Ecology

Photosynthetic pigmentsBased on the analysis of variance, data of Photosynthetic pigments as presented in Table 1 indicate that photosynthetic pigments as chlorophyll a (Chl. a) had non-significant for three Canola genotypes AD201 (G1), Topaz (G2) and SemuDNK 234/84 (G3), but chlorophyll b (Chl. b) and chlorophyll a/b ratio (Chl. a/b) had significant difference for three genotypes. Chl. a, Chl. b and Chl. a/b were positively responded to different N application i.e. without nitrogen fertilization (control F0), 95 kg N ha−1 (F1), 120 kg N ha−1 (F2) and 142 kg N ha−1 (F3) (without yeast); and integrated between nitrogen fertilization and yeast extract (YE) treatments as follows: 95 kg N ha−1 + YE (F4), 120 kg N ha−1 + YE (F5) and 142 kg N ha−1 (F6) (with yeast), data indicated that F5 and F6 gave the highest values of Chl. a and Chl. a/b ratio and lowest values of Chl. b Table 1. Interaction data showed that three Canola genotypes that were fertilized with N without yeast or with yeast had a slight difference with statistically significant in chl. a. The highest values of Chl. an obtained by G2 under F5 treatment followed by G1 under F6 treatments. In respect to Chl. a/b ratio, statistical analysis showed that Interaction between Canola genotypes treated with N applications without or with yeast had a significant difference whereas the highest values were recorded when Canola genotypes G3 and G2 fertilized with F6 and F5 with slight differences. While the interaction was significant between N treatments and Canola genotypes for Chl. b. and Canola genotype (G1) gave the highest value when treated with F1. Generally, F6 and F5 improve the contents of chl. a and chl. a/b ratio for three Canola genotypes Table 1. Chl. contents were increased in plants grown under middle and high N conditions as compared with plants grown under low N conditions, which significantly affected photochemical processes20. N is a fundamental element for leaf plants, insufficient N supply lead to decreased photosynthetic rate in plants21, this occurs to many factors such as a decrease in pigment degradation22, reduction in stomatal conductance23 and a decline in the light and dark reaction of photosynthesis. Canola is a nitrophilous plant, wherein a high concentration of NO3 in the culture media results in higher Chl. contents in the plant leave compared with controls20. The Chl. a/b ratio can be a valuable indicator of N element within a leaf because this ratio must be positively related to the ratio of PSII cores to light-harvesting chlorophyll-protein complex (LHCII)24. LHCII contains the majority of Chl. b, consequently it has a lower Chl a/b ratio than other Chl. binding proteins associated with PSII25. Thus, Chl. a/b ratios should increase with decreasing N availability, especially under high light conditions26, the Chl. a/b ratio and the ratio of PSII to Chl. are independent of N availability for spinach27, and lower Chl. a/b ratios were noticed when plants were subjected to low N28, while Kitajima and Hogan29 revealed that the Chl. a/b ratio increased when Chl. content decreased in response to N restriction in photosynthetic cotyledons in leaves of seedlings of four tropical woody species in the Bignoniaceae, and Bungard et al.30 demonstrated that there is a tiny response in Chl. a/b ratios to light or N. The yeast includes bio-regulators i.e. plant growth regulators and endogenous plant hormones, which enhance photosynthesis, also it produces 5-Aminolevulinic acid which is vital to tetrapyrrole biosynthesis and biochemical processes in plants, including heme and Chl. biosynthesis25.Table 1 Photosynthetic pigments for the three Canola genotypes under different N applications without and with yeast extract.Full size tableYield and its attributesComparing of mean data through the Duncan Multiple Range Test in the probability level of 5%, data showed significant differences among the Canola genotypes for the highest plant (cm), branches number/plant, and pods number/plant. On contrary, there wasn’t a significant difference for seed number/pods, seed yield (t ha−1), biological yield (t ha−1), and harvest index, wherein G2 gave the highest value for the highest plant (cm). In the same trend, G2 gave the highest values of branches No./plant and pods No./plant followed by G3 for the previous two treats Table 2. All examined N without or with yeast caused a significant difference in yield and its attributes, wherein F6 positively affected on abovementioned traits and gave the highest values on the highest plant (cm), branches No./plant, pods No./plant, seed No./pods, seed yield (t ha−1), and harvest index. While the highest values of biological yield (t ha−1) were obtained with F3, F6, and F5, respectively Table 2.Table 2 Growth, yield and its attributes for the three Canola genotypes under different N applications without and with yeast extract.Full size tableThe interaction between the Canola genotype and different N rates without or with yeast extract as shown in Table 2, demonstrated a significant difference. Data showed that the highest values of plant height and pods No./plant were recorded by G2 under F6 and the highest values of branches No./plant, seed No./pods, and seed yield (t ha−1) got by G3 and G2 under F6. There was a slight difference with statistically significant biological yield (t ha−1) and highest values established by G1 under F3 and F6; and G2 and G3 under F3, F5, and F6 respectively; and the highest values of harvest index recorded by G1, G2 and G3. under F6. Generally, data proved that 142 kg N/ h−1 + YE (F6) was enhanced the yield and its components of three Canola genotypes i.e. AD201 (G1), Topaz (G2), and SemuDNK 234/84 (G3). Many researchers reported that there are significant differences among Canola varieties and growth and yield traits are significantly increased by increasing N rates11. Increasing N fertilizer rates significantly increased most of the yield and its components31, N enhances metabolites synthesized by the plant which leads to more transformation of photosynthesis to reproductive parts, and induces different physiological mechanisms to access the nutrient32. Yeast extract as bio-fertilizer had a significant and positive effect on plant height and yield traits of Canola. The role of bread yeast in increasing the growth and yield traits; may be due to the content of yeast to many important nutrients elements i.e. N, Mg, Ca, Zn, Cu, and Fe, and the production of some growth regulators such as Auxin and Gibberellin and cytokinin which is necessary for plant biological processers especially photosynthesis and cell division and elongation33. Also, Yeast extract had stimulatory effects on cell division and enlargement, protein and nucleic acid synthesis, and chlorophyll formation34, in addition to its content of cryoprotective agent, i.e. sugars, protein, amino acids, and also several vitamins35. Consequently, it improves growth, flowering, and fruit set and formation and increases yield34.Correlation of Canola seed yield and chlorophyll a/b ratioPartial correlation coefficients of Canola seed yield and Chl. a/b ratio is given in Fig. 1. This result showed that seed yield was positively correlated with Chl. a/b ratio when the amount of N applied without or with yeast extract is increased. Chl. a/b ratio can be an important indicator of N within a leaf, this ratio must be positively related to photosynthesis and biological processers which reflect on seed yield.Figure 1Correlation of Canola seed yield (t/h) and chlorophyll a/b ratio as affected by different nitrogen rates without and with yeast extract.Full size imageCorrelation of Canola seed yield and its attributesCorrelations of seed yield and yield components of Canola are a function of the plant height, number of branches/plant, number of pods/plant, and number of seeds/pod as shown in Fig. 2a–d. These results proved that grain yield was strongly positively correlated with some of the abovementioned traits when N fertilization increased without or with yeast extract. Sufficient N contributes to enhance physiological processes, improves growth, flowering, seed formation, and the seed yield finally.Figure 2(a) Correlation of Canola seed yield (t/h) and plant height (cm) as affected by different nitrogen rates without and with yeast extract, (b) Correlation of Canola seed yield (t/h) and branch No/plant as affected by different nitrogen rates without and with yeast extract, (c) Correlation of Canola seed yield (t/h) and pods No/ plant as affected by different nitrogen rates without and with yeast extract, and (d) Correlation of Canola seed yield (t/h) and seeds No/ pod as affected by different nitrogen rates without and with yeast extract.Full size imageChemical propertiesRegarding results of the oil yield (t ha−1), seed oil %, protein %, N % in seed, and N% in straw as presented in Table 3, data showed significant differences among three Canola genotypes; AD201 (G1), Topaz (G2) and SemuDNK 234/84 (G3), excepted oil yield had non-significant difference. G1 was surpassed in oil %; G2, G3 surpassed in protein % and N % in seed, and G3 surpassed in N% in straw. Different N fertilization applies without or with yeast extract had a significant effect on the abovementioned traits, wherein F6 treatment gave the highest oil yield, protein %, N % in seed, and N% in straw, while seed oil % significantly increased with F1 and F4 treatments. There was significant interaction concerning with abovementioned traits, Table 3, as well as the highest values of seed oil yield (t ha−1), protein % in seeds, and nitrogen % in seeds were obtained with G1, G2, and G3 when treated with F6. Wherein the highest values of oil % were obtained by G1 under F1 and F4 treatments. Concerning N% in straw was increased by increasing the rate of N fertilizer application and the highest value was recorded by adding F6 to G336. Seed oil percentage was decreased by increasing nitrogen rates; the effect of interaction between Canola cultivars and nitrogen fertilization treatments was significant on seed oil. % High rates of N led to decreases in seed oil % and increase in protein concentrations in Canola seed37, the increase in seed protein % because N is an integral part of protein and the protein of Canola.Table 3 Effect of different N applications without and with yeast extract on oil yield, oil %, protein %, N % in seed and N% in straw for the three Canola genotypes.Full size tableCorrelation of Canola seed yield and seed oil percentageA strong negative correlation was detected between seed oil percentage as shown in Fig. 3. The result indicates that seed oil percentage decreases with increasing in different N fertilization rates without or with yeast extract. That’s a negative correlation between seed yield and seed oil %; it might be due to N application which results in delaying maturity leading to poor seed filling and a greater proportion of green seed38.Figure 3Correlation of Canola seed yield (t h−1) and oil % as affected by different nitrogen rates without and with yeast extract.Full size imagePhysico-chemical properties of Canola oilThe effects of different N application rates without or with yeast extract on Canola genotypes on physico-chemical properties i.e. Acid value (mg g−1), saponification number (mg g−1) and peroxide value (mg kg−1) were shown in Table 4. Data of chemical properties of Canola oil showed significant differences among Canola genotypes, the highest acid value and peroxide value were obtained from G2 followed by G1 and G3, respectively, while the highest saponification number was obtained by G3 followed by G1 and G2, respectively.Table 4 Oil properties for three Canola genotypes under different N applications without and with yeast extract.Full size tableData had significant differences among different N application rates without or with yeast extract, by increasing the N rated from F0 to F6 caused decreases in Acid value, Saponification number, and peroxide value. Also, data showed a significant interaction between Canola genotypes and different N application rates without or with yeast extract for all abovementioned traits, wherein the highest values of saponification number were obtained by G1 and G3 under F0 treatment. In addition, the highest values of peroxide value and the acid value were obtained by G2 with F0. The acid value is a physicochemical indicator38, wherein oils which have higher acid value posse poor quality39, on another hand, Low acid value of Canola genotype shows their higher oil quality. The peroxide value varied between 7.1 and 9.06 meq. O2/kg indicates that the tested vegetable oils are fresh, and the lowest initial peroxide value is suitable for consumption40. High saponification value indicated that Canola oil possesses normal triglycerides and may be useful in the production of liquid soap and shampoo41. Saponification number was significantly different among genotypes and a higher nitrogen rate resulted in an increase in the unsaponifiable matter and led to a decrease in oil acid value and saponification value42.Fatty acids composition percentages in Canola oilThe main values of fatty acids composition percentages in Canola oil were determined and calculated in the second season Table 5. Gas–liquid chromatographic analysis showed that, saturated fatty acids (Palmitic, 16:0, Stearic, 18:0, Arachidic, 20:0, and Behenic, 22:0) represent about 9.1 of the total fatty acids. Palmitic was the dominant acid among the saturated ones. In respect of unsaturated fatty acids i.e., Oleic acid (18:1), Linoleic (18:2), Linolenic (18:3), and Erucic (22:1), they all represent about 90.9% of total fatty acids. Therefore, Oleic acid (18:1) was the major fatty acid in Canola oil (59.43%) followed by Linoleic (20.80%) and Linolenic (9.02%). Erucic acid was less than 2%.Table 5 Saturated and unsaturated fatty acids (%) in seeds of the three Canola genotypes and different N applications without and with yeast extract.Full size tableData in Table 5, showed slight differences in saturated fatty acids between Canola varieties. AD201(G1) variety contained more amount of Palmitic (4.78%) and Stearic (1.52%) acids followed by Topaz (G2) for Palmitic and SemuDNK 234/84 (V3) for Stearic. However, Behenic acid (1.20%) was higher in G3 than G2 (1.17%), while G2 was the highest in Arachidic acid than G3 variety. These results are in line with those obtained by El Habbasha et al.43. They reported that AD 201, Silvo, and Topas (G2) were different in their oil contents of saturated and unsaturated fatty acids. Canola varieties were also slightly differed in their content of the unsaturated fatty acids Table 5, G3 variety contained more amounts of Oleic (60.36%) acid followed by the G2 variety. G1 recorded the lowest amount of Oleic acid (58.36%) in comparison with the other two varieties. On the other hand, G1 showed a high increment in Linoleic and Linolenic acids followed by G3 for Linoleic and Linolenic acids. The second oil quality breeding objective is to reduce the percentage of Linolenic acid from the percent 8–10% to less than 3% while maintaining or increasing the level of Linoleic acid44. Lower Linolenic acid is desired to improve the storage characteristics of the oil, while higher Linolenic acid content may be nutritionally desirable. Similar observations were reported by Ref.45. Topaz variety recorded the highest value for Erucic acid (1.77%) followed by AD201 variety, whereas Semu DNK gave the lowest value (1.45%). The increase in Erucic acid content in the Topaz variety may be due to the decrease in Oleic acid content46. Stated that the concentrations of Oleic and Erucic acids were negatively correlated and a high Oleic acid concentration ( > 50%) was always associated with a low Erucic acid concentration ( More

