Accuracy Assessment of NOAA\u27s Florida Keys Benthic Habitat Map

This report describes the methodologies, analyses, and results for an independent accuracy assessment of a thematic benthic habitat map produced by NOAA for the Florida Keys. It is an analysis of four regional accuracy assessments. Over the course of the Florida Keys mapping project, NOAA amended part of the classification scheme. The original scheme for mapping benthic cover was a tiered approach where certain benthic cover categories were given priority over others (e.g. coral was most important). Recently, this was modified to a dominant benthic cover scheme where the habitat is characterized by the single most dominant cover type and all habitats are characterized for percent cover of coral. The data and data analyses from Walker and Foster (2009 and 2010) were used to evaluate the accuracy of the reclassified map for Regions Of Interest (ROI) 1 and 2. New data were collected for ROIs 3 and 4 as part of this report. All four regions were combined and analyzed to determine total map accuracy. Data were collected in January 2009 at ROI 1 (eastern Lower Keys), in June 2009 at ROI 2 (western Lower Keys), in September 2012 and February, March, and May 2013 at ROI 3 (back country), and in May 2013 at ROI 4 (Key Largo) (Figure 1). A total of 2029 sampling stations were visited, of which 1969 were used in the accuracy assessment. The sites were selected using a stratified random sampling protocol that equally distributed sampling points amongst the detailed structure categories. Most sites were sampled by deploying a weighted drop camera with the vessel drifting in idle and recording 30-120 seconds of dGPS-referenced video. The shallowest sites were sampled by snorkel, waverunner, or kayak, using a hand-held dGPS for navigation and a housed camera to record video. Each sampling station was given a Detailed Structure, Biological, and Coral Cover assignment in the field. These field classifications were reevaluated post-survey during a systematic review of video and photographic data, designed to ensure consistency within classifications. The efficacy of the benthic habitat map was assessed by a number of classification metrics derived from error matrices of the Major and Detailed levels of Geomorphological Structure and Biological Cover. The overall, producer’s, and user’s accuracies were computed directly from the error matrices. The analyses of the combined ROIs 1 – 4 gave an overall accuracy of the benthic habitat map of 90.4% and 84.6% at the Major and Detailed levels of Structure respectively, and 85.1% and 76.5% at the Major and Detailed levels of cover. The known map proportions, i.e. relative areas of mapped classes, were used to remove the bias introduced to the producer’s and user’s accuracies by differential sampling intensity (points per unit area). The overall accuracy at the Major and Detailed levels of Structure changed to 92.6% and 85.9%. The overall accuracy at the Major and Detailed levels of cover changed to 83.9% and 77.5%. The overall accuracies were also adjusted to the number of map categories using the Tau coefficient. Tau is a measure of the improvement of the classification scheme over a random assignment of polygons to categories, bounded between -1 (0% overall accuracy for 2 map categories) and 1 (100% accuracy for any number of categories). The Tau coefficients were 0.807 ± 0.026 and 0.829 ± 0.018 at the Major and Detailed levels of Structure, and 0.814 ± 0.020 and 0.745 ± 0.020 at the Major and Detailed levels of cover. Percent coral cover was classified for every polygon, thus coral cover was evaluated separately. Total accuracy for Coral in all habitats for all ROIs was 89.6% and 93.4% after adjusting for map marginal proportions. This calculation, however, was not realistic because it evaluated coral cover in non-coral habitat which inflated the number of correct sites. To account for this, coral cover was also evaluated at only those sites found to be Coral Reef and Hardbottom habitats. Total map accuracy for mapping coral cover on Coral Reef and Hardbottom habitats was 79.8%, and 82.7% after adjusting for habitat proportions. The accuracy varied greatly between the two coral categories present. User’s and Producer’s accuracies for Coral 0% - \u3c10% were near or equal to 90%. Conversely, Coral 10% - \u3c50% user’s and producer’s accuracies were 54.3% and 66.5% respectively. Adjusted producer’s accuracy was reduced to 55.2%. The adjustment for map proportions was very relevant here due to the large disparity of area between the two classes. The map contained 658.5 km² of Coral 0% - \u3c10% and 39.8 km² of Coral 10% - \u3c50%. Further 583 of AA points on Coral Reef and Hardbottom habitat were in Coral 0% - \u3c10% and 219 were in Coral 10% - \u3c50%. Interestingly, there were no mapped polygons of Coral 50% - \u3c90% and 90% - 100%. There was confusion between coral classes where 88 locations mapped as Coral 10% - \u3c50% were actually Coral 0% - \u3c10% and 60 locations mapped as Coral 0% - \u3c10% were found to be Coral 10% - \u3c50%. Confusion between 11 locations that were mapped as Coral 10% - \u3c50% were actually Coral 50% - \u3c90% and 1 location mapped as Coral 10% - \u3c50% was found to be Coral 90% - 100%. These sites were all located in the patch reefs of Hawk Channel. It is unknown if these sites met the minimum mapping unit criteria, but the field data indicated high coral cover at these locations. The relatively low adjusted producer’s accuracy for Coral 10% - \u3c50% (55.2%) suggests that not all higher coral cover areas were captured in the map. Furthermore the relatively low user’s accuracy (54.3%) indicates that the areas of Coral 10% - \u3c50% portrayed in the map are highly variable. Combining all the results into a total map accuracy assessment gave a sense of how the overall map portrays the seascape. However, it should be noted that large gaps in map coverage exist, especially between Marathon and Key Largo, a 137 km stretch. The results given in the appendices are more representative of their specific regions. ROIs 1 and 2 covered most of the lower Keys and their results are a good representation of map accuracy for that region. ROI 3 covered the Backcountry which had higher accuracies, presumably due to a reduced diversity of habitats and lack of coral cover. ROI 4 is a good representation of the upper Keys map accuracy. It is difficult to know which assessment best represents the middle Keys. The landscape is more similar to the upper Keys, but Hawk Channel becomes deeper and more turbid

