Philippa Kaur

RESEARCH BRIEFINGS
02 November 2022

The world’s largest tropical peatland complex is in the central Congo Basin. A drying of the climate between 5,000 and 2,000 years ago triggered decomposition of peat in the Congo Basin and emission of carbon into the atmosphere. The tipping point at which drought results in carbon release might accelerate future climate change if regional droughts become more common. More

Study site and future climate treatmentOur study site is located at the Bad Lauchstädt Field Research Station, Bad Lauchstädt, Germany (51° 22060 N, 11° 50060 E), which belongs to the Helmholtz Centre for Environmental Research–UFZ. Long-term mean annual precipitation in the area is 489 mm and the mean annual temperature is 8.9 °C (ref. 32). During 2018 and 2019, Europe experienced a record-setting drought that was especially severe in 2018 (refs. 33,34); the mean annual precipitation at our study site in 2018 and 2019 was 254 mm and 353 mm, respectively, whereas 2017 was a more normal year, with a mean annual precipitation of 403 mm. Mean annual temperatures were above average: 2017, 10.5 °C; 2018, 10.8 °C; 2019, 11.2 °C (data from the weather station at the Bad Lauchstädt field station). The soils in the study area are fertile Haplic Chernozem type32,35.Our eDiValo experiment was conducted in the GCEF, which was designed to investigate climate change effects under different land-use scenarios32. We used 10 ‘extensively’ used pastures of the GCEF in our experiment; that is, 384-m2 (16 × 24 m) areas of grassland (hereafter called ‘pastures’) that were grazed by a flock of 20 sheep 2–3 times each year. Grazing was implemented as short-time high-intensity grazing events, each lasting 24 h (ref. 32). This type of high-intensity but short-term grazing is considered better in maintaining species richness as it gives plants more time to recover between grazing events36. It is also a recommended management type for nature conservation areas in Germany37. Vegetation in the pastures was species-rich grassland vegetation that is typical of drier regions of central Germany32,38. The whole GCEF was fenced to exclude native large mammalian herbivores (for example, deer); however, European hare (Lepus europaeus), wood mice (Apodemus sylvaticus) and voles (Microtus arvalis) are common at the site.Our experimental design was originally intended to test the dependence of light competition on nutrient and herbivory under current and future climatic scenarios. Although we included both climate treatments in our data, climate was never significant for richness and Shannon diversity, either alone or in interaction with other factors, and our focus was therefore on the other treatments. Five of the above random pastures received future climatic treatment which was based on different dynamic regional climate models for Germany, all predicting an increased mean temperature by approximately 2 °C year-round, strongly decreased summer precipitation and slightly increased spring and autumn precipitation (https://www.regionaler-klimaatlas.de/) (ref. 32). Passive night-time (after sunset and before sunrise) warming through the use of roller blinds attached to the GCEF roof and eastern and western wall structures was used to increase the air temperature. In each spring (1 March–31 May) and autumn (1 September–30 November), future climate plots received 110% of the ambient rainfall and in the summer (1 June–31 August), they received 80% of the ambient rainfall. The precipitation treatment was adjusted weekly and compensated for a possible night-time reduction in rainfall due to temperature treatment. A detailed description of the future climate treatment is provided in a previous report32.Fertilization, herbivore exclusion and light additionWe first tested whether adding light can offset the negative effect of fertilization on plant diversity. In May 2017, we established a full-factorial experiment of fertilization and light addition. Within each 10 pastures (5 in ambient climatic conditions, 5 in future climatic conditions), we established 4 plots of 1.4 × 1.4 m, separated by a 1-m buffer zone (hereafter called ‘blocks’), in total 40 plots and 10 blocks. At the time the experiment was established, vegetation in the whole experimental area (that is, in a block of 4 plots and the surrounding 1-m area) was trimmed to a height of 5 cm to make conditions uniform and the whole area was temporarily fenced to let the experiment establish and fertilization effects develop. The temporary fence was removed in August when the herbivore exclusion treatment was started. Therefore, there was no grazing by sheep in the experimental plots in the summer of 2017. Two randomly chosen plots received fertilizer treatment and two were controls. For the former (fertilizer-treatment plots), slow-release granular NPK fertilizer (a mixture of Haifa Multicote 2 M 40-0-0 40% N; Triple Super Phosphate (TSP) 45% P205; and potassium sulfate fertilizer 50% K2O, 45% SO3) was added twice per growing season, in a total of 10 g N, 10 g P and 10 g K per m² (see ref. 3 for a similar protocol that is used in grasslands worldwide). In 2017, the first fertilization was done at the beginning of June right after establishing the experiment and the second fertilization was done at the beginning of July. In the subsequent years, the first fertilization was done at the beginning of the growing season (late March–April) and the second fertilization was done in June. In 2019, two previously unfertilized plots were accidentally fertilized and were thereafter treated as fertilized plots. To manipulate light, 1.4 × 1.4-m plots were further divided into two subplots, 0.7 m × 1.4 m each, and one of these was randomly assigned to the light-addition treatment, resulting in 80 subplots (Fig. 1). We installed two 120-cm-long and 3.5-cm-wide recently developed LED lamps (C65, Valoya) parallel to each other and at a 28-cm distance from each other to each light-addition subplot. To increase light for the small understory plants that are the most likely to suffer from competition for light, we installed the lamps 10 cm above the smallest plants. The lamps were gradually uplifted over the course of the growing season to follow the growth of the smallest plants. As our light-addition treatment was intended to mimic natural sunlight (that is, making a gap in a dense vegetation and allowing the sunshine in), we chose the spectrum of the lamps to include all wavelengths of sunlight, including small amounts of ultraviolet and infrared. Each lamp added roughly 350–400 µmol and did not alter the air or aboveground soil surface temperature (Fig. 1b), which is an improvement on previous studies12. Each year, we added light during the active growing season: the lamps were switched on early in the spring (March–April), when temperatures were clearly above zero, and switched off and removed when temperatures dropped close to zero in November–December and aboveground plant parts had died and formed litter. Each day, the lamps were set to switch on two hours after sunrise, and to switch off two hours before sunset, and when the temperature exceeded 28 °C to prevent overheating. We did not install unpowered lamps to unlighted plots because our modern, narrow LED lamps caused minimal disturbance (see below) and no heating (Fig. 1b), and because unpowered lamps would have added an artefact in that they create shade that does not occur when the lamps are on in lighted plots.At the end of August 2017, after running the fertilization–light-addition experiment for one growing season, we expanded the experiment by implementing the herbivore exclusion treatment in a full-factorial combination with the other treatments. Two of the previously established 1.4 m × 1.4-m plots, one with and one without the fertilization treatment, were randomly allotted to the herbivore (sheep) exclusion treatment and fenced with rectangular metal fences of 1.8 m × 1.8 m, 82 cm height and 10 cm mesh size. At the same time, the temporary fence established in May 2017 was removed from around the whole experimental area, allowing the grazing of sheep in unfenced plots. The fences did not exclude mice, voles and hares. For the time of each grazing event, lamps in grazed subplots were removed and switched off in the ungrazed subplots. Uplifting the lamps from grazed plots did not cause disturbance because vegetation in grazed plots was always short and did not reach above the lamps. Inside exclosures, lamps were always kept in place during the growing season, and plants could freely grow around and above them.Plant community and trait samplingIn July 2017, we established 50 cm × 50-cm permanent quadrats in every subplot for plant community sampling. We visually estimated the per cent areal cover for all species occurring in the quadrats, and litter cover, from the beginning of June to mid-June 2019, when the vegetation was at its peak biomass. The 2017 sampling happened later, in mid-July, because vegetation in all plots and surrounding areas was trimmed to a height of 5 cm at the time of the establishment of the experiment at the end of May, and it took later for vegetation to reach its peak biomass. In 2018, the effects of drought were devastating, and most plants had senesced or died before the planned sampling date; we therefore omitted the year 2018. At the beginning of each growing season—that is, when the lamps were installed and switched on—there was very little live biomass in the plots, and the maximum height of existing plants was approximately 5 cm (in all plots). During the peak biomass the maximum plant height was up to approximately 1 m; however, it varied greatly between the treatments and was especially low in grazed plots. All vegetation surveys were done by the same trained and experienced person with a minimum estimate threshold of 0.1%. We used plant cover data to calculate species richness and Shannon diversity.In May–June 2020, we measured plant height (centimetres), SLA (leaf area in square millimetres per milligram of dry mass), foliar C:N (based on the per cent C and N in plant leaves) and LWC (leaf water content as 1,000 − LDMC (the ratio of leaf dry mass to saturated fresh mass), expressed as milligrams per gram39) for most species occurring in the experimental plots, and complemented the trait data from the TRY Plant Trait Database40,41,42 (v.5.0; https://www.try-db.org/TryWeb/Home.php) and for one species one trait value from another source9. The trait data were collected from seven to ten individuals per species from the study site or close areas; the collection and handling followed standard protocols39. We chose these traits because they are widely documented to be associated with responsiveness to soil nutrients, herbivory and light9,26,27,43,44,45,46. We used all traits as, although they partially reflect similar ecological adaptations (for example, leaf economics spectrum43), they could also potentially reflect independent and distinctive processes, and differently mediate the responses of species to our treatments. For example, SLA and LWC in our dataset correlated weakly (r2 = 0.16), but were to a greater extent uncorrelated (Extended Data Table 6), and could function differently, for example, in light capture and drought tolerance26,39. In 2017, our trait data covered on average 97.7–98.6% of the total cover in the plots, the value slightly differing depending on the trait as we did not have all traits for all species. Our own trait collections covered on average 96.6–97.6% and TRY data covered on average 0.9–2% of