Effectively inferring overall spatial distribution pattern of species in a map when exact coordinate information is missing

Mapping and comparing biodiversity in space is a routine task ecological research. However, accurate mapping requires exact distributional information as inputs. When geographical coordinate missing or when only coarse distribution of species available at the quadrat plot level, how can we effectively infer compare spatial patterns with reasonable accuracy while controlling confounding impacts scale dependency? A crossing-scale coefficient variation (CCV) calculated for three simple diversity indices, Shannon, Gini–Simpson reciprocal-Simpson evenness from quadrat-based data over varying sampling grain sizes subtly measuring overall pattern survey map. Extensive numerical simulations showed that, organisms was regular, CCV tended to be very small, all indices. By contrast, highly aggregate, large. Finally, an intermediate observed organism random. To this end, indices detect subtle change on aggregate regular distribution. Among index most insensitive both population size number used. Compared statistic metrics that coordinates inputs, useful metric assessing across plots which each individual are unavailable. Empirical applications plants habitat patches extensively managed by humans present pattern, being counterintuitive usual expectation cultivated planted land should pattern. The study recommends one effective tool, combination exploring single under quadrat-sampling scheme. proposed method particularly suitable handle finest-scale missing.