Changes of straw degradation characteristics at different culture stagesCorn straw degradation ratioCorn straw weight loss in M44 at F1 reached 35.90% at 15 ℃ for 21 days, which was greater than that at F5, F8, and F11 by 2.33%, 3.01%, and 3.35%, respectively. There were no significant differences between F8 and F11(Fig. 1).Figure 1Corn straw degradation ratio was measured at different culture stages. The same small letter means there was no significant difference, and different small letters indicate significant differences at p  More

Selection of environmental-biotic events to be studiedIn global warming events associated with mass extinctions, the current environmental changes are similar to those recorded during the end-Ordovician, end-Guadalupian, and end-Permian mass extinctions. Therefore, I analyzed global surface temperature anomalies, mercury pollution concentrations, and deforestation percentages in these three mass extinctions and in the current crisis. The asteroid impact at the K–Pg boundary and nuclear war cause the formation of stratospheric soot aerosols distributed globally, thus inducing sunlight reductions and global cooling (impact winter and nuclear winter). I also analyzed stratospheric soot aerosols as a possible cause of future extinctions.Most likely case and worst caseThe most likely case corresponds to the reduction of CO2 emissions resulting from human conduct, the protection of forests, and the introduction of anti-pollution measures in the future under the Paris Agreement on Climate change and Sustainable Development Goals (SDGs). The worst case corresponds to the scenario in which humans fail to stop increasing global surface temperatures, pollution, and deforestation until 2100–2200 CE.I use the average of the RCP4.5 and RCP6.0 cases in the Intergovernmental Panel on Climate Change (IPCC)8 as the most likely case of GHG emissions, representing the middle of the four potential GHG emissions cases (RCP2.6, 4.5, 6.0, and 8.5) in Fifth Assessment Report of the IPCC8, approximately corresponding to the middle of SSP2-4.5 and SSP3-7.0 in Sixth Assessment Report of the IPCC9. The timing of decreased global GHG emissions is 2060–2080 CE. Therefore, I use the average GHG emissions and global surface temperature anomalies of the RCP4.5 and RCP6.0 cases as the most likely values and those of the RCP8.5 case as the worst-case scenario, marked by stopping GHG emissions from 2090 to 2100 CE8,9, as this case corresponds to the highest GHG emissions8,9.Surface temperature anomaly, environment, and extinction magnitude dataData on surface temperature anomalies and extinction percentages are from Kaiho4. Changes in industrial GHG emissions and global surface temperature anomalies are sourced from the Fifth and Sixth Assessment Report of the IPCC8,9.Pollution can be represented by mercury concentrations measured in sedimentary rocks recording mass extinctions8 and in recent sediments deposited in seas and lakes25,26 because mercury is toxic to plants and animals and because its sources include volcanic eruptions, meteorite impacts, and the combustion of fossil fuels10,33, which are common sources of pollutants, and because it can be commonly measured from sedimentary rocks recording mass extinctions33. The mercury concentration is related to the CO2 emission amount during global warming because of the common sources of mercury and CO2 (volcanism and fossil fuel combustion influencing global warming). Thus, the future mercury concentrations are estimated based on the CO2 emission amounts estimated by the IPCC8,9. Since mercury and the other pollutants mainly come from oil, coal, and vegetation33, the amount of mercury released should change in parallel with industrial CO2 emissions because there is a good correlation between mercury and CO2 emissions11.Deforestation occurs by the expansion of agricultural areas and urban areas, which are strongly related to human populations13,28. Thus, future deforestation percentages are estimated based on estimated future population data27 (Supplementary Table S2). The severity of deforestation in each event is expressed by the occupancy % of the deforested area in the pre-event forest area in (i) the Permian–Triassic transition marked by the largest mass extinction based on plant fossil records24 and (ii) 2005–2015 CE as a representative of the Anthropocene epoch12,13,28 based on the actual forest area relative to the pre-agriculture phase before 4000 BP. Deforestation is related to the human population because agriculture and urbanization have caused deforestation13,28. I estimate the past and future deforestation percentage using human population data in the past and future21 based on the parallel growth of the human population and deforestation13,28.Amount of stratospheric soot was calculated using a method of Kaiho and Oshima34 (Supplementary Table S1). I obtained global surface temperature anomaly caused by stratospheric soot using Fig. 5 of Kaiho and Oshima34.I then use those data to estimate the future extinction magnitude based on the assumption that the Earth and contemporary life at the time of each crisis are more or less mutually comparable throughout time and to the present day.I estimate the magnitude of the species animal extinction crisis between 2000 and 2500 CE using Figs. 1, 2 and Supplementary Tables S1 and S2 in each cause under the most likely case and worst case under three nuclear war scenarios (zero, minor, and major; Fig. 2d)15 in the PETM and mass extinction cases, respectively (Supplementary Tables S3, S4; Fig. 3). Finally, I estimate the magnitude of current animal extinction crisis by the four causes as an average of the species extinction magnitude by the four causes in Fig. 3. I use two different contribution rates of temperature anomalies, pollution, deforestation, and stratospheric soot by nuclear wars, 1:0.2:0.1:1 for marine animals and 1:0.5:1:1 for terrestrial tetrapods (different contribution case considering lower influence of pollution and deforestation to marine animals rather than terrestrial animals) and 1:1:1:1 for marine animals and 1:1:1:1 for terrestrial tetrapods (equal contribution case considering high influence of pollution and deforestation to marine animals via rain and soil erosion) (Supplementary Tables S5–S9). These contribution rates are estimated as end-members to show ranges of animal species extinction magnitude (%). More

Madhusudan Katti says ecology would benefit from including perspectives from all of Earth’s inhabitants.Credit: Marc Hall

Decolonizing science

Science is steeped in injustice and exploitation. Scientific insights from marginalized people have been erased, natural history specimens have been taken without consent and genetics data have been manipulated to back eugenics movements. Without acknowledgement and redress of this legacy, many people from minority ethnic groups have little trust in science and certainly don’t feel welcome in academia — an ongoing barrier to the levels of diversity that many universities claim to pursue.
In the next of a short series of articles about decolonizing the biosciences, Madhusudan Katti suggests five shifts that ecologists need to make to unravel the effects of colonization on their field. Katti, an evolutionary ecologist at North Carolina State University in Raleigh, would also like to see stronger inclusion of uncredentialed experts and Indigenous communities in research.

Last year, my colleagues and I wrote a paper highlighting five shifts that would help to decolonize ecology (C. H. Trisos et al. Nature Ecol. Evol. 5, 1205–1212; 2021). Ecologists need to improve how they incorporate varied perspectives, approaches and interpretations from the diverse peoples inhabiting Earth’s natural environments. The five shifts are: the individual need to decolonize one’s mind; understand the history of colonization and how it shaped Western ecology; facilitate access to and dissemination of data; recognize diverse scientific expertise; and establish inclusive research groups. Although it can be difficult to make reforms given how resistant institutions are to change, we are optimistic because we have received invitations to speak on these issues. People are ready for these conversations.
Decolonizing science toolkit
My colleagues and I developed a workshop around the five shifts. We have conducted the workshop at my institution, and at the annual conference of the Society for Integrative and Comparative Biology. For each of the shifts, I have participants brainstorm and write down challenges and solutions that might lead to progress in these areas for their own research departments or institutions. We address them, shuffle groups and suggest policy changes and future action.Some organizations are already moving forward with some low-hanging fruit, such as making data and published results more accessible. However, open-access publishing models put an even greater burden of publication costs on authors and perpetuate inequalities, because early-career researchers and those in the global south often can’t afford them.The most contentious area tends to be the reluctance of academia to accept non-credentialed expertise such as traditional knowledge. Universities are in the business of giving out credentials in the form of degrees. If academia no longer requires a PhD, that can be a challenge to that model. There are also few, if any, incentives or rewards to spend time working towards decolonizing academia, even though it takes time and effort away from furthering individual careers.As an Indian American, I would like to see institutions expand antiracism conversations rather than introduce new checklists of things to do. For example, at annual meetings, it would be great to see scientific societies make more connections with the Indigenous communities where we work and invite them to share their perspectives.
This interview has been edited for length and clarity. More

Beniston, M., Diaz, H. F. & Bradley, R. S. Climatic change at high elevation sites: An overview. Clim. Change 36, 233–251 (1997).Article 

Google Scholar 
Chape, S., Spalding, M. & Jenkins, M. The world’s protected areas: Status, values, and prospects in the twenty-first century. Bioscience 59(7), 623–624 (2009).
Google Scholar 
Körner, C. Mountain biodiversity, its causes and function. Ambio 33, 11–17 (2004).Article 

Google Scholar 
Körner, C. et al. A global inventory of mountains for bio-geographical applications. Alp. Bot. 127, 1–15 (2017).Article 

Google Scholar 
Forero-Medina, G., Joppa, L. & Pimm, S. L. Constraints to species’ elevational range shifts as climate changes. Conserv. Biol. 25, 163–171 (2011).Article 
PubMed 

Google Scholar 
Urban, M. C., Tewksbury, J. J. & Sheldon, K. S. On a collision course: Competition and dispersal differences create no-analogue communities and cause extinctions during climate change. Proc. R. Soc. B 279, 2072–2080 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Freeman, B. G., Scholer, M. N., Ruiz-Gutierrez, V. & Fitzpatrick, J. W. Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community. Proc. Natl. Acad. Sci. 115, 11982–11987 (2018).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024 (2011).Article 
CAS 
PubMed 
ADS 

Google Scholar 
Lenoir, J. & Svenning, J. C. Climate-related range shifts: A global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2015).Article 

Google Scholar 
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).Article 
CAS 
PubMed 
ADS 

Google Scholar 
Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl. Acad. Sci. 117, 4211–4217 (2020).Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e200114 (2016).Article 

Google Scholar 
Orians, G. H. & Milewski, A. V. Ecology of Australia: The effects of nutrient-poor soils and intense fires. Biol. Rev. 82, 393–423 (2007).Article 
PubMed 

