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
1
AND
DENNIS A. LAPOINTE2
U.S. Geological Survey, Wisconsin Cooperative Wildlife Research Unit, University of Wisconsin, Madison, Wisconsin 53706 USA
2
U.S. Geological Survey, Pacific Island Ecosystems Research Center, Hawai‘i National Park, Hawai‘i 96718 USA
3
University 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 (crosssectional) 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: Ó 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]
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SAMUEL ET AL.
affect host adaptation, extinction risk, and
genetic variation (Altizer et al. 2003, Poulin
2007, Wolinska and King 2009, Lachish et al.
2013). In turn ecological heterogeneity can alter
disease dynamics and shape the interaction and
coevolution between hosts and parasites (Ostfeld
et al. 2005, Poulin 2007, Real and Biek 2007,
Wolinska and King 2009, Lachish et al. 2013).
Characteristically, vector-borne pathogens are
generalists that infect multiple hosts; therefore,
heterogeneity in host resistance can also alter
species-specific risk of infection and community
dynamics (Altizer et al. 2003, Keesing et al. 2006).
Studies on avian blood parasites, especially
Plasmodium species, have shown that host prevalence varies temporally, spatially, and among
sympatric species due to climatic, host susceptibility, vector abundance, or vector habitat availability (Merilä et al. 1995, Sol et al. 2000, Wood et
al. 2007, Loiseau et al. 2010, Sehgal et al. 2011,
Lachish et al. 2013). An improved understanding
of how these biotic and abiotic factors influence
host-parasite interactions is of particular conservation relevance given rapid changes in the
world’s climate and increasing habitat fragmentation. Understanding the ecological drivers of
vector borne diseases in wildlife also has strong
implications for transmission and potential control of similar diseases to humans.
Avian malaria, a worldwide disease caused by
the blood parasite Plasmodium relictum, was likely
brought to Hawai‘i in the early 20th Century
(Laird and van Riper 1981, van Riper et al. 1986,
Atkinson and LaPointe 2009a). Malaria, together
with the previously introduced avian pox virus
(Avipoxvirus spp.), posed a major new threat to
immunologically naı̈ve Hawaiian birds (Warner
1968). Both avian diseases are readily transmitted
by the southern house mosquito (Culex quinquefasciatus), which was introduced as early as 1826
(Halford 1954, Hardy 1960). A wave of native
bird extinctions during the 1920s and 1930s has
been attributed to avian malaria, and native birds
below 1,500 m elevation continue to be at risk
from malaria (Goff and van Riper 1981, van Riper
et al. 1986). Above that elevation mosquitoes are
rare, allowing native forest birds to survive. Later
studies reported many endemic Hawaiian species, especially Hawaiian honeycreepers, are
highly susceptible to avian malaria, effective
disease transmitters, and chronically-infected,
INTRODUCTION
Infectious diseases can have significant impacts on biological conservation and biodiversity
(Daszak et al. 2000, Dobson and Foufopoulos
2001). The increasing emergence of wildlife
diseases with potential threats to ecological
systems, as well as domestic animal and human
health, emphasize the importance of understanding disease dynamics and associated risks to
biological conservation and human health. As
invasive species, human development, and climate change alter host or vector communities
and habitats, this information is increasingly
essential for long-range conservation planning
for species that face significant climate (Harvell et
al. 2002) and environmental change, particularly
for threatened or endangered species. However,
determining wildlife disease dynamics including
rates of infection, drivers of transmission, vector
feeding preferences, and host mortality presents
significant challenges (McCallum et al. 2001,
Wobeser 2008). The importance of these epizootiological parameters for understanding hostpathogen dynamics, population effects, and on
host-pathogen evolution have long been recognized, but are infrequently addressed (Scott 1988,
Oli et al. 2006, Murray et al. 2009, Lachish et al.
2011a). Quantifying epizootiological parameters
in wildlife populations is difficult because methods used in human epidemiology seldom apply
(McCallum et al. 2001, Caley and Hone 2004,
Lachish et al. 2011a); however, recent applications of multi-state mark-recapture models have
provided an important tool to assess infection
dynamics and population impacts (Faustino et al.
2004, Senar and Conroy 2004, Conn and Cooch
2009, Atkinson and Samuel 2010) while accounting for differential capture heterogeneity (Jennelle et al. 2007).
In addition to having direct wildlife conservation and management implications, diseases can
profoundly affect ecological and evolutionary
processes. For example, host and parasite diversity can influence disease prevalence, host and
parasite abundance, and evolutionary outcomes
(Holt and Pickering 1985, Altizer et al. 2003,
Keesing et al. 2006). When pathogens impose a
significant fitness cost on their hosts, spatial and
temporal variation in the risk of infection can
generate differential selection pressures that
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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, Atkinson
and Samuel 2010). In contrast, malaria has
minimal impact on the survival of non-native
birds, which also have a limited period of
effective disease transmission. Because of their
high susceptibility, prior workers hypothesized
that malaria played a key role in historical
population declines of native birds (Warner
1968, van Riper et al. 1986, Atkinson et al.
1995). However, data that quantify disease
transmission or address the demographic consequences of malaria in native Hawaiian birds are
limited. This information is critical for assessing
disease risks, developing conservation strategies
(Hobbelen et al. 2012), and predicting the future
impact of climate on disease and birds (Benning
et al. 2002, Atkinson and LaPointe 2009b).
The goal of our study was to investigate
species and elevation patterns in malaria infection of native birds along a 1700 m altitudinal
gradient on the Island of Hawai‘i. We focused on
three species of Hawaiian honeycreepers that
differ in their susceptibility to malaria and
movement across the landscape. We used serology and change in disease status (susceptible to
recovered) of captured and marked birds to
identify susceptible and recovered (chronically
infected and immune) birds. We combined multistate capture-recapture (longitudinal) models
with age-prevalence (cross-sectional) models to
simultaneously estimate disease transmission
and mortality, survival, and capture rates (Atkinson and Samuel 2010). This novel approach is
advantageous because age-prevalence models
can potentially estimate disease transmission
from single capture (cross-sectional) data for
known age animals, but multi-state models
require recapture of marked individuals. Estimation of these epizootiological and demographic
parameters was conducted using a Bayesian
state-space model of capture and recapture data
(Kery and Schaub 2012, King 2012). Unlike past
studies, this approach also allowed us to estimate
species-specific patterns of malaria infection,
mortality, and population impacts across the
altitudinal gradient in Hawai‘i.
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METHODS
Study species and area
We studied three of the most abundant
honeycreepers remaining in Hawaiian forests.
The Iiwi (Vestiaria coccinea) is highly susceptible
to avian malaria (Atkinson et al. 1995) while
Hawai‘i Amakihi (Hemignathus virens) and Apapane (Himatione sanguinea sanguinea) are moderately susceptible and chronically infected
individuals are believed important reservoirs
for this disease (Atkinson et al. 2000, Yorinks
and 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 seasonal
or ephemeral nectar resources; in contrast, the
more generalist Amakihi is relatively sedentary
throughout the year (Scott et al. 1986, Ralph and
Fancy 1995, Fancy and Ralph 1997, Hart et al.
2011). These species also differ in their ability to
exploit high quality food resources (Pimm and
Pimm 1982).
Our evaluation involves four longitudinal
studies of avian malaria infection on the Island
of Hawai‘i. The study area comprises approximately 1100 km2 on the eastern flanks of Mauna
Loa volcano in the southeast corner of Hawai‘i
(Appendix: Fig. A1). The Biocomplexity study
was conducted as part of a collaborative research
effort (NSF grant DEB 0083944) on the Biocomplexity of Introduced Avian Diseases in Hawai‘i.
