Philippa Kaur

Gartland, L. A., Firth, J. A., Laskowski, K. L., Jeanson, R. & Ioannou, C. C. Sociability as a personality trait in animals: Methods, causes and consequences. Biol. Rev. https://doi.org/10.1111/brv.12823 (2021).Article 

Google Scholar 
Bergmüller, R. & Taborsky, M. Adaptive behavioural syndromes due to strategic niche specialization. BMC Ecol. 7, 12. https://doi.org/10.1186/1472-6785-7-12 (2007).Article 

Google Scholar 
Massen, J. J. M., Sterck, E. H. M. & de Vos, H. Close social associations in animals and humans: Functions and mechanisms of friendship. Behaviour 147, 1379–1412. https://doi.org/10.1163/000579510X528224 (2010).Article 

Google Scholar 
Haller, J., Harold, G., Sandi, C. & Neumann, I. D. Effects of adverse early-life events on aggression and anti-social behaviours in animals and humans. J. Neuroendocrinol. 26, 724–738. https://doi.org/10.1111/jne.12182 (2014).Article 
CAS 

Google Scholar 
Carlson Bruce, A. Early life experiences have complex and long-lasting effects on behavior. Proc. Natl. Acad. Sci. 114, 11571–11573. https://doi.org/10.1073/pnas.1716037114 (2017).Article 
ADS 
CAS 

Google Scholar 
Zablocki-Thomas, P. B. et al. Personality and performance are affected by age and early life parameters in a small primate. Ecol. Evol. 8, 4598–4605. https://doi.org/10.1002/ece3.3833 (2018).Article 

Google Scholar 
Langenhof, M. R. & Komdeur, J. Why and how the early-life environment affects development of coping behaviours. Behav. Ecol. Sociobiol. 72, 34–34. https://doi.org/10.1007/s00265-018-2452-3 (2018).Article 

Google Scholar 
Daros, R. R., Costa, J. H. C., von Keyserlingk, M. A. G., Hötzel, M. J. & Weary, D. M. Separation from the dam causes negative judgement bias in dairy calves. PLoS One 9, e98429. https://doi.org/10.1371/journal.pone.0098429 (2014).Article 
ADS 
CAS 

Google Scholar 
Grześkowiak, ŁM. et al. Impact of early-life events on the susceptibility to Clostridium difficile colonisation and infection in the offspring of the pig. Gut Microbes 10, 251–259. https://doi.org/10.1080/19490976.2018.1518554 (2019).Article 

Google Scholar 
Schmauss, C., Lee-McDermott, Z. & Medina, L. R. Trans-generational effects of early life stress: The role of maternal behavior. Sci. Rep. 4, 4873. https://doi.org/10.1038/srep04873 (2014).Article 
ADS 
CAS 

Google Scholar 
Brask, J. B., Ellis, S. & Croft, D. P. Animal social networks: An introduction for complex systems scientists. J. Complex Netw. 9, cnab001. https://doi.org/10.1093/comnet/cnab001 (2021).Article 

Google Scholar 
Almeling, L., Hammerschmidt, K., Sennhenn-Reulen, H., Freund, A. M. & Fischer, J. Motivational shifts in aging monkeys and the origins of social selectivity. Curr. Biol. 26, 1744–1749. https://doi.org/10.1016/j.cub.2016.04.066 (2016).Article 
CAS 

Google Scholar 
Borgeaud, C., Sosa, S., Sueur, C. & Bshary, R. The influence of demographic variation on social network stability in wild vervet monkeys. Anim. Behav. 134, 155–165. https://doi.org/10.1016/j.anbehav.2017.09.028 (2017).Article 

Google Scholar 
Cantor, M. et al. The importance of individual-to-society feedbacks in animal ecology and evolution. J. Anim. Ecol. 90, 27–44. https://doi.org/10.1111/1365-2656.13336 (2021).Article 

Google Scholar 
Sosa, S., Sueur, C. & Puga-Gonzalez, I. Network measures in animal social network analysis: Their strengths, limits, interpretations and uses. Methods Ecol. Evol. 12, 10–21. https://doi.org/10.1111/2041-210X.13366 (2021).Article 

Google Scholar 
Farine, D. R. & Whitehead, H. Constructing, conducting and interpreting animal social network analysis. J. Anim. Ecol. 84, 1144–1163. https://doi.org/10.1111/1365-2656.12418 (2015).Article 

Google Scholar 
Neethirajan, S. & Kemp, B. Social network analysis in farm animals: Sensor-based approaches. Animals 11, 434. https://doi.org/10.3390/ani11020434 (2021).Article 

Google Scholar 
Whitehead, H. Analyzing Animal Societies. University of Chicago Press, Chicago, IL, USA (2008).
Smith, J. E. & Pinter-Wollman, N. Observing the unwatchable: Integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data. J. Anim. Ecol. 90, 62–75. https://doi.org/10.1111/1365-2656.13362 (2021).Article 

Google Scholar 
Chen, S., Ilany, A., White, B. J., Sanderson, M. W. & Lanzas, C. Spatial-temporal dynamics of high-resolution animal networks: What can we learn from domestic animals?. PLoS One 10, e0129253. https://doi.org/10.1371/journal.pone.0129253 (2015).Article 
CAS 

Google Scholar 
Atton, N., Galef, B. J., Hoppitt, W., Webster, M. M. & Laland, K. N. Familiarity affects social network structure and discovery of prey patch locations in foraging stickleback shoals. Proc. R. Soc. B Biol. Sci. 281, 20140579. https://doi.org/10.1098/rspb.2014.0579 (2014).Article 
CAS 

Google Scholar 
Ilany, A. & Akçay, E. Personality and social networks: A generative model approach. Integr. Comp. Biol. 56, 1197–1205. https://doi.org/10.1093/icb/icw068 (2016).Article 

Google Scholar 
Romano, V. et al. Modeling infection transmission in primate networks to predict centrality-based risk. Am. J. Primatol. 78, 767–779. https://doi.org/10.1002/ajp.22542 (2016).Article 

Google Scholar 
Ren, K., Bernes, G., Hetta, M. & Karlsson, J. Tracking and analysing social interactions in dairy cattle with real-time locating system and machine learning. J. Syst. Archit. 116, 102139. https://doi.org/10.1016/j.sysarc.2021.102139 (2021).Article 

Google Scholar 
Boyland, N. K., Mlynski, D. T., James, R., Brent, L. J. N. & Croft, D. P. The social network structure of a dynamic group of dairy cows: From individual to group level patterns. Appl. Anim. Behav. Sci. 174, 1–10. https://doi.org/10.1016/j.applanim.2015.11.016 (2016).Article 

Google Scholar 
Chopra, K. et al. Proximity interactions in a permanently housed dairy herd: Network structure, consistency, and individual differences. Front. Vet. Sci. 7, 583715 (2020).Article 

Google Scholar 
Šárová, R. et al. Pay respect to the elders: Age, more than body mass, determines dominance in female beef cattle. Anim. Behav. 86, 1315–1323. https://doi.org/10.1016/j.anbehav.2013.10.002 (2013).Article 

Google Scholar 
Foris, B., Haas, H. G., Langbein, J. & Melzer, N. Familiarity influences social networks in dairy cows after regrouping. J. Dairy Sci. 104, 3485–3494. https://doi.org/10.3168/jds.2020-18896 (2021).Article 
CAS 

Google Scholar 
Foris, B., Zebunke, M., Langbein, J. & Melzer, N. Comprehensive analysis of affiliative and agonistic social networks in lactating dairy cattle groups. Appl. Anim. Behav. Sci. 210, 60–67. https://doi.org/10.1016/j.applanim.2018.10.016 (2019).Article 

Google Scholar 
de Freslon, I., Martínez-López, B., Belkhiria, J., Strappini, A. & Monti, G. Use of social network analysis to improve the understanding of social behaviour in dairy cattle and its impact on disease transmission. Appl. Anim. Behav. Sci. 213, 47–54. https://doi.org/10.1016/j.applanim.2019.01.006 (2019).Article 

Google Scholar 
Bolt, S. L., Boyland, N. K., Mlynski, D. T., James, R. & Croft, D. P. Pair housing of dairy calves and age at pairing: Effects on weaning stress, health production and social networks. PLoS One 12, e0166926. https://doi.org/10.1371/journal.pone.0166926 (2017).Article 
CAS 

Google Scholar 
Koene, P. & Ipema, B. Social networks and welfare in future animal management. Animals (Basel) 4, 93–118. https://doi.org/10.3390/ani4010093 (2014).Article 

Google Scholar 
Raussi, S. et al. The formation of preferential relationships at early age in cattle. Behav. Proc. 84, 726–731. https://doi.org/10.1016/j.beproc.2010.05.005 (2010).Article 

Google Scholar 
Weary, D. M., Jasper, J. & Hötzel, M. J. Understanding weaning distress. Appl. Anim. Behav. Sci. 110, 24–41. https://doi.org/10.1016/j.applanim.2007.03.025 (2008).Article 

Google Scholar 
Lopes, P. C., Block, P. & König, B. Infection-induced behavioural changes reduce connectivity and the potential for disease spread in wild mice contact networks. Sci. Rep. 6, 31790. https://doi.org/10.1038/srep31790 (2016).Article 
ADS 
CAS 

Google Scholar 
Ripperger, S. P., Stockmaier, S. & Carter, G. G. Tracking sickness effects on social encounters via continuous proximity sensing in wild vampire bats. Behav. Ecol. 31, 1296–1302. https://doi.org/10.1093/beheco/araa111 (2020).Article 

Google Scholar 
McGuirk, S. M. & Peek, S. F. Timely diagnosis of dairy calf respiratory disease using a standardized scoring system. Anim. Health Res. Rev. 15, 145–147. https://doi.org/10.1017/s1466252314000267 (2014).Article 

Google Scholar 
Callan, R. J. & Garry, F. B. Biosecurity and bovine respiratory disease. Vet. Clin. N. Am. Food Anim. Pract. 18, 57–77. https://doi.org/10.1016/S0749-0720(02)00004-X (2002).Article 

Google Scholar 
Sewio. Tag Leonardo iMU/Personal. https://docs.sewio.net/docs/tag-leonardo-imu-personal-30146967.html (2022).Barker, Z. E. et al. Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle. J. Dairy Sci. 101, 6310–6321. https://doi.org/10.3168/jds.2016-12172 (2018).Article 
CAS 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing.Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R package. J. Stat. Softw. 33, 1–22. https://doi.org/10.18637/jss.v033.i02 (2010).Article 

Google Scholar 
Franks, D. W., Weiss, M. N., Silk, M. J., Perryman, R. J. Y. & Croft, D. P. Calculating effect sizes in animal social network analysis. Methods Ecol. Evol. 12, 33–41. https://doi.org/10.1111/2041-210X.13429 (2021).Article 

Google Scholar 
Bates, D., Mächler, 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 (2015).Article 

Google Scholar 
Bell, D. C., Atkinson, J. S. & Carlson, J. W. Centrality measures for disease transmission networks. Soc. Netw. 21, 1–21. https://doi.org/10.1016/S0378-8733(98)00010-0 (1999).Article 

Google Scholar 
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x (1995).Article 
MathSciNet 
MATH 

Google Scholar 
PercieduSert, N. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol. 18, e3000411. https://doi.org/10.1371/journal.pbio.3000411 (2020).Article 
CAS 

Google Scholar 
Hulbert, L. E. & Moisá, S. J. Stress, immunity, and the management of calves. J. Dairy Sci. 99, 3199–3216. https://doi.org/10.3168/jds.2015-10198 (2016).Article 
CAS 

Google Scholar 
Sweeney, B. C., Rushen, J., Weary, D. M. & de Passillé, A. M. Duration of weaning, starter intake, and weight gain of dairy calves fed large amounts of milk. J. Dairy Sci. 93, 148–152. https://doi.org/10.3168/jds.2009-2427 (2010).Article 
CAS 

Google Scholar 
Rault, J.-L. Friends with benefits: Social support and its relevance for farm animal welfare. Appl. Anim. Behav. Sci. 136, 1–14. https://doi.org/10.1016/j.applanim.2011.10.002 (2012).Article 

Google Scholar 
Ishiwata, T., Kilgour, R. J., Uetake, K., Eguchi, Y. & Tanaka, T. Choice of attractive conditions by beef cattle in a Y-maze just after release from restraint. J. Anim. Sci. 85, 1080–1085. https://doi.org/10.2527/jas.2006-405 (2007).Article 
CAS 

Google Scholar 
Ede, T., von Keyserlingk, M. A. G. & Weary, D. M. Social approach and place aversion in relation to conspecific pain in dairy calves. PLoS One 15, e0232897. https://doi.org/10.1371/journal.pone.0232897 (2020).Article 
CAS 

Google Scholar 
Cantor, M. C., Renaud, D. L., Neave, H. W. & Costa, J. H. C. Feeding behavior and activity levels are associated with recovery status in dairy calves treated with antimicrobials for Bovine Respiratory Disease. Sci. Rep. 12, 4854. https://doi.org/10.1038/s41598-022-08131-1 (2022).Article 
ADS 
CAS 

Google Scholar 
Kappeler, P. M., Cremer, S. & Nunn, C. L. Sociality and health: Impacts of sociality on disease susceptibility and transmission in animal and human societies. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370, 20140116. https://doi.org/10.1098/rstb.2014.0116 (2015).Article 

Google Scholar 
Ezenwa, V. O. et al. Host behaviour–parasite feedback: An essential link between animal behaviour and disease ecology. Proc. R. Soc. B Biol. Sci. 283, 20153078. https://doi.org/10.1098/rspb.2015.3078 (2016).Article 
CAS 

Google Scholar 
Klein, S. L. Parasite manipulation of the proximate mechanisms that mediate social behavior in vertebrates. Physiol. Behav. 79, 441–449. https://doi.org/10.1016/S0031-9384(03)00163-X (2003).Article 
ADS 
CAS 

Google Scholar 
LavistaFerres, J. M. et al. Social connectedness and movements among communities of giraffes vary by sex and age class. Anim. Behav. 180, 315–328. https://doi.org/10.1016/j.anbehav.2021.08.008 (2021).Article 

Google Scholar 
VanderWaal, K. L., Wang, H., McCowan, B., Fushing, H. & Isbell, L. A. Multilevel social organization and space use in reticulated giraffe (Giraffa camelopardalis). Behav. Ecol. 25, 17–26. https://doi.org/10.1093/beheco/art061 (2014).Article 

Google Scholar 
Sato, S. & Wood-Gush, D. G. Observations on creche behaviour in suckler calves. Behav. Process. 15, 333–343. https://doi.org/10.1016/0376-6357(87)90017-9 (1987).Article 
CAS 

Google Scholar 
Lecorps, B., Kappel, S., Weary, D. M. & von Keyserlingk, M. A. G. Social proximity in dairy calves is affected by differences in pessimism. PLoS One 14, e0223746. https://doi.org/10.1371/journal.pone.0223746 (2019).Article 
CAS 

Google Scholar 
Carslake, C., Occhiuto, F., Vázquez-Diosdado, J. A. & Kaler, J. Repeatability and predictability of calf feeding behaviors—Quantifying between- and within-individual variation for precision livestock farming. Front. Vet. Sci. 9, 827124 (2022).Article 

Google Scholar 
Occhiuto, F., Vázquez-Diosdado, J. A., Carslake, C. & Kaler, J. Personality and predictability in farmed calves using movement and space-use behaviours quantified by ultra-wideband sensors. R. Soc. Open Sci. 9, 212019. https://doi.org/10.1098/rsos.212019 (2022).Article 
ADS 

Google Scholar 
Carslake, C., Occhiuto, F., Vázquez-Diosdado, J. A. & Kaler, J. Indication of a personality trait in dairy calves and its link to weight gain through automatically collected feeding behaviours. Sci. Rep. 12, 19425. https://doi.org/10.1038/s41598-022-24076-x (2022).Article 
ADS 
CAS 

Google Scholar 
Planas-Sitjà, I., Deneubourg, J.-L. & Cronin, A. L. Variation in personality can substitute for social feedback in coordinated animal movements. Commun. Biol. 4, 469. https://doi.org/10.1038/s42003-021-01991-9 (2021).Article 

