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Appl Environ Microbiol. 2009 December; 75(23): 7378–7384.
Published online 2009 October 2. doi:  10.1128/AEM.01900-09
PMCID: PMC2786408

Influence of Environmental Gradients on the Abundance and Distribution of Mycobacterium spp. in a Coastal Lagoon Estuary[down-pointing small open triangle]

Abstract

Environmental mycobacteria are of increasing concern in terms of the diseases they cause in both humans and animals. Although they are considered to be ubiquitous in aquatic environments, few studies have examined their ecology, and no ecological studies of coastal marine systems have been conducted. This study uses indirect gradient analysis to illustrate the strong relationships that exists between coastal water quality and the abundance of Mycobacterium spp. within a U.S. mid-Atlantic embayment. Mycobacterium species abundance and water quality conditions (based on 16 physical and chemical variables) were examined simultaneously in monthly samples obtained at 18 Maryland and Virginia coastal bay stations from August 2005 to November 2006 (n = 212). A quantitative molecular assay for Mycobacterium spp. was evaluated and applied, allowing for rapid, direct enumeration. By using indirect gradient analysis (environmental principal-components analysis), a strong linkage between eutrophic conditions, characterized by low dissolved-oxygen levels and elevated nutrient concentrations, and mycobacteria was determined. More specifically, a strong nutrient response was noted, with all nitrogen components and turbidity measurements correlating positively with abundance (r values of >0.30; P values of <0.001), while dissolved oxygen showed a strong negative relationship (r = −0.38; P = 0.01). Logistic regression models developed using salinity, dissolved oxygen, and total nitrogen showed a high degree of concordance (83%). These results suggest that coastal restoration and management strategies designed to reduce eutrophication may also reduce total mycobacteria in coastal waters.

Environmental mycobacteria, or nontuberculous mycobacteria (NTM), include all species of mycobacteria other than those in the Mycobacterium tuberculosis complex and M. leprae. In general, NTM are aerobic, acid-fast, gram-positive, non-spore-forming, nonmotile organisms found as free-living saprophytes in soil and water (12, 14, 20, 21, 35). However, several members of this group can cause serious disease in humans, including pulmonary infections, cervical lymphadenitis, ulcerative necrosis, skin infections, and disseminated infections associated primarily with autoimmune disorders (12, 29). For example, disseminated infection with the Mycobacterium avium complex can occur in up to 40% of late-stage AIDS patients in developed countries (43). NTM can also have costly and problematic effects on wild and domesticated animals (17, 23). Thus, understanding the sources and reservoirs of these bacteria has become a priority in recent years (12, 34).

While the mode of infection has been poorly established for many cases involving NTM, water is commonly implicated as either a source or a vector (12, 43). NTM are considered to be ubiquitous in the environment and have been cultured globally from samples obtained from freshwaters and marine natural waters (12), swimming pools and hot tubs (11, 25), and drinking water supplies (12, 13), among others. However, only a limited number of attempts have been made to examine the association of their distribution and abundance with environmental parameters (1, 21, 24). The abundance of the M. avium complex was found to correlate positively with water temperature and levels of zinc and humic and fulvic acids and negatively with the dissolved-oxygen content and pH in brown-water swamps in the southeastern United States (24). In a study of Finnish brook waters, acidic conditions, along with the presence of peatlands, chemical oxygen demand, increased precipitation, water color, and concentrations of several metals, were found to favor total NTM (20, 21). However, recent efforts with samples from the Rio Grande River in the United States found positive correlations with the presence of coliforms and Escherichia coli counts and negative correlations with chemical toxicity and water temperature in this alkaline, oligotrophic system (1). Although system-specific differences may be apparent, no attempts to examine mycobacterial ecology in marine and estuarine systems have been reported to date.