For the Biocomplexity study, nine 1-km2 study
sites were established along an altitudinal gradient from 25 to 1800 m above sea level and
stratified into three major disease ‘‘zones’’ based
on elevations identified by van Riper et al. (1986).
We had two study sites (SOL and CJR) at high
elevation (.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 a
0.5-ha mid-elevation (1200 m) study site at
Kilauea Volcano (KV) from 1992 to 1998. These
data were originally reported in Atkinson and
Samuel (2010), but we conducted a reanalysis
using new statistical models (below) and interpret the results in the larger context of the avian
malaria on Hawai‘i. The Kulani study was
conducted on a 0.5-ha high elevation (1765 m)
site from February 1992 to July 1994 concurrently
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with the KV study. The Ainahou site (AIN) was
located at mid-elevation (915 m) in Hawaii
Volcanoes National Park. Ainahou was an
operating cattle ranch from 1941 until 1971 and
is an open understory, mesic ohia forest (Kilpatrick et al. 2006a). All study sites were in mesicwet forest (840–4200 mm annual rainfall) dominated by ohia (Metrosideros polymorpha), the
primary canopy tree and food source for nectarivorous honeycreepers in Hawai‘i. There were
broad similarities in substrate age, rainfall, and
vegetation for sites within the same altitudinal
zone. Intensity of disease transmission varies
across this landscape primarily driven by seasonal and altitudinal differences in both temperature and rainfall that affect abundance of Culex
quinquefasciatus and the intrinsic incubation of P.
relictum (Ahumada et al. 2004, Ahumada et al.
2009, Samuel et al. 2011). Mean monthly temperatures range from 248C at low elevation to
approximately 138C at high elevation.
cloacal protuberance, plumage characteristics,
and measurements of wing chord, culmen length
and tarsus length (Pyle 1997; USGS, unpublished
data). We determined age as hatch year (HY),
second year (SY), or after-second-year (ASY)
based on plumage (Fancy et al. 1993); SY and
ASY birds were collectively considered as adults
(AD). We obtained blood samples (,1% of body
weight) by jugular venipuncture with a heparinized 28.5-gauge insulin syringe. Blood smears
were prepared and fixed in absolute methanol in
the field. Remaining blood was transferred to
microhematocrit tubes and centrifuged to separate plasma, which was frozen at 708C for
serological analysis. All captures and blood
samples were collected under approved Animal
Care and Use Protocols through the U.S. Geological Survey–National Wildlife Health Center,
Madison, WI (1992–1998) or the University of
Hawaii–Manoa (2000–2006).
Testing for malarial infection
Banding and blood sampling
Within 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 21–28
days as the humoral and cellular immune
systems respond (van Riper et al. 1986, Atkinson
et al. 1995, Atkinson et al. 2000, Yorinks and
Atkinson 2000). Antibodies can develop as early
as 7 days PI (Atkinson et al. 2001b). Mortality
typically occurs between 19-30 days PI (Atkinson
et al. 1995, Atkinson et al. 2000, Yorinks and
Atkinson 2000). Native birds that survive are
chronically infected, have immunity to rechallenge with P. relictum, and probably remain
infectious to mosquitoes throughout their life
(Atkinson et al. 2001a), although detection by
microscopy is inconsistent (Jarvi et al. 2002).
Therefore, we used a combination of blood
smears and serology to determine the malarial
infection status of captured birds. Blood smears
were stained with buffered 6% Giemsa, pH 7, for
one hour, rinsed with tap water, and dried. We
scanned 100 microscope fields (approximately
30,000 to 50,000 erythrocytes) with a 403
objective to identify erythrocytic stages of the
parasite. For plasma samples collected as part of
the Biocomplexity and Ainahou studies, we used
an enzyme-linked immunosorbent assay (ELISA)
(Graczyk et al. 1993) to detect antibodies to
Mist-netting was conducted monthly in each of
the nine Biocomplexity study sites, from January
2002 through June 2005, using 18–24 mist-nets at
a height of 6 m (Woodworth et al. 2005). Nets
were operated for approximately 6 hours each
day between 06:30 and 14:00 HST, for 3–4 days/
month. At KV, we captured native and nonnative forest birds by mist-netting at 1–3 month
intervals from January 1992 to June 1998 at 16
fixed locations. Eleven net sites were operated
during the entire study, with five additional nets
added in August 1992 to increase the number of
captures. At Kulani, we captured birds at
monthly intervals from February 1992 to July
1994 at 13 fixed net sites within the study site. At
Ainahou, we mist-netted, banded, and bled birds
for 4 days on alternate weeks from December
2001 to December 2004. A varying number of
nets were set at each session depending upon
number of bird captures, availability of bird
banders, and intensity of tree bloom. Depending
on weather conditions, nets were operated from
1-2 h after sunrise to mid-afternoon on 3–5
consecutive days during each sampling period.
For all studies, captured birds were banded with
US Fish and Wildlife Service numbered aluminum leg bands for subsequent identification. Sex
of birds was determined by brood patch or
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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 þ 1
True state at time t
Susceptible
SR
Recovered
S
(1 w ) 3 s
0
0
Susceptible
Recovered
Dead
SR
w
3 (1 m) 3 s
sR
0
Dead
R
SR
S
1–((1 w ) 3 s þwSR 3 (1 m) 3 sR)
1 sR
1
recovered because active birds are likely malaria
survivors.
erythrocytic stages of P. relictum and identify
strongly positive and negative samples. Samples
with ELISA values (mean absorbance of triplicate
samples the mean absorbance of triplicate
negative controls)/(mean absorbance of triplicate
positive controls mean absorbance of triplicate
negative controls) 3 100) between 15 and 65 were
considered equivocal and further tested by
immunoblot (Atkinson et al. 2001a). Plasma
samples from the Kulani and KV studies were
tested by immunoblot alone. Sensitivity and
specificity of serological tests are comparable to
PCR (Jarvi et al. 2002, VanderWerf et al. 2006)
and helped distinguish acute from chronic
infections where parasitemias are too low to be
consistently detected by microscopy or PCR
(Atkinson et al. 2001a, Jarvi et al. 2002).
Captured birds were considered susceptible
(antibody and parasitemia negative) or recovered/chronically infected (antibody positive, parasitemia positive or negative). Birds with acute
infections were antibody negative and parasite
positive (Atkinson et al. 2001a), but these made
up less than 5% of captures (Appendix: Table
A1). Acutely infected birds were unlikely to be
captured as clinical signs of infection include
reduced activity, acute morbidity, or mortality
(Yorinks and Atkinson 2000, Atkinson and
Samuel 2010). For analysis, we considered
acutely infected birds that were captured to be
Epizootiological modeling
We analyzed longitudinal (capture-recapture)
data using Bayesian state-space multi-state models (Kery and Schaub 2012, King 2012). We
combined longitudinal estimation of disease
transmission with a discrete cumulative ageprevalence model (Atkinson and Samuel 2010)
to simultaneously estimate time-specific (i ) or
seasonal (winter ¼ Wi, spring ¼ Sp, summer ¼ Su,
and fall ¼ Fa) capture rate (piS, piR), transition
probability (w iSR) from susceptible (S) to recovered (R), malaria fatality (m), AD survival of
recovered (siR) and susceptible birds (siS) for each
season-time period (i ¼ 11, 13, 16 and 26 for
Kulani (KUL), Biocomplexity, Ainahou (AIN),
and KV studies, respectively) (Tables 1–3).
