Forest fires are complex spatiotemporal phenomena influenced by local interactions, environmental conditions, and human intervention. In this study, we propose a hybrid modeling framework that integrates a probabilistic cellular automaton (CA) for fire propagation with mobile intelligent agents for active suppression. The fire dynamics are governed by neighbor-dependent stochastic rules modulated by vegetation density and wind intensity, while suppression agents dynamically move toward burning sites and probabilistically extinguish fires. We conduct systematic multi-scenario experiments to examine (1) the impact of wind speed, (2) vegetation density, and (3) the number of suppression agents on fire evolution. Results reveal nonlinear relationships between these parameters and suppression efficiency, including threshold effects and diminishing returns in agent scaling. The model demonstrates how spatial structure, stochasticity, and adaptive control jointly shape fire outcomes. Our framework provides a flexible platform for studying coupled human-environment dynamics and offers insights into optimal resource allocation for wildfire management.
The total number of species on Earth is a fundamental metric in ecology and conservation biology, yet after three centuries of taxonomic effort estimates span two orders of magnitude (2 million to over 1 billion), with little sign of convergence. This persistent uncertainty stems from three intertwined challenges: the preponderance of rare and undersampled species, the methodological incommensurability of extrapolation, decompositional, and molecular approaches, and the lack of a universal species concept for microorganisms. This study proposes the Hierarchical Weighted Cross-Calibration (HWCC) framework, a four-layer probabilistic integration method that synthesizes domain-specific expert estimates, higher-taxon regression constraints, statistical lower bounds (Chao-class estimators), and molecular correction factors derived from eDNA metabarcoding. The seven essential dimensions of the estimation problem are systematically reviewed and quantified: (1) calibration of the known-species baseline, (2) efficacy testing of classical macroecological extrapolations, (3) statistical inference of unseen species, (4) domain-decomposed estimation for major trophic and habitat guilds, (5) quantification of taxonomic dark matter revealed by high-throughput sequencing, (6) dynamic correction for net species loss under contemporary extinction rates, and (7) philosophical and operational reconstruction of the microbial species concept. Monte Carlo propagation of all quantified uncertainties (106 iterations) yields a median global eukaryotic species richness of 71 million, with a 90 % credible interval of 65—78 million. When prokaryotic molecular operational units based on a 95 % average nucleotide identity threshold are included, the interval broadens to 72—86 million. The estimate represents a 7- to 8-fold upward revision from the widely cited 8.7 million and implies that the denominator for current extinction-rate calculations, and thus the magnitude of unrecognized "dark extinctions", has been underestimated by an order of magnitude. The HWCC framework is openly structured for iterative refinement with new data and provides a probability density distribution rather than a single-point estimate, enabling risk-explicit incorporation into global conservation targets and biodiversity scenarios.
The geographical patterning of species diversity, most famously the latitudinal gradient, one of the oldest and most celebrated patterns in ecology, still lacks a unified mechanistic explanation. Existing hypotheses, including the species-energy hypothesis, the environmental heterogeneity hypothesis, the area hypothesis, neutral theory, metabolic theory, maximum entropy theory, and historical-evolutionary hypotheses, each capture a critical dimension of diversity generation. Yet none alone can explain why regions with identical energy inputs can differ so dramatically in species richness. After systematically reviewing these classical and cutting-edge theories, I propose a novel integrative framework: the Niche-Energy-Time triadic synergy hypothesis (NET hypothesis) in present paper. The NET hypothesis starts from three irreducible ultimate constraints: a thermodynamic constraint, available energy flux (E) sets the upper limit on the total biomass and number of individuals a community can sustain; a structural constraint, multidimensional niche space volume (H) determines the fineness with which that energy flow can be partitioned among species; and a historical constraint, the effective evolutionary and community assembly time (T) determines the degree to which that niche space has been filled. I argue that species diversity is an emergent outcome of these three constraints acting as a serial filter not a linear function of any single factor. I present the core mathematical structure of the NET hypothesis, demonstrate its logical necessity by deriving it from population energy allocation, the niche-width-species-number trade-off, and the macro-dynamics of speciation-extinction balance, and show its power to unify a wide range of classical diversity patterns, including the latitudinal gradient, elevational patterns, and island species-area relationships, as well as anomalous cases. The NET hypothesis does not overturn existing hypotheses but embeds energy, heterogeneity, area, and time into the E, H, and T dimensions, revealing the synergistic mechanisms by which they act as necessary but not sufficient conditions. It provides a testable, quantifiable, mechanistic foundation for predicting biodiversity change and guiding conservation planning.
A hierarchical indicator system and scoring sytem to assess plant species' adaptation to climate change was constructed in present study. The AI-driven assessment tool based on the systems was developed. It features with (1) Multi-dimensional adaptive capacity analysis. (2) Support for multiple AI providers (DeepSeek, Google Gemini). (3) Real-time progress tracking. (4) Comprehensive scoring system. (5) Actionable recommendations for conservation and management. By integrating physiological, biological, morphological, and genetic perspectives, it offers actionable insights for conservation planning and ecosystem management.
Understanding the global distribution of species is crucial for their conservation, utilization, and management of the species. In present study, the AI-driven web tool, Species Distribution Finder, was developed. The Species Distribution Finder is a front-end HTML/JavaScript application that enables users to query an AI model (DeepSeek or Google Gemini) to obtain detailed species geographic distribution data. The Species Distribution Finder guides the AI to return structured, exhaustive lists of species occurrence locations (countries, provinces/states, counties, cities, etc.). It acts as a structured front-end AI querying interface which uses precise prompt engineering for scientific comprehensiveness, provides fluid, user- friendly interaction, and enables reproducible, AI-driven biodiversity data exploration. It can be used as a real-time finder for global geographic distribution of any species.
