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    Public interest in individual study animals can bolster wildlife conservation

    Benson, E. S. Sci. Context 29, 107–128 (2016).Article 
    PubMed 

    Google Scholar 
    Buckmaster, C. A. Lab Anim. 44, 237 (2015).Article 

    Google Scholar 
    Kelly, M. J. et al. J. Zool. 244, 473–488 (1998).Article 

    Google Scholar 
    Spagnuolo, O. S. B., Lemerle, M. A., Holekamp, K. E. & Wiesel, I. Mamm. Biol. https://doi.org/10.1007/s42991-022-00309-4 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    California Department of Fish and Wildlife. Mountain lion P-22 compassionately euthanized following complete health evaluation results. wildlife.ca.gov, https://wildlife.ca.gov/News/mountain-lion-p-22-compassionately-euthanized-following-complete-health-evaluation-results (17 December 2022).Road Ecology Center, UC Davis. California roadkill observation system, https://www.wildlifecrossing.net/california/ (accessed 19 December 2022).Wong-Parodi, G. & Feygina, I. Environ. Commun. 15, 571–593 (2021).Article 

    Google Scholar 
    Carmi, N., Arnon, S. & Orion, N. J. Environ. Educ. 46, 183–201 (2015).Article 

    Google Scholar 
    Manfredo, M. J., Urquiza-Haas, E. G., Don Carlos, A. W., Bruskotter, J. T. & Dietsch, A. M. Biol. Conserv. 241, 108297 (2020).Article 

    Google Scholar 
    Schueler, D. S. & Newberry, M. G. III Appl. Environ. Educ. Commun. 19, 259–273 (2020).Article 

    Google Scholar 
    Jennings, L. Public gets to name Dallas Zoo’s baby giraffe. Dallas Zoo https://zoohoo.dallaszoo.com/2014/11/05/public-gets-to-name-dallas-zoos-baby-giraffe/ (5 November 2014).Verma, A., van der Wal, R. & Fischer, A. Ambio 44(Suppl 4), 648–660 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Macdonald, D. W., Jacobsen, K. S., Burnham, D., Johnson, P. J. & Loveridge, A. J. Animals 6, 26 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jones, M. D., Shanahan, E. A. & McBeth, M. K. The Science of Stories: Applications of the Narrative Policy Framework in Public Policy Analysis (Palgrave MacMillan, 2014). More

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    Temperature, species identity and morphological traits predict carbonate excretion and mineralogy in tropical reef fishes

    Animal collection and holding for this project was conducted under Marine Research Permit RE-19–28 issued by the Ministry of Natural Resources, Environment, and Tourism of the Republic of Palau (10.03.2019), Marine Research/Collection Permit and Agreement 62 issued by the Koror State Government (08.10.2019), Queensland Government GBRMPA Marine Parks Permit G14/36689.1, Queensland Government DNPRSR Marine Parks Permits QS2014/MAN247 and QS2014/MAN247a, Queensland Government General Fisheries Permit 168991, Queensland Government DAFF Animal Ethics approval CA2013/11/733, approval by The Bahamas Department of Marine Resources, approval by the Animal Care Officer of both the University of Bremen and the Leibniz Centre for Tropical Marine Research (ZMT), and in accordance with UK and Germany animal care guidelines.Sample collectionWe collected fish carbonate samples at four study locations across three tropical and subtropical regions: Eleuthera (24°50’N, 76°20’W), The Bahamas, between 2009 and 201127,37; Heron Reef (23°27’S, 151°55’E) and Moreton Bay (27°29’S, 153°24’E) in Queensland, Australia, in 2014 and 201528; and Koror (7°20’N, 134°28’E), Palau, during November and December 2019. These are located within four distinct marine biogeographic provinces and three realms (Tropical Atlantic, Central Indo-Pacific, and Temperate Australasia)43. At each location fish were collected using barrier nets, dip nets, clove oil or hook and line, and immediately transferred to aquaria facilities at the Cape Eleuthera Institute, Heron Island and Moreton Bay Research Stations, and the Palau International Coral Reef Center. Fish were held in a range of tanks (60, 400, or 1400 L in the Bahamas, 10, 60, 100, 120, or 400 L in Heron Island and Moreton Bay, and 8, 80, 280, or 400 L in Palau) of suitable dimensions for different fish sizes ( 5). Each sample was titrated with 0.01–0.5 N HCl (with continuous aeration with CO2-free air) until the end point (grey-lavender; pH~4.80) was reached and stable for at least 10 min. If the sample was over-titrated (pink), 0.01–0.1 N NaOH was added to titrate back to the end point and the amount of base used was subtracted from the amount of acid. Acid and base were added using an electronic multi-dispenser pipette (Eppendorf Repeater ®E3X, Eppendorf, Hamburg, Germany) with a precision of  ± 1 ({{{{{rm{mu }}}}}})L. Additionally, the pH of several samples was monitored using a pH microelectrode (Mettler Toledo InLab Micro) to ascertain the correctness of the colorimetric end point. The amount of carbonate in the sample was then calculated using Eq. (1). The method was validated using certified reference material (Alkalinity Standard Solution, 25,000 mg/L as CaCO3, HACH) and the accuracy in the determination of solid samples was verified using certified CaCO3 powder (Suprapur, ≥ 99.95% purity, Merck) samples (60–500 ({{{{{rm{mu }}}}}})g) and resulted in 96.53 ± 1.94% accuracy (mean ± SE; n = 8).To compare values obtained with the two titration methods we further analysed 12 samples collected at Lizard Island, Australia, in February 2016. Samples were collected at 24 h intervals from one individual of Lethrinus atkinsoni (f. Lethrinidae, body mass: 245 g), a group of five Lutjanus fulvus (f. Lutjanidae, mean body mass: 21 g), and an individual of Cephalopholis cyanostigma (f. Serranidae, body mass: 295 g), following the procedures described above. During sample collection water temperature ranged from 29.1 °C during the night to 32.6 °C during the day, with an average of ~31 °C, mean salinity was 35.4, and pHNBS ranged from 8.13 to 8.21. To compare the amount of carbonate measured by the two methods we added carbonate samples to 20 ml ultrapure water and disaggregated crystals via sonication. We then used a Metrohm Titrando autotitrator and Metrohm Aquatrode pH electrode to measure initial pH of the suspension of carbonates, then titrated each sample of carbonate in two stages. Firstly, they were titrated down to pH 4.80 using 0.1 M HCl, adding 20 µl increments of acid until this was sufficient to keep pH below 4.80 for 10 min whilst bubbling with CO2-free air. This first stage was comparable to the single end point titration used for samples collected in Palau. Secondly, whilst continuing to bubble with CO2-free air, further acid was added to the sample until it reached pH 3.89 and was stable for 1 min. Then 0.1 M NaOH was added to the samples to return them to the initial pH. For all samples the first end point titration (to pH 4.80) yielded slightly higher values for carbonate content than the second double titration. The ratio between the two methods (single end point/double titration) was 1.08 ± 0.01 (mean ± SE; range: 1.04–1.14; Supplementary Table 2). As we found a small but consistent difference between the two methods, all following analyses were initially performed on the actual data obtained with the double titration for samples from Australia and The Bahamas, and the single end point titration for samples from Palau. Then, to assess the robustness of the results, we repeated the analyses after applying a correction factor of 1.08 to the excretion rates of Palauan fishes (that used the single end point titration method). All results were consistent and robust to the measured difference between the titration methods (Supplementary Figs. 8, 9).Finally, measurements of multiple samples from each individual collected over periods of 18–169 h (median: 64 h) were combined to produce an average individual excretion rate in ({{{{{rm{mu }}}}}})mol h−1. For fish held in groups, carbonate excretion rates per individual (of average biomass) were obtained by averaging the total excretion rate of the group across the sampling period and dividing it by the number of individuals in the tank. Excretion rates obtained from fish groups thus evened the intraspecific variability within tanks, and are therefore more robust than those directly obtained from fish held individually. This aspect was considered in our models by fitting weighted regressions (see the “Statistical modelling” section). In total, we measured the carbonate excretion rates of 382 individual fishes arranged in 192 groups (i.e., independent observations), representing 85 species from 35 families across three tropical regions (180 individuals from 29 species in Australia, 90 individuals from 10 species in the Bahamas, and 112 individuals from 46 species in Palau; Supplementary Table 1).We assume that during the sampling of carbonates fishes were close to their resting metabolic rate and that their carbonate excretion rates are representative of fish at rest. Although the ratio of tank volume to fish volume in our study (median ~660; inter-quartile range ~180–1700) typically greatly exceeds the guideline ideal range for measuring resting metabolic rate (20–50)85, fishes were fasted prior to and throughout sampling, and in most instances their movement was somewhat constrained by tank volume. Fasting reduces metabolic rate in all animals, including fish, as they do not undergo energy-intensive digestive processes and use energy reserves to support vital processes, triggering metabolic changes in many tissues and reducing activity levels86,87. Additionally, other than the carbonate syphoning ( More

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    Taxonomic composition, community structure and molecular novelty of microeukaryotes in a temperate oligomesotrophic lake as revealed by metabarcoding

    Pawlowski, J. et al. CBOL Protist working group: barcoding eukaryotic richness beyond the animal, plant, and fungal kingdoms. PLOS Biol. 10, e1001419 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    del Campo, J. et al. The others: our biased perspective of eukaryotic genomes. Trends Ecol. Evol. 29, 252–259 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Handbook of the Protists (Springer, 2017). https://doi.org/10.1007/978-3-319-28149-0.Lang, B. F., O’Kelly, C., Nerad, T., Gray, M. W. & Burger, G. The closest unicellular relatives of animals. Curr. Biol. 12, 1773–1778 (2002).Article 
    CAS 
    PubMed 

    Google Scholar 
    del Campo, J. et al. Ecological and evolutionary significance of novel protist lineages. Eur. J. Protistol. 55, 4–11 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grau-Bové, X. et al. Dynamics of genomic innovation in the unicellular ancestry of animals. Life 6, e26036 (2017).
    Google Scholar 
    Gawryluk, R. M. R. et al. Non-photosynthetic predators are sister to red algae. Nature 572, 240–243 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gabr, A., Grossman, A. R. & Bhattacharya, D. Paulinella, a model for understanding plastid primary endosymbiosis. J. Phycol. 56, 837–843 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gao, Z., Karlsson, I., Geisen, S., Kowalchuk, G. & Jousset, A. Protists: Puppet masters of the rhizosphere microbiome. Trends Plant Sci. 24, 165–176 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Caron, D. A. New accomplishments and approaches for assessing protistan diversity and ecology in natural ecosystems. Bioscience 59, 287–299 (2009).Article 

    Google Scholar 
    Gooday, A. J., Schoenle, A., Dolan, J. R. & Arndt, H. Protist diversity and function in the dark ocean: Challenging the paradigms of deep-sea ecology with special emphasis on foraminiferans and naked protists. Eur. J. Protistol. 75, 125721 (2020).Article 
    PubMed 

    Google Scholar 
    Stoecker, D. K., Johnson, M. D., de Vargas, C. & Not, F. Acquired phototrophy in aquatic protists. Aquat. Microb. Ecol. 57, 279–310 (2009).Article 

    Google Scholar 
    Strom, S. L., Benner, R., Ziegler, S. & Dagg, M. J. Planktonic grazers are a potentially important source of marine dissolved organic carbon. Limnol. Oceanogr. 42, 1364–1374 (1997).Article 
    ADS 
    CAS 

    Google Scholar 
    Orsi, W. D. et al. Identifying protist consumers of photosynthetic picoeukaryotes in the surface ocean using stable isotope probing. Environ. Microbiol. 20, 815–827 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Corno, G. & Jürgens, K. Direct and indirect effects of protist predation on population size structure of a bacterial strain with high phenotypic plasticity. Appl. Environ. Microbiol. 72, 78–86 (2006).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mahé, F. et al. Parasites dominate hyperdiverse soil protist communities in Neotropical rainforests. Nat. Ecol. Evol. 1, 91 (2017).Article 
    PubMed 

    Google Scholar 
    Ruppert, K. M., Kline, R. J. & Rahman, M. S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 17, e00547 (2019).Article 

    Google Scholar 
    Epstein, S. & López-García, P. “Missing” protists: a molecular prospective. Biodivers. Conserv. 17, 261–276 (2008).Article 