Although we used open population models we
did not account for emigration from our study
sites; therefore, we calculated apparent survival
which underestimates true survival (White and
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.
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.
Observation at time t
True state
at time t
Susceptible
Recovered
Dead
Captured in
susceptible state
S
p
0
0
Captured in
recovered state
0
pR
0
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True state
at time t
Not
captured
S
Susceptible
Recovered
Dead
1p
1 pR
1
5
True state at time t þ 1
Susceptible
SR
(1 w )
0
0
Recovered
(1 m) 3 w
1
0
Dead
SR
0
0
1
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SAMUEL ET AL.
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
the same year. We also estimated separate
capture, malaria fatality (m), and malaria infection rates for HY birds, but assumed SY and ASY
birds were similar (Tables 1 and 3). For known
age birds capture rates have no biological
meaning because they are based on captures of
birds know to be alive. We estimated annual
survival and malaria infection rates as the
product of the seasonal survival SY ¼ sWi 3 sSp
3 sSu 3 sFa or seasonal infection IY ¼ (1 (1 IWi )
(1 ISp) (1 ISu) (1 IFa)) (Atkinson and Samuel
2010), respectively. We also determined the
annual population mortality rate from malaria
M ¼ IY 3 S/H 3 m (Atkinson and Samuel 2010) for
HY birds (S/H ¼ 1.0; 100% susceptible at hatch)
and for AD (S/H ¼ no. of susceptible AD
captured in winter/no. of AD captured in winter).
We enhanced model parameter estimates using
Bayesian priors for the estimated malaria fatality
rates (m) for the three native species based on
experimental laboratory studies from high and
low 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(survivors þ 1, fatalities þ 1)) to estimate priors
based on the number of survivors and fatalities
in laboratory studies for each species: Iiwi b(2, 9),
Apapane b(6, 4), Amakihi b(13, 19), and lowelevation Amakihi b(11, 2). Because our primary
focus was on disease processes we also used the
95% confidence intervals on annual survival rates
from previous studies (Woodworth and Pratt
2009: Table 8.1) to limit model estimates of
seasonal non-malaria survival as follows: Iiwi
uniform (0.81, 0.91), Apapane uniform (0.83,
0.97), Amakihi uniform (0.90, 0.95). Preliminary
analysis showed these annual survival priors had
little effect on disease related parameter estimates.
Models were parameterized so malaria incidence was equal to the transition from susceptible to recovered status (Ii ¼ wiSR), and occurred
prior to survival of recovered birds (Kery and
Schaub 2012) which allowed us to estimate the
malaria 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) for
seasonal, time-specific, or constant capture, survival, and infection rates for each native species
(Amakihi, Apapane, and Iiwi ) and elevation
Model description
Model 1
A
Model 2
A
Model 3
A
Model 4
A
Model 5
A
Model 6
A
Model 7
A
Model 8
A
Model 9
A
Model 10
A
Model 11
A
Model 12
A
Model 13
A
Model 14
A
Model 15
A
pt,S6¼R Awt Ast,S6¼R Jpt,S6¼R Jwt
pt,S¼R Awt Ast,S6¼R Jpt,S¼R Jwt
pt,S¼R Awt Ast,S¼R Jpt,S¼R Jwt
pt,S¼R Aw Ast,S6¼R Jpt,S¼R Jw
pt,S¼R Awt AsS6¼R Jpt,S¼R Jwt
pt,S¼R Awt AsS¼R Jpt,S¼R Jwt
pt,S¼R Aw Ast,S¼R Jpt,S¼R Jw
pS¼R Awt Ast,S¼R JpS¼R Jwt
pt,S¼R Aw AsS6¼R Jpt,S¼R Jw
pS¼R Awt AsS6¼R JpS¼R Jwt
pS¼R Awt AsS¼R JpS¼R Jwt
pt,S¼R w AsS¼R Jpt,S¼R
pS¼R Aw Ast,S¼R JpS¼R Jw
pS¼R Aw AsS6¼R JpS¼R Jw
pS¼R Aw AsS¼R JpS¼R Jw
Notes: Model descriptions are based on capture (p),
transition (w), and survival (s) parameters. Subscripts indicate
the type of parameter heterogeneity; t indicates parameters
are estimated for each time interval, S ¼ R indicates parameter
estimates are similar for susceptible and recovered birds, and
S 6¼ R indicates all estimates are different. Superscripts
represent age-specific parameter estimates for either AD or
HY; no superscript indicates a common parameter for both
AD and HY. In addition, malaria mortality rate (m) was
estimated for all models. For example, model pt,S¼R Awt sS6¼R
J
wt has time specific capture probabilities that are equal for
susceptible and recovered birds and for AD ¼ A and HY ¼ J,
time-specific and age-specific transition (infection) probabilities, and constant survival probabilities across time that differ
between susceptible and recovered birds.
Burnham 1999).
Because acutely infected individuals were
seldom captured and have relatively high mortality, we estimated the transition probabilities of
susceptible individuals to recovered (chronically
infected). For age-prevalence modelling, we
estimated cumulative age-prevalence parameters
(piS, piR and wiSR) conditioned on the first capture
(survival ¼ 1) of known age (HY and SY) birds
which are susceptible at birth (hatch). We
randomly assigned a birth season for each HY
and SY bird based a species- and elevationspecific multinomial distribution of AD birds
with brood patches captured during the Biocomplexity study (Appendix: Table A2). SY birds
were randomly assigned a season in the year
prior to capture. HY birds were assigned a
season that preceded their capture season within
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from the Biocomplexity, KV, Ainahou, and
Kulani studies. We used the Bayesian Information Criterion (BIC, Schwarz 1978) to compare
alternative models and identify parsimonious
models for capture, survival, and transition
probabilities. Estimated model parameters are
reported as posterior mean estimates and 95%
Bayesian Credible Intervals (BCI). Because transient (non-residents) animals are common in
most avian populations and their presence
negatively biases survival rates, we evaluated
capture data for the presence of transient AD
birds (Pradel et al. 1997) using TEST 3G.SR in
program U-CARE (Pradel et al. 2005). When
transient birds were important we removed first
captures of AD birds for model selection and to
estimate model parameters (Pradel et al. 1997).
We ran models with 30,000 MCMC replications
for burn-in and an additional 20,000 MCMC
replications for model convergence and to
estimate deviance and calculate BIC ¼ Deviance
þ Ln(N) 3 K, where N is the number of birds and
K is the number of model parameters (Link and
Barker 2010). Final model parameters were
estimated using 70,000 Markov Chain Monte
Carlo (MCMC) burn-in replications and 30,000
MCMC replications. Model convergence was
assessed based on visual convergence of the
MCMC replicates, smoothness of the posterior
parameter distributions, and the MCMC error for
each parameter being ,100-fold smaller than the
posterior standard deviation. We used generalized v2 methods for binomial variables (Sauer
and Williams 1989) to evaluate species and
elevation differences in malaria infection and
survival.
We also used fp to calculate an index of the
relative contribution of native species (in pairs) to
infecting susceptible mosquitoes IMj,k ¼ fpj,k 3
IBj/IBk, where IB is the number of infected birds
for each species calculated as the proportion of
infectious birds 3 P. Our approach assumes that
avian species have equal likelihood of becoming
infected by mosquitoes, which is supported by
experimental studies showing 98% infection of all
three species from a single bite by an infectious
mosquito (Samuel et al. 2011: Appendix B; C. T.
Atkinson, unpublished data). Both equations were
solved using Bayesian analyses which included
the species and elevation specific parameters and
variances on the right-hand-side of the equations.
RESULTS
Prevalence and intensity of infection
We obtained blood samples from 5,353 Hawai‘i Amakihi, 2,116 Apapane, and 1,046 Iiwi
during four studies on Kilauea and Mauna Loa
Volcanoes during 1992–1998 and 2001–2005
(Appendix: Table A1). Overall prevalence of
malaria based on parasitemia and serology at
first capture ranged from 2.6% (27/1,046) in Iiwi
to 40% (837/2,116) in Apapane and 39% (2,072/
5,353) in Hawai‘i Amakihi. Prevalence was
strongly influenced by elevation, with lowest
prevalence of infection at high elevation (2.2% for
Iiwi, 7.8% for Apapane, and 1.5% for Amakihi )
followed by mid elevation (20% for Iiwi, 60% for
Apapane, and 17% for Amakihi ), and low
elevation studies (no captures for Iiwi, 100% for
Apapane, and 85% for Amakihi ). We classified
.97% of the malarial infections as chronic
(recovered) and the remaining birds were acutely
infected (Appendix: Table A1), but were classified as recovered in our analyses.
Vector-host interaction
Because densities of adult mosquitoes are low
and collection of blood fed Culex is difficult in
Hawai‘i, especially at mid and high elevations,
we estimated vector feeding preference based on
the relative annual infection rates in AD birds
adjusted for their population density. We estimated the relative vector preference of bird
species j over k for each pair of species for midand high-elevation Biocomplexity sites: fpj,k ¼ Ij/
Pj/Ik/Pk, where I and P are the annual infection
rates (IY ) and population density for each species,
respectively. In this calculation fpj,k is an estimated odds ratio for preference of species j to k.
v www.esajournals.org
Species-specific patterns—Amakihi
Low-elevation forests.—Amakihi were the only
species with sufficient captures to evaluate
epizootiological patterns across all three elevations (Appendix: Table A1). Amakihi in lowelevation forests were also the only population of
native birds with regular capture of acutely
infected birds (3.7% of captures). In low-elevation
forests the capture data was best represented by
Model 12 (Table 5) with equal time-specific
capture rates for susceptible and recovered birds
7
June 2015 v Volume 6(6) v Article 104
SAMUEL ET AL.
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.
DBayesian information criterion (BIC)
Hawai’i Amakihi
Apapane
Iiwi
Model number
Low
Mid
AIN
High
KUL
Mid
KV
High
KUL
High
KUL
Model
Model
Model
Model
Model
Model
Model
Model
Model
Model
Model
Model
Model
Model
Model
291
244
193
111
143
143
24
482
25
453
436
0
324
310
304
411
275
226
182
164
157
117
148
64
102
92
63
56
3
0
335
851
686
350
817
893
274
1125
0
1223
1240
289
691
646
507
370
262
205
170
140
150
105
216
54
202
136
142
47
6
0
253
159
120
107
90
90
71
66
45
39
37
67
25
0
0
493
371
295
243
233
220
164
199
199
149
131
10
61
2
0
715
670
433
254
434
417
136
537
6
552
532
0
148
95
94
543
407
331
270
261
257
202
215
41
154
133
90
54
11
0
364
231
175
151
133
129
92
119
49
74
68
41
42
6
0
344
201
142
114
98
96
59
13
12
73
72
5
44
0
1
221
150
95
91
79
67
41
181
0
185
143
17
186
159
159
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Notes: For Hawai‘i Amakihi transient adults were removed for all sites except mid-elevation. For Apapane transient adults
were removed at KV. For Iiwi transient adults were removed at all sites. Transient adults were removed based on U-CARE test
G3.SR (P , 0.05).
that differed between AD and HY birds, constant
malaria infection that was equal for AD and HY,
and constant non-malaria survival that was
similar for disease classes. We found significant
(Z ¼ 7.07, P , 0.001) evidence of transient birds
and removed first captures of AD. Annual
population 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 avoid
malaria infection annually (Table 6 and Fig. 1).
AD seasonal capture rates were highest in
summer and fall (Table 7).
Mid-elevation forests.—Most captures of Amakihi in mid-elevation forests were from the
Ainahou study, and most of the captures were
susceptible birds (Appendix: Table A1). For the
Biocomplexity sites, Models 14 and 15 (DBIC ¼ 3)
provided the best fit (Table 5) with time-constant
malaria infection rates, and capture and survival
rates that were time-constant and similar for
susceptible and recovered birds. We found no
evidence for transient birds in the data (P ¼ 0.30).
AD capture rates were approximately 0.10 and
constant throughout the year (Table 7). Annual
population survival without malaria mortality
was 0.70 (0.66, 0.79). Annual (0.33) infection rates
for AD were moderate, but infection rates were
higher for HY (0.68) (Table 6 and Fig. 1).
For the Ainahou study, Model 9 provided the
best fit to this much larger data set and we found
significant evidence (P , 0.001) of transient
birds, so first capture of AD birds were removed
(Table 5). A simplified model with seasonal
capture rates for AD and HY birds and equal
survival for susceptible and recovered birds
provided an improved fit to the capture data (D
BIC ¼ 61). AD seasonal capture rates were
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
AD seasonal malaria infection (Ii )
HY seasonal malaria infection (Ii )
HY annual malaria infection (IY ) HY malaria fatality (m)
AD non-malaria survival (Sy)à
0.48 6 0.03
0.10 6 0.04
0.26 6 0.06
0.68 6 0.10
0.73 6 0.05
0.70 6 0.04
AIN
0.09
0.27
0.72
0.86
0.68
6
6
6
6
6
0.01
0.01
0.02
0.02
0.02
High
0.02
0.37
0.84
0.87
0.74
6
6
6
6
6
0.01
0.04
0.04
0.03
0.04
KUL
0.05 6 0.03
0.31 6 0.07
0.76 6 0.10
0.73 6 0.05
Annual malaria infection rate IY (see text).
à Annual non-malaria survival estimates: the annual survival probability with malaria fatality removed SY (see text).
v www.esajournals.org
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June 2015 v Volume 6(6) v Article 104
SAMUEL ET AL.
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).
the best fit for both studies (Table 5). Model 15
with seasonal capture rates for AD and HY birds
provided a modest increase in model fit (DBIC ¼
8) for the Kulani data. For the Biocomplexity
study, AD capture rates were approximately 0.15
and constant during the year. At Kulani AD
capture rates were lowest in spring and highest
during fall and winter (Table 7). Annual population survival without malaria mortality was
0.73–0.74 for these two studies. Annual (0.09–
0.17) malaria infection rates were low for AD
birds 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–
highest in fall and winter (Table 7). Annual (0.29)
infection rates for AD were moderate. Higher
malaria infection and fatality rates for HY birds
(Table 6 and Fig. 1) indicates that malaria
dynamics may be much different than in AD
birds. Annual population survival without malaria mortality was 0.70 (0.66, 0.79).
