Philippa Kaur

Cropland-mapping extent and time intervalsThe global boundaries for the cropland mapping were informed by the US Geological Survey (USGS) Global Food Security-Support Analysis Data at 30 m (GFSAD)11. The cropland mapping extent was defined using the geographic 1° × 1° grid. We included every 1° × 1° grid cell that contains cropland area according to the GFSAD. Small islands were excluded due to the absence of Landsat geometrically corrected data (Supplementary Fig. 1).The cropland mapping was performed at 4-year intervals (2000–2003, 2004–2007, 2008–2011, 2012–2015 and 2016–2019). Use of a long interval (rather than a single year) increased the number of clear-sky satellite observations in the time-series, which improves representation of land-surface phenology and the accuracy of cropland detection. For each 4-year interval, we mapped an area as cropland if a growing crop was detected during any of these years. In this way, we implemented the criterion of the maximum fallow length: if an area was not used as cropland for >4 years, it was not included in the cropland map for the corresponding time interval.Landsat dataWe employed the global 16-day normalized surface reflectance Landsat Analysis Ready Data (Landsat ARD19) as input data for cropland mapping. The Landsat ARD were generated from the entire Landsat archive from 1997 to 2019. The Landsat top-of-atmosphere reflectance was normalized using globally consistent MODIS surface reflectance as a normalization target. Individual Landsat images were aggregated into 16-day composites by prioritizing clear-sky observations.For each 4-year interval, we created a single annualized gap-free 16-day observation time-series. For each 16-day interval, we selected the observation with the highest near-infrared reflectance value (to prioritize observations with the highest vegetation cover) from 4 years of Landsat data. Observations contaminated by haze, clouds and cloud shadows, as indicated by the Landsat ARD quality layer, were removed from the analysis. If no clear-sky data were available for a 16-day interval, we filled the missing reflectance values using linear interpolation.The annualized, 16-day time-series within each 4-year interval were transformed into a set of multitemporal metrics that provide consistent land-surface phenology inputs for global cropland mapping. Metrics include selected ranks, inter-rank averages and amplitudes of surface reflectance and vegetation index values, and surface reflectance averages for selected land-surface phenology stages defined by vegetation indices (that is, surface reflectance for the maximum and minimum greenness periods). The multitemporal metrics methodology is provided in detail19,38. The Landsat metrics were augmented with elevation data39. In this way, we created spatially consistent inputs for each of the 4-year intervals. The complete list of input metrics is presented in Supplementary Table 1.Global cropland mappingGlobal cropland mapping included three stages that enabled extrapolation of visually delineated cropland training data to a temporally consistent, global cropland map time-series using machine learning. At all three stages, we employed bagged decision tree ensembles40 as a supervised classification algorithm that used class presence and absence data as the dependent variables, and a set of multitemporal metrics as independent variables at a Landsat ARD pixel scale. The bagged decision tree results in a per-pixel cropland probability layer, which has a threshold of 0.5 to obtain a cropland map.The first stage consisted of performing individual cropland classifications for a set of 924 Landsat ARD 1° × 1° tiles for the 2016–2019 interval (Supplementary Fig. 1). The tiles were chosen to represent diverse global agriculture landscapes. Classification training data (cropland class presence and absence) were manually selected through visual interpretation of Landsat metric composites and high-resolution data from Google Earth. An individual supervised classification model (bagged decision trees) was calibrated and applied to each tile.At the second stage, we used the 924 tiles that had been classified as cropland/other land and the 2016–2019 metric set to train a series of regional cropland mapping models. The classification was iterated by adding training tiles and assessing the results until the resulting map was satisfactory. We then applied the regional models to each of the preceding 4-year intervals, thus creating a preliminary time-series of global cropland maps.At the third stage, we used the preliminary global cropland maps as training data to generate temporally consistent global cropland data. As the regional models applied at the second stage were calibrated using 2016–2019 data alone, classification errors may arise due to Landsat data inconsistencies before 2016. The goal of this third stage was to create a robust spatiotemporally consistent set of locally calibrated cropland detection models. For each 1° × 1° Landsat ARD tile (13,451 tiles total), we collected training data for each 4-year interval from the preliminary cropland extent maps within a 3° radius of the target tile, with preference to select stable cropland and non-cropland pixels as training. Training data from all intervals were used to calibrate a single decision tree ensemble for each ARD tile. The per-tile models were then applied to each time interval, and the results were post-processed to remove single cropland class detections and omissions within time-series and eliminate cropland patches More

