Philippa Kaur

Rabbits have had a disastrous impact on Australian agriculture and native plants.Credit: Bettman/Getty

A genomic analysis has helped to show that Australia’s invasive rabbit population probably originated from a shipment of two dozen wild English rabbits that arrived near Melbourne on Christmas Day, 1859. The study also finds that the herd’s wild ancestry probably gave it an advantage over previous arrivals.Rabbits have invaded most of the Australian continent and have had a disastrous impact on ecosystems, threatening some 300 species of plants and animals, and causing hundreds of millions of dollars’ worth of damage to the agriculture industry a year. “That single event triggered this enormous catastrophe, ecologically and economically, in Australia,” says Francis Jiggins, an evolutionary geneticist at the University of Cambridge, UK, and study co-author.Breeding like rabbitsHistorical records suggest that the first European rabbits (Oryctolagus cuniculus) in Australia arrived in Sydney in 1788, with the first colonizers. Ships bringing rabbits continued to dock along the coast for decades, but it wasn’t until the second half of the nineteenth century that the population expanded significantly, spreading across the country at a rate of 100 kilometres a year.Historical records also suggest that the rabbit expansion followed a shipment of animals that arrived for a certain Thomas Austin at Barwon Park, southwest of what is now Melbourne. His brother had trapped them around their family property in Baltonsborough in southwest England.Joel Alves, an evolutionary geneticist at the University of Oxford, UK, and his colleagues wanted to find out whether genomic data corroborated the records. They analysed genetic data from 179 wild rabbits caught across Australia and in New Zealand, France and the United Kingdom, as well as 8 domestic rabbits of different breeds.They found that most rabbits in mainland Australia were genetically similar, with mixed wild and domestic ancestry. Australian rabbits also shared more rare alleles with rabbits from southwest England than with those from elsewhere in the United Kingdom, suggesting they originated in Baltonsborough. Looking specifically at mitochondrial DNA, which is inherited from the mother, the researchers concluded that most mainland Australian rabbits descended from about five females, introduced from Europe.The researchers also found that the rabbits’ genetic diversity declined the further from Barwon Park the animals were caught, and that alleles that are rare or absent in wild rabbits increased. The researchers say these patterns are consistent with the idea that most rabbits across Australia originated from Barwon Park. The team report their findings in the Proceedings of the National Academy of Sciences on 22 August1.“This is a very exciting paper on a very important and well-studied topic,” says Martin Nuñez, who researches ecological invasions at the University of Houston in Texas. Using genetics to understand how unwanted animal invasions start can help to predict future invasions, he says.Perfect stormOverall, the team says that the rabbits’ wild ancestry was an important factor in triggering their invasion of the continent. “Wild rabbits are different,” says Alves. They exhibit traits such as fleeing stressful environments and burrow-digging, meaning that they were probably better at evading predators and surviving in difficult terrain than are domestic rabbits, he says. Historical records suggest that Austin requested wild rabbits, and that previous arrivals were largely domestic breeds.The expansion of Australian pastoral lands and widespread suppression of predators around that time would have also helped their expansion. “It was like a perfect storm,” says Alves. “You have the right rabbits in the right place at the right time, with the right changes in the environment.”“The genetic analyses appear very sound,” says rabbit geneticist Amy Iannella, a consultant based in Adelaide, Australia. She adds that although the country’s rabbit populations probably originated in Barwon Park, their rapid expansion might have been aided by people transporting the animals to other parts of the country, where they also began spreading. Rabbits are typically communal animals that rely on shelter for survival and juveniles rarely travel further than 1 kilometre, she says. “The idea of rabbits moving fast enough at the invasion front to colonize Australia so quickly from a single release, well that feels extreme to me, given what we know about rabbit ecology.” More

