Ecology

SubjectsIn total, twenty-two black-capped chickadees (nine males and 13 females) were tested between May and December 2019, and 16 black-capped chickadees (seven males and nine females) completed the experiment. One male and one female failed to learn Pretraining, and one female failed to learn Non-differential training (see descriptions below for training information); as a result, all three were removed from the experiment. In addition, one male and two females died of natural causes during the course of the study (see Ethics Declaration). For all birds, sex was determined by deoxyribonucleic acid analysis of blood samples37. All birds were captured in Edmonton (North Saskatchewan River Valley, 53.53°N, 113.53°W; Mill Creek Ravine, 53.52°N, 113.47°W), Alberta, Canada in January 2018 and January 2019 and were at least one year of age at capture, verified by examining outer tail rectrices38.Prior to the current experiment, all chickadees were individually housed in Jupiter Parakeet cages (30 × 40 × 40 cm; Rolf C. Hagen, Inc., Montreal, QB, Canada) in a single colony room. Therefore, birds did not have physical contact with each other, but did have visual and auditory contact. Birds had ad libitum access to food (Mazuri Small Bird Maintenance Diet; Mazuri, St. Louis, MO, USA), water with vitamins supplemented on alternating days (Monday, Wednesday, Friday; Prime Vitamin Supplement; Hagen, Inc.), a cup containing grit, and a cuttlebone. Additional nutritional supplements included three to five sunflower seeds daily, one superworm (Zophobas morio) three times a week, and a mixture of hard-boiled eggs and greens (spinach or parsley) twice a week. The colony rooms were maintained at approximately 20 °C and on a light:dark cycle that followed the natural light cycle for Edmonton, Alberta, Canada.One bird had previous experience with one operant experiment involving chick-a-dee calls but showed no difference in responding in comparison to the naïve birds. The remaining 15 birds had no previous experimental experience with black-capped chickadee-produced fee-bee songs or any experimental paradigm.Recordings of acoustic stimuliThe following acoustic stimuli were used in our previous published operant study which indicated that male and female chickadees can identify individual females via their song36. Stimuli included the songs of six female black-capped chickadees. All females were captured in Edmonton (North Saskatchewan River Valley, 53.53°N, 113.53°W; Mill Creek Ravine, 53.52°N, 113.47°W), Alberta, Canada in January 2010, 2011, 2012, and 2014, and all females were at least one year of age at capture, verified by examining outer tail rectrices38. Four females were recorded in Spring 2012 and two females were recorded in Fall 2014. Each recording session lasted approximately 1 h and all recordings took place after colony lights turned on at 08:00, specifically at 8:15. All females were recorded in silence, individually, within their respective colony room cages. Colony room cages were placed in sound-attenuating chambers for recording (1.7 m × 0.84 m × 0.58 m; Industrial Acoustics Company, Bronx, NY). An AKG C 1000S (AKG Acoustics, Vienna, Austria) microphone (positioned 0.1 m above and slightly behind the cage) was connected to a Marantz PMD670 (Marantz America, Mahwah, NJ) digital recorder (16 bit, 44,100 Hz sampling rate) and was used for all recordings. Audio recordings were analyzed and cut into individual files (songs) using SIGNAL 5.03.11 software (Engineering Design, Berkley, CA, USA).Acoustic stimuliFor the current study, a total of 150 vocalizations were used as stimuli, these vocalizations were comprised of 25 fee-bee songs produced by each of six recorded female chickadees. We ensured that all 150 were of high quality, meaning no audible interference, and all stimuli were bandpass filtered (lower bandpass 500 Hz, upper bandpass 14,000 Hz) using GoldWave version 6.31 (GoldWave, Inc., St. John’s, NL, Canada) in order to reduce any background noise outside of the song stimuli spectrum. For each song stimulus, 5 ms of silence was added to the leading and trailing portion of the vocalization and each stimulus was tapered to remove transients, in addition amplitude was equalized peak to peak using SIGNAL 5.03.11 software. When triggered, stimuli were presented at approximately 75 dB peak SPL as measured by a calibrated Brüel & Kjær Type 2239 (Brüel & Kjær Sound & Vibration Measurement A/S, Nærum, Denmark) sound pressure meter (A-weighting, slow response), a level that corresponds with the natural chickadee vocalizations amplitudes39,40,41. All dB measurements were made at the level of the request perch where birds trigger stimuli and where birds are required to remain for the length of the stimuli and all dB measurements refer to SPL.Noise stimuliAnthropogenic noise stimuli were originally created and used by Potvin and MacDougall-Shackleton42 and by Potvin, Curcio, Swaddle, and MacDougall-Shackleton43. The stimuli were recorded from an urban area in Melbourne, Victoria, Australia and other anthropogenic noise stimuli of various trains, cars, motorcycles, and lawnmowers downloaded from Soundbible.com were used. Within Victoria44 and Alberta45,46, urban traffic noise averages 60–80 dB SPL. The files used varied in length, with those recorded in Melbourne all being 10 min in length and those downloaded from Soundbible.com varying between 1–10 minutes42,43. In total 10 tracks were used with 30 total minutes of noise stimuli. Three anthropogenic noise conditions were used in the study, including Silence (no noise), Low noise (anthropogenic noise stimuli played at ~ 40 dB peak SPL), and High noise (anthropogenic noise stimuli played at ~ 75 dB peak SPL) replicating the variation of traffic noise experienced in urban areas42,43. For the Low and High noise conditions the 10 tracks repeated on a randomized loop during data collection (natural light of light/dark cycle) with, thus noise exemplars overlapped songs by chance, to further emulate urban areas. Noise stimuli had natural variations and modulations in frequency and amplitude over the course of the sound files. All dB measurements for noise stimuli included in this study refer to SPL. See Fig. 1 for female song and traffic noise stimuli spectrograms and power spectra.Figure 1(A) Spectrogram of a female fee-bee song in silence. (B) Power spectrum of female fee-bee song in silence . (C) Spectrogram of female fee-bee song in low noise. (D) Power spectrum of female fee-bee song (black) in low noise (grey). (E) Spectrogram of female fee-bee song in high noise. (F) Power spectrum of female fee-bee song (black) in high noise (grey).Full size imageApparatusFor the duration of the experiment, birds were housed individually in modified colony room cages (30 × 40 × 40 cm; described above) which were placed inside a ventilated, sound-attenuating operant chamber. See Fig. 2 for illustration of operant conditioning chamber. All chambers were lit with a full spectrum LED bulb (3 W, 250 lm E26, Not-Dim, 5000 K; Lohas LED, Chicago, IL, USA), and maintained the natural light:dark cycle for Edmonton, Alberta. Each cage within each operant chamber contained two perches and an additional perch fitted with an infrared sensor (i.e., the request perch). See Fig. 2C. Each cage also contained a water bottle, grit cup, and cuttlebone See Fig. 2G-2H. Birds had ad libitum access to water (with vitamins supplemented on alternating days; Monday, Wednesday, Friday), grit, and cuttlebone and were provided two superworms daily (a morning and afternoon worm). An opening (11 × 16 cm) located on the left side of the cage allowed the birds to access a motorized feeder, with a red LED light, and equipped with an infrared sensor47. See Fig. 2B,D–F. The purpose of the sensor was so that food was only available as a reward for correct responses to auditory stimuli during the operant discrimination task. We should note that performance of the discrimination task is required for access to food and thus maintains motivation. For operation and data collection, a personal computer connected to a single-board computer48 scheduled trials and recorded responses to stimuli. Stimuli were played from a personal computer hard drive through a Cambridge Integrated Amplifier (model A300 or Azur 640A; Cambridge Audio, London, England). Data is downloaded once a day in order to reduce stress on subjects as all equipment must be tested following download, requiring contact with subjects. Stimuli played in the chamber through a Fostex full-range speaker (model FE108 Σ or FE108E Σ; Fostex Corp., Japan; frequency response range 80–18,000 Hz) located beside the feeder. See Sturdy and Weisman49 for a detailed description of the apparatus. See Fig. 2 for an illustration of the operant conditioning chamber set-up.Figure 2Illustration of the operant conditioning chamber, including: (A) speaker, (B) automated feeder, (C) request perch fitted with infrared photo-beam assembly, (D) feeder cup, (E) electrical inputs, (F) red LED, (G) water bottle, (H) and cuttlebone. Also shown is the feeder opening, and additional perches. To simplify, the sketch the front and floor of the chamber, and the enclosure’s acoustic lining are not included.Full size imageProcedureOperant conditioningOur current operant conditioning go/no-go set-up is used to understand how birds perceive auditory stimuli. By training the birds to respond to particular stimuli and withhold responding to other stimuli we can compare responses to both types of stimuli. The go/no-go paradigm requires the birds to learn which stimuli require correct responses (go), providing reinforcement (food), and which stimuli require birds to withhold responding (no-go), resulting in the avoidance of punishment (lights out).The current study follows nine stages, after learning to use the operant conditioning set-up, birds then go through Non-differential training (stage 1) where they will be exposed to all stimuli that will be used in the experiment and to ensure that the birds respond to the stimuli equivalently. Then birds complete Discrimination training (stage 2) where birds on two categories of sounds. One category is rewarded, the other category is punished. Then the Discrimination-85 (stage 3) phase prepares birds for future trials where there is no reward nor punishment. After this point, birds will follow three series (Silence; Low; High) of Discrimination-85 with noise (stage 4, 6, 8) and a corresponding Probe with noise (stage 5, 7, 9), meaning that that each subject will repeated the two discrimination tasks three times with different noise conditions, with the order of noise conditions randomized among individuals. The detailed procedures for each stage are described in the following.Non-differential trainingThe purpose of Non-differential training is to engender a high level of responding on all trials, across all stimuli. Once a bird learned to use the request perch fitted with a sensor as well as learned to use the feeder to obtain food then