiducial coils located on the nasion and the left and right pre-auricular points. Data were sampled at 600 Hz, and a third-order spatial gradient and an anti-aliasing low-pass filter of 150 Hz were applied.Participants’ MRI data were acquired in a 3.0 T MRI scanner  using a 12-channel head coil. Radio-opaque markers were placed at the MEG fiducial points, allowing for later MEG-MRI co-registration. T1-weighted MRI scans were obtained with the 3D SAG MPRAGE sequence .www.mathworks.com/products/matlab/). Trials were epoched from −1500 to 2000 ms, relative to the onset of the No-go stimulus. Data were filtered offline between 1 and 150 Hz using a fourth-order Butterworth bandpass filter. A notch filter was also applied to remove line noise and its harmonic (60 and 120 Hz). Physiological artefacts were removed by using independent component analysis to decompose the data into independent components, then manually identifying and rejecting components characteristic of eyeblinks and heartbeats. Trials containing signals exceeding 2000 fT or head motion exceeding 5 mm from the median head position were deemed artefactual and removed. Only correct trials were analysed.We analysed the MEG data using FieldTrip  in MATLATo generate the forward model, each participant’s MRI data were co-registered to their MEG data using the fiducials, then used to calculate a subject-specific head model based on the single-shell method . Source z scores using the values in the baseline window (− 500 to 0 ms), then averaged over our time window of interest, 0–400 ms. Connectivity matrices containing these normalized wPLI values were then subjected to statistical analysis to examine within- and between-group differences in network connectivity.To assess connectivity, we took the weighted phase lag index (wPLI) between each pairwise connection . wPLI is a reliable measure of phase synchronization which is robust to noise and volume conduction artefacts and has demonstrated good statistical power in detecting non-zero phase coherence . wPLI vaz-transformed hit rate and the z-transformed false alarm rate: d′ = z(hit rate) − z. Correct No-go trials were considered as hits, while incorrect Go trials were deemed as false alarms. All behavioural data were analysed using linear mixed effects models, as implemented by the nlme package, in R 3.5.0 . Significant results are reported at p < 0.05, and effect sizes for linear mixed effects models were calculated as outlined by Brysbaert and Stevens [We examined the effects of group (control vs. ASD), rater (self vs. informant) and IQ, as well as the interaction between group and rater, on Inhibit scale scores of the BRIEF-A. We also investigated the effects of group (control vs. ASD), condition (Inhibition vs. Vigilance), age, and IQ, in addition to the interaction between group and condition, on participants’ accuracy on our Go/No-go task. D-prime (d′) was used as a measure of accuracy or ability to distinguish between Go and No-go stimuli when withholding a response. It was calculated by taking the difference between the  Stevens  and West Stevens .t tests to detect networks that showed increased connectivity in the Inhibition vs. Vigilance condition in each group , as well as between groups . To determine these networks, the t tests were applied at each pairwise connection and thresholded at values exceeding t = 2.708 . The largest network of contiguous suprathreshold connections was then subjected to permutation testing (5000 permutations), whereby an empirical null distribution of maximal network size was established by shuffling group labels. A family-wise error (FWE) corrected p value was calculated, signifying the probability of finding a network of equivalent or greater size, given the number of permutations, if the null hypothesis were true. Significant networks are reported at pFWE < 0.05 and visualized using BrainNet Viewer [We used the Network-Based Statistic toolbox  to identt Viewer .https://www.r-project.org/). Significant results are reported at p < 0.05. Both unstandardized and standardized regression coefficients are reported as measures of effect size.We explored the relations between any networks showing significant group differences and participants’ everyday inhibitory control, ASD symptom severity, and task performance. Specifically, we tested whether mean network connectivity values in these networks would predict any of these measures, and whether group status moderated this effect. Self-rated scores on the Inhibit scale of the BRIEF-A were used as an estimate of inhibitory control. SRS-2 self-rated Total scores were taken as an indication of severity of ASD symptoms. Task performance was determined using d′ scores. Analyses were performed in R 3.5.0  = 3.95, p = 0.054, d = 0.25). However, scores