Effects of epiphytic lichens on host preference of the vascular epiphyte Tillandsia usneoides
نویسندگان
چکیده
We investigated the potential for nonvascular epiphytic species (primarily lichens) to affect the quality of different host tree species for the vascular epiphyte Tillandsia usneoides in the southeastern USA. Different host tree species had substantially different abundances of Tillandsia, and these abundances were correlated with the composition of nonvascular epiphyte communities. In greenhouse experiments Tillandsia grew significantly faster on the branches of Quercus irginiana (a species with very high natural abundances of Tillandsia) when the dominant lichen on Q. irginiana was left intact than when the lichen was removed from the branches. In laboratory experiments, extracts from Cryptothecia rubrocincta, a lichen that was 10 times more common on poor host species for Tillandsia than on good host species, reduced Tillandsia seedling survival and growth in comparison to extracts from other species and rainwater. In field experiments, lichens increased the proportion of Tillandsia seeds and vegetative strands that adhered to the trunk of Ilex opaca (a poor Tillandsia host), but lichens did not affect propagule adherence to Q. irginiana. Our results are by no means exhaustive of the possibilities, but they suggest that the structure and diversity of vascular and nonvascular epiphytic communities that grow in different tree species may not be simply the product of host tree characteristics, but may also be influenced by interactions among the epiphytes themselves.
منابع مشابه
Deep Sequencing-Identified Kanamycin-Resistant Paenibacillus sp. Strain KS1 Isolated from Epiphyte Tillandsia usneoides (Spanish Moss) in Central Florida, USA
Paenibacillus sp. strain KS1 was isolated from an epiphyte, Tillandsia usneoides (Spanish moss), in central Florida, USA. Here, we report a draft genome sequence of this strain, which consists of a total of 398 contigs spanning 6,508,195 bp, with a G+C content of 46.5% and comprising 5,401 predicted coding sequences.
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