Ecology

Avital, E. & Jablonka, E. Animal Traditions: Behavioural Inheritance in Evolution. (Cambridge University Press, 2000).Bonduriansky, R. & Day, T. Nongenetic inheritance and its evolutionary implications. Annu. Rev. Ecol. Evol. Syst. 40, 103–125 (2009).
Google Scholar 
Mousseau, T. A. & Fox, C. W. Maternal Effects as Adaptations. (Oxford University Press, 1998).Mousseau, T. A. & Fox, C. W. The adaptive significance of maternal effects. Trends Ecol. Evol. 13, 403–407 (1998b).CAS 
PubMed 

Google Scholar 
Jablonka, E. & Lamb, M. J. Evolution in four dimensions. Genetic, Epigenetic, Behavioral and Symbolic Variation in the History of Life. Revised Edition. (MIT Press, 2014).Uller, T. Developmental plasticity and the evolution of parental effects. Trends Ecol. Evol. 23, 432–438 (2018).
Google Scholar 
Bell, A. M. & Hellmann, J. K. An integrative framework for understanding the mechanisms and multigenerational consequences of transgenerational plasticity. Annu. Rev. Ecol. Evol. Syst. 50, 97–118 (2019).
Google Scholar 
Marshall, D. J. & Uller, T. When is a maternal effect adaptive? Oikos 116, 1957–1963 (2007).
Google Scholar 
Yin, J., Zhou, M., Lin, Z., Li, Q. Q. & Zhang, Y.-Y. Transgenerational effects benefit offspring across diverse environments: a meta-analysis in plants and animals. Ecol. Lett. 22, 1976–1986 (2019).PubMed 

Google Scholar 
Wolf, J. B. & Wade, M. J. What are maternal effects (and what are they not)? Philos. Trans. R. Soc. B 364, e1115 (2008).
Google Scholar 
Kilner, R. M. et al. Parental effects alter the adaptive value of an adult behavioural trait. eLife 4, e07340 (2015).PubMed 
PubMed Central 

Google Scholar 
McNamara, J. M., Dall, S. R. X., Hammerstein, P. & Leimar, O. Detection vs. selection: integration of genetic, epigenetic and environmental cues in fluctuating environments. Ecol. Lett. 19, 1267–1276 (2016).PubMed 

Google Scholar 
Deas, J. B., Blondel, L. & Extavour, C. G. Ancestral and offspring nutrition interact to affect life-history traits in Drosophila melanogaster. Proc. R. Soc. B 286, 20182778 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stamps, J. A. & Bell, A. M. Combining information from parental and personal experiences: simple processes generate diverse outcomes. PLoS ONE 16, e0250540 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Agrawal, A. A., Laforsch, C. & Tollrian, R. Transgenerational induction of defences in animals and plants. Nature 401, 60–63 (1999).CAS 

Google Scholar 
Remy, J. J. Stable inheritance of an acquired behavior in Caenorhabditis elegans. Curr. Biol. 20, R877–R878 (2010).CAS 
PubMed 

Google Scholar 
Shama, L. N. S. & Wegner, K. M. Grandparental effects in marine sticklebacks: transgenerational plasticity across multiple generations. J. Evol. Biol. 27, 2297–2307 (2014).CAS 
PubMed 

Google Scholar 
Crocker, K. C. & Hunter, M. D. Environmental causes and transgenerational consequences of ecdysteroid hormone provisioning in Acheta domesticus. J. Insect Physiol. 109, 69–78 (2018).CAS 
PubMed 

Google Scholar 
Sarker, G. & Peleg-Raibstein, D. Maternal overnutrition induces long-term cognitive deficits across several generations. Nutrients 11, 7 (2019).CAS 

Google Scholar 
Hellmann, J. K., Carlsson, E. R. & Bell, A. M. Sex-specific plasticity across generations II: grandpaternal effects are lineage specific and sex specific. J. Anim. Ecol. 89, 2800–2819 (2020).PubMed 
PubMed Central 

Google Scholar 
Mahaq, O. The effects of dietary edible bird nest supplementation on learning and memory functions of multigenerational mice. Brain Behav. 10, e01817 (2020).PubMed 
PubMed Central 

Google Scholar 
Ranade, S. C. et al. Different types of nutritional deficiencies affect different domains of spatial memory function checked in a radial arm maze. Neuroscience 152, 859–866 (2008).CAS 
PubMed 

Google Scholar 
De Souza, A. S., Fernandes, F. S., do Carmo, T. & das Gracas, M. Effects of maternal malnutrition and postnatal nutritional rehabilitation on brain fatty acids, learning, and memory. Nutr. Rev. 69, 132–144 (2011).PubMed 

Google Scholar 
Munch, K. L. et al. Maternal effects impact decision-making in a viviparous lizard. Biol. Lett. 14, 20170556 (2018).PubMed 
PubMed Central 

Google Scholar 
Li, C. et al. The learning ability and memory retention of broiler breeders: 2 transgenerational effects of reduced balanced protein diet on reward-based learning. Animal 13, 1260–1268 (2019).CAS 
PubMed 

Google Scholar 
Boogert, N. J., Zimmer, C. & Spencer, K. A. Pre- and post-natal stress have opposing effects on social information use. Biol. Lett. 9, 20121088 (2013).PubMed 
PubMed Central 

Google Scholar 
Xia, S.-Z., Liu, L., Feng, C.-H. & Guo, A.-K. Nutritional effects on operant visual learning in Drosophila melanogaster. Physiol. Behav. 62, 263–271 (1997).CAS 
PubMed 

Google Scholar 
Eaton, L., Edmonds, E. J., Henry, T. B., Snellgrove, D. L. & Sloman, K. A. Mild maternal stress disrupts associative learning and increases aggression in offspring. Horm. Behav. 71, 10–15 (2015).CAS 
PubMed 

Google Scholar 
Costa, C. P. et al. Care-giver identity impacts offspring development and performance in an annually social bumble bee. BMC Ecol. Evol. 21, 20 (2021).PubMed 
PubMed Central 

Google Scholar 
Roche, D. P., McGhee, K. E. & Bell, A. M. Maternal predator-exposure has lifelong consequences for offspring learning in three-spined sticklebacks. Biol. Lett. 8, 932–935 (2012).PubMed 
PubMed Central 

Google Scholar 
Feng, S., McGhee, K. E. & Bell, A. M. Effect of maternal predator exposure on the ability of stickleback offspring to generalize a learned colour-reward association. Anim. Behav. 107, 61–69 (2015).PubMed 
PubMed Central 

Google Scholar 
Ghio, S. C., Leblanc, A. B., Audet, C. & Aubin-Horth, N. Effects of maternal stress and cortisol exposure at the egg stage on learning, boldness and neophobia in brook trout. Behaviour 153, 1639–1663 (2016).
Google Scholar 
Tariel, J., Plenet, S. & Luquet, E. How do developmental and parental exposures to predation affect personality and immediate behavioural plasticity in the snail Physa acuta? Proc. R. Soc. B 287, 20201761 (2020).PubMed 
PubMed Central 

Google Scholar 
Dinh, H. et al. Transgenerational effects of parental diet on offspring development and disease resistance in flies. Front. Ecol. Evol. 9, 606993 (2021).
Google Scholar 
Bilkó, A., Altbäcker, V. & Hudson, R. Transmission of food preference in the rabbit: The means of information transfer. Physiol. Behav. 56, 907–912 (1994).PubMed 

Google Scholar 
Oostindjer, M., Bolhuis, J. E., van den Brand, H., Roura, E. & Kemp, B. Prenatal flavor exposure affects growth, health and behavior of newly weaned piglets. Physiol. Behav. 99, 579–586 (2010).CAS 
PubMed 

Google Scholar 
Wells, D. L. & Hepper, P. G. Prenatal olfactory learning in the domestic dog. Anim. Behav. 72, 681–686 (2006).
Google Scholar 
Hepper, P. G. Fetal memory: does it exist? What does it do? Acta Paediatr. 85, 16–20 (1996).
Google Scholar 
Gowri, V., Dion, E., Viswanath, A., Monteiro Piel, F. & Monteiro, A. Transgenerational inheritance of learned preferences for novel host plant odors in Bicyclus anynana butterflies. Evolution 73, 2401–2414 (2019).CAS 
PubMed 

Google Scholar 
Peralta-Quesada, P. C. & Schausberger, P. Prenatal chemosensory learning by the predatory mite Neoseiulus californicus. PLoS ONE 7, e53229 (2012).PubMed 
PubMed Central 

Google Scholar 
Nieberding, C. M., van Dyck, H. & Chittka, L. Adaptive learning in non-social insects: from theory to field work, and back. Curr. Opin. Insect Sci. 27, 75–81 (2018).PubMed 

Google Scholar 
Momen, F. M. & El Saway, S. A. Biology and fee18lopemenviour of the predatory mite Amblyseius swirskii (Acari: Phytoseiidae). Acarologia 33, 199–204 (1993).
Google Scholar 
Wimmer, D., Hoffmann, D. & Schausberger, P. Prey suitability of Western flower thrips, Frankliniella occidentalis, and onion thrips, Thrips tabaci, for the predatory mite Amblyseius swirskii. Biocontrol Sci. Technol. 18, 533–542 (2008).
Google Scholar 
Vangansbeke, D. et al. Supplemental food for Amblyseius swirskii in the control of thrips: feeding friend or foe? Pest Manag. Sci. 72, 466–473 (2016).CAS 
PubMed 

Google Scholar 
Delisle, J. F., Brodeur, J. & Shipp, L. Evaluation of various types of supplemental food for two species of predatory mites, Amblyseius swirskii and Neoseiulus cucumeris (Acari: Phytoseiidae). Exp. Appl. Acarol. 65, 483–494 (2015).CAS 
PubMed 

Google Scholar 
Christiansen, I. C., Szin, S. & Schausberger, P. Benefit-cost trade-offs of early learning in foraging predatory mites Amblyseius swirskii. Sci. Rep. 6, 23571 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Schausberger, P., Davaasambuu, U., Saussure, S. & Christiansen, I. C. Categorizing experience-based foraging plasticity in mites: age dependency, primacy effects and memory persistence. R. Soc. Open Sci. 5, 172110 (2018).PubMed 
PubMed Central 

Google Scholar 
Seiter, M. & Schausberger, P. Constitutive and operational variation of learning in foraging predatory mites. PLoS ONE 11, e0166334 (2016).PubMed 
PubMed Central 

Google Scholar 
Schausberger, P., Seiter, M. & Raspotnig, G. Innate and learned responses of foraging predatory mites to polar and non-polar fractions of thrips’ chemical cues. Biol. Control 151, 104371 (2020).CAS 

Google Scholar 
Seiter, M. & Schausberger, P. Maternal intraguild predation risk affects offspring anti-predator behavior and learning in mites. Sci. Rep. 5, 15046 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, Z. M. Transgenerational influence of sensorimotor training on offspring behavior and its neural basis in Drosophila. Neurobiol. Learn. Mem. 131, 166–175 (2016).PubMed 

Google Scholar 
Jahanbazi, M., Sedaratian-Jahromi, A. & Ghane-Jahromi, M. Comparative study of predation, preference and switching behaviors of two predatory mite Neoseiulus californicus and Amblyseius swirskii (Acari: Phytoseiidae). Int. J. Pest Manag. https://doi.org/10.1080/09670874.2021.1944699 (2021).Margulies, C., Tully, T. & Dubnau, J. Deconstructing memory in Drosophila. Curr. Biol. 15, R700–R713 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mery, F. & Kawecki, T. J. A cost of long-term memory in Drosophila. Science 308, 1148 (2005).CAS 
PubMed 

Google Scholar 
Schausberger, P., Walzer, A., Hoffmann, D. & Rahmani, H. Food imprinting revisited: early learning in foraging predatory mites. Behaviour 147, 883–897 (2010).
Google Scholar 
Schausberger, P. & Peneder, S. Non-associative versus associative learning by foraging predatory mites. BMC Ecol. 17, 2 (2017).PubMed 
PubMed Central 

Google Scholar 
Stephens, D. W. & Krebs, J. R. Foraging Theory. (Princeton University Press, 1986).Mendel, D. & Schausberger, P. Diet-dependent intraguild predation between the predatory mites Neoseiulus californicus and Neoseiulus cucumeris. J. Appl. Entomol. 135, 311–319 (2011).
Google Scholar 
Somer, R. A. & Thummel, C. S. Epigenetic inheritance of metabolic state. Curr. Opin. Genet. Dev. 27, 43–47 (2014).CAS 
PubMed 

Google Scholar 
Bonduriansky, R. & Crean, A. J. What are condition-transfer effects and how can they be detected? Methods Ecol. Evol. 9, 450–456 (2018).
Google Scholar 
Engqvist, L. & Reinhold, K. Adaptive parental effects and how to estimate them: a comment to Bonduriansky and Crean. Methods Ecol. Evol. 9, 457–459 (2018).
Google Scholar 
Melis, R. et al. Effect of freezing and drying processes on the molecular traits of edible yellow mealworm. Innov. Food Sci. Emerg. Technol. 48, 138–149 (2018).CAS 

Google Scholar 
Singh, Y., Cullere, M., Kovitvadhi, A., Chundang, P. & Dalle Zotte, A. Effect of different killing methods on physicochemical traits, nutritional characteristics, in vitro human digestibility and oxidative stability during storage of the house cricket (Acheta domesticus L.). Innov. Food Sci. Emerg. Technol. 65, 102444 (2020).CAS 

Google Scholar 
Grafen, A. On the uses of data on lifetime reproductive success. Philos. Trans. R. Soc. B 363, 1635–1645 (1988).
Google Scholar 
Monaghan, P. Early growth conditions, phenotypic development and environmental change. Philos. Trans. R. Soc. B 363, 1635–1645 (2008).
Google Scholar 
English, S., Fawcett, T. W., Higginson, A. D., Trimmer, P. C. & Uller, T. Adaptive use of information during growth can explain long-term effects of early life experiences. Am. Nat. 187, 620–632 (2016).PubMed 