significantly differed between raters  = 7.17, p = 0.011, d = 0.13), such that participants reported having more inhibitory control problems than their informants did  = 0.044, p = 0.84, d = 0.002), measured by d′. Both groups showed decreased accuracy during the Inhibition condition compared to the Vigilance condition  = 197.81, p < 0.0001, d = 1.02; Fig. Adults with and without ASD performed equally well on the Go/No-go task (pFWE < 0.001). A network in the alpha band with hubs in the left thalamus, left ventromedial prefrontal cortex (vmPFC), and right SPL, and which involved the right IFG, was also engaged (pFWE = 0.004). A right-lateralized network in the beta band was additionally recruited (pFWE = 0.037), with the right middle frontal gyrus (MFG), fusiform gyrus, and putamen showing the greatest degree.Control adults displayed greater functional connectivity in the Inhibition than the Vigilance condition in the theta, alpha, and beta frequency bands between 0–400 ms  or beta (pFWE = 0.534) band.Adults with ASD only showed greater connectivity for the Inhibition versus Vigilance condition in a network in the theta band (pFWE = 0.038), such that mean connectivity in this network in the Inhibition condition was lower in adults with ASD than controls. The node with the highest degree in this network was the right IFG, which showed decreased connectivity with the left superior temporal gyrus (STG), fusiform gyrus, thalamus, and hippocampus. Adults with ASD did not show greater connectivity than controls in the alpha band, and the two groups did not differ significantly in terms of connectivity in either direction in the theta or beta band .Adults with ASD demonstrated decreased connectivity, compared to controls, in a network in the alpha band between 0–400 ms for the Inhibition condition relative to the Vigilance condition . Neither mean connectivity in this network nor its interaction with group was significantly predictive of self-rated Total scores on the SRS-2 or task performance  in networks recruited in the Inhibition > Vigilance condition in control and ASD groups.Additional file 3: A list of brain regions in the network demonstrating significant differences between the control and ASD groups in the alpha band.Additional file 4: Figure S1. A graph illustrating the relationship between mean network connectivity in the alpha-band network that was significantly different between groups and SRS-2 self-rated Total t scores.Additional file 5: Figure S2. A plot of the event-related fields elicited by the control and ASD groups during correct No-go trials in the Inhibition condition.


Sphagnum annual growth and production, and (5) annual growth of the dominant tree species (Picea mariana). We placed five permanent plots in each of six bogs located at different distances from the center of oil sand extraction and sampled these for 2 years (2018 and 2019). We compared line intercept with point intercept plant assessments using NMDS ordination, concluding that both methods provide comparable data. These data indicated that each of our six bog sites differ in key species abundances. Structural differences were apparent for the six sites between years. These differences were mostly driven by changes in Vaccinium oxycoccos, not the dominant shrubs. We developed allometric growth equations for the dominant two shrubs . Equations developed for each of the six sites produced growth values that were not different from one another nor from one developed using data from all sites. Annual growth of R. groenlandicum differed between sites, but not years, whereas growth of C. calyculata differed between the 2 years with more growth in 2018 compared with 2019. In comparison, Sphagnum plant density and stem bulk density both had strong site differences, with stem mass density higher in 2019. When combined, annual production of S. fuscum was greater in 2019 at three sites and not different at three of the sites. Chlorophyll and phaeophytin concentrations from the epiphytic lichen Evernia mesomorpha also differed between sites and years. This protocol for field assessments of five key plant/lichen response variables indicated that both site and year are factors that must be accounted for in future assessments. A portion of the site variation was related to patterns of N and S deposition.Bogs are nutrient poor, acidic ecosystems that receive their water and nutrients entirely from precipitation (= ombrogenous) and as a result are sensitive to nutrient loading from atmospheric sources. Bogs occur frequently on the northern Alberta landscape, estimated to cover 6% of the Athabasca Oil Sands Area. As a result of oil sand extraction and processing, emissions of nitrogen (N) and sulfur (S) to the atmosphere have led to increasing N