Skip to main content

Co-infection patterns in the ectoparasitic community affecting the Iberian ibex Capra pyrenaica



Sarcoptic mange is one of the main parasitic diseases affecting the Iberian ibex Capra pyrenaica. Scabietic animals suffer a decline in body condition and reproductive fitness and in severe cases may die. Although several previous studies of the pathology of this disease and the physiological changes it produces in ibex have been carried out in recent years, our knowledge of the relationship between Sarcoptes scabiei and other ectoparasites of this host is still limited.


We analysed 430 Iberian ibex skin samples. Ectoparasites were removed, counted and identified. Mite (S. scabiei) numbers were obtained after digesting the skin samples in a 5% KOH solution. We modelled mite numbers in terms of host sex and age, site, year, season and the presence of other ectoparasites such as ticks and lice using generalized linear mixed models (GLMMs) and ectoparasite co-occurrence patterns using two different models: the probabilistic model species co-occurrence and the generalized linear latent variable model (GLLVM).


The ectoparasite community was mainly composed of S. scabiei, six ticks (Haemaphysalis sulcata, Haemaphysalis punctata, Rhipicephalus bursa, Rhipicephalus turanicus, Dermacentor marginatus and Ixodes ricinus) and two lice (Bovicola crassipes and Linognathus stenopsis). Adult male ibex harboured more mites than females. Mite numbers varied greatly spatially and seasonally and increased with the presence of other parasites. Some positive co-occurrence relationships between pairs of different ectoparasites were observed, particularly between ticks. The presence of S. scabiei negatively affected lice and H. sulcata numbers.


Sarcoptic mange has spread above all in ibex populations in and around the Mediterranean Basin, where it is now found in almost a third of its host’s range. Mite numbers varied seasonally and spatially and were higher in male hosts. The presence of S. scabiei had a negative effect on lice numbers but favoured the presence of ticks.

Graphical Abstract


Sarcoptic mange affects wild Caprinae throughout Eurasia [1,2,3]. In the Iberian ibex (Capra pyrenaica), outbreaks of this parasitic disease have been frequently recorded in the literature since the late 1980s, and much effort has since been dedicated to investigating the effects of sarcoptic mange at the individual and population levels in this particular host. As a consequence, certain aspects of the biology [4], ecology [5, 6], epidemiology [7, 8], physiology [9,10,11,12], pathology [13], genetics [14,15,16], diagnostic methods [17, 18] and management [19, 20] of this disease in this Caprinae species have only recently been explored. Briefly, a catabolic process leads ibex to lose weight [5, 10] and causes lesions on the skin and in inner organs (which are reversible), which are compounded by secondary infections [13] and a loss of reproductive fitness [9, 11]. After reaching its chronic phase, mange may kill hosts, although some ibex develop a degree of resistance [21, 22].

Hosts may become concurrently infected with several other micro- and macro-parasites [23], as occurs in Iberian ibex [24]. Parasites usually interact with each other, and these relationships may be antagonistic for at least one of the parasites or beneficial for one or both interacting parasites [25]. Pedersen and Fenton (2007) categorized a range of mechanisms that drive parasite interactions, ranging from reciprocal competition (i.e. for shared resources) to reciprocal facilitation (e.g. indirectly linked to the host’s immune response). Interactions between parasites may be similarly influenced by host traits such as behaviour, ecology, exposure history and pathologies [26] that affect the transmission [23, 27], distribution [28] and load patterns of parasites [29]. Morbidity induced by one parasite can affect host exposure to others, even if they are antagonistic [30], and mortality induced by one parasite can reduce the number of hosts available for other parasite species [31]. Moreover, the pattern of ectoparasite species co-occurrence varies over time and space [32].

Recently, Carvalho et al. [33] studied ectoparasite communities in ibex from the Sierra Nevada Natural Space (southern Spain). Such communities become richer more quickly in scabietic animals than in healthy ones. According to these authors, Sarcoptes scabiei infestations act in tandem with the off-host environment and host sex, which define the prevalence and abundance of lice and ticks. Bovicola crassipes was more prevalent in healthy animals, whereas Linognathus stenopsis was particularly prevalent in scabietic hosts with a severe clinical presentation.

The aims of this study were to (i) determine the occurrence of S. scabiei and other ectoparasites in ibex skin samples from different sites in Spain within the context of a monitoring programme of this disease; (ii) model the number of mites as a function of certain host and extrinsic factors; and (iii) analyse the co-occurrence patterns between these ectoparasites. We hypothesized the following: sarcoptic mange currently spreads through the Iberian Peninsula in parallel to host spread; epidemiology of sarcoptic mange in the Iberian Peninsula follows patterns found in intensively studied ibex population in Sierra Nevada (southern Spain); host alopecia caused by sarcoptic mange negatively affects lice (permanent ectoparasites attached to the host’s hair) but not ticks, which are temporal ectoparasites; the immune reaction caused by haematophagous parasites (e.g., ticks and sucking lice) may affect the presence of other ectoparasites.


