Ecological systems have a high level of complexity combined with stability and rich biodiversity. Recently, the analysis of their properties and evolution has been pushed forward on a basis of concept of mutualistic networks that provides a detailed understanding of their features being linked to a high nestedness of t…
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48 results for “ecological networks”
StatEcoNet models species distribution using neural networks to correct observation errors.
problem Correcting observation errors in wildlife surveys for accurate species distribution modeling.
method StatEcoNet integrates a graphical generative model with neural networks to address SDM challenges.
result StatEcoNet outperforms traditional methods on simulated and real datasets.
Sparse Network Inference under Imperfect Detection and its Application to Ecological Networksstat.ML
Framework for inferring latent structure from sparse, imperfectly detected bipartite networks.
problem Recovering latent structure from sparse, imperfectly detected bipartite networks in ecology.
method Structured sparse nonnegative low-rank factorization with detection probability estimation and ADMM-based algorithm.
result Improved recovery of latent factors and structure compared to existing methods.
Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait-matching for determining species in…
Classification is an important supervised machine learning method, which is necessary and challenging issue for ecological research. It offers a way to classify a dataset into subsets that share common patterns. Notably, there are many classification algorithms to choose from, each making certain assumptions about the …
The study examines how language models learn to represent the world, identifying conditions for ecological veridicality.
problem Understanding when language models learn to represent the world accurately and how this learning process can fail.
method Analyzes the Bayes-optimal next-token cross-entropy decomposition and the role of training ecology in shaping model representations.
result The minimum-complexity zero-excess solution is the quotient partition by training equivalence, and this solution is not preserved in in-context learning or per-task adaptation.
The rise in online social networking has brought about a revolution in social relations. However, its effects on offline interactions and its implications for collective well-being are still not clear and are under-investigated. We study the ecology of online and offline interaction in an evolutionary game framework wh…
Machine learning methods have had spectacular success on numerous problems. Here we show that a prominent class of learning algorithms - including Support Vector Machines (SVMs) -- have a natural interpretation in terms of ecological dynamics. We use these ideas to design new online SVM algorithms that exploit ecologic…
The paper uses a graph autoencoder to learn unbiased plant-pollinator interaction embeddings.
problem Sampling bias in citizen science data affects ecological network analysis.
method Bipartite graph variational autoencoder with HSIC for fairness.
result The method mitigates sampling bias and provides unbiased embeddings.
From Ecology to Finance (and Back?): Recent Advancements in the Analysis of Bipartite Networksphysics.soc-ph
Bipartite networks provide an insightful representation of many systems, ranging from mutualistic networks of species interactions to investment networks in finance. The analysis of their topological structures has revealed the ubiquitous presence of properties which seem to characterize many - apparently different - s…
Hybrid model improves forest growth predictions.
problem Misspecified assumptions in mechanistic models.
method Forest Informed Neural Networks (FINN) combining DVM and DNN.
result DNN learned improved growth process functional form.
Time Series Vector Autoregression Prediction of the Ecological Footprint based on Energy Parametersphysics.soc-ph
Sustainability became the most important component of world development, as countries worldwide fight the battle against the climate change. To understand the effects of climate change, the ecological footprint, along with the biocapacity should be observed. The big part of the ecological footprint, the carbon footprin…
Study uses machine learning to predict predator-prey dynamics without prior knowledge.
problem Predicting predator-prey interactions without prior knowledge of the system.
method Applied Neural Ordinary Differential Equations (Neural ODEs) and Universal Differential Equations (UDEs) to the Lotka-Volterra model.
result UDEs outperform Neural ODEs in predicting predator-prey dynamics, especially in noisy data.
The network structure of city-firm relationsphysics.soc-ph
How are economic activities linked to geographic locations? To answer this question, we use a data-driven approach that builds on the information about location, ownership and economic activities of the world's 3,000 largest firms and their almost one million subsidiaries. From this information we generate a bipartite …
Graph neural networks help assess how global changes affect plant-pollinator networks.
problem Interpreting GNN results to understand how global changes impact plant-pollinator networks.
method Simulation study and application on Spipoll dataset to assess effects of global changes on pollination networks.
result GNNs can detect interactive effects between covariates and plant genera on pollination network connectivity.
BayesFlow learns complex models using neural networks.
problem Estimating parameters in complex, non-likelihood models.
method Invertible neural networks for global Bayesian inference.
result Global probabilistic mapping from data to parameters.
