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 documented wild edible plant diversity and traditional knowledge in northern Ethiopia. Forty species were identified, mainly trees and shrubs. Knowledge varied by gender, age, and occupation, with key informants showing greater expertise. Wild plants provide critical seasonal food security but face threats from deforestation, agriculture, and overgrazing.

This study analyzed plant community succession in Mediterranean ecosystems affected by climate stress and wildfires. Results show that early stages are dominated by abiotic filters, while biotic interactions grow over time. Biodiversity increases with succession, but frequent disturbances limit recovery, shaping long-term functional and phylogenetic community structure.

This research examines Rotterdam’s urban tree planting practices and their impact on air quality and drought resilience. Findings show that current reliance on single-species tree lines reduces resilience, while greater species and functional diversity improves pollutant removal and climate adaptability. The study recommends transitioning to mixed-species, biodiversity-focused urban forestry strategies.

Australia’s wildlife is hard to count due to difficult terrain and vast landscapes. This research uses remote sensing—camera traps, audio recorders, drones, and satellites—combined with AI and mathematical models to understand animal presence, habitat choices, and detectability. The goal is faster, more accurate population monitoring to guide conservation.

Iowa’s prairies are nearly gone, but restored prairies may cool local climates through evaporative cooling. Deep-rooted, structurally diverse plants increase water transfer to the atmosphere, reducing surface and air temperatures. Using drones, LiDAR, and flux towers, the researcher quantifies prairie cooling as a climate-mitigation tool.

The Mississippi River relies on dams for commercial navigation, but these structures block fish migration and damage ecosystems and local fishing economies. This research uses hydrodynamic modelling to test fish-passage designs, such as bypass channels, showing how they can reconnect habitats, support biodiversity, and allow economic and ecological goals to coexist.

My research uses artificial intelligence to detect water pollution by analysing DNA traces left by aquatic species. Instead of relying on visual signs or costly expert identification, supervised machine learning reads species patterns to determine water quality. The method is faster, cheaper, and more accurate than traditional analysis.