Study area, sample collection and processing

In 2002–2022, 430 Iberian ibex skin samples (217 from males, 160 from females and 53 samples lacking information about sex) were provided by the staff of the Sierra Nevada Natural Space and the Fundación Artemisan (Ciudad Real, Spain). Samples were collected from legally hunted ibex harvested in Andalucía, Aragón, Castilla-La Mancha, Castilla-León and Región de Murcia (Fig. 1). Therefore, no approval by an ethics committee was necessary. A square 10 × 10-cm skin sample was removed from the withers of each shot animal, placed in a plastic bag, labelled and then frozen until analysis.

Fig. 1
figure 1

Geographical origin of the samples analysed in this study. Red dots: Sarcoptes-positive; blue dots: Sarcoptes-negative

Each sample was inspected for ectoparasites, which were collected, counted and fixed in 70% ethanol. Lice and ticks were identified to species level using available morphological keys [5, 34,35,36,37]. A 2.5 × 2.5-cm portion of each skin sample was removed and digested in a 5% KOH solution overnight at 45 °C [4] and the number of mites was recorded.

Statistical analysis

The database (n = 430) included 13 variables: ‘mite number’ or number of Sarcoptes specimens, host ‘sex’ and ‘age’, ‘site’ (where the host was shot), ‘year’ and ‘season’, when the sample was taken: ‘autumn’ (October–December), ‘winter’ (January–March), ‘spring’ (April–June) and ‘summer’ (July–September), and ‘others’ including the number of parasites other than S. scabiei on the host: two lice species (‘B. crassipes’ and ‘L. stenopsis’) and five tick species (‘Haemaphysalis sulcata’, ‘Haemaphysalis punctata’, ‘Rhipicephalus bursa’, ‘Rhipicephalus turanicus’ and ‘Dermacentor marginatus’). Ixodes ricinus was not included in the analyses because it was present only (one adult female) in one host. Like the case of Rhipicephalus turanicus in which there are only three individuals.

All statistical analysis was carried out using R version 4.2.2. [38]. We also compared the number of Sarcoptes on Iberian ibex between seasons and sites separately for each year considered. Due to the lack of normality in the residuals of the ANOVA for both season and site, we used the Kruskal–Wallis test. This test was used to perform a comparison between the distributional form of the seasonal groups and site with certain simplifications; comparisons were carried out on the medians to detect significant differences in the number of mites between seasonal and site medians. A Dunn test was performed to check the multiple comparisons after the Kruskal–Wallis test. We used the kruskal.test() function to conduct the Kruskal–Wallis test with the stats package, while the Dunn test post hoc comparisons were estimated with dunnTest(). Generalized linear mixed models (GLMMs) were employed to determine whether sex, age, year, season and the presence of other parasites affected mite densities. We also considered the variable ‘site’ as a random factor in the model to avoid pseudoreplication [39]. The dependent variable (‘mite number’) had an excess of zeros, which required the use of zero-inflated distributions. Before fitting zero-inflated models, we carried out a zero-inflated test with the testZeroInflation() function in the DHARMa package in R [40], given that the presence of many zeros does not necessarily mean that there was a zero-inflation problem [41]. Zero-inflated Poisson and zero-inflated negative binomial mixed models were fitted using the glmmTMB() function of the glmmTMB package in R [42]. The conditional and marginal \({R}^{2}\) values based on [43] were obtained using the package performance [44]. There was no substantial correlation between explanatory variables when variation inflation factor (VIF) values were < 5 [45]. VIFs were obtained using the check_collinearity () function the of package performance [44]. We plotted the standardized estimates and random effects using the plot_model() function in the sjPlot package [46].

The first approach used to estimate the patterns of co-occurrence based on probabilistic model species co-occurrence [47] used the cooccur() function of the package cooccur [48]. This analysis uses a hypergeometric distribution to calculate the probabilities that a lower or higher value of co-occurrence may or may not be randomly obtained.

The second approach, the joint species distribution modelling framework, uses generalized linear latent variable models (GLLVMs) to assess how parasite community composition is influenced by environmental variation while taking into account patterns of species co-occurrence [49, 50]. We fitted GLLVMs using the gllvm() function of the gllvm package [51] in R, which incorporates the latent variables derived from the Laplace approximation method implemented through Template Model Builder [52]. The function gllvm() fits pure latent variable models (PLVMs) in which species occurrence data are regressed only against the latent variables [53].

Correlation between species co-occurrence could be due to residual correlation (e.g. unknown variables, biotic interactions, etc.), which can be accounted for by the latent variables in the PLVM [54]. The strength and sign of correlations between species co-infection were checked at a 5% significance level. A goodness-of-fit test was checked graphically with the summary() function of the gllvm package using a normal qq-plot of the residuals and the Dunn–Smyth residuals [55].