The Electronic and Superconducting Properties of Oxygen-Ordered MgB2 compounds of the form Mg2B3Ox

Possible candidates for the Mg2B3Ox nanostructures observed in bulk of polycrystalline MgB2 (Ref.1) have been studied using a combination of Z-contrast imaging, electron energy loss spectroscopy (EELS) and first-principles calculations. The electronic structures, phonon modes, and electron phonon coupling parameters are calculated for two oxygen-ordered MgB2 compounds of composition Mg2B3O and Mg2B3O2, and compared with those of MgB2. We find that the density of states for both Mg2B3Ox structures show very good agreement with EELS, indicating that they are excellent candidates to explain the observed coherent oxygen precipitates. Incorporation of oxygen reduces the transition temperature and gives calculated TC values of 18.3 K and 1.6 K for Mg2B3O and Mg2B3O2, respectively.Comment: Submitted to PR

Characterizing and Determining the Extent of Coral Reefs and Associated Resources in Southeast Florida through the Acquisition of High-Resolution Bathymetry and Benthic Habitat Mapping

Effective marine resource management begins with knowing the distribution of resources within the region. Minimal data, and thus limited knowledge, exists about the reef resources of Martin County. The marine benthic habitats in Martin County need to be mapped to characterize and quantify the distribution of its coral and other benthic communities, therefore, the Florida Department of Environmental Protection – Coral Reef Conservation Program, FL Fish and Wildlife Research Institute and the National Coral Reef Institute at Nova Southeastern University have partnered to expand upon previous mapping efforts to identify and classify the benthic habitats in the southeast Florida region (Miami-Dade, Broward, Palm Beach, and Martin Counties). The maps will provide critical information needed to understand the extent of the coral reef habitat throughout Martin County and the southeast Florida region. They will enable managers to enforce impact avoidance and assist in the development of conservation action strategies. Updating the existing maps is also essential to the region to monitor changes to the resources and provide current data for management decisions. Southeastern Florida has a very dynamic marine system influenced by high energy weather systems (e.g. hurricanes), ship groundings, various construction projects, and artificial reef deployment which change the morphology of the sea floor and thus affect the benthic habitats. Existing Broward benthic habitat maps were drawn based on 2001 LIDAR data, therefore a new LIDAR survey in Broward County will facilitate updating these maps. The Broward LIDAR dataset was collected by Tenix LADS Inc. between July and August 2008. The data were obtained and processed into high resolution hill-shaded topographic maps. Detailed information regarding this survey can be obtained by contacting Ken Banks at Broward County’s Environmental Protection and Growth Management Department Natural Resources Planning and Management Division. The marine benthic habitats in Martin County were mapped using the same combined technique approach as was done in the other southeast Florida counties (Walker, Riegl, and Dodge 2008). The mapping area extended seaward from shore to the 30 m depth contour where possible and covered an area of ~350 sq km. Image-based analyses in deeper water were not ideal in Martin County due to poor water clarity; therefore, a high resolution (4 m) LIDAR bathymetric survey was conducted to image the sea floor. This effort was conducted in two phases. Phase 1, where a LIDAR bathymetric survey of the seafloor was conducted, and Phase 2 where habitat maps were created by outlining and defining the features within the bathymetric survey. Phase 1 mapping began when the project area in Martin County was flown in December 2008 by Blom Aerofilms, Ltd. LIDAR for the project area was acquired over a period of four days and included both topographic and bathymetric LIDAR as well as vertical aerial imagery. These data were processed by Blom. Deliverables for the project included cleaned point cloud, DTM 5m grid, hillshaded geotifs, seabed reflectance data, and 25cm GSD orthophotos. Gaps in the initial LIDAR data coverage were evident mainly due to poor water quality, temporal, and meteorological conditions. Of the total 341.5 km² surveyed, 51.5 km² contained data holidays and coverage gaps; 15% of the total survey area. Therefore Blom Aerofilms re-flew the areas with major gaps in December 2009 coincident with other work in the United States. The re-flights included a collection of similar data types. The re-flight scheduling and data processing significantly delayed the project, thus a no-cost extension was granted by FWC to extend the project to December 2012. Benthic