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    Evaluating changes in growth and pigmentation of Cladosporium cladosporioides and Paecilomyces variotii in response to gamma and ultraviolet irradiation

    Gamma source and dose modelingThe general literature contains conflicting results on whether the energies of photons interacting with fungi affects the radiotrophic response. As such, we sought to control critical variables while irradiating the fungi with ionizing radiation from a sealed Cs-137 source and a UV source. The Cs-137 source emitted a photon at 662 keV along with other lower energy photons near 30 keV (Table S1).A review of previous studies was conducted to identify the gamma dose rate and total dose that should be targeted for exposure (Table S2). Those dose rates ranged from 600,000 rad delivered in 1.5 h to 0.08 rad delivered in 16 h. Even among studies examining the same fungi attributes, the total dose varied dramatically. For the present study, we used a Health Physics code to target a 50-rad dose over a one-week exposure. This dose was selected as it changes blood count observed in most humans24. We hypothesized that this dose would induce physiological changes in the fungi without causing a high rate of lethality. A MicroShield (Grove Software, Inc.) model was created to identify the quantity of radioactive material and distance between source and sample necessary to achieve the dose of 50 rad in seven days. From a sensitivity analysis of the MicroShield model, it was determined that ~ 350 µCi of Cs-137 would create a dose rate of ~ 50 rad in seven days (Fig. 1; Table S3), if placed 1.8 cm from the surface of the fungi. It should be noted that Microshield values are often conservative and likely underestimate the actual dose on target. In addition, 50 rad falls in the middle of the large range for energies previously reported in the general literature (Table S2).Figure 1Time required on target to achieve an exposure of 50 rad determined in MicroShield and based on an activity of ~ 350 µCi for Cs-137 source and the vertical distance between the source and fungus.Full size imageThe dose from the Cs-137 source on the fungal mycelium is also dependent on the radial growth of the fungus from the center plug used to initiate growth. As the fungus grows away from the source, the leading edge will experience a lower total dose of radiation. Although a uniform dose would have been ideal, a source with activity sufficient to create a uniform radiation field would have initiated a variety of safety controls deemed impractical for this experiment. The background radiation dose at the testing site in Albuquerque is approximately 10 µrem h−1; the dose at the outermost area of the Petri dish was measured at 65,553 µrad h−1. As this dose was primarily from gamma emissions, rad and rem can be considered equivalent. To validate the simulation, a dose rate study was performed using thermoluminescent dosimeters (TLD) placed at varying distances from the center of the source. The TLD placed directly under the source measured ~ 100 rad over the seven-day exposure, which is double the prediction from the simulation (50 rad; Fig. 2A). However, at a radial distance of 3.5 cm, the measured and estimated total dose over seven days were much closer, 12.3 and 11.4 rad, respectively. A comparison of the measured and estimated dose on target demonstrated a non-linear correlation (Fig. 2B), in which the simulation better approximated the dose at larger radial distances from the source.Figure 2(A) The total gamma dose on the fungal mycelial at 7 days as a function of the radial distance from the central mycelium plug based on empirical measurements (-●-) and estimated from simulations (-○-). (B) Observed correlation between the measured and estimated doses at varying radial distances.Full size imageIn order to normalize the energy deposited in the fungi from Cs-137 and the UV lamp sources, the units of MeV g−1 s−1 were selected for additional simulations. Monte Carlo N-Particle transport code (MCNP) simulations were used to determine this quantity for the Cs-137. The materials and geometry of the Petri dish and fungus used for these simulations are shown in Fig. 3. The Cs-137 was simulated as a point source located 1.5 cm from the top of the fungi. The Petri dish was set on a bakelite table. The setup was located in the center of a notional 5 m × 5 m × 5 m room with 30 cm thick concrete walls and filled with air. Leads bricks set on the table surrounded the petri dish and source. The International Commission on Radiological Protection (ICRP) material definitions did not contain data for fungal mycelia. Thus, we selected for skin as the closest approximation of the properties of the fungal mycelium25. This simulation gave a result for the energy deposited per particle as 6.53 × 10–4 MeV g−1, which for a 350 μCi activity, the rate of energy deposition was determined to be 7907 MeV g−1 s−1.Figure 3Top (upper left) and side (upper right) view of the Petri dish and fungi materials and distances used to determine energy deposition rates in MCNP. The overall geometry used for the radiation transport simulations, including the lead bricks, is shown from the top down (lower left) and from the side (lower right).Full size imageUV source and irradiationOur intent was to match the energy absorbed by the fungi to control for all variables except the photon energy difference between the Cs-137 source and UV lamp. The spectrum of energies emitted from the Cs-137 source varied significantly from those of the UV lamp, which in this case was a 30 W deuterium lamp that emitted from 185 to 400 nm (Fig. S1). This wide bandwidth represented photon energies ranging from 3.1 to 6.7 eV. The bandwidth of the UV exposure was limited to 300–350 nm using a 50-nm bandpass filter centered at 325 nm to ensure that incident photons would be in the UV energy range and not form ozone. Because we chose to match the overall energy deposited from the UV source to the gamma source it was necessary to attenuate the beam to the right power level. We assumed that all the UV energy would be absorbed near the surface rather than in the bulk since the fungi were melanized. This simplified the calculations and reduced risk, given the challenge of accurately estimating the absorbance of the fungi. The power deposited by the gamma source was calculated as the rate of energy deposition was determined to be 7907 MeV g−1 s−1 (1.3 nW g−1 s−1). Given the initial size of the plug was 1 cm in diameter, the desired lamp fluence needed to be ~ 2.8 nW cm−2. Across the spectrum of interest, the lamp power was determined to be 3.202 × 10–4 mW, thus requiring an attenuation of 8.7 × 10–9 (OD 8.06), reducing the lamp power to ~ 3 pW cm−2 and achieving a reasonably close power density to the target. Due to the sensitivity of UV detectors, the required power densities could not be measured directly. Alternatively, we measured the neutral density filters to verify the prescription was indeed correct.Response of P. variotii to irradiationUniform plugs (~ 5-mm in diameter) of actively growing mycelia of P. variotii were cut using the end of a Pasteur pipette and transferred a Petri plate containing potato dextrose agar (PDA) one day prior to initiating exposure experiments. The diameter of the mycelium was measured from four images, separated by precisely six hours, over the course of seven days and used to measure the growth rate. Differences in the pigmentation of the fungi under the different conditions was quantified in Fiji26 through analysis of grayscale images collected at day seven, following the method described by Brilhante et al.27 A ratiometric value was derived from the grayscale values and the white background, which corrected for variations in lighting across or between images.Significant differences in the pigmentation but not growth rates of P. variotii were associated with exposure to UV and gamma to irradiation, based on One-Way ANOVA analyses (Fig. 4A; Table S4). P. variotii is a ubiquitous filamentous fungus commonly inhabiting soil, decaying plants, and food products and was reported to be present on the surface of the walls of Unit-4 at ChNPP22,28. P. variotii is also a common food contaminant and is resistant to high temperature and metals29,30, despite being more sensitive to gamma irradiation than other fungi such as Aspergillus fumigatus31. In the present study, we hypothesized that positive radiation-induced effects in P. variotii would result in enhanced growth rates due to gamma irradiation. Across all conditions, the average growth rate of P. variotii was ~ 5.6 ± 0.9 mm d−1 (mean ± standard deviation). While the growth rate of P. variotii exposed to gamma irradiation was greater compared with the control and UV-irradiated samples (Fig. 4A), the difference in the mean growth rates was not significant (P = 0.255) by ANOVA.Figure 4(A) Growth rate and pigmentation of control (orange square), gamma- (blue square), and UV- (red square) irradiated cultures of P. variotti (mean ± standard deviation). (B) Estimated total irradiation dose experienced by the mycelial as a function of the distance from the central source. Exponential decay fit: − 3.6 + 105.7*exp(− 0.75*x); Adjusted R2 = 0.998. (C) Graphical representation of the irradiation dose based on the growth rate and duration of exposure for zones of mycelia as a function of radial distance from the central plug.Full size imageWe also hypothesized that the pigmentation of P. variotii would increase with exposure to gamma and UV irradiation. While P. variotti does not produce melanin, it does produce a pigment, Ywa1, from a polyketide synthesis (PKS) gene cluster and has been shown to protect the fungus against UV-C irradiation28. In some melanized fungi, Ywa1 serves as precursor and can be hydrolyzed to 1,3,6,8-tetrahydroxynaphthalen (T4HN). T4HN may then be converted to 1,8-dihydroxynaphthalene (1,8-DHN) melanin through the DHN pathway32. However, Lim et al.28 concluded that P. variotii does not produce true melanin as the pigmentation was maintained when the DHN-melanin pathway was inhibited. Significant differences in the pigmentation of P. variotii were observed among the three different sample types (P  More