    Google Scholar 
    López-García, P., Rodríguez-Valera, F., Pedrós-Alió, C. & Moreira, D. Unexpected diversity of small eukaryotes in deep-sea Antarctic plankton. Nature 409, 603–607 (2001).Article 
    ADS 
    PubMed 

    Google Scholar 
    Lovejoy, C., Massana, R. & Pedrós-Alió, C. Diversity and distribution of marine microbial eukaryotes in the Arctic Ocean and adjacent seas. Appl. Environ. Microbiol. 72, 3085–3095 (2006).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Worden, A. Z., Cuvelier, M. L. & Bartlett, D. H. In-depth analyses of marine microbial community genomics. Trends Microbiol. 14, 331–336 (2006).Article 
    CAS 
    PubMed 

    Google Scholar 
    Countway, P. D. et al. Distinct protistan assemblages characterize the euphotic zone and deep sea (2500 m) of the western North Atlantic (Sargasso Sea and Gulf Stream). Environ. Microbiol. 9, 1219–1232 (2007).Article 
    CAS 
    PubMed 

    Google Scholar 
    Massana, R. & Pedrós-Alió, C. Unveiling new microbial eukaryotes in the surface ocean. Curr. Opin. Microbiol. 11, 213–218 (2008).Article 
    PubMed 

    Google Scholar 
    Alexander, E. et al. Microbial eukaryotes in the hypersaline anoxic L’Atalante deep-sea basin. Environ. Microbiol. 11, 360–381 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol. Ecol. 19, 21–31 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Logares, R. et al. Patterns of rare and abundant marine microbial eukaryotes. Curr. Biol. 24, 813–821 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    de Vargas, C. et al. Eukaryotic plankton diversity in the sunlit ocean. Science 348, 150 (2015).Article 

    Google Scholar 
    Fell, J. W., Scorzetti, G., Connell, L. & Craig, S. Biodiversity of micro-eukaryotes in Antarctic Dry Valley soils with More

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    Cell aggregation is associated with enzyme secretion strategies in marine polysaccharide-degrading bacteria

    Strains belonging to the same species display distinct growth dynamics on the marine polysaccharide alginateWe first quantified the growth dynamics of the 12 Vibrionaceae strains (Supplementary Table 1) on alginate in well-mixed batch cultures. Growth of populations was initiated at approximately the same inoculum density (105 colony forming units (c.f.u.) ml−1). We tracked the growth dynamics by measuring the optical density at 600 nm and compared the maximum population size reached over the course of 36 h (Fig. 1 and S1). We found significant differences in the maximal optical density achieved by different strains within each species (Fig. 1 and S1). In V. splendidus, strains 12B01 and FF6 reached a lower maximum population size compared to strains 1S124 and 13B01 (Fig. 1 and S1A). In V. cyclitrophicus, strain ZF270 reached a lower maximum population size compared to strains 1F175, 1F111, and ZF28 (Fig. 1 and S1A). Similarly, in V. sp. F13, strain 9ZC77 reached a lower maximum population size than strains 9CS106, 9ZC13, and ZF57 (Fig. 1 and S1A). These findings suggest that some strains are limited in their growth abilities in well-mixed environments, perhaps as a consequence of differences in the amount and activity of enzymes they release (Supplementary Table 1).Fig. 1: Vibrionaceae strains differ in their growth dynamics on the marine polysaccharide alginate under well-mixed conditions.Maximum optical density (measured at 600 nm) achieved by populations of strains belonging to Vibrio splendidus, Vibrio cyclitrophicus, and Vibrio sp. F13 during the course of a 36 h growth cycle on the same concentration (0.1% weight/volume) of the polysaccharide alginate. Points and error bars indicate the mean of measurements across populations within each ecotype (npopulations = 3) and the 95% confidence interval (CI), respectively. Different letters indicate statistically significant differences between strains within one species (One-way ANOVA and Dunnett’s post-hoc test; V. splendidus: p  More

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    Disentangling the causes of temporal variation in the opportunity for sexual selection

    Darwin, C. The Descent of Man and Selection in Relation to Sex. (John Murray, 1871).Andersson, M. Sexual Selection. (Princeton University Press, 1994).Shuster, S. & Wade, M. J. Mating Systems and Strategies. (Princeton University Press, 2003).Gosden, T. P. & Svensson, E. I. Spatial and temporal dynamics in a sexual selection mosaic. Evolution 62, 845–856 (2008).Article 
    PubMed 

    Google Scholar 
    Kasumovic, M. M., Bruce, M. J., Andrade, M. C. B. & Herberstein, M. E. Spatial and temporal demographic variation drives within-season fluctuations in sexual selection. Evolution 62, 2316–2325 (2008).Article 
    PubMed 

    Google Scholar 
    Mobley, K. B. & Jones, A. G. Environmental, demographic, and genetic mating system variation among five geographically distinct dusky pipefish (Syngnathus floridae) populations. Mol. Ecol. 18, 1476–1490 (2009).Article 
    PubMed 

    Google Scholar 
    Hoffer, J. N., Mariën, J., Ellers, J. & Koene, J. M. Sexual selection gradients change over time in a simultaneous hermaphrodite. eLife 6, e25139 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sih, A., Montiglio, P.-O., Wey, T. W. & Fogarty, S. Altered physical and social conditions produce rapidly reversible mating systems in water striders. Behav. Ecol. 28, 632–639 (2017).Article 

    Google Scholar 
    Preston, B. T., Stevenson, I. R., Pemberton, J. M. & Wilson, K. Dominant rams lose out by sperm depletion. Nature 409, 681–682 (2001).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Cornwallis, C. K. & Uller, T. Towards an evolutionary ecology of sexual traits. Trends Ecol. Evol. 25, 145–152 (2010).Article 
    PubMed 

    Google Scholar 
    Forsgren, E., Amundsen, T., Borg, A. A. & Bjelvenmark, J. Unusually dynamic sex roles in a fish. Nature 429, 551–554 (2004).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Hare, R. M. & Simmons, L. W. Sexual selection maintains a female-specific character in a species with dynamic sex roles. Behav. Ecol. 32, 609–616 (2021).Article 

    Google Scholar 
    Fox, R. J., Donelson, J. M., Schunter, C., Ravasi, T. & Gaitán-Espitia, J. D. Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change. Philos. Trans. R. Soc. B 374, 20180174 (2019).Article 

    Google Scholar 
    Ingleby, F. C., Hunt, J. & Hosken, D. J. The role of genotype-by-environment interactions in sexual selection. J. Evol. Biol. 23, 2031–2045 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Lindström, J., Pike, T. W., Blount, J. D. & Metcalfe, N. B. Optimization of resource allocation can explain the temporal dynamics and honesty of sexual signals. Am. Nat. 174, 515–525 (2009).Article 
    PubMed 

    Google Scholar 
    Janicke, T., David, P. & Chapuis, E. Environment-dependent sexual selection: Bateman’s parameters under varying levels of food availability. Am. Nat. 185, 756–768 (2015).Article 
    PubMed 

    Google Scholar 
    Morimoto, J., Pizzari, T. & Wigby, S. Developmental environment effects on sexual selection in male and female Drosophila melanogaster. PLoS ONE 11, e0154468 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cattelan, S., Evans, J. P., Garcia-Gonzalez, F., Morbiato, E. & Pilastro, A. Dietary stress increases the total opportunity for sexual selection and modifies selection on condition-dependent traits. Ecol. Lett. 23, 447–456 (2020).Article 
    PubMed 

    Google Scholar 
    Glavaschi, A., Cattelan, S., Grapputo, A. & Pilastro, A. Imminent risk of predation reduces the relative strength of postcopulatory sexual selection in the guppy. Philos. Trans. R. Soc. B 375, 20200076 (2020).Article 

    Google Scholar 
    Clark, D. C., DeBano, S. J. & Moore, A. J. The influence of environmental quality on sexual selection in Nauphoeta cinerea (Dictyoptera: Blaberidae). Behav. Ecol. 8, 46–53 (1997).Article 

    Google Scholar 
    Emlen, S. & Oring, L. Ecology, sexual selection and the evolution of mating systems. Science 197, 215–223 (1977).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Liker, A., Freckleton, R. P. & Székely, T. The evolution of sex roles in birds is related to adult sex ratio. Nat. Commun. 4, 1–6 (2013).Article 

    Google Scholar 
    Wacker, S. et al. Operational sex ratio but not density affects sexual selection in a fish. Evolution 67, 1937–1949 (2013).Article 
    PubMed 

    Google Scholar 
    Wacker, S., Ness, M. H., Östlund-Nilsson, S. & Amundsen, T. Social structure affects mating competition in a damselfish. Coral Reefs 36, 1279–1289 (2017).Article 
    ADS 

    Google Scholar 
    Janicke, T. & Morrow, E. H. Operational sex ratio predicts the opportunity and direction of sexual selection across animals. Ecol. Lett. 21, 384–391 (2018).Article 
    PubMed 

    Google Scholar 
    Procter, D. S., Moore, A. J. & Miller, C. W. The form of sexual selection arising from male-male competition depends on the presence of females in the social environment. J. Evol. Biol. 25, 803–812 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Eldakar, O. T., Dlugos, M. J., Pepper, J. W. & Wilson, D. S. Population structure mediates sexual conflict in Water striders. Science 326, 816–816 (2009).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Martin, A. M., Festa-Bianchet, M., Coltman, D. W. & Pelletier, F. Demographic drivers of age-dependent sexual selection. J. Evol. Biol. 29, 1437–1446 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Pilakouta, N. & Ålund, M. Sexual selection and environmental change: what do we know and what comes next? Curr. Zool. 67, 293–298 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kahn, A. T., Dolstra, T., Jennions, M. D. & Backwell, P. R. Y. Strategic male courtship effort varies in concert with adaptive shifts in female mating preferences. Behav. Ecol. 24, 906–913 (2013).Article 

    Google Scholar 
    Jordan, L. A. & Brooks, R. C. Recent social history alters male courtship preferences. Evolution 66, 280–287 (2012).Article 
    PubMed 

    Google Scholar 
    Wilson, D. R., Nelson, X. J. & Evans, C. S. Seizing the opportunity: Subordinate male fowl respond rapidly to variation in social context. Ethology 115, 996–1004 (2009).Article 

    Google Scholar 
    Gwynne, D. T., Bailey, W. J. & Annells, A. The sex in short supply for matings varies over small Spatial scales in a Katydid (Kawanaphila nartee, Orthoptera: Tettigoniidae). Behav. Ecol. Sociobiol. 42, 157–162 (1998).Article 

    Google Scholar 
    Fedina, T. Y. & Lewis, S. M. Female mate choice across mating stages and between sequential mates in flour beetles. J. Evol. Biol. 20, 2138–2143 (2007).Article 
    CAS 
    PubMed 

    Google Scholar 
    Clark, H. L. & Backwell, P. R. Y. Temporal and spatial variation in female mating preferences in a fiddler crab. Behav. Ecol. Sociobiol. 69, 1779–1784 (2015).Article 

    Google Scholar 
    Serbezov, D., Bernatchez, L., Olsen, E. M. & Vøllestad, L. A. Mating patterns and determinants of individual reproductive success in brown trout (Salmo trutta) revealed by parentage analysis of an entire stream living population. Mol. Ecol. 19, 3193–3205 (2010).Article 
    PubMed 

    Google Scholar 
    Gerlach, N. M., McGlothlin, J. W., Parker, P. G. & Ketterson, E. D. Reinterpreting Bateman gradients: multiple mating and selection in both sexes of a songbird species. Behav. Ecol. 23, 1078–1088 (2012).Article 

    Google Scholar 
    Dubuc, C., Ruiz-Lambides, A. & Widdig, A. Variance in male lifetime reproductive success and estimation of the degree of polygyny in a primate. Behav. Ecol. 25, 878–889 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Breuer, T. et al. Variance in the male reproductive success of western gorillas: acquiring females is just the beginning. Behav. Ecol. Sociobiol. 64, 515–528 (2010).Article 

    Google Scholar 
    Germain, R. R., Hallworth, M. T., Kaiser, S. A., Sillett, T. S. & Webster, M. S. Variance in within-pair reproductive success influences the opportunity for selection annually and over the lifetimes of males in a multi-brooded songbird. Evolution 75, 915–930 (2021).Article 
    PubMed 

    Google Scholar 
    Lande, R. & Arnold, S. J. The measurement of selection on correlated characters. Evolution 37, 1210–1226 (1983).Article 
    PubMed 