Google Scholar 
Miller, R. R. & Polack, C. W. Sources of maladaptive behavior in ‘normal’ organisms. Behav. Process. 154, 4–12 (2018).
Google Scholar 
Schausberger, P. Inter-and intraspecific predation on immatures by adult females in Euseius finlandicus, Typhlodromus pyri and Kampimodromus aberrans (Acari, Phytoseiidae). Exp. Appl. Acarol. 21, 131–150 (1997).
Google Scholar 
Walzer, A. & Schausberger, P. Non-consumptive effects of predatory mites on thrips and its host plant. Oikos 118, 934–940 (2009).
Google Scholar 
Walzer, A., Paulus, H. & Schausberger, P. Ontogenetic shifts in intraguild predation on thrips by phytoseiid mites: the relevance of body size and diet specialization. Bull. Entomol. Res. 94, 577–588 (2004).CAS 
PubMed 

Google Scholar 
Vangansbeke, D., Duarte, M. V. A. & De Clercq, P. Cold-born killers: exploiting temperature-size rule enhances predation capacity of a predatory mite. Pest Manag. Sci. 76, 1841–1846 (2020).CAS 
PubMed 

Google Scholar 
Krantz, G. W. & Walter, D. E. A Manual of Acarology 3rd edn (Texas Tech University Press, 2008).Croft, B. A., Luh, H.-K. & Schausberger, P. Larval size relative to larval feeding, cannibalism of larvae, egg or adult female size and larval–adult setal patterns among 13 phytoseiid mite species. Exp. Appl. Acarol. 23, 599–610 (1999).
Google Scholar  More

Jamieson, A. J., Fujii, T., Mayor, D. J., Solan, M. & Priede, I. G. Hadal trenches: the ecology of the deepest places on Earth. Trends Ecol. Evol. 25, 190–197 (2010).PubMed 

Google Scholar 
Stewart, H. A. & Jamieson, A. J. Habitat heterogeneity of hadal trenches: considerations and implications for future studies. Prog. Oceanogr. 161, 47–65 (2018).ADS 

Google Scholar 
Zhu, G. et al. Along-strike variation in slab geometry at the southern Mariana subduction zone revealed by seismicity through ocean bottom seismic experiments. Geophys. J. Int. 218, 2122–2135 (2019).ADS 

Google Scholar 
Bao, R. et al. Tectonically-triggered sediment and carbon export to the Hadal zone. Nat. Commun. 9, 121 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Kioka, A. et al. Megathrust earthquake drives drastic organic carbon supply to the hadal trench. Sci. Rep. 9, 1553 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Luo, M., Gieskes, J., Chen, L. Y., Shi, X. F. & Chen, D. F. Provenances, distribution, and accumulation of organic matter in the southern Mariana Trench rim and slope: implication for carbon cycle and burial in hadal trenches. Mar. Geol. 386, 98–106 (2017).ADS 
CAS 

Google Scholar 
Glud, R. N. et al. High rates of microbial carbon turnover in sediments in the deepest oceanic trench on Earth. Nat. Geosci. 6, 284–288 (2013).ADS 
CAS 

Google Scholar 
Liu, S. & Peng, X. Organic matter diagenesis in hadal setting: insights from the pore-water geochemistry of the Mariana Trench sediments. Deep Sea Res. I 147, 22–31 (2019).CAS 

Google Scholar 
Nunoura, T. et al. Microbial diversity in sediments from the bottom of the Challenger Deep, the Mariana Trench. Microbes Environ. 33, 186–194 (2018).PubMed 
PubMed Central 

Google Scholar 
Wang, Y. et al. Genomics insights into ecotype formation of ammonia-oxidizing archaea in the deep ocean. Environ. Microbiol. 21, 716–729 (2019).CAS 
PubMed 

Google Scholar 
Nunoura, T. et al. Molecular biological and isotopic biogeochemical prognoses of the nitrification-driven dynamic microbial nitrogen cycle in hadopelagic sediments. Environ. Microbiol. 15, 3087–3107 (2013).CAS 
PubMed 

Google Scholar 
Mason, E. et al. Volatile metal emissions from volcanic degassing and lava–seawater interactions at Kīlauea Volcano, Hawai’i. Commun. Earth Environ. 2, 79 (2021).ADS 

Google Scholar 
Sun, R. et al. Methylmercury produced in upper oceans accumulates in deep Mariana Trench fauna. Nat. Commun. 11, 3389 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kalia, K. & Khambholja, D. B. in Handbook of Arsenic Toxicology (ed. Flora, S. J. S.) Ch. 28 (Elsevier, 2015).Welty, C. J., Sousa, M. L., Dunnivant, F. M. & Yancey, P. H. High-density element concentrations in fish from subtidal to hadal zones of the Pacific Ocean. Heliyon 4, e00840 (2018).PubMed 
PubMed Central 

Google Scholar 
Oremland, R. S. & Stolz, J. F. The ecology of arsenic. Science 300, 939–944 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Popowich, A., Zhang, Q. & Le, X. C. Arsenobetaine: the ongoing mystery. Natl Sci. Rev. 3, 451–458 (2016).CAS 

Google Scholar 
Hoffmann, T. et al. Arsenobetaine: an ecophysiologically important organoarsenical confers cytoprotection against osmotic stress and growth temperature extremes. Environ. Microbiol. 20, 305–323 (2018).CAS 
PubMed 

Google Scholar 
Steinbauer, M. J. et al. Topography-driven isolation, speciation and a global increase of endemism with elevation. Glob. Ecol. Biogeogr. 25, 1097–1107 (2016).
Google Scholar 
Hoffmann, A. A. & Hercus, M. J. Environmental stress as an evolutionary force. Bioscience 50, 217–226 (2000).
Google Scholar 
Cui, G., Li, J., Gao, Z. & Wang, Y. Spatial variations of microbial communities in abyssal and hadal sediments across the Challenger Deep. PeerJ 7, e6961 (2019).PubMed 
PubMed Central 

Google Scholar 
Hiraoka, S. et al. Microbial community and geochemical analyses of trans-trench sediments for understanding the roles of hadal environments. ISME J. 14, 740–756 (2020).CAS 
PubMed 

Google Scholar 
Morono, Y. et al. Aerobic microbial life persists in oxic marine sediment as old as 101.5 million years. Nat. Commun. 11, 3626 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, X. et al. Metagenomics reveals microbial diversity and metabolic potentials of seawater and surface sediment from a hadal biosphere at the Yap Trench. Front. Microbiol. 9, 2402 (2018).PubMed 
PubMed Central 

Google Scholar 
Logares, R. et al. Metagenomic 16S rDNA Illumina tags are a powerful alternative to amplicon sequencing to explore diversity and structure of microbial communities. Environ. Microbiol. 16, 2659–2671 (2014).CAS 
PubMed 

Google Scholar 
Zhou, Z. et al. Genome- and community-level interaction insights into carbon utilization and element cycling functions of Hydrothermarchaeota in hydrothermal sediment. mSystems 5, e00795-00719 (2020).
Google Scholar 
Dombrowski, N., Teske, A. P. & Baker, B. J. Expansive microbial metabolic versatility and biodiversity in dynamic Guaymas Basin hydrothermal sediments. Nat. Commun. 9, 4999 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Dong, X. et al. Metabolic potential of uncultured bacteria and archaea associated with petroleum seepage in deep-sea sediments. Nat. Commun. 10, 1816 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Laso-Pérez, R. et al. Anaerobic degradation of non-methane alkanes by “Candidatus Methanoliparia” in hydrocarbon seeps of the Gulf of Mexico. mBio 10, e01814–e01819 (2019).PubMed 
PubMed Central 

Google Scholar 
Gao, Z. M. et al. In situ meta-omic insights into the community compositions and ecological roles of hadal microbes in the Mariana Trench. Environ. Microbiol. 21, 4092–4108 (2019).CAS 
PubMed 

Google Scholar 
Varliero, G., Bienhold, C., Schmid, F., Boetius, A. & Molari, M. Microbial diversity and connectivity in deep-sea sediments of the South Atlantic polar front. Front. Microbiol. 10, 665 (2019).Su, X. et al. Identifying and predicting novelty in microbiome studies. mBio 9, e02099-02018 (2018).
Google Scholar 
Jing, G. et al. Microbiome Search Engine 2: a platform for taxonomic and functional search of global microbiomes on the whole-microbiome level. mSystems 6, e00943-00920 (2021).
Google Scholar 
Baltar, F., Zhao, Z. H. & Herndl, G. J. Potential and expression of carbohydrate untilization by marine fungi in the global ocean. Microbiome 9, 106 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Quemener, M. et al. Meta-omics highlights the diversity, activity and adaptations of fungi in deep oceanic crust. Environ. Microbiol. 22, 3950–3967 (2020).CAS 

Google Scholar 
Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. 38, 1079–1086 (2020).CAS 
PubMed 

Google Scholar 
Almeida, A. et al. A new genomic blueprint of the human gut microbiota. Nature 568, 499–504 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Giovannoni, S. J., Cameron Thrash, J. & Temperton, B. Implications of streamlining theory for microbial ecology. ISME J. 8, 1553–1565 (2014).PubMed 
PubMed Central 

Google Scholar 
Bobay, L. M. & Ochman, H. The evolution of bacterial genome architecture. Front. Genet. 8, 72 (2017).PubMed 
PubMed Central 

Google Scholar 
Huang, L. et al. dbCAN-seq: a database of carbohydrate-active enzyme (CAZyme) sequence and annotation. Nucleic Acids Res. 46, D516–D521 (2018).CAS 
PubMed 

Google Scholar 
Xu, Y., Ge, H. & Fang, J. Biogeochemistry of hadal trenches: Recent developments and future perspectives. Deep Sea Res. II Top. Stud. Oceanogr. 155, 19–26 (2018).ADS 
CAS 

Google Scholar 
Jørgensen, B. B. & Boetius, A. Feast and famine — microbial life in the deep-sea bed. Nat. Rev. Microbiol. 5, 770–781 (2007).PubMed 

Google Scholar 
Pérez Castro, S. et al. Degradation of biological macromolecules supports uncultured microbial populations in Guaymas Basin hydrothermal sediments. ISME J. 15, 3480–3497 (2021).Rastelli, E. et al. Drivers of bacterial α- and β-diversity patterns and functioning in subsurface hadal sediments. Front. Microbiol. 10, 2609 (2019).Vetter, Y. A. & Deming, J. W. Extracellular enzyme-activity in the Arctic northeast water polynya. Mar. Ecol. Prog. Ser. 114, 23–34 (1994).ADS 
CAS 

Google Scholar 
Li, J. et al. Recycling and metabolic flexibility dictate life in the lower oceanic crust. Nature 579, 250–255 (2020).ADS 
CAS 

Google Scholar 
Kikuchi, G., Motokawa, Y., Yoshida, T. & Hiraga, K. Glycine cleavage system: reaction mechanism, physiological significance, and hyperglycinemia. Proc. Jpn. Acad. 84, 246–263 (2008).CAS 

Google Scholar 
Chakraborty, A. et al. Hydrocarbon seepage in the deep seabed links subsurface and seafloor biospheres. Proc. Natl Acad. Sci. USA 117, 11029–11037 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu, J. et al. Proliferation of hydrocarbon-degrading microbes at the bottom of the Mariana Trench. Microbiome 7, 47 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Xue, C.-X. et al. Insights into the vertical stratification of microbial ecological roles across the deepest seawater column on Earth. Microorganisms 8, 1309 (2020).CAS 
PubMed Central 

Google Scholar 
Thamdrup, B. et al. Anammox bacteria drive fixed nitrogen loss in hadal trench sediments. Proc. Natl Acad. Sci. USA 118, e2104529118 (2021).CAS 
PubMed 

Google Scholar 
Wu, J. et al. Unexpectedly high diversity of anammox bacteria detected in deep-sea surface sediments of the South China Sea. FEMS Microbiol. Ecol. 95, fiz013 (2019).Kartal, B. et al. Molecular mechanism of anaerobic ammonium oxidation. Nature 479, 127–130 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Maalcke, W. J. et al. Characterization of anammox hydrazine dehydrogenase, a key N2-producing enzyme in the global nitrogen cycle. J. Biol. Chem. 291, 17077–17092 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kartal, B. et al. How to make a living from anaerobic ammonium oxidation. FEMS Microbiol. Rev. 37, 428–461 (2013).CAS 
PubMed 

Google Scholar 
Oshiki, M., Ali, M., Shinyako-Hata, K., Satoh, H. & Okabe, S. Hydroxylamine-dependent anaerobic ammonium oxidation (anammox) by “Candidatus Brocadia sinica”. Environ. Microbiol. 18, 3133–3143 (2016).CAS 
PubMed 

Google Scholar 
Mateos, L. M. et al. in Advances in Applied Microbiology (eds Sariaslani, S. & Gadd, G. M.) Ch. 4 (Academic Press, 2017).Ben Fekih, I. et al. Distribution of arsenic resistance genes in prokaryotes. Front. Microbiol. 9, 2473 (2018).Wang, P. P., Sun, G. X. & Zhu, Y. G. Identification and characterization of arsenite methyltransferase from an archaeon, methanosarcina acetivorans C2A. Environ. Sci. Technol. 48, 12706–12713 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Masuda, H., Yoshinishi, H., Fuchida, S., Toki, T. & Even, E. Vertical profiles of arsenic and arsenic species transformations in deep-sea sediment, Nankai Trough, offshore Japan. Prog. Earth Planet Sci. 6, 28 (2019).ADS 

