Exploring the role of urban vegetation in ecosystem-based adaptation strategies in the coastal cityscape of Campeche, Mexico

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Romero-Uribe, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7686968/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Predicted population growth and urbanization in low-elevation coastal zones threaten natural ecosystems, causing landscape fragmentation, biodiversity loss, and reduced ecosystem services, such as climate regulation, crucial for coastal communities. These impacts undermine the capacity of communities to cope with climate change, including rising urban temperatures. Ecosystem-based adaptation (EbA) can address ecological and social challenges simultaneously. This study aimed to identify areas where green infrastructure projects could maximize benefits for landscape connectivity and temperature regulation in Campeche, Gulf of Mexico. Between 1995 and 2023, 1,249 ha of urban vegetation were lost, reducing access to green areas to under 10% in a quarter of neighborhoods and decreasing functional landscape connectivity by over 54%. During the same period, average temperatures rose from 29.5°C to 33°C. Areas without forest cover and functional connectivity (439 ha) exhibited significantly higher mean temperatures (33.43°C, STD 0.52) than vegetated areas (31.84°C, STD 0.49). Priority intervention sites were identified, covering 8.4% of the urban area, allowing recovery of temperature regulation services and enhancing landscape connectivity. These findings provide actionable insights for urban planning and EbA strategies in coastal cities facing climate change, highlighting the importance of preserving and restoring urban vegetation to maintain ecological functions and human wellbeing. Urbanization Coastal city Connectivity dynamics Climate regulation Green space loss Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The expected rise in population and urbanization in coastal regions, which will lead to an increase in coastal assets, is closely tied to forthcoming socioeconomic development (Hoornweg & Pope, 2017 ; Merkens, Reimann, Hinkel, & Vafeidis, 2016 ). Low elevation coastal zones (LECZ), defined as regions with an elevation less than 10 m above sea level and constituting 2% of the Earth's land area (McGranahan, Balk, & Anderson, 2007 ), are particularly susceptible to these dynamics. Currently, the global LECZ are inhabited by 898 million people, with projections suggesting a potential increase to 1.2 billion depending on socioeconomic scenarios, highlighting the swift rise in vulnerability to coastal hazards (Reimann, Vafeidis, & Honsel, 2023 ). Moreover, urbanization patterns in these areas significantly shape global transformations, profoundly affecting ecosystems and their essential services (Chen, Zhou, & Liang, 2023 ; Peng, Zhao, Guo, Pan, & Liu, 2017 ). The increasing populations in these zones also extend the consequences of urban expansion beyond mere demographic shifts, influencing the intricate network of ecological connections and functions (Sahraoui et al., 2021 ). The expansion of urban areas along coastlines not only affects the resistance and resilience of coastal ecosystems but also fragments natural habitats, leading to a loss of habitat connectivity and biodiversity (Aguilera & González, 2023 ; Mulinge, 2023 ). This urban growth also has diverse effects on various ecosystem services, such as habitat provision, nutrient cycling, and climate regulation, among others (Liu, He, Gao, Zhan, & Yang, 2023 ; Richards & Friess, 2017 ). Therefore, sustainable urban planning and conservation measures are key in addressing these challenges and ensuring the coexistence of urban development with the preservation of essential coastal ecosystem services and functions (Aguilera & González, 2023 ). Climate regulation, a critical ecosystem service, is facilitated by vegetation through shading, evapotranspiration, and the creation of microclimates (Manoli et al., 2019 ; Richards & Edwards, 2017 ), influencing local temperature patterns, heat absorption, and overall climate conditions within urban environments (Heckbert, Costanza, Poloczanska, & Richardson, 2011 ). However, urban growth, characterized by increased impervious surfaces and reduced natural areas, induces the Urban Heat Island (UHI) effect, increasing ambient and surface temperature relative to surrounding non-urban areas (Bai et al., 2017 ; Kotharkar & Bagade, 2018 ). This effect has detrimental consequences on human health, especially among vulnerable populations, such as the elderly and outdoor workers (Artiola, Reynolds, & Brusseau, 2019 ). Hence, creating green spaces, including water bodies, can help to mitigate the UHI effect by providing cooler environments and fostering more comfortable living conditions (De Carvalho & Szlafsztein, 2019 ; Ramsay et al., 2023 ; Wong, Alias, Aghamohammadi, Aghazadeh, & Sulaiman, 2017 ). More frequent and intense heatwaves, changing rainfall patterns, and more extreme weather events are expected as climate change effects (IPCC, 2023 ). These changes will have significant impacts on the environment, ecosystems, and human societies (Gabric, 2023 ). Furthermore, extreme heat events, exacerbated by the UHI effect, will directly impact public health, especially in urban areas (Founda & Santamouris, 2017 ; Kotharkar & Bagade, 2018 ). Thus, addressing the predicted temperature rise requires comprehensive efforts, including the reduction of greenhouse gas emissions and the implementation of adaptation strategies that can help mitigate these impacts. Within the context of urbanization and climate change in coastal regions, the ecosystem-based adaptation (EbA) approach emerges as a promising strategy rooted in sustainable management of ecosystems (Ma & Jiang, 2023 ). This comprehensive framework integrates ecological and human-centered considerations, recognizing the intricate connections between functional ecosystems and the resilience of human communities (Nalau et al., 2018 ; Scarano, 2017 ). A crucial component of EbA involves the greening of urban spaces, emphasizing the importance of incorporating nature into urban landscapes (Reid et al., 2018 ). Due to the limited availability of resources, urban greening initiatives depend on selecting where these efforts can yield the most significant positive socioecological outcomes. Prioritizing nature-based actions is crucial in shaping public policies for heightened effectiveness, particularly those grounded in nature (Van den Bosch & Sang, 2017 ). For example, selecting sites where green infrastructure could increase temperature regulation capacity and landscape connectivity could be an efficient resource allocation and an EbA measure. From an environmental perspective, strategically located urban green infrastructure enhances landscape functional connectivity by linking scattered vegetation fragments within the city, therefore allowing some species to find food and refuge areas in a potentially hostile environment (Wang et al., 2021 ). Simultaneously, the same infrastructure can contribute to residents’ quality of life mitigating some of the UHI effects (Bannerji & Bhanja, 2023 ; Li & Chen, 2023 ; Liang & Gong, 2020 ; Tam, Gough, & Mohsin, 2015 ). This integrated approach enables targeted and effective interventions, fostering ecosystem health, biodiversity conservation, and community well-being amid the challenges of urbanization. This study assesses the effect of urban growth dynamics on the temperature regulation of Campeche City, to identify optimal sites for urban green infrastructure efforts under an EbA approach. First, the spatial configuration of land use and vegetation changes in 28 years (between 1995 and 2023) was analyzed. Then, the effects of urban growth on landscape connectivity and temperature dynamics were evaluated. Finally, priority zones for green infrastructure efforts were identified, and recommendations were provided to support informed decision-making and promote sustainable development initiatives in coastal cities facing temperature changes driven by dynamic environmental factors. Materials and methods Study area The city of Campeche, designated as a UNESCO Cultural Heritage of Humanity since 1999,is located on the coastal plain of the Gulf of Mexico in the southwest region of the Yucatán Peninsula (90°32'22.737" W, 19°50'41.331" N). This city spans an area of 52.36 km² (INEGI, 2022 ) (see Fig. 1 ). The topography of the area is characterized by a relatively flat terrain, with elevations ranging from 8 to 100 ms above sea level and exhibiting gentle slopes. In contrast to neighboring cities in the Yucatán Peninsula, which often rest upon the remnants of ancient Mayan settlements, the urban layout of Campeche City reflects significant influences from colonial historical, strategic, commercial, and security functions. Regarding demographic dynamics, in 2020, the municipality of Campeche experienced an average annual growth rate of 1.3%, slightly surpassing the national average of 1.2%. Over the past quarter-century, the city's population has seen a notable increase of 40%, rising from 178,160 residents in 1995 to 249,623 inhabitants in 2020 (INEGI, 1995 , 2020 ). The climate in the city of Campeche is warm subhumid with summer rainfall patterns. The region experiences an average annual temperature of 27.1°C, with monthly averages ranging from 24.7°C to 28.4°C in the coldest and hottest months, respectively (INEGI, 2021 ). Additionally, the annual precipitation averages at 1,125.3 mm, with recorded extremes ranging from 665.3 mm in the driest year to 1,965.9 mm in the wettest year. The rainy season in Campeche usually occurs during August and September. On the other hand, the dry season is during March and April (INEGI, 2021 ). Campeche is expected to be affected by climate change. Climate models project a potential increase in the annual mean temperature of 2.5°C to 4°C between the historical period (1961–2000) and the distant future (2075–2099), accompanied by heightened minimum and maximum temperatures. Furthermore, these projections forecast alterations in precipitation patterns, particularly affecting the state's southern region (CCPY, 2014 ). Landscape connectivity analysis Structural and functional connectivity were assessed using a reference land use and land cover map for years 1995 and 2023. Structural and functional connectivity metrics were calculated, along with surface temperature analysis, to identify priority areas for intervention Land-use and land-cover map Land use and land cover (LULC) maps were generated based on orthophotos for 1995 and satellite images for 2023. The 1995 orthophoto, with a 2-meter resolution, was obtained from the Mexican National Institute of Statistics and Geography (INEGI), and the 2023 satellite image from the Copernicus program. The image classification was performed with an object-based approach and the Random Trees classifier of Trimble eCognition® Developer 9.0. Three categories were defined: areas with tree cover (native and exotic, such as parks), grasslands, and urban areas. The classification process for the year 2023 adhered to the recommendations of Campbell et al. ( 2015 ) to ensure accuracy, using approximately 200 samples for training and validation, obtained from field visits and recent Google Earth images. Furthermore, the 2023 sampling units were reinterpreted and adjusted through visual inspection to reflect 1995 conditions to address potential discrepancies over time. The final maps were validated using the mentioned samples, applying an area-based error matrix and Kappa index, following the guidelines of Congalton and Green ( 2019 ). Structural and functional connectivity metrics The spatial configuration of different types of green spaces was analysed using LULC maps previously obtained. We calculated both class-level and landscape-level metrics using Fragstats v4.2.1 software (McGarigal, Cushman, & Ene, 2012 ) for the years 1995 and 2023. The class-level metrics included class area (CA) in ha, the number of patches (NP), and the mean patch size (Area MN) in ha. Landscape-level metrics included the Largest Patch Index (LPI; equals the area of the largest patch of the corresponding patch type divided by total landscape area, multiplied by 100 to convert to a percentage) and the Simpson's Diversity Index (SIDI; equals 1 minus the sum of the squared proportional abundances of each patch type). The LPI, which ranges from 0 to 100, measures how much a landscape is dominated by a single patch. A higher LPI value indicates that the landscape consists mostly of one continuous patch, while a lower value suggests a more fragmented landscape with multiple patches. Meanwhile, the SIDI, measured on a scale from 0 to 1, represents a spectrum from complete homogeneity (0) to infinite diversity (1). These two measures are complementary; while class-level metrics treat vegetation elements as independent entities, landscape-level indices provide insights into their interactions (Gustafson & Parker, 1992 ; Weaver & Shannon, 1949 ). Additionally, we assessed functional connectivity to understand how individual species behave in response to the physical structure of the landscape, following Alonso et al. ( 2017 ). This approach was selected because landscape connectivity, defined as the degree to which the landscape facilitates species movement among its elements, is influenced by the spatial arrangement, shape, and extent of vegetation fragments, directly