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Herpetological Conservation and Biology 10(1):149-160.Submitted: 16 June 2014; Accepted: 3 December 2014; Published: 14 June 2015.

Effects of Processing Time onWhole Blood and PlasmaSamples from Loggerhead Sea Turtles (Caretta caretta) for

1H-NMR-basedMetabolomics

Jennifer N. Niemuth1–5, Craig A. Harms1–4 and Michael K. Stoskopf1–4

1Department of Clinical Sciences, College of Veterinary Medicine, North Carolina State University, 1060William Moore Drive, Raleigh, North Carolina 27607, USA

2Environmental Medicine Consortium, North Carolina State University, 1060 William Moore Drive, Raleigh,North Carolina 27607, USA

3Center for Marine Sciences and Technology, North Carolina State University, 303 College Circle, MoreheadCity, North Carolina 28557, USA

4Fisheries, Wildlife, and Conservation Biology Program, College of Natural Resources, North Carolina StateUniversity, 3120 Jordan Hall, Raleigh, North Carolina 27695, USA

5Corresponding author, e-mail: jennifer_niemuth@ ncsu.edu

Abstract.—The use of metabolomics in veterinary medicine is growing, but still tends to be limitedto laboratory or hospital settings. In this study, we examined the effects of delayed processing timefor Loggerhead Sea Turtle (Caretta caretta) whole blood and plasma samples under field conditions.We froze aliquots of heparinized whole blood samples immediately post-collection and held them tobe processed for plasma at four time-points (median 22, 39, 56, and 72 min) post-collection. Therewere several significant differences between C. caretta whole blood and plasma samples with no appar-ent processing time association. These differences were largely related to erythrocyte metabolism andthe intracellular concentration of certain metabolites within erythrocytes. Variability in results fromplasma samples did not increase with time of delay to processing within the first three time-pointsof collection. The goals of an individual study and processing requirements should be considered todetermine if whole blood or plasma is most appropriate to answer the study questions. While we donot recommend purposefully waiting to process samples collected in the field for nuclear magneticresonance (NMR)-based metabolomics studies, if samples are consistently processed at a time withinapproximately 40–50 min of collection, their usefulness should not be disregarded due to processingtime.

Key Words.—nuclear magnetic resonance spectroscopy; NMR; sample handling; sample processing

Introduction

Metabolomics is an emerging disciplinethat studies the status of metabolites, thesmall molecules involved in the physiologyof metabolism. These compounds include sug-ars, amino acids, fatty acids, and other smallmolecules critical to homeostasis, growth, andadaptation to environmental conditions. The useof metabolomics in veterinary medicine is rel-atively rare (Jones and Cheung 2007; Kirwan

2013; Schock et al. 2013) and tends to be repre-sented by studies using animal species as modelsfor human disease (Whitfield et al. 2005; Zhanget al. 2008; Banerjee et al. 2012). As awarenessof the techniques has grown, clinical and labo-ratory studies using metabolomic techniques toanswer domestic animal and wildlife researchquestions have begun to appear, but in generalrely on captive individuals and/or sampling oc-curring in a controlled, laboratory setting (Viantet al. 2003; Lin et al. 2007; Tikunov et al. 2010;

Copyright c© 2015. Jennifer Niemuth.All Rights Reserved. 149

Niemuth et al.—-Effect of processing time on blood samples form Loggerhead Sea Turtles.

Dove et al. 2012). An important challenge to us-ing metabolomic techniques effectively to answerquestions about free-ranging wildlife is to under-stand how robust the data and information ob-tained are to variability that can be introducedunder the constraints of collecting samples underfield conditions.

The biological end products of metabolism de-grade after a sample is collected from a livinganimal (Ross et al. 2007). For example, Tikunovet al. (2013) showed that the slope of the changein lactate concentration in rat liver samples isgreater than 1.0 mM/sec/g from 0 to 120 secondsuntil freezing. Given the sensitivity of nuclearmagnetic resonance (NMR)-based metabolomics,efficient sample handling and processing is a con-cern with regards to accuracy and reproducibilityof results (Beckonert et al. 2007).

Processing techniques for metabolomic studiesthat are effective in the laboratory or clinic arenot necessarily practical in a field setting. Lim-ited sample processing, such as centrifugation,may not be feasible and/or may be delayed dueto a variety of reasons. It is commonly under-stood that after a sample is collected, metabolismshould be rapidly suppressed because changescan occur on a millisecond scale (Beckonert et al.2007). This is referred to as quenching the sam-ple and is typically accomplished by immediatelyfreezing samples in liquid nitrogen (Beckonert etal. 2007). However, due to its low boiling point,weight, and the need for a vacuum flask, liquidnitrogen can be logistically difficult to use in thefield.

