Philippa Kaur

All the results were reported relative to the control, unless specifically stated to the contrary or for clarity.Growth of cucumber plants in response to different biofertilizersSoilThere was no significant difference in cucumber growth before microbial fertilizer was applied. However, some microbial fertilizers significantly increased cucumber height and stem diameter when they were applied within 4 weeks from when the seedlings were planted (Fig. 1a,b,e,f). In the second week, SHZ and SMF increased plant height by 11.2 and 9.5%, respectively. In the third week, S267, SBS, SBH, SM and SHZ increased plant height by 12.0, 13.8, 15.0, 20.5 and 26.9%, respectively (Fig. 1a). In the fourth and fifth weeks, some treatments significantly increased cucumber height. In the second and third weeks, S267 significantly increased stem diameter by 21.2 and 16.8% (Fig. 1b).Figure 1Effect of different biofertilizer treatments on the growth of cucumber seedlings produced in soil or substrate in a greenhouse. S267 = Trichoderma Strain 267 added to soil; SBH = Bacillus subtilis and T. harzianum biofertilizers added to soil; SBS = B. subtilis biofertilizer added to the soil; SM = Compound biofertilizer added to soil; SHZ = T. harzianum biofertilizer added to soil; SCK = Untreated soil. US267 = T.267 biofertilizer added to substrate; USBH = B. subtilis and T. harzianum biofertilizers added to substrate; USBS = B. subtilis biofertilizer added to substrate; USM = Compound biofertilizer added to substrate; USHZ = T. harzianum biofertilizer added to substrate; USCK = Untreated substrate.Full size imageOver the subsequent 5 weeks, some microbial fertilizer treatments decreased cucumber height and stem diameter (Fig. 1g,h).SubstrateThere were no significant differences in cucumber growth before microbial fertilizer microbial fertilizer was applied (Fig. 1c,d,g,h). However, within 4 weeks of applying the microbial fertilizer, each biofertilizer treatment applied significantly increased cucumber height (Fig. 1c). US267 and USHZ significantly increased cucumber height by 39.8–75.4% and 56.1–86.1%, respectively. US267, USM and USHZ significantly increased the stem diameter by 76.8–108.9%, 71.1–97.6% and 80.4–122.4%, respectively (Fig. 1d).Over the subsequent 5 weeks, US267, USM and USHZ treatments continued to significantly increase cucumber height and stem diameter (Fig. 1g,h).Changes in the taxonomic composition of soil-borne fungal pathogensSoilBiofertilizers application significantly reduced the taxonomic composition of soil-borne fungal pathogens at different times during the cucumber growth period (Tables 1 and 2). Fusarium spp. were significantly reduced (T, 63.8% reduction, P  More

Dargie, G. C. et al. Age, extent and carbon storage of the central Congo Basin peatland complex. Nature 542, 86–90 (2017).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Crezee, B. et al. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. Nat. Geosci. 15, 639–644 (2022).Runge, J. in Large Rivers (ed. Gupta, A.) 293–309 (Wiley, 2008).Davenport, I. J. et al. First evidence of peat domes in the Congo Basin using LiDAR from a fixed-wing drone. Remote Sens. 12, 2196 (2020).Article 
ADS 

Google Scholar 
Dargie, G. C. et al. Congo Basin peatlands: threats and conservation priorities. Mitig. Adapt. Strateg. Glob. Chang. 24, 669–686 (2018).Young, D. M. et al. Misinterpreting carbon accumulation rates in records from near-surface peat. Sci. Rep. 9, 17939 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Young, D. M., Baird, A. J., Gallego-Sala, A. V. & Loisel, J. A cautionary tale about using the apparent carbon accumulation rate (aCAR) obtained from peat cores. Sci. Rep. 11, 9547 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sebag, D. et al. Monitoring organic matter dynamics in soil profiles by ‘Rock-Eval pyrolysis’: bulk characterization and quantification of degradation. Eur. J. Soil Sci. 57, 344–355 (2006).Article 
CAS 

Google Scholar 
Sebag, D. et al. Dynamics of soil organic matter based on new Rock-Eval indices. Geoderma 284, 185–203 (2016).Article 
ADS 
CAS 

Google Scholar 
Girkin, N. T. et al. Spatial variability of organic matter properties determines methane fluxes in a tropical forested peatland. Biogeochemistry 142, 231–245 (2019).Article 
CAS 
PubMed 

Google Scholar 
Dargie, G. C. Quantifying and Understanding the Tropical Peatlands of the Central Congo Basin. PhD thesis, Univ. Leeds (2015).Spiker, E. C. & Hatcher, P. G. Carbon isotope fractionation of sapropelic organic matter during early diagenesis. Org. Geochem. 5, 283–290 (1984).Article 
CAS 

Google Scholar 
Chave, J. et al. Regional and seasonal patterns of litterfall in tropical South America. Biogeosciences 7, 43–55 (2010).Article 
ADS 

Google Scholar 
Dommain, R. et al. Forest dynamics and tip-up pools drive pulses of high carbon accumulation rates in a tropical peat dome in Borneo (Southeast Asia). J. Geophys. Res. 120, 617–640 (2015).Article 
CAS 

Google Scholar 
Wotzka, H.-P. in Grundlegungen: Beiträge zur europäischen und afrikanischen Archäologie fűr Manfred K. H. Eggert (ed. Wotzka, H.-P.) 271–289 (Francke, 2006).Saulieu, G. D. et al. Archaeological evidence for population rise and collapse between ~2500 and ~500 cal. yr BP in Western Central Africa. Afr. Archéol. Arts 17, 11–32 (2021).
Google Scholar 
Sachse, D. et al. Molecular paleohydrology: interpreting the hydrogen-isotopic composition of lipid biomarkers from photosynthesizing organisms. Annu. Rev. Earth Planet. Sci. 40, 221–249 (2012).Article 
ADS 
CAS 

Google Scholar 
Collins, J. A. et al. Estimating the hydrogen isotopic composition of past precipitation using leaf-waxes from western Africa. Quat. Sci. Rev. 65, 88–101 (2013).Article 
ADS 

Google Scholar 
Schefuß, E., Schouten, S. & Schneider, R. R. Climatic controls on central African hydrology during the past 20,000 years. Nature 437, 1003–1006 (2005).Article 
ADS 
PubMed 

Google Scholar 
Kelly, T. J. et al. The vegetation history of an Amazonian domed peatland. Palaeogeogr. Palaeoclimatol. Palaeoecol. 468, 129–141 (2017).Article 

Google Scholar 
Swindles, G. T. et al. Ecosystem state shifts during long-term development of an Amazonian peatland. Global Change Biol. 24, 738–757 (2018).Article 
ADS 

Google Scholar 
Dommain, R., Couwenberg, J. & Joosten, H. Development and carbon sequestration of tropical peat domes in south-east Asia: links to post-glacial sea-level changes and Holocene climate variability. Quat. Sci. Rev. 30, 999–1010 (2011).Article 
ADS 

Google Scholar 
Lottes, A. L. & Ziegler, A. M. World peat occurrence and the seasonality of climate and vegetation. Palaeogeogr. Palaeoclimatol. Palaeoecol. 106, 23–37 (1994).Article 

Google Scholar 
Moutsamboté, J. M. Ecological, Phytogeographic and Phytosociological Study of Northern Congo (Plateaus, Bowls, Likouala and Sangha). PhD thesis, Univ. Marien Ngouabi (2012).Dingman, S. L. Fluvial Hydrology (W. H. Freeman, 1984).Swindles, G. T., Morris, P. J., Baird, A. J., Blaauw, M. & Plunkett, G. Ecohydrological feedbacks confound peat-based climate reconstructions. Geophys. Res. Lett. 39, L11401 (2012).Article 
ADS 

Google Scholar 
Morris, P. J., Baird, A. J., Young, D. M. & Swindles, G. T. Untangling climate signals from autogenic changes in long-term peatland development. Geophys. Res. Lett. 42, 10,788–10,797 (2015).Article 

Google Scholar 
Young, D. M., Baird, A. J., Morris, P. J. & Holden, J. Simulating the long-term impacts of drainage and restoration on the ecohydrology of peatlands. Water Resour. Res. 53, 6510–6522 (2017).Article 
ADS 

Google Scholar 
Weldeab, S., Lea, D. W., Schneider, R. R. & Andersen, N. Centennial scale climate instabilities in a wet early Holocene West African monsoon. Geophys. Res. Lett. 34, L24702 (2007).Article 
ADS 

Google Scholar 
Collins, J. A. et al. Rapid termination of the African Humid Period triggered by northern high-latitude cooling. Nat. Commun. 8, 1372 (2017).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Garcin, Y. et al. Early anthropogenic impact on Western Central African rainforests 2,600 y ago. Proc. Natl. Acad. Sci. USA 115, 3261–3266 (2018).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Vincens, A. et al. Changement majeur de la végétation du lac Sinnda (vallée du Niari, Sud-Congo) consécutif à l’assèchement climatique holocène supérieur: apport de la palynologie. C. R. Acad. Sci. Paris Sér. II 318, 1521–1526 (1994).
Google Scholar 
Elenga, H. et al. Diagramme pollinique holocène du lac Kitina (Congo): mise en évidence de changements paléobotaniques et paléoclimatiques dans le massif forestier du Mayombe. C. R. Acad. Sci. Paris Sér. II 323, 403–410 (1996).CAS 

Google Scholar 
Ngomanda, A., Neumann, K., Schweizer, A. & Maley, J. Seasonality change and the third millennium BP rainforest crisis in southern Cameroon (Central Africa). Quat. Res. 71, 307–318 (2009).Article 

Google Scholar 
Maley, J. et al. Late Holocene forest contraction and fragmentation in central Africa. Quat. Res. 89, 43–59 (2018).Article 

Google Scholar 
Bayon, G. et al. Intensifying weathering and land use in Iron Age Central Africa. Science 335, 1219–1222 (2012).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Giresse, P., Maley, J. & Chepstow-Lusty, A. Understanding the 2500 yr BP rainforest crisis in West and Central Africa in the framework of the Late Holocene: pluridisciplinary analysis and multi-archive reconstruction. Global Planet. Change 192, 103257 (2020).Article 