Pretraining began. During Pretraining, birds were trained to respond to a 1 s tone (1,000 Hz) in order to receive access to food. Pretraining occurred over an approximately 15-day period in order to allow acclimatization to the chamber, feeder, and speaker. Following Pretraining was Non-differential training. During Non-differential training, birds received food for responding to all fee-bee song stimuli. All trials began when a bird landed on the request perch and remained on the perch for between 900–1100 ms, at which point a randomly-selected song stimulus played. Songs were presented in random order from trial to trial until all 150 stimuli had been triggered and played without replacement; once all 150 stimuli were played, a new random sequence initiated. In the event that the bird left the request perch during a stimulus presentation, the trial was deemed interrupted, and resulted in a 30 s lights out of the operant chamber. If the bird entered the feeder within 1 s after the stimulus (any stimulus) was played, it was given 1 s access to food, followed by a 30 s intertrial interval. If a bird remained on the request perch during the stimulus presentation and the 1 s following the completion of the stimulus, then the bird received a 60 s intertrial interval with the lights on. Birds continued on Non-differential training until they completed six 450-trial blocks at ≥ 60% responding on average to all stimuli, at least four 450-trial blocks at ≤ 3% difference in responding to future rewarded versus future unrewarded Discrimination stimuli, at least four 450-trial blocks at ≤ 3% difference in responding to future rewarded versus unrewarded Discrimination stimuli. Then following a day of free feed (during which birds had ad libitum access to a food cup) birds completed a second round of Non-differential training in which they completed at least one 450-trial block that met each of the above requirements. A 450-trial block consisted of the bird experiencing each of the 150 stimuli three times. For the current study the average time to complete Non-differential training ranged from 10 to 41 days (M = 21.43, SD = 9).Discrimination trainingDiscrimination training procedures included only 114 out of the 150 training stimuli that were previously presented in non-differential training, and responses to these stimuli were now differentially reinforced. Specifically, correct responses to half of the stimuli (“rewarded stimuli”, S+) were positively reinforced with 1 s access to food, and incorrect responses to the other half (“unrewarded stimuli”, S−) were instead punished with a 30-s intertrial interval of lights off within the operant chamber. In regard to criterion, Discrimination training continued until a bird completed six 342-trial blocks with a discrimination ratio between their respective S+ and S− of greater than 0.80 with the last two blocks being consecutive. For discrimination ratio calculations see Response Measures below.The current subjects were randomly assigned to either a True category discrimination group (n = 10) or Pseudo category discrimination group (n = 6). Furthermore, chickadees in the True category discrimination group were divided into two subgroups: (a) True 1 (n = 5; three females and two males) discriminated between 57 rewarded fee-bee songs produced by three individual female chickadees (S+) and 57 unrewarded fee-bee songs produced by another three individual female chickadees (S−); and (b) True 2 (n = 5; two females and three males) discriminated between the same songs with opposite rewards, properly, the 57 rewarded (S+) fee-bee songs were the S− from True 1 and the 57 unrewarded (S−) fee-bee songs were the S+ from True 1. For birds in the True category discrimination the average number of blocks completed per day for Discrimination training ranged from 2.4–4.4 blocks (3.3 ± 0.7 blocks).In similitude, the Pseudo category discrimination group was divided into two subgroups: (a) Pseudo 1 (n = 3; two females and one male) discriminated between 57 randomly-selected rewarded (S+) fee-bee songs and 57 randomly-selected unrewarded (S−) fee-bee songs; and (b) the second subgroup Pseudo 2 (n = 3; one female and two males) discriminated between the same songs with opposite rewards, meaning, the 57 rewarded (S+) fee-bee songs were the S− from Pseudo 1 and the 57 unrewarded (S−) fee-bee songs were the S+ from Pseudo 1 (S+) fee-bee songs and 57 randomly-selected unrewarded (S−) fee-bee songs. To explicate, the purpose of the two Pseudo groups was to include a control in which subjects are required to memorize each vocalization independent of the producer rather than be trained to categorize songs according to individual chickadees as the True groups have been. All birds remained in their respective groups (True 1 and 2; Pseudo 1 and 2) for the duration of the study. For birds in the Pseudo category discrimination the average number of blocks completed per day for Discrimination training ranged from 3.3–6.1 blocks (4.34 ± 1.2 blocks).Discrimination-85 phaseDiscrimination-85 was identical to the above Discrimination training except that rewarded songs were reinforced with a reduced probability, P = 0.85. Therefore, for 15% of trials when a rewarded stimulus was played and a bird correctly responded, no access to food was triggered. Instead, a 30 s lights on intertrial interval occurred. The change in reinforcement occurs in order to prepare birds for Probe trials in which novel song stimuli were neither rewarded with access to food nor unrewarded with a lights out, instead nothing occurs. Discrimination-85 continued until birds completed two consecutive 342-trial blocks with a discrimination ratio of at least 0.80.Discrimination-85 phase with noiseAll subjects followed three series (Silence; Low; High) of Discrimination-85 with noise and a corresponding Probe with noise and the order of noise stimuli was randomly-selected for each bird. Discrimination-85 with noise was identical to the Discrimination-85 phase except one of the three noise stimuli conditions (Silence; Low noise, 40 dB SPL; High noise, 75 dB SPL) was played over the song stimuli. The noise stimuli condition was randomly-selected for each bird. Each bird went through three series of Discrimination-85 with noise (Silence; Low; High) until reaching criteria: two consecutive 342-trial blocks with a discrimination ratio of at least 0.80. Here, we were interested in how the addition of noise would impact discrimination between rewarded and unrewarded female song stimuli.Probe phase with noiseFollowing each Discrimination-85 phase with noise was a corresponding Probe phase with noise. During Probe the reinforcement contingencies from Discrimination-85 were maintained. In addition to the 114 stimuli from Discrimination training, this stage included 12 novel fee-bee songs (i.e., Probe stimuli), two from each of the six individual females. For True groups, six of these novel songs were categorized as P + and the other six as P-, based on whether they were produced by the same birds as the S+ or the S− training stimuli. For Pseudo groups, the novel songs were not assigned to categories. For both groups, the 12 novel stimuli were neither rewarded (no food access) nor unrewarded (no lights out). The birds completed six 126-trial blocks in which the 114 familiar discrimination stimuli repeated once per block and the 12 probe sequences played once per block. In addition, one of the three noise stimuli conditions (Silence; Low noise, 40 dB SPL; High noise, 75 dB SPL) was played over the song stimuli, and each bird went through three series of Probe with noise (Silence; Low; High) which corresponded with the birds previous Discrimination-85 phase with noise condition. Thus, all birds completed all three Discrimination-85 phases with noise conditions followed by the corresponding Probe with noise conditions, and the order of noise stimuli condition was randomly-selected for each bird. In Probe phases we are interested if subjects can categorize novel stimuli to previously rewarded or unrewarded female birds.Response measuresFor each 342-block trial during training (Discrimination-85 with noise; Probe with noise), proportion response was calculated (R + /(N-I)): R + represents the number of trials in which the bird went to the feeder, N represents the total number of trials, and I represents the number of interrupted trials in which the bird left the perch before the entire stimulus played. For Discrimination training and the Discrimination-85 phase, a discrimination ratio was calculated by dividing the mean proportion response to all S+ stimuli by the mean proportion response to S+ stimuli plus the mean proportion response to S− stimuli. A discrimination ratio = 0.50 specifies equal response to rewarded (S+) and unrewarded (S−) stimuli, a discrimination ratio = 1.00 specifies a perfect discrimination between S+ and S− stimuli. We also collected data regarding the number of blocks and days per stage (Discrimination training; Discrimination-85 training with noise) in order to examine the latency of discrimination learning.Statistical analysesAll statistical analyses were conducted using SPSS (Version 20, Chicago, SPSS Inc.). In order to compare the number of trials needed to reach criterion and the discrimination ratios between True and Pseudo groups for Discrimination Training we conducted an analysis of variance (ANOVA). For Discrimination-85 with noise (Silence, Low noise, High noise), an ANOVA was conducted to compare the number of trials needed to reach criterion and the discrimination ratios between True and Pseudo groups. We also conducted post-hoc tests in order to reveal any sex differences between groups.And for Discrimination-85 with noise and Probes with noise repeated measures ANOVA was conducted to compare proportion response to training stimuli and probe stimuli between True groups and Pseudo groups. Lastly, we conducted post-hoc tests in order to reveal any differences in the number of trials to reach criterion during Discrimination training and to Discrimination-85 with noise.Ethics declarationThroughout the experiment, birds remained in the testing apparatus to minimize the transport and handling of each bird. One male and two female subjects died from natural causes during operant training. Following the experiment, healthy birds were returned to the colony room for use in future experiments.All procedures were conducted in accordance with the Canadian Council on Animal Care (CCAC) Guidelines and Policies with approval from the Animal Care and Use Committee for Biosciences for the University of Alberta (AUP 1937), which is consistent with the Animal Care Committee Guidelines for the Use of Animals in Research. Birds were captured and research was conducted under an Environment Canada Canadian Wildlife Service Scientific permit (#13-AB-SC004), Alberta Fish and Wildlife Capture and Research permits (#56,066 and #56,065), and the City of Edmonton Parks permit. All methods are reported in accordance with ARRIVE guidelines. More

