Pigeault et al Raw Data - Determinants of haemosporidian single- and co-infection risks in western Palearctic birds.xlsx

Determinants of haemosporidian single- and co-infection risks in western Palearctic birds Co-infections with multiple pathogens are common in the wild and may act as a strong selective pressure on both host and parasite evolution. Yet, contrary to single infection, the factors that shape co-infection risk are largely under-investigated. Here, we explored the extent to which bird ecology and phylogeny impact single- and co-infection probabilities by haemosporidian parasites using large datasets from museum collections and a Bayesian phylogenetic modelling framework. While both phylogeny and species attributes (e.g. size of the geographic range, life-history strategy, migration) were relevant predictors of co-infection risk, these factors were less pertinent in predicting the probability of being single infected. Our study suggests that co-infection risk is under a stronger deterministic control than single-infection risk. These results underscore the combined influence of host evolutionary history and species attributes in determining single- and co-infection pattern providing new avenues regarding our ability to predict infection risk in the wild. 1- Avian samples and parasites detection: Our data set includes 1361 samples of 151 species encompassing 44 families and 18 orders (see Appendix 1, Table S1). Sampling was conducted on salvaged birds that were obtained between 1990 and 2019. and consisted of tissues (muscle and liver) stored in 85% ethanol at 4°C at the Cantonal Museum of Zoology in Lausanne (855 specimens) and in 90% ethanol at -20°C at the Natural History Museum of Geneva (506 specimens). For each individual, parasites (i.e. Haemoproteus, Leucocytozoon and Plasmodium) were detected from tissue samples using molecular methods. Specifically, a nested PCR (Hellgren et al. 2004, J. Parasitol., 90, 797–802) was performed in triplicates on all samples after DNA was extracted from tissues using a DNeasy Blood & Tissue Kit (Qiagen, Switzerland) following the manufacturer's instructions. Nested PCR products were visualized on agarose gels after electrophoresis to identify infected samples. This nested PCR protocol does not allow to detect co-infections between parasites of the genera Haemoproteus and Plasmodium. Therefore, all positive samples were sequenced in both directions as in van Rooyen et al. (2013, Malar. J., 12, 40) and identified by performing a local BLAST search in the MalAvi database (Bensch et al. 2009, Mol. Ecol. Resour., 9, 1353–1358). Co-infections by Haemoproteus and Plasmodium were identified by analyzing double nucleotide peaks on sequence chromatographs. We re-amplified and re-sequenced all the samples for which the chromatograph could not reliably identify the parasite sequences. All sequences were edited using Geneious v8.0.5. Birds not infected by any parasites were classified as “not infected”, birds infected with a single parasite genus were classified as “single infected” and those infected with at least two different genera as “co-infected” (Pigeault et al. 2018, Int. J. Parasitol., 48, 1079–1087).2- Life-history strategies, ecological and behavioral characteristics We used published trait data to position each species along the slow-fast continuum of life-history variation (Storchová & Hořák 2018, Glob. Ecol. Biogeogr., 27, 400–406). More specifically, bird position was represented by the first axis of a principal component analysis (PCA) performed on nine variables describing bird reproductive traits (clutch size, number of broods per year, average length, width and weight of the egg, incubation period, fledging age, age at first breeding) and maximum lifespan (Appendix 1, Table S1). The first axis (Dim.1_Slow_Fast) explained 62.7% of the variability and represented a gradient going from fast (negative values) to slow (positive values) life-history strategies.The trophic niche of bird species was estimated using 35 variables describing the diet during the breeding season (Pearman et al. 2014, Glob. Ecol. Biogeogr., 23, 414–424). Specifically, we considered 14 variables characterizing diet, nine variables characterizing food acquisition behavior, nine variables characterizing the substrate from which food is taken (e.g. air, water surface, mud, canopy) and three variables characterizing the daily foraging period (Appendix 1, Table S2). As in Pearman et al. (2014), we also included body weight as a surrogate for total energy requirements. These variables were scored as either 0 or 1, with the exception of body weight, which was scored as the average weight of individuals during the breeding season (Pearman et al. 2014, Glob. Ecol. Biogeogr., 23, 414–424). Trophic niches were represented by the scores of each species along the first two axes of a Hill-Smith ordination (denoted OA; Hill & Smith 1976, Taxon, 25, 249–255). These axes roughly corresponded to the structure (from open to forest habitats; OA1 = 19.3%) and the height (from underwater and ground to foraging in trees or in flight; OA2 = 12.6%) of the foraging environment.The remaining traits (nest type and migration status) were extracted from Storchová & Hořák (2018). Nest type was categorized as either “open” or “closed” while migration status was categorized as “sedentary” (species living in the same area in both the breeding and the non-breeding season), “migratory” (species migrates between breeding and non-breeding season) and “facultative migrant” (species makes irregular shifts in breeding and/or nonbreeding season).3- Climatic niche breadth, climatic niche position and geographic range size. Estimating species climatic realized niches (i.e. the set of suitable environmental conditions accessible to the species and constrained by biotic interactions; Jackson and Overpeck, 2000, pbio, 26, 194–220) requires data for the full geographical range of species together with the corresponding environmental variables (Guisan et al., 2017, Cambridge University Press). For each species, we estimated its climatic niche by cross-referencing IUCN range maps with climatic data. Climatic layers for 19 bioclimatic variables were extracted from worldclim at a 10’ resolution (roughly 340 km² at the equator). From these variables, we performed a PCA from which we extracted the two first axes, which explained 55% and 19% of the total variance, respectively. To obtain the climatic niche of each species, we first projected the environmental values corresponding to the geographic range coordinates falling inside the IUCN polygon within the two-dimensional space defined by the two PCA axes. We then used a kernel density estimator (KDE) to delineate species envelopes. KDE have proved useful to characterize complex and potentially irregular shapes (Blonder et al. 2014, Glob. Ecol. Biogeogr., 23, 595–609) and are increasingly used to characterize climatic niches (e.g. Broennimann et al. 2012, Glob. Ecol. Biogeogr., 21, 481–497). For each species, the bandwidth of the KDE was estimated from the data using a Hpi multivariate generalization of the plug-in bandwidth selector (Wand & Jones 1994, CRC Press, London). Envelopes were then defined as the minimum threshold of probability density that included 99% of points (to leave out environmentally atypical occurrences). From species envelopes, we extracted its area (niche breadth) and computed its centroid as the mean of point coordinates falling inside the delimited niche. We then extracted the coordinates of the centroid on each of the two PCA axes. To test the robustness of our results we used two other algorithms to delineate species realized niches: convex hulls and alpha hulls. The area of the geographic range was directly extracted from IUCN polygons.