Google Scholar 
Schefuß, E. et al. Hydrologic control of carbon cycling and aged carbon discharge in the Congo River basin. Nat. Geosci. 9, 687–690 (2016).Article 
ADS 

Google Scholar 
Hoyt, A. M., Chaussard, E., Seppalainen, S. S. & Harvey, C. F. Widespread subsidence and carbon emissions across Southeast Asian peatlands. Nat. Geosci. 13, 435–440 (2020).Article 
ADS 
CAS 

Google Scholar 
Deshmukh, C. S. et al. Conservation slows down emission increase from a tropical peatland in Indonesia. Nat. Geosci. 14, 484–490 (2021).Article 
ADS 
CAS 

Google Scholar 
Köhler, P., Nehrbass-Ahles, C., Schmitt, J., Stocker, T. F. & Fischer, H. A 156 kyr smoothed history of the atmospheric greenhouse gases CO2, CH4, and N2O and their radiative forcing. Earth Syst. Sci. Data 9, 363–387 (2017).Article 
ADS 

Google Scholar 
Jiang, Y. et al. Widespread increase of boreal summer dry season length over the Congo rainforest. Nat. Clim. Change 9, 617–622 (2019).Article 

Google Scholar 
Cook, K. H., Liu, Y. & Vizy, E. K. Congo Basin drying associated with poleward shifts of the African thermal lows. Clim. Dyn. 54, 863–883 (2020).Article 

Google Scholar 
Bennett, A. C. et al. Resistance of African tropical forests to an extreme climate anomaly. Proc. Natl. Acad. Sci. USA 118, e2003169118 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sullivan, M. J. P. et al. Long-term thermal sensitivity of Earth’s tropical forests. Science 368, 869–874 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
García-Palacios, P. et al. Evidence for large microbial-mediated losses of soil carbon under anthropogenic warming. Nat. Rev. Earth Environ. 2, 585–585 (2021).Article 
ADS 

Google Scholar 
Cobb, A. R. et al. How temporal patterns in rainfall determine the geomorphology and carbon fluxes of tropical peatlands. Proc. Natl. Acad. Sci. USA 114, E5187–E5196 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Feng, X., Porporato, A. & Rodriguez-Iturbe, I. Changes in rainfall seasonality in the tropics. Nat. Clim. Change 3, 811–815 (2013).Article 
ADS 

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

Google Scholar 
Xu, J. R., Morris, P. J., Liu, J. G. & Holden, J. PEATMAP: refining estimates of global peatland distribution based on a meta-analysis. Catena 160, 134–140 (2018).Article 

Google Scholar 
Paillard, D., Labeyrie, L. & Yiou, P. Macintosh program performs time-series analysis. Eos Trans. AGU 77, 379 (1996).Article 
ADS 

Google Scholar 
Blaauw, M. & Christen, J. A. Flexible paleoclimate age–depth models using an autoregressive gamma process. Bayesian Anal. 6, 457–474 (2011).Article 
MathSciNet 
MATH 

Google Scholar 
Blaauw, M. et al. rbacon: age–depth modelling using Bayesian statistics. R package version 2.5.7 (2021); https://cran.r-project.org/web/packages/rbacon/index.html.Hogg, A. G. et al. SHCal20 Southern Hemisphere calibration, 0–55,000 years cal BP. Radiocarbon 62, 759–778 (2020).Article 
CAS 

Google Scholar 
Reimer, P. et al. The IntCal20 Northern Hemisphere radiocarbon age calibration curve (0–55 kcal BP). Radiocarbon 62, 725–757 (2020).Article 
CAS 

Google Scholar 
Reuter, H., Gensel, J., Elvert, M. & Zak, D. Evidence for preferential protein depolymerization in wetland soils in response to external nitrogen availability provided by a novel FTIR routine. Biogeosciences 17, 499–514 (2020).Article 
ADS 
CAS 

Google Scholar 
Kuhry, P. & Vitt, D. H. Fossil carbon/nitrogen ratios as a measure of peat decomposition. Ecology 77, 271–275 (1996).Article 

Google Scholar 
Hornibrook, E. R. C., Longstaffe, F. J. & Fyfe, W. S. Evolution of stable carbon isotope compositions for methane and carbon dioxide in freshwater wetlands and other anaerobic environments. Geochim. Cosmochim. Acta 64, 1013–1027 (2000).Article 
ADS 
CAS 

Google Scholar 
Broder, T., Blodau, C., Biester, H. & Knorr, K. H. Peat decomposition records in three pristine ombrotrophic bogs in southern Patagonia. Biogeosciences 9, 1479–1491 (2012).Article 
ADS 
CAS 

Google Scholar 
Biester, H., Knorr, K. H., Schellekens, J., Basler, A. & Hermanns, Y. M. Comparison of different methods to determine the degree of peat decomposition in peat bogs. Biogeosciences 11, 2691–2707 (2014).Article 
ADS 
CAS 

Google Scholar 
Leifeld, J., Klein, K. & Wüst-Galley, C. Soil organic matter stoichiometry as indicator for peatland degradation. Sci. Rep. 10, 7634 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hodgkins, S. B. et al. Tropical peatland carbon storage linked to global latitudinal trends in peat recalcitrance. Nat. Commun. 9, 3640 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Chimner, R. A. & Ewel, K. C. A tropical freshwater wetland: II. Production, decomposition, and peat formation. Wetlands Ecol. Manage. 13, 671–684 (2005).Article 

Google Scholar 
Lafargue, E., Marquis, F. & Pillot, D. Rock-Eval 6 applications in hydrocarbon exploration, production, and soil contamination studies. Oil Gas Sci. Technol. 53, 421–437 (1998).CAS 

Google Scholar 
Behar, F., Beaumont, V. & Penteado, H. L. D. Rock-Eval 6 technology: performances and developments. Oil Gas Sci. Technol. 56, 111–134 (2001).Article 
CAS 

Google Scholar 
Disnar, J. R., Guillet, B., Keravis, D., Di-Giovanni, C. & Sebag, D. Soil organic matter (SOM) characterization by Rock-Eval pyrolysis: scope and limitations. Org. Geochem. 34, 327–343 (2003).Article 
CAS 

Google Scholar 
Marzi, R., Torkelson, B. E. & Olson, R. K. A revised carbon preference index. Org. Geochem. 20, 1303–1306 (1993).Article 
CAS 

Google Scholar 
Eglinton, G. & Hamilton, R. J. Leaf epicuticular waxes. Science 156, 1322–1334 (1967).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Sauer, P. E., Eglinton, T. I., Hayes, J. M., Schimmelmann, A. & Sessions, A. L. Compound-specific D/H ratios of lipid biomarkers from sediments as a proxy for environmental and climatic conditions. Geochim. Cosmochim. Acta 65, 213–222 (2001).Article 
ADS 
CAS 

Google Scholar 
Waelbroeck, C. et al. Sea-level and deep water temperature changes derived from benthic foraminifera isotopic records. Quat. Sci. Rev. 21, 295–305 (2002).Article 
ADS 

Google Scholar 
Han, J. & Calvin, M. Hydrocarbon distribution of algae and bacteria, and microbiological activity in sediments. Proc. Natl. Acad. Sci. U.S.A. 64, 436–443 (1969).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nakagawa, T. et al. Dense-media separation as a more efficient pollen extraction method for use with organic sediment/deposit samples: comparison with the conventional method. Boreas 27, 15–24 (1998).Article 

Google Scholar 
Stone, B. C. A synopsis of the African Species of Pandanus. Ann. Missouri Bot. Gard. 60, 260–272 (1973).Article 

Google Scholar 
African Plant Database (version 3.4.0) (Conservatoire et Jardin Botaniques de la Ville de Genève and South African National Biodiversity Institute, accessed January 2022); http://africanplantdatabase.ch.Polhill, R. M., Nordal, I., Kativu, S. & Poulsen, A. D. Flora of Tropical East Africa 1st edn (CRC Press, 1997).Hawthorne, D. et al. Global Modern Charcoal Dataset (GMCD): a tool for exploring proxy-fire linkages and spatial patterns of biomass burning. Quat. Int. 488, 3–17 (2018).Article 

Google Scholar 
Stevenson, J. & Haberle, S. Macro Charcoal Analysis: A Modified Technique Used by the Department of Archaeology and Natural History. Palaeoworks Technical Paper No. 5 (PalaeoWorks, Department of Archaeology and Natural History, Research School of Pacific and Asian Studies, Australian National University, 2005).Tierney, J. E., Pausata, F. S. R. & deMenocal, P. B. Rainfall regimes of the Green Sahara. Sci. Adv. 3, e1601503 (2017).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Shanahan, T. M. et al. The time-transgressive termination of the African Humid Period. Nat. Geosci. 8, 140–144 (2015).Article 
ADS 
CAS 

Google Scholar 
Ladd, S. N. et al. Leaf wax hydrogen isotopes as a hydroclimate proxy in the Tropical Pacific. J. Geophys. Res. 126, e2020JG005891 (2021).
Google Scholar 
Dansgaard, W. Stable isotopes in precipitation. Tellus 16, 436–468 (1964).Article 
ADS 

Google Scholar 
Munksgaard, N. C. et al. Data Descriptor: daily observations of stable isotope ratios of rainfall in the tropics. Sci. Rep. 9, 14419 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Aggarwal, P. K. et al. Proportions of convective and stratiform precipitation revealed in water isotope ratios. Nat. Geosci. 9, 624–629 (2016).Article 
CAS 

Google Scholar 
Zwart, C. et al. The isotopic signature of monsoon conditions, cloud modes, and rainfall type. Hydrol. Processes 32, 2296–2303 (2018).Article 
ADS 

Google Scholar 
Jackson, B., Nicholson, S. E. & Klotter, D. Mesoscale convective systems over Western Equatorial Africa and their relationship to large-scale circulation. Mon. Weather Rev. 137, 1272–1294 (2009).Article 
ADS 

