Warming Drives Ecological Community Changes Linked to Host-Associated Microbiome Dysbiosis

Articles https://doi.org/10.1038/s41558-020-0899-5

Warming drives ecological community changes linked to host-associated microbiome dysbiosis

Sasha E. Greenspan 1 ✉ , Gustavo H. Migliorini 2, Mariana L. Lyra 3, Mariana R. Pontes 4,5, Tamilie Carvalho 4,5, Luisa P. Ribeiro 4,5, Diego Moura-Campos 4,5, Célio F. B. Haddad3, Luís Felipe Toledo 4, Gustavo Q. Romero 6 and C. Guilherme Becker1

Anthropogenic climate warming affects many biological systems, ranging in scale from microbiomes to biomes. In many animals, warming-related fitness depression appears more closely linked to changes in ecological community interactions than to direct thermal stress. This biotic community framework is commonly applied to warming studies at the scale of ecosystems but is rarely applied at the scale of microbiomes. Here, we used replicated bromeliad microecosystems to show warming effects on tadpole gut microbiome dysbiosis mediated through biotic community interactions. Warming shifted environmental bacteria and arthropod community composition, with linkages to changes in microbial recruitment that promoted dysbiosis and stunted tadpole growth. Tadpole growth was more strongly associated with cascading effects of warming on gut dysbiosis than with direct warming effects or indirect effects on food resources. These results suggest that assessing warming effects on animal health requires an ecological community perspective on microbiome structure and function.

limate warming is a global-scale threat to animal health and optimal richness and composition for pathogen defence and lower fitness1, predominantly through changes in ecological com- indices of dysbiosis, referring to disruption to the normal symbiotic munity interactions2. For instance, warming-mediated shifts host–microbe relationships in the microbiome24. This work sug- C 3 4,5 6,7 in phenology , habitat suitability , food webs and pathogen pres- gests that the functioning of host microbiomes depends on healthy sure8 may interfere with nutrition, reproduction, competitive ability, ecosystem dynamics. Thus, ecosystem disturbances such as climate dispersal and disease defences. Thus, using an ecological commu- warming have the potential to disrupt the community interactions nity framework to investigate effects of climate warming on animal that contribute to microbiome integrity and host fitness. health may reveal important trends that are undetectable outside of We investigated warming effects on amphibian gut bacterial this community context. microbiome dysbiosis mediated through biotic community interac- The host microbiome is essential to many aspects of animal health, tions, a pathway that has received remarkably little attention from including digestion, development, immunity and stress responses9. ecologists. Specifically, we used naturally colonized tank bromeli- Most previous studies investigating warming effects on the micro- ads as model microecosystems (Fig. 1a) to test the hypothesis that biome were not designed to disentangle ecological community fac- warming could influence host fitness through a cascade of effects in tors driving temperature-mediated microbiome shifts10–12, instead which (1) warming influences community assembly of arthropods primarily exploring direct temperature responses of host-associated and/or environmental bacteria, (2) warming-mediated changes bacteria12–16 or indirect responses mediated through host physiol- in the biotic community increase host dysbiosis and (3) dysbiosis ogy17. However, warming effects on biotic community interactions reduces host fitness. To quantify microbiome dysbiosis, we used may be important factors predicting host-associated microbiome the metric known as bacterial dispersion. Dysbiosis is defined as integrity in real-world ecosystems. For instance, warming may alter perturbation of the microbiome that disrupts the symbiotic host– environmental bacterial community composition18,19 or arthropod microbe relationship25. In many cases, dysbiosis is a stochastic pro- community structure, or increase arthropod reproductive rates or cess, leading to increased microbial variability among hosts26. Thus, metabolism, with altered feeding rates leading to changes in the dispersion, a measure of microbial variability, is routinely used as a pool of environmental bacteria20,21 and, subsequently, recruitment measure of dysbiosis26–28. to the microbiome. We placed nursery-raised tank bromeliads in a tropical for- Recent work has begun to resolve how microbiome structure and est in Brazil and used heaters to manipulate water temperature in function are influenced by ecological community interactions19,22–24. bromeliad tanks, with half of the bromeliads exposed to ambient For example, interactions among naturalistic arthropod and bacte- temperatures and the other half warmed up to 6 °C above ambient ria communities within bromeliad tanks had a conditioning effect temperatures, on the basis of predicted climate change scenarios on the environmental bacterial community, enhancing the pool of for 2100 (Fig. 1a,b)29. Bromeliads were then colonized by natural pathogen-inhibiting bacteria and reducing host colonization by communities of arthropods and bacteria for 70 d (hereafter ‘com- transient taxa24. As a result, tadpole microbiome assemblages had munity assembly phase’). We then transferred the bromeliads to a

