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Abstract Future changes to tropical cyclone (TC) climate have the potential to dramatically impact the social and economic landscape of coastal communities. Paleoclimate modeling and paleohurricane proxy development offer exciting opportunities to understand how TC properties (like frequency) change in response to climate variability on long time scales. However, sampling biases in proxies make it difficult to ascertain whether signals in paleohurricane records are related to climate variability or just stochasticity. Short observations and simulation biases prevent TC models from capturing the full range of climate variability and TC characteristics. Integration of these two data types can help address these uncertainties. Robust data model comparison in paleotempestology will require (a) simulating TCs using new paleoclimate data assimilation products and climate model ensembles, (b) building a central repository of open access paleohurricane proxies, (c) compiling paleohurricane records, and (d) filling key gaps in the existing paleohurricane networks. Incorporating the combined information from both paleohurricane proxies and paleo TC simulations into risk assessments for coastal communities could help improve adaptation strategies.
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Impediments to Understanding Seagrasses’ Response to Global Change
Uncertainties from sampling biases present challenges to ecologists and evolutionary biologists in understanding species sensitivity to anthropogenic climate change. Here, we synthesize possible impediments that can constrain research to assess present and future seagrass response from climate change. First, our knowledge of seagrass occurrence information is prevalent with biases, gaps and uncertainties that can influence inferences on species response to global change. Second, research on seagrass diversity has been focused on species-level metrics that can be measured with data from the present – but rarely accounting for the shared phylogenetic relationships and evolutionary distinctiveness of species despite species evolved and diversified from shared ancestors. Third, compared to the mass production of species occurrence records, computational tools that can analyze these datasets in a reasonable amount of time are almost non-existent or do not scale well in terms of computer time and memory. These impediments mean that scientists must work with incomplete information and often unrepresentative data to predict how seagrass diversity might change in the future. We discuss these shortfalls and provide a framework for overcoming the impediments and diminishing the knowledge gaps they generate.
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- Award ID(s):
- 2031928
- PAR ID:
- 10283328
- Date Published:
- Journal Name:
- Frontiers in Marine Science
- Volume:
- 8
- ISSN:
- 2296-7745
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fmars.2021.608867
