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UPRLIMET is our response to a need for a consistent method for predicting the upper extent of trout in all streams across land ownerships within our region. By developing and implementing the model using LiDAR-derived flowline hydrography, we offer a standardized, spatially explicit, spatially contiguous (where LiDAR hydrography is available), and high-quality fish-distribution layer based on the probability of fish presence. UPRLIMET maps both the probability of trout and the upper limit of trout across landscapes, ownerships, and jurisdictions, and better captures the upper extent of fish in headwater reaches relative to previous approaches allowing for a cross-boundary distribution map on which decision-makers and managers can base policies and regulations.This work provides a transferable prediction modeling framework for systematically and comprehensively estimating the upper distribution limit of fish, which could be calibrated and implemented in watersheds and for fish species around the globe. Although the dependency on LiDAR-derived data here may be seen as a limitation to broader implementation of this method, the method is scalable to any resolution, and LiDAR is becoming increasingly ubiquitous in the United States through the U.S. Geological Survey 3D Elevation Program, which is funding LiDAR acquisitions across the United States. Furthermore, LiDAR data is available globally via data from GEDI and ICESAT-2 satellites that offer coarser resolution (~ 25-m) data that are still superior to either ASTER or SRTM derived-DEMs26, 27.Minimizing prediction errors for the upper limit of trout is important to decision support and management planning because it ensures that forest-harvest regulations and management prescriptions are aligned. It is important to note that the prediction error estimates from this study are derived from the NSpCV process, except for models using 20% slope thresholds or unaltered parameterization of Fransen’s model13, because it is likely that the NSpCV estimates are conservative. They tended to overestimate error, as evidenced by the fact that the Refit model (i.e. Fransen’s optimal model13 refit to our data) exhibited a larger MAE than the unchanged optimal Fransen model13. This unexpected result was likely due to applying the NSpCV routine on the Refit model, resulting in the use of many intermediate models to characterize predictive performance using randomized subsets of independent training and test data. In contrast, the optimal Fransen model13 was developed independently using the data in this study and thus error could be evaluated directly without subsampling imposed by NSpCV.The relatively low error for the two-stage model that becomes UPRLIMET suggests that it more accurately characterizes the upper limit of fish than all other models considered in this study, including the Fransen model13, which has been used for estimating upper limit of fish regionally. Although some of the models exhibited relatively small differences in error relative to the model that became UPRLIMET, small differences in predicted upper limit locations when considered in aggregate across multiple watersheds can potentially alter management decisions and expected outcomes. Differences in predictive performance and error between UPRLIMET and the optimal Fransen model13 are likely attributed to high-accuracy hydrography and hydro-topographic data (as LiDAR-derived DEMs were not available in western Washington in 2006), which allowed a finer-scale of analysis (i.e., 5-m vs 10-m reaches). Additionally, the fact that UPRLIMET was fit to data solely from western Oregon likely offers predictive performance gains when applied to western Oregon when compared to the Fransen model13 that was fit to data from western Washington.Quantifying the predicted accuracy associated with applying UPRLIMET to western Washington will require new data and is outside the intended scope of this study. However, we think it is reasonable to infer findings from UPRLIMET across regions with similar climatic and hydro-topographic conditions including northwestern California, western Oregon, western Washington, and southwestern British Columbia, especially given the broad availability of LiDAR-derived DEMs. This conclusion is supported the fact that both the Fransen13 and Refit models produced similar logistic regression coefficients (Data S5) and similar Matthews Correlation Coefficients (Data S6), suggesting that feature space of the two models is similar. This evidence is further corroborated by the high degree of overlap observed among the distributions of each of the four predictor variables for both western Oregon and Washington. We acknowledge that UPRLIMET does not contain identical predictor variables to Fransen’s model13 but maintain that