Ecology

1.
Sparks, J. S. et al. The covert world of fish biofluorescence: a phylogenetically widespread and phenotypically variable phenomenon. PLoS ONE 9, e83259 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 
2.
Wucherer, M. F. & Michiels, N. K. A fluorescent chromatophore changes the level of fluorescence in a reef fish. PLoS ONE 7, e37913 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

3.
Gruber, D. F. et al. Biofluorescence in catsharks (Scyliorhinidae): fundamental description and relevance for elasmobranch visual ecology. Sci. Rep. 6, 24751 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
Gruber, D. F. & Sparks, J. S. First observation of fluorescence in marine turtles. Am. Mus. Novit. 3845, 1–8 (2015).
Article  Google Scholar 

5.
Kohler, A. M., Olson, E. R., Martin, J. G. & Anich, P. S. Ultraviolet fluorescence discovered in New World flying squirrels (Glaucomys). J. Mammal. 100, 21–30 (2019).
Article  Google Scholar 

6.
Jeng, M.-L. Biofluorescence in terrestrial animals, with emphasis on fireflies: a review and field observation in Bioluminescence—Analytical Applications and Basic Biology 1–16 (Hirobumi Suzuki, IntechOpen, 2019).

7.
Evtukh, G. Fluorescence among Fraterculinae subfamily. Pyccкий opнитoлoгичecкий жypнaл 28, 2134–2142 (2019).
Google Scholar 

8.
Wilkinson, B. P., Johns, M. E. & Warzybok, P. Fluorescent ornamentation in the Rhinoceros Auklet Cerorhinca monocerata. Ibis 161, 694–698 (2019).
Article  Google Scholar 

9.
Arnold, K., Owens, I. P. & Marshall, N. J. Fluorescent signalling in parrots. Science 295, 92 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Barreira, A., Lagorio, M. G., Lijtmaer, D., Lougheed, S. & Tubaro, P. Fluorescent and ultraviolet sexual dichromatism in the blue-winged parrotlet. J. Zool. 288, 135–142 (2012).
Article  Google Scholar 

11.
Goutte, S. et al. Intense bone fluorescence reveals hidden patterns in pumpkin toadlets. Sci. Rep. 9, 1–8 (2019).
CAS  Article  Google Scholar 

12.
Taboada, C., Brunetti, A. E., Alexandre, C., Lagorio, M. G. & Faivovich, J. Fluorescent frogs: a herpetological perspective. S. Am. J. Herpetol. 12, 1–13 (2017).
Article  Google Scholar 

13.
Taboada, C. et al. Naturally occurring fluorescence in frogs. Proc. Nat. Acad. Sci. USA 114, 3672–3677 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Deschepper, P., Jonckheere, B. & Matthys, J. A light in the dark: the discovery of another fluorescent frog in the Costa Rican rainforests. Wilderness Environ. Med. 29, 4212134–2142422 (2018).
Google Scholar 

15.
Lamb, J. Y. & Davis, M. P. Salamanders and other amphibians are aglow with biofluorescence. Sci. Rep. 10, 1–7 (2020).
Article  CAS  Google Scholar 

16.
Thompson, M. E., Saporito, R., Ruiz-Valderrama, D. H., Medina-Rangel, G. F. & Donnelly, M. A. A field-based survey of fluorescence in tropical tree frogs using an LED UV-B flashlight. Herpetol. Notes 12, 987–990 (2019).
Google Scholar 

17.
Gray, R. J. Biofluorescent lateral patterning on the Mossy Bushfrog (Philautus macroscelis): the first report of biofluorescence in a rhacophorid frog. Herpetol. Notes 12, 363–364 (2019).
Google Scholar 

18.
Munoz, D. Plethodon cinereus (Eastern Red-backed Salamander) Fluorescence. Herpetol. Rev. 49, 512–513 (2018).
Google Scholar 

19.
Tah, M.M.T.-M., Puan, C. L., Chuang, M.-F., Othman, S. N. & Borzée, A. First record of ultraviolet fluorescence in Bent-toed Gecko Cyrtodactylus quadrivirgatus (Gekkonidae: Sauria). Herpetol. Notes 13, 211–212 (2020).
Google Scholar 

20.
Sloggett, J. J. Field observations of putative bone-based fluorescence in a gecko. Curr. Zool. 64, 319–320 (2018).
PubMed  PubMed Central  Article  Google Scholar 

21.
Prötzel, D. et al. Widespread bone-based fluorescence in chameleons. Sci. Rep. 8, 698 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

22.
Maitland, D. & Hart, A. A fluorescent vertebrate: the Iberian Worm-lizard Blanus cinereus (Amphisbaenidae). Herpetol. Rev. 39, 50 (2008).
Google Scholar 

23.
Andrews, K., Reed, S. M. & Masta, S. E. Spiders fluoresce variably across many taxa. Biol. Lett. 3, 265–267 (2007).
PubMed  PubMed Central  Article  Google Scholar 

24.
Macel, M.-L. et al. Sea as a color palette: the ecology and evolution of fluorescence. Zool. Lett. 6, 1–11 (2020).
Article  Google Scholar 

25.
Salih, A., Larkum, A., Cox, G., Kühl, M. & Hoegh-Guldberg, O. Fluorescent pigments in corals are photoprotective. Nature 408, 850–853 (2000).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

26.
Kloock, C. T., Kubli, A. & Reynolds, R. Ultraviolet light detection: a function of scorpion fluorescence. J. Arachnol. 38, 441–445 (2010).
Article  Google Scholar 

27.
Haddock, S. H. & Dunn, C. W. Fluorescent proteins function as a prey attractant: experimental evidence from the hydromedusa Olindias formosus and other marine organisms. Biol. Open 4, 1094–1104 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

28.
Gandía-Herrero, F., García-Carmona, F. & Escribano, J. Botany: floral fluorescence effect. Nature 437, 334 (2005).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

29.
Mazel, C., Cronin, T., Caldwell, R. & Marshall, N. Fluorescent enhancement of signaling in a mantis shrimp. Science 303, 51 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

30.
Lim, M. L., Land, M. F. & Li, D. Sex-specific UV and fluorescence signals in jumping spiders. Science 315, 481 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Kloock, C. T. A comparison of fluorescence in two sympatric scorpion species. J. Photochem. Photobiol. B 91, 132–136 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

32.
Michiels, N. K. et al. Red fluorescence in reef fish: a novel signalling mechanism?. BMC Ecol. 8, 1–16 (2008).
Article  Google Scholar 

33.
Gerlach, T., Sprenger, D. & Michiels, N. K. Fairy wrasses perceive and respond to their deep red fluorescent coloration. Proc. R. Soc. B 281, 20140787 (2014).
PubMed  Article  PubMed Central  Google Scholar 

34.
Lagorio, M. G., Cordon, G. B. & Iriel, A. Reviewing the relevance of fluorescence in biological systems. Photochem. Photobiol. Sci. 14, 1538–1559 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Bachman, C. H. & Ellis, E. H. Fluorescence of bone. Nature 206, 1328–1331 (1965).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

36.
Rebouças, R. et al. Is the conspicuous dorsal coloration of the Atlantic forest pumpkin toadlets aposematic?. Salamandra 55, 39–47 (2019).
Google Scholar 

37.
Werner, Y. L. Ecological comments on some gekkonid lizards of the Namib Desert, South West Africa. Modoqua 1977, 157–169 (1977).
Google Scholar 

38.
Russell, A. & Bauer, A. Substrate excavation in the Namibian web-footed gecko, Palmatogecko rangei Andersson 1908, and its ecological significance. Trop. Zool. 3, 197–207 (1990).
Article  Google Scholar 

39.
Vitt, L. J. & Caldwell, J. P. Herpetology: An Introductory Biology of Amphibians and Reptiles 776 (Academic Press, London, 2013).
Google Scholar 

40.
Schmidt, W. J. Die Chromatophoren der Reptilienhaut. Arch. Mikrosk. Anat. 90, 98–259 (1918).
Article  Google Scholar 

41.
Szydłowski, P., Madej, J. P. & Mazurkiewicz-Kania, M. Histology and ultrastructure of the integumental chromatophores in tokay gecko (Gekko gecko) (Linnaeus, 1758) skin. Zoomorphology 136, 233–240 (2017).
PubMed  PubMed Central  Article  Google Scholar 

42.
Saenko, S. V., Teyssier, J., Van Der Marel, D. & Milinkovitch, M. C. Precise colocalization of interacting structural and pigmentary elements generates extensive color pattern variation in Phelsuma lizards. BMC Biol. 11, 105 (2013).
PubMed  PubMed Central  Article  CAS  Google Scholar 

43.
Teyssier, J., Saenko, S. V., Van Der Marel, D. & Milinkovitch, M. C. Photonic crystals cause active colour change in chameleons. Nat. Commun. 6, 6368 (2015).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Avallone, B., Tizzano, M., Cerciello, R., Buglione, M. & Fulgione, D. Gross anatomy and ultrastructure of Moorish Gecko, Tarentola mauritanica skin. Tissue Cell 51, 62–67 (2018).
PubMed  Article  PubMed Central  Google Scholar 

45.
Morrison, R. L., Sherbrooke, W. C. & Frost-Mason, S. K. Temperature-sensitive, physiologically active iridophores in the lizard Urosaurus ornatus: an ultrastructural analysis of color change. Copeia 1996, 804–812 (1996).
Article  Google Scholar 

46.
Polewski, K., Zinger, D., Trunk, J., Monteleone, D. C. & Sutherland, J. C. Fluorescence of matrix isolated guanine and 7-methylguanine. J. Photochem. Photobiol. B 24, 169–177 (1994).
CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Turrisi, R. et al. Stokes shift/emission efficiency trade-off in donor–acceptor perylenemonoimides for luminescent solar concentrators. J. Mater. Chem. A 3, 8045–8054 (2015).
CAS  Article  Google Scholar 

48.
Suzuki, K. et al. Reevaluation of absolute luminescence quantum yields of standard solutions using a spectrometer with an integrating sphere and a back-thinned CCD detector. Phys. Chem. Chem. Phys. 11, 9850–9860 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Szydłowski, P., Madej, J. P. & Mazurkiewicz-Kania, M. Ultrastructure and distribution of chromatophores in the skin of the leopard gecko (Eublepharis macularius). Acta Zool. 97, 370–375 (2016).
Article  Google Scholar 

50.
Hibbitts, T. J., Pianka, E. R., Huey, R. B. & Whiting, M. J. Ecology of the common barking gecko (Ptenopus garrulus) in southern Africa. J. Herpetol. 39, 509–515 (2005).
Article  Google Scholar 

51.
Olivier, J. Spatial distribution of fog in the Namib. J. Arid Environ. 29, 129–138 (1995).
ADS  Article  Google Scholar 

52.
Gottlieb, T. R., Eckardt, F. D., Venter, Z. S. & Cramer, M. D. The contribution of fog to water and nutrient supply to Arthraerua leubnitziae in the central Namib Desert, Namibia. J. Arid Environ. 161, 35–46. https://doi.org/10.1016/j.jaridenv.2018.11.002 (2019).
ADS  Article  Google Scholar 

53.
Prötzel, D. D. Palmatogecko—ein sozialer Gecko?. Reptilia 107, 4–5 (2014).
Google Scholar 

54.
Nørgaard, T., Henschel, J. R. & Wehner, R. The night-time temporal window of locomotor activity in the Namib Desert long-distance wandering spider, Leucorchestris arenicola. J. Comp. Physiol. A 192, 365–372 (2006).
Article  Google Scholar 

55.
Roth, L. S. & Kelber, A. Nocturnal colour vision in geckos. Proc. R. Soc. B 271, 485–487 (2004).
Article  Google Scholar 

56.
Pinto, B. J., Nielsen, S. V. & Gamble, T. Transcriptomic data support a nocturnal bottleneck in the ancestor of gecko lizards. Mol. Phylogenet. Evol. 141, 106639 (2019).
PubMed  Article  PubMed Central  Google Scholar 

57.
Iriel, A. & Lagorio, M. G. Implications of reflectance and fluorescence of Rhododendron indicum flowers in biosignaling. Photochem. Photobiol. Sci. 9, 342–348 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Spurr, A. R. A low-viscosity epoxy resin embedding medium for electron microscopy. J. Ultrastruct. Res. 26, 31–43 (1969).
CAS  PubMed  Article  PubMed Central  Google Scholar 

59.
Richardson, K., Jarett, L. & Finke, E. Embedding in epoxy resins for ultrathin sectioning in electron microscopy. Stain Technol. 35, 313–323 (1960).
CAS  PubMed  Article  PubMed Central  Google Scholar 

60.
Reynolds, E. S. The use of lead citrate at high pH as an electron-opaque stain in electron microscopy. J. Cell Biol. 17, 208 (1963).
CAS  PubMed  PubMed Central  Article  Google Scholar 

61.
Rueden, C. T. et al. Image J2: ImageJ for the next generation of scientific image data. BMC Bioinform. 18, 529 (2017).
Article  Google Scholar 

62.
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2020).

