Hasil untuk "Ecology"

P. Hosten, T. Allen, T. Hoekstra

P. Dayton

A. Naess

S. Bottoms, A. Franks, P. Kramer

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R. Catchpole, D. Fautin, Douglas J. Futuymqa et al.

J. Bernardo

S. Pickett, M. Cadenasso

J. Endler, A. Basolo

J. K. Cronk, M. Fennessy

L. Mech, L. Boitani

C. Brown

B. McNab, James H. Brown

R. May, S. Levin, G. Sugihara

D. Stephens, Joel s. Brown, R. Ydenberg

D. Raubenheimer, S. Simpson, D. Mayntz

Mingxi Zou, Jiaxiang Chen, Aotian Luo et al.

Conventional financial strategy evaluation relies on isolated backtests in static environments. Such evaluations assess each policy independently, overlook correlations and interactions, and fail to explain why strategies ultimately persist or vanish in evolving markets. We shift to an ecological perspective, where trading strategies are modeled as adaptive agents that interact and learn within a shared market. Instead of proposing a new strategy, we present FinEvo, an ecological game formalism for studying the evolutionary dynamics of multi-agent financial strategies. At the individual level, heterogeneous ML-based traders-rule-based, deep learning, reinforcement learning, and large language model (LLM) agents-adapt using signals such as historical prices and external news. At the population level, strategy distributions evolve through three designed mechanisms-selection, innovation, and environmental perturbation-capturing the dynamic forces of real markets. Together, these two layers of adaptation link evolutionary game theory with modern learning dynamics, providing a principled environment for studying strategic behavior. Experiments with external shocks and real-world news streams show that FinEvo is both stable for reproducibility and expressive in revealing context-dependent outcomes. Strategies may dominate, collapse, or form coalitions depending on their competitors-patterns invisible to static backtests. By reframing strategy evaluation as an ecological game formalism, FinEvo provides a unified, mechanism-level protocol for analyzing robustness, adaptation, and emergent dynamics in multi-agent financial markets, and may offer a means to explore the potential impact of macroeconomic policies and financial regulations on price evolution and equilibrium.

Tianyu Song, Van-Doan Duong, Thi-Phuong Le et al.

Accurate identification of wood species plays a critical role in ecological monitoring, biodiversity conservation, and sustainable forest management. Traditional classification approaches relying on macroscopic and microscopic inspection are labor-intensive and require expert knowledge. In this study, we explore the application of deep learning to automate the classification of ten wood species commonly found in Vietnam. A custom image dataset was constructed from field-collected wood samples, and five state-of-the-art convolutional neural network architectures--ResNet50, EfficientNet, MobileViT, MobileNetV3, and ShuffleNetV2--were evaluated. Among these, ShuffleNetV2 achieved the best balance between classification performance and computational efficiency, with an average accuracy of 99.29\% and F1-score of 99.35\% over 20 independent runs. These results demonstrate the potential of lightweight deep learning models for real-time, high-accuracy species identification in resource-constrained environments. Our work contributes to the growing field of ecological informatics by providing scalable, image-based solutions for automated wood classification and forest biodiversity assessment.

Davide Zanchetta, Deepak Gupta, Sofia Moschin et al.

Ecosystems frequently display the coexistence of diverse species under resource competition, typically resulting in skewed distributions of rarity and abundance. A potential driver of such coexistence is environmental fluctuations that favor different species over time. How to include and treat such temporal variability in existing consumer-resource models is still an open problem. In this work, we study correlated temporal fluctuations in species' resource uptake rates -- i.e. metabolic strategies -- within a stochastic consumer-resource framework. In a biologically relevant regime, we are able to find analytically the species abundance distributions through the path integral formalism. Our results reveal that stochastic dynamic metabolic strategies induce community structures that align more closely with empirical ecological observations. Within this framework, ecological communities show a higher diversity than expected under static competitive scenarios. We find that all species become extinct when the ratio of the number of species to the number of resources exceeds a critical threshold. Conversely, diversity peaks at intermediate values of the same ratio. Furthermore, when metabolic strategies of different species are different on average, maximal biodiversity is achieved for intermediate values of the amplitude of fluctuations. This work establishes a robust theoretical framework for exploring how temporal dynamics and stochasticity drive biodiversity and community structure.

Preet Mishra, Shyam Kumar, Sorokhaibam Cha Captain Vyom et al.

Evolutionary changes impacts interactions among populations and can disrupt ecosystems by driving extinctions or by collapsing population oscillations, posing significant challenges to biodiversity conservation. This study addresses the ecological rescue of a predator population threatened by a mutant prey population using optimal control method. To study this, we proposed a model which incorporates genotypically structured prey comprising of wild-type, heterozygous and mutant prey types and predator population. We proved that this model has both local and global existence and uniqueness of solutions ensuring the model robustness. Then, we applied optimal control method along with Pontryagin Maximum Principle by incorporating a control input in the model to minimize the mutant population and subsequently to stabilize the ecosystem. The numerical results clearly reveal that the undesired dynamics of the model can be controlled showing the suppression of the mutant, rescues the predator, and restores the oscillatory dynamics of the system. These findings demonstrate the efficacy of the control strategy and provide a mathematical framework for managing such ecological disruptions