affecting species abundance and distribution. Functional connectivity was assessed with the probability of connectivity index (PC), which integrates habitat patch area and interconnectivity through graph theory. The PC index (Eq. 1 ) quantifies connectivity on a scale from 0 (no connectivity) to 1 (maximum connectivity). While functional connectivity was assessed based on species-specific dispersal distances (average, median, or maximum dispersion), structural connectivity was determined by the spatial distribution of tree cover fragments in the landscape (Saura, 2013 ). $$\:PC=\frac{{\sum\:}_{i=1}^{n}{\sum\:}_{j=1}^{n}{a}_{1}\times\:{a}_{2}\times\:{p}_{ij}^{*}}{{A}_{L}^{2}}=\frac{{PC}_{num}}{{A}_{L}^{2}}$$ 1 where \(\:PC\) is the connectivity probability, \(\:{p}_{ij}^{*}\) is the probability of the maximum path, and \(\:\:{A}_{L}\) is the landscape area (habitat and non-habitat). Also, we quantified the contribution of individual woody elements to overall connectivity using the delta PC (dPC), which reflects the relative importance of each fragment. The dPC value of a specific vegetation fragment, reflects the disparity in connectivity between the original landscape and the landscape without that fragment, offering a measure of the fragment's relative importance (Calabrese & Fagan, 2004 ). This map exclusively focuses on the woody vegetation category, given its societal benefits (i.e., ecosystem services), including air quality improvement (e.g., dust and aerosol retention), sound and microclimate regulation, and provision of food and habitat for diverse species. Additionally, woody vegetation is particularly suitable for monitoring and regulation (e.g., enforcement and penalization). Although shrubs and grasslands are not depicted, we recognize their essential role in providing ecosystem services (Rudd, Vala, & Schaefer, 2002 ). It is worth noting that the movement threshold between fragments for both the PC and dPC indices was set at 200 m, as observations showed that the number of components at this distance equals 1. This threshold implies that any species capable of moving this distance can access all available tree cover fragments within the city of Campeche. Finally, we conducted a scenario analysis focusing on fragments of 5 ha, as this area represented the upper range among zones classified from very low to medium connectivity. Our primary objective was to identify key zones within these areas, to determine which elements are critical for conservation, and to assess where conservation efforts should be strengthened. Landsat surface temperature The Landsat surface temperature (LST) refers to measurements of the Earth's surface temperature obtained from Landsat series satellites. Unlike air temperature, LST represents the temperature recorded at the ground surface or land cover, including vegetation, buildings, or bodies of water (Laraby & Schott, 2018 ). To assess the temperature of the city of Campeche, we used Landsat images from 1995, 1998, 2000, 2003, 2011, 2013, 2015, 2018, 2020, and 2023, obtained from various missions of Landsat (4, 5, 7, and 8). Image selection criteria included downloading all available images for the month of May and ensuring no precipitation at least 3 days prior (validated with climatological stations of CONAGUA for the period 4038 and 4003, as well as data from the AccuWeather application) to eliminate the influence of precipitation on the images. The calculation was executed using the SCP 7.10.6 - Matera plugin (Congedo, 2021 ) within the QGIS 3.22.1-Białowieża software. Identification of priority areas for intervention To determine priority areas for intervention to achieve outcomes, we pinpointed tree fragments with low functional connectivity and rising temperatures. First, the areas with the lowest connectivity values were digitally mapped on-screen. Then, a surface temperature analysis, from 1995 to 2023, was conducted to assess temperature variations in these identified areas. Results Structural and functional connectivity Our LULC maps exhibited high overall accuracy, with rates of 86% and Kappa indices of 0.83 in 1995, increasing to 89% and 0.86 in 2023. The structural connectivity analysis revealed that the tree cover in the city of Campeche, initially at 2,671.83 ha in 1995, decreased to 2,031.14 ha by 2023, indicating a net loss of 640.4 ha (see Table 1 ). The expansion of the city led to varied processes of tree cover change, with some areas experiencing loss while others gained coverage (Fig. 2 a). Notably, between 1995–2023, there was a recovery of 269 ha of tree cover within the city, prominently observed in trees grown among houses, road medians, and city parks (Fig. 2 b). Additionally, around 934 ha of tree cover shifted to urban areas, primarily due to house demolitions and vegetation removal for house yards. Another significant change involved the conversion of 535 ha from grassland to urban areas (Fig. 2 a). Table 1 Description of the structural connectivity metrics applied at class and landscape levels in Campeche City. Class level Landscape level 1995 CA (ha) NP AREA_MN (ha) LPI SIDI Tree cover 2,671.83 5,918 0.54 7.40 0.61 Grass 1,023.31 7,621 0.13 Urban area 1,540.53 2,914 0.53 2023 Tree cover 2,031.14 8,352 0.31 5.07 0.59 Grass 581.57 1,632 1.35 Urban area 2,623.26 1,940 1.35 CA = class area, NP = number of patches, AREA_MN = mean patch size, LPI = largest patch index, and SIDI = Simpson’s diversity index. Between 1995 and 2023, the city of Campeche underwent a significant transformation in its land use pattern. As shown in Table 1 , in 1995, tree cover dominated the landscape in terms of area, but by 2023, the urban area had become the primary and dominant category, indicating a substantial shift in the land use dynamics of the city. Additionally, our study identified an increase in fragmentation given the increase in the number of tree patches (Table 1 ). Moreover, the average fragment area decreased 43% according to the data in Table 1 , highlighting a notable reduction in surface area. The LPI indicated that the largest forest patch has shrunk in proportion to the total landscape (Table 1 ). This trend was corroborated by decrease of the SIDI, indicating a decline in land use categories and a clear shift towards the urban area of Campeche. Our study on functional connectivity revealed that, with a distance threshold of 200 m, all tree elements in the city of Campeche are interconnected. As a result of the analysis of functional connectivity, PC values of 0.24 and 0.11 were obtained for 1995 and 2023, respectively. This shows that the probability of connectivity experienced a substantial 54% decrease in that period Among the 8,352 identified tree cover patches in 2023 (Table 1 ), four fragments located in the north, south, and west of the city are crucial for connectivity, representing 37% of the total tree cover (areas in red in Fig. 3 a). Furthermore, using the dPC model with a 5-hectare criterion, we identified 21 key tree cover fragments essential for connectivity (areas in red in Fig. 3 b). These fragments formed a corridor spanning the central part of the city from east to west, as depicted in Fig. 3 b. Landsat surface temperature The temperature dynamics in the city of Campeche between 1995 and 2023 revealed a range from 19°C to 42°C. Notably, there was a gradual increase in the average temperature of the city over this period, rising from 21.5°C in 1995 to 25.2°C in 2023. A significant temperature surge, observed in 2013, persisted until 2023, with an overall increase of at least 1°C across the entire city (Fig. 4 ). In 2023, the temperature contrast between the urban area and tree cover approached 1°C, with the urban area recording 33.73°C and tree cover at 32.81°C. Conversely, in 1995, the temperature difference between tree cover and the urban area was smaller, at 0.33°C, with the urban area temperature at 29.67°C and tree cover at 29.34°C. Identification of priority attention areas By utilizing the dPC connectivity map encompassing all tree cover fragments, we identified areas with the lowest connectivity values, pinpointing 16 priority zones for enhancing tree coverage (Fig. 5 ). Remarkably, these zones exhibited less than 10% tree coverage as of 2023. Furthermore, due to urban expansion, these areas diminished from 209 ha in 1995 to 37 ha in 2023, accompanied by a temperature increase from 29.7°C to 33.43°C within these zones. Discussion The rapid surge in urbanization, particularly in coastal low-lying regions, poses imminent threats to biodiversity, habitat connectivity, and essential ecosystem services. Our findings align with observed trends in other cities experiencing urban sprawl, resulting in deforestation and subsequent temperature increases (Amani-Beni, Zhang, Xie, & Shi, 2019 ). Consequently, it becomes imperative to develop EbA strategies that prioritize areas for conservation and emphasize connectivity (Scarano, 2017 ). This approach has been explored in other urban settings, demonstrating positive effects on both diversity and, notably, temperature regulation (Nyathi, Ndlovu, & Hadebe, 2024 ). Structural and functional connectivity The transformation from tree cover dominance to urban area prevalence signifies a fundamental shift in the land use pattern of the city. In 1995, tree cover dominated the landscape, shaping the visual and ecological identity of the city. However, by 2023, urban areas had taken precedence, becoming the primary and dominant land use category. This shift is indicative of extensive urbanization, leading to the replacement of natural green spaces with built environments (Amani-Beni et al., 2019 ). The consequences extend beyond visual changes and could have an impact on biodiversity, ecosystem services, and overall, the environmental resilience (De Carvalho & Szlafsztein, 2019 ; Liu et al., 2023 ). Understanding this shift is essential for effective urban planning and conservation efforts, especially in the context of climate change and the challenges posed by altered land use patterns. The increased fragmentation in the study area pose significant challenges in current urban settings driven by rapid expansion and development. Ecologically, this phenomenon could result in biodiversity decline as natural habitats are replaced by urban infrastructure, reducing habitable space, and deteriorating the remaining habitats due to pollution, traffic, and various human disturbances, hindering species movement, and risking extinction (Gibb & Hochuli, 2002 ; Gomes, Ribeiro, & Carretero, 2011 ; Hernando, Velázquez, Valbuena, Legrand, & García-Abril, 2017 ; McKinney, 2008 ). Socially, the diminishing availability of green spaces affects public health, contributing to stress and reduced physical activity (Lee & Maheswaran, 2011 ; WHO, 2017 ). Finally, from an environmental standpoint, urban green spaces play a crucial role in mitigating the urban heat island effect (Kotharkar & Bagade, 2018 ). Addressing these challenges requires comprehensive urban planning strategies prioritizing preservation, preventing further fragmentation, and creating new green spaces, recognizing the interconnected nature of ecological, social, and environmental factors. While all tree fragments remain interconnected within a 200 m threshold, the study results show a loss of connectivity, leading to the creation of isolated fragments of vegetation that hinder the movement of species (Gomes et al., 2011 ; McKinney, 2008 ; Riley et al., 2003 ; Vogt et al., 2017 ). Research conducted in other cities, e.g. in Xalapa, Mexico, recognized for its green urban features, reveals a shorter connectivity distance of 100 meters (Von Thaden et al., 2021 ). Beyond the direct effects on biodiversity, the loss and fragmentation of tree cover compromise vital ecosystem services, such as carbon sequestration and temperature regulation, affecting both the natural environment and human well-being (Mao et al., 2019 ; Song, Kim, Mayer, He, & Tian, 2020 ). Effective urban planning strategies must prioritize the preservation of green corridors, the creation of wildlife-friendly spaces, and measures to enhance connectivity between habitats to mitigate these ecological challenges and promote sustainable urban ecosystems. Temperature dynamics The results highlight a substantial increase in mean temperature in Campeche (3.7°C during the analyzed period). This temperature surge is crucial for a coastal city, potentially affecting factors like sea levels and community resilience to climate changes. Other studies, like Almadrones-Reyes & Dagamac ( 2023 ) in the megacity of Manila, Philippines, noted a comparable trend, registering a 4.02°C mean temperature increase over a relatively shorter span from 2001 to 2019. These collective findings emphasize the urgency of understanding and adapting to temperature variations in coastal urban areas for sustainable development and climate resilience. The city experienced a gradual temperature rise, with a noticeable surge in 2013, leading to a near 1°C difference between urban areas and tree cover in 2023. The temperature differences between urban and tree-covered areas constitute a critical dimension in the findings of this study. Urban areas, characterized by impervious surfaces, experience higher temperatures compared to areas with abundant tree cover, known as the urban heat island effect (Estoque & Murayama, 2017 ; Kotharkar & Bagade, 2018 ; Song et al., 2020 ). The results also emphasize the role of green spaces in mitigating heat stress (Manoli et al., 2019 ; Richards & Edwards, 2017 ), as impervious surfaces consistently exhibit higher surface temperature, particularly during summer, compared to other land uses (Song et al., 2020 ). For instance, Amani-Beni et al. ( 2019 ), found that for each 10% rise in the green space ratio, the land surface temperature decreases by 0.4°C. Additionally, for every kilometer increase in distance from the forest park, the land surface temperature tends to increase by 0.15°C. Therefore, understanding these temperature dynamics is crucial for urban planning, informing strategies to enhance green infrastructure, manage heat island effects, and create resilient, climate-responsive urban environments. Priority areas for tree coverage enhancement and EbA strategies Identifying sixteen priority areas for tree coverage enhancement, where less than 10% coverage exists and increased temperatures are attributed to urban expansion, holds significant implications for urban planning and conservation efforts in the context of climate change. Particularly noteworthy is the absence of natural protected areas within the city, which puts crucial zones at risk. This underscores the urgency of strategic urban planning to safeguard vulnerable areas and emphasizes the importance of integrating conservation strategies within urban development plans. The identification of crucial fragments, areas within the city landscape that play an important role in maintaining functional connectivity, is pivotal in understanding the dynamics of urban green spaces (Pascual-Hortal & Saura, 2007 ). As key nodes in the ecological network, these fragments contribute significantly to landscape connectivity, support biodiversity, and regulate surface temperature. Identifying them can inform strategic decision-making for intervention and restoration efforts, fostering sustainable urban development. Study limitations Data constraints and potential biases may influence the robustness of our findings, highlighting the need for future research to address these gaps and explore additional aspects of urban green space dynamics. One primary limitation is the inability to distinguish between plant species, resulting in the classification of both native and exotic species under the broad category of tree cover. This distinction is crucial, as exotic species may not support local biodiversity or provide the same ecosystem services as native vegetation. Future studies should incorporate species-level classifications to better assess their ecological contributions. Additionally, our land surface temperature (LST) analysis was based solely on summer daytime remotely sensed data. While this approach captures peak urban heat stress, a more comprehensive understanding of urban green space cooling mechanisms would require incorporating seasonal and nighttime temperature data. Expanding the temporal scope of LST assessments could improve the accuracy of urban climate resilience planning. Another significant limitation is the lack of consideration for socioeconomic factors, such as marginalization and income disparities (Huang et al., 2011 ), which are known to influence vegetation distribution in many urban areas. Integrating socioeconomic variables into future analyses would enhance the applicability of EbA strategies by ensuring equitable green space planning. Lastly, the applicability of our results may be limited to climates similar to that of Campeche. While our findings contribute to the broader understanding of urban green space dynamics, caution is needed when extrapolating them to regions with different environmental conditions. Despite these constraints, our analytical approach remains a valid and valuable tool for understanding landscape dynamics, particularly in assessing forest recovery and deforestation processes. Addressing these limitations in future research will further refine urban ecological planning and climate adaptation strategies. Conclusions Our study examined the evolution of urban green spaces in the city of Campeche, providing a comprehensive analysis of structural and functional connectivity changes, alongside temperature dynamics. By establishing a robust knowledge base, our findings support informed urban planning and EbA strategies in the face of accelerating urbanization and climate change. The results reveal a significant transition from predominantly tree cover to urban areas in the period analyzed (640.4 ha). This land-use change has led to increased fragmentation and a reduction in the average fragment size, with profound ecological, social, and environmental implications. Although tree vegetation fragments remain interconnected within a 200 m threshold, a decline in connectivity probability suggests disruptions to natural connectivity, exacerbating biodiversity challenges by fostering isolated tree patches and altering community composition and ecosystem dynamics. The city has also experienced a gradual rise in temperatures. The thermal differences between green and non-green areas underscores the critical role of green spaces in mitigating heat stress, offering valuable insights for climate-responsive urban planning. Furthermore, the identification of sixteen priority zones—characterized by less than 10% tree cover and heightened temperatures due to urban expansion—pinpointing the key areas for intervention. Emphasizing the importance of specific tree fragments in maintaining functional connectivity provides a foundation for strategic conservation efforts and sustainable urban development. Beyond confirming the anticipated impacts of urbanization on green spaces, this study sheds light on the interconnected dynamics of land use change, connectivity, and temperature variations. These findings emphasize the urgent need for adaptive urban planning and targeted EbA strategies to address the challenges posed by rapid urban expansion and climate change. Notably, these critical tree fragments lack formal protection, as they are not designated as natural protected areas (ANP) or private conservation zones. To enhance functional connectivity, we recommend increasing tree cover, particularly near the coastline, to strengthen ecological linkages and climate resilience. Declarations Author Contribution Conceptualization, D.L.; methodology, D.L. and J.J.V.T.; software, J.J.V.T.; validation, J.J.V.T., C.M.; formal analysis, J.J.V.T., D.L. and H.M.R.U.; investigation, J.J.V.T., D.L., VC; writing—original draft preparation D.L.; writing—review and editing, J.J.V.T., D.L., H.M.R.U., VC., G.S.B.C., C.M., and R.S.; visualization, J.J.V.T. and D.L.; supervision, G.S.B.C. and R.S; project administration VC, R.S. All authors reviewed the manuscript. Acknowledgments This study was supported by The David & Lucile Packard Foundation ( https://www.packard.org/ ) under grant number 2022–73909 for the project Coastal Green Infrastructure as an Ecosystem-based Adaptation Strategy in the Yucatán Peninsula. RS was the beneficiary of this funding. References Aguilera, M. A., & González, M. G. (2023). Urban infrastructure expansion and artificial light pollution degrade coastal ecosystems, increasing natural-to-urban structural connectivity. Landscape Urban Plann, 229 , 104609. Almadrones-Reyes, K. J., & Dagamac, N. H. A. (2023). Land-use/land cover change and land surface temperature in Metropolitan Manila, Philippines using landsat imagery. GeoJournal, 88 (2), 1415-1426. Alonso, A. M., Finegan, B., Brenes, C., Günter, S., & Palomeque, X. (2017). Evaluación de la conectividad estructural y funcional en el corredor de conservación Podocarpus-Yacuambi, Ecuador. Caldasia, 39 (1), 140-156. Amani-Beni, M., Zhang, B., Xie, G.-D., & Shi, Y. (2019). Impacts of urban green landscape patterns on land surface temperature: Evidence from the adjacent area of Olympic Forest Park of Beijing, China. Sustainability, 11 (2), 513. Artiola, J., Reynolds, K., & Brusseau, M. (2019). Urban and household pollution Environ Pollut Sci (pp. 311-326): Elsevier. Bai, X., McPhearson, T., Cleugh, H., Nagendra, H., Tong, X., et al. (2017). Linking urbanization and the environment: Conceptual and empirical advances. Annu Rev Environ Resour, 42 (1), 215-240. Bannerji, P., & Bhanja, R. (2023). Analysing the Relationship Between Rising Urban Heat Islands and Climate Change of Howrah Sadar Subdivision in the Past Two Decades Using Geospatial Indicators Ecological Footprints of Climate Change: Adaptive Approaches and Sustainability (pp. 543-558): Springer. Calabrese, J. M., & Fagan, W. F. (2004). A comparison‐shopper's guide to connectivity metrics. Front Ecol Environ, 2 (10), 529-536. Campbell, M., Congalton, R. G., Hartter, J., & Ducey, M. (2015). Optimal land cover mapping and change analysis in northeastern Oregon using Landsat imagery. Photogramm Eng Remote Sens, 81 (1), 37-47. CCPY. (2014). Escenarios de Cambio Climático para el Estado de Campeche. . Estrategia de Cambio Climático de la Península de Yucatán. Retrieved from http://www.ccpy.gob.mx/agenda-campeche/escenarios.php Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: principles and practices : CRC press. Congedo, L. (2021). Semi-Automatic Classification Plugin: A Python tool for the download and processing of remote sensing images in QGIS. J Open Source Softw, 6 (64), 3172. Chen, W., Zhou, T., & Liang, J. (2023). Urbanization, ecosystem services, and their interactive coercive relationship in Hunan Province, China. Environ Sci Pollut Res, 30 (2), 3416-3431. De Carvalho, R. M., & Szlafsztein, C. F. (2019). Urban vegetation loss and ecosystem services: The influence on climate regulation and noise and air pollution. Environ Pollut, 245 , 844-852. Estoque, R. C., & Murayama, Y. (2017). Monitoring surface urban heat island formation in a tropical mountain city using Landsat data (1987–2015). ISPRS J Photogramm Remote Sens, 133 , 18-29. Founda, D., & Santamouris, M. (2017). Synergies between Urban Heat Island and Heat Waves in Athens (Greece), during an extremely hot summer (2012). Sci Rep, 7 (1), 10973. Gabric, A. J. (2023). The climate change crisis: a review of its causes and possible responses. Atmosphere, 14 (7), 1081. Gibb, H., & Hochuli, D. F. (2002). Habitat fragmentation in an urban environment: large and small fragments support different arthropod assemblages. Biol Conserv, 106 (1), 91-100. Gomes, V., Ribeiro, R., & Carretero, M. A. (2011). Effects of urban habitat fragmentation on common small mammals: species versus communities. Biodivers Conserv, 20 , 3577-3590. Gustafson, E. J., & Parker, G. R. (1992). Relationships between landcover proportion and indices of landscape spatial pattern. Landscape Ecol, 7 , 101-110. Heckbert, S., Costanza, R., Poloczanska, E., & Richardson, A. (2011). 12.10–Climate regulation as a service from estuarine and coastal ecosystems. Treatise Estuar Coast Sci , 199-216. Hernando, A., Velázquez, J., Valbuena, R., Legrand, M., & García-Abril, A. (2017). Influence of the resolution of forest cover maps in evaluating fragmentation and connectivity to assess habitat conservation status. Ecol Indic, 79 , 295-302. Hoornweg, D., & Pope, K. (2017). Population predictions for the world’s largest cities in the 21st century. Environ Urbanization, 29 (1), 195-216. Huang, C., Vaneckova, P., Wang, X., FitzGerald, G., Guo, Y., et al. (2011). Constraints and barriers to public health adaptation to climate change: a review of the literature. Am J Prev Med, 40 (2), 183-190. INEGI. (1995). Conteo de Población y Vivienda 1995. Retrieved from https://www.inegi.org.mx/programas/ccpv/1995/ INEGI. (2020). Conteo de Población y Vivienda 2020. Retrieved from https://www.inegi.org.mx/programas/ccpv/2020/ INEGI. (2021). Anuario Estadístico y Geográfico, Campeche. Retrieved from https://www.inegi.org.mx/contenidos/app/areasgeograficas/resumen/resumen_04.pdf INEGI. (2022). Marco Geoestadístico Nacional, diciembre 2022. Retrieved from https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=889463770541 IPCC. (2023). Climate Change 2022 - Mitigation of Climate Change: Working Group III Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (C. Intergovernmental Panel on Climate Ed.). Cambridge: Cambridge University Press. Kotharkar, R., & Bagade, A. (2018). Evaluating urban heat island in the critical local climate zones of an Indian city. Landscape Urban Plann, 169 , 92-104. Laraby, K. G., & Schott, J. R. (2018). Uncertainty estimation method and Landsat 7 global validation for the Landsat surface temperature product. Remote Sensing of Environment, 216 , 472-481. Lee, A. C., & Maheswaran, R. (2011). The health benefits of urban green spaces: a review of the evidence. Journal of public health, 33 (2), 212-222. Li, K., & Chen, Y. (2023). Characterizing the indicator-based, day-and-night, and climate-based variations in response of surface urban heat island during heat wave across global 561 cities. Sustainable Cities and Society, 99 , 104877. Liang, L., & Gong, P. (2020). Urban and air pollution: a multi-city study of long-term effects of urban landscape patterns on air quality trends. Sci Rep, 10 (1), 18618. Liu, H., He, B.