Google Scholar 
Sorí, R., Nieto, R., Vicente-Serrano, S. M., Drumond, A. & Gimeno, L. A Lagrangian perspective of the hydrological cycle in the Congo River basin. Earth Syst. Dynam. 8, 653–675 (2017).Article 
ADS 

Google Scholar 
International Atomic Energy Agency–World Meteorological Organization Global Network of Isotopes in Precipitation: The GNIP Database (accessed May 2020); https://nucleus.iaea.org/wiser/index.aspx.Sachse, D., Dawson, T. E. & Kahmen, A. Seasonal variation of leaf wax n-alkane production and δ2H values from the evergreen oak tree, Quercus agrifolia. Isotopes Environ. Health Stud. 51, 124–142 (2015).Article 
CAS 
PubMed 

Google Scholar 
Huang, X., Zhao, B., Wang, K., Hu, Y. & Meyers, P. A. Seasonal variations of leaf wax n-alkane molecular composition and δD values in two subtropical deciduous tree species: results from a three-year monitoring program in central China. Org. Geochem. 118, 15–26 (2018).Article 
CAS 

Google Scholar 
Botev, Z. I., Grotowski, J. F. & Kroese, D. P. Kernel density estimation via diffusion. Ann. Stat. 38, 2916–2957 (2010).Article 
MathSciNet 
MATH 

Google Scholar 
Albrecht, R., Sebag, D. & Verrecchia, E. Organic matter decomposition: bridging the gap between Rock-Eval pyrolysis and chemical characterization (CPMAS 13C NMR). Biogeochemistry 122, 101–111 (2015).Article 
CAS 

Google Scholar 
Matteodo, M. et al. Decoupling of topsoil and subsoil controls on organic matter dynamics in the Swiss Alps. Geoderma 330, 41–51 (2018).Article 
ADS 
CAS 

Google Scholar 
Malou, O. P. et al. The Rock-Eval® signature of soil organic carbon in arenosols of the Senegalese groundnut basin. How do agricultural practices matter? Agr. Ecosyst. Environ. 301, 107030 (2020).Article 
CAS 

Google Scholar 
Thoumazeau, A. et al. A new in-field indicator to assess the impact of land management on soil carbon dynamics. Geoderma 375, 114496 (2020).Article 
ADS 
CAS 

Google Scholar 
Cranwell, P. A. Diagenesis of free and bound lipids in terrestrial detritus deposited in a lacustrine sediment. Org. Geochem. 3, 79–89 (1981).Article 
CAS 

Google Scholar 
Ofiti, N. O. E. et al. Warming promotes loss of subsoil carbon through accelerated degradation of plant-derived organic matter. Soil Biol. Biochem. 156, 108185 (2021).Article 
CAS 

Google Scholar 
Stuiver, M. & Reimer, P. J. Extended 14C data base and revised CALIB 3.0 14C age calibration program. Radiocarbon 35, 215–230 (1993).Article 

Google Scholar  More

Bobbink, R. et al. Ecol. Appl. 20, 30–59 (2010).Article 
PubMed 

Google Scholar 
Olff, H. & Ritchie, M. E. Trends Ecol. Evol. 13, 261–265 (1998).Article 
PubMed 

Google Scholar 
DeMalach, N., Zaady, E. & Kadmon, R. Ecol. Lett. 20, 60–69 (2017).Article 
PubMed 

Google Scholar 
Borer, E. T. et al. Nature 508, 517–520 (2014).Article 
PubMed 

Google Scholar 
Harpole, W. S. et al. Nature 537, 93–96 (2016).Article 
PubMed 

Google Scholar 
Eskelinen, A., Harpole, W. S., Jessen, M.-T., Virtanen, R. & Hautier, Y. Nature https://doi.org/10.1038/s41586-022-05383-9 (2022).Article 

Google Scholar 
Koerner, S. E. et al. Nature Ecol. Evol. 2, 1925–1932 (2018).Article 
PubMed 

Google Scholar 
Chesson, P. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).Article 

Google Scholar 
Coley, P. D., Bryant, J. P. & Chapin, F. S. Science 230, 895–899 (1985).Article 
PubMed 

Google Scholar 
Hautier, Y., Niklaus, P. A. & Hector, A. Science 324, 636–638 (2009).Article 
PubMed 

Google Scholar 
Allan, E. & Crawley, M. J. Ecol. Lett. 14, 1246–1253 (2011).Article 
PubMed 

Google Scholar  More

The link between SDGs and NRRPThe Italian National Recovery and Resilience Plan (NRRP) is part of the Next Generation EU (NGEU) program, the 750-billion-euro package, consisting of about half of grants, agreed by the European Union in response to the pandemic crisis. The main component of the NGEU program is the Recovery and Resilience Facility (RRF), which has a duration of six years, from 2021 to 2026, and a total size of €672.5 billion (€312.5 billion grants, the remaining €360 billion loans at subsidized rates).The Plan is developed around three strategic axes shared at European level: digitalization and innovation, ecological planning and social inclusion.The missions of the NRRP are as follows:

Mission 1: Digitalization, innovation, competitiveness, culture and tourism

Mission 2: Green revolution and ecological transition

Mission 3: Infrastructure for sustainable mobility

Mission 4: Education and research

Mission 5: Cohesion and inclusion

Mission 6: Health.

With the aim of encouraging the debate on the use of sustainability indicators for monitoring the progress of the PNRR, a mapping of the correspondences between the 17 Sustainable Development Goals and the 6 Missions provided for by the NRRP is proposed (Fig. 1). In this way it is possible to identify the SDGs indicators that can be useful tools for achieving the missions of the NRRP.Figure 1Relationships between SDGs indicators and NRRP missions.Full size imageOf particular interest for the purposes of our work is Mission 2 (Green Revolution and Ecological Transition) of NRRP. It provides for investments and reforms for the circular economy and to improve waste management, strengthen separate collection infrastructure and modernize or develop new waste treatment plants. Substantial tax incentives are provided to increase the energy efficiency of buildings, to achieve progressive decarbonization, to increase the use of renewable energy sources. In addition, the Mission devotes resources to enhancing the capacity of electricity grids, their reliability, security, and flexibility (Smart Grid) and water infrastructure. The Mission also includes the issues of territorial security, with prevention and restoration interventions in the face of significant hydrogeological risks, the protection of green areas and biodiversity, and those related to the elimination of water and soil pollution, and the availability of water resources.The main components of this mission are:

M2C1: Circular economy and sustainable agriculture

M2C2: Renewable energy, hydrogen, grid, and sustainable mobility

M2C3: Energy efficiency and upgrading of buildings

M2C4: Protection of land and water resources.

The analysis of Mission 2 (Green Revolution and Ecological Transition) finds ample space in the SDGs creating important interconnections between the different indicators present in the individual Goals and the objectives of the Mission itself.The SDGs indicators to support the NRRPThe SDGs indicators selected for the analysis of Mission 2 (Green Revolution and Ecological Transition) of the NRRP, are descripted in Table 1. We considered 13 indicators, selected from Goals 2, 6, 7, 11, 12 and 15 which may be of significant interest for the achievement of Mission 2. These indicators will then be attributed to the individual components of the mission.Table 1 Goal, indicators, measures e source of SDGs data.Full size tableThe indicators were chosen based on their relevance to the objectives of the mission and on the availability of data on a regional basis. For each main component we can use the following indicators:

M2C1: Circular economy and sustainable agriculture:

– Share of utilized agricultural area invested by organic crops

– Growth rate of organic crops

– Delivery of municipal waste to landfill.

– Separate waste collection

M2C2: Renewable energy, hydrogen, grid and sustainable mobility:

M2C3: Energy efficiency and upgrading of buildings

M2C4: Protection of land and water resources

– Irregularities in water distribution

– Sealing and soil consumption per capita

– Soil sealing from artificial cover

– Fragmentation of the natural and agricultural territory

– Incidence of urban green areas on the urbanized surface of cities.

