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Related Experiment Video

Updated: Jan 18, 2026

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

Quantifying drivers of biodiversity change through increasing data availability and improved analytical frameworks.

Timothy C Bonebrake¹, Eugene Yu Hin Yau¹

¹School of Biological Sciences, The University of Hong Kong, Hong Kong SAR, China.

The Journal of Animal Ecology

|September 11, 2025

Summary

This summary is machine-generated.

Researchers developed a new framework to analyze biodiversity data, revealing how climate and habitat changes impact moth communities differently across environments and species traits. This helps identify key threats to biodiversity.

Keywords:

biodiversity change climate change habitat joint species distribution model species distribution model traits

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Last Updated: Jan 18, 2026

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

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Published on: July 11, 2025

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Watershed Planning within a Quantitative Scenario Analysis Framework

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Published on: July 24, 2016

Area of Science:

Ecology
Biodiversity Science
Conservation Biology

Background:

Species distribution is critical for understanding extinction risk and biodiversity patterns.
Identifying the primary drivers of species range changes is challenging due to data limitations and complex interactions.
Existing biodiversity datasets often have spatial and temporal gaps, hindering comprehensive analysis.

Purpose of the Study:

To present a novel analytical framework for analyzing biodiversity data with spatiotemporal gaps.
To identify the dominant drivers of species distributions and potential threats to biodiversity.
To investigate how the importance of climate and habitat drivers varies across different environments and species traits.

Main Methods:

Combined joint species distribution modelling (jSDM) with variance partitioning.
Utilized a nation-wide macro-moth monitoring dataset from Finland.
Applied the framework to analyze biodiversity-monitoring data with spatiotemporal gaps.

Main Results:

The study successfully identified dominant drivers of species distributions in the Finnish moth community.
Results demonstrated strong context dependence, showing that the relative importance of climate and habitat varies.
Environmental dependency and functional dependency of drivers were observed across different moth groups and habitats.

Conclusions:

The developed framework effectively analyzes biodiversity data, even with spatiotemporal gaps.
Understanding the context-dependent drivers of biodiversity change is crucial for effective conservation.
Expanding this approach to other datasets will enhance our ability to manage biodiversity threats and slow species loss.