and S deposition that have the potential to alter the structure and function of these traditionally nutrient-poor ecosystems. At present, no detailed protocol is available for monitoring potential change of these sensitive ecosystems. We propose a user-friendly protocol that will monitor potential plant and lichen responses to future environmental inputs of nutrients and provide a structured means for collecting annual data. The protocol centers on measurement of five key plant/lichen attributes, including changes in (1) plant abundances, (2) dominant shrub annual growth and primary production, (3) lichen health estimated through chlorophyll/phaeophytin concentrations, (4) The online version of this article (10.1007/s10661-020-08645-z) contains supplementary material, which is available to authorized users. Sphagnum-dominated, acidic, nutrient poor, peat-accumulating ecosystems found in subarctic, boreal, and cool-temperate regions, mainly in the northern hemisphere (Vitt 2 (or about 20%) of the land surface in Alberta, Saskatchewan, and Manitoba  groenlandicum or Chamaedaphne calyculata, and a hummocky ground layer mostly dominated by one species of peat moss - Sphagnum fuscum  are abundant on tree branches  in bog porewaters  deposition leads to changes in ecosystem structure and function, including altered growth and species composition of 2 with bogs covering 6% of the area  At the higher levels, Sphagnum fuscum net primary production (NPP) was inhibited, dominant shrub and Picea mariana aboveground NPP were stimulated, with increased root biomass and production, and Sphagnum species relative abundance changed. Higher N deposition also increased the abundance of Rhododendron groenlandicum and Andromeda polifolia and to vascular plants in general, with an increase in shrub leaf N concentrations in Andromeda polifolia, Chamaedaphne calyculata, Vaccinium oxycoccos (= Oxycoccus microcarpus), V. vitis-idaea, and Picea mariana  For the plant community—over a multi-year time frame with different field personnel, is a line transect method of plant abundance assessment appropriate for monitoring? (2) Can a line transect with numerous contiguous points of measurement be reduced to a more limited point transect method? (3) For plant growth and production—can we develop an easy, non-destructive, field-based method for assessing plant annual growth and production utilizing the 1–2 dominant shrub species present at each site? (4) Using only one lichen species (Evernia mesomorpha), can we develop a suitable technique for using lichen chlorophyll/phaeophytin ratios as an indicator of lichen health? (5) Using cranked wires how variable is annual growth and NPP of Sphagnum? (6) Can we use leader length of Picea mariana to assess variability in growth of this species? Here we assess the outcomes of these protocols over a 2-year time period for a series of six bog sites in northeastern Alberta, Canada, and provide evidence of whether the variability in responses can be related to growing season N and S deposition from oil sands activities.These unique attributes of bogs  and their responses to environmental conditions from atmospheric deposition make them target ecosystems for monitoring changes in atmospheric inputs of nutrients. In particular, N inputs are a primary stressor of concern in many regions affected by anthropogenic disturbances  trifolium, and a ground layer of 100% mosses dominated by Sphagnum fuscum, with occasional hummocks of Pleurozium schreberi and S. capillifolium and lawns of S. angustifolium. The lichen, Evernia mesomorpha, is abundant on tree branches. At the top of the bog water table, porewater mean pH at these six bog sites ranged from 3.81 to 4.69 and mean reduced (corrected for pH) electrical conductivity ranged from 18 to 131 μS cm−1. Regional climate is characterized by 164–221 mm growing season precipitation, a mean July daily temperature of 14.5–15.1 °C, and a growing season aridity index (PET/Precipitation) of 1.7–2.6  are a major feature of Alberta’s landscape  without evidence of disturbance. For each transect, permanent, plastic survey stakes were placed at the ends and the transect delineated by a cloth measuring tape held straight and anchored at ground level by the stakes at each end of the transect. Species presence was recorded at every centimeter along the 300-cm line. A ruler, kept vertically straight, was used to assist in determining whether a living vascular plant, bryophyte, or ground lichen was present or absent at a given point. Data were not transformed and were analyzed as absolute frequency (number of hits/300). In order to determine if point-intercept data would mirror the data from the 300 cm line transect, frequency data from 30 and 60 evenly spaced points along the transect were