Ecology

1.
Dressler, R. L. In Phylogeny and Classification of the Orchid Family (ed. Dressler, R. L.) 7–13 (Cambridge University Press, Cambridge, 1994).
Google Scholar 
2.
Delforge, P. In Orchids of Europe, Nord Africa and the Middle East (ed. Delforge, P.) 67–68 (A & C Black Publishers, London, 2001).
Google Scholar 

3.
Barman, D. & Devadas, R. Climate change on orchid population and conservation strategies: a review. J. Crop Weed. 9(12), 1–12 (2013).
Google Scholar 

4.
Fay, M. F. Orchid conservation: how can we meet the challenges in the twenty-first century. Bot. Stud. 5, 1–6 (2018).
Google Scholar 

5.
Brovkin, V. Climate–vegetation interaction. J. Phys. IV FRANCE 12, 57–72 (2002).
Google Scholar 

6.
Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148. https://doi.org/10.1038/nature02121 (2004).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

7.
Anderson, M. G. & Ferree, C. E. Conserving the stage: climate change and the geophysical underpinnings of species diversity. PLoS ONE 5(7), e11554 (2010).
ADS  PubMed  PubMed Central  Google Scholar 

8.
Bálint, M. et al. Cryptic biodiversity loss linked to global climate change. Nat. Clim. Change. 1, 313–318. https://doi.org/10.1038/nclimate1191 (2011).
ADS  Article  Google Scholar 

9.
Kolanowska, M. Niche conservatism and the future potential range of Epipactis helleborine (Orchidaceae). PLoS ONE 8(10), e77352. https://doi.org/10.1371/journal.pone.0077352 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

10.
Kolanowska, M. The naturalization status of African Spotted Orchid (Oeceoclades maculata) in Neotropics. Plant Biosyst. 148(5), 1049–1055. https://doi.org/10.1080/11263504.2013.824042 (2014).
Article  Google Scholar 

11.
Kolanowska, M. & Konowalik, K. Niche conservatism and future changes in the potential area coverage of Arundina graminifolia, an invasive orchid species from Southeast Asia. Biotropica 46(2), 157–165. https://doi.org/10.1111/btp.12089 (2014).
Article  Google Scholar 

12.
Konowalik, K. & Kolanowska, M. Climatic niche shift and possible future spread of the invasive South African Orchid Disa bracteata in Australia and adjacent areas. PeerJ. 6, e6107. https://doi.org/10.7717/peerj.6107 (2018).
Article  PubMed  PubMed Central  Google Scholar 

13.
Kolanowska, M. et al. Global warming not so harmful for all plants – response of holomycotrophic orchid species for the future climate change. Sci. Rep. 7, 12704. https://doi.org/10.1038/s41598-017-13088-7 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Naczk, A. & Kolanowska, M. Glacial refugia and future habitat coverage of selected Dactylorhiza representatives (Orchidaceae). PLoS ONE 10(11), e0143478. https://doi.org/10.1371/journal.pone.0143478 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

15.
Kolanowska, M. & Rykaczewski, M. From the past to the future – glacial refugia, current distribution patterns and future potential range changes of Diodonopsis (Orchidaceae) representatives. Lankesteriana. 17(2), 315–327 (2017).
Google Scholar 

16.
Wang, H. H. et al. Species distribution modelling for conservation of an endangered endemic orchid. AoB Plants 7, plv039 (2015).
PubMed  PubMed Central  Google Scholar 

17.
Tsiftsis, S., Djordjević, V. & Tsiripidis, I. Neottia cordata (Orchidaceae) at its southernmost distribution border in Europe: threat status and effectiveness of Natura 2000 Network for its conservation. J. Nat. Conserv. 48, 27–35 (2019).
Google Scholar 

18.
Vollering, J., Schuiteman, A., de Vogel, E., van Vugt, R. & Raes, N. Phytogeography of New Guinean orchids: patterns of species richness and turnover. J. Biogeogr. 43(1), 204–214 (2016).
Google Scholar 

19.
Reina-Rodríguez, G. A., Rubiano Mejía, J. E., Castro Llanos, F. A. & Soriano, I. Orchid distribution and bioclimatic niches as a strategy to climate change in areas of tropical dry forest in Colombia. Lankesteriana 17(1), 17–47 (2017).
Google Scholar 

20.
Gogol-Prokurat, M. Predicting habitat suitability for rare plants at local spatial scales using a species distribution model. Ecol. Appl. 21, 33–47. https://doi.org/10.1890/09-1190.1 (2011).
Article  PubMed  Google Scholar 

21.
Dudley, T. L. & Bean, D. W. Tamarisk biocontrol, endangered species risk and resolution of conflict through riparian restoration. Biocontrol 57, 331–347. https://doi.org/10.1007/s10526-011-9436-9 (2012).
Article  Google Scholar 

22.
Antúnez, P. et al. The potential distribution of tree species in three periods of time under a climate change scenario. Forests 9(10), 628. https://doi.org/10.3390/f9100628 (2018).
Article  Google Scholar 

23.
Wilson, C. D., Roberts, D. & Reid, N. Applying species distribution modeling to identify areas of high conservation value for endangered species: a case study using Margaritifera margaritifera (L.). Biol. Cons. 144, 821–829 (2011).
Google Scholar 

24.
Koch, R., Almeida-Cortez, J. S. & Kleinschmit, B. Revealing areas of high nature conservation importance in a seasonally dry tropical forest in Brazil: combination of modelled plant diversity hot spots and threat patterns. J. Nat. Conserv. 35, 24–39 (2017).
Google Scholar 

25.
Spiers, J. A., Oatham, M. P., Rostant, L. V. & Farrell, A. D. Applying species distribution modelling to improving conservation based decisions: a gap analysis of Trinidad and Tobago’s endemic vascular plants. Biodivers. Conserv. 27(11), 2931–2949 (2018).
Google Scholar 

26.
Ramírez, S. R., Gravendeel, B., Singer, R. B., Marshall, C. R. & Pierce, N. E. Dating the origin of the Orchidaceae from a fossil orchid with its pollinator. Nature 448, 1042–1045 (2007).
ADS  PubMed  Google Scholar 

27.
Conran, J. G., Bannister, J. M. & Lee, D. E. Earliest orchid macrofossils: early Miocene Dendrobium and Earina (Orchidaceae: Epidendroideae) from New Zealand. Am. J. Bot. 96(2), 466–474 (2009).
PubMed  Google Scholar 

28.
Kenny, J. 2008. Orchids of Trinidad and Tobago (ed. Kenny, J.) 1–127 (Prospect Press, 2008).

29.
Swarts, N. D. & Dixon, K. W. Terrestrial orchid conservation in the age of extinction. Ann. Bot. 104(3), 543–556 (2009).
PubMed  PubMed Central  Google Scholar 

30.
Teketay, D. History, botany and ecological requirements of coffee. Walia 20, 28–50 (1999).
Google Scholar 

31.
Tupac, O. J., Ackerman, J. D. & Bayman, P. Diversity and host specificity of endophytic Rhizoctonia-like fungi from tropical orchids. Am. J. Bot. 89(11), 1852–1858 (2002).
Google Scholar 

32.
Pellegrino, G., Luca, A. & Bellusci, F. Relationships between orchid and fungal biodiversity: mycorrhizal preferences in Mediterranean orchids. Plant Biosyst. 150(2), 180–189 (2016).
Google Scholar 

33.
Suárez, J. P. & Kottke, I. Main fungal partners and different levels of specificity of orchid mycorrhizae in the tropical mountain forests of Ecuador. Lankesteriana 16(2), 299–305 (2016).
Google Scholar 

34.
Senthilkumar, S. Mycorrhizal fungi of endangered orchid species in Kolli, a part of eastern ghats, South India. Lankesteriana 7, 15–156 (2003).
Google Scholar 

35.
Pereira, O. L., Rollemberg, C. L., Borges, A. C., Matsuoka, K. & Kasuya, M. C. M. Epulorhiza epiphytica sp. nov. isolated from mycorrhizal roots of epiphytic orchids in Brazil. Mycoscience 44, 153–155 (2003).
Google Scholar 

36.
Tedersoo, L. Biogeography of mycorrhizal symbiosis (Springer, Cham, 2017).
Google Scholar 

37.
Waud, M., Brys, R., Van Landuyt, W., Lievens, B. & Jacquemyn, H. Mycorrhizal specificity does not limit the distribution of an endangered orchid species. Mol. Ecol. 26(6), 1687–1701 (2017).
CAS  PubMed  Google Scholar 

38.
van der Cingel, N. A. An atlas of orchid pollination: America, Africa, Asia and Australia (A.A. Balkema Publishers, Rotterdam, 2001).
Google Scholar 

39.
Pansarin, E. R. & Maria do Carmo, E. A. Biologia reprodutiva e polinização de duas espécies de Polystachya Hook. no Sudeste do Brasil: evidência de pseudocleistogamia em Polystachyeae (Orchidaceae). Rev. Bras. Bot. 29(3), 423–432 (2006).

