Revealing the drivers of parasite community assembly: using avian haemosporidians to model global dynamics of parasite species turnover

Abstract

Why do some regions share more or fewer species than others? Community assembly relies on the ability of individuals to disperse, colonize and thrive in new regions. Therefore, many distinct factors, such as geographic distance and environmental features, can determine the odds of a species colonizing a new environment. For parasites, host community composition (i.e. resources) also plays a key role in their ability to colonize a new environment as they rely on their hosts to complete their life cycle. Thus, variation in host community composition and environmental conditions should determine parasite turnover among regions. Here, we explored the global drivers of parasite turnover using avian malaria and malaria‐like (haemosporidian) parasites. We compiled global databases on avian haemosporidian lineages distributions, environmental conditions, avian species distributions and functional traits, and ran generalized dissimilarity models to uncover the main drivers of parasite turnover. We demonstrated that haemosporidian parasite turnover is mainly driven by geographic distance followed by host functional traits, environmental conditions and host distributions. The main host functional traits associated with high parasite turnover were the predominance of resident (i.e. non‐migratory) species and strong territoriality, while the most important climatic drivers of haemosporidian turnover were mean temperature and temperature seasonality. Overall, we established the importance of geographic distance as a key predictor of ecological dissimilarity and showed that host resources influence parasite turnover more strongly than environmental conditions. We also evidenced that parasite turnover is most pronounced among tropical and less interconnected regions (i.e. regions with mostly territorial and non‐migratory hosts). Our findings provide a robust foundation for the prediction of avian pathogen spread and the emergence of infectious diseases.

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Accepted 4 January 2023
doi: 10.1111/ecog.06634
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2023: e06634
Why do some regions share more or fewer species than others? Community assembly
relies on the ability of individuals to disperse, colonize and thrive in new regions.
erefore, many distinct factors, such as geographic distance and environmental fea-
tures, can determine the odds of a species colonizing a new environment. For parasites,
host community composition (i.e. resources) also plays a key role in their ability to col-
onize a new environment as they rely on their hosts to complete their life cycle. us,
variation in host community composition and environmental conditions should deter-
mine parasite turnover among regions. Here, we explored the global drivers of parasite
turnover using avian malaria and malaria-like (haemosporidian) parasites. We com-
piled global databases on avian haemosporidian lineages distributions, environmental
conditions, avian species distributions and functional traits, and ran generalized dis-
similarity models to uncover the main drivers of parasite turnover. We demonstrated
that haemosporidian parasite turnover is mainly driven by geographic distance fol-
lowed by host functional traits, environmental conditions and host distributions. e
main host functional traits associated with high parasite turnover were the predomi-
nance of resident (i.e. non-migratory) species and strong territoriality, while the most
important climatic drivers of haemosporidian turnover were mean temperature and
temperature seasonality. Overall, we established the importance of geographic distance
as a key predictor of ecological dissimilarity and showed that host resources inuence
parasite turnover more strongly than environmental conditions. We also evidenced
that parasite turnover is most pronounced among tropical and less interconnected
regions (i.e. regions with mostly territorial and non-migratory hosts). Our ndings
provide a robust foundation for the prediction of avian pathogen spread and the emer-
gence of infectious diseases.