extracted and compared with the 300-point line transect.We used a 300-cm-long line transect, with the transect usually divided into two portions in order to fit on a single large Evernia mesomorpha were collected from each plot in paper bags. Lichen thalli were dried at room temperature and kept at − 20 °C until chlorophyll and phaeophytin analysis. Prior to analysis, 20 mg of lichen tissue  were taken from 10 randomly selected thalli for each plot, for a total of 50 samples per site. To extract chlorophyll and determine percent phaeophytin, samples were extracted using dimethyl sulfoxide (DMSO). Acetone has also been used for this extraction  of the two dominant species at each of five plots—outside of our 1-m2 quadrats—for the six sites to develop allometric equations as a means for non-destructive estimation of NPP. After drying the growth segments at 55 °C for 1 week, we quantified dry mass and determined the mass per new growth segment (MPS). For each new growth segment, we measured the shoot length (mm), the number of leaves, and the length and maximum width (mm) of each leaf. During the drying process, R. groenlandicum leaves slightly curled, thus affecting leaf width measurements. Using measurements of living R. groenlandicum leaves from our 2018 and 2019 field data, we were able to predict the width and area of dried R. groenlandicum leaves:2) and LL is the leaf length (mm),2), and LL is the leaf length (mm). Adapted from Wieder et al. (−1) for R. groenlandicum and C. calyculata at each site  and stem mass density (SMD), with SMD defined as the mass of 1-cm lengths of Sphagnum stems beneath the capitula per square meter. We used the cranked wire method  and remeasured them in late September or early October. To measure SMD, we collected surface cores (6.5 cm diam.) from S. fuscum hummocks in each plot in early July of each year. From each core, all capitula were removed and counted to determine S. fuscum plant density (plants m2). Seventy stems of S. fuscum were selected, cut into 2-cm lengths, and weighed after drying at 55 °C for 5 days. SMD was calculated as the average mass of a 1-cm stem of an individual Sphagnum plant multiplied by plant density.Net primary production (NPP) of od Clymo  to measuPicea mariana (black spruce). Stand dynamics of this species have been examined by Wieder et al.  for one growing season was quantified by measuring from the second most recent bud scale to the most recent bud scale, thus our data for leader length are for the 2018 growing season.Bogs of the region are dominated by a single tree species r et al. . Mature Analyses for plant community composition were based on absolute frequency data from each plot from each site. To determine differences between sites and changes in plant community composition between years, we used Bray-Curtis similarity nonmetric multidimensional scaling (NMDS) in Primer 7  and at every 10 cm . Within each year (2018 and 2019), we used a one-way PERMANOVA to test whether the subsets differed from the entire transect .Evernia mesomorpha chlorophyll a and phaeophytin a concentrations, aboveground Rhododendron groenlandicum and Chamaedaphne calyculata abundance, MPS and NPP, and Picea mariana leader length were performed in JMP (Picea mariana leader length was tested between sites using a one-way nested (subsamples nested within plots) analysis of variance (ANOVA) design and Tukey’s pairwise post hoc comparisons when data passed the Shapiro-Wilks test for normality . Evernia mesomorpha chlorophyll and phaeophytin were analyzed using a two-way ANOVA  with subsamples nested within plot (random effect) and with Tukey’s pairwise post hoc comparisons. Shrub abundance, MPS, and NPP were analyzed using a two-way ANOVA  with subplots nested within plots (random effect) and Tukey’s pairwise post hoc comparisons when data passed the Shapiro-Wilks test for normality . Non-normal data were either square-root (2018 vs. 2019 Rhododendron groenlandicum predicted NPP), cube-root , or log-transformed  to improve homoscedasticity. To compare the site-specific MPS algorithms with the overall algorithm, ANCOVA was used to test for differences in slopes  were analyzed with a nested ANOVA; a posteriori means comparisons were carried out using Tukey’s HSD. For linear growth and NPP, we report means ± standard errors of the plots (n = 5 per site), not of the individual cranked wires.4+-N, NO3−-N, DIN, SO4−2-S, and distance from the midpoint between the Syncrude and Suncor upgrader stacks using linear regression.We used atmospheric deposition data for 2018–2019 growing seasons from R. K. Wieder  for JPH4, McKay, McMurray, Anzac, Horse Creek, and Kearl. Deposition data from the six sites were collected using ion exchange resin collectors , and one lichen. About 41% of the species