Google Scholar 
Stockmaier, S., Bolnick, D. I., Page, R. A. & Carter, G. G. Sickness effects on social interactions depend on the type of behaviour and relationship. J. Anim. Ecol. 89, 1387–1394. https://doi.org/10.1111/1365-2656.13193 (2020).Article 

Google Scholar 
Smith, L. A., Swain, D. L., Innocent, G. T., Nevison, I. & Hutchings, M. R. Considering appropriate replication in the design of animal social network studies. Sci. Rep. 9, 7208. https://doi.org/10.1038/s41598-019-43764-9 (2019).Article 
ADS 
CAS 

Google Scholar 
Shin, D. H., Kang, H. M. & Seo, S. Social relationships enhance the time spent eating and intake of a novel diet in pregnant Hanwoo (Bos taurus coreanae) heifers. PeerJ 5, e3329. https://doi.org/10.7717/peerj.3329 (2017).Article 
CAS 

Google Scholar  More

Our approach uses hypothetical 30 cm fixed depth samples taken at three successive time points (t0, t1, and t2) with prescribed changes in SOC (1.4% to 1.6%) and BD (1.5–1.1 g cm−3) over these time points (Table 1). The 30 cm soil depth is the common international standard for sampling and analysis required for SOC stock assessment and adhered to by carbon accounting and market organizations6,18. The changes we adopted (a 27% decrease in BD and a 14% increase in SOC) while relatively large, are consistent with those reported in the literature. For example, Reganold and Palmer reported a 25% decrease in BD (1.2–0.9 g cm−3) in neighboring farms with differing management practices23, and Syswerda et al. observed a 17% increase in SOC concentration (10.4–12.2 g C kg soil−1) when converting from a conventionally to organically managed row crop rotation21.Table 1 Hypothetical changes in bulk density (BD) and soil organic carbon (SOC) concentration in 30 cm fixed depth samples at time points t0, t1 and t2 along with calculated values of SOC stock and total soil mass and mineral soil mass.Full size tableIn Table 1, the total soil mass, mineral soil mass, and the SOC stock of the fixed depth samples were calculated by equations as described in the introduction from our prescribed changes in BD and SOC values.ScenariosWe compared hypothetical ESM correction scenarios with our 30 cm fixed depth sample at each time point (Table 2, Figs. 2, 3).Table 2 Hypothetical ESM scenarios showing variation with depth for bulk density (BD) and soil organic carbon (SOC) at each sampling time point, along with the sample depth intervals investigated.Full size tableFigure 2Flow chart of the definition, sampling, and SOC stock correction for a theoretical data set at time points t0, t1, and t2 for scenarios s1 with linear distributions of BD and SOC and s2 with a linear increase in BD and exponential decrease in SOC with depth. Scenario s2 is sampled at (a) 10 cm, (b) 15 cm, and (c) 30 cm intervals.Full size imageFigure 3Scenarios (S1 and S2), showing (a) bulk density variation (BD, g cm−3), and (b) soil organic carbon (SOC, %) variation by depth (0–30 cm) at each time point (t0, t1, and t2). For scenario 2, the single 30 cm depth interval was used (2c). See Table 1 and 2 for details.Full size imageScenario 1We carried out the ESM correction on a 30 cm sample and assumed that the sample was homogenous throughout the profile, with constant SOC and BD values at each time point.To correct for the error in SOC stock estimation when using fixed depth soil sampling, we used  Eqs.2a, 2b and 2c that consider changes in BD28,35. The adjusted soil depth resulting from the change in BD is calculated as:$${mathrm{M}}_{mathrm{n}}= {mathrm{M}}_{mathrm{i}}$$
(2a)
$${mathrm{D}}_{mathrm{a}}*{mathrm{BD}}_{mathrm{n}}*left(1-mathrm{k}*{mathrm{SOC}}_{mathrm{n}}right)={mathrm{D}}_{mathrm{i}}*{mathrm{BD}}_{mathrm{i}}*left(1-mathrm{k}*{mathrm{SOC}}_{mathrm{i}}right)$$
(2b)
$${mathrm{D}}_{mathrm{a}}={mathrm{D}}_{mathrm{i}}*frac{{mathrm{BD}}_{mathrm{i}}}{{mathrm{BD}}_{mathrm{n}}}*frac{1-mathrm{k}*{mathrm{SOC}}_{mathrm{i}}}{1-mathrm{k}*{mathrm{SOC}}_{mathrm{n}}}$$
(2c)
where Mi = Initial mineral soil mass per area (left[frac{M}{{L}^{2}}right]) , Mn = New mineral soil mass per area (left[frac{M}{{L}^{2}}right]) , Da = Adjusted soil surface depth (left[Lright]) , BDi = Initial bulk density (left[frac{M}{{L}^{3}}right]) , BDn = New bulk density (left[frac{M}{{L}^{3}}right]) , SOCi = Initial SOC as a decimal percent (left[frac{M}{M}right]) , SOCn = New SOC as a decimal percent (left[frac{M}{M}right]) , Di = Initial depth (left[Lright]).To conform with Eq. (2a), an increase in SOC over time results in a displacement of some soil mineral mass from the sample, whereas a decrease in SOC over time requires some soil mineral mass to be replaced34. Multiplying the BD by the mineral fraction of the soil (left(1-mathrm{k}*{mathrm{SOC}}right)) for each time point allowed us to compare equivalent mineral mass28. The effect of a change in SOC on mineral mass is small, with a 1% change in SOC equating to approximately a 2% change in apparent depth. This adjustment relates SOC per unit of mineral mass of the fine fraction ( 2 mm)20. The corrected apparent depth can then be used to calculate the corrected SOC stock of a single layer, fixed depth sample (Eq. 3).$$SO{C}_{stock}={D}_{a}*BD*SOC$$
(3)
Scenario 2In ESM correction scenarios 2a, 2b, and 2c, we imposed variable, dynamic BD and SOC values with depth over time (Table 2, Figs. 2, 3). To investigate these profiles, we determined the SOC and BD values throughout the soil depth by separating the soil into one (1) cm depth increments (i.e., 0–1 cm, 1–2 cm, etc.). We refer to this calculated incremental profile as the scenario 2 baseline. We assumed that our prescribed SOC concentration varied with depth following an exponential decay. To represent this decay, we simulated the global average distribution of SOC concentration with depth on crop land36, following the distribution from Hobley and Wilson37 (Eq. 4),$$SOCleft(dright)=SO{C}_{infty }+left(SO{C}_{o}-SO{C}_{infty }right)times {e}^{-dk}$$
(4)
where SOC (d) is the SOC concentration at depth (d), ({SOC}_{infty }) is the infinity SOC concentration, SOC0 is the SOC concentration at the soil surface, and k is the decay rate. We solved for the decay rate, initial SOC0, and infinity ({SOC}_{infty }) to fit the global average distribution for the 30 cm profile36 and then scaled the SOC concentration to our 30 cm fixed depth sample’s average SOC (1.4%) at t0 (Fig. 3).In scenarios 2a, 2b, and 2c, the BD increased linearly with depth38,39. At the initial time point (t0), we varied the BD values by ± 10% of the BD average over the 30 cm depth, such that for example, BD at t0 (profile average of 1.5 g cm−3) was 1.35 g cm−3 and 1.65 g cm−3 for the upper (0–1 cm) and lower (29–30 cm) depth increment, respectively. For each sequential time point, as the average BD decreased, the soil expanded. To determine the expansion, the depth of the initial sample (e.g., at t0) that filled the 30 cm depth in the subsequent sample (e.g., at t1) was calculated as the initial depth multiplied by the ratio of the average initial BD over the average new BD (e.g., 1.5/1.3 = 1.15 for t0/t1).The linear increase in BD with depth of each following time point maintained the average BD of scenario 1. We then varied the new BD by ± the percent change in the average BD between the time periods (see annotated scripts “main.R” and “functions.R” in Supplementary Material 1 for the development of the theoretical dataset). We then divided each initial BD increment (using soil mass for every 1 cm depth increment) by the new BD in the expanded increment (using soil mass for every  > 1 cm depth increment) to determine the expanded depth of each increment. The SOC value at the initial time represented the same, now expanded, ( > 1 cm) increments, as SOC is a ratio of mass. We used a linear decay rate that was twice that of the percent change in BD between time points to maintain an average BD that was consistent with scenario 1. To model the subsequent fixed depth sample, the BD and SOC concentration values of this expanded soil profile were then interpolated back to the 30 × 1 cm increments of the scenario 2 baseline depth. This calculation preserved the prescribed average BD of the new time point by only expanding the initial SOC concentration.We adjusted the SOC concentration of the next time point to maintain the average SOC concentrations of the 30 cm fixed depth sample, (see annotated scripts “main.R” and “functions.R” in Supplementary Material 1). Because the BD changed between time points and because the SOC stock in the 30 cm fixed depth sample was known, we determined the change in SOC stock between time points by subtracting the average SOC stock in the prior sample from the new sample. We then weighted this change across the 30 cm profile using the distribution of the global soil SOC in the top 30 cm to simulate SOC stratification with reduced tillage or agricultural intensification40. We then multiplied this change by the BD to convert back to SOC concentration and added the delta ((Delta )) SOC value to the prior sample. A worked example is shown in Supplementary Material 2 “Correction Example”.At each time point we split the soil profile at 10 cm and 15 cm depth intervals to create samples for scenarios 2a (3 soil intervals) and 2b (2 soil intervals), respectively. Note that scenario 2c is mathematically equivalent to scenario 1—with only one sample depth interval (30 cm) the sample contains no data on varying SOC or BD. The samples for 2a and 2b were generated by summing the total mass per area and SOC stock values from the scenario 2 baseline to produce single sample values of total soil mass per area and SOC concentration values per depth interval (as would be determined in a laboratory) and calculating BD and mineral mass.In scenario 2, any required additional mineral mass and the associated SOC values were ‘placed’ at the base of the sample to represent a soil profile that had expanded below the fixed 30 cm depth. To account for this, we calculated the increase in adjusted sample depth and accumulated additional soil mineral mass with the lowest sample depth interval of each split sample (Eqs. 5 and 6).$$mathrm{Delta D}={D}_{a}-{D}_{i}$$
(5)
$$SO{C}_{stock}={(D}_{1}*B{D}_{1}*SO{C}_{1}+dots + {(D}_{j}+Delta D)*B{D}_{j}*SO{C}_{j}))*10^2 (mathrm{g}/mathrm{cm}^{2})/(mathrm{Mg}/mathrm{ha})$$
(6)
where (mathrm{ Delta D}) is the apparent change in depth needed to generate the same mineral mass of the initial sample and the subscript j is the number of sample depth intervals from 1 to j.Varying BD linearly with depth introduces additional complexity in the calculation of the apparent depth. Each sample depth interval may expand (or contract in cases not explored here) at differing rates. Here, the over or under sampling of soil mineral mass is no longer constant with depth and the correction for apparent depth (Da) is estimated with linear interpolation using the BD of each sampling depth interval (i.e., 10 cm, 15 cm, or 30 cm). To do so, we calculated the mineral mass in each depth interval, determined their difference between the initial sample time point and new sample time point, and converted the change in mineral mass to a depth, where:$${mathrm{D}}_{mathrm{a}}={mathrm{D}}_{mathrm{i}}+frac{left(mathrm{sum}left({mathrm{D}}_{mathrm{ij}}*{mathrm{BD}}_{mathrm{ij}}*(1-mathrm{k}*{mathrm{soc}}_{mathrm{ij}}right))- mathrm{sum}left({mathrm{D}}_{mathrm{nj}}*{mathrm{BD}}_{mathrm{nj}}*(1-mathrm{k}*{mathrm{soc}}_{nmathrm{j}})right)right)}{{mathrm{BD}}_{{mathrm{nj}}_{mathrm{bottom}}}*1-mathrm{k}*{mathrm{soc}}_{n{mathrm{j}}_{mathrm{bottom}}}}$$
(7)
where jbottom is the lowest sample depth interval, and other terms are as previous. Using Eqs. (5), (6), and (7), with variable BD and SOC values, SOC stock can be corrected using samples split into the 10 cm and 15 cm sampling depth intervals. More

