Bonilla Bedoya, Santiago

Preferred name

Main Affiliation

Centro de Investigación para el Territorio y el Hábitat Sostenible

Quito

santiagobonilla@uti.edu.ec

ORCID

0000-0002-2464-4500

Scopus Author ID

55360745200

Now showing 1 - 10 of 32

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From smartphones to satellites: Integrating citizen science and high-resolution environmental data for mapping plant invasions in urban mountain landscapes
(Elsevier BV, 2026-11)
Bonilla Bedoya, Santiago
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Salazar-Valenzuela, David
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Oleas Gallo, Nora Helena
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Buenaño, María Belén
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Quijano-Abril, Mario
Predicting the distribution of invasive species in urban mountain ecosystems is important for biodiversity conservation and effective management. We developed a sequential two-stage species distribution modelling framework to characterize habitat suitability for the invasive vine Thunbergia alata in the Metropolitan District of Quito and to evaluate the contribution of citizen science records, systematic field surveys, and multi-source environmental data. In the first stage, 637 iNaturalist occurrence records retained after quality control were combined with 12 bioclimatic variables from WorldClim to develop a preliminary MaxEnt model. The resulting habitat suitability map was classified into five categories and used to design a stratified field survey across the predicted suitability gradient. We sampled 1009 locations, obtaining 450 confirmed presences and 559 independent absence records. In the second stage, the field-derived occurrence data were used to develop a refined MaxEnt model incorporating the bioclimatic variables together with elevation, slope, land cover, VIIRS nighttime lights, NDVI, and the red spectral band. Environmental predictors were standardized to a common 5 m. spatial resolution and screened for multicollinearity. Both models were evaluated using receiver-operating characteristic analyses and AUC, with 10 replicate runs and 25% of occurrence records withheld for testing. Mean AUC values were 0.851 ± 0.012 and 0.902 ± 0.008 for the preliminary and refined models, respectively. In the preliminary model, habitat suitability was primarily associated with broad-scale climatic variables, particularly precipitation seasonality and temperature-related variables. In the refined model, nighttime light intensity and elevation showed the highest contributions and permutation importance, while NDVI and climatic variables also contributed to model predictions. Independent field validation showed that 47.2% of absence observations occurred in the two lowest suitability classes, whereas 33.5% occurred in areas classified as highly or very highly suitable. The refined model therefore captured substantial spatial variation in habitat suitability while also revealing areas of mismatch between predicted suitability and field observations. These results demonstrate the value of sequentially integrating citizen science, systematic field observations, and fine-resolution environmental predictors to improve invasive species mapping. This framework provides a practical approach for supporting targeted monitoring and management of invasive plants in complex urban mountain ecosystems. © 2026 The Authors.
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Tourism transport capacity under wildfire risk: A framework for highly visited protected areas
(Elsevier BV, 2026-09)
Ortega, M.
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Bonilla Bedoya, Santiago
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Molina, J.R.
Ecotourism is a major driver of rural development, particularly in natural protected areas. Existing approaches to estimating sustainable tourism capacity in these areas have focused primarily on physical, ecological, perceptual, and economic dimensions. However, these frameworks do not explicitly incorporate risks to people, vehicles, and infrastructure under wildfire scenarios. Given the global rise in large wildfires, this study develops a framework to quantify tourism transport capacity in a highly visited protected area. Tourism transport fire capacity is defined as the maximum number of vehicles permitted within a given sector to minimize wildfire exposure and potential impacts on infrastructure and assets. This capacity is determined by the interaction among structural infrastructure risk, tourism transport fire exposure, and the fire spread index. Under extreme fire spread conditions, results indicate that the number of vehicles during summer weekends should be reduced by 28–34% relative to current levels. Furthermore, when flame lengths exceed 4.3 m, 83.3% of the vegetation-free buffer strips tested in experimental burns fail to prevent impacts on infrastructure or vehicles. This study advances a risk-informed framework for integrating wildfire dynamics into tourism transport capacity assessments. The proposed approach should be incorporated into protected area management planning guidelines, in alignment with evolving fire regimes and regulatory requirements.
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A transition in the theoretical and methodological paradigms for the rehabilitation of hardened pyroclastic formations in tropical Andean landscapes
(Wiley, 2026-06-09)
Bonilla Bedoya, Santiago
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Zalakeviciute, Rasa
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Mejía‐Coronel, Danilo
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Silva, Gilson F.
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Molina, Juan R.
