This thesis examines how octopuses respond to climate change at a molecular level, focusing on ocean acidification and RNA editing. Rising temperatures harm octopus reproduction, growth, and survival, while acidification produces mixed effects—some species show stress, yet others demonstrate resilience. Cephalopods overall appear more tolerant of acidification than fish, raising questions about the mechanisms behind this adaptability. Thousands of acidification-responsive edits disproportionately affect C2H2 zinc finger regulators, altering predicted binding targets, including nuclear pore components implicated in stress responses.

This research uses drone imagery and a hybrid AI model to classify rangeland cover as green vegetation, dead vegetation, or bare soil. Combining two neural network approaches achieved 96% accuracy while requiring only simple, low-cost sensors. The method enables fast, large-scale monitoring to combat invasive shrubs and support sustainable land management.

This project uses hive sound recordings and machine learning to detect early signs of bee swarming. By identifying acoustic differences between swarming and stable colonies, the system predicts swarming with 93% accuracy. This enables beekeepers to intervene early, prevent colony loss, and even create new healthy colonies.

This study examined how intestinal parasite diversity changes with habitat dryness using Guinean baboons and West African crocodiles as models. Through DNA metabarcoding of 258 samples, multiple parasite species—including some zoonotic—were identified. Results showed that parasite richness decreases with increasing aridity, especially in terrestrial hosts, highlighting ecological and public health implications in climate-sensitive regions.

This study examines how early competition influences growth and structure in young mixed forests. Results show that competition strongly affects height, biomass allocation, and species interactions. Managing competition early is crucial for maintaining diversity, reducing dominance, and building climate-resilient forests, making early interventions more effective and cost-efficient.

This study reviews mangroves of the Americas and their vulnerability to climate change. Mangroves are vital carbon sinks, biodiversity hotspots, and coastal protectors, but face threats from deforestation, pollution, and urban expansion. Effective conservation requires ecosystem-based restoration, improved management, and reduced human pressures to ensure long-term resilience.

This study analyzed long-term changes in forest composition in the Spanish Iberian Range using National Forest Inventory data and Landsat imagery. Results show a significant shift from monospecific to mixed forests, with mixed stands nearly doubling over three decades. Satellite-derived vegetation indices successfully detected these temporal dynamics.

This study examines whether burned area from forest fires in Portugal can be predicted using satellite, weather, and time-series data. Results show human-caused fires dominate over natural ones, and predictive models achieved around 95% accuracy, demonstrating strong potential for improving resource allocation and fire management strategies.

Pakistan is highly vulnerable to climate change due to low forest cover, rising temperatures, glacier melting, floods, droughts, and agricultural decline. With only 4.2 million hectares of forest, impacts are severe. Government initiatives like the 10 Billion Tree Tsunami and mangrove restoration aim to improve resilience and environmental sustainability.

This project applies dendrochronological methods to restored Andean forests in Colombia. It evaluates whether tropical species form annual rings, models their growth over time, and compares results with long-term plot data. The study focuses on Juglans neotropica, Cedrela montana, and Quercus humboldtii, integrating wood anatomy and climatic analysis.