they are similar enough in purpose that it is reasonable to assume that the feature space similarities are retained.When we undertook this study, we hypothesized that a prediction model based on RF would offer superior predictive performance over those based on LR, given the availability of 67 predictor variables and RF’s demonstrated superior predictive performance in ecological applications23,24,25. However, our results suggests no improvement is offered by including more than four of the 67 environmental predictors examined, and that no clear advantage is offered by employing the more complex RF model, as evidenced by the top three of the top five prediction models being four-variable LR model algorithms (Fig. 3; Data S3.) The general importance of these variables to so many models is likely due to the strong linear relationships in the response of fish or no fish in logit space given the slopes of the curves in the partial dependence profiles (Fig. 4). This finding is congruent with the fundamental premise of LR, which is to explain and predict a response with a functional relationship, whereas RF deliberately focuses only on maximizing prediction accuracy with many decision trees28. Additional advantages to prediction models based on LR include the following: relatively better extrapolation performance over RF29, the simplicity of transferring a LR model to another processing platform using the model coefficients (versus the black box of RF decisions), and the immensely reduced computational processing times associated with LR model fitting and prediction. These advantages are especially key to this work, where there may be a desire to implement the model on other landscapes without the requisite expertise in doing so using the R software30. However, there are tradeoffs, as LR is more sensitive to the influence of outliers and multi-collinearity among variables, and overfitting is an increasing concern as the number of predictor variables increase, whereas RF tends to be robust to these concerns, but is more likely to produce a high-variance, low-bias prediction model31.Although there is no single, general explanation for distribution limits of species32, the intersection of stream size, slope, and elevation together locate the upper limit of fish. Stream size corresponds to major ecosystem changes along a stream continuum including for energy sources, ecosystem metabolism, habitat characteristics, and biodiversity33, as well as the upper distribution limit of fish, as shown here. As expected, stream size accounts for the top two variables in the model suggesting that it is the major driver of the upper distribution limit of fish with the probability of trout increasing with increasing upstream stream length and upstream drainage area. Our finding proposes that downstream stream reaches are more likely to have fish. Although the underlying mechanisms have multiple influences, factors related to increasing stream size, such as increasing habitat size, habitat complexity, stability, or temperature variability34 have been shown to be important. Similarly, stream size is the most sensitive factor in intrinsic potential models for Chinook Salmon (O. tshawytscha35). Slope, the next variable of importance influencing the upper extent of fish, exerts control on physical habitats in streams, including channel morphology, hydraulics, sediment transport, substrate, and habitat36. Steep slopes drastically prevent trout from reaching areas above waterfalls or impassable chutes of over 25% slope, but trout can be found in streams channels without barriers at slopes as high as 28%7, 14, 37. Other fishes, such as Coho Salmon (O. kisutch) and steelhead (O. mykiss) are generally not found above 12% slope38. Interestingly, survival of fishes that make it upstream or are introduced above barriers may be facilitated by a geomorphic setting that is less prone to debris flows and other episodic sediment fluxes and has a greater resilience to flooding resulting from wider valley and greater floodplain connectivity39. Elevation or vertical topographic position may indirectly integrate broad influences of other landscape-scale or climate factors or also indirectly capture stream size, influencing the likelihood of fish presence. Frequently, species richness increases at lower elevations40, and we suggest that elevation also contributes to species distribution limits, as is the case for the Endangered Species Act listed Bull Trout (Salvelinus confluentus)41. The multiple factors associated with elevation correspond to the relationship found for stream size that smaller streams are less likely to have fish. Ultimately, the intersection of stream size, slope, and elevation guide us to finding the upper extent of fish in streams.Physical influences have been proposed to be more limiting to fish distributions