The ecological baseline in TirezThe geology and the climate of the Tirez region favored the generation and maintenance of a type of hypersaline habitat characterized by extreme seasonality: the sulfate-chlorine waters, with sodium and magnesium cations, showed significant seasonal variations15. The alkaline pH, the low oxidant value for the redox potential of the water column and the highly reduced sediments imposed extreme conditions (see Table 1 and Supplementary Information for details). This extreme seasonality requires to define a valid representative ecological baseline to compare the ecology of the lagoon between 2002 and 2021 and, in this way, set the basis to proposing our model of ecological succession with increasing dryness as a “time-analog” for early Mars. Taxonomic data from 2002 is a snapshot of the community during one season, so we include in our discussion the results presented by Montoya et al. (2013) from a sample campaign carried out in 2005, because they16 analyzed both water and sediment and during both the wet and dry seasons.We consider here only the results obtained by Montoya et al.16 by gene cloning, since those obtained by isolation and sequencing are not comparable. At the level of large groups, no major seasonal differences were observed: Pseudomonadota, followed by Bacteroidetes, were the dominant phyla, in both water and sediments, and both in the dry and the rainy seasons; although Alphaproteobacteria was the dominant class in water, while Gammaproteobacteria was dominant in sediments (in both dry and rainy seasons). With respect to the archaeal domain, all the identified sequences were affiliated to Halobacteriales order, mainly Halorubrum (water) and Halobacterium (sediment), both within Halobacteriaceae family. We can consider these results presented in Montoya et al.16 as the “ecological baseline” for Tirez, however taken with a grain of salt, because only 43 bacterial and 35 archaeal sequences, including rainy and dry seasons and water and sediments, were considered for analysis.Prokaryotic diversity in 2002As can be expected for an extreme environment, the bacterial diversity detected in 2002 was low, although we cannot exclude the possibility that this may reflect the limitation of DNA sequencing techniques at the time. 59% of the obtained clones in the then-wet sediments corresponded to the Malaciobacter genus. Malaciobacter (previously named17 Arcobacter) is an aerotolerant Epsilonproteobacteria. Species within this genus are moderately halophilic, e.g., M. halophilus, capable to grow in up to 4% NaCl. Even though the role that Malaciobacter can play in the environment is not known, it seems to thrive in aquatic systems, like sewage, with a high organic matter content17: e.g., M. canalis, M. cloacae, or M. defluvii.After Malaciobacter-like clones, the next most numerous group belongs to the phylum Bacillota (27% of the sequenced clones; Table 2). Under stressful environmental conditions, members of the genus Virgibacillus produce endospores, a very useful property in an extreme and variable environment (ionic strength, temperature, light intensity), easy to compare with early Mars. Endospores facilitate species survival, allowing them to overcome drastic negative changes, like dry periods, and to germinate when the conditions are favorable again. The closest identified species was the halotolerant V. halodenitrificans, but with low homology, not far from other halotolerant (e.g., V. dokdonensis) or halophilic (e.g., V. marismortui) species within the same genus. The other Gram-positive clones belong to the order Clostridiales. These clones cluster in two taxonomic units related with the strictly anaerobic genus Tissierella.Despite the abundance of Pseudomonadota, their biodiversity was very low, reduced to only two genera within the Epsilon- and Delta-proteobacteria. Six sequences affiliated to Deltaproteobacteria, and clustered in one OTU (salB38, similarity 96.6% with Desulfotignum), were retrieved. Its presence in anaerobic media rich in sulfates (Table 1) seems reasonable. In fact, sulfate-reducing activity was detected using a specific enrichment assay.Finally, one taxon belonging to the phylum Spirochaetota (previously named Spirochaetes) was identified. The presence of Spirochaetota in this system is not strange because members of the genus Spirochaeta are very often found in mud and anaerobic marine environments rich in sulfates18. Moreover, the closest species to SalB63, although with a low similarity of 87%, was Spirochaeta bajacaliforniensis, a spirochete isolated19 from a microbial mat in Laguna Figueroa (Baja California), an extensive hypersaline lagoon with high gypsum content, very similar, although much bigger, than Tirez lagoon.The diversity within the domain Archaea was very low in 2002. The phylogenetic analysis of 96 clones indicate that they correspond to one specie belonging to the obligate halophile genus Methanohalophilus. Their high similarity (99.3%) with several species of Methanohalophilus, such as M. portucalensis (isolated from sediments of a solar saltern in Portugal), M. mahii (isolated from sediments of the Great Salt Lake), or M. halophilus (isolated20 from a cyanobacterial mat at Hamelin Pool, Australia), makes impossible its adscription to any particular species level. Methanohalophilus is strictly methylotrophic, which is consistent with this environment, given that the methylotrophic methanogenesis pathway, non-competitive at low-salt conditions, is predominant at high saline concentrations21. We further confirmed methanogenic activity in Tirez by the measurement of methane by gas chromatography in enrichment cultures.Prokaryotic diversity in context of other studies between 2002 and 2021It was challenging to establish a timeline for the succession of the populations involved, because the scarcity of data harvested and published so far from Tirez. However, combining our results with the few data available in Montoya et al.16 and Preston et al.22 on samplings carried out on 2005 and 2017, respectively, we can see a clear predominance of the phylum Pseudomonadota: Epsilonproteobacteria, i.e. Arcobacter-like, and Deltaproteobacteria, mainly sulfate-reducing bacteria (this work, sampling 2002), and Gammaproteobacteria16 when Tirez maintained a water film, to eventually a final predominance of Gammaproteobacteria, e.g. Chromatiales and Pseudomonadales, in the dry Tirez (this work, 2020 sampling). The Bacillales order has remained widely represented both in the wet and dry Tirez.Regarding the archaeal domain, the few references available (Refs.16,22; this work) confirm that the members of the Halobacteriaceae family are well adapted to both the humid and dry ecosystems of Tirez, being predominant in both conditions. Preston et al.22 found that the second most abundant group of archaea in the dry sediments of Tirez was the Methermicoccaceae family, within the Methanosarcinales order, Methanomicrobia class. Taking into account the results obtained in the dry Tirez (Preston et al.22; and this work, sample 2020), the methanogenic archaea have decreased drastically through time, probably due to salt stress and the competition with sulfate-reducing bacteria.Prokaryotic diversity in 2021From a metabolic point of view, most of the bacteria present today in the sediment are chemoorganotrophs, anaerobes, and halophilic or halotolerant. Scarce information is available about the predominant OTU, Candidate Division OP1. The OP1 division was one of the main bacterial phyla in a sulfur-rich sample in the deepest analyzed samples from the Red Sea sediments under brine pools23. In addition, the phylogenetically related Candidate division KB1 has been observed in deep-sea hypersaline anoxic basins at Orca Basin (Gulf of Mexico), and other hypersaline environments24. Eight of the nine genera identified show coverage greater than 1% of the sequences: i.e., Rubinisphaera, Halothiobacillus, Thiohalophilus, Anaerobacillus/Halolactibacillus, Halomonas, Halothermothrix, and Aliifodinibius are halophilic or halotolerant genera13,25.Regarding archaea, our analyses reveal archaeal groups that seem to thrive in sediments from extreme environments, e.g., marine brine pools/deep water anoxic basins or hypersaline lakes. The most abundant OTU, Thermoplasmata KTK4A, was found prominent and active in the sediment of Lake Strawbridge, a hypersaline lake in Western Australia26, and in soda-saline lakes in China27. The creation of a Candidatus Haloplasmatales, a novel order to include KTK4A-related Thermoplasmata, has been proposed27. On the other hand, both in the aforementioned soda-saline lakes in China27 and in a sulfur-rich section of the sediments from below the Red Sea brine pools23, retrieved sequences were assigned to Marine Benthic Groups B, D, and E. Finally, in the section of nitrogen-rich sediments from the aforementioned Red Sea brine pools, the unclassified lineage ST-12K10A represented the most abundant archaeal group. In the Tirez Lagoon sediment after desiccation, all Methanomicrobia readings belonged to this group.The significance and implications of an ecosystem characterized in 2021 by high diversity, high inequality, and lack of isolated representatives, resides in that Tirez is today an ecosystem in which many (most) of the species/OTUs present are dormant, and they do not play any metabolic role. Hence the high percentage of raretons, greater than 80% for both bacteria and archaea, which are actually present in the lagoon but with only one or two copies each. Only those species adapted to the conditions imposed by the extreme environment are able to actually thrive, and consequently only a few species carry out all the metabolic activity. We conclude that the microbiota in Tirez today represents an ecosystem with a high resilience capacity in the face of environmental changes that may occur.We want to clearly highlight that the technique available in 2002 to study the microbiota of the Tirez lagoon only allowed to obtain a low-resolution image, but that was the state-of-the-art procedure at the time, and the Tirez lagoon cannot be sampled again with the conditions back in 2002, which no longer exist and are not expected to return. Although we have kept in storage several samples of water and sediment from the 2002 Tirez lagoon, it is reasonable to assume that those laboratory microcosms would have chemically and microbiologically changed during the last 20 years, and as such no longer represent reliable replicas of the original lagoon, so we cannot use them for the purposes of this work. Therefore, we are aware that any comparisons of the 2002 laboratory results with the much more robust results obtained by Illumina in 2021 need to be taken with a grain of salt. With all the precautions required, in a high-level, first-order comparison, the most noticeable difference between 2002 and 2021 is a drastic change in the microbial Tirez population. Only some OTUs within Bacillales (Virgibacillus/Anaerobacillus), sulfate-reducing Deltaproteobacteria (Desulfotignum/Desulfobacteraceae-Desulfovibrio), and Spirochaetes are shared among the 2002 and 2021 samples. This comparison is enough for the purposes of this work, as we are interested in the evolution of the lagoon system as a whole to establish a “time-analog” with the wet-to-dry transition on early Mars, and not in the particular outcome of each and every OTU in Tirez. With the results at hand, we conclude that, since 2002, the lacustrine microbiota has shifted to one more adapted to the extreme conditions in the dry sediments, derived from the gradual and persistent desiccation concluding ca. 7 years ago (i.e., completely desiccated in 2015), such as lack of light, absence of oxygen, and lack of water availability. This shift has likely been triggered because organisms that were originally in the lagoon but at low abundance in 2002 became dominant as they were better adapted to desiccation, and because the incoming of new microorganisms transported by birds or wind28.Lipid biomarkers analysis of the desiccated lake sedimentsThe analysis of cell membrane-derived lipid compounds on the dry lake sediments at present allow to provide another perspective of the microbial communities inhabiting the Tirez lagoon, by contributing additional information about the ecosystem and depositional environment. It is important to note that, analyzing only the 2021 lake sediments, we cannot differentiate between lipidic biomarkers of the microorganisms inhabiting Tirez in 2002 and before from those left behind by the microorganisms living in the dried sediments today. Instead, the analyses of lipid biomarkers provide clues about the different microorganisms that have populated Tirez through time, including both older communities inhabiting the former aqueous system and also younger communities better adapted to the present dry conditions. Thus, the lipid biomarkers analysis can be considered as a time-integrative record of the microbial community inhabiting Tirez during the last decades.Based on the molecular distribution of lipid biomarkers, the presence of gram-positive bacteria was inferred from the relative abundance of the monounsaturated alkanoic acid C18:1[ω9], or iso/anteiso pairs of alkanoic acids from 12 to 17 carbons29 with dominance of i/a-C15:0 and i/a-C17:0 (Fig. 3B). In contrast, generally ubiquitous alkanoic acids such as C16:1[ω7], C18:1[ω7], or C18:2[ω6] suggested a provenance rather related to gram-negative bacteria30. The combined detection of the i/a-C15:0 and i/a-C17:0 acids, with dominance of the iso over the anteiso congeners, together with other biomarkers such as the mid-chain branched 10Me16:0, the monounsaturated C17:1, or the cyclopropyl Cy17:0 and Cy19:0 acids, may be associated with a community of SRB31 in today´s dry sediments of Tirez. Specifically, most of those alkanoic acids have been found in a variety of Deltaproteobacteria and/or Bacteroidota (previously named Bacteroidetes). The presence of archaea was deduced from the detection of prominent peaks of archaeol in the polar fraction32 (Fig. 3C), as well as squalene and relatives (dihydrosqualene and tetrahydrosqualene) in the apolar fraction33 (Fig. 3A). Squalene and a variety of unsaturated derivatives are present in the neutral lipid fractions of many archaea with high abundances in saline lakes34. The relative abundance of autotrophs over heterotrophs35 can be estimated by the ratio of the autotrophically-related pristane and phytane over the both autotrophically- and heterotrophically-produced n-C17 and n-C18 alkanes ([Pr + Ph]/[n-C17 + n-C18]). A ratio of 0.56 in the Tirez sediments suggest the presence of a relevant proportion of heterotrophs in the ancient lacustrine system.Furthermore, the lipid biomarkers analysis was able to detect compounds specific of additional microbial sources, such as cyanobacteria36 (n-C17, C17:1, or 7Me-C15 and 7Me-C17), microalgae and/or diatoms (phytosterols37; or C20:5, and C22:6 alkanoic acids30), and other photoautotrophs (phytol and potentially degradative compounds such as pristane and phytane31). A relatively higher preservation of the cell-membrane remnants (i.e., lipids) compared to the DNA-composing nucleic acids may contribute to explain the lack of detection of cyanobacteria, diatoms and microalgae, and other phototrophs by DNA analysis (a deficit in our results shared with Montoya et al.16, and Preston et al.22). Although abundant in higher plants38, sterols such as those detected here (i.e., the sterols campesterol, stigmasterol, and β-sitosterol, as well as ergosterol) are also major sterols in some microalgal classes37 (such as Bacillariophyceae, Chrysophyceae, Euglenophyceae, Eustigmatophyceae, Raphidophyceae, Xanthophyceae, and Chlorophyceae), cyanobacteria (β-sitosterol), and fungi (ergosterol39).The carbon isotopic composition of lipid biomarkers provides a rapid screening of the carbon metabolism in a system, by recognizing the principal carbon fixation pathways used by autotrophs. The range of δ13C values measured in the Tirez sediments (from − 33.9 to − 16.1‰) denotes a mixed use of different carbon assimilation pathways, involving mostly the reductive pentose phosphate (a.k.a. Calvin–Benson–Bassham or just Calvin) cycle (from − 19 to − 30‰), and in lesser extent the reductive acetyl-CoA (a.k.a. Wood–Ljungdahl) pathway (from − 28 to − 44‰), and/or the reverse tricarboxylic acid (rTCA) cycle (from − 12 to − 21‰).The lipids synthesized by microorganisms using the Calvin or reductive acetyl-CoA pathway are typically depleted relative to the bulk biomass, particularly those produced via de latter pathway. In the dry Tirez sediments, the majority of the lipid compounds are more depleted in 13C than the bulk biomass (Fig. 4). In particular, the branched alkane DiMeC18 (Fig. 4A) and the SRB-indicative 10Me16:0 acid (Fig. 4B) showed the most depleted δ13C values and suggested the use of the reductive acetyl-CoA pathway. The rest of lipid compounds showed isotopic signatures (from − 16.1 to − 31.4‰) compatible with the prevalence of the Calvin pathway. These values may directly reflect the autotrophic activity of microorganisms fixing carbon via the Calvin cycle or heterotrophic activity of microorganisms growing on their remnants. Thus, the saturated and linear alkyl chains of lipids (i.e., n-alkanes, n-alkanoic acids, and n-alkanols) showing the most negative δ13C values (e.g., alkanes n-C17 and C17:1; or acid C18:1[ω7]) reflect prokaryotic sources of Calvin-users autotrophs (e.g., cyanobacteria or purple sulfur bacteria), while the rest of compounds with slightly less negative δ13C values instead stem from the autotrophic activity of eukaryotes also users of the Calvin cycle (unsaturated fatty acids and sterols) or from the metabolism of heterotrophs such as SRB (iso/anteiso-, other branched, and cyclopropyl fatty acids) and haloarchaea (isoprenoids, phytanol, and archaeol). All in all, the compound-specific isotope composition of the dry sediments in the today´s Tirez lagoon may indirectly reflect the prevailing autotrophic mechanisms in the present lacustrine system of Tirez, by showing isotopic signatures of secondary lipids similar to their carbon source40.In addition, the use of a number of lipid molecular ratios or proxies allow further characterization of the lacustrine ecosystem and depositional environment. For example, the average chain length of the n-alkanes (24.1) suggests a relevant presence of eukaryotic biomass in the lacustrine sediments, since long-chained alkanes ( > C20) are known to originate from epicuticular leaf waxes in higher plants41. Highlighting the relevance of eukaryotes and their ecological roles is one of the major contributions of this work, because previous studies on the microbial ecology of hypersaline environments have been focused primarily on prokaryotes42.The proportion of odd n-alkanes of high molecular chain (i.e., n-C27, n-C29, and n-C31) over even n-alkanes of low molecular chain (i.e., n-C15, n-C17, and n-C19) provides an estimate of the relative abundance of terrigenous over aqueous biomass43, which in Tirez is TAR = 1.8. The Paq index may also be used to differentiate the proportion of terrigenous versus aquatic (emergent and submerged) plant biomass44. A Paq of 0.3 in the Tirez sediments from 2021 supported the relative abundance of land plants. Finally, the depositional environment in the lacustrine system of Tirez may be also characterized analyzing the ratio of pristane over phytane (Pr/Ph), which is higher than 1 when phytol degrades to pristane under oxic conditions45. Assuming that both isoprenoids in the Tirez sediments derived from phytol31, according to their similarly depleted δ13C (Fig. 4A), we can conclude that the sediments in the Tirez lagoon were deposited under predominantly oxic conditions (i.e., Pr/Ph ratio of 1.1).In summary, the lipid biomarkers study revealed useful information about the depositional environment and lacustrine ecosystem, including the presence of active or past autotrophic metabolisms involving prokaryotes (e.g., cyanobacteria and purple sulfur bacteria) and eukaryotes (plants, diatoms and other microalgae), as well as heterotrophic metabolisms of likely SRB and haloarchaea growing on Calvin-users exudates. These results are quite in agreement with the microbial community previously reported16 in sediments from the wet and dry seasons: abundant Gammaproteobacteria and Alphaproteobacteria, together with Algae and Cyanobacteria, dinoflagellates and filamentous fungi, Bacillota, Actinomycetota (previously named Actinomycetes), and a halophilic sulfate-reducing Deltaproteobacteria.Tirez as the first astrobiological “time-analog” for early MarsEarly Mars most likely had a diversity of environments in terms of pH, redox conditions, geochemistry, temperature, and so on. Field research in terrestrial analog environments contribute to understand the habitability of this diversity of environments on Mars in the past, because terrestrial analogues are places on Earth characterized by environmental, mineralogical, geomorphological, or geochemical conditions similar to those observed on present or past Mars9. Therefore, so far analogs have been referred to terrestrial locations closely similar to any of the geochemical environments that have been inferred on Mars, i.e., they are “site-analogs” that represent snapshots in time: one specific environmental condition at a very specific place and a very specific time. Because of this, each individual field analog site cannot be considered an adequate representation of the changing martian environmental conditions through time. Here we introduce the concept of astrobiological “time-analog”, referred to terrestrial analogs that may help understand environmental transitions and the related possible ecological successions on early Mars. In this sense, they should be “time-resolved analogs”: dynamic analog environments where we can analyze changes over time. To the best of our knowledge, this is the first study that looks at the environmental microbiology of a Mars astrobiological analog site over a significant and long period of change, and try to understand the ecological successions to put them in the context of martian environmental evolution.As Mars lost most of its surface water at the end of the Hesperian5,9,12, this wet-to-dry global transition can be considered the major environmental perturbation in the geological history of Mars, and therefore merits to be the first one to be assigned a “time-analog” for its better understanding and characterization. The drying of Mars was probably a stepwise process, characterized by multiple transitions between drier and wetter environments12,47, and therefore the seasonal fluctuations and eventual full desiccation of Tirez represent a suitable analog to better understand possible ecological transitions during the global desiccation of most of the Mars’s surface before the Amazonian (beginning 3.2 Ga).To introduce Tirez as the first Mars astrobiological “time-analog” of the wet-to-dry transition on early Mars, the objective of this study was threefold: first, we wanted to identify the dominant prokaryotic microorganisms in the active Tirez lagoon 20 years ago, a unique hypersaline ecosystem with an ionic composition different from that of marine environments, and therefore potentially analogous to ancient saline lacustrine environments on Mars during the Noachian and into the Hesperian46,47. Our results provide a preliminary basis to hypothesize how the microbial communities on the Noachian Mars could have developed in salty environments with dramatically fluctuating water availability. The requirement to deal with important variations in ionic strength and water availability, involving at times the complete evaporation of the water, could have represented additional constraints48 for microorganisms on early Mars.The second objective of this investigation was the identification of the microbial community inhabiting the desiccated Tirez sediments today, after all the water was lost, as a potential analog to desiccated basins on Mars at the end of the Hesperian1,3,4,47. Our results suggest that hypothetical early microbial communities on early Mars, living with relative abundance of liquid water during the Noachian, would have been forced to adapt to increasingly desiccating surface environments, characterized by extreme conditions derived from the persistent dryness and lack of water availability. Our investigation in Tirez suggest that hypothetical microorganisms at the end of the Hesperian would have needed to evolve strategies similar to those of microorganisms on Earth adapted to living at very low water activity49, to thrive in the progressively desiccating sediments.And the third objective of this investigation was the identification of the lipidic biomarkers left behind by the microbial communities in Tirez, as a guide to searching and identifying the potential leftovers of a hypothetical ancient biosphere on Mars. Lipids (i.e., fatty acids and other biosynthesized hydrocarbons) are structural components of cell membranes bearing recognized higher resistance to degradation relative to other biomolecules, thus with potential to reconstruct paleobiology in a broader temporal scale than more labile molecules50. Our results reinforce the notion that lipidic biomarkers should be preferred targets in the search for extinct and/or extant life on Mars precisely because they are so recalcitrant. More