In this study, we examined the effects of de-layed processing time for Loggerhead Sea Tur-tle (Caretta caretta) plasma samples and wholeblood samples requiring minimal processing un-der field conditions, using dry ice to quench thesamples. We hypothesized that there would besignificant differences between whole blood sam-ples and plasma samples and that as process-ing time increased, the metabolic differences be-tween the whole blood sample and the plasmasamples would become more pronounced. We

also hypothesized that variability among repli-cate plasma samples would increase with delaysin processing time.

Materials andMethods

Whole blood and plasma sampleprocessing.—We used whole blood sam-ples, collected for diagnostic purposes fromfive C. caretta of undetermined sex undergoingroutine examination prior to release from theKaren Beasley Sea Turtle Rescue and Rehabil-itation Center, Topsail Island, North Carolina,USA in this study. We collected blood from thedorsal cervical sinus into heparinized syringes(sodium heparin). All of the turtles had beenfed a mixture of fish and squid at approximately0700–0800 the morning their blood was drawn.

We immediately divided each sample into a0.2–0.3 mL whole blood aliquot and four aliquotsof approximately 0.4–0.6 mL to be processed forplasma. Each whole blood aliquot was immedi-ately placed in a cryogenic vial, put on dry ice,and recorded as the A sample. We placed theremaining four aliquots from each animal in Ep-pendorf microcentrifuge tubes and left at ambienttemperature. We then processed these at each offour processing time-points, intended to be 15min (B), 30 min (C), 45 min (D), and 60 min (E)after blood collection. We processed aliquots bycentrifugation at 3,000 × g for three minutes justprior to each time-point. The plasma was thenseparated, placed in a cryogenic vial, put on dryice, and the time recorded.

We determined packed cell volume (PCV) bycentrifuging heparinized blood in a capillary tubewith no additives. We determined total protein(TP) concentration with a refractometer using theplasma fraction from the centrifuged capillarytube. Sampling occurred on 23 May 2012 fromapproximately 1100 to 1500. The maximum airtemperature that day was 28◦ C (Sandy RunNational Oceanic & Atmospheric AdministrationStation, North Carolina, USA). We transportedthe samples on dry ice, transferred them the same

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day to an ultra-low freezer (-80◦C), and storedthem until analysis approximately 12 weeks later.

NMR data acquisition.—We soaked Multi-Screen filter plates with Ultracel-10 membranes(Millipore Ltd., Carrigtwohill, Ireland) indistilled water for two days with water changesevery 24 h and then washed by filtering Milli-Qwater (Merck KGaA, Darmstadt, Germany)through the plate four times to remove theglycerol preservative. Plates were kept moistwith Milli-Q water until use. We preparedsamples for NMR analysis by pipetting 50 µLof sample into a pre-prepared filter plate. Wecentrifuged the plate with samples at 3,000 × gfor 60 min at 21◦ C. We added Milli-Q water(100 µL) and we repeated centrifugation underthe same parameters. The filtrate was lyophilizedand resuspended in 50 µL deuterium oxide with20 mM phosphate buffer, 0.1 mM trimethylsilylpropionate (TSP), and 1 mM formate. We usedformate as an additional reference standard, asTSP can bind to plasma proteins affecting theresultant signal (Beckonert et al. 2007). Weused a Varian Inova 600 MHz multinuclearINOVA NMR spectrometer (Varian MedicalSystems, Palo Alto, California, USA) equippedwith a Protasis microcoil NMR probe (ProtasisCorporation, Marlboro, Massachusetts, USA) toobtain 1D, 1H-NMR spectra at 25◦ C with a 1.1sec acquisition time. The sweep width of 7,195Hz acquired 8,191 complex points and 1,024transients (Macomber 1998; Viant et al. 2008).

Data processing, analysis, andinterpretation.—We processed NMR spec-tra using ACD Labs 12.0 1D NMR Processor(Advanced Chemistry Development, Toronto,Ontario, Canada). We zero-filled spectra to16,000 points and Fourier transformed it. Thebaseline and phasing was corrected automati-cally and adjusted by hand, if necessary. Wereferenced all spectra to the internal standardTSP peak at 0 ppm (Hz/MHz) chemical shift.We identified metabolites by comparison to

reference data available through the HumanMetabolome database (Wishart et al. 2009) andChenomx NMR Suite 7.7 (Chenomx, Edmonton,Alberta, Canada).

