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Variation in microclimateMicroclimate at the tower varied across the daily sampling period with temperatures highest and relative humidity lowest around midday and the early afternoon hours, although the time of peak temperature and nadir humidity varied by height (Fig. 3a, b). Mean temperature was highest at ground level at 11:30 (30.0 °C) when it was on average 0.2 °C hotter than at 9 m, whereas at 5 m and 9 m, it was highest at 13:30 (29.7 °C and 30.3 °C, respectively). The inverse was true for mean relative humidity, which was lowest at ground level at 11:30 (83.8%) and lowest at 5 m and 9 m at 13:30 (80.1% and 76.4%, respectively). Both variables showed substantial overlap in means and standard errors across the sampled heights during the morning hours, before diverging in the afternoon. For comparison, we extracted microclimate data from the corresponding sampling period in the BG-Sentinel trap study15, which revealed clear differences in temperature and humidity at each height sampled (Fig. 3c, d). BG-Sentinel traps were often hung beneath the forest canopy where it was considerably cooler and more humid than at the treefall gap, particularly at ground level.Figure 3Variation in microclimate by height and collection method. (a) and (b) show the mean temperature (temp,°C) + / − 1 standard error (S.E.) and relative humidity (RH, %) + / − 1 standard error (S.E.) for net collections made at the tower between 10:00 and 15:00 in this study. (c) and (d) show corresponding data extracted from the BG-Sentinel trap study15.Full size imageCommunity composition of diurnally active, anthropophilic mosquitoesA total of 2146 adult mosquitoes representing seven genera and 34 species were collected using nets (Fig. 4a), of which 99.8% (2142/2146) were female and 99.7% (2140/2146) were identified to species level. Mosquito abundance was similar at ground level and 9 m but was slightly lower at 5 m, while species richness was higher at ground level (28 species), than at 5 m (18 species) and 9 m (22 species). Psorophora was the most abundant genus (1231 mosquitoes, 57.4% of the total catch), followed by Haemagogus (32.3%), and Sabethes (6.6%). The genera Limatus (1.4%), Culex (1.2%), Wyeomyia (1.0%), and Onirion ( 0.1 for both comparisons). A linear regression showed that, across all heights, lag to first approach decreased significantly as Hg. janthinomys abundance increased (DF = 1, F = 52.1, P  More

More detailed methods can be found in the supplementary material. Data from this experiment on the characterisation of the microbial community and its response to climate change has been previously published in Scanes et al.12, therefore, the present study focussed on the interaction of metabolic processes with the microbiome. We examined the links between climate change, metabolism, genotype and microbiome of the Sydney rock oyster, Saccostrea glomerata20. Nine oyster aquacultural breeding lineages (labelled as genotype-lines A–I) of S. glomerata, which are known to differ in their resilience to climate change12 were exposed to ambient and elevated temperature and PCO2 treatments. All seawater used in acclimation and experimental exposure was collected from Little Beach, Port Stephens (152°9′30.00″E, 32°42′43.03″S), filtered through canister filters to a nominal 5 µm, and stored onsite in 38,000 L polyethylene tanks as a stock of filtered seawater.Approximately 72 individual S. glomerata, from each of the nine families (A-I) were collected from intertidal leases in Cromarty Bay, Port Stephens (152° 4′0.69″E, 32°43′19.69″S). Oysters were held on private leases so a collection permit was not required. Oysters were collected in September 2019 for experiments, meaning all oysters were 22 months old when experiments began. Oysters were placed into a 2000 L fibreglass tank and maintained at 24 °C, a salinity of 35 ppt and ambient PCO2 (pH 8.18) for two weeks to acclimate to laboratory conditions. Following acclimation, oysters from each genotype-line were divided among twelve 750 L polyethylene tanks filled with 400 L of filtered seawater (5 µm) at a density of 54 oysters per tank, with each genotype-line represented by six replicate individuals. Treatments consisted of orthogonal combinations of two PCO2 concentrations (ambient [400 µatm]; elevated [1000 µatm]) and two temperature treatments (24 and 28 °C). Each combination was replicated across three tanks. Treatments were selected to represent temperatures and PCO2 concentrations predicted for 2080–2100 by the IPCC27 and reflect measured changes in estuary temperatures reported from