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    Comparative efficacy of phosphorous supplements with phosphate solubilizing bacteria for optimizing wheat yield in calcareous soils

    United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development (United Nations, 2015).
    Google Scholar 
    Salimpour, S., Khavazi, K., Nadian, H., Besharati, H. & Miransari, M. Enhancing phosphorous availability to canola (Brassica napus L.) using P solubilizing and sulfur oxidizing bacteria. Plant Biol. 6, 629–642 (2010).
    Google Scholar 
    Ezawa, T., Smith, S. E. & Smith, F. A. P metabolism and transport in AM fungi. Plant Soil 244, 221–230 (2002).CAS 
    Article 

    Google Scholar 
    Halajnia, A., Haghnia, G. H., Fotovat, A. & Khorasani, R. Phosphorus fractions in calcareous soils amended with P fertilizer and cattle manure. Geoderma 150, 209–213 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    Adnan, M. et al. Coupling phosphate-solubilizing bacteria with phosphorus supplements improve maize phosphorus acquisition and growth under lime induced salinity stress. Plants 9, 900 (2020).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    Khan, A. A., Jilani, G., Akhtar, M. S., Naqvi, S. M. S. & Rasheed, M. Phosphorus solubilizing bacteria, occurrence, mechanisms and their role in crop production. J. Agric. Biol. Sci. 1, 48–58 (2009).
    Google Scholar 
    Torrent, J., Barron, V. & Schwertmann, U. Phosphate adsorption and desorption by goethites differing in crystal morphology. Soil Sci. Soc. Am. J. 54, 1007–1012 (1990).ADS 
    Article 

    Google Scholar 
    Rehim, A. Band-application of phosphorus with farm manure improves phosphorus use efficiency, productivity, and net returns of wheat on sandy clay loam soil. Turk. J. Agric. For. 40, 319–326 (2016).CAS 
    Article 

    Google Scholar 
    Bieleski, R. L. Phosphate pools, phosphate transport and phosphate availability. Annu. Rev. Plant Physiol. 24, 225–252 (1973).CAS 
    Article 

    Google Scholar 
    Goldstein, A. H. Recent progress in understanding the molecular genetics and biochemistry of calcium phosphate solubilization by gram negative bacteria. Biol. Agric. Hortic. 12, 185–193 (1995).Article 

    Google Scholar 
    Lopez-Bucio, J., Vega, O. M., Guevara-Garcıa, A. & Herrera-Estrella, L. Enhanced phosphorus uptake in transgenic tobacco plants that overproduce citrate. Nat. Biotechnol. 18, 450–453 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Tilman, D. et al. Forecasting agriculturally driven global environmental change. Science 292, 281–284 (2001).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Sato, S., Solomon, D., Hyl, C., Ketterings, Q. M. & Lehmann, J. Phosphorus speciation in manure and manure-amended soils using XANES spectroscopy. Environ. Sci. Technol. 39, 7485–74919 (2000).ADS 
    Article 
    CAS 

    Google Scholar 
    Brady, N. C., Weil, R. R. & Weil, R. R. The Nature and Properties of Soils Vol. 13, 662–710 (Prentice Hall, 2008).
    Google Scholar 
    Adnan, M. et al. Coupling phosphate solubilizing bacteria with Phosphorus supplements improve maize phosphorus acquisition and growth under lime induced salinity stress. Plants 9, 900 (2020).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    Caravaca, F., Alguacil, M. M., Azcon, R., Diaz, G. & Roldan, A. Comparing the effectiveness of mycorrhizal inoculum and amendment with sugar beet, rock phosphate and Aspergillus niger to enhance field performance of the leguminous shrub Dorycnium pentaphyllum L.. Appl. Soil Ecol. 25, 169–180 (2004).Article 

    Google Scholar 
    Zaidi, A., Khan, M., Ahemad, M. S., Oves, M. & Wani, P. A. Recent advances in plant growth promotion by phosphate-solubilizing microbes. In Microbial Strategies for Crop Improvement (eds Khan, M. S. et al.) 23–50 (Springer, 2009).Chapter 

    Google Scholar 
    Illmer, P., Barbato, A. & Schinner, F. Solubilization of hardly-soluble AlPO4 with P-solubilizing microorganism. Soil Biol. Biochem. 27, 265–270 (1995).CAS 
    Article 

    Google Scholar 
    Ryan, P. R., Delhaize, E. & Jones, D. L. Function and mechanism of organic anion exudation from plant roots. Annu. Rev. Plant Biol. 52, 527–560 (2001).CAS 
    Article 

    Google Scholar 
    Chen, Y. P. et al. Phosphate solubilizing bacteria from subtropical soil and their tricalcium phosphate solubilizing abilities. Appl. Soil Ecol. 34, 33–41 (2006).Article 

    Google Scholar 
    Adnan, M. et al. Integration of poultry manure and phosphate solubilizing bacteria improved availability of Ca bound P in calcareous soils. 3 Biotech 9, 368 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    He, Z. & Zhu, J. Microbial utilization and transformation of phosphate adsorbed by variable charged minerals. Soil Biol. Biochem. 30, 917–923 (1988).Article 

    Google Scholar 
    Kucey, R. M. N. Effect of Penicillium bilajion the solubility and uptake of P and micronutrients from soil by wheat. Can. J. Soil Sci. 68, 261–270 (1988).CAS 
    Article 

    Google Scholar 
    Bünemann, E. K., Bossio, D. A., Smithson, P. C., Frossard, E. & Oberson, A. Microbial community composition and substrate use in a highly weathered soil as affected by crop rotation and P fertilization. Soil Biol. Biochem. 36, 889–901 (2004).Article 
    CAS 

    Google Scholar 
    McGill, W. B. & Cole, C. V. Comparative aspects of cycling of organic C, N, S and P through soil organic matter. Geoderma 26, 267–268 (1981).ADS 
    CAS 
    Article 

    Google Scholar 
    Chaiharn, M. & Lumyong, S. Screening and optimization of indole-3-acetic acid production and phosphate solubilization from rhizobacteria aimed at improving plant growth. Curr. Microbiol. 62, 173–181 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kucey, R. M. N., Janzen, H. H. & Legett, M. E. Microbially mediated increases in plant-available phosphorus. Adv. Agron. 42, 198–228 (1989).
    Google Scholar 
    Rodriguez, H. & Fraga, R. Phosphate solubilizing bacteria and their role in plant growth promotion. Biotechnol. Adv. 17, 319–339 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    Xiao, Y., Wang, X., Chen, W. & Huang, Q. Isolation and identification of three potassium-solubilizing bacteria from rape rhizospheric soil and their effects on ryegrass. Geomicrobiol. J. 34, 873–880 (2017).CAS 
    Article 

    Google Scholar 
    Sugihara, S., Funakawa, S., Kilasara, M. & Kosaki, T. Dynamics of microbial biomass nitrogen in relation to plant nitrogen uptake during the crop growth period in a dry tropical cropland in Tanzania. Soil Sci. Plant Nutr. 56, 105–114 (2010).CAS 
    Article 

    Google Scholar 
    Jalili, F. et al. Isolation and characterization of ACC deaminase producing fluorescent pseudomonads, to alleviate salinity stress on canola (Brassica napus L.) growth. J. Plant Physiol. 166, 667–674 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Tiwari, V. N., Lehri, L. K. & Pathak, A. N. Effect of inoculating crops with phospho-microbes. Exp. Agric. 25, 47–50 (1989).Article 