Discovery and historiography of USNM PAL V 11367With basic image enhancement tools (e.g., Adobe Photoshop), we were able to better resolve the original but faded specimen label in the collections associated with USNM PAL V 11367 (Fig. 1 and Related file 1). Specifically, we were able to make the now-faded handwritten notes legible (Fig. 1A,B), revealing critical information about the specimen. The widespread availability of image enhancement for faded fieldnotes and labels provides a new source of information for uncovering legacy issues in museum collections (e.g.21,22,23), especially in cases where locality data or collecting information cannot be well resolved.Accession files with this specimen (Related file 1) show that it was gifted from Arthur M. Ames to the United States National Museum (now the National Museum of Natural History, Smithsonian Institution) on 15 October 1925, and approved by George P. Merrill, head curator of geology from 1917 to 1929. Prior to its accession to the museum, an anonymous individual identified the tooth as belonging to Desmostylus hesperus. Forty years later, on 17 November 1965, Charles A. Repenning reidentified this specimen as Paleoparadoxia sp. (Fig. 1A,B), an assertion that was incorporated into its catalog information. According to the label, USNM PAL V 11367 was collected in the city of Corona, Riverside County, California, yet no precise information of its geological provenance was recorded. On the backside of the label, there are notes (Fig. 1B) referring to the US Geologic Survey Corona South 7.5′ quadrangle map for Riverside and Orange counties, California24. However, no geographic location, exact horizon, nor lithology was stated, and the specimen’s collector, A. M. Ames, lived in Santa Barbara, California but died on 25 August 193921,22,23.In nearly a century after its discovery, the only mention of USNM PAL V 11367 was by Panofsky25, who listed it in a catalog of desmostylian tooth specimens used as a comparative basis for a mandible restoration of the “Stanford specimen” N. repenningi. Panofsky25 identified USNM PAL V 11367 as a left m2 with six main cusps, with no additional cusps (Table 1 in25), while also stating that this specimen has “an open lake in the center of each of the seven cusps” (25: p. 103). The inconsistency of this description differs from our own, which we attribute to differences in morphological criteria or a typographic error.Geological horizon and age of USNM PAL V 11367In this paper, we refer to the “Topanga” Formation following recent studies20,26,27 of this geologic unit. This formation was originally based on a sequence of marine sandstones exposed in an anticline just west of Old Topanga Canyon in the central Santa Monica Mountains of Los Angeles County, California28. After its initial description, the name of the formation was applied to a much thicker and heterogeneous sequence of sedimentary and volcanic rocks29. Campbell et al.30 compiled the history and chronology of changes in usage of “Topanga” in the Miocene stratigraphic nomenclature in Southern California, showing that the criteria of continuous deposition and shared provenance were not demonstrated in every instance. Campbell et al.30 argued that strata assigned to the Topanga Formation in the Los Angeles Basin and eastern Ventura Basin areas are different from other units that have also been referred to the Topanga Formation in Orange County or in the Santa Monica Mountains of Los Angeles and Ventura counties. To distinguish these units, here we follow recent studies20,26,27 and use the name of “Topanga” Formation for the early to middle Miocene rocks bearing fossil marine mammals20,26,31,32,33 in Southern California.According to the collections records (Fig. 1), USNM PAL V 11367 was collected in the city of Corona, Riverside County, California, USA. This city is in the western part of Riverside County, comprising an approximate area of 100 km234. Previously, Panofsky25 suggested that USNM PAL V 11367 would have derived from the Temblor Formation (14.8 to 15.8 Ma35), likely as a guess based on the prevalence of desmostylian teeth recovered from this unit in central California, yet today there are no Temblor Formation outcrops mapped near Corona24,36; the closest Temblor outcrops are located in Fresno and Kern counties37, approximately 200 km away.The geologic maps of Riverside County24,36,38 indicate that the city limits of Corona encompass a wide variety of sedimentary rocks from the Jurassic to the Holocene in age, but only a few marine deposits, such as the Jurassic Bedford Canyon Formation and the middle Miocene “Topanga” Formation are exposed24,39. Specifically, the marine sandstones of the “Topanga” Formation occur within the fault zone at the southeast and northwest of Corona.Outside of Riverside County, the “Topanga” Formation has yielded a diverse assemblage of fossil marine vertebrates in Southern California20,26,31, including desmostylians referred to Desmostylus hesperus and Paleoparadoxia sp. in Orange County (Supplementary 1). USNM PAL V 11367 represents the second reported fossil marine mammal from Riverside County. Previously, an isolated record of “Cetacea indet.” was mentioned from the Zanclean stage Imperial Formation40 and Supplementary Data 2), which is exposed far east of Corona’s city limits.In assessing the age of the “Topanga” Formation in Southern California, Boessenecker and Churchill26,31 argued that the land mammals (late Hemingfordian North American Land Mammal Age, represented by Aepycamelus, Copemys and Merychippus; 17.5–15.9 Ma35,41), benthic foraminifera, fossil mollusks, and K/Ar dating all placed the age range between 17.5 and 15 Ma for this geological unit41 in Orange County. More recently, Velez-Juarbe20 revised the age of “Topanga” Formation in this county to 16.5–14.5 Ma based on new foraminiferal zones presented in Ogg et al.42.We propose that USNM PAL V 11367 derives from exposures of the “Topanga” Formation in Riverside County. If this mapped unit in Riverside can be correlated with “Topanga” Formation units in Orange County, it would imply a middle Miocene age, likely 16.5–14.5 Ma20, and given the morphological similarities of this isolated tooth with more complete paleoparadoxiid material in Orange County with stronger age constraints, we think a middle Miocene age for USNM PAL V 11367 is warranted. Given the reduced distribution of outcrops of the “Topanga” Formation24,36 in Corona, we identify two potential localities for USNM PAL V 11367 (Fig. 3). These two localities are situated in urbanized areas, less than 21 km apart, in the northwest and the southeast corners of Corona’s city limits (see Fig. 3B). Both are notably less than 40 km apart from the type locality of N. cecilialina in Orange County, but we urge skepticism for a direct correlation as the marine units of Riverside County requires detailed stratigraphic revision to determine their age constraints; they likely belong to a different depositional basin than “Topanga” Formation exposures in westward Southern California counties.Morphological variation and potential diversity of PaleoparadoxiidaeOur comparisons reveal considerable morphological variation in the arrangement and number of dental cusps across Paleoparadoxiidae (Fig. 4). The cusps arrangement for the m2-3 of Archaeoparadoxia and Paleoparadoxia were previously reported by Inuzuka et al.43 (Fig. 4B), but the addition of another specimen (USNM PAL V 11367) reveals larger morphological variability than previously known for the genus Neoparadoxia (Fig. 4C). Specifically, the holotype of N. cecilialina displays slightly different configurations between its right and left m2, driven mainly by the position of the hypoconulid in occlusal view (Fig. 4C). USNM PAL V 11367, the second known Neoparadoxia m2 (or the first m3), is comparable in size and shape with the same teeth in the type specimen of N. cecilialina, especially the right m2. Both the Smithsonian and LACM specimens display a horizontal alignment of the extra cusp, the hypoconulid, and the entoconid; nevertheless, USNM PAL V 11367 shows a tighter configuration, lacking a wide internal spacing between cusps characteristic of the type specimen of N. cecilialina (Fig. 4C). Given the known ontogenetic changes that affect the dental nomenclature in desmostylians32,44, the addition of more comparative material should help discriminate between competing statements of homology45. The identification of USNM PAL V 11367 from the “Topanga” Formation of Corona represents a second diagnostic record of Neoparadoxia from three separate Middle Miocene units in Southern California, reaffirming its presence as a Middle Miocene taxon: USNM PAL V 11367 from the “Topanga” Formation of Riverside County; Neoparapdoxia (LACM 6920) from the Altamira Shale46; Neoparadoxia from the Topanga Formation of Orange County46,47; and the holotype of N. cecilialina from the lower part of Monterey Formation in the Capistrano syncline, Orange County46. It is possible that other records of Palaeoparadoxiidae from Orange County (e.g.47) and elsewhere in California may represent Neoparadoxia. For example, Awalt et al.32 noted that a palaeoparadoxiid from Orange County identified by Panofsky as Paleoparadoxia sp. (LACM 131889)25 is better referred to Paleoparadoxidae sp., pending a more detailed evaluation of this material, which differs in clear ways from N. ceciliana. One of the benefits of continued descriptive work on desmostylian material from well-constrained stratigraphic contexts in Southern California will be the biostratigraphic opportunities for cross-basin comparisons, especially for exposures of the “Topanga” Formation.Parham et al.46 emphasized that Neoparadoxia occurs widely in middle Miocene units across California: besides the aforementioned ones, Parham et al.46 noted records of this genus from the Sharktooth Hill Bonebed (LACM 120023), the Altamira Shale (LACM 6920), and the Ladera Sandstone15 (UCMP 81302). To date, Neoparadoxia is only known from California, yet it is likely that other paleoparadoxiid material tentatively assigned to other genera may expand the geographic range of this taxon. Interestingly, on the west side of the Pacific (Russia–Japan) and some parts of the east side of the Pacific (Oregon–Washington), Desmostylus spp. and paleoparadoxiids rarely co-occurred from the same formation48,49, yet there are many geological units in South California where desmostylids and paleoparadoxiids co-occurred (e.g., Santa Margarita Formation50,51, Rosarito Beach Formation52, Tortugas Formation51, and Temblor Formation3,4). The abundance of new material from the “Topanga” Formation from Orange and Riverside counties should contribute to the discussion of desmostylian environmental preferences48,53.Lastly, like other marine mammal lineages, desmostylian body sizes reached their maximum body size late in their evolutionary history54. By the middle to late Miocene, desmostylians were the largest herbivorous marine mammals along the North Pacific coastlines54, although they likely competed ecologically with co-occurring sirenians, which later eclipsed desmostylians in body size and survived until historical times in the North Pacific Ocean55. Specifically, in the “Topanga” Formation of Orange County, desmostylians co-occurred with sirenians such as Metaxytherium arctodites56, an ecological association that likely was repeated elsewhere in the mid-Miocene of California (e.g., coeval deposits of the Round Mountain Silt). Given the improving stratigraphic picture of Southern California marine mammal-bearing localities, future work on desmostylian paleoecology could test hypotheses of competition with taxonomic co-occurrence data grounded in strong comparative taphonomic and sedimentological frameworks. More