The SDGs indicators at the level of territorial distribution in ItalyWe carry out a first analysis by territorial distribution for the different sets of main components of Mission 2.From a first analysis of the M2C1 indicators (Circular Economy and Sustainable Agriculture) it emerges that the share of agricultural area destined for organic crops is greater, especially in the Center and in the South of Italy. In 2019, the extent of organic farming in Italy reached 15.8% of the utilized agricultural area, almost double the EU average. However, the annual growth rate of the areas converted to organic farming or in the process of conversion (+ 1.8%) is the lowest since 2012 and is negative in the South, where for the second consecutive year there is a decrease (− 2.1% in the 2-year period 2017–2019). The dynamics of organic farming is an index of the spread of sustainable agricultural practices, which must be accompanied by measures that also consider the pressure on the environment generated by agriculture (Table 2).Table 2 M2C1 indicators—Circular economy and sustainable agriculture by territorial distribution (year 2019).Full size tableAlso, in the Central and Southern Italy area there is the greatest delivery of waste to landfills. Waste cycle management is crucial for living conditions and global health. The share of municipal waste landfilled is steadily decreasing at national level. In 2019, in fact, the part sent to landfill is equal to 20.9% of the total, down compared to the previous year (21.5%). The separate collection of municipal waste represents a further important step in view of the objective of reducing the amount of waste returned to the environment and, more specifically, of the delivery of waste to landfills. The 18.5 million tons of differentiated RU in 2019 represent 61.3% of national production, a share almost doubled compared to ten years ago and up from last year by 3.1 percentage points. Despite the evident progress, Italy is still marked by a considerable delay compared to the regulatory objectives, having not yet reached, in 2019, the target of 65% of separate collection planned for 2012. Critical issues are also observed in relation to the substantial territorial gaps, which disadvantage the Center and the South compared to the North, despite the distances have been reduced in recent years.
Regarding the M2C2 Mission (Renewable Energy, Hydrogen, Network and Sustainable Mobility), national and international energy policies have been committed for years to the enhancement of renewable energy sources, with the aim of decarbonizing the economy and guaranteeing the commitments made in the field of climate change. In 2019, one year after the expiry of the objectives of the European Union’s Climate-Energy Package, fourteen Member States, including Italy, exceeded the target assigned at national level. In Italy, the overall share of energy from renewable sources in gross final consumption (CFL) of energy, equal to 18.2% (Table 3), a percentage slightly lower than the average of the EU27 (19.7%), is placed for the sixth consecutive year above the 17% target set for our country. However, for Italy to achieve the ambitious programs defined by the 2020 National Integrated Energy and Climate Plan, which set a 30% target for renewables by 2030, a further boost to production from renewable sources is necessary. The resources introduced by the National Recovery and Resilience Plan (NRRP) to achieve the “green revolution and ecological transaction” include significant investments in the energy field, focusing, among other components, on a further strengthening of the Sources from Renewable energy (FER).Table 3 M2C2 indicators—Renewable energy, hydrogen, network and sustainable mobility by territorial distribution (year 2019).Full size tableThe M2C3 Mission (Energy Efficiency and Upgrading of Buildings) devotes resources to enhancing the capacity of electricity grids, their reliability, safety, and flexibility (Smart Grid). Consistent with the objectives of reducing energy consumption pursued by European policies, the Italian figure for 2019 confirms the process of reducing Italian energy intensity, which marks a further contraction of 1.3%, reaching an overall negative balance compared to the last decade of 11.8%, with an average annual rate of change of − 1.2% (Table 4). The reduction in energy intensity is largely attributable to the effect of the measures in favor of energy efficiency, which, between 2011 and 2019, resulted in energy savings of 12 Mtoe/year, equal to 77% of the 2020 target set by the National Action Plan for Energy Efficiency 2017. A further acceleration of energy efficiency is expected, in the coming years, because of the investment plan envisaged by the NRRP, also linked to the redevelopment of the public and private building stock. At the sectoral level, the reduction in energy intensity is driven by improvements in industry, which, despite the slight increase in the last year, in 2019, with 92 toes per million euros, shows a decrease compared to 2009 of 17%, with an average annual rate of change of − 1.8%.Table 4 M2C3 indicators—Energy efficiency and requalification of buildings by territorial distribution (year 2019).Full size tableThe M2C4 Mission (Protection of the territory and water resources) also includes the issues of territorial safety, with prevention and recovery interventions, the protection of green areas and those related to the elimination of water and soil pollution.Italy is among the European countries of the Mediterranean area that use groundwater, springs and wells the most; these represent the most important resource of fresh water for drinking water use on the Italian territory (84.8% of the total withdrawn). The efficiency of municipal drinking water distribution networks has been steadily deteriorating since 2008 for more than half of the regions. The share of families who complain of irregularities in the water supply service in their home is stable (equal to 8.6% in 2019) with more accentuated values in the Center and South of Italy (Table 5).Table 5 M2C4 indicators—Protection of land and water resources by territorial distribution (year 2019).Full size tableLand degradation, understood as loss of ecological functionality, is monitored through the dynamics of land consumption, which Italy has committed to zero by 2030 with the National Strategy for Sustainable Development (2017). The “consumed” soil is that occupied by urbanization and made impermeable by artificial roofing (soil sealing). Excessive fragmentation of open spaces, however, is also a factor of degradation, since the barriers made up of buildings and infrastructures interrupt the continuity of ecosystems, making even unoccupied but not large enough spaces ecologically inert and unproductive. Moreover, in a fragile territory such as Italy, land consumption is also a significant factor of hydrogeological risk and deterioration of the landscape. The index of sealing and land consumption per capita in 2019 increases for the fifth consecutive year, resulting in 357 m2 per inhabitant. The soil sealed by artificial covers is equal to 7.1% of the national territory (8.5% in the North, 6.7% in the Center, 5.9% in the South).According to Ispra estimates, 44.3% of Italy’s natural and agricultural land has a high or very high degree of fragmentation. A joint representation of the variations in fragmentation and soil sealing over the last two years summarizes recent trends in land consumption and their impact on the environment and landscape.A further objective for 2030 is to provide universal access to safe, inclusive, and accessible public green spaces, for women and children, the elderly, and people with disabilities. In 2019 the incidence of urban green areas on the urbanized surface of cities is equal to 8.5% in Italy with slightly higher values in the North and less elevated in the South. More

Butchart, S. H. M. et al. Shortfalls and solutions for meeting national and global conservation area targets. Conserv. Lett. 8, 329–337 (2015).Article 

Google Scholar 
McDonald-Madden, E. et al. ‘True’ conservation progress. Science 323, 43–44 (2009).Article 
CAS 
PubMed 

Google Scholar 
Corson, C. et al. Everyone’s Solution? Defining and redefining protected areas at the Convention on Biological Diversity. Conservation and Society 190–202. https://www.jstor.org/stable/26393154?seq=1#metadata_info_tab_contents (Accessed 1st March 2022) (2014).Caro, T. & Berger, J. Can behavioural ecologists help establish protected areas?. Philos. Trans. R. Soc. B 374, 20180062 (2019).Article 

Google Scholar 
Barnes, M. D. et al. Wildlife population trends in protected areas predicted by national socio-economic metrics and body size. Nat. Commun. 7, 12747 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Watson, J. E. M. et al. Bolder science needed now for protected areas. Conserv. Biol. 30, 243–248 (2016).Article 
PubMed 

Google Scholar 
Joppa, L. N. & Pfaff, A. High and far: Biases in the location of protected areas. PLoS One 4, e8273 (2009).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Runge, C. A., Martin, T. G., Possingham, H. P., Willis, S. G. & Fuller, R. A. Conserving mobile species. Front. Ecol. Environ. 12, 395–402 (2014).Article 

Google Scholar 
Thirgood, S. et al. Can parks protect migratory ungulates? The case of the Serengeti wildebeest. Anim. Conserv. 7, 113–120 (2004).Article 

Google Scholar 
Craigie, I. D. et al. Large mammal population declines in Africa’s protected areas. Biol. Conserv. 143, 2221–2228 (2010).Article 

Google Scholar 
Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).Article 

Google Scholar 
Hansen, A. J. & DeFries, R. Ecological mechanisms linking protected areas to surrounding lands. Ecol. Appl. 17, 974–988 (2007).Article 
PubMed 

Google Scholar 
Beresford, A. E. et al. Poor overlap between the distribution of Protected Areas and globally threatened birds in Africa. Anim. Conserv. 14, 99–107 (2011).Article 

Google Scholar 
Haynes, G. Elephants (and extinct relatives) as earth-movers and ecosystem engineers. Geomorphology 157–158, 99–107 (2012).Article 
ADS 

Google Scholar 
Terborgh, J., Davenport, L. C., Ong, L. & Campos-Arceiz, A. Foraging impacts of Asian megafauna on tropical rain forest structure and biodiversity. Biotropica 50, 84–89 (2018).Article 

Google Scholar 
Galanti, V., Preatoni, D., Martinoli, A., Wauters, L. A. & Tosi, G. Space and habitat use of the African elephant in the Tarangire-Manyara ecosystem, Tanzania: Implications for conservation. Mamm. Biol. 71, 99–114 (2006).Article 

Google Scholar 
Williams, C. et al. Elephas maximus. The IUCN Red List of Threatened Species. e.T7140A45818198. https://doi.org/10.2305/IUCN.UK.2020-3.RLTS.T7140A45818198.en. Accessed on 15 February 2022. (2020)Stokke, S. & Du Toit, J. T. Sexual segregation in habitat use by elephants in Chobe National Park, Botswana. Afr. J. Ecol. 40, 360–371 (2002).Article 

Google Scholar 
Chowdhury, S. et al. Protected areas in South Asia: Status and prospects. Sci. Total Environ. 811, 152316 (2022).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Goswami, V. R. et al. Community-managed forests and wildlife-friendly agriculture play a subsidiary but not substitutive role to protected areas for the endangered Asian elephant. Biol. Conserv. 177, 74–81 (2014).Article 

Google Scholar 
Fernando, C., Weston, M. A., Corea, R., Pahirana, K. & Rendall, A. R. Asian elephant movements between natural and human-dominated landscapes mirror patterns of crop damage in Sri Lanka. Oryx https://doi.org/10.1017/S0030605321000971 (2022).Article 

Google Scholar 
Santini, L., Saura, S. & Rondinini, C. Connectivity of the global network of protected areas. Divers. Distrib. 22, 199–211 (2016).Article 

Google Scholar 
Brennan, A. et al. Functional connectivity of the world’s protected areas. Science 376, 1101–1104 (2022).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Kumar, M. A. & Singh, M. Behavior of Asian elephant (Elephas maximus) in a land-use mosaic: Implications for human-elephant coexistence in the Anamalai Hills, India. Wildl. Biol. Pract. 6, 69–80 (2010).
Google Scholar 
Rathnayake, C. W. M., Jones, S., Soto-Berelov, M. & Wallace, L. Human–elephant conflict and land cover change in Sri Lanka. Appl. Geogr. 143, 102685 (2022).Article 

Google Scholar 
Chan, A. N. et al. Landscape characteristics influence ranging behavior of Asian elephants at the human-wildlands interface in Myanmar. Mov. Ecol. 10, 1–15 (2022).Article 

Google Scholar 
Magioli, M. et al. Land-use changes lead to functional loss of terrestrial mammals in a Neotropical rainforest. Perspect. Ecol. 19, 161–170 (2021).
Google Scholar 
Fernando, P. et al. The future of Asian elephant conservation: Setting sights beyond protected area boundaries. in Conservation Biology in Asia 252–260 (2006).Kumar, M. A., Vijayakrishnan, S. & Singh, M. Whose habitat is it anyway? Role of natural and anthropogenic habitats in conservation of charismatic species. Trop. Conserv. Sci. 11, 194008291878845 (2018).Article 

Google Scholar 
Sirua, H. Nature above people: Rolston and “fortress” conservation in the South. Ethics Environ. 11, 71–96 (2006).Article 