    Google Scholar 
    Pal, S. S. Interaction of an acid tolerant strain of phosphate solubilizing bacteria with a few acid tolerant crops. Plant Soil 213, 221–230 (1999).MathSciNet 
    Article 

    Google Scholar 
    Afzal, A., Ashraf, M., Asad, S. A. & Faroog, M. Effect of phosphate solubilizing microorganism on phosphorus uptake, yield and yield traits of wheat (Triticum aestivum L.) in rainfed area. Int. J. Agric. Biol. 7, 207–209 (2005).
    Google Scholar 
    Bolan, N. S., Naidu, R., Mahimairajaand, S. & Baskaran, S. Influence of low-molecular-weight organic acids on the solubilization of phosphates. Biol. Fertil. Soils 18, 311–319 (1994).CAS 
    Article 

    Google Scholar 
    Mihoub, A., Amin, A. E. E. A. Z., Motaghian, H. R., Saeed, M. F. & Naeem, A. Citric acid (CA)–modified biochar improved available phosphorus concentration and its half-life in a P-fertilized calcareous sandy soil. J. Soil Sci. Plant Nutr. 22(1), 465–474 (2022).CAS 
    Article 

    Google Scholar 
    Adnan, M., Shah, Z., Sharif, M. & Rahman, H. Liming induces carbon dioxide (CO2) emission in PSB inoculated alkaline soil supplemented with different phosphorus sources. Environ. Sci. Pollut. Res. 25(10), 9501–9509 (2018).CAS 
    Article 

    Google Scholar 
    Amin, A. E. E. A. Z. & Mihoub, A. Effect of sulfur-enriched biochar in combination with sulfur-oxidizing bacterium (Thiobacillus spp.) on release and distribution of phosphorus in high calcareous p-fixing soils. J. Soil Sci. Plant Nutr. 21(3), 2041–2047 (2021).CAS 
    Article 

    Google Scholar 
    Tawaraya, K., Hirose, R. & Wagatsuma, T. Inoculation of arbuscularmycorrhizal fungi can substantially reduce phosphate fertilizer application to Alliumfis-tulosum L. and achieve marketable yield underfield condition. Biol. Fertil. Soils 48, 839–843 (2012).Article 

    Google Scholar 
    Islam, M. T. & Hossain, M. M. Plant probiotics in phosphorus nutrition in crops, with special reference to rice. In Bacteria in Agrobiology, Plant Probiotics (ed. Maheshwari, D. K.) 325–363 (Springer, 2012).Chapter 

    Google Scholar 
    Amruthesh, K. N., Raj, S. N., Kiran, B., Shetty, H. S. & Reddy, M. S. Growth promotion by plant growth-promoting rhizobacteria in some economically important crop plants. In Sixth International PGPR Workshop, 5–10 October, Calicut, India, 97–103 (2003).Kumar, S. et al. Impacts of nitrogen rate and landscape position on soils and switchgrass root growth parameters. Agron. J. 111, 1046–1059 (2019).CAS 
    Article 

    Google Scholar 
    Mihoub, A. & Boukhalfa-Deraoui, N. Performance of different phosphorus fertilizer types on wheat grown in calcareous sandy soil of El-Menia, Southern Algeria. Asian J. Crop Sci. 6, 383–391 (2014).Article 

    Google Scholar 
    Piccini, D. & Azcon, R. Effect of phosphate solubilizing bacteria and vesicular-arbuscular mycorrhizal fungi on the utilization of Bayovar rock phosphate by alfalfa plants using a sand-vermiculite medium. Plant Soil 50, 45–50 (1987).Article 

    Google Scholar 
    Dwivedi, B. S., Singh, V. K. & Dwivedi, V. Application of phosphate rock, with or without Aspergillus awamori inoculation, to meet phosphorus demands of rice–wheat systems in the Indo Gangetic plains of India. Aus. J. Exp. Agric. 44, 1041–1050 (2004).CAS 
    Article 

    Google Scholar 
    Saad, O. A. O. & Hammad, A. M. M. Fertilizing wheat plants with rock phosphate combined with phosphate dissolving bacteria and V.A mycorrhiza as alternate for ca–superphosphate. Ann. Agric. Sci. Cairo 43, 445–460 (1998).
    Google Scholar 
    Chabot, R. & Antoun, H. Growth promotion of maize and lettuce by phosphate solubilizing Rhizobium leguminosarum. Plant Soil. 184, 311–321 (1996).CAS 
    Article 

    Google Scholar 
    Kundu, B. S. & Gaur, A. C. Rice response to inoculation with N2 fixing and P solubilizing microorganisms. Plant Soil. 79, 227–234 (1984).CAS 
    Article 

    Google Scholar 
    Sharma, G. D., Thakur, R., Raj, S., Kauraw, D. L. & Kulhare, P. S. Impact of integrated nutrient management on yield, nutrient uptake, protein content of wheat (Triticum aestivum) and soil fertility in a typic Haplustert. Bioscan 8, 1159–1164 (2013).CAS 

    Google Scholar 
    Afzal, A. & Asghari, B. Rhizobium and phosphate solubilizing bacteria improve the yield and phosphorus uptake in wheat (Triticum aestivum). Int. J. Agric. Biol. 10, 85–88 (2008).CAS 

    Google Scholar 
    Jalili, G. et al. Enhancing crop growth, nutrients availability, economics and beneficial rhizosphere micro flora through organic and bio fertilizers. Ann. Microbiol. 57(2), 177–183 (2007).Article 

    Google Scholar 
    Sharma, S. N. & Prasad, R. Yield and P uptake by rice and wheat grown in a sequence as influenced by phosphate fertilization with diammonium phosphate and Mussoorie rock phosphate with or without crop residues and phosphate solubilizing bacteria. J. Agric. Sci. 141, 359–369 (2003).CAS 
    Article 

    Google Scholar 
    Vyas, P. & Gulati, A. Organic acid production in vitro and plant growth promotion in maize under controlled environment by phosphate-solubilizing fluorescent Pseudomonas. BMC Microbiol. 9, 174 (2009).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Mukherjee, P. K. & Rai, R. K. Sensitivity of P uptake to change in root growth and soil volume as influenced by VAM, PSB and P levels in wheat and chickpeas. Ann. Agric. Res. 20, 528–530 (1999).
    Google Scholar 
    Egamberdiyeva, D. Proc. Inst. Microbiol. Tashkent, Uzekistan (2004).Mihoub, A., Daddi Bouhoun, M., Naeem, A. & Saker, M. L. Low-molecular weight organic acids improve plant availability of phosphorus in different textured calcareous soils. Arch. Agron. Soil Sci. 63, 1023–1034 (2017).CAS 
    Article 

    Google Scholar 
    Thakuria, D. et al. Characterization and screening of bacteria from rhizosphere of rice grown in acidic soils of Assam. Curr. Sci. 86, 978–985 (2004).
    Google Scholar 
    Mamta, P. et al. Stimulatory effect of phosphate solubilizing bacteria on plant growth, stevioside and rebaudioside-A content of Stevia rebaudiana Bertoni. Appl. Soil Ecol. 46, 222–229 (2010).Article 

    Google Scholar 
    Banik, S. B. K. Solubilization of inorganic phosphate and production of organic acids by micro-organisms isolated in sucrose tricalcium phosphate agar plate. Zentralblat. Bakterol. Parasilenkl. Infektionskr. Hyg. 136, 478–486 (1981).CAS 

    Google Scholar 
    Stevenson, F. J. Cycles of Soil: Carbon, Nitrogen, Phosphorus, Sulfur, Micro-nutrients (Wiley, 2005).
    Google Scholar 
    Ekin, Z. Performance of phosphorus solubilizing bacteria for improving growth and yield of sun flower (Helianthus annuus L.) in the presence of phosphorus fertilizer. Afr. J. Biotechnol. 9, 3794–3800 (2010).CAS 

    Google Scholar 
    Zabihi, H. R., Savaghebi, G. R., Khavazi, K., Ganjali, A. & Miransari, M. Pseudomonas bacteria and phosphorus fertilization, affecting wheat (Triticum aestivum L.) yield and P uptake under green house and field conditions. Acta Physiol. Plant 33, 145–152 (2010).Article 