Site descriptionThe field experiment was initiated in 2013 at the Yongchun County, Fujian Province, China (25°12′37″ N, 118°10′24″ E), using the two rotations of vegetables and rice (Fig. 1). The site is in the north of the Tropic of Cancer, with a typical subtropical marine monsoon climate, sufficient sunshine, and average annual solar radiation 462.26 kJ/cm2. The climate is mild and humid, with average annual temperature 16–21 °C and average annual rainfall about 1400 mm. Agricultural production allows for the cultivation of three crops annually. The soil of the test field was lateritic red soil.Figure 1Location of the field experiment site.Full size imageExperiment designThe experiment was conducted over 9 years from 2013 to 2021. Soil samples were collected before the experiment began to determine the main physical and chemical properties of the soil in the test plot, which were: organic matter content 19.96 g/kg, total nitrogen 2.25 g/kg, total phosphorus 1.31 g/kg, total potassium 27.86 g/kg, alkaline hydrolyzable nitrogen 107.73 mg/kg, available phosphorus 60.35 mg/kg, available potassium 116 mg/kg and soil pH 5.54. The test site was a rectangular field, 26 m long and 9 m wide, divided into 15 test blocks, each 5 m long and 2.8 m wide. Cement ridges were used to separate the test blocks, and irrigation drainage ditches were set outside the blocks. A protective isolation strip 1 m wide was formed around the test site. The experiment included two crop rotations: (I) rotation P–B–O: P, kidney bean (Phaseolus vulgaris L.), B, mustard (Brassica juncea L.), O, rice (Oryza sativa L.); and II) rotation P–B–V: P, kidney bean (P. vulgaris L.), B, mustard (B. juncea L.), V, cowpea (Vigna unguiculata L.). Four fertilizer treatments were selected: (1) recommended fertilization (RF) used with rotation P–B–O; (2) recommended fertilization (RF) used with P–B–V; (3) conventional fertilization (CF) used with P–B–O; (4) conventional fertilization (CF) used with P–B–V. A randomized complete block experimental design with three replications was used in the field study. The fertilization amounts used for treatments RF and CF are shown in Table 1. Under the CF, the amount of fertilizer applied to crops in each season is determined according to the years of fertilization habits of local farmers. The fertilization amount of crops in each season under the RF was calculated according to the measured basic soil fertility combined with the fertilization model of previous studies. The fertilization amount of crops in each season under the CF in this study is obtained by investigating the local farmers. The data on the fertilization amount of crops in each season under the RF is cited from the research report of Zhang et al.23. Urea (N 46%) was the nitrogen fertilizer, calcium superphosphate (P2O5 12%) was the phosphorus fertilizer, and potassium chloride (K2O 60%) was the potassium fertilizer. All phosphorus fertilizer applied to crops in each season was used as base fertilizer, and nitrogen and potassium fertilizer were applied separately as base fertilizer (40% of the total fertilization) and topdressing (60% of the total fertilization). The topdressing method was that nitrogen and potassium fertilizer for kidney bean and cowpea were applied twice, 30% of the fertilization amount each time; nitrogen and potassium fertilizer for mustard was applied three times, 20% of the fertilization amount each time; nitrogen fertilizer for rice was applied at two different growing stages, 50% of the fertilization amount at the tillering stage and 10% of the fertilization amount at the panicle stage; potassium fertilizer was applied once, using 60% of the fertilization amount. The first crop, kidney bean, was sown in early September and harvested in November. The second crop mustard, was sown in early December and harvested in February of the following year. The third crop, rice or cowpea, was sown in early April and harvested in July.Table 1 Fertilization rate of each treatment in the long term crop rotation experiment (kg/hm2).Full size tableData analysis and methodsYield stability analysis was conducted for the 9 years period using three different approaches. First, the coefficient of variation (CV) was calculated to give a measure of the temporal variability of yield for each treatment:$$CV=frac{upsigma }{Y}*100 {%}$$
(1)
where σ is the standard deviation of average crop yield in each year, and Y is the average crop yield in each year. A low value of CV indicates little variation, which implies that interannual difference in crop yield in the experimental plot is small and the yield is relatively stable over the years of the experimental period.A second yield stability indicator is the sustainable yield index (SYI), which is calculated by Singh et al.25:$$SYI=frac{mathrm{Y}-upsigma }{{Y}_{max}}$$
(2)
where Y is the average annual crop yield, σ is the standard deviation of the average annual crop yield, and YMax is the maximum annual crop yield. A high value of SYI indicates a greater capacity of the soil to sustain a particular crop yield over time.The third stability measure is Wricke’s ecovalence index (Wi2), which was calculated individually for each crop management system by Wricke26:$${Wi}^{2}={sum }_{j=1}^{q}({x}_{ij}-{{m}_{i}-{m}_{j}+m)}^{2}$$
(3)
where xij is the yield for treatment i in year j, mi is the yield for treatment i across all years, mj is the yield for year j across all treatments, and m is the average yield for all treatments across all years. When Wi2 is close to 0, the yield for treatment i is very stable.Analysis of crop yield trendsA simple linear regression analysis of grain yield (slopes and P values) over the years was performed to identify the yield trend (Choudhary et al.27):$$Y=a+bt$$
(4)
where Y is the crop yield (t/ha), a is a constant, t is the time in years, and b is the slope, or magnitude of the yield trend (annual rate of change in yield).Analysis of variance (ANOVA) was performed using MATLAB R2019b in order to compare crop yields in the long term experiment. Yield stability and univariate linear regression equations were created and statistically analyzed using the software toolbox. The coefficients of variation for yields, yield sustainability indexes, and graphs presented in this paper were calculated and drawn using MATLAB; differences were considered to be significant when P  More

Henneron, L., Cros, C., Picon-Cochard, C., Rahimian, V. & Fontaine, S. Plant economic strategies of grassland species control soil carbon dynamics through rhizodeposition. J. Ecol. 108, 528–545 (2020).CAS 
Article 

Google Scholar 
Arft, A. M. et al. Responses of tundra plants to experimental warming: meta-analysis of the international tundra experiment. Ecol. Monogr. 69, 491–511 (1999).
Google Scholar 
Bloom, A. A., Exbrayat, J.-F., van der Velde, I. R., Feng, L. & Williams, M. The decadal state of the terrestrial carbon cycle: global retrievals of terrestrial carbon allocation, pools, and residence times. Proc. Natl Acad. Sci. USA 113, 1285–1290 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ma, H. et al. The global distribution and environmental drivers of aboveground versus belowground plant biomass. Nat. Ecol. Evol. 5, 1110-+ (2021).PubMed 
Article 

Google Scholar 
Mokany, K., Raison, R. J. & Prokushkin, A. S. Critical analysis of root: shoot ratios in terrestrial biomes. Glob. Change Biol. 12, 84–96 (2006).ADS 
Article 

Google Scholar 
Shipley, B. & Meziane, D. The balanced-growth hypothesis and the allometry of leaf and root biomass allocation. Funct. Ecol. 16, 326–331 (2002).Article 

Google Scholar 
Eziz, A. et al. Drought effect on plant biomass allocation: a meta‐analysis. Ecol. Evolution 7, 11002–11010 (2017).Article 

