DOI: 10.5061/dryad.sf7m0cgdf
title: Network specificity decreases community stability and competition among avian haemosporidian parasites and their hosts

description: Aim: Parasites play a fundamental role in shaping ecological communities
 and influencing trophic interactions. Understanding the factors that drive
 parasite impacts on community structure and stability (i.e., resilience to
 disturbances) is crucial for predicting disease dynamics and implementing
 effective conservation strategies. In this study, using avian malaria and
 malaria-like parasites as a model system, we investigated the relationship
 between specificity, community stability, and parasite vulnerability and
 their association with host diversity and climate. Location: Global Time
 period: 2009-2023 Major taxa studied: Avian malaria and malaria-like
 parasites. Methods: By compiling occurrence data from a global avian
 haemosporidian parasite database (MalAvi), we constructed a comprehensive
 dataset encompassing 60 communities. We utilized a phylogenetic model
 approach to predict missing host-parasite interactions, enhancing the
 accuracy of our analyses. Network analyses based on bipartite interactions
 provided measures of network specificity, stability, modularity, parasite
 competition, and vulnerability to extinction. Results: We found that the
 high network specificity reduced community stability and decreased
 competition among parasites. Furthermore, we found that parasite
 vulnerability decreased with increasing community stability, highlighting
 the importance of community stability in host-parasite interactions for
 long-term parasite persistence. When exploring the influence of local host
 diversity and climate conditions on host-parasite community stability, we
 demonstrated that increasing host biodiversity and precipitation reduces
 parasite competition. Conversely, higher temperature raises competition
 among parasites. Conclusion: These findings provide valuable insights into
 the mechanisms underlying parasite impacts on communities and the
 interplay between specificity, community stability, and environmental
 factors. Further, we reveal the role of climate in shaping host-parasite
 interactions. By unravelling the complexities of parasite-mediated
 interactions, our research substantially improves the current knowledge of
 the importance of specificity as a modulator of interactions in bipartite
 networks.
description: <em>Data compilation</em>       
 In June 2023 we extracted haemosporidian occurrence data from
 MalAvi. MalAvi (http://130.235.244.92/Malavi/) is an online, freely
 available global database on avian haemosporidian parasites
 (<em>Plasmodium</em>, <em>Haemoproteus,</em> and
 <em>Leucocytozoon</em>) (Bensch et al., 2009). The database
 comprises &gt;15,000 entries, each representing a recorded malaria
 infection in an individual bird, with information on parasite lineage
 identity, bird species identity, and geographical location. Each entry in
 our dataset consisted of an individual link of an identified malaria
 lineage in an identified host species from a given geographical locality.
 However, the absence of a parasite lineage from a particular host species
 may reflect inadequate sampling, rather than a true host-parasite
 incompatibility.  <em>Network analyses and
 specificity </em> All analyses were performed in
 the R environment version 4.3 (R Core Team, 2023). Each community was
 composed of all parasite lineages and host species occurring within a
 given geographical locality including the potential missing links.
 Communities were defined as cell grids of equal (5x5 degrees, total =
 2592) dimensions harboring 20 or more distinct parasite lineages and at
 least 10 distinct host species. Grids harboring fewer than 20 distinct
 parasite lineages, or less than 10 host species were excluded from our
 analyses. It is important to note that the majority of the grids in our
 study represent regions with scarce sampling (e.g., Africa and Asia)
 and/or regions with no haemosporidian parasites (e.g., oceans and
 Antarctica) as they cover the entire globe. We chose the cell grid size
 that provides the optimal compromise between the number of cells that met
 the inclusion criteria mentioned above, and the spatial extent that could
 reasonably be expected to allow for species interactions. We acknowledge
 that the area covered by a 5X5 degrees cell encompasses diverse habitats
 and that not all bird species will occupy the entire cell; thus, it is at
 the upper end of what may be considered a community. The use of
 geographical grids to delimit communities is consistent with previous
 studies on avian haemosporidian parasites (de Angeli Dutra et al., 2023)
 and well established in ecology (Bousquin, 2021; Rangel et al.,
 2018). As detailed below, network specificity
 (<em>H<sub>2</sub>’</em>) and stability (measured
 as nestedness and modularity), parasite inter-specific competition, and
 vulnerability to extinction (measured as niche overlap and extinction
 slope, respectively) were calculated using the package “bipartite” in R
 (Dormann et al., 2008). Network modularity was calculated by comparing
 observed modularity with the partition that maximizes modularity (Beckett,
 2016). Network specificity
 (<em>H<sub>2</sub>’</em>) was estimated as the
 degree of specialization or partitioning among both host and parasite
 species within a bipartite network based on their interaction patterns
 (Blüthgen et al., 2006). This metric is useful to compare the degree of
 specialization across distinct interaction webs as it considers both
 parties of a bipartite network. Likewise,  nestedness is also a
 network-level index (i.e., considers both parties, hosts, and parasites,
 of a bipartite network). It reflects the degree to which specialist
 parasites interact with a subset of the hosts used by progressively more
 generalist parasites. Here, we calculated nestedness based on decreasing
 fill (or DF) and paired overlap (Almeida-Neto et al., 2008). Since nested
 networks are cohesive and robust and nestedness is a network-level
 measurement (Bascompte, 2007), we used this metric to estimate community
 stability. Additionally, in certain cases, modularity can be associated
 with higher stability (Grilli et al., 2016), therefore we also evaluated
 the role of network specificity on modularity. Here, we calculated
 modularity using label propagation and multi-step agglomeration to attempt
 to maximize modularity in binary bipartite networks applying the LPAwb+
 algorithm (Beckett, 2016).   On the other hand, niche
 overlap and extinction slope metrics only take into account one party of
 the bipartite networks: the parasites. Niche overlap measures the
 similarity in resource utilization between two or more species within a
 community (Dormann et al., 2008). In our analyses, it measures the degree
 to which parasites share host species. As a result, communities with low
 overlap in host utilization by parasites are subject to generally lower
 levels of interspecific competition. The extinction slope estimates the
 impact of removing a species from a bipartite network. It measures the
 coextinction of parasites as a consequence of repeated removals of host
 species from the network (Dormann et al., 2008). In the selected
 algorithm, the host species infected by fewer parasites are removed first.
