Oyster fisheries collapse in Florida’s Apalachicola Bay: When Ideology, not science, guides policy

The politics behind the collapse of the oyster fishery in Apalachicola Bay are impossible to ignore.  Especially given Tea Party Governor Rick Scott’s appeal to the federal government (Small Business Association or SBA) for emergency funds to bail out the communities with economies devastated by the collapse.

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Rick Scott has repeated the following mantra again and again, wanting to “Balance the budget — without gimmicks, one-time revenues, borrowed funds, temporary funds, or tax increases.” It inspired his refusal of federal money (loans and grants) in the following scenarios:

So why is he taking federal money now? To make a short term payment to communities in an economic crisis, but this is near-sighted policy as usual.

The real solution to fisheries in a state of collapse is a longterm and sustained investment in restoring the affected ecosystems to a pre-crisis state. Scott and other Tea Partiers are constantly berating environmental management as frivolous spending, or they criticize the science/data as a way to delay restorations or cleanups to infinity. The fact of the matter is that investing in ecological restorations is critical in a state like Florida, with an economy that is so intrinsically tied  to the ecological health of its costal ecosystems.

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If not outright restoration, then the ecosystem services provided by fresh water sources must be economically values in terms of dollar amounts. Step one would be to review the necessity of them dams along the Chattahoochee and Flint rivers. These are the freshwater sources that the oysters depend on. Who is benefiting from the ecosystem services provided by the freshwater they dam up? If the disproportionate benefit accrues in terms of economic revenue, then it is these private interests who need to make damage payments to the communities with crashing fisheries.

Florida does not make science driven policy based on an accurate assessment of natural capital, and its value in dollars. In my opinion, it is the only way to make ideological Tea Partiers begin to realize the return on investment that healthy, restored ecosystems can make to the Florida economy.

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Sea Turtle Nesting Habits & Socioeconomic Indicators in R (part II)

Last time I looked at correlations between actual nest building and socioeconomic variables like population in South Florida’s coastal counties, and per capita income. This time I am looking for relationships between emergence of the same three sea turtle species (loggerhead, green, and leatherbacks noted in their latin names in the code). Emergence means coming onto the beach and returning into the ocean, laying eggs or not. I thought that counties with lower populations would see more emergence because beaches would probably be emptier at night. I ran the correlations and got pretty non-remarkable correlations for population. The correlations were mild to moderate for per capita income by county. It was  0.33 for loggerhead emergence and per capita income by county, .25 for greens, and .37 for leatherbacks. I ran the linear regressions and the result was disappointing. No stars and high p-values. I have attached the .csv and the R code in case you want to play around. Best for people learning R. As I said in my last blog entry, the data could tell us more if it looked at emergence through time and socioeconomic trends related more closely to direct, shore-front development.

Excel data file here:  sea.turtle.nests

R code below:

#take cor of emergence and per capita income
cor(sea.turtle.nests$C..carettaNON.NESTING.EMERGENCE , sea.turtle.nests$per.capita.income)
cor(sea.turtle.nests$C..mydasNON.NESTING.EMERGENCE , sea.turtle.nests$per.capita.income)
cor(sea.turtle.nests$D..coriaceaNON.NESTING.EMERGENCE , sea.turtle.nests$per.capita.income)
#run linear regressions of emergence as a function of per capita income
loggerhead.reg <- lm(C..carettaNON.NESTING.EMERGENCE ~ per.capita.income , data=sea.turtle.nests)
summary(loggerhead.reg)
#Coefficients:
#Estimate Std. Error t value Pr(>|t|)
#(Intercept) -5122.3617 10669.0678 -0.480 0.641
#per.capita.income 0.4263 0.3732 1.142 0.278
#Residual standard error: 9566 on 11 degrees of freedom
#Multiple R-squared: 0.1061, Adjusted R-squared: 0.02479
#F-statistic: 1.305 on 1 and 11 DF, p-value: 0.2776