Google Scholar 
Dunivin, T. K., Yeh, S. Y. & Shade, A. A global survey of arsenic-related genes in soil microbiomes. BMC Biol. 17, 45 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Teske, A. et al. The Guaymas Basin hiking guide to hydrothermal mounds, chimneys, and microbial mats: complex seafloor expressions of subsurface hydrothermal circulation. Front. Microbiol. 7, 75 (2016).O’Day, P. A., Vlassopoulos, D., Root, R. & Rivera, N. The influence of sulfur and iron on dissolved arsenic concentrations in the shallow subsurface under changing redox conditions. Proc. Natl Acad. Sci. USA 101, 13703–13708 (2004).ADS 
PubMed 
PubMed Central 

Google Scholar 
Galinski, E. A. Osmoadaptation in bacteria. Adv. Microb. Physiol. 37, 273–328 (1995).CAS 

Google Scholar 
Papini, C. M., Pandharipande, P. P., Royer, C. A. & Makhatadze, G. I. Putting the piezolyte hypothesis under pressure. Biophys. J. 113, 974–977 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Caumette, G., Koch, I. & Reimer, K. J. Arsenobetaine formation in plankton: a review of studies at the base of the aquatic food chain. J. Environ. Monit. 14, 2841–2853 (2012).CAS 
PubMed 

Google Scholar 
Whaley-Martin, K. J., Koch, I., Moriarty, M. & Reimer, K. J. Arsenic speciation in blue mussels (Mytilus edulis) along a highly contaminated arsenic gradient. Environ. Sci. Technol. 46, 3110–3118 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Oremland, R. S. et al. Anaerobic oxidation of arsenite in Mono Lake water and by a facultative, arsenite-oxidizing chemoautotroph, strain MLHE-1. Appl. Environ. Microbiol. 68, 4795–4802 (2002).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rhine, E. D., Phelps, C. D. & Young, L. Y. Anaerobic arsenite oxidation by novel denitrifying isolates. Environ. Microbiol. 8, 899–908 (2006).CAS 
PubMed 

Google Scholar 
Rhine et al. LY. The arsenite oxidase genes (aroAB) in novel chemoautotrophic arsenite oxidizers. Biochem. Biophys. Res. Commun. 354, 662–667 (2007).CAS 
PubMed 

Google Scholar 
Saunders, J. K., Fuchsman, C. A., Mckay, C. & Rocap, G. Complete arsenic-based respiratory cycle in the marine microbial communities of pelagic oxygen-deficient zones. Proc. Natl Acad. Sci. USA 116, 9925–9930 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Couture, R. M., Sekowska, A., Fang, G. & Danchin, A. Linking selenium biogeochemistry to the sulfur‐dependent biological detoxification of arsenic. Environ. Microbiol. 14, 1612–1623 (2012).CAS 
PubMed 

Google Scholar 
Zhang, Y. & Gladyshev, V. N. Trends in selenium utilization in marine microbial world revealed through the analysis of the Global Ocean Sampling (GOS) project. PLoS Genet. 4, e1000095 (2008).PubMed 
PubMed Central 

Google Scholar 
Peng, T., Lin, J., Xu, Y.-Z. & Zhang, Y. Comparative genomics reveals new evolutionary and ecological patterns of selenium utilization in bacteria. ISME J. 10, 2048–2059 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Labunskyy, V. M., Hatfield, D. L. & Gladyshev, V. N. Selenoproteins: molecular pathways and physiological roles. Physiol. Rev. 94, 739–777 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Yin, K., Wang, Q., Lv, M. & Chen, L. Microorganism remediation strategies towards heavy metals. Chem. Eng. J. 360, 1553–1563 (2019).CAS 

Google Scholar 
O’Day, P. A., Vlassopoulos, D., Root, R. & Rivera, N. The influence of sulfur and iron on dissolved arsenic concentrations in the shallow subsurface under changing redox conditions. Proc. Natl Acad. Sci. USA 101, 13703–13708 (2004).ADS 
PubMed 
PubMed Central 

Google Scholar 
Chen, S. F., Zhou, Y. Q., Chen, Y. R. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, 884–890 (2018).
Google Scholar 
Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).PubMed 
PubMed Central 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).CAS 
PubMed 

Google Scholar 
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang, Y., Gilna, P. & Li, W. Z. Identification of ribosomal RNA genes in metagenomic fragments. Bioinformatics 25, 1338–1340 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).ADS 
MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhou, Y. Microbiomes in the Challenger Deep slope and bottom-axis sediments. Zenodo https://doi.org/10.5281/zenodo.6061243 (2022).Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jing, G. C. et al. Parallel-META 3: comprehensive taxonomical and functional analysis platform for efficient comparison of microbial communities. Sci. Rep. 7, 40371 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, Y. W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).CAS 
PubMed 

Google Scholar 
Kang, D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).PubMed 
PubMed Central 

Google Scholar 
Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).CAS 
PubMed 

Google Scholar 
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).PubMed 
PubMed Central 

Google Scholar 
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chaumeil, P. A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).PubMed 
PubMed Central 

Google Scholar 
Mende, D. R., Sunagawa, S., Zeller, G. & Bork, P. Accurate and universal delineation of prokaryotic species. Nat. Methods 10, 881–887 (2013).CAS 
PubMed 

Google Scholar 
Yamada, K. D., Tomii, K. & Katoh, K. Application of the MAFFT sequence alignment program to large data-reexamination of the usefulness of chained guide trees. Bioinformatics 32, 3246–3251 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Capella-Gutierrez, S., Silla-Martinez, J. M. & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
PubMed 

Google Scholar 
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pachiadaki, M. G. et al. Major role of nitrite-oxidizing bacteria in dark ocean carbon fixation. Science 358, 1046–1051 (2017).ADS 
CAS 
PubMed 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 

Google Scholar 
Perry, M. heatmaps: flexible heatmaps for functional genomics and sequence features. R package version 1.14.0 (Bioconductor, 2020).Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).PubMed 
PubMed Central 

Google Scholar 
Aramaki, T. et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2019).PubMed Central 

Google Scholar 
Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726–731 (2016).CAS 
PubMed 

Google Scholar 
Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).CAS 
PubMed 

Google Scholar 
Zhou, Y. Microbiomes in the Challenger Deep slope and bottom-axis sediments. Figshare https://doi.org/10.6084/m6089.figshare.12979709 (2022). More

Concentration of TPHs in surface soilsStatistical results of TPHs concentrations at different geographic oilfields were showed in Fig. 2, and grid regional distribution of TPHs in YC Oilfield surface soils (Y6–Y25) were shown in Fig. 3. Results are given as mean value of triplicate analysis of each sample. The results of TPHs concentration in soil samples showed that the three oilfields all suffered from varying degrees of petroleum pollution, and 60.92% of the 47 sampling points was significantly higher than the soil critical value (500 mg/kg). The average concentration of the TPHs in each study areas conformed to be in the following law: SL Oilfield (average: 5.36 × 103 mg/kg) ( >) NY Oilfield (average: 1.73 × 103 mg/kg) ( >) YC Oilfield (average: 1.37 × 103 mg/kg). The highest concentration of the TPHs were found in SL Oilfield surface soils, ranging from 1.21 × 102 to 6.66 × 104 mg/kg, and NY Oilfield had the second highest TPHs concentrations in the range from 15.82 to 7.42 × 103 mg/kg. The concentrations of TPHs in YC Oilfield ranged from 12.34 to 5.38 × 103 mg/kg. The petroleum contamination mainly derived from abandoned and working oil wells. S4 and S8 soils were collected near the abandoned oil well and working oil well, respectively, and had the highest concentration of TPHs up to 5.28 × 104 and 6.66 × 104 mg/kg. Y1, N8 near the abandoned oil well also had high concentration of TPHs with 5.39 × 103 and 7.42 × 103 mg/kg, respectively. Pollution caused by grounded crude oil in exploitation process has been a serious problem in oilfield area. Our previous research reported that the TPHs content in Dagang Oilfield soils collected adjacent to working oil wells were about 20-folds higher than that in corn soils and living area soils25. Concentration contour map of TPHs in YC Oilfield by grid sampling method showed that regional pollution in the northwest and southeast area are more serious than other sites. Y6 near the gas station and Y15, Y21, Y23 adjacent to the working oil wells have higher concentration (2.12 × 103–5.34 × 103 mg/kg) of TPHs than other farmland and grass soils. Previous study reported that the concentrations of TPHs ranged 7.0 × 102–4.0 × 103 mg/kg in oil exploitation areas of the loess plateau region (34°20′N,107°10′E), showing a similar pollution level with this study26.Figure 2The concentration of TPHs in three oilfield soils.Full size imageFigure 3Grid regional distribution of TPHs in YC Oilfield.Full size imageThe percentage composition of total PAHs, SHs and polar components of petroleum hydrocarbons were shown in Table 1. In general, the dominant petroleum component was saturated hydrocarbons in all soils, accounting more than 50%. Yet, the percentage proportion of PAHs and SHs in contamination soils adjacent to working and abandon oil wells were significantly different (p  BbF (14.16–21.87%) ≫ BaA, Chr, InP, and BkF (less than 10%). This result aligned to the previous study that the contribution of individual PAHs to the TEQs of ∑PAH16 was BaP (45%)  > DBA (33%) in urban surface dust of Xi’an city, China46. Therefore, contamination control should priority focus on the individual PAHs of BaP, DBA, BbF in these areas. In addition, the ecological risk with abandoned time ranging 0–15 years has been assessed, and the descriptive statistic TEQBap of PAHs was shown in Supporting Information, Table S6. The highest TEQs of ∑PAH16 and ∑PAH7 with mean of 1422.27 μg/kg and 1400.48 μg/kg, respectively, were present in soils adjacent to abandoned oil well with abandoned time of 0—5 years. And the TEQs of ∑PAH16 and ∑PAH7 decreased with the abandoned time though the percentage proportion of PAHs increased. The TEQs of ∑PAH16 and ∑PAH7 were close between abandoned time of 5–10 years and 10—15 years while both had high content. It demonstrated that high ecological risk was persistent in abandoned oil well areas over abandoned time of 15 years, and basically stable after 5 years. Therefore, abandoned oil well areas need to be blocked to prevent PAHs entering the external environment, and combine physical–chemical technology for petroleum remediation instead of simple weathering biological processes.Table 3 Descriptive statistic TEQBap of PAHs in different sampling area.Full size tableAs referred the PAHs standard of Dutch soil, TEQs of ∑PAH7 was 32.02 μg/kg, calculated by ten individual PAHs times TEFs. In this study, the mean TEQs of ∑PAH7 were about 35- and 10-folds of Dutch soil in petro-related area soils and grassland soils, indicating a high and medium ecological risk in these soils respectively. However, the mean TEQs of ∑PAH7 in farmland soils (18.80 μg/kg) was below Dutch soil, presenting a low potential ecological risk. It should be noted that the minimum of TEQs of ∑PAH7 in grassland soil was 26.24 μg/kg less than TEQs of ∑PAH7 in Dutch soil, but it was vulnerable affected by the surrounding soils with high TEQs of ∑PAH7. In this study, except the farmland soils, TEQs of ∑PAH7 exhibited higher TEQ values than those reported soils in Santiago, Chile47 and Nepal24, and road dust in Tianjin, China48. Overall, the most threat of ecological risk in petro-related soils caused by the anthropogenic PAHs input, such like oil leakage, oil refining, and fossil energy combustion. Preventing oil spills accident and developing the remediation methods are the main significant ways to reduce the ecological risks in these areas. The medium ecological risk in grassland might result from the migration of PAHs via rainfall pathway. Therefore, establishment the oil-blocking isolation zones is the critical way for medium ecological risk areas to control petroleum inflow. Even though the low ecological risk was identified in farmland soils, PAHs source analysis indicated that the biomass combustion should be controlled in these areas. More

Lipkind, D. et al. Stepwise acquisition of vocal combinatorial capacity in songbirds and human infants. Nature 498, 104–108 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Goldstein, M., King, A. P. & West, M. J. Social interaction shapes babbling: testing parallels between birdsong and speech. Proc. Natl Acad. Sci. USA 100, 8030–8035 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fehér, O., Ljubičić, I., Suzuki, K., Okanoya, K. & Tchernichovski, O. Statistical learning in songbirds: from self-tutoring to song culture. Phil. Trans. R. Soc. B 372, 20160053 (2017).PubMed 
PubMed Central 

Google Scholar 
Tchernichovski, O., Lints, T., Mitra, P. P. & Nottebohm, F. Vocal imitation in zebra finches is inversely related to model abundance. Proc. Natl Acad. Sci. USA 96, 12901–12904 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tchernichovski, O. Dynamics of the vocal imitation process: how a zebra finch learns its song. Science 291, 2564–2569 (2001).CAS 

Google Scholar 
Fehér, O., Wang, H., Saar, S., Mitra, P. P. & Tchernichovski, O. De novo establishment of wild-type song culture in the zebra finch. Nature 459, 564–568 (2009).PubMed 
PubMed Central 

Google Scholar 
Takahashi, D. et al. The developmental dynamics of marmoset monkey vocal production. Science 349, 734–738 (2015).CAS 

Google Scholar 
Takahashi, D. Y., Liao, D. A. & Ghazanfar, A. A. Vocal learning via social reinforcement by infant marmoset monkeys. Curr. Biol. 27, 1844–1852.E6 (2017).Takahashi, D. Y., Fenley, A. R. & Ghazanfar, A. A. Early development of turn-taking with parents shapes vocal acoustics in infant marmoset monkeys. Phil. Trans. R. Soc. B 371, 20150370 (2016).PubMed 
PubMed Central 

Google Scholar 
Gultekin, Y. B. & Hage, S. R. Limiting parental interaction during vocal development affects acoustic call structure in marmoset monkeys. Sci. Adv. 4, eaar4012 (2018).PubMed 
PubMed Central 

