Modelling the response of urban lichens to broad-scale changes in air pollution and climate

• Autor(a) principal: Rocha, Bernardo
• Data de Publicação: 2022
• Outros Autores: Matos, Paula, Giordani, Paolo, Piret, Lõhmus, Branquinho, Cristina, Casanelles-Abella, Joan, Aleixo, Cristiana, Deguines, Nicolas, Hallikma, Tiit, Laanisto, Lauri, Moretti, Marco, Alós Ortí, Marta, Samson, Roeland, Tryjanowski, Piotr, Pinho, Pedro
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10451/55254
• topic: Urban macroecology; Spatial scales; Atmospheric pollution; Ecological indicator; Biodiversity-based metrics; Trait-based diversity

To create more resilient cities, it is important that we understand the effects of the global change drivers in cities. Biodiversity-based ecological indicators (EIs) can be used for this, as biodiversity is the basis of ecosystem structure, composition, and function. In previous studies, lichens have been used as EIs to monitor the effects of global change drivers in an urban context, but only in single-city studies. Thus, we currently do not understand how lichens are affected by drivers that work on a broader scale. Therefore, our aim was to quantify the variance in lichen biodiversity-based metrics (taxonomic and trait-based) that can be explained by environmental drivers working on a broad spatial scale, in an urban context where local drivers are superimposed. To this end, we performed an unprecedented effort to sample epiphytic lichens in 219 green spaces across a continental gradient from Portugal to Estonia. Twenty-six broad-scale drivers were retrieved, including air pollution and bio-climatic variables, and their dimensionality reduced by means of a principal component analysis (PCA). Thirty-eight lichen metrics were then modelled against the scores of the first two axes of each PCA, and their variance partitioned into pollution and climate components. For the first time, we determined that 15% of the metric variance was explained by broad-scale drivers, with broad-scale air pollution showing more importance than climate across the majority of metrics. Taxonomic metrics were better explained by air pollution, as expected, while climate did not surpass air pollution in any of the trait-based metric groups. Consequently, 85% of the metric variance was shown to occur at the local scale. This suggests that further work is necessary to decipher the effects of climate change. Furthermore, although drivers working within cities are prevailing, both spatial scales must be considered simultaneously if we are to use lichens as EIs in cities at continental to global scales.