Update and Synthesis of Higher Trophic Level Research in
Florida Bay, 2003 to PresentJoan A. Browder
Southeast Fisheries Science CenterNOAA Fisheries Service
Miami, FL
Continuing InvestigationsSubject Approach Entity InvestigatorLobster, sponge, octocoral
Model, field, lab
ODU, UF, FWRI
Butler, Behringer, Hunt, Zito, Childress
Spotted seatrout
Field NOAA, UM-CIMAS
Kelble, Browder
Shrimp Model, field NOAA, UM-CIMAS, USGS
Robblee, Criales, Johnson, Browder
Spoonbill & wetland fish
HSI, field Audubon, NOAA
Lorenz, Bartell, Nuttle, Serafy
Crocodile HSI, Field UF Mazzotti, Brandt
New InitiativesSubject Approach Entity Investigator
Gobies Field, lab USGS Schofield
Fishery stocks HSI, field UM Ault, Smith
Forage fish Lab FIU Rand, Bachmann
Hardbottom Field FWRI Tellier, Bertelsen
Oysters Field FGCU Volety, Savarese
Rivulus Field USGS McIvor, Silverman
Mesozooplankton
Chris Kelbleand
Peter Ortner
Increased abundance and number of functional groups at oceanic and higher salinities is contrary to previous lab and field observations (Putland and Iverson 2007, Greenwald and Hurlbert 1993).
This increase in mesozooplankton at hypersalinity must be due to increased prey availability or decreased predation.
Anchoa mitchelli, Western Florida Bay
Date1/1994 1/1995 1/1996 1/1997 1/1998 1/1999
Abun
danc
e (lo
g 10 no
100
0m-2
+1)
0
1
2
3
4
Anchoa mitchelli, Central Florida Bay
Date1/1994 1/1995 1/1996 1/1997 1/1998 1/1999
Abu
ndan
ce (l
og10
no
1000
m-2
+1)
0
1
2
3
4North Central BaySouth Central Bay
Anchoa mitchelli, Eastern Florida Bay
Date1/1995 1/1996 1/1997 1/1998 1/1999
Abun
danc
e (lo
g 10 no
100
0m-2
+1)
0
1
2
3
4The distinct mesozooplankton community 1994 through May 1997 was accompanied by a high abundance of Anchoa mitchilli, which became the dominant planktivorous fish in Florida Bay (Thayer et al 1999).
Mesozooplankton Conclusions
• Mesozooplankton in Florida Bay respond to regional and bay-wide processes.– A distinct short-term mesozooplankton assemblage
follows a tropical cyclone.• Salinity is the water quality parameter most
strongly correlated with mesozooplankton, and reduced salinities decrease mesozooplankton abundance and diversity.– Top-down control may be important.
• If Everglades Restoration results in decreased salinities, it is likely to result in decreased mesozooplankton diversity and abundance (dependent upon predator response).