1.Paustian, K. et al. Climate-smart soils. Nature 532, 49–57 (2016).
Google Scholar 
2.Jackson, R. B. et al. The ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls. Annu. Rev. Ecol. Evol. Syst. 48, 419–445 (2017).
Google Scholar 
3.Lal, R. Global potential of soil carbon sequestration to mitigate the greenhouse effect. CRC Crit. Rev. Plant Sci. 22, 151–184 (2003).
Google Scholar 
4.Scurlock, J. M. O. & Hall, D. O. The global carbon sink: a grassland perspective. Glob. Chang. Biol. 4, 229–233 (1998).
Google Scholar 
5.Pellegrini, A. F. A. et al. Fire frequency drives decadal changes in soil carbon and nitrogen and ecosystem productivity. Nature 553, 194–198 (2018).
Google Scholar 
6.Grace, J. et al. Productivity and carbon fluxes of tropical savannas. J. Biogeogr. 33, 387–400 (2006).
Google Scholar 
7.Walker, X. J. et al. Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature 572, 520–523 (2019).
Google Scholar 
8.Jones, M. W., Santín, C., van der Werf, G. R. & Doerr, S. H. Global fire emissions buffered by the production of pyrogenic carbon. Nat. Geosci. 12, 742–747 (2019).
Google Scholar 
9.Bodí, M. B. et al. Wildland fire ash: production, composition and eco-hydro-geomorphic effects. Earth Sci. Rev. 130, 103–127 (2014).
Google Scholar 
10.Certini, G., Nocentini, C., Knicker, H., Arfaioli, P. & Rumpel, C. Wildfire effects on soil organic matter quantity and quality in two fire-prone Mediterranean pine forests. Geoderma 167–168, 148–155 (2011).
Google Scholar 
11.Jiménez-Morillo, N. T. et al. Fire effects in the molecular structure of soil organic matter fractions under Quercus suber cover. Catena 145, 266–273 (2016).
Google Scholar 
12.Certini, G. Effects of fire on properties of forest soils: a review. Oecologia 143, 1–10 (2005).
Google Scholar 
13.Lehmann, J. et al. Australian climate–carbon cycle feedback reduced by soil black carbon. Nat. Geosci. 1, 832–835 (2008).
Google Scholar 
14.Santin, C. et al. Towards a global assessment of pyrogenic carbon from vegetation fires. Glob. Chang. Biol. 22, 76–91 (2016).
Google Scholar 
15.Czimczik, C. I. & Masiello, C. A. Controls on black carbon storage in soils. Global Biogeochem. Cycles https://doi.org/10.1029/2006GB002798 (2007).16.Bird, M. I., Wynn, J. G., Saiz, G., Wurster, C. M. & McBeath, A. The pyrogenic carbon cycle. Annu. Rev. Earth Planet. Sci. 43, 273–298 (2015).
Google Scholar 
17.Randerson, J. T., Chen, Y., van der Werf, G. R., Rogers, B. M. & Morton, D. C. Global burned area and biomass burning emissions from small fires. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2012JG002128 (2012).18.Archibald, S., Lehmann, C. E. R., Gómez-Dans, J. L. & Bradstock, R. A. Defining pyromes and global syndromes of fire regimes. Proc. Natl Acad. Sci. USA 110, 6442–6447 (2013).
Google Scholar 
19.Bond, W. J., Woodward, F. I. & Midgley, G. F. The global distribution of ecosystems in a world without fire. New Phytol. 165, 525–538 (2005).
Google Scholar 
20.Chuvieco, E. et al. Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sens. Environ. 225, 45–64 (2019).
Google Scholar 
21.Bowman, D. M. J. S. et al. Fire in the Earth system. Science 324, 481–484 (2009).
Google Scholar 
22.Bowman, D. M. J. S. et al. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 1, 500–515 (2020).
Google Scholar 
23.Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).
Google Scholar 
24.Nave, L. E., Vance, E. D., Swanston, C. W. & Curtis, P. S. Fire effects on temperate forest soil C and N storage. Ecol. Appl. 21, 1189–1201 (2011).
Google Scholar 
25.McKee, W. H. Changes in Soil Fertility Following Prescribed Burning on Coastal Plain Pine Sites Research Paper-RE-234 (US Department of Agriculture, 1982).26.Fynn, R. W. S., Haynes, R. J. & O’Connor, T. G. Burning causes long-term changes in soil organic matter content of a South African grassland. Soil Biol. Biochem. 35, 677–687 (2003).
Google Scholar 
27.Roscoe, R., Buurman, P., Velthorst, E. J. & Pereira, J. A. A. Effects of fire on soil organic matter in a “cerrado sensu-stricto” from southeast Brazil as revealed by changes in δ13C. Geoderma 95, 141–160 (2000).
Google Scholar 
28.Phillips, D. H., Foss, J. E., Buckner, E. R., Evans, R. M. & FitzPatrick, E. A. Response of surface horizons in an oak forest to prescribed burning. Soil Sci. Soc. Am. J. 64, 754–760 (2000).
Google Scholar 
29.Walker, X. J. et al. Fuel availability not fire weather controls boreal wildfire severity and carbon emissions. Nat. Clim. Chang. 10, 1130–1136 (2020).
Google Scholar 
30.Hartford, R. & Frandsen, W. When it’s hot, it’s hot… or maybe it’s not! (Surface flaming may not portend extensive soil heating). Int. J. Wildland Fire 2, 139–144 (1992).
Google Scholar 
31.Pellegrini, A. F. A. et al. Frequent burning causes large losses of carbon from deep soil layers in a temperate savanna. J. Ecol. 108, 1426–1441 (2020).
Google Scholar 
32.Wardle, D. A., Hörnberg, G., Zackrisson, O., Kalela-Brundin, M. & Coomes, D. A. Long-term effects of wildfire on ecosystem properties across an island area gradient. Science 300, 972–975 (2003).
Google Scholar 
33.Mack, M. C. et al. Carbon loss from boreal forest wildfires offset by increased dominance of deciduous trees. Science 372, 280–283 (2021).
Google Scholar 
34.Pellegrini, A. F. A., Hoffmann, W. A. & Franco, A. C. Carbon accumulation and nitrogen pool recovery during transitions from savanna to forest in central Brazil. Ecology 95, 342–352 (2014).
Google Scholar 
35.Johnson, D. W. & Curtis, P. S. Effects of forest management on soil C and N storage: meta analysis. For. Ecol. Manage. 140, 227–238 (2001).
Google Scholar 
36.González-Pérez, J. A., González-Vila, F. J., Almendros, G. & Knicker, H. The effect of fire on soil organic matter—a review. Environ. Int. 30, 855–870 (2004).
Google Scholar 
37.Scharenbroch, B. C., Nix, B., Jacobs, K. A. & Bowles, M. L. Two decades of low-severity prescribed fire increases soil nutrient availability in a Midwestern, USA oak (Quercus) forest. Geoderma 183–184, 80–91 (2012).
Google Scholar 
38.Boyer, W. D. & Miller, J. H. Effect of burning and brush treatments on nutrient and soil physical properties in young longleaf pine stands. For. Ecol. Manage. 70, 311–318 (1994).
Google Scholar 
39.Martin, A., Mariotti, A., balesdent, J., Lavelle, P. & Vuattoux, R. Estimate of organic matter turnover rate in a savanna soil by 13C natural abundance measurements. Soil Biol. Biochem. 22, 517–523 (1990).
Google Scholar 
40.McKee, W. H. & Lewis, C. E. Influence of burning and grazing on soil nutrient properties and tree growth on a Georgia coastal plain site after 40 years. In Proc. Second Biennial Southern Silvicultural Research Station Conference (Ed. Jones, E. P. J.) 79–86 (US Department of Agriculture, 1983).41.Neill, C., Patterson, W. A. & Crary, D. W. Responses of soil carbon, nitrogen and cations to the frequency and seasonality of prescribed burning in a Cape Cod oak-pine forest. For. Ecol. Manage. 250, 234–243 (2007).
Google Scholar 
42.Russell-Smith, J., Whitehead, P. J., Cook, G. D. & Hoare, J. L. Response of Eucalyptus-dominated savanna to frequent fires: lessons from Munmarlary, 1973–1996. Ecol. Monogr. 73, 349–375 (2003).
Google Scholar 
43.Guinto, D. F., Xu, Z. H., House, A. P. N. & Saffigna, P. G. Soil chemical properties and forest floor nutrients under repeated prescribed-burning in eucalypt forests of south-east Queensland, Australia. N. Z. J. For. Sci. 31, 170–187 (2001).
Google Scholar 
44.Köster, K., Berninger, F., Lindén, A., Köster, E. & Pumpanen, J. Recovery in fungal biomass is related to decrease in soil organic matter turnover time in a boreal fire chronosequence. Geoderma 235–236, 74–82 (2014).
Google Scholar 
45.O’Donnell, J. A. et al. The effect of fire and permafrost interactions on soil carbon accumulation in an upland black spruce ecosystem of interior Alaska: implications for post-thaw carbon loss. Glob. Chang. Biol. 17, 1461–1474 (2011).
Google Scholar 
46.Butnor, J. R. et al. Vertical distribution and persistence of soil organic carbon in fire-adapted longleaf pine forests. For. Ecol. Manage. 390, 15–26 (2017).
Google Scholar 
47.Sollins, P., Homann, P. & Caldwell, B. A. Stabilization and destabilization of soil organic matter: mechanisms and controls. Geoderma 74, 65–105 (1996).
Google Scholar 
48.Six, J., Conant, R. T., Paul, E. A. & Paustian, K. Stabilization mechanisms of soil organic matter: implications for C-saturation of soils. Plant Soil 241, 155–176 (2002).
Google Scholar 
49.Shi, Z. et al. The age distribution of global soil carbon inferred from radiocarbon measurements. Nat. Geosci. 13, 555–559 (2020).
Google Scholar 
50.Schmidt, M. W. I. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).
Google Scholar 
51.Lutzow, M. V. et al. Stabilization of organic matter in temperate soils: mechanisms and their relevance under different soil conditions – a review. Eur. J. Soil Sci. 57, 426–445 (2006).
Google Scholar 
52.Keiluweit, M., Wanzek, T., Kleber, M., Nico, P. & Fendorf, S. Anaerobic microsites have an unaccounted role in soil carbon stabilization. Nat. Commun. 8, 1771 (2017).
Google Scholar 
53.Six, J., Bossuyt, H., Degryze, S. & Denef, K. A history of research on the link between (micro)aggregates, soil biota, and soil organic matter dynamics. Soil Tillage Res. 79, 7–31 (2004).
Google Scholar 
54.Mataix-Solera, J., Cerdà, A., Arcenegui, V., Jordán, A. & Zavala, L. M. Fire effects on soil aggregation: a review. Earth Sci. Rev. 109, 44–60 (2011).
Google Scholar 
55.Chen, H. Y. H. & Shrestha, B. M. Stand age, fire and clearcutting affect soil organic carbon and aggregation of mineral soils in boreal forests. Soil Biol. Biochem. 50, 149–157 (2012).
Google Scholar 
56.Arocena, J. M. & Opio, C. Prescribed fire-induced changes in properties of sub-boreal forest soils. Geoderma 113, 1–16 (2003).
Google Scholar 
57.Jian, M., Berhe, A. A., Berli, M. & Ghezzehei, T. A. Vulnerability of physically protected soil organic carbon to loss under low severity fires. Front. Environ. Sci. 6, 66 (2018).
Google Scholar 
58.Debano, L. F. The role of fire and soil heating on water repellency in wildland environments: a review. J. Hydrol. 231, 195–206 (2000).
Google Scholar 
59.Hallett, P. D. et al. Disentangling the impact of AM fungi versus roots on soil structure and water transport. Plant Soil 314, 183–196 (2009).
Google Scholar 
60.Bardgett, R. D., Mommer, L. & De Vries, F. T. Going underground: root traits as drivers of ecosystem processes. Trends Ecol. Evol. 29, 692–699 (2014).
Google Scholar 
61.Hartnett, D. C., Potgieter, A. F. & Wilson, G. W. T. Fire effects on mycorrhizal symbiosis and root system architecture in southern African savanna grasses. Afr. J. Ecol. 42, 328–337 (2004).
Google Scholar 
62.Eom, A.-H., Hartnett, D. C., Wilson, G. W. T. & Figge, D. A. H. The effect of fire, mowing and fertilizer amendment on arbuscular mycorrhizas in tallgrass prairie. Am. Midl. Nat. 142, 55–70 (1999).
Google Scholar 
63.Sankey, J. B. et al. Climate, wildfire, and erosion ensemble foretells more sediment in western USA watersheds. Geophys. Res. Lett. 44, 8884–8892 (2017).
