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    Predicting the potential for zoonotic transmission and host associations for novel viruses

    Data collectionVirus-host data was collated from various sources. Major sources for the association databases included data shared by Olival et al4., Pandit et al.3, and Johnson et al.13. In data provided by Olival et al (assessed September 2019), host-virus associations have been assigned a score, based on detection methods and tests that are specific and more reliable. We used associations that have been identified as the most reliable (stringent data) from Olival et al4. In addition, a query in GenBank was run to parse out hosts reported for each GenBank submission for viruses presented in each of these three databases. Initially, for each virus name, taxonomic ID was identified using entrez.esearch function in biopython package. The taxonomic ID helped linked to the GenBank databases, identify the ICTV lineage and associated data in PubMed20,21. NCBI TaxID closely follows the ICTV database, but some recent changes in ICTV might not always be reflected in NCBI, so we manually checked names to ensure matching. This included virus genus and family information along with a standard virus name. Host data were aggregated based on the taxonomic ID and associated standard name. Finally, for each virus, a search was completed in PubMed to compile the number of hits related to the virus and their vertebrate hosts using the search terms below. The number of PubMed hits (PMH1) were used as a proxy for sampling bias3,13. The virus-host association data source is presented in supplementary code and data files (https://zenodo.org/record/5899054).$$ searchterm= (+virus_name+,[Title/Abstract])\ ANDleft(host,OR,hosts,OR,reservoir,OR,reservoirs,OR right.\ wild,OR,wildlife,OR,domestic,OR,animal,OR,animals,OR\ mammal,OR,bird,OR,birds,OR,aves,OR,avian,OR,avians\ left. OR,vertebrate,OR,vertebrates,OR,surveillance,OR,sylvaticright)$$Along with the PubMed terms we also queried the nucleotide database on PubMed using the taxonomic ID to find the number of GenBank entries for these viruses (PMH2). A correlation analysis between the PMH1 and PMH2 of well-recognized known viruses showed a high correlation with each other for us to safely use GenBank hits for novel viruses during the prediction stage of the model (Fig. S32).Development of ({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}})
    a. Centrality measures of observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}}))To test if centrality measures (degree centrality, betweenness centrality, eigenvector centrality, clustering coefficient) for viral nodes in the observed network (({G}_{c})) vary significantly between viral families, we firstly used the Kolmogorov-Smirnov (KS) test. KS test is routinely used to identify distances between cumulative distribution functions of two probability distributions and is largely used to compare degree distributions of networks22,23. For each viral family, distributions of centrality measures (degree centrality, betweenness centrality, and eigenvector centrality) and clustering coefficient within the observed network (({G}_{c})) were compared with the distribution of all nodes in the network using the two-tailed KS test. Secondly, a linear regression model with virus family as a categorical variable and the number of PubMed hits as a covariate to adjust for sampling bias were fitted to understand associations of viral families with centrality measures.$${centrality},{measure}={beta }_{0}{intercept}+{{beta }_{1}{Viral}{family}}_{{categorical}}+{beta }_{2}{PubMed},{hits}$$After fitting the model, node-level permutations were implemented. For each random permutation, the output variable was randomly assigned to covariate values and the model was re-fitted. Finally, a p-value was calculated by comparing the distribution of coefficients from permutations with the original model coefficient.Network topology feature selectionUsing the observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}})), multiple network topological features for all node (virus) pairs were calculated. The following are topological network features calculated. Features data type, definition and methods to calculate these features are presented in Table S3.1. The Jaccard coefficient: a commonly used similarity metric between nodes in information retrieval, is also called an intersection of over the union for two nodes in the network. In the unipartite network generated here, it represents the proportion of common neighbor viruses from the union of neighbor viruses for two nodes. Neighbor viruses are defined as viruses with which the virus shares at least a single host.2. Adamic/Adar (Frequency-Weighted Common Neighbors): Is the sum of inverse logarithmic degree centrality of the neighbors shared by two nodes in the network24. The concept of Adamic Adar index is a weighted common neighbors for viruses in the network. Within network prediction, the index assumes that viruses with large neighborhoods have a less significant impact while predicting a connection between two viruses compared with smaller neighborhoods.Both Jaccard and Adamic Adar coefficients have been routinely used for generalized network prediction and have shown high accuracy in predicting missing links in networks, specifically bipartite networks25, the information flowing through neighborhoods formed by two nodes might not always be enough to have similar predictive power in an unipartite network. This warrants use of other topology features along with neighborhood-based features.3. Resource allocation: Similarity score of two nodes defined by the weights of common neighbors of two nodes. Resource allocation is another measure to quantify the closeness of two nodes in the network and hence to understand the similarity of hosts they infect.4. Preferential attachment coefficients: The mechanism of preferential attachment can be used to generate evolving scale-free networks, where the probability that a new link is connected to node x is proportional to k26.5. Betweenness centrality: For a node in the network betweenness centrality is the sum of the fraction of all-pairs shortest paths that pass through it. The feature that we used for training the supervised learning model was the absolute difference between of betweenness centralities of two nodes. The difference between the betweenness centrality represents the difference in the sharing observed by two viruses in the pair.6. Degree centrality: The degree centrality for a node v is the fraction of nodes it is connected to. The feature that we used for training the supervised learning model was the absolute difference between degree centralities of two nodes. Unlike the difference in the betweenness centrality, the difference in degree centrality only looks at the difference in the number of observed host sharing.7. Network clustering: All nodes were classified into community clusters using Louvain methods27. A binary feature variable was generated to describe if both the nodes in the pair were part of the same cluster or not. If both viruses are from the same cluster, it represents a similar host predilection than when both viruses are not from the same cluster hence accounting for the evolutionary predilection of viruses (or virus families) to infect a certain type of host.These topological network characteristics come with certain limitations when it comes to the unipartite network of viruses with links formed due to shared hosts and might not truly represent the flow of information between nodes as compared to a bipartite network. Therefore, to account for these limitations, we use multiple network features as weak learners in our model building characteristics summarizing the network through the use of several quantitative metrics. In addition to this, we estimated the feature importance of these metrics in predicting missing links between viruses to quantify the information pasting through these links.Pearson’s correlation coefficients were calculated to identify highly correlated features and for choosing features for model training (Fig. S33). Virological features included in model training were categorical variables describing the virus family of both the nodes in the pair, followed by a binary variable if both the viruses belong to the same virus family. During the model development, PubMed hits generated three predictive features for each pair of viruses on which model training and predictions were conducted. These included two features representing PubMed hits for the two viruses in the pair (PubMedV1, PubMedV2) and the absolute difference between PubMedV1 and PubMedV2 to account for differences in sampling bias between the two viruses.Cross-validation and fitting generalized boosting machine (GBMs) modelsA nested-cross-validation was implemented for the binary model while simple cross-validation was implemented for the multiclass model (multiple output categories). The parameters of the binary model were first hyper-tuned using a cross-validated grid-search method. Values were tested using a grid search to find the best-performing model parameters that showed the highest sensitivity (recall). The parameters tested for hypertuning and their performance are provided in the supplementary material (supplementary results and Table S5). For further cross-validation of the overall binary model, all the viruses were randomly assigned to five groups. For each fold, the viruses assigned to a group were dropped from the data, and a temporary training network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{t}}}}}}}{{{{{boldsymbol{)}}}}}}) was constructed, assuming that this represented the current observed status of the virus-host community. For all possible pairs in ({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{t}}}}}}}) (both that sharing and not sharing any hosts) ten topological and viral characteristics were calculated as training features (Table S4). Categorical features were one-hot-encoded and numeric features were scaled. An XGBClassifier model with binary: logistic family was trained using the feature dataset to predict if virus pairs share hosts (1,0 encoded output). The cross-validation was also used to determine the optimum decision threshold for determining binary classification (Fig. S6) and a precision-recall curve was used to identify positive predictive value and sensitivity at the optimum threshold (Fig. S8).The multiclass model was implemented in the same way, creating an observed network (({G}_{c})) based on species-level sharing of hosts and randomly dropping viruses to generate a training network (({G}_{t})) to train the XGboost model. The output variables were generated based on the taxonomical orders of shared hosts. A pair of viruses can share multiple hosts, hence we trained a multioutput-multiclass model. Humans were considered an independent category of taxonomical order (label) and were given a separate label from primates. For fine-tuning the multiclass model, we started with the best performing parameters of the binary model and manually tested 5 combinations of model parameters by adjusting values of the learning rate, number of estimators, maximum depth, and minimum child weight (Supplementary code and results).We used three methods to estimate the importance of features for our binary model. Specifically, improvement in accuracy brought by branching based on the feature (gain), the percentage of times the feature appears in the XGboost tree model (weight), and the relative number of observations related to the specific feature (cover). Results for feature importance are shown in supplementary results (Fig. S10).Missing links for novel viruses, binary and multiclass predictionThe wildlife surveillance data represented a sampling of 99,379 animals (94,723 wildlife, 4656 domesticated animals) conducted in 34 countries around the world between 2009–2019 (Table S6)1. Specimens were tested using conventional Rt-PCR, Quantitative PCR, Sanger sequencing, and Next Generation Sequencing protocols to detect viruses from 28 virus families or taxonomic groups (Table S7). Testing resulted in 951 novel monophyletic clusters of virus sequences (referred to as novel viruses henceforth). Within 951 novel viruses, 944 novel viruses had vertebrate hosts that were identified with certainty based on barcoding methods and field identification. Host species identification was confirmed by cytochrome b (cytb) DNA barcoding using DNA extracted from the samples28. We predicted the shared host links between novel viruses and known viruses using binary and multiclass models in the following steps. Out of 944 novel viruses discovered in the last ten years, we were able to generate predictions for 531 novel viruses that were detected in species already classified as hosts within the network. The remaining 413 viruses were the first detection of any virus in that species and thus host associations could not be informed by the observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{C}}}}}}})) data.1. A new node representing the novel virus was inserted in the observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}})). Using the list of species in which the novel virus was detected, new edges were created with known viruses that are also known to be found in those hosts. This generated a temporary network for the novel virus (({{{{{{boldsymbol{G}}}}}}}_{{temp}})). If the novel virus was not able to generate any edges with known viruses, meaning the host in which they have been found was never found positive for any known virus, predictions were not performed.2. Using ({{{{{{boldsymbol{G}}}}}}}_{{temp}}) feature values were calculated for the novel virus (betweenness centrality, clustering, and degree). For all possible pairs of the novel virus with known viruses that are not yet connected with each other through an edge in ({{{{{{boldsymbol{G}}}}}}}_{{temp}}) a feature dataset was generated (Jaccard coefficient(novel virus, known virus), the difference in betweenness centrality of the novel virus and known virus, if the novel virus and known virus were in the same cluster, the difference in degree centrality(novel virus, known virus), if the novel virus and known virus were from same virus family, the difference in PubMed hits(novel virus, known virus), PubMed hits for the novel virus, PubMed hits for the known virus). Studies and nucleotide sequences for novel viruses are expected to be published and shared on PubMed’s Nucleotide database and in various peer-reviewed publications. Data associated with GenBank accession numbers and nucleotide sequences for novel viruses are presented in Supplementary Data 3 and Supplementary Data 4 respectively. At the time of development of the model, data for all viruses was not shared in a format that would reflect on PubMed’s database, we decided to use the number of unique species the virus was detected in the last ten years of wildlife surveillance conducted by the USAID PREDICT project. These detections will be reflected in PubMed’s Nucleotide database and search term eventually, hence we considered them as a proxy for search terms conducted for known viruses. Currently, evaluation of the effects of this substitution of PubMed hits with the number of detections for novel viruses is not possible with limited data on novel viruses but needs to be reevaluated as more studies are published on these novel viruses. To further evaluate the association between PubMed hits through search term and Genbank hits, we ran a generalized linear regression model with PubMed hits as dependent variable and Genbank hits as intendent variable, accounting for virus families.$${{PubMed}}_{{Search}}left({log }right)={beta }_{0}{intercept}+{{beta }_{1}{Virus}{family}}_{{categorical}}+{beta }_{2}{Genbank},{hits},({log })$$The results indicated that Genbank hits had statistically significant predictive value in predicting PubMed hits (β = 0.72, p  More

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    Permian hypercarnivore suggests dental complexity among early amniotes