1Department of Biological Sciences, The University of Alabama, Tuscaloosa, AL, USA. 2Programa de Pós-graduação em Biologia Animal, Universidade Estadual Paulista ‘Júlio de Mesquita Filho’, São José do Rio Preto, Brazil. 3Department of Biodiversity and Aquaculture Center (CAUNESP), Universidade Estadual Paulista, Rio Claro, Brazil. 4Laboratório de História Natural de Anfíbios Brasileiros (LaHNAB), Departamento de Biologia Animal, Universidade Estadual de Campinas, Campinas, Brazil. 5Programa de Pós-Graduação em Ecologia, Instituto de Biologia, Universidade Estadual de Campinas, Campinas, Brazil. 6Departamento de Biologia Animal, Universidade Estadual de Campinas, Campinas, Brazil. ✉e-mail: [email protected]

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a c ↑ Fast growth rate Slow growth rate ↓

Tadpole gut bacterial dispersion

Temperature sensor

Tadpole Heater

T °C 1 3 Temperature controller Aedes 2

Cool Warm Bacteria Tank bromeliad

n = 5 n = 5 n = 5 n = 5 n = 5 n = 5

Ambient → ambient Ambient → ambient Ambient → ambient Ambient → +4 °C +4 °C → ambient +4 °C → +4 °C

n = 5 n = 5 n = 5 n = 5 n = 5 n = 5 Ambient → +2 °C +2 °C → ambient +2 °C → +2 °C Ambient → +6 °C +6 °C → ambient +6 °C → +6 °C

Community assembly phase → laboratory phase

Fig. 1 | Bromeliad set-up and main experimental findings. a, Bromeliad heating system and focal components of each bromeliad microecosystem. b, Temperature treatments for 60 bromeliads distributed in five replicate spatial blocks. Each bromeliad is divided in half, representing the temperature treatment during the community assembly phase (left) and laboratory phase (right) of the experiment. c, Conceptual diagram illustrating three biotic pathways through which warming influenced tadpole gut bacterial dispersion and growth, with lower bacterial dispersion depicted as an ordered colour spectrum and higher bacterial dispersion depicted as scrambled colours.

laboratory (hereafter ‘laboratory phase’) to reduce daily tempera- Warming shifted community assembly of environmental ture fluctuations, added omnivorous but primarily algae-eating bacteria larvae (tadpoles) of the bromeliad-specialist frog species Ololygon Warming-mediated shifts in unweighted UniFrac community com- perpusilla (Lutz & Lutz, 1939) as model hosts30 and switched half position of environmental bacteria were associated with higher of the bromeliads to the opposite temperature treatment (ambient Jaccard dispersion of tadpole gut bacteria (Figs. 1c (red pathway no. to warmed or vice versa; Fig. 1b). Our fully crossed experimen- 1) and 2, Supplementary Fig. 1 and Supplementary Tables 1a,b, 2a,b, tal design allowed us to disentangle (1) indirect effects of warm- 3a,b and 4). We detected 313 amplicon sequence variants (ASVs) ing on host dysbiosis and fitness, specifically through natural, that were unique to the environmental bacterial community (domi- temperature-mediated community assembly, from (2) direct effects nated by order Burkholderiales within phylum Proteobacteria), 277 of warming on host dysbiosis, algal food production and growth. ASVs that were unique to tadpole gut samples (dominated by order Piecewise structural equation models (SEMs) revealed cascad- Clostridiales within phylum Fermicutes) and 388 ASVs that were ing effects of warming on tadpole microbiome dysbiosis through shared between these two groups (dominated by diverse orders several distinct, non-mutually exclusive biotic pathways and linear within phylum Proteobacteria). Seven ASVs were detected in at least mixed-effects models (LME) supported the trends in our SEMs. By 90% of tadpole gut samples, including three ASVs from the animal testing for links among temperature, biotic community structure, gut-associated families Ruminococcaceae and Veillonellaceae (order dysbiosis and host fitness, our study sheds new light on the impacts Clostridiales), three ASVs from the environment-associated fami- of global change on animal health and ecosystem integrity. lies Methylocystaceae (order Rhizobiales) and Microbacteriaceae

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a b 200 200 Tadpole Tadpole growth growth 75 75 Chlorophyll a Chlorophyll a Growth (mg) Growth (mg) –50 concentration –50 concentration 0.4 0.6 0.8 –0.3758 0.4 0.6 0.8 –0.3758 Jaccard dispersion Jaccard dispersion Tadpole gut Tadpole gut 0.8 bacterial 0.8 bacterial dispersion dispersion 0.6 0.6