habitat maps were produced by delineating seafloor features evident in multiple datasets including the 2008 and 2009 high resolution LIDAR bathymetry and aerial photography collected from Phase 1. Phase 2 started in April 2010 and continued until August 2012. The habitats were classified according to established NOAA guidelines in coordination with the NOS Coral Mapping Program and use a similar classification scheme when possible. Of the 374 km² seafloor mapped in Martin County, the polygon totals indicated 95.2% was Sand, 4.1% was Coral Reef and Colonized Pavement, and 0.7% was Other Delineations. The Martin County benthic habitat morphology is very different than the other counties further south. Hardbottom habitats are sparse outside of a shallow, near shore area around St. Lucie Inlet and a few thin deep ridge lines which taper or are buried further north. All of these features are thought to be cemented beach dunes submerged during the last Holocene sea level transgression. Although not confirmed by coring, they do not appear to be composed of a coral-derived framework and they do not exhibit any morphologic signs of historic reef growth like the spur and groove formations of the Outer Reef which terminates in Palm Beach County near Lake Worth inlet (Banks et al. 2007; Walker 2012). The most extensive, deep hardbottom was the northern end of the Deep Ridge Complex which extends from Palm Beach into southern Martin for about 2 km before it appears to be covered with sediments. Only small, thin portions of the tallest ridges are exposed further north. In southern Martin there are three shore-parallel deep ridge lines. The first deep ridge, nicknamed Three Holes, is located approximately 2 km from shore in 18 m water depth and extends approximately 3.5 km northward in a mostly continuous arrangement. The second deep ridge appears at the same latitude that Three Holes terminates, but it is approximately 6 km from shore in 22 m of water. This feature extends northward in a mostly continuous fashion for about 6 km. The third deep ridge, nicknamed 7-Mile Ledge, is the most conspicuous deep hardbottom feature. Despite its name, in southern Martin this feature is located approximately 6 km (~ 4 miles) from shore in 22 m of water. This is also its widest portion at just about 0.5 km. This ridge extends northward over 23 km with relatively few (4) small breaks or gaps. At its northern terminus, it is located about 12.8 km (8 miles) from shore in 25 m water depth. Shallow hardbottom habitats extended throughout much of the county, but the majority of the habitat existed near St. Lucie inlet. This was comprised of two habitats, Colonized Pavement-Shallow and Ridge-Shallow. The differences between their delineations were mainly morphological. The Ridge-Shallow has an obvious linear morphology and usually contains higher relief, at least at larger scales. The Colonized Pavement-Shallow is typically lower relief and has no distinct linear morphology. The shallow Martin County ridges extend 2.5 km north of the inlet and 11.5 km south in a shore-parallel orientation. The eastern side resides in about 10m depth, it crests near 3m and the western side remains shallow in some parts and drops back to 10m in others. The shallow colonized pavement is located westward of the shallow ridge in waters 10m to 4m deep, sloping upward toward shore. As with other features along the northern Florida Reef Tract, these ridges terminate at the shoreline. The northern terminus is known as Bath Tub Reef and the southern end slips under the shoreline just off Bridge Road on Jupiter Island. Small portions of shallow ridge appear north of the inlet off Jensen Beach. These appear to be ephermeral communities affected by high wave energy and shifting sediments. Beach construction, storm activity, and natural littoral drift all have an effect on the type and arrangement of near shore sea floor habitats and depending on their magnitudes may cause large-scale changes through time. Approximately 357 km² were identified as unconsolidated sediments that contained different sediment features that were not part of the mapping. The most evident features were large sand dunes throughout the county extending to the northeast. In the south, these dunes appear to be partially or totally burying portions of deep ridge habitats. Elevation profiles revealed these features were up to 11 m high extending over 2.25 miles wide. Little is known about the movement of these features, but given the dynamic environment and the frequently high currents, it is likely that they are migrating across the seafloor, including over the deep ridges. In collaboration with FWC, FDEP-CRCP, and NCRI, NOAA funded quantitative ground truthing to provide a rigorous determination of habitat types beyond qualitative efforts and valuable information