Resistance of H. zea populations to Cry1AcTo measure variation in resistance of H. zea populations across field locations during 2017 and 2018, larval offspring of insects collected from non-Bt maize were subjected to a diet-overlay bioassay containing a diagnostic concentration of Cry1Ac (29 µg/cm2) corresponding to the mean LC95 of four Cry1Ac susceptible H. zea populations. Overall, larval survivorship varied significantly among years (Fig. 1).Figure 1Survival of H. zea larvae collected in 2017 and 2018 following exposure to 29 µg/cm2 Cry1Ac in diet-overlay assay. Dashed line is mean survival of a known susceptible field population. ***Years significantly different F = 25.38; df = 1, 58; P  More

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OverviewOur study investigates the patterns and drivers of global marine plankton diversity by simultaneously modeling the spatial distribution of 860 phyto- and zooplankton species, based on the widest and most recent compilations of in situ observations available. These observations were associated with various sets of relevant predictors to train a range of statistical species distribution models (SDMs) on a monthly resolution. The SDMs were used to estimate contemporary and future levels of global surface species richness (SR) for total plankton, phytoplankton and zooplankton. We explore how, and why, global phyto- and zooplankton SR and community composition are affected by future climate change under the RCP8.5 scenario of greenhouse gas (GHG) emissions. We also summarize regional patterns of climate change impacts on plankton diversity by clustering the global ocean and examine how hotspots of climate change impacts might overlap with the current provision of marine ecosystem services. All data manipulation and analyses were performed under the R programming language39. The R packages used are mentioned below in their corresponding section.Plankton species observationsFirst, to model global, open ocean plankton diversity from species-level field observations, comparable datasets of phytoplankton and zooplankton occurrences (i.e., presences) had to be compiled. We refer to as open ocean all those regions where the seafloor depth exceeds 200 m. We made use of the large dataset of phytoplankton occurrences recently compiled by Righetti et al.11. For zooplankton, a new dataset was compiled following the same methodology. Both occurrence datasets were based on publicly available data from online biodiversity repositories, as well as some additional published datasets. The R packages mainly used for implementing the datasets are those constituting the tidyverse package.Phytoplankton occurrences
For the phytoplankton occurrences used here, Righetti et al.40 compiled data from various sources: the Global Biodiversity Information Facility (GBIF; https://www.gbif.org), the Ocean Biogeographic Information System (OBIS; https://www.obis.org), the data from Villar et al.41, and the MAREDAT initiative42. Righetti et al.40 gathered >106 presences from nearly 1300 species sampled through various methodologies within the monthly climatological mixed-layer depth, at an average depth of 5.41 ± 6.95 m (mean ± sd), between 1800 and 2015. The species names were corrected and harmonized following the reference list of Algaebase (http://www.algaebase.org/) and were further validated by expert opinion. The final species list spanned most of the extant phytoplankton taxa composing the biodiversity of the euphotic zone of the global ocean. Fossil records, sedimentary records, and occurrences associated with senseless metadata were removed. This dataset has been mined to effectively obtain phytoplankton SR estimates for the global open ocean that were: (i) robust to sampling spatial-temporal biases11, and (ii) validated against independent data11.

