Avian malaria in Hawaiian forest birds: infection and population

Avian malaria in Hawaiian forest birds:infection and population impacts across species and elevations

MICHAEL D. SAMUEL,1,� BETHANY L. WOODWORTH,2,4 CARTER T. ATKINSON,2

PATRICK J. HART,3 AND DENNIS A. LAPOINTE2

1U.S. Geological Survey, Wisconsin Cooperative Wildlife Research Unit, University of Wisconsin, Madison, Wisconsin 53706 USA2U.S. Geological Survey, Pacific Island Ecosystems Research Center, Hawai‘i National Park, Hawai‘i 96718 USA

3University of Hawai‘i, Hilo, Hawai‘i 96720 USA

Citation: Samuel, M. D., B. L. Woodworth, C. T. Atkinson, P. J. Hart, and D. A. LaPointe. 2015. Avian malaria in

Hawaiian forest birds: infection and population impacts across species and elevations. Ecosphere 6(6):104. http://dx.doi.

org/10.1890/ES14-00393.1

Abstract. Wildlife diseases can present significant threats to ecological systems and biological diversity,

as well as domestic animal and human health. However, determining the dynamics of wildlife diseases and

understanding the impact on host populations is a significant challenge. In Hawai‘i, there is ample

circumstantial evidence that introduced avian malaria (Plasmodium relictum) has played an important role

in the decline and extinction of many native forest birds. However, few studies have attempted to estimate

disease transmission and mortality, survival, and individual species impacts in this distinctive ecosystem.

We combined multi-state capture-recapture (longitudinal) models with cumulative age-prevalence (cross-

sectional) models to evaluate these patterns in Apapane, Hawai‘i Amakihi, and Iiwi in low-, mid-, and

high-elevation forests on the island of Hawai‘i based on four longitudinal studies of 3–7 years in length. We

found species-specific patterns of malaria prevalence, transmission, and mortality rates that varied among

elevations, likely in response to ecological factors that drive mosquito abundance. Malaria infection was

highest at low elevations, moderate at mid elevations, and limited in high-elevation forests. Infection rates

were highest for Iiwi and Apapane, likely contributing to the absence of these species in low-elevation

forests. Adult malaria fatality rates were highest for Iiwi, intermediate for Amakihi at mid and high

elevations, and lower for Apapane; low-elevation Amakihi had the lowest malaria fatality, providing

strong evidence of malaria tolerance in this low-elevation population. Our study indicates that hatch-year

birds may have greater malaria infection and/or fatality rates than adults. Our study also found that

mosquitoes prefer feeding on Amakihi rather than Apapane, but Apapane are likely a more important

reservoir for malaria transmission to mosquitoes. Our approach, based on host abundance and infection

rates, may be an effective alternative to mosquito blood meal analysis for determining vector-host contacts

when mosquito densities are low and collection of blood-fed mosquitoes is impractical. Our study supports

the hypothesis that avian malaria has been a primary factor influencing the elevational distribution and

abundance of these three species, and likely limits other native Hawaiian species that are susceptible to

malaria.

Key words: avian malaria; Bayesian state-space models; Culex quinquefasciatus; disease mortality; Hawaii; Hemignathus

virens; Himatione sanguinea; mosquitoes; Plasmodium relictum; multistate model; Vestiaria coccinea; wildlife disease.

Received 14 October 2014; revised 15 January 2015; accepted 21 January 2015; final version received 23 March 2015;

published 30 June 2015. Corresponding Editor: D. P. C. Peters.

Copyright: � 2015 Samuel et al. This is an open-access article distributed under the terms of the Creative Commons

Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the

original author and source are credited. http://creativecommons.org/licenses/by/3.0/4 Present address: Department of Environmental Studies, University of New England, Biddeford, Maine 04005 USA.

� E-mail: [email protected]

v www.esajournals.org 1 June 2015 v Volume 6(6) v Article 104

INTRODUCTION

Infectious diseases can have significant im-pacts on biological conservation and biodiversity(Daszak et al. 2000, Dobson and Foufopoulos2001). The increasing emergence of wildlifediseases with potential threats to ecologicalsystems, as well as domestic animal and humanhealth, emphasize the importance of understand-ing disease dynamics and associated risks tobiological conservation and human health. Asinvasive species, human development, and cli-mate change alter host or vector communitiesand habitats, this information is increasinglyessential for long-range conservation planningfor species that face significant climate (Harvell etal. 2002) and environmental change, particularlyfor threatened or endangered species. However,determining wildlife disease dynamics includingrates of infection, drivers of transmission, vectorfeeding preferences, and host mortality presentssignificant challenges (McCallum et al. 2001,Wobeser 2008). The importance of these epizo-otiological parameters for understanding host-pathogen dynamics, population effects, and onhost-pathogen evolution have long been recog-nized, but are infrequently addressed (Scott 1988,Oli et al. 2006, Murray et al. 2009, Lachish et al.2011a). Quantifying epizootiological parametersin wildlife populations is difficult because meth-ods used in human epidemiology seldom apply(McCallum et al. 2001, Caley and Hone 2004,Lachish et al. 2011a); however, recent applica-tions of multi-state mark-recapture models haveprovided an important tool to assess infectiondynamics and population impacts (Faustino et al.2004, Senar and Conroy 2004, Conn and Cooch2009, Atkinson and Samuel 2010) while account-ing for differential capture heterogeneity (Jen-nelle et al. 2007).

In addition to having direct wildlife conserva-tion and management implications, diseases canprofoundly affect ecological and evolutionaryprocesses. For example, host and parasite diver-sity can influence disease prevalence, host andparasite abundance, and evolutionary outcomes(Holt and Pickering 1985, Altizer et al. 2003,Keesing et al. 2006). When pathogens impose asignificant fitness cost on their hosts, spatial andtemporal variation in the risk of infection cangenerate differential selection pressures that

affect host adaptation, extinction risk, andgenetic variation (Altizer et al. 2003, Poulin2007, Wolinska and King 2009, Lachish et al.2013). In turn ecological heterogeneity can alterdisease dynamics and shape the interaction andcoevolution between hosts and parasites (Ostfeldet al. 2005, Poulin 2007, Real and Biek 2007,Wolinska and King 2009, Lachish et al. 2013).Characteristically, vector-borne pathogens aregeneralists that infect multiple hosts; therefore,heterogeneity in host resistance can also alterspecies-specific risk of infection and communitydynamics (Altizer et al. 2003, Keesing et al. 2006).Studies on avian blood parasites, especiallyPlasmodium species, have shown that host prev-alence varies temporally, spatially, and amongsympatric species due to climatic, host suscepti-bility, vector abundance, or vector habitat avail-ability (Merila et al. 1995, Sol et al. 2000, Wood etal. 2007, Loiseau et al. 2010, Sehgal et al. 2011,Lachish et al. 2013). An improved understandingof how these biotic and abiotic factors influencehost-parasite interactions is of particular conser-vation relevance given rapid changes in theworld’s climate and increasing habitat fragmen-tation. Understanding the ecological drivers ofvector borne diseases in wildlife also has strongimplications for transmission and potential con-trol of similar diseases to humans.

Avian malaria, a worldwide disease caused bythe blood parasite Plasmodium relictum, was likelybrought to Hawai‘i in the early 20th Century(Laird and van Riper 1981, van Riper et al. 1986,Atkinson and LaPointe 2009a). Malaria, togetherwith the previously introduced avian pox virus(Avipoxvirus spp.), posed a major new threat toimmunologically naıve Hawaiian birds (Warner1968). Both avian diseases are readily transmittedby the southern house mosquito (Culex quinque-fasciatus), which was introduced as early as 1826(Halford 1954, Hardy 1960). A wave of nativebird extinctions during the 1920s and 1930s hasbeen attributed to avian malaria, and native birdsbelow 1,500 m elevation continue to be at riskfrom malaria (Goff and van Riper 1981, van Riperet al. 1986). Above that elevation mosquitoes arerare, allowing native forest birds to survive. Laterstudies reported many endemic Hawaiian spe-cies, especially Hawaiian honeycreepers, arehighly susceptible to avian malaria, effectivedisease transmitters, and chronically-infected,


SAMUEL ET AL.

life-long reservoirs of disease (Atkinson et al.1995, Atkinson et al. 2000, Atkinson et al.2001a, b, Atkinson and LaPointe 2009b, Atkinsonand Samuel 2010). In contrast, malaria hasminimal impact on the survival of non-nativebirds, which also have a limited period ofeffective disease transmission. Because of theirhigh susceptibility, prior workers hypothesizedthat malaria played a key role in historicalpopulation declines of native birds (Warner1968, van Riper et al. 1986, Atkinson et al.1995). However, data that quantify diseasetransmission or address the demographic conse-quences of malaria in native Hawaiian birds arelimited. This information is critical for assessingdisease risks, developing conservation strategies(Hobbelen et al. 2012), and predicting the futureimpact of climate on disease and birds (Benninget al. 2002, Atkinson and LaPointe 2009b).

The goal of our study was to investigatespecies and elevation patterns in malaria infec-tion of native birds along a 1700 m altitudinalgradient on the Island of Hawai‘i. We focused onthree species of Hawaiian honeycreepers thatdiffer in their susceptibility to malaria andmovement across the landscape. We used serol-ogy and change in disease status (susceptible torecovered) of captured and marked birds toidentify susceptible and recovered (chronicallyinfected and immune) birds. We combined multi-state capture-recapture (longitudinal) modelswith age-prevalence (cross-sectional) models tosimultaneously estimate disease transmissionand mortality, survival, and capture rates (At-kinson and Samuel 2010). This novel approach isadvantageous because age-prevalence modelscan potentially estimate disease transmissionfrom single capture (cross-sectional) data forknown age animals, but multi-state modelsrequire recapture of marked individuals. Estima-tion of these epizootiological and demographicparameters was conducted using a Bayesianstate-space model of capture and recapture data(Kery and Schaub 2012, King 2012). Unlike paststudies, this approach also allowed us to estimatespecies-specific patterns of malaria infection,mortality, and population impacts across thealtitudinal gradient in Hawai‘i.

METHODS

Study species and areaWe studied three of the most abundant

honeycreepers remaining in Hawaiian forests.The Iiwi (Vestiaria coccinea) is highly susceptibleto avian malaria (Atkinson et al. 1995) whileHawai‘i Amakihi (Hemignathus virens) and Apa-pane (Himatione sanguinea sanguinea) are moder-ately susceptible and chronically infectedindividuals are believed important reservoirsfor this disease (Atkinson et al. 2000, Yorinksand Atkinson 2000, Atkinson et al. 2001a,Atkinson and Samuel 2010, Atkinson et al.2013). Iiwi and Apapane are highly mobile,traveling across elevations in search of seasonalor ephemeral nectar resources; in contrast, themore generalist Amakihi is relatively sedentarythroughout the year (Scott et al. 1986, Ralph andFancy 1995, Fancy and Ralph 1997, Hart et al.2011). These species also differ in their ability toexploit high quality food resources (Pimm andPimm 1982).

Our evaluation involves four longitudinalstudies of avian malaria infection on the Islandof Hawai‘i. The study area comprises approxi-mately 1100 km2 on the eastern flanks of MaunaLoa volcano in the southeast corner of Hawai‘i(Appendix: Fig. A1). The Biocomplexity studywas conducted as part of a collaborative researcheffort (NSF grant DEB 0083944) on the Biocom-plexity of Introduced Avian Diseases in Hawai‘i.For the Biocomplexity study, nine 1-km2 studysites were established along an altitudinal gradi-ent from 25 to 1800 m above sea level andstratified into three major disease ‘‘zones’’ basedon elevations identified by van Riper et al. (1986).We had two study sites (SOL and CJR) at highelevation (.1650 m), four (COO, CRA, PUU,WAI) at mid elevation (1000–1300 m), and three(BRY, MAL, NAN) at low elevation (,300 m).The second study focused on Apapane within a0.5-ha mid-elevation (1200 m) study site atKilauea Volcano (KV) from 1992 to 1998. Thesedata were originally reported in Atkinson andSamuel (2010), but we conducted a reanalysisusing new statistical models (below) and inter-pret the results in the larger context of the avianmalaria on Hawai‘i. The Kulani study wasconducted on a 0.5-ha high elevation (1765 m)site from February 1992 to July 1994 concurrently


SAMUEL ET AL.

with the KV study. The Ainahou site (AIN) waslocated at mid-elevation (915 m) in HawaiiVolcanoes National Park. Ainahou was anoperating cattle ranch from 1941 until 1971 andis an open understory, mesic ohia forest (Kilpa-trick et al. 2006a). All study sites were in mesic-wet forest (840–4200 mm annual rainfall) dom-inated by ohia (Metrosideros polymorpha), theprimary canopy tree and food source for nectar-ivorous honeycreepers in Hawai‘i. There werebroad similarities in substrate age, rainfall, andvegetation for sites within the same altitudinalzone. Intensity of disease transmission variesacross this landscape primarily driven by sea-sonal and altitudinal differences in both temper-ature and rainfall that affect abundance of Culexquinquefasciatus and the intrinsic incubation of P.relictum (Ahumada et al. 2004, Ahumada et al.2009, Samuel et al. 2011). Mean monthly temper-atures range from 248C at low elevation toapproximately 138C at high elevation.

Banding and blood samplingMist-netting was conducted monthly in each of

the nine Biocomplexity study sites, from January2002 through June 2005, using 18–24 mist-nets ata height of 6 m (Woodworth et al. 2005). Netswere operated for approximately 6 hours eachday between 06:30 and 14:00 HST, for 3–4 days/month. At KV, we captured native and non-native forest birds by mist-netting at 1–3 monthintervals from January 1992 to June 1998 at 16fixed locations. Eleven net sites were operatedduring the entire study, with five additional netsadded in August 1992 to increase the number ofcaptures. At Kulani, we captured birds atmonthly intervals from February 1992 to July1994 at 13 fixed net sites within the study site. AtAinahou, we mist-netted, banded, and bled birdsfor 4 days on alternate weeks from December2001 to December 2004. A varying number ofnets were set at each session depending uponnumber of bird captures, availability of birdbanders, and intensity of tree bloom. Dependingon weather conditions, nets were operated from1-2 h after sunrise to mid-afternoon on 3–5consecutive days during each sampling period.For all studies, captured birds were banded withUS Fish and Wildlife Service numbered alumi-num leg bands for subsequent identification. Sexof birds was determined by brood patch or

cloacal protuberance, plumage characteristics,and measurements of wing chord, culmen lengthand tarsus length (Pyle 1997; USGS, unpublisheddata). We determined age as hatch year (HY),second year (SY), or after-second-year (ASY)based on plumage (Fancy et al. 1993); SY andASY birds were collectively considered as adults(AD). We obtained blood samples (,1% of bodyweight) by jugular venipuncture with a heparin-ized 28.5-gauge insulin syringe. Blood smearswere prepared and fixed in absolute methanol inthe field. Remaining blood was transferred tomicrohematocrit tubes and centrifuged to sepa-rate plasma, which was frozen at �708C forserological analysis. All captures and bloodsamples were collected under approved AnimalCare and Use Protocols through the U.S. Geo-logical Survey–National Wildlife Health Center,Madison, WI (1992–1998) or the University ofHawaii–Manoa (2000–2006).

Testing for malarial infectionWithin 4 days post-infection (PI) susceptible

native birds develop detectable parasitemias(pre-patent period), which peak 12–16 days PI(the ‘‘crisis’’), and begin a rapid decline by 21–28days as the humoral and cellular immunesystems respond (van Riper et al. 1986, Atkinsonet al. 1995, Atkinson et al. 2000, Yorinks andAtkinson 2000). Antibodies can develop as earlyas 7 days PI (Atkinson et al. 2001b). Mortalitytypically occurs between 19-30 days PI (Atkinsonet al. 1995, Atkinson et al. 2000, Yorinks andAtkinson 2000). Native birds that survive arechronically infected, have immunity to rechal-lenge with P. relictum, and probably remaininfectious to mosquitoes throughout their life(Atkinson et al. 2001a), although detection bymicroscopy is inconsistent (Jarvi et al. 2002).