    All vertebrates examined in this study and histologically sampled (Supplementary Table 1) exhibit polyphyodonty and dentine growth lines (Figs. 2–4 and Supplementary Figs. 2–9) that are morphologically consistent with the incremental lines of von Ebner of extant mammalian and crocodilian teeth: alternating opaque zones, line trajectories paralleling the pulp cavity, and widths ranging between 1 and 30 mm18. All functional teeth were continuously replaced through the development of the replacement tooth, lingual to the functional tooth, resulting in resorption of its base and shedding.Fig. 2: Incremental lines of Mesenosaurus efremovi.a ROMVP 85502, lingual view of fragmented dentary with dashed red lines through the plane of the LL section of the functional and replacement teeth. b Whole view of tooth family LL section near crown apex. c Closeup view of functional tooth LL cross-section showing incremental lines, white arrows. d Closeup view of replacement tooth TR cross-section showing incremental lines, white arrows.Full size imageFig. 3: Incremental lines of Dimetrodon cf. D. limbatus.a Lateral view of Dimetrodon. b ROMVP 85510, maxillary tooth family, photographed in lingual view showing the plane of LL section through the functional tooth and replacement tooth. c Whole view of longitudinal LL section near the crown apex of functional and replacement tooth. d Closeup view of functional tooth LL cross-section showing incremental lines, white arrows. e Closeup view of replacement tooth LL cross-section showing incremental lines, white arrows. Skull drawing was modified from Reisz42 and Brink and Reisz43.Full size imageFig. 4: Incremental lines of Edaphosaurus sp.a Lateral view of Edaphosaurus. b USNM PAL 706602, maxillary tooth family, photographed in lingual view showing the plane of LL section through the functional tooth and replacement tooth. c Whole view of longitudinal LL section near crown apex of functional and replacement tooth. d Closeup view of functional tooth LL cross-section showing incremental lines, white arrows. Skull drawing was modified from Romer and Price41 and Modesto44.Full size imageReplacement pattern in Mesenosaurus efremovi
    Replacement in the gracile predator Mesenosaurus efremovi from the Richards Spur locality (Fig. 1) appears to occur as a wave in alternating tooth positions, with every other functional tooth in a sequence undergoing replacement during one event. Gaps in the tooth row represent stages in the replacement cycle when the old tooth has been shed, but the replacement tooth has not yet become functional and is not ankylosed to the jawbone. Frequently, these small replacement teeth are lost during fossilization, but in the case of the Dolese Mesenosaurus, preservation is so exquisite that these unattached replacement teeth are preserved, often in place (Fig. 1e). We found that numerous specimens of M. efremovi have tooth families containing a functional tooth and a single replacement tooth lingual to it, but one maxilla (ROMVP 85456) was observed to have a tooth family containing a functional tooth and two successive replacement teeth (Fig. 1c).The replacement rate found in one tooth family within an M. efremovi dentary was 39 days (ROMVP 85502; Fig. 2), and 34 days for the left maxilla (ROMVP 85443; Supplementary Fig. 2). Replacement rates of three tooth families (mx10, mx12, and mx15) for ROMVP 85457 were estimated to be 46, 36, and 35 days. Thus, the replacement rate for M. efremovi does not appear to vary significantly in one specimen across tooth position, size, or ontogenetic age of tooth.Replacement pattern in other synapsidsIn contrast to the availability of many Mesenosaurus specimens for destructive sampling, other taxa are exceedingly rare, and few specimens were available for destructive analysis. Thus, only a single maxilla of the apex predator Dimetrodon with a replacement tooth in position was available (Fig. 3). The functional tooth had a total of 459 incremental lines, whereas the replacement tooth had a total of 354 lines, resulting in a replacement rate of 105 days. In contrast, the maxillary tooth for the basal sphenacodont Haptodus, was calculated to have functional tooth longevity of approximately 152 days and since neither a replacement tooth nor a resorption pit was present, the minimum replacement rate is 152 days.Similarly, relatively little material was available for the larger varanopid predator Watongia meieri which is only known from the holotype material, with a resorption pit on one of the two teeth (mx19) on a maxillary fragment, but both teeth were missing the crown apex; thus, only a minimum age could be determined using the incremental line counts. The tooth with the resorption pit was determined to be a minimum of 81 days old, while the adjacent tooth not in the process of being replaced was approximately 68 days old. A second maxillary tooth with a resorption pit at mx18 was determined to be 145 days old. Additionally, one complete tooth with no resorption pit was longitudinally LL sectioned and estimated to be 108 days old.One maxilla of the small, very rare herbivorous caseid Oromycter was available for destructive sampling (Supplementary Fig. 3). The tooth with a resorption pit in position mx07 was determined to have a total of 506 incremental lines, whereas the tooth without a resorption pit (mx09) had a total of 426 incremental lines. For the mx09 tooth family, the missing replacement tooth was estimated to have 115 incremental lines, resulting in an approximate replacement rate of 391 days.The left dentary of the large herbivorous caseid Ennatosaurus, known only from five specimens, exhibited two posterior teeth with resorption pits on positions d08 and d07 (Supplementary Fig. 4). Tooth position d08 had a visibly larger and more developed resorption pit, with the functional tooth having a total of 628 incremental lines, whereas d07 had a smaller resorption pit and a total of 567 incremental lines. The missing replacement teeth for both d07 and d08 were estimated to have 136 and 169 incremental lines, resulting in a replacement rate of approximately 431 and 459 days, respectively.One maxilla of the herbivorous edaphosaurid Edaphosaurus had a resorption pit at tooth position mx09 (Fig. 4) and was estimated to have a total of 506 incremental lines. The adjacent tooth at position mx10 had no resorption pit and was determined to have a total of 429 lines. For the mx09 tooth family, the missing replacement tooth was estimated to have 131 incremental lines, resulting in a replacement rate of 381 days.Replacement pattern in early and extant reptilesFor the insectivorous parareptile Delorhynchus the functional tooth had a total of 147 incremental lines, while the replacement tooth had 43 lines (Supplementary Fig. 5), resulting in a replacement rate of 104 days. For the other parareptile Colobomycter the premaxillary functional tooth had a total of 157 incremental lines, whereas the replacement tooth had a total of 59 lines, resulting in a replacement rate of 98 days (Supplementary Fig. 6). For the omnivorous eureptile Captorhinus, the functional tooth was 146 days, and the replacement tooth was 69 days, resulting in a replacement rate of approximately 77 days. For the other eureptile, the highly specialized insectivore Opisthodontosaurus, the maximum tooth age for positions d04 to d07 was 151, 155, 206, and 258, respectively (Supplementary Fig. 7). Although no replacement teeth were present, it was possible to use the resorption pit heights to estimate the replacement rates of 182 and 193 days for d06 and d07, respectively. These rates, although different from Captorhinus are not unexpected since this small, close relative of Captorhinus has a very odd, unusual dentition, specialized for feeding on harder shelled invertebrates.In addition to the above Paleozoic amniotes, two skulls were examined for the extant varanid lizards, Varanus bengalensis and Varanus komodoensis, as well as shed teeth of the latter were also available for study and comparison. The maxillary bone of Varanus bengalensis carried dentition showing six replacement events, but only the mx04 tooth position was sectioned. The functional tooth was determined to have 188 incremental lines, and since a continuous record for the replacement tooth’s incremental lines was not visible, the replacement rate was estimated based on its entire dentine area divided by the functional tooth’s mean line width. The estimated replacement rate for V. bengalensis was approximately 110 days. Unlike M. efremovi, the base of the teeth is characterized by plicidentine, and neither tooth serrations (ziphodonty; Supplementary Fig. 8) nor resorption pits were observed for V. bengalensis.Similar to Mesenosaurus, Varanus komodoensis, a highly endangered varanid lizard, exhibits ziphodonty on both the mesial and distal tooth surfaces and provides a valuable comparison with the fossil taxon. Two isolated teeth of an adult individual that were in the process of attachment, but not yet ankylosed with the jawbone, were sectioned. The age of the first tooth was determined to have 106 lines, and the second tooth had approximately 135 lines. A third isolated shed tooth (due to resorption from replacement tooth or from the processing of food)29 provided by the Toronto Zoo was determined to have approximately 227 incremental lines. Thus, from the age of initial tooth attachment to the age of shedding, a tooth appears to be functional for an average of 107 days. Additionally, as in Mesenosaurus, the adult skull of V. komodoensis (ROM R7565) showed that each tooth position exhibited multiple replacement teeth for both the dentary and the maxilla, also confirmed by the data from Auffenberg30.Replacement pattern in a stem amnioteFor the representative carnivorous stem amniote Seymouria (Supplementary Fig. 9) the functional tooth was determined to have a maximum of 171 incremental lines, while the missing replacement tooth was estimated to have had approximately 36 lines. Thus, the estimated replacement rate for Seymouria was calculated to be 135 days.Replacement rate and body massThere seems to be no significant relationship between replacement rate and body mass (kg) for the taxa examined (Supplementary Fig. 10). Although the largest body sized taxon Ennatosaurus had the longest replacement rate, but the other large species had varying rates, while the smallest taxa (Captorhinus, Delorhynchus, Colobomycter, and Opisthodontosaurus) all have varying replacement rates. Instead, replacement rates appear to be related to feeding behaviour since the herbivorous synapsids all exhibited long replacement rates and great tooth longevities (Fig. 5).Fig. 5: Rates of tooth replacement and age across a range of taxa.a Relationship between the total number of incremental lines of von Ebner (age) for the functional tooth and the tooth families replacement rate or period (days). The symbols indicate the type of feeding behaviour, with circles representing carnivory, triangles representing herbivory, square representing insectivory, and diamond representing omnivory. b Phylogenetic tree of all taxa (n = 11) used in the analyses, displaying the age in millions of years ago (length of bars) and tooth longevity (gradient in branch colours). c Phylogenetic tree of all taxa (n = 9) used in the analyses, displaying the age in millions of years ago (mya) (length of bars) and tooth replacement rate (gradient in branch colours). Reconstructed using the ‘contMap’ function in the ‘phytools’ R package. The tree was modified from Maddin, Evans, and Reisz45 and Reisz and Sues12. Source data are provided as a Source Data file.Full size image More

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    Spatial distribution pattern of dominant tree species in different disturbance plots in the Changbai Mountain

    Wiegand, T., Gunatilleke, S. & Gunatilleke, N. Species Associations in a Heterogeneous Sri Lankan Dipterocarp Forest. Am. Nat. 170, E77–E95. https://doi.org/10.1890/06-1350.1 (2007).Article 
    PubMed 

    Google Scholar 
    Zhang, J. et al. Spatial patterns and associations of six congeneric species in an old-growth temperate forest. Acta Oecol. 11, 29–38. https://doi.org/10.1016/j.actao.2009.09.005 (2010).ADS 
    Article 

    Google Scholar 
    Pretzsch, H. et al. Comparison between the productivity of pure and mixed stands of Norway spruce and European beech along an ecological gradient. Ann. For. Sci. 67, 712–712. https://doi.org/10.1051/forest/2010037 (2010).Article 

    Google Scholar 
    Zhu, J., Kang, H., Tan, H., Xu, M. & Wang, J. Natural regeneration characteristics ofPinus sylvestris var.mongolica forests on sandy land in Honghuaerji, China. J. For. Res. 16, 253–259. https://doi.org/10.1007/BF02858184 (2005).Felton, A., Felton, A. M., Wood, J. & Lindenmayer, D. B. Vegetation structure, phenology, and regeneration in the natural and anthropogenic tree-fall gaps of a reduced-impact logged subtropical Bolivian forest. For. Ecol. Manage. 235, 186–193. https://doi.org/10.1016/j.foreco.2006.08.011 (2006).Article 

    Google Scholar 
    Man, R., Kayahara, G. J., Rice, J. A. & MacDonald, G. B. Eleven-year responses of a boreal mixedwood stand to partial harvesting: Light, vegetation, and regeneration dynamics. For. Ecol. Manage. 255, 697–706. https://doi.org/10.1016/j.foreco.2007.09.043 (2008).Article 

    Google Scholar 
    Xiang, W., Lei, X. & Zhang, X. Modelling tree recruitment in relation to climate and competition in semi-natural Larix-Picea-Abies forests in northeast China. For. Ecol. Manage. 382, 100–109. https://doi.org/10.1016/j.foreco.2016.09.050 (2016).Article 