Zooplankton occurrences
A new dataset of global zooplankton species occurrences was compiled in a comparable fashion to that put together for phytoplankton. Prior to retrieving the occurrence data online, we first identified the phyla (Order/Class/Family) that comprise the bulk of extant oceanic zooplankton communities: Copelata (i.e., appendicularians), Ctenophora, Cubozoa (i.e., box jellyfish), Euphausiidae (i.e., krill), Foraminifera, Gymnosomata (i.e., sea angels, pteropods), Hydrozoa (i.e. jellyfish), Hyperiidea (i.e., amphipods), Myodocopina (i.e., ostracods), Mysidae (i.e., small pelagic shrimps resembling krill), Neocopepoda, Podonidae and Penilia avirostris (i.e., cladocerans), Sagittoidea (i.e., chaetognaths), Scyphozoa (i.e., jellyfish), Thaliacea (i.e., salps, doliolids and pyrosomes), Thecosomata (i.e., sea snails, pteropods), and four families of pelagic Polychaeta (i.e., worms) that are often found in the zooplankton and whose species are known to display holoplanktonic lifecycles (Tomopteridae, Alciopidae, Lopadorrhynchidae, Typhloscolecidae). The presence data associated with species belonging to these groups were retrieved from OBIS and GBIF between the 12/04/2018 and the 18/04/2018 using online queries via the R packages RPostgreSQL, robis and rgbif. Since the Neocopepoda infra-class comprise several thousands of benthic and parasitic taxa43, a preliminary selection of the non-parasitic planktonic species had to be carried out prior to the downloading using the species list of Razouls et al.43 as a reference. The spatial distributions of the groups cited above were first inspected using GBIF’s and OBIS’s online mapping tools to evaluate the potential number of overlapping observations between the two databases. As a result of their relatively low contributions to total observations/diversity, and very high overlap between databases, the occurrences of Cladocera and Polychaeta were retrieved from OBIS only (which usually harbors more occurrences). On top of the data collected from OBIS and GBIF, the copepod occurrences from Cornils et al.44 and the pteropod occurrences from the MAREDAT initiative45 were added to the dataset. This initial collection of zooplankton observations gathered 4,899,151 occurrences worldwide.
Then, similar criteria as Righetti et al.11,40 were applied to progressively remove those presences that would be discordant with estimates of contemporary open ocean zooplankton diversity. The number of observations and species discarded after each main step and for each initial dataset are reported in Supplementary Data 1. We discarded records that: (i) presented at least one missing spatial coordinate, (ii) were associated with an incomplete sampling date (d/m/y), (iii) were associated with a year of collection older than 1800, (iv) were not associated with any sampling depth, (v) were not identified down to the species level, and (vi) were issued from drilling holes or sediment core data. For step (vi), a list of keywords (Supplementary Note 3) was used to identify the names of those original datasets that contained either fossil or sedimentary records. These first steps resulted in the removal of 1,766,783 occurrences (~36%). Like for phytoplankton, the remaining occurrences were associated with surface salinity values from the World Ocean Atlas (WOA) 201346 and bathymetry levels from the National Oceanic and Atmospheric Administration (NOAA) using the marmap R package. Occurrences associated with salinity levels 500 m. The average depth was used when maximal depth was not provided in the metadata. Therefore, the maximal depth of a zooplankton species occurrence allowed in our dataset is 500 m. This way, we tried to account for the zooplankton community that frequently performs diel vertical migration across the euphotic zone or the mixed layer, and that often co-occurs with species inhabiting surface layers. This removed 109,582 (~6%) occurrences. Next, for each phylum, OBIS and GBIF datasets were merged and the list of species names were extracted. Every species name was then carefully examined and compared to the taxonomic reference list of the World Register of Marine Species (WoRMS; http://www.marinespecies.org) for all taxa but copepods, for which we used the taxonomic reference of Razouls et al.43. This way, we rigorously harmonized and corrected the species names across all datasets. In addition, we used the notes and attributes of WoRMS to identify whether species were holoplanktonic or meroplanktonic (i.e., those species that have at least one benthic phase in their life cycle). Jellyfish species usually display a fixed polyp phase during their life cycle, therefore we used the dataset of Gibbons et al.47 to remove the species that were not holoplanktonic. Overall, these steps discarded 37,234 occurrences (~2%). One of two duplicate occurrences were removed from the dataset if they displayed the same species name, sampling depth, sampling date, and if they occurred within the same 0.25° x 0.25° cell grid. This last step removed 900,446 occurrences (~54%), highlighting the high overlap between the two main data sources. The remaining 766,033 presences were binned into the monthly 1° x 1° grid cell of the WOA to match the spatial resolution of the environmental predictors. The average maximum (±std) sampling depth was 73 ± 109 m and the average sampling year was 1985 ± 21. Observation densities were spatially biased towards the North Atlantic Ocean and the Southern Ocean (Supplementary Figure 1). The data reflected the historical seasonal sampling bias towards spring and summer. In the northern hemisphere, observations were equally distributed from March to October but constituted 78% of the data. In the southern hemisphere, 75% of occurrences were sampled between November and March. The final dataset gathered occurrences for 2034 different species (576 genera, 161 families) spanning all the major zooplankton phyla and several size classes. The only notable missing taxa are those belonging to the Cercozoa and Radiozoa because they present little to no species-level observations in online biodiversity repositories as they have been historically overlooked by traditional sampling techniques48.
Contemporary environmental conditionsA comprehensive set of environmental variables that are known to affect the physiology and constrain the distribution of plankton was prepared to define the candidate predictors for the SDMs. The R packages mainly used were raster and ncdf4. First, twelve primary variables that are relevant for modeling the distribution of both phytoplankton and zooplankton taxa were identified49,50,51,52. These were then aggregated into twelve monthly climatologies at a 1° x 1° resolution (i.e., the spatial cell grid of the WOA). The first six primary variables were sea surface temperature (SST, °C), sea surface salinity (SSS), nitrates (NO3-), phosphates (PO43-) and silicic acid (Si(OH)4) surface concentrations (µM), as well as dissolved oxygen concentration (dO2, ml l−1). Oxygen limitations and oxygen minimum zones (OMZ) are key factors controlling the horizontal and vertical distribution of zooplankton53,54. However, the effects of oxygen are often confounded with those of temperature because surface oxygen scales linearly with SST on a global scale. Therefore, dO2 at 175 m depth was used instead of surface dO2. For all six variables, the twelve monthly climatologies of the WOA13v2 (https://www.nodc.noaa.gov/OC5/woa13/woa13data.html) were used. In addition, satellite observations stemming from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS; https://oceancolor.gsfc.nasa.gov/) over the 1997 to 2007 time period were used to derive monthly climatologies of Photosynthetically Active Radiation (PAR; µmol m−2 s−1) and chlorophyll (Chl; mg m−3), the latter serving as a proxy for surface phytoplankton biomass. Monthly climatologies of mixed-layer depth (MLD, m) based on the temperature criterion of55 from the Argo floats data (http://mixedlayer.ucsd.edu/) were also considered. Climatologies of surface wind stress (m s−1) were obtained from the Cross-Calibrated Multi-Platform56, using data from 1987 to 2011 (https://podaac.jpl.nasa.gov/). Climatologies of surface carbon dioxide partial pressure (pCO2; atm) were obtained from the Surface Ocean CO2 Atlas (SOCATv2; https://www.socat.info/) and made available by Landschützer et al.57. Lastly, a variable depicting sub-mesoscale dynamics and the strength of sea currents was derived from the daily satellite altimetry observations over the 1993–2012 period (https://cds.climate.copernicus.eu/#!/home): mean Eddy Kinetic Energy (EKE, m2 s−2). EKE was computed from the northward and eastward components of surface geostrophic seawater velocity (assuming the sea level as geoid), following the method of Qiu & Chen58. Such variable enabled us to account for the potentially important role of sub-mesoscale activity in structuring plankton biodiversity59.Then, nine secondary predictors were derived from some of the predictors described above. PAR over the MLD (MLPAR, µmol m−2 s−1) was calculated following Brun et al.49 An estimate of annual range of SST (dSST) was added by computing the difference between the warmest and the coldest temperature across the 12 months. The excess of nitrate to phosphate (N*, µM) relative to the Redfield ratio was computed as [NO3-] −16[PO43-]. Changes in N* represent varying conditions of denitrification and remineralization from N2-fixing organisms7. The excess of silicates to nitrates (Si*=[Si(OH)4]-[NO3-], µM) was also computed to represent regions where silicates are in excess compared to what diatoms would need to use up the nitrates7. Si*  > 0 are indicative of conditions where diatoms can grow healthy. Since the distribution of macronutrients concentrations, chlorophyll concentration, and EKE values were all skewed towards lower values, we considered their logarithmic values (logNO3, logPO4, logSiOH4, logEKE, and logChl), based on either natural log or base 10, as additional predictors because they were much closer to a normal distribution.Species distribution modelingSDMs refer to a wide range of statistical algorithms that link an observed biological response variable (i.e., presence-only, presence/absence, abundance) to contextual environmental variables in the form of a response curve60. The latter is used to explore how a species’ environmental niche is realized in space and time24. In short, SDMs mainly rely on the following assumptions: (i) species distributions are not strongly limited by dispersal at a macroecological scale, an assumption valid for plankton considering the very strong connectivity of ocean basins through surface current on decadal scales61,62, which enables plankton species to display very large spatial ranges;11,47,60 (ii) species distributions are primarily shaped by the combinations of environmental factors that define the conditions allowing a species to develop. The latter assumption has been supported on macroecological scales, where the imprint of biological interactions (and dispersal) has been found to be relatively small63,64. Neither comparable abundance data nor presence/absence data were available from our datasets. In addition, presence-only data are less sensitive to discrepancies in species detection across various plankton sampling techniques. Therefore, based on species presences, we developed an exhaustive SDM framework to estimate plankton diversity patterns from an ensemble modeling approach65 that addresses the underlying main sources of uncertainties66,67.We follow the methodology developed in ref. 11, but simplify this approach to accommodate the limited predictor availability in the future model projections (see section “Choice of environmental predictors”), and the large number of diverse species we model in this work (sections “Background data (pseudo-absences)”, “Choice of environmental predictors”, and “SDMs evaluation and projections of monthly plankton species community composition”). We further derive SR based on habitat suitability rather than observed presence–absence data (section “Ensemble projection of global plankton species richness”). All methodological choices led to a minimization of computational cost and model complexity, while preserving all crucial patterns reported in ref. 11. Each methodological choice was carefully evaluated against other options, see sections below.
Background data (pseudo-absences)
Since we aimed at training correlative SDMs to model species distributions from presence data and environmental predictors, background data had to be simulated to indicate those conditions where a species is likely not to occur (i.e., pseudo-absences68). The generation of background data is a critical step in niche modeling experiments, and though no single optimal method has been identified by the niche modeling community, this step must address the important spatial and temporal sampling biases inherent to field-based observations. To do so, we made use of the target-group approach of Philipps et al.69, which has been shown to efficiently model phytoplankton distributions11. This method was found appropriate for our study because it generates background data according to the density distribution of the presence data, and therefore it: (i) does not induce additional bias to the initial biases in the presence data; and (ii) does not misclassify regions lacking observations (e.g., South Atlantic and Subtropical Pacific, Supplementary Fig. 1) as regions of absences.
For phytoplankton, we followed the background selection procedure described in Righetti et al.11. The authors used either the total pool of occurrences as a target-group, or defined three target groups based on the taxonomic groups contributing most to species diversity and observations (Bacillariophyta, Dinoflagellata, and Haptophyta). Background data of each species were randomly drawn based on the monthly resolved 1° x 1° occurrences of both their corresponding target groups, after applying an environmental stratification based on the SST and MLD gradients. This way, a species’ background is located at the sites where its lack of presence is most likely to reflect an actual absence. For each species, ten times more background data than presences were generated following the guidelines of Barbet-Massin et al.68. The amount of background data sampled from a specific SSTxMLD stratum was proportional to the number of monthly 1° x 1° cells provided by the target-group in this very stratum, thereby reflecting original sampling efforts. Both the total target-group background data (drawn from all sampling sites together) and the group-specific target background data were considered for our study, but both led to comparable estimates of phytoplankton species diversity (but see Fig. S3B of ref. 11).
The same method was applied for zooplankton species. First, we defined target groups based on their sampling distribution and broad taxonomic classification: Arthropoda (mainly copepods, but also krill and amphipods, that are sampled through similar techniques), Pteropoda, Chaetognatha, Cnidaria, Ctenophora, Chordata, Foraminifera, and Annelida. Unfortunately, the last three target groups displayed too few occurrences for drawing ten times more background data than presences. Consequently, their background data were drawn from the total pool of occurrences. Ctenophora also showed very few observations so they were merged with the Cnidaria as they are often considered together as jellyfish and collected in similar ways.
Total and target-group background data were drawn for all zooplankton species presenting more than 100 presences to run preliminary SDMs based on a preliminary set of predictors (Supplementary Fig. 2). These SDMs were then used to project preliminary diversity patterns for the four months that represent each season in the northern hemisphere (April, July, October, and January). These projections were examined for every group, and the predictive skills of the SDMs were evaluated using a repeated ten times split-sample test (see below). These tests showed that the total target-group background and the group target-group background converged towards SDMs of comparable skills and similar diversity patterns, except for the Chaetognatha, for which the target-group background leads to models of much poorer predictive skills (Supplementary Fig. 2). Furthermore, both background choices led to very similar latitudinal diversity gradients for phytoplankton (Fig. S3B in ref. 11). Therefore, to generate diversity patterns that are robust and consistent across the two trophic levels, the total target background data were used as standard background. The phytoplankton diversity pattern obtained with the total target background approach was only slightly lower in the Indo-Pacific and at very high latitudes11. Once they were generated, all background data were matched with monthly values for the 21 environmental variables described above.

Choice of the SDMs algorithms
The choice of the statistical method is a main source of uncertainty when projecting biodiversity scenarios through niche modeling66,67. Therefore, an ensemble forecasting strategy was adopted based on four types of SDMs that cover the range of algorithms types and model complexity that are commonly used:67,69 Generalized Linear Models (GLM), Generalized Additive Models (GAM), Random Forest (RF), and Artificial Neural Networks (ANN). The level of complexity of those models was constrained to avoid model overfitting70, a common pitfall when dealing with noisy and spatially biased data. SDMs including numerous predictors and parameterization features are more likely to fit spurious relationships and to be less transferable70,71. Consequently, the number of predictors was limited relative to the number of presences (see below) and the SDMs were tuned to fit relatively simple response curves. The GLM followed a binomial logit link, including linear and quadratic terms, and a stepwise bi-directional predictor selection procedure. The GAM also followed a binomial logit link. Smoothing terms with five dimensions, estimated by penalized regression splines without penalization to zero for single variables, were applied. Interaction levels between environmental predictors were set to zero for both GLM and GAM. The RF included 750 trees, and terminal node size was fixed at 10 to avoid having single occurrences as end members of some trees. The number of variables randomly sampled as candidates at each tree split (mtry parameter) was equal to the number of predictors used divided by three. The numbers of units in the hidden layers of the ANN, as well as the decay parameter, were optimized through five different cross-validations and a maximum of 200 iterations. Background data were weighted inverse-proportional to that of presence data (total weight = 1).