40.
Chakraborty, D. et al. Selecting populations for non-analogous climate conditions using universal response functions: the case of Douglas-Fir in Central Europe. PLoS ONE 10(8), e0136357 (2015).
PubMed  PubMed Central  Google Scholar 

41.
Broennimann, O. & Guisan, A. Predicting current and future biological invasions: both native and invaded ranges matter. Biol. Lett. 4, 585–589 (2008).
PubMed  PubMed Central  Google Scholar 

42.
Abrams, M. D. Adaptations of forest ecosystems to air pollution and climate change. Tree Physiol. 31, 258–261 (2011).
PubMed  Google Scholar 

43.
Atwater, D. Z., Ervine, C. & Barney, J. N. Climatic niche shifts are common in introduced plants. Nat. Ecol. Evol. 2, 34–43 (2018).
PubMed  Google Scholar 

44.
Konowalik, K. & Kolanowska, M. Climatic niche shift and possible future spread of the invasive South African Orchid Disa bracteata in Australia and adjacent areas. PeerJ 6, e6107 (2018).
PubMed  PubMed Central  Google Scholar 

45.
Early, R. & Sax, D. F. Climatic niche shifts between species’ native and naturalized ranges raise concern for ecological forecasts during invasions and climate change. Global Ecol. Biogeogr. 23, 1356–1365 (2014).
Google Scholar 

46.
Baranow, P. & Mytnik-Ejsmont, J. Two new species of Polystachya Hook. (Orchidaceae) from Africa. Plant Syst Evol. 281, 11–16 (2009).
Google Scholar 

47.
Mytnik-Ejsmont, J. & Baranow, P. Taxonomic study of Polystachya Hook. (Orchidaceae) from Asia. Plant Syst. Evol. 290, 57–63 (2010).
Google Scholar 

48.
Russell, A. et al. Phylogenetics and cytology of a pantropical orchid genus Polystachya (Polystachyinae, Vandeae, Orchidaceae): Evidence from plastid DNA sequence data. Taxon 59(2), 389–404 (2010).
Google Scholar 

49.
McCartney, C. African affinities, part I: the surprising relationship of some of Florida’s wild orchids. Orchids 69(2), 130–139 (2010).
Google Scholar 

50.
Mytnik-Ejsmont, J. A monograph of the subtribe Polystachyinae Schltr. (Orchidaceae) (Wydawnictwo Uniwersytetu Gdańskiego, Gdańsk, 2011).
Google Scholar 

51.
GBIF Occurrence Download; https://doi.org/10.15468/dl.ks410t (2018).

52.
Phillips, S. J., Dudík, M. & Schapire, R. E. A maximum entropy approach to species distribution modeling. In: ICML ’04. Proceedings of the Twenty-First International Conference on Machine learning. 655–662 (ACM, New York, 2004).

53.
Phillips, S. J., Anderson, R. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Google Scholar 

54.
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
Google Scholar 

55.
Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Model. 222, 1810–1819 (2011).
Google Scholar 

56.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Google Scholar 

57.
WorldClim (version 1.4) www.worldclim.org

58.
Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33, 607–611 (2010).
Google Scholar 

59.
Chung, M. Y. et al. Comparison of genetic variation between northern and southern populations of Lilium cernuum (Liliaceae): Implications for Pleistocene refugia. PLoS ONE 13(1), e0190520. https://doi.org/10.1371/journal.pone.0190520 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

60.
Kim, S. H. et al. Phylogeography and ecological niche modeling reveal reduced genetic diversity and colonization patterns of skunk cabbage (Symplocarpus foetidus; Araceae) from Glacial Refugia in Eastern North America. Front. Plant Sci. 9, 648. https://doi.org/10.3389/fpls.2018.00648 (2018).
Article  PubMed  PubMed Central  Google Scholar 

61.
Moss, R. et al. Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies. (Intergovernmental Panel on Climate Change, 2008)

62.
Weyant, J. et al. Report of 2.6 Versus 2.9 Watts/m2 RCPP Evaluation Panel (IPCC Secretariat, 2009).

63.
Sohel, S. I., Akhter, S., Ullah, H., Haque, E. & Rana, P. Predicting impacts of climate change on forest tree species of Bangladesh: evidence from threatened Dysoxylum binectariferum (Roxb.) Hook.f. ex Bedd. (Meliaceae). Forest 10(1), 154–160 (2016).
Google Scholar 

64.
Sony, R. K., Sen, S., Kumar, S., Sen, M. & Jayahari, K. M. Niche models inform the effects of climate change on the endangered Nilgiri Tahr (Nilgiritragus hylocrius) populations in the southern Western Ghats, India. Ecol. Eng. 120, 355–363 (2018).
Google Scholar 

65.
Mason, S. J. & Graham, N. E. Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves statistical significance and interpretation. Q. J. R. Meteorol. Soc. 128, 2145–2166 (2002).
ADS  Google Scholar 

66.
Evangelista, P. H. et al. Modelling invasion for a habitat generalist and a specialist plant species. Divers. Distrib. 14, 808–817 (2008).
Google Scholar 

67.
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
Google Scholar 

68.
Hijmans, R. J., Phillips, S., Leathwick, J. & Elith J. Dismo: Species Distribution Modeling. R package version 1.1-4. https://cran.r-project.org/package=dismo (2017)

69.
Phillips, S. B., Aneja, V. P., Kang, D. & Arya, S. P. Modelling and analysis of the atmospheric nitrogen deposition in North Carolina. Int. J. Glob. Environ. Issues 6, 231–252 (2006).
Google Scholar 

70.
Warren, D. L., Glor, R. E. & Turelli, M. Environmental nicheequivalency versus conservatism: quantitative approaches toniche evolution. Evolution 62, 2868–2883. https://doi.org/10.1111/evo.2008.62.issue-11 (2008).
Article  PubMed  Google Scholar 

71.
Schoener, T. W. The Anolis lizards of Bimini: resource partitioning in a complex fauna. Ecology 49, 704–726. https://doi.org/10.2307/1935534 (1968).
Article  Google Scholar 

72.
Heibl, C. & Calenge, C. Phyloclim: integrating phylogenetics and climatic Niche modeling. R package version 0.9-4 https://cran.rproject.org/web/packages/phyloclim/phyloclim.pdf (2015).

73.
Kremen, C. et al. Aligning conservation priorities across taxa in Madagascar with high-resolution planning tools. Science 320, 222–226 (2008).
ADS  CAS  PubMed  Google Scholar 

74.
Leps, J. & Smilauer, P. Multivariate Analysis of Ecological Data Using CANOCO (Cambridge University Press, Cambridge, 2003).
Google Scholar 

75.
Peterson, A. T. et al. Ecological Niches and Geographic Distributions (MPB-49) (Princeton University Press, Princeton, 2011).
Google Scholar  More

In-flight dosing system
The system used in this work is detailed in Fig. 1. It can be broken down into three primary modules: (1) a “coarse tracking” system that uses a pair of stereoscopic cameras to identify the three dimensional location of a target subject, which is passed on to (2) the “fine tracking” system that uses a single higher speed camera and a fast scanning mirror (FSM) to keep the target in the middle of the field of view (FOV) of the camera using a proportional-integral-derivative (PID) control loop, and (3) the laser dosing system that fires the laser pulse, which is co-aligned with the fine tracking system to ensure the laser pulse is accurately applied to the subject even while it is moving. For both the coarse and fine tracking systems, subjects are identified by the size of their silhouettes generated from near infrared LED back-illumination or reflection. As demonstrated in Fig. 1b, the subject cages were 20 cm cubes constructed from clear acrylic, but with the side facing the tracking and dosing systems made of borosilicate glass, placed two meters away from the FSM. A high-speed video camera (Vision Research Phantom) was set up to record a small subset of experiments at 2000 frames per second as well. For all experiments, each cage contained only a single subject to be illuminated, or “dosed,” with the laser, and conditions were set such that a subject could be dosed only when it was at least 2.5 to 5 cm away from any wall of the cage to ensure the subject was flying normally, rather than taking wing or alighting.
Figure 1

Schematics and images of in-flight dosing setup. (a) Schematic (not to scale) of primary dosing system components and communication lines among them. Note that the mirror control and 3D position subsystems were run on separate kernels within the same PC. (b) Image of complete system setup including the test cage location, LED backlighting, and control electronics. Cameras for coarse and fine tracking, the fast scanning mirror (FSM), and dosing laser path are contained in the white circle and better seen in (c) close-up image of core tracking and dosing system components and alignment among them. Red line represents fine tracking image path, green line the dosing laser path (laser not shown), and yellow line the combined fine tracking and dosing laser path.

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Prior to commencing the laser dosing experiments, a number of parameters were defined to analyze the results, as detailed in Table 1 and Fig. 2. The key outcome, in line with our previous study15 and with WHO guidelines on insecticide treatments17, was whether the subjects were alive or disabled (i.e. dead or moribund) 24 h after the treatment. To characterize how well the subjects were tracked during the laser pulse, the tracking error was defined as how far the insect’s centroid was from the center of the fine tracking camera’s FOV. Other parameters defined in Table 1 relate to the “occlusion factor,” or how much of the laser beam’s energy (assuming a Gaussian profile) overlapped with the target’s outline, as demonstrated in Fig. 2. From the coarse tracking system’s output of xyz position of the target, we could also define velocity, speed, and both linear and angular acceleration of the subjects before, during, and after the laser pulse.
Table 1 Definitions of all parameters tracked or calculated for each in-flight dosing event.
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Figure 2

Representative fine tracking camera images of A. stephensi silhouettes. In each frame, the approximate outline of the thorax and abdomen is drawn (thick black lines) according to a set pixel intensity threshold. The centroid of this region is then calculated (intersection of red crosshairs) and compared with the center of the camera’s field of view (green dot) to determine the current tracking error and provide input to the fine tracking PID loop controlling the direction of the scanning mirror. The green circle around the green dot represents the spot size of the laser (2.5 mm diameter for all images shown here); occlusion factor represents how much of this laser spot (assuming a Gaussian profile) overlaps with the body of the subject. The images in (a) demonstrate a typical time course for a single subject in 1 ms intervals, and those in (b) show representative depictions of tracking errors ranging from 1 to 5 pixels for various subjects.

