Dan Last

Dan has been inspired by nature since childhood — fascinated by wild landscapes and motivated to protect the environments that sustain us. He completed both his bachelor’s and master’s degrees in electrical engineering, a field that strengthened his analytical abilities and exposed him to advanced computational methods. Yet, he felt a growing desire to apply those skills directly to environmental challenges. This led him to pivot toward environmental physics, where he now pursues research rooted in the desert ecosystems he loves.

Alongside his academic work, he is an active environmental advocate and entrepreneur. He engages in grassroots sustainability initiatives in Beer Sheva, focusing on community involvement and practical solutions to local environmental issues. He also contributes to environmental policy research through collaborations with both municipal and national institutions.

Driven by curiosity and a commitment to real‑world impact, he aims to combine scientific insight with community action — helping to ensure a more resilient and sustainable future for dryland environments and the people who depend on them.

Research Project: Vegetation spatial adaptation to heterogeneous dry ecosystems: model and remote-sensing studies

The spontaneous emergence of spatial heterogeneity in biological systems, explained by pattern formation theory, is a widespread phenomenon. Such a phenomenon is clearly visible in satellite imagery of deserts and savannas and is frequently reproduced in numerical model studies. Vegetation patchiness has been shown to play a critical role in shaping the nature and likelihood of regime shifts, as well as enhancing ecosystem resilience. While some spatial patterns arise from geological and historical processes, others result from self-organization in vegetation, often in interaction with heterogeneous background conditions, such as topography, seasonal water channels, and soil composition. This interplay can produce unique spatial structures and alter ecological responses to climatic stress.

The primary goal of the project  is to model a variety of dryland ecosystems embedded within heterogeneous landscapes and to assess how such heterogeneity affects vegetation patterning and ecosystem resilience. The model will be studied by direct numerical simulations that follow time-dependent responses, and numerical continuation, which allows the calculation of bifurcation diagrams. Remote sensing and empirical data will be used to validate model predictions. The main case studies include salinity-forced vegetation patterns in Kenya,  modeling of pine tree forests on the edge of the desert, and acacia tree distributions along desert water channels in a hyper-arid region in Israel. Preliminary results of the Kenya case suggest a legacy effect of vegetation pattern formation, where early vegetation patterning induced salinity patterns that imposed spatial constraints on vegetation regrowth following droughts. These results are suggested by remote-sensing data analysis as well as model studies.