Philippa Kaur

Isotope labelling and phylogenetic analysis of a natural marine bacterioplankton population at seaMediterranean seawater was collected during August 2017 (station N1200, 32.45° N, 34.37 °E) from 11 depths by Niskin bottles and divided into triplicate 250 ml polycarbonate bottles. Two bottles from each depth were labelled with 1 mM sodium bicarbonate-13C and 1 mM ammonium-15N chloride (Sigma-Aldrich), and all three bottles (two labelled and one control) were incubated at the original depth and station at sea for 3.5 h around mid-day. The stable isotopes were chosen to enable direct comparison of C and N uptake in single cells, and the short incubation time was chosen to minimize isotope dilution and potential recycling and transfer of 13C and 15N between community members25. After incubation, bottles were brought back on board and the incubations were stopped by fixing with 2× electron-microscopy-grade glutaraldehyde (2.5% final concentration) and stored at 4 °C until sorting analysis. Cell sorting, NanoSIMS analyses and the calculation of uptake rates were performed as described in Roth-Rosenberg et al.26.DNA collection and extraction from seawaterSamples for DNA were collected on 0.22 µm Sterivex filters (Millipore). Excess water was removed using a syringe, 1 ml lysis buffer (40 mM EDTA, 50 mM Tris pH 8.3, and 0.75 M sucrose) was added and both ends of the filter were closed with parafilm. Samples were kept at −80 °C until extraction. DNA was extracted by using a semi-automated protocol including manual chemical cell lysis before automated steps using the QIAamp DNA Mini Protocol: DNA Purification from Blood or Body Fluids (Spin Protocol, starting from step 6, at the BioRap unit, Faculty of Medicine, Technion). The manual protocol began with thawing the samples, then the storage buffer was removed using a syringe and 170 µl lysis buffer added to the filters. Thirty microlitres of Lysozyme (20 mg ml−1) were added to the filters and incubated at 37 °C for 30 min. After incubation, 20 µl proteinase K and 200 µl buffer AL (from the Qiagen kit) were added to the tube for 1 h at 56 °C (with agitation). The supernatant was transferred to a new tube, and DNA was extracted using the QIAcube automated system. All DNA samples were eluted in 100 μl DNA-free distilled water.ITS PCR amplificationPCR amplification of the ITS was carried out with specific primers for Prochlorococcus CS1_16S_1247F (5′-ACACTGACGACATGGTTCTACACGTACTACAATGCTACGG) and Cs2_ITS_Ar (5′-TACGGTAGCAGAGACTTGGTCTGGACCTCACCCTTATCAGGG)21,22. The first PCR was performed in triplicate in a total volume of 25 μl containing 0.5 ng of template, 12.5 μl of MyTaq Red Mix (Bioline) and 0.5 μl of 10 μM of each primer. The amplification conditions comprised steps at 95 °C for 5 min, 28/25 (16 S/ITS) cycles at 95 °C for 30 s, 50 °C for 30 s and 72 °C for 1 min followed by one step of 5 min at 72 °C. All PCR products were validated on a 1% agarose gel, and triplicates were pooled. Subsequently, a second PCR amplification was performed to prepare libraries. These were pooled and after a quality control sequenced (2 × 250 paired-end reads) using an Illumina MiSeq sequencer. Library preparation and pooling were performed at the DNA Services facility, Research Resources Center, University of Illinois at Chicago. MiSeq sequencing was performed at the W.M. Keck Center for Comparative and Functional Genomics at the University of Illinois at Urbana-Champaign.ITS sequence processingPaired-end reads were analysed using the Dada2 pipeline46. The quality of the sequences per sample was examined using the Dada2 ‘plotQualityProfile’ command. Quality filtering was performed using the Dada2 ‘filterAndTrim’ command with parameters for quality filtering truncLen=c(290,260), maxN=0, maxEE=c(2,2), truncQ=2, rm.phix=TRUE, trimLeft=c(20,20). Following error estimation and dereplication, the Dada2 algorithm was used to correct sequences. Merging of the forward and reverse reads was done with minimum overlap of 4 bp. Detection and removal of suspected chimaeras was done with command ‘removeBimeraDenovo’. In total, 388,417 sequences in 484 amplicon sequence variants were counted. The amplicon sequence variants were aligned in MEGA6 (ref. 47), and the first ~295 nucleotides, corresponding to the 16S gene, were trimmed. The ITS sequences were then classified using BLASTn against a custom database of ITS sequences from cultured Prochlorococcus and Synechococcus strains as well as from uncultured HL and LL clades.Individual-based modelPlanktonIndividuals.jl (v0.1.9) was used to run the individual-based simulations48. Briefly, the cells fix inorganic carbon through photosynthesis and nitrogen, phosphorus and DOC from the water column into intracellular quotas and grow until division or grazing. Cell division is modelled as a probabilistic function of cell size. Grazing is represented by a quadratic probabilistic function of cell population. Cells consume nutrient resources, which are represented as Eulerian, density-based tracers. A full documentation of state variables and model equations are available online at https://juliaocean.github.io/PlanktonIndividuals.jl/dev/. Equations related to mixotrophy are shown below as an addition to the online documentation.$$V_{{mathrm{DOC}}} = V_{{mathrm{DOC}}}^{{mathrm{max}}} cdot {{mathrm{max}}}left( {0.0,{{mathrm{min}}}left( {1.0,,frac{{q_{mathrm{C}}^{{mathrm{max}}} – q_{mathrm{C}}}}{{q_{mathrm{C}}^{{mathrm{max}}} – q_{mathrm{C}}^{{mathrm{min}}}}}} right)} right) cdot frac{{{mathrm{DOC}}}}{{{mathrm{DOC}} + K_{{mathrm{DOC}}}^{{mathrm{sat}}}}}$$