Therefore, we used a combination of bloodsmears and serology to determine the malarialinfection status of captured birds. Blood smearswere stained with buffered 6% Giemsa, pH 7, forone hour, rinsed with tap water, and dried. Wescanned 100 microscope fields (approximately30,000 to 50,000 erythrocytes) with a 403objective to identify erythrocytic stages of theparasite. For plasma samples collected as part ofthe Biocomplexity and Ainahou studies, we usedan enzyme-linked immunosorbent assay (ELISA)(Graczyk et al. 1993) to detect antibodies to


SAMUEL ET AL.

erythrocytic stages of P. relictum and identifystrongly positive and negative samples. Sampleswith ELISA values (mean absorbance of triplicatesamples � the mean absorbance of triplicatenegative controls)/(mean absorbance of triplicatepositive controls� mean absorbance of triplicatenegative controls)3 100) between 15 and 65 wereconsidered equivocal and further tested byimmunoblot (Atkinson et al. 2001a). Plasmasamples from the Kulani and KV studies weretested by immunoblot alone. Sensitivity andspecificity of serological tests are comparable toPCR (Jarvi et al. 2002, VanderWerf et al. 2006)and helped distinguish acute from chronicinfections where parasitemias are too low to beconsistently detected by microscopy or PCR(Atkinson et al. 2001a, Jarvi et al. 2002).

Captured birds were considered susceptible(antibody and parasitemia negative) or recov-ered/chronically infected (antibody positive, par-asitemia positive or negative). Birds with acuteinfections were antibody negative and parasitepositive (Atkinson et al. 2001a), but these madeup less than 5% of captures (Appendix: TableA1). Acutely infected birds were unlikely to becaptured as clinical signs of infection includereduced activity, acute morbidity, or mortality(Yorinks and Atkinson 2000, Atkinson andSamuel 2010). For analysis, we consideredacutely infected birds that were captured to be

recovered because active birds are likely malariasurvivors.

Epizootiological modelingWe analyzed longitudinal (capture-recapture)

data using Bayesian state-space multi-state mod-els (Kery and Schaub 2012, King 2012). Wecombined longitudinal estimation of diseasetransmission with a discrete cumulative age-prevalence model (Atkinson and Samuel 2010)to simultaneously estimate time-specific (i ) orseasonal (winter¼Wi, spring¼ Sp, summer¼ Su,and fall ¼ Fa) capture rate (pi

S, piR), transition

probability (wiSR) from susceptible (S) to recov-

ered (R), malaria fatality (m), AD survival ofrecovered (si

R) and susceptible birds (siS) for each

season-time period (i ¼ 11, 13, 16 and 26 forKulani (KUL), Biocomplexity, Ainahou (AIN),and KV studies, respectively) (Tables 1–3).Although we used open population models wedid not account for emigration from our studysites; therefore, we calculated apparent survivalwhich underestimates true survival (White and

Table 1. State-space transitions for malaria infection in adult Hawaiian forest birds from the true state at time t to

the true state at time t þ1.

True state at time t

True state at time t þ 1

Susceptible Recovered Dead

Susceptible (1 � wSR) 3 sS wSR 3 (1 � m) 3 sR 1–((1 � wSR) 3 sSþwSR 3 (1 � m) 3 sR)Recovered 0 sR 1 � sR

Dead 0 0 1

Table 2. Capture probabilities for Hawaiian forest birds

at time t. Capture rates for hatch-year birds may be

different than capture rates for adult birds.

True stateat time t

Observation at time t

Captured insusceptible state

Captured inrecovered state

Notcaptured

Susceptible pS 0 1 � pS

Recovered 0 pR 1 � pR

Dead 0 0 1

Table 3. State-space transitions for malaria infection in

hatch-year (HY) and second-year (SY) Hawaiian

forest birds from the true state at time t to the true

state at time t þ 1 used to estimate age-prevalence

infection rates. HY and SY birds are included only

for first capture; therefore, they have a conditional

non-malaria survival probability of 1.0. We assumed

that transition rates for SY birds were identical to

AD, but we allowed different transition rates for HY

birds. See text for further details and definition of

parameters.

True stateat time t

True state at time t þ 1

Susceptible Recovered Dead

Susceptible (1 � wSR) (1 � m) 3 wSR 0Recovered 0 1 0Dead 0 0 1


SAMUEL ET AL.

Burnham 1999).Because acutely infected individuals were

seldom captured and have relatively high mor-tality, we estimated the transition probabilities ofsusceptible individuals to recovered (chronicallyinfected). For age-prevalence modelling, weestimated cumulative age-prevalence parameters(pi

S, piR and wi

SR) conditioned on the first capture(survival ¼ 1) of known age (HY and SY) birdswhich are susceptible at birth (hatch). Werandomly assigned a birth season for each HYand SY bird based a species- and elevation-specific multinomial distribution of AD birdswith brood patches captured during the Biocom-plexity study (Appendix: Table A2). SY birdswere randomly assigned a season in the yearprior to capture. HY birds were assigned aseason that preceded their capture season within

the same year. We also estimated separatecapture, malaria fatality (m), and malaria infec-tion rates for HY birds, but assumed SY and ASYbirds were similar (Tables 1 and 3). For knownage birds capture rates have no biologicalmeaning because they are based on captures ofbirds know to be alive. We estimated annualsurvival and malaria infection rates as theproduct of the seasonal survival SY ¼ sWi 3 sSp3 sSu3 sFa or seasonal infection IY¼ (1� (1� IWi )(1� ISp) (1� ISu) (1� IFa)) (Atkinson and Samuel2010), respectively. We also determined theannual population mortality rate from malariaM¼ IY3S/H3m (Atkinson and Samuel 2010) forHY birds (S/H ¼ 1.0; 100% susceptible at hatch)and for AD (S/H ¼ no. of susceptible ADcaptured in winter/no. of AD captured in winter).We enhanced model parameter estimates usingBayesian priors for the estimated malaria fatalityrates (m) for the three native species based onexperimental laboratory studies from high andlow elevation Amakihi, Apapane, and Iiwi(Atkinson et al. 1995, Atkinson et al. 2000,Yorinks and Atkinson 2000, Samuel et al. 2011,Atkinson et al. 2013). We used a beta distribution(b(survivorsþ 1, fatalitiesþ 1)) to estimate priorsbased on the number of survivors and fatalitiesin laboratory studies for each species: Iiwi b(2, 9),Apapane b(6, 4), Amakihi b(13, 19), and low-elevation Amakihi b(11, 2). Because our primaryfocus was on disease processes we also used the95% confidence intervals on annual survival ratesfrom previous studies (Woodworth and Pratt2009: Table 8.1) to limit model estimates ofseasonal non-malaria survival as follows: Iiwiuniform (0.81, 0.91), Apapane uniform (0.83,0.97), Amakihi uniform (0.90, 0.95). Preliminaryanalysis showed these annual survival priors hadlittle effect on disease related parameter esti-mates.

Models were parameterized so malaria inci-dence was equal to the transition from suscepti-ble to recovered status (Ii ¼ wi

SR), and occurredprior to survival of recovered birds (Kery andSchaub 2012) which allowed us to estimate themalaria fatality rate (m) as part of the transition(Tables 1 and 3). We used OpenBugs (Lunn et al.2009) to evaluate 15 models (Table 4) forseasonal, time-specific, or constant capture, sur-vival, and infection rates for each native species(Amakihi, Apapane, and Iiwi) and elevation

Table 4. Alternative models and description for AD

Hawaiian forest birds with malaria transition based

on all HY and AD birds (see text for details).

