Springs Ecology Baseline Assessment – Standard...

Springs Ecology Baseline Assessment –

Standard Operating Procedures

Prepared by

The Howard T. Odum Florida Springs Institute

June 2015

Springs Ecology Baseline Standard Operating Procedures

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Table of Contents Figures ....................................................................................................................................... ii

Tables ........................................................................................................................................ iii

Introduction ....................................................................................................................4

Description of Sampling Methods ................................................................................4

Introduction .............................................................................................................................. 4

Insolation and PAR.................................................................................................................. 6

Underwater Light Transmission............................................................................................ 8

Stream Discharge and Current Velocity ............................................................................... 8

Stream Segment Morphometry.............................................................................................. 9

Secchi Disk Visibility ............................................................................................................... 9

Oxygen Diffusion Rate .......................................................................................................... 11

Weather Station ...................................................................................................................... 13

Water Quality ......................................................................................................................... 13

Biology ..........................................................................................................................15

General Biological Structure................................................................................................. 15

Plant Community Characterization .................................................................................... 15

General Faunal Observations............................................................................................... 16

Macroinvertebrates ................................................................................................................ 16

Adult Aquatic Insects ............................................................................................................ 16

Snails ........................................................................................................................................ 17

Fish Surveys............................................................................................................................ 17


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Turtles ...................................................................................................................................... 18

Human Use ............................................................................................................................. 19

Ecosystem Level Monitoring ......................................................................................22

Ecosystem Metabolism.......................................................................................................... 22

Oxygen Diffusion................................................................................................................... 24

Nutrient Assimilation............................................................................................................ 26

Community Export ................................................................................................................ 26

Literature Cited.............................................................................................................28

Table of Exhibits Figures

Figure 1. Device to continuously measure photosynthetically available radiation

(PAR), LI-COR sensors on top, data logger in box....................................................... 7

Figure 2. Underwater LI COR sensor used to continuously measure PAR, cable

goes to data logger. ........................................................................................................... 7

Figure 3. Stream depth and velocity measurement along a cross-section of the

Ichetucknee River. ............................................................................................................. 9

Figure 4. Oxygen diffusion dome, with oxygen meter and nitrogen tank in

background....................................................................................................................... 11

Figure 5. Image of data sonde housing with holes in the housing allowing water

to pass through while locking well cap and cable lock provide security. .............. 14

Figure 6. Trap used to collect adult aquatic insects as they emerge from the water ....... 17

Figure 7. Daily pattern of water-dependent human use observed at Wekiwa

Springs on Sunday, August 12, 2007 (from WSI 2007)............................................... 21


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Figure 8. Example determination of ecosystem metabolism based on upstream-

downstream dissolved oxygen data (from WSI 2007). .............................................. 25

Figure 9. Image of plankton net capturing suspended material with flow meter

upstream ........................................................................................................................... 27

Tables

Table 1. Marsh-McBirney Flo-Mate cross sectional flow example ..................................... 10

Table 2. Oxygen diffusion rate calculation examples ........................................................... 12

Table 3. Water quality constituents analyzed for each study spring ................................. 14


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Introduction The Howard T. Odum Florida Springs Institute (FSI) is embarking on a project to document existing baseline ecological conditions in the springs of Florida. Data collected for this baseline assessment, in combination with ecological data from previous studies will be used to provide a continuing record of changes, both positive and negative, in Florida’s endangered springs and spring runs.

This Standard Operating Procedures (SOP) will be used to provide consistent and valid data for documenting springs ecology. This is a living document and will be revised from time-to-time to upgrade and add new methods.

Description of Sampling Methods Introduction

Florida’s springs and spring runs are physically quite variable. Ecological sampling efforts were applied to this diverse group of spring ecosystems in as consistent a fashion as practical. Also, biological systems vary considerably due to seasonal changes in sunlight, temperature, and precipitation. Seasonal variation is reduced somewhat in spring-fed aquatic ecosystems due to their groundwater supply. These natural groundwater discharges demonstrate relatively consistent water temperature, inflow volume, and water chemistry (Odum 1957). The one major environmental factor that is seasonally variable in springs is the input of solar energy. This seasonal variability is considered in data collection and analysis.

