Ecology

1.Binford, L. R. Constructing Frames of Reference: An Analytical Method for Archaeological Theory Building Using Hunter-Gatherer and Environmental Data Sets (Univ. of California Press, 2001).2.Kelly, R. L. The Lifeways of Hunter-Gatherers: The Foraging Spectrum (Cambridge Univ. Press, 2013).3.Tallavaara, M., Eronen, J. T. & Luoto, M. Productivity, biodiversity, and pathogens influence the global hunter-gatherer population density. Proc. Natl Acad. Sci. USA 115, 1232–1237 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Eriksson, A. et al. Late Pleistocene climate change and the global expansion of anatomically modern humans. Proc. Natl Acad. Sci. USA 109, 16089–16094 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Gurven, M. D. & Davison, R. J. Periodic catastrophes over human evolutionary history are necessary to explain the forager population paradox. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1902406116 (2019).6.Tallavaara, M., Luoto, M., Korhonen, N., Järvinen, H. & Seppä, H. Human population dynamics in Europe over the Last Glacial Maximum. Proc. Natl Acad. Sci. USA 112, 8232–8237 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Bradshaw, C. J. A. et al. Minimum founding populations for the first peopling of Sahul. Nat. Ecol. Evol. 3, 1057–1063 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Kavanagh, P. H. et al. Hindcasting global population densities reveals forces enabling the origin of agriculture. Nat. Hum. Behav. 2, 478–484 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Porter, C. C. & Marlowe, F. W. How marginal are forager habitats? J. Archaeol. Sci. 34, 59–68 (2007).Article 

Google Scholar 
10.Reyes-García, V. & Pyhälä, A. Hunter-Gatherers in a Changing World (Springer International Publishing, 2017).11.Lee, R. B. & Daly, R. The Cambridge Encyclopedia of Hunters and Gatherers (Cambridge Univ. Press, 1999).12.Kitanishi, K. Seasonal changes in the subsistence activities and food intake of the Aka hunter-gatherers in northeastern Congo. Afr. Study Monogr. 16, 73–118 (1995).
Google Scholar 
13.Timmermann, A. & Friedrich, T. Late Pleistocene climate drivers of early human migration. Nature 538, 92–95 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Keeley, L. H. Hunter-gatherer economic complexity and ‘population pressure’: a cross-cultural analysis. J. Anthropol. Archaeol. 7, 373–411 (1988).15.Fisher, J. B., Huntzinger, D. N., Schwalm, C. R. & Sitch, S. Modeling the terrestrial biosphere. Annu. Rev. Environ. Resour. 39, 91–123 (2014).Article 

Google Scholar 
16.Pachzelt, A., Forrest, M., Rammig, A., Higgins, S. I. & Hickler, T. Potential impact of large ungulate grazers on African vegetation, carbon storage and fire regimes. Glob. Ecol. Biogeogr. 24, 991–1002 (2015).Article 

Google Scholar 
17.Zhu, D. et al. The large mean body size of mammalian herbivores explains the productivity paradox during the Last Glacial Maximum. Nat. Ecol. Evol. 2, 640–649 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Dyble, M., Thorley, J., Page, A. E., Smith, D. & Migliano, A. B. Engagement in agricultural work is associated with reduced leisure time among Agta hunter-gatherers. Nat. Hum. Behav. 3, 792–796 (2019).19.Hill, K., Kaplan, H., Hawkes, K. & Hurtado, A. M. Men’s time allocation to subsistence work among the Ache of eastern Paraguay. Hum. Ecol. 13, 29–47 (1985).Article 

Google Scholar 
20.Hill, K., Hawkes, K., Hurtado, M. & Kaplan, H. Seasonal variance in the diet of Ache hunter-gatherers in eastern Paraguay. Hum. Ecol. 12, 101–135 (1984).CAS 
Article 

Google Scholar 
21.Marlowe, F. W. et al. Honey, Hadza, hunter-gatherers, and human evolution. J. Hum. Evol. 71, 119–128 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Cordain, L. et al. Plant–animal subsistence ratios and macronutrient energy estimations in worldwide hunter-gatherer diets. Am. J. Clin. Nutr. 71, 682–692 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Gurven, M. & Kaplan, H. Longevity among hunter-gatherers: a cross-cultural examination. Popul. Dev. Rev. 33, 321–365 (2007).Article 

Google Scholar 
24.Klein Goldewijk, K., Beusen, A. & Janssen, P. Long-term dynamic modeling of global population and built-up area in a spatially explicit way: HYDE 3.1. Holocene 20, 565–573 (2010).Article 

Google Scholar 
25.Marlowe, F. W. Hunter-gatherers and human evolution. Evol. Anthropol. 14, 54–67 (2005).Article 

Google Scholar 
26.Burger, J. R. & Fristoe, T. S. Hunter-gatherer populations inform modern ecology. Proc. Natl Acad. Sci. USA 115, 1137–1139 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.Hurtado, A. M. & Hill, K. R. Seasonality in a foraging society: variation in diet, work effort, fertility, and sexual division of labor among the Hiwi of Venezuela. J. Anthropol. Res. 46, 293–346 (1990).Article 

Google Scholar 
28.Wilmsen, E. N. Studies in diet, nutrition, and fertility among a group of Kalahari Bushmen in Botswana. Soc. Sci. Inf. 21, 95–125 (1982).Article 

Google Scholar 
29.Lee, R. B. in Man the Hunter (eds. Lee, R. B. & DeVore, I.) 30–48 (Aldine de Gruyter, 1968).30.Hamilton, M. J., Milne, B. T., Walker, R. S. & Brown, J. H. Nonlinear scaling of space use in human hunter-gatherers. Proc. Natl Acad. Sci. USA 104, 4765–4769 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Messer, E. Anthropological perspectives on diet. Annu. Rev. Anthropol. 13, 205–249 (1984).Article 

Google Scholar 
32.Testart, A. et al. The significance of food storage among hunter-gatherers: residence patterns, population densities, and social Inequalities [and Comments and Reply]. Curr. Anthropol. 23, 523–537 (1982).Article 

Google Scholar 
33.Winterhalder, B. Diet choice, risk, and food sharing in a stochastic environment. J. Anthropol. Archaeol. 5, 369–392 (1986).Article 

Google Scholar 
34.Kelly, R. L., Pelton, S. R. & Robinson, E. in Towards a Broader View of Hunter-Gatherer Sharing (eds Lavi, N. & Friesem, D. E.) Ch. 10 (McDonald Institute for Archaeological Research, 2019).35.Joannes-Boyau, R. et al. Elemental signatures of Australopithecus africanus teeth reveal seasonal dietary stress. Nature 572, 112–115 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Smits, S. A. et al. Seasonal cycling in the gut microbiome of the Hadza hunter-gatherers of Tanzania. Science 357, 802–806 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Barnosky, A. D. Assessing the causes of Late Pleistocene extinctions on the continents. Science 306, 70–75 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Henrich, J. Demography and cultural evolution: how adaptive cultural processes can produce maladaptive losses—the Tasmanian case. Am. Antiq. 69, 197–214 (2004).Article 

Google Scholar 
39.Powell, A., Shennan, S. & Thomas, M. G. Late Pleistocene demography and the appearance of modern human behavior. Science 324, 1298–1301 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.D’Alpoim Guedes, J. A., Crabtree, S. A., Bocinsky, R. K. & Kohler, T. A. Twenty-first century approaches to ancient problems: climate and society. Proc. Natl Acad. Sci. USA 113, 14483–14491 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
41.Cegielski, W. H. & Rogers, J. D. Rethinking the role of Agent-based modeling in archaeology. J. Anthropol. Archaeol. 41, 283–298 (2016).Article 

Google Scholar 
42.Axtell, R. L. et al. Population growth and collapse in a multiagent model of the Kayenta Anasazi in long house valley. Proc. Natl Acad. Sci. USA 99, 7275–7279 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Hayden, B. Research and development in the stone age: technological transitions among hunter-gatherers. Curr. Anthropol. 22, 519–548 (1981).Article 

Google Scholar 
44.Itkonen, T. I. Suomen Lappalaiset Vuoteen 1945. Ensimmäinen Osa (WSOY, 1848).45.Kirby, K. R. et al. D-PLACE: a global database of cultural, linguistic and environmental diversity. PLoS ONE 11, e0158391 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
46.MODIS NPP (MOD17A3) (NTSG, accessed 12 March 2015); http://files.ntsg.umt.edu/data/NTSG_Products/MOD17/47.Defries, R.S. et al. ISLSCP II Continuous Fields of Vegetation Cover, 1992–1993 (ORNL DAAC, 2009); https://doi.org/10.3334/ORNLDAAC/93148.Bodesheim, P., Jung, M., Gans, F., Mahecha, M. D. & Reichstein, M. Upscaled diurnal cycles of land–atmosphere fluxes: a new global half-hourly data product. Earth Syst. Sci. Data 10, 1327–1365 (2018).Article 

Google Scholar 
49.Šímová, I. & Storch, D. The enigma of terrestrial primary productivity: measurements, models, scales and the diversity–productivity relationship. Ecography 40, 239–252 (2017).Article 