green.reg <- lm(C..mydasNON.NESTING.EMERGENCE ~ per.capita.income , data=sea.turtle.nests)
summary(green.reg)
#Estimate Std. Error t value Pr(>|t|)
#(Intercept) -475.84094 1672.65952 -0.284 0.781
#per.capita.income 0.04852 0.05851 0.829 0.425
#Residual standard error: 1500 on 11 degrees of freedom
#Multiple R-squared: 0.05885, Adjusted R-squared: -0.02671
#F-statistic: 0.6878 on 1 and 11 DF, p-value: 0.4245

leather.reg <- lm(D..coriaceaNON.NESTING.EMERGENCE ~ per.capita.income , data=sea.turtle.nests)
summary(leather.reg)
#Coefficients:
#Estimate Std. Error t value Pr(>|t|)
#(Intercept) -47.376772 57.792547 -0.820 0.430
#per.capita.income 0.002672 0.002021 1.322 0.213
#Residual standard error: 51.82 on 11 degrees of freedom
#Multiple R-squared: 0.1371, Adjusted R-squared: 0.05862
#F-statistic: 1.747 on 1 and 11 DF, p-value: 0.2131

Scenario Planning and Sea Level Rise

Today I sat in on a webinar offered by the National Parks Service on scenario planning for climate change, adaptation, and sea level rise. Coincidentally, the class that I TA for Professor Larry Susskind at MIT just had a lecture/discussion on scenario planning. I want to write a quick and easy piece reflecting on these two information sessions and scenario planning, mainly for coastal systems.

1. Scenario Planning is a lot of work, but payoffs (i.e. added resilience to future disasters) are huge: Can government agencies sink large amounts of time into the up front research needed to craft a range of scenarios? Can the governance structures of federal agencies manage to implement measures to account for a range of scenarios based on detailed socio-economic and natural drivers for change? These responses would build capacity to respond to any foreseen scenario. Based on the NPS presentation, this agency activity is in its early phases but is showing results.  An example from the webinar were the thousands of parking spaces in Sandy Hook that experienced mass overwashing and sedimentation post-storm. What do we do with the sediment to reinforce areas heavily eroded and not alter ecosystem processes on the beach? Another example was West Pond in the Gateway National Recreation area, a hotspot for birders. Once this was breached by storm surge, the Park Service had to decide immediately to repair the breech or not, and account for the brackish water intrusion. This mean looking at faunal communities pre-storm, and the implications for their nesting and breeding if the brackish water remained.

2. What people who live nearby want may be different from ecosystem-based management: A scenario like West Pond could apply here. Perhaps birders would want the freshwater ecosystem restored. But the change to brackish water was natural. Yet the Gateway National Recreation Area is heavily managed. You can see how this balancing between ecosystem functioning and services to people can create difficult decisions.

3. Scenario planning takes a lot of data, but it proves extremely useful for longterm decision-making: The webinar’s case-based look at scenario planning for climate change on the coast zeroed in on the National Park Service’s work post Hurricane Sandy. Enormous amounts of data were required to realize a range of scenarios for cases: ecological monitoring data, biological date and drivers of change, Best Available Flood Hazard data (BAFH), Flood Insurance Rate Map (FIRM) data, and the list goes on. The most interesting point that the presenters made was that given dynamic natural change, these datasets often are moving targets. For example, if the 100 year flood plain is revised to show a difference of 7 feet pre and post storm, as it was in one of the examples they gave, building critical infrastructure 1-2 feet above it becomes an iterative process. All of this is to allow scenarios to reveal what outcomes are pretty much going to happen.

Sea Turtle Nesting Habits in R

I pulled together a small and simple data set to have a look at turtle nesting on South Florida’s beaches, and to see if it had any relationships to some easy-to-find socioeconomic data. I looked at nesting habits for greens, loggerheads, and leatherbacks to see if you could create linear regressions with population by county, median household income, number of households by county, and per capita income by county.

The highest correlation I got was between leatherback nests and per capita income, but it bore no statistically significant relationship upon running the regression. I put my R code here with comments for people like me who are learning to play around in R.

