Habitat Patch Size and Landscape Structure Inuence, Although Weakly, the Parasite Richness of an Arboreal Folivorous-frugivorous Primate in Anthropogenic Landscapes

Context Anthropogenic habitat disturbances that affect the ecology and behavior of parasites and hosts can either facilitate or compromise their interactions and modulate the parasite richness. Objectives We assessed if the size of the habitat patch, the composition and conguration of the landscape (forest cover, patch density and mean distance to the nearest patch) and host group size inuence the parasite richness of brown howler monkeys (Alouatta guariba clamitans) inhabiting forest fragments immersed in an anthropogenic matrix. Methods We collected fecal samples from 60 howler monkey groups inhabiting distinct forest fragments (one group/fragment) from January to July 2019. We used generalized linear models to assess the power of the independent variables in predicting parasite richness at the patch- and patch-landscape scales. We found 10 parasite taxa (ve basal eukaryotes, four nematodes and one platyhelminth), nine of which also infect humans or domestic animals. Overall parasite richness showed an inverse relationship with habitat patch size and forest cover, and a direct relationship with the mean distance to the nearest patch and group size. Patch-landscape metrics and host group size also inuenced the infection with parasites with direct cycle and transmission via ingestion of the infective stage in the arboreal environment or with parasites with indirect cycle and transmission via ingestion of intermediate hosts. However, all signicant models presented low weight. suggest their

such as plantations of exotic trees, roofs and open elds. Therefore, the matrix surrounding the habitat patch can directly or indirectly change the parasitic dynamics. A matrix composed of human settlements and pastures, for example, may increase the bidirectional exchange of parasites between howler monkeys and humans and domestic animals. Howlers may get infected with these shared generalist gastrointestinal parasites when they use matrix elements or when people, cattle, dogs, cats and other domestic animals defecate inside the forest fragment. Consequently, an approach at the patchlandscape-scale (Arroyo-Rodríguez and Fahrig 2014; Galán-Acedo et al. 2019a) that allows to assess the relationship between parasite richness and the characteristics of the composition and con guration of the patch-landscape can help identifying the factors that modulate parasite-host relationships in populations isolated in habitat patches immersed in anthropogenic matrices.
In this study, we identi ed the gastrointestinal parasites of brown howler monkey (Alouatta guariba clamitans) groups inhabiting forest fragments immersed in an anthropogenic matrix. We assessed the relationship between parasite richness (overall and by life cycle) and characteristics of the fragments and the landscapes. We classi ed parasites according to their life cycles and modes of transmission into those with (a) direct life cycle and transmission via ingestion of the infective stage in the forest canopy (e.g. Trypanoxyuris), (b) direct cycle and transmission via ingestion of the infective stage on the ground (e.g. Ascaris, Eimeria, Endolimax, Entamoeba, Giardia, Iodamoeba, Isospora and Trichuris) and (c) indirect cycle and transmission via ingestion of intermediate hosts (e.g. Bertiella, Moniezia and Paragoninus; Stuart et al. 1998; Kowalewski and Gillespie 2009; Solórzano-García and Pérez-Ponce de Léon 2018). On one hand, we assumed in our approach at the patch level that the interaction of howlers with matrix elements is not su ciently strong to increase their parasite richness. Therefore, howler-parasite interactions would be limited by the size of the habitat patch. On the other hand, we assumed in our approach at the patch-landscape level that howlers' use of the matrix surrounding forest fragments exposes them to a greater parasite richness. Alternatively, in the absence of matrix use, a greater presence of domestic animals and humans inside forest fragments can also increase the parasite richness of howlers if they share parasites.

Study region and groups
We run this study from January to July 2019 in the rural region of Viamão, Rio Grande do Sul state, Brazil, near the southern limit of the distribution of brown howler monkeys (Culot et al. 2019). The landscape of the region is composed of a mosaic of forest fragments with varying levels of disturbance, crops, pastures and rural and suburban human settlements. At the patch-scale, we analyzed fecal samples of 60 groups of howlers (2-9 individuals, mean=5, SD=2) that inhabited 60 isolated forest fragments (1.2-257 ha, mean=25.8, SD=50.5, median=6.7, Fig. 1a). We analyzed independent landscapes surrounding 32 of these forest fragments for the analysis at the patch-landscape-scale (Fig. 1b). Howler group size in this subsample ranged from two to seven individuals (mean=5, SD=2, N=32).

Fecal sample collection and parasitological analysis
We collected 295 fecal samples from all individuals of the 60 groups once for the analysis at the patchscale. We used the subsample of 32 groups above for the analysis at the patch-landscape-scale. We collected ca. 2 g of material from the center of each stool to avoid contamination with larvae, eggs and oocysts found on the forest oor (Gillespie 2006) using disposable wooden spatulas. We pooled all individual samples of each howler group for assessing their parasite richness and preserved them in 10% formalin. This pooling increases the likelihood of detecting the group's parasites because the release of eggs, oocysts and larvae is not continuous; that is, while a parasite of a given host may lay eggs in a given day, a conspeci c parasite in another host individual from the same species may not (Gillespie 2006). Therefore, the likelihood of sampling all parasite taxa may increase with an increase in the number of stools composing a group's fecal pool. We included the number of fecal samples (=howler group size) per patch or patch-landscape in the modelling to assess its potential effect on the patterns of parasite richness. We transported the fecal samples in ice within 8 h of collection and stored them in a refrigerator at ca. 2ºC until analysis, which took place after one to eight months of collection.
We used the otation and the centrifuge-sedimentation in formalin ethyl-acetate techniques (De Carli 2001) to separate eggs, oocysts, cysts, larvae and adult parasites from the fecal remains of 4 g of each group's fecal pool. We analyzed the slides under an Olympus CH30 stereoscopic microscope using 200x magni cation lenses.
We classi ed the parasite richness (number of parasite species) of each fecal pool into four categories:

Sampling design
We treated each forest fragment as a sampling unit in the patch-scale approach. We estimated fragment area (size) using polygons