Google Scholar 
64.Van Oost, K. et al. Legacy of human-induced C erosion and burial on soil-atmosphere C exchange. Proc. Natl Acad. Sci. USA 109, 19492–19497 (2012).
Google Scholar 
65.Kleber, M. et al. Mineral-organic associations: formation, properties, and relevance in soil environments. Advances in Agronomy 130, 1–140 (2015).
Google Scholar 
66.Torn, M. S., Trumbore, S. E., Chadwick, O. A., Vitousek, P. M. & Hendricks, D. M. Mineral control of soil organic carbon storage and turnover. Nature 389, 170–173 (1997).
Google Scholar 
67.Baldock, J. A. & Skjemstad, J. O. Role of the soil matrix and minerals in protecting natural organic materials against biological attack. Org. Geochem. 31, 697–710 (2000).
Google Scholar 
68.Kaiser, K. & Guggenberger, G. Mineral surfaces and soil organic matter. Eur. J. Soil Sci. 54, 219–236 (2003).
Google Scholar 
69.Knicker, H. How does fire affect the nature and stability of soil organic nitrogen and carbon? A review. Biogeochemistry 85, 91–118 (2007).
Google Scholar 
70.Ketterings, Q. M., Bigham, J. M. & Laperche, V. Changes in soil mineralogy and texture caused by slash-and-burn fires in Sumatra, Indonesia. Soil Sci. Soc. Am. J. 64, 1108–1117 (2000).
Google Scholar 
71.Ulery, A. L., Graham, R. C. & Bowen, L. H. Forest fire effects on soil phyllosilicates in California. Soil Sci. Soc. Am. J. 60, 309–315 (1996).
Google Scholar 
72.Fernández, I., Cabaneiro, A. & Carballas, T. Organic matter changes immediately after a wildfire in an atlantic forest soil and comparison with laboratory soil heating. Soil Biol. Biochem. 29, 1–11 (1997).
Google Scholar 
73.Heckman, K., Campbell, J., Powers, H., Law, B. & Swanston, C. The influence of fire on the radiocarbon signature and character of soil organic matter in the Siskiyou national forest, Oregon, USA. Fire Ecol. 9, 40–56 (2013).
Google Scholar 
74.Knicker, H., González-Vila, F. J. & González-Vázquez, R. Biodegradability of organic matter in fire-affected mineral soils of Southern Spain. Soil Biol. Biochem. 56, 31–39 (2013).
Google Scholar 
75.Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Glob. Chang. Biol. 19, 988–995 (2013).
Google Scholar 
76.Kögel-Knabner, I. The macromolecular organic composition of plant and microbial residues as inputs to soil organic matter: fourteen years on. Soil Biol. Biochem. 105, A3–A8 (2017).
Google Scholar 
77.Neff, J., Harden, J. & Gleixner, G. Fire effects on soil organic matter content, composition, and nutrients in boreal interior Alaska. Can. J. For. 35, 2178–2187 (2005).
Google Scholar 
78.Harden, J. W. et al. Chemistry of burning the forest floor during the FROSTFIRE experimental burn, interior Alaska, 1999. Glob. Biogeochem. Cycles https://doi.org/10.1029/2003GB002194 (2004).79.DeLuca, T. H. & Aplet, G. H. Charcoal and carbon storage in forest soils of the Rocky Mountain West. Front. Ecol. Environ. 6, 18–24 (2008).
Google Scholar 
80.Preston, C. M. & Schmidt, M. W. I. Black (pyrogenic) carbon in boreal forests: a synthesis of current knowledge and uncertainties. Biogeosci. Discuss. 3, 211–271 (2006).
Google Scholar 
81.Krishnaraj, S. J., Baker, T. G., Polglase, P. J., Volkova, L. & Weston, C. J. Prescribed fire increases pyrogenic carbon in litter and surface soil in lowland Eucalyptus forests of south-eastern Australia. For. Ecol. Manage. 366, 98–105 (2016).
Google Scholar 
82.Singh, N., Abiven, S., Torn, M. S. & Schmidt, M. W. I. Fire-derived organic carbon in soil turns over on a centennial scale. Biogeosciences 9, 2847–2857 (2012).
Google Scholar 
83.Knicker, H., Almendros, G., González-Vila, F. J., Martin, F. & Lüdemann, H. D. 13C- and 15N-NMR spectroscopic examination of the transformation of organic nitrogen in plant biomass during thermal treatment. Soil Biol. Biochem. 28, 1053–1060 (1996).
Google Scholar 
84.Waldrop, M. P. & Harden, J. W. Interactive effects of wildfire and permafrost on microbial communities and soil processes in an Alaskan black spruce forest. Glob. Chang. Biol. 14, 2591–2602 (2008).
Google Scholar 
85.Pellegrini, A. F. A. et al. Repeated fire shifts carbon and nitrogen cycling by changing plant inputs and soil decomposition across ecosystems. Ecol. Monogr. 90, e01409 (2020).
Google Scholar 
86.Wang, Q., Zhong, M. & Wang, S. A meta-analysis on the response of microbial biomass, dissolved organic matter, respiration, and N mineralization in mineral soil to fire in forest ecosystems. For. Ecol. Manage. 271, 91–97 (2012).
Google Scholar 
87.Dooley, S. R. & Treseder, K. K. The effect of fire on microbial biomass: a meta-analysis of field studies. Biogeochemistry 109, 49–61 (2012).
Google Scholar 
88.Beringer, J. et al. Fire impacts on surface heat, moisture and carbon fluxes from a tropical savanna in northern Australia. Int. J. Wildland Fire 12, 333–340 (2003).
Google Scholar 
89.Dove, N. C. & Hart, S. C. Fire reduces fungal species richness and in situ mycorrhizal colonization: a meta-analysis. Fire Ecol. 13, 37–65 (2017).
Google Scholar 
90.Pressler, Y., Moore, J. C. & Cotrufo, M. F. Belowground community responses to fire: meta-analysis reveals contrasting responses of soil microorganisms and mesofauna. Oikos 128, 309–327 (2019).
Google Scholar 
91.Holden, S. R., Gutierrez, A. & Treseder, K. K. Changes in soil fungal communities, extracellular enzyme activities, and litter decomposition across a fire chronosequence in Alaskan boreal forests. Ecosystems 16, 34–46 (2013).
Google Scholar 
92.Gongalsky, K. B. et al. Forest fire induces short-term shifts in soil food webs with consequences for carbon cycling. Ecol. Lett. 24, 438–450 (2021).
Google Scholar 
93.Wardle, D. A., Nilsson, M.-C. & Zackrisson, O. Fire-derived charcoal causes loss of forest humus. Science 320, 629 (2008).
Google Scholar 
94.Whitman, T. et al. Soil bacterial and fungal response to wildfires in the Canadian boreal forest across a burn severity gradient. Soil Biol. Biochem. 138, 107571 (2019).
Google Scholar 
95.Harden, J. W. et al. The role of fire in the boreal carbon budget. Glob. Chang. Biol. 6, 174–184 (2000).
Google Scholar 
96.Smith, H. G., Sheridan, G. J., Lane, P. N. J., Nyman, P. & Haydon, S. Wildfire effects on water quality in forest catchments: a review with implications for water supply. J. Hydrol. 396, 170–192 (2011).
Google Scholar 
97.Clemmensen, K. E. et al. Carbon sequestration is related to mycorrhizal fungal community shifts during long-term succession in boreal forests. New Phytol. 205, 1525–1536 (2015).
Google Scholar 
98.Pellegrini, A. F. A. et al. Low-intensity frequent fires in coniferous forests transform soil organic matter in ways that may offset ecosystem carbon losses. Glob. Chang. Biol. 27, 3810–3823 (2021).
Google Scholar 
99.Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).
Google Scholar 
100.Bossio, D. A. et al. The role of soil carbon in natural climate solutions. Nat. Sustain. 3, 391–398 (2020).
Google Scholar 
101.Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, 6497 (2020).
Google Scholar 
102.Walker, R. B., Coop, J. D., Parks, S. A. & Trader, L. Fire regimes approaching historic norms reduce wildfire-facilitated conversion from forest to non-forest. Ecosphere 9, e02182 (2018).
Google Scholar 
103.Wieder, W. R., Boehnert, J., Bonan, G. B. & Langseth, M. Regridded Harmonized World Soil Database v1.2 (Oak Ridge National Laboratory, 2014); https://doi.org/10.3334/ORNLDAAC/1247104.Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).
Google Scholar 
105.Oliveras, I. et al. Effects of fire regimes on herbaceous biomass and nutrient dynamics in the Brazilian savanna. Int. J. Wildland Fire 22, 368–380 (2013).
Google Scholar 
106.Newland, J. A. & DeLuca, T. H. Influence of fire on native nitrogen-fixing plants and soil nitrogen status in ponderosa pine – Douglas-fir forests in western Montana. Can. J. For. Res. 30, 274–282 (2000).
Google Scholar 
107.Bormann, B. T., Homann, P. S., Darbyshire, R. L. & Morrissette, B. A. Intense forest wildfire sharply reduces mineral soil C and N: the first direct evidence. Can. J. For. Res. 38, 2771–2783 (2008).
Google Scholar 
108.Reich, P. B., Peterson, D. W., Wedin, D. A. & Wrage, K. Fire and vegetation effects on productivity and nitrogen cycling across a forest-grassland continuum. Ecology 82, 1703–1719 (2001).
Google Scholar 
109.O’Neill, K. P., Richter, D. D. & Kasischke, E. S. Succession-driven changes in soil respiration following fire in black spruce stands of interior Alaska. Biogeochemistry 80, 1–20 (2006).
Google Scholar 
110.Köster, E. et al. Changes in fluxes of carbon dioxide and methane caused by fire in Siberian boreal forest with continuous permafrost. J. Environ. Manage. 228, 405–415 (2018).
Google Scholar 
111.Zhao, H., Tong, D. Q., Lin, Q., Lu, X. & Wang, G. Effect of fires on soil organic carbon pool and mineralization in a Northeastern China wetland. Geoderma 189–190, 532–539 (2012).
Google Scholar 
112.Kuzyakov, Y., Friedel, J. & Stahr, K. Review of mechanisms and quantification of priming effects. Soil Biol. Biochem. 32, 1485–1498 (2000).
Google Scholar 
113.Wang, J., Xiong, Z. & Kuzyakov, Y. Biochar stability in soil: meta‐analysis of decomposition and priming effects. Glob. Change Biol. Bioenergy 8, 512–523 (2016).
Google Scholar 
114.Pellegrini, A. F. A. et al. Decadal changes in fire frequencies shift tree communities and functional traits. Nat. Ecol. Evol. 5, 504–512 (2021).
Google Scholar 
115.Peterson, D. W., Reich, P. B., Wrage, K. J. & Franklin, J. Plant functional group responses to fire frequency and tree canopy cover gradients in oak savannas and woodlands. J. Veg. Sci. 18, 3–12 (2007).
Google Scholar 
116.Reisser, M., Purves, R. S., Schmidt, M. W. I. & Abiven, S. Pyrogenic carbon in soils: A literature-based inventory and a global estimation of its content in soil organic carbon and stocks. Front. Earth Sci. 4, 80 (2016).
Google Scholar 
117.Loades, K. W., Bengough, A. G., Bransby, M. F. & Hallett, P. D. Planting density influence on fibrous root reinforcement of soils. Ecol. Eng. 36, 276–284 (2010).
Google Scholar 
118.Balshi, M. S. et al. The role of historical fire disturbance in the carbon dynamics of the pan-boreal region: a process-based analysis. J. Geophys. Res. 112, G02029 (2007).
Google Scholar 
119.Aaltonen, H. et al. Forest fires in Canadian permafrost region: the combined effects of fire and permafrost dynamics on soil organic matter quality. Biogeochemistry 143, 257–274 (2019).
Google Scholar 
120.Treseder, K. K., Mack, M. C. & Cross, A. Relationships among fires, fungi, and soil dynamics in Alaskan boreal forests. Ecol. Appl. 14, 1826–1838 (2004).
Google Scholar 
121.Kelly, J. et al. Boreal forest soil carbon fluxes one year after a wildfire: effects of burn severity and management. Glob. Chang. Biol. 27, 4181–4195 (2021).
Google Scholar 
122.Aaltonen, H. et al. Temperature sensitivity of soil organic matter decomposition after forest fire in Canadian permafrost region. J. Environ. Manage. 241, 637–644 (2019).
Google Scholar  More

1.Tihelka et al. Angiosperm pollinivory in a Cretaceous beetle. Nat. Plants. 7, 445–451 (2021).Article 

Google Scholar 
2.Halbritter et al. Illustrated Pollen Terminology (Springer, 2018).3.Friis, E. M., Pedersen, K. R. & Crane, P. R. Early Flowers and Angiosperm Evolution (Cambridge Univ. Press, 2011).4.Seyfullah et al. Revealing the diversity of amber source plants from the Early Cretaceous Crato Formation, Brazil. BMC Evol. Biol. 20, 107 (2020).Article 

Google Scholar 
5.Nation, L. Insect Physiology and Biochemistry (CRC Press, 2002).6.Lupia, R., Herendeen, P. S. & Keller, J. A. A new fossil flower and associated coprolites: evidence for angiosperm–insect interactions in the Santonian (Late Cretaceous) of Georgia, USA. Int. J. Plant Sci. 163, 675–686 (2002).Article 

Google Scholar 
7.Procheş, Ş. & Johnson, S. D. Beetle pollination of the fruit-scented cones of the South African cycad Stangeria eriopus. Am. J. Bot. 96, 1722–1730 (2009).Article 

Google Scholar 
8.Shanker, C., Mohan, M., Sampathkumar, M., Lydia, C. & Katti, G. Functional significance of Micraspis discolor (F.) (Coccinellidae: Coleoptera) in the rice ecosystem. J. Appl. Entomol. 137, 601–609 (2013).Article 

Google Scholar 
9.Lehane, M. J. Peritrophic matrix structure and function. Annu. Rev. Entomol. 42, 525–550 (1997).CAS 
Article 

Google Scholar 
10.Hegedus, D., Erlandson, M., Gillott, C. & Toprak, U. New insights into peritrophic matrix synthesis, architecture, and function. Annu. Rev. Entomol. 54, 285–302 (2009).CAS 
Article 

Google Scholar 
11.Klavins, S. D., Kellogg, D. W., Krings, M., Taylor, E. L. & Taylor, T. N. Coprolites in a Middle Triassic cycad pollen cone: evidence for insect pollination in early cycads? Evol. Ecol. Res. 7, 479–488 (2005).
Google Scholar 
12.Friis, E. M., Pedersen, K. R. & Crane, P. R. Early angiosperm diversification: the diversity of pollen associated with angiosperm reproductive structures in Early Cretaceous floras from Portugal. Ann. Missouri Bot. Garden 86, 259–296 (1999).Article 

Google Scholar 
13.Brenner, G. J. The Spores and Pollen of the Potomac Group of Maryland (Waverly Press, 1963).14.Labandeira, C. C. The paleobiology of pollination and its precursors. Paleontol. Soc. Pap. 6, 233–270 (2000).Article 

Google Scholar 
15.Peris et al. False Blister Beetles and the expansion of gymnosperm–insect pollination modes before angiosperm dominance. Curr. Biol. 27, 897–904 (2017).CAS 
Article 

Google Scholar  More

Humans have infected a wide range of animals with SARS-CoV-2 viruses1–5, but the establishment of a new natural animal reservoir has not been observed. Here, we document that free-ranging white-tailed deer (Odocoileus virginianus) are highly susceptible to infection with SARS-CoV-2 virus, are exposed to a range of viral diversity from humans, and are capable of sustaining transmission in nature. SARS-CoV-2 virus was detected by rRT-PCR in more than one-third (129/360, 35.8%) of nasal swabs obtained from Odocoileus virginianus in northeast Ohio (USA) during January-March 2021. Deer in 6 locations were infected with 3 SARS-CoV-2 lineages (B.1.2, B.1.582, B.1.596). The B.1.2 viruses, dominant in humans in Ohio at the time, infected deer in four locations. Probable deer-to-deer transmission of B.1.2, B.1.582, and B.1.596 viruses was observed, allowing the virus to acquire amino acid substitutions in the spike protein (including the receptor-binding domain) and ORF1 that are infrequently seen in humans. No spillback to humans was observed, but these findings demonstrate that SARS-CoV-2 viruses have the capacity to transmit in US wildlife, potentially opening new pathways for evolution. There is an urgent need to establish comprehensive “One Health” programs to monitor deer, the environment, and other wildlife hosts globally. More

