Ecology

1.Roselló-Mora R, Amann R. The species concept for prokaryotes. FEMS Microbiol Rev. 2001;25:39–67.
Google Scholar 
2.Certini G, Campbell CD, Edwards AC. Rock fragments in soil support a different microbial community from the fine earth. Soil Biol Biochem. 2004;36:1119–28.CAS 

Google Scholar 
3.Carson JK, Rooney D, Gleeson DB, Clipson N. Altering the mineral composition of soil causes a shift in microbial community structure. FEMS Microbiol Ecol. 2007;61:414–23.CAS 
PubMed 

Google Scholar 
4.Uroz S, Kelly LC, Turpault M, Lepleux C, Frey-Klett P. The mineralosphere concept: mineralogical control of the distribution and function of mineral-associated bacterial communities. Trends Microbiol. 2015;23:751–62.CAS 
PubMed 

Google Scholar 
5.Ahmed E, Hugerth LW, Logue JB, Brüchert V, Andersson AF, Holmström SJ. Mineral type structures soil microbial communities. Geomicrobiol J 2017;34:538–45.CAS 

Google Scholar 
6.Whitman T, Neurath R, Perera A, Chu-Jacoby I, Ning D, Zhou J, et al. Microbial community assembly differs across minerals in a rhizosphere microcosm. Environ Microbiol. 2018;20:4444–60.CAS 
PubMed 

Google Scholar 
7.Kandeler E, Gebala A, Boeddinghaus RS, Müller K, Rennert T, Soares M, et al. The mineralosphere—succession and physiology of bacteria and fungi colonising pristine minerals in grassland soils under different land-use intensities. Soil Biol Biochem. 2019;136:107534.CAS 

Google Scholar 
8.Hassink J, Bouwman LA, Zwart KB, Bloem J, Brussaard L. Relationships between soil texture, physical protection of organic-matter, soil biota, and C-mineralization and N-mineralization in grassland soils. Geoderma 1993;57:105–28.CAS 

Google Scholar 
9.Mayer LM, Schick LL, Hardy KR, Wagai R, McCarthy J. Organic matter in small mesopores in sediments and soils. Geochim Cosmochim Acta. 2004;68:3868–72.
Google Scholar 
10.Chenu C, Stotzky G. Interaction between microorganisms and soil particles: an overview. In: Huang PM, Bollag JM, Senesi N, editors. Interactions between soil particles and microorganism: impact on the terrestrial ecosystem. New York: Wiley; 2002. p. 3–40.11.Hemkemeyer M, Pronk GJ, Heister K, Kögel-Knabner I, Martens R, Tebbe CC. Artificial soil studies reveal domain-specific preferences of microorganisms for the colonisation of different soil minerals and particle size fractions. FEMS Microbiol Ecol. 2014;90:770–82.CAS 
PubMed 

Google Scholar 
12.Six J, Elliott ET, Paustian K. Soil macroaggregate turnover and microaggregate formation: a mechanism for C sequestration under no-tillage agriculture. Soil Biol Biochem. 2000;32:2099–103.CAS 

Google Scholar 
13.Totsche KU, Amelung W, Gerzabek MH, Guggenberger G, Klumpp E, Knief C, et al. Microaggregates in soils. J Plant Nutr Soil Sci. 2018;181:104–36.CAS 

Google Scholar 
14.Rasmussen C, Southard RJ, Horwath WR. Litter type and soil minerals control temperate forest soil carbon response to climate change. Glob Change Biol 2008;14:2064–80.
Google Scholar 
15.Hemingway JD, Rothman DH, Grant KE, Rosengard SZ, Eglinton TI, Derry LA, et al. Mineral protection regulates long-term global preservation of natural organic carbon. Nature 2019;570:228–31.CAS 
PubMed 

Google Scholar 
16.Ranjard L, Richaume A. Quantitative and qualitative microscale distribution of bacteria in soil. Res Microbiol. 2001;152:707–16.CAS 
PubMed 

Google Scholar 
17.Poll C, Thiede A, Wermbter N, Sessitsch A, Kandeler E. Micro-scale distribution of microorganisms and microbial enzyme activities in a soil with long-term organic amendment. Eur J Soil Sci. 2003;54:715–24.
Google Scholar 
18.Neumann D, Heuer A, Hemkemeyer M, Martens R, Tebbe CC. Response of microbial communities to long-term fertilization depends on their microhabitat. FEMS Microbiol Ecol. 2013;86:71–84.CAS 
PubMed 

Google Scholar 
19.Nie M, Pendall E, Bell C, Wallenstein MD. Soil aggregate size distribution mediates microbial climate change feedbacks. Soil Biol Biochem. 2014;68:357–365.CAS 

Google Scholar 
20.Chenu C, Hassink J, Bloem J. Short-term changes in the spatial distribution of microorganisms in soil aggregates as affected by glucose addition. Biol Fertil Soils. 2001;34:349–56.CAS 

Google Scholar 
21.Saidy AR, Smernik RJ, Baldock JA, Kaiser K, Sanderman J. The sorption of organic carbon onto differing clay minerals in the presence and absence of hydrous iron oxide. Geoderma. 2013;209:15–21.
Google Scholar 
22.Mikutta R, Kleber M, Torn MS, Jahn R. Stabilization of soil organic matter: association with minerals or chemical recalcitrance? Biogeochemistry 2006;77:25–56.CAS 

Google Scholar 
23.Gadd GM. Metals, minerals and microbes: geomicrobiology and bioremediation. Microbiology. 2010;156:609–43.CAS 
PubMed 

Google Scholar 
24.Lehmann J, Kleber M. The contentious nature of soil organic matter. Nature 2015;528:60–68.CAS 
PubMed 

Google Scholar 
25.Sokol NW, Sanderman J, Bradford MA. Pathways of mineral‐associated soil organic matter formation: integrating the role of plant carbon source, chemistry, and point of entry. Glob Change Biol. 2019;25:12–24.
Google Scholar 
26.Skjemstad JO, Janik LJ, Head MJ, McClure SG. High energy ultraviolet photo‐oxidation: a novel technique for studying physically protected organic matter in clay‐and silt‐sized aggregates. J Soil Sci. 1993;44:485–99.CAS 

Google Scholar 
27.Goldfarb KC, Karaoz U, Hanson CA, Santee CA, Bradford MA, Treseder KK, et al. Differential growth responses of soil bacterial taxa to carbon substrates of varying chemical recalcitrance. Front Microbiol. 2011;2:1–10.
Google Scholar 
28.Kleber M, Sollins P, Sutton R. A conceptual model of organo-mineral interactions in soils: self-assembly of organic molecular fragments into zonal structures on mineral surfaces. Biogeochemistry 2007;85:9–24.
Google Scholar 
29.Torn MS, Trumbore SE, Chadwick OA, Vitousek PM, Hendricks DM. Mineral control of soil organic carbon storage and turnover content. Nature 1997;389:3601–3.
Google Scholar 
30.Dahlgren RA, Saigusa M, Ugolini FC. The nature, properties and management of volcanic soils. Adv Agron. 2004;82:113–82.CAS 

Google Scholar 
31.Mikutta R, Kleber M, Jahn R. Poorly crystalline minerals protect organic carbon in clay subfractions from acid subsoil horizons. Geoderma 2005;128:106–15.CAS 

Google Scholar 
32.Keiluweit M, Bougoure JJ, Nico PS, Pett-Ridge J, Weber PK, Kleber M. Mineral protection of soil carbon counteracted by root exudates. Nat Clim Change. 2015;5:588–95.CAS 

Google Scholar 
33.Rasmussen C, Throckmorton H, Liles G, Heckman K, Meding S, Horwath WR. Controls on soil organic carbon partitioning and stabilization in the California Sierra Nevada. Soil Syst. 2018;2:1–18.
Google Scholar 
34.Zhou Z, Wang C, Luo Y. Meta-analysis of the impacts of global change factors on soil microbial diversity and functionality. Nat Comm. 2020;11:1–10.CAS 

Google Scholar 
35.Hungate BA, Mau RL, Schwartz E, Caporaso JG, Dijkstra P, van Gestel N, et al. Quantitative microbial ecology through stable isotope probing. Appl Environ Microb. 2015;81:7570–81.CAS 

Google Scholar 
36.Hayer M, Schwartz E, Marks JC, Koch BJ, Morrissey EM, Schuettenberg AA, et al. Identification of growing bacteria during litter decomposition in freshwater through H218O quantitative stable isotope probing. Environ Microbiol Rep. 2016;8:975–82.CAS 
PubMed 

Google Scholar 
37.Papp K, Hungate BA, Schwartz E. Microbial rRNA synthesis and growth compared through quantitative stable isotope probing with H218O. Appl Environ Microbiol. 2018;84:1–17.
Google Scholar 
38.Finley BK, Dijkstra P, Rasmussen C, Schwartz E, Liu XA, van Gestel N, et al. Soil mineral assemblage and substrate quality effects on microbial priming. Geoderma2018;322:38–47.CAS 

Google Scholar 
39.Rasmussen C, Southard RJ, Horwath WR. Mineral control of organic carbon mineralization in a range of temperate conifer forest soils. Glob Change Biol. 2006;12:834–47.
Google Scholar 
40.Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Rohland N, Reich D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 2012;22:939–46.CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590–6.
Google Scholar 
45.Morrissey EM, Mau RL, Schwartz E, McHugh TA, Dijkstra P, Koch BJ, et al. Bacterial carbon use plasticity, phylogenetic diversity and the priming of soil organic matter. ISME J. 2017;11:1890–9.PubMed 
PubMed Central 

Google Scholar 
46.R Core Team. R: a language and environment for statistical computiong. Vienna: R Foundation for Statistical Computing; 2021. https://www.R-project.org/.47.Dowle M, Srinivasan A. data.table: Extensions of ‘data.frame’. R package version 1.13.6. 2020.48.Oksanen J, Blanchet FG, Kindt R, Legendre P, O’hara RB, Simpson GL, et al. Vegan: community ecology package. R package version 1.17-4. 2010. http://cran.r-project.org.49.Morrissey EM, Mau RL, Hayer M, Liu XJ, Schwartz E, Dijkstra P, et al. Evolutionary history constrains microbial traits across environmental variation. Nat Ecol Evol. 2019;3:1064–9.PubMed 

Google Scholar 
50.Pinheiro J, Bates D, DebRoy S, Sarkar D. R Core Team. nlme: linear and nonlinear mixed effects models. R package version 3. 1–137, 2018. https://CRAN.R-project.org/package=nlme .51.Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 2018;35:526–8.
Google Scholar 
52.Barter RL, Yu B. Superheat: an R package for creating beautiful and extendable heatmaps for visualizing complex data. J Comput Graph Stat. 2018;27:910–22.PubMed 
PubMed Central 

Google Scholar 
53.Demoling F, Figueroa D, Bååth E. Comparison of factors limiting bacterial growth in different soils. Soil Biol Biochem. 2007;39:2485–95.CAS 

Google Scholar 
54.Kaiser K, Zech W. Sorption of dissolved organic nitrogen by acid subsoil horizons and individual mineral phases. Eur J Soil Sci. 2000;51:403–11.CAS 

Google Scholar 
55.Barnhisel RI, Bertsch PM. Chlorites and hydroxy-interlayered vermiculite and smectite. In: Dixon JB, Weed SB editors. Minerals in soils environments, 2nd edn. Madison: Soil Science Society of America, Inc.; 1989. p. 729–88.56.Zunino H, Borie F, Aguilera S, Martin JP, Haider K. Decomposition of C-14- labeled glucose, plant and microbial products and phenols in volcanic ash-derived soils of Chile. Soil Biol Biochem. 1982;14:37–43.CAS 

Google Scholar 
57.Baldock JA, Nelson PN. In: Sumner ME editor. Handbook of soil science. Boca Raton: CRC Press; 2000. B25–B84.58.Matus F, Rumpel C, Neculman R, Panichini M, Mora ML. Soil carbon storage and stabilisation in andic soils: a review. Catena. 2014;120:102–10.CAS 

Google Scholar 
59.Nottingham AT, Griffiths H, Chamberlain PM, Stott AW, Tanner EVJ. Soil priming by sugar and leaf-litter substrates: a link to microbial groups. Appl Soil Ecol. 2009;42:183–90.
Google Scholar 
60.McMahon SK, Williams MA, Bottomley PJ, Myrold DD. Dynamics of microbial communities during decomposition of carbon-13 labeled ryegrass fractions in soil. Soil Sci Soc Am J 2005;69:1238–47.CAS 

Google Scholar 
61.Vieira S, Sikorski J, Gebala A, Boeddinghaus RS, Marhan S, Rennert T, et al. Bacterial colonization of minerals in grassland soils is selective and highly dynamic. Environ Microbiol. 2020;22:917–33.CAS 
PubMed 

Google Scholar 
62.Mille-Lindblom C, Fischer H, Tranvik LJ. Antagonism between bacteria and fungi: substrate competition and a possible tradeoff between fungal growth and tolerance towards bacteria. Oikos 2006;113:233–42.
Google Scholar  More