The following protocols are applied in this study to facilitate temporal and seasonal spring variation:

• The focus of each spring baseline ecological study is a spring segment which includes the spring pool or a portion of the spring run. Each study segment has a measurable inflow and outflow dominated by recently discharged artesian groundwater;

• Each spring study area is large enough to average out small-scale variation and to allow significant and measurable changes in dissolved oxygen and other biologically-active water quality parameters;

• Preferred sampling segments for collection of continuous field parameter data, as well as water quality, discharge, export, and plant and animal diversity and population data include: the spring vent area to the downstream edge of the spring pool or an intermediate point in the spring


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run that integrates all principal spring vents in a spring group, or a spring run segment that allows measurement of a relatively homogenous area of spring run habitat with constant flow and physical characteristics (such as shading by trees, water depths, channel width, dominant plant communities, etc.).

Spring ecosystem data collection includes as many environmental variables as practical. The following ecological metrics can be measured in many spring segments:

Physical Environment

• Insolation and photosynthetically active radiation (PAR) and underwater light transmission of PAR

• Stream discharge (water level and flow) and stream velocity

• Secchi disk visibility

• Segment morphometry (area and volume)

• Atmospheric oxygen diffusion rate as a function of velocity

• Water quality field parameters (temperature, pH, dissolved oxygen, conductance)

Water Chemistry

• Water chemistry (total Kjeldahl nitrogen, nitrate+nitrite nitrogen, ammonia nitrogen, soluble reactive phosphorus, total phosphorus, chloride, chlorophyll, color, and turbidity. Total nitrogen and organic nitrogen will be calculated.)

• Nitrogen to phosphorus ratios will be calculated

Biology

• Plant community characterization (species, coverage, algal depth)

• Macrofauna observations (species and counts)

• Adult aquatic insects (species and emergence rates)

• Human uses

Ecosystem Level


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• Ecosystem metabolism metrics (gross primary productivity, net primary productivity, community respiration, P/R ratio, ecological efficiency)

• Nutrient assimilation

• Community export (fine particulate export)

Physical Environment

Insolation and PAR

Inputs of solar radiation are measured as Photosynthetically Active Radiation (PAR) using a LI-COR brand sensors LI-200SA (surface quantum sensor), and LI-192 (underwater quantum sensor). Total solar radiation (insolation) is measured with a LI-COR pyrometer (LI-190SA). Light sensors are placed in a representative area of the spring segment with respect to trees and other possible obstructions. Figure 1 and Figure 2 provides a typical light sensor installation in a spring run.

Data from the light sensors are recorded using a LI-1400 data logger every 15-minutes. During deployment the underwater sensor is cleaned as necessary (primarily to remove attached algae or floating plants). The resulting PAR data are downloaded to a computer and utilized in metabolism Excel spreadsheets to calculate primary productivity and photosynthetic efficiency.


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Figure 1. Device to continuously measure photosynthetically available radiation (PAR), LI-COR

sensors on top, data logger in box

Figure 2. Underwater LI COR sensor used to continuously measure PAR, cable goes to data logger.


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Underwater Light Transmission

PAR underwater light transmission and attenuation coefficients are measured at the upstream and downstream ends of each spring segment. A LI-200SA sensor is used to measure PAR energy reaching the water surface, while an underwater LI-COR LI-192 sensor is used to measure PAR energy at multiple water depths. The underwater PAR sensor is attached to a weighted frame and readings are logged at 15 to 30 cm (0.5 to 1 ft) depth intervals from the surface to the bottom of the water column (typically in an unshaded location). Measurements at each depth are collected following at least a ten second stabilization period. Light extinction (attenuation) coefficients are calculated from these data using the Lambert-Beer equation (Wetzel 2001):

Iz = Io(e-kz) [Equation 1]

Where:

Iz = PAR at depth z

Io = PAR at the water surface

k = diffuse attenuation coefficient, m-1

z = water depth, m

Stream Discharge and Current Velocity

Stream discharge and velocity are measured at the upstream and downstream ends of each spring segment using a Marsh-McBirney Flo-Mate portable flow meter. At each location, a fiberglass tape is stretched across the stream channel perpendicular to the flow direction, allowing depth and velocity to be measured in at least 15 to 25 evenly-spaced segments (Figure 3). At water depths less than 76 cm (2.5 ft), velocity is measured at 0.6 of the water column. For water depths greater than 76 cm (2.5 ft), velocity is measured at 0.2 and 0.8 fractional depths of the water column. For each of the resulting cross-section sub-segments, velocity is multiplied by width and depth to calculate sub-segment discharge. The cumulative discharge of all cross-section sub-segments is calculated using an Excel spreadsheet (Table 1).


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Figure 3. Stream depth and velocity measurement along a cross-section of the Ichetucknee River.

Stream Segment Morphometry

Segment depths are measured by use of a recording depth finder linked to a global positioning system (GPS). These data are processed using Bentley PowerCivil™ software to extrapolate the wetted surface area and volume of each spring study segment. Nominal hydraulic residence times are calculated in a spreadsheet for each spring segment based on these estimated water volumes and the upstream and downstream flow estimates.

Secchi Disk Visibility

Water clarity is rapidly assessed using Secchi disk visibility, the distance were the disk disappears from sight. In spring systems, this distance is commonly greater than the depth of the water column and Secchi disk visibility must be measured horizontally. Secchi distance is measured with a 50 centimeter diameter white disk attached to the end of a tape measure and held below the surface of the water. A skin diver then extends the tape until the disk was no longer visible. Daily measurements are typically made between 10 A.M. and 3 P.M. when the sun is overhead within the study segment.


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Table 1. Marsh-McBirney Flo-Mate cross sectional flow example


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Oxygen Diffusion Rate

Atmospheric oxygen diffusion rates are estimated through the use of the floating dome technique of Copeland and Duffer (1964) and modified by McKeller (1975). To measure oxygen diffusion, a dome-shaped plastic container of known volume is used. This dome floats on the water surface while an oxygen sensor remains in the headspace contained within (Figure 4). To measure diffusion, the dome is inverted and filled with water, and then nitrogen gas is purged into the dome to expel the water. This results in an anoxic atmosphere which gradually equilibrated with oxygen diffusing up from the water below. A field meter capable of logging dissolved oxygen concentrations (e.g., YSI 556 MPS) is used to record oxygen levels at 2 to 5 minute intervals. Both stream velocity and water depth are measured at the location of the diffusion dome. Experiments are typically run for one to 2 hours, allowing multiple measures to be made over the course of a day in different locations. Diffusion rates are calculated using a spreadsheet (Table 2). A regression between oxygen diffusion rate and current velocity is developed to estimate diffusion rates for correction of ecosystem metabolism estimates.

Figure 4. Oxygen diffusion dome, with oxygen meter and nitrogen tank in background.


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Table 2. Oxygen diffusion rate calculation examples


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Weather Station

Local area weather (rainfall, air temperature, solar radiation, and evapotranspiration) will be estimated using the University of Florida – Florida Automated Weather Network (FAWN, http://fawn.ifas.ufl.edu/). The FAWN network includes a total of 44 weather stations throughout Florida reporting weather data at 15-minute increments.

Water Quality

During segment sampling, field variables (water temperature, dissolved oxygen concentration, oxygen percent saturation, pH, conductivity and specific conductance) are measured and logged at 30 minute intervals using YSI 6920 data sondes.

Oxygen data are collected using optical sensors with automated wipers, which improve calibration and reduce instrument drift during deployment. Data sondes are deployed near the middle of the water column at the upstream and downstream ends of each study segment for periods of at least 72-hours (Figure 5). Data sondes are calibrated prior to deployment and subsequent to their retrieval for each sampling period following the manufacturers protocol. Field water quality variables are also measured using hand-held YSI 556 MPS meters adjacent to data sonde deployments to spot-check data sonde calibration.