Google Scholar 
Keerthipriya, P. et al. Musth and its effects on male–male and male–female associations in Asian elephants. J. Mammal. 101, 259–270 (2020).Article 

Google Scholar 
Eisenberg, J. F., Mckay, G. M. & Jainudeen, M. R. Reproductive behavior of the Asiatic elephant (Elephas maximus maximus). Behaviour 38, 193–225 (1971).Article 
CAS 
PubMed 

Google Scholar 
Fernando, P. et al. Ranging behavior of the Asian elephant in Sri Lanka. Mamm. Biol. Zeitschrift für Säugetierkd. 73, 2–13 (2008).Article 

Google Scholar 
Hollister-Smith, J. A., Alberts, S. C. & Rasmussen, L. E. L. Do male African elephants, Loxodonta africana, signal musth via urine dribbling?. Anim. Behav. 76, 1829–1841 (2008).Article 

Google Scholar 
LaDue, C. A., Vandercone, R. P. G., Kiso, W. K. & Freeman, E. W. Behavioral characterization of musth in Asian elephants (Elephas maximus): Defining progressive stages of male sexual behavior in in-situ and ex-situ populations. Appl. Anim. Behav. Sci. 251, 105639 (2022).Article 

Google Scholar 
LaDue, C. A., Goodwin, T. E. & Schulte, B. A. Concentration-dependent chemosensory responses towards pheromones are influenced by receiver attributes in Asian elephants. Ethology 124, 387–399 (2018).Article 

Google Scholar 
Goldenberg, S. Z., de Silva, S., Rasmussen, H. B., Douglas-Hamilton, I. & Wittemyer, G. Controlling for behavioural state reveals social dynamics among male African elephants, Loxodonta africana. Anim. Behav. 95, 111e119 (2014).Article 

Google Scholar 
Chave, E. et al. Variation in metabolic factors and gonadal, pituitary, thyroid, and adrenal hormones in association with musth in African and Asian elephant bulls. Gen. Comp. Endocrinol. 276, 1–13 (2019).Article 
CAS 
PubMed 

Google Scholar 
Glaeser, S. S. et al. Characterization of longitudinal testosterone, ocrtisol, and musth in male Asian Elephants (Elephas maximus), effects of aging, and adrenal responses to social changes and health events. Animals 12, 1332 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
de Silva, S., Ranjeewa, A. D. G. & Kryazhimskiy, S. The dynamics of social networks among female Asian elephants. BMC Ecol. 11, 17 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Nandini, S., Keerthipriya, P. & Vidya, T. N. C. Group size differences may mask underlying similarities in social structure: A comparison of female elephant societies. Behav. Ecol. 29, 145–159 (2018).Article 

Google Scholar 
de Silva, S. & Wittemyer, G. A comparison of social organization in Asian elephants and African Savannah elephants. Int. J. Primatol. 33, 1125–1141 (2012).Article 

Google Scholar 
Nandini, S., Keerthipriya, P. & Vidya, T. N. C. Seasonal variation in female Asian elephant social structure in Nagarahole-Bandipur, southern India. Anim. Behav. 134, 135–145 (2017).Article 

Google Scholar 
de Silva, S., Schmid, V. & Wittemyer, G. Fission-fusion processes weaken dominance networks among female Asian elephants in a productive habitat. Behav. Ecol. 28, 243–252 (2017).Article 

Google Scholar 
de Silva, S., Ranjeewa, A. D. G. & Weerakoon, D. Demography of Asian elephants (Elephas maximus) at Uda Walawe National Park, Sri Lanka based on identified individuals. Biol. Conserv. 144, 1742–1752 (2011).Article 

Google Scholar 
Ginsberg, J. R. & Young, T. P. Measuring association between individuals or groups in behavioural studies. Anim. Behav. 44, 377–379 (1992).Article 

Google Scholar 
Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal, Complex Syst. 5, 1–9 (2014).
Google Scholar 
Liechti, J. I. & Bonhoeffer, S. A time resolved clustering method revealing longterm structures and their short-term internal dynamics. arXiv:1912.04261 (2020).Farine, D. R. & Whitehead, H. Constructing, conducting and interpreting animal social network analysis. J. Anim. Ecol. 84, 1144–1163 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Wikramanayake, E. D. et al. An ecology-based method for defining priorities for large mammal conservation: The tiger as case study. Conserv. Biol. 12, 865–878 (2008).Article 

Google Scholar 
Chundawat, R. S., Sharma, K., Gogate, N., Malik, P. K. & Vanak, A. T. Size matters: Scale mismatch between space use patterns of tigers and protected area size in a tropical dry forest. Biol. Conserv. 197, 146–153 (2016).Article 

Google Scholar 
Tucker, M. A. et al. Moving in the Anthropocene: Global reductions in terrestrial mammalian movements. Science 359, 466–469 (2018).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Karanth, K. K. & DeFries, R. Nature-based tourism in Indian protected areas: New challenges for park management. Conserv. Lett. 4, 137–149 (2011).Article 

Google Scholar 
Brown, J. L. et al. Comparative endocrinology of testicular, adrenal and thyroid function in captive Asian and African elephant bulls. Gen. Comp. Endocrinol. 151, 153–162 (2007).Article 
CAS 
PubMed 

Google Scholar 
Slotow, R. et al. Older bull elephants control young males. Nature 408, 425–426 (2000).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Poole, J. H., Lee, P. C., Njiraini, N. & & Moss, C. J. Longevity, competition, and musth: A long-term perspective on male reproductive strategies. in The Amboseli Elephants: A Long‐Term Perspective on a Long‐Lived Mammal 272–286 (2011).Poole, J. H. Announcing intent: The aggressive state of musth in African elephants. Anim. Behav. 37, 153–155 (1989).Article 

Google Scholar 
Poole, J. H. Mate guarding, reproductive success and female choice in African elephants. Anim. Behav. 37, 842–849 (1989).Article 

Google Scholar 
Poole, J. H. Rutting behavior in elephants: The phenomenon of musth in African elephants. Anim. Behav. 102, 283–316 (1987).
Google Scholar 
Foley, A. M. et al. Reproductive effort and success of males in scramble-competition polygyny: Evidence for trade-offs between foraging and mate search. J. Anim. Ecol. 87, 1600–1614 (2018).Article 
PubMed 

Google Scholar 
Fernando, P., Leimgruber, P., Prasad, T. & Pastorini, J. Problem-elephant translocation: Translocating the problem and the elephant?. PLoS One 7, e50917 (2012).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Archie, E. A., Morrison, T. A., Foley, C. A. H., Moss, C. J. & Alberts, S. C. Dominance rank relationships among wild female African elephants, Loxodonta africana. Anim. Behav. 71, 117–127 (2006).Article 

Google Scholar 
Wittemyer, G. & Getz, W. M. Hierarchical dominance structure and social organization in African elephants, Loxodonta africana. Anim. Behav. 73, 671–681 (2007).Article 

Google Scholar 
Wittemyer, G., Getz, W. M., Vollrath, F. & Douglas-Hamilton, I. Social dominance, seasonal movements, and spatial segregation in African elephants: A contribution to conservation behavior. Behav. Ecol. Sociobiol. 61, 1919–1931 (2007).Article 

Google Scholar 
Gunaryadi, D., Sugiyo, & Hedges, S. Community-based human-elephant conflict mitigation: The value of an evidence-based approach in promoting the uptake of effective methods. PLoS One 12, e0173742 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Wilson, G. et al. Between a rock and a hard place: Rugged terrain features and human disturbance affect behaviour and habitat use of Sumatran elephants in Aceh, Sumatra, Indonesia. Biodivers. Conserv. 30, 597–618 (2021).Article 

Google Scholar 
de Silva, S. et al. Demographic variables for wild Asian elephants using longitudinal observations. PLoS One 8, e82788 (2013).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
LaDue, C. A., Eranda, I., Jayasinghe, C. & Vandercone, R. P. G. Mortality patterns of Asian elephants in a region of human–elephant conflict. J. Wildl. Manag. 85, 794–802 (2021).Article 

Google Scholar 
Ram, A. K. et al. Tracking forest loss and fragmentation between 1930 and 2020 in Asian elephant (Elephas maximus) range in Nepal. Sci. Rep. 11, 1–13 (2021).Article 
ADS 

Google Scholar 
Neupane, D., Kwon, Y., Risch, T. S. & Johnson, R. L. Changes in habitat suitability over a two decade period before and after Asian elephant recolonization. Glob. Ecol. Conserv. 22, e01023 (2020).Article 

Google Scholar 
de Silva, S. & Leimgruber, P. Demographic tipping points as early indicators of vulnerability for slow-breeding megafaunal populations. Front. Ecol. Evol. 7, 171 (2019).Article 

Google Scholar 
Rodrigo, M. Farmers move to occupy a critical elephant corridor in Sri Lanka. Mongabay (2021).de la Torre, J. A. et al. There will be conflict—Agricultural landscapes are prime, rather than marginal, habitats for Asian elephants. Anim. Conserv. 24, 720–732 (2021).Article 

Google Scholar 
Rood, E., Ganie, A. A. & Nijman, V. Using presence-only modelling to predict Asian elephant habitat use in a tropical forest landscape: Implications for conservation. Divers. Distrib. 16, 975–984 (2010).Article 

Google Scholar 
Evans, L. J., Goossens, B., Davies, A. B., Reynolds, G. & Asner, G. P. Natural and anthropogenic drivers of Bornean elephant movement strategies. Glob. Ecol. Conserv. 22, e00906 (2020).Article 

Google Scholar 
Morales-Hidalgo, D., Oswalt, S. N. & Somanathan, E. Status and trends in global primary forest, protected areas, and areas designated for conservation of biodiversity from the Global Forest Resources Assessment 2015. For. Ecol. Manag. 352, 68–77 (2015).Article 

Google Scholar 
Ellis, E. C. et al. People have shaped most of terrestrial nature for at least 12,000 years. Proc. Natl. Acad. Sci. U.S.A. 118, 1–8 (2021).Article 