south eastern Australia20.Once oysters were transferred to experimental tanks, the PCO2 level and temperature were steadily increased in elevated exposure tanks over one week until the experimental treatment level was reached. The elevated CO2 level was maintained using a pH negative feedback system (Aqua Medic, Aqacenta Pty Ltd, Kingsgrove, NSW, Australia; accuracy ± 0.01 pH units) bubbling food grade CO2 (BOC Australia) through a mixing chamber and into each tank, previously described in18. These PCO2 levels corresponded to a mean ambient pHNBS of (8.18 ± 0.01) and at elevated CO2 levels a mean pHNBS of (7.84 ± 0.01). Temperature was increased and then maintained using 1000 W aquarium heaters in each tank. Oysters were then exposed to their respective treatments for a further four weeks. Oysters were checked daily for mortality; no dead oysters were found in any tanks during the four-week exposure period.Haemolymph sampling for DNA extractionFollowing exposure to experimental conditions, haemolymph was taken from two replicate oysters, from each genotype-line, from each tank for microbial analysis following the methods previously described in Scanes et al.,12. This amounted to six individuals from each genotype-line, in each treatment. Each oyster was opened using an autoclave sterilised shucking knife, ensuring that the pericardial cavity was not ruptured. Excess fluid was tipped off the tissue surface and 200–300 µL of haemolymph was extracted from the pericardial cavity using a new sterile 1 mL needled syringe (Terumo Co.). Samples from two oysters were transferred to two new pre-labelled DNA/RNA free 1 mL tubes (Eppendorf Co.) and immediately frozen at − 80 °C where they were stored until DNA extraction.We used 16 s rRNA amplicon sequencing to characterise the bacterial microbiome of S. glomerata haemolymph following the methods previously described in Scanes et al.12. DNA was extracted from 216 oyster haemolymph samples (9 genotype-lines × 4 treatments × 3 replicate tanks × 2 replicate oysters per tank) using the Qiagen DNeasy Blood and Tissue Kit (Qiagen Australia, Chadstone, VIC), according to the manufacturer’s instructions. The bacterial microbiome of the oyster haemolymph was characterised with 16S rRNA amplicon sequencing, using the 341F (CCTACGGGNGGCWGCAG) and 805R (GACTACHVGGGTATCTAATCC) primer pair28 targeting the V3-V4 variable regions of the 16S rRNA gene with the following cycling conditions: 95 °C for 3 min, 25 cycles of 95 °C for 30 s, 55 °C for 30 s and 72 °C for 30 s, and a final extension at 72 °C for 5 min. Amplicons were sequenced on the Illumina Miseq platform (2 × 300 bp) following the manufacturer’s guidelines at the Ramaciotti Centre for Genomics, University of New South Wales. Raw data files in FASTQ format were deposited in NCBI Sequence Read Archive (SRA) under Bioproject number PRJNA663356.Sequence analysisRaw demultiplexed data was processed using the Quantitative Insights into Microbial Ecology (QIIME 2 version 2019.1.0) pipeline. Briefly, paired-end sequences were imported (qiime tools import), trimmed and denoised using DADA2 (version 2019.1.0). Sequences were identified at the single nucleotide threshold (Amplicon Sequence Variants; ASV) and taxonomy was assigned using the classify-sklearn QIIME 2 feature classifier against the Silva v138 database29. Sequences identified as chloroplasts or mitochondria were also removed. Cleaned data were then rarefied at 6,500 counts per sample.Physiological analysisWe measured physiological variables relating to oyster haemolymph metabolic function. These were: extracellular pH (pHe), extracellular CO2 concentrations (PCO2e) and the whole oyster metabolic rate (MR) measured as a standardised rate of oxygen consumption. Physiological measurements were taken from two oysters from each genotype-line in each tank (methods followed that of Parker et al.16,30 and Scanes et al.18). Oysters were immediately opened without rupturing the pericardial cavity. Haemolymph samples were drawn from the interstitial fluid filling the pericardial cavity chamber of an opened oyster using a sealed 1 mL needled syringe. A 0.2 mL sample was drawn carefully to avoid aeration of the haemolymph. Half of the sample was then immediately transferred to an Eppendorf tube where pHe of the sample was measured at 20 °C using a micro pH probe (Metrohm 827 biotrode). The remaining haemolymph was transferred to a gas analyser (CIBA Corning 965) to determine total CO2 (CCO2). The micro pH probe was calibrated prior to use with NBS standards at the acclimation temperature and the gas analyser was calibrated using manufacturer guidelines. Two