Keywords: avian haemosporidians, avian malaria, community assembly, functional
and taxonomic diversity, parasite dispersal, parasite turnover
Revealing the drivers of parasite community assembly: using
avian haemosporidians to model global dynamics of parasite
species turnover
Daniela de Angeli Dutra ✉1, Rafael Barros Pereira Pinheiro 2, Alan Fecchio 3 and Robert Poulin1
1Dept of Zoology, Univ. of Otago, Dunedin, New Zealand
2Depto de Biologia Animal, Univ. Estadual de Campinas, Campinas, Brazil
3Centro de Investigación Esquel de Montaña y Estepa Patagónica (CIEMEP), CONICET – Univ. Nacional de la Patagonia San Juan Bosco, Esquel,
Chubut, Argentina
Correspondence: Daniela de Angeli Dutra (danideangeli@live.com)
Research article
10

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Introduction
Some species are globally widespread and occur across mul-
tiple ecological communities, whereas most biodiversity is
geographically restricted, but what determines species turn-
over among distinct regions? Niche theory predicts species
inhabit a subset of conditions and resources they can toler-
ate and/or in which they thrive. According to Hutchinson
(1957), a species’ niche is a multidimensional space in which
all environmental conditions and resources required for its
persistence are met. For parasites (i.e. organisms that exploit
another to complete their life cycle), however, resource limi-
tation is mainly related to the presence of suitable hosts and,
in some cases, vectors (i.e. mobile blood-feeding invertebrates
involved in the transmission of pathogens (Wilsonetal. 2017,
Mestreetal. 2020)). Indeed, host specicity (i.e. spectrum of
hosts a parasite can infect) has been directly linked with their
geographic and environmental ranges (Krasnovet al. 2005,
de Angeli Dutraetal. 2021a), indicating generalist parasites
are more likely to expand their range and disperse globally.
Moreover, host specicity can vary across distinct climatic
conditions (Fecchioet al. 2019a), suggesting parasite func-
tional traits might shift according to habitat features. As a
result, similar host communities living in similar environ-
mental conditions should harbor similar parasite community
composition (Fecchioetal. 2019b).
Nonetheless, parasite colonization and assembly are con-
strained by limitations other than just host species community
composition. For instance, host community and parasite dis-
similarity tend to increase with increasing geographic distance
(Nekola and White 1999, Tuomistoetal. 2003, Dallas and
Poisot 2018, Martinsetal. 2021), due to dispersal restrictions
of organisms and the presence of geographic barriers (Nekola
and White 1999). In the case of parasites, the ability to infect
highly mobile hosts (e.g. migratory species) could expand their
distribution by increasing their dispersal ability and opportu-
nities to invade new host communities and environments (de
Angeli Dutraetal. 2021b, Poulin and de Angeli Dutra 2021).
However, at the same time, this dispersal mechanism comes
with a challenge: adaptation to new environmental conditions
and resources (Poulin and de Angeli Dutra 2021). Hence,
even if parasites reach a new region via host dispersal, parasite
colonization could be prevented by inadequate climatic con-
ditions and/or absence of suitable new hosts or vectors. For
this reason, multiple factors can contribute to parasite com-
munity composition similarity or dissimilarity among distinct
regions/communities across the globe.
Parasites comprise many of the most successful and
prevalent organisms in nature, accounting for a third of the
world’s biodiversity (Claytonetal. 2015, Mestreetal. 2020).
Nonetheless, due to the nature of parasite interactions with
their hosts, the drivers of parasite community composition
similarity and turnover are very particular (Mestre et al.
2020). Parasites can have major impacts on community
structure and energy ow through direct and indirect cas-
cading eects on host tness, population dynamics, ecosys-
tem functions and extinction risk (Dunne 2002, Kurisetal.
2008, Laerty et al. 2008). For these reasons, elucidating
the mechanisms underpinning parasite community compo-
sition similarity and turnover among regions is important
to predict parasite species distribution worldwide, estimate
parasite impact on regional host communities and forecast
distributional shifts in response to anthropogenic changes.
For instance, limited parasite turnover could indicate that
dierent regions are prone to colonization by multiple dis-
tinct parasite species, whereas high turnover might reect
local parasite specialization. erefore, we aimed to identify
the drivers of parasite community composition similarity and
turnover worldwide, using avian malaria and malaria-like
parasites as a model system.
