On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning. (arXiv:2401.02989v1 [q-bio.QM])

The article explores the use of species distribution modeling and the challenges associated with limited data on confirmed species absence. It discusses the common approach of employing pseudo-absences as negative samples and highlights the underexplored application of pseudo-absences in multi-species neural networks. The significant class imbalance between species presences and pseudo-absences is identified as a key issue, along with the complexity of selecting the optimal combination of pseudo-absence types. The paper proposes integrating pseudo-absences in the training of multi-species neural networks through modifications to the loss function, addressing both the class imbalance and the choice of pseudo-absence types. The authors also suggest a strategy for setting loss weights using spatial block cross-validation with presence-only data. The approach is evaluated using a benchmark dataset from six different regions, demonstrating improved results compared to competing approaches.

The Importance of Pseudo-Absences in Multi-Species Distribution Models

Species distribution modeling is a valuable tool for understanding the complex relationship between environmental conditions and species occurrences. However, the availability of data often presents limitations, particularly in terms of confirmed species absence. To address this issue, researchers commonly employ pseudo-absences, which are geographic locations designated as negative samples.

While pseudo-absences are well-established for single-species distribution models, their application in multi-species neural networks has been largely unexplored. One of the main challenges in using pseudo-absences is the significant class imbalance between species presences and pseudo-absences. Furthermore, the existence of different types of pseudo-absences adds complexity to the selection process.

Tackling the Challenge: Integrating Pseudo-Absences in Multi-Species Neural Networks

In this study, we propose an innovative approach that effectively addresses these challenges by integrating pseudo-absences into the training of multi-species neural networks through modifications to the loss function. By assigning different weights to the distinct terms of the loss function, we can tackle both the class imbalance and the choice of pseudo-absence types.

Additionally, we propose a strategy to set these loss weights using spatial block cross-validation with presence-only data. This approach allows us to evaluate the performance of our model on independent presence-absence data from six different regions.

Evaluating the Proposed Approach

To assess the effectiveness of our approach, we applied it to a benchmark dataset containing presence-absence data from six different regions. We compared our results to those obtained using competing approaches.

The evaluation revealed that our modified multi-species neural network, incorporating pseudo-absences and weighted loss function, outperformed other approaches. The inclusion of pseudo-absences allowed for a more comprehensive understanding of the distribution patterns of multiple species, leading to improved predictive accuracy.

Conclusion: Enhancing Multi-Species Distribution Models

By integrating pseudo-absences into multi-species neural networks and adjusting the loss function, we can address the challenges posed by limited data and class imbalance. This innovative approach offers a more comprehensive understanding of species distributions and improves the accuracy of predictions.

“Our results highlight the importance of considering pseudo-absences in multi-species distribution modeling and emphasize the need for further exploration and development in this area. This approach has the potential to enhance our understanding of complex ecological relationships and contribute to conservation efforts.”

Species distribution modeling is a crucial tool for understanding how environmental conditions influence the occurrence of species. However, the data available for such modeling often lacks information on confirmed species absence and is limited to opportunistic presence-only observations. To overcome this limitation, researchers commonly use pseudo-absences, which are specific geographic locations designated as negative samples. While the use of pseudo-absences is well-established for single-species distribution models, their application in multi-species neural networks is not well-explored.

One of the challenges in using pseudo-absences in multi-species neural networks is the significant class imbalance between species presences and pseudo-absences. This issue is often ignored, leading to biased results. Additionally, there are different types of pseudo-absences, such as random and target-group background points, which further complicate the selection process. Determining the optimal combination of pseudo-absence types is difficult and depends on the characteristics of the data, including geographic biases.

In this paper, the authors propose a solution to these challenges by integrating pseudo-absences into the training of multi-species neural networks through modifications to the loss function. By assigning different weights to the distinct terms of the loss function, the authors address both the class imbalance and the choice of pseudo-absence types. This adjustment helps to improve the accuracy and reliability of species distribution models.

Furthermore, the authors suggest a strategy for setting these loss weights using spatial block cross-validation with presence-only data. This approach allows for a more robust evaluation of the model’s performance and helps to ensure that the weights are appropriately calibrated.

To evaluate their approach, the authors used a benchmark dataset consisting of independent presence-absence data from six different regions. The results of their study demonstrated improved performance compared to competing approaches. This suggests that incorporating pseudo-absences and modifying the loss function can lead to more accurate and reliable multi-species distribution models.

Overall, this paper provides valuable insights into the challenges associated with using pseudo-absences in multi-species neural networks for species distribution modeling. The proposed approach offers a promising solution to these challenges and opens up new possibilities for improving our understanding of the intricate relationship between environmental conditions and species occurrences. Further research in this area could explore the applicability of this approach to different datasets and species as well as investigate potential extensions or refinements to the methodology.
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