We identified eight ectoparasite species other than S. scabiei on the Iberian ibex skin samples: six tick species (H. sulcata, H. punctata, R. bursa, R. turanicus, D. marginatus and I. ricinus), together with a biting louse (B. crassipes) and a sucking louse (L. stenopsis). Table 1 summarizes their taxonomy, feeding habits and temporal relationship with their hosts.

Table 1 Relationship between ectoparasites found in this study, including their taxonomic group (at the family level), feeding habits and the temporality of their relationship with hosts

Sarcoptes scabiei was the most prevalent ectoparasite, affecting more than 46% of sampled animals. Nevertheless, the prevalence of mange varied significantly according to host origin: < 5% in Málaga and Salamanca provinces to > 50% in Granada, Jaén and Murcia provinces. Rhipicephalus bursa was found in almost 11% of sampled hosts and was the most abundant tick species. Bovicola crassipes was the most prevalent lice species, although L. stenopsis was the most abundant. The prevalence and mean intensity (± standard deviation) of each ectoparasite are shown in Table 2.

Table 2 Basic epidemiological data of the ectoparasites found on Iberian ibex

The Kruskal–Wallis test (Fig. 2) detected significant differences between seasons (\({\chi }^{2}\)=9.5233, P-value = 0.0231) and sites (\({\chi }^{2}\)=59.723, P-value < 0.0001). Post hoc multiple comparisons with the Dunn test showed statistically significant differences. The top plot shows differences between winter and summer, while the bottom plot shows differences between Granada, Jaén and Murcia and the other localities.

Fig. 2
figure 2

Variation of mite numbers in different seasons and sites from which skin samples originated

Goodness of fit of GLMMs and PLVMs was checked graphically (see Additional file 1: Figs. S1, S2 and S3) and we found that a zero-inflated Poisson distribution fitted better than the zero-inflated negative binomial distribution in the GLMMs. The zero-inflation test gave a ratioObsSim value of 2.0194, where a value of ratioObsSim > 1 means that there are more zeros than expected (also known as zero-inflation), as in our case. The zero-inflated Poisson GLMM found significant differences for all the parameters considered in the model and all P-values were below the 5% significance level (i.e. sex, age, year, season and others; see Table 3 and Fig. 3). VIF values were all < 3 for all the explanatory variables in the GLMM (Additional file 1: Table S1), so our model did not have multicollinearity problems. According to the coefficients of the model, autumn was the season with the highest number of mites on hosts (Fig. 4), males (particularly older ones) harboured more mites than females, and the presence of other parasites (ticks and/or lice) was negatively affected by the number of mites on ibex (see Table 3 and Fig. 3). Figure 3 depicts the random effects by levels.

Table 3 Summary of the results of estimates for zero-inflated Poisson GLMM models
Fig. 3
figure 3

Influence of fixed and random factors on the number of Sarcoptes mites in Iberian ibex (Capra pyrenaica); estimated coefficients (and 95% confidence intervals) of the covariates for the explanatory variables; overlapping 95% confidence intervals 0 (solid vertical line) indicate a non-significant coefficient

Fig. 4
figure 4

Monthly dynamics of Sarcoptes scabiei numbers. Points refer to the monthly mean value for mite numbers; bars represent the standard deviation; the blue line represents the smoothed average values; the grey area is the associated 95% confidence intervals

The co-occurrence analysis carried out by the methodology implemented in the cooccur package found six pairs of combinations. Figure 5A shows the percentage of species pairs that were classified as positive, negative or random for all species, and also illustrates whether the species tended to have predominantly positive or negative interactions. Additionally, this graph shows whether these interactions were uniformly distributed, since the bars are arranged in increasing (or decreasing) order. We found four positive, two negative and nine random or undefined associations (Fig. 5B); only significant associations are shown and events without co-occurrence data were removed. The positive associations were R. bursaD. marginatus, R. bursaH. punctata, R. bursaB. crassipes and D. marginatusB. crassipes, while the negative associations were S. scabieiH. sulcata and S. scabieiB. crassipes. The rest of the associations were classified as random (Fig. 5B). Figure 5C shows the observed and expected values of the co-occurrences and the degree to which the pairs of parasite species deviate from their expected levels of co-occurrence.

Fig. 5
figure 5

A The percent of total positive, negative or random pairings for each species. B Heat map representing the positive, negative or random species associations (co-occurrence). C Scatter plot with the observed vs expected co-occurrence. Positive, negative and random pairs of species are represented by coloured points

The co-occurrence analysis carried out with PLVM shows that the patterns of co-occurrence between parasite species could be attributed to the effects produced between the parasites themselves. Figure 6 shows the correlations between the parasite species. We found the same associations as with the previous methodology, as well as six fresh ones. The positive associations were B. crassipesH. sulcata, D. marginatusH. sulcata, B. crassipesL. stenopsis and L. stenopsisH. sulcata, while the negative associations were S. scabieiD. marginatus and S. scabieiL. stenopsis.

