DOI: 10.5061/dryad.gmsbcc2xg
title: Parasite abundance-occupancy relationships across biogeographic regions: Joint effects of niche breadth, host availability, and climate

description: Aim Changing biodiversity and environmental conditions may allow
 multi-host pathogens to spread amongst host species and affect prevalence.
 There are several widely acknowledged theories about mechanisms that may
 influence variation in pathogen prevalence, including the controversially
 debated dilution effect and abundance-occupancy relationship hypotheses.
 Here, we explore such abundance-occupancy relationships for unique
 lineages of three vector-borne avian blood parasite genera (the avian
 malaria parasite Plasmodium and the related haemosporidian parasites
 Parahaemoproteus and Leucocytozoon) across biogeographical regions.
 Location Nearctic-Neotropical and Palearctic-Afrotropical regions Methods
 We compiled a cross-continental dataset of 17,116 bird individuals
 surveyed from 46 bird assemblages across the Nearctic-Neotropical and
 Palearctic-Afrotropical regions and explored relationships between local
 parasite lineage prevalence and host assemblage metrics in a Bayesian
 random regression framework. Results Most lineages from these three genera
 infected ≥5 host species and exhibited clear phylogenetic or functional
 host specificity. Lineage prevalence from all three genera increased with
 host range, but also with higher degrees of specialisation to
 phylogenetically or functionally related host species. Local avian
 community features were also found to be important drivers of prevalence.
 For example, bird species richness was positively correlated with lineage
 prevalence for Plasmodium and Leucocytozoon, whereas higher relative
 abundances of the main host species were associated with lower prevalence
 for Plasmodium and Parahaemoproteus but higher prevalence for
 Leucocytozoon. Conclusions Our results broadly support several of the
 leading hypotheses about mechanisms that influence pathogen prevalence,
 including the niche breadth hypothesis in that higher avian host species
 diversity and broader host range amplifies prevalence through increasing
 ecological opportunities and the trade-off hypotheses in that
 specialisation among subsets of available host species may increase
 prevalence. Furthermore, the three studied avian haemosporidian genera
 exhibited different abundance-occupancy relationships across the major
 global climate gradients and in relation to host availability, emphasising
 that these relationships do not strictly follow common rules for
 vector-borne parasites with different life histories.
description: To examine the prevalence-host specificity relationships of
 unique haemosporidian lineages in bird assemblages, we systematically
 compiled field and molecular screening data from studies that reported
 individual-level haemosporidian infection status and parasite
 cytochrome-<em>b</em> (cyt-<em>b</em>) sequences
 in bird assemblages with reasonably large sample sizes (≥ 30 individuals
 and more than five host species surveyed at a single location) based on a
 dataset updated from our previous study (Fecchio et al., 2021). The
 assembled data set included 36,896 individual birds with recorded
 presence-absence of infection with the haemosporidian genera
 <em>Plasmodium</em>, <em>Parahaemoproteus</em>,
 and <em>Leucocytozoon</em>, and all infections confirmed by
 molecular sequencing of a 477 bp nucleotide cyt-<em>b</em>
 fragment (Bensch, Perez-Tris, Waldenström, & Hellgren, 2004).
 Sequence identities were verified with a local BLAST against the MalAvi
 database (Bensch, Hellgren, & Perez-Tris, 2009). We then matched
 nucleotide sequences to respective amino-acid sequences using the R
 package Biostrings (Pagès, Aboyoun, Gentleman, & DebRoy, 2024),
 reducing the number of 2,070 unique nucleotide lineages to 1,983
 ‘functional lineages’ based on amino acid sequences. We acknowledge that
 because we only have data for a fragment of the cyt-<em>b</em>
 sequence, these lineage assignments may not necessarily represent the true
 functional lineage or species diversity, but is assumed to be the best
 possible representation of data currently available, amid the recent onset
 of genomic sequencing of haemosporidians (Videvall, 2019).