    Google Scholar 
    Klug, H., Heuschele, J., Jennions, M. D. & Kokko, H. The mismeasurement of sexual selection. J. Evol. Biol. 23, 447–462 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Jennions, M. D., Kokko, H. & Klug, H. The opportunity to be misled in studies of sexual selection. J. Evol. Biol. 25, 591–598 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Krakauer, A. H., Webster, M. S., Duval, E. H., Jones, A. G. & Shuster, S. M. The opportunity for sexual selection: not mismeasured, just misunderstood. J. Evol. Biol. 24, 2064–2071 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hebets, E. A., Stafstrom, J. A., Rodriguez, R. L. & Wilgers, D. J. Enigmatic ornamentation eases male reliance on courtship performance for mating success. Anim. Behav. 81, 963–972 (2011).Article 

    Google Scholar 
    Fitzpatrick, J. L. & Lüpold, S. Sexual selection and the evolution of sperm quality. Mol. Hum. Reprod. 20, 1180–1189 (2014).Article 
    PubMed 

    Google Scholar 
    Jones, A. G. On the opportunity for sexual selection, the Bateman gradient and the maximum intensity of sexual selection. Evolution 63, 1673–1684 (2009).Article 
    PubMed 

    Google Scholar 
    Henshaw, J. M., Kahn, A. T. & Fritzsche, K. A rigorous comparison of sexual selection indexes via simulations of diverse mating systems. Proc. Natl Acad. Sci. USA 113, E300–E308 (2016).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Evans, J. P. & Garcia-Gonzalez, F. The total opportunity for sexual selection and the integration of pre- and post-mating episodes of sexual selection in a complex world. J. Evol. Biol. 29, 2338–2361 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Downhower, J. F., Blumer, L. S. & Brown, L. Opportunity for selection: an appropriate measure for evaluating variation in the potential for selection? Evolution 41, 1395–1400 (1987).Article 
    PubMed 

    Google Scholar 
    Klug, H. & Stone, L. More than just noise: Chance, mating success, and sexual selection. Ecol. Evol. 11, 6326–6340 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Anthes, N., Häderer, I. K., Michiels, N. K. & Janicke, T. Measuring and interpreting sexual selection metrics: evaluation and guidelines. Methods Ecol. Evol. 8, 918–931 (2016).Article 

    Google Scholar 
    Klug, H., Lindström, K. & Kokko, H. Who to include in measures of sexual selection is no trivial matter. Ecol. Lett. 13, 1094–1102 (2010).Article 
    PubMed 

    Google Scholar 
    Collet, J. M., Dean, R. F., Worley, K., Richardson, D. S. & Pizzari, T. The measure and significance of Bateman’s principles. Proc. R. Soc. B 281, 20132973 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Collet, J., Richardson, D. S., Worley, K. & Pizzari, T. Sexual selection and the differential effect of polyandry. Proc. Natl Acad. Sci. USA 109, 8641–8645 (2012).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    McDonald, G. C., Spurgin, L. G., Fairfield, E. A., Richardson, D. S. & Pizzari, T. Pre- and postcopulatory sexual selection favor aggressive, young males in polyandrous groups of red junglefowl. Evolution 71, 1653–1669 (2017).Article 
    PubMed 

    Google Scholar 
    Morimoto, J. et al. Sex peptide receptor-regulated polyandry modulates the balance of pre- and post-copulatory sexual selection in Drosophila. Nat. Commun. 10, 283 (2019).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shuster, S. M., Willen, R. M., Keane, B. & Solomon, N. G. Alternative mating tactics in socially monogamous prairie voles, Microtus ochrogaster. Front. Ecol. Evol. 7, 7 (2019).Article 

    Google Scholar 
    Dowling, J. & Webster, M. S. Working with what you’ve got: unattractive males show greater mate-guarding effort in a duetting songbird. Biol. Lett. 13, 20160682 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pizzari, T. & McDonald, G. C. Sexual selection in socially structured, polyandrous populations: Some insights from the fowl. Adv. Study Behav. 51, 77–141 (2019).Article 

    Google Scholar 
    Archer, M. S. & Elgar, M. A. Female preference for multiple partners: sperm competition in the hide beetle, Dermestes maculatus (DeGeer). Anim. Behav. 58, 669–675 (1999).Article 
    CAS 
    PubMed 

    Google Scholar 
    Qvarnström, A. & Forsgren, E. Should females prefer dominant males? Trends Ecol. Evol. 13, 498–501 (1998).Article 
    PubMed 

    Google Scholar 
    Webster, M. S., Tarvin, K. A., Tuttle, E. M. & Pruett-Jones, S. Promiscuity drives sexual selection in a socially monogamous bird. Evolution 61, 2205–2211 (2007).Article 
    PubMed 

    Google Scholar 
    Brunton, D. H. Energy expenditure in reproductive effort of male and female Killdeer (Charadrius vociferus). Auk 105, 553–564 (1988).Article 

    Google Scholar 
    Johnson, L. S., Hicks, B. G. & Masters, B. S. Increased cuckoldry as a cost of breeding late for male house wrens (Troglodytes aedon). Behav. Ecol. 13, 670–675 (2002).Article 

    Google Scholar 
    Boinski, S. Mating patterns in squirrel monkeys (Saimiri oerstedi): implications for seasonal sexual dimorphism. Behav. Ecol. Sociobiol. 21, 13–21 (1987).Article 

    Google Scholar 
    McDonald, G. C., Spurgin, L. G., Fairfield, E. A., Richardson, D. S. & Pizzari, T. Differential female sociality is linked with the fine-scale structure of sexual interactions in replicate groups of red junglefowl, Gallus gallus. Proc. R. Soc. B 286, 20191734 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Carleial, R. et al. Temporal dynamics of competitive fertilization in social groups of red junglefowl (Gallus gallus) shed new light on avian sperm competition. Philos. Trans. R. Soc. B 375, 20200081 (2020).Article 

    Google Scholar 
    Lessells, C. M. & Birkhead, T. R. Mechanisms of sperm competition in birds: mathematical models. Behav. Ecol. Sociobiol. 27, 325–337 (1990).Article 

    Google Scholar 
    Taborsky, T., Oliveira, R. F. & Brockmann, H. J. The Evolution of Alternative Reproductive Tactics: Concepts and Questions. in Alternative Reproductive Tactics: An Integrative Approach (Cambridge University Press, 2008).Ghislandi, P. G. et al. Resource availability, mating opportunity and sexual selection intensity influence the expression of male alternative reproductive tactics. J. Evol. Biol. 31, 1035–1046 (2018).Article 
    PubMed 

    Google Scholar 
    Lehtonen, T. K., Wong, B. B. M. & Lindström, K. Fluctuating mate preferences in a marine fish. Biol. Lett. 6, 21–23 (2010).Article 
    PubMed 

    Google Scholar 
    Chaine, A. S. & Lyon, B. E. Adaptive plasticity in female mate choice dampens sexual selection on male ornaments in the lark bunting. Science 319, 459–462 (2008).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

    Google Scholar 
    Oklander, L. I., Kowalewski, M. & Corach, D. Male reproductive strategies in black and gold howler monkeys (Alouatta caraya). Am. J. Primatol. 76, 43–55 (2014).Article 
    PubMed 

    Google Scholar 
    Pröhl, H. & Hödl, W. Parental investment, potential reproductive rates, and mating system in the strawberry dart-poison frog, Dendrobates pumilio. Behav. Ecol. Sociobiol. 46, 215–220 (1999).Article 

    Google Scholar 
    Turnell, B. R. & Shaw, K. L. High opportunity for postcopulatory sexual selection under field conditions. Evolution 69, 2094–2104 (2015).Article 
    PubMed 

    Google Scholar 
    Gill, L. F., van Schaik, J., von Bayern, A. M. P. & Gahr, M. L. Genetic monogamy despite frequent extrapair copulations in “strictly monogamous” wild jackdaws. Behav. Ecol. 31, 247–260 (2020).Article 
    PubMed 

    Google Scholar 
    Carleial, R., McDonald, G. C. & Pizzari, T. Dynamic phenotypic correlates of social status and mating effort in male and female red junglefowl, Gallus gallus. J. Evol. Biol. 33, 22–40 (2020).Article 
    PubMed 

    Google Scholar 
    McDonald, G. C. & Pizzari, T. Structure of sexual networks determines the operation of sexual selection. Proc. Natl Acad. Sci. USA 115, E53–E61 (2018).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Janicke, T., Häderer, I. K., Lajeunesse, M. J. & Anthes, N. Darwinian sex roles confirmed across the animal kingdom. Sci. Adv. 2, e1500983 (2016).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Webster, M. S., Pruett-Jones, S., Westneat, D. F. & Arnold, S. J. Measuring the effects of pairing success, extra-pair copulations and mate quality on the opportunity for sexual selection. Evolution 49, 1147–1157 (1995).PubMed 

    Google Scholar 
    Etches, R. J. Reproduction in Poultry. (CABI, 1996).Schielzeth, H. Simple means to improve the interpretability of regression coefficients: Interpretation of regression coefficients. Methods Ecol. Evol. 1, 103–113 (2010).Article 

    Google Scholar 
    Løvlie, H., Cornwallis, C. K. & Pizzari, T. Male mounting alone reduces female promiscuity in the fowl. Curr. Biol. 15, 1222–1227 (2005).Article 
    PubMed 

    Google Scholar 
    Berglund, A. Many mates make male pipefish choosy. Behaviour 132, 213–218 (1995).Article 

    Google Scholar 
    Carleial, R., Pizzari, T., Richardson, D. S. & McDonald, G. C. Data for: Disentangling the causes of temporal variation in the opportunity for sexual selection. figshare Dataset (2023) https://doi.org/10.6084/m9.figshare.21902133.v1.McLain, D. K., Burnette, L. B. & Deeds, D. A. Within season variation in the intensity of sexual selection on body size in the bug Margus obscurator (Hemiptera Coreidae). Ethol. Ecol. Evol. 5, 75–86 (1993).Article 

    Google Scholar 
    Schlicht, E. & Kempenaers, B. Effects of social and extra-pair mating on sexual selection in Blue tits (Cyanistes caeruleus). Evolution 67, 1420–1434 (2013).PubMed 

    Google Scholar  More

  • in

    Pathways of degradation in rangelands in Northern Tanzania show their loss of resistance, but potential for recovery

    Asner, G. P., Elmore, A. J., Olander, L. P., Martin, R. E. & Harris, A. T. Grazing systems, ecosystem responses, and global change. Annu. Rev. Environ. Resour. 29, 261–299 (2004).Article 

    Google Scholar 
    Millenium Ecosystem Assessment Board. Ecosystems and Human Well-Being: Wetlands and Water: Synthesis (Island Press, Washington, DC, 2005).Lind, J., Sabates-Wheeler, R., Caravani, M., Kuol, L. B. D. & Nightingale, D. M. Newly evolving pastoral and post-pastoral rangelands of Eastern Africa. Pastoralism 10, 24 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hoffman, T. & Vogel, C. Climate change impacts on African rangelands. Rangelands 30, 12–17 (2008).Article 

    Google Scholar 
    Joyce, L. A. et al. Climate change and North American rangelands: Assessment of mitigation and adaptation strategies. Rangeland Ecol. Manage. 66, 512–528 (2013).Article 

    Google Scholar 
    Stringer, L. C., Reed, M. S., Dougill, A. J., Seely, M. K. & Rokitzki, M. Implementing the UNCCD: Participatory challenges. Nat. Resour. Forum 31, 198–211 (2007).Article 

    Google Scholar 
    Vågen, T.-G., Winowiecki, L. A., Tondoh, J. E., Desta, L. T. & Gumbricht, T. Mapping of soil properties and land degradation risk in Africa using MODIS reflectance. Geoderma 263, 216–225 (2016).Article 
    ADS 

    Google Scholar 
    Stevens, N., Lehmann, C. E. R., Murphy, B. P. & Durigan, G. Savanna woody encroachment is widespread across three continents. Glob. Chang. Biol. 23, 235–244 (2017).Article 
    ADS 
    PubMed 

    Google Scholar 
    Muñoz, P. et al. Land degradation, poverty and inequality (2019).Bond, W. & Keeley, J. Fire as a global ‘herbivore’: the ecology and evolution of flammable ecosystems. Trends Ecol. Evol. 20, 387–394 (2005).Article 
    PubMed 