    Google Scholar 
    Zhang, M., Liu, Y., Guo, W., Kang, X. & Zhao, H. Spatial associations and species collocation of dominant tree spscies in a natural spruce-fir mixed forest of Changbai Mountains in Northeastern China. Appl. Ecol. Env. Res. 17, 6213–6225. https://doi.org/10.15666/aeer/1703_62136225 (2019).Garbarino, M., Weisberg, P. J. & Motta, R. Interacting effects of physical environment and anthropogenic disturbances on the structure of European larch (Larix decidua Mill.) forests. For. Ecol. Manag. 257, 1794–1802. https://doi.org/10.1016/j.foreco.2008.12.031 (2009).Gourlet-Fleury, S. et al. Silvicultural disturbance has little impact on tree species diversity in a Central African moist forest. For. Ecol. Manage. 304, 322–332. https://doi.org/10.1016/j.foreco.2013.05.021 (2013).Article 

    Google Scholar 
    Yu, D. & Han, S. Ecosystem service status and changes of degraded natural reserves—A study from the Changbai Mountain Natural Reserve China. Ecosyst. Serv. 20, 56–65. https://doi.org/10.1016/j.ecoser.2016.06.009 (2016).Article 

    Google Scholar 
    Moreau, G. et al. Long-term tree and stand growth dynamics after thinning of various intensities in a temperate mixed forest. For. Ecol. Manage. 473, 118311. https://doi.org/10.1016/j.foreco.2020.118311 (2020).Article 

    Google Scholar 
    Yan, Y., Zhang, C., Wang, Y., Zhao, X. & Gadow, K. Drivers of seedling survival in a temperate forest and their relative importance at three stages of succession. Ecol. Evol. 5, 4287–4299. https://doi.org/10.1002/ece3.1688 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bai, F. et al. Long-term protection effects of national reserve to forest vegetation in 4 decades: Biodiversity change analysis of major forest types in Changbai Mountain Nature Reserve China. Sci. China Ser. C 51, 948–958. https://doi.org/10.1007/s11427-008-0122-9 (2008).Article 

    Google Scholar 
    Liu, Q., Li, X., Ma, Z. & Takeuchi, N. Monitoring forest dynamics using satellite imagery—a case study in the natural reserve of Changbai Mountain in China. For. Ecol. Manage. 210, 25–37. https://doi.org/10.1016/j.foreco.2005.02.025 (2005).Article 

    Google Scholar 
    Hao, H. et al. Patches structure succession based on spatial point pattern features in semi-arid ecosystems of the water-wind erosion crisscross region. Glob. Ecol. Conserv. 12, 158–165. https://doi.org/10.1016/j.gecco.2017.11.001 (2017).Article 

    Google Scholar 
    Das Gupta, S. & Pinno, B. D. Spatial patterns and competition in trees in early successional reclaimed and natural boreal forests. Acta Oecol. 92, 138–147. https://doi.org/10.1016/j.actao.2018.05.003 (2018).Hao, Z., Zhang, J., Song, B., Ye, J. & Li, B. Vertical structure and spatial associations of dominant tree species in an old-growth temperate forest. For. Ecol. Manage. 252, 1–11. https://doi.org/10.1016/j.foreco.2007.06.026 (2007).Article 

    Google Scholar 
    Zhao, H., Kang, X., Guo, Z., Yang, H. & Xu, M. Species interactions in spruce-fir mixed stands and implications for enrichment planting in the Changbai Mountains China. Mount. Res. Dev. 32, 187–196. https://doi.org/10.1659/MRD-JOURNAL-D-11-00125.1 (2012).Article 

    Google Scholar 
    Li, Y., Hui, G., Wang, H., Zhang, G. & Ye, S. Selection priority for harvested trees according to stand structural indices. iForest 10, 561–566, DOI: https://doi.org/10.3832/ifor2115-010 (2017).Zhang, Y., Drobyshev, I., Gao, L., Zhao, X. & Bergeron, Y. Disturbance and regeneration dynamics of a mixed Korean pine dominated forest on Changbai Mountain North-Eastern China. Dendrochronologia 32, 21–31. https://doi.org/10.1016/j.dendro.2013.06.003 (2014).Article 

    Google Scholar 
    Zhang, M. et al. Community stability for spruce-fir forest at different succession stages in Changbai Mountains, Northeast China. Chin. J. Appl. Ecol. 26, 1609–1616. https://doi.org/10.13287/j.1001-9332.20150331.024 (2015).Gong, Z., Kang, X. & Gu, L. Quantitative division of succession and spatial patterns among different stand developmental stages in Changbai Mountains. J. Mt. Sci. 16, 2063–2078. https://doi.org/10.1007/s11629-018-5142-8 (2019).Article 

    Google Scholar 
    Hu, Y., Min, Z., Gao, Y. & Feng, Q. Effects of selective cutting on stand growth and structure for natural mixed spruce (Picea koraiensis )-Fir (Abies nephrolepis) forests. Scientia Silvae Sinicae 47, 15–24. https://doi.org/10.11707/j.1001-7488.20110203 (2011).Article 

    Google Scholar 
    Hubbell, S. P. Light-gap disturbances, recruitment limitation, and tree diversity in a neotropical forest. Science 283, 554–557. https://doi.org/10.1126/science.283.5401.554 (1999).Seidler, T. G. & Plotkin, J. B. Seed dispersal and spatial pattern in tropical trees. PLoS Biol. 4, e344. https://doi.org/10.1371/journal.pbio.0040344 (2006).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ghalandarayeshi, S., Nord-Larsen, T., Johannsen, V. K. & Larsen, J. B. Spatial patterns of tree species in Suserup Skov—a semi-natural forest in Denmark. For. Ecol. Manage. 406, 391–401. https://doi.org/10.1016/j.foreco.2017.10.020 (2017).Article 

    Google Scholar 
    Harms, K. E., Wright, S. J., Calderón, O., Hernández, A. & Herre, E. A. Pervasive density-dependent recruitment enhances seedling diversity in a tropical forest. Nature 404, 493–495. https://doi.org/10.1038/35006630 (2000).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Wiegand, T., Gunatilleke, C. V. S., Gunatilleke, I. A. U. N. & Huth, A. How individual species structure diversity in tropical forests. Proc. Natl. Acad. Sci. 104, 19029–19033. https://doi.org/10.1073/pnas.0705621104 (2007).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhang, T., Yan, Q., Wang, J. & Zhu, J. Restoring temperate secondary forests by promoting sprout regeneration: Effects of gap size and within-gap position on the photosynthesis and growth of stump sprouts with contrasting shade tolerance. For. Ecol. Manage. 429, 267–277. https://doi.org/10.1016/j.foreco.2018.07.025 (2018).Article 

    Google Scholar 
    Zhang, M., Kang, X., Meng, J. & Zhang, L. Distribution patterns and associations of dominant tree species in a mixed coniferous-broadleaf forest in the Changbai Mountains. J. Mt. Sci. 12, 659–670. https://doi.org/10.1007/s11629-013-2795-1 (2015).Article 

    Google Scholar 
    Navarro-Cerrillo, R. M. et al. Structure and spatio-temporal dynamics of cedar forests along a management gradient in the Middle Atlas Morocco. For. Ecol. Manag. 289, 341–353. https://doi.org/10.1016/j.foreco.2012.10.011 (2013).Article 

    Google Scholar 
    Condit, R. Spatial patterns in the distribution of tropical tree species. Science 288, 1414–1418. https://doi.org/10.1126/science.288.5470.1414 (2000).del Río, M. et al. Characterization of the structure, dynamics, and productivity of mixed-species stands: Review and perspectives. Eur. J. For. Res. 135, 23–49. https://doi.org/10.1007/s10342-015-0927-6 (2016).Article 

    Google Scholar 
    Wiegand, K., Jeltsch, F. & Ward, D. Do spatial effects play a role in the spatial distribution of desert-dwelling Acacia raddiana ?. J. Veg. Sci. 11, 473–484. https://doi.org/10.2307/3246577 (2000).Article 

    Google Scholar 
    Hui, G. & Pommerening, A. Analysing tree species and size diversity patterns in multi-species uneven-aged forests of Northern China. For. Ecol. Manage. 316, 125–138. https://doi.org/10.1016/j.foreco.2013.07.029 (2014).Article 

    Google Scholar 
    Graz, F. P. The behaviour of the species mingling index M sp in relation to species dominance and dispersion. Eur. J. For. Res. 123, 87–92. https://doi.org/10.1007/s10342-004-0016-8 (2004).Article 

    Google Scholar 
    Zhang, M. Spatial association and optimum adjacent distribution of trees in a mixed coniferous-broadleaf forest in northeastern China. Appl. Ecol. Environ. Res. 15, 1551–1564. https://doi.org/10.15666/aeer/1503_15511564 (2017).Hou, J. H., Mi, X. C., Liu, C. R. & Ma, K. P. Spatial patterns and associations in a Quercus-Betula forest in northern China. J. Veg. Sci. 15, 407–414. https://doi.org/10.1111/j.1654-1103.2004.tb02278.x (2004).Article 

    Google Scholar 
    Boyden, S., Binkley, D. & Shepperd, W. Spatial and temporal patterns in structure, regeneration, and mortality of an old-growth ponderosa pine forest in the Colorado Front Range. For. Ecol. Manage. 219, 43–55. https://doi.org/10.1016/j.foreco.2005.08.041 (2005).Article 

    Google Scholar 
    Li, J., Niu, S. & Liu, Y. Forest Ecology. Higher Education Press, (2010).Hui, G. et al. Theory and practice of structure-based forest management. Science Press, (2020).Gong, Z. et al. Interspecific association among arbor species in two succession stages of spruce-fir conifer and broadleaved mixed forest in Changbai Mountains, northeastern China. J. Beijing For. Univ. 33, 28–33 (2011).
    Google Scholar 
    Suzuki, S. N., Kachi, N. & Suzuki, J.-I. Development of a local size hierarchy causes regular spacing of trees in an even-aged Abies Forest: Analyses using spatial autocorrelation and the mark correlation function. Ann. Bot. 102, 435–441. https://doi.org/10.1093/aob/mcn113 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shao, G. et al. Integrating stand and landscape decisions for multi-purposes of forest harvesting. For. Ecol. Manage. 207, 233–243. https://doi.org/10.1016/j.foreco.2004.10.029 (2005).Article 

    Google Scholar 
    Dai, L. et al. Changes in forest structure and composition on Changbai Mountain in Northeast China. Ann. For. Sci. 68, 889–897. https://doi.org/10.1007/s13595-011-0095-x (2011).Article 

    Google Scholar 
    Liu, Y. et al. Determining suitable selection cutting intensities based on long-term observations on aboveground forest carbon, growth, and stand structure in Changbai Mountain, Northeast China. Scand. J. For. Res. 29, 436–454. https://doi.org/10.1080/02827581.2014.919352 (2014).CAS 
    Article 

    Google Scholar 
    K. von Gadow and & Hui, G. Y. Characterizing Forest spatial structure and diversity. Proc. of an international workshop organized at the University of Lund, Sweden, 20–30 (2001).Baddeley, A. & Turner, R. spatstat: An R Package for Analyzing Spatial Point Patterns. J. Stat. Soft. 12, 1–42. https://doi.org/10.18637/jss.v012.i06 (2005).Illian, J., Penttinen, A., Stoyan, H. & Stoyan, D. Statistical Analysis and Modelling of Spatial Point Patterns: Illian/Statistical Analysis and Modelling of Spatial Point Patterns. John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470725160 (2007).Wiegand, T. & Moloney, K. A. Handbook of Spatial Point-Pattern Analysis in Ecology. Chapman and Hall/CRC. https://doi.org/10.1201/b16195 (2013).Martínez, I., Wiegand, T., González-Taboada, F. & Obeso, J. R. Spatial associations among tree species in a temperate forest community in North-western Spain. For. Ecol. Manage. 260, 456–465. https://doi.org/10.1016/j.foreco.2010.04.039 (2010).Article 

    Google Scholar 
    Wang, X. et al. Species associations in an old-growth temperate forest in north-eastern China. J. Ecol. 98, 674–686. https://doi.org/10.1111/j.1365-2745.2010.01644.x (2010).Article 

    Google Scholar 
    Getzin, S., Wiegand, T. & Hubbell, S. P. Stochastically driven adult–recruit associations of tree species on Barro Colorado Island. Proc. R. Soc. B. 281, 20140922. https://doi.org/10.1098/rspb.2014.0922 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nakashizuka, T. Species coexistence in temperate, mixed deciduous forests. Trends Ecol. Evol. 16, 205–210 (2001).CAS 
    Article 