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Initial results with 532 nm laser
Initial experiments were conducted with A. stephensi subjects and a 532 nm laser (Verdi, Coherent) using parameters for power (3 W), pulse duration (25 ms), and laser spot diameter (2.5 mm) that were defined in our previous work15 as an optimal combination of potential cost and mortality performance. As seen in Supplemental Video 1, the system could be quite effective in disabling a flying mosquito with these parameters. Of note from the video, along with flight tracking data for numerous trials (not shown), the 25 ms pulse duration appeared to be short enough that the mosquitoes did not perceptibly alter their flight pattern during the pulse to throw off the tracking algorithm. From Fig. 3a,b, though, the initial system did not have consistent enough tracking performance, such that survival of the subjects was almost entirely dictated by how well the fine tracking system kept the target near the center of its FOV. From this initial experiment on a sample of 80 subjects, we set limits for mean and maximum tracking error during the laser pulse as 2 and 3 pixels, respectively, where each pixel represents ~ 250 μm. Figure 3c,d demonstrates that the system was able to achieve and maintain this performance after modifying the PID loop parameters, and that there was no longer any association between tracking performance and mortality at these conditions. These criteria were evaluated and assured for all mortality data reported in this manuscript (i.e. a Kruskal–Wallis test indicated no significant differences in tracking errors among subjects that survived or were disabled by the laser pulses). Further, Supplemental Fig. S1 shows that flight behavior, in particular speed and linear acceleration, did correlate with tracking accuracy, but that the system was able to meet the noted tracking requirements even at the extremes of flight behavior that could be observed in this study given the restricted flight volumes (though the typical values seen here largely align with available data for other Anopheles mosquitoes18,19).
Figure 3

Influence of mean and maximum tracking errors on mortality outcomes. Initial mortality outcomes using LD90 conditions with a 532 nm laser from previous anesthetized work showed strong correlation with (a) mean and (b) maximum tracking errors over the 25 ms pulse duration. After setting and achieving new tracking error targets of less than 2 pixels mean and 3 pixels max (see Fig. 2b for depictions of overlap between the laser spot and the subject for various error magnitudes), identical experiments no longer showed any correlation with (c) mean or (d) maximum tracking errors. Moribund and dead outcomes were grouped in (c) and (d), labeled “Disabled,” and subsequent experiments since they both represent functional kills and are grouped as such in WHO guidelines for insecticide trials.

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Mortality results were analyzed as in Fig. 4. Each data point represents the mortality outcomes (i.e. percentage of targets that were dead or moribund at 24 h, assuming acceptable control mortality  2 × that of the other experiments at 445 or 532 nm. Note that this greater LD90 fluence value for the smaller spot still represented a lower pulse energy than required for the larger spots given the spot size differential.
Table 2 List of in-flight dosing experimental conditions and resulting LD90 fluence values for A. stephensi subjects.
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Figure 5

Dose–response curves for all experiments from Table 2, other than the two constant pulse energy tests. The x-axis is plotted on a log scale to allow clear visualization of the curves at both low and high fluence values. Individual data points and error bars not shown for the sake of clarity. The intersections of the dashed horizontal line at 90% mortality with the dose–response curves indicate the LD90 points, which are also presented in Table 2, along with pseudo R2 values for the logistic regression fits.

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Dosing with near infrared wavelengths
Two near infrared (NIR) laser sources were examined as well—a custom constructed 1,064 nm (1 μm) fiber laser with a maximum output of 30 W and a commercial 1,570 nm (1.5 μm) fiber laser (IPG Photonics) with a maximum deliverable output of 11 W. The right-hand side of Fig. 5 shows the dose–response curves for these laser sources in addition to those using visible wavelengths. All of the infrared experiments resulted in significantly higher LD90 fluence levels compared with the visible lasers, and given the log scale of Fig. 5, varying experimental conditions with the 1 μm source led to comparatively larger changes in LD90 compared with the visible sources. As with the blue diode, the small spot (1.5 mm) had the highest LD90 fluence, but unlike the visible lasers, the larger spot (4 mm) offered a lower LD90 fluence compared with the baseline 2.5 mm. For the 1.5 μm source, there was insufficient mortality at the maximum power available from this source with a 2.5 mm spot and 25 ms pulse duration, so Fig. 5 shows a curve using slightly longer pulse durations to make up the additional fluence required. From the available data, it appears that the LD90 fluence levels for the 1 μm and 1.5 μm sources are at least at reasonably comparable levels.
Effects of longer pulse durations with lower power
We then further explored the effects of increasing the pulse duration to determine whether this could relax the optical power required from the laser source, given that laser cost is correlated with output power. Figure 6a,b show the results for the 532 nm and 1,064 nm sources, respectively, with spot diameters of 2.5 mm and pulse duration set to 25, 50, or 100 ms. In all cases, the optical power was adjusted according to the pulse duration to supply a constant pulse energy, and therefore fluence at approximately the LD75 level determined from previous experiments, which was chosen to ensure a low likelihood of any data point being 100% mortality (i.e., a saturated signal). As evidenced from Fig. 6a and Table 3, at 532 nm there was a significant drop off in mortality at the longer pulse durations, although the effect is smaller going from 50 to 100 ms compared with the initial drop from 25 to 50 ms. Similar, but smaller magnitude effects were seen with the 1 μm source in Fig. 6b and Table 3. Supplemental Fig. S2 shows that the tracking accuracy for these experiments somewhat mirrors the mortality performance, with a notable decrement from 25 to 50 ms but no significant change from 50 to 100 ms.
Figure 6

Mortality results for experiments with constant pulse energy achieved by proportionally adjusting the power according to pulse duration (25, 50, and 100 ms). Dosing was performed with both the (a) 532 nm and (b) 1,064 nm lasers, both using a 2.5 mm spot size, and set to the approximate LD75 fluence value from the dose–response curves (solid curves with dashed line 95% confidence intervals of logistic regression fit) from the corresponding 25 ms experiments. Error bars on individual points represent 95% confidence intervals of exact binomial probabilities. Statistical differences among mortality at the various pulse durations summarized in Table 3. The slight offsets on the x axis in (a) stem from slight changes in the beam spot size as a function of power.

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Table 3 Results of χ2 tests for mortality equivalence among experiments with constant pulse energy but varied amounts of power and pulse duration (25, 50, and 100 ms).
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Comparing dosing performance across other insects
Using the same system and procedures, experiments were also run on SWD and ACP subjects as a way of cursorily exploring laser-insect interaction for species of interest besides A. stephensi. For SWD, a full dose–response curve was created for the 1 μm laser with typical exposure settings (2.5 mm spot diameter and 25 ms pulse duration). The LD90 fluence from this work was 8.6 J/cm2, compared with 12.9 J/cm2 for A. stephensi under the same conditions. Tracking performance on SWD subjects was comparable to that for A. stephensi, given that flight parameters such as speed and acceleration were comparable as well. With ACP subjects, inducing them to fly in the cages was very difficult. As such, we could not process a sufficient number of subjects to create a proper dose–response curve. Based on the limited data available, and as demonstrated in Supplemental Video 2, the system as set for the mosquito LD90 condition with the 1 μm laser, 2.5 mm spot diameter and 25 ms pulse duration was effective in tracking and disabling the ACP subjects, despite their very different flying behavior (greater speeds and accelerations, limited durations) relative to the other test species.
Longer range dosing system demonstration
As a final proof of principle test, a long-range version of the system from Fig. 1 was configured. This system worked at a distance of 30 m instead of 2 m, facilitated primarily by modifying the optics used for the coarse and fine tracking systems. Given the reduced flexibility of this system, it was only used with the 1 μm laser with a 2.5 mm spot diameter and 25 ms pulse duration. Rather than acquiring new dose–response curves, which was impractical given the setup’s location in a building ~ 30 km away from the insect-housing chamber, we verified the efficacy of the system using the LD90 conditions established by short-range dosing. Supplemental Video 3 shows this system dosing an A. stephensi subject from the same stock as used for short-range testing, and Supplemental Video 4 shows the same for a wild-sourced Culex pipiens mosquito obtained in a local pond. Note that the wild C. pipiens was substantially larger in size than the lab-reared A. stephensi. In all of the limited number of tests of this system for both species (10 A. stephensi and 5 C. pipiens), the mortality rate was 100%. More

1.
Yang, J., Kloepper, J. W. & Ryu, C. M. Rhizosphere bacteria help plants tolerate abiotic stress. Trends Plant Sci. 14, 1–4 (2009).
ADS  CAS  PubMed  Google Scholar 
2.
Kearl, J. et al. Salt-tolerant halophyte rhizosphere bacteria stimulate growth of alfalfa in salty soil. Front. Microbiol. 10, 1849. https://doi.org/10.3389/fmicb.2019.01849 (2019).
Article  PubMed  PubMed Central  Google Scholar 

3.
Khan, N. et al. Comparative physiological and metabolic analysis reveals a complex mechanism involved in drought tolerance in Chickpea (Cicer arietinum L.) induced by PGPR and PGRs. Sci. Rep. 9, 2097. https://doi.org/10.1038/s41598-019-38702-8 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

4.
Compant, S., Samad, A., Faist, H. & Sessitsch, A. A review on the plant microbiome: ecology, functions, and emerging trends in microbial application. J. Adv. Res. 19, 29–37 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

5.
Bruto, M., Prigent-Combaret, C., Muller, D. & Moënne-Loccoz, Y. Analysis of genes contributing to plant-beneficial functions in plant growth-promoting rhizobacteria and related Proteobacteria. Sci. Rep. 4, 6261. https://doi.org/10.1038/srep06261 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

6.
Timmusk, S. Perspectives and challenges of microbial application for crop improvement. Front. Plant Sci. 8, 49. https://doi.org/10.3389/fpls.2017.00049 (2017).
Article  PubMed  PubMed Central  Google Scholar 

7.
Sessitsch, A., Pfaffenbichler, N. & Mitter, B. Microbiome applications from lab to field: facing complexity. Trends Plant Sci. 24, 194–198 (2019).
CAS  PubMed  Google Scholar 

8.
Olanrewaju, O. S., Glick, B. R. & Babalola, O. O. Mechanisms of action of plant growth promoting bacteria. World J. Microb. Biot 33, 197. https://doi.org/10.1007/s11274-017-2364-9 (2017).
CAS  Article  Google Scholar 

9.
Gupta, A. et al. Whole genome sequencing and analysis of plant growth promoting bacteria isolated from the rhizosphere of plantation crops coconut, cocoa and arecanut. PLoS ONE 9, e104259. https://doi.org/10.1371/journal.pone.0104259 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

10.
Pérez-Jaramillo, J. E., Carrión, V. J., de Hollander, M. & Raaijmakers, J. M. The wild side of plant microbiomes. Microbiome 6, 143. https://doi.org/10.1186/s40168-018-0519-z (2018).
Article  PubMed  PubMed Central  Google Scholar 

