dataesr/scientific-paragraphs-categorization huggingface dataset:
(Forest Pests, 1955;Tomilova, 1973;a (see Materials and Methods) and our records (in the latter case the species are marked with †). 2This column contains identification number (ID) of Siberian samples in the BOLD database, the reliability (%) of molecular diagnostics, and the origin of the reference specimens: AT, Austria; CA, Canada; CZ, Czech Republic; DE, Germany; FI, Finland; FR, France; NL, the Netherlands; NO, Norway. 3Trophicspecialization:M,monophagous on Betula; O, oligophagous; P, polyphagous (see Materials and Methods). 4Hostplantsarelisted based on the literature data (see Materials and Methods); doubtful indications of host plants in Russian literature (Forest Pests, 1955; Tomilova, 1962, 1972; Dovnar-Zapolsky and Tomilova, 1978; Barannik, 1981; Kuznetsov, 1981; Gusev, 1984; Yanovsky, 2003) which need to be verified are given in parentheses; our data are given in square brackets; * introduced plant species; # for notes on the identity of O.
ACKNOWLEDGMENTS We are grateful to  M. A. Tomoshevich  and  E. V. Banaeva  ( Novosibirsk ) for help in organizing regular research in the  Central Siberian Botanical Garden , to  E. A. Akulov  and  V. V. Shishov  ( Krasnoyarsk ) for collecting some specimens in Krasnoyarsk and Yakutsk, to  S. A. Krivets  (Tomsk) and  B. A. Korotyaev  ( St. Petersburg ) for confirming the identification of Curculionidae, to  E. van Nieukerken  and  C. Doorenweerd  (the  Netherlands ) for identification of Nepticulidae, to  P. Triberti  ( Italy ) for identification of Gracillariidae, to  A. Roques  (France) and the colleagues from the  University of Guelph (Canada)  for the possibility of performing DNA barcoding of insects. We also thank  I. A. Mikhailova  ( Krasnoyarsk ) for designing the map,  Yu. N. Baranchikov  ( Krasnoyarsk ) for fruitful discussion, and the anonymous reviewer for valuable comments which helped us to improve the manuscript.
Phylogeny and Range Simulations We jointly simulated trees {# spp. = 20, 50, 100, 150, 200, 250} and biogeographies under the dispersal-extinctioncladogenesis model of biogeographical evolution (i. e. , DEC+J, with the inclusion of founder event speciation) in BioGeoBEARS  (Ree and Smith 2008; Matzke 2014). Briefly, the DEC+J model is a model of range evolution in which species ranges change along the branches of a phylogeny as a function of dispersal and local extinction and are inherited by daughter taxa at speciation according to several possible cladogenetic scenarios (see more details in Supplementary Methods available on Dryad at http://dx. doi. org/10. 5061/dryad. ch0vn). For each tree, we started with a single ancestral species occupying one of ten equidistant regions, and simulated trees with constant rates of speciation and local extinction.
We considered different biogeographic scenarios by varying the rate of dispersal events between ranges ("high" and "low" dispersal; see details in Supplementary Methods available on Dryad) and the probability that speciation events occur in sympatry versus allopatry ("high" and "low" sympatric speciation; Supplementary Methods available on Dryad). Each of these simulations resulted in a phylogeny (the tree of extant species) and its associated biogeography (the set of regions in which each lineage occurred throughout the history of the clade). Lineages were identified as sympatric if they cooccurred in at least one of the ten geographic regions, and allopatric if they did not co-occur in any. We simulated four biogeographic scenarios (combinations of low or high dispersal and low or high sympatric speciation) for each tree size.
The resulting biogeographies span scenarios where sympatric speciation is common and dispersal is low (e. g. , lizards on islands) to scenarios where allopatric speciation is the main mode of speciation and dispersal between regions is high (e. g. , birds on continents). These parameter combinations produced a range of realistic proportions of sister taxa that are sympatric (Supplementary Fig. S1A  available on Dryad) and a range of realistic differences in age between sympatric and allopatric sister taxa  (Pigot and Tobias 2014; Supplementary Fig. S1B available on Dryad) , at least for animal taxa (but see  Anacker and Strauss 2014  for different patterns in plants). In defining sympatry as any overlap, the mean magnitude of range overlap fell between 33% and 42% across all tree sizes and simulation parameters (Supplementary Fig.
