Ecology

1.Johnson, M. T. J. & Munshi-South, J. Evolution of life in urban environments. Science 358, 8327 (2017).Article 
CAS 

Google Scholar 
2.Alberti, M. et al. Global urban signatures of phenotypic change in animal and plant populations. Proc. Natl. Acad. Sci. U.S.A. 114, 8951–8956 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Alberti, M., Marzluff, J. & Hunt, V. M. Urban driven phenotypic changes: Empirical observations and theoretical implications for eco-evolutionary feedback. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160029 (2017).Article 

Google Scholar 
4.Chamberlain, D. E. et al. Avian productivity in urban landscapes: A review and meta-analysis. Ibis 151, 1–18 (2009).Article 

Google Scholar 
5.Miles, L. S., Rivkin, L. R., Johnson, M. T. J., Munshi-South, J. & Verrelli, B. C. Gene flow and genetic drift in urban environments. Mol. Ecol. 28, 4138–4151 (2019).PubMed 
Article 

Google Scholar 
6.Lowry, H., Lill, A. & Wong, B. B. M. Behavioural responses of wildlife to urban environments. Biol. Rev. 88, 537–549 (2013).PubMed 
Article 

Google Scholar 
7.McKinney, M. L. Effects of urbanization on species richness: A review of plants and animals. Urban Ecosyst. 11, 161–176 (2008).Article 

Google Scholar 
8.Devictor, V., Julliard, R., Couvet, D., Lee, A. & Jiguet, F. Functional homogenization effect of urbanization on bird communities. Conserv. Biol. 21, 741–751 (2007).PubMed 
Article 

Google Scholar 
9.McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 127, 247–260 (2006).Article 

Google Scholar 
10.Salmón, P., Watson, H., Nord, A. & Isaksson, C. Effects of the urban environment on oxidative stress in early life: Insights from a cross-fostering experiment. Integr. Comp. Biol. https://doi.org/10.1093/icb/icy099 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
11.Chatelain, M., Drobniak, S. M. & Szulkin, M. The association between stressors and telomeres in non-human vertebrates: A meta-analysis. Ecol. Lett. 23, 381–398 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Seress, G. & Liker, A. Habitat urbanization and its effects on birds. Acta Zool. Acad. Sci. Hung. 61, 373–408 (2015).Article 

Google Scholar 
13.Isaksson, C. Impact of urbanization on birds. In Bird Species (ed. Tietze, D. T.) 235–257 (Springer, 2018).Chapter 

Google Scholar 
14.Ouyang, J. Q. et al. A new framework for urban ecology: An integration of proximate and ultimate responses to anthropogenic change. Integr. Comp. Biol. https://doi.org/10.1093/icb/icy110 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
15.Meillère, A. et al. Corticosterone levels in relation to trace element contamination along an urbanization gradient in the common blackbird (Turdus merula). Sci. Total Environ. 566–567, 93–101 (2016).ADS 
PubMed 
Article 
CAS 

Google Scholar 
16.Chatelain, M. et al. Urban metal pollution explains variation in reproductive outputs in great tits and blue tits. Sci. Total Environ. 776, 145966 (2021).ADS 
CAS 
Article 

Google Scholar 
17.Santangelo, J. S. et al. Urban environments as a framework to study parallel evolution. In Urban Evolutionary Biology (eds Szulkin, M. et al.) 36–53 (Oxford University Press, 2020).Chapter 

Google Scholar 
18.Rivkin, L. R. et al. A roadmap for urban evolutionary ecology. Evol. Appl. 12, 384–398 (2019).PubMed 
Article 

Google Scholar 
19.Szulkin, M., Garroway, C. J., Corsini, M., Kotarba, A. Z. & Dominoni, D. How to quantify urbanisation when testing for urban evolution? In Urban Evolutionary Biology (eds Szulkin, M. et al.) (Oxford University Press, 2020).Chapter 

Google Scholar 
20.McDonnell, M. J. & Pickett, S. T. A. Ecosystem structure and function along urban-rural gradients: An unexploited opportunity for ecology. Ecology 71, 1232–1237 (1990).Article 

Google Scholar 
21.Bai, X. et al. Linking urbanization and the environment: Conceptual and empirical advances. Annu. Rev. Environ. Resour. 42, 215–240 (2017).Article 

Google Scholar 
22.Boyd, R. S. Heavy metal pollutants and chemical ecology: Exploring new frontiers. J. Chem. Ecol. 36, 46–58 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Dauwe, T., Janssens, E., Pinxten, R. & Eens, M. The reproductive success and quality of blue tits (Parus caeruleus) in a heavy metal pollution gradient. Environ. Pollut. 136, 243–251 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Eeva, T., Ahola, M. & Lehikoinen, E. Breeding performance of blue tits (Cyanistes caeruleus) and great tits (Parus major) in a heavy metal polluted area. Environ. Pollut. 157, 3126–3131 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Stauffer, J., Panda, B., Eeva, T., Rainio, M. & Ilmonen, P. Telomere damage and redox status alterations in free-living passerines exposed to metals. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2016.09.131 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
26.Fritsch, C., Jankowiak, Ł & Wysocki, D. Exposure to Pb impairs breeding success and is associated with longer lifespan in urban European blackbirds. Sci. Rep. 9, 486 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.Nriagu, J. O. Global inventory of natural and anthropogenic emissions of trace metals to the atmosphere. Nature 279, 409–411 (1979).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Duan, J. & Tan, J. Atmospheric heavy metals and arsenic in China: Situation, sources and control policies. Atmos. Environ. 74, 93–101 (2013).ADS 
CAS 
Article 

Google Scholar 
29.Celik, E., Durmus, A., Adizel, O. & Nergiz Uyar, H. A bibliometric analysis: What do we know about metals(loids) accumulation in wild birds? Environ. Sci. Pollut. Res. 28, 10302–10334 (2021).CAS 
Article 

Google Scholar 
30.Bichet, C. et al. Urbanization, trace metal pollution, and malaria prevalence in the house sparrow. PLoS ONE 8, e53866 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Gragnaniello, S. et al. Sparrows as possible heavy-metal biomonitors of polluted environments. Bull. Environ. Contam. Toxicol. 66, 719–726 (2001).CAS 
PubMed 
Article 

Google Scholar 
32.Hofer, C., Gallagher, F. J. & Holzapfel, C. Metal accumulation and performance of nestlings of passerine bird species at an urban brownfield site. Environ. Pollut. 158, 1207–1213 (2010).CAS 
PubMed 
Article 

Google Scholar 
33.Nam, D.-H. & Lee, D.-P. Monitoring for Pb and Cd pollution using feral pigeons in rural, urban, and industrial environments of Korea. Sci. Total Environ. 357, 288–295 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
34.Roux, K. E. & Marra, P. P. The presence and impact of environmental lead in passerine birds along an urban to rural land use gradient. Arch. Environ. Contam. Toxicol. 53, 261–268 (2007).CAS 
PubMed 
Article 

Google Scholar 
35.Scheifler, R. et al. Lead concentrations in feathers and blood of common blackbirds (Turdus merula) and in earthworms inhabiting unpolluted and moderately polluted urban areas. Sci. Total Environ. 371, 197–205 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
36.Manjula, M., Mohanraj, R. & Devi, M. P. Biomonitoring of heavy metals in feathers of eleven common bird species in urban and rural environments of Tiruchirappalli, India. Environ. Monit. Assess. 187, 267 (2015).PubMed 
Article 
CAS 

Google Scholar 
37.Zarrintab, M. & Mirzaei, R. Tissue distribution and oral exposure risk assessment of heavy metals in an urban bird: Magpie from Central Iran. Environ. Sci. Pollut. Res. 25, 17118–17127 (2018).CAS 
Article 

Google Scholar 
38.Binkowski, ŁJ. & Meissner, W. Levels of metals in blood samples from Mallards (Anas platyrhynchos) from urban areas in Poland. Environ. Pollut. 178, 336–342 (2013).CAS 
PubMed 
Article 

Google Scholar 
39.Orłowski, G. et al. Residues of chromium, nickel, cadmium and lead in rook Corvus frugilegus eggshells from urban and rural areas of Poland. Sci. Total Environ. 490, 1057–1064 (2014).ADS 
PubMed 
Article 
CAS 

Google Scholar 
40.Kekkonen, J., Hanski, I. K., Väisänen, R. A. & Brommer, J. E. Levels of heavy metals in house sparrows (Passer domesticus) from urban and rural habitats of southern Finland. Ornis Fennica 89, 91 (2012).
Google Scholar 
41.Jaspers, V. L. B., Covaci, A., Herzke, D., Eulaers, I. & Eens, M. Bird feathers as a biomonitor for environmental pollutants: Prospects and pitfalls. TrAC Trends Anal. Chem. https://doi.org/10.1016/j.trac.2019.05.019 (2019).Article 

Google Scholar 
42.Dijkstra, L. & Poelman, H. Cities in Europe: The new OECD-EC definition. Reg. Focus 16, 1–3 (2012).
Google Scholar 
43.Svensson, L. Identification Guide to European Passerines (British Trust for Ornithology, 1992).
Google Scholar 
44.Jenni, L. & Winkler, R. Moult and Ageing of European Passerines (Academic Press, 1994).
Google Scholar 
45.Greenwood, P. J., Harvey, P. H. & Perrins, C. M. The role of dispersal in the great tit (Parus major): The causes, consequences and heritability of natal dispersal. J. Anim. Ecol. 48, 123 (1979).Article 

Google Scholar 
46.Harvey, P. H., Greenwood, P. J. & Perrins, C. M. Breeding area fidelity of great tits (Parus major). J. Anim. Ecol. 48, 305 (1979).Article 

Google Scholar 
47.Ortego, J., García-Navas, V., Ferrer, E. S. & Sanz, J. J. Genetic structure reflects natal dispersal movements at different spatial scales in the blue tit, Cyanistes caeruleus. Anim. Behav. 82, 131–137 (2011).Article 

Google Scholar 
48.Tufto, J., Ringsby, T., Dhondt, A. A., Adriaensen, F. & Matthysen, E. A parametric model for estimation of dispersal patterns applied to five passerine spatially structured populations. Am. Nat. 165, E13–E26 (2005).PubMed 
Article 

Google Scholar 
49.Miles, L. S., Carlen, E. J., Winchell, K. M. & Johnson, M. T. J. Urban evolution comes into its own: Emerging themes and future directions of a burgeoning field. Evol. Appl. 14, 3–11 (2021).PubMed 
Article 

Google Scholar 
50.Moll, R. J. et al. What does urbanization actually mean? A framework for urban metrics in wildlife research. J. Appl. Ecol. 56, 1289–1300 (2019).Article 

Google Scholar 
51.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2018).52.Lee, L. & Helsel, D. Statistical analysis of water-quality data containing multiple detection limits: S-language software for regression on order statistics. Comput. Geosci. 31, 1241–1248 (2005).ADS 
CAS 
Article 

Google Scholar 
53.Salgado, C. M., Azevedo, C., Proença, H., Vieira, S. M. Noise versus outliers. In Secondary Analysis of Electronic Health Records, 163–183 (ed MIT Critical Data) (Springer, 2016).Chapter 

Google Scholar 
54.Betts, M. M. The food of titmice in Oak Woodland. J. Anim. Ecol. 24, 282 (1955).Article 

Google Scholar 
55.Newton, I. & Brockie, K. The Migration Ecology of Birds (Elsevier/Acad. Press, 2008).
Google Scholar 
56.Greenwood, P. J. & Harvey, P. H. The natal and breeding dispersal of birds. Annu. Rev. Ecol. Syst. 13, 1–21 (1982).Article 

Google Scholar 
57.Sakamoto, Y., Ishiguro, M. & Kitagawa, G. Akaike Information Criterion Statistics Vol. 81 (D. Reidel, 1986).MATH 

Google Scholar 
58.Lenth, R. V. Least-squares means: The R package lsmeans. J. Stat. Softw. 69, 1–33 (2016).Article 

Google Scholar 
59.Grömping, U. Relative importance for linear regression in R : The package relaimpo. J. Stat. Softw. https://doi.org/10.18637/jss.v017.i01 (2006).Article 

Google Scholar 
60.Pacyna, E. G. et al. Mercury emissions to the atmosphere from anthropogenic sources in Europe in 2000 and their scenarios until 2020. Sci. Total Environ. 370, 147–156 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.Frantz, A. et al. Contrasting levels of heavy metals in the feathers of urban pigeons from close habitats suggest limited movements at a restricted scale. Environ. Pollut. 168, 23–28 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Eens, M., Pinxten, R., Verheyen, R. F., Blust, R. & Bervoets, L. Great and blue tits as indicators of heavy metal contamination in terrestrial ecosystems. Ecotoxicol. Environ. Saf. 44, 81–85 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Dauwe, T. et al. Great and blue tit feathers as biomonitors for heavy metal pollution. Ecol. Indic. 1, 227–234 (2002).CAS 
Article 

Google Scholar 
64.Janssens, E., Dauwe, T., Bervoets, L. & Eens, M. Heavy metals and selenium in feathers of great tits (Parus major) along a pollution gradient. Environ. Toxicol. Chem. 20, 2815–2820 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Burger, J. Metals in avian feathers: bioindicators of environmental pollution. Rev. Environ. Contam. Toxicol. 5, 203–311 (1993).
Google Scholar 
66.Chatelain, M., Gasparini, J., Jacquin, L. & Frantz, A. The adaptive function of melanin-based plumage coloration to trace metals. Biol. Lett. 10, 20140164–20140164 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Bańbura, M. et al. Egg size variation in blue tits Cyanistes caeruleus and great tits Parus major in relation to habitat differences in snail abundance. Acta Ornithol. 45, 121–129 (2010).Article 

Google Scholar 
68.Scheuhammer, A. M. Influence of reduced dietary calcium on the accumulation and effects of lead, cadmium, and aluminum in birds. Environ. Pollut. 94, 337–343 (1996).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Dauwe, T., Snoeijs, T., Bervoets, L., Blust, R. & Eens, M. Calcium availability influences lead accumulation in a passerine bird. Anim. Biol. 56, 289–298 (2006).Article 

Google Scholar 
70.Snoeijs, T. et al. The combined effect of lead exposure and high or low dietary calcium on health and immunocompetence in the zebra finch. Environ. Pollut. 134, 123–132 (2005).CAS 
PubMed 
Article 

Google Scholar 
71.McCabe, E. B. Age and sensitivity to lead toxicity: A review. Environ. Health Perspect. 29, 29–33 (1979).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Chatelain, M., Gasparini, J. & Frantz, A. Do trace metals select for darker birds in urban areas? An experimental exposure to lead and zinc. Glob. Change Biol. 22, 2380 (2016).ADS 
Article 

Google Scholar 
73.Chatelain, M., Gasparini, J. & Frantz, A. Trace metals, melanin-based pigmentation and their interaction influence immune parameters in feral pigeons (Columba livia). Ecotoxicology. https://doi.org/10.1007/s10646-016-1610-5 (2016).Article 
PubMed 

Google Scholar 
74.Chatelain, M., Frantz, A., Gasparini, J. & Leclaire, S. Experimental exposure to trace metals affects plumage bacterial community in the feral pigeon. J. Avian Biol. https://doi.org/10.1111/jav.00857 (2015).Article 

Google Scholar 
75.Chatelain, M., Pessato, A., Frantz, A., Gasparini, J. & Leclaire, S. Do trace metals influence visual signals? Effects of trace metals on iridescent and melanic feather colouration in the feral pigeon. Oikos. https://doi.org/10.1111/oik.04262 (2017).Article 

Google Scholar 
76.Watson, H., Videvall, E., Andersson, M. N. & Isaksson, C. Transcriptome analysis of a wild bird reveals physiological responses to the urban environment. Sci. Rep. 7, 44180 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Harris, S. E. & Munshi-South, J. Signatures of positive selection and local adaptation to urbanization in white-footed mice (Peromyscus leucopus). Mol. Ecol. https://doi.org/10.1101/038141 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
78.Koivula, M. J. & Eeva, T. Metal-related oxidative stress in birds. Environ. Pollut. 158, 2359–2370 (2010).CAS 
PubMed 
Article 

Google Scholar 
79.Korashy, H. M. et al. Gene expression profiling to identify the toxicities and potentially relevant human disease outcomes associated with environmental heavy metal exposure. Environ. Pollut. 221, 64–74 (2017).CAS 
PubMed 
Article 

Google Scholar 
80.Ghalambor, C. K., McKAY, J. K., Carroll, S. P. & Reznick, D. N. Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Funct. Ecol. 21, 394–407 (2007).Article 

Google Scholar 
81.Garcia, C. M., Suárez-Rodríguez, M. & López-Rull, I. Becoming citizens: Avian adaptations to urban life. In Ecology and Conservation of Birds in Urban Environments (eds Murgui, E. & Hedblom, M.) 91–112 (Springer, 2017).Chapter 

Google Scholar 
82.Goiran, C., Bustamante, P. & Shine, R. Industrial Melanism in the Seasnake Emydocephalus annulatus. Curr. Biol. 27, 2510–2513 (2017).CAS 
PubMed 
Article 

Google Scholar 
83.Obukhova, N. Polymorphism and phene geography of the blue rock pigeon in Europe. Russ. J. Genet. 43, 492–501 (2007).CAS 
Article 

Google Scholar 
84.Jacquin, L. et al. A potential role for parasites in the maintenance of color polymorphism in urban birds. Oecologia 173, 1089–1099 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
85.Gomes, W. R. et al. Polymorphisms of genes related to metabolism of lead (Pb) are associated with the metal body burden and with biomarkers of oxidative stress. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 836, 42–46 (2018).PubMed 
Article 

Google Scholar 
86.Sekovanić, A., Jurasović, J. & Piasek, M. Metallothionein 2A gene polymorphisms in relation to diseases and trace element levels in humans. Arch. Ind. Hyg. Toxicol. 71, 27–47 (2020).
Google Scholar  More

In addition to the raw data, we provide two means of exploring the Avian Diet Database and extracting species- or prey-specific summaries. The first is through the website https://aviandiet.unc.edu where users can enter a bird species name to explore a summary of diet information known for that species, or a prey name to explore which bird species are known to eat that prey taxon. We also provide an R package (‘aviandietdb’) for exploring the database, which should be loaded in R by typing:
install.packages(“devtools”)

library(devtools)

devtools::install_github(“ahhurlbert/aviandietdb”)

library(aviandietdb)
Three useful R functions for summarizing records in the database are detailed below.dbSummary().Example usage:
dbSummary()
This function returns the total number of database records, the unique number of bird species, and the unique number of publications summarized in the Diet Database. In addition, it provides a tally of the number of records by bird species listed in alphabetical order, as well as a summary for each bird family in the American Birding Association (ABA) Checklist (version 8.0.6a) of 1) the number of species in the family in the database, 2) the total number of species in the family based on the ABA checklist, and 3) the percent of the family represented based on the species expected in North America. This information on taxonomic coverage is also provided in Online-only Table 2.speciesSummary().Example usage:
speciesSummary(“Bald Eagle”, by = ”Order”)
This function provides a summary of the total number of records and total number of studies available in the database for this species, along with a summary of how those records are distributed across seasons, years, and geographic regions. The number of records are also summarized by taxonomic level to which prey were identified and by analysis type (by number of items, weight or volume, occurrence, or unspecified). Finally, for each analysis type, the mean fraction of diet is given for each prey category at the hierarchical taxonomic level specified with the “by” argument. This is an overall mean, averaged across year, region, and season. If the original data source indicated that specific parts of the prey taxon were consumed (e.g. fruit, seed, vegetation, etc.) then they are listed in the Prey_Part field.dietSummary().Example usage:
dietSummary(“Bald Eagle”, season = ”summer”, region = ”California”, yearRange = c(1940, 1970), by = ”Order”, dietType = ”Items”)
This function allows one to specify season, region, a year range, analysis type, and taxonomic level for prey summarization, and then provides the mean fraction of diet information based on all studies meeting the stated criteria.dietSummaryByPrey().Example usage:
dietSummaryByPrey(“Lepidoptera”, preyLevel = ”Order”, dietType = ”Items”, yearRange = c(1985, 2000), season = ”summer”, preyStage = ”larva”, speciesMean = TRUE)
This function provides a list of all bird species that consume a particular prey taxon in decreasing order of importance. In addition to providing the prey taxon name, you must also specify the taxonomic level (preyLevel) of that name. Like dietSummary(), this function allows one to specify season, region, a year range, and analysis type. There are two additional arguments not present in dietSummary(). One is preyStage, which specifies the life stage of the prey item (if applicable) for which a summary should be conducted. By default (‘any’), diet records will be included regardless of prey stage. Alternatively, one can specify that the summary should only be conducted for records including the terms ‘larva’, ‘adult’, or ‘pupa’ in the Diet Database’s ‘Prey_Stage’ field. This is most relevant for Lepidoptera and a few other insect groups, where one might want to single out the importance of caterpillars or other larvae, for example.By specifying speciesMean = TRUE, only a single value is returned for each bird species that is known to consume a specified prey taxon which represents the average across all analyses meeting the season, region, and year criteria. If speciesMean is FALSE, then each analysis of a bird species which meets the specified criteria will be listed separately.Example code and output is available in the Github README.md document (https://github.com/ahhurlbert/aviandietdb). More

1.Laurance, W. F., Sayer, J. & Cassman, K. G. Agricultural expansion and its impacts on tropical nature. Trends Ecol. Evol. 29, 107–116 (2014).Article 

Google Scholar 
2.Bélanger, J. & Pilling, D. (eds) The State of the World’s Biodiversity for Food and Agriculture (FAO Commission on Genetic Resources for Food and Agriculture, 2019).3.Rockström, J. et al. Sustainable intensification of agriculture for human prosperity and global sustainability. Ambio 46, 4–17 (2017).Article 

Google Scholar 
4.Pretty, J. et al. Global assessment of agricultural system redesign for sustainable intensification. Nat. Sustain. 1, 441–446 (2018).Article 

Google Scholar 
5.Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571, 257–260 (2019).CAS 
Article 

Google Scholar 
6.Tilman, D. Benefits of intensive agricultural intercropping. Nat. Plants 6, 604–605 (2020).Article 

Google Scholar 
7.Gomez, A. A. & Gomez, K. A. Multiple Cropping in the Humid Tropics of Asia (IDRC, 1983).8.Li, L. et al. Diversity enhances agricultural productivity via rhizosphere phosphorus facilitation on phosphorus-deficient soils. Proc. Natl Acad. Sci. USA 104, 11192–11196 (2007).CAS 
Article 

Google Scholar 
9.Li, C. J. et al. Syndromes of production in intercropping impact yield gains. Nat. Plants 6, 653–660 (2020).Article 

Google Scholar 
10.Xu, Z. et al. Intercropping maize and soybean increases efficiency of land and fertilizer nitrogen use; a meta-analysis. Field Crops Res. 246, 107661 (2020).Article 

Google Scholar 
11.Zhu, Y. Y. et al. Genetic diversity and disease control in rice. Nature 406, 718–722 (2000).CAS 
Article 

Google Scholar 
12.Li, W. X. et al. Effects of intercropping and nitrogen application on nitrate present in the profile of an Orthic Anthrosol in Northwest China. Agric. Ecosyst. Environ. 105, 483–491 (2005).CAS 
Article 

Google Scholar 
13.Manevski, K., Borgesen, C. D., Andersen, M. N. & Kristensen, I. S. Reduced nitrogen leaching by intercropping maize with red fescue on sandy soils in North Europe: a combined field and modeling study. Plant Soil 388, 67–85 (2015).CAS 
Article 

Google Scholar 
14.Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).CAS 
Article 

Google Scholar 
15.Roscher, C. et al. Identifying population- and community-level mechanisms of diversity–stability relationships in experimental grasslands. J. Ecol. 99, 1460–1469 (2011).Article 

Google Scholar 
16.Zhou, B. R. et al. Plant functional groups asynchrony keep the community biomass stability along with the climate change—a 20-year experimental observation of alpine meadow in eastern Qinghai-Tibet Plateau. Agric. Ecosyst. Environ. 282, 49–57 (2019).Article 

Google Scholar 
17.Schnabel, F. et al. Drivers of productivity and its temporal stability in a tropical tree diversity experiment. Glob. Change Biol. 25, 4257–4272 (2019).Article 

Google Scholar 
18.Pohl, M., Alig, D., Körner, C. & Rixen, C. Higher plant diversity enhances soil stability in disturbed alpine ecosystems. Plant Soil 324, 91–102 (2009).CAS 
Article 

Google Scholar 
19.Pérès, G. et al. Mechanisms linking plant community properties to soil aggregate stability in an experimental grassland plant diversity gradient. Plant Soil 373, 285–299 (2013).Article 

Google Scholar 
20.Gould, I. J., Quinton, J. N., Weigelt, A., De Deyn, G. B. & Bardgett, R. D. Plant diversity and root traits benefit physical properties key to soil function in grasslands. Ecol. Lett. 19, 1140–1149 (2016).Article 

Google Scholar 
21.Lange, M. et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat. Commun. 6, 6707 (2015).CAS 
Article 

Google Scholar 
22.Dybzinski, R., Fargione, J. E., Zak, D. R., Fornara, D. & Tilman, D. Soil fertility increases with plant species diversity in a long-term biodiversity experiment. Oecologia 158, 85–93 (2008).Article 

Google Scholar 
23.Tisdall, J. M. & Oades, J. M. Organic matter and water-stable aggregates in soils. J. Soil Sci. 33, 141–163 (1982).CAS 
Article 

Google Scholar 
24.Amézketa, E. Soil aggregate stability: a review. J. Sustain. Agric. 14, 83–151 (1999).Article 

Google Scholar 
25.Six, J., Bossuyt, H., Degryze, S. & Denef, K. A history of research on the link between (micro)aggregates, soil biota, and soil organic matter dynamics. Soil Tillage Res. 79, 7–31 (2004).Article 

Google Scholar 
26.Tiemann, L. K., Grandy, A. S., Atkinson, E. E., Marin-Spiotta, E. & McDaniel, M. D. Crop rotational diversity enhances belowground communities and functions in an agroecosystem. Ecol. Lett. 18, 761–771 (2015).CAS 
Article 

Google Scholar 
27.Christensen, B. T. & Johnston, A. E. in Developments in Soil Science (eds Gregorich, E. G. & Carter, M. R.) 399–430 (Elsevier, 1997).28.Fornara, D. A. & Tilman, D. Soil carbon sequestration in prairie grasslands increased by chronic nitrogen addition. Ecology 93, 2030–2036 (2012).Article 

Google Scholar 
29.Cong, W. F. et al. Plant species richness promotes soil carbon and nitrogen stocks in grasslands without legumes. J. Ecol. 102, 1163–1170 (2014).CAS 
Article 

Google Scholar 
30.Zhang, W. F. et al. Closing yield gaps in China by empowering smallholder farmers. Nature 537, 671–674 (2016).CAS 
Article 

Google Scholar 
31.Wan, N. F. et al. Global synthesis of effects of plant species diversity on trophic groups and interactions. Nat. Plants 6, 503–510 (2020).Article 

Google Scholar 
32.Isbell, F. et al. Benefits of increasing plant diversity in sustainable agroecosystems. J. Ecol. 105, 871–879 (2017).Article 

Google Scholar 
33.Status of the World’s Soil Resources Main Report (FAO and ITPS, 2015).34.Jensen, E. S. & Williamson, S. in Replacing Chemicals with Biology: Phasing Out Highly Hazardous Pesticides with Agroecology (eds Watts, M. & Williamson, S.) 162–167 (Pestice Action Network International, 2015).35.Bender, S. F., Wagg, C. & van der Heijden, M. G. A. An underground revolution: biodiversity and soil ecological engineering for agricultural sustainability. Trends Ecol. Evol. 31, 440–452 (2016).Article 

Google Scholar 
36.Zhang, R. Z. et al. Response of the arbuscular mycorrhizal fungi diversity and community in maize and soybean rhizosphere soil and roots to intercropping systems with different nitrogen application rates. Sci. Total Environ. 740, 139810 (2020).CAS 
Article 

Google Scholar 
37.Bao, S. D. Analysis on Soil and Agricultural Chemistry (China Agricultural Press, 2005).38.Kemper, W. D. & Rosenau, R. C. Aggregate Stability and Size Distribution: Methods of Soil Analysis, Part 1 Physical and Mineralogical Methods (American Society of Agronomy, 1986).39.Willey, R. W. Evaluation and presentation of intercropping advantages. Exp. Agric. 21, 119–133 (1985).Article 

Google Scholar 
40.van Ruijven, J. & Berendse, F. Contrasting effects of diversity on the temporal stability of plant populations. Oikos 116, 1323–1330 (2007).Article 

Google Scholar 
41.The Price Division, National Development and Reform Commission of China The National Agricultural Products Cost–Benefit Compilation of Information (China Statistics Press, 2010–2019).42.R Core Team R: A Language and Environment for Statistical Computing v.3.6.2 (R Foundation for Statistical Computing, 2019). More

1.Tourism and visitor management in protected areas: guidelines for sustainability. (IUCN, International Union for Conservation of Nature, 2018). https://doi.org/10.2305/IUCN.CH.2018.PAG.27.en.2.Pickering, C. M., Hill, W., Newsome, D. & Leung, Y.-F. Comparing hiking, mountain biking and horse riding impacts on vegetation and soils in Australia and the United States of America. J. Environ. Manag. 91, 551–562 (2010).Article 

Google Scholar 
3.Huddart, D. & Stott, T. Outdoor Recreation Environmental Impacts and Management (Springer International Publishing, 2019) https://doi.org/10.1007/978-3-319-97758-4.Book 

Google Scholar 
4.Marion, J. L., Leung, Y.-F., Eagleston, H. & Burroughs, K. A review and synthesis of recreation ecology research findings on visitor impacts to wilderness and protected natural areas. J. Forest. 114, 352–362 (2016).Article 

Google Scholar 
5.Monz, C. A. et al. Assessment and monitoring of recreation impacts and resource conditions on mountain summits: Examples from the Northern Forest, USA. Mt. Res. Dev. 30, 332–343 (2010).Article 

Google Scholar 
6.Salesa, D. & Cerdà, A. Soil erosion on mountain trails as a consequence of recreational activities. A comprehensive review of the scientific literature. J. Environ. Manag. 271, 110990 (2020).CAS 
Article 

Google Scholar 
7.Barros, A., Aschero, V., Mazzolari, A., Cavieres, L. A. & Pickering, C. M. Going off trails: How dispersed visitor use affects alpine vegetation. J. Environ. Manag. 267, 110546 (2020).Article 

Google Scholar 
8.Cole, D. N. & Monz, C. A. Impacts of camping on vegetation: Response and recovery following acute and chronic disturbance. Environ. Manag. 32, 693–705 (2003).Article 

Google Scholar 
9.Andrés-Abellán, M. et al. Impacts of visitors on soil and vegetation of the recreational area ‘Nacimiento del Río Mundo’ (Castilla-La Mancha, Spain). Environ. Monit. Assess. 101, 55–67 (2005).PubMed 

Google Scholar 
10.Lathrop, E. W. The effect of vehicle use on desert vegetation. In Environmental Effects of Off-Road Vehicles (eds Webb, R. H. & Wilshire, H. G.) 153–166 (Springer New York, 1983) https://doi.org/10.1007/978-1-4612-5454-6_8.Chapter 

Google Scholar 
11.Abd El-Wahab, R. H., Al-Rashed, A. R. & Al-Dousari, A. Influences of physiographic factors, vegetation patterns and human impacts on aeolian landforms in arid environment. Arid Ecosyst. 8, 97–110 (2018).Article 

Google Scholar 
12.Abdullah, M. M., Feagin, R. A., Musawi, L., Whisenant, S. & Popescu, S. The use of remote sensing to develop a site history for restoration planning in an arid landscape: Developing site history using remote sensing. Restor. Ecol. 24, 91–99 (2016).Article 

Google Scholar 
13.Kariuki, S., Gallery, R. E., Sparks, J. P., Gimblett, R. & McClaran, M. P. Soil microbial activity is resistant to recreational camping disturbance in a Prosopis dominated semiarid savanna. Appl. Soil Ecol. 147, 103424 (2020).Article 

Google Scholar 
14.Ballantyne, M. & Pickering, C. M. The impacts of trail infrastructure on vegetation and soils: Current literature and future directions. J. Environ. Manag. 164, 53–64 (2015).Article 

Google Scholar 
15.Marion, J. L. & Cole, D. N. Spatial and temporal variation in soil and vegetation impacts on campsites. Ecol. Appl. 6, 520–530 (1996).Article 

Google Scholar 
16.Favretto, N., Luedeling, E., Stringer, L. C. & Dougill, A. J. Valuing ecosystem services in semi-arid rangelands through stochastic simulation. Land Degrad. Dev. 28, 65–73 (2017).Article 

Google Scholar 
17.MalekiSadabadi, Z., Ejtehadi, H., Abrishamchi, P., Vaezi, J. & Erfanian Taleii Noghan, M. B. Comparative study of autecological, morphological, anatomical and karyological characteristics of Acanthophyllum ejtehadii Mahmoudi & Vaezi (Caryophyllaceae): A rare endemic in Iran. Taiwania 62, 321–330 (2017).
Google Scholar 
18.Noroozi, J. et al. Endemic diversity and distribution of the Iranian vascular flora across phytogeographical regions, biodiversity hotspots and areas of endemism. Sci. Rep. 9, 12991 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Erfanian, M. B., Ejtehadi, H., Vaezi, J. & Moazzeni, H. Plant community responses to multiple disturbances in an arid region of northeast Iran. Land Degrad. Dev. 30, 1554–1563 (2019).Article 

Google Scholar 
20.Erfanian, M. B., Sagharyan, M., Memariani, F. & Ejtehadi, H. Predicting range shifts of three endangered endemic plants of the Khorassan-Kopet Dagh floristic province under global change. Sci. Rep. 11, 9159 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Memariani, F. Khorassan-Kopet Dagh Mountains. In Plant biogeography and vegetation of high mountains of central and south-west Asia (ed. Noroozi, J.) (Springer, 2020).
Google Scholar 
22.Noroozi, J. et al. Hotspots within a global biodiversity hotspot—areas of endemism are associated with high mountain ranges. Sci. Rep. 8, 10345 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
23.Manafzadeh, S., Staedler, Y. M. & Conti, E. Visions of the past and dreams of the future in the Orient: The Irano-Turanian region from classical botany to evolutionary studies. Biol. Rev. 92, 1365–1388 (2017).PubMed 
Article 

Google Scholar 
24.Erfanian, M. B. et al. Plant community responses to environmentally friendly piste management in northeast Iran. Ecol. Evol. 9, 8193–8200 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
25.District 9 of Mashhad municipality. Introducing the Khorshid Park. District 9 of Mashhad municipality https://zone9.mashhad.ir/media_gallery/6505295 (2020).26.Djamali, M. et al. Application of the global bioclimatic classification to Iran: Implications for understanding the modern vegetation and biogeography. Ecol. Mediterr. 37, 91–114 (2011).Article 

Google Scholar 
27.Hamedian, M. Investigation of Plant Biodiversity in Najafi Mountains, Mashhad, Khorassan Razavi Province (Ferdowsi University of Mashhad, 2015).
Google Scholar 
28.Kent, M. Vegetation Description and Data Analysis (Wiley, 2012).
Google Scholar 
29.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).MATH 
Book 

Google Scholar 
30.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019).31.de Mendiburu, F. agricolae: Statistical Procedures for Agricultural Research (2020).32.Legendre, P. & Legendre, L. F. J. Numerical Ecology (Elsevier, 2012).MATH 

Google Scholar 
33.Oksanen, J. et al. vegan: Community Ecology Package. (2019).34.Chao, A. et al. Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).Article 

Google Scholar 
35.Chao, A. & Jost, L. Coverage-based rarefaction and extrapolation: Standardizing samples by completeness rather than size. Ecology 93, 2533–2547 (2012).PubMed 
Article 

Google Scholar 
36.Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: An R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).Article 

Google Scholar 
37.Jin, Y. & Qian, H. V. PhyloMaker: An R package that can generate very large phylogenies for vascular plants. Ecography https://doi.org/10.1111/ecog.04434 (2019).Article 

Google Scholar 
38.Barber, N. A. et al. Grassland restoration characteristics influence phylogenetic and taxonomic structure of plant communities and suggest assembly mechanisms. J. Ecol. 107, 2105–2120 (2019).Article 

Google Scholar 
39.Chao, A., Chiu, C.-H. & Jost, L. Unifying species diversity, phylogenetic diversity, functional diversity, and related similarity and differentiation measures through hill numbers. Annu. Rev. Ecol. Evol. Syst. 45, 297–324 (2014).Article 

Google Scholar 
40.Chao, A., Chiu, C.-H. & Jost, L. Phylogenetic diversity measures based on Hill numbers. Philos. Trans. R. Soc. B Biol. Sci. 365, 3599–3609 (2010).Article 

Google Scholar 
41.Chao, A. et al. Rarefaction and extrapolation of phylogenetic diversity. Methods Ecol. Evol. 6, 380–388 (2015).Article 

Google Scholar 
42.Barros, A. & Marina Pickering, C. How networks of informal trails cause landscape level damage to vegetation. Environ. Manag. 60, 57–68 (2017).Article 

Google Scholar 
43.Kissling, M., Hegetschweiler, K. T., Rusterholz, H.-P. & Baur, B. Short-term and long-term effects of human trampling on above-ground vegetation, soil density, soil organic matter and soil microbial processes in suburban beech forests. Appl. Soil. Ecol. 42, 303–314 (2009).Article 

Google Scholar 
44.Mingyu, Y., Hens, L., Xiaokun, O. & Wulf, R. D. Impacts of recreational trampling on sub-alpine vegetation and soils in Northwest Yunnan, China. Acta Ecol. Sin. 29, 171–175 (2009).Article 

Google Scholar 
45.Pickering, C. M. & Growcock, A. J. Impacts of experimental trampling on tall alpine herbfields and subalpine grasslands in the Australian Alps. J. Environ. Manag. 91, 532–540 (2009).Article 

Google Scholar 
46.Roovers, P., Verheyen, K., Hermy, M. & Gulinck, H. Experimental trampling and vegetation recovery in some forest and heathland communities. Appl. Veg. Sci. 7, 111–118 (2004).Article 

Google Scholar 
47.Jägerbrand, A. K. & Alatalo, J. M. Effects of human trampling on abundance and diversity of vascular plants, bryophytes and lichens in alpine heath vegetation, Northern Sweden. Springerplus 4, 95 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
48.Ballantyne, M. & Pickering, C. M. Recreational trails as a source of negative impacts on the persistence of keystone species and facilitation. J. Environ. Manag. 159, 48–57 (2015).Article 

Google Scholar 
49.Hill, W. & Pickering, C. M. Vegetation associated with different walking track types in the Kosciuszko alpine area, Australia. J. Environ. Manag. 78, 24–34 (2006).Article 

Google Scholar 
50.Wilkerson, E. & Whitman, A. Recreation trails in Maine and New Hampshire: A comparison of notorized, non-motorized, and non-mechanized trails. In Proceedings of the 2009 Northeastern Recreation Research Symposium, Vol. 1, 214–222 (U.S. Department of Agriculture, 2010).51.Karim, M. N. & Mallik, A. U. Roadside revegetation by native plants. Ecol. Eng. 32, 222–237 (2008).Article 

Google Scholar 
52.Lembrechts, J. J., Milbau, A. & Nijs, I. Alien roadside species more easily invade alpine than lowland plant communities in a subarctic mountain ecosystem. PLoS ONE 9, e89664 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
53.Lembrechts, J. J. et al. Mountain roads shift native and non-native plant species’ ranges. Ecography 40, 353–364 (2017).Article 

Google Scholar 
54.Farrell, T. A. & Marion, J. L. The protected area visitor impact management (PAVIM) framework: A simplified process for making management decisions. J. Sustain. Tour. 10, 31–51 (2002).Article 

Google Scholar 
55.Jim, C. Y. Camping impacts on vegetation and soil in a Hong Kong country park. Appl. Geogr. 7, 317–332 (1987).Article 

Google Scholar 
56.Nylund, M., Haapanen, A., Kellomäki, S. & Nylund, L. Deterioration of forest ground vegetation and decrease of radial growth of trees on camping sites. Silva Fenn. 13, 343–356 (1979).Article 

Google Scholar 
57.Lembrechts, J. J. et al. Disturbance is the key to plant invasions in cold environments. Proc. Natl. Acad. Sci. 113, 14061–14066 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Nãsi, R. et al. Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sens. 7, 15467–15493. https://doi.org/10.3390/rs71115467 (2015).ADS 
Article 

Google Scholar 
2.Navarro, A. et al. The application of unmanned aerial vehicles (UAVs) to estimate above-ground biomass of mangrove ecosystems. Remote Sens. Environ. 242, 111747. https://doi.org/10.1016/j.rse.2020.111747 (2020).ADS 
Article 

Google Scholar 
3.Reis, B. P. et al. Management recommendation generation for areas under forest restoration process through images obtained by UAV and LiDAR. Remote Sens. 11, 1508. https://doi.org/10.3390/rs11131508 (2019).ADS 
Article 

Google Scholar 
4.Saarinen, N. et al. Assessing biodiversity in boreal forests with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sens. 10, 338. https://doi.org/10.3390/rs10020338 (2018).ADS 
Article 

Google Scholar 
5.Casapia, X. T. et al. Identifying and quantifying the abundance of economically important palms in tropical moist forest using UAV imagery. Remote Sens. 12, 9. https://doi.org/10.3390/rs12010009 (2019).ADS 
Article 

Google Scholar 
6.Li, L. et al. Quantifying understory and overstory vegetation cover using UAV-based RGB imagery in forest plantation. Remote Sens. 12, 298. https://doi.org/10.3390/rs12020298 (2020).ADS 
Article 

Google Scholar 
7.dos Santos, A. A. et al. Assessment of CNN-based methods for individual tree detection on images captured by RGB cameras attached to UAVs. Sensors 19, 3595. https://doi.org/10.3390/s19163595 (2019).ADS 
Article 
PubMed Central 

Google Scholar 
8.Miyoshi, G. T., Imai, N. N., Tommaselli, A. M. G., de Moraes, M. V. A. & Honkavaara, E. Evaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse Atlantic forest. Remote Sens. 12, 244. https://doi.org/10.3390/rs12020244 (2020).ADS 
Article 

Google Scholar 
9.Morales, G. et al. Automatic segmentation of Mauritia flexuosa in unmanned aerial vehicle (UAV) imagery using deep learning. Forests 9, 736. https://doi.org/10.3390/f9120736 (2018).Article 

Google Scholar 
10.Voss, M. & Sugumaran, R. Seasonal effect on tree species classification in an urban environment using hyperspectral data, LiDAR, and an object- oriented approach. Sensors 8, 3020–3036. https://doi.org/10.3390/s8053020 (2008).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
11.Andersen, H.-E., Reutebuch, S. E. & McGaughey, R. J. A rigorous assessment of tree height measurements obtained using airborne lidar and conventional field methods. Can. J. Remote Sens. 32, 355–366. https://doi.org/10.5589/m06-030 (2006).ADS 
Article 

Google Scholar 
12.Ganz, S., Käber, Y. & Adler, P. Measuring tree height with remote sensing—A comparison of photogrammetric and LiDAR data with different field measurements. Forests 10, 694. https://doi.org/10.3390/f10080694 (2019).Article 

Google Scholar 
13.Csillik, O., Cherbini, J., Johnson, R., Lyons, A. & Kelly, M. Identification of citrus trees from unmanned aerial vehicle imagery using convolutional neural networks. Drones 2, 39. https://doi.org/10.3390/drones2040039 (2018).Article 

Google Scholar 
14.Berveglieri, A., Imai, N. N., Tommaselli, A. M., Casagrande, B. & Honkavaara, E. Successional stages and their evolution in tropical forests using multi-temporal photogrammetric surface models and superpixels. ISPRS J. Photogram. Remote Sens. 146, 548–558. https://doi.org/10.1016/j.isprsjprs.2018.11.002 (2018).ADS 
Article 

Google Scholar 
15.Cao, J. et al. Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens. 10, 89. https://doi.org/10.3390/rs10010089 (2018).ADS 
Article 

Google Scholar 
16.Weinstein, B. G., Marconi, S., Bohlman, S., Zare, A. & White, E. Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks. Remote Sens. 11, 1309. https://doi.org/10.3390/rs11111309 (2019).ADS 
Article 

Google Scholar 
17.Torres, D. L. et al. Applying fully convolutional architectures for semantic segmentation of a single tree species in urban environment on high resolution UAV optical imagery. Sensors 20, 563. https://doi.org/10.3390/s20020563 (2020).ADS 
Article 

Google Scholar 
18.Liu, L., Song, B., Zhang, S. & Liu, X. A novel principal component analysis method for the reconstruction of leaf reflectance spectra and retrieval of leaf biochemical contents. Remote Sens. 9, 1113. https://doi.org/10.3390/rs9111113 (2017).ADS 
Article 

Google Scholar 
19.Maschler, J., Atzberger, C. & Immitzer, M. Individual tree crown segmentation and classification of 13 tree species using airborne hyperspectral data. Remote Sens. 10, 1218. https://doi.org/10.3390/rs10081218 (2018).ADS 
Article 

Google Scholar 
20.Hennessy, A., Clarke, K. & Lewis, M. Hyperspectral classification of plants: A review of waveband selection generalisability. Remote Sens. 12, 113. https://doi.org/10.3390/rs12010113 (2020).Article 

Google Scholar 
21.Hamraz, H., Contreras, M. A. & Zhang, J. Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds. Sci. Rep. 7, 1–9. https://doi.org/10.1038/s41598-017-07200-0 (2017).CAS 
Article 

Google Scholar 
22.Cho, M. A. et al. Mapping tree species composition in south African savannas using an integrated airborne spectral and LiDAR system. Remote Sens. Environ. 125, 214–226. https://doi.org/10.1016/j.rse.2012.07.010 (2012).ADS 
Article 

Google Scholar 
23.Apostol, B. et al. Species discrimination and individual tree detection for predicting main dendrometric characteristics in mixed temperate forests by use of airborne laser scanning and ultra-high-resolution imagery. Sci. Total Environ. 698, 134074. https://doi.org/10.1016/j.scitotenv.2019.134074 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
24.Immitzer, M., Atzberger, C. & Koukal, T. Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sens. 4, 2661–2693. https://doi.org/10.3390/rs4092661 (2012).ADS 
Article 

Google Scholar 
25.Franklin, S. E. & Ahmed, O. S. Deciduous tree species classification using object-based analysis and machine learning with unmanned aerial vehicle multispectral data. Int. J. Remote Sens. 39, 5236–5245. https://doi.org/10.1080/01431161.2017.1363442 (2017).Article 

Google Scholar 
26.Dalponte, M., Orka, H. O., Gobakken, T., Gianelle, D. & Naesset, E. Tree species classification in boreal forests with hyperspectral data. IEEE Trans. Geosci. Remote Sens. 51, 2632–2645. https://doi.org/10.1109/tgrs.2012.2216272 (2013).ADS 
Article 

Google Scholar 
27.Guimarães, N. et al. Forestry remote sensing from unmanned aerial vehicles: A review focusing on the data, processing and potentialities. Remote Sens. 12, 1046. https://doi.org/10.3390/rs12061046 (2020).ADS 
Article 

Google Scholar 
28.Kattenborn, T., Eichel, J. & Fassnacht, F. E. Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery. Sci. Rep. 9, 1–9. https://doi.org/10.1038/s41598-019-53797-9 (2019).CAS 
Article 

Google Scholar 
29.Onishi, M. & Ise, T. Explainable identification and mapping of trees using UAV RGB image and deep learning. Sci. Rep. 11, 1–15. https://doi.org/10.1038/s41598-020-79653-9 (2021).CAS 
Article 

Google Scholar 
30.Näsi, R. et al. Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft. Urban For. Urban Green. 30, 72–83. https://doi.org/10.1016/j.ufug.2018.01.010 (2018).Article 

Google Scholar 
31.Nezami, S., Khoramshahi, E., Nevalainen, O., Pölönen, I. & Honkavaara, E. Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks. Remote Sens. 12, 1070. https://doi.org/10.3390/rs12071070 (2020).ADS 
Article 

Google Scholar 
32.Nevalainen, O. et al. Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sens. 9, 185. https://doi.org/10.3390/rs9030185 (2017).ADS 
Article 

Google Scholar 
33.Raczko, E. & Zagajewski, B. Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images. Eur. J. Remote Sens. 50, 144–154. https://doi.org/10.1080/22797254.2017.1299557 (2017).Article 

Google Scholar 
34.Tuominen, S. et al. Assessment of classifiers and remote sensing features of hyperspectral imagery and stereo-photogrammetric point clouds for recognition of tree species in a forest area of high species diversity. Remote Sens. 10, 714. https://doi.org/10.3390/rs10050714 (2018).ADS 
Article 

Google Scholar 
35.Xie, Z., Chen, Y., Lu, D., Li, G. & Chen, E. Classification of land cover, forest, and tree species classes with ZiYuan-3 multispectral and stereo data. Remote Sens. 11, 164. https://doi.org/10.3390/rs11020164 (2019).ADS 
Article 

Google Scholar 
36.Maxwell, A. E., Warner, T. A. & Fang, F. Implementation of machine-learning classification in remote sensing: an applied review. Int. J. Remote Sens. 39, 2784–2817. https://doi.org/10.1080/01431161.2018.1433343 (2018).ADS 
Article 

Google Scholar 
37.Osco, L. P. et al. Predicting canopy nitrogen content in citrus-trees using random forest algorithm associated to spectral vegetation indices from UAV-imagery. Remote Sens. 11, 2925. https://doi.org/10.3390/rs11242925 (2019).ADS 
Article 

Google Scholar 
38.Marrs, J. & Ni-Meister, W. Machine learning techniques for tree species classification using co-registered LiDAR and hyperspectral data. Remote Sens. 11, 819. https://doi.org/10.3390/rs11070819 (2019).ADS 
Article 

Google Scholar 
39.Imangholiloo, M. et al. Characterizing seedling stands using leaf-off and leaf-on photogrammetric point clouds and hyperspectral imagery acquired from unmanned aerial vehicle. Forests 10, 415. https://doi.org/10.3390/f10050415 (2019).Article 

Google Scholar 
40.Pham, T., Yokoya, N., Bui, D., Yoshino, K. & Friess, D. Remote sensing approaches for monitoring mangrove species, structure, and biomass: Opportunities and challenges. Remote Sens. 11, 230. https://doi.org/10.3390/rs11030230 (2019).ADS 
Article 

Google Scholar 
41.Ma, L. et al. Deep learning in remote sensing applications: A meta-analysis and review. ISPRS J. Photogram. Remote Sens. 152, 166–177. https://doi.org/10.1016/j.isprsjprs.2019.04.015 (2019).ADS 
Article 

Google Scholar 
42.Safonova, A. et al. Detection of fir trees (Abies sibirica) damaged by the bark beetle in unmanned aerial vehicle images with deep learning. Remote Sens. 11, 643. https://doi.org/10.3390/rs11060643 (2019).ADS 
Article 

Google Scholar 
43.Kamilaris, A. & Prenafeta-Boldú, F. X. Deep learning in agriculture: A survey. Comput. Electron. Agric. 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016 (2018).Article 

Google Scholar 
44.Khamparia, A. & Singh, K. M. A systematic review on deep learning architectures and applications. Exp. Syst. 36, e12400. https://doi.org/10.1111/exsy.12400 (2019).Article 

Google Scholar 
45.Sothe, C. et al. Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data. GISci. Remote Sens. 57, 369–394. https://doi.org/10.1080/15481603.2020.1712102 (2020).Article 

Google Scholar 
46.Redmon, J. & Farhadi, A. Yolov3: An incremental improvement (2018). arXiv:1804.02767.47.Lin, T.-Y., Goyal, P., Girshick, R., He, K. & Dollár, P. Focal loss for dense object detection (2018). arXiv:1708.0200248.Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks (2016). arXiv:1506.0149749.Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition (2015). arXiv:1409.155650.Sylvain, J.-D., Drolet, G. & Brown, N. Mapping dead forest cover using a deep convolutional neural network and digital aerial photography. ISPRS J. Photogram. Remote Sens. 156, 14–26. https://doi.org/10.1016/j.isprsjprs.2019.07.010 (2019).ADS 
Article 

Google Scholar 
51.Hartling, S., Sagan, V., Sidike, P., Maimaitijiang, M. & Carron, J. Urban tree species classification using a WorldView-2/3 and LiDAR data fusion approach and deep learning. Sensors 19, 1284. https://doi.org/10.3390/s19061284 (2019).ADS 
Article 
PubMed Central 

Google Scholar 
52.Culman, M., Delalieux, S. & Tricht, K. V. Individual palm tree detection using deep learning on RGB imagery to support tree inventory. Remote Sens. 12, 3476. https://doi.org/10.3390/rs12213476 (2020).ADS 
Article 

Google Scholar 
53.Aburasain, R. Y., Edirisinghe, E. A. & Albatay, A. Palm tree detection in drone images using deep convolutional neural networks: Investigating the effective use of YOLO v3. In Digital Interaction and Machine Intelligence, 21–36, https://doi.org/10.1007/978-3-030-74728-2_3 (Springer International Publishing, 2021).54.Bortolotto, I. M., Damasceno-Junior, G. A. & Pott, A. Preliminary list of native food plants from mato grosso do sul, brazil. Iheringia, Série Botânica 73, 101–116 (2018). https://doi.org/10.21826/2446-8231201873s10155.van der Hoek, Y., Solas, S. Á. & Peñuela, M. C. The palm Mauritia flexuosa, a keystone plant resource on multiple fronts. Biodiver. Conserv. 28, 539–551. https://doi.org/10.1007/s10531-018-01686-4 (2019).Article 

Google Scholar 
56.Agostini-Costa, T. d. S., Faria, J. P., Naves, R. V. & Vieira, R. F. Espécies Nativas da Flora Brasileira de Valor Econômico Atual ou Potencial Plantas para o Futuro – Região Centro-Oeste (Ministério do Meio Ambiente – MMA, 2016).57.Djerriri, K., Ghabi, M., Karoui, M. S. & Adjoudj, R. Palm trees counting in remote sensing imagery using regression convolutional neural network. In IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 2627–2630 (2018). https://doi.org/10.1109/IGARSS.2018.851918858.Osco, L. P. et al. A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery. ISPRS J. Photogram. Remote Sens. 160, 97–106. https://doi.org/10.1016/j.isprsjprs.2019.12.010 (2020).ADS 
Article 

Google Scholar 
59.Goldman, E. et al. Precise detection in densely packed scenes (2019). arXiv:1904.0085360.Holm, J. A., Miller, C. J. & Cropper, W. P. Population dynamics of the dioecious amazonian palm Mauritia flexuosa: Simulation analysis of sustainable harvesting. Biotropica 40, 550–558. https://doi.org/10.1111/j.1744-7429.2008.00412.x (2008).Article 

Google Scholar 
61.Zhao, H., Shi, J., Qi, X., Wang, X. & Jia, J. Pyramid scene parsing network (2017). arXiv:1612.01105 More

Soil chemical propertiesThe total soil carbon and nitrogen content, pH and total petroleum hydrocarbons (TPH) in the soils of the study sites are presented in Table 1. The acidity of the soil at the UF site varied from 4.4 to 5.1, the nitrogen content varied from 0.65 to 1.45% and the carbon content varied from 20 to 45%, which is typical for soils of the taiga zone31. The acidity of the soils in sites contaminated with TPH was generally slightly higher and varied from 4.6 to 5.6 (Table 1). The nitrogen and carbon content were significantly (p  More

Batrachochytrium salamandrivorans (B. salamandrivorans) culture conditions and zoospore isolationB. salamandrivorans type strain (AMFP 13/01)8 was grown in tryptone-gelatin hydrolysate-lactose (TGhL) broth and incubated for 5−7 days at 15 °C. Zoospores were harvested by replacing the TGhL broth with distilled water. The collected water was filtered through a sterile mesh filter with pore size 10 µm (Pluristrainer, PluriSelect) to remove sporangia. Zoospore viability and mobility were confirmed using light microscopy.Salamander skin lysate binding assayBinding of B. salamandrivorans spores to the protein or carbohydrate fractions from fire salamander (Salamandra salamandra) skin was tested by treating fire salamander sloughed skin lysates enzymatically with glycoside hydrolases, followed by protein precipitation. An overview of the skin lysate binding assay is shown in Supplementary Fig. 3.To collect the sloughed skin, ten captive-bred adult fire salamanders were housed at 15 ± 1 °C on moist tissue. The sloughed skin samples were ground with liquid nitrogen into a fine powder and then homogenized, using 3 ml RadioImmunoprecipitation assay (RIPA) buffer (Sigma-Aldrich) per gram of tissue. Samples were incubated for 1 h at 4 °C, centrifuged at 27.000 × g for 10 min and the supernatant was subsequently collected. Protein concentration was determined using the PierceTM BCA Protein Assay Kit (Thermo Fisher Scientific). The obtained skin lysate was equally divided, one part was treated with Protein Deglycosylation Mix II and two parts were kept as crude skin lysates. Protein Deglycosylation Mix II (New England BioLabs) was used to remove N-linked and O-linked glycans from glycoproteins. According to the manufacturer’s instructions, 5 µl 10× Deglycosylation Mix Buffer I and 5 µl Protein Deglycosylation Mix II were added to 40 µl skin lysate. The mixture was incubated at 37 °C for 16 h. Protein precipitation was conducted on the redundant Protein Deglycosylation Mix II treated and crude skin lysates. The precipitation was performed by slowly adding saturated ammonium sulfate solution to the skin lysates to achieve a final concentration of 75%. Samples were then centrifuged at 21.130 × g for 30 min to separate the precipitated proteins from the supernatant. The precipitated protein pellets were resuspended in 300 µl of 0.05 M carbonate−bicarbonate coating buffer (3.7 g NaHCO3, 0.64 g Na2CO3, 1 L distilled water, pH 9.6). Each skin lysate solution was adjusted to the volume of 300 µl by adding a coating buffer. One hundred µl of each skin lysate solution was coated in each well of 96-well polystyrene microtiter plates (MaxiSorpTM plate, Thermo Fisher Scientific) in three technical replicates. As controls, coating buffer (negative control) and 75% ammonium sulfate solution were also coated on the 96-well plates. After incubation at 4 °C for 24 h the coated plates were washed three times with washing buffer (0.01 M PBS-Tween 20, pH 7.4) and blocked with 1% BSA overnight at 4 °C. Plates were then again washed three times with washing buffer and three times with distilled water. One hundred µl of B. salamandrivorans zoospore suspension (1 × 107 zoospores per ml) were added in each well. Plates were incubated for 20 min at 15 °C and washed five times with distilled water to remove the unbound zoospores. Digital photographs were taken through via an inverted light microscope at 100 × magnification. Five pictures were taken for each well and zoospores in each photograph were counted in a blind fashion. Three independent repeats of the experiment were conducted (biological replicates).Carbohydrate binding assayTo further determine which carbohydrates expressed on fire salamander sloughed skin can mediate the binding of B. salamandrivorans zoospores, B. salamandrivorans binding against four carbohydrates; N-acetylglucosamine (GlcNAc), N-acetylgalactosamine (GalNAc), mannose, and lactose was tested. The three monosaccharides and the disaccharide (Sigma-Aldrich) were dissolved and thereafter diluted in coating buffer to achieve a concentration of 5% (w/v). Then they were coated in triplicate wells by incubating at 4 °C for 24 h42. Plates were rinsed three times with washing buffer and blocked with 1% BSA overnight at 4 °C.Hundred μl of B. salamandrivorans zoospore suspension (1 × 107 zoospores per ml) was added in each well and incubated for 20 min at 15 °C. After washing the wells five times with distilled water to remove unbound zoospores, the plates were evaluated using a light microscope. Digital photographs were taken at 100 × magnification. Five pictures were taken for each well and zoospores in each photograph were counted in a blind fashion. Three independent repeats of the experiment were conducted (biological replicates).In this experiment the highest level of B. salamandrivorans spores binding to lactose was observed. Lactose is a dissacharide consisting of glucose and galactose. Therefore, in the following experiments galactose, glucose and their derivatives will be tested separately.Carbohydrate chemotaxis testChemotaxis of B. salamandrivorans toward free carbohydrates was tested as previously explained (Supplementary Fig. 4)12. The sugars D-Glucose (Sigma-Aldrich), D-mannose (Sigma-Aldrich), Lactose (Sigma-Aldrich), and D-galactose (Sigma-Aldrich) were tested as attractant for B. salamandrivorans. The monosaccharides instead of the amide derivatives were used in this experiment to exclude any chemotactic signalling activity of the amides. Sugars were dissolved in distilled water, filter sterilized, and tested at a 0.1 M concentration. Hematocrit capillaries (75 mm length; Hirschmann laborgeräte, Eberstadt, Germany) were filled with 60 µl carbohydrate solution, vehicle control capillaries with 60 µl sterile distilled water. To prevent leakage, the capillaries were sealed with wax plugs (Hirschmann laborgeräte, Eberstadt, Germany) at one side. Each capillary was swiped on the outside with lens paper (Kimtech Science, Kimberley Clark, Roswell, GA, USA) to remove possible attractant spillover. Capillaries were incubated in 400 µl inoculum containing 106 B. salamandrivorans zoospores in water and placed in a holder inclined about 65° upwards. The assay was incubated for 90 min at 15 °C, after which the capillaries were removed and swiped again at the outside to remove B. salamandrivorans zoospores possibly adhering on the outside. Inocula were checked for motility of the zoospores using an inverted microscope (Olympus CKX 41, Hamburg, Germany). Contents of the capillaries were collected and centrifuged for 2 min at 16.000 × g. The supernatant was removed as much as possible. The pellet was suspended in 100 µl Prepman Ultra Sample Preparation reagent (Applied Biosystems, Life Technologies Europe, Ghent, Belgium) and DNA was extracted according to the manufacturer’s guidelines. For each sample, the number of B. salamandrivorans zoospores was quantified using quantitative real-time PCR (qPCR)41, and data were analyzed using the Bio-Rad CFX manager 3.1. The primers and probe can be found in Supplementary Table 11. Within each assay, all carbohydrates and negative controls were tested at least in triplicate (technical replicates) and three independent repeats of the assay were performed (biological replicates).Carbohydrate transcriptome testRNA preparation: total RNA was isolated from B. salamandrivorans zoospores treated with different carbohydrates. Therefore, newly released zoospores (less than 2 h after induction of spore release by adding water) were harvested from 175 cm2 cell culture flasks by replacing the TGhL broth with distilled water, which was filtered using a sterile mesh filter with pore size 10 µm (Pluristrainer, PluriSelect). Six-biological replicates containing 4 × 107 zoospores were obtained. Each biological replicate consisted of a pool of spores harvested from three cell culture flasks. Per biological replicate, the spores were divided into 4 eppendorfs (107 zoospores/eppendorf) which were treated for 1 h at 15 °C with H2O (control), 50 mM (D-galactose), 50 mM (D-glucose), or 50 mM (D-mannose) (Supplementary Fig. 5). After 1 h, the zoospores were centrifuged for 5 min at 4.000 × g at 15 °C to remove the supernatant, after which RNA was extracted using the RNeasy mini kit (Qiagen)18. The RNA was treated with Turbo™ DNase (Ambion), following the manufacturer’s instructions. RNA degradation and contamination were monitored on 1% agarose gels. The RNA purity was checked using the NanoPhotometer® spectrophotometer (IMPLEN, CA, USA). Finally, the RNA integrity and quantitation were assessed using the RNA Nano 6000 assay kit of the Bioanalyzer 2100 system (Agilent Technologie, CA, USA).Library preparation for transcriptome sequencing: Whole-transcriptome sequencing libraries were constructed and sequenced on the Illumina HiSeq platform (Novogen, China). A total amount of 1 μg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, USA) following the manufacturer’s recommendations and index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer (5X). First-strand cDNA was synthesized using random hexamer primer and M-MuLV Reverse Transcriptase (RNase H-). Second strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3′ ends of DNA fragments, NEBNext Adaptor with hairpin loop structure was ligated to prepare for hybridization. In order to select cDNA fragments of preferentially 150−200 bp in length, the library fragments were purified with AMPure XP system (Beckman Coulter, Beverly, USA). Then 3 μl USER Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at 37 °C for 15 min followed by 5 min at 95 °C before PCR. Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers, and Index (X) Primer. At last, PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system.Clustering and sequencing: The clustering of the index-coded samples was performed on a cBot Cluster Generation System using PE Cluster Kit cBot-HS (Illumina) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina platform and paired-end reads were generated.Quality analysis, mapping, and assembly: Raw data (raw reads) of FASTQ format were first processed through fastp (version 0.20.0). In this step, clean data (clean reads) were obtained by removing reads containing adapter and poly-N sequences and reads with low quality from raw data. At the same time, Q20, Q30, and GC content of the clean data were calculated (Supplementary Table 12). All the downstream analyses were based on the clean data with high quality. Reference genome and gene model annotation files were downloaded from genome website browser (NCBI/UCSC/Ensembl) directly. Paired-end clean reads were mapped to the B. salamandrivorans reference genome using HISAT2 (version 2.0.5) software18. Featurecounts (version 1.5.0-p3) were used to count the read numbers mapped to each gene, including known and novel genes (Supplementary Table 13). And then RPKM (reads per kilobase per million) of each gene was calculated based on the length of the gene and reads count mapped to this gene.Gene expression, differential expression, enrichment, and coexpression- analysis: Differential expression analysis was performed using the DESeq2 R package43. The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate (FDR). Genes with an adjusted P-value < 0.05 found by DESeq2 were assigned as differentially expressed. Protein domains were annotated with PFAM version 27 and 33 and KEGG domains, Gene Ontology (GO) enrichment analysis of differentially expressed genes was implemented by the clusterProfiler R package44 and dcGOR R package45. GO terms with corrected P-value less than 0.05 were considered significantly enriched by differential expressed genes. ClusterProfiler R package44 was also used to test the statistical enrichment of differentially expressed genes in KEGG pathways.Detection of protease activityThe influence of carboydrate exposure on protease activity of B. salamandrivorans zoospores was assessed. Therefore, zoospores were harvested from 175 cm2 cell culture flasks by replacing the TGhL broth with distilled water, which was filtered using a sterile mesh filter with pore size 10 µm (Pluristrainer, PluriSelect). A pool containing approximately 5 × 107 zoospores/ ml was obtained. 200 µl of the spore suspension (107 spores) was added to eppendorfs containing 200 µl H2O (H2O; n = 3), 200 µl 100 mM D-Glucose (Glc; n = 3), 200 µl 100 mM D-mannose (Man; n = 3), 200 µl 100 mM D-galactose (Gal; n = 3), or as a control, 200 µl H2O containing protease inhibitor mix (P8215, Sigma-Aldrich) (PI; n = 3). After 1.5 h at 15 °C, the zoospores were centrifuged for 5 min at 4.000 × g at 15 °C and the supernatant was collected. Protease activity in the supernatant was analyzed using the Pierce Fluorescent Protease Assay Kit (Thermo Fisher Scientific), according to the manufacturer’s instructions. Three independent repeats of the experiment were performed (biological replicates).Identification of B. salamandrivorans lectin genesPotential candidates of carbohydrate-binding molecules (CBMs) were identified in the B. salamandrivorans (AMFP) genome listed in the NCBI database (Bioproject PRJNA311566).B. salamandrivorans (AMFP 13/01) coding regions from the single annotated genome present on NCBI database (Bioproject PRJNA311566) were used to single out potential lectin genes of interest that could serve as genes of carbohydrate-binding proteins. The lectin candidates were identified with BLASTp (BLAST + 2.9.0) over the FungiDB database (constituting 199 candidates, database accessed 1st March 2018) using the stringent e-value cutoff of 1e−50 to avoid spurious hits46,47.From these, five candidates that referred to lectins and carbohydrate-binding were manually selected using the NCBI CDD (v3.16) conserved domain software with default settings48.Expression of two of these genes (BSLG_00833 and BSLG_02674) was confirmed by a previous mRNA expression analysis (Bioproject PRJNA311566)18.AnimalsThe animal experiments were performed following the European law and with the approval of the ethical committee of the Faculty of Veterinary Medicine (Ghent University EC) (EC2015/86). Only captive bred animals were used. Fire salamander larvae belonging to different life stages49 were used in a B. salamandrivorans infection trial.For lectin-histochemical staining, skin samples were collected from amphibian species Salamandra salamandra (n = 10), Ichthyosaura alpestris (n = 12), Lissotriton helveticus (n = 13), Pleurodeles waltl (n = 11), Lissotriton boscai (n = 3), Alytes obstetricans (n = 10), Cynops pyrrhogaster (n = 3), Triturus anatolicus (n = 3), Triturus marmoratus (n = 3), Calotriton asper (n = 10), Bombina variegata (n = 5), Rana temporaria (n = 10), Epidalea calamita (n = 5), Pelobates fuscus (n = 5) and Salamandra lanzai (n = 3). Tail or toe clips, ventral and dorsal skin samples were collected from animals that were euthanized with natrium pentobarbital 20% (KELA). The collected samples were immediately fixed in Bouin’s solution for 24 h.Mucosome samples were collected by bathing animals in HPLC-grade water for 1 h from 21 amphibian species (different animals as the ones used for the tissueclips), namely Lissotriton helveticus (n = 3), Pleurodeles waltl (n = 3), Lissotriton boscai (n = 3), Triturus anatolicus (n = 3), Triturus marmoratus (n = 3), Cynops pyrrhogaster (n = 3), Ichthyosaura alpestris (n = 3), Salamandra salamandra (n = 3), Lyciasalamandera helverseni (n = 3), Speleomantes strinatii (n = 2), Paramesotriton hongkongensis (n = 2), Plethodon glutinosus (n = 2), Chioglossa lusitanica (n = 3), Pachyhynobius shangchengensis (n = 3), Calotriton asper (n = 3), Salamandra algira (n = 3), Salamandra lanzai (n = 2), Alytes obstetricans (n = 3), Bombina variegata (n = 2), Epidalea calamita (n = 3) and Pelobates fuscus (n = 3).Exposure of fire salamander larvae and metamorphs to B. salamandrivorans Twenty-two early-stage and 26 late-stage larvae49,50 were inoculated with 1.5 × 105 B. salamandrivorans spores per ml water during 24 h. Ten days after the inoculation all the early-stage and sixteen late-stage larvae were euthanized. The two hind legs were analyzed by qPCR to detect the B. salamandrivorans GE load. A tail clip was stained with fluorescein-labelled RCA I (see below). Ten late-stage larvae were further kept until five weeks after metamorphosis.Six one-week-old fire salamander metamorphs were inoculated with 1 ml of water containing 1.5 × 105 spores for 24 h. The animals were euthanized 10 days after inoculation. The two hind legs were analyzed by qPCR to detect the B. salamandrivorans GE load. A tail clip was stained with fluorescein labelled RCA I (see below).Lectin-histochemical stainingFluorescein labelled RCA I (Ricinus communis agglutinin I) (Vector Laboratories) and Con A (Concanavalin A) has been used to detect the expression of galactose and mannose or glucose in the epidermis of amphibians38.After 24 h fixation in Bouin’s medium (Sigma-Aldrich), samples were washed first with tap water until the water ran colourless, then washed for 24 h in 70% ethanol saturated with lithium carbonate (Sigma-Aldrich) to remove picric acid. Tissues were then dehydrated in a graded ethanol series, cleared in xylene, embedded in paraffin, and sectioned in 4−6 µm slices. Before lectin staining, the sections were deparaffinized in xylene and hydrated in a series of ethyl alcohols. For better presenting the carbohydrate antigens, we performed antigen retrieval by submerging slides in citrate buffer (10 mM citric acid, pH 6.0) and heat treating in microwave (850 W for 3.5 min plus 450 W for 10 min). The slides were rinsed with PBS (0.01 M, pH 7.4) and immersed in 1% BSA (Sigma-Aldrich) for 15 min, to prevent non-specific lectin binding. Subsequently, the sections were incubated with either lectin RCA I (15 µg/ml) or lectin Con A (5 µg/ml) for 30 min. Lectins were diluted with lectin binding buffer (10 mM Hepes, 0.15 M NaCl, pH 7.5). As a negative control, lectin RCA I was mixed with 200 mM galactose, and lectin Con A was mixed with 200 mM mannose + 200 mM glucose, before incubating with skin sections to inhibit lectin binding. For positive control, a slide of fire salamander ventral skin sample for RCA staining, and midewife toad ventral skin sample for Con A staining, was included in each experiment. The slides were then washed in PBS, and cell nuclei were stained with 10 µg/ml Hoechst 33342 Solution (Invitrogen). Coverslips were mounted with ProlongTM Gold Antifade Reagent (Invitrogen). Staining results were observed using a Leica fluorescence microscope under 10× magnification, with a 450−490 nm BP excitation filter for lectin staining and a 355−425 nm BP excitation filter for Hoechst staining. Staining pictures were taken using Leica Application Suite (LAS) X software. The lectin staining intensities were classified as intense (3), strong (2), weak (1), or negative (0) staining (Supplementary Fig. 6). Experimental positive and negative controls were defined as intense (3) and negative (0) stained, respectively, and other slides were then evaluated in comparison to the set parameters. Hoechst staining results were paired with corresponding lectin staining results, making it easier to discern the tissue structure from the dark background. The fluorescent intensities were scored by three reviewers, respectively scoring the same dataset of pictures blinded three separate times, and the mean value was taken as the final result.Free galactose, mannose, and total carbohydrates in amphibian mucosomeMucus was collected from 21 amphibian species (see above). The animal body surface and volume of bathing water were calculated as follows: surface area of anuran species in cm2 = 9.9* (mass in g)^(0.56), surface area of urodelan species in cm2 = 8.42* (mass in g)^(0.694), and the quantity of HPLC-grade water to add to both anuran and urodelan species was determined by dividing the surface area by 4), and animals were bathed in respective amounts of HPLC-grade water for 1 h40,51. Animal washes were collected and concentrated by SpeedVac Vacuum Concentrators (Thermo Fisher Scientific) to 100 µl. The quantities of free galactose, mannose, and total carbohydrates in 100 µl of concentrated animal wash were measured using the Galactose Assay Kit (Abcam), Mannose ELISA Kit (Aviva Systems Biology), and Total Carbohydrates Assay Kit (Abcam), as per instructions. Concentrations of free galactose, mannose, and total carbohydrates in animal washes were divided by animal body surface to get the final results of sugar concentrations per square centimetre of the body surface.Statistical analysisStatistical analyses of fire salamander skin lysate binding assay, carbohydrate-binding assay, chemotaxis assay, and protease activity assay were performed using R version 4.0.3. To account for the experimental design, Generalized Linear Mixed Models (GLMM, R library lme452) were used, specifying a nested random effect whereby technical replicates are nested within biological replicates. Count data were modelled first using a Poisson distribution, but as significant overdispersion was present in the data, a negative binomial error structure was implemented. For the protease activity assay, data do not represent counts and a log transformation on the raw values were used to ensure normality of model residuals (Shapiro-Wilk W  > 0.95) allowing a Gaussian error structure (i.e., a Linear Mixed Model (LMM). To test for differences between categories, the (G)LMMs were directly fed to the glht function of the R library multcomp53, setting up contrasts for Tukey’s all-pair comparisons, resulting in Bonferroni-corrected p-values adjusted for multiple testing. Statistical analyses of the larvae infection trial were performed in R version 4.0.0, with tidyverse54 version 1.3.0, MASS55 version 7.3-51.6, VGAM56 version 1.1-3, DHARMa57 version 0.3.1 and glmmTMB58 version 1.0.2.1. Infection loads of larvae and metamorphs were compared, using the Wilcoxon rank sum test, formula Chytrid GE load ~ larvae vs metamorph status, from the stats package. The correlation between larvae Ricinus communis agglutinin (RCA) scoring (1 = weak staining, 2 = strong staining, 3 = intense staining) and infection load was performed using the glm() function on log-transformed genomic equivalents with formula log10(B. salamandrivorans load in Genomic equivalents)~ RCA score, treating RCA score as an ordered factor with guassian distribution. As non-transformed chytrid loads showed zero-inflation and overdispersion, we also fit a generalized linear model with negative binomial distribution (GE load ~ RCA score) with RCA score as an ordered factor, using glmmTMB with a zero-inflation model (~ RCA score), which showed a comparable positive correlation between RCA score and GE load (conditional model coefficient = 5.67, p = 0.003, zero-inflation model coefficient = −2.21, p = 0.016). Residuals and chi-square test indicated the negative binomial model was not a significant improvement and so the simpler generalized linear model on transformed data was included. RCA scoring and larval stage prediction probabilities in Fig. 4c were generated by polr(RCA score ~ life stage) from MASS. Model fit and appropriateness was tested using Chisq test (p = 0.003), the model fit compared favourably to a more complex multinomial logit model and a model fit based on 70% of the data predicted 70−75% of remaining data (when data repeatedly sampled with different seeds, with the final model fit to all data).The regression and correlation analyses of different amphibian species were performed in SPSS (IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY, USA). Correlations of RCA scores with B. salamandrivorans infection peak loads, mortality rates, and percentage of free galactose were calculated by two-tailed Point-Biserial Correlation (p  More

WHERE I WORK
04 October 2021

The Māori meeting house that’s also a research lab

Ocean Mercier researches how Indigenous knowledge and Western science can help resolve environmental issues.

James Mitchell Crow

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James Mitchell Crow

James Mitchell Crow is a freelance writer in Melbourne, Australia.

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Ocean Mercier is an associate professor at the Victoria University of Wellington, Aotearoa, New Zealand.Credit: Chevron Hassett for Nature

The wharenui behind me in the photograph is in the heart of Victoria University of Wellington, where I lead the school of Māori studies. The detailed carvings, paintings and weavings are a library of traditional knowledge and understanding. The tongues poking from the carved faces on the meeting house might look fierce, but the Māori primarily had an oral culture, and the tongue symbolizes knowledge. The bigger the tongue, the more history, narrative and knowledge there is.I am Māori, and descend from the Ngāti Porou tribe. I research the nexus of Māori knowledge and Western science, and how we can draw the best from both knowledge systems to resolve environmental issues.In 2016, the town of Havelock North suffered a disease outbreak caused by livestock faeces seeping into groundwater. We aim to prevent a recurrence through a better understanding of groundwater and springs. Before the affected area began to be drained for agriculture in the 1870s, it was swampland, and Māori people travelled on the waterways. We might find written reports on spring flow going back 70 years, but Māori knowledge can go back nearly 1,000 years. We are looking at ways to access the knowledge captured in carvings and oral histories — mainly by talking to people who could point out features such as where they swam as a child or gathered eels or cress — to tell us where water once flowed.Another project looks at marine heatwaves, including changes in ocean currents due to climate change. Māori ancestors journeyed across these seas. There is knowledge of ocean currents there, if we can unlock it.In the geometric panels in the photograph, the white triangular ‘teeth’ symbolize strength though unity. I think of Māori knowledge as helping to constrain the scientific data so that they can make better predictions. We want to get to a place where the wider research community realizes that we can’t solve these climate problems with one knowledge system alone.

Nature 598, 228 (2021)
doi: https://doi.org/10.1038/d41586-021-02697-y

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