Philippa Kaur

Clapham, P. J. Encyclopedia of Marine Mammals 489–492 (Elsevier, 2018).Book 

Google Scholar 
Calambokidis, J. et al. Movements and population structure of humpback whales in the North Pacific. Mar. Mamm. Sci. 17, 769–794. https://doi.org/10.1111/j.1748-7692.2001.tb01298.x (2001).Article 

Google Scholar 
Rosenbaum, H. C. et al. First circumglobal assessment of Southern Hemisphere humpback whale mitochondrial genetic variation and implications for management. Endangered Species Res. 32, 551–567. https://doi.org/10.3354/esr00822 (2017).Article 

Google Scholar 
Darling, J. D. & Sousa-Lima, R. S. Songs indicate interaction between humpback whale (Megaptera novaeangliae) populations in the western and eastern South Atlantic Ocean. Mar. Mamm. Sci. 21, 557–566 (2005).Article 

Google Scholar 
Marcondes, M. C. C. et al. The Southern Ocean Exchange: Porous boundaries between humpback whale breeding populations in southern polar waters. Sci. Rep. 11, 23618. https://doi.org/10.1038/s41598-021-02612-5 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Witteveen, B. H., Foy, R. J., Wynne, K. M. & Tremblay, Y. Investigation of foraging habits and prey selection by humpback whales (Megaptera novaeangliae) using acoustic tags and concurrent fish surveys. Mar. Mamm. Sci. 24, 516–534. https://doi.org/10.1111/j.1748-7692.2008.00193.x (2008).Article 

Google Scholar 
Barendse, J. et al. Migration redefined? Seasonality, movements and group composition of humpback whales Megaptera novaeangliae off the west coast of South Africa. Afr. J. Mar. Sci. 32, 1–22 (2010).Article 

Google Scholar 
Findlay, K. P. et al. Humpback whale “super-groups” – A novel low-latitude feeding behaviour of Southern Hemisphere humpback whales (Megaptera novaeangliae) in the Benguela Upwelling System. PLoS One 12, e0172002. https://doi.org/10.1371/journal.pone.0172002 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Barendse, J. et al. Transit station or destination? Attendance patterns, movements and abundance estimate of humpback whales off west South Africa from photographic and genotypic matching. Afr. J. Mar. Sci. 33, 353–373 (2011).Article 

Google Scholar 
Schall, E. et al. Multi-year presence of humpback whales in the Atlantic sector of the Southern Ocean but not during El Niño. Commun. Biol. 4, 1–7. https://doi.org/10.1038/s42003-021-02332-6 (2021).Article 

Google Scholar 
Amaral, A. R. et al. Population genetic structure among feeding aggregations of humpback whales in the Southern Ocean. Mar. Biol. 163, 1–13. https://doi.org/10.1007/s00227-016-2904-0 (2016).Article 

Google Scholar 
Schall, E. et al. Humpback whale song recordings suggest common feeding ground occupation by multiple populations. Sci. Rep. 11, 1–13. https://doi.org/10.1038/s41598-021-98295-z (2021).ADS 
CAS 
Article 

Google Scholar 
International Whaling Commission. Annex H: Report of the Sub-Committee on Other Southern Hemisphere Whale Stocks. (2016).Payne, R. & Guinee, L. N. Humpback whale (Megaptera novaeangliae) songs as an indicator of “stocks”. Commun. Behav. Whales 20, 333–358 (1983).
Google Scholar 
Riekkola, L. et al. Application of a multi-disciplinary approach to reveal population structure and Southern Ocean feeding grounds of humpback whales. Ecol. Indic. 89, 455–465. https://doi.org/10.1016/j.ecolind.2018.02.030 (2018).Article 

Google Scholar 
Herman, L. M. The multiple functions of male song within the humpback whale (Megaptera novaeangliae) mating system: Review, evaluation, and synthesis. Biol. Rev. 92, 1795–1818. https://doi.org/10.1111/brv.12309 (2017).Article 
PubMed 

Google Scholar 
Garland, E. C. et al. Humpback Whale song on the Southern Ocean feeding grounds: Implications for cultural transmission. PLoS One 8, e79422. https://doi.org/10.1371/journal.pone.0079422 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McSweeney, D., Chu, K., Dolphin, W. & Guinee, L. North Pacific humpback whale songs: A comparison of southeast Alaskan feeding ground songs with Hawaiian wintering ground songs. Mar. Mamm. Sci. 5, 139–148. https://doi.org/10.1111/j.1748-7692.1989.tb00328.x (1989).Article 

Google Scholar 
Van Opzeeland, I. C. et al. Towards collective circum-antarctic passive acoustic monitoring: The southern ocean hydrophone network (SOHN). Polarforschung 83, 47–61 (2013).
Google Scholar 
Gridley, T., Silva, M., Wilkinson, C., Seakamela, S. & Elwen, S. H. Song recorded near a super-group of humpback whales on a mid-latitude feeding ground off South Africa. J. Acoust. Soc. Am. 143, 298–304 (2018).ADS 
Article 

Google Scholar 
Ross-Marsh, E., Elwen, S. H., Prinsloo, A., James, B. & Gridley, T. Singing in South Africa: Monitoring the occurrence of humpback whale (Megaptera novaeangliae) song near the Western Cape. Bioacoustics 30, 163–179 (2021).Article 

Google Scholar 
Garland, E. C. et al. Population structure of humpback whales in the western and central South Pacific Ocean as determined by vocal exchange among populations. Conserv. Biol. 29, 1198–1207. https://doi.org/10.1111/cobi.12492 (2015).Article 
PubMed 

Google Scholar 
Bombosch, A. et al. Predictive habitat modelling of humpback (Megaptera novaeangliae) and Antarctic minke (Balaenoptera bonaerensis) whales in the Southern Ocean as a planning tool for seismic surveys. Deep Sea Res. Part 1 Oceanogr. Res. Pap. 91, 101–114 (2014).Article 

Google Scholar 
El-Gabbas, A., Van Opzeeland, I., Burkhardt, E. & Boebel, O. Static species distribution models in the marine realm: The case of baleen whales in the Southern Ocean. Divers. Distrib. 27, 1536–1552. https://doi.org/10.1111/ddi.13300 (2021).Article 

Google Scholar 
Schall, E. et al. Large-scale spatial variabilities in the humpback whale acoustic presence in the Atlantic sector of the Southern Ocean. R. Soc. Open Sci. 7, 201347. https://doi.org/10.1098/rsos.201347 (2020).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
International Whaling Commission. Report of the scientific committee. Annex G. Report of the sub-committee on comprehensive assessment of southern hemisphere humpback whales. Appenix4. Initial alternative hypotheses for the distribution of humpack breeding stocks on the feeding grounds. Report of the International Whlaing Commission 48, 181 (1998).International Whaling Commission. Report on the workshop on the comprehensive assessment of Southern Hemisphere humpback whales. J. Cetacean Res. Manage. Spec. Issue 3, 1–50 (2011).
Google Scholar 
Winn, H. E. & Winn, L. K. Song of Humpback Whale Megaptera-Novaeangliae in West-Indies. Mar. Biol. 47, 97–114. https://doi.org/10.1007/Bf00395631 (1978).Article 

Google Scholar 
Payne, K. & Payne, R. Large-scale changes over 19 years in songs of Humpback Whales in Bermuda. Z. Tierpsychol. 68, 89–114 (1985).Article 

Google Scholar 
Thomisch, K. et al. Temporal patterns in the acoustic presence of baleen whale species in a presumed breeding area off Namibia. Mar. Ecol. Prog. Ser. 620, 201–214 (2019).ADS 
Article 

Google Scholar 
Buchan, S. J., Stafford, K. M. & Hucke-Gaete, R. Seasonal occurrence of southeast Pacific blue whale songs in southern Chile and the eastern tropical Pacific. Mar. Mamm. Sci. 31, 440–458. https://doi.org/10.1111/mms.12173 (2015).Article 

Google Scholar 
Ross-Marsh, E., Elwen, S., Prinsloo, A., James, B. & Gridley, T. Singing in South Africa: Monitoring the occurrence of humpback whale (Megaptera novaeangliae) song near the Western Cape. Bioacoustics https://doi.org/10.1080/09524622.2019.1710254 (2020).Article 

Google Scholar 
Cholewiak, D. M., Sousa-Lima, R. S. & Cerchio, S. Humpback whale song hierarchical structure: Historical context and discussion of current classification issues. Mar. Mamm. Sci. 29, E312–E332. https://doi.org/10.1111/mms.12005 (2013).Article 

Google Scholar 
M_Map: A Mapping Package for MATLAB v. 1.4m. (2020).Raven Pro: Interactive sound analysis software. Version 1.6 ([Ithaca (NY)]: The Cornell Lab of Ornithology. Accessed 1 Mar 2018 (2022).Schall, E., Roca, I. & Van Opzeeland, I. Acoustic metrics to assess humpback whale song unit structure from the Atlantic sector of the Southern ocean. J. Acoust. Soc. Am. 149, 4649–4658. https://doi.org/10.1121/10.0005315 (2021).ADS 
Article 
PubMed 

Google Scholar 
Dice, L. R. Measures of the amount of ecologic association between species. Ecology 26, 297–302. https://doi.org/10.2307/1932409 (1945).Article 

Google Scholar 
R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2018).Suzuki, R., Terada, Y. & Shimodaira, H. pvclust: Hierarchical clustering with P-values via multiscale bootstrap resampling. R package version 2.2-0 (2019).Kohonen, T. Median strings. Pattern Recogn. Lett. 3, 309–313. https://doi.org/10.1016/0167-8655(85)90061-3 (1985).ADS 
Article 

Google Scholar 
Garland, E. C. et al. Improved versions of the Levenshtein distance method for comparing sequence information in animals’ vocalisations: Tests using humpback whale song. Behaviour 149, 1413–1441. https://doi.org/10.1163/1568539x-00003032 (2012).Article 

Google Scholar 
Van der Loo, M. P. The stringdist package for approximate string matching. R J. 6, 111–122 (2014).Article 

