Inferring multispecies distributional aggregation level from limited line transect‐derived biodiversity data

  1. Ecologists have generally concluded that species distributions are not random (e.g. aggregate), based on single‐species studies that applied single‐species–based statistical methods, like the negative binomial model. Although it is common knowledge that some specific species in an ecological community present aggregate distributions, this does not necessarily imply that the entire community presents an aggregate distribution. Studying community‐level distributional aggregation patterns requires different statistical methods.
  2. Herein, by utilizing a novel conspecific‐encounter index derived from a multiple species Markov transition model that accounts for the non‐independent sampling of consecutive individuals along line transects, we were able to show that tree assemblages in tropical forest ecosystems can present a strong signal of extensive distributional interspersion.
  3. This interesting result is not unexpected, given the fact that neighbouring tree individuals in highly diverse tropical forests are usually of different species, resulting in strong niche packing or interspersed patterns. In contrast, for the amphibian assemblages surveyed from southwestern China and central‐south Vietnam, the conspecific‐encounter index was found to be consistently high, implying that amphibian communities tend to be highly aggregate in space.
  4. Conclusively, using the conspecific‐encounter index derived from the Markov non‐independent sampling model, we provide a legible definition of community‐level distributional aggregation as an interspersed or cluster‐like distribution of different species. This definition is not idiosyncratic, as it is coincident with the definition of the contagion index used in landscape ecology. To this end, the model used in this paper establishes a framework explicitly linking community ecology and landscape ecology from a multi‐object perspective.
Youhua Chen, Tsung-Jen Shen, Hoang Van Chung, Shengchao Shi, Jianping Jiang, Richard Condit, and Stephen P. Hubbell
Methods in Ecology and Evolution