Avian malaria and malaria-like (haemosporidian) parasites
are cosmopolitan vector-borne parasites which are among
the most prevalent and diverse wildlife parasites, comprising
almost 4000 lineages (Bensch et al. 2009). Avian haemo-
sporidians are mainly represented by three distinct genera:
Plasmodium, Haemoproteus and Leucocytozoon (Valkiūnas
2005). ese parasites have been intensively studied in the
past two decades and represent an ideal system to investi-
gate the mechanisms shaping parasite turnover due to the
comprehensive data freely available online on parasite dis-
tribution and diversity (Benschetal. 2009) as well as host
distribution and functional traits (Wilman et al. 2014,
Dufouretal. 2020). Here, we test how environmental condi-
tions (i.e. temperature and precipitation patterns) and host
species distribution and functional traits (e.g. range size, ter-
ritoriality, migratory status and body mass) relate to global
parasite community dissimilarity while also controlling for
geographic and host phylogenetic distances. We predict that
1) parasite species turnover increases as a function of increas-
ing dissimilarity among communities (i.e. greater variation in
environmental and host features) and 2) host species distribu-
tion is a key driver of parasite community composition. Our
results identify the main factors shaping avian haemosporid-
ian dissimilarity and turnover worldwide and quantify their
relative importance.
Material and methods
Datasets
We obtained datasets from multiple openly available
sources. Haemosporidian (i.e. Plasmodium, Haemoproteus
and Leucocytozoon) data were extracted from MalAvi
(http://130.235.244.92/Malavi/) (Benschetal. 2009) using
the function extract_table from the ‘malaviR’ package in R
(www.r-project.org). MalAvi compiles records of haemospo-
ridian parasites for each site sampled; sites with fewer than
10 records were excluded from the analyses. is rst dataset
contains information on the haemosporidian lineage identity,
infected host species and site (with geographic coordinates)
where each parasite lineage was recorded (Fig. 1). Bird distri-
bution data were acquired from BirdLife International (www.
birdlife.org/) (BirdLife International and Handbook of the

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Birds of the World (2020)) and bird functional traits (i.e.
body mass, range size and territoriality) were obtained from
Open Traits datasets (https://opentraits.org/datasets.html)
(Wilmanetal. 2014). We used data published by Dufouretal.
(2020) to determine bird migratory category. e authors
classify migratory birds in three categories depending on the
distance the species migrate (short, variable or long) or as
resident species. Multiple datasets containing host functional
traits data were used to allow us to explore a wider range of
functional traits in our analyses. Lastly, environmental data
were extracted from Wordclim (https://worldclim.org/) using
the function getData from the ‘raster’ package in R and reso-
lution equal to 10 km. is last dataset is composed of 19
distinct environmental features relating to temperature and
precipitation measures. We classied each locality where hae-
mosporidians were recorded based on their geographic coor-
dinates by clustering localities into geographic cell grids of 5
× 5 degrees. We then considered those geographic cell grids
as distinct geographic units, each characterized by the occur-
rence of particular haemosporidian lineages, bird species and
their traits and environmental conditions.
Data preparation
Initially, in order to combine all three datasets, we used prin-
cipal component analyses (PCA) to reduce the dimensions of
our data. We performed two PCAs: one for environmental
data using all 19 variables extracted from Worldclim and a
second PCA for all the functional traits. We then extracted
the individual values for the rst axis (53% and 72% of vari-
ance explained, respectively), which were each incorporated
into the bird distribution raster le as a new layer. In order to
transform all functional traits variables into a numeric format,
we used the function dummy.data.frame from the ‘dummies’
package. is function converts a categorical variable and
returns a numerical matrix. en, we transformed each PCA
into a raster format and combined them into a unique raster
le. en, we employed this new three-layer raster object as
our predictor data in a unique full data model. e three pre-
dictor datasets (bird distribution, bird functional traits and
environmental data) clustered in a raster le were converted
into a site-pair data format using the function formatsitepair
from the ‘gdm’ package. is function inputs geographic and
other predictor data to create a site-pair table, which is the
format required to run generalized dissimilarity models. is
new set of site-pair data contains distance (i.e. dissimilarity)
and weight columns (i.e. importance of predictors) for each
variable and site. ese data were then included in a single
generalized dissimilarity model that evaluated 1) bird func-
tional traits, 2) bird distribution, 3) environmental features
and 4) pairwise geographic distances among sites, as predic-
tors of haemosporidian community composition variation.