    Google Scholar 
    Mugglestone, M. & Renshaw, E. Spectral tests of randomness for spatial point patterns. Environ. Ecol. Stat. 237–251. https://doi.org/10.1023/A:1011339607376 (2001).Stoyan, D. & Stoyan, H. Fractals, random shapes, and point fields: methods of geometrical statistics. Wiley, (1994).Liu, P. et al. Competition and facilitation co-regulate the spatial patterns of boreal tree species in Kanas of Xinjiang, northwest China. For. Ecol. Manage. 467, 118167. https://doi.org/10.1016/j.foreco.2020.118167 (2020).Article 

    Google Scholar 
    Wiegand, T., Moloney, A. & Rings, K. circles, and null-models for point pattern analysis in ecology. Oikos 104, 209–229. https://doi.org/10.1111/j.0030-1299.2004.12497.x (2004).Article 

    Google Scholar  More

  • in

    Effects of vegetation spatial pattern on erosion and sediment particle sorting in the loess convex hillslope

    Zhao, B. H. et al. Spatial distribution of soil organic carbon and its influencing factors under the condition of ecological construction in a hilly-gully watershed of the Loess Plateau China. Geoderma 296, 10–17 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    Shi, P. et al. Soil respiration and response of carbon source changes to vegetation restoration in the Loess Plateau China. Sci. Total Environ. 707, 135507 (2019).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    Zhang, Y. et al. Effects of farmland conversion on the stoichiometry of carbon, nitrogen, and phosphorus in soil aggregates on the Loess Plateau of China. Geoderma 351, 188–196 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    Chang, E. H. et al. Using water isotopes to analyze water uptake during vegetation succession on abandoned cropland on the Loess Plateau China. CATENA 181, 104095 (2019).Article 

    Google Scholar 
    Chang, E. H. et al. The impact of vegetation successional status on slope runoff erosion in the Loess Plateau of China. Water 11, 2614 (2019).CAS 
    Article 

    Google Scholar 
    Sun, L. Y., Zhou, J. L., Cai, Q. G., Liu, S. X. & Xiao, J. G. Comparing surface erosion processes in four soils from the Loess Plateau under extreme rainfall events. Int. Soil Water Conse. 9, 520–531 (2021).Article 

    Google Scholar 
    Wang, R. et al. Effects of gully head height and soil texture on gully headcut erosion in the Loess Plateau of China. CATENA 207, 105674 (2021).Article 

    Google Scholar 
    Wei, H., Zhao, W. W. & Wang, H. Effects of vegetation restoration on soil erosion on the Loess Plateau: A case study in the Ansai watershed. Int. J. Environ. Res. Pub He. 18, 6266 (2021).Article 

    Google Scholar 
    Zhang, X., Li, P., Li, Z. B., Yu, G. Q. & Li, C. Effects of precipitation and different distributions of grass strips on runoff and sediment in the loess convex hillslope. CATENA 162, 130–140 (2018).Article 

    Google Scholar 
    Foster, G. R., Huggins, L. F. & Meyer, L. D. A laboratory study of rill hydraulics: II Shear Stress Relationships. T Asabe. 27, 797–804 (1984).Article 

    Google Scholar 
    Zhu, B. B., Zhou, Z. C. & Li, Z. B. Soil erosion and controls in the slope-gully system of the Loess Plateau of China: A review. Front. Environ. Sci. 9, 657030 (2021).Article 

    Google Scholar 
    Wang, H., Wang, J. & Zhang, G. H. Impact of landscape positions on soil erodibility indices in typical vegetation-restored slope-gully systems on the Loess Plateau of China. CATENA 201, 105235 (2021).Article 

    Google Scholar 
    Chang, X. G. et al. Determining the contributions of vegetation and climate change to ecosystem WUE variation over the last two decades on the Loess Plateau China. Forests 12, 1442 (2021).Article 

    Google Scholar 
    Li, B. B. et al. Deep soil moisture limits the sustainable vegetation restoration in arid and semi-arid Loess Plateau. Geoderma 399, 115122 (2021).ADS 
    Article 

    Google Scholar 
    Dong, L. B. et al. Effects of vegetation restoration types on soil nutrients and soil erodibility regulated by slope positions on the Loess Plateau. J. Environ. Manage. 302, 113985 (2022).CAS 
    PubMed 
    Article 

    Google Scholar 
    Shi, P. et al. Effects of grass vegetation coverage and position on runoff and sediment yields on the slope of Loess Plateau China. Agric. Water Manage. 259, 107231 (2022).Article 

    Google Scholar 
    Xia, L. et al. Soil moisture response to land use and topography across a semi-arid watershed: Implications for vegetation restoration on the Chinese Loess Plateau. J. Mt Sci. 19, 103–120 (2022).Article 

    Google Scholar 
    Chen, Y. X. et al. Soil enzyme activities of typical plant communities after vegetation restoration on the Loess Plateau China. Appl. Soil Ecol. 170, 104292 (2022).Article 

    Google Scholar 
    Qiu, L. J. et al. Quantifying spatiotemporal variations in soil moisture driven by vegetation restoration on the Loess Plateau of China. J. Hydrol. 600, 126580 (2021).Article 

    Google Scholar 
    Fang, H. Y., Li, Q. Y. & Cai, Q. G. A study on the vegetation recovery and crop pattern adjustment on the Loess Plateau of China. Afr. J. Microbiol. Res. 5, 1414–1419 (2011).Article 

    Google Scholar 
    Hu, C. J., Fu, B. J., Liu, G. H., Jin, T. T. & Guo, L. Vegetation patterns influence on soil microbial biomass and functional diversity in a hilly area of the Loess Plateau China. J. Soil Sedim. 10, 1082–1091 (2010).CAS 
    Article 

    Google Scholar 
    Sun, C. L., Chai, Z. Z., Liu, G. B. & Xue, S. Changes in species diversity patterns and spatial heterogeneity during the secondary succession of grassland vegetation on the Loess Plateau China. Front. Plant Sci. 8, 1465 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Xu, J. X. Threholds in vegetation-precipitation relationship and the implications in restoration of vegetation on the Loesee Plateau China. Acta Ecol. Sin. 25, 1233–1239 (2005).
    Google Scholar 
    Yang, X., Shao, M. A., Li, T. C. G, M. & Chen, M. Y. Community characteristics and distribution patterns of soil fauna after vegetation restoration in the northern Loess Plateau. Ecol. Indic. 122, 107236 (2021).Bullock, M. S., Nelson, S. D. & Kemper, W. D. Soil cohesion as affected by freezing, water content, time and tillage. Soil Sci. Soc. Am. J. 52, 70–776 (1988).Article 

    Google Scholar 
    Wang, T. et al. Effects of freeze-thaw on soil erosion processes and sediment selectivity under simulated rainfall. J. Arid Land. 9, 34–243 (2017).
    Google Scholar 
    Su, Y. Y., Li, P., Ren, Z. P., Xiao, L. & Zhang, H. Freeze–thaw effects on erosion process in loess slope under simulated rainfall. J. Arid Land. 12, 937–949 (2020).Article 

    Google Scholar 
    Slattery, M. C. & Burt, T, P. Particle size characteristics of suspended sediment in hillslope runoff and stream flow. Earth Surf. Proc. Land. 22, 705–719 (1997).Wu, F. Z., Shi, Z. H., Yue, B. J. & Wang, L. Particle characteristics of sediment in erosion on hillslope. Acta Pedol. Sin. 49, 1235–1240 (2012).
    Google Scholar 
    Issa, O. M., Bissonnais, Y. L. & Planchon, O. Soil detachment and transport on field-and laboratory-scale interrill areas: Erosion processes and the size-selectivity of eroded sediment. Earth Surf. Proc. Land. 31, 929–939 (2006).ADS 
    Article 

    Google Scholar 
    Shi, Z. H. et al. Soil erosion processes and sediment sorting associated with transport mechanisms on steep slopes. J. Hydrol. 454–455, 123–130 (2012).Article 

    Google Scholar 
    Koiter, A. J., Owens, P. N. & Petticrew, E. L. The behavioural characteristics of sediment properties and their implications for sediment fingerprinting as an approach for identifying sediment sources in river basins. Earth Sci. Rev. 125, 24–42 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    Pan, C. Z. & Shang, G. Z. P. Runoff hydraulic characteristics and sediment generation in sloped grassplots under simulated rainfall conditions. J. Hydrol. 331, 178–185 (2006).ADS 
    Article 

    Google Scholar 
    Pan, C. Z. & Shang, G. Z. P. The effects of ryegrass roots and shoots on loess erosion under simulated rainfall. CATENA 2007(70), 350–355 (2007).
    Google Scholar 
    Zheng, M. G., Cai, Q. G., Wang, C. F. & Liu, J. G. Effect of vegetation and other measures for soil and water conservation on runoff-sediment relationship in watershed scale. J. Hydraul. Eng. 38, 47–53 (2007).
    Google Scholar 
    Wei, X. et al. Flow characteristics of convex composite slopes of loess under vegetation cover. Trans. Chin. Soc. Agric. Eng. 30, 147–154 (2014).CAS 

    Google Scholar 
    Wang, L. et al. Rainfall kinetic energy controlling erosion processes and sediment sorting on steep hillslopes: A case study of clay loam soil from the Loess Plateau China. J. Hydrol. 512, 168–176 (2014).ADS 
    Article 

    Google Scholar 
    Li, M., Yao, W. Y., Ding, W. F., Yang, J. F. & Chen, J. N. Effect of grass coverage on sediment yield in the hillslope-gully side erosion system. J. Geogr. Sci. 19, 321–330 (2009).Article 

    Google Scholar 
    Benito, E., Santiago, J. L., Blas, E. D. & Varela, M. E. Deforestation of water-repellent soils in Galicia (NW Spain): Effects on surface runoff and erosion under simulated rainfall. Earth Surf. Proc. Land. 28, 145–155 (2003).ADS 
    Article 

    Google Scholar 
    Han, P. & Li, X. X. Study on soil erosion and vegetation effect on soil conservation in the Yellow River Basin. J. Basic Sci. Eng. 16, 181–190 (2008).
    Google Scholar 
    Bissonnais, Y. L. Aggregate stability and assessment of soil crustability and erodibility: I. Theory and methodology. Eur. J. Soil Sci. 47, 425–437 (1996).Zhang, X., Yu, G. Q., Li, Z. B. & Li, P. Experimental study on slope runoff, erosion and sediment under different vegetation types. Water Resour. Manag. 28, 2415–2433 (2014).Article 

    Google Scholar 
    Xu, G. C. et al. Temporal and spatial characteristics of soil water content in diverse soil layers on land terraces of the Loess Plateau China. CATENA 158, 20–29 (2017).Article 

    Google Scholar 
    Yu, Y. et al. Land preparation and vegetation type jointly determine soil conditions after long-term land stabilization measures in a typical hilly catchment, Loess Plateau of China. J. Soil Sedim. 17, 144–156 (2017).CAS 
    Article 

    Google Scholar 
    Dou, Y. X., Yang, Y., An, S. S. & Zhu, Z. L. Effects of different vegetation restoration measures on soil aggregate stability and erodibility on the Loess Plateau China. CATENA 185, 104294 (2020).CAS 
    Article 

    Google Scholar 
    He, J., Shi, X. Y. & Fu, Y. J. Identifying vegetation restoration effectiveness and driving factors on different micro-topographic types of hilly Loess Plateau: From the perspective of ecological resilience. J. Environ. Manage. 289, 112562 (2021).PubMed 
    Article 

    Google Scholar 
    Qiu, D. X., Gao, P., Mu, X. M. & Zhao, B. L. Vertical variations and transport mechanism of soil moisture in response to vegetation restoration on the Loess Plateau of China. Hydrol. Process. 35, e14397 (2021).
    Google Scholar 
    Zhang, G. H., Liu, G. B., Wang, G. L. & Wang, Y. X. Effects of Vegetation cover and rainfall intensity on sediment-bound nutrient loss, size composition and volume fractal dimension of sediment particles. Pedosphere 21, 676–684 (2011).CAS 
    Article 

    Google Scholar 
    Gu, Z. J. et al. Estimating the effect of Pinus massoniana Lamb plots on soil and water conservation during rainfall events using vegetation fractional coverage. CATENA 109, 225–233 (2013).Article 

    Google Scholar 
    Comprehensive analysis of relationship between vegetation attributes and soil erosion on hillslopes in the Loess Plateau of China. Environ Earth Sci. 72, 1721–1731 (2014).Zhao, G. J., Mu, X. M., Wen, Z. M., Wang, F. & Gao, P. Soil erosion, conservation, and eco-environment changes in the loess plateau of China. Land Degrad. Dev. 24, 499–510 (2013).Article 

    Google Scholar 
    Zhang, L., Wang, J. M., Bai, Z. K. & Lv, C. J. Effects of vegetation on runoff and soil erosion on reclaimed land in an opencast coal-mine dump in a loess area. CATENA 128, 44–53 (2015).Article 

    Google Scholar 
    Wei, W., Pan, D. L. & Feng, J. Tradeoffs between soil conservation and soil-water retention: The role of vegetation pattern and density. Land Degrad. Dev. 33, 18–27 (2021).Article 

    Google Scholar 
    Asadi, H., Ghadiri, H., Rose, C. W., Yu, B. & Hussein, J. An investigation of flow-driven soil erosion processes at low streampowers. J. Hydrol. 342, 134–142 (2007).ADS 
    Article 

    Google Scholar 
    Shi, Z. H., Yan, F. L., Li, L., Li, Z. X. & Cai, C. F. Interrill erosion from disturbed and undisturbed samples in relation to topsoil aggregate stability in red soils from subtropical China. CATENA 81, 240–248 (2010).Article 

    Google Scholar 
    Zhou, J. et al. Effects of precipitation and restoration vegetation on soil erosion in a semi-arid environment in the Loess Plateau China. CATENA 137, 1–11 (2016).Article 

    Google Scholar 
    Han, Z. M. et al. Effects of vegetation restoration on groundwater drought in the Loess Plateau China. J. Hydrol. 591, 125566 (2020).Article 

    Google Scholar 
    Liang, Y., Jiao, J. Y., Tang, B. Z., Cao, B. T. & Li, H. Response of runoff and soil erosion to erosive rainstorm events and vegetation restoration on abandoned slope farmland in the Loess Plateau region China. J. Hydrol. 584, 124694 (2020).Article 

    Google Scholar  More

  • in

    Increasing sensitivity of dryland vegetation greenness to precipitation due to rising atmospheric CO2

    Pascolini-Campbell, M., Reager, J. T., Chandanpurkar, H. A. & Rodell, M. A 10 per cent increase in global land evapotranspiration from 2003 to 2019. Nature 593, 543–547 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhang, Y., Parazoo, N. C., Williams, A. P., Zhou, S. & Gentine, P. Large and projected strengthening moisture limitation on end-of-season photosynthesis. Proc. Natl Acad. Sci. 117, 9216–9222 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Berg, A., Sheffield, J. & Milly, P. C. D. Divergent surface and total soil moisture projections under global warming. Geophys. Res. Lett. 44, 2016GL071921 (2017).
    Google Scholar 
    Williams, A. P. et al. Large contribution from anthropogenic warming to an emerging North American megadrought. Science 368, 314–318 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Liu, Y., Kumar, M., Katul, G. G., Feng, X. & Konings, A. G. Plant hydraulics accentuates the effect of atmospheric moisture stress on transpiration. Nat. Clim. Change 10, 691–695 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    Guan, K. et al. Photosynthetic seasonality of global tropical forests constrained by hydroclimate. Nat. Geosci. 8, 284–289 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    Dannenberg, M. P., Wise, E. K. & Smith, W. K. Reduced tree growth in the semiarid United States due to asymmetric responses to intensifying precipitation extremes. Sci. Adv. 5, eaaw0667 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhou, L. et al. Widespread decline of Congo rainforest greenness in the past decade. Nature 509, 86–90 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Humphrey, V. et al. Sensitivity of atmospheric CO 2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Wang, X. et al. A two-fold increase of carbon cycle sensitivity to tropical temperature variations. Nature 506, 212–215 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654 (2004).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Maurer, G. E., Hallmark, A. J., Brown, R. F., Sala, O. E. & Collins, S. L. Sensitivity of primary production to precipitation across the United States. Ecol. Lett. 23, 527–536 (2020).PubMed 
    Article 

    Google Scholar 
    Hsu, J. S., Powell, J. & Adler, P. B. Sensitivity of mean annual primary production to precipitation. Glob. Change Biol. 18, 2246–2255 (2012).ADS 
    Article 

    Google Scholar 
    Zuidema, P. A. et al. Recent CO2 rise has modified the sensitivity of tropical tree growth to rainfall and temperature. Glob. Change Biol. 26, 4028–4041 (2020).ADS 
    Article 

    Google Scholar 
    Bansal, S., James, J. J. & Sheley, R. L. The effects of precipitation and soil type on three invasive annual grasses in the western United States. J. Arid Environ. 104, 38–42 (2014).ADS 
    Article 

    Google Scholar 
    Konings, A. G., Williams, A. P. & Gentine, P. Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation. Nat. Geosci. 10, 284–288 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    O’Connor, J. C. et al. Forests buffer against variations in precipitation. Glob. Change Biol., 27, 4686–4696 (2021).Schuldt, B. et al. Change in hydraulic properties and leaf traits in a tall rainforest tree species subjected to long-term throughfall exclusion in the perhumid tropics. Biogeosciences 8, 2179–2194 (2011).ADS 
    Article 

    Google Scholar 
    Zhang, W. et al. Ecosystem structural changes controlled by altered rainfall climatology in tropical savannas. Nat. Commun. 10, 671 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Adams, M. A., Buckley, T. N., Binkley, D., Neumann, M. & Turnbull, T. L. CO2, nitrogen deposition and a discontinuous climate response drive water use efficiency in global forests. Nat. Commun. 12, 5194 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Abel, C. et al. The human–environment nexus and vegetation–rainfall sensitivity in tropical drylands. Nat. Sustain. 4, 25–32 (2021).Green, J. K. et al. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nat. Geosci. 10, 410–414 (2017).ADS 
    MathSciNet 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Lian, X. et al. Multifaceted characteristics of dryland aridity changes in a warming world. Nat. Rev. Earth Environ. 2, 232–250 (2021).ADS 
    Article 

    Google Scholar 
    Zhang, W., Brandt, M., Guichard, F., Tian, Q. & Fensholt, R. Using long-term daily satellite based rainfall data (1983–2015) to analyze spatio-temporal changes in the sahelian rainfall regime. J. Hydrol. 550, 427–440 (2017).ADS 
    Article 

    Google Scholar 
    Martens, B. et al. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10, 1903–1925 (2017).ADS 
    Article 

    Google Scholar 
    Huntzinger, D. N. et al. The North American carbon program multi-scale synthesis and terrestrial model intercomparison project – part 1: overview and experimental design. Geosci. Model Dev. 6, 2121–2133 (2013).ADS 
    Article 

    Google Scholar 
    Porporato, A., Daly, E. & Rodriguez-Iturbe, I. Soil water balance and ecosystem response to climate change. Am. Naturalist 164, 625–632 (2004).Article 

    Google Scholar 
    Good, S. P., Moore, G. W. & Miralles, D. G. A mesic maximum in biological water use demarcates biome sensitivity to aridity shifts. Nat. Ecol. Evol. 1, 1883 (2017).PubMed 
    Article 

    Google Scholar 
    Donohue, R. J., Roderick, M. L., McVicar, T. R. & Yang, Y. A simple hypothesis of how leaf and canopy-level transpiration and assimilation respond to elevated CO 2 reveals distinct response patterns between disturbed and undisturbed vegetation: vegetation responses to elevated CO2. J. Geophys. Res. Biogeosci. 122, 168–184 (2017).CAS 
    Article 

    Google Scholar 
    Milly, P. C. D. & Dunne, K. A. Potential evapotranspiration and continental drying. Nat. Clim. Change 6, 946 (2016).ADS 
    Article 

    Google Scholar 
    Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R. & Donohue, R. J. Hydrologic implications of vegetation response to elevated CO 2 in climate projections. Nat. Clim. Change 9, 44–48 (2019).ADS 
    Article 
    CAS 

    Google Scholar 
    Wolf, A., Anderegg, W. R. L. & Pacala, S. W. Optimal stomatal behavior with competition for water and risk of hydraulic impairment. Proc. Natl Acad. Sci. 113, E7222–E7230 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Guerrieri, R. et al. Disentangling the role of photosynthesis and stomatal conductance on rising forest water-use efficiency. Proc. Natl Acad. Sci. USA 116, 16909–16914 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Anderegg, W. R. L. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    González de Andrés, E. et al. Tree-to-tree competition in mixed European beech-Scots pine forests has different impacts on growth and water-use efficiency depending on site conditions. J. Ecol. 106, 59–75 (2018).Article 
    CAS 

    Google Scholar 
    Donohue, R. J., Roderick, M. L., McVicar, T. R. & Farquhar, G. D. Impact of CO2 fertilization on maximum foliage cover across the globe’s warm, arid environments. Geophys. Res. Lett. 40, 3031–3035 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    Gonsamo, A. et al. Greening drylands despite warming consistent with carbon dioxide fertilization effect. Glob. Change Biol. 27, 3336–3349 (2021).Article 

    Google Scholar 
    Mankin, J. S., Smerdon, J. E., Cook, B. I., Williams, A. P. & Seager, R. The curious case of projected twenty-first-century drying but greening in the American West. J. Clim. 30, 8689–8710 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Fatichi, S. et al. Partitioning direct and indirect effects reveals the response of water-limited ecosystems to elevated CO2. Proc. Natl Acad. Sci. 113, 12757–12762 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ainsworth, E. A. & Rogers, A. The response of photosynthesis and stomatal conductance to rising [CO2]: mechanisms and environmental interactions: Photosynthesis and stomatal conductance responses to rising [CO2]. Plant, Cell Environ. 30, 258–270 (2007).CAS 
    Article 

    Google Scholar 
    Morgan, J. A. et al. C4 grasses prosper as carbon dioxide eliminates desiccation in warmed semi-arid grassland. Nature 476, 202–205 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Duursma, R. A. et al. On the minimum leaf conductance: its role in models of plant water use, and ecological and environmental controls. N. Phytologist 221, 693–705 (2019).Article 

    Google Scholar 
    Ukkola, A. M. et al. Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation. Nat. Clim. Change 6, 75–78 (2015).ADS 
    Article 

    Google Scholar 
    Thompson, S. E., Harman, C. J., Heine, P. & Katul, G. G. Vegetation-infiltration relationships across climatic and soil type gradients: vegetation-infiltration relationships. J. Geophys. Res. 115, G02023 (2010).ADS 

    Google Scholar 
    Norby, R. J. & Zak, D. R. Ecological lessons from free-air CO2 enrichment (FACE) experiments. Annu. Rev. Ecol. Evol. Syst. 42, 181–203 (2011).Article 

    Google Scholar 
    Fatichi, S., Leuzinger, S. & Körner, C. Moving beyond photosynthesis: from carbon source to sink-driven vegetation modeling. N. Phytologist 201, 1086–1095 (2014).CAS 
    Article 

    Google Scholar 
    Cui, J. et al. Vegetation forcing modulates global land monsoon and water resources in a CO2-enriched climate. Nat. Commun. 11, 5184 (2020).Gedney, N. et al. Detection of a direct carbon dioxide effect in continental river runoff records. Nature 439, 835–838 (2006).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Cui, J. et al. Vegetation response to rising CO2 amplifies contrasts in water resources between global wet and dry land Areas. Geophys. Res. Lett. 48, e2021GL094293 (2021).Yang, Y. et al. Low and contrasting impacts of vegetation CO2 fertilization on global terrestrial runoff over 1982–2010: Accounting for aboveground and belowground vegetation-CO2 effects. Hydrol. Earth Syst. Sci. 25, 3411–3427 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    Keenan, T. F. et al. A constraint on historic growth in global photosynthesis due to increasing CO2. Nature 600, 253–258 (2022).ADS 
    Article 
    CAS 

    Google Scholar 
    Sang, Y. et al. Comment on “Recent global decline of CO 2 fertilization effects on vegetation photosynthesis”. Science 373, eabg4420 (2021).PubMed 
    Article 
    CAS 

    Google Scholar 
    Jump, A. S. et al. Structural overshoot of tree growth with climate variability and the global spectrum of drought‐induced forest dieback. Glob. Change Biol. 23, 3742–3757 (2017).ADS 
    Article 

    Google Scholar 
    Zhang, Y., Keenan, T. F. & Zhou, S. Exacerbated drought impacts on global ecosystems due to structural overshoot. Nat. Ecol. Evol. 5, 1490–1498 (2021).Ahlstrom, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    Pinzon, J. E. & Tucker, C. J. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).ADS 
    Article 

    Google Scholar 
    Tian, F. et al. Evaluating temporal consistency of long-term global NDVI datasets for trend analysis. Remote Sens. Environ. 163, 326–340 (2015).ADS 
    Article 

    Google Scholar 
    Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sens. 5, 927–948 (2013).ADS 
    Article 

    Google Scholar 
    Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).Schneider, U. et al. GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl Climatol. 115, 15–40 (2014).ADS 
    Article 

    Google Scholar 
    Prado, R. & West, M. Time series: modeling, computation, and inference (CRC Press, 2010).West, M. & Harrison, J. Bayesian forecasting and dynamic models (Springer, 1997).Liu, Y., Kumar, M., Katul, G. G. & Porporato, A. Reduced resilience as an early warning signal of forest mortality. Nat. Clim. Chang. 9, 880–885 (2019).ADS 
    Article 

    Google Scholar 
    Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).ADS 
    Article 

    Google Scholar  More

  • in

    Adaptive phenotypic plasticity is under stabilizing selection in Daphnia

    Scheiner, S. M. Genetics and evolution of phenotypic plasticity. Annu. Rev. Ecol. Syst. 24, 35–68 (1993).Article 

    Google Scholar 
    Via, S. et al. Adaptive phenotypic plasticity: consensus and controversy. Trends Ecol. Evol. 10, 212–217 (1995).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ghalambor, C. K. et al. Adaptive versus non‐adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Funct. Ecol. 21, 394–407 (2007).Article 

    Google Scholar 
    King, J. G. & Hadfield, J. D. The evolution of phenotypic plasticity when environments fluctuate in time and space. Evol. Lett. 3, 15–27 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Newman, R. A. Genetic variation for phenotypic plasticity in the larval life history of spadefoot toads (Scaphiopus couchii). Evolution 48, 1773–1785 (1994).PubMed 

    Google Scholar 
    Nussey, D. H. et al. Selection on heritable phenotypic plasticity in a wild bird population. Science 310, 304–306 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    Scheiner, S. Selection experiments and the study of phenotypic plasticity 1. J. Evol. Biol. 15, 889–898 (2002).Article 

    Google Scholar 
    Ghalambor, C. K. et al. Non-adaptive plasticity potentiates rapid adaptive evolution of gene expression in nature. Nature 525, 372–375 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Reger, J. et al. Predation drives local adaptation of phenotypic plasticity. Nat. Ecol. Evol. 2, 100–107 (2018).PubMed 
    Article 

    Google Scholar 
    Sommer, R. J. Phenotypic plasticity: from theory and genetics to current and future challenges. Genetics 215, 1–13 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Brakefield, P. M. & Reitsma, N. Phenotypic plasticity, seasonal climate and the population biology of Bicyclus butterflies (Satyridae) in Malawi. Ecol. Entomol. 16, 291–303 (1991).Article 

    Google Scholar 
    Rountree, D. & Nijhout, H. Hormonal control of a seasonal polyphenism in Precis coenia (Lepidoptera: Nymphalidae). J. Insect Physiol. 41, 987–992 (1995).CAS 
    Article 

    Google Scholar 
    Scheiner, S. M. & Holt, R. D. The genetics of phenotypic plasticity. X. Variation versus uncertainty. Ecol. Evol. 2, 751–767 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bonamour, S. et al. Phenotypic plasticity in response to climate change: the importance of cue variation. Philos. Trans. R. Soc. B 374, 20180178 (2019).Article 

    Google Scholar 
    Fox, R.J., Donelson, J. M., Schunter, C., Ravasi, T. & Gaitán-Espitia, J. D. Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change. Philos. Trans. R. Soc. B https://doi.org/10.1098/rstb.2018.0174 (2019).Auld, J. R., Agrawal, A. A. & Relyea, R. A. Re-evaluating the costs and limits of adaptive phenotypic plasticity. Proc. R. Soc. B 277, 503–511 (2010).PubMed 
    Article 

    Google Scholar 
    Murren, C. J. et al. Constraints on the evolution of phenotypic plasticity: limits and costs of phenotype and plasticity. Heredity 115, 293–301 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yampolsky, L. Y., Schaer, T. M. & Ebert, D. Adaptive phenotypic plasticity and local adaptation for temperature tolerance in freshwater zooplankton. Proc. R. Soc. B 281, 20132744 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Schmid, M. & Guillaume, F. The role of phenotypic plasticity on population differentiation. Heredity 119, 214–225 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Charlesworth, B., Lande, R. & Slatkin, M. A neo-Darwinian commentary on macroevolution. Evolution 36, 474–498 (1982).PubMed 

    Google Scholar 
    Lynch, M. The rate of morphological evolution in mammals from the standpoint of the neutral expectation. Am. Nat. 136, 727–741 (1990).Article 

    Google Scholar 
    Kingsolver, J. G. & Pfennig, D. W. Patterns and power of phenotypic selection in nature. Bioscience 57, 561–572 (2007).Article 

    Google Scholar 
    West-Eberhard, M. J. Developmental plasticity and the origin of species differences. Proc. Natl Acad. Sci. USA 102, 6543–6549 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Turelli, M. & Barton, N. Polygenic variation maintained by balancing selection: pleiotropy, sex-dependent allelic effects and G × E interactions. Genetics 166, 1053–1079 (2004).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Charlesworth, B. Causes of natural variation in fitness: evidence from studies of Drosophila populations. Proc. Natl Acad. Sci. USA 112, 1662–1669 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Noble, D. W., Radersma, R. & Uller, T. Plastic responses to novel environments are biased towards phenotype dimensions with high additive genetic variation. Proc. Natl Acad. Sci. USA 116, 13452–13461 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Draghi, J. A. & Whitlock, M. C. Phenotypic plasticity facilitates mutational variance, genetic variance, and evolvability along the major axis of environmental variation. Evolution 66-9, 2891–2902 (2012).Article 

    Google Scholar 
    Houle, D. How should we explain variation in the genetic variance of traits? Genetica 102, 241–253 (1998).PubMed 
    Article 

    Google Scholar 
    Tollrian, R. Predator‐induced morphological defenses: costs, life history shifts, and maternal effects in Daphnia pulex. Ecology 76, 1691–1705 (1995).Article 

    Google Scholar 
    Agrawal, A. A., Laforsch, C. & Tollrian, R. Transgenerational induction of defences in animals and plants. Nature 401, 60–63 (1999).CAS 
    Article 

    Google Scholar 
    Tollrian, R. Neckteeth formation in Daphnia pulex as an example of continuous phenotypic plasticity: morphological effects of Chaoborus kairomone concentration and their quantification. J. Plankton Res. 15, 1309–1318 (1993).Article 

    Google Scholar 
    Dennis, S. et al. Phenotypic convergence along a gradient of predation risk. Proc. R. Soc. B 278, 1687–1696 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hammill, E. & Beckerman, A. P. Reciprocity in predator–prey interactions: exposure to defended prey and predation risk affects intermediate predator life history and morphology. Oecologia 163, 193–202 (2010).PubMed 
    Article 

    Google Scholar 
    Hammill, E., Rogers, A. & Beckerman, A. P. Costs, benefits and the evolution of inducible defences: a case study with Daphnia pulex. J. Evol. Biol. 21, 705–715 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Barnard-Kubow, K. et al. Polygenic variation in sexual investment across an ephemerality gradient in Daphnia pulex. Mol. Bio. Evol. 39, msac121 (2022).Article 

    Google Scholar 
    Deng, H.-W. & Lynch, M. Inbreeding depression and inferred deleterious-mutation parameters in Daphnia. Genetics 147, 147–155 (1997).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Seyfert, A. L. et al. The rate and spectrum of microsatellite mutation in Caenorhabditis elegans and Daphnia pulex. Genetics 178, 2113–2121 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Xu, S. et al. High mutation rates in the mitochondrial genomes of Daphnia pulex. Mol. Biol. Evol. 29, 763–769 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Collyer, M. L. & Adams, D. C. Phenotypic trajectory analysis: comparison of shape change patterns in evolution and ecology. Hystrix 24, 75 (2013).
    Google Scholar 
    Adams, D.C., Collyer, M., Kaliontzopoulou, A. & Sherratt, E. et al. Geomorph: software for geometric morphometric analyses (University of New England, 2016); https://hdl.handle.net/1959.11/21330Adams, D. C. & Collyer, M. L. Comparing the strength of modular signal, and evaluating alternative modular hypotheses, using covariance ratio effect sizes with morphometric data. Evolution 73, 2352–2367 (2019).PubMed 
    Article 

    Google Scholar 
    Richards, C. L., Bossdorf, O. & Pigliucci, M. What role does heritable epigenetic variation play in phenotypic evolution? BioScience 60, 232–237 (2010).Article 

    Google Scholar 
    Latta, L. C. IV et al. The phenotypic effects of spontaneous mutations in different environments. Am. Nat. 185, 243–252 (2015).PubMed 
    Article 

    Google Scholar 
    Lind, M. I. et al. The alignment between phenotypic plasticity, the major axis of genetic variation and the response to selection. Proc. R. Soc. B 282, 20151651 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Laforsch, C. & Tollrian, R. Inducible defenses in multipredator environments: cyclomorphosis in Daphnia cucullata. Ecology 85, 2302–2311 (2004).Article 

    Google Scholar 
    Weiss, L. C., Leimann, J. & Tollrian, R. Predator-induced defences in Daphnia longicephala: location of kairomone receptors and timeline of sensitive phases to trait formation. J. Exp. Biol. 218, 2918–2926 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tollrian, R. & Harvell, C.D. The Ecology and Evolution of Inducible Defenses (Princeton Univ. Press, 1999).Lande, R. Adaptation to an extraordinary environment by evolution of phenotypic plasticity and genetic assimilation. J. Evol. Biol. 22, 1435–1446 (2009).PubMed 
    Article 
    CAS 

    Google Scholar 
    Via, S. & Lande, R. Genotype–environment interaction and the evolution of phenotypic plasticity. Evolution 39, 505–522 (1985).PubMed 
    Article 

    Google Scholar 
    Kvist, J. et al. Temperature treatments during larval development reveal extensive heritable and plastic variation in gene expression and life history traits. Mol. Ecol. 22, 602–619 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Siepielski, A. M. et al. Differences in the temporal dynamics of phenotypic selection among fitness components in the wild. Proc. R. Soc. B 278, 1572–1580 (2011).PubMed 
    Article 

    Google Scholar 
    Muschick, M. et al. Adaptive phenotypic plasticity in the Midas cichlid fish pharyngeal jaw and its relevance in adaptive radiation. BMC Evol. Biol. 11, 116 (2011).Salzburger, W. Understanding explosive diversification through cichlid fish genomics. Nat. Rev. Genet. 19, 705–717 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Halligan, D. L. & Keightley, P. D. Spontaneous mutation accumulation studies in evolutionary genetics. Annu. Rev. Ecol. Evol. Syst. 40, 151–172 (2009).Article 

    Google Scholar 
    Houle, D., Morikawa, B. & Lynch, M. Comparing mutational variabilities. Genetics 143, 1467–1483 (1996).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Eberle, S. et al. Hierarchical assessment of mutation properties in Daphnia magna. G3 Genes Genomes Genetics 8, 3481–3487 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stenseth, N. C. et al. Ecological effects of climate fluctuations. Science 297, 1292–1296 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    Burgmer, T., Hillebrand, H. & Pfenninger, M. Effects of climate-driven temperature changes on the diversity of freshwater macroinvertebrates. Oecologia 151, 93–103 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Yan, N. D. et al. Long-term trends in zooplankton of Dorset, Ontario, lakes: the probable interactive effects of changes in pH, total phosphorus, dissolved organic carbon, and predators. Can. J. Fish. Aquat. Sci. 65, 862–877 (2008).CAS 
    Article 

