HEMATOLOGIC CHARACTERISTICS OF THE WOYLIE (BETTONGIA PENICILLATA OGILBYI ) Carlo Pacioni, 1,4 Ian D. Robertson, 1 Marika Maxwell, 2 Jason van Weenen, 3 and Adrian F. Wayne 2 1 School of Veterinary and Biomedical Sciences, Murdoch University, Murdoch, 6150, Western Australia, Australia 2 Department of Environment and Conservation, Science Division, Manjimup, 6258, Western Australia, Australia 3 Department of Environment, Water and Natural Resources, Threatened Species Unit, Adelaide, 5001, South Australia, Australia 4 Corresponding author (email: [email protected]) ABSTRACT: An accurate assessment of animal health is fundamental to disease investigation in wildlife. Blood samples (n5609) from several populations of the endangered woylie or brush-tailed bettong (Bettongia penicillata ogilbyi), collected between March 2006 and April 2010 in Western Australia and South Australia, were used to establish hematologic reference ranges. Differences between populations, sexes, and seasons were also investigated. Significant sex differences in hematocrit, red blood cell, total white blood cell, neutrophil, lymphocyte, and eosinophil counts were evident in at least one population. Generally, males had higher hematocrit and blood cell concentrations than did females. A positive association of the erythron parameters with rainfall was also detected. The hematologic characteristics of woylie populations described in this study greatly increase knowledge of the health status in these populations. The data also represent a baseline to enable monitoring and detection of changes in the health status in these populations as well as representing a valid dataset for comparison with hematologic investigations in other macropods and marsupials. Key words: Bettong, health assessment, hematology, macropods, seasonality, wild populations. INTRODUCTION Health assessment and disease investi- gations are dependent on accurate detec- tion of variation in health status. With wildlife this can be challenging because it may be difficult to establish reference ranges for physiologic parameters and define what can be considered ‘‘normal’’ (Wobeser, 2007). While emerging infec- tious diseases associated with high mor- tality rates are of great concern (Daszak and Cunningham, 1999), clinical condi- tions that result in chronic diseases but low fatality rates, or that have sublethal effects, can also profoundly affect popula- tions (Spalding and Forrester, 1993). Sensitive diagnostic tools are especially important to detect diseases that are carried subclinically by individuals. The importance of such tools is further high- lighted by the difficulty in sampling wild animals throughout all stages of a patho- logic process. This is because sampling may be biased toward healthier animals, as affected animals become increasingly rare or undetectable with the advance of the disease. Therefore, linking what might seem an unremarkable finding to a prelim- inary stage of a serious clinical condition can be critical to successful diagnosis. In several wildlife studies hematologic changes, which were the only detectable abnormality in the study, were associated with reduced survival despite the inability to identify clearly the underlining patho- logic process (e.g., Mathews et al., 2006). Hematology is believed to be a reliable indicator of general health in many mammals (Coles, 1986; Kerr, 2002; Clark, 2004). This is also true for macropods, despite the limited research on hemato- logic responses in these species and the fact that animals with severe clinical signs might not show hematologic changes of the magnitude expected in eutherian mammals (Vogelnest and Portas, 2008). We describe the hematologic character- istics of the woylie (Bettongia penicillata ogilbyi), an Australian macropod that has recently undergone a dramatic population decline (approximately 90% between 1999 DOI: 10.7589/2011-09-275 Journal of Wildlife Diseases, 49(4), 2013, pp. 816–830 # Wildlife Disease Association 2013 816
Transcript
HEMATOLOGIC CHARACTERISTICS OF THE WOYLIE (BETTONGIA
PENICILLATA OGILBYI )
Carlo Pacioni,1,4 Ian D. Robertson,1 Marika Maxwell,2 Jason van Weenen,3 and Adrian F.Wayne2
1 School of Veterinary and Biomedical Sciences, Murdoch University, Murdoch, 6150, Western Australia, Australia2 Department of Environment and Conservation, Science Division, Manjimup, 6258, Western Australia, Australia3 Department of Environment, Water and Natural Resources, Threatened Species Unit, Adelaide, 5001, South Australia,Australia4 Corresponding author (email: [email protected])
ABSTRACT: An accurate assessment of animal health is fundamental to disease investigation inwildlife. Blood samples (n5609) from several populations of the endangered woylie or brush-tailedbettong (Bettongia penicillata ogilbyi), collected between March 2006 and April 2010 in WesternAustralia and South Australia, were used to establish hematologic reference ranges. Differencesbetween populations, sexes, and seasons were also investigated. Significant sex differences inhematocrit, red blood cell, total white blood cell, neutrophil, lymphocyte, and eosinophil countswere evident in at least one population. Generally, males had higher hematocrit and blood cellconcentrations than did females. A positive association of the erythron parameters with rainfall wasalso detected. The hematologic characteristics of woylie populations described in this study greatlyincrease knowledge of the health status in these populations. The data also represent a baseline toenable monitoring and detection of changes in the health status in these populations as well asrepresenting a valid dataset for comparison with hematologic investigations in other macropodsand marsupials.