about the composition of the benthic communities for resource management. This was accomplished in August 2012. Data were collected on 16 sites: 7 Ridge-Deep sites, 5 Ridge-Shallow sites, and 4 Colonized Pavement-Shallow sites. The sites were distributed across the seascape as much as possible to provide data on all the main hardbottom habitats and account for latitudinal variation. A cluster analysis and corresponding non-metric, multi-dimensional scaling (MDS) plot showed that the sites were more similar than not, yet subtle distinctions were evident when the sites were categorized by habitat. The Ridge-Deep sites all plotted on one side of the graph and the two shallow habitats on the other, showing there are likely differences between shallow and deep habitats. Furthermore apart from one site, colonized pavement and ridge did not cluster, indicating a wide range of benthic communities between shallow sites. A summary of the mean percent cover data by habitat showed many differences in cover. Turf algae were more abundant on the shallow colonized pavement (41.4% ± 11.1) and ridge (52.4% ± 19.6) than the deep ridge (19.1% ± 9.5) and vice versa for cyanobacteria. Cover varied greatly within habitat categories and most cover types were low (\u3e 5%) making it difficult to detect differences at the habitat level. Although percent cover between habitats was muddled by within-habitat variability, the number of biotic cover categories (e.g. macroalgae, hydroids, coral) were significantly different. Colonized Pavement-Shallow had significantly fewer biotic cover categories (5.5 ± 0.96 SEM) than the Ridge-Shallow (7 ± 0.45 SEM) and Ridge-Deep (7.4 ± 0.72 SEM). The number of biotic categories ranged from 4 to 8 on the Colonized Pavement-Shallow, from 6 to 8 on the Ridge-Shallow, and from 4 to 9 on the Ridge-Deep. This indicates the shallow colonized pavement may have less taxonomic diversity than the other habitats. Rugosity significantly varied between habitats. The Ridge-Shallow mean rugosity significantly higher than the Colonized Pavement-Shallow which was significantly higher than the Ridge-Deep. This result was not surprising because feature relief (albeit at a larger scale) was one of the main criteria used to distinguish between the two shallow habitats. Although univariate differences between habitats were found (e.g. MDS separation, rugosity, number of biotic categories), multivariate differences of cover types and amounts among sites were not statistically strong between the habitat categories. A one-way analysis of similarity (ANOSIM) was performed to statistically determine the strength of the site categorization by habitat. The strongest result was between the Ridge-Deep and Ridge-Shallow indicating these were most different and supporting the MDS results, however the difference was not very strong. Furthermore the results between Deep-Ridge and Colonized Pavement-Shallow and between Colonized Pavement-Shallow and Ridge-Shallow were very weak. The lack of strong ANOSIM groupings was likely due to not distinguishing between algal species. Although no species data were collected, it was recognized anecdotally that the algal communities between the deep and shallow hard bottoms were distinct. Previous research showing distinct differences in the macroalgal communities in southeast Florida supports these observations (Lapointe 2007). Lapointe’s data show that shallow ridge sites had a large component of Phaeophyta cover (\u3e 50% during certain times) that was not present in the deep habitats, where Chlorophyta was dominant. This was further exemplified by the five sites on the Deep Ridge Complex in north Palm Beach that were dominated by Chlorophyta and Rhodophyta and had very little Phaeophyta if any. Therefore, if macroalgal communities were distinguished in the Martin County quantitative ground truthing, it is likely that the cluster analysis between habitats would have been much more robust. The MDS plot scatter indicated there may be a cross-shelf pattern to the communities in the Nearshore Ridge Complex ((NRC) combination of Ridge-Shallow and Colonized Pavement-Shallow habitats). A site located on the eastern side of the shallow ridge had a distinct community comprised mostly of macroalgae, turf algae, and palythoa. Sites associated with the shallowest top portion of the ridge (the crest) were most similar to each other. And all of the other shallow sites located on the western side of the shallow ridge grouped in a central axis. It is likely that the distinct ridge profile is providing different conditions across the shelf that are shaping the benthic communities. This could account for larger within-habitat variations because the shallow ridge was not divided into separate habitats to account for the differences across the fore-ridge, crest, and back-ridge

Serum metabolomic profiles in dogs with chronic enteropathy

Background Metabolic profiles differ between healthy humans and those with inflammatory bowel disease. Few studies have examined metabolic profiles in dogs with chronic enteropathy (CE). Hypothesis Serum metabolic profiles of dogs with CE are significantly different from those of healthy dogs. Animals Fifty-five dogs with CE and 204 healthy controls. Methods A cross-sectional study. The serum concentrations of 99 metabolites measured using a canine-specific proton nuclear magnetic resonance spectroscopy platform were studied. A 2-sample unpaired t-test was used to compare the 2 study samples. The threshold for significance was set at P < .05 with a Bonferroni correction for each metabolite group. Results Nineteen metabolites and 18 indices of lipoprotein composition were significantly different between the CE and healthy dogs. Four metabolites were significantly higher in dogs with CE, including phenylalanine (mean and SD) (healthy: 0.0417 mmol/L; [SD] 0.0100; CE: 0.0480 mmol/L; SD: 0.0125; P value:Peer reviewe

Seasonal variation in serum metabolites of northern European dogs

Background Metabolic profiling identifies seasonal variance of serum metabolites in humans. Despite the presence of seasonal disease patterns, no studies have assessed whether serum metabolites vary seasonally in dogs. Hypothesis There is seasonal variation in the serum metabolite profiles of healthy dogs. Animals Eighteen healthy, client-owned dogs. Methods A prospective cohort study. Serum metabolomic profiles were assessed monthly in 18 healthy dogs over a 12-month period. Metabolic profiling was conducted using a canine-specific proton nuclear magnetic resonance spectroscopy platform, and the effects of seasonality were studied for 98 metabolites using a cosinor model. Seasonal component was calculated, which describes the seasonal variation of each metabolite. Results We found no evidence of seasonal variation in 93 of 98 metabolites. Six metabolites had statistically significant seasonal variance, including cholesterol (mean 249 mg/dL [6.47 mmol/L] with a seasonal component amplitude of 9 mg/dL [0.23 mmol/L]; 95% confidence interval [CI] 6-13 mg/dL [0.14-0.33 mmol/L], P < .008), with a peak concentration of 264 mg/dL (6.83 mmol/L) in June and trough concentration of 236 mg/dL (6.12 mmol/L) in December. In contrast, there was a significantly lower concentration of lactate (mean 20 mg/dL [2.27 mmol/L] with a seasonal component amplitude of 4 mg/dL [0.42 mmol/L]; 95% CI 2-6 mg/dL [0.22-0.62 mmol/L], P < .001) during the summer months compared to the winter months, with a peak concentration of 26 mg/dL (2.9 mmol/L) in February and trough concentration of 14 mg/dL (1.57 mmol/L) in July. Conclusions and Clinical Importance We found no clear evidence that seasonal reference ranges need to be established for serum metabolites of dogs.Peer reviewe