Castillo, C. P. et al. Agricultural Land Abandonment in the EU within 2015-2030. (Joint Research Centre (Seville site), 2018).van der Zanden, E. H., Verburg, P. H., Schulp, C. J. E. & Verkerk, P. J. Trade-offs of European agricultural abandonment. Land Use Policy 62, 290–301 (2017).Article 

Google Scholar 
Sun, Z. et al. Dietary change in high-income nations alone can lead to substantial double climate dividend. Nat. Food 3, 29–37 (2022).Article 
CAS 

Google Scholar 
Rasmussen, L. V. et al. Social-ecological outcomes of agricultural intensification. Nat. Sustainability 1, 275–282 (2018).Article 

Google Scholar 
Arneth, A. et al. Post-2020 biodiversity targets need to embrace climate change. Proc. Natl. Acad. Sci. 117, 30882–30891 (2020).Article 
CAS 

Google Scholar 
Seddon, N., Turner, B., Berry, P., Chausson, A. & Girardin, C. A. J. Grounding nature-based climate solutions in sound biodiversity science. Nat. Clim. Change 9, 84–87 (2019).Article 

Google Scholar 
Rey Benayas, J. M. & Bullock, J. M. Restoration of biodiversity and ecosystem services on agricultural land. Ecosystems 15, 883–899 (2012).Article 

Google Scholar 
Malhi, Y. et al. The role of large wild animals in climate change mitigation and adaptation. Current Biology 32, R181–R196 (2022).Article 
CAS 

Google Scholar 
Perino, A. et al. Rewilding complex ecosystems. Science 364, eaav5570 (2019).Article 
CAS 

Google Scholar 
Zhou, Y. et al. Limited increases in savanna carbon stocks over decades of fire suppression. Nature 603, 445–449 (2022).Article 
CAS 

Google Scholar 
Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, eaaz7005 (2020).Article 
CAS 

Google Scholar 
Svenning, J.-C. Rewilding should be central to global restoration efforts. One Earth 3, 657–660 (2020).Article 

Google Scholar 
Dandy, N. & Wynne-Jones, S. Rewilding forestry. Forest Policy Econ. 109, 101996 (2019).Article 

Google Scholar 
Reino, L. et al. Does afforestation increase bird nest predation risk in surrounding farmland. Forest Ecol. Manag. 260, 1359–1366 (2010).Article 

Google Scholar 
Pausas, J. G. & Bond, W. J. Alternative biome states in terrestrial ecosystems. Trends Plant Sci. 25, 250–263 (2020).Article 
CAS 

Google Scholar 
Bakker, E. S. et al. Combining paleo-data and modern exclosure experiments to assess the impact of megafauna extinctions on woody vegetation. Proc. Natl. Acad. Sci. 113, 847–855 (2016).Article 
CAS 

Google Scholar 
Johnston, C. M. T. & Radeloff, V. C. Global mitigation potential of carbon stored in harvested wood products. Proceedings of the National Academy of Sciences 116, 14526–14531 (2019).Article 
CAS 

Google Scholar 
Shukla, P. R. et al. Climate Change and Land: an IPCC special report on climate change, desertification, landdegradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. Intergovernmental Panel on Climate Change (2019).Hong, S. et al. Divergent responses of soil organic carbon to afforestation. Nat. Sustainability 3, 694–700 (2020).Article 

Google Scholar 
Bala, G. et al. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl. Acad. Sci. 104, 6550–6555 (2007).Article 
CAS 

Google Scholar 
Rohatyn, S., Yakir, D., Rotenberg, E. & Carmel, Y. Limited climate change mitigation potential through forestation of the vast dryland regions. Science 377, 1436–1439 (2022).Article 
CAS 

Google Scholar 
Beer, C., Zimov, N., Olofsson, J., Porada, P. & Zimov, S. Protection of permafrost soils from thawing by increasing herbivore density. Sci Rep-Uk 10, 4170 (2020).Article 
CAS 

Google Scholar 
Johnson, C. N. et al. Can trophic rewilding reduce the impact of fire in a more flammable world. Philos. Trans. R. Soc. B: Biological Sci. 373, 20170443 (2018).Article 

Google Scholar 
Kristensen, J. A., Svenning, J.-C., Georgiou, K. & Malhi, Y. Can large herbivores enhance ecosystem carbon persistence? Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2021.09.006 (2021).Granado-Díaz, R., Villanueva, A. J. & Gómez-Limón, J. A. Willingness to accept for rewilding farmland in environmentally sensitive areas. Land Use Policy 116, 106052 (2022).Article 

Google Scholar 
Broughton, R. K. et al. Long-term woodland restoration on lowland farmland through passive rewilding. PloS one 16, e0252466 (2021).Article 
CAS 

Google Scholar 
Carver, S. et al. Guiding principles for rewilding. Conserv. Biology 35, 1882–1893 (2021).Article 

Google Scholar  More

Data reportingThe data underpinning this study is a compilation of existing datasets and therefore, no statistical methods were used to predetermine sample size, the experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. The measurements were taken from distinct samples, repeated measurements from the same sites were averaged in the main analysis.Inclusion & ethicsData were primarily collected from individual archives of contributing co-authors. The data collection initiative was openly announced via the mailing list of the 10th International Seminar on Apterygota and via social media (Twitter, Researchgate). In addition, colleagues from less explored regions (Africa, South America) were contacted via personal networks of the initial authors group and literature search. All direct data providers who collected and standardised the data were invited as co-authors with defined minimum role (data provision and cleaning, manuscript editing and approval). For unpublished data, people who were directly involved in sorting and identification of springtails, including all local researchers, were invited as co-authors. Principal investigators were normally not included as co-authors, unless they contributed to conceptualisation and writing of the manuscript. All co-authors were informed and invited to contribute throughout the research process—from the study design and analysis to writing and editing. The study provided an inclusive platform for researchers around the globe to network, share and test their research ideas.Data acquisitionBoth published and unpublished data were collected, using raw data whenever possible entered into a common template. In addition, data available from Edaphobase47 was included. The following minimum set of variables was collected: collectors, collection method (including sampling area and depth), extraction method, identification precision and resources, collection date, latitude and longitude, vegetation type (generalized as grassland, scrub, woodland, agriculture and other for the analysis), and abundances of springtail taxa found in each soil sample (or sampling site). Underrepresented geographical areas (Africa, South America, Australia and Southeast Asia) were specifically targeted by a literature search in the Web of Science database using the keywords ‘springtail’ or ‘Collembola’, ‘density’ or ‘abundance’ or ‘diversity’, and the region of interest; data were acquired from all found papers if the minimum information listed above was provided. All collected datasets were cleaned using OpenRefine v3.3 (https://openrefine.org) to remove inconsistencies and typos. Geographical coordinates were checked by comparing the dataset descriptions with the geographical coordinates. In total, 363 datasets comprising 2783 sites were collected and collated into a single dataset (Supplementary Fig. 1).Calculation of community parametersCommunity parameters were calculated at the site level. Here, we defined a site as a locality that hosts a defined springtail community, is covered by a certain vegetation type, with a certain management, and is usually represented by a sampling area of up to a hundred metres in diameter, making species co-occurrence and interactions plausible. To calculate density, numerical abundance in all samples was averaged and recalculated per square metre using the sampling area. Springtail communities were assessed predominantly during active vegetation periods (i.e., spring, summer and autumn in temperate and boreal biomes, and summer in polar biomes). Our estimations of community parameters therefore refer to the most favourable conditions (peak yearly densities). This seasonal sampling bias is likely to have little effect on our conclusions, since most springtails survive during cold periods38,48. Finally, we used mean annual soil temperatures49 to estimate the seasonal mean community metabolism (described below) and tested for the seasonal bias in additional analysis (see Linear mixed-effects models).All data analyses were conducted in R v. 4.0.250 with RStudio interface v. 1.4.1103 (RStudio, PBC). Data was transformed and visualised with tidyverse packages51,52, unless otherwise mentioned. Background for the global maps was acquired via the maps package53,54. To calculate local species richness, we used data identified to species or morphospecies level (validated by the expert team). Since the sampling effort varied among studies, we extrapolated species richness using rarefaction curves based on individual samples with the Chao estimator51,52 in the vegan package53. For some sites, sample-level data were not available in the original publications, but site-level averages were provided, and an extensive sampling effort was made. In such cases, we predicted extrapolated species richness based on the completeness (ratio of observed to extrapolated richness) recorded at sites where sample-level data were available (only sites with 5 or more samples were used for the prediction). We built a binomial model to predict completeness in sites where no sample-level data were available using latitude and the number of samples taken at a site as predictors: glm(Completeness~N_samples*Latitude). We found a positive effect of the number of samples (Chisq = 1.97, p = 0.0492) and latitude (Chisq = 2.07, p = 0.0391) on the completeness (Supplementary Figs. 17–19). We further used this model to predict extrapolated species richness on the sites with pooled data (435 sites in Europe, 15 in Australia, 6 in South America, 4 in Asia, and 3 in Africa).To calculate biomass, we first cross-checked all taxonomic names with the collembola.org checklist55 using fuzzy matching algorithms (fuzzyjoin R package56) to align taxonomic names and correct typos. Then we merged taxonomic names with a dataset on body lengths compiled from the BETSI database57, a personal database of Matty P. Berg, and additional expert contributions. We used average body lengths for the genus level (body size data on 432 genera) since data at the species level were not available for many morphospecies (especially in tropical regions), and species within most springtail genera had similar body size ranges. Data with no genus-level identifications were excluded from the analysis. Dry and fresh body masses were calculated from body length using a set of group-specific length-mass regressions (Supplementary Table 1)58,59 and the results of different regressions applied to the same morphogroup were averaged. Dry mass was recalculated to fresh mass using corresponding group-specific coefficients58. We used fresh mass to calculate individual metabolic rates60 and account for the mean annual topsoil (0–5 cm) temperature at a given site61. Group-specific metabolic coefficients for insects (including springtails) were used for the calculation: normalization factor (i0) ln(21.972) [J h−1], allometric exponent (a) 0.759, and activation energy (E) 0.657 [eV]60. Community-weighted (specimen-based) mean individual dry masses and metabolic rates were calculated for each sample and then averaged by site after excluding 10% of maximum and 10% of minimum values to reduce impact of outliers. To calculate site-level biomass and community metabolism, we summed masses or metabolic rates of individuals, averaged them across samples, and recalculated them per unit area (m2).Parameter uncertaintiesOur biomass and community metabolism approximations contain several assumptions. To account for the uncertainty in the length-mass and mass-metabolism regression coefficients, in addition to the average coefficients, we also used maximum (average + standard error) and minimum coefficients (average—standard error; Supplementary Table 1) in all equations to calculate maximum and minimum estimations of biomass and community metabolism reported in the main text. Further, we ignored latitudinal variation in body sizes within taxonomic groups62. Nevertheless, latitudinal differences in springtail density (30-fold), environmental temperature (from −16.0 to +27.6 °C in the air and from −10.2 to +30.4 °C in the soil), and genus-level community compositions (there are only few common genera among polar regions and the tropics)55 are higher than the uncertainties introduced by indirect parameter estimations, which allowed us to detect global trends. Although most springtails are concentrated in the litter and uppermost soil layers20, their vertical distribution depends on the particular ecosystem63. Since sampling methods are usually ecosystem-specific (i.e. sampling is done deeper in soils with developed organic layers), we treated the methods used by the original data collectors as representative of a given ecosystem. Under this assumption, we might have underestimated the number of springtails in soils with deep organic horizons, so our global estimates are conservative and we would expect true global density and biomass to be slightly higher. To minimize these effects, we excluded sites where the estimations were likely to be unreliable (see data selection below).Data selectionOnly data collection methods allowing for area-based recalculation (e.g. Tullgren or Berlese funnels) were used for analysis. Data from artificial habitats, coastal ecosystems, caves, canopies, snow surfaces, and strong experimental manipulations beyond the bounds of naturally occurring conditions were excluded (Supplementary Fig. 1). To ensure data quality, we performed a two-step quality check: technical selection and expert evaluation. Collected data varied according to collection protocols, such as sampling depth and the microhabitats (layers) considered. To technically exclude unreliable density estimations, we explored data with a number of diagnostic graphs (Supplementary Table 2; Supplementary Figs. 12–20) and filtered it, excluding the following: (1) All woodlands where only soil or only litter was considered; (2) All scrub ecosystems where only ground cover (litter or mosses) was considered; (3) Agricultural sites in temperate zones where only soil with sampling depth 90% of cases were masked on the main maps; for the map with density-species richness visualisation, two corresponding masks were applied (Fig. 2).To estimate spatial variability of our predictions while accounting for the spatial sampling bias in our data (Fig. 1a) we performed a spatially stratified bootstrapping procedure. We used the relative area of each IPBES79 region (i.e., Europe and Central Asia, Asia and the Pacific, Africa, and the Americas) to resample the original dataset, creating 100 bootstrap resamples. Each of these resamples was used to create a global map, which was then reduced to create mean, standard deviation, 95% confidence interval, and coefficient of variation maps (Supplementary Figs. 4–7).Global biomass, abundance, and community metabolism of springtails were estimated by summing predicted values for each 30 arcsec pixel10. Global community metabolism was recalculated from joule to mass carbon by assuming 1 kg fresh mass = 7 × 106 J80, an average water proportion in springtails of 70%58, and an average carbon concentration of 45% (calculated from 225 measurements across temperate forest ecosystems)81. We repeated the procedure of global extrapolation and prediction for biomass and community metabolism using minimum and maximum estimates of these parameters from regression coefficient uncertainties (see Parameter uncertainties).Path analysisTo reveal the predictors of springtail communities at the global scale, we performed a path analysis. After filtering the selected environmental variables (see above) according to their global availability and collinearity, 13 variables were used (Supplementary Fig. 9b): mean annual air temperature, mean annual precipitation (CHELSA database67), aridity (CGIAR database68), soil pH, sand and clay contents combined (sand and clay contents were co-linear in our dataset), soil organic carbon content (SoilGrids database73), NDVI (MODIS database72), human population density (GPWv4 database74), latitude, elevation69, and vegetation cover reported by the data providers following the habitat classification of European Environment Agency (woodland, scrub, agriculture, and grasslands; the latter were coded as the combination of woodland, scrub, and agriculture absent). Before running the analysis, we performed the Rosner’s generalized extreme Studentized deviate test in the EnvStats package82 to exclude extreme outliers and we z-standardized all variables (Supplementary R Code).Separate structural equation models were run to predict density, dry biomass, community metabolism, and local species richness in the lavaan package83. To account for the spatial clustering of our data in Europe, instead of running a model for the entire dataset, we divided the data by the IPBES79 geographical regions and selected a random subset of sites for Eurasia, such that only twice the number of sites were included in the model as the second-most represented region. We ran the path analysis 99 times for each community parameter with different Eurasian subsets (density had n = 723 per iteration, local species richness had n = 352, dry biomass had n = 568, and community metabolism had n = 533). We decided to keep the share of the Eurasian dataset larger than other regions to increase the number of sites per iteration and validity of the models. The Eurasian dataset also had the best data quality among all regions and a substantial reduction in datasets from Eurasia would result in a low weight for high-quality data. We additionally ran a set of models in which the Eurasian dataset was represented by the same number of sites as the second-most represented region, which yielded similar effect directions for all factors, but slightly higher variations and fewer consistently significant effects. In the paper, only the first version of analysis is presented. To illustrate the results, we averaged effect sizes for the paths across all iterations and presented the distribution of these effect sizes using mirrored Kernel density estimation (violin) plots. We marked and discussed effects that were significant at p  More