Figure1. Sampled localities included in the analysis. Color scale illustrates spatial variation in bird species richness worldwide. Crosses
depict parasite distribution from a total of 100 regions and 207 localities (including oshore islands) extracted from the MalAvi database
(http://130.235.244.92/Malavi/).

Page 4 of 10
Generalized dissimilarity modeling
In order to determine the drivers of variation in haemosporid-
ian species community composition worldwide, we employed
generalized dissimilarity models (GDM) (Ferrieretal. 2007,
Mokanyetal. 2022) using the function gdm from the ‘gdm’
package in R. In this model, parasite community composi-
tion dissimilarity among regions was the response variable;
and geographic distance, bird distribution and the rst axes
of the PCAs comprising both environmental conditions and
bird functional traits were used as explanatory variables. After
tting the GDM, we estimated the contribution of each of
the four predictor variables toward explaining the deviation
of parasite dissimilarity using variation partitioning analyses.
For this, we used the function gdm.partition.deviance. Since
there is a limitation of three variables for this function we ran
the analyses twice. First, we evaluated the variation explained
by host functional traits, host distribution and environmental
features while excluding geographic distance. Second, with
the same procedure, we explored the variation explained by
bird distribution and functional traits clustered together,
environmental features and geographic distance. Afterwards,
we assessed the signicance of each predictor for each model
using the function gdm.varImp. is function estimates the
deviance explained from the tted model and compares it
to the one derived from randomized models (n = 50). e
given p-value represents the proportion of randomized mod-
els that explained better the deviance than the actual predic-
tor data (Mokanyet al. 2022). Finally, we evaluated the t
of each GDM using the function gdm.crossvaliation. Model
tting and individual predictors were evaluated visually and
by summarizing the GDM. e height of the splines (i.e.
function composed of pieces of simple functions joined to
provide a suitable degree of smoothness to the GDM) illus-
trated the degree of parasite composition turnover among
regions. All analyses were repeated for each parasite genus (i.e.
Plasmodium, Haemoproteus and Leucocytozoon) separately.
One additional model incorporating beta phylogenetic
diversity of hosts as a covariable was run. To estimate host
beta phylogenetic diversity among regions we created a host
occurrence matrix, downloaded a le containing 10 000
global bird phylogenetic trees (AllBirdsHackett1.tre) from
https://birdtree.org/ and randomly selected one phylogenetic
tree. en, we calculated an index of beta-diversity based on
the phylogenetic distances among species present in each
region and used a PCA to reduce the dimensions of our data.
However, since the addition of host phylogenetic relation-
ships did not contribute to further explain the variance in
our data, they were not incorporated in most of our analyses
(Results and Supporting information).
Results
Haemosporidian richness varied between ve and 276 dis-
tinct lineages per regions (i.e. dened as a geographic cell
grid of 5 × 5 degrees, n = 100) and the maximum number of
parasite records per region was 934. In general, we observed
a mean of 36 distinct parasite lineages and 71 records per
region. Regional bird richness (i.e. number of bird species
per region) varied from four to 349, with a mean of 97 bird
species. We found that geographic distance is the main pre-
dictor of spatial shifts in parasite community composition (p
< 0.01), followed by host functional traits (p < 0.01), envi-
ronmental features (p < 0.01) and, lastly, host geographic
distribution (p = 0.02) (Fig. 2, Table 1A–C, model signi-
cance p < 0.01). To assess all environmental features and host
functional traits in a unique model, dimension reduction was
performed for both variables using PCAs. Interestingly, para-
site species turnover responds dierently to these predictors.
For example, parasite turnover is mostly associated with tem-
perature features (Fig. 3A), as mean temperature and tem-
perature seasonality values (i.e. the variables that make the
greatest contribution to negative and positive values in the
PC1, respectively). Our results demonstrate that regions with
warmer, rainier and less seasonal climate (negative values in
Fig. 3A) show greater turnover of parasite lineages among
localities than regions with lower temperature and precipi-
tation, and high temperature seasonality (positive values in
Fig. 3A). Dissimilarity in parasite community composition
among warmer and wetter regions accounts for most of the
turnover associated with environmental features (Fig. 2).