Fig. 6
figure 6

Correlations between parasite abundance due to latent variables based on the PLVM. The strength and significance of correlations (i.e. 95% confidence interval not overlapping zero) are represented by solid colours, while transparent ones represent non-significant correlations

In short, both probabilistic models and PLVM suggest that the patterns of co-occurrence between the six ectoparasite species can be attributed to the effects produced by the parasites themselves. Most of the significant correlations between the different ectoparasite pairs were positive—for example, those between the ticks; on the other hand, the presence of S. scabiei negatively affected the number of individuals of the lice species and of D. marginatus and H. sulcata (Figs. 5 and 6).


Our sampling method (including data on ectoparasites from 10 cm2 skin samples from host withers), despite being standardized, may represent a limitation of this study, as ectoparasites may be unevenly distributed over the skin surface. In fact, this may explain the large number of zeroes in our database.

Other arthropod species such as Dermacentor reticulatus, Hyalomma lusitanicum, Psoroptes sp., Trombicula sp. [56], Straelensia cynotis [57] and Pulex irritans [58] have been reported to parasitize Iberian ibex. These taxa were not included in our analyses due to their very low prevalence (only one or a very few cases) and lack of data on mite numbers, as they were found in other research projects.

Geographically, sarcoptic mange mainly affects the ibex populations in the Mediterranean Basin but does reach the north-west of the Iberian Peninsula (Riaño, Castilla-León) as well. Currently, this disease is present in over 28% of the distribution range of C. pyrenaica [59]. It spread throughout the whole of the Sierra Nevada mountain range in the 10 years following the detection of the first cases (1992), with an estimated mean front spread speed of nearly 9 km/year [60]. Moreover, given that the Iberian ibex is currently expanding its range [61], a similar trend in the future distribution of mange is to be expected.

The prevalence values obtained for the different host locations (provinces) must be interpreted with caution since most samples were not obtained randomly and mangy animals were more likely to be selectively removed in different areas for humanitarian reasons and/or to manage ibex density and mange spread. In fact, a decreasing trend in mange prevalence has recently been reported in the ibex population from Sierra Nevada [22] despite the fact that more than 58% of samples from this location were positive for S. scabiei.

The epidemiological trend observed in our study fits that previously reported for the Sierra Nevada Natura Space (southern Spain) [4]\(.\) As expected, male ibex harboured more mites than females. This is due to physiological differences between the sexes, in particular in relation to the activity of sex steroid hormones such as testosterone, which has an immunosuppressive effect [4, 6]. Seasonal dynamics of mite numbers seem to be related to the concentrations of these hormones, with higher mite numbers—particularly larvae—coinciding with the host rutting season [62].

Nakagawa’s conditional \({R}^{2}\) for the selected zero-inflated Poisson GLMM explained 87.2% of the variance in the number of mites (Table 3). Information regarding other factors such as host body weight, kidney fat index (KFI) [4], immune response [8, 63] and temperature and humidity [64], among others, could improve this model in future.

Community resilience closely depends on the nature and strength of interspecific interactions [65]. The predominant pattern of species association within a community will determine the pattern of the community structure such that, for example, if most species associations are positive, the community will be structured aggregatively, with the frequency of species co-occurrence being greater than expected under random species assemblage. However, if these associations are negative, then the community structure is segregative, with the frequency of species co-occurrence being smaller than expected under random assemblage [66].

It is likely that competitive interactions between ticks and other haematophagous ectoparasites will occur due to competition for blood as a food resource [29]. Nevertheless, in our case, most of the significant interspecific associations between ibex ectoparasites were positive, so the community structure is aggregative and stable [33]. Aggregative patterns such as those shown by most of the tick species in our study suggest apparent facilitation mediated by the host. This facilitation could be explained by host immunodepression due to infection by multiple parasites [23, 67]. Establishing different types of immune responses is likely to be more costly than developing just one specific type of response [68]. Consequently, the effectiveness of energy allocation to immune defence will decrease as the diversity of parasite attacks increases [69]. Tick feeding induces a complex immune response in hosts [70]. Competition between tick species could also be reduced by temporal differences in emergence and/or attachment to hosts as a kind of segregation [71]. Co-occurrence between different lice taxa has not often been reported [72]. In our case, B. crassipes and L. stenopsis do not compete for food since their diet is quite different (Table 1). Again, the immune response developed by the host due to the haematophagous nature of L. stenopsis could facilitate the presence of B. crassipes.

As expected, the presence of S. scabiei negatively affected the presence of both lice species [33]. Mange induces alopecia in hosts, thereby reducing the ability of lice to remain attached to hosts. Nevertheless, these mites were also negatively associated with D. marginatus and H. sulcata, which suggests a cross-effective immune response in hosts [25].

As ibex are also infected by endoparasites, most of the possible interactions throughout the whole parasite community remain unexplored, which constitutes a challenge for future research.