Google Scholar 
50.Bontemps, S. et al. Consistent global land cover maps for climate modelling communities: current achievements of The ESA Land Cover CCI. In Proc. ESA Living Planet Symposium 2013 (ESA, 2013).51.Bliege Bird, R. & Bird, D. W. Why women hunt—risk and contemporary foraging in a western desert aboriginal community. Curr. Anthropol. 49, 655–693 (2008).Article 

Google Scholar 
52.Bliege Bird, R., Codding, B. F. & Bird, D. W. What explains differences in men’s and women’s production? Hum. Nat. 20, 105–129 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
53.Reyes-García, V., Díaz-Reviriego, I., Duda, R., Fernández-Llamazares, Á. & Gallois, S. ‘Hunting otherwise’—women’s hunting in two contemporary forager-horticulturalist societies. Hum. Nat. 31, 203–221 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
54.Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochem. Cycles 19, GB1015 (2005).55.Hamilton, M. J., Lobo, J., Rupley, E., Youn, H. & West, G. B. The ecological and evolutionary energetics of hunter‐gatherer residential mobility. Evol. Anthropol. 25, 124–132 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Abrams, P. A. & Ginzburg, L. R. The nature of predation: prey dependent, ratio dependent or neither? Trends Ecol. Evol. 15, 337–341 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Winterhalder, B., Baillargeon, W., Cappelletto, F., Randolph Daniel, I. & Prescott, C. The population ecology of hunter-gatherers and their prey. J. Anthropol. Archaeol. 7, 289–328 (1988).Article 

Google Scholar 
58.Illius, A. W. & O’Connor, T. G. Resource heterogeneity and ungulate population dynamics. Oikos 89, 283–294 (2000).Article 

Google Scholar 
59.Golley, F. B. Energy values of ecological materials. Ecology 42, 581–584 (1961).Article 

Google Scholar 
60.Herbers, J. M. Time resources and laziness in animals. Oecologia 49, 252–262 (1981).PubMed 
Article 
PubMed Central 

Google Scholar 
61.Raichlen, D. A. et al. Sitting, squatting, and the evolutionary biology of human inactivity. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1911868117 (2020).62.Abrams, H., Jr. in Food and Evolution (eds Harris, M. & Ross, E.) 207–223 (Temple Univ. Press, 1987).63.Hanya, G. & Aiba, S. Fruit fall in tropical and temperate forests: implications for frugivore diversity. Ecol. Res. 25, 1081–1090 (2010).Article 

Google Scholar 
64.Gherardi, L. A. & Sala, O. E. Global patterns and climatic controls of belowground net carbon fixation. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2006715117 (2020).65.van Zonneveld, M. et al. Human diets drive range expansion of megafauna-dispersed fruit species. Proc. Natl Acad. Sci. USA 115, 3326–3331 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
66.Max R., Hannah R. & Esteban Ortiz-Ospina World Population Growth (GCDL, 2020); https://ourworldindata.org/world-population-growth67.Pontzer, H. et al. Metabolic acceleration and the evolution of human brain size and life history. Nature 533, 390 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Viovy, N. CRUNCEP Version 7—Atmospheric Forcing Data for the Community Land Model (Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, 2018); https://doi.org/10.5065/PZ8F-F01769.Sobol, I. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Simul. 55, 271–280 (2001).Article 

Google Scholar  More

Two databases including yield, management, and weather data for maize (n = 17,013) and soybean (n = 24,848) involving US crop performance trials conducted in 28 states between 2016 to 2018 for maize and between 2014 to 2018 for soybean, were developed (Fig. 1). Crop yield and management data were obtained from publicly available variety performance trials which are typically performed yearly in several locations across each state (see methods for more information). Final databases were separated in training (80% of database) and testing (20% of database) datasets using stratified sampling by year, use of irrigation, and soil type. For each crop, an extreme gradient boosting (XGBoost, see methods for more information) algorithm to estimate yield based on soil type and weather conditions (E), seed traits (G) and management practices (M) was developed (see variables listed in Tables S1 and S2 for maize and soybean, respectively, and data science workflow in Fig. S1).Figure 1Locations where maize and soybean trials were performed during the examined period. The map was developed in ArcGIS Pro 2.8.0 (https://www.esri.com).Full size imageThe developed algorithms exhibited a high degree of accuracy when estimating yield in independent datasets (test dataset not used for model calibration) (Fig. 2). For maize, the root mean square error (RMSE) and mean absolute error (MAE) was a respective 4.7 and 3.6% of the dataset average yield (13,340 kg/ha). For soybean, the respective RMSE and MAE was 6.4 and 4.9% of the dataset average yield (4153 kg/ha). As is evident in the graphs (Fig. 2), estimated yields exhibited a high degree of correlation with actual yields for both algorithms in the independent datasets. For maize and soybean, 72.3 and 60% of cases in the test dataset deviated less than 5% from actual yields, respectively. Maximum deviation for maize and soybean reached 43 and 70%, respectively. Data points with deviations greater than 15% from actual yield were 1.5% in maize and 3.6% in soybean databases. These results suggest that the developed algorithms can accurately estimate maize and soybean yields utilizing database-generated information involving reported environmental, seed genetic, and crop management variables.Figure 2Actual versus algorithm-derived maize (left) and soybean (right) yield in test datasets. Black solid line indicates y = x, red short-dashed lines, black dashed lines, and red long-dashed lines indicate ± 5, 10, and 15% deviation from the y = x line. RMSE, root mean square error; MAE, mean absolute error; r2, coefficient of determination; n = number of observations. Each observation corresponds to a yield of an individual cropping system in a specific environment (location-year).Full size imageIn contrast to statistical models, ML algorithms can be complex, and the effect of single independent variables may not obvious. However, accumulated local effects (ALE) plots14 can aid the understanding and visualization of important and possibly correlated features in ML algorithms. For both crops, indicatively important variables included sowing date, seeding rate, nitrogen fertilizer (for maize), row spacing (for soybean) and June to September cumulative precipitation (Fig. 3). Across the entire region and for both crops, the algorithm-derived trends suggest that above average yields occur in late April to early May sowing dates, but sharply decrease thereafter. Similar responses have been observed in many regional studies across the US for both, maize15,16,17,18 and soybean19. Similarly, simulated yield curves due to increasing seeding rate are in close agreement with previous maize20,21 and soybean22 studies. The maize algorithm has captured the increasing yield due to increasing N fertilizer rate. The soybean algorithm suggests that narrower row spacing resulted in above average yield compared to wider spacing. Such response has been observed in many regions across the US23. Season cumulative precipitation between 400 and 700 mm resulted in above average yields for both crops.Figure 3Accumulated local effect plots for maize sowing date (A), seeding rate (B), Nitrogen fertilizer rate (C), and cumulative precipitation between June and September (mm) (D), and soybean sowing date (E), seeding rate (F), row spacing (G), and cumulative precipitation between June and September (mm) (H).Full size imageThe responses in the ALE plots (Fig. 3) suggest that these algorithms have captured the general expected average responses for important single features. Nevertheless, our databases include hundreds of locations with diverse environments across the US and site-specific crop responses which may vary due to components of the G × E × M interaction. We argue that, instead of examining a single or low-order management interactions, site-specific evaluation of complex high order interactions (a.k.a. cropping systems) can reveal yield differences that current research approaches cannot fully explore and quantify. For example, sowing date exerts a well-known impact on maize and soybean yield. For each crop separately, by creating a hypothetical cropping system (a single combination of all management and traits in Tables S1 and S2) in a randomly chosen field in south central Wisconsin (latitude = 43.34, longitude = -89.38), and by applying the developed algorithms, we can generate estimates of maize and soybean yield. For that specific field and cropping system (out of the vast number of management combinations a farmer can choose from), maize yield with May 1st sowing was 711 kg/ha greater (6% increase) than June sowing (Fig. 4A). By creating scenarios with 256 background cropping system choices (Table S3), the resultant algorithm-derived yield estimate difference for the same sowing date contrast (averaged across varying cropping systems) was smaller but still positive (3% increase), although the range of possible yield differences was wider (Fig. 4B). However, when comparing, instead of averaging, the estimated yield potential among the simulated cropping systems, a 2903 kg/ha yield difference (25% difference) was observed (Fig. 4C). Interestingly, when focusing on the early sown fields that were expected to exhibit the greatest yield, the same yield difference was observed (Fig. 4D). This result shows that sub-optimal background management can mitigate the beneficial effect of early sowing (Table S4).Figure 4Maize yield difference (in kg/ha and percentage) due to sowing date (May 1st vs. June 1st) for a single identical background cropping system (A), maize yield difference due to sowing date when averaged across 256 (3 years × 256 cropping systems = 768 year-specific yields) (B), maize yield variability in each of the 256 cropping systems (C), and maize yield variability in each of the 128 cropping systems with early sowing (D). Soybean yield difference due to sowing date (May 1st vs June 1st) for a single identical background cropping system (E), soybean yield difference due to sowing date when averaged across 128 (5 years × 128 cropping systems = 640 year-specific yields) (F), soybean yield in each of the 128 cropping systems (G) and soybean yield variability due in each of the 64 cropping systems with early sowing (H). Within each panel, the horizontal red and grey lines indicate the boxplot with maximum and minimum yield, respectively. In the left four panels, boxes delimit first and third quartiles; solid lines inside boxes indicate median and green triangles indicate means. Upper and lower whiskers extend to maximum and minimum yields. Each maize and soybean cropping system is a respective 8-way and a 7-way interaction of management practices in a randomly chosen field in Wisconsin, USA (Table S3 and S5, respectively).Full size imageIn the case of soybean, a May 1st sowing resulted in greater yield (588 kg/ha; a 14% increase) than a June 1st in the single background cropping system (Fig. 4E). The result was consistent when yield differences due to sowing date were averaged across 128 background cropping system choices (Table S5) (Fig. 4F). Similar to what was observed in maize, among all cropping systems, yield varied by 1704 kg/ha (44% difference) (Fig. 4G). When focusing only on the early sown fields, a 1181 kg/ha yield difference (27% yield increase) was observed (Fig. 4H). In agreement with maize, this result highlights the importance of accounting for sub-optimal background management which can mitigate the beneficial effect of early sowing (Table S6).We note here the ability of farmers to change management practices can be limited due to an equipment constraint (e.g., change planter unit row width) or simply impossible (e.g., change the previous year’s crop). Thus, recommended management practices that were evaluated in studies that used specific background management may not be applicable in some instances. The benefits of the foregoing approach, which involves extensive up-to-date agronomic datasets and high-level computational programing, can have important and immediate implications in future agricultural trials. Our approach allows for more precise examination of complex management interactions in specific environments (soil type and growing season weather) across the US (region covered in Fig. 1). The ability to extract single management practice information (even across cropping systems) is also possible by utilizing ALE plots, or by calculation of the frequency at which a given level/rate of a management practice appeared among the highest yielding cropping systems (Tables S4 and S6).Among all available 30-d weather variables, many were strongly correlated in both crop databases (Figs. S2 and S3 for maize and soybean, respectively). Models using all 30-d interval variables with r  More