On the other hand, moderate to high host community
composition dissimilarity is increasingly associated with
turnover in parasite composition (Fig. 2F). Hence, regions
with greater turnover of birds among localities must also pres-
ent higher turnover of haemosporidians (Fig. 2F), which sug-
gests parasite turnover increases in response to turnover of
their avian hosts. Further, the main functional traits associ-
ated with haemosporidian turnover among regions were the
presence of resident birds and strong territoriality; these were
the traits that contributed the most to positive values in the
PC1 (Fig. 3B). For those regions, shifts in the proportion
of territorial and migratory birds between regions are asso-
ciated with the greatest values of haemosporidian turnover.
Moreover, regions harboring mostly large-bodied and/or
migratory birds (i.e. the traits that present the greatest contri-
bution to negative values in the PC1) showed the lowest rates
of parasite turnover when compared to regions where birds
present similar functional traits values in the PC1 (Fig. 3B).
is suggests regions where migratory birds are abundant
share more parasites among themselves (lower turnover) than
regions harboring mostly resident birds. Naturally, regions
that are the most distinct for any of the features considered
also harbor the most distinct parasite fauna. In addition, we
also observed a large overlap in deviance explanation among
the variables analyzed, which is evidenced by the small
increase in the variation explained when combining variables
(Fig. 4, Table 1A, B and Supporting information). Overall,
similar patterns were observed when analysing each parasite
genus separately (Supporting information, models signi-
cance p < 0.01); however, when evaluating Haemoproteus and
Leucocytozoon separately, bird distribution was not a signi-
cant predictor of haemosporidian turnover.

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It is important to note that the addition of host beta phy-
logenetic diversity did not increase the amount of variation
explained by our data (Supporting information). For this rea-
son, our main models were run without taking into account
the eects of host phylogenetic relationships on haemospo-
ridian turnover. Again, the two major variables associated
with haemosporidian turnover were geographic distance fol-
lowed by host functional traits (p < 0.01).
Discussion
Species turnover and community assembly are ruled by three
main lters, which include species’ dispersal ability, abiotic
tolerance and the pressures of interactions they face with
other organisms (e.g. parasite–host interactions) (Vellend
2010). Here, we quantied the relative inuence of these l-
ters in driving parasite turnover worldwide using avian hae-
mosporidians as a model. We found that geographic distance
was the main driver of global haemosporidian turnover, indi-
cating that dispersal ability may be the main constraint on
parasite range. We also demonstrated that avian host func-
tional diversity is a more important determinant of parasite
turnover than host distribution (particularly in the case of
Leucocytozoon parasites, Supporting information), since bird
functional traits explained parasite turnover better than bird
distribution in all models. Finally, we also revealed that cli-
matic tolerance plays a minor role in shaping parasite species
community composition worldwide, as environmental fea-
tures (mainly temperature) explained only 19% of the varia-
tion in our main analyses. Nonetheless, our models explained
only around 33–52% of total deviance in parasite commu-
nity composition, suggesting there are many other lters
associated with haemosporidian turnover, possibly related
with vector distribution. Overall, we show that, despite the
fact that abiotic and biotic lters seem highly correlated, dis-
persal ability and host resources are the principal forces shap-
ing the spatial turnover of haemosporidian parasite species.
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Environmental Features
Bird Distribution
Bird Functional Traits
f(Bird Functional Traits)
f(Bird Distribution)
Predicted Ecological Distance
(A) (B) (C)
(D) (E) (F)
Predicted Compositional Dissimilarity
Observed Compositional Dissimilarity
Observed Compositional Dissimilarity
Geographic Distance
f(Geographic Distance)
f(Environmental Features)
Figure2. (A) Observed haemosporidian turnover as a function of GDM predicted ecological distance (i.e. calculated based on the dissimi-
larity of all variables included in the GDM – geographic distance, environmental features, bird functional traits and distribution). (B)
Observed haemosporidian turnover as a function of GDM-predicted dissimilarity (i.e. expected turnover of haemosporidians). (C–F)
I-splines illustrating the tted relationship between GDM predictor variables (i.e. x-axis) and I-splines transformed variables (i.e. y-axis).