• Documented changes in sponge community structure
• Documented changes in juvenile lobster population structure,shelter use, & seasonal recruitment at local, regional, & Keys-wide scales
• Hypothesized ecological linkages: blooms → sponges → lobster
1991-1992 Sponge Die-off Studies by Butler & Colleagues
Photo Credit: Rod Bertelsen
Hard-bottom Monitoring: 2002 - 2007Sites• 132 sites in 2002; 32 -40 sites in 2003-2007
Methods• surveyed annually in June/July• 4 permanent 2 x 25m transects/site• 16 permanent 1m2 quadrats/site
Measurements• Abundance of 55 taxa (24 spp. sponge)• Size structure selected sponges & octocorals• Lobster population structure & disease
2002 - 20072002 Only
Pre-bloom & Post-bloom HB Surveys 2007 -2008
Survey locations
• 18 sites chosen from central region of ODU / FMRI hard-bottom monitoring sites
• Sessile fauna surveys: July & Oct 2007• Lobster surveys: July 2007 & Mar 2008
Moderate ImpactsSevere ImpactsModerate ImpactsSevere ImpactsModerate ImpactsSevere Impacts
Loggerhead sponges: ↓ 67%Vase sponges: ↓ 90%Other sponges: ↓ 50%Commercial sponges: ↓ 95%
Severe Impacts- 22 of 24 sponge species killed
Severe Impacts- 22 of 24 sponge species killed
Loggerhead sponges: ↓ 100%Vase sponges: ↓ 100%Other sponges: ↓ 90%Commercial sponges: ↓ 100%
Moderate ImpactSevere ImpactModerate ImpactSevere Impact
Little or No Impact
-0.06 -0.04 -0.02 0.00 0.02 0.04 0.06-0.12
-0.08
-0.04
0.00
0.04
0.08
IvAasp
Ti
SggSg
GE
SbdSv
Other
Hl
Sp_Cmplx
Is
Cv
Ic
Sb
Lsp ApspNe Csp
Adsp
CaHm
Other_smHsp
Tc
NOXNH4
TNDINTON
TPSRP
APA
CHLA
TOC
TURB
SAL
TEMPDO
Group 2
Group 1
Group 3
-0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
Iv
Aasp
Ti
SggSg
GE
Sbd
SvOtherHl
Sp_CmplxIs
Cv
Ic
Sb
Lsp
ApspNe
Csp Adsp
CaHm
Other_smHsp
TcNOX
NH4
TN
DIN
TONTP
SRPAPA
CHLATOC
TURBSAL
TEMP
DO Group 1
Group 2
Group 3
ODU / FMRI Hard-bottom Monitoring Sites
Canonical Correspondence Analysis of water quality (FIU) and sponge community distribution (ODU/FMRI) data at sites throughout the region reveal three sponge groups with similar water quality requirements or tolerances.
• Group 1: low TP, DO, TEMP, and TURB and high variability in all N measures
• Group 2: low levels of DIN, TON, and TOC and high variability in P and ChlA
• Group 3: high Nox, high salinity and highly variable salinity and DO
Water Quality - Sponge Distribution Association
Butler & Weisz unpub.
Summary of 2007 Bloom Effects on Sponges
• Impact of blooms on hard-bottom communities appears to be similar to that in 1991-1992, although bloom genesis different
• Sponge die-off widespread in middle Florida Keys (200 km2 area) and full recovery will take decades if no further blooms
• Sponge tolerance may be related to species-specific difference in filtration efficiencies
• Ecosystem filtration capacity and habitat structure is greatly diminished in impacted areas, with cascading effects on juvenilelobster abundance and aggregation with possible effects on theirpredator-prey and disease dynamics
Butler & Behringer unpubl.
Sponge & Octocoral Salinity Tolerance Experiments:
•All sponges tested survived sub-optimal salinities better in winter than summer; the reverse was true of octocorals.
•Sponge & Octocoral survival was similar whether exposed to low salinity for a few days or a few weeks (i.e., press vs. pulse experiments).
0
25
50
75
100
C. alloclada H. lachne I. campana I. variabilis S. vesparium
15 ppt 25 ppt 30 ppt 35 ppt
% S
urv
ived
Species
0
25
50
75
100
C. alloclada H. lachne I. campana I. variabilis S. vesparium
15 ppt 25 ppt 35 ppt 45 ppt
% S
urv
ived
Species
Butler unpubl. data
Angular Sea WhipPurple Sea Plume
n = 6 - 16 per treatment
15 20 453525
NoneSurvived
Salinity (psu)
20
40
60
80
100
0
Perc
ent S
urvi
val
n = 8 per treatment
15 20 4535
NoneSurvived
25
NoneSurvived
NoneSurvived NA
Salinity (psu)
Spongessummer
winter
Octocorals
summer winter
Behringer & Butler 2006 Oecologia
Stable isotope analysis of representative benthic macrofauna from hard-bottom habitat in Florida Bay suggests that:• algae, not seagrass, is the major source of primary productivity for hard-bottom higher trophic webs (sponge, mollusc, echinoderm, lobster)
• effect of plankton blooms on trophic structure not evident ~ 5 yrs later
Viral Transmission: Laboratory Trials• Inoculation: 95% transmission in 80d
• Ingestion: 42% transmission in 80 d
• Waterborne: 10 – 50% transmission in 80d;effectiveness declines with lobster size & distance
No transmission tosympatric decapodsvia inoculation
Spotted SpinyLobster
Spider Crab Stone Crab
• Contact: 11 – 63% transmission in 80d;transmission decreases withsize
Contact Transmission
Healthy lobsters can detect &avoid diseased lobsters about2 weeks before they becomeinfectous.
