Work

Rarefaction profiles for repeated biodiversity surveys: integrating temporal variability and trend along sample-based accumulation curves

Year: 2026

Type: article

Abstract: Repeated biodiversity surveys are often summarised by one diversity value per survey date, although temporal change may be expressed differently at the scale of individual sampling units, across the full sampled community, or along the accumulation process linking the two. We introduce temporal rarefaction profiles as a computational extension of sample-based rarefaction for repeated ecological surveys. For each survey date, a rarefaction curve is calculated by accumulating comparable sampling units. Temporal variability and temporal trend are then estimated at each number of accumulated units, (m), from (m = 1), representing the expected diversity of one sampling unit, to (m = N), representing sampled gamma diversity within the defined sampling focus. The approach is metric-general and can be applied to taxonomic diversity, functional diversity and directional community descriptors, provided that the target quantity can be calculated for pooled subsets of sampling units. We provide a reproducible R implementation that returns survey-specific rarefaction curves, temporal variability profiles, temporal trend profiles, alpha- and sampled-gamma summaries, whole-profile summaries and contrasts among groups. We illustrate the method using a 16-year vegetation dataset from the Monte Labbro long-term grassland experiment in southern Tuscany, Italy. Ungrazed plots showed higher temporal variability and stronger negative temporal trends in richness and Shannon diversity than grazed plots. Functional responses differed from taxonomic responses: functional richness was more temporally variable in grazed plots and tended to increase at the sampled-gamma scale, whereas community-weighted mean trait displacement was more variable and increased more strongly through time in ungrazed plots. Temporal rarefaction profiles therefore provide a reproducible way to analyse where, along sample-based accumulation curves, biodiversity change is variable, directional or both.

Source: Ecological Informatics

DOI: 10.1016/j.ecoinf.2026.104113

Authors: Giovanni Bacaro, Emanuele Fanfarillo, Simona Maccherini

Institutions: University of Trieste, University of Siena

Language: English

Topic: Ecology and Vegetation Dynamics Studies

Subfield: Nature and Landscape Conservation

Field: Environmental Science

Domain: Physical Sciences

SDG: Life on land

Open Access status: gold

Estimated APC paid: $2,770