Ecology

1.Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science (80- ) 287, 1770–1774 (2000).CAS 
Article 

Google Scholar 
2.Wall, D. H. & Nielsen, U. N. Biodiversity and ecosystem services: is it the same below ground?. Nat. Educ. Knowl. 12, 3–8 (2012).
Google Scholar 
3.FAO. Global Forest Resources Assessment 2015: Desk Reference. http://www.fao.org/3/a-i4808e.pdf (2015).4.Filloy, J., Zurita, G. A., Corbelli, J. M. & Bellocq, M. I. On the similarity among bird communities: testing the influence of distance and land use. Acta Oecol. 36, 333–338 (2010).ADS 
Article 

Google Scholar 
5.Santoandré, S., Filloy, J., Zurita, G. A. & Bellocq, M. I. Ant taxonomic and functional diversity show differential response to plantation age in two contrasting biomes. For. Ecol. Manag. 437, 304–313 (2019).Article 

Google Scholar 
6.Calviño-Cancela, M. Effectiveness of eucalypt plantations as a surrogate habitat for birds. For. Ecol. Manag. 310, 692–699 (2013).Article 

Google Scholar 
7.Santoandré, S., Filloy, J., Zurita, G. A. & Bellocq, M. I. Taxonomic and functional β-diversity of ants along tree plantation chronosequences differ between contrasting biomes. Basic Appl. Ecol. 41, 1–12 (2019).Article 

Google Scholar 
8.Corbelli, J. M. et al. Integrating taxonomic, functional and phylogenetic beta diversities: interactive effects with the biome and land use across taxa. PLoS ONE 10, 1–17 (2015).Article 
CAS 

Google Scholar 
9.Phifer, C. C., Knowlton, J. L., Webster, C. R., Flaspohler, D. J. & Licata, J. A. Bird community responses to afforested eucalyptus plantations in the Argentine pampas. Biodivers. Conserv. https://doi.org/10.1007/s10531-016-1126-6 (2016).Article 

Google Scholar 
10.Tererai, F., Gaertner, M., Jacobs, S. M. & Richardson, D. M. Eucalyptus invasions in riparian forests: effects on native vegetation community diversity, stand structure and composition. For. Ecol. Manag. 297, 84–93 (2013).Article 

Google Scholar 
11.Brancalion, P. H. S. et al. Intensive silviculture enhances biomass accumulation and tree diversity recovery in tropical forest restoration. Ecol. Appl. 29, 1–12 (2019).Article 

Google Scholar 
12.Zhang, C., Liu, G., Xue, S. & Wang, G. Soil bacterial community dynamics reflect changes in plant community and soil properties during the secondary succession of abandoned farmland in the Loess Plateau. Soil Biol. Biochem. 97, 40–49 (2016).CAS 
Article 

Google Scholar 
13.Zhu, Y., Wang, Y. & Chen, L. Effects of non-native tree plantations on the diversity of understory plants and soil macroinvertebrates in the Loess Plateau of China. Plant Soil 446, 357–368 (2019).Article 
CAS 

Google Scholar 
14.Zhang, W. et al. Plant functional composition and species diversity affect soil C, N, and P during secondary succession of abandoned farmland on the Loess Plateau. Ecol. Eng. 122, 91–99 (2018).Article 

Google Scholar 
15.Munévar, A., Rubio, G. D. & Andrés, G. Changes in spider diversity through the growth cycle of pine plantations in the semi-deciduous Atlantic forest: the role of prey availability and abiotic conditions. For. Ecol. Manag. 424, 536–544 (2018).Article 

Google Scholar 
16.Vega, E., Baldi, G., Jobbágy, E. G. & Paruelo, J. Land use change patterns in the Río de la Plata grasslands: the influence of phytogeographic and political boundaries. Agric. Ecosyst. Environ. 134, 287–292 (2009).Article 

Google Scholar 
17.Ntshuxeko, V. E. & Ruwanza, S. Physical properties of soil in Pine elliottii and Eucalyptus cloeziana plantations in the Vhembe biosphere, Limpopo Province of South Africa. J. For. Res. https://doi.org/10.1007/s11676-018-0830-3 (2018).Article 

Google Scholar 
18.Kerr, T. F. & Ruwanza, S. Does Eucalyptus grandis invasion and removal affect soils and vegetation in the Eastern Cape Province, South Africa?. Austral. Ecol. 41, 328–338 (2016).Article 

Google Scholar 
19.Zhang, D. J., Zhang, J., Yang, W. Q. & Wu, F. Z. Potential allelopathic effect of Eucalyptus grandis across a range of plantation ages. Ecol. Res. 25, 13–23 (2010).Article 

Google Scholar 
20.Díaz, S. & Cabido, M. Vive la difference: plant functional diversity matters to ecosystem processes: plant functional diversity matters to ecosystem processes. Trends Ecol. Evol. 16, 646–655 (2001).Article 

Google Scholar 
21.Petchey, O. L. & Gaston, K. J. Functional diversity: back to basics and looking forward. Ecol. Lett. 9, 741–758 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Luck, G. W., Lavorel, S., Mcintyre, S. & Lumb, K. Improving the application of vertebrate trait-based frameworks to the study of ecosystem services. J. Anim. Ecol. 81, 1065–1076 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
23.Lindenmayer, D. et al. Richness is not all: how changes in avian functional diversity reflect major landscape modification caused by pine plantations. Divers. Distrib. 21, 836–847 (2015).Article 

Google Scholar 
24.Whittaker, R. H. Vegetation of the Siskiyou Mountains, Oregon and California. Ecol. Monogr. 30, 280–338 (1960).Article 

Google Scholar 
25.Swenson, N. G. Functional and Phylogenetic Ecology in R. Use R! (2014). https://doi.org/10.1007/978-1-4614-9542-0.26.Vaccaro, A. S., Filloy, J. & Bellocq, M. I. What land use better preserves taxonomic and functional diversity of birds in a grassland biome?. Avian Conserv. Ecol. 14, 1 (2019).Article 

Google Scholar 
27.Blair, J., Nippert, J. & Briggs, J. Grassland Ecology. Ecology and the Environment vol. 8 (Springer, 2014).28.Nic Lughadha, E. et al. Measuring the fate of plant diversity: towards a foundation for future monitoring and opportunities for urgent action. Philos. Trans. R. Soc. B Biol. Sci. 360, 359–372 (2005).CAS 
Article 

Google Scholar 
29.Marteinsdóttir, B. & Eriksson, O. Trait-based filtering from the regional species pool into local grassland communities. J. Plant Ecol. 7, 347–355 (2014).Article 

Google Scholar 
30.Salgado Negret, B. La Ecología Funcional como aproximación al estudio, manejo y conservación de la biodiversidad: protocolos y aplicaciones. La ecología funcional como aproximación al estudio, manejo y conservación de la biodiversidad: protocolos y aplicaciones (2015).31.Barbier, S., Gosselin, F. & Balandier, P. Influence of tree species on understory vegetation diversity and mechanisms involved—a critical review for temperate and boreal forests. For. Ecol. Manag. 254, 1–15 (2008).Article 

Google Scholar 
32.Zhang, D., Zhang, J., Yang, W., Wu, F. & Huang, Y. Plant and soil seed bank diversity across a range of ages of Eucalyptus grandis plantations afforested on arable lands. Plant Soil 376, 307–325 (2014).CAS 
Article 

Google Scholar 
33.Zhang, C. & Fu, S. Allelopathic effects of eucalyptus and the establishment of mixed stands of eucalyptus and native species. For. Ecol. Manag. 258, 1391–1396 (2009).Article 

Google Scholar 
34.Florentine, S. K. & Fox, J. E. D. Allelopathic effects of Eucalyptus victrix L. on Eucalyptus species and grasses. Allelopath. J. 11, 77–83 (2003).
Google Scholar 
35.Jobbágy, E. et al. Forestación en pastizales: hacia una visión integral de sus oportunidades y costos ecológicos. Agrociencia X, 109–124 (2006).36.Ruwanza, S., Gaertner, M., Esler, K. J. & Richardson, D. M. Allelopathic effects of invasive Eucalyptus camaldulensis on germination and early growth of four native species in the Western Cape South Africa. South. For. 77, 91–105 (2015).Article 

Google Scholar 
37.Suggitt, A. J. et al. Habitat microclimates drive fi ne-scale variation in extreme temperatures. Oikos https://doi.org/10.1111/j.1600-0706.2010.18270.x (2011).Article 

Google Scholar 
38.Zellweger, F., Roth, T., Bugmann, H. & Bollmann, K. Beta diversity of plants, birds and butterflies is closely associated with climate and habitat structure. Glob. Ecol. Biogeogr. 26, 898–906 (2017).Article 

Google Scholar 
39.Silveira, L. & Alonso, J. Runoff modifications due to the conversion of natural grasslands to forests in a large basin in Uruguay. Hidrol. Process. 329, 320–329 (2009).ADS 
Article 

Google Scholar 
40.Mendoza, C. A., Gallardo, J. F., Turrión, M. B., Pando, V. & Aceñolaza, P. G. Dry weight loss in leaves of dominant species in a successional sequence of the Mesopotamian Espinal (Argentina). For. Syst. 26, 1–10 (2017).
Google Scholar 
41.Rodriguez, E. E., Aceñolaza, P. G., Perea, E. L. & Galán de Mera, A. A phytosociological analysis of Butia yatay (Arecaceae) palm groves and gallery forests in Entre Rios, Argentina. Aust. J. Bot. https://doi.org/10.1071/BT16140 (2017).Article 

Google Scholar 
42.Piwczyński, M., Puchałka, R. & Ulrich, W. Influence of tree plantations on the phylogenetic structure of understorey plant communities. For. Ecol. Manag. 376, 231–237 (2016).Article 

Google Scholar 
43.Csecserits, A. et al. Tree plantations are hot-spots of plant invasion in a landscape with heterogeneous land-use. Agric. Ecosyst. Environ. 226, 88–98 (2016).Article 

Google Scholar 
44.Amazonas, N. T. et al. High diversity mixed plantations of Eucalyptus and native trees: an interface between production and restoration for the tropics. For. Ecol. Manag. 417, 247–256 (2018).Article 

Google Scholar 
45.Verstraeten, G. et al. Understorey vegetation shifts following the conversion of temperate deciduous forest to spruce plantation. For. Ecol. Manag. 289, 363–370 (2013).Article 

Google Scholar 
46.Grass, I., Brandl, R., Botzat, A., Neuschulz, E. L. & Farwig, N. Contrasting taxonomic and phylogenetic diversity responses to forest modifications: comparisons of taxa and successive plant life stages in south African scarp forest. PLoS ONE 10, 1–20 (2015).Article 
CAS 

Google Scholar 
47.Wu, J. et al. Should exotic Eucalyptus be planted in subtropical China: insights from understory plant diversity in two contrasting Eucalyptus chronosequences. Environ. Manag. 56, 1244–1251 (2015).ADS 
Article 

Google Scholar 
48.Jin, D. et al. High risk of plant invasion in the understory of eucalypt plantations in South China. Sci. Rep. 5, 18492 (2016).ADS 
Article 
CAS 

Google Scholar 
49.Haughian, S. R. & Frego, K. A. Short-term effects of three commercial thinning treatments on diversity of understory vascular plants in white spruce plantations of northern New Brunswick. For. Ecol. Manag. 370, 45–55 (2016).Article 

Google Scholar 
50.Smith, G. F., Iremonger, S., Kelly, D. L., O’Donoghue, S. & Mitchell, F. J. G. Enhancing vegetation diversity in glades, rides and roads in plantation forests. Biol. Conserv. 136, 283–294 (2007).Article 

Google Scholar 
51.Aceñolaza, P. G., Rodriguez, E. E. & Diaz, D. Efecto de prácticas de manejo silvícola sobre la diversidad vegetal bajo plantaciones de Eucalyptus grandis. In 4to Congreso Forestal Argentino y Latinoamericano (2013).52.Connell, J. H. & Slatyer, R. O. Mechanisms of succession in natural communities and their role in community stability and organization. Am. Nat. 111, 1119–1144 (1977).Article 