oysters were sampled per genotype-line in each replicate tank. Partial pressure of CO2 in haemolymph (PCO2e) was calculated from the CCO2 using the modified Henderson-Hasselbalch equation according to Heisler31,32. Metabolic rate (MR) was determined using a closed respiratory system as previously described in Parker et al.16 and Scanes et al.18. Briefly, MR was measured in two oysters per genotype-line, per tank by placing oysters in a closed 500 mL glass chamber containing filtered seawater (5 µm) set at the correct treatment conditions. Oxygen concentrations were then measured within the chamber using a fibre optic dipping probe (PreSens dipping probe DP-PSt3, AS1 Ltd, Regensburg, Germany) and recorded (15 s intervals) until the oxygen concentration had been reduced by 20%, the time taken to reduce oxygen by 20% was recorded. Oysters were removed from the chambers, opened and the tissue was dried at 70 °C for 72 h. Tissue was then weighed on an electronic balance (± 0.001 g), and MR was calculated using Eq. (1):$$MR = frac{{left[ {V_{r} times Delta {text{C}}_{W} O_{2} } right]}}{{Delta t times {text{bw}}}}$$
(1)

where MR is oxygen consumption normalised to 1 g of dry tissue mass (mg O2 g−1 dry tissue mass h−1), Vr is the volume of the respiratory chamber minus the volume of the oyster (L), ΔCWO2 is the change in water oxygen concentration measured (mg O2L−1), Δt is the measuring time (h), bw is the dry tissue mass (g). Equation is modified from Parker et al.16.Data analysisIt was not possible to measure all variables in each oyster, but rather three individuals were needed to fulfil one replicate set of measurements. PCO2e and pHe could be measured in the same individual however, MR and the microbiome were measured in separate individuals. This meant that measurements were taken from 6 oysters per genotype-line, per replicate tank (each measurement replicated twice). To align physiological data with microbiome data we took a conservative approach where data from PCO2e and pHe, MR and the microbiome were randomly matched to individuals from the same genotype-line and replicate tank. This gave us the best approximation and is conservative because it increased variability compared to taking all measurements from the same individual. ANOVA was used to determine the significant (n = 210; P  More

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Study systemWe conducted our study in Caracoles Ranch, located in Doñana National Park (SW Spain 37° 04′ N, 6° 18′ W). The study area has a Mediterranean climate with mild winters and an average 50-year annual rainfall of 550–570 mm. Vegetation is dominated by annual grassland species, with no perennial species present. A subtle topographic gradient (slope 0.16%) is enough to generate vernal pools at the lower border of the ranch from winter (November–January) to spring (March–May), while upper parts do not get flooded except in exceptionally wet seasons. In our study, an extreme flooding event occurred during the growing season of 2018. A strong soil salinity–humidity gradient is structured along this topographic gradient.In September 2014, we established nine plots of 8.5 m × 8.5 m along a 1 km × 200 m area. Three of these nine plots were located in the upper part of the topographic gradient, three at the middle, and the last three at the lower part. The average distance between these three locations was 300 m and the average distance between plots within each location was 30 m (minimum distance 20 m). In addition, each plot was divided into 36 subplots of 1 m × 1 m with aisles of 0.5 m in between to allow access to subplots where measurements were taken (total of 324 subplots). This spatial design was established to parameterize population models including an intrinsic fecundity component and the effect of intra- and interspecific pairwise interactions. Specifically, the core of the observations involved measuring, for each focal individual, per germinant viable seed production as a function of the number and identity of neighbors within a radius of 7.5 cm including individuals of the same species. This radius is a standard distance used in previous studies to measure competitive interactions among annual plant species29,34, and has been validated to capture the outcome of competition interactions at larger scales (1 m²) under locally homogeneous environmental conditions35. From November 2014 to September 2019, we sampled 19 species present in the study area each year. We sampled one individual per subplot for widespread species and several individuals per subplot when species were rare (max. 324 individuals/species). This sampling design ensured that all species are balanced in terms of number of observations, and that we capture the full range of observed spatial interactions among species across the study area. Furthermore, we obtained independent estimates of seed survival and seed germination rates in 2016 (see17 for details on obtaining these rates). These 19 species belong to disparate taxonomic families and exhibit contrasted functional profiles along the growing season (Supplementary Table 1). The earliest species with small size and open flowers, such as C. fuscatum (Asteraceae), peak at beginning of the growing season (February), while late species with succulent leaves, such as S. soda (Amaranthaceae) and S. splendens (Amaranthaceae), grow during summer and peak at the end of the growing season (September-October). All these species represent up to 99% of plant cover in the study area.Estimating species interaction networks and intrinsic growth ratesWe estimated the effect of nearby individuals on individual fecundity via a Ricker model of population dynamics, which allowed us to estimate the strength of positive or negative interactions among pair of species, and therefore, to build a matrix of interactions among species. This approach has been previously applied to study annual plant systems under Mediterranean-type climates36, and it has also recently been shown to have several advantages compared to other formulations34. For example, this model implemented using a negative-binomial distribution for individual fecundities is more flexible in terms of modeling over-dispersion than a Poisson model, while maintaining predictions as positive integers. The model is of the form$${F}_{i,t}={lambda }_{i}{e}^{-({sum }^{}{alpha }_{i,j}{N}_{j,t})}$$
(1)
where ({lambda }_{i}) is the number of seeds produced by species i in the absence of interactions, ({alpha }_{i,j}) is the per capita effect of species j over species i (which can be positive or negative, thus allowing both competitive and facilitative effects), and ({N}_{j,t}) is the number of individuals of species j within 7.5 cm of the focal individual at timestep t. We fitted this model to the empirical data using Bayesian multilevel models with a negative-binomial distribution34. For model fitting, we used non-informative priors with MCMC settings of 5000 iterations (of which 2500 were warm-up) and 6 chains. The model was implemented using the brms R package37. The effect of changes in environmental conditions on species persistence can be phenomenologically evaluated by allowing models to vary in their estimates of species’ intrinsic growth rates and the reorganization of species interactions38. In our case, to evaluate the effect of environmental heterogeneity on species persistence (Question 1), we developed two complementary models. In both cases, we modeled the observed viable seed production per individual as a function of the identity and abundance of neighboring species. For the model assuming that plant species interact within a homogeneous environment across plots, we pooled together observations from the whole study area, and allowed the intercept and slope of the relationships to vary across years by including year as a random effect. Thus, the ({lambda }_{i}) and ({alpha }_{i,j}) values in Eq. 1 vary across years, but are homogeneous for the whole study area. We used the means from the obtained posterior distributions as estimates in the subsequent analyses. For the model that assumes that heterogeneous environments across space and time impact species population dynamics, we included an additional crossed random effect “plot”, thus obtaining spatially and temporally differentiated seed production in the absence of neighbors (({lambda }_{i})) and interaction coefficients (({alpha }_{i,j})). Importantly, our modeling approach does not evaluate the magnitude per se of the spatiotemporal variability in our system. It rather evaluates the response of plant species to changes in environmental conditions through their effects on vital rates and interaction coefficients (see39,40,41 for similar approaches). Likewise, this approach does not model the spatial dynamics of the community or spatially explicit mechanisms such as dispersal, but rather uses observed spatially explicit associations of individuals to infer their vital rates and interaction coefficients. In the following, we refer to the two developed models as “homogeneous parameterization” and “heterogeneous parameterization”, respectively (Fig. 1).The statistical methodology generates a posterior distribution of estimates for each parameter inferred, i.e., for each