Google Scholar 
Zerbini, A. et al. Migration and summer destinations of humpback whales (Megaptera novaeangliae) in the western South Atlantic Ocean. J. Cetacean Res. Manage. Spec. Issue 3, 113–118. https://doi.org/10.3354/meps313295 (2011).Article 

Google Scholar 
Rosenbaum, H. C., Maxwell, S. M., Kershaw, F. & Mate, B. Long-range movement of Humpback Whales and their overlap with anthropogenic activity in the South Atlantic Ocean. Conserv. Biol. 28, 604–615. https://doi.org/10.1111/cobi.12225 (2014).Article 
PubMed 

Google Scholar 
Reisinger, R. R. et al. Combining regional habitat selection models for large-scale prediction: Circumpolar habitat selection of Southern Ocean humpback whales. Remote Sens. 13, 2074. https://doi.org/10.3390/rs13112074 (2021).ADS 
Article 

Google Scholar 
Garland, E. C. & McGregor, P. K. Cultural transmission, evolution, and revolution in vocal displays: Insights from bird and whale song. Front. Psychol. 11, 2387. https://doi.org/10.3389/fpsyg.2020.544929 (2020).Article 

Google Scholar 
Findlay, K. P. et al. Humpback whale “super-groups”—a novel low-latitude feeding behaviour of Southern Hemisphere humpback whales (Megaptera novaeangliae) in the Benguela Upwelling System. PLoS One https://doi.org/10.1371/journal.pone.0172002 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Owen, K. et al. Effect of prey type on the fine-scale feeding behaviour of migrating east Australian humpback whales. Mar. Ecol. Prog. Ser. 541, 231–244. https://doi.org/10.3354/meps11551 (2015).ADS 
Article 

Google Scholar 
Riekkola, L., Andrews-Goff, V., Friedlaender, A., Zerbini, A. N. & Constantine, R. Longer migration not necessarily the costliest strategy for migrating humpback whales. Aquat. Conserv. Mar. Freshwat. Ecosyst. 30, 937–948. https://doi.org/10.1002/aqc.3295 (2020).Article 

Google Scholar 
Torres, L. G. A sense of scale: Foraging cetaceans’ use of scale-dependent multimodal sensory systems. Mar. Mamm. Sci. 33, 1170–1193. https://doi.org/10.1111/mms.12426 (2017).Article 

Google Scholar 
Horton, T. W. et al. Straight as an arrow: Humpback whales swim constant course tracks during long-distance migration. Biol. Lett. 7, 674–679. https://doi.org/10.1098/rsbl.2011.0279 (2001).Article 

Google Scholar 
Au, W. W. L. et al. Acoustic properties of humpback whale songs. J. Acoust. Soc. Am. 120, 1103–1110. https://doi.org/10.1121/1.2211547 (2006).ADS 
Article 
PubMed 

Google Scholar 
Dunlop, R. A., Cato, D. H., Noad, M. J. & Stokes, D. M. Source levels of social sounds in migrating humpback whales (Megaptera novaeangliae). J. Acoust. Soc. Am. 134, 706–714. https://doi.org/10.1121/1.4807828 (2013).ADS 
Article 
PubMed 

Google Scholar 
Cheeseman, T. et al. Advanced image recognition: A fully automated, high-accuracy photo-identification matching system for humpback whales. Mamm. Biol. https://doi.org/10.1007/s42991-021-00180-9 (2021).Article 

Google Scholar 
Felix, F. et al. A new case of interoceanic movement of a humpback whale in the Southern Hemisphere: The El Nino Link. Aquat. Mamm. 46, 578–584. https://doi.org/10.1578/AM.46.6.2020.578 (2020).Article 

Google Scholar 
Pomilla, C. & Rosenbaum, H. C. Against the current: An inter-oceanic whale migration event. Biol. Lett. 1, 476–479 (2005).Article 

Google Scholar 
Stevick, P. T. et al. A quarter of a world away: Female humpback whale moves 10,000 km between breeding areas. Biol. Lett. 7, 299–302. https://doi.org/10.1098/rsbl.2010.0717 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Nicol, S. Krill, currents, and sea ice: Euphausia superba and its changing environment. Bioscience 56, 111–120. https://doi.org/10.1641/0006-3568(2006)056[0111:Kcasie]2.0.Co;2 (2006).Article 

Google Scholar 
Atkinson, A., Siegel, V., Pakhomov, E. & Rothery, P. Long-term decline in krill stock and increase in salps within the Southern Ocean. Nature 432, 100–103. https://doi.org/10.1038/nature02996 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142–147. https://doi.org/10.1038/s41558-018-0370-z (2019).ADS 
Article 

Google Scholar 
Loeb, V. J. & Santora, J. A. Climate variability and spatiotemporal dynamics of five Southern Ocean krill species. Prog. Oceanogr. 134, 93–122 (2015).ADS 
Article 

Google Scholar 
Marrari, M., Daly, K. L. & Hu, C. Spatial and temporal variability of SeaWiFS chlorophyll a distributions west of the Antarctic Peninsula: Implications for krill production. Deep Sea Res. Part II 55, 377–392. https://doi.org/10.1016/j.dsr2.2007.11.011 (2008).ADS 
Article 

Google Scholar 
Sremba, A. L., Hancock-Hanser, B., Branch, T. A., LeDuc, R. L. & Baker, C. S. Circumpolar diversity and geographic differentiation of mtDNA in the critically endangered Antarctic Blue Whale (Balaenoptera musculus intermedia). PLoS One 7, e32579. https://doi.org/10.1371/journal.pone.0032579 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bortolotto, G. A., Danilewicz, D., Andriolo, A., Secchi, E. R. & Zerbini, A. N. Whale, whale, everywhere: Increasing abundance of Western South Atlantic Humpback Whales (Megaptera novaeangliae) in their wintering grounds. PLoS One 11, e0164596. https://doi.org/10.1371/journal.pone.0164596 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Félix, F., Castro, C. & Laake, J. L. Abundance and survival estimates of the southeastern Pacific humpback whale stock from 1991–2006 photo-identification surveys in Ecuador. J. Cetacean Res. Manage. https://doi.org/10.47536/jcrm.vi.303 (2020).Article 

Google Scholar 
Ward, E., Zerbini, A. N., Kinas, P. G., Engel, M. H. & Andriolo, A. Estimates of population growth rates of humpback whales (Megaptera novaeangliae) in the wintering grounds off the coast of Brazil (Breeding Stock A). J. Cetacean Res. Manage. https://doi.org/10.47536/jcrm.vi3.323 (2020).Article 

Google Scholar 
Seyboth, E. et al. Influence of krill (Euphausia superba) availability on humpback whale (Megaptera novaeangliae) reproductive rate. Mar. Mammal Sci. https://doi.org/10.1111/mms.12805 (2021).Article 

Google Scholar 
Cai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Clim. Change 4, 111–116 (2014).ADS 
Article 

Google Scholar 
Santora, J. A., Reiss, C. S., Loeb, V. J. & Veit, R. R. Spatial association between hotspots of baleen whales and demographic patterns of Antarctic krill Eupahusia superba suggests size-dependent predation. Mar. Ecol. Prog. Ser. 405, 255–269 (2010).ADS 
Article 

Google Scholar 
Friedlaender, A. S., Lawson, G. L. & Halpin, P. N. Evidence of resource partitioning between humpback and minke whales around the western Antarctic Peninsula. Mar. Mamm. Sci. 25, 402–415 (2009).Article 

Google Scholar 
Reid, K., Brierley, A. S. & Nevitt, G. A. An initial examination of relationships between the distribution of whales and antarctic krill Euphausia superba at South Georgia. J. Cetacean Res. Manage. 2, 143–149 (2000).
Google Scholar 
Nicol, S. et al. Southern Ocean iron fertilization by baleen whales and Antarctic krill. Fish Fish. 11, 203–209. https://doi.org/10.1111/j.1467-2979.2010.00356.x (2010).Article 

Google Scholar 
Teschke, K., Pehlke, H., Deininger, M., Jerosch, K. & Brey, T. Scientific background document in support of the development of a CCAMLR MPA in the Weddell Sea (Antarctica)-Version 2016. (2016).Teschke, K. et al. Planning marine protected areas under the CCAMLR regime—the case of the Weddell Sea (Antarctica). Mar. Policy 124, 104370 (2021).Article 

Google Scholar  More

Tracing the symptomology of predation through macroscopic plaquesA culture bioassay (Expanded Microcoleus Mortality Assay, or EMMA) (Fig. 1 and see Materials and Methods) based on the capacity of a soil to induce complete mortality in the foundational biocrust cyanobacterium Microcoleus vaginatus helped us trace the pathogen detected in biocrust production facilities to the development of cm-sized plaques, or zones of cyanobacterial clearing, in natural biocrusts. These plaques were revealed to the naked eye (Fig. 2) when the soil was wet (i.e., after a rain event), as impacted areas would fail to green up by the migration of cyanobacteria to the surface21, enabling us to detect and quantify them with relative ease. Soil samples obtained from such plaques (n = 30) from different sites (n = 6; Table S1) in the US Southwest were invariably EMMA + , and the pathogens always filterable with pore sizes 0.45–1 µm but not larger, and always insensitive to the eukaryotic inhibitor cycloheximide, indicating the agent’s prokaryotic nature and small size, while paired samples from asymptomatic areas just outside the plaques were always EMMA- (Table S2). These end-point EMMA solutions never gave rise to cyanobacterial re-growth upon further incubation and maintained its infectivity of fresh cyanobacterial cultures for up to 6 months. A one-time, small-scale sampling across a plaque at intervals of 2 mm using microcoring22 showed that the boundary of the visible plaque demarcated exactly the end of infectivity, samples 0–2 mm outside the plaque proving non-infective. Further, inoculation of healthy, natural biocrusts with EMMA + suspensions resulted in the local development of biocrust plaques, and soil from these plaques was itself EMMA + , in partial fulfillment of Koch’s postulates. Yet, standard microbiological plating failed to yield any isolates that were EMMA + (we tested 30 unique isolates), even though standard plating with similar isolation efforts can successfully cultivate a large portion of heterotrophs from biocrusts23.Fig. 1: EMMA bioassay (Expanded Microcoleus Mortality Assay), used to study biocrust pathogens.a Typical visual progression of a positive EMMA inoculated with soil or culture to be tested, as used to test for pathogenicity to Microcoleus vaginatus PPC 9802 in the field and in enrichments. b Typical degradation of cyanobacterial biomass during an EMMA displayed through electron microscopy: healthy Microcoleus vaginatus PPC 9802 filaments (top) display abundant photosynthetic membranes (white arrows), peptidoglycan cross-walls (yellow arrows) and carboxysomes (green arrow). As infection proceeds (downwards), patent degradation of intracellular structures follows, leaving only cellular ghosts in the form of peptidoglycan wall remnants (yellow arrows), including the characteristically enlarged peptidoglycan “bumper” of terminal cells (red arrow). Intracellular bacilloid bacteria can sometimes be observed (blue arrow). Cyanobacterial cultures lose all viability. Scale bars = 1 µm. n = 250 images from 4 independent experiments. c Assay modification used in flow cytometry/cell sorting, showing enrichments positive for predation in the top two rows and those negative for predation below. d Test and controls in EMMA to ensure prokaryotic nature of the disease agent.Full size imageFig. 2: Symptomology in nature: biocrust plaques.Main: Macroscopic view of a soil surface colonized by cyanobacterial biocrusts and impacted by multiple plaques as taken after a rain in a quadrat used for field surveys. Insert: Close-up of a single plaque, showing well-demarcated boundaries and a typical central area of new cyanobacterial colonization.Full size imageCultivation, identification, and salient genomic traits of the cyanobacterial pathogenTo study these organisms, we turned to enrichment of pathogen/prey co-cultures based on repeated passages through EMMA and differential size filtration combined with dilution-to-extinction approaches, followed by purification with flow cytometry/cell sorting. The process was monitored by 16S rRNA gene amplicon sequencing, and eventually yielded a highly enriched co-culture of the cyanobacterium with a genetically homogenous (one single Amplicon Sequence Variant) population that made up more than 80% of reads (Fig. 3 a, b). We name the organism represented by this ASV Candidatus Cyanoraptor togatus. That it corresponds indeed to the predator is supported by the fact that of the 17 ASV’s detected in the final enrichment, only 10 were consistently detected at all infectious stages in the process and, among these, only our candidate ASV steadily increased in relative abundance through the enrichment process (Fig. 3 a, b). This final enrichment of C. togatus, LGM-1, constitutes the basis for downstream biological and molecular analyses. Its ASV was most similar to little-known members of the family Chitinophagaceae in the phylum Bacteroidetes. LGM-1’s genome was sequenced and assembled into a single 3.3 Mb contig with 1,781 putative and 1,328 hypothetical genes (Table S3), though most proteins had low identity (Fig. 4: Compiled paired ratios of functional parameters and compositional (relative) abundance in biocrusts across plaque boundaries (circles), red bars denoting the medians for each group of ratios, and bar background color denoting the p-values that the median is significantly different from unity (Wilcoxon paired ratio two-sided tests), where gray is non-significant (p  >  0.1), light orange is 0.05   > p   p  More

Genetic structure of the parental populationBased on the lnPD and ΔK values obtained using STRUCTURE, we identified two genetic groups within the DHS Acer population (Supplementary Fig. S1). The q value from STRUCTURE analysis represents the proportion of ancestral origin28 (Fig. 2a). Among the 70 individual trees, 72.9% were assigned a q value smaller than 0.1 or larger than 0.9, thereby signifying a typical bimodal distribution (Fig. 2b). Individuals with q value greater than 0.9 and consistent genetic origin from the NEA region were defined as the NEA lineage (hereafter “NEA-DHS”), whereas those with values less than 0.1 and with consistent genetic origin from the SEA region were defined as the SEA lineage (hereafter “SEA-DHS”). Individuals with intermediate q value between 0.1 and 0.9 were defined as hybrid genetic types (hereafter “Hybrid-DHS”). Accordingly, we identified 27 SEA-DHS (38.6%), 24 NEA-DHS (34.3%), and 19 Hybrid-DHS (27.1%) (Fig. 2b).Figure 2Genetic structure of the parental and offspring population. (a) Bar plots illustrating the genetic composition of the adult (leaf) and offspring (fruit) populations in the Daheishan National Nature Reserve (DHS). Each individual is represented by a line partitioned into color segments corresponding to its ancestral proportion. Red color represents the ancestral proportion of Southern East Asia lineage. Green color represents the ancestral proportion of Northern East Asia lineage. Black lines in bar plots of leaf population separate individuals with ancestral proportion (q value) bigger than 0.9 or smaller than 0.1 from hybrids (0.1  0.5) produced by the SEA-DHS were obtained from a single tree, which was identified as SEA-DHS based on the DHS-only dataset, although it was indicated to be Hybrid-DHS based on the whole-range dataset. The Hybrid-DHS maternal trees produced 17.6% pure SEA-DHS seeds, 57.6% pure NEA-DHS seeds, and 24.7% hybrid seeds.Flowering phenologyThe sexual system of Acer has four phenotypes: duodichogamous, protogynous, protandrous, and male31. Hence, there are three functional sex types: (1) “Male I” flowers open earlier than “Female” flowers, with mature stamens, no style, and ovary; (2) “Female” flowers have mature pistils, short filaments, and indehiscence anthers; (3) “Male II” flowers open later than “Female” flowers, with mature stamens, ovaries, and separated stigmas. Duodichogamy is characterized by “Male I,” “Female,” and “Male II” types; protandry by “Male I” and “Female” types; and protogyny by “Female” and “Male II” types31.During the flowering season, we monitored a total of 10,074 flowers produced by 29 trees (Fig. 2d), among which one tree (SEA-DHS) was protandrous, four trees (three Hybrid-DHS and one NEA-DHS) were protogynous, and the remaining 24 trees were duodichogamous. We observed that the blooming phenology of SEA-DHS and NEA-DHS differed significantly to most assessed phenological indices, with a single exception being a marginally significant difference in the peak blooming time of Male I (Table 1). Compared with NEA-DHS, SEA-DHS were characterized by significantly later flowering phenology, with Male I commencement and cessation of blooming being on average two and three days later, respectively. Similarly, the commencement, peak, and cessation of Female occurred later by averages of 4, 4, and 5 days, respectively, whereas those of Male II occurred later by 5, 4, and 5 days, respectively. Furthermore, the duration of blooming was significantly longer in the SEA-DHS group than in the NEA-DHS group by three days. In the case of Hybrid-DHS, the values obtained for all assessed phenological indices were intermediate between those of the two parental types. Among these, the values of the six indices differed significantly from one or the other parental types, with the majority (5/6) differing from those of the SEA-DHS. Thus, phenologically, Hybrid-DHS appeared to be closer to NEA-DHS.Table 1 Flowering phenology of SEA-DHS, Hybrid-DHS, and NEA-DHS.Full size tableHowever, despite the differing phenology of the SEA-DHS and NEA-DHS, we observed instances of overlap in the blooming periods of male or female flowers in one genetic type with those of flowers of the opposite sex in another genetic type. For example, the peak of Female among NEA-DHS (11.67 ± 0.67) was found to coincide with the peak of Male I (11.44 ± 1.06; p = 0.879) in SEA-DHS. Similarly, Female blooming in the SEA-DHS peaked (16.11 ± 1.09) just 1 d after the peak of Male II (15.50 ± 0.43) in the NEA-DHS (p = 0.667), which at this time still retained an abundance of male flowers in bloom. In contrast, we detected no overlapping phenology with respect to the blooming of Male I of NEA-DHS or Male II of SEA-DHS with the Female in another genetic type.Morphological variation of leaves and fruitLeaves Among the eight leaf indices, all except InfectionRatio were significantly different between lineages. Generally, the leaves of NEA-DHS were found to have seven lobes, whereas those of SEA-DHS were typically five lobed (Lobes#), thereby contributing to significantly larger leaves in NEA-DHS than in SEA-DHS (TotalArea). Furthermore, NEA-DHS leaves had shorter and wider central lobes (CentralLength and CentralWidth), as well as an earlier and narrower inflection of the central lobes (InflectionLength and InflectionWidth), compared with those of SEA-DHS (Table 2). Six indices had correlation coefficients of less than 0.7, which were used for principal component analysis (PCA) analysis (Supplementary Table S2). The first two axes of the PCA were found to explain 63.7% of the variation in leaf morphology (Fig. 3a), with InflectionLength, CentralLength, and CentralRatio contributing the most to the first axis (38.2%), whereas TotalArea contributed the most to the second axis (25.5%) (Supplementary Table S3). The leaves of SEA-DHS and NEA-DHS plants were largely clustered in separate groups (Fig. 3a). However, all indices were continuous variables with large overlaps between the lineages (Table 2). For example, NEA-DHS had a significantly larger leaf area (21.06–88.70 cm2) than SEA-DHS (11.34–70.09 cm2). The shape of the central lobe is another major leaf trait that distinguishes between the two species. NEA-DHS had a shorter and wider central lobe (CentralRatio:0.67–2.49), while SEA-DHS had a longer and narrower central lobe (CentralRatio:0.9–3.46).Table 2 Morphological variation in the leaves and fruits of Acer trees in the Daheishan National Nature Reserve.Full size tableFigure 3Morphological variation in the leaves (a) and fruits (b) of southern and northern East Asia lineages of the Acer species complex in the Daheishan National Nature Reserve based on principal component analysis. SEA-DHS: Southern East Asia lineage of the Acer species complex in the DHS; NEA-DHS: Northern East Asia lineage of the Acer species complex in the DHS; Hybrid-DHS: hybrids between SEA-DHS and NEA-DHS lineages.Full size imageWith regard to Hybrid-DHS, the leaves were morphologically intermediate between those of the two parental types (Fig. 3a), as were the values of the assessed morphological trait indices (Table 2).Fruits 11 indices of fruits were significantly different between lineages. NEA-DHS tend to be characterized by smaller fruits (FruitLength and FruitWidth), seeds (SeedLength, SeedWidth and JunctionWidth), and fruit wings (WingLength and WingWidth). Moreover, the seed wings of NEA-DHS fruits are typically oriented at an obtuse angle, whereas those of SEA-DHS fruits tend to be aligned at a right angle (FruitAngle). The length ratio of the wing and seed (Wing:Seed) was larger in NEA-DHS than in SEA-DHS (1.24 vs 1.06, respectively, Table 2). Eight indices had correlation coefficients of less than 0.7, which were retained for PCA analysis (Supplementary Table S4). The first two axes of the PCA explained 58.4% of the variation in fruit morphology (Fig. 3b), with JunctionWidth and SeedLength contributing the most to the first axis (35.1%), whereas SeedRatio and WingRatio contributed the most to the second axis (23.3%) (Supplementary Table S3). The fruits of SEA-DHS and NEA-DHS plants were largely clustered in separate groups, with most fruits of SEA-DHS having negative values in Axis 1, while most fruits of NEA-DHS having positive values (Fig. 3b). Both JunctionWidth and SeedLength in Axis 1 reflect the size of the seed. NEA-DHS had smaller seed (SeedLength: 0.63–1.21 cm, SeedWidth:0.43–0.75 cm), while larger seed in SEA-DHS (SeedLength:0.79–1.49 cm, SeedWidth:0.49–0.93 cm). All indices were continuous variables with large overlaps between the lineages (Table 2).The morphology of Hybrid-DHS fruits was generally intermediate between that of the two parental types (Fig. 3b), as reflected in the values of the different morphological traits. The exceptions in this regard were FruitLength, WingLength, as well as two ratio indices (SeedRatio and WingRatio), with hybrid trees typically producing longer fruit with longer fruit wings (Table 2).Ecological niche divergence between NEA and SEAWe found a positive correlation between q value from Structure analysis and altitude (Pearson’s r = 0.83, p  670 m), whereas SEA-DHS was clustered at the foothill ( More

Darwin, C. On the Origin of Species. Facsimile of the First Edition (Harvard University Press, 1859).
Google Scholar 
Grafen, A. The phylogenetic regression. Philos. Trans. R. Soc. Lond. B Biol. Sci. 326, 119–157 (1989).ADS 
CAS 
PubMed 

Google Scholar 
Sillero, N., Reis, M., Vieira, C. P., Vieira, J. & Morales-Hojas, R. Niche evolution and thermal adaptation in the temperate species Drosophila americana. J. Evol. Biol. 27, 1549–1561 (2014).CAS 
PubMed 

Google Scholar 
Ramos, R. et al. Global spatial ecology of three closely-related gadfly petrels. Sci. Rep. 6, 23447 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kumar, B., Cheng, J., Ge, D., Xia, L. & Yang, Q. Phylogeography and ecological niche modeling unravel the evolutionary history of the Yarkand hare, Lepus yarkandensis (Mammalia: Leporidae), through the Quaternary. BMC Evol. Biol. 19, 113 (2019).PubMed 
PubMed Central 

Google Scholar 
Wiens, J. J. & Graham, C. H. Niche conservatism: Integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. 36, 519–539 (2005).
Google Scholar 
Losos, J. B. Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species. Ecol. Lett. 11, 995–1003 (2008).PubMed 

Google Scholar 
Crisp, M. D. & Cook, L. G. Phylogenetic niche conservatism: What are the underlying evolutionary and ecological causes?. New Phytol. 196, 681–694 (2012).PubMed 

Google Scholar 
Qian, H. & Ricklefs, R. E. Geographical distribution and ecological conservatism of disjunct genera of vascular plants in eastern Asia and eastern North America. J. Ecol. 92, 253–265 (2004).
Google Scholar 
Vitt, L. J., Zani, P. A. & Espósito, M. C. Historical ecology of Amazonian lizards: Implications for community ecology. Oikos 87, 286–294 (1999).
Google Scholar 
Rice, N. H., Martínez-Meyer, E. & Peterson, A. T. Ecological niche differentiation in the Aphelocoma jays: A phylogenetic perspective. Biol. J. Linn. Soc. 80, 369–383 (2003).
Google Scholar 
Jost, L. Explosive local radiation of the genus Teagueia (Orchidaceae) in the Upper Pastaza Watershed of Ecuador. Lyonia 7, 42–47 (2004).
Google Scholar 
Antonelli, A., Verola, C. F., Parisod, C. & Gustafsson, A. L. S. Climate cooling promoted the expansion and radiation of a threatened group of South American orchids (Epidendroideae: Laeliinae). Biol. J. Linn. Soc. 100, 597–607 (2010).
Google Scholar 
Johnson, S. D., Linder, H. P. & Steiner, K. E. Phylogeny and radiation of pollination systems in Disa (Orchidaceae). Am. J. Bot. 85, 402–411 (1998).CAS 
PubMed 

Google Scholar 
Kolanowska, M., Grochocka, E. & Konowalik, K. Phylogenetic climatic niche conservatism and evolution of climatic suitability in Neotropical Angraecinae (Vandeae, Orchidaceae) and their closest African relatives. PeerJ 5, e3328 (2017).PubMed 
PubMed Central 

Google Scholar 
Dressler, R. L., Blanco, M. A., Pupulin, F. & Neubig, K. M. Proposal to conserve the name Sobralia (Orchidaceae) with a conserved type. Taxon 60, 907–908 (2011).
Google Scholar 
Baranow, P., Dudek, M. & Szlachetko, D. L. Brasolia, a new genus highlighted from Sobralia (Orchidaceae). Plant Syst. Evol. 303, 853–871 (2017).CAS 

Google Scholar 
Dressler, R. L. The major sections or groups within Sobralia, with four new species from Panama and Costa Rica, S. crispissima, S. gloriana, S. mariannae and S. nutans. Lankesteriana 5, 9–15 (2002).
Google Scholar 
Pridgeon, A. M., Cribb, P. J., Chase, M. W. & Rasmussen, F. N. Genera Orchidacearum Vol. 4: Epidendroideae Part 1 (Oxford University Press, 2005).
Google Scholar 
Van der Cingel, N. A. An Atlas of Orchid Pollination: America, Africa, Asia and Australia (Balkema, 2001).
Google Scholar 
Dodson, C. H. Why are there so many orchid species. Lankesteriana 7, 99–103 (2003).
Google Scholar 
Van Der Pijl, L. & Dodson, C. H. Orchid Flowers: Their Pollination and Evolution (University of Miami Press, 1966).
Google Scholar 
Neubig, K. M. Systematics of Tribe Sobralieae (Orchidaceae): Phylogenetics, Pollination, Anatomy, and Biogeography of a Group of Neotropical Orchids (University of Florida, 2012).
Google Scholar 
Neubig, K. M. et al. Preliminary molecular phylogenetics of Sobralia and relatives (Orchidaceae; Sobralieae). Lankesteriana 11, 307–317 (2011).
Google Scholar 
Ramírez, S. R., Roubik, D. W., Skov, C. & Pierce, N. E. Phylogeny, diversification patterns and historical biogeography of euglossine orchid bees (Hymenoptera: Apidae). Biol. J. Linn. Soc. 100, 552–572 (2010).
Google Scholar 
Gregory-Wodzicki, K. M. Uplift history of the Central and Northern Andes: A review. Geol. Soc. Am. Bull. 112, 1091–1105 (2000).ADS 

Google Scholar 
Sundell, K. E., Saylor, J. E., Lapen, T. J. & Horton, B. K. Implications of variable late Cenozoic surface uplift across the Peruvian central Andes. Sci. Rep. 9, 4877 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Mescua, J. F. et al. Middle to late miocene contractional deformation in Costa Rica triggered by plate geodynamics. Tectonics 36, 2936–2949 (2017).ADS 

Google Scholar 
Kolanowska, M., Mystkowska, K., Kras, M., Dudek, M. & Konowalik, K. Evolution of the climatic tolerance and postglacial ranges of the most primitive orchids (Apostasioideae) within Sunduland, Wallacea and Sahul. PeerJ 4, e2384 (2016).PubMed 
PubMed Central 

Google Scholar 
Arnal, P. et al. The evolution of climate tolerance in conifer-feeding aphids in relation to their host’s climatic niche. Ecol. Evol. 9, 11657–11671 (2019).PubMed 
PubMed Central 

Google Scholar 
Zangiabadi, S., Zaremaivan, H., Brotons, L., Mostafavi, H. & Ranjbar, H. Using climatic variables alone overestimate climate change impacts on predicting distribution of an endemic species. PLoS ONE 16, e0256918. https://doi.org/10.1371/journal.pone.0256918 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Soberón, J. & Peterson, A. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers. Inform. https://doi.org/10.17161/bi.v2i0.4 (2005).Article 

Google Scholar 
Jiménez-Valverde, A., Lobo, J. & Hortal, J. Not as good as they seem: The importance of concepts in species distribution modelling. Divers. Distrib. 14, 885–890. https://doi.org/10.1111/j.1472-4642.2008.00496.x (2008).Article 

Google Scholar 
Bonetti, M. F. & Wiens, J. J. Evolution of climatic niche specialization: a phylogenetic analysis in amphibians. Proc. Biol. Sci. 281, 20133229. https://doi.org/10.1098/rspb.2013.3229 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
George, P. M., Walter, E. W. & Yeuh-Lih, Y. Realized versus fundamental niche functions in a model of chaparral response to climatic change. Ecol. Modell. 7, 261–277 (1992).
Google Scholar 
Hijmans, R. J., Schreuder, M., Cruz, J. & Guarino, L. Using GIS to check co-ordinates of genebank accessions. Genet. Resour. Crop Evol. 46, 291–296 (1999).
Google Scholar 
Phillips, S. J., Dudík, M. & Schapire, R. E. A maximum entropy approach to species distribution modeling. In ICML ’04. Proceedings of the Twenty-First International Conference on MACHINE LEARNing, 655–662 (ACM, New York, 2004).Phillips, S. J., Anderson, R. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190, 231–259 (2006).
Google Scholar 
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
Google Scholar 
Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Modell. 222, 1810–1819 (2011).
Google Scholar 
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
Brown, J. L. SDMtoolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5, 694–700 (2014).
Google Scholar 
Feng, X., Park, D. S., Liang, Y., Pandey, R. & Papeş, M. Collinearity in ecological niche modeling: Confusions and challenges. Ecol. Evol. https://doi.org/10.1002/ece3.5555 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Hosmer, D. W. & Lemeshow, S. Applied Logistic Regression (Wiley, 2000).MATH 