Key words: Bettong, health assessment, hematology, macropods, seasonality, wild populations.
INTRODUCTION
Health assessment and disease investi-gations are dependent on accurate detec-tion of variation in health status. Withwildlife this can be challenging because itmay be difficult to establish referenceranges for physiologic parameters anddefine what can be considered ‘‘normal’’(Wobeser, 2007). While emerging infec-tious diseases associated with high mor-tality rates are of great concern (Daszakand Cunningham, 1999), clinical condi-tions that result in chronic diseases butlow fatality rates, or that have sublethaleffects, can also profoundly affect popula-tions (Spalding and Forrester, 1993).Sensitive diagnostic tools are especiallyimportant to detect diseases that arecarried subclinically by individuals. Theimportance of such tools is further high-lighted by the difficulty in sampling wildanimals throughout all stages of a patho-logic process. This is because samplingmay be biased toward healthier animals, asaffected animals become increasingly rare
or undetectable with the advance of thedisease. Therefore, linking what mightseem an unremarkable finding to a prelim-inary stage of a serious clinical conditioncan be critical to successful diagnosis.
In several wildlife studies hematologicchanges, which were the only detectableabnormality in the study, were associatedwith reduced survival despite the inabilityto identify clearly the underlining patho-logic process (e.g., Mathews et al., 2006).Hematology is believed to be a reliableindicator of general health in manymammals (Coles, 1986; Kerr, 2002; Clark,2004). This is also true for macropods,despite the limited research on hemato-logic responses in these species and thefact that animals with severe clinical signsmight not show hematologic changes ofthe magnitude expected in eutherianmammals (Vogelnest and Portas, 2008).
We describe the hematologic character-istics of the woylie (Bettongia penicillataogilbyi), an Australian macropod that hasrecently undergone a dramatic populationdecline (approximately 90% between 1999
DOI: 10.7589/2011-09-275 Journal of Wildlife Diseases, 49(4), 2013, pp. 816–830# Wildlife Disease Association 2013
816
and 2010; Wayne et al., 2009, 2013;Groom, 2010). Based on the availableevidence, it was hypothesized that preda-tors or disease may be the primary causesof the decline (Wayne, 2006). Therefore, itwas particularly important to detectchanges in the health status of individualsfrom monitored populations. We establishreliable hematologic reference ranges,investigate differences in hematologicparameters between populations and sex-es, and evaluate the influence of climaticconditions on woylie hematology. Thesefindings are discussed and compared withcurrently available hematologic informa-tion for other marsupials.