Google Scholar 
Yan, Z. et al. Biomass allocation in response to nitrogen and phosphorus availability: Insight from experimental manipulations of Arabidopsis thaliana. Front. Plant Sci. 10, 598 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Li, C. et al. Precipitation and nitrogen addition enhance biomass allocation to aboveground in an alpine steppe. Ecol. Evol. 9, 12193–12201 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Piao, S. et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature 451, 49–52 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kim, J.-S. et al. Reduced North American terrestrial primary productivity linked to anomalous Arctic warming. Nat. Geosci. 10, 572–576 (2017).ADS 
CAS 
Article 

Google Scholar 
Lin, D., Xia, J. & Wan, S. Climate warming and biomass accumulation of terrestrial plants: a meta-analysis. N. Phytol. 188, 187–198 (2010).Article 

Google Scholar 
Fernandez, C. W. et al. Ectomycorrhizal fungal response to warming is linked to poor host performance at the boreal-temperate ecotone. Glob. Change Biol. 23, 1598–1609 (2017).ADS 
Article 

Google Scholar 
Keller, J. A. & Shea, K. Warming and shifting phenology accelerate an invasive plant life cycle. Ecology 102, e03219 (2020).PubMed 

Google Scholar 
Cavagnaro, R. A., Oyarzabal, M., Oesterheld, M. & Grimoldi, A. A. Screening of biomass production of cultivated forage grasses in response to mycorrhizal symbiosis under nutritional deficit conditions. Grassl. Sci. 60, 178–184 (2014).Article 

Google Scholar 
Rasheed, M. U. et al. The responses of shoot-root-rhizosphere continuum to simultaneous fertilizer addition, warming, ozone and herbivory in young Scots pine seedlings in a high latitude field experiment. Soil Biol. Biochem. 114, 279–294 (2017).CAS 
Article 

Google Scholar 
Xu, M., Liu, M., Xue, X. & Zhai, D. Warming effects on plant biomass allocation and correlations with the soil environment in an alpine meadow, China. J. Arid Land 8, 773–786 (2016).Article 

Google Scholar 
Zhou, X., Talley, M. & Luo, Y. Biomass, litter, and soil respiration along a precipitation gradient in southern great plains, USA. Ecosystems 12, 1369–1380 (2009).CAS 
Article 

Google Scholar 
Hertel, D., Strecker, T., Mueller-Haubold, H. & Leuschner, C. Fine root biomass and dynamics in beech forests across a precipitation gradient – is optimal resource partitioning theory applicable to water-limited mature trees? J. Ecol. 101, 1183–1200 (2013).Article 

Google Scholar 
Zhou, L. et al. Responses of biomass allocation to multi-factor global change: a global synthesis. Agriculture, Ecosyst. Environ. 304, 107115 (2020).CAS 
Article 

Google Scholar 
Poorter, H. et al. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. N. Phytol. 193, 30–50 (2012).CAS 
Article 

Google Scholar 
Gorka, S., Dietrich, M., Mayerhofer, W., Gabriel, R. & Kaiser, C. Rapid transfer of plant photosynthates to soil bacteria via ectomycorrhizal hyphae and its interaction with nitrogen availability. Front. Microbiol. 10, 168 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, W. et al. Root morphology and mycorrhizal symbioses together shape nutrient foraging strategies of temperate trees. Proc. Natl Acad. Sci. USA 113, 8741–8746 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Terrer, C. et al. A trade-off between plant and soil carbon storage under elevated CO2. Nature 591, 599–603 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Averill, C., Dietze, M. C. & Bhatnagar, J. M. Continental-scale nitrogen pollution is shifting forest mycorrhizal associations and soil carbon stocks. Glob. Change Biol. 24, 4544–4553 (2018).ADS 
Article 

Google Scholar 
Cheng, L. et al. Mycorrhizal fungi and roots are complementary in foraging within nutrient patches. Ecology 97, 2815–2823 (2016).PubMed 
Article 

Google Scholar 
Averill, C. & Hawkes, C. V. Ectomycorrhizal fungi slow soil carbon cycling. Ecol. Lett. 19, 937–947 (2016).PubMed 
Article 

Google Scholar 
Hollister, R. D. & Flaherty, K. J. Above- and below-ground plant biomass response to experimental warming in northern Alaska. Appl. Vegetation Sci. 13, 378–387 (2010).
Google Scholar 
Johnson, N. C., Rowland, D. L., Corkidi, L. & Allen, E. B. Plant winners and losers during grassland N-eutrophication differ in biomass allocation and mycorrhizas. Ecology 89, 2868–2878 (2008).PubMed 
Article 

Google Scholar 
Xia, J., Yuan, W., Wang, Y. P. & Zhang, Q. Adaptive carbon allocation by plants enhances the terrestrial carbon sink. Sci. Rep. 7, 3341 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Litton, C. M. & Giardina, C. P. Below-ground carbon flux and partitioning: global patterns and response to temperature. Funct. Ecol. 22, 941–954 (2008).Article 

Google Scholar 
Wang, P. et al. Belowground plant biomass allocation in tundra ecosystems and its relationship with temperature. Environ. Res. Lett. 11, 055003 (2016).ADS 
Article 
CAS 

Google Scholar 
Hovenden, M. J. et al. Warming and elevated CO2 affect the relationship between seed mass, germinability and seedling growth in Austrodanthonia caespitosa, a dominant Australian grass. Glob. Change Biol. 14, 1633–1641 (2008).ADS 
Article 

Google Scholar 
Olszyk, D. M. et al. Whole-seedling biomass allocation, leaf area, and tissue chemistry for Douglas-fir exposed to elevated CO2 and temperature for 4 years. Can. J. For. Res. 33, 269–278 (2003).CAS 
Article 

Google Scholar 
Parmesan, C. & Hanley, M. E. Plants and climate change: complexities and surprises. Ann. Bot. 116, 849–864 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Hagedorn, F., Gavazov, K. & Alexander, J. M. Above- and belowground linkages shape responses of mountain vegetation to climate change. Science 365, 1119-+ (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Pinto, R. S. & Reynolds, M. P. Common genetic basis for canopy temperature depression under heat and drought stress associated with optimized root distribution in bread wheat. Theor. Appl. Genet. 128, 575–585 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rasse, D. P., Rumpel, C. & Dignac, M. F. Is soil carbon mostly root carbon? Mechanisms for a specific stabilisation. Plant Soil 269, 341–356 (2005).CAS 
Article 

Google Scholar 
Phillips, R. P., Brzostek, E. & Midgley, M. G. The mycorrhizal-associated nutrient economy: a new framework for predicting carbon-nutrient couplings in temperate forests. N. Phytol. 199, 41–51 (2013).CAS 
Article 

Google Scholar 
Wieder, W. R., Cleveland, C. C., Smith, W. K. & Todd-Brown, K. Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441–444 (2015).ADS 
CAS 
Article 

Google Scholar 
Wang, P., Huang, K. & Hu, S. Distinct fine‐root responses to precipitation changes in herbaceous and woody plants: a meta‐analysis. N. Phytol. 225, 1491–1499 (2020).Article 