Historically, researchers have relied on culture-based techniques for detection and enumeration of mycobacteria from environmental samples (1, 20, 21, 43). Because of the slow growth of many mycobacteria, culture from environmental samples requires decontamination, which can severely impact both the quantity and diversity of species recovered (18, 19). Recently, quantitative PCR (qPCR) has gained favor as a means of rapidly enumerating organisms or genes in environmental samples (5, 15, 38, 40). This method allows for the continuous monitoring of the reaction through the use of fluorescent reporter molecules or DNA stains. Because of this strategy, the reaction can be evaluated at the peak of the exponential phase, reducing errors of reagent depletion and assay efficiency associated with end point reads. Quantification is based on the principle that the amount of the starting template is directly proportional to the number of cycles required to reach the peak of the exponential phase, and is evaluated through the preparation of standards.

Like many coastal lagoon estuaries, the shallow embayments bordering the Maryland and Virginia seaboard are highly susceptible to anthropogenic influence, as they are visited by millions of people annually for vacation and water-related recreation (44). While eutrophication and degraded environmental conditions have been generally linked to factors or organisms which can ultimately influence human health, little attention has been given to the response of bacteria (16, 45). In this paper, we describe our efforts to examine environmental influences on the abundance and distribution of NTM in a dynamic estuarine system.

MATERIALS AND METHODS

Site description.

Maryland and Virginia coastal bays are located in the mid-Atlantic region and are typical of shallow lagoon estuaries along the Atlantic seaboard (Fig. (Fig.1).1). Narrow barrier islands extending from the Delaware line through the Virginia region protect shallow, wind-driven waters (mean depth, 1.2 m). The surrounding watershed is relatively small (452 km2) and is dominated by mixed agricultural and forestland cover at the southern end, with areas of intense development in the northern reaches. The focus of this effort was the southern coastal bays, which comprise three distinct basins: Sinepuxent, Chincoteague, and Newport Bays (Fig. (Fig.1).1). In contrast to the northern reaches, the barrier islands protecting these areas are undeveloped and managed by the National Park Service (NPS) and the State of Maryland. Because of poor tidal flushing (with only two inlets), residence times for nutrients are protracted (4). The majority of nutrient inputs are from nonpoint sources, with agriculture estimated to contribute up to two-thirds of the total (3).

FIG. 1.
Maryland and Virginia's coastal embayments. Numbered circles represent sampling locations visited monthly by the NPS's Assateague Island water quality monitoring program. mi, miles.

Sample sites.

The NPS conducts monthly water quality sampling of the southern coastal bays adjacent to Assateague Island National Seashore. Grab samples are collected at a 0.5-m depth from 18 stations in Chincoteague, Newport, and Sinepuxent Bays (Fig. (Fig.1)1) for full nutrient and chlorophyll analyses, while depth profiles are examined for physical data. Nutrient analysis for this program is conducted by Analytical Services, Horn Point Laboratory, University of Maryland, in accordance with Environmental Protection Agency guidelines, while physical parameters are measured in situ with a datasonde (YSI Incorporated, Yellow Springs, OH). For this study, samples for bacterial analysis were collected concurrently with samples for nutrient analysis from August to November 2005 and April to November 2006. At each location, a 500-ml water sample from a 0.5-m depth was collected into a sterile Nalgene container. Samples were gently shaken and inverted to mix and then filtered immediately in the field. A total of 200 ml of each sample was passed through a 0.22-μm Sterivex filter (Millipore, Billerica, MA) attached to a sterile 60-ml Luer-Lok syringe (Becton, Dickinson and Co., Franklin Lakes, NJ). Water was completely removed from each filter, with filter housings wrapped tightly in Parafilm and stored in Whirl-Pak bags on dry ice. For samples from heavily turbid areas, water was filtered until no more could pass through and the volume was noted to the nearest milliliter. The minimum filtered volume in this study was 100 ml (n = 4). Filter housings were subsequently stored at −80°C until extraction.

DNA purification.