    Google Scholar 
    Gulati, A., Rahi, P. & Vyas, P. Characterization of phosphate-solubilizing fluorescent Pseudomonas from the rhizosphere of seabuckthorn growing in the cold deserts of Himalayas. Curr. Microbiol. 56, 73–79 (2007).PubMed 
    Article 
    CAS 

    Google Scholar 
    Kloepper, J. W., Lifshitz, R. & Zablotowicz, R. M. Free-living bacterial inocula for enhancing crop productivity. Trends Biotechnol. 7, 39–44 (1989).Article 

    Google Scholar 
    Satchell, J. E. Ecology and environment in the United Arab Emirates. J. Arid. Environ. 1, 201–226 (1978).ADS 
    Article 

    Google Scholar 
    Biswas, D. R. Nutrient recycling potential of rock phosphate and waste mica enriched compost on crop productivity and changes in soil fertility under potato–soybean cropping sequence in an Inceptisol of Indo-Gangetic Plains of India. Nutr. Cycl. Agroecosyst. 89, 15–30 (2011).Article 

    Google Scholar 
    Mitra, S. et al. Effect of integrated nutrient management on fiber yield, nutrient uptake and soil fertility in jute (Corchorus olitorius). Indian J. Anim. Sci. 80(9), 801–804 (2010).
    Google Scholar 
    Laxminarayana, K. Effect of integrated use of inorganic and organic manures on soil properties, yield and nutrient uptake of rice in Ultisols of Mizoram. J. Indian Soc. Soil Sci. 54, 120–123 (2006).
    Google Scholar 
    Sanyal, S. K. & De Datta, S. K. Chemistry of phosphorus transformations in soil. Adv. Soil Sci. 16, 1–120 (1991).CAS 

    Google Scholar 
    Briedis, C. et al. Soil organic matter pools and carbon-protection mechanisms in aggregate classes influenced by surface liming in a no-till system. Geoderma 170, 80–88 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    Bronick, C. J. & Lal, R. Soil structure and management: A review. Geoderma 124, 3–22 (2005).ADS 
    CAS 
    Article 

    Google Scholar 
    Krieg, N. R. & Holt, J. G. Bergey’s Manual of Systemetic Bacteriology Vol. 1, 984 (Williams & Wilkin, 1984).
    Google Scholar 
    Holt, J. G. et al. (eds) Bergey’s Manual of Determinative Bacteriology 9th edn, 787 (The Williams & Wilkin, 1994).
    Google Scholar 
    Gordon, R. E., Haynes, W. C. & Pang, C. N. The Genus Bacillus. Agricultural Handbook. No. 427 283 (Department of Agriculture, 1973).
    Google Scholar 
    Nautiyal, C. S. An efficient microbiological growth medium for screening phosphate solubilizing microorganisms. FEMS Microbiol. Lett. 170(1), 265–270 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    Nelson, D. W. & Sommers, L. E. Total carbon, organic carbon, and organic matter. In Methods of Soil Analysis, Part 2 2nd edn, Vol. 14 (ed. Page, A. L.) 961–1010 (Wiley, 1996).
    Google Scholar 
    Eivazi, F. & Tabatabai, M. Phosphatases in soils. Soil Biol. Biochem. 9, 167–172 (1977).CAS 
    Article 

    Google Scholar 
    Alexander, D. B. & Zuberer, D. A. Use of chrome azurol S reagents to evaluate siderophore production by rhizosphere bacteria. Biol. Fertil. Soils 12, 39–45 (1991).CAS 
    Article 

    Google Scholar 
    Vincet, J. M. A. Manual for the Practical Study of the Root-Nodule Bacteria; IBPH and Book No. 15 (Blackwell Scientific Publication, 1970).
    Google Scholar 
    Alagawadi, A. R. & Gaur, A. C. Associative effect of Rhizobium and phosphate solubilizing bacteria on the yield and nutrient uptake of chickpea. Plant Soil. 105, 241–246 (1988).Article 

    Google Scholar 
    Satyaprakash, M., Nikitha, T., Reddi, E. U. B., Sadhana, B. & Vani, S. S. Phosphorous and phosphate solubilising bacteria and their role in plant nutrition. Int. J. Curr. Microbiol. Appl. Sci. 6, 2133–2144 (2017).CAS 
    Article 

    Google Scholar 
    Wu, S. C., Cao, Z. H., Li, Z. G., Cheung, K. C. & Wong, M. H. Effects of biofertilizer containing N-fixer, P and K solubilizers and AM fungi on maize growth: A greenhouse trial. Geoderma 125, 155–166 (2005).ADS 
    Article 

    Google Scholar 
    Thomas, G. W. Soil pH and soil acidity. In Methods of Soil Analysis, Part 3, Chemical Methods Vol. 5 (eds Sparks, D. L. et al.) 475–490 (Wiley, 1996).
    Google Scholar 
    Rhoades, J. D. Salinity, electrical conductivity and total dissolved solids. In Methods of Soil Analysis, Part 3, Chemical Methods Vol. 5 (eds Sparks, D. L. et al.) 417–435 (Soil Science Society of America, 1996).
    Google Scholar 
    Bremner, J. M. & Breitenbeck, G. A. A simple method for determination of ammonium in semi-micro Kjeldahl analysis of soil and plant material using a block digestor. Commun. Soil Sci. Plant Anal. 14, 905–913 (1983).CAS 
    Article 

    Google Scholar 
    Ryan, J., Estefan, G. & Rashid, A. Soil and Plant Analysis Laboratory Manual 2nd edn, 172 (The National Agricultural Research Center (NARC), 2001).
    Google Scholar 
    Olsen, S. R., Cole, C. V., Watanabe, F. S. & Dean, L. A. Estimation of Available Phosphorus in Soils by Extraction with Sodium Bicarbonate (No. 939) (Department of Agriculture Circular, 1954).
    Google Scholar 
    Loeppert, R. H. & Suarez, D. L. Carbonate and gypsum. In Methods of Soil Analysis, Part 3, Chemical Methods Vol. 9 (eds Sparks, D. L. et al.) 181–197 (Soil Science Society of America, 1996).
    Google Scholar 
    Bahadur, L., Tiwari, D. D., Mishra, J. & Gupta, B. R. Effect of integrated nutrient management on yield, microbial population and changes in soil properties under rice-wheat cropping system in sodic soil. J. Indian Soc. Soil Sci. 60(4), 326–329 (2012).CAS 

    Google Scholar 
    Nelson, D. W. & Sommers, L. E. Total carbon, organic carbon, and organic matter. In Methods of Soil Analysis, Part 2 2nd edn, Vol. 9 (eds Sparks, D. L. et al.) 961–1010 (Soil Science Society of America, 1996).
    Google Scholar 
    Richards, L. A. Diagnosis and improvement of saline and alkali soils. LWW 78(2), 154 (1954).
    Google Scholar 
    Steel, R. G. D. & Torrie, J. H. Principles and Procedures of Statistics, a Biometrical Approach 195–233 (McGraw Hill, 1996).MATH 

    Google Scholar  More

  • in

    Nitrogen balance and efficiency as indicators for monitoring the proper use of fertilizers in agricultural and livestock systems