63.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis 2nd edn. (Springer, Berlin, 2016).
Google Scholar  More

In this section, the methodology for the fluorescence spectra acquisition of the in-vivo cochineal in its natural ambient is described. The optical setup used is our own design that guarantees the detection of low levels of fluorescence present in this study. In addition, the wild cochineal reproduction model employed to ensure the existence of the samples is described. This study proposes a fluorescence standard based on a commonly used disk-shaped carmine colored pencil segment because of the non-availability of a commercial one in our laboratory.
Optical setup for fluorescence spectra measurements
The optical setup for detecting fluorescence spectra consists of (1) a power supply, (2) an excitation source, (3) a dichroic mirror, (4) a 10 × microscope objective lens, (5) a homemade sample-holder, (6) a mechanical positioning device with micrometric movements in x, y and z, (7) a 5 × microscope objective lens, (8) multi-mode optical fiber, (9) a miniature fiber optic spectrometer and (10) computer equipment. The previous elements were arranged as shown in Fig. 5.
Figure 5

Optical experimental setup for detecting fluorescence spectra: (1) power supply, (2) laser source emitting at 532 nm, (3) dichroic mirror, (4) 10 × microscope objective, (5) sample-holder with the biological specimen depicted in yellow color, (6) a mechanical positioning device with micrometric movements in x, y and z directions, (7) 5 × microscope objective, (8) optical fiber, (9) fiber optic spectrometer and (10) desktop computer showing a typical spectrum of carminic acid within a cochineal.

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The power supply (M30-TP305E, Shanghai MCP Corp.) provides a constant voltage of 3.5 V to the excitation source. The excitation source is a commercial laser pointer (KG-303-6) emitting at a wavelength of 532 nm and nominal power of 1 W. This wavelength is found within the absorption band of carminic acid diluted in methanol19, and as was demonstrated by Cárdenas23, this wavelength allows the excitation of the fluorescence of natural carminic acid from wild cochineal in-vivo. The dichroic mirror (DMLP550, Thorlabs, Inc.) is tilted at an angle of 45° with respect to the direction of laser beam propagation allowing the excitation light to be reflected towards the aperture of the 10 × microscope objective lens (M-10X, Newport, Co.) which in turn focuses this radiation on a plane where the sample is placed. The specimen to be studied is fixed on the sample holder which in turn is magnetically coupled to the mechanical positioning device (M-900, Newport, Co.), that together with a micrometric base (High-Performance Linear Stage, Newport, Co.) allows the cochineal to be placed at the focal point of the excitation beam. Both the fraction of the fluorescent light emitted isotropically by the cochineal and the fraction of the light reflected diffusely by this insect enter the 10 × objective lens and leave it as a collimated beam towards the dichroic mirror. The first fraction of light is transmitted approximately 85% to the second microscope objective lens (M-5X, Newport, Co.) while the second one is strongly blocked by the dichroic mirror. A 400 µm diameter optical fiber (QP400-2-SR, Ocean Optics, Inc.) is placed at the focal distance from this latter microscope objective to collect the fluorescence radiation and transmit it to the input port of the miniature fiber optic spectrometer (Exemplar, B&W Tek, LLC.) which allows for the spectral decomposition of the fluorescence radiation for its later processing. This spectrometer is connected through a USB port to a desktop computer where the BWSpec (B&W Tek, LLC.) software is installed, provided by the same manufacturer of this device, by means of which the acquisition of the spectra is carried out with an integration time of 200 ms. This software allows the user to save the spectra in plain text files (.txt) for further processing of the acquired information.
The use of the dichroic mirror in the optical setup allows for adequate discrimination of the excitation and emission wavelengths. It shows about a 90% reflection in the range of 380–535 nm, the range in which the wavelength of the excitation beam is found (λexc = 532 nm), whereas for wavelengths in the range of 565–800 nm it presents an 85% transmission. We expect that the in-vivo cochineal fluorescence emission should be mainly located in this last spectral region, based on the findings reported in previous research works for solutions of carminic acid in methanol19,20,23.
Carminic acid fluorescence standard
As a part of this research, we proposed the use of a segment from a carmine colored pencil (Ekuz Carmín, AZOR) as a fluorescence standard due to the absence of a commercial one in our laboratory. This homemade fluorescence standard was used to calibrate the spectra acquisition optical setup. The standard was obtained by cutting a 5 mm segment of the carmine colored pencil with a razor blade (Stainless blade, Dorco, Co.) to achieve a finer cut. Then, this segment was situated inside a plastic piece in an annulus-shape to provide protection and handling during fluorescence measurements. Finally, its face of greater diameter was polished with fine grit sandpaper (B-99 1000, Fandeli, México) to get a smooth plane surface. The opposite face was adhered to the sample-holder while the polished one was placed perpendicular to the optical axis of the 10 × objective lens so that the excitation light impinges on it allowing the optimization of the fluorescence signal recorded by the optical setup.
In order to test the temporal stability of the carminic acid fluorescence standard, a continuous acquisition of fluorescence spectra was performed by using the optical setup previously shown in Fig. 5. To do so, the excitation light source was turned on 30 min before the measurements. Then, the spectra acquisition was done one after the other by allowing the excitation light hits on the standard to detect its fluorescence, immediately blocking this radiation while its spectrum was recorded, and so on until 10 spectra were recorded.
Biological samples and reproduction model
The cochineal samples used in this research come from a cladode infected with this pest donated by farmers from the Sociedad Cooperativa Productora Agropecuaria de Nopal Tlanalapa S.C. de R. L. de C.V., from the state of Hidalgo, Mexico.
For its survival, this pest was reproduced in the Biomedical Optics Laboratory of the Polytechnic University of Tulancingo, according to the reproduction model illustrated in Fig. 6, in a small-implemented orchard of plastic pots. In these pots were planted healthy adult cladodes of approximate dimensions of 40 × 20 × 2 cm; these cladodes were intentionally infected with this pest.
Figure 6

Reproduction model of the cochineal pest in a pot with adult cladodes near an infected cladode with active wild cochineal. Observe, in the center of the pot and between the two healthy cladodes, the infected cladode.

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As observed, in a plastic pot with a diameter approximately of 20 cm, two healthy cladodes were planted with the cladode infected with the cochineal pest placed between them. The spread of the pest is carried out through the points of contact between cladodes, which in this case were the thorns of each specimen, the separation distance between them was 1.5–3 cm approximately. Since the first stage of cochineal pest is mobile, during migration to other parts of the cladode, some cochineals fell to the ground infecting it from the base.
If we consider that in literature it has been reported that during oviposition females deposit about 419 eggs4 and we note that in our reproduction model both sides of the infected cladode have about 30 colonies conformed of 1–4 adult cochineal per side, then it is assumed that the spread of the pest towards the healthy cladodes with an infected cladode is enough to contaminate. This was visually verified by observing that during the course of two days both cladodes were already contaminated. In other variants of the reproduction model it was verified that a cladode with the pest is enough to infect four to five adult cladodes approximately of similar dimensions to those used in this study. This reproduction procedure allows us to have several groups of pots where the cochineals are in different maturation stages. The reproduction model proposed in this work is similar to the previously reported reproduction methods14,15,28,29 where the planting of the cladodes is also done in pots, however, the pest infestation is carried out in a different way from the one proposed in this work.
In order for the pest to maintain an appropriate lifecycle, the pots are taken out the laboratory to expose the cladodes to sunlight during the day, taking care to avoid the rain and predators that could put the cochineals at risk. In the evening, approximately at 6 o’clock, the pots are returned to their resting place in the laboratory for their care, conservation and adequate control for this study. To be specific, in this study a photoperiod of 9 ± 1 h of direct solar radiation was stablished, to later leave the pots inside the laboratory in absolute darkness, until the next day. This photoperiod was applied for 3 months, for covering the life cycle of the cochineals (90 days approx.). A script development in MATLAB R2014a (The MathWorks, Inc.) software was performed to process the solar radiation detected by the UPT weather station (Vantage Pro2, Davis Instruments Corp.), as shown in Fig. 7, during the last five days of photoperiod from June 01st to 05th, which correspond also to the end of life cycle of the cochineal.
Figure 7

Solar radiation as a function of time (09:00–18:00 h) of the last five days of photoperiod and life cycle of the cochineal. The maximum solar radiation was between 13:00 and 14:20 h, recording a maximum solar radiation of 1037 to 1322 W/m2.

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Note that the photoperiod was established in the time range of 09:00–18:00 h, (0–540 min in graphs), corresponding to the time in which the reproduction model was exposed to direct sunlight, so that at the end of the day was placed again inside the laboratory. Figure 7 shows the following data: date, time of maximum solar radiation, also in minutes, and the value of solar radiation [W/m2], corresponding to each graph of this figure.
The values of temperature and humidity, external (blue line) and internal (red line), recorded by the UPT weather station during the fluorescence spectra acquisition period for each set presented in this work, were processed in a script developed in MATLAB R2014a (The MathWorks, Inc.) software. In Fig. 8 are shown temperature and humidity as a function of time in a temporal range of 35 min, approximately.
Figure 8

Temperature and humidity (external and internal) registered by the UPT weather station (Vantage Pro2, Davis Instruments Corp.) during the experimental fluorescence measurements for each cochineal set.

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The measurement periods of each cochineal set, comprise around 35 min, where most of the cases were performed from 18:10 to 20:15 h. In this figure, data of temperature and humidity recorded by the UPT weather station were plotted, both external and internal with respect to the CIDETyP (Centro de Investigación, Desarrollo Tecnológico, Transferencia de Tecnología y Posgrado) building where the Biomedical Optics Laboratory is located. In Table 3 the temperature and humidity related to six replicates of our experiment are shown, since the cochineal life cycle (approximately 90 days) to the spectral measurement of the cochineal sets (set 0–5). These data are arranged by stage, date, temperature and humidity. These two lasts are in turn divided into the information collected from an online weather service page (WeatherOnline Online Services, www.weatheronline.mx) and by the UPT weather station (Vantage Pro2, Davis Instruments Corp., www.davisinstruments.com). The values, corresponding to temperature and humidity recorded by the UPT weather station, are the result of an average of the data plotted in Fig. 8.
Observe that, in Table 3 exist differences between the temperature and humidity provided by the online weather service page and the UPT weather station. In this regard, since the data from the UPT weather station contain local information of the environment where both the growth of the pest in the reproduction model and the experimental fluorescence measurements were made, these values of temperature and humidity have been adopted in this work.
Selection of the samples to study spectrally
For this study 3 live-female cochineals of different sizes were selected, of around 480 µm for the small cochineal, 540 µm for the medium cochineal and 790 µm for the largest cochineal. These sizes were determined with a script developed in MATLAB R2014a (The MathWorks, Inc.) software reported in the work of Cárdenas23. We assumed that the cochineals are infesting the cladode, in other words feeding from the sap, given their morphological features and size observed and compared with those referred in the available literature4.
Sample preparation
In order to prepare the biological samples to be studied spectrally, a segment of approximately 6 mm long was cut from one of the infected cladodes planted in the laboratory. The three cochineals previously classified in different sizes were found in this portion as shown in Fig. 9a, where, according to their lifecycle, they are in the stage of nymph I (crawler) and II, that is, in their early growth stages with 23 days old. This cladode portion was fixed on the round base of a plastic cylinder with a glue stick (Lápiz adhesivo DIXON, 36 g). The other end of the round base was inserted inside of an aluminum ring to which two magnets were attached with adhesive (Kola Loka Brocha, 5 GRS) to the sides with 90° in respect to the other, which allows for the proper placement of the sample in the pinhole mount of the magnetic positioning device during fluorescence measurements. Figure 9b shows the lateral view of the sample-holder, where it can be seen that the plastic round base and the sample protrude from the aluminum ring. This device allows us to keep considerable distances and displacements between the sample and the 10 × objective microscope lens during fluorescence measurements as shown in Fig. 9c.
Figure 9

Sample. (a) Top view of the biological sample composed by a portion of cladode with presence of female cochineals classified in sizes S, M and L, indicated by yellow arrows. (b) Homemade sample-holder for the in-vivo measurement of fluorescence spectra from cochineals in their natural habitat, composed by a cylindrical plastic base where the biological sample is adhered on its top surface and an aluminum ring with two disk-shaped magnets for immobilization purposes. (c) Picture taken during the in-vivo fluorescence study of the cochineals in their natural habitat, when the cladode portion with the cochineals in nymph I and II stages is fitted on the sample-holder of the experimental setup.

