ArticlePloS one2018
Germination fitness of two temperate epiphytic ferns shifts under increasing temperatures and forest fragmentation.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ferns are an important component of ecosystems around the world. Studies of the impacts that global changes may have on ferns are scarce, yet emerging studies indicate that some species may be particularly sensitive to climate change. The lack of research in this subject is much more aggravated in the case of epiphytes, and especially those that live under temperate climates. A mathematical model was developed for two temperate epiphytic ferns in order to predict potential impacts on spore germination kinetics, in response to different scenarios of global change, coming from increasing temperature and forest fragmentation. Our results show that an increasing temperature will have a negative impact over the populations of these temperate epiphytic ferns. Under unfragmented forests the germination percentage was comparatively less influenced than in fragmented patches. This study highlight that, in the long term, populations of the studied epiphytic temperate ferns may decline due to climate change. Overall, epiphytic fern communities will suffer changes in diversity, richness and dominance. Our study draws attention to the role of ferns in epiphytic communities of temperate forests, emphasizing the importance of considering these plants in any conservation strategy, specifically forest conservation. From a methodological point of view, the model we propose could be easily used to dynamically monitor the status of ecosystems, allowing the quick prediction of possible future scenarios, which is a crucial issue in biodiversity conservation decision-making.
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