Isolates and typingThe isolates characterised as well as strain affiliations, geographic origins and clinical presentations are summarised in Table 1. Autopsy images showing typical aspects of putrid infections in some animals are shown in Fig. 1. The complete microarray hybridisation patterns are provided as Supplemental file 2 and some relevant features will be discussed in the descriptions of the respective strains. While all German isolates yielded hybridisation signals for lukF/S-PV, frequently only weak positive or ambiguous results for the lukS-PV probe were observed. This prompted further investigations, including the detection of PVL by lateral flow assay21 (Table 1) and whole genome sequencing (see below).Table 1 Details of animals, isolates and strains.Full size tableFigure 1Pathological lesions of Eurasian beavers (C. fiber) infected with BVL-positive S. aureus. (A) Severe suppurative necrotizing pneumonia (animal B); (B) severe suppurative pyelonephritis (animal G); (C) caseous lymphadenitis, popliteal lymph node (animal E); (D) urinary bladder with pyuria (animal C).Full size imagePhenotypic and genotypic resistance properties of the S. aureus isolatesAntimicrobial susceptibility testing revealed that all beaver isolates from Germany were susceptible to all antimicrobial agents tested. The distribution of minimal inhibitory concentration (MIC) values and test ranges are displayed in Supplemental File 3a. The phenotypic data corresponded well with microarray data, since none of the corresponding resistance genes was identified. In contrast, two of the Austrian isolates showed macrolide resistance with one of them also being lincosamide resistant. One isolate also exhibited tetracycline resistance. These phenotypes corresponded with the detection of genes erm(A), erm(C) and tet(M), respectively (Supplemental file 2 and 3b).The chromosomal variant of the metallothiol transferase gene fosB was present in all CC1956 isolates. Sequence analysis revealed a frame shift at position 108 creating a stop codon at positions (pos.) 146.0.148 compared to the reference sequence (N315, GenBank BA000018.3 [2,389,328.0.2,389,747]). This resulted in a truncated protein of 48 amino acids (aa) rather than 139 aa as for the original fosB gene product. The mutation was present in all available sequences (i.e., Oxford Nanopore and Illumina of WT19 as well as Illumina of WT63, WT64, WT66, WT67a, WT67b, WT68, WT69, WT70, WT71, WT110 and WT111). While fosB was originally implicated in fosfomycin resistance, it appears to be linked to certain CCs. Indeed, it was also present in the CC8 and CC12 beaver isolates (B2, B3, B4) as well as in the reference sequences of the respective CCs (Supplemental File 2). The fosB gene was absent from the CC49 isolate WT65 and from the CC49 reference sequence of Tager 104, GenBank CP012409.1, as well as from the CC398 isolate B1. Moreover, all sequenced isolates (from animals A to G) harboured a gene designated tet(38), encoding a major facilitator superfamily permease. While this gene was implicated in low-level tetracycline resistance when overexpressed22, its mere presence certainly is not associated with phenotypic tetracycline resistance as it can be found in virtually every S. aureus genome.Biocide susceptibility testing of the CC49/1956 isolates revealed unimodal MIC distributions (Supplemental File 3b), with ranges encompassing not more than three to four dilution steps for each of the biocides (benzalkonium chloride, 0.00003–0.00025%; polyhexanide, 0.000125–0.0005%; chlorhexidine, 0.00006–0.00025% and octenidine, 0.00006–0.00025% with percentages given as mass per volume). The four remaining isolates showed MIC values of 0.0000125–0.00025% for benzalkonium chloride, 0.0005–0.001% for polyhexanide, 0.00006–0.000125% for chlorhexidine, and 0.000125–0.00025% for octenidine.The chromosomal heavy metal resistance markers arsB/R and czrB were detected by hybridisation in all four CC1956 isolates tested as well as in the CC49 isolate. This was confirmed by sequencing. There was no evidence for plasmid- or SCC-borne heavy metal resistance markers.The sequence of the phage-borne leukocidin genes in WT19 and WT65As mentioned above, CC49/CC1956 beaver isolates yielded occasionally ambiguous hybridisation intensities for lukS-PV probes prompting further investigation assuming that the specifically designed oligonucleotides were not able to bind optimally at the target due to mismatches, i.e., allelic variants. Sequencing revealed the presence of distinct alleles of phage-borne leukocidin genes (Figs. 2a/b and 3a/b). The sequences from the two sequenced beaver isolates were identical to each other despite their origin from different prophages in different CCs. In general, the beaver alleles, hitherto referred to as “Beaver Leukocidin” or BVL, lukF/S-BV, appeared to be closer related to the PVL genes from human strains of S. aureus than to those from ruminants and horses (see Figs. 2a/b and 3a/b and the percentages of homologies as provided in Supplemental File 4). There was no evidence for recombination/chimerism in lukF-BV and lukS-BV as mismatches compared to other sequences were evenly distributed across the entire sequences. Sequences of lukF-BV and lukS-BV were also related but clearly distinct from core genomic lukF/S-int of S. intermedius/pseudintermedius.Figure 2(a) Alignment of the lukF-BV sequences, of other phage-borne leukocidin F component sequences from S. aureus and of lukF-int from S. intermedius/pseudintermedius. (b) Alignment of the amino acid sequences of the corresponding lukF gene products.Full size imageFigure 3(a) Alignment of the lukS-BV sequences, of other phage-borne leukocidin S component sequences from S. aureus and of lukS-int from S. intermedius/pseudintermedius. (b) Alignment of the amino acid sequences of the corresponding lukS gene products.Full size image
lukF/S-BV and the agr locusTwo isolates from one animal, WT110 and WT111 (Table 1), differed in hemolysis on Columbia blood agar and were thus handled separately although array analysis eventually revealed the same strain affiliations. They also differed in BVL production as shown by lateral flow tests. Sequencing using both, Illumina and Oxford nanopore technologies, revealed a substitution from A to T in position 706 of the agrA gene that results in a premature stop codon at position 236 of the agrA gene product (Supplemental File 5) suggesting that agr played a role in the observed phenotype and the regulation of BVL.Core genome and genomic islands of the CC1956 isolate WT19As revealed by array experiments (Supplemental File 1) and confirmed by genome sequencing of WT19, CC1956 isolates presented with agr IV alleles and capsule type 5. They were positive for cna, but they lacked seh and egc enterotoxin genes, ORF CM14 as well as sasG. Leukocidin genes lukX/Y, lukD/E and lukF/S-hlg were present. This is also in accordance with previously sequenced BVL-negative CC1959 isolates (SAMEA3251370, SAMEA3251372, SAMEA3251377, SAMEA3251376, SAMEA3251380; Supplemental File 2).The WT19 genome (Supplemental Files 6a and 6b) harboured two uncharacterised enterotoxin genes (pos. 1,940,148..1,940,900 and pos. 1,939,378..1,940,121). Both were also found in DAR4145 (CC772) where they also formed a genomic island at approximately the same position within the genome (GenBank CP010526.1: RU53_RS09775, pos. 1,968,336..1,969,061 and RU53_RS09780, pos. 1,969,088..1,969,840). One of these two genes (“seu2” = RU53_RS09780) was covered by the second array-based assay23 and it was found in all four isolates tested with this array.Mobile genetic elements in the CC1956 isolate WT19The lukF/S-BV prophage was integrated into the lipase 2 gene (lip2, “geh”, “sal3”, “salip35”, GenBank CP000253.1 [314,326..316,398]), and spanned pos. 322,629 to 365,636. Besides leukocidin genes, it also included genes associated with the different modules of a typical Siphoviridae genome (lysogeny, DNA metabolism, packaging and capsid morphogenesis, tail morphogenesis, host cell lysis24,25; see Supplemental File 7/Fig. 4).Figure 4Schematic representation of the aligned sequences of the lukF/S-BV prophages from WT19 and WT65.Full size imageFurthermore, there was a small pathogenicity island at pos. 869,706 to 884,748 that included pif encoding a phage interference protein, a gene for a small terminase subunit, genes for “putative proteins” as well as a gene (scn2) coding for a paralog of a complement inhibitor SCIN family protein and a gene for a variant of the von Willebrand factor binding protein Vwb (vwb3). Thus, it is considered a staphylococcal pathogenicity island (SaPI) related to the one in S0385, GenBank AM990992.1.Another prophage integrated between rpmF and isdB, pos. 1,107,447 to 1,146,132. A third prophage was located between a truncated nikB and Q5HG37, pos. 1,425,279 to 1,481,870. Finally, there was a forth prophage between Q5HDU4 and sarV (actually interrupting an MFS transporter between those genes), pos. 2,340,832 to 2,386,591. This prophage sequence corresponded to the phage that was detected by nanopore sequencing after induction by Mitomycin C (see below and Supplemental File 8).Phage morphology and sequencing of phages from the CC1956 isolate WT19In three separate preparations, large numbers of phages were observed that were well contrasted with uranyl acetate and with phosphotungstic acid. Phages had elongated capsids. The non-contractile thin tails were straight or slightly curved and ended in a bulb-shaped base plate. Based on these characteristics, they were assigned to the order Caudovirales, family Siphoviridae.Capsids were measured in 40 phages, tails in 34 and base plates in 33 phages. Based on these measurements, two distinct populations could be differentiated (Fig. 5). In one (Fig. 5A), the prolate, distinctly pentagonal capsids averaged 39 ± 5 nm (range 32–46 nm) in diameter and 92 ± 8 nm (range 80–104 nm) in length. Tails were 276 ± 20 nm (range 243–310 nm) long, had a diameter of 11 ± 1 nm (range 10–12 nm) and had a stacked discs appearance. Their baseplates were 16 nm (range 16–31 nm) by 27 nm (range 19–33 nm). The other population (Fig. 5B) had elongated oval capsids with a maximal diameter of 55 ± 2 nm (range 51–60 nm) diameter and 93 ± 5 nm (range 85–100 nm) length. Their tails measured 287 ± 12 nm (range 275–313 nm) in length and 9 ± 1 nm (8–10) in diameter and had a rail-road-track morphology. Dimensions of baseplates were 25 nm (range 21–30 nm) by 29 nm (range 23–39 nm).Figure 5Transmission electron micrograph of two distinct prolate phages resulting from Mitomycin C treatment of S. aureus CC1956 isolate WT19. A, Phage particle with pentagonal 38 nm in diameter capsid and a 12 nm thick tail with stacked disc appearance; B, Two phage particles (1, 2) with oval capsids of 55 nm in diameter and 9 nm thick tails with rail-road-track morphology. The base plate is separated from the tail by a transversal disc (arrow). Negative contrast preparation with uranyl acetate. Bars = 100 nm.Full size imageOxford Nanopore sequencing of one of these phage preparations (Supplemental File 8) yielded just one circular contig with a coverage of 724. Its sequence was identical to that of the forth prophage, between Q5HDU4 and sarV, except for a loss of a single triplet out of a total length of 46,387 nt.Core genome and genomic islands of the CC49 isolate WT65The CC49 isolate carried agr group II alleles and capsule type 5. It was positive for sasG, but lacked seh and egc enterotoxin genes, ORF CM14 and the collagen adhesion gene cna. A truncated copy of the enterotoxin S gene (GenBank CP000046, pos. 2,203,972.0.2,204,196) was found as well as leukocidin genes lukG/H = lukX/Y, lukD/E and lukF/S-hlg. With regard to presence and alleles of chromosomal markers such as MSCRAMM or ssl genes, the genome of WT65 (Supplemental Files 7a and 7b) is closely related to the CC49 reference sequences such as Tager 104, GenBank CP012409.1 (Supplemental File 2).Mobile genetic elements in the CC49 isolate WT65One prophage was integrated into the lip2 gene spanning pos. 311,401 to 354,724. The prophage included the lukF/S-BV genes as well as genes associated with the different modules of a typical Siphoviridae genome (Supplemental File 7/Fig. 4). Sequences corresponding to the lysogeny and replication modules were clearly different compared to the lukF/S-BV-prophage in the CC1956 isolate WT19 while approximately the second half of the two respective prophage sequences (the lower part of the alignment in Fig. 4) were virtually identical in gene content, order and orientation.Other mobile genetic elements (Supplemental File 9a/b) included a small pathogenicity island, pos. 402,133 to 416,237 (between rpsR encoding 30S ribosomal protein S18 and its terminator), that included hypothetical proteins, a gene of a terminase small subunit, vwb3 (encoding a “von Willebrand factor” binding protein) and the scn2 gene (putative paralog of complement inhibitor). Between the genes ktrB and groL, pos. 2,029,208 to 2,042,866, another SaPI was identified that contained additional, slightly different copies of vwb3 and scn2 genes as well as terminase small subunit, integrase and excisionase (xis-AIO21657) genes. Finally, five genes between pos.1,334,169 and 1,339,503 were annotated as phage capsid genes although no other phage-related genes were found in this region.Phage morphology and sequencing of phages from the CC49 isolate WT65Four separate phage preparations were examined. In one of them, few phage-like structures were detected. These findings could not be confirmed in the following preparations. Thus, they were interpreted as artefacts, also given that it was not possible to induce a sufficient amount of phages for Oxford Nanopore sequencing. More

NATURE PODCAST
22 December 2021

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In this episode:01:12 “Oh powered flight”In the first of our festive songs, We pay tribute to NASA’s Ingenuity craft — which took the first powered flight on another planet earlier this year. Lyrics by Noah Baker and performed by The Simon Langton School choir, directed by Emily Renshaw-Kidd.Scroll to the bottom of the page for the lyrics.Video: Flying a helicopter on Mars: NASA’s IngenuityNews: Lift off! First flight on Mars launches new way to explore worlds07:40 Communicating complex science with common wordsIn this year’s festive challenge, our competitors try to describe some of the biggest science stories of the year, using only the 1,000 most commonly used words in the English language. Find out how they get on …Test your skills communicating complex science with simple words with the Up-Goer Five Text Editor18:04 Alphafold oh AlphafoldOur second song brings some Hanukkah magic to Deep Mind’s protein-solving algorithm Alphafold. Lyrics by Kerri Smith and Noah Baker, arranged and performed by Phil Self.Scroll to the bottom of the page for the lyrics.News: ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures21:01 Nature’s 10Every year, Nature’s 10 highlights some of the people who played key roles in science. We hear about a few of the people who made the 2021 list.Nature’s 10 — Ten people who helped shape science in 2021