Model number Model description

Model 1 Apt,S6¼RAwt

Ast,S6¼RJpt,S 6¼R

Jwt

Model 2 Apt,S¼RAwt

Ast,S6¼RJpt,S¼R

Jwt

Model 3 Apt,S¼RAwt

Ast,S¼RJpt,S¼R

Jwt

Model 4 Apt,S¼RAw Ast,S6¼R

Jpt,S¼RJw

Model 5 Apt,S¼RAwt

AsS 6¼RJpt,S¼R

Jwt

Model 6 Apt,S¼RAwt

AsS¼RJpt,S¼R

Jwt

Model 7 Apt,S¼RAw Ast,S¼R

Jpt,S¼RJw

Model 8 ApS¼RAwt

Ast,S¼RJpS¼R

Jwt

Model 9 Apt,S¼RAw AsS 6¼R

Jpt,S¼RJw

Model 10 ApS¼RAwt

AsS6¼RJpS¼R

Jwt

Model 11 ApS¼RAwt

AsS¼RJpS¼R

Jwt

Model 12 Apt,S¼R w AsS¼RJpt,S¼R

Model 13 ApS¼RAw Ast,S¼R

JpS¼RJw

Model 14 ApS¼RAw AsS6¼R

JpS¼RJw

Model 15 ApS¼RAw AsS¼R

JpS¼RJw

Notes: Model descriptions are based on capture (p),transition (w), and survival (s) parameters. Subscripts indicatethe type of parameter heterogeneity; t indicates parametersare estimated for each time interval, S¼R indicates parameterestimates are similar for susceptible and recovered birds, andS 6¼ R indicates all estimates are different. Superscriptsrepresent age-specific parameter estimates for either AD orHY; no superscript indicates a common parameter for bothAD and HY. In addition, malaria mortality rate (m) wasestimated for all models. For example, model pt,S¼R

Awt sS 6¼RJwt has time specific capture probabilities that are equal forsusceptible and recovered birds and for AD¼A and HY¼ J,time-specific and age-specific transition (infection) probabil-ities, and constant survival probabilities across time that differbetween susceptible and recovered birds.


SAMUEL ET AL.

from the Biocomplexity, KV, Ainahou, andKulani studies. We used the Bayesian Informa-tion Criterion (BIC, Schwarz 1978) to comparealternative models and identify parsimoniousmodels for capture, survival, and transitionprobabilities. Estimated model parameters arereported as posterior mean estimates and 95%Bayesian Credible Intervals (BCI). Because tran-sient (non-residents) animals are common inmost avian populations and their presencenegatively biases survival rates, we evaluatedcapture data for the presence of transient ADbirds (Pradel et al. 1997) using TEST 3G.SR inprogram U-CARE (Pradel et al. 2005). Whentransient birds were important we removed firstcaptures of AD birds for model selection and toestimate model parameters (Pradel et al. 1997).We ran models with 30,000 MCMC replicationsfor burn-in and an additional 20,000 MCMCreplications for model convergence and toestimate deviance and calculate BIC ¼ DevianceþLn(N)3K, where N is the number of birds andK is the number of model parameters (Link andBarker 2010). Final model parameters wereestimated using 70,000 Markov Chain MonteCarlo (MCMC) burn-in replications and 30,000MCMC replications. Model convergence wasassessed based on visual convergence of theMCMC replicates, smoothness of the posteriorparameter distributions, and the MCMC error foreach parameter being ,100-fold smaller than theposterior standard deviation. We used general-ized v2 methods for binomial variables (Sauerand Williams 1989) to evaluate species andelevation differences in malaria infection andsurvival.

Vector-host interactionBecause densities of adult mosquitoes are low

and collection of blood fed Culex is difficult inHawai‘i, especially at mid and high elevations,we estimated vector feeding preference based onthe relative annual infection rates in AD birdsadjusted for their population density. We esti-mated the relative vector preference of birdspecies j over k for each pair of species for mid-and high-elevation Biocomplexity sites: fpj,k ¼ Ij/Pj/Ik/Pk, where I and P are the annual infectionrates (IY) and population density for each species,respectively. In this calculation fpj,k is an esti-mated odds ratio for preference of species j to k.

We also used fp to calculate an index of therelative contribution of native species (in pairs) toinfecting susceptible mosquitoes IMj,k ¼ fpj,k 3

IBj/IBk, where IB is the number of infected birdsfor each species calculated as the proportion ofinfectious birds 3 P. Our approach assumes thatavian species have equal likelihood of becominginfected by mosquitoes, which is supported byexperimental studies showing 98% infection of allthree species from a single bite by an infectiousmosquito (Samuel et al. 2011: Appendix B; C. T.Atkinson, unpublished data). Both equations weresolved using Bayesian analyses which includedthe species and elevation specific parameters andvariances on the right-hand-side of the equations.

RESULTS

Prevalence and intensity of infectionWe obtained blood samples from 5,353 Ha-

wai‘i Amakihi, 2,116 Apapane, and 1,046 Iiwiduring four studies on Kilauea and Mauna LoaVolcanoes during 1992–1998 and 2001–2005(Appendix: Table A1). Overall prevalence ofmalaria based on parasitemia and serology atfirst capture ranged from 2.6% (27/1,046) in Iiwito 40% (837/2,116) in Apapane and 39% (2,072/5,353) in Hawai‘i Amakihi. Prevalence wasstrongly influenced by elevation, with lowestprevalence of infection at high elevation (2.2% forIiwi, 7.8% for Apapane, and 1.5% for Amakihi )followed by mid elevation (20% for Iiwi, 60% forApapane, and 17% for Amakihi ), and lowelevation studies (no captures for Iiwi, 100% forApapane, and 85% for Amakihi ). We classified.97% of the malarial infections as chronic(recovered) and the remaining birds were acutelyinfected (Appendix: Table A1), but were classi-fied as recovered in our analyses.

Species-specific patterns—AmakihiLow-elevation forests.—Amakihi were the only

species with sufficient captures to evaluateepizootiological patterns across all three eleva-tions (Appendix: Table A1). Amakihi in low-elevation forests were also the only population ofnative birds with regular capture of acutelyinfected birds (3.7% of captures). In low-elevationforests the capture data was best represented byModel 12 (Table 5) with equal time-specificcapture rates for susceptible and recovered birds


SAMUEL ET AL.

that differed between AD and HY birds, constantmalaria infection that was equal for AD and HY,and constant non-malaria survival that wassimilar for disease classes. We found significant(Z ¼ 7.07, P , 0.001) evidence of transient birdsand removed first captures of AD. Annualpopulation survival (malaria mortality excluded)was 0.73 (95% BCI ¼ 0.63, 0.83). Seasonal (0.48)and annual (0.92) infection rates were high,indicating that few Amakihi (8%) would avoidmalaria infection annually (Table 6 and Fig. 1).AD seasonal capture rates were highest insummer and fall (Table 7).

Mid-elevation forests.—Most captures of Ama-kihi in mid-elevation forests were from theAinahou study, and most of the captures weresusceptible birds (Appendix: Table A1). For theBiocomplexity sites, Models 14 and 15 (DBIC¼ 3)provided the best fit (Table 5) with time-constant

malaria infection rates, and capture and survivalrates that were time-constant and similar forsusceptible and recovered birds. We found noevidence for transient birds in the data (P¼ 0.30).AD capture rates were approximately 0.10 andconstant throughout the year (Table 7). Annualpopulation survival without malaria mortalitywas 0.70 (0.66, 0.79). Annual (0.33) infection ratesfor AD were moderate, but infection rates werehigher for HY (0.68) (Table 6 and Fig. 1).

For the Ainahou study, Model 9 provided thebest fit to this much larger data set and we foundsignificant evidence (P , 0.001) of transientbirds, so first capture of AD birds were removed(Table 5). A simplified model with seasonalcapture rates for AD and HY birds and equalsurvival for susceptible and recovered birdsprovided an improved fit to the capture data (DBIC ¼ 61). AD seasonal capture rates were

Table 5. Alternative models and D BIC values for AD Hawaiian forest birds with malaria transition based on all

HY and AD birds. See text for details and Table 4 for model descriptions.

Model number

DBayesian information criterion (BIC)

Hawai’i Amakihi Apapane Iiwi

Low Mid AIN High KUL Mid KV High KUL High KUL

Model 1 291 411 335 370 253 493 715 543 364 344 221Model 2 244 275 851 262 159 371 670 407 231 201 150Model 3 193 226 686 205 120 295 433 331 175 142 95Model 4 111 182 350 170 107 243 254 270 151 114 91Model 5 143 164 817 140 90 233 434 261 133 98 79Model 6 143 157 893 150 90 220 417 257 129 96 67Model 7 24 117 274 105 71 164 136 202 92 59 41Model 8 482 148 1125 216 66 199 537 215 119 13 181Model 9 25 64 0 54 45 199 6 41 49 12 0Model 10 453 102 1223 202 39 149 552 154 74 73 185Model 11 436 92 1240 136 37 131 532 133 68 72 143Model 12 0 63 289 142 67 10 0 90 41 5 17Model 13 324 56 691 47 25 61 148 54 42 44 186Model 14 310 3 646 6 0 2 95 11 6 0 159Model 15 304 0 507 0 0 0 94 0 0 1 159

Notes: For Hawai‘i Amakihi transient adults were removed for all sites except mid-elevation. For Apapane transient adultswere removed at KV. For Iiwi transient adults were removed at all sites. Transient adults were removed based on U-CARE testG3.SR (P , 0.05).