Sterile pliers were used to forcibly crack the plastic outer coverings of the Sterivex filters. Filters were then removed from the frame by cutting five lengthwise strips (approximately 6.0 mm long) with sterile razor blades. Strips were then folded lengthwise, and all five were placed into a PowerSoil kit bead tube (Mo Bio Laboratories, Inc., Carlsbad, CA). The manufacturer's protocol was modified to obtain constant volumes, increase cell lysis, and maximize removal from the filters. Sixty microliters of the manufacturer's solution C1 was added to each bead tube, which was then incubated at 70°C and shaken at 500 rpm in an Eppendorf thermomixer (Eppendorf AG, Hamburg, Germany) for 10 min, removed, inverted, subjected to a benchtop vortex at maximum speed, and reincubated under the same conditions for an additional 10 min. Following incubation, 700 μl of phenol-chloroform-isoamyl alcohol (PCI) was added to each tube to dissolve the filters and all tubes were subjected to a vortex at maximum speed for 10 min. Tubes were then centrifuged at 10,000 × g for 30 s, and 800 μl of supernatant was removed for further DNA extraction per the manufacturers' instructions.

qPCR.

A genus-level assay targeting the internal transcribed spacer region and partial 23S gene, developed by Bruijnesteijn van Coppenraet et al. (6), was evaluated for use on environmental samples and optimized for efficiency and sensitivity. Primer and probe sequences were as follows: forward primer, 5′-GGG GTGTGGTGTTTGAG-3′; reverse primer, 5′-CTCCCACGTCCTTCATC-3′; and probe, 5′-(6-carboxyfluorescein)-TGGATAGTGGTTGCGAGCATC-(black hole quencher 1)-3′. Every sample was evaluated for the presence of inhibitors by using a unique manufactured control (33). While we have used a variant of this control internally in multiplex formats, for the samples described in this study, a separate reaction was performed for inhibition. qPCR for Mycobacterium spp. was performed by using a mixture of 2.50 μl of 10× PCR buffer (Invitrogen Corporation, Carlsbad, CA), 1.50 μl of 50.00 mM MgCl2 (Invitrogen), 0.50 μl of a 10.00 mM deoxynucleoside triphosphate solution (a mixture of all deoxynucleoside triphosphates at equal concentrations; Roche Diagnostics, Inc., Madison, WI), 0.50 μl of each primer at a 10.00 μM concentration, 0.50 μl of a 10.00 μM 6-carboxyfluorescein-labeled probe, and 0.25 μl of 5-U/μl Platinum hot-start Taq (Invitrogen) per reaction. DNase/RNase-free water was added in a quantity sufficient for a 25-μl total reaction volume. Two-stage qPCR cycling parameters were as follows: (i) initial denaturation of the template at 95°C for 180 s and (ii) 40 cycles of denaturation at 95°C for 30 s and combined annealing and extension at 62°C for 40 s. Following qPCR, some amplification products were run on a 1.5% agarose gel at 84 V for 1 h 45 min to ensure that proper products were being amplified by comparison to a known molecular weight marker.

Bacterial strains.

The primers and probe used in this assay had been evaluated previously against 36 species representing several genera (6). In this study, an additional 20 species of mycobacteria and several other genera were evaluated to ensure specificity (Table (Table1).1). Strains were received from the ATCC or the Virginia-Maryland Regional College of Veterinary Medicine at the University of Maryland. Mycobacterium strains were maintained on Middlebrook 7H10 agar slants supplemented with Middlebrook oleic acid-albumin-dextrose-catalase enrichment. All standard curve development, assay optimization, and repeatability experiments were conducted with M. marinum (ATCC 927).

TABLE 1.
Family, genera, and species used to examine assay specificity in previous studies (6, 7) and in the present study

Assay performance/standard curves.