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1.Yamada, N., Fukuda, H., Ogawa, H., Saito, H. & Suzumura, M. Heterotrophic bacterial production and extracellular enzymatic activity in sinking particulate matter in the western North Pacific Ocean. Front. Microbiol. 3, 379. https://doi.org/10.3389/fmicb.2012.00379 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
2.del Giorgio, P., Cole, J. & Cimbleris, A. Respiration rates in bacteria exceed phytoplankton production in unproductive aquatic systems. Nature 385, 148–151 (1997).ADS 

Google Scholar 
3.Teira, E. et al. Sample dilution and bacterial community composition influence empirical leucine-to-carbon conversion factors in surface waters of the world’s oceans. Appl. Environ. Microbiol. 81, 8224–8232 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Dobal-Amador, V. et al. Vertical stratification of bacterial communities driven by multiple environmental factors in the waters (0–5000 m) off the Galician coast (NW Iberian margin). Deep-Sea Res. I(114), 1–11 (2016).
Google Scholar 
5.Kirchman, D., Ducklow, H. W. & Mitchell, R. Estimates of bacterial growth from changes in uptake rates and biomass. Appl. Environ. Microbiol. 44, 1296–1307 (1982).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
6.Simon, M. & Azam, F. Protein content and protein synthesis rates of planktonic marine bacteria. Mar. Ecol. Prog. Ser. 51, 201–213 (1989).ADS 
CAS 

Google Scholar 
7.Ducklow, H. Bacterial production and biomass in the ocean. In Microbial Ecology of the Oceans (ed. Kirchman, D.) 85–120 (Wiley, 2000).
Google Scholar 
8.Varela, M. M., Bode, A., Morán, X. A. G. & Valencia, J. Dissolved organic nitrogen (DON) release and bacterial activity in the upper layers of the Atlantic Ocean. Microb. Ecol. 51, 487–500 (2006).CAS 
PubMed 

Google Scholar 
9.Calvo-Díaz, A. & Morán, X. A. G. Empirical leucine-to-carbon conversion factors for estimating heterotrophic bacterial production: Seasonality and predictability in a temperate coastal ecosystem. Appl. Environ. Microbiol. 75, 3216–3221 (2009).ADS 
PubMed 
PubMed Central 

Google Scholar 
10.Alonso-Sáez, L., Pinhassi, J., Pernthaler, J. & Gasol, J. M. Leucine-to-carbon empirical conversion factor experiments: Does bacterial community structure have an influence?. Environ. Microbiol. 12, 2988–2997 (2010).PubMed 

Google Scholar 
11.Gasol, J. M. et al. Mesopelagic prokaryotic bulk and single-cell heterotrophic activity and community composition in the NW Africa-Canary Islands coastal-transition zone. Prog. Oceanogr. 83, 189–196 (2009).ADS 

Google Scholar 
12.Baltar, F., Aristegui, J., Gasol, J. M. & Herndl, G. Prokaryotic carbon utilization in the dark ocean: Growth efficiency, leucine-to-carbon conversion factors, and their relation. Aquat. Microb. Ecol. 60, 227–232 (2010).
Google Scholar 
13.Varela, M. M., Rodríguez-Ramos, T., Guerrero-Feijóo, E. & Nieto-Cid, M. Changes in activity and community composition shape bacterial responses to size-fractionated marine DOM. Front. Microbiol. 11, 586148. https://doi.org/10.3389/fmicb.2020.586148 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
14.Sarmento, H., Morana, C. & Gasol, J. M. Bacterioplankton niche partitioning in the use of phytoplankton-derived dissolved organic carbon: Quantity is more important than quality. ISME J. 10, 2582–2592 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Guerrero-Feijóo, E. et al. Optical properties of dissolved organic matter relate to different depth-specific patterns of archaeal and bacterial community structure in the North Atlantic Ocean. FEMS Microbiol. Ecol. 93, 1–14 (2017).
Google Scholar 
16.Helms, J. R. et al. Absorption spectral slopes and slope ratios as indicators of molecular weight, source and photobleaching of chromophoric dissolved organic matter. Limnol. Oceanogr. 51, 2170–2180 (2008).
Google Scholar 
17.Stedmon, C. A. & Nelson, N. B. The optical properties of DOM in the ocean. In Biogeochemistry of Marine Dissolved Organic Matter (eds Hansell, D. A. & Carlson, C. A.) 481–508 (Academic Press, 2015).
Google Scholar 
18.Nieto-Cid, M., Álvarez-Salgado, X. A. & Pérez, F. F. Microbial and photochemical reactivity of fluorescent dissolved organic matter in a coastal upwelling system. Limnol. Oceanogr. 51, 1391–1400 (2006).ADS 
CAS 

Google Scholar 
19.Rodríguez-Ramos, T., Nieto-Cid, M., Auladell, A., Guerrero-Feijóo, E. & Varela, M. M. Vertical niche partitioning of archaea and bacteria linked to shifts in dissolved organic matter quality and hydrography in North Atlantic waters. Front. Mar. Sci. 8, 673171. https://doi.org/10.3389/fmars.2021.673171 (2021).Article 

Google Scholar 
20.Bode, A., Álvarez-Osorio, M. T., Cabanas, J. M., Miranda, A. & Varela, M. Recent trends in plankton and upwelling intensity off Galicia (NW Spain). Prog. Oceanogr. 83, 342–350 (2009).ADS 

Google Scholar 
21.Teira, E. et al. Plankton carbon budget in a coastal wind-driven upwelling station off A Coruña (NW Iberian Peninsula). Mar. Ecol. Prog. Ser. 265, 31–43 (2003).ADS 
CAS 

Google Scholar 
22.Ruiz-Villarreal, M. et al. Oceanographic conditions in North and Northwest Iberia and their influence on the Prestige oil spill. Mar. Pollut. Bull. 53, 220–238 (2006).CAS 
PubMed 

Google Scholar 
23.Lavin, A. et al. The Bay of Biscay: The encountering of the ocean and the shelf. In The Sea, Volume 14B: The Global Coastal Ocean (eds Robinson, A. & Brink, K.) 993–1001 (Harvard University Press, 2006).
Google Scholar 
24.Pedrós-Alió, C., Calderón-Paz, J. I., Guixa-Boixereu, N., Estrada, M. & Gasol, J. M. Bacterioplankton and phytoplankton biomass and production during summer stratification in the northwestern Mediterranean Sea. Deep-Sea Res. I(46), 985–1019 (1999).
Google Scholar 
25.Barbosa, A. B. et al. Short-term variability of heterotrophic bacterioplankton during upwelling off the NW Iberian margin. Prog. Oceanogr. 51, 339–359 (2001).ADS 

Google Scholar 
26.Morán, X. A., Gasol, J. M., Pedrós-Alió, C. & Estrada, M. Partitioning of phytoplankton organic carbon production and bacterial production along a coastal-offshore gradient in the NE Atlantic during different hydrographic regimes. Aquat. Microb. Ecol. 29, 239–252 (2002).
Google Scholar 
27.Martínez-García, S. et al. Differential responses of phytoplankton and heterotrophic bacteria to organic and inorganic nutrient additions in coastal waters off the NW Iberian Peninsula. Mar. Ecol. Prog. Ser. 416, 17–33 (2010).ADS 

Google Scholar 
28.Li, X., Xu, J., Shi, Z., Li, Q. & Li, R. Variability in the empirical leucine-to-carbon conversion factors along an environmental gradient. Acta Oceanol. Sin. 37, 77–82 (2018).
Google Scholar 
29.Doval, M. D., Nogueira, E. & Pérez, F. F. Spatio-temporal variability of the thermohaline and biogeochemical properties and dissolved organic carbon in a coastal embayment affected by upwelling: The Ría de Vigo (NW Spain). J. Mar. Sys. 14, 135–150 (1998).
Google Scholar 
30.Valencia, J. et al. Variations in planktonic bacterial biomass and production, and phytoplankton blooms off A Coruña (NW Spain). Sci. Mar. 67, 143–157 (2003).
Google Scholar 
31.Bode, A., Álvarez-Osorio, M. T. & Varela, M. Phytoplankton and macrophyte contributions to littoral food webs in the Galician upwelling estimated from stable isotopes. Mar. Ecol. Prog. Ser. 318, 89–102 (2006).ADS 
CAS 

Google Scholar 
32.Bode, A., Varela, M., Canle, M. & González, N. Dissolved and particulate organic nitrogen in shelf waters of northern Spain during spring. Mar. Ecol. Prog. Ser. 214, 43–54 (2001).ADS 
CAS 