Our data evidence that sarcoptic mange is spreading across the Iberian Peninsula, parallel to host dispersal, as had been hypothesized. As previously reported for the Sierra Nevada ibex population, the number of mites and therefore the effects of this disease are biased toward host males and have a clear seasonal pattern; therefore, our starting hypothesis is also confirmed. Sarcoptes scabiei, together with five tick and two lice species, form a stable ectoparasite community in which the presence of mites usually favours the presence of ticks but constrains lice numbers, confirming our hypothesis in this regard. Some authors suggest performing manipulative experiments (e.g., involving the extirpation of one ectoparasite species) to confirm the reliability of such interspecific associations between ectoparasites) [33]. Nevertheless, such experiments are logistically challenging, as they need completely specific methods for removing a particular ectoparasitic species with no effects for the remaining ones. Further studies will allow us to assess potential co-infection patterns between S. scabiei and ibex endoparasites.

Availability of data and materials

Data are available from the authors upon reasonable request and with permission from the Fundación Artemisan.


  1. Pence DB, Ueckermann E. Sarcoptic mange in wildlife. Rev Sci Tech OIE. 2002;21:385–98.

    Article  CAS  Google Scholar 

  2. Turchetto S, Obber F, Rossi L, D’Amelio S, Cavallero S, Poli A, et al. Sarcoptic mange in wild Caprinae of the Alps: could pathology help in filling the gaps in knowledge? Front Vet Sci. 2020;7:193.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Pérez JM, Granados JE, Espinosa J, Ráez-Bravo A, López-Olvera JR, Rossi L, et al. Biology and management of sarcoptic mange in wild Caprinae populations. Mammal Rev. 2021;51:82–94.

    Article  Google Scholar 

  4. Castro I, de la Fuente A, Fandos P, Cano-Manuel FJ, Granados JE, Soriguer RC, et al. On the population biology of Sarcoptes scabiei infesting Iberian ibex (Capra pyrenaica). Int J Acarol. 2016;42:7–11.

    Article  Google Scholar 

  5. Carvalho J, Granados JE, López-Olvera JR, Cano-Manuel FJ, Pérez JM, Fandos P, et al. Sarcoptic mange breaks up bottom-up regulation of body condition in a large herbivore population. Parasit Vectors. 2015;8:572.

    Article  PubMed  PubMed Central  Google Scholar 

  6. López-Olvera JR, Serrano E, Armenteros A, Pérez JM, Fandos P, Carvalho J, et al. Sex-biased severity of sarcoptic mange at the same biological cost in a sexually dimorphic ungulate. Parasit Vectors. 2015;8:583.

    Article  PubMed  PubMed Central  Google Scholar 

  7. León-Vizcaíno L, Ruiz de Ybáñez MR, Cubero MJ, Ortiz JM, Espinosa J, Pérez L, et al. Sarcoptic mange in Spanish ibex from Spain. J Wildl Dis. 1999;1999:647–59.

    Article  Google Scholar 

  8. Sarasa M, Rambozzi L, Rossi L, Meneguz PG, Serrano E, Granados JE, et al. Sarcoptes scabiei: specific immune response to sarcoptic mange in the Iberian ibex Capra pyrenaica depends on previous exposure and sex. Exp Parasitol. 2010;124:265–71.

    Article  CAS  PubMed  Google Scholar 

  9. Sarasa M, Serrano E, Soriguer RC, Granados JE, Fandos P, Gonzalez G, et al. Negative effect of the arthropod parasite, Sarcoptes scabiei, on testes mass in Iberian ibex, Capra pyrenaica. Vet Parasitol. 2011;175:306–12.

    Article  PubMed  Google Scholar 

  10. Pérez JM, Serrano E, Soriguer RC, González FJ, Sarasa M, Granados JE, et al. Distinguishing disease effects from environmental effects in a mountain ungulate: seasonal variation in body weight, hematology and serum chemistry among Iberian Ibex (Capra pyrenaica) affected by sarcoptic mange. J Wildl Dis. 2015;51:148–56.

    Article  PubMed  Google Scholar 

  11. Espinosa J, Granados JE, Cano-Manuel FJ, López-Olvera JR, Ráez-Bravo A, Romero D, et al. Sarcoptes scabiei alters follicular dynamics in female Iberian ibex through a reduction in body weight. Vet Parasitol. 2017;243:151–6.

    Article  PubMed  Google Scholar 

  12. Pérez JM, Molina L, Ureña-Gutiérrez B, Espinosa J, López-Montoya AJ, Boos M, et al. Individual stress responses to Sarcoptes scabiei infestation in Iberian ibex, Capra pyrenaica. Gen Comp Endocrinol. 2019;281:1–6.

    Article  CAS  PubMed  Google Scholar 

  13. Espinosa J, Ráez-Bravo A, López-Olvera JR, Pérez JM, Lavín S, Tvarijonaviciute A, et al. Histopathology, microbiology and the inflammatory process associated with Sarcoptes scabiei infection in the Iberian ibex, Capra pyrenaica. Parasit Vectors. 2017;10:596.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Alasaad S, Soglia D, Spalenza V, Maione S, Soriguer RC, Pérez JM, et al. Is ITS-2 rDNA suitable marker for genetic characterization of Sarcoptes mites from different wild animals in different geographic areas? Vet Parasitol. 2009;159:181–5.