    Google Scholar 
    Lehmann, C. E. R., Archibald, S. A., Hoffmann, W. A. & Bond, W. J. Deciphering the distribution of the savanna biome. New Phytol. 191, 197–209 (2011).Article 
    PubMed 

    Google Scholar 
    Staver, A. C., Archibald, S. & Levin, S. A. The global extent and determinants of savanna and forest as alternative biome states. Science 334, 230–232 (2011).Article 
    ADS 
    CAS 
    MATH 
    PubMed 

    Google Scholar 
    Fuhlendorf, S. D., Fynn, R. W. S., McGranahan, D. A. & Twidwell, D. Heterogeneity as the basis for rangeland management in Rangeland Systems: Processes, Management and Challenges, Springer Series on Environmental Management (ed. Briske, D. D.), 169–196 (Springer International Publishing, 2017).Liao, C., Agrawal, A., Clark, P. E., Levin, S. A. & Rubenstein, D. I. Landscape sustainability science in the drylands: mobility, rangelands and livelihoods. Landsc. Ecol. 35, 2433–2447 (2020).Article 

    Google Scholar 
    Galvin, K. A. Transitions: pastoralists living with change. Annu. Rev. Anthropol. 38, 185–198 (2009).Article 

    Google Scholar 
    López-i Gelats, F., Fraser, E. D. G., Morton, J. F. & Rivera-Ferre, M. G. What drives the vulnerability of pastoralists to global environmental change? A qualitative meta-analysis. Glob. Environ. Change 39, 258–274 (2016).Obiri, J. F. Invasive plant species and their disaster-effects in dry tropical forests and rangelands of Kenya and Tanzania. Jàmbá: Journal of Disaster Risk Studies 3, 417–428 (2011).Kioko, J., Kiringe, J. W. & Seno, S. O. Impacts of livestock grazing on a savanna grassland in Kenya. J. Arid Land 4, 29–35 (2012).Article 

    Google Scholar 
    Kotiaho, J. S. et al. The IPBES assessment report on land degradation and restoration. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem (2018).Western, D., Mose, V. N., Worden, J. & Maitumo, D. Predicting extreme droughts in savannah Africa: A comparison of proxy and direct measures in detecting biomass fluctuations, trends and their causes. PLoS One 10, e0136516 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dai, A. Drought under global warming: a review. WIREs Climate Change 2, 45–65 (2011).Article 

    Google Scholar 
    Holechek, J. L., Cibils, A. F., Bengaly, K. & Kinyamario, J. I. Human population growth, African pastoralism, and rangelands: A perspective. Rangeland Ecol. Manage. 70, 273–280 (2017).Article 

    Google Scholar 
    Midgley, G. F. & Bond, W. J. Future of African terrestrial biodiversity and ecosystems under anthropogenic climate change. Nat. Clim. Chang. 5, 823–829 (2015).Article 
    ADS 

    Google Scholar 
    Hill, M. J. & Guerschman, J. P. The MODIS global vegetation fractional cover product 2001–2018: Characteristics of vegetation fractional cover in grasslands and savanna woodlands. Remote Sensing 12, 406 (2020).Article 
    ADS 

    Google Scholar 
    Lake, P. S. Resistance, resilience and restoration. Ecol. Manage. Restor. 14, 20–24 (2013).Article 

    Google Scholar 
    Hodgson, D., McDonald, J. L. & Hosken, D. J. What do you mean, ‘resilient’?. Trends Ecol. Evol. 30, 503–506 (2015).Article 
    PubMed 

    Google Scholar 
    Tilman, D. & Downing, J. A. Biodiversity and stability in grasslands. Nature 367, 363–365 (1994).Article 
    ADS 

    Google Scholar 
    Fedrigo, J. K. et al. Temporary grazing exclusion promotes rapid recovery of species richness and productivity in a long-term overgrazed Campos grassland. Restor. Ecol. 26, 677–685 (2018).Article 

    Google Scholar 
    Ruppert, J. C. et al. Quantifying drylands’ drought resistance and recovery: the importance of drought intensity, dominant life history and grazing regime. Glob. Chang. Biol. 21, 1258–1270 (2015).Article 
    ADS 
    PubMed 

    Google Scholar 
    Homewood, K. M. Policy, environment and development in African rangelands. Environ. Sci. Policy 7, 125–143 (2004).Article 

    Google Scholar 
    Caro, T. & Davenport, T. R. B. Wildlife and wildlife management in Tanzania. Conserv. Biol. 30, 716–723 (2016).Article 
    PubMed 

    Google Scholar 
    Bollig, M. & Schulte, A. Environmental change and pastoral perceptions: degradation and indigenous knowledge in two African pastoral communities. Hum. Ecol. 27, 493–514 (1999).Article 

    Google Scholar 
    Veldhuis, M. P. et al. Cross-boundary human impacts compromise the Serengeti-Mara ecosystem. Science 363, 1424–1428 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Nicholson, S. E. Climate and climatic variability of rainfall over Eastern Africa. Rev. Geophys. 55, 590–635 (2017).Article 
    ADS 

    Google Scholar 
    2012 Population and Housing Census (National Bureau of Statistics, Ministry of Finance, 2013).Kiffner, C., Nagar, S., Kollmar, C. & Kioko, J. Wildlife species richness and densities in wildlife corridors of Northern Tanzania. J. Nat. Conserv. 31, 29–37 (2016).Article 

    Google Scholar 
    Foley, C. A. H. & Faust, L. J. Rapid population growth in an elephant Loxodonta africana population recovering from poaching in Tarangire National Park, Tanzania. Oryx 44, 205–212 (2010).Article 

    Google Scholar 
    Kebacho, L. L. Large-scale circulations associated with recent interannual variability of the short rains over East Africa. Meteorol. Atmos. Phys. 134, 10 (2021).Article 
    ADS 

    Google Scholar 
    Wainwright, C. M., Finney, D. L., Kilavi, M., Black, E. & Marsham, J. H. Extreme rainfall in East Africa, October 2019-January 2020 and context under future climate change. Weather 76, 26–31 (2021).Article 
    ADS 

    Google Scholar 
    Abukari, H. & Mwalyosi, R. B. Comparing pressures on national parks in Ghana and Tanzania: The case of mole and Tarangire National Parks. Global Ecol. Conserv. 15, e00405 (2018).Article 

    Google Scholar 
    Kaswamila, A. An analysis of the contribution of community wildlife management areas on livelihood in Tanzania. Sustain. Natl. Res. Manag. 139–54 (2012).NTRI. Maps | NTRI – Northern Tanzania Rangelands Initiative. https://www.ntri.co.tz/maps/ (2016). Accessed: 2021-3-29.Mworia, J., Kinyamario, J. & John, E. Impact of the invader Ipomoea hildebrandtii on grass biomass, nitrogen mineralisation and determinants of its seedling establishment in Kajiado, Kenya. Afr. J. Range Forage Sci. 25, 11–16 (2008).Article 

    Google Scholar 
    Manyanza, N. M. & Ojija, F. Invasion, impact and control techniques for invasive Ipomoea hildebrandtii on Maasai steppe rangelands. NATO Adv. Sci. Inst. Ser. E Appl. Sci. 17, 12 (2021).Thaiyah, A. G. et al. Acute, sub-chronic and chronic toxicity of Solanum incanum L in sheep in Kenya. Kenya Veterinarian 35, 1–8 (2011).
    Google Scholar 
    Roques, K. G., O’Connor, T. G. & Watkinson, A. R. Dynamics of shrub encroachment in an African savanna: relative influences of fire, herbivory, rainfall and density dependence. J. Appl. Ecol. 38, 268–280 (2001).Article 

    Google Scholar 
    Riginos, C. & Herrick, J. E. Monitoring rangeland health: a guide for pastoralists and other land managers in Eastern Africa. Version II (2010).Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007).QGIS Development Team. QGIS Geographic Information System. QGIS Association (2022).Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).Article 
    ADS 

    Google Scholar 
    Didan, K. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006 [Data set] (NASA EOSDIS Land Processes DAAC, 2015).Friedl, M. & Sulla-Menashe, D. MCD12Q1 MODIS/Terra+ Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2019).Vermote, E. MOD09A1 MODIS/Terra Surface Reflectance 8-day L3 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC 10 (2015).Funk, C. et al. The climate hazards infrared precipitation with stations–a new environmental record for monitoring extremes. Scientific Data 2, 1–21 (2015).Article 

    Google Scholar 
    Zeileis, A. & Grothendieck, G. zoo: S3 infrastructure for regular and irregular time series. arXiv:math/0505527 (2005).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2016).Scaramuzza, P. & Barsi, J. Landsat 7 scan line corrector-off gap-filled product development in Proceeding of Pecora 16, 23–27 (2005).
    Google Scholar 
    Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).Article 
    ADS 

    Google Scholar 
    Rikimaru, A., Roy, P. S. & Miyatake, S. Tropical forest cover density mapping. Trop. Ecol. 39–47 (2002).Diek, S., Fornallaz, F., Schaepman, M. E. & De Jong, R. Barest pixel composite for agricultural areas using landsat time series. Remote Sensing 9, 1245 (2017).Article 
    ADS 

    Google Scholar 
    Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H. & Sorooshian, S. A modified soil adjusted vegetation index. Remote Sens. Environ. 48, 119–126 (1994).Article 
    ADS 

    Google Scholar 
    Adams, B. et al. Mapping forest composition with Landsat time series: An evaluation of seasonal composites and harmonic regression. Remote Sensing 12, 610 (2020).Article 
    ADS 

    Google Scholar 
    Nwanganga, F. & Chapple, M. Practical machine learning in R (John Wiley and Sons, Indianapolis, 2020).Adam, E., Mutanga, O., Odindi, J. & Abdel-Rahman, E. M. Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers. Int. J. Remote Sens. 35, 3440–3458 (2014).Article 

    Google Scholar 
    Mansour, K., Mutanga, O., Adam, E. & Abdel-Rahman, E. M. Multispectral remote sensing for mapping grassland degradation using the key indicators of grass species and edaphic factors. Geocarto Int. 31, 477–491 (2016).Article 

    Google Scholar 
    Hunter, F. D. L., Mitchard, E. T. A., Tyrrell, P. & Russell, S. Inter-Seasonal time series imagery enhances classification accuracy of grazing resource and land degradation maps in a savanna ecosystem. Remote Sensing 12, 198 (2020).Article 
    ADS 

    Google Scholar 
    Yang, L. et al. Estimating surface downward shortwave radiation over china based on the gradient boosting decision tree method. Remote Sensing 10, 185 (2018).Article 
    ADS 

    Google Scholar 
    Pham, T. D. et al. Estimating mangrove Above-Ground biomass using extreme gradient boosting decision trees algorithm with fused Sentinel-2 and ALOS-2 PALSAR-2 data in Can Gio biosphere reserve, Vietnam. Remote Sensing 12, 777 (2020).Article 
    ADS 

    Google Scholar 
    Adobe Inc. Adobe illustrator.Lenth, R. V. emmeans: Estimated marginal means, aka Least-Squares means. R package version 1.5.4 (2021).Royall, R. M. The effect of sample size on the meaning of significance tests. Am. Stat. 40, 313–315 (1986).MATH 

    Google Scholar 
    Rue, H., Martino, S. & Chopin, N. Approximate bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. Series B Stat. Methodol. 71, 319–392 (2009).Lindgren, F. & Rue, H. Bayesian spatial modelling with R-INLA. J. Stat. Softw. 63, 1–25 (2015).Article 

    Google Scholar 
    Bakka, H. et al. Spatial modelling with R-INLA: A review. arXiv:1802.06350 [stat] (2018).Lobora, A. L. et al. Modelling habitat conversion in Miombo woodlands: Insights from Tanzania. J. Land Use Sci. 1747423X.2017.1331271 (2017).Bright, E. A., Rose, A. N., Urban, M. L. & McKee, J. LandScan 2017 High-Resolution global population data set. Tech. Rep., Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States) (2018).Gilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci Data 5, 180227 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yang, Y., Fang, J., Ma, W. & Wang, W. Relationship between variability in aboveground net primary production and precipitation in global grasslands. Geophys. Res. Lett. 35 (2008).Guo, Q. et al. Spatial variations in aboveground net primary productivity along a climate gradient in Eurasian temperate grassland: effects of mean annual precipitation and its seasonal distribution. Glob. Chang. Biol. 18, 3624–3631 (2012).Article 
    ADS 

