1 Seagrasses Along the Texas Coast: Using GIS to Examine Differences from Sara Wilson 12/07/2012 Department of Marine Science University of Texas at Austin CE 394K GIS in Water Resources
2 Wilson 2 Table of Contents Part I Introduction page 3 Part II Differences in Seagrass Percent Cover page 6 Halophila engelmanii page 6 Ruppia maritima page 8 Syringodium filiforme page 9 Thalassia testudinum page 12 Halodule wrightii page 15 All seagrass page 18 Part III Differences in the Light Attenuation Coefficient K d and Comparisons to Seagrass Percent Cover page 21 Part IV Differences in Salinity and Comparisons to Seagrass Percent Cover page 25 Part V Differences in ph and Comparisons to Seagrass Percent Cover page 28 Part VI Differences in Dissolved Oxygen and Comparisons to Seagrass Percent Cover page 31 Part VII Conclusions page 34 Part VIII Acknowledgements page 37 Part IX References page 38
3 Wilson 3 Part I - Introduction Texas coastal waters are home to extensive seagrass beds (approximately 235,000 total acres in 1994) that provide a variety of ecosystem services (TWPD 1999). Over 90% of the total acreage of seagrasses occurs within the Nueces/Corpus Christi/Redfish Bays, Baffin Bay, and the Laguna Madre (TPWD 1999) along the Southeast coast of Texas. For this reason, a monitoring plan for these areas was proposed within the Seagrass Conservation Plan published by Texas Parks and Wildlife in Under the supervision of Dr. Ken Dunton at the University of Texas Marine Science Institute, a three-tier monitoring approach began in the summer of 2011 and continued through Figure 1: Sampling Station Locations.
4 Wilson 4 This monitoring program examined both biotic and abiotic parameters at 567 sampling stations within 4 systems- the Mission-Aransas National Estuarine Research Reserve (MANERR), Corpus Christi Bay (CCBAY), and the Upper (ULM) and Lower (LLM) portions of the Laguna Madre (Figure 1). In this report, the MANERR and CCBAY stations are considered together. Biotic parameters that this monitoring program evaluated in situ were seagrass percent cover (%), species composition, and canopy height (cm) (four measurements per sampling station, one from each corner of the boat). Abiotic parameters measured in situ included salinity (ppt), temperature ( C), percent surface irradiance (% SI), light attenuation (K d ), ph, chlorophyll a (µg/l), and dissolved oxygen (mg/l). In the laboratory, water samples were analyzed to find total suspended solids (mg/l). Additionally, stable isotope analysis for δ 13 C ( ) and δ 15 N ( ) was done for seagrass samples. The purpose of this project was to use geographic information systems (GIS) in order to visually assess changes in seagrass percent cover in Texas between 2011 and 2012 for each species as well as the total percent cover. Additionally, differences from 2011 to 2012 for light attenuation, salinity, ph, and dissolved oxygen were graphed, and compared to the differences observed in seagrass percent cover. Each section includes a discussion of how the designated parameter may influence seagrass growth. As a result of this project, predictions can be made for seagrass coverage in 2013, and any areas of concern where seagrass percent cover has declined from will become obvious. All of the interpolations in this report were prepared using Inverse-Distance Weight (IDW) interpolation techniques, with the IDW (Spatial Analyst) tool in ArcGIS Performing an IDW interpolation was ideal since these monitoring efforts involved relatively dense sampling over the four study sites (ArcGIS Resource Center). By using a shapefile from
5 Wilson 5 the National Oceanic and Atmospheric Administration that showed extent of the seagrasses (obtained through aerial photography), and the Extract by Mask (Spatial Analyst) tool in ArcGIS, all the interpolations were extracted so that their extent only covered that of the seagrass beds. Interpolations were created for both 2011 and 2012, then the 2011 interpolation was subtracted from the 2012 interpolation using the Minus (Spatial Analyst) tool, in order to create the difference rasters. Positive values in these rasters are indicative of an increase in seagrass percent cover, while negative values indicate a decrease.
