Urban biodiversity: assessment methods and conservation strategies

Aim. A comparative analysis of the effectiveness of biodiversity conservation measures in ur­ban areas using the example of Singapore, Cape Town, Berlin, and Moscow was conducted using the author's biodiversity index (IB) as a tool.

Methodology. The study is based on the calculation of the author's Biodiversity Index (IB), developed on the principles of the Singapore Index (CBI) and considering the availability of data for key aspects: species richness (birds, plants), the proportion of native flora and endemic species, the area of green areas and the level of air pollution (PM2.5). The data is obtained from open sources (eBird, GBIF, iNaturalist, OSM, OpenAQ, WHO), official urban statistics and published sources.

Results. The IB calculation revealed significant differences in the obtained indicators: Cape Town (83 points) — the maximum score is due to the high proportion of endemics (68%) and species richness, despite the limited area of green areas (22%). Singapore (51 points), when technological solutions compensate for the low proportion of native species (47%) and endemic (15%). Moscow (46 points), where a large area of green areas (54%) is com­bined with a low species diversity of plants (1,647 species) and endemism (5%). Berlin (44 points) has the most developed network of eco-corridors (44% of the territory), but it has limited biodiversity (1527 plant species, 2% endemic). Correlation analysis showed a strong positive relationship between IB and the proportion of endemic species (r = +0.997, p<0,001) and the species richness of the flora (r = +0.980, p<0,01), and a significant nega­tive relationship with the area of green areas (r = -0.938, p<0.01), indicating the priority of quality (naturalness, conservation of endemic species) over quantity (a formal approach to urban greening) in solving biodiversity conservation tasks.

Research implications. The developed Biodiversity Index (IB) can be used as a tool for com­paring cities in different natural and climatic conditions. By applying a relative estimation methodology to calculate the index, such as normalizing indicators to the maximum value in the sample and using weighting coefficients, differences in the initial biological potential of territories can be offset. This makes it possible to assess the effectiveness of urban poli­cies in biodiversity conservation and develop targeted recommendations based on regional specificities. The information gathered through this process can help identify features of model cities, such as the low proportion of native species in Singapore, the fragmentation of habitats in Cape Town, and the prevalence of decorative species in Moscow's landscaping. Established patterns (the priority of preserving natural communities dominated by native species over the expansion of artificial plantings) provide a scientific basis for biodiversity conservation strategies. These patterns are linked to the Sustainable Development Goals (SDG 11 — which focuses on urban environment quality, SDG 13 — which addresses pol­lution impact monitoring, and SDG 15 — which aims to conserve biodiversity). These links allow us to use information to improve national assessment systems, such as integrating quality indicators into the Russian Urban Environment Quality Index.

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