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Similar non-native and invasive flora, such as the fever tree (pictured) are found in regions previously occupied by the same European empire.Credit: Alamy

In 1860, a British expedition raided the highland forests of South America, looking for a hot commodity: Cinchona seeds. The bark of these ‘fever’ trees produces the anti-malarial compound quinine, and the British Empire sought a stable source of the drug for its soldiers and civil service in India. After cultivation in the United Kingdom, young Cinchona trees were planted across southern India and what is now Sri Lanka.The British quinine scheme failed — instead, a species introduced to Java, now part of Indonesia, by the Dutch Empire later dominated the global market — but Cinchona trees are still common in parts of India.Such botanical legacies of imperial rule are common, finds a study published on 17 October in Nature Ecology & Evolution1. Regions that were once occupied by the same European colonial power — such as India and Sri Lanka — tend to have similar species of non-native and invasive plants. The longer the regions were occupied, the more their populations of invasive species resemble each other, the research found.Alien floraThe link between European colonialism and invasive species is intuitive, and has been noted by other researchers, says Bernd Lenzner, a macro-ecologist at the University of Vienna who led the study. To test the association, his team turned to the Global Naturalized Alien Flora database, which maps the distribution of nearly 14,000 invasive plant species.
The imperial roots of climate science
Across more than 1,100 regions, including 404 islands, the researchers found that regions once occupied by the British Empire had more similarities in their invasive flora than did ‘artificial’ empires that the team assembled from random regions. This was also the case for regions once part of the Dutch Empire (former Spanish and Portuguese colonies had alien-plant compositions similar to those of the artificial empires).Climate and geography play an important part in explaining the overlap in the diversity of invasive species, modelling by Lenzner’s team found, but so does the length of time regions were occupied by an imperial power. Regions that were central to trade, such as southern India for the British Empire and Indonesia for the Dutch Empire, formed clusters with considerable overlap in invasive-plant composition.The analysis did not look at when individual plant species were introduced or why. But anecdotally, many of the plants that were commonly taken to former empires were once of economic value and their populations were probably established on purpose, says Lenzner.Global trade impactsThe study’s conclusions might be “super obvious”, but they have important implications for conservation, says Nussaïbah Raja, a palaeontologist at Friedrich-Alexander University of Erlangen–Nürnberg in Erlangen, Germany. “We should be taking this history into consideration when we think about management of species.” Appreciating the history of introduced plants — as well as their place in today’s ecosystems — could help conservationists to handle future changes in biodiversity, such as those driven by climate change, Raja adds.Global trade is beginning to overwrite the colonial legacy of introduced plants. For example, the analysis showed similarities between invasive plant populations in Fujian, China, and some parts of Australia. Although both places were once connected by the British Empire, more recent global trade might also be partly responsible for the overlap.“We are still seeing these imprints of the colonial-empire legacies from centuries ago,” Lenzner says. “So what we’re doing and the species we’re redistributing today will be visible far into the future.” More

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Benthic organism exudate collectionsExudate collections from benthic organisms were conducted on board the R/V Walton Smith in November 2018 in Lameshur Bay, St. John, U.S. Virgin Islands within the Virgin Islands National Park. In brief, we collected six species of benthic organisms (n = 6 specimens), incubated these organisms in separate containers for 8 h, and harvested the incubation water to characterize the composition of dissolved metabolites in their exudates. A description of the exudate collections is included below (additional details available in Supplementary Methods).Before each organism experiment, 58 l of surface (non-reef) seawater was collected ~1 mile offshore (18 17.127° N, 064 44.312° W, 31.6 m depth). Cells and particles were removed using peristaltic pressure through a 0.2 µm filter (47 mm, Omnipore, EMD Millipore Corporation, Billerica, MA, USA) using metabolomics-grade tubing and this filtrate (filtered seawater) was collected for the incubations. Additionally, two to three, 2 l filtrate subsets per experiment were acidified with concentrated hydrochloric acid (final concentration 1% volume/volume) and subjected to solid-phase-extraction (SPE) using a negative vacuum pressure of –3.7 to –5 100xkPA in Hg, to serve as controls. Before SPE, 6 ml, 1 gm Bond Elut PPL cartridges (Agilent, Santa Clara, CA, USA) were pre-conditioned with 6 ml of 100% HPLC-grade methanol.For the experiments, six species of benthic organisms were collected from reefs around Lameshur Bay by SCUBA divers. Experiments were completed on three stony corals (Porites astreoides, Siderastrea