Evology models US equity mutual funds interactions for investment strategies.
problem Understanding complex interactions in financial markets.
method Agent-based model (ABM) of US stock market participants and their strategies.
result Trading strategies interact with other market participants and conditions.
Common Structure Discovery in Collections of Bipartite Networks: Application to Pollination Systemsstat.ML
A new model detects common patterns in pollination networks.
problem Comparing organization of bipartite networks to understand community structure.
method colBiSBM, a family of probabilistic models for collections of bipartite networks.
result The method uncovers shared ecological roles and partitions networks.
Decodes neural activity to assess latent states in real-world driving tasks.
problem Understanding latent states during complex tasks in natural settings.
method Domain-generalized models trained on controlled lab paradigms applied to ecologically valid driving tasks.
result Changes in neural activity correlate with changes in behavior and task performance.
Study finds cryptocurrency market diversity patterns inconsistent with neutral models.
problem Cryptocurrency market diversity patterns not consistent with neutral models.
method Analysis borrowing methods from ecology, focusing on diversity patterns and community structure.
result Cryptocurrency market diversity patterns not consistent with neutral models, suggesting strong interactions between species.
We combine fine-grained spatially referenced census data with the vote outcomes from the 2016 US presidential election. Using this dataset, we perform ecological inference using distribution regression (Flaxman et al, KDD 2015) with a multinomial-logit regression so as to model the vote outcome Trump, Clinton, Other / …
A new spin on optimal portfolios and ecological equilibriacond-mat.stat-mech
New insights into optimal portfolios and ecological equilibria reveal surprising complexity.
problem Optimal portfolio construction with ecological constraints.
method Computational analysis of multispecies Lotka-Volterra equations with unit rank interaction matrices.
result Logarithm of the average number of solutions grows as \(N^{2/3}\), with most likely solutions being much smaller.
Market inefficiencies arise from density-dependent returns in a noisy environment.
problem Market inefficiencies and excess volatility.
method Developed a market model using ecological concepts.
result Market dynamics are density-dependent, leading to inefficiencies.
Earth observation embeddings can convert discrete biome maps into continuous representations that better capture ecological variation.
problem Biome maps impose categorical boundaries that compress continuous variation in biotic communities.
method Fit a linear classifier on Earth observation embeddings to predict biome labels.
result Continuous biome representation outperforms discrete biome labels for predicting species occurrence.
SGNNs use simulations to train neural networks, improving scientific forecasting and interpretability.
problem Combining precise theory and machine learning for robust scientific modeling.
method Pretraining neural networks on diverse mechanistic simulations as training data.
result SGNNs outperform data-driven and physics-constrained models in forecasting and interpretability.
Networks are a fundamental model of complex systems throughout the sciences, and network datasets are typically analyzed through lower-order connectivity patterns described at the level of individual nodes and edges. However, higher-order connectivity patterns captured by small subgraphs, also called network motifs, de…
The methods of statistical physics are widely used for modelling complex networks. Building on the recently proposed Equilibrium Expectation approach, we derive a simple and efficient algorithm for maximum likelihood estimation (MLE) of parameters of exponential family distributions - a family of statistical models, th…
We introduce the nonparametric metadata dependent relational (NMDR) model, a Bayesian nonparametric stochastic block model for network data. The NMDR allows the entities associated with each node to have mixed membership in an unbounded collection of latent communities. Learned regression models allow these memberships…
A review of ML and DL for ecological data analysis.
problem Understanding the strengths and limitations of ML and DL in ecological research.
method Historical overview, algorithm families, differences, universal principles, and emerging trends.
result ML and DL excel in prediction tasks but are still debated for causal inference.
Improved forecasting of suicide attempts using LSGPs for patients with little data.
problem Challenges in predicting suicide attempts due to their rarity and patient heterogeneity.
method Introduced Latent Similarity Gaussian Processes (LSGPs) to capture patient heterogeneity.
result LSGPs outperform baseline models, even without kernel-design, and offer new insights into patient similarity.