    Article  CAS  PubMed  Google Scholar 

  15. Rasero R, Rossi L, Soglia D, Maione S, Sacchi P, Rambozzi L, et al. Host taxon-derived Sarcoptes mite in European wild animals revealed by microsatellite markers. Biol Conserv. 2010;143:1269–77.

    Article  Google Scholar 

  16. Moroni B, Angelone S, Pérez JM, Molinar Min AR, Pasquetti M, Tizzani P, et al. Sarcoptic mange in wild ruminants in Spain: solving the epidemiological enigma using microsatellite markers. Parasit Vectors. 2021;9:379.

    Article  CAS  Google Scholar 

  17. Ráez-Bravo A, Granados JE, Serrano E, Dellamaria D, Casais R, Rossi L, et al. Evaluation of three enzyme-linked immunosorbent assays for sarcoptic mange diagnosis and assessment in the Iberian ibex, Capra pyrenaica. Parasit Vectors. 2016;9:558.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Valldeperes M, Granados JE, Pérez JM, Castro I, Ráez-Bravo A, Fandos P, et al. How sensitive and specific is the visual diagnosis of sarcoptic mange in free-ranging Iberian ibexes? Parasit Vectors. 2019;12:405.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Espinosa J, López-Olvera JR, Cano-Manuel FJ, Fandos P, Pérez JM, López-Graells C, et al. Guidelines for managing captive Iberian ibex herds for conservation purposes. J Nat Conserv. 2017;40:24–32.

    Article  Google Scholar 

  20. Espinosa J, Pérez JM, Raéz-Bravo A, Fandos P, Cano-Manuel FJ, Soriguer RC, et al. Recommendations for the management of sarcoptic mange in free-ranging Iberian ibex populations. Anim Biodiv Conserv. 2020;43:137–49.

    Article  Google Scholar 

  21. Alasaad S, Granados JE, Fandos P, Cano-Manuel FJ, Soriguer RC, Pérez JM. The use of radio-collars for monitoring wildlife diseases: a case study from Iberian ibex affected by Sarcoptes scabiei in Sierra Nevada, Spain. Parasit Vectors. 2013;6:242.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Pérez JM, López-Montoya AJ, Cano-Manuel FJ, Soriguer RC, Fandos P, Granados JE. Development of resistance to sarcoptic mange in Ibex. J Wildl Manage. 2022;86:e22224.

    Article  Google Scholar 

  23. Cox FEG. Concomitant infections, parasites and immune responses. Parasitology. 2001;122:S23–38.

    Article  PubMed  Google Scholar 

  24. Pérez JM, Meneguz PG, Dematteis A, Rossi L, Serrano E. Parasites and conservation biology: the ‘ibex-ecosystem.’ Biodiv Conserv. 2006;15:2033–47.

    Article  Google Scholar 

  25. Pedersen AB, Fenton A. Emphasizing the ecology in parasite community ecology. TREE. 2007;22:133–9.

    Article  PubMed  Google Scholar 

  26. Johnson PT, Buller ID. Parasite competition hidden by correlated co-infection: using surveys and experiments to understand parasite interactions. Ecology. 2011;92:535–41.

    Article  PubMed  Google Scholar 

  27. Lello J, Norman RA, Boag B, Hudson PJ, Fenton A. Pathogen interactions, population cycles, and phase shifts. Am Nat. 2008;171:176–82.

    Article  PubMed  Google Scholar 

  28. Ng YL, Hamdan NES, Tuen AA, Mohd-Azlan J, Chong YL. Co-infections of ectoparasite species in synanthropic rodents of western Sarawak, Malaysian Borneo. Trop Biomed. 2017;34:723–31.

    CAS  PubMed  Google Scholar 

  29. Hoffmann S, Horak IG, Bennett NC, Lutermann H. Evidence for interspecific interactions in the ectoparasite infracommunity f a wild mammal. Parasit Vectors. 2016;9:58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Karvonen A, Sepälä O, Valtonen ET. Host immunization shapes interspecies associations in trematode parasites. J Anim Ecol. 2009;78:945–52.

    Article  PubMed  Google Scholar 

  31. Jolles AE, Ezenwa VO, Etienne RS, Turner WC, Olff H. Interactions between macroparasites and microparasites drive infection patterns in free-ranging African buffalo. Ecology. 2008;89:2239–50.

    Article  PubMed  Google Scholar 

  32. Krasnov BR, Vinarski MV, Korallo-Vinarskaya NP, Shenbrot GI, Khokhlova IS. Species associations in arthropod ectoparasite infracommunities are spatially and temporally variable and affected by environmental factors. Ecol Entomol. 2021;46:1254–65.