11.
Song, J. Y. et al. Genome sequence of the Plant Growth-Promoting Rhizobacterium Bacillus sp. strain JS. J. Bacteriol. 19, 14. https://doi.org/10.1128/JB.00676-12 (2012).
CAS  Article  Google Scholar 

12.
Duan, J., Jiang, W., Cheng, Z., Heikkila, J. J. & Glick, B. R. The complete genome sequence of the Plant Growth-Promoting bacterium Pseudomonas sp. UW4. PLoS ONE 8, e58640. https://doi.org/10.1371/journal.pone.0058640 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

13.
Li, S. et al. Complete genome sequence of Paenibacillus polymyxa SQR-21, a plant growth-promoting rhizobacterium with antifungal activity and rhizosphere colonization ability. Genome Announc. 2, e00281-e314. https://doi.org/10.1128/genomeA.00281-14 (2014).
Article  PubMed  PubMed Central  Google Scholar 

14.
Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).
CAS  PubMed  Google Scholar 

15.
Penrose, D. M. & Glick, B. R. Methods for isolating and characterizing ACC deaminase-containing plant growth-promoting rhizobacteria. Physiol. Plantarum 118, 10–15 (2003).
CAS  Google Scholar 

16.
Lo, K. J., Lin, S. S., Lu, C. W., Kuo, C. H. & Liu, C. T. Whole-genome sequencing and comparative analysis of two plant-associated strains of Rhodopseudomonas palustris (PS3 and YSC3). Sci. Rep. 8, 12769. https://doi.org/10.1038/s41598-018-31128-8 (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

17.
Liu, W. et al. Whole genome analysis of halotolerant and alkalotolerant plant growth-promoting rhizobacterium Klebsiella sp. D5A. Sci. Rep. 6, 20–22 (2016).
Google Scholar 

18.
Andrés-Barrao, C. et al. Complete genome sequence analysis of Enterobacter sp. SA187, a plant multi-stress tolerance promoting endophytic bacterium. Front. Microbiol. 8, 2023. https://doi.org/10.3389/fmicb.2017.02023 (2017).
Article  PubMed  PubMed Central  Google Scholar 

19.
Esmaeel, Q. et al. Draft genome sequence of plant growth-promoting Burkholderia sp. strain BE12, isolated from the rhizosphere of maize. Genome Announc. 6, e00299-18. https://doi.org/10.1128/genomeA.00299-18 (2018).
Article  PubMed  PubMed Central  Google Scholar 

20.
Wang, Z. et al. Draft genome analysis offers insights into the mechanism by which Streptomyces chartreusis WZS021 increases drought tolerance in sugarcane. Front. Microbiol. 10, 3262. https://doi.org/10.3389/fmicb.2018.03262 (2019).
Article  Google Scholar 

21.
Matteoli, F. P. et al. Genome sequencing and assessment of plant growth-promoting properties of a Serratia marcescens strain isolated from vermicompost. BMC Genomics 19, 750. https://doi.org/10.1186/s12864-018-5130-y (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

22.
Yu, Z. et al. Complete genome sequence of the nitrogen-fixing bacterium Azospirillum humicireducens type strain SgZ-5T. Stand. Genomic Sci. 13, 28. https://doi.org/10.1186/s40793-018-0322-2 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

23.
Westerman, R. L. Soil Testing and Plant Analysis. SSSA Book Series 3 3rd edn. (Madison, SSSA, 1990).
Google Scholar 

24.
Bharti, N., Pandey, S. S., Barnawal, D., Patel, V. K. & Kalra, A. Plant growth promoting rhizobacteria Dietzia natronolimnaea modulates the expression of stress responsive genes providing protection of wheat from salinity stress. Sci. Rep. 6, 34768. https://doi.org/10.1038/srep34768 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

25.
Sharma, S., Kulkarni, J. & Jha, B. Halotolerant rhizobacteria promote growth and enhance salinity tolerance in peanut. Front. Microbiol. 7, 1600. https://doi.org/10.3389/fmicb.2016.01600 (2016).
Article  PubMed  PubMed Central  Google Scholar 

26.
Gupta, S. & Pandey, S. ACC Deaminase producing bacteria with multifarious plant growth promoting traits alleviates salinity stress in French Bean (Phaseolus vulgaris) plants. Front. Microbiol. 10, 1506. https://doi.org/10.3389/fmicb.2019.01506 (2019).
Article  PubMed  PubMed Central  Google Scholar 

27.
Pan, J. et al. The growth promotion of two salt-tolerant plant groups with PGPR inoculation: a meta-analysis. Sustainability 11, 378. https://doi.org/10.3390/su11020378 (2019).
CAS  Article  Google Scholar 

28.
Vurukonda, S. S. K. P., Vardharajula, S., Shrivastava, M. & SkZ, A. Multifunctional Pseudomonas putida strain FBKV2 from arid rhizosphere soil and its growth promotional effects on maize under drought stress. Rhizosphere 1, 4–13 (2016).
Google Scholar 

29.
Marasco, R. et al. Salicornia strobilacea (synonym of Halocnemum strobilaceum) grown under different tidal regimes selects rhizosphere bacteria capable of promoting plant growth. Front. Microbiol. 7, 1286. https://doi.org/10.3389/fmicb.2016.01286 (2016).
Article  PubMed  PubMed Central  Google Scholar 

30.
Mukhtar, S. et al. Impact of soil salinity on the microbial structure of halophyte rhizosphere microbiome. World J. Microb. Biot. 34, 136. https://doi.org/10.1007/s11274-018-2509-5 (2018).
CAS  Article  Google Scholar 

31.
Patten, C. L. & Glick, B. R. Role of Pseudomonas putida indoleacetic acid in development of the host plant root system Appl. Environ. Microbiol. 68(3795), 3801 (2002).
Google Scholar 

32.
Etesami, H., Alikhani, H. A. & Hosseini, H. M. Indole-3-acetic acid (IAA) production trait, a useful screening to select endophytic and rhizosphere competent bacteria for rice growth promoting agents. MethodsX 2, 72–78 (2015).
PubMed  PubMed Central  Google Scholar 

33.
Abbamondi, G. R. et al. Plant growth-promoting effects of rhizospheric and endophytic bacteria associated with different tomato cultivars and new tomato hybrids. Chem. Biol. Technol. Agric. 3, 1. https://doi.org/10.1186/s40538-015-0051-3 (2016).
CAS  Article  Google Scholar 

34.
Gupta, S. & Pandey, S. Unravelling the biochemistry and genetics of ACC deaminase-An enzyme alleviating the biotic and abiotic stress in plants. Plant gene 18, 100175. https://doi.org/10.1016/j.plgene.2019.100175 (2019).
CAS  Article  Google Scholar 

35.
Khamna, S., Yokota, A., Peberdy, J. F. & Lumyong, S. Indole-3-acetic acid production by Streptomyces sp. isolated from some Thai medicinal plant rhizosphere soils. EurAsian J. BioSciences 4, 23–32 (2010).
CAS  Google Scholar 

36.
Spaepen, S. & Vanderleyden, J. Auxin and plant-microbe interactions. C. S. H. Perspect. Biol. 3, a001438. https://doi.org/10.1101/cshperspect.a001438 (2011).
CAS  Article  Google Scholar 

37.
Patten, C. L. & Glick, B. R. Bacterial biosynthesis of indole-3-acetic acid. Can. J. Microbiol. 42, 207–220 (1996).
CAS  PubMed  Google Scholar 

38.
Majeed, A., Abbasi, K. M., Hameed, S., Imran, A. & Rahim, N. Isolation and characterization of plant growth-promoting rhizobacteria from wheat rhizosphere and their effect on plant growth promotion. Front. Microbiol. 6, 198. https://doi.org/10.3389/fmicb.2015.00198 (2015).
Article  PubMed  PubMed Central  Google Scholar 

39.
Apine, O. A. & Jadhav, J. P. Optimization of medium for indole-3-acetic acid production using Pantoea agglomerans strain PVM. J. Appl. Microbiol. 110, 1235–1244 (2011).
CAS  PubMed  Google Scholar 

40.
Checcucci, A. et al. Role and regulation of ACC deaminase gene in Sinorhizobium melilotr: Is it a symbiotic, rhizospheric or endophytic gene?. Front. Genet. 8, 6. https://doi.org/10.3389/fgene.2017.00006 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

41.
Belimov, A. A. et al. Cadmium-tolerant plant growth-promoting bacteria associated with the roots of Indian mustard (Brassica juncea L. Czern.). Soil Biol. Biochem. 37, 241–250 (2005).
CAS  Google Scholar 

42.
Khan, N. A., Khan, M. I. R., Ferrante, A. & Poor, P. Editorial: ethylene: a key regulatory molecule in plants. Front. Plant Sci. 8, 1782. https://doi.org/10.3389/fpls.2017.01782 (2017).
Article  PubMed  PubMed Central  Google Scholar 

43.
Glick, B. R. Bacteria with ACC deaminase can promote plant growth and help to feed the world. Microbiol. Res. 169, 30–39 (2014).
CAS  PubMed  Google Scholar 

44.
Wang, Z. et al. Isolation and characterization of a phosphorus-solubilizing bacterium from rhizosphere soils and its colonization of Chinese Cabbage (Brassica campestris ssp. chinensis). Front. Microbiol. 8, 1270. https://doi.org/10.3389/fmicb.2017.01270 (2017).
Article  PubMed  PubMed Central  Google Scholar 

45.
Dinesh, R. et al. Isolation, characterization, and evaluation of multi-trait plant growth promoting rhizobacteria for their growth promoting and disease suppressing effects on ginger. Microbiol. Res. 173, 34–43 (2015).
PubMed  Google Scholar 

46.
Niu, X., Song, L., Xiao, Y. & Ge, W. Drought-tolerant plant growth-promoting rhizobacteria associated with foxtail millet in a semi-arid agroecosystem and their potential in alleviating drought stress. Front. Microbiol. 8, 2580. https://doi.org/10.3389/fmicb.2017.02580 (2018).
Article  PubMed  PubMed Central  Google Scholar 

