Inferring macro‐ecological patterns from local presence/absence data. Issue 11 (8th August 2019)

• Record Type: Journal Article
• Authors: Tovo, Anna; Formentin, Marco; Suweis, Samir; Stivanello, Samuele; Azaele, Sandro; Maritan, Amos
• Is Part Of: Oikos. Volume 128:Issue 11(2019)
• Page Start: 1641
• Page End: 1652
• Publication Date: 2019-08-08
• Subjects: biodiversity patterns -- spatial ecology -- species–abundance distribution -- species–accumulation curve -- upscaling biodiversity patterns; Ecology -- Periodicals; 570
• DOI: 10.1111/oik.06754
• Languages: English
• ISSNs: 0030-1299

Abstract

Abstract : Biodiversity provides support for life, vital provisions, regulating services and has positive cultural impacts. It is therefore important to have accurate methods to measure biodiversity, in order to safeguard it when we discover it to be threatened. For practical reasons, biodiversity is usually measured at fine scales whereas diversity issues (e.g. conservation) interest regional or global scales. Moreover, biodiversity may change across spatial scales. It is therefore a key challenge to be able to translate local information on biodiversity into global patterns. Many databases give no information about the abundances of a species within an area, but only its occurrence in each of the surveyed plots. In this paper, we introduce an analytical framework (implemented in a ready‐to‐use R code) to infer species richness and abundances at large spatial scales in biodiversity‐rich ecosystems when species presence/absence information is available on various scattered samples (i.e. upscaling). This framework is based on the scale‐invariance property of the negative binomial. Our approach allows to infer and link within a unique framework important and well‐known biodiversity patterns of ecological theory, such as the species accumulation curve (SAC) and the relative species abundance (RSA) as well as a new emergent pattern, which is the relative species occupancy (RSO). Our estimates are robust and accurate, as confirmed by tests performed on both in silico‐generated and real forests. We demonstrate the accuracy of our predictions using data from two well‐studied forest stands. Moreover, we compared our results with other popular methods proposed in the literature to infer species richness from presence to absence data and we showed that our framework gives better estimates. It has thus important applications to biodiversity research and conservation practice.