Google Scholar 
Mason, S. J. & Graham, N. E. Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves statistical significance and interpretation. Q. J. R. Meteorol. Soc. 128, 2145–2166 (2002).ADS 

Google Scholar 
Evangelista, P. H. et al. Modelling invasion for a habitat generalist and a specialist plant species. Divers. Distrib. 14, 808–817 (2008).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2022).Warren, D. L. et al. ENMTools 1.0: An R package for comparative ecological biogeography. Ecography 44, 504–511 (2021).
Google Scholar 
Schoener, T. W. The Anolis lizards of Bimini: Resource partitioning in a complex fauna. Ecology 49, 704–726 (1968).
Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evolution 62, 2868–2883 (2008).PubMed 

Google Scholar 
Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497 (2012).
Google Scholar 
Heibl, C. & Calenge, C. Phyloclim: integrating phylogenetics and climatic niche modeling. R package version 0.9-4. http://CRAN.R-project.org/package=phyloclim (2013).Evans, M. E., Smith, S. A., Flynn, R. S. & Donoghue, M. J. Climate, niche evolution, and diversification of the ‘“bird-cage”’ evening primroses (Oenothera, sections Anogra and Kleinia). Am. Nat. 173, 225–240 (2009).PubMed 

Google Scholar 
Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
PubMed 

Google Scholar 
Galtier, N., Gouy, M. & Gautier, C. SeaView and Phylo_win, two graphic tools for sequence alignment and molecular phylogeny. Comput. Appl. Biosci. 12, 543–548 (1996).CAS 
PubMed 

Google Scholar 
Edgar, R. MUSCLE: Mulitiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nylander, J. A. A. MrModeltest v2 (Uppsala University, 2004).
Google Scholar 
Ronquist, F. & Huelsenbeck, J. P. MRBAYES: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).CAS 
PubMed 

Google Scholar 
Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Givnish, T. et al. Orchid phylogenomics and multiple drivers of their extraordinary diversification. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2015.1553 (2015).Article 
PubMed 
PubMed Central 

Google Scholar  More

Statistical characteristics of rill cross-sectional asymmetry (RCA)The rill cross-sectional asymmetry (RCA) is a key parameter in describing rill morphology and includes the asymmetry ratio of the width (Aw) and the asymmetry ratio of the area (Aa). It reflects the differences in certain aspects of natural conditions resulting in inconsistent development speeds on both sides of a rill cross-section. The cross-section was classified as left-biased if Aw, Aa < 0, quasi-symmetrical if Aw, Aa = 0, and right skewed if Aw, Aa > 0. The left/right deflection reflects that erosion on the right happened faster than on the left, so the slope on the left is not as steep as on the right. The results of this study show that asymmetry is a common phenomenon in the cross-section of a rill. The Aw ranged from − 1.77 to 1.97, with an average value of − 0.034. There were 374 cross-sections whose RCA was less than or equal to 0, meaning that 53% of the cross-sections were right-biased. The Aa ranged from − 1.81 to 1.71, with an average of − 0.046. There were 374 cross-sections with an RCA of less than or equal to 0, meaning that 53% of the cross-sections were right-biased (Fig. 1).Figure 1Statistical characteristics of the rill cross-sectional asymmetry (RCA).Full size imageFigure 2 shows that there are four Aw groups in the interval (− 1.7, − 1.5), 53 groups in the interval (− 1.5, − 1.0), 144 groups in the interval (− 1.0, − 0.5), 173 groups in the interval (− 0.5, 0), 174 groups in the interval (0, 0.5), 120 groups in the interval (0.5, 1.0), 39 groups in the interval (1.0, 1.5), and five groups in the interval (1.5, 2). The Aa has 15 groups in the interval (− 1.8, − 1.5), 63 groups in the interval (− 1.5, − 1.0), 130 groups in the interval (− 1.0, − 0.5), 166 groups in the interval (− 0.5, 0), 161 groups in the interval (0, 0.5), 110 groups in the interval (0.5, 1.0), 53 groups in the interval (1.0, 1.5), and 14 groups in the interval (1.5, 2). The RCA of most cross-sections is concentrated in the interval (− 0.5, 0.5). This interval of Aw contains 491 cross-sections, accounting for 68.96% of the total. There are 470 cross-sections in this interval of Aa, accounting for 66.01% of the total. This indicates that, although the rill cross-section exhibits some asymmetry, the difference between both sides of the section is small (Fig. 2).Figure 2Distribution characteristics of the RCA.Full size imageThe influence of a single topographic factor on the RCACorrelation analyses of the Aw, Aa, and the slope difference on both sides (B), rill length (L), rill slope length (I), rill head catchment area (A), difference between the catchment areas of both sides (R), rill bending coefficient (K), and location of the section angle of turning of the rill (J) were carried out. The results show that the main factors that have a significant linear correlation with the Aw and the Aa are B (p < 0.01), with correlation coefficients of 0.32 and 0.22, respectively (Fig. 3). That is, the greater the difference in slope between the two sides, the more asymmetric the rill cross-section. R also has a significant linear correlation with the Aw (p < 0.05), with a correlation coefficient of 0.07. This means that the greater the difference in the catchments between the left and right sides of the rill, the greater the asymmetry of the rill cross-section. However, other topographic factors have no significant correlation with the RCA.Figure 3Correlation between rill cross-sectional asymmetry (RCA) and topographic factors.Full size imageB is the difference in slope between the left and right sides of the rill cross-section catchment area. The closer B gets to 0, the smaller the difference in slope between the left and right sides of the rill cross-section catchment area. When the catchment area slope on the right side of the cross-section is greater than that on the left side, B < 0; and when the catchment area slope on the left side of the cross-section is greater than that on the right side, B > 0. Grouping B reveals that the average RCA increases as B increases (Fig. 4). When B is (− 30, − 20), Aw is − 0.48 and Aa is − 0.38; when B is (− 20, − 10), Aw is − 0.36 and Aa is − 0.31; when B is (− 10, 0), Aw is − 0.23 and Aa is − 0.22; when B is (0, 10), Aw is 0.21 and Aa is 0.16; when B is (10, 20), Aw is 0.47 and Aa is 0.40; and when B is (20, 40), Aw is 0.31 and Aa is 0.13. These are relatively low values because this group only has two sets of cross-sections which cannot represent the characteristics of interval B. The sign of the RCA is the same as the sign of B. The directionality of the RCA is significantly affected by B. When the slope of the left catchment area is large, RCA > 0, and the rill cross-section appears to be left-biased; when the slope of the right catchment area is large, RCA < 0, and the cross-section appears to the righ-biased.Figure 4The asymmetry of different B values.Full size imageThe influence of multiple topographic factors on the RCAIn order to explore the influence of multiple topographic factors on the RCA, principal component analysis (PCA) was used to extract the main feature components of the topographic data. The PCA results show that the nine topographic factors can be reflected by two principal components at 61.84% (characteristic value: 3.117+1.211=4.328 variables) (Table 1). Therefore, the analysis of the first two principal components could reflect most of the information from all the data.Table 1 Calculation results of topographic factor principal component analysis (PCA).Full size tableThe contribution rate of the first principal component is 44.534%. The characteristic is that the factor variables have high positive loads for the four factors L, I, A, and K. L has the largest contribution rate at 88.5%, followed by A, I, and K, at 87.5%, 81.1%, and 60.2%, respectively. Therefore, the first component represents the rill slope and rill shape.The contribution rate of the second principal component is 17.303%. The characteristic is that the factor variables have high positive loads for the three factors B, J, and R. B has the largest contribution rate at 83.5%, followed by J and R, at 57.4% and 55.7%, respectively. Therefore, the second component represents the effect of the difference between the two sides of the rill.Based on the correlation between the topographic factors and the RCA of a rill cross-section in the Yuanmou dry-hot valley area, the following was observed: asymmetry in rill cross-sections is ubiquitous. The distribution range of Aw is − 1.77 to 1.97, the average value is − 0.034, and the cross-section that is right-biased accounts for 53%. A correlation analysis of the RCA and seven topographic factors shows that B has a significant positive correlation with the Aw and Aa (p < 0.01), the average RCA increases as B increases, and the directionality of the RCA is affected by B. When B > 0, RCA > 0, and the rill cross-section appears to the left; when B < 0, RCA < 0, and the cross-section appears to the right. The difference in catchment area between the sides has a significant linear correlation with the Aw (p < 0.05). Other single topographic factors have no significant correlation with the RCA. Principal component analysis and calculations show that the first principal component represents the influence of the rill slope surface and rill shape on the rill cross-sectional asymmetry. The contribution rate is 44.534%, which is characterized by a high positive load on the L, I, A, and K factors. The second principal component represents the effect of the difference between the two sides of the rill. The contribution rate is 44.534%, which is characterized by a high positive load on the B, J, and R factors. More