47.
Singh, V. K., Singh, A. K., Singh, P. P. & Kumar, A. Interaction of plant growth promoting bacteria with tomato under abiotic stress: a review. Agricult. Ecosyst. Environ. 267, 129–140 (2018).
CAS  Google Scholar 

48.
Grover, M., Bodhankar, S., Maheswari, M. & Srinivasarao, C. Actinomycetes as mitigators of climate change and abiotic stress. In Plant Growth Promoting Actinobacteria: A New Avenue for Enhancing the Productivity and Soil Fertility of Grain Legumes (eds Subramaniam, G. et al.) 203–212 (Springer, Berlin, 2016).
Google Scholar 

49.
Sang, M. K., Jeong, J. J., Kim, J. & Kim, K. D. Growth promotion and root colonisation in pepper plants by phosphate-solubilising Chryseobacterium sp strain ISE14 that suppresses Phytophthora blight. Ann. Appl. Biol. 172, 208–223 (2018).
CAS  Google Scholar 

50.
Xiao, X., Fan, M., Wang, E., Chen, W. & Wei, G. Interactions of plant growth-promoting rhizobacteria and soil factors in two leguminous plants. Appl. Microbiol. Biot. 101, 8485–8497 (2017).
CAS  Google Scholar 

51.
Abdelkrim, S. et al. Effect of Pb-resistant plant growth-promoting rhizobacteria inoculation on growth and lead uptake by Lathyrus sativus. J. Basic Microb. 58, 579–589 (2018).
CAS  Google Scholar 

52.
Liu, Y. et al. Characterization of Lysobacter capsici strain NF87–2 and its biocontrol activities against phytopathogens. Eur. J. Plant Pathol. 155, 859–869 (2019).
CAS  Google Scholar 

53.
Edgar, R. C. Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences. PeerJ. https://doi.org/10.7717/peerj.4652 (2018).
Article  PubMed  PubMed Central  Google Scholar 

54.
Lee, Z. M., Bussema, C. III. & Schmidt, T. M. rrnDB: documenting the number of rRNA and tRNA genes in bacteria and archaea. Nucleic Acids Res. 37, D489–D493 (2008).
PubMed  PubMed Central  Google Scholar 

55.
Shen, X., Hu, H., Peng, H., Wang, W. & Zhang, X. Comparative genomic analysis of four representative plant growth-promoting rhizobacteria in Pseudomonas. BMC Genomics 14, 271. https://doi.org/10.1186/1471-2164-14-271 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

56.
Niazi, A. et al. Genome analysis of Bacillus amyloliquefaciens subsp. Plantarum UCMB5113: a rhizobacterium that improves plant growth and stress management. PloS ONE 9, e104651. https://doi.org/10.1371/journal.pone.0104651 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

57.
Riggs, P. J., Chelius, M. K., Leonardo, I. A., Kaeppler, S. M. & Triplett, E. W. Enhanced maize productivity by inoculation with diazotrophic bacteria. Funct. Plant Biol. 28, 829–836 (2001).
Google Scholar 

58.
Saleem, M., Arshad, M., Hussain, S. & Bhatti, A. S. Perspective of plant growth promoting rhizobacteria (PGPR) containing ACC deaminase in stress agriculture. J. Ind. Microbiol. Biot. 34, 635–648 (2007).
CAS  Google Scholar 

59.
Dorjey, S., Gupta, V. & Razdan, V. K. Evaluation of Pseudomonas fluorescens isolates for the management of Fusarium oxysporum f.sp. lycopersici and Rhizoctonia solani causing wilt complex in tomato. Indian Phytopathol. 70, 127–130 (2017).
Google Scholar 

60.
Dardanelli, M. S. et al. Changes in flavonoids secreted by Phaseolus vulgaris roots in the presence of salt and the plant growth-promoting rhizobacterium Chryseobacterium balustinum. Appl. Soil Ecol. 75, 31–38 (2012).
Google Scholar 

61.
Ogut, M., Er, F. & Brohi, A. Excessive phosphorus fertilization does not increase cadmium concentrations in soil or carrots (Daucus carota L.) grown in Konya (Turkey). Acta Agr. Scand B-S.P. 60, 420–426 (2010).
CAS  Google Scholar 

62.
Rodríguez, H., Fraga, R., Gonzalez, T. & Bashan, Y. Genetics of phosphate solubilization and its potential applications for improving plant growth-promoting bacteria. Plant Soil 287, 15–21 (2006).
Google Scholar 

63.
Kalayu, G. Phosphate solubilizing microorganisms: Promising approach as biofertilizers. Int. J. Agron. 2019, 4917256. https://doi.org/10.1155/2019/4917256 (2019).
CAS  Article  Google Scholar 

64.
Mellidou, I. et al. Silencing S-adenosyl-L-methionine decarboxylase (SAMDC) in Nicotiana tabacum points at a polyamine-dependent trade-off between growth and tolerance responses. Front. Plant Sci. 7, 379. https://doi.org/10.3389/fpls.2016.00379 (2016).
Article  PubMed  PubMed Central  Google Scholar 

65.
Michael, A. J. Biosynthesis of polyamines and polyamine-containing molecules. Biochem. J. 473, 2315–2329 (2016).
CAS  PubMed  Google Scholar 

66.
Nascimento, F. X., Hernández, A. G., Glick, B. R. & Rossi, M. J. Plant growth-promoting activities and genomic analysis of the stress-resistant Bacillus megaterium STB1, a bacterium of agricultural and biotechnological interest. Biotechnol. Rep. 25, e00406. https://doi.org/10.1016/j.btre.2019.e00406 (2020).
Article  Google Scholar 

67.
Goswami, R. et al. Optimization of growth determinants of a potent cellulolytic bacterium isolated from lignocellulosic biomass for enhancing biogas production. Clean Technol. Envir. 18, 1565–1583 (2016).
CAS  Google Scholar 

68.
Vokou, D., Giannakou, U., Kontaxi, C. & Vareltzidou, S. Axios. Aliakmon and gallikos delta complex, Νorthern Greece. In Encyclopedia of Wetlands, Vol. 4 World Wetlands (eds Finlayson, M. et al.) (Springer, Berlin, 2016).
Google Scholar 

69.
Mellidou, I., Keulemans, J., Kanellis, A. & Davey, M. W. Regulation of fruit ascorbic acid concentrations in high and low vitamin C tomato cultivars during ripening. BMC Plant Biol. 12, 239–258 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

70.
Bouyoucos, G. J. Hydrometer method improved for making particle size analysis of soils. Agron. J. 54, 464–465 (1962).
Google Scholar 

71.
Sparks, D. L. Methods of Soil Analysis – Part 3 – Chemical Methods, SSSA Book Series 5 (ASA. Madison, WI, SSSA, 1996).
Google Scholar 

72.
Karamanoli, K., Bouligaraki, P., Constantinidou, H. I. & Lindow, S. E. Polyphenolic compounds on leaves limit iron availability and affect growth of epiphytic bacteria. Ann. Appl. Biol. 159, 99–108 (2011).
CAS  Google Scholar 

73.
Eevers, N. et al. Optimization of isolation and cultivation of bacterial endophytes through addition of plant extract to nutrient media. Microb. Biotechnol. 8, 707–715 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

74.
Louden, B. C., Haarmann, D. & Lynne, A. M. Use of blue agar CAS assay for siderophore detection. J. Microbiol. Biol. Educ. 12, 51. https://doi.org/10.1128/jmbe.v12i1.249 (2011).
Article  PubMed  PubMed Central  Google Scholar 

75.
García, C. A., De Rossi, B. P., Alcaraz, E., Vay, C. & Franco, M. Siderophores of Stenotrophomonas maltophilia: detection and determination of their chemical nature. Rev. Argent. Microbiol. 44, 150–154 (2012).
PubMed  Google Scholar 

76.
Dworkin, M. & Foster, J. Experiments with some microorganisms which utilize ethane and hydrogen. J. Bacteriol. 75, 592–601 (1958).
CAS  PubMed  PubMed Central  Google Scholar 

77.
Hontzeas, N., Zoidakis, J., Glick, B. R. & Abu-Omar, M. M. Expression and characterization of 1-aminocyclopropane-1-carboxylate deaminase from the rhizobacterium Pseudomonas putida UW4: a key enzyme in bacterial plant growth promotion. Biochim. Biophys. Acta 1703, 11–19 (2004).
CAS  PubMed  Google Scholar 

78.
Bradford, M. A rapid and sensitive method for the quantitation of micro gram quantities of protein utilizing the principle of protein—dye binding. Anal. Biochem. 72, 248–258 (1976).
CAS  Google Scholar 

79.
Nautiyal, C. S. An efficient microbiological growth medium for screening phosphate solubilizing microorganisms. FEMS Microbiol. Lett. 170, 265–270 (1999).
CAS  PubMed  Google Scholar 

80.
Luziatelli, F., Ficca, A. G., Colla, G., Švecová, E. B. & Ruzzi, M. Foliar application of vegetal-derived bioactive compounds stimulates the growth of beneficial bacteria and enhances microbiome biodiversity in lettuce. Front. Plant Sci. 10, 60. https://doi.org/10.3389/fpls.2019.00060 (2019).
Article  PubMed  PubMed Central  Google Scholar 

81.
Herlemann, D. P. R. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

82.
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
CAS  PubMed  Google Scholar 

83.
Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 2, W537–W544 (2018).
Google Scholar 

84.
Li, D. et al. MEGAHIT v.10: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 1, 3–11 (2016).
Google Scholar 

85.
Seeman, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 15, 2068–2069 (2014).
Google Scholar 

86.
Thompson, J. D., Gibson, T. J. & Higgins, D. G. Multiple sequence alignment using ClustalW and ClustalX. Curr Protoc Bioinform. https://doi.org/10.1002/0471250953.bi0203s00 (2002).
Article  Google Scholar 

87.
Hall, T. A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl. Acid S. 41, 95–98 (1999).
CAS  Google Scholar 

88.
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

89.
Felsenstein, J. Confidence limits on phylogenies: an approach using the Bootstrap. Evolution 39, 783–791 (1985).
PubMed  PubMed Central  Google Scholar 

