Long-term Trends in Carabids over 25 Years – Community and Population Level Analyses
ARPHA Conference Abstracts(2019)
Abstract
In times of insect decline, long-term data become more and more important. Such data allow insights into long-term trends and an analysis of possible drivers underlying temporal changes of community and population structure. Using data from 25 years of continuous ground beetle trapping in an ancient woodland located in a large nature reserve in Northern Germany, we analysed temporal changes at both community and population level and identified potential underlying drivers. Ground beetle species significantly declined over time but biomass and number of trapped individuals remained constant. As the habitat was kept stable und unchanged in the last 25 years we also study the influence of external drivers such as climatic variables on phenology and population trends of the most-abundant species. We discuss our results in light of the ongoing insect decline and climate change.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
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
Related Papers
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
GPU is busy, summary generation fails
Rerequest