Google Scholar 
Goswami, V. R., Vasudev, D. & Oli, M. K. The importance of conflict-induced mortality for conservation planning in areas of human–elephant co-occurrence. Biol. Conserv. 176, 191–198 (2014).Article 

Google Scholar 
de Silva, S. & Leimgruber, P. Demographic tipping points as early indicators of vulnerability for slow-breeding megafaunal populations. Front. Ecol. Evol. 7, 1–13 (2019).Article 
CAS 

Google Scholar 
Hettiarachchi, K. ‘Gathering’ shuns ‘brimming’ Minneriya. The Sunday Times (2021).Srinivasaiah, N., Kumar, V., Vaidyanathan, S., Sukumar, R. & Sinha, A. All-male groups in Asian elephants: A novel, adaptive social strategy in increasingly anthropogenic landscapes of southern India. Sci. Rep. 9, 1–11 (2019).Article 
ADS 
CAS 

Google Scholar 
de Silva, E. M. K. et al. Feasibility of using convolutional neural networks for individual-identification of wild Asian elephants. Mamm. Biol. https://doi.org/10.1007/S42991-021-00206-2 (2022).Article 

Google Scholar 
de Silva, S. The Elephant Attribute Recording System (EARS): A tool for individual-based research on Asian elephants. Gajah 40, 46 (2014).
Google Scholar 
Jainudeen, M. R., Katongole, C. B. & Short, R. V. Plasma testosterone levels in relation to musth and sexual activity in the male Asiatic elephant, Elephas maximus. J. Reprod. Fertil. 29, 99–103 (1972).Article 
CAS 
PubMed 

Google Scholar 
Farine, D. R. Animal social network inference and permutations for ecologists in R using asnipe. Methods Ecol. Evol. 4, 1187–1194 (2013).Article 

Google Scholar 
Whitehead, H. Analyzing Animal Societies (University of Chicago Press, 2008).Book 

Google Scholar 
Bates, D., Mächler, M., Bolker, B., & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Dekker, D., Krackhardt, D. & Snijders, T. A. B. Sensitivity of MRQAP tests to collinearity and autocorrelation conditions. Psychometrika 72, 563–581 (2007).Article 
MathSciNet 
PubMed 
PubMed Central 
MATH 

Google Scholar  More

Last year, female researchers received Aus$95 million less than male researchers in investigator grants from the Australian National Health and Medical Research Council.Credit: Lisa Maree Williams/Getty

Australian research agency introduces ‘Game-changing’ gender quotasIn an attempt to achieve gender equity, Australia’s leading health and medical research funding organization plans to award half of its research grants for its largest funding programme to women and non-binary applicants, starting next year.The National Health and Medical Research Council (NHMRC) announced the move last month. It will apply to researchers at the mid-career and senior level applying for the agency’s investigator grants, which fund research and salaries. Grants will also be fixed at Aus$400,000 (US$252,000) per year for five years. Many countries struggle to achieve gender equity in research funding, and the NHMRC will be one of the first agencies to introduce gender quotas at this scale, say researchers.“It’s game-changing,” says Anna-Maria Arabia, chief executive of the Australian Academy of Science in Canberra. The plan “directly removes a barrier that’s historically led to attrition in the research workforce and has led to the significant under-representation of women at senior levels”, she says.In 2021, 254 investigator grants were awarded, worth Aus$400 million in total. But when two researchers in Melbourne reviewed the data, they found that men had received 23% more of the grants, worth an extra Aus$95 million, than had women. There was an outcry from researchers. This year, the agency conducted its own review of investigator-grant outcomes from the past three years and found that the biggest gap was among the most senior researchers. A subsequent discussion paper and consultations with researchers informed the latest decision.The NHMRC has been working for a decade to address gender inequity in its grant funding. For example, in 2017, it introduced ‘structural priority funding’, which reserves extra money — around 8% of the overall grant budget — for high-quality ‘near-miss’ research applications led by women.But this did not address the gender imbalance among the most established researchers. In 2021, only 20% of the applicants in this group were women.The council will be looking to see whether awarding equal numbers of grants by gender leads to an increase in the number of senior women applying for leadership grants.No-fishing zone boosts tuna catch ratesLarge no-fishing areas can drive the recovery of commercially valuable fish species, a study suggests. Researchers examined ten years’ worth of fisheries data from the vicinity of Papahānaumokuākea Marine National Monument, a 1.5-million-square-kilometre protected area off the northwestern Hawaiian islands.They found that after the area expanded in 2016, catch rates — the number of fish caught for every 1,000 hooks deployed — went up (S. Medoff et al. Science 378, 313–316; 2022). The increases were greater the closer the boats were to the no-fishing zone. At up to 100 nautical miles, the catch rate for yellowfin tuna (Thunnus albacares) increased by 54%, and that for bigeye tuna (Thunnus obesus) by 12%. The size of the protected area probably played a part in the positive effects, as did the fact that it runs from west to east, allowing tropical fish to move in their preferred temperature range without leaving the zone.

SOURCE: S. Medoff et al. More

Ground-truth surveys of seagrass habitatTo obtain georeferenced field data on benthic cover levels from habitats of the Bahama Banks, we employed two similar, in-water survey and image approaches: (1) swimmer-based photo-transects; and (2) tow board photo transects (Supplementary Fig. 6), resulting in a total of 2542 surveys.For (1), free-divers swam over the bottom of the seafloor at a fixed height with a digital camera (Canon 5D mIV, GoPro Hero) set to capture images manually. Photographs were captured using automatic settings in a 1.0 m × 1.0 m footprint, 1.5 m above the seafloor following [39]. A center console vessel was used to run the transects at distances of 5–7 km, whereby the free-diver would capture successive photos at a horizontal distance of between 400–800 m, and the location was logged using either a handheld GPS (Garmin GPS 73) or a boat-mounted GPS with a depth sounder (Garmin EchoMap DV). Transect locations were chosen based on a priori local expert knowledge of varying benthic cover in the region. Surveyed areas included: southern New Providence (24.948862°, −77.387834°), southeast of New Providence (24.980265°, −77.229168°), south of Rose Island (25.066268°, −77.160063°), the middle Great Bahama Bank (24.735355°, −77.212998°), and the northern Exumas (24.729973°, −76.889488°). For (2), snorkeling observers were pulled from a research vessel on tow boards affixed with underwater action cameras (GoPro Hero 3+) traveling at ~1 m/s. The start and end of a tow were delineated with either a handheld GPS (Garmin eTrex 30) or a boat mounted GPS with depth-finder (Garmin EchoMap DV), and tows proceeded in a straight line recorded by the GPS. Cameras recorded images at 0.5 Hz throughout the tow, starting in conjunction with creating a waypoint. Samples (i.e., paired image and geolocated point) were sub-selected from the tow once movement began, at the midpoint of a tow, and immediately before movement stopped. Images were manually quality controlled such that if a selected image contained obstructions or was out of focus, the nearest clear image was selected to replace it. If no images within 10 s were clear (i.e., 10 m maximum spatial error), the sample was discarded. If the GPS track contained gaps or segments larger than 10 m, only images/point pairs at the start and end waypoints were sampled.Surveys focused on historical fishing grounds for queen conch (Lobatus gigas) between 2015 and 2018 following the sampling design and methods of ref. 32. A stratified random design was used to allocate 6000 m2 of observation effort into each cell of a 1’ by 1’ grid placed over each fishing ground. This effort was split into multiple tows between 200 and 1000 m in length, thus images were separated by at least 100 m.Fishing grounds extended from the edge of a deepwater sound to between 7 and 10 km up the bank and were limited to the depths used by freediving fishers. Surveyed fishing grounds included: the Exumas (24.382207°, −76.631058°), the southwestern Berry Islands (25.455529°, −78.014214°), south of Bimini (25.375592°, −79.187609°), the Grassy Cays (23.666864°, −77.383547°), the Joulter Cays (25.321297°, −78.109251°) and the southeast tip of the Tongue of the Ocean (23.376417°, −76.621943°). For details on image processing, see section on remote sensing below.Sediment coringTo gather the sediment cores analyzed for organic carbon content on the Bahama Banks, we collected samples from various benthic habitats that included varying densities of seagrass habitat (Thalassia testidinum and Syringodium filiforme). We percussed, via SCUBA, an acrylic cylinder tube perpendicular to the seafloor into marine sediment until rejection at various penetration depths up to 30 cm. The sample was then extracted vertically from the marine sediment and capped at the bottom to avoid loss of material. This sample was then transported vertically through the water column to a research vessel where it was removed from the coring device and immediately capped on top with an air-tight cap. Compression rates were negligible (~5 cm) across the first 5 cores, and as such were not subsequently measured. The samples were then labeled, photographed, geotagged, and the first 30 centimeters of each core was extruded. To complete the extrusion process, we placed each sample on top of a capped piston device in the same orientation as collection (deepest portion of collected sediment still on the bottom). The bottom cap was removed to thread the acrylic cylinder tube onto the piston device and then was lowered to various measured lengths to collect corresponding depth sections of the sediment core. These sections were sliced (every 1–5 centimeters), labeled, and placed into whirl pack bags to collect the wet weight of each sample. All samples were then frozen and stored for future laboratory analyses. All samples were dried in a laboratory oven at 55 °C for 48 h until constant dry weights were reached. The samples were then weighed to collect their corresponding dry weights. The dry bulk density (DBD) was calculated by diving the sample dry weight (g) by the sample volume (cm3). The samples were then further ground with a mortar and pestle until a homogeneous fine grain size was achieved. Sediment samples collected from the Exuma Cays (142 samples from 16 cores) were analyzed for Corg content. Sediment samples were weighed accurately into silver capsules and acidified with 4% HCl until no effervescence was detected in two consecutive cycles. The samples were then dried in a 60 °C oven overnight, encapsulated into tin capsules and analyzed using an Organic Elemental Analyzer Flash 2000 (Thermo Fisher Scientific, Massachusetts, USA). We then conducted a standard loss on ignition (LOI) methodology at our laboratory facility (Braintree, Massachusetts, USA) for all the samples. Each sample was subsequently sub sampled with 5–15 grams of representative material and placed into a ceramic crucible to collect its mass. The crucibles were then loaded into a separate muffle laboratory oven and heated at 550 °C for 6 h. Upon completion of this muffle, the crucibles were then immediately weighed to collect the LOI of organic material from each sample, defined as the weight lost in the muffle (g) divided by the subsample dry weight (g). A fitted regression between the Corg and LOI from the Exuma Cays cores was generated (Supplementary Fig. 7), and then used to predict the sediment Corg contents from LOI measurements in the Grand Bahama cores. Sediment Corg stocks were quantified by multiplying Corg and DBD data by soil depth increment (1–5 cm) of the sampled soil cores. The cores from the Exuma Cays (15 cm) and Grand Bahama (30 cm) were collected with different depths, we therefore fitted a regression between Corg stock in 15 cm-depth and Corg stock in 30 cm-depth for the Grand Bahama cores (Supplementary Fig. 8) and used this regression to extrapolate Corg stock of the Exuma Cays cores into 30 cm-depth. Moreover, to allow direct comparison among other studies27, the Corg stock per unit area was standardized to 1 m-thick deposits by multiplying 100/30.Tiger shark taggingThe research and protocols conducted in this study complies with relevant ethical regulations as approved by the Carleton University Animal Care Committee. The shark data used in this paper were collected as part of a multi-year, long-term research program evaluating the interannual behavior and physiology of large sharks throughout the coastal waters of The Commonwealth of The Bahamas23. All sharks were captured using standardized circle-hook drumlines33 on the Great and Little Bahama Banks throughout the country, focusing efforts in three primary locations: off New Providence Island, the Exuma Cays, and off West End, Grand Bahama, from 2011–2019. All sharks were secured alongside center console research vessels and local dive boats, where their sex, morphometric measurements, and blood samples were taken. A mark-recapture identification tag was applied to the shark at the base of the dorsal fin. Some of the sharks sampled in the present study were also tagged with a coded acoustic transmitter which was surgically implanted ventrally into the peritoneal cavity and then sutured, as part of a concurrent study on shark habitat use and residency within the region23.Pop-off archival satellite tags were affixed to eight tiger sharks (seven female, one male; 298 ± 28 cm total length; mean ± SD) in The Bahamas from 2011–2019, permitting measurements of swimming depth and water temperature recorded at either 4-min (Sea-Tag MODS, Desert Star Systems LCC, USA) or 10-s intervals (miniPAT tags, Wildlife Computers, USA). Pop-off satellite tags were inserted into the dorsal musculature of the sharks using stainless steel anchors and tethers. All pop-off satellite tags were either recovered manually, permitting access to the full time-series, or popped-off and transmitted their data to an Earth-orbiting Argos satellite, resulting in a subset of the full time-series (transmission frequencies: 2.5 min [miniPAT], 10 min [PSATGEO], daily average [Sea-Tag MOD]). Tiger shark positions were estimated from the satellite data using tag-specific proprietary state space algorithms from Wildlife Computers (GPE3; based on ref. 34) and Desert Star Systems35. With miniPAT tags, positions were further filtered to remove the least reliable positions ( More