    Google Scholar 
    Wang, X., Yue, Y. & Faraway, J. J. Bayesian Regression Modeling with INLA (Chapman and Hall/CRC, 2018).Côté, I. M. & Darling, E. S. Rethinking ecosystem resilience in the face of climate change. PLoS Biol. 8, e1000438 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    O’Loughlin, J. et al. Climate variability and conflict risk in East Africa, 1990–2009. Proc. Natl. Acad. Sci. 109, 18344–18349 (2012).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ongoma, V., Chen, H., Gao, C., Nyongesa, A. M. & Polong, F. Future changes in climate extremes over Equatorial East Africa based on CMIP5 multimodel ensemble. Nat. Hazards 90, 901–920 (2018).Article 

    Google Scholar 
    Homewood, K. & Rodgers, W. A. Pastoralism, conservation and the overgrazing controversy. Conservation in Africa: People, policies and practice 111–128 (1987).Scoones, I. Exploiting heterogeneity: habitat use by cattle in dryland Zimbabwe. J. Arid Environ. 29, 221–237 (1995).Article 
    ADS 

    Google Scholar 
    Goldman, M. J. & Riosmena, F. Adaptive capacity in Tanzanian Maasailand: Changing strategies to cope with drought in fragmented landscapes. Glob. Environ. Change 23, 588–597 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Selemani, I. S. & Others. Communal rangelands management and challenges underpinning pastoral mobility in Tanzania: a review. Livestock Res. Rural Dev. 26, 1–12 (2014).Middleton, N. Rangeland management and climate hazards in drylands: dust storms, desertification and the overgrazing debate. Nat. Hazards 92, 57–70 (2018).Article 

    Google Scholar 
    Sallu, S. M., Twyman, C. & Stringer, L. C. Resilient or vulnerable livelihoods? Assessing livelihood dynamics and trajectories in rural Botswana. Ecology and Society 15 (2010).Oba, G. & Lusigi, W. J. An overview of drought strategies and land use in African pastoral systems (Agricultural Administration Unit, Overseas Development Institute, 1987).Russell, S., Tyrrell, P. & Western, D. Seasonal interactions of pastoralists and wildlife in relation to pasture in an African savanna ecosystem. J. Arid Environ. 154, 70–81 (2018).Article 
    ADS 

    Google Scholar 
    Girvetz, E. et al. Future climate projections in Africa: Where are we headed? In The Climate-Smart Agriculture Papers: Investigating the Business of a Productive, Resilient and Low Emission Future 15–27 (Springer International Publishing, 2019).Lyon, B. & DeWitt, D. G. A recent and abrupt decline in the East African long rains. Geophys. Res. Lett. 39 (2012).Liebmann, B. et al. Climatology and interannual variability of boreal spring wet season precipitation in the Eastern Horn of Africa and implications for its recent decline. J. Clim. 30, 3867–3886 (2017).Article 
    ADS 

    Google Scholar 
    Shongwe, M. E., van Oldenborgh, G. J., van den Hurk, B. & van Aalst, M. Projected changes in mean and extreme precipitation in Africa under global warming. part II: East Africa. J. Clim. 24, 3718–3733 (2011).Dunning, C. M., Black, E. & Allan, R. P. Later wet seasons with more intense rainfall over Africa under future climate change. J. Clim. 31, 9719–9738 (2018).Article 
    ADS 

    Google Scholar 
    Rowell, D. P., Booth, B. B. B., Nicholson, S. E. & Good, P. Reconciling past and future rainfall trends over East Africa. J. Clim. 28, 9768–9788 (2015).Article 
    ADS 

    Google Scholar 
    Vizy, E. K. & Cook, K. H. Mid-Twenty-First-Century changes in extreme events over Northern and Tropical Africa. J. Clim. 25, 5748–5767 (2012).Article 
    ADS 

    Google Scholar 
    Gebremeskel Haile, G. et al. Droughts in East Africa: Causes, impacts and resilience. Earth-Sci. Rev. 193, 146–161 (2019).Kendon, E. J. et al. Enhanced future changes in wet and dry extremes over Africa at convection-permitting scale. Nat. Commun. 10, 1794 (2019).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Finney, D. L. et al. Effects of explicit convection on future projections of mesoscale circulations, rainfall, and rainfall extremes over Eastern Africa. J. Clim. 33, 2701–2718 (2020).Article 
    ADS 

    Google Scholar 
    Prins, H. H. T. & Loth, P. E. Rainfall patterns as background to plant phenology in Northern Tanzania. J. Biogeogr. 15, 451–463 (1988).Article 

    Google Scholar 
    Ngondya, I. B., Treydte, A. C., Ndakidemi, P. A. & Munishi, L. K. Invasive plants: ecological effects, status, management challenges in Tanzania and the way forward. J. Biodivers. Environ. Sci. (JBES) 10, 204–217 (2017).
    Google Scholar 
    Drusch, M. et al. Sentinel-2: ESA’s optical High-Resolution mission for GMES operational services. Remote Sens. Environ. 120, 25–36 (2012).Article 
    ADS 

    Google Scholar 
    Rapinel, S. et al. Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities. Remote Sens. Environ. 223, 115–129 (2019).Article 
    ADS 

    Google Scholar 
    Li, W. et al. Accelerating savanna degradation threatens the Maasai Mara socio-ecological system. Glob. Environ. Change 60, 102030 (2020).Article 

    Google Scholar 
    Wonkka, C. L., Twidwell, D., Franz, T. E., Taylor, C. A. & Rogers, W. E. Persistence of a severe drought increases desertification but not woody dieback in semiarid savanna. Rangeland Ecol. Manage. 69, 491–498 (2016).Article 

    Google Scholar 
    Vierich, H. I. D. & Stoop, W. A. Changes in West African savanna agriculture in response to growing population and continuing low rainfall. Agric. Ecosyst. Environ. 31, 115–132 (1990).Article 

    Google Scholar 
    Fynn, R. W. S. & O’Connor, T. G. Effect of stocking rate and rainfall on rangeland dynamics and cattle performance in a semi-arid savanna, South Africa. J. Appl. Ecol. 37, 491–507 (2000).Article 

    Google Scholar 
    Wang, S., Chen, W., Xie, S. M., Azzari, G. & Lobell, D. B. Weakly supervised deep learning for segmentation of remote sensing imagery. Remote Sensing 12, 207 (2020).Article 
    ADS 

    Google Scholar 
    Alananga, S., Makupa, E. R., Moyo, K. J., Matotola, U. C. & Mrema, E. F. Land administration practices in Tanzania: A replica of past mistakes. Journal of Property, Planning and Environmental Law (2019).Huggins, C. Village land use planning and commercialization of land in Tanzania. LANDac Research Brief 1 (2016).Stein, H., Maganga, F. P., Odgaard, R., Askew, K. & Cunningham, S. The formal divide: Customary rights and the allocation of credit to agriculture in Tanzania. J. Dev. Stud. 52, 1306–1319 (2016).Article 

    Google Scholar 
    Hall, D. G. M., Reeve, M. J., Thomasson, A. J. & Wright, V. F. Water retention, porosity and density of field soils (No. Tech. Monograph N9, 1977).Moore, D. C. & Singer, M. J. Crust formation effects on soil erosion processes. Soil Sci. Soc. Am. J. 54, 1117–1123 (1990).Article 
    ADS 

    Google Scholar 
    Cotler, H. & Ortega-Larrocea, M. P. Effects of land use on soil erosion in a tropical dry forest ecosystem, Chamela watershed, Mexico. Catena 65, 107–117 (2006).Article 

    Google Scholar 
    Bach, E. M., Baer, S. G., Meyer, C. K. & Six, J. Soil texture affects soil microbial and structural recovery during grassland restoration. Soil Biol. Biochem. 42, 2182–2191 (2010).Article 
    CAS 

    Google Scholar 
    Butz, R. J. Traditional fire management: historical fire regimes and land use change in pastoral East Africa. Int. J. Wildland Fire 18, 442–450 (2009).Article 

    Google Scholar  More

  • in

    Exploring soil bacterial diversity in different micro-vegetational habitats of Dachigam National Park in North-western Himalaya

    Hatton, P. J., Castanha, C., Torn, M. S. & Bird, J. A. Litter type control on soil C and N stabilization dynamics in a temperate forest. Glob. Change Biol. 21(3), 1358–1367. https://doi.org/10.1111/gcb.12786 (2015).Article 
    ADS 

    Google Scholar 
    Lladó, S., López-Mondéjar, R. & Baldrian, P. Forest soil bacteria: Diversity, involvement in ecosystem processes, and response to global change. Microbiol. Mol. Biol. Rev. 81(2), e00063–16. https://doi.org/10.1128/mmbr.00063-16 (2017).Article 
    CAS 

    Google Scholar 
    Ranjard, L. & Richaume, A. Quantitative and qualitative microscale distribution of bacteria in soil. Res. Microbiol. 152(8), 707–716. https://doi.org/10.1016/S0923-2508(01)01251-7 (2001).Article 
    CAS 

    Google Scholar 
    Nannipieri, P., Badalucco, L., Benbi, D. K., & Nieder, R. Handbook of processes and modelling in the soil-plant system. Biological Processes, 57–82 (2003).Wixon, D. L. & Balser, T. C. Complexity, climate change and soil carbon: A systems approach to microbial temperature response. Syst. Res. Behav. Sci. 26(5), 601–620. https://doi.org/10.1002/sres.995 (2009).Article 

    Google Scholar 
    Van Der Heijden, M. G., Bardgett, R. D. & Van Straalen, N. M. The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 11(3), 296–310. https://doi.org/10.1111/j.1461-0248.2007.01139.x (2008).Article 

    Google Scholar 
    Tisdall, J. M. Possible role of soil microorganisms in aggregation in soils. Plant Soil 159, 115–121. https://doi.org/10.1007/BF00000100 (1994).Article 

    Google Scholar 
    Ingham, E. R. Soil biology primer, Chapter 4: Soil fungus. Soil and Water Conservation 22–23 (Soil and Water Conservation Society, 2009).
    Google Scholar 
    Stevens, W. B., Sainju, U. M., Caesar, A. J., West, M. & Gaskin, J. F. Soil-aggregating bacterial community as affected by irrigation, tillage, and cropping system in the northern great plains. Soil Sci. 179(1), 11–20 (2014).Article 
    ADS 

    Google Scholar 
    Islam, K. R. Lecture on Soil Physics, Personal Collection of K. Islam (Ohio State University, 2008).
    Google Scholar 
    López-Mondéjar, R., Zühlke, D., Becher, D., Riedel, K. & Baldrian, P. Cellulose and hemicellulose decomposition by forest soil bacteria proceeds by the action of structurally variable enzymatic systems. Sci. Rep. 6(1), 25279. https://doi.org/10.1038/srep25279 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Wardle, D. A., Nilsson, M. C. & Zackrisson, O. Fire-derived charcoal causes loss of forest humus. Science 320(5876), 629–629. https://doi.org/10.1126/science.1154960 (2008).Article 
    ADS 
    CAS 

    Google Scholar 
    Shelobolina, E., Roden, E., Benzine, J. & Xiong, M. Y. Using phyllosilicate-Fe (II)-oxidizing soil bacteria to improve Fe and K plant nutrition. U.S. Patent Application 14/924,397 (Wisconsin Alumni Research Foundation, 2016).
    Google Scholar 
    Kumar, A., & Verma, J. P. The role of microbes to improve crop productivity and soil health. In Ecological Wisdom Inspired Restoration Engineering 249–265. https://doi.org/10.1007/978-981-13-0149-0_14 (2019).Dick, W. Lecture on Biochemistry Process in Soil Microbiology, Personal Collection of W. Dick (The Ohio State University School of Environment and Natural Resources, 2009).
    Google Scholar 
    Reed, S. C., Cleveland, C. C. & Townsend, A. R. Functional ecology of free-living nitrogen fixation: A contemporary perspective. Annu. Rev. Ecol. Evol. Syst. 42, 489–512. https://doi.org/10.1146/annurev-ecolsys-102710-145034 (2011).Article 

    Google Scholar 
    Sylvia, D. M., Fuhrmann, J. J., Hartel, P. G. & Zuberer, D. A. Principles and Applications of Soil Microbiology (No. QR111 S674 2005) 2nd edn. (Prentice Hall, 2005).
    Google Scholar 
    Torsvik, V., Daae, F. L., Sandaa, R. A. & Øvreås, L. Novel techniques for analysing microbial diversity in natural and perturbed environments. J. Biotechnol. 64(1), 53–62. https://doi.org/10.1016/s0168-1656(98)00103-5 (1998).Article 
    CAS 