Google Scholar 
33.Lønborg, C. & Álvarez-Salgado, X. A. Tracing dissolved organic matter cycling in the eastern boundary of the temperate North Atlantic using absorption and fluorescence spectroscopy. Deep Res. Part I 85, 35–46 (2014).
Google Scholar 
34.Lønborg, C., Yokokawa, T., Herndl, G. & Álvarez-Salgado, X. A. Production and degradation of fluorescent dissolved organic matter in surface waters of the eastern North Atlantic Ocean. Deep Res. Part I(96), 28–37 (2015).
Google Scholar 
35.Lønborg, C., Davidson, K., Álvarez-Salgado, X. A. & Miller, A. E. J. Bioavailability 904 and bacterial degradation rates of dissolved organic matter in a temperate coastal area 905 during an annual cycle. Mar. Chem. 113, 219–226 (2009).
Google Scholar 
36.Teira, E. et al. Plankton carbon budget in a coastal wind-driven upwelling station off A Coruna (NW Iberian Peninsula). Mar. Ecol. Prog. Ser. 265, 31–43 (2003).ADS 
CAS 

Google Scholar 
37.Álvarez-Salgado, X. A., Arístegui, J., Barton, E. D. & Hansell, D. A. Contribution of upwelling filaments to offshore carbon export in the subtropical Northeast Atlantic Ocean. Limnol. Oceanogr. 52, 1287–1292 (2007).ADS 

Google Scholar 
38.Álvarez-Salgado, X. A. et al. Off-shelf fluxes of labile materials by an upwelling filament in the NW Iberian Upwelling System. Prog. Oceanogr. 51, 321–337 (2001).ADS 

Google Scholar 
39.del Giorgio, P. A. et al. Coherent patterns in bacterial growth, growth efficiency, and leucine metabolism along a northeastern Pacific inshore-offshore transect. Limnol. Oceanogr. 56, 1–16 (2011).ADS 

Google Scholar 
40.Aristegui, J., Gasol, J. M., Duarte, C. M. & Herndl, G. J. Microbial oceanography of the dark ocean’s pelagic realm. Limnol. Oceanogr. 54, 1501–1529 (2009).ADS 
CAS 

Google Scholar 
41.Herndl, G. J. & Reinthaler, T. Microbial control of the dark end of the biological pump. Nat. Geosci. 6, 718–724 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Landry, Z., Swan, B. K., Herndl, G. J., Stepanauskas, R. & Giovannoni, S. J. SAR202 genomes from the dark ocean predict pathways for the oxidation of recalcitrant dissolved organic matter. MBio 8, e00413-17. https://doi.org/10.1128/mBio.00413-17 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
43.Catalá, T. et al. Dissolved Organic Matter (DOM) in the open Mediterranean Sea. I. Basin–wide distribution and drivers of chromophoric DOM. Prog. Oceanogr. 165, 35–51 (2018).ADS 

Google Scholar 
44.Bjørnsen, P. K. & Kuparinen, J. Determination of bacterioplankton biomass, net production and growth efficiency in the Southern Ocean. Mar. Ecol. Prog. Ser. 71, 185–194 (1991).ADS 

Google Scholar 
45.Weinbauer, M. G. et al. Synergistic and antagonistic effects of viral lysis and protistan grazing on bacterial biomass, production and diversity. Environ. Microbiol. 9(3), 777–788 (2007).CAS 
PubMed 

Google Scholar 
46.Evans, C. et al. Shift from carbon flow through the microbial loop to the viral shunt in coastal Antarctic waters during austral summer. Microorganisms 9, 460 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Gasol, J. M., Zweifel, U., Peters, F., Fuhrman, J. D. & Hagström, H. Significance of size and nucleic acid content heterogeneity as measured by flow cytometry in Natural Planktonic Bacteria. Appl. Environ. Microbiol. 65, 104475–104483 (1999).ADS 

Google Scholar 
48.Calvo-Díaz, A. & Morán, X. A. G. Seasonal dynamics of picoplankton in shelf waters of the southern Bay of Biscay. Aquat. Microb. Ecol. 42, 159–174 (2006).
Google Scholar 
49.Norland, S. The relationship between biomass and volume of bacteria. In Handbook of Methods in Aquatic Microbial Ecology (eds Kemp, P. F. et al.) 303–307 (CRC Press, 1993).
Google Scholar 
50.Kirchman, D., Knees, E. & Hodson, R. Leucine incorporation and its potential as a measure of protein synthesis by bacteria in natural aquatic systems. Appl. Environ. Microbiol. 49, 599–607 (1985).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Smith, D. C. & Azam, F. A simple, economical method for measuring bacterial protein synthesis rates in seawater using 3H-leucine. Mar. Microb. Food Webs 6, 107–114 (1992).
Google Scholar 
52.Massana, R. et al. Vertical distribution and phylogenetic characterization of marine planktonic Archaea in the Santa Barbara Channel. Appl. Environ. Microbiol. 63, 50–56 (1997).ADS 
CAS 
PubMed 
PubMed Central 

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

Google Scholar 
54.Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).PubMed 
PubMed Central 

Google Scholar 
55.R Core Team. R: A language and environment for statistical computing http://www.r-project.org (2018).56.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 

Google Scholar 
57.Huse, S. M., Welch, D. M., Morrison, H. G. & Sogin, M. L. Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environ. Microbiol. 12, 1889–1898 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.4–6 http://www.CRAN.R-project.org/package=vegan (2018).59.Álvarez-Salgado, X. A. & Miller, A. E. J. Simultaneous determination of dissolved organic carbon and total dissolved nitrogen in seawater by high temperature catalytic oxidation: Conditions for precise shipboard measurements. Mar. Chem. 62, 325–333 (1998).
Google Scholar 
60.Green, S. A. & Blough, N. V. Optical absorption and fluorescence properties of chromophoric dissolved organic matter in natural waters. Limnol. Oceanogr. 29, 1903–1916 (1994).ADS 

Google Scholar 
61.Shapiro, S. S. & Wilk, M. B. An analysis of variance test for normality (complete samples). Biometrika 52, 591–611 (1965).MathSciNet 
MATH 

Google Scholar 
62.Pearson, K. Notes on the history of correlation. Biometrika 13, 25–45 (1920).
Google Scholar 
63.Addinsoft XLSTAT statistical and data analysis solution http://www.xlstat.com (2020).64.Burnham, K. & Anderson, D. Information and Likelihood Theory: A Basis for Model Selection and Inference in Model Selection and Inference: A Practical Information-Theoretic Approach 60–64 (Springer, 2002).65.Clarke, K. Nonparametric multivariate analyses of changes in community structure. Austral Ecol. 18, 117–143 (1993).
Google Scholar 
66.Quinn, T. P. et al. A field guide for the compositional analysis of any-omics data. GigaScience 8, giz107. https://doi.org/10.1093/gigascience/giz107 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

1.Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).ADS 
CAS 
PubMed 

Google Scholar 
2.Chaudhary, A., Burivalova, Z., Koh, L. P. & Hellweg, S. Impact of forest management on species richness: global meta-analysis and economic trade-offs. Sci. Rep. 6, 23954 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Edwards, F. A., Edwards, D. P., Hamer, K. C. & Davies, R. G. Impacts of logging and conversion of rainforest to oil palm on the functional diversity of birds in Sundaland. Ibis 155, 313–326 (2013).
Google Scholar 
4.Bicknell, J. E., Struebig, M. J. & Davies, Z. G. Reconciling timber extraction with biodiversity conservation in tropical forests using reduced-impact logging. J. Appl. Ecol. 52, 379–388 (2015).PubMed 
PubMed Central 

Google Scholar 
5.Tews, J. et al. Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures: animal species diversity driven by habitat heterogeneity. J. Biogeogr. 31, 79–92 (2004).
Google Scholar 
6.Robles, H. et al. Sylvopastoral management and conservation of the middle spotted woodpecker at the south-western edge of its distribution range. For. Ecol. Manag. 242, 343–352 (2007).
Google Scholar 
7.Aleixo, A. Effects of selective logging on a bird community in the brazilian atlantic forest. Condor 101, 537–548 (1999).
Google Scholar 
8.Robles, H., Ciudad, C. & Matthysen, E. Tree-cavity occurrence, cavity occupation and reproductive performance of secondary cavity-nesting birds in oak forests: the role of traditional management practices. For. Ecol. Manag. 261, 1428–1435 (2011).
Google Scholar 
9.Burivalova, Z. et al. Avian responses to selective logging shaped by species traits and logging practices. Proc. R. Soc. B. 282, 20150164 (2015).PubMed 
PubMed Central 

Google Scholar 
10.Wiebe, K. L. Nest sites as limiting resources for cavity-nesting birds in mature forest ecosystems: a review of the evidence. J. Field Ornithol. 82, 239–248 (2011).
Google Scholar 
11.Politi, N., Hunter, M. & Rivera, L. Assessing the effects of selective logging on birds in Neotropical piedmont and cloud montane forests. Biodivers. Conserv. 21, 3131–3155 (2012).
Google Scholar 
12.Bergner, A. et al. Influences of forest type and habitat structure on bird assemblages of oak (Quercus spp.) and pine (Pinus spp.) stands in southwestern Turkey. For. Ecol. Manag. 336, 137–147 (2015).
Google Scholar 
13.van der Hoek, Y., Gaona, G. V. & Martin, K. The diversity, distribution and conservation status of the tree-cavity-nesting birds of the world. Divers. Distrib. 23, 1120–1131 (2017).
Google Scholar 
14.Aitken, K. E. H. & Martin, K. The importance of excavators in hole-nesting communities: availability and use of natural tree holes in old mixed forests of western Canada. J. Ornithol. 148, 425–434 (2007).
Google Scholar 
15.Cockle, K. L., Martin, K. & Wesołowski, T. Woodpeckers, decay, and the future of cavity-nesting vertebrate communities worldwide. Front. Ecol. Environ. 9, 377–382 (2011).
Google Scholar 
16.Schaaf, A. A. et al. Tree use, niche breadth and overlap for excavation by woodpeckers in subtropical piedmont forests of Northwestern Argentina. Acta Ornithol. 55 (2020).17.Sekercioglu, C. H. Effects of forestry practices on vegetation structure and bird community of Kibale National Park, Uganda. Biol. Conserv. 12 (2002).18.Stratford, J. A. & Robinson, W. D. Gulliver travels to the fragmented tropics: geographic variation in mechanisms of avian extinction. Front. Ecol. Environ. 3, 85–92 (2005).
Google Scholar 
19.Moore, R. P., Robinson, W. D., Lovette, I. J. & Robinson, T. R. Experimental evidence for extreme dispersal limitation in tropical forest birds. Ecol. Lett. 11, 960–968 (2008).CAS 
PubMed 