the total cover. In 2019, the whole trait data covered on average 99.5% of the total cover in the plots, again slightly depending on the trait. Our own trait collections covered on average 94.2–96.5% and TRY data covered on average 2.7–5.3% of the total cover.Abiotic environmental measurementsWe measured several soil and other environmental properties from the experimental plots. Light availability (photosynthetically active radiation; PAR) in unlighted and lighted (under lamps) subplots was measured using LI-190R and LI-250A meters (LI-COR), approximately 7–10 cm under the lamps and 15–20 cm above ground level. We measured light availability from the same distance to the ground in unlighted plots. Measurements of light availability were done in mid-July 2020 on three consecutive cloudless days around noon. Note that in grazed plots, light levels between lighted and unlighted plots are more similar than inside exclosures (Fig. 1), because herbivores keep the vegetation short, and natural sunlight can therefore reach under the lamps where the light measurements were taken. Air temperature and humidity were recorded from unlighted and lighted (under lamps) subplots using loggers (HOBO MX2301A, Onset Computer Cooperation) that were installed approximately 7 cm under the lamps and to the same height from the ground in unlighted plots, and were replicated under different combinations of fertilization, herbivore exclusion and light addition in ambient climatic conditions three times (n = 3). The logger data were collected in May 2019 before the effects of drought were visible.Statistical analysisWe analysed our data in two steps. First, to test whether competition for light mediates the effect of fertilization on diversity, we analysed the effects of fertilization and light and their interaction on species richness and Shannon diversity using data from 2017, when the herbivore exclusion treatment had not yet been implemented. We also analysed the effects of treatment on total vegetation cover and litter cover. We fit LME models in which diversity (species richness and Shannon diversity), total cover and litter cover, each in their own model, were explained by fertilization, light addition and their interaction (fixed variables). All treatments were categorical variables with two levels (treated and untreated). In each model, subplot was nested within plot, which was nested within block (nested random variable). We simplified the models using the anova() function for model comparison in the nlme and lme4 packages in R (ref. 47) (on the basis of log likelihood ratio tests; P ≥ 0.05; Extended Data Table 2). This was done to uncover the significance of the main effects and interaction terms, to avoid overparametrization47,48 and to provide model-derived parameter estimates for the figures (Extended Data Table 5). However, we also provide full model results that are qualitatively similar to the results of simplified models (Extended Data Tables 3 and 4); therefore, model choice did not affect our conclusions. Climate treatment was included in all original models but was never significant for richness and diversity, and was not considered further. Total cover and litter results for 2017 are reported in Extended Data Figs. 1a,b and 3a). As there was heterogeneity in the variance structure between treatments, we used the varIdent() function in the nlme package in R to allow each treatment combination to have a different variance. Model fit was inspected using model diagnostic plots in the package nlme. In the full design with climate included, the number of replicates per treatment combination was ten.Second, to include herbivore exclusion to the experimental design and to test whether competition for light mediates the effect of herbivore exclusion on diversity, and whether competition for light, herbivory and fertilization interact, we analysed the effects of herbivore exclusion, fertilization, light and their interactions on species richness and Shannon diversity using data from 2019. All treatments were categorical variables with two levels (treated and untreated). We also analysed the effects of treatment on total vegetation cover and litter cover. We fit similar models to those described above, except that herbivore exclusion was an additional fixed factor in the models. We simplified the models, used the varIdent() function to account for heteroscedasticity and checked the model fit using model diagnostic plots, as above. Climate treatment was included in all original models but was significant for litter cover only, and was not considered further. In the full design with climate included, the number of replicates per treatment combination was five.To further assess which plant traits increased the probability of species benefiting from the addition of light, we first created a binary response variable: those species that increased from unlighted to lighted plots (that is, had a higher value in a lighted than an unlighted plot) were given a value of 1 and those that did not were given a value of 0. This response variable takes into account rare species that emerged or persisted in the lighted plots but were absent in the unlighted plots (that is, species gains and losses) and changes in small, subordinate species (those that are likely to benefit from light addition) with small but consistently trait-dependent changes in response to light. It is also in line with our species richness analyses, as species gains and losses ultimately determine richness responses. We did not use different indexes (for example, lnRR or RII) because these could not handle multiple zero values and species losses or gains (that is, species having zero cover in either unlighted or lighted subplots). Second, we fit GLME models with a binomial error structure (family = “binomial”, link = “logit”) in which a probability of a species increasing from unlighted to lighted plots was explained by categorical experimental treatments (fertilization, herbivore exclusion and their interactions), traits (SLA, height, LWC, foliar C:N), and interactions between the treatments and traits. Each trait was analysed in its own model as some of the traits were correlated (Extended Data Table 6), and to avoid overly complex models and overparametrization47,48. We included all species for which we had traits in the models. As we calculated the increase in cover from unlighted to lighted plots, our smallest experimental unit in trait analyses was a plot (not a subplot, unlike in other analyses). As there were several species in the same plots, we nested species within plots, and plots within blocks. We similarly simplified the models to include only significant variables (on the basis of χ2 tests; P ≥ 0.05). We did not include a crossed random effect for species in the models because the full models with a more complex random structure did not converge; however, when we refitted the simplified models with a crossed random effect for species, we found that the models converged (with scaled data) and that the significance of the effects remained qualitatively the same. Climate was included in all original models but was never significant. In addition, C:N and height did not predict the responsiveness of species to light in either year (P ≥ 0.13 for both); results are therefore not shown. In the full design with climate included, the number of replicates per treatment combination was five; however, the number of observations was greater (see Fig. 4 and Extended Data Fig. 4). To make sure that our results for SLA and LWC were not influenced by whether they were analysed in separate models or in the same model, or by the order in which they were in the models, we also performed analyses in which both SLA and LWC were included (in both orders). Results remained qualitatively similar and are not discussed further.Furthermore, to check whether our trait results were driven primarily by species gains and losses or changes in abundance, we ran additional trait analyses for which we calculated the change in cover between lighted and unlighted subplots (cover in lighted subplot − cover in unlighted subplot), and analysed the ‘change’ with otherwise similar trait models to those described above, except that we used Gaussian error structure. With this index, which gives a disproportionate importance to the abundant species, we found that traits were poor predictors of changes in cover between lighted and unlighted plots (all interactions were non-significant, P  > 0.05, except for a marginally significant C:N × fertilization interaction in 2017 that was no longer visible in 2019; results not shown; codes and data available in the Dryad repository). We also analysed presence–absence-based species losses and gains. In these models, each species was given a value of 1 when it was present in the lighted subplot but absent from the unlighted subplot; otherwise, these models were similar to the binomial trait models described above. These models produced, to a large extent, similar results to our models using the probability of increase in response to light as a response variable (results not shown; codes and data available in the Dryad repository). These additional analyses and results support using the probability of increase in response to light as our response variable, rather than abundance-based metrics, as it includes both gains and losses and abundance aspects, and is therefore a general test that is well suited to assessing species gains and extinctions and changes in subordinate species.All statistical analyses were performed using R v. 4.0.0 (ref. 49). We used the nlme package (v.3.1.147) for LME models50, the lme4 package (v.1.1.23) for GLME models51, and the car package52 for P values (v.3.07).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Birds and housing62 males and 8 females of house sparrows were caught with mist nets in September and October 2019 in several sites in Kraków, Poland. Before releasing them to the outdoor aviary located on the campus of the Jagiellonian University, Kraków, Poland, each bird was weighed and banded with a metal band. The aviary measured 3.5 m in width, 10.0 m in length, 2.5 m in height, and was outfitted with trees, bushes, perches, wooden shelters, a water source, and food dishes. Initially, birds were maintained with water and a mixture of seeds: wheat, barley, millet, and sunflower seeds, provided ad libitum. Additionally, they had access to sand with shells and sepia.Experimental designAfter a few weeks of acclimation to captivity, the aviary was divided into two separate parts (3.5 × 5 m): aviary no. 1 (A1) and aviary no. 2 (A2). At the same time male individuals were assigned to two crossed experimental treatments, ensuring that in each aviary birds originated from all sampled populations. The experiment comprised of two different treatments conducted simultaneously—one designed to simulate a deficiency in an environmental factor influencing colouration (the quality of available food), the other—to introduce physiological stress and facilitate trade-offs in the allocation of resources limited by the first treatment (an immune response induced by a bacteria-derived compound, S1).The dietary manipulation was achieved by feeding one group of birds with a low-quality protein food (diet reduced in exogenous amino acids, namely phenylalanine and tyrosine content, which are precursors essential for melanin synthesis; PT-reduced diet), and the other one with a wholesome diet (control diet). At the same time, two levels of immune challenge were achieved within each dietary group, by injecting half of the birds with either lipopolysaccharide (LPS) from the cell wall of Escherichia coli, or a 0.9% saline vehicle (as a control). Four females were placed in each group of males to alleviate interspecific conflicts occurring in all-male sparrow flocks, but they did not take part in the experiments. After three weeks of experiment, birds housed in A1 were moved to A2, whereas birds from A2 were moved to A1.Immune challengeBefore receiving injections, birds were first weighed and then transferred from the outdoor aviary to the laboratory. 