patrickbdevaney/mia9 · Datasets at Hugging Face

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Florida Bay out of several algorithms considered by Pereira et al.
(2019). This study utilized delineation instead of an algorithm
derived from satellite data to map the sediment plume because
the goal of this study was to map the extent of the plume, not SSC.
Furthermore, the shallow waters and algal blooms within Florida
Bay make isolating a sediment plume difficult and require an
algorithm to be derived from extensive field sampling, which was
not available for this study. Future work will focus on building
upon the work done by Hajigholizadeh and Melesse (2017) to
create an algorithm and threshold that maps the sediment plume
within Florida Bay.
Seagrass Cover
Seagrass data was obtained from the Fish Habitat Assessment
Program (FHAP), established through the Comprehensive
Everglades Restoration Plan’s (CERP) Restoration, Coordination
and Verification (RECOVER) program to “provide information
for the spatial assessment and resolution of inter-annual
variability in seagrass communities, and to establish a baseline
to monitor responses of seagrass communities to water
management alterations associated with CERP activities” (Hall
et al., 2016; Hall and Durako, 2019). Monitoring for FHAP is
conducted once a year in May–June (with the exception of 2015
when monitoring occurred after the die-off in November) at
30 sites within 17 basins across Florida Bay. At each site, eight
0.5 × 0.5 m quadrats are deployed and benthic macrophyte cover
is quantified using the Braun-Blanquet (BB) method (Hall et al.,
2016). The BB method is a rapid and highly repeatable visual
assessment technique that has been employed in Florida Bay for
over two decades (Fourqurean et al., 2001; Furman et al., 2018).
The scoring system is as follows: 0 = no presence, 0.1 = 1 shoot,
0.5 = less than 5 shoots, 1 = many shoots but <5% cover, 2 = 5–
25% cover, 3 = 25–50% cover, 4 = 50–75% cover, 5 = 75–100%
cover. The BB score for total seagrass is then averaged for each
site. Our study utilized 30 sites in Johnson Basin and 30 sites
in Rankin Basin surveyed each year for a total of 720 seagrass
measurements. To determine the relationship between seagrass
cover and sediment plume extent, the total seagrass cover from
the 30 sites within each basin was averaged to create one BB score
per year for each basin.
Data Analyses
To determine the extent of the sediment plume and how it
changed over time, the two classes were combined and the area
of the plume was calculated for each time step (Figure 3C).
A Generalized Additive Model (GAM) was used to model plume
size across years using the R package “mgcv” (Wood, 2017). Two
models were run in preliminary analyses: one with seasonality
and one without seasonality to determine whether seasonality
was a significant driver of plume size. A breakpoint analysis was
run to determine years in which plume size significantly changed
over the 12 years.
In order to relate plume extent to changes in seagrass cover,
plume expansion and contraction within Johnson and Rankin
Basins were investigated. Shapefiles of Rankin and Johnson were
used to determine the proportion of each basin the plume covered
Frontiers in Marine Science \| www.frontiersin.org 5 July 2021 \| Volume 8 \| Article 633240
Rodemann et al. Sediment Plume and Seagrass Resilience
within each image. A breakpoint analysis was also run on the
Rankin and Johnson Basin time series individually to identify the
years in which the plume coverage within each basin significantly
changed. For all of the breakpoint analyses, the optimal number
of breakpoints in the data was determined by the minimum
Bayesian Information Criterion (BIC; Bai and Perron, 2003).
Breakpoint analyses were done with the R package “strucchange”
(Zeileis et al., 2002, 2003).
In order to examine the interaction of plume expansion and
seagrass cover, an analysis of variance (ANOVA) was used to
test for differences in the proportion of each basin covered
by the sediment plume and seagrass cover before and after
the breakpoints between each basin. A Tukey’s HSD was run
to identify which time periods significantly differed. Pearson’s
correlation tests were run to test the relationship between
the proportion of each basin covered by the sediment plume
and seagrass cover. Only spring images (n = 12, includes the
November measurement after the seagrass die-off) were included
in the seagrass ANOVA and correlation analyses since seagrass
cover was only monitored once a year in May–June. ANOVA and
correlation analyses were done in R v 4.0.3 (R Core Team, 2020).
RESULTS
Accuracy of Sediment Plume Delineation
The areal extent of the sediment plume in western Florida Bay
increased over the period of the study (2008–2020). At its largest,
the sediment plume covered an area of 249.2 km2
, increasing
108% from a minimum of 119.6 km2 during the period of
observation (Supplementary Table 1). The overall accuracy of
satellite imagery plume delineations tested with grab samples
over 2017–2020 was 80.5% (Table 1). However, the majority
(69.2%) of that error was due to lower turbidity measurements
in the deeper, southern portion of our study area (around
Rabbit Key Basin), where the bottom can be obscured by lighter
sediment loads due to depth. The overall accuracy increased to
93.1% when the deeper, southern area was excluded from the
accuracy assessment.
Sediment Plume Expansion Across the
Study Area
When considering the full spatial extent of the study, we
observed a significant, non-linear increase of the plume over the
period examined. The GAM results found that yearly variation
TABLE 1 \| Summary of accuracy assessment of images from 2017 until 2020
using grab sample data provided by ENP.