90.
Wu, S., Zhu, Z., Fu, L., Niu, B. & Li, W. WebMGA: a customizable web server for fast metagenomic sequence analysis. BMC Genomics 12, 444. https://doi.org/10.1186/1471-2164-12-444 (2011).
Article  PubMed  PubMed Central  Google Scholar 

91.
Kamou, N. N. et al. Isolation screening and characterisation of local beneficial rhizobacteria based upon their ability to suppress the growth of Fusarium oxysporum f. sp. radicis-lycopersici and tomato foot and root rot. Biocontrol Sci. Technol. 25(8), 928–949 (2015).
Google Scholar  More

Total P and available P
Fertilizer application significantly (P  0.05). The highest increase in available P concentration in NPK + S treatment observed in the 60–100 cm soil depth, with the increase of 111% and 115% in 60–80 cm and 80–100 cm soil depths, respectively, over CK treatment.
Figure 2

Effect of long-term application of chemical fertilizer, organic manure, and straw on total phosphorus (P) and available P concentrations. * indicates significant difference at P  3.6%) was associated with OM treatment, especially at the 0–20 and 20–40 cm soil depths (Fig. 3). The PAC values under NPK, NPK + S and OM treatments increased by 7.6%, 4.5% and 11.5% in the 0–20 cm soil depth and 4.2%, 1.3%, and 5.8% in 20–40 cm soil depth, respectively as compared to the CK treatment. However, PAC value for soil depth below 40 cm showed the trend, NPK  More

NEWS AND VIEWS
09 September 2020

How can the decline in global biodiversity be reversed, given the need to supply food? Computer modelling provides a way to assess the effectiveness of combining various conservation and food-system interventions to tackle this issue.

Brett A. Bryan &

Brett A. Bryan is at the Centre for Integrative Ecology, Deakin University, Melbourne, Victoria 3125, Australia.
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Carla L. Archibald

Carla L. Archibald is at the Centre for Integrative Ecology, Deakin University, Melbourne, Victoria 3125, Australia.

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Nature is in trouble, and its plight will probably become even more precarious unless we do something about it1. Writing in Nature, Leclère et al.2 quantify what might be needed to reverse this deeply worrying path while also feeding people’s increasingly voracious appetites. The authors’ answer is to team ambitious conservation measures with food-system transformation in the hope of reversing the trend of global terrestrial biodiversity loss.

By nature, we mean the diversity of life that has evolved over billions of years to exist in dynamic balance with Earth’s biophysical environment and the ecosystems present. Nature contributes to human well-being in many ways, and the services it provides, such as carbon sequestration by plants or pollination by insects, could impose a vast cost if lost3. Although the slow and long-term decline of Earth’s biodiversity4 is often overshadowed by climate change, and more recently by the COVID-19 pandemic, the loss of biodiversity is no less of a risk than those posed by the other challenges. Many would argue that the effect of biodiversity losses could surpass the combined impacts of climate change and COVID-19.
More and more, the realization is growing that, as a planet, we are what we eat. Human demand for food is accelerating with the ever-increasing global population (projected to approach 10 billion by 2050), and each successive generation is wealthier and consumes more resource-intensive diets than did the previous one5. Trying to balance this rapidly rising demand against the limited amount of land available for crops and pasture sets agriculture and nature (Fig. 1) on a collision course6. As Leclère and colleagues show, a bold and integrated strategy is required immediately to turn this around.

Figure 1 | A bean field bordering a rainforest reserve near Sorriso, Brazil.Credit: Florian Plaucheur/AFP/Getty

Taking a long view out to the year 2100, Leclère et al. present a global modelling study assessing the ability of ambitious conservation and food-system intervention scenarios to reverse the decline, or, as they call it, “bending the curve”, of biodiversity losses resulting from changes in agricultural land use and management. Projections of future land use and biodiversity are uncertain, and when these models are combined, this uncertainty is compounded. One of the great innovations of Leclère and colleagues’ work is in embracing this uncertainty by combining an ensemble of four global land-use models and eight global biodiversity models and measuring the performance of future land-use scenarios in terms of higher-level model-independent metrics such as the amount of biodiversity loss avoided.
Importantly, the study also included a baseline (termed BASE) scenario — the world expected without interventions — and Leclère et al. used this to gauge the effectiveness of the intervention scenarios. Although it is not a focus of the paper, it’s worth pausing to ponder the sobering picture painted by this business-as-usual future largely bereft of birdsong and insect chirp.
Choosing to act now can make a difference to nature’s plight. Most (61%) of the model combinations run by the authors indicated that implementing ambitious conservation actions led to a positive uptick in the biodiversity curve by 2050. Such conservation actions included: extending the global conservation network by establishing protected nature reserves; restoring degraded land; and basing future land-use decisions on comprehensive landscape-level conservation planning. This comprehensive conservation strategy avoids more than half (an average of 58%) of the biodiversity losses expected if nothing is done, but also leads to a hike in food prices.

When conservation actions were teamed with a range of equally ambitious food-system interventions, the prognosis for global biodiversity in the model was improved further. Including both supply- and demand-side measures, these approaches included boosting agricultural yields, having an increasingly globalized food trade, reducing food waste by half, and the global adoption of healthy diets by halving meat consumption. These combined measures of conservation and food-systems actions avoided more than two-thirds of future biodiversity losses, with the integrated action portfolio (combining all actions) avoiding an average of 90% of future biodiversity losses. Almost all models predicted a biodiversity about-face by mid-century. These food-system measures also avoided adverse outcomes for food affordability.
Leclère and colleagues’ work complements the current global climate-change scenario framework (tools for future planning by governments and others, including scenarios called shared socio-economic pathways, which integrate future socio-economic projections with greenhouse-gas emissions), and represents the most comprehensive incorporation of biodiversity into this scenario framing7 so far. However, a major limitation of the present study is that it does not consider the potential impact of climate change on biodiversity. This raises an internal inconsistency because, on the one hand, the baseline scenario considers land-use, social and economic changes under approximately 4 °C of global heating by 21008, yet, on the other hand, it does not consider the profound effect of warming on plant and animal populations and the ecosystems they comprise9. Also absent from the models were other threats to biodiversity, including harvesting, hunting and invasive species10. Although Leclère and colleagues recognized these limitations and assigned them a high priority for future research, unfortunately for us all, omitting these key threats probably means that the authors’ estimates of biodiversity’s plight and the effectiveness of integrated global conservation and food-system action are overly optimistic. To truly bend the curve, Leclère and colleagues’ integrated portfolio will need to be substantially expanded to address the full range of threats to biodiversity.
Although the models say that a better future is possible, is the combination of the multiple ambitious conservation and food-system interventions considered by Leclère et al. a realistic possibility? Achieving each one of the conservation and food-system actions would require a monumental coordinated effort from all nations. And even if the global community were to get its act together in prioritizing conservation and food-system transformation, would such efforts come in time and be enough to save our planet’s natural legacy? We certainly hope so.

doi: 10.1038/d41586-020-02502-2

References

1.
Díaz, S. et al. Science 366, eaax3100 (2019).

2.
Leclère, D. et al. Nature https://doi.org/10.1038/s41586-020-2705-y (2020).

3.
Costanza, R. et al. Glob. Environ. Change Hum. Policy Dimens. 26, 152–158 (2014).

4.
Butchart, S. H. M. et al. Science 328, 1164–1168 (2010).

5.
Springmann, M. et al. Nature 562, 519–525 (2018).

6.
Montesino Pouzols, F. et al. Nature 516, 383–386 (2014).

7.
Kok, M. T. J. et al. Biol. Conserv. 221, 137–150 (2018).

8.
Leclère, D. et al. Towards Pathways Bending the Curve of Terrestrial Biodiversity Trends Within the 21st Century https://doi.org/10.22022/ESM/04-2018.15241 (Int. Inst. Appl. Syst. Analysis, 2018).

9.
Warren, R., Price, J., Graham, E., Forstenhaeusler, N. & VanDerWal, J. Science 360, 791–795 (2018).

10.
Driscoll, D. A. et al. Nature Ecol. Evol. 2, 775–781 (2018).

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Properties of permafrost soils
All sites contained syngenetic permafrost in which the active layer and the uppermost permafrost have experienced numerous freeze-thaw cycles since formation during the Holocene22. Permafrost organic matter radiocarbon ages ranged from 7850 ± 30 to 830 ± 20 y B.P. (Supplementary Data), with the western sites containing the oldest SOM and the northern Hudson Bay peatlands containing the youngest SOM.
For organic layers, permafrost soil C:N atomic ratios (14.50 [12.61–19.67], median [25th–75th]) were lower and H:C atomic ratios (0.13 [0.13–0.14]) were greater relative to the active layer, 23.89 [19.33–29.50] and 0.14 [0.12–0.15], respectively (Supplementary Fig. 1). Similarly for mineral layers, permafrost C:N ratios were lower (12.0 [3.23–18.09]) and H:C ratios higher (0.24 [0.15–0.86]) compared to the active layer, 16.21 [13.67–18.87] and 0.17 [0.15–0.21], respectively. For both thermal layers, in organic layers C:N ratios were higher and H:C ratios were lower than in mineral layers.
These stoichiometric properties are typical of boreal and tundra soils (Supplementary Fig. 1)23. The higher C:N and very low H:C ratios of the organic layers relative to mineral layers suggest higher contents of condensed aromatic structures originating from peat24. Permafrost layers displayed lower C:N properties suggesting different SOM composition (e.g., lignin, tannins, lipids, sugars or amino acids) and an enrichment in microbial biomass relative to the active layer24. The absence of a downward trend of C:N and H:C within the permafrost (Supplementary Fig. 1), except at Daring Lake, indicates that soil development and microbial processing were effectively halted soon after permafrost aggradation23.
Active layer and permafrost yields of DOC and nitrogen
DOC content correlated with soil C content in both active layer (r2 [log–log] = 0.748, P  More

1.
Hoekstra, A. Y. & Wiedmann, T. O. Humanity’s unsustainable environmental footprint. Science 344, 1114–1117. https://doi.org/10.1126/science.1248365 (2014).
ADS  CAS  Article  PubMed  Google Scholar 
2.
Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 736–746. https://doi.org/10.1126/science.1259855 (2015).
ADS  CAS  Article  Google Scholar 

3.
Wang, H. et al. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388, 1459–1544. https://doi.org/10.1016/S0140-6736(16)31012-1 (2016).
Article  Google Scholar 

4.
Grizzetti, B. et al. Human pressures and ecological status of European rivers. Sci. Rep. 7, 205. https://doi.org/10.1038/s41598-017-00324-3 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

5.
Hoekstra, A. Y. & Mekonnen, M. M. The water footprint of humanity. Proc. Natl. Acad. Sci. 109, 3232–3237. https://doi.org/10.1073/pnas.1109936109 (2012).
ADS  Article  PubMed  Google Scholar 

6.
Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561. https://doi.org/10.1038/nature09440 (2010).
ADS  CAS  Article  PubMed  Google Scholar 

7.
Millennium Ecosystem Assessment. Ecosystems and Human Well-being. A Framework for Assessment https://pdf.wri.org/ecosystems_human_wellbeing.pdf (2003).