    Google Scholar 
    Roesch, L. F. W. et al. Pyrosequencing enumerates and contrasts soil microbial diversity. ISME J. 1(4), 283–290. https://doi.org/10.1038/ismej.2007.53 (2007).Article 
    CAS 

    Google Scholar 
    Rousk, J., Brookes, P. C. & Bååth, E. The microbial PLFA composition as affected by pH in an arable soil. Soil Biol. Biochem. 42(3), 516–520. https://doi.org/10.1016/j.soilbio.2009.11.026 (2010).Article 
    CAS 

    Google Scholar 
    Brockett, B. F., Prescott, C. E. & Grayston, S. J. Soil moisture is the major factor influencing microbial community structure and enzyme activities across seven biogeoclimatic zones in western Canada. Soil Biol. Biochem. 44(1), 9–20. https://doi.org/10.1016/j.soilbio.2011.09.003 (2012).Article 
    CAS 

    Google Scholar 
    Urbanová, M., Šnajdr, J. & Baldrian, P. Composition of fungal and bacterial communities in forest litter and soil is largely determined by dominant trees. Soil Biol. Biochem. 84, 53–64. https://doi.org/10.1016/j.soilbio.2015.02.011 (2015).Article 
    CAS 

    Google Scholar 
    Binkley, D. & Vitousek, P. M. Soil nutrient availability. In Plant Physiological, Field Methods and Instrumentation (eds Pearey, R. W. et al.) 75–96 (Champan and Hall, 1989).Chapter 

    Google Scholar 
    Ruess, J. O. & Innis, G. S. A grassland nitrogen flow simulation mode. Ecology 58, 348–429. https://doi.org/10.2307/1935612 (1977).Article 

    Google Scholar 
    Kumar, M., Sharma, C. M. & Rajwar, G. S. Physico-chemical properties of forest soil along altitudinal gradient in Garhwal Himalaya. J. Hill Res. 17(2), 60–64 (2004).
    Google Scholar 
    Smit, E. et al. Diversity and seasonal fluctuations of the dominant members of the bacterial soil community in a wheat field as determined by cultivation and molecular methods. Appl. Environ. Microbiol. 67(5), 2284–2291. https://doi.org/10.1128/AEM.67.5.2284-2291.2001 (2001).Article 
    ADS 
    CAS 

    Google Scholar 
    Qazi, P. H. Bioprospecting Himalayan microbial diversity. ENVIS Newsletter on Himalayan Ecology 12(4). http://gbpihedenvis.nic.in/ENVIS%20Newsletter/vol%2012(4).pdf (2015).Pradhan, S. et al. Bacterial biodiversity from Roopkund glacier, Himalayan Mountain ranges, India. Extremophiles 14, 377–395. https://doi.org/10.1007/s00792-010-0318-3 (2010).Article 
    CAS 

    Google Scholar 
    Shivaji, S. et al. Bacterial diversity of soil in the vicinity of Pindari glacier, Himalayan Mountain ranges, India, using culturable bacteria and soil 16S rRNA gene clones. Extremophiles 15, 1–22. https://doi.org/10.1007/s00792-010-0333-4 (2011).Article 
    CAS 

    Google Scholar 
    Das, J. & Dangar, T. K. Microbial population dynamics, especially stress tolerant Bacillus thuringiensis, in partially anaerobic rice field soils during post-harvest period of the Himalayan, island, brackish water and coastal habitats of India. World J. Microbiol. Biotechnol. 24, 1403–1410. https://doi.org/10.1007/s11274-007-9620-3 (2008).Article 

    Google Scholar 
    Lyngwi, N. A., Koijam, K., Sharma, D. & Joshi, S. R. Cultivable bacterial diversity along the altitudinal zonation and vegetation range of tropical Eastern Himalaya. Rev. Biol. Trop. 61(1), 467–490. https://doi.org/10.15517/rbt.v61i1.11141 (2013).Article 

    Google Scholar 
    Pandey, S., Singh, S., Yadav, A. N., Nain, L. & Saxena, A. K. Phylogenetic diversity and characterization of novel and efficient cellulase producing bacterial isolates from various extreme environments. Biosci. Biotechnol. Biochem. 77(7), 1474–1480. https://doi.org/10.1271/bbb.130121 (2013).Article 
    CAS 

    Google Scholar 
    Venkatachalam, S., Gowdaman, V. & Prabagaran, S. R. Culturable and culture-independent bacterial diversity and the prevalence of cold-adapted enzymes from the Himalayan Mountain ranges of India and Nepal. Microb. Ecol. 69, 472–491. https://doi.org/10.1007/s00248-014-0476-4 (2015).Article 
    CAS 

    Google Scholar 
    Saxena, A. K., Yadav, A. N., Kaushik, R., Tyagi, S. P., & Shukla, L. Biotechnological applications of microbes isolated from cold environments in agriculture and allied sectors. In International Conference on Low Temperature Science and Biotechnological Advances, Vol. 104 (Society of Low Temperature Biology, 2015).Singh, R. N. et al. First high-quality draft genome sequence of a plant growth promoting and cold active enzyme producing psychrotrophic Arthrobacter agilis strain L77. Stand. Genom. Sci. 11, 1–9. https://doi.org/10.1186/s40793-016-0176-4 (2016).Article 
    CAS 

    Google Scholar 
    Mushtaq, H. et al. Biochemical characterization and functional analysis of heat stable high potential protease of Bacillus amyloliquefaciens strain HM48 from soils of Dachigam National Park in Kashmir Himalaya. Biomolecules 11(1), 117. https://doi.org/10.3390/biom11010117 (2021).Article 
    CAS 

    Google Scholar 
    Maharana, A. K. & Ray, P. Isolation and screening of cold active extracellular enzymes producing psychrotrophic bacteria from soil of Jammu City. Biosci. Biotechnol. Res. Asia 10(1), 267–273. https://doi.org/10.13005/bbra/1120 (2013).Article 

    Google Scholar 
    Rehakova, K., Chlumska, Z. & Dolezal, J. Soil cyanobacterial and microalgal diversity in dry mountains of Ladakh, NW Himalaya, as related to site, altitude, and vegetation. Microb. Ecol. 62, 337–346. https://doi.org/10.1007/s00248-011-9878-8 (2011).Article 
    CAS 

    Google Scholar 
    Rehakova, K. et al. Bacterial community of cushion plant Thylacospermum ceaspitosum on elevational gradient in the Himalayan cold desert. Front. Microbiol. 6, 304. https://doi.org/10.3389/fmicb.2015.00304 (2015).Article 

    Google Scholar 
    Gupta, P. & Vakhlu, J. Culturable bacterial diversity and hydrolytic enzymes from Drass, a cold desert in India. Afr. J. Microbiol. Res. 9, 1866–1876. https://doi.org/10.5897/AJMR2015.7424 (2015).Article 

    Google Scholar 
    Yadav, A. N. et al. Culturable diversity and functional annotation of psychrotrophic bacteria from cold desert of Leh Ladakh (India). World J. Microbiol. Biotechnol. 31, 95–108. https://doi.org/10.1007/s11274-014-1768-z (2015).Article 
    CAS 

    Google Scholar 
    Farooq, S., Nazir, R., Ganai, B. A., Mushtaq, H. & Dar, G. J. Psychrophilic and psychrotrophic bacterial diversity of Himalayan Thajwas glacial soil, India. Biologia 77, 203–213. https://doi.org/10.1007/s11756-021-00915-6 (2022).Article 
    CAS 

    Google Scholar 
    Ahmad, N., Johri, S., Abdin, M. Z. & Qazi, G. N. Molecular characterization of bacterial population in the forest soil of Kashmir, India. World J. Microbiol. Biotechnol. 25, 107–113. https://doi.org/10.1007/s11274-008-9868-2 (2009).Article 
    CAS 

    Google Scholar 
    Thakur, D., Yadav, A., Gogoi, B. K. & Bora, T. C. Isolation and screening of Streptomyces in soil of protected forest areas from the states of Assam and Tripura, India, for antimicrobial metabolites. J. Mycol. Méd. 17(4), 242–249. https://doi.org/10.1016/j.mycmed.2007.08.001 (2007).Article 

    Google Scholar 
    Rina, K., Hiral, P., Payal, P., Dharaiya, N. & Patel, R. K. Study on microbial diversity of Wild Ass Sanctuary, Little Rann of Kutch, Gujarat, India. ICFAI Univ. J. Life Sci. 3(1), 34–41 (2009).
    Google Scholar 
    Das, S., Saikia, P., Baruah, P. P. & Chakraborty, A. Isolation and identification of soil bacteria collected from Dibru-Saikhowa, the National Park and Biosphere Reserve Forest of Assam, India. Int. J. Sci. Res. (IJSR), 1937–1940 (2016).De Mandal, S., Lalremsanga, H. T. & Kumar, N. S. Bacterial diversity of Murlen National Park located in Indo-Burman Biodiversity hotspot region: A metagenomic approach. Genom. Data 5, 25–26. https://doi.org/10.1016/j.gdata.2015.04.025 (2015).Article 

    Google Scholar 
    Megha, B., Sejal, P., Puja, P. & Jasrai, Y. T. Isolation and identification of soil microflora of national parks of Gujarat, India. Int. J. Curr. Microbiol. Appl. Sci. 4(3), 421–429 (2015).
    Google Scholar 
    Kumar, A., Singh, R. D., Patra, A. K., Sahu, S. K. & Singh, M. Impact of oak and pine canopy cover on soil biochemical and microbial indicators of Binsar Wildlife Sanctuary in the Western Himalaya, India. J. Pure Appl. Microbiol. 11(3), 1599–1607. https://doi.org/10.22207/JPAM.11.3.47 (2017).Article 
    CAS 

    Google Scholar 
    Dhiman, P., Mehta, J. P., Singh, P. & andSharesthBaldotra, S. S.,. Effect of prescribe fire on bacterial abundance and their enzymatic activity in burnt and unburnt soil of Chilla Forest, Raja Ji National Park, Uttarakhand, India. Plant Arch. 18(1), 1125–1128 (2018).
    Google Scholar 
    Behera, P. et al. Spatial and temporal heterogeneity in the structure and function of sediment bacterial communities of a tropical mangrove forest. Environ. Sci. Pollut. Res. 26, 3893–3908 (2019).Article 
    CAS 

    Google Scholar 
    Sharma, P. & Thakur, D. Antimicrobial biosynthetic potential and diversity of culturable soil actinobacteria from forest ecosystems of Northeast India. Sci. Rep. 10(1), 1–18. https://doi.org/10.1038/s41598-020-60968-6 (2020).Article 
    CAS 

    Google Scholar 
    Dar, G. H., Bhagat, R. C. & Khan, M. A. Biodiversity of the Kashmir Himalaya (Valley Book House, 2002).
    Google Scholar 
    Shameem, S. A., Kangroo, N. I. & Bhat, G. A. Comparative assessment of edaphic features and herbaceous diversity in lower Dachigam national park, Kashmir, Himalaya. J. Ecol. Nat. Environ. 3(6), 196–204 (2011).
    Google Scholar 
    Thakur, M., Sharma, L. K., Charoo, S. A. & Sathyakumar, S. Conflict bear translocation: Investigating population genetics and fate of bear translocation in Dachigam National Park, Jammu and Kashmir, India. PLoS One 10, e0132005. https://doi.org/10.1371/journal.pone.0132005 (2015).Article 
    CAS 

    Google Scholar 
    Ahmad, K., Qureshi, Q., Agoramoorthy, G. & Nigam, P. Habitat use patterns and food habits of the Kashmir red deer or Hangul (Cervus elaphus hanglu) in Dachigam National Park, Kashmir, India. Ethol. Ecol. Evol. 28(1), 85–101. https://doi.org/10.1080/03949370.2015.1018955 (2016).Article 