Google Scholar 
Laurance, W. F. et al. The 10 Australian ecosystems most vulnerable to tipping points. Biol. Cons. 144, 1472–1480 (2011).Article 

Google Scholar 
Rahbek, C. et al. Humboldt’s enigma: What causes global patterns of mountain biodiversity?. Science 365, 1108–1113 (2019).Article 
CAS 
PubMed 
ADS 

Google Scholar 
Williams, S. E., Bolitho, E. E. & Fox, S. Climate change in Australian tropical rainforests: An impending environmental catastrophe. Proc. R. Soc. Lond. B 270, 1887–1892 (2003).Article 

Google Scholar 
Mahony, M.J. The amphibians. in Remnants of Gondwana: A Natural and Social History of the Gondwana Rainforests of Australia. (eds. Kitching, R.L., Braithwaite, R., & Cavanaugh, J.) (Surrey Beatty & Sons, 2010).Kooyman, R. M., Watson, J. & Wilf, P. Protect Australia’s gondwana rainforests. Science 367, 1083–1083 (2020).Article 
PubMed 
ADS 

Google Scholar 
Narsey, S. et al. (2020). Impact of climate change on cloud forests in the Gondwana Rainforests of Australia World Heritage Area. Earth Systems and Climate Change Hub Report.Newell, D. An update on frog declines from the forests of subtropical eastern Australia in Status of Conservation and Decline of Amphibians: Australia, New Zealand, and Pacific Islands (eds. Heatwole H. and Rowley J. L.) 29–37 (CSIRO, 2018).DAWE. Bushfire Impacts Vol. 2021 (Commonwealth Department of Agriculture Water and Environment, 2020).
Google Scholar 
Collins, L. et al. The 2019/2020 mega-fires exposed Australian ecosystems to an unprecedented extent of high-severity fire. Environ. Res. Lett. 16, 044029 (2021).Article 
ADS 

Google Scholar 
Filkov, A. I., Ngo, T., Matthews, S., Telfer, S. & Penman, T. D. Impact of Australia’s catastrophic 2019/20 bushfire season on communities and environment: Retrospective analysis and current trends. J. Saf. Sci. Resil. 1, 44–56 (2020).
Google Scholar 
Blunden, J. & Arndt, D. S. State of the climate in 2019. Bull. Am. Meteor. Soc. 101, S1–S429 (2020).Article 

Google Scholar 
Zhongming, Z., Linong, L., Wangqiang, Z. & Wei, L. AR6 Climate Change 2021: The Physical Science Basis (Springer, 2021).
Google Scholar 
Laidlaw, M. J., McDonald, W. J. F., Hunter, R. J., Putland, D. A. & Kitching, R. L. The potential impacts of climate change on Australian subtropical rainforest. Aust. J. Bot. 59, 440–449 (2011).Article 

Google Scholar 
Blaustein, A. R. et al. Direct and indirect effects of climate change on amphibian populations. Diversity 2, 281–313 (2010).Article 

Google Scholar 
Li, Y., Cohen, J. M. & Rohr, J. R. Review and synthesis of the effects of climate change on amphibians. Integr. Zool. 8, 145–161 (2013).Article 
PubMed 

Google Scholar 
Carey, C. & Alexander, M. A. Climate change and amphibian declines: Is there a link?. Divers. Distrib. 9, 111–121 (2003).Article 

Google Scholar 
Cohen, J. M., Civitello, D. J., Venesky, M. D., McMahon, T. A. & Rohr, J. R. An interaction between climate change and infectious disease drove widespread amphibian declines. Glob. Change Biol. 25, 927–937 (2019).Article 
ADS 

Google Scholar 
Geyle, H. M. et al. Red hot frogs: Identifying the Australian frogs most at risk of extinction. Pac. Conserv. Biol. 28, 211–223 (2021).Article 

Google Scholar 
Gillespie, G. R. et al. Status and priority conservation actions for Australian frog species. Biol. Conserv. 247, 108543 (2020).Article 

Google Scholar 
Almeida, A. M. et al. Prediction scenarios of past, present, and future environmental suitability for the Mediterranean species Arbutus unedo L. Sci. Rep. 12, 1–15 (2022).Article 

Google Scholar 
Lima, V. P. et al. Climate change threatens native potential agroforestry plant species in Brazil. Sci. Rep. 12, 1–14 (2022).Article 
ADS 

Google Scholar 
Tiwari, S. et al. Modelling the potential risk zone of Lantana camara invasion and response to climate change in eastern India. Ecol. Process. 11(1), 1–13 (2022).Article 

Google Scholar 
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).Article 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

Google Scholar 
Galante, P. J. et al. The challenge of modeling niches and distributions for data-poor species: a comprehensive approach to model complexity. Ecography 41, 726–736 (2018).Article 

Google Scholar 
Li, J. et al. Climate refugia of snow leopards in High Asia. Biol. Conserv. 203, 188–196 (2016).Article 

Google Scholar 
Searcy, C. A. & Shaffer, B. H. Do ecological niche models accurately identify climatic determinants of species ranges?. Am. Nat. 187, 423–435 (2016).Article 
PubMed 

Google Scholar 
Melo-Merino, S. M., Reyes-Bonilla, H. & Lira-Noriega, A. Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence. Ecol. Model. 415, 108857 (2020).Article 

Google Scholar 
Anstis, M. Tadpoles and Frogs of Australia (New Holland Publishers Pty Limited, 2017).
Google Scholar 
Knowles, R., Mahony, M., Armstrong, J. & Donnellan, S. Systematics of sphagnum frogs of the Genus Philoria (Anura: Myobatrachidae) in Eastern Australia, with the description of two new species. Rec. Aust. Mus. 56, 57–74 (2004).Article 

Google Scholar 
Mahony, M. J. et al. A new species of Philoria (Anura: Limnodynastidae) from the uplands of the Gondwana Rainforests world heritage area of eastern Australia. Zootaxa 5104, 209–241 (2022).Article 
PubMed 

Google Scholar 
Bolitho, L. J., Rowley, J. J. L., Hines, H. B. & Newell, D. Occupancy modelling reveals a highly restricted and fragmented distribution in a threatened montane frog (Philoria kundagungan) in subtropical Australian rainforests. Aust. J. Zool. 67, 231–240 (2021).Article 

Google Scholar 
Heard, G. et al. Post-fire impact assessment for priority frogs: northern Philoria. (NESP Threatened Species Recovery Hub Project 8.1.3 report, Brisbane, 2021).Vanderwal, J. All Future Climate Layers for Australia: 1 km Resolution (James Cook University, 2012).
Google Scholar 
Torkkola, J. J., Chauvenet, A. L. M., Hines, H. & Oliver, P. M. Distributional modelling, megafires and data gaps highlight probable underestimation of climate change risk for two lizards from Australia’s montane rainforests. Austral Ecol. 47(2), 365–379 (2021).Article 

Google Scholar 
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
Geoscience, A. Digital Elevation Model (DEM) 25 Metre Grid of Australia derived from LiDAR. (Geoscience Australia, 2015).Thuiller, W., Georges, D., Engler, R. & Breiner, F. (2014). biomod2: Ensemble platform for species distribution modeling. R package version 3.1-64. http://CRANR-project.org/package=biomod2. Accessed Feb 2021.Feng, X., Park, D. S., Liang, Y., Pandey, R. & Papeş, M. Collinearity in ecological niche modeling: Confusions and challenges. Ecol. Evol. 9, 10365–10376 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Thuiller, W. BIOMOD: Optimising predictions of species distributions and projecting potential future shifts under global change. Glob. Change Biol. 9, 1353–1362 (2003).Article 
ADS 

Google Scholar 
MacKenzie, D. I., Nichols, J. D., Hines, J. E., Knutson, M. G. & Franklin, A. B. Estimating site occupancy, colonisation, and local extinction when a species is detected imperfectly. Ecology 84, 2200–2207 (2003).Article 

Google Scholar 
Schwalm, C. R., Glendon, S. & Duffy, P. B. RCP8.5 tracks cumulative CO2 emissions. Proc. Natl. Acad. Sci. 117, 19656–19657 (2020).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).Article 
CAS 
PubMed 
ADS 

Google Scholar 
Campos-Cerqueira, M. & Mitchell Aide, T. Lowland extirpation of anuran populations on a tropical mountain. PeerJ 2017, 1–10 (2017).
Google Scholar 
Pounds, J. A., Fogden, M. P. L. & Campbell, J. H. Biological response to climate change on a tropical mountain. Nature 398, 611–615 (1999).Article 
CAS 
ADS 

Google Scholar 
Raxworthy, C. J. et al. Extinction vulnerability of tropical montane endemism from warming and upslope displacement: A preliminary appraisal for the highest massif in Madagascar. Glob. Change Biol. 14, 1703–1720 (2008).Article 
ADS 

Google Scholar 
Fordham, D. A. et al. Extinction debt from climate change for frogs in the wet tropics. Biol. Lett. 12, 20160236 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Hoffmann, E. P., Williams, K., Hipsey, M. R. & Mitchell, N. J. Drying microclimates threaten persistence of natural and translocated populations of threatened frogs. Biodivers. Conserv. 30(1), 15–34 (2020).Article 

Google Scholar 
Scheele, B. C., Driscoll, D. A., Fischer, J. & Hunter, D. A. Decline of an endangered amphibian during an extreme climatic event. Ecosphere 3, 101 (2012).Article 

Google Scholar 
Legge, S. et al. Rapid assessment of the biodiversity impacts of the 2019–2020 Australian megafires to guide urgent management intervention and recovery and lessons for other regions. Divers. Distrib. 28, 571–591 (2022).Article 

Google Scholar 
Canadell, J. G. et al. Multi-decadal increase of forest burned area in Australia is linked to climate change. Nat. Commun. 12, 6921 (2021).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Hisano, M., Searle, E. B. & Chen, H. Y. H. Biodiversity as a solution to mitigate climate change impacts on the functioning of forest ecosystems. Biol. Rev. 93, 439–456 (2018).Article 
PubMed 

Google Scholar 
Holz, A., Wood, S. W., Veblen, T. T. & Bowman, D. M. J. S. Effects of high-severity fire drove the population collapse of the subalpine Tasmanian endemic conifer Athrotaxis cupressoides. Glob. Change Biol. 21, 445–458 (2015).Article 
ADS 

Google Scholar 
Hutley, L. B., Doley, D., Yates, D. J. & Boonsaner, A. Water balance of an australian subtropical rainforest at altitude: The ecological and physiological significance of intercepted cloud and fog. Aust. J. Bot. 45, 311–329 (1997).Article 

Google Scholar 
Godfree, R. C. et al. Implications of the 2019–2020 megafires for the biogeography and conservation of Australian vegetation. Nat. Commun. 12, 1023 (2021).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Hennessy, K. et al. Climate Change Impacts on Fire-Weather in South-East Australia (Commonwealth Scientific and Industrial Research Organisation, 2005).
Google Scholar 
Moriondo, M. et al. Potential impact of climate change on fire risk in the Mediterranean area. Clim. Res. 31, 85–95 (2006).Article 

Google Scholar 
Pitman, A. J., Narisma, G. T. & McAneney, J. The impact of climate change on the risk of forest and grassland fires in Australia. Clim. Change 84, 383–401 (2007).Article 
ADS 

Google Scholar 
Caughley, G. Directions in conservation biology. J. Anim. Ecol. 63, 215–244 (1994).Article 

Google Scholar 
Scheele, B. C. et al. Conservation translocations for amphibian species threatened by chytrid fungus: A review, conceptual framework, and recommendations. Conserv. Sci. Pract. 3, e524 (2021).
Google Scholar 
Rudin-Bitterli, T. S., Evans, J. P. & Mitchell, N. J. Geographic variation in adult and embryonic desiccation tolerance in a terrestrial-breeding frog. Evolution 74, 1186–1199 (2020).Article 
CAS 
PubMed 