• Visibly infected after 4 – 6 wks (black line)
• Infectious to others after 8 wks (red bars)
• Healthy lobsters avoid diseased conspecifics 4 – 6 wkspost-innoculation (blue bars)
Vira
l Tra
nsm
issi
on to
H
ealth
y Lo
bste
rs (%
)
0
10
20
30
40
50
60
70
80
2 weeks 4 weeks 6 weeks 8 weeks
Weeks After Inoculation
% of InoculatedLobsters Visibly Diseased
% o
f Hea
lthy
Lobs
ters
A
void
ing
Infe
cted
Lob
ster
s
0
10
20
30
40
50
60
70
80
90
100
2 weeks 4 weeks 6 weeks 8 weeks
Weeks After Inoculation
random
Behringer, Butler, & Shields 2006 Nature
(Paris, Butler, Cowen, unpubl.data)
Lobster Caribbean matrix spawning: June 2004
Where do Florida’s Lobster Come From and Go To?
• Simulations using a coupled bio-physical oceanographic model developed for reef fish (modified for lobster to include larval behavior & PLD) are less surprising for Florida than elsewhere in Caribbean and suggest:
• Florida’s lobster recruits come from all over the Caribbean …
… whereas the larvae spawned by lobsters in Florida are lost from the system
Fish and Invertebrate Assessment Network (FIAN)
Michael B. RobbleeJoan A. Browder
CERP-RECOVER-MAP
Region-wide Pink Shrimp Density, by MAP Collection
Fish and Invertebrate Monitoring Network (FIAN)
Pink Shrimp Abundance Metrics Over Time in Johnson Key Basin
Fall Pink Shrimp Density vs. Salinity in Johnson Key Basin
CrossCross--shelf temperature and larval concentrationsshelf temperature and larval concentrations
-160 -140 -120 -100 -80 -60 -40 -20 0
Distance Offshore (km)
-30
-20
-10
0
De
pth
(m
)
24
26
28
30
oCDT ONMQ
WindsWindsWind do not explain upwelling at MQ, large scale forces, intrusions of Loop Current, meso- small scale eddies may be related with this phenomenon.
Wind stress - DT CMAN station
June 15 - July 7 (days)
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7Win
d st
ress
(dyn
e cm
-2)
-0.9
-0.6
-0.3
0.0
0.3
0.6
tx ty
Criales et al. (2007, MEPS)
A shallow and cool thermocline at MQ with a high concentration of larvae
Linear internal tides (LIT)A shallow thermocline with strong density
gradients and high frequency motions of ~ 12 h
Strong vertical shear, vertical turbulent mixing
High concentration of larvae at the shallow thermocline
LIT don’t contribute to onshore transport but convergent current may concentrate larvae at the upper layer
From 17:30h to 15:30h
Temperature
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
Dept
h (
m)
From 17:30h to 15:30h
Salinity
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
From 17:30hto 15:30h
Density
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
Marquesas, July 3-4, 2004
Temperature, salinity Temperature, salinity and density and density
C urren t shear (10 -4 s -2 )
0 10 20 30 40
(du /dz)2
(dv/dz)2
R ichardson num bers0.0 0 .5 1 .0 1 .5 2 .0
D ens ity grad ient (kg m -3)-0 .2-0 .10 .0
Dep
th (m
)
0
5
10
15
20
Density, shear and Richardson numbersDensity, shear and Richardson numbers
Cur
spd
(cm
s-1
)
-60
-30
0
30
60
0
40
80
120
0
100
200
300
0
40
80
120
160
Protozoeae Myses Postlarvae
17192123 1 3 5 7 9 111315
Larv
al fl
ux(l
* 100
m-2
s-1
)
-6
-3
0
3
6
July 3-4, 2004 (h)
17192123 1 3 5 7 9 111315 17192123 1 3 5 7 9 111315
(+) onshore
(-) offshore
Larval fluxes and onshore transport
Dep
th (m
)
-12
-8
-4
0
WMD
Evidence of a Flood Tidal Transport (FTT) a type of STSTfor myses and postlarvae at ~ 80 km from nursery grounds
Investigation of Factors Influencing the Distribution of Clown and Code Gobies
• The hypothesis was that M. gulosus would be more tolerant to salinity shifts than G. robustum.