    Site descriptionThe experiment was conducted at the Beef Cattle Research Center of the Institute of Animal Science/APTA/SAA, Sertãozinho, São Paulo, Brazil (21°08′16″ S e 47°59′25″ W, average altitude 548 m), during two consecutive years. The climate in this region is Aw according to the Köppen’s classification, characterized as humid tropical, with a rainy season during summer and drought during winter. The meteorological data is reported in Fig. 1. The soil in the experimental area is classified as an Oxisol42. Before the experiment, soil samples were collected for chemical characterization (Table 4), which was performed following the methodology described in Van Raij et al.43. Samples were collected in 18 experimental paddocks, at the depths of 0- to 10- and 10- to 20-cm layers, from 10 distinct sampling points in each paddock, in order to create one composite sample per unit, totaling 36 samples analyzed.Figure 1Meteorological data during the study period, obtained from the meteorological station located at Centro de Pesquisa de Bovinos de Corte, Instituto de Zootecnia/Agência Paulista de Tecnologia dos Agronegócios (APTA)/Secretaria de Agricultura e Abastecimento de São Paulo (SAA), Sertãozinho, São Paulo, Brazil.Full size imageTable 4 Chemical attributes of the soil in the experimental area, before installing the experiment (November 2015).Full size tableThe nitrogen total (Nt) content was determined by the micro-Kjeldahl method44, and the soil nitrogen stocks (SN) were calculated using the following equation below, according to Veldkamp et al.45.$${text{SN }}left[ {{text{Mg ha}}^{ – 1} {text{ at a given depth}}} right], = ,({text{concentration }} times {text{ BD}}, times ,{1}/{1}0),$$ where concentration refers to the Nt concentration at a given depth (g kg−1), BD is the bulk density at a certain depth (average 1.24 kg dm−3), and 1 is the layer thickness (cm).Description of treatments and managementsThe experiment was carried out in a 16-ha area, divided into 18 paddocks of 0.89 ha each (Fig. 2), organized in a randomized blocks design with three replicates and six treatments, namely conventional crop system with grain maize production (CROP), conventional livestock system with beef cattle production in pasture using Marandu grass (LS), and four ICLS for the production of intercropped maize grain with beef cattle pasture. All production systems were sowed in December 2015, under a no-tillage system. The fertilization recommendations in the systems were based on the recommendation presented in the Boletim 10046.Figure 2Localization and representation of the area of the experiment carried out in the study. Google Earth version Pro was used to construct the map (http://www.google.com/earth/index.html).Full size imageIn the CROP system, the maize Pioneer P2830H was cultivated, sowed in a spacing of 75 cm and sowing density of 70 thousand plants. Applications of 32 kg ha−1 of nitrogen (urea), 112 kg ha−1 of P2O5 (single superphosphate) and 64 kg ha−1 of KCl (potassium chloride) were performed. Complementarily, a topdressing fertilization was made using 80 kg ha−1 of nitrogen (urea) and 80 kg ha−1 of KCl. Sowing was carried out for two consecutive years (December 2015 and 2016), providing two harvests of maize grains (May 2016 and 2017), and between one harvest and the other, the soil remained in fallow without any cover crop. The total amount of fertilizer applied in two years was 224 kg ha−1 of nitrogen (urea), 224 kg ha−1 of P2O5 (single superphosphate) and 288 kg ha−1 of KCl (potassium chloride).For the LS treatment, Urochloa brizantha (Hoechst. ex A. Rich) R.D. Webster cv. Marandu (syn. Brachiaria brizantha cv. Marandu) was sowed in a spacing of 37.5 cm, with a density of 5 kg ha−1 of seeds (76% of crop value) for the pasture assemblage. Marandu grass seeds were mixed with the planting fertilizer, applying 32 kg ha−1 of nitrogen (urea), 112 kg ha−1 of P2O5 (as single superphosphate) and 64 kg ha−1 of KCl. Applications of 40 kg ha−1 of nitrogen, 10 kg ha−1 of P2O5 and 40 kg ha−1 of KCl were also performed as topdressing fertilization in October 2016 and March 2017. 90 days after sowing, the pasture was ready to be grazed (March 2016). Three grazing periods were carried out in continuous stocking systems, with the first period between March and April 2016, the second period between August and October 2016 and the third between November 2016 and December 2017. The total amount for 2 years was 112 kg ha−1 of nitrogen (urea), 132 kg ha−1 of P2O5 (single superphosphate) and 144 kg ha−1 of KCl (potassium chloride).The same cultivar, spacing, sowing density and fertilization rates described in the CROP treatment were used in all ICLS, as well as the same density of Marandu grass seeds and topdressing fertilization adopted in the pasture of the LS treatment. The total amount for two years was 192 kg ha−1 of nitrogen (urea), 132 kg ha−1 of P2O5 (single superphosphate) and 224 kg ha−1 of KCl (potassium chloride). In ICLS-1, Marandu grass was sowed in lines simultaneously with maize, while in ICLS-2, the sowing was also simultaneous, but the application of an under-dose of 200 mL of the herbicide Nicosulfuron was used, 20 days after seedlings emergence. In the ICLS-3, Marandu grass seeds were sown the time of topdressing fertilization of maize, thus the grass seeds were mixed with the fertilizer, and sowing was carried out in the interlines of maize, using a minimum cultivator. In ICLS-4, the sowing of Marandu grass was performed simultaneously with maize, but the grass seeds were sowed in both rows and inter-rows of maize, resulting in a spacing of 37.5 cm. In this treatment, the application of 200 mL of the herbicide Nicosulfuron was adopted, 20 days after seedlings emergence.In all ICLS treatments, maize harvest was carried out in May 2016. Ninety days after harvesting the plants, the pastures were ready to be grazed. Therefore, two grazing periods were made in continuous stocking, being the first period between August and October 2016 and the second period between November 2016 and December 2017. The method for animal stocking in treatments LS and ICLS was continuous with a stocking rate (put and take) being defined according to Mott47. Caracu beef cattle with 14 months of age were used at the beginning of the experiment, with an average body weight of 335 ± 30 kg.Estimations of the nutrient balance (NB) and nutrient use efficiency (NUE)In this study, the inputs and outputs of N were assessed at the farm level48,49. The NB was calculated by the equation below19,45,50.$${text{NB}}_{{text{N}}} = {text{ Input}}_{{text{N}}} {-}{text{ Output}}_{{text{N}}}$$As for the NUE, this parameter was evaluated as defined by the EU Nitrogen Expert Panel51, being calculated as the ratio between outputs and inputs of nitrogen.$${text{NUE}}_{{text{N}}} = , left[ {{text{Output}}_{{text{N}}} /{text{ Input}}_{{text{N}}} } right]$$where NB is the nutrient balance, N is nitrogen, Input is the N concentration in the mineral fertilizer (urea), Output is the nitrogen concentration in export (maize grain and animal tissue), and NUE is the use efficiency of the nutrient.The amount of N exported in maize grains, the grain production results (Table 2) were multiplied by the mean value of N, consulted in Crampton and Harris52.In order to estimate the amounts of nutrient exported by the animals in their tissues, the values of live weight gain were considered [kg ha-1 of live weight (PV)] (Table 2), as well as the nitrogen values of the tissue, according to the methodology proposed by Rasmussen et al.21. Those authors reported that for animals weighting less than 452 kg/PV, it represents 2.7%, while heavier animals have a 2.4% nitrogen content representation of their body weight.The inputs and outputs of N in each production system are represented in Figs. 3, 4 and 5. Biological N fixation, atmospheric deposition, denitrification, leaching, rainfall, and volatilization and absorption of ammonia were not considered in the calculation of NB.Figure 3Representation of inputs and outputs of nitrogen and organic residues generated in the crop system.Full size imageFigure 4Representation of inputs and outputs of nitrogen and organic residues generated in the livestock system.Full size imageFigure 5Representation of inputs and outputs of nitrogen and organic residues generated in the integrated systems.Full size imageData for animal tissue, animal excreta, and N concentration in grains were obtained from key manuscripts from the scientific literature in order to estimate the N balance.Calculation of nitrogen quantity and valuation of organic residuesThe amount of N in the organic residues was determined as a function of the system (Figs. 3, 4, 5). The residue considered in the CROP was the straw derived from maize, while for LS it was the litter deposited (LD) in the grass Marandu, and animal manure (feces and urine). The ICLS were considered as the straw, LD, and animal manure.The N concentration in straw and LD was determined following the methods of AOAC (1990). Straw was sampled immediately after maize grain harvest, using a 1-m2 frame in the field. The material was collected in two spots of the plot that were chosen randomly. All straw deposited on the soil was sampled, weighted and dried in an oven with air circulation (60 °C) until constant weight, for the determination of dry matter in kg of straw per hectare (Table 2). The LD in the pasture system (Table 2) was analyzed according to Rezende et al.53.In order to estimate the daily amount of excreta, we considered the stocking rate adopted in the experiment (Table 2) and the values proposed by Haynes and Williams54. According to those authors, adult beef cattle can defecate on average 13 times a day and urinate 10 times a day, totaling a daily amount of 28.35 kg of feces and 19 L of urine.The valuation was calculated based on the mean value of urea for the last 10 years in the fertilizer market55,56,57, namely $0.28 kg−1 ha−1 of urea, and considering the loss of nitrogen by volatilization, which according to Freney et al.58 and Subair et al.59 can reach up to 28%.Statistical analysisThe experiment was assembled in a randomized blocks design. The model adopted for the analysis of all response variables included the block’s and treatments fixed effects (3 blocks and 6 treatments), in addition to the random error. Statistical analysis were carried out by the function “dbc()” of the package “ExpDes.pt” of the software R Development Core Team60, and the mean values were compared by the Tukey’s test at a 5% probability level. More