Choice of environmental predictors
To select for parsimonious and ecologically relevant sets of environmental predictors, a three-stage hierarchical selection framework was developed: (i) the distribution of the predictors’ values fitted to the presences were compared to their realized distribution between the main ocean basins to check whether one predictor could bias SDMs outputs towards a particular basin; (ii) pair-wise rank correlations between variables were examined, and one of two collinear variables was discarded where necessary; and (iii) models were trained to evaluate the explanatory power of several predictors sets of increasing parsimony, and rank the predictors within those sets at species-level. This selection procedure was carried out by separating phytoplankton from zooplankton since: (i) the two groups show different sampling distributions, and (ii) their niche dimensions might differ because of differences in their lifecycles (few days for phytoplankton, months to years for zooplankton) and biological requirements (photoautotrophy vs. heterotrophy and respiration). For those tests, only well-observed species with >100 occurrences were selected (nphytoplankton = 328; nzooplankton = 372). Ultimately, to account for the uncertainty in predictors choice, several final sets of predictors were defined based on the steps of the selection framework, and ensemble forecasting was adopted again (i.e., diversity estimates will be averaged across the sets of predictors).
Removal of variables impacted by sampling imbalances across ocean basins: Imbalance of sampling effort in geographical space can lead to sampling imbalance in environmental space if portions of an environmental gradient are strongly connected to an ocean basin that has been surveyed more extensively than others. To avoid such issues, the distributions of the annual values of the predictors were examined between the main basins (Arctic, Southern, Pacific, Indian and Atlantic Oceans). The most spatially imbalanced predictors were SSS and pCO2: the former is on average higher in the Atlantic Ocean, while the latter exhibits many of its most extreme values in the Peruvian upwelling system (Supplementary Note 3). The Peruvian upwelling is a hotspot of phytoplankton observations with clearly skewed observations (and the number of species sampled) towards pCO2 values  > 400 atm (Supplementary Note 3). Plus, the pCO2 data do not cover the Arctic Ocean, the Mediterranean Sea, and the Red Sea. Consequently, pCO2 was discarded from the list of predictors to avoid strong sampling bias effects on SDM projections. A majority of the zooplankton data are concentrated in the Atlantic Ocean (Supplementary Fig. 1). As a result, the distribution of SSS values fitted to zooplankton occurrences is skewed towards SSS values >35 (Supplementary Note 3). As SSS is commonly used as a predictor for modeling the distribution of zooplankton47,48,49, we wanted to further examine its potential to act as a basin indicator rather than a predictor meant to represent an actual environmental control on species distribution. To do so, we performed ensemble SDMs projections for the zooplankton species, based on three variables sets: (i) without SSS, (ii) with SSS, and (iii) with Longitude (0°–360°) instead of SSS. Variables sets (ii) and (iii) led to very similar global zooplankton SR patterns with hotspots in the Atlantic Ocean. On the contrary, (i) led to more balanced zooplankton SR between basins without significantly lowering SDMs skills (Supplementary Note 3). We interpreted this as a bias in environmental space towards the conditions prevailing in the Atlantic Ocean, therefore we chose to discard SSS from the list of predictors.
Removal of collinear variables: Strong correlations among predictors can mislead the ranking of variable importance in SDMs72, so it has become common practice to exclude one of two variables that are highly collinear. Pair-wise Spearman’s rank correlation coefficients (⍴) were computed based on the predictors’ values fitted to the presences. When two variables exhibited a |⍴|  > 0.70, the one displaying the distribution closest to a normal distribution was kept. From phytoplankton occurrences, we identified two clusters of strongly correlated variables: one comprising MLD, PAR, MLPAR (by construction), and wind stress (but PAR and MLD were only correlated at ⍴ = −0.66); and the other one comprising [NO3-], [PO43-] and their logged values. Similar clusters were found from zooplankton data, except that PAR was slightly less correlated to Wind stress (⍴ = −0.66) and MLD (⍴ = −0.58), and that [NO3-], [PO43-], plus their logged versions, showed stronger correlations with SST (⍴ = −0.80). As [NO3-] is a key factor for structuring planktonic systems7, and because we aimed to keep the variables sets as consistent as possible between species, logNO3 was kept as a candidate predictor. The variables retained for phytoplankton were: SST, dSST, logEKE, Si*, N*, logSiOH4, logNO3, logChl, wind stress, PAR, MLPAR, and MLD. The last four variables were kept to explore the outcome from alternative choices in the variables sets (but see below). The variables retained for zooplankton were the same but with the addition of dO2.
Examination of the explanatory power of predictors sets and ranking of predictors: To further evaluate which subset of these variable subsets are key to model species distributions, GLM and RF were performed for each species for several sets of decreasing complexity (from ten to five predictors), and the adjusted R2 of the models, as well as the ranking of predictors within each set, was extracted (Supplementary Note 1). GLM and RF were used here because they are part of the SDMs that will be used for projections afterwards and because they represent maximally different inherent model complexities among the SDM types used73. For GLM, predictor importance was determined according to their absolute t-statistic using the caret R package. For RF, predictor importance was based on the Gini index, which measures the mean decrease in node impurity by summing over the number of splits (across all trees) that includes a variable, proportionally to the number of samples it splits. The ranger R package was used for assessing variable importance with RF models. To keep the variables ranks comparable across predictors sets, rank values were normalized to their maximum. For each model type, the distributions of the models’ R2 and the distribution of the predictors’ ranks were examined for phytoplankton and zooplankton separately. The same was done between the main groups constituting the phytoplankton (Bacillariophyta, Dinoflagellata and Haptophyta) and the zooplankton (Copepoda, Chaetognatha, Pteropoda, Malacostraca, Jellyfish, Chordata and Foraminifera). This allowed us to identify the most important predictors for modeling the species distributions and to evaluate if a decrease in the models’ skill was linked to the removal of certain variables. Group patterns allowed us to test whether different groups differed in their main environmental drivers.
For phytoplankton species, 14 sets of variables were examined (Supplementary Note 1). The first nine aimed to test: (i) the impact of alternative choices between variables that were identified as collinear (wind vs. MLPAR vs. MLD + PAR); (ii) the impact of progressively discarding variables that initially presented lower ranks (logEKE, Si*), and (iii) the impact of choosing logNO3 over logSiOH4, two variables representing global macronutrients availability and that present relatively high correlation coefficient (⍴ = 0.59). The last five sets of predictors (10–14) aimed to test the impact of alternatively removing those variables that presented relatively high ranks in the previous sets: SST, dSST, N*, logSiOH4, logChl, PAR. In a similar fashion, 15 sets of variables were tested for zooplankton (Supplementary Note 1). The first ten aimed to test: (i) the impact of choosing wind stress over MLPAR or over MLD + PAR; (ii) the impact of selecting PAR over MLD; (iii) the impact of discarding Si*, N*, logEKE; and (iv) the impact of choosing logNO3 over logSiOH4 (⍴ = 0.64). The last five sets of predictors (11–15) aimed to test the impact of alternatively discarding the top five predictors: SST, dSST, dO2, logSiOH4, and logChl.
GLM and RF converged towards similar median variable rankings and evidenced high inter-species variability (Supplementary Note 1). For total phytoplankton, GLM identified the following median ranking across all species: SST  > N*  >logChl  > logSiOH4 and dSST  > logNO3  > logEKE  > Si* > the PAR/MLD/MLPAR/wind stress cluster. RF ranked predictors in the following median order: SST and N*  >logChl  > dSST  > logSiOH4  > logEKE and logNO3  > Si* > the PAR/MLD/MLPAR/wind stress cluster. Yet, both GLM and RF also identified PAR as a major predictor for Haptophyta, which does not appear in the rankings for total phytoplankton because Haptophyta represented ~9% of species composition only. Since adding PAR does not alter the models’ R2 for the Bacillariophyta and Dinoflagellata, it was retained for the final predictors sets. For total zooplankton, GLM ranked predictors in the following median order across all species: SST  > dSST and logSiOH4  > logChl and logEKE  > dO2 and logNO3  > N*  >Si* and MLPAR  > wind stress  > PAR and MLD. RF identified the following median ranks: SST  > dSST and dO2  > logNO3  > logSiOH4  > logChl and logEKE  > N* and Si*  > PAR  > wind stress  > MLD and MLPAR. These median rankings reflected those of the Copepoda since they represented >70% of all zooplankton species. Again, rankings displayed high variance, reflecting high inter-species variability. Overall, based on all the results shown above, eight different final predictors sets were kept for modeling the distribution of phytoplankton (n = 4) and zooplankton (n = 4) consistently. In contrast to ref. 11, predictor ensembles were defined across all species rather than for each species. This was due to multiple reasons: (i) predictor availability for future model projections was limited and did not allow for species-specific variable choices, (ii) computational constraints with regard to the total number of ensemble members that could be projected, (iii) the five sets already contain those predictors that explain a majority of the variability in most models, (iv) recent findings from Righetti et al. (in prep.) that the uncertainty due to predictor choice is low for models with optimized background selection.
Phytoplankton:

1.

SST, dSST, logChl, N*, PAR, and logNO3

2.

SST, dSST, logChl, N*, PAR, and logSiOH4

3.

SST, dSST, logChl, N*, PAR, logNO3 and Si*

4.

SST, dSST, logChl, PAR, and logNO3

Zooplankton:

1.

SST, dSST, dO2, logChl, and logNO3

2.

SST, dSST, dO2, logChl, and logSiOH4

3.

SST, dSST, dO2, logChl, logSiOH4, and N*

4.

SST, dSST, dO2, logChl, logNO3, and Si*

SDMs evaluation and projections of monthly plankton species community composition
Only species with more than 75 presences were considered for modeling plankton species distributions (nphytoplankton = 348; nzooplankton = 541) because we aimed to achieve a relatively high presence-to-predictors ratio (~15, which is the ratio achieved for a species with 75 presences and five to six predictors) to be more conservative than Righetti et al.11 (i.e., minimum 24 presences) since we aimed to project the SDMs in future conditions based on a pool of species for which we have high confidence. This is in line with Guisan et al.60 who suggest to maintain at least a ratio of ten. For each species, each SDM, and each set of predictors, presences and background data were randomly split into a training set (80%) and a testing set (20%) and these evaluation tests were repeated ten times. Therefore, 160 (four SDM types x four predictor sets x ten separate evaluation runs) models were trained per species, resulting in a total of 142,240 SDMs. Model skill was evaluated based on two widely used metrics: the True Skills Statistic (TSS74) and the Area Under the Curve (AUC60). TSS values range between −1 and 1, with null values indicating that models perform no better than at random. AUC ranges between 0 and 1, with values 0.30 were retained for the final ensemble projections (Supplementary Fig. 3).
In total, 860 species were considered as successfully modeled (nphytoplankton = 336; nzooplankton = 524; Supplementary Data 2). For those, each of the 160 SDMs was projected onto the twelve monthly climatologies of its corresponding predictors set and the projections were averaged over the ten cross-evaluation runs. This way, we obtained global maps of monthly mean presence probability for each of the 16 SDM x predictor set combinations. These maps are to be interpreted as habitat suitability patterns that highlight the regions of the global ocean where the environmental conditions are most favorable for a species to develop. Habitat suitability maps were not converted to binary presence–absence maps as probabilistic outputs provide more gradual responses that should better reflect the very dynamic occupancy patterns of plankton and that are better suited than threshold approaches for our purposes75,76,77. For each grid cell of the global ocean, the probabilistic estimates of species habitat suitability were stacked to obtain monthly estimates of species composition. All SDMs were trained, evaluated, and projected using the biomod2 R package.