Introduction Global ecological restoration commitments face political, economic, and technical challenges that limit their implementation and long‐term success. Among the technical constraints, the lack of effective management and monitoring plans highlights the need for tools that support efficient and accurate spatial management of restoration landscapes. Objectives This study provides theoretical and methodological contributions to digital soil mapping (DSM) for the classification and local prediction of suitable microsites for vegetation establishment, with the aim of improving restoration efficiency in hardened pyroclastic formations ( cangahua ) of the Tropical Andes. Methods Unmanned aerial vehicles equipped with multispectral cameras were used to collect environmental data in a mountainous peri‐urban landscape dominated by cangahua . In a ravine sub‐landscape, 350 soil samples were collected at a depth of 0.15 m. Soil organic carbon (SOC) was analyzed and predictively modeled using two machine‐learning algorithms: Random Forest (RF) and Convolutional Neural Networks (CNNs). Results SOC exhibited substantial spatial variability, with minimum, mean, and maximum values of 0.69%, 2.48 ± 0.71%, and 4.33%, respectively. In DSM predictions of SOC, the CNN model showed better performance ( r 2 = 34.81%) than the RF model (out‐of‐bag error‐based coefficient of determination, r 2 OOB = 24%). The most influential variables in the CNN model were the red, near‐infrared, and blue spectral bands. Conclusions DSM proved effective for identifying spatial patterns of SOC and suitable restoration microsites in cangahua landscapes. At local scales, this approach provides valuable information on landscape structure, micro‐topography, and ecological and historical memory, improving understanding of how these factors interact to influence soil properties.
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Spatial and temporal trends of surface urban heat islands in the Andean city of Cuenca, Ecuador
(Elsevier BV, 2026-09)
Alvarez, Hermel
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Zalakeviciute, Rasa
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Bonilla Bedoya, Santiago
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Mejía C, Danilo
This study aims to assess the long-term spatio-temporal dynamics and magnitude of the Surface Urban Heat Island (SUHI) in the intermediate, high-altitude Andean city of Cuenca, Ecuador (∼2560 m a.s.l.). This research addresses a significant gap in urban climatology by analyzing the SUHI evolution over 34 years, utilizing Landsat data from 1989 to 2022. The analysis focused on the correlation between Landsat-derived Land Surface Temperature (LST) and key biophysical indices: the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI), which serve as proxies for characterizing surface cooling and warming mechanisms, respectively. The results confirm a strong and intensifying SUHI effect in Cuenca, characterized by a net increase of 1.28 °C in SUHI Intensity (SUHII) over the study period, rising from 4.47 °C (1989) to 5.75 °C (2022). Urban areas consistently exhibited mean temperatures 4.47–5.75 °C higher than surrounding unurbanized regions. The mechanisms driving this thermal anomaly are confirmed by strong correlations: LST exhibits a negative correlation with NDVI (R2 = 0.22 to 0.59), suggesting that vegetative cover provides evaporative cooling, while the strong positive correlation with NDBI (R2 = 0.47 to 0.59) indicates that the increase in impervious, thermally massive surfaces contributes significantly to SUHI intensification. These findings provide specific evidence for the magnitude and long-term trend of surface warming within a high-altitude Andean environment, underscoring the necessity of adopting climate-responsive urban planning measures centered on green infrastructure and surface material selection to mitigate the escalating SUHI effect in this context. Copyright © 2026. Published by Elsevier Ltd.
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Sentinel satellite data monitoring of air pollutants with interpolation methods in Guayaquil, Ecuador
(2023)
Mejía C. D.
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Alvarez H.
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Zalakeviciute R.
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Macancela D.
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Sanchez C.
In Ecuador, there is a limitation on air quality monitoring due to the cost of monitoring networks. Although air quality monitoring stations are instruments for air measurement, they do not cover an entire city due to their scope. Satellite remote sensing is now an effective tool to study atmospheric pollutants and has been applied to continuously assess a region and overcome the limitations of fixed stations. Despite the application of satellite data for air quality monitoring, there are some limitations, such as measurement frequency, cloud cover and wide spatial resolution, which do not allow the assessment of air pollution in cities. Therefore, downscaling, applying interpolation methods, is essential for continuous air quality monitoring at smaller scales. For this research, Nitrogen Dioxide (NO2) data from the Sentinel-5 satellite percussor was used in the city of Guayaquil for January–December 2020, which is considered before, during and after the COVID-19 quarantine. This mid-size port city does not have a permanent monitoring network, which prevents us from knowing the air quality. Due to the limitation of pixel size, this study used satellite data to apply interpolation techniques and reduce pixels to assess air quality. Two categories of interpolation were selected: deterministic and stochastic. The empirical Bayesian kriging (EBK) interpolation obtained a R2 of 0.9546, which was superior to the other methods applied. Therefore, the EBK method had the best accuracy for tropospheric NO2 concentration. Finally, the method used in this research can help monitor air quality in cities lacking continuous monitoring networks, as the reduction of the pixel size gives us a better pattern of pollutants. © 2023 The Authors