MATERIALS AND METHODS
We trapped 1,248 woylies using standardprotocols (Department of Environment andConservation [DEC] Science Division, 2008)
in several indigenous and translocated popu-lations in Western Australia and South Aus-tralia (Fig. 1). The two genetically distinctpopulations (Perup and Kingston; 34u109S,116u359E and 34u089S, 116u219E, respectively;Fig. 2) within the contiguous jarrah (Eucalyp-tus marginata) forest of the Upper Warrenregion (Pacioni et al., 2011) were sam-pled intensively between March 2006 andApril 2010. The population in KarakamiaWildlife Sanctuary (Karakamia, 31u499S,116u159E) was sampled in July 2006and November 2007. The populations inDryandra woodland (Dryandra, 32u479S,116u559E), Tutanning Nature Reserve (Tutan-ning, 32u319S, 117u189E), and Batalling StateForest (Batalling, 33u299S, 116u329E) weresampled in November 2006. In South Aus-tralia, St. Peter Island (SPI, 32u169S,133u359E) and Venus Bay Island (33u129S,134u409E) populations were sampled in June2007. After a population crash in Venus BayConservation Park (Venus Bay CP, 33u139S,134u409E), woylies were moved temporarily toMonarto Zoo (Adelaide) and blood sampleswere taken shortly after this move in Decem-ber 2006. Individuals were identified by eartags or microchips. Weight, head length, sex,reproductive status (including presence ofpouch young), age (juvenile, subadult, oradult), and body and coat condition (as a scorefrom 1 to 5) were also recorded. The completedataset was not always available for individualscaptured in populations other than Perup andKingston.
FIGURE 1. Geographic location of sampled woy-lie (Bettongia penicillata ogilbyi) populations inWestern Australia and South Australia (S. A.; inset).Modified with permission from Pacioni et al., 2011.
FIGURE 2. Forest blocks sampled within theUpper Warren region, Western Australia (Repro-duced with permission from Pacioni et al., 2011).
PACIONI ET AL.—WOYLIE (BETTONGIA PENICILLATA OGILBYI) HEMATOLOGY 817
Blood samples were collected from thelateral tail vein of a subset of individuals(Table 1), mixed with ethylenediaminetetra-acetic acid in commercial tubes, and chilled onwet ice in the field. Blood smears were madeat the time of collection and air dried. InUpper Warren, animals were sampled imme-diately after removing the animals from thetraps early in the morning. In South Australia,animals were processed throughout the nightwhile in Karakamia, animals were removedfrom traps during the night and kept in bagsuntil early morning when blood samples werecollected.
Blood samples were processed at MurdochUniversity Clinical Pathology Laboratory with-in 36 hr of collection as recommended formacropods (Hulme-Moir et al., 2006). Differ-ential white blood cell (WBC) counts werecarried out manually by examination of bloodsmears using light microscopy while other
parameters (Table 1) were measured with anautomatic hematology analyzer (ADVIA-120)using multispecies software (Bayer diagnosticsdivision, Tarrytown, New York, USA). Plateletcount and mean platelet volume were notincluded in analyses because aggregates ofplatelets were frequent on blood smears.Packed cell volume (after centrifugation of acapillary tube) and mean corpuscular hemo-globin concentration (MCHC) were plottedand graphically checked for consistency withhematocrit (HCT; calculated from red bloodcell [RBC] count and mean cell volume[MCV]) and corpuscular hemoglobin concen-tration mean (CHCM; an MCHC directmeasurement determined by flowcytometricsignal; Bosch et al., 1992), respectively. Whendiscordant, blood samples were removed fromthe dataset (Bosch et al., 1992) as weresamples that showed macroscopic signs ofhemolysis. Total protein (TP) concentration
TABLE 1. Sample size (n), mean, standard deviation (SD), and 5th and 95th percentiles of hematologicparameters in woylie (Bettongia penicillata ogilbyi) populations.
a RBC 5 red blood cells; HGB 5 hemoglobin concentration; HCT 5 hematocrit; MCV 5 mean cell volume; MCH 5
mean corpuscular hemoglobin; CHCM 5 corpuscular hemoglobin concentration mean; WBC 5 total white blood cells;Neutro 5 neutrophils; Lympho 5 lymphocytes; Mono 5 monocytes; Baso 5 basophils; TP 5 total proteinconcentration; FBG 5 fibrinogen concentration.
b Perc 5 percentiles.c Btst 5 bootstrapped.
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was assessed by refractometry and fibrinogen(FBG) concentration by heat precipitation(Coles, 1986).
Although the ratio of neutrophils to lym-phocytes (N:L) has been used as an indicatorof health in some marsupial studies (e.g.,Presidente and Correa, 1981), it was not usedhere because of variability within individualsand inconsistent results, especially in macro-pods (McKenzie et al., 2002; Clark, 2004;Young and Deane, 2006).