31 house sparrows (from both dietary groups) were injected intraperitoneally with 0.026 mg LPS (serotype O55:B5, Sigma-Aldrich) diluted in 0.1 mL of 0.9% saline vehicle, so that each bird received a dose of ca. 1 mg/kg body mass, which had previously been shown to induce sickness behaviour in another passerine, the white-crowned sparrow, Zonotrichia leucophrys55. 31 control males were injected with the same volume (0.1 mL) of 0.9% saline vehicle. All individuals were injected twice throughout the experiment with an interval of three weeks between the injections. Birds were always injected at the same time in the morning and early afternoon (between 9:00 am and 12:30 pm).Diet manipulationDuring the six weeks of the experiment (S1), birds received synthetic diet ad libitum, which constituted of a mixture of protein (WPC80, free amino acids and whey protein isolate BiPRO GMP 9000 (Agropur Inc., Appleton, USA)), fats, carbohydrates, and fiber30. The ingredients were thoroughly mixed to produce small pellets (6 mm in diameter) that the sparrows consumed readily. The experimental diet had phenylalanine and tyrosine at 42% (N = 32) of their level in the control diet (N = 30)30. The food pellets were prepared by ZooLab (zoolab.pl/en/home, Sędziszów, Poland). Each bird was weighed before and after the experiment to monitor potential effects of diet on body mass of each animal. Following the experiment, during the next three consecutive days, the amount of food consumed by passerines within every 24 h (starting from 10 am each day to 10 am next day) was noted for both compartments of the aviary. Because of different numbers of individuals per aviary, an overall weight of food consumed in A1 and A2 was calculated per individual, respectively.Feathers samplingMoult of the black bib feathers was stimulated at the end of the moulting period occurring in natural conditions in early November. At day 1 of the dietary/immunological experiment (S1) a small area of the bib (around 25 mm2) was plucked from each male sparrow held in A1. At day 2 the same procedure was performed on individuals from A2. The time difference is orders of magnitude smaller than the timescale of feather growth and hence it would not affect the results in any way.Because the feather growth rate may differ during melanogenesis, with consequences for final colouration (if feathers grow at a faster rate, pigments may be deposited over a larger surface and therefore result in less intense colouration56, we measured the rate of feather development during the course of the experiment. After three weeks of the experiment, three feathers from the upper, central, and lower region of the previously plucked bib were plucked once again. The mass of the collected feathers was determined to the nearest 0.01 mg (XP26 Micro Balance, Mettler-Toledo, Greinfensee, Switzerland). The experiment was completed after six weeks after fully regrown and developed feathers from the bib and PC2 were sampled the second time (S1). Three feathers from the central part of previously plucked bib region were collected to perform transmission electron microscopy (TEM) imaging, whereas the feathers obtained from the rest of the regrown bib area were subjected to electron paramagnetic resonance (EPR) spectroscopy and feather microstructure analyses (greater spatial density of melanized barbs or barbules may affect colouration17.Feathers measurementsReflectance measurementsAn USB4000 spectrophotometer (range 300–700 nm) with the PX-2 Pulsed Xenon Lamp (Ocean Optics, Dunedin, FL, USA) and a bifurcated probe with 7 × 400 μm optical fibres, equipped with a permanently attached 3 mm long black collar, was used to quantify the brightness of the bib feathers collected at the end of the experiment. The measurements were taken with 90 ms integration time and the probe held at 90° to a feather’s surface. Calibration measurements of a Spectralon white standard (Ocean Optics. Largo, FL, USA) were taken every 15 min during measurements. The order in which the samples were measured was randomized in terms of belonging to the experimental group. From each sample (N = 62), seven feathers were chosen and stacked in one pile on a piece of black paper. Ten reflectance measurements were taken on each pile, avoiding distal, brighter parts of the feathers. The obtained spectra were averaged and smoothed in the package ‘pavo’57. Brightness was calculated as a sum of the reflectance values over all wavelengths of a spectrum, and its lower values were interpreted as those indicative of a more melanin-rich feathers (i.e., absorbing more light).Feather developmentEach feather (3 per individual; N = 62 individuals) was laid on a white card and covered by a microscope slide to flatten the naturally curved feathers. Digital photographs were taken using camera (Canon EOS 7D) and imported to ImageJ v1.52a Software (National Institutes of Health, USA). The lengths of fully developed and undeveloped (still in sheath) parts of each feather were measured. To estimate the degree of a feather’s development, the length of the developed part of the vane was divided by its total length (quill with rachis plus the developed vane, Fig. 4A).Figure 4House sparrow feathers sampled from bib after three weeks of the experiment. Feathers during development (A), a TEM cross-sections of feather sampled from bib after the experiment (B).Full size imageFeather densityBarb density measurements were performed on the sampled regrown black bib feathers (N = 2–3 for each individual; N = 62 individuals), but because of their sparser structure we calculated the number of non-down (i.e., rigid) barbs on both sides of the vane, and divided this number by two (to obtain an average single-sided number of barbs) and then by the length of the rachis.Melanosome density (TEM)Feathers sampled from the bib of male sparrows (N = 62) were fixed for transmission electron microscopy (TEM) analysis in a mixture of 0.25 M sodium hydroxide and 0.1% Tween for 20 to 30 min on a bench-top shaker. Next, the feathers were treated with formic acid and ethanol in the ratio of 2:3 for 2.5 h and dehydrated twice for 20 min in 100% ethanol. Samples were embedded in a mixture of the PolyBed 812 resin (20 ml), DDSA (9 ml), NMA (12 ml) and DMP-30 (0.82 ml). Resin infiltration was gradual from 15% resin content in ethanol through 50%, 70% to 100% without alcohol. Each step lasted for 24 h. Then, the feathers were placed in silicone embedding moulds (Agar Scientific) and transferred to an oven. The polymerization proceeded at the temperature of 60 °C for 16 h. The epoxy resin blocks were then trimmed to get rid of excess resin. The surface of each block was prepared by its trimming, starting from the end of the feather, to approximately 5 mm using a glass knife. Next, ultrathin sections (70 nm) were cut with a diamond knife (DIATOME A. G., Berno, Switzerland) on a microtome (UC7, Leica, Wetzlar, Germany) and collected on single slot grids coated with a formvar film. The sections were then contrasted in uranyl acetate and lead citrate for 3 min. They were viewed and photographed with a transmission electron microscope (TEM) JEOL 2100HT (Jeol Ltd, Tokyo, Japan) for the purpose of investigating the number and density of the embedded pigment granules. For each individual three photographs of the cross-sections from a similar feather region were selected. Melanosome density was measured as the number of melanin granules observed in the barb cross-section divided by its area. Images were analysed using Adobe Photoshop (cross-sections area) and ImageJ (number of melanosomes, Fig. 4B).Melanin content: electron paramagnetic resonance (EPR) spectroscopyQuality and quantity of melanin pigments58 in individual feather samples obtained from the bib of house sparrows (N = 57) were characterized using a Varian E3 spectrometer (Varian, Sunnyvale, LA, USA) equipped with a rectangular resonance (TE 102) cavity. Five milligrams of feathers per individual were placed inside the Wilmad finger quartz dewar WG-816-Q (Rototec-Spintec GmbH, Griesheim, Germany). Prior to inserting the vessel into the resonance cavity of the EPR spectrometer, feathers were pressed down the quartz finger to a height of approximately 0.5 cm to ensure comparable volumes of each sample. Measurements were performed at room temperature, at X-band (9.26–9.27 GHz frequency), using the following parameters: magnetic field range 3240–3340 Gs, microwave power 1 mW, modulation frequency 100 kHz, modulation amplitude and time constant—5 Gs and 0.3 s for quantitative analysis, 1 Gs and 0.1 s for qualitative analysis. An EPR signal was recorded as its first derivative, averaged from three consecutive scans, lasting 160 s each (giving a total of 480 s of scan time per EPR spectrum). Then, the following parameters were measured: peak-to-peak amplitude, area under the microwave absorption curve (the integral intensity of the recorded signal) and linewidth of the EPR absorption curve (ΔH;59).Statistical analysesStatistical analysis was performed in R (version 4.0.2,60) using a two-way ANOVA test, with bird’s diet (control vs. PT-reduced) and applied immune challenges (LPS vs. saline-injections) as the independent variables. The following parameters were used as the dependent variables: feathers reflectance (brightness), feather growth rate, feather density (number of barbs per mm), and melanisation level (expressed as the EPR spectrum amplitude measured in arbitrary units [a.u.]). The density of melanosomes was analysed by fitting a linear mixed-effects model. In this model, melanosome density was used as the dependent variable, with diet, immunological challenge, and slice ID as independent variables, and individual ID as a random-effect term. Additionally, to assess the reliability of measurements, the intraclass correlation coefficient (i.e., technical repeatability) was calculated. The models’ residuals were checked for normality and homoscedasticity. Mean food consumption per individual was analysed by the Friedman test. Body mass before and after the experiment was analysed by fitting a linear mixed-effect model. Body mass was used as the dependent variable, whereas diet, immunological challenge, and time as the independent variables, and individual ID as a random-effect term. The model included the following interaction terms: time × diet, time × injection, and diet × injection, and was reduced by removing the non-significant interactions. Results are reported with appropriate statistical tests and estimates (accompanied by standard errors) signifying relevant factor contrasts (relative to the reference group, which in all analyses was diet: control; injection: LPS, body mass: before experiment).
Ethical noteAll applicable national and institutional guidelines for the care and use of animals were followed. The research was performed under permit no. 25/2019 (with a supplementary permit no. 78/2020) from the 2nd Local Institutional Animal Care and Use Committee in Kraków. More