(1)
$$f_{{mathrm{PS}}} = frac{{P_{mathrm{S}}}}{{P_{mathrm{S}} + V_{{mathrm{DOC}}}}}$$
(2)
$$V_{{mathrm{DOC}}} = 0,,{mathrm{if}},f_{{mathrm{PS}}} < f_{{mathrm{PS}}}^{{mathrm{min}}}$$ (3) where VDOC is the cell-specific DOC uptake rate (mol C cell−1 s−1), (V_{{mathrm{DOC}}}^{{mathrm{max}}}) is the maximum cell-specific DOC uptake rate (mol C cell−1 s−1), (q_{mathrm{C}}^{{mathrm{max}}}) is the maximum cell carbon quota (mol C cell−1), (q_{mathrm{C}}^{{mathrm{min}}}) is the minimum cell carbon quota (mol C cell−1). The maximum and minimum functions here is used to keep qC between (q_{mathrm{C}}^{{mathrm{min}}}) and (q_{mathrm{C}}^{{mathrm{max}}}). (K_{{mathrm{DOC}}}^{{mathrm{sat}}}) is the half-saturation constant for DOC uptake (mol C m−3). fPS is the fraction of fixed C originating from photosynthesis (PS, mol C cell−1 s−1). DOC uptake stops when fPS is smaller than (f_{{mathrm{PS}}}^{{mathrm{min}}})(minimum fraction of fixed C originating form photosynthesis, 1% by default) according to laboratory studies of Prochlorococcus that showed that they cannot survive long exposure to darkness (beyond several days) even when supplied with organic carbon sources13. (1 − fPS) is also shown in Fig. 3 as the contribution of DOC uptake.We set up two separate simulations; each of them has a population of either an obligate photo-autotroph or a mixotroph that also consumes DOC. The initial conditions and parameters (Supplementary Table 3) are the same for the two simulations except the ability of mixotrophy. The simulations were run with a timestep of 1 min for 360 simulated days to achieve a steady state. We run the two simulations for multiple times in order to get the range of the stochastic processes.Evaluation of autotrophic growth ratesWe evaluated the carbon-specific, daily-averaged carbon fixation rate, ℙ as a function of light intensity (I, µE), following Platt et al.33:$${Bbb P} = frac{1}{{Delta t}}{int}_0^{Delta t} {frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}} P_{mathrm{S}}^{{mathrm{Chl}}}left( {1 - e^{ - alpha _{{mathrm{Chl}}}I/P_{mathrm{S}}^{{mathrm{Chl}}}}} right)e^{ - beta _{{mathrm{Chl}}}I/P_{mathrm{S}}^{{mathrm{Chl}}}}Delta t$$ (4) Here, (P_{mathrm{S}}^{{mathrm{Chl}}}), αChl and βChl are empirically determined coefficients representing the chlorophyll-a-specific carbon fixation rate (mol C (mol Chl)−1 s−1), the initial slope of the photosynthesis–light relationship and photo-inhibition effects at high photon fluxes, respectively. We impose empirically determined values for (P_{mathrm{S}}^{{mathrm{Chl}}}), αChl and βChl from the published study of Moore and Chisholm24. The natural Prochlorococcus community comprises HL and LL ecotypes, which have different values of (P_{mathrm{S}}^{{mathrm{Chl}}}), αChl and βChl, and the community growth rate is expected to be between that of HL extremes and LL extremes. Therefore, we use photo-physiological parameters for an HL-adapted ecotype (MIT9215), acclimated at 70 µmol photons m−2 s−1 and an LL-adapted ecotype (MIT9211), acclimated 9 µmol photons m−2 s−1. The models with these values are shown as the different lines in Fig. 2b,d. I is the hourly PAR, estimated by scaling the observed noon value at each depth with a diurnal variation evaluated from astronomical formulae based on geographic location and time of year37,38.(frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}) is the molar chlorophyll-a to carbon ratio, which is modelled as a function of growth rate and light intensity using the Inomura34 model (equation 17 therein) where parameters were calibrated with laboratory data from Healey49. In addition, the maximum growth rate ((mu _{{mathrm{max}}}^I)) based on macromolecular allocation is also estimated using the Inomura model (equation 30 therein). An initial guess of the growth rate and the empirically informed light intensity are used to estimate (frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}), which is then used to evaluate the light-limited, photoautotrophic growth rate$${Bbb V}_{mathrm{C}}^{{mathrm{auto}}} = min left( {{Bbb P} - K_{mathrm{R}},mu _{{mathrm{max}}}^I} right)$$ (5) from which the (frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}) is again updated. The light-limited growth rate is used to re-evaluate