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    Biodegradable sensors are ready to transform autonomous ecological monitoring

    Rundel, P. W., Graham, E. A., Allen, M. F., Fisher, J. C. & Harmon, T. C. New Phytol. 182, 589–607 (2009).Article 

    Google Scholar 
    Gibb, R., Browning, E., Glover‐Kapfer, P. & Jones, K. E. Methods Ecol. Evol. 10, 169–185 (2019).Article 

    Google Scholar 
    O’Connell, A. F. (ed) Camera Traps in Animal Ecology: Methods and Analyses. Vol. 271 (Springer, 2011).Hale, R. C., Seeley, M. E., Guardia, M. J. L., Mai, L. & Zeng, E. Y. J. Geophys. Res. Oceans 125, e2018JC014719 (2020).Article 

    Google Scholar 
    Widmer, R., Oswald-Krapf, H., Sinha-Khetriwal, D., Schnellmann, M. & Böni, H. Environ. Impact Assess. Rev. 25, 436–458 (2005).Article 

    Google Scholar 
    Hwang, S.-W. et al. Science 337, 1640–1644 (2012).CAS 
    Article 

    Google Scholar 
    Ashammakhi, N. et al. Adv. Funct. Mater. 31, 2104149 (2021).Boutry, C. M. et al. Nat. Biomed. Eng. 3, 47–57 (2019).CAS 
    Article 

    Google Scholar 
    Boutry, C. M. et al. Nat. Electron. 1, 314–321 (2018).Article 

    Google Scholar 
    Hori, K., Inami, A., Kan, T. & Onoe, H. In Proc. 21st International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers) 863–866 (IEEE, Orlando, 2021).Dincer, C. et al. Adv. Mater. 31, 1806739 (2019).Article 

    Google Scholar 
    Kocer, B. B. et al. In Proc. Aerial Robotic Systems Physically Interacting with the Environment (AIRPHARO) 1–8 (IEEE, Biograd na Moru, 2021).Pandolfi, C. & Izzo, D. Bioinspir. Biomim. 8, 025003 (2013).Article 

    Google Scholar 
    Wiesemüller, F., Miriyev, A. & Kovac, M. In Proc. Aerial Robotic Systems Physically Interacting with the Environment (AIRPHARO) 1–6 (IEEE, Biograd na Moru, 2021).Boutry, C. M. et al. Sens. Actuators A Phys. 189, 344–355 (2013).CAS 
    Article 

    Google Scholar 
    Tsang, M., Armutlulu, A., Martinez, A. W., Allen, S. A. B. & Allen, M. G. Microsyst. Nanoeng. 1, 15024 (2015).CAS 
    Article 

    Google Scholar 
    Lee, G. et al. Adv. Energy Mater. 7, 1700157 (2017).Article 

    Google Scholar 
    Dagdeviren, C. et al. Small 9, 3398–3404 (2013).CAS 
    Article 

    Google Scholar 
    Sadasivuni, K. K. et al. J. Mater. Sci. Mater. Electron. 30, 951–974 (2019).CAS 
    Article 

    Google Scholar 
    Luvisi, A., Panattoni, A. & Materazzi, A. Comput. Electron. Agric. 123, 135–141 (2016).Article 

    Google Scholar 
    Yin, L. et al. Adv. Mater. 26, 3879–3884 (2014).CAS 
    Article 

    Google Scholar 
    Demetillo, A. T., Japitana, M. V. & Taboada, E. B. Sustain. Environ. Res. 29, 12 (2019).CAS 
    Article 

    Google Scholar 
    Salvatore, G. A. et al. Adv. Funct. Mater. 27, 1702390 (2017).Article 

    Google Scholar 
    Farinha, A., Zufferey, R., Zheng, P., Armanini, S. F. & Kovac, M. IEEE Robot. Autom. Lett. 5, 6623–6630 (2020).Article 

    Google Scholar 
    Miriyev, A. & Kovač, M. Nat. Mach. Intell. 2, 658–660 (2020).Article 

    Google Scholar 
    Kang, S.-K., Koo, J., Lee, Y. K. & Rogers, J. A. Acc. Chem. Res. 51, 988–998 (2018).CAS 
    Article 

    Google Scholar 
    Goel, V., Luthra, P., Kapur, G. S. & Ramakumar, S. S. V. J. Polym. Environ. 29, 3079–3104 (2021).CAS 
    Article 

    Google Scholar  More

  • in

    Struggling to keep pace

    Brondizio, E. S., Settele, J., Díaz, S. & Ngo, H. T. Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. (IPBES, 2019).Tingley, M. W., Monahan, W. B., Beissinger, S. R. & Moritz, C. Proc. Natl Acad. Sci. USA 106(Suppl 2), 19637–19643 (2009).CAS 
    Article 

    Google Scholar 
    Schloss, C. A., Nuñez, T. A. & Lawler, J. J. Proc. Natl Acad. Sci. USA 109, 8606–8611 (2012).CAS 
    Article 

    Google Scholar 
    Senior, R. A., Hill, J. K. & Edwards, D. P. Nat. Clim. Chang. 9, 623–626 (2019).Article 

    Google Scholar 
    Viana, D. S. & Chase, J. M. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01814-y (2022).Article 

    Google Scholar 
    Sauer, J. R. et al. Condor 119, 576–593 (2017).Article 

    Google Scholar 
    Nowak, L., Schleuning, M., Bender, I. M. A., Kissling, W. D. & Fritz, S. A. Divers. Distrib. https://doi.org/10.1111/ddi.13518 (2022).Article 

    Google Scholar 
    Allen, C. D. et al. For. Ecol. Manage. 259, 660–684 (2010).Article 

    Google Scholar 
    Janis, C. M., Damuth, J. & Theodor, J. M. Proc. Natl Acad. Sci. USA 97, 7899–7904 (2000).CAS 
    Article 

    Google Scholar 
    Stuart-Smith, R. D., Mellin, C., Bates, A. E. & Edgar, G. J. Nat. Ecol. Evol. 5, 656–662 (2021).Article 

    Google Scholar 
    Watanabe, Y. Y. Ecol. Lett. 19, 907–914 (2016).Article 

    Google Scholar 
    Bladon, A. J. et al. J. Anim. Ecol. 89, 2440–2450 (2020).Article 

    Google Scholar 
    Claramunt, S., Hong, M. & Bravo, A. Biotropica https://doi.org/10.1111/btp.13109 (2022).Article 

    Google Scholar 
    Zurell, D., Gallien, L., Graham, C. H. & Zimmermann, N. E. J. Biogeogr. 45, 1459–1468 (2018).Article 

    Google Scholar 
    Bowler, D. E., Heldbjerg, H., Fox, A. D., O’Hara, R. B. & Böhning-Gaese, K. J. Anim. Ecol. 87, 1034–1045 (2018).Article 

    Google Scholar 
    Warren, D. L., Cardillo, M., Rosauer, D. F. & Bolnick, D. I. Trends Ecol. Evol. 29, 572–580 (2014).Article 

    Google Scholar 
    Gómez, C., Tenorio, E. A., Montoya, P. & Cadena, C. D. Proc. R. Soc. Lond. B. Biol. Sci. 283, 20152458 (2016).
    Google Scholar 
    Amano, T., Lamming, J. D. L. & Sutherland, W. J. Bioscience 66, 393–400 (2016).Article 