Google Scholar 
Gultekin, Y. B. & Hage, S. R. Limiting parental feedback disrupts vocal development in marmoset monkeys. Nat. Commun. 8, 14046 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jarvis, E. D. Evolution of vocal learning and spoken language. Science 366, 50–54 (2019).CAS 

Google Scholar 
Snowdon, C. T. Learning from monkey “talk”. Science 355, 1120–1122 (2017).CAS 

Google Scholar 
Malik, K. Rights and wrongs. Nature 406, 675–676 (2000).
Google Scholar 
Wise, S. M. & Goodall, J. Rattling the Cage: Toward Legal Rights for Animals (Da Capo Press, 2017).Grayson, L. Animals in Research: For and Against (British Library, 2000).Nater, A. et al. Morphometric, behavioral, and genomic evidence for a new orangutan species. Curr. Biol. 27, 3487–3498.E10 (2017).CAS 

Google Scholar 
Estrada, A. et al. Impending extinction crisis of the world’s primates: why primates matter. Sci. Adv. 3, e1600946 (2017).PubMed 
PubMed Central 

Google Scholar 
Ross, S. et al. Inappropriate use and portrayal of chimpanzees. Science 319, 1487 (2008).CAS 

Google Scholar 
Wich, S. A. et al. Land-cover changes predict steep declines for the Sumatran orangutan (Pongo abelii). Sci. Adv. 2, e1500789 (2016).PubMed 
PubMed Central 

Google Scholar 
Wich, S. A. et al. Understanding the impacts of land-use policies on a threatened species: is there a future for the Bornean orangutan? PLoS ONE 7, e49142 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wich, S. A. et al. Will oil palm’s homecoming spell doom for Africa’s great apes? Curr. Biol. https://doi.org/10.1016/j.cub.2014.05.077 (2014).Fitch, T. W. Empirical approaches to the study of language evolution. Psychon. Bull. Rev. 24, 3–33 (2017).Hauser, M. D. et al. The mystery of language evolution. Front. Psychol. https://doi.org/10.3389/fpsyg.2014.00401 (2014)Corballis, M. C. Crossing the Rubicon: behaviorism, language, and evolutionary continuity. Front. Psychol. 11, 653 (2020).PubMed 
PubMed Central 

Google Scholar 
Berwick, R. C. & Chomsky, N. All or nothing: no half-merge and the evolution of syntax. PLoS Biol. 17, e3000539 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolhuis, J. J. & Wynne, C. D. Can evolution explain how minds work? Nature 458, 832–833 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hayes, K. J. & Hayes, C. The intellectual development of a home-raised chimpanzee. Proc. Am. Phil. Soc. 95, 105–109 (1951).
Google Scholar 
Premack, D. Language in chimpanzee? Science 172, 808–822 (1971).CAS 
PubMed 
PubMed Central 

Google Scholar 
Terrace, H., Petitto, L., Sanders, R. & Bever, T. Can an ape create a sentence? Science 206, 891–902 (1979).CAS 

Google Scholar 
Patterson, F. & Linden, E. The Education of Koko (Holt, Rinehart and Winston, 1981).Leavens, D. A., Bard, K. A. & Hopkins, W. D. BIZARRE chimpanzees do not represent “the chimpanzee”. Behav. Brain Sci. 33, 100–101 (2010).
Google Scholar 
Lameira, A. R. Bidding evidence for primate vocal learning and the cultural substrates for speech evolution. Neurosci. Biobehav. Rev. 83, 429–439 (2017).
Google Scholar 
Lameira, A. R. et al. Speech-like rhythm in a voiced and voiceless orangutan call. PLoS ONE 10, e116136 (2015).PubMed 
PubMed Central 

Google Scholar 
Lameira, A. R. & Shumaker, R. W. Orangutans show active voicing through a membranophone. Sci. Rep. 9, 12289 (2019).PubMed 
PubMed Central 

Google Scholar 
Lameira, A. R., Hardus, M. E., Mielke, A., Wich, S. A. & Shumaker, R. W. Vocal fold control beyond the species-specific repertoire in an orangutan. Sci. Rep. 6, 30315 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lameira, A. R. et al. Orangutan (Pongo spp.) whistling and implications for the emergence of an open-ended call repertoire: a replication and extension. J. Acoust. Soc. Am. 134, 2326–2335 (2013).
Google Scholar 
Perlman, M. & Clark, N. Learned vocal and breathing behavior in an enculturated gorilla. Anim. Cogn. 18, 1165–1179 (2015).
Google Scholar 
Wich, S. et al. A case of spontaneous acquisition of a human sound by an orangutan. Primates 50, 56–64 (2009).
Google Scholar 
Lameira, A. R., Maddieson, I. & Zuberbuhler, K. Primate feedstock for the evolution of consonants. Trends Cogn. Sci. 18, 60–62 (2014).
Google Scholar 
Lameira, A. R. The forgotten role of consonant-like calls in theories of speech evolution. Behav. Brain Sci. 37, 559–560 (2014).
Google Scholar 
Boë, L.-J. et al. Which way to the dawn of speech? Reanalyzing half a century of debates and data in light of speech science. Sci. Adv. 5, eaaw3916 (2019).PubMed 
PubMed Central 

Google Scholar 
Boë, L. J. et al. Evidence of a vocalic proto-system in the baboon (Papio papio) suggests pre-hominin speech precursors. PLoS ONE 12, e0169321 (2017).PubMed 
PubMed Central 

Google Scholar 
Fitch, T. W., Boer, B., Mathur, N. & Ghazanfar, A. A. Monkey vocal tracts are speech-ready. Sci. Adv. 2, e1600723 (2016).PubMed 
PubMed Central 

Google Scholar 
Pereira, A. S., Kavanagh, E., Hobaiter, C., Slocombe, K. E. & Lameira, A. R. Chimpanzee lip-smacks confirm primate continuity for speech-rhythm evolution. Biol. Lett. 16, 20200232 (2020).PubMed 
PubMed Central 

Google Scholar 
Lameira, A. R. et al. Proto-consonants were information-dense via identical bioacoustic tags to proto-vowels. Nat. Hum. Behav. 1, 0044 (2017).
Google Scholar 
Lameira, A. R. et al. Orangutan information broadcast via consonant-like and vowel-like calls breaches mathematical models of linguistic evolution. Biol. Lett. 17, 20210302 (2021).PubMed 
PubMed Central 

Google Scholar 
Watson, S. K. et al. Nonadjacent dependency processing in monkeys, apes, and humans. Sci. Adv. 6, eabb0725 (2020).PubMed 
PubMed Central 

Google Scholar 
Lameira, A. R. & Call, J. Time-space–displaced responses in the orangutan vocal system. Sci. Adv. 4, eaau3401 (2018).PubMed 
PubMed Central 

Google Scholar 
Belyk, M. & Brown, S. The origins of the vocal brain in humans. Neurosci. Biobehav. Rev. 77, 177–193 (2017).
Google Scholar 
Crockford, C., Wittig, R. M. & Zuberbuhler, K. Vocalizing in chimpanzees is influenced by social-cognitive processes. Sci. Adv. 3, e1701742 (2017).PubMed 
PubMed Central 

Google Scholar 
Taglialatela, J. P., Reamer, L., Schapiro, S. J. & Hopkins, W. D. Social learning of a communicative signal in captive chimpanzees. Biol. Lett. 8, 498–501 (2012).PubMed 
PubMed Central 

Google Scholar 
Russell, J. L., Joseph, M., Hopkins, W. D. & Taglialatela, J. P. Vocal learning of a communicative signal in captive chimpanzees, Pan troglodytes. Brain Lang. 127, 520–525 (2013).PubMed 
PubMed Central 

Google Scholar 
Hopkins, W. D. et al. Genetic factors and orofacial motor learning selectively influence variability in central sulcus morphology in chimpanzees (Pan troglodytes). J. Neurosci. 37, 5475–5483 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Staes, N. et al. FOXP2 variation in great ape populations offers insight into the evolution of communication skills. Sci. Rep. 7, 16866 (2017).PubMed 
PubMed Central 

Google Scholar 
Martins, P. T. & Boeckx, C. Vocal learning: beyond the continuum. PLoS Biol. 18, e3000672 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Watson, S. K. et al. Vocal learning in the functionally referential food grunts of chimpanzees. Curr. Biol. 25, 495–499 (2015).CAS 

Google Scholar 
Hopkins, W. D., Taglialatela, J. P. & Leavens, D. A. Chimpanzees differentially produce novel vocalizations to capture the attention of a human. Anim. Behav. 73, 281–286 (2007).PubMed 
PubMed Central 

Google Scholar 
Bianchi, S., Reyes, L. D., Hopkins, W. D., Taglialatela, J. P. & Sherwood, C. C. Neocortical grey matter distribution underlying voluntary, flexible vocalizations in chimpanzees. Sci. Rep. 6, 34733 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wich, S. A. et al. Call cultures in orangutans? PLoS ONE 7, e36180 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Crockford, C., Herbinger, I., Vigilant, L. & Boesch, C. Wild chimpanzees produce group-specific calls: a case for vocal learning? Ethology 110, 221–243 (2004).
Google Scholar 
Whiten, A. et al. Cultures in chimpanzees. Nature 399, 682–685 (1999).CAS 

Google Scholar 
van Schaik, C. P. et al. Orangutan cultures and the evolution of material culture. Science 299, 102–105 (2003).
Google Scholar 
Whiten, A. Culture extends the scope of evolutionary biology in the great apes. Proc. Natl Acad. Sci. USA 114, 7790–7797 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Koops, K., Visalberghi, E. & van Schaik, C. The ecology of primate material culture. Biol. Lett. 10, 20140508 (2014).PubMed 
PubMed Central 

Google Scholar 
Kalan, A. K. et al. Chimpanzees use tree species with a resonant timbre for accumulative stone throwing. Biol. Lett. 15, 20190747 (2019).PubMed 
PubMed Central 

Google Scholar 
Hardus, M., Lameira, A. R., Van Schaik, C. P. & Wich, S. A. Tool use in wild orangutans modifies sound production: a functionally deceptive innovation? Proc. R. Soc. B https://doi.org/10.1098/rspb.2009.1027 (2009).Lameira, A. R. et al. Population-specific use of the same tool-assisted alarm call between two wild orangutan populations (Pongo pygmaeus wurmbii) indicates functional arbitrariness. PLoS ONE 8, e69749 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hohmann, G. & Fruth, B. Culture in bonobos? Between‐species and within‐species variation in behavior. Curr. Anthropol. 44, 563–571 (2003).
Google Scholar 
Robbins, M. M. et al. Behavioral variation in gorillas: evidence of potential cultural traits. PLoS ONE 11, e0160483 (2016).PubMed 
PubMed Central 

Google Scholar 
Kühl, H. S. et al. Human impact erodes chimpanzee behavioral diversity. Science 363, 1453–1455 (2019).
Google Scholar 
van Schaik, C. P. Fragility of Traditions: the disturbance hypothesis for the loss of local traditions in orangutans. Int. J. Primatol. 23, 527–538 (2002).
Google Scholar 
Delgado, R. A. & van Schaik, C. P. The behavioral ecology and conservation of the orangutan (Pongo pygmaeus): a tale of two islands. Evol. Anthropol. 9, 201–218 (2000).
Google Scholar 
van Schaik, C. The socioecology of fission–fusion sociality in orangutans. Primates 40, 69–86 (1999).
Google Scholar 
Nater, A. et al. Sex-biased dispersal and volcanic activities shaped phylogeographic patterns of extant orangutans (genus: Pongo). Mol. Biol. 28, 2275–2288 (2011).CAS 

Google Scholar 
Arora, N. et al. Parentage-based pedigree reconstruction reveals female matrilineal clusters and male-biased dispersal in nongregarious Asian great apes, the Bornean orangutans (Pongo pygmaeus). Mol. Ecol. 21, 3352–3362 (2012).CAS 

Google Scholar 
Kavanagh, E. et al. Dominance style is a key predictor of vocal use and evolution across nonhuman primates. R. Soc. Open Sci. 8, 210873 (2021).PubMed 
PubMed Central 

Google Scholar 
Husson, S. et al. in Orangutans: Geographic Variation in Behavioral Ecology and Conservation (eds Wich, S. et al.) Ch. 6 (Oxford Univ. Press, 2009).van Noordwijk, M. A. et al. in Orangutans: Geographic Variation in Behavioral Ecology and Conservation (eds Wich, S. et al.) Ch. 12 (Oxford Univ Press, 2009).Singleton, I., Knott, C., Morrogh-Bernard, H., Wich, S. & van Schaik, C. P. in Orangutans: Geographic Variation in Behavioral Ecology and Conservation (eds Wich, S. et al.) Ch. 13 (Oxford Univ. Press, 2009).Wich, S. et al. Life history of wild Sumatran orangutans (Pongo abelii). J. Hum. Evol. 47, 385–398 (2004).CAS 

Google Scholar 
Wich, S. et al. in Orangutans: Geographic Variation in Behavioral Ecology and Conservation (eds Wich, S. et al.) Ch. 5 (Oxford Univ. Press, 2009).Shumaker, R. W., Wich, S. A. & Perkins, L. Reproductive life history traits of female orangutans (Pongo spp.). Primate Reprod. Aging 36, 147–161 (2008).CAS 