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Measuring procedure of the fluorescence spectra
The in-vivo fluorescence spectra measurements from the cochineals were performed using the optical setup described at the beginning of this section under laboratory conditions of complete darkness, at a temperature range from 20 to 25 °C as well as a relative humidity range from 42 to 80%. Additionally, it was necessary to turn on the excitation laser 30 min before the measurements began in order to achieve a stable fluorescence signal. During this time the output of the laser was obstructed with a dark piece of cardboard to avoid photodegradation of the sample, which was already placed in the sample-holder. Subsequently, the sample was approached to the edge of a dark colored, 374 µm thin plastic sheet, located just at the focal distance of the objective lens. Knowing the thickness of this sheet allows for the sample to be placed at the focal distance of the microscope objective once the plastic sheet is removed by moving properly the sample with the micrometric mechanism along the z-axis. This guarantees that recorded spectra of the cochineal are carried out “correctly”, that is, when the excitation focal point is on the highest part of the surface of this insect.
Once in this location, 10 fluorescence spectra were acquired from each one of the cochineals selected and located in the cladode portion. Three displacements of the sample holder were performed on the z-axis, moving the sample away from and then back toward to the focus plane of the laser excitation beam. Finally, these fluorescence spectra were averaged using a script development in MATLAB R2014a (The MathWorks, Inc.) software, obtaining a smoothed average spectrum of each cochineal as well as the standard deviation between them.
Figure 9c shows a lateral view of the way in which the fluorescence measurements of the in-vivo cochineals were performed. This view, of the interaction area of the excitation beam with a cochineal, allow us to observe that the spot size of the excitation laser is wide enough to cover completely the body of the cochineal.
Statistical analysis
Ten fluorescence spectra were acquired from each cochineal of different size that was present in the biological samples. These spectra were processed with a script developed by the authors in MATLAB R2014a (The MathWorks, Inc.) software, obtaining a smoothed average spectrum of each cochineal as well as its standard deviation. Figures for comparative fluorescence spectra of cochineals of different sizes show average values of fluorescence intensity and its standard deviation (errors bars). Control of reference fluorescence spectra of the background medium (cladode) and a homemade fluorescence standard were taken in all the experiments. Experiments were replicated in five sets of biological samples containing cochineals of similar sizes as is shown in Fig. 10 (see Table 1, which is complementary to this figure below). Statistical analysis of the fluorescence intensity at four wavelengths namely 620, 640, 660 nm and 760 nm were carried out using ANOVA test for the three cochineal sizes (small, medium and large) in each of the five available samples. The statistical values were computed using the Minitab 16 (Minitab, LLC.) software, where the computed p values  More

Overall similar genomic and functional repertoires of specialist and generalist wasps
We first sequenced and de novo assembled the reference genomes of Lh and Lb. The generalist species Lh was fully sequenced with PacBio long reads (Supplementary Table 1), which yielded a 487 Mb genome assembly with high continuity (N50 = 2.18 Mb; Supplementary Table 2). The specialist species Lb was sequenced and assembled with 170-fold Illumina read coverage and paired-end sequencing data from five long-insert libraries (up to 13 Kb; Supplementary Table 3). Although the assembled Lb genome had a lower N50 size (480 Kb) and smaller genome size (324 Mb), both the Lh and Lb genomes showed high completeness based on BUSCO and CEGMA assessments (Supplementary Table 2). The difference in genome sizes between Lh and Lb was likely determined by their repeat contents, as 51.47% of the Lh genome was annotated as repeat content in comparison with a 33.79% ratio in the Lb genome (Supplementary Table 4). The average GC contents of the Lh and Lb genomes were 27 and 26%, respectively, indicating that Leptopilina genomes are remarkably AT-rich. Interestingly, unlike the uniform distribution in Lb, the Lh genome shows a secondary peak enriched with scattered genomic windows of remarkably low GC content (16%; Supplementary Fig. 2).
We identified 11,881 and 11,054 protein-coding genes as the official gene sets for Lh and Lb, respectively (Supplementary Table 5). These gene sets were mainly generated by an integrated pipeline, and those retained had evidence from either a full-length transcriptome of pooled developmental stages or high-confidence homology with Insecta genes (see “Methods”). Ortholog analyses across 10 hymenopteran genomes suggested that the genomes of both these parasitoid species maintained a typical hymenopteran gene repertoire (Supplementary Fig. 3). Compared with those of other sequenced insects, the gene numbers of these two Leptopilina species are relatively small, largely due to their low proportions of patchy genes that were not vertically inherited along speciation (Supplementary Fig. 3). We manually annotated several representative gene families or pathways that are strongly associated with the biology of parasitoid wasps (Fig. 1c). The genomes of Leptopilina encode more olfactory receptor (OR) genes than those of other parasitoids except Nasonia vitripennis, while they encode the fewest gustatory receptor (GR) genes. Genes associated with metabolic and immune pathways in Leptopilina were shown to be more or less the same as those in other hymenopteran species. Because consistent patterns within a phylogenetic context were not available, we did not gain informative insights into the gene family variations underlying the divergence between Leptopilina and other Parasitoida species.
The divergence time between the two Leptopilina species was estimated to be approximately 40 Mya, and those between the sublineages of Parasitoida (e.g., Cynipoidea and Chalcidoidea) were estimated to be as distant as hundreds of millions of years (Fig. 1c), consistent with the tremendous diversification of parasitoids17,18. Thus we focused on making detailed comparisons within Leptopilina. The gene repertoires of Lh and Lb shared 8050 (~75%) orthologous pairs (Fig. 1d) with an average sequence identity of 85.6% at the amino acid (aa) level. We calculated the ratio of synonymous-to-nonsynonymous substitutions (dN/dS) to characterize the genes and functional modules associated with the divergence between Lh and Lb. Only four pathways were found to have significantly elevated dN/dS values (Z-test P  More

1.
Murphy, G. E. & Romanuk, T. N. A meta-analysis of declines in local species richness from human disturbances. Ecol. Evol. 4, 91–103 (2014).
PubMed  Article  Google Scholar 
2.
Said, M. Y. et al. Effects of extreme land fragmentation on wildlife and livestock population abundance and distribution. J. Nat. Cons. 34, 151–164 (2016).
Article  Google Scholar 

3.
Frankham, R., Ballou, J. D. & Briscoe, D. A. Introduction to Conservation Genetics 1–617 (Cambridge University Press, Cambridge, 2002).
Google Scholar 

4.
Lacy, R. C. Importance of genetic variation to the viability of mammalian populations. J. Mammal. 78, 320–335 (1997).
Article  Google Scholar 

5.
Johnson, W. E. et al. Genetic restoration of the Florida panther. Science 329, 1641–1645 (2010).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
Marín, J. C. et al. Mitochondrial phylogeography and demographic history of the vicuna: Implications for conservation. Heredity 99, 70–80 (2007).
PubMed  Article  CAS  Google Scholar 

7.
Karamanlidis, A. A. & Dendrinos, P. Monachus monachus. The IUCN Red List of Threatened Species 2015: e.T13653A45227543 (2015).

8.
Karamanlidis, A. A. et al. The Mediterranean monk seal Monachus monachus: Status, biology, threats, and conservation priorities. Mamm. Rev. 46, 92–105 (2016).
Article  Google Scholar 

9.
Karamanlidis, A. A., Adamantopoulou, S., Tounta, E. & Dendrinos, D. Monachus monachus Eastern Mediterranean subpopulation. The IUCN Red List of Threatened Species 2019, e.T120868935A120869697 (2019).

10.
Dendrinos, D. et al. LIFE-IP 4 NATURA: Integrated actions for the conservation and management of Natura 2000 sites, species, habitats and ecosystems in Greece. Deliverable Action A.1: Action Plan for the Mediterranean monk seal (Monachus monachus). 1–105; Annexes 112p (Hellenic Ministry of Environment and Energy, 2020).

11.
Piggott, M. P. & Taylor, A. C. Remote collection of animal DNA and its applications in conservation management and understanding the population biology of rare and cryptic species. Wildl. Res. 30, 1–13 (2003).
Article  Google Scholar 

12.
Pastor, T. et al. Genetic diversity and differentiation between the two remaining populations of the critically endangered Mediterranean monk seal. Anim. Conserv. 10, 461–469 (2007).
Article  Google Scholar 

13.
Karamanlidis, A. A. et al. Shaping species conservation strategies using mtDNA analysis: The case of the elusive Mediterranean monk seal (Monachus monachus). Biol. Conserv. 193, 71–79 (2016).
Article  Google Scholar 

14.
Selkoe, K. A. & Toonen, R. J. Microsatellites for ecologists: A practical guide to using and evaluating microsatellite markers. Ecol. Lett. 9, 615–629 (2006).
PubMed  Article  Google Scholar 

15.
Rey-Iglesia, A. et al. Mitogenomics of the endangered Mediterranean monk seal (Monachus monachus) reveals dramatic loss of diversity and supports historical gene-flow between Atlantic and eastern Mediterranean populations. Zool. J. Linn. Soc. 20, 1–13 (2020).
Google Scholar 

16.
Dayon, J. et al. Development and characterization of nineteen microsatellite loci for the endangered Mediterranean monk seal Monachus monachus. Mar. Biodiv. 50, 1–7 (2020).

17.
Brandström, M. & Ellegren, H. Genome-wide analysis of microsatellite polymorphism in chicken circumventing the ascertainment bias. Gen. Res. 18, 881–887 (2008).
Article  CAS  Google Scholar 

18.
Allendorf, F. W. & Luikart, G. Conservation and the Genetics of Populations (Wiley, New York, 2009).
Google Scholar 

19.
Tokarska, M., Pertoldi, C., Kowalczyk, R. & Perzanowski, K. Genetic status of the European bison Bison bonasus after extinction in the wild and subsequent recovery. Mamm. Rev. 41, 151–162 (2011).
Article  Google Scholar 

20.
Casas-Marce, M. et al. Spatio-temporal dynamics of genetic variation in the Iberian Lynx along its path to extinction reconstructed with ancient DNA. Mol. Biol. Evol. 34, 2893–2907 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Hasselgren, M. et al. Genetic rescue in an inbred Arctic fox (Vulpes lagopus) population. Proc. R. Soc. Lond. Ser. B. Biol. Sci. 285, 20172814 (2018).
Google Scholar 

22.
Mills, L. S. & Allendorf, F. W. The One-Migrant-per-Generation rule in conservation and management. Conserv. Biol. 10, 1509–1518 (1996).
Article  Google Scholar 

23.
Heber, S. et al. The genetic rescue of two bottlenecked South Island robin populations using translocations of inbred donors. Proc. R. Soc. Lond. Ser. B. Biol. Sci. 280, 20122228 (2013).
CAS  Google Scholar 

24.
Greenbaum, G., Templeton, A. R., Zarmi, Y. & Bar-David, S. Allelic richness following population founding events—A stochastic modeling framework incorporating gene flow and genetic drift. PLoS ONE 9, e115203 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

25.
Vucetich, J. A. & Waite, T. A. Is one migrant per generation sufficient for the genetic management of fluctuating populations?. Anim. Conserv. 3, 261–266 (2000).
Article  Google Scholar 

26.
Waples, R. S. in: Population viability analysis (eds S.R. Beissinger & D.R. McCullough), 147–168 (University of Chicago Press, 2002).

27.
Lynch, M. & Lande, R. The critical effective size for a genetically secure population. Anim. Cons. For. 1, 70–72 (1998).
Article  Google Scholar 

28.
Johnson, C. N. Sex-biased philopatry and dispersal in mammals. Oecologia 69, 626–627 (1986).
ADS  CAS  PubMed  Article  Google Scholar 

29.
Oosthuizen, W. C. et al. Dispersal and dispersion of southern elephant seals in the Kerguelen province, Southern Ocean. Antarct.sSci. 23, 567–577 (2011).
ADS  Article  Google Scholar 

30.
Fabiani, A., Galimberti, F., Sanvito, S. & Hoelzel, A. R. Relatedness and site fidelity at the southern elephant seal, Mirounga leonina, breeding colony in the Falkland Islands. Anim. Behav. 72, 617–626 (2006).
Article  Google Scholar 

31.
Hofmeyr, G. J. G., Kirkman, S. P., Pistorius, P. A. & Bester, M. N. Natal site fidelity by breeding female southern elephant seals in relation to their history of participation in the winter haulout. Afr. J. Mar. Sci. 34, 373–382 (2012).
Article  Google Scholar 

32.
Dendrinos, P. et al. Pupping habitat use in the Mediterranean monk seal: A long-term study. Mar. Mamm. Sci. 23, 615–628 (2007).
Article  Google Scholar 

33.
Gücü, A. C., Gücü, G. & Orek, H. Habitat use and preliminary demographic evaluation of the critically endangered Mediterranean monk seal (Monachus monachus) in the Cilician Basin (Eastern Mediterranean). Biol. Conserv. 116, 417–431 (2004).
Article  Google Scholar 

34.
Valtonen, M. et al. Causes and consequences of fine-scale population structure in a critically endangered freshwater seal. BMC Ecol. 14, 22 (2014).
PubMed  PubMed Central  Article  Google Scholar 

35.
Karamanlidis, A. A., Dendrinos, P., Tounta, E. & Kotomatas, S. Monitoring human activity in an area dedicated to the protection of the endangered Mediterranean monk seal. Coast. Manag. 32, 293–306 (2004).
Article  Google Scholar 

36.
Karamanlidis, A. A. et al. An interview-based approach to assess seal—small-scale fishery interactions informs the conservation strategy of the endangered Mediterranean monk seal. Aquat. Cons. Mar. Freshw. Ecos. 30, 928–936 (2020).
Article  Google Scholar 

37.
Karamanlidis, A. A. Establishment of the “Hellenic Monk Seal Register”. Final report of a grant award from the Marine Mammal Commission to MOm/Hellenic Society for the Study and Protection of the Monk seal. 1–25 (2017).

38.
Stoffel, M. A. et al. Demographic histories and genetic diversity across pinnipeds are shaped by human exploitation, ecology and life-history. Nat. Commun. 9, 1–12 (2018).
Article  CAS  Google Scholar 

39.
Gaubert, P. et al. Insights from 180 years of mitochondrial variability in the endangered Mediterranean monk seal (Monachus monachus). Mar. Mamm. Sci. 35, 1489–1511 (2019).
Article  Google Scholar 

40.
Frankham, R. Genetics and extinction. Biol. Conserv. 126, 131–140 (2005).
Article  Google Scholar 

41.
Hoelzel, A. R. et al. Impact of a population bottleneck on symmetry and genetic diversity in the northern elephant seal. J. Evol. Biol. 15, 567–575 (2002).
Article  Google Scholar 

42.
Allen, P. J., Amos, W., Pomeroy, P. P. & Twiss, S. D. Microsatellite variation in grey seals (Halichoerus grypus) shows evidence of genetic differentiation between two British breeding colonies. Mol. Ecol. 4, 653–662 (1995).
CAS  PubMed  Article  Google Scholar 

43.
Goodman, S. J. Molecular population genetics of the European harbour seal (Phoca vitulina) with reference to the 1988 phocine distemper virus epizootic PhD thesis thesis, University of Cambridge, (1995).