0.4 –0.3667 –0.3865 Jaccard dispersion –0.3 0 0.3 0.4 Jaccard dispersion –0.3 0 0.3 UniFrac PC1 Environmental Environmental bacteria UniFrac PC1 bacteria community community 0.3332 composition composition 0.2930

0.2879 –0.3964 –0.2942 –0.1802 0.3 0.3 Community Community Laboratory 0 Laboratory assembly 0 assembly temperature –0.3 temperature

temperature UniFrac PC1 temperature UniFrac PC1 –0.3 –0.6 20 26 32 0 20 40 Assembly 0.5613 0.8 Aedes 0.1788 temperature (°C) 0.4 40 abundance 40 0.6 –0.2197 0 20 20 0.4 Jaccard dispersion

–0.4 0 0.4 0 Aedes abundance 0

composition PC1 –0.4 Arthropod Aedes abundance Jaccard arthropod 20 26 32 18 22 26 20 26 32 community Jaccard arthropod Aedes abundance Temperature (°C) Temperature (°C) composition composition PC1 Temperature (°C)

Fig. 2 | Piecewise SEMs. SEMs assessed direct and indirect associations between temperature, arthropod community composition, environmental bacteria community composition, tadpole gut bacterial dispersion, chlorophyll a concentration (a proxy for algal food availability) and tadpole growth. a,b, We characterized arthropod assemblages at the community level (a) and individual species (A. albopictus) level (b). Numbers are standardized coefficients. Unsupported paths shown in grey were removed to improve model fit. Scatterplots depict key associations. PC1, principal coordinate 1.

(order Actinomycetales) and one ASV from the genus Desulfovibrio contained higher abundances of an ASV from the genus Zoogloea (order Desulfovibrionales), containing members isolated from both (order Rhodocyclales; Supplementary Table 5). gut and environmental sources. Warming shifted community assembly of arthropods Warming shifted mosquito–bacteria interactions Warming-mediated shifts in Jaccard community composition Warming was a positive predictor of larval Aedes mosquito abun- of arthropods (see Supplementary Table 6) were associated with dance (Figs. 1c (orange pathway no. 2) and 2b and Supplementary higher Jaccard dispersion of tadpole gut bacteria (Figs. 1c (pink Table 1b). Aedes abundance was most strongly predicted by the pathway no. 3) and 2a and Supplementary Tables 1a, 2d and 3e). interaction between community assembly temperature and Community-level arthropod composition was not a well-supported laboratory temperature (Supplementary Tables 2c and 3c). predictor of environmental bacterial community composition Specifically, bromeliads that were warmer in the community (Fig. 2a and Supplementary Table 2a). Arthropod community struc- assembly phase contained more mosquito larvae. In contrast, ture did not directly predict tadpole growth, indicating weak effects bromeliads that were warmer in the laboratory phase contained of arthropods as predators or prey of tadpoles (Fig. 2). Tadpole gut fewer mosquito larvae but this negative relationship was stron- bacterial dispersion was consistently a negative predictor of tadpole ger for bromeliads exposed to higher community assembly tem- growth (Figs. 1c and 2 and Supplementary Table 1a,b). Laboratory peratures. In turn, higher abundances of Aedes predicted shifts temperature and chlorophyll a concentration (a proxy for algal food in unweighted UniFrac community composition of environmen- availability) were not well-supported predictors of tadpole gut bac- tal bacteria that were associated with higher Jaccard dispersion terial dispersion or growth (Fig. 2). of tadpole gut bacteria (Figs. 1c (orange pathway no. 2) and 2b and Supplementary Tables 1b, 2b and 3d). LEfSe (linear discrimi- Discussion nant analysis effect size method) revealed that bromeliads carry- Changing ecological community interactions appear to be the ing relatively high numbers of mosquito larvae contained higher strongest drivers of diminishing species fitness parameters under abundances of two unidentified ASVs from the widely occurring global change2. We used tank bromeliads as model microecosystems orders of environmental bacteria classified as Cytophagales and to test for cascading effects of warming on microbiome integrity Xanthomonadales (Supplementary Table 5). In contrast, brome- through shifts in ecological community structure. Warming shifted liads carrying no mosquito larvae at the end of the experiment aquatic bacteria and arthropod communities with linkages to