Ensemble projection of global plankton species richness
For every SDM x predictor set combination and every month, we summed the species habitat suitability to estimate monthly SR. Then, annual average SR was estimated for each cell. The annual average was preferred over the annual integral because of the high latitudes that presented a lot of missing values in winter because of the lower coverage of satellite products. Since this diversity estimate is the sum of habitat suitability indices, it is to be interpreted as the amount of SR that the monthly/annual average environmental conditions should be able to sustain (i.e., potential SR). This way we obtained 16 estimates of annual SR for total plankton (all 860 species together), phytoplankton and zooplankton. Ensemble projections of annual SR were then obtained for these three categories by averaging the annual SR estimates.
As the biological data used to train the SDMs span several decades (mostly between the 1970s and 2000s), our diversity estimates are integrative of changes in SR and species composition (i.e., changes in beta diversity) that occurred during these decades. The phytoplankton species modeled are mainly members of the Bacillariophyceae (45.8%), and the Dinoflagellata (45.8%), which usually rank among the large marine microalgae. Therefore, the phytoplankton SR estimates shown here should be mainly representative of the microphytoplankton (20–200 µm) rather than smaller size fractions. Nearly half (51.9%) of the zooplankton species modeled are Copepoda, making it the most represented groups in the zooplankton SR patterns followed by: Malacostraca (crustacean macrozooplankton such as Euphausiids and Amphipods; 13.9%), Jellyfish (13.1%), Foraminifera (5%), Chaetognatha (5%), Pteropods (4%), Chordata (3%). The last 4% of species modeled are a mix of Annelids and Branchiopods. The full list of the species modeled as well as their taxonomic classification is given in the Supplementary Data 2.
We underline that the 860 species modeled are those for which we have enough records for training reliable SDMs. Therefore, these species are likely to be those that are the most frequently detected by conventional sampling and identification techniques, either because: (i) they are the ones dominating total plankton abundance, which makes their collection more likely; or (ii) they are larger species (in terms of cell volume or body length), which would facilitate their sampling and identification under the binocular or recent imaging systems. We acknowledge that our approach does not allow us to account for rare taxa and thus under samples the true diversity of the marine plankton. Nonetheless, we argue that our approach does allow us to estimate global plankton diversity patterns as the species dominating plankton abundances are those carrying biogeographical information30, meaning their distribution and abundance patterns can be correlated to environmental gradients. Meanwhile, the patterns of rare and non-dominant species, which constitute the majority local SR, exhibit no biogeographical signature30. This has been supported in the previous study of Righetti et al.11 where the authors showed that global SR patterns were robust to the progressive exclusion of taxa with relatively few records.
We also acknowledge our estimates of species distribution might be biased by imbalances in species detection and sampling effort between sampling cruises, as those rely on a wide range of collection and identification methodologies. We argue that such biases are particularly significant when relying on abundance data, and that we mitigate them by: (i) converting all observations to presences and aggregating them onto a 1° x 1° grid; (ii) modeling SR as an emergent property overlapping the distribution of single species with equal weighting rather than modeling SR directly, in which case diversity estimates would be highly sensitive to sampling effort imbalances; (iii) by designing the SDMs in a way that accounts for spatial and temporal sampling biases in geographical and environmental space; and (iv) by tuning down the complexity of the SDMs (i.e., reduced number of features and predictors) in order to avoid model overfitting70.
Future environmental conditions in the global surface oceanThe future monthly fields of the selected environmental predictors were obtained from the projections for the 2012–2100 period of five ESM simulations for the IPCC’s RCP8.5 scenario from the MARine Ecosystem Model Intercomparison Project (MAREMIP, http://pft.ees.hokudai.ac.jp/maremip/index.shtml78) and/or the Coupled Model Intercomparison Project 5 (CMIP579). The model ensemble contained the following five ESMs (with their embedded ocean and ecosystem models indicated indicated in brackets after the semicolons): Community Earth System Model version 1 (CESM1, POP-BEC), Geophysical Fluid Dynamics Laboratory Earth System Model with Modular Ocean Model version 4 (GFDL-ESM2M; MOM-TOPAZ), Institut Pierre Simon Laplace Climate Model version 5A-LR (IPSL-CM5A-LR; NEMO-PISCES), Centre National de Recherches Météorologiques Climate Model version 5 (CNRM-CM5; NEMO-PISCES) and the Model for Interdisciplinary Research on Climate version 5 (MIROC5; MRI.COM-MEM). All ESMs were fully-coupled except for MIROC5 for which the ocean model was forced by the atmospheric component. All of the projections used here were benchmarked, quality-controlled and described in the previous multi-model comparison studies of Laufkötter et al.26,80. Considering the scope of the present study, we refer to these authors’ previous extensive descriptions for the full detail of the ESMs used here. Taken together, the present five ESM ensemble gathers models of various sensitivity to future climate forcing, and thus provides a wide range of alternative environmental conditions projected for the future surface ocean. With the present ESM ensemble, we account for the variability in the choice of the climate model, which is known to be a significant source of uncertainty in biodiversity projections; this source being consistently lower than those associated with SDM choice, though28,66,67.The monthly projections of the five selected ESMs were interpolated on the 1° x 1° cell grid of the WOA (i.e., the one used to train our SDMs) over the 2012–2100 period for all the nine chosen environmental predictors. To obtain future monthly climatologies that span a comparable amount of temporal variability as the in situ climatologies used to train the SDMs (~20 years), a baseline and an end-of-century time periods were first defined (2012–2031 and 2081–2100, respectively) for every ESM projection run. The 12 monthly climatologies were derived based on the models’ monthly projections and monthly anomalies were computed by subtracting the baseline values to the end-of-century ones. For dSST (i.e. annual range of SST), the annual maximum of SST was derived from the monthly climatologies and the difference between the baseline and the end-of-century dSST provided the delta value. These anomalies can be either positive or negative and they represent the difference in the predictors’ condition due to future climate change under the RCP8.5 GHG concentration scenario25. To obtain the final conditions prevailing in the surface ocean for the end-of-century period, the delta values were simply added to the in situ climatologies representing the conditions in the contemporary ocean. The SDMs of the 860 plankton species successfully modeled were then projected onto these future monthly climatologies for each of the ESM. This way, we estimate the monthly probability-based species composition in the future global ocean for each of the 80 combinations of SDMs (n = 4), ESMs (n = 5), and predictor set (n = 4). Overall, our ensemble forecast approach65 generates an unprecedented set of 825,600 species-level estimates of global future habitat suitability patterns. Finally, mean annual SR and community composition were calculated for total plankton, phytoplankton and zooplankton for each of the 80 possible combinations of projections, as described in section “Ensemble projection of global plankton species richness”.Analyses
Ensemble projections of changes in species richness, community composition turnover, and changes in species associations between the contemporary and the future ocean
For each of the 80 projection combinations described above, the mean annual SR estimates for the contemporary ocean were subtracted to their corresponding mean annual SR estimates for the future ocean to compute the percentage difference in mean annual SR (%∆SR) for total plankton, phyto- and zooplankton. The %∆SR represents the emergent change in SR caused by future climate change(s) through changes in species-level habitat suitability patterns. While changes in SR indicate climate change impacts on plankton alpha diversity, these do not inform us on the potential impacts on beta diversity (i.e., changes in community composition81). A community that experiences the replacement of all its constituting species by an equivalent number of newcomers will display a 100% rate of community turnover but no changes in SR. To investigate the amplitude of global plankton species turnover triggered by climate change, we examined future total turnover in annual species composition using Jaccard’s dissimilarity index while decomposing its two additive components: nestedness (i.e., changes in SR) and true turnover (ST), which indicates the % of species that will be replaced in a community27 using the betapart R package.
To do so, the mean annual species habitat suitability patterns used to estimate the ensemble changes in SR had to be converted to presence–absence maps as the Jaccard’s dissimilarity index requires binary inputs80. A range of thresholds (0.10 to 0.80, by steps of 0.01) was first explored for each SDM type (GLM, GAM, ANN, and RF) to infer threshold-based annual SR patterns. Then, we quantified the similarity of the threshold-based annual SR vs. the probability-based annual SR using Spearman’s rank correlation coefficient (⍴) and ordinary linear regressions (R2) to identify the range of thresholds that best match the probability-based estimates. The 0.25–0.40 range provided the most similar global SR patterns for GLMs, GAMs and ANNs (all ⍴  > 0.95, and all R2  > 0.90). The 0.10–0.25 range was chosen for RF models. These ranges largely overlap with the species mean probability thresholds that maximize the TSS/AUC evaluation metrics, which are commonly used to convert habitat suitability into presence–absence maps. However, the maximizing-TSS approach tends to underestimate the natural gradual response of organisms to environmental variations, which is particularly problematic when dealing with SR patterns of widely-dispersed and climate-sensitive ectotherms such as the plankton75,76,77. Therefore, we chose to rely on a range of thresholds instead as it enables us to account for a wider range of possible realizations of community composition and better reflect the dynamic occupancy patterns inherent to planktonic taxa. Consequently, we derived ST estimates in annual species composition for each of the SDM-dependent threshold mentioned above and every of the 80 annual projections combinations, for total plankton, phyto- and zooplankton separately. Again, the ensemble projection in annual ST was derived by averaging those projections.
We further examined how climate change could impact not only community composition but also those species associations within the community that represent potential biotic interactions, which support ecosystem functioning3,20,21. Based on the mean annual species composition estimates used to compute ST rates, a text analysis algorithm82,83 was used to identify pairs of species, which co-occur more frequently than expected given their individual occurrence. In short, the text analysis algorithm assigns an association score to each possible pair of two plankton species in all grid cells based on a likelihood ratio (LLR)82. The latter compares the probability of two species co-occurring together to the probability of one species occurring without their partner (i.e., two alternative probabilities), or when both are projected as absent, based on a combination of Shannon’s entropy indices (H’). LLR values are >0 and they scale with the significance level of the projected species association, whether it is a positive (co-occurrence) or a negative (one-sided occurrence or co-absence) association. To disentangle between those two cases, when a species pair displayed an observed co-occurrence frequency lower than the product of the one-sided occurrence frequencies normalized to sample size, its LLR value was multiplied by −1. This way, we can identify those species pairs whose co-occurrence probability is lower than the products of the two one-sided occurrence probabilities (LLR  More