44.
Coltman, D. W., Bowen, W. D. & Wright, J. M. PCR primers for harbour seal (Phoca vitulina concolour) microsatellites amplify polymorphic loci in other pinniped species. Mol. Ecol. 5, 161–163 (1996).
CAS  PubMed  Article  Google Scholar 

45.
Goodman, S. J. Patterns of extensive genetic differentiation and variation among European harbor seals (Phoca vitulina vitulina) revealed using microsatellite DNA polymorphisms. Mol. Biol. Evol. 15, 104–118 (1998).
CAS  PubMed  Article  Google Scholar 

46.
Pastor, T. et al. Low genetic variability in the highly endangered Mediterranean monk seal. J. Hered. 95, 291–300 (2004).
CAS  PubMed  Article  Google Scholar 

47.
Schultz, J. K., Marshall, A. J. & Pfunder, M. Genome-wide loss of diversity in the critically endangered Hawaiian monk seal. Diversity 2, 863–880 (2010).
CAS  Article  Google Scholar 

48.
Mihnovets, A. N. et al. A novel microsatellite multiplex assay for the endangered Hawaiian monk seal (Neomonachus schauinslandi). Con. Gen. Res. 8, 91–95 (2016).
Article  Google Scholar 

49.
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure multilocus genotype data. Genetics 155, 945–959 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

50.
Jombart, T., Devillard, S. & Durfour, A.-B. Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity 101, 92–103 (2008).
CAS  PubMed  Article  Google Scholar 

51.
Upton, G. & Fingleton, B. Spatial Data Analysis by Example. Volume 1: Point Pattern and Quantitative Data (Wiley, New York, 1985).
Google Scholar 

52.
R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2020).

53.
Jombart, T. adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).
CAS  Article  Google Scholar 

54.
Forbes, S. H. & Hogg, J. T. Assessing population structure at high levels of differentiation: Microsatellite comparisons of bighorn sheep and large carnivores. Anim. Cons. For. 2, 223–233 (1999).
Article  Google Scholar 

55.
Hardy, O. J. & Vekemans, X. SPAGeDi: A versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol. Ecol. Notes 2, 618–620 (2002).
Article  CAS  Google Scholar 

56.
Loiselle, B. A., Sork, V. L., Nason, J. & Graham, C. Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubicaceae). Am. J. Bot. 82, 1420–1425 (1995).
Article  Google Scholar 

57.
Nei, M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89, 583–590 (1978).
CAS  PubMed  PubMed Central  Google Scholar 

58.
Engels, W. R. Exact tests for Hardy–Weinberg proportions. Genetics 183, 1431–1441 (2009).
PubMed  PubMed Central  Article  Google Scholar 

59.
HWxtest: Exact Tests for Hardy–Weinberg proportions. R package version 1.1.7. (2016).

60.
Karamanlidis, A. A. et al. History-driven population structure and assymetric gene flow in a recovering large carnivore at the rear-edge of its European range. Heredity 120, 168–182 (2018).
CAS  PubMed  Article  Google Scholar 

61.
Wang, J. COANCESTRY: A program for simulating, estimating and analysing relatedness and inbreeding coefficients. Mol. Ecol. Res. 11, 141–145 (2011).
Article  Google Scholar 

62.
Waples, R. S. A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Conserv. Genet. 7, 167–184 (2006).
Article  Google Scholar 

63.
Waples, R. S. & Do, C. H. I. Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: A largely untapped resource for applied conservation and evolution. Evol. Appl. 3, 244–262 (2010).
PubMed  Article  Google Scholar 

64.
Do, C. et al. NeEstimator V2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Res. 14, 209–214 (2014).
CAS  Article  Google Scholar 

65.
Waples, R. S. & Do, C. LdNe: A program for estimating effective population size from data on linkage disequilibrium. Mol. Ecol. Res. 8, 753–756 (2008).
Article  Google Scholar  More

1.
Pereira, H. M. et al. Scenarios for global biodiversity in the 21st century. Science 1, 1–7. https://doi.org/10.1126/science.1196624 (2010).
CAS  Article  Google Scholar 
2.
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

3.
Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

4.
Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).
CAS  Article  Google Scholar 

5.
Sodhi, N. S. & Brook, B. W. Southeast Asian Biodiversity in Crisis (Cambridge University Press, Cambridge, 2006).
Google Scholar 

6.
Fischer, J. & Lindenmayer, D. B. Landscape modification and habitat fragmentation: A synthesis. Glob. Ecol. Biogeogr. 16, 265–280 (2007).
Article  Google Scholar 

7.
Murcia, C. Forest fragmentation and the pollination of neotropical plants. For. Patches Trop. Landsc. 1, 19–36 (1996).
Google Scholar 

8.
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).
PubMed  PubMed Central  Article  Google Scholar 

9.
Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Zhu, K., Woodall, C. W. & Clark, J. S. Failure to migrate: Lack of tree range expansion in response to climate change. Glob. Change Biol. 18, 1042–1052 (2012).
ADS  Article  Google Scholar 

11.
Deb, J. C., Phinn, S., Butt, N. & McAlpine, C. A. The impact of climate change on the distribution of two threatened Dipterocarp trees. Ecol. Evol. 7, 2238–2248 (2017).
PubMed  PubMed Central  Article  Google Scholar 

12.
Garcia, K., Lasco, R., Ines, A., Lyon, B. & Pulhin, F. Predicting geographic distribution and habitat suitability due to climate change of selected threatened forest tree species in the Philippines. Appl. Geogr. 44, 12–22 (2013).
Article  Google Scholar 

13.
Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435 (2013).
PubMed  PubMed Central  Article  Google Scholar 

14.
McShea, W. J. What are the roles of species distribution models in conservation planning?. Environ. Conserv. 41, 93–96 (2014).
Article  Google Scholar 

15.
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Article  Google Scholar 

16.
Miettinen, J., Shi, C. & Liew, S. C. Deforestation rates in insular Southeast Asia between 2000 and 2010: Deforestation in insular Southeast Asia 2000–2010. Glob. Change Biol. 17, 2261–2270 (2011).
ADS  Article  Google Scholar 

17.
Sodhi, N. S. et al. The state and conservation of Southeast Asian biodiversity. Biodivers. Conserv. 19, 317–328 (2010).
Article  Google Scholar 

18.
Yusuf, A. A. & Francisco, H. Climate change vulnerability mapping for Southeast Asia. (2009).

19.
Ambal, R. G. R. et al. Key biodiversity areas in the Philippines: priorities for conservation. J. Threat. Taxa 4, 2788–2796 (2012).
Article  Google Scholar 

20.
Feeley, K. J. & Silman, M. R. The data void in modeling current and future distributions of tropical species. Glob. Change Biol. 17, 626–630 (2011).
ADS  Article  Google Scholar 

21.
Ramos, L. T., Torres, A. M., Pulhin, F. B. & Lasco, R. D. Developing a georeferenced database of selected threatened forest tree species in the Philippines. Philipp. J. Sci. 141, 165–177 (2012).
Google Scholar 

22.
Liu, D. S., Iverson, L. R. & Brown, S. Rates and patterns of deforestation in the Philippines: application of geographic information system analysis. For. Ecol. Manag. 57, 1–16 (1993).
Article  Google Scholar 

23.
Shively, G. & Pagiola, S. Agricultural intensification, local labor markets, and deforestation in the Philippines. Environ. Dev. Econ. 9, 241–266 (2004).
Article  Google Scholar 

24.
Ashton, P. S. Dipterocarpaceae. Dipterocarpaceae. 9, 237–552 (1982).
Google Scholar 

25.
De Guzman, E. D., Umali, R. M. & Sotalbo, E. D. Guide to Philippine Flora and Fauna, Vol. 3: Dipterocarps, Non-Dipterocarps. Nat. Resour. Manag. Cent. Minist. Nat. Resour. Univ. Philipp. (1986).

26.
Fernando, E. S., Suh, M. H., Lee, J. & Lee, D. K. Forest formations of the Philippines. (ASEAN-Korea Environmental Cooperation Unit, 2008).

27.
Tuck, S. L. et al. The value of biodiversity for the functioning of tropical forests: insurance effects during the first decade of the Sabah biodiversity experiment. Proc. R. Soc. B Biol. Sci. 283, 20161451 (2016).
Article  Google Scholar 

28.
Brearley, F. Q., Banin, L. F. & Saner, P. The ecology of the Asian dipterocarps. Plant Ecol. Divers. 9, 429–436 (2016).
Article  Google Scholar 

29.
Schulte, A. Dipterocarp forest ecosystem theory based on matter balance and biodiversity. in Dipterocarp Forest Ecosystems: Towards Sustainable Management 3–28 (1996).

30.
Anderegg, W. R., Kane, J. M. & Anderegg, L. D. Consequences of widespread tree mortality triggered by drought and temperature stress. Nat. Clim. Change 3, 30 (2013).
ADS  Article  Google Scholar 

31.
Granados, A. Ecological Effects of Disrupting Plant-Animal Interactions (University of British Columbia, Vancouver, 2017).
Google Scholar 

32.
Schleuning, M. et al. Ecological networks are more sensitive to plant than to animal extinction under climate change. Nat. Commun. 7, 13965 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Albrecht, J. et al. Correlated loss of ecosystem services in coupled mutualistic networks. Nat. Commun. 5, 3810 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

34.
Keppel, G. et al. The capacity of refugia for conservation planning under climate change. Front. Ecol. Environ. 13, 106–112 (2015).
Article  Google Scholar 

35.
Kettle, C. J. Ecological considerations for using dipterocarps for restoration of lowland rainforest in Southeast Asia. Biodivers. Conserv. 19, 1137–1151 (2010).
Article  Google Scholar 

36.
Titeux, N. et al. Biodiversity scenarios neglect future land-use changes. Glob. Change Biol. 22, 2505–2515 (2016).
ADS  Article  Google Scholar 

37.
Pimm, S. L., Jenkins, C. N. & Li, B. V. How to protect half of Earth to ensure it protects sufficient biodiversity. Sci. Adv. 4, 2616 (2018).
ADS  Article  Google Scholar 

38.
Watson, J. E. M., Dudley, N., Segan, D. B. & Hockings, M. The performance and potential of protected areas. Nature 515, 67–73 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Ashcroft, M. B. Identifying refugia from climate change. J. Biogeogr. 1, 1407–1413. https://doi.org/10.1111/j.1365-2699.2010.02300.x (2010).
Article  Google Scholar 

40.
Graham, V., Baumgartner, J. B., Beaumont, L. J., Esperón-Rodríguez, M. & Grech, A. Prioritizing the protection of climate refugia: designing a climate-ready protected area network. J. Environ. Plan. Manag. 1, 1–19. https://doi.org/10.1080/09640568.2019.1573722 (2019).
Article  Google Scholar 

41.
Mair, L. et al. Land use changes could modify future negative effects of climate change on old-growth forest indicator species. Divers. Distrib. 24, 1416–1425 (2018).
Article  Google Scholar 

42.
Methorst, J., Böhning-Gaese, K., Khaliq, I. & Hof, C. A framework integrating physiology, dispersal and land-use to project species ranges under climate change. J. Avian Biol. 48, 1532–1548 (2017).
Article  Google Scholar 

43.
Segan, D. B., Murray, K. A. & Watson, J. E. M. A global assessment of current and future biodiversity vulnerability to habitat loss–climate change interactions. Glob. Ecol. Conserv. 5, 12–21 (2016).
Article  Google Scholar 

44.
Milanesi, P., Della Rocca, F. & Robinson, R. A. Integrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models. Ecol. Evol. 10, 1087–1092 (2020).
PubMed  Article  PubMed Central  Google Scholar 

45.
Faurby, S. & Araújo, M. B. Anthropogenic range contractions bias species climate change forecasts. Nat. Clim. Change 8, 252–256 (2018).
ADS  Article  Google Scholar 

46.
Peterson, A. T., Cobos, M. E. & Jiménez-García, D. Major challenges for correlational ecological niche model projections to future climate conditions: Climate change, ecological niche models, and uncertainty. Ann. N. Y. Acad. Sci. 1429, 66–77 (2018).
ADS  PubMed  Article  PubMed Central  Google Scholar 

47.
Scheele, B. C., Foster, C. N., Banks, S. C. & Lindenmayer, D. B. Niche Contractions in declining species: Mechanisms and consequences. Trends Ecol. Evol. 32, 346–355 (2017).
PubMed  Article  PubMed Central  Google Scholar 

48.
PAGASA. Daily Rainfall and Temperature. http://bagong.pagasa.dost.gov.ph/climate/climate-monitoring#daily-rainfall-and-temperature (2019).

49.
GBIF. GBIF Occurrence Download. https://doi.org/10.15468/dl.cetigh (2020).

50.
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).
Article  Google Scholar 

51.
DAO. Updated national list of threatened Philippine plants and their categories. Dep. Environ. Nat. Resour. Repub. Philipp. Quezon City Manila (2017).