upstream, such as near the upper extent of fish, whereas biological factors are probably more important downstream33. Although 67 environmental predictor variables representing geologic, soil, climatic, and hydro-topographic conditions at local and patch scales are evaluated (Data S1), only the hydro-topographic variables of stream size, slope, and elevation are important to predicting the upper limit of fish in UPRLIMET. In fact, the top 9 models (Fig. 3; Data S3) relied on just four to five hydro-topographic variables, most of which were patch-scale variables or elevation at 1000 m, all of which incorporate a broader extent of influence. This suggests that local scale variables that contribute to fish limits, including slope or riparian influences may need to be further explored. In addition, some of the remaining 63 variables present in UPRLIMET, such as precipitation and air temperature, are important drivers of within-network trout distributions and contribute to their connectivity. Some of these predictor variables appear in the 10th ranked 26-variable RF-O-SR1 model (Data S2; Data S4; Data S8), but the influence appears to be dubious for isolating the upper limit and explaining variation in fish occurrence because MAE of upper limit was substantially higher than the 9 models with lower MAEs (Fig. 3; Data S3), and the lower MCC of the associated RF-O sub-model (Data S6). It is likely that other combinations of the 67 predictor variables, including precipitation, may be more important when this model development and evaluation framework is applied elsewhere, especially if those areas contain fishes or are places that are vulnerable to changing water temperatures and streamflow regimes. In addition, biological factors may be a concern in other watersheds, including invasive species and fish stocking which can limit the longitudinal distribution and the upstream extent of fishes.Given the large geographic extent of this study, we expected other variables such as precipitation to be more important drivers, however due to a combination of a wet water year, a lack of precipitation gradient in the study area, coarse grain data, and location of fish in streams this was not the case. For example, 2017 was a wetter than normal water year53, and it may be that the gradient of precipitation variation in western Oregon was not strong enough to explain the variation in the spatial distribution of trout occurrence. All climate data, including the precipitation data were sourced from relatively coarse-scale (800 m) PRISM data. The inability to adequately downscale precipitation to characterize how precipitation truly varies within and between patches, especially along elevational gradients, likely confounded how the model interprets the influence of precipitation. Trout occurrence was on perennial streams, which is likely far enough downstream of locations where variation in precipitation was the dominant influence on streamflow permanence and consequently would not have been a factor.Stream network structure plays a key role in the upper limits of fish. Upper limits for fish can occur at either lateral or terminal points13 and when mapping these points, differences were seen for UPRLIMET relative to other datasets. Lateral limits end in the tributary stream just above where it connects with a mainstem stream. Terminal limits include both mid-stream terminal limits where fish drop out in the middle of a stream channel owing to a soft (i.e., transient barrier or puttering out) or hard (i.e., waterfall) edge, and confluence terminal limits where the upper limit of fish ends at the confluence. For example, when closely examining the 14 watersheds where we have overlapping information across various datasets and models, UPRLIMET and the Fransen optimal model13 exhibit substantial agreement in their lateral limits. However, the largest differences are in their terminal ends, especially terminal mid-stream limits, probably owing to hydro-topographic changes that contribute to fish occurrence at confluences, which are more pronounced than mid-stream. Accordingly, the logic in the stopping rule is likely important in identifying specific upper extent of fish distributions in reaches that end mid-stream.Differences among databases for the upper distribution limits of fish come from both the upper limit points and depiction of fish-bearing reaches, underscoring the importance of having a shared map with common coverage of the fish extent across landscapes and ownerships. Differences among mapped distributions can result from source information, relating to whether it is modeled or occurrence data. Models, such as UPRLIMET, can be applied across a broad extent based on model parameters and training data, thereby offering broad coverage for distributions (and quantifiable error) across the