The modified extraction protocol and the assay were examined for performance. Recovery of DNA was estimated by first running a boiled cell suspension through the PCR assay and gel purifying. The DNA content was measured using a NanoDrop 1000 instrument (Thermo Scientific, Watham, MA) (27). A standard curve was then developed by making serial dilutions from 1010 to 101 gene copies. Results from subsequent qPCR extractions from samples with known numbers of target gene copies were compared to the above-mentioned standard curve in triplicate to evaluate extraction efficiency.

For standard curves, a series of dilutions of cell suspensions in Butterfield's phosphate-buffered saline were made from pure cultures in active growth phases. Aliquots of 200 μl from each suspension were plated onto Middlebrook 7H10 agar plates with Middlebrook oleic acid-albumin-dextrose-catalase enrichment in replicate to determine cell counts. Two filters were processed for each dilution by spiking 200 ml of natural, estuarine water with 1 ml of the dilution, and all filters were processed as described above. Estuarine water for standard curves was screened prior to being used to ensure the absence of background concentrations of target organisms. Extracted DNA for all dilutions was then run according to the above-described qPCR parameters, and the cycle threshold was plotted against the number of total cells in the extraction to determine the standard curve. Multiple standard curves generated were used to estimate assay efficiency (E) by using the following formula: E = −1 + 10(−1/slope) (36).

Statistical analysis.

A variety of statistical approaches to explain bacterial concentrations in relation to water quality characteristics were explored. The physical and chemical environmental dynamics of the study area were first characterized by principal-components analysis (PCA) using the Proc Princomp procedure in software from SAS (Cary, NC). Indirect environmental gradient analysis was then conducted by graphically overlaying the PCA biplot of the first two water quality principal components (PC1 and PC2) with sample bacterial abundance. Stepwise logistic regression analysis (with Proc Logistic in SAS software) was used to construct two models, one using water quality PC1 and PC2 as potential predictors of bacterial abundance and the other model using the originally measured water quality variables as potential predictor variables. Because of the ubiquitous nature of members of the genus Mycobacterium in estuarine waters, we examined environmental conditions associated with elevated bacterial abundance rather than presence. Logistical models were used to predict the occurrence of a Mycobacterium concentration at or above the sample data set's 4th quartile level (>45 cells/ml). Final model selection was based on Akaike's information criterion (AIC) and concordance. Probability plots were generated based on the 5th and 95th quantiles for the total nitrogen (TN) data by using the following equation: P = elogit/(1 + elogit), where logit = β0 + β1χ1 + β2χ2 + β3χ3. Simple correlation was also examined for comparison to data in the literature by using Proc Corr in SAS software. Spatial and temporal trends were examined using multivariate general linear models with Proc GLM in SAS software, with subsequent least-square-means comparison. All count data were log transformed prior to analysis and back transformed for reporting of means and confidence intervals (CI). For the spatial analysis, embayments were defined by the watershed boundary, with Chincoteague Bay being further divided into northern and southern regions to examine latitudinal trends and reduce the discrepancy in size among watersheds. Seasons were defined as spring (April and May), summer (June, July, and August), and fall (September, October, and November).

RESULTS

Assay performance.

The specificity of the assay was evaluated in the present study with 20 species of mycobacteria and 2 species of vibrios in additional to the organisms used in the previous evaluation of the assay (6). In all cases, only members of the genus Mycobacterium were amplified (Table (Table1).1). Recovery estimates for our modified Mo Bio extraction protocol averaged 39.6% ± 8.5% (standard deviation; n = 40) over a 6-log range of starting DNA template. This relatively low variability was also demonstrated in filter-to-filter repeatability, determined by analyzing two samples from the sample bottle (R2 = 0.98; n = 11 bottles). However, when replicate bottles were filled at the same location, results were highly variable (R2 = 0.66; n = 24 replicates). Replicate qPCRs yielded an R2 value of 0.96 (n = 38 samples). The generation of standard curves from spiked water samples through extraction, rather than from serial dilutions of DNA preparations, is also a measure of repeatability since variable recovery would yield poor fit. Assay efficiency measured from standard curves averaged 90.57% ± 9.08% (standard deviation; n = 3), with a sensitivity of less than 1 cell/ml in a 200-ml water sample. Using our modified extraction protocol, we did not encounter inhibitors in any of our environmental samples.

Environmental gradient analysis.

Collectively, the first two PCs resulting from the water quality PCA accounted for 47 and 19% of the variance within that multivariate data set. Subsequent PCs were not retained for further analysis because PC1 and PC2 described a majority (66%, collectively) of the system's environmental variance and because no other PCs possessed eigenvalues of >1.0 (Fig. (Fig.2).2). As evident in the water quality variable loadings on each of these PCs, PC1 describes the eutrophication gradient that exists among samples across space and season, with positive PC1 scores associated with high-nutrient and low-dissolved-oxygen samples and negative scores representing the opposite conditions. PC2 represents a gradient of salinity. Individual stations were evenly distributed along both PCs, providing a continuum from well-oxygenated, low-nutrient waters to eutrophic conditions for PC1 and from mesohaline to marine waters for PC2.

FIG. 2.
PCA of water quality variables and Mycobacterium species abundance in the coastal bays. Circle radii represent relative abundances of mycobacteria. PC1 explains 47% of the total variation, which follows a eutrophication gradient. PC2 explains ...

Mycobacterium species distribution.

Mycobacterium spp. were detected at 96% of stations sampled, with estimated concentrations ranging from 0 to 103 cells/ml. Estimated abundance varied both spatially and temporally during the study period, with a greater degree of model variance explained by embayment (85%) than by season (15%; P < 0.0001; F = 20.21; 5,211 df). Spatially, the mean concentration of Mycobacterium spp. in Newport Bay (123.97 cells/ml; CI, 80.64 to 192.48 cells/ml) was almost an order of magnitude higher than those in the other embayments (P < 0.0001, F = 28.62; 3,211 df) (Fig. (Fig.3).3). Mean estimated abundance generally increased with latitude, with concentrations being lowest in southern Chincoteague Bay (12.18 cells/ml; CI, 9.49 to 15.96 cells/ml) (Fig. (Fig.3).3). Temporally, concentrations were depressed in the spring (to 17.28 cells/ml; CI, 12.18 to 24.77 cells/ml; P < 0.05) but did not differ significantly between summer (29.67 cells/ml; CI, 22.65 to 38.47 cells/ml) and fall (38.5 cells/ml; CI, 31.2 to 47.47 cells/ml; P > 0.05).

FIG. 3.
Box-and-whisker plot of estimated Mycobacterium concentrations by embayment. NB, Newport Bay; SB, Sinepuxent Bay; NCB, northern Chincoteague Bay; SCB, southern Chincoteague Bay. Labeling of boxes with the same letter denotes a lack of significance difference ...

Environmental associations/model development.

The results from incorporating cell concentrations into the environmental gradient analysis suggest a consistent orientation of elevated Mycobacterium species densities with more nutrient-rich and lower-salinity environments (Fig. (Fig.2).2). Correlation analysis further demonstrates this relationship, with significant positive correlations found for increased temperature, turbidity, and nitrogen and phosphorus components (Table (Table2).2). Negative correlations were found for the dissolved-oxygen content, secchi depth, and salinity (Table (Table2).2). Logistic regression models were developed using PC scores and combinations of individual water quality parameters (Table (Table3).3). Although the composite variables PC1 and PC2 allow for adequate model development (71.9% concordance), more robust and sensitive models were obtained using salinity and oxygen (81.4%) and salinity, oxygen, and TN (83.8%) (Table (Table3).3). Parameter estimates for the final model are as follows: intercept, 16.57 ± 4.52; salinity, −0.48 ± 0.11; dissolved oxygen, −0.75 ± 0.25; and TN, 0.04 ± 0.01. Probability plots were generated based on the 5th and 95th quantiles of the TN data for visualization of the interaction of the three variables (Fig. (Fig.44).