Butchart, S. H. M. et al. Shortfalls and solutions for meeting national and global conservation area targets. Conserv. Lett. 8, 329–337 (2015).Article 

Google Scholar 
McDonald-Madden, E. et al. ‘True’ conservation progress. Science 323, 43–44 (2009).Article 
CAS 
PubMed 

Google Scholar 
Corson, C. et al. Everyone’s Solution? Defining and redefining protected areas at the Convention on Biological Diversity. Conservation and Society 190–202. https://www.jstor.org/stable/26393154?seq=1#metadata_info_tab_contents (Accessed 1st March 2022) (2014).Caro, T. & Berger, J. Can behavioural ecologists help establish protected areas?. Philos. Trans. R. Soc. B 374, 20180062 (2019).Article 

Google Scholar 
Barnes, M. D. et al. Wildlife population trends in protected areas predicted by national socio-economic metrics and body size. Nat. Commun. 7, 12747 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Watson, J. E. M. et al. Bolder science needed now for protected areas. Conserv. Biol. 30, 243–248 (2016).Article 
PubMed 

Google Scholar 
Joppa, L. N. & Pfaff, A. High and far: Biases in the location of protected areas. PLoS One 4, e8273 (2009).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Runge, C. A., Martin, T. G., Possingham, H. P., Willis, S. G. & Fuller, R. A. Conserving mobile species. Front. Ecol. Environ. 12, 395–402 (2014).Article 

Google Scholar 
Thirgood, S. et al. Can parks protect migratory ungulates? The case of the Serengeti wildebeest. Anim. Conserv. 7, 113–120 (2004).Article 

Google Scholar 
Craigie, I. D. et al. Large mammal population declines in Africa’s protected areas. Biol. Conserv. 143, 2221–2228 (2010).Article 

Google Scholar 
Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).Article 

Google Scholar 
Hansen, A. J. & DeFries, R. Ecological mechanisms linking protected areas to surrounding lands. Ecol. Appl. 17, 974–988 (2007).Article 
PubMed 

Google Scholar 
Beresford, A. E. et al. Poor overlap between the distribution of Protected Areas and globally threatened birds in Africa. Anim. Conserv. 14, 99–107 (2011).Article 

Google Scholar 
Haynes, G. Elephants (and extinct relatives) as earth-movers and ecosystem engineers. Geomorphology 157–158, 99–107 (2012).Article 
ADS 

Google Scholar 
Terborgh, J., Davenport, L. C., Ong, L. & Campos-Arceiz, A. Foraging impacts of Asian megafauna on tropical rain forest structure and biodiversity. Biotropica 50, 84–89 (2018).Article 

Google Scholar 
Galanti, V., Preatoni, D., Martinoli, A., Wauters, L. A. & Tosi, G. Space and habitat use of the African elephant in the Tarangire-Manyara ecosystem, Tanzania: Implications for conservation. Mamm. Biol. 71, 99–114 (2006).Article 

Google Scholar 
Williams, C. et al. Elephas maximus. The IUCN Red List of Threatened Species. e.T7140A45818198. https://doi.org/10.2305/IUCN.UK.2020-3.RLTS.T7140A45818198.en. Accessed on 15 February 2022. (2020)Stokke, S. & Du Toit, J. T. Sexual segregation in habitat use by elephants in Chobe National Park, Botswana. Afr. J. Ecol. 40, 360–371 (2002).Article 

Google Scholar 
Chowdhury, S. et al. Protected areas in South Asia: Status and prospects. Sci. Total Environ. 811, 152316 (2022).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Goswami, V. R. et al. Community-managed forests and wildlife-friendly agriculture play a subsidiary but not substitutive role to protected areas for the endangered Asian elephant. Biol. Conserv. 177, 74–81 (2014).Article 

Google Scholar 
Fernando, C., Weston, M. A., Corea, R., Pahirana, K. & Rendall, A. R. Asian elephant movements between natural and human-dominated landscapes mirror patterns of crop damage in Sri Lanka. Oryx https://doi.org/10.1017/S0030605321000971 (2022).Article 

Google Scholar 
Santini, L., Saura, S. & Rondinini, C. Connectivity of the global network of protected areas. Divers. Distrib. 22, 199–211 (2016).Article 

Google Scholar 
Brennan, A. et al. Functional connectivity of the world’s protected areas. Science 376, 1101–1104 (2022).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Kumar, M. A. & Singh, M. Behavior of Asian elephant (Elephas maximus) in a land-use mosaic: Implications for human-elephant coexistence in the Anamalai Hills, India. Wildl. Biol. Pract. 6, 69–80 (2010).
Google Scholar 
Rathnayake, C. W. M., Jones, S., Soto-Berelov, M. & Wallace, L. Human–elephant conflict and land cover change in Sri Lanka. Appl. Geogr. 143, 102685 (2022).Article 

Google Scholar 
Chan, A. N. et al. Landscape characteristics influence ranging behavior of Asian elephants at the human-wildlands interface in Myanmar. Mov. Ecol. 10, 1–15 (2022).Article 

Google Scholar 
Magioli, M. et al. Land-use changes lead to functional loss of terrestrial mammals in a Neotropical rainforest. Perspect. Ecol. 19, 161–170 (2021).
Google Scholar 
Fernando, P. et al. The future of Asian elephant conservation: Setting sights beyond protected area boundaries. in Conservation Biology in Asia 252–260 (2006).Kumar, M. A., Vijayakrishnan, S. & Singh, M. Whose habitat is it anyway? Role of natural and anthropogenic habitats in conservation of charismatic species. Trop. Conserv. Sci. 11, 194008291878845 (2018).Article 

Google Scholar 
Sirua, H. Nature above people: Rolston and “fortress” conservation in the South. Ethics Environ. 11, 71–96 (2006).Article 

Google Scholar 
Keerthipriya, P. et al. Musth and its effects on male–male and male–female associations in Asian elephants. J. Mammal. 101, 259–270 (2020).Article 

Google Scholar 
Eisenberg, J. F., Mckay, G. M. & Jainudeen, M. R. Reproductive behavior of the Asiatic elephant (Elephas maximus maximus). Behaviour 38, 193–225 (1971).Article 
CAS 
PubMed 

Google Scholar 
Fernando, P. et al. Ranging behavior of the Asian elephant in Sri Lanka. Mamm. Biol. Zeitschrift für Säugetierkd. 73, 2–13 (2008).Article 

Google Scholar 
Hollister-Smith, J. A., Alberts, S. C. & Rasmussen, L. E. L. Do male African elephants, Loxodonta africana, signal musth via urine dribbling?. Anim. Behav. 76, 1829–1841 (2008).Article 

Google Scholar 
LaDue, C. A., Vandercone, R. P. G., Kiso, W. K. & Freeman, E. W. Behavioral characterization of musth in Asian elephants (Elephas maximus): Defining progressive stages of male sexual behavior in in-situ and ex-situ populations. Appl. Anim. Behav. Sci. 251, 105639 (2022).Article 

Google Scholar 
LaDue, C. A., Goodwin, T. E. & Schulte, B. A. Concentration-dependent chemosensory responses towards pheromones are influenced by receiver attributes in Asian elephants. Ethology 124, 387–399 (2018).Article 

Google Scholar 
Goldenberg, S. Z., de Silva, S., Rasmussen, H. B., Douglas-Hamilton, I. & Wittemyer, G. Controlling for behavioural state reveals social dynamics among male African elephants, Loxodonta africana. Anim. Behav. 95, 111e119 (2014).Article 

Google Scholar 
Chave, E. et al. Variation in metabolic factors and gonadal, pituitary, thyroid, and adrenal hormones in association with musth in African and Asian elephant bulls. Gen. Comp. Endocrinol. 276, 1–13 (2019).Article 
CAS 
PubMed 

Google Scholar 
Glaeser, S. S. et al. Characterization of longitudinal testosterone, ocrtisol, and musth in male Asian Elephants (Elephas maximus), effects of aging, and adrenal responses to social changes and health events. Animals 12, 1332 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
de Silva, S., Ranjeewa, A. D. G. & Kryazhimskiy, S. The dynamics of social networks among female Asian elephants. BMC Ecol. 11, 17 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Nandini, S., Keerthipriya, P. & Vidya, T. N. C. Group size differences may mask underlying similarities in social structure: A comparison of female elephant societies. Behav. Ecol. 29, 145–159 (2018).Article 

Google Scholar 
de Silva, S. & Wittemyer, G. A comparison of social organization in Asian elephants and African Savannah elephants. Int. J. Primatol. 33, 1125–1141 (2012).Article 

Google Scholar 
Nandini, S., Keerthipriya, P. & Vidya, T. N. C. Seasonal variation in female Asian elephant social structure in Nagarahole-Bandipur, southern India. Anim. Behav. 134, 135–145 (2017).Article 

Google Scholar 
de Silva, S., Schmid, V. & Wittemyer, G. Fission-fusion processes weaken dominance networks among female Asian elephants in a productive habitat. Behav. Ecol. 28, 243–252 (2017).Article 

Google Scholar 
de Silva, S., Ranjeewa, A. D. G. & Weerakoon, D. Demography of Asian elephants (Elephas maximus) at Uda Walawe National Park, Sri Lanka based on identified individuals. Biol. Conserv. 144, 1742–1752 (2011).Article 

Google Scholar 
Ginsberg, J. R. & Young, T. P. Measuring association between individuals or groups in behavioural studies. Anim. Behav. 44, 377–379 (1992).Article 

Google Scholar 
Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal, Complex Syst. 5, 1–9 (2014).
Google Scholar 
Liechti, J. I. & Bonhoeffer, S. A time resolved clustering method revealing longterm structures and their short-term internal dynamics. arXiv:1912.04261 (2020).Farine, D. R. & Whitehead, H. Constructing, conducting and interpreting animal social network analysis. J. Anim. Ecol. 84, 1144–1163 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Wikramanayake, E. D. et al. An ecology-based method for defining priorities for large mammal conservation: The tiger as case study. Conserv. Biol. 12, 865–878 (2008).Article 

Google Scholar 
Chundawat, R. S., Sharma, K., Gogate, N., Malik, P. K. & Vanak, A. T. Size matters: Scale mismatch between space use patterns of tigers and protected area size in a tropical dry forest. Biol. Conserv. 197, 146–153 (2016).Article 

Google Scholar 
Tucker, M. A. et al. Moving in the Anthropocene: Global reductions in terrestrial mammalian movements. Science 359, 466–469 (2018).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Karanth, K. K. & DeFries, R. Nature-based tourism in Indian protected areas: New challenges for park management. Conserv. Lett. 4, 137–149 (2011).Article 