    Google Scholar 
    Jammu and Kashmir Forest Department (JKFD). Handbook of Forest Statistics (Jammu and Kashmir Forest Department, 2011).
    Google Scholar 
    Anderson, J. M. & Ingram, J. S. I. A Handbook of Methods 62–65 (CAB International, 1993).
    Google Scholar 
    Joshi, S. R., Chauhan, M. A. N. J. U., Sharma, G. D. & Mishra, R. R. Effect of deforestation on microbes, VAM fungi and their enzymatic activity in Eastern Himalaya. In Studies in Himalayan Ecobiology 141–152 (Today and Tommorows Publication, 1991).
    Google Scholar 
    Jackson, M. L. Soil Chemical Analysis 151–154 (Prentice-Hall, 1958). https://doi.org/10.1002/jpln.19590850311.Book 

    Google Scholar 
    Gardner, W. H. Water content. Methods of soil analysis: Part 1. Phys. Mineral. Methods 5, 493–544 (1986).
    Google Scholar 
    Walkley, A. & Black, I. A. Estimation of soil organic carbon by the chromic acid titration method. Soil Sci. 37(1), 29–38 (1934).Article 
    ADS 
    CAS 

    Google Scholar 
    Bremner, J. M. Determination of nitrogen in soil by the Kjeldahl method. J. Agric. Sci. 55(1), 1–23 (1960).Article 

    Google Scholar 
    Coursey, D. G. & Eggins, H. O. W. Microorganismes responsables de l’altération de l’huile de palme pendant le stockage. Oléagineux 16, 227–233 (1961).CAS 

    Google Scholar 
    Kumar, R., Acharya, C. & Joshi, S. R. Isolation and analyses of uranium tolerant Serratia marcescens strains and their utilization for aerobic uranium U (VI) bioadsorption. J. Microbiol. 49, 568–574. https://doi.org/10.1007/s12275-011-0366-0 (2011).Article 
    CAS 

    Google Scholar 
    Team, R. C. R: A language and environment for statistical computing. https://www.R-project.org (R Foundation for Statistical Computing, 2017).Bergey, D. H. & Holt, J. G. Bergey’s Manual of Determinative Bacteriology (Lippincott Williams & Wilkins, 1994).
    Google Scholar 
    Gürtler, V. & Stanisich, V. A. New approaches to typing and identification of bacteria using the 16S–23S rDNA spacer region. Microbiology 142(1), 3–16 (1996).Article 

    Google Scholar 
    Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 1–9 (2001).
    Google Scholar 
    Sorensen, T. A. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biol. Skar. 5, 1–34 (1948).
    Google Scholar 
    Muhumuza, M. & Balkwill, K. Factors affecting the success of conserving biodiversity in national parks: A review of case studies from Africa. Int. J. Biodivers. https://doi.org/10.1155/2013/798101 (2013).Article 

    Google Scholar 
    Yaqoob, A., Yunus, M., Bhat, G. A. & Singh, D. P. Phytodiversity and seasonal variations in the soil characteristics of shrublands of Dachigam National Park, Jammu and Kashmir, India. Clim. Change Environ. Sustain. 3(2), 137–143. https://doi.org/10.5958/2320-642X.2015.00015.0 (2015).Article 

    Google Scholar 
    Mir, Z. R., Noor, A., Habib, B. & Veeraswami, G. G. Seasonal population density and winter survival strategies of endangered Kashmir gray langur (Semnopithecus ajax) in Dachigam National Park, Kashmir, India. Springer Plus 4, 1–8. https://doi.org/10.1186/s40064-015-1366-z (2015).Article 
    CAS 

    Google Scholar 
    Buchan, G. D. Soil temperature regime. In Soil and Environmental Analysis: Physical Methods (eds Smith, K. A. & Mullins, C.) 539–594 (Marcel Dekker, 2001).
    Google Scholar 
    Buchan, G. D. Temperature effects in soil. In Encyclopedia of Agrophysics, Encyclopedia of Earth Sciences Series (Springer, 2011).
    Google Scholar 
    Chiemeka, I. U. Soil temperature profile at Uturu, Nigeria. Pac. J. Sci. Technol. 11(1), 478–482 (2010).
    Google Scholar 
    Decker, K. L. M., Wang, D., Waite, C. & Scherbatskoy, T. Snow removal and ambient air temperature effects on forest soil temperatures in northern Vermont. Soil Sci. Soc. Am. J. 67(4), 1234–1242. https://doi.org/10.2136/sssaj2003.1234 (2003).Article 
    ADS 
    CAS 

    Google Scholar 
    Abu-Hamdeh, N. H. & Reeder, R. C. Soil thermal conductivity effects of density, moisture, salt concentration, and organic matter. Soil Sci. Soc. Am. J. 64(4), 1285–1290. https://doi.org/10.2136/sssaj2000.6441285x (2000).Article 
    ADS 
    CAS 

    Google Scholar 
    Lu, S., Ren, T., Gong, Y. & Horton, R. An improved model for predicting soil thermal conductivity from water content at room temperature. Soil Sci. Soc. Am. J. 71(1), 8–14. https://doi.org/10.2136/sssaj2006.0041 (2007).Article 
    ADS 
    CAS 

    Google Scholar 
    Elizbarashvili, E. S., Urushadze, T. F., Elizbarashvili, M. E., Elizbarashvili, S. E. & Schaefer, M. K. Temperature regime of some soil types in Georgia. Eurasian Soil Sci. 43(4), 427–435. https://doi.org/10.1134/S1064229310040083 (2010).Article 
    ADS 

    Google Scholar 
    Walter, H. & Burnett, J. H. Ecology of Tropical and Subtropical Vegetation Vol. 539, xviii+-539 (Oliver and Boyd, 1971).
    Google Scholar 
    Callaway, R. M. Positive interactions and interdependence in plant communities. Springer Science Business Media https://doi.org/10.1007/978-1-4020-6224-7 (2007).Article 

    Google Scholar 
    Song, Y. et al. Effects of vegetation height and density on soil temperature variations. Chin. Sci. Bull. 58(8), 907–912. https://doi.org/10.1007/s11434-012-5596-y (2013).Article 

    Google Scholar 
    Dimri, B. M., Singh, S. B., Baneriee, S. K. & Singh, B. Relation of age and dominance of tree species with soil chemical attributes in Kalimpong and Kurseong District of West Bengal. Indian For. 113(4), 307–311 (1987).
    Google Scholar 
    Jackson, R. B., Mooney, H. A. & Schulze, E. D. A global budget for fine root biomass, surface area, and nutrient contents. Proc. Natl. Acad. Sci. 94(14), 7362–7366. https://doi.org/10.1073/pnas.94.14.7362 (1997).Article 
    ADS 
    CAS 

    Google Scholar 
    Wilson, S. D. Competition between grasses and woody plants. In Population Biology of Grasses (ed. Cheplick, G. P.) 231–254 (Cambridge University Press, 1998).Chapter 

    Google Scholar 
    Reth, S., Reichstein, M. & Falge, E. The effect of soil water content, soil temperature, soil pH-value and the root mass on soil CO2 efflux—A modified model. Plant Soil 268, 21–33. https://doi.org/10.1007/s11104-005-0175-5 (2005).Article 
    CAS 

    Google Scholar 
    Zinke, P. J. The pattern of influence of individual forest trees on soil properties. Ecology 43(1), 130–133 (1962).Article 

    Google Scholar 
    Patric, J. H. Forest management and nutrient cycling in eastern hardwoods Vol. 324 (Forest Service, US Department of Agriculture, Northeastern Forest Experiment Station, 1975).
    Google Scholar 
    Mroz, G. D., Jurgensen, M. F. & Frederick, D. J. Soil nutrient changes following whole tree harvesting on three northern hardwood sites. Soil Sci. Soc. Am. J. 49(6), 1552–1557. https://doi.org/10.2136/sssaj1985.03615995004900060044x (1985).Article 
    ADS 

    Google Scholar 
    Maggs, J. & Hewett, B. Organic C and nutrients in surface soils from some primary rainforests, derived grasslands and secondary rainforests on the Atherton Tableland in North East Queensland. Soil Res. 31(3), 343–350 (1993).Article 
    CAS 

    Google Scholar 
    Hart, S. C. & Perry, D. A. Transferring soils from high-to low-elevation forests increases nitrogen cycling rates: Climate change implications. Glob. Change Biol. 5(1), 23–32 (1999).Article 
    ADS 

    Google Scholar 
    Atlas, R. M. Diversity of microbial communities. Adv. Microb. Ecol., 1–47 (1984).Dimitriu, P. A. & Grayston, S. J. Relationship between soil properties and patterns of bacterial β-diversity across reclaimed and natural boreal forest soils. Microb. Ecol. 59, 563–573. https://doi.org/10.1007/s00248-009-9590-0 (2010).Article 

    Google Scholar 
    Bele, S. S. Soil Testing and Soil Microbiology 79–108 (Satyam Publishers and Distributors, 2014). https://doi.org/10.1007/s11356-018-3927-5.Book 

    Google Scholar 
    Cattelan, A. J., Hartel, P. G. & Fuhrmann, J. J. Bacterial composition in the rhizosphere of nodulating and non-nodulating soybean. Soil Sci. Soc. Am. J. 62(6), 1549–1555. https://doi.org/10.2136/sssaj1998.03615995006200060011x (1998).Article 
    ADS 
    CAS 

    Google Scholar 
    Silva, P. D. & Nahas, E. Bacterial diversity in soil in response to different plans, phosphate fertilizers and liming. Braz. J. Microbiol. 33, 304–310 (2002).Article 

    Google Scholar 
    Begum, K. et al. Isolation and characterization of bacteria with biochemical and pharmacological importance from soil samples of Dhaka City. Dhaka Univ. J. Pharm. Sci. 16(1), 129–136. https://doi.org/10.3329/dujps.v16i1.33390 (2017).Article 

    Google Scholar 
    Liu, D., Liu, Y., Fang, S. & Tian, Y. Tree species composition influenced microbial diversity and nitrogen availability in rhizosphere soil. Plant Soil Environ. 61(10), 438–443. https://doi.org/10.17221/94/2015-PSE (2015).Article 
    CAS 

    Google Scholar 
    Chodak, M., Klimek, B., Azarbad, H. & Jaźwa, M. Functional diversity of soil microbial communities under Scots pine, Norway spruce, silver birch and mixed boreal forests. Pedobiologia 58(2–3), 81–88 (2015).Article 

    Google Scholar 
    Gartzia-Bengoetxea, N., Kandeler, E., de Arano, I. M. & Arias-González, A. Soil microbial functional activity is governed by a combination of tree species composition and soil properties in temperate forests. Appl. Soil. Ecol. 100, 57–64 (2016).Article 

    Google Scholar 
    Shameem, S. A., Mushtaq, H., Wani, A. A., Ahmad, N. & Hai, A. Phytodiversity of herbaceous vegetation in disturbed and undisturbed forest ecosystems of Pahalgam valley, Kashmir Himalaya, India. Br. J. Environ. Clim. Change 7(3), 148–167 (2017).Article 

    Google Scholar 
    Felske, A., Wolterink, A., Van Lis, R. & Akkermans, A. D. Phylogeny of the main bacterial 16S rRNA sequences in Drentse A grassland soils (The Netherlands). Appl. Environ. Microbiol. 64(3), 871–879. https://doi.org/10.1128/aem.64.3.871-879.1998 (1998).Article 
    ADS 
    CAS 

    Google Scholar 
    Chodak, M., Gołębiewski, M., Morawska-Płoskonka, J., Kuduk, K. & Niklińska, M. Soil chemical properties affect the reaction of forest soil bacteria to drought and rewetting stress. Ann. Microbiol. 65, 1627–1637. https://doi.org/10.1007/s13213-014-1002-0 (2015).Article 
    CAS 

    Google Scholar 
    Lugo, M. A. et al. Arbuscular mycorrhizal fungi and rhizospheric bacteria diversity along an altitudinal gradient in South American Puna grassland. Microb. Ecol. 55, 705–713. https://doi.org/10.1007/s00248-007-9313-3 (2008).Article 
    CAS 

    Google Scholar 
    Wang, Q., Wang, S., Fan, B. & Yu, X. Litter production, leaf litter decomposition and nutrient return in Cunninghamia lanceolata plantations in south China: Effect of planting conifers with broadleaved species. Plant Soil 297, 201–211. https://doi.org/10.1007/s11104-007-9333-2 (2007).Article 
    CAS 

    Google Scholar 
    Nüsslein, K. & Tiedje, J. M. Soil bacterial community shift correlated with change from forest to pasture vegetation in a tropical soil. Appl. Environ. Microbiol. 65(8), 3622–3626. https://doi.org/10.1128/aem.65.8.3622-3626.1999 (1999).Article 
    ADS 

