Abstract

Species interactions are susceptible to anthropogenic changes in ecosystems, but this has been poorly investigated in a spatially explicit manner in the case of plant parasitism, such as the omnipresent hemiparasitic mistletoe-host plant interactions. Analyzing such interactions at a large spatial scale may advance our understanding of parasitism patterns over complex landscapes. Combining high-resolution airborne imaging spectroscopy and LiDAR, we studied hemiparasite incidence within and among tree host stands to examine the prevalence and spatial distribution of hemiparasite load in ecosystems. Specifically, we aimed to assess: (1) detection accuracy of mistletoes on their oak hosts; (2) hemiparasitism prevalence within host tree canopies depending on tree height, and (3) spatial variation in hemiparasitism across fragmented woodlands, in a low-diversity mediterranean oak woodland in California, USA. We identified mistletoe infestations with 55-96% accuracy, and detected significant differences in remote-sensed spectra between oak trees with and without mistletoe infestation. We also found that host canopy height had little influence on infestation degree, whereas landscape-level variation showed consistent; non-random patterns: isolated host trees had twice the infestation load than did trees located at the core of forest fragments. Overall, we found that canopy exposure (i.e., lower canopy density or proximity to forest edge) is more important than canopy height for mistletoe infestation, and that by changing landscape structure, parasitic prevalence increased with woodland fragmentation. We conclude that reducing fragmentation in oak woodlands will minimize anthropogenic impact on mistletoe infestation at the landscape level. We argue that advanced remote sensing technology can provide baselines to quantitatively analyze and monitor parasite-host trajectories in light of global environmental change, and that this is a promising approach to be further tested in other temperate and tropical forests.

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