Google Scholar 
20.Woltmann, S. Bird community responses to disturbance in a forestry concession in lowland Bolivia. 16.21.Strubbe, D. & Matthysen, E. Experimental evidence for nest-site competition between invasive ring-necked parakeets (Psittacula krameri) and native nuthatches (Sitta europaea). Biol. Conserv. 142, 1588–1594 (2009).
Google Scholar 
22.Rivera, L., Politi, N. & Bucher, E. H. Nesting habitat of the Tucuman Parrot Amazona tucumana in an old-growth cloud-forest of Argentina. Bird Conserv. Int. 22, 398–410 (2012).
Google Scholar 
23.Schaaf, A. A., Tallei, E., Politi, N. & Rivera, L. Cavity-tree use and frequency of response to playback by the Tropical Screech-Owl in northwestern Argentina. NBC 14, 99–107 (2019).
Google Scholar 
24.Schepps, J., Lohr, L. & Martin, T. E. Does tree hardness influence nest-tree selection by primary cavity nesters?. Auk 116, 658–665 (1999).
Google Scholar 
25.Rudolph, D. C., Conner, R. N. & Turner, J. Competition for red-cockaded woodpecker roost and nest cavities: effects of resin age and entrance diameter. Wilson Bull. 102(1), 23–36 (1990).
Google Scholar 
26.Drever, M. C. & Martin, K. Response of woodpeckers to changes in forest health and harvest: implications for conservation of avian biodiversity. For. Ecol. Manag. 259, 958–966 (2010).
Google Scholar 
27.Styring, A. R. & Hussin, M. Z. Effects of logging on woodpeckers in a Malaysian rain forest: the relationship between resource availability and woodpecker abundance. J. Trop. Ecol. 20, 495–504 (2004).
Google Scholar 
28.Ruggera, R. A., Schaaf, A. A., Vivanco, C. G., Politi, N. & Rivera, L. O. Exploring nest webs in more detail to improve forest management. For. Ecol. Manag. 372, 93–100 (2016).
Google Scholar 
29.Ibarra, J. T., Martin, M., Cockle, K. L. & Martin, K. Maintaining ecosystem resilience: functional responses of tree cavity nesters to logging in temperate forests of the Americas. Sci. Rep. 7, 4467 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
30.Dı́az, S., Cabido, M. Vive la différence: plant functional diversity matters to ecosystem processes. Trends Ecol. Evolut. 16, 646–655 (2001).31.Córdova-Tapia, F. & Zambrano, L. Functional diversity in community ecology. ECOS 24, 78–87 (2015).
Google Scholar 
32.Leaver, J., Mulvaney, J., Ehlers Smith, D. A., Ehlers Smith, Y. C. & Cherry, M. I. Response of bird functional diversity to forest product harvesting in the Eastern Cape, South Africa. For. Ecol. Manag. 445, 82–95 (2019).33.Georgiev, K. B. et al. Salvage logging changes the taxonomic, phylogenetic and functional successional trajectories of forest bird communities. J. Appl. Ecol. 57, 1103–1112 (2020).
Google Scholar 
34.Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Google Scholar 
35.Kassen, R. The experimental evolution of specialists, generalists, and the maintenance of diversity: experimental evolution in variable environments. J. Evolut. Biol. 15, 173–190 (2002).
Google Scholar 
36.Scherer-Lorenzen, M. Biodiversity and ecosystem functioning: basic principles. Struct. Funct. 10 (2005).37.Devictor, V., Julliard, R. & Jiguet, F. Distribution of specialist and generalist species along spatial gradients of habitat disturbance and fragmentation. Oikos 117, 507–514 (2008).
Google Scholar 
38.Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).PubMed 

Google Scholar 
39.Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).PubMed 

Google Scholar 
40.Schaaf, A. A. et al. Functional diversity of tree cavities for secondary cavity-nesting birds in logged subtropical Piedmont forests of the Andes. For. Ecol. Manag. 464, 118069 (2020).
Google Scholar 
41.Lindenmayer, D. B., Margules, C. R. & Botkin, D. B. Indicators of biodiversity for ecologically sustainable forest management. Conserv. Biol. 14, 941–950 (2000).
Google Scholar 
42.Gregory, R. D. et al. The generation and use of bird population indicators in Europe. Bird Conserv. Int. 18, S223–S244 (2008).
Google Scholar 
43.Prado, D. E. Seasonally dry forests of tropical South America: from forgottenecosystems to a new phytogeographic unit. Edinb. J. Bot. 57, 437–461 (2000).
Google Scholar 
44.Arias, M. Estadísticas climatológicas de la Provincia de Salta. Dirección de Medio Ambiente y Recursos Naturales, Provincia de Salta, Estación Experimental Agropecuaria Salta, Inta. (1996).45.Brown, A. D. & Malizia, L. R. Las Selvas Pedemontanas de las Yungas. Ciencia hoy 14, 52–63 (2004).
Google Scholar 
46.Politi, N., Hunter, M. Jr. & Rivera, L. Nest selection by cavity-nesting birds in subtropical montane forests of the andes: implications for sustainable forest management. Biotropica 41, 354–360 (2009).
Google Scholar 
47.Politi, N., Hunter, M. & Rivera, L. Availability of cavities for avian cavity nesters in selectively logged subtropical montane forests of the Andes. For. Ecol. Manag. 260, 893–906 (2010).
Google Scholar 
48.Eliano, P. M., Badinier, C. & Malizia, L. R. Manejo forestal sustentable en Yungas: protocolo para el desarrollo de un plan de manejo forestal e implementación en una finca piloto. Ediciones del Subtrópico, San Miguel de Tucumán (2009).49.Ralph, C. J., Droege, S. & Sauer, J. R. Managing and monitoring birds using point counts: standards and applications 1: 3-8 (1995).50.Hill, D. Handbook of biodiversity methods: survey, evaluation and monitoring (Cambridge University Press, 2005).
Google Scholar 
51.Ibarra, J. T. & Martin, K. Biotic homogenization: loss of avian functional richness and habitat specialists in disturbed Andean temperate forests. Biol. Conserv. 192, 418–427 (2015).
Google Scholar 
52.Schaaf, A. A. et al. Identification of tree groups used by secondary cavity-nesting birds to simplify forest management in subtropical forests. J. For. Res. 31, 1417–1424 (2020).
Google Scholar 
53.Blendinger, P. G. & Álvarez, M. E. Aves de la Selva Pedemontana de las Yungas australes. In: Selva Pedemontana de las Yungas. Historia Natural, Ecología y Manejo de un Ecosistema en Peligro. (Eds AD Brown, A. D et al.) 233–272 (2009).54.Wilman, H. et al. EltonTraits 1.0: Species-level foraging attributes of the world’s birds and mammals: Ecol. Arch. Ecol. 95, 2027–2027 (2014).55.del Hoyo, J. A., Sargatal, J., Christie, D. A. & de Juana, E. Handbook of the Birds of the World Alive. (Lynx Edicions, 2017).56.Schaaf, A. A. et al. Influence of logging on nest density and nesting microsites of cavity-nesting birds in the subtropical forests of the Andes. For. Int. J. For. Res. https://doi.org/10.1093/forestry/cpab032 (2021).Article 

Google Scholar 
57.Mason, N. W. H., Mouillot, D., Lee, W. G. & Wilson, J. B. Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos 111, 112–118 (2005).
Google Scholar 
58.R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/ (2016).59.Laliberté, E., Legendre, P. & Shipley, B. FD: measuring functional diversity (FD) from multiple traits, and other tools for functional ecology. http://cran.r-project.org/web/packages/FD (2011).60.Ghadiri Khanaposhtani, M., Kaboli, M., Karami, M., Etemad, V. & Baniasadi, S. Effects of logged and unlogged forest patches on avifaunal diversity. Environ. Manag. 51, 750–758 (2013).61.Tilman, D. The influence of functional diversity and composition on ecosystem processes. Science 277, 1300–1302 (1997).CAS 

Google Scholar 
62.Mouchet, M. A., Villéger, S., Mason, N. W. H. & Mouillot, D. Functional diversity measures: an overview of their redundancy and their ability to discriminate community assembly rules: functional diversity measures. Funct. Ecol. 24, 867–876 (2010).
Google Scholar 
63.Mackey, B. et al. Policy options for the world’s primary forests in multilateral environmental agreements. Conserv. Lett. 8, 139–147 (2015).
Google Scholar 
64.Petchey, O. L. & Gaston, K. J. Functional diversity: back to basics and looking forward. Ecol. Lett. 9, 741–758 (2006).PubMed 

Google Scholar 
65.Azeria, E. T. et al. Differential response of bird functional traits to post-fire salvage logging in a boreal forest ecosystem. Acta Oecol. 37, 220–229 (2011).ADS 

Google Scholar  More