Urban ecosystems represent complex adaptive systems shaped by anthropogenic pressures, invasive species, and spatial heterogeneity. The expansion of metropolitan areas has led to significant alterations in species composition, ecological connectivity, and biogeographical patterns, necessitating sophisticated modeling approaches to predict, manage, and mitigate ecological impacts. Concurrently, advances in information technology, particularly microservice architectures, have enabled unprecedented capabilities for scalable, resilient, and maintainable data systems. This paper explores the intersection of ecological modeling and computational infrastructure by examining the integration of zero-downtime .NET Core microservices into urban ecological research platforms. Drawing on case studies from plant invasions, seed dispersal mechanisms, and urban biodiversity dynamics, we develop a framework that unites rigorous ecological theory with cutting-edge software engineering practices.

We begin with a detailed review of ecological principles governing urban biota, focusing on plant fecundity, dispersal heterogeneity, and the role of propagule pressure in shaping invasion dynamics (Simberloff, 2009; Reaser et al., 2007; Schurr et al., 2008). Subsequently, we discuss the challenges associated with ecological data collection, storage, and analysis, highlighting the limitations of traditional monolithic database systems and the potential for service-oriented architectures to enhance computational efficiency and reliability. We emphasize the advantages of zero-downtime migration strategies for microservices, which allow continuous operation of research platforms during system updates and feature integration (.NET Core Microservices for Zero-Downtime AuthHub Migrations, 2025).

Methodologically, we adopt a hybrid approach that synthesizes ecological survey data, remote sensing-derived land cover metrics (Stys et al., 2004), and climate simulations to model plant distribution and urban habitat connectivity. Statistical modeling is performed using the R programming environment (R Development Core Team, 2010), leveraging multivariate regression, spatial autocorrelation analysis, and stochastic simulation frameworks. Ecological observations are interpreted in the context of species-specific traits, such as the germination constraints of Ficus aurea (Swagel et al., 1997) and canopy seed rain dynamics in tropical cloud forests (Sheldon & Nadkarni, 2013).

Our findings indicate that integrating microservice-based computational frameworks significantly enhances the scalability and responsiveness of urban ecological models. Service-oriented designs facilitate modular updates, parallel processing of spatially heterogeneous datasets, and robust error handling, thereby supporting high-resolution simulations of urban biodiversity dynamics. Furthermore, by combining propagule pressure models with advanced software architectures, we demonstrate a predictive capacity for potential invasion hotspots and the efficacy of mitigation strategies.

In the discussion, we critically evaluate the interplay between ecological theory and computational implementation, addressing limitations such as data sparsity, cross-scale inference challenges, and the complexities of urban-rural gradient effects on species interactions (Seabloom et al., 2006; Song et al., 2012). We propose a set of best practices for designing resilient urban ecological research infrastructures, emphasizing the importance of cross-disciplinary collaboration between ecologists, software engineers, and urban planners. The paper concludes with an outlook on the evolving role of microservice architectures in ecological research, advocating for continued integration of computational resilience and ecological insight to advance sustainable urban development.

Urban ecology, Microservices, Zero-downtime migration, Plant invasions

Seabloom, E. W., J. W. Williams, D. Slayback, D. M. Stoms, J. H. Viers, and A. P. Dobson. 2006. Human impacts, plant invasion, and imperiled plant species in California. Ecological Applications 16:1338–1350.

Serrato, A., G. Ibarra-Manrı´quez, and K. Oyama. 2004. Biogeography and conservation of the genus Ficus (Moraceae) in Mexico. Journal of Biogeography 31:475–485.

Schurr, F. M., O. Steinitz, and R. Nathan. 2008. Plant fecundity and seed dispersal in spatially heterogeneous environments: models, mechanisms and estimation. Journal of Ecology 96:628–640.

Simberloff, D. 2009. The role of propagule pressure in biological invasions. Annual Review of Ecology, Evolution, and Systematics 40:81.

Swagel, E. N., A. V. H. Bernhard, and G. S. Ellmore. 1997. Substrate water potential constraints on germination of the strangler fig Ficus aurea (Moraceae). American Journal of Botany 84:716.

Song, L., Liu, W., & Nadkarni, N. M. 2012. Response of non-vascular epiphytes to simulated climate change in a montane moist evergreen broad-leaved forest in southwest China. Biological Conservation,152, 127-135.

Stracey, C. M., and S. K. Robinson. In press. Does nest predation shape urban bird communities? Studies in Avian Biology.

R Development Core Team. 2010. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org

Stys, B., R. Kautz, D. Reed, M. Kertis, R. Kawula, C. Keller, and A. Davis. 2004. Florida vegetation and land cover data derived from 2003 Landsat ETM+ imagery. Florida Fish and Wildlife Conservation Commission, Tallahassee, Florida, USA.

McPherson, J. R. 1999. Studies in urban ecology: strangler figs in the urban parklands of Brisbane, Queensland, Australia. Australian Geographical Studies 37:214–229.

Sheldon, K. S., & Nadkarni, N. M. 2013. Spatial and temporal variation of seed rain in the canopy and on the ground of a tropical cloud forest. Biotropica. 45(5), 549-556.

Reaser, J. K., L. A. Meyerson, and B. Von Holle. 2007. Saving camels from straws: how propagule pressure-based prevention policies can reduce the risk of biological invasion. Biological Invasions 10:1085–1098.

.NET Core Microservices for Zero-Downtime AuthHub Migrations. (2025). European Journal of Engineering and Technology Research, 10(5), 1-4. https://doi.org/10.24018/ejeng.2025.10.5.3288.