6 Wilson 6 Part II Differences in Seagrass Percent Cover Evaluating seagrass percent coverage is one of the most time- and cost-efficient ways to monitor both seagrass extent and health. Assessing station-specific differences in percent cover each year may be a good way to determine which stations are experiencing changes. However, it should be noted that certain parameters such as currents or winds can affect the exact position and orientation of the boat in situ, which in turn affects exactly where percent cover estimates are taken. For these reasons, large discrepancies between 2011 and 2012 data sometimes occur, and it is difficult to assess whether this is due to subpar positioning of the boat, or due to actual changes in seagrass extent. Similarly, some beds contain large bare areas (e.g. areas with propeller scars), so a 0% cover assessment of that area may not accurately reflect the surrounding percent cover. It is for precisely these reasons that we assess percent cover four times per station, and over so many locations. For this report, the average percent cover from the four assessments at each station was used for interpolations. Five species of seagrasses can be found in Texas: Halophila engelmanii, Ruppia maritima, Syringodium filiforme, Thalassia testudinum, and Halodule wrightii. The dominant species is H. wrightii, with some areas showing moderate amounts of S. filiforme or T. testudinum, and relatively few stations with R. maritima or H. engelmanii. Figure 2 shows the relative percent cover of each species across the three systems (average of 2011 and 2012 data). Halophila engelmanii The presence of the seagrass Halophila engelmanii was reported at only 3% of sampling stations in both 2011 and 2012 (n=18). In 2011 H. engelmanii was observed at 2 MANERR
7 Wilson 7 MANERR/CCBAY Species Distribution H. wrightii T. testudinum S. filiforme H. engelmanii R. maritima ULM Species Distribution H. wrightii T. testudinum S. filiforme H. engelmanii R. maritima LLM Species Distribution H. wrightii T. testudinum S. filiforme H. engelmanii R. maritima Figure 2: Species Distribution of Seagrass in the MANERR/CCBAY, ULM, and LLM. Reported values were averaged from 2011 and 2012.
8 Wilson 8 stations, 2 CCBAY stations, and 14 ULM stations. During 2012, a slightly different pattern was seen, with H. engelmanii observed at 1 MANERR station, 10 CCBAY stations and 7 ULM stations. Due to the small extent of H. engelmanii throughout Texas coastal waters, the interpolations are not included in this report. However, it should be noted that percent cover of H. engelmanii in these areas is most likely underestimated. Growing only a few centimeters in height, H. engelmanii may be difficult to observe amongst mixed beds with other grasses, whose taller canopies effectively hide the short H. engelmanii from view(wilson, personal observation). However, if shading of this H. engelmanii is occurring, the other seagrasses that are present would be present in such high amounts that any further contribution to total percent cover the H. engelmanii would make is negligible. Ruppia maritima The presence of Ruppia maritima was observed at only 7% of sampling stations in 2011 (n=41),and at only 4% of stations in 2012 (n=25). During 2011, R. maritima was observed at 11 MANERR stations, 6 CCBAY stations, 6 ULM stations, and 18 LLM stations. During 2012, R. maritima was observed at 8 MANERR stations, 6 CCBAY stations, 2 ULM stations, and 9 LLM stations. Because of the small extent of R. maritima, the interpolations of percent coverage are not included in this report. It is likely that percent cover of R. maritima is underestimated as well, though for a different reason than H. engelmanii. The blades of R. maritima, when young (short), are very similar in appearance to those of young Halodule wrightii, the dominant seagrass along the Texas coast (Wilson, pers. obs.). A novice field assistant may mistakenly identify R. maritima as H. wrightii. While a sincere effort is made to send seasoned field assistants to collect the data, this is not always possible due to time constraints of the sampling season, and for other
9 Wilson 9 reasons outside of our control. Hence, it should suffice to say that any R. maritima being mistaken for H. wrightii will at least be contributing to the assessment of overall seagrass percent cover. Syringodium filiforme Mission-Aransas National Estuarine Research Reserve and Corpus Christi Bay The seagrass Syringodium filiforme was observed at 14% of sampling stations in both 2011 and 2012 (n=81 and n=80). Figure 3 shows S. filiforme coverage in the MANERR and CCBAY sampling stations (n=18 and n=22). Most of the area show slight increases in S. filiforme percent cover, with the exception of a patch around Aransas Pass that shows decline. Some scattered areas did not show any change in S. filiforme percent cover, such as the west coast of San Jose Island and areas to the northwest of Port Aransas. Figure 3: MANERR and CCBAY Differences in S. filiforme Percent Cover from
10 Wilson 10 Upper Laguna Madre Figure 4 shows S. filiforme coverage in the ULM sampling stations (n=47 and n=41). The southern region of this site shows no changes in S. filiforme percent cover. This is to be expected, due to the fact that S. filiforme has not occurred in these areas in 2011 or 2012 (Wilson, unpub. data). The rest of the ULM shows variation between slight to moderate increases and slight to moderate decreases. Figure 4: ULM Differences in S. filiforme Percent Cover from
11 Wilson 11 Lower Laguna Madre Figure 5 shows S. filiforme coverage in the LLM sampling stations (n=16 and n=17). The great majority of LLM showed no change in S. filiforme percent cover, which is again due to the fact that S. filiforme has not grown in these areas from (Wilson, unpub. data). The southern regions of the LLM generally show slight increases in percent cover. Figure 5: LLM Differences in S. filiforme Percent Cover from