52.
IUCN. IUCN Red List of Threatened Species. (IUCN, Geneva, 2019).

53.
Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).
Article  Google Scholar 

54.
Newbold, T. Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models. Prog. Phys. Geogr. 34, 3–22 (2010).
Article  Google Scholar 

55.
Yackulic, C. B. et al. Presence-only modelling using MAXENT: when can we trust the inferences?. Methods Ecol. Evol. 4, 236–243 (2013).
Article  Google Scholar 

56.
Pelser, P. B., Barcelona, J. F. & Nickrent, D. L. Co’s Digital Flora of the Philippines. (2011).

57.
IPNI. The International Plant Names Index. http://www.ipni.org (2020).

58.
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).
PubMed  PubMed Central  Article  Google Scholar 

59.
Dormann, C. F. Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Glob. Ecol. Biogeogr. 16, 129–138 (2007).
Article  Google Scholar 

60.
Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
Article  Google Scholar 

61.
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
ADS  Article  Google Scholar 

62.
Kamworapan, S. & Surussavadee, C. Evaluation of CMIP5 global climate models for simulating climatological temperature and precipitation for Southeast Asia. Adv. Meteorol. 2019, 1–18 (2019).
Article  Google Scholar 

63.
Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change 109, 213 (2011).
ADS  CAS  Article  Google Scholar 

64.
PAGASA. Observed and Projected Climate Change in the Philippines. (2018).

65.
Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

66.
R Core Team. R: A language and environment for statistical computing. R Found. Stat. Comput. Vienna Austria 55, 275–286 (2013).

67.
Hijmans, R. J. & Etten, J. V. Geographic analysis and modeling with raster data. R Package Version 2, 1–25 (2012).
Google Scholar 

68.
Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).
Article  Google Scholar 

69.
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
Article  Google Scholar 

70.
Townsend Peterson, A., Papeş, M. & Eaton, M. Transferability and model evaluation in ecological niche modeling: A comparison of GARP and Maxent. Ecography 30, 550–560 (2007).
Article  Google Scholar 

71.
Breiner, F. T., Nobis, M. P., Bergamini, A. & Guisan, A. Optimizing ensembles of small models for predicting the distribution of species with few occurrences. Methods Ecol. Evol. 9, 802–808 (2018).
Article  Google Scholar 

72.
Zhu, G. P. & Peterson, A. T. Do consensus models outperform individual models? Transferability evaluations of diverse modeling approaches for an invasive moth. Biol. Invasions 19, 2519–2532 (2017).
Article  Google Scholar 

73.
Araujo, M. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).
PubMed  Article  PubMed Central  Google Scholar 

74.
Hannemann, H., Willis, K. J. & Macias-Fauria, M. The devil is in the detail: unstable response functions in species distribution models challenge bulk ensemble modelling. Glob. Ecol. Biogeogr. 25, 26–35 (2016).
Article  Google Scholar 

75.
Hao, T., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. Ecography https://doi.org/10.1111/ecog.04890 (2020).
Article  Google Scholar 

76.
Roberts, D. R. et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929 (2017).
Article  Google Scholar 

77.
Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol. Evol. 5, 1198–1205 (2014).
Article  Google Scholar 

78.
Fourcade, Y., Besnard, A. G. & Secondi, J. Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. Glob. Ecol. Biogeogr. 27, 245–256 (2018).
Article  Google Scholar 

79.
Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. A single-algorithm ensemble approach to estimating suitability and uncertainty: Cross-time projections for four Malagasy tenrecs. Divers. Distrib. 23, 196–208 (2017).
Article  Google Scholar 

80.
Shcheglovitova, M. & Anderson, R. P. Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes. Ecol. Model. 269, 9–17 (2013).
Article  Google Scholar 

81.
Iturbide, M. et al. A framework for species distribution modelling with improved pseudo-absence generation. Ecol. Model. 312, 166–174 (2015).
Article  Google Scholar 

82.
VanDerWal, J., Shoo, L. P., Graham, C. & Williams, S. E. Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?. Ecol. Model. 220, 589–594 (2009).
Article  Google Scholar 

83.
Chefaoui, R. M. & Lobo, J. M. Assessing the effects of pseudo-absences on predictive distribution model performance. Ecol. Model. 210, 478–486 (2008).
Article  Google Scholar 

84.
Liu, C., Newell, G. & White, M. The effect of sample size on the accuracy of species distribution models: Considering both presences and pseudo-absences or background sites. Ecography 42, 535–548 (2019).
Article  Google Scholar 

85.
Morales, N. S., Fernández, I. C. & Baca-González, V. MaxEnt’s parameter configuration and small samples: Are we paying attention to recommendations? A systematic review. PeerJ 5, e3093 (2017).
PubMed  PubMed Central  Article  Google Scholar 

86.
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: an open-source release of Maxent. Ecography 40, 887–893 (2017).
Article  Google Scholar 

87.
Radosavljevic, A. & Anderson, R. P. Making better Maxent models of species distributions: complexity, overfitting and evaluation. J. Biogeogr. 41, 629–643 (2014).
Article  Google Scholar 

88.
Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342 (2011).
PubMed  Article  PubMed Central  Google Scholar 

89.
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?: How to use pseudo-absences in niche modelling?. Methods Ecol. Evol. 3, 327–338 (2012).
Article  Google Scholar 

90.
Anderson, R. P. & Gonzalez, I. Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent. Ecol. Model. 222, 2796–2811 (2011).
Article  Google Scholar 

91.
Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49 (1997).
Article  Google Scholar 

92.
Velasco, J. A. & González-Salazar, C. Akaike information criterion should not be a “test” of geographical prediction accuracy in ecological niche modelling. Ecol. Inform. 51, 25–32 (2019).
Article  Google Scholar 

93.
Vignali, S., Barras, A. & Braunisch, V. SDMtune: Species distribution model selection. R Package Version 101 (2019) https://github.com/ConsBiol-unibern/SDMtune.

94.
Liu, C., Newell, G. & White, M. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 6, 337–348 (2016).
PubMed  Article  PubMed Central  Google Scholar 

95.
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS): Assessing the accuracy of distribution models. J. Appl. Ecol. 43, 1223–1232 (2006).
Article  Google Scholar 

96.
Somodi, I., Lepesi, N. & Botta-Dukát, Z. Prevalence dependence in model goodness measures with special emphasis on true skill statistics. Ecol. Evol. 7, 863–872 (2017).
PubMed  PubMed Central  Article  Google Scholar 

97.
Warren, D. L., Matzke, N. J. & Iglesias, T. L. Evaluating species distribution models with discrimination accuracy is uninformative for many applications. https://doi.org/10.1101/684399 (2019)

98.
Angelstam, P. Conservation of communities—the importance of edges, surroundings and landscape mosaic structure. in Ecological principles of nature conservation 9–70 (Springer, 1992).

99.
Waldhardt, R., Simmering, D. & Otte, A. Estimation and prediction of plant species richness in a mosaic landscape. Landsc. Ecol. 19, 211–226 (2004).
Article  Google Scholar 

100.
Fischer, J. & Lindenmayer, D. B. Small patches can be valuable for biodiversity conservation: two case studies on birds in southeastern Australia. Biol. Conserv. 106, 129–136 (2002).
Article  Google Scholar 

101.
Struebig, M. J. et al. Targeted conservation to safeguard a biodiversity hotspot from climate and land-cover change. Curr. Biol. 25, 372–378 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

102.
UNEP-WCMC. World database on protected areas. UNEP WCMC Camb. UK (2018).

103.
LP DAAC. Global 30 arc-second elevation data set GTOPO30. Land Process Distrib. Act. Arch. Cent. (2004) http://edcdaac.usgs.gov/gtopo30/gtopo30.asp.

104.
Amaral, A. G., Munhoz, C. B. R., Walter, B. M. T., Aguirre-Gutiérrez, J. & Raes, N. Richness pattern and phytogeography of the Cerrado herb-shrub flora and implications for conservation. J. Veg. Sci. 28, 848–858 (2017).
Article  Google Scholar 

105.
Kanagaraj, R. et al. Predicting range shifts of Asian elephants under global change. Divers. Distrib. https://doi.org/10.1111/ddi.12898 (2019).
Article  Google Scholar 

106.
De Alban, J. D. et al. High Conservation Value Areas as a strategic approach for protected area management in the Philippines. in 1–10 (Asian Association on Remote Sensing, 2015).

107.
IUCN. IUCN Red List Categories and Criteria: Version 3.1. (IUCN, Gland, 2012).

108.
Fuller, R. A. et al. Replacing underperforming protected areas achieves better conservation outcomes. Nature 466, 365 (2010).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

109.
Davis, K. F., Yu, K., Rulli, M. C., Pichdara, L. & D’Odorico, P. Accelerated deforestation driven by large-scale land acquisitions in Cambodia. Nat. Geosci. 8, 772–775 (2015).
ADS  CAS  Article  Google Scholar 

110.
Parmesan, C. & Hanley, M. E. Plants and climate change: complexities and surprises. Ann. Bot. 116, 849–864 (2015).
PubMed  PubMed Central  Article  Google Scholar 

111.
Walck, J. L., Hidayati, S. N., Dixon, K. W., Thompson, K. E. N. & Poschlod, P. Climate change and plant regeneration from seed. Glob. Change Biol. 17, 2145–2161 (2011).
ADS  Article  Google Scholar 

112.
Corlett, R. T. Seed dispersal distances and plant migration potential in tropical East Asia. Biotropica 41, 592–598 (2009).
Article  Google Scholar 

113.
Smith, J. R. et al. Predicting dispersal of auto-gyrating fruit in tropical trees: a case study from the Dipterocarpaceae. Ecol. Evol. 5, 1794–1801 (2015).
PubMed  PubMed Central  Article  Google Scholar 

114.
Hoffmann, S., Irl, S. D. H. & Beierkuhnlein, C. Predicted climate shifts within terrestrial protected areas worldwide. Nat. Commun. 10, 4787 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

115.
Bonn, A. & Gaston, K. J. Capturing biodiversity: Selecting priority areas for conservation using different criteria. Biodivers. Conserv. 14, 1083–1100 (2005).
Article  Google Scholar 

116.
Hannah, L. et al. Protected area needs in a changing climate. Front. Ecol. Environ. 5, 131–138 (2007).
Article  Google Scholar 

117.
Carvalho, S. B., Brito, J. C., Crespo, E. G., Watts, M. E. & Possingham, H. P. Conservation planning under climate change: Toward accounting for uncertainty in predicted species distributions to increase confidence in conservation investments in space and time. Biol. Conserv. 144, 2020–2030 (2011).
Article  Google Scholar 

118.
Lemes, P. & Loyola, R. D. Accommodating species climate-forced dispersal and uncertainties in spatial conservation planning. PLoS ONE 8, e54323 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

119.
Suzuki, E. & Ashton, P. S. Sepal and nut size ratio of fruits of Asian Dipterocarpaceae and its implications for dispersal. J. Trop. Ecol. 12, 853–870 (1996).
Article  Google Scholar 

120.
Ball, I. R., Possingham, H. P. & Watts, M. Marxan and relatives: software for spatial conservation prioritisation. Spat. Conserv. Prioritisation Quant. Methods Comput. Tools 1, 185–195 (2009).
Google Scholar 

121.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2016).
Google Scholar  More

1.
Morner, T., Obendorf, D. L., Artois, M. & Woodford, M. H. Surveillance and monitoring of wildlife diseases. Rev. Sci. Tech. 21(1), 67–76 (2002).
CAS  PubMed  Article  Google Scholar 
2.
Daszak, P., Cunningham, A. A. & Hyatt, A. D. Emerging infectious diseases of wildlife–threats to biodiversity and human health. Science 287(5452), 443–449 (2000).
ADS  CAS  PubMed  Article  Google Scholar 

3.
Bagagli, E. et al. Isolation of Paracoccidioides brasiliensis from armadillos (Dasypus noveminctus) captured in an endemic area of paracoccidioidomycosis. Am. J. Trop. Med. Hyg. 58(4), 505–512 (1998).
CAS  PubMed  Article  Google Scholar 

4.
Truman, R. Leprosy in wild armadillos. Leprosy Rev. 76(3), 198–208 (2005).
PubMed  Article  Google Scholar 

5.
da Silva, M. B. et al. Evidence of zoonotic leprosy in Pará, Brazilian Amazon, and risks associated with human contact or consumption of armadillos. PLOS Neglect. Trop. D. 12(6), e0006532. https://doi.org/10.1371/journal.pntd.0006532 (2018).
MathSciNet  CAS  Article  Google Scholar 

6.
Rodrigues, T. F., Mantellatto, A. M., Superina, M. & Chiarello, A. G. Ecosystem services provided by armadillos. Biol. Rev. 95(1), 1–21 (2020).
Article  Google Scholar 

7.
Jiang, Y. et al. Phylogeny, ecology and taxonomy of systemic pathogens and their relatives in Ajellomycetaceae (Onygenales): Blastomyces, Emergomyces, Emmonsia, Emmonsiellopsis. Fungal Divers. 90(1), 245–291 (2018).
Article  Google Scholar 

8.
Schwartz, I. S. et al. 50 years of Emmonsia disease in humans: The dramatic emergence of a cluster of novel fungal pathogens. PLoS Pathog. 11(11), e1005198. https://doi.org/10.1371/journal.ppat.1005198 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

9.
Moraes, M. A., Gomes, M. I. & Vianna, L. M. S. Pulmonary adiaspiromycosis: Casual finding in a deceased yellow fever patient [in Portuguese]. Rev. Soc. Bras. Med. Trop. 34(1), 83–85 (2001).
CAS  PubMed  Article  Google Scholar 