S1C ,D available on Dryad), which falls well within the range of overlap of sympatric taxa defined under commonly used minimum threshold values applied to continuous indices of range overlap (e. g. ,  Pigot and Tobias 2014; Tobias et al. 2014). For each combination of tree sizes and DEC parameter combinations (n = 24), we performed 100 simulations, resulting in a bank of 2400 trees with associated biogeographies. ) a trait evolving via divergent character displacement, c) a trait evolving via convergent character displacement, and d) a species interaction that exists at present due to pairwise trait similarity. For simulation details, see the main text and Supplementary Methods of the Supplementary Material available on Dryad. drawn toward) one another. In divergent character displacement, trait divergence is driven by pairwise similarity in that same trait z; in convergent character displacement however, convergence in trait z (e.
When both m and = 0, this model reduces to BM. For additional simulation details, see Supplementary Methods available on Dryad. We use a lineage-based "phenomenological" model for our simulations rather than an individual-based model to have the computational ability to produce data sets of a size comparable to the maximum sometimes reached in empirical comparative phylogenetic studies (i. e. , often reaching several hundreds of species). Models derived from microevolutionary first principles (e. g. ,  Grether et al. 2009; Nuismer and Harmon 2015)  generate similar patterns of sympatric shifts resulting from character displacement, and using such a model here would be much more computationally intensive, therefore restricting the range of parameter values that can be studied. For simplicity, this model also omits the effect of a species' geographic structure and the effect of gene flow between distinct populations on the evolution of the mean species phenotype.
2016 ; https://github. com/hmorlon/ PANDA), which could easily be modified to do so. Divergent character displacement. -We simulated data sets with divergent character displacement by setting y = z in Eq. 1 such that trait divergence is driven by pairwise similarity in that trait. Biologically, this could represent a feeding trait that covaries with resource use (e. g. , bill shape in Galápagos finches, Grant and Grant 2011) and which would directly affect interspecific competition. To assess whether each method could detect divergent character displacement when it occurred and did not erroneously detect character displacement when it was absent, we simulated data sets both with repulsion {m = 2} and without repulsion {m = 0} (see Supplementary Methods available on Dryad). We also simulated data sets with { = 2} and without { = 0} the OU process.
We simulated resource-use traits under both BM ( 2 resource = 0. 5, resource = 0) and OU ( 2 resource = 0. 5, resource = 2, resource = 0) models. For the signal trait, we simulated data sets both with convergence {m =-0. 25} and without convergence {m = 0}. We did not include attraction toward a stable peak for the signal trait (i. e. , was held constant at 0). As above, we held 2 = 0. 5 and z 0 = 0, though we held constant at 10, since smaller values result in rapid, cladewise convergence in traits. To analyze the effect of the maximum strength of convergence, we ran another set of simulations on 100-species trees varying m {m = 0, -0. 1, -0. 25, -0. 5} (see Supplementary Methods available on Dryad). The resource trait (y) and signal trait (z) were modeled as unlinked and genetically uncorrelated.
Phylogenetic Tests Among our tests of character displacement (both divergent and convergent), the "correlation" tests involved assessing the significance of the relationship between phenotypic similarity and coexistence, using either the "full" data set (all species pairs) or the "sister taxa" subset obtained by culling sister taxa from trees with 150 tips (Appendix 1, Diagram S1). To the full data sets, we applied standard nonphylogenetic regression analyses that ignore phylogenetic nonindependence (Appendix 1), the raw and phylogenetically permuted partial Mantel tests (Appendix 1), phylogenetic linear mixed models (PLMMs, Appendix 1), and the simulation approach (Appendix 1, Supplementary Methods available on Dryad). To the sister-taxa data sets, we applied nonphylogenetic regression analyses (Appendix 1), PLMMs (Appendix 1), the simulation approach (Appendix 1), sister-taxa GLMs (Appendix 1), and fit process based models in EvoRAG (Appendix 1, Supplementary Methods available on Dryad).