• M. gulosus inhabits northeastern Florida Bay where salinity is more variable than in the habitat over which G. robustum is distributed.
• To test this hypothesis, acute (e.g., “plunge-type”) salinity tolerance tests were performed with both species.
• Both species were remarkably tolerant to rapid shifts in salinity, and there was no compelling evidence for differences in acute salinity tolerance.
Clown goby occurs more in seagrass than sand in absence of code goby, but more in sand than seagrass in presence of code goby.
Code goby occurs more in seagrass than in sand whether or not clown goby is present.
Clown goby
Code goby
Evidence for species interactions and dominance:
Code goby is the dominant species.
Juvenile Spotted Seatrout Monitoring Areas
Chris Kelble, Allyn Powell, Joan Browder
Juvenile spotted Seatrout, Cynoscion nebulosus, density and frequency of occurrence vary interannually and geographically
Frequency of occurrence is inversely related to salinity in 3 of the 4 sub-regions, indicating susceptibility to changes in hydrology.
Temperature bins (C)
Trou
t den
sity
<26.0 26.0-28.7 28.7-30.5 >30.50.00.51.01.52.02.5
estimate+/- 2 S.E.
Salinity bins (ppt)
Trou
t den
sity
<33.0 33.0-36.4 36.4-39.5 >39.50.00.51.01.52.02.5
Seagrass biomass bins (dry g/m^2)
Trou
t den
sity
<17.9 17.9-45.8 45.8-93.9 >93.90.00.51.01.52.02.5
Based on AIC, the best GLM for predicting seatrout density included all three explanatory variables (water temperature, salinity, and seagrass biomass). Estimated seatrout density increased with greater temperature, decreased with greater salinity, and increased but saturated with greater seagrass biomass.
Fish-Habitat Suitability usingGeneralized Linear Models
Example: Spotted Seatrout (Cynoscion nebulosus)General Model
Fish Abundance = f (Habitat Variables)Data
Source: FWC fishery-independent surveys
Location: Charlotte Harbor
Time Period: 1989-2000
Season: Late summer through fall
Sampling Effort: n=106 to 307 each year-season
Key Biological Data: Catch-per-unit-effort (CPUE) and length composition
Key Environmental Data: Depth, bottom type, salinity, temperature
Target Seatrout Lifestage: Early juvenile (age 1-4 months, length 15-80 mm
Key Processing Step: CPUE data inter-calibrated among different samplinggears (seines, trawls, dropnets, etc.)
Jerald S. Ault, Steven G. Smith & William B. Perry
Two-Stage Model Fitting Procedure
Two-Stage Generalized Linear Model (GLM) Development1) Develop single habitat variable functions, e.g.,
p = f (depth) or u= f ( salinity)
Rationale: Two-stage estimation necessary due to high frequency of zerocatches common in fishery-independent survey data
Stage 1: Logistic regression model using presence-absence data (p),p = f (habitat variables)
Stage 2: Generalized linear regression model using non-zero CPUE data (u),u = f (habitat variables)
Overall Prediction Model: CPUE = p x u
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
Depth (m)
logi
t_p
(a)
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
N V
Bottom Vegetation
logi
t_p
(b)
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38
Salinity
logi
t_p
(c)
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
Temperature (C)
logi
t_p
(d)
Logistic Regression Functions (presence-absence data)Spotted Seatrout
Dots = mean values for class intervals
n=1,739
n=1,772 n=1,858
n=1,847
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Depth (m)
logu
(a)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
N V
Bottom Vegetation
logu
(b)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38
Salinity
logu
(c)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
Temperature (C)
logu
(d)
GLM Regression Functions (non-zero CPUE data)Spotted Seatrout
Dots = mean values for class intervals; n=509 for each function.