Community detection is considered as a fundamental task in analyzing social networks. Even though many techniques have been proposed for community detection, most of them are based exclusively on the connectivity structures. However, there are node features in real networks, such as gender types in social networks, fee…
Reflective of income and wealth distributions, philanthropic gifting appears to follow an approximate power-law size distribution as measured by the size of gifts received by individual institutions. We explore the ecology of gifting by analysing data sets of individual gifts for a diverse group of institutions dedicat…
A policy compass indicates the direction in which an institution is going in terms of three general qualities. The three qualities are: suppression, harmony and passion. Any formal institution can develop a policy compass to examine the discrepancy between what the institution would like to do (suggested in its mandate…
Aspects of a phase transition in high-dimensional random geometrycond-mat.stat-mech
High-dimensional random geometry shows phase transitions in various problems.
problem Phase transitions in high-dimensional random geometry.
method Analysis of various financial, optimization, and ecological problems.
result Links between seemingly distant fields and further ramifications.
The global financial system can be represented as a large complex network in which banks, hedge funds and other financial institutions are interconnected to each other through visible and invisible financial linkages. Recently, a lot of attention has been paid to the understanding of the mechanisms that can lead to a b…
Heterogeneous GNN improves species distribution modeling.
problem Predicting species occurrences and habitat suitability using environmental factors.
method Graph Neural Networks (GNN) for presence-only species distribution modeling.
result Heterogeneous GNN model outperforms single-species SDMs and baseline models.
Networks have in recent years emerged as an invaluable tool for describing and quantifying complex systems in many branches of science. Recent studies suggest that networks often exhibit hierarchical organization, where vertices divide into groups that further subdivide into groups of groups, and so forth over multiple…
We consider a problem of ecological inference, in which individual-level covariates are known, but labeled data is available only at the aggregate level. The intended application is modeling voter preferences in elections. In Rosenman and Viswanathan (2018), we proposed modeling individual voter probabilities via a log…
Modeling vessel speed to balance efficiency and environmental risks in Arctic shipping.
problem Balancing vessel speed with environmental and ecological risks in Arctic shipping.
method Inverse control constrained optimization framework with risk parameters estimated from AIS data.
result Distinct decision-making patterns across vessel types and navigational statuses, with varying sensitivity to ice and whale risks.
Exact results on power-law distributions in systems with resets.
problem Understanding power-law distributions in systems with resets.
method Exact mathematical analysis of multiplicative processes with resets.
result Power-law distributions are built up over time and their moments behave quantitatively determined by parameters.
With globalization, countries are more connected than before by trading flows, which currently amount to at least 36 trillion dollars. Interestingly, approximately 30-60 percent of global exports consist of intermediate products. Therefore, the trade flow network of a particular product with high added values can be re…
The Gestalt laws of perceptual organization, which describe how visual elements in an image are grouped and interpreted, have traditionally been thought of as innate despite their ecological validity. We use deep-learning methods to investigate whether natural scene statistics might be sufficient to derive the Gestalt …
The paper explores how innovative financing solutions boost Moroccan businesses' performance.
problem Market volatility, ecological transitions, and technological change pose challenges to business sustainability.
method Examines innovative financing solutions like venture capital, green finance, crowdfunding, and blockchain.
result Embracing innovative financial strategies can transform business challenges into opportunities.
Intermittency and Localizationphysics.soc-ph
In this paper, we show how simple logistic growth that was studied intensively during the last 200 years in many domains of science could be extended in a rather simple way and with these extensions is capable to produce a collection of behaviors widely observed in an enormous number of real-life systems in Economics, …
The 2008 financial crisis illustrated the need for a thorough, functional understanding of systemic risk in strongly interconnected financial structures. Dynamic processes on complex networks being intrinsically difficult, most recent studies of this problem have relied on numerical simulations. Here we report analytic…
Critiques causal reductionism in financial studies, suggesting alternative approaches.
problem Limitations of unidirectional causation in self-referencing systems like finance.
method Critical assessment of causal inference in empirical finance, using ecological models.
result Current financial tools may be limited to ex post inference, especially in reflexive contexts.
Neural likelihood approximates integer time series data efficiently.
problem Inference of parameters for integer-valued stochastic processes is challenging.
method Constructs a neural likelihood approximation for inference of parameters from time series data.
result Accurately approximates the true posterior with significant computational speed-ups.
Within the last fifteen years, network theory has been successfully applied both to natural sciences and to socioeconomic disciplines. In particular, bipartite networks have been recognized to provide a particularly insightful representation of many systems, ranging from mutualistic networks in ecology to trade network…