Google Scholar 
53.Pedley, S. M. & Dolman, P. M. Multi-taxa trait and functional responses to physical disturbance. J. Anim. Ecol. 83, 1542–1552 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
54.Birkhofer, K., Smith, H. G., Weisser, W. W., Wolters, V. & Gossner, M. M. Land-use effects on the functional distinctness of arthropod communities. Ecography (Cop.) https://doi.org/10.1111/ecog.01141 (2015).Article 

Google Scholar 
55.Mangels, J., Fiedler, K., Schneider, F. D. & Blu, N. Diversity and trait composition of moths respond to land-use intensification in grasslands : generalists replace specialists. Biodivers. Conserv. https://doi.org/10.1007/s10531-017-1411-z (2017).Article 

Google Scholar 
56.Morello, J., Matteucci, S. D., Rodriguez, A. F. & Silva, M. Ecorregiones y complejos ecosistemicos argentino. (2012).57.Cabrera, Á. Fitogeografía de la República Argentina. Bol. Soc. Argent. Bot. 14, 1–42 (1971).
Google Scholar 
58.Rodriguez, E. E., Aceñolaza, P. G., Picasso, G. & Gago, J. Plantas del bajo Rio Uruguay: árboles, arbustos, herbáceas, lianas y epifitas. (2018).59.Bilenca, D. & Miñarro, F. Identificación de Áreas Valiosas de Pastizal (AVPs) en las Pampas y Campos de Argentina Uruguay y sur de Brasil. Vasa https://doi.org/10.1007/s13398-014-0173-7.2 (2004).Article 

Google Scholar 
60.Inta. Plan de Tecnologia Regional 2009–2011. INTA Cent. Reg. Entre Rios (2011).61.Aguerre, M. et al. Manual para productores de Eucaliptos de la Mesopotamia Argentina. (1995).62.Aparicio, J. L., Larocca, F. & Dalla Tea, F. Silvicultura de establecimiento de Eucalyptus grandis. IDIA XXI, Revista de Información sobre Investigación y Desarrollo Agropecuario 66–69 (2005).63.Vilela, E., Leite, H. G. & Jaffe, K. Level of economic damage for leaf-cutting ants (Hymenoptera: Formicidae) in Eucalyptus plantations in Brazil. Sociobiology 42, 1–10 (2015).
Google Scholar 
64.Larroca, F., Dalla Tea, F. & Aparicio, J. L. Técnicas de implantación y manejo de eucaliptus para pequeños y medianos forestadores en Entre Ríos y Corrientes. in XIX Jornadas Forestales de Entre Ríos. (2004).65.Burkart, A. Flora ilustrada de la provincia de Entre Ríos. (INTA, 1969).66.Burkart, A. Flora ilustrada de Entre Ríos (Argentina). Parte 2 Gramíneas. Colección Científica del INTA (1969).67.Peyras, M., Vespa, N. I., Bellocq, M. I. & Zurita, G. A. Quantifying edge effects : the role of habitat contrast and species specialization. J. Insect Conserv. 17, 807–820 (2013).Article 

Google Scholar 
68.Werenkraut, V., Fergnani, P. N. & Ruggiero, A. Ants at the edge: a sharp forest-steppe boundary influences the taxonomic and functional organization of ant species assemblages along elevational gradients in northwestern Patagonia (Argentina). Biodivers. Conserv. 24, 287–308 (2015).Article 

Google Scholar 
69.Diaz, S., Cabido, M. & Casanoves, F. Plant functional traits and environmental filters at a regional scale. J. Veg. Sci. 9, 113–122 (1998).Article 

Google Scholar 
70.Grime, J. P. Benefits of plant diversity to ecosystems: immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).Article 

Google Scholar 
71.Carreño-Rocabado, G. et al. Land-use intensification effects on functional properties in tropical plant communities. Ecol. Appl. https://doi.org/10.1007/s11548-012-0737-y (2015).Article 

Google Scholar 
72.Pérez-Harguindeguy, N. et al. New Handbook for standardized measurment of plant functional traits worldwide. Aust. J. Bot. 61, 167–234 (2013).Article 

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

Google Scholar 
74.Legendre, P. & Legendre, L. F. J. Numerical Ecology. (Elsevier, 2012).75.Kembel, S. W. et al. Package ‘ picante ’: Integrating Phylogenies and Ecology. Cran-R 1–55 (2018). https://doi.org/10.1093/bioinformatics/btq166 >.License.76.Swenson, N. G., Anglada-Cordero, P. & Barone, J. A. Deterministic tropical tree community turnover: evidence from patterns of functional beta diversity along an elevational gradient. Proc. R. Soc. B Biol. Sci. 278, 877–884 (2011).Article 

Google Scholar 
77.Cribari-Neto, F. & Zeileis, A. Journal of Statistical Software. J. Stat. Softw. 34, 1–24 (2010).Article 

Google Scholar 
78.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).MATH 

Google Scholar 
79.Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

Google Scholar 
80.Grace, J. B. Structural Equation Modeling and Natural Systems. (Cambridge University Press, 2006).81.Fan, Y. et al. Applications of structural equation modeling (SEM) in ecological studies: an updated review. Ecol. Process. 5, 19 (2016).ADS 
Article 

Google Scholar 
82.Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
83.Lefcheck, J., Byrnes, J. & Grace, J. Package ‘ piecewiseSEM ’. R (2019).84.Brown, A. M. et al. The fourth-corner solution – using predictive models to understand how species traits interact with the environment. Methods Ecol. Evol. 5, 344–352 (2014).Article 

Google Scholar 
85.Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference and Prediction. (2009).86.Barton, K. Package ‘MuMIn’.Multi-Model Inference. (2018).87.Dawson, S. K. et al. Plant traits of propagule banks and standing vegetation reveal flooding alleviates impacts of agriculture on wetland restoration. J. Appl. Ecol. 54, 1907–1918 (2017).Article 

Google Scholar 
88.QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. (2019). http://qgis.osgeo.org More

Study area and parcel selectionThe study was conducted in Castro Verde Special Protection Area (SPA), located in southern Portugal (Fig. 1). The climate is Mediterranean, with hot summers (30–35 °C on average in July) and mild winters (averaging 5–8 °C in January), and over 75% of annual rainfall (500–600 mm) concentrated in October–March. The landscape is flat or gently undulating (100–300 m), mainly dominated by open areas used for rainfed pastures (ca. 60%) and annual crops (ca. 25%), and to a less extent by open woodlands (ca. 7%)15.Figure 1(a) Location of the study area within the Castro Verde Special Protected Area (SPA), southern Portugal. (b) Distribution of the 27 sheep (dark grey polygons) and 23 cattle (light grey polygons) grazing parcels and (c) Sampling scheme applied to each parcel surveyed. Bird counts were done at the centroid of the parcel (white dot) whereas vegetation sampling was performed at the indicated 10 points (black dots). The area covered with pastures and annual crops (derived from CORINE land cover 2018—https://land.copernicus.eu/pan-european/corine-land-cover/clc2018) is shown in yellow. The map was done using the version 3.10.0 of QGIS—https://qgis.org/en/site/index.html.Full size imageSince 1995, part of the study area has benefited from a CAP agri-environment aiming to protect the traditional farming system16. This scheme provides financial support to farmers for agricultural practices considered favourable to conservation, including the traditional rotation of cereals and fallows, the maintenance of low stocking rates (usually related with sheep grazing systems), and sowing of crops benefiting grassland birds16. However, in recent years the traditional farming system has been declining, with many farmers converting to specialized livestock systems, mainly, cattle grazing systems, with an increase of stocking rates7,15.Parcel selection started by identifying grasslands grazed by either sheep or cattle, based on parcel-level statistical information from 2010 provided by the Portuguese Ministry of Agriculture7. To minimize potentially confounding effects of adjacent land uses (edge effects) and other non-crop elements within parcels on bird assemblages, we excluded parcels less than 100 m from shrubland or forested areas, with shrub and tree cover  > 5% and with a minimum size of 10 ha. In January 2019 we visited 100 pre-selected parcels which were grazed by either sheep or cattle in 2010 in order to confirm the parcel land use in the agricultural year of 2018/2019, aiming to sample a balanced proportion of 50 sheep and cattle grazed parcels. Additional livestock information for the agricultural year of 2018/2019 was obtained during systematic visits to targeted parcels (see “Grazing Regime” section from Methods). We ended up with 23 cattle parcels and 27 sheep parcels (Fig. 1).Bird and vegetation dataBreeding birds were sampled twice in each parcel during 7–16 April and 1–15 May 2019 respectively, always by the same observer (R.F.R). This was done to take into account species-specific breeding phenology in the area (early and late breeders)17 and minimize bias due to other factors (like weather or disturbance). Sampling was conducted using standardized 10 min point counts18 carried out at the central point of the parcel (Fig. 1). As the open terrain allowed for high visibility, a large detection radius was used, and all birds detected within 100 m of the central point were identified and counted. This radius is roughly similar to the one previously used for characterizing bird populations in the region19. All counts were carried out in the first four hours after sunrise and in the last two hours before sunset, with none in heavy or persistent rain, or in strong wind conditions. To estimate bird species richness and occurrences in each parcel, we pooled the data from the two counts. Species-level analyses focused on the six most common species, which occurred in  > 30% of the parcels (see Supplementary Table S1). In addition to presence/absence, we also estimated population densities, using the count which yielded the highest estimate of density for each species (assuming this is the best indicator of population density, given the potential phenology and detectability biases above mentioned). Bird densities were based on the number of males simultaneously detected and expressed as breeding pairs/10 ha or males/10 ha (in the case of Little Bustard Tetrax tetrax and Common Quail Coturnix coturnix). Categorization to the genus level was made for the Crested and Thekla larks (Galerida cristata and G. theklae) due to difficulties in correctly identifying all individuals of these two very similar species in the field.Vegetation height and cover were measured once in each parcel, between April 22 and May 6. Vegetation height was estimated in a set of ten 3 m radius plots defined inside the 100 m buffer (Fig. 1). In each plot, ten measurements of vegetation height were taken at random locations, for a total of 100 measurements per parcel. Vegetation height was measured using a 50 cm ruler and was defined as the highest point of vegetation projection within 3 cm of the ruler20. All values were estimated to the nearest half centimeter. When no vegetation was present (bare soil, soil litter, rocks or animal dung) the height was set to zero (0) but these measurements were not considered to estimate the mean height of the sward. Vegetation cover was measured inside a 50 × 50 cm quadrat placed at each of the ten grid points, by visual estimation to the nearest 5% of the percentage of the quadrat area covered by vegetation21 (Fig. 1). Vegetation height and cover measurements were averaged within each parcel.Grazing regimeThe number and type of livestock in each parcel as well as the extent of the grazing period since the start of the year (2019) were gathered from interviews (Supplementary Information S1) to land managers during 1–15 May 2019. This information was further validated, and corrected in a few cases, through field checks during regular visits (made at two-week intervals) to the parcels (see “Bird and vegetation data” section from Methods). Three grazing regime indicators were estimated for the whole period (January–May 2019): livestock type (either sheep or cattle), animal density, and grazing pressure. The animal density in each parcel was calculated as the average density (animals per hectare) of any species (regardless of being sheep or cattle) that grazed the parcel during the 5-months period. Stocking Rate translated animal density into livestock unit (LU) per hectare (LU/ha), between January and May, according to the following criteria: one adult bovine = 1 LU; bovine aged  More

1.McGowan, P. J. K., Traylor-Holzer, K. & Leus, K. IUCN guidelines for determining when and how ex situ management should be used in species conservation. Conserv. Lett. 10, 361–366 (2017).Article 