Sampling and genotypingWe collected hair and cheek swab samples from 77 men from geographically and linguistically distinct groups of the Caucasus: Kartvelian speakers from Georgia and Turkey, Northeast Caucasian speakers and Turkic speakers from the Russian Federation and Armenian speakers from Georgia’s southern province of Javakheti, descendants of the families displaced from Mush and Erzurum provinces of eastern Turkey in the early nineteenth century (Table 1, Fig. 1). To maximize the representativeness of the genetic signature of each population, the samples were collected from locals with no ancestors from outside of the respective ethnic/geographic population over the last three generations. DNA was extracted from follicles of 10–12 male chest hairs and cheek swab samples. Extraction was performed using Qiagen DNeasy Blood and Tissue kit, following the manufacturer’s recommendations (Qiagen, Valencia, CA, USA). The DNA samples were genotyped for 693,719 autosomal and 17,678 X-chromosomal SNPs by Family Tree DNA (FTDNA—Gene By Gene, Ltd, Houston, TX, www.familytreedna.com).Table 1 Modern study populations of the Caucasus. Latitude and longitude georeference population hubs.Full size tableFigure 1The distribution of the study populations: averaged centroids of ancient populations (uniquely colored points in the main map, see Table 2 for details) and hubs of the modern Caucasian populations (identified in the inset map, see Table 1 for details). Glacial human refugia extracted from Gavashelishvili and Tarkhnishvili5 are shaded in purple. The map is generated using QGIS Desktop 3.10.6-A Coruña (https://qgis.org).Full size imageOur dataset of modern Caucasian genotypes was supplemented with published 10 modern Mbuti (Supplementary Table S1) and 122 Upper Paleolithic-Mesolithic human genotypes, retrieved as a part of 1240 K dataset from David Reich’s Lab website, Harvard University (https://reich.hms.harvard.edu/downloadable-genotypes-present-day-and-ancient-dna-data-compiled-published-papers; see Supplementary Table S2 for details). The ancient genotypes were selected such that they either dated from the LGM or fell within the glacial refugia identified by Gavashelishvili and Tarkhnishvili5. We did so in order to maximize the genetic signature of potential refugial populations in our analysis. We divided the ancient genotypes into 2000-year-long intervals, and then grouped each of these intervals into geographic units (hereafter ancient populations, Table 2, Fig. 1). The modern and ancient genotypes were merged using PLINK 1.9 (PLINK 1.9: www.cog-genomics.org/plink/1.9/27.Table 2 Ancient study populations. The ancient genotypes are divided into 2000-year-long intervals, and then each of these intervals is grouped into geographic units (i.e. ancient populations). Age, latitude and longitude are averaged across each ancient population (see Supplementary Table S2 for details).Full size tableEthics statementThe research team members, through their contacts in the studied communities, inquired whether locals would voluntarily participate in genetic research that would help clarify the genetic makeup of the Caucasus. A verbal agreement was made with volunteer donors of DNA samples, according to which the results would be communicated, electronically or in hard copy, with participants individually. Participants were informed that, upon the completion of the lab work, the research would be published without mentioning the names of sample donors. Those who agreed provided us with the envelopes containing their chest hairs or cheek swab samples, with the birthplace of their ancestors (last three generations) written on the envelope or a piece of paper. In accordance with the preferences of the sample donors, the agreement was verbal and not written. The envelopes and papers are stored as evidence of voluntary provision of the samples and the related information. Analysis of data was done anonymously, using only location and ethnic information; only the first and third authors of the manuscript had access to names associated with the samples. Therefore, this study was based on noninvasive and nonintrusive sampling (volunteers provided hair and swab samples they collected themselves), and the information destined for open publication does not contain any personal information. The study methodology and the procedure of verbal consent was discussed in detail with and approved by the members of the Ilia State University Commission for Ethical Issues before the field survey started, and the commission decided that formal ethical approval was not needed for conducting this study. This is confirmed in a letter from the commission chairman, a copy of which has been provided to the journal editor as part of the submission process.Genetic affinity and geographyFirst, we measured genetic affinity between the modern Caucasian populations, and between the modern populations and the ancient populations of hunter-gatherers, and then tested whether the genetic affinity between these populations was determined by geographic features. Data were mapped using QGIS Desktop 3.10.6-A Coruña, whereas graphs were created using the “ggplot2” package28 in R version 3.5.229.To evaluate genetic affinities and structure of the modern populations, we used Wright’s fixation index (Fst), inbreeding coefficient, admixture analysis and the principal component analysis (PCA). For these procedures we filtered the raw SNP genotypes in PLINK 1.9, first removing all SNPs with the minor allele frequency  0.3, calculated in windows of 50 bp size and 10 bp steps (–maf 0.05 –indep-pairwise 50 10 0.3). Since all individuals in our dataset possess a single copy of the X-chromosome, we did not expect any differential ploidy bias, and SNPs on the X were treated similarly to those on the autosomes. Fst pairwise values were calculated using the smartpca program of EIGENSOFT30 with default parameters, inbreed: YES, and fstonly: YES. The relationship between the modern populations based on Fst values was visualized by constructing a neighbor-joining tree using the “ape” package31 in R version 3.5.2. The average and standard deviation of the inbreeding coefficient for each population was calculated using “fhat2” estimate of PLINK 1.9. The LD pruned genotypes were used in ADMIXTURE 1.3.032, performed in unsupervised mode in order to infer the population structure from the modern individuals. The number of clusters (k) was varied from 2 to 7 and the fivefold cross-validation error was calculated for each k33. We conducted principal components analysis in the smartpca program of EIGENSOFT30, using default parameters and the lsqproject: YES and numoutlieriter: 0 options. Eigenvectors of principal components were inferred with the modern populations from the Caucasus, while the ancient populations were then projected onto the PCA plots. We also assessed the relatedness between sampled individuals using kinship coefficients estimated by KING34.To quantify genetic affinities between the modern and ancient populations, we used the programs qp3Pop and qpDstat in the ADMIXTOOLS suite (https://github.com/DReichLab35 for f3- and f4-statistics, respectively. f3-statistics of the form f3(X,Y,Outgroup) measure the amount of shared genetic drift of populations X and Y after their divergence from an outgroup. We used an ancient population and a modern Caucasian population for X, Y and Mbuti as an outgroup. f4-statistics of the form f4(Outgroup,Test;X,Y) show if population Test is equally related to X and Y or shares an excess of alleles with either of the two. In the f4-statistic calculation we used Mbuti for Outgroup, a modern population of the Caucasus for Test, and X and Y for contemporaneous ancient populations. This meant that f4  0 indicated higher genetic affinity between the test population and Y.To quantify geographic features, we derived least-cost paths and measured least-cost distances (LCD) between the modern and ancient populations using the Least Cost Path Plugin for QGIS. The computation of LCD considers a friction grid that is a raster map where each cell indicates the relative difficulty (or cost) of moving through that cell. A least-cost path minimizes the sum of frictions of all cells along the path, and this sum is the least-cost distance (LCD). For impedance to human movement and expansion, we used 15 geographic features (Table 3). All gridded geographic features (i.e. raster layers) were resampled to a resolution of 1 km using the nearest-neighbor assignment technique. All possible subsets of the 15 geographic features, that did not cancel out each other, were used to calculate different variables of LCD. We assumed that most human movements occurred during climate warming events when the earth’s surface was not dramatically different from that of today, and hence used the current data of the geographic features.Table 3 Geographic features used in combinations to calculate least-cost distances (LCD) between ancient populations and modern Caucasians.Full size tableLinking genetic affinity and geographyGeneralized additive models (GAMs) were used to fit the outgroup f3-statistic to time and variously calculated LCD between the modern and ancient populations using the “mgcv” package36 in R version 3.5.2. Time between the modern and ancient populations was measured in BP (years before present, defined by convention as years before 1950 CE). We used GAMs because without any assumptions they are able to find nonlinear and non-monotonic relationships. GAMs were fitted using a Gamma family with a log link function. Penalized thin plate regression splines were used to represent all the smooth terms. The restricted maximum likelihood (REML) estimation method was implemented to estimate the smoothing parameter because it is the most robust of the available GAM methods36.Model and variable selection were performed by exploring LCD, time BP and the interaction term. The predictive power of the models was evaluated through a tenfold cross-validation. The cross-validation of many models was handled through R’s parallelization capabilities37,38. The best model was selected by the mean squared error of the cross-validation. Akaike’s Information Criterion (AIC) is generally used as a means for model selection. However, we preferred cross-validation for model selection because AIC a priori assumes that simpler models with the high goodness of fit are more likely to have the higher predictive power, while cross-validation without any a priori assumptions measures the predictive performance of a model by efficiently running model training and testing on the available data.We additionally validated the effect of different subsets of geographic features by assessing the relationship between statistically significant values of f4-statistic (i.e. |Z| > 3) and each subset. The relationship between f4-statistic of the form of f4(Outgroup,Test;X,Y) and geographic features was determined by measuring the agreement between the negative/positive signs of f4-statistic and the difference in LCD (LCD.D) for each pair of contemporaneous ancient populations X and Y. LCD.D was calculated as (LCD1–LCD2), where LCD1 was least-cost distance between the test population and X, and LCD2 was least-cost distance between the test population and Y. LCD.D  0 indicated less least-cost distance between Test and Y. So, the same sign of f4 and LCD.D values indicated agreement between geographic proximity and genetic affinity. We used Cohen’s kappa39 to measure the agreement.In order to test if geographic features (Table 3) accounted for present-day genetic differentiation in the Caucasus, we measured the relationship between Fst and LCD across the modern populations using the Mantel test in the “vegan” package40 in R version 3.5.2. In addition, we checked whether contribution from ancient samples was related to today’s genetic differentiation. To do so, we calculated median of f3-statistic of ancient populations of each geographic grouping (e.g. the following 6 populations made up one group: Balkans 39,950–41,950 BP, Balkans 37,950–39,950 BP, Balkans 31,950–33,950 BP, Balkans 9950–11,950 BP, Balkans 7950–9950 BP, Balkans 5950–7950 BP). Then we measured the manhattan distance of f3 median values of all combinations of the geographic groupings between the modern populations and compared the results to Fst and LCD using the Mantel test. More

