Asset Details
Do you wish to reserve the book?
Computational analysis and modeling of climate impact on Pteridium aquilinum (L.) populations
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Computational analysis and modeling of climate impact on Pteridium aquilinum (L.) populations
Do you wish to request the book?
Computational analysis and modeling of climate impact on Pteridium aquilinum (L.) populations
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Computational analysis and modeling of climate impact on Pteridium aquilinum (L.) populations
2025
Overview
Pteridium aquilinum
is a medicinally important fern with a limited range in northern Iran, increasingly threatened by climate change. Using morphological, genetic, and environmental data, we assessed differentiation, adaptive capacity, and vulnerability across 11 populations. Factor analysis of mixed data (FAMD) identified stipe indument, pinnule shape, and pinnae number as key traits distinguishing populations. Redundancy and association analyses (RDA/CCA) revealed strong links between both morphological and genetic variation and climatic gradients, particularly temperature and humidity, indicating local adaptation. Several SCoT loci were detected as adaptive outliers. Spatial PCA showed that variation is shaped by both global and local spatial factors, forming clines and local variants. Populations varied in sensitivity and adaptive capacity; populations 2, 3, 7, and 8 exhibited the lowest adaptive indices and highest vulnerability. Connectivity modeling suggested that while some populations (e.g., 2, 4, and 6) may maintain or slightly improve connectivity, others risk isolation under future climates. Structural equation modeling (SEM) indicated a positive genetic contribution to adaptation, while differential equation modeling (DEM) predicted logistic growth with temporary instability and genetic decline in vulnerable groups. Overall, findings highlight spatially uneven adaptive responses and recommend targeted conservation through connectivity enhancement, assisted gene flow, and ex-situ preservation of adaptive genotypes.
