Philippa Kaur

Britto, D. T., Siddiqi, M. Y., Glass, A. D. M. & Kronzucker, H. J. Futile transmembrane NH4+ cycling: A cellular hypothesis to explain ammonium toxicity in plants. PNAS 98(7), 4255–4258 (2001).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Britto, D. T. & Konzucker, H. J. NH4 + toxicity in higher plants: a critical review. J. Plant Physiol. 159, 567–584 (2002).CAS 
Article 

Google Scholar 
Müller, T., Walter, B., Wirtz, A. & Burkovski, A. Ammonium toxicity in bacteria. Curr. Microbiol. 52, 400–406 (2006).PubMed 
Article 
CAS 

Google Scholar 
Mayes, M. A., Alexander, H. C., Hopkins, D. L. & Latvaitis, P. B. Acute and chronic toxicity of ammonia to freshwater fish: a site-specific study. Environ. Toxicol. Chem. 5, 437–442 (1986).CAS 
PubMed 
Article 

Google Scholar 
Archer, M. C. Hazards of nitrate, nitrite, and n-nitroso compounds in human nutrition. Nutr. Toxicol. 1, 329–381 (2012).
Google Scholar 
Brione, E., Martin, G. & Morvan, J. Non-destructive technique for elimination of nutrients from pig manure, 33–37. In Horan, N. J., Lowe, P. & Stentiford, E. I. (ed.), Nutrient removal from wastewaters. Techonomic Publishing Co. (Lancaster 1994).Butler, D., Friedler, E. & Gatt, K. Characterising the quantity and quality of domestic wastewater inflows. Wal. Sci. Tech. 31(7), 13–24 (1995).CAS 
Article 

Google Scholar 
Mahne, I., Prinčič, A. & Megušar, F. Nitrification/denitrification in nitrogen high-strength liquid wastes. Water Res. 30, 2107–2111 (1996).CAS 
Article 

Google Scholar 
Arp, D. J., Sayavedra-Soto, L. A. & Hommes, N. G. Molecular biology and biochemistry of ammonia oxidation by Nitrosomonas Europaea. Arch. Microbiol. 178, 250–255. https://doi.org/10.1007/s00203-002-0452-0 (2002).CAS 
Article 
PubMed 

Google Scholar 
Dalton, H. Ammonia oxidation by the methane oxidizing bacterium Methylococcuscapsulatus strain bath. Arch. Microbiol. 114(3), 273–279 (1977).CAS 
Article 

Google Scholar 
Daum, M. et al. Physiological and molecular biological characterization of ammonia oxidation of the heterotrophic nitrifier pseudomonas putida. Curr Microbiol 37, 281–288 (1998).CAS 
PubMed 
Article 

Google Scholar 
Do, H. et al. Simultaneous effect of temperature, cyanide and ammonia-oxidizing bacteria concentrations on ammonia oxidation. J. Ind. Microbiol. Biotechnol. 35, 1331–1338. https://doi.org/10.1007/s10295-008-0415-9 (2008).MathSciNet 
CAS 
Article 
PubMed 

Google Scholar 
Lin, Y. et al. Physiological and molecular biological characteristics of heterotrophic ammonia oxidation by Bacillus sp. LY. World J. Microbiol. Biotechnol. 26, 1605–1612 (2010).ADS 
CAS 
Article 

Google Scholar 
Snider, M. J. & Wolfenden, R. The rate of spontaneous decarboxylation of amino acids. J. Am. Chem. Soc. 122(46), 11507–11508 (2000).CAS 
Article 

Google Scholar 
Zamora, R., León, M. M. & Hidalgo, F. J. Oxidative versus non-oxidative decarboxylation of amino acids: conditions for the preferential formation of either strecker aldehydes or amines in amino acid/lipid-derived reactive carbonyl model systems. J. Agric. Food Chem. 63(36), 8037–8043 (2015).CAS 
PubMed 
Article 

Google Scholar 
Perez, M. et al. The relationship among tyrosine decarboxylase and agmatine deiminase pathways in enterococcus faecalis. Front. Microbiol. https://doi.org/10.3389/fmicb.2017.02107/full (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Chen, B. Y., Lin, K. W., Wang, Y. M. & Yen, C. Y. Revealing interactive toxicity of aromatic amines to azo dye decolorizer Aeromonas hydrophila. J. Hazard Mater. 166(1), 187–194 (2009).CAS 
PubMed 
Article 

Google Scholar 
Greim, H., Bury, D., Klimisch, H. J., Oeben-Negele, M. & Ziegler-Skylakakis, K. Toxicity of aliphatic amines: structure-activity relationship. Chemosphere 36(2), 271–295. https://doi.org/10.1016/s0045-6535(97)00365-2 (1998).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Newsome, L. D., Johnson, D. E., Lipnick, R. L., Broderius, S. J. & Russom, C. L. A QSAR study of the toxicity of amines to the fathead minnow. Sci. Total Environ. 109–110, 537–551 (1991).ADS 
PubMed 
Article 

Google Scholar 
Pinheiro, H. M., Touraud, E. & Thomas, O. Aromatic amines from azo dye reduction: status review with emphasis on direct UV spectrophotometric detection in textile industry wastewaters. Dyes Pigm. 61, 121–139 (2004).CAS 
Article 

Google Scholar 
Poste, A. E., Grung, M. & Wright, R. F. Wright Amines and amine-related compounds in surface waters: A review of sources, concentrations and aquatic toxicity. Sci. Total Environ. 481, 274–279 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ramos, E. U., Vaal, M. A. & Hermens, J. L. M. Interspecies sensitivity in the aquatic toxicity of aromatic amines. Environ. Toxicol. Pharmacol. 11(3–4), 149–158 (2002).CAS 
PubMed 
Article 

Google Scholar 
Nicholas, G. A., Peter, J. B., Milton, W. G. & Stan, V. G. Microbial decomposition of wood in streams: distribution of microflora and factors affecting [14C] lignocellulose mineralization. Appl. Environ. Microbiol. 46(6), 1409–1416 (1983).Article 

Google Scholar 
Okabe, S., Kindaichi, T. & Ito, T. Fate of 14C-labeled microbial products derived from nitrifying bacteria in autotrophic nitrifying biofilms. Appl. Environ. Microbiol. 71(7), 3987–3994 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Qiao, Z. et al. Microbial Heterotrophic Nitrification-Aerobic Denitrification Dominates Simultaneous Removal of Aniline and Ammonium in Aquatic Ecosystems. Water Air Soil Pollut. https://doi.org/10.1007/s11270-020-04476-3 (2020).Article 

Google Scholar 
Celik, A. Oxytetracycline and paracetamol biodegradation performance in the same enriched feed medium with aerobic nitrification/anaerobic denitrification SBR. Bioprocess Biosyst. Eng. 44, 1649–1658 (2021).CAS 
PubMed 
Article 

Google Scholar 
Spataru, P., Povar, I., Mosanu, E. & Trancalan, A. Study of stable nitrogen forms in natural surface waters in the presence of mineral substrates. Chem. J. Moldova 10, 26–32 (2015).CAS 
Article 

Google Scholar 
Spataru, P. et al. Influence of the interaction of calcium carbonate particles with surfactants on the degree of water pollution in small rivers. Ecol. Process. https://doi.org/10.1186/s13717-017-0086-4#article-dates-history (2017).Article 

Google Scholar 
Spataru, P., Povar, I., Lupascu, T., Alder, A. C. & Mosanu, E. Study of nitrogen forms in seasonal dynamics and kinetics of nitrification and denitrification in Prut and Nistru river waters. Environ. Eng. Manag. J. 17(7), 1711–1719 (2018).CAS 
Article 

Google Scholar 
Cui, Z. G., Cui, Y. Z., Cui, C. F., Chen, Z. & Binks, B. P. Aqueous foams stabilized by in situ surface activation of CaCO3 Nanoparticles via Adsorption of Anionic Surfactant. Langmuir 26(15), 12567–12574 (2010).CAS 
PubMed 
Article 

Google Scholar 
Cui, Z.-G., Cui, C.-F., Zhu, Y. & Binks, B. P. Multiple phase inversion of emulsions stabilized by in situ surface activation of CaCO3 nanoparticles via adsorption of Fatty acids. Langmuir 28(1), 314–320 (2012).PubMed 
Article 
CAS 

Google Scholar 
Tanaka, T. et al. Biodegradation of endocrine-disrupting chemical aniline by microorganisms. J. Health Sci. 55(4), 625–630 (2009).CAS 
Article 

Google Scholar 
Ahmed, S. et al. Isolation and characterization of a bacterial strain for aniline degradation. Afr. J. Biotechnol. 9(8), 1173–1179 (2010).CAS 
Article 

Google Scholar 
Spataru, P. Transformations of organic substances in surface waters of Republic of Moldova. PhD Dissertation, State University of Moldova (2011).Sandu, M. et al. The dynamic of nitrification process in the presence of cationic surfactants. Proc. SIMI Bucharest 1, 277–281 (2007).
Google Scholar 
Spataru, P., Fernandez, F., Povar, I. & Spataru, T. Behavior of nitrogen soluble forms in natural water in the presence of anionic and cationic surfactants and mineral substrates. Adv. Sci. Eng. 11(2), 70–77. https://doi.org/10.32732/ase.2019.11.2.70 (2019).Article 