 The present algorithm was selected since those species are expected to
 have the weakest impact on community stability and extinction slope is a
 metric of parasite vulnerability to their hosts’ extinction.  
 All network metric values were standardized among distinct
 communities using null models and Z-scores (i.e., a numerical measurement
 that describes the position of a given value according to the mean) to
 account for the fact that the different community networks differ widely
 in the number of nodes (diversity of hosts and parasites) and the number
 of interactions (biases in sampling effort and/or prevalence). This method
 corrects for those biases as we used null models to create a thousand
 different and reshuffled networks. Our null models created shuffled binary
 matrices, assigning interspecific interactions according to
 species-specific probabilities based on abundance of host species while
 ensuring that all hosts and parasites still had at least one interaction.
 This procedure was repeated a thousand times for each host-parasite
 network. All those new matrices were used to calculate randomized network
 metrics (e.g., nestedness, niche overlap, extinction slope, and
 specificity). After we obtained a thousand random values of each network
 metric for each host-parasite network, we used them to calculate Z-scores.
 Therefore, the final network metric values obtained and used in the
 analyses represent how the original value compares to randomly generated
 values, i.e., if the parasites compete as much, are as likely to get
 extinct or whether our networks are more, less, or equally specific and
 stable, as they would be by chance alone based solely on their number of
 nodes and interactions.  <em>Local biodiversity
 and climate conditions</em> Local host taxonomic
 and functional diversity and climatic conditions were calculated for each
 community evaluated. Host taxonomic diversity was calculated based on
 avian occurrence data extracted from the BirdLife International database.
 Host functional traits for each bird species were extracted from the Open
 Traits Network database (https://opentraits.org/datasets.html) (Wilman et al., 2014), migratory status followed classification by Dufour et al., 2020. We used Hill numbers to estimate both diversity metrics using the package hillR in R (Chao et al., 2014). Hill numbers are a common metric of diversity used in community ecology. Taxonomic diversity is measured as Shannon entropy. In our study, it refers to the level of uncertainty in identifying the host species of a randomly selected parasite. On the other hand, functional diversity was calculated as the sum of the pairwise distances (i.e., variety in host functional attributes evaluated: migratory status, territoriality, body weight, diet and habitat) among the host species that parasites infect in each cell grid (Chao et al., 2014). We extracted climate data obtained from Worldclim (https://worldclim.org/) at a resolution of 10km for the whole grid area. To do so, we used the "getData" function from the "raster" package in R climate data (Hijmans, 2023). Values obtained across each grid were averaged so that our climatic metrics took into consideration conditions for the entire cell. The Worldclim dataset consists of 19 distinct environmental attributes, encompassing measurements related to temperature and precipitation. To reduce the dimensions of our climatic data, we ran two Principal Component Analysis (PCAs) separating the variables for temperature variables (Bio1-11) and precipitation variables (Bio12-19). PCAs for temperature and precipitation were performed separately to provide a clearer interpretation of the principal components for each variable. This is important in understanding the dominant patterns of variability in temperature and precipitation independently. We added the first axis of both PCAs (which explained 78% and 60% of the variance, respectively) as explanatory variables in the Bayesian models.
description: # Network specificity decreases community stability and competition among
 avian haemosporidian parasites and their hosts
 [https://doi.org/10.5061/dryad.sf7m0cgdf](https://doi.org/10.5061/dryad.sf7m0cgdf) ## Description of the data and file structure Our data represent parasite-host community metrics on bipartite network structure, taxonomic and functional host diversity and local climate conditions. Metadata describes briefly each variable and contain information on source and unit for all values. ## Sharing/Access information This is a section for linking to other ways to access the data, and for linking to sources the data is derived from, if any. Links to other publicly accessible locations of the data: * MalAvi ([http://130.235.244.92/Malavi/)](http://130.235.244.92/Malavi/\)) * Open Traits datasets ([https://opentraits.org/datasets.html)](https://opentraits.org/datasets.html\)) * BirdLife International ([http://datazone.birdlife.org/species/requestdis)](http://datazone.birdlife.org/species/requestdis\))