Florida Bay out of several algorithms considered by Pereira et al.

(2019). This study utilized delineation instead of an algorithm

derived from satellite data to map the sediment plume because

the goal of this study was to map the extent of the plume, not SSC.

Furthermore, the shallow waters and algal blooms within Florida

Bay make isolating a sediment plume difficult and require an

algorithm to be derived from extensive field sampling, which was

not available for this study. Future work will focus on building

upon the work done by Hajigholizadeh and Melesse (2017) to

create an algorithm and threshold that maps the sediment plume

within Florida Bay.

Seagrass Cover

Seagrass data was obtained from the Fish Habitat Assessment

Program (FHAP), established through the Comprehensive

Everglades Restoration Plan’s (CERP) Restoration, Coordination

and Verification (RECOVER) program to “provide information

for the spatial assessment and resolution of inter-annual

variability in seagrass communities, and to establish a baseline

to monitor responses of seagrass communities to water

management alterations associated with CERP activities” (Hall

et al., 2016; Hall and Durako, 2019). Monitoring for FHAP is

conducted once a year in May–June (with the exception of 2015

when monitoring occurred after the die-off in November) at

30 sites within 17 basins across Florida Bay. At each site, eight

0.5 × 0.5 m quadrats are deployed and benthic macrophyte cover

is quantified using the Braun-Blanquet (BB) method (Hall et al.,

2016). The BB method is a rapid and highly repeatable visual

assessment technique that has been employed in Florida Bay for

over two decades (Fourqurean et al., 2001; Furman et al., 2018).

The scoring system is as follows: 0 = no presence, 0.1 = 1 shoot,

0.5 = less than 5 shoots, 1 = many shoots but <5% cover, 2 = 5–

25% cover, 3 = 25–50% cover, 4 = 50–75% cover, 5 = 75–100%

cover. The BB score for total seagrass is then averaged for each

site. Our study utilized 30 sites in Johnson Basin and 30 sites

in Rankin Basin surveyed each year for a total of 720 seagrass

measurements. To determine the relationship between seagrass

cover and sediment plume extent, the total seagrass cover from

the 30 sites within each basin was averaged to create one BB score

per year for each basin.

Data Analyses

To determine the extent of the sediment plume and how it

changed over time, the two classes were combined and the area

of the plume was calculated for each time step (Figure 3C).

A Generalized Additive Model (GAM) was used to model plume

size across years using the R package “mgcv” (Wood, 2017). Two

models were run in preliminary analyses: one with seasonality

and one without seasonality to determine whether seasonality

was a significant driver of plume size. A breakpoint analysis was

run to determine years in which plume size significantly changed

over the 12 years.

In order to relate plume extent to changes in seagrass cover,

plume expansion and contraction within Johnson and Rankin

Basins were investigated. Shapefiles of Rankin and Johnson were

used to determine the proportion of each basin the plume covered

Frontiers in Marine Science | www.frontiersin.org 5 July 2021 | Volume 8 | Article 633240

Rodemann et al. Sediment Plume and Seagrass Resilience

within each image. A breakpoint analysis was also run on the

Rankin and Johnson Basin time series individually to identify the

years in which the plume coverage within each basin significantly

changed. For all of the breakpoint analyses, the optimal number

of breakpoints in the data was determined by the minimum

Bayesian Information Criterion (BIC; Bai and Perron, 2003).

Breakpoint analyses were done with the R package “strucchange”

(Zeileis et al., 2002, 2003).

In order to examine the interaction of plume expansion and

seagrass cover, an analysis of variance (ANOVA) was used to

test for differences in the proportion of each basin covered

by the sediment plume and seagrass cover before and after

the breakpoints between each basin. A Tukey’s HSD was run

to identify which time periods significantly differed. Pearson’s

correlation tests were run to test the relationship between

the proportion of each basin covered by the sediment plume

and seagrass cover. Only spring images (n = 12, includes the

November measurement after the seagrass die-off) were included

in the seagrass ANOVA and correlation analyses since seagrass

cover was only monitored once a year in May–June. ANOVA and

correlation analyses were done in R v 4.0.3 (R Core Team, 2020).

RESULTS

Accuracy of Sediment Plume Delineation

The areal extent of the sediment plume in western Florida Bay

increased over the period of the study (2008–2020). At its largest,

the sediment plume covered an area of 249.2 km2

, increasing

108% from a minimum of 119.6 km2 during the period of

observation (Supplementary Table 1). The overall accuracy of

satellite imagery plume delineations tested with grab samples

over 2017–2020 was 80.5% (Table 1). However, the majority

(69.2%) of that error was due to lower turbidity measurements

in the deeper, southern portion of our study area (around

Rabbit Key Basin), where the bottom can be obscured by lighter

sediment loads due to depth. The overall accuracy increased to

93.1% when the deeper, southern area was excluded from the

accuracy assessment.

Sediment Plume Expansion Across the

Study Area

When considering the full spatial extent of the study, we

observed a significant, non-linear increase of the plume over the

period examined. The GAM results found that yearly variation

TABLE 1 | Summary of accuracy assessment of images from 2017 until 2020

using grab sample data provided by ENP.