Google Scholar 
Ashcroft, M. B. Identifying refugia from climate change. J. Biogeogr. 37, 1407–1413 (2010).
Google Scholar 
Keppel, G. et al. Refugia: Identifying and understanding safe havens for biodiversity under climate change. Glob. Ecol. Biogeogr. 21, 393–404 (2012).Article 

Google Scholar 
Selwood, K. E. & Zimmer, H. C. Refuges for biodiversity conservation: A review of the evidence. Biol. Conserv. 245, 108502 (2020).Article 

Google Scholar  More

Each sedimentary sequence from the three excavated ponds (Tampolove [TAMP], Ankatoke [ANKA], and Andranobe [ANDR]) includes a layer of clay (defined as zone 2), which separates the surface soil formation (zone 1) from the underlying fossiliferous muddy sand and bedrock (zone 3, Figs. S4–S7 & S9). Details regarding the composition of this sediment and its microfossils are given in Appendix-Results-Excavation (Figs. S9–S12).Subfossils and chronologyCoastal survey recovered mostly zebu bones on exposed sandy surfaces, some pygmy hippo and giant tortoise bones on the margins of shallow ponds, and giant tortoise carapace under overhanging limestone outcrops (Appendix-Results-Survey, Fig. S3). A high proportion of surface bone failed 14C analysis (~ 55%, Table S1), yet the successfully analyzed specimens (n = 8) span up to 3390–3220 calibrated years before present (cal BP, PSUAMS 8681, 3150 ± 15 14C BP, a hippo molar). Pond deposits that are relatively deep include bones that cover a relatively long period of time (Figs. S14–S16, Dataset S6). This span ranges from ~ 6000 years at TAMP (~ 120 cm deep) to ~ 2500 years at ANDR (~ 100 cm deep), with the oldest bones present in the fossiliferous sedimentary zone 3 and scarce bones in the overlying clay (zone 2).Zone 3Most bones in this layer are relatively intact and include readily identifiable pygmy hippo long bones and cranial fragments (e.g., Fig. S13a,f), giant tortoise carapace and plastron fragments (Fig. S13d), ratite eggshell and long bones (Fig. S13c,m), and crocodile scutes, cranial fragments, and teeth (Fig. S13b). Scarce bones of a duck (genus Anas) were recovered at ANDR. Remains of subfossil lemurs were scarce or absent, but they may be represented by an unknown type of bone fragment identified through protein fingerprinting (ANDR-1-5-55, Dataset S3). The widespread success of collagen extraction from these bones attests to the excellent preservation of organics in this zone. ANKA also includes keratin (mostly in the form of crocodile claws, e.g., Fig. S13i), as well as two rounded agates found associated with ratite eggshell (Fig. S13m).Remains of a juvenile pygmy hippo were recovered from both TAMP and ANDR (a femur and tibia, respectively, Dataset S3). The epiphyses of some of the pygmy hippo long bones have gnaw marks (Fig. S13f), and none of the bones include chop marks. In association with these bones towards the top of this zone are some large ( > 1 cm diameter) charcoal fragments and scarce bones of bushpig (Fig. S13k) and zebu (Fig. S13e). Protein fingerprinting identified a screened fragment of a non-zebu bovid in ANKA zone 3 and confirmed that a tentatively identified bushpig canine fragment (ANKA 1-4-151) belonged to a hippo. This zone at TAMP and ANDR also includes occasional mangrove whelk (Terebralia palustris) shells (Fig. S13g). These whelks currently live at least ~ 500 m distant from these ponds, and whelk shells at ANDR each have an irregular hole above the operculum.The span of time represented by bones in zone 3 ranges up to ~ 4000 years (~ 6000–2000 cal BP at TAMP, Fig. S14). Confirmed introduced animal bones from zone 3 failed direct 14C analysis. There are multiple examples of directly 14C-dated bone in close stratigraphic association that nonetheless differ in age by  > 1000 years, and there are a couple of examples of bones from the same individual that are separated stratigraphically. For example, two giant tortoise carapace and plastron fragments from TAMP that have indistinguishable 14C ages are separated by 22 cm of sediment (PSUAMS 8670 comes from 112 cm depth, and PSUAMS 8668 comes from 90 cm depth).Although ANKA produced what is thus far the oldest directly 14C dated pygmy hippo bone from a coastal subfossil site (PSUAMS 9383, 4380 ± 25 BP, 5030–4840 cal BP), the mean calibrated age of hippos from the Tampolove excavations (n = 11, x̄ = 2858 cal BP, SD = 972 yr) is significantly less than that of the giant tortoises (n = 9, x̄ = 4582 cal BP, SD = 705 yr, t(18) = − 4.4, p  2000 years older than a closely associated charcoal sample (38 cm depth, PSUAMS 8849, 575 ± 30 14C BP, 630–510 cal BP), which makes this molar comparable in age to bone from zone 3. Consequently, the youngest directly 14C-dated ancient bone from the Tampolove excavations comes from the lowermost zone 3: a pygmy hippo’s vertebra recovered at 90 cm depth at TAMP (PSUAMS 8730, 1865 ± 15 14C BP, 1819–1705 cal BP). Though poorly constrained in time, the deposition of zone 2 sediment came sometime within the past two millennia, which witnessed marine regression and dry intervals recorded in both the δ18O record of a nearby speleothem27 and the salinization of a nearby pan36. Previously directly 14C-dated bone collected around Tampolove attests to the local persistence of at least pygmy hippos and giant tortoises until the start of the last millennium (n = 15), and an atlas from Lamboara/Lamboharana is in fact the most recent confidently dated pygmy hippo bone from the island (PSUAMS 5629, 1100 ± 15 14C BP, 980–930 cal BP).Figure 4Cutmarked pygmy hippo femur recovered from Tampolove during recent excavation at ~ 40 cm depth (TAMP-1-2-61, above), and previously-recovered and directly 14C-dated (~ 3500 and 1600 cal BP37) cutmarked pygmy hippo femora from the nearby site of Lamboara/Lamboharana that are currently housed in the National Museum of Natural History in Paris (MAD 1709 & MAD 1710, below). Four views highlight three locations of cutmarks on the broken shaft of TAMP-1-2-61, and the inset frames show 20 × magnification of these areas, with corresponding orientations given by red lines. Note that the false color insets of TAMP-1-2-61 are meant to highlight linear edges and crevices, and the overview photos of all three femur fragments are on the same scale.Full size imageZone 1A fragment of iron (from TAMP, 16 cm depth) and sparse ceramic fragments (from ANKA, 3 & 9 cm depth) are present only in zone 1, and three 14C dates from TAMP and ANKA suggest that these specimens span the past ~ 200 years (Figs. S14–S15).CharcoalThe directly 14C dated charcoal spans all three stratigraphic zones yet consistently dates to the past millennium (Figs. S14–16). Multiple charcoal samples from different excavated ponds have practically indistinguishable 14C ages (Table S2), and much of the charcoal from Tampolove formed during peaks in the deposition of macrocharcoal at nearby Namonte (17 km distant; Fig. 5A). The onset of directly 14C-dated charcoal deposition approximately coincides with a decrease in Asafora speleothem δ18O values and with multiple directly 14C-dated first and final local occurrences of large animals. While directly 14C dated charcoal is limited to the past millennium, microcharcoal particles were abundant in all TAMP sediment samples (x̄ ± SD = 2.0 × 106 ± 2.8 × 106 particles). Additionally, microcharcoal is relatively abundant near the bottom of TAMP and ANKA, which contains bones that span ~ 6000–2000 cal BP (Fig. 5B).Figure 5Records of fire, drought, and faunal turnover from the vicinity of Tampolove within the past 1200 years, with dashed horizontal lines for reference (5A), and macrocharcoal concentrations from the excavated ponds, with depth intervals containing directly 14C-dated charcoal that spans the past millennium marked in red (5B). The past 1200 years includes the entire summed calibrated distribution of the 10 directly dated prebomb charcoal fragments from the Tampolove excavations. The calibrated probability distributions associated with the latest dates from endemic megafauna bone (giant tortoises and pygmy hippos) and earliest dates from introduced animal bone (zebu cattle and bushpigs) are shown as black distributions, and 95% of each distribution is bracketed. Considering directly dated remains within the past 4 ka from hippos (n = 26), giant tortoises (n = 18), and zebu (n = 9) and the assumption that bones were deposited uniformly over time, the grey distributions and bracketed 95% credible intervals give estimates of extirpation and arrival times. As in Fig. 3, the red line on the Asafora record follows from BCPA.Full size image More

Caro, T., Izzo, A., Reiner, R. C., Walker, H. & Stankowich, T. The function of zebra stripes. Nat. Commun. 5, 3535 (2014).Article 
PubMed 

Google Scholar 
Merilaita, S., Scott-Samuel, N. E. & Cuthill, I. C. How camouflage works. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160341 (2017).Article 

Google Scholar 
Rowland, H. M. From Abbott Thayer to the present day: what have we learned about the function of countershading? Philos. Trans. R. Soc. B Biol. Sci. 364, 519–527 (2009).Article 

Google Scholar 
Rogalla, S. et al. The evolution of darker wings in seabirds in relation to temperature-dependent flight efficiency. J. R. Soc. Interface 18, 20210236.Malling Olsen, K. Gulls of the World. (Princeton University Press, 2018).Jawor, J. M. & Breitwisch, R. Melanin Ornaments, Honesty, and Sexual Selection. Auk 120, 249–265 (2003).Article 

Google Scholar 
Field, D. J. et al. Melanin Concentration Gradients in Modern and Fossil Feathers. PLOS ONE 8, e59451 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
McNamara, M. E. et al. Decoding the Evolution of Melanin in Vertebrates. Trends Ecol. Evol. 36, 430–443 (2021).Article 
CAS 
PubMed 

Google Scholar 
Dufour, P. et al. Plumage colouration in gulls responds to their non-breeding climatic niche. Glob. Ecol. Biogeogr. 29, 1704–1715 (2020).Article 

Google Scholar 
Hassanalian, M., Abdelmoula, H., Ben Ayed, S. & Abdelkefi, A. Thermal impact of migrating birds’ wing color on their flight performance: Possibility of new generation of biologically inspired drones. J. Therm. Biol. 66, 27–32 (2017).Article 
CAS 
PubMed 

Google Scholar 
Hassanalian, M., Throneberry, G., Ali, M., Ben Ayed, S. & Abdelkefi, A. Role of wing color and seasonal changes in ambient temperature and solar irradiation on predicted flight efficiency of the Albatross. J. Therm. Biol. 71, 112–122 (2018).Article 
CAS 
PubMed 

Google Scholar 
Spear, L. B. & Ainley, D. G. Flight behaviour of seabirds in relation to wind direction and wing morphology. Ibis 139, 221–233 (1997).Article 

Google Scholar 
Sullivan, T. N., Meyers, M. A. & Arzt, E. Scaling of bird wings and feathers for efficient flight. Sci. Adv. 5, eaat4269.Pennycuick, C. J. Modelling the Flying Bird. (Elsevier, 2008).Buffo, J., Fritschen, L. J. & Murphy, J. L. Direct Solar Radiation on Various Slopes from 0 to 60 Degrees North Latitude. (Pacific Northwest Forest and Range Experiment Station, Forest Service, U.S. Department of Agriculture, 1972).Hansen, T. F. Stabilizing Selection and the Comparative Analysis of Adaptation. Evolution 51, 1341–1351 (1997).Article 
PubMed 

Google Scholar 
Hansen, T. F., Pienaar, J. & Orzack, S. H. A Comparative Method for Studying Adaptation to a Randomly Evolving Environment. Evolution 62, 1965–1977 (2008).PubMed 