FIG. 4.
Probability of occurrence of elevated Mycobacterium concentrations (>45 cells/ml) as a function of salinity and oxygen at the 5th and 95th quantiles of total nitrogen (TN) levels observed in samples obtained during the 2005-to-2006 sampling period. ...
TABLE 2.
Spearman's rank correlation coefficients for water quality variables and log bacterial concentrations in coastal bay water samples
TABLE 3.
Logistic models evaluated for the probability of occurrence of Mycobacterium spp. in the coastal baysa

DISCUSSION

This study is the first examination of the ecology of environmental mycobacteria in estuarine/marine waters. The results quantitatively depict that the concentration of Mycobacterium spp. varies in association with the environmental gradients of salinity and eutrophication. These relationships were strong enough to support accurate probabilistic models (83% concordance) that have great potential for use in predicting future abundance. Eutrophication, which was shown to be a useful predictor of mycobacterial concentrations in this study, is a well-recognized human-induced stressor of coastal ecosystems. Therefore, the findings of this study provide a greater understanding of how human activities influence coastal ecosystem conditions by influencing the distribution and abundance of marine/estuarine bacteria.

Of the few efforts to examine environmental mycobacterial ecology, this is the first to rely strictly on a molecular approach (1, 20, 21, 43). It is well known that mycobacterial culture is not optimal for all species and that decontamination procedures required to accommodate slow-growing species from environmental samples can dramatically reduce the abundance and diversity of what is cultured (18, 19). Molecular techniques such as qPCR have become very common in studies of the diversity and abundance of aquatic bacterioplankton (5, 9, 15, 38, 40). While progress is being made (31, 32, 37, 42), one issue that remains for PCR in general is the ability to separate live from dead cells. DNA may remain intact in bacteria for days to weeks (31). Because we were unable to address this issue during the time period in which the samples were collected, our quantitative data may represent an overestimate of live-cell abundance. However, our technique is also capable of detecting species and strains that are not culturable. These differences in methodology most likely account for the log elevation of concentration estimates reported here compared to those reported elsewhere (1, 21).

Recovery estimates for our modified Mo Bio protocol are similar to or better than many reported in the literature (27, 28). Previously reported estimates range from undetectable to 43.3%, with averages around 15%. In our efforts, the Mo Bio PowerSoil kit consistently gave average yields of 30% or better with our modifications. The most significant modification is the addition of PCI. PCI is commonly applied for DNA cleanup and protein removal but also fully dissolves the polyethersulfone filter in minutes (Byron Crump, Horn Point Laboratory, University of Maryland, personal communication), erasing concerns of recovery from filtration materials. While the benefits are apparent, there is DNA loss associated with PCI extraction (2), affecting total yield. However, the combined protocol yields repeatable results, allowing for strong standard curve development from extraction and good assay efficiency for environmental matrices. Because our qPCR approach uses standards which are prepared by the same extraction protocol used for the test samples, repeatability is more important than optimization for high yield.

The primers used in this study target the internal transcribed spacer region of the 16S to 23S rRNA gene and are genus specific (6). While many of the mycobacteria may be distinguished by sequencing (6, 22, 39), obtaining species-specific information in this initial coastal bay system study was not attempted. Determining species is extremely important in the study of mycobacterial ecology and should be pursued in future efforts. Environmental mycobacteria have been largely ignored in the literature in comparison to their more noteworthy relatives (e.g., M. tuberculosis and M. leprae). Thus, uncertainties remain as to whether many of the potentially pathogenic species are endemic to the aquatic environment or merely use it as a vector (12). Indeed, many are capable of persistence in free-living amoebae, which has been postulated to be an adaptive mechanism for mammalian cell virulence (41). Our results offer an initial attempt to understand the ecology of NTM and provide a strong basis for future study.