Table 6. Model parameter estimates with Bayesian standard errors for Hawai‘i Amakihi by elevation. See text for

description of study sites and final models.

Model parameter Low Mid AIN High KUL

AD seasonal malaria infection (Ii ) 0.48 6 0.03 0.10 6 0.04 0.09 6 0.01 0.02 6 0.01 0.05 6 0.03HY seasonal malaria infection (Ii ) 0.26 6 0.06 0.27 6 0.01 0.37 6 0.04 0.31 6 0.07HY annual malaria infection (IY)� 0.68 6 0.10 0.72 6 0.02 0.84 6 0.04 0.76 6 0.10HY malaria fatality (m) 0.86 6 0.02 0.87 6 0.03AD non-malaria survival (Sy)� 0.73 6 0.05 0.70 6 0.04 0.68 6 0.02 0.74 6 0.04 0.73 6 0.05

� Annual malaria infection rate IY (see text).� Annual non-malaria survival estimates: the annual survival probability with malaria fatality removed SY (see text).


SAMUEL ET AL.

highest in fall and winter (Table 7). Annual (0.29)infection rates for AD were moderate. Highermalaria infection and fatality rates for HY birds(Table 6 and Fig. 1) indicates that malariadynamics may be much different than in ADbirds. Annual population survival without ma-laria mortality was 0.70 (0.66, 0.79).

High-elevation forests.—Amakihi were frequent-ly captured in high-elevation forests, but fewbirds (,2%) had acute or chronic malarialinfection (Appendix: Table A1). We found strongevidence (P , 0.03) for transient Amakihi in boththe Biocomplexity and Kulani studies and re-moved first captures of AD birds. Model 15 was

the best fit for both studies (Table 5). Model 15with seasonal capture rates for AD and HY birdsprovided a modest increase in model fit (DBIC¼8) for the Kulani data. For the Biocomplexitystudy, AD capture rates were approximately 0.15and constant during the year. At Kulani ADcapture rates were lowest in spring and highestduring fall and winter (Table 7). Annual popu-lation survival without malaria mortality was0.73–0.74 for these two studies. Annual (0.09–0.17) malaria infection rates were low for ADbirds compared to other elevations, but high(0.76–0.84) for HY birds (Table 6 and Fig. 1).Mortality from malaria infection was 0.60 (0.43–

Fig. 1. Annual malaria infection rates (IY in Tables 6 and 8) in low-, mid-, and high-elevation forest in Hawaii

for Biocomplexity (unlabeled), Ainahou (AIN), Kilauea Volcano (KV), and Kulani (KUL) study sites. Mean

infection rates with 95% Bayesian Credible Intervals for Amakihi (solid rectangles), Apapane (solid diamonds),

and Iiwi (solid triangles).

Table 7. Adult seasonal capture rates and Bayesian standard errors for Hawai’i Amakihi, Apapane, and Iiwi at

Biocomplexity (Low, Mid, High), Ainahou (AIN), Kilauea Volcano (KV), and Kulani (KUL) study sites on

Hawai‘i.

Elevation or Study Winter Spring Summer Fall

Hawai’i AmakihiLow 0.067 6 0.063 0.081 6 0.029 0.231 6 0.037 0.397 6 0.039Mid 0.099 6 0.020 0.099 6 0.020 0.099 6 0.020 0.099 6 0.020AIN 0.210 6 0.019 0.021 6 0.006 0.126 6 0.014 0.278 6 0.022High 0.151 6 0.015 0.151 6 0.015 0.151 6 0.015 0.151 6 0.015KUL 0.326 6 0.072 0.092 6 0.040 0.208 6 0.066 0.409 6 0.058

ApapaneMid 0.032 6 0.010 0.008 6 0.006 0.034 6 0.012 0.130 6 0.230KV 0.107 6 0.013 0.013 6 0.005 0.032 6 0.008 0.263 6 0.021High 0.074 6 0.012 0.002 6 0.002 0.047 6 0.011 0.095 6 0.016KUL 0.056 6 0.019 0.012 6 0.008 0.012 6 0.013 0.229 6 0.060

IiwiHigh 0.120 6 0.038 0.036 6 0.020 0.122 6 0.040 0.357 6 0.065KUL 0.230 6 0.045 0.017 6 0.012 0.069 6 0.029 0.416 6 0.051


SAMUEL ET AL.

0.76) in AD birds at the Biocomplexity sites, buthigher (0.75; 0.63, 0.87) at Kulani (Fig. 2B).Malaria fatality was higher in HY birds (0.85;0.77, 0.92) than AD birds at the Biocomplexitysites, but AD and HY malaria mortality was notdifferent at Kulani (Table 6). These patterns showthat malaria dynamics and impact are likelymore substantial in HY Amakihi.

Comparisons among elevations.—AD Amakihipopulations in low-elevation forests experiencegreater annual malaria transmission than mid-(v2¼ 122, P , 0.001) or high-elevation forests (v2

¼ 177, P , 0.001) and transmission was greater(v2 ¼ 6.3, P ¼ 0.012) at mid elevation than highelevation (Fig. 1). We found no evidence ofdifferent AD malaria fatality between mid- andhigh- elevation Amakihi (v2¼ 1.08, P¼ 0.3), withan average rate of 68% (Fig. 2B). Malaria fatalitywas lowest in low-elevation Amakihi (2.5%)compared to mid (v2 ¼ 182, P , 0.001), high (v2

¼ 86, P , 0.001), or the combined mortality ratesof mid- and high-elevation birds (v2 ¼ 236, P ,

0.001). The malaria fatality rate in low-elevationforests was substantially lower than the priorfrom experimental studies (Fig. 2A). Malariafatality rates among mid- and high-elevation

studies were slightly higher than prior estimatesfrom laboratory experiments; except for the high-elevation Biocomplexity site where fatality wasnearly identical (Fig. 2B). Malaria populationimpacts were least in low-elevation AD Amakihicompared with mid (v2¼ 24, P , 0.001) and highelevations (v2 ¼ 2.79, P , 0.095), but midelevation was not different from high elevation(v2 ¼ 2.02, P , 0.155). Malaria reduced the low-elevation AD population ,1% annually, andreduced the HY population by 1–4%. Withaverage malaria fatality rate of 0.68 for AD (Fig.2) the annual population mortality from avianmalaria in mid-elevation forests was 15% (10–21%) for AD and likely much higher (44–62%) forHY birds. Estimated annual population impactswere relatively small for AD (5.3% and 12%)Amakihi populations at high-elevation studysites, respectively. However, malaria impactswere likely higher for high-elevation HY birds(73% and 57%).

Species-specific patterns—ApapaneFew Apapane were captured in low-elevation

forests, and all these birds had previouslysurvived malaria infection (Appendix: Table

Fig. 2. Bayesian prior and posterior estimates (solid diamonds) and 95% Bayesian Credible Intervals (solid

lines) of malaria fatality rates (m in Tables 1 and 3) for Biocomplexity (Low, Mid, and High), Ainahou (AIN), and

Kulani (KUL) studies for (A) low-elevation Amakihi, (B) mid- and high-elevation Amakihi. Average estimates

were calculated using mean and variance for each study.


SAMUEL ET AL.

A1), making it impossible to estimate epizootio-logical parameters for low-elevation Apapane,which are rare in these forests (Spiegel et al.2006). By comparison, captures of Apapane werefrequent in mid- and high-elevation forestswhere only a few birds were acutely infectedwith malaria. Capture rates of AD Apapanevaried seasonally at all study sites and weretypically highest in fall and lowest in spring(Table 7). For the mid-elevation KV study wefound moderate evidence for transient birds (Z¼1.84, P ¼ 0.066) and removed first AD captures.For all other studies there was no evidence oftransient birds (P . 0.05).