Generally, 2.5th and 97.5th percentiles areused to establish normal hematologic referenceranges (Lumsden and Mullen, 1978; Solberg,1987; National Committee for Clinical Labora-tory Standards, 1995); however, a more conser-vative approach was used due to the lack ofclinical history of individuals and the relativelysmall sample size in some populations. Refer-ence ranges for the adult age class (only) wereestablished as the 5th and 95th percentiles ofeach parameter distribution after removingoutliers (Lumsden and Mullen, 1978). Addi-tional criteria were used for inclusion of animalstrapped within the Upper Warren region tocalculate the reference ranges: valid morpho-logic measurements, an average body and coat
condition score equal to or higher than 3.5; abiometric index above the 5th percentile (seebelow); and lastly, animals had to be alive (i.e.,retrapped) at the following trapping session.
The biometric index was calculated as theratio of the weight:head length. The minimumthreshold to consider a woylie in ‘‘good’’condition was established by calculating the5th percentile, after controlling for sex, andremoving outliers and females with pouchyoung. Biometric indices were analyzed onlyfor woylies trapped at Kingston, Perup, andKarakamia because morphometric data wereinsufficient from other populations.
When there were statistical differencesbetween populations or sexes, these groupswere considered separately. Only individualsthat had blood parameters within the estab-lished reference ranges were retained forfurther analyses.
Statistical analysis
All statistical analyses were carried out inSPSS version 19 (SPSS Inc., Chicago, Illinois,USA) and when multiple samples for the sameindividual were present, one was randomly
PACIONI ET AL.—WOYLIE (BETTONGIA PENICILLATA OGILBYI) HEMATOLOGY 819
selected for inclusion in the analysis to complywith the assumption of independence. Thedistributions of variables were plotted andinspected, and the Kolmogorov-Smirnov testand Z-test were used to confirm a normaldistribution following Field’s (2009) guide-lines. Variables that were not normally distrib-uted were transformed (square root). Thehomogeneity of variance was tested with theLevene’s test.
Lack of compliance with the assumptionsfor the multivariate analysis of varianceprevented the use of this statistical approach(Stevens, 2002). Therefore, two-way analysis ofvariances (ANOVAs) were used to comparethe mean differences between populations andgenders for RBC, hemoglobin (HGB), HCT,TP, WBC, neutrophil, lymphocyte, monocyte,and eosinophil counts. Hochberg’s GT2 posthoc tests were used to follow up differencesbetween groups, and parametric t-tests wereused to explore differences between sexeswithin the same populations. When theassumption of homogeneity of variances wasnot met, multiple one-way ANOVAs were usedand Welch’s robust tests were consideredwhen the variances were still unequal betweengroups. In these cases, Games-Howell posthoc tests were used (Field, 2009). Thenonparametric Mann-Whitney U-test or Krus-kal-Wallis test were used to compare two ormore groups respectively when variables didnot have a normal distribution and to investi-gate differences in rainfall within the sameseasons between years and the two UpperWarren populations.
Seasonal changes could not be directlyinvestigated because fewer samples wereobtained in winter and summer. Nevertheless,associations between selected hematologicparameters and rainfall (Bureau of Meteorol-ogy, 2010) were considered. These analyseswere limited to animals trapped within 15 kmof the weather stations within Perup andKingston (weather stations 9616 and 9906,respectively). Initially, Spearman’s rho coeffi-cients (rs) were calculated using two rainfallvariables: monthly rainfall and the averagerainfall for the 2-mo period including thesampling month and the month prior tosampling. Only the variable that showed thestrongest correlation with the hematologicparameters was used in further analyses (seebelow). Finally, rainfall, ‘‘period of the year’’(cold or warm), and population of origin wereentered one at a time in a hierarchical multipleregression model. In doing so, the effect ofeach variable on the hematologic parameterswas calculated and partialled out. Significantimprovement of the model was evaluated with
an F-ratio test. Only the final models arereported (i.e., including only variables thatmade a significant contribution). Standardguidelines were followed to verify compliancewith assumptions (Durbin and Watson, 1951;Myers, 1990; Menard, 1995; Tabachnick andFidell, 2007).
Statistical significance was set to 0.05.However, control of type I error (rejectingthe null hypothesis when it is true) wasachieved with the Benjamini-Hochberg ap-proach (Benjamini and Hochberg, 1995). Thisapproach was applied only when the statisticaltests involved analysis of the same variable indifferent groups.