-j., Gao, S., Zhan, Q., & Yang, C. (2023). Influence of non-urban reference delineation on trend estimate of surface urban heat island intensity: A comparison of seven methods. Remote Sensing of Environment, 296 , 113735. Ma, Y., & Jiang, Y. (2023). Ecosystem-based adaptation to address urbanization and climate change challenges: the case of China’s sponge city initiative. Climate Policy, 23 (2), 268-284. Manoli, G., Fatichi, S., Schläpfer, M., Yu, K., Crowther, T. W., et al. (2019). Magnitude of urban heat islands largely explained by climate and population. Nature, 573 (7772), 55-60. Mao, D., He, X., Wang, Z., Tian, Y., Xiang, H., et al. (2019). Diverse policies leading to contrasting impacts on land cover and ecosystem services in Northeast China. J Clean Prod, 240 , 117961. McGarigal, K., Cushman, S. A., & Ene, E. (2012). FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. Computer software program produced by the authors at the University of Massachusetts, Amherst, 15 , 153-162. McGranahan, G., Balk, D., & Anderson, B. (2007). The rising tide: assessing the risks of climate change and human settlements in low elevation coastal zones. Environ Urbanization, 19 (1), 17-37. McKinney, M. L. (2008). Effects of urbanization on species richness: a review of plants and animals. Urban ecosystems, 11 , 161-176. Merkens, J.-L., Reimann, L., Hinkel, J., & Vafeidis, A. T. (2016). Gridded population projections for the coastal zone under the Shared Socioeconomic Pathways. Global and Planetary Change, 145 , 57-66. Mulinge, J. (2023). Effects of Environmental change on species diversity. Int J Biol, 3 (1), 43-53. Nalau, J., Becken, S., Schliephack, J., Parsons, M., Brown, C., et al. (2018). The role of indigenous and traditional knowledge in ecosystem-based adaptation: A review of the literature and case studies from the Pacific Islands. Weather Clim Soc, 10 (4), 851-865. Nyathi, D., Ndlovu, J., & Hadebe, S. (2024). Challenges of Implementing Urban Ecosystem-Based Adaptation to Climate Change in Developing Countries: A Systematic Review. In S. Ghosh, S. Majumdar, & A. Cheshmehzangi (Eds.), Cities of Tomorrow: Urban Resilience and Climate Change Preparedness (pp. 249-266). Singapore: Springer Nature Singapore. Pascual-Hortal, L., & Saura, S. (2007). Impact of spatial scale on the identification of critical habitat patches for the maintenance of landscape connectivity. Landscape Urban Plann, 83 (2-3), 176-186. Peng, J., Zhao, M., Guo, X., Pan, Y., & Liu, Y. (2017). Spatial-temporal dynamics and associated driving forces of urban ecological land: A case study in Shenzhen City, China. Habitat International, 60 , 81-90. Ramsay, E. E., Duffy, G. A., Burge, K., Taruc, R. R., Fleming, G. M., et al. (2023). Spatio-temporal development of the urban heat island in a socioeconomically diverse tropical city. Environ Pollut, 316 , 120443. Reid, H., Bourne, A., Muller, H., Podvin, K., Scorgie, S., et al. (2018). A framework for assessing the effectiveness of ecosystem-based approaches to adaptation Resilience (pp. 207-216): Elsevier. Reimann, L., Vafeidis, A. T., & Honsel, L. E. (2023). Population development as a driver of coastal risk: Current trends and future pathways. Cambridge Prisms: Coastal Futures, 1 , e14. Richards, D. R., & Edwards, P. J. (2017). Quantifying street tree regulating ecosystem services using Google Street View. Ecol Indic, 77 , 31-40. Richards, D. R., & Friess, D. A. (2017). Characterizing coastal ecosystem service trade-offs with future urban development in a tropical city. Environmental management, 60 (5), 961-973. Riley, S. P., Sauvajot, R. M., Fuller, T. K., York, E. C., Kamradt, D. A., et al. (2003). Effects of urbanization and habitat fragmentation on bobcats and coyotes in southern California. Conserv Biol, 17 (2), 566-576. Rudd, H., Vala, J., & Schaefer, V. (2002). Importance of backyard habitat in a comprehensive biodiversity conservation strategy: a connectivity analysis of urban green spaces. Restor Ecol, 10 (2), 368-375. Sahraoui, Y., Leski, C. D. G., Benot, M.-L., Revers, F., Salles, D., et al. (2021). Integrating ecological networks modelling in a participatory approach for assessing impacts of planning scenarios on landscape connectivity. Landscape Urban Plann, 209 , 104039. Saura, S. (2013). Métodos y herramientas para el análisis de la conectividad del paisaje y su integración en los planes de conservación. Avances en el análisis espacial de datos ecológicos: aspectos metodológicos y aplicados , 355. Scarano, F. R. (2017). Ecosystem-based adaptation to climate change: concept, scalability and a role for conservation science. Perspectives in Ecology and Conservation, 15 (2), 65-73. Song, P., Kim, G., Mayer, A., He, R., & Tian, G. (2020). Assessing the ecosystem services of various types of urban green spaces based on i-Tree Eco. Sustainability, 12 (4), 1630. Tam, B. Y., Gough, W. A., & Mohsin, T. (2015). The impact of urbanization and the urban heat island effect on day to day temperature variation. Urban Climate, 12 , 1-10. Van den Bosch, M., & Sang, Å. O. (2017). Urban natural environments as nature-based solutions for improved public health–A systematic review of reviews. Environ Res, 158 , 373-384. Vogt, J., Gillner, S., Hofmann, M., Tharang, A., Dettmann, S., et al. (2017). Citree: A database supporting tree selection for urban areas in temperate climate. Landscape Urban Plann, 157 , 14-25. Von Thaden, J., Badillo-Montaño, R., Lira-Noriega, A., García-Ramírez, A., Benítez, G., et al. (2021). Contributions of green spaces and isolated trees to landscape connectivity in an urban landscape. Urban For Urban Green, 64 , 127277. Wang, S., Wu, M., Hu, M., Fan, C., Wang, T., et al. (2021). Promoting landscape connectivity of highly urbanized area: An ecological network approach. Ecol Indic, 125 , 107487. Weaver, W., & Shannon, C. E. (1949). The mathematical theory of communication . Urbana, Illinois, USA: University of Illinois Press. WHO. (2017). Urban green spaces: a brief for action. Retrieved from https://iris.who.int/bitstream/handle/10665/344116/9789289052498-eng.pdf Wong, L. P., Alias, H., Aghamohammadi, N., Aghazadeh, S., & Sulaiman, N. M. N. (2017). Urban heat island experience, control measures and health impact: A survey among working community in the city of Kuala Lumpur. Sustainable cities and society, 35 , 660-668. 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1","display":"","copyAsset":false,"role":"figure","size":144708,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy area: City of Campeche with land use and land cover maps for 1995 and 2023.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7686968/v1/23dde15c48eedc925a84d4d5.png"},{"id":93567867,"identity":"db696671-25e7-4372-8664-5872cb891e59","added_by":"auto","created_at":"2025-10-15 08:40:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":148640,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLand cover changes in Campeche (1995–2023). \u003c/strong\u003e(A) Transitions, (B) examples of satellite imagery.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7686968/v1/4bb400e782bd79518874373f.png"},{"id":93567869,"identity":"ab562281-a89b-4723-a319-9c787c187aaf","added_by":"auto","created_at":"2025-10-15 08:40:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":191947,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSignificance of forest patches for connectivity using dPC with a 200 m threshold. \u003c/strong\u003e(A) All forest patches, (B) Forest patches \u0026lt;5 ha\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7686968/v1/28bd8e44f2e9e788ff3a7284.png"},{"id":93566463,"identity":"2ab35d55-fe10-4c2a-bd4e-d5f338487ed7","added_by":"auto","created_at":"2025-10-15 08:32:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":117566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurface temperature transition during the 1995–2023 period.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7686968/v1/cad63c6b62d0747512d0e083.png"},{"id":93567868,"identity":"351d42e3-b4e4-4696-9ca2-c219b8bbbe53","added_by":"auto","created_at":"2025-10-15 08:40:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":120511,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap highlighting blue areas with the lowest dPC values, emphasizing the need for connectivity strategies.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7686968/v1/eaeb87e22d6dea6b5ec0b5de.png"},{"id":101795193,"identity":"f4bc7d26-95ac-4b29-bf02-bb9ca5a0fb59","added_by":"auto","created_at":"2026-02-03 16:41:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1580521,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7686968/v1/fba7d484-25e3-4a31-a58e-dc9d8e219355.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the role of urban vegetation in ecosystem-based adaptation strategies in the coastal cityscape of Campeche, Mexico","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe expected rise in population and urbanization in coastal regions, which will lead to an increase in coastal assets, is closely tied to forthcoming socioeconomic development (Hoornweg \u0026amp; Pope, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Merkens, Reimann, Hinkel, \u0026amp; Vafeidis, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Low elevation coastal zones (LECZ), defined as regions with an elevation less than 10 m above sea level and constituting 2% of the Earth's land area (McGranahan, Balk, \u0026amp; Anderson, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), are particularly susceptible to these dynamics. Currently, the global LECZ are inhabited by 898\u0026nbsp;million people, with projections suggesting a potential increase to 1.2\u0026nbsp;billion depending on socioeconomic scenarios, highlighting the swift rise in vulnerability to coastal hazards (Reimann, Vafeidis, \u0026amp; Honsel, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, urbanization patterns in these areas significantly shape global transformations, profoundly affecting ecosystems and their essential services (Chen, Zhou, \u0026amp; Liang, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Peng, Zhao, Guo, Pan, \u0026amp; Liu, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The increasing populations in these zones also extend the consequences of urban expansion beyond mere demographic shifts, influencing the intricate network of ecological connections and functions (Sahraoui et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe expansion of urban areas along coastlines not only affects the resistance and resilience of coastal ecosystems but also fragments natural habitats, leading to a loss of habitat connectivity and biodiversity (Aguilera \u0026amp; Gonz\u0026aacute;lez, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mulinge, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This urban growth also has diverse effects on various ecosystem services, such as habitat provision, nutrient cycling, and climate regulation, among others (Liu, He, Gao, Zhan, \u0026amp; Yang, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Richards \u0026amp; Friess, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, sustainable urban planning and conservation measures are key in addressing these challenges and ensuring the coexistence of urban development with the preservation of essential coastal ecosystem services and functions (Aguilera \u0026amp; Gonz\u0026aacute;lez, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Climate regulation, a critical ecosystem service, is facilitated by vegetation through shading, evapotranspiration, and the creation of microclimates (Manoli et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Richards \u0026amp; Edwards, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), influencing local temperature patterns, heat absorption, and overall climate conditions within urban environments (Heckbert, Costanza, Poloczanska, \u0026amp; Richardson, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, urban growth, characterized by increased impervious surfaces and reduced natural areas, induces the Urban Heat Island (UHI) effect, increasing ambient and surface temperature relative to surrounding non-urban areas (Bai et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kotharkar \u0026amp; Bagade, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This effect has detrimental consequences on human health, especially among vulnerable populations, such as the elderly and outdoor workers (Artiola, Reynolds, \u0026amp; Brusseau, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Hence, creating green spaces, including water bodies, can help to mitigate the UHI effect by providing cooler environments and fostering more comfortable living conditions (De Carvalho \u0026amp; Szlafsztein, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ramsay et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wong, Alias, Aghamohammadi, Aghazadeh, \u0026amp; Sulaiman, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMore frequent and intense heatwaves, changing rainfall patterns, and more extreme weather events are expected as climate change effects (IPCC, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These changes will have significant impacts on the environment, ecosystems, and human societies (Gabric, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, extreme heat events, exacerbated by the UHI effect, will directly impact public health, especially in urban areas (Founda \u0026amp; Santamouris, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kotharkar \u0026amp; Bagade, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, addressing the predicted temperature rise requires comprehensive efforts, including the reduction of greenhouse gas emissions and the implementation of adaptation strategies that can help mitigate these impacts. Within the context of urbanization and climate change in coastal regions, the ecosystem-based adaptation (EbA) approach emerges as a promising strategy rooted in sustainable management of ecosystems (Ma \u0026amp; Jiang, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This comprehensive framework integrates ecological and human-centered considerations, recognizing the intricate connections between functional ecosystems and the resilience of human communities (Nalau et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Scarano, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A crucial component of EbA involves the greening of urban spaces, emphasizing the importance of incorporating nature into urban landscapes (Reid et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Due to the limited availability of resources, urban greening initiatives depend on selecting where these efforts can yield the most significant positive socioecological outcomes. Prioritizing nature-based actions is crucial in shaping public policies for heightened effectiveness, particularly those grounded in nature (Van den Bosch \u0026amp; Sang, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For example, selecting sites where green infrastructure could increase temperature regulation capacity and landscape connectivity could be an efficient resource allocation and an EbA measure. From an environmental perspective, strategically located urban green infrastructure enhances landscape functional connectivity by linking scattered vegetation fragments within the city, therefore allowing some species to find food and refuge areas in a potentially hostile environment (Wang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Simultaneously, the same infrastructure can contribute to residents\u0026rsquo; quality of life mitigating some of the UHI effects (Bannerji \u0026amp; Bhanja, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li \u0026amp; Chen, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liang \u0026amp; Gong, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tam, Gough, \u0026amp; Mohsin, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This integrated approach enables targeted and effective interventions, fostering ecosystem health, biodiversity conservation, and community well-being amid the challenges of urbanization.\u003c/p\u003e\u003cp\u003eThis study assesses the effect of urban growth dynamics on the temperature regulation of Campeche City, to identify optimal sites for urban green infrastructure efforts under an EbA approach. First, the spatial configuration of land use and vegetation changes in 28 years (between 1995 and 2023) was analyzed. Then, the effects of urban growth on landscape connectivity and temperature dynamics were evaluated. Finally, priority zones for green infrastructure efforts were identified, and recommendations were provided to support informed decision-making and promote sustainable development initiatives in coastal cities facing temperature changes driven by dynamic environmental factors.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy area\u003c/h2\u003e\u003cp\u003eThe city of Campeche, designated as a UNESCO Cultural Heritage of Humanity since 1999,is located on the coastal plain of the Gulf of Mexico in the southwest region of the Yucat\u0026aacute;n Peninsula (90\u0026deg;32'22.737\" W, 19\u0026deg;50'41.331\" N). This city spans an area of 52.36 km\u0026sup2; (INEGI, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The topography of the area is characterized by a relatively flat terrain, with elevations ranging from 8 to 100 ms above sea level and exhibiting gentle slopes. In contrast to neighboring cities in the Yucat\u0026aacute;n Peninsula, which often rest upon the remnants of ancient Mayan settlements, the urban layout of Campeche City reflects significant influences from colonial historical, strategic, commercial, and security functions.\u003c/p\u003e\u003cp\u003eRegarding demographic dynamics, in 2020, the municipality of Campeche experienced an average annual growth rate of 1.3%, slightly surpassing the national average of 1.2%. Over the past quarter-century, the city's population has seen a notable increase of 40%, rising from 178,160 residents in 1995 to 249,623 inhabitants in 2020 (INEGI, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1995\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe climate in the city of Campeche is warm subhumid with summer rainfall patterns. The region experiences an average annual temperature of 27.1\u0026deg;C, with monthly averages ranging from 24.7\u0026deg;C to 28.4\u0026deg;C in the coldest and hottest months, respectively (INEGI, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, the annual precipitation averages at 1,125.3 mm, with recorded extremes ranging from 665.3 mm in the driest year to 1,965.9 mm in the wettest year. The rainy season in Campeche usually occurs during August and September. On the other hand, the dry season is during March and April (INEGI, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCampeche is expected to be affected by climate change. Climate models project a potential increase in the annual mean temperature of 2.5\u0026deg;C to 4\u0026deg;C between the historical period (1961\u0026ndash;2000) and the distant future (2075\u0026ndash;2099), accompanied by heightened minimum and maximum temperatures. Furthermore, these projections forecast alterations in precipitation patterns, particularly affecting the state's southern region (CCPY, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLandscape connectivity analysis\u003c/h3\u003e\n\u003cp\u003eStructural and functional connectivity were assessed using a reference land use and land cover map for years 1995 and 2023. Structural and functional connectivity metrics were calculated, along with surface temperature analysis, to identify priority areas for intervention\u003c/p\u003e\n\u003ch3\u003eLand-use and land-cover map\u003c/h3\u003e\n\u003cp\u003eLand use and land cover (LULC) maps were generated based on orthophotos for 1995 and satellite images for 2023. The 1995 orthophoto, with a 2-meter resolution, was obtained from the Mexican National Institute of Statistics and Geography (INEGI), and the 2023 satellite image from the Copernicus program. The image classification was performed with an object-based approach and the Random Trees classifier of Trimble eCognition\u0026reg; Developer 9.0. Three categories were defined: areas with tree cover (native and exotic, such as parks), grasslands, and urban areas. The classification process for the year 2023 adhered to the recommendations of Campbell et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) to ensure accuracy, using approximately 200 samples for training and validation, obtained from field visits and recent Google Earth images. Furthermore, the 2023 sampling units were reinterpreted and adjusted through visual inspection to reflect 1995 conditions to address potential discrepancies over time. The final maps were validated using the mentioned samples, applying an area-based error matrix and Kappa index, following the guidelines of Congalton and Green (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eStructural and functional connectivity metrics\u003c/h3\u003e\n\u003cp\u003eThe spatial configuration of different types of green spaces was analysed using LULC maps previously obtained. We calculated both class-level and landscape-level metrics using Fragstats v4.2.1 software (McGarigal, Cushman, \u0026amp; Ene, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) for the years 1995 and 2023. The class-level metrics included class area (CA) in ha, the number of patches (NP), and the mean patch size (Area MN) in ha. Landscape-level metrics included the Largest Patch Index (LPI; equals the area of the largest patch of the corresponding patch type divided by total landscape area, multiplied by 100 to convert to a percentage) and the Simpson's Diversity Index (SIDI; equals 1 minus the sum of the squared proportional abundances of each patch type). The LPI, which ranges from 0 to 100, measures how much a landscape is dominated by a single patch. A higher LPI value indicates that the landscape consists mostly of one continuous patch, while a lower value suggests a more fragmented landscape with multiple patches. Meanwhile, the SIDI, measured on a scale from 0 to 1, represents a spectrum from complete homogeneity (0) to infinite diversity (1). These two measures are complementary; while class-level metrics treat vegetation elements as independent entities, landscape-level indices provide insights into their interactions (Gustafson \u0026amp; Parker, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Weaver \u0026amp; Shannon, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1949\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, we assessed functional connectivity to understand how individual species behave in response to the physical structure of the landscape, following Alonso et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This approach was selected because landscape connectivity, defined as the degree to which the landscape facilitates species movement among its elements, is influenced by the spatial arrangement, shape, and extent of vegetation fragments, directly affecting species abundance and distribution.\u003c/p\u003e\u003cp\u003eFunctional connectivity was assessed with the probability of connectivity index (PC), which integrates habitat patch area and interconnectivity through graph theory. The PC index (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) quantifies connectivity on a scale from 0 (no connectivity) to 1 (maximum connectivity). While functional connectivity was assessed based on species-specific dispersal distances (average, median, or maximum dispersion), structural connectivity was determined by the spatial distribution of tree cover fragments in the landscape (Saura, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:PC=\\frac{{\\sum\\:}_{i=1}^{n}{\\sum\\:}_{j=1}^{n}{a}_{1}\\times\\:{a}_{2}\\times\\:{p}_{ij}^{*}}{{A}_{L}^{2}}=\\frac{{PC}_{num}}{{A}_{L}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:PC\\)\u003c/span\u003e\u003c/span\u003e is the connectivity probability, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{ij}^{*}\\)\u003c/span\u003e\u003c/span\u003e is the probability of the maximum path, and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{A}_{L}\\)\u003c/span\u003e\u003c/span\u003e is the landscape area (habitat and non-habitat).\u003c/p\u003e\u003cp\u003eAlso, we quantified the contribution of individual woody elements to overall connectivity using the delta PC (dPC), which reflects the relative importance of each fragment. The dPC value of a specific vegetation fragment, reflects the disparity in connectivity between the original landscape and the landscape without that fragment, offering a measure of the fragment's relative importance (Calabrese \u0026amp; Fagan, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This map exclusively focuses on the woody vegetation category, given its societal benefits (i.e., ecosystem services), including air quality improvement (e.g., dust and aerosol retention), sound and microclimate regulation, and provision of food and habitat for diverse species. Additionally, woody vegetation is particularly suitable for monitoring and regulation (e.g., enforcement and penalization). Although shrubs and grasslands are not depicted, we recognize their essential role in providing ecosystem services (Rudd, Vala, \u0026amp; Schaefer, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIt is worth noting that the movement threshold between fragments for both the PC and dPC indices was set at 200 m, as observations showed that the number of components at this distance equals 1. This threshold implies that any species capable of moving this distance can access all available tree cover fragments within the city of Campeche.\u003c/p\u003e\u003cp\u003eFinally, we conducted a scenario analysis focusing on fragments of 5 ha, as this area represented the upper range among zones classified from very low to medium connectivity. Our primary objective was to identify key zones within these areas, to determine which elements are critical for conservation, and to assess where conservation efforts should be strengthened.\u003c/p\u003e\n\u003ch3\u003eLandsat surface temperature\u003c/h3\u003e\n\u003cp\u003eThe Landsat surface temperature (LST) refers to measurements of the Earth's surface temperature obtained from Landsat series satellites. Unlike air temperature, LST represents the temperature recorded at the ground surface or land cover, including vegetation, buildings, or bodies of water (Laraby \u0026amp; Schott, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To assess the temperature of the city of Campeche, we used Landsat images from 1995, 1998, 2000, 2003, 2011, 2013, 2015, 2018, 2020, and 2023, obtained from various missions of Landsat (4, 5, 7, and 8). Image selection criteria included downloading all available images for the month of May and ensuring no precipitation at least 3 days prior (validated with climatological stations of CONAGUA for the period 4038 and 4003, as well as data from the AccuWeather application) to eliminate the influence of precipitation on the images. The calculation was executed using the SCP 7.10.6 - Matera plugin (Congedo, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) within the QGIS 3.22.1-Białowieża software.