12 Wilson 12 Thalassia testudinum Mission-Aransas National Estuarine Research Reserve and Corpus Christi Bay The seagrass Thalassia testudinum was observed at 27% of sampling stations in 2011 (n=157) and at 26% of stations in 2012 (n=148). Figure 6 shows T. testudinum coverage in the MANERR and CCBAY sampling stations (n=59 and n=58). This map is quite varied, showing a few areas to the south with no changes in percent cover, and slight to moderate increase or decrease in the rest of the region. Figure 6: MANERR and CCBAY Differences in T. testudinum Percent Cover from
13 Wilson 13 Upper Laguna Madre Figure 7 shows T. testudinum coverage in the ULM sampling stations (n=1 and n=0). The very northernmost decreases in the map are actually part of the MANERR/CCBAY map shown in Figure 6, and are simply present in this map for spatial context. Hence, only the one station that had T. testudinum in 2011 (seen near the Nueces and Kluberg county line) showed decrease in 2012, while the rest of the area showed no change due to the fact that no T. testudinum was present here in 2011 or 2012 (Wilson, unpub. data). Figure 7: ULM Differences in T. testudinum Percent Cover from
14 Wilson 14 Lower Laguna Madre Figure 8 shows T. testudinum coverage in the LLM sampling stations (n=97 and n=90). The upper third of the LLM region shows slight increase in seagrass percent cover, and the lower third shows areas of both slight to moderate increase or decrease in seagrass percent cover. The central third of this region showed no change in T. testudinum percent cover, which is due to the fact that no T. testudinum grew in this area from 2011 to 2012 (Wilson, unpub. data). Figure 8: LLM Differences in T. testudinum Percent Cover from
15 Wilson 15 Halodule wrightii Mission-Aransas National Estuarine Research Reserve and Corpus Christi Bay The seagrass Halodule wrightii was observed at 67% of sampling stations in both 2011 and 2012 (n=380 and 385). Figure 9 shows H. wrightii coverage in the MANERR and CCBAY sampling stations (n=101 and n=95). Some increases are seen along the west coast of San Jose Island, and a large decrease can be seen at one station on the west coast of Mustang Island. All other areas showed small increases or decreases in H. wrightii percent cover. Figure 9: MANERR and CCBAY Differences in H. wrightii Percent Cover from
16 Wilson 16 Upper Laguna Madre Figure 10 shows H. wrightii coverage in the ULM sampling stations (n=127 and n=126). The majority of stations experienced an increase in H. wrightii cover, with only a few decreases, generally towards the North. Figure 10: ULM Differences in H. wrightii Percent Cover from
17 Wilson 17 Lower Laguna Madre Figure 11 shows H. wrightii coverage in the LLM sampling stations (n=152 and n=164). There are a few areas of very substantial decreases, mostly right along the eastern shore of the mainland, which should be watched carefully in the future. The central third of the LLM shows light to moderate declines in percent cover, with most of the rest of the area showing light to moderate increases. The areas near the bottom of the map showing no change in percent cover indicate areas where no H. wrightii is present (Wilson, unpub. data). Figure 11: LLM Differences in H. wrightii Percent Cover from
18 Wilson 18 All seagrass Mission-Aransas National Estuarine Research Reserve and Corpus Christi Bay Seagrass (all species included) was observed at 89% of sampling stations in 2011 (n=506) and at 90% of sampling stations in 2012 (n=511). Figure 12 shows seagrass coverage in the MANERR and CCBAY sampling stations (n=134). Increases and decreases in these areas are very station-specific. Increases are seen in the northern sites along the West Coast of San Jose Island as well as most of the southern part of this area. Stations with both increase and decrease in percent cover are scattered throughout the central stations in this area. Figure 12: MANERR and CCBAY Differences in Total Seagrass Percent Cover from
19 Wilson 19 Upper Laguna Madre Figure 13 shows seagrass coverage in the ULM sampling stations (n=137 and n=136). Almost all stations seemed to experience small increases in percent coverage. These beds are likely healthy and probably growing to fill in their more sparse areas with new shoots. Figure Figure 13: 4 ULM Differences in Total Seagrass Percent Cover from
20 Wilson 20 Lower Laguna Madre Figure 14 shows seagrass coverage in the LLM sampling stations (n=235 and n=241). Predictably, the same area that showed declines in both T. testudinum and H. wrightii has experienced decreases in total seagrass percent cover. There are several locations showing substantial declines, but also areas showing substantial increases. Changes in percent cover in this region seem to be quite variable by station. Figure 14: LLM Differences in Total Seagrass Percent Cover from
21 Wilson 21 Part III Differences in the Light Attenuation Coefficient K d and Comparisons to Seagrass Percent Cover Light attenuation in water refers to the intensity of light that is lost as the light penetrates through the water column. Seagrasses need high light levels to reach them so that they can photosynthesize and grow, so minimal attenuation of light is optimal. Generally, areas where the light attenuation coefficient K d has a value below 0.69 are best for seagrasses (Dunton et. al 2005). Attenuation varies by station and also over time, since tides and currents are constantly circulating suspended materials in the water. Due to these variations, five light measurements were taken at each site, both at the surface and at depth if a site was > 70 cm (measurements could not be taken at sites with water depths < 30 cm due to instrument limitations). The five measurements of light (μmol photons m -2 s -1 ) were averaged to determine a surface light value and a depth light value. From here, K d was determined using the following equation: K d = - ln(i z /I 0 ) / d I z refers to the light at depth and I 0 refers to light at the surface, and d refers to depth in meters (always 0.3 for our calculations since the two sensors were fixed exactly 30cm apart). All measurements were taken using an LI-190SA quantum sensor. Decreases in K d reflect less light being attenuated, and hence more light available to reach the seagrasses. For this reason, the legends in Figures depict decreases in K d in shades of green, which may correspond to increases in total seagrass percent cover that are also noted in shades of green (Figures 12-14).