were found at all six sites; similarity for vascular plants was high with eight of the vascular plants found at all sites, but low for bryophytes, with only one species occurring at all sites , and sites differed . The six sites all differed from one another . Differences between sites are evident on the NMDS ordination —most abundant to the lower right at some Anzac and McMurray plots, Kalmia polifolia (OR1B)—abundant to the lower right at other Anzac plots, and Rhododendron groenlandicum (OR1C)—more abundant to the upper portion of the ordination at Kearl, McKay, and Horse Creek. Likewise, Vaccinium oxyxoccos is more abundant at these latter sites (upper portion of ordination) . Pohlia nutans  and Rubus chamaemorus  are most abundant at McKay (to the upper right). Two plots at JPH4 were dominated by Sphagnum angustifolium , while all others had S. fuscum as the dominant moss . When all six sites are considered together, abundance of Rhododendron groenlandicum was 1.2 times higher than the combined abundances of Chamaedaphne calyculata, Kalmia polifolia, and Andromeda polifolia .The years (2018 and 2019) differed  to 52% (Horse Creek and McKay) of the overall dissimilarity between years). Dominant shrub species contributed to the overall dissimilarity—with R. groenlandicum (16%), C. calyculata (8%), and K. polifolia (4%) higher in 2018. Also, R. groenlandicum abundances were different between years , but C. calyculata did not differ between the 2 years   to 34% (McMurray) lower vascular plant abundance; these higher 2018 values were largely due to differences in estimates of p < 0.05). The subsets of 30 and 60 points also were not different from one another or the complete 300-point set .Plot locations based on 30 and 60 points and those from data extracted from the 300-cm line transect for 2018 (not shown) and 2019 cluster close together on the NMDS ordination Fig. . Similara and b concentrations for Evernia mesomorpha differed between sites , and year , with interactions between site and year . Overall, E. mesomorpha at Anzac (2019), JPH4, and McMurray had greater chlorophyll concentrations than at the remaining three sites. Chlorophyll concentrations were greater in 2019 than in 2018 at Anzac, and Horse Creek  and year , with interactions between site and year  Although percent phaeophytin a was greater in 2018 than 2019 overall , Horse Creek, Kearl, and McKay (2019) had the highest percent phaeophytin contents.Percent phaeophytin all Fig. , this dia to phaeophytin a in E. mesomorpha differed between sites , and year , with interactions between site and year . High ratios were recorded at Anzac (2019), JPH4, McKay (2018), and McMurray (2019). Ratios between years were only different at Anzac (2019) and McKay (2019) . A comparison between the predicted MPS  and actual dry weight (mass type) for R. groenlandicum (for 2019) revealed that site  and the interaction between site and mass  were significant, while mass type was not . Predicted MPS and actual dry weight were not different from one another except for Kearl .Slopes for allometric growth equations modeled for each of the six sites were not different from one another or from the combined algorithm for 2019 , between sites , and as interaction was detected between site and year . Sites JPH4 and Kearl were different between 2018 and 2019 and MPS lower in 2019 than 2018 at JPH4 and higher in 2019 than 2018 at Kearl  at 115 g m−2 year−1 and differed between sites  and between years , with an interaction between site and year   Fig. . Both siRhododendron groenlandicum, slopes for modeled allometric growth equations  for C. calyculata were not different for 2019 . A comparison of modeled C. calyculata MPS and actual dry weight for 2019, site , mass type , and the interaction between site and mass  were not different. In particular, there were no differences between predicted MPS and dry weight for any of the five sites  ,with an interaction between site and year . On average, C. calyculata MPS was two times higher in 2018 (mean = 0.116 g segment−1 year−1) than 2019 (mean = 0.058 g segment−1 year−1). Specifically, MPS was higher in 2018 than 2019 at JPH4, McKay, and McMurray  and year  were different, and the interaction between site and year  was significant. As with MPS, NPP was 2.1 times higher—on average—in 2018 (mean = 15.73 g m−2 year−1) than 2019 (mean = 7.37 g m−2 year−1); however, among the five sites, only JPH4 and McMurray were different between years. Likewise, few differences were significant between sites, with only Horse Creek different from JPH4 (2018)  Fig. .Fig. 13 −2, with no difference between 2018 and 2019 (p = 0.367), differences between sites (p = 0.0003), and a nonsignificant year by site interaction (p = 0.134). Highest plant density was at