Driven by the land-to-river and upstream-to-downstream WBIF, biodiversity information across terrestrial and aquatic biomes could be detected in riverine water eDNA6,16, and the monitoring effectiveness of riverine water eDNA relies on the transportation effectiveness of corresponding WBIF6,17,18,19,20. The transportation effectiveness of WBIF mainly relies on the transport capacity, degradation rate, and environmental filtration of WBIF15,21,22,23, which can vary with different seasons and weather conditions26. We hypothesized that the monitoring effectiveness would vary with the seasons and weather conditions. In the present case, the bacterial community richness in riparian soil did not vary with season, whereas the bacterial community composition in riverine water was richest in the autumn, followed by the summer (Figs. 2, 3). The transportation effectiveness of riparian-to-river and upstream-to-downstream WBIF in spring frozen days was significantly lower than in summer rainy days and autumn cloudy days (Tables 1, 2, Supplementary Tables S3, S4). Considering the insufficient read depth on the riverine water samples of summer and autumn groups (Supplementary Fig. S1), the riverine water bacterial community richness and the riparian-to-river transportation effectiveness on summer and autumn were already underestimated. It indicates that the monitoring effectiveness varied with different seasons and weather conditions, and summer and autumn were the optimal seasons, along with rainy days being the optimal weather condition, for using riverine water eDNA to simultaneously monitor the holistic biodiversity information in riverine sites and riparian sites.The biodiversity information detected by water eDNA could originate from living and dead organisms23,26. The detection of biodiversity information that originates from a living organism mainly depends on the dispersal of this living organism11,20. The detection of biodiversity information that originates from a dead organism mainly depends on its transport capacity and degradation rate12,22,29. In summer and autumn, as driven by active organisms, more eDNA was input into the river system. In particular, the surface runoff caused by rain can input more eDNA from terrestrial soil into the river system and can preserve them in soil aggregates30. In the present study, the highest proportion of bacteria in riparian soil was detected in riverine water in summer and autumn, and the rain promoted this phenomenon (Fig. 3 and Table 1, Supplementary Table S3). The proportion of effective upstream-to-downstream WBIF was significantly higher in summer and autumn than in spring, as well as being higher on rainy days than on cloudy days (Table 2). eDNA (originated from dead organisms) degrades over time in a logistic manner (a half-life time)12,22,27,31, which was described in this study as degrading by half-life distance in a lotic system, which integrates the transport capacity and the degradation rate. In the present work, as driven by runoff discharge and flow velocity (Supplementary Table S1), the half-life distance of noneffective WBIF was significantly farther in the summer than in autumn and in spring (Table 2).The biodiversity information monitoring effectiveness of riverine water eDNA, as approximated by the transportation effectiveness of WBIF, was impacted by the eDNA degradation rate in WBIF, and there were taxonomy-specific eDNA degradation rates27, species-specific eDNA degradation rates17, and form-specific eDNA degradation rates28. We hypothesized that the monitoring effectiveness of riverine water eDNA would vary with taxonomic communities. In the present case, the results revealed the detection of a significantly higher monitoring effectiveness of riverine water eDNA (both riparian-to-river and downstream-to-upstream) for bacterial communities than for eukaryotic communities (Tables 3, 4). Considering the insufficient read depth on the bacterial community (16S rRNA gene, Supplementary Fig. S2), the detection capacity on bacterial group was already underestimated. A significantly higher monitoring effectiveness of riverine water eDNA was found for micro-eukaryotic communities (fungi) than for overall eukaryotic communities (including micro- and macro-organisms) (Tables 3, 4). This indicates that the monitoring effectiveness varied with different taxonomic communities, and the effectiveness of monitoring eukaryotic communities was significantly lower than for monitoring bacterial communities; in addition, the effectiveness of monitoring macrobe communities was significantly lower than for monitoring microbe communities.eDNA surveys that are based on metabarcoding can actually acquire information across the taxonomic tree of life5,6,11,32,33. However, eDNA that originates from different taxonomic groups has a different probability of being left in the environment and input into water6,8,9,34. van Bochove et al. inferred that the eDNA contained inside of cells and mitochondria is especially resilient against degradation (i.e., intracellular vs. extracellular effects)28. In the present case, more bacteria than eukaryotes and more microorganisms than macroorganisms (both OTU and species levels) in riparian soil could be detected in riverine water (Table 3). The half-life distance of noneffective WBIF for bacteria (detected by the 16 s RNA gene) was much farther than that for unicellular eukaryotes (detected by the ITS gene, which is mainly unicellular), than that for multicellular eukaryotes (as detected by the CO1 gene, which is mainly multicellular) (Table 4). We inferred that the eDNA contained inside of bacterial cells was more resilient against degradation than that contained inside of unicellular eukaryotic cells (i.e., prokaryotic cells vs. eukaryotic cells), as well as compared to the eDNA contained inside of multicellular eukaryotic cells or extracellular mitochondria (i.e., unicellular eukaryotic cells vs. multicellular eukaryotic cells or extracellular mitochondria).In previous studies, the effectiveness of using water eDNA to monitor terrestrial organisms was indicated by the detection probability8,9,34, and the effectiveness of using downstream water eDNA to monitor upstream organisms was indicated by the detectable distance7,12,17,19,20,35. In this study, we approximated the biodiversity information monitoring effectiveness by the WBIF transportation effectiveness and proposed its assessment framework, in which we described the riparian-to-river monitoring effectiveness with the proportion of biodiversity information in riparian soil that was detected by using riverine water eDNA samples. Additionally, we described the downstream-to-upstream monitoring effectiveness with the proportion of biodiversity information in upstream site water eDNA samples that was detected by 1-km downstream site water eDNA samples, and the runoff distance of that 50% of dead bioinformation (i.e., the bioinformation labeling the biological material that lacked life activity and fertility) could be monitored. These indicators provided new usable assessment tools for designing monitoring projects and for evaluating monitoring results.In the optimal monitoring season and weather condition (a summer rainy day) in the Shaliu river basin on the Qinghai–Tibet Plateau, by using riverine water eDNA, we were able to monitor as much as 87.95% of bacterial species, 76.18% of fungal species, and 53.52% of eukaryotic species from riparian soil, along with as much as 98.69% of bacterial species, 95.71% of fungal species, and 92.41% of eukaryotic species from 1 km upstream (Table 4). The half-life distance of the noneffective WBIF was respectively 17.82 km, 5.96 km, and 5.02 km for bacteria, fungi, and metazoans at the species level (Table 4). When considering the fact that the monitoring effectiveness of eDNA can not only vary with season, weather, and taxonomic communities, but can also vary with rivers and watersheds with different environmental conditions12,17,19,23, more studies on the monitoring effectiveness for each taxonomic community in other watersheds with different environmental conditions are needed.eDNA metabarcoding surveys are relatively cheaper, more efficient, and more accurate than traditional surveys in aquatic systems10,13, although this is certainly not true in all circumstances36. Sales et al. show that the detection probability of using riverine water eDNA to monitor the semi-aquatic and terrestrial mammals in natural lotic ecosystems in the UK was 40–67%, which provided comparable results to conventional survey methods per unit of survey effort for three species (water vole, field vole and red deer); in other words, the results from 3 to 6 water replicates would be equivalent to the results from 3 to 5 latrine surveys and 5–30 weeks of single camera deployment9. In the current case, the riverine water eDNA samples detected 53.52% of eukaryotic species from riparian soil samples. As the bioinformation in WBIF includes the biodiversity information of all taxonomic communities, the information of all taxonomic communities could be monitored by using riverine water eDNA, although variability in monitoring effectiveness exists among different taxonomic communities. We anticipate that, in future biodiversity research, conservation, and management, we will be able to efficiently monitor and assess the aquatic and terrestrial biodiversity by simply using riverine water eDNA samples.In summary, to test the idea of using riverine water eDNA to simultaneously monitor aquatic and terrestrial biodiversity, we proposed a monitoring effectiveness assessment framework, in which the land-to-river monitoring effectiveness was indicated by detection probability, and the upstream-to-downstream monitoring effectiveness was described by the detection probability per kilometer runoff distance and by the half-life distance of dead bioinformation. In our case study, in the Shaliu River watershed on the Qinghai-Tibet Plateau, and on summer rainy days, 43–76% of species information in riparian sites could be detected in adjacent riverine water eDNA samples, 92–99% of species information from upstream sites could be detected in a 1-km downstream eDNA sample, and the half-life distances of dead bioinformation for bacteria was approximately 13–19 km and was approximately 4–6 km for eukaryotes. The indicators in the assessment framework that describe the monitoring effectiveness provide usable assessment tools for designing monitoring projects and for evaluating monitoring results. In future ecological research, biodiversity conservation, and ecosystem management, riverine water eDNA may be a general diagnostic procedure for routine watershed biodiversity monitoring and assessment. More

1.Dingemanse, N. J. & Wolf, M. Recent models for adaptive personality differences: A review. Phil. Trans. R. Soc. B 365, 3947–3958. https://doi.org/10.1098/rstb.2010.0221 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
2.Wolf, M., van Doorn, G., Leimar, O. & Weissing, F. J. Life-history trade-offs favour the evolution of animal personalities. Nature 447, 581–584. https://doi.org/10.1038/nature05835 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
3.Dingemanse, N. J., Both, C., Drent, P. J. & Tinbergen, J. M. Fitness consequences of avian personalities in a fluctuating environment. Proc. R. Soc. B. 271, 847–852. https://doi.org/10.1098/rspb.2004.2680 (2004).Article 
PubMed 
PubMed Central 

Google Scholar 
4.Sih, A. & Bell, A. M. Insights for behavioral ecology from behavioral syndromes. Adv. Study Behav. 38, 227–281. https://doi.org/10.1016/S0065-3454(08)00005-3 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
5.Sih, A., Bell, A. M. & Johnson, J. C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378. https://doi.org/10.1016/j.tree.2004.04.009 (2004).Article 
PubMed 

Google Scholar 
6.Drent, P. J., van Oers, K. & van Noordwijk, A. J. Realized heritability of personalities in the great tit (Parus major). Proc. R. Soc. B. 270, 45–51. https://doi.org/10.1098/rspb.2002.2168 (2003).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Sol, D., Griffin, A. S., Bartomeus, I. & Boyce, H. Exploring or avoiding novel food resources? The novelty conflict in an invasive bird. PLoS ONE 6, e19535. https://doi.org/10.1371/journal.pone.0019535 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Dammhahn, M., Mazza, V., Schirmer, A., Göttsche, C. & Eccard, J. C. Of city and village mice: Behavioural adjustments of striped field mice to urban environments. Sci. Rep. 10, 13056. https://doi.org/10.1038/s41598-020-69998-6 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
9.Sih, A. & Del Giudice, M. Linking behavioural syndromes and cognition: A behavioural ecological perspective. Phil. Trans. R. Soc. B 367, 2762–2772. https://doi.org/10.1098/rstb.2012.0216 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Stoewe, M. & Kotrschal, K. Behavioural phenotypes may determine whether social context facilitates or delays novel object exploration in ravens (Corvus corax). J. Ornithol. 148, S179–S184. https://doi.org/10.1007/s10336-007-0145-1 (2007).Article 

Google Scholar 
11.Guillette, L. M., Reddon, A. R., Hoeschele, M. & Sturdy, C. B. Sometimes slower is better: Slow-exploring birds are more sensitive to changes in a vocal discrimination task. Proc. R. Soc. B 278, 767–773. https://doi.org/10.1098/rspb.2010.1669 (2011).Article 
PubMed 

Google Scholar 
12.Dochtermann, N. A., Schwab, T. & Sih, A. The contribution of additive genetic variation to personality variation: Heritability of personality. Proc. R. Soc. B 282, 20142201. https://doi.org/10.1098/rspb.2014.2201 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
13.Van Oers, K., De Jong, G., Van Noordwijk, A. J., Kempenaers, B. & Drent, P. J. Contribution of genetics to the study of animal personalities: A review of case studies. Behaviour 142, 1185–1206. https://doi.org/10.1163/156853905774539364 (2005).Article 

Google Scholar 
14.Van Oers, K. & Mueller, J. C. Evolutionary genomics of animal personality. Phil. Trans. R. Soc. B 365, 3991–4000. https://doi.org/10.1098/rstb.2010.0178 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
15.Croston, R., Branch, C. L., Kozlovsky, D. Y., Dukas, R. & Pravosudov, V. V. Heritability and the evolution of cognitive traits. Behav. Ecol. 26, 1447–1459. https://doi.org/10.1093/beheco/arv088 (2015).Article 

Google Scholar 
16.Quinn, J. L., Cole, E. F., Reed, T. E. & Morand-Ferron, J. Environmental and genetic determinants of innovativeness in a natural population of birds. Phil. Trans. R. Soc. B 371, 20150184. https://doi.org/10.1098/rstb.2015.0184 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Evans, J., Boudreau, K. & Hyman, J. Behavioural syndromes in urban and rural populations of song sparrows. Ethology 116, 588–595. https://doi.org/10.1111/j.1439-0310.2010.01771.x (2010).Article 

Google Scholar 
18.Bókony, V., Kulcsár, A., Tóth, Z. & Liker, A. Personality traits and behavioral syndromes in differently urbanized populations of house sparrows (Passer domesticus). PLoS ONE 7, 36639. https://doi.org/10.1371/journal.pone.0036639 (2007).ADS 
CAS 
Article 

Google Scholar 
19.Charmantier, A., Deyeyrier, V., Lambrechts, M., Perret, S. & Grégoire, A. Urbanization is associated with divergence in pace-of-life in great tits. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2017.00053 (2017).Article 

Google Scholar 
20.Isaksson, C., Rodewald, A. D. & Gil, D. Editorial: Behavioural and ecological consequences of urban life in birds. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2018.00050 (2018).Article 

Google Scholar 
21.Audet, J.-N., Ducatez, S. & Lefebvre, L. The town bird and the country bird: Problem solving and immunocompetence vary with urbanization. Behav. Ecol. 27, 637–644. https://doi.org/10.1093/beheco/arv201 (2016).Article 

Google Scholar 
22.Miranda, A. C., Schielzeth, H., Sonntag, T. & Partecke, J. Urbanization and its effects on personality traits: A result of microevolution or phenotypic plasticity. Glob. Change Biol. 19, 2634–2644. https://doi.org/10.1111/gcb.12258 (2013).ADS 
Article 

Google Scholar 
23.Riyahi, S., Björklund, M., Mateos-Gonzalez, F. & Senar, J. C. Personality and urbanization: Behavioural traits and DRD4 SNP830 polymorphisms in great tits in Barcelona city. J. Ethol. 35, 101–108. https://doi.org/10.1007/s10164-016-0496-2 (2017).Article 

Google Scholar 
24.Schinka, J. A., Letsch, E. A. & Crawford, F. C. DRD4 and novelty seeking: Results of meta-analyses. Am. J. Med. Genet. 114, 643–648. https://doi.org/10.1002/ajmg.10649 (2002).CAS 
Article 
PubMed 