High-elevation forests.—Amakihi were frequently captured in high-elevation forests, but few
birds (,2%) had acute or chronic malarial
infection (Appendix: Table A1). We found strong
evidence (P , 0.03) for transient Amakihi in both
the Biocomplexity and Kulani studies and removed first captures of AD birds. Model 15 was
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
Hawai’i Amakihi
Low
Mid
AIN
High
KUL
Apapane
Mid
KV
High
KUL
Iiwi
High
KUL
Winter
Spring
Summer
Fall
0.067
0.099
0.210
0.151
0.326
6
6
6
6
6
0.063
0.020
0.019
0.015
0.072
0.081
0.099
0.021
0.151
0.092
6
6
6
6
6
0.029
0.020
0.006
0.015
0.040
0.231
0.099
0.126
0.151
0.208
6
6
6
6
6
0.037
0.020
0.014
0.015
0.066
0.397
0.099
0.278
0.151
0.409
6
6
6
6
6
0.039
0.020
0.022
0.015
0.058
0.032
0.107
0.074
0.056
6
6
6
6
0.010
0.013
0.012
0.019
0.008
0.013
0.002
0.012
6
6
6
6
0.006
0.005
0.002
0.008
0.034
0.032
0.047
0.012
6
6
6
6
0.012
0.008
0.011
0.013
0.130
0.263
0.095
0.229
6
6
6
6
0.230
0.021
0.016
0.060
0.120 6 0.038
0.230 6 0.045
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0.036 6 0.020
0.017 6 0.012
9
0.122 6 0.040
0.069 6 0.029
0.357 6 0.065
0.416 6 0.051
June 2015 v Volume 6(6) v Article 104
SAMUEL ET AL.
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.
0.76) in AD birds at the Biocomplexity sites, but
higher (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 Biocomplexity
sites, but AD and HY malaria mortality was not
different at Kulani (Table 6). These patterns show
that malaria dynamics and impact are likely
more substantial in HY Amakihi.
Comparisons among elevations.—AD Amakihi
populations in low-elevation forests experience
greater annual malaria transmission than mid(v2 ¼ 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 high
elevation (Fig. 1). We found no evidence of
different AD malaria fatality between mid- and
high- elevation Amakihi (v2 ¼ 1.08, P ¼ 0.3), with
an average rate of 68% (Fig. 2B). Malaria fatality
was 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 rates
of mid- and high-elevation birds (v2 ¼ 236, P ,
0.001). The malaria fatality rate in low-elevation
forests was substantially lower than the prior
from experimental studies (Fig. 2A). Malaria
fatality rates among mid- and high-elevation
v www.esajournals.org
studies were slightly higher than prior estimates
from laboratory experiments; except for the highelevation Biocomplexity site where fatality was
nearly identical (Fig. 2B). Malaria population
impacts were least in low-elevation AD Amakihi
compared with mid (v2 ¼ 24, P , 0.001) and high
elevations (v2 ¼ 2.79, P , 0.095), but mid
elevation was not different from high elevation
(v2 ¼ 2.02, P , 0.155). Malaria reduced the lowelevation AD population ,1% annually, and
reduced the HY population by 1–4%. With
average malaria fatality rate of 0.68 for AD (Fig.
2) the annual population mortality from avian
malaria in mid-elevation forests was 15% (10–
21%) for AD and likely much higher (44–62%) for
HY birds. Estimated annual population impacts
were relatively small for AD (5.3% and 12%)
Amakihi populations at high-elevation study
sites, respectively. However, malaria impacts
were likely higher for high-elevation HY birds
(73% and 57%).
Species-specific patterns—Apapane
Few Apapane were captured in low-elevation
forests, and all these birds had previously
survived malaria infection (Appendix: Table
10
June 2015 v Volume 6(6) v Article 104
SAMUEL ET AL.
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.
Apapane
Model parameter
Mid
KV
AD seasonal malaria infection (Ii )
HY seasonal malaria infection (Ii )
HY annual malaria infection (IY ) HY malaria fatality (m)
AD non-malaria survival (Sy)à
0.27 6 0.04
0.24 6 0.02
0.49 6 0.02
0.65 6 0.05
Iiwi
High
0.03
0.32
0.79
0.89
0.50
6
6
6
6
6
0.01
0.04
0.03
0.04
0.02
KUL
0.08
0.32
0.78
0.81
0.51
6
6
6
6
6
0.04
0.06
0.08
0.07
0.04
High
KUL
0.08 6 0.03
0.31 6 0.05
0.77 6 0.07
0.05 6 0.02
0.20 6 0.03
0.60 6 0.06
0.55 6 0.07
0.60 6 0.07
Annual malaria infection rate IY (see text).
à Annual non-malaria survival estimates: the annual survival probability with malaria fatality removed SY (see text).
A1), making it impossible to estimate epizootiological parameters for low-elevation Apapane,
which are rare in these forests (Spiegel et al.
2006). By comparison, captures of Apapane were
frequent in mid- and high-elevation forests
where only a few birds were acutely infected
with malaria. Capture rates of AD Apapane
varied seasonally at all study sites and were
typically highest in fall and lowest in spring
(Table 7). For the mid-elevation KV study we
found moderate evidence for transient birds (Z ¼
1.84, P ¼ 0.066) and removed first AD captures.
For all other studies there was no evidence of
transient birds (P . 0.05).
Mid-elevation forests.—Model 12 provided the
best fit for Apapane captures for KV, while
Model 15 was the best fit for the Biocomplexity
study (Table 5). For the Biocomplexity and KV
studies, seasonal capture rates provided a substantial improvement in model fit; DBIC ¼ 49 and
.200, respectively. Annual (0.67 and 0.71)
malaria infection rates for AD and HY birds
were similar for both mid-elevation studies
(Table 8 and Fig. 1) demonstrating a high
likelihood of susceptible Apapane becoming
infected annually. Annual population survival
without malaria mortality was 0.49–0.65 for these
two studies.
High-elevation forests.—Apapane were frequently captured in high-elevation forests, but
acutely infected birds were rare and the number
of recovered birds was limited (Appendix: Table
A1). For the Biocomplexity and Kulani studies,
differing seasonal capture rates provided a
substantial improvement in the fit for Model 15;
DBIC ¼ 69 and 10, respectively. Annual (0.10 and
0.28) malaria infection rates for AD Apapane at
Biocomplexity and Kulani studies indicated a
low to moderate risk of malarial infection (Fig. 1).
v www.esajournals.org
AD malaria fatality (0.42 vs. 0.43) was nearly
identical for both studies (Fig. 3A). However,
annual infection and fatality rates were higher for
HY birds indicating malaria dynamics and
impacts where greater than for AD birds.
Estimated annual AD survival rate was 0.50–
0.51.
Comparisons among elevations.—AD Apapane
had similar rates of annual malaria infection for
mid- (v2 ¼ 0.4, P ¼ 0.52) and high-elevation (v2 ¼
2.2, P ¼ 0.14) studies (Fig. 1). However, malaria
infection (v2 ¼ 50, P , 0.001) was lower in highelevation forests. Except for the mid-elevation
Biocomplexity study, malaria fatality was slightly
higher than experimental infection. The two
high-elevation studies had posterior estimates
that were nearly identical to the prior (Fig. 3A),
suggesting that capture data were generally
insufficient to improve fatality estimates. The
average AD malaria fatality across all sites was
47%, suggesting malaria reduced the AD and HY
population approximately 10% and 28–35%
annually in mid-elevation forests and 5–12% in
high-elevation AD populations. Malaria fatality
and population impacts were much higher in
high-elevation HY Apapane, indicating that HY
birds may have higher malaria impacts than AD
Apapane at this elevation.