22 Wilson 22 Mission-Aransas National Estuarine Research Reserve and Corpus Christi Bay The area along the central western coast of Mustang Island shows a large increase in light attenuation (Figure 15), and parts of this area also showed a substantial decrease in total seagrass percent cover (Figure 12). However, some areas near Port Aransas and Aransas Pass that showed decreases in seagrass percent cover (Figure 12) showed only slight increases in attenuation (Figure 15). Areas with substantial decreases in light attenuation, such as Copano Bay and to the West of Port Aransas (Figure 15) did not experience substantial increases in seagrass percent cover (Figure 12). Figure 15: MANERR and CCBAY Differences in the Light Attenuation Coefficient (K d ) from
23 Wilson 23 Upper Laguna Madre Increases in K d were observed in the southern parts of the Upper Laguna Madre (Figure 16), although these areas showed general increases in seagrass percent cover (Figure 13). Areas with large decreases in attenuation near the center of Figure 16 did not experience substantial increases in seagrass percent cover (Figure 13). However, the general trend in ULM seems to be a decrease in attenuation (Figure 16), and we do see a general trend of increased seagrass percent cover as well (Figure 13). Figure 16: ULM Differences in the Light Attenuation Coefficient (K d ) from
24 Wilson 24 Lower Laguna Madre The general trend in LLM was an increase in light attenuation (Figure 17). There is a thin strip in the northern part of the region that showed decreases in light attenuation that did correspond to increases in seagrass percent cover (Figure 14). However, since the levels of light attenuation are so varied throughout the rest of the area (Figure 17), no real correlation to differences in seagrass percent cover can be seen (Figure 14). Figure 17: LLM Differences in the Light Attenuation Coefficient (K d ) from
25 Wilson 25 Part IV Differences in Salinity and Comparisons to Seagrass Percent Coverage Since the Laguna Madre is a hypersaline estuary, the Texas coast is an interesting study area for salinity and how it may be affecting seagrass growth. From , salinity ranged from ppt in the MANERR/CCBAY region, from ppt in the ULM region, and from ppt in the LLM region. Mission-Aransas National Estuarine Research Reserve and Corpus Christi Bay Salinity in the MANERR/CCBAY region showed a general decrease from (Figure 18). However, areas showing the most decrease did not correspond to areas with substantial seagrass percent cover decreases (Figure 12). Similarly, areas where salinity did increase, specifically Copano Bay (Figure 18), did not correspond to increases in seagrass percent cover (Figure 12). Figure 18: MANERR and CCBAY Differences in Salinity from
26 Wilson 26 Upper Laguna Madre Salinity in the ULM showed large decreases in the southern stations and the northernmost stations (Figure 19). However, these areas did show substantial decreases in seagrass percent cover (Figure 13). Areas showing moderately increased salinity along the northwestern shore of Padre Island (Figure 19) showed only slight increases in seagrass percent cover (Figure 13). Figure 19: ULM Differences in Salinity from
27 Wilson 27 Lower Laguna Madre Very large decreases in salinity can again be seen in the LLM (Figure 20). However, the areas where salinity shows the greatest decrease do not necessarily show substantial decrease in seagrass percent cover (Figure 14). Figure 20: LLM Differences in Salinity from
28 Wilson 28 Part V Differences in ph and Comparisons to Seagrass Percent Coverage Seagrasses seem to be relatively tolerant to small changes in ph. From , ph ranged from in the MANERR/CCBAY region, from in the ULM region, and from in the LLM region. Mission-Aransas National Estuarine Research Reserve and Corpus Christi Bay Interestingly, areas of ph increase in the MANERR/CCBAY region are distinctly separated from areas with ph decrease (Figure 21). The lower areas of ph decline do not correspond to any decrease in seagrass percent cover (Figure 12), although some sites to the northeast of Aransas Pass do show decreases in percent cover. Areas of ph increase towards the center of this region showed both increases and decreases in seagrass percent cover (Figure 12). Figure 21: MANERR and CCBAY Differences in ph from
29 Wilson 29 Upper Laguna Madre Generally, large decreases in ph were seen throughout the ULM, with the exception of areas in the northern half that are located to the east of a chain of spoil islands, which due to these islands likely experience less water exchange (Figure 22). The aforementioned areas showed some increase in seagrass percent cover (Figure 13), however the general increase in seagrass percent cover observed was seen throughout the other areas with decreases in ph (Figure 22). Figure 22: ULM Differences in ph from
30 Wilson 30 Lower Laguna Madre In contrast to the ULM and most of the MANERR/CCBAY regions, the LLM region showed many areas with increases in ph (Figure 23). Two large areas of ph decrease are seen in the center of the region and to the south. This southern drop in ph corresponds to decreases in seagrass percent cover (Figure 14). The only other areas of decreased seagrass percent cover occurred directly to the north of the central area showing the drop in ph (Figure 23). Figure 23: LLM Differences in ph from