Regular structures and isothermal theoremFor networks where each node represents a single individual, the isothermal theorem of evolutionary graph theory shows that the fixation probability is the same as the fixation probability of a well-mixed population if the temperature distribution is homogeneous across the whole population1. The temperature of a node defined as the sum over all the weights leads to that node. This theorem extends to structured meta-populations for any migration probability (lambda ): If the underlying structure of the meta-population that connects the patches is a regular network and the local population size is identical in each patch, the temperature of all individuals is identical, regardless of the value of the migration probability. Therefore, the fixation probability in a population with such a structure is the same as the fixation probability in a well-mixed population of the same total population size (N=sum _{j=1}^M N_j), given by ( phi _{mathrm{wm}}^N(r)).Small migration regimeIf the migration probability is small enough such that the time between two subsequent migration events (( sim frac{1}{lambda } )) is much longer than the absorption time within any patch, then at the time of each migration event we may suppose that the meta-population is in a homogeneous configuration22,28. In other words, the low migration regime is an approximation in which we neglect the probability that the meta-population is not in a homogeneous configuration at the time of migration events. We define a homogeneous configuration of the meta-population as a configuration in which in all patches either all individuals are mutants, or all are wild-types.Therefore, instead of having (2^N) states, where N is the population size, the system has only (2^M) states, where M is the number of patches. Thus, we can calculate the fixation probability exactly as in the case of a standard evolutionary graph model where each node represents a single individual but with a modified transition probabilities.In a network with homogeneous patches, in order to increase the number of homogeneous mutant-patches one individual mutant needs to migrate to one of its neighbouring homogeneous wild-type-patches and reaches fixation there. For example if node j is occupied by mutants and one of its neighbouring patches, node k, is occupied by wild-types, the probability that one mutant individual from patch j migrates to patch k and reaches fixation there is (frac{lambda }{mathrm{deg} (j)}phi _{mathrm{wm}}^{N_{k}}(r) ), where (mathrm{deg} (j) ) is the degree of node j to take into account that the mutant can move to different patches. This is analogous to the probability that one mutant in node j replaces one wild-type in node k ,(T^{jrightarrow k}), in the network of individuals.Similarly, if node j is occupied by wild-types and one of its neighbouring patches, node j, is occupied by mutants the probability that one wild-type individual from patch j migrates to patch k and reaches fixation there equals to (frac{lambda }{mathrm{deg} (j)}phi _{mathrm{wm}}^{N_{k}}(1/r) ) where (mathrm{deg} (j) ). Overall, we can move from network of individuals to the network of homogeneous patches by replacing the transition probabilities with the product of migration and fixation probabilities.Two-patch meta-populationThe simplest non-trivial case is the fixation probability in a two-patch meta-population with different local size for small migration probability (lambda ). If the migration probability (lambda ) is very small, a new mutant first needs to take over its own patch and only then the first migrant arrives in the second patch. To be more precise, the time between two migration events has to be much higher than the typical time that it takes for the migrant to take over the patch or go extinct again38. In this case, we can divide the dynamics into two phases: A first phase in which a mutant invades one patch and a second phase in which a homogeneous patch of mutants invades the whole meta-population. Assume a new mutation arises in patch 1. Only if this mutant reaches fixation in patch 1, it also has a chance to reach fixation in patch 2. When patch 1 consists of only mutants and patch 2 consists of only wild-types, there are two possibilities for the ultimate fate of the mutant:

(i)

Eventually, the offspring of one mutant selected from patch 1 for reproduction will migrate to patch 2 and reach fixation there. The wild-type goes extinct. This happens with probability ( frac{N_1 r}{N_1 r+N_2} phi _{mathrm{wm}}^{N_2}(r)).

(ii)

Eventually, the offspring of one wild-type selected from patch 2 for reproduction will migrate to patch 1 and the mutant goes extinct. This occurs with probability ( frac{N_2}{N_1r+N_2} phi _{mathrm{wm}}^{N_1}(tfrac{1}{r})).