DataSpatial and temporal rangeWe focused on a geographic area that is defined by a cropping window with the corner points P1 (Lon = −180, Lat = 25) and P2 (Lon = −52, Lat = 80), covering the majority of the North American continent (e.g., Fig. 3). We focused on the last 30 Myr, a time span encompassing most of our available sites with paleovegetation information (Supplementary Fig. 1). From the following data sources, we only selected those data points that fall within this spatiotemporal range.Our approach described below required discretizing the input data of past vegetation labels and fossil occurrences into time-bins. For this, we chose the age boundaries of geological stages defined in the International Chronostratigraphic Chart, v2020/0345, since these stages are expected to represent meaningful temporal units for analyzing both faunal and floral patterns. A total of 17 geological stages fell within our selected time frame of the last 30 Myr. We discretized the ages of all data points (vegetation data and fossil occurrences) that fell within a given stage by setting them to the midpoint of the respective stage.Paleovegetation dataWe reviewed a large body of peer-reviewed literature containing paleovegetation reconstructions and compiled a database of 331 sites with paleovegetation data for North America (Supplementary Data 1). These sites represent individual vegetation reconstructions based on fossil evidence (phytoliths, pollen, macrofossil assemblages) of distinct locations in time and space. We condensed the vegetation interpretation of the compiled vegetation data, which in many cases described specific vegetation ecosystem components, into the broader labels “open” versus “closed” vegetation. This resulted in 180 sites being labeled as closed and 151 as open, their dating rounded to the midpoint of the nearest geological stage (Supplementary Data 1). For several of these sites we found multiple vegetation reconstructions in the reviewed literature, for example when multiple sediment samples were taken from the same horizon of a given formation, belonging to the same geological stage. We treated these spatiotemporal duplicates as a single data point, excluding sites with mixed vegetation information (i.e., containing both open and closed vegetation reconstructions).Current vegetation dataTo supplement the limited number of paleovegetation sites, we compiled data about the current vegetation within our study area. In order to obtain current vegetation patterns, we downloaded the SYNMAP Global Potential Vegetation data29. As for the paleovegetation data, we collapsed the more detailed biome data into broader categories by coding the SYNMAP biome IDs  More

Reaka-Kudla, M. L. The global biodiversity of coral reefs: a comparison with rain forests. Biodivers. II. Underst. Prot. Our Biol. Resour. 2, 551 (1997).
Google Scholar 
Connell, J. H. Population ecology of reef-building corals. in Biology and Geology of Coral Reefs (eds. Jones, O. A. & Endean, R.) 205–245 (Academic Press, 1973). doi:https://doi.org/10.1016/B978-0-12-395526-5.50015-8.Berumen, M. L. et al. The status of coral reef ecology research in the Red Sea. Coral Reefs 32, 737–748 (2013).ADS 
Article 

Google Scholar 
Hughes, T. P., Graham, N. A., Jackson, J. B., Mumby, P. J. & Steneck, R. S. Rising to the challenge of sustaining coral reef resilience. Trends Ecol. Evol. 25, 633–642 (2010).Article 
PubMed 

Google Scholar 
Edmunds, P. J. & Riegl, B. Urgent need for coral demography in a world where corals are disappearing. Mar. Ecol. Prog. Ser. 635, 233–242 (2020).ADS 
Article 

Google Scholar 
Pisapia, C. et al. Projected shifts in coral size structure in the Anthropocene. Adv Mar Biol 87, 31–60 (2020).Article 
PubMed 

Google Scholar 
Meesters, E. et al. Colony size-frequency distributions of scleractinian coral populations: spatial and interspecific variation. Mar. Ecol. Prog. Ser. 209, 43–54 (2001).ADS 
Article 

Google Scholar 
Riegl, B. et al. Demographic mechanisms of reef coral species winnowing from communities under increased environmental stress. Front. Mar. Sci. 4, 344 (2017).Article 

Google Scholar 
Pisapia, C., Burn, D. & Pratchett, M. Changes in the population and community structure of corals during recent disturbances (February 2016-October 2017) on Maldivian coral reefs. Sci. Rep. 9, 1–12 (2019).CAS 
Article 

Google Scholar 
Dietzel, A., Bode, M., Connolly, S. R. & Hughes, T. P. Long-term shifts in the colony size structure of coral populations along the Great Barrier Reef. Proc. R. Soc. B 287, 20201432 (2020).PubMed Central 
Article 
PubMed 

Google Scholar 
Lachs, L. et al. Linking population size structure, heat stress and bleaching responses in a subtropical endemic coral. Coral Reefs 40, 777–790 (2021).Article 

Google Scholar 
McClanahan, T., Ateweberhan, M. & Omukoto, J. Long-term changes in coral colony size distributions on Kenyan reefs under different management regimes and across the 1998 bleaching event. Mar. Biol. 153, 755–768 (2008).Article 

Google Scholar 
Grimsditch, G. et al. Variation in size frequency distribution of coral populations under different fishing pressures in two contrasting locations in the Indian Ocean. Mar. Environ. Res. 131, 146–155 (2017).CAS 
Article 
PubMed 

Google Scholar 
Bak, R. P. & Meesters, E. H. Coral population structure: the hidden information of colony size-frequency distributions. Mar. Ecol. Prog. Ser. 162, 301–306 (1998).ADS 
Article 

Google Scholar 
Hughes, T. & Jackson, J. Do corals lie about their age? Some demographic consequences of partial mortality, fission, and fusion. Science 209, 713–715 (1980).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Hughes, T. P. & Jackson, J. Population dynamics and life histories of foliaceous corals. Ecol. Monogr. 55, 141–166 (1985).Article 

Google Scholar 
Soong, K. Colony size as a species character in massive reef corals. Coral Reefs 12, 77–83 (1993).ADS 
Article 

Google Scholar 
Bak, R. P. & Meesters, E. H. Population structure as a response of coral communities to global change. Am. Zool. 39, 56–65 (1999).Article 

Google Scholar 
Adjeroud, M., Pratchett, M. S., Kospartov, M. C., Lejeusne, C. & Penin, L. Small-scale variability in the size structure of scleractinian corals around Moorea, French Polynesia: patterns across depths and locations. Hydrobiologia 589, 117–126 (2007).Article 

Google Scholar 
Adjeroud, M., Mauguit, Q. & Penin, L. The size-structure of corals with contrasting life-histories: A multi-scale analysis across environmental conditions. Mar. Environ. Res. 112, 131–139 (2015).CAS 
Article 
PubMed 

Google Scholar 
Bauman, A. G. et al. Variation in the size structure of corals is related to environmental extremes in the Persian Gulf. Mar. Environ. Res. 84, 43–50 (2013).CAS 
Article 
PubMed 

Google Scholar 
Smith, L., Devlin, M., Haynes, D. & Gilmour, J. A demographic approach to monitoring the health of coral reefs. Mar. Pollut. Bull. 51, 399–407 (2005).CAS 
Article 
PubMed 

Google Scholar 
Lowe, R. J. & Falter, J. L. Oceanic forcing of coral reefs. Annu. Rev. Mar. Sci. 7, 43–66 (2015).ADS 
Article 

Google Scholar 
Thornborough, K., Davies, P. Reef flats. Encycl. Mod. Coral Reefs 869–876 (2011).Camp, E. F. et al. The future of coral reefs subject to rapid climate change: lessons from natural extreme environments. Front. Mar. Sci. 5, 4 (2018).Article 

Google Scholar 
Bellwood, D. R. et al. The role of the reef flat in coral reef trophodynamics: Past, present, and future. Ecol. Evol. 8, 4108–4119 (2018).PubMed Central 
Article 
PubMed 

Google Scholar 
Pineda, J. et al. Two spatial scales in a bleaching event: Corals from the mildest and the most extreme thermal environments escape mortality. Limnol. Oceanogr. https://doi.org/10.4319/lo.2013.58.5.1531 (2013).Article 

Google Scholar 
Riegl, B. M., Bruckner, A. W., Rowlands, G. P., Purkis, S. J. & Renaud, P. Red Sea coral reef trajectories over 2 decades suggest increasing community homogenization and decline in coral size. PLoS ONE 7, e38396 (2012).ADS 
CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
Riegl, B., Berumen, M. & Bruckner, A. Coral population trajectories, increased disturbance and management intervention: A sensitivity analysis. Ecol. Evol. https://doi.org/10.1002/ece3.519 (2013).Article 
PubMed Central 
PubMed 

Google Scholar 
Loya, Y. The red sea coral Stylophora pistillata is an r strategist. Nature https://doi.org/10.1038/259478a0 (1976).Article 
PubMed 

Google Scholar 
Lozano-Cortés, D. F. & Berumen, M. L. Colony size-frequency distribution of pocilloporid juvenile corals along a natural environmental gradient in the Red Sea. Mar. Pollut. Bull. 105, 546–552 (2016).Article 
CAS 
PubMed 

Google Scholar 
Ellis, J. et al. Cross shelf benthic biodiversity patterns in the Southern Red Sea. Sci. Rep. 7, 1–14 (2017).Article 
CAS 

Google Scholar 
Furby, K. A., Bouwmeester, J. & Berumen, M. L. Susceptibility of central Red Sea corals during a major bleaching event. Coral Reefs 32, 505–513 (2013).ADS 
Article 

Google Scholar 
Monroe, A. A. et al. In situ observations of coral bleaching in the central Saudi Arabian Red Sea during the 2015/2016 global coral bleaching event. PLoS ONE 13, e0195814 (2018).PubMed Central 
Article 
CAS 
PubMed 

Google Scholar 
Davis, K. et al. Observations of the thermal environment on Red Sea platform reefs: A heat budget analysis. Coral Reefs 30, 25–36 (2011).ADS 
Article 

Google Scholar 
Liu, G. et al. Reef-scale thermal stress monitoring of coral ecosystems: new 5-km global products from NOAA Coral Reef Watch. Remote Sens. 6, 11579–11606 (2014).ADS 
Article 

Google Scholar 
Voolstra, C. R. et al. Standardized short-term acute heat stress assays resolve historical differences in coral thermotolerance across microhabitat reef sites. Glob. Change Biol. https://doi.org/10.1111/gcb.15148 (2020).Article 

Google Scholar 
Abràmoff, M. D., Magalhães, P. J. & Ram, S. J. Image processing with ImageJ. Biophotonics Int. 11, 36–42 (2004).
Google Scholar 
Morais, J., Morais, R. A., Tebbett, S. B., Pratchett, M. S. & Bellwood, D. R. Dangerous demographics in post-bleach corals reveal boom-bust versus protracted declines. Sci. Rep. 11, 1–7 (2021).Article 
CAS 