NatUre ClImate Change | www.nature.com/natureclimatechange Articles NATure ClImATe CHAnge dysbiosis in the microbiome of O. perpusilla tadpoles and stunted bacteria, fungi and protozoans38. Our results suggest that increased tadpole growth, potentially from loss or inhibition of key functional mosquito development under warming increased rates of Zoogloea groups of bacteria linked to physiological processes. Physiological consumption by larval mosquitos. Alternatively, increased mosquito rates including growth rates strongly depend on temperature in development could have altered water chemistry parameters that amphibians and other ectotherms31. In our study, however, tadpole subsequently inhibited growth of Zoogloea. Reduced abundances growth was strongly influenced by cascading effects of warming on of these bacteria could weaken ecosystem functions such as waste gut bacterial dysbiosis but was not detectably influenced directly by processing and sequestration of waste-associated microorganisms37, laboratory temperature or indirectly by increased growth of algal leaving tadpoles vulnerable to gut dysbiosis through exposure to food resources or altered predator–prey interactions at warmer opportunistic bacteria from the environment. Our previous work temperatures. Moreover, accounting for direct temperature effects with experimental bromeliad microecosystems demonstrated that in the laboratory allowed us to rule out acclimation stress as a interactions between a few dominant arthropod species and envi- potential driver of tadpole fitness differences between temperature ronmental bacteria promoted growth of antimicrobial bacteria taxa treatments. Our results highlight the importance of community in the aquatic environment and reduced host colonization by tran- context in assessing effects of warming on ecosystems and species. sient bacterial taxa24. Our current study reveals that warming may One pathway by which warming was linked to increased tadpole disrupt these interactions between dominant arthropod species and gut bacterial dispersion was through changes in the species com- aquatic bacteria that are critical in conditioning the environmental position of environmental bacterial communities within bromeliad bacterial pool for recruitment of a healthy tadpole microbiome. tanks (see red pathway no. 1 in Fig. 1c). This result is consistent We also detected effects of warming on tadpole gut bacterial with previous findings that environmental bacterial communities dispersion through changes in arthropod community composi- shift in response to warming18,20. There is abundant research show- tion, without direct evidence that warming-mediated changes in ing that warming effects on environmental bacterial communi- the arthropod community altered environmental bacterial commu- ties have downstream effects on biogeochemical cycles18,20 but our nity composition (see pink pathway no. 3 in Fig. 1c). Arthropods in results suggest additional direct links to vertebrate health. Shifts in aquatic ecosystems are known to facilitate bacterial growth through bacterial community composition in response to temperature typi- nutrient (faeces) input and fragmentation of detritus, a relationship cally stem from certain taxa disappearing and new species becom- that may optimize aquatic ecosystem functioning including nitro- ing established32. While tadpoles of O. perpusilla primarily feed on gen flux and decomposition20. However, warming may increase algae, they also consume other microorganisms, presumably includ- feeding rates of arthropods on bacteria, reducing bacterial den- ing bacteria, in proportion to their occurrence in the environment30. sity and thereby weakening the facilitative interactions between Thus, turnover of environmental bacterial taxa may alter propor- macroinvertebrates and bacteria that promote ecosystem functions of mutualistic, commensal and pathogenic taxa, creating tioning, including within bromeliad and pitcher plant microeco- new interactions in the host-associated bacterial community that systems20,39,40. Another ecosystem function that could be dependent offset the balance of the microbiome. Temperature may also alter on interactions between arthropods and bacteria is maintenance of the functional potential of bacterial taxa in the microbiome14 or