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Data and scripts for Co-phylogeny, narrow host breadth and local conditions drive highly specialized bird-haemosporidian associations in West-Central African sky islands

This document includes the raw datafiles, host and parasite phylogenies and r-code use to conduct analyses for "Co-phylogeny, narrow host breadth and local conditions drive highly specialized bird-haemosporidian associations in West-Central African sky islands". Please see the readme file to get more detailed information about each file.

Fig. 4 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy

Fig. 4. Predicted mean and standard error body condition scores show associations with infection presence and season, with co-infected buffalo in much lower condition in the early wet season (Table S2). Coccidia infection status is represented with C– and C+; nematode infection status is represented with N– and N+.

Fig. 5 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy

Fig. 5. Season and co-infection differences in nematode aggregation. (a) Aggregation patterns in calves (b) and non-calves. (c) In non-calves, the distribution of nematode parasites in the late wet season shows that k is not significantly different in coccidia positive vs. negative buffalo. (d) In the early wet season coccidia positive buffalo have a truncated distribution, resulting in significantly reduced aggregation. Arrows indicate nematode intensity values in the tail of the distribution of coccidia negative buffalo. Coccidia infection status is represented with C– and C+.

Fig. 3 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy

Fig. 3. Patterns of parasite egg/oocyst counts with co-infection for (a) nematodes and (b) coccidia. (c), the mean nematode intensity in calves is higher in early wet season than in the late wet season independent of co-infection with coccidia. (d) Co-infection with coccidia alters the seasonal patterns of nematode intensity in non-calf buffalo (>1 year, juvenile through senescent). Calf vs. non-calf division is based on model paramters (Table 1).

Fig. 2 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy

Fig. 2. Age specific patterns of parasite prevalence with co-infection. (a) Prevalence of nematodes is higher in buffalo co-infected with coccidia (C+) compared to coccidia negative buffalo (C–) in all age categories (N = 33, 318, 166, 272, 162 for calf, juvenile, subadult, adult and senescent C– buffalo; N = 58, 237, 55, 54, 20 for C+ buffalo). (b) Prevalence of coccidia is higher in buffalo co-infected with nematodes (N+) compared to nematode negative buffalo (N–) in calf, juvenile, subadult, and senescent buffalo but not adult buffalo (N = 13, 107, 92, 144, 56 for calf, juvenile, subadult, adult and senescent N– buffalo; N = 38, 448, 129, 208, 100 for N+ buffalo).

Fig. 1 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy

Fig. 1. Age, sex and seasonal patterns of infection. Both parasites had the highest (a) prevalence (sample size for calf, juvenile, subadult, adult, and senescent respectively: N = 91, 555, 221, 326, 182) and (b) mean intensity in calves and juveniles (nematode N = 78, 448, 129, 208, 100; coccidia N = 58, 237, 55, 60, 14). (c) Males had lower estimated nematode prevalence and (d) higher estimated coccidia intensity compared to female buffalo. (e) The estimated nematode prevalence, coccidia prevalence, and (f) mean coccidia intensity were all increased in the early wet season compared to the late wet season. ‡Indicates significant differences at p <0.05.

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Fig. 2 in Microclimate and host body condition influence mite population growth in a wild bird-ectoparasite system

Fig. 2. Distribution of nest mite population sizes estimated when nests were placed in a Berlese funnel after nestlings had fledged. All nests began the experiment with the same population size (100 live mites), mimicking identical transmission, but ending population sizes 30–35 days later were highly variable. This suggests that factors of the nest environment or hosts may be playing an important role in mite population growth.

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Fig. 4 in Microclimate and host body condition influence mite population growth in a wild bird-ectoparasite system

Fig. 4. The relationship between the substrate the nest was built on: concrete, metal, or wood (y-axis) and the number of mites estimated in the field when chicks were 12 days old. Nests built on wooden substrates had significantly more mites compared to nests built on concrete or metal substrates. This graph was made using raw data, but models reported in the text included site as a random effect.

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Fig. 3 in Microclimate and host body condition influence mite population growth in a wild bird-ectoparasite system

Fig. 3. Relationship between the number of non-mite arthropods (x-axis) and nest mites (y-axis) that were recovered when experimental nests were removed from the field after nestlings fledged and placed in a Berlese funnel. Nests with more arthropods had significantly fewer nest mites. This graph was made using raw data, but models reported in text had a Poisson distribution and included site as a random effect.

Fig. 6 in Interactions of cranial helminths in the European polecat (Mustela putorius): Implications for host body condition

Fig. 6. Marginal effects plot of the gamma generalised linear model of the Zeroaltered gamma model, predicting kidney fat weight as a function of snout-vent length and sex of the host. The colour of the 95% confidence interval corresponds to the sex of the same colour. The plot is based on the most parsimonious model identified after model selection (see Table 3). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3 in Interactions of cranial helminths in the European polecat (Mustela putorius): Implications for host body condition

Fig. 3. Marginal effects plot of a logistic regression model predicting the presence (a) of T. acutum as a function of sex of the host and the presence/absence of S. nasicola, the other parasite, (b) of S. nasciola as a function of sex of the host and (c) of S. nasciola as a function of age of the host and the presence/absence of T. acutum. The 95% confidence intervals are shown as error bars or in grey. The plots is based on the most parsimonious model identified after model selection (see Table 1).