Microscopic origins of charge transport in triphenylene systems

We study the effects of molecular ordering on charge transport at the mesoscale level in a layer of approximate to 9000 hexa-octyl-thio-triphenylene discotic mesogens with dimensions of approximate to 20 x 20 x 60 nm(3). Ordered (columnar) and disordered isotropic morphologies are obtained from a combination of atomistic and coarse-grained molecular-dynamics simulations. Electronic structure codes are used to find charge hopping rates at the microscopic level. Energetic disorder is included through the Thole model. Kinetic Monte Carlo simulations then predict charge mobilities. We reproduce the large increase in mobility in going from an isotropic to a columnar morphology. To understand how these mobilities depend on the morphology and hopping rates, we employ graph theory to analyze charge trajectories by representing the film as a charge-transport network. This approach allows us to identify spatial correlations of molecule pairs with high transfer rates. These pairs must be linked to ensure good transport characteristics or may otherwise act as traps. Our analysis is straightforward to implement and will be a useful tool in linking materials to device performance, for example, to investigate the influence of local inhomogeneities in the current density. Our mobility-field curves show an increasing mobility with field, as would be expected for an organic semiconductor

Microscopic origins of charge transport in triphenylene systems

We study the effects of molecular ordering on charge transport at the mesoscale level in a layer of approximate to 9000 hexa-octyl-thio-triphenylene discotic mesogens with dimensions of approximate to 20 x 20 x 60 nm(3). Ordered (columnar) and disordered isotropic morphologies are obtained from a combination of atomistic and coarse-grained molecular-dynamics simulations. Electronic structure codes are used to find charge hopping rates at the microscopic level. Energetic disorder is included through the Thole model. Kinetic Monte Carlo simulations then predict charge mobilities. We reproduce the large increase in mobility in going from an isotropic to a columnar morphology. To understand how these mobilities depend on the morphology and hopping rates, we employ graph theory to analyze charge trajectories by representing the film as a charge-transport network. This approach allows us to identify spatial correlations of molecule pairs with high transfer rates. These pairs must be linked to ensure good transport characteristics or may otherwise act as traps. Our analysis is straightforward to implement and will be a useful tool in linking materials to device performance, for example, to investigate the influence of local inhomogeneities in the current density. Our mobility-field curves show an increasing mobility with field, as would be expected for an organic semiconductor

Full Genome Characterization of the Culicoides-Borne Marsupial Orbiviruses: Wallal Virus, Mudjinbarry Virus and Warrego Viruses

Viruses belonging to the species Wallal virus and Warrego virus of the genus Orbivirus were identified as causative agents of blindness in marsupials in Australia during 1994/5. Recent comparisons of nucleotide (nt) and amino acid (aa) sequences have provided a basis for the grouping and classification of orbivirus isolates. However, full-genome sequence data are not available for representatives of all Orbivirus species. We report full-genome sequence data for three additional orbiviruses: Wallal virus (WALV); Mudjinabarry virus (MUDV) and Warrego virus (WARV). Comparisons of conserved polymerase (Pol), sub-core-shell 'T2' and core-surface 'T13' proteins show that these viruses group with other Culicoides borne orbiviruses, clustering with Eubenangee virus (EUBV), another orbivirus infecting marsupials. WARV shares <70% aa identity in all three conserved proteins (Pol, T2 and T13) with other orbiviruses, consistent with its classification within a distinct Orbivirus species. Although WALV and MUDV share <72.86%/67.93% aa/nt identity with other orbiviruses in Pol, T2 and T13, they share >99%/90% aa/nt identities with each other (consistent with membership of the same virus species - Wallal virus). However, WALV and MUDV share <68% aa identity in their larger outer capsid protein VP2(OC1), consistent with membership of different serotypes within the species - WALV-1 and WALV-2 respectively

RadSTraM: Radiological Source Tracking and Monitoring, Phase II Final Report

This report focuses on the technical information gained from the Radiological Source Tracking and Monitoring (RadSTraM) Phase II investigation and its implications. The intent of the RadSTraM project was to determine the feasibility of tracking radioactive materials in commerce, particularly International Atomic Energy Agency (IAEA) Category 3 and 4 materials. Specifically, Phase II of the project addressed tracking radiological medical isotopes in commerce. These categories of materials are susceptible to loss or theft but the problem is not being addressed by other agencies