 We obtained data on bird species' phylogenetic relationships
 from the open-source Birdtree.org phylogenetic supertree (Jetz, Thomas,
 Joy, Hartmann, & Mooers, 2012), using a consensus tree from a
 random selection of 100 possible tree topologies from the supertree's
 ‘Ericsson All Species’ Bayesian posterior distribution (available at
 Birdtree.org/subsets/). Bird species names from field data were revised
 according to the taxonomy used in these trees. Phylogenetic distances
 among pairs of bird species were calculated as mean pairwise distance
 across the selected trees. For all bird species, we
 obtained data on their body mass and the proportional use of ten diet
 categories and seven foraging habitats from the EltonTraits v1.0 database
 (Wilman et al., 2014). We quantified pairwise functional distances using a
 Gower's distance matrix (Gower, 1971; Pavoine, Vallet, Dufour,
 Gachet, & Daniel, 2009). For analysis, phylogenetic and functional
 distance matrices were scaled (dividing by the maximum for each
 matrix). To analyse the prevalence of parasite lineages
 within local assemblages in context of host specificity, we focused on
 sufficiently well sampled lineages that allowed reasonable estimates of
 prevalence and host specificity in a given local context. For this, we
 filtered the cleaned data and selected only those data for lineages that
 have been reported infecting ≥ 8 host individuals in local assemblages
 with a total ≥ 10 host individuals and ≥ 5 host species sampled (a sample
 size of ten translates to an ~ 80% probability of detecting a parasite
 with a true prevalence of 15%, and we assume a record of eight infections
 within a community of ≥ 5 host species to provide reasonable insights into
 the variation in host specificity of different lineages). This resulted in
 a dataset of 144 unique lineage-location records from 46 different
 locations and 17,116 sampled bird individuals, of which 2,830 individuals
 were recorded as infected. Locations were in the zoogeographical regions
 of the Nearctic, Palearctic, Neotropics, and Afrotropics (a single lineage
 from the Saudi-Arabian region was included in the Afrotropics group for
 analysis) (<strong>Tables S1, S2, Figure S1</strong>). The
 number of bird species sampled at the different locations ranged from 6 to
 115 (mean 51± SD 22), the number of locally recorded and infected host
 species for the different lineages ranged from 1 to 21 (mean 5 ± SD  4)
 (<strong>Figure S2</strong>), comprising 1% to 69% of the
 locally sampled bird species. According to host range estimates (Chao-1
 species richness estimates based on the frequency different bird species
 were recorded as host), the true local host ranges were likely larger than
 the number of recorded host species (species recorded to be infected by a
 lineage)(<strong>Figure S3</strong>), whereas the number of
 recorded host species was not correlated with the respective sample sizes
 (Spearman’s r = 0.05) and the proportion of host individuals in a sample
 was only weakly correlated with sample sizes (Spearman’s r = 0.31); we
 therefore assumed that sample sizes were sufficient for
 inference. <strong><em>Host assemblage and
 environmental metrics</em></strong> Within
 a local context, each haemosporidian lineage was recorded from a number of
 infected individuals that comprised a subset of the sampled bird
 individuals, which, in turn, can be considered a random sample of the
 local bird assemblage. To summarize key aspects of the infected and
 sampled species assemblage as proxies of aspects of host specificity and
 host availability, we computed the following metrics:
 1) <em>Phylogenetic host specificity (B.phyl)</em> –
 host specificity calculated as a model-based estimate of the relative
 difference in phylogenetic distances among all pairs of infected versus
 sampled bird individuals. We regressed all possible pairwise phylogenetic
 distances <em>d</em> against the binary categorical classifier
 <em>C</em> of whether such distance was computed for any pair
 of infected individuals versus any pair of sampled and uninfected
 individuals (i.e. a binary indicator variable, where ‘1’ indicates the
 pair of sampled bird individuals that is infected; ‘0’ indicates that they
 are uninfected) in a linear model, such that:
             d ~ Ɲ(µ + β<sub>phyl</sub>C,
 σ<sup>2</sup>)               (eqn. 1).