Species-specific patterns—Iiwi
High-elevation forests.—Iiwi were not captured
in low-elevation forests and only a few were
captured in mid-elevations forests (Appendix:
Table A1), restricting epizootiological models to
high elevations. These patterns correspond with
Iiwi abundance measured during bird surveys
(Gorresen et al. 2009, Hart et al. 2011). Capture of
either acutely and chronically infected birds at
high-elevation sites was infrequent (2%). We
11
June 2015 v Volume 6(6) v Article 104
SAMUEL ET AL.
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.
indicating that malaria may have a more
substantial impact on HY Iiwi populations.
Based on 93% average fatality, malaria reduced
high-elevation AD and HY Iiwi populations by
approximately 16–20% and 55–73% annually,
respectively. The annual non-malaria survival
rate for Iiwi was 0.55–0.60 (Table 8).
found strong evidence for transient birds (P ,
0.005) for both studies and removed first
captures of AD Iiwi for model estimation. For
Biocomplexity sites, Models 14 and 15 best fit the
capture data (DBIC ¼ 1). We found that Model 15
with seasonal capture rates substantially improved model fit (DBIC ¼ 78). For the Kulani
study, Model 9 best fit our data (Table 5), and it
was also improved by using seasonal capture
rates and equal survival for susceptible and
recovered birds (DBIC ¼ 58). Seasonal captures
of AD birds for both studies was highest in fall
and smaller in spring (Table 7). Seasonal and
annual malaria infection rates were similar for
both studies and indicated a moderate risk of
annual malaria infection (0.23) for AD Iiwi (Fig.
1). Malaria mortality for AD and HY Iiwi
averaged 0.93 (0.87, 0.98%); similar to the 95%
fatality reported by Atkinson et al. (1995) for
experimental studies (Fig. 3B). Malaria fatality in
Iiwi was the highest for any native birds (Figs. 2
and 3). Malaria infection rates were much higher
for HY Iiwi for these studies (Table 8 and Fig. 1),
v www.esajournals.org
Patterns among species
To evaluate species differences we compared
annual malaria infection rates for the same
elevation (Fig. 1). We were unable to make
species comparisons in low-elevation forests
because few Apapane and no Iiwi were found
at this elevation (Fig. 1). For AD birds in midelevation forests, annual malaria infection rates
for Apapane from the Biocomplexity (0.71) and
KV studies (0.67) were higher (v22 ¼ 35, P , 0.001)
than for Amakihi from Biocomplexity (0.33) and
Ainahou (0.30). In high-elevation forests, annual
malaria infection rates were similar (v25 ¼ 7.6, P ¼
0.18) among Amakihi, Apapane, and Iiwi. AD
malaria fatality (Fig. 2B) for mid- and high12
June 2015 v Volume 6(6) v Article 104
SAMUEL ET AL.
elevation Amakihi (66%) was greater (v2 ¼ 16, P
, 0.001) than Apapane (47%), but mortality for
Iiwi (93%) was greater (v2 ¼ 1247, P , 0.001) than
low-elevation Amakihi (3%), mid-elevation and
high-elevation Amakihi (v2 ¼ 48, P , 0.001), or
Apapane (v2 ¼ 92, P , 0.001). Thus, malaria
mortality was greatest in Iiwi, least in lowelevation Amakihi, and intermediate in midand high-elevation Amakihi and Apapane. Annual malaria impacts on AD birds in midelevation forests were greater for Amakihi
(15%) than Apapane (6%) (v2 ¼ 7.1, P ¼ 0.008).
In high-elevation forests malaria impacts were
similar (v2 ¼ 0.003, P ¼ 0.96) for AD Amakihi
(9%) and Apapane (8%). However, malaria
impacts were highest (v2 ¼ 4.4, P ¼ 0.036) for
AD Iiwi (21%) compared to Amakihi and
Apapane.
space model (Kery and Schaub 2012, King 2012)
to combine longitudinal and cross-sectional
capture-recapture models (Atkinson and Samuel
2010) to estimate malaria infection and mortality
rates, and population impacts of three species of
Hawaiian honeycreeper. This approach allowed
us to enhance traditional multi-state models by
incorporating single captures of known age birds
using an age-prevalence method (Caley and
Hone 2004, Heisey et al. 2006). Further, the
Bayesian framework allowed us to separate
infection and disease mortality; therefore, directly estimate disease impacts on each avian species.
It also allowed us to incorporate prior information on malaria mortality and annual survival
rates. An important assumption in combining
multi-state and age-prevalence data is similarity
in rates of disease infection and fatality. Our
experience showed that including age-prevalence
data was most helpful when disease infection
and fatality rates for known age birds (primarily
HY birds in our case) were similar to those for
AD birds. We found this situation for lowelevation Amakihi and mid-elevation Apapane
which had improved estimates of malaria fatality
(Figs. 2 and 3) and infection. However, when one
or both of these rates differ for HY birds there
was limited or no benefit from age-prevalence
data. In this situation disease and capture
parameters for HY birds may be highly correlated and estimation of infection or fatality rates for
HY birds may be confounded. As a result, it is
only appropriate to conclude that the disease
process is operating differently in the known age
individuals (a.k.a., young birds). Further, incorporating age-prevalence data with multi-state
models may also be beneficial when infection
rates are high (so that prevalence changes rapidly
with age) or when capture rates are low because
many individuals will have insufficient recaptures to estimate state transition rates.
Our study demonstrates that transmission of
avian malaria, susceptibility, and population
impact depends on the combination of both
elevation and species in Hawai‘i. Our results
show that malaria infection is lowest in highelevation forests where climate is less favourable
for mosquito populations and malaria parasite
development (Ahumada et al. 2004, Samuel et al.
2011). As a result, these high-elevation forests
currently provide a relatively disease-free refuge
Vector and host interaction
Culex mosquitoes appeared to prefer feeding
on Amakihi over Apapane in mid-elevation
forests (fp ¼ 3.15; 1.31, 5.79) where malaria
transmission was moderate. In high-elevation
forests, mosquitoes preferred feeding on Iiwi
over Apapane (fp ¼ 10.3; 2.57, 29.7). In these
forests Iiwi were somewhat favored over Amakihi (fp ¼ 3.96; 0.79, 13.3), but Amakihi and
Apapane had similar feeding preference (fp ¼
3.76; 0.58, 12.0); these preferences were not
statistically significant, likely because malaria
infection rates were low and poorly estimated.
Apapane were more likely to produce newly
infected mosquitoes than Amakihi at mid (IM ¼
7.23; 3.94, 17.8) and high (IM ¼ 3.29; 0.95, 25.1)
elevations. There was no difference between
high-elevation Iiwi and Amakihi (5.95; 0.84,
22.4) or Apapane (IM ¼ 1.15; 0.22, 3.7) in their
relative contribution to mosquito infections,
perhaps partly due to low infection rates.
DISCUSSION
Over the past three decades evidence has
accumulated about the prevalence and pathogenicity of avian malaria in native Hawaiian
species. However, we previously knew little
about malaria transmission patterns and disease
impacts on survivorship for different species of
honeycreepers, and across the elevation gradients found on Hawai‘i. We used a Bayesian statev www.esajournals.org
13
June 2015 v Volume 6(6) v Article 104
SAMUEL ET AL.
for susceptible honeycreepers (Ahumada et al.
2004, Atkinson and LaPointe 2009b). However,
daily and seasonal movements of birds to lowerelevation forests in search of nectar resources
(Ralph and Fancy 1995) and/or upslope movement of infected mosquitoes (Freed and Cann
2013) likely contribute to the malaria infection we
observed in these high-elevation populations. In
contrast, annual malaria infection was 2–4 fold
greater in mid-elevation forests and 1.5–2 times
greater in Apapane than Amakihi. In lowelevation Amakihi, malaria infection was .
90% which is more than three-fold greater than
mid-elevation and more than eight-fold greater
than high-elevation forests. We found that
malaria fatality was highest in Iiwi, followed by
Amakihi and Apapane in mid and high elevations, and lowest in low-elevation Amakihi. We
also found no differences in survival rates
between susceptible and chronically infected
birds during any studies, in contrast to results
from Kilpatrick et al. (2006a) for Amakihi at the
Ainahou site. The combination of high malaria
transmission and fatality for Iiwi (and mosquito
feeding preference), but also for Apapane and
mid-elevation Amakihi, likely explains why these
honeycreepers are absent from low-elevation
forests (Samuel et al. 2011).
Our results confirm laboratory studies (Atkinson et al. 2013) that malaria mortality in lowelevation Amakihi is lower (3%) than in mid- and
high-elevation Amakihi (42–68%), Apapane (16–
43%), or Iiwi (93%). Apparent adaptation
through tolerance to malaria provides a demographic advantage that allows low-elevation
Amakihi to increase in abundance, despite high
levels of malaria infection (Woodworth et al.
2005, Samuel et al. 2011, Atkinson et al. 2013).
Our estimated malaria fatality and annual
survival rates generally agree with previous
laboratory and field studies on Apapane, Iiwi,
and Amakihi from mid- and high-elevation
forests (Atkinson et al. 1995, Ralph and Fancy
1995, Yorinks and Atkinson 2000, Kilpatrick et al.
2006a, Woodworth and Pratt 2009). We note that
our malaria fatality and non-malaria survival
estimates were influenced by prior experimental
data or constrained to correspond with previous
results, respectively. However, we found lower
malaria fatality for low-elevation Amakihi (3%
vs. 17%; Atkinson et al. 2014), but similar fatality
v www.esajournals.org
for Iiwi (93% vs. 95%) compared to previous
laboratory experiments (Atkinson et al. 1995). A
surprising pattern in our results was the apparent higher level of malaria infection and/or
malaria fatality we found in young birds. As
indicated above, specific disease parameter estimates in this situation may be unreliable;
however, these findings indicate that malaria
may have a more substantial impact on young
birds than on adults. This finding is supportive of
previous studies that have suggested that juvenile birds are more susceptible to malarial
infection (van Riper et al. 1994) and requires
further investigation in the Hawaiian ecosystem.
Although we found that malaria fatality was
difficult to estimate, in part because we had
generally low capture rates, it provides a key
parameter in understanding host fitness and
evolutionary response (Lachish et al. 2011b),
and predicting demographic impacts of malaria
infection in Hawaiian birds (Samuel et al. 2011).
Previous studies on avian malaria have shown
spatial differences in Plasmodium prevalence (Sol
et al. 2000, Wood et al. 2007, Loiseau et al. 2010,
Sehgal et al. 2011, Lachish et al. 2013). Our study
builds on these results by demonstrating that
pathogen prevalence is driven by both elevation
differences in transmission pressure combined
with species-specific disease susceptibility. Together, these two key factors affect the distribution and abundance of endemic Hawaiian
honeycreepers, restricting most malaria susceptible species to high-elevation forests on Kaua‘i,
Maui, and Hawai‘i (Scott et al. 1986, van Riper et
al. 1986, Atkinson and LaPointe 2009a). These
spatial differences in parasite-mediated selection
pressure coupled with spatial host genetic
structure and gene flow can significantly influence host adaptation or maintaining genetic
diversity (Poulin 2007, Wolinska and King
2009). In Hawaii, the spatial gradient in selection
pressure has apparently produced two opposing
outcomes—local adaptation of low-elevation
Amakihi for malaria-tolerance (Woodworth et
al. 2005, Foster et al. 2007, Atkinson et al. 2013) or
local extinction of highly susceptible species like
Iiwi from low and mid-elevation forests (van
Riper et al. 1986). As climate warms and malaria
infection risk increases in Hawai‘i (Benning et al.
2002, Atkinson and LaPointe 2009b, Atkinson et
al. 2014) it is uncertain whether other endemic
14
June 2015 v Volume 6(6) v Article 104
SAMUEL ET AL.
honeycreepers will be able to evolve tolerance to
malaria. Further research is needed to determine
the suite of biotic and abiotic factors that facilitate
the coexistence of pathogens and hosts (via either
resistance or tolerance) and how these factors
interact with host demography and movement
across a landscape of heterogeneous selection
pressures. The factors driving these outcomes
have long been a focus of ecological study
(Anderson and May 1991), but are not well
understood. In Hawai‘i these processes likely
involve host susceptibility and demographic
recovery, vector feeding preferences, heterogeneous selection pressure, host genetic diversity,
and host gene flow. Understanding the importance of these factors is particularly critical for
endemic Hawaiian species that continue to
undergo steep population declines and range
restrictions as climate warms. Like human
malaria, spatial patterns of avian malaria are
driven by exogenous temperatures, altitude,
rainfall, and suitable habitat for larval mosquitoes 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 may
provide a seminal model to understand the
environmental and ecological drivers of human
malaria and to evaluate alternative control
strategies (Samuel et al. 2011, LaPointe et al.
2012).
Heterogeneous encounter rates between hosts
and parasites are important determinants of
parasite prevalence across host species and
disease transmission dynamics, and have significant consequences on host fitness (Kilpatrick et
al. 2006b, Medeiros et al. 2013). Previous studies
have relied on vector blood meals to evaluate
mosquito feeding preferences (Kilpatrick et al.
2006b, Hamer et al. 2009, Simpson et al. 2012,
Medeiros et al. 2013). However, in Hawai‘i the
low abundance of mosquitos in mid- and highelevation forests (LaPointe 2000) and their low
rate of malarial infection (LaPointe 2000, Samuel
et al. 2011) make capturing blood-fed mosquitoes
challenging. As an alternative, we used relative
host infection rates and relative host abundance
to assess host-vector contact rates. In mid- and
high-elevation forests, we found that mosquitoes
preferred feeding on Iiwi, then Amakihi, followed by Apapane. Despite the preference for
feeding on Amakihi, Apapane were much more
v www.esajournals.org
likely to produce newly infected mosquitoes and
therefore are a more important reservoir species,
likely because they are less susceptible to malaria
mortality, more abundant, and have higher
malarial infection rates than other native species.
These results suggest that Apapane may serve as
a significant reservoir host that enables higher
rates of disease transmission to more vulnerable
species such as Iiwi, and thereby facilitate
apparent competition where one host indirectly
competes with others via disease transmission
(Holt and Pickering 1985, McCallum and Dobson
1995). Our study identifies the need for research,
especially on other Hawaiian islands, to better
understand the factors that influence host-vector
encounters (e.g., mosquito feeding preference,
host defensive behavior, roosting locations) and
are potential drivers of differential malaria
transmission among species and how these
factors influence mosquito infection, the relative
impacts of malaria on native species, and
composition of the Hawaiian forest bird community.