    Google Scholar 
    Hackl, E., Zechmeister-Boltenstern, S., Bodrossy, L. & Sessitsch, A. Comparison of diversities and compositions of bacterial populations inhabiting natural forest soils. Appl. Environ. Microbiol. 70(9), 5057–5065. https://doi.org/10.1128/AEM.70.9.5057-5065.2004 (2004).Article 
    ADS 
    CAS 

    Google Scholar 
    Chan, C. et al. Vegetation cover of forest, shrub and pasture strongly influences soil bacterial community structure as revealed by 16S rRNA gene T-RFLP analysis. FEMS Microbiol. Ecol. 64(3), 449–458. https://doi.org/10.1111/j.1574-6941.2008.00488.x (2008).Article 
    CAS 

    Google Scholar 
    Adamczyk, B., Kitunen, V. & Smolander, A. Protein precipitation by tannins in soil organic horizon and vegetation in relation to tree species. Biol. Fertil. Soils 45(1), 55–64. https://doi.org/10.1007/s00374-008-0308-0 (2008).Article 
    CAS 

    Google Scholar 
    Kanerva, S., Kitunen, V., Loponen, J. & Smolander, A. Phenolic compounds and terpenes in soil organic horizon layers under silver birch, Norway spruce and Scots pine. Biol. Fertil. Soils 44(4), 547–556. https://doi.org/10.1007/s00374-007-0234-6 (2008).Article 
    CAS 

    Google Scholar 
    Ushio, M., Balser, T. C. & Kitayama, K. Effects of condensed tannins in conifer leaves on the composition and activity of the soil microbial community in a tropical montane forest. Plant Soil 365(1), 157–170. https://www.jstor.org/stable/42952341 (2013).Lomolino, M. V. Elevation gradients of species-density: Historical and prospective views. Glob. Ecol. Biogeogr. 10(1), 3–13. https://doi.org/10.1046/j.1466-822x.2001.00229.x (2001).Article 

    Google Scholar 
    Thomson, B. C. et al. Vegetation affects the relative abundances of dominant soil bacterial taxa and soil respiration rates in an upland grassland soil. Microb. Ecol. 59(2), 335–343. https://doi.org/10.1007/s00248-009-9575-z (2010).Article 

    Google Scholar 
    May, R. M. Patterns of species abundance and diversity. In Ecology and Evolution of Communities (eds Cody, M. L. & Diamond, J. M.) 81–120 (Harvard University, 1975).
    Google Scholar 
    Kapur, M. & Jain, R. K. Microbial diversity: Exploring the unexplored. World Federation of Culture Collection Newsletter 39, 12–16 (2004).
    Google Scholar 
    Bryant, J. A. et al. Microbes on mountainsides: Contrasting elevational patterns of bacterial and plant diversity. Proc. Natl. Acad. Sci. 105(Suppl 1), 11505–11511 (2008).Article 
    ADS 
    CAS 

    Google Scholar 
    Fierer, N. et al. Microbes do not follow the elevational diversity patterns of plants and animals. Ecology 92(4), 797–804. https://doi.org/10.1890/10-1170.1 (2011).Article 

    Google Scholar  More

  • in

    Diagnosing destabilization risk in global land carbon sinks

    Fernández-Martínez, M. et al. Global trends in carbon sinks and their relationships with CO2 and temperature. Nat. Clim. Change 9, 73–79 (2019).Article 
    ADS 

    Google Scholar 
    Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Dakos, V. et al. Slowing down as an early warning signal for abrupt climate change. Proc. Natl Acad. Sci. USA 105, 14308–14312 (2008).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gasser, T. et al. Path-dependent reductions in CO2 emission budgets caused by permafrost carbon release. Nat. Geosci. 11, 830–835 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Bastos, A. et al. Contrasting effects of CO2 fertilization, land-use change and warming on seasonal amplitude of Northern Hemisphere CO2 exchange. Atmos. Chem. Phys. 19, 12361–12375 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Pugh, T. A. M. et al. Role of forest regrowth in global carbon sink dynamics. Proc. Natl Acad. Sci. USA 116, 4382–4387 (2019).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wang, S. et al. Recent global decline of CO2 fertilization effects on vegetation photosynthesis. Science 370, 1295–1300 (2020).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Peñuelas, J. et al. Assessment of the impacts of climate change on Mediterranean terrestrial ecosystems based on data from field experiments and long-term monitored field gradients in Catalonia. Environ. Exp. Bot. 152, 49–59 (2018).Article 

    Google Scholar 
    Terrer, C. et al. Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nat. Clim. Change 9, 684–689 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Gatti, L. V. et al. Amazonia as a carbon source linked to deforestation and climate change. Nature 595, 388–393 (2021).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Carpenter, S. R. & Brock, W. A. Rising variance: a leading indicator of ecological transition. Ecol. Lett. 9, 311–318 (2006).Article 
    CAS 
    PubMed 

    Google Scholar 
    Dakos, V., Nes, E. H. & Scheffer, M. Flickering as an early warning signal. Theor. Ecol. 6, 309–317 (2013).Article 

    Google Scholar 
    Sillmann, J., Daloz, A. S., Schaller, N. & Schwingshackl, C. in Climate Change 3rd edn (ed. Letcher, T. M.) 359–372 (Elsevier, 2021).Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Wang, X. et al. A two-fold increase of carbon cycle sensitivity to tropical temperature variations. Nature 506, 212–215 (2014).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Barnosky, A. D. et al. Approaching a state shift in Earth’s biosphere. Nature 486, 52–58 (2012).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Buermann, W. et al. Climate-driven shifts in continental net primary production implicated as a driver of a recent abrupt increase in the land carbon sink. Biogeosciences 13, 1597–1607 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Luyssaert, S. et al. CO2 balance of boreal, temperate, and tropical forests derived from a global database. Glob. Change Biol. 13, 2509–2537 (2007).Article 
    ADS 

    Google Scholar 
    Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).Article 
    PubMed 

    Google Scholar 
    Fernández-Martínez, M. et al. Nutrient availability as the key regulator of global forest carbon balance. Nat. Clim. Change 4, 471–476 (2014).Article 
    ADS 

    Google Scholar 
    Fernández-Martínez, M. et al. Spatial variability and controls over biomass stocks, carbon fluxes and resource-use efficiencies in forest ecosystems. Trees Struct. Funct. 28, 597–611 (2014).Article 

    Google Scholar 
    Ciais, P. et al. Five decades of northern land carbon uptake revealed by the interhemispheric CO2 gradient. Nature 568, 221–225 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Tilman, D., Lehman, C. L. & Thomson, K. T. Plant diversity and ecosystem productivity: theoretical considerations. Proc. Natl Acad. Sci. USA 94, 1857–1861 (1997).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    de Mazancourt, C. et al. Predicting ecosystem stability from community composition and biodiversity. Ecol. Lett. 16, 617–625 (2013).Article 
    PubMed 

    Google Scholar 
    Sakschewski, B. et al. Resilience of Amazon forests emerges from plant trait diversity. Nat. Clim. Change 6, 1032–1036 (2016).Article 
    ADS 

    Google Scholar 
    Fernández‐Martínez, M. et al. The role of climate, foliar stoichiometry and plant diversity on ecosystem carbon balance. Glob. Change Biol. 26, 7067–7078 (2020).Article 
    ADS 

    Google Scholar 
    Musavi, T. et al. Stand age and species richness dampen interannual variation of ecosystem-level photosynthetic capacity. Nat. Ecol. Evol. 1, 0048 (2017).Article 

    Google Scholar 
    Anderegg, W. R. L. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    IPBES: Summary for Policymakers. In The Global Assessment Report on Biodiversity and Ecosystem Services (eds Díaz, S. et al.) 1–56 (IPBES, 2019).Heath, J. P. Quantifying temporal variability in population abundances. Oikos 115, 573–581 (2006).Article 

    Google Scholar 
    Fernández-Martínez, M., Vicca, S., Janssens, I. A., Martín-Vide, J. & Peñuelas, J. The consecutive disparity index, D, as measure of temporal variability in ecological studies. Ecosphere 9, e02527 (2018).Article 

    Google Scholar 
    Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc Natl Acad Sci USA 104, 5925–5930 (2007).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ackerman, D. E., Chen, X. & Millet, D. B. Global nitrogen deposition (2° × 2.5° grid resolution) simulated with GEOS-Chem for 1984–1986, 1994–1996, 2004–2006, and 2014–2016 (University of Minnesota, 2018); https://conservancy.umn.edu/handle/11299/197613.Harris, I., Jones, P. D. D., Osborn, T. J. J. & Lister, D. H. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2013).Article 

    Google Scholar 
    Graven, H. D. et al. Enhanced seasonal exchange of CO2 by northern ecosystems since 1960. Science 341, 1085–1089 (2013).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Wang, K. et al. Causes of slowing-down seasonal CO2 amplitude at Mauna Loa. Glob. Change Biol. 26, 4462–4477 (2020).Article 
    ADS 

    Google Scholar 
    Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Liang, J. et al. Positive biodiversity–productivity relationship predominant in global forests. Science 354, aaf8957–aaf8957 (2016).Article 
    PubMed 

    Google Scholar 
    Gessner, M. O. et al. Diversity meets decomposition. Trends Ecol. Evol. 25, 372–380 (2010).Article 
    PubMed 

    Google Scholar 
    Peguero, G. et al. Fast attrition of springtail communities by experimental drought and richness–decomposition relationships across Europe. Glob. Change Biol. 25, 2727–2738 (2019).Article 
    ADS 

    Google Scholar 
    Díaz, S. & Cabido, M. Vive la différence: plant functional diversity matters to ecosystem processes. Trends Ecol. Evol. 16, 646–655 (2001).Article 

    Google Scholar 
    Cardinale, B. J. Biodiversity improves water quality through niche partitioning. Nature 472, 86–91 (2011).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Scheffer, M. Critical Transitions in Nature and Society (Princeton University Press, 2009).Ostfeld, R. & Keesing, F. Pulsed resources and community dynamics of consumers in terrestrial ecosystems. Trends Ecol. Evol. 15, 232–237 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    Chevallier, F. et al. CO2 surface fluxes at grid point scale estimated from a global 21 year reanalysis of atmospheric measurements. J. Geophys. Res. 115, D21307 (2010).Article 
    ADS 

    Google Scholar 
    Chevallier, F. et al. Toward robust and consistent regional CO2 flux estimates from in situ and spaceborne measurements of atmospheric CO2. Geophys. Res. Lett. 41, 1065–1070 (2014).Article 
    ADS 
    CAS 

    Google Scholar 
    Rödenbeck, C., Houweling, S., Gloor, M. & Heimann, M. CO2 flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport. Atmos. Chem. Phys. 3, 1919–1964 (2003).Article 
    ADS 

    Google Scholar 
    Rödenbeck, C., Zaehle, S., Keeling, R. & Heimann, M. How does the terrestrial carbon exchange respond to interannual climatic variations? A quantification based on atmospheric CO2 data. Biogeosciences 15, 2481–2498 (2018).Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).Article 
    ADS 

    Google Scholar 
    Fernández‐Martínez, M. & Peñuelas, J. Measuring temporal patterns in ecology: the case of mast seeding. Ecol. Evol. 11, 2990–2996 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wood, S. N. Generalized Additive Models: An introduction with R 2nd edn (Chapman and Hall/CRC, 2017).Ohlson, J. A. & Kim, S. Linear Valuation Without OLS: The Theil–Sen Estimation Approach (SSRN, 2015); https://ssrn.com/abstract=2276927.Komsta, L. Package mblm, 0.12.1: Median-based linear models (2013).Keeling, C. D. et al. in A History of Atmospheric CO2 and its effects on Plants, Animals, and Ecosystems (eds Ehleringer, J. R. et al.) 83–113 (Springer Verlag, 2005).Leroux, B. G., Lei, X. & Breslow, N. in Statistical Models in Epidemiology, the Environment and Clinical Trials (eds Halloran, M. & Berry, D.) 179–191 (Springer-Verlag, 2000).Lee, D. CARBayes: an R package for Bayesian spatial modeling with conditional autoregressive priors. J. Stat. Softw. 55, 1–24 (2013).Article 

    Google Scholar 
    Gonzalez, A. et al. Scaling‐up biodiversity–ecosystem functioning research. Ecol. Lett. 15, ele.13456 (2020).
    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). More