Heterogenous graph neural networks for species distribution modeling

• URL: http://arxiv.org/abs/2503.11900v2
• Date: Sun, 27 Apr 2025 05:26:16 GMT
• Title: Heterogenous graph neural networks for species distribution modeling
• Authors: Lauren Harrell, Christine Kaeser-Chen, Burcu Karagol Ayan, Keith Anderson, Michelangelo Conserva, Elise Kleeman, Maxim Neumann, Matt Overlan, Melissa Chapman, Drew Purves,
• Abstract summary: We introduce a novel presence-only Species Distribution Model (SDM) with graph neural networks (GNN)<n>In our model, species and locations are treated as two distinct node sets, and the learning task is predicting detection records as the edges that connect locations to species.<n>We evaluate the potential of this methodology on the six-region dataset compiled by National Center for Ecological Analysis and Synthesis (NCEAS) for benchmarking SDMs.
• Score: 6.423278804632857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Species distribution models (SDMs) are necessary for measuring and predicting occurrences and habitat suitability of species and their relationship with environmental factors. We introduce a novel presence-only SDM with graph neural networks (GNN). In our model, species and locations are treated as two distinct node sets, and the learning task is predicting detection records as the edges that connect locations to species. Using GNN for SDM allows us to model fine-grained interactions between species and the environment. We evaluate the potential of this methodology on the six-region dataset compiled by National Center for Ecological Analysis and Synthesis (NCEAS) for benchmarking SDMs. For each of the regions, the heterogeneous GNN model is comparable to or outperforms previously-benchmarked single-species SDMs as well as a feed-forward neural network baseline model.

Related papers

• NeuroADDA: Active Discriminative Domain Adaptation in Connectomic [3.241925400160274]
  We introduce NeuroADDA, a method that combines optimal domain selection with source-free active learning to adapt pretrained backbones to a new dataset.<n>NeuroADDA consistently outperforms training from scratch across diverse datasets and fine-tuning sample sizes.
  arXiv  Detail & Related papers  (2025-03-08T12:40:30Z)
• Multi-Scale and Multimodal Species Distribution Modeling [4.022195138381868]
  Species distribution models (SDMs) aim to predict the distribution of species relating occurrence data with environmental variables. Recent applications of deep learning to SDMs have enabled new avenues, specifically the inclusion of spatial data. We develop a modular structure for SDMs that allows us to test the effect of scale in both single- and multi-scale settings. Results on the GeoLifeCLEF 2023 benchmark indicate that considering multimodal data and learning multi-scale representations leads to more accurate models.
  arXiv  Detail & Related papers  (2024-11-06T15:57:20Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• LD-SDM: Language-Driven Hierarchical Species Distribution Modeling [9.620416509546471]
  We focus on the problem of species distribution modeling using global-scale presence-only data. To capture a stronger implicit relationship between species, we encode the taxonomic hierarchy of species using a large language model. We propose a novel proximity-aware evaluation metric that enables evaluating species distribution models.
  arXiv  Detail & Related papers  (2023-12-13T18:11:37Z)
• Supervised Training of Siamese Spiking Neural Networks with Earth's Mover Distance [4.047840018793636]
  This study adapts the highly-versatile siamese neural network model to the event data domain. We introduce a supervised training framework for optimizing Earth's Mover Distance between spike trains with spiking neural networks (SNN)
  arXiv  Detail & Related papers  (2022-02-20T00:27:57Z)
• Siamese Neural Network with Joint Bayesian Model Structure for Speaker Verification [54.96267179988487]
  We propose a novel Siamese neural network (SiamNN) for speaker verification. Joint distribution of samples is first formulated based on a joint Bayesian (JB) based generative model. We further train the model parameters with the pair-wised samples as a binary discrimination task for speaker verification.
  arXiv  Detail & Related papers  (2021-04-07T09:17:29Z)
• StatEcoNet: Statistical Ecology Neural Networks for Species Distribution Modeling [8.534315844706367]
  This paper focuses on a core task in computational sustainability and statistical ecology: species distribution modeling (SDM) In SDM, the occurrence pattern of a species on a landscape is predicted by environmental features based on observations at a set of locations. To address the unique challenges of SDM, this paper proposes a framework called StatEcoNet.
  arXiv  Detail & Related papers  (2021-02-17T02:19:00Z)
• Statistical model-based evaluation of neural networks [74.10854783437351]
  We develop an experimental setup for the evaluation of neural networks (NNs) The setup helps to benchmark a set of NNs vis-a-vis minimum-mean-square-error (MMSE) performance bounds. This allows us to test the effects of training data size, data dimension, data geometry, noise, and mismatch between training and testing conditions.
  arXiv  Detail & Related papers  (2020-11-18T00:33:24Z)
• Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing [88.30867628592112]
  Graph neural networks (GNNs) are emerging machine learning models on graphs. Permutation-equivariance and proximity-awareness are two important properties highly desirable for GNNs. We show that existing GNNs, mostly based on the message-passing mechanism, cannot simultaneously preserve the two properties. In order to preserve node proximities, we augment the existing GNNs with node representations.
  arXiv  Detail & Related papers  (2020-09-05T16:46:56Z)
• Bayesian Graph Neural Networks with Adaptive Connection Sampling [62.51689735630133]
  We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) The proposed framework not only alleviates over-smoothing and over-fitting tendencies of deep GNNs, but also enables learning with uncertainty in graph analytic tasks with GNNs.
  arXiv  Detail & Related papers  (2020-06-07T07:06:35Z)
• Stochastic Graph Neural Networks [123.39024384275054]
  Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning. Current GNN architectures assume ideal scenarios and ignore link fluctuations that occur due to environment, human factors, or external attacks. In these situations, the GNN fails to address its distributed task if the topological randomness is not considered accordingly.
  arXiv  Detail & Related papers  (2020-06-04T08:00:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.