31 Wilson 31 Part VI Differences in Dissolved Oxygen and Comparisons to Seagrass Percent Coverage High dissolved oxygen (D.O.) concentrations generally indicate healthy marine/estuarine systems, so D.O. concentration may be a good indicator of seagrass health. D.O. ranges from mg/l in the MANERR/CCBAY region, from mg/l in the ULM region, and from mg/l in the LLM region. Mission-Aransas National Estuarine Research Reserve and Corpus Christi Bay Many stations in the MANERR/CCBAY showed substantial decreases in dissolved oxygen (Figure 24). Surprisingly, the general spread of areas with decreased D.O. seem to correspond quite well with areas where seagrasses experienced an increase in percent cover (Figure 12). Likewise, areas of increased D.O. concentrations (between Port Aransas and Aransas Pass) correspond well to areas of decreases in seagrass percent cover (Figure 12). Figure 24: MANERR and CCBAY Differences in D.O. from
32 Wilson 32 Upper Laguna Madre Two isolated areas in the north of the ULM experienced substantial increases in D.O. concentration (Figure 25), and these areas showed varied differences in seagrass percent cover. The area towards the center of the region experienced an increase in seagrass percent cover, while the more northern area of increased D.O. showed slight decreases in seagrass percent cover (Figure 13). The general trend of large increases in D.O. is mostly correlated to light to moderate increases in seagrass percent cover (Figure 13). Figure 25: ULM Differences in D.O. from
33 Wilson 33 Lower Laguna Madre Similarly to the MANERR/CCBAY region, areas of increased D.O. concentration in the LLM (Figure 26) seem to generally correspond to areas of decreases in seagrass percent cover (Figure 14). The North and South of the LLM show substantial increases in D.O. concentration, while the central-south part of LLM shows mostly substantial decreases in D.O. concentration (Figure 26). Figure 26: LLM Differences in D.O. from
34 Wilson 34 Part VII Conclusions Seagrass Percent Cover The seagrass H. engelmanii showed extremely minimal cover and hardly any change between R. maritima was present at only about half the number of stations in 2012 than it was in S. filiforme showed mostly growth but some decline, the majority of this was < 25 % increase/decrease in percent cover. T. testudinum showed areas of light-moderate increase in percent cover, and also areas of light-moderate decrease. H. wrightii, the dominant species of seagrass in Texas, showed mostly decreases in percent cover in the MANERR/CCBAY, but mostly increases in the ULM and LLM. H. wrightii was the only seagrass to show multiple areas of both moderate to high increase and decrease in percent cover. Total seagrass cover followed a similar pattern, showing areas of moderate to high decrease in the MANERR/CCBAY and parts of the LLM, but a general pattern of light to moderate increase throughout the ULM and the rest of the LLM. Areas of high increases in percent cover were also observed in the northern region of the LLM. Light Attenuation Changes in light attenuation did not correspond well to changes in seagrass percent cover. It is likely that the seagrasses are fairly hearty with regards to changes in light attenuation. Also, attenuation readings can change from day to day based on the amount of suspended material in the water column at any given time, and so perhaps not much can be learned from only one sampling per station once a year. It is not surprising, therefore, that comparing this instantaneous attenuation measurement to seagrass percent cover, which is more or less constant throughout the
35 Wilson 35 season, does not show any correlation. Ideally, average light attenuation could be measured over an extended period of time and the noise of day-to-day changes removed so that a more accurate long-term representation of water column light levels could be compared with changes in seagrass percent cover for a more meaningful analysis. Salinity Changes in salinity from do not seem to have much correlation to changes in seagrass percent cover. Areas of these systems that experience freshwater inflow likely experience large fluctuations in salinity that vary with rainfall and river run-off. Perhaps examination of specific salinity values rather than the amount of change could give a better representation of why seagrasses are growing in their respective locations. Also, salinity tolerance is seagrasses is species-specific, so examination of individual species rather than total cover may yield better conclusions about salinity tolerance. Dissolved Oxygen Concentration Surprisingly, the D.O. interpolations yielded more or less opposite patterns than the seagrass percent cover interpolations did. Decreases in D.O. corresponded fairly well with increases in seagrass percent coverage. However, D.O. is highly variable, even at small temporal scales. Similarly to light attenuation, using only one value per year to represent D.O. for a sampling station is an inaccurate representation of the fluctuation that is almost certainly occurring. The time of day can greatly influence the amount of D.O. in the water, and perhaps sampling protocols should be re-visited so that D.O. is only recorded during specified times of the morning, before the seagrasses senesce and release oxygen in the afternoons.