For initial analysis, we selected preliminarydark regions from -5 to 0.5 ppm, 4.75 to 4.8ppm, and 9 to 15 ppm. These dark regions elimi-nate areas without metabolites and the water peakfrom analysis. We applied more restrictive darkregions (Appendix A) prior to statistical analy-ses to minimize counting any statistically signif-icant differences in bins containing only noise.We used intelligent bucket integration to dividethe spectra into bins with a width of 0.04 ppm.We normalized individual samples by dividingeach bin value by the sum of the bins of the sam-ple. We calculated median values for each bin ateach time-point and we compared bin differencesbetween time-points using a two-tailed, paired-comparison, exact permutation tests (α = 0.1).

Results

Sample processing.—We divided each samplewith the intent of freezing an aliquot of wholeblood immediately (A) and aliquots of plasma 15min (B), 30 min (C), 45 min (D), and 60 min (E)after collection from the animal. However, thedifficulty of processing samples during routineclinical activity under field conditions did notpermit the desired precision. As a result, thoughwe froze the initial whole blood aliquot of eachsample immediately after collection (within ap-proximately one minute) to represent the first (A)time-point, subsequent aliquots were frozen atmedian times of B = 22 min (20–22 interquartilerange [IQR]), C = 39 min (39–40 IQR), D = 56min (55–57 IQR), and E = 72 min (70–73 IQR)after blood collection.

The turtles had PCVs and TP concentrationsthat ranged from 28–37% and 5.2–6.4 g/dL,respectively. Complete blood counts and plasmabiochemistry panels identified values outside ofthe expected baseline range in some animals,particularly plasma calcium concentrations and

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Table 1. List of metabolites identified in the 1H-NMR spectra of whole blood and plasma samples from C.caretta. Time-point A represents whole blood results, while subsequent time-points represent plasma samples(median times after whole blood collection) at: B = 22 min (20–22 interquartile range [IQR]), C = 39 min(39–40 IQR), D = 56 min (55–57 IQR), and E = 72 min (70–73 IQR).aIdentified in a single individual at thistime-point.

Metabolite A B C D EAcetate � � � � �

Adenosine �

Alanine �

Creatine phosphate � � � � �

Formate � � � � �

Glucose � � � � �

Glutathione �

Hydroxyaetone � � � �

Lactate � � � � �

Leucine � � � � �

Malonate � � � � a �

Methanol �

Myo-Inositol �

Taurine �

Trimethylamine �

Trimethylamine N-oxide � � � �

Valine � � � � �

aspartate aminotransferase activity (Craig Harms,unpubl. data). However, careful considerationof these variations did not identify impacts thatwere germane to our analysis considering thescope of this study.

Limited qualitative metabolite profiling.—Spectra from whole blood samples had moremetabolite diversity than the spectra of plasmasamples (Table 1). The most noteworthy of thesedifferences was the absence of glutathione in theplasma samples (Fig. 1).

Statistical analysis.—Examining the medianintegral value of each normalized bin (n = 218)by sample group after accounting for our initialdark regions demonstrated results similar to thequalitative analysis. Each of the plasma sampletime-points appeared to have relatively little vari-ation, while there were several regions where thewhole blood sample group appeared to be dif-

ferent (Fig. 2). The major metabolites in theseregions of difference included valine, lactate, glu-tathione, creatine phosphate, taurine, and glucose(Fig. 3).

Using our restrictive dark regions, the numberof bins (n = 84) with significant differences (P< 0.1) between whole blood and plasma sam-ples was 11, 13, 13, and 16 for time-points B, C,D, and E, respectively. Major metabolites identi-fied in these statistically different areas includeglutathione (RBC > plasma), creatine phosphate(RBC > plasma), and glucose (RBC < plasma).A slight decrease in plasma glucose over time(Fig. 3) was noted. The number of bins with sig-nificant differences (P < 0.1) for time pairingsdid not change consistently with time, with zero,one, seven, and one significant differences forthe pairing B/C, B/E, C/E, and D/E, respectively.The B/D and C/D pairings each had 15 differentbins (P < 0.1).