Google Scholar 
Ma, Z. et al. Evolutionary history resolves global organization of root functional traits. Nature 555, 94–97 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Prieto, I., Armas, C. & Pugnaire, F. I. Water release through plant roots: new insights into its consequences at the plant and ecosystem level. N. Phytol. 193, 830–841 (2012).Article 

Google Scholar 
Bai, W. et al. Increased temperature and precipitation interact to affect root production, mortality, and turnover in a temperate steppe: implications for ecosystem C cycling. Glob. Change Biol. 16, 1306–1316 (2010).ADS 
Article 

Google Scholar 
Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 5077 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Turner, B. L. Resource partitioning for soil phosphorus: a hypothesis. J. Ecol. 96, 698–702 (2008).CAS 
Article 

Google Scholar 
Phillips, L. A., Ward, V. & Jones, M. D. Ectomycorrhizal fungi contribute to soil organic matter cycling in sub-boreal forests. ISME J. 8, 699–713 (2014).CAS 
PubMed 
Article 

Google Scholar 
Gonzalez-Meler, M. A., Silva, L. B. C., Dias-De-Oliveira, E., Flower, C. E. & Martinez, C. A. Experimental air warming of a stylosanthes capitata, vogel dominated tropical pasture affects soil respiration and nitrogen dynamics. Front. Plant Sci. 8, 46 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Carrillo, Y., Pendall, E., Dijkstra, F. A., Morgan, J. A. & Newcomb, J. M. Response of soil organic matter pools to elevated CO2 and warming in a semi-arid grassland. Plant Soil 347, 339–350 (2011).CAS 
Article 

Google Scholar 
An, J. et al. Physiological and growth responses to experimental warming in first-year seedlings of deciduous tree species. Turkish J. Agriculture Forestry 41, 175–182 (2017).CAS 
Article 

Google Scholar 
Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Liu, R., Li, Y., Wang, Y., Ma, J. & Cieraad, E. Variation of water use efficiency across seasons and years: Different role of herbaceous plants in desert ecosystem. Sci. Total Environ. 647, 827–835 (2018).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Duarte, A. G. & Maherali, H. A meta-analysis of the effects of climate change on the mutualism between plants and arbuscular mycorrhizal fungi. Ecol. Evol. 12, https://doi.org/10.1002/ece3.8518 (2022).Bastos, A. & Fleischer, K. Fungi are key to CO2 response of soil. Nature 591, 532–534 (2021).ADS 
Article 
CAS 

Google Scholar 
Wang, X., Peng, L. & Jin, Z. Effects of AMF inoculation on growth and photosynthetic physiological characteristics of Sinocalycanthus chinensis under conditions of simulated warming. Acta Ecologica Sin. 36, 5204–5214 (2016).CAS 

Google Scholar 
Tedersoo, L., Bahram, M. & Zobel, M. How mycorrhizal associations drive plant population and community biology. Science 367, 6480 (2020).Article 
CAS 

Google Scholar 
Jing, X. et al. The links between ecosystem multifunctionality and above- and belowground biodiversity are mediated by climate. Nat. Commun. 6, 8159–8159 (2015).ADS 
PubMed 
Article 

Google Scholar 
Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).PubMed 
Article 
CAS 

Google Scholar 
IPCC. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change 1535 (Cambridge University Press, 2021).Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Task, G. Global Gridded Surfaces of Selected Soil Characteristics (IGBP-DIS) (2000).Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).Article 

Google Scholar 
Luo, Y. Q., Hui, D. F. & Zhang, D. Q. Elevated CO2 stimulates net accumulations of carbon and nitrogen in land ecosystems: A meta-analysis. Ecology 87, 53–63 (2006).PubMed 
Article 

Google Scholar 
Rosenberg, M. S., Adams, D. C. & Gurevitch, J. MetaWin: Statistical Software for Meta-analysis (Sinauer Associates, Incorporated, 2000).Kembel, S. W. et al. Picante: integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2018).Article 
CAS 

Google Scholar 
Calcagno, V. & De, C. M. Glmulti: an R package for easy automated model selection with (generalized) linear models. J. Stat. Softw. 34, https://doi.org/10.18637/jss.v034.i12 (2010).Pinheiro, J. C., Bates, D. J., Debroy, S. D. & Sakar, D. nlme: Linear and nonlinear mixed effects models. R. package version 3, 1–117 (2009).
Google Scholar 
Viechtbauer, W. Metafor: meta-analysis package for R. J. Stat. Softw. 2010, 1–10 (2010).
Google Scholar 
Rosseel, Y. Lavaan: an R package for structural equation modeling. J. Stat. Softw. 48, https://doi.org/10.18637/jss.v048.i02 (2012). More