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of priority areas for intervention\u003c/h2\u003e\u003cp\u003eTo determine priority areas for intervention to achieve outcomes, we pinpointed tree fragments with low functional connectivity and rising temperatures. First, the areas with the lowest connectivity values were digitally mapped on-screen. Then, a surface temperature analysis, from 1995 to 2023, was conducted to assess temperature variations in these identified areas.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStructural and functional connectivity\u003c/h2\u003e\u003cp\u003eOur LULC maps exhibited high overall accuracy, with rates of 86% and Kappa indices of 0.83 in 1995, increasing to 89% and 0.86 in 2023. The structural connectivity analysis revealed that the tree cover in the city of Campeche, initially at 2,671.83 ha in 1995, decreased to 2,031.14 ha by 2023, indicating a net loss of 640.4 ha (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The expansion of the city led to varied processes of tree cover change, with some areas experiencing loss while others gained coverage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Notably, between 1995\u0026ndash;2023, there was a recovery of 269 ha of tree cover within the city, prominently observed in trees grown among houses, road medians, and city parks (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Additionally, around 934 ha of tree cover shifted to urban areas, primarily due to house demolitions and vegetation removal for house yards. Another significant change involved the conversion of 535 ha from grassland to urban areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescription of the structural connectivity metrics applied at class and landscape levels in Campeche City.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eClass level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eLandscape level\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1995\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCA (ha)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eNP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eAREA_MN (ha)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eLPI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eSIDI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTree cover\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,671.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGrass\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,023.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7,621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUrban area\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,540.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2023\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTree cover\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,031.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8,352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGrass\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e581.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUrban area\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,623.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCA\u0026thinsp;=\u0026thinsp;class area, NP\u0026thinsp;=\u0026thinsp;number of patches, AREA_MN\u0026thinsp;=\u0026thinsp;mean patch size, LPI\u0026thinsp;=\u0026thinsp;largest patch index, and SIDI\u0026thinsp;=\u0026thinsp;Simpson\u0026rsquo;s diversity index.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBetween 1995 and 2023, the city of Campeche underwent a significant transformation in its land use pattern. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, in 1995, tree cover dominated the landscape in terms of area, but by 2023, the urban area had become the primary and dominant category, indicating a substantial shift in the land use dynamics of the city. Additionally, our study identified an increase in fragmentation given the increase in the number of tree patches (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Moreover, the average fragment area decreased 43% according to the data in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, highlighting a notable reduction in surface area. The LPI indicated that the largest forest patch has shrunk in proportion to the total landscape (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This trend was corroborated by decrease of the SIDI, indicating a decline in land use categories and a clear shift towards the urban area of Campeche.\u003c/p\u003e\u003cp\u003eOur study on functional connectivity revealed that, with a distance threshold of 200 m, all tree elements in the city of Campeche are interconnected. As a result of the analysis of functional connectivity, PC values of 0.24 and 0.11 were obtained for 1995 and 2023, respectively. This shows that the probability of connectivity experienced a substantial 54% decrease in that period Among the 8,352 identified tree cover patches in 2023 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), four fragments located in the north, south, and west of the city are crucial for connectivity, representing 37% of the total tree cover (areas in red in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Furthermore, using the dPC model with a 5-hectare criterion, we identified 21 key tree cover fragments essential for connectivity (areas in red in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). These fragments formed a corridor spanning the central part of the city from east to west, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLandsat surface temperature\u003c/h2\u003e\u003cp\u003eThe temperature dynamics in the city of Campeche between 1995 and 2023 revealed a range from 19\u0026deg;C to 42\u0026deg;C. Notably, there was a gradual increase in the average temperature of the city over this period, rising from 21.5\u0026deg;C in 1995 to 25.2\u0026deg;C in 2023. A significant temperature surge, observed in 2013, persisted until 2023, with an overall increase of at least 1\u0026deg;C across the entire city (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In 2023, the temperature contrast between the urban area and tree cover approached 1\u0026deg;C, with the urban area recording 33.73\u0026deg;C and tree cover at 32.81\u0026deg;C. Conversely, in 1995, the temperature difference between tree cover and the urban area was smaller, at 0.33\u0026deg;C, with the urban area temperature at 29.67\u0026deg;C and tree cover at 29.34\u0026deg;C.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of priority attention areas\u003c/h2\u003e\u003cp\u003eBy utilizing the dPC connectivity map encompassing all tree cover fragments, we identified areas with the lowest connectivity values, pinpointing 16 priority zones for enhancing tree coverage (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Remarkably, these zones exhibited less than 10% tree coverage as of 2023. Furthermore, due to urban expansion, these areas diminished from 209 ha in 1995 to 37 ha in 2023, accompanied by a temperature increase from 29.7\u0026deg;C to 33.43\u0026deg;C within these zones.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe rapid surge in urbanization, particularly in coastal low-lying regions, poses imminent threats to biodiversity, habitat connectivity, and essential ecosystem services. Our findings align with observed trends in other cities experiencing urban sprawl, resulting in deforestation and subsequent temperature increases (Amani-Beni, Zhang, Xie, \u0026amp; Shi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, it becomes imperative to develop EbA strategies that prioritize areas for conservation and emphasize connectivity (Scarano, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This approach has been explored in other urban settings, demonstrating positive effects on both diversity and, notably, temperature regulation (Nyathi, Ndlovu, \u0026amp; Hadebe, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStructural and functional connectivity\u003c/h2\u003e\u003cp\u003eThe transformation from tree cover dominance to urban area prevalence signifies a fundamental shift in the land use pattern of the city. In 1995, tree cover dominated the landscape, shaping the visual and ecological identity of the city. However, by 2023, urban areas had taken precedence, becoming the primary and dominant land use category. This shift is indicative of extensive urbanization, leading to the replacement of natural green spaces with built environments (Amani-Beni et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The consequences extend beyond visual changes and could have an impact on biodiversity, ecosystem services, and overall, the environmental resilience (De Carvalho \u0026amp; Szlafsztein, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Understanding this shift is essential for effective urban planning and conservation efforts, especially in the context of climate change and the challenges posed by altered land use patterns.\u003c/p\u003e\u003cp\u003eThe increased fragmentation in the study area pose significant challenges in current urban settings driven by rapid expansion and development. Ecologically, this phenomenon could result in biodiversity decline as natural habitats are replaced by urban infrastructure, reducing habitable space, and deteriorating the remaining habitats due to pollution, traffic, and various human disturbances, hindering species movement, and risking extinction (Gibb \u0026amp; Hochuli, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Gomes, Ribeiro, \u0026amp; Carretero, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hernando, Vel\u0026aacute;zquez, Valbuena, Legrand, \u0026amp; Garc\u0026iacute;a-Abril, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; McKinney, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Socially, the diminishing availability of green spaces affects public health, contributing to stress and reduced physical activity (Lee \u0026amp; Maheswaran, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; WHO, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Finally, from an environmental standpoint, urban green spaces play a crucial role in mitigating the urban heat island effect (Kotharkar \u0026amp; Bagade, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Addressing these challenges requires comprehensive urban planning strategies prioritizing preservation, preventing further fragmentation, and creating new green spaces, recognizing the interconnected nature of ecological, social, and environmental factors.\u003c/p\u003e\u003cp\u003eWhile all tree fragments remain interconnected within a 200 m threshold, the study results show a loss of connectivity, leading to the creation of isolated fragments of vegetation that hinder the movement of species (Gomes et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; McKinney, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Riley et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Vogt et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Research conducted in other cities, e.g. in Xalapa, Mexico, recognized for its green urban features, reveals a shorter connectivity distance of 100 meters (Von Thaden et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Beyond the direct effects on biodiversity, the loss and fragmentation of tree cover compromise vital ecosystem services, such as carbon sequestration and temperature regulation, affecting both the natural environment and human well-being (Mao et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Song, Kim, Mayer, He, \u0026amp; Tian, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Effective urban planning strategies must prioritize the preservation of green corridors, the creation of wildlife-friendly spaces, and measures to enhance connectivity between habitats to mitigate these ecological challenges and promote sustainable urban ecosystems.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eTemperature dynamics\u003c/h2\u003e\u003cp\u003eThe results highlight a substantial increase in mean temperature in Campeche (3.7\u0026deg;C during the analyzed period). This temperature surge is crucial for a coastal city, potentially affecting factors like sea levels and community resilience to climate changes. Other studies, like Almadrones-Reyes \u0026amp; Dagamac (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in the megacity of Manila, Philippines, noted a comparable trend, registering a 4.02\u0026deg;C mean temperature increase over a relatively shorter span from 2001 to 2019. These collective findings emphasize the urgency of understanding and adapting to temperature variations in coastal urban areas for sustainable development and climate resilience.