Geochemistry and ecotoxicology of AMDAMD systems are an important source of metal/metalloid pollution to the receiving hydrosphere with devastating consequences on the biological drivers of affected ecosystems. Environmental menaces of AMD have not been exhaustively reported worldwide. Scanty information exists across Africa and many developing economies. The homogenised mixture of detached biofilm and AMD samples from a derelict coal mine at three sampling periods were assayed for geochemical delineation and analysed for pollution intensity against reference background geochemical values. The measured values of the physical properties and contents of selected HMs in drains from a coal mine in Nigeria were as presented in Supplementary Table A.1. Virtually all the measured parameters exceeded the permissible limits of WHO guidelines for potable water. The AMD water was acidic (pH = 3.1 ± 0.265), and contained characteristic anions that are common to AMD including dissolved sulphides (1.37 ± 0.233 mg l−1), sulphates (313.0 ± 15.9 mg l−1), carbonate (253.0 ± 22.4 mg l−1) and nitrate (86.6 ± 41.0 mg l−1) above the allowable limits of WHO. Although the acidic pH of AMD in the present study compares well with those associated with mines in Russia14, more extreme acidic pH values have been reported in other climes. Negative pH values of − 1.56 and − 3.6 were observed in AMD from Iberian Pyrite Belt20 and Richmond Mine at Iron Mountain, USA21, respectively. The values of physicochemical parameters associated with the AMD from Onyeama were similar to data reported for other mine wastewaters in Nigeria22 and elsewhere4. It is known that sulphide minerals, in presence of water and oxygen, oxidise to sulphate as observed in the elevated sulphate concentration (313 ± 15.9 mg l−1) in the present study. The low pH observed in the AMD is due to the formation of sulphuric acid from sulphate in presence of protons (H+). This consequently causes the leaching of metal/metalloid ions into the drains. The concentrations of dissolved organic matter in AMD tends to be relatively low ( Co  > Pb  > As  > Ni  > Cr  > Fe (Table 1). Enrichment of five HMs was exceptionally high (Cd  > Co  > Pb  > As  > Ni), while Cr and Fe were very high and moderately enriched the AMD water, respectively. The astronomically high contamination and enrichment factors of the AMD signified the enrichment potentials the AMD portends on receiving surface waters. The AMD from the Onyeama coal mine has been reportedly impacting the water qualities of rivers within the location25. It is assumed that the extremely high concentrations of toxic metals/metalloids in the AMD dilutes out upon discharges into nearby rivers, contaminating the surface water and raising the bioavailable metals/metalloids beyond safe thresholds. Further reports of toxic metals/metalloids enrichment of surface waters via inflow of AMDs from other mines in Nigeria26 and other climes3,4,27 are worrisome and oblige mitigations.Table 1 Physico-chemistry, pollution and ecological impact determinants of heavy metals and metalloid contained in the AMD from coal mine.Full size tableThe HMs-enriched environments inadvertently exert ecotoxicity unto the drivers of the ecosystems. The level of HMs accumulation to the organic matter in the AMD, through geo-accumulation (Igeo) index of Fe (7.60 ± 0.779) to Cd (20.9 ± 0.075) (Supplementary Table A.2), was very severe and in a similar order to CF. It possibly implies organic matter in the AMD harbours the mobile toxic metal/metalloid concentrations and make them available to the food web28. Thus, biomagnification of the toxic metals/metalloids along the trophic level becomes palpable and a challenge to the biota of any surface water receiving the AMD and to public health21,28. Ecological risk assessments define and categorise the pollution status of ecosystems with the HMs contained in the AMD. Based on the potential ecological risk factor (Er), Cd exerted an extremely high-risk index (36.3 ± 1.96 × 106), and none of the metals/metalloids exercised less than 1000 risk index (Supplementary Table A.2). All the HMs/metalloid contained in the AMD posed very high ecological risks and could be categorised in the order of Cd  > Co  > Pb  > As  > Ni  > Cr  > Fe. The modified potential ecological risk factor (MEr), however, stipulated that five HMs posed a very high risk in the order: Cd  > Co  > Pb  > As  > Ni, whereas Cr and Fe were determined to be of considerate and low risks, respectively. The HMs exerted high risk to the AMD ecosystem as calculated by ecological risk quotient (RQ) in the order: Pb  > Cd  > As  > Ni  > Co  > Fe  > Cr. The ecological risk index of all the HMs as a whole was very high (375,000 ± 22,400) index as stipulated by the modified potential ecological risk index (Table 1). The prodigiously high ecological risks indexes of the HMs/metalloid in the AMD indicated grave danger the AMD would portend on the surface- and ground-waters.Microbial community structure of AMD from Onyeama coal mineA total of 26,160 and 40,403 valid (filtered) sequence reads were obtained for bacteria and eukarya, respectively, after a quality check of biofilm-water amplicon sequence data. The valid sequences were clustered into 2036 and 1002 operational taxonomic units (OTUs) of bacteria and eukarya domains of life, respectively, as presented in Table 2. Microbial community structures are sensitive descriptors of ecological stressors pivotal to understanding ecosystem functions29. The number of clustered high quality, non-chimeric sequences as OTUs based on UCLUST and CD-HIT against the sequence reads was depicted as asymptotic rarefaction curves (Supplementary Fig. A.1). The curves revealed that higher numbers of OTUs were delineated from valid sequence reads of 16S rRNA genes, unlike the lesser number of OTUs obtained from valid sequence reads of ITS2 region located between 5.8S and 28S rRNA genes of eukaryotes. The OTU richness observed in the rarefaction curves established coverage of the majority of species and was further validated with the richness and diversity estimations presented in Table 2. Despite the higher number of valid sequence reads obtained from the amplified ITS2 (40,403) than that of 16S rRNA genes (26,160), the observed OTUs were more in 16S rRNA genes (2036) than those of ITS2 (1002). More than 99.8% and about 98.5% of the microbial community in AMD from the Onyeama coal mine represented eukarya and bacteria OTUs, respectively, based on estimated Good’s library coverage. The coverage degree of the MiSeq sequencing corroborated the rarefaction curves. Furthermore, the estimated OTU richness (based on higher values obtained from ACE, Chao1 and JackKnife indexes) showed that bacterial phylotypes were richer than those of eukarya. Alpha diversity indexes (NPShannon, Shannon, and inverse Simpson) phylogenetic diversity index revealed that bacteria in the AMD were more diverse than eukarya OTUs.Table 2 Alpha diversity of microbiome evenness, richness and varieties of species in the sediments.Full size tableTaxonomy and phylogeny of microbial OTUs in AMD from coal mineThe taxonomic composition and relative abundances of the AMD microbiome, as shown in Fig. 1, revealed that the bacterial community spanned 10 phyla whose sequence reads were at least 1% (Fig. 1a). Whereas the eukarya domain of life (with sequence reads ≥ 1%) found in the AMD include Fungi, Plantae and Animalia kingdoms (Fig. 1b). Ascomycota, unclassified Fungi phylum (Fungi_p), Basidiomycota, and Mucoromycota represented Fungi kingdom, while Ciliophora and Arthropoda phyla were Animalia and Chlorophyta phylum epitomised Plantae kingdom. Association of the domain Eukarya (comprising Alveolates, Chlorophyta and Fungi as observed in this study) with AMD is reported to a lesser extent when compared with Bacteria30. The Fungi, largely represented by Ascomycota and Basidiomycota, are primarily found in sub-surface low-pH biofilms thriving in AMD31. While the Alveolates are suggested to have acted as primary/secondary consumers, the amoebae were secondary grazers in the AMD ecosystem29,32. Fungi taxa must have participated in carbon cycling as the main decomposers in the microbial community of the AMD. The taxonomic composition and relative abundance of phyla regarded as ‘Others’ (sequence reads  50%). Evolutionary analyses were conducted in MEGA6.Full size imageUrease-producing bacteria instigate insoluble metal-carbonate micro-precipitation through urease activity16. The growth-time courses and urease activities of the bacteria consortium in simulated AMD were presented as curves (Fig. 5). It was observed that the impact of high concentrations of HMs cocktails was not pronounced beyond the early 6 h post-inoculation, which was regarded as the lag phase. The bacteria consortium might have activated necessary genes needed to tolerate and sequester the metals/metalloids toxicity during the lag phase without cell multiplications. Afterwards, the bacteria consortium grew steadily with the production of urease, based on increasing measurement of urease activity, as incubation continued. At 30 h post-inoculation, 245.3 (± 23.7) U ml−1 activity of urease was observed in broth without a toxic metal cocktail. However, more urease activity (255 ± 7.6 U ml−1) by the bacteria consortium was observed in medium amended with low concentrations of metal cocktails unlike lesser activities of 235 (± 7.6) U ml−1 and 193.7 (± 10.7) U ml−1 associated with medium and high metal concentrations, respectively. As the growth remains stationary and pH further increased to  > 8.2, urease activities were at least 253 U ml−1 in all the cultures. Although urease activities at acidic pH have been reported in acid-tolerant human pathogens19, the findings in this report were assumedly the first amongst bacterial strains from AMD-polluted environments. The urease activities at acidic pH compared favourably with activities at alkaline pH in previous studies7,16,42,44. Moreover, the pH of the culture system kept increasing, alleviating the acidity condition that initially prevailed in the AMD system.Figure 5Growth kinetics of bacterial consortium via viable counts extrapolated into optical density at 600 nm wavelength (a) and growth-dependent urease activity of bacterial consortium (b) in TGYM broth without heavy metals (HMs) cocktail, and with low, medium, and high concentrations of HMs cocktails. Low HMs concentrations cocktail comprised (per liter) Cd, 27.9 mg; Pb, 118.7 mg; Co, 16.2 mg; Ni, 16.2 mg; and As, 61.5 mg. While medium HMs concentration contained (per liter) Cd, 55.7 mg; Pb, 237.3 mg; Co, 32.4 mg; Ni, 32.3 mg; and As, 123.1 mg. High HMs concentration contained (per liter) Cd, 139.3 mg; Pb, 593.3 mg; Co, 81.1 mg; Ni, 80.7 mg; and As, 307.6 mg. The mean pH at the beginning of experiment was 3.5 and rose to 8.2–8.4 at 48 h post-inoculation. Growth kinetics at exponential growth phase are in the inserts of panel (a), where ‘Td’ represents doubling time and ‘K’ is the growth rate at exponential growth phase. Error bars represent standard error mean (SEM) of triplicate experiments. The culture conditions were as explained in the “Methods” Section (Growth kinetics and urease activity of bacteria consortium; Determination of bacterial growth-dependent HMs/metalloid sequestration in simulated and natural AMD).Full size imageInterestingly, urease activity was observed in low quantity at acidic pH, unlike higher activity when the pH inclined towards alkaline (Fig. 5). It is proposed that urea finds its way into Onyeama coal mine drains through runoff from agricultural soils fortified with urea fertilizers and animal manures, which are common agricultural practices in Nigeria. The products of urea hydrolysis might have equilibrated in water to form bicarbonate, ammonium and hydroxyl ions that serially increased the culture pH. Ultimately, the bicarbonate equilibrium might have shifted to form carbonate ions (HCO3− + H+ + 2NH4+ + 2OH− ↔ CO32− + NH4+  + 2H2O) that enhanced the metal-carbonate micro-precipitation (Me2+  + Cell → Cell-Me2+ + CO32− → Cell-MeCO3). The gradual increase in pH could have further indulged the formation of CO32− from HCO3−, leading to metal-CO3 precipitation around cells and in culture media. Bicarbonates enrichment with inherent ammonia production was thought to have provided additional acid neutralization of the AMD. The growth kinetics after the presumed lag phase in the early 6 h to late exponential phase at 18 h showed that a low concentration of HMs cocktails did not have an impact on the growth of the bacteria consortium. Consequently, the bacteria consortium exhibited excellent sequestration of multi-component toxic HMs in both the simulated toxic metal-rich AMD and the actual AMD obtained from the Onyeama coal mine (Table 3).Table 3 Growth associated sequestration and precipitation of heavy metals/metalloid cocktail and AMD from Onyeama coal mine.Full size tableThe bacteria consortium displayed more than 94% efficiency of Cd and Pb sequestration in natural AMD, while 100% efficiency was observed in all the simulated AMD treatments (Table 3). Low performance was found with Ni and As, but not less than 70% sequestration efficiency was observed in all treatments. Efficient sequestrations of toxic metals, up to 100% removal efficiency of most toxic metals, observed with the bacteria consortium were similar to findings in a previous study13. Mixed-bacterial cultures are known to be able to perform more complex tasks and survive in more unstable environments than a monoculture. Nevertheless, 89.3–98% removal efficiencies of Ni, Pb, Co, and Cd from solution have been reportedly achievable with urease-producing Sporosarcina koreensis45. Similarly, Bacillus sp. KK1 reportedly mitigated lead-contaminated mines tailings containing mobile Pb (1050 mg kg−1) to form insoluble precipitates of PbS and PbSiO334. Growth-dependent sequestration of HMs cocktails by the bacteria consortium was adduced to be via precipitation. The weight of the precipitates was evaluated to be proportional to concentrations of HMs cocktail present. The bacteria consortium was observed to drive the formation of as much as 15.6 (± 0.92) mg ml−1 precipitates (Table 3) that were assumed to be in form of HMs-carbonates in TGYM supplemented with high concentrations of HMs cocktail within 24 h post-inoculation. In natural AMD bio-stimulated with urea and seeded with bacteria consortium for 24 h, 10.5 (± 0.52) mg ml−1 HMs precipitates was observed unlike 8.57 (± 2.52) mg ml−1 precipitates obtained from natural AMD toxic metals sequestration without urea fortification. It appeared that the quantity of toxic metal precipitate was proportional to quantities of available toxic metals, which corresponded to the number of heterogeneous nucleation sites on the surface of the bacterial cells. Omoregie et al.42 reported a relatively similar quantum of precipitation as CaCO3 with species of ureolytic Firmicutes isolated from limestone caves. As such, there was no correlation between urease activity and quantum of toxic metal precipitation since there is a likelihood that other metabolic activities may be linked to urease activities. Nevertheless, the bioremediation strategies demonstrated in the present study exhibited excellent toxic metal sequestrations unlike insignificant (p  > 0.05) natural attenuation process of the autochthonous community without augmentation with bacteria consortium and stimulation with nutrients (as presented in Table 3).In conclusion, AMD from the Onyeama coal mine is a point source of pollution to the surrounding environments because of its richness in anions and toxic metals/metalloids. It has a high potential of enriching the receiving hydrosphere with toxic metals/metalloids and exerts severe ecological risks (Er  > 320) with Cd and Pb wielding a huge critical risk index (38.1 ± 2.18 × 106) on the biological elements of the ecosystems. The dominance of Proteobacteria (50.8%), Bacteroidetes (18.9%), Ascomycota (60.8%), and Ciliophora (12.6%) characterised the microbial community of the AMD, where unclassified OTUs occurred mostly among the species. Enrichment of the AMDs skewed the bacterial community as depicted in the alpha diversity indexes against that of coal AMD leading to the selection of bacteria consortium with an excellent potential of stemming the toxicants in the AMD. The bacteria consortium efficiently removed toxic metals/metalloids ( > 70%) through precipitation and simultaneously neutralised AMD acidity. The bacteria consortium exhibited appreciable urease activity ( > 190 U ml−1), through which the precipitation was assumed possible via the formation of metal/metalloid-carbonates. The bacteria consortium is suggested to be a sustainable biotechnological candidate in designing a bioremediation strategy for decommissioning AMD before discharge into the surrounding environment. More