Google Scholar 
Freund, C., Rahman, E. & Knott, C. Ten years of orangutan-related wildlife crime investigation in West Kalimantan, Indonesia. Am. J. Primatol. 79, 22620 (2016).
Google Scholar 
van Noordwijk, M. A. & van Schaik, C. P. Development of ecological competence in Sumatran orangutans. Am. J. Phys. Anthropol. 127, 79–94 (2005).
Google Scholar 
Knot, C. D. et al. The Gunung Palung Orangutan Project: Twenty-five years at the intersection of research and conservation in a critical landscape in Indonesia. Biol. Conserv. 255, 108856 (2021).
Google Scholar 
Guillermo, S.-B., Gershenson, C. & Fernández, N. A package for measuring emergence, self-organization, and complexity based on shannon entropy. Front. Robot. AI 4, 174102 (2017).
Google Scholar 
Santamaría-Bonfil, G., Fernández, N. & Gershenson, C. Measuring the complexity of continuous distributions. Entropy 18, 72 (2016).
Google Scholar 
Kalan, A. K., Mundry, R. & Boesch, C. Wild chimpanzees modify food call structure with respect to tree size for a particular fruit species. Anim. Behav. 101, 1–9 (2015).
Google Scholar 
Fedurek, P. & Slocombe, K. E. The social function of food-associated calls in male chimpanzees. Am. J. Primatol. 75, 726–739 (2013).
Google Scholar 
Luef, E., Breuer, T. & Pika, S. Food-associated calling in gorillas (Gorilla g. gorilla) in the wild. PLoS ONE 11, e0144197 (2016).PubMed 
PubMed Central 

Google Scholar 
Clay, Z. & Zuberbuhler, K. Food-associated calling sequences in bonobos. Anim. Behav. 77, 1387–1396 (2009).
Google Scholar 
Hardus, M. E. et al. in Orangutans: Geographic Variation in Behavioral Ecology and Conservation (eds Wich, S. et al.) Ch. 4 (Oxford Univ. Press, 2009).Wich, S. A. et al. Forest fruit production is higher on Sumatra than on Borneo. PLoS ONE 6, e21278 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lameira, A. R. & Wich, S. Orangutan long call degradation and individuality over distance: a playback approach. Int. J. Primatol. 29, 615–625 (2008).
Google Scholar 
Lameira, A. R., Delgado, R. & Wich, S. Review of geographic variation in terrestrial mammalian acoustic signals: human speech variation in a comparative perspective. J. Evolut. Psychol. 8, 309–332 (2010).
Google Scholar 
Lameira, A. R. et al. Predator guild does not influence orangutan alarm call rates and combinations. Behav. Ecol. Sociobiol. 67, 519–528 (2013).
Google Scholar 
Derex, M. & Mesoudi, A. Cumulative cultural evolution within evolving population structures. Trends Cogn. Sci. 24, 654–667 (2020).
Google Scholar 
Scerri, E. M. et al. Did our species evolve in subdivided populations across Africa, and why does it matter? Trends Ecol. Evol. 33, 582–594 (2018).PubMed 
PubMed Central 

Google Scholar 
Kaya, F. et al. The rise and fall of the Old World savannah fauna and the origins of the African savannah biome. Nat. Ecol. Evol. 2, 241–246 (2018).PubMed 
PubMed Central 

Google Scholar 
Bobe, R. The expansion of grassland ecosystems in Africa in relation to mammalian evolution and the origin of the genus Homo. Palaeogeogr. Palaeoclimatol. Palaeoecol. 207, 399–420 (2004).
Google Scholar 
Zhu, D., Galbraith, E. D., Reyes-García, V. & Ciais, P. Global hunter-gatherer population densities constrained by influence of seasonality on diet composition. Nat. Ecol. Evol. 5, 1536–1545 (2021).PubMed 
PubMed Central 

Google Scholar 
DeCasien, A. R., Williams, S. A. & Higham, J. P. Primate brain size is predicted by diet but not sociality. Nat. Ecol. Evol. 1, 0112 (2017).
Google Scholar 
Mauricio, G.-F. & Gardner, A. Inference of ecological and social drivers of human brain-size evolution. Nature 557, 554–557 (2018).
Google Scholar 
Lindenfors, P., Wartel, A. & Lind, J. ‘Dunbar’s number’ deconstructed. Biol. Lett. 17, 20210158 (2021).PubMed 
PubMed Central 

Google Scholar 
Schuppli, C., van Noordwijk, M., Atmoko, S. U. & van Schaik, C. Early sociability fosters later exploratory tendency in wild immature orangutans. Sci. Adv. 6, eaaw2685 (2020).PubMed 
PubMed Central 

Google Scholar 
Schuppli, C. et al. Observational social learning and socially induced practice of routine skills in immature wild orangutans. Anim. Behav. 119, 87–98 (2016).
Google Scholar 
Jaeggi, A. V. et al. Social learning of diet and foraging skills by wild immature Bornean orangutans: implications for culture. Am. J. Primatol. 72, 62–71 (2010).PubMed 
PubMed Central 

Google Scholar 
Schuppli, C. et al. The effects of sociability on exploratory tendency and innovation repertoires in wild Sumatran and Bornean orangutans. Sci. Rep. 7, 15464 (2017).PubMed 
PubMed Central 

Google Scholar 
Ehmann, B. et al. Immature wild orangutans acquire relevant ecological knowledge through sex-specific attentional biases during social learning. PLoS Biol. 19, e3001173 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Meijaard, E. et al. Declining orangutan encounter rates from Wallace to the present suggest the species was once more abundant. PLoS ONE 5, e12042 (2010).PubMed 
PubMed Central 

Google Scholar 
Marshall, A. J. et al. The blowgun is mightier than the chainsaw in determining population density of Bornean orangutans (Pongo pygmaeus morio) in the forests of East Kalimantan. Biol. Conserv. 129, 566–578 (2006).
Google Scholar 
Gail, C.-S., Miran, C.-S., Singleton, I. & Linkie, M. Raiders of the lost bark: orangutan foraging strategies in a degraded landscape. PLoS ONE 6, e20962 (2011).
Google Scholar 
Schuppli, C. & van Schaik, C. P. Animal cultures: how we’ve only seen the tip of the iceberg. Evol. Hum. Sci. 1, e2 (2019).
Google Scholar 
Langergraber, K. E. et al. Vigilant, generation times in wild chimpanzees and gorillas suggest earlier divergence times in great ape and human evolution. Proc. Natl Acad. Sci. USA 109, 15716–15721 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fernández, N., Maldonado, C. & Gershenson, C. in Guided Self-Organization: Inception (ed Prokopenko, M.) 19–51 (Springer Berlin Heidelberg, 2014).JAST Team, JASP (Univ. of Amsterdam, 2020).R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2009).Auguie, B. gridExtra: Functions in grid graphics. R version 0.9.1 (2012).Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
Google Scholar 
Korthauer, K. et al. A practical guide to methods controlling false discoveries in computational biology. Genome Biol. 20, 118 (2019).PubMed 
PubMed Central 

Google Scholar  More

Pimm, S. L. et al. Emerging technologies to conserve biodiversity. Trends Ecol. Evol. 30, 685–696 (2015).Article 

Google Scholar 
Marvin, D. C. et al. Integrating technologies for scalable ecology and conservation. Glob. Ecol. Conserv. 7, 262–275 (2016).Article 

Google Scholar 
Wall, J., Wittemyer, G., Klinkenberg, B. & Douglas-Hamilton, I. Novel opportunities for wildlife conservation and research with real-time monitoring. Ecol. Appl. 24, 593–601 (2014).Article 

Google Scholar 
Snaddon, J., Petrokofsky, G., Jepson, P. & Willis, K. J. Biodiversity technologies: tools as change agents. Biol. Lett. 9, 20121029 (2013).Article 

Google Scholar 
Pettorelli, N., Safi, K., Turner, W. Satellite remote sensing, biodiversity research and conservation of the future. Philos. Trans. R. Soc. B Biol. Sci. 369, 20130190 (2014).Ripperger, S. P. et al. Thinking small: Next-generation sensor networks close the size gap in vertebrate biologging. PLOS Biol. 18, e3000655 (2020).CAS 
Article 

Google Scholar 
Xu, H., Wang, K., Vayanos, P. & Tambe, M. Strategic coordination of human patrollers and mobile sensors with signaling for security games. 8 (2018).Liu, Y. et al. AI for Earth: Rainforest conservation by acoustic surveillance. 2 (2019).Joppa, L. N. Technology for nature conservation: an industry perspective. Ambio 44, 522–526 (2015).Article 

Google Scholar 
Koh, L. P. & Wich, S. A. Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Trop. Conserv. Sci. 5, 121–132 (2012).Article 

Google Scholar 
Hahn, N. et al. Unmanned aerial vehicles mitigate human–elephant conflict on the borders of Tanzanian Parks: a case study. Oryx 51, 513–516 (2017).Article 

Google Scholar 
Pomerantz, A. et al. Real-time DNA barcoding in a rainforest using nanopore sequencing: opportunities for rapid biodiversity assessments and local capacity building. GigaScience 7, (2018).Van Doren, B. M. & Horton, K. G. A continental system for forecasting bird migration. Science 361, 1115–1118 (2018).ADS 
Article 

Google Scholar 
Howson, P. Building trust and equity in marine conservation and fisheries supply chain management with blockchain. Mar. Policy 115, 103873 (2020).Article 

Google Scholar 
Speaker, T. et al. A global community-sourced assessment of the state of conservation technology. Conserv. Biol. cobi. https://doi.org/10.1111/cobi.13871 (2022).Article 

Google Scholar 
Pearce, J. M. Building research equipment with free Open-Source Hardware. Science 337, 1303–1304 (2012).ADS 
CAS 
Article 

Google Scholar 
Gibb, R., Browning, E., Glover-Kapfer, P. & Jones, K. E. Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring. Methods Ecol. Evol. 10, 169–185 (2019).Article 

Google Scholar 
current constraints and future priorities for development. Glover-Kapfer, P., Soto-Navarro, C. A. & Wearn, O. R. Camera-trapping version 3.0. Remote Sens. Ecol. Conserv. 5, 209–223 (2019).Article 

Google Scholar 
Norouzzadeh, M. S. et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci. 115, E5716–E5725 (2018).CAS 
Article 

Google Scholar 
Berger-Tal, O. & Lahoz-Monfort, J. J. Conservation technology: the next generation. Conserv. Lett. 11, 1–6 (2018).Article 

Google Scholar 
Hill, A. P. et al. AudioMoth: Evaluation of a smart open acoustic device for monitoring biodiversity and the environment. Methods Ecol. Evol. 9, 1199–1211 (2018).Article 

Google Scholar 
Zárybnická, M., Kubizňák, P., Šindelář, J. & Hlaváč, V. Smart nest box: a tool and methodology for monitoring of cavity-dwelling animals. Methods Ecol. Evol. 7, 483–492 (2016).Article 

Google Scholar 
Kalmár, G. et al. Animal-Borne Anti-Poaching System. in Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services 91–102 (ACM, 2019). https://doi.org/10.1145/3307334.3326080.Weise, F. J. et al. Lions at the gates: Trans-disciplinary design of an early warning system to improve human-lion coexistence. Front. Ecol. Evol. 6, 242 (2019).Article 

Google Scholar 
Beery, S., Van Horn, G. & Perona, P. Recognition in Terra Incognita. in Proceedings of the European Conference on Computer Vision (ECCV) (eds. Ferrari, V., Hebert, M., Sminchisescu, C. & Weiss, Y.) 472–489 (Springer International Publishing, 2018). https://doi.org/10.1007/978-3-030-01270-0_28.Crego, R. D., Masolele, M. M., Connette, G. & Stabach, J. A. Enhancing animal movement analyses: spatiotemporal matching of animal positions with remotely sensed data using google earth engine and R. Remote Sens. 13, 4154 (2021).ADS 
Article 

Google Scholar 
Gorelick, N. et al. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).ADS 
Article 

Google Scholar 
Vulcan. EarthRanger. https://earthranger.com.Ahumada, J. A. et al. Wildlife insights: A platform to maximize the potential of camera trap and other passive sensor wildlife data for the planet. Environ. Conserv. 47, 1–6 (2020).MathSciNet 
Article 

Google Scholar 
Lahoz-Monfort, J. J. et al. A call for international leadership and coordination to realize the potential of conservation technology. Bioscience 69, 823–832 (2019).Article 

Google Scholar 
Group Gets – AudioMoth. https://groupgets.com/manufacturers/open-acoustic-devices/products/audiomoth.Kulits, P., Wall, J., Bedetti, A., Henley, M. & Beery, S. ElephantBook: A semi-automated human-in-the-loop system for elephant re-identification. in ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS) 88–98 (ACM, 2021). https://doi.org/10.1145/3460112.3471947.Pardo, L. E. et al. Snapshot Safari: A large-scale collaborative to monitor Africa’s remarkable biodiversity. South Afr. J. Sci. 117, (2021).Iacona, G. et al. Identifying technology solutions to bring conservation into the innovation era. Front. Ecol. Environ. 17, 591–598 (2019).Article 

Google Scholar 
Cooper, R. G. What’s next?: After stage-gate. Res.-Technol. Manag. 57, 20–31 (2014).ADS 

Google Scholar 
Cooper, R. G. The drivers of success in new-product development. Ind. Mark. Manag. 76, 36–47 (2019).Article 

Google Scholar 
Pearce, J. M. The case for open source appropriate technology. Environ. Dev. Sustain. 14, 425–431 (2012).Article 

Google Scholar 
Mair, J., Battilana, J. & Cardenas, J. Organizing for society: A typology of social entrepreneuring models. J. Bus. Ethics 111, 353–373 (2012).Article 

Google Scholar 
Meissner, D. Public-private partnership models for science, technology, and innovation cooperation. J. Knowl. Econ. 10, 1341–1361 (2019).Article 

Google Scholar 
Likert, R. A technique for the measurement of attitudes. Arch. Psychol. 22, 1–55.Mayer, A. L. & Wellstead, A. M. Questionable survey methods generate a questionable list of recommended articles. Nat. Ecol. Evol. 2, 1336–1337 (2018).Article 

