Soil nematode abundance and functional group composition at a global scale

Soil organisms are a crucial part of the terrestrial biosphere. Despite their importance for ecosystem functioning, few quantitative, spatially explicit models of the active belowground community currently exist. In particular, nematodes are the most abundant animals on Earth, filling all trophic levels in the soil food web. Here we use 6,759 georeferenced samples to generate a mechanistic understanding of the patterns of the global abundance of nematodes in the soil and the composition of their functional groups. The resulting maps show that 4.4 ± 0.64 × 1020 nematodes (with a total biomass of approximately 0.3 gigatonnes) inhabit surface soils across the world, with higher abundances in sub-Arctic regions (38% of total) than in temperate (24%) or tropical (21%) regions. Regional variations in these global trends also provide insights into local patterns of soil fertility and functioning. These high-resolution models provide the first steps towards representing soil ecological processes in global biogeochemical models and will enable the prediction of elemental cycling under current and future climate scenarios.

Saved in:
Bibliographic Details
Main Authors: Hoogen, Johan van den, Geisen, Stefan, Routh, Devin, Ferris, Howard, Traunspurger, Walter, Wardle, David A., Goede, Ron G. M. de, Adams, Byron J., Ahmad, Wasim, Andriuzzi, Walter S., Bardgett, Richard D., Bonkowski, Michael, Campos-Herrera, R., Cares, Juvenil E., Caruso, Tancredi, Brito Caixeta, Larissa de, Chen, Xiaoyun, Costa, Sofia R., Creamer, Rachel, Cunha Castro, José Mauro da, Dam, Marie, Djigal, Djibril, Escuer, M., Griffiths, Bryan S., Gutiérrez, Carmen, Hohberg, Karin, Kalinkina, Daria, Kardol, Paul, Kergunteuil, Alan, Korthals, Gerard, Krashevska, Valentyna, Kudrin, Alexey A., Li, Qi, Liang, Wenju, Magilton, Matthew, Marais, Mariette, Rodríguez Martín, José Antonio, Matveeva, Elizaveta, Mayad, El Hassan, Mulder, Christian, Mullin, Peter, Neilson, Roy, Nguyen, T. A. Duong, Nielsen, Uffe N., Okada, Hiroaki, Palomares Rius, Juan E., Pan, Kaiwen, Peneva, Vlada, Pellissier, Loïc, Pereira da Silva, Julio Carlos, Pitteloud, Camille, Powers, Thomas O., Powers, Kirsten, Quist, Casper W., Rasmann, Sergio, Sánchez Moreno, Sara, Scheu, Stefan, Setälä, Heikki, Sushchuk, Anna, Tiunov, Alexei V., Trap, Jean, Putten, Wim van der, Vestergård, Mette, Villenave, Cecile, Waeyenberge, Lieven, Wall, Diana H., Wilschut, Rutger, Wright, Daniel G., Yang, Jiue-in, Crowther, Thomas Ward
Other Authors: Netherlands Organization for Scientific Research
Format: artículo biblioteca
Language:English
Published: 2019-08-08
Subjects:Biogeography, Ecological modelling, Machine learning,
Online Access:http://hdl.handle.net/10261/193342
http://dx.doi.org/10.13039/501100000270
http://dx.doi.org/10.13039/501100002261
http://dx.doi.org/10.13039/501100001807
http://dx.doi.org/10.13039/501100000923
http://dx.doi.org/10.13039/501100002367
http://dx.doi.org/10.13039/501100003593
http://dx.doi.org/10.13039/501100000780
http://dx.doi.org/10.13039/501100004901
http://dx.doi.org/10.13039/501100001871
http://dx.doi.org/10.13039/501100001655
http://dx.doi.org/10.13039/501100001659
http://dx.doi.org/10.13039/100011150
http://dx.doi.org/10.13039/501100003329
http://dx.doi.org/10.13039/501100001809
http://dx.doi.org/10.13039/100000001
Tags: Add Tag
No Tags, Be the first to tag this record!