landscape, ownerships, and jurisdictions. However, models are limited by accuracy and fit. As such, they can incorrectly predict distributions in some areas, especially if there are prediction features not yet trained with the model data where prediction would require extrapolation of the model. This makes both the training dataset and modeled extent important considerations, as models are only as good as the data used to develop them. Updating UPRLIMET with new data as it becomes available will help to expand the prediction domain, improve accuracy, and allow the model to do more interpolation than extrapolation.Distributions based on occurrence information depend heavily on data availability, data quality, and access. Differences in data availability can lead to inconsistent coverage across landscapes and ownerships, with high coverage in some watersheds and low to no coverage in others. Inconsistent coverage can lead to errors that are difficult to quantify across landscapes, ownerships, and survey crews. Occurrence information also depends on the ability to survey watersheds and gain access across ownership types, including on private lands that do not have the same assurances of access as public lands, resulting in information asymmetry42, 43. Data quality also depends on the spatial accuracy of the points of uppermost fish, which are a function of GPS quality and error, and can drastically change the modeled results, as these points are used in the training dataset. Differences among mapped distribution limits also result from differences in field protocols on designating last fish. For example, some crews note fish distribution limits where they visually see the last fish, whereas others note it upstream of where they saw last fish, based on habitat features that would limit fish. With the advent of LiDAR-derived DEMs and associated LiDAR-derived stream hydrography, like those available in much of western Oregon, have revealed additional flowlines in watersheds compared to previous topographic maps, which adds more potential tributaries to survey for fish-distribution assessments. When these new previously unmapped tributaries are paired with a model, such as UPRLIMET, a common information set is available across landowners, managers, and agencies for the upper extent of fish. This helps policymakers determine where to apply regulations that support fisheries and forest management, based on the upper fish limit.Next steps for applying and expanding the model include addressing current data gaps. More information and observations about the upper distribution limits of fish beyond western Oregon would be needed to properly expand the spatial scope of the model. The upper extent of fish is at the detection limit of many current technologies, including global nativation satellite system (GNSS), geographic information systems (GIS), and LiDAR, especially in forested landscapes. Better precision of GNSS coordinates from observations would help greatly. From an ecological perspective, we could focus on fish distribution limits that vary seasonally or interannually to better understand which stream features and hydrologic parameters influence those endpoints. We also need information related to locations of barriers, including culverts, waterfalls, and knickpoints to understand their influence on contemporary distributions. Incorporating variables representing riparian conditions as well as leveraging higher-resolution DEMs ( More

Honey bee colony loss and parasites across space and timeHoney bee colony loss strongly depends on spatio-temporal factors33,42, which in turn have to be jointly modeled with other stressors. Focusing on CONUS climatic regions, defined by the National Centers for Environmental Information40 (see Fig. 1), this is supported by the box plots in Fig. 2 which depict appropriately normalized honey bee colony loss (upper panel) and presence of V. destructor (lower panel) quarterly between 2015 and 2021. Specifically, Fig. 2a highlights that the first quarter generally accounts for a higher and more variable proportion of losses. Average losses are typically lower and less dispersed during the second quarter, and then tend to increase again during the third and fourth quarters. The Central region, which reports the highest median losses during the first quarter (larger than 20%) exemplifies this pattern, which is in line with existing studies that link overwintering with honey bee colony loss6,29,30,31,32,33,43. On the other hand, the West North Central region follows a different pattern, where losses are typically lower during the first quarter and peak during the third. This holds, albeit less markedly, also for Northwest and Southwest regions. These differing patterns are also depicted in Fig. 3, which shows the time series of normalized colony loss for each state belonging