Biological and Geographical Sources of Variation in Leaf Economic and Hydraulic Traits Throughout the Los Angeles Megacity

Abstract:

Leaf functional traits act as driving forces on each other, as well as being driven by abiotic factors. Leaf Nitrogen Content (LNC), Specific Leaf Area (SLA) and maximum conductance to water vapor (gwmax) are well researched, yet their relationship to each other and the climate is still not fully understood. Urban setting provides a unique look into trait relationships because of the high plant biodiversity, distribution of plants outside their native climate conditions, well-watered conditions that remove a key abiotic factor and soil nutrient availability varies due to atmospheric nitrogen deposition. We predict owing to these conditions, both LNC and gwmax will increase in across the climate gradient of the LA area.

Ten tree species were sampled from green spaces across the L.A Megacity in three geographic areas of focus: coastal (Irvine), inland (Riverside) and desert (Palm Springs), exhibiting a gradient of increasing MAT, and nitrogen deposition peaking inland. Leaf samples were processed for N, and gwmax was quantified through stomatal characteristics. LNC increased between the coastal and inland region (mean diff. -0.324; p=0.007). For two case study species, gwmax in the genus’ Jacaranda and Pyrus show a contrasting positive and negative correlation with LNC (Jacaranda: p=0.037, Pyrus: p<.001). The varying relationship of N and gwmax may show that economic and hydraulic traits tradeoffs are dependent on the evolutionary characteristics of the species (Drought deciduous sub-tropical Jacaranda’s large compound leaves, and average sampled SLA: 94.05; winter deciduous temperate Pyrus’ simple leaf with average SLA: 61.78). The increase in N along one section of the gradient, but not the other, potentially shows the driving force of the N deposition may outweigh the forces of MAT. If the variation in driving forces behind economic and hydraulic traits can be identified, there will be a significant increase in the predictive power of how urban vegetation responds to climatic shifts.