Community ecology in the age of multivariate multiscale spatial analysis

By Stéphane Dray, Raphaël Pélissier, Pierre Couteron, Marie-Josée Fortin, Pierre Legendre, P. Peres‐Neto, Edwige Bellier, Roger S. Bivand, F. Guillaume Blanchet, M. de Cáceres and 7 more. 2012

655 citations recorded by the source corpus.

Subfields: Ecology, Evolution, Behavior and Systematics, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/3c4e00cb3c61f16b8ee80bec08de0c7e984b133b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology, Evolution, Behavior and Systematics

1. A. Norberg
2. Otso Ovaskainen
3. N. Abrego
4. Gleb Tikhonov
5. David B. Dunson
6. J. Heino
7. T. Datry
8. J. Alahuhta

In Global and Planetary Change

1. J. Heino
2. J. Soininen
3. J. Alahuhta
4. Adriano S. Melo
5. F. Guillaume Blanchet
6. Otso Ovaskainen
7. K. Tolonen
8. Luis Mauricio Bini

In Nature and Landscape Conservation

1. J. Heino
2. J. Soininen
3. Luis Mauricio Bini
4. T. Erős
5. N. Abrego
6. J. Alahuhta
7. Otso Ovaskainen
8. A. Norberg

Citations in the corpus

• Local environment and space drive multiple facets of stream macroinvertebrate beta diversity (2018, methodology): “The db‐MEM spatial variables were obtained using the function PCNM in the R package PCNM (Legendre, Borcard, Blanchet, & Dray, 2012).”
• Testing the Mantel statistic with a spatially‐constrained permutation procedure (2019, background): “…2011): with The variable x can thus be entirely decomposed on the orthogonal basis of MEM as follows: This decomposition allows to define a scalogram (Dray et al., 2012), depicting the multiscale structure of x, where each MEM explains a proportion of the variance of x equal to cor2 ( vi, x ) .”
• Integrating dispersal proxies in ecological and environmental research in the freshwater realm (2017, background): “This offers important 442 flexibility to model complex spatial phenomena (Griffith and Peres-Neto 2006), such as 443 variation of community structure (Dray et al. 2012).”
• The distance decay of similarity in communities of ectomycorrhizal fungi in different ecosystems and scales (2013, background): “Patterns of distance decay and the underlying processes are essential for understanding the function and conservation of ecosystems (Legendre, Borcard & Peres-Neto 2005) and modelling niche-neutral effects on species distribution (Dray et al. 2012).”
• How to make more out of community data? A conceptual framework and its implementation as models and software. (2017, background): “While such approaches provide insights into patterns of diversity and community composition at different spatiotemporal scales (Legendre et al. 2005; Dray et al. 2012; Legendre & Gauthier 2014), they provide little quantitative insight into the relative contributions of different assembly processes.”