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Figure 1. (a, upper) Representative 1H-NMR spectrum of a whole blood sample from a 57.2 kg Carettacaretta of undetermined sex. Glutathione (*) was identified only in whole blood. Other peaks, for reference:lactate (red), acetate (purple), creatine phosphate (green), taurine (blue), and the alpha anomer peak of glucose(orange). The water peak has been removed. (b, lower) 1H-NMR spectrum of a plasma sample from the sameturtle, processed and frozen 22 min after the whole blood sample. Spectra were zero-filled to 16,000 pointsand Fourier transformed. The baseline and phasing was corrected automatically and adjusted by hand, ifnecessary. Both spectra were referenced to the internal standard TSP peak at 0 ppm (Hz/MHz) chemical shift.Formate (1 mM) was included as an additional reference standard. The y-axis is indicative of intensity.

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Figure 2. Representative 1H-NMR spectra from a 57.2 kg Caretta caretta of undetermined sex over time.Time-point A represents whole blood results, while subsequent time-points represent plasma samples (mediantimes after whole blood collection) at: B = 22 min (20–22 interquartile range [IQR]), C = 39 min (39–40IQR), D = 56 min (55–57 IQR), and E = 72 min (70–73 IQR). Labeled peaks, for reference: lactate (red),acetate (purple), creatine phosphate (green), taurine (blue), and the alpha anomer peak of glucose (orange).Spectra were zero-filled to 16,000 points and Fourier transformed. The baseline and phasing was correctedautomatically and adjusted by hand, if necessary. All spectra were referenced to the TSP peak at 0 ppm(Hz/MHz) chemical shift. Formate (1 mM) was included as an additional reference standard. The y-axis isindicative of intensity.

Discussion

We examined the metabolic effects of delayedprocessing time for C. caretta plasma samplesand whole blood samples under field conditions.Our results demonstrate the promising applica-tion of dry ice as a quenching method, some ofthe difficulties of using whole blood samples, andthe difficulty of processing samples in a timelyfashion in the field, especially when personnelor equipment may be limiting. The study alsoshowed the metabolic profile of C. caretta whole

blood is different from plasma and provided ini-tial evidence that the possible clinical applicationof plasma samples should not be discounted sim-ply due to processing delays common in fieldsituations.

While use of dry ice in the field was safe andconvenient, we have found that our method offreezing samples within their cryogenic vialscould have been improved. We noted that sam-ples did not freeze immediately, even when pipet-ted directly into a plastic tube kept on dry ice. Wehave now adopted a method initially described

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Figure 3. Integral values of two normalized bins of glucose from five Caretta caretta categorized by time-point. Intelligent bucket integration was used to divide the spectra into bins with a width of 0.04 ppm.Individual samples were normalized by dividing each bin value by the sum of the sample’s bins. Integralvalues correlate to metabolite concentration. Time-point A represents whole blood results, while subsequenttime-points represent plasma samples (median times after whole blood collection) at: B = 22 min (20–22interquartile range [IQR]), C = 39 min (39–40 IQR), D = 56 min (55–57 IQR), and E = 72 min (70–73 IQR).

in the field of theriogenology (Nagase and Niwa1964; Krause and Grove 1967), which involvesfreezing the sample directly within a well drilledinto a slab of dry ice. Biofluid samples placed indirect contact with dry ice freeze spherically andare easily removed from the well with forceps. Asecondary benefit is that fluid samples frozen inthis way remain separated in their sample tubesand can be stored with multiple aliquots (fromthe same subject) per tube and/or fractured toobtain an aliquot without the need to thaw theremainder of the sample.

We had expected freezing whole blood to -80◦

C and thawing to result in complete or near com-plete hemolysis. However, examination of undi-luted thawed, whole blood samples via hemo-cytometer had too many erythrocytes to count(Jennifer Niemuth, unpubl. data). Because the

whole blood samples were frozen and filteredprior to analysis, the final analyzed sample wasactually a partial whole blood lysate with plasma.We expect some of the variation we observed be-tween samples could be due to differences in theproportion of red blood cells lysed in processing.Similarly, the absolute number of cells availableto lyse would vary with the PCV. We recommendthat studies measure PCV and TP at the timeof sampling and use either mechanical homoge-nization or sonication to ensure that the cellularcomponents of thawed samples are completelylysed to reduce variation between samples fromdifferent individuals or even time-points on thesame individual. Evaluation of the lysed sampleusing a hemocytometer to confirm complete celllysis prior to performing NMR analysis is alsoadvisable.