    Google Scholar 
    Reed, T. E., Schindler, D. E. & Waples, R. S. Interacting effects of phenotypic plasticity and evolution on population persistence in a changing climate. Conserv. Biol. 25, 56–63 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    ASTM, Standard Guide for Conducting Acute Toxicity Tests with Fishes, Macroinvertebrates, and Amphibians (American Society for Testing and Materials, 1988).Baym, M. et al. Inexpensive multiplexed library preparation for megabase-sized genomes. PLoS ONE 10, e0128036 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhang, J. et al. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).MarkDuplicates v.2.20 (Broad Institute, 2019); http://broadinstitute.github.io/picardMcKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2018).Zheng, X. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–3328 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Beckerman, A. P., Rodgers, G. M. & Dennis, S. R. The reaction norm of size and age at maturity under multiple predator risk. J. Anim. Ecol. 79, 1069–1076 (2010).PubMed 
    Article 

    Google Scholar 
    Naraki, Y., Hiruta, C. & Tochinai, S. Identification of the precise kairomone-sensitive period and histological characterization of necktooth formation in predator-induced polyphenism in Daphnia pulex. Zool. Sci. 30, 619–625 (2013).Article 

    Google Scholar 
    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Scrucca, L. et al. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J. 8, 289 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2018).Ben-Shachar, M. S., Lüdecke, D. & Makowski, D. effectsize: estimation of effect size indices and standardized parameters. J. Open Source Softw. 5, 2815 (2020).Article 

    Google Scholar 
    Collyer, M. L. & Adams, D. C. RRPP: an r package for fitting linear models to high‐dimensional data using residual randomization. Methods Ecol. Evol. 9, 1772–1779 (2018).Article 

    Google Scholar 
    Collyer, M., Adams, D. & and Collyer, M.M. RRPP: linear model evaluation with randomized residuals in a permutation procedure. R package version 1.3 https://CRAN.R-project.org/package=RRPP (2021).Smirnov, P. robcor: Robust correlations. R package version 0.1-6.1 https://CRAN.R-project.org/package=ropcor (2014).Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).Article 

    Google Scholar 
    Yang, J. et al. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).Villanueva, R., Chen, Z. & Wickham, H. ggplot2: Elegant Graphics for Data Analysis Using the Grammar of Graphics (Springer-Verlag, 2016).Wilke, C. cowplot: Streamlined plot theme and plot annotations for ‘ggplot2’. R package version 0.9. 2 https://CRAN.R-project.org/package=cowplot (2020).Dowle, M. et al. data.table: Extension of ‘data.frame‘. R package version 1.14.0 https://CRAN.R-project.org/package=data.table (2021).Daniel, M. foreach: Provides foreach looping construct. R package version 1.5.1 https://CRAN.R-project.org/package=foreach (2020).Weston, S. doMC: Foreach parallel adaptor for ‘parallel’. R package version 1.3.7 https://CRAN.R-project.org/package=doMC (2020).Clarke, E. & Sherrill-Mix, S. Ggbeeswarm: Categorical scatter (violin point) plots. R package version 0.6. 0 https://CRAN.R-project.org (2017).Garnier, S. et al. viridis: Default color maps from ‘matplotlib’. R package version 0.5.1 (2018). More

  • in

    Ecological analysis of Pavlovian fear conditioning in rats

    Watson, J. B. & Morgan, J. J. B. Emotional reactions and psychological experimentation. Am. J. Psychol. 28, 163–174 (1917).Article 

    Google Scholar 
    Watson, J. B. & Rayner, R. Conditioned emotional reactions. J. Exp. Psychol. 3, 1–14 (1920).Article 

    Google Scholar 
    LeDoux, J. Fear and the brain: where have we been, and where are we going. Biol. Psychiatry 44, 1229–1238 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    Fendt, M. & Fanselow, M. S. The neuroanatomical and neurochemical basis of conditioned fear. Neurosci. Biobehav. Rev. 23, 743–760 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    Maren, S. & Quirk, G. J. Neuronal signalling of fear memory. Nat. Rev. Neurosci. 5, 844–852 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bouton, M. E., Mineka, S. & Barlow, D. H. A modern learning theory perspective on the etiology of panic disorder. Psychol. Rev. 108, 4–32 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kim, J. J. & Jung, M. W. Neural circuits and mechanisms involved in Pavlovian fear conditioning: a critical review. Neurosci. Biobehav. Rev. 30, 188–202 (2006).PubMed 
    Article 

    Google Scholar 
    Watson, J. B. Psychology as the behaviorist views it. Psychological Rev. 20, 158–177 (1913).Article 

    Google Scholar 
    Pavlov, I. P. Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex (Oxford University Press, 1927).Guthrie, E. R. Conditioning as a principle of learning. Psychological Rev. 37, 412–428 (1930).Article 

    Google Scholar 
    Kamin, L. J. in Miami Symposium on the Prediction of Behavior (ed. Jones, M. R.) 9–33 (University of Miami Press, 1968).Rescorla, R. A. Probability of shock in the presence and absence of CS in fear conditioning. J. Comp. Physiol. Psychol. 66, 1–5 (1968).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wagner, A. R., Logan, F. A., Haberlandt, K. & Price, T. Stimulus selection in animal discrimination learning. J. Exp. Psychol. 76, 171–180 (1968).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rescorla, R. A. & Wagner, A. R. A Theory of Pavlovian Conditioning: Variations in the Effectiveness of Reinforcement and Nonreinforcement 64–99 (Appleton-Century-Crofts, 1972).Josselyn, S. A. & Tonegawa, S. Memory engrams: recalling the past and imagining the future. Science 367, https://doi.org/10.1126/science.aaw4325 (2020).Tovote, P., Fadok, J. P. & Luthi, A. Neuronal circuits for fear and anxiety. Nat. Rev. Neurosci. 16, 317–331 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Haubensak, W. et al. Genetic dissection of an amygdala microcircuit that gates conditioned fear. Nature 468, 270–276 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Foa, E. B. & Rothbaum, B. O. Treating the Trauma of Rape: Cognitive Behavioral Therapy for PTSD (Guilford Press, 1998).Butler, A. C., Chapman, J. E., Forman, E. M. & Beck, A. T. The empirical status of cognitive-behavioral therapy: a review of meta-analyses. Clin. Psychol. Rev. 26, 17–31 (2006).PubMed 
    Article 

    Google Scholar 
    Delgado, M. R., Olsson, A. & Phelps, E. A. Extending animal models of fear conditioning to humans. Biol. Psychol. 73, 39–48 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mahan, A. L. & Ressler, K. J. Fear conditioning, synaptic plasticity and the amygdala: implications for posttraumatic stress disorder. Trends Neurosci. 35, 24–35 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Craske, M. G. et al. What is an anxiety disorder? Focus 9, 20 (2011).
    Google Scholar 
    LeDoux, J. E. The Emotional Brain: the Mysterious Underpinnings of Emotional Life (Simon & Schuster, 1996).Fanselow, M. S. From contextual fear to a dynamic view of memory systems. Trends Cogn. Sci. 14, 7–15 (2010).PubMed 
    Article 

    Google Scholar 
    Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation—a review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

    Google Scholar 
    Bednekoff, P. A. Foraging in the Face of Danger 305–329 (University of Chicago Press, 2007).Stephens, D. W. Decision ecology: foraging and the ecology of animal decision making. Cogn. Affect Behav. Neurosci. 8, 475–484 (2008).PubMed 
    Article 

    Google Scholar 
    Beckers, T., Krypotos, A. M., Boddez, Y., Effting, M. & Kindt, M. What’s wrong with fear conditioning? Biol. Psychol. 92, 90–96 (2013).PubMed 
    Article 

    Google Scholar 
    Mobbs, D. & Kim, J. J. Neuroethological studies of fear, anxiety, and risky decision-making in rodents and humans. Curr. Opin. Behav. Sci. 5, 8–15 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Pellman, B. A. & Kim, J. J. What can ethobehavioral studies tell us about the Brain’s fear system. Trends Neurosci. 39, 420–431 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Thorndike, E. Biological Lectures from the Marine Laboratory at Woods’ Holl, USA, for 1899. Nature 62, 411 (1900).Bolles, R. C. Species-specific defense reactions and avoidance learning. Psychol. Rev. 77, 32–48 (1970).Choi, J. S. & Kim, J. J. Amygdala regulates risk of predation in rats foraging in a dynamic fear environment. Proc. Natl Acad. Sci. USA 107, 21773–21777 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zambetti, P. R., Schuessler, B. P. & Kim, J. J. Sex differences in foraging rats to naturalistic aerial predator stimuli. iScience 16, 442–452 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yilmaz, M. & Meister, M. Rapid innate defensive responses of mice to looming visual stimuli. Curr. Biol. 23, 2011–2015 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Papes, F., Logan, D. W. & Stowers, L. The vomeronasal organ mediates interspecies defensive behaviors through detection of protein pheromone homologs. Cell 141, 692–703 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tolman, E. C. Cognitive maps in rats and men. Psychol. Rev. 55, 189–208 (1948).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wilensky, A. E., Schafe, G. E. & LeDoux, J. E. The amygdala modulates memory consolidation of fear-motivated inhibitory avoidance learning but not classical fear conditioning. J. Neurosci. 20, 7059–7066 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lee, T. & Kim, J. J. Differential effects of cerebellar, amygdalar, and hippocampal lesions on classical eyeblink conditioning in rats. J. Neurosci. 24, 3242–3250 (2004).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stiedl, O. & Spiess, J. Effect of tone-dependent fear conditioning on heart rate and behavior of C57BL/6N mice. Behav. Neurosci. 111, 703–711 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Guimaraes, F. S., Hellewell, J., Hensman, R., Wang, M. & Deakin, J. F. Characterization of a psychophysiological model of classical fear conditioning in healthy volunteers: influence of gender, instruction, personality and placebo. Psychopharmacology 104, 231–236 (1991).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mackintosh, N. J. The Psychology of Animal Learning (Academic Press, 1974).Bouton, M. E. Learning and Behavior (Sinauer Associates 2007).Sheafor, P. J. “Pseudoconditioned” jaw movements of the rabbit reflect associations conditioned to contextual background cues. J. Exp. Psychol. Anim. Behav. Process 1, 245–260 (1975).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rescorla, R. A. Behavioral studies of Pavlovian conditioning. Annu. Rev. Neurosci. 11, 329–352 (1988).CAS 
    PubMed 
    Article 

    Google Scholar 
    Thompson, R. F. & Krupa, D. J. Organization of memory traces in the mammalian brain. Annu. Rev. Neurosci. 17, 519–549 (1994).CAS 
    PubMed 
    Article 

    Google Scholar 
    Fanselow, M. S. & Wassum, K. M. The origins and organization of vertebrate pavlovian conditioning. Cold Spring Harb. Perspect. Biol. 8, a021717 (2015).PubMed 
    Article 

    Google Scholar 
    Lee, H. J., Berger, S. Y., Stiedl, O., Spiess, J. & Kim, J. J. Post-training injections of catecholaminergic drugs do not modulate fear conditioning in rats and mice. Neurosci. Lett. 303, 123–126 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Palgi, Y., Gelkopf, M. & Berger, R. The inoculating role of previous exposure to potentially traumatic life events on coping with prolonged exposure to rocket attacks: a lifespan perspective. Psychiatry Res. 227, 296–301 (2015).PubMed 
    Article 

    Google Scholar 
    Somer, E. et al. Israeli civilians under heavy bombardment: prediction of the severity of post-traumatic symptoms. Prehosp. Disaster Med. 24, 389–394 (2009).PubMed 
    Article 

    Google Scholar 
    Alexander, B. K., Beyerstein, B. L., Hadaway, P. F. & Coambs, R. B. Effect of early and later colony housing on oral ingestion of morphine in rats. Pharm. Biochem. Behav. 15, 571–576 (1981).CAS 
    Article 

    Google Scholar 
    Gage, S. H. & Sumnall, H. R. Rat Park: how a rat paradise changed the narrative of addiction. Addiction 114, 917–922 (2019).PubMed 
    Article 

    Google Scholar 
    Fanselow, M. S. & Lester, L. S. A Functional Behavioristic Approach to Aversively Motivated Behavior: Predatory Imminence as a Determinant of the Topography of Defensive Behavior 185–212 (Lawrence Erlbaum Associates Inc, 1988).Cain, C. & LeDoux, J. Brain mechanisms of Pavlovian and instrumental aversive conditioning. Handb. Behav. Neurosci. 17, 103–124 (2008).Article 

    Google Scholar 
    Choi, J. S., Cain, C. K. & LeDoux, J. E. The role of amygdala nuclei in the expression of auditory signaled two-way active avoidance in rats. Learn Mem. 17, 139–147 (2014).Article 