R Code

loggerhead.nest <- sea.turtle.nests$C..carettaNEST
loggerhead.emergence <- sea.turtle.nests$C..carettaNON.NESTING.EMERGENCE
green.nest <- sea.turtle.nests$C..mydasNEST
green.emergence <- sea.turtle.nests$C..mydasNON.NESTING.EMERGENCE
leather.nest <- sea.turtle.nests$D..coriaceaNEST
leather.emergence <- sea.turtle.nests$D..coriaceaNON.NESTING.EMERGENCE
#make latin to common name labeling
plot(loggerhead.nest ~ population , data=sea.turtle.nests)
plot(loggerhead.nest ~ log(population) , data=sea.turtle.nests)
#population alone looked off, took the log
plot(green.nest ~ log(population) , data=sea.turtle.nests)
plot(leather.nest ~ log(population) , data=sea.turtle.nests)
#took plots for all three species
cor(loggerhead.nest,sea.turtle.nests$population)
#0.03736234
cor(loggerhead.nest,sea.turtle.nests$per.capita.income)
#0.2318836
cor(loggerhead.nest,sea.turtle.nests$median.household.income)
#-0.04984773
cor(green.nest,sea.turtle.nests$population)
#0.007499322
cor(green.nest,sea.turtle.nests$per.capita.income)
#0.2130996
cor(leather.nest,sea.turtle.nests$population)
#0.01959502
cor(leather.nest,sea.turtle.nests$per.capita.income)
#0.4036139
leather.lm <- lm(leather.nest ~ sea.turtle.nests$per.capita.income , data=sea.turtle.nests)
summary(leather.lm)
#Coefficients:
#Estimate Std. Error t value Pr(>|t|)
#(Intercept) -2.083e+02 2.397e+02 -0.869 0.403
#sea.turtle.nests$per.capita.income 1.227e-02 8.385e-03 1.463 0.171
#p-value: 0.1714
#Adjusted R-squared: 0.0868

Figure 1: leatherback nests and log of population: no linear relationship

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Figure 2 Loggerhead nest and log population

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Figure 3 Green nests and log population

Rplot3

Getting better data to reflect development along the ocean front would probably help here, also looking at nesting habits through time .

Biodiversity in a restored salt marsh: managed versus unmanaged habitats

Introduction

An ecological restoration is an iterative process where a degraded ecosystem is brought back to its previous, healthier state (Walters 1997; Stankey 2005). It is a multi step, non-linear process that I simplified into the following diagram:

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The purpose behind a restoration is to restore ecological services that a previously productive ecosystem delivered to society before it was degraded. Ecosystem services provided by the tidal salt marsh (one of the most highly productive ecosystem types on the planet) include water filtration, fish and wildlife habitat, pollution filtration, storm water retention, shoreline erosion protection, and recreation (Wohlgemuth 1990). Restored marshes should be able to provide these same ecological services at a rate comparable to their historic form.

The dominant physical characteristic of a tidal salt marsh is a periodic, predictable tidal inundation. Therefore, reinstituting traditional flood-drainage patterns in a tidal wetland is the first step in the restoration process. As time passes and development increases, dikes, dams, and inadequately planned water storage areas diminish the historical hydrodynamic patterns of the tidal marsh. This has implications for the salinity, surface elevation, and plant and animal communities that originally inhabited the ecosystem. In other words, plants and animals that characterize tidal wetlands require a tidal shift to survive. When this changes, new species move in and colonize, altering the state and productive capabilities of the wetland.

The history of salt marsh restoration in New England begins in the 1970s. The first step in these restorations was breeching dams and floodgates that previously stemmed the tides in the salt marshes. The aim was recreating historical hydrological dynamics in hopes that plants and animals characterizing the marsh in healthier states would return. The Massachusetts Department of Conservation and Recreation documents the significant impact that restoring historical hydrological flooding has on invasive species. This is important because invasives reduce both habitat and fisheries populations. When historical flooding is restored, water salinity increases, and invasive plants such as Phragmites see their populations diminished (Robinson 2002). Alternatively they can be deliberatively removed. Biodiversity maintenance is another reason why plants that tend to choke out other natively occurring species (like Phragmites) need to be removed. Biodiversity means plants with a larger variety of ecological functions inhabit the marsh and provide a wider range of ecosystem services.