10.
Mendes, M. O. et al. Acute conjunctivitis with episcleritis and anterior uveitis linked to adiaspiromycosis and freshwater sponges, Amazon region, Brazil, 2005. Emerg. Infect. Dis. 15(4), 633 (2009).
PubMed  PubMed Central  Article  Google Scholar 

11.
Emmons, C. W. & Ashburn, L. L. The isolation of Haplosporangium parvum n. sp. and Coccidioides immitis from wild rodents. Their relationship to coccidioidomycosis. Public Health Rep. 57(46), 1715–1727 (1942).
Article  Google Scholar 

12.
Doby-Dubois, M., Chevrel, M. L., Doby, J. M. & Louvet, M. First human case of adiaspiromycosis by Emmonsia crescens, Emmons and Jellison 1960 [in french]. Bull. Soc. Pathol. Exot. 57, 240–244 (1964).
CAS  Google Scholar 

13.
Danesi, P. et al. Molecular diagnosis of Emmonsia-like fungi occurring in wild animals. Mycopathologia 185, 51–65 (2020).
CAS  PubMed  Google Scholar 

14.
Peres, L. C., Figueiredo, F., Peinado, M. & Soares, F. A. Fulminant disseminated pulmonary adiaspiromycosis in humans. Am. J. Trop. Med. Hyg. 46(2), 146–1450 (1992).
CAS  PubMed  Article  Google Scholar 

15.
Mörner, T., Avenäs, A. & Mattsson, R. Adiaspiromycosis in a European beaver from Sweden. J. Wildl. Dis. 35(2), 367–370 (1999).
PubMed  Article  Google Scholar 

16.
Simpson, V. R. & Gavier-Widen, D. Fatal adiaspiromycosis in a wild Eurasian otter (Lutra lutra). Vet. Rec. 147(9), 239–241 (2000).
CAS  PubMed  Article  Google Scholar 

17.
Pusterla, N. et al. Disseminated pulmonary adiaspiromycosis caused by Emmonsia crescens in a horse. Equine Vet. J. 34(7), 749–752 (2002).
CAS  PubMed  Article  Google Scholar 

18.
Kenyon, C. et al. A dimorphic fungus causing disseminated infection in South Africa. N. Engl. J. Med. 369(15), 1416–1424 (2013).
CAS  PubMed  Article  Google Scholar 

19.
Santos, V. M. D., Fatureto, M. C., Saldanha, J. C. & Adad, S. J. Pulmonary adiaspiromycosis: Report of two cases. Rev. Soc. Bras. Med. Trop. 33(5), 483–488 (2000).
PubMed  Article  Google Scholar 

20.
Moraes, M. A. & Gomes, M. I. Human adiaspiromycosis: Cicatricial lesions in mediastinal lymph nodes [in Portuguese]. Rev. Soc. Bras. Med. Trop. 37(2), 177–178 (2004).
PubMed  Article  Google Scholar 

21.
Pfaller, M. A. & Diekema, D. J. Unusual fungal and pseudofungal infections of humans. J. Clin. Microbiol. 43(4), 1495–14504 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Ministério da Saúde do Brasil. Ed. 3. Risk Classification of Biological Agents [in Portuguese]. Brasil. Ministério da Saúde. Secretaria de Ciência, Tecnologia e Insumos Estratégicos. Departamento do Complexo Industrial e Inovação em Saúde, 48 (2017). ISBN 978-85-334-2547-7.

23.
Richini-Pereira, V. B., Bosco, S. M. G., Theodoro, R. C., Barrozo, L. & Bagagli, E. Road-killed wild animals: A preservation problem useful for eco-epidemiological studies of pathogens. J. Venom. Anim. Toxins Incl. Trop. Dis. 16(4), 607–613 (2010).
Article  Google Scholar 

24.
Jellison, W. L. & Lord, R. D. Adiaspiromycosis in Argentine mammals. Mycologia 56(3), 374–383 (1964).
Article  Google Scholar 

25.
Ascensão, F., Yogui, D., Alves, M., Medici, E. P. & Desbiez, A. Predicting spatiotemporal patterns of road mortality for medium-large mammals. J. Environ. 248, 109320. https://doi.org/10.1016/j.jenvman.2019.109320 (2019).
Article  Google Scholar 

26.
Cáceres, N. C. Biological characteristics influence mammal roadkill in an Atlantic Forest-Cerrado interface in south-western Brazil. Ital. J. Zool. 78(3), 379–389 (2011).
Article  Google Scholar 

27.
Ascensão, F., Desbiez, A. L., Medici, E. P. & Bager, A. Spatial patterns of road mortality of medium–large mammals in Mato Grosso do Sul, Brazil. Wildlife Res. 44(2), 135–1346 (2017).
Article  Google Scholar 

28.
Ribeiro, P., Miranda, J. S. & de Araújo, D. R. The effect of roadkills on the persistence of xenarthran populations: the case of the Brazilian Cerrado. Edentata 18, 51–61 (2017).
Google Scholar 

29.
Zhu, X., Xu, J. M., Marsh, C. M., Hines, M. K. & Dein, F. J. Machine learning for zoonotic emerging disease detection.  In International Conference on Machine Learning. Proc. 2011 Workshop on Machine Learning for Global Challenges, Bellevue, WA, USA. http://pages.cs.wisc.edu/~jerryzhu/pub/WHER.pdf (2011).

30.
Desbiez, A. L. J., Massocato, G. F., Kluyber, D. & Santos, R. C. F. Unraveling the cryptic life of the southern naked-tailed armadillo, Cabassous unicinctus squamicaudis (Lund, 1845), in a Neotropical wetland: home range, activity pattern, burrow use and reproductive behaviour. Mamm. Biol. 91(1), 95–103 (2018).
Article  Google Scholar 

31.
Borman, A. M., Simpson, V. R., Palmer, M. D., Linton, C. J. & Johnson, E. M. Adiaspiromycosis due to Emmonsia crescens is widespread in native British mammals. Mycopathologia 168(4), 153–163 (2009).
PubMed  Article  Google Scholar 

32.
Muñoz, J. F., McEwen, J. G., Clay, O. K. & Cuomo, C. A. Genome analysis reveals evolutionary mechanisms of adaptation in systemic dimorphic fungi. Sci. Rep. 8(1), 1–13 (2018).
Article  CAS  Google Scholar 

33.
Munson, L. et al. Necropsy of Wild Animals (Wildlife Health Center, School of Veterinary Medicine, University of California Davis, Davis, 2006).
Google Scholar 

34.
Abràmoff, M. D., Magalhães, P. J. & Ram, S. J. Image processing with ImageJ. Biophotonics Intern. 11(7), 36–42 (2004).
Google Scholar 

35.
Sambrook, J., Fitsch, E. F. & Maniatis, T. Molecular cloning. In A Laboratory Manual (eds Sambrook, J. et al.) 65–69 (Cold Spring Harbor, New York, 1989).
Google Scholar 

36.
Sacristán, C. et al. Identification of novel gammaherpesviruses in a South American fur seal (Arctocephalus australis) with ulcerative skin lesions. J. Wildl. Dis. 54(3), 592–596 (2018).
PubMed  Article  CAS  Google Scholar 

37.
Coutinho, S. D. A. et al. Malassezia japonica is part of the cutaneous microbiome of free-ranging golden-headed lion tamarins (Leontopithecus chrysomelas–Kuhl, 1820). Med. Mycol. J. 58(1), 133–136 (2020).
CAS  Article  Google Scholar 

38.
Kumar, S. et al. Molecular diversity of terpene synthases in the liverwort Marchantia polymorpha. Plant Cell. 28(10), 2632–2650 (2016).
CAS  PubMed  PubMed Central  Google Scholar  More

Staining of POM in presence of fine sand and coarse silt
Compared to the unstained control (unstained sample mean peak grey value ± standard deviation: 3686 ± 1557), all staining treatments caused a slight shift of the central grey value peak to the right, towards 3757 ± 1272 and 4421 ± 1646 for the PMA and PbAc treatments, respectively. This central peak represents X-ray attenuation by coarse silt and POM (Fig. 2a). However, no increased X-ray attenuation was observed in coarse silt in the reconstructed CT-sections (Fig. 3). Instead, the grey value distributions of pin-pointed POM particles (Fig. 3) confirmed that staining did cause a clear shift in POM’s grey values, viz. from 1000–4000 to 6000–18,000. This contrast enhancement of POM was more intense for AgNO3, PbAc and Pb(NO3)2 treatments compared to PMA (Fig. 3) and caused the very clear increase in the histogram’s right tail (grey values  > 7500), which was absent with PMA treatment (Fig. 2a). Staining with Pb2+ or Ag+ also resulted in broad grey value peaks of POM that overlapped those of the coarse silt (grey value range: 2500–5000) and fine sand particles (grey value range: 5000–7500), represented by the second and third peak on the bulk soil histograms (Fig. 2a). However, the histogram peak maxima of the AgNO3 (11,118), PbAc (13,829), Pb(NO3)2 (14,255) and PMA (9678) stained POM particles (Fig. 3) clearly exceeded the grey value range of both the coarse silt and fine sand, which suggests that CT grey value based discrimination of POM from these mineral fractions should be feasible. The smaller peak shift in the POM histograms for PMA (Fig. 3) shows that PMA was less efficient in increasing attenuation of POM and this could be ascribed to the lower atomic mass (AM) of Mo (95.9) compared to Pb (207.2). Likewise, the effect of Ag (AM 107.9) was closer to that of Mo. Next to atomic mass, the affinity for binding to OM likely also differs between the contrast agents. While Ag+ and Pb2+ bind with the organic functional groups of POM via ionic bonds, PMA is known to interact with conjugated unsaturated bonds37.
Figure 2

Grey scale histogram (0–16,000) for 16 bit images of (a) the fine sand + coarse silt + POM soil samples, (b) the fine sand + fine silt + POM mixtures and (c) the fine sand + clay + POM mixtures. The unstained control treatment (black) and the stained AgNO3 (red), PbAc (blue), Pb(NO3)2 (purple) and PMA (green) treatments. (d) Grey scale histogram (0–16,000) for 16 bit images of OsO4 stained fine sand + coarse silt + POM (fSa + cSi + POM; red), fine sand + fine silt + POM (fSa + fSi + POM; blue) and fine sand + clay + POM (fSa + C + POM; black) mixtures. The equivalent control treatments are indicated by dotted lines.

Full size image

Figure 3

Grey scale histograms (0–40,000) of operator-supervised pin-pointed POM particles in 16 bit images of fine sand + coarse silt + POM mixtures: the unstained control treatment (black) and the stained AgNO3 (red), PbAc (blue), Pb(NO3)2 (purple) and PMA (green) treatments. For each treatment, a two dimensional grey scale image representing a segment of a horizontal slice of the fine sand + coarse silt + POM mixtures is included (image contrast was enhanced in this figure). An unstained POM particle in the control treatment and stained POM particles in the AgNO3, PbAc, Pb(NO3)2 and PMA treatments are indicated by the red arrows.

Full size image

There was no shift towards higher grey values in the third peak in the soil core histograms, representing fine sand (Fig. 2a). In addition, we did not find traces of Pb, Ag or Mo on any SEM–EDX scanned fine sand or coarse silt particles (Supplementary Fig. S1). Visual inspection of horizontal reconstructed slices revealed a clear discrimination of the stained POM particles vs. the soil mineral phase. SEM–EDX scans confirmed substantial Mo, Pb or Ag EDX-peaks on randomly chosen POM particles, directly confirming the successful selective staining of POM by all agents (Supplementary Fig. S1). Smaller mineral patches on the surface of POM were visible in the SEM-images (Supplementary Fig. S2), but our analysis demonstrates that these were not responsible for Mo, Pb or Ag-staining of the POM. Stained POM could also be discriminated from several other high density particles such as glauconite, since the latter were not only characterized by higher grey values32 but also a different grey value pattern. The SEM–EDX spectra of some dense mineral particles moreover revealed Zr peaks, suggesting these to be ZrO2 or Zr-silicate. In conclusion, the experiment demonstrates that Pb(NO3)2, AgNO3 and PbAc are able to selectively bind with POM in fine sand + coarse silt mixtures. However the potential to raise POM contrast in such soil mixtures by treatment with PMA is smaller.
Staining of POM in presence of fine silt and clay
PMA did not appear to stain fine silt, with no shifts of peaks in the bulk soil histogram (Fig. 2B), no visible impact on contrast of the soil mineral phase (Supplementary Fig. S3) and again no discernible Mo peaks in the SEM–EDX spectra of mineral particles (Supplementary Fig. S4 and Supplementary Fig. S5). In contrast, AgNO3, PbAc and Pb(NO3)2 appeared rather non-selective for POM (Fig. 2B). Indeed, the staining increased the grey values of histogram peaks corresponding to fine silt (Fig. 2b), which can be seen from shifting fine silt histogram peaks from a grey value of 4303 to 4702–5577. The increase of the fine silt grey values following staining even resulted in a partial (Pb(NO3)2 and AgNO3) or complete (PbAc) overlap with the sand fraction histogram peak. The grey value histogram right tail was also much enlarged by Pb(NO3)2, AgNO3 and PbAc treatment and increasingly overlapped with the grey value range of stained POM (6000–18,000) (Fig. 3). This overlap was clearly an effect of staining of fine silt, as was also apparent in the CT-sections (Supplementary Fig. S3). The shift in histogram grey values following staining in the fine sand + clay mixtures was similar to that in the sand + fine silt mixtures, but with a larger shift in the right tail of the Pb2+ and Ag+-histograms to grey values between 7500 and  > 12,000 (Fig. 2c). However, we did not detect Pb or Ag EDX peaks on pinpointed fine silt or clay particles (Supplementary Fig. S4). But as inspection of CT-sections (Fig. 4) demonstrated that large patches of the mixtures were stained by AgNO3, PbAc and Pb(NO3)2, it is well possible that such discrete stained areas were coincidently not sampled in our ancillary unsystematic SEM-analysis of subsamples. Regardless, overlap in grey value-ranges of Pb2+ and Ag+ stained POM and stained clay particles clearly impedes proper segmentation of both phases. As was the case for fine silt, PMA treatment did not cause any shift in histogram peaks of mineral particles (Fig. 2c) and there was no visible impact on mineral phase attenuation in the CT-sections (Fig. 4). This also suggests that Mo has a higher potential than Pb2+ and Ag+ to bind to OM solely and not to mineral surfaces. Indeed, this may be a result of the neutral Mo(VI)O3 in PMA, while the cations Pb2+ and Ag+ may adsorb to negatively charged mineral surfaces. However, Chenu and Plante24 did not detect any sorption of both Pb and Ag on pure clay minerals (vermiculite, illite, kaolinite). Despite minerals identified in our clay fraction being kaolinite and smectite, sorption of Pb (as observed by Chenu and Plante24) or Ag onto Al and Fe oxides and hydroxides in the clay fraction could have occurred.
Figure 4

Two dimensional grey scale image representing a horizontal slice of the fine sand + clay + POM mixtures: the control treatment and the stained AgNO3, PbAc, Pb(NO3)2 and PMA treatments (image contrast was enhanced in this figure). Clusters of stained clay particles are clearly observable as brighter structures in the AgNO3, PbAc and Pb(NO3)2 treatment.