We did not perform Mantel tests on the sister-taxa data because such tests require complete matrices and distance matrices with data for only sister taxa would mostly contain empty cells (i. e. , all those cells that correspond to nonsister taxa species pairs). We compared the fit of process-based phenotypic models with and without species interactions (BM, OU, diversity dependent, and MC models; see Appendix 1 and Supplementary Methods available on Dryad) to the full data sets from divergence scenarios using the R packages geiger  (Pennell et al. 2014 ) and RPANDA  (Morlon et al. 2016). We acknowledge that diversity-dependent models were not designed to analyze character displacement per se, but because they incorporate interspecific interactions, we hypothesized that (and wanted to test if) they could be useful in doing so.
We did not apply process-based models to convergence scenarios because the necessary model fitting tools have yet to be developed (see Discussion). Our tests of predictors of species interactions involved assessing the significance of the relationship between phenotypic similarity and species interactions (i. e. , whether the species interact where they occur in sympatry). Since the response variable is binary, we fit nonphylogenetic logistic regressions, logistic PLMMs, and employed the simulation approach (see Supplementary Methods available on Dryad). We did not perform Mantel tests or sister-taxa analyses because the species pair matrix was incomplete (species that do not coexist cannot interact) and typically too few sister taxa occurred in sympatry for regression analysis.
Divergent Character Displacement When all possible pairwise comparisons are included in analyses, the ability of most methods to detect divergent character displacement in simulated data sets depends on the presence of the OU process. As expected, nonphylogenetic regression analyses have high Type I error rates [Fig. 2Ai ,iv, Supplementary Fig. S2Ai ,iv available on Dryad (NB: throughout, results for low sympatric speciation biogeographies are plotted in the main text and high sympatric speciation biogeographies in the supplement)]. When the OU process is present ( = 2), all phylogenetic methods generally have low Type I error rates and high power (Fig. 2Aiv -vi, Supplementary Fig. S2iv -vi and Tables  available on  Dryad ). However, when there is no pull toward a peak ( = 0), the Type I error rate is higher for Mantel tests (Fig.
2Ai ,ii, Supplementary Fig. S2i ,ii available on Dryad), and the power is much lower for all methods, though the pppMantel and raw Mantel perform better than the simulation and PLMM methods (Fig. 2Aiii , Supplementary Fig. S2iii  available on Dryad). Repulsion is easier to detect against an OU background of traits converging toward a common optimum than against a background of traits diverging under BM, likely because the repulsion process is more active when species occupy similar trait space (Supplementary Figs. S3 and S4 available on Dryad). High rates of sympatric speciation and dispersal tend to slightly decrease the power of all methods (Supplementary Fig. S2iii ,vi and Tables  available on Dryad ). The ability to detect divergence was relatively similar for m = 1 and m = 2, but declined for m = 10 (Supplementary Fig.
S5  available on Dryad). This is due to a positive relationship between the ability to detect character displacement and the ratio of :m (Supplementary Fig. S6  available on Dryad), resulting from a higher absolute  magnitude of repulsion when both processes are present (Supplementary Figs. S4 and S6 available on Dryad), indicating that this ratio impacts the ability to detect divergence more than the raw value of m. For several analyses using only sister-taxa comparisons, there is a high probability of falsely concluding that character displacement occurred in data sets simulated under BM and, to a lesser extent, OU, when data are analyzed with simple linear regressions or PLMMs (Fig. 2Bi ,iv, Supplementary Fig. S2Bi ,iv available on Dryad). As with the whole-tree approach, the power tends to increase and Type I error rate tends to decrease in data sets with attraction toward a single-stationary peak (Fig.
2Biv -vi, Supplementary Fig. S2Biv -vi available on Dryad). However, the overall power to infer the presence of divergence was low (<0. 8) with analyses conducted on sister taxa (Fig. 2Biii ,vi, Supplementary Fig. S2Biii ,vi available on Dryad, Table  2 ), regardless of the analytical approach used. Inferences were generally better when dispersal was high, which may reflect the elevated observed divergence in high-dispersal scenarios (Supplementary Fig. S3  available on Dryad). Allopatric speciation increased the probability of Type I error (e. g. , Fig. 2Bi-ii ). For the phylogenetic trait model-fitting analyses, BM and OU were generally correctly chosen when they were the generating models (i. e. , when m = 0 and when = 0 or 2, respectively, Fig. 3 , Supplementary Fig. S8  available on Dryad).
