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In this paper, we propose a novel Total Electron Content (TEC) map prediction model, named ED‐AttConvLSTM, using a Convolutional Long Short‐Term Memory (ConvLSTM) network and attention mechanism based on encoder‐decoder structure. The inclusion of the attention mechanism enhances the efficient utilization of spatiotemporal features extracted by the ConvLSTM, emphasizing the significance of crucial spatiotemporal features in the prediction process and, as a result, leading to an enhancement in predictive performance. We conducted experiments in East Asia (10°N–45°N, 90°E−130°E). The ED‐AttConvLSTM was trained and evaluated using the International GNSS Service TEC maps over a period of six years from 2013 to 2015 (high solar activity years) and 2017 to 2019 (low solar activity years). We compared our ED‐AttConvLSTM with IRI‐2016, COPG, LSTM, GRU, ED‐ConvLSTM and ED‐ConvGRU. The results indicate that our model surpasses the comparison models in forecasting both high and low solar activity years, across most months and UT moments in a day. Moreover, our model exhibits notably superior prediction performance during the most severe phases of a magnetic storm when compared to the comparison models. Subsequently, we then also discuss how the prediction performance of our model is affected by latitude. Finally, we discuss the diminishing performance of our model in multi‐day predictions, demonstrating that its reliability for forecasts ranging from one to 4 days in advance. Beyond the fifth day, there is a pronounced decline in the model's performance. Plain Language Summary: High precision prediction of Total Electron Content (TEC) is of great significance for improving the accuracy of global satellite navigation systems. In this paper, we introduced the attention mechanism into the ionospheric TEC map prediction model to adaptively weight the ionospheric TEC spatiotemporal features, highlighting the contribution of important spatiotemporal features in TEC map prediction. Results showed that the prediction performance of our model is improved compared with the other six models. Key Points: Introducing ED‐AttConvLSTM, a novel Total Electron Content (TEC) map prediction model utilizing Convolutional Long Short‐Term Memory and an attention mechanism in an encoder‐decoder frameworkThe incorporation of attention mechanisms markedly decreased root mean square error in TEC map predictions for 2015 and 2019Our model excels over 6 state‐of‐the‐art models, affirming its robustness and reliability for TEC map prediction [ABSTRACT FROM AUTHOR]

Mounting observational records demonstrate human‐caused faunal decline in recent decades, while accumulating archaeological evidence suggests an early biodiversity impact of human activities during the Holocene. A fundamental question arises concerning whether modern wildlife population declines began during early human disturbance. Here, we performed a population genomic analysis of six common forest birds in East Asia to address this question. For five of them, demographic history inference based on 25–33 genomes of each species revealed dramatic population declines by 4‐ to 48‐fold over millennia (e.g. 2000–5000 thousand years ago). Nevertheless, summary statistics detected nonsignificant correlations between these population size trajectories and Holocene temperature variations, and ecological niche models explicitly predicted extensive range persistence during the Holocene, implying limited demographic consequence of Holocene climate change. Further analyses suggest high negative correlations between the reconstructed population declines and human disturbance intensities and indicate a potential driver of human activities. These findings provide a deep‐time and large‐scale insight into the recently recognized avifaunal decline and support an early origin hypothesis of human effects on biodiversity. Overall, our study sheds light on the current biodiversity crisis in the context of long‐term human–environment interactions and offers a multi‐evidential framework for quantitatively assessing the ecological consequences of human disturbance. [ABSTRACT FROM AUTHOR]