Guidetti, R. & Bertolani, R. B. Tardigrade taxonomy: An updated check list of the taxa and a list of characters for their identification. Zootaxa 845, 1–46. https://doi.org/10.11646/zootaxa.845.1.1 (2005).Article 

Google Scholar 
Degma, P. & Guidetti, R. Notes to the current checklist of Tardigrada. Zootaxa 1579, 41–53. https://doi.org/10.11646/zootaxa.1579.1.2 (2007)Article 

Google Scholar 
Vicente, F. & Bertolani, R. Considerations on the taxonomy of the phylum Tardigrada. Zootaxa 3626, 245–248. https://doi.org/10.11646/zootaxa.3626.2.2 (2013).Article 

Google Scholar 
Degma, P. & Guidetti, R. Actual checklist of Tardigrada species. (Version 41: Edition: 16-05-2022). (2009–2022).Ramazzotti, G. & Maucci, W. Il phylum Tardigrada. III edizione riveduta e aggiornata. Mem. Ist. Ital. Idrobiol. 41, 1–1012 (1983).
Google Scholar 
Beasley, C. W. The phylum Tardigrada. in English Translation P. 3rd edn (eds Ramazzotti, G. & Maucci, W.) 1–1014 (Abilene, USA, 1995).Nelson, D. R., Guidetti, R., Rebecchi, L., Kaczmarek, Ł. & McInnes, S. Phylum Tardigrada. in Thorp and Covich’s Freshwater Invertebrates 505–522 (Elsevier, 2020). https://doi.org/10.1016/B978-0-12-804225-0.00015-0.Da Cunha, A. X. & do Nascimento-Ribeiro, F. A fauna de Tardígrados da Ilha da Madeira. Mem. Estud. Mus. Zool. Univ. Coimbra 1–24 (1962).Fontoura, P., Pilato, G. & Lisi, O. Tardigrada from Santo Antão Island (Archipelago of Cape Verde, West Africa) with the description of a new species. Zootaxa 2838, 30–40. https://doi.org/10.11646/zootaxa.2838.1.2 (2011).Article 

Google Scholar 
Gąsiorek, P., Vončina, K. & Michalczyk, Ł. Echiniscus testudo (Doyère, 1840) in New Zealand: Anthropogenic dispersal or evidence for the ‘Everything is Everywhere’ hypothesis?. N. Z. J. Zool. 46, 174–181. https://doi.org/10.1080/03014223.2018.1503607 (2019).Article 

Google Scholar 
Guidetti, R., Schill, R. O., Bertolani, R., Dandekar, T. & Wolf, M. New molecular data for tardigrade phylogeny, with the erection of Paramacrobiotus gen. nov. J. Zool. Syst. Evol. 47, 315–321. https://doi.org/10.1111/j.1439-0469.2009.00526.x (2009).Article 

Google Scholar 
Kaczmarek, Ł, Gawlak, M., Bartels, P. J., Nelson, D. R. & Roszkowska, M. Revision of the genus Paramacrobiotus Guidetti et al., 2009 with the description of a new species, re-descriptions and a key. Ann. Zool. 67, 627–656. https://doi.org/10.3161/00034541ANZ2017.67.4.001 (2017).Article 

Google Scholar 
Marley, N. J. et al. A clarification for the subgenera of Paramacrobiotus Guidetti, Schill, Bertolani, Dandekar and Wolf, 2009, with respect to the International Code of Zoological Nomenclature. Zootaxa 4407, 130–134. https://doi.org/10.11646/zootaxa.4407.1.9 (2018).Article 
CAS 

Google Scholar 
Guidetti, R., Cesari, M., Bertolani, R., Altiero, T. & Rebecchi, L. High diversity in species, reproductive modes and distribution within the Paramacrobiotus richtersi complex (Eutardigrada, Macrobiotidae). Zool. Lett. 5, 1–28. https://doi.org/10.1186/s40851-018-0113-z (2019).Article 

Google Scholar 
Stec, D., Krzywański, Ł, Zawierucha, K. & Michalczyk, Ł. Untangling systematics of the Paramacrobiotus areolatus species complex by an integrative redescription of the nominal species for the group, with multilocus phylogeny and species delineation in the genus Paramacrobiotus. Zool. J. Linn. Soc. 188, 694–716. https://doi.org/10.1093/zoolinnean/zlz163 (2020).Article 

Google Scholar 
Murray, J. Scottish Tardigrada, a review of our present knowledge. Ann. Scot. Nat. Hist. 78, 88–95 (1911).
Google Scholar 
Murray, J. XXV.—Arctic Tardigrada, collected by Wm. S. Bruce. Trans. R. Soc. Edinb. 45, 669–681 (1907).Article 

Google Scholar 
Ramazzotti, G. Tre nouve specie di Tardigradi ed altre specie poco comuni. Atti Soc. Nat. Milano 10, 284–291 (1956).
Google Scholar 
Schill, R. O., Förster, F., Dandekar, T. & Wolf, M. Using compensatory base change analysis of internal transcribed spacer 2 secondary structures to identify three new species in Paramacrobiotus (Tardigrada). Org. Divers. Evol. 10, 287–296. https://doi.org/10.1007/s13127-010-0025-z (2010).Article 

Google Scholar 
Kaczmarek, Ł et al. Integrative description of bisexual Paramacrobiotus experimentalis sp. Nov. (Macrobiotidae) from republic of Madagascar (Africa) with microbiome analysis. Mol. Phylogenet. Evol. 145, 106730. https://doi.org/10.1016/j.ympev.2019.106730 (2020).Article 

Google Scholar 
Bertolani, R. Partenogenesi geografica triploide in un Tardigrado (Macrobiotus richtersi). Rend. Acc. Naz. Lincei. Ser. 8, 487–489 (1971).
Google Scholar 
Bertolani, R. Sex ratio and geographic parthenogenesis in Macrobioutus (Tardigrada). Experientia 28, 94–95. https://doi.org/10.1007/BF01928285 (1972).Article 

Google Scholar 
Bertolani, R. L. partenogenesi nei Tardigradi. Boll. Zool. 39, 577–581. https://doi.org/10.1080/11250007209431414 (1972).Article 

Google Scholar 
Bertolani, R. Cytology and Reproductive Mechanisms in Tardigrades. I. 93–114 (East Tennesse State University Press, Johnson City, 1982).
Google Scholar 
Lemloh, M., Brümmer, F. & Schill, R. O. Life-history traits of the bisexual tardigrades Paramacrobiotus tonollii and Macrobiotus sapiens. J. Zool. Syst. Evol. Res. 49, 58–61. https://doi.org/10.1111/j.1439-0469.2010.00599.x (2011).Article 

Google Scholar 
Guil, N. & Giribet, G. A comprehensive molecular phylogeny of tardigrades-adding genes and taxa to a poorly resolved phylum-level phylogeny. Cladistics 28, 21–49. https://doi.org/10.1111/j.1096-0031.2011.00364.x (2012).Article 

Google Scholar 
Kosztyła, P. et al. Experimental taxonomy confirms the environmental stability of morphometric traits in a taxonomically challenging group of microinvertebrates. Zool. J. Linn. Soc. 178, 765–775. https://doi.org/10.1111/zoj.12409 (2016).Article 

Google Scholar 
Kaczmarek, Ł et al. New records of Antarctic Tardigrada with comments on iterpopulation variability of the Paramacrobiotus fairbanksi Schill, Förster, Dandekar and Wolf, 2010. Diversity 12, 108. https://doi.org/10.3390/d12030108 (2020).Article 

Google Scholar 
Stec, D., Vecchi, M., Calhim, S. & Michalczyk, Ł. New multilocus phylogeny reorganises the family Macrobiotidae (Eutardigrada) and unveils complex morphological evolution of the Macrobiotus hufelandi group. Mol. Phylogenet. Evol. 160, 106987. https://doi.org/10.1016/j.ympev.2020.106987 (2021).Article 

Google Scholar 
Stec, D., Smolak, R., Kaczmarek, Ł & Michalczyk, Ł. An integrative description of Macrobiotus paulinae sp. Nov. (Tardigrada: Eutardigrada: Macrobiotidae: hufelandi group) from Kenya. Zootaxa 4052, 501–526. https://doi.org/10.11646/zootaxa.4052.5.1 (2015).Article 

Google Scholar 
Bryce, D. On some moss-dwelling Cathypnadae; with descriptions of five new species. Sci. Gossip Lond. 28, 271–275 (1892).
Google Scholar 
Casquet, J., Thebaud, C. & Gillespie, R. G. Chelex without boiling, a rapid and easy technique to obtain stable amplifiable DNA from small amounts of ethanol-stored spiders. Mol. Ecol. Resour. 12(1), 136–141. https://doi.org/10.1111/j.1755-0998.2011.03073.x (2012).Article 
CAS 

Google Scholar 
Stec, D., Kristensen, R. M. & Michalczyk, Ł. An integrative description of Minibiotus ioculator sp. nov. from the Republic of South Africa with notes on Minibiotus pentannulatus Londoño et al., 2017 (Tardigrada: Macrobiotidae). Zool. Anz. 286, 117–134. https://doi.org/10.1016/j.jcz.2020.03.007 (2020).Article 

Google Scholar 
Stec, D., Zawierucha, K. & Michalczyk, Ł. An integrative description of Ramazzottius subanomalus (Biserov, 1985 (Tardigrada) from Poland. Zootaxa 4300, 403–420. https://doi.org/10.11646/zootaxa.4300.3.4 (2017).Article 

Google Scholar 
Mironov, S. V., Dabert, J. & Dabert, M. A new feather mite species of the genus Proctophyllodes Robin, 1877 (Astigmata: Proctophyllodidae) from the Long-tailed Tit Aegithalos caudatus (Passeriformes: Aegithalidae)—Morphological description with DNA barcode data. Zootaxa 3253, 54–61. https://doi.org/10.11646/zootaxa.3253.1.2 (2012).Article 

Google Scholar 
White, T. J., Bruns, T., Lee, S. & Taylor, J. PCR Protocols: A Guide to Methods and Application 315–322. https://doi.org/10.1016/B978-0-12-372180-8.50042-1 (Academic Press, 1990).Book 

Google Scholar 
Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. Phylogenetic uncertainty. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 

Google Scholar 
Vecchi, M. & Stec, D. Integrative descriptions of two new Macrobiotus species (Tardigrada, Eutardigrada, Macrobiotidae) from Mississippi (USA) and Crete (Greece). ZSE 97, 281–306. https://doi.org/10.3897/zse.97.65280 (2021).Article 

Google Scholar 
Thulin, G. Über die phylogenie und das system der. Hereditas 11, 207–266. https://doi.org/10.1111/j.1601-5223.1928.tb02488.x (1928).Article 

Google Scholar 
Stec, D. Mesobiotus datanlanicus sp. nov., a new tardigrade species (Macrobiotidae: Mesobiotus harmsworthi group) from Lâm Đồng Province in Vietnam. Zootaxa 4679, 164–180. https://doi.org/10.11646/zootaxa.4679.1.10 (2019).Article 

Google Scholar 
Katoh, K. MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. NAR 30, 3059–3066. https://doi.org/10.1093/nar/gkf436 (2002).Article 
CAS 

Google Scholar 
Katoh, K. & Toh, H. Recent developments in the MAFFT multiple sequence alignment program. Brief. Bioinform. 9, 286–298. https://doi.org/10.1093/bib/bbn013 (2008).Article 
CAS 

Google Scholar 
Vaidya, G., Lohman, D. J. & Meier, R. SequenceMatrix: Concatenation software for the fast assembly of multi-gene datasets with character set and codon information. Cladistics 27, 171–180. https://doi.org/10.1111/j.1096-0031.2010.00329.x (2011).Article 

Google Scholar 
Lanfear, R., Calcott, B., Ho, S. Y. & Guindon, S. PartitionFinder: Combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol. Biol. Evol. 29(6), 1695–1701. https://doi.org/10.1093/molbev/mss020 (2012).Article 
CAS 

Google Scholar 
Xia, X., Xie, Z., Salemi, M., Chen, L. & Wang, Y. An index of substitution saturation and its application. Mol. Phylogenet. Evol. 26, 1–7. https://doi.org/10.1016/S1055-7903(02)00326-3 (2003).Article 
CAS 

Google Scholar 
Xia, X. & Lemey, P. Assessing substitution saturation with DAMBE. In The Phylogenetic Handbook (eds Lemey, P. et al.) 615–630. https://doi.org/10.1017/CBO9780511819049.022 (Cambridge University Press, 2009).Chapter 

Google Scholar 
Ronquist, F. & Huelsenbeck, J. P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574. https://doi.org/10.1093/bioinformatics/btg180 (2003).Article 
CAS 

Google Scholar 
Rambaut, A., Suchard, M. A., Xie, D. & Drummond, A. J. Tracer v1. 6. 2014. (2015).Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313. https://doi.org/10.1093/bioinformatics/btu033 (2014).Article 
CAS 

Google Scholar 
Bandelt, H., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48. https://doi.org/10.1093/oxfordjournals.molbev.a026036 (1999).Article 
CAS 

Google Scholar 
Ehrenberg, C. G. Beitrag zur Bestimmung des Stationären Mikroskopischen Lebens in bis 20,000 Fuss Alpenhöhe. (1859).Guil, N. & Guidetti, R. A new species of Tardigrada (Eutardigrada: Macrobiotidae) from Iberian Peninsula and Canary Islands (Spain). Zootaxa 889, 1–11. https://doi.org/10.11646/zootaxa.889.1.1 (2005).Article 

Google Scholar 
Plate, L. H. Beiträge zur Naturgeschichte der Tardigraden. Zool. Jahrb. Abteilung Anat. Ontog. Tiere 3, 487–550. https://doi.org/10.5962/bhl.part.1265 (1889).Article 

Google Scholar 
Kaczmarek, Ł, Kayastha, P., Roszkowska, M., Gawlak, M. & Mioduchowska, M. Integrative redescription of the Minibiotus intermedius (Plate, 1888)—The type species of the genus Minibiotus R.O. Schuster, 1980. Diversity 14, 356. https://doi.org/10.3390/d14050356 (2022).Article 
CAS 

Google Scholar 
Londoño, R., Daza, A., Lisi, O. & Quiroga, S. New species of waterbear Minibiotus pentannulatus (Tardigrada: Macrobiotidae) from Colombia. Rev. Mex. Biodivers. 88, 807–814. https://doi.org/10.1016/j.rmb.2017.10.040 (2017).Article 

Google Scholar 
Vecchi, M. et al. Macrobiotus naginae sp. nov., a new Xerophilous Tardigrade species from Rokua Sand Dunes (Finland). Zool. Stud. 61, e22 (2022).
Google Scholar 
Stec, D., Dudziak, M. & Michalczyk, Ł. Integrative descriptions of two new Macrobiotidae species (Tardigrada: Eutardigrada: Macrobiotoidea) from French Guiana and Malaysian Borneo. Zool. Stud. 59, e23 (2020).
Google Scholar 
Stec, D., Roszkowska, M., Kaczmarek, Ł & Michalczyk, Ł. Paramacrobiotus lachowskae, a new species of Tardigrada from Colombia (Eutardigrada: Parachela: Macrobiotidae). N. Z. J. Zool. 45, 43–60. https://doi.org/10.1080/03014223.2017.1354896 (2018).Article 

Google Scholar 
Sugiura, K., Matsumoto, M. & Kunieda, T. Description of a model tardigrade Paramacrobiotus metropolitanus sp. nov. (Eutardigrada) from Japan with a summary of its life history, reproduction and genomics. Zootaxa 5134, 92–112. https://doi.org/10.11646/zootaxa.5134.1.4 (2022).Article 

Google Scholar 
Tumanov, D. V. Three new species of Macrobiotus (Eutardigrada, Macrobiotidae, tenuis-group) from Tien Shan (Kirghizia) and Spitsbergen. J. Limnol. 66, 40. https://doi.org/10.4081/jlimnol.2007.s1.40 (2007).Article 

Google Scholar 
Zawierucha, K., Kolicka, M. & Kaczmarek, Ł. Re-description of the Arctic tardigrade Tenuibiotus voronkovi (Tumanov, 2007 (Eutardigrada; Macrobiotidea), with the first molecular data for the genus. Zootaxa 4196, 498. https://doi.org/10.11646/zootaxa.4196.4.2 (2016).Article 

Google Scholar 
Stec, D., Tumanov, D. T. & Kristensen, R. M. Integrative taxonomy identifies two new tardigrade species (Eutardigrada: Macrobiotidae) from Greenland. EJT 614, 1–40. https://doi.org/10.5852/ejt.2020.614 (2020).Article 

Google Scholar 
Fontaneto, D., Flot, J.-F. & Tang, C. Q. Guidelines for DNA taxonomy, with a focus on the meiofauna. Mar. Biodiv. 45, 433–451. https://doi.org/10.1007/s12526-015-0319-7 (2015).Article 

Google Scholar 
Zhang, J., Kapli, P., Pavlidis, P. & Stamatakis, A. A general species delimitation method with applications to phylogenetic placements. Bioinformatics 29, 2869–2876. https://doi.org/10.1093/bioinformatics/btt499 (2013).Article 
CAS 

Google Scholar 
Puillandre, N., Brouillet, S. & Achaz, G. ASAP: Assemble species by automatic partitioning. Mol. Ecol. Resour. 21, 609–620. https://doi.org/10.1111/1755-0998.13281 (2021).Article 

Google Scholar 
Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T. & Calcott, B. PartitionFinder 2: New methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. 34(3), 772–773. https://doi.org/10.1093/molbev/msw260 (2017).Article 
CAS 

Google Scholar 
Roszkowska, M., Stec, D., Gawlak, M. & Kaczmarek, Ł. An integrative description of a new tardigrade species Mesobiotus romani sp. nov. (Macrobiotidae: harmsworthi group) from the Ecuadorian Pacific coast. Zootaxa 4450, 550–564. https://doi.org/10.11646/zootaxa.4450.5.2 (2018).Article 

Google Scholar 
Pilato, G. & Binda, M. G. Definition of families, subfamilies, genera and subgenera of the Eutardigrada, and keys to their identification. Zootaxa 2404, 1–54. https://doi.org/10.11646/zootaxa.2404.1.1 (2010).Article 

Google Scholar 
Kaczmarek, Ł & Michalczyk, Ł. The Macrobiotus hufelandi group (Tardigrada) revisited. Zootaxa 4363, 101–123. https://doi.org/10.11646/zootaxa.4363.1.4 (2017).Article 

Google Scholar 
Michalczyk, Ł & Kaczmarek, Ł. A description of the new tardigrade Macrobiotus reinhardti (Eutardigrada: Macrobiotidae, harmsworthi group) with some remarks on the oral cavity armature within the genus Macrobiotus Schultze. Zootaxa 331, 1–24. https://doi.org/10.11646/zootaxa.331.1.1 (2003).Article 

Google Scholar 
Kaczmarek, Ł, Cytan, J., Zawierucha, K., Diduszko, D. & Michalczyk, Ł. Tardigrades from Peru (South America), with descriptions of three new species of Parachela. Zootaxa 3790, 357–379. https://doi.org/10.11646/zootaxa.3790.2.5 (2014).Article 

Google Scholar 
Kiosya, Y., Pogwizd, J., Matsko, Y., Vecchi, M. & Stec, D. Phylogenetic position of two Macrobiotus species with a revisional note on Macrobiotus sottilei Pilato, Kiosya, Lisi & Sabella, 2012 (Tardigrada: Eutardigrada: Macrobiotidae). Zootaxa 4933, 113–135. https://doi.org/10.11646/zootaxa.4933.1.5 (2021).Article 

Google Scholar 
Pilato, G. Analisi di nuovi caratteri nello studio degli Eutardigradi. Animalia 8, 51–57 (1981).
Google Scholar 
Michalczyk, Ł & Kaczmarek, Ł. The Tardigrada Register: a comprehensive online data repository for tardigrade taxonomy. J. Limnol. 72, e22. https://doi.org/10.4081/jlimnol.2013.s1.e22 (2013).Article 

Google Scholar 
Bertolani, R. et al. Phylogeny of Eutardigrada: New molecular data and their morphological support lead to the identification of new evolutionary lineages. Mol. Phylogenet. Evol. 76, 110–126. https://doi.org/10.1016/j.ympev.2014.03.006 (2014).Article 

Google Scholar 
Perry, E., Miller, W. R. & Kaczmarek, Ł. Recommended abbreviations for the names of genera of the phylum Tardigrada. Zootaxa 4608, 145. https://doi.org/10.11646/zootaxa.4608.1.8 (2019).Article 

Google Scholar 
Degma, P., Michalczyk, Ł & Kaczmarek, Ł. Macrobiotus derkai, a new species of Tardigrada (Eutardigrada, Macrobiotidae, huziori group) from the Colombian Andes (South America). Zootaxa 1731, 1–23. https://doi.org/10.11646/zootaxa.1731.1.1 (2008).Article 

Google Scholar 
Kaczmarek, Ł, Michalczyk, Ł & Diduszko, D. Some tardigrades from Siberia (Russia, Baikal region) with a description of Macrobiotus garynahi sp. nov. (Eutardigrada: Macrobiotidae: richtersi group). Zootaxa 1053, 35–45. https://doi.org/10.11646/zootaxa.1053.1.3 (2005).Article 

Google Scholar 
Michalczyk, Ł & Kaczmarek, Ł. Macrobiotus huziori, a new species of Tardigrada (Eutardigrada: Macrobiotidae) from Costa Rica (Central America). Zootaxa 1169, 47–59. https://doi.org/10.11646/zootaxa.1169.1.3 (2006).Article 

Google Scholar 
Michalczyk, L. & Kaczmarek, L. A new species Macrobiotus magdalenae (Tardigrada: Eutardigrada: Macrobiotidae, richtersi group) from Costa Rican rain forest (Central America). N. Z. J. Zool. 33, 189–196. https://doi.org/10.1080/03014223.2006.9518444 (2006).Article 

Google Scholar 
Michalczyk, Ł, Kaczmarek, Ł & Węglarska, B. Macrobiotus sklodowskae sp. nov. (Tardigrada: Eutardigrada: Macrobiotidae, richtersi group) from Cyprus. Zootaxa 1371, 45–56. https://doi.org/10.11646/zootaxa.1371.1.4 (2006).Article 

Google Scholar 
Tumanov, D. V. Notes on the Tardigrada of Thailand, with a description of Macrobiotus alekseevi sp. nov. (Eutardigrada, Macrobiotidae). Zootaxa 999, 1–6. https://doi.org/10.11646/zootaxa.999.1.1 (2005).Article 

Google Scholar 
Doyère, M. Memoire sur les tardigrades. Ann. Sci. Nat Zool. Ser. 2, 269–362 (1840).
Google Scholar 
Richters, F. Tardigrada. In Handbuch der Zoologie Vol. 3 (eds Kükenthal, W. & Krumbach, T.) 58–61 (Walter de Gruyter & Co., Berlin and Leipzig, 1926).
Google Scholar 
Stec, D., Cancellario, T. & Fontaneto, D. Diversification rates in Tardigrada indicate a decreasing tempo of lineage splitting regardless of reproductive mode. Org. Divers. Evol. 22(4), 965–974. https://doi.org/10.1007/s13127-022-00578-4 (2022).Article 

Google Scholar 
Dellicour, S. & Flot, J.-F. The hitchhiker’s guide to single-locus species delimitation. Mol. Ecol. Resour. 18, 1234–1246. https://doi.org/10.1111/1755-0998.12908 (2018).Article 

Google Scholar 
Magoga, G., Fontaneto, D. & Montagna, M. Factors affecting the efficiency of molecular species delimitation in a species-rich insect family. Mol. Ecol. Resour. 21, 1475–1489. https://doi.org/10.1111/1755-0998.13352 (2021).Article 
CAS 

Google Scholar 
Kaczmarek, Ł, Michalczyk, Ł & McInnes, S. J. Annotated zoogeography of non-marine Tardigrada. Part I: Central America. Zootaxa 3763, 1–62. https://doi.org/10.11646/zootaxa.3763.1.1 (2014).Article 

Google Scholar 
Kaczmarek, Ł, Michalczyk, Ł & McInnes, S. J. Annotated zoogeography of non-marine Tardigrada. Part II: South America. Zootaxa 3923, 1–107. https://doi.org/10.11646/zootaxa.3923.1.1 (2015).Article 

Google Scholar 
Kaczmarek, Ł, Michalczyk, Ł & Mcinnes, S. J. Annotated zoogeography of non-marine Tardigrada. Part III: North America and Greenland. Zootaxa 4203, 1–249. https://doi.org/10.11646/zootaxa.4203.1.1 (2016).Article 

Google Scholar 
Mcinnes, S. J., Michalczyk, Ł & Kaczmarek, Ł. Annotated zoogeography of non-marine Tardigrada. Part IV: Africa. Zootaxa 4284, 1. https://doi.org/10.11646/zootaxa.4284.1.1 (2017).Article 

Google Scholar 
Michalczyk, Ł, Kaczmarek, Ł & Mcinnes, S. J. Annotated zoogeography of non-marine Tardigrada. Part V: Australasia. Zootaxa 5107, 1–119. https://doi.org/10.11646/zootaxa.5107.1.1 (2022).Article 

Google Scholar 
Pilato, G., Claxton, S. & Binda, M. G. Tardigrades from Australia. III. Echiniscus marcusi and Macrobiotus peteri, new species of tardigrades from New South Wales. Animalia 16, 43–48 (1989).
Google Scholar 
Pilato, G., Binda, M. G. & Lisi, O. Eutardigrada from New Zealand, with descriptions of two new species. N. Z. J. Zool. 33, 49–63. https://doi.org/10.1080/03014223.2006.9518430 (2006).Article 

Google Scholar 
Bartels, P. J., Pilato, G., Lisi, O. & Nelson, D. R. Macrobiotus (Eutardigrada, Macrobiotidae) from the Great Smoky Mountains National Park, Tennessee/North Carolina, USA (North America): Two new species and six new records. Zootaxa 2022, 45–57. https://doi.org/10.11646/zootaxa.2022.1.4 (2009).Article 

Google Scholar 
Binda, M. G., Pilato, G., Moncada, E. & Napolitano, A. Some tardigrades from Central Africa with the description of two new species: Macrobiotus ragonesei and M. priviterae (Eutardigrada Macrobiotidae). Trop. Zool. 14, 233–242. https://doi.org/10.1080/03946975.2001.10531155 (2001).Article 

Google Scholar 
Pilato, G., Binda, M. G. & Lissi, O. Notes on tardigrades of the Seychelles with the description of two new species. Ital. J. Zool. 71, 171–178 (2004).Article 

Google Scholar 
Pilato, G., Binda, M. G. & Lisi, O. Three new species of eutardigrades from the Seychelles. N. Z. J. Zool. 33, 39–48. https://doi.org/10.1080/03014223.2006.9518429 (2006).Article 

Google Scholar 
Pilato, G., Binda, M. G. & Lisi, O. Notes on tardigrades of the Seychelles with the description of three new species. Ital. J. Zool. 71, 171–178. https://doi.org/10.1080/11250000409356569 (2004).Article 

Google Scholar 
Pilato, G., Binda, M. G. & Catanzaro, R. Remarks on some tardigrades of the African fauna with the description of three new species of Macrobiotus Schultze 1834. Trop. Zool. 4, 167–178. https://doi.org/10.1080/03946975.1991.10539487 (1991).Article 

Google Scholar 
Maucci, W. & Durante Pasa, M. V. Tardigradi muscicoli delle Isole Andamane. Boll. Mus. Civ. St. Nat. Verona 7, 281–291 (1980).
Google Scholar 
Iharos, G. Neuere Daten zur Kenntnis der Tardigraden-Fauna von Neuguinea. Opusc. Zool. Bp. 11, 65–73 (1973).
Google Scholar 
Binda, M. G. & Pilato, G. Macrobiotus savai and Macrobiotus humilis, two new species of tardigrades from Sri Lanka. Boll. Accad. Gioenia Sci. Nat. Catania 34, 101–111 (2001).
Google Scholar 
Pilato, G. Macrobiotus centesimus, new species of eutardigrade from the South America. Boll. Accad. Gioenia Sci. Nat. Catania 33, 97–101 (2000).
Google Scholar 
Daza, A., Caicedo, M., Lisi, O. & Quiroga, S. New records of tardigrades from Colombia with the description of Paramacrobiotus sagani sp. nov. and Doryphoribius rosanae sp. nov. Zootaxa 4362, 29–50. https://doi.org/10.11646/zootaxa.4362.1.2 (2017).Article 

Google Scholar 
Claps, M. C. & Rossi, G. C. Tardígrados de Uruguay, con descripción de dos nuevas especies (Echiniscidae, Macrobiotidae). Iheringia Sér. Zool. 83, 17–22 (1997).
Google Scholar 
Iharos, G. Neue tardigraden-arten aus ungarn (neuere beitrage zur kenntnis der tardigraden-fauna ungarns. 6.). Acta Zool. Acad. Sci. Hung. 12(1–2), 111 (1966).
Google Scholar 
Pilato, G., Kiosya, Y., Lisi, O. & Sabella, G. New records of Eutardigrada from Belarus with the description of three new species. Zootaxa 3179, 39–60. https://doi.org/10.11646/zootaxa.3179.1.2 (2012).Article 

Google Scholar 
Pasa, D. & Maucci, W. Moss Tardigrada from the Scandinavian Peninsula. in Second International Symposium on Tardigrada, Vol. 79(25). 47–85 (1979).Lisi, O., Binda, M. G. & Pilato, G. Eremobiotus ginevrae sp. nov. and Paramacrobiotus pius sp. nov., two new species of Eutardigrada. Zootaxa 4103, 344–360. https://doi.org/10.11646/zootaxa.4103.4.3 (2016).Article 

Google Scholar 
Biserov, V. I. Macrobiotus lorenae sp. n., a new species of Tardigrada (Eutardigrada Macrobiotidae) from the Russian Far East. Arthr Sel. 5, 145–149 (1996).
Google Scholar 
Biserov, V. I. Tardigrades of the Caucasus with a taxonomic analysis of genus Ramazzottius. Zool. Anz. 236, 139–159 (1997).
Google Scholar 
Morek, W. et al. Redescription of Milnesium alpigenum Ehrenberg, 1853 (Tardigrada: Apochela) and a description of Milnesium inceptum sp. nov., a tardigrade laboratory model. Zootaxa 4586(1), 35. https://doi.org/10.11646/zootaxa.4586.1.2 (2019).Article 

Google Scholar 
Morek, W., Surmacz, B., López-López, A. & Michalczyk, Ł. “Everything is not everywhere”: Time-calibrated phylogeography of the genus Milnesium (Tardigrada). Mol. Ecol. 30, 3590–3609. https://doi.org/10.1111/mec.15951 (2021).Article 

Google Scholar 
Mogle, M. J., Kimball, S. A., Miller, W. R. & McKown, R. D. Evidence of avian-mediated long-distance dispersal in American tardigrades. PeerJ 6, e5035. https://doi.org/10.7717/peerj.5035 (2018).Article 

Google Scholar 
Vuori, T., Calhim, S. & Vecchi, M. A lift in snail’s gut provides an efficient colonization route for tardigrades. Ecology 103, e3702. https://doi.org/10.1002/ecy.3702 (2022).Article 

Google Scholar 
Książkiewicz, Z. & Roszkowska, M. Experimental evidence for snails dispersing tardigrades based on Milnesium inceptum and Cepaea nemoralis species. Sci. Rep. 12(4421), 1–10. https://doi.org/10.1038/s41598-022-08265-2 (2022).Article 
ADS 
CAS 