When = 0 and m >0, the MC model with biogeography is consistently the best-fit model (Fig. 3A , Supplementary Fig. S8A  available on Dryad). When m >0 and = 2, the diversity dependent exponential (DD exp ) model with biogeography was favored over other models in most scenarios (Fig. 3B , Supplementary Fig. S8B  available on Dryad), with positive rate parameters estimated in the maximum likelihood solution (Supplementary Fig. S9  available on Dryad). The biogeographic scenario did not greatly affect the outcome of model fitting, though correct models were slightly more supported when dispersal was high (Supplementary Fig. S10  available on Dryad), in agreement with the observed magnitude of repulsion (Supplementary Fig. S3  available on Dryad). Although the models are less identifiable when m = 10 and = 2 (Fig.
3 , Supplementary Fig. S8  available on Dryad), this results from variation in the :m ratio-there is a ratio of :m around which these models cannot be distinguished (Supplementary Fig. S11  available on Dryad). Process-based models fit to sister-taxa data sets in EvoRAG did not mistakenly identify an effect of species interactions when they were absent (Supplementary Fig. S4A  available on Dryad, C, Table  2 ), but they were unable to identify the effect of competition when = 0 (Supplementary Fig. S4B  available on Dryad, Table  2 ). However, as with process-based models fit to the whole phylogeny, when data were simulated with both repulsion and a pull toward a stable peak, a model where evolutionary rates vary linearly with the number of sympatric taxa is often the best-fit model, though generally with only a marginally lower AICc value (i.
Convergent Character Displacement As with divergent character displacement, with all pairwise species combinations, the ability of most methods to detect convergent character displacement depends on the presence of the OU process on the resource-use trait: data sets simulated with convergent character displacement and an OU pull on resourceuse traits were more likely to be statistically significant (Fig. 4A. vi, Supplementary Fig. S12A. vi available on Dryad) across all methods than those simulated with convergent character displacement and no OU pull on resource-use traits (Fig. 4A. iii, Supplementary Fig. S12A. iii available on Dryad). Again, this is likely because the presence of the OU process in the resource-use trait amplifies the magnitude of convergence (Supplementary Figs. S13 and S14 available on Dryad).
Overall, however, only the simulation approach had substantial power (>0. 80) to detect convergent character displacement (Table  1 ), and only in trees with 100 or more tips and data sets with the OU process in the simulated resource-use trait. Indeed, the nonphylogenetic regressions often (spuriously) detected divergence rather than the simulated convergence, especially in smaller trees (Fig. 4Ai  vs. Aii, Supplementary Fig. S12Aiv ,ii and Tables  available on Dryad ). Both types of Mantel tests were unable to detect convergence, in fact having a higher Type I error rate (detecting divergence in BM simulated data sets, Supplementary Tables available on Dryad) than power. As with divergent character displacement, there was a tendency for higher power in lower dispersal scenarios. The power to detect convergence generally increased with increasingly negative values of m, the maximum strength of attraction in the signal trait when species are identical in the resource-use trait (Supplementary Fig.
S15  available on Dryad), though as m gets large, the probability that all species converge on the same trait value increases, especially when resource = 2 (see Supplementary Methods available on Dryad). Regardless of whether resource-use traits are simulated under OU or BM, when there is no convergence, nonphylogenetic regressions and PLMMs used for analyses of sister taxa data sets tend to have high Type I error rates, though these analyses return an erroneous inference of divergence, rather than convergence, between sister taxa (Fig. 4Bi ,ii,iv,v, Supplementary Fig. S12Bi ,ii,iv,v available on Dryad, Table  2 , Supplementary Tables  available on Dryad ). Sister-taxa analyses had overall very low power (<0. 6) to detect convergence when it did exist, and nonphylogenetic regressions often detected divergence, rather than convergence (Table  2 , Supplementary Tables  available on Dryad ).
e. , BM was the best-fit model when m = 0, Supplementary Fig. S14A ,C available on Dryad, Table  2 ), but they could not detect an effect of species interactions when convergence was present, at least for the number of sister taxa in this study, as OU was the best-fit model when m =-0. 25 (Supplementary Fig. S14B, C  available on Dryad, Table  2 ).