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The artificial environment, diet, and persis-tent physical abnormalities in some animals usedin this study precludes consideration of theseindividuals as representative of normal wildC. caretta. Routine hematologic and biochem-ical panels identified values outside of the ex-pected baseline range in some animals, particu-larly plasma calcium concentrations and aspar-tate aminotransferase activity (Craig Harms, un-publ. data). Considering this, we focused on sub-jectively evaluating spectra quality and majorqualitative changes over time with only limitedmetabolite profiling and no comparisons betweenindividuals. Additionally, 2-dimensional NMRexperiments could be added in the future for ad-ditional confirmation of peak identification.

In general, the resultant spectra from wholeblood samples had more metabolite diversity thanthe spectra of plasma samples. The most note-worthy of these differences, was the absence ofglutathione in the plasma samples. This is notcompletely unexpected, as glutathione has beenfound to be concentrated as the major intracel-lular antioxidant in human erythrocytes (Rosen-thal 2009). In concert with the pentose phosphatepathway, the glutathione defense system protectsthe red blood cell from hemolysis (Rosenthal2009). Subjectively, changes over time in theplasma metabolic profile appeared minor.

The results of two-tailed, paired-comparison,exact permutation tests support the whole bloodsamples having significant differences comparedto plasma samples at all time-points. However,unlike our initial hypothesis, there did not ap-pear to be a trend of increasing differences as-sociated with increasing sample processing time.This result and the sample median integral valuessuggests that while there are differences betweenwhole blood and plasma, the differences over thefirst 3 time-points would be expected to affectinterpretation of only a few metabolites, suchas glutathione or taurine, at the level of clinicalrelevance. A future study examining time-pairedwhole blood and plasma samples may furtherelucidate these differences.

The slight decrease in plasma glucose overtime was expected. Glycolysis in erythrocytescontinues in vitro and longer sample handlingtimes prior to processing for standard clinicalpathologic tests results in lower glucose concen-trations over time (Stockham and Scott 2008).Lower concentrations of glucose in the wholeblood samples were unexpected and are likely anartifact of the processing methods. It is possiblethat intact erythrocytes continued to have somelevel of enzyme activity during freezing, whilestored at -80◦ C, and after thawing. Retained en-zymatic activity, as well as changes in mineralsand metabolites, has been demonstrated duringstorage of rat and canine plasma and serum sam-ples used for routine clinical pathologic testing(Thoresen et al. 1995; Cray et al. 2009). Duringsample preparation, which included 120 min ofcentrifugation at 21◦ C, contact between plasmaand intact erythrocytes may be expected to de-crease glucose concentrations by 5–10% per hour(Stockham and Scott 2008). Also, erythrocyteglucose concentrations are approximately 76%that of plasma (Stockham and Scott 2008) andthe presence and/or lysis of erythrocytes duringsample preparation may have contributed to thelower glucose concentrations in the whole bloodsamples.

Lactate, an end-product of anaerobic glycoly-sis in erythrocytes (Stockham and Scott 2008),was increased in the whole blood samples andsupports our hypothesis that erythrocytes weremetabolically active, either during storage and/orduring sample preparation. Some hemolysis ofthe whole blood samples was observed and wouldalso be expected to release erythrocyte enzymes,including lactate dehydrogenase, which could actto increase sample lactate concentrations (Stock-ham and Scott 2008).

Creatine concentrations and creatininemetabolism are higher in erythrocytes versusserum due to a specific transporter (Jiao et al.1998; Wyss and Kaddurah-Daouk 2000). Thismay explain the higher concentration in thewhole blood samples. It has been suggested that

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taurine plays a key role in avian osmoregulationbased on avian erythrocyte taurine concentrationsbeing approximately 100-fold greater than thosein mammals (Shihabi et al. 1989). It is possiblereptiles may have a similar mechanism, whichwould account for the higher concentration inwhole blood samples.

With regard to our other hypothesis thatvariability would increase with increasedprocessing time, comparing the number of binswith statistically significant differences betweeneach possible time-point pairing for plasmasamples failed to support the hypothesis. Themajority of pairings had few to no statisticallysignificant differences. The number of bins withstatistically significant differences (P < 0.1) forthe pairs did not change consistently with time.However, the B/D and C/D pairings each had 15statistically different bins (P < 0.1) suggestingthat some aspect of time-point D may have beencontributed to this result. Plasma samples did notappear to become more significantly different asprocessing time increased. These results suggestthat clinically relevant metabolomic results maystill be obtained within approximately 40–50min after blood collection.