Anzac, and lowest plant density was at JPH4 (Table S. fuscum ranged from 7.4 to 19.6 mg cm−3 and was higher in 2019 than in 2018 (p = 0.016), with differences between sites (p = 0.002), and a nonsignificant year by site interaction (p = 0.057) (Table Plant density varied from 3.5 to 6.1 plants cm p = 0.13. Highest p = 0.13. Highest−1and exhibited a significant year by site interaction (p < 0.0001). Linear growth was highest at JPH4 in 2018 and lowest at Horse Creek in 2018 (Table S. fuscum ranged from 93.8 to 380.3 g m−2 year−1 (Table p < 0.0001). At Kearl, Anzac, and Horse Creek, NPP was lower in 2018 than in 2019, with no difference between the 2 years at the other sites . Picea mariana individuals at Horse Creek had the shortest leader lengths compared with all other sites. The second shortest leader lengths were found at McKay, with no differences between McKay, Kearl, and McMurray. Although not significantly different from trees at Anzac, trees at JPH4 had the longest leader lengths. Leader lengths at Anzac, Kearl, and McMurray were not different from one another  . Between Oct. 15, 2018, and May 15, 2019, 49% of the N deposition and 32% of the S deposition fell during the non-growing season.Over the past 20 years, gaseous emissions from oil sands operations have deposited NHRhododendron groenlandicum abundance (r2 = 0.51) decreased with increases in NH4+-N and DIN deposition ; however, mass per segment showed no change  as well as mass per segment (r2 = 0.39) and NPP  and DIN (r2 = 0.47), along with a decrease in phaeophytin (r2 = 0.35)  and stem mass density ; however, Sphagnum NPP was not affected , with six of the seven positive and the remaining negative phaeophytin one related to the positive chlorophyll increase in Evernia . Other species also contributed to the decrease in abundance ranging from 4 to 16% lower; however, these lower abundances in 2018 were not consistent between sites. For example, the dominant species, Rhododendron groenlandicum, was higher at two sites, but only slightly higher at three sites and slightly lower at one site; however, the overall effect of this variation was a 17% increase in R. groenlandicum abundance when all sites are considered together. We determined that our 2-year line transect data were sufficiently sensitive to show that bog communities vary, both between sites and over time. We suggest that a line transect method has appropriate sensitivity for assessing changes in bog plant communities, but that site variability must be taken into account in future monitoring by assessing changes at individual sites over a temporal frame.Plant community composition Figs. , and 4: Data collection in the field for line transects is time-consuming and rather tedious. We analyzed our transect data to determine if we could reduce the effort in the field by reducing the line transect data to points along the line transect. Surprisingly, point observation methods yielded no differences from the line transect and provide ordinations that position plots from point and line transects in close proximity to one another for both 2018 and 2019 Fig. . We sugga/b concentrations were variable, differing some two-fold (ca. 0.7 to 1.7 mg g−1 chlorophyll) and in general, formed two groups: Anzac-2019, JPH4, and McMurray in one group with relatively high chlorophyll concentrations, while Anzac-2018, Horse Creek, Kearl, and McKay had low concentrations. Three of the six sites had significant differences between years. Likewise, phaeophytin varied from ca. 30% to over 60% between sites with two mostly similar groups and year differences as those of chlorophyll a/b concentrations. Groups and years with high chlorophyll content had low phaeophytin concentrations. The ratios of chlorophyll to phaeophytin were less variable, but overall showed the same patterns.Lichen chlorophyll and phaeophytin Figs. , and 7: Rhododendron growth and ANPP  differed from McMurray (0.12) with a low value. We produced algorithms for MPS for the six individual sites and an algorithm for all sites combined. Slopes for individual site algorithms were not different values from the overall one; thus, one generalized algorithm appears to be sufficient for all of our sites (r2 = 0.85), and probably useful for all bog sites in the region.PP Figs. , and 10:Rhododendron at this site was also lower  are somewhat higher than those reported by Wieder et al.  and Kearl (higher in 2019) had differing MPS values. The difference at JPH4 between 2018 and 2019 seems real as the variation is within that from other sites and although not significant, the abundance of Rhododendron. Stem densities varied from 174 (Anzac) to 355 (Kearl) stems m−2 in 2018 and from 205 (Anzac) to 409 (Kearl) stems m−2 in 2019. Five of the six sites had stem counts from 1 