Dataset Submitted

Model predictions of the total canopy cover and species composition of seagrass beds (including potential recovery areas) within the Florida Gulf Coast under contemporary environmental conditions (1997-2017) and two sea-level rise (SLR) scenarios (+50cm and +100cm above current)

Authors: Jennifer McHenry, Andrew Rassweiler, and Sarah E. Lester
Identified On: Mar 16 2023 14:27 UTC
DOI: https://doi.org/10.7266/yg39rrhx
UDI: H3.x875.000:0002
Project Information

Funded By:
National Academies of Sciences Gulf Research Program

Funding Cycle:
Healthy Ecosystems 3

Research Group:
Developing an Integrated Monitoring and Assessment Framework for Evaluating Ecosystem Service Outcomes from Seagrass Restoration in the Gulf of Mexico

Point of Contact

Jennifer McHenry
University of Victoria
jennmchenry1@gmail.com

Data Collection Period
1997-01-01
to
2100-12-31
Theme keywords

seagrass beds, Thalassia testudinum, Syringodium filiforme, Halodule wrightii, seagrass restoration, sea-level rise, model predictions, spatial model

Abstract:

These spatial datasets describe the total canopy cover and species composition of seagrasses in the Florida Gulf Coast at the 1-hectare scale according to model predictions for the contemporary period (mean annual summertime; 1997-2017) and two sea-level rise scenarios (SLR), including +50cm and +100cm above contemporary levels. Model predictions are parsed into separate data layers representing confirmed seagrass beds (i.e., areas that have been previously surveyed and found to have seagrass beds by Florida Fish and Wildlife Conservation Commission aerial surveys between 1985 and 2019) and potential recovery areas (i.e., areas where seagrass beds are absent from FWCC maps but could potentially survive if restored according to seagrass model predictions). For more information about the methods used to develop model predictions see McHenry et al. (2021). Paper Citation: McHenry, J., Rassweiler, A., Hernan, G., Uejio, C. K., Pau, S., Dubel, A. K., & Lester, S. E. (2021). Modelling the biodiversity enhancement value of seagrass beds. Diversity and Distributions, 00, 1–14. https://doi.org/10.1111/ddi.13379 Interactively view these datasets through our "Seagrass Biodiversity Enhancement Explorer" App deployed using RShiny (http://jenni fermchenry.shiny apps.io/seagrass_bev)

Suggested Citation:

Jennifer McHenry, Andrew Rassweiler, and Sarah E. Lester. Model predictions of the total canopy cover and species composition of seagrass beds (including potential recovery areas) within the Florida Gulf Coast under contemporary environmental conditions (1997-2017) and two sea-level rise (SLR) scenarios (+50cm and +100cm above current). Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/yg39rrhx

Purpose:

Seagrass beds are declining globally and are increasingly vulnerable to sea level rise (SLR). We examined the distribution, total cover, and species composition of seagrass beds in the Florida Gulf Coast under different modeled scenarios to understand the potential consequences of environmental changes and proposed managment decisions for seagrass communities and the rich biodiversity they support.

Data Parameters and Units:

Seagrass cover predictions account for the areal percent coverage of all seagrass species found within a 25 by 25 cm quadrat. Probability of occurence predictions describe the distrbituion of the three most dominant seagrass species found in the Florida Gulf Coast

Methods:

We used generalized additive mixed models (GAMMs) to describe the distribution, total cover and species composition of seagrass beds and to estimate their effects on spatial patterns of faunal species richness under three scenarios. Specifically, we: (a) quantified the biodiversity enhancement value of current seagrass beds, (b) inferred the biodiversity value of potential restoration areas and (c) projected potential changes in the distribution and biodiversity enhancement value of seagrass beds due to SLR using low (+0.50 m) and high (+1.0 m) SLR forecasts for 2100.