\u003c/p\u003e\u003cp\u003eThe city experienced a gradual temperature rise, with a noticeable surge in 2013, leading to a near 1\u0026deg;C difference between urban areas and tree cover in 2023. The temperature differences between urban and tree-covered areas constitute a critical dimension in the findings of this study. Urban areas, characterized by impervious surfaces, experience higher temperatures compared to areas with abundant tree cover, known as the urban heat island effect (Estoque \u0026amp; Murayama, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kotharkar \u0026amp; Bagade, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The results also emphasize the role of green spaces in mitigating heat stress (Manoli et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Richards \u0026amp; Edwards, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), as impervious surfaces consistently exhibit higher surface temperature, particularly during summer, compared to other land uses (Song et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For instance, Amani-Beni et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), found that for each 10% rise in the green space ratio, the land surface temperature decreases by 0.4\u0026deg;C. Additionally, for every kilometer increase in distance from the forest park, the land surface temperature tends to increase by 0.15\u0026deg;C. Therefore, understanding these temperature dynamics is crucial for urban planning, informing strategies to enhance green infrastructure, manage heat island effects, and create resilient, climate-responsive urban environments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003ePriority areas for tree coverage enhancement and EbA strategies\u003c/h2\u003e\u003cp\u003eIdentifying sixteen priority areas for tree coverage enhancement, where less than 10% coverage exists and increased temperatures are attributed to urban expansion, holds significant implications for urban planning and conservation efforts in the context of climate change. Particularly noteworthy is the absence of natural protected areas within the city, which puts crucial zones at risk. This underscores the urgency of strategic urban planning to safeguard vulnerable areas and emphasizes the importance of integrating conservation strategies within urban development plans. The identification of crucial fragments, areas within the city landscape that play an important role in maintaining functional connectivity, is pivotal in understanding the dynamics of urban green spaces (Pascual-Hortal \u0026amp; Saura, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). As key nodes in the ecological network, these fragments contribute significantly to landscape connectivity, support biodiversity, and regulate surface temperature. Identifying them can inform strategic decision-making for intervention and restoration efforts, fostering sustainable urban development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eStudy limitations\u003c/h2\u003e\u003cp\u003eData constraints and potential biases may influence the robustness of our findings, highlighting the need for future research to address these gaps and explore additional aspects of urban green space dynamics.\u003c/p\u003e\u003cp\u003eOne primary limitation is the inability to distinguish between plant species, resulting in the classification of both native and exotic species under the broad category of tree cover. This distinction is crucial, as exotic species may not support local biodiversity or provide the same ecosystem services as native vegetation. Future studies should incorporate species-level classifications to better assess their ecological contributions.\u003c/p\u003e\u003cp\u003eAdditionally, our land surface temperature (LST) analysis was based solely on summer daytime remotely sensed data. While this approach captures peak urban heat stress, a more comprehensive understanding of urban green space cooling mechanisms would require incorporating seasonal and nighttime temperature data. Expanding the temporal scope of LST assessments could improve the accuracy of urban climate resilience planning.\u003c/p\u003e\u003cp\u003eAnother significant limitation is the lack of consideration for socioeconomic factors, such as marginalization and income disparities (Huang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which are known to influence vegetation distribution in many urban areas. Integrating socioeconomic variables into future analyses would enhance the applicability of EbA strategies by ensuring equitable green space planning.\u003c/p\u003e\u003cp\u003eLastly, the applicability of our results may be limited to climates similar to that of Campeche. While our findings contribute to the broader understanding of urban green space dynamics, caution is needed when extrapolating them to regions with different environmental conditions.\u003c/p\u003e\u003cp\u003eDespite these constraints, our analytical approach remains a valid and valuable tool for understanding landscape dynamics, particularly in assessing forest recovery and deforestation processes. Addressing these limitations in future research will further refine urban ecological planning and climate adaptation strategies.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study examined the evolution of urban green spaces in the city of Campeche, providing a comprehensive analysis of structural and functional connectivity changes, alongside temperature dynamics. By establishing a robust knowledge base, our findings support informed urban planning and EbA strategies in the face of accelerating urbanization and climate change.\u003c/p\u003e\u003cp\u003eThe results reveal a significant transition from predominantly tree cover to urban areas in the period analyzed (640.4 ha). This land-use change has led to increased fragmentation and a reduction in the average fragment size, with profound ecological, social, and environmental implications. Although tree vegetation fragments remain interconnected within a 200 m threshold, a decline in connectivity probability suggests disruptions to natural connectivity, exacerbating biodiversity challenges by fostering isolated tree patches and altering community composition and ecosystem dynamics.\u003c/p\u003e\u003cp\u003eThe city has also experienced a gradual rise in temperatures. The thermal differences between green and non-green areas underscores the critical role of green spaces in mitigating heat stress, offering valuable insights for climate-responsive urban planning. Furthermore, the identification of sixteen priority zones\u0026mdash;characterized by less than 10% tree cover and heightened temperatures due to urban expansion\u0026mdash;pinpointing the key areas for intervention. Emphasizing the importance of specific tree fragments in maintaining functional connectivity provides a foundation for strategic conservation efforts and sustainable urban development.\u003c/p\u003e\u003cp\u003eBeyond confirming the anticipated impacts of urbanization on green spaces, this study sheds light on the interconnected dynamics of land use change, connectivity, and temperature variations. These findings emphasize the urgent need for adaptive urban planning and targeted EbA strategies to address the challenges posed by rapid urban expansion and climate change.\u003c/p\u003e\u003cp\u003eNotably, these critical tree fragments lack formal protection, as they are not designated as natural protected areas (ANP) or private conservation zones. To enhance functional connectivity, we recommend increasing tree cover, particularly near the coastline, to strengthen ecological linkages and climate resilience.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, D.L.; methodology, D.L. and J.J.V.T.; software, J.J.V.T.; validation, J.J.V.T., C.M.; formal analysis, J.J.V.T., D.L. and H.M.R.U.; investigation, J.J.V.T., D.L., VC; writing\u0026mdash;original draft preparation D.L.; writing\u0026mdash;review and editing, J.J.V.T., D.L., H.M.R.U., VC., G.S.B.C., C.M., and R.S.; visualization, J.J.V.T. and D.L.; supervision, G.S.B.C. and R.S; project administration VC, R.S. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThis study was supported by The David \u0026amp; Lucile Packard Foundation (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.packard.org/\u003c/span\u003e\u003cspan address=\"https://www.packard.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) under grant number 2022\u0026ndash;73909 for the project Coastal Green Infrastructure as an Ecosystem-based Adaptation Strategy in the Yucat\u0026aacute;n Peninsula. RS was the beneficiary of this funding.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAguilera, M. A., \u0026amp; Gonz\u0026aacute;lez, M. G. (2023). Urban infrastructure expansion and artificial light pollution degrade coastal ecosystems, increasing natural-to-urban structural connectivity. \u003cem\u003eLandscape Urban Plann, 229\u003c/em\u003e, 104609.\u003c/li\u003e\n\u003cli\u003eAlmadrones-Reyes, K. J., \u0026amp; Dagamac, N. H. A. (2023). Land-use/land cover change and land surface temperature in Metropolitan Manila, Philippines using landsat imagery. \u003cem\u003eGeoJournal, 88\u003c/em\u003e(2), 1415-1426. \u003c/li\u003e\n\u003cli\u003eAlonso, A. M., Finegan, B., Brenes, C., G\u0026uuml;nter, S., \u0026amp; Palomeque, X. (2017). Evaluaci\u0026oacute;n de la conectividad estructural y funcional en el corredor de conservaci\u0026oacute;n Podocarpus-Yacuambi, Ecuador. \u003cem\u003eCaldasia, 39\u003c/em\u003e(1), 140-156. \u003c/li\u003e\n\u003cli\u003eAmani-Beni, M., Zhang, B., Xie, G.-D., \u0026amp; Shi, Y. (2019). Impacts of urban green landscape patterns on land surface temperature: Evidence from the adjacent area of Olympic Forest Park of Beijing, China. \u003cem\u003eSustainability, 11\u003c/em\u003e(2), 513.\u003c/li\u003e\n\u003cli\u003eArtiola, J., Reynolds, K., \u0026amp; Brusseau, M. (2019). Urban and household pollution \u003cem\u003eEnviron Pollut Sci\u003c/em\u003e (pp. 311-326): Elsevier.\u003c/li\u003e\n\u003cli\u003eBai, X., McPhearson, T., Cleugh, H., Nagendra, H., Tong, X., et al. (2017). Linking urbanization and the environment: Conceptual and empirical advances. \u003cem\u003eAnnu Rev Environ Resour, 42\u003c/em\u003e(1), 215-240.\u003c/li\u003e\n\u003cli\u003eBannerji, P., \u0026amp; Bhanja, R. (2023). Analysing the Relationship Between Rising Urban Heat Islands and Climate Change of Howrah Sadar Subdivision in the Past Two Decades Using Geospatial Indicators \u003cem\u003eEcological Footprints of Climate Change: Adaptive Approaches and Sustainability\u003c/em\u003e (pp. 543-558): Springer.\u003c/li\u003e\n\u003cli\u003eCalabrese, J. M., \u0026amp; Fagan, W. F. (2004). A comparison‐shopper\u0026apos;s guide to connectivity metrics. \u003cem\u003eFront Ecol Environ, 2\u003c/em\u003e(10), 529-536.\u003c/li\u003e\n\u003cli\u003eCampbell, M., Congalton, R. G., Hartter, J., \u0026amp; Ducey, M. (2015). Optimal land cover mapping and change analysis in northeastern Oregon using Landsat imagery. \u003cem\u003ePhotogramm Eng Remote Sens, 81\u003c/em\u003e(1), 37-47. \u003c/li\u003e\n\u003cli\u003eCCPY. (2014). Escenarios de Cambio Clim\u0026aacute;tico para el Estado de Campeche. . \u003cem\u003eEstrategia de Cambio Clim\u0026aacute;tico de la Pen\u0026iacute;nsula de Yucat\u0026aacute;n.\u003c/em\u003e Retrieved from http://www.ccpy.gob.mx/agenda-campeche/escenarios.php\u003c/li\u003e\n\u003cli\u003eCongalton, R. G., \u0026amp; Green, K. (2019). \u003cem\u003eAssessing the accuracy of remotely sensed data: principles and practices\u003c/em\u003e: CRC press.\u003c/li\u003e\n\u003cli\u003eCongedo, L. (2021). Semi-Automatic Classification Plugin: A Python tool for the download and processing of remote sensing images in QGIS. \u003cem\u003eJ Open Source Softw, 6\u003c/em\u003e(64), 3172.\u003c/li\u003e\n\u003cli\u003eChen, W., Zhou, T., \u0026amp; Liang, J. (2023). Urbanization, ecosystem services, and their interactive coercive relationship in Hunan Province, China. \u003cem\u003eEnviron Sci Pollut Res, 30\u003c/em\u003e(2), 3416-3431. \u003c/li\u003e\n\u003cli\u003eDe Carvalho, R. M., \u0026amp; Szlafsztein, C. F. (2019). Urban vegetation loss and ecosystem services: The influence on climate regulation and noise and air pollution. \u003cem\u003eEnviron Pollut, 245\u003c/em\u003e, 844-852.\u003c/li\u003e\n\u003cli\u003eEstoque, R. C., \u0026amp; Murayama, Y. (2017). Monitoring surface urban heat island formation in a tropical mountain city using Landsat data (1987\u0026ndash;2015). \u003cem\u003eISPRS J Photogramm Remote Sens, 133\u003c/em\u003e, 18-29.\u003c/li\u003e\n\u003cli\u003eFounda, D., \u0026amp; Santamouris, M. (2017). Synergies between Urban Heat Island and Heat Waves in Athens (Greece), during an extremely hot summer (2012). \u003cem\u003eSci Rep, 7\u003c/em\u003e(1), 10973.\u003c/li\u003e\n\u003cli\u003eGabric, A. J. (2023). The climate change crisis: a review of its causes and possible responses. \u003cem\u003eAtmosphere, 14\u003c/em\u003e(7), 1081.\u003c/li\u003e\n\u003cli\u003eGibb, H., \u0026amp; Hochuli, D. F. (2002). Habitat fragmentation in an urban environment: large and small fragments support different arthropod assemblages. \u003cem\u003eBiol Conserv, 106\u003c/em\u003e(1), 91-100.\u003c/li\u003e\n\u003cli\u003eGomes, V., Ribeiro, R., \u0026amp; Carretero, M. A. (2011). Effects of urban habitat fragmentation on common small mammals: species versus communities. \u003cem\u003eBiodivers Conserv, 20\u003c/em\u003e, 3577-3590.\u003c/li\u003e\n\u003cli\u003eGustafson, E. J., \u0026amp; Parker, G. R. (1992). Relationships between landcover proportion and indices of landscape spatial pattern. \u003cem\u003eLandscape Ecol, 7\u003c/em\u003e, 101-110.\u003c/li\u003e\n\u003cli\u003eHeckbert, S., Costanza, R., Poloczanska, E., \u0026amp; Richardson, A. (2011). 12.10\u0026ndash;Climate regulation as a service from estuarine and coastal ecosystems. \u003cem\u003eTreatise Estuar Coast Sci\u003c/em\u003e, 199-216. \u003c/li\u003e\n\u003cli\u003eHernando, A., Vel\u0026aacute;zquez, J., Valbuena, R., Legrand, M., \u0026amp; Garc\u0026iacute;a-Abril, A. (2017). Influence of the resolution of forest cover maps in evaluating fragmentation and connectivity to assess habitat conservation status. \u003cem\u003eEcol Indic, 79\u003c/em\u003e, 295-302.\u003c/li\u003e\n\u003cli\u003eHoornweg, D., \u0026amp; Pope, K. (2017). Population predictions for the world\u0026rsquo;s largest cities in the 21st century. \u003cem\u003eEnviron Urbanization, 29\u003c/em\u003e(1), 195-216.