36 Wilson 36 General Conclusions IDW interpolation seems to be a good way to represent changes in seagrass percent cover visually, since the viewer can quickly get a sense of what change is occurring and at what scale. For water quality parameters, IDW interpolations are a good idea in theory, however for much of the data in this monitoring project they do not seem to be appropriate. Water quality indicators such as light attenuation and D.O., at least, are highly varied based on the time of day, so interpolating one value from each year does not give an accurate picture of what is going on. Interpolation may be a better method if more long-term averages were used rather than these once-a-year values, which many times indicated sharp increases or declines, but which in reality were probably much more smooth and much less varied from year to year.
37 Wilson 37 Part VIII Acknowledgements I would like to thank my professor, Dr. Maidment for all his advice on my project, and for doing an excellent job teaching CE 394 K, G.I.S. in Water Resources, along with Dr. Tarboton from Utah State University and Dr. Kilic from the University of Nebraska-Lincoln. I would also like to thank our T.A., Gonzalo Espinoza for all of his help with my project. Tim Whiteaker was extremely helpful in providing me with the shapefile I used to create my interpolation masks, as well as other basemaps and his own interpolations from 2011 data to reference. Allison Wood showed me how to manipulate my data in Excel much more efficiently than I had been doing, which saved me a lot of time. Finally, I would like to thank my advisor Dr. Dunton for advice on where to begin with this project as well as the rest of the Dunton lab for their field efforts to collect this dataset.
38 Wilson 38 Part IX References ArcGIS Resource Center. IDW (Spatial Analyst). help.arcgis.com. ESRI. 29 June Web. 29 November < 009z m htm> Dunton, K.H., Kopecky, A.L. and Maidment, D Monitoring design criteria and biological indicators for seagrass conservation in Texas coastal waters. Final report for Regional Environmental Monitoring and Assessment Program, EPA. Texas Parks and Wildlife (TPWD) Seagrass conservation plan for Texas. TPWD: Resource Protection Division, Austin, TX, USA.
Seagrasses: Vital Habitat in Texas Estuaries Cindy Hobson Water Resources Branch Coastal Fisheries Division Texas Parks and Wildlife Department February 17, 2015 Why Care About Seagrass? Sustain coastal
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Helicopter C.E.R. Teacher Pages 45 Minutes Objective Students will conduct an experiment to determine if wing length will affect the descent time of a paper helicopter. Students will analyze their data
Effects of climate change on fish spawning grounds and larvae drift Frode Vikebø firstname.lastname@example.org Risør 15.08.2012 Objectives What are the prerequisites for modelling drift, growth and survival of early stages
Chapter 10 Lecture Outline The Restless Oceans Focus Question 10.1 How does the Coriolis effect influence ocean currents? The Ocean s Surface Circulation Ocean currents Masses of water that flow from one
Purpose The purpose of this document is to improve the understanding and transparency of the Commission s stock assessment process and results. It is the first of several that will be developed throughout
GCSE GEOGRAPHY YR 11 KNOWLEDGE BOOK FIELDWORK PHYSICAL STUDY Page 1 PHYSICAL STUDY: HARD ENGINEERING IS CONTROLLING LONGSHORE DRIFT AT SHERINGHAM The information here is what students MUST know. If you
ACUTE TEMPERATURE TOLERANCE OF JUVENILE CHINOOK SALMON FROM THE MOKELUMNE RIVER Charles H. Hanson, Ph.D. Hanson Environmental, Inc. SUMMARY A series of static acute tests were performed to determine the
Name: Section: Ocean Currents Unit (Topic 9A-1) page 1 Ocean Currents Unit (4 pts) Ocean Currents An ocean current is like a river in the ocean: water is flowing traveling from place to place. Historically,
WWW.ARBORSCI.COM Lab: Velocity () By Dr. Joel Bryan OBJECTIVES: Determine average velocity (speed). Predict the relative velocities (speeds) of the two objects traveling in the same and in the opposite
UNITED STATES DEPARTMENT OF COMMERCE National Oceanic and Atmospheric Administration NATIONAL MARINE FISHERIES SERVICE Northwest Fisheries Science Center Fish Ecology Division 2725 Montlake Boulevard East
Habitat use, site fidelity, and growth of juvenile black sea bass, Centropristis striata, in the Maryland Coastal Bays using mark-recapture Rebecca Peters and Paulinus Chigbu University of Maryland Eastern
Water circulation in Dabob Bay, Washington: Focus on the exchange flows during the diurnal tide transitions Jeong-in Kang School of Oceanography University of Washington (206) 349-7319 email@example.com
Assessment Chapter Test B The Movement of Ocean Water USING KEY TERMS Use the terms from the following list to complete the sentences below. Each term may be used only once. Some terms may not be used.