Google Scholar 
Roulin, A. Condition-dependence, pleiotropy and the handicap principle of sexual selection in melanin-based colouration. Biol. Rev. 91, 328–348 (2016).Article 
PubMed 

Google Scholar 
Rayner, J. M. V. FORM AND FUNCTION IN AVIAN FLIGHT. in Current Ornithology vol. 5 1–66 (Plenum Press, 1988).Schreiber, E. A. & Burger, J. Biology of Marine Birds. (CRC Press, 2001).Clusella Trullas, S., van Wyk, J. H. & Spotila, J. R. Thermal melanism in ectotherms. J. Therm. Biol. 32, 235–245 (2007).Article 

Google Scholar 
Shamoun-Baranes, J. & van Loon, E. Energetic influence on gull flight strategy selection. J. Exp. Biol. 209, 3489–3498 (2006).Article 
PubMed 

Google Scholar 
Pennycuick, C. J. & Lighthill, M. J. The flight of petrels and albatrosses (procellariiformes), observed in South Georgia and its vicinity. Philos. Trans. R. Soc. Lond. B Biol. Sci. 300, 75–106 (1982).Article 

Google Scholar 
Rogalla, S., Shawkey, M. D. & D’Alba, L. Thermal effects of plumage coloration. Ibis 164, 933–948 (2022).Article 

Google Scholar 
Flinks, H. & Salewski, V. Quantifying the effect of feather abrasion on wing and tail lengths measurements. J. Ornithol. 153, 1053–1065 (2012).Article 

Google Scholar 
Hill, G. E. Sexiness, Individual Condition, and Species Identity: The Information Signaled by Ornaments and Assessed by Choosing Females. Evol. Biol. 42, 251–259 (2015).Article 

Google Scholar 
Sonsthagen, S. A. et al. Recurrent hybridization and recent origin obscure phylogenetic relationships within the ‘white-headed’ gull (Larus sp.) complex. Mol. Phylogenet. Evol. 103, 41–54 (2016).Article 
PubMed 

Google Scholar 
Howell, S. & Dunn, J. A reference guide to gulls of the Americas. (Houghton Mifflin Company, 2007).Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).Article 
CAS 
PubMed 

Google Scholar 
Tobias, J. A. et al. AVONET: morphological, ecological and geographical data for all birds. Ecol. Lett. 25, 581–597 (2022).Article 
PubMed 

Google Scholar 
Yalden, D. Wing area, wing growth and wing loading of Common Sandpipers Actitis hypoleucos. Wader Study Group Bull. 119, 84–88 (2012).
Google Scholar 
Ho, L. S. T. & Ane, C. A linear-time algorithm for Gaussian and non-Gaussian trait evolution models. Syst. Biol. 63, 397–408 (2014).Article 
PubMed 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (2021).Cooper, N., Thomas, G. H., Venditti, C., Meade, A. & Freckleton, R. P. A cautionary note on the use of Ornstein Uhlenbeck models in macroevolutionary studies. Biol. J. Linn. Soc. 118, 64–77 (2016).Article 

Google Scholar 
Harmon, L. J. Phylogenetic Comparative Methods: Learning from Trees. (CreateSpace Independent Publishing Platform, 2018).Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
Douma, J. C. & Weedon, J. T. Analysing continuous proportions in ecology and evolution: a practical introduction to beta and Dirichlet regression. Methods Ecol. Evol. 10, 1412–1430 (2019).Article 

Google Scholar 
Li, M. & Bolker, B. wzmli/phyloglmm: First release of phylogenetic comparative analysis in lme4-verse. https://doi.org/10.5281/zenodo.2639887 (2019).Smithson, M. & Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11, 54–71 (2006).Article 
PubMed 

Google Scholar 
Goumas, M. Dark wing pigmentation as a mechanism for improved flight efficiency in the Larinae. Zenodo, https://doi.org/10.5281/zenodo.7156454 (2022). More

Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).Article 

Google Scholar 
Tscharntke, T. et al. Global food security, biodiversity conservation and the future of agricultural intensification. Biol. Conserv. 151, 53–59 (2012).Article 

Google Scholar 
DeFries, R. S., Foley, J. A. & Asner, G. P. Land-use choices: balancing human needs and ecosystem function. Front. Ecol. Environ. 2, 249–257 (2004).Article 

Google Scholar 
Matson, P. A., Parton, W. J., Power, A. G. & Swift, M. J. Agricultural intensification and ecosystem properties. Science 277, 504–509 (1997).Article 

Google Scholar 
Zabel, F. et al. Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity. Nat. Commun. 10, 2844 (2019).Article 

Google Scholar 
Tsiafouli, M. A. et al. Intensive agriculture reduces soil biodiversity across Europe. Glob. Change Biol. 21, 973–985 (2015).Article 

Google Scholar 
Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl Acad. Sci. USA 108, 20260–20264 (2011).Article 

Google Scholar 
Mehrabi, Z., Ellis, E. C. & Ramankutty, N. The challenge of feeding the world while conserving half the planet. Nat. Sustain. 1, 409–412 (2018).Article 

Google Scholar 
Kopittke, P. M., Menzies, N. W., Wang, P., McKenna, B. A. & Lombi, E. Soil and the intensification of agriculture for global food security. Environ. Int. 132, 105078 (2019).Article 

Google Scholar 
Pereira, P., Bogunovic, I., Muñoz-Rojas, M. & Brevik, E. C. Soil ecosystem services, sustainability, valuation and management. Curr. Opin. Environ. Sci. Health 5, 7–13 (2018).Article 

Google Scholar 
Bai, Z. G., Dent, D. L., Olsson, L. & Schaepman, M. E. Proxy global assessment of land degradation. Soil Use Manage. 24, 223–234 (2008).Article 

Google Scholar 
Stockmann, U., Minasny, B. & McBratney, A. B. How fast does soil grow? Geoderma 216, 48–61 (2014).Article 

Google Scholar 
Wall, D. H., Nielsen, U. N. & Six, J. Soil biodiversity and human health. Nature 528, 69–76 (2015).Article 

Google Scholar 
Wilhelm, R. C., van Es, H. M. & Buckley, D. H. Predicting measures of soil health using the microbiome and supervised machine learning. Soil Biol. Biochem. 164, 108472 (2022).Article 

Google Scholar 
König, S., Vogel, H.-J., Harms, H. & Worrich, A. Physical, chemical and biological effects on soil bacterial dynamics in microscale models. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2020.00053 (2020).Six, J., Frey, S. D., Thiet, R. K. & Batten, K. M. Bacterial and fungal contributions to carbon sequestration in agroecosystems. Soil Sci. Soc. Am. J. 70, 555–569 (2006).Article 

Google Scholar 
Status of the World’s Soil Resources (SWSR) — Main Report, 650 (FAO/Intergovernmental Technical Panel on Soils, 2015).Singh, B. K., Bardgett, R. D., Smith, P. & Reay, D. S. Microorganisms and climate change: terrestrial feedbacks and mitigation options. Nat. Rev. Microbiol. 8, 779–790 (2010).Article 

Google Scholar 
Gougoulias, C., Clark, J. M. & Shaw, L. J. The role of soil microbes in the global carbon cycle: tracking the below-ground microbial processing of plant-derived carbon for manipulating carbon dynamics in agricultural systems. J. Sci. Food Agric. 94, 2362–2371 (2014).Article 

Google Scholar 
Naylor, D. et al. Soil microbiomes under climate change and implications for carbon cycling. Annu. Rev. Environ. Resour. 45, 29–59 (2020).Article 

Google Scholar 
Berg, I. A. Ecological aspects of the distribution of different autotrophic CO2 fixation pathways. Appl. Environ. Microbiol. 77, 1925–1936 (2011).Article 

Google Scholar 
Yuan, H., Ge, T., Chen, C., O’Donnell, A. G. & Wu, J. Significant role for microbial autotrophy in the sequestration of soil carbon. Appl. Environ. Microbiol. 78, 2328–2336 (2012).Article 

Google Scholar 
Stevenson, F. J. Humus Chemistry: Genesis, Composition, Reactions 2nd edition (Wiley, 1994).Liang, C., Amelung, W., Lehmann, J. & Kästner, M. Quantitative assessment of microbial necromass contribution to soil organic matter. Glob. Change Biol. 25, 3578–3590 (2019).Article 

Google Scholar 
Crowther, T. W. et al. Biotic interactions mediate soil microbial feedbacks to climate change. Proc. Natl Acad. Sci. USA 112, 7033–7038 (2015).Article 

Google Scholar 
Angel, R., Claus, P. & Conrad, R. Methanogenic archaea are globally ubiquitous in aerated soils and become active under wet anoxic conditions. ISME J. 6, 847–862 (2012).Article 

Google Scholar 
Conrad, R. The global methane cycle: recent advances in understanding the microbial processes involved. Environ. Microbiol. Rep. 1, 285–292 (2009).Article 

Google Scholar 
Saunois, M. et al. The global methane budget 2000–2017. Earth Syst. Sci. Data 12, 1561–1623 (2020).Article 

Google Scholar 
Dutta, H. & Dutta, A. The microbial aspect of climate change. Energy Ecol. Environ. 1, 209–232 (2016).Article 

Google Scholar 
Hu, H.-W., Chen, D. & He, J.-Z. Microbial regulation of terrestrial nitrous oxide formation: understanding the biological pathways for prediction of emission rates. FEMS Microbiol. Rev. 39, 729–749 (2015).Article 

Google Scholar 
Tian, H. et al. A comprehensive quantification of global nitrous oxide sources and sinks. Nature 586, 248–256 (2020).Article 

Google Scholar 
Marschner, P. in Nutrient Cycling in Terrestrial Ecosystems (eds Petra, M. & Zdenko, R.) 159–182 (Springer, 2007).Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive Earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).Article 

Google Scholar 
Saccá, M. L., Barra Caracciolo, A., Di Lenola, M. & Grenni, P. Soil Biological Communities and Ecosystem Resilience (eds Martin, L., Paola, G. & Mauro, G.) 9–24 (Springer, 2017).Jetten, M. S. M. The microbial nitrogen cycle. Environ. Microbiol. 10, 2903–2909 (2008).Article 

Google Scholar 
Kuypers, M. M. M., Marchant, H. K. & Kartal, B. The microbial nitrogen-cycling network. Nat. Rev. Microbiol. 16, 263–276 (2018).Article 

Google Scholar 
Canfield, D. E., Glazer, A. N. & Falkowski, P. G. The evolution and future of Earth’s nitrogen cycle. Science 330, 192–196 (2010).Article 

Google Scholar 
Clark, I. M., Hughes, D. J., Fu, Q., Abadie, M. & Hirsch, P. R. Metagenomic approaches reveal differences in genetic diversity and relative abundance of nitrifying bacteria and archaea in contrasting soils. Sci. Rep. 11, 15905 (2021).Article 

Google Scholar 
Philippot, L., Hallin, S. & Schloter, M. in Advances in Agronomy Vol. 96, 249–305 (Academic, 2007).Hayatsu, M., Tago, K. & Saito, M. Various players in the nitrogen cycle: diversity and functions of the microorganisms involved in nitrification and denitrification. Soil Sci. Plant Nutr. 54, 33–45 (2008).Article 

Google Scholar 
Mackey, K. R. M. & Paytan, A. in Encyclopedia of Microbiology 3rd edition (ed. Moselio, S.) 322–334 (Academic, 2009).Richardson, A. E., Barea, J.-M., McNeill, A. M. & Prigent-Combaret, C. Acquisition of phosphorus and nitrogen in the rhizosphere and plant growth promotion by microorganisms. Plant. Soil. 321, 305–339 (2009).Article 

Google Scholar 
Richardson, A. E. & Simpson, R. J. Soil microorganisms mediating phosphorus availability. Plant. Physiol. 156, 989–996 (2011).Article 

