NEGATIVE FREQUENCY DEPENDENCE AND THE IMPORTANCE OF SPATIAL SCALE

By Jane Molofsky, James D. Bever, J. Antonovics, T. Newman. 2002

57 citations recorded by the source corpus.

Subfields: Genetics, Global and Planetary Change, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/6644caa802b4f3845fd0a7eea10687bd23b67ec3

How researchers see this paper

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

In Genetics

1. A. Fajardo
2. Eliot J. B. McIntire

In Global and Planetary Change

1. A. Fajardo
2. James D. Bever
3. Eliot J. B. McIntire

Citations in the corpus

• Negative plant-soil feedback and species coexistence (2005, background): “Although inferences about plant community processes are difficult to obtain on the basis of only spatio-temporal patterns (Molofsky et al. 2002), the model presented here suggests an experimentally testable mechanism for these common, but poorly understood, plant community dynamics.”
• Soil community feedback and the coexistence of competitors: conceptual frameworks and empirical tests. (2003, background): “…models indicate that the spatial scale of dispersal and interaction can have strong effects on the spatial structure expected under negative frequency dependence, but spatial scale does not alter the expectation that negative feedback can maintain diversity in a community (Molofsky et al., 2002).”
• Beyond description: the active and effective way to infer processes from spatial patterns. (2009, background): “…a single precise pattern (e.g., Schurr et al. 2004, Fajardo and McIntire 2007), nonrandom processes can create highly structured patterns (e.g., Molofsky et al. 2002, Broquet et al. 2006), and the impact of pattern on process may not act at the same scales as the impact of process on pattern…”
• A comparison of methods for the statistical analysis of spatial point patterns in plant ecology (2006, background): “Finally, it is important to realise that just because we characterise a spatial pattern as random (the typical null model) does not necessarily mean that the processes generating it are, of themselves, random (e.g. see Molofsky et al. 2002).”