西古北界鸟类血孢子虫单感染与共感染风险的决定因素
多种病原体共感染在野生环境中十分普遍，可对宿主与寄生虫的演化形成强烈的选择压力。然而，与单感染不同，目前针对共感染风险影响因子的研究仍相对匮乏。本研究依托馆藏标本的大型数据集与贝叶斯系统发育建模框架，探讨了鸟类生态学特征与系统发育地位对血孢子虫（haemosporidian）单感染与共感染概率的影响程度。
研究发现，系统发育与物种属性（如地理分布范围大小、生活史策略、迁徙行为等）均可作为共感染风险的有效预测因子，但这些因子对单感染概率的预测能力相对较弱。本研究表明，共感染风险相较于单感染风险，受到更强的确定性调控。上述结果强调了宿主演化历史与物种属性共同决定单感染与共感染模式，为我们预测野生动物感染风险提供了新的研究方向。

1. 鸟类样本与寄生虫检测
本数据集涵盖151个物种的1361份样本，涉及44个科、18个目（详见附录1表S1）。采样对象为1990年至2019年间收集的死亡鸟类，样本包括保存于洛桑州立动物博物馆（Cantonal Museum of Zoology, Lausanne）85%乙醇、4℃环境下的855份组织样本（肌肉与肝脏），以及日内瓦自然历史博物馆（Natural History Museum of Geneva）90%乙醇、-20℃环境下的506份组织样本。
针对每只个体，我们通过分子方法从组织样本中检测寄生虫，即血孢子虫属（Haemoproteus）、住白细胞虫属（Leucocytozoon）与疟原虫属（Plasmodium）。具体而言，采用DNeasy血液与组织试剂盒（DNeasy Blood & Tissue Kit，Qiagen, 瑞士）按照制造商说明书提取组织DNA后，对所有样本进行三轮重复的巢式PCR扩增（Hellgren等，2004，《Journal of Parasitology》，90卷，797–802页）。电泳后通过琼脂糖凝胶可视化巢式PCR产物，以鉴定感染样本。该巢式PCR方案无法鉴定血孢子虫属（Haemoproteus）与疟原虫属（Plasmodium）之间的共感染。因此，我们对所有阳性样本进行双向测序，参考van Rooyen等（2013，《Malaria Journal》，12卷，40页）的方法，通过在MalAvi数据库（MalAvi）中进行本地BLAST搜索完成物种鉴定。血孢子虫属与疟原虫属的共感染通过分析序列色谱图中的双重核苷酸峰进行判定。对于无法通过色谱图可靠鉴定寄生虫序列的样本，我们均进行了重新扩增与测序。所有序列均使用Geneious v8.0.5进行编辑。未感染任何寄生虫的鸟类被归类为“未感染”，仅感染单一寄生虫属的鸟类被归类为“单感染”，感染至少两种不同寄生虫属的鸟类被归类为“共感染”（Pigeault等，2018，《International Journal for Parasitology》，48卷，1079–1087页）。