Google Scholar 
Hall, V. R. & Hughes, T. P. Reproductive strategies of modular organisms: Comparative studies of reef-building corals. Ecology https://doi.org/10.2307/2265514 (1996).Article 

Google Scholar 
Rinkevich, B. & Loya, Y. Reproduction of the Red Sea coral Stylophora pistillata. 2. Synchronization in breeding and seasonality of planulae shedding. Mar. Ecol. Prog. Ser. 1, 145–152 (1979).ADS 
Article 

Google Scholar 
Komsta, L. & Novomestky, F. Moments, cumulants, skewness, kurtosis and related tests. R Package Version 14, (2015).Anderson, M., Gorley, R. & Clarke, K. PERMANOVA+ for PRIMER: guide to software and statistical methods. Primer-E Plymouth UK (2008).Meziere, Z. et al. Stylophora under stress: A review of research trends and impacts of stressors on a model coral species. Sci. Total Environ. 816, 151639 (2022).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Rinkevich, B. & Loya, Y. Reproduction of the Red Sea coral Stylophora pistillata 1. Gonads and planulae. Mar. Ecol. Prog. Ser. 1, 133–144 (1979).ADS 
Article 

Google Scholar 
Nishikawa, A., Katoh, M. & Sakai, K. Larval settlement rates and gene flow of broadcast-spawning (Acropora tenuis) and planula-brooding (Stylophora pistillata) corals. Mar. Ecol. Progress Ser. https://doi.org/10.3354/meps256087 (2003).Article 

Google Scholar 
Monroe, A. Genetic differentiation across multiple spatial scales of the Red Sea of the corals Stylophora pistillata and Pocillopora verrucosa. M.S. thesis, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia (2015).Gouezo, M. et al. Relative roles of biological and physical processes influencing coral recruitment during the lag phase of reef community recovery. Sci. Rep. 10, 1–12 (2020).ADS 
Article 
CAS 

Google Scholar 
Boco, S. R., Cabansag, J. B. P., Jamodiong, E. A. & Ticzon, V. S. Size-frequency distributions of scleractinian coral (Porites spp.) colonies inside and outside a marine reserve in Leyte Gulf, central Philippines. Reg. Stud. Mar. Sci. 35, 101147 (2020).
Google Scholar 
River, G. F. & Edmunds, P. J. Mechanisms of interaction between macroalgae and scleractinians on a coral reef in Jamaica. J. Exp. Mar. Biol. Ecol. 261, 159–172 (2001).Article 
PubMed 

Google Scholar 
Kuffner, I. B. et al. Inhibition of coral recruitment by macroalgae and cyanobacteria. Mar. Ecol. Prog. Ser. 323, 107–117 (2006).ADS 
Article 

Google Scholar 
Hughes, T. & Jackson, J. Do corals lie about their age? Some demographic consequences of partial mortality, fission, and fusion. Science 209, 713–715 (1980).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Lewis, J. B. Abundance, distribution and partial mortality of the massive coral Siderastrea siderea on degrading coral reefs at Barbados West Indies. Mar. Pollut. Bull. 34, 622–627 (1997).CAS 
Article 

Google Scholar 
Meesters, E. H., Wesseling, I. & Bak, R. P. Coral colony tissue damage in six species of reef-building corals: partial mortality in relation with depth and surface area. J. Sea Res. 37, 131–144 (1997).ADS 
Article 

Google Scholar 
Meesters, E. H., Wesseling, I. & Bak, R. P. Partial mortality in three species of reef-building corals and the relation with colony morphology. Bull. Mar. Sci. 58, 838–852 (1996).
Google Scholar 
Graham, J. & Van Woesik, R. The effects of partial mortality on the fecundity of three common Caribbean corals. Mar. Biol. 160, 2561–2565 (2013).Article 

Google Scholar 
Rinkevich, B. & Loya, Y. Intraspecific competitive networks in the Red Sea coral Stylophora pistillata. Coral Reefs https://doi.org/10.1007/BF00571193 (1983).Article 

Google Scholar 
Takabayashi, M. & Hoegh-Guldberg, O. Ecological and physiological differences between two colour morphs of the coral Pocillopora damicornis. Mar. Biol. 123, 705–714 (1995).Article 

Google Scholar 
Innis, T., Cunning, R., Ritson-Williams, R., Wall, C. & Gates, R. Coral color and depth drive symbiosis ecology of Montipora capitata in Kāne ‘ohe Bay, O ‘ahu, Hawai ‘i. Coral Reefs 37, 423–430 (2018).ADS 
Article 

Google Scholar 
Gochfeld, D., Ansley, M., Ankisetty, S. & Aeby, G. Antibacterial chemical resistance to disease in the Hawaiian coral Montipora capitata. Planta Med. 80, CL31 (2014).Article 

Google Scholar 
Shore-Maggio, A., Callahan, S. M. & Aeby, G. S. Trade-offs in disease and bleaching susceptibility among two color morphs of the Hawaiian reef coral Montipora capitata. Coral Reefs 37, 507–517 (2018).ADS 
Article 

Google Scholar 
Dove, S. G., Takabayashi, M. & Hoegh-Guldberg, O. Isolation and partial characterization of the pink and blue pigments of pocilloporid and acroporid corals. Biol. Bull. 189, 288–297 (1995).CAS 
Article 
PubMed 

Google Scholar 
Hume, B. C. C., Mejia-Restrepo, A., Voolstra, C. R. & Berumen, M. L. Fine-scale delineation of Symbiodiniaceae genotypes on a previously bleached central Red Sea reef system demonstrates a prevalence of coral host-specific associations. Coral Reefs https://doi.org/10.1007/s00338-020-01917-7 (2020).Article 

Google Scholar  More

Ecological networks: Linking structure to dynamics in food webs. (Oxford University Press, 2006).Adaptive food webs: Stability and transitions of real and model ecosystems. (Cambridge University Press, 2018).Pimm, S. L. Food Webs (Springer, 1982).Book 

Google Scholar 
Adaptive Food Webs: Stability and Transitions of Real and Model Ecosystems. (Cambridge University Press, 2017). doi:https://doi.org/10.1017/9781316871867.da Mata, A. S. Complex Networks: A Mini-review. Braz. J. Phys. 50, 658–672 (2020).ADS 
Article 

Google Scholar 
Zhang, W. Fundamentals of Network Biology. (World Scientific (Europe), 2018). https://doi.org/10.1142/q0149.Reichman, O. J., Jones, M. B. & Schildhauer, M. P. Challenges and opportunities of open data in ecology. Science 331, 703–705 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Farley, S. S., Dawson, A., Goring, S. J. & Williams, J. W. situating ecology as a big-data science: Current advances, challenges, and solutions. Bioscience 68, 563–576 (2018).Article 

Google Scholar 
Osawa, T. Perspectives on biodiversity informatics for ecology. Ecol. Res. 34, 446–456 (2019).Article 

Google Scholar 
Shin, N. et al. Toward more data publication of long-term ecological observations. Ecol. Res. 35, 700–707 (2020).Article 

Google Scholar 
Pringle, R. M. & Hutchinson, M. C. Resolving food-web structure. Annu. Rev. Ecol. Evol. Syst. 51, 55–80 (2020).Article 

Google Scholar 
Derocles, S. A. P. et al. Biomonitoring for the 21st Century: Integrating Next-Generation Sequencing Into Ecological Network Analysis. in Advances in Ecological Research vol. 58 1–62 (Elsevier, 2018).Vacher, C. et al. Learning ecological networks from next-generation sequencing data. in Advances in Ecological Research vol. 54, 1–39 (Elsevier, 2016).Evans, D. M., Kitson, J. J. N., Lunt, D. H., Straw, N. A. & Pocock, M. J. O. Merging DNA metabarcoding and ecological network analysis to understand and build resilient terrestrial ecosystems. Funct. Ecol. 30, 1904–1916 (2016).Article 

Google Scholar 
Pocock, M. J. O. et al. A vision for global biodiversity monitoring with citizen science. in Advances in Ecological Research vol. 59, 169–223 (Elsevier, 2018).Sultana, M. & Storch, I. Suitability of open digital species records for assessing biodiversity patterns in cities: A case study using avian records. J. Urban Ecol. 7, juab014 (2021).Article 

Google Scholar 
Amano, T., Lamming, J. D. L. & Sutherland, W. J. Spatial gaps in global biodiversity information and the role of citizen science. Bioscience 66, 393–400 (2016).Article 

Google Scholar 
Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Conserv. 213, 280–294 (2017).Article 

Google Scholar 
Fontaine, C. et al. The ecological and evolutionary implications of merging different types of networks: Merging networks with different interaction types. Ecol. Lett. 14, 1170–1181 (2011).PubMed 
Article 

Google Scholar 
Martinson, H. M. & Fagan, W. F. Trophic disruption: A meta-analysis of how habitat fragmentation affects resource consumption in terrestrial arthropod systems. Ecol. Lett. 17, 1178–1189 (2014).PubMed 
Article 

Google Scholar 
Marczak, L. B., Thompson, R. M. & Richardson, J. S. Meta-analysis: Trophic level, Habitat, and productivity shape the food web effects of resource subsidies. Ecology 88, 140–148 (2007).PubMed 
Article 

Google Scholar 
McCary, M. A., Mores, R., Farfan, M. A. & Wise, D. H. Invasive plants have different effects on trophic structure of green and brown food webs in terrestrial ecosystems: A meta-analysis. Ecol. Lett. 19, 328–335 (2016).PubMed 
Article 

Google Scholar 
Cirtwill, A. R., Stouffer, D. B. & Romanuk, T. N. Latitudinal gradients in biotic niche breadth vary across ecosystem types. Proc. R. Soc. B Biol. Sci. 282, 20151589 (2015).Article 
CAS 