Water chemistry samples are collected at the beginning and end of each study period, at both the upstream and downstream end of each spring run segment. Water chemistry samples are collected as sub-surface grabs. A rinsed water collection bottle is used to collect water samples from about 30 cm (1 ft) below the water surface and used to fill acid-preserved sample bottles. Following collection, samples are placed in an ice-filled cooler and delivered to the analytical laboratory for analysis within 24 hours. Water depth and field variables (temperature, dissolved oxygen, pH, and specific conductance using a YSI model 556 MPS) are also recorded during all water chemistry sampling events.

Water chemistry samples are analyzed for the constituents listed in Table 3 by Advanced Environmental Labs, Gainesville FL, (FDOH certified laboratory # E82620).


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Figure 5. Image of data sonde housing with holes in the housing allowing water to pass through

while locking well cap and cable lock provide security.

Table 3. Water quality constituents analyzed for each study spring


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Biology

General Biological Structure

Spring run sediments are characterized at a minimum of three equally-spaced intervals in each spring segment. In general, these locations include the upstream end of the segment, the middle of the segment, and the downstream end of the segment. Sediment samples are collected from three replicate Petite Ponar dredge samples at each station and combined and analyzed for Kjeldahl nitrogen and total phosphorus. Sediment particle size is analyzed on material from each sampling location.

Habitat quality is assessed within a minimum of three (“upper”, “middle”, and “lower”) 100-meter long sub-segments in the spring run study segment to quantify coverage of biologically-productive habitats (roots, rock, aquatic vegetation, woody debris, and leaf material). Habitat Assessment evaluates eight attributes known to have potentially important effects aquatic invertebrates:

• substrate diversity,

• substrate availability,

• water velocity,

• habitat smothering,

• artificial channelization,

• bank stability,

• riparian buffer zone width, and

• riparian vegetation quality.

Twenty points are possible for each parameter. The total score is placed into one of four categories: Optimal (≥ 120), Suboptimal (81-119), Marginal (41-80), and Poor (≤ 40).

Plant Community Characterization

The distribution and percent cover of aquatic plant communities (macroalgae and submerged aquatic vegetation) in each of the study segments is visually estimated during the sampling events. Aquatic vegetative cover is documented for each spring


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run segment at a minimum of three sampling points (typically upstream, middle, and downstream). Observed plants are identified to species or lowest practicable taxonomic classification and are categorized according to functional group (floating aquatic, submersed aquatic, emergent, or benthic algae). No quantitative plant biomass samples are collected. The thickness of benthic filamentous algae communities is measured with a ruler. Within each spring study area, a general sketch of the aquatic plant community is made.

General Faunal Observations

While on site at each spring, the observed macrofauna (mammals, birds, reptiles, and amphibians) are identified to the lowest possible taxonomic level and their numbers quantified and recorded.

Macroinvertebrates

Macroinvertebrate sampling is performed at all sites using the Stream Condition Index (SCI) methodology. The SCI procedure involves sampling productive habitats using 20 sweeps of a US 30 mesh D-framed dip net. The sweeps are apportioned across productive habitats which had been identified during the Habitat Assessment (see above) leaving some sweeps to be taken in “minor” habitats (e.g., sand, silt). Aquatic macroinvertebrates are sorted to the lowest possible taxonomic order and placed in an alcohol-filled jar for further identification.

Adult Aquatic Insects

Aquatic insects are characterized based on collections made of adults as they emerge from the water. Insect emergence is measured through the use of floating pyramidal traps, each with a sampling area of 0.25 m2 (Figure 6). The design is based on traps used for midge and mosquito sampling from wetland and aquatic environments (Walton et al. 1999). Each trap is constructed of wood and has four sides covered with fiberglass window screen. Flotation is provided by foam “noodles” attached along the bottom wooden supports. The traps work under the premise that insects emerging into the trap generally seek the highest spot and in the process travel through an inverted funnel into a 500 mL jar inverted over the end of the funnel. A total of ten traps are deployed at locations along the periphery of the spring pool and run. At each location the substrate, vegetation cover, and water depth is noted. Traps are deployed and the jars containing the emergent insects collected at 24 hour intervals for a total of three collections during the study period. Insect identifications are made to the lowest practical taxonomic level. The number of insects captured in traps is used to calculate emergence rates and extrapolated across the wetted area of the study segments.