to Central and West North Central regions – with the smoothed conditional means highlighted in black and red, respectively. Figure 2b shows that also the presence of V. destructor tends to follow a specific pattern; in most regions it increases from the first to the third quarter, and then it decreases in the fourth – with the exception of the Southwest region, where it keeps increasing. This is most likely because most beekeepers try to get V. destructor levels low by fall, so that colonies are as healthy as possible going into winter, and also because of the population dynamics of V. destructor alongside honey bee colonies – i.e., their presence typically increases as the colony grows and has more brood cycles, since this parasite develops inside honey bee brood cells44,45. The West region (which encompasses only California since Nevada was missing in the honey bee dataset; see Data) reports high levels of V. destructor throughout the year, with very small variability. A comparison of Fig. 2a and b shows that honey bee colony loss and the presence of V. destructor tend to be higher than the corresponding medians during the third quarter, suggesting a positive association. This is further confirmed in Fig. 4, which shows a scatter plot of normalized colony loss against V. destructor presence, documenting a positive association in all quarters. Although with the data at hand we are not able to capture honey bee movement across states, as well as intra-quarter losses and honey production, these preliminary findings can be useful to support commercial beekeeper strategies and require further investigation.Figure 2Empirical distribution of honey bee (Apis mellifera) colony loss (a) and Varroa destructor presence (b) across quarters (the first one being January-March) and climatic regions; red dashed lines indicate the overall medians. (a) Box plots of normalized colony loss (number of lost colonies over the maximum number of colonies) for each quarter of 2015–2021 and each climatic region. At the contiguous United States level, this follows a stable pattern across the years, with higher and more variable losses during the first quarter (see Supplementary Figs. S2-S6), but some regions do depart from this pattern (e.g., West North Central). (b) Box plots of normalized V. destructor presence (number of colonies affected by V. destructor over the maximum number of colonies) for each quarter of 2015–2021 and each climatic region. The maximum number of colonies is defined as the number of colonies at the beginning of a quarter, plus all colonies moved into that region during the same quarter.Full size imageFigure 3Comparison of normalized honey bee (Apis mellifera) colony loss (number of lost colonies over the maximum number of colonies) between Central and West North Central climatic regions for each quarter of 2015–2021 (the first quarter being January-March). (a) Trajectory of each state belonging to Central (yellow) and West North Central (blue) climatic regions. (b) Smoothed conditional means for each of the two sets of curves based on a locally weighted running line smoother where the width of the sliding window is equal to 0.2 and corresponding standard error bands are based on a 0.95 confidence level46.Full size imageFigure 4Scatter plot of normalized honey bee (Apis mellifera) colony loss (number of lost colonies over the maximum number of colonies) against normalized Varroa destructor presence (number of colonies affected by V. destructor over the maximum number of colonies) for each state and each quarter of 2015–2021 (the first quarter being January-March). Points are color-coded by quarter, and ordinary least squares fits (with corresponding standard error bands based on a 0.95 confidence level) computed by quarter are superimposed to visualize the positive association.Full size imageUp-scaling weather dataThe data sets available to us for weather related variables had a much finer spatio-temporal resolution (daily and on a (4 times 4) kilometer grid) than the colony loss data (quarterly and at the state level). Therefore, we aggregated the former to match the latter. For similar data up-scaling tasks, sums or means are commonly employed to summarize the variables available at finer resolution47. The problem with aggregating data in such a manner is that one only preserves information on the “center” of the distributions – thus losing a potentially considerable amount of information. To retain richer weather related information in our study, we considered additional summaries capturing more complex characteristics, e.g., the tails of the distributions or their entropy, to ascertain whether they may help in predicting honey bee colony loss. Within each state and quarter we therefore computed, in addition to means, indexes such as standard deviation, skewness, kurtosis, (L_2)-norm (or energy), entropy and tail indexes48. This was done for minimum and maximum temperatures, as well as precipitation data (see Data processing for details).Next, as a first way to validate the proposed weather data up-scaling approach, we performed a likelihood ratio test between nested models. Specifically, we considered a linear regression for colony loss (see Statistical model) and compared an ordinary least squares fit comprising all the computed indexes as predictors (the full model) against one comprising only means and standard deviations (the reduced model). The test showed that the use of additional indexes provides a statistically significant improvement in the fit (p-(text {value}=0.03)). This test, which can be replicated for other choices of models and estimation methods (see Supplementary Table S5), supports the use of our up-scaling approach.Figure 5 provides a spatial representation of (normalized) honey bee colony losses and of three indexes relative to the minimum temperature distribution; namely, mean, kurtosis and skewness (these all turn out to be relevant predictors based on subsequent analyses; see Table 1). For each of the four quantities, the maps are color-coded by state based on the median of first quarter values over the period 2015-2021 (first quarters typically have the highest losses, but similar patterns can be observed for other quarters; see Supplementary Figs. S12-S14). Notably, the indexes capture characteristics of the within-state distributions of minimum temperatures that do vary geographically. For example, considering minimum temperature, skewness is an index that (broadly speaking) provides information on whether the data tends to accumulate at one end or the other of the observed range of minimum temperatures (i.e., a positive/negative skewness indicates that the data accumulates towards the lower/upper range, respectively). On the other hand, kurtosis is an index that captures the presence of “extreme” values in the tails of the data (i.e., a low/high value of kurtosis indicates that the tail minimum temperatures are relatively close/very far from the typical minimum temperatures). With this in mind, going back to Fig. 5, we can see that minimum temperatures in states in the north-west present large kurtosis (a prevalence of extreme values in the tails) and negative skewness (a tendency to accumulate towards the upper values of the minimum temperature range), while the opposite is true for states in the south-east. More generally, the mean minimum temperature separates northern vs southern states, kurtosis is higher for states located in the central band of the CONUS, and skewness separates western vs eastern states.We further note that the states with lower losses during the first quarter (e.g., Montana and Wyoming) do not report extreme values in any of the considered indexes. Although these states are generally characterized by low minimum temperatures, these are somewhat “stable” (they do not show marked kurtosis or skewness in their distributions) – perhaps allowing honey bees and beekeepers to adapt to more predictable conditions. On the other hand, states with higher losses during the first quarter such as New Mexico have higher minimum temperatures as well as marked kurtosis, and thus higher chances of extreme minimum temperatures – which may indeed affect honey bee behavior and colony loss. Overall, across all quarters of the years 2015-2021, we found that normalized colony losses and mean minimum temperatures are negatively associated (the Pearson correlation is -0.17 with a p-(text {value} More

Dinosauria Owen, 1842Theropoda Marsh, 1881Dromaeosauridae Matthew and Brown, 1922Halszkaraptorinae Cau et al., 2017Revised diagnosisSmall dromaeosaurids that possess dorsoventrally flattened premaxillae, premaxillary bodies perforated by many neurovascular foramina, enlarged and closely packed premaxillary teeth that utilized delayed replacement patterns, reduced anterior maxillary teeth, dorsolateral placement of retracted external nares, greatly elongated cervical vertebrae, anterior cervical vertebrae with round lobes formed by the postzygapophyses, horizontal zygapophyses, and pronounced zygapophyseal laminae in the anterior caudal vertebrae, mediolaterally compressed ulnae with sharp posterior margins, second and third metacarpals with similar thicknesses, shelf-like supratrochanteric processes on the ilia, elongated fossae that border posterolateral ridges on the posterodistal surfaces of the femoral shafts, and third metatarsals in which the proximal halves are unconstricted and anteriorly convex.Natovenator polydontus gen. et sp. nov.HolotypeMPC-D 102/114 (Institute of Paleontology, Mongolian Academy of Sciences, Ulaanbaatar, Mongolia) is a mostly articulated skeleton with a nearly complete skull (See Supplementary Table 1 