LOGAN MARTIN RESERVOIR MANAGEMENT REPORT 2008 Prepared by E. Daniel Catchings District Fisheries Supervisor Robert O. Andress District Fisheries Biologist Department of Conservation and Natural Resources
NAME: Using Darts to Simulate the Distribution of Electrons in a 1s Orbital Introduction: The quantum theory is based on the mathematical probability of finding an electron in a given three dimensional
Seagrasses of the Virgin Islands Seagrasses are flowering plants that live underwater. These marine plants resemble the land species of grasses in that they have long blade-like leaves. Seagrasses grow
DJO-1401 Simulation of Wind Variation for the WPI Kite-Powered Water Pump A Major Qualifying Project Report Submitted to the Faculty of the WORCESTER POLYTECHNIC INSTITUTE in Partial Fulfillment of the
Educat i onser i es Di ssol vedoxygengui de Dissolved Oxygen Guide Introduction Dissolved oxygen probes provide a convenient approach to essentially direct measurement of molecular oxygen. The membrane
Variability of surface transport in the Northern Adriatic Sea from Finite-Size Lyapunov Exponents" Maristella Berta Marseille, 29 November 2011 Outline Geographical setting (winds and circulation of Adriatic
Taking Your Class for a Walk, Randomly Daniel Kaplan Macalester College Oct. 27, 2009 Overview of the Activity You are going to turn your students into an ensemble of random walkers. They will start at
Home Team Scoring Advantage in the First Inning Largely Due to Time By David W. Smith Presented June 26, 2015 SABR45, Chicago, Illinois Throughout baseball history, the home team has scored significantly
SECTION 2 HYDROLOGY AND FLOW REGIMES In this section historical streamflow data from permanent USGS gaging stations will be presented and discussed to document long-term flow regime trends within the Cache-Bayou
Rainfall in the GLOBE Africa Region: A GLOBE Data Exploration Purpose Through explorations of GLOBE rain depth data from Africa, students learn about seasonal patterns in locations affected by monsoons.
Make a Marigram Overview: In this lesson, students briefly examine the use of acoustics for data collection then use Microsoft Excel to analyze tide gauge data. Basic knowledge of Microsoft Excel is recommended.
Ch 15 Earth s Oceans SECTION 15.1 An Overview of Oceans 38 points In your textbook, read about modern oceanography. For each item write the word that meets the description. (5 points) 1. German research
NAFO Sci. Coun. Studies, 29: 23 29 A Combined Recruitment Index for Demersal Juvenile Cod in NAFO Divisions 3K and 3L David C. Schneider Ocean Sciences Centre, Memorial University St. John's, Newfoundland,
DOW# 69-0696 West Twin Lake West Twin Lake has a surface area of 120.4 acres and is located on the east side of St. Louis County Road 31, south of Highway 2. West Twin Lake has a maximum depth of 18 feet
Chapter The Dynamic Ocean An ocean current is the mass of ocean water that flows from one place to another. 16.1 The Composition of Seawater Surface Circulation Surface Currents Surface currents are movements
El Niño Lecture Notes There is a huge link between the atmosphere & ocean. The oceans influence the atmosphere to affect climate, but the atmosphere also influences the ocean, which can also affect climate.
