## 4 Results
### Ablation Study  
We conducted an ablation study to analyse the impact of each component of the AnimalEnvNet model. The key components considered include the LSTM module, the CNN module, the Attention Mechanism, and the Feature Fusion with Dimensionality Reduction module. To assess the contribution of each component, we designed the following ablation experiments:  
1. AnimalEnvNet: The complete AnimalEnvNet model with all components included.
2. A: Directly combines the outputs of LSTM and CNN without using the Attention Mechanism.
3. B: Replaces the CNN module with simple fully connected layers.
4. C: Replaces the LSTM module with simple fully connected layers.
5. D: Removes the Feature Fusion with Dimensionality Reduction module. Directly concatenates the outputs of LSTM and CNN without feature fusion.  
We evaluated the performance of each model using three metrics: MAE, the convergence rate, and the training time. The results of the ablation study are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **MAE** & **Convergence Rate** &
\begin{tabular}{c} **Training Time** \\ **(Seconds/Batch)** \\ \end{tabular} \\ \hline AnimalEnvNet & 28.4 & 55 batches & 49 \\ A & 40.2 & 80 batches & 45 \\ B & 55.3 & 100 batches & 35 \\ C & 60.7 & 120 batches & 30 \\ D & 45.1 & 90 batches & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study results.  
Figure 12: Average steps of agent in single episode.  
Figure 13: Comparison of differences between actual and predicted trajectories. (**A**–**D**) represent four different moments.  
The MAE of AnimalEnvNet is 28.4 m, indicating the highest prediction accuracy. Removing the Attention Mechanism increases the MAE to 40.2 m, demonstrating the importance of the Attention Mechanism in focusing on relevant features. Removing the CNN module significantly increases the MAE to 55.3 m, highlighting the critical role of spatial feature extraction. The model without the LSTM module has the highest MAE at 60.7 m, indicating the importance of LSTM in capturing temporal dependencies. Removing the Feature Fusion module increases the MAE to 45.1 m, showing the benefit of combining features from different modalities.  
In terms of convergence rate, the complete model reaches stable performance within 55 batches, while the models without the Attention Mechanism, CNN, LSTM, and Feature Fusion module require 80, 100, 120, and 90 batches, respectively. This indicates that all components contribute to faster convergence, with LSTM and CNN being the most critical. Regarding training time, the complete model takes an average of 49 s per batch. The simplified models show a reduction in training time due to their reduced complexity, but this comes at the cost of increased MAE and longer convergence times. The ablation study highlights the significance of each component in the AnimalEnvNet model. The LSTM module is crucial for capturing temporal dependencies, the CNN module is essential for spatial feature extraction, the Attention Mechanism enhances the model's ability to focus on important features, and the Feature Fusion module effectively combines multimodal data. Removing any of these components results in a substantial drop in performance, confirming their importance in the model.