RESULTS
Influence of populations and sex
All hematologic parameters were nor-mally distributed except for monocyte,eosinophil, and basophil concentrationswithin each population and sex, andexcepting lymphocyte counts for males inVenus Bay CP and females in SPI. Thesquare root transformations of monocyteand eosinophil concentrations were nor-mally distributed and used for statisticalanalyses. After removal of hemolyticsamples and outliers, sample sizes forKarakamia, Dryandra, Tutanning, Batal-ling, and Venus Bay Island were very small(n,20), and data from these populationswere not included in any statistical analysisexcept for the calculation of ‘‘overallspecies’’ reference ranges (see below).
All the considered hematologic param-eters were significantly different betweenpopulations (Table 2) except for monocyteconcentrations. Within the erythron panel,post hoc analyses revealed that eachpopulation differed significantly from anyother population except for Perup andVenus Bay CP, where only HGB differedsignificantly (Table 3). Within the leuko-cyte panel, South Australia populationsshowed a significant difference from eachother only in the lymphocyte concentra-tion, while Kingston and Perup were notdifferent between each other and, instead,differed from Venus Bay CP and SPI inmost parameters (Table 3).
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Several parameters showed a significantdifference between sexes (Table 2), al-though the comparisons within populationsdid not always remain significant afterBenjamini-Hochberg correction. Kingstonwas the only population where more thanone hematologic parameter was significantlydifferent between sexes (Table 2), withmales having higher cell counts (Table 4).This trend was also present in the otherpopulations with the exception of Venus BayCP (Tables 3 and 5). Due to these statisticaldifferences, reference ranges were calculat-ed separately for each population. More-over, when at least one parameter wassignificantly different between males andfemales, the 5th and 95th percentiles werereported separately (Table 1 and 5).
Reference ranges were also calculatedby combining all available data along with1,000 bootstrapped estimates as suggestedfor hematologic reference ranges whenn.100 (Table 1; Linnet, 2000). Theseshould represent the likely overall speciesranges for blood parameters. This wasdone to provide indicative ranges by which
populations not studied here might begenerally compared.
Climatic influence
The monthly rainfall in Upper Warrenranged from 0 to 162 mm (Fig. 3) withsummer months being the driest, asexpected. No significant difference wasfound for the same season between yearsand between the two populations in theUpper Warren region. The monthly rain-fall had significant correlations with allhematologic parameters except TP, neu-trophil, monocyte, and eosinophil concen-trations. This variable was then used in thehierarchical multiple regression analysesand, from here on, is referred to as‘‘rainfall.’’
There was a significant positive associ-ation between rainfall and RBC counts,hemoglobin concentration, and HCT. Thepopulation of origin still had a significanteffect despite controlling for the rainfalleffect (Table 5). The WBC and lympho-cyte counts were negatively associatedwith rainfall while neutrophil and eosino-
TABLE 2. Statistical significance (P-values) of hematologic differences between sexes and populations of thewoylie (Bettongia penicillata ogilbyi). Significant P-values are indicated in boldface (after Benjamini-Hochberg correction [Benjamini and Hochberg, 1995]).
a RBC 5 red blood cells; HGB 5 hemoglobin concentration; HCT 5 hematocrit; TP 5 total protein concentration; WBC5 total white blood cells; CP 5 Conservation Park; SPI 5 St. Peter Island.
b Comparisons between sexes within population.c Welch’s robust tests of equality of means.d Two-way analysis of variance.e Mann-Whitney U-test.f Kruskal-Wallis test.g Square root of raw data.
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TABLE 3. Statistical significance (P-value) of post hoc tests of hematologic parameters between woylie(Bettongia penicillata ogilbyi) populations. Post hoc tests for monocyte concentrations are not reportedbecause a nonsignificant difference was found between populations in the univariate analysis.a
Population Parameter Group Perup Kingston Venus Bay CP SPI
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phil concentrations were associated onlywith the period of the year (Table 5).Lastly, TP was also (and uniquely) associ-ated with the period of the year (Table 5).