i CHAPTER 1 INTRODUCTION TO RELIABILITY ii CHAPTER-1 INTRODUCTION 1.1 Introduction: In the present scenario of global competition and liberalization, it is imperative that Indian industries become fully
GEOS 513 ENSO: Past, Present and Future Jessica Conroy Department of Geosciences Stephen W. Bieda, III Department of Atmospheric Sciences February 22, 2006: Regional Teleconnections (Observations) References:
Air Pressure and Wind 19.1 Understanding Air Pressure Air Pressure Defined Air pressure is the pressure exerted by the weight of air. Air pressure is exerted in all directions down, up, and sideways. The
Basic Emergency Planning Awareness for Acadia National Park Rock Climbing Enthusiasts Otter Cliffs Climbing Area Lina Dailide-Custodio GEOINT Student, Penn State University 1. Introduction Since the 1880s,
Historical Analysis of Montañita, Ecuador for April 6-14 and March 16-24 Prepared for the ISA by Mark Willis and the Surfline Forecast and Science Teams Figure 1. Perfect Right- hander at Montañita, Ecuador
Aaron Vo 11/4/15 A03 Wednesday 2-5 PM Group 7/HS LAB 6- Discovering how ph/heat Change Affects Solubility I. PURPOSE/OBJECTIVE: The purpose of lab 6 was to observe how altering the ph or heat to proteins
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Characterising the status of the Western Port recreational fishery in relation to biodiversity values: Phase 1 Greg Jenkins and Simon Conron November 2015 1 Contents Executive Summary... 7 Introduction...
Transactions of the Illinois State Academy of Science received 9/14/99 (2000), Volume 93, #2, pp. 165-173 accepted 1/16/00 Status and Distribution of the Bobcat (Lynx rufus) in Illinois Alan Woolf 1, Clayton
A Tracer Investigation of Aerated Water Dispersion and Tidal Exchange in the San Joaquin River and Stockton Ship Channel Prepared for: Jones & Stokes Sacramento, California Contact: Russ Grimes Prepared
Aquatic Plant Management and Importance to Sport Fisheries Presentation to Michigan Inland Lakes Convention May 2014 Mike Maceina Professor Emeritus School of Fisheries, Aquaculture, and Aquatic Sciences
Challenge 7 See if you can determine what the following magnified photos are. Number your paper to 5. The Answers: EXPERIMENTAL DESIGN Science answers questions with experiments DEFINE THE PROBLEM Begin
Oryx Populations at White Sands Missile Range New Mexico Supercomputing Challenge Final Report April 2, 2008 Team 68 Melrose High School Team Members: Kyle Jacobs Richard Rush Randall Rush Teachers: Alan
ROCKWALL CENTRAL APPRAISAL DISTRICT WILDLIFE MANAGEMENT SPECIAL VALUATION GUIDELINES A SUPPLEMENT TO THE STATE OF TEXAS GUIDELINES FOR QUALIFICATION OF AG LAND IN WILDLIFE MANAGEMENT USE These guidelines
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UNDERSTANDING A DIVE COMPUTER by S. Angelini, Ph.D. Mares S.p.A. Dive Computer UNDERSTANDING A DIVE COMPUTER The decompression algorithm in a dive computer is an attempt to replicate the effects of a dive
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Seismic Survey Designs for Converted Waves James A. Musser* GMG/AXIS Inc., Denver, CO 1720 Red Cloud Road, Longmont, CO, 80501, USA firstname.lastname@example.org ABSTRACT Designing converted wave 3D surveys is considerably
STRUCTURED INQUIRY: Investigating Surface Area to Volume Ratio in Cells Introduction: All organisms are composed of cells. The size and shape of a cell determines how well it can deliver nutrients to its
Northeast Atlantic Mackerel, Handlines Northeast Atlantic Mackerel, Handlines Content last updated 3rd Apr 2017 Stock: Mackerel (Scomber scombrus) in subareas 1 7 and 14, and in divisions 8.a e and 9.a
Discerning Patterns: Does the North Atlantic oscillate? Climate variability, or short term climate change, can wreak havoc around the world. Dramatic year to year shifts in weather can have unanticipated
THE POLARIMETRIC CHARACTERISTICS OF BOTTOM TOPOGRAPHY RELATED FEATURES ON SAR IMAGES Taerim Kim Professor, Ocean System Eng. Dept. Kunsan University Miryong Dong San 68, Kunsan, Jeonbuk, Korea, email@example.com
RANCHING Wildlife Texas White-Tailed Deer 2017 Hunting Forecast During most summers, I take a short break and head to Colorado, Wyoming, or somewhere out west to enjoy a respite from the hot South Texas
Chapter Air Pressure and Wind 19.1 Understanding Air Pressure Air Pressure Defined Air pressure is the pressure exerted by the weight of air. 19.1 Understanding Air Pressure Air Pressure Defined Air pressure
Chapter 1 - Secondary Ports?? If you can work with secondary ports try the exercise on page 13. Applicable references for this section are YN on Secondary Ports and NH chapter 7. The Tiller CD interactive