Google Scholar 
Reifferscheid, G., Buchinger, S., Cao, Z. & Claus, E. Identification of mutagens in freshwater sediments by the Ames-fluctuation assay using nitroreductase and acetyltransferase overproducing test strains. Environ. Mol. Mutagen. https://doi.org/10.1002/em.20638 (2011).Osadchyy, V., Nabyvanets, B., Linnik, P., Osadcha, N. & Nabyvanets, Y. Characteristics of Surface Water Quality in Processes Determining Surface Water Chemistry, Springer Link, 1–9. https://doi.org/10.1007/978-3-319-42159-9_1 (2016).Matveeva, N. P., Klimenko, O. A. & Trunov, N. M. Simulation of self-purification of natural treatment of organic pollutants in the laboratory, Gidrometeoizdat, Leningrad, 26–31 (in Russian) (1988).ISO 7150-1:2001.Water quality – Determination of ammonium – Spectrometric method.ISO 8466-1:1990. Water quality – Calibration and evaluation of analytical methods and estimation of performance characteristics, 1: Statistical evaluation of the linear calibration function.SR ISO 7890-3:2000 Water quality – The determination of the content of nitrates, 3: The spectrometric method with sulfosalicylic acid.SM SR EN 26777:2006 Water quality – determination of the content of nitrites. The method of the spectrometry of molecular absorption.Sandu, M. et al. Method for nitrate determination in water in the presence of nitrite. Chem. J. Moldova 9, 8–13 (2014).CAS 
Article 

Google Scholar 
Bentzon-Tilia, M. et al. Significant N2 fixation by heterotrophs, photoheterotrophs and heterocystous cyanobacteria in two temperate estuaries. ISME 9, 273–285 (2015).CAS 
Article 

Google Scholar 
Farnelid, H. et al. Active nitrogen-fixing heterotrophic bacteria at and below the chemocline of the central Baltic Sea. ISME J. 7, 1413–1423. https://doi.org/10.1038/ismej.2013.26 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nagatani, H., Shimizu, M. & Valentine, R. C. The mechanism of ammonia assimilation in nitrogen fixing bacteria. Arch. Mikrobiol. 79, 164–175 (1971).CAS 
PubMed 
Article 

Google Scholar 
Allen, A. E., Booth, M. G., Verity, P. G. & Frischer, M. E. Influence of nitrate availability on the distribution and abundance of heterotrophic bacterial nitrate assimilation genes in the Barents Sea during summer. Aquat. Microb. Ecol. 39, 247–255 (2005).Article 

Google Scholar 
Davidson, K. et al. The influence of the balance of inorganic and organic nitrogen on the trophic dynamics of microbial food webs. Limnol. Oceanogr. 52(5), 2147–2163 (2007).ADS 
CAS 
Article 

Google Scholar 
Domingues, R. B., Barbosa, A. B., Sommer, U. & Galvão, H. M. Ammonium, nitrate and phytoplankton interactions in a freshwater tidal estuarine zone: potential effects of cultural eutrophication. Aquat. Sci. 73, 331–343. https://doi.org/10.1007/s00027-011-0180-0 (2011).CAS 
Article 

Google Scholar 
Hollibaugh, J. T., Gifford, S., Sharma, S., Bano, N. & Moran, M. A. Metatranscriptomic analysis of ammonia-oxidizing organisms in an estuarine bacterioplankton assemblage. ISME J. 5, 866–878 (2011).CAS 
PubMed 
Article 

Google Scholar 
Yang, J. et al. Ecogenomics of zooplankton community reveals ecological threshold of ammonia nitrogen. Environ. Sci. Technol. 51, 3057–3064 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Jones, Z. L., Jasper, J. T., Sedlak, D. L. & Sharpa, J. O. Sulfide-Induced dissimilatory nitrate reduction to ammonium supports anaerobic Ammonium oxidation (anammox) in an open-water unit process wetland. Appl. Environ. Microbiol. 83(15), 1–14 (2017).Article 

Google Scholar 
Nizzoli, D., Carraro, E., Nigro, V. & Viaroli, P. Effect of organic enrichment and thermal regime on denitrification and dissimilatory nitrate reduction to ammonium (DNRA) in hypolimnetic sediments of two lowland lakes. Water Res. 4, 2715–2724 (2010).Article 
CAS 

Google Scholar 
Rutting, T., Boeckx, P., Muller, C. & Klemedtsson, L. Assessment of the importance of dissimilatory nitrate reduction to ammonium for the terrestrial nitrogen cycle. Biogeosciences 8, 1779–1791 (2011).ADS 
Article 
CAS 

Google Scholar 
Roberts, K. L., Kessler, A. J., Grace, M. R. & Cook, P. L. M. Increased rates of dissimilatory nitrate reduction to ammonium (DNRA) under oxic conditions in a periodically hypoxic estuary. Cosmochim. Acta 133, 313–324 (2014).ADS 
CAS 
Article 

Google Scholar 
Broman, E. et al. Active DNRA and denitrification in oxic hypereutrophic waters. Water Res. 194, 116954 (2021).CAS 
PubMed 
Article 

Google Scholar 
Han, X., Peng, S., Zhang, L., Lu, P. & Zhang, D. The Co-occurrence of DNRA and Anammox during the anaerobic degradation of benzene under denitrification. Chemosphere 247, 125968 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Rantanen, P.-L. et al. Decreased natural organic matter in water distribution decreases nitrite formation in non-disinfected conditions, via enhanced nitrite oxidation. Water Res. X 9, 100069 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Raimonet, M., Cazier, T., Rocher, V. & Laverman, A. M. Nitrifying kinetics and the persistence of nitrite in the Seine River, France. J. Environ. Qual. https://doi.org/10.2134/jeq2016.06.0242 (2017).Article 
PubMed 

Google Scholar 
Philip, S., Laanbroek, H. J. & Verstraete, W. Origin, causes and effects of increased nitrite concentrations in aquatic environments. Rev. Environ. Sci. Biotechnol. 1, 115–141. https://doi.org/10.1023/A:1020892826575 (2002).Article 

Google Scholar 
Baneshi, M. M. et al. Aniline bio-adsorption from aqueous solutions using dried activated sludge: Aniline bio-adsorption from aqueous solutions using dried activated sludge. Poll. Res. 36(3), 403–409 (2017).CAS 

Google Scholar 
Börnick, H., Eppinger, P., Grischek, T. & Worch, E. Simulation of biological degradation of aromatic amines in river bed sediments. Water Res. 35(3), 619–624 (2001).PubMed 
Article 

Google Scholar 
Norzaee, S., Djahed, B., Khaksefidi, R. & Mostafapour, F. K. Photocatalytic degradation of aniline in water using CuO nanoparticles. Water Supply 66(3), 178–185 (2017).Article 

Google Scholar 
Paździor, K. et al. Integration of nanofiltration and biological degradation of textile wastewater containing azo dye. Chemosphere 75, 250–255 (2009).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Babcock, R. W., Chen, W., Ro, K. S., Mah, R. A. & Stenstrom, M. K. Enrichment and kinetics of biodegradation of 1-naphthylamine in activated sludge. Appl. Microbiol. Biotechnol. 39, 264–269 (1993).CAS 
Article 

Google Scholar 
Shin, K. A. & Spain, J. C. Pathway and evolutionary implications of diphenylamine biodegradation by Burkholderia sp. Strain JS667. Appl. Microbiol. Biotechnol. 75(9), 2694–2704 (2009).ADS 
CAS 

Google Scholar  More

Effects of aqueous plant extracts on germination and early growth of B. bipinnata by in vitro bioassaysSeed germination and seedling growth of B. bipinnata were investigated after treatment with DT, RC, PT, and JG aqueous extracts to explore the allelopathic effects of these plant species. The pH of the aqueous extracts corresponded to 6.62 for DL, 5.59 for RC, 7.20 for PT, and 7.42 for JG, with no significant difference in pH values between DL and RC extracts or between PT and JG extracts; however, the pH of DL and RC extracts differed significantly (p  1000 cm−1 were attributed to the C − H out-of-plane bending vibration of aliphatic alkenes and aromatic benzene rings49,50.The range between 1800 and 600 cm−1 of the infrared spectra was selected for the PCA, as it is the most representative region of the differences present in the spectra. In the PC1 versus PC2 score plot (Fig. 6), representing 85.78% of the total variance, it is possible to observe the separation of the samples into three distinct groups. The samples of DL and RC extracts formed two distinct groups, since they showed a significant separation in the PC1 axis, with positive and negative scores for PC1, respectively. The samples of JG and PT extracts formed a single group, remaining superimposed and located close to the zero value of PC1, indicating intermediate spectral characteristics in relation to the DL and RC extracts. These results may be correlated with the allelopathic activity of these extracts, since the RC extract showed better performance, followed by the JG and PT extracts, with intermediate performance, and the DL extract showed lower activity compared to the others.Figure 6PCA score plot (PC1 × PC2) of D. lacunifera (DL), R. communis (RC), P. tuberculatum (PT), and J. gossypiifolia (JG) extracts.Full size imageThe PC1 loading plot (Fig. S1) has as main contributors the negative bands associated with signals at approximately 1732, 1595, 1404, 1200–1025, 1049, and 780–600 cm−1, which significantly contributed to the separation of RC extract samples that presented greater intensity than in DL extract samples. On the other hand, the positive bands in PC1 in the region of 780–970 cm−1 were more intense in DL extracts. When evaluating the negative region of the PC1 loading plot, it is possible to observe that the functional groups responsible for the discrimination are probably those present in flavonoids and phenolic acids, corroborating the data in the literature that demonstrate the identification of these compound classes in RC leaves, such as gallic acid, quercetin, gentisic acid, rutin, epicatechin, ellagic acid, etc.51,52,53.The presence of flavonoids can be observed due to the stretching of C=O at approximately 1732 cm−1, C=C of aromatics at 1600 cm−1, C–O at 1200–1000 cm−1, and O–H at 3284–3174 cm−1. Phenolic acids can be verified due to stretching of the O–H of carboxylic acid, C=O and aromatic ring, as well as the C − H out-of-plane bending vibration of aromatic benzene ring at  More

Daskalova, G. N. et al. Science 368, 1341–1347 (2020).CAS 
Article 

Google Scholar 
Cardinale, B. J. et al. Nature 486, 59–67 (2012).CAS 
Article 

Google Scholar 
Hector, A. & Bagchi, R. Nature 448, 188–190 (2007).CAS 
Article 

Google Scholar 
Fanin, N. et al. Nat. Ecol. Evol. 2, 269–278 (2018).Article 

Google Scholar 
Duffy, J. E. Front. Ecol. Environ. 7, 437–444 (2009).Article 

Google Scholar 
Manning, P. et al. Adv. Ecol. Res. 61, 323–356 (2019).Article 

Google Scholar 
Lefcheck, J. S. et al. Nat. Commun. 6, 6936 (2015).CAS 
Article 

Google Scholar 
Soliveres, S. et al. Nature 536, 456–459 (2016).CAS 
Article 

Google Scholar 
Moi, D. A. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01827-7 (2022).Article 

Google Scholar 
Venter, O. et al. Sci. Data 3, 160067 (2016).Article 

Google Scholar 
Allan, E. et al. Proc. Natl Acad. Sci. USA 111, 308–313 (2014).CAS 
Article 

Google Scholar 
Manning, P. et al. Nat. Ecol. Evol. 2, 427–436 (2018).Article 

Google Scholar 
Gamfeldt, L. et al. Nat. Commun. 4, 1340 (2013).Article 

Google Scholar 
Schuldt, A. et al. Nat. Commun. 9, 2989 (2018).Article 

Google Scholar 
Jochum, M. et al. Nat. Ecol. Evol. 4, 1485–1494 (2020).Article 

Google Scholar 
Dudgeon, D. et al. Biol. Rev. 81, 163–182 (2005).Article 

Google Scholar 
Blois, J. L. et al. Proc. Natl Acad. Sci. USA 110, 9374–9379 (2013).CAS 
Article 

Google Scholar 
França, F. et al. J. Appl. Ecol. 53, 1098–1105 (2016).Article 

Google Scholar 
Ewers, R. M. et al. Nat. Commun. 6, 6836 (2015).CAS 
Article 

Google Scholar 
Reich, P. B. et al. Science 336, 589–592 (2012).CAS 
Article 

Google Scholar  More

Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Syst. 37, 637–669 (2006).
Google Scholar 
Forrest, J. & Miller-Rushing, A. J. Toward a synthetic understanding of the role of phenology in ecology and evolution. Philos. Trans. R. Soc. B Biol. Sci. 365, 3101–3112 (2010).
Google Scholar 
Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change/631/158/2165/2457/631/158/2039/129/141/139 letter. Nat. Clim. Chang. 8 (2018).Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Mushegian, A. A. et al. Ecological mechanism of climate-mediated selection in a rapidly evolving invasive species. Ecol. Lett. 24, 698–707 (2021).PubMed 
PubMed Central 

Google Scholar 
Visser, M. E. & Both, C. Shifts in phenology due to global climate change: the need for a yardstick. Proc. R. Soc. B Biol. Sci. 272, 2561–2569 (2005).
Google Scholar 
Mayor, S. J. et al. Increasing phenological asynchrony between spring green-up and arrival of migratory birds. Sci. Rep. 7, 1–10 (2017).ADS 

Google Scholar 
Beard, K. H., Kelsey, K. C., Leffler, A. J. & Welker, J. M. The missing angle: Ecosystem consequences of phenological mismatch. Trends Ecol. Evol. 34 (2019).Youngflesh, C. et al. Migratory strategy drives species-level variation in bird sensitivity to vegetation green-up. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01442-y (2021).PubMed 

Google Scholar 
Forrest, J. R. Complex responses of insect phenology to climate change. Curr. Opin. Insect Sci. 17 (2016).Crimmins, T. M. et al. Short-term forecasts of insect phenology inform pest management. Ann. Entomol. Soc. Am. 113 (2020).Brakefield, P. M. Geographical variability in, and temperature effects on, the phenology of Maniola jurtina and Pyronia tithonus (Lepidoptera, Satyrinae) in England and Wales. Ecol. Entomol. 12 (1987).Dell, D., Sparks, T. H. & Dennis, R. L. H. Climate change and the effect of increasing spring temperatures on emergence dates of the butterfly Apatura iris (Lepidoptera: Nymphalidae). Eur. J. Entomol. 102, 161–167 (2005).
Google Scholar 
Van Der Kolk, H. J., Wallisdevries, M. F. & Van Vliet, A. J. H. Using a phenological network to assess weather influences on first appearance of butterflies in the Netherlands. Ecol. Indic. 69 (2016).Abarca, M. et al. Inclusion of host quality data improves predictions of herbivore phenology. Entomol. Exp. Appl. 166 (2018).Abarca, M. & Lill, J. T. Latitudinal variation in the phenological responses of eastern tent caterpillars and their egg parasitoids. Ecol. Entomol. 44 (2019).Karlsson, B. Extended season for northern butterflies. Int. J. Biometeorol. 58, 691–701 (2014).ADS 
PubMed 

Google Scholar 
Kharouba, H. M., Paquette, S. R., Kerr, J. T. & Vellend, M. Predicting the sensitivity of butterfly phenology to temperature over the past century. Glob. Chang. Biol. 20 (2014).Diamond, S. E., Frame, A. M., Martin, R. A. & Buckley, L. B. Species’ traits predict phenological responses to climate change in butterflies. Ecology 92 (2011).Diamond, S. E. et al. Unexpected phenological responses of butterflies to the interaction of urbanization and geographic temperature. Ecology 95 (2014).Cayton, H. L., Haddad, N. M., Gross, K., Diamond, S. E. & Ries, L. Do growing degree days predict phenology across butterfly species?. Ecology 96, 1473–1479 (2015).
Google Scholar 
Stewart, J. E., Illán, J. G., Richards, S. A., Gutiérrez, D. & Wilson, R. J. Linking inter-annual variation in environment, phenology, and abundance for a montane butterfly community. Ecology 101 (2020).Roy, D. B. et al. Similarities in butterfly emergence dates among populations suggest local adaptation to climate. Glob. Chang. Biol. 21 (2015).Dennis, R. L. H. et al. Turnover and trends in butterfly communities on two British tidal islands: Stochastic influences and deterministic factors. J. Biogeogr. 37, 2291–2304 (2010).
Google Scholar 
Sparks, T. H. & Yates, T. J. The effect of spring temperature on the appearance dates of British butterflies 1883–1993. Ecography (Cop.). 20 (1997).Michielini, J. P., Dopman, E. B. & Crone, E. E. Changes in flight period predict trends in abundance of Massachusetts butterflies. Ecol. Lett. 24, 249–257 (2021).PubMed 

Google Scholar 
Zografou, K. et al. Species traits affect phenological responses to climate change in a butterfly community. Sci. Rep. 11 (2021).Belitz, M. W., Larsen, E. A., Ries, L. & Guralnick, R. P. The accuracy of phenology estimators for use with sparsely sampled presence-only observations. Methods Ecol. Evol. 11, 1273–1285 (2020).
Google Scholar 
Van Strien, A. J., Plantenga, W. F., Soldaat, L. L., Van Swaay, C. A. M. & WallisDeVries, M. F. Bias in phenology assessments based on first appearance data of butterflies. Oecologia 156, 227–235 (2008).ADS 
PubMed 

Google Scholar 
Pollard, E. A method for assessing changes in the abundance of butterflies. Biol. Conserv. 12 (1977).Taron, D. & Ries, L. Butterfly Monitoring for Conservation. in Butterfly Conservation in North America 35–57 (Springer Netherlands, 2015). https://doi.org/10.1007/978-94-017-9852-5_3.Schmucki, R. et al. A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes. J. Appl. Ecol. 53, 501–510 (2016).
Google Scholar 
Prudic, K., Oliver, J., Brown, B. & Long, E. Comparisons of citizen science data-gathering approaches to evaluate urban butterfly diversity. Insects 9, 186 (2018).PubMed Central 

Google Scholar 
Prudic, K. L. et al. eButterfly: Leveraging massive online citizen science for butterfly conservation. Insects 8 (2017).Barve, V. V. et al. Methods for broad-scale plant phenology assessments using citizen scientists’ photographs. Appl. Plant Sci. 8 (2020).Seltzer, C. Making biodiversity data social, shareable, and scalable: Reflections on iNaturalist & citizen science. Biodivers. Inf. Sci. Stand. 3 (2019).Wittmann, J., Girman, D. & Crocker, D. Using inaturalist in a coverboard protocol to measure data quality: Suggestions for project design. Citiz. Sci. Theory Pract. 4 (2019).Dorazio, R. M. Accounting for imperfect detection and survey bias in statistical analysis of presence-only data. Glob. Ecol. Biogeogr. 23 (2014).Ries, L., Zipkin, E. F. & Guralnick, R. P. Tracking trends in monarch abundance over the 20th century is currently impossible using museum records. In Proceedings of the National Academy of Sciences of the United States of America vol. 116 (2019).Larsen, E. A. & Shirey, V. Method matters: Pitfalls in analysing phenology from occurrence records. Ecol. Lett. https://doi.org/10.1111/ele.13602 (2021).PubMed 
PubMed Central 

Google Scholar 
de Keyzer, C. W., Rafferty, N. E., Inouye, D. W. & Thomson, J. D. Confounding effects of spatial variation on shifts in phenology. Glob. Chang. Biol. 23 (2017).Cima, V. et al. A test of six simple indices to display the phenology of butterflies using a large multi-source database. Ecol. Indic. 110, 105885 (2020).
Google Scholar 
Zipkin, E. F. et al. Addressing data integration challenges to link ecological processes across scales. Front. Ecol. Environ. 19 (2021).Polgar, C. A., Primack, R. B., Williams, E. H., Stichter, S. & Hitchcock, C. Climate effects on the flight period of Lycaenid butterflies in Massachusetts. Biol. Conserv. 160 (2013).Brooks, S. J. et al. The influence of life history traits on the phenological response of British butterflies to climate variability since the late-19th century. Ecography (Cop.) 40, 1152–1165 (2017).
Google Scholar 
van Strien, A. J., van Swaay, C. A. M., van Strien-van Liempt, W. T. F. H., Poot, M. J. M. & WallisDeVries, M. F. Over a century of data reveal more than 80% decline in butterflies in the Netherlands. Biol. Conserv. 234 (2019).Boggs, C. L. The fingerprints of global climate change on insect populations. Curr. Opin. Insect Sci. 17 (2016).Belitz, M. et al. Climate drivers of adult insect activity are conditioned by life history traits. Authorea Prepr. (2021).Kellner, K. F. & Swihart, R. K. Accounting for imperfect detection in ecology: A quantitative review. PLoS ONE 9 (2014).Park, D. S., Newman, E. A. & Breckheimer, I. K. Scale gaps in landscape phenology: challenges and opportunities. Trends Ecol. Evol. 36 (2021).Kerr, J. T., Vincent, R. & Currie, D. J. Lepidopteran richness patterns in North America. Écoscience 5, 448–453 (1998).
Google Scholar 
Taylor, S. D., Meiners, J. M., Riemer, K., Orr, M. C. & White, E. P. Comparison of large-scale citizen science data and long-term study data for phenology modeling. Ecology 100 (2019).Isaac, N. J. B. et al. Data integration for large-scale models of species distributions. Trends Ecol. Evol. 35 (2020).Miller, D. A. W., Pacifici, K., Sanderlin, J. S. & Reich, B. J. The recent past and promising future for data integration methods to estimate species’ distributions. Methods Ecol. Evol. 10 (2019).Fletcher, R. J. et al. A practical guide for combining data to model species distributions. Ecology https://doi.org/10.1002/ecy.2710 (2019).PubMed 

Google Scholar 
Wepprich, T., Adrion, J. R., Ries, L., Wiedmann, J. & Haddad, N. M. Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA. bioRxiv https://doi.org/10.1101/613786 (2019).
Google Scholar 
Crossley, M. S. et al. Recent climate change is creating hotspots of butterfly increase and decline across North America. Glob. Chang. Biol. 27, 2702–2714 (2021).CAS 
PubMed 

Google Scholar 
Forister, M. L. et al. Fewer butterflies seen by community scientists across the warming and drying landscapes of the American West. Science (80-) 371, 1042–1045 (2021).ADS 
CAS 

Google Scholar 
Macgregor, C. J. et al. Climate-induced phenology shifts linked to range expansions in species with multiple reproductive cycles per year. Nat. Commun. 10, (2019).Kerr, N. Z. et al. Developmental trap or demographic bonanza? Opposing consequences of earlier phenology in a changing climate for a multivoltine butterfly. Glob. Chang. Biol. 26, (2020).Belth, J. E. Butterflies of Indiana: A field guide. Butterflies of Indiana: A Field Guide (2012).Betros, B. A Photographic Field Guide to the Butterflies in the Kansas City Region (Kansas City Star Books, 2008).
Google Scholar 
Bouseman, J. K., Sternburg, J. G. & Wiker, J. R. Field guide to the skipper butterflies of Illinois. (Illinois Natural History Survey Manual 11, 2006).Clark, A. H. The butterflies of the District of Columbia and vicinity. Bull. United States Natl. Museum (1932).Glassberg, J. Butterflies through Binoculars: Boston—New York—Washington Region (Oxford University Press, 1993).
Google Scholar 
Glassberg, J. Butterflies through Binoculars: The East—A Field Guide to the Butterflies of Eastern North America (Oxford University Press, 1999).
Google Scholar 
Iftner, D. C., Shuey, J. A. & Calhoun, J. V. Butterflies and skippers of Ohio (Ohio State University, 1992).
Google Scholar 
Jeffords, M. R., Post, S. L. & Wiker, J. Butterflies of Illinois: a field guide (Illinois Natural History Survey, 2019).
Google Scholar 
Schlicht, D. W., Downey, J. C. & Nekola, J. C. The butterflies of Iowa (University of Iowa Press, 2007).
Google Scholar 
Schmucki, R., Harrower, C. A. & Dennis, E. B. rbms: Computing generalised abundance indices for butterfly monitoring count data. R package version 1.1.0. https://github.com/RetoSchmucki/rbms (2021).GBIF. GBIF Occurrence download. https://doi.org/10.15468/dl.1erh15 (2019).Thornton, P. E. et al. Daymet: Daily surface weather data on a 1-km grid for North America, version 3. ORNL DAAC. (Oak Ridge, TN, 2017).Baskerville, G. L. & Emin, P. Rapid estimation of heat accumulation from maximum and minimum temperatures. Ecology 50, (1969).R Development Core Team, R. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing vol. 1 409 (2011).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest: Tests for random and fixed effects for linear mixed effect models (lmer objects of lme4 package). R package version (2014).Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4 (2013).Kahle, D. & Wickham, H. ggmap: Spatial visualization with ggplot2. R J 5 (2013). More

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

Google Scholar 
Di Marco, M., Venter, O., Possingham, H. P. & Watson, J. E. M. Changes in human footprint drive changes in species extinctions risk. Nat. Commun. 9, 4621 (2018).PubMed 
PubMed Central 

Google Scholar 
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).CAS 
PubMed 

Google Scholar 
Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Google Scholar 
Lefcheck, J. S. et al. Biodiversity enhances ecosystem multifunctionality across trophic levels and habitats. Nat. Commun. 6, 6936 (2015).CAS 
PubMed 

Google Scholar 
Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016).CAS 
PubMed 

Google Scholar 
Schuldt, A. et al. Biodiversity across trophic levels drive multifunctionality in highly diverse forests. Nat. Commun. 9, 2989 (2018).PubMed 
PubMed Central 

Google Scholar 
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 211–220 (2020).
Google Scholar 
Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).CAS 
PubMed 

Google Scholar 
Allan, E. et al. Land use intensification alters ecosystem multifunctionality via loss of biodiversity and changes to functional composition. Ecol. Lett. 18, 834–843 (2015).PubMed 
PubMed Central 

Google Scholar 
Jing, X. et al. The links between ecosystem multifunctionality and above- and belowground biodiversity are mediated by climate. Nat. Commun. 6, 8159 (2015).PubMed 

Google Scholar 
Fanin, N. et al. Consistent effects of biodiversity loss on multifunctionality across contrasting ecosystems. Nat. Ecol. Evol. 2, 269–278 (2018).PubMed 

Google Scholar 
Hautier, Y. et al. Local loss and spatial homogenization of plant diversity reduce ecosystem multifunctionality. Nat. Ecol. Evol. 2, 50–56 (2018).PubMed 

Google Scholar 
Venter, O. et al. Global terrestrial human footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).PubMed 
PubMed Central 

Google Scholar 
Allan, E. et al. Interannual variation in land-use intensity enhances grassland multidiversity. Proc. Natl Acad. Sci. USA 111, 308–313 (2014).CAS 
PubMed 

Google Scholar 
Moi, D. A. et al. Regime shifts in a shallow lake over 12 years: consequences for taxonomic and functional diversities, and ecosystem multifunctionality. J. Anim. Ecol. 91, 551–565 (2022).PubMed 

Google Scholar 
Moi, D. A. et al. Multitrophic richness enhances ecosystem multifunctionality of tropical shallow lakes. Funct. Ecol. 35, 942–954 (2021).CAS 

Google Scholar 
Byrnes, J. E. K. et al. Investigating the relationship between biodiversity and ecosystem multifunctionality: challenges and solutions. Methods Ecol. Evol. 5, 111–124 (2014).
Google Scholar 
Li, F. et al. Human activitiesʼ fingerprint on multitrophic biodiversity and ecosystem functions across a major river catchment in China. Glob. Change Biol. 26, 6867–6879 (2020).
Google Scholar 
Enquist, B. J. et al. The megabiota are disproportionately importante for biosphere functioning. Nat. Commun. 11, 699 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Eisenhauer, N. et al. A multitrophic perspective on biodiversity–ecosystem functioning research. Adv. Ecol. Res. 61, 1–54 (2019).PubMed 
PubMed Central 

Google Scholar 
Agostinho, A. A., Thomaz, S. M. & Gomes, L. C. Threats for biodiversity in the floodplain of the Upper Paraná River: effects of hydrological regulation by dams. Ecohydrol. Hydrobiol. 4, 255–268 (2004).
Google Scholar 
Chiaravalloti, R. M., Homewood, K. & Erikson, K. Sustainability and land tenure: who owns the floodplain in the Pantanal, Brazil? Land Use Policy 64, 511–524 (2017).
Google Scholar 
Pelicice, F. M. et al. Large-scale degradation of the Tocantins–Araguaia River Basin. Environ. Manag. 68, 445–452 (2021).
Google Scholar 
Malekmohammadi, B. & Jahanishakib, F. Vulnerability assessment of wetland landscape ecosystem services using driver-pressure-state-impact-response (DPSIR) model. Ecol. Indic. 82, 293–303 (2017).
Google Scholar 
McIntyre, P. B. et al. Fish extinctions alter nutrient recycling in tropical freshwaters. Proc. Natl Acad. Sci. USA 104, 4461–4466 (2006).
Google Scholar 
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).CAS 
PubMed 

Google Scholar 
Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).
Google Scholar 
Heino, J. et al. Lakes in the era of global change: moving beyond single-lake thinking in maintaining biodiversity and ecosystem services. Biol. Rev. 96, 89–106 (2020).PubMed 

Google Scholar 
Bridgewater, P. & Kim, R. E. The Ramsar conservation on wetlands at 50. Nat. Ecol. Evol. 5, 268–270 (2020).
Google Scholar 
Romero, G. Q. et al. Pervasive decline of subtropical aquatic insects over 20 years driven by water transparency, non-native fish and stoichiometric imbalance. Biol. Lett. 17, 20210137 (2021).PubMed 
PubMed Central 

Google Scholar 
Lansac-Tôha, F. M. et al. Scale-depedent patterns of metacommunity structuring in aquatic organisms across floodplain systems. J. Biogeogr. 48, 872–885 (2021).
Google Scholar 
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
Google Scholar 
Weiss, K. C. B. & Ray, C. A. Unifying functional trait approaches to understand the assemblage of ecological communities: synthesizing taxonomic divides. Ecography 42, 2012–2020 (2019).
Google Scholar 
Laliberté, E. & Legendre, R. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).PubMed 

Google Scholar 
Mackereth, F. J. H, Heron, J & Talling, J. F. Water Analysis: Some Revised Methods for Limnologists. Publication No. 36 (Freshwater Biological Association, 1978).Golterman, H. L., Clymo, R. S. & Ohnstad, M. A. M. Methods for Physical and Chemical Analysis of Freshwaters (Blackwell Scientific Publications, 1978).Bernhardt, E. S. et al. The metabolic regimes of flowing waters. Limnol. Oceanogr. 63, S99–S118 (2018).
Google Scholar 
Sun, J. & Liu, D. Geometric models for calculating cell biovolume and surface area for phytoplankton. J. Plankt. Res. 25, 1331–1346 (2003).
Google Scholar 
Froese, R. & Pauly, D. FishBase (2018); www.fishbase.orgPorter, K. G. & Feig, Y. S. The use of DAPI for identifying and counting aquatic microflora1. Limnol. Oceanogr. 25, 943–948 (1980).
Google Scholar 
Manning, P. et al. Redifining ecosystem multifunctionality. Nat. Ecol. Evol. 2, 427–436 (2018).PubMed 

Google Scholar 
Hijmans, R. J. & van Etten, J. raster: Geographic analysis and modeling with raster data. R version 2.0–12 https://rspatial.org/raster (2012).World Urbanization Prospects: The 2020 Revision: Highlights (United Nations, 2020).Junk, W. J. et al. Brazilian wetlands: their definition, delineation, and classification for research, sustainable management, and protection. Aquat. Conserv. Mar. Freshwater Ecosyst. 24, 5–22 (2013).
Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and nonlinear mixed effects models. R version 3.1.137 https://CRAN.Rproject.org/package=nlme (2018).K. Barton, MuMIn: Model selection and model averaging based on information criteria (AICc and alike). R version 1–1 https://CRAN.R-project.org/package=MuMIn (2014).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).Schielzeth, H. Simple means to improve the interpretability ofregression coefficients. Meth. Ecol. Evol. 1, 103–113 (2010).
Google Scholar 
Aiken, L. S. & West, S. G. Multiple Regression: Testing and Interpreting Interactions (Sage Publications, 1991).Rosseel, Y. lavaan: an R package for structural equation modeling. J. Stat. Softw. 48, 1–36 (2015).
Google Scholar 
Grace, J. B. & Bollen, K. A. Representing general theoretical concepts in structural equation models: the role of composite variables. Environ. Ecol. Stat. 15, 191–213 (2008).
Google Scholar 
R Development Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). More

Effect of different organic fertilizers on the growth of Pear-jujubeEffect of different organic fertilizers on the bearing branch length of Pear-jujubeJujube-bearing branch has the dual role of fruiting and photosynthesis32,33. It can be seen from Fig. 1 that different organic fertilizer treatments have a significant impact on the growth of jujube-bearing branches. Among them, the longest jujube-bearing branch in the SC treatment is 20.17 cm, which is significantly higher than that in CK and CF; the jujube-bearing branch length in the SC, SM and BM treatment are increased by 34%, 23% and 25% compared with that in CK, and the difference is significant (P  SM  > SC  > CK. Among them, the density of light of BM is the largest. It reaches 38.06 mol/(m2 d). CF, SC, SM and BM respectively increase by 11.54%, 8.09%, 7.96% and 15.13% compared with CK, and the difference is significant. The canopy transmittance of jujube is BM  CF  > SM  > SC. The highest Tr of BM reaches 8.66 µmol/moL. It may be related to higher LAI, and the instantaneous water use efficiency of SC is highest, which reaches 3.30%. The WUEp of CF, SC, SM and BM treatments increase by 22.4%, 64.2%, 44.3% and 30.8%, respectively, compared with that of CK. It reaches a significant difference level (P  SM  > BM  > CF  > CK. Compared with CK (9.37%), the SC, SM, BM, and CF increased by 3.69, 3.18, 1.11 and 0.40% points, respectively. Organic fertilizer is beneficial to increase the water content of the soil. Among them, soybean cake fertilizer (SC) has the largest increase, which is significantly different from CK (P  SM  > SC  > CF  > CK. The RWC of BM reaches 94.20%, which is significantly different from CK (P  SM  > BM  > CK. The total flavonoid content of SC reaches 14.35 mg/kg, which is 24.57% higher than that of CK. The total flavonoid content of SM and BM increase by 17.01% and 9.2%, respectively, compared with that of CK. Moreover, each treatment is significantly different from CK (P  More

Suding, K. et al. Committing to ecological restoration. Science 348, 638–640 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Chazdon, R. L. Landscape restoration, natural regeneration, and the forests of the future. mobt 102, 251–257 (2017).
Google Scholar 
Crouzeilles, R., Lorini, M. L. & Grelle, C. Applying graph theory to design networks of protected areas: using inter-patch distance for regional conservation planning. Natureza Conservaçao Rev. Brasileira de Conservaçao da Natureza 9, 219–224 (2011).
Google Scholar 
Crouzeilles, R., Lorini, M. L. & Grelle, C. E. V. The importance of using sustainable use protected areas for functional connectivity. Biol. Cons. 159, 450–457 (2013).Article 

Google Scholar 
Arroyo-Rodríguez, V. et al. Designing optimal human-modified landscapes for forest biodiversity conservation. Ecol. Lett. 23, 1404–1420 (2020).PubMed 
Article 

Google Scholar 
O’Farrell, P. J. & Anderson, P. M. Sustainable multifunctional landscapes: a review to implementation. Curr Opin Environ. Sustain. 2, 59–65 (2010).Article 

Google Scholar 
Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).Article 

Google Scholar 
César, R. G. et al. It is not just about time: agricultural practices and surrounding forest cover affect secondary forest recovery in agricultural landscapes. Biotropica 53, 496–508 (2021).Article 

Google Scholar 
Crouzeilles, R. et al. A new approach to map landscape variation in forest restoration success in tropical and temperate forest biomes. J. Appl. Ecol. 56, 2675–2686 (2019).Article 

Google Scholar 
Villard, M.-A. & Metzger, J. P. Beyond the fragmentation debate: a conceptual model to predict when habitat configuration really matters. J. Appl. Ecol. 51, 309–318 (2014).Article 

Google Scholar 
Taylor, P. D., Fahrig, L. & With, K. A. Landscape connectivity: a return to the basics. in Connectivity Conservation (eds. Crooks, K. R. & Sanjayan, M.) 29–43 (Cambridge University Press, 2006).Tischendorf, L. & Fahrig, L. On the usage and measurement of landscape connectivity. Oikos 90, 7–19 (2000).Article 

Google Scholar 
McRae, B. H., Hall, S. A., Beier, P. & Theobald, D. M. Where to restore ecological connectivity? Detecting barriers and quantifying restoration benefits. PLoS ONE 7, e52604 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Torrubia, S. et al. Getting the most connectivity per conservation dollar. Front. Ecol. Environ. 12, 491–497 (2014).Article 

Google Scholar 
Crouzeilles, R. et al. A global meta-analysis on the ecological drivers of forest restoration success. Nat. Commun. 7, 11666 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Leal-Ramos, D. et al. Forest and connectivity loss drive changes in movement behavior of bird species. Ecography 43, 1203–1214 (2020).Article 

Google Scholar 
Pérez-Cárdenas, N. et al. Effects of landscape composition and site land-use intensity on secondary succession in a tropical dry forest. For. Ecol. Manage. 482, 118818 (2021).Article 

Google Scholar 
Holl, K. D., Reid, J. L., Chaves-Fallas, J. M., Oviedo-Brenes, F. & Zahawi, R. A. Local tropical forest restoration strategies affect tree recruitment more strongly than does landscape forest cover. J. Appl. Ecol. 54, 1091–1099 (2017).Article 

Google Scholar 
Holl, K. D., Zahawi, R. A., Cole, R. J., Ostertag, R. & Cordell, S. Planting seedlings in tree islands versus plantations as a large-scale tropical forest restoration strategy. Restor. Ecol. 19, 470–479 (2011).Article 

Google Scholar 
Cole, R. J., Holl, K. D. & Zahawi, R. A. Seed rain under tree islands planted to restore degraded lands in a tropical agricultural landscape. Ecol. Appl. 20, 1255–1269 (2010).CAS 
PubMed 
Article 

Google Scholar 
Zahawi, R. A., Holl, K. D., Cole, R. J. & Reid, J. L. Testing applied nucleation as a strategy to facilitate tropical forest recovery. J. Appl. Ecol. 50, 88–96 (2013).Article 

Google Scholar 
Reid, J. L., Kormann, U., Zarrate-Chary, D., Holl, K. D. & Zahawi, R. A. Predicting toucan-mediated seed dispersal in tropical forest restoration. Ecosphere (In press).Zahawi, R. A. et al. Proximity and abundance of mother trees affects recruitment patterns in a long-term tropical forest restoration study. Ecography 44,1826–1837 (2021).Lehouck, V. et al. Habitat disturbance reduces seed dispersal of a forest interior tree in a fragmented African cloud forest. Oikos 118, 1023–1034 (2009).Article 

Google Scholar 
Fahrig, L. Ecological responses to habitat fragmentation per se. Annu. Rev. Ecol. Evol. Syst. 48, 1–23 (2017).Article 

Google Scholar 
Fahrig, L. et al. Is habitat fragmentation bad for biodiversity?. Biol. Cons. 230, 179–186 (2019).Article 

Google Scholar 
Schupp, E. W., Jordano, P. & Gómez, J. M. Seed dispersal effectiveness revisited: a conceptual review. New Phytol. 188, 333–353 (2010).PubMed 
Article 

Google Scholar 
Rogers, H. S., Donoso, I., Traveset, A. & Fricke, E. C. Cascading impacts of seed disperser loss on plant communities and ecosystems. Annu. Rev. Ecol. Evol. Syst. 52, 641–666 (2021).Article 

Google Scholar 
Howe, H. F. & Smallwood, J. Ecology of seed dispersal. Annu. Rev. Ecol. Syst. 13, 201–228 (1982).Article 

Google Scholar 
Holdridge, L. R., Grenke, W. C., Hatheway, W. H., Liang, T. & Tosi, J. A. J. Forest environments in tropical life zones: a pilot study (Pergamon Press, 1971).
Google Scholar 
Zahawi, R. A., Duran, G. & Kormann, U. Sixty-seven years of land-use change in Southern Costa Rica. PLoS ONE 10, e0143554 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Holl, K. D. et al. Applied nucleation facilitates tropical forest recovery: Lessons learned from a 15-year study. J. Appl. Ecol. 57, 2316–2328 (2020).Article 

Google Scholar 
Reid, J. L., Mendenhall, C. D., Rosales, J. A., Zahawi, R. A. & Holl, K. D. Landscape context mediates avian habitat choice in tropical forest restoration. PLoS ONE 9, e90573 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Buchanan, G. M., Donald, P. F. & Butchart, S. H. M. Identifying priority areas for conservation: a global assessment for forest-dependent birds. PLoS ONE 6, e29080 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Carrara, E. et al. Impact of landscape composition and configuration on forest specialist and generalist bird species in the fragmented Lacandona rainforest, Mexico. Biol. Conser. 184, 117–126 (2015).Article 

Google Scholar 
Chao, A. & Shen, T. J. Program SPADE (Species Prediction and Diversity Estimation). Program and User’s Guide. (http://chao.stat.nthu.edu.tw, 2010).Chazdon, R. L. et al. A novel statistical method for classifying habitat generalists and specialists. Ecology 92, 1332–1343 (2011).PubMed 
Article 

Google Scholar 
de Souza, R. P. & Válio, I. F. M. Seed size, seed germination, and seedling survival of Brazilian tropical tree species differing in successional status. Biotropica 33, 447–457 (2001).Article 

Google Scholar 
Werden, L. K., Holl, K. D., Rosales, J. A., Sylvester, J. M. & Zahawi, R. A. Effects of dispersal- and niche-based factors on tree recruitment in tropical wet forest restoration. Ecol. Appl. 30, e02139 (2020).PubMed 

Google Scholar 
Mendenhall, C. D., Shields-Estrada, A., Krishnaswami, A. J. & Daily, G. C. Quantifying and sustaining biodiversity in tropical agricultural landscapes. PNAS 113, 14544–14551 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jesus, F. M., Pivello, V. R., Meirelles, S. T., Franco, G. A. D. C. & Metzger, J. P. The importance of landscape structure for seed dispersal in rain forest fragments. J. Veg. Sci. 23, 1126–1136 (2012).Article 

Google Scholar 
Galán-Acedo, C., Arroyo-Rodríguez, V., Estrada, A. & Ramos-Fernández, G. Drivers of the spatial scale that best predict primate responses to landscape structure. Ecography 41, 2027–2037 (2018).Article 

Google Scholar 
Pardini, R., de Souza, S. M., Braga-Neto, R. & Metzger, J. P. The role of forest structure, fragment size and corridors in maintaining small mammal abundance and diversity in an Atlantic forest landscape. Biol. Cons. 124, 253–266 (2005).Article 

Google Scholar 
Forman, R. T. T. & Godron, M. Landscape ecology. (Wiley, 1986).QGIS Development Team. QGIS Geographic Information System. (Open Source Geospatial Foundation, 2016).Gillies, C. S. & Clair, C. C. S. Riparian corridors enhance movement of a forest specialist bird in fragmented tropical forest. PNAS 105, 19774–19779 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Harvey, C. A., Tucker, N. I. & Estrada, A. Live fences, isolated trees, and windbreaks: tools for conserving biodiversity in fragmented tropical landscapes. in Agroforestry and biodiversity conservation in tropical landscapes 261–289 (2004).Harvey, C. A. et al. Contribution of live fences to the ecological integrity of agricultural landscapes. Agric. Ecosyst. Environ. 111, 200–230 (2005).Article 

Google Scholar 
Saura, S., Bodin, Ö. & Fortin, M.-J. EDITOR’S CHOICE: Stepping stones are crucial for species’ long-distance dispersal and range expansion through habitat networks. J. Appl. Ecol. 51, 171–182 (2014).Article 

Google Scholar 
He, H. S., DeZonia, B. E. & Mladenoff, D. J. An aggregation index (AI) to quantify spatial patterns of landscapes. Landscape Ecol. 15, 591–601 (2000).Article 

Google Scholar 
Radford, J. Q., Bennett, A. F. & Cheers, G. J. Landscape-level thresholds of habitat cover for woodland-dependent birds. Biol. Cons. 124, 317–337 (2005).Article 

Google Scholar 
Pires, A. S., Lira, P. K., Fernandez, F. A. S., Schittini, G. M. & Oliveira, L. C. Frequency of movements of small mammals among Atlantic Coastal Forest fragments in Brazil. Biol. Conserv. 108, 229–237 (2002).Article 

Google Scholar 
Holbrook, K. M. Home range and movement patterns of toucans: implications for seed dispersal. Biotropica 43, 357–364 (2011).Article 

Google Scholar 
Şekercioğlu, Ç. H. et al. Tropical countryside riparian corridors provide critical habitat and connectivity for seed-dispersing forest birds in a fragmented landscape. J Ornithol 156, 343–353 (2015).Article 

Google Scholar 
Eigenbrod, F., Hecnar, S. J. & Fahrig, L. Sub-optimal study design has major impacts on landscape-scale inference. Biol. Conserv. 144, 298–305 (2011).Article 

Google Scholar 
McGarigal, K., Cushman, S. A. & Ene, E. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. (2012).Jackson, H. B. & Fahrig, L. Are ecologists conducting research at the optimal scale?. Global Ecol. Biogeography 24, 52–63 (2015).Article 

Google Scholar 
Jackson, H. B. & Fahrig, L. What size is a biologically relevant landscape?. Landscape Ecol 27, 929–941 (2012).Article 

Google Scholar 
McGarigal, K., Wan, H. Y., Zeller, K. A., Timm, B. C. & Cushman, S. A. Multi-scale habitat selection modeling: a review and outlook. Landscape Ecol 31, 1161–1175 (2016).Article 

Google Scholar 
Huais, P. Y. multifit: an R function for multi-scale analysis in landscape ecology. Landscape Ecol 33, 1023–1028 (2018).Article 

Google Scholar 
R Development Core Team. R: a language and environment for statistical computing. (R Foundation for Statistical Computing, 2019).Crawley, M. J. Statistical modelling in the R book. (John Wiley & Sons Ltd., 2007).Leite, M. de S., Tambosi, L. R., Romitelli, I. & Metzger, J. P. Landscape ecology perspective in restoration projects for biodiversity conservation: a review. Natureza & Conservação 11, 108–118 (2013).Neter, J., Kutner, M. H., Nachtsheim, C. J. & Wasserman, W. Applied linear statistical models. (McGraw-Hill/Irwin, 1996).Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: a practical information-theoretic approach. (Springer, 2002).Calcagno, V. & Mazancourt, C. glmulti: an R package for easy automated model selection with (generalized) linear models. J. Stat. Soft. 34, 1–29 (2010).Article 

Google Scholar 
Giam, X. & Olden, J. D. Quantifying variable importance in a multimodel inference framework. Methods Ecol. Evol. 7, 388–397 (2016).Article 

Google Scholar 
Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
PubMed 
Article 

Google Scholar 
Andrén, H. Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos 71, 355–366 (1994).Article 

Google Scholar 
Fagan, M. E., DeFries, R. S., Sesnie, S. E., Arroyo-Mora, J. P. & Chazdon, R. L. Targeted reforestation could reverse declines in connectivity for understory birds in a tropical habitat corridor. Ecol. Appl. 26, 1456–1474 (2016).PubMed 
Article 

Google Scholar 
Reid, J. L. & Holl, K. D. Arrival ≠ survival. Restor. Ecol. 21, 153–155 (2013).Article 

Google Scholar 
Pejchar, L. et al. Birds as agents of seed dispersal in a human-dominated landscape in southern Costa Rica. Biol. Cons. 141, 536–544 (2008).Article 

Google Scholar 
Norden, N. et al. Is temporal variation of seedling communities determined by environment or by seed arrival? A test in a neotropical forest. J. Ecol. 95, 507–516 (2007).Article 

Google Scholar 
Tabarelli, M., Lopes, A. V. & Peres, C. A. Edge-effects drive tropical forest fragments towards an early-successional system. Biotropica 40, 657–661 (2008).Article 

Google Scholar 
Lôbo, D., Leão, T., Melo, F. P. L., Santos, A. M. M. & Tabarelli, M. Forest fragmentation drives Atlantic forest of northeastern Brazil to biotic homogenization. Divers. Distrib. 17, 287–296 (2011).Article 

Google Scholar 
Costa, J. B. P., Melo, F. P. L., Santos, B. A. & Tabarelli, M. Reduced availability of large seeds constrains Atlantic forest regeneration. Acta Oecologica 39, 61–66 (2012).ADS 
Article 

Google Scholar 
Miguet, P., Jackson, H. B., Jackson, N. D., Martin, A. E. & Fahrig, L. What determines the spatial extent of landscape effects on species?. Landscape Ecol 31, 1177–1194 (2016).Article 

Google Scholar  More

Chaudhary, G. & Singh, S. K. Global status of genetically modified crops and its commercialization: environmental issues in logistics and manufacturing. (2019).Zwahlen, C., Hilbeck, A., Gugerli, P. & Nentwig, W. Degradation of the Cry1Ab protein within transgenic Bacillus thuringiensis corn tissue in the field. Mol. Ecol. 12, 765–775 (2010).Article 

Google Scholar 
Kamota, A., Muchaonyerwa, P. & Mnkeni, P. N. S. Decomposition of surface-applied and soil-incorporated Bt maize leaf litter and Cry1Ab protein during winter fallow in South Africa. Pedosphere 24, 251–257 (2014).CAS 
Article 

Google Scholar 
Xue, K., Diaz, B. R. & Thies, J. E. Stability of Cry3Bb1 protein in soils and its degradation in transgenic corn residues. Soil Biol. Biochem. 76, 119–126 (2014).CAS 
Article 

Google Scholar 
Griffiths, N. A. et al. Occurrence, leaching, and degradation of Cry1Ab protein from transgenic maize detritus in agricultural streams. Sci. Total Environ. 592, 97–105 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Wang, B. F., Yin, J. Q., Wu, F. C., Jiang, Z. L. & Song, X. Y. Field decomposition of Bt-506 maize leaves and its effect on Collembola in the black soil region of Northeast China. Glob. Ecol. Conserv. https://doi.org/10.1016/j.gecco.2021.e01480 (2021).Article 

Google Scholar 
Shu, Y. H., Zhang, Y. Y., Zeng, H., Zhang, Y. H. & Wang, J. W. Effects of Cry1Ab Bt maize straw return on bacterial community of earthworm Eisenia Fetida. Chemosphere 173, 1–13 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Čerevková, A., Miklisová, D., Szoboszlay, M. S., Tebbe, C. C. & Cagáň, L. The responses of soil nematode communities to Bt maize cultivation at four field sites across Europe. Soil Biol. Biochem. 119, 194–202 (2018).Article 
CAS 

Google Scholar 
Liu, T. et al. Root and detritus of transgenic Bt crop did not change nematode abundance and community composition but enhanced trophic connections. Sci. Total Environ. 644, 822–829 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Domínguez, M. T., Holthof, E., Smith, A. R., Koller, E. & Emmett, B. A. Contrasting response of summer soil respiration and enzyme activities to long-term warming and drought in a wet shrubland (NE Wales, UK). Appl. Soil Ecol. 110, 151–155 (2016).Article 

Google Scholar 
Zhang, Q. F. et al. Are the combined effects of warming and drought on foliar C:N:P:K stoichiometry in a subtropical forest greater than their individual effects?. Forest Ecol. Manag. 448, 256–266 (2019).Article 

Google Scholar 
Chen, Q., Niu, B., Hu, Y., Luo, T. & Zhang, G. Warming and increased precipitation indirectly affect the composition and turnover of labile-fraction soil organic matter by directly affecting vegetation and microorganisms. Sci. Total Environ. 714, 136787.1-136787.9 (2020).
Google Scholar 
Dai, A. Drought under global warming: A review. Wiley Interdiscip. Rev. Clim. Change 2, 45–65 (2011).Article 

Google Scholar 
Martin, J. T., Pederson, G. T., Woodhouse, C. A., Cook, E. R. & King, J. Increased drought severity tracks warming in the United States’ largest river basin. Proc. Natl. Acad. Sci. USA 117, 201916208 (2020).
Google Scholar 
Ma, S., Zhu, C. & Liu, J. Combined impacts of warm central equatorial pacific sea surface temperatures and anthropogenic warming on the 2019 severe drought in east China. Adv. Atmos. Sci. 37, 1149–1163 (2020).Article 

Google Scholar 
Peñuelas, J. et al. Nonintrusive field experiments show different plant responses to warming and drought among sites, seasons, and species in a north–south European gradient. Ecosystems 7, 598–612 (2004).Article 

Google Scholar 
Sardans, J., Peñuelas, J. & Estiarte, M. Warming and drought alter soil phosphatase activity and soil P availability in a Mediterranean shrubland. Plant Soil 289, 227–238 (2006).CAS 
Article 

Google Scholar 
Viciedo, D. O., Prado, R., Martinez, C. A., Habermann, H. & Piccolo, M. Short-term warming and water stress affect Panicum maximum Jacq. stoichiometric homeostasis and biomass production. Sci. Total Environ. 681, 267–274 (2019).ADS 
Article 
CAS 

Google Scholar 
Meeran, K. et al. Warming and elevated CO2 intensify drought and recovery responses of grassland carbon allocation to soil respiration. Glob. Change Biol. 27, 3230–3243 (2021).Article 

Google Scholar 
Lang, B., Rall, B. C., Scheu, S. & Brose, U. Effects of environmental warming and drought on size-structured soil food webs. Oikos 123, 1224–1233 (2014).Article 

Google Scholar 
Pold, G., Melillo, J. M. & Deangelis, K. M. Two decades of warming increases diversity of a potentially lignolytic bacterial community. Front. Microbiol. 6, 480 (2010).
Google Scholar 
Séneca, J. et al. Composition and activity of nitrifier communities in soil are unresponsive to elevated temperature and CO2, but strongly affected by drought. ISME J. 14, 1–16 (2020).Article 
CAS 

Google Scholar 
Santos, A. et al. Water stress alters lignin content and related gene expression in two sugarcane genotypes. J. Agric. Food Chem. 63, 4708 (2015).CAS 
PubMed 
Article 

Google Scholar 
Albert, K. R. et al. Effects of elevated CO2, warming and drought episodes on plant carbon uptake in a temperate heath ecosystem are controlled by soil water status. Plant Cell Environ. 34, 1207–1222 (2011).CAS 
PubMed 
Article 

Google Scholar 
Peñuelas, J. et al. Nonintrusive field experiments show different plant responses to warming and drought among sites, seasons, and species in a north-south European gradient. Ecosystems 7, 598–612 (2004).Article 

Google Scholar 
Zhu, E., Cao, Z., Jia, J., Liu, C. & Feng, X. Inactive and inefficient: Warming and drought effect on microbial carbon processing in alpine grassland at depth. Glob. Change Biol. https://doi.org/10.1111/gcb.15541 (2021).Article 

Google Scholar 
Sardans, J., Peñuelas, J. & Estiarte, M. Changes in soil enzymes related to C and N cycle and in soil C and N content under prolonged warming and drought in a Mediterranean shrubland. Appl. Soil Ecol. 39, 223–235 (2008).Article 

Google Scholar 
Xu, G. L. et al. Seasonal exposure to drought and air warming affects soil Collembola and Mites. PLoS ONE 7, e43102 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chang, L. et al. Warming limits daytime but not nighttime activity of epigeic microarthropods in Songnen grasslands. Appl. Soil Ecol. 141, 79–83 (2019).Article 

Google Scholar 
Dai, A. G., Trenberth, K. E. & Qian, T. T. A global dataset of palmer drought severity index for 1870–2002: Relationship with soil moisture and effects of surface warming. J. Hydrometeorol. 5, 1117–1130 (2004).ADS 
Article 

Google Scholar 
Bongaarts, J. Intergovernmental panel on climate change special report on global warming of 1.5 °C Switzerland: IPCC, 2018. Popul. Dev. Rev. 45, 251–252 (2019).Article 

Google Scholar 
Bellinger, P.F., Christiansen, K. A. & Janssens, F. Checklist of the Collembola of the World. 1996–2019. http://www.collembola.org (Accessed 10 Sept 2021).Hopkin, S. P. Biology of the Springtails (Insecta:Collembola) 1–330 (Oxford University Press, 1997).
Google Scholar 
Rusek, J. Biodiversity of Collembola and their functional role in the ecosystem. Biodivers. Conserv. 7, 1207–1219 (1998).Article 

Google Scholar 
Filser, J. The role of Collembola in carbon and nitrogen cycling in soil. Pedobiologia 46, 234–245 (2002).
Google Scholar 
Endlweber, K. & Scheu, S. Effects of Collembola on root properties of two competing ruderal plant species. Soil Biol. Biochem. 38, 2025–2031 (2006).CAS 
Article 

Google Scholar 
Rebek, E. J., Hogg, D. B. & Young, D. K. Effect of four cropping systems on the abundance and diversity of epedaphic Springtails (Hexapoda: Parainsecta: Collembola) in southern Wisconsin. Environ. Entomol. 31, 37–46 (2002).Article 

Google Scholar 
Santorufo, L. et al. An assessment of the influence of the urban environment on collembolan communities in soils using taxonomy- and trait-based approaches. Appl. Soil Ecol. 78, 48–56 (2014).Article 

Google Scholar 
Rossetti, I. et al. Isolated cork oak trees affect soil properties and biodiversity in a Mediterranean wooded grassland. Agric. Ecosyst. Environ. 202, 203–216 (2015).Article 

Google Scholar 
Hönemann, L., Zurbrügg, C. & Nentwig, W. Effects of Bt-corn decomposition on the composition of the soil meso- and macrofauna. Appl. Soil Ecol. 40, 203–209 (2008).Article 

Google Scholar 
Arias-Martín, M. et al. Effects of three-year cultivation of Cry1Ab-expressing Bt maize on soil microarthropod communities. Agric. Ecosyst. Environ. 220, 125–134 (2016).Article 
CAS 

Google Scholar 
Song, X. Y. et al. Use of taxonomic and trait-based approaches to evaluate the effects of transgenic Cry1Ac corn on the community characteristics of soil Collembola. Environ. Entomol. 48, 263–269 (2019).PubMed 
Article 

Google Scholar 
Thibaud, J. M. Intermue ettemperatures lethales chez les insects collemboles arthropleones. II.—Isotomidae, Entomobryidae et Tomoceridae. Rev. Ecol. Biol. Sol. 14, 267–278 (1977).
Google Scholar 
Eisenbeis, G. & Wichard, W. Atlas on the Biology of Soil Arthropods 200–228 (Springer, 1987).Book 

Google Scholar 
Wang, B. F., Wu, F. C., Yin, J. Q., Jiang, Z. L. & Song, X. Y. Use of taxonomic and trait-based approaches to evaluate the effect of Bt maize expressing cry1Ie protein on non-target Collembola: A case study in Northeast China. Insects. https://doi.org/10.3390/insects12020088 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Chang, L., Song, X. Y., Wang, B. F., Wu, D. H. & Reddy, G. Effect of Bt corn (Bt 38) cultivation on community structure of Collembola. Ann. Entomol. Soc. Am. 113, 1–5 (2020).CAS 
Article 

Google Scholar 
Al-Deeb, M., Wilde, G. E., Blair, J. M. & Todd, T. C. Effect of Bt corn for corn rootworm control on nontarget soil microarthropods and nematodes. Environ. Entomol. 32, 859–865 (2003).Article 

Google Scholar 
Bitzer, R. J., Rice, M. E., Pilcher, C. D., Pilcher, C. L. & Lam, W. F. Biodiversity and community structure of epedaphic and euedaphic springtails (Collembola) in transgenic rootworm Bt maize. Environ. Entomol. 34, 1346–1376 (2005).Article 

Google Scholar 
Yang, Y. et al. Toxicological and biochemical analyses demonstrate no toxic effect of Cry1C and Cry2A to Folsomia candida. Sci. Rep. 5, 15619 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jiang, Z., Zhou, L., Wang, B. F., Wang, D. M. & Song, X. Y. Toxicological and biochemical analyses demonstrate no toxic effect of Bt maize on the Folsomia candida. PLoS ONE 15, e0232747 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Frouz, J., Elhottová, D., Helingerová, M. & Kocourek, F. The effect of bt corn on soil invertebrates, soil microbial community and decomposition rates of corn post-harvest residues under field and laboratory conditions. J. Sustain. Agric. 32, 645–655 (2008).Article 

Google Scholar 
Daghighi, E., Filser, J., Koehler, H. & Kesel, R. Long-term succession of Collembola communities in relation to climate change and vegetation. Pedobiologia 64, 25–38 (2017).Article 

Google Scholar 
Chang, L. et al. Green more than brown food resources drive the effect of simulated climate change on Collembola: A soil transplantation experiment in Northeast China. Geoderma 392, 115008 (2021).ADS 
CAS 
Article 

Google Scholar 
Convey, P., Block, W. & Peat, H. J. Soil arthropods as indicators of water stress in Antarctic terrestrial habitats. Glob. Change Biol. 9, 1718–1730 (2003).ADS 
Article 

Google Scholar 
Alvarez, T., Frampton, G. K. & Goulson, D. The effects of drought upon epigeal Collembola from arable soils. Agric. For. Entomol. 1, 243–248 (2015).Article 

Google Scholar 
Fountain, M. T. & Hopkin, S. P. Folsomia candida (collembola): A “standard” soil arthropod. Annu. Rev. Entomol. 50, 201–222 (2005).CAS 
PubMed 
Article 

Google Scholar 
Holmstrup, M. Water relations and drought sensitivity of Folsomia candida eggs (Collembola: Isotomidae). Eur. J. Entomol. 116, 229–234 (2019).Article 

Google Scholar 
Meehan, M. L., Barreto, C., Turnbull, M. S., Bradley, R. L. & Lindo, Z. Response of soil fauna to simulated global change factors depends on ambient climate conditions. Pedobiologia 83, 150672 (2020).Article 

Google Scholar 
Harte, J., Rawa, A. & Price, V. Effects of manipulated soil microclimate on mesofaunal biomass and diversity. Soil Biol. Biochem. 28, 313–322 (1996).CAS 
Article 

Google Scholar 
Lindberg, N. Soil fauna and global change: responses to experimental drought, irrigation, fertilisation and soil warming. Acta Universitatis Agriculturae Sueciae Silvestria 37, + Papers I-IV (2003).Bokhorst, S. et al. Extreme winter warming events more negatively impact small rather than large soil fauna: shift in community composition explained by traits not taxa. Global Change Biolo. 18, 1152–1162 (2012).Macfadyen, A. Improved funnel-type extractors for soil arthropods. J. Anim. Ecol. 30, 171–184 (1961).Article 

Google Scholar 
Christiansen, K. A. & Bellinge, P. F. The Collembola of North America, North of the Rio Grande: A Taxonomic Analysis 2nd edn. (Grinnell College, 1998).
Google Scholar 
Fjellberg, A. The Collembola of Fennoscandia and Denmark. Part II: Entomobryomorpha and Symphypleona. In Fauna Entomologica Scandinavica, Vol. 42, 1−264 (Koninklijke Brill, 2007).Potapov, M. Synopses on Palaearctic Collembola: Isotomidae. Abhandlungen und Berichte des Naturkundemuseums, Görlitz, Poland 73, 1–603 (2001).
Google Scholar 
Yin, W. Y. Pictorial Keys to Soil Animals of China. 282−292, 592−600 (Science Press, 1998).Grime, J. P. Benefits of plant diversity to ecosystems: Immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).Article 

Google Scholar 
Cerabolini, B., Pierce, S., Luzzaro, A. & Ossola, A. Species evenness affects ecosystem processes in situ via diversity in the adaptive strategies of dominant species. Plant Ecol. 207, 333–345 (2010).Article 

Google Scholar  More