DISCUSSION
Influence of populations and gender
The reference ranges we calculated hada higher concentration of RBC accompa-nied by lower MCV, mean corpuscularhemoglobin (MCH), and CHCM than didthe reference ranges of other macropods(Clark, 2004; see Vogelnest and Portas,2008 for a review). These findings are alsoconsistent with preliminary woylie hema-tologic investigations (Clark, 2008). Nev-ertheless, the leukocyte panel is broadlysimilar to other macropods (Clark, 2004;Vogelnest and Portas, 2008). In manymarsupials, lymphocytes are the mostabundant leukocyte in the peripheralblood (Clark, 2004) but this is notnecessarily the case in macropods (Vo-gelnest and Portas, 2008). Lymphocyteconcentrations were significantly differentbetween woylie populations. In mostpopulations, neutrophils were evidentlythe most common WBC type while inthe Upper Warren populations counts oflymphocytes and neutrophils were similar.
It is not clear whether the differencesbetween woylie populations in the leuko-cyte panel are due to physiologic differenc-es, sampling biases, or different immunesystem stimulations. Fear and pain canincrease neutrophil and lymphocyte con-centrations in marsupials (Clark, 2004), andthe differences in trapping and handlingprotocols could be responsible for thesediscrepancies. Similarly, environmentalfluctuations or captivity (animals fromVenus Bay CP) may partially account forthese differences. Nevertheless, the twoUpper Warren populations were sampledduring their decline or shortly after, whilethe other wild populations (with .10samples) were demographically stable.The association of the immune systemresponses with woylie populations under-going decline are consistent with thehypothesis that immunologic stressors maybe involved. Preliminary results that indi-cate the prevalence of gastrointestinal andblood parasites in woylies from the UpperWarren were significantly higher than thosefrom Karakamia (Parker et al., 2008) furthersupport this hypothesis.
Differences between sexes in the RBCcount are consistent with other marsupials:an increased erythrocyte concentration wasfound in male allied rock-wallabies, Petro-
Population Parameter Group Perup Kingston Venus Bay CP SPI
a CP 5 Conservation Park; SPI 5 St. Peter Island; RBC 5 red blood cells; HGB 5 hemoglobin concentration; HCT 5
hematocrit; TP 5 total protein concentration; WBC 5 total white blood cells.b (—) 5 not applicable.c The square root of raw variable (for eosinophils) was used in the analysis.
TABLE 3. Continued.
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gale assimilis (Spencer and Speare, 1992),greater gliders, Petauroides volans (Viggersand Lindenmayer, 2001), common andmountain brushtail possums, Trichosurusvulpecula and Trichosurus caninum (Pre-sidente and Correa, 1981; Viggers andLindenmayer, 1996), and tammar wallabies,Macropus eugenii in winter (McKenzie etal., 2002). In contrast to this study, no
differences were reported in leukocyteparameters between genders in previousmarsupial hematologic studies (Presidenteand Correa, 1981; Spencer and Speare,1992; Viggers and Lindenmayer, 1996;McKenzie et al., 2002). The sex differencesbetween woylie could be related to differenthormonal profiles and behavioral charac-teristics as well as to an increased suscep-
TABLE 4. Sample size (n), mean, standard deviation (SD), and 5th and 95th percentiles of hematologicparameters, by gender, in woylie (Bettongia penicillata ogilbyi) populations.a
a RBC 5 red blood cells; HGB 5 hemoglobin concentration; HCT 5 hematocrit; MCV 5 mean cell volume; MCH 5
mean corpuscular hemoglobin; CHCM 5 corpuscular hemoglobin concentration mean; WBC 5 total white blood cells;Neutro 5 neutrophils; Lympho 5 lymphocytes; Mono 5 monocytes; Baso 5 basophils; TP 5 total proteinconcentration; FBG 5 fibrinogen concentration; Perc 5 percentiles.Perc 5 percentiles.
b (—) 5 FBG not established for Venus Bay Conservation Park (CP) and St. Peter Island.
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tibility of one sex to specific pathogens. Thiscould be particularly true for eosinophilconcentrations and could explain the trendof higher eosinophil concentrations inmales. Males of various mammalian speciesare more prone to heavier parasite infesta-tions (Wilson et al., 2002), and increasedeosinophil counts is a common hematologicresponse in these situations (Kerr, 2002).
Climatic influence
A robust understanding of the effect ofseason on hematologic parameters ofmarsupials, particularly macropods, is
restricted by the small number of pub-lished studies, small sample sizes withinseasons, no or few repeated surveys overseveral continuous years, and variation inthe climatic regions in which the studieswere conducted. Our results are notimmune to these issues. Nevertheless,changes in the erythron panel for woylieswere consistent with the seasonal hema-tologic changes in tammar wallabies(McKenzie et al., 2002), western greykangaroos, Macropus fuliginosus (Algaret al., 1988), agile wallabies, Macropusagilis (Stirrat, 2003), and mountain brush-
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tail possums (Barnett et al., 1979). Similarchanges in HGB and HCT in quokkas,Setonix brachyurus, RBC and HGB ineuros, Macropus robustus, and TP in agilewallabies (Stirrat, 2003) were also report-ed and it was inferred that nutrition was
responsible for these seasonal fluctuations(Barker, 1961; Ealey and Main, 1967;Shield, 1971). Rain influences the nutrientcontent of plants (Ealey and Main, 1967);increased nutrient content in plants,especially proteins, was positively corre-lated with increased erythron values ineuros (Ealey and Main, 1967). Seasonalchanges in diet and food quantity andquality may also explain seasonal hemato-logic changes in the Upper Warrenpopulations: fungi, the main diet ofwoylies, are more prevalent in winter andautumn than in spring and summer(Zosky, 2012). If this hypothesis is correct,it is not surprising that once the effect ofthe rain was partialled out, the period ofthe year did not significantly influence theprediction of the majority of the erythronvalues. Nevertheless, no quantitative as-sessment of the relationship between thenutritional composition of diets and he-matologic profiles has been carried out inthe woylie, and similar fluctuations havebeen proven not to be associated withnutrition in the western grey kangaroo(Algar et al., 1988). The significant influ-ence of the variable ‘‘population’’ on RBCand HGB also indicates that other factors
TABLE 5. Relationships of rainfall, period of the year (colder months versus warmer months), andpopulation, with hematologic parameters, for the woylie (Bettongia penicillata ogilbyi). Each line reportsstandardized correlation coefficient and, between brackets, R2 change for the variable retained in thehierarchical multiple regression models. Variables not retained in the model are indicated with dashes (—).When only one variable was retained, Spearman’s rho correlation coefficient is reported.
Parametera n Rainfall Year periodb Populationc P-valued
a RBC 5 red blood cells; HGB 5 hemoglobin concentration; HCT 5 hematocrit; TP 5 total protein concentration; WBC5 total white blood cells.
b Codified as ‘‘0’’ for colder months (March to August) and ‘‘1’’ for warmer months (September to February).c Codified as ‘‘1’’ for Perup and ‘‘2’’ for Kingston.d Statistical significance of the final model (analysis of variance) or Spearman’s rho correlation.e The square roots of raw variables were used in the analysis.
Kingston) in the Upper Warren region, WesternAustralia.
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influence these parameters, which are notsimply explained by rainfall and period ofthe year alone. For example, nematodeinfestations in macropods resulted inanemia (Arundel et al., 1977), and hemo-or endoparasitism could result in seasonalfluctuation in RBC indices correspondingwith seasonal changes in parasite load.
The interpretation of leukocyte variationin association with rainfall and the period ofthe year presented a challenge. It is likelythat multiple factors are responsible forthese hematologic changes and a direct,simple explanation is unlikely.
Similar patterns to those found in thisstudy were identified in adult tammarwallabies; nevertheless, none of thesedifferences were statistically significant(McKenzie et al., 2002)—a likely functionof small sample sizes. In mountain brushtailpossums and agile wallabies, parasitism wassuggested as a possible explanation ofseasonal variation in the concentration ofeosinophils (Viggers and Lindenmayer,1996; Stirrat, 2003). Increased exposure toparasites and vectors, or migration of larvaeduring the warmer months, could similarlyexplain the change in eosinophil countsfound in woylies.
