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Agnan, Y./ Probst, A./ Séjalon-Delmas, N. 2016: Evaluation of lichen species resistance to atmospheric metal pollution by coupling diversity and bioaccumulation approaches: A new bioindication scale for French forested areas. - Ecological Indicators 72: 99-110. [RLL List # 244 / Rec.# 37982]
Abstract: In order to evaluate the metal resistance or sensitivity of lichen species and improve the bioindication scales, we studied lichens collected in eight plottings in French and Swiss remote forest areas. A total of 92 corticolous species was sampled, grouped in 54 lichen genera and an alga. Various ecological variables were calculated to characterize the environmental quality – including lichen diversity, lichen abundance, and Shannon index –, as well as lichen communities. Average ecological features were estimated for each study site and each of the following variables – light, temperature, continentality, humidity, substrate pH, and eutrophication – and they corresponded to lichen communities. Based on lichen frequencies, we calculated the index of atmospheric purity (IAP) and lichen diversity value (LDV). These two bioindication indices were closely related to lichen diversity and lichen abundance, respectively, due to their calculation formula. It appeared that LDV, which measures lichen abundance, was a better indicator of metal pollution than IAP. Coupling lichen diversity and metal bioaccumulation in a canonical correspondence analysis, we evaluated the resistance/sensitivity to atmospheric metal pollution for the 43 most frequent lichen species. After validation by eliminating possible influences of acid and nitrogen pollutions, we proposed a new scale to distinguish sensitive species (such as Physconia distorta, Pertusaria coccodes, and Ramalina farinacea) from resistant species (such as Lecanactis subabietina, Pertusaria leioplaca, and Pertusaria albescens) to metal pollution, adapted to such forested environment.
– doi:10.1016/j.ecolind.2016.08.006

URL: http://www.sciencedirect.com/science/article/pii/S1470160X16304678?np=y

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