Therefore, the probability that a single mutant arising in patch 1 reaches fixation in the entire population is $$begin{aligned} phi _{mathrm{wm}}^{N_1}(r) frac{frac{N_1 r}{N_1 r+N_2} phi _{mathrm{wm}}^{N_2}(r)}{frac{N_1 r}{N_1 r+N_2} phi _{mathrm{wm}}^{N_2}(r)+frac{N_2}{N_1r+N_2} phi _{mathrm{wm}}^{N_1}left( tfrac{1}{r}right) }=phi _{mathrm{wm}}^{N_1}(r) phi _{mathrm{wm}}^{N_2}(r) frac{1 }{ phi _{mathrm{wm}}^{N_2}(r) +frac{N_2}{N_1} frac{1}{r}phi _{mathrm{wm}}^{N_1} left( tfrac{1}{r}right) }. end{aligned}$$
(3a)
Similarly the probability that a mutant arising in patch 2 takes over the whole population equals$$begin{aligned} phi _{mathrm{wm}}^{N_2}(r) phi _{mathrm{wm}}^{N_1}(r) frac{1 }{phi _{mathrm{wm}}^{N_1}(r)+frac{N_1}{N_2} frac{1}{r} phi _{mathrm{wm}}^{N_2}left( tfrac{1}{r}right) }. end{aligned}$$
(3b)
If we assume that the mutant arises in a patch with a probability proportional to the patch size, the average fixation probability (phi _{bullet !!-!!bullet }) in a two patch population for small migration probability is the weighted sum of Eqs. (3a) and (3b),$$begin{aligned} phi _{bullet !!-!!bullet }&= phi _{mathrm{wm}}^{N_1}(r) phi _{mathrm{wm}}^{N_2}(r) nonumber \&quad times left( frac{frac{N_1}{N_1+N_2} }{ phi _{mathrm{wm}}^{N_2}(r) +frac{N_2}{N_1} frac{1}{r}phi _{mathrm{wm}}^{N_1}left( tfrac{1}{r}right) } +frac{frac{N_2}{N_1+N_2} }{ phi _{mathrm{wm}}^{N_1}(r) +frac{N_1}{N_2} frac{1}{r} phi _{mathrm{wm}}^{N_2}left( tfrac{1}{r}right) }right) . end{aligned}$$
(4)
In the case of neutrality, (r=1), we recover (phi _{bullet !!-!!bullet } = frac{1}{N_1+N_2})—the fixation probability in a population of the total size of the two patches. For identical patch sizes, ( N_1=N_2 ), Eq. (4) simplifies to$$begin{aligned} phi _{bullet !!-!!bullet } = left( phi _{mathrm{wm}}^{N_1}(r)right) ^2 frac{1}{phi _{mathrm{wm}}^{N_1}(r)+frac{1}{r} phi _{mathrm{wm}}^{N_1}left( tfrac{1}{r}right) } = phi _{mathrm{wm}}^{2 N_1}(r), end{aligned}$$
(5)
where the simplification to the fixation probability within a single population of size (2N_1) reflects the validity of the isothermal theorem.For (N_1 ne N_2), we approximate Eq. (4) for weak and strong selection. Let us first consider highly advantageous mutants, (r gg 1). In this case, we have (phi _{mathrm{wm}}^{N_1}(r) gg phi _{mathrm{wm}}^{N_1}(tfrac{1}{r})) and thus we can neglect the possibility that a wild-type takes over a mutant patch if patch sizes are sufficiently large. The probability (phi _{bullet !!-!!bullet } ) then becomes a weighted average reflecting patch sizes. For identical patch size (N_1=N_2 = N/2), it reduces to (phi _{bullet !!-!!bullet } approx phi _{mathrm{wm}}^{N_1}(r)=phi _{mathrm{wm}}^{N/2}(r)). In other words, taking over the first patch is sufficient to make fixation in the entire population certain. For patches of very different size, (N_1 gg N_2), we have (N approx N_1) and find (phi _{bullet !!-!! bullet } approx phi _{mathrm{wm}}^{N}(r), ) which implies that fixation is driven by the fixation process in the larger patch, regardless of where the mutant arises. Note that there is a difference between the case of identical patch size and very different patch size . The case of highly disadvantageous mutants, (r ll 1), can be handled in a very similar way.Next, we consider weak selection, (r approx 1). We can approximate the fixation probability as (phi _{mathrm{wm}}^{N}(r^{pm 1}) approx frac{1}{N} pm frac{N-1}{2N} (r-1)). With this, we find$$begin{aligned} phi _{bullet !!-!!bullet } approx frac{1}{N_1+N_2} +frac{1}{2} left( 1 – frac{1}{N_1+N_2} -frac{(N_1-N_2)^2}{(N_1^2+N_2^2)^2} N_1 N_2right) (r-1). end{aligned}$$
(6)
For identical patch size (N_1=N_2 = N/2), this reduces to$$begin{aligned} phi _{bullet !!-!!bullet } approx tfrac{1}{N} +tfrac{N-1}{2N} (r-1), end{aligned}$$
(7)
which is the known result for a single population of size (N=N_1+N_2). When patches have very different size, (N_1 gg N_2) such that (N approx N_1), we recover the same result. Thus, the difference between the fixation probability of a two-patch meta-population with identical patch size and the fixation probability of a two-patch meta-population with very different patch size that we found for highly advantageous mutants is no longer observed for weak selection.When migration probabilities become larger, our approximation is no longer valid and we need to rely on numerical approaches. Figure 2 illustrates the difference between the fixation probability of a two-patch structure meta-population and the equivalent well-mixed population of size (N_1+N_2 ) when migration is low using Eq. (4) and comparing with the numerical approach in Ref.39.While the fixation probability of the two-patch meta-population is very close to the fixation probability of the well-mixed population40, a close inspection reveals an interesting property: For low migration probabilities and (N_1 ne N_2), the two patch structure is a suppressor of selection in the original sense of Lieberman et al.1: For advantageous mutations, (r >1), it decreases the fixation probability, whereas for disadvantageous mutations, (r1) and negative for (r1 ) the minimum fixation probability occurs when the two patch sizes are identical, ( N_1=N_2=N/2 ). Similarly, for fitness values ( r More

1.Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton Univ. Press, 2001).2.Wills, C. et al. Nonrandom processes maintain diversity in tropical forests. Science 311, 527–531 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Evol. Syst. 31, 343–366 (2000).Article 

Google Scholar 
4.Ollerton, J. Pollinator diversity: distribution, ecological function, and conservation. Annu. Rev. Ecol. Evol. Syst. 48, 353–376 (2017).Article 

Google Scholar 
5.Vamosi, J. C. et al. Pollination decays in biodiversity hotspots. Proc. Natl Acad. Sci. USA 103, 956–961 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Bennett, J. M. et al. Land use and pollinator dependency drives global patterns of pollen limitation in the Anthropocene. Nat. Commun. 11, 3999 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Potts, S. G. et al. Global pollinator declines: trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).PubMed 
Article 

Google Scholar 
8.Vamosi, J. C., Magallon, S., Mayrose, I., Otto, S. P. & Sauquet, H. Macroevolutionary patterns of flowering plant speciation and extinction. Annu. Rev. Plant Biol. 69, 685–706 (2018).CAS 
PubMed 
Article 

Google Scholar 
9.Ollerton, J., Winfree, R. & Tarrant, S. How many flowering plants are pollinated by animals? Oikos 120, 321–326 (2011).Article 

Google Scholar 
10.Rodger, J. G. et al. 2021 Widespread vulnerability of plant seed production to pollinator decline. Sci. Adv. (in the press).11.Pimm, S. L., Jones, H. L. & Diamond, J. On the risk of extinction. Am. Nat. 132, 757–785 (1988).Article 

Google Scholar 
12.Sargent, R. D. & Ackerly, D. D. Plant–pollinator interactions and the assembly of plant communities. Trends Ecol. Evol. 23, 123–130 (2008).PubMed 
Article 

Google Scholar 
13.Benadi, G. & Pauw, A. Frequency dependence of pollinator visitation rates suggests that pollination niches can allow plant species coexistence. J. Ecol. 106, 1892–1901 (2018).Article 

Google Scholar 
14.Bruno, J. F., Stachowicz, J. J. & Bertness, M. D. Inclusion of facilitation into ecological theory. Trends Ecol. Evol. 18, 119–125 (2003).Article 

Google Scholar 
15.Benadi, G., Bluthgen, N., Hovestadt, T. & Poethke, H. J. Population dynamics of plant and pollinator communities: stability reconsidered. Am. Nat. 179, 157–168 (2012).PubMed 
Article 

Google Scholar 
16.Moeller, D. A. Facilitative interactions among plants via shared pollinators. Ecology 85, 3289–3301 (2004).Article 

Google Scholar 
17.Bergamo, P. J., Susin Streher, N., Traveset, A., Wolowski, M. & Sazima, M. Pollination outcomes reveal negative density-dependence coupled with interspecific facilitation among plants. Ecol. Lett. 23, 129–139 (2020).PubMed 
Article 

Google Scholar 
18.Barrett, S. C. H. The evolution of plant sexual diversity. Nat. Rev. Genet. 3, 274–284 (2002).CAS 
PubMed 
Article 

Google Scholar 
19.Ashman, T. L. & Arceo-Gómez, G. Toward a predictive understanding of the fitness costs of heterospecific pollen receipt and its importance in co-flowering communities. Am. J. Bot. 100, 1061–1070 (2013).PubMed 
Article 

Google Scholar 
20.Moreira-Hernández, J. I. & Muchhala, N. Importance of pollinator-mediated interspecific pollen transfer for angiosperm evolution. Annu. Rev. Ecol. Evol. Syst. 50, 191–217 (2019).Article 

Google Scholar 
21.Ashman, T. L. et al. Pollen limitation of plant reproduction: ecological and evolutionary causes and consequences. Ecology 85, 2408–2421 (2004).Article 

Google Scholar 
22.Tur, C., Saez, A., Traveset, A. & Aizen, M. A. Evaluating the effects of pollinator-mediated interactions using pollen transfer networks: evidence of widespread facilitation in south Andean plant communities. Ecol. Lett. 19, 576–586 (2016).CAS 
PubMed 
Article 

Google Scholar 
23.Levin, D. A. & Anderson, W. W. Competition for pollinators between simultaneously flowering species. Am. Nat. 104, 455–467 (1970).Article 

Google Scholar 
24.Ashman, T. L., Alonso, C., Parra-Tabla, V. & Arceo-Gómez, G. Pollen on stigmas as proxies of pollinator competition and facilitation: complexities, caveats and future directions. Ann. Bot. 125, 1003–1012 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Lloyd, D. G. Some reproductive factors affecting the selection of self-fertilization in plants. Am. Nat. 113, 67–79 (1979).MathSciNet 
Article 

Google Scholar 
26.Sargent, R. D. & Otto, S. P. The role of local species abundance in the evolution of pollinator attraction in flowering plants. Am. Nat. 167, 67–80 (2006).PubMed 
Article 

Google Scholar 
27.Adler, P. B., Fajardo, A., Kleinhesselink, A. R. & Kraft, N. J. B. Trait-based tests of coexistence mechanisms. Ecol. Lett. 16, 1294–1306 (2013).PubMed 
Article 

Google Scholar 
28.Armbruster, W. S. The specialization continuum in pollination systems: diversity of concepts and implications for ecology, evolution and conservation. Funct. Ecol. 31, 88–100 (2017).Article 

Google Scholar 
29.Minnaar, C., Anderson, B., de Jager, M. L. & Karron, J. D. Plant–pollinator interactions along the pathway to paternity. Ann. Bot. 123, 225–245 (2019).PubMed 
Article 

Google Scholar 
30.Kantsa, A. et al. Disentangling the role of floral sensory stimuli in pollination networks. Nat. Commun. 9, 1041 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
31.Fang, Q. & Huang, S. Q. A directed network analysis of heterospecific pollen transfer in a biodiverse community. Ecology 94, 1176–1185 (2013).PubMed 
Article 