Google Scholar 
Li, J.-t. et al. A comprehensive synthesis unveils the mysteries of phosphate-solubilizing microbes. Biol. Rev. 96, 2771–2793 (2021).Article 

Google Scholar 
Kobae, Y. Dynamic phosphate uptake in arbuscular mycorrhizal roots under field conditions. Front. Env. Sci. https://doi.org/10.3389/fenvs.2018.00159 (2019).Oberson, A. & Joner, E. J. in Organic Phosphorus in the Environment (eds Turner, B. L. et al.) 133–164 (CABI, 2005).Compant, S., Samad, A., Faist, H. & Sessitsch, A. A review on the plant microbiome: ecology, functions, and emerging trends in microbial application. J. Adv. Res. 19, 29–37 (2019).Article 

Google Scholar 
Eichmann, R., Richards, L. & Schäfer, P. Hormones as go-betweens in plant microbiome assembly. Plant J. 105, 518–541 (2021).Article 

Google Scholar 
Nascimento, F. X., Hernandez, A. G., Glick, B. R. & Rossi, M. J. The extreme plant-growth-promoting properties of Pantoea phytobeneficialis MSR2 revealed by functional and genomic analysis. Environ. Microbiol. 22, 1341–1355 (2020).Article 

Google Scholar 
Valliere, J. M., Wong, W. S., Nevill, P. G., Zhong, H. & Dixon, K. W. Preparing for the worst: utilizing stress-tolerant soil microbial communities to aid ecological restoration in the Anthropocene. Ecol. Solut. Evid. 1, e12027 (2020).Article 

Google Scholar 
Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant–microbiome interactions: from community assembly to plant health. Nat. Rev. Microbiol. 18, 607–621 (2020).Article 

Google Scholar 
Rolfe, S. A., Griffiths, J. & Ton, J. Crying out for help with root exudates: adaptive mechanisms by which stressed plants assemble health-promoting soil microbiomes. Curr. Opin. Microbiol. 49, 73–82 (2019).Article 

Google Scholar 
Costa, O. Y. A., Raaijmakers, J. M. & Kuramae, E. E. Microbial extracellular polymeric substances: ecological function and impact on soil aggregation. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.01636 (2018).Sharma, A. et al. Phytohormones regulate accumulation of osmolytes under abiotic stress. Biomolecules 9, 285 (2019).Article 

Google Scholar 
Singh, D. P. et al. Microbial inoculation in rice regulates antioxidative reactions and defense related genes to mitigate drought stress. Sci. Rep. 10, 4818 (2020).Article 

Google Scholar 
Bárzana, G., Aroca, R., Bienert, G. P., Chaumont, F. & Ruiz-Lozano, J. M. New insights into the regulation of aquaporins by the arbuscular mycorrhizal symbiosis in maize plants under drought stress and possible implications for plant performance. Mol. Plant Microbe Interact. 27, 349–363 (2014).Article 

Google Scholar 
Gamalero, E. & Glick, B. R. Bacterial modulation of plant ethylene levels. Plant Physiol. 169, 13–22 (2015).Article 

Google Scholar 
Le Pioufle, O., Ganoudi, M., Calonne-Salmon, M., Ben Dhaou, F. & Declerck, S. Rhizophagus irregularis MUCL 41833 improves phosphorus uptake and water use efficiency in maize plants during recovery from drought stress. Front. Plant Sci. https://doi.org/10.3389/fpls.2019.00897 (2019).Begum, N. et al. Role of arbuscular mycorrhizal fungi in plant growth regulation: implications in abiotic stress tolerance. Front. Plant Sci. https://doi.org/10.3389/fpls.2019.01068 (2019).Köhl, J., Kolnaar, R. & Ravensberg, W. J. Mode of action of microbial biological control agents against plant diseases: relevance beyond efficacy. Front. Plant Sci. https://doi.org/10.3389/fpls.2019.00845 (2019).Hu, L. et al. Root exudate metabolites drive plant–soil feedbacks on growth and defense by shaping the rhizosphere microbiota. Nat. Commun. 9, 2738 (2018).Article 

Google Scholar 
Granato, E. T., Meiller-Legrand, T. A. & Foster, K. R. The evolution and ecology of bacterial warfare. Curr. Biol. 29, R521–R537 (2019).Article 

Google Scholar 
Shah, P. A. & Pell, J. K. Entomopathogenic fungi as biological control agents. Appl. Microbiol. Biotechnol. 61, 413–423 (2003).Article 

Google Scholar 
Soares, F. Ed. F., Sufiate, B. L. & de Queiroz, J. H. Nematophagous fungi: far beyond the endoparasite, predator and ovicidal groups. Agric. Nat. Resour. 52, 1–8 (2018).
Google Scholar 
Nordbring-Hertz, B., Jansson, H.-B. & Tunlid, A. in eLS (Wiley, 2011); https://doi.org/10.1002/9780470015902.a0000374.pub3.Tian, B., Yang, J. & Zhang, K.-Q. Bacteria used in the biological control of plant-parasitic nematodes: populations, mechanisms of action, and future prospects. FEMS Microbiol. Ecol. 61, 197–213 (2007).Article 

Google Scholar 
Shafi, J., Tian, H. & Ji, M. Bacillus species as versatile weapons for plant pathogens: a review. Biotechnol. Biotechnol. Equip. 31, 446–459 (2017).Article 

Google Scholar 
Bravo, A., Likitvivatanavong, S., Gill, S. S. & Soberón, M. Bacillus thuringiensis: a story of a successful bioinsecticide. Insect Biochem. Mol. Biol. 41, 423–431 (2011).Article 

Google Scholar 
Schnepf, E. et al. Bacillus thuringiensis and its pesticidal crystal proteins. Microbiol. Mol. Biol. Rev. 62, 775–806 (1998).Article 

Google Scholar 
Wei, J.-Z. et al. Bacillus thuringiensis crystal proteins that target nematodes. Proc. Natl Acad. Sci. USA 100, 2760–2765 (2003).Article 

Google Scholar 
Flury, P. et al. Insect pathogenicity in plant-beneficial pseudomonads: phylogenetic distribution and comparative genomics. ISME J. 10, 2527–2542 (2016).Article 

Google Scholar 
Vurukonda, S. S. K. P., Giovanardi, D. & Stefani, E. Plant growth promoting and biocontrol activity of Streptomyces spp. as endophytes. Int. J. Mol. Sci. 19, 952 (2018).Article 

Google Scholar 
Whipps, J. M. Microbial interactions and biocontrol in the rhizosphere. J. Exp. Bot. 52, 487–511 (2001).Article 

Google Scholar 
MacLeod, M., Arp, H. P. H., Tekman, M. B. & Jahnke, A. The global threat from plastic pollution. Science 373, 61–65 (2021).Article 

Google Scholar 
Sharma, A. et al. Worldwide pesticide usage and its impacts on ecosystem. SN Appl. Sci. 1, 1446 (2019).Article 

Google Scholar 
Gworek, B., Kijeńska, M., Wrzosek, J. & Graniewska, M. Pharmaceuticals in the soil and plant environment: a review. Water Air Soil Pollut. 232, 145 (2021).Article 

Google Scholar 
Tang, F. H. M., Lenzen, M., McBratney, A. & Maggi, F. Risk of pesticide pollution at the global scale. Nat. Geosci. 14, 206–210 (2021).Article 

Google Scholar 
Zumstein, M. T. et al. Biodegradation of synthetic polymers in soils: tracking carbon into CO2 and microbial biomass. Sci. Adv. 4, eaas9024 (2018).Article 

Google Scholar 
Singh, B. & Singh, K. Microbial degradation of herbicides. Crit. Rev. Microbiol. 42, 245–261 (2016).
Google Scholar 
Teng, Y. & Chen, W. Soil microbiomes — a promising strategy for contaminated soil remediation: a review. Pedosphere 29, 283–297 (2019).Article 

Google Scholar 
Vogt, C. & Richnow, H. H. in Geobiotechnology II: Energy Resources, Subsurface Technologies, Organic Pollutants and Mining Legal Principles (eds Schippers, A. et al.) 123–146 (Springer, 2014).Mishra, S. et al. Recent advanced technologies for the characterization of xenobiotic-degrading microorganisms and microbial communities. Front. Bioeng. Biotechnol. https://doi.org/10.3389/fbioe.2021.632059 (2021).Rolli, E. et al. ‘Cry-for-help’ in contaminated soil: a dialogue among plants and soil microbiome to survive in hostile conditions. Environ. Microbiol. 23, 5690–5703 (2021).Article 

Google Scholar 
Wilpiszeski, R. L. et al. Soil aggregate microbial communities: towards understanding microbiome interactions at biologically relevant scales. Appl. Environ. Microbiol. https://doi.org/10.1128/aem.00324-19 (2019).Blott, S. J. & Pye, K. Particle size scales and classification of sediment types based on particle size distributions: review and recommended procedures. Sedimentology 59, 2071–2096 (2012).Article 

Google Scholar 
Totsche, K. U. et al. Microaggregates in soils. J. Plant Nutr. Soil Sci. 181, 104–136 (2018).Article 

Google Scholar 
Martin, J. P., Martin, W. P., Page, J. B., Raney, W. A. & de Ment, J. D. in Advances in Agronomy Vol. 7 (ed. Norman, A. G.) 1–37 (Academic, 1955).Chotte, J.-L. in Microorganisms in Soils: Roles in Genesis and Functions (eds Varma, A. & Buscot, F.) 107–119 (Springer, 2005).Oades, J. M. Soil organic matter and structural stability: mechanisms and implications for management. Plant. Soil. 76, 319–337 (1984).Article 

Google Scholar 
Six, J., Elliott, E. T. & Paustian, K. Soil macroaggregate turnover and microaggregate formation: a mechanism for C sequestration under no-tillage agriculture. Soil Biol. Biochem. 32, 2099–2103 (2000).Article 

Google Scholar 
Six, J., Bossuyt, H., Degryze, S. & Denef, K. A history of research on the link between (micro)aggregates, soil biota, and soil organic matter dynamics. Soil Tillage Res. 79, 7–31 (2004).Article 

Google Scholar 
Schlüter, S. et al. Microscale carbon distribution around pores and particulate organic matter varies with soil moisture regime. Nat. Commun. 13, 2098 (2022).Article 

Google Scholar 
Acosta, J. A., Martínez-Martínez, S., Faz, A. & Arocena, J. Accumulations of major and trace elements in particle size fractions of soils on eight different parent materials. Geoderma 161, 30–42 (2011).Article 

Google Scholar 
Sessitsch, A., Weilharter, A., Gerzabek, M. H., Kirchmann, H. & Kandeler, E. Microbial population structures in soil particle size fractions of a long-term fertilizer field experiment. Appl. Environ. Microbiol. 67, 4215–4224 (2001).Article 

Google Scholar 
Zhang, Q. et al. Fatty-acid profiles and enzyme activities in soil particle-size fractions under long-term fertilization. Soil Sci. Soc. Am. J. 80, 97–111 (2016).Article 

Google Scholar 
Hemkemeyer, M., Christensen, B. T., Martens, R. & Tebbe, C. C. Soil particle size fractions harbour distinct microbial communities and differ in potential for microbial mineralisation of organic pollutants. Soil Biol. Biochem. 90, 255–265 (2015).Article 

Google Scholar 
Briar, S. S. et al. The distribution of nematodes and soil microbial communities across soil aggregate fractions and farm management systems. Soil Biol. Biochem. 43, 905–914 (2011).Article 

Google Scholar 
Hemkemeyer, M., Dohrmann, A. B., Christensen, B. T. & Tebbe, C. C. Bacterial preferences for specific soil particle size fractions revealed by community analyses. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.00149 (2018).Hemkemeyer, M., Christensen, B. T., Tebbe, C. C. & Hartmann, M. Taxon-specific fungal preference for distinct soil particle size fractions. Eur. J. Soil Biol. 94, 103103 (2019).Article 