Mid-elevation forests.—Model 12 provided thebest fit for Apapane captures for KV, whileModel 15 was the best fit for the Biocomplexitystudy (Table 5). For the Biocomplexity and KVstudies, seasonal capture rates provided a sub-stantial improvement in model fit; DBIC¼ 49 and.200, respectively. Annual (0.67 and 0.71)malaria infection rates for AD and HY birdswere similar for both mid-elevation studies(Table 8 and Fig. 1) demonstrating a highlikelihood of susceptible Apapane becominginfected annually. Annual population survivalwithout malaria mortality was 0.49–0.65 for thesetwo studies.

High-elevation forests.—Apapane were fre-quently captured in high-elevation forests, butacutely infected birds were rare and the numberof recovered birds was limited (Appendix: TableA1). For the Biocomplexity and Kulani studies,differing seasonal capture rates provided asubstantial improvement in the fit for Model 15;DBIC¼ 69 and 10, respectively. Annual (0.10 and0.28) malaria infection rates for AD Apapane atBiocomplexity and Kulani studies indicated alow to moderate risk of malarial infection (Fig. 1).

AD malaria fatality (0.42 vs. 0.43) was nearlyidentical for both studies (Fig. 3A). However,annual infection and fatality rates were higher forHY birds indicating malaria dynamics andimpacts where greater than for AD birds.Estimated annual AD survival rate was 0.50–0.51.

Comparisons among elevations.—AD Apapanehad similar rates of annual malaria infection formid- (v2¼ 0.4, P¼ 0.52) and high-elevation (v2¼2.2, P ¼ 0.14) studies (Fig. 1). However, malariainfection (v2¼ 50, P , 0.001) was lower in high-elevation forests. Except for the mid-elevationBiocomplexity study, malaria fatality was slightlyhigher than experimental infection. The twohigh-elevation studies had posterior estimatesthat were nearly identical to the prior (Fig. 3A),suggesting that capture data were generallyinsufficient to improve fatality estimates. Theaverage AD malaria fatality across all sites was47%, suggesting malaria reduced the AD and HYpopulation approximately 10% and 28–35%annually in mid-elevation forests and 5–12% inhigh-elevation AD populations. Malaria fatalityand population impacts were much higher inhigh-elevation HY Apapane, indicating that HYbirds may have higher malaria impacts than ADApapane at this elevation.

Species-specific patterns—IiwiHigh-elevation forests.—Iiwi were not captured

in low-elevation forests and only a few werecaptured in mid-elevations forests (Appendix:Table A1), restricting epizootiological models tohigh elevations. These patterns correspond withIiwi abundance measured during bird surveys(Gorresen et al. 2009, Hart et al. 2011). Capture ofeither acutely and chronically infected birds athigh-elevation sites was infrequent (2%). We

Table 8. Model parameter estimates with Bayesian standard errors for Hawaiian Apapane and Iiwi by elevation.

See text for description of study sites and final models.

Model parameter

Apapane Iiwi

Mid KV High KUL High KUL

AD seasonal malaria infection (Ii ) 0.27 6 0.04 0.24 6 0.02 0.03 6 0.01 0.08 6 0.04 0.08 6 0.03 0.05 6 0.02HY seasonal malaria infection (Ii ) 0.32 6 0.04 0.32 6 0.06 0.31 6 0.05 0.20 6 0.03HY annual malaria infection (IY)� 0.79 6 0.03 0.78 6 0.08 0.77 6 0.07 0.60 6 0.06HY malaria fatality (m) 0.89 6 0.04 0.81 6 0.07AD non-malaria survival (Sy)� 0.49 6 0.02 0.65 6 0.05 0.50 6 0.02 0.51 6 0.04 0.55 6 0.07 0.60 6 0.07

� Annual malaria infection rate IY (see text).� Annual non-malaria survival estimates: the annual survival probability with malaria fatality removed SY (see text).


SAMUEL ET AL.

found strong evidence for transient birds (P ,

0.005) for both studies and removed firstcaptures of AD Iiwi for model estimation. ForBiocomplexity sites, Models 14 and 15 best fit thecapture data (DBIC¼ 1). We found that Model 15with seasonal capture rates substantially im-proved model fit (DBIC ¼ 78). For the Kulanistudy, Model 9 best fit our data (Table 5), and itwas also improved by using seasonal capturerates and equal survival for susceptible andrecovered birds (DBIC ¼ 58). Seasonal capturesof AD birds for both studies was highest in falland smaller in spring (Table 7). Seasonal andannual malaria infection rates were similar forboth studies and indicated a moderate risk ofannual malaria infection (0.23) for AD Iiwi (Fig.1). Malaria mortality for AD and HY Iiwiaveraged 0.93 (0.87, 0.98%); similar to the 95%fatality reported by Atkinson et al. (1995) forexperimental studies (Fig. 3B). Malaria fatality inIiwi was the highest for any native birds (Figs. 2and 3). Malaria infection rates were much higherfor HY Iiwi for these studies (Table 8 and Fig. 1),

indicating that malaria may have a moresubstantial impact on HY Iiwi populations.Based on 93% average fatality, malaria reducedhigh-elevation AD and HY Iiwi populations byapproximately 16–20% and 55–73% annually,respectively. The annual non-malaria survivalrate for Iiwi was 0.55–0.60 (Table 8).

Patterns among speciesTo evaluate species differences we compared

annual malaria infection rates for the sameelevation (Fig. 1). We were unable to makespecies comparisons in low-elevation forestsbecause few Apapane and no Iiwi were foundat this elevation (Fig. 1). For AD birds in mid-elevation forests, annual malaria infection ratesfor Apapane from the Biocomplexity (0.71) andKV studies (0.67) were higher (v2

2¼ 35, P , 0.001)than for Amakihi from Biocomplexity (0.33) andAinahou (0.30). In high-elevation forests, annualmalaria infection rates were similar (v2

5 ¼ 7.6, P¼0.18) among Amakihi, Apapane, and Iiwi. ADmalaria fatality (Fig. 2B) for mid- and high-

Fig. 3. Bayesian prior and posterior estimates (solid diamonds) and 95% Bayesian Credible Intervals (solid

lines) of malaria fatality rates (m in Tables 1 and 3) for Biocomplexity (Low, Mid, and High), Kilauea Volcano

(KV), and Kulani (KUL) studies for (A) mid- and high-elevation Apapane, and (B) high-elevation Iiwi. Average

estimates were calculated using mean and variance for each study.


SAMUEL ET AL.

elevation Amakihi (66%) was greater (v2 ¼ 16, P, 0.001) than Apapane (47%), but mortality forIiwi (93%) was greater (v2¼ 1247, P , 0.001) thanlow-elevation Amakihi (3%), mid-elevation andhigh-elevation Amakihi (v2 ¼ 48, P , 0.001), orApapane (v2 ¼ 92, P , 0.001). Thus, malariamortality was greatest in Iiwi, least in low-elevation Amakihi, and intermediate in mid-and high-elevation Amakihi and Apapane. An-nual malaria impacts on AD birds in mid-elevation forests were greater for Amakihi(15%) than Apapane (6%) (v2 ¼ 7.1, P ¼ 0.008).In high-elevation forests malaria impacts weresimilar (v2 ¼ 0.003, P ¼ 0.96) for AD Amakihi(9%) and Apapane (8%). However, malariaimpacts were highest (v2 ¼ 4.4, P ¼ 0.036) forAD Iiwi (21%) compared to Amakihi andApapane.

Vector and host interactionCulex mosquitoes appeared to prefer feeding

on Amakihi over Apapane in mid-elevationforests (fp ¼ 3.15; 1.31, 5.79) where malariatransmission was moderate. In high-elevationforests, mosquitoes preferred feeding on Iiwiover Apapane (fp ¼ 10.3; 2.57, 29.7). In theseforests Iiwi were somewhat favored over Ama-kihi (fp ¼ 3.96; 0.79, 13.3), but Amakihi andApapane had similar feeding preference (fp ¼3.76; 0.58, 12.0); these preferences were notstatistically significant, likely because malariainfection rates were low and poorly estimated.Apapane were more likely to produce newlyinfected mosquitoes than Amakihi at mid (IM ¼7.23; 3.94, 17.8) and high (IM ¼ 3.29; 0.95, 25.1)elevations. There was no difference betweenhigh-elevation Iiwi and Amakihi (5.95; 0.84,22.4) or Apapane (IM ¼ 1.15; 0.22, 3.7) in theirrelative contribution to mosquito infections,perhaps partly due to low infection rates.