Maximum height of each variable indicates the total degree of haemosporidian turnover predicted by that specic predictor. e shape of
the splines illustrates the rate of turnover over each predictor gradient.

Page 6 of 10
Table 1. (A) Deviance in haemosporidian parasite composition variation explained by bird distribution (p = 0.02), bird functional traits (p <
0.01) and environmental features (p < 0.01). (B) Deviance in haemosporidian parasite composition variation explained by bird distribution
and functional traits (clustered as ‘Bird data’), environmental features and geographic distance (p < 0.01). (C) Variable importance (i.e. per-
cent change in deviance explained in the full model) and p-value.
Set of variables – A Deviance explained
Bird distribution 15.38
Environmental features 19.61
Bird functional traits 31.15
Bird distribution and environmental features 25.01
Bird distribution and functional traits 33.76
Bird functional traits and environmental features 37.88
All variables 38.75
Set of variables – B Deviance explained
Bird data 33.76
Environmental features 19.61
Geographic distance 39.23
Bird data and environmental features 38.75
Bird data and geographic distance 48.66
Environmental features and geographic distance 47.39
All variables 51.04
Set of variables – C Importance
Geographic distance 24.09 (p < 0.01)
Bird distribution 0.87 (p = 0.02)
Bird functional trait 3.98 (p < 0.01)
Environmental features 4.67 (p < 0.01)
53 %
PC1
(A) (B)
Coordinates in PC1 Coordinates in PC1
72 %
PC1
dist: resident
dist: variable
territ: weak
range size
territ: strong
dist: long
body mass
MeanTemperatures
Temperature Seasonality
Mean Precipitation
Environmental Variables Functional Traits
Figure3. Contribution of each variable of principal component analyses (PCA) for (A) environmental features and (B) host functional traits
variability worldwide. Font size represents the variable’s contribution to the rst axis of the PCA. Variables with negative values are illus-
trated in red whereas blue is used for variables that had positive values in the PCAs. Territ = territoriality strength, dist = migration distance.
See Supporting information for contribution values for each variable. Bio2, Bio15, distance of migration short and territoriality none are
not shown because their contribution was less than 1%. Vertical position of variables is arbitrary. Information regarding environmental
features and host functional traits was extracted from WorldClim and OpenTraits and Dufouretal. (2020), respectively.

Page 7 of 10
Parasites rely on their hosts to complete their life cycle,
with the presence of the latter in an area also associated with
environmental features. Specically, the range and turnover
of parasites are also constrained by their hosts’ tolerance
to abiotic conditions (Mestreetal. 2020). Here, we dem-
onstrated a great overlap in the deviance explained by host
and environmental features. Since haemosporidians coevolve
with their hosts (Pachecoetal. 2018, de Angeli Dutraetal.
2022), they are in turn constantly under selection to adapt to
the environmental conditions those hosts inhabit or migrate
into. Consequently, the linked eects of environmental and
host features on parasite turnover might be the outcome of
similar selective pressures for abiotic tolerance faced mutu-
ally by both haemosporidian parasites and their avian hosts
through their evolutionary history.
Moreover, despite the large overlap in deviance explained
by host functional traits and geographic distribution, the rst
emerges as a better predictor of parasite turnover. e two
main functional traits associated with parasite turnover are
the proportion of resident species in the local avian fauna
and the degree of territoriality among those species. Regions
inhabited by generally larger-bodied hosts present lower hae-
mosporidian turnover. Host body mass is positively associ-
ated with both haemosporidian prevalence (Scheuerlein and
Ricklefs 2004, Filion etal. 2020, Fecchio etal. 2021) and
richness (Arriero and Møller 2008), therefore the presence
of large-bodied hosts can shape local parasite species com-
position by providing a suitable resource for multiple hae-
mosporidian species, enabling similar parasite lineages to
colonize multiple communities. A high proportion of large-
bodied hosts in a broad region facilitates higher similarity of
parasite lineages among localities within the region. On the
other hand, migration can homogenize parasite composition
through parasite dispersal (Møller and Szép 2011, Ellisetal.