Google Scholar 
32.Baldwin, B. G. Origins of plant diversity in the California floristic province. Annu. Rev. Ecol. Evol. Syst. 45, 347–369 (2014).Article 

Google Scholar 
33.Bascompte, J., Jordano, P., Melian, C. J. & Olesen, J. M. The nested assembly of plant–animal mutualistic networks. Proc. Natl Acad. Sci. USA 100, 9383–9387 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Thomson, J. D., Fung, H. F. & Ogilvie, J. E. Effects of spatial patterning of co-flowering plant species on pollination quantity and purity. Ann. Bot. 123, 303–310 (2019).PubMed 
Article 

Google Scholar 
35.Rezende, E. L., Lavabre, J. E., Guimaraes, P. R., Jordano, P. & Bascompte, J. Non-random coextinctions in phylogenetically structured mutualistic networks. Nature 448, 925–928 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
36.Song, C. L., Rohr, R. P. & Saavedra, S. Why are some plant–pollinator networks more nested than others? J. Anim. Ecol. 86, 1417–1424 (2017).PubMed 
Article 

Google Scholar 
37.Hegland, S. J., Nielsen, A., Lazaro, A., Bjerknes, A. L. & Totland, O. How does climate warming affect plant–pollinator interactions? Ecol. Lett. 12, 184–195 (2009).PubMed 
Article 

Google Scholar 
38.Ohlemuller, R. et al. The coincidence of climatic and species rarity: high risk to small-range species from climate change. Biol. Lett. 4, 568–572 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Arceo-Gómez, G., Kaczorowski, R. L. & Ashman, T.-L. A network approach to understanding patterns of coflowering in diverse communities. Int. J. Plant Sci. 179, 569–582 (2018).Article 

Google Scholar 
40.Koski, M. H. et al. Plant–flower visitor networks in a serpentine metacommunity: assessing traits associated with keystone plant species. Arthropod Plant Interact. 9, 9–21 (2015).Article 

Google Scholar 
41.Arceo-Gómez, G. et al. Patterns of among- and within-species variation in heterospecific pollen receipt: the importance of ecological generalization. Am. J. Bot. 103, 396–407 (2016).PubMed 
Article 
CAS 

Google Scholar 
42.Chao, A. et al. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).Article 

Google Scholar 
43.R Core Team. R: A Language and Environment for Statistical Computing, https://www.R-project.org/ (R Foundation for Statistical Computing, 2019).44.Arceo-Gómez, G., Alonso, C., Ashman, T.-L. & Parra-Tabla, V. Variation in sampling effort affects the observed richness of plant–plant interactions via heterospecific pollen transfer: implications for interpretation of pollen transfer networks. Am. J. Bot. 105, 1601–1608 (2018).PubMed 
Article 

Google Scholar 
45.Hayes, R. A., Cullen N., Kaczorowski R. L., O’Neill E. M. & Ashman T-L. A community-wide description and key of pollen from co-flowering plants of the serpentine seeps of Mclaughlin Reserve. Madrono (in the press).46.Dafni, A. Pollination Ecology: a Practical Approach (Oxford Univ. Press, 1992).47.McMurdie, P. J. & Holmes, S. Waste NOT, want not: why rarefying microbiome data is inadmissible. PLoS Comp. Biol. 10, e1003531 (2014).ADS 
Article 
CAS 

Google Scholar 
48.Qian, H. & Jin, Y. An updated megaphylogeny of plants, a tool for generating plant phylogenies and an analysis of phylogenetic community structure. J. Plant Ecol. 9, 233–239 (2016).Article 

Google Scholar 
49.Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
50.Hinchliff, C. E. et al. Synthesis of phylogeny and taxonomy into a comprehensive tree of life. Proc. Natl Acad. Sci. USA 112, 12764–12769 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 
PubMed 
Article 

Google Scholar 
52.Michonneau, F., Brown, J. W. & Winter, D. J. rotl: an R package to interact with the Open Tree of Life data. Methods Ecol. Evol. 7, 1476–1481 (2016).Article 

Google Scholar 
53.Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
54.Le, S., Josse, J. & Husson, F. FactoMineR: an R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).Article 

Google Scholar 
55.Dormann, C. F., Gruber, B. & Fruend, J. Introducing the bipartite package: analysing ecological networks. R News 8, 8–11 (2008).
Google Scholar 
56.Feinsinger, P., Spears, E. E. & Poole, R. W. A simple measure of niche breadth. Ecology 62, 27–32 (1981).Article 

Google Scholar 
57.Horn, H. S. Measurement of “overlap” in comparative ecological studies. Am. Nat. 100, 419–424 (1966).Article 

Google Scholar 
58.Almeida-Neto, M., Guimaraes, P., Guimaraes, P. R., Loyola, R. D. & Ulrich, W. A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117, 1227–1239 (2008).Article 

Google Scholar 
59.Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal 1695, 1–9 (2006).
Google Scholar 
60.Patefield, W. Algorithm AS 159: an efficient method of generating random R × C tables with given row and column totals. Appl. Stat. 30, 91–97 (1981).MATH 
Article 

Google Scholar 
61.Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5–5, https://CRAN.R-project.org/package=vegan (2019).62.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).63.Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating networks. Presented at the Third international AAAI Conference on Weblogs and Social Media (2009).64.Arceo-Gómez, G., Kaczorowski, R. L., Patel, C. & Ashman, T. L. Interactive effects between donor and recipient species mediate fitness costs of heterospecific pollen receipt in a co-flowering community. Oecologia 189, 1041–1047 (2019).ADS 
PubMed 
Article 

Google Scholar 
65.Keck, F., Rimet, F., Bouchez, A. & Franc, A. phylosignal: an R package to measure, test, and explore the phylogenetic signal. Ecol. Evol. 6, 2774–2780 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
66.Orme, D. et al. caper: comparative analyses of phylogenetics and evolution in R. R package version 1.0.1, https://CRAN.R-project.org/package=caper (2018).67.Barrett, S. C. H. The evolution of plant sexual diversity. Nat. Rev. Genet. 3, 274–284 (2002).CAS 
PubMed 
Article 

Google Scholar 
68.Fort, H., Vazquez, D. P. & Lan, B. L. Abundance and generalisation in mutualistic networks: solving the chicken-and-egg dilemma. Ecol. Lett. 19, 4–11 (2016).PubMed 
Article 

Google Scholar 
69.Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: linear and nonlinear mixed effects models. R package version 3.1-143, https://CRAN.R-project.org/package=nlme (2019).70.Lefcheck, J. S. & Freckleton, R. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2015).Article 

Google Scholar 
71.Fox, J. & Weisberg, S. An R companion to Applied Regression, 3rd edition (Sage, 2019).72.Blüthgen, N., Menzel, F. & Blüthgen, N. Measuring specialization in species interaction networks. BMC Ecol. 6, 9 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
73.Shipley, B. The AIC model selection method applied to path analytic models compared using a d-separation test. Ecology 94, 560–564 (2013).PubMed 
Article 

Google Scholar 
74.van der Bijl, W. phylopath: easy phylogenetic path analysis in R. PeerJ 6, e4718 (2018).PubMed 
PubMed Central 
Article 

Google Scholar  More

NEWS AND VIEWS
08 September 2021

Pollination advantage of rare plants unveiled

An analysis of plant–pollinator interactions reveals that the presence of abundant plant species favours the pollination of rare species. Such asymmetric facilitation might promote the coexistence of species in diverse plant communities.

Marcelo A. Aizen

 ORCID: http://orcid.org/0000-0001-9079-9749

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Marcelo A. Aizen

Marcelo A. Aizen is at the Research Institute for Biodiversity and the Environment (INIBIOMA), National University of Comahue – CONICET, 8400 San Carlos de Bariloche, Río Negro, Argentina, and at the Institute for Advanced Study, Berlin, Germany.

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Species diversification results from the balance between the formation of new species (speciation) and the loss of existing ones (extinction). The tremendous proliferation of different life forms on Earth can be attributed to both high rates of speciation and low rates of extinction. Flowering plants — a group called angiosperms — are one of the most diverse groups of non-mobile organism. There are approximately 352,000 plant species, nearly 90% of which depend, to various extents, on insects and other animals for pollination and seed production1. These animal pollinators have been key to the unstoppable diversification of the angiosperms, starting at least 120 million years ago, with pollinators promoting speciation by acting as potent selection agents for a plethora of flower traits2,3. Pollinators also aid species persistence by enabling pollen transfer between relatively distant individuals in sparse plant populations4. Writing in Nature, Wei et al.5 report that, for plant species that flower at the same time, pollinators mediate interactions that might facilitate species coexistence in diverse plant communities.