Woolhouse MEJ, Gowtage-Sequeria S. Host range and emerging and reemerging pathogens. Emerg Infect Dis. 2005;11:1842–7.Article 
PubMed 
PubMed Central 

Google Scholar 
Allen T, Murray KA, Zambrana-Torrelio C, Morse SS, Rondinini C, Di Marco M, et al. Global hotspots and correlates of emerging zoonotic diseases. Nat Commun. 2017;8:1–10.Article 
CAS 

Google Scholar 
Van Kerkhove MD, Ly S, Holl D, Guitian J, Mangtani P, Ghani AC, et al. Frequency and patterns of contact with domestic poultry and potential risk of H5N1 transmission to humans living in rural Cambodia. Influenza Other Respir Viruses. 2008;2:155–63.Article 
PubMed 
PubMed Central 

Google Scholar 
Gaythorpe KAM, Hamlet A, Cibrelus L, Garske T, Ferguson NM. The effect of climate change on yellow fever disease burden in Africa. eLife. 2020;9:1–27.Article 

Google Scholar 
Faust CL, McCallum HI, Bloomfield LSP, Gottdenker NL, Gillespie TR, Torney CJ, et al. Pathogen spillover during land conversion. Ecol Lett. 2018;21:471–83.Article 
PubMed 

Google Scholar 
Gog J, Woodroffe R, Swinton J. Disease in endangered metapopulations: The importance of alternative hosts. Proc R Soc B Biol Sci. 2002;269:671–6.Article 

Google Scholar 
Jones BA, Grace D, Kock R, Alonso S, Rushton J, Said MY, et al. Zoonosis emergence linked to agricultural intensification and environmental change. Proc Natl Acad Sci USA. 2013;110:8399–404.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
White LA, Forester JD, Craft ME. Disease outbreak thresholds emerge from interactions between movement behavior, landscape structure, and epidemiology. Proc Natl Acad Sci USA. 2018;115:7374–9.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Altizer S, Bartel R, Han BA. Animal migration and infectious disease risk. Science. 2011;331:296–302.Article 
CAS 
PubMed 

Google Scholar 
Ludwig SC, Roos S, Bubb D, Baines D. Long-term trends in abundance and breeding success of red grouse and hen harriers in relation to changing management of a Scottish grouse moor. Wildl Biol. 2017;2017:wlb.00246.Article 

Google Scholar 
Newton I. Weather-related mass-mortality events in migrants. Ibis. 2007;149:453–67.Article 

Google Scholar 
Ropert-Coudert Y, Kato A, Meyer X, Pellé M, MacIntosh AJJ, Angelier F, et al. A complete breeding failure in an Adélie penguin colony correlates with unusual and extreme environmental events. Ecography. 2015;38:111–3.Article 

Google Scholar 
Newmark WD, Stanley TR. Habitat fragmentation reduces nest survival in an Afrotropical bird community in a biodiversity hotspot. Proc Natl Acad Sci USA. 2011;108:11488–93.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tuyttens FaM, Macdonald DW, Rogers LM, Cheeseman CL, Roddam AW. Comparative study on the consequences of culling badgers (Meles meles) on biometrics, population dynamics and movement. J Anim Ecol. 2000;69:567–80.Article 

Google Scholar 
Frafjord K. Influence of reproductive status: Home range size in water voles (Arvicola amphibius). PLoS ONE. 2016;11:1–13.Article 

Google Scholar 
Begg CM, Begg KS, Du Toit JT, Mills MGL. Spatial organization of the honey badger Mellivora capensis in the southern Kalahari: Home-range size and movement patterns. J Zool. 2005;265:23–35.Article 

Google Scholar 
Bronikowski AM, Cords M, Alberts SC, Altmann J, Brockman DK, Fedigan LM, et al. Female and male life tables for seven wild primate species. Sci Data. 2016;3:1–8.Article 

Google Scholar 
Mitchell GW, Woodworth BK, Taylor PD, Norris DR. Automated telemetry reveals age specific differences in flight duration and speed are driven by wind conditions in a migratory songbird. Mov Ecol. 2015;3:1–13.Article 

Google Scholar 
Frankish CK, Manica A, Phillips RA. Effects of age on foraging behavior in two closely related albatross species. Mov Ecol. 2020;8:1–17.Article 

Google Scholar 
Tirpak JM, Giuliano WM, Allen TJ, Bittner S, Edwards JW, Friedhof S, et al. Ruffed grouse-habitat preference in the central and southern Appalachians. Ecol Manag. 2010;260:1525–38.Article 

Google Scholar 
Zhu WW, Garber PA, Bezanson M, Qi XG, Li BG. Age- and sex-based patterns of positional behavior and substrate utilization in the golden snub-nosed monkey (Rhinopithecus roxellana). Am J Primatol. 2015;77:98–108.Article 
PubMed 

Google Scholar 
Tian H, Yu P, Bjørnstad ON, Cazelles B, Yang J, Tan H, et al. Anthropogenically driven environmental changes shift the ecological dynamics of hemorrhagic fever with renal syndrome. PLOS Pathog. 2017;13:e1006198.Article 
PubMed 
PubMed Central 

Google Scholar 
George DB, Webb CT, Farnsworth ML, O’Shea TJ, Bowen RA, Smith DL, et al. Host and viral ecology determine bat rabies seasonality and maintenance. Proc Natl Acad Sci USA. 2011;108:10208–13.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
van Dijk JG, Verhagen JH, Wille M, Waldenström J. Host and virus ecology as determinants of influenza A virus transmission in wild birds. Curr Opin Virol. 2018;28:26–36.Article 
PubMed 

Google Scholar 
Chong R, Shi M, Grueber CE, Holmes EC, Hogg CJ, Belov K, et al. Fecal Viral Diversity of Captive and Wild Tasmanian Devils Characterized Using Virion-Enriched Metagenomics and Metatranscriptomics. J Virol. 2019;93:e00205–19.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
François S, Pybus OG. Towards an understanding of the avian virome. J Gen Virol. 2020;101:785–90.Article 
PubMed 
PubMed Central 

Google Scholar 
Springer A, Fichtel C, Al-Ghalith GA, Koch F, Amato KR, Clayton JB, et al. Patterns of seasonality and group membership characterize the gut microbiota in a longitudinal study of wild Verreaux’s sifakas (Propithecus verreauxi). Ecol Evol. 2017;7:5732–45.Article 
PubMed 
PubMed Central 

Google Scholar 
Aivelo T, Laakkonen J, Jernvall J. Population-and individual-level dynamics of the intestinal microbiota of a small primate. Appl Environ Microbiol. 2016;82:3537–45.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
van Dongen WFD, White J, Brandl HB, Moodley Y, Merkling T, Leclaire S, et al. Age-related differences in the cloacal microbiota of a wild bird species. BMC Ecol. 2013;13:11.Cleaveland S, Laurenson MK, Taylor LH. Diseases of humans and their domestic mammals: Pathogen characteristics, host range and the risk of emergence. Philos Trans R Soc B Biol Sci. 2001;356:991–9.Article 
CAS 