for measurements).Locality and horizonBaruungoyot Formation (Upper Cretaceous), Hermiin Tsav, Omnogovi Province, Mongolia13 (Supplementary Fig. 5).EtymologyNatovenator, from the Latin nato (swim) and venator (hunter), in reference to the hypothesized swimming behaviour and piscivorous diet of the new taxon; polydontus, from the Greek polys (many) and odous (tooth) in reference to the unusually many teeth.DiagnosisA small halszkaraptorine dromaeosaurid with the following autapomorphies: wide groove delimited by a pair of ridges on the anterodorsal surface of the premaxilla, premaxilla with an elongated internarial process that overlies nasal and extends posterior to the external naris, 13 premaxillary teeth with large and incisiviform crowns, first three anteriormost maxillary teeth are greatly reduced and are clustered together with the following tooth without any separations by interdental septa, anteroposteriorly long external naris (about 30% of the preorbital skull length), paroccipital process with a anteroposteriorly broad dorsal surface, elongate maxillary process of the palatine that extends anteriorly beyond the middle of the antorbital fenestra, pterygoid with a deep fossa on the medial surface of the quadrate ramus, distinct posterolaterally oriented projection on the lateral surface of atlas, absence of pleurocoels in cervical vertebrae (not confirmed in the missing fifth cervical centrum), posterolaterally oriented and nearly horizontal proximal shafts in the dorsal ribs, hourglass-shaped metacarpal II with distinctly concave medial and lateral surfaces.DescriptionThe skull of Natovenator is nearly complete, although the preorbital region has been affected by compression and is slightly offset from the rest of the skull (Figs. 1c, d, 2a–d and Supplementary Figs. 1, 2). Near the tip of the snout, the premaxilla is marked by a broad groove. The body of the premaxilla is also dorsoventrally low and is perforated by numerous foramina that lead into a complex network of neurovascular chambers (Supplementary Fig. 1b) as in Halszkaraptor4. Similarly, the external naris is positioned posteriorly and is level with the premaxilla-maxilla contact (Fig. 2a, b), although it is marginally behind this position in Halszkaraptor4. It is also dorsally placed compared to those of other non-avian theropods and faces dorsolaterally. The exceptionally long external naris and accordingly elongated internarial process of Natovenator (Fig. 2c) are unique among dromaeosaurids but comparable to those in aquatic toothed birds14 as well as in therizinosaurs15,16. The frontal is similar to those of other halszkaraptorines4,17 in that it is vaulted to accommodate a large orbit and has little contribution to the supratemporal fossa. A sharp nuchal crest is formed by the parietal and the squamosal (Supplementary Fig. 2a–e). The latter also produces a shelf that extends over the quadrate head as in other dromaeosaurids18. The paroccipital process curves gently on the occiput and has a broad dorsal surface that tapers laterally (Fig. 2f and Supplementary Fig. 2b, e). Its ventrolateral orientation is reminiscent of Mahakala17 but is different from the more horizontal paroccipital process of Halszkaraptor4. The occipital condyle is long and constricted at its base. A shallow dorsal tympanic recess on the lateral wall of the braincase is different from the deep one of Mahakala17. The palatine is tetraradiate with a greatly elongated maxillary process, which extends anteriorly beyond the level of the mid-antorbital fenestra. The pterygoid is missing its anterior portion (Fig. 2g and Supplementary Fig. 2a–e). A deep fossa on the medial surface of the thin quadrate ramus is not seen in any other dromaeosaurids. The mandibles of Natovenator preserve most of the elements, especially those on the left side (Fig. 1a, b, d and Supplementary Figs. 1a, 2). Each jaw is characterized by a slender dentary with nearly parallel dorsal and ventral margins, a surangular partially fused with the articular, a distinctive surangular shelf, and a fan-shaped retroarticular process that protrudes dorsomedially. The upper dentition of Natovenator is heterodont as the premaxillary teeth are morphologically distinct from the maxillary teeth (Fig. 2a, b, e and Supplementary Fig. 1a, c). There are unusually numerous premaxillary teeth tightly packed without any separation of the alveoli by bony septa. The roots of the teeth are long, and the crowns are tall and incisiviform as in Halszkaraptor4. Moreover, the large replacement teeth in the premaxilla suggest that the replacement of the premaxillary teeth was delayed as in Halszkaraptor4. However, the number of teeth in each premaxilla is 13 in Natovenator, whereas it is only 11 in Halszkaraptor4. In the maxilla, the three most anterior maxillary teeth are markedly shorter than the