siderea, and Psuedodiploria strigosa), two octocorals (Plexaura homomalla and Gorgonia ventalina), and one encrusting alga (Ramicrusta textilis) (Table S1). P. astreoides, S. siderea, and R. textilis were held in a seawater table for 24 h (hrs) before the incubations and colonies from the other three species were held for 2-3 h due to timing constraints. Coral and algal fragments were generally small (2.5-5.0 cm in length).For each incubation, nine, acid-washed, 10 l polycarbonate bins (with lids) containing filtered seawater (4 l) were secured into an illuminated aquarium table (Prime HD, Aqua illumination, Bethlehem, PA, USA) (Photosynthetically Active Radiation = ~350–600 µmol quanta m−2 s−1). Air bubblers with sterilized Fluorinated Ethylene Propylene (FEP) tubing (890 Tubing, Nalgene, Thermo Scientific, Waltham, MA, USA) were used to inject air into each bin. Surface seawater was circulated through the aquarium table to maintain reef seawater temperature (29.5 °C). Six colonies/fragments of one species were randomly placed into 6 bins. The other 3 bins were reserved for control incubations containing filtered seawater only. A sensor (8 K HOBO/PAR loggers; Onset, Wareham, MA) monitored temperature and light conditions (data not shown). At the end of each 8 h experiment, colonies/fragments were wrapped in combusted aluminum foil and flash frozen in a charged dry shipper. The water in all incubations was re-filtered (as outlined above) and 2 l of each filtrate were acidified and subjected to SPE as described above. SPE cartridges were wrapped in combusted aluminum foil, placed in Whirl-Pak (Nasco, Madison, WI, USA) bags, and frozen at –20 °C.Metabolomics analyses and data processingAt the Woods Hole Oceanographic Institution (WHOI), metabolites were eluted from the thawed cartridges into combusted, borosilicate test tubes using 100% methanol (Optima grade) within 3 months of collection. The eluents were transferred into combusted amber 8 ml vials and nearly dried using a vacuum centrifuge. Samples were reconstituted in 200 µL of 95:5 (v/v) Milli-Q (MQ, Millipore Sigma, Burlington, MA, USA) water: acetonitrile with a deuterated standard mix added as an internal control (Table S2), vortexed, and prepared for targeted and untargeted metabolomics analyses in both positive and negative ion modes as described previously [16]. Samples prepared for untargeted analyses were further diluted (1:200) with the reconstitution solvent. A pooled sample (technical replicate) was made by combining aliquots from all samples and was injected repeatedly to assess instrument drift over the course of the run and for downstream sample processing. Samples prepared for targeted metabolomics were analyzed using an ultra-high performance liquid chromatography system (UHPLC; Accela Open Autosampler and Accela 1250 Pump, Thermo Scientific, Waltham, MA, USA) coupled to a heated electrospray ionization source (H-ESI) and a triple stage quadrupole mass spectrometer (TSQ Vantage, Thermo Scientific), operated in selected reaction monitoring (SRM) mode. Samples prepared for untargeted metabolomics were analyzed with a UHPLC system (Vanquish UHPLC, Thermo Scientific) coupled to an ultra-high resolution mass spectrometer (Orbitrap Fusion Lumos, Thermo Scientific). MS/MS spectra were collected in a data-dependent manner using higher energy collisional dissociation (HCD) with a normalized collision energy of 35% (detailed methods provided in [16]). A Waters Acquity HSS T3 column (2.1 × 100 mm, 1.8 μm) equipped with a Vanguard pre-column was used for chromatographic separation at 40 °C for targeted and untargeted analyses. Sample order was randomized and the pooled sample was analyzed after every six samples.For targeted metabolomics analysis, tandem MS/MS data files were converted into .mzML files using msconvert and processed with El-MAVEN [49]. Calibration curves for each compound (8 points each) were constructed based on the integrated peak areas using El-MAVEN. The concentrations of metabolites in the original samples were determined by dividing each concentration by the volume of the filtrate that passed through each PPL column. Finally, metabolite concentrations above the limits of detection and quantification were corrected for extraction efficiency using in-house values determined using standard protocols [50]. Statistical analyses of targeted metabolite concentrations were conducted using Welch’s independent t-tests and ANOVAs or Wilcoxon rank sum tests if data were not normally distributed (additional details in Supplementary Methods). We determined the mass of each colony and conducted Pearson correlations to investigate if colony size significantly correlated with concentrations of targeted metabolites, but no correlations were found.For the untargeted metabolomics analyses, raw files containing MS1 and MS/MS data were converted into .mzML files using msconvert and processed using XCMS [51]. Ion modes were analyzed separately. Before processing with XCMS, the R package AutoTuner [52] was used to find XCMS processing parameters appropriate for the data. In XCMS, the CentWave algorithm picked peaks using a gaussian fit. The specific parameters for peak picking for both ion modes were: noise = 10,000, peak-width = 3–15, ppm = 15, prefilter = c(2,168.600), integrate = 2, mzdiff = –0.005, snthresh = 10. Obiwarp was used to adjust retention times and this step was followed by correspondence analysis. 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