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Figure 6. Trap used to collect adult aquatic insects as they emerge from the water

Snails

Quantitative Snail Population Surveys are performed to target the snail populations in each spring segment. A minimum of ten stations are sampled, evenly distributed along each segment. A one quarter meter square frame is used to delineate each sample location and three replicate counts of visible snails are made at each station. All snails are identified to the lowest practical taxonomic unit. A small number of snails are collected for measurement and weighing to provide data for biomass estimates.

Fish Surveys

Visual surveys of the fish communities are made in each segment of the study springs where visibility is adequate. In addition, dip-net sampling is made in the segment to collect small fish in densely vegetated habitats. Data from dip-net collections is primarily used to augment the species list developed for each spring. Multiple daily surveys (typically three) of fish communities are made by two to three people using snorkel and/or SCUBA gear. The two fish observers start at an upstream location and work their way downstream. Each observer partitions the spring pool or run into equal halves. Observers note the species (or lowest practical taxonomy) of all observed fish, and these observations are called out to a data


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recorder, who follows along in a boat. Following each survey, observers estimate the total length (average and range) by fish species. Fish density is calculated by dividing the average number of individuals counted by the area sampled. Biomass of fish species is estimated using published length-weight relationships (Schneider et al. 2000) and average species total lengths and numbers. Fish assemblage diversity is calculated using the Shannon-Wiener diversity index on the calculated densities of individual species (Zar 1984). In addition, the proportion of the assemblage that is exotic and the proportion that can be described by each functional group (herbivores, invertivores, and piscivores) are calculated for each station and sampling date.

Measures of fish abundance and diversity are correlated with in-situ dissolved oxygen concentrations using Spearman rank correlation. Spearman rank correlation is a nonparametric test in which the variation in two variables is compared from their respective ranks. The magnitude of the data points in each variable is ranked. The sum of squared differences in ranks is used to produce the Spearman rank correlation coefficient, which is compared to a table of significance to determine the p-value (Zar 1984).

Turtles

Quantitative monitoring of the aquatic turtle community utilization is conducted for each spring segment. During each sampling event, turtle censuses are conducted by snorkeling the entire spring run segment and capturing all observed turtles by hand or net.

After hand capture, data are collected on species, turtle weight, carapace length, carapace width, plastron length, shell height, and sex (using sexual differences in tail length and forefoot claw length). At the time of initial capture, turtles are permanently and uniquely marked for later identification. Turtles are marked either by drilling or filing unique patterns in their marginal scutes, a technique which does not harm the turtles and has been used for decades in turtle population studies. Softshell turtles, which lack hard scutes, cannot be marked with this method and data are collected on individuals without marking. However, tattooing with liquid nitrogen has begun to be used for this species (personal communication with Eric Munscher).

Estimates of the population densities for the turtle species in the spring run are calculated using mark-recapture techniques and measured weights by species. In each season (spring, summer, fall, winter), censuses are performed over multiple days (2-4). The resulting sample period is divided into an initial period and a second sampling period. These two censuses taken close together in time allow calculation of the Lincoln-Peterson abundance estimates (Equation 2, Bolen and Robinson 1999) which estimates the population size for each turtle species. In this technique, the


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species’ population size is estimated from the number of turtles observed in each census and the proportion of the turtles recaptured in the second census. For species that have few captures and recaptures in a sample period, population estimates were not calculated due the low sample size.