the (frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}). Repeating this sequence until the values converge, ({Bbb V}_{mathrm{C}}^{{mathrm{auto}}}) and (frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}) are solved iteratively.The nitrogen-specific uptake rate of fixed nitrogen (day−1) is modelled as$${Bbb V}_{{{mathrm{N}}}} = {Bbb V}_{mathrm{N}}^{{mathrm{max}}}frac{1}{{Q_{mathrm{N}}}}frac{N}{{N + K_{{{mathrm{N}}}}}}$$ (6) where values of the maximum uptake rate, ({Bbb V}_{mathrm{N}}^{{mathrm{max}}}), and half-saturation, KN, are determined from empirical allometric scalings35, along with a nitrogen cell quota QN from Bertilsson et al.39.The P-limited growth rate, or the phosphorus-specific uptake rate of phosphate (day−1), is modelled as$${Bbb V}_{mathrm{P}} = {Bbb V}_{mathrm{P}}^{{mathrm{max}}}frac{1}{{Q_{mathrm{P}}}}frac{{{mathrm{PO}_{4}}^{3 - }}}{{{mathrm{PO}_{4}}^{3 - } + K_{mathrm{P}}}}$$ (7) where values of the maximum uptake rate, ({Bbb V}_{mathrm{P}}^{{mathrm{max}}}). and half-saturation, KP, are determined from empirical allometric scalings35, along with a nitrogen cell quota QP from Bertilsson et al.39.Iron uptake is modelled as a linear function of cell surface area (SA), with rate constant ((k_{{mathrm{Fe}}}^{{mathrm{SA}}})) following Lis et al.36.$${Bbb V}_{{mathrm{Fe}}} = k_{{mathrm{Fe}}}^{{mathrm{SA}}} cdot {mathrm{SA}}frac{1}{{Q_{{mathrm{Fe}}}}}{mathrm{Fe}}$$ (8) The potential light-, nitrogen-, phosphorus- and iron-limited growth rates (({Bbb V}_{mathrm{C}},{Bbb V}_{mathrm{N}},{Bbb V}_{mathrm{P}},{Bbb V}_{{mathrm{Fe}}})) were evaluated at each depth in the water column and the minimum is the local modelled photo-autotrophic growth rate estimate, assuming no mixotrophy (Fig. 2b,d, blue lines). Parameters used in this evaluation are listed in Supplementary Table 2.An important premise of this study is that heterotrophy is providing for the shortfall in carbon under very low light conditions, but not nitrogen. It is known that Prochlorococcus can assimilate amino acids9 and therefore the stoichiometry of the heterotrophic contribution might alter the interpretations. However, it is also known that Prochlorococcus can exude amino acids40, which might cancel out the effects on the stoichiometry of Prochlorococcus.For the estimates of phototrophic growth rate from local environmental conditions (Fig. 2) we employed photo-physiological parameters from laboratory cultures of Prochlorococcus24. For the purposes of this study, we have assumed that the photosynthetic rates predicted are net primary production, which means that autotrophic respiration has been accounted for in the measurement. However, the incubations in that study were of relatively short timescale (45 min), which might suggest they are perhaps more representative of gross primary production. If this is the case, our estimates of photo-autotrophic would be even lower after accounting for autotrophic respiration, and thus would demand a higher contribution from heterotrophic carbon uptake. In this regard, our estimates might be considered a lower bound for organic carbon assimilation.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Our results show that GoM BFT larval survey samples can provide the crucial mark events for eventual CKMR estimates of adult abundance. The adult parents marked by larval samples can be directly recaptured in the fishery many years later as POPs, and indirectly through their progeny in future samples of larvae, as evidenced by the two cross-cohort HSPs (XHSPs) recovered in this study, which imply that a parent survived and spawned in the GoM in consecutive years. As more cohorts are sampled in future, the growing number of XHSPs could be used to estimate average adult survival rates, in addition to helping with the estimation of adult abundance31, as is now done for southern blue tuna40.There is a modest level of sibship within our 2016 samples, and a high level (involving over half the samples) in 2017, but it turns out not to be high enough to cause serious problems for POP-based CKMR. High sibship per se does not lead to bias in CKMR by virtue of the statistical construction of the estimate, but it does increase variance, which can be summarized through a reduction in effective sample size. In a POP-based CKMR model, our effective sample size would be about 75% of nominal for the two years combined, or 66% of nominal for the targeted sampling of 2017. Since it is actually the product of adult and juvenile sample sizes which drives precision in CKMR14, one way to think about the 75% is that we will need about 33% more adult samples to achieve a given precision on abundance estimates than if we had somehow been able to collect the same number of “independent” juvenile samples (i.e. without oversampling siblings). That increase is appreciable but entirely achievable; for WBFT, it is logistically much easier to collect more feeding-ground