to 54 (Kearl) stems greater in 2019, with one site (McMurray slightly lower (− 16), but overall stem counts did not differ in the 2 years. These stem densities are higher compared with those reported from . Actual NPP from our six sites varied from 25 g m−2 year−1 (McMurray 2019) to 70 g m−2 year−1 (Kearl 2018) and 110 g m−2 year−1 (Kearl 2019). Both MPS and stem density are key components of NPP for Rhododendron. Our predicted MPS plus stem density count predicted wide variation in NPP between sites, but little variation between years , suggesting that like our line transect analysis, comparisons over time must be addressed for individual sites.Comparing our six bog sites to the Mariana bog site used in Wieder et al.  revealedChamaedaphne growth and ANPP . Our algorithm results are similar to those of Rhododendron (r2 = 0.91) with an overall algorithm sufficient to predict MPS for Chamaedaphne  compared with those from 2019. NPP for Chamaedaphne were much lower than those for Rhododendron, varying from 2.5 (Horse Creek 2019) to 23 g m−2 year−1 (JPH4 2018) and are comparable to those from Mariana Lake Bog . PredicSphagnum NPP  having long leaders (> 4 cm) vs. Horse Creek having short ones (< 2 cm). We are unable to make a recommendation based on these limited data, and measurements in additional years are necessary.We examined leader lengths of −1 year−1for N and 0.4 kg ha−1 year−1 for S, with our six sites varying from 0.82– to 1.51 kg ha−1 year−1 for N and 1.33 to 4.80 kg ha−1 year−1for S. These increases in N, although 1.4 to 2.5 fold higher than regional background levels remain relatively low given that the critical load recommended by Wieder et al. . We found that between 35 and 76% of the variability present in these nine responses could be explained by N or S deposition.Responses to increases in S appear to be limited with only Sphagnum at higher N concentrations. Over the 2 years of our study we have assessed the responses of two shrubs, the epiphytic lichen Evernia mesomorpha and Sphagnum fuscum, to assess the responses in six boreal bogs in order to develop a protocol for monitoring future effects of changing atmospheric N and S deposition regimes. The ombrogenous condition of bog ecosystems provides a unique opportunity for these assessments; however, at present no structured protocol has been available. We conclude the following: (1) Bog communities are species poor with high degree of similarity for vascular plant occurrences; however, differences in bryophyte species and variation in vascular plant abundances  indicate that bog sites are individually distinct, and monitoring over a temporal framework must take this site variation into account. (2) At the plot level, both line-intercept and point-intercept methods of assessing plant abundances provided similar results, with almost all plots within a site having high similarity and indicating that individual sites have highly uniform structure. (3) Sites also are variable between years especially in shrub abundances that couple to differences in annual growth and ANPP. Also, variable between years and sites is Sphagnum NPP. (4) We developed allometric growth equations for non-destructively predicting annual MPS and ANNP for two dominant shrubs. These equations predicted differences in annual NPP between years (higher in 2018 compared with 2019) and between many of the sites. (5) We found the lichen Evernia mesomorpha was abundant at all of the sites and easy to collect in the field. Laboratory analyses of chlorophyll and phaeophytin contents were straight-forward and yielded data that indicate clear differences between years and sites. (6) Picea mariana leader lengths are variable among the sites, but we found it difficult to find suitable trees and to be sure that we measured the correct bud scars; thus, we have less confidence in this measure. Based on these results we recommend a monitoring protocol of (a) sites with 5 permanent plots each with (b) 30 or 60 point-intercepts along a 300-cm line transect followed by multivariate analysis, (c) two 1-m2 plots non-destructively sampled annually for Rhododendron groenlandicum and Chamaedaphne calyculata annual growth, (d) Evernia mesomorpha sampled for chlorophyll/phaeophytin analysis, and (e) NPP of Sphagnum fuscum determined from cranked wires. Additionally, variation in many responses at the site level are correlated to patterns of N deposition in the oil sands area, suggesting that attributes of shrubs, the lichen Evernia mesomorpha and Sphagnum fuscum, are useful indicators of changes in N, and to a lesser extent, S deposition.Considerable research conducted across Europe has provided widespread agreement that increasing N deposition has negative consequences for bog structure and function ESM 2(PDF 119 kb)