    Google Scholar 
    Steimer, T. The biology of fear- and anxiety-related behaviors. Dialogues Clin. Neurosci. 4, 231–249 (2002).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Fanselow, M. S. The role of learning in threat imminence and defensive behaviors. Curr. Opin. Behav. Sci. 24, 44–49 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Fanselow, M. S. Associative vs topographical accounts of the immediate shock freezing deficit in rats—implications for the response selection-rules governing species-specific defensive reactions. Learn. Motiv. 17, 16–39 (1986).Article 

    Google Scholar 
    Landeira-Fernandez, J., DeCola, J. P., Kim, J. J. & Fanselow, M. S. Immediate shock deficit in fear conditioning: effects of shock manipulations. Behav. Neurosci. 120, 873–879 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hull, C. L. A functional interpretation of the conditioned reflex. Psychol. Rev. 36, 498–511 (1929).Article 

    Google Scholar 
    Lazarus, A. A. Behavior Therapy and Beyond (McGraw-Hill Companies, 1971).Öhman, A. & Mineka, S. Fears, phobias, and preparedness: toward an evolved module of fear and fear learning. Psychol. Rev. 108, 483–522 (2001).PubMed 
    Article 

    Google Scholar 
    Lee, H. & Kim, J. J. Amygdalar NMDA receptors are critical for new fear learning in previously fear-conditioned rats. J. Neurosci. 18, 8444–8454 (1998).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Rapid evolution of a novel protective symbiont into keystone taxon in Caenorhabditis elegans microbiota

    Samuel, B. S., Rowedder, H., Braendle, C., Félix, M. A. & Ruvkun, G. Caenorhabditis elegans responses to bacteria from its natural habitats. Proc. Natl. Acad. Sci. USA 113, E3941–E3949 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Oliver, K. M., Smith, A. H. & Russell, J. A. Defensive symbiosis in the real world: Advancing ecological studies of heritable, protective bacteria in aphids and beyond. Funct. Ecol. 28, 341–355 (2014).
    Google Scholar 
    King, K. C. Defensive symbionts. Curr. Biol. 29, R78–R80 (2019).CAS 
    PubMed 

    Google Scholar 
    Foster, K. R., Schluter, J., Coyte, K. Z. & Rakoff-Nahoum, S. The evolution of the host microbiome as an ecosystem on a leash. Nature 548, 43–51 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ford, S. A., Kao, D., Williams, D. & King, K. C. Microbe-mediated host defence drives the evolution of reduced pathogen virulence. Nat. Commun. 7, 13430 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Litvak, Y. et al. Commensal Enterobacteriaceae protect against Salmonella colonization through oxygen competition. Cell Host Microbe 25, 128–139 (2019).CAS 
    PubMed 

    Google Scholar 
    Pimentel, A. C., Cesar, C. S., Martins, M. & Cogni, R. The antiviral effects of the symbiont bacteria Wolbachia in insects. Front. Immunol. 11, 626329 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Becker, M. H., Brucker, R. M., Schwantes, C. R., Harris, R. N. & Minbiole, K. P. C. The bacterially produced metabolite violacein is associated with survival of amphibians infected with a lethal fungus. Appl. Environ. Microbiol. 75, 6635–6638 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bates, K. A., Bolton, J. S. & King, K. C. A globally ubiquitous symbiont can drive experimental host evolution. Mol. Ecol. 30, 3882–3892 (2021).CAS 
    PubMed 

    Google Scholar 
    Dahan, D., Preston, G. M., Sealey, J. & King, K. C. Impacts of a novel defensive symbiosis on the nematode host microbiome. BMC Microbiol. 20, 1–10 (2020).
    Google Scholar 
    Banerjee, S., Schlaeppi, K. & van der Heijden, M. G. A. Keystone taxa as drivers of microbiome structure and functioning. Nat. Rev. Microbiol. 16, 567–576 (2018).CAS 
    PubMed 

    Google Scholar 
    Zheng, Y. et al. Exploring biocontrol agents from microbial keystone taxa associated to suppressive soil: A new attempt for a biocontrol strategy. Front. Plant Sci. 12, 655673 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Tudela, H., Claus, S. P. & Saleh, M. Next generation microbiome research: Identification of keystone species in the metabolic regulation of host-gut microbiota interplay. Front. Cell Dev. Biol. 9, 719072 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Mateos-Hernández, L. et al. Anti-tick microbiota vaccine impacts Ixodes ricinus performance during feeding. Vaccine 8, 1–21 (2020).
    Google Scholar 
    Mateos-Hernández, L. et al. Anti-microbiota vaccines modulate the tick microbiome in a taxon-specific manner. Front. Immunol. 12, 704621 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Dirksen, P. et al. The native microbiome of the nematode Caenorhabditis elegans: Gateway to a new host-microbiome model. BMC Biol. 14, 38 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Berg, M. et al. Assembly of the Caenorhabditis elegans gut microbiota from diverse soil microbial environments. ISME J. 10, 1998–2009 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Zhang, F. et al. Caenorhabditis elegans as a model for microbiome research. Front. Microbiol. 8, 485 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    King, K. C. et al. Rapid evolution of microbe-mediated protection against pathogens in a worm host. ISME J. 10, 1915–1924 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Faust, K. & Raes, J. Microbial interactions: From networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).CAS 
    PubMed 

    Google Scholar 
    Layeghifard, M., Hwang, D. M. & Guttman, D. S. Disentangling interactions in the microbiome: A network perspective. Trends Microbiol. 25, 217–228 (2017).CAS 
    PubMed 

    Google Scholar 
    Röttjers, L. & Faust, K. From hairballs to hypotheses–biological insights from microbial networks. FEMS Microbiol. Rev. 42, 761–780 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, e1002352 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 685–688 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hou, Y. et al. Hierarchical microbial functions prediction by graph aggregated embedding. Front. Genet. 11, 608512 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Montalvo-Katz, S., Huang, H., Appel, M. D., Berg, M. & Shapira, M. Association with soil bacteria enhances p38-dependent infection resistance in Caenorhabditis elegans. Infect. Immun. 81, 514–520 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 7, 852–857 (2019).
    Google Scholar 
    Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 1–17 (2018).
    Google Scholar 
    Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 12, 635–645 (2014).CAS 
    PubMed 

    Google Scholar 
    Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    RStudio Team. RStudio: Integrated Development for R (RStudio, PBC, 2020).
    Google Scholar 
    Bastian, M., Heymann, S. & Jacomy, M. Gephi: An open-source software for exploring and manipulating networks. Third International AAAI Conference on Weblogs and Social Media (2009).Lhomme, S. NetSwan: Network Strengths and Weaknesses Analysis. R Pack Version (2015).Peschel, S., Müller, C. L., von Mutius, E., Boulesteix, A. L. & Depner, M. NetCoMi: Network construction and comparison for microbiome data in R. Brief Bioinform. 22, bbaa290 (2021).PubMed 

    Google Scholar 
    Kanehisa, M. Goto, S, KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tatusov, R. L., Galperin, M. Y., Natale, D. A. & Koonin, E. V. The COG database: A tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28, 33–36 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 46, D633–D639 (2018).CAS 
    PubMed 

    Google Scholar 
    Fernandes, A. D. et al. Unifying the analysis of high-throughput sequencing datasets: Characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2, 15 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Lin, H. & Peddada, S. D. Analysis of microbial compositions: A review of normalization and differential abundance analysis. npj Biofilms Microbiomes 6, 60 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Ploner, A. Heatplus: Heatmaps with Row and/or Column Covariates and Colored Clusters. R package version 3.2. (2021).Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423, 623–656 (1948).Pielou, E. C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144 (1966).ADS 

    Google Scholar 
    Fisher, R. A., Corbet, A. S. & Williams, C. B. The relation between the number of species and the number of individuals in a random sample of an animal population. J. Anim. Ecol. 12, 42 (1943).
    Google Scholar 
    Ford, S. A. & King, K. C. Harnessing the power of defensive microbes: Evolutionary implications in nature and disease control. PLoS Pathog. 12, e1005465 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Gibbons, S. M. Keystone taxa indispensable for microbiome recovery. Nat. Microbiol. 5, 1067–1068 (2020).CAS 
    PubMed 

    Google Scholar 
    Wu-Chuang, A. et al. Thermostable keystone bacteria maintain the functional diversity of the Ixodes scapularis microbiome under heat stress. Microb. Ecol. https://doi.org/10.1007/s00248-021-01929-y (2021).Article 
    PubMed 

    Google Scholar 
    Ford, S. A. & King, K. C. In vivo microbial coevolution favors host protection and plastic downregulation of immunity. Mol. Biol. Evol. 38, 1330–1338 (2021).CAS 
    PubMed 

    Google Scholar 
    Banerjee, S. et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 13, 1722–1736 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Gao, Q. et al. The microbial network property as a bio-indicator of antibiotic transmission in the environment. Sci. Total Environ. 758, 143712 (2021).ADS 
    CAS 
    PubMed 

    Google Scholar 
    de Vries, F. T. et al. Soil bacterial networks are less stable under drought than fungal networks. Nat. Commun. 9, 3033 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    de Morais, U. L. A look at the way we look at complex networks’ robustness and resilience. https://arxiv.org/ftp/arxiv/papers/1909/1909.06448.pdf (2017).Carlson, J. M. & Doyle, J. Complexity and robustness. Proc. Natl. Acad. Sci. USA 99, 2538–2545 (2002).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Estrada-Peña, A., Cabezas-Cruz, A. & Obregón, D. Resistance of tick gut microbiome to anti-tick vaccines, pathogen infection and antimicrobial peptides. Pathogens 9, 309 (2020).PubMed Central 

    Google Scholar 
    Neelakanta, G., Sultana, H., Fish, D., Anderson, J. F. & Fikrig, E. Anaplasma phagocytophilum induces Ixodes scapularis ticks to express an antifreeze glycoprotein gene that enhances their survival in the cold. J. Clin. Investig. 120, 3179–3190 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dey, A. K., Gel, Y. R. & Poor, H. V. What network motifs tell us about resilience and reliability of complex networks. Proc. Natl. Acad. Sci. USA 116, 19368–19373 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nemergut, D. R. et al. Patterns and processes of microbial community assembly. Microbiol. Mol. 77, 342–356 (2013).
    Google Scholar 
    Coyte, K. Z., Rao, C., Rakoff-Nahoum, S. & Foster, K. R. Ecological rules for the assembly of microbiome communities. PLoS Biol. 19, e3001116 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: Networks, competition, and stability. Science 350, 663–666 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    McLoughlin, K., Schluter, J., Rakoff-Nahoum, S., Smith, A. L. & Foster, K. R. Host selection of microbiota via differential adhesion. Cell Host Microbe 19, 550–559 (2016).CAS 
    PubMed 

    Google Scholar 
    Sheridan, K. J. et al. Ergothioneine biosynthesis and functionality in the opportunistic fungal pathogen, Aspergillus fumigatus. Sci. Rep. 6, 1–17 (2016).
    Google Scholar 
    Rothfork, J. M. et al. Inactivation of a bacterial virulence pheromone by phagocyte-derived oxidants: New role for the NADPH oxidase in host defense. Proc. Natl. Acad. Sci. USA 101, 13867–13872 (2004).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gaupp, R., Ledala, N. & Somerville, G. A. Staphylococcal response to oxidative stress. Front. Cell. Infect. Microbiol. Microbiol. 2, 33 (2012).
    Google Scholar 
    Matchado, M. S. et al. Network analysis methods for studying microbial communities: A mini review. Comput. Struct. Biotechnol. J. 19, 2687–2698 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jiang, D. et al. Microbiome multi-omics network analysis: Statistical considerations, limitations, and opportunities. Front. Genet. 10, 995 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gao, C. et al. Co-occurrence networks reveal more complexity than community composition in resistance and resilience of microbial communities. Nat. Commun. 13, 3867 (2022).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mammeri, M. et al. Cryptosporidium parvum infection depletes butyrate producer bacteria in goat kid microbiome. Front. Microbiol. 16, 548737 (2020).
    Google Scholar 
    Foo, J. L., Ling, H., Lee, Y. S. & Chang, M. W. Microbiome engineering: Current applications and its future. Biotechnol. J. 12, 1600099 (2017).Inda, M. E., Broset, E., Lu, T. K. & de la Fuente-Nunez, C. Emerging frontiers in microbiome engineering. Trends Immunol. 40, 952–973 (2019). More