Fig. 5 in Interactions of cranial helminths in the European polecat (Mustela putorius): Implications for host body condition

Fig. 5. Marginal effects plot of the zero hurdle model of the Zero-altered gamma model, predicting the presence of kidney fat as a function of (a) snout-vent length, (b) abundance of T. acutum, (c) abundance of S. nasicola and (d) sex of the host. The 95% confidence intervals are shown in grey. The plot is based on the most parsimonious model identified after model selection (see Table 3).

Fig. 2 in Interactions of cranial helminths in the European polecat (Mustela putorius): Implications for host body condition

Fig. 2. Geographic origin of the skulls of the European polecat (Mustela putorius) analysed in this study. The size of the pie charts is indicative of the number of skulls analysed per locality and the contents of the pie charts are indicative of the infestation status of the corresponding animals. SKJ: Skrjabingylus nasicola, TRO: Troglotrema acutum. The numbers are indicative of the major landscape unit of origin of the samples.

Fig. 1 in Interactions of cranial helminths in the European polecat (Mustela putorius): Implications for host body condition

Fig. 1. Dorsal views of European polecat (Mustela putorius) skulls with or without infestations of the cranial helminths Skrjabingylus nasicola and Troglotrema acutum. (a) Skull of a non-infested one-year-old male. (b) Skull of a threeyear-old male with lesions in the frontal bone resulting from an infestation with T. acutum. (c) Lesions in both postorbital processes and the rear of the frontal bone of a skull of a two-year-old male infested with both parasites. (d) Lesion in the right postorbital process of a skull of a two-year-old male infested with both parasites. (e) Skull of a three-year-old female with lesions in both postorbital processes resulting from an infestation with S. nasicola. (f) Lesions in both postorbital processes and the frontal bone of a skull of a four-year-old female infested with both parasites. (g) Skull of a two-year-old female infested with T. acutum that is characterised by large perforations in the frontal bone and exposure of the frontal sinus and the nasal cavity. (h) Skull of a four-year-old male with perforations in and distensions of the frontal bone resulting from an infestation with T. acutum. (i) Skull of a five-year-old male characterised by multiple perforations and distended and sponge-like appearance of the bones across the whole of the frontal dorsal cranium.

Fig. 4 in Interactions of cranial helminths in the European polecat (Mustela putorius): Implications for host body condition

Fig. 4. Marginal effects plot of logistic regression model predicting the presence of skull damage as a function of sex of the host, the abundance of S. nasciola and a selection of T. acutum abundances. The colour of the 95% confidence interval corresponds to the T. acutum abundance of the same colour. The plot is based on the most parsimonious model identified after model selection (see Table 2). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

FIGURE 3 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

FIGURE 3 Variation in HeV AUC from flying foxes. (A) The forest plot displays annual estimates and 95% confidence intervals ordered by latitude and year; points are scaled by the inverse sampling variance. The horizontal axis uses a modulus transformation to accommodate wide upper bounds of some confidence intervals. (B) Fitted values and 95% confidence intervals for the top GAM, with raw data (scaled by inverse sampling variance) and modelled means coloured by roost type. Transparency denotes AUC derived from truncated time series (≤20 weeks)

FIGURE 2 Fitted HeV urine pool prevalence and 95 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

FIGURE 2 Fitted HeV urine pool prevalence and 95% confidence intervals from the most parsimonious GAMM with week, seasonal interactions with roost type and previous food shortages, and an adjustment for relative abundance of Pteropus alecto. Weekly data are overlaid, coloured by roost type, and sized by corresponding P. alecto relative abundance. Thin lines show the fitted curves from the random factor smooth including each roost per year

FIGURE 1 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

FIGURE 1 Spatiotemporal variation in HeV shedding for the nine Australian flying fox roosts sampled from 2012 through 2014. Curve height indicates the weekly proportion of HeV-positive urine pools, with roosts shown in order of latitude and coloured by roost type. Ticks show sampling time points. Dark grey shading indicates regional acute food shortage events, and dashed lines with light grey shading indicate the Austral winter (i.e. June through August)

FIGURE 4 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

FIGURE 4 Spatiotemporal variation in regional HeV spillover events during the flying fox surveillance period (2012–2014) and its relationship with HeV AUC. (a) Maps display the annual distributions of spillovers (coloured by year) in relation to the nine analysed roosts. (b) Modelled relationships between AUC and spillover counts are shown with fitted values and 95% confidence intervals from GAMs for 50, 100, 200, 300, 400 and 500 km buffers of each roost. Raw data are overlaid and scaled by the inverse of the sampling variance for AUC