Conclusions.—We found that there were sev-eral significant differences between C. carettawhole blood and plasma samples analyzed for 1H-NMR-based metabolomics. These differenceswere suspected to be largely related to continuedmetabolism of intact erythrocytes and intracel-lular concentration of certain metabolites withinerythrocytes. The goals of an individual study,along with processing requirements, should beconsidered to determine which sample type maybe most appropriate to answer the study ques-tions.

This study contributes to our understanding ofhow rapidly samples collected for NMR-basedmetabolomics will degrade post-collection. Inlaboratory settings, where the researcher hasgreater control over study subjects, environmen-tal conditions, and sampling conditions, we be-

lieve it is prudent to minimize the amount of timesamples are held post-collection prior to process-ing. However, our results provide initial evidencethat collection of samples under field conditionscan still yield biologically useful data, even whenplasma cannot be separated for up to approxi-mately 40–50 min. While we do not recommendpurposefully waiting to process samples collectedin the field for 1H-NMR-based metabolomicsstudies, if samples are consistently processed ata time within 40-50 min of collection, their clini-cal usefulness should not be disregarded due toprocessing time.

Acknowledgements.—The authors thank JeanBeasley and the Karen Beasley Sea Turtle Res-cue and Rehabilitation Center for their cooper-ation in sample collection and support of thisstudy and the Macdonald laboratory and Uni-versity of North Carolina Metabolomics Facilityat The Hamner Institute for use of their mag-net and assistance in NMR technique. This studywas supported in part by funding from the NorthCarolina Aquariums. Funding for this work wasprovided by a Southeast Climate Science Centergraduate fellowship awarded to J.N.N.

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Table A1. Restrictive dark regions applied prior tostatistical analyses to minimize the effect of noise.

Region No. Region (ppm)1 -0.500–0.9202 1.360–1.4503 1.500–1.6704 1.760–1.9105 1.940–2.1306 2.200–2.5007 2.580–2.7208 2.750–2.8909 4.150–4.29010 4.320–4.36011 4.380–4.42012 4.450–4.63013 4.670–5.22014 5.250–6.07015 6.100–6.85016 7.000–7.15017 7.600–7.70018 7.760–8.26019 8.270–8.34020 8.360–8.45021 8.470–10.791

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Jennifer N. Niesmuth is a doctoral candidate inNorth Carolina State University’s (NCSU) Fisheries,Wildlife, and Conservation Biology program withinthe College of Natural Resources and Collegeof Veterinary Medicine. She received her BS(Wildlife Ecology) and DVM from the Universityof Wisconsin-Madison. Her current work is focusedon using NMR-based metabolomics to describethe physiologic changes of cold stun syndrome insea turtles and determining the potential impact ofclimate change on this syndrome. (Photographed byJennifer N. Niemuth).

CraigA. Harms directs the Marine Health Program atNCSU’s Center for Marine Sciences and Technology.He conducts clinically applied research on health anddiseases of aquatic and nondomestic species in thecourse of delivering veterinary services and supportat the NC Aquariums, the Karen Beasley Sea TurtleRescue and Rehabilitation Center, marine mammaland sea turtle stranding networks, area researchaquaculture facilities, and Morehead City/Beaufortarea marine laboratories. He received his DVM fromIowa State University, PhD (Immunology) fromNCSU, and is a Diplomate of the American Collegeof Zoological Medicine. His recent work has included

identification of novel and emerging aquatic animalpathogens, meeting unique anesthetic challenges forin-water sensory biology research, assessing andmitigating physiologic impact of capture techniquesfor wildlife research, welfare concerns for strandedlarge whales, and pharmacokinetics in nondomesticspecies. (Photographed by Ohiopyle AdventurePhotography).

Michael K. Stoskopf directs the EnvironmentalMedicine Consortium at NCSU and participates ac-tively in the inter-college Fisheries, Wildlife, and Con-servation Biology and Marine Sciences programs. Heis a professor of wildlife and aquatic health in the Col-lege of Veterinary Medicine’s Department of ClinicalSciences with appointments in Forestry (College ofNatural Resources), Biomedical Engineering (Collegeof Engineering), and Toxicology (College of Agricul-tural and Life Sciences). He received his DVM fromColorado State University, PhD (Environmental andBiochemical Toxicology) from Johns Hopkins Uni-versity, and is a Diplomate, and former President, ofthe American College of Zoological Medicine. Hisresearch focuses on population, ecosystem and land-scape approaches to health management of wildlifespecies broadly defined to include aquatic and ma-rine species including invertebrates and vertebrates.(Photographed by Wendy Savage).

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