Lincoln-Peterson formula:

N = (M x C) / R [Equation 2]

Where:

N = estimated population size

M = number of turtles marked in the initial sample

C = number of turtles marked in the second sample

R = number of recaptured turtles in the second sample

A 95% confidence interval of population estimates used these formulas:

Upper 95% CI = N + 1.96 [√ (((M2 x C x (C - R)) / R3)] [Equation 3]

Lower 95% CI = N - 1.96 [√ (((M2 x C x (C - R)) / R3)] [Equation 4]

For the lower 95% confidence interval, a value of zero turtles is presented for the lower limit if the calculation using these equations returned a negative population size.

To get estimates of aquatic turtle population density (as individuals/hectare), the population size estimate by species is divided by the average surface area of the spring run segment. Aquatic turtle biomass estimates (in kg/hectare) are calculated by multiplying population density for a species by the average body mass (in kg) measured from surveys for that species.

Human Use

For publically-owned springs total human use is estimated by entry data collected from the managing agency. For privately-owned springs, the owner is contacted and entry data are requested. Attendance data are used to calculate the average number (and standard deviation) of visitors for the period of record. In addition, the total number of visitors per year is calculated.


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Detailed observations of human use are made throughout the time that the study spring is visited. These observations are made for the visible portions of the spring pool, run, and surrounding upland areas. The count area is referred to as the “observation area”. Primary water contact activities are categorized as: wading (less than waist deep), bathing (greater than waist deep and less than neck deep), swimming, snorkeling, SCUBA diving, tubing, canoeing/kayaking, power boating, tour boating, and fishing. Primary out-of-water activities included: sitting, walking, sunbathing, and nature study.

For each of these activity categories, the counts of all persons within the observation area are made at about 15 minute intervals. Individual counts are multiplied by 0.25 hours (15 minutes) to estimate the average person-hours throughout the period of observation. An example of actual human use data from Wekiwa Spring is provided in Figure 7. The area under the curve is equal to the total human-use during the sampling period and is reported in units of person-hours as follows:

= person-hours [Equation 4]

Where:

T = time (hours)

A = area observed (ha)

t1 = time (start)

t2 = time (finish)


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Figure 7. Daily pattern of water-dependent human use observed at Wekiwa Springs on Sunday, August 12, 2007 (from WSI 2007)


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Person-hour estimates are in turn divided by the total observation interval in hours to estimate an average number of persons involved in in-water and out-of-water activities for each day of observation. Water and upland areas within the zone of observation were estimated from maps and aerial photographs to normalize data on a per-area basis:

Human-Use Density = no. persons/area counted [Equation 5]

The resulting data were tabulated and reported as the average number of persons

and human-use density (persons per area) basis by activity and location.

Ecosystem Level Monitoring

Ecosystem Metabolism

Ecosystem metabolism is calculated in each spring segment using an Excel spreadsheet adaptation of the upstream/downstream dissolved oxygen (DO) change methods of H.T. Odum (1957a, 1957b). This method estimates and subtracts upstream from downstream DO mass fluxes corrected for atmospheric diffusion to determine the metabolic oxygen rate-of-change of the aquatic ecosystem. Dissolved oxygen mass inputs typically include spring discharges, atmospheric diffusion into the water column (when DO is less than 100% saturation), accretion from other undocumented stream or spring seep inflows, and the release of DO as a by-product of aquatic plant photosynthesis. Oxygen losses include diffusion from the water column to the atmosphere (under super-saturated conditions), the metabolic respiration of the aquatic microbial, plant, and animal communities, and sediment biological oxygen demand.

The downstream DO concentration measured at any time is the net effect of these gains and losses as shown in the following conceptual equation:

Δ DO = GPP – CR + Din + A [Equation 6]

Where:

Δ DO = DO rate-of-change, g O2/m2/d

GPP = gross primary productivity, g O2/m2/d

CR = community respiration, g O2/m2/d


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Din = diffusion into the water under unsaturated conditions, g O2/m2/d

A = accrual of DO from other spring boils, g O2/m2/d

The dissolved oxygen measurements used to estimate segment ecosystem metabolism are collected at the upstream and downstream end of each study segment at 30 minute intervals using recording YSI 6920 data sondes with optical DO sensors.