    Google Scholar 
    Rosenberg, K. V. et al. Science 366, 120–124 (2019).CAS 
    Article 

    Google Scholar 
    Howard, C. et al. Divers. Distrib. 26, 1442–1455 (2020).Article 

    Google Scholar  More

  • in

    Guiding large-scale management of invasive species using network metrics

    Banks, N. C., Paini, D. R., Bayliss, K. L. & Hodda, M. The role of global trade and transport network topology in the human-mediated dispersal of alien species. Ecol. Lett. 18, 188–199 (2015).
    Google Scholar 
    Epanchin-Niell, R. et al. Controlling invasive species in complex social landscapes. Front. Ecol. Environ. 8, 210–216 (2009).
    Google Scholar 
    Charles, H. & Dukes, J. S. in Biological Invasions (ed. Nentwig, W.) 217–237 (Springer, 2007). https://doi.org/10.1007/978-3-540-36920-2_13Gallardo, B., Clavero, M., Sánchez, M. & Vilà, M. Global ecological impacts of invasive species in aquatic ecosystems. Glob. Change Biol. 22, 151–163 (2016).
    Google Scholar 
    Diagne, C. et al. High and rising economic costs of biological invasions worldwide. Nature 592, 571–576 (2021).CAS 

    Google Scholar 
    Sardain, A., Sardain, E. & Leung, B. Global forecasts of shipping traffic and biological invasions to 2050. Nat. Sustain. 2, 274–282 (2019).
    Google Scholar 
    Epanchin-Niell, R. S. & Hastings, A. Controlling established invaders: integrating economics and spread dynamics to determine optimal management. Ecol. Lett. 13, 528–541 (2010).
    Google Scholar 
    Chades, I. et al. General rules for managing and surveying networks of pests, diseases, and endangered species. Proc. Natl. Acad. Sci. USA 108, 8323–8328 (2011).CAS 

    Google Scholar 
    Epanchin-Niell, R. S. & Wilen, J. E. Optimal spatial control of biological invasions. J. Environ. Econ. Manag. 63, 260–270 (2012).
    Google Scholar 
    Epanchin-Niell, R. S. & Wilen, J. E. Individual and cooperative management of invasive species in human-mediated landscapes. Am. J. Agric. Econ. 97, 180–198 (2015).
    Google Scholar 
    Aadland, D., Sims, C. & Finnoff, D. Spatial dynamics of optimal management in bioeconomic systems. Comput. Econ. 45, 545–577 (2015).
    Google Scholar 
    Baker, C. M. Target the source: optimal spatiotemporal resource allocation for invasive species control. Conserv. Lett. 10, 41–48 (2017).
    Google Scholar 
    Bushaj, S., Büyüktahtakın, İ. E., Yemshanov, D. & Haight, R. G. Optimizing surveillance and management of emerald ash borer in urban environments. Nat. Res. Model. 34, e12267 (2021).
    Google Scholar 
    Fischer, S. M., Beck, M., Herborg, L.-M. & Lewis, M. A. Managing aquatic invasions: optimal locations and operating times for watercraft inspection stations. J. Environ. Manag. 283, 111923 (2021).
    Google Scholar 
    Büyüktahtakın, İ. E. & Haight, R. G. A review of operations research models in invasive species management: state of the art, challenges, and future directions. Ann. Oper. Res. 271, 357–403 (2018).
    Google Scholar 
    Epanchin-Niell, R. S. Economics of invasive species policy and management. Biol. Invasions 19, 3333–3354 (2017).
    Google Scholar 
    Bodin, Ö. et al. Improving network approaches to the study of complex social–ecological interdependencies. Nat. Sustain. 2, 551–559 (2019).CAS 

    Google Scholar 
    Nowzari, C., Precaido, V. M. & Pappas, G. J. Analysis and control of epidemics: a survey of spreading processes on complex networks. IEEE Control Syst. 36, 26–46 (2016).
    Google Scholar 
    Newman, M. E. J. Spread of epidemic disease on networks. Phys. Rev. E 66, 016128 (2002).CAS 

    Google Scholar 
    Kempe, D., Kleinberg, J. & Tardos, E. Maximizing the spread of influence through a social network. In Proc. 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 137–146 (ACM Press, 2003).Pastor-Satorras, R. & Vespignani, A. Immunization of complex networks. Phys. Rev. E 65, 036104 (2002).
    Google Scholar 
    Pastor-Satorras, R., Castellano, C., Van Mieghem, P. & Vespignani, A. Epidemic processes in complex networks. Rev. Mod. Phys. 87, 925–979 (2015).
    Google Scholar 
    Holme, P., Kim, B. J., Yoon, C. N. & Han, S. K. Attack vulnerability of complex networks. Phys. Rev. E 65, 056109 (2002).
    Google Scholar 
    Muirhead, J. R. & Macisaac, H. J. Development of inland lakes as hubs in an invasion network. J. Appl. Ecol. 42, 80–90 (2005).
    Google Scholar 
    de la Fuente, B., Saura, S. & Beck, P. S. Predicting the spread of an invasive tree pest: the pine wood nematode in southern europe. J. Appl. Ecol. 55, 2374–2385 (2018).
    Google Scholar 
    Minor, E. S. & Urban, D. L. A graph-theory framework for evaluating landscape connectivity and conservation planning. Conserv. Biol. 22, 297–307 (2008).
    Google Scholar 
    Morel-Journel, T., Assa, C. R., Mailleret, L. & Vercken, E. Its all about connections: hubs and invasion in habitat networks. Ecol. Lett. 22, 313–321 (2019).
    Google Scholar 
    Perry, G. L. W., Moloney, K. A. & Etherington, T. R. Using network connectivity to prioritise sites for the control of invasive species. J. Appl. Ecol. 54, 1238–1250 (2017).
    Google Scholar 
    Kvistad, J. T., Chadderton, W. L. & Bossenbroek, J. M. Network centrality as a potential method for prioritizing ports for aquatic invasive species surveillance and response in the Laurentian Great Lakes. Manag. Biol. Invasions 10, 403 (2019).
    Google Scholar 
    Haight, R. G., Kinsley, A. C., Kao, S.-Y., Yemshanov, D. & Phelps, N. B. Optimizing the location of watercraft inspection stations to slow the spread of aquatic invasive species. Biol. Invasions 23, 3907–3919 (2021).
    Google Scholar 
    McEachran, M. C. et al. Stable isotopes indicate that zebra mussels (Dreissena polymorpha) increase dependence of lake food webs on littoral energy sources. Freshw, Biol. 64, 183–196 (2019).CAS 

    Google Scholar 
    Karatayev, A. Y., Burlakova, L. E. & Padilla, D. K. in Invasive Aquatic Species of Europe. Distribution, Impacts and Management (eds Leppäkoski, E. et al.) 433–446 (Springer, 2002).Prescott, T. H., Claudi, R. & Prescott, K. L. Impact of Dreissenid mussels on the infrastructure of dams and hydroelectric power plants. In Quagga and Zebra Mussels (eds Nalepa, T. F. & Schloesser, D. W.) 243–258 (CRC Press, 2013).Invasive Species of Aquatic Plants and Wild Animals in Minnesota: Annual Report for 2020 (Minnesota Department of Natural Resources, 2020).Kanankege, K. S., Alkhamis, M. A., Phelps, N. B. & Perez, A. M. A probability co-kriging model to account for reporting bias and recognize areas at high risk for zebra mussels and eurasian watermilfoil invasions in Minnesota. Front. Vet. Sci. 4, 231 (2018).
    Google Scholar 
    Mallez, S. & McCartney, M. Dispersal mechanisms for zebra mussels: population genetics supports clustered invasions over spread from hub lakes in Minnesota. Biol. Invasions 20, 2461–2484 (2018).
    Google Scholar 
    Kao, S.-Y. Z. et al. Network connectivity of Minnesota waterbodies and implications for aquatic invasive species prevention. Biol. Invasions 23, 3231–3242 (2021).
    Google Scholar 
    Kleinberg, J. M. Authoritative sources in a hyperlinked environment. In Proc. 9th Annual ACM-SIAM Symposium on Discrete Algorithms 668–677 (1998).McDonald-Madden, E. et al. Using food-web theory to conserve ecosystems. Nat. Commun. 7, 10245 (2016).CAS 