Google Scholar 
25.Chen, C. S., Burton, M., Greenberger, E. & Dmitrieva, J. Population migration and the variation of Dopamine D4 Receptor (DRD4) allele frequencies around the globe. Evol. Hum. Behav. 20, 309–324. https://doi.org/10.1016/S1090-5138(99)00015-X (1999).Article 

Google Scholar 
26.Shimada, M. K. et al. Polymorphism in the second intron of dopamine receptor D4 gene in humans and apes. Biochem. Biophys. Res. Commun. 316, 1186–1190. https://doi.org/10.1016/j.bbrc.2004.03.006 (2004).CAS 
Article 
PubMed 

Google Scholar 
27.Fidler, A. E. et al. Drd4 gene polymorphisms are associated with personality variation in a passerine bird. Proc. R. Soc. B. 274, 1685–1691. https://doi.org/10.1098/rspb.2007.0337 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Mueller, J. C. et al. Haplotype structure, adaptive history and associations with exploratory behaviour of the DRD4 gene region in four great tit (Parus major) populations. Mol. Ecol. 22, 2797–2809. https://doi.org/10.1111/mec.12282 (2013).CAS 
Article 
PubMed 

Google Scholar 
29.Korsten, P. et al. Association between DRD4 gene polymorphism and personality variation in great tits: A test across four wild populations. Mol. Ecol. 19, 832–843. https://doi.org/10.1111/j.1365-294X.2009.04518.x (2010).CAS 
Article 
PubMed 

Google Scholar 
30.Jiang, W., Shang, S. & Su, Y. Genetic influences on insight problem solving: The role of catechol-O-methyltransferase polymorphisms. Front. Psychol. 6, 1569. https://doi.org/10.3389/fpsyg.2015.01569 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Hopkins, W. et al. Genetic influences on receptive joint attention in chimpanzees (Pan troglodytes). Sci. Rep. 4, 3774. https://doi.org/10.1038/srep03774 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Fitzpatrick, M. J. et al. Candidate genes for behavioural ecology. Trends Ecol. Evol. 20, 96–104. https://doi.org/10.1016/j.tree.2004.11.017 (2005).Article 
PubMed 

Google Scholar 
33.Munafo, M. R., Brown, S. M. & Harkless, K. C. Serotonin transporter (5-HTTLPR) genotype and amygdala activation: A meta-analysis. Biol. Psychiatry 63, 852–857. https://doi.org/10.1016/j.biopsych.2007.08.016 (2008).CAS 
Article 
PubMed 

Google Scholar 
34.Staes, N. et al. Serotonin receptor 1A variation is associated with anxiety and agonistic behavior in chimpanzees. Mol. Biol. Evol. 36, 1418–1429. https://doi.org/10.1093/molbev/msz061 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Mueller, J. C. et al. Behaviour-related DRD4 polymorphisms in invasive bird populations. Mol. Ecol. 23, 2876–2885. https://doi.org/10.1111/mec.12763 (2014).CAS 
Article 
PubMed 

Google Scholar 
36.Timm, K., Tilgar, V. & Saag, P. DRD4 gene polymorphism in great tits: Gender-specific association with behavioural variation in the wild. Behav. Ecol. Sociobiol. 69, 729–735. https://doi.org/10.1007/s00265-015-1887-z (2015).Article 

Google Scholar 
37.Riyahi, S., Sánchez-Delgado, M., Calafell, F., Monk, D. & Senar, J. C. Combined epigenetic and intraspecific variation of the DRD4 and SERT genes influence novelty seeking behaviour in great tit Parus major. Epigenetics 10, 516–525. https://doi.org/10.1080/15592294.2015.1046027 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
38.Holtmann, B. et al. Population differentiation and behavioural association of the two ‘personality’ genes DRD4 and SERT in dunnocks (Prunella modularis). Mol. Ecol. 25, 706–722. https://doi.org/10.1111/mec.13514 (2016).CAS 
Article 
PubMed 

Google Scholar 
39.Krause, E. T., Kjaer, J. B., Lüders, C. & van Phi, L. A polymorphism in the 5′-flanking region of the serotonin transporter (5-HTT) gene affects fear-related behaviors of adult domestic chickens. Behav. Brain Res. 14, 92–96. https://doi.org/10.1016/j.bbr.2017.04.051 (2017).CAS 
Article 

Google Scholar 
40.Timm, K., van Oers, K. & Tilgar, V. SERT gene polymorphisms are associated with risk-taking behaviour and breeding parameters in wild great tits. J. Exp. Biol. 221, jeb171595. https://doi.org/10.1242/jeb.171595 (2018).Article 
PubMed 

Google Scholar 
41.Timm, K., Koosa, K. & Tilgar, V. The serotonin transporter gene could play a role in anti-predator behaviour in a forest passerine. J. Ethol. 37, 221–227. https://doi.org/10.1007/s10164-019-00593-7 (2019).Article 

Google Scholar 
42.Berger, M., Gray, J. A. & Roth, B. L. The expanded biology of serotonin. Annu. Rev. Med. 60, 355–366. https://doi.org/10.1146/annurev.med.60.042307.110802 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
43.Lesch, K. P. & Merschdorf, U. Impulsivity, aggression, and serotonin: A molecular psychobiological perspective. Behav. Sci. Law 18, 581–604 (2000).CAS 
Article 

Google Scholar 
44.Duke, A. A., Bègue, L., Bell, R. & Eisenlohr-Moul, T. Revisiting the serotonin-aggression relation in humans: A meta-analysis. Psychol. Bull. 139, 1148–1172. https://doi.org/10.1037/a0031544 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
45.Ferrari, P. F., Palanza, P., Parmigiani, S., de Almeida, R. M. & Miczek, K. A. Serotonin and aggressive behavior in rodents and nonhuman primates: Predispositions and plasticity. Eur. J. Pharmacol. 526, 259–273. https://doi.org/10.1016/j.ejphar.2005.10.002 (2005).CAS 
Article 
PubMed 

Google Scholar 
46.Bacqué-Cazenave, J. et al. Serotonin in animal cognition and behavior. Int. J. Mol. Sci. 21, 1649. https://doi.org/10.3390/ijms21051649 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
47.Walker, S. C. et al. Selective prefrontal serotonin depletion impairs acquisition of a detour-reaching task. Eur. J. Neurosci. 23, 3119–3123. https://doi.org/10.1111/j.1460-9568.2006.04826.x (2006).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
48.Cools, R., Roberts, A. C. & Robbins, T. W. Serotoninergic regulation of emotional and behavioural control processes. Trends Cogn. Sci. 12, 31–40. https://doi.org/10.1016/j.tics.2007.10.011 (2008).Article 
PubMed 

Google Scholar 
49.Rudnick, G. & Sandtner, W. Serotonin transport in the 21st century. J. Gen. Physiol. 151, 1248–1264. https://doi.org/10.1085/jgp.201812066 (2018).CAS 
Article 

Google Scholar 
50.Lesch, K. P. et al. Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region. Science 274, 1527–1531. https://doi.org/10.1126/science.274.5292.1527 (1996).ADS 
CAS 
Article 
PubMed 

Google Scholar 
51.Sen, S., Burmeister, M. & Ghosh, D. Meta-analysis of the association between a serotonin transporter promoter polymorphism (5- HTTLPR) and anxiety-related personality traits. Am. J. Med. Genet. 127, 85–89. https://doi.org/10.1002/ajmg.b.20158 (2004).Article 

Google Scholar 
52.Karg, K., Burmeister, M., Shedden, K. & Sen, S. The serotonin transporter promoter variant (5-HTTLPR), stress, and depression meta-analysis revisited: Evidence of genetic moderation. Arch. Gen. Psychiatry 68, 444–454. https://doi.org/10.1001/archgenpsychiatry.2010.189 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
53.Beversdorf, D. Q. et al. Influence of serotonin transporter SLC6A4 genotype on the effect of psychosocial stress on cognitive performance: An exploratory pilot study. Cogn. Behav. Neurol. 31, 79–85. https://doi.org/10.1097/WNN.0000000000000153 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
54.Canli, T. & Lesch, P.-K. Long story short: The serotonin transporter in emotion regulation and social cognition. Nat. Neurosci. 10, 1103–1109. https://doi.org/10.1038/nn1964 (2007).CAS 
Article 
PubMed 

Google Scholar 
55.Jarrell, H. et al. Polymorphisms in the serotonin reuptake transporter gene modify the consequences of social status on metabolic health in female rhesus monkeys. Physiol. Behav. 93, 807–819. https://doi.org/10.1016/j.physbeh.2007.11.042 (2008).CAS 
Article 
PubMed 

Google Scholar 
56.Bennett, A. et al. Early experience and serotonin transporter gene variation interact to influence primate CNS function. Mol. Psychiatry 7, 118–122. https://doi.org/10.1038/sj.mp.4000949 (2002).CAS 
Article 
PubMed 

Google Scholar 
57.Golebiowska, J. et al. Serotonin transporter deficiency alters socioemotional ultrasonic communication in rats. Sci. Rep. 9, 20283. https://doi.org/10.1038/s41598-019-56629-y (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
58.Thys, B. et al. The serotonin transporter gene and female personality variation in a free-living passerine. Sci. Rep. 11, 8577. https://doi.org/10.1038/s41598-021-88225-4 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
59.Audet, J.-N. et al. Divergence in problem-solving skills is associated with differential expression of glutamate receptors in wild finches. Sci. Adv. 4, eaao6369. https://doi.org/10.1126/sciadv.aao6369 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
60.Grunst, A. S., Grunst, M. L., Pinxten, R. & Eens, M. Personality and plasticity in neophobia levels vary with anthropogenic disturbance but not toxic metal exposure in urban great tits. Sci. Total Environ. 656, 997–1009. https://doi.org/10.1016/j.scitotenv.2018.11.383 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
61.Grunst, A. S., Grunst, M. L., Pinxten, R. & Eens, M. Sources of individual variation in problem-solving performance in urban great tits (Parus major): Exploring effects of metal pollution, urban disturbance and personality. Sci. Tot. Environ. 749, 141436. https://doi.org/10.1016/j.scitotenv.2020.141436 (2020).CAS 
Article 

Google Scholar 
62.Thys, B. et al. The female perspective of personality in a wild songbird: Repeatable aggressiveness relates to exploration behavior. Sci. Rep. 7, 7656. https://doi.org/10.1038/s41598-017-08001-1 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
63.Grunst, A. S. et al. An important personality trait varies with blood and plumage metal concentrations in a free-living songbird. Environ. Sci. Technol. 53, 10487–10496. https://doi.org/10.1021/acs.est.9b03548 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
64.Grunst, A. S. et al. Variation in personality traits across a metal pollution gradient in a free-living songbird. Sci. Total Environ. 630, 668–678. https://doi.org/10.1016/j.scitotenv.2018.02.19 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
65.Laucht, M. et al. Interaction between the 5-HTTLPR serotonin transporter polymorphism and environmental adversity for mood and anxiety psychopathology: Evidence from a high-risk community sample of young adults. Int. J. Neuropharmacol. 12, 737–747. https://doi.org/10.1017/S1461145708009875 (2009).CAS 
Article 

Google Scholar 
66.Wang, Z. et al. Genome-wide gene by lead exposure interaction analysis identifies UNC5D as a candidate gene for neurodevelopment. Environ. Health 16, 81. https://doi.org/10.1186/s12940-017-0288-3 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
67.Grunst, A. S., Grunst, M. L., Pinxten, R. & Eens, M. Proximity to roads, but not exposure to metal pollution, is associated with accelerated developmental telomere shortening in nestling great tits. Environ. Pollut. 256, 113373. https://doi.org/10.1016/j.envpol.2019.113373 (2019).CAS 
Article 
PubMed 

Google Scholar 
68.Dingemanse, N. J. et al. Repeatability and heritability of exploratory behaviour in great tits from the wild. Anim. Behav. 64, 929–937. https://doi.org/10.1006/anbe.2002.2006 (2002).Article 

Google Scholar 
69.Solé, X. et al. SNPStats: A web tool for the analysis of association studies. Bioinformatics 22, 1928–1929. https://doi.org/10.1093/bioinformatics/bti283 (2005).Article 

Google Scholar 
70.Hecht, M., Bromberg, Y. & Rost, B. Better prediction of functional effects for sequence variants from VarI-SIG 2014: Identification and annotation of genetic variants in the context of structure, function and disease. BMC Genom. 16, S1. https://doi.org/10.1186/1471-2164-16-S8-S1 (2015).CAS 
Article 

Google Scholar 
71.Choi, Y. & Chan, A. PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 31, 2745–2747. https://doi.org/10.1093/bioinformatics/btv195 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
72.Omasits, U., Ahrens, C. H., Müller, S. & Wollscheid, B. Protter: Interactive protein feature visualization and integration with experimental proteomic data. Bioinformatics 30(6), 884–886. https://doi.org/10.1093/bioinformatics/btt607 (2014).CAS 
Article 
PubMed 

Google Scholar 
73.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2019). URL https://www.R-project.org/.74.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2014).Article 

Google Scholar 
75.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26. https://doi.org/10.18637/jss.v082.i13 (2017).Article 

Google Scholar 
76.Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644. https://doi.org/10.1111/2041-210X.12797 (2017).Article 

Google Scholar 
77.Harrison, X. A. Using observation-level random effects to model overdispersion in count data in ecology and evolution. PeerJ 2, e616. https://doi.org/10.7717/peerj.616 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
78.Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.4.3.01 (2019). https://CRAN.R-project.org/package=emmeans.79.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300. https://doi.org/10.2307/2346101 (1995).MathSciNet 
Article 
MATH 

Google Scholar 
80.Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142. https://doi.org/10.1111/j.2041-210x.2012.00261.x (2013).Article 

