Paper List
-
Exactly Solvable Population Model with Square-Root Growth Noise and Cell-Size Regulation
This paper addresses the fundamental gap in understanding how microscopic growth fluctuations, specifically those with size-dependent (square-root) no...
-
Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
-
Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
-
MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
-
The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
-
A Theoretical Framework for the Formation of Large Animal Groups: Topological Coordination, Subgroup Merging, and Velocity Inheritance
This paper addresses the core problem of how large, coordinated animal groups form in nature, challenging the classical view of gradual aggregation by...
-
ANNE Apnea Paper
This paper addresses the core challenge of achieving accurate, event-level sleep apnea detection and characterization using a non-intrusive, multimoda...
-
DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor
This paper addresses the critical bottleneck of managing and organizing the growing volume of differential expression and functional enrichment analys...
EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
Concordia University | Algoma University
The 30-Second View
IN SHORT: This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts under rapidly changing environmental conditions, moving beyond static models to operational, data-driven decision support.
Innovation (TL;DR)
- Methodology First transformer-based model applied to ecological and climate risk forecasting in Africa, using sequence-to-point prediction (12-month environmental sequences → next-month species occurrence) with explicit temporal dependency modeling via self-attention.
- Methodology Integration of continual learning (rehearsal + EWC) into biodiversity forecasting, enabling model updates with new data streams without catastrophic forgetting, crucial for non-stationary climate impacts.
- Biology Operational near-term forecasting paradigm (monthly to seasonal) that requires no future climate projections, using observed environmental sequences to predict immediate conservation-relevant shifts, bridging geophysical forecasting architectures with species distribution modeling.
Key conclusions
- EcoCast achieves macro-averaged F1 score of 0.65 and PR-AUC of 0.72 on 2023 holdout data for five African bird species, representing +34 and +43 percentage point improvements respectively over Random Forest baseline (F1=0.31, PR-AUC=0.29).
- Transformer architecture successfully captures critical temporal dependencies: annual seasonality via positional encoding, lagged environmental responses (2-4 month delays), and cross-species ecological signals through joint multi-label training.
- The framework demonstrates operational feasibility with monthly forecast updates using near-real-time data (ERA5 available within 5 days, final data 2-3 months later), enabling alignment with conservation planning cycles rather than static decadal projections.
Abstract: Increasing climate change and habitat loss are driving unprecedented shifts in species distributions. Conservation professionals urgently need timely, high-resolution predictions of biodiversity risks, especially in ecologically diverse regions like Africa. We propose EcoCast, a spatio-temporal model designed for continual biodiversity and climate risk forecasting. Utilizing multisource satellite imagery, climate data, and citizen science occurrence records, EcoCast predicts near-term (monthly to seasonal) shifts in species distributions through sequence-based transformers that model spatio-temporal environmental dependencies. The architecture is designed with support for continual learning to enable future operational deployment with new data streams. Our pilot study in Africa shows promising improvements in forecasting distributions of selected bird species compared to a Random Forest baseline, highlighting EcoCast's potential to inform targeted conservation policies. By demonstrating an end-to-end pipeline from multi-modal data ingestion to operational forecasting, EcoCast bridges the gap between cutting-edge machine learning and biodiversity management, ultimately guiding data-driven strategies for climate resilience and ecosystem conservation throughout Africa.