Upstream and downstream dissolved oxygen data are each shifted by one-half of the estimated travel time between the upstream and downstream stream segment stations and an oxygen rate-of-change curve is prepared. Areas, volumes, current velocities and diffusion measurements are used to estimate ecosystem metabolism. Water surface area is estimated for each of the study segments using the survey methods described earlier in this SOP and corrected hourly using an estimated stage: area relationship. Average velocities are estimated from the stage: volume relationship and spring discharge measurements. Nominal travel times for the water mass are estimated based on the length of the spring run and the estimated hourly current velocities.

This DO rate-of-change curve is corrected for atmospheric diffusion based on measured percent oxygen saturation in the water, and oxygen diffusion rates corrected for water velocity. The corrected oxygen rate-of-change curve for each 24-hour period is used to estimate gross primary productivity (GPP), community respiration (CR), net primary productivity (NPP), production/respiration (P/R) ratio, and ecological efficiency. Figure 8 illustrates these metabolism measurements based on development of a typical oxygen rate-of-change curve.

Descriptions of the ecosystem metabolism parameters follow below:

• Gross primary productivity (GPP) is estimated as the entire area under the oxygen rate-of-change curve, calculated by extending the nighttime corrected oxygen rate-of-change through the daylight hours and estimating the entire area under the daytime curve in g O2/m2/d. GPP is a measure of all aquatic plant productivity occurring below the water surface within the stream segment. GPP includes primary productivity of both algae (including photosynthetic bacteria) and submerged vascular plants.

• Community respiration (CR) is the average of the corrected nighttime oxygen rate-of-change values in g O2/m2/d. CR is a measure of the total dark metabolism of the entire submerged ecosystem within each stream segment. CR includes the respiration of all microbes in the sediments and water column, respiration of bacteria, algae, and plants in the water column, and


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respiration of all aquatic animals, including protozoans, macroinvertebrates, crustaceans, and fish.

• Net primary productivity (NPP) is equal to the difference between these two estimates (GPP-CR). NPP provides an estimate of the net fixed carbon that remains each day after the respiratory needs of the aquatic ecosystem are met. CR may be higher than GPP in some streams and during some periods of time, indicating that there are unmeasured inputs of fixed carbon or losses of fixed carbon that were previously stored in the ecosystem.

• The P/R ratio or ecological quotient is equal to GPP/CR. A P/R ratio of one indicates that production and consumption are equally balanced. A ratio greater than one indicates an autotrophic aquatic ecosystem while a value less than one indicates a heterotrophic ecosystem.

• Photosynthetic efficiency (PE) is equal to the rate of gross primary productivity divided by the incident PAR during a specified time interval. It estimates the overall efficiency of an aquatic ecosystem to utilize the visible fraction of incident solar radiation, the principal forcing function for autotrophic stream ecosystems. PAR reaching the plant level is estimated based on river stage, the plant community characterization data for segment depth, and the light attenuation coefficient estimated for each sampling event. PE is reported as PAR Efficiency by dividing GPP in O2/m2/d by mol/m2/d, resulting in units of g O2/mol. PAR Efficiency is also reported as a percentage using the conversion factors employed by Knight (1980; 1983): 4.22 Kcal/g O2 and 52.27 Kcal/mole of photons (McCree 1972).

Oxygen Diffusion

Diffusion of dissolved oxygen has been previously measured in situ in a number of Florida spring runs following the methods of Copeland and Duffer (1964) and McKellar (1975). The rate of oxygen diffusion is measured using the floating dome technique in conjunction with water depth and flow velocity measurements during multiple sampling events, allowing development of a regression between oxygen diffusion rate and current velocity. An existing regression between dissolved oxygen diffusion and current velocity developed from the Silver River, Wekiva River, Rock Springs Run, Alexander Springs Run, and Juniper Run is used to estimate diffusion in the absence of site-specific data.


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Figure 8. Example determination of ecosystem metabolism based on upstream-downstream dissolved oxygen data (from WSI 2007).


