Shannon H´ (entropia)

The Shannon H' (entropy) calculator quantifies species diversity in a community, combining richness (number of species) and evenness (distribution of abundances). The H' index is computed as the negative sum of each species' proportion (pi) multiplied by its natural logarithm. Higher values indicate greater diversity, while values near zero suggest dominance by a few species. This tool is widely used in ecology, conservation biology, and environmental studies to compare habitats, assess impacts, and monitor biodiversity changes.

Operation is straightforward: input the abundances or proportions of each species (as integers or decimals summing to 1), and the calculator applies the formula H' = -Σ(pi * ln(pi)). For count data, it first computes relative proportions. The result is a dimensionless number reflecting the uncertainty in the identity of a randomly sampled individual. The higher the H', the more unpredictable and diverse the community. The tool can also display Pielou's evenness (J' = H'/ln(S)), ranging from 0 to 1, indicating how evenly abundances are distributed.

Use this calculator when analyzing species inventories in terrestrial or aquatic ecosystems, comparing areas before and after disturbances, or studying environmental gradients. It is common in fieldwork, theses, and environmental impact reports. The Shannon index is sensitive to rare species, but less so than Simpson's index; thus it is preferred when giving weight to less abundant species. Remember that H' depends on sample size and spatial scale, so comparisons are only valid among communities sampled with similar effort.

Important caveats: never compare H' calculated with different logarithms (ln vs log10) without conversion; values may vary. Interpretation must consider ecological context: an H' of 3.5 in a tropical forest may be low if the expected value is 4.5. Additionally, the index assumes all species are equally detectable, which is rarely true. For communities with many rare species, undersampling bias can underestimate H'. Finally, use evenness alongside H' for a more complete analysis.