Google Scholar 
Brown, J. L. et al. Comparative endocrinology of testicular, adrenal and thyroid function in captive Asian and African elephant bulls. Gen. Comp. Endocrinol. 151, 153–162 (2007).Article 
CAS 
PubMed 

Google Scholar 
Slotow, R. et al. Older bull elephants control young males. Nature 408, 425–426 (2000).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Poole, J. H., Lee, P. C., Njiraini, N. & & Moss, C. J. Longevity, competition, and musth: A long-term perspective on male reproductive strategies. in The Amboseli Elephants: A Long‐Term Perspective on a Long‐Lived Mammal 272–286 (2011).Poole, J. H. Announcing intent: The aggressive state of musth in African elephants. Anim. Behav. 37, 153–155 (1989).Article 

Google Scholar 
Poole, J. H. Mate guarding, reproductive success and female choice in African elephants. Anim. Behav. 37, 842–849 (1989).Article 

Google Scholar 
Poole, J. H. Rutting behavior in elephants: The phenomenon of musth in African elephants. Anim. Behav. 102, 283–316 (1987).
Google Scholar 
Foley, A. M. et al. Reproductive effort and success of males in scramble-competition polygyny: Evidence for trade-offs between foraging and mate search. J. Anim. Ecol. 87, 1600–1614 (2018).Article 
PubMed 

Google Scholar 
Fernando, P., Leimgruber, P., Prasad, T. & Pastorini, J. Problem-elephant translocation: Translocating the problem and the elephant?. PLoS One 7, e50917 (2012).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Archie, E. A., Morrison, T. A., Foley, C. A. H., Moss, C. J. & Alberts, S. C. Dominance rank relationships among wild female African elephants, Loxodonta africana. Anim. Behav. 71, 117–127 (2006).Article 

Google Scholar 
Wittemyer, G. & Getz, W. M. Hierarchical dominance structure and social organization in African elephants, Loxodonta africana. Anim. Behav. 73, 671–681 (2007).Article 

Google Scholar 
Wittemyer, G., Getz, W. M., Vollrath, F. & Douglas-Hamilton, I. Social dominance, seasonal movements, and spatial segregation in African elephants: A contribution to conservation behavior. Behav. Ecol. Sociobiol. 61, 1919–1931 (2007).Article 

Google Scholar 
Gunaryadi, D., Sugiyo, & Hedges, S. Community-based human-elephant conflict mitigation: The value of an evidence-based approach in promoting the uptake of effective methods. PLoS One 12, e0173742 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Wilson, G. et al. Between a rock and a hard place: Rugged terrain features and human disturbance affect behaviour and habitat use of Sumatran elephants in Aceh, Sumatra, Indonesia. Biodivers. Conserv. 30, 597–618 (2021).Article 

Google Scholar 
de Silva, S. et al. Demographic variables for wild Asian elephants using longitudinal observations. PLoS One 8, e82788 (2013).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
LaDue, C. A., Eranda, I., Jayasinghe, C. & Vandercone, R. P. G. Mortality patterns of Asian elephants in a region of human–elephant conflict. J. Wildl. Manag. 85, 794–802 (2021).Article 

Google Scholar 
Ram, A. K. et al. Tracking forest loss and fragmentation between 1930 and 2020 in Asian elephant (Elephas maximus) range in Nepal. Sci. Rep. 11, 1–13 (2021).Article 
ADS 

Google Scholar 
Neupane, D., Kwon, Y., Risch, T. S. & Johnson, R. L. Changes in habitat suitability over a two decade period before and after Asian elephant recolonization. Glob. Ecol. Conserv. 22, e01023 (2020).Article 

Google Scholar 
de Silva, S. & Leimgruber, P. Demographic tipping points as early indicators of vulnerability for slow-breeding megafaunal populations. Front. Ecol. Evol. 7, 171 (2019).Article 

Google Scholar 
Rodrigo, M. Farmers move to occupy a critical elephant corridor in Sri Lanka. Mongabay (2021).de la Torre, J. A. et al. There will be conflict—Agricultural landscapes are prime, rather than marginal, habitats for Asian elephants. Anim. Conserv. 24, 720–732 (2021).Article 

Google Scholar 
Rood, E., Ganie, A. A. & Nijman, V. Using presence-only modelling to predict Asian elephant habitat use in a tropical forest landscape: Implications for conservation. Divers. Distrib. 16, 975–984 (2010).Article 

Google Scholar 
Evans, L. J., Goossens, B., Davies, A. B., Reynolds, G. & Asner, G. P. Natural and anthropogenic drivers of Bornean elephant movement strategies. Glob. Ecol. Conserv. 22, e00906 (2020).Article 

Google Scholar 
Morales-Hidalgo, D., Oswalt, S. N. & Somanathan, E. Status and trends in global primary forest, protected areas, and areas designated for conservation of biodiversity from the Global Forest Resources Assessment 2015. For. Ecol. Manag. 352, 68–77 (2015).Article 

Google Scholar 
Ellis, E. C. et al. People have shaped most of terrestrial nature for at least 12,000 years. Proc. Natl. Acad. Sci. U.S.A. 118, 1–8 (2021).Article 

Google Scholar 
Goswami, V. R., Vasudev, D. & Oli, M. K. The importance of conflict-induced mortality for conservation planning in areas of human–elephant co-occurrence. Biol. Conserv. 176, 191–198 (2014).Article 

Google Scholar 
de Silva, S. & Leimgruber, P. Demographic tipping points as early indicators of vulnerability for slow-breeding megafaunal populations. Front. Ecol. Evol. 7, 1–13 (2019).Article 
CAS 

Google Scholar 
Hettiarachchi, K. ‘Gathering’ shuns ‘brimming’ Minneriya. The Sunday Times (2021).Srinivasaiah, N., Kumar, V., Vaidyanathan, S., Sukumar, R. & Sinha, A. All-male groups in Asian elephants: A novel, adaptive social strategy in increasingly anthropogenic landscapes of southern India. Sci. Rep. 9, 1–11 (2019).Article 
ADS 
CAS 

Google Scholar 
de Silva, E. M. K. et al. Feasibility of using convolutional neural networks for individual-identification of wild Asian elephants. Mamm. Biol. https://doi.org/10.1007/S42991-021-00206-2 (2022).Article 

Google Scholar 
de Silva, S. The Elephant Attribute Recording System (EARS): A tool for individual-based research on Asian elephants. Gajah 40, 46 (2014).
Google Scholar 
Jainudeen, M. R., Katongole, C. B. & Short, R. V. Plasma testosterone levels in relation to musth and sexual activity in the male Asiatic elephant, Elephas maximus. J. Reprod. Fertil. 29, 99–103 (1972).Article 
CAS 
PubMed 

Google Scholar 
Farine, D. R. Animal social network inference and permutations for ecologists in R using asnipe. Methods Ecol. Evol. 4, 1187–1194 (2013).Article 

Google Scholar 
Whitehead, H. Analyzing Animal Societies (University of Chicago Press, 2008).Book 

Google Scholar 
Bates, D., Mächler, M., Bolker, B., & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Dekker, D., Krackhardt, D. & Snijders, T. A. B. Sensitivity of MRQAP tests to collinearity and autocorrelation conditions. Psychometrika 72, 563–581 (2007).Article 
MathSciNet 
PubMed 
PubMed Central 
MATH 

Google Scholar  More

Dargie, G. C. et al. Age, extent and carbon storage of the central Congo Basin peatland complex. Nature 542, 86–90 (2017).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Crezee, B. et al. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. Nat. Geosci. 15, 639–644 (2022).Runge, J. in Large Rivers (ed. Gupta, A.) 293–309 (Wiley, 2008).Davenport, I. J. et al. First evidence of peat domes in the Congo Basin using LiDAR from a fixed-wing drone. Remote Sens. 12, 2196 (2020).Article 
ADS 

Google Scholar 
Dargie, G. C. et al. Congo Basin peatlands: threats and conservation priorities. Mitig. Adapt. Strateg. Glob. Chang. 24, 669–686 (2018).Young, D. M. et al. Misinterpreting carbon accumulation rates in records from near-surface peat. Sci. Rep. 9, 17939 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Young, D. M., Baird, A. J., Gallego-Sala, A. V. & Loisel, J. A cautionary tale about using the apparent carbon accumulation rate (aCAR) obtained from peat cores. Sci. Rep. 11, 9547 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sebag, D. et al. Monitoring organic matter dynamics in soil profiles by ‘Rock-Eval pyrolysis’: bulk characterization and quantification of degradation. Eur. J. Soil Sci. 57, 344–355 (2006).Article 
CAS 

Google Scholar 
Sebag, D. et al. Dynamics of soil organic matter based on new Rock-Eval indices. Geoderma 284, 185–203 (2016).Article 
ADS 
CAS 

Google Scholar 
Girkin, N. T. et al. Spatial variability of organic matter properties determines methane fluxes in a tropical forested peatland. Biogeochemistry 142, 231–245 (2019).Article 
CAS 
PubMed 

Google Scholar 
Dargie, G. C. Quantifying and Understanding the Tropical Peatlands of the Central Congo Basin. PhD thesis, Univ. Leeds (2015).Spiker, E. C. & Hatcher, P. G. Carbon isotope fractionation of sapropelic organic matter during early diagenesis. Org. Geochem. 5, 283–290 (1984).Article 
CAS 

Google Scholar 
Chave, J. et al. Regional and seasonal patterns of litterfall in tropical South America. Biogeosciences 7, 43–55 (2010).Article 
ADS 

Google Scholar 
Dommain, R. et al. Forest dynamics and tip-up pools drive pulses of high carbon accumulation rates in a tropical peat dome in Borneo (Southeast Asia). J. Geophys. Res. 120, 617–640 (2015).Article 
CAS 

Google Scholar 
Wotzka, H.-P. in Grundlegungen: Beiträge zur europäischen und afrikanischen Archäologie fűr Manfred K. H. Eggert (ed. Wotzka, H.-P.) 271–289 (Francke, 2006).Saulieu, G. D. et al. Archaeological evidence for population rise and collapse between ~2500 and ~500 cal. yr BP in Western Central Africa. Afr. Archéol. Arts 17, 11–32 (2021).
Google Scholar 
Sachse, D. et al. Molecular paleohydrology: interpreting the hydrogen-isotopic composition of lipid biomarkers from photosynthesizing organisms. Annu. Rev. Earth Planet. Sci. 40, 221–249 (2012).Article 
ADS 
CAS 

Google Scholar 
Collins, J. A. et al. Estimating the hydrogen isotopic composition of past precipitation using leaf-waxes from western Africa. Quat. Sci. Rev. 65, 88–101 (2013).Article 
ADS 

Google Scholar 
Schefuß, E., Schouten, S. & Schneider, R. R. Climatic controls on central African hydrology during the past 20,000 years. Nature 437, 1003–1006 (2005).Article 
ADS 
PubMed 

Google Scholar 
Kelly, T. J. et al. The vegetation history of an Amazonian domed peatland. Palaeogeogr. Palaeoclimatol. Palaeoecol. 468, 129–141 (2017).Article 

Google Scholar 
Swindles, G. T. et al. Ecosystem state shifts during long-term development of an Amazonian peatland. Global Change Biol. 24, 738–757 (2018).Article 
ADS 

Google Scholar 
Dommain, R., Couwenberg, J. & Joosten, H. Development and carbon sequestration of tropical peat domes in south-east Asia: links to post-glacial sea-level changes and Holocene climate variability. Quat. Sci. Rev. 30, 999–1010 (2011).Article 
ADS 

Google Scholar 
Lottes, A. L. & Ziegler, A. M. World peat occurrence and the seasonality of climate and vegetation. Palaeogeogr. Palaeoclimatol. Palaeoecol. 106, 23–37 (1994).Article 

Google Scholar 
Moutsamboté, J. M. Ecological, Phytogeographic and Phytosociological Study of Northern Congo (Plateaus, Bowls, Likouala and Sangha). PhD thesis, Univ. Marien Ngouabi (2012).Dingman, S. L. Fluvial Hydrology (W. H. Freeman, 1984).Swindles, G. T., Morris, P. J., Baird, A. J., Blaauw, M. & Plunkett, G. Ecohydrological feedbacks confound peat-based climate reconstructions. Geophys. Res. Lett. 39, L11401 (2012).Article 
ADS 

Google Scholar 
Morris, P. J., Baird, A. J., Young, D. M. & Swindles, G. T. Untangling climate signals from autogenic changes in long-term peatland development. Geophys. Res. Lett. 42, 10,788–10,797 (2015).Article 

Google Scholar 
Young, D. M., Baird, A. J., Morris, P. J. & Holden, J. Simulating the long-term impacts of drainage and restoration on the ecohydrology of peatlands. Water Resour. Res. 53, 6510–6522 (2017).Article 
ADS 

Google Scholar 
Weldeab, S., Lea, D. W., Schneider, R. R. & Andersen, N. Centennial scale climate instabilities in a wet early Holocene West African monsoon. Geophys. Res. Lett. 34, L24702 (2007).Article 
ADS 

Google Scholar 
Collins, J. A. et al. Rapid termination of the African Humid Period triggered by northern high-latitude cooling. Nat. Commun. 8, 1372 (2017).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Garcin, Y. et al. Early anthropogenic impact on Western Central African rainforests 2,600 y ago. Proc. Natl. Acad. Sci. USA 115, 3261–3266 (2018).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Vincens, A. et al. Changement majeur de la végétation du lac Sinnda (vallée du Niari, Sud-Congo) consécutif à l’assèchement climatique holocène supérieur: apport de la palynologie. C. R. Acad. Sci. Paris Sér. II 318, 1521–1526 (1994).
Google Scholar 
Elenga, H. et al. Diagramme pollinique holocène du lac Kitina (Congo): mise en évidence de changements paléobotaniques et paléoclimatiques dans le massif forestier du Mayombe. C. R. Acad. Sci. Paris Sér. II 323, 403–410 (1996).CAS 

Google Scholar 
Ngomanda, A., Neumann, K., Schweizer, A. & Maley, J. Seasonality change and the third millennium BP rainforest crisis in southern Cameroon (Central Africa). Quat. Res. 71, 307–318 (2009).Article 

Google Scholar 
Maley, J. et al. Late Holocene forest contraction and fragmentation in central Africa. Quat. Res. 89, 43–59 (2018).Article 

Google Scholar 
Bayon, G. et al. Intensifying weathering and land use in Iron Age Central Africa. Science 335, 1219–1222 (2012).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Giresse, P., Maley, J. & Chepstow-Lusty, A. Understanding the 2500 yr BP rainforest crisis in West and Central Africa in the framework of the Late Holocene: pluridisciplinary analysis and multi-archive reconstruction. Global Planet. Change 192, 103257 (2020).Article 

Google Scholar 
Schefuß, E. et al. Hydrologic control of carbon cycling and aged carbon discharge in the Congo River basin. Nat. Geosci. 9, 687–690 (2016).Article 
ADS 

Google Scholar 
Hoyt, A. M., Chaussard, E., Seppalainen, S. S. & Harvey, C. F. Widespread subsidence and carbon emissions across Southeast Asian peatlands. Nat. Geosci. 13, 435–440 (2020).Article 
ADS 
CAS 

Google Scholar 
Deshmukh, C. S. et al. Conservation slows down emission increase from a tropical peatland in Indonesia. Nat. Geosci. 14, 484–490 (2021).Article 
ADS 
CAS 

Google Scholar 
Köhler, P., Nehrbass-Ahles, C., Schmitt, J., Stocker, T. F. & Fischer, H. A 156 kyr smoothed history of the atmospheric greenhouse gases CO2, CH4, and N2O and their radiative forcing. Earth Syst. Sci. Data 9, 363–387 (2017).Article 
ADS 

Google Scholar 
Jiang, Y. et al. Widespread increase of boreal summer dry season length over the Congo rainforest. Nat. Clim. Change 9, 617–622 (2019).Article 

Google Scholar 
Cook, K. H., Liu, Y. & Vizy, E. K. Congo Basin drying associated with poleward shifts of the African thermal lows. Clim. Dyn. 54, 863–883 (2020).Article 

Google Scholar 
Bennett, A. C. et al. Resistance of African tropical forests to an extreme climate anomaly. Proc. Natl. Acad. Sci. USA 118, e2003169118 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sullivan, M. J. P. et al. Long-term thermal sensitivity of Earth’s tropical forests. Science 368, 869–874 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
García-Palacios, P. et al. Evidence for large microbial-mediated losses of soil carbon under anthropogenic warming. Nat. Rev. Earth Environ. 2, 585–585 (2021).Article 
ADS 

Google Scholar 
Cobb, A. R. et al. How temporal patterns in rainfall determine the geomorphology and carbon fluxes of tropical peatlands. Proc. Natl. Acad. Sci. USA 114, E5187–E5196 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Feng, X., Porporato, A. & Rodriguez-Iturbe, I. Changes in rainfall seasonality in the tropics. Nat. Clim. Change 3, 811–815 (2013).Article 
ADS 

Google Scholar 
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Xu, J. R., Morris, P. J., Liu, J. G. & Holden, J. PEATMAP: refining estimates of global peatland distribution based on a meta-analysis. Catena 160, 134–140 (2018).Article 

Google Scholar 
Paillard, D., Labeyrie, L. & Yiou, P. Macintosh program performs time-series analysis. Eos Trans. AGU 77, 379 (1996).Article 
ADS 

Google Scholar 
Blaauw, M. & Christen, J. A. Flexible paleoclimate age–depth models using an autoregressive gamma process. Bayesian Anal. 6, 457–474 (2011).Article 
MathSciNet 
MATH 

Google Scholar 
Blaauw, M. et al. rbacon: age–depth modelling using Bayesian statistics. R package version 2.5.7 (2021); https://cran.r-project.org/web/packages/rbacon/index.html.Hogg, A. G. et al. SHCal20 Southern Hemisphere calibration, 0–55,000 years cal BP. Radiocarbon 62, 759–778 (2020).Article 
CAS 

Google Scholar 
Reimer, P. et al. The IntCal20 Northern Hemisphere radiocarbon age calibration curve (0–55 kcal BP). Radiocarbon 62, 725–757 (2020).Article 
CAS 

Google Scholar 
Reuter, H., Gensel, J., Elvert, M. & Zak, D. Evidence for preferential protein depolymerization in wetland soils in response to external nitrogen availability provided by a novel FTIR routine. Biogeosciences 17, 499–514 (2020).Article 
ADS 
CAS 

Google Scholar 
Kuhry, P. & Vitt, D. H. Fossil carbon/nitrogen ratios as a measure of peat decomposition. Ecology 77, 271–275 (1996).Article 

Google Scholar 
Hornibrook, E. R. C., Longstaffe, F. J. & Fyfe, W. S. Evolution of stable carbon isotope compositions for methane and carbon dioxide in freshwater wetlands and other anaerobic environments. Geochim. Cosmochim. Acta 64, 1013–1027 (2000).Article 
ADS 
CAS 

Google Scholar 
Broder, T., Blodau, C., Biester, H. & Knorr, K. H. Peat decomposition records in three pristine ombrotrophic bogs in southern Patagonia. Biogeosciences 9, 1479–1491 (2012).Article 
ADS 
CAS 

Google Scholar 
Biester, H., Knorr, K. H., Schellekens, J., Basler, A. & Hermanns, Y. M. Comparison of different methods to determine the degree of peat decomposition in peat bogs. Biogeosciences 11, 2691–2707 (2014).Article 
ADS 
CAS 

Google Scholar 
Leifeld, J., Klein, K. & Wüst-Galley, C. Soil organic matter stoichiometry as indicator for peatland degradation. Sci. Rep. 10, 7634 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hodgkins, S. B. et al. Tropical peatland carbon storage linked to global latitudinal trends in peat recalcitrance. Nat. Commun. 9, 3640 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Chimner, R. A. & Ewel, K. C. A tropical freshwater wetland: II. Production, decomposition, and peat formation. Wetlands Ecol. Manage. 13, 671–684 (2005).Article 

Google Scholar 
Lafargue, E., Marquis, F. & Pillot, D. Rock-Eval 6 applications in hydrocarbon exploration, production, and soil contamination studies. Oil Gas Sci. Technol. 53, 421–437 (1998).CAS 

Google Scholar 
Behar, F., Beaumont, V. & Penteado, H. L. D. Rock-Eval 6 technology: performances and developments. Oil Gas Sci. Technol. 56, 111–134 (2001).Article 
CAS 

Google Scholar 
Disnar, J. R., Guillet, B., Keravis, D., Di-Giovanni, C. & Sebag, D. Soil organic matter (SOM) characterization by Rock-Eval pyrolysis: scope and limitations. Org. Geochem. 34, 327–343 (2003).Article 
CAS 

Google Scholar 
Marzi, R., Torkelson, B. E. & Olson, R. K. A revised carbon preference index. Org. Geochem. 20, 1303–1306 (1993).Article 
CAS 

Google Scholar 
Eglinton, G. & Hamilton, R. J. Leaf epicuticular waxes. Science 156, 1322–1334 (1967).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Sauer, P. E., Eglinton, T. I., Hayes, J. M., Schimmelmann, A. & Sessions, A. L. Compound-specific D/H ratios of lipid biomarkers from sediments as a proxy for environmental and climatic conditions. Geochim. Cosmochim. Acta 65, 213–222 (2001).Article 
ADS 
CAS 

Google Scholar 
Waelbroeck, C. et al. Sea-level and deep water temperature changes derived from benthic foraminifera isotopic records. Quat. Sci. Rev. 21, 295–305 (2002).Article 
ADS 

Google Scholar 
Han, J. & Calvin, M. Hydrocarbon distribution of algae and bacteria, and microbiological activity in sediments. Proc. Natl. Acad. Sci. U.S.A. 64, 436–443 (1969).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nakagawa, T. et al. Dense-media separation as a more efficient pollen extraction method for use with organic sediment/deposit samples: comparison with the conventional method. Boreas 27, 15–24 (1998).Article 

Google Scholar 
Stone, B. C. A synopsis of the African Species of Pandanus. Ann. Missouri Bot. Gard. 60, 260–272 (1973).Article 

Google Scholar 
African Plant Database (version 3.4.0) (Conservatoire et Jardin Botaniques de la Ville de Genève and South African National Biodiversity Institute, accessed January 2022); http://africanplantdatabase.ch.Polhill, R. M., Nordal, I., Kativu, S. & Poulsen, A. D. Flora of Tropical East Africa 1st edn (CRC Press, 1997).Hawthorne, D. et al. Global Modern Charcoal Dataset (GMCD): a tool for exploring proxy-fire linkages and spatial patterns of biomass burning. Quat. Int. 488, 3–17 (2018).Article 

Google Scholar 
Stevenson, J. & Haberle, S. Macro Charcoal Analysis: A Modified Technique Used by the Department of Archaeology and Natural History. Palaeoworks Technical Paper No. 5 (PalaeoWorks, Department of Archaeology and Natural History, Research School of Pacific and Asian Studies, Australian National University, 2005).Tierney, J. E., Pausata, F. S. R. & deMenocal, P. B. Rainfall regimes of the Green Sahara. Sci. Adv. 3, e1601503 (2017).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Shanahan, T. M. et al. The time-transgressive termination of the African Humid Period. Nat. Geosci. 8, 140–144 (2015).Article 
ADS 
CAS 

Google Scholar 
Ladd, S. N. et al. Leaf wax hydrogen isotopes as a hydroclimate proxy in the Tropical Pacific. J. Geophys. Res. 126, e2020JG005891 (2021).
Google Scholar 
Dansgaard, W. Stable isotopes in precipitation. Tellus 16, 436–468 (1964).Article 
ADS 

Google Scholar 
Munksgaard, N. C. et al. Data Descriptor: daily observations of stable isotope ratios of rainfall in the tropics. Sci. Rep. 9, 14419 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Aggarwal, P. K. et al. Proportions of convective and stratiform precipitation revealed in water isotope ratios. Nat. Geosci. 9, 624–629 (2016).Article 
CAS 

Google Scholar 
Zwart, C. et al. The isotopic signature of monsoon conditions, cloud modes, and rainfall type. Hydrol. Processes 32, 2296–2303 (2018).Article 
ADS 

Google Scholar 
Jackson, B., Nicholson, S. E. & Klotter, D. Mesoscale convective systems over Western Equatorial Africa and their relationship to large-scale circulation. Mon. Weather Rev. 137, 1272–1294 (2009).Article 
ADS 

Google Scholar 
Sorí, R., Nieto, R., Vicente-Serrano, S. M., Drumond, A. & Gimeno, L. A Lagrangian perspective of the hydrological cycle in the Congo River basin. Earth Syst. Dynam. 8, 653–675 (2017).Article 
ADS 

Google Scholar 
International Atomic Energy Agency–World Meteorological Organization Global Network of Isotopes in Precipitation: The GNIP Database (accessed May 2020); https://nucleus.iaea.org/wiser/index.aspx.Sachse, D., Dawson, T. E. & Kahmen, A. Seasonal variation of leaf wax n-alkane production and δ2H values from the evergreen oak tree, Quercus agrifolia. Isotopes Environ. Health Stud. 51, 124–142 (2015).Article 
CAS 
PubMed 

Google Scholar 
Huang, X., Zhao, B., Wang, K., Hu, Y. & Meyers, P. A. Seasonal variations of leaf wax n-alkane molecular composition and δD values in two subtropical deciduous tree species: results from a three-year monitoring program in central China. Org. Geochem. 118, 15–26 (2018).Article 
CAS 

Google Scholar 
Botev, Z. I., Grotowski, J. F. & Kroese, D. P. Kernel density estimation via diffusion. Ann. Stat. 38, 2916–2957 (2010).Article 
MathSciNet 
MATH 

Google Scholar 
Albrecht, R., Sebag, D. & Verrecchia, E. Organic matter decomposition: bridging the gap between Rock-Eval pyrolysis and chemical characterization (CPMAS 13C NMR). Biogeochemistry 122, 101–111 (2015).Article 
CAS 

Google Scholar 
Matteodo, M. et al. Decoupling of topsoil and subsoil controls on organic matter dynamics in the Swiss Alps. Geoderma 330, 41–51 (2018).Article 
ADS 
CAS 

Google Scholar 
Malou, O. P. et al. The Rock-Eval® signature of soil organic carbon in arenosols of the Senegalese groundnut basin. How do agricultural practices matter? Agr. Ecosyst. Environ. 301, 107030 (2020).Article 
CAS 

Google Scholar 
Thoumazeau, A. et al. A new in-field indicator to assess the impact of land management on soil carbon dynamics. Geoderma 375, 114496 (2020).Article 
ADS 
CAS 

Google Scholar 
Cranwell, P. A. Diagenesis of free and bound lipids in terrestrial detritus deposited in a lacustrine sediment. Org. Geochem. 3, 79–89 (1981).Article 
CAS 

Google Scholar 
Ofiti, N. O. E. et al. Warming promotes loss of subsoil carbon through accelerated degradation of plant-derived organic matter. Soil Biol. Biochem. 156, 108185 (2021).Article 
CAS 

Google Scholar 
Stuiver, M. & Reimer, P. J. Extended 14C data base and revised CALIB 3.0 14C age calibration program. Radiocarbon 35, 215–230 (1993).Article 

Google Scholar  More

All the results were reported relative to the control, unless specifically stated to the contrary or for clarity.Growth of cucumber plants in response to different biofertilizersSoilThere was no significant difference in cucumber growth before microbial fertilizer was applied. However, some microbial fertilizers significantly increased cucumber height and stem diameter when they were applied within 4 weeks from when the seedlings were planted (Fig. 1a,b,e,f). In the second week, SHZ and SMF increased plant height by 11.2 and 9.5%, respectively. In the third week, S267, SBS, SBH, SM and SHZ increased plant height by 12.0, 13.8, 15.0, 20.5 and 26.9%, respectively (Fig. 1a). In the fourth and fifth weeks, some treatments significantly increased cucumber height. In the second and third weeks, S267 significantly increased stem diameter by 21.2 and 16.8% (Fig. 1b).Figure 1Effect of different biofertilizer treatments on the growth of cucumber seedlings produced in soil or substrate in a greenhouse. S267 = Trichoderma Strain 267 added to soil; SBH = Bacillus subtilis and T. harzianum biofertilizers added to soil; SBS = B. subtilis biofertilizer added to the soil; SM = Compound biofertilizer added to soil; SHZ = T. harzianum biofertilizer added to soil; SCK = Untreated soil. US267 = T.267 biofertilizer added to substrate; USBH = B. subtilis and T. harzianum biofertilizers added to substrate; USBS = B. subtilis biofertilizer added to substrate; USM = Compound biofertilizer added to substrate; USHZ = T. harzianum biofertilizer added to substrate; USCK = Untreated substrate.Full size imageOver the subsequent 5 weeks, some microbial fertilizer treatments decreased cucumber height and stem diameter (Fig. 1g,h).SubstrateThere were no significant differences in cucumber growth before microbial fertilizer microbial fertilizer was applied (Fig. 1c,d,g,h). However, within 4 weeks of applying the microbial fertilizer, each biofertilizer treatment applied significantly increased cucumber height (Fig. 1c). US267 and USHZ significantly increased cucumber height by 39.8–75.4% and 56.1–86.1%, respectively. US267, USM and USHZ significantly increased the stem diameter by 76.8–108.9%, 71.1–97.6% and 80.4–122.4%, respectively (Fig. 1d).Over the subsequent 5 weeks, US267, USM and USHZ treatments continued to significantly increase cucumber height and stem diameter (Fig. 1g,h).Changes in the taxonomic composition of soil-borne fungal pathogensSoilBiofertilizers application significantly reduced the taxonomic composition of soil-borne fungal pathogens at different times during the cucumber growth period (Tables 1 and 2). Fusarium spp. were significantly reduced (T, 63.8% reduction, P  More

Bobbink, R. et al. Ecol. Appl. 20, 30–59 (2010).Article 
PubMed 

Google Scholar 
Olff, H. & Ritchie, M. E. Trends Ecol. Evol. 13, 261–265 (1998).Article 
PubMed 

Google Scholar 
DeMalach, N., Zaady, E. & Kadmon, R. Ecol. Lett. 20, 60–69 (2017).Article 
PubMed 

Google Scholar 
Borer, E. T. et al. Nature 508, 517–520 (2014).Article 
PubMed 

Google Scholar 
Harpole, W. S. et al. Nature 537, 93–96 (2016).Article 
PubMed 

Google Scholar 
Eskelinen, A., Harpole, W. S., Jessen, M.-T., Virtanen, R. & Hautier, Y. Nature https://doi.org/10.1038/s41586-022-05383-9 (2022).Article 

Google Scholar 
Koerner, S. E. et al. Nature Ecol. Evol. 2, 1925–1932 (2018).Article 
PubMed 

Google Scholar 
Chesson, P. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).Article 

Google Scholar 
Coley, P. D., Bryant, J. P. & Chapin, F. S. Science 230, 895–899 (1985).Article 
PubMed 

Google Scholar 
Hautier, Y., Niklaus, P. A. & Hector, A. Science 324, 636–638 (2009).Article 
PubMed 

Google Scholar 
Allan, E. & Crawley, M. J. Ecol. Lett. 14, 1246–1253 (2011).Article 
PubMed 

Google Scholar  More

Schimper, A. F. W. Plant Geography upon a Physiological Basis (Clarendon Press, 1903).Alexander, J. M., Diez, J. M. & Levine, J. M. Novel competitors shape species’ responses to climate change. Nature 525, 515–518 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
HilleRisLambers, J., Harsch, M. A., Ettinger, A. K., Ford, K. R. & Theobald, E. J. How will biotic interactions influence climate change-induced range shifts? Ann. N. Y. Acad. Sci. 1297, 112–125 (2013).PubMed 

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

Google Scholar 
Loarie, S. R., Weiss, S. B., Hamilton, H., Branciforte, R. & Kraft, N. J. B. The geography of climate change: implications for conservation biogeography. Divers. Distrib. 16, 476–487 (2010).Article 

Google Scholar 
Callaway, R. M. et al. Positive interactions among alpine plants increase with stress. Nature 417, 844–848 (2002).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Dybzinski, R. & Tilman, D. Resource use patterns predict long‐term outcomes of plant competition for nutrients and light. Am. Nat. 170, 305–318 (2007).Article 
PubMed 

Google Scholar 
Hautier, Y., Niklaus, P. A. & Hector, A. Competition for light causes plant biodiversity loss after eutrophication. Science 324, 636–638 (2009).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Levine, J. M. & HilleRisLambers, J. The importance of niches for the maintenance of species diversity. Nature 461, 254–257 (2009).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Kraft, N. J. B., Godoy, O. & Levine, J. M. Plant functional traits and the multidimensional nature of species coexistence. Proc. Natl Acad. Sci. USA 112, 797–802 (2015).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Knapp, A. K. et al. Rainfall variability, carbon cycling, and plant species diversity in a mesic grassland. Science 298, 2202–2205 (2002).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Sandel, B. et al. Contrasting trait responses in plant communities to experimental and geographic variation in precipitation. New Phytol. 188, 565–575 (2010).Article 
PubMed 

Google Scholar 
Esch, E. H., Ashbacher, A. C., Kopp, C. W. & Cleland, E. E. Competition reverses the response of shrub seedling mortality and growth along a soil moisture gradient. J. Ecol. 106, 2096–2108 (2018).Article 

Google Scholar 
Alon, M. & Sternberg, M. Effects of extreme drought on primary production, species composition and species diversity of a Mediterranean annual plant community. J. Veg. Sci. 30, 1045–1061 (2019).Article 

Google Scholar 
Chesson, P. Updates on mechanisms of maintenance of species diversity. J. Ecol. 106, 1773–1794 (2018).Article 

Google Scholar 
Barabás, G., D’Andrea, R. & Stump, S. M. Chesson’s coexistence theory. Ecol. Monogr. 88, 277–303 (2018).Article 

Google Scholar 
Ellner, S. P., Snyder, R. E., Adler, P. B. & Hooker, G. An expanded modern coexistence theory for empirical applications. Ecol. Lett. 22, 3–18 (2019).Article 
ADS 
PubMed 

Google Scholar 
Adler, P., Hillerislambers, J. & Levine, J. A niche for neutrality. Ecol. Lett. 10, 95–104 (2007).Article 
PubMed 

Google Scholar 
Germain, R. M., Mayfield, M. M. & Gilbert, B. The ‘filtering’ metaphor revisited: competition and environment jointly structure invasibility and coexistence. Biol. Lett. 14, 20180460 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Pau, S. et al. Predicting phenology by integrating ecology, evolution and climate science. Glob. Change Biol. 17, 3633–3643 (2011).Article 
ADS 

Google Scholar 
Fargione, J. & Tilman, D. Niche differences in phenology and rooting depth promote coexistence with a dominant C4 bunchgrass. Oecologia 143, 598–606 (2005).Article 
ADS 
PubMed 

Google Scholar 
Godoy, O., Kraft, N. J. B. & Levine, J. M. Phylogenetic relatedness and the determinants of competitive outcomes. Ecol. Lett. 17, 836–844 (2014).Article 
PubMed 

Google Scholar 
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).Article 
ADS 
PubMed 

Google Scholar 
Kunstler, G. et al. Plant functional traits have globally consistent effects on competition. Nature 529, 204–207 (2016).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Diffenbaugh, N. S., Swain, D. L. & Touma, D. Anthropogenic warming has increased drought risk in California. Proc. Natl Acad. Sci. USA 112, 3931–3936 (2015).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Swain, D. L., Langenbrunner, B., Neelin, J. D. & Hall, A. Increasing precipitation volatility in twenty-first-century California. Nat. Clim. Change 8, 427–433 (2018).Article 
ADS 

Google Scholar 
Chesson, P. Geometry, heterogeneity and competition in variable environments. Phil. Trans. R. Soc. Lond. B 330, 165–173 (1990).Article 
ADS 

Google Scholar 
Aronson, J., Kigel, J., Shmida, A. & Klein, J. Adaptive phenology of desert and Mediterranean populations of annual plants grown with and without water stress. Oecologia 89, 17–26 (1992).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Santa Barbara County Public Works water resources hydrology: historical rainfall data: daily and monthly rainfall. County of Santa Barbara http://www.countyofsb.org/pwd/water/downloads/hydro/421dailys.pdf (2019).Kandlikar, G. S., Kleinhesselink, A. R. & Kraft, N. J. B. Functional traits predict species responses to environmental variation in a California grassland annual plant community. J. Ecol. 110, 833–844 (2022).Article 
CAS 

Google Scholar 
Cleland, E. E. et al. Sensitivity of grassland plant community composition to spatial vs. temporal variation in precipitation. Ecology 94, 1687–1696 (2013).Article 
PubMed 

Google Scholar 
Usinowicz, J. et al. Temporal coexistence mechanisms contribute to the latitudinal gradient in forest diversity. Nature 550, 105–108 (2017).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Kandlikar, G. S., Johnson, C. A., Yan, X., Kraft, N. J. B. & Levine, J. M. Winning and losing with microbes: how microbially mediated fitness differences influence plant diversity. Ecol. Lett. 22, 1178–1191 (2019).PubMed 

Google Scholar 
Kleinhesselink, A. R., Kraft, N. J. B., Pacala, S. W. & Levine, J. M. Detecting and interpreting higher order interactions in ecological communities. Ecol. Lett. 25, 1604–1617 (2022).Article 
PubMed 

Google Scholar 
Saavedra, S. et al. A structural approach for understanding multispecies coexistence. Ecol. Monogr. 87, 470–486 (2017).Article 

Google Scholar 
Levine, J. I., Levine, J. M., Gibbs, T. & Pacala, S. W. Competition for water and species coexistence in phenologically structured annual plant communities. Ecol. Lett. 25, 1110–1125 (2022).Article 
PubMed 

Google Scholar 
Farrior, C. E. et al. Resource limitation in a competitive context determines complex plant responses to experimental resource additions. Ecology 94, 2505–2517 (2013).Article 
PubMed 

Google Scholar 
Harrison, S., Grace, J. B., Davies, K. F., Safford, H. D. & Viers, J. H. Invasion in a diversity hotspot: exotic cover and native richness in the Californian serpentine flora. Ecology 87, 695–703 (2006).Article 
PubMed 

Google Scholar 
Pérez-Harguindeguy, N. et al. New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 61, 167–234 (2013).Article 

Google Scholar 
Godoy, O. & Levine, J. M. Phenology effects on invasion success: insights from coupling field experiments to coexistence theory. Ecology 95, 726–736 (2014).Article 
PubMed 

Google Scholar  More