Google Scholar 
81.Lüdecke, D., Makowski, D., Waggoner, P. & Patil, I. performance: Assessment of Regression Models Performance. R package version 0.4.6 (2020). https://CRAN.R-project.org/package=performance.82.Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. R package version 0.2.6 (2019). https://CRAN.R-project.org/package=DHARMa.83.Mikros, E. & Diallinas, G. Tales of tails in transporters. Open Biol. 9, 190083. https://doi.org/10.1098/rsob.190083 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
84.Kern, C. et al. The N teminus specifies the switch between transporter modes of the human serotonin transporter. J. Biol. Chem. 292, 3603–3613. https://doi.org/10.1074/jbc.M116.771360 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
85.Visser, M. E., Van Noordwijk, A. J., Tinbergen, J. M. & Lessells, C. M. Warmer springs lead to mistimed reproduction in great tits (Parus major). Proc. R. Soc. B 265, 1867–1870. https://doi.org/10.1098/rspb.1998.0514 (1998).Article 
PubMed Central 

Google Scholar 
86.Hunt, R., Sauna, Z. E., Ambudkar, S. V., Gottesman, M. M. & Kimchi-Sarfaty, C. Silent (Synonymous) SNPs: Should we care about them? In Single Nucleotide Polymorphisms Methods in Molecular Biology (Methods and Protocols) Vol. 578 (ed. Komar, A.) (Humana Press, 2009). https://doi.org/10.1007/978-1-60327-411-1_2.Chapter 

Google Scholar 
87.Grunst, A.S., Grunst, M.L. & Staes, N., Bert, T., Pinxten, R., Eens, M. Data for: Serotonin Transporter (SERT) Polymorphisms, Personality and Problem-Solving in Urban Great Tits. (Dryad Digital Repository, 2021). More

1.Piao, S. et al. Glob. Change Biol. 25, 1922–1940 (2019).Article 

Google Scholar 
2.Richardson, A. D. et al. Nature 560, 368–371 (2018).CAS 
Article 

Google Scholar 
3.Ma, H. et al. Nat. Ecol. Evol. 5, 1110–1122 (2021).Article 

Google Scholar 
4.Mokany, K., Raison, R. J. & Prokushkin, A. S. Glob. Change Biol. 12, 84–96 (2006).Article 

Google Scholar 
5.Radville, L., McCormack, M. L., Post, E. & Eissenstat, D. M. J. Exp. Bot. 67, 3617–3628 (2016).CAS 
Article 

Google Scholar 
6.Liu, H. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-021-01244-x (2021).7.Freschet, G. T. et al. New Phytol. 232, 1123–1158 (2021).Article 

Google Scholar 
8.Clemmensen, K. E. et al. Science 339, 1615–1618 (2013).CAS 
Article 

Google Scholar 
9.Sokol, N. W. & Bradford, M. A. Nat. Geosci. 12, 46–53 (2019).CAS 
Article 

Google Scholar 
10.Jones, D. L., Nguyen, C. & Finlay, R. D. Plant Soil 321, 5–33 (2009).CAS 
Article 

Google Scholar 
11.Abramoff, R. Z. & Finzi, A. C. New Phytol. 205, 1054–1061 (2015).Article 

Google Scholar 
12.Warren, J. M. et al. New Phytol. 205, 59–78 (2015).Article 

Google Scholar 
13.Blume-Werry, G., Wilson, S. D., Kreyling, J. & Milbau, A. New Phytol. 209, 978–986 (2016).CAS 
Article 

Google Scholar 
14.Sloan, V. L., Fletcher, B. J. & Phoenix, G. K. J. Ecol. 104, 239–248 (2016).CAS 
Article 

Google Scholar 
15.Fu, Y. H. et al. Nature 526, 104–107 (2015).CAS 
Article 

Google Scholar  More

1.Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).
Google Scholar 
2.Forrest, J. & Miller-Rushing, A. Toward a synthetic understanding of the role of phenology in ecology and evolution. Philos. Trans. R. Soc. B 365, 3101–3112 (2010).
Google Scholar 
3.Lane, J. E., Kruuk, L., Charmantier, A., Murie, J. O. & Dobson, F. S. Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature 489, 554–557 (2012).CAS 

Google Scholar 
4.Richardson, A. D. et al. Ecosystem warming extends vegetation activity but heightens vulnerability to cold temperatures. Nature 560, 368–371 (2018).CAS 

Google Scholar 
5.Abramoff, R. Z. & Finzi, A. C. Are above- and below-ground phenology in sync? New Phytol. 205, 1054–1061 (2015).
Google Scholar 
6.Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).
Google Scholar 
7.Smithwick, E., Lucash, M. S., Mccormack, M. L. & Sivandran, G. Improving the representation of roots in terrestrial models. Ecol. Model. 291, 193–204 (2014).CAS 

Google Scholar 
8.Warren, J. M. et al. Root structural and functional dynamics in terrestrial biosphere models – evaluation and recommendations. New Phytol. 205, 59–78 (2015).
Google Scholar 
9.Ma, H., Mo, L., Crowther, T. W., Maynard, D. S. & Zohner, C. M. The global distribution and environmental drivers of aboveground versus belowground plant biomass. Nat. Ecol. Evol. 5, 1110–1122 (2021).
Google Scholar 
10.Neumann, R. B. & Cardon, Z. G. The magnitude of hydraulic redistribution by plant roots: a review and synthesis of empirical and modeling studies. New Phytol. 194, 337–352 (2012).
Google Scholar 
11.Lucas, M., Schlueter, S., Vogel, H.-J. & Vetterlein, D. Roots compact the surrounding soil depending on the structures they encounter. Sci. Rep. 9, 16236 (2019).
Google Scholar 
12.Oades, J. M. The role of biology in the formation, stabilization and degradation of soil structure. Geoderma 56, 377–400 (1993).
Google Scholar 
13.Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).CAS 

Google Scholar 
14.Roslin, T., Anto, L., Hllfors, M., Meyke, E. & Ovaskainen, O. Phenological shifts of abiotic events, producers and consumers across a continent. Nat. Clim. Change 11, 241–248 (2021).
Google Scholar 
15.Radville, L., McCormack, M. L., Post, E. & Eissenstat, D. M. Root phenology in a changing climate. J. Exp. Bot. 67, 3617–3628 (2016).CAS 

Google Scholar 
16.Blume-Werry, G., Jansson, R. & Milbau, A. Root phenology unresponsive to earlier snowmelt despite advanced above‐ground phenology in two subarctic plant communities. Funct. Ecol. 31, 1493–1502 (2017).
Google Scholar 
17.Wilson, J. B. A review of evidence on the control of shoot:root ratio, in relation to models. Ann. Bot. 61, 433–449 (1988).
Google Scholar 
18.Schwieger, S., Kreyling, J., Milbau, A. & Blume-Werry, G. Autumnal warming does not change root phenology in two contrasting vegetation types of subarctic tundra. Plant Soil 424, 145–156 (2018).CAS 

Google Scholar 
19.Liu, H., Lu, C., Wang, S., Ren, F. & Wang, H. Climate warming extends growing season but not reproductive phase of terrestrial plants. Glob. Ecol. Biogeogr. 30, 950–960 (2021).
Google Scholar 
20.Steinaker, D. F., Wilson, S. D. & Peltzer, D. A. Asynchronicity in root and shoot phenology in grasses and woody plants. Glob. Change Biol. 16, 2241–2251 (2010).
Google Scholar 
21.Keenan, T. F. et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Change 4, 598–604 (2014).CAS 

Google Scholar 
22.Thakur, M. P. Climate warming and trophic mismatches in terrestrial ecosystems: the green–brown imbalance hypothesis. Biol. Lett. 16, 20190770 (2020).
Google Scholar 
23.Wang, H. et al. Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change. Ecol. Lett. 23, 701–710 (2020).
Google Scholar 
24.Chuine, I. A united model for budburst of trees. J. Theor. Biol. 2007, 337–347 (2000).
Google Scholar 
25.Lim, P. O., Kim, H. J. & Gil Nam, H. Leaf senescence. Annu. Rev. Plant Biol. 58, 115–136 (2007).CAS 

Google Scholar 
26.Reich, P. B., Walters, M. & Ellsworth, D. Leaf life-span in relation to leaf, plant, and stand characteristics among diverse ecosystems. Ecol. Monogr. 62, 365–392 (1992).
Google Scholar 
27.Körner, C. & Basler, D. Phenology under global warming. Science 327, 1461–1462 (2010).
Google Scholar 
28.Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).CAS 

Google Scholar 
29.Wolkovich, E. M. et al. Warming experiments underpredict plant phenological responses to climate change. Nature 485, 494–497 (2012).CAS 

Google Scholar 
30.López-Bucio, J., Cruz-Ramírez, A. & Herrera-Estrella, L. The role of nutrient availability in regulating root architecture. Curr. Opin. Plant Biol. 6, 280–287 (2003).
Google Scholar 
31.Friedl, M. A. et al. Global land cover mapping from MODIS: algorithms and early results. Remote Sens. Environ. 83, 287–302 (2002).
Google Scholar 
32.Lian, X. et al. Summer soil drying exacerbated by earlier spring greening of northern vegetation. Sci. Adv. 6, eaax0255 (2020).
Google Scholar 
33.Hollister, R. D., Webber, P. J. & Bay, C. Plant response to temperature in northern Alaska: implications for predicting vegetation change. Ecology 86, 1562–1570 (2005).
Google Scholar 
34.Song, J. et al. A meta-analysis of 1,119 manipulative experiments on terrestrial carbon-cycling responses to global change. Nat. Ecol. Evol. 3, 1309–1320 (2019).
Google Scholar 
35.Collins, C. G. et al. Experimental warming differentially affects vegetative and reproductive phenology of tundra plants. Nat. Commun. https://doi.org/10.1038/s41467-021-23841-2 (2021).36.Reyes-Fox, M. et al. Elevated CO2 further lengthens growing season under warming conditions. Nature 510, 259–267 (2014).CAS 

Google Scholar 
37.Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. B 365, 3227–3246 (2010).
Google Scholar 
38.Wingler, A. & Hennessy, D. Limitation of grassland productivity by low temperature and seasonality of growth. Front. Plant Sci. 7, 1130 (2016).
Google Scholar 
39.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).
Google Scholar 
40.Wang, P., Huang, K. & Hu, S. Distinct fine-root responses to precipitation changes in herbaceous and woody plants: a meta-analysis. New Phytol. 225, 1491–1499 (2020).
Google Scholar 
41.Arft, A. et al. Responses of tundra plants to experimental warming: meta-analysis of the international tundra experiment. Ecol. Monogr. 69, 491–511 (1999).
Google Scholar 
42.Fu, Y. S. et al. Variation in leaf flushing date influences autumnal senescence and next year’s flushing date in two temperate tree species. Proc. Natl Acad. Sci. USA 111, 7355–7360 (2014).CAS 

Google Scholar 
43.Seastedt, T. & Knapp, A. Consequences of nonequilibrium resource availability across multiple time scales: the transient maxima hypothesis. Am. Nat. 141, 621–633 (1993).CAS 

Google Scholar 
44.Bai, E. et al. A meta-analysis of experimental warming effects on terrestrial nitrogen pools and dynamics. New Phytol. 199, 441–451 (2013).
Google Scholar 
45.Sakai, A. & Larcher, W. Frost Survival of Plants: Responses and Adaptation to Freezing Stress (Springer‐Verlag, 1987).46.Zani, D., Crowther, T. W., Mo, L., Renner, S. S. & Zohner, C. M. Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science 370, 1066–1071 (2020).CAS 

Google Scholar 
47.Luo, Y. Terrestrial carbon-cycle feedback to climate warming. Annu. Rev. Ecol. Evol. Syst. 38, 683–712 (2007).
Google Scholar 
48.Hijmans, R. J., Ca Meron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2010).
Google Scholar 
49.Sloan, V. L., Fletcher, B. J. & Phoenix, G. K. Contrasting synchrony in root and leaf phenology across multiple sub‐Arctic plant communities. J. Ecol. 104, 239–248 (2016).CAS 

Google Scholar 
50.Kou, L. et al. Nitrogen deposition increases root production and turnover but slows root decomposition in Pinus elliottii plantations. New Phytol. 218, 1450–1461 (2018).
Google Scholar 
51.Adams, D. C., Gurevitch, J. & Rosenberg, M. S. Resampling tests for meta-analysis of ecological data. Ecology 78, 1277–1283 (1997).
Google Scholar 
52.Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Soft. 36, 1–48 (2010).
Google Scholar 
53.Kattge, J. et al. TRY plant trait database-enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).
Google Scholar 
54.De Martonne, E. Une nouvelle fonction climatologique: l’indice d’aridité. La MétéOrol. 2, 449–458 (1926).
Google Scholar 
55.Breiman, L. Classification and Regression Trees (Routledge, 2017).56.Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2/3, 18–22 (2002).
Google Scholar 
57.Terrer, C. et al. Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nat. Clim. Change 10, 696–697 (2020).
Google Scholar 
58.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).59.Liu, H. et al. Supporting data for ‘Phenological mismatches between above- and belowground plant responses to climate warming’. Figshare https://figshare.com/s/1f086364114021cd80d9 (2021). More

1.Baines SB, Pace ML. The production of dissolved organic matter by phytoplankton and its importance to bacteria: patterns across marine and freshwater systems. Limnol Oceanogr. 1991;36:1078–90.
Google Scholar 
2.Williams PJLeB. Heterotrophic bacteria and the dynamics of dissolved organic material. In: Kirchman DL (ed). Microbial Ecology of the Oceans, 1st edn. New York: Wiley-Liss; 2000. p. 153–200.3.Thornton DCO. Dissolved organic matter (DOM) release by phytoplankton in the contemporary and future ocean. Eur J Phycol. 2014;49:20–46.CAS 

Google Scholar 
4.Nagata T. Organic matter-bacteria interactions in seawater. In: Kirchman DL, (ed). Microbial Ecology of the Oceans. Hoboken: John Wiley and Sons, Inc; 2008. p. 207–41.
Google Scholar 
5.Kujawinski EB. The impact of microbial metabolism on marine dissolved organic matter. Ann Rev Mar Sci. 2011;3:567–99.PubMed 

Google Scholar 
6.Azam F, Fenchel T, Field JG, Gray JS, Meyerreil LA, Thingstad F. The ecological role of water-column microbes in the sea. Mar Ecol Prog Ser. 1983;10:257–63.
Google Scholar 
7.Cole JJ, Findlay S, Pace ML. Bacterial production in fresh and saltwater ecosystems – a cross-system overview. Mar Ecol Prog Ser. 1988;43:1–10.
Google Scholar 
8.Moran MA, Kujawinski EB, Stubbins A, Fatland R, Aluwihare LI, Buchan A, et al. Deciphering ocean carbon in a changing world. Proc Nat Acad Sci. 2016;113:3143–51.CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Becker KW, Collins JR, Durham BP, Groussman RD, White AE, Fredricks HF, et al. Daily changes in phytoplankton lipidomes reveal mechanisms of energy storage in the open ocean. Nat Comm. 2018;9:5179.
Google Scholar 
10.Boysen AK, Carlson LT, Durham BP, Groussman RD, Aylward FO, Ribalet F, et al. Diel oscillations of particulate metabolites reflect synchronized microbial activity in the North Pacific Subtropical Gyre. bioRxiv. 2020: 2020.05.09.086173.11.Durham BP, Boysen AK, Carlson LT, Groussman RD, Heal KR, Cain KR, et al. Sulfonate-based networks between eukaryotic phytoplankton and heterotrophic bacteria in the surface ocean. Nat Microbiol. 2019;4:1706–15.CAS 
PubMed 

Google Scholar 
12.Burney CM, Davis PG, Johnson KM, Sieburth JM. Diel relationships of microbial trophic groups and in situ dissolved carbohydrate dynamics in the Caribbean Sea. Mar Biol. 1982;67:311–22.CAS 

Google Scholar 
13.Gasol JM, Doval MD, Pinhassi J, Calderon-Paz JI, Guixa-Boixareu N, Vaque D, et al. Diel variations in bacterial heterotrophic activity and growth in the northwestern Mediterranean Sea. Mar Ecol Prog Ser. 1998;164:107–24.
Google Scholar 
14.Kuipers B, van Noort GJ, Vosjan J, Herndl GJ. Diel periodicity of bacterioplankton in the euphotic zone of the subtropical Atlantic Ocean. Mar Ecol Prog Ser. 2000;201:13–25.
Google Scholar 
15.Ottesen EA, Young CR, Gifford SM, Eppley JM, Marin R, Schuster SC, et al. Multispecies diel transcriptional oscillations in open ocean heterotrophic bacterial assemblages. Science 2014;345:207–12.CAS 
PubMed 

Google Scholar 
16.Aylward FO, Eppley JM, Smith JM, Chavez FP, Scholin CA, DeLong EF. Microbial community transcriptional networks are conserved in three domains at ocean basin scales. Proc Nat Acad Sci. 2015;112:5443–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
17.Frischkorn KR, Haley ST, Dyhrman ST. Coordinated gene expression between Trichodesmium and its microbiome over day–night cycles in the North Pacific Subtropical Gyre. ISME J 2018;12:997–1007.PubMed 
PubMed Central 

Google Scholar 
18.Seymour JR, Amin SA, Raina JB, Stocker R. Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships. Nat Microbiol. 2017;2:17065.CAS 
PubMed 

Google Scholar 
19.Bjornsen PK. Phytoplankton exudation of organic-matter – why do healthy cells do it. Limnol Oceanogr. 1988;33:151–4.
Google Scholar 
20.Fogg GE. The ecological significance of extracellular products of phytoplankton photosynthesis. Bot Mar. 1983;26:3–14.CAS 

Google Scholar 
21.Amin SA, Hmelo LR, van Tol HM, Durham BP, Carlson LT, Heal KR, et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature 2015;522:98–101.CAS 
PubMed 

Google Scholar 
22.Durham BP, Dearth SP, Sharma S, Amin SA, Smith CB, Campagna SR, et al. Recognition cascade and metabolite transfer in a marine bacteria‐phytoplankton model system. Environ Microbiol. 2017;19:3500–13.CAS 
PubMed 

Google Scholar 
23.Guerrini F, Mazzotti A, Boni L, Pistocchi R. Bacterial-algal interactions in polysaccharide production. Aquat Micro Ecol. 1998;15:247–53.
Google Scholar 
24.Armbrust EV, Berges JA, Bowler C, Green BR, Martinez D, Putnam NH, et al. The genome of the diatom Thalassiosira pseudonana: ecology, evolution, and metabolism. Science 2004;306:79–86.CAS 
PubMed 

Google Scholar 
25.Moran MA, Buchan A, Gonzalez JM, Heidelberg JF, Whitman WB, Kiene RP, et al. Genome sequence of Silicibacter pomeroyi reveals adaptations to the marine environment. Nature 2004;432:910–3.CAS 
PubMed 

Google Scholar 
26.Uitz J, Claustre H, Gentili B, Stramski D. Phytoplankton class-specific primary production in the world’s oceans: Seasonal and interannual variability from satellite observations. Global Biogeochem Cycles. 2010;24.27.Buchan A, LeCleir GR, Gulvik CA, Gonzalez JM. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat Rev Microbiol. 2014;12:686–98.CAS 
PubMed 

Google Scholar 
28.Luo HW, Moran MA. Evolutionary ecology of the marine Roseobacter clade. Microbiol Mol Biol Rev. 2014;78:573–87.PubMed 
PubMed Central 

Google Scholar 
29.Nowinski B, Moran MA. Niche dimensions of a marine bacterium are identified using invasion studies in coastal seawater. Nat Microbiol. 2021;6:524.CAS 
PubMed 

Google Scholar 
30.Denger K, Lehmann S, Cook AM. Molecular genetics and biochemistry of N-acetyltaurine degradation by Cupriavidus necator H16. Microbiology 2011;157:2983–91.CAS 
PubMed 

Google Scholar 
31.Schulz A, Stoveken N, Binzen IM, Hoffmann T, Heider J, Bremer E. Feeding on compatible solutes: a substrate-induced pathway for uptake and catabolism of ectoines and its genetic control by EnuR. Environ Microbiol. 2017;19:926–46.CAS 
PubMed 

Google Scholar 
32.Crossette E, Gumm J, Langenfeld K, Raskin L, Duhaime M, Wigginton K. Metagenomic quantification of genes with internal standards. mBio. 2021;12:e03173-20.PubMed 
PubMed Central 

Google Scholar 
33.Gifford SM, Becker JW, Sosa OA, Repeta DJ, DeLong EF. Quantitative transcriptomics reveals the growth-and nutrient-dependent response of a streamlined marine methylotroph to methanol and naturally occurring dissolved organic matter. mBio. 2016;7:e01279-16.PubMed 
PubMed Central 

Google Scholar 
34.Moran MA, Satinsky B, Gifford SM, Luo HW, Rivers A, Chan LK, et al. Sizing up metatranscriptomics. ISME J 2013;7:237–43.CAS 
PubMed 

Google Scholar 
35.Guillard RRL, Hargraves PE. Stichochrysis immobilis is a diatom, not a chyrsophyte. Phycologia 1993;32:234–6.
Google Scholar 
36.Uchimiya M, Tsuboi Y, Ito K, Date Y, Kikuchi J. Bacterial substrate transformation tracked by stable-isotope-guided NMR metabolomics: application in a natural aquatic microbial community. Metabolites 2017;7:52.PubMed Central 

Google Scholar 
37.Lewis IA, Schommer SC, Markley JL. rNMR: open source software for identifying and quantifying metabolites in NMR spectra. Mag Res Chem. 2009;47:S123–S6.CAS 

Google Scholar 
38.Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu YF, et al. HMDB 3.0-the human metabolome database in 2013. Nuc Acids Res 2013;41:D801–D7.CAS 

Google Scholar 
39.Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE, Lin J, et al. BioMagResBank Nuc Acids Res. 2008;36:D402–D8.CAS 

Google Scholar 
40.Toukach PV, Egorova KS. Carbohydrate structure database merged from bacterial, archaeal, plant and fungal parts. Nuclic Acids Res. 2016;44:D1229–D36.CAS 

Google Scholar 
41.Landa M, Burns AS, Durham BP, Esson K, Nowinski B, Sharma S, et al. Sulfur metabolites that facilitate oceanic phytoplankton-bacteria carbon flux. ISME J. 2019;13:2536–50.CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Boroujerdi AFB, Lee PA, DiTullio GR, Janech MG, Vied SB, Bearden DW. Identification of isethionic acid and other small molecule metabolites of Fragilariopsis cylindrus with nuclear magnetic resonance. Anal Bioanal Chem. 2012;404:777–84.CAS 
PubMed 

Google Scholar 
43.Walejko JM, Chelliah A, Keller-Wood M, Gregg A, Edison AS. Global metabolomics of the placenta reveals distinct metabolic profiles between maternal and fetal placental tissues following delivery in non-labored women. Metabolites 2018;8:10.PubMed Central 

Google Scholar 
44.Schwämmle V, Jensen ON. VSClust: feature-based variance-sensitive clustering of omics data. Bioinformatics 2018;34:2965–72.PubMed 

Google Scholar 
45.Thaben PF, Westermark PO. Detecting rhythms in time series with RAIN. J Biol Rhythms. 2014;29:391–400.PubMed 
PubMed Central 

Google Scholar 
46.Welsh J (2020). CirHeatmap. Available from: https://github.com/joadwe/cirheatmap.47.Landa M, Burns AS, Roth SJ, Moran MA. Bacterial transcriptome remodeling during sequential co-culture with a marine dinoflagellate and diatom. ISME J. 2017;11:2677–90.CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Satinsky BM, Gifford SM, Crump BC, Moran MA Use of internal standards for quantitative metatranscriptome and metagenome analysis. In: DeLong EF (ed). Methods in Enzymology. 2013. 531: p. 237-50.49.Anders S, Pyl PT, Huber W. HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics 2015;31:166–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.PubMed 
PubMed Central 

Google Scholar 
51.Becker S, Tebben J, Coffinet S, Wiltshire K, Iversen MH, Harder T, et al. Laminarin is a major molecule in the marine carbon cycle. Proc Nat Acad Sci. 2020;117:6599–607.CAS 
PubMed 
PubMed Central 

Google Scholar 
52.Neidhardt F, Ingraham J, Schaechter S Physiology of the bacterial cell: a molecular approach. Massachusetts: Sinauer Associates Inc.; 1990.53.Lidbury I, Murrell JC, Chen Y. Trimethylamine N-oxide metabolism by abundant marine heterotrophic bacteria. Proc Nat Acad Sci. 2014;111:2710–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Mayer J, Huhn T, Habeck M, Denger K, Hollemeyer K, Cook AM. 2,3-Dihydroxypropane-1-sulfonate degraded by Cupriavidus pinatubonensis JMP134: purification of dihydroxypropanesulfonate 3-dehydrogenase. Microbiology 2010;156:1556–64.CAS 
PubMed 

Google Scholar 
55.Mou XZ, Sun SL, Rayapati P, Moran MA. Genes for transport and metabolism of spermidine in Ruegeria pomeroyi DSS-3 and other marine bacteria. Aquat Micro Ecol. 2010;58:311–21.
Google Scholar 
56.Biller SJ, Coe A, Roggensack SE, Chisholm SW Heterotroph interactions alter Prochlorococcus transcriptome dynamics during extended periods of darkness. mSystems. 2018; 3 https://doi.org/10.1128/mSystems.00040-18.57.Harding L, Meeson B, Prézelin B, Sweeney B. Diel periodicity of photosynthesis in marine phytoplankton. Mar Biol. 1981;61:95–105.
Google Scholar 
58.Harding L, Prezelin B, Sweeney B, Cox J. Diel oscillations of the photosynthesis-irradiance (PI) relationship in natural assemblages of phytoplankton. Mar Biol. 1982;67:167–78.
Google Scholar 
59.Blough NV, Zepp RG Reactive oxygen species in natural waters. Active oxygen in chemistry. Dordrecht: Springer; 1995. p. 280–333.60.Zafiriou OC, Joussot-Dubien J, Zepp RG, Zika RG. Photochemistry of natural waters. Environ Sci Technol. 1984;18:358A–71A.CAS 

Google Scholar 
61.Ziegelhoffer EC, Donohue TJ. Bacterial responses to photo-oxidative stress. Nat Rev Microbiol. 2009;7:856–63.CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Lubin EA, Henry JT, Fiebig A, Crosson S, Laub MT. Identification of the PhoB regulon and role of PhoU in the phosphate starvation response of Caulobacter crescentus. J Bacteriol. 2016;198:187–200.CAS 
PubMed 

Google Scholar 
63.Yang C, Huang TW, Wen SY, Chang CY, Tsai SF, Wu WF, et al. Genome-wide PhoB binding and gene expression profiles reveal the hierarchical gene regulatory network of phosphate starvation in Escherichia coli. Plos One. 2012;7:e47314.CAS 
PubMed 
PubMed Central 

Google Scholar 
64.Hsieh YJ, Wanner BL. Global regulation by the seven-component Pi signaling system. Curr Opin Microbiol. 2010;13:198–203.CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Muratore D, Boysen AK, Harke MJ, Becker KW, Casey JR, Coesel SN, et al. Community-scale synchronization and temporal partitioning of gene expression, metabolism, and lipid biosynthesis in oligotrophic ocean surface waters. bioRxiv. 2020: 2020.05.15.098020.66.Giedroc DP. Hydrogen peroxide sensing in Bacillus subtilis: it is all about the (metallo)regulator. Mol Microbiol. 2009;73:1–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Wagner GP, Kin K, Lynch VJ. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 2012;131:281–5.CAS 
PubMed 

Google Scholar 
68.Weinitschke S, Sharma PI, Stingl U, Cook AM, Smits TH. Gene clusters involved in isethionate degradation by terrestrial and marine bacteria. Appl Environ Microbiol. 2010;76:618–21.CAS 
PubMed 

Google Scholar 
69.Nikaido H. Molecular basis of bacterial outer membrane permeability revisited. Microbiol Mol Biol Rev. 2003;67:593–656.CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Hellebust JA. Excretion of some organic compounds by marine phytoplankton 1. Limnol Oceanogr. 1965;10:192–206.
Google Scholar 
71.Behrenfeld MJ, Halsey KH, Milligan AJ. Evolved physiological responses of phytoplankton to their integrated growth environment. Philos Trans R Soc B: Biol Sci. 2008;363:2687–703.CAS 

Google Scholar 
72.Kiene RP, Linn LJ, Bruton JA. New and important roles for DMSP in marine microbial communities. J Sea Res. 2000;43:209–24.CAS 

Google Scholar 
73.Fredrickson KA, Strom SL. The algal osmolyte DMSP as a microzooplankton grazing deterrent in laboratory and field studies. J Plankton Res. 2009;31:135–52.
Google Scholar 
74.Sunda W, Kieber DJ, Kiene RP, Huntsman S. An antioxidant function for DMSP and DMS in marine algae. Nature 2002;418:317–20.CAS 
PubMed 

Google Scholar 
75.Lidbury I, Kimberley G, Scanlan DJ, Murrell JC, Chen Y. Comparative genomics and mutagenesis analyses of choline metabolism in the marine Roseobacter clade. Environ Microbiol. 2015;17:5048–62.CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Cunliffe M. Purine catabolic pathway revealed by transcriptomics in the model marine bacterium Ruegeria pomeroyi DSS-3. FEMS Microbiol Ecol. 2016;92:fiv150.PubMed 

Google Scholar 
77.Durham BP, Sharma S, Luo HW, Smith CB, Amin SA, Bender SJ, et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc Nat Acad Sci. 2015;112:453–7.CAS 
PubMed 

Google Scholar  More

1.Beissinger, S. R. & Westphal, M. I. On the use of demographic models of population viability in endangered species management. J. Wildl. Manag. 62, 821–841 (1998).
Google Scholar 
2.Campana, S. Accuracy, precision and quality control in age determination, including a review of the use and abuse of age validation methods. J. Fish. Biol. 59, 197–242 (2001).
Google Scholar 
3.Polanowski, A. M., Robbins, J., Chandler, D. & Jarman, S. N. Epigenetic estimation of age in humpback whales. Mol. Ecol. Resour. 14, 976–987 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Jarman, S. N. et al. Molecular biomarkers for chronological age in animal ecology. Mol. Ecol. 24, 4826–4847 (2015).CAS 
PubMed 

Google Scholar 
5.Thompson, M. J., vonHoldt, B., Horvath, S. & Pellegrini, M. An epigenetic aging clock for dogs and wolves. Aging 9, 1055–1068 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
6.De Paoli-Iseppi, R. et al. Measuring animal age with DNA methylation: from humans to wild animals. Front. Genet. 8, 106 (2017).PubMed 
PubMed Central 

Google Scholar 
7.Bell, C. G. et al. DNA methylation aging clocks: challenges and recommendations. Genome Biol. 20, 249 (2019).PubMed 
PubMed Central 

Google Scholar 
8.Field, A. E. et al. DNA methylation clocks in aging: categories, causes, and consequences. Mol. Cell 71, 882–895 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Horvath, S. & Raj, K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 19, 371–384 (2018).CAS 
PubMed 

Google Scholar 
10.Petkovich, D. A. et al. Using DNA methylation profiling to evaluate biological age and longevity interventions. Cell Metab. 25, 954–960 e956 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Stubbs, T. M. et al. Multi-tissue DNA methylation age predictor in mouse. Genome Biol. 18, 68 (2017).PubMed 
PubMed Central 

Google Scholar 
12.Wang, T. et al. Epigenetic aging signatures in mice livers are slowed by dwarfism, calorie restriction, and rapamycin treatment. Genome Biol. 18, 57 (2017).PubMed 
PubMed Central 

Google Scholar 
13.Nussey, D. H., Froy, H., Lemaitre, J. F., Gaillard, J. M. & Austad, S. N. Senescence in natural populations of animals: widespread evidence and its implications for bio-gerontology. Ageing Res. Rev. 12, 214–225 (2013).PubMed 

Google Scholar 
14.Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).PubMed 
PubMed Central 

Google Scholar 
15.Voisin, S. et al. An epigenetic clock for human skeletal muscle. J. Cachexia Sarcopenia Muscle https://doi.org/10.1002/jcsm.12556 (2020).16.De Paoli-Iseppi, R. et al. Age estimation in a long-lived seabird (Ardenna tenuirostris) using DNA methylation-based biomarkers. Mol. Ecol. Resour. 19, 411–425 (2019).PubMed 

Google Scholar 
17.Ito, H., Udono, T., Hirata, S. & Inoue-Murayama, M. Estimation of chimpanzee age based on DNA methylation. Sci. Rep. 8, 9998 (2018).PubMed 
PubMed Central 

Google Scholar 
18.Chen, B. H. et al. DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging 8, 1844–1865 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Christiansen, L. et al. DNA methylation age is associated with mortality in a longitudinal Danish twin study. Aging Cell 15, 149–154 (2016).CAS 
PubMed 

Google Scholar 
20.Horvath, S. et al. Decreased epigenetic age of PBMCs from Italian semi‐ supercentenarians and their offspring. Aging 7, 1159–1170 (2018).
Google Scholar 
21.Marioni, R. E. et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 16, 25 (2015).PubMed 
PubMed Central 

Google Scholar 
22.Perna, L. et al. Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort. Clin. Epigenetics 8, 64 (2016).PubMed 
PubMed Central 

Google Scholar 
23.Mitchell, C., Schneper, L. M. & Notterman, D. A. DNA methylation, early life environment, and health outcomes. Pediatr. Res. 79, 212–219 (2016).CAS 
PubMed 

Google Scholar 
24.Pérez, R. F., Santamarina, P., Fernández, A. F., & Fraga, M. F. Epigenetics and Lifestyle: The Impact of Stress, Diet, and Social Habits on Tissue Homeostasis. In Epigenetics and Regeneration (ed. Palacios, D.) pp. 461–489 (Academic Press, 2019).25.Szyf, M., Tang, Y. Y., Hill, K. G. & Musci, R. The dynamic epigenome and its implications for behavioral interventions: a role for epigenetics to inform disorder prevention and health promotion. Transl. Behav. Med. 6, 55–62 (2016).PubMed 
PubMed Central 

Google Scholar 
26.Lee, R. S. et al. Chronic corticosterone exposure increases expression and decreases deoxyribonucleic acid methylation of Fkbp5 in mice. Endocrinology 151, 4332–4343 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Zannas, A. S. et al. Lifetime stress accelerates epigenetic aging in an urban, African American cohort: relevance of glucocorticoid signaling. Genome Biol. 16, 266 (2015).PubMed 
PubMed Central 

Google Scholar 
28.Biemont, C. Inbreeding effects in the epigenetic era. Nat. Rev. Genet. 11, 234 (2010).CAS 
PubMed 

Google Scholar 
29.Venney, C. J., Johansson, M. L. & Heath, D. D. Inbreeding effects on gene-specific DNA methylation among tissues of Chinook salmon. Mol. Ecol. 25, 4521–4533 (2016).CAS 
PubMed 

Google Scholar 
30.Vergeer, P., Wagemaker, N. C. & Ouborg, N. J. Evidence for an epigenetic role in inbreeding depression. Biol. Lett. 8, 798–801 (2012).PubMed 
PubMed Central 

Google Scholar 
31.Han, W. et al. Genome-wide analysis of the role of DNA methylation in inbreeding depression of reproduction in Langshan chicken. Genomics 112, 2677–2687 (2020).CAS 
PubMed 

Google Scholar 
32.Thompson, M. J. et al. A multi-tissue full lifespan epigenetic clock for mice. Aging 10, 2832–2854 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Zhang, Q. et al. Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Med. 11, 54 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Snir, S., Farrell, C. & Pellegrini, M. Human epigenetic ageing is logarithmic with time across the entire lifespan. Epigenetics 14, 912–926 (2019).PubMed 
PubMed Central 

Google Scholar 
35.Pinho, G. M. et al. Hibernation slows epigenetic aging in yellow-bellied marmots. Preprint at bioRxiv https://doi.org/10.1101/2021.03.07.434299 (2021).36.Moehlman, P. D. Equids: Zebras, Asses, and Horses Status Survey and Conservation Action Plan Vol. 37, 190 pp (IUCN/SSC Equid Specialist Group, 2002).37.Moehlman, P. D. & King, S. R. B. IUCN SSC Equid Specialist Group 2020 Report. https://www.iucn.org/commissions/ssc-groups/mammals/mammals-a-e/equid (2020).38.Rubinacci, S., Ribeiro, D. M., Hofmeister, R. & Delaneau, O. Efficient phasing and imputation of low-coverage sequencing data using large reference panels. Nat. Genet. 53, 120–126 (2021).CAS 
PubMed 

Google Scholar 
39.Ceballos, F. C., Hazelhurst, S. & Ramsay, M. Runs of homozygosity in sub-Saharan African populations provide insights into complex demographic histories. Hum. Genet. 138, 1123–1142 (2019).CAS 
PubMed 

Google Scholar 
40.Curik, I., Ferenčaković, M. & Sölkner, J. Inbreeding and runs of homozygosity: a possible solution to an old problem. Livest. Sci. 166, 26–34 (2014).
Google Scholar 
41.Anderson, J. A. et al. The costs of competition: high social status males experience accelerated epigenetic aging in wild baboons. eLife 10, e66128 (2020).
Google Scholar 
42.McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. https://doi.org/10.1038/nbt.1630 (2010).43.Gronniger, E. et al. Aging and chronic sun exposure cause distinct epigenetic changes in human skin. PLoS Genet. 6, e1000971 (2010).PubMed 
PubMed Central 

Google Scholar 
44.Robeck, T. R. et al. Multi-species and multi-tissue methylation clocks for age estimation in toothed whales and dolphins. Commun. Biol. https://doi.org/10.1038/s42003-021-02179-x (2021).45.Jonsson, H. et al. Speciation with gene flow in equids despite extensive chromosomal plasticity. Proc. Natl Acad. Sci. USA 111, 18655–18660 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Vilstrup, J. T. et al. Mitochondrial phylogenomics of modern and ancient equids. PLoS One 8, e55950 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Jensen-Seaman, M. I. & Hooper-Boyd, K. A. in Encyclopedia of Life Sciences (ELS) (John Wiley & Sons, Ltd., 2008).48.Farrell, C., Snir, S. & Pellegrini, M. The epigenetic pacemaker—modeling epigenetic states under an evolutionary framework. Bioinformatics https://doi.org/10.1093/bioinformatics/btaa585 (2020).49.Snir, S. & Pellegrini, M. An epigenetic pacemaker is detected via a fast conditional expectation maximization algorithm. Epigenomics 10, 695–706 (2018).CAS 
PubMed 

Google Scholar 
50.Charlesworth, B. & Hughes, K. A. Age-specific inbreeding depression and components of genetic variance in relation to the evolution of senescence. Proc. Natl Acad. Sci. USA 93, 6140–6145 (1996).CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Fox, C. W. Inbreeding depression increases with maternal age. Evolut. Ecol. Res. 12, 961–972 (2010).
Google Scholar 
52.Benton, C. H. et al. Inbreeding intensifies sex- and age-dependent disease in a wild mammal. J. Anim. Ecol. 87, 1500–1511 (2018).PubMed 

Google Scholar 
53.Mayne, B., Berry, O., Davies, C., Farley, J. & Jarman, S. A genomic predictor of lifespan in vertebrates. Sci. Rep. 9, 17866 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
54.McClain, A. T. & Faulk, C. The evolution of CpG density and lifespan in conserved primate and mammalian promoters. Aging 10, 561–572 (2018).
Google Scholar 
55.Alpi, A. F., Pace, P. E., Babu, M. M. & Patel, K. J. Mechanistic insight into site-restricted monoubiquitination of FANCD2 by Ube2t, FANCL, and FANCI. Mol. Cell 32, 767–777 (2008).CAS 
PubMed 

Google Scholar 
56.Kannan, M. B., Solovieva, V. & Blank, V. The small MAF transcription factors MAFF, MAFG, and MAFK: current knowledge and perspectives. Biochim. Biophys. Acta 1823, 1841–1846 (2012).CAS 
PubMed 

Google Scholar 
57.Li, Z. et al. PBX3 is an important cofactor of HOXA9 in leukemogenesis. Blood 121, 1422–1431 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Malecki, M. T. et al. Mutations in NEUROD1 are associated with the development of type 2 diabetes mellitus. Nat. Genet. 23, 323–328 (1999).CAS 
PubMed 

Google Scholar 
59.Ding, Q., Joshi, P. S., Xie, Z. H., Xiang, M. & Gan, L. BARHL2 transcription factor regulates the ipsilateral/contralateral subtype divergence in postmitotic dI1 neurons of the developing spinal cord. Proc. Natl Acad. Sci. USA 109, 1566–1571 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
60.Mo, Z., Li, S., Yang, X. & Xiang, M. Role of the Barhl2 homeobox gene in the specification of glycinergic amacrine cells. Development 131, 1607–1618 (2004).CAS 
PubMed 

Google Scholar 
61.Giampietro, C. et al. The alternative splicing factor Nova2 regulates vascular development and lumen formation. Nat. Commun. 6, 8479 (2015).CAS 
PubMed 

Google Scholar 
62.Yano, M., Hayakawa-Yano, Y., Mele, A. & Darnell, R. B. Nova2 regulates neuronal migration through an RNA switch in disabled-1 signaling. Neuron 66, 848–858 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
63.Deneen, B. et al. The transcription factor NFIA controls the onset of gliogenesis in the developing spinal cord. Neuron 52, 953–968 (2006).CAS 
PubMed 

Google Scholar 
64.Hiraike, Y. et al. NFIA co-localizes with PPARgamma and transcriptionally controls the brown fat gene program. Nat. Cell Biol. 19, 1081–1092 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Caricasole, A., Sala, C., Roncarati, R., Formenti, E. & Terstappen, G. C. Cloning and characterization of the human phosphoinositide-specific phospholipase C-beta 1 (PLCβ1). Biochim. Biophys. Acta 1517, 63–72 (2000).CAS 
PubMed 

Google Scholar 
66.McOmish, C. E., Burrows, E. L., Howard, M. & Hannan, A. J. PLC-beta1 knockout mice as a model of disrupted cortical development and plasticity: behavioral endophenotypes and dysregulation of RGS4 gene expression. Hippocampus 18, 824–834 (2008).CAS 
PubMed 

Google Scholar 
67.Mittelstaedt, T., Alvarez-Baron, E. & Schoch, S. RIM proteins and their role in synapse function. Biol. Chem. 391, 599–606 (2010).CAS 
PubMed 

Google Scholar 
68.Schoch, S. et al. RIM1α forms a protein scaffold for regulating neurotransmitter release at the active zone. Nature 415, 321–326 (2002).CAS 
PubMed 

Google Scholar 
69.Lu, A. T. et al. Universal DNA methylation age across mammalian tissues. Preprint at bioRxiv https://doi.org/10.1101/2021.01.18.426733 (2021).70.Nishikawa, K. et al. Maf promotes osteoblast differentiation in mice by mediating the age-related switch in mesenchymal cell differentiation. J. Clin. Invest. 120, 3455–3465 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Saidak, Z., Hay, E., Marty, C., Barbara, A. & Marie, P. J. Strontium ranelate rebalances bone marrow adipogenesis and osteoblastogenesis in senescent osteopenic mice through NFATc/Maf and Wnt signaling. Aging Cell 11, 467–474 (2012).CAS 
PubMed 

Google Scholar 
72.McClay, J. L. et al. A methylome-wide study of aging using massively parallel sequencing of the methyl-CpG-enriched genomic fraction from blood in over 700 subjects. Hum. Mol. Genet. 23, 1175–1185 (2014).CAS 
PubMed 

Google Scholar 
73.Ambeskovic, M. et al. Ancestral stress programs sex-specific biological aging trajectories and non-communicable disease risk. Aging 12, 3828–3847 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Burger, C., Lopez, M. C., Baker, H. V., Mandel, R. J. & Muzyczka, N. Genome-wide analysis of aging and learning-related genes in the hippocampal dentate gyrus. Neurobiol. Learn Mem. 89, 379–396 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Horvath, S. et al. DNA methylation clocks show slower progression of aging in naked mole-rat queens. Preprint at bioRxiv https://doi.org/10.1101/2021.03.15.435536 (2021).76.Rapoport, S. I., Primiani, C. T., Chen, C. T., Ahn, K. & Ryan, V. H. Coordinated expression of phosphoinositide metabolic genes during development and aging of human dorsolateral prefrontal cortex. PLoS One 10, e0132675 (2015).PubMed 
PubMed Central 

Google Scholar 
77.Dube, J. B. et al. Genetic determinants of “cognitive impairment, no dementia”. J. Alzheimers Dis. 33, 831–840 (2013).CAS 
PubMed 

Google Scholar 
78.Hinney, A. et al. Genetic variation at the CELF1 (CUGBP, elav-like family member 1 gene) locus is genome-wide associated with Alzheimer’s disease and obesity. Am. J. Med. Genet. B Neuropsychiatr. Genet. 165B, 283–293 (2014).PubMed 

Google Scholar 
79.Ntalla, I. et al. Replication of established common genetic variants for adult BMI and childhood obesity in Greek adolescents: the TEENAGE study. Ann. Hum. Genet. 77, 268–274 (2013).PubMed 
PubMed Central 

Google Scholar 
80.Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
81.Gao, Z. et al. Neurod1 is essential for the survival and maturation of adult-born neurons. Nat. Neurosci. 12, 1090–1092 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
82.Badawi, Y. & Nishimune, H. Presynaptic active zones of mammalian neuromuscular junctions: Nanoarchitecture and selective impairments in aging. Neurosci. Res. 127, 78–88 (2018).CAS 
PubMed 

Google Scholar 
83.Tollervey, J. R. et al. Analysis of alternative splicing associated with aging and neurodegeneration in the human brain. Genome Res. 21, 1572–1582 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
84.Kim, B. H., Nho, K. & Lee, J. M., Alzheimer’s Disease Neuroimaging, I. Genome-wide association study identifies susceptibility loci of brain atrophy to NFIA and ST18 in Alzheimer’s disease. Neurobiol. Aging 102, 200 e201–200 e211 (2021).
Google Scholar 
85.Horvath, S. et al. DNA methylation aging and transcriptomic studies in horses. Preprint at bioRxiv https://doi.org/10.1101/2021.03.11.435032 (2021).86.Benayoun, B. A., Pollina, E. A. & Brunet, A. Epigenetic regulation of ageing: linking environmental inputs to genomic stability. Nat. Rev. Mol. Cell Biol. 16, 593–610 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
87.Quach, A. et al. Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging 9, 419–446 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
88.Crary-Dooley, F. K. et al. A comparison of existing global DNA methylation assays to low-coverage whole-genome bisulfite sequencing for epidemiological studies. Epigenetics 12, 206–214 (2017).PubMed 
PubMed Central 

Google Scholar 
89.Reed, K., Poulin, M. L., Yan, L. & Parissenti, A. M. Comparison of bisulfite sequencing PCR with pyrosequencing for measuring differences in DNA methylation. Anal. Biochem. 397, 96–106 (2010).CAS 
PubMed 

Google Scholar 
90.Tost, J., Dunker, J. & Gut, I. G. Analysis and quantification of multiple methylation variable positions in CpG islands by Pyrosequencing. Biotechniques 35, 152–156 (2003).CAS 
PubMed 

Google Scholar 
91.Karesh, W. B. in Zoo and Wild Animal Medicine: Current Therapy (eds Fowler Murray, E. & Eric Miller, R.) 298−308 (Saunders Elsevier, 2008).92.Chiou, K. L. & Bergey, C. M. Methylation-based enrichment facilitates low-cost, noninvasive genomic scale sequencing of populations from feces. Sci. Rep. 8, 1975 (2018).PubMed 
PubMed Central 

Google Scholar 
93.Orkin, J. D. et al. The genomics of ecological flexibility, large brains, and long lives in capuchin monkeys revealed with fecalFACS. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2010632118 (2021).94.Snyder-Mackler, N. et al. Efficient genome-wide sequencing and low-coverage pedigree analysis from noninvasively collected samples. Genetics 203, 699–714 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
95.Harley, E. H., Knight, M. H., Lardner, C., Wooding, B. & Gregor, M. The Quagga project: progress over 20 years of selective breeding. South African J. Wildlife Res. https://doi.org/10.3957/056.039.0206 (2009).96.Arneson, A. et al. A mammalian methylation array for profiling methylation levels at conserved sequences Preprint at bioRxiv https://doi.org/10.1101/2021.01.07.425637 (2021).97.Kalbfleisch, T. S. et al. Improved reference genome for the domestic horse increases assembly contiguity and composition. Commun. Biol. 1, 197 (2018).PubMed 
PubMed Central 

Google Scholar 
98.Wade, C. M. et al. Genome sequence, comparative analysis, and population genetics of the domestic horse. Science https://doi.org/10.1126/science.1178158 (2009).99.Zhou, W., Triche, T. J. Jr., Laird, P. W. & Shen, H. SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions. Nucleic Acids Res. 46, e123 (2018).PubMed 
PubMed Central 

Google Scholar 
100.Bocklandt, S. et al. Epigenetic predictor of age. PLoS One 6, e14821 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
101.Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
102.Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).PubMed 
PubMed Central 

Google Scholar 
103.R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).104.Van Rossum, G. & Drake, F. L. Python 3 Reference Manual (CreateSpace, 2009).105.Larison, B. et al. Population structure, inbreeding and stripe pattern abnormalities in plains zebras. Mol. Ecol. 30, 379–390 (2021).CAS 
PubMed 

Google Scholar 
106.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows−Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
107.Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at https://arxiv.org/abs/1207.3907 (2012).108.Freed, D., Aldana, R., Weber, J. A. & Edwards, J. S. The Sentieon Genomics Tools—A fast and accurate solution to variant calling from next-generation sequence data. Preprint at bioRxiv https://doi.org/10.1101/115717 (2017).109.Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 

Google Scholar 
110.Meyermans, R., Gorssen, W., Buys, N. & Janssens, S. How to study runs of homozygosity using PLINK? A guide for analyzing medium density SNP data in livestock and pet species. BMC Genomics 21, 94 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
111.Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
112.McQuillan, R. et al. Runs of homozygosity in European populations. Am. J. Hum. Genet. 83, 359–372 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
113.Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).
Google Scholar 
114.Zeileis, A. Econometric computing with HC and HAC covariance matrix estimators. J. Stat. Softw. 11, 1–17 (2004).
Google Scholar 
115.Zeileis, A., Köll, S. & Graham, N. Various versatile variances: an object-oriented implementation of clustered covariances in R. J. Stat. Softw. 95, 1–36 (2020).
Google Scholar 
116.Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9, 559 (2008).
Google Scholar 
117.Stouffer, S. A., Suchman, E. A., DeVinney, L. C., Star, S. A. & Williams, R. M. J. Adjustment During Army Life (Princeton University Press, 1949). More

1.Schmidt-Nielsen, K. Scaling: Why is Animal Size So Important? (Cambrige University Press, 1984).Book 

Google Scholar 
2.Sheridan, J. A. & Bickford, D. Shrinking body size as an ecological response to climate change. Nat. Clim. Change 1, 401–406. https://doi.org/10.1038/nclimate1259 (2011).ADS 
Article 

Google Scholar 
3.Yom-Tov, Y., Heggberget, T. M., Wiig, O. & Yom-Tov, S. Body size changes among otters, Lutra lutra, in Norway: The possible effects of food availability and global warming. Oecologia 150, 155–160. https://doi.org/10.1007/s00442-006-0499-8 (2006).ADS 
Article 
PubMed 

Google Scholar 
4.Bergmann, C. Ueber die Verhältnisse der Wärmeökonomie der Tiere zu ihrer Grösse. Gött Stud. 3, 595–708 (1847).
Google Scholar 
5.Dehnel, A. Studies on the genus Sorex L.. Ann. Univ. Mariae Curie Sklodowska 5, 17–102 (1949).
Google Scholar 
6.Foster, J. B. Evolution of mammals on islands. Nature 202, 234–235. https://doi.org/10.1038/202234a0 (1964).ADS 
Article 

Google Scholar 
7.Mayr, E. Geographical character gradients and climatic adaptation. Evolution 10, 105–108. https://doi.org/10.1111/j.1558-5646.1956.tb02836.x (1956).Article 

Google Scholar 
8.Allen, J. A. The Influence of physical conditions in the genesis of species. Radic. Rev. 1, 108–140 (1877).
Google Scholar 
9.Blackburn, T. M., Gaston, K. J. & Loder, N. Geographic gradients in body size: A clarification of Bergmann’s rule. Divers. Distrib. 5, 165–174. https://doi.org/10.1046/j.1472-4642.1999.00046.x (1999).Article 

Google Scholar 
10.Riemer, K., Guralnick, R. P. & White, E. P. No general relationship between mass and temperature in endothermic species. Elife 7, 16. https://doi.org/10.7554/eLife.27166 (2018).Article 

Google Scholar 
11.Ashton, K. G. Patterns of within-species body size variation of birds: Strong evidence for Bergmann’s rule. Glob. Ecol. Biogeogr. 11, 505–523. https://doi.org/10.1046/j.1466-822X.2002.00313.x (2002).Article 

Google Scholar 
12.Meiri, S. & Dayan, T. On the validity of Bergmann’s rule. J. Biogeogr. 30, 331–351. https://doi.org/10.1046/j.1365-2699.2003.00837.x (2003).Article 

Google Scholar 
13.Reig, S. Geographic variation in pine marten (Martes martes) and beech marten (M. foina) in Europe. J. Mammal. 73, 744–769. https://doi.org/10.2307/1382193 (1992).Article 

Google Scholar 
14.Blackburn, T. M. & Hawkins, B. A. Bergmann’s rule and the mammal fauna of northern North America. Ecography 27, 715–724. https://doi.org/10.1111/j.0906-7590.2004.03999.x (2004).Article 

Google Scholar 
15.Diniz, J. A. F., Bini, L. M., Rodriguez, M. A., Rangel, T. & Hawkins, B. A. Seeing the forest for the trees: Partitioning ecological and phylogenetic components of Bergmann’s rule in European Carnivora. Ecography 30, 598–608. https://doi.org/10.1111/j.2007.0906-7590.04988.x (2007).Article 

Google Scholar 
16.Hoy, S. R., Peterson, R. O. & Vucetich, J. A. Climate warming is associated with smaller body size and shorter lifespans in moose near their southern range limit. Glob. Change Biol. 24, 2488–2497. https://doi.org/10.1111/gcb.14015 (2018).ADS 
Article 

Google Scholar 
17.Martin, J. M., Mead, J. I. & Barboza, P. S. Bison body size and climate change. Ecol. Evol. 8, 4564–4574. https://doi.org/10.1002/ece3.4019 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
18.Ozgul, A. et al. The dynamics of phenotypic change and the shrinking sheep of St. Kilda. Science 325, 464–467. https://doi.org/10.1126/science.1173668 (2009).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
19.Prokosch, J., Bernitz, Z., Bernitz, H., Erni, B. & Altwegg, R. Are animals shrinking due to climate change? Temperature-mediated selection on body mass in mountain wagtails. Oecologia 189, 841–849. https://doi.org/10.1007/s00442-019-04368-2 (2019).ADS 
Article 
PubMed 

Google Scholar 
20.Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055. https://doi.org/10.1038/nature08649 (2009).ADS 
CAS 
Article 
PubMed 

Google Scholar 
21.Schloss, C. A., Nunez, T. A. & Lawler, J. J. Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. Proc. Natl. Acad. Sci. U.S.A. 109, 8606–8611. https://doi.org/10.1073/pnas.1116791109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
22.Williams, J. E. & Blois, J. L. Range shifts in response to past and future climate change: Can climate velocities and species’ dispersal capabilities explain variation in mammalian range shifts? J. Biogeogr. 45, 2175–2189. https://doi.org/10.1111/jbi.13395 (2018).Article 

Google Scholar 
23.Gordon, C. J. Effects of ambient temperature and exposure to 2450-MHz microwave radiation of evaporative heat loss in the mouse. J. Microw. Power Electromagn. Energy 17, 145–150 (1982).CAS 

Google Scholar 
24.Zub, K., Piertney, S., Szafranska, P. A. & Konarzewski, M. Environmental and genetic influences on body mass and resting metabolic rates (RMR) in a natural population of weasel Mustela nivalis. Mol. Ecol. 21, 1283–1293. https://doi.org/10.1111/j.1365-294X.2011.05436.x (2012).Article 
PubMed 

Google Scholar 
25.Leyequien, E., de Boer, W. F. & Cleef, A. Influence of body size on coexistence of bird species. Ecol. Res. 22, 735–741. https://doi.org/10.1007/s11284-006-0311-6 (2007).Article 

Google Scholar 
26.Briscoe, N. J., Krockenberger, A., Handasyde, K. A. & Kearney, M. R. Bergmann meets Scholander: Geographical variation in body size and insulation in the koala is related to climate. J. Biogeogr. 42, 791–802. https://doi.org/10.1111/JBI.12445 (2015).Article 

Google Scholar 
27.Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: A third universal response to warming? Trends Ecol. Evol. 26, 285–291. https://doi.org/10.1016/J.TREE.2011.03.005 (2011).Article 
PubMed 

Google Scholar 
28.Reyer, C. et al. Projections of regional changes in forest net primary productivity for different tree species in Europe driven by climate change and carbon dioxide. Ann. For. Sci. 71, 211–225. https://doi.org/10.1007/s13595-013-0306-8 (2014).Article 

Google Scholar 
29.Laidre, K. L. et al. Transient benefits of climate change for a high-Arctic polar bear (Ursus maritimus) subpopulation. Glob. Change Biol. 26, 6251–6265. https://doi.org/10.1111/gcb.15286 (2020).ADS 
Article 

Google Scholar 
30.Yunger, J. A. Response of two low-density populations of Peromyscus leucopus to increased food availability. J. Mammal. 83, 267–279. https://doi.org/10.1644/1545-1542(2002)083%3c0267:rotldp%3e2.0.co;2 (2002).Article 

Google Scholar 
31.Monterroso, P., Francisco, D. R., Lukacs, P. M., Alves, P. C. & Ferreras, P. Ecological traits and the spatial structure of competitive coexistence among carnivores. Ecology. https://doi.org/10.1002/ecy.3059 (2020).Article 
PubMed 

Google Scholar 
32.Dayan, T. & Simberloff, D. Ecological and community-wide character displacement: The next generation. Ecol. Lett. 8, 875–894. https://doi.org/10.1111/j.1461-0248.2005.00791.x (2005).Article 

Google Scholar 
33.Creel, S. & Creel, N. M. Limitation of African wild dogs by competition with larger carnivores. Conserv. Biol. 10, 526–538. https://doi.org/10.1046/j.1523-1739.1996.10020526.x (1996).Article 

Google Scholar 
34.Wereszczuk, A. & Zalewski, A. Spatial niche segregation of sympatric stone marten and pine marten—Avoidance of competition or selection of optimal habitat? PLoS ONE 10, e0139852. https://doi.org/10.1371/journal.pone.0139852 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Pereboom, V. et al. Movement patterns, habitat selection, and corridor use of a typical woodland-dweller species, the European pine marten (Martes martes), in fragmented landscape. Can. J. Zool. 86, 983–991. https://doi.org/10.1139/Z08-076 (2008).Article 

Google Scholar 
36.Virgos, E., Zalewski, A., Rosalino, L. M. & Mergey, M. Habitat ecology of Martens species in Europe. A review of the evidence. In Biology and Conservation of Martens, Sables and Fishers: A New Synthesis (eds Aubry, K. B. et al.) 255–266 (Cornell University Press, 2012).
Google Scholar 
37.Goszczyński, J., Posłuszny, M., Pilot, M. & Gralak, B. Patterns of winter locomotion and foraging in two sympatric marten species: Martes martes and Martes foina. Can. J. Zool. 85, 239–249. https://doi.org/10.1139/Z06-212 (2007).ADS 
Article 

Google Scholar 
38.Larroque, J., Ruette, S., Vandel, J. M. & Devillard, S. Where to sleep in a rural landscape? A comparative study of resting sites pattern in two syntopic Martes species. Ecography 38, 1129–1140. https://doi.org/10.1111/ecog.01133 (2015).Article 

Google Scholar 
39.Monakhov, V. G. & Hamilton, M. J. Spatial trends in the size structure of pine Marten Martes martes Linnaeus, 1756 (Mammalia: Mustelidae) within the species range. Russ. J. Ecol. 51, 250–259. https://doi.org/10.1134/s1067413620030108 (2020).CAS 
Article 

Google Scholar 
40.Meiri, S., Dayan, T. & Simberloff, D. Carnivores, biases and Bergmann’s rule. Biol. J. Linn. Soc. 81, 579–588. https://doi.org/10.1111/j.1095-8312.2004.00310.x (2004).Article 

Google Scholar 
41.Keinath, D. A. et al. A global analysis of traits predicting species sensitivity to habitat fragmentation. Glob. Ecol. Biogeogr. 26, 115–127. https://doi.org/10.1111/geb.12509 (2017).Article 

Google Scholar 
42.Bailey, L. D. et al. Using different body size measures can lead to different conclusions about the effects of climate change. J. Biogeogr. 47, 1687–1697. https://doi.org/10.1111/jbi.13850 (2020).Article 

Google Scholar 
43.Buskirk, S. W. & Harlow, H. J. Body-fat dynamics of the American marten (Martes americana) in winter. J. Mammal. 70, 191–193. https://doi.org/10.2307/1381687 (1989).Article 

Google Scholar 
44.Wereszczuk, A.et al. Various responses of pine marten
morphology and demography to temporal climate changes and primary productivity. PREPRINT (Version 1) available at
Research Square https://doi.org/10.21203/rs.3.rs-1021314/v1 (2021)45.Desy, E. A. & Batzli, G. O. Effects of food availability and predation on prairie vole demography—A field experiment. Ecology 70, 411–421. https://doi.org/10.2307/1937546 (1989).Article 

Google Scholar 
46.Geist, V. Bergmann rule is invalid. Can. J. Zool. 65, 1035–1038. https://doi.org/10.1139/z87-164 (1987).Article 

Google Scholar 
47.Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563. https://doi.org/10.1126/science.1082750 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
48.Svensson, B. M., Carlsson, B. A. & Melillo, J. M. Changes in species abundance after seven years of elevated atmospheric CO2 and warming in a Subarctic birch forest understorey, as modified by rodent and moth outbreaks. PeerJ 6, e4843. https://doi.org/10.7717/peerj.4843 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
49.Zalewski, A., Jedrzejewski, W. & Jedrzejewska, B. Mobility and home range use by pine martens (Martes martes) in a Polish primeval forest. Ecoscience 11, 113–122. https://doi.org/10.1080/11956860.2004.11682815 (2004).Article 

Google Scholar 
50.Krebs, C. J., Cowcill, K., Boonstra, R. & Kenney, A. J. Do changes in berry crops drive population fluctuations in small rodents in the southwestern Yukon? J. Mammal. 91, 500–509. https://doi.org/10.1644/09-mamm-a-005.1 (2010).Article 

Google Scholar 
51.Selas, V., Kobro, S. & Sonerud, G. A. Population fluctuations of moths and small rodents in relation to plant reproduction indices in southern Norway. Ecosphere 4, 1–11. https://doi.org/10.1890/es13-00228.1 (2013).Article 

Google Scholar 
52.Yom-Tov, Y., Yom-Tov, S. & Jarrell, G. Recent increase in body size of the American marten Martes americana in Alaska. Biol. J. Linn. Soc. 93, 701–707. https://doi.org/10.1111/j.1095-8312.2007.00950.x (2008).Article 

Google Scholar 
53.Caryl, F. M., Quine, C. P. & Park, K. J. Martens in the matrix: the importance of nonforested habitats for forest carnivores in fragmented landscapes. J. Mammal. 93, 464–474. https://doi.org/10.1644/11-mamm-a-149.1 (2012).Article 

Google Scholar 
54.Zalewski, A. Factors affecting the duration of activity by pine martens (Martes martes) in the Bialowieza National Park, Poland. J. Zool. 251, 439–447. https://doi.org/10.1111/j.1469-7998.2000.tb00799.x (2000).Article 

Google Scholar 
55.Zalewski, A. Factors affecting selection of resting site type by pine marten in primeval deciduous forests (Bialowieza National Park, Poland). Acta Theriol. 42, 271–288. https://doi.org/10.4098/AT.arch.97-29 (1997).Article 

Google Scholar 
56.Gilbert, J. H., Zollner, P. A., Green, A. K., Wright, J. L. & Karasov, W. H. Seasonal field metabolic rates of American martens in Wisconsin. Am. Midl. Nat. 162, 327–334. https://doi.org/10.1674/0003-0031-162.2.327 (2009).Article 

Google Scholar 
57.Zub, K., Szafranska, P. A., Konarzewski, M. & Speakman, J. R. Effect of energetic constraints on distribution and winter survival of weasel males. J. Anim. Ecol. 80, 259–269. https://doi.org/10.1111/j.1365-2656.2010.01762.x (2011).Article 
PubMed 

Google Scholar 
58.Hantak, M. M., McLean, B. S., Li, D. & Guralnick, R. P. Mammalian body size is determined by interactions between climate, urbanization, and ecological traits. Commun. Biol. https://doi.org/10.1038/s42003-021-02505-3 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
59.Yom-Tov, Y., Yom-Tov, S. & Baagoe, H. Increase of skull size in the red fox (Vulpes vulpes) and Eurasian badger (Meles meles) in Denmark during the twentieth century: An effect of improved diet? Evol. Ecol. Res. 5, 1037–1048 (2003).
Google Scholar 
60.Wereszczuk, A., Leblois, R. & Zalewski, A. Genetic diversity and structure related to expansion history and habitat isolation: Stone marten populating rural-urban habitats. BMC Ecol. 17, 46. https://doi.org/10.1186/s12898-017-0156-6 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
61.Phillips, B. L., Brown, G. P., Webb, J. K. & Shine, R. Invasion and the evolution of speed in toads. Nature 439, 803. https://doi.org/10.1038/439803a (2006).ADS 
CAS 
Article 
PubMed 

Google Scholar 
62.Sidorovich, V., Kruuk, H. & Macdonald, D. W. Body size, and interactions between European and American mink (Mustela lutreola and M. vison) in Eastern Europe. J. Zool. 248, 521–527. https://doi.org/10.1017/s0952836999008110 (1999).Article 

Google Scholar 
63.Pagh, S., Hansen, M. S., Jensen, B., Pertoldi, C. & Chriel, M. Variability in body mass and sexual dimorphism in Danish red foxes (Vulpes vulpes) in relation to population density. Zool. Ecol. 28, 1–9. https://doi.org/10.1080/21658005.2017.1409997 (2018).Article 

Google Scholar 
64.Zalewski, A. & Bartoszewicz, M. Phenotypic variation of an alien species in a new environment: The body size and diet of American mink over time and at local and continental scales. Biol. J. Linn. Soc. 105, 681–693. https://doi.org/10.1111/j.1095-8312.2011.01811.x (2012).Article 

Google Scholar 
65.Balestrieri, A. et al. Range expansion of the pine marten (Martes martes) in an agricultural landscape matrix (NW Italy). Mamm. Biol. 75, 412–419. https://doi.org/10.1016/j.mambio.2009.05.003 (2010).Article 

Google Scholar 
66.Rosellini, S., Osorio, E., Ruiz-Gonzalez, A., Isabel, A. P. & Barja, I. Monitoring the small-scale distribution of sympatric European pine martens (Martes martes) and stone martens (Martes foina): A multievidence approach using faecal DNA analysis and camera-traps. Wildl. Res. 35, 434–440. https://doi.org/10.1071/wr07030 (2008).Article 

Google Scholar 
67.Delibes, M. Interspecific competition and the habitat of the stone marten Martes foina (Erxleben 1777) in Europe. Acta Zool. Fennica 174, 229–231 (1983).
Google Scholar 
68.Zabala, J., Zuberogoitia, I. & Antonio Martinez-Climent, J. Testing for niche segregation between two abundant carnivores using presence-only data. Folia Zool. 58, 385–395 (2009).
Google Scholar 
69.Jacob, D. et al. Climate impacts in Europe under +1.5 degrees C global warming. Earths Fut. 6, 264–285. https://doi.org/10.1002/2017ef000710 (2018).ADS 
Article 

Google Scholar 
70.Fewster, R. M., Buckland, S. T., Siriwardena, G. M., Baillie, S. R. & Wilson, J. D. Analysis of population trends for farmland birds using generalized additive models. Ecology 81, 1970–1984. https://doi.org/10.2307/177286 (2000).Article 

Google Scholar 
71.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (Chapman and Hall/CRC, 2017).Book 

Google Scholar 
72.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).73.Lenssen, N. J. L. et al. Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos. 124, 6307–6326. https://doi.org/10.1029/2018jd029522 (2019).ADS 
Article 

Google Scholar  More