Google Scholar 
2.Conde, D. A., Flesness, N., Colchero, F., Jones, O. R. & Scheuerlein, A. An emerging role of zoos to conserve biodiversity. Science 331, 1390–1391 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Lacy, R. C. Conservation Genetics in the Age of Genomics (eds Amato, G., DeSalle, R., Ryder, O. A. & Rosenbaum, H. C.) (Columbia Univ. Press, 2009).4.Frankham, R. Genetic adaptation to captivity in species conservation programs. Mol. Ecol. 17, 325–333 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Jule, K. R., Leaver, L. A. & Lea, S. E. G. The effects of captive experience on reintroduction survival in carnivores: a review and analysis. Biol. Conserv. 141, 355–363 (2008).Article 

Google Scholar 
6.Araki, H., Cooper, B. & Blouin, M. S. Genetic effects of captive breeding cause a rapid, cumulative fitness decline in the wild. Science 318, 100–103 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Lacy, R. C., Alaks, G. & Walsh, A. Evolution of Peromyscus leucopus mice in response to a captive environment. PLOS One 8, e72452 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Milot, E., Perrier, C., Papillon, L., Dodson, J. J. & Bernatchez, L. Reduced fitness of Atlantic salmon released in the wild after one generation of captive breeding. Evol. Appl. 6, 472–485 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Frankham, R. Where are we in conservation genetics and where do we need to go? Conserv. Genet. 11, 661–663 (2010).Article 

Google Scholar 
10.Williams, S. E. & Hoffman, E. A. Minimizing genetic adaptation in captive breeding programs: a review. Biol. Conserv. 142, 2388–2400 (2009).Article 

Google Scholar 
11.Christie, M. R., Marine, M. L., Fox, S. E., French, R. A. & Blouin, M. S. A single generation of domestication heritably alters the expression of hundreds of genes. Nat. Commun. 7, 10676 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Farquharson, K. A., Hogg, C. J. & Grueber, C. E. A meta-analysis of birth-origin effects on reproduction in diverse captive environments. Nat. Commun. 9, 1055 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
13.Christie, M. R., Marine, M. L., French, R. A. & Blouin, M. S. Genetic adaptation to captivity can occur in a single generation. Proc. Natl Acad. Sci. USA 109, 238–242 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Matos, M. Maternal effects can inflate rate of adaptation to captivity. Proc. Natl Acad. Sci. USA 109, e2380 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Grueber, C. E., Laws, R. J., Nakagawa, S. & Jamieson, I. G. Inbreeding depression accumulation across life-history stages of the endangered takahe. Conserv. Biol. 24, 1617–1625 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Harrisson, K. A. et al. Lifetime fitness costs of inbreeding and being inbred in a critically endangered bird. Curr. Biol. 29, 2711–2717 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Boakes, E. H., Wang, J. & Amos, W. An investigation of inbreeding depression and purging in captive pedigreed populations. Heredity 98, 172–182 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Kennedy, E. S., Grueber, C. E., Duncan, R. P. & Jamieson, I. G. Severe inbreeding depression and no evidence of purging in an extremely inbred wild species – the Chatham Island black robin. Evolution 68, 987–995 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Frankham, R., Ballou J. D., Briscoe D. A. Introduction to Conservation Genetics 2nd edn, (Cambridge Univ. Press, 2010).20.Hedrick, P. W. & Kalinowski, S. T. Inbreeding depression in conservation biology. Annu. Rev. Ecol. Syst. 31, 139–162 (2000).Article 

Google Scholar 
21.Fa, J. E., Gusset, M., Flesness, N. & Conde, D. A. Zoos have yet to unveil their full conservation potential. Anim. Conserv. 17, 97–100 (2014).Article 

Google Scholar 
22.Martin, T. E., Lurbiecki, H., Joy, J. B. & Mooers, A. O. Mammal and bird species held in zoos are less endemic and less threatened than their close relatives not held in zoos. Anim. Conserv. 17, 89–96 (2014).Article 

Google Scholar 
23.Fisher, D. O. & Owens, I. P. F. The comparative method in conservation biology. Trends Ecol. Evol. 19, 391–398 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Conde, D. A. et al. Data gaps and opportunities for comparative and conservation biology. Proc. Natl Acad. Sci. USA 116, 9658–9664 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Species 360. Zoological Information Management System (ZIMS) http://zims.species360.org (2018).26.Charlesworth, D. & Willis, J. H. The genetics of inbreeding depression. Nat. Rev. Genet. 10, 783–796 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Packer, C., Tatar, M. & Collins, A. Reproductive cessation in female mammals. Nature 392, 807–811 (1998).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Farquharson, K. A., Hogg, C. J. & Grueber, C. E. Pedigree analysis reveals a generational decline in reproductive success of captive Tasmanian devil (Sarcophilus harrisii): implications for captive management of threatened species. J. Hered. 108, 488–495 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Hammerly, S. C., de la Cerda, D. A., Bailey, H. & Johnson, J. A. A pedigree gone bad: increased offspring survival after using DNA-based relatedness to minimize inbreeding in a captive population. Anim. Conserv. 19, 296–303 (2016).Article 

Google Scholar 
30.Woodworth, L. M., Montgomery, M. E., Briscoe, D. A. & Frankham, R. Rapid genetic deterioration in captive populations: causes and conservation implications. Conserv. Genet. 3, 277–288 (2002).CAS 
Article 

Google Scholar 
31.Fraser, D. J. et al. Population correlates of rapid captive-induced maladaptation in a wild fish. Evol. Appl. 12, 1305–1317 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
32.Frankham, R. & Loebel, D. A. Modeling problems in conservation genetics using captive Drosophila populations: rapid genetic adaptation to captivity. Zoo. Biol. 11, 333–342 (1992).Article 

Google Scholar 
33.Lacy, R. C. Loss of genetic diversity from managed populations: interacting effects of drift, mutation, immigration, selection, and population subdivision. Conserv. Biol. 1, 143–158 (1987).Article 

Google Scholar 
34.Mason, G. et al. Plastic animals in cages: behavioural flexibility and responses to captivity. Anim. Behav. 85, 1113–1126 (2013).Article 

Google Scholar 
35.Courtney Jones, S. K. & Byrne, P. G. What role does heritability play in transgenerational phenotypic responses to captivity? Implications for managing captive populations. Zoo. Biol. 36, 397–406 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Kokko, H. & Jennions, M. D. The Evolution of Parental Care (eds Royle, N. J., Smiseth, P. T. & Kölliker, M.) (Oxford Univ. Press, 2012).37.Grueber, C. E., Hogg, C. J., Ivy, J. A. & Belov, K. Impacts of early viability selection on management of inbreeding and genetic diversity in conservation. Mol. Ecol. 24, 1645–1653 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
38.Wells, J. C. Commentary: paternal and maternal influences on offspring phenotype: the same, only different. Int J. Epidemiol. 43, 772–774 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Calkins, E. S., Fuller, T. K., Asa, C. S., Sievert, P. R. & Coonan, T. J. Factors influencing reproductive success and litter size in captive island foxes. J. Wildl. Manag. 77, 346–351 (2013).Article 

Google Scholar 
40.Hogg, C. J. et al. Influence of genetic provenance and birth origin on productivity of the Tasmanian devil insurance population. Conserv. Genet. 16, 1465–1473 (2015).Article 

Google Scholar 
41.O’Grady, J. J. et al. Realistic levels of inbreeding depression strongly affect extinction risk in wild populations. Biol. Conserv. 133, 42–51 (2006).Article 

Google Scholar 
42.Hoeck, P. E. A., Wolak, M. E., Switzer, R. A., Kuehler, C. M. & Lieberman, A. A. Effects of inbreeding and parental incubation on captive breeding success in Hawaiian crows. Biol. Conserv. 184, 357–364 (2015).Article 

Google Scholar 
43.Menotti-Raymond, M. & O’Brien, S. J. Dating the genetic bottleneck of the African cheetah. Proc. Natl Acad. Sci. USA 90, 3172–3176 (1993).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Brüniche-Olsen, A., Jones, M. E., Austin, J. J., Burridge, C. P. & Holland, B. R. Extensive population decline in the Tasmanian devil predates European settlement and devil facial tumour disease. Biol. Lett. 10, 20140619 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
45.Hedrick, P. W. & Fredrickson, R. J. Captive breeding and the reintroduction of Mexican and red wolves. Mol. Ecol. 17, 344–350 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Hogg, C. J. et al. Founder relationships and conservation management: empirical kinships reveal the effect on breeding programmes when founders are assumed to be unrelated. Anim. Conserv. 22, 348–361 (2019).Article 

Google Scholar 
47.Ivy, J. A. & Lacy, R. C. A comparison of strategies for selecting breeding pairs to maximize genetic diversity retention in managed populations. J. Hered. 103, 186–196 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Norman, A. J., Putnam, A. S. & Ivy, J. A. Use of molecular data in zoo and aquarium collection management: benefits, challenges, and best practices. Zoo. Biol. 38, 106–118 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Leberg, P. L. & Firmin, B. D. Role of inbreeding depression and purging in captive breeding and restoration programmes. Mol. Ecol. 17, 334–343 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
50.Tennenhouse, E. M., Weladji, R. B., Holand, Ø. & Nieminen, M. Timing of reproductive effort differs between young and old dominant male reindeer. Ann. Zool. Fenn. 49, 152–160 (2012). 159.Article 

Google Scholar 
51.L’Italien, L. et al. Mating group size and stability in reindeer Rangifer tarandus: the effects of male characteristics, sex ratio and male age structure. Ethology 118, 783–792 (2012).Article 

Google Scholar 
52.Imlay, T. L., Steiner, J. C. & Bird, D. M. Age and experience affect the reproductive success of captive Loggerhead Shrike (Lanius ludovicianus) subspecies. Can. J. Zool. 95, 547–554 (2017).Article 

Google Scholar 
53.Henry, M. D., Hankerson, S. J., Siani, J. M., French, J. A. & Dietz, J. M. High rates of pregnancy loss by subordinates leads to high reproductive skew in wild golden lion tamarins (Leontopithecus rosalia). Horm. Behav. 63, 675–683 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
54.Descamps, S., Boutin, S., Berteaux, D. & Gaillard, J.-M. Age-specific variation in survival, reproductive success and offspring quality in red squirrels: evidence of senescence. Oikos 117, 1406–1416 (2008).Article 

Google Scholar 
55.Ruiz-López, M. J., Espeso, G., Evenson, D. P., Roldan, E. R. S. & Gomendio, M. Paternal levels of DNA damage in spermatozoa and maternal parity influence offspring mortality in an endangered ungulate. Proc. R. Soc. B 277, 2541–2546 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
56.Ripple, W. J. et al. Extinction risk is most acute for the world’s largest and smallest vertebrates. Proc. Natl Acad. Sci. USA 114, 10678–10683 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Kellermann, V., Hoffmann, A. A., Overgaard, J., Loeschcke, V. & Sgrò, C. M. Plasticity for desiccation tolerance across Drosophila species is affected by phylogeny and climate in complex ways. Proc. R. Soc. B 285, 20180048 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
58.Mellor, E., McDonald Kinkaid, H. & Mason, G. Phylogenetic comparative methods: harnessing the power of species diversity to investigate welfare issues in captive wild animals. Zoo. Biol. 37, 369–388 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
59.Araki, H., Cooper, B. & Blouin, M. S. Carry-over effect of captive breeding reduces reproductive fitness of wild-born descendants in the wild. Biol. Lett. 5, 621–624 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
60.Christie, M. R., Ford, M. J. & Blouin, M. S. On the reproductive success of early-generation hatchery fish in the wild. Evol. Appl. 7, 883–896 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
61.González, A., Quevedo, M. Á. & Cuadrado, M. Comparison of reproductive success between parent-reared and hand-reared northern bald ibis Geronticus eremita in captivity during Proyecto Eremita. J. Zoo. Aquar. Res. 8, 246–252 (2020).
Google Scholar 
62.Lacy, R. C., Ballou, J. D. & Pollak, J. P. PMx: software package for demographic and genetic analysis and management of pedigreed populations. Methods Ecol. Evol. 3, 433–437 (2012).Article 

Google Scholar 
63.Ballou, J. D., Lacy R. C., Pollak J. P. PMx: software for demographic and genetic analysis and mangement of pedigreed populations. Chicago Zoological Society (2010).64.Ballou, J. Genetics and Conservation: a Reference for Managing Wild Animal and Plant Populations (eds Schonewald-Cox, C. M., Chambers, S. M., MacBryde, B., Thomas, W. L.) (The Benjamin/Cummings Publishing Company Inc., 1983).65.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing https://www.R-project.org/ (2018).66.Tacutu, R. et al. Human ageing genomic resources: new and updated databases. Nucleic Acids Res. 46, D1083–D1090 (2017).PubMed Central 
Article 
CAS 

Google Scholar 
67.Eager, C. D. standardize: tools for standardizing variables for regression in R. https://CRAN.R-project.org/package=standardize (2017).68.Michonneau, F., Brown, J. W. & Winter, D. J. rotl: an R package to interact with the Open Tree of Life data. Methods Ecol. Evol. 7, 1476–1481 (2016).Article 

Google Scholar 
69.Hinchliff, C. E. et al. Synthesis of phylogeny and taxonomy into a comprehensive tree of life. Proc. Natl Acad. Sci. USA 112, 12764–12769 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 48 (2015).Article 

Google Scholar 
71.Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. https://CRAN.R-project.org/package=DHARMa (2019).72.Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
73.Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. Multimodel inference in ecology and evolution: challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Barton, K. MuMIn: multi-model inference. R package https://CRAN.R-project.org/package=MuMIn (2018).75.IUCN. The IUCN Red List of Threatened Species. Version 2020-2 https://www.iucnredlist.org (2020).76.Wright, S. Evolution in Mendelian populations. Genetics 16, 97–159 (1931).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Metabolic profiling of EP and LE root exudates and root periderm samplesHigh performance liquid chromatography (HPLC) analysis of root exudates and root periderm reported the presence of five bioactive NQs. The identified NQs included shikonin (SK), acetylshikonin (AS); isobutyrylshikonin (IBS); β, β-dimethylacrylshikonin (DMAS); and isovalerylshikonin (IVS) (Fig. 1a–d). This suggests that SK and its derivatives accumulate in the rhizosphere of both EP and LE via root exudation. Though all the five NQs were found to be exuded in the rhizosphere however they varied quantitatively among EP and LE species. LE samples had higher SK and its derivatives production compared to EP (Figs. S5–S6). Our results also displayed quantitative variations in SK and its derivatives production among two soil types (Table 1a,b). However, regardless of variation, SK, AS, DMAS, and IVS were consistently present among all the samples.Figure 1Images and chromatograms representing qualitative and quantitative variation of SK and its derivatives production in root periderm extracts. Chromatograms of root extracts of E. plantagenium (EP) and L.erythrorhizon (LE) specimens grown in Peat potting artificial soil (a) EP.PP, (c) LE.PP; and Natural campus soil (b) EP.NC, (d) LE.NC. Resulting peaks correspond to shikonin (SK), acetylshikonin (AS); isobutylshikonin (IBS); β, β-dimethylacrylshikonin (DMAS); and isovalerylshikonin (IVS). Chromatogram for each sample represents a composite sample of 3–4 individual plants. Figure represents only one replicate for each sample while the rest of the two replicates for each sample with standard chromatogram are provided in Fig. S5.Full size imageTable 1 Quantitative analysis of shikonin and its derivatives via HPLC in (a) root periderm; (b) root exudates samples of E. plantagineum (EP) and L.erythrorhizon (LE).Full size tablePacBio sequence reads statistics and taxonomic profilingAfter quality filtering, removal of chimera, chloroplast and mitochondrial sequences, approximately 165,570 high quality sequences (Tags) were obtained. Tags were clustered into 14,429 microbial operational taxonomic units (OTUs) at a 97% sequence similarity cutoff level (Table S2). All OTUs with species annotation are summarized in Table S3. Taxonomic profiling for taxonomic affiliations revealed Proteobacteria, Bacteroidetes, Planctomycetes, Cyanobacteria, Acidobacteria, and Actinobacteria to be the dominant phyla among all the samples (Fig. S7). These 6 phyla accounted for 70.97–96.61% of the total microbial OTUs. The Proteobacterial microbes mainly belonged to Classes Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria that accounted for 13.94–40.54% of the total microbes (Table S4).Host plant genetics are the drivers for distinct microbiomeTo identify the effects of host plant genetics on microbial acquisition, microbial community composition of bulk soil was compared with root and rhizospher soils of EP and LE. α-diversity estimates revealed a significantly higher observed species richness (Sobs), and shannon diversity for bulk soil (Fig. 3a,b; Table S5). This indicates that bulk soil serves as a reservoir for microbial acquisition in other rhizo-compartments. At different taxonomic levels, microbes associated with Proteobacteria, Planctomycetes, Bacteroidetes and Cyanobacteria were all present in relatively higher abundance in EP and LE rhizo-compartments compared to bulk soil in two different soil types (Fig. 2a; Table S6). Wilcox test also displayed quantitative variation in microbial acquisition at order level. For example, compared to bulk soil, Flavobacteriales, Sphingomonadales, and Verrucomicrobiales had a relatively higher abundance in EP rhizosphere, while Caulobacterales, and Sphingomonadales were significantly higher in LE rhizosphere (Fig. S8, P  More

1.Hardoim, P. R. et al. The hidden world within plants: Ecological and evolutionary considerations for defining functioning of microbial endophytes. Microbiol. Mol. Biol. Rev. 79, 293–320 (2015).Article 

Google Scholar 
2.Zhang, H. W., Song, Y. C. & Tan, R. X. Biology and chemistry of endophytes. Nat. Prod. Rep. 23, 753–771 (2006).CAS 
Article 

Google Scholar 
3.Köberl, M., Schmidt, R., Ramadan, E. M., Bauer, R. & Berg, G. The microbiome of medicinal plants: Diversity and importance for plant growth, quality and health. Front. Microbiol. 4, 400 (2013).Article 

Google Scholar 
4.Soto, M. J., Domínguez-Ferreras, A., Pérez-Mendoza, D., Sanjuán, J. & Olivares, J. Mutualism versus pathogenesis: The give-and-take in plant–bacteria interactions. Cell. Microbiol. 11, 381–388 (2009).CAS 
Article 

Google Scholar 
5.Leff, J. W., Del Tredici, P., Friedman, W. E. & Fierer, N. Spatial structuring of bacterial communities within individual Ginkgo biloba trees. Environ. Microbiol. 17, 2352–2361. https://doi.org/10.1111/1462-2920.12695 (2015).Article 
PubMed 

Google Scholar 
6.Berg, G., Rybakova, D., Grube, M. & Köberl, M. The plant microbiome explored: Implications for experimental botany. J. Exp. Bot. 67, 995–1002. https://doi.org/10.1093/jxb/erv466 (2016).CAS 
Article 
PubMed 

Google Scholar 
7.Cregger, M. A. et al. The Populus holobiont: Dissecting the effects of plant niches and genotype on the microbiome. Microbiome 6, 31. https://doi.org/10.1186/s40168-018-0413-8 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Wang, Y., Liu, Y., Wu, Q., Yao, X. & Cheng, Z. Rapid and sensitive determination of major active ingredients and toxic components in Ginkgo biloba leaves extract (EGb 761) by a validated UPLC–MS-MS method. J. Chromatogr. Sci. 55, 459–464. https://doi.org/10.1093/chromsci/bmw206 (2017).CAS 
Article 
PubMed 

Google Scholar 
9.Mesquita, T. R. R. et al. Cardioprotective action of Ginkgo biloba extract against sustained β-adrenergic stimulation occurs via activation of M2/NO pathway. Front. Pharmacol. 8, 220. https://doi.org/10.3389/fphar.2017.00220 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Woelk, H., Arnoldt, K. H., Kieser, M. & Hoerr, R. Ginkgo biloba special extract EGb 761® in generalized anxiety disorder and adjustment disorder with anxious mood: A randomized, double-blind, placebo-controlled trial. J. Psychiatr. Res. 41, 472–480. https://doi.org/10.1016/j.jpsychires.2006.05.004 (2007).CAS 
Article 
PubMed 

Google Scholar 
11.Rojas, P., Montes, P., Rojas, C., Serrano-Garcia, N. & Rojas-Castaneda, J. C. Effect of a phytopharmaceutical medicine, Ginko biloba extract 761, in an animal model of Parkinson’s disease: Therapeutic perspectives. Nutrition 28, 1081–1088. https://doi.org/10.1016/j.nut.2012.03.007 (2012).CAS 
Article 
PubMed 

Google Scholar 
12.Tan, M.-S. et al. Efficacy and adverse effects of Ginkgo biloba for cognitive impairment and dementia: A systematic review and meta-analysis. J. Alzheimer’s Dis. 43, 589–603 (2015).CAS 
Article 

Google Scholar 
13.Kennedy, D. O., Jackson, P. A., Haskell, C. F. & Scholey, A. B. Modulation of cognitive performance following single doses of 120 mg Ginkgo biloba extract administered to healthy young volunteers. Hum. Psychopharm. Clin. 22, 559–566. https://doi.org/10.1002/hup.885 (2007).Article 

Google Scholar 
14.Yao, Z.-X., Han, Z., Drieu, K. & Papadopoulos, V. Ginkgo biloba extract (Egb 761) inhibits β-amyloid production by lowering free cholesterol levels. J. Nutr. Biochem. 15, 749–756. https://doi.org/10.1016/j.jnutbio.2004.06.008 (2004).CAS 
Article 
PubMed 

Google Scholar 
15.Chen, D., Sun, S., Cai, D. & Kong, G. Induction of mitochondrial-dependent apoptosis in T24 cells by a selenium (Se)-containing polysaccharide from Ginkgo biloba L. leaves. Int. J. Biol. Macromol. 101, 126–130 (2017).CAS 
Article 

Google Scholar 
16.Hamdoun, S. & Efferth, T. Ginkgolic acids inhibit migration in breast cancer cells by inhibition of NEMO sumoylation and NF-κB activity. Oncotarget 8, 35103 (2017).Article 

Google Scholar 
17.Fei, R. et al. Purified polysaccharide from Ginkgo biloba leaves inhibits P-selectin-mediated leucocyte adhesion and inflammation. Acta Pharmacol. Sin. 29, 499–506 (2008).CAS 
Article 

Google Scholar 
18.Mahadevan, S. & Park, Y. Multifaceted therapeutic benefits of Ginkgo biloba L.: Chemistry, efficacy, safety, and uses. J. Food Sci. 73, R14–R19 (2008).CAS 
Article 

Google Scholar 
19.Zimmermann, M., Colciaghi, F., Cattabeni, F. & Di Luca, M. Ginkgo biloba extract: From molecular mechanisms to the treatment of Alzheimer’s disease. Cell. Mol. Biol. 48, 613–623 (2002).CAS 
PubMed 

Google Scholar 
20.van Beek, T. A. & Montoro, P. Chemical analysis and quality control of Ginkgo biloba leaves, extracts, and phytopharmaceuticals. J. Chromatogr. A 1216, 2002–2032 (2009).Article 

Google Scholar 
21.Lu, X. et al. Combining metabolic profiling and gene expression analysis to reveal the biosynthesis site and transport of ginkgolides in Ginkgo biloba L.. Front. Plant Sci. 8, 872. https://doi.org/10.3389/fpls.2017.00872 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
22.Mancuso, C. & Santangelo, R. Panax ginseng and Panax quinquefolius: From pharmacology to toxicology. Food Chem. Toxicol. 107, 362–372 (2017).CAS 
Article 

Google Scholar 
23.Karmazyn, M., Moey, M. & Gan, X. T. Therapeutic potential of Ginseng in the management of cardiovascular disorders. Drugs 71, 1989–2008. https://doi.org/10.2165/11594300-000000000-00000 (2011).CAS 
Article 
PubMed 