premaxillary teeth and the more posterior maxillary teeth. This pattern is also observed in Halszkaraptor, although the number of shorter maxillary teeth differs as it has two reduced ones7. Both the maxillary and dentary teeth have sharp fang-like crowns that lack serrations. Although posteriormost parts are poorly preserved, there are at least 23 alveoli in each of the maxilla and dentary, which suggests high numbers of teeth in both elements.The neck of Natovenator, as preserved, is twisted and includes ten elongated cervical vertebrae, although most of the 5th cervical is missing (Figs. 1, 3a–d). This elongation of the cervicals results in a noticeably longer neck than those of most dromaeosaurids and is estimated to be longer than the dorsal series. It is, however, proportionately shorter than that of Halszkaraptor, which has a neck as long as its dorsal and sacral vertebra combined4. Another peculiarity in the neck of the Natovenator is a pronounced posterolaterally extending projection on the neurapophysis of the atlas (Fig. 3a and Supplementary Fig. 2b, c, e). The postzygapophyses of each anterior cervical are fused into a single lobe-like process as in Halszkaraptor4. Pleurocoels are absent in the cervical vertebrae. In contrast, Halszkaraptor has pleurocoels on its 7th–9th cervicals4. A total of 12 dorsal vertebrae are preserved (Figs. 1a, b, 3e, 4a and Supplementary Figs. 3a–d). They all lack pleurocoels, and their parapophyses on the anterior and mid-dorsals are placed high on the anterodorsal end of each centrum. Interestingly, the positions of the parapophyses are similar to those of hesperornithiforms19,20,21 rather than other dromaeosaurids such as Deinonychus22 or Velociraptor23. The preserved dorsal ribs, articulated with the second to seventh dorsals, are flattened and posteriorly oriented (Figs. 1, 3e, 4a–d). The proximal shafts are also nearly horizontal, which is indicative of a dorsoventrally compressed ribcage. Each proximal caudal vertebra has a long centrum and horizontal zygapophyses with expanded laminae (Fig. 3f and Supplementary Fig. 3e–i), all of which are characters shared with other halszkaraptorines4,17. The forelimb elements are partially exposed (Figs. 1a, b, 2a–d, 3e, g). The nearly complete right humerus is proportionately short and distally flattened like that of Halszkaraptor4. The shaft of the ulna is mediolaterally compressed to produce a sharp posterior margin as in Halszkaraptor4 and Mahakala17. Metacarpal III is robust and is only slightly longer than metacarpal II. Similarly, metacarpal III is almost as thick and long as other second metacarpals of other halszkaraptorines4,17. The femur has a long ridge on its posterior surface, which is another characteristic shared among halszkaraptorines4. Typically for a dromaeosaurid, metatarsals II and III have ginglymoid distal articular surfaces (Fig. 3h and Supplementary Fig. 4f, h). The ventral surface of metatarsal III is invaded by a ridge near the distal end, unlike other halszkaraptorines (Fig. 3h)4,5,17,24.Phylogenetic analysisThe phylogenetic analysis found more than 99,999 most parsimonious trees (CI = 0.23, RI = 0.55) with 6574 steps. Deinonychosaurian monophyly is not supported by the strict consensus tree (Supplementary Fig. 6). Instead, Dromaeosauridae was recovered as a sister clade to a monophyletic clade formed by Troodontidae and Avialae, which is consistent with the results of Cau et al.4 and Cau7. Halszkaraptorinae is positioned at the base of Dromaeosauridae as in Cau et al.4, although there are claims that dromaeosaurid affinities of halszkaraptorines are not well supported25. Nine (seven ambiguous and two unambiguous) synapomorphies support the inclusion of Halszkaraptorinae in Dromaeosauridae. The two unambiguous synapomorphies are the anterior tympanic recess at the same level as the basipterygoid process and the presence of a ventral flange on the paroccipital process. A total of 20 synapomorphies (including one unambiguous synapomorphy) unite the four halszkaraptorines, including Natovenator (Supplementary Fig. 7). In Halszkaraptorinae, Halszkaraptor is the earliest branching taxon, and the remaining three taxa form an unresolved clade supported by three ambiguous synapomorphies (characters 121/1, 569/0, and 1153/1). Two of these synapomorphies are related to the paroccipital process (characters 121 and 569), which is not preserved in Hulsanpes5,24. The other is the presence of an expansion on the medial margin of the distal half of metatarsal III, which is not entirely preserved in the Natovenator. When scored as 0 for this character, Natovenator branches off from the unresolved clade. It suggests that the medial expansion of the dorsal surface of metatarsal III could be a derived character among halszkaraptorines. More

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