Spatial complexity of ecological communities: Bridging the gap between probabilistic and non-probabilistic uncertainty measures

Recently, it has become well appreciated that disorder-based measures of biological diversity,such as Shannon’s entropy, fail to adequately capture the structural complexity ofan ecological community. The contributions of spatial complexity to community structureare however quantifiable if we consider, for example, the degree of spatial co-occurrencebetween species. The larger and more intricate these correlations, the more structurallycomplex the community. We suggest that Juh´asz-Nagy information-theoretical functionsoffer an adequate basis for themeasurement of structural complexity of plant communities.However, whereas Juh´asz-Nagy’s developed his model solely in terms of classical probabilisticuncertainty, we show that these functions are based both on traditional probabilisticconcepts and on non-probabilistic elements borrowed from fuzzy set theory. Therefore,the proposed representation of community structure offers an interesting way for linkingprobabilistic uncertainty and fuzzy uncertainty. It also turns out that Juh´asz-Nagyinformation-theoretical functions fit previously established theoretical definitions of ecologicalcomplexity. We illustrate the utility of the proposed functions to the multi-scaleanalysis of disturbed and undisturbed plant communities.