Global biodiversity continues to decline under the combined pressures of habitat loss, climate change, pollution, and overexploitation, placing nearly one million species at risk of extinction (Diaz et al., 2019). This alarming trend calls urgent and transformative actions. Since their emergence in the nineteenth century, protected areas have become a cornerstone of biodiversity conservation, originally established to preserve natural landscapes and wildlife (Pacifici et al., 2020; Watson et al., 2014). The Kunming-Montreal Global Biodiversity Framework has reaffirmed the importance of area-based conservation by setting an ambitious target of effectively conserving and managing at least 30% of the planet’s terrestrial, inland water, and coastal and marine areas by 2030 (Stephens, 2023; Neugarten et al., 2024). However, many scientists remain skeptical about the feasibility of this target, citing the limitations of the previous Aichi Targets and the risk of repeating similar shortcomings (Phang et al., 2020; Rochette et al., 2014; Watson et al., 2023).

Recent research emphasizes that effectively safeguarding areas of high ecological and biological value requires more than simply increasing the number or total area of protected sites (Gurney et al., 2023; Li et al., 2023; Maxwell et al., 2020). Studies highlight that a focus purely on spatial extent can inadvertently lead to the downgrading of existing protected areas in favor of establishing new sites that may lack ecological significance (Jonas et al., 2024; Sovinc and Kržič, 2025). In practice, protected areas are often designated in locations with low socio-economic conflict rather than where threatened species are most concentrated (Visconti et al., 2019; Dasgupta et al., 2025). To fulfill global conservation commitments and reverse biodiversity loss, conservation strategies must prioritize ecological representativeness and management effectiveness. This includes ensuring that protected areas encompass viable populations and their required habitats, not merely meeting area-based quotas (Barnes et al., 2018; Cui et al., 2025). Zoning approaches should thus emphasize the identification and protection of critical habitats, integrating species’ spatial needs into the functional layout of conservation areas (Herrera-Montes, 2018; Pressey et al., 2007; Rotich, 2012). Therefore, zoning should not only delineate areas for conservation and sustainable use, but also emphasize the protection of key habitats, forming a foundation for effective biodiversity conservation.

Understanding population dynamics and spatial distribution is essential for identifying biologically important areas and assessing the effectiveness of protected area design (Williams et al., 2017). However, conducting wildlife censuses is often labor-intensive, time-consuming, and logistically challenging (Witmer, 2005). Over the past few decades, aerial surveys have become widely used to estimate the abundance and distribution of various terrestrial and marine species, including rhinoceroses (Goddard, 1967), seabirds (Buckland et al., 2012), seals (Cunningham et al., 2010), turtles (Fernandes et al., 2025), manatee (Sanchez-Galan et al., 2025) and dugongs (Preen, 2004). Satellite remote sensing has further expanded our ability to monitor biodiversity at large spatial scales. Early efforts relied on indirect indicators such as guano stains (Schwaller et al., 1989), vegetation degradation, or burrows (Loffler and Margules, 1980) to infer animal presence and abundance.

Since the launch of Ikonos in 1999, a growing number of very high-resolution (VHR) satellite platforms with sub-meter resolution have enabled direct detection of individual animals (Wang et al., 2019). The use of VHR imagery for wildlife monitoring offers key advantages—high spatial and temporal resolution, broad coverage, minimal habitat disturbance, and retrospective analysis through archival data. These capabilities make VHR imagery a powerful tool for studying animal population dynamics and for conservation planning. Case studies involving whales, seals, and albatrosses have demonstrated its feasibility and accuracy in estimating population size, mapping habitat use, and assessing conservation outcomes (Attard et al., 2025; Cubaynes et al., 2024; Duporge et al., 2025; Fretwell et al., 2017; Zhao et al., 2021). Unlike most previous applications that focus on species detection, our approach links remote sensing data directly with reserve zoning performance, offering a novel framework for species-oriented conservation planning. Extensive archives of VHR imagery generated over the past two decades provide a long-term record of species distributions and habitats, offering valuable information for conservation at relatively low cost.

In this study, we apply a species-oriented conservation approach to assess and improve the spatial zoning of a national nature reserve in China, focusing on the wintering behavior of the whooper swan (Cygnus cygnus). Using a series of archived VHR satellite images, we estimated the abundance and distribution of wintering swans from 2008 to 2016, with a particular focus on the 2015–2016 overwintering season. By comparing swan spatial distribution with the reserve’s existing core, buffer, and experimental zones, we identified spatial mismatches between functional zoning and actual animal habitat use. Based on these findings, we propose a rezoning strategy that aligns protected areas more closely with the ecological needs of the target species. This approach illustrates how satellite-derived animal distribution data can support evidence-informed zoning assessments. The findings offer practical guidance for protected area managers by illustrating how satellite-derived animal distribution data can inform adaptive zoning and enhance conservation outcomes. The case study further underscores the broader value of integrating animal remote sensing into protected area management, providing insights for evidence-based conservation and contributing to the 30 × 30 global biodiversity target.

Aligning the spatial configuration of protected areas with the ecological requirements of target species is a fundamental principle in conservation science and practice (Dudley, 2008; Geldmann et al., 2013; Rodrigues and Cazalis, 2020). Remote sensing has emerged as a powerful tool in conservation, offering opportunities to optimize protected area networks and evaluate the effectiveness of conservation interventions (Rose et al., 2015). This study provides a case-based demonstration of how satellite-derived data on species abundance and distribution can be directly applied to assess and refine spatial zoning, highlighting the potential of wildlife remote sensing for advancing species-oriented conservation planning.