Model Development (cont.)2) Combine single variable functions into multiple variable functions:
p = f (depth, bottom type, salinity, temperature) for logistic regression and
u= f ( depth, bottom type, salinity, temperature) for GLM regression
Florida Bay CPUE MappingUse fish-habitat regression models to predict CPUE for each 200 x 200 m
grid cell of the digital composite habitat map
Mud Banks
ModelGrid
200 x 200 m
ENP Boundary
Seagrass
Hardbottom
No Vegetation
Florida Bay Benthic Habitats
Rsmas_grid_g.shp-1.332 - -1.056
-1.056 - -0.952
-0.952 - -0.862
-0.862 - -0.789
-0.789 - -0.724
-0.724 - -0.659
-0.659 - -0.592
-0.592 - -0.527
-0.527 - -0.464
-0.464 - -0.4
-0.4 - -0.315
-0.315 - -0.207
-0.207 - -0.083
-0.083 - 0.017
FloridaBay
Florida Bay Bathymetry
Hsm_resp.shp0
0
0.001 - 1
1.001 - 20
20.001 - 33.29
33.3 - 33.8
33.8 - 34.3
34.3 - 35.2
35.2 - 35.7
35.7 - 36.1
36.1 - 36.6
36.6 - 37.2
37.2 - 37.3
37.3 - 37.7
37.7 - 37.8
37.8 - 38
38 - 38.1
38.1 - 38.3
38.3 - 38.4
38.4 - 38.6
38.6 - 38.8
38.8 - 39.1
39.1 - 39.2
39.2 - 40.7
40.7 - 40.8
40.8 - 41.7
41.7 - 41.8
41.8 - 41.9
41.9 - 42.2
42.2 - 43.2
43.2 - 45.7
45.7 - 45.8
45.8 - 46.5
46.5 - 50
50 - 53.1
53.1 - 54.5
54.5 - 62.7
62.7 - 72.1
72.1 - 72.8
FATHOM – Salinity April 2001
Hsm_resp.shp0
0
0.001 - 1
1.001 - 20
20.001 - 33.299
33.3 - 33.8
33.8 - 34.3
34.3 - 35.2
35.2 - 35.7
35.7 - 36.1
36.1 - 36.6
36.6 - 37.2
37.2 - 37.3
37.3 - 37.7
37.7 - 37.8
37.8 - 38
38 - 38.1
38.1 - 38.3
38.3 - 38.4
38.4 - 38.6
38.6 - 38.8
38.8 - 39.1
39.1 - 39.2
39.2 - 40.7
40.7 - 40.8
40.8 - 41.7
41.7 - 41.8
41.8 - 41.9
41.9 - 42.2
42.2 - 43.2
43.2 - 45.7
45.7 - 45.8
45.8 - 46.5
46.5 - 50
50 - 53.1
53.1 - 54.5
54.5 - 62.7
62.7 - 72.1
72.1 - 72.8
72.8 - 79.6
FATHOM:Salinity September 2001
Hsm_resp.shp0
0
0.001 - 1
1.001 - 20
20.001 - 33.299
33.3 - 33.8
33.8 - 34.3
34.3 - 35.2
35.2 - 35.7
35.7 - 36.1
36.1 - 36.6
36.6 - 37.2
37.2 - 37.3
37.3 - 37.7
37.7 - 37.8
37.8 - 38
38 - 38.1
38.1 - 38.3
38.3 - 38.4
38.4 - 38.6
38.6 - 38.8
38.8 - 39.1
39.1 - 39.2
39.2 - 40.7
40.7 - 40.8
40.8 - 41.7
41.7 - 41.8
41.8 - 41.9
41.9 - 42.2
42.2 - 43.2
43.2 - 45.7
45.7 - 45.8
45.8 - 46.5
46.5 - 50
50 - 53.1
53.1 - 54.5
54.5 - 62.7
62.7 - 72.1
72.1 - 72.8
72.8 - 79.6
FATHOM – Salinity June 2001
Hsm_resp.shp0 - 0.05
0.05 - 0.1
0.1 - 0.15
0.15 - 0.2
0.2 - 0.25
0.25 - 0.3
0.3 - 0.35
0.35 - 0.4
0.4 - 0.45
0.45 - 0.5
0.5 - 0.55
, 0.6
0.6 - 0.65
0.65 - 0.7
0.7 - 0.75
0.75 - 0.8
0.8 - 0.85
0.85 - 0.9
0.9 - 0.95
0.95 - 1
FloridaBay
Predicted Florida Bay Habitat SuitabilityJuvenile Seatrout -- June 2001
Hsm_resp.shp0 - 0.05
0.05 - 0.1
0.1 - 0.15
0.15 - 0.2
0.2 - 0.25
0.25 - 0.3
0.3 - 0.35
0.35 - 0.4
0.4 - 0.45
0.45 - 0.5
0.5 - 0.55
, 0.6
0.6 - 0.65
0.65 - 0.7
0.7 - 0.75
0.75 - 0.8
0.8 - 0.85
0.85 - 0.9
0.9 - 0.95
0.95 - 1
FloridaBay
Predicted Florida Bay Habitat SuitabilityJuvenile Seatrout – September 2001