Convergence is a common feature of evolution and has great effect on the succession of microbial communities. For natural microbial communities such as the microbiome of gut [1], soil [2], sediment [3], rhizosphere [4], and phyllosphere [5], convergence generally means that different communities converge towards a similar species composition, which is accompanied by species loss and acquisition. Such a convergence can be reproduced in simplified synthetic communities [6,7,8], or even in single-species populations, in which convergence can still be achieved at sub-species level [9, 10]. Unlike the convergence of natural microbial community, those experiments carried out in a sterile laboratory environment only involves the loss of species. Specifically, the main manifestation of convergence in the synthetic community containing stably coexisting species lies in that the relative proportion of species tend to become consistent [7, 8]. Nonetheless, synthetic community opens a window for us to investigate the ecological mechanism. Previous studies of synthetic communities have revealed that the convergence of bacterial community can be regulated by pH [11], mortality [12], and particularly nutrient availability [13, 14]. Most existing studies focus on the changes in species proportions, but there is a lack of in-depth understanding of the gene expression changes driven by the community species interaction.In this study, we constructed a synthetic community with two model microorganisms, Escherichia coli K-12 (EC) and Pseudomonas putida KT2440 (PP), and reproduced a convergent community assembly in closed broth-culture system. In monocultures, the growth curves of both E. coli and P. putida fitted well with the bacterial growth model, and fell into a logarithmic phase at the first 4 h of bacterium culture and a stationary phase at subsequent 20 h (after the first 4 h) (Fig. 1a). When same quantities of bacteria were grown in cocultures, their quantities were basically similar to those in monocultures, particularly in the logarithmic phase (Fig. 1b–d). By contrast, the quantities of minority species in cocultures continued to increase, and they were close to the quantities in monocultures at 24 h post co-cultivation (Fig. 1b–d). Besides, statistical analysis showed that the quantities of P. putida in all three cocultures were overall greater than that in monoculture, while E. coli quantities were no more than its monoculture (Fig. 1b–d), suggesting that P. putida has a negative effect on the growth of E. coli, but E. coli promotes that of P. putida.Fig. 1: Convergence of community structure and gene expression.a–d Growth curves of E. coli and P. putida in monoculture (a) and the “1:1000”, “1:1”, “1000:1” cocultures (b–d). In b–d subplots, the growth curves of monocultures were placed on the background layer (dashed lines), and the significant differences in cell quantity between coculture and corresponding monoculture were shown (ns, non-significant; *p  More

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