Departamento de Biología, Escuela Politécnica Nacional del Ecuador, Ladrón de Guevara E11-253 y Andalucía, Quito, EcuadorSelene BáezBiology and Geology, Physics and Inorganic Chemistry, Universidad Rey Juan Carlos, Calle Tulipán s/n, Móstoles, Madrid, SpainLuis Cayuela & Guillermo Bañares de DiosDepartamento de Biología, Área de Botánica, Universidad Autónoma de Madrid, Madrid, Calle Darwin 2, ES–28049, Madrid, SpainManuel J. Macía, Celina Ben Saadi, Julia G. de Aledo & Laura Matas-GranadosCentro de Investigación en Biodiversidad y Cambio Global (CIBC-UAM), Universidad Autónoma de Madrid, Calle Darwin 2, ES–28049, Madrid, SpainManuel J. MacíaEscuela de Ciencias Agrícolas, Pecuarias y del Medio Ambiente, Universidad Nacional Abierta a Distancia de Colombia, Sede José Celestino Mutis, Cl. 14 Sur 14-23, Bogotá, ColombiaEsteban Álvarez-DávilaInstituto Experimental de Biología Luis Adam Briancon, Universidad Mayor Real y Pontificia San Francisco Xavier de Chuquisaca, Dalence 235, Sucre, BoliviaAmira Apaza-QuevedoDepartamento de Ciencias Biológicas y Agropecuarias, Universidad Técnica Particular de Loja, Ecuador. San Cayetano Alto s/n. Paris y Marcelino Chamagnat, 1101608, Loja, EcuadorItziar Arnelas & Carlos Iván EspinosaDepartamento de Biología. Grupo de Biología de Páramos y Ecosistemas Andinos, Universidad de Nariño, Calle 18 # 50-02 Ciudadela Universitaria Torobajo, Pasto, ColombiaNatalia Baca-Cortes, Marian Cabrera & María Elena Solarte-CruzDepartment of Environment, CAVElab – Computational and Applied Vegetation Ecology, Ghent University, Coupure links 653, B-9000, Gent, BelgiumMarijn Bauters & Hans VerbeeckInstituto de Ecología Regional, Universidad Nacional de Tucumán, CONICET, Residencia Universitaria Horco Molle, Edificio Las Cúpulas, 4107, Tucumán, ArgentinaCecilia BlundoHerbario UIS, Escuela de Biología, Universidad Industrial de Santander, Carrera. 27, calle 9a, Bucaramanga, ColombiaFelipe CastañoHerbario Nacional de Bolivia, Instituto de Ecología, Universidad Mayor de San Andrés, Calle 27 s/n, La Paz, BoliviaLeslie Cayola, Alfredo Fuentes, M. Isabel Loza & Carla MaldonadoCenter for Conservation and Sustainable Development, Missouri Botanical Garden, 4344 Shaw Blvd., St. Louis, MO, 63110, USALeslie Cayola, William Farfán-Rios, Alfredo Fuentes, M. Isabel Loza & J. Sebastián TelloSchool of Geography, University of Leeds, Leeds, LS2 9JT, UKBelén FadriqueLiving Earth Collaborative, Washington University, 1 Brookings Drive, St. Louis, MO, 63130, USAWilliam Farfán-RiosDepartment of Biology, University of Florida, 876 Newell Drive, ZIP 32611, Gainesville, Florida, USAClaudia Garnica-DíazInstituto de Investigación de Recursos Biológicos Alexander von Humboldt, Calle 28 A # 15-09, Bogotá, ColombiaMailyn González, Ana Belén Hurtado & Natalia NordenConservación Internacional, Colombia, Carrea 13 # 71-41, Bogotá, ColombiaDiego GonzálezInstitute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Am Kirchtor 1, D-06108, Halle, GermanyIsabell Hensen & Denis LippokEscuela de Ingeniería Agronómica, Universidad de Cuenca, Av. 12 de Abril y Av. Loja s/n, Cuenca, EcuadorOswaldo JadánGlobal Tree Conservation Program and the Center for Tree Science, The Morton Arboretum, Lisle, IL, 60532-1293, USAM. Isabel LozaFacultad de Ciencias Agrarias, Universidad Nacional de Jujuy, Alberdi 47, San Salvador de Jujuy, CP 4600, Jujuy, ArgentinaLucio MaliziaDepartment of Biology, Washington University, 1 Brookings Drive, St. Louis, MO, 63130, USAJonathan A. MyersAMAP (Botanique et Modélisation de l’Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université  de Montpellier, TA-A51/PS, Boulevard de la Lironde, 34398 cedex 5, Montpellier, FranceImma Oliveras MenorEnvironmental Change Institute, School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, UKImma Oliveras Menor & Greta WeithmannPlant Ecology and Ecosystems Research, University of Goettingen, Untere Karspüle 2, 37073, Goettingen, GermanyKerstin Pierick & Jürgen HomeierInstituto de Investigaciones para el Desarrollo Forestal (Indefor), Vía los Chorros de Milla, Mérida, VenezuelaHirma Ramírez-AnguloDepartamento de Biología, Universidad Nacional de Colombia, Cra 45 #26-85, Bogotá, ColombiaBeatriz Salgado-NegretSenckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberganlage 25, 60325, Frankfurt, GermanyMatthias SchleuningDepartment of Biology, Wake Forest University, Winston-Salem, NC, 27109, USAMiles SilmanWildlife Conservation Society (WCS), 2300 Southern Boulevard Bronx, New York, 10460, USAEmilio VilanovaFaculty of Resource Management, HAWK University of Applied Sciences and Arts, Büsgenweg 1 A, 37077, Goettingen, GermanyJürgen HomeierCentre of Biodiversity and Sustainable Land Use (CBL), University of Goettingen, Goettingen, GermanyJürgen HomeierL.C., J.H., M.J.M. and S.B. conceived the idea. S.B., L.C., M.J.M., J.A.M. and J.S.T. obtained funding and coordinated the L.E.C. and iDiv workshops. L.C., S.B., J.H. and K.P. compiled the data sets and performed data quality checks. L.C., S.B., J.H. and K.P. conceived and developed the figures. S.B., J.H. and L.C. wrote the manuscript. The rest of authors (ordered alphabetically) contributed data, revised and agreed on the final version of the manuscript. More

Thorson, G. Reproductive and larval ecology of marine bottom invertebrates. Biol. Rev. 25, 1–45 (1950).CAS 
PubMed 
Article 

Google Scholar 
Weersing, K. & Toonen, R. J. Population genetics, larval dispersal, and connectivity in marine systems. Mar. Ecol. Progr. Ser. 393, 1–12 (2009).ADS 
Article 

Google Scholar 
Hedgecock, D. Is gene flow from pelagic larval dispersal important in the adaptation and evolution of marine invertebrates?. Bull. Mar. Sci. 39, 550–564 (1986).
Google Scholar 
Jenkins, S. R. & Hawkins, S. J. Barnacle larval supply to sheltered rocky shores: a limiting factor?. Hydrobiologia 503, 143–151 (2003).Article 

Google Scholar 
Pineda, J., Hare, J. A. & Sponaugle, S. Consequences for population connectivity. Oceanography 20, 22–39 (2007).Article 

Google Scholar 
Shanks, A. L. Mechanisms of cross-shelf dispersal of larval invertebrates and fish. In Ecology of Marine Invertebrate Larvae (ed. McEdward, L. R.) 324–367 (CRC, Boca Raton, 1995).
Google Scholar 
Shanks, A. L. Pelagic larval duration and dispersal distance revisited. Biol. Bull. 216, 373–385 (2009).PubMed 
Article 

Google Scholar 
Bradford, R. W., Griffin, D. & Bruce, B. D. Estimating the duration of the pelagic phyllosoma phase of the southern rock lobster, Jasus edwardsii (Hutton). Mar. Freshw. Res. 66, 213–219 (2015).Article 

Google Scholar 
Mileikovsky, S. A. Speed of active movement of pelagic larvae of marine bottom invertebrates and their ability to regulate their vertical position. Mar. Biol. 23, 11–17 (1973).Article 

Google Scholar 
Garrison, L. P. Vertical migration behavior and larval transport in brachyuran crabs. Mar. Ecol. Progr. Ser. 176, 103–113 (1999).ADS 
Article 

Google Scholar 
Morgan, S. G. & Fisher, J. L. Larval behavior regulates nearshore retention and offshore migration in an upwelling shadow and along the open coast. Mar. Ecol. Progr. Ser. 404, 109–126 (2010).ADS 
Article 

Google Scholar 
Cowen, R. K. & Castro, L. R. Relation of coral reef fish larval distributions to island scale circulation around Barbados, west indies. Bull. Mar. Sci. 54, 228–224 (1994).
Google Scholar 
Rudorff, C. A. G., Lorenzzetti, J. A., Gherardia, D. F. M. & Lins-Oliveira, J. E. Modeling spiny lobster larval dispersion in the Tropical Atlantic. Fish. Res. 96, 206–215 (2009).Article 

Google Scholar 
Allee, W. C. Studies in marine ecology. IV. The effect of temperature in limiting the geographic range of invertebrates of the Woods Hole littoral. Ecology 4, 341–354 (1923).Article 

Google Scholar 
Burton, R. S. Intraspecific phylogeography across the Point Conception biogeographic boundary. Evolution 52, 734–745 (1998).PubMed 
Article 

Google Scholar 
Lancellotti, D. A. & Vasquez, J. A. Biogeographical patterns of benthic macroinvertebrates in the southeastern Pacific littoral. J. Biogeogr. 26, 1001–1006 (1999).Article 

