Urban wetlands are essential to the longstanding health and well-being of cities. Acknowledged as rich in biodiversity and highly productive ecosystems, they provide ecosystem services represented in aspects such as air purification, urban climate regulation, physical and mental health, recreation, and contemplation, among a wide variety of other goods and services on which the quality of life of the inhabitants of large cities such as Bogota depends largely. We used cellular automata to model and simulate urban wetland changes in Bogota, Colombia. The study applied the coupled Markov-Future Land Use Simulation (FLUS) model to simulate and analyze land use/land cover (LULC) change over 20 years. First, we used an orthomosaic (1998) and two WorldView-2 satellite images (2004 and 2010), to detect land cover changes. Then, using the artificial neural network FLUS module, we calculated the relationships between land classes and associated drivers and estimated the probability of occurrence of each land class. Finally, we applied Intensity Analysis to examine the observed and projected LULC change (1998–2034). Results indicate that gains in areas of crops and pastures are at the expense of wetlands. In addition, simulation outputs show that wetlands will likely represent less than 2% of the total study area in 2034, representing a 14% decrease in 24 years. The importance of this project lies in its potential contribution to the decision-making process within the city and as an instrument of natural resource management. Additionally, the results of this study could contribute to the United Nations Sustainable Development Goal 6, “Clean water and sanitation," and climate change mitigation.

Human well-being relies on the presence and role of pollinators, as they contribute to the vitality of ecosystems, support the reproduction of wild plants, increase crop yields, and strengthen overall food security. While wild bee populations are dwindling due to climate and environmental change, there has been a notable 45% rise globally in the number of managed honey bee (Apis mellifera) colonies over the past five decades. Given their economic significance and their relative ease of tracking, honey bees have the potential to serve as bioindicators of global pollinator health. Consequently, honey bees have emerged as a keystone species requiring protection and conservation efforts. Here, we investigate the intricate relationship between air quality, environmental factors, and honey bee mortality across Canada and the United States. Using statistical and machine learning modeling, our findings underscore the honey bee’s role as a bioindicator. We found that air quality is an important predictor of honey bee mortality. The risk of honey bee mortality increased with poor air quality (ozone and Air Quality Health Index) but was substantially reduced in regions with greater vegetation availability (Normalized Difference Vegetation Index). Therefore, our study offers a beacon of hope: improving management practices by increasing greenery can significantly mitigate the impact of deteriorating air quality on honey bees, providing a vital solution to safeguard our essential pollinators.

This study analyzes the outcomes of Cellular Automata (CA) with different neighborhood sizes and spatial resolution configurations on the performance of the Future Land Use Simulation (FLUS) model. The analysis is executed using three analogic images to extract the land use/land cover in Bogota, Colombia, for three years: 1998, 2004, and 2010. The FLUS model has an Artificial Neuronal Network model, which was used for calculating the relationships between the land uses and the associated drivers and to estimate the probability of occurrence of each land use. Whenever a CA is used to model and simulate, sensitivity analysis (SA) becomes a crucial step in CA modeling to understand better the influence of parameters’ changes in the simulation outcomes. Therefore, the SA is conducted by varying the neighborhood sizes between 3 × 3, 5 × 5, and 7 × 7 for 5 and 30 meters. In addition, cross-classification maps, Area Under the Curve (AUC) of the Total Operating Characteristic, landscape metrics, the figure of merit, Fuzzy Kappa, and disagreement metrics were calculated to assess how well the model performed. High AUC values and low disagreement results show that, in general, the model performed well, and the accuracy of the outputs improves with a 3 × 3 neighborhood size and 5 meters spatial resolution. This study provides a broad assessment approach to the different methods that must be considered to evaluate the sensitivity of CA models in the simulation of urban wetlands’ spatial-temporal evolution.

Species distribution modeling helps understand how environmental factors influence species distribution, creating profiles to predict presence in unexplored areas and assess ecological impacts. This study examined the habitat use and population ecology of the Chilean dolphin in Seno Skyring, Chilean Patagonia. We used three models—random forest (RF), generalized linear model (GLM), and artificial neural network (ANN)—to predict dolphin distribution based on environmental and biotic data like water temperature, salinity, and fish farm density. Our research has determined that the RF model is the most precise tool for predicting the habitat preferences of Chilean dolphins. The results indicate that these dolphins are primarily located within six kilometers of the coast, strongly correlating with areas featuring numerous fish farms, sheltered waters close to the shore with river inputs, and shallow productive zones. This suggests a potential association between dolphin presence and fish-farming activities. These findings can guide targeted conservation measures, such as regulating fish-farming practices and protecting vital coastal areas to improve the survival prospects of the Chilean dolphin. Given the extensive fish-farming industry in Chile, this research highlights the need for greater knowledge and comprehensive conservation efforts to ensure the species’ long-term survival. By understanding and mitigating the impacts of fish farming and other human activities, we can better protect the habitat and well-being of Chilean dolphins.

Human well-being relies significantly on the presence and role of pollinators, as they contribute to the vitality of ecosystems, support the reproduction of wild plants, increase crop yields, and strengthen overall food security​1​. While wild bee populations are dwindling​2​ due to climate and environmental shifts, there has been a notable 45% rise globally in managed honey bee colonies over the past five decades​3​. Given their economic significance, this focus on Apis Mellifera is attributed to their ability to serve as bioindicators of global pollinator health and ease of tracking. Consequently, honey bees have emerged as a keystone species requiring protection and conservation efforts. Here, we investigate the intricate relationship between air quality, vegetation density, wind speed, and honey bee mortality across North America. Using descriptive statistics and machine learning techniques, our findings underscored the honey bee's role as a bioindicator, with a higher Air Quality Index strongly correlating to increased mortality. Conversely, elevated greenness demonstrated a robust correlation with reduced mortality. Our results show how air quality and greenness strongly drive honey bee survival. Overall, our study offered a beacon of hope: increasing greenery within the region facing poor air quality and improving management tools and practices can significantly alleviate the impact of deteriorating air quality, providing a vital solution to safeguard our essential pollinators.