Predicting Interspecific Interactions Although all three methods used to identify traits that are causally related to interspecific interactions had high power ( 0. 8, Table  1 , Supplementary Tables available on Dryad) to do so in the parameter space explored here (Fig. 5ii ,iv, Supplementary Fig. S16ii ,iv available on Dryad), only the simulation approach had both high power and a low Type I error rate (Table  1 ), whereas nonphylogenetic regressions and PLMMs had fairly high Type I error rates (Table  1 ) when interactions were simulated based on similarity in a trait other than the measured one (Fig. 5i ,iii, Supplementary Fig. S16i ,iii available on Dryad). The power to detect an effect of trait similarity on species interactions was not greatly affected by the presence of an additional, unmeasured trait that also affected the interaction (Supplementary Fig.
DISCUSSION As open-access databases with species range, trait, and phylogenetic data rapidly expand, investigators are able to test hypotheses about the relationships between interspecific interactions and phenotypic evolution at an unprecedented scale. Understanding the relative strengths and weaknesses of phylogenetic comparative methods available for testing such hypotheses is thus paramount. We found that many currently used methods for detecting causal relationships between interspecific interactions and species phenotypes suffer from severe limitations (Tables  1, 2 ). Overall, standard methods are better at detecting divergent character displacement when divergence does not drive unbounded trait evolution (i. e. , when selection acts against extreme phenotypes, as can be modeled by the OU process).
Consistent with previous reports  (Harmon and Glor 2010; Guillot and Rousset 2013) , Mantel tests had high Type I error rates and both standard and pppMantel tests have low power (Table  1 , Fig. 2Ai , Supplementary Fig. S2Ai  available on Dryad). We found that several analytical tools used on sister-taxa data sets have high Type I error rates (   Notes: Values refer to the range of type I error rates and power levels, averaged across biogeographic scenarios, and scenarios where or resource = 0 or 2. Power refers to only those statistically significant tests in the appropriate tail (i. e. , in the upper tail for divergent character displacement and lower tail for convergent character displacement). Since no method has both low Type-I error rates and high power, we caution against using sister-taxa approaches to test for character displacement.
*Type I error rate calculated as the proportion of data sets simulated without divergent character displacement for which a model that includes a linear dependency on the number of sympatric lineages-BM linear or OU linear_beta -was chosen by model selection (i. e. , for which AICc = 0 and AICc for all other models >2). † Power calculated as the proportion of data sets simulated with divergent character displacement for which either BM linear or OU linear_beta was chosen by model selection. Dryad), which would lead investigators to conclude that divergent character displacement had occurred when, in fact, it had not, and no statistical approaches for sistertaxa analyses have a reasonable combination of Type I error and power. Given the lack of a method that has reasonable Type I error rate and power, we discourage empiricists from using sister-taxa approaches to study character displacement.
For data sets simulated with the OU process, the diversity-dependent exponential (DD exp , see Appendix 1) model with biogeography was the best-fit model, and similarly a model with a linear relationship between evolutionary rates and the number of sympatric taxa often fit sister-taxa data sets, though with much lower power overall (Supplementary Fig S4 available on Dryad, Table  2 ). In the DD exp model, rates of trait evolution vary exponentially with the number of sympatric lineages through time, thereby incorporating the effect of interspecific interactions on the rate of trait of evolution but not explicitly modeling the process of character displacement acting on the mean trait values. It may nonetheless provide a useful proxy for detecting patterns that are similar to those left by character displacement, in the absence of a process-based model that incorporates both attraction toward an optimum trait value and divergent character displacement.
2010; Weir and Mursleen 2013) , the maximum likelihood estimates of the rate parameters for this model were positive, rather than negative, when both divergence and the OU process were present (Supplementary Fig. S9  available on Dryad). This is consistent with our finding of increasing evolutionary rates with increasing species richness (Supplementary Figs. S3, S4 and S7 available on Dryad) in this scenario. An increase in the rate of evolutionary changes in trait values toward the present likely results from selection not only restricting species to certain trait space but also partitioning that space. The resulting adaptive landscape is therefore changing rapidly, causing accelerating evolutionary rates as lineages fill this increasingly constrained space.