 <em>Ɲ(μ, σ2)</em> represents the Gaussian
 distributions with mean <em>μ</em> and variance
 <em>σ<sup>2</sup></em> of the linear model, the
 coefficient estimate β<sub>phyl</sub> represents the
 difference between observed and potential host distances. Values of
 β<sub>phyl</sub><em> </em>&lt;0 indicate
 smaller distances between infected individuals than those in the entire
 sample and therefore stronger host specificity, a value of &gt; 0
 suggests that a lineage infects more distantly related hosts than expected
 (Wells, Gibson, & Clark, 2019). Model-based
 β<sub>phyl</sub> estimates of means and SDs computed with the
 glm() function in R (R Core Team, 2023) independently for each lineage are
 considered for defining the uncertainty and priors in our Bayesian random
 regression to compute the possible relationship between lineage prevalence
 and specificity (refer to statistical analysis). Host diversity calculated
 as Rao’s quadratic entropy (a measure of within and among community
 diversity of infected versus all sampled individuals from different bird
 species that takes phylogenetic relationships into account) correlated
 with <em>B.phyl </em>(Spearman r &lt; 0.7) and was
 therefore not considered in analyses. 2)
 <em>Functional host specificity
 </em>(<em>B.func</em>)  – host specificity calculated as
 a model-based estimate of the relative difference in functional distances
 among all pairs of infected versus sampled bird individuals, using the
 same concept as for computing phylogenetic specificity described
 above. 3) <em>Main host availability
 </em>(<em>mainH<sub>avail</sub></em>) – the
 availability of known suitable main host individuals within a local
 assemblage. This metric is a probabilistic estimate of the proportion of
 main host individuals based on the number of individuals from the main
 host species (i.e. the single host species for which the largest number of
 infected birds were recorded within a local assemblage) and the total
 number of individuals sampled from a local assemblage. We used a
 multinomial model linked to a Dirichlet prior to model the proportion of
 available host individuals belonging to the main host as described
 below. 4) <em>Host range
 </em>(<em>HR.Chao</em>) – we computed host range as the
 Chao species richness estimate based on the locally sampled number of
 infected bird individuals from different species; host range estimates
 were computed with the package iNEXT (Hsieh, Ma, & Chao,
 2016). 5) <em>Bird species richness
 </em>(<em>SpRich</em>) – We measured local bird species
 richness of terrestrial birds based on a map that summarizes species
 distributions from BirdLife International range maps
 (https://biodiversitymapping.org/; refer also to Jenkins, Pimm, &
 Joppa, 2013). This measure counts all terrestrial birds (potentially
 available as host) regardless of whether they have been sampled or
 not. To<strong> </strong>account for key
 climatic differences across locations that are likely to affect many
 aspects of host (and vector) species, we selected six climate variables
 from the WorldClim database of gridded climate data at a 0.01-degree
 resolution ((Fick & Hijmans, 2017), 2017; http://world
 clim.org/version2)(bio1: annual mean temperature, bio4: temperature
 seasonality, bio7: temperature annual range, bio12: annual precipitation,
 bio14: precipitation of driest month, bio15: precipitation seasonality
 based on coefficient of variation).
description: # Parasite abundance-occupancy relationships across biogeographic regions:
 joint effects of niche breadth, host availability, and climate
