Robbi's 4th PhD paper, titled 'Evaluating static and dynamic landscape connectivity modelling using a 25-year remote sensing time series' has been published in Landscape Ecology.
Summary of our findings:
• Although remotely sensed time series data represents a promising and increasingly available resource for studying dynamic ecosystems, many landscape ecology studies continue to model landscape connectivity at single or limited moments in time.
• In dynamic dryland ecosystems, these static approaches ignore often significant temporal variation in habitat availability and connectedness, and potentially risk giving misleading insights into the importance of individual habitats over time
• In this study, we used a 25-year remote sensing time series to assess whether static landscape connectivity analyses are able to identify similar important areas for connectivity as analyses based on dynamic habitat data
• Our findings revealed large differences between static and dynamic habitat prioritisations that varied significantly between region, spatial scale and hydroclimatic conditions, demonstrating that static habitat datasets can serve as poor surrogates for dynamic time series habitat data
• Our findings indicate that where possible, ecologists assessing landscape connectivity should utilise dynamic time series habitat data with temporal resolutions that correspond with the natural dynamics of their study area.
• Remote sensing data represents a particularly promising source of spatially and temporally consistent time series habitat data, and research devoted to combining these datasets with advanced graph theory models is likely to provide further valuable insights into landscape connectivity in dynamic ecosystems.
Bishop-Taylor, R., M.G. Tulbure, M. Broich. (2018). Evaluating static and dynamic landscape connectivity modelling using a 25-year remote sensing time series. Landscape Ecology. https://doi.org/10.1007/s10980-018-0624-1