Stress associated with extreme temper-atures was suggested as a possible mech-anism responsible for changes in theconcentration of neutrophil and lympho-cytes in the brushtail possum (Baker andGemmell, 1999). Seasonal fluctuations ofWBC could support this hypothesis (lowerlymphocyte and higher neutrophil countsin winter). However, the fact that neutro-phil counts varied significantly with theperiod of the year, whereas lymphocytesdid not, argues against a simple stress-related response.
Implication for conservation, management, andfurther studies
We provide important baseline data toevaluate woylie health and a valid basis ofcomparison for other hematologic investi-gations in macropods. The establishedreference ranges will facilitate the inter-
pretation of results in ongoing and futuredisease investigations in this species. Withdue consideration of the limitations of suchstudies in wild animals (e.g., lack ofindividual’s clinical history; unpredictableenvironmental variability), our sample sizeis among the largest ever used to establishhematologic reference ranges in macropodsand, more generally, in marsupials (Mel-rose et al., 1987; Haynes and Skidmore,1991; Spencer and Speare, 1992; Svenssonet al., 1998; McKenzie et al., 2002; Clark,2004; Wicks and Clark, 2005; Clark andSpencer, 2006; Bennett et al., 2007; Reisset al., 2008; Vogelnest and Portas, 2008).
There were strong differences betweenpopulations and, therefore, we recom-mend the use of reference ranges calcu-lated for the population of origin. Whenevaluating the health of newly establishedpopulations (e.g., translocated popula-tions) or newly studied populations, widerranges should be used (i.e., overall spe-cies). This study also represents a furthercontribution toward the understanding ofassociations between hematologic changesand environment variability. A clear pat-tern was evident in the erythron withrespect to rainfall, which was consistentwith other studies in marsupials.
Unprecedented fluctuations in the leu-kocyte panel were also found (i.e., associ-ations between leukocytes and rainfall andperiod of the year). While we acknowledgethe limitations of our study, we argue thatthe results from ours and other studiessuggest that future researchers shouldconcurrently assess how resources andparasites (endo-, ecto-, and hemoparasites)vary in relation to climate variation and,ultimately, what are the hematologic re-sponses of the host to these factors. Inwestern ringtail possums, Pseudocheirusoccidentalis, increased WBC counts wereassociated with reduced survival (Clarke,2011), indicating the potential importancethat these fluctuations may have for indi-vidual and population health. Understand-ing the causes of these variations will also
PACIONI ET AL.—WOYLIE (BETTONGIA PENICILLATA OGILBYI) HEMATOLOGY 827
improve management and possibly increasesurvival of wild macropod populations.
ACKNOWLEDGMENTS
We thank the Australian Academy ofScience, South Coast Natural Resource Man-agement Inc., the Woylie Conservation andResearch Project (WCRP) and Department ofEnvironment and Conservation, Science Divi-sion (C.P. PhD Student Stipend), and BauxiteResources Limited for financial support. Asincere thank you to the many people whocontributed by the collection of field data andsamples for this study including C. Ward, C.Vellios, N. Marlow, N. Thomas, P. Orell, andF. Kirkpatrick, all from the DEC; J. Williamsfrom the Australian Wildlife Conservancy; B.Dalzel, R. Sleep, D. Armstrong, and A. Clarkefrom the Department of Environment andNatural Resources (South Australia) and W.Boardman and I. Smith from ZoosSA; as wellas all of the DEC Donnelly District personneland volunteers of the Woylie Conservation andResearch Project. We are much obliged to P.Davies (DEC) for preparing many of thegraphic aids. We are grateful to the staff of theMurdoch University Clinical Pathology Labo-ratory who processed the hematologic samplesincluding P. Clark, G. Allen, and J. Robertson.Special thanks to P. Clark who conductedpreliminary analyses and J. Stayt who foundthe time to discuss with us the interpretationof the hematologic profiles of certain individ-uals. We deeply value the contribution of M.Calver (Murdoch University) in advising onstatistical analysis as well as the usefuldiscussions and advice from P. Eden and A.Reiss (Perth Zoo). We are particularly gratefulto S. Trocini, T. Jacobs, and two anonymousreviewers for providing useful comments onearly drafts of this manuscript and to J. Clarkeand K. Zosky for sharing their (at the time)unpublished results.
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Submitted for publication 22 September 2011.Accepted 22 February 2013.
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