High uncertainty over the future of tidal marsh birds under current sea-level rise projections

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Jan 04, 2021
Author: 
Brian T. Klingbeil, Jonathan B. Cohen, Maureen D. Correll, Christopher R. Field, Thomas P. Hodgman, Adrienne I. Kovach, Erika E. Lentz, Brian J. Olsen, W. Gregory Shriver, Whitney A. Wiest, and Chris S. Elphick

 

Abstract

Sea-level rise (SLR) is projected to increase dramatically with profound effects on tidal marshes, yet uncertainty stemming from underlying climate change scenarios, model specifications, and temporal scale is a major hurdle to conservation planning. We compared likely effects of SLR for 2030 and 2050 under static inundation and dynamic response model predictions for the northeastern USA, where tidal marshes experience elevated rates of SLR compared to global averages. Static inundation and dynamic response models of SLR, which differ in how they incorporate uncertainty associated with local processes and biophysical feedbacks, have historically been applied at different scales, and generally differ in spatial and temporal predictions of marsh vulnerability. We used population estimates for five tidal marsh bird species of conservation concern to predict patterns of population change for each SLR model and examined how uncertainty affects planning decisions for these species. Static inundation and dynamic response models differed markedly in their predictions for 2030, yet both models predicted with reasonable certainty that only 10–15% of tidal marsh in northeastern USA is likely (> 66% chance; as defined by the IPCC) to remain by 2050. Most (85–90%) of the marsh is predicted to be as likely as not (33–66% chance) to disappear, representing high potential for the loss of habitat for > 85% of current populations of four of the five bird species. We propose a planning approach using guidelines established by the IPCC to categorize uncertainty associated with marsh loss due to SLR and apply it to prioritize key sites for preservation. 

Article published in Biodiversity and Conservation

 

Associated SESYNC Researcher(s): 
DOI for citing: 
https://doi.org/10.1007/s10531-020-02098-z
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