A third consideration when restoring tidal marshes in New England is the historical digging of mosquito ditches. Settlers dug mosquito ditches centuries ago to drain the marsh and lower mosquito populations, as they are disease vectors. The ditches are straight, narrow, separated by intervals of approximately 50m, and dug on 90% of the tidal wetlands on the Atlantic coast (Harrington et al. 1984). Unfortunately, the unintended consequence of removing water where mosquito larva could reproduce also reduced habitat where mosquito-eating fish could live, especially the killfish (Harrington et al. 1984).

This study examines the plant communities along the mosquito ditches in the restored Ipswitch Salt Marsh and compares them to those that lay along naturally meandering creeks in the same marsh. Figure 1 shows the salt marsh’s location in comparison to Crane Beach. Figure 2 shows the extent of the naturally occurring creeks as well as the mosquito ditches.  Figure 3 shows these features colorized, with blue for the natural creeks and orange for the mosquito ditches.

Figure 1

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Figure 2

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Figure 3

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Methods

To examine plants along mosquito ditches versus natural creeks, we selected two habitats and randomly sampled a smaller plot within the habitat. We sampled with 2 replications. This involved a random toss of a square meter sampling hoop. After the random toss demarcated our sample, we next measured average plant density, plant height, and species diversity. We used small shovels do examine root development and composition. Figure 4 shows the sampling hoop and a sampling space.

Figure 4

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Results

The quantitative results are displayed in the four tables below, two for each sampled habitat.

Mosquito Ditch Sample 1

 

Species Mean height Approximate density
Spartina patens 30.48 cm 60%
Spartina Alterniflora 45.72 cm 30%
Salicornia virginica 20.32 cm 2%
Distichlis spicta 26.26 cm 8%

Range of distance between plants: 2.54-7.62 cm

Area of visible ground: 30% of sample ring

Average depth of roots: 15.24 cm


Mosquito Ditch Sample 2

 

species mean height approximate density
Spartina patens n/a 0%
Spartina Alterniflora 63.5 cm 97%
Salicornia virginica 29.21 cm 3%
Distichlis spicta n/a 0%

Range of distance between plants:2.54-8.89 cm

Area of visible ground: 20% of sample ring

Average depth of roots: 15.24 cm, but less dense than sample one with more visible soil

Natural Creek Sample 1

species mean height approximate density
Spartina patens 46.99 cm 9%
Spartina Alterniflora 111.76 cm 90%
Salicornia virginica 35.56 cm 1%
Distichlis spicta n/a 0%

Range of distance between plants: 10.16-15.24 cm

Area of visible ground: 40% of sample ring

Average depth of roots: 22.86 cm

 

Qualitative Comparative Observations:

-This sample shows more mature plants more spread out

-The surface has more variability in elevation

-Leaves are bigger and plants are taller and thicker
-Really densely packed roots, lots of fine hairs and rhizomes, almost could not pull them out

-The soil is an iron sulphur compound with far more clays

Natural Creek Sample 2

 

species mean height approximate density
Spartina patens 44.50 cm 70%
Spartina Alterniflora 116.86 cm 30%
Salicornia virginica n/a 0%
Distichlis spicta n/a 0%

Range of distance between plants: 2.54-7.62 cm

Area of visible ground: 20% of sample ring

Average depth of roots: 16.24 cm

 

Qualitative Comparative Observations:

-More mature plants, higher, broader leaves

– far more biomass

-When you don’t get down to root level, the density appears to be 100% plant coverage with no visible ground

-Roots have a lots of fine hairs and more of a range of rhizomes

Discussion

In both samples taken from the natural creek bank, the plants were markedly more mature. They were not only larger, but also with thicker stems and broader leaves. When you examined them from directly on top, you could see no visible ground. On the other hand, the plants near the mosquito ditches were smaller, and when examined from directly on top, a lot of visible ground was present. The quantitative differences in biomass were remarkable, with the natural creeks having significantly more. The roots were more matured as well, with a greater range of functional types (roots, rhizomes) and density occurring near the natural creeks. Figure 5 shows a root sample taken by the natural creeks. The study’s significance is that along more managed areas, plants mature less and less biomass (and productivity) is present. Given the information presented in the introduction on how sensitive plant communities are to inundation, I would hypothesize that unnatural inundation attributes were to blame for the lackluster plant communities that occur around managed ditches.

Figure 5

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