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Nutrient Assimilation

Nutrient assimilation/dissimilation rates for total nitrogen, nitrate, ammonia, total phosphorus, and soluble reactive phosphorus, are estimated for each springs segment by calculating upstream-downstream changes in nutrient mass. Average nutrient mass inputs and outputs are estimated based on average water chemistry concentrations and flows over the period of each study. Positive nutrient mass changes indicate assimilation/dissimilation of nutrients, while negative changes indicate an increase in nutrient mass with travel of the spring flow downstream.

Community Export

Community export of particulate suspended matter is quantified for each stream segment using a plankton net suspended in the current at mid-depth (Figure 9). The mesh size on the plankton net was 153 µm. Three replicate plankton net samples are collected at the upstream and downstream end of each segment. Sample material collected in the plankton net is rinsed into a sample bottle and returned to the laboratory for wet, dry, and ash-free (combusted at an oven temperature of 450 °C) dry weight analyses. As samples were collected, the velocity of the water at the mouth of the net was measured as was the time of net deployment. This allows the volume of water passing through the net to be calculated. The amount of material collected is expressed on a volume and area (based on upstream wetted-area) basis. Particulate export results are reported as dry weight (DW) and ash-free dry weight (AFDW) per upstream area per time (g DW/m2/d and g AFDW/m2/d, respectively). Overall particulate export for each study segment is calculated as the difference between the upstream and downstream export rates.


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Figure 9. Image of plankton net capturing suspended material with flow meter upstream


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Literature Cited Bentley PowerCivil Software. Software to calculate area and volume. Exton, PA. Website: http://www.bentley.com

Bolen, E.G. and W.L. Robinson. 1999. Wildlife Ecology and Management. Prentice Hall, Upper Saddle River, New Jersey.

Copeland, B.J. and W.R. Duffer. 1964. Use of a clear plastic dome to measure gaseous diffusion rates in natural waters. Limnology and Oceanography 9:494-499.

Florida Automated Weather Network (FAWN). Website: http://fawn.ifas.ufl.edu/

Knight, R. L. 1983. Energy Basis of Ecosystem Control at Silver Springs, Florida, pp. 161-179 In T. D. Fontaine and S. M. Bartell [eds.], Dynamics of Lotic Ecosystems. Ann Arbor Science.

Knight, R.L. 1980. Energy Basis of Control in Aquatic Ecosystems. Ph.D. Dissertation, University of Florida, Gainesville, FL. 200 pp.

LI-COR Biosciences. LI-COR Quantum Sensors for the measure of photosynthetically active radiation. Lincoln, NE. Website: http://www.licor.com

Marsh-McBirney, Inc. Flo-Mate Model 2000 portable flow meter. Frederick, MD. Website: http://www.marsh-mcbirney.com

McCree, K.J. 1972. Test of Current Definitions of Photosynthetically Active Radiation against Leaf Photosynthesis Data. Agricultural Meteorology 10:443-453.

McKellar, H.N. 1975. Metabolism and Models of Estuarine Bay Ecosystems Affected by a Coastal Power Plant. Ph.D. Dissertation. University of Florida, Gainesville, Florida.

Odum, H.T. 1957a. Trophic Structure and Productivity of Silver Springs, Florida. Ecological Monographs. Volume 27 (1): 55-112.

Odum, H.T. 1957b. Primary Production Measurements in Eleven Florida Springs and a Marine Turtle-Grass Community. Limnology and Oceanography 2:85-97.

Schneider, J.C., P.W. Larrman, and H. Gowing. 2000. Length-weight relationships, Chapter 17, In Schneider, J.C. [Ed.] Manual of Fisheries Survey Methods II: With Periodic Updates. Michigan Department of Natural Resources, Fisheries Special Report 25, Ann Arbor, MI.


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Walton, W.E., P.D. Workman, and J.B. Keiper. 1999. An inexpensive collapsible pyramidal emergence trap for the assessment of wetland insect populations. Proceedings and Papers of the Sixty-Seventh Conference of the California Mosquito Control Association. 15-17.

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