Google Scholar 
Archie, K. M., Dilling, L., Milford, J. B. & Pampel, F. C. Climate Change and Western Public Lands: a Survey of U.S. Federal Land Managers on the Status of Adaptation Efforts. Ecol. Soc. 17 (2012).Jimenez, M. F. et al. Underrepresented faculty play a disproportionate role in advancing diversity and inclusion. Nat. Ecol. Evol. 3, 1030–1033 (2019).Article 

Google Scholar 
Christensen, R. ordinal – Regression Models for Ordinal Data. R package version 2019.12-10. (2019).R Core Team. R: A language and environment for statistical computing. (2020).Arnold, T. W. Uninformative parameters and model selection using Akaike’s information criterion. J. Wildl. Manag. 74, 1175–1178 (2010).Article 

Google Scholar 
QSR International Pty Ltd. Nvivo 12 Pro. (2020).Glesne, C. Making words fly: Developing understanding through interviewing. Becom. Qual. Res. Introd. 3, (2006).Creswell, J. W. & Creswell, J. D. Research design: Qualitative, quantitative, and mixed methods approaches. (Sage publications 2017). More

Balami, S., Vašutová, M., Godbold, D., Kotas, P. & Cudlín, P. Soil fungal communities across land use types. Forest Biogeosci. For. 13, 548–558 (2020).
Google Scholar 
Deacon, J. Fungal Biology (Wiley, 2009).
Google Scholar 
Ruiz-Almenara, C., Gándara, E. & Gómez-Hernández, M. Comparison of diversity and composition of macrofungal species between intensive mushroom harvesting and non-harvesting areas in Oaxaca, Mexico. PeerJ 7, e8325 (2019).PubMed 
PubMed Central 

Google Scholar 
Moore, J. C. et al. Detritus, trophic dynamics and biodiversity. Ecol. Lett. 7, 584–600 (2004).ADS 

Google Scholar 
Egli, S. Mycorrhizal mushroom diversity and productivity—an indicator of forest health?. Ann. For. Sci. 68, 81–88 (2011).
Google Scholar 
Westover, K. M. & Bever, J. D. Mechanisms of plant species coexistence: Roles of rhizosphere bacteria and root fungal pathogens. Ecology 82, 3285–3294 (2001).
Google Scholar 
Deacon, J. Fungal Biology (Wiley, 2006).
Google Scholar 
Fernandez, C. W., Nguyen, N. H. U. H., Stefanski, A. & Han, Y. Ectomycorrhizal fungal response to warming is linked to poor host performance at the boreal-temperate ecotone. Glob. Chang. Biol. 23, 1598–1609 (2017).ADS 
PubMed 

Google Scholar 
Heilmann-Clausen, J. et al. A fungal perspective on conservation biology. Conserv. Biol. 29, 61–68 (2015).PubMed 

Google Scholar 
Shay, P.-E., Winder, R. S. & Trofymow, J. A. Nutrient-cycling microbes in coastal Douglas-fir forests: Regional-scale correlation between communities, in situ climate, and other factors. Front. Microbiol. 6, 5897 (2015).
Google Scholar 
van der Heijden, M. G. A., Bardgett, R. D. & van Straalen, N. M. The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 11, 296–310 (2008).PubMed 

Google Scholar 
Richter, A., Schöning, I., Kahl, T., Bauhus, J. & Ruess, L. Regional environmental conditions shape microbial community structure stronger than local forest management intensity. For. Ecol. Manag. 409, 250–259 (2018).
Google Scholar 
Monkai, J., Hyde, K. D., Xu, J. & Mortimer, P. E. Diversity and ecology of soil fungal communities in rubber plantations. Fungal Biol. Rev. 31, 1–11 (2017).
Google Scholar 
White, F. Vegetation of Africa—a descriptive memoir to accompany the UNESCO/AETFAT/UNSO vegetation map of Africa, Natural Resources Research Report XX. U.N. Educational, Scientific and Cultural Organization, Paris (1983).Aynekulu, E. et al. Plant diversity and regeneration in a disturbed isolated dry Afromontane forest in northern Ethiopia. Folia Geobot. 51, 115–127 (2016).
Google Scholar 
Wassie, A., Sterck, F. J. & Bongers, F. Species and structural diversity of church forests in a fragmented Ethiopian Highland landscape. J. Veg. Sci. 21, 938–948 (2010).
Google Scholar 
Alem, D., Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Survey of macrofungal diversity and analysis of edaphic factors influencing the fungal community of church forests in Dry Afromontane areas of Northern Ethiopia. For. Ecol. Manag. 496, 119391 (2021).
Google Scholar 
Aerts, R. et al. Conservation of the Ethiopian church forests: Threats, opportunities and implications for their management. Sci. Total Environ. 551–552, 404–414 (2016).ADS 
PubMed 

Google Scholar 
Wassie, A., Teketay, D. & Powell, N. Church forests in North Gonder administrative zone, Northern Ethiopia. For. Trees Livelihoods 15, 349–373 (2005).
Google Scholar 
Wsaaie, A., Teketay, D. & Powell, N. Church forests in North Gondar Administative Zone, Northern Ethioopia. For. Trees Livelihoods 15, 349–373 (2005).
Google Scholar 
Lemenih, M. & Bongers, F. Dry forests of Ethiopia and their silviculture. In Silviculture in the Tropics, Tropical Forestry 8 (ed. S. G€unter et al.) 261–272 (Springer, Heidelberg, 2011). https://doi.org/10.1007/978-3-642-19986-8_17.Fernández, A., Sánchez, S., García, P. & Sánchez, J. Macrofungal diversity in an isolated and fragmented Mediterranean Forest ecosystem. Plant Biosyst. Int. J. Deal. Asp. Plant Biol. 154, 139–148 (2020).
Google Scholar 
Peay, K. G. & Bruns, T. D. Spore dispersal of basidiomycete fungi at the landscape scale is driven by stochastic and deterministic processes and generates variability in plant-fungal interactions. New Phytol. 204, 180–191 (2014).PubMed 

Google Scholar 
Burgess, N. D., Hales, J. D. A., Ricketts, T. H. & Dinerstein, E. Factoring species, non-species values and threats into biodiversity prioritisation across the ecoregions of Africa and its islands. Biol. Conserv. 127, 383–401 (2006).
Google Scholar 
Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Fungal community succession and sporocarp production following fire occurrence in Dry Afromontane forests of Ethiopia. For. Ecol. Manag. 398, 37–47 (2017).
Google Scholar 
Větrovský, T. et al. GlobalFungi, a global database of fungal occurrences from high-throughput-sequencing metabarcoding studies. Sci. Data 7, 1–14 (2020).
Google Scholar 
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science (80-. ). 346 (2014).Hawksworth, D. L. Global species numbers of fungi: are tropical studies and molecular approaches contributing to a more robust estimate?. Biodivers. Conserv. 21, 2425–2433 (2012).
Google Scholar 
Crous, P. W. et al. How many species of fungi are there at the tip of Africa?. Stud. Mycol. 55, 13–33 (2006).PubMed 
PubMed Central 

Google Scholar 
Martínez, M. L. et al. Effects of land use change on biodiversity and ecosystem services in tropical montane cloud forests of Mexico. For. Ecol. Manag. 258, 1856–1863 (2009).
Google Scholar 
Phillips, H. et al. The effects of global change on soil faunal communities: a meta-analytic approach. Res. Ideas Outcomes 5 (2019).Riutta, T. et al. Experimental evidence for the interacting effects of forest edge, moisture and soil macrofauna on leaf litter decomposition. Soil Biol. Biochem. 49, 124–131 (2012).CAS 

Google Scholar 
Rantalainen, M., Haimi, J., Fritze, H., Pennanen, T. & Setala, T. Soil decomposer community as a model system in studying the effects of habitat fragmentation and habitat corridors. Soil Biol. Biochem. 40, 853–863 (2008).CAS 

Google Scholar 
Newsham, K. K. et al. Relationship between soil fungal diversity and temperature in the maritime Antarctic. Nat. Clim. Chang. 6, 182–186 (2016).ADS 

Google Scholar 
Bahram, M., Põlme, S., Kõljalg, U., Zarre, S. & Tedersoo, L. Regional and local patterns of ectomycorrhizal fungal diversity and community structure along an altitudinal gradient in the Hyrcanian forests of northern Iran. New Phytol. 193, 465–473 (2012).PubMed 

Google Scholar 
Rousk, J. et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 4, 1340–1351 (2010).PubMed 

Google Scholar 
Krüger, C. et al. Plant communities rather than soil properties structure arbuscular mycorrhizal fungal communities along primary succession on a mine spoil. Front. Microbiol. 8, 1–16 (2017).
Google Scholar 
Bahram, M., Peay, K. G. & Tedersoo, L. Local-scale biogeography and spatiotemporal variability in communities of mycorrhizal fungi. New Phytol. 205, 1454–1463 (2015).CAS 
PubMed 

Google Scholar 
Li, P. et al. Spatial variation in soil fungal communities across paddy fields in Subtropical China. mSystems 5 (2020).Grilli, G., Urcelay, C. & Galetto, L. Forest fragment size and nutrient availability: Complex responses of mycorrhizal fungi in native–exotic hosts. Plant Ecol. 213, 155–165 (2012).
Google Scholar 
Fernández, C., Vega, J. A. & Fonturbel, T. Shrub Resprouting Response After Fuel Reduction Treatments: Comparison of Prescribed Burning, Clearing and Mastication (Elsevier, 2013).
Google Scholar 
Tedersoo, L., Sadam, A., Zambrano, M., Valencia, R. & Bahram, M. Low diversity and high host preference of ectomycorrhizal fungi in Western Amazonia, a neotropical biodiversity hotspot. ISME J. 4, 465–471 (2010).PubMed 

Google Scholar 
Glassman, S. I., Wang, I. J. & Bruns, T. D. Environmental filtering by pH and soil nutrients drives community assembly in fungi at fine spatial scales. Mol. Ecol. 26, 6960–6973 (2017).CAS 
PubMed 

Google Scholar 
Colwell, R. K. EstimateS: statistical estimation of species richness and shared species from samples. Version 9. User’s Guide and application published at: http://purl.oclc.org/estimates (2013).Purvis, A. & Hector, A. Getting the measure of biodiversity. Nature 405, 212–219 (2000).CAS 
PubMed 

Google Scholar 
Pan, W. et al. DNA polymerase preference determines PCR priming efficiency. BMC Biotechnol. 14, 10 (2014).PubMed 
PubMed Central 

Google Scholar 
Kirk, P. M., Cannon, P. F., Minter, D. W. & J.A, S. Dictionary of the Fungi (The Centre for Agriculture and Bioscience International (CABI), 2008).Rossman, A., Samuel, G., Rogerson, C. & Lowen, R. Genera of bionectriaceae, hypocreaceae and nectriaceae (hypocreales, ascomycetes). Stud. Mycol. 42, 1–260 (1999).
Google Scholar 
Samuels, G. Trichoderma: A review of biology and systematics of the genus. Mycol. Res. 923–935 (1996).Alem, D. et al. Soil fungal communities and succession following wildfire in Ethiopian dry Afromontane forests, a highly diverse underexplored ecosystem. For. Ecol. Manag. 474, 118328 (2020).
Google Scholar 
Muleta, D., Woyessa, D. & Teferi, Y. Mushroom consumption habits of Wacha Kebele residents, southwestern Ethiopia. Glob. Res. J. Agric. Biol. Sci. 4, 6–16 (2013).
Google Scholar 
Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Edible wild mushrooms of Ethiopia: Neglected non-timber forest products. Rev. Fitotec. Mex. 40, 391–397 (2017).
Google Scholar 
Tedersoo, L. et al. Disentangling global soil fungal diversity. Science (80-) 346, 1052–1053 (2014).
Google Scholar 
Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Fungal community succession and sporocarp production following fire occurrence in Dry Afromontane forests of Ethiopia. For. Ecol. Manag. 398 (2017).Dang, P. et al. Changes in soil fungal communities and vegetation following afforestation with Pinus tabulaeformis on the Loess Plateau. Ecosphere 9 (2018).Gilbert, G. S., Ferrer, A. & Carranza, J. Polypore fungal diversity and host density in a moist tropical forest. Biodivers. Conserv. 11, 947–957 (2002).
Google Scholar 
Kottke, I., Beck, A., Oberwinkler, F., Homeier, J. & Neill, D. Arbuscular endomycorrhizas are dominant in the organic soil of a neotropical montane cloud forest. J. Trop. Ecol. 20, 125–129 (2004).
Google Scholar 
Barnes, C. J., Van der Gast, C. J., Burns, C. A., McNamara, N. P. & Bending, G. D. Temporally variable geographical distance effects contribute to the assembly of root-associated fungal communities. Front. Microbiol. 7, 1–13 (2016).
Google Scholar 
Tian, J. et al. Environmental factors driving fungal distribution in freshwater lake sediments across the Headwater Region of the Yellow River, China. Sci. Rep. 8, 4–11 (2018).
Google Scholar 
Rosales-Castillo, J. et al. Fungal community and ligninolytic enzyme activities in Quercus deserticola Trel. litter from forest fragments with increasing levels of disturbance. Forests 9, 11 (2017).
Google Scholar 
Kuhar, F., Barroetaveña, C. & Rajchenberg, M. New species of Tomentella (Thelephorales) from the Patagonian Andes forests. Mycologia 108, 780–790 (2016).CAS 
PubMed 

Google Scholar 
Alem, D., Dejene, T., Oria-de-Rueda, J. A., Geml, J. & Martín-Pinto, P. Soil fungal communities under Pinus patula Schiede ex Schltdl. & Cham. Plantation forests of different ages in Ethiopia. Forests 11, 1109 (2020).
Google Scholar 
Tedersoo, L. et al. Terrestrial and lignicolous macrofungi. ISME J. 10, 1228–1239 (2016).
Google Scholar 
Ruiz, R., Decock, C., Saikawa, M., Gene, J. & Guarro, J. Polyschema obclaviformis sp. Nov., and some new records of hyphomycetes from Cuba. Cryptogam. Mycol. 21, 215–220 (2000).
Google Scholar 
Kaygusuz, O. New locality records of Trichoglossum hirsutum (Geoglossales: Geoglossaceae) based on molecular analyses, and prediction of its potential distribution in Turkey. Curr. Res. Environ. Appl. Mycol. 10, 443–456 (2020).
Google Scholar 
Mayer, P. M. Ecosystem and decomposer effects on litter dynamics along an old field to old-growth forest successional gradient. Acta Oecol. 33, 222–230 (2008).ADS 

Google Scholar 
Krishna, M. P. & Mohan, M. Litter decomposition in forest ecosystems: A review. Energy Ecol. Environ. 2, 236–249 (2017).
Google Scholar 
Kirschbaum, M. U. F. The temperature dependence of soil organic matter decomposition, and the effect of global warming on soil organic C storage. Soil Biol. Biochem. 27, 753–760 (1995).CAS 

Google Scholar 
Mayer, P. M., Tunnell, S. J., Engle, D. M., Jorgensen, E. E. & Nunn, P. Invasive grass alters litter decomposition by influencing macrodetritivores. Ecosystems 8, 200–209 (2005).
Google Scholar 
Epstein, H. E., Burke, I. C. & Lauenroth, W. K. Regional patterns of decomposition and primary production rates in the U.S. great plains. Ecology 83, 320 (2002).
Google Scholar 
Sharon, R., Degani, G. & Warburg, M. Comparing the soil macro-fauna in two oak-wood forests: Does community structure differ under similar ambient conditions?. Pedobiologia (Jena). 45, 355–366 (2001).
Google Scholar 
Clocchiatti, A., Hannula, S. E., van den Berg, M., Korthals, G. & de Boer, W. The hidden potential of saprotrophic fungi in arable soil: Patterns of short-term stimulation by organic amendments. Appl. Soil Ecol. 147, 103434 (2020).
Google Scholar 
Drenovsky, R., Vo, D., Graham, K. & Scow, K. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Microb. Ecol. 48, 424–430 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lauber, C., Hamady, M., Knigh, R. & Fierer, N. Pyrosequencing based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ullah, S. et al. The response of soil fungal diversity and community composition to long-term fertilization. Appl. Soil Ecol. 140, 35–41 (2019).
Google Scholar 
Bååth, E. & Anderson, T.-H. Comparison of soil fungal/bacterial ratios in a pH gradient using physiological and PLFA-based techniques. Soil Biol. Biochem. 35, 955–963 (2003).
Google Scholar 
Zhang, T., Wang, N.-F., Liu, H.-Y., Zhang, Y.-Q. & Yu, L.-Y. Soil pH is a key determinant of soil fungal community composition in the Ny-Ålesund Region, Svalbard (high arctic). Front. Microbiol. 7 (2016).Tian, D. et al. Effects of nitrogen deposition on soil microbial communities in temperate and subtropical forests in China. Sci. Total Environ. 607–608, 1367–1375 (2017).ADS 
PubMed 

Google Scholar 
Zhao, A. et al. Influences of canopy nitrogen and water addition on am fungal biodiversity and community composition in a mixed deciduous forest of China. Front. Plant Sci. 9 (2018).He, J. et al. Greater diversity of soil fungal communities and distinguishable seasonal variation in temperate deciduous forests compared with subtropical evergreen forests of eastern China. FEMS Microbiol. Ecol. 93, 1–12 (2017).
Google Scholar 
Shi, L. et al. Variation in forest soil fungal diversity along a latitudinal gradient. Fungal Divers. 64, 305–315 (2014).
Google Scholar 
Gebeyehu, G., Soromessa, T., Bekele, T. & Teketay, D. Plant diversity and communities along environmental, harvesting and grazing gradients in dry afromontane forests of Awi Zone, northwestern Ethiopia. Taiwania 64, 307–320 (2019).
Google Scholar 
Zegeye, H., Teketay, D. & Kelbessa, E. Diversity and regeneration status of woody species in Tara Gedam and Abebaye forests, northwestern Ethiopia. J. For. Res. 22, 315–328 (2011).
Google Scholar 
Abere, F., Belete, Y., Kefalew, A. & Soromessa, T. Carbon stock of Banja forest in Banja district, Amhara region, Ethiopia: An implication for climate change mitigation. J. Sustain. For. 36, 604–622 (2017).
Google Scholar 
Masresha, G., Soromessa, T. & Kelbessa, E. Status and species diversity of Alemsaga Forest, Northwestern Ethiopia 14 (2015).Rudolph, S., Maciá-Vicente, J. G., Lotz-Winter, H., Schleuning, M. & Piepenbring, M. Temporal variation of fungal diversity in a mosaic landscape in Germany. Stud. Mycol. 89, 95–104 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
De la Varga, H., Águeda, B., Martínez-Peña, F., Parladé, J. & Pera, J. Quantification of extraradical soil mycelium and ectomycorrhizas of Boletus edulis in a Scots pine forest with variable sporocarp productivity. Mycorrhiza 22, 59–68 (2012).PubMed 

Google Scholar 
Voříšková, J. & Baldrian, P. Fungal community on decomposing leaf litter undergoes rapid successional changes. ISME J. 7, 477–486 (2013).PubMed 

Google Scholar 
Reeuwijk, L. Procedures for Soil Analysis (International Soil Reference and Information Centre, 2002).
Google Scholar 
Walkley, A. & Black, I. A. An examination of the digestion method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci. 34, 29–38 (1934).ADS 

Google Scholar 
Kim, J., Kreller, C. R. & Greenberg, M. M. Preparation and analysis of oligonucleotides containing the C4’-oxidized abasic site and related mechanistic probes. J. Org. Chem. 70, 8122–8129 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kim, H. T. Soil sampling, preparation and analysis. 139–145 (1996).Bouyoucos, G. H. A reclamation of the hydrometer for making mechanical analysis. Soil. Agro. J. 43, 434–438 (1951).CAS 

Google Scholar 
Ihrmark, K., Bödeker, I. & Cruz-Martinez, K. New primers to amplify the fungal ITS2 region—evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677 (2012).CAS 
PubMed 

Google Scholar 
White, T. ., Bruns, S., Lee, S. & Taylor, J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. in PCR Protocols: A Guide to Methods and Applications (eds. Innis, M. A., Gelfand, D. H., Sninsky, J. J. & White, T. J.) 315–322 (Academic Press, 1990).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10 (2011).
Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277 (2013).PubMed 

Google Scholar 
Põlme, S. et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105 (2020).Hedberg, I. & Edwards, S. Flora of Ethiopia and Eritria (1989).Collins, C. G., Stajich, J. E., Weber, S. E., Pombubpa, N. & Diez, J. M. Shrub range expansion alters diversity and distribution of soil fungal communities across an alpine elevation gradient. Mol. Ecol. 27, 2461–2476 (2018).PubMed 
PubMed Central 

Google Scholar 
Schön, M. E., Nieselt, K. & Garnica, S. Belowground fungal community diversity and composition associated with Norway spruce along an altitudinal gradient. PLoS ONE 13, e0208493 (2018).PubMed 
PubMed Central 

Google Scholar 
Castaño, C. et al. Changes in fungal diversity and composition along a chronosequence of Eucalyptus grandis plantations in Ethiopia. Fungal Ecol. 39, 328–335 (2019).
Google Scholar 
Shannon, C. E. & Weaver, W. The Mathematical Theory of Communication (University of Illinois Press, 1949).MATH 

Google Scholar 
Kent, M. & Coker, P. Vegetation Description and Analysis: A Practical Approach (Belhaven Press, 1993).
Google Scholar 
Magurran, A. E. Ecological Diversity and Its Measurement (Princeton University Press, 1988).
Google Scholar 
Jost, L., Chao, A. & Chazdon, R. Compositional similarity and β (beta) diversity. in Biological Diversity. Frontiers in Measurement and Assessment (eds. A.E., Magurran & B.J., M.) 66–84 (Oxford University Press, 2011).Kindt, R. & Coe, R. Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies. (World Agroforestry Centre (ICRAF), 2005).R Core Team. A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria, 2020).Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R. C. Nlme: Linear and Nonlinear Mixed Effects Models. R Package Version 3.1-128. http://CRAN.R-project.org/package=nlme (2016).Tóthmérész, B. Comparison of different methods for diversity ordering. J. Veg. Sci. 6, 283–290 (1995).
Google Scholar 
Clarke, K. R., Gorley, R. N., Somerfield, P. J. & Warwick, R. M. Change in marine communities: an approach to statistical analysis and interpretation. (PRIMER-E, Plymouth, 2014).Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar  More

Climate change is affecting arctic ecosystems through temperature increase1, hydrological changes2, earlier snowmelt3,4, and the associated increase in growing season length5. Annual arctic air temperature has been increasing at more than double the magnitude of the global mean air temperature increase1, and terrestrial snow cover in June has decreased by 15.2% per decade from 1981 to 20194. Warming is the main driver of the earlier start of the growing season, and the greening of the Arctic6,7,8. Arctic greening is associated with enhanced vegetation height, biomass, cover, and abundance9. However, the complexity of arctic systems reveals an intricate patchwork of landscape greening and browning8,10,11, with browning linked to a variety of stresses to vegetation8 including water stress12,13. The interconnected changes in temperature, soil moisture, snowmelt timing, etc. can have important effects on the carbon sequestered by arctic ecosystems14. The reservoir of carbon in arctic soil and vegetation depends on the interaction of two main processes: (1) changes in net CO2 uptake by vegetation; and (2) increased net loss of CO2 (from vegetation and soil) to the atmosphere via respiration. Therefore, defining the response of both plant productivity and ecosystem respiration to environmental changes is needed to predict the sensitivity of the net CO2 fluxes of arctic systems to climate change.An earlier snowmelt, and a longer growing season, do not necessarily translate into more carbon sequestered by high latitude ecosystems5. There is a large disagreement on the response of plant productivity and the net CO2 uptake to early snowmelt in tundra ecosystems15,16,17,18,19. A warmer and longer growing season might not result in more net CO2 uptake if CO2 loss from respiration increases16, particularly later in the season, and surpasses the CO2 sequestered by enhanced plant productivity in northern ecosystems16,20. Moreover, snowmelt timing and the growing season length greatly affect hydrologic conditions of arctic soils21, as well as plant productivity22. Longer non-frozen periods earlier in the year23 and earlier vegetation greening can increase evapotranspiration (ET), resulting in lower summer soil moisture24,25,26. The complexity in the hydrology of tundra systems arises from the tight link between the water drainage and the presence and depth of permafrost. The presence of permafrost reduces vertical water losses, preventing soil drainage in northern wetlands during most of the summer despite low precipitation input27. Increasing rainfall28 and increased permafrost degradation can increase soil wetness in continuous permafrost regions2. Further permafrost degradation (e.g. ice-wedge melting) can increase hydrologic connectivity leading to increased lateral drainage of the landscape and subsequent soil drying2,29.Given the importance of soil moisture in affecting the carbon balance of arctic ecosystems, and its links with snowmelt timing, in this study, we investigated the correlation between summer fluxes of CO2 (i.e., net ecosystem exchange (NEE), gross primary productivity (GPP) ecosystem respiration (ER)), ET, and environmental drivers such as soil moisture and snowmelt timing, while controlling for the other most important drivers of photosynthesis and respiration (i.e. solar radiation and air temperature). We expected earlier snowmelt to be correlated with larger ET and lower soil moisture, particularly during peak and late season, consistent with drying associated with a longer growing season. The lower soil moisture with earlier snowmelt should result in a negative correlation between snowmelt timing and GPP, particularly during the peak and late season (when we expect the most water stress), and in a positive correlation between snowmelt timing and ER during the entire growing season. This soil moisture limitation to plant productivity should result in lower net cumulative CO2 sequestration during the entire summer, because of lower plant productivity if these ecosystems are water-limited due to lower soil moisture with earlier snowmelt.Testing the impact of snowmelt timing on the carbon dynamics and hydrology of tundra ecosystemsThe 11 sites were selected as among the longest-running tower sites in the circumpolar Arctic (including 6 to 19 years of fluxes per site and a total of 119 site-years of summer (June to August) eddy covariance CO2 flux data, Table S1). All sites lie in the zone of continuous permafrost. The sites are representative of dominant tundra vegetation classes (wetland, graminoids, and shrub tundra), together accounting for 31% of all tundra vegetation types (Fig. 130 and Supplementary Information). Given the complex interactions among different variables (many covarying together), we used a variety of statistical analyses to identify the association between standardized anomalies of NEE, GPP, ER, and ET, and standardized anomalies of the main environmental controls during different periods of the summer corresponding to various stages in seasonal phenology (early season: June, peak season: July, and late season: August). We used a partial correlation analysis to identify if the timing of the snowmelt associates with anomalies of ET, soil moisture, NEE, GPP, ER, atmospheric vapor pressure deficit (VPD), or the Bowen ratio (the ratio between Sensible Heat (H) and Latent Heat (LE)) while statistically controlling for the main meteorological forcing such as air temperature and solar radiation (Methods). Identifying the correlation between ET (and the Bowen ratio) and snowmelt timing is a way to assess water limitation to ecosystems (in addition to testing their response to soil moisture changes), as H, and therefore, the Bowen ratio, are expected to increase with surface drying31,32. To identify the association between snowmelt timing, the main environmental variables (i.e., air temperature and solar radiation), and NEE, GPP, ER, and ET over time, we performed a maximum covariance analysis (MCA) on the monthly median standardized anomalies from 2004 to 2019 (a time period when data for most of the sites were available). MCA allowed us to find patterns in two space–time datasets that are highly correlated using a cross-covariance matrix26. We retained sites as the unit of variation (i.e., by estimating the standardized anomalies by site for each of the indicated variables, see “Methods”), hence the results of the MCA integrated the site level relationships between each of the variables over time). The goal of this analysis was to identify the most important environmental drivers associated with NEE, GPP, and ER across all the sites over time. MCA is particularly appropriate for this study as it can handle data with gaps and unequal lengths in the datasets. We also tested the relative importance of the abovementioned environmental drivers on the monthly median GPP, ER, and NEE using a linear mixed effect model, including site as a random effect to account for the site-to-site variability. The MCA and the mixed model analyses were conducted to test the relative importance of snowmelt and other variables at different times of the season. Finally, to evaluate the water balance at different times of the season, we estimated the difference between Potential Evapotranspiration (PET) and the actual ET, and the difference between precipitation (PPT) and ET for each of the sites, years, and months (e.g. June, July, and August). This study did not attempt to describe the long-term temporal changes in the anomalies of snowmelt and carbon fluxes, given the short data record available for some of the sites (i.e. less than 10 years, Table S1), but instead focused on understanding the association between environmental variables and the carbon balance at different times of the season. More details of these analyses are included in the Methods.Figure 1Study sites. Locations of the 11 eddy covariance flux tower sites used in this study. Light blue regions delineate the total Circumpolar Arctic Vegetation Map (CAVM), green regions delineate the subset of CAVM vegetation types represented in this study (including all the vegetation types listed in Table S1). This map was created using QGIS.org, 2020, QGIS 3.10. Geographic Information System User Guide. QGIS Association: https://docs.qgis.org/3.10/en/docs/user_manual/index.html. The dataset used in the map was the CAVM map: CAVM Team. 2003. Circumpolar Arctic Vegetation Map. (1:7,500,000 scale), Conservation of Arctic Flora and Fauna (CAFF) Map No. 1. U.S. Fish and Wildlife Service, Anchorage, Alaska. ISBN: 0-9767525-0-6, ISBN-13: 978-0-9767525-0-9.Full size imageInfluence of snowmelt timing on NEE, GPP, ER, and hydrological status of tundra ecosystemsOnce statistically controlling for solar radiation and air temperature (in the partial correlation analysis, see “Methods”), we observed a significant positive relationship between the snowmelt timing anomalies and NEE anomalies (i.e. earlier snowmelt was associated with a higher net CO2 sequestration) in June and July, but a negative correlation in August (Fig. 2a, Table 1). A significant relationship was also found between snowmelt date anomalies and GPP anomalies, with more positive GPP anomalies (i.e. higher plant productivity) with earlier snowmelt in June and July, and more negative GPP anomalies with earlier snowmelt in August (Fig. 2b, Table 1). Earlier snowmelt was associated with significantly higher ER in both June and July, but there was no significant relationship in August (Fig. 2c, Table 1), suggesting that the late-season correlation between NEE and snowmelt timing was mostly driven by the lower GPP and with earlier snowmelt in August. The MCA analysis showed that the anomalies in snowmelt timing had the highest squared covariance fraction (SCF) with the monthly median anomalies of GPP, NEE, and ER in June and July, and the lowest in August over the 2004–2019 period (Fig. 3). A similar result was observed in the linear mixed effect model, which showed a significant relationship between snowmelt date and GPP, and NEE, in all summer months, higher ({R}_{m}^{2}) between the snowmelt date and GPP in June and July, and no significant relationship between snowmelt date and ER in August (Table S3). In late season, other environmental variables had a higher covariance with the GPP, NEE, and ER anomalies than the snowmelt timing (Fig. 3, Table S3).Figure 2Relationships between the indicated median monthly anomalies using partial correlation analysis accounting for solar radiation and air temperature anomalies (retaining site as the unit of variation). Given that the interaction term between “month” and snowmelt timing was significant, we included the correlation coefficients and P of the regressions for each of the indicated months separately in each panel (also included in Table 1). Negative values indicate CO2 uptake and positive values CO2 release into the atmosphere, and shaded areas are 95% confidence intervals.Full size imageTable 1 Significance (P) and Pearson’s correlation coefficient (r) of the relationships between the indicated monthly median standardized anomalies for June, July, and August retaining site as a unit of variation.Full size tableFigure 3Squared covariance fraction (SCF) of each couple of the indicated variables for the maximum covariance analysis (MCA) of the monthly median anomalies of GPP, ER, and NEE in June, July, and August. The first pair of singular vectors are the phase-space directions when projected that have the largest possible cross-covariance. The singular vectors describe the patterns in the anomalies that are linearly correlated. A higher SCF indicates a stronger association over time between the indicated variables.Full size imageOur results are consistent with the discrepancy between the observed increase in the maxNDVI over the last four decades and the time-integrated (TI) NDVI which instead has plateaued in the last two decades and even decreased over the last 10 years in several northern arctic ecosystems33. TI-NDVI considers the length of the growing season and phenological variations34 and, therefore, better integrates vegetation development during the entire growing season. Moisture was shown to be important for the NDVI trends33,35. Given the potential water limitation to summer carbon uptake in northern ecosystems12,23,24,25, we tested if an earlier snowmelt was associated with a decrease in soil moisture, which would affect GPP and NEE. We only observed a significant correlation between soil moisture anomalies and snowmelt date anomalies in June (i.e. higher soil moisture with earlier snowmelt, Fig. S1a, Table S2), but no significant correlation in July and August (Fig. S1a, Table S2). The higher soil moisture with earlier snowmelt in June is consistent with surface inundation after snowmelt36,37 and earlier soil thawing resulting in higher soil moisture (i.e., soil moisture is low while soils are frozen). A similar result was observed for the ET anomalies. Higher ET with earlier snowmelt in June (Fig. S1b) could be the result of surface inundation after snowmelt32. The standardized NEE anomalies were significantly correlated with the soil moisture anomalies in each of the summer months (Fig. S1d, Table S2). However, the relationship between the GPP (and ER anomalies) and soil moisture anomalies was only significant in June (Fig. S1e,f, Table S2) suggesting an earlier activation of the vegetation with earlier soil thaw (and the associated higher soil moisture). A higher water loss from ET in early season (Fig. S1b) could have resulted in the drying of the surface moss layer with the progression of the summer, which would have been consistent with the observed lower GPP and the lower net CO2 sequestration with earlier snowmelt observed in August (Fig. 2a,b, Table 1). A potential moisture limitation to plant productivity might have been consistent also with the higher SCF of NEE, or GPP and VPD anomalies in August than in June and July (Fig. 3). However, no significant relationship between ET (or soil moisture) and snowmelt date anomalies was observed in July and August (Fig. S1a,b) contrary to what would be expected if drying occurred following earlier snowmelt. No significant relationship was found between VPD anomalies and snowmelt date anomalies in any of the summer months (P = 0.14 in a partial correlation considering air temperature and solar radiation anomalies). Finally, surface drying should result in an increase in the Bowen ratio anomalies with the progression of the summer, given that H increases with a decrease in water table and surface drying32,38. However, the Bowen ratio showed no correlation with the standardized snowmelt date anomalies in any of the summer months (Fig. S1c, Table S2), and presented similar values in all the summer months (Fig. S2a). The lack of correlation between the soil moisture, VPD, Bowen ratio, and snowmelt date anomalies suggests that an earlier snowmelt did not result in significant surface drying in the sites of this study. The median PET-ET and PPT-ET for all years and sites included in this analysis (Fig.S2 b,c) was slightly higher in August, similar to reports by others for the Russian arctic tundra38,39, further supporting a lack of soil moisture limitation in late season. Although these analyses do not consider runoff, which can be significant21,26, overall our results do not suggest that an earlier snowmelt resulted in a water stress that significantly limited plant productivity in these arctic ecosystems over continuous permafrost.The correlation between the anomalies in the August GPP and snowmelt timing is consistent with earlier senescence in northern plant species (e.g. Eriophorum vaginatum, a dominant species across these tundra types) compared to southern species growing in the same location in a common garden experiment40. The phenotypic variation was shown to be persisting for decades41, and ecotypes may be unable to extend their effective growth period or take advantage of a longer growing season40. Several studies across different plant functional types have shown that once plant growth is initiated after the snowmelt in northern ecosystems, it continues only for a fixed number of days until the occurrence of senescence42,43,44. Therefore, the lower GPP in August with earlier snowmelt might not be linked to water limitation on photosynthesis later in the season, but rather to an earlier senescence arising from the endogenous rhythms of growth and senescence, that plant functional types living in these extreme conditions have developed over decades. On a broader scale, earlier senescence with an earlier start of the growing season after snowmelt in northern ecosystems is also consistent with an earlier spring zero-crossing date and an earlier autumn zero-crossing date of the mean detrended seasonal CO2 variations at Barrow, AK, USA (NOAA ESRL: https://www.esrl.noaa.gov/gmd/ccgg/obspack/) during 2013–2017 compared to 1980–19845. The spring and autumn zero-crossing date is the time when the detrended seasonal CO2 variations intersect the zero line in spring and autumn respectively and can be used as an indicator for the start and end of the net CO2 uptake by vegetation45,46. On the other hand, NDVI measurements show both an earlier start of the season and a later end of season for 2008–2012 compared to 1982–19865. The disagreement between the detrended seasonal atmospheric CO2 concentration showing an earlier autumn zero-crossing date and the NDVI measurements showing a later end of the season has been explained by the increase in respiration in the fall20. The disagreement between atmospheric CO2 concentration trends (showing an earlier autumn zero-crossing date), and NDVI (showing a later end of the season,5 may also be explained by the challenges in using NDVI as a proxy for plant productivity in these arctic systems. The relationship between NDVI and CO2 flux and plant productivity is highly variable and non-linear in arctic ecosystems47. While some arctic ecosystems have shown that NDVI was strongly correlated with GPP (explaining 75% of the variation in GPP48, other studies showed that NDVI was either not significantly correlated with GPP and NEE49 or was only able to explain a minor fraction (maximum of 25%) of the variation in NEE and GPP in some arctic tundra ecosystems after accounting for the seasonal variation50,51.In conclusion, earlier snowmelt was associated with greater net CO2 uptake and higher GPP in early and peak seasons, but with less net CO2 uptake and lower GPP later in the summer, in the studied arctic tundra ecosystems. We did not find evidence of a late-season water limitation to GPP with earlier snowmelt. Although several hypotheses can be forwarded to explain the link between snowmelt and late season declines in plant productivity and carbon uptake, the current literature does not provide a definitive explanation (schematic Fig. 4). Future studies should investigate the potential interaction of different processes explaining the response of the carbon dynamics in the Arctic to earlier snowmelt and reconstruct the temporal changes in the carbon balance from these systems. The link between the long-term changes in the CO2 fluxes and NDVI in tundra ecosystems needs closer examination. Studies should investigate if higher NDVI is definitively associated with higher net CO2 uptake. Greening of the Arctic might not necessarily translate into more net CO2 uptake, as early and peak season carbon gains might be offset by a late-season CO2 loss, and respiration might counterbalance the increase in plant productivity. A better understanding of the processes driving these temporal changes is a fundamental step in advancing our prediction of the response of the arctic CO2 balance to changing climate.Figure 4Schematic of the effect of earlier snowmelt on NEE, GPP, and ER at different times of the season. Earlier snowmelt results in an earlier activation of the vegetation, higher plant productivity, and higher net carbon uptake in June and July. This earlier activation could result in more carbon loss and lower plant productivity with earlier snowmelt in August, potentially related to either environmental stress, or to earlier senescence. Photo credit: Donatella Zona.Full size image More

Of which at the third instar, the external morphology of larvae is quite similar; thus, the morphological identification used to differentiate between its genera or species, generally includes cephalophalyngeal skeleton, anterior spiracle, and posterior spiracles. The morphology of the posterior spiracle is one of the important characteristics for identification. A typical morphology of the posterior spiracle of third stage larvae was shown in Fig. 2. Based on studying under light microscopy, the posterior spiracle of M. domestica was clearly distinguished from the others. On the other hand, the morphology of the posterior spiracle of C. megacephala and A. rufifacies was quite similar. For C. megacephala and C. rufifacies, the peritreme, a structure encircling the three spiracular openings (slits), was incomplete and slits were straight as shown Fig. 2A,B, respectively. The complete peritreme encircling three slits was found in L. cuprina and M. domestica as shown in Fig. 2C,D, respectively. However, only the slits of M. domestica were sinuous like the M-letter (Fig. 2D). Their morphological characteristics found in this study were like the descriptions in the previous reports23,24,25.Figure 2Morphology of posterior spiracles of four different fly species after inverting the image colors; (A) Chrysomya (Achoetandrus) ruffifacies, (B) Chrysomya megacephala, (C) Lucilia cuprina, (D) Musca domestica.Full size imageFor model training, four of the CNN models used for species-level identification of fly maggots provided 100% accuracy rates and 0% loss. Number of parameter (#Params), model speed, model size, macro precision, macro recall, f1-score, and support value were also presented in Table 1. The result demonstrated that the AlexNet model provided the best performance in all indicators when compared among four models. The AlexNet model used the least number of parameters while the Resnet101 model used the most. For model speed, the AlexNet model provided the fastest speed, while the Densenet161 model provided the slowest speed. For the model size, the AlexNet model was the smallest, while the Resnet101 model was the largest which corresponded to the number of parameters used. Macro precision, macro recall, f1-score and support value of all models were the same.Table 1 Comparison of model size, speed, and performances of each studied model (The text in bold indicates the best value in each category).Full size tableAs the training results presented in the supplementary data (Fig. S1), all models provided 100% accuracy and 0% loss in the early stage of training ( More