2015, de Angeli Dutraetal. 2021b) and parasite–host net-
work connectance (de Angeli Dutraetal. 2021c). A greater
proportion of migrants – and, consequently, fewer resident
birds – could also reduce parasite turnover because of the
greater likelihood parasites will be transported among com-
munities harboring multiple migratory host species. us,
the mechanisms through which host functional traits struc-
ture parasite turnover vary, and distinct traits can have con-
trasting outcomes on parasite turnover.
Climate can also drive parasite turnover by shaping vector
abundance and diversity (Benningetal. 2002, Atkinsonetal.
2014, Clarketal. 2018, de La Torreetal. 2022). Here, we
found that parasite turnover among regions with higher tem-
peratures and precipitation rates, and low temperature sea-
sonality, are the highest worldwide. is could be because
haemosporidians use ectothermic hematophagous insects as
vectors; as a result, these parasites must tolerate local tempera-
tures to survive and reproduce (Valkiūnas 2005). Temperature
is, therefore, the main driver of mosquito development due to
its inuence on metabolic rate (Chandrasegaranetal. 2020).
Local temperature drives the developmental rate of vectors of
haemosporidian parasites (Mordecaietal. 2013) and unsuit-
able temperature conditions constrain parasite sporogonic
development within their vectors (Lapointe et al. 2010).
us, parasite turnover across localities in colder and drier
regions with high temperature seasonality might be lower
due to temperature placing constraints on parasite and vec-
tor development, limiting the diversity of parasites capable
of persisting in those environments. Overall, environmental
features have the potential to shape parasite turnover by con-
straining direct and indirect eects on parasite, host and vec-
tor development.
Previous research has evaluated factors driving haemospo-
ridian turnover at local and regional scales (Williamsonetal.
2019, McNew et al. 2021, de La Torre et al. 2022,
Fecchioetal. 2022). Some found that geographic distance
was not the principal driver of parasite turnover. Instead,
researchers observed that host distribution (de La Torreetal.
2022) and environmental features (Williamsonetal. 2019,
McNew et al. 2021) were the main determinants of dis-
similarity among parasite communities. Precipitation and
elevation seem the most important drivers of haemosporid-
ian parasite turnover in the Peruvian Andes (McNewet al.
Bird distribution (15%)
Bird distribution & funtional traits (34%)
Environmental
Features (20%)
Environmental
Features (20%)
All v
ariables = 39%
All variables = 51%
(A)
(B)
Bird functional traits (31%)
Geographical distance (39%)
Figure4. (A) Diagram illustrating the proportion of deviance in
haemosporidian parasites turnover explained by host functional
traits, environmental features and host distribution. (B) Diagram
illustrating the proportion of deviance in haemosporidian parasites
turnover explained by host functional traits and distribution clus-
tered, environmental features and geographic distance.

Page 8 of 10
2021) and in North American sky islands mountain ranges
(Williamsonetal. 2019), respectively. On the other hand, de
La Torreetal. (2022) also observed that host resources were
the most important drivers of haemosporidian turnover in
the Brazilian Atlantic Forest. However, contrary to the nd-
ings of this study, de La Torreetal. (2022) demonstrated that
host community composition was a more relevant driver of
parasite turnover than host functional traits. All these studies
have explored haemosporidian turnover dynamics in a spe-
cic type of environment (e.g. Tropical Andes in Peru, sky
island mountain ranges in the USA and Atlantic Rain Forest
in Brazil); consequently, the variation in biotic and abiotic
diversity expected among the sites evaluated is likely much
lower than that seen at a global scale. is may explain the
discrepancy between past research ndings and the conclu-
sions of the present study. Indeed, past research on large-scale
parasite turnover has conrmed this pattern among haemo-
sporidians and other parasite taxa (Dallas and Poisot 2018,
Fecchioetal. 2019b, 2022, Martinsetal. 2021). erefore,
geographic distance might only play an important role in
shaping parasite turnover at larger scales or when there is a
higher degree of heterogeneity among sites, whereas land-
scape and host features are the most important factors deter-
mining parasite turnover at smaller scales or among more
homogeneous environmental conditions.