Google Scholar 
Christensen, B. T. & Olesen, J. E. Nitrogen mineralization potential of organomineral size separates from soils with annual straw incorporation. Eur. J. Soil Sci. 49, 25–36 (1998).Article 

Google Scholar 
Christensen, B. T. Decomposability of organic matter in particle size fractions from field soils with straw incorporation. Soil Biol. Biochem. 19, 429–435 (1987).Article 

Google Scholar 
Luo, G. et al. Long-term fertilisation regimes affect the composition of the alkaline phosphomonoesterase encoding microbial community of a vertisol and its derivative soil fractions. Biol. Fertil. Soils 53, 375–388 (2017).Article 

Google Scholar 
Mummey, D., Holben, W., Six, J. & Stahl, P. Spatial stratification of soil bacterial populations in aggregates of diverse soils. Microb. Ecol. 51, 404–411 (2006).Article 

Google Scholar 
Ranjard, L. et al. Heterogeneous cell density and genetic structure of bacterial pools associated with various soil microenvironments as determined by enumeration and DNA fingerprinting approach (RISA). Microb. Ecol. 39, 263–272 (2000).
Google Scholar 
Raynaud, X. & Nunan, N. Spatial ecology of bacteria at the microscale in soil. PLoS One 9, e87217 (2014).Article 

Google Scholar 
Rillig, M. C., Muller, L. A. H. & Lehmann, A. Soil aggregates as massively concurrent evolutionary incubators. ISME J. 11, 1943–1948 (2017).Article 

Google Scholar 
Trivedi, P. et al. Soil aggregation and associated microbial communities modify the impact of agricultural management on carbon content. Environ. Microbiol. 19, 3070–3086 (2017).Article 

Google Scholar 
Borer, B., Tecon, R. & Or, D. Spatial organization of bacterial populations in response to oxygen and carbon counter-gradients in pore networks. Nat. Commun. 9, 769 (2018).Article 

Google Scholar 
Kong, A. Y. Y., Hristova, K., Scow, K. M. & Six, J. Impacts of different N management regimes on nitrifier and denitrifier communities and N cycling in soil microenvironments. Soil Biol. Biochem. 42, 1523–1533 (2010).Article 

Google Scholar 
Bhattacharyya, S. S. et al. Soil carbon sequestration, greenhouse gas emissions, and water pollution under different tillage practices. Sci. Total Environ. 826, 154161 (2022).Article 

Google Scholar 
Zhang, W. et al. Differences in the nitrous oxide emission and the nitrifier and denitrifier communities among varying aggregate sizes of an arable soil in China. Geoderma 389, 114970 (2021).Article 

Google Scholar 
Tilman, D. et al. Forecasting agriculturally driven global environmental change. Science 292, 281–284 (2001).Article 

Google Scholar 
Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R. & Polasky, S. Agricultural sustainability and intensive production practices. Nature 418, 671–677 (2002).Article 

Google Scholar 
Hartmann, M., Frey, B., Mayer, J., Mader, P. & Widmer, F. Distinct soil microbial diversity under long-term organic and conventional farming. ISME J. 9, 1177–1194 (2015).Article 

Google Scholar 
Degrune, F. et al. The pedological context modulates the response of soil microbial communities to agroecological management. Front. Ecol. Environ. 7, 261 (2019).Article 

Google Scholar 
Longepierre, M. et al. Limited resilience of the soil microbiome to mechanical compaction within four growing seasons of agricultural management. ISME Commun. 1, 44 (2021).Article 

Google Scholar 
Delitte, M., Caulier, S., Bragard, C. & Desoignies, N. Plant microbiota beyond farming practices: a review. Front. Sustain. Food Syst. https://doi.org/10.3389/fsufs.2021.624203 (2021).Hobbs, P. R., Sayre, K. & Gupta, R. The role of conservation agriculture in sustainable agriculture. Phil. Trans. R. Soc. B 363, 543–555 (2008).Article 

Google Scholar 
Van den Putte, A., Govers, G., Diels, J., Gillijns, K. & Demuzere, M. Assessing the effect of soil tillage on crop growth: a meta-regression analysis on European crop yields under conservation agriculture. Eur. J. Agron. 33, 231–241 (2010).Article 

Google Scholar 
Six, J. et al. Soil organic matter, biota and aggregation in temperate and tropical soils — effects of no-tillage. Agronomie 22, 755–775 (2002).Article 

Google Scholar 
Young, I. M. & Ritz, K. Tillage, habitat space and function of soil microbes. Soil Tillage Res. 53, 201–213 (2000).Article 

Google Scholar 
Degrune, F. et al. Temporal dynamics of soil microbial communities below the seedbed under two contrasting tillage regimes. Front. Microbiol. 8, 1127 (2017).Article 

Google Scholar 
Pittelkow, C. M. et al. Productivity limits and potentials of the principles of conservation agriculture. Nature 517, 365–368 (2015).Article 

Google Scholar 
Babin, D. et al. Impact of long-term agricultural management practices on soil prokaryotic communities. Soil Biol. Biochem. https://doi.org/10.1016/j.soilbio.2018.11.002 (2018).Article 

Google Scholar 
Srour, A. Y. et al. Microbial communities associated with long-term tillage and fertility treatments in a corn–soybean cropping system. Front. Microbiol. https://doi.org/10.3389/fmicb.2020.01363 (2020).Cania, B. et al. Site-specific conditions change the response of bacterial producers of soil structure-stabilizing agents such as exopolysaccharides and lipopolysaccharides to tillage intensity. Front. Microbiol. https://doi.org/10.3389/fmicb.2020.00568 (2020).Cooper, H. V., Sjögersten, S., Lark, R. M. & Mooney, S. J. To till or not to till in a temperate ecosystem? Implications for climate change mitigation. Environ. Res. Lett. 16, 054022 (2021).Article 

Google Scholar 
Mangalassery, S. et al. To what extent can zero tillage lead to a reduction in greenhouse gas emissions from temperate soils? Sci. Rep. 4, 4586 (2014).Article 

Google Scholar 
Abdalla, M. et al. Conservation tillage systems: a review of its consequences for greenhouse gas emissions. Soil Use Manage. 29, 199–209 (2013).Article 

Google Scholar 
Six, J. et al. The potential to mitigate global warming with no-tillage management is only realized when practised in the long term. Glob. Change Biol. 10, 155–160 (2004).Article 

Google Scholar 
van Kessel, C. et al. Climate, duration, and N placement determine N2O emissions in reduced tillage systems: a meta-analysis. Glob. Change Biol. 19, 33–44 (2013).Article 

Google Scholar 
Hamza, M. A. & Anderson, W. K. Soil compaction in cropping systems: a review of the nature, causes and possible solutions. Soil Tillage Res. 82, 121–145 (2005).Article 

Google Scholar 
Schäffer, B., Stauber, M., Mueller, T. L., Muller, R. & Schulin, R. Soil and macro-pores under uniaxial compression. I. Mechanical stability of repacked soil and deformation of different types of macro-pores. Geoderma 146, 183–191 (2008).Article 

Google Scholar 
Hartmann, M. et al. Resistance and resilience of the forest soil microbiome to logging-associated compaction. ISME J. 8, 226–244 (2014).Article 

Google Scholar 
Sitaula, B. K., Hansen, S., Sitaula, J. I. B. & Bakken, L. R. Methane oxidation potentials and fluxes in agricultural soil: effects of fertilisation and soil compaction. Biogeochemistry 48, 323–339 (2000).Article 

Google Scholar 
Sitaula, B. K., Hansen, S., Sitaula, J. I. B. & Bakken, L. R. Effects of soil compaction on N2O emission in agricultural soil. Chemosphere Glob. Change Sci. 2, 367–371 (2000).Article 

Google Scholar 
Beckett, C. T. S. et al. Compaction conditions greatly affect growth during early plant establishment. Ecol. Eng. 106, 471–481 (2017).Article 

Google Scholar 
Reichert, J. M., Suzuki, L. E. A. S., Reinert, D. J., Horn, R. & Håkansson, I. Reference bulk density and critical degree-of-compactness for no-till crop production in subtropical highly weathered soils. Soil Tillage Res. 102, 242–254 (2009).Article 

Google Scholar 
von Wilpert, K. & Schäffer, J. Ecological effects of soil compaction and initial recovery dynamics: a preliminary study. Eur. J. For. Res. 125, 129–138 (2006).Article 

Google Scholar 
Tim Chamen, W. C., Moxey, A. P., Towers, W., Balana, B. & Hallett, P. D. Mitigating arable soil compaction: a review and analysis of available cost and benefit data. Soil Tillage Res. 146, 10–25 (2015).Article 

Google Scholar 
Beillouin, D., Ben-Ari, T., Malézieux, E., Seufert, V. & Makowski, D. Positive but variable effects of crop diversification on biodiversity and ecosystem services. Glob. Change Biol. 27, 4697–4710 (2021).Article 

Google Scholar 
Smith, R. G., Gross, K. L. & Robertson, G. P. Effects of crop diversity on agroecosystem function: crop yield response. Ecosystems 11, 355–366 (2008).Article 

Google Scholar 
Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).Article 

Google Scholar 
Galindo-Castañeda, T., Lynch, J. P., Six, J. & Hartmann, M. Improving soil resource uptake by plants through capitalizing on synergies between root architecture and anatomy and root-associated microorganisms. Front. Plant Sci. https://doi.org/10.3389/fpls.2022.827369 (2022).Venter, Z. S., Jacobs, K. & Hawkins, H.-J. The impact of crop rotation on soil microbial diversity: a meta-analysis. Pedobiologia 59, 215–223 (2016).Article 

Google Scholar 
Stefan, L., Hartmann, M., Engbersen, N., Six, J. & Schöb, C. Positive effects of crop diversity on productivity driven by changes in soil microbial composition. Front. Microbiol. 12, 660749 (2021).Article 

Google Scholar 
Peralta, A. L., Sun, Y., McDaniel, M. D. & Lennon, J. T. Crop rotational diversity increases disease suppressive capacity of soil microbiomes. Ecosphere 9, e02235 (2018).Article 

Google Scholar 
Abdalla, M. et al. A critical review of the impacts of cover crops on nitrogen leaching, net greenhouse gas balance and crop productivity. Glob. Change Biol. 25, 2530–2543 (2019).Article 

Google Scholar 
Bacq-Labreuil, A., Crawford, J., Mooney, S. J., Neal, A. L. & Ritz, K. Cover crop species have contrasting influence upon soil structural genesis and microbial community phenotype. Sci. Rep. 9, 7473 (2019).Article 

Google Scholar 
Kong, A. Y. Y. & Six, J. Microbial community assimilation of cover crop rhizodeposition within soil microenvironments in alternative and conventional cropping systems. Plant Soil 356, 315–330 (2012).Article 

Google Scholar 
Kim, N., Zabaloy, M. C., Guan, K. & Villamil, M. B. Do cover crops benefit soil microbiome? A meta-analysis of current research. Soil Biol. Biochem. 142, 107701 (2020).Article 

Google Scholar 
Alahmad, A. et al. Cover crops in arable lands increase functional complementarity and redundancy of bacterial communities. J. Appl. Ecol. 56, 651–664 (2019).Article 

Google Scholar 
Cloutier, M. L. et al. Fungal community shifts in soils with varied cover crop treatments and edaphic properties. Sci. Rep. 10, 6198 (2020).Article 

Google Scholar 
Finney, D. M., Buyer, J. S. & Kaye, J. P. Living cover crops have immediate impacts on soil microbial community structure and function. J. Soil Water Conserv. 72, 361–373 (2017).Article 

Google Scholar 
Vukicevich, E., Lowery, T., Bowen, P., Úrbez-Torres, J. R. & Hart, M. Cover crops to increase soil microbial diversity and mitigate decline in perennial agriculture. A review. Agron. Sustain. Dev. 36, 48 (2016).Article 

Google Scholar 
Sanz-Cobena, A. et al. Do cover crops enhance N2O, CO2 or CH4 emissions from soil in Mediterranean arable systems? Sci. Total Environ. 466-467, 164–174 (2014).Article 

Google Scholar 
Basche, A. D., Miguez, F. E., Kaspar, T. C. & Castellano, M. J. Do cover crops increase or decrease nitrous oxide emissions? A meta-analysis. J. Soil Water Conserv. 69, 471–482 (2014).Article 

Google Scholar 
Tribouillois, H., Constantin, J. & Justes, E. Cover crops mitigate direct greenhouse gases balance but reduce drainage under climate change scenarios in temperate climate with dry summers. Glob. Change Biol. 24, 2513–2529 (2018).Article 

Google Scholar 
Vanlauwe, B. et al. Integrated soil fertility management: operational definition and consequences for implementation and dissemination. Outlook Agric. 39, 17–24 (2010).Article 

Google Scholar 
Barzman, M. et al. Eight principles of integrated pest management. Agron. Sustain. Dev. 35, 1199–1215 (2015).Article 

Google Scholar 
Francioli, D. et al. Mineral vs. organic amendments: microbial community structure, activity and abundance of agriculturally relevant microbes are driven by long-term fertilization strategies. Front. Microbiol. https://doi.org/10.3389/fmicb.2016.01446 (2016).Lentendu, G. et al. Effects of long-term differential fertilization on eukaryotic microbial communities in an arable soil: a multiple barcoding approach. Mol. Ecol. 23, 3341–3355 (2014).Article 

Google Scholar 
Lori, M., Symnaczik, S., Mäder, P., De Deyn, G. & Gattinger, A. Organic farming enhances soil microbial abundance and activity — a meta-analysis and meta-regression. PLoS One 12, e0180442 (2017).Article 

Google Scholar 
Bebber, D. P. & Richards, V. R. A meta-analysis of the effect of organic and mineral fertilizers on soil microbial diversity. Appl. Soil Ecol. 175, 104450 (2022).Article 

Google Scholar 
Rillig, M. C., Tsang, A. & Roy, J. Microbial community coalescence for microbiome engineering. Front. Microbiol. https://doi.org/10.3389/fmicb.2016.01967 (2016).Loaiza Puerta, V., Pujol Pereira, E. I., Wittwer, R., van der Heijden, M. & Six, J. Improvement of soil structure through organic crop management, conservation tillage and grass-clover ley. Soil Tillage Res. 180, 1–9 (2018).Article 

Google Scholar 
Řezáčová, V. et al. Organic fertilization improves soil aggregation through increases in abundance of eubacteria and products of arbuscular mycorrhizal fungi. Sci. Rep. 11, 12548 (2021).Article 

Google Scholar 
Fonte, S. J., Kong, A. Y. Y., van Kessel, C., Hendrix, P. F. & Six, J. Influence of earthworm activity on aggregate-associated carbon and nitrogen dynamics differs with agroecosystem management. Soil Biol. Biochem. 39, 1014–1022 (2007).Article 

Google Scholar 
Fu, B., Chen, L., Huang, H., Qu, P. & Wei, Z. Impacts of crop residues on soil health: a review. Environ. Pollut. Bioavailab. 33, 164–173 (2021).Article 

Google Scholar 
Blanco-Canqui, H. & Lal, R. Crop residue removal impacts on soil productivity and environmental quality. Crit. Rev. Plant Sci. 28, 139–163 (2009).Article 

Google Scholar 
Yang, H. et al. Wheat straw return influences nitrogen-cycling and pathogen associated soil microbiota in a wheat–soybean rotation system. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.01811 (2019).Enebe, M. C. & Babalola, O. O. Soil fertilization affects the abundance and distribution of carbon and nitrogen cycling genes in the maize rhizosphere. AMB Express 11, 24 (2021).Article 

Google Scholar 
Skinner, C. et al. The impact of long-term organic farming on soil-derived greenhouse gas emissions. Sci. Rep. 9, 1702 (2019).Article 

Google Scholar 
Lazcano, C., Zhu-Barker, X. & Decock, C. Effects of organic fertilizers on the soil microorganisms responsible for N2O emissions: a review. Microorganisms https://doi.org/10.3390/microorganisms9050983 (2021).Tilston, E. L., Pitt, D. & Groenhof, A. C. Composted recycled organic matter suppresses soil-borne diseases of field crops. N. Phytol. 154, 731–740 (2002).Article 

Google Scholar 
Bonanomi, G., Antignani, V., Capodilupo, M. & Scala, F. Identifying the characteristics of organic soil amendments that suppress soilborne plant diseases. Soil Biol. Biochem. 42, 136–144 (2010).Article 

Google Scholar 
Briceño, G., Palma, G. & Durán, N. Influence of organic amendment on the biodegradation and movement of pesticides. Crit. Rev. Environ. Sci. Technol. 37, 233–271 (2007).Article 

Google Scholar 
Lehmann, J. & Joseph, S. Biochar for Environmental Management: Science, Technology and Implementation (Routledge, 2015).Wang, D., Fonte, S. J., Parikh, S. J., Six, J. & Scow, K. M. Biochar additions can enhance soil structure and the physical stabilization of C in aggregates. Geoderma 303, 110–117 (2017).Article 

Google Scholar 
Wang, J., Xiong, Z. & Kuzyakov, Y. Biochar stability in soil: meta-analysis of decomposition and priming effects. GCB Bioenergy 8, 512–523 (2016).Article 

Google Scholar 
Lehmann, J. et al. Biochar effects on soil biota — a review. Soil Biol. Biochem. 43, 1812–1836 (2011).Article 

Google Scholar 
Liu, X., Shi, Y., Zhang, Q. & Li, G. Effects of biochar on nitrification and denitrification-mediated N2O emissions and the associated microbial community in an agricultural soil. Environ. Sci. Pollut. Res. 28, 6649–6663 (2021).Article 

Google Scholar 
Zhang, L. et al. Effects of biochar application on soil nitrogen transformation, microbial functional genes, enzyme activity, and plant nitrogen uptake: a meta-analysis of field studies. GCB Bioenergy 13, 1859–1873 (2021).Article 

Google Scholar 
Deb, D., Kloft, M., Lässig, J. & Walsh, S. Variable effects of biochar and P solubilizing microbes on crop productivity in different soil conditions. Agroecol. Sustain. Food Syst. 40, 145–168 (2016).Article 

Google Scholar 
Li, X., Wang, T., Chang, S. X., Jiang, X. & Song, Y. Biochar increases soil microbial biomass but has variable effects on microbial diversity: a meta-analysis. Sci. Total Environ. 749, 141593 (2020).Article 

Google Scholar 
Yoo, G., Lee, Y. O., Won, T. J., Hyun, J. G. & Ding, W. Variable effects of biochar application to soils on nitrification-mediated N2O emissions. Sci. Total Environ. 626, 603–611 (2018).Article 

Google Scholar 
Verhoeven, E. et al. Toward a better assessment of biochar–nitrous oxide mitigation potential at the field scale. J. Environ. Qual. 46, 237–246 (2017).Article 

Google Scholar 
He, Y. et al. Effects of biochar application on soil greenhouse gas fluxes: a meta-analysis. GCB Bioenergy 9, 743–755 (2017).Article 

Google Scholar 
Wang, W. et al. Biochar application alleviated negative plant–soil feedback by modifying soil microbiome. Front. Microbiol. https://doi.org/10.3389/fmicb.2020.00799 (2020).Duan, M. et al. Effects of biochar on reducing the abundance of oxytetracycline, antibiotic resistance genes, and human pathogenic bacteria in soil and lettuce. Environ. Pollut. 224, 787–795 (2017).Article 

Google Scholar 
Liu, Y., Lonappan, L., Brar, S. K. & Yang, S. Impact of biochar amendment in agricultural soils on the sorption, desorption, and degradation of pesticides: a review. Sci. Total Environ. 645, 60–70 (2018).Article 

Google Scholar 
du Jardin, P. Plant biostimulants: definition, concept, main categories and regulation. Sci. Hortic. 196, 3–14 (2015).Article 

Google Scholar 
Le Mire, G. et al. Review: implementing plant biostimulants and biocontrol strategies in the agroecological management of cultivated ecosystems. Biotechnol. Agron. Soc. Environ. 20, 1–15 (2016).
Google Scholar 
Leggett, M. et al. Soybean response to inoculation with Bradyrhizobium japonicum in the United States and Argentina. Agron. J. 109, 1031–1038 (2017).Article 

Google Scholar 
Coniglio, A., Mora, V., Puente, M. & Cassán, F. in Microbial Probiotics for Agricultural Systems: Advances in Agronomic Use (eds Zúñiga-Dávila, D. et al.) 45–70 (Springer, 2019).Alori, E. T., Dare, M. O. & Babalola, O. O. in Sustainable Agriculture Reviews (ed. Lichtfouse, E.) 281–307 (Springer, 2017).Rillig, M. C. & Mummey, D. L. Mycorrhizas and soil structure. N. Phytol. 171, 41–53 (2006).Article 

Google Scholar 
Mawarda, P. C., Le Roux, X., Dirk van Elsas, J. & Salles, J. F. Deliberate introduction of invisible invaders: a critical appraisal of the impact of microbial inoculants on soil microbial communities. Soil. Biol. Biochem. 148, 107874 (2020).Article 

Google Scholar 
Liu, X., Le Roux, X. & Salles, J. F. The legacy of microbial inoculants in agroecosystems and potential for tackling climate change challenges. iScience 25, 103821 (2022).Article 

Google Scholar 
Cornell, C. et al. Do bioinoculants affect resident microbial communities? A meta-analysis. Front. Agron. https://doi.org/10.3389/fagro.2021.753474 (2021).Bender, S. F., Schlaeppi, K., Held, A. & Van der Heijden, M. G. A. Establishment success and crop growth effects of an arbuscular mycorrhizal fungus inoculated into Swiss corn fields. Agric. Ecosyst. Environ. 273, 13–24 (2019).Article 

Google Scholar 
Schreiter, S. et al. Soil type-dependent effects of a potential biocontrol inoculant on indigenous bacterial communities in the rhizosphere of field-grown lettuce. FEMS Microbiol. Ecol. 90, 718–730 (2014).Article 

Google Scholar 
Mueller, U. G. & Sachs, J. L. Engineering microbiomes to improve plant and animal health. Trends Microbiol. 23, 606–617 (2015).Article 

Google Scholar 
Kennedy, T. L., Suddick, E. C. & Six, J. Reduced nitrous oxide emissions and increased yields in California tomato cropping systems under drip irrigation and fertigation. Agric. Ecosyst. Environ. 170, 16–27 (2013).Article 

Google Scholar 
Fonte, S. J., Barrios, E. & Six, J. Earthworms, soil fertility and aggregate-associated soil organic matter dynamics in the Quesungual agroforestry system. Geoderma 155, 320–328 (2010).Article 

Google Scholar 
Pauli, N., Barrios, E., Conacher, A. J. & Oberthür, T. Soil macrofauna in agricultural landscapes dominated by the Quesungual slash-and-mulch agroforestry system, western Honduras. Appl. Soil. Ecol. 47, 119–132 (2011).Article 

Google Scholar 
Eichorst, S. A. et al. Advancements in the application of NanoSIMS and Raman microspectroscopy to investigate the activity of microbial cells in soils. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiv106 (2015).Musat, N., Musat, F., Weber, P. K. & Pett-Ridge, J. Tracking microbial interactions with NanoSIMS. Curr. Opin. Biotechnol. 41, 114–121 (2016).Article 

Google Scholar 
Bronick, C. J. & Lal, R. Soil structure and management: a review. Geoderma 124, 3–22 (2005).Article 

Google Scholar  More