Mapping an Indicator Species of Sea-Level Rise along the Forest–Marsh Ecotone

Bryanna Norlin, Andrew E. Scholl, Andrea L. Case and Timothy J. Assal ()
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Bryanna Norlin: Department of Geography, Kent State University, Kent, OH 44242, USA
Andrew E. Scholl: Department of Geography, Kent State University, Kent, OH 44242, USA
Andrea L. Case: Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
Timothy J. Assal: Department of Geography, Kent State University, Kent, OH 44242, USA

Land, 2024, vol. 13, issue 10, 1-20

Abstract: Atlantic White Cedar ( Chamaecyparis thyoides ) (AWC) anchors a globally threatened ecosystem that is being impacted by climate change, as these trees are vulnerable to hurricane events, sea-level rises, and increasing salinity at the forest–marsh ecotone. In this study, we determined the current amount and distribution of AWC in an area that is experiencing sea-level rises that are higher than the global average rate. We used a combination of a field investigation and aerial photo interpretation to identify known locations of AWC, then integrated Sentinel-1 and 2A satellite data with abiotic variables into a species distribution model. We developed a spectral signature of AWC to aid in our understanding of phenology differences from nearby species groups. The selected model had an out-of-bag error of 7.2%, and 8 of the 11 variables retained in the final model were derived from remotely sensed data, highlighting the importance of including temporal data to exploit divergent phenology. Model predictions were strong in live AWC stands and, accurately, did not predict live AWC in stands that experienced high levels of mortality after Hurricane Sandy. The model presented in this study provides high utility for AWC management and tracking mortality dynamics within stands after disturbances such as hurricanes.

Keywords: species distribution modeling; Chamaecyparis thyoides; sea-level rise; ecotone; ghost forests (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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