The field of ecological research is experiencing significant advancements in modeling and data analysis. Recent developments are focused on improving the accuracy and interpretability of models, as well as enhancing their ability to handle complex and heterogeneous data. Notably, researchers are exploring new methods for converting qualitative causal loop diagrams into dynamic models, and for bridging the gap between farm-level economic decisions and landscape-scale spatial planning. Additionally, there is a growing interest in applying machine learning and deep learning techniques to ecological data, including species distribution modeling and predictive soil mapping. These innovations have the potential to revolutionize the field of ecology and improve our understanding of complex environmental systems. Noteworthy papers include: Diagrams-to-Dynamics (D2D), which proposes a method for converting causal loop diagrams into exploratory system dynamics models. Bridging Farm Economics and Landscape Ecology, which introduces a novel hierarchical optimization framework for aligning farm incentives with biodiversity goals. CISO, a deep learning-based method for species distribution modeling conditioned on incomplete species observations, which enables predictions to be conditioned on a flexible number of species observations alongside environmental variables.
Advances in Ecological Modeling and Data Analysis
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Diagrams-to-Dynamics (D2D): Exploring Causal Loop Diagram Leverage Points under Uncertainty
Bridging Farm Economics and Landscape Ecology for Global Sustainability through Hierarchical and Bayesian Optimization
Formal Concept Analysis: a Structural Framework for Variability Extraction and Analysis
CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations
A Comparative Study of Feature Selection in Tsetlin Machines
ESNERA: Empirical and semantic named entity alignment for named entity dataset merging
Application of association rule mining to assess forest species distribution in Italy considering abiotic and biotic factors
Enhancing Systematic Interoperability: Convergences and Mismatches between Web 3.0 and the EU Data Act
Heterogeneity in Entity Matching: A Survey and Experimental Analysis
Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases
Fuzzy-Pattern Tsetlin Machine
Differentiable Cyclic Causal Discovery Under Unmeasured Confounders
A Framework for FAIR and CLEAR Ecological Data and Knowledge: Semantic Units for Synthesis and Causal Modelling
Towards Universal Neural Inference
Peer Effect Estimation in the Presence of Simultaneous Feedback and Unobserved Confounders
Modern Neural Networks for Small Tabular Datasets: The New Default for Field-Scale Digital Soil Mapping?
OpenFPL: An open-source forecasting method rivaling state-of-the-art Fantasy Premier League services
xRFM: Accurate, scalable, and interpretable feature learning models for tabular data
Advances in Logic-Based Entity Resolution: Enhancing ASPEN with Local Merges and Optimality Criteria
Emerging Skycube