ArticleScientific data2026
Full-elevational gradient dataset on moth diversity and abundance in a temperate mountain range.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Full-elevational gradient dataset on moth diversity and abundance in a temperate mountain range.Scientific data · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
Climate change is reshaping ecosystems worldwide, yet our ability to quantify its long-term impact across taxa is limited by a lack of reliable and comparable data. Here, we present a systematically collected long-term dataset spanning nearly a decade (2012-2021), documenting the diversity, abundance, and distribution of 439 moth species (Lepidoptera: Heterocera) from the Czech part of the Giant Mountains, a region entirely protected as Krkonoše National Park. Using standardised light traps, we sampled 982 localities across an area of 550 km², yielding a total of 64,776 specimens. Localities are accompanied by in-situ assessments of vegetation characteristics and management regimes, complemented by topographical derivatives and ecosystem information retrieved post-hoc from open spatial data. The dataset provides a valuable resource for investigating spatial and temporal patterns in moth diversity and abundance, as well as for evaluating the effects of different management practices, supporting both basic and applied research.
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