\u003c/li\u003e\n\u003cli\u003eHuang, C., Vaneckova, P., Wang, X., FitzGerald, G., Guo, Y., et al. (2011). Constraints and barriers to public health adaptation to climate change: a review of the literature. \u003cem\u003eAm J Prev Med, 40\u003c/em\u003e(2), 183-190. \u003c/li\u003e\n\u003cli\u003eINEGI. (1995). Conteo de Poblaci\u0026oacute;n y Vivienda 1995. Retrieved from https://www.inegi.org.mx/programas/ccpv/1995/\u003c/li\u003e\n\u003cli\u003eINEGI. (2020). Conteo de Poblaci\u0026oacute;n y Vivienda 2020. Retrieved from https://www.inegi.org.mx/programas/ccpv/2020/\u003c/li\u003e\n\u003cli\u003eINEGI. (2021). Anuario Estad\u0026iacute;stico y Geogr\u0026aacute;fico, Campeche. Retrieved from https://www.inegi.org.mx/contenidos/app/areasgeograficas/resumen/resumen_04.pdf\u003c/li\u003e\n\u003cli\u003eINEGI. (2022). Marco Geoestad\u0026iacute;stico Nacional, diciembre 2022. Retrieved from https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=889463770541\u003c/li\u003e\n\u003cli\u003eIPCC. (2023). \u003cem\u003eClimate Change 2022 - Mitigation of Climate Change: Working Group III Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\u003c/em\u003e (C. Intergovernmental Panel on Climate Ed.). Cambridge: Cambridge University Press.\u003c/li\u003e\n\u003cli\u003eKotharkar, R., \u0026amp; Bagade, A. (2018). Evaluating urban heat island in the critical local climate zones of an Indian city. \u003cem\u003eLandscape Urban Plann, 169\u003c/em\u003e, 92-104.\u003c/li\u003e\n\u003cli\u003eLaraby, K. G., \u0026amp; Schott, J. R. (2018). Uncertainty estimation method and Landsat 7 global validation for the Landsat surface temperature product. \u003cem\u003eRemote Sensing of Environment, 216\u003c/em\u003e, 472-481.\u003c/li\u003e\n\u003cli\u003eLee, A. C., \u0026amp; Maheswaran, R. (2011). The health benefits of urban green spaces: a review of the evidence. \u003cem\u003eJournal of public health, 33\u003c/em\u003e(2), 212-222.\u003c/li\u003e\n\u003cli\u003eLi, K., \u0026amp; Chen, Y. (2023). Characterizing the indicator-based, day-and-night, and climate-based variations in response of surface urban heat island during heat wave across global 561 cities. \u003cem\u003eSustainable Cities and Society, 99\u003c/em\u003e, 104877.\u003c/li\u003e\n\u003cli\u003eLiang, L., \u0026amp; Gong, P. (2020). Urban and air pollution: a multi-city study of long-term effects of urban landscape patterns on air quality trends. \u003cem\u003eSci Rep, 10\u003c/em\u003e(1), 18618.\u003c/li\u003e\n\u003cli\u003eLiu, H., He, B.-j., Gao, S., Zhan, Q., \u0026amp; Yang, C. (2023). Influence of non-urban reference delineation on trend estimate of surface urban heat island intensity: A comparison of seven methods. \u003cem\u003eRemote Sensing of Environment, 296\u003c/em\u003e, 113735.\u003c/li\u003e\n\u003cli\u003eMa, Y., \u0026amp; Jiang, Y. (2023). Ecosystem-based adaptation to address urbanization and climate change challenges: the case of China\u0026rsquo;s sponge city initiative. \u003cem\u003eClimate Policy, 23\u003c/em\u003e(2), 268-284.\u003c/li\u003e\n\u003cli\u003eManoli, G., Fatichi, S., Schl\u0026auml;pfer, M., Yu, K., Crowther, T. W., et al. (2019). Magnitude of urban heat islands largely explained by climate and population. \u003cem\u003eNature, 573\u003c/em\u003e(7772), 55-60.\u003c/li\u003e\n\u003cli\u003eMao, D., He, X., Wang, Z., Tian, Y., Xiang, H., et al. (2019). Diverse policies leading to contrasting impacts on land cover and ecosystem services in Northeast China. \u003cem\u003eJ Clean Prod, 240\u003c/em\u003e, 117961.\u003c/li\u003e\n\u003cli\u003eMcGarigal, K., Cushman, S. A., \u0026amp; Ene, E. (2012). FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. \u003cem\u003eComputer software program produced by the authors at the University of Massachusetts, Amherst, 15\u003c/em\u003e, 153-162.\u003c/li\u003e\n\u003cli\u003eMcGranahan, G., Balk, D., \u0026amp; Anderson, B. (2007). The rising tide: assessing the risks of climate change and human settlements in low elevation coastal zones. \u003cem\u003eEnviron Urbanization, 19\u003c/em\u003e(1), 17-37.\u003c/li\u003e\n\u003cli\u003eMcKinney, M. L. (2008). Effects of urbanization on species richness: a review of plants and animals. \u003cem\u003eUrban ecosystems, 11\u003c/em\u003e, 161-176.\u003c/li\u003e\n\u003cli\u003eMerkens, J.-L., Reimann, L., Hinkel, J., \u0026amp; Vafeidis, A. T. (2016). Gridded population projections for the coastal zone under the Shared Socioeconomic Pathways. \u003cem\u003eGlobal and Planetary Change, 145\u003c/em\u003e, 57-66.\u003c/li\u003e\n\u003cli\u003eMulinge, J. (2023). Effects of Environmental change on species diversity. \u003cem\u003eInt J Biol, 3\u003c/em\u003e(1), 43-53.\u003c/li\u003e\n\u003cli\u003eNalau, J., Becken, S., Schliephack, J., Parsons, M., Brown, C., et al. (2018). The role of indigenous and traditional knowledge in ecosystem-based adaptation: A review of the literature and case studies from the Pacific Islands. \u003cem\u003eWeather Clim Soc, 10\u003c/em\u003e(4), 851-865.\u003c/li\u003e\n\u003cli\u003eNyathi, D., Ndlovu, J., \u0026amp; Hadebe, S. (2024). Challenges of Implementing Urban Ecosystem-Based Adaptation to Climate Change in Developing Countries: A Systematic Review. In S. Ghosh, S. Majumdar, \u0026amp; A. Cheshmehzangi (Eds.), \u003cem\u003eCities of Tomorrow: Urban Resilience and Climate Change Preparedness\u003c/em\u003e (pp. 249-266). Singapore: Springer Nature Singapore.\u003c/li\u003e\n\u003cli\u003ePascual-Hortal, L., \u0026amp; Saura, S. (2007). Impact of spatial scale on the identification of critical habitat patches for the maintenance of landscape connectivity. \u003cem\u003eLandscape Urban Plann, 83\u003c/em\u003e(2-3), 176-186.\u003c/li\u003e\n\u003cli\u003ePeng, J., Zhao, M., Guo, X., Pan, Y., \u0026amp; Liu, Y. (2017). Spatial-temporal dynamics and associated driving forces of urban ecological land: A case study in Shenzhen City, China. \u003cem\u003eHabitat International, 60\u003c/em\u003e, 81-90.\u003c/li\u003e\n\u003cli\u003eRamsay, E. E., Duffy, G. A., Burge, K., Taruc, R. R., Fleming, G. M., et al. (2023). Spatio-temporal development of the urban heat island in a socioeconomically diverse tropical city. \u003cem\u003eEnviron Pollut, 316\u003c/em\u003e, 120443.\u003c/li\u003e\n\u003cli\u003eReid, H., Bourne, A., Muller, H., Podvin, K., Scorgie, S., et al. (2018). A framework for assessing the effectiveness of ecosystem-based approaches to adaptation \u003cem\u003eResilience\u003c/em\u003e (pp. 207-216): Elsevier.\u003c/li\u003e\n\u003cli\u003eReimann, L., Vafeidis, A. T., \u0026amp; Honsel, L. E. (2023). Population development as a driver of coastal risk: Current trends and future pathways. \u003cem\u003eCambridge Prisms: Coastal Futures, 1\u003c/em\u003e, e14.\u003c/li\u003e\n\u003cli\u003eRichards, D. R., \u0026amp; Edwards, P. J. (2017). Quantifying street tree regulating ecosystem services using Google Street View. \u003cem\u003eEcol Indic, 77\u003c/em\u003e, 31-40.\u003c/li\u003e\n\u003cli\u003eRichards, D. R., \u0026amp; Friess, D. A. (2017). Characterizing coastal ecosystem service trade-offs with future urban development in a tropical city. \u003cem\u003eEnvironmental management, 60\u003c/em\u003e(5), 961-973.\u003c/li\u003e\n\u003cli\u003eRiley, S. P., Sauvajot, R. M., Fuller, T. K., York, E. C., Kamradt, D. A., et al. (2003). Effects of urbanization and habitat fragmentation on bobcats and coyotes in southern California. \u003cem\u003eConserv Biol, 17\u003c/em\u003e(2), 566-576.\u003c/li\u003e\n\u003cli\u003eRudd, H., Vala, J., \u0026amp; Schaefer, V. (2002). Importance of backyard habitat in a comprehensive biodiversity conservation strategy: a connectivity analysis of urban green spaces. \u003cem\u003eRestor Ecol, 10\u003c/em\u003e(2), 368-375.\u003c/li\u003e\n\u003cli\u003eSahraoui, Y., Leski, C. D. G., Benot, M.-L., Revers, F., Salles, D., et al. (2021). Integrating ecological networks modelling in a participatory approach for assessing impacts of planning scenarios on landscape connectivity. \u003cem\u003eLandscape Urban Plann, 209\u003c/em\u003e, 104039. \u003c/li\u003e\n\u003cli\u003eSaura, S. (2013). M\u0026eacute;todos y herramientas para el an\u0026aacute;lisis de la conectividad del paisaje y su integraci\u0026oacute;n en los planes de conservaci\u0026oacute;n. \u003cem\u003eAvances en el an\u0026aacute;lisis espacial de datos ecol\u0026oacute;gicos: aspectos metodol\u0026oacute;gicos y aplicados\u003c/em\u003e, 355. \u003c/li\u003e\n\u003cli\u003eScarano, F. R. (2017). Ecosystem-based adaptation to climate change: concept, scalability and a role for conservation science. \u003cem\u003ePerspectives in Ecology and Conservation, 15\u003c/em\u003e(2), 65-73.\u003c/li\u003e\n\u003cli\u003eSong, P., Kim, G., Mayer, A., He, R., \u0026amp; Tian, G. (2020). Assessing the ecosystem services of various types of urban green spaces based on i-Tree Eco. \u003cem\u003eSustainability, 12\u003c/em\u003e(4), 1630.\u003c/li\u003e\n\u003cli\u003eTam, B. Y., Gough, W. A., \u0026amp; Mohsin, T. (2015). The impact of urbanization and the urban heat island effect on day to day temperature variation. \u003cem\u003eUrban Climate, 12\u003c/em\u003e, 1-10.\u003c/li\u003e\n\u003cli\u003eVan den Bosch, M., \u0026amp; Sang, \u0026Aring;. O. (2017). Urban natural environments as nature-based solutions for improved public health\u0026ndash;A systematic review of reviews. \u003cem\u003eEnviron Res, 158\u003c/em\u003e, 373-384.\u003c/li\u003e\n\u003cli\u003eVogt, J., Gillner, S., Hofmann, M., Tharang, A., Dettmann, S., et al. (2017). Citree: A database supporting tree selection for urban areas in temperate climate. \u003cem\u003eLandscape Urban Plann, 157\u003c/em\u003e, 14-25. \u003c/li\u003e\n\u003cli\u003eVon Thaden, J., Badillo-Monta\u0026ntilde;o, R., Lira-Noriega, A., Garc\u0026iacute;a-Ram\u0026iacute;rez, A., Ben\u0026iacute;tez, G., et al. (2021). Contributions of green spaces and isolated trees to landscape connectivity in an urban landscape. \u003cem\u003eUrban For Urban Green, 64\u003c/em\u003e, 127277.\u003c/li\u003e\n\u003cli\u003eWang, S., Wu, M., Hu, M., Fan, C., Wang, T., et al. (2021). Promoting landscape connectivity of highly urbanized area: An ecological network approach. \u003cem\u003eEcol Indic, 125\u003c/em\u003e, 107487.\u003c/li\u003e\n\u003cli\u003eWeaver, W., \u0026amp; Shannon, C. E. (1949). \u003cem\u003eThe mathematical theory of communication\u003c/em\u003e. Urbana, Illinois, USA: University of Illinois Press.\u003c/li\u003e\n\u003cli\u003eWHO. (2017). Urban green spaces: a brief for action. Retrieved from https://iris.who.int/bitstream/handle/10665/344116/9789289052498-eng.pdf\u003c/li\u003e\n\u003cli\u003eWong, L. P., Alias, H., Aghamohammadi, N., Aghazadeh, S., \u0026amp; Sulaiman, N. M. N. (2017). Urban heat island experience, control measures and health impact: A survey among working community in the city of Kuala Lumpur. \u003cem\u003eSustainable cities and society, 35\u003c/em\u003e, 660-668.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Urbanization, Coastal city, Connectivity dynamics, Climate regulation, Green space loss","lastPublishedDoi":"10.21203/rs.3.rs-7686968/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7686968/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePredicted population growth and urbanization in low-elevation coastal zones threaten natural ecosystems, causing landscape fragmentation, biodiversity loss, and reduced ecosystem services, such as climate regulation, crucial for coastal communities. These impacts undermine the capacity of communities to cope with climate change, including rising urban temperatures. Ecosystem-based adaptation (EbA) can address ecological and social challenges simultaneously. This study aimed to identify areas where green infrastructure projects could maximize benefits for landscape connectivity and temperature regulation in Campeche, Gulf of Mexico. Between 1995 and 2023, 1,249 ha of urban vegetation were lost, reducing access to green areas to under 10% in a quarter of neighborhoods and decreasing functional landscape connectivity by over 54%. During the same period, average temperatures rose from 29.5\u0026deg;C to 33\u0026deg;C. Areas without forest cover and functional connectivity (439 ha) exhibited significantly higher mean temperatures (33.43\u0026deg;C, STD 0.52) than vegetated areas (31.84\u0026deg;C, STD 0.49). Priority intervention sites were identified, covering 8.4% of the urban area, allowing recovery of temperature regulation services and enhancing landscape connectivity. These findings provide actionable insights for urban planning and EbA strategies in coastal cities facing climate change, highlighting the importance of preserving and restoring urban vegetation to maintain ecological functions and human wellbeing.\u003c/p\u003e","manuscriptTitle":"Exploring the role of urban vegetation in ecosystem-based adaptation strategies in the coastal cityscape of Campeche, Mexico","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 08:32:25","doi":"10.21203/rs.3.rs-7686968/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9af69b6a-7b22-4bfb-8c8b-a0a33731a93d","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-03T16:39:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 08:32:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7686968","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7686968","identity":"rs-7686968","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}