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Vulnerability assessment of natural heritage in the North-Eastern Ecuadorian Amazon using land use cover and nature protection status
(2024)
J. Durango-Cordero
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M. Saqalli
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Bonilla Bedoya, Santiago
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A. Elger
This study evaluates the vulnerability of natural heritage in the Northeast Ecuadorian Amazon (NEA), from the perspective of intrinsic vulnerability. Therefore, two proxy indicators of vulnerability were established: (i) the ecological integrity vulnerability index (EIVI), indicating the potential loss of ecological integrity, assessed by protection status (2018); (ii) the biological diversity vulnerability index (BVI), deriving the potential loss of biological diversity, using land cover/land use (2014). Biodiversity values were derived with land cover-related biodiversity data, and (iii) and compared with spatial congruence/correlation between (i) and (ii). The EIVI was directly derived using protection status, which defines vulnerability levels by ranking them according to corresponding levels of anthropic development allowance. A robust relationship between land use classes and species richness was found for both endemic vascular plants in neotropical regions and multi-taxa in tropical forests worldwide, validating the BVI. Furthermore, the moderate spatial correlation between protection status and land cover (r = 0.52) may be partially explained by 3,060 km2 of unprotected primary forest. Overall, 42 % of the NEA have a high degree of congruence between potential biodiversity and protection status. In conclusion, this study provides insights into nature conservation efforts in areas with sparse biodiversity data using readily available geographic information.
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Urban soil management in the strategies for adaptation to climate change of cities in the Tropical Andes
(2022)
Bonilla Bedoya, Santiago
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Ángel Herrera, M.
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Vaca, A.
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Salazar, L.
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Zalakeviciute, R.
The unique characteristics of a city amplify the impacts of climate change; therefore, urban planning in the 21st century is challenged to apply mitigation and adaptation strategies that ensure the collective well-being. Despite advances in monitoring urban environmental change, research on the application of adaptation-oriented criteria remains a challenge in urban planning in the Global South. This study proposes to include urban land management as a criterion and timely strategy for climate change adaptation in the cities of the Tropical Andes. Here, we estimate the distribution of the soil organic carbon stock (OCS) of the city of Quito (2,815 m.a.s.l.; population 2,011,388; 197.09 km2) in the following three methodological moments: i) field/laboratory: city-wide sampling design established to collect 300 soil samples (0–15 cm) and obtain data on organic carbon (OC) concentrations in addition to 30 samples for bulk density (BD); ii) predictors: geographic, spectral and anthropogenic dimensions established from 17 co-variables; and iii) spatial modeling: simple multiple regression (SMRM) and random forest (RFM) models of organic carbon concentrations and density as well as OCS stock estimation. We found that the spatial modeling techniques were complementary; however, SMRM showed a relatively higher fit both (OC: r2 = 20%, BD: r2 = 16%) when compared to RFM (OC: r2 = 8% and BD: r2 = 5%). Thus, soil carbon stock (0–0.15 m) was estimated with a spatial variation that fluctuated between 9.89 and 21.48 kg/m2; whereas, RFM showed fluctuations between 10.38 and 17.67 kg/m2. We found that spatial predictors (topography, relative humidity, precipitation, temperature) and anthropogenic predictors (population density, roads, vehicle traffic, land cover) positively influence the model, while spatial predictors have little influence and show multicollinearity with relative humidity. Our research suggests that urban land management in the 21st century provides key information for adaptation and mitigation strategies aimed at coping with global and local climate variations in the cities of the Tropical Andes. © 2022 Elsevier B.V.
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Modelling the relationship between urban expansion processes and urban forest characteristics: An application to the Metropolitan District of Quito
(2020)
Bonilla Bedoya, Santiago
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Mora A., Vaca A.
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Estrella A.
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Herrera M.Á.
The rapid process of global urbanisation engenders changes in urban socio-ecological systems and in the landscape structure. However, the future processes of urban expansion in Latin American cities has been little studied even though the wellbeing of its citizens will depend on territorial management and on planning the provision of ecosystemic benefits and services. This research, considering different socio-ecological dimensions, proposed to determine the causes of potential urban expansion, analysing the dimensions and possible predictors that would explain the expansion of a high Andean city and its influence on peri-urban forest landscapes. To develop a model that integrates the complexity of the system, we used the following five dimensions: biophysics, land cover and management, infrastructure and services, socio-economics, and landscape metrics, and we opted for a binomial analysis through a spatial logistic regression model developed from 33 predictors. Considering the odd radio of the model, we observe that the independent increase in predictors, including building blocks, drinking water, sewerage, waste collection, average