Overall, we found patterns of malaria transmission across elevations that provide further
support that climate, based on an elevational
gradient, drives avian malaria through mosquito
population dynamics and malaria parasite development rates within the host mosquito (Ahumada et al. 2004, Ahumada et al. 2009, Atkinson
and Samuel 2010, LaPointe et al. 2010). As a
result, increased temperatures from global warming (Benning et al. 2002, Harvell et al. 2002, Freed
et al. 2005, Atkinson and LaPointe 2009b) are
likely to increase the severity and frequency of
malaria transmission at higher elevations in
Hawai‘i. Increasing temperatures may eliminate
high-elevation refugia that currently protect
many Hawaiian bird populations, and increase
malaria transmission and epizootics in midelevation forests (LaPointe et al. 2012). There is
recent evidence this may already be taking place
on Kaua‘i (Atkinson et al. 2014). This combination of factors will likely produce further
reductions and extinctions of native Hawaiian
birds, particularly for threatened species with
small, fragmented populations in high-elevation
forests and for species with high susceptibility to
malaria such as the Iiwi. In addition, higher
transmission of malaria in mid-elevation forests
may mean substantial declines in honeycreepers
15
June 2015 v Volume 6(6) v Article 104
SAMUEL ET AL.
Jacobi, and B. L. Woodworth, editors. Conservation
biology of Hawaiian forest birds. Yale University
Press, New Haven, Connecticut, USA.
Atkinson, C. T., and D. A. LaPointe. 2009b. Introduced
avian disease, climate change, and the future of
Hawaiian honeycreepers. Journal of Avian Medicine and Surgery 23:53–63.
Atkinson, C. T., J. K. Lease, B. M. Drake, and N. P.
Shema. 2001b. Pathogenicity, serological responses,
and diagnosis of experimental and natural malarial
infections in native Hawaiian thrushes. Condor
103:209–218.
Atkinson, C. T., K. S. Saili, R. B. Utzurrum, and S. I.
Jarvi. 2013. Experimental evidence for evolved
tolerance to avian malaria in a wild population of
low elevation Hawai‘i ‘Amakihi (Hemignathus
virens). Ecohealth 10:366–375.
Atkinson, C. T., and M. D. Samuel. 2010. Avian malaria
(Plasmodium relictum) in native Hawaiian forest
birds: epizootiology and demographic impacts on
‘Apapane (Himatione sanguinea). Journal of Avian
Biology 41:357–366.
Atkinson, C. T., R. B. Utzurrum, D. A. LaPointe, R. J.
Camp, L. H. Crampton, J. T. Foster, and T. W.
Giambelluca. 2014. Changing climate and the
altitudinal range of avian malaria in the Hawaiian
Islands—an ongoing conservation crisis on the
island of Kaua‘i. Global Change Biology 20:2426–
2436.
Atkinson, C. T., K. L. Woods, R. J. Dusek, L. S. Sileo,
and W. M. Iko. 1995. Wildlife disease and
conservation in Hawaii: pathogenicity of avian
malaria (Plasmodium relictum) in experimentally
infected Iiwi (Vestiaria coccinea). Parasitology
111:S59–S69.
Altizer, S., D. Harvell, and E. Friedie. 2003. Rapid
evolutionary dynamics and disease threats to
biodiversity. Trends in Ecology and Evolution
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Balls, M. J., R. Bødker, C. J. Thomas, W. Kisinza, H. A.
Msangeni, and S. W. Lindsay. 2004. Effect of
topography on the risk of malaria infection in the
Usambara Mountains, Tanzania. Royal Society of
Tropical Medicine and Hygiene 98:400–408.
Benning, T. L., D. LaPointe, C. T. Atkinson, and P. M.
Vitousek. 2002. Interactions of climate change with
biological invasions and land use in the Hawaiian
Islands: modeling the fate of endemic birds using a
geographic information system. Proceedings of the
National Academy of Science 99:14246–14249.
Caley, P., and J. Hone. 2004. Disease transmission
between and within species, and the implications
for disease control. Journal of Applied Ecology
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Conn, P. B., and E. G. Cooch. 2009. Multistate capturerecapture analysis under imperfect state observation: an application to disease models. Journal of
with moderate levels of susceptibility to avian
malaria, such as the Hawai‘i Amakihi. The
greatest challenge will be development of disease
control strategies that conserve the native Hawaiian species while considering the future
threats that may occur as climate warms.
ACKNOWLEDGMENTS
This research was funded through the U.S. Geological Survey’s Wildlife, Invasive Species, and Natural
Resource Protection Programs and a Biocomplexity
grant from the National Science Foundation
(DEB0083944). We also wish to thank our technical
staff, postdoctoral researchers, and numerous research
interns whose hard work and dedication made this
research possible. The use of trade names or products
does not constitute endorsement by the U.S. Government. This study was performed under the animal care
and use protocols approved at the University of
Hawai‘i, Manoa. E. Paxton and V. Henaux provided
many valuable comments that improved the paper.
The Department of Forest and Wildlife Ecology at the
University of Wisconsin-Madison provide assistance
with publication costs.
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SUPPLEMENTAL MATERIAL
APPENDIX
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.
Number captured
Hawai’i Amakihi
Apapane
Iiwi
Elevation
Study site
Age
S
I
R
Tot
S
I
R
Total
S
I
R
Tot
Low-elevation
NAN
AD
HY
AD
HY
AD
HY
25
21
105
38
45
38
272
0
3
72
34
0
0
0
0
8
8
13
0
25
15
69
0
0
1
0
0
0
1
0
221
28
291
54
880
45
1519
1
1
13
6
0
0
0
1
254
57
409
92
950
98
1860
1
4
86
40
0
0
1
1
0
0
0
0
0
0
0
15
27
25
19
2
3
23
4
205
181
0
0
0
0
0
0
0
0
3
1
0
0
0
0
0
0
0
1
1
6
1
1
0
10
103
12
24
5
11
0
45
3
520
34
1
1
6
1
1
0
10
118
42
50
24
13
3
68
7
725
225
0
0
0
0
0
0
0
0
1
0
0
2
0
10
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
3
0
0
0
0
0
0
0
0
0
2
0
0
2
0
13
3
400
50
472
2
1
3
2
1
0
9
1575
1103
2811
192
69
172
38
169
42
682
504
128
33
248
92
186
97
784
4
0
0
2
0
757
14
2
23
2
22
1
64
1265
142
35
273
94
208
98
850
16
120
0
177
44
433
229
1003
0
0
0
3
0
4
1
0
4
0
12
3
20
20
121
0
184
44
445
232
1026
MAL
BRY
Mid-elevation
Total
COO
CRA
WAI
PUU
KV
AIN
High-elevation
Total
CJR
SOL
KUL
AD
HY
AD
HY
AD
HY
AD
HY
AD
HY
AD
HY
AD
HY
AD
HY
AD
HY
Total
1175
1053
2337
189
68
169
36
168
42
672
2
1
0
0
0
1
2
3
Notes: Age based on first capture for individual birds or each capture (for total captures). See Fig. A1 for locations of study
sites on the Island of Hawaii.
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.
Proportion of adult females
with brood patches
Species
Hawai’i Amakihi
Apapane
Iiwi
Elevation Winter Spring Summer Fall
Low
Mid
High
Mid
High
High
019
0.20
0.22
0.30
0.41
0.52
0.54
0.60
0.78
0.50
0.59
0.48
0.27
0.20
0.0
0.20
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Notes: Proportions were used to randomly assign hatching
season to each HY and SY bird captured at that elevation.
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June 2015 v Volume 6(6) v Article 104
SAMUEL ET AL.
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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June 2015 v Volume 6(6) v Article 104