8.
Carpenter, S. R., Stanley, E. H. & Vander Zanden, M. J. State of the world’s freshwater ecosystems: physical, chemical, and biological changes. Annu. Rev. Environ. Resour. 36, 75–99. https://doi.org/10.1146/annurev-environ-021810-094524 (2011).
Article  Google Scholar 

9.
Richmond, E. K. et al. A diverse suite of pharmaceuticals contaminates stream and riparian food webs. Nat. Commun. 9, 4491. https://doi.org/10.1038/s41467-018-06822-w (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

10.
Maes, J. et al. An indicator framework for assessing ecosystem services in support of the EU Biodiversity Strategy to 2020. Ecosyst. Serv. 17, 14–23. https://doi.org/10.1016/j.ecoser.2015.10.023 (2016).
Article  Google Scholar 

11.
Anzaldua, G. et al. Getting into the water with the ecosystem services approach: the DESSIN ESS evaluation framework. Ecosyst. Serv. 30, 318–326. https://doi.org/10.1016/j.ecoser.2017.12.004 (2018).
Article  Google Scholar 

12.
Van Vliet, M. T. H., Florke, M. & Wada, Y. Quality matters for water scarcity. Nat. Geosci. 10, 800–802. https://doi.org/10.1038/NGEO3047 (2017).
ADS  Article  Google Scholar 

13.
Bernhardt, E. S., Rosi, E. J. & Gessner, M. O. Synthetic chemicals as agents of global change. Front. Ecol. Environ. 15, 84–90. https://doi.org/10.1002/fee.1450 (2017).
Article  Google Scholar 

14.
Global Chemicals Outlook II—from legacies to innovative solutions: implementing the 2030 agenda for sustainable development. Synthesis report https://wedocs.unep.org/bitstream/handle/20.500.11822/28113/GCOII.pdf?sequence=1&isAllowed=y (2019).

15.
Birk, S. et al. Impacts of multiple stressors on freshwater biota across spatial scales and ecosystems. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-020-1216-4 (2020).
Article  PubMed  Google Scholar 

16.
A guide to SDG interactions. From sciene to implementation https://council.science/publications/a-guide-to-sdg-interactions-from-science-to-implementation/ (2017).

17.
EC. Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH). Off. J. Eur. Union L 396, 1–848 https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:02006R01907-20140410&from=EN (2006).

18.
Geiser, K. Chemicals Without Harm. Policies for a Sustainable World (MIT Press, Cambridge, 2015).
Google Scholar 

19.
Escher, B. I., Stapleton, H. M. & Schymanski, E. L. Tracking complex mixtures of chemicals in our changing environment. Science 367, 388–392. https://doi.org/10.1126/science.aay6636 (2020).
ADS  CAS  Article  PubMed  Google Scholar 

20.
UNESCO. Solving the puzzle: the ecosystem approach and biosphere reserves https://unesdoc.unesco.org/ark:/48223/pf0000119790 (2000).

21.
Nõges, P., van de Bund, W., Cardoso, A. C., Solimini, A. G. & Heiskanen, A. S. Assessment of the ecological status of European surface waters: a work in progress. Hydrobiologia 633, 197–211. https://doi.org/10.1007/s10750-009-9883-9 (2009).
CAS  Article  Google Scholar 

22.
Tsakiris, G. The status of the European waters in 2015: a review. Environ. Process. 2, 543–557. https://doi.org/10.1007/s40710-015-0079-1 (2015).
Article  Google Scholar 

23.
Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Off. J. Eur. Commun. L 327, 1–72 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ:L:2000:2327:TOC (2000).

24.
Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674. https://doi.org/10.1038/s41586-019-1684-3 (2019).
ADS  CAS  Article  PubMed  Google Scholar 

25.
Rockström, J. et al. A safe operating space for humanity. Nature 461, 472–475. https://doi.org/10.1038/461472a (2009).
ADS  CAS  Article  PubMed  Google Scholar 

26.
Clean Water Rule: Definition of “Waters of the United States”. Federal Register 80, 37054–37127, Monday, June 29, 2015/Rules https://www.govinfo.gov/content/pkg/FR-2015-06-29/pdf/2015-13435.pdf (2015).

27.
C&L Inventory. Database containing classification and labelling information on notified and registered substances received from manufacturers and importers https://echa.europa.eu/information-on-chemicals/cl-inventory-database (accessed March 4, 2019) (2019).

28.
Posthuma, L., de Zwart, D. & Dyer, S. D. Chemical mixtures affect freshwater species assemblages: from problems to solutions. Curr. Opin. Environ. Sci. Health 11, 78–89. https://doi.org/10.1016/j.coesh.2019.09.002 (2019).
Article  Google Scholar 

29.
The Water Framework Directive and the Floods Directive: Actions towards the ‘good status’ of EU water and to reduce flood risks. Communication from the Commission to the European Parliament and the Council, 9.3.2015. COM(2015) 120 final https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52015DC0120 (2015).

30.
EC. Fitness check of the Water Framework Directive, Groundwater Directive, Environmental Quality Standards Directive and Floods Directive https://ec.europa.eu/environment/water/fitness_check_of_the_eu_water_legislation/documents/Water%20Fitness%20Check%20-%20SWD(2019)439%20-%20web.pdf. 1–176 (2019).

31.
Arle, J., Mohaupt, V. & Kirst, I. Monitoring of surface waters in Germany under the Water Framework Directive—a review of approaches, methods and results. Water 8, 217. https://doi.org/10.3390/w8060217 (2016).
Article  Google Scholar 

32.
Drakvik, E. et al. Statement paper on advancing the assessment of chemical mixtures and their risks for human health and the environment. Environ. Int. 134, 105267. https://doi.org/10.1016/j.envint.2019.105267 (2020).
CAS  Article  PubMed  Google Scholar 

33.
Brack, W. et al. High-resolution mass spectrometry to complement monitoring and track emerging chemicals and pollution trends in European water resources. Environ. Sci. Eur. 31, 62. https://doi.org/10.1186/s12302-019-0230-0 (2019).
Article  Google Scholar 

34.
Van Gils, J. et al. The European Collaborative Project SOLUTIONS developed models to provide diagnostic and prognostic capacity and fill data gaps for chemicals of emerging concern. Environ. Sci. Eur. 31, 72. https://doi.org/10.1186/s12302-019-0248-3 (2019).
Article  Google Scholar 

35.
van Gils, J. et al. Computational material flow analysis for thousands of chemicals of emerging concern in European waters. J. Hazard. Mater. https://doi.org/10.1016/j.jhazmat.2020.122655 (2020).
Article  PubMed  Google Scholar 

36.
Pistocchi, A. et al. Assessment of the effectiveness of reported Water Framework Directive Programmes of Measures. Part III—JRC Pressure Indicators v.2.0: nutrients, urban runoff, flow regime and hydromorphological alteration https://doi.org/10.2760/325451 (2018).

37.
EC. Directive 2013/39/EU of the European Parliament and of the Council of 12 August 2013 amending Directives 2000/60/EC and 2008/105/EC as regards priority substances in the field of water policy. Off. J. Eur. Union L 226, 1–17 https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2013:2226:0001:0017:EN:PDF (2013).

38.
EEA. European waters—assessment of status and pressures https://www.eea.europa.eu/publications/state-of-water (2018).

39.
Dulio, V. et al. Emerging pollutants in the EU: 10 years of NORMAN in support of environmental policies and regulations. Environ. Sci. Eur. 30, 5. https://doi.org/10.1186/s12302-018-0135-3 (2018).
Article  PubMed  PubMed Central  Google Scholar 

40.
Guidelines for the health risk assessment of chemical mixtures Fed. Reg. 51 185, 34014–34025 https://www.epa.gov/sites/production/files/32014-34011/documents/chem_mix_31986.pdf (1986).

41.
Calamari, D. & Vighi, M. A proposal to define quality objectives for aquatic life for mixtures of chemical substances. Chemosphere 25, 531–542. https://doi.org/10.1016/0045-6535(92)90285-Y (1992).
ADS  Article  Google Scholar 

42.
Technical guidance for deriving environmental quality standards. Common Implementation Strategy for the Water framework Directive (2000/60/EC)—Guidance Document No. 27 https://circabc.europa.eu/sd/a/ba6810cd-e611-4f72-9902-f0d8867a2a6b/Guidance%20No%2027%20-%20Deriving%20Environmental%20Quality%20Standards%20-%20version%202018.pdf (2011).