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doi: https://doi.org/10.1038/d41586-021-02375-z

References1.Ollerton, J., Winfree, R. & Tarrant, S. Oikos 120, 321–326 (2011).Article 

Google Scholar 
2.Hernández-Hernández, T. & Wiens, J. J. Am. Nat. 195, 948–963 (2020).PubMed 
Article 

Google Scholar 
3.van der Niet, T. & Johnson, S. D. Trends Ecol. Evol. 27, 353–361 (2012).PubMed 
Article 

Google Scholar 
4.Bawa, K. S. Trends Ecol. Evol. 10, 311–312 (1995).PubMed 
Article 

Google Scholar 
5.Wei, N. et al. Nature https://doi.org/10.1038/s41586-021-03890-9 (2021).Article 

Google Scholar 
6.Hegland, S. J., Grytnes, J.-A. & Totland, Ø. Ecol. Res. 24, 929–936 (2009).Article 

Google Scholar 
7.Sargent, R. D. & Ackerly, D. D. Trends Ecol. Evol. 23, 123–130 (2008).PubMed 
Article 

Google Scholar 
8.Ghazoul, J. J. Ecol. 94, 295–304 (2006).Article 

Google Scholar 
9.Tur, C., Sáez, A., Traveset, A. & Aizen, M. A. Ecol. Lett. 19, 576–586 (2016).PubMed 
Article 

Google Scholar 
10.Bergamo, P. J., Streher, N. S., Traveset, A., Wolowski, M. & Sazima, M. Ecol. Lett. 23, 129–139 (2020).PubMed 
Article 

Google Scholar 
11.Morales, C. L. & Traveset, A. Crit. Rev. Plant Sci. 27, 221–238 (2008).Article 

Google Scholar 
12.Silvertown, J., Franco, M., Pisanty, I. & Mendoza, A. J. Ecol. 81, 465–476 (1993).Article 

Google Scholar 
13.IPBES. The Assessment Report on Pollinators, Pollination and Food Production of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2016).
Google Scholar 
14.Knight, T. M. et al. Annu. Rev. Ecol. Syst. 36, 467–497 (2005).Article 

Google Scholar 
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1.Schwinning, S. The ecohydrology of roots in rocks. Ecohydrology 3, 238–245 (2010).
Google Scholar 
2.Rose, K., Graham, R. & Parker, D. Water source utilization by Pinus jeffreyi and Arctostaphylos patula on thin soils over bedrock. Oecologia 134, 46–54 (2003).CAS 
PubMed 
Article 
ADS 

Google Scholar 
3.Rempe, D. M. & Dietrich, W. E. Direct observations of rock moisture, a hidden component of the hydrologic cycle. Proc. Natl Acad. Sci. USA 115, 2664–2669 (2018).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
4.Schwinning, S. A critical question for the critical zone: how do plants use rock water? Plant Soil 454, 49–56 (2020).Article 
CAS 

Google Scholar 
5.Fan, Y. et al. Hillslope hydrology in global change research and Earth system modeling. Wat. Resour. Res. 55, 1737–1772 (2019).Article 
ADS 

Google Scholar 
6.Brantley, S. L. et al. Reviews and syntheses: on the roles trees play in building and plumbing the critical zone. Biogeosciences 14, 5115–5142 (2017).CAS 
Article 
ADS 

Google Scholar 
7.Chaney, N. W. et al. POLARIS soil properties: 30-m probabilistic maps of soil properties over the contiguous United States. Wat. Resour. Res. 55, 2916–2938 (2019).Article 
ADS 

Google Scholar 
8.Uhlig, D., Schuessler, J. A., Bouchez, J., Dixon, J. L. & Blanckenburg, F. V. Quantifying nutrient uptake as driver of rock weathering in forest ecosystems by magnesium stable isotopes. Biogeosciences 14, 3111–3128 (2017).CAS 
Article 
ADS 

Google Scholar 
9.Wald, J. A., Graham, R. C. & Schoeneberger, P. J. Distribution and properties of soft weathered bedrock at 1 m depth in the contiguous United States. Earth Surf. Process. Landf. 38, 614–626 (2013).Article 
ADS 

Google Scholar 
10.Nimmo, J. R., Creasey, K. M., Perkins, K. S. & Mirus, B. B. Preferential flow, diffuse flow, and perching in an interbedded fractured-rock unsaturated zone. Hydrogeol. J. 25, 421–444 (2017).CAS 
Article 
ADS 

Google Scholar 
11.Leshem, B. Resting roots of Pinus halepensis: structure, function, and reaction to water stress. Bot. Gaz. 131, 99–104 (1970).Article 

Google Scholar 
12.Hahm, W. J. et al. Low subsurface water storage capacity relative to annual rainfall decouples Mediterranean plant productivity and water use from rainfall variability. Geophys. Res. Lett. 46, 6544–6553 (2019).Article 
ADS 

Google Scholar 
13.Hahm, W. J. et al. Lithologically controlled subsurface critical zone thickness and water storage capacity determine regional plant community composition. Wat. Resour. Res. 55, 3028–3055 (2019).Article 
ADS 

Google Scholar 
14.Eggemeyer, K. D. & Schwinning, S. Biogeography of woody encroachment: why is mesquite excluded from shallow soils? Ecohydrology 2, 81–87 (2009).CAS 
Article 

Google Scholar 
15.Madakumbura, G. D. et al. Recent California tree mortality portends future increase in drought-driven forest die-off. Environ. Res. Lett. 15, 124040 (2020).Article 
ADS 

Google Scholar 
16.McDowell, N. G. et al. Mechanisms of a coniferous woodland persistence under drought and heat. Environ. Res. Lett. 14, 045014 (2019).CAS 
Article 
ADS 

Google Scholar 
17.McEvoy, D. J., Pierce, D. W., Kalansky, J. F., Cayan, D. R. & Abatzoglou, J. T. Projected changes in reference evapotranspiration in California and Nevada: implications for drought and wildland fire danger. Earths Future 8, e2020EF001736 (2020).Article 
ADS 

Google Scholar 
18.Hauwert, N. M. & Sharp, J. M. Measuring autogenic recharge over a karst aquifer utilizing eddy covariance evapotranspiration. J. Water Resour. Prot. 6, 869–879 (2014).Article 

Google Scholar 
19.Spawn, S. A., Sullivan, C. C., Lark, T. J. & Gibbs, H. K. Harmonized global maps of above and belowground biomass carbon density in the year 2010. Sci. Data 7, 112 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
20.Goulden, M. L. & Bales, R. C. California forest die-off linked to multi-year deep soil drying in 2012–2015 drought. Nat. Geosci. 12, 632–637 (2019).CAS 
Article 

Google Scholar 
21.Hahm, W. J. et al. Oak transpiration drawn from the weathered bedrock vadose zone in the summer dry season. Wat. Resour. Res. 56, e2020WR027419 (2020).Article 
ADS 

Google Scholar 
22.Cannon, W. A. The Root Habits of Desert Plants 131 (Carnegie Institute of Washington, 1911).23.Daily reservoir storage summary. California Department of Water Resources https://info.water.ca.gov/cgi-progs/reservoirs/RES (2020).24.USGS water use data for California. United States Geological Society https://waterdata.usgs.gov/ca/nwis/water_use/ (2020).25.David, T., Ferreira, M., Cohen, S., Pereira, J. & David, J. Constraints on transpiration from an evergreen oak tree in southern Portugal. Agric. For. Meteorol. 122, 193–205 (2004).Article 
ADS 

Google Scholar 
26.Querejeta, J. I., Estrada-Medina, H., Allen, M. F. & Jimenez-Osornio, J. J. Water source partitioning among trees growing on shallow karst soils in a seasonally dry tropical climate. Oecologia 152, 26–36 (2007).PubMed 
Article 
ADS 

Google Scholar 
27.Carrière, S. D. et al. The role of deep vadose zone water in tree transpiration during drought periods in karst settings—insights from isotopic tracing and leaf water potential. Sci. Total Environ. 699, 134332 (2020).Article 
CAS 

Google Scholar 
28.Rambal, S. Water balance and pattern of root water uptake by a Quercus coccifera L. evergreen scrub. Oecologia 62, 18–25 (1984).CAS 
PubMed 
Article 
ADS 

Google Scholar 
29.Montaldo, N. et al. Rock water as a key resource for patchy ecosystems on shallow soils: digging deep tree clumps subsidize surrounding surficial grass. Earths Future 9, e2020EF001870 (2021).Article 
ADS 

Google Scholar 
30.Corona, R. & Montaldo, N. On the transpiration of wild olives under water-limited conditions in a heterogeneous ecosystem with shallow soil over fractured rock. J. Hydrol. Hydromech. 68, 338–350 (2020).Article 

Google Scholar 
31.Nardini, A. et al. Water ‘on the rocks’: a summer drink for thirsty trees? New Phytol. 229, 199–212 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Ruiz, L. et al. Water balance modelling in a tropical watershed under deciduous forest (Mule Hole, India): regolith matric storage buffers the groundwater recharge process. J. Hydrol. 380, 460–472 (2010).Article 
ADS 

Google Scholar 
33.Ding, Y., Nie, Y., Chen, H., Wang, K. & Querejeta, J. I. Water uptake depth is coordinated with leaf water potential, water-use efficiency and drought vulnerability in karst vegetation. New Phytol. 229, 1339–1353 (2021).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Dawson, T. E., Hahm, W. J. & Crutchfield-Peters, K. Digging deeper: what the critical zone perspective adds to the study of plant ecophysiology. New Phytol. 226, 666–671 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
35.Salve, R., Rempe, D. M. & Dietrich, W. E. Rain, rock moisture dynamics, and the rapid response of perched groundwater in weathered, fractured argillite underlying a steep hillslope. Wat. Resour. Res. 48, W11528 (2012).Article 
ADS 

Google Scholar 
36.Harsch, M. A., Hulme, P. E., McGlone, M. S. & Duncan, R. P. Are treelines advancing? A global meta-analysis of treeline response to climate warming. Ecol. Lett. 12, 1040–1049 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
37.Kapnick, S. & Hall, A. Causes of recent changes in western North American snowpack. Clim. Dyn. 38, 1885–1899 (2012).Article 

Google Scholar 
38.Tune, A. K., Druhan, J. L., Wang, J., Bennett, P. C. & Rempe, D. M. Carbon dioxide production in bedrock beneath soils substantially contributes to forest carbon cycling. J. Geophys. Res. Biogeosci. 125, e2020JG005795 (2020).CAS 
Article 
ADS 

Google Scholar 
39.Hasenmueller, E. A. et al. Weathering of rock to regolith: the activity of deep roots in bedrock fractures. Geoderma 300, 11–31 (2017).CAS 
Article 
ADS 

Google Scholar 
40.Yang, L. et al. A new generation of the United States National Land Cover Database: requirements, research priorities, design, and implementation strategies. ISPRS J. Photogramm. Remote Sens. 146, 108–123 (2018).41.Soil Survey Staff Gridded National Soil Survey Geographic (gNATSGO) Database for the Conterminous United States (USDA, 2019); https://nrcs.app.box.com/v/soils42.QGIS Development Team QGIS Geographic Information System (Open Source Geospatial Foundation, 2019); http://qgis.org43.O’Geen, A. T. et al. Southern Sierra Critical Zone Observatory and Kings River Experimental Watersheds: a synthesis of measurements, new insights, and future directions. Vadose Zone J. 17, 180081 (2018).Article 
CAS 

Google Scholar 
44.Anderson, M. A., Graham, R. C., Alyanakian, G. J. & Martynn, D. Z. Late summer water status of soils and weathered bedrock in a giant sequoia grove. Soil Sci. 160, 415–422 (1995).CAS 
Article 
ADS 

Google Scholar 
45.Hubbert, K. R., Graham, R. C. & Anderson, M. A. Soil and weathered bedrock: components of a Jeffrey pine plantation substrate. Soil Sci. Soc. Am. J. 65, 1255–1262 (2001).CAS 
Article 
ADS 

Google Scholar 
46.Bornyasz, M., Graham, R. & Allen, M. Ectomycorrhizae in a soil-weathered granitic bedrock regolith: linking matrix resources to plants. Geoderma 126, 141–160 (2005).Article 
ADS 

Google Scholar 
47.Sternberg, P., Anderson, M., Graham, R., Beyers, J. & Tice, K. Root distribution and seasonal water status in weathered granitic bedrock under chaparral. Geoderma 72, 89–98 (1996).Article 
ADS 

Google Scholar 
48.Graham, R. C., Sternberg, P. D. & Tice, K. R. Morphology, porosity, and hydraulic conductivity of weathered granitic bedrock and overlying soils. Soil Sci. Soc. Am. J. 61, 516–522 (1997).CAS 
Article 
ADS 

Google Scholar 
49.McCole, A. A. & Stern, L. A. Seasonal water use patterns of Juniperus ashei on the Edwards Plateau, Texas, based on stable isotopes in water. J. Hydrol. 342, 238–248 (2007).Article 
ADS 

Google Scholar 
50.Schwinning, S. The water relations of two evergreen tree species in a karst savanna. Oecologia 158, 373–383 (2008).PubMed 
Article 
ADS 

Google Scholar 
51.McCormick, E. L. et al. Dataset for “Evidence for widespread woody plant use of water stored in bedrock”. Hydroshare https://doi.org/10.4211/hs.a2f0d5fd10f14cd189a3465f72cba6f3 (2021).52.Jackson, R. B. et al. A global analysis of root distributions for terrestrial biomes. Oecologia 108, 389–411 (1996).CAS 
PubMed 
Article 
ADS 

Google Scholar 
53.Schenk, H. J. & Jackson, R. B. The global biogeography of roots. Ecol. Monogr. 72, 311–328 (2002).Article 

Google Scholar 
54.Schenk, H. J. & Jackson, R. B. Rooting depths, lateral root spreads and below-ground/above-ground allometries of plants in water-limited ecosystems. J. Ecol. 90, 480–494 (2002).Article 

Google Scholar 
55.Fan, Y., Miguez-Macho, G., Jobbagy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
56.Daly, C. et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. 28, 2031–2064 (2008).Article 

Google Scholar 
57.Daly, C., Smith, J. I. & Olson, K. V. Mapping atmospheric moisture climatologies across the conterminous United States. PLoS ONE 10, e0141140 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
58.Zhang, Y. et al. Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017. Remote Sens. Environ. 222, 165–182 (2019).Article 
ADS 

Google Scholar 
59.Gan, R. et al. Use of satellite leaf area index estimating evapotranspiration and gross assimilation for Australian ecosystems. Ecohydrology 11, e1974 (2018).Article 

Google Scholar 
60.Dralle, D. N., Hahm, W. J., Chadwick, K. D., McCormick, E. L. & Rempe, D. M. Technical note: accounting for snow in the estimation of root-zone water storage capacity from precipitation and evapotranspiration fluxes. Hydrol. Earth Syst. Sci. 25, 2861–2867 (2021).Article 
ADS 

Google Scholar 
61.Wang-Erlandsson, L. et al. Global root zone storage capacity from satellite-based evaporation. Hydrol. Earth Syst. Sci. 20, 1459–1481 (2016).Article 
ADS 

Google Scholar 
62.Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).Article 
ADS 

Google Scholar 
63.Singh, C., Wang-Erlandsson, L., Fetzer, I., Rockstrom, J. & van der Ent, R. Rootzone storage capacity reveals drought coping strategies along rainforest savanna transitions. Environ. Res. Lett. 15, 124021 (2020).CAS 
Article 
ADS 

Google Scholar 
64.Hall, D., Riggs, G. & Salomonson, V. MODIS/Terra Snow Cover Daily L3 Global 500m Grid, Version 6 [Data set] (NASA National Snow and Ice Data Center Distributed Active Archive Center, 2016).65.Friedl, M. & Sulla-Menashe, D. MCD12Q1 MODIS/Terra+ Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006 [Data set] (NASA EOSDIS Land Processes DAAC, 2015).66.Peel, M. C., Finlayson, B. L. & Mcmahon, T. A. Updated world map of the Koppen–Geiger climate classification. Hydrol. Earth Syst. Sci. Discuss. 4, 439–473 (2007).ADS 

Google Scholar 
67.Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

Google Scholar 
68.Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
69.Niemeyer, R. J. et al. Spatiotemporal soil and saprolite moisture dynamics across a semi-arid woody plant gradient. J. Hydrol. 544, 21–35 (2017).Article 
ADS 

Google Scholar 
70.Pedrazas, M. A. et al. The relationship between topography bedrock weathering and water storage across a sequence of ridges and valleys. J. Geophys. Res. Earth Surf. 126, e2020JF005848 (2021).ADS 

Google Scholar 
71.Arkley, R. J. Soil moisture use by mixed conifer forest in a summer-dry climate. Soil Sci. Soc. Am. J. 45, 423–427 (1981).Article 
ADS 

Google Scholar 
72.Zwieniecki, M. A. & Newton, M. Water-holding characteristics of metasedimentary rock in selected forest ecosystems in southwestern Oregon. Soil Sci. Soc. Am. J. 60, 1578–1582 (1996).CAS 
Article 
ADS 

Google Scholar 
73.Hellmers, H., Horton, J. S., Juhren, G. & O’Keefe, J. Root systems of some chaparral plants in southern California. Ecology 36, 667–678 (1955).Article 

Google Scholar 
74.Cardella Dammeyer, H., Schwinning, S., Schwartz, B. F. & Moore, G. W. Effects of juniper removal and rainfall variation on tree transpiration in a semi-arid karst: evidence of complex water storage dynamics. Hydrol. Process. 30, 4568–4581 (2016).Article 
ADS 

Google Scholar 
75.Twidwell, D. et al. Drought-induced woody plant mortality in an encroached semi-arid savanna depends on topoedaphic factors and land management. Appl. Veg. Sci. 17, 42–52 (2013).Article 

Google Scholar 
76.Davis, E. A. Root system of shrub live oak in relation to water yield by chaparral. Proceedings of the 1977 Meetings of the Arizona Section of the American Water Resources Association and the Hydrology Section of the Arizona Academy of Sciences. Hydrol. Water Resour. Ariz. Southwest 7, 241–248 (1977).
Google Scholar 
77.West, A. G., Hultine, K. R., Burtch, K. G., & Ehleringer, J. R. Seasonal variations in moisture use in a piñon–juniper woodland. Oecologia 153, 787–798 (2007).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
78.Seyfried, M. S. & Wilcox, B. P. Soil water storage and rooting depth: key factors controlling recharge on rangelands. Hydrol. Process. 20, 3261–3275 (2006).Article 
ADS 

Google Scholar 
79.Dietrich, W. E. & Dunne, T. Sediment budget for a small catchment in mountainous terrain. Zeitschrift Für Geomorphologie 29, 191–206 (1978).
Google Scholar 
80.Litvak, M. E., Schwinning, S. & Heilman, J. L. in Ecosystem Function in Savannas (eds Hill, M. J. & Hanan, N. P.) 117–134 (2010). More

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