Google Scholar 
Wille M, Shi M, Hurt AC, Klaassen M, Holmes EC. RNA virome abundance and diversity is associated with host age in a bird species. Virology. 2021;561:98–106.Article 
CAS 
PubMed 

Google Scholar 
Negrey JD, Thompson ME, Langergraber KE, Machanda ZP, Mitani JC, Muller MN, et al. Demography, life-history trade-offs, and the gastrointestinal virome of wild chimpanzees. Philos Trans R Soc B Biol Sci. 2020;375:20190613.Article 

Google Scholar 
Hill SC, Manvell RJ, Schulenburg B, Shell W, Wikramaratna PS, Perrins C, et al. Antibody responses to avian influenza viruses in wild birds broaden with age. Proc R Soc B Biol Sci. 2016;283:20162159.Article 

Google Scholar 
Perrins CM, Ogilvie MA. A study of the Abbotsbury mute swans (Cygnus olor). Wildfowl. 1981;32:35–47.
Google Scholar 
Perrins CM, McCleery RH, Ogilvie MA. A study of the breeding Mute Swans Cygnus olor at Abbotsbury. Wildfowl. 1994;45:1–14.
Google Scholar 
Perrins C. Survival rates of young mute swans Cygnus olor. Wildfowl Suppl. 1991;45:95–103.
Google Scholar 
McCleery RH, Perrins C, Wheeler D, Groves S. Population structure, survival rates and productivity of mute swans breeding in a colony at Abbotsbury, Dorset, England. Waterbirds Waterbird Soc. 2002;25:201.
Google Scholar 
Matrozis R A 30-year (1988–2017) study of Mute Swans Cygnus olor in Riga, Latvia. Wildfowl. 2019;14:164–77.Charmantier A, Perrins C, McCleery RH, Sheldon BC. Quantitative genetics of age at reproduction in wild swans: Support for antagonistic pleiotropy models of senescence. Proc Natl Acad Sci USA. 2006;103:6587–92.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hill SC, Hansen R, Watson S, Coward V, Russell C, Cooper J, et al. Comparative micro-epidemiology of pathogenic avian influenza virus outbreaks in a wild bird population. Philos Trans R Soc B Biol Sci. 2019;374:20180259.Cotten M, Oude Munnink B, Canuti M, Deijs M, Watson SJ, Kellam P, et al. Full genome virus detection in fecal samples using sensitive nucleic acid preparation, deep sequencing, and a novel iterative sequence classification algorithm. PLoS ONE. 2014;9:e93269.Article 
PubMed 
PubMed Central 

Google Scholar 
Boom R, Sol CJA, Salimans MMM, Janses CL, Wertheim Van Dillen PME, Van Der Noordaa J. Rapid and simple method for purification of nucleic acids R. J Clin Microbiol. 1990;28:495–503.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Endoh D, Mizutani T, Kirisawa R, Maki Y, Saito H, Kon Y, et al. Species-independent detection of RNA virus by representational difference analysis using non-ribosomal hexanucleotides for reverse transcription. Nucleic Acids Res. 2005;33:1–11.Article 

Google Scholar 
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMB. 2011;17:10–12.
Google Scholar 
Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008;18:821–9.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2014;12:59–60.Article 
PubMed 

Google Scholar 
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.Article 
CAS 
PubMed 

Google Scholar 
Langmead B Aligning short sequencing reads with Bowtie. Curr Protoc Bioinforma. 2010; Chapter 11: Unit 11.7.Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinforma Oxf Engl. 2012;28:1647–9.Article 

Google Scholar 
Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30:3059–66.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Muhire BM, Varsani A, Martin DP SDT: A virus classification tool based on pairwise sequence alignment and identity calculation. PLoS ONE. 2014;9:e108277.Darriba D, Taboada GL, Doallo R, Posada D. ProtTest 3: fast selection of best-fit models of protein evolution. Bioinforma Oxf Engl. 2011;27:1164–5.Article 
CAS 

Google Scholar 
Stamatakis A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kapoor A, Simmonds P, Lipkin WI, Zaidi S, Delwart E. Use of nucleotide composition analysis to infer hosts for three novel picorna-like viruses. J Virol. 2010;84:10322–8.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wood DE, Salzberg SL Kraken: Ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 2014;15:R46.Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. PeerJ Comput Sci. 2017;3:e104.Article 

Google Scholar 
Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. Ribosomal Database Project: Data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42:633–42.Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. 2019. R Foundation for Statistical Computing, Vienna, Austria.RStudio Team. RStudio: Integrated Development for R. 2015. Boston, MA, USA.Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.Article 

Google Scholar 
Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46.
Google Scholar 
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57:289–300.
Google Scholar 
Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann Stat. 2001;29:1165–88.Article 

Google Scholar 
Oakley BB, Lillehoj HS, Kogut MH, Kim WK, Maurer JJ, Pedroso A, et al. The chicken gastrointestinal microbiome. FEMS Microbiol Lett. 2014;360:100–12.Article 
CAS 
PubMed 

Google Scholar 
Waite DW, Taylor MW. Characterizing the avian gut microbiota: Membership, driving influences, and potential function. Front Microbiol. 2014;5:1–12.Article 

Google Scholar 
Waite DW, Taylor MW. Exploring the avian gut microbiota: Current trends and future directions. Front Microbiol. 2015;6:1–12.Article 

Google Scholar 
Zell R, Delwart E, Gorbalenya AE, Hovi T, King AMQ, Knowles NJ, et al. ICTV virus taxonomy profile: Picornaviridae. J Gen Virol. 2017;98:2421–2.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cotmore SF, Agbandje-McKenna M, Canuti M, Chiorini JA, Eis-Hubinger AM, Hughes J, et al. ICTV virus taxonomy profile: Parvoviridae. J Gen Virol. 2019;100:367–8.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bosch A, Guix S, Krishna N, Méndez E, Monroe SS, Pantin-Jackwood M, et al. Astroviridae. In: King A, Adams M, Carstens E, Lefkowitz E (eds). Virus taxonomy. Classification and nomenclature of viruses: ninth report of the International Committee on the Taxonomy of Viruses. 2011. Elsevier, London, pp 953-9.Risely A. Applying the core microbiome to understand host–microbe systems. J Anim Ecol. 2020;89:1549–58.Article 
PubMed 

Google Scholar 
Piepenbring AK, Enderlein D, Herzog S, Kaleta EF, Heffels-Redmann U, Ressmeyer S, et al. Pathogenesis of avian bornavirus in experimentally infected Cockatiels. Emerg Infect Dis. 2012;18:234–41.Article 
PubMed 
PubMed Central 

Google Scholar 
Anzil AP, Blinzinger K, Mayr A. Persistent Borna virus infection in adult hamsters. Arch Für Gesamt Virusforsch. 1973;40:52–57.Article 
CAS 

Google Scholar 
Heffels-Redmann U, Enderlein D, Herzog S, Piepenbring A, Bürkle M, Neumann D, et al. Follow-Up Investigations on Different Courses of Natural Avian Bornavirus Infections in Psittacines. Avian Dis. 2012;56:153–9.Article 
PubMed 

Google Scholar 
Rubbenstroth D, Brosinski K, Rinder M, Olbert M, Kaspers B, Korbel R, et al. No contact transmission of avian bornavirus in experimentally infected cockatiels (Nymphicus hollandicus) and domestic canaries (Serinus canaria forma domestica). Vet Microbiol. 2014;172:146–56.Article 
PubMed 

Google Scholar 
Olsen I The Family Fusobacteriaceae. In: Rosenberg E, Delong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Firmicutes and Tenericutes, 4th ed. 2014. pp 109-32.Imhoff JF The Family Chlorobiaceae. In: Rosenberg E, Delong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Other Major Lineages of Bacteria and The Archaea, 4th ed. 2014. pp 501-14.Cho JC The Family Lentisphaeraceae. In: Rosenberg E, Delong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Other Major Lineages of Bacteria and The Archaea, 4th ed. 2014. pp 705-10.Karami A, Sarshar M, Ranjbar R, Zanjani RS The Phylum Spirochaetaceae. In: Rosenberg E, Delong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Other Major Lineages of Bacteria and The Archaea, 4th ed. 2014. pp 915-29.McBride MJ The Family Flavobacteriaceae. In: Rosenberg E, Delong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Other Major Lineages of Bacteria and The Archaea, 4th ed. 2014. pp 643-76.Van Dijk JGB, Hoye BJ, Verhagen JH, Nolet BA, Fouchier RAM, Klaassen M. Juveniles and migrants as drivers for seasonal epizootics of avian influenza virus. J Anim Ecol. 2014;83:266–75.Article 
PubMed 

Google Scholar 
Chevalier V, Marsot M, Molia S, Rasamoelina H, Rakotondravao R, Pedrono M, et al. Serological evidence of West Nile and Usutu viruses circulation in domestic and wild birds in wetlands of Mali and Madagascar in 2008. Int J Environ Res Public Health. 2020;17:1998.Guy, JS Turkey Viral Hepatitis. Diseases of Poultry, 12th Edition. 2008. Wiley Blackwell, pp 426-30.Davies ZG, Fuller RA, Dallimer M, Loram A, Gaston KJ. Household factors influencing participation in bird feeding activity: a national scale analysis. PLOS ONE. 2012;7:e39692.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Shutt JD, Trivedi UH, Nicholls JA. Faecal metabarcoding reveals pervasive long-distance impacts of garden bird feeding. Proc R Soc B Biol Sci. 2021;288:20210480.Article 

Google Scholar 
Minich JJ, Sanders JG, Amir A, Humphrey G, Gilbert JA, Knight R. Quantifying and understanding well-to-well contamination in microbiome research. mSystems. 2019;4:e00186–19.Article 
PubMed 
PubMed Central 

Google Scholar  More