    Article  Google Scholar 

  33. Carvalho J, Serrano, Pettorelli N, Granados JE, Habela MA, Olmeda S, et al. Sarcoptes scabiei infestation does not alter the stability of ectoparasite communities. Parasit Vectors 2016;9:379. Doi:

  34. Rodríguez F, Jiménez A, Martín-Mateo MP. Primeras citas de malófagos parásitos de Capra pyrenaica hispanica. Nouv Rev Entomol. 1980;10:363–71.

    Google Scholar 

  35. Pajot FX. Les poux (Insecta, Anoplura) de la région afrotropicale. Paris: IRD Éditions; 2000.

    Google Scholar 

  36. Habela M, Peña J, Corchero E, Sevilla RG. Garrapatas y hemoparásitos transmitidos de interes veterinario en España. Manual práctico para su identificación, 1st ed. Madrid: Schering Plough Animal Health; 2000.

  37. Estrada-Peña A, Mihalca AD, Petney TN, editors. Ticks of Europe and North Africa. Cham: Springer International Publishing; 2017.

    Google Scholar 

  38. R Core Team (2022). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Accessed 20 Jan 2023.

  39. Zuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM. Mixed effects models and extensions in ecology with R. New York: Springer; 2009.

    Book  Google Scholar 

  40. Hartig F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R Package Version 0.2 4; 2019. Accessed 20 Jan 2023.

  41. Warton DI. Many zeros does not mean zero inflation: comparing the goodness-of-fit of parametric models to multivariate abundance data. Environmetrics. 2005;16:275–89.

    Article  Google Scholar 

  42. Brooks ME, Kristensen K, Van Benthem KJ, Magnusson A, Berg CW, Nielsen A, et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 2017;9:378–400.

  43. Nakagawa S, Johnson PCD, Schielzeth H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J Roy Soc Interface. 2017;14:134.

    Article  Google Scholar 

  44. Lüdecke D, Makovski D, Wagonner P, Patil I. Assessment of regression models performance. R package; 2020. Accessed 20 Jan 2023.

  45. James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning: with applications in R. New York: Springer; 2013.

    Book  Google Scholar 

  46. Lüdecke D. sjPlot: Data visualization for statistics in social science. R package version 2.8.12; 2022. Accessed 20 Jan 2023.

  47. Veech JA. A probabilistic model for analysing species co-occurrence. Glob Ecol Biogeogr. 2013;22:252–60.

    Article  Google Scholar 

  48. Griffith DM, Veech JA, Marsh CJ. cooccur: probabilistic species co-occurrence analysis in R. J Stat Softw. 2016;69:1–17.

    Article  Google Scholar 

  49. Hui FKC, Taskinen S, Pledger S, Foster SD, Warton DI. Model-based approaches to unconstrained ordination. Methods Ecol Evol. 2015;6:399–411.

    Article  Google Scholar 

  50. Warton DI, Blanchet FG, O’Hara ROO, Taskinen S, Walker SC, Hui FKC. So many variables: joint modeling in community ecology. Trends Ecol Evol. 2015;30:766–79.

    Article  PubMed  Google Scholar 

  51. Niku J, Brooks W, Herliansyah R, Hui FKC, Taskinen S, Warton DI. gllvm: generalized linear latent variable models. R package version 1.1.7; 2019. Accessed 20 Jan 2023.

  52. Kristensen K, Nielsen A, Berg CW, Skaug H, Bell BM. TMB: automatic differentiation and laplace approximation. J Stat Software. 2016;70:1–21.

    Article  Google Scholar 

  53. Niku J, Brooks W, Herliansyah R, Hui FKC, Taskinen S, Warton DI. Efficient estimation of generalized linear latent variable models. PLoS ONE. 2019;14:e0216129.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Niku J, Hui FKC, Taskinen S, Warton DI. gllvm: Fast analysis of multivariate abundance data with generalized linear latent variable models in R. Methods Ecol Evol. 2019;10:2173–82.

    Article  Google Scholar 

  55. Dunn PK, Smyth GK. Randomized quantile residuals. J Comput Graph Stat. 1996;5:236–44.

    Article  Google Scholar 

  56. Pérez JM, Meneguz PG, Dematteis A, Rossi L, Serrano E. Parasites and conservation biology: the ‘ibex-ecosystem. Biodivers Conserv. 2006;15:2033–47.

    Article  Google Scholar 

  57. Cevidanes A, Pérez JM, Mentaberre G, Lavín S, Velarde R. First report of Straelensia cynotis Fain and Le Net, 2000 (Trombidiformes: Leeuwenhoekiidae) parasitizing Capra pyrenaica (Artiodactyla: Bovidae) with histopathological analysis. Int J Acarol. 2019;45:214–6.

    Article  Google Scholar 

  58. López AJ. Nuevas citas para la fauna de sifonápteros ibéricos. Final Degree Dissertation. Jaén University; 2019.

  59. Granados JE; López-Olvera JR; Lavín S; Fandos P; Soriguer RC; Mentaberre G; Valldeperes M; Cano-Manuel León FJ; Espinosa JE; Ráez A; Pérez JM; Estruch J; Velarde R; Prieto P. La sarna en la península ibérica In: Castillo Contreras R, Fuentes Rodríguez E, Villanueva LF, Sánchez García C (Eds.): Cabra montés en España: aspectos clave sobre salud, genética, caza y gestión. La Trébere, Ciudad Real, pp. 139–141; 2022.