adult samples than to collect more larvae, and at present there is no known practical way to collect large numbers of older, more dispersed, and thus more independent, juvenile western origin bluefin tuna (WBFT).This study was motivated by the concern that sibship might be a serious impediment to use of WBFT larvae for CKMR. High levels of sibship have been found in larval collections for other taxa despite a pelagic larval phase, suggesting that abiotic factors can impede random mixing of larvae after a spawning event41. Our larval samples were only a few days old (4–11) and thus had little time to disperse since fertilization; our concern beforehand was that each tow might sample the offspring of a very small number of adults (one spawning group in one night), and in 2017 that repeatedly towing the same water mass might simply be resampling the same “family”. In practice, though, the cumulative effect was limited. Samples were not dominated by progeny from just a few adults; the maximum DPG size (i.e., number of offspring from any one adult) was 5, which is under 2% of the larval sample size. There are several possible reasons for this finding. First, plankton sample tows are typically standardized to a ten-minute duration, covering on average about 0.3 nautical miles. Based on continuous plankton cameras42, each tow is likely to tow through multiple patches of zooplankton, and therefore potentially multiple patches of BFT larvae. Second, spawning aggregations of BFT may contain many adults. For example, on the spawning grounds near the Balearic Islands in the Mediterranean, purse seine fisheries target spawning fish and individual net sets routinely capture upwards of 500 mature individuals43. These numbers suggest that BFT spawner aggregations can be quite large, although the number of individuals that contribute gametes to a single spawning event may be lower. The results of this study pose intriguing scenarios for understanding BFT larval ecology and spawning behavior, which could be explored with larger sample sizes paired with data on oceanographic conditions, direct observation of spawning aggregations, and modeling to compare observed and predicted dispersal. The results of this study are based on just two years of sampling, and numerous practical and theoretical challenges remain to fully understand BFT reproduction in the GoM.Our sibship impact calculations assume use of an unmodified adult-size-based CKMR POP model, where each juvenile is compared to each adult taking into account the latter’s size (e.g.,14). That will give unbiased estimates, which we regard as essential in a CKMR model. However, for WBFT the estimates are not fully statistically efficient, in that some adults receive more statistical weight than others because they are marked more often (by having a large DPG), and thus variance might not be the lowest achievable. Modifying the model to fix that would be simple in a “cartoon” CKMR setting where all adults are identical (e.g., Fig. 1 of14), simply by first condensing each DPG to a single representative, then only using those representatives (rather than all the larvae) in POP comparisons. Each marked parent then receives the same weight, giving maximum efficiency. For the cartoon, this condensed-DPG model still gives an unbiased estimate of abundance, because each DPG has one parent of given sex, and the chance of any sampled cartoon adult of that sex being that parent is 1/N. The DPG-condensed effective sample size is simply half the number of distinct parents, which would be a little larger than the effective sample sizes for the unmodified model shown in Table 3; e.g., in 2017, 504/2 = 252 versus 209. However, no such straightforward improvement is available for an adult-size-based CKMR model such as is needed for WABFT. Using condensed DPGs directly would bias the juvenile sampling against larger more-fecund adults, whose DPGs will tend on average to be larger and thus to experience disproportionate condensation. Those adults would be marked less often by the DPG-condensed juveniles than the model assumes, violating the basic requirements for unbiased CKMR in14. A more sophisticated model might be able to combine unbiasedness with higher efficiency but, since the unmodified adult-size-based POP model that we expect to use is unbiased and only mildly inefficient (at worst 209/252 = 83% efficient, in 2017) there seems no particular need for extra complications at present. However, that may not hold true if we eventually move to a POP + XHSP model, where the impact on unmodified CKMR variance is worse (though there is still no bias, for the same reason as with POPs). Intuitively, the biggest impact that a DPG of size 5 can have in a POP model is to suddenly raise the number of POPs by 5 if its parent happens to be sampled; within a useful total of, say, 75 POPs, the influence is not that large. But if two DPGs both of size 5 in different cohorts happen to share a parent, then the total of XHSPs suddenly jumps by 25— likely a substantial proportion of total XHSPs. Supplementary Material B also includes effective sample size formulae for a simplified XHSP-only model, which demonstrate the increased impact of within-cohort sibship; for our WBFT samples, it turns out that the XHSP-effective size is slightly lower for the targeted 2017 samples (110) than for the 2016 samples (130), unlike the POP-only effective size. Dropping from a maximum theoretical effective sample size of 252 (half the number of DPGs) down to 110 would be rather inefficient and would increase the number of years of sampling required to yield a useful XHSP dataset. This motivates developing a modified POP + XHSP model that retains unbiasedness without sacrificing too much efficiency. In principle, that can be done by condensing each DPG but then conditioning its comparison probabilities on the DPG’s original size, in accordance with the framework in14. This is a topic for subsequent research, and the results will inform future sampling strategy decisions for WBFT.One potential difficulty for western BFT CKMR might occur if a substantial proportion of animals reaching maturity are the offspring of “Western” (in genetic terms) adults who persistently spawn in the western North Atlantic but outside the GoM. However, as long as the adults marked by GoM larvae are well mixed at the time of sampling with any western adults that do spawn outside of the GoM, the total POP-based population estimate of genetically-western BFT from CKMR will remain unbiased. Given evidence from tagging of widespread adult movements within the western North Atlantic2, good mixing in the sampled feeding grounds seems likely; so, even if successful non-GoM western BFT spawning really is commonplace, there should not be a problem with relying on GoM larvae for at least the POP component of CKMR14.Studies of fish early life history have long been considered to have great potential to provide novel insight into the unique population dynamics of fishes44,45,46. Sampling efforts aimed at estimating fish recruitment dynamics have spawned a diversity of larval survey programs. Examples of these long-term programs include the California Cooperative Oceanic Fisheries Investigations, International Council for the Exploration of the Sea (ICES) surveys in the North Atlantic and adjacent areas, Southeast Monitoring and Assessment Program (SEAMAP) in the GoM, Ecosystem Monitoring (EcoMon) in the Northeast U.S., and numerous others, many of which provide indices of larval abundance widely used in fisheries and ecosystem assessments. Yet, as a result of the inherent patchiness of larvae42, sampling variability, and highly variable density dependent mortality45, fisheries scientists have often struggled to determine how larval surveys relate to the adult fish populations. Inclusion of estimates of sibship among larvae collected in surveys could refine estimates of adult spawning stock biomass estimated from these surveys.The results of this study also represent products of decades of work and coordination in obtaining high-quality DNA from larval specimens. Key steps to successful genotyping of larvae include ensuring that larvae are preserved, sorted, and handled in 95% non-denatured ethanol. In addition, strict instrument cleaning protocols must be followed, and stomachs should be removed or avoided (this study used larval tails and, when possible, eyes to avoid cross contamination of prey contents, including possible congeners and other BFT individuals). Exposure to hot lamps during the sorting and dissection processes should also be minimized to ensure that DNA quality is sufficiently high for genotyping-by-sequencing. Although the tissues available for genetic analysis were limited by the needs of other experiments that required BFT tissues, otoliths, gut contents, and other information from the same larvae, we were able to successfully genotype most larvae greater than 6 mm SL and identify thousands of informative SNPs. The lower size limit of larvae could likely be decreased if whole specimens were available for genotyping, although the use of younger larvae could increase the incidence of sibship.In summary, while we observed both FSPs and HSPs in larval collections, with elevated sibship overall and with siblings being more prevalent within tows and in nearby tows, the level of sibship was sufficiently low that collections of GoM BFT larvae can still provide the critical genetic mark of parental genotypes required for CKMR. Our results demonstrate a crucial proof of concept and are the first step towards an operational CKMR modelling estimate of spawning stock abundance for western BFT. More

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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

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