    Google Scholar 
    Bossenbroek, J. M., Kraft, C. E. & Nekola, J. C. Prediction of long-distance dispersal using gravity models: zebra mussel invasion of inland lakes. Ecol. Appl. 11, 1778–1788 (2001).
    Google Scholar 
    Leung, B., Bossenbroek, J. M. & Lodge, D. M. Boats, pathways, and aquatic biological invasions: estimating dispersal potential with gravity models. Biol. Invasions 8, 241–254 (2006).
    Google Scholar 
    Beger, M. et al. Integrating regional conservation priorities for multiple objectives into national policy. Nat. Commun. 6, 8208 (2015).Runting, R. K. et al. Larger gains from improved management over sparing–sharing for tropical forests. Nat. Sustain. 2, 53–61 (2019).
    Google Scholar 
    Kinsley, A. C. et al. AIS Explorer: prioritization for watercraft inspections. A decision-support tool for aquatic invasive species management. J. Environ. Manage. 314, 115037 (2022).
    Google Scholar 
    Vander Zanden, M. J. & Olden, J. D. A management framework for preventing the secondary spread of aquatic invasive species. Can. J. Fish. Aquat. Sci. 65, 1512–1522 (2008).
    Google Scholar 
    Kanankege, K. S. et al. Lessons learned from the stakeholder engagement in research: application of spatial analytical tools in one health problems. Front. Vet. Sci. 7, 254 (2020).
    Google Scholar 
    Kroetz, K. & Sanchirico, J. The bioeconomics of spatial-dynamic systems in natural resource management. Annu. Rev. Resour. Econ. 7, 189–207 (2015).
    Google Scholar 
    Cade, B. S. & Noon, B. R. A gentle introduction to quantile regression for ecologists. Front. Ecol. Environ. 1, 412–420 (2003).
    Google Scholar 
    Koenker, R. in Asymptotic Statistics (eds Mandl, P. & Hušková, M.) 349–359 (Springer, 1994).Ashander, J. Analysis code and data for ‘Guiding large-scale management of invasive species using network metrics’. figshare https://doi.org/10.6084/m9.figshare.14402447 (2021). More

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    Rising ecosystem water demand exacerbates the lengthening of tropical dry seasons

    Climate and land cover dataOur study of tropical dry season dynamics required climatic variables with high temporal resolution (i.e., daily) and full coverage of tropic regions. To reduce uncertainties associated with the choice of precipitation (P) and evapotranspiration (Ep or E) datasets, we used an ensemble of eight precipitation products, three reanalysis-based products for Ep, and one satellite-based land E product. These precipitation datasets were derived four gauge-based or satellite observation (CHIRPS58, GPCC59, CPC-U60 and PERSIANN-CDR61), three reanalyses (ERA-562, MERRA-263, and PGF64) and a multi-source weighted ensemble product (MSWEP v2.865). The potential evapotranspiration (Ep) was calculated using the FAO Penman–Monteith equation66 (Eqs. (1, 2)), which requires meteorological inputs of wind speed, net radiation, air temperature, specific humidity, and surface pressure. We derived these meteorological variables from the three reanalysis products (ERA-5, MERRA-2, and GLDAS-2.067). Since PGF reanalysis lacked upward short- and long-wave radiation output and thus net radiation, we used available meteorological outputs from GLDAS-2.0 instead, which was forced entirely with the PGF input data.$${Ep}=frac{0.408cdot triangle cdot left({R}_{n}-Gright)+gamma cdot frac{900}{T+273}cdot {u}_{2}cdot left({e}_{s}-{e}_{a}right)}{triangle +{{{{{rm{gamma }}}}}}cdot left(1+0.34cdot {u}_{2}right)}$$
    (1)
    $${VPD}={e}_{s}-{e}_{a}=0.6108cdot {e}^{frac{17.27cdot T}{T+237.3}}cdot left(1-frac{{RH}}{100}right)$$
    (2)
    Where Ep is the potential evapotranspiration (mm day−1). Rn is net radiation at the surface (MJ m−2 day−1), T is mean daily air temperature at 2 m height (°C), ({u}_{2}) is wind speed at 2 m height (m s−1), ((,{e}_{s}-{e}_{a})) is the vapor pressure deficit of the air (kPa), ({RH}) is the relative air humidity near surface (%), ∆ is the slope of the saturation vapor pressure-temperature relationship (kPa °C−1), γ is the psychrometric constant (kPa °C−1), G is the soil heat flux (MJ m−2 day−1, is often ignored for daily time steps G ≈ 0).We derived the daily evapotranspiration data from the Global Land Evaporation Amsterdam Model (GLEAM v3.3a68), which is a set of algorithms dedicated to developing terrestrial evaporation and root-zone soil moisture data. GLEAM fully assimilated the satellite-based soil moisture estimates from ESA CCI, microwave L-band vegetation optical depth (VOD), reanalysis-based temperature and radiation, and multi-source precipitation forcings. The direct assimilation of observed soil moisture allowed us to detect true soil moisture dynamic and its impacts on evapotranspiration. Besides, the incorporation of VOD, which is closely linked to vegetation water content69,70, allowed us to detect the effect of water stress, heat stress, and vegetation phenological constraints on evaporation. Other observation-driven ET products from remote-sensing physical estimation and flux-tower are not included due to their low temporal resolution (i.e., monthly)71 or short duration72,73. ET outputs of reanalysis products are not considered in our analysis, because the assimilation systems lack explicit representation of inter-annual variability of vegetation activities and thus may not fully capture hydrological response to vegetation changes62,63,67.We used land cover maps for the year 2001 from the Moderate-Resolution Imaging Spectroradiometer (MODIS, MCD12C1 C574) based on the IGBP classification scheme to exclude water-dominated and sparely-vegetated pixels (like Sahara, Arabian Peninsula). All climate and land cover datasets mentioned above were remapped to a common 0.25° × 0.25° grid and unified to daily resolution. The main characteristics of the datasets mentioned above are summarized in Supplementary Table 1.Outputs of CMIP6 simulationsTo understand how modeled dry season changes compare with observed changes, we analyzed outputs from the “historical” (1983-2014) runs of 34 coupled models participating in the 6th Coupled Model Inter-comparison Project75 (CMIP6, Supplementary Table 3). We used these models because they offered daily outputs of all climatic variables needed for our analysis, including precipitation, latent heat (convert to E), and multiple meteorological variables for Ep (air temperature, surface specific humidity, wind speed, and net radiation). All outputs were remapped to a common 1.0° × 1.0° grid and unified to daily resolution.Defining dry season length and timingFor each grid cell and each dry season definition (P  More

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    Network metrics guide good control choices

    The management of introduced species, whether kudzu or zebra mussels, is costly and complex. Now, a paper reports a workable, effective solution that harnesses network analyses of ecological phenomena.Invasive species can pose severe economic and environmental problems, costing more than US$1 trillion worldwide since 1970 (ref. 1). Yet managing this human-driven issue is difficult in itself. The regions involved can be vast — entire continents or countries, for instance — while budgets are typically limited. As well, the sites potentially affected and management options can be numerous. Real systems (for example, all the lakes in the United States) can have thousands of locations that could potentially be infested. By contrast, considering just 40 locations means dealing theoretically with over 1 trillion unique combinations (240) where management could be applied (for instance, to reduce the number of invasive species leaving infested areas or entering uninfested ones). Given these constraints, a key problem is how and where to deploy control measures such as invasive-species removal. While sophisticated optimization approaches exist2, which use mathematical rules to exclude most suboptimal combinations and quickly zoom in to which locations should be managed to minimize new invasions, these algorithms are generally unfeasible for very large systems. Now, writing in Nature Sustainability, Ashander et al.3 demonstrate that simpler network metrics revealing linkages between patches can provide solutions that are often comparable to the more complex optimization algorithms. More