DISCUSSION

Over the past three decades evidence hasaccumulated about the prevalence and pathoge-nicity of avian malaria in native Hawaiianspecies. However, we previously knew littleabout malaria transmission patterns and diseaseimpacts on survivorship for different species ofhoneycreepers, and across the elevation gradi-ents found on Hawai‘i. We used a Bayesian state-

space model (Kery and Schaub 2012, King 2012)to combine longitudinal and cross-sectionalcapture-recapture models (Atkinson and Samuel2010) to estimate malaria infection and mortalityrates, and population impacts of three species ofHawaiian honeycreeper. This approach allowedus to enhance traditional multi-state models byincorporating single captures of known age birdsusing an age-prevalence method (Caley andHone 2004, Heisey et al. 2006). Further, theBayesian framework allowed us to separateinfection and disease mortality; therefore, direct-ly estimate disease impacts on each avian species.It also allowed us to incorporate prior informa-tion on malaria mortality and annual survivalrates. An important assumption in combiningmulti-state and age-prevalence data is similarityin rates of disease infection and fatality. Ourexperience showed that including age-prevalencedata was most helpful when disease infectionand fatality rates for known age birds (primarilyHY birds in our case) were similar to those forAD birds. We found this situation for low-elevation Amakihi and mid-elevation Apapanewhich had improved estimates of malaria fatality(Figs. 2 and 3) and infection. However, when oneor both of these rates differ for HY birds therewas limited or no benefit from age-prevalencedata. In this situation disease and captureparameters for HY birds may be highly correlat-ed and estimation of infection or fatality rates forHY birds may be confounded. As a result, it isonly appropriate to conclude that the diseaseprocess is operating differently in the known ageindividuals (a.k.a., young birds). Further, incor-porating age-prevalence data with multi-statemodels may also be beneficial when infectionrates are high (so that prevalence changes rapidlywith age) or when capture rates are low becausemany individuals will have insufficient recap-tures to estimate state transition rates.

Our study demonstrates that transmission ofavian malaria, susceptibility, and populationimpact depends on the combination of bothelevation and species in Hawai‘i. Our resultsshow that malaria infection is lowest in high-elevation forests where climate is less favourablefor mosquito populations and malaria parasitedevelopment (Ahumada et al. 2004, Samuel et al.2011). As a result, these high-elevation forestscurrently provide a relatively disease-free refuge


SAMUEL ET AL.

for susceptible honeycreepers (Ahumada et al.2004, Atkinson and LaPointe 2009b). However,daily and seasonal movements of birds to lower-elevation forests in search of nectar resources(Ralph and Fancy 1995) and/or upslope move-ment of infected mosquitoes (Freed and Cann2013) likely contribute to the malaria infection weobserved in these high-elevation populations. Incontrast, annual malaria infection was 2–4 foldgreater in mid-elevation forests and 1.5–2 timesgreater in Apapane than Amakihi. In low-elevation Amakihi, malaria infection was .

90% which is more than three-fold greater thanmid-elevation and more than eight-fold greaterthan high-elevation forests. We found thatmalaria fatality was highest in Iiwi, followed byAmakihi and Apapane in mid and high eleva-tions, and lowest in low-elevation Amakihi. Wealso found no differences in survival ratesbetween susceptible and chronically infectedbirds during any studies, in contrast to resultsfrom Kilpatrick et al. (2006a) for Amakihi at theAinahou site. The combination of high malariatransmission and fatality for Iiwi (and mosquitofeeding preference), but also for Apapane andmid-elevation Amakihi, likely explains why thesehoneycreepers are absent from low-elevationforests (Samuel et al. 2011).

Our results confirm laboratory studies (Atkin-son et al. 2013) that malaria mortality in low-elevation Amakihi is lower (3%) than in mid- andhigh-elevation Amakihi (42–68%), Apapane (16–43%), or Iiwi (93%). Apparent adaptationthrough tolerance to malaria provides a demo-graphic advantage that allows low-elevationAmakihi to increase in abundance, despite highlevels of malaria infection (Woodworth et al.2005, Samuel et al. 2011, Atkinson et al. 2013).Our estimated malaria fatality and annualsurvival rates generally agree with previouslaboratory and field studies on Apapane, Iiwi,and Amakihi from mid- and high-elevationforests (Atkinson et al. 1995, Ralph and Fancy1995, Yorinks and Atkinson 2000, Kilpatrick et al.2006a, Woodworth and Pratt 2009). We note thatour malaria fatality and non-malaria survivalestimates were influenced by prior experimentaldata or constrained to correspond with previousresults, respectively. However, we found lowermalaria fatality for low-elevation Amakihi (3%vs. 17%; Atkinson et al. 2014), but similar fatality

for Iiwi (93% vs. 95%) compared to previouslaboratory experiments (Atkinson et al. 1995). Asurprising pattern in our results was the appar-ent higher level of malaria infection and/ormalaria fatality we found in young birds. Asindicated above, specific disease parameter esti-mates in this situation may be unreliable;however, these findings indicate that malariamay have a more substantial impact on youngbirds than on adults. This finding is supportive ofprevious studies that have suggested that juve-nile birds are more susceptible to malarialinfection (van Riper et al. 1994) and requiresfurther investigation in the Hawaiian ecosystem.Although we found that malaria fatality wasdifficult to estimate, in part because we hadgenerally low capture rates, it provides a keyparameter in understanding host fitness andevolutionary response (Lachish et al. 2011b),and predicting demographic impacts of malariainfection in Hawaiian birds (Samuel et al. 2011).

Previous studies on avian malaria have shownspatial differences in Plasmodium prevalence (Solet al. 2000, Wood et al. 2007, Loiseau et al. 2010,Sehgal et al. 2011, Lachish et al. 2013). Our studybuilds on these results by demonstrating thatpathogen prevalence is driven by both elevationdifferences in transmission pressure combinedwith species-specific disease susceptibility. To-gether, these two key factors affect the distribu-tion and abundance of endemic Hawaiianhoneycreepers, restricting most malaria suscepti-ble species to high-elevation forests on Kaua‘i,Maui, and Hawai‘i (Scott et al. 1986, van Riper etal. 1986, Atkinson and LaPointe 2009a). Thesespatial differences in parasite-mediated selectionpressure coupled with spatial host geneticstructure and gene flow can significantly influ-ence host adaptation or maintaining geneticdiversity (Poulin 2007, Wolinska and King2009). In Hawaii, the spatial gradient in selectionpressure has apparently produced two opposingoutcomes—local adaptation of low-elevationAmakihi for malaria-tolerance (Woodworth etal. 2005, Foster et al. 2007, Atkinson et al. 2013) orlocal extinction of highly susceptible species likeIiwi from low and mid-elevation forests (vanRiper et al. 1986). As climate warms and malariainfection risk increases in Hawai‘i (Benning et al.2002, Atkinson and LaPointe 2009b, Atkinson etal. 2014) it is uncertain whether other endemic


SAMUEL ET AL.

honeycreepers will be able to evolve tolerance tomalaria. Further research is needed to determinethe suite of biotic and abiotic factors that facilitatethe coexistence of pathogens and hosts (via eitherresistance or tolerance) and how these factorsinteract with host demography and movementacross a landscape of heterogeneous selectionpressures. The factors driving these outcomeshave long been a focus of ecological study(Anderson and May 1991), but are not wellunderstood. In Hawai‘i these processes likelyinvolve host susceptibility and demographicrecovery, vector feeding preferences, heteroge-neous selection pressure, host genetic diversity,and host gene flow. Understanding the impor-tance of these factors is particularly critical forendemic Hawaiian species that continue toundergo steep population declines and rangerestrictions as climate warms. Like humanmalaria, spatial patterns of avian malaria aredriven by exogenous temperatures, altitude,rainfall, and suitable habitat for larval mosqui-toes to complete their life cycle (Balls et al. 2004,Pascual et al. 2008, Grillet et al. 2010, Grillet et al.2014). As a result avian malaria in Hawaii mayprovide a seminal model to understand theenvironmental and ecological drivers of humanmalaria and to evaluate alternative controlstrategies (Samuel et al. 2011, LaPointe et al.2012).

Heterogeneous encounter rates between hostsand parasites are important determinants ofparasite prevalence across host species anddisease transmission dynamics, and have signif-icant consequences on host fitness (Kilpatrick etal. 2006b, Medeiros et al. 2013). Previous studieshave relied on vector blood meals to evaluatemosquito feeding preferences (Kilpatrick et al.2006b, Hamer et al. 2009, Simpson et al. 2012,Medeiros et al. 2013). However, in Hawai‘i thelow abundance of mosquitos in mid- and high-elevation forests (LaPointe 2000) and their lowrate of malarial infection (LaPointe 2000, Samuelet al. 2011) make capturing blood-fed mosquitoeschallenging. As an alternative, we used relativehost infection rates and relative host abundanceto assess host-vector contact rates. In mid- andhigh-elevation forests, we found that mosquitoespreferred feeding on Iiwi, then Amakihi, fol-lowed by Apapane. Despite the preference forfeeding on Amakihi, Apapane were much more

likely to produce newly infected mosquitoes andtherefore are a more important reservoir species,likely because they are less susceptible to malariamortality, more abundant, and have highermalarial infection rates than other native species.These results suggest that Apapane may serve asa significant reservoir host that enables higherrates of disease transmission to more vulnerablespecies such as Iiwi, and thereby facilitateapparent competition where one host indirectlycompetes with others via disease transmission(Holt and Pickering 1985, McCallum and Dobson1995). Our study identifies the need for research,especially on other Hawaiian islands, to betterunderstand the factors that influence host-vectorencounters (e.g., mosquito feeding preference,host defensive behavior, roosting locations) andare potential drivers of differential malariatransmission among species and how thesefactors influence mosquito infection, the relativeimpacts of malaria on native species, andcomposition of the Hawaiian forest bird commu-nity.

Overall, we found patterns of malaria trans-mission across elevations that provide furthersupport that climate, based on an elevationalgradient, drives avian malaria through mosquitopopulation dynamics and malaria parasite de-velopment rates within the host mosquito (Ahu-mada et al. 2004, Ahumada et al. 2009, Atkinsonand Samuel 2010, LaPointe et al. 2010). As aresult, increased temperatures from global warm-ing (Benning et al. 2002, Harvell et al. 2002, Freedet al. 2005, Atkinson and LaPointe 2009b) arelikely to increase the severity and frequency ofmalaria transmission at higher elevations inHawai‘i. Increasing temperatures may eliminatehigh-elevation refugia that currently protectmany Hawaiian bird populations, and increasemalaria transmission and epizootics in mid-elevation forests (LaPointe et al. 2012). There isrecent evidence this may already be taking placeon Kaua‘i (Atkinson et al. 2014). This combina-tion of factors will likely produce furtherreductions and extinctions of native Hawaiianbirds, particularly for threatened species withsmall, fragmented populations in high-elevationforests and for species with high susceptibility tomalaria such as the Iiwi. In addition, highertransmission of malaria in mid-elevation forestsmay mean substantial declines in honeycreepers


SAMUEL ET AL.

with moderate levels of susceptibility to avianmalaria, such as the Hawai‘i Amakihi. Thegreatest challenge will be development of diseasecontrol strategies that conserve the native Ha-waiian species while considering the futurethreats that may occur as climate warms.

ACKNOWLEDGMENTS

This research was funded through the U.S. Geolog-ical Survey’s Wildlife, Invasive Species, and NaturalResource Protection Programs and a Biocomplexitygrant from the National Science Foundation(DEB0083944). We also wish to thank our technicalstaff, postdoctoral researchers, and numerous researchinterns whose hard work and dedication made thisresearch possible. The use of trade names or productsdoes not constitute endorsement by the U.S. Govern-ment. This study was performed under the animal careand use protocols approved at the University ofHawai‘i, Manoa. E. Paxton and V. Henaux providedmany valuable comments that improved the paper.The Department of Forest and Wildlife Ecology at theUniversity of Wisconsin-Madison provide assistancewith publication costs.

LITERATURE CITED

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SUPPLEMENTAL MATERIAL

APPENDIX

Table A2. Proportion of adult female native Hawaiian

birds with active brood patches by species and

season captured in low-, mid-, and high-elevation

forests during 2001–2003 at nine Biocomplexity sites.

Species Elevation

Proportion of adult femaleswith brood patches

Winter Spring Summer Fall

Hawai’i Amakihi Low 019 0.54 0.27 0.0Mid 0.20 0.60 0.20 0.0High 0.22 0.78 0.0 0.0

Apapane Mid 0.30 0.50 0.20 0.0High 0.41 0.59 0.0 0.0

Iiwi High 0.52 0.48 0.0 0.0

Notes: Proportions were used to randomly assign hatchingseason to each HY and SY bird captured at that elevation.

Table A1. Capture of susceptible (S), acutely infected (I), recovered (R), and total adult (AD) and hatch-year (HY)

native Hawaiian forest birds at Biocomplexity (2002–2004), KV (1992–1998), AIN (2001–2004), and KUL (1992–

1994) study sites, species, age, and elevation.

Elevation Study site Age

Number captured

Hawai’i Amakihi Apapane Iiwi

S I R Tot S I R Total S I R Tot

Low-elevation NAN AD 25 8 221 254 0 0 1 1 0 0 0 0HY 21 8 28 57 0 0 1 1 0 0 0 0

MAL AD 105 13 291 409 0 0 6 6 0 0 0 0HY 38 0 54 92 0 0 1 1 0 0 0 0

BRY AD 45 25 880 950 0 0 1 1 0 0 0 0HY 38 15 45 98 0 0 0 0 0 0 0 0

Total 272 69 1519 1860 0 0 10 10 0 0 0 0Mid-elevation COO AD 0 0 1 1 15 0 103 118 0 0 0 0

HY 3 0 1 4 27 3 12 42 1 0 1 2CRA AD 72 1 13 86 25 1 24 50 0 0 0 0

HY 34 0 6 40 19 0 5 24 0 0 0 0WAI AD 0 0 0 0 2 0 11 13 2 0 0 2

HY 0 0 0 0 3 0 0 3 0 0 0 0PUU AD 0 1 0 1 23 0 45 68 10 0 3 13

HY 0 0 1 1 4 0 3 7 3 0 0 3KV AD 205 0 520 725

HY 181 0 34 225AIN AD 1175 400 1575

HY 1053 50 1103Total 2337 2 472 2811 504 4 757 1265 16 0 4 20

High-elevation CJR AD 189 1 2 192 128 0 14 142 120 0 1 121HY 68 0 1 69 33 0 2 35 0 0 0 0

SOL AD 169 0 3 172 248 2 23 273 177 3 4 184HY 36 0 2 38 92 0 2 94 44 0 0 44

KUL AD 168 1 169 186 22 208 433 12 445HY 42 0 42 97 1 98 229 3 232

Total 672 1 9 682 784 2 64 850 1003 3 20 1026

Notes: Age based on first capture for individual birds or each capture (for total captures). See Fig. A1 for locations of studysites on the Island of Hawaii.


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Fig. A1. Map of Biocomplexity (solid diamonds), Kilauea Volcano (KV solid circle), Ainahou (AIN solid star),

and Kulani (KUL solid triangle) study sites located on the eastern slope of Mauna Loa and Kilauea Volcanoes on

the Island of Hawai‘i. Biocomplexity study sites are Bryson’s (BRY), Malama Ki (MAL), Nanawale (NAN) at low

elevation, Crater (CRA), Cooper (COO), Pu’u (PUU), and Waiakea (WAI) at mid elevation, and C.J. Ralph (CJR)

and Solomon (SOL) at high elevation. See text and Appendix tables for additional information on study sites.


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Documents

Avian malaria in Hawaiian forest birds: infection and population