Google Scholar 
Hormazabal, S., Shaffer, G. & Leth, O. Coastal transition zone off Chile. J. Geophys. Res. 109, C01021 (2004).ADS 

Google Scholar 
Mcdonald, A. M. The global ocean circulation: a hydrographic estimate and regional analysis. Prog. Oceanogr. 41, 281–382 (1998).ADS 
Article 

Google Scholar 
Montecino, V. & Lange, C. B. The Humboldt Current System: Ecosystem components and processes, fisheries, and sediment studies. Progr. Oceanogr. 83, 65–79 (2009).ADS 
Article 

Google Scholar 
Haye, P. A. et al. Phylogeographic structure in benthic marine invertebrates of the southeast Pacific Coast of Chile with differing dispersal potential. PLoS ONE 9, e88613 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kelly, R. P. & Palumbi, S. R. Genetic structure among 50 species of the northeastern Pacific rocky intertidal community. PLoS ONE 5, e8594 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gaylord, B. & Gaines, S. D. Temperature or transport? Range limits in marine species mediated solely by flow. Am. Nat. 155, 769–789 (2000).PubMed 
Article 

Google Scholar 
Wares, J. P., Gaines, S. D. & Cunningham, C. W. A comparative study of asymmetric migration events across a marine biogeographic boundary. Evolution 55, 295–306 (2001).CAS 
PubMed 
Article 

Google Scholar 
Rumrill, S. S. Natural mortality of marine invertebrate larvae. Ophelia 32, 163–198 (1990).Article 

Google Scholar 
Jenkins, S. R., Marshall, D. & Fraschetti, S. Settlement and recruitment. In Marine Hard Bottom Communities, Ecological Studies Vol. 206 (ed. Wahl, M.) 177–190 (Springer, Berlin, 2009).Chapter 

Google Scholar 
Marino, I. A. M. et al. Genetic heterogeneity in populations of the Mediterranean shore crab, Carcinus aestuarii (Decapoda, Portunidae), from the Venice Lagoon. Estuar. Coast. Shelf. Sci. 87, 135–144 (2010).ADS 
CAS 
Article 

Google Scholar 
Sernapesca. Estadística de pesca de Chile. http://www.sernapesca.cl/informes/estadisticas (2022).Nation JD (1975) The Genus Cancer: Crustacea: Brachyura): Systematics, biogeography and fossil record. Nat. Hist. Mus. Los Angeles County Sci, Bull. 23 (1975).Pardo, L. M., Fuentes, J. P., Olguin, A. & Orensanz, J. M. L. Reproductive maturity in the edible Chilean crab Cancer edwardsii: methodological and management considerations. J. Mar. Biol. Assoc. U. K. 89, 1627–1634 (2009).Article 

Google Scholar 
Rojas-Hernández, N., Veliz, D. & Pardo, L. M. Use of novel microsatellite markers for population and paternity analysis in the commercially important crab Metacarcinus edwardsii. Mar. Biol. Res. 10, 839–844 (2014).Article 

Google Scholar 
Pardo, L. M., Riveros, M. P., Fuentes, J. P., Rojas-Hernández, N. & Veliz, D. An effective sperm competition avoidance strategy in crabs drives genetic monogamy despite evidence of polyandry. Behav. Ecol. Sociobiol. 70, 73–81 (2016).Article 

Google Scholar 
Pardo, L. M. et al. High fishing intensity reduces females’ sperm reserve and brood fecundity in a eubrachyuran crab subject to sex- and size biased harvest. ICES J. Mar. Sci. 74, 2459–2469 (2017).Article 

Google Scholar 
Pardo, L. M., Mora-Vásquez, P. & Garcés-Vargas, J. Asentamiento diario de megalopas de jaibas del género Cancer en un estuario micromareal. Lat. Am. J. Aquat. Res. 40, 142–152 (2012).Article 

Google Scholar 
Pardo, L. M., Rubilar, P. R. & Fuentes, J. P. North Patagonian estuaries appear to function as nursery habitats for marble crab (Metacarcinus edwardsii). Reg. Stud. Mar. Sci. 36, 101315 (2020).
Google Scholar 
Quintana, R. Larval development of the Edible crab, Cancer edwardsi Bell, 1835 under laboratory conditions (Decapoda, Brachyura). Rep. USA Mar. Biol. Inst. 5, 1–19 (1983).
Google Scholar 
Rojas-Hernández, N., Veliz, D., Riveros, M. P., Fuentes, J. P. & Pardo, L. M. Highly connected populations and temporal stability in allelic frequencies of a harvested crab from southern Pacific. PLoS ONE 11, e0166029 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Strub, P. T., James, C., Montecino, V., Rutllant, J. A. & Blanco, J. L. Ocean circulation along the southern Chile transition region (38°–46°S): Mean, seasonal and interannual variability, with a focus on 2014–2016. Progr. Oceanogr. 172, 159–198 (2019).ADS 
Article 

Google Scholar 
Beerli, P., Mashayekhi, S., Sadeghi, M., Khodaei, M. & Shaw, K. Population genetic inference with MIGRATE. Curr. Protoc. Bioinform. 68, e87 (2019).Article 

Google Scholar 
Kilian, A. et al. Diversity arrays technology: A generic genome profiling technology on open platforms. Methods Mol. Biol. 888, 67–89 (2012).PubMed 
Article 

Google Scholar 
Chang, C. C. et al. Second-generation PLINK: Rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Weiss, M. et al. Influence of temperature on the larval development of the edible crab, Cancer pagurus. J. Mar Biol. Assoc. UK 89, 753–759 (2009).CAS 
Article 

Google Scholar 
Pampoulie, C. et al. A pilot genetic study reveals the absence of spatial genetic structure in Norway lobster (Nephrops norvegicus) on fishing grounds in Icelandic waters. ICES J. Mar. Sci. 68, 20–25 (2011).Article 

Google Scholar 
Costlow, J. D. J. & Bookhout, C. G. The larval development of Callinectes sapidus Rathbun reared in the laboratory. Biol. Bull. 116, 373–396 (1959).Article 

Google Scholar 
Ungfors, A., McKeown, N. J., Shaw, P. W. & Andre, C. Lack of spatial genetic variation in the edible crab (Cancer pagurus) in the Kattegat – Skagerrak area. ICES J. Mar. Sci. 66, 462–469 (2009).Article 

Google Scholar 
Lacerda, A. L. F. et al. High connectivity among blue crab (Callinectes sapidus) populations in the Western South Atlantic. PLoS ONE 11, e0153124 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Taylor, M. S. & Hellberg, M. E. Comparative phylogeography in a genus of coral reef fishes: biogeographic and genetic concordance in the Caribbean. Mol. Ecol. 15, 695–707 (2006).CAS 
PubMed 
Article 

Google Scholar 
Arranz, V., Fewster, R. M. & Lavery, S. D. Geographic concordance of genetic barriers in New Zealand coastal marine species. Aquat. Conserv. Mar. Freshw. Ecosyst. 31, 3607–3625 (2021).Article 