land size, the Interspersion and Juxtaposition Index (IJI) and Largest Patch Index (LPI), and the constant behaviour of the others predictors, would increase the probability of a potential urbanisation of the territory. Similarly, the independent increase in predictors, including the presence of protected areas, the presence of protected forests, land cover, unemployment, and the Shannon Diversity Index(SHDI), reduce the probability of the urbanisation process. Our results suggest that the territorial vulnerability from a potential urbanisation process is strongly related to an increase in infrastructure, services, and the average size of properties variables. Moreover, the landscape with the greatest potential for urbanisation presents an adequate intercalation of the different patches that compose it. However, the presence of variables such as protected areas and protective forests, in addition to monitoring indicators such as landscape diversity and mitigation strategies, could be considered to focus the analysis on the current dynamics of urbanisation processes in Latin America. © 2019 Elsevier Ltd
Scopus© Citations 31 69
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Mapping 50 years of contribution to the development of soil quality biological indicators
(2023)
Bonilla Bedoya, Santiago
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Valencia K.
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Herrera M.Á.
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López-Ulloa M.
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Donoso D.A.
Biological indicators of soil quality express the capacity of a soil to maintain its ecosystem functions and services between socio-ecosystem inflection thresholds; therefore, they are determinants in management and land use decisions. However, their development until a few decades ago was limited for several reasons: reductionism and early development of other dimensions, such as physical and chemical indicators or their methodological complexity, thus affecting the importance given to biological factors and the integral evaluation of soil quality or health. Thus, this review presents a mapping of the scientific contributions of the last 50 years oriented to the theoretical and methodological development of biological indicators of soil quality, identifying their development and application in these decades. We conducted a bibliometric analysis that allowed us to present an overview of the field with respect to scientific production: temporality, geographical origin, institutional origin, journals that promote the development of the field, articles with greater influence by citation in the field of study, and the co-occurrences of these indicators in research. This analysis was complemented at the second stage by a systematic review of the literature with the greatest impact by citation. We found 2320 scientific papers distributed mainly in the United States (17.8%), China (12.2%), Brazil (8.3%), India (6.3%), and European Mediterranean countries, such as Spain, France, and Italy (14.2%). Our review showed 25 biological indicators with the highest occurrence; for example, microbial biomass (1 1 8), enzymatic activity (90), and organic matter (78); other indicators, such as earthworms, nematodes, or springtails, are also reported. All indicators showed relationships, to a greater or lesser extent, with soil biodiversity and its functions in the landscape. Important advances in soil indicators have developed gradually in the last few decades, with scientific efforts mainly concentrated in developed and emerging countries. In the last decade, the production curve continues with a growth trend., and research questions in the field revolve around the linkage of diversity and function from a molecular point of view. The scope goes beyond productivity, manifesting the real need to conserve and manage the ecosystem services of a limited and non-renewable natural resource. Pioneering research should begin to report on the scope of soil biological monitoring and its influence on policy, management, and land use. Finally, the promotion of research networks with developing countries can foster the development of regional and local soil monitoring policies in these regions. © 2023 The Author(s)
Scopus© Citations 5 33
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The effect of national protest in Ecuador on PM pollution
(2021)
Zalakeviciute R.
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Alexandrino K.
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Mejia D.
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Bastidas M.G.
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Oleas N.H.
Particulate matter (PM) accounts for millions of premature deaths in the human population every year. Due to social and economic inequality, growing human dissatisfaction manifests in waves of strikes and protests all over the world, causing paralysis of institutions, services and circulation of transport. In this study, we aim to investigate air quality in Ecuador during the national protest of 2019, by studying the evolution of PM2.5 (PM ≤ 2.5 µm) concentrations in Ecuador and its capital city Quito using ground based and satellite data. Apart from analyzing the PM2.5 evolution over time to trace the pollution changes, we employ machine learning techniques to estimate these changes relative to the business-as-usual pollution scenario. In addition, we present a chemical analysis of plant samples from an urban park housing the strike. Positive impact on regional air quality was detected for Ecuador, and an overall − 10.75 ± 17.74% reduction of particulate pollution in the capital during the protest. However, barricade burning PM peaks may contribute to a release of harmful heavy metals (tire manufacture components such as Co, Cr, Zn, Al, Fe, Pb, Mg, Ba and Cu), which might be of short- and long-term health concerns. © 2021, The Author(s).
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