SearcharxivSearch

arXiv · 2509.18217

Stochastic Dynamics of Urban Predator-Prey Systems: Integrating Human Disturbance and Functional Responses

Abstract

Urban ecosystems exhibit complex predator-prey dynamics increasingly disrupted by anthropogenic disturbances (e.g., noise, habitat fragmentation). Classical Lotka-Volterra (LV) models fail to capture these human-induced stressors, and integrated frameworks incorporating functional responses, stochasticity, and spatial dynamics remain scarce. We develop a comprehensive stochastic model to quantify how human disturbance reshapes predator-prey interactions in urban landscapes, using rat-cat systems as a case study. Our framework extends the LV model to incorporate: (i) human disturbance as an external mortality factor, (ii) Holling Type III functional responses to model predation saturation and prey refugia, (iii) multiplicative noise and periodic forcing to capture stochastic disturbance regimes, and (iv) spatial diffusion across fragmented habitats. We non-dimensionalize the system to generalize dynamics and analyze stability, bifurcations, and noise-induced transitions. Numerical simulations (MATLAB) reveal three key outcomes: (1) Human disturbance disrupts classical oscillations, inducing quasi-periodic cycles and elevating extinction risks; (2) Stochasticity lowers collapse thresholds by 25% compared to deterministic predictions; (3) Spatial diffusion drives pattern formation (e.g., disturbance shadows, prey hotspots) through habitat coupling. Results highlight the extreme vulnerability of urban wildlife to anthropogenic pressures, demonstrating how disturbance intensity governs system stability. The model provides a predictive framework for conservation strategies, emphasizing refuge enhancement and phased interventions synchronized with population cycles.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Arhonefe Joseph Ogethakpo, Ozioma Ogoegbulem, Sarduana Joshua Apanapudor, Helen Onovwerosuoke Sanubi. 2025-09-21. Stochastic Dynamics of Urban Predator-Prey Systems: Integrating Human Disturbance and Functional Responses. https://doi.org/10.11648/j.ajam.20251304.15

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Evolution as fitness landscape navigation: concepts, measures, and emerging questions

Fitness landscapes are mappings between genotypes, phenotypes, and fitness that shape evolution. In recent years, empirical work and theoretical models have greatly advanced our understanding of how populations navigate rugged fitness landscapes. Here, we provide a timely review of the theoretical aspects of this field. Its rapidly growing literature employs a wide range of terms, which are sometimes used ambiguously or inconsistently. We therefore begin by defining the major concepts and the field's vocabulary, highlighting our own terminology choices wherever needed. We then review key results on the relationships between epistasis, ruggedness, accessibility, and navigability for genotype-fitness maps, highlighting several complex and sometimes counterintuitive connections that have emerged. Further, we review how the conserved structural properties of the underlying genotype-phenotype map, which can lead to the formation of large connected neutral networks of genotypes, influence dynamics on fitness landscapes. We then compare the two levels to study landscape navigation: the level of genotype-phenotype maps and the level of genotype-fitness maps. Our review leads us to propose a new measure of navigability, based on evolutionary outcomes, that is broadly applicable and overcomes limitations of existing measures. Finally, we highlight examples from the smaller body of work that relaxes the common assumption of fitness-monotonic paths on static landscapes, and discuss how this can fundamentally change the nature of fitness landscape navigation. Throughout the review, we identify directions for future work to fill existing gaps and to synthesize the disparate strands of research within the field.

q-bio.PE

Best Matches in Phylogenetic Networks

Best match graphs (BMGs) were introduced in mathematical phylogenetics to describe the concept of closest relatives for related genes (leaves of rooted tree) in different organisms (defining leaf colors). We generalize this concept here to leaf-colored rooted networks, where least common ancestors are in general neither unique nor comparable. We characterize BMGs of rooted networks as those vertex-colored digraphs that are properly colored and satisfy an easy-to-check condition that we call the sicor-in-hub property. BMGs can be recognized in linear time and an explaining network can be constructed in quadratic time. Analogous results are obtained for reciprocal best match graphs (RBMGs), where an edge $\{x,y\}$ corresponds to pairs of vertices with different color that are mutually closest relatives.

q-bio.PE

Exact Counts of Binary Phylogenetic Networks with Four Reticulations

Phylogenetic networks provide a flexible framework for representing reticulate evolutionary processes, such as hybridization, introgression, recombination, and horizontal gene transfer. However, their combinatorial complexity makes even basic enumeration problems difficult. Building on our previous work for networks with up to three reticulations, we derive an explicit closed-form formula for the number of unrestricted rooted binary phylogenetic networks with four reticulations on \(n\) labeled taxa. Our approach is based on tree-component graphs. We classify the 79 possible component graphs corresponding to networks with four reticulations into ten groups. We then enumerate the networks associated with each group by combining known counts of one-component networks, forests, and networks with fewer reticulations. Summing these contributions yields the desired formula. This result extends the exact enumeration of unrestricted binary phylogenetic networks to four reticulations and further demonstrates the effectiveness of component graphs for systematically organizing and counting increasingly complex network classes.

q-bio.PE