intrinsic fecundity rate (({lambda }_{i})) and interaction coefficient (({alpha }_{i,j})). These means, by definition, do not capture the full variability obtained with the statistical model, and may potentially be biased, especially for species pairs that have comparatively few observations. To ensure that our results were not biased by using the posterior mean as a fixed value in subsequent analyses, we replicated our analyses using random samples from the posterior distributions instead of the mean values. We generated 100 random draws from each parameterization and compared the obtained curves to the ones derived from the posterior means (Supplementary Note 1 and Supplementary Fig. 3).Finally, we assume that the study system presents a rich soil seed bank but we do not explicitly model its direct influence on driving the spatial pattern of species interactions or intrinsic vital rates: rather we use fixed field estimates of seed survival and germination rates in our modeling framework (see section “Analyzing species persistence”). This assumption implies that we cannot evaluate the contribution of a spatially or temporally varying seed bank to the shape of CARs and SARs, which remains an open question for future studies.Analyzing species persistenceTo analyze which species are predicted to persist and coexist with others in our system, we built communities based on the species’ spatial location. At the smallest spatial scale, given a community of S species observed in the field in a given plot and a given year, we calculated the persistence of each species within every community combination, from 2 species to S. Thus, we obtained for each species, plot, and year, two estimates of persistence, one from the homogeneous and another from the heterogeneous parameterization. To scale-up our predictions of species persistence at increasingly large areas, we aggregated species composition and persistence patterns from increasing numbers of plots. We consistently evaluated species persistence using a structuralist approach because prior work has shown it is compatible with the model used to estimate interaction coefficients (Eq. 1)14. Specifically, for a given community we first used the strength of sign of intra- and interspecific interactions to compute its feasibility domain (note that the structuralist approach can accommodate different signs in the interaction coefficients). Broadly speaking the feasibility domain is the structural analog of niche differences, and it represents the possible range of intrinsic species growth rates compatible with the persistence of individual species and of the entire community14. Indeed, the larger the feasibility domain, the larger the likelihood of species to persist. Yet, computing the feasibility domain does not tell us which species can persist. To obtain such information, we need to check whether the vector containing the observed differences in intrinsic growth rates between species fits within the limits of the feasibility domain. If so, then all species are predicted to coexist. If not, then one or more particular species is predicted to be excluded (see14 for a graphical representation).In order to quantify the feasibility of ecological communities, the intrinsic growth rates and interaction coefficients must be formulated according to a linear Lotka-Volterra model, or an equivalent formulation14. We transformed the parameters obtained from Eq. 1 to an equivalent Lotka-Volterra formulation with the following expression (Supplementary Note 2):$${r}_{i}={log}left(frac{1-(1-{g}_{i}){s}_{i}}{{g}_{i}}right)+{lambda }_{i}$$
(2)
where ({g}_{i}) is the seed germination rate of species i and ({s}_{i}) is its seed survival rate. Thus, we quantified the feasibility of our communities using the ri intrinsic growth rates from Eq. 2 and the ({alpha }_{i,j}) coefficients, which are not modified. For our main analyses, we used empirical estimates of seed survival and germination rates. We further explored the influence of these vital rates in the transformed intrinsic growth rates in Supplementary Note 2 (see also Supplementary Fig. 4 and Supplementary Table 4).The structuralist methodology further allowed us to dissect which specific configuration of species interactions is behind species persistence in our system (Question 2), among three possibilities: first, a given species may be able to persist by itself, and hinder the long-term persistence of neighboring species (category dominant). Second, pairs of species may be able to coexist through direct interactions (category coexistence of species pairs). The classic example of two-species