Full size image

While manual pin-pointing of the PMA stained POM was not impeded, automated POM extraction from the CT volume may still prove to be challenging due to the lack of image contrast between POM and mineral soil particles. Very recent work by Lammel et al.38 applied a machine learning segmentation tool in synchrotron-based soil CT volumes but experienced limited success. Piccoli et al.39 suggested that an operator-based ability for the selection of thresholds may still result in the most accurate segmentation of POM in soil.
Impact on sample structure
Bulk sample histogram evaluation (Fig. 2a) of the fine sand + coarse silt samples demonstrated that the grey value peak of the pore space (unstained sample mean peak grey value ± standard deviation: 1765 ± 609) had slightly shifted following staining with Pb(NO3)2 (1930 ± 386), AgNO3 (1923 ± 392) and PbAc (2204 ± 314), and to a lesser extent with PMA (1801 ± 611). There was also a reduction in the pore space peak height for the Pb(NO3)2, AgNO3 and PbAc stained treatments of 16%, 27% and 42%, respectively. In the PbAc treatment, the pore space peak disappeared nearly completely, whereas this drop was smaller for AgNO3 and much smaller for Pb(NO3)2. In contrast, no such decline in pore space peak was observed in the PMA-stained samples. A decrease in pore volume (Fig. 5) for the Pb(NO3)2, AgNO3 and PbAc treatments matched the trend in reduction of pore peak height. In addition, opposite trends of peak height increases existed for the second, coarse silt peak (+ 6%, 13%, 25%, respectively) and for the third, fine sand peak (+ 18%, 19% and 33%, respectively). Combined, these observations demonstrate that the observed changes in pore space and mineral phase peak areas are the result of the compaction of the samples by the Pb(NO3)2, AgNO3 and PbAc treatments.
Figure 5

Total X-ray µCT visible porosity in the fine sand + coarse silt + POM mixtures: the unstained control treatment (black) and the stained AgNO3 (red), PbAc (blue), Pb(NO3)2 (purple) and PMA (green) treatments.

Full size image

Given that peak height reductions were very different for Pb(NO3)2 and PbAc, the deterioration of soil structure could not be solely related to the heavy-element applied (both containing Pb2+). In addition, the decreased porosity is unlikely to have been predominantly due to the addition of NO3−, as the addition of Pb(NO3)2 would have impacted structure to a greater extent than AgNO3 which contains less (0.5×) NO3− (solutions were all at 1 M). Deterioration of the sample structure was therefore more likely to be a physical phenomenon. One possible mechanism could be that an increased underpressure, as a result of perfusion of a more viscous agent, could have more severely disrupted the structure of the soil mixtures. However, an increasing degree of structure distortion could not be related to a higher viscosity of the staining solutions (considered at 0.01 or 0.1 M40,41,42). Hence, the results in this study did not allow identification of the exact origin of this artifact. As a consequence, caution has to be taken when perfusing soil samples with liquid staining agents since deterioration of soil structure would alter spatial location of the POM as well. Thus, these results indicate that soil structure validation is still required as long as the specific cause for deterioration is not identified.
The Pb(NO3)2, AgNO3 and PbAc treatment histograms of both the fine silt (Fig. 2b) and clay (Fig. 2c) mixtures also had a smaller and broader pore space peak. It is likely that these are both derived from increased occurrence of partial volume effects (PVE) from chemical staining. It is thought that compaction following staining resulted in more voxels containing both pore space and mineral particles. With a voxel resolution of 7 µm this increased the number of pore space voxels with an intermediate grey value (2000–4000). The order in grey value increase magnitude for the fine silt and clay samples was similar as for the coarse silt mixtures: PbAc  > Pb(NO3)2  > AgNO3. Chemical staining had an increasingly stronger effect on pore space X-ray attenuation for the finer particle size mixtures, probably because of more intensive binding of Ag+ and Pb2+ on their much larger reactive surfaces.
In this study the combination of the structural degradation and the overlap in grey value ranges of POM and the mineral fractions render these staining agents unsuitable for staining natural soils. However, further development and testing of the staining method may reduce the impact on soil structure to a minimum. PMA treated silt and clay samples were not structurally degraded and no undesirable shifts of mineral particles’ grey value ranges were observed. However, the artificial soil samples are not fully representative of naturally structured soil and further testing could also rule out degradation of natural soil structure by perfusion with PMA solutions. We expect structural integrity of natural soil samples to exceed that of the ‘loose’ soil mixtures tested in this work.
Performance of PMA compared to gaseous OsO4 staining
Inspection of horizontal CT-sections suggested that OsO4 (Supplementary Fig. S6a–c) did not increase X-ray attenuation of mineral material. This is also suggested by the absence of a shift to the left of mineral fraction peaks (grey value 2500–7000), following OsO4 treatment (Fig. 2d).
The grey value distribution obtained via manual pin-pointing of POM particles demonstrated that the grey value interval of OsO4-stained POM (Fig. 6) corresponded to the right tail of the soil mixture histograms. This effect was larger but still comparable to the POM grey value shift obtained by PMA treatment. This outcome is a likely result of both staining agents targeting unsaturated bonds in e.g. hydrocarbons or proteins, and the higher atomic mass of Os (190.2) compared to Mo (95.9). Because of the significant health risks when using OsO4, PMA appears to be a suitable alternative. The increase in POM grey values following both PMA and OsO4 staining showed a sufficient differentiation of POM particles from the mineral fraction, at least for manual pin-pointing. However, single threshold-based segmentation of PMA- or OsO4-stained POM in the X-ray CT volumes obtained with this lab-scale polychromatic X-ray µCT system did not appear possible. More sophisticated segmentation algorithms are required, as was also very recently suggested by Piccoli et al.39. We propose to develop self-learning algorithms (e.g. incorporate machine learning) that consider the local grey value patterns of POM in combination with morphological characteristics for a more objective and faster segmentation of the stained POM. By using self-learning algorithms, the expert intervention to segment POM particles would be reduced to an absolute minimum and decrease further over time. In addition, technological development will very likely further enhance the X-ray CT resolution, which will strongly improve the morphological characterization of finer grained POM.
Figure 6

Grey scale histogram (0–25,000) for pin-pointed POM particles in the 16 bit images of PMA (green) or OsO4 (black) stained fine sand + clay + POM mixtures.

Full size image

Alternatively, the occurrence of a K-edge in the X-ray absorption spectrum of molybdenum could be exploited to discriminate POM in CT images. Rawlins et al.33 and Peth et al.31 have previously used the occurrence of a K-edge in the X-ray absorption spectrum of Os by scanning soil with synchrotron µCT at photon energies immediately below and beyond the K-edge successfully. However, the K-edge of Mo is situated at 20 keV, an energy level at which most X-rays may be attenuated by the soil mineral fraction, thereby probably making a dual energy approach similar to that used for Os challenging for non-synchrotron scanners and probably also for synchrotrons. Very recently, Lammel et al.38 identified gaseous iodide (I2) as a plausible candidate for selective staining of OM in soil for use with synchrotron scanners. However, they did not fully demonstrate its selectivity for OM versus silt and clay sized mineral particles, nor in X-ray µCT soil volumes obtained with non-synchrotron scanners.
The findings presented here demonstrate that laboratory based X-ray µCT scanners may also enable the segmentation of OsO4-stained POM from mineral particles, provided that better segmentation tools are developed. This opens up new possibilities for a more widespread application of the OsO4 staining technique due to much better availability of laboratory based X-ray µCT scanners and throughput time of samples. More

1.
Huang, H. Study on mechanized production engineering mode for paddy rice in double-cropping areas in south china. Published doctorial dissertation, China Agricultural University, Beijing. https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFD1214&filename=1014223520.nh (2014).
2.
Gao, Y. M., Yan, T. & Liu, W. J. Research and progress of direct rice seeding mechanization at home and abroad. Agric. Sci. Technol. Equip. 1, 28–29. https://doi.org/10.16313/j.cnki.nykjyzb.2013.01.020 (2013).
Article  Google Scholar 

3.
Fawzy, S., Osman, A. I., Doran, J. & Rooney, D. W. Strategies for mitigation of climate change: A review. Environ. Chem. Lett. 6, 2069–2094. https://doi.org/10.1007/s10311-020-01059-w (2020).
CAS  Article  Google Scholar 

4.
Hussain, S. et al. Rice production under climate change: Adaptations and mitigating strategies. In Environment, Climate, Plant and Vegetation Growth (eds Fahad, S. et al.) 659–686 (Springer, Berlin, 2020).
Google Scholar 

5.
Vicente-Serrano, S. M., Quiring, S. M., Pena-Gallardo, M., Yuan, S. S. & Dominguez-Castro, F. A review of environmental droughts: Increased risk under global warming?. Earth Sci. Rev. 201, 102953. https://doi.org/10.1016/j.earscirev.2019.102953 (2020).
Article  Google Scholar 

6.
Ault, T. R. On the essentials of drought in a changing climate. Science 6488, 256–260. https://doi.org/10.1126/science.aaz5492 (2020).
ADS  CAS  Article  Google Scholar 

7.
Zhang, L. X. & Zhou, T. J. Drought over east Asia: A review. J. Clim. 8, 3375–3399. https://doi.org/10.1175/JCLI-D-14-00259.1 (2015).
ADS  Article  Google Scholar 

8.
Zhang, X. et al. Urban drought challenge to 2030 sustainable development goals. Sci. Total Environ. 693, 133536. https://doi.org/10.1016/j.scitotenv.2019.07.342 (2019).
ADS  CAS  Article  PubMed  Google Scholar 

9.
Chakraborty, D. et al. A global analysis of alternative tillage and crop establishment practices for economically and environmentally efficient rice production. Sci. Rep. 7, 9342. https://doi.org/10.1038/s41598-017-09742-9 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

10.
Peng, S. B., Tang, Q. Y. & Zou, Y. B. Current status and challenges of rice production in China. Plant Prod. Sci. 12, 3–8. https://doi.org/10.1626/pps.12.3 (2009).
Article  Google Scholar 

11.
Sun, L. M. et al. Implications of low sowing rate for hybrid rice varieties under dry direct-seeded rice system in central China. Field Crops Res. 175, 87–95. https://doi.org/10.1016/j.fcr.2015.02.009 (2015).
Article  Google Scholar 

12.
Farooq, M. et al. Rice direct seeding: Experiences, challenges and opportunities. Soil Tillage Res. 111, 87–98. https://doi.org/10.1016/j.still.2010.10.008 (2011).
Article  Google Scholar 

13.
Sandhu, N. et al. Deciphering the genetic basis of root morphology, nutrient uptake, yield, and yield-related traits in rice under dry direct-seeded cultivation systems. Sci. Rep. 9, 9334. https://doi.org/10.1038/s41598-019-45770-3 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Liu, H. Y. et al. Dry direct-seeded rice as an alternative to transplanted-flooded rice in Central China. Agron. Sustain. Dev. 35, 285–294. https://doi.org/10.1007/s13593-014-0239-0 (2015).
Article  Google Scholar 

15.
Muhammad, S. et al. The effect of different weed management strategies on the growth and yield of direct-seeded dry rice (Oryza sativa). Planta Daninha. 34, 57–64. https://doi.org/10.1590/S0100-83582016340100006 (2016).
Article  Google Scholar 

16.
Kakumanu, K. R., Kotapati, G. R., Nagothu, U. S., Kuppanan, P. & Kallam, S. R. Adaptation to climate change and variability: A case of direct seeded rice in Andhra Pradesh, India. J. Water Clim. Change. 10, 419–430. https://doi.org/10.2166/wcc.2018.141 (2019).
Article  Google Scholar 

17.
Yamane, K. et al. Seed vigour contributes to yield improvement in dry direct-seeded rainfed lowland rice. Ann Appl. Biol. 172, 100–110. https://doi.org/10.1111/aab.12405 (2018).
CAS  Article  Google Scholar 

18.
Nakano, H., Hattori, I. & Morita, S. Dry matter yield response to seeding rate and row spacing in direct-seeded and double-harvested forage rice. Jpn. Agric. Res. Q. 53, 255–264. https://doi.org/10.6090/jarq.53.255 (2019).
CAS  Article  Google Scholar 

19.
Sun, C. L. et al. Implications of low sowing rate for hybrid rice varieties under drydirect-seeded rice system in Central China. Field Crops Res. 175, 87–95. https://doi.org/10.1016/j.fcr.2015.02.009 (2015).
Article  Google Scholar 

20.
Jabran, K. et al. Mulching improves water productivity, yield and quality of fine rice under water-saving rice production systems. J. Agron. Crop Sci. 201, 389–400. https://doi.org/10.1111/jac.12099 (2015).
Article  Google Scholar 

21.
Fawibe, O. O., Hiramatsu, M., Taguchi, Y., Wang, J. & Isoda, A. Grain yield, water-use efficiency, and physiological characteristics of rice cultivars under drip irrigation with plastic-film-mulch. J. Crop Improv. 34, 414–436. https://doi.org/10.1080/15427528.2020.1725701 (2020).
Article  Google Scholar 

22.
He, H. B. et al. Rice performance and water use efficiency under plastic mulching with drip irrigation. PLoS ONE 8, e83103. https://doi.org/10.1371/journal.pone.0083103 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

23.
Farooqi, Z. U. R., Sabir, M., Zeeshan, N., Naveed, K. & Hussain, M. M. Enhancing carbon sequestration using organic amendments and agricultural practices. In Carbon Capture, Utilization and Sequestration (ed. Agarwal, R. K.) 17–35 (IntechOpen, London, 2018).
Google Scholar 

24.
Fuss, S. et al. Negative emissions-part 2: Costs, potentials and side effects. Environ. Res. Lett. 6, 063002. https://doi.org/10.1088/1748-9326/aabf9f (2018).
ADS  CAS  Article  Google Scholar 

25.
Li, Y. S. et al. Influence of continuous plastic film mulching on yield, water use efficiency and soil properties of rice fields under non-flooding condition. Soil Tillage Res. 93, 370–378. https://doi.org/10.1016/j.still.2006.05.010 (2007).
Article  Google Scholar 

26.
Huang, Y., Liu, Q., Jia, W. Q., Yan, C. R. & Wang, J. Agricultural plastic mulching as a source of microplastics in the terrestrial environment. Environ. Pollut. 260, 114096. https://doi.org/10.1016/j.envpol.2020.114096 (2020).
CAS  Article  PubMed  Google Scholar 

27.
Yu, Q. et al. Distribution, abundance and risks of microplastics in the environment. Chemosphere 249, 126059. https://doi.org/10.1016/j.chemosphere.2020.126059 (2020).
ADS  CAS  Article  PubMed  Google Scholar 

28.
Yan, X. Y. et al. Downward transport of naturally-aged light microplastics in natural loamy sand and the implication to the dissemination of antibiotic resistance genes. Environ. Pollut. 262, 114270. https://doi.org/10.1016/j.envpol.2020.114270 (2020).
CAS  Article  PubMed  Google Scholar 

29.
Geyer, R., Jambeck, J. R. & Law, K. L. Production, use, and fate of all plastics ever made. Sci. Adv. 7, e1700782. https://doi.org/10.1126/sciadv.1700782 (2017).
ADS  CAS  Article  Google Scholar 

30.
Osman, A. I. et al. Pyrolysis kinetic modelling of abundant plastic waste (PET) and in-situ emission monitoring. Environ. Sci. Eur. 1, 112. https://doi.org/10.1186/s12302-020-00390-x (2020).
CAS  Article  Google Scholar 

31.
Kumar, U. S. U. et al. Neem leaves extract based seaweed bio-degradable composite films with excellent antimicrobial activity for sustainable packaging material. BioResources 1, 700–713. https://doi.org/10.15376/biores.14.1.700-713 (2019).
CAS  Article  Google Scholar 

32.
Qasim, U. et al. Renewable cellulosic nanocomposites for food packaging to avoid fossil fuel plastic pollution: A review. Environ. Chem. Lett. https://doi.org/10.1007/s10311-020-01090-x (2020).
Article  Google Scholar 

33.
Wang, Y. J., He, K., Zhang, J. B. & Chang, H. Y. Environmental knowledge, risk attitude, and households’ willingness to accept compensation for the application of degradable agricultural mulch film: Evidence from rural China. Sci. Total Environ. 744, 140616. https://doi.org/10.1016/j.scitotenv.2020.140616 (2020).
ADS  CAS  Article  PubMed  Google Scholar 

34.
Cirujeda, A. et al. Biodegradable mulch instead of polyethylene for weed control of processing tomato production. Agron. Sustain. Dev. 32, 889–897. https://doi.org/10.1007/s13593-012-0084-y (2012).
CAS  Article  Google Scholar 

35.
Yin, M. H., Li, Y. N., Fang, H. & Chen, P. P. Biodegradable mulching film with an optimum degradation rate improves soil environment and enhances maize growth. Agric. Water Manag. 216, 127–137. https://doi.org/10.1016/j.agwat.2019.02.004 (2019).
Article  Google Scholar 

36.
Daryanto, S., Wang, L. X. & Jacinthe, P. A. Can ridge-furrow plastic mulching replace irrigation in dryland wheat and maize cropping systems?. Agric. Water Manag. 190, 1–5. https://doi.org/10.1016/j.agwat.2017.05.005 (2017).
Article  Google Scholar 

37.
Qin, S. H., Zhang, J. L., Dai, H. L., Wang, D. & Li, D. M. Effect of ridge–furrow and plastic-mulching planting patterns on yield formation and water movement of potato in a semi-arid area. Agric. Water Manag. 131, 87–94. https://doi.org/10.1016/j.agwat.2013.09.015 (2014).
Article  Google Scholar 

38.
Fan, Y. L. et al. Effects of ridge and furrow film mulching on soil environment and yield under potato continuous cropping system. Plant Soil Environ. 65, 523–529. https://doi.org/10.17221/481/2019-PSE (2019).
Article  Google Scholar 

39.
Fan, T. L. et al. Film mulched furrow-ridge water harvesting planting improves agronomic productivity and water use efficiency in rainfed areas. Agric. Water Manag. 217, 1–10. https://doi.org/10.1016/j.agwat.2019.02.031 (2019).
Article  Google Scholar 

40.
Diaz-Perez, J. C. Root zone temperature, plant growth and yield of broccoli [Brassica oleracea (plenck) var. italica] as affected by plastic film mulches. Sci. Hortic. 123, 156–163. https://doi.org/10.1016/j.scienta.2009.08.014 (2009).
Article  Google Scholar 

41.
Gholamhoseini, M., Dolatabadian, A. & Habibzadeh, F. Ridge-furrow planting system and wheat straw mulching effects on dryland sunflower yield, soil temperature, and moisture. Agron. J. 111, 3383–3392. https://doi.org/10.2134/agronj2019.02.0097 (2019).
CAS  Article  Google Scholar 

42.
Mo, F. et al. Alternating small and large ridges with full film mulching increase linseed (Linum usitatissimum L.) productivity and economic benefit in a rainfed semiarid environment. Field Crops Res. 219, 120–130. https://doi.org/10.1016/j.fcr.2018.01.036 (2018).
Article  Google Scholar 

43.
Gu, X. B., Li, Y. N., Du, Y. D. & Yin, M. H. Ridge-furrow rainwater harvesting with supplemental irrigation to improve seed yield and water use efficiency of winter oilseed rape (Brassica napus L.). J. Integr. Agric. 16, 1162–1172. https://doi.org/10.1016/S2095-3119(16)61447-8 (2017).
Article  Google Scholar 

44.
Mo, F., Wang, J. Y., Xiong, Y. C., Nguluu, S. N. & Li, F. M. Ridge-furrow mulching system in semiarid Kenya: A promising solution to improve soil water availability and maize productivity. Eur. J. Agron. 80, 124–136. https://doi.org/10.1016/j.eja.2016.07.005 (2016).
Article  Google Scholar 

45.
Zhang, X. D. et al. Ridge-furrow mulching system regulates diurnal temperature amplitude and wetting-drying alternation behavior in soil to promote maize growth and water use in a semiarid region. Field Crops Res. 233, 121–130. https://doi.org/10.1016/j.fcr.2019.01.009 (2019).
Article  Google Scholar 

46.
Li, F. M., Wang, J., Xu, J. Z. & Xu, H. L. Productivity and soil response to plastic film mulching durations for spring wheat on entisols in the semiarid loess plateau of China. Soil Tillage Res. 78, 9–20. https://doi.org/10.1016/j.still.2003.12.009 (2004).
CAS  Article  Google Scholar 

47.
Li, C. J. et al. Towards the highly effective use of precipitation by ridge-furrow with plastic film mulching instead of relying on irrigation resources in a dry semi-humid area. Field Crops Res. 188, 62–73. https://doi.org/10.1016/j.fcr.2016.01.013 (2016).
ADS  Article  Google Scholar 

48.
Li, Y. Z. et al. The effect of tillage on nitrogen use efficiency in maize (Zea mays L.) in a ridge–furrow plastic film mulch system. Soil Tillage Res. 195, 104409. https://doi.org/10.1016/j.still.2019.104409 (2019).
Article  Google Scholar 

49.
Zheng, J., Fan, J. L., Zou, Y. F., Chau, H. W. & Zhang, F. C. Ridge-furrow plastic mulching with a suitable planting density enhances rainwater productivity, grain yield and economic benefit of rainfed maize. J. Arid Land. 12, 181–198. https://doi.org/10.1007/s40333-020-0001-1 (2020).
CAS  Article  Google Scholar 

50.
Zhao, H. et al. Ridge-furrow with full plastic film mulching improves water use efficiency and tuber yields of potato in a semiarid rainfed ecosystem. Field Crops Res. 161, 137–148. https://doi.org/10.1016/j.fcr.2014.02.013 (2014).
ADS  Article  Google Scholar 

51.
Ren, X., Chen, X. & Jia, Z. Effect of rainfall collecting with ridge and furrow on soil moisture and root growth of corn in semiarid northwest China. J. Agron. Crop Sci. 196, 109–122. https://doi.org/10.1111/j.1439-037X.2009.00401.x (2010).
Article  Google Scholar 

52.
Dong, W. L. et al. Ridge and furrow systems with film cover increase maize yields and mitigate climate risks of cold and drought stress in continental climates. Field Crops Res. 207, 71–78. https://doi.org/10.1016/j.fcr.2017.03.003 (2017).
Article  Google Scholar 

53.
Tian, Y., Su, D. R., Li, F. M. & Li, X. L. Effect of rainwater harvesting with ridge and furrow on yield of potato in semiarid areas. Field Crops Res. 84, 385–391. https://doi.org/10.1016/S0378-4290(03)00118-7 (2003).
Article  Google Scholar 

54.
Zhang, X. D. et al. Ridge-furrow mulching system drives the efficient utilization of key production resources and the improvement of maize productivity in the loess plateau of China. Soil Tillage Res. 190, 10–21. https://doi.org/10.1016/j.still.2019.02.015 (2019).
Article  Google Scholar 

55.
Li, R., Hou, X. Q., Jia, Z. K. & Han, Q. F. Soil environment and maize productivity in semi-humid regions prone to drought of Weibei Highland are improved by ridge-and-furrow tillage with mulching. Soil Tillage Res. 196, 104476. https://doi.org/10.1016/j.still.2019.104476 (2020).
Article  Google Scholar 

56.
Gu, X. B., Li, Y. N. & Du, Y. D. Film-mulched continuous ridge-furrow planting improves soil temperature, nutrient content and enzymatic activity in a winter oilseed rape field, northwest China. J. Arid Land. 10, 362–374. https://doi.org/10.1007/s40333-018-0055-5 (2018).
Article  Google Scholar 

57.
Li, M. Study on dynamic of maize (Zea mays L.) yield, soil water and soil carbon under the dry-farming plastic mulching system of ridge and furrow. Published doctorial dissertation, LanZhou University, Lanzhou. https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFDTEMP&filename=1020655864.nh (2020).

58.
Liu, X. E. et al. Film-mulched ridge-furrow management increases maize productivity and sustains soil organic carbon in a dryland cropping system. Soil Sci. Soc. Am. J. 4, 1434–1441. https://doi.org/10.2136/sssaj2014.04.0121 (2014).
CAS  Article  Google Scholar 

59.
Wang, Y. P. et al. Multi-site assessment of the effects of plastic-film mulch on the soil organic carbon balance in semiarid areas of China. Agric. For. Meteorol. 228, 42–51. https://doi.org/10.1016/j.agrformet.2016.06.016 (2016).
ADS  Article  Google Scholar 

60.
Li, S. P., Cai, Z. C., Yang, H. & Wang, J. K. Effects of long-term fertilization and plastic film covering on some soil fertility and microbial properties. Acta Ecol. Sin. 5, 2489–2498 (2009).
Google Scholar  More