 [https://doi.org/10.5061/dryad.gmsbcc2xg](https://doi.org/10.5061/dryad.gmsbcc2xg) ## Description of the data and file structure | Variable | Description | | :-------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Site.ID | ID fof the Site where birds have been captured | | Lineage | Name of the haemosporidian lineage (corresponding to names used in the MalAvi database) | | Genus | Genus of haemosporidian lineage | | Seq.CytB | DNA sequence of the lineage/sample based on a cytochrome B sequence | | seq.AminoA | Amino-acid sequence matched to DNA sequence using the R package Biostrings | | seq.AminoA.ID | Amino-acid sequence ID | | nsamp\_all | Number of all sampled bird individuals from the local assemblage | | nsamp\_host | Number of sampled bird individuals recorded to be a host | | nsamp\_infect | Number of sampled bird individuals infected | | nspec\_all | Number of all sampled bird species | | nspec\_host | Number of sampled bird species recorded as host species (either in local assemblage or elsewhere) | | nspec\_infect | Number of sampled bird species infected | | HostRange\_Chao\_mean | Mean of the host range estimate based on Chao1 species richness estimator | | HostRange\_Chao\_sd | One SD of the host range estimate based on Chao1 species richness estimator | | Lineage\_nobs | Number of haemosporidian lineages observed | | B.ind.phyl | Mean of the  estimate of phylogenetic host specificity (B.phyl) | | B.ind.phyl.sd | One SD  of the  estimate of phylogenetic host specificity (B.phyl) | | B.ind.func | Mean of the  estimate of functional host specificity (B.func) | | B.ind.func.sd | One SD  of the  estimate of functional host specificity (B.func) | | nsamp\_host.main | Number of all sampled bird individuals from the main host species (main host species (i.e. the single host species for which the largest number of infected birds were recorded within a local assemblage)) | | nsamp\_infect.main | Number of all sampled bird individuals from the main host species infected  | | Longitude | Longitude value of the geographic coordinate fo the study site (WGS84)  | | Latitude | Latitude value of the geographic coordinate fo the study site (WGS84)  | | Country | Country where the study site is located | | realm | Zoogeographical region based on Holt et al. 2013 (doi: 10.1126/science.1228282) | | Bird.specrich | Local bird species richness of terrestrial birds based on a map that summarizes species distributions from BirdLife International range maps ([https://biodiversitymapping.org/](https://biodiversitymapping.org/)) | ## Access information Table of data sources and countries of origin of the data compiled for this study. For each dataset, the geographical coordinates, the country, and the total number of bird individuals and species in the data set as well as links to published work is listed. | **Longitude** | **Latitude** | **Country** | **No. of bird individuals** | **No. of bird species** | **Data source/owner** | | :------------ | :----------- | :------------------------ | :-------------------------- | :---------------------- | :----------------------------------------------------------------------------------------------------------------------------- | | -148.23181 | 63.32385 | USA | 48 | 13 | [https://doi.org/10.1111/1365-2656.13089](https://doi.org/10.1111/1365-2656.13089) | | -149.29083 | 65.28899 | USA | 95 | 20 | [https://doi.org/10.1111/1365-2656.13089](https://doi.org/10.1111/1365-2656.13089) | | -37.26666667 | -6.566666667 | Brazil | 176 | 38 | [https://doi.org/10.1007/s00248-023-02283-x](https://doi.org/10.1007/s00248-023-02283-x) | | -38.682305 | -9.73225 | Brazil | 97 | 36 | [https://doi.org/10.1017/S0031182022001317 ](https://doi.org/10.1017/S0031182022001317)  | | -41 | -16.43333333 | Brazil | 164 | 48 | [https://doi.org/10.1016/j.ijpara.2021.01.001 ](https://doi.org/10.1016/j.ijpara.2021.01.001)  | | -42.13333333 | -19.78333333 | Brazil | 126 | 33 | [https://doi.org/10.1016/j.ijpara.2021.01.001 ](https://doi.org/10.1016/j.ijpara.2021.01.001)  | | -45.73333333 | -15.16666667 | Brazil | 127 | 26 | [https://doi.org/10.1111/mec.15094](https://doi.org/10.1111/mec.15094) | | -46.73280556 | -23.99369444 | Brazil | 403 | 68 | [https://doi.org/10.1017/S0031182022001317 ](https://doi.org/10.1017/S0031182022001317)  | | -47.613628 | -15.546044 | Brazil | 790 | 53 | [https://doi.org/10.1111/mec.15094](https://doi.org/10.1111/mec.15094) | | -57.49573611 | -30.20556389 | Brazil | 244 | 63 | [https://doi.org/10.1111/1365-2656.13117 ](https://doi.org/10.1111/1365-2656.13117)  | | -59.68274444 | -13.82132778 | Brazil | 136 | 42 | [https://doi.org/10.1111/mec.15094](https://doi.org/10.1111/mec.15094) | | -6.97 | 31.72 | Morocco | 75 | 17 | [https://doi.org/10.1111/mec.15545 ](https://doi.org/10.1111/mec.15545) | | -71.35 | -42.91666667 | Argentina | 688 | 25 | [https://doi.org/10.1017/S0031182022001317 ](https://doi.org/10.1017/S0031182022001317)  | | -72.16 | -13.19933333 | Peru | 30 | 15 | [https://doi.org/10.1111/ele.13263](https://doi.org/10.1111/ele.13263) | | -75.486 | 39.984 | USA | 1451 | 62 | [https://doi.org/10.1007/s00248-023-02283-x ](https://doi.org/10.1007/s00248-023-02283-x)  | | -77.4167 | -6.7167 | Peru | 419 | 115 | [https://doi.org/10.1111/ele.13263](https://doi.org/10.1111/ele.13263) | | -77.55 | -6.5833 | Peru | 433 | 113 | [https://doi.org/10.1111/ele.13263](https://doi.org/10.1111/ele.13263) | | -77.6833 | -6.6833 | Peru | 169 | 56 | [https://doi.org/10.1111/ele.13263](https://doi.org/10.1111/ele.13263) | | -77.73333333 | -2.116666667 | Ecuador | 343 | 88 | [https://doi.org/10.1016/j.ijpara.2015.08.001](https://doi.org/10.1016/j.ijpara.2015.08.001) | | -78.64518 | -8.38685 | Peru | 85 | 20 | [https://doi.org/10.1111/ele.13263](https://doi.org/10.1111/ele.13263) | | -78.77826667 | -7.398033333 | Peru | 135 | 37 | [https://doi.org/10.1111/ele.13263](https://doi.org/10.1111/ele.13263) | | -79.2333 | -5.1 | Peru | 161 | 62 | [https://doi.org/10.1111/ele.13263](https://doi.org/10.1111/ele.13263) | | -83.69 | 35.95 | USA | 127 | 26 | [https://doi.org/10.1073/pnas.1515309112](https://doi.org/10.1073/pnas.1515309112) | | -84.12 | 35.61 | USA | 156 | 32 | [https://doi.org/10.1073/pnas.1515309112](https://doi.org/10.1073/pnas.1515309112) | | -85.34944 | 42.32667 | USA | 381 | 46 | [https://doi.org/10.1073/pnas.1515309112](https://doi.org/10.1073/pnas.1515309112) | | -86.75175926 | 39.06638889 | USA | 500 | 34 | [https://doi.org/10.1073/pnas.1515309112](https://doi.org/10.1073/pnas.1515309112) | | -87.760704 | 41.746465 | USA | 2023 | 52 | [https://doi.org/10.1073/pnas.1515309112](https://doi.org/10.1073/pnas.1515309112) | | -89.22 | 35.12 | USA | 625 | 44 | [https://doi.org/10.1073/pnas.1515309112](https://doi.org/10.1073/pnas.1515309112) | | -89.71083 | 30.4025 | USA | 157 | 10 | [https://doi.org/10.1073/pnas.1515309112](https://doi.org/10.1073/pnas.1515309112) | | -91.0374 | 37.12555 | USA | 1438 | 49 | [https://doi.org/10.1073/pnas.1515309112](https://doi.org/10.1073/pnas.1515309112) | | -96.45972222 | 47.95611111 | USA | 150 | 13 | [https://doi.org/10.1007/s00248-023-02283-x  ](https://doi.org/10.1007/s00248-023-02283-x)  | | -99.25152 | 19.24604 | Mexico | 83 | 27 | [https://doi.org/10.1645/18-130  ](https://doi.org/10.1007/s00248-023-02283-x)  | | 13.433 | 55.6833 | Sweden | 2905 | 64 | [https://doi.org/10.1111/oik.07280 ](https://doi.org/10.1111/oik.07280) | | 20.9285 | -0.1703 | Democratic Republic Congo | 118 | 30 | Shannon Hackett & Heather Skeen, | | 30.3806 | 0.3587 | Uganda | 136 | 44 | Shannon Hackett & Heather Skeen, | | 30.4289 | 0.5061 | Uganda | 124 | 31 | Shannon Hackett & Heather Skeen, | | 33.45 | -10.86666667 | Malawi | 206 | 90 | [https://doi.org/10.1371/journal.pone.0121254 ](https://doi.org/10.1371/journal.pone.0121254) | | 33.8 | -10.58333333 | Malawi | 95 | 30 | [https://doi.org/10.1371/journal.pone.0121254 ](https://doi.org/10.1371/journal.pone.0121254) | | 33.96666667 | -10.73333333 | Malawi | 93 | 26 | [https://doi.org/10.1371/journal.pone.0121254 ](https://doi.org/10.1371/journal.pone.0121254) | | 34.0506 | -18.4667 | Mozambique | 192 | 43 | Shannon Hackett & Heather Skeen, | | 36.8778 | 0.0061 | Kenya | 61 | 14 | Wanyoike Wamiti, National Museums of Kenya, Nairobi & Jason Weckstein, Drexel University, Philadelphia, unpublished data, 2003 | | 46.2315433 | 40.05055 | Karabakh | 37 | 6 | [https://doi.org/10.1111/mec.15545 ](https://doi.org/10.1111/mec.15545) | | 46.24 | 38.9 | Armenia | 72 | 18 | [https://doi.org/10.1111/mec.15545 ](https://doi.org/10.1111/mec.15545) | | 8.9772 | 9.8756 | Nigeria | 458 | 59 | [https://doi.org/10.1111/j.1365-294X.2007.03227.x](https://doi.org/10.1111/j.1365-294X.2007.03227.x) | | -49.21348 | -1.11058 | Brazil | 288 | 98 | [https://doi.org/10.1016/j.ympev.2023.107828](https://doi.org/10.1016/j.ympev.2023.107828) | | -64.72866 | -7.41419 | Brazil | 296 | 97 | [https://doi.org/10.1016/j.ympev.2023.107828](https://doi.org/10.1016/j.ympev.2023.107828) |