Monitoring British Upland Ecosystems With the Use of Landscape Structure as an Indicator for State-and-Transition Models

Remote sensing and landscape ecology concepts can provide a useful framework for state-and-transition models (STM) in order to quantify thresholds at different scales, and provide useful information for scientists, land managers, and conservationists in relation to resilience management. The overall aim of this research was to develop a spatially explicit STM to quantify thresholds based on the scale of disturbance processes impacting a grazing system. Specific objectives were to develop a conceptual STM framework for upland grazing ecosystems, to quantify spatial dynamics of stable and degraded pastures, and to assess threshold occurrence. Color aerial photography from Armboth Fell in the English Lake District National Park (United Kingdom) was classified into bare rock, dwarf shrub heath (DSH), and grassland/degraded wet heath (GDWH) in four pastures with different degrees of grazing pressure. Vegetation communities from these pastures were combined with soils, climate, and landform data to create a conceptual STM framework. Each pasture was sampled with 2-ha plots to quantify DSH and GDWH spatial structure. The proposed STM consisted of two reference and three alternative states. Low-grazing-pressure areas showed significantly higher percentage of DSH cover with larger contiguous patches and lower patch density than high-grazing-pressure areas. Breakpoints, considered to be thresholds, in mean patch area were identified in our data when DSH percentage cover was < 63% and GDWH, > 77%. The present study has shown the value of a robust, reliable, and repeatable approach to identify landscape dynamics and integrate it with field data to inform a conceptual STM framework for upland grazing ecosystems. It also demonstrates the importance of selecting scales relevant to the predominant disturbance process to test for threshold occurrence, and how this approach can be integrated with current assessment methods for resilience management.