WeChat Mini Program
Old Version Features

Identifying Climatic Refugia by Integrating Continuous Heterogeneity and Discrete Classifications of Variables

Ecosystem Health and Sustainability(2024)

Cited 0|Views12
Abstract
Climate diversity is essential for safeguarding biological diversity against climate change. Two planning approaches based on continuous heterogeneity or discrete classification have previously been implemented to identify climatic refugia. However, little is known about the performance of the integration of the 2 measurements for identifying climatic refugia. Using the case of Yunnan in southwest China, we examined the relationship between 2 measurements of climatic heterogeneity: the continuous climatic heterogeneity index (CCHI) and the variety of climatic units (VCU). We then identified climatic-heterogeneity refugia focusing only on CCHIs and the comprehensive climate-diversity refugia integrating CCHIs with the rarity and endemism of climatic units. Last, we assessed the coverages of these 2 sets of refugia for current high conservation-value areas, indicated by 5 existing biodiversity priority conservation area (PCA) schemes. The composite-CCHI and VCU demonstrated substantial different distributions, and the climatic heterogeneity level assessed by VCU was higher than that of composite-CCHI. The composite-CCHI levels were significantly positively correlated with the coverage percentages of the 5 PCAs. The Jaccard similarity index between climatic-heterogeneity refugia and climate-diversity refugia at a 30% conservation target was 0.26. The climate-diversity refugia coverages for the 5 biodiversity PCAs were consistently higher than those of climatic-heterogeneity refugia. Existing nature reserves covered 18.6% of the 5% climate-diversity refugia. Our analyses suggest that CCHI is more effective than VCU in revealing climatic heterogeneity and indicating current high conservation-value areas. Integrating continuous climatic heterogeneity with the rarity and endemism of climatic units serves as an optimal approach for identifying climate-diversity refugia.
More
Translated text
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined