Applying the maximum entropy principle to neural networks enhances multi-species distribution models (2412.19217v3)

Abstract: The rapid expansion of citizen science initiatives has led to a significant growth of biodiversity databases, and particularly presence-only (PO) observations. PO data are invaluable for understanding species distributions and their dynamics, but their use in a Species Distribution Model (SDM) is curtailed by sampling biases and the lack of information on absences. Poisson point processes are widely used for SDMs, with Maxent being one of the most popular methods. Maxent maximises the entropy of a probability distribution across sites as a function of predefined transformations of variables, called features. In contrast, neural networks and deep learning have emerged as a promising technique for automatic feature extraction from complex input variables. Arbitrarily complex transformations of input variables can be learned from the data efficiently through backpropagation and stochastic gradient descent (SGD). In this paper, we propose DeepMaxent, which harnesses neural networks to automatically learn shared features among species, using the maximum entropy principle. To do so, it employs a normalised Poisson loss where for each species, presence probabilities across sites are modelled by a neural network. We evaluate DeepMaxent on a benchmark dataset known for its spatial sampling biases, using PO data for calibration and presence-absence (PA) data for validation across six regions with different biological groups and covariates. Our results indicate that DeepMaxent performs better than Maxent and other leading SDMs across all regions and taxonomic groups. The method performs particularly well in regions of uneven sampling, demonstrating substantial potential to increase SDM performances. In particular, our approach yields more accurate predictions than traditional single-species models, which opens up new possibilities for methodological enhancement.

• The paper introduces DeepMaxent, a novel method integrating deep learning neural networks with the Maximum Entropy principle to improve species distribution models using presence-only data.
• DeepMaxent employs a normalized Poisson loss function and learns shared latent features across species, enhancing feature extraction and addressing sampling bias for more accurate predictions.
• Evaluations show DeepMaxent consistently outperforms conventional methods like Maxent, particularly in regions with uneven data sampling, offering potential for better conservation and ecological modeling.

Overview of the Paper "Applying the Maximum Entropy Principle to Multi-Species Neural Networks Improves Species Distribution Models"

The paper under consideration explores a novel methodology aimed at enhancing Species Distribution Models (SDMs), pivotal tools in ecology used to predict where species are located geographically, by employing neural networks driven by the Maximum Entropy (Maxent) principle—a statistical method widely used for making inferences based on presence-only (PO) data. The researchers introduce DeepMaxent, an innovative integration of deep learning with the established Maxent framework. Their primary aim is to leverage the automatic feature extraction capabilities of neural networks to overcome the challenges associated with sampling biases and unaccounted absence data in PO observations.

Methodological Advancements

DeepMaxent represents a significant methodological innovation by implementing a normalized Poisson loss function within a neural network framework. This approach models the probability of species presence across locations, improving the feature extraction inherent in traditional Maxent models. Notably, DeepMaxent adaptively learns shared features among multiple species, which is advantageous for multi-species distribution modeling. Deep learning's capacity for modeling complex transformations of environmental variables is effectively harnessed here to address the biases typically affecting conventional SDMs that primarily rely on Maxent or similar PO data models.

Key Components:

• Normalized Poisson Loss: The method models presence probabilities, applying backpropagation and stochastic gradient descent (SGD) to adjust neural network parameters.
• Sampling Bias Correction: The integration of a target-group background (TGB) correction strategy addresses the spatial clustering bias by utilizing a targeted subset of related species to model unobserved areas effectively.
• Joint Latent Features: The ability to learn a collective latent representation across species allows the method to make informed predictions even for species with limited data points.

Evaluative Metrics and Comparative Performance

The researchers meticulously evaluated DeepMaxent using a comprehensive dataset comprising multiple biological groups across six distinct geographic regions. The model's evaluation leveraged PO data for training and presence-absence (PA) data for validation, with performance primarily assessed through the Area Under the ROC Curve (AUC). The findings assert that DeepMaxent consistently outperforms conventional Maxent and other state-of-the-art SDM methodologies, especially in regions characterized by uneven data sampling.

Implications and Future Directions

The implications of employing DeepMaxent for species distribution forecasting are multifaceted. Practically, the approach has the potential to improve conservation strategies by providing more accurate and unbiased predictions of species distributions. Theoretically, it paves the way for enriched environmental feature learning, extending the utility of SDMs beyond traditional application boundaries. Given its scalability and compatibility with large datasets, DeepMaxent could serve as a robust tool for integrating other types of ecological data, including standardized data sources, to enhance model reliability further.

Forward-looking Perspectives:

• Integration with Other Data Sources: Future research could explore incorporating additional data types, such as temporal shifts or climate variations, into the model to assess longitudinal changes in species distributions.
• Enhancing Model Robustness: By implementing techniques from integrated species distribution modeling, the model could leverage standardized observational datasets alongside opportunistic PO data to improve detection and sampling bias corrections.
• Applications in Conservation Policy: The adoption of DeepMaxent in real-world policy-making could be explored, informing dynamic habitat protection efforts and biodiversity assessments.

This research exemplifies a meaningful progression in ecological modeling by merging deep learning with ecological insights, aiming to provide a nuanced understanding of species distribution in the face of environmental complexities and data constraints.