coexistence is when the stabilizing effect of niche differences that arise because intraspecific competition exceeds interspecific competition overcome fitness differences41. Lastly, species may only be able to coexist in more complex communities (category multispecies coexistence)23, thanks to the effect of indirect interactions on increasing the feasible domain of the community14. A classic example of multispecies coexistence is a rock–paper–scissors configuration in which the three species coexist because no species is best at competing for all resources24,42. Because species may be predicted to persist under different configurations in a given community, we assigned their persistence category to the simplest community configuration. For instance, if we predicted that a three-species combination coexists as well as each of the three pairs separately, we assigned these species to the coexistence of pairs category26,43. Finally, if a species is not predicted to persist but it is observed in the system, we classify it as naturally transient, that is, it will tend to become locally extinct no matter what its surrounding community. In order to ascertain our classification of species as transient, we further analyzed whether these species shared ecological traits known to be common to transient species. In particular, a pervasive characteristic of transient species is their comparatively small population sizes. We explored the relationship between our classification as transient and species abundance through a logistic regression with logit link (supplementary Table 3).In addition to our main analyses, based on the structuralist approach, we explored the local stability44 of the observed communities, which evaluates their asymptotic response to infinitesimal perturbations, and thus provides a complementary view to the potential coexistence of the system (Supplementary Note 3).Species–area and coexistence–area relationshipsTo answer Question 3, we obtained standard SARs for each year, by calculating the average diversity observed when moving from 1 plot (72 m²) to 9 plots (650 m²) of our system. In the classification of SAR types proposed by Scheiner et al.45, the curves from our system are thus of type III-B, i.e., plots in a non-contiguous grid, with diversity values obtained using averages from all possible combinations of plots. Likewise, the yearly CARs were built taking the average number of coexisting species in each combination from 1 to 9 plots. In this case, a species was taken to persist in a given area if it was persisting alone or if it was part of at least one coexisting community within that area. We obtained CARs for the two parameterizations, i.e., assuming homogeneous interaction coefficients and individual fecundity throughout the study area, or explicitly including spatial variability in these terms. We fitted the CARs from Fig. 2 to power-law functions and obtained their associated parameters (Supplementary Table 2) using the mmSAR v1.0 package in R46. In the last step of the analyses, to evaluate the role of species identity in driving these empirical fits of CARs, we compared them to two complementary null models that reshuffle the strengths of per capita interactions between species pairs across the interaction matrix. In particular, as baseline we took the CARs from the homogeneous parameterization, in order to have a unique interaction strength value per species pair. In the first null model, and taking the inferred interaction matrix from a given plot and year, we redistributed the pairwise interaction coefficients randomly. That is, we fixed the number of species observed in a certain plot and year, as well as the structure of the interaction matrix, but randomized the magnitude of observed pairwise interactions (both intra and interspecific interactions) in that community. The second null model is similar, but keeping the diagonal coefficients of the interaction matrix, i.e., the intraspecific terms, fixed. While the first null model accounted for the effect of interspecific competitive responses, as well as self-limiting processes on driving CARs, the second null maintained self-limiting processes fixed by avoiding changes in the diagonal elements of our interaction matrices. We ran 100 replicates of each model for each year, and obtained the average CARs across replicates. All analyses were carried out in R v3.6.3, using packages tidyverse47 v.1.3.1 for data manipulation and visualization, and foreach48 v1.5.1 and doParallel49 v1.0.16 for parallelizing computationally intensive calculations.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More