Historically, the whooper swan was prevalent across Rongcheng’s coastal lagoons and bays, with reports of over 10,000 individuals in the 1960s and 1990s. However, due to human activities such as sea reclamation and aquaculture pond construction, their presence in other coastal waters has dwindled over the past two decades, making the reserve their final refuge in the region. Yuehu Lagoon, part of the reserve, annually hosted approximately 3,000 to 4,000 swans between 1988 and 1994, with numbers ranging from 595 to 1086 during the 2015–2016 overwintering period. This declining trend is confirmed by our findings. The population’s fluctuations across different locations during the overwinter period, reflecting shifts in habitat preference, are attributed to variations in food availability, ice coverage, and other factors, a pattern also validated by other studies (Jia et al., 2019; Rees et al., 1997). In December 2015, the swans in Yuehu Lagoon fed on seagrass seedlings in shallow waters, but the population dwindled, possibly due to ice formation and seedling consumption. The relatively stable swan numbers on the northern shores of Yuehu and at Yandunjiao might be due to feeding by reserve staff and tourists.

Although the reserve was initially zoned to protect water bodies, our findings reveal a mismatch between current zoning boundaries and key habitats used by whooper swans. In particular, critical mudflat areas were excluded from the core zones despite their significance for resting and foraging, especially during the winter months when water bodies are frozen. Major congregation areas—including the northern and western shores of Yuehu Lagoon and the shoreline at Yandunjiao—currently fall within buffer or experimental zones. This spatial misalignment undermines the reserve’s effectiveness in meeting national conservation standards. We therefore recommend revising the zoning scheme to designate these swan aggregation areas as core zones.

Habitat use patterns observed during the study also reflect ongoing shifts in ecological suitability. Yangyuchi Bay, which previously hosted large numbers of swans, now supports few individuals, possibly due to seagrass loss or habitat degradation. Unlike Yuehu Lagoon, where seagrass restoration efforts have been documented (Zhang et al., 2015), no similar initiatives have been implemented in Yangyuchi Bay. Our results suggest the need to initiate habitat assessments and potential restoration in this area. The growing swan presence on the western shore—a relatively undisturbed area shaped by a small river—may indicate an emerging habitat preference. Conversely, the sustained high numbers at sites receiving artificial feeding suggest an ongoing reliance on human support. For long-term sustainability, reserve management should prioritize the enhancement of natural habitats over artificial feeding.

While this study demonstrates the effectiveness of VHR satellite imagery for wildlife monitoring and conservation planning, several limitations should be acknowledged. The method is most effective for species that aggregate in open, unvegetated habitats; its applicability is limited in areas with dense vegetation or canopy cover. The applicability of the approach also depends on the characteristics of the species and the surrounding environment. It performs best for large-bodied and high-contrast species that can be clearly distinguished from the background in open coastal wetlands, mudflats or grasslands. Although this study focuses on whooper swans, the general framework can be applied to other colonial or flocking species, provided that habitat openness and target contrast are sufficient for reliable detection. For more complex ecosystems or species with limited spectral contrast, additional sensors or analytical techniques may be required to achieve accurate identification. Image availability, weather conditions, and acquisition timing may also affect detection accuracy. Furthermore, the cost of commercial VHR imagery may pose challenges for routine monitoring. Future work should explore the integration of emerging technologies such as low-cost high-resolution satellites, unmanned aerial systems, and machine learning algorithms (Hollings et al., 2018) to broaden the method’s applicability across species and ecosystems.

Artificial intelligence has been increasingly applied to image-based wildlife surveys in recent years, and its performance in recognizing large, well-defined targets in aerial and ground imagery has improved rapidly, as evidenced by recent reviews and empirical studies demonstrating significant progress in animal detection across aerial, drone and satellite platforms (Xu et al., 2024; Wu et al., 2023; Axford et al., 2024). However, recent reviews and empirical studies suggest that automated detection methods may struggle when targets occupy only a few pixels, when background complexity is high, or when animals cluster densely, conditions that are common for colonial waterbirds and marine mammals in satellite scenes (Nikouei et al., 2025; Hua and Chen, 2025). Under these conditions, AI-based detection methods may merge adjacent individuals or fail to separate them from the background with sufficient accuracy. These constraints are a key reason why AI techniques were not adopted in the present study, and why a brightness-based workflow provided more reliable results for identifying individual swans. As analytical tools continue to advance, future work should examine how AI methods can be adapted to improve small-target detection and to distinguish tightly aggregated individuals in satellite images.

This study highlights the practical value of integrating satellite-based wildlife monitoring with protected area management. By using very high-resolution (VHR) satellite imagery to estimate the abundance and distribution of whooper swans, we demonstrated how remote sensing data can support adaptive spatial zoning and identify mismatches between designated conservation zones and actual habitat use. The findings emphasize the need for species-oriented zoning strategies that account for seasonal shifts, habitat degradation, and human-induced changes. Incorporating animal remote sensing into conservation planning offers a scalable, non-invasive, and cost-effective approach to enhance biodiversity protection, especially for migratory species. Our results provide a replicable framework for aligning conservation targets with ecological realities and advancing the implementation of global biodiversity goals.