Google Scholar 
24.Predy, G. N. et al. Efficacy of an extract of North American ginseng containing poly-furanosyl-pyranosyl-saccharides for preventing upper respiratory tract infections: A randomized controlled trial. Can. Med. Assoc. J. 173, 1043–1048. https://doi.org/10.1503/cmaj.1041470 (2005).Article 

Google Scholar 
25.Yuan, C.-S., Wang, C.-Z., Wicks, S. M. & Qi, L.-W. Chemical and pharmacological studies of saponins with a focus on American ginseng. J. Ginseng Res. 34, 160 (2010).CAS 
Article 

Google Scholar 
26.Yang, W.-Z., Hu, Y., Wu, W.-Y., Ye, M. & Guo, D.-A. Saponins in the genus Panax L. (Araliaceae): A systematic review of their chemical diversity. Phytochemistry 106, 7–24. https://doi.org/10.1016/j.phytochem.2014.07.012 (2014).CAS 
Article 
PubMed 

Google Scholar 
27.Solieri, L., Dakal, T. C. & Giudici, P. Next-generation sequencing and its potential impact on food microbial genomics. Ann. Microbiol. 63, 21–37 (2013).CAS 
Article 

Google Scholar 
28.Ercolini, D. High-throughput sequencing and metagenomics: Moving forward in the culture-independent analysis of food microbial ecology. Appl. Environ. Microbiol. 79, 3148–3155 (2013).CAS 
Article 

Google Scholar 
29.Metzker, M. L. Sequencing technologies—the next generation. Nat. Rev. Genet. 11, 31 (2010).CAS 
Article 

Google Scholar 
30.Riesenfeld, C. S., Schloss, P. D. & Handelsman, J. Metagenomics: Genomic analysis of microbial communities. Annu. Rev. Genet. 38, 525–552 (2004).CAS 
Article 

Google Scholar 
31.Robinson, R. J. et al. Endophytic bacterial community composition in wheat (Triticum aestivum) is determined by plant tissue type, developmental stage and soil nutrient availability. Plant Soil 405, 381–396. https://doi.org/10.1007/s11104-015-2495-4 (2016).CAS 
Article 

Google Scholar 
32.Vorholt, J. A. Microbial life in the phyllosphere. Nat. Rev. Microbiol 10, 828–840. https://doi.org/10.1038/nrmicro2910 (2012).CAS 
Article 
PubMed 

Google Scholar 
33.Dasgupta, M. G. et al. Diversity of bacterial endophyte in Eucalyptus clones and their implications in water stress tolerance. Microbiol. Res. 241, 126579. https://doi.org/10.1016/j.micres.2020.126579 (2020).CAS 
Article 
PubMed 

Google Scholar 
34.Knief, C. et al. Metaproteogenomic analysis of microbial communities in the phyllosphere and rhizosphere of rice. ISME J. 6, 1378–1390. https://doi.org/10.1038/ismej.2011.192 (2012).CAS 
Article 
PubMed 

Google Scholar 
35.Idris, R., Trifonova, R., Puschenreiter, M., Wenzel, W. W. & Sessitsch, A. Bacterial communities associated with flowering plants of the Ni hyperaccumulator Thlaspi goesingense. Appl. Environ. Microbiol. 70, 2667–2677. https://doi.org/10.1128/aem.70.5.2667-2677.2004 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
36.Rastogi, G. et al. Leaf microbiota in an agroecosystem: Spatiotemporal variation in bacterial community composition on field-grown lettuce. Isme J. 6, 1812–1822. https://doi.org/10.1038/ismej.2012.32 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Bulgarelli, D. et al. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488, 91–95. https://doi.org/10.1038/nature11336 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
38.Kielak, A. M., Cipriano, M. A. P. & Kuramae, E. E. Acidobacteria strains from subdivision 1 act as plant growth-promoting bacteria. Arch. Microbiol. 198, 987–993. https://doi.org/10.1007/s00203-016-1260-2 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Fuerst, J. A. & Sagulenko, E. Beyond the bacterium: Planctomycetes challenge our concepts of microbial structure and function. Nat. Rev. Microbiol 9, 403–413. https://doi.org/10.1038/nrmicro2578 (2011).CAS 
Article 
PubMed 

Google Scholar 
40.Wiegand, S., Jogler, M. & Jogler, C. On the maverick planctomycetes. FEMS Microbiol. Rev. 42, 739–760. https://doi.org/10.1093/femsre/fuy029 (2018).CAS 
Article 
PubMed 

Google Scholar 
41.Kim, H. et al. High population of Sphingomonas species on plant surface. J. Appl. Microbiol. 85, 731–736. https://doi.org/10.1111/j.1365-2672.1998.00586.x (1998).Article 

Google Scholar 
42.Delmotte, N. et al. Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. Proc. Natl. Acad. Sci. 106, 16428–16433. https://doi.org/10.1073/pnas.0905240106 (2009).ADS 
Article 
PubMed 

Google Scholar 
43.Kampfer, P., Busse, H. J., McInroy, J. A. & Glaeser, S. P. Sphingomonas zeae sp nov., isolated from the stem of Zea mays. Int. J. Syst. Evol. Microbiol. 65, 2542–2548. https://doi.org/10.1099/ijs.0.000298 (2015).CAS 
Article 
PubMed 

Google Scholar 
44.Xie, C.-H. & Yokota, A. Sphingomonas azotifigens sp. nov., a nitrogen-fixing bacterium isolated from the roots of Oryza sativa. Int. J. Syst. Evol. Microbiol. 56, 889–893. https://doi.org/10.1099/ijs.0.64056-0 (2006).CAS 
Article 
PubMed 

Google Scholar 
45.Videira, S. S., De Araujo, J. L. S., Da Silva Rodrigues, L., Baldani, V. L. D. & Baldani, J. I. Occurrence and diversity of nitrogen-fixing Sphingomonas bacteria associated with rice plants grown in Brazil. FEMS Microbiol. Lett. 293, 11–19. https://doi.org/10.1111/j.1574-6968.2008.01475.x (2009).CAS 
Article 
PubMed 

Google Scholar 
46.Innerebner, G., Knief, C. & Vorholt, J. A. Protection of Arabidopsis thaliana against leaf-pathogenic Pseudomonas syringae by Sphingomonas strains in a controlled model system. Appl. Environ. Microbiol. 77, 3202–3210. https://doi.org/10.1128/aem.00133-11 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
47.Khan, A. L. et al. Bacterial endophyte Sphingomonas sp LK11 produces gibberellins and IAA and promotes tomato plant growth. J. Microbiol. 52, 689–695. https://doi.org/10.1007/s12275-014-4002-7 (2014).CAS 
Article 
PubMed 

Google Scholar 
48.Asaf, S., Numan, M., Khan, A. L. & Al-Harrasi, A. Sphingomonas: From diversity and genomics to functional role in environmental remediation and plant growth. Crit. Rev. Biotechnol. 40, 138–152. https://doi.org/10.1080/07388551.2019.1709793 (2020).CAS 
Article 
PubMed 

Google Scholar 
49.Ali, A. et al. Biotransformation of benzoin by Sphingomonas sp. LK11 and ameliorative effects on growth of Cucumis sativus. Arch. Microbiol. 201, 591–601. https://doi.org/10.1007/s00203-019-01623-1 (2019).CAS 
Article 
PubMed 

Google Scholar 
50.Chhetri, G., Kim, J., Kim, I., Kim, H. & Seo, T. Hymenobacter setariae sp. nov., isolated from the ubiquitous weedy grass Setaria viridis. Int. J. Syst. Evol. Microbiol. 70, 3724–3730. https://doi.org/10.1099/ijsem.0.004226 (2020).CAS 
Article 
PubMed 

Google Scholar 
51.Dai, Y. et al. Wheat-associated microbiota and their correlation with stripe rust reaction. J. Appl. Microbiol. 128, 544–555. https://doi.org/10.1111/jam.14486 (2020).CAS 
Article 
PubMed 

Google Scholar 
52.Buczolits, S. et al. Classification of three airborne bacteria and proposal of Hymenobacter aerophilus sp nov. Int. J. Syst. Evol. Microbiol. 52, 445–456. https://doi.org/10.1099/00207713-52-2-445 (2002).CAS 
Article 
PubMed 

Google Scholar 
53.Su, S. Y. et al. Hymenobacter kanuolensis sp nov., a novel radiation-resistant bacterium. Int. J. Syst. Evol. Microbiol. 64, 2108–2112. https://doi.org/10.1099/ijs.0.051680-0 (2014).CAS 
Article 
PubMed 

Google Scholar 
54.Dimitrijevic, S. et al. Plant growth-promoting bacteria elevate the nutritional and functional properties of black cumin and flaxseed fixed oil. J. Sci. Food Agric. 98, 1584–1590. https://doi.org/10.1002/jsfa.8631 (2018).CAS 
Article 
PubMed 

Google Scholar 
55.Yang, R. X., Fan, X. J., Cai, X. Q. & Hu, F. P. The inhibitory mechanisms by mixtures of two endophytic bacterial strains isolated from Ginkgo biloba against pepper phytophthora blight. Biol. Control 85, 59–67. https://doi.org/10.1016/j.biocontrol.2014.09.013 (2015).Article 

Google Scholar 
56.Islam, M. N., Choi, J. & Baek, K. H. Control of foodborne pathogenic bacteria by endophytic bacteria isolated from Ginkgo biloba L. Foodborne Pathog. Dis. 16, 661–670. https://doi.org/10.1089/fpd.2018.2496 (2019).CAS 
Article 
PubMed 

Google Scholar 
57.Datta, S. et al. Endophytic bacteria in xenobiotic degradation In Microbial endophytes (eds. Kumar, A. & Singh, V. K.) 125–156 (Woodhead Publishing, 2020).58.Newmaster, S. G., Grguric, M., Shanmughanandhan, D., Ramalingam, S. & Ragupathy, S. DNA barcoding detects contamination and substitution in North American herbal products. BMC Med. 11, 222 (2013).Article 

Google Scholar 
59.Gao, Z. et al. Derivative technology of DNA barcoding (Nucleotide Signature and SNP Double Peak methods) detects adulterants and substitution in Chinese patent medicines. Sci. Rep. 7, 5858. https://doi.org/10.1038/s41598-017-05892-y (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
60.Ichim, M. C. & de Boer, H. J. A review of authenticity and authentication of commercial ginseng herbal medicines and food supplements. Front. Pharmacol. https://doi.org/10.3389/fphar.2020.612071 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
61.Dhivya, S. et al. Validated identity test method for Ginkgo biloba NHPs using DNA-based species-specific hydrolysis PCR probe. J. AOAC Int. 102, 1779–1786. https://doi.org/10.5740/jaoacint.18-0319 (2019).CAS 
Article 
PubMed 

Google Scholar 
62.Singh, A., Bajpai, V., Srivastava, M., Arya, K. R. & Kumar, B. Rapid screening and distribution of bioactive compounds in different parts of Berberis petiolaris using direct analysis in real time mass spectrometry. J. Pharm. Anal. 5, 332–335 (2015).CAS 
Article 

Google Scholar 
63.Kim, H. K., Choi, Y. H. & Verpoorte, R. NMR-based metabolomic analysis of plants. Nat. Protoc. 5, 536 (2010).CAS 
Article 

Google Scholar 
64.Lundberg, D. S. et al. Defining the core Arabidopsis thaliana root microbiome. Nature 488, 86–90 (2012).ADS 
CAS 
Article 

Google Scholar 
65.Lundberg, D. S., Yourstone, S., Mieczkowski, P., Jones, C. D. & Dangl, J. L. Practical innovations for high-throughput amplicon sequencing. Nat. Methods 10, 999–1002 (2013).CAS 
Article 