Of anthropogenically influenced change, eutrophication is considered to be the largest aquatic pollution problem in the United States (8, 30). It has long been held that nutrient overenrichment results in increased heterotrophy; however, only recently has evidence to support this claim been provided (26). Heterotrophic bacteria are responsible for many important processes in estuarine systems (e.g., N2 fixation and nitrification, etc.); however, the community is also composed of several species know to be pathogenic to humans and aquatic animals. Mallin et al. (26) found that the combination of inorganic and organic phosphorus directly stimulates bacterial growth in Atlantic coastal black-water streams, while algal communities exhibit greater responses to nitrogen. This finding suggests that bacteria may be phosphorus limited, in contrast to algal communities, which may respond more directly to nitrogenous inputs. For our data, TN served as a better predictor variable than phosphorus. While this result may seem to contradict the findings of Mallin et al. (26), TN and total phosphorus in the coastal bays were highly correlated with each other (r = 0.47; P < 0.0001), thus potentially masking the functional relationship. Heterotrophic bacteria may also respond indirectly to nitrogen inputs through the use of algal by-products as carbon substrates for enhanced growth and the decomposition of dying blooms. Either pathway may enhance the biological oxygen demand and subsequently reduce oxygen availability in the water column in direct response to overall nutrient loading (26).

The association of environmental mycobacteria with low dissolved-oxygen contents and organically rich areas in freshwater systems has been reported previously (20, 24). Our data offer further support for this general association with degraded environments. Concentrations of Mycobacterium spp. were elevated in Newport Bay, which ranks among the highest in nitrogen concentration among the coastal bay systems and fails to meet water quality thresholds for sea grass survival (10). Overall, the surface oxygen concentration correlated negatively with abundance and served as a strong predictor variable (Table (Table2).2). In the coastal bays, surface oxygen concentrations varied from 10.74 to 4.40 mg/liter during the time period examined and were strongly negatively correlated with the surface temperature (r = −0.685; P < 0.0001). As demonstrated in our environmental gradient analysis, 47% of the system variation is explained along a eutrophication gradient which is represented by a continuum from a high dissolved-oxygen concentration to nutrient-rich waters. It is likely that oxygen itself does not regulate the abundance of these organisms but rather serves as the strongest composite indicator of physical and environmental factors and the biological oxygen demand.

Because of the strength of the association of NTM with eutrophication and salinity gradients, we were able to develop several logistic regression models, all with greater than 70% concordance (Table (Table3).3). This approach is advantageous because it allows for predictive modeling of the probability of occurrence or, in our case, elevated concentrations (>45 cells/ml). While there remain cultural and technical issues that may limit the application of pathogen forecasting in general, at a minimum our approach affords the ability to spatially and temporally identify regions of interest in the coastal bays. With future efforts in model refinement and further distinction of mycobacterial species, it is anticipated that we will move closer to this goal. It is apparent from our data, however, that management efforts focused on nutrient and sediment reduction may also reduce the abundance and distribution of environmental mycobacteria.

Acknowledgments

We are grateful for the technical assistance of and provision of internal control by Andy Depaola, Jessica Nordstrom, and George Blackstone (U.S. FDA, Dauphin Island, AL). We also extend our gratitude to Ana Baya for access to her extensive library of mycobacterial isolates. We are indebted to Byron Crump (Horn Point Laboratory, University of Maryland) for his ideas concerning DNA extraction from filters. We are also grateful to past and present NOAA personnel Heath Kelsey, Howard Townsend, and A. K. Leight for statistical advice and geographic information system assistance. We thank the water quality monitoring crew of the NPS for collection of samples and provision of water quality data.

This work was partially funded by NOAA's Oceans and Human Health Initiative.

Footnotes

[down-pointing small open triangle]Published ahead of print on 2 October 2009.

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