It is important to note, however, that the use and com-
bination of multiple datasets comes with an increased risk
of intrinsic errors, such as typos and divergence in species
names (Nekola and Horsák 2022). Furthermore, data com-
prising bird distribution do not take into consideration the
abundance of host species, which could be an important bias
in our analyses. We also did not check for richness gradients
directly; however, the addition of bird species distribution
to the models indirectly takes this gradient into account, as
bird diversity generally follows richness gradients (Jetzetal.
2012). Another relevant bias in our analyses is the concen-
tration of haemosporidian samples in certain regions of the
globe (e.g. Europe and Americas) whilst some other regions
(e.g. Asia and Africa) lack data. Finally, due to absence of
global data on the competent vectors of haemosporidians,
our analyses could not consider vector distribution or their
functional traits as well as parasite functional traits as poten-
tial determinants of haemosporidian turnover.
We demonstrated that, on a global spatial scale, avian
hosts are the main predictor of the turnover of haemosporid-
ian species across localities, after geographic distance (Table
1B). More specically, we observed that haemosporidian spe-
cies turnover among localities in regions harboring multiple
migratory species is reduced, whereas this turnover is more
pronounced in regions inhabited mostly by territorial and
resident birds. We also demonstrated that temperature is the
major climatic condition structuring haemosporidian turn-
over, with mean temperature values being the main factor
associated with spatial shifts in haemosporidian composition.
At the same time, since lower temperature values are associated
with less pronounced haemosporidian turnover among local-
ities within environmentally similar regions, temperate/high
latitude regions should present more similar parasite commu-
nity composition across localities than tropical regions. We
also reveal that host functional traits, distribution and climate
eects on haemosporidian parasite turnover are intrinsically
inter-dependent. Finally, as geographic distance is the main
predictor of haemosporidian species turnover, we conclude
that dispersal limitations and ecological gradients may be
major forces driving variation in parasite species composition
globally, followed by resource (i.e. hosts) availability.
Acknowledgments – We thank the MalAvi and Elton Traits curators
for maintaining the database and for making all data available, as
well as all researchers who shared their data. We are also grateful to
BirdLife International for providing the data required to conduct
our research.
Funding – Daniela Dutra was supported by a doctoral scholarship
from the University of Otago, New Zealand. Rafael Pinheiro was
supported by a postdoctoral scholarship from Fundação de Amparo
à Pesquisa do Estado de São Paulo (Fapesp), Brazil (FAPESP,
2020/06771-2).
Author contributions
Daniela de Angeli Dutra: Conceptualization (lead); Data
curation (lead); Formal analysis (lead); Investigation (lead);
Project administration (lead); Writing – original draft (lead).
Rafael Barros Pereira Pinheiro: Formal analysis (support-
ing); Investigation (supporting); Methodology (support-
ing); Writing – review and editing (equal). Alan Fecchio:
Conceptualization (supporting); Data curation (support-
ing); Writing – review and editing (equal). Robert Poulin:
Conceptualization (supporting); Investigation (supporting);
Supervision (lead); Writing – review and editing (equal).
Transparent peer review
e peer review history for this article is available at https://
publons.com/publon/10.1111/ecog.06634.
Data availability statement
Some of the data that support the ndings of this study are
openly available in MalAvi (http://130.235.244.92/Malavi/),
Open Traits datasets (https://opentraits.org/datasets.html)
and as Supporting information for Dufour et al. (2020).
BirdLife International and Handbook of the Birds of the
World (2020), Bird species distribution maps of the world,
ver. 2020.1, can be accessed at http://datazone.birdlife.org/
species/requestdis. Data and R code necessary to perform
our analyses are available from the Dryad Digital Repository:
https://doi.org/10.5061/dryad.w3r2280vm (de Angeli
Dutraetal. 2023).
Supporting information
e Supporting information associated with this article is
available with the online version.

Page 9 of 10
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