Google Scholar 
Ayre, D. J., Minchinton, T. E. & Perrin, C. Does life history predict past and current connectivity for rocky intertidal invertebrates across a marine biogeographic barrier?. Mol. Ecol. 18, 1887–1903 (2009).CAS 
PubMed 
Article 

Google Scholar 
Barber, P. H., Erdmann, M. V. & Palumbi, S. R. Comparative phylogeography of three codistributed stomatopods: origins and timing of regional lineage diversification in the coral triangle. Evolution 60, 1825–1839 (2006).PubMed 
Article 

Google Scholar 
Macaya, E. C. & Zuccarello, G. C. Genetic structure of the giant kelp Macrocystis pyrifera along the southeastern Pacific. Mar. Ecol. Progr. Ser. 420, 103–112 (2010).ADS 
Article 

Google Scholar 
Ruiz, M., Tarifeño, E., Llanos-Rivera, A., Padget, C. & Campos, B. Efecto de la temperatura en el desarrollo embrionario y larval del mejillón, Mytilus galloprovincialis (Lamarck 1819). Rev. Biol. Mar. Oceanogr. 431, 51–61 (2008).
Google Scholar 
Toro, J. E., Castro, G. C., Ojeda, J. A. & Vergara, A. M. Allozymic variation and differentiation in the Chilean blue mussel, Mytilus chilensis, along its natural distribution. Genet. Mol. Biol. 29, 174–179 (2006).CAS 
Article 

Google Scholar 
Araneda, C., Larraín, M. A., Hecht, B. & Narum, S. Adaptive genetic variation distinguishes Chilean blue mussels (Mytilus chilensis) from different marine environments. Ecol. Evol. 6, 3632–3644 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Disalvo, L. H. Observations on the larval and post-metamorphic life of Concholepas concholepas (Bruguière, 1789) in laboratory culture. Veliger 30, 358–368 (1988).
Google Scholar 
Cardenas, L., Castilla, J. C. & Viard, F. Hierarchical analysis of the population genetic structure in Concholepas concholepas, a marine mollusk with a long-lived dispersive larva. Mar. Ecol. 37, 359–369 (2016).ADS 
Article 

Google Scholar 
Domingues, C. P., Creer, S., Taylor, M. I., Queiroga, H. & Carvalho, G. R. Genetic structure of Carcinus maenas within its native range: larval dispersal and oceanographic variability. Mar. Ecol. Progr. Ser. 410, 111–123 (2010).ADS 
Article 

Google Scholar 
Domingues, C. P., Creer, S., Taylor, M. I., Queiroga, H. & Carvalho, G. R. Temporal genetic homogeneity among shore crab (Carcinus maenas) larval events supplied to an estuarine system on the Portuguese northwest coast. Heredity 106, 832–840 (2011).CAS 
PubMed 
Article 

Google Scholar 
Vadopalas, B., Pietsch, T. & Friedman, C. The proper name for the geoduck: resurrection of Panopea generosa Gould, 1850, from the synonymy of Panopea abrupta (Conrad, 1849) (Bivalvia: Myoida: Hiatellidae). Malacologia 52, 169–173 (2010).Article 

Google Scholar 
Cassista, M. C. & Hart, M. W. Spatial and temporal genetic homogeneity in the Arctic surfclam (Mactromeris polynyma). Mar. Biol. 152, 569–579 (2007).Article 

Google Scholar 
Li, G. & Hedgecock, D. Genetic heterogeneity, detected by PCR-SSCP, among samples of larval Pacific oysters (Crassostrea gigas) supports the hypothesis of large variance in reproductive success. Can. J. Fish. Aquat. Sci. 55, 1025–1033 (1998).CAS 
Article 

Google Scholar 
Schmidt, P. S., Phifer-Rixey, M., Taylor, G. M. & Christner, J. Genetic heterogeneity among intertidal habitats in the flat periwinkle, Littorina obtusata. Mol. Ecol. 16, 2393–2404 (2007).CAS 
PubMed 
Article 

Google Scholar 
Dambach, J., Raupach, M. J., Leese, F., Schwarzer, J. & Engler, J. O. Ocean currents determine functional connectivity in an Antarctic deep-sea shrimp. Mar. Ecol. 37, 1336–1344 (2016).ADS 
CAS 
Article 

Google Scholar 
Reid, K. et al. Secondary contact and asymmetrical gene flow in a cosmopolitan marine fish across the Benguela upwelling zone. Heredity 117, 307–315 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hu, Z.-M., Zhang, J., Lopez-Bautista, J. & Duan, D.-L. Asymmetric genetic exchange in the brown seaweed Sargassum fusiforme (Phaeophyceae) driven by oceanic currents. Mar. Biol. 160, 1407–1414 (2013).Article 

Google Scholar 
Xuereb, A. et al. Asymmetric oceanographic processes mediate connectivity and population genetic structure, as revealed by RADseq, in a highly dispersive marine invertebrate (Parastichopus californicus). Mol. Ecol. 27, 2347–2364 (2018).PubMed 
Article 

Google Scholar 
Becker, R. A. & Wilks, A. R. R version by Ray Brownrigg. mapdata: Extra Map Databases. R package version 2.3.0. (2018b).Becker, R.A. & Wilks, A. R. R version by Ray Brownrigg. Enhancements by TP Minka and A Deckmyn.maps: Draw Geographical Maps. R package version 3.3.0. https://CRAN.R-project.org/package=maps (2018a).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2022).
Google Scholar 
Grube, B., Unmack, P. J., Berry, O. F. & Georges, A. dartr: An R package to facilitate analysis of SNP data generated from reduced representation genome sequencing. Mol. Ecol. Resour. 18, 691–699 (2018).Article 

Google Scholar 
Foll, M. & Gaggiotti, O. E. A genome scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180, 977–993 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Flanagan, S. P. & Jones, A. G. Constraints on the Fst-heterozygosity outlier approach. J. Hered 108, 561–573 (2017).PubMed 
Article 

Google Scholar 
Keenan, K., McGinnity, P., Cross, T. F., Crozier, W. W. & Prodohl, P. A. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol Evol 4, 782–788 (2013).Article 

Google Scholar 
Belkhir, K., Borsa, P., Chikhi, L., Raufaste, N. & Bonhomme, F. GENETIX 4.05, Logiciel sous Windows pour la Genetique des Populations. Laboratoire Genome, Populations, Interactions, CNRS UMR 5000 (Université de Montpellier II, Montpellier, France, 2000).Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pritchard, J. K., Wen, X. & Falush, D. Documentation for Structure Software: Version 2.3. University of Oxford http://pritch.bsd.uchicago.edu/structure.html (2010).Beerli, P. & Felsenstein, J. Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach. Proc. Natl. Acad. Sci. U.S.A. 98, 4563–4568 (2001).ADS 
CAS 
PubMed 
PubMed Central 
MATH 
Article 

Google Scholar 
Petkova, D., Novembre, J. & Stephens, M. Visualizing spatial population structure with estimated effective migration surfaces. Nat. Genet. 48, 94–100 (2016).CAS 
PubMed 
Article 

Google Scholar  More

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

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