Google Scholar  More

Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 

Google Scholar 
Spehn, E. M., Rudmann-Maurer, K. & Körner, C. Mountain biodiversity. Plant Ecol. Divers. 4, 301–302 (2011).
Google Scholar 
Körner, C. et al. A global inventory of mountains for bio-geographical applications. Alp Bot. 127, 1–15 (2017).
Google Scholar 
Hoorn, C. et al. (eds) Mountains, Climate and Biodiversity (Wiley, 2018).
Google Scholar 
Huang, S., Meijers, M. J. M., Eyres, A., Mulch, A. & Fritz, S. A. Unravelling the history of biodiversity in mountain ranges through integrating geology and biogeography. J. Biogeogr. 46, 1777–1791 (2019).
Google Scholar 
Perrigo, A., Hoorn, C. & Antonelli, A. Why mountains matter for biodiversity. J. Biogeogr. 47, 315–325 (2020).
Google Scholar 
Rahbek, C. et al. Humboldt’s enigma: What causes global patterns of mountain biodiversity?. Science 365, 1108–1113 (2019).ADS 
CAS 

Google Scholar 
Antonelli, A. et al. Amazonia is the primary source of Neotropical biodiversity. Proc. Natl. Acad. Sci. USA 115, 6034–6039 (2018).ADS 
CAS 

Google Scholar 
Merckx, V. S. F. T. et al. Evolution of endemism on a young tropical mountain. Nature 524, 347–350 (2015).ADS 
CAS 

Google Scholar 
Fjeldsa, J., Bowie, R. C. K. & Rahbek, C. The role of mountain ranges in the diversification of birds. Annu. Rev. Ecol. Evol. Syst. 43, 249–265 (2012).
Google Scholar 
Badgley, C. et al. Biodiversity and topographic complexity: Modern and geohistorical perspectives. Trends Ecol. Evol. 32, 211–226 (2017).
Google Scholar 
Körner, C. Mountain biodiversity, its causes and function. Ambio 33, 11 (2004).
Google Scholar 
Antonelli, A. et al. An engine for global plant diversity: Highest evolutionary turnover and emigration in the American tropics. Front. Genet. 6, 130 (2015).
Google Scholar 
Chazot, N. et al. Into the Andes: Multiple independent colonizations drive montane diversity in the Neotropical clearwing butterflies Godyridina. Mol. Ecol. 25, 5765–5784 (2016).
Google Scholar 
Stebbins, G. L. Flowering Plants: Evolution Above the Species Level (Belknap Press of Harvard University Press, 1974).
Google Scholar 
Rangel, T. F. et al. Modeling the ecology and evolution of biodiversity: Biogeographical cradles, museums, and graves. Science 361, 5452 (2018).
Google Scholar 
Chapman, F. M. The relationships and distribution of the warblers of the genus Compsothlypis: A contribution to the study of the origin of Andean bird life. Auk 42(2), 193–208 (1925).
Google Scholar 
Endler, J. A. Geographic variation, speciation, and clines. Genet. Res. 1, b1–b3 (1978).
Google Scholar 
Baert, L. & Maelfait, J. P. A contribution to the knowledge of the spider fauna of Galápagos (Ecuador). Bull. Koninklijk Belg. Instit. Nat. Entomol. 56, 93–123 (1986).
Google Scholar 
Desender, K., Baert, L. & Maelfait, J. P. Distribution and speciation of carabid beetles in the Galápagos Archipelago (Ecuador). Bull. Inst. R. Sci. Natl. Belg. 62, 57–65 (1992).
Google Scholar 
Patton, J. L. & Smith, M. F. mtDNA phylogeny of Andean mice: A test of diversification across ecological gradients. Evolution 46, 174 (1992).CAS 

Google Scholar 
Nevado, B., Contreras-Ortiz, N., Hughes, C. & Filatov, D. A. Pleistocene glacial cycles drive isolation, gene flow and speciation in the high-elevation Andes. New Phytol 219, 779–793 (2018).
Google Scholar 
Winger, B. M. & Bates, J. M. The tempo of trait divergence in geographic isolation: Avian speciation across the Marañon Valley of Peru. Evolution 69, 772–787 (2015).
Google Scholar 
Hazzi, N. A., Moreno, J. S., Ortiz-Movliav, C. & Palacio, R. D. Biogeographic regions and events of isolation and diversification of the endemic biota of the tropical Andes. Proc. Natl. Acad. Sci. USA. 115, 7985–7990 (2018).ADS 
CAS 

Google Scholar 
Palma, R. E., Marquet, P. A. & Boric-Bargetto, D. Inter-and intraspecific phylogeography of small mammals in the Atacama Desert and adjacent areas of northern Chile. J. Biogeogr. 32(11), 1931–1941 (2005).
Google Scholar 
Beckman, E. J. & Witt, C. C. Phylogeny and biogeography of the New World siskins and goldfinches: Rapid, recent diversification in the Central Andes. Mol. Phylogenet. Evol. 87, 28–45 (2015).
Google Scholar 
Drummond, C. S., Eastwood, R. J., Miotto, S. T. S. & Hughes, C. E. Multiple continental radiations and correlates of diversification in Lupinus (Leguminosae): Testing for key innovation with incomplete taxon sampling. Syst. Biol. 61, 443–460 (2012).
Google Scholar 
Hutter, C. R., Lambert, S. M. & Wiens, J. J. Rapid diversification and time explain amphibian richness at different scales in the tropical Andes, Earth’s most biodiverse hotspot. Am. Nat. 190, 828–843 (2017).
Google Scholar 
Toussaint, E. F. A. et al. Flight over the Proto-Caribbean seaway: Phylogeny and macroevolution of Neotropical Anaeini leafwing butterflies. Mol. Phylogenet. Evol. 137, 86–103 (2019).
Google Scholar 
Acevedo, A. A. Historical biogeography, phylogenetic diversity and evolution of body size in Pristimantis, the world’s most diverse amphibian genus. Doctoral thesis, Fac. Ciencias Biológicas, Pontificia Universidad Católica de Chile (2021).Lagomarsino, L. P., Condamine, F. L., Antonelli, A., Mulch, A. & Davis, C. C. The abiotic and biotic drivers of rapid diversification in A ndean bellflowers (Campanulaceae). New Phytol. 210, 1430–1442 (2016).
Google Scholar 
Fjeldsa, J. & Rahbek, C. Diversification of tanagers, a species rich bird group, from lowlands to montane regions of South America. Integr. Comp. Biol. 46(1), 72–81 (2006).CAS 

Google Scholar 
Struwe, L., Haag, S., Heiberg, E. & Grant, J. R. Andean speciation and vicariance in Neotropical Macrocarpaea (Gentianaceae-Helieae). Ann. Mol. Bot. Gard. 96, 450–469 (2009).
Google Scholar 
Hutter, C. R., Guayasamin, J. M. & Wiens, J. J. Explaining Andean megadiversity: The evolutionary and ecological causes of glassfrog elevational richness patterns. Ecol. Lett. 16, 1135–1144 (2013).
Google Scholar 
Santos, J. C. et al. Amazonian amphibian diversity is primarily derived from late Miocene Andean lineages. PLoS Biol. 7, e1000056 (2009).
Google Scholar 
Luebert, F. & Weigend, M. Phylogenetic insights into Andean plant diversification. Front. Ecol. Evol. 2, 27 (2014).
Google Scholar 
Chazot, N. et al. Renewed diversification following Miocene landscape turnover in a Neotropical butterfly radiation. Glob. Ecol. Biogeogr. 28, 1118–1132 (2019).
Google Scholar 
Esquerré, D., Brennan, I. G., Catullo, R. A., Torres-Pérez, F. & Keogh, J. S. How mountains shape biodiversity: The role of the Andes in biogeography, diversification, and reproductive biology in South America’s most species-rich lizard radiation (Squamata: Liolaemidae). Evolution 73, 214–230 (2019).
Google Scholar 
Garzione, C. N. et al. Rise of the Andes. Science 320, 1304–1307 (2008).ADS 
CAS 

Google Scholar 
Hoorn, C. et al. Amazonia through time: Andean uplift, climate change, landscape evolution, and biodiversity. Science 330, 927–931 (2010).ADS 
CAS 

Google Scholar 
Brumfield, R. T. & Edwards, S. V. Evolution into and out of the Andes: A Bayesian analysis of historical diversification in Thamnophilus antshrikes. Evolution 61, 346–367 (2007).CAS 

Google Scholar 
Pennington, R. T. et al. Contrasting plant diversification histories within the Andean biodiversity hotspot. Proc. Natl. Acad. Sci. USA. 107, 13783–13787 (2010).ADS 
CAS 

Google Scholar 
Antonelli, A. & Sanmartín, I. Why are there so many plant species in the Neotropics?. Taxon 60, 403–414 (2011).
Google Scholar 
Hughes, C. & Eastwood, R. Island radiation on a continental scale: Exceptional rates of plant diversification after uplift of the Andes. Proc. Natl. Acad. Sci. USA. 103, 10334–10339 (2006).ADS 
CAS 

Google Scholar 
Madriñán, S., Cortés, A. J. & Richardson, J. E. Páramo is the world’s fastest evolving and coolest biodiversity hotspot. Front. Genet. 4, 192 (2013).
Google Scholar 
Upham, N. S., Ojala-Barbour, R., Brito, M. J., Velazco, P. M. & Patterson, B. D. Transitions between Andean and Amazonian centers of endemism in the radiation of some arboreal rodents. BMC Evol. Biol. 13, 191 (2013).
Google Scholar 
Hughes, C. E. & Atchison, G. W. The ubiquity of alpine plant radiations: From the Andes to the Hengduan mountains. New Phytol. 207, 275–282 (2015).
Google Scholar 
Givnish, T. J. et al. Adaptive radiation, correlated and contingent evolution, and net species diversification in Bromeliaceae. Mol. Phylogenet. Evol. 71, 55–78 (2014).
Google Scholar 
Horton, B. K. Sedimentary record of Andean Mountain building. Earth Sci. Rev. 178, 279–309 (2018).ADS 
CAS 

Google Scholar 
Gianni, G. M. et al. Northward propagation of Andean genesis: Insights from Early Cretaceous synorogenic deposits in the Aysén-Río Mayo basin. Gondwana Res. 77, 238–259 (2020).ADS 
CAS 

Google Scholar 
Boschman, L. M. Andean Mountain building since the Late Cretaceous: A paleoelevation reconstruction. Earth Sci. Rev. 220, 103640 (2021).
Google Scholar 
Gentry, A. H. Patterns of neotropical plant species diversity. Evol. Biol. 15, 1–84 (1982).
Google Scholar 
Gregory-Wodzicki, K. M. Uplift history of the Central and Northern Andes: A review. Geol. Soc. Am. Bull. 112, 1091–1105 (2000).ADS 

Google Scholar 
Pérez-Escobar, O. A. et al. Recent origin and rapid speciation of Neotropical orchids in the world’s richest plant biodiversity hotspot. New Phytol. 215(2), 891–905 (2017).
Google Scholar 
Alhajeri, B. H., Schenk, J. J. & Steppan, S. J. Ecomorphological diversification following continental colonization in muroid rodents (Rodentia: Muroidea). Biol. J. Linn. Soc. 117, 463–481 (2016).
Google Scholar 
Burgin, C. J., Colella, J. P., Kahn, P. L. & Upham, N. S. How many species of mammals are there?. J. Mammal. 99, 1–14 (2018).
Google Scholar 
Parada, A., Pardiñas, U. F. J., Salazar-Bravo, J., D’Elía, G. & Palma, R. E. Dating an impressive Neotropical radiation: Molecular time estimates for the Sigmodontinae (Rodentia) provide insights into its historical biogeography. Mol. Phylogenet. Evol. 66, 960–968 (2013).
Google Scholar 
Schenk, J. J. & Steppan, S. J. The role of geography in adaptive radiation. Am. Nat. 192, 415–431 (2018).
Google Scholar 
Pardiñas, U. F. J. et al. Morphological disparity in a hyperdiverse mammal clade: A new morphotype and tribe of Neotropical cricetids. Zool. J. Linn. Soc. 196, 1013–1038 (2022).
Google Scholar 
Reig, O. A. Distribuição geográfica e história evolutiva dos roedores muroideos sulamericanos (Cricetidae: Sigmodontinae). Rev. Bras. Genét. 7, 333–365 (1984).
Google Scholar 
Reig, O. A. Diversity Patterns and Differentiation of High Andean Rodents. High Altitude Tropical Biogeography 404–438 (Oxford University Press, 1986).
Google Scholar 
Maestri, R., Upham, N. S. & Patterson, B. D. Tracing the diversification history of a Neogene rodent invasion into South America. Ecography 42, 683–695 (2019).
Google Scholar 
Engel, S. R., Hogan, K. M., Taylor, J. F. & Davis, S. K. Molecular systematics and paleobiogeography of the South American sigmodontine rodents. Mol. Biol. Evol. 15(1), 35–49 (1998).CAS 

Google Scholar 
Parada, A., D’Elía, G. & Palma, R. E. The influence of ecological and geographical context in the radiation of Neotropical sigmodontine rodents. BMC Evol. Biol. 15(1), 1–17 (2015).
Google Scholar 
Leite, R. N. et al. In the wake of invasion: Tracing the historical biogeography of the South American cricetid radiation (Rodentia, Sigmodontinae). PLoS ONE 9, e100687 (2014).ADS 

Google Scholar 
Vilela, J. F., Mello, B., Voloch, C. M. & Schrago, C. G. Sigmodontine rodents diversified in South America prior to the complete rise of the Panamanian Isthmus. J. Zool. Syst. Evol. Res. 52, 249–256 (2014).
Google Scholar 
Ronez, C., Martin, R. A., Kelly, T. S., Barbière, F. & Pardiñas, U. F. J. A brief critical review of sigmodontine rodent origins, with emphasis on paleontological data. Mastozool. Neotrop 28, 001–026 (2021).
Google Scholar 
Maestri, R. & Patterson, B. D. Patterns of species richness and turnover for the South American Rodent Fauna. PLoS ONE 11, e0151895 (2016).
Google Scholar 
Smith, M. F. & Patton, J. L. Phylogenetic relationships and the radiation of sigmodontine rodents in South America: Evidence from cytochrome b. J. Mamm. Evol. 6(2), 89–128 (1999).
Google Scholar 
Udvardy, M. D. & Udvardy, M. D. F. A Classification of the Biogeographical Provinces of the World Vol. 8 (International Union for Conservation of Nature and Natural Resources, 1975).
Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on earth. Bioscience 51, 933 (2001).
Google Scholar 
Kreft, H. & Jetz, W. A framework for delineating biogeographical regions based on species distributions: Global quantitative biogeographical regionalizations. J. Biogeogr. 37, 2029–2053 (2010).
Google Scholar 
Patton, J. L. et al. (eds) Mammals of South America, Volume 2: Rodents (University of Chicago Press, 2015).
Google Scholar 
Marsh, C. J. et al. Expert range maps of global mammal distributions harmonised to three taxonomic authorities. J. Biogeogr. 49, 979–992 (2022).
Google Scholar 
Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 

Google Scholar 
Tamura, K., Stecher, G. & Kumar, S. MEGA11: Molecular evolutionary genetics analysis version 11. Mol. Biol. Evol. 38, 3022–3027 (2021).CAS 

Google Scholar 
Parada, A., Hanson, J. & D’Eiía, G. Ultraconserved elements improve the resolution of difficult nodes within the rapid radiation of neotropical Sigmodontine Rodents (Cricetidae: Sigmodontinae). Syst. Biol. 70, 1090–1100 (2021).
Google Scholar 
Steppan, S. J. & Schenk, J. J. Muroid rodent phylogenetics: 900-species tree reveals increasing diversification rates. PLoS ONE 12, e0183070 (2017).
Google Scholar 
Gonçalves, P. R. et al. Unraveling deep branches of the Sigmodontinae Tree (Rodentia: Cricetidae) in Eastern South America. J Mammal Evol 27, 139–160 (2020).
Google Scholar 
Steppan, S. J., Adkins, R. M. & Anderson, J. Phylogeny and divergence-date estimates of rapid radiations in muroid rodents based on multiple nuclear genes. Syst. Biol. 53, 533–553 (2004).
Google Scholar 
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 