  60. Granados JE, Cano-Manuel J, Castillo A, Fandos P, Pérez JM, Alasaad S et al. Evolución de la sarcoptidosis en la población de cabra montés de Sierra Nevada. In: Granados JE, Cano-Manuel FJ, Fandos P, Cadenas R (eds.) II Congreso Internacional del género Capra en Europa, pp. 42–47. Consejería de Medio Ambiente, Granada; 2007.

  61. Acevedo P, Cassinello J, Gortázar C. The Iberian ibex is under an expansion trend but displaced to suboptimal habitats by the presence of extensive goat livestock in central Spain. Biodiv Conserv. 2006;16:3361–76.

    Article  Google Scholar 

  62. Pérez JM, Castro I, Granados JE, Cano-Manuel FJ, Fandos P, Espinosa J, et al. Does Sarcoptes scabiei synchronize its breeding cycle with that of the Iberian Ibex, Capra pyrenaica? Int J Acarol. 2017;43:199–203.

    Article  Google Scholar 

  63. He R, Gu X, Lai W, Peng X, Yang G. Transcriptome-microRNA analysis of Sarcoptes scabiei and host immune response. PLoS ONE. 2017;12:e0177733.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Pérez JM, Ruiz-Martínez I, Granados JE, Soriguer RC, Fandos P. The dynamics of sarcoptic mange in the ibex population of Sierra Nevada in Spain – Influence of climatic factors. J Wild Res. 1997;2:86–9.

    Google Scholar 

  65. Pimm S. The complexity and stability of ecosystems. Nature. 1984;307:321–6.

    Article  Google Scholar 

  66. Gotelli NJ. Null model analysis of species co-occurrence patterns. Ecology. 2000;81:2606–21.[2606:NMAOSC]2.0.CO;2.

    Article  Google Scholar 

  67. Bush AO, Holmes JC. Intestinal helminths of lesser scaup ducks: patterns of association. Can J Zool. 1986;64:132–41.

    Article  Google Scholar 

  68. Taylor LH, Mackinnon MJ, Read AF. Virulence of mixed-clone and single-clone infections of the rodent malaria Plasmodium chabaudi. Evolution. 1998;52:583–91.

    Article  PubMed  Google Scholar 

  69. Jokela J, Schmid-Hempel P, Rigby MC. Dr. Pangloss restrained by the Red Quinn—steps towards a unified defence theory. Oikos. 2000;89:267–74.

    Article  Google Scholar 

  70. Brossard M, Wikel SK. Tick immunobiology. Parasitology. 2004;129:S161–76.

    Article  CAS  PubMed  Google Scholar 

  71. Anderson K, Ezenwa VO, Jolles AE. Tick infestation patterns in free ranging African buffalo (Syncercus caffer): effects of host innate immunity and niche segregation among tick species. Int J Parasitol Parasit Wildl. 2013;2:1–9.

    Article  Google Scholar 

  72. Oniki-Willis Y, Willis EO, Lopes LE, Rózsa L. Museum-based research on the lice (Insecta: Phthiraptera) infestations of hummingbirds (Aves: Trochilidae)—prevalence, genus richness and parasite associations. Diversity. 2023;15:54.

    Article  Google Scholar 

Download references


The research activities of the authors were partially funded by the Andalusian Research, Development and Innovation Plan (PAIDI), Junta de Andalucía (RNM-118 and RNM-175 groups) and by the Universidad de Jaén (action 1b).


This study was funded by the Fundación Artemisan (contract 2022_042).

Author information

Authors and Affiliations



MJFM, RCC, AJLM and JMP conceived and designed the study. RC and JEG obtained the samples. MJFM and JMP processed the samples. MJFM, FJM and JMP identified the ectoparasites. AJLM and MJFM analysed the data. All authors read and approved the final manuscript.

Corresponding author

Correspondence to María J. Fernández-Muñoz.

Ethics declarations

Ethics approval and consent to participate

This study complies with the Spanish and Andalusian laws regarding bioethics and animal welfare (ref. 21/03/2022/048).

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

Variation inflation factors (VIFs) with the zero-inflated Poisson GLMMs. Figure S1. Diagnostic plot of the residuals of the zero-inflated Poisson GLMM. Figure S2. Diagnostic plot of the residuals of the zero-inflated negative binomial GLLVM. Figure S3. Diagnostic plot of the residuals of the zero-inflated Poisson GLLVM.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fernández-Muñoz, M.J., Castillo-Contreras, R., Pérez, J.M. et al. Co-infection patterns in the ectoparasitic community affecting the Iberian ibex Capra pyrenaica. Parasites Vectors 16, 172 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: