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Midpoint attractor models resolve the mid-elevation peak in Himalayan plant species richness

Cite this dataset

Macek, Martin et al. (2021). Midpoint attractor models resolve the mid-elevation peak in Himalayan plant species richness [Dataset]. Dryad.


The midpoint attractor models (MPA) of species richness integrate a unimodal environmental favourability gradient and neutral effects forced by geometric constraints and thus extend ecologically neutral mid-domain model. However, both alternative MPA algorithms assume that underlying environmental favourability peaks within the modeling domain. Here, we used elevational distribution data for 1054 plant species occurring in NW Himalaya to explore species richness gradients and MPA performance in species groups defined by biogeography, taxonomy and life form. MPA models achieved an excellent fit, but the two MPA algorithms produced contrasting estimates of midpoint attractor location, especially for species groups with richness originating in lowlands. Therefore, we propose a modification of the MPA model accounting for the environmental favourability peak outside the study domain to reflect these situations. Biogeographic origin was more decisive for midpoint attractor location than taxonomic or life-form classification, indicating relatively low climatic niche conservatism in plants.


Data file contains information on vertical range limits for 1054 vascular plants recorded in West Himalayas, Ladakh territory, India. Regional range limits for Ladakh are based on field survey by L. Klimeš and J. Doležal. Continental range limits in adjacent regions were compiled from published records in: the Flora of Pakistan (, the Flora of China (, the Flora of Nanga Parbat (Dickoré & Nüsser, 2000), The Himalayan Uplands Plant database (Dickoré, 2011), and the Global Biodiversity Information Facility (GBIF, Records from the GBIF were rounded to nearest hundred. Unreliable outliers based on historical records (e.g. proclaimed elevation more than 1000 m apart from other records) were not taken into account and the next closest, reliable occurrence extreme was used instead.

Further, it contains implementation of midpoint attractor models in R language, which was used to analyze this data.


Czech Academy of Sciences, Award: RVO 67985939

Czech Science Foundation, Award: GACR 21-26883S