Google Scholar 
Chernomor, O., von Haeseler, A. & Minh, B. Q. Terrace aware data structure for phylogenomic inference from supermatrices. Syst. Biol. 65, 997–1008 (2016).
Google Scholar 
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).CAS 

Google Scholar 
Bouckaert, R. et al. BEAST 2: A software platform for bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537 (2014).
Google Scholar 
Gavryushkina, A., Welch, D., Stadler, T. & Drummond, A. J. Bayesian inference of sampled ancestor trees for epidemiology and fossil calibration. PLoS Comput. Biol. 10, e1003919 (2014).ADS 

Google Scholar 
Heath, T. A., & Moore, B. R. Bayesian inference of species divergence times. Bayesian phylogenetics: Methods, algorithms, and applications, 277–318 (2014).Douglas, J., Zhang, R. & Bouckaert, R. Adaptive dating and fast proposals: Revisiting the phylogenetic relaxed clock model. PLoS Comput Biol 17, e1008322 (2021).ADS 
CAS 

Google Scholar 
Bouckaert, R. R. & Drummond, A. J. bModelTest: Bayesian phylogenetic site model averaging and model comparison. BMC Evol. Biol. 17, 42 (2017).
Google Scholar 
Barido-Sottani, J., Aguirre-Fernández, G., Hopkins, M. J., Stadler, T. & Warnock, R. Ignoring stratigraphic age uncertainty leads to erroneous estimates of species divergence times under the fossilized birth–death process. Proc. R. Soc. B. 286, 20190685 (2019).
Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 

Google Scholar 
Maliet, O., Hartig, F. & Morlon, H. A model with many small shifts for estimating species-specific diversification rates. Nat. Ecol. Evol. 3, 1086–1092 (2019).
Google Scholar 
Maliet, O. & Morlon, H. Fast and accurate estimation of species-specific diversification rates using data augmentation. Syst. Biol. 71, 353–366 (2022).CAS 

Google Scholar 
Gelman, A. & Rubin, D. B. A single series from the Gibbs sampler provides a false sense of security. Bayesian Stat. 4(1), 625–631 (1992).
Google Scholar 
Rabosky, D. L. et al. BAMMtools: An R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods Ecol. Evol. 5, 701–707 (2014).
Google Scholar 
Ree, R. H., Moore, B. R., Webb, C. O. & Donoghue, M. J. A likelihood framework for inferring the evolution of geographic range on phylogenetic trees. Evolution 59, 2299–2311 (2005).
Google Scholar 
Ree, R. H. & Sanmartín, I. Conceptual and statistical problems with the DEC +J model of founder-event speciation and its comparison with DEC via model selection. J. Biogeogr. 45, 741–749 (2018).
Google Scholar 
Matzke, N. J. Model selection in historical biogeography reveals that founder-event speciation is a crucial process in Island Clades. Syst. Biol. 63, 951–970 (2014).
Google Scholar 
Matzke, N. J. Statistical comparison of DEC and DEC +J is identical to comparison of two ClaSSE submodels, and is therefore valid. J. Biogeogr. 49, 1805–1824 (2022).
Google Scholar 
Matzke, N. J. Probabilistic Historical Biogeography: New Models for Founder-Event Speciation, Imperfect Detection, and Fossils Allow Improved Accuracy and Model-Testing (University of California, 2013).
Google Scholar 
Tripp, E. A. & McDade, L. A. A rich fossil record yields calibrated phylogeny for acanthaceae (lamiales) and evidence for marked biases in timing and directionality of intercontinental disjunctions. Syst. Biol. 63, 660–684 (2014).
Google Scholar 
Matos-Maraví, P. et al. Mesoamerica is a cradle and the Atlantic Forest is a museum of Neotropical butterfly diversity: Insights from the evolution and biogeography of Brassolini (Lepidoptera: Nymphalidae). Biol. J. Lin. Soc. 133, 704–724 (2021).
Google Scholar 
Caetano, D. S., O’Meara, B. C. & Beaulieu, J. M. Hidden state models improve state-dependent diversification approaches, including biogeographical models. Evolution 72, 2308–2324 (2018).
Google Scholar 
Beaulieu, J. M. & O’Meara, B. C. Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. Syst. Biol. 65, 583–601 (2016).
Google Scholar 
Schenk, J. J., Rowe, K. C. & Steppan, S. J. Ecological opportunity and incumbency in the diversification of repeated continental colonizations by muroid rodents. Syst. Biol. 62(6), 837–864 (2013).
Google Scholar 
Percequillo, A. R. et al. Tempo and mode of evolution of oryzomyine rodents (Rodentia, Cricetidae, Sigmodontinae): A phylogenomic approach. Mol. Phylogenet. Evol. 159, 107120 (2021).
Google Scholar 
Pacheco, V. R., Patton, J. L. & D’elía, G. Tribe Thomasomyini Steadman and Ray, 1982. In Mammals of South America Vol. 2 (eds Patton, J. L. et al.) 571–574 (The University of Chicago Press, 2015).
Google Scholar 
Salazar-Bravo, J., Pardiñas, U. F., Zeballos, H., & Teta, P. Description of a new tribe of sigmodontine rodents (Cricetidae: Sigmodontinae) with an updated summary of valid tribes and their generic contents. Museum of Texas Tech University 338 (2016).Pardiñas, U. F. et al. Morphological disparity in a hyperdiverse mammal clade: A new morphotype and tribe of Neotropical cricetids. Zool. J. Linnean Soc. 196, 1013–1038 (2022).
Google Scholar 
Pardiñas, U. F., Voglino, D. & Galliari, C. A. Miscellany on Bibimys (Rodentia, Sigmodontinae), a unique akodontine cricetid. Mastozool. Neotrop. 24(1), 241–250 (2017).
Google Scholar 
Salazar-Bravo, J., Pardiñas, U. F. J. & D’Elía, G. A phylogenetic appraisal of Sigmodontinae (Rodentia, Cricetidae) with emphasis on phyllotine genera: Systematics and biogeography. Zool. Scr. 42, 250–261 (2013).
Google Scholar 
Pardiñas, U. F. J., Lessa, G., Teta, P., Salazar-Bravo, J. & Câmara, E. M. V. C. A new genus of sigmodontine rodent from eastern Brazil and the origin of the tribe Phyllotini. J. Mamm. 95, 201–215 (2014).
Google Scholar 
Edler, D., Guedes, T., Zizka, A., Rosvall, M. & Antonelli, A. Infomap bioregions: Interactive mapping of biogeographical regions from species distributions. Syst. Biol. 1, 087 (2016).
Google Scholar 
Johnson, T. C. et al. Late pleistocene desiccation of lake victoria and rapid evolution of cichlid fishes. Science 273, 1091–1093 (1996).ADS 
CAS 

Google Scholar 
Azevedo, J. A. R. et al. Museums and cradles of diversity are geographically coincident for narrowly distributed Neotropical snakes. Ecography 43, 328–339 (2020).
Google Scholar 
Rosauer, D. F. & Jetz, W. Phylogenetic endemism in terrestrial mammals: Mammal phylogenetic endemism. Glob. Ecol. Biogeogr. 24, 168–179 (2015).
Google Scholar 
Peyton, B. Ecology, distribution, and food habits of spectacled bears, Tremarctos ornatus, in Peru. J. Mammal. 61, 639–652 (1980).
Google Scholar 
Patterson, B. D., Solari, S. & Velazco, P. M. The role of the Andes in the diversification and biogeography of Neotropical mammals. In Bones, Clones, and Biomes: The History and Geography of Recent Neotropical Mammals (eds Patterson, B. D. & Costa, L. P.) (Springer, 2012).
Google Scholar 
Tribe, C. J. The Neotropical Rodent Genus’ Rhipidomys’(Cricetidae: Sigmodontinae): A Taxonomic Revision (University of London, 1996).
Google Scholar 
Percequillo, A. R. Sistemática de Oryzomys Baird, 1858: Definição dos Grupos de Espécies e Revisão do Grupo Albigularis (Rodentia, Sigmodontinae) (Doctoral dissertation, Tese de Doutorado) (Universidade de São Paulo, 2003).
Google Scholar 
Brito, J. et al. A new genus of oryzomyine rodents (Cricetidae, Sigmodontinae) with three new species from montane cloud forests, western Andean cordillera of Colombia and Ecuador. PeerJ 8, e10247 (2020).
Google Scholar 
Valencia-Pacheco, E., Avaria-Llautureo, J., Munoz-Escobar, C., Boric-Bargetto, D. & Hernandez, C. E. Geographic patterns of richness distribution of rodents species from the Oryzomyini tribe (Rodentia: Sigmodontinae) in South America: Evaluating the importance of colonization and extinction processes. Rev. Chil. Hist. Nat. 84(3), 365–377 (2011).
Google Scholar 
Pine, R. H., Timm, R. M. & Weksler, M. A newly recognized clade of trans-Andean Oryzomyini (Rodentia: Cricetidae), with description of a new genus. J. Mammal. 93(3), 851–870 (2012).
Google Scholar 
Prado, J. R. & Percequillo, A. R. Geographic distribution of the genera of the tribe Oryzomyini (Rodentia: Cricetidae: Sigmodontinae) in South America: Patterns of distribution and diversity. Arq. Zool. 44(1), 1–120 (2013).
Google Scholar 
Prado, J. R. et al. Species richness and areas of endemism of oryzomyine rodents (Cricetidae, Sigmodontinae) in South America: An NDM/VNDM approach. J. Biogeogr. 42(3), 540–551 (2015).
Google Scholar 
Voss, R. S. A new species of Thomasomys (Rodentia: Muridae) from eastern Ecuador, with remarks on mammalian diversity and biogeography in the Cordillera Oriental. Am. Mus. Novit. 2003(3421), 1–47 (2003).
Google Scholar 
Brito, J. et al. Diversidad insospechada en los Andes de Ecuador: Filogenia del grupo “cinereus” de Thomasomys y descripción de una nueva especie (Rodentia, Cricetidae). Mastozool. Neotrop. 26(2), 308–330 (2019).
Google Scholar 
Rodríguez-Serrano, E., Palma, R. E. & Hernández, C. E. The evolution of ecomorphological traits within the Abrothrichini (Rodentia: Sigmodontinae): A Bayesian phylogenetics approach. Mol. Phylogenet. Evol. 48(2), 473–480 (2008).
Google Scholar 
Villagrán, C. & Hinojosa, L. F. Historia de los bosques del sur de Sudamérica, II: Análisis fitogeográfico. Rev. Chil. Hist. Nat. 70(2), 1–267 (1997).ADS 

Google Scholar 
Pardinas, U. F., Teta, P., D’elía, G. & Lessa, E. P. The evolutionary history of sigmodontine rodents in Patagonia and Tierra del Fuego. Biol. J. Lin. Soc. 103(2), 495–513 (2011).
Google Scholar 
Yepes, J. Consideraciones sobre el género “Andinomys” (Cricetinae) y descripción de una forma nueva. In Anales del Museo Argentino de Ciencias Naturales “Bernardino Rivadavia” (Vol. 38, 333–348) (1935).Salazar-Bravo, J. & Jayat, J. P. Genus Andinomys Thomas, 1902. Mamm. S. Am. 2, 75–77 (2015).
Google Scholar 
Pacheco, V. & Patton, J. L. A new species of the Puna mouse, genus Punomys Osgood, 1943 (Muridae, Sigmodontinae) from the Southeastern Andes of Peru. Z. Saugetierkunde 60(2), 85–96 (1995).
Google Scholar 
Salazar-Bravo, J., Miralles-Salazar, J., Rico-Cernohorska, A. & Vargas, J. First record of Punomys (Rodentia: Sigmodontinae) in Bolivia. Mastozool. Neotrop. 18(1), 143–146 (2011).
Google Scholar 
Rolland, J., Condamine, F. L., Beeravolu, C. R., Jiguet, F. & Morlon, H. Dispersal is a major driver of the latitudinal diversity gradient of C arnivora. Glob. Ecol. Biogeogr. 24(9), 1059–1071 (2015).
Google Scholar 
Pyron, R. A. & Wiens, J. J. Large-scale phylogenetic analyses reveal the causes of high tropical amphibian diversity. Proc. R. Soc. B Biol. Sci. 280(1770), 20131622 (2013).
Google Scholar 
Meseguer, A. S. et al. Reconstructing deep-time palaeoclimate legacies in the clusioid Malpighiales unveils their role in the evolution and extinction of the boreotropical flora. Glob. Ecol. Biogeogr. 27(5), 616–628 (2018).
Google Scholar 
Brumfield, R. T. & Edwards, S. V. Evolution into and out of the Andes: A Bayesian analysis of historical diversification in Thamnophilus antshrikes. Evolution 61(2), 346–367 (2007).CAS 

Google Scholar 
Ribas, C. C., Moyle, R. G., Miyaki, C. Y. & Cracraft, J. The assembly of montane biotas: Linking Andean tectonics and climatic oscillations to independent regimes of diversification in Pionus parrots. Proc. R. Soc. B 274(1624), 2399–2408 (2007).
Google Scholar 
Bonaccorso, E. Historical biogeography and speciation in the Neotropical highlands: Molecular phylogenetics of the jay genus Cyanolyca. Mol. Phylogenet. Evol. 50(3), 618–632 (2009).CAS 

Google Scholar 
McGuire, J. A., Witt, C. C., Altshuler, D. L. & Remsen, J. V. Phylogenetic systematics and biogeography of hummingbirds: Bayesian and maximum likelihood analyses of partitioned data and selection of an appropriate partitioning strategy. Syst. Biol. 56(5), 837–856 (2007).CAS 

Google Scholar 
Rheindt, F. E., Christidis, L. & Norman, J. A. Habitat shifts in the evolutionary history of a Neotropical flycatcher lineage from forest and open landscapes. BMC Evol. Biol. 8(1), 1–18 (2008).
Google Scholar 
Percequillo, A. R., Weksler, M. & Costa, L. P. Comments on oryzomyine biogeography. Zool. J. Linn. Soc. 161(2), 357–390 (2011).
Google Scholar 
Weksler, M. Tribe Oryzomyini Vorontsov, 1959. Mamm. S. Am. 2, 291–293 (2015).
Google Scholar 
Haag, T. et al. Phylogenetic relationships among species of the genus Calomys with emphasis on South American lowland taxa. J. Mammal. 88(3), 769–776 (2007).
Google Scholar 
Badgley, C. Tectonics, topography, and mammalian diversity. Ecography 33(2), 220–231 (2010).
Google Scholar 
Simpson, G. G. Species density of North American recent mammals. Syst. Zool. 13(2), 57–73 (1964).
Google Scholar  More

The growth behaviour of Linnemannia is strain-specificMost strains showed comparable morphological characteristics on both media as well as in pure and co-culture. However, Linnemannia solitaria and Entomortierella galaxiae produced more aerial mycelium on PDA compared to LcA. There was more/less aerial mycelium in co-cultures with P. helmanticensis compared to pure cultures depending on the strain (Fig. 1, SI Fig. S3).The comparison of Linnemannia and E. galaxiae daily radial growth rates did not support a difference between these genera (p ≥ 0.3). The overall linear model indicated that the fungal daily growth rates mainly differed among species (Table 1). In addition, the effect of strains highlighted the heterogeneity among strains within species (Fig. 2, SI Figs. S4, S5). Although there was no relevant main effect of medium on the daily radial growth rate of the fungi, the medium did affect the fungi in a strain-specific manner (Table 1, Fig. 2, SI Figs. S4, S5). On nutrient poor LcA, the fungal daily radial growth rates were reduced for all species, except for L. solitaria, which grew better on LcA (SI Figs. S3, S4).Table 1 The effect of experimental factors on the fungal daily radial growth rate.Full size tableFigure 2Daily radial growth rate of pure Linnemannia and Entomortierella cultures as well as co-cultures with P. helmanticensis on nutrient rich PDA medium. (a) L. exigua, (b) L. gamsii, (c) L. hyalina, (d) L. sclerotiella, (e) L. solitaria, (f) E. galaxiae.Full size imageThe main effect of co-plating P. helmanticensis on radial growth rate was small, yet significant (0.7%, p  More