Google Scholar 
66.Illumina. 16S Metagenomic Sequencing Library Preparation. Preparing 16S Ribosomal RNA Gene Amplicons for the Illumina MiSeq System (Part 15044223 Rev. B). (2013), Accessed 07-2017, available at https://support.illumina.com/documents/documentation/chemistry_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf.67.Chong, J., Liu, P., Zhou, G. & Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15, 799–821. https://doi.org/10.1038/s41596-019-0264-1 (2020).CAS 
Article 
PubMed 

Google Scholar 
68.Dhariwal, A. et al. MicrobiomeAnalyst: A web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 45, W180–W188. https://doi.org/10.1093/nar/gkx295 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
69.Hulsen, T., de Vlieg, J. & Alkema, W. BioVenn—A web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genom. 9, 488. https://doi.org/10.1186/1471-2164-9-488 (2008).CAS 
Article 

Google Scholar 
70.vegan: Community Ecology Package v. 2.5-6 (2019), available at https://cran.r-project.org/web/packages/vegan/index.html71.Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30. https://doi.org/10.1093/nar/28.1.27 (2000).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

P. aeruginosa produces novel glycolipids in response to Pi stressTo determine changes in the membrane lipidome in response to P-stress, the model P. aeruginosa strain PAO1 was grown in minimal medium under high (1 mM) or low Pi (50 µM) conditions (Fig. 1a). The latter condition elicited strong alkaline phosphatase activity, measured through the liberation of para-nitrophenol (pNP) from pNPP (Fig. 1b), this being a strong indication that cells were P-stressed. Analysis of membrane lipid profiles using high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS) revealed the presence of several new lipids under Pi stress conditions (Fig. 1c). Thus, during Pi-replete growth (1 mM phosphate), the lipidome is dominated by two glycerophospholipids: PG (eluted at 6.8 min) and PE (eluted at 12.2 min). During Pi-stress a lipid species with mass to charge ratio (m/z) of 623 and 649 were also found, with MS fragmentation resulting in a 131 m/z peak, a diagnostic ion for the amino-acid containing ornithine lipid. This is consistent with previous reports of ornithine lipids in the P. aeruginosa membrane in response to Pi stress [29, 30].Fig. 1: Lipidomics analysis uncovers novel glycolipid formation in Pseudomonas aeruginosa strain PAO1 in response to phosphorus limitation.a Growth of strain PAO1 WT in minimal medium A containing 1 mM phosphate (+Pi, blue) or 50 µM phosphate (−Pi, black) over 12 h. Data are the average of three independent replicates. b Liberation of para-nitrophenol (pNP) from para-nitrophenol phosphate (pNPP) through alkaline phosphatase activity, under Pi-replete (1 mM, black) and Pi-deplete (50 µM, yellow) conditions. Error bars represent the standard deviation of three independent replicates. c Representative chromatograms in negative ionisation mode of the P. aeruginosa lipidome when grown under phosphorus stress (−Pi, black) compared to growth under phosphorus sufficient conditions (+Pi, orange). PG phosphatidylglycerol, PE phosphatidylethanolamine, OL ornithine lipids. Lower panel: extracted ion chromatograms of three new glycolipid species in P. aeruginosa which are only produced during Pi-limitation (black, 1 mM; orange, 50 µM). MGDG monoglucosyldiacylglycerol, GADG glucuronic acid-diacylglycerol and UGL unconfirmed glycolipid. d Mass spectrometry fragmentation spectra of three glycolipid species present under Pi stress in P. aeruginosa, at retention times of 7.7 (m/z 774.7), 8.7 (m/z 786.7) and 9.8 (m/z 788.6) minutes, respectively. Each spectrum depicts an intact lipid mass with an ammonium (NH4+) adduct exhibiting neutral loss of a head group, yielding diacylglycerol (DAG) (595 m/z). Further fragmentation yields monoacylglycerols (MAG) with C16:0 or C18:1 fatty acyl chains.Full size imageFurther to ornithine lipids, three unknown lipids eluting at 7.7, 8.7 and 9.8 min, were only present under Pi stress conditions (Fig. 1c). Using several rounds of MS fragmentation (MSn), with a quadrupole ion trap MS, fragmentation patterns characteristic of glycolipids were found for all three peaks. For each peak of interest, the most predominant lipid masses of 774.7, 786.8 and 788.6 m/z were analysed by MSn in positive ionisation mode (Fig. 1d). In each case, an initial head group was lost leaving a significant signal of 595.6 m/z, the mass of the glycolipid building block diacylglycerol (DAG). Further fragmentation leads to the loss of either fatty acyl chain from DAG, leaving monoacylglycerols of 313.2 and 339.3 m/z. Two monoacylglycerols with different masses are produced as a result of the original lipid containing 16:0 and 18:1 fatty acids (313.2 and 339.3 m/z monoacylglycerols, respectively). To further elucidate the identity of the peaks, a search for a neutral loss of a polar head group was carried out. Thus, the intact masses of 774.7 and 788.6 m/z in positive ionisation mode leads to the loss of a head group of −179 and −193 m/z, which corresponds to a hexose- and a glucuronate- group, respectively (Fig. 1d), suggesting the occurrence of novel monoglucosyldiacylglycerol (MGDG) and glucuronic acid diacylglycerol (GADG) glycolipids in P. aeruginosa. The third glycolipid peak at 8.7 min remains an unknown lipid with intact mass of 786.8 m/z (hereafter designated as a putative unknown glycolipid, UGL). Together, these data confirm the production of new glycolipids in P. aeruginosa in response to Pi stress.Comparative proteomics uncover the lipid renovation pathway in P. aeruginosa
To determine the proteomic response of P. aeruginosa to phosphorus limitation, and identify the genes involved in glycolipid formation, strain PAO1 was cultivated under high and low Pi conditions for 8 h and the cellular proteome then analysed. A total of 2844 proteins were detected, 175 of which were found to be differentially regulated by Pi availability (Fig. 2a, Table S1). In line with previous transcriptomic studies of strain PAO1 [18], major phosphorus acquisition mechanisms were highly expressed under Pi stress conditions, e.g. the Pi-specific transporter PstSCAB, the two-component regulator PhoBR (Table S1) [31].Fig. 2: Comparative multi-omic analyses for the identification of the PlcP-Agt pathway responsible for glycolipid formation in Pseudomonas aeruginosa strain PAO1.a Volcano plot depicting differentially expressed proteins when comparing Pi-replete and Pi-deplete conditions. Significantly upregulated proteins when under Pi stress are shown in red (left), and those that are significantly upregulated when Pi is sufficient are in green (right). Significance was accepted when the false discovery rate (FDR) was More

IACUC approval: all work described below was approved by the Boise State Institutional Animal Care and Use Committee: AC15-021.Site layoutWe selected 20 sites, across five drainages, within the Pioneer Mountains of Idaho—matched for elevation and riparian habitat. We split these 20 sites into 10 noise playback sites, and 10 control sites (Fig. 1A; S1). The control sites ranged from quiet, slow-moving streams to relatively loud whitewater torrents. Noise playback sites, on the other hand, were relatively quiet (not whitewater) sites, where we broadcast loud whitewater river recordings with speaker arrays hung from towers (Fig. S1; S2; S3; S4; see supplementary information for more details on noise file creation, playback equipment, and experimental setup). At five of the noise playback sites we broadcast normal river noise (hereafter referred to as ‘river noise’ sites), and at the other five noise sites we broadcast spectrally-altered river recordings (hereafter referred to as “shifted noise” sites).Our field sites were oriented along the riparian zone, with data collection occurring at three primary locations within each site (Fig. S1): (1) roughly in the middle of the speaker tower systems, (2) at a shorter distance from the middle location (mean: 198.2 ± 54.5 m SD; range: 117.6–384.5 m), and (3) and a longer distance from the middle location (in the opposite direction from the nearer location; mean: 312.7 ± 64.7 m SD; range: 249.1–479.6 m). Thus, sites were approximately 510.9 ± 98.3 m long (range: 374.7–850.6 m), along the riparian corridor. All control sites were, at minimum, 1 km apart along the riparian corridor from any noise site, to maintain acoustic independence (see Fig. 1A; S1).Data collectionBirds
We conducted three-minute avian point counts between one half hour before sunrise and 6 h after sunrise (roughly 0530–1130 h). During the project, we conducted 1330 point-counts from 28 May to 20 July 2017 and 1639 point-count events occurred from 7 May to 24 July in 2018.
Caterpillar deploymentWe deployed a total of 720 clay caterpillars throughout the 2018 breeding season. Forty caterpillars were glued to stems and branches of trees between 1 and 2.5 m high at each site (Fig. S8). Twenty caterpillars surrounded the middle point count location at each site (a set of 10 were placed upstream, and another set of 10 were placed downstream starting from the middle ARU location), while the other twenty were at upstream and downstream sampling locations (10 each at upstream and downstream locations). We placed each caterpillar along the riparian corridor, at least 1 m apart from each other30. See Supplementary information for details on caterpillar predation scoring.Bird trait analysisWe performed a trait-based analysis to understand the mechanistic patterns of bird distributions in our study paradigm. Avian vocal frequencies and body mass were collected from Hu and Cardoso 2009, Cardoso 2014, and Francis 201516,31,32. When multiple sources contained data, the values were averaged. There were a few cases where none of those sources contained a vocal frequency or mass measurement for species of interest. Thus, representative songs were downloaded from the Macaulay Library of the Cornell Lab of Ornithology based on recording quality and geographical relevance (MacGillivray’s warbler: ML42249; dusky flycatcher: ML534684; red-naped sapsuckers: ML6956), and analyzed with Avisoft SASLab Pro to obtain a peak frequency measure. Mass measurements for these ‘missing’ birds were taken from the ‘All about birds’ webpage of the Cornell Lab of Ornithology.BatsMeasuring and identifying bat callsWe measured bat activity using Song Meter 3 (hereafter “SM3”) recording units (Wildlife Acoustics Inc., Massachusetts, USA) equipped with a single SMU (Wildlife Acoustics Inc.) ultrasonic microphone. One recording unit was used at each site and we pseudo-randomly rotated the unit between the three point-count locations so that each location was monitored for at least 21 days. We mounted microphones on metal conduit at a height of ~3 m, oriented perpendicular to the ground and facing away from the stream to optimize recording conditions (Fig. S9; S10; see Supplementary information for more information).Robotic insectsWe used a modified version of Lazure and Fenton’s26 apparatus to present bats with a fluttering target (Fig. S12). This consisted of a 3 cm2 piece of masking tape affixed to a metal rod [30.48 cm length × 3.25 mm diameter], which itself was connected to a 12-volt brushed DC motor (AndyMark 9015 12 V, AndyMark Inc., Kokomo, IN, USA). The no-load revolution speed of these motors (267 Hz) falls within the range of wingbeat frequency measured in Chironomidae27,33, a group that is an important food source for many North American bat species34.We attached each motor to a tripod made of PVC piping and positioned the tripod such that the target was approximately 1.2 m above the ground. Each motor was powered by a 12 V battery (35Ah AGM; DURA12-35C, Duracell) which was controlled by a programmable 12 V timer (CN101, FAVOLCANO) to automatically start and stop the motor each night. The rotors were powered for 2 h following sunset.Prey-sound speaker playbackWe created a playlist composed of several insect acoustic cues to present gleaning bats: a beetle (Tenebrio molitor) walking on dried grass, a cricket (Acheta domesticus) walking on leaves, mealworm larvae (Tenebrio molitor) on leaves, fall field cricket (Gryllus pennsylvanicus) calls, and fork-tailed bush katydid (Scudderia furcata) calls. The cricket and katydid calls were sourced from the Macaulay Library (ML527360 and ML107505, respectively).Experimental setup for bat foraging testsMost sites received two rotors (Fig. S12) and two speakers (Fig. S13): one of each at the center of the site, and one of each at approximately 125 m from the center of the site (in opposite directions in order to have tests in a range of acoustic environments), placed roughly 10 m from the edge of the riparian zone. Rotors and speakers at the center locations were separated by at least 50 m. The exception to this setup were the four positive control (loud whitewater river) sites, which only received a single rotor and speaker separated by 50 m because of logistical difficulties of accessing those sites. We paired each rotor and speaker with an SM2BAT + bat detector equipped with an SMX-US microphone (Wildlife Acoustics Inc.)35, using tripods to elevate the microphones approximately 1 m off the ground and ~1 m from the speaker/rotor. We programmed the bat detectors with a gain of 36 dB and a trigger level of 18 dB to limit recordings to bats that were passing within the immediate vicinity. To allow for a comparison of activity between speakers and rotors, bat activity was only considered for the first two hours following sunset.Bat trait analysisWe collected bat foraging behavior and peak echolocation frequency information to use as predictors in a phylogenetically controlled trait analysis (Tables S8; S13). We based our behavioral foraging classifications on the categories of Ratcliffe et al.36 and followed the classifications of Gordon et al.37 where possible, and others38,39,40,41,42,43 where necessary. We extracted peak echolocation frequency from the 2017 and 2018 SM3 field recordings and employed two controls to decrease variability in call parameters potentially introduced via this method. First, we selected only recordings made on control sites in 2017 and 2018 (n = 740,848 calls), as echolocation call characteristics may be affected by local acoustic environments (e.g., Bunkley et al.)22. Secondly, we averaged all call parameters per species per hour at each site to decrease the possible effects of few individuals driving measurements. This resulted in 9538 species-hours of recordings, which themselves were averaged per species (Table S13).Quantifying environmental variablesWe used long-term monitoring of the acoustic environment (via Roland R05 recorders) to calculate daily sound pressure level (L50 dBA) and median frequency (kHz) values for each location (see supplementary information for details on quantification of all predictor variables).Sound pressure level (SPL)We converted 106,769 h of long-term ARU recordings into daily-averaged median sound pressure levels (L50; measured as dBA rel. 20 µPa) see refs. 13,44 using custom software ‘AUDIO2NVSPL’ and ‘Acoustic Monitoring Toolbox’ (Damon Joyce, Natural Sounds and Night Skies Division, National Park Service).Acoustic environment spectrumWe used custom software45 in the programming language R and the package ‘FFmpeg’ in command prompt to convert 106,769 h of long-term recordings into 71,282 individual 3-minute files starting each hour of the day (Fig. S5). Thus 24, 3-min files were created per acoustic recording location per day (one for every hour). We then used the packages “tuneR” and “seewave” to read in and measure the median frequency of sound files, respectively45,46,47. These hourly metrics were then averaged by date to create a daily metric.StatisticsAll models of abundance, activity, and foraging transects were generalized linear mixed effects models (glmm) in R48 using the package ‘lme4’49,50 or ‘glmmTMB’51. All distribution families were selected based on theoretical sampling processes of the data, models were checked for collinearity (VIF scores)52, and model fits were visually checked with residual plots (see supplemental R code)53.Bird abundance and bat activity
Model predictors and covariates
Both bird and bat models had the following variables in a glmm: site and bird/bat species were random effects terms and sound pressure level (dBA L50), sound spectrum (median frequency), the interaction between sound pressure level and spectrum, elevation, percent riparian vegetation, ordinal date (and a quadratic version of this), and year as fixed effects. While year is sometimes used as a random-effect term, it is suggested to be used as a fixed effect if fewer than five levels exist for that factor, as variance estimates become imprecise54,55. Additionally, moon phase was a fixed effect in the bat models56, while spectral overlap (the absolute difference between sound spectrum and bird species vocalization frequencies) and the interaction between sound pressure level and spectral overlap were fixed effects in bird models.
We attempted to fit both sound pressure level and spectrum as having random slopes for each species, yet both bat and bird models would not converge with such complex model structure. Thus, we followed group models with individual species models (see Supplementary information).

Model family distribution and link function
For both bird and bat counts, we used a negative binomial distribution with a log link, rather than a Poisson distribution, because data were over-dispersed. We plotted variance-mean relationships and residuals of multiple models to select the appropriate variance structure, and compared these with AIC to select the best-fitting distribution (see R script for further justification of these methods)54.

Individual species models
Individual species models were parameterized the same as above (except without the species term). All 12 bat species (see Tables S6; S10) and 26 of the most common birds (see Tables S2; S9) were modeled individually to be able to interpret model parameter estimates, with complex interactions, for each species.
Clay caterpillar predationWe modeled caterpillar predation with a glmm (binomial family; logit link function), using the number of individual scorers as weights in the model. Like the bird abundance model, we used site as a random effect and sound pressure level (dBA L50), spectral frequency (median), elevation, percent riparian vegetation, ordinal date, and year as fixed effects (Table S4). Additionally, the predicted number of birds at a site were modeled as fixed effects to control for varying amounts of foraging birds on the landscape.Robotic moths and prey-sound speakersRobotic moth and prey-sound speaker models were parameterized exactly the same as the overall bat activity model. That is, the model was fit with a negative binomial family (log link) with site and species as random effects and sound pressure level (dBA L50), sound spectrum (median frequency), the interaction between sound pressure level and spectrum, moon phase, elevation, percent riparian vegetation, ordinal date (and a quadratic version of this), and year as fixed effects. Additionally, the predicted number of bats at a site were modeled as fixed effects to control for varying amounts of foraging bats on the landscape.Trait analysesWe performed trait analyses with phylogenetic generalized least squares (PGLS) to control for relatedness while predicting species responses to noise12. We performed PGLS analyses with the gls function in the R package nlme57, and accounted for error in the response variable with a fixed-variance weighting function of one divided by the square root of the standard error of the response estimate58,59. We accounted for phylogenetic structure by estimating Pagel’s λ60. When λ estimates fell outside of the zero to 1 range, we fixed λ at the nearest boundary. For bird models, we used a pruned consensus tree from a recent class-wide phylogeny61. For bats, we used a pruned mammalian tree62. We used initial global models with all traits as variables that explained the responses to sound pressure level (SPL; birds and bats), spectral overlap with birdsong (birds), background frequency (bats), and the interaction between SPL and each measure of frequency (birds and bats). We then used AIC model selection63 to choose top models in explaining these patterns. Models with dAIC ≤4 are included in Table S3 (birds) and Table S8 (bats), and the top model is interpreted in the main text.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.Malthus, T. An Essay on the Principle of Population (J. Johnson Publisher, London, 1798).
Google Scholar 
2.Nicholson, A. J. The balance of animal populations. J. Anim. Ecol. 2(1), 132–588 (1933).Article 

Google Scholar 
3.Andrewartha, H. G. & Birch, L. C. The distribution and abundance of animals (University of Chicago Press, 1954).
Google Scholar 
4.Turchin, P. Complex Population Dynamics: A Theoretical/Empirical Synthesis. Monographs in Population Biology Vol. 35 (Princeton University Press, 2003).MATH 

Google Scholar 
5.Bellows, T. S. & Hassell, M. P. Theories and mechanisms of natural population regulation. In Handbook of Biological Control (eds Bellows, T. S. & Fisher, T. W.) 17–44 (Academic Press, 1999).
Google Scholar 
6.Cock, M. J. W. et al. Do new access and benefit sharing procedures under the convention on biological diversity threaten the future of biological control?. Biocontrol 55, 199–218. https://doi.org/10.1007/s10526-009-9234-9 (2010).Article 

Google Scholar 
7.Biondi, A., Guedes, R. N. C., Wan, F. H. & Desneux, N. Ecology, worldwide spread, and management of the invasive South American tomato pinworm, Tuta absoluta: past, present, and future. Annu. Rev. Entomol. 63, 239–258. https://doi.org/10.1146/annurev-ento-031616-034933 (2018).CAS 
Article 
PubMed 

Google Scholar 
8.Ferracini, C. et al. Natural enemies of Tuta absoluta in the Mediterranean basin, Europe and South America. Biocontrol Sci. Technol. 29, 578–609. https://doi.org/10.1080/09583157.2019.1572711 (2019).Article 

Google Scholar 
9.Guedes, N.C., Picanco M. Tuta absoluta in South America: pest status, management & insecticide resistance. Proceedings of the EPPO/IOBC/FAO/NEPPO Joint International Symposium on Management of Tuta absoluta (tomato borer). Agadir, Marocco, Nov. 16–18, 2011, 15–16 (2011).10.Urbaneja, A., Monton, H. & Mollá, O. Suitability of the tomato borer Tuta absoluta as prey for Macrolophus pygmaeus and Nesidiocoris tenuis. J. Appl. Entomol. 133, 292–296 (2009).Article 

Google Scholar 
11.Parra, J. R. P. & Zucchi, R. A. Trichogramma in Brazil: feasibility of use after 20 years of research. Neotrop. Entomol. 33, 271–281 (2004).Article 

Google Scholar 
12.Pérez-Hedo, M. & Urbaneja, A. The zoophytophagous predator Nesidiocoris tenuis: a successful but controversial biocontrol agent in tomato crops. In Advances in Insect Control and Resistance Management (eds Horowitz, A. R. & Ishaaya, I.) 121–138 (Springer, Dordrecht, 2016).
Google Scholar 
13.Mollá, O., Biondi, A., Alonso-Valiente, M. & Urbaneja, A. A comparative life history study of two mirid bugs preying on Tuta absoluta and Ephestia kuehniella eggs on tomato crops: implications for biological control. Biocontrol 59, 175–183. https://doi.org/10.1007/s10526-013-9553-8 (2014).Article 

Google Scholar 
14.Bajonero, J.G. Tuta absoluta (Meyrick, 1917) (Lepidoptera: Gelechiidae): adequação de uma dieta artificial e avaliação do seu controle biológico com Trichogramma pretiosum Riley em tomateiro. PhD Thesis, Universidade de São Paulo Escola Superior de Agricultura “Luiz de Queiroz”, Piracicaba, Sao Paolo, Brazil, p. 87 (2016).15.Calvo, F. J., Lorente, M. J., Stansly, P. A. & Belda, J. E. Pre-plant release of Nesidiocoris tenuis and supplementary tactics for control of Tuta absoluta and Bemisia tabaci in greenhouse tomato. Entomol. Exp. Appl. 143, 111–119 (2012).Article 

Google Scholar 
16.van Lenteren, J. C. et al. Pest kill rate as aggregate evaluation criterion to rank biological control agents: a case study with Neotropical predators of Tuta absoluta on tomato. Bull. Entomol. Res. 109, 812–820. https://doi.org/10.1017/S0007485319000130 (2019).CAS 
Article 
PubMed 

Google Scholar 
17.Tommasini, M. G., van Lenteren, J. C. & Burgio, G. Biological traits and predation capacity of four Orius species on two prey species. Bull. Insectol. 57, 79–94 (2004).
Google Scholar 
18.van Lenteren, J. C. Ecology: Cool Science, But Does It Help? 44 (Wageningen University, Wageningen, 2010).
Google Scholar 
19.Biondi, A., Desneux, N., Amiens-Desneux, E., Siscaro, G. & Zappalà, L. Biology and developmental strategies of the Palaearctic parasitoid Bracon nigricans (Hymenoptera: Braconidae) on the Neotropical moth Tuta absoluta (Lepidoptera: Gelechiidae). J. Econ. Entomol. 106, 1638–1647 (2013).Article 

Google Scholar 
20.Bin, F., Vinson, S. B. Efficacy assessment in egg parasitoids (Hymenoptera): proposal for a unified terminology. In Trichogramma and other egg Parasitoids (eds. Wajnberg E. & Vinson S.B.). Proceedings 3rd International Symposium, San Antonio, Texas, pp. 175–179 (1990).21.Abram, P. K., Brodeur, J., Urbaneja, A. & Tena, A. Nonreproductive effects of insect parasitoids on their hosts. Annu. Rev. Entomol. 64, 259–276. https://doi.org/10.1146/annurev-ento-011118-111753 (2019).CAS 
Article 
PubMed 

Google Scholar 
22.Chailleux, A., Droui, A., Bearez, P. & Desneux, N. Survival of a specialist natural enemy experiencing resource competition with an omnivorous predator when sharing the invasive prey Tuta absoluta. Ecol. Evol. 7, 8329–8337 (2017).Article 

Google Scholar 
23.Han, P. et al. Bottom-up efects of irrigation, fertilization and plant resistance on Tuta absoluta: implications for Integrated Pest Management. J. Pest Sci. 92, 1359–1370. https://doi.org/10.1007/s10340-018-1066-x (2019).Article 

Google Scholar 
24.Calvo, F. J., Bolckmans, K. & Belda, J. E. Release rate for a pre-plant application of Nesidiocoris tenuis for Bemisia tabaci control in tomato. Biocontrol 57, 809–817 (2012).Article 

Google Scholar 
25.van Lenteren, J. C., Bueno, V. H. P., Calvo, F. J., Calixto, A. M. & Montes, F. C. Comparative effectiveness and injury to tomato plants of three Neotropical mirid predators of Tuta absoluta (Lepidoptera: Gelechiidae). J. Econ. Entomol. 111, 1080–1086. https://doi.org/10.1093/jee/toy057 (2018).Article 
PubMed 

Google Scholar 
26.Calvo, F. J., Soriano, J. D., Stansly, P. A. & Belda, J. E. Can the parasitoid Necremnus tutae (Hymenoptera: Eulophidae) improve existing biological control of the tomato leafminer Tuta aboluta (Lepidoptera: Gelechiidae). Bull. Entomol. Res. 406, 502–511. https://doi.org/10.1017/S0007485316000183 (2016).CAS 
Article 

Google Scholar 
27.Crisol-Marinez, E. & van der Blom, J. Necremnus tutae (Hymenoptera, Eulophidae) is widespread and efficiently controls Tuta absoluta in tomato greenhouses in SE Spain. IOBC/WPRS Bull. 147, 22–29 (2019).
Google Scholar 
28.Castañé, C., van der Blom, J. & Nicot, P. C. Tomatoes. In Integrated Pest And Disease Management In Greenhouse Crops (eds Gullino, M. L. et al.) 487–511 (Springer, Switzerland, 2020). https://doi.org/10.1007/978-3-030-22304-5_17.
Google Scholar 
29.Knapp, M., Palevsky, E. & Rapisarda, C. Insect and mite pests. In Integrated Pest and Disease Management in Greenhouse Crops (eds Gullino, M. L. et al.) 101–144 (Springer, Switzerland, 2020). https://doi.org/10.1007/978-3-030-22304-5_17.
Google Scholar 
30.van Lenteren, J. C., Alomar, O., Ravensberg, W. J. & Urbaneja, A. Biological control agents for control of pests in greenhouses. In Integrated Pest And Disease Management In Greenhouse Crops (eds Gullino, M. L. et al.) 409–440 (Springer, Switzerland, 2020).
Google Scholar 
31.Pratissoli, D. & de Carvalho, J.D. Guia de Campo: Pragas da Cultura do Tomateiro. Alegre, ES: NUDEMAFI, Centro de Ciências Agrárias, UFES, 35pp. (Série Técnica/NUDEMAFI, ISSN 2359-4179; 1) (2015).32.Bodino, N., Ferracini, C. & Tavella, L. Functional response and age-specific foraging behaviour of Necremnus tutae and N. cosmopterix, native natural enemies of the invasive pest Tuta absoluta in Mediterranean area. J. Pest Sci. 92, 1467–1478. https://doi.org/10.1007/s10340-018-1025-6 (2019).Article 

Google Scholar 
33.Pérez-Hedo, M., Riahi, C. & Urbaneja, A. Use of zoophytophagous mirid bugs in horticultural crops: current challenges and future perspectives. Pest Manag. Sci. 77, 33–42 (2021).Article 

Google Scholar 
34.Wheeler, A. G. Jr. & Krimmel, B. A. Mirid (Hemiptera: Heteroptera) specialists of sticky plants: adaptations, interactions and ecological implications. Annu. Rev. Entomol. 60, 393–414 (2015).CAS 
Article 

Google Scholar 
35.Bueno, V. H. P., Lins, J. C., Silva, D. B. & van Lenteren, J. C. Is predation of Tuta absoluta by three Neotropical mirid predators affected by tomato lines with different densities in glandular trichomes?. Arthropod-Plant Int. 13, 41–48. https://doi.org/10.1007/s11829-018-9658-1 (2019).Article 

Google Scholar 
36.Pérez-Hedo, M., Arias-Sanguino, A. M. & Urbaneja, A. Induced tomato plant resistance against Tetranychus urticae triggered by the phytophagy of Nesidiocoris tenuis. Front. Plant Sci. 9, 1419. https://doi.org/10.3389/fpls.2018.01419 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
37.Barra-Bucarei, L., Devotto Moreno, L. & Iglesis, A. F. Biological control in Chile. In Biological Control in Latin America and the Caribbean: Its Rich History and Bright Future (eds van Lenteren, J. C. et al.) 108–123 (CAB International, Wallingford, 2020).
Google Scholar 
38.López, S. N., OrozcoMuñoz, A., Andorno, A. V., Cuello, E. M. & Cagnotti, C. L. Predatory capacity of Tupiocoris cucurbitaceus (Hemiptera Miridae) on several pests of tomato. Bull. Insectol. 72, 201–205 (2019).
Google Scholar 
39.Fauvel, G., Malausa, J. C. & Kaspar, B. Etude en laboratoire des principales caracteristiques biologiques de Macrolophus caliginosus (Heteroptera: Miridae). Entomophaga 32, 529–543 (1987).Article 

Google Scholar 
40.Mollá, O., Montón, H., Vanaclocha, P., Beitia, F. & Urbaneja, A. Predation by the mirids Nesidiocoris tenuis and Macrolophus pygmaeus on the tomato borer Tuta absoluta. IOBC/WPRS Bull. 49, 203–208 (2009).
Google Scholar 
41.Sánchez, J. A., Lacasa, A., Arnó, J., Castañé, C. & Alomar, O. Life history parameters for Nesidiocoris tenuis (Reuter) (Het., Miridae) under different temperature regimes. J. Appl. Entomol. 133, 125–132. https://doi.org/10.1111/j.1439-0418.2008.01342.x (2009).Article 

Google Scholar 
42.Sánchez, J. A., La-Spina, M. & Lacasa, A. Numerical response of Nesidiocoris tenuis (Hemiptera: Miridae) preying on Tuta absoluta (Lepidoptera: Gelechiidae) in tomato crops. Eur. J. Entomol. 11, 387–395 (2014).Article 

Google Scholar 
43.Arnó, J. et al. Tuta absoluta, a new pest in IPM tomatoes in the northeast of Spain. IOBC/WPRS Bull. 49, 203–208 (2009).
Google Scholar 
44.Mahdavi, T. S., Madadi, H. & Biondi, A. Predation and reproduction of the generalist predator Nabis pseudoferus preying on Tuta absoluta. Entomol. Exp. Appl. 168, 732–741. https://doi.org/10.1111/eea.12975 (2020).Article 

Google Scholar 
45.Salas Gervassio, N. G., Aquino, D., Vallina, C., Biondi, A. & Luna, M. G. A re-examination of Tuta absoluta parasitoids in South America for optimized biological control. J. Pest Sci. 92, 1343–1357. https://doi.org/10.1007/s10340-018-01078-1 (2019).Article 

Google Scholar 
46.Idriss, G. E. A., Mohamed, S. A., Khamis, F., Du Plessis, H. & Ekesi, S. Biology and performance of two indigenous larval parasitoids on Tuta absoluta (Lepidoptera: Gelechiidae) in Sudan. Biocontrol Sci. Technol. 28, 614–628. https://doi.org/10.1080/09583157.2018.1477117 (2018).Article 

Google Scholar 
47.Cagnotti, C. L., Riquelme Virgala, M., Botto, E. N. & López, S. N. Dispersion and persistence of Trichogrammatoidea bactrae (Nagaraja) over Tuta absoluta (Meyrick), in tomato greenhouses. Neotrop. Entomol. 47, 553–559. https://doi.org/10.1007/s13744-017-0573-4 (2018).CAS 
Article 
PubMed 

Google Scholar 
48.Pratissoli, D. Bioecologia de Trichogramma pretiosum Riley, 1879, nas traças, Scrobipalpuloides absoluta (Meyrick, 1917) e Phthorimaea operculella (Zeller, 1873), em tomateiro. Piracicaba: Doutorado – Escola Superior de Agricultura “Luiz de Queiroz”/USP, p. 153 (1995).49.Pratissoli, D. & Parra, J. R. P. Fertility life table of Trichogramma pretiosum (Hym., Trichogrammatidae) in eggs of Tuta absoluta and Phthorimaea operculella (Lep., Gelechiidae) at different temperatures. J. Appl. Entomol. 124, 339–342 (2000).Article 

Google Scholar 
50.Riquelme Virgala, M. B. & Botto, E. N. Estudios biológicos de Trichogrammatoidea bactrae Nagaraja (Hymenoptera: Trichogrammatidae), parasitoide de huevos de Tuta absoluta Meyrick (Lepidoptera: Gelechiidae). Neotrop. Entomol. 36, 612–617 (2010).Article 

Google Scholar 
51.Aigbedion-Atalor, P.O, Abuelgasim Mohamed, S., Hill, M.P., Zalucki, M.P., Azrag, A.G.A., Srinivasan, R. & Ekesi, S. Host stage preference and performance of Dolichogenidea gelechiidivoris (Hymenoptera: Braconidae), a candidate for classical biological control of Tuta absoluta in Africa. Biol. Control. Preprint at https://doi.org/https://doi.org/10.1016/j.biocontrol.2020.104215 (2020).52.Guleria, P., Sharma, P. L., Verma, S. C. & Chandel, R. S. Life history traits and host-killing rate of Neochrysocharis formosa on Tuta absoluta. Biocontrol 65, 401–411. https://doi.org/10.1007/s10526-020-10016-z (2020).CAS 
Article 

Google Scholar 
53.Calvo, F. J., Soriano, J. D., Bolckmans, K. & Belda, J. E. Host instar suitability and life-history parameters under different temperature regimes of Necremnus artynes on Tuta absoluta. Biocontrol Sci. Technol. 23, 803–815 (2013).Article 

Google Scholar 
54.Nieves, E. L., Pereyra, P. C., Luna, M. G., Medone, P. & Sánchez, N. E. Laboratory population parameters and field impact of the larval endoparasitoid Pseudapanteles dignus (Hymenoptera: Braconidae) on its host Tuta absoluta (Lepidoptera: Gelechiidae) in tomato crops in Argentina. J. Econ. Entomol. 108, 1553–1559 (2015).Article 

Google Scholar 
55.Luna, M. G., Wada, V. & Sánchez, N. E. Biology of Dineulophus phtorimaeae (Hymenoptera: Eulophidae), and field interaction with Pseudapanteles dignus (Hymenoptera: Braconidae), larval parasitoids of Tuta absoluta (Lepidoptera: Gelechiidae) in tomato. Ann. Entomol. Soc. Am. 106, 936–942 (2010).Article 

Google Scholar 
56.Savino, V., Coviella, C. E. & Luna, M. G. Reproductive biology and functional response of Dineulophus phtorimaeae a natural enemy of the tomato moth Tuta absoluta. J. Insect Sci. 12, 1–14 (2012).Article 

Google Scholar 
57.Birch, L. C. The intrinsic rate of natural increase of an insect population. J. Anim. Ecol. 17, 15–26 (1948).Article 

Google Scholar 
58.Lotka, A. J. Relation between birth rates and death rates. Science 26, 21–22 (1907).ADS 
CAS 
Article 

Google Scholar 
59.Lotka, A. J. & Sharpe, F. R. A problem in age distribution. Philos. Mag. 6(21), 339–345 (1911).MATH 

Google Scholar 
60.Dalgaard, P. Introductory Statistics With R 2nd edn. (Springer, New York, 2008).Book 

Google Scholar  More