Effects of Hurricane-Induced Fragmentation of Mangrove Forest on
Habitat and Fish
Carole C. McIvor1, Noah Silverman,1,2
and Victor Levesque3
1 USGS, FISC, St Petersburg, FL2 ETI Professionals, Tampa FL3 USGS, FISC, Tampa, FL
Funding: USGS Global Climate Change, USGS Priority Ecosystem Science
Hurricanes convert mangrove forests to mudflats:
Does it matter to fish?
Hurricanes convert mangrove forests to mudflats:
Does it matter to fish?
Big Sable Creek complex, SW FloridaBig Sable Creek complex, SW Florida
Nek
ton
(indi
vidu
als)
100
m-3
of w
ater
Mangrove Mudflat
Wet
Dry
Per volume of water, mangrove forests contain higher numbers and greater biomass of fish than do mudflatsn
u
ConclusionsHurricane-Driven Fragmentation of Mangroves
Found
• Water-column fish replaced benthic fish on mudflats
• Decreased fish density, biomass
Given
• Rising sea level
• Probable increased hurricane intensity
Expect
• Increased forest fragmentation at landfall
• Decreasing fisheries productivity at impact site
ConclusionsHurricane-Driven Fragmentation of Mangroves
Found
• Water-column fish replaced benthic fish on mudflats
• Decreased fish density, biomass
Given
• Rising sea level
• Probable increased hurricane intensity
Expect
• Increased forest fragmentation at landfall
• Decreasing fisheries productivity at impact site
OverviewContinuing and new research is sharpening our understanding of the processes that shape the ecology of Florida Bay and the influence of salinity on the distribution and abundance of faunal indicators—essential to CERP.Specific thresholds, preferences, or optima for individual species that can be directly used in evaluation of alternative CERP scenarios:
SpongesLittle fishes (Rand and Bachmann ) -- the FIU salinity tolerance experiments But rainwater killifish still neededMore trials at upper limits of salinity needed
Future work should focus especially on acquiring and improving specific information about the relationship of species and communities to salinity, as well as other potential influencing factors (e.g., nutrients, turbidity) on faunal species and communities that may be changed by CERP. Basic ecological studies such those on hardbottom habitat are needed for other important habitats of Florida Bay.More research and monitoring attention should be given to the lower southwest Florida coast:
Whitewater Bay to Lostmans RiverChokoloskee BayPicayune Strannd (Fakahatchee, Faka Union, and Pumpkin)
Acknowledgments and Apologies
I would particularly like to thank Savanna Howington of Everglades National Park for sharing information and materials from the National Park Service CESI Program. I also thank the many investigators who provided slides to illustrate discussion of their work. I apologize for any inadvertent slips in interpretation of their work. I also apologize for any omissions.