2. 生活史策略、生态与行为特征
我们采用已发表的功能性状数据，将每个物种置于生活史变异的快慢连续谱上（Storchová & Hořák，2018，《Global Ecology and Biogeography》，27卷，400–406页）。具体而言，鸟类在该连续谱上的位置通过主成分分析（PCA）的第一轴得分表征，该分析基于9项描述鸟类繁殖性状的变量（窝卵数、每年窝数、卵的平均长度、宽度与重量、孵化期、离巢日龄、首次繁殖年龄）以及最大寿命（详见附录1表S1）。第一轴（Dim.1_Slow_Fast）解释了62.7%的变异，代表了从快生活史（负值）到慢生活史（正值）的梯度。
鸟类物种的营养生态位通过35项描述繁殖期饮食的变量进行估算（Pearman等，2014，《Global Ecology and Biogeography》，23卷，414–424页）。具体而言，我们纳入了14项描述饮食的变量、9项描述食物获取行为的变量、9项描述食物获取基质（如空气、水面、泥地、树冠层）的变量以及3项描述每日觅食时段的变量（详见附录1表S2）。参考Pearman等（2014）的研究，我们同时将体重作为总能量需求的替代指标。除体重以繁殖期个体平均体重计分外，其余变量均以0或1计分。营养生态位通过每个物种在希尔-史密斯排序（Hill-Smith ordination，记为OA；Hill & Smith，1976，《Taxon》，25卷，249–255页）前两个轴上的得分表征。这两个轴大致对应觅食环境的结构（从开阔生境到森林生境；OA1=19.3%）与高度（从水下、地面到树上或飞行中觅食；OA2=12.6%）。
其余性状（巢型与迁徙状态）提取自Storchová & Hořák（2018）的研究。巢型分为“开放式”与“封闭式”两类；迁徙状态分为“定居性”（繁殖期与非繁殖期均栖息于同一区域的物种）、“迁徙性”（在繁殖期与非繁殖期之间进行迁徙的物种）与“兼性迁徙”（在繁殖期和/或非繁殖期进行不定向移动的物种）。

3. 气候生态位宽度、气候生态位位置与地理分布范围大小
估算物种的实际气候生态位（即物种可利用且受生物相互作用约束的适宜环境条件集合；Jackson与Overpeck，2000，《pbio》，26卷，194–220页）需要物种完整地理分布范围的数据与对应的环境变量数据（Guisan等，2017，剑桥大学出版社）。我们针对每个物种，通过交叉参考IUCN分布范围地图与气候数据估算其气候生态位。19个生物气候变量的气候图层从世界气候数据库（WorldClim）以10’分辨率提取（赤道处分辨率约为340 km²）。基于这些变量进行主成分分析，提取前两个轴，分别解释总方差的55%与19%。
为获取每个物种的气候生态位，我们首先将IUCN分布多边形内的地理范围坐标对应的环境值投影至由两个PCA轴定义的二维空间中。随后采用核密度估计器（KDE）划定物种生态位包络。核密度估计器已被证实可有效表征复杂且可能不规则的形状（Blonder等，2014，《Global Ecology and Biogeography》，23卷，595–609页），并日益广泛应用于气候生态位的表征（如Broennimann等，2012，《Global Ecology and Biogeography》，21卷，481–497页）。针对每个物种，我们采用插件带宽选择器的Hpi多元推广方法从数据中估算KDE的带宽（Wand & Jones，1994，CRC出版社，伦敦）。随后将包含99%样本点的最小概率密度阈值定义为生态位包络（以排除非典型的环境出现记录）。从物种生态位包络中，我们提取其面积（即生态位宽度），并以包络内所有点坐标的均值计算其质心，进而提取该质心在两个PCA轴上的坐标。
为验证研究结果的稳健性，我们采用另外两种算法划定物种实际气候生态位：凸包算法与α凸包算法。地理分布范围的面积直接从IUCN分布多边形中提取。