43.
Posthuma, L. & De Zwart, D. Encyclopedia of Toxicology 3rd edn, Vol. 4, 363–368 (Elsevier Inc., Academic Press, 2014).
Google Scholar 

44.
Birk, S. et al. Three hundred ways to assess Europe’s surface waters: an almost complete overview of biological methods to implement the Water Framework Directive. Ecol. Ind. 18, 31–41. https://doi.org/10.1016/j.ecolind.2011.10.009 (2012).
Article  Google Scholar 

45.
De Zwart, D. & Posthuma, L. Complex mixture toxicity for single and multiple species: proposed methodologies. Environ. Toxicol. Chem. 24, 2665–2676. https://doi.org/10.1897/04-639r.1 (2005).
Article  PubMed  Google Scholar 

46.
Lyche Solheim, A. et al. A new broad typology for rivers and lakes in Europe: Development and application for large-scale environmental assessments. Sci. Total Environ. 697, 134043. https://doi.org/10.1016/j.scitotenv.2019.134043 (2019).
ADS  CAS  Article  Google Scholar 

47.
Cade, B. S. & Noon, B. R. A gentle introduction to quantile regression for ecologists. Front. Ecol. Environ. 1, 412–420 https://www.jstor.org/stable/3868138 (2003).

48.
Vermeulen, R., Schymanski, E. L., Barabási, A.-L. & Miller, G. W. The exposome and health: where chemistry meets biology. Science 367, 392–396. https://doi.org/10.1126/science.aay3164 (2020).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

49.
Posthuma, L., van Gils, J., Zijp, M. C., van de Meent, D. & de Zwart, D. Species sensitivity distributions for use in environmental protection, assessment, and management of aquatic ecosystems for 12 386 chemicals. Environ. Toxicol. Chem. 38, 905–917. https://doi.org/10.1002/etc.4373 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

50.
Hoondert, R. P. J., Oldenkamp, R., de Zwart, D., van de Meent, D. & Posthuma, L. QSAR-based estimation of Species Sensitivity Distribution parameters: an exploratory investigation. Environ. Toxicol. Chem. 38, 2764–2770. https://doi.org/10.1002/etc.4601 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

51.
Williams, A. J. et al. The CompTox Chemistry Dashboard: a community data resource for environmental chemistry. J. Chem. Inform. 9, 61. https://doi.org/10.1186/s13321-017-0247-6 (2017).
CAS  Article  Google Scholar 

52.
Blum, C. et al. The concept of sustainable chemistry: key drivers for the transition towards sustainable development. Sustain. Chem. Pharm. 5, 94–104. https://doi.org/10.1016/j.scp.2017.01.001 (2017).
CAS  Article  Google Scholar 

53.
Kostal, J., Voutchkova-Kostal, A., Anastas, P. T. & Zimmerman, J. B. Identifying and designing chemicals with minimal acute aquatic toxicity. Proc. Natl. Acad. Sci. U.S.A. 112, 6289–6294. https://doi.org/10.1073/pnas.1314991111 (2015).
ADS  CAS  Article  PubMed  Google Scholar 

54.
Saouter, E. et al. Environmental footprint: update of Life Cycle Impact Assessment methods—ecotoxicity freshwater, human toxicity cancer, and non-cancer https://doi.org/10.2760/178544 (2018).

55.
Rapport, D. & Friend, A. Towards a comprehensive framework for environmental statistics. A stress-response approach https://www.worldcat.org/title/towards-a-comprehensive-framework-for-environmental-statistics-a-stress-response-approach/oclc/21772350 (1979).

56.
Kaika, M. & Page, B. The EU Water Framework Directive: part 1. European policy-making and the changing topography of lobbying. Eur. Environ. 13, 314–327. https://doi.org/10.1002/eet.331 (2003).
Article  Google Scholar 

57.
Page, B. & Kaika, M. The EU Water Framework Directive: part 2. Policy innovation and the shifting choreography of governance. Eur. Environ. 13, 328–343. https://doi.org/10.1002/eet.332 (2003).
Article  Google Scholar 

58.
Elosegi, A., Gessner, M. O. & Young, R. G. River doctors: learning from medicine to improve ecosystem management. Sci. Total Environ. 595, 294–302. https://doi.org/10.1016/j.scitotenv.2017.03.188 (2017).
ADS  CAS  Article  PubMed  Google Scholar 

59.
Kortenkamp, A. & Faust, M. Regulate to reduce chemical mixture risk. Science 361, 224–226. https://doi.org/10.1126/science.aat9219 (2018).
ADS  CAS  Article  PubMed  Google Scholar 

60.
Voulvoulis, N., Arpon, K. D. & Giakoumis, T. The EU Water Framework Directive: from great expectations to problems with implementation. Sci. Total Environ. 575, 358–366. https://doi.org/10.1016/j.scitotenv.2016.09.228 (2017).
ADS  CAS  Article  PubMed  Google Scholar 

61.
Giakoumis, T. & Voulvoulis, N. The transition of EU water policy towards the Water Framework Directive’s integrated river basin management paradigm. Environ. Manag. 62, 819–831. https://doi.org/10.1007/s00267-018-1080-z (2018).
ADS  Article  Google Scholar 

62.
Suter, G. W., Traas, T. P. & Posthuma, L. In Species Sensitivity Distributions in Ecotoxicology, Ch 21 (eds Posthuma, L. et al.) 437–474 (CRC Press, Boca Raton, 2002).
Google Scholar 

63.
Kortenkamp, A. et al. Common assessment framework for HRA and ERA higher tier assessments including fish and drinking water and multi-species ERA via SSD, population-level ERA via IBM and food web vulnerability ERA. SOLUTIONS Deliverable D18.1 https://www.solutions-project.eu/wp-content/uploads/2018/11/D18.1_SOLUTIONS-D18_1-after-peer-review-clean-V2_Kortenkamp_chm_with_annex.pdf (2018).

64.
Posthuma, L., De Zwart, D., Keijzers, R. & Postma, J. Water systems analysis with the ecological key factor ‘toxicity’. Part 2. Calibration. Toxic pressure and ecological effects on macrofauna in the Netherlands (in Dutch) https://www.stowa.nl/sites/default/files/assets/PUBLICATIES/Publicaties%202016/STOWA%202016-15/STOWA%202016-15B.pdf (STOWA, Amersfoort, the Netherlands, 2016).

65.
Posthuma, L. & De Zwart, D. Predicted effects of toxicant mixtures are confirmed by changes in fish species assemblages in Ohio, USA, rivers. Environ. Toxicol. Chem. 25, 1094–1105. https://doi.org/10.1897/05-305r.1 (2006).
CAS  Article  PubMed  Google Scholar 

66.
Posthuma, L. & De Zwart, D. Predicted mixture toxic pressure relates to observed fraction of benthic macrofauna species impacted by contaminant mixtures. Environ. Toxicol. Chem. 31, 2175–2188. https://doi.org/10.1002/etc.1923 (2012).
CAS  Article  PubMed  Google Scholar 

67.
Berger, E., Haase, P., Oetken, M. & Sundermann, A. Field data reveal low critical chemical concentrations for river benthic invertebrates. Sci. Total Environ. 544, 864–873. https://doi.org/10.1016/j.scitotenv.2015.12.006 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

68.
Posthuma, L. et al. Mixtures of chemicals are important drivers of impacts on ecological status in European surface waters. Environ. Sci. Eur. 31, 71. https://doi.org/10.1186/s12302-019-0247-4 (2019).
Article  Google Scholar 

69.
Zijp, M. C., Posthuma, L. & Van de Meent, D. Definition and applications of a versatile chemical pollution footprint methodology. Environ. Sci. Technol. 48, 10588–10597. https://doi.org/10.1021/es500629f (2014).
ADS  CAS  Article  PubMed  Google Scholar 

70.
Bjørn, A., Diamond, M., Birkved, M. & Hauschild, M. Z. Chemical footprint method for improved communication of freshwater ecotoxicity impacts in the context of ecological limits. Environ. Sci. Technol. 48, 13253–13262. https://doi.org/10.1021/es503797d (2014).
ADS  CAS  Article  PubMed  Google Scholar 

71.
Kapo, K. E. et al. iSTREEM®: an approach for broad-scale in-stream exposure assessment of “down-the-drain” chemicals. Integr. Environ. Assess. Manag. 12, 782–792. https://doi.org/10.1002/ieam.1793 (2016).
Article  PubMed  Google Scholar 

72.
Donnelly, C., Arheimer, B., Capell, R., Dahne, J. & Stromqvist, J. Regional overview of nutrient load in Europe—challenges when using a large-scale model approach, E-HYPE. Understanding fresh-water quality problems in a changing world https://iahs.info/uploads/dms/15569.361%2049-58.pdf (2013).

73.
Posthuma, L., Suter, G. W. I. & Traas, T. P. Species Sensitivity Distributions in Ecotoxicology (CRC-Press, Boca Raton, 2002).
Google Scholar 

74.
Drescher, K. & Bödeker, W. Assessment of the combined effects of substances—the relationship between concentration addition and independent action. Biometrics 51, 716–730. https://doi.org/10.2307/2532957 (1995).
MathSciNet  Article  MATH  Google Scholar 

75.
EEA. WISE WFD database at https://www.eea.europa.eu/data-and-maps/data/wise-wfd-3 (2012).

76.
Globevnik, L., Koprivsek, M. & Snoj, L. Metadata to the MARS spatial database. Freshw. Metadata J. 21, 1–7. https://doi.org/10.15504/fmj.2017.21 (2017).
Article  Google Scholar 

77.
Birk, S. et al. Intercalibrating classifications of ecological status: Europe’s quest for common management objectives for aquatic ecosystems. Sci. Total Environ. 454–455, 490–499. https://doi.org/10.1016/j.scitotenv.2013.03.037 (2013).
ADS  CAS  Article  PubMed  Google Scholar 

78.
Zijp, M. C., Huijbregts, M. A. J., Schipper, A. M., Mulder, C. & Posthuma, L. Identification and ranking of environmental threats with ecosystem vulnerability distributions. Sci. Rep. 7, 9298. https://doi.org/10.1038/s41598-017-09573-8 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar  More

Genome structure of DhNV
The circular genome of DhNV is 119,754 bp and contains 106 hypothetical ORFs, with 56 on the positive strand and 50 on the negative strand (Fig. 1). Fifty-nine of the ORFs met our comparative e-value threshold of  More