Google Scholar 
Fortuna, M. A., Ortega, R. & Bascompte, J. The Web of Life. ArXiv14032575 Q-Bio (2014).Brose, U. et al. Predator traits determine food-web architecture across ecosystems. Nat. Ecol. Evol. 3, 919–927 (2019).PubMed 
Article 

Google Scholar 
Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: A multilayered relationship. Trends Ecol. Evol. 27, 19–26 (2012).PubMed 
Article 

Google Scholar 
Keyes, A. A., McLaughlin, J. P., Barner, A. K. & Dee, L. E. An ecological network approach to predict ecosystem service vulnerability to species losses. Nat. Commun. 12, 1586 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Peng, J. et al. Linking ecosystem services and circuit theory to identify ecological security patterns. Sci. Total Environ. 644, 781–790 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Su, Y. et al. Modeling the optimal ecological security pattern for guiding the urban constructed land expansions. Urban For. Urban Green. 19, 35–46 (2016).Article 

Google Scholar 
Kowarik, I. Novel urban ecosystems, biodiversity, and conservation. Environ. Pollut. 159, 1974–1983 (2011).CAS 
PubMed 
Article 

Google Scholar 
Di Marco, M., Watson, J. E. M., Venter, O. & Possingham, H. P. Global biodiversity targets require both sufficiency and efficiency. Conserv. Lett. 9, 395–397 (2016).Article 

Google Scholar 
Kim, K.-H. & Pauleit, S. Landscape character, biodiversity and land use planning: The case of Kwangju City Region, South Korea. Land Use Policy 24, 264–274 (2007).Article 

Google Scholar 
Young, J. et al. Towards sustainable land use: Identifying and managing the conflicts between human activities and biodiversity conservation in Europe. Biodivers. Conserv. 14, 1641–1661 (2005).Article 

Google Scholar 
Dardonville, M., Urruty, N., Bockstaller, C. & Therond, O. Influence of diversity and intensification level on vulnerability, resilience and robustness of agricultural systems. Agric. Syst. 184, 102913 (2020).Article 

Google Scholar 
Oliver, T. H. et al. Biodiversity and resilience of ecosystem functions. Trends Ecol. Evol. 30, 673–684 (2015).PubMed 
Article 

Google Scholar 
Lau, M. K., Borrett, S. R., Baiser, B., Gotelli, N. J. & Ellison, A. M. Ecological network metrics: Opportunities for synthesis. Ecosphere 8, e01900 (2017).Article 

Google Scholar 
Newman, M. E. J. Networks. (Oxford University Press, 2018).Levine, S. Several measures of trophic structure applicable to complex food webs. J. Theor. Biol. 83, 195–207 (1980).ADS 
Article 

Google Scholar 
Guimarães, P. R. The structure of ecological networks across levels of organization. Annu. Rev. Ecol. Evol. Syst. 51, 433–460 (2020).Article 

Google Scholar 
Dormann, C. F., Frund, J., Bluthgen, N. & Gruber, B. Indices, graphs and null models: Analyzing bipartite ecological networks. Open Ecol. J. 2, 7–24 (2009).Article 

Google Scholar 
Jordán, F., Benedek, Z. & Podani, J. Quantifying positional importance in food webs: A comparison of centrality indices. Ecol. Model. 205, 270–275 (2007).Article 

Google Scholar 
Jordán, F., Liu, W. & Davis, A. J. Topological keystone species: Measures of positional importance in food webs. Oikos 112, 535–546 (2006).Article 

Google Scholar 
Jordán, F., Okey, T. A., Bauer, B. & Libralato, S. Identifying important species: Linking structure and function in ecological networks. Ecol. Model. 216, 75–80 (2008).Article 

Google Scholar 
Jiang, L. Determination of keystone species in CSM food web: A topological analysis of network structure. Netw. Biol. 5, 13 (2015).
Google Scholar 
Abarca-Arenas, L. G., Franco-Lopez, J., Peterson, M. S., Brown-Peterson, N. J. & Valero-Pacheco, E. Sociometric analysis of the role of penaeids in the continental shelf food web off Veracruz. Mexico Based By-catch Fish. Res. 87, 46–57 (2007).
Google Scholar 
Abascal-Monroy, I. M. et al. Functional and structural food web comparison of Terminos Lagoon, Mexico in Three Periods (1980, 1998, and 2011). Estuaries Coasts 39, 1282–1293 (2016).Article 

Google Scholar 
McDonald-Madden, E. et al. Using food-web theory to conserve ecosystems. Nat. Commun. 7, 10245 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Windsor, F. M. et al. Identifying plant mixes for multiple ecosystem service provision in agricultural systems using ecological networks. J. Appl. Ecol. 58, 2770–2782 (2021).Article 

Google Scholar 
Klaise, J. & Johnson, S. The origin of motif families in food webs. Sci. Rep. 7, 16197 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Estrada, E. Characterization of topological keystone species. Ecol. Complex. 4, 48–57 (2007).Article 

Google Scholar 
Thompson, R. M. & Townsend, C. R. Impacts on stream food webs of native and exotic forest: An intercontinental comparison. Ecology 84, 145–161 (2003).Article 

Google Scholar 
Bascompte, J., Melian, C. J. & Sala, E. Interaction strength combinations and the overfishing of a marine food web. Proc. Natl. Acad. Sci. 102, 5443–5447 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dunne, J. A. et al. The roles and impacts of human hunter-gatherers in North Pacific marine food webs. Sci. Rep. 6, 21179 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gauzens, B., Legendre, S., Lazzaro, X. & Lacroix, G. Food-web aggregation, methodological and functional issues. Oikos 122, 1606–1615 (2013).Article 

Google Scholar 
Patonai, K. & Jordán, F. Aggregation of incomplete food web data may help to suggest sampling strategies. Ecol. Model. 352, 77–89 (2017).Article 

Google Scholar 
Thompson, R. M. & Townsend, C. R. Is resolution the solution?: The effect of taxonomic resolution on the calculated properties of three stream food webs. Freshw. Biol. 44, 413–422 (2000).Article 

Google Scholar 
Abarca-Arenas, L. G. & Ulanowicz, R. E. The effects of taxonomic aggregation on network analysis. Ecol. Model. 149, 285–296 (2002).Article 

Google Scholar 
Jordán, F. & Osváth, G. The sensitivity of food web topology to temporal data aggregation. Ecol. Model. 220, 3141–3146 (2009).Article 

Google Scholar 
European Commission. Communication from the commission to the european parliament, the council, the european economic and social committee and the committee of the regions: EU Biodiversity Strategy for 2030 Bringing nature back into our lives. Preprint at https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020DC0380 (2020).European Parliament. European Parliament resolution of 9 June 2021 on the EU Biodiversity Strategy for 2030: Bringing nature back into our lives (P9_TA(2021)0277). Preprint at https://www.europarl.europa.eu/doceo/document/TA-9-2021-0277_EN.html (2021).Felson, A. J. & Ellison, A. M. Designing (for) Urban Food Webs. Front. Ecol. Evol. 9, 582041 (2021).Article 

Google Scholar 
Warren, P. et al. Urban food webs: Predators, prey, and the people who feed them. Bull. Ecol. Soc. Am. 87, 387–393 (2006).Article 

Google Scholar 
De Montis, A., Ganciu, A., Cabras, M., Bardi, A. & Mulas, M. Comparative ecological network analysis: An application to Italy. Land Use Policy 81, 714–724 (2019).Article 

Google Scholar 
Poisot, T. et al. Mangal—making ecological network analysis simple. Ecography 39, 384–390 (2016).Article 

Google Scholar 
Morris, Z. B., Weissburg, M. & Bras, B. Ecological network analysis of urban–industrial ecosystems. J. Ind. Ecol. 25, 193–204 (2021).Article 

Google Scholar 
Chamberlain, S. A. & Szöcs, E. taxize: Taxonomic search and retrieval in R. F1000 Research 2, 191 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Hagberg, A. A., Schult, D. A. & Swart, P. J. Exploring network structure, dynamics, and function using networkX. in Proceedings of the 7th Python in Science Conference (eds. Varoquaux, G., Vaught, T. & Millman, J.) 11–15 (2008).Scotti, M. & Jordán, F. Relationships between centrality indices and trophic levels in food webs. Community Ecol. 11, 59–67 (2010).Article 

Google Scholar 
Gouveia, C., Móréh, Á. & Jordán, F. Combining centrality indices: Maximizing the predictability of keystone species in food webs. Ecol. Indic. 126, 107617 (2021).Article 

Google Scholar 
Allesina, S. & Pascual, M. Googling Food Webs: Can an Eigenvector Measure Species’ Importance for Coextinctions?. PLoS Comput. Biol. 5, e1000494 (2009).ADS 
MathSciNet 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Patro, S. G. K. & Sahu, K. K. Normalization: A preprocessing stage. https://doi.org/10.48550/ARXIV.1503.06462(2015).Reback, J. et al. pandas-dev/pandas: Pandas 1.2.3. (Zenodo, 2021). 10.5281/ZENODO.4572994.Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).Article 

Google Scholar 
Waskom, M. et al. mwaskom/seaborn: v0.11.1 (December 2020). (Zenodo, 2020). 10.5281/ZENODO.4379347.Girvan, M. & Newman, M. E. J. Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99, 7821–7826 (2002).ADS 
MathSciNet 
CAS 
PubMed 
PubMed Central 
MATH 
Article 

Google Scholar 
Rosvall, M., Axelsson, D. & Bergstrom, C. T. The map equation. Eur. Phys. J. Spec. Top. 178, 13–23 (2009).Article 

Google Scholar 
Gao, P. & Kupfer, J. A. Uncovering food web structure using a novel trophic similarity measure. Ecol. Inform. 30, 110–118 (2015).Article 

Google Scholar 
Gauzens, B., Thébault, E., Lacroix, G. & Legendre, S. Trophic groups and modules: Two levels of group detection in food webs. J. R. Soc. Interface 12, 20141176 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Rudiger, P. et al. holoviz/holoviews: Version 1.14.2. (Zenodo, 2021). 10.5281/ZENODO.4581995.Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).MathSciNet 
MATH 

Google Scholar  More