How useful is the prediction of mortality of Pinus edulis in the semi-arid and monsoonal climate of New Mexico, USA to predict mortality of Pinus sylvestris in the semi-arid and Mediterranean climate of Spain? Different ecosystems have different soil and climates; different species have different structure, physiology and response to drought. Mechanistic models can be extrapolated with the necessary condition that the biological processes they represent remain similar (C3 versus C4). However, a distinction should be made between studying resilience and mortality. Using a SPAC model to mechanistically model a living tree and explore the resilience of its fluxes is different from mechanistically modeling mortality (Hawkes, 2000). For both, many challenges lie ahead to predicting mortality and resilience of trees globally (Hawkes, 2000). I limit the following discussion to two important ones: phloem and mortality datasets.
Phloem has the potential to reconcile the carbon cycles and water cycles and help define a less arbitrary threshold for mortality (see Part I). In most SPAC models the carbon
and water fluxes also interact through water potential gradient along the soil-plant- atmosphere continuum (Hölttä et al., 2014). Phloem transports the carbon produced in the leaves to lower tree organs for growth, maintenance and abiotic defense. Ignoring the downward water flux that occurs in phloem tissues might occult another mechanism of mortality, which is the loss of conductivity in the phloem and hence of energy distribution capacities (McDowell & Sevanto, 2010; Sala et al., 2010). Several SPAC models including phloem were recently developed (Thompson & Holbrook, 2003; Hölttä et al., 2005; Hölttä et al., 2017) but their development remain slow due to the difficulty of creating phloem datasets (Hölttä et al., 2014) and their integration into resilience study has not been done yet.
Predicting mortality based on mechanistic models requires a validation dataset, and a verification dataset, especially when incorporated into Dynamic Global Vegetation Models (DGVM). Mortality datasets of good quality are rare and often focused on one species (Hawkes, 2000; Anderegg et al., 2015a). Producing datasets to study ‘regular mortality’ (result of competition; Lee, 1971) requires long monitoring of an ecosystem, often beyond the timing of academic projects. Collecting data to study ‘irregular’ mortality (result of external disturbance; Lee, 1971) requires financial and operational support, especially when mortality results from short-extreme drought. Obstacles to create datasets for these two mortality situations might be overcome with technology. Global monitoring of earth systems has improved with remote sensing. The analysis of two satellite products - vegetation optical death (VOD) and Vegetation Indices (NDVI) – has helped monitor vegetation dynamics and phenology (Andela et al., 2013; Liu et al.,
2018). Further development could help to quantify vegetation mortality with a high temporal resolution. However, the spatial resolution of these two products cannot allow remote differentiation between species.
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