high densities of bacteria that promote microbiome stability. In our the likelihood that hosts will recruit certain taxa to fulfil particular study, warming-mediated changes in community-level arthropod physiological functions10 but our experimental design allowed us to composition may have increased consumption of bacteria, reduc- specifically pinpoint strong and detrimental temperature effects on ing bacterial densities without detectably altering environmental the microbiome mediated through microbial reservoirs in the envi- bacterial community composition. In turn, lower environmental ronment. Bacterial communities are expected to show some natural bacterial densities could increase stochasticity in recruitment to the variability in response to the myriad biotic and abiotic fluctuations microbiome, increasing bacterial dispersion. of daily and annual cycles33,34. For example, seasonal temperature Microbiome dysbiosis is associated with increased susceptibility shifts, which sometimes included seasonal warming, were associ- to disease19,25. Thus, one implication of our study is that dysbiosis ated with changes in microbiome richness hypothesized to help stemming from altered community interactions under warming protect frogs from pathogenic microbes10,11,35. However, our results may reduce host resistance to pathogens. Climate warming and suggest that community assembly under more extreme warming emerging pathogens are two of the greatest contemporary threats to events may lead to disturbances in the microbiome. wildlife populations41. These stressors may interact synergistically Warming also increased rates of colonization by a dominant through several pathways, resulting in changes to pathogen viru- filter-feeding arthropod species, with apparent cascading effects lence or changes in host immune function that may threaten the on environmental bacterial community composition, tadpole gut stability of animal populations. For example, infection with the fun- bacterial dispersion and tadpole growth (see orange pathway no. 2 gus Batrachochytrium dendrobatidis lowered heat tolerance in frog in Fig. 1c). Warm community assembly temperatures followed by hosts, suggesting that infectious diseases could increase host vul- cool laboratory temperatures yielded relatively high abundances nerability to climate warming42,43. Likewise, frogs were particularly of Aedes mosquito larvae. In contrast, warm community assembly susceptible to B. dendrobatidis following temperature drops in the temperatures followed by warm laboratory temperatures yielded wake of short-term heat waves predicted under climate change sce- relatively low abundances of mosquito larvae. This pattern sug- narios44. Therefore, climate variability associated with global change gests that warming facilitated efficient mosquito reproduction may exacerbate the effects of wildlife diseases44–46. By disentangling with high rates of egg hatching in the field accompanied by high specific biotic pathways linking warming to dysbiosis, our results rates of adult emergence in the laboratory. This warming-mediated help to paint a more complete picture of the interactions between increase in aspects of the mosquito reproductive cycle (for example, climate change and disease that may radiate through many levels of egg hatching and larval development) drove changes in environ- ecological organization. mental bacterial community composition, which most prominently Of the limited studies focused on proximate causes of localized included decreases in the bacterial genus Zoogloea, with down- climate-linked extinctions, few suggest direct links between extinc- stream increases in tadpole gut bacterial dispersion and decreased tion and thermal stress2. Rather, changing biotic interactions are tadpole growth. Zoogloea is commonly found in freshwater habitats predicted to be the most common proximate cause of climate-linked and is known for its role in water purification through formation of extinction and have been deemed an urgent research priority given a gelatinous matrix that helps separate organic pollutants from puri- the relative scarcity of this type of study1,2. Our research conducted fied water36,37. Aedes larvae filter-feed on microorganisms including with naturally colonized, replicated bromeliad microecosystems

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Methods analysis. We measured tadpole body mass on day 0 (day that bromeliads were Model system. As naturally occurring and replicable microecosystems, transferred from the forested site to the laboratory) and day 13 and estimated tank bromeliads are ideal model systems for ecological and thermal biology tadpole growth by subtracting body mass on day 0 from body mass on day 13. experiments. Tese relatively small water sources, formed by tightly overlapping By the end of the laboratory phase, tadpole development ranged from Gosner’s leaf rosettes, can support fully functional microecosystems comprised of rich biotic stages 25 to 39, stages characterized by consistent feeding behaviours and body 30,56 communities linked through complex food webs47–50. Among the most dominant proportions, preceding breakdown of the larval mouthparts and tail resorption . components of bromeliad food webs are abundant communities of bacteria51 We sampled environmental bacterial communities in each bromeliad on days as well as worms, leeches and arthropods (for example, crustaceans, aquatic 0 and 13 by swabbing the submerged, inner leaf surfaces with a sterile swab. We hemipterans, and larval and adult insects) from diverse feeding groups including focused our environmental bacteria sampling on this leaf biofilm because tadpoles herbivores, detritivores, flter-feeders and predators52. Bromeliads also function as were consistently observed in contact with leaf surfaces rather than swimming in obligate and refugial habitats for many amphibian species53,54. the water column. Swabs and tadpole intestines were stored at −20 °C until further processing. We also measured chlorophyll a concentration in each bromeliad Experimental procedures. We obtained 60 potted Alcantarea imperialis tank on day 13 as a proxy for abundance of algal food resources available for tadpoles bromeliads of similar age and size from a nursery in Campinas, São Paulo, Brazil. (Aquafluore, Turner Designs). To remove invertebrates and detritus, we rinsed and air-dried each bromeliad. Our On day 13, we collected all arthropods in each bromeliad and preserved focal vertebrate ectotherm species for the experiment was O. perpusilla, the most arthropod samples in ethanol for identification and quantification. We identified abundant bromeliad-breeding amphibian species in Brazil’s Atlantic Forest55. As all arthropods to taxonomic family or genus. We used two different metrics to larvae, this omnivorous frog species has a highly generalist diet containing diverse characterize the arthropod assemblages. First, we calculated a community-level flora and fauna in proportion to their occurrence in the environment, with algae metric of arthropod assemblages. Specifically, we used presence/absence data comprising the dominant food item30. We collected tadpoles at Gosner’s stage 25 for each arthropod species observed in each bromeliad to calculate Jaccard from bromeliads in the municipality of São Luiz do Paraitinga, São Paulo, Brazil, distance between samples using the vegdist function in the vegan package in 57,58 over 2 d before the start of the laboratory phase of the experiment56. Program R . We then performed a principal coordinates analysis on the Jaccard 58 Before the laboratory phase, our goal was to facilitate natural assembly of distance matrix using the cmdscale function in the stats package in Program R . microorganism and arthropod communities in bromeliad tanks under a warming Finally, we extracted values from the first principal coordinate axis as a measure gradient (community assembly phase). For 70 d (September to December) of qualitative community-wide arthropod composition. The first principal leading up to the laboratory phase, we kept the bromeliads in a forest fragment coordinate axis explained 18.19% of the variation among samples. As a second in Campinas, São Paulo, Brazil (−22.8316°, −46.9651°), containing vegetation metric, we characterized arthropod assemblages using abundance of a dominant representative of Brazil’s Atlantic Forest and Cerrado. We manipulated water taxon, on the basis of our previous work demonstrating a strong influence of the temperature in each bromeliad tank with three heaters (1 W each; 110 V), most abundant arthropod taxa on environmental bacteria and downstream host 24 each connected to a controller (Delta) and a temperature sensor (Thermopar). fitness variables . For this metric, we used abundance of the mosquito larva A. Controllers were connected to a human machine interface (HMI, Delta) through albopictus (filter-feeder), the most common mosquito found in the bromeliads which we programmed, monitored and extracted bromeliad tank temperatures. (mean ± s.d. = 5 ± 8; maximum = 44) and the most dominant taxon by biomass. Heaters were submerged in the bromeliad tanks and adjusted temperatures in real time on the basis of programmed target temperatures. The HMI recorded water Bacterial sequencing and bioinformatics. We used 16S ribosomal RNA gene temperature in each bromeliad tank every 30 min. amplicon sequencing to characterize bacterial communities in bromeliads and For the community assembly phase, half of the bromeliads (n = 30) were tadpole intestines. To extract bacterial DNA from swab and gut samples, we used assigned to an ambient temperature treatment (no temperature manipulation) and the DNeasy Blood and Tissue Kit (Qiagen), with minor modifications to the the other half (n = 30) were assigned to the warming gradient. The target warming manufacturer’s protocol to increase DNA yield, including increasing the incubation treatments were 2, 4 and 6 °C above ambient temperature (Supplementary Table 7). time in the lysis stage to overnight. To amplify and sequence bacterial DNA, we We selected warming treatments with the aim of producing a warming gradient followed the Earth Microbiome Project 16S Illumina Amplicon Protocol, which relevant to climate change scenarios for 2100 predicted by the Intergovernmental targets the V4 region of the 16S rRNA gene using universal primers 515 F and Panel on Climate Change29. The bromeliads were distributed among five replicate 806 R, using a dual index approach59–61. We amplified samples in duplicate using spatial blocks, each containing six bromeliads exposed to ambient temperatures Phire Hot Start II DNA Polymerase (Finnzyme) and combined duplicate PCR and six bromeliads exposed to warming, with two bromeliads per target warming products. We visualized amplicons in 1% agarose gel to confirm consistent gel temperature (Fig. 1b). The placement of each bromeliad within each block was band strength and then pooled samples in equal volumes to generate the amplicon determined randomly. Each block was controlled by a separate temperature library. We purified the library using the QIAquick Gel Extraction Kit (Qiagen) controller. All bromeliads received natural rainfall and similar falling litter from and measured DNA concentration of the purified sample using the Qubit 2.0 a homogenous tree canopy comprised of the species Leucaena leucocephala. We fluorometer with the dsDNA Broad-Range Assay Kit (ThermoFisher Scientific). monitored bromeliads daily and did not observe any vertebrates in bromeliad tanks The library was sequenced using an Illumina MiSeq (2 × 250 base pairs, bp) during the community assembly phase. sequencer at the Tufts University Core Facility (TUCF Genomics). For the laboratory phase, we transferred the bromeliads from the forested We used QIIME 2 2019.1 (ref. 62) for initial processing of bacterial sequences, site to a temperature-controlled laboratory, added one tadpole to each bromeliad using only forward reads trimmed to 150 bp (ref. 63). We used the q-score plugin tank (haphazardly assigned to achieve similar average tadpole mass and genetic to filter low-quality reads. We used the deblur workflow64 to cluster sequences variability across treatments) and manipulated water temperature in bromeliad into ASVs. We aligned sequences (alignment plugin) with MAFFT65 and tanks using the same heating system and the same target warming gradient (Fig. 1b constructed a phylogenetic tree (phylogeny plugin) using FastTree with QIIME and Supplementary Table 7). The aim for the laboratory temperature manipulation default parameters66. We assigned taxonomy (feature-classifier plugin) with the was to test for indirect effects of warming on tadpole dysbiosis through changes in classify‐sklearn naive Bayes taxonomy classifier and the Greengenes 13.8 reference aquatic community composition while accounting for direct effects of warming on sequence database67–69. We discarded sequences identified as chloroplasts or tadpole fitness. Thus, within each of the five replicate spatial blocks, half (n = 6) of mitochondria and ASVs with fewer than 0.005% (482 sequence reads) of the the bromeliads were exposed to temperature treatments matching the community 9,607,077 total sequence reads in the dataset70. To standardize read counts across assembly phase (ambient to ambient or warming to warming) and half (n = 6) were samples, we rarefied samples to 2,800 reads (Supplementary Figure 2). None of switched to the other treatment (ambient to warming or warming to ambient) for the PCR negative controls (reactions without template DNA) was retained after the laboratory phase (Fig. 1b). Thus, each block contained bromeliads exposed to filtering and rarefaction. Final sample sizes are provided in Supplementary Table 8. ambient temperatures during both phases of the study (n = 3), warming during For analyses of alpha diversity, we calculated ASV richness, Faith’s phylogenetic both phases of the study (n = 3), ambient temperatures during the community diversity71 and Shannon diversity for each environmental bacteria and tadpole gut assembly phase and warming during the laboratory phase (n = 3) and warming sample. We also identified core ASVs from tadpole gut samples, defined as the set during the community assembly phase and ambient temperatures during the of ASVs detected in at least 90% of samples. For analyses of bacterial community laboratory phase (n = 3; Fig. 1b). composition, we used QIIME 2 to perform principal coordinates analyses on For each phase of the experiment (community assembly phase and laboratory unweighted and weighted UniFrac72, Jaccard and Bray–Curtis distances between phase), we calculated the average daily water temperature in each bromeliad samples. For each distance metric, we then extracted the position of each sample tank and used the mean of the daily averages for each bromeliad as a continuous on the first principal coordinate axis as a measure of community composition. For estimate of temperature in statistical analyses (Supplementary Table 7). The aquatic environmental bacteria samples collected at the end of the 70-d community average of mean daily temperature variance during the community assembly phase assembly phase (day 0), the first principal coordinate axis explained 14.93% was 6 °C across temperature treatments. The average of mean daily temperature (unweighted UniFrac), 48.06% (weighted UniFrac), 9.53% (Jaccard) and 10.02% variance during the laboratory phase was ≤0.5 °C. Each day throughout both (Bray–Curtis) of the variation among samples. For aquatic environmental bacteria phases of the study, we used mineral water to replace water in bromeliad tanks lost samples collected at the end of the laboratory phase (day 13), the first principal from evaporation. coordinate axis explained 11.56% (unweighted UniFrac) and 7.14% (Jaccard) of After the 13-d laboratory phase, we euthanized tadpoles by decapitating and the variation among samples. In addition, we used Jaccard distances between pithing and dissected the intestines from each tadpole for gut bacterial community samples to calculate bacterial dispersion for each tadpole gut sample as an index of

NatUre ClImate Change | www.nature.com/natureclimatechange NATure ClImATe CHAnge Articles dysbiosis (betadisper function in vegan package in Program R)57,58. Dispersion is platform (https://huttenhower.sph.harvard.edu/galaxy/) with default parameters. estimated by reducing original distances to principal coordinates and calculating To compare environmental bacterial community composition among community the non-Euclidean distance between each object and the group centroid. Because assembly temperature treatments, we used four temperature classes: ≤22 °C we treated temperature as a continuous variable in statistical analyses, we (n = 27), >22 °C to ≤24 °C (n = 9), >24 °C to ≤26 °C (n = 14) and >26 °C (n = 9). To calculated dispersion for all samples treated as a single group. visualize the LEfSe results, we constructed a heatmap with the heatmap.2 function from the gplots package in Program R (ref. 78). To compare environmental bacterial Statistical analysis. To identify the most appropriate metric of bacterial community composition in bromeliads containing differing numbers of Aedes community structure for our study, we first assessed effects of temperature mosquito larvae, we used three mosquito classes based on natural breaks in the on aquatic environmental bacteria community structure in the community data distribution: 0 larvae (n = 20), 1−5 larvae (n = 20) and >5 larvae (n = 19). assembly phase. At the end of the 70-d community assembly period (day 0), qualitative composition (unweighted UniFrac and Jaccard) of the aquatic bacterial Research animals. All experimental procedures were approved by the University of community differed by temperature treatment (LME (unweighted UniFrac): Alabama Animal Ethics Committee (protocol 18-06-1264), University of Campinas β = −0.029; t = −3.46; P = 0.001; LME (Jaccard): β = −0.039; t = −4.61; P < 0.0001; Animal Ethics Committee (protocol 4460-1(A)/2019 CEUA – UNICAMP) and see below). In contrast, temperature did not influence ASV richness, Shannon the Brazilian Ministry of Environment MMA/SISBIO (collecting permit 67637; diversity, phylogenetic diversity or community composition accounting for relative SISGEN AF469C5). abundance of taxa (weighted UniFrac and Bray–Curtis). Qualitative measures of beta diversity (for example, unweighted UniFrac distance) are appropriate Reporting Summary. Further information on research design is available in the for assessing effects of variables that restrict bacterial growth and survival, such Nature Research Reporting Summary linked to this article. as temperature73. UniFrac indices are powerful tools for estimating differences between bacterial communities because they consider the evolutionary history Data availability shared between groups of taxa, which may not be fully reflected by measuring 72,74 Sequence data that support the findings of this study have been deposited in the gene sequence similarity . Thus, we focus on unweighted UniFrac distance as NCBI Sequence Read Archive with the accession code PRJNA613682. Other data a measure of environmental bacterial community composition in subsequent that support the findings of this study are available from the corresponding author analyses. All subsequent analyses use metrics of ecological community upon reasonable request. composition that were measured at the end of the laboratory phase of the experiment (day 13) to incorporate the fully crossed experimental design. We used piecewise SEM (psem function in the piecewise SEM package in Program R) to assess the direct and indirect associations between temperature, References arthropod community composition, environmental bacterial community 47. Dézerald, O. et al. Food-web structure in relation to environmental gradients composition, tadpole gut bacterial dispersion, chlorophyll a concentration (a and predator–prey ratios in tank-bromeliad ecosystems. 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68. DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database Acknowledgements and workbench compatible with ARB. Appl. Environ. Microbiol. 72, We thank R. Bell, T. Bernabe, M. Bletz, T. Jenkinson, R. Martins, D. Medina, W. Neely 5069–5072 (2006). and R. Salla Jacob. São Paulo Research Foundation (FAPESP) provided grants to M.L.L. 69. McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks (grant no. 2017/26162-8), L.P.R. (grant nos. 2018/23622-0 and 2016/25358-3), L.F.T. for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, (grant nos. 2016/25358-3 and 2019/18335-5), C.F.B.H. (grant no. 2013/50741-7) and 610–618 (2012). G.Q.R. (grant nos. 2017/09052-4 and 2018/12225-0). National Council for Scientific 70. Bokulich, N. A. et al. Quality-fltering vastly improves diversity estimates and Technological Development (CNPq) provided research fellowships to L.F.T. (grant from Illumina amplicon sequencing. Nat. Methods 10, 57–59 (2013). no. 300896/2016-6), C.F.B.H. (grant no. 306623/2018-8) and G.Q.R. The Royal Society 71. Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. provided a Newton Advanced Fellowship to G.Q.R. (grant no. NAF\R2\180791). 61, 1–10 (1992). 72. Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for Author contributions comparing microbial communities. Appl. Environ. Microbiol. 71, C.G.B. and S.E.G. designed the study. All authors carried out the study. C.G.B., S.E.G. 8228–8235 (2005). and G.Q.R. analysed the data. S.E.G. drafted the manuscript. All authors critically revised 73. Lozupone, C. A., Hamady, M., Kelley, S. T. & Knight, R. Quantitative and the manuscript. qualitative β diversity measures lead to diferent insights into factors that structure microbial communities. Appl. Environ. Microbiol. 73, 1576–1585 (2007). Competing interests 74. Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J. & Knight, R. The authors declare no competing interests. UniFrac: an efective distance metric for microbial community comparison. ISME J. 5, 169–172 (2011). Additional information 75. Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in R Supplementary information Supplementary information is available for this paper at for ecology. Methods Ecol. Evol. 7, 573–579 (2016). https://doi.org/10.1038/s41558-020-0899-5. 76. Deegan, J. On the occurrence of standardized regression coefcients greater Correspondence and requests for materials should be addressed to S.E.G. than one. Educ. Psychol. Meas. 38, 873–888 (1978). 77. JMP v.14.0.0 (SAS Institute, 2019). Peer review information Nature Climate Change thanks Obed Hernandez-Gomez and 78. Warnes, G. et al. gplots: various R programming tools for plotting data. R the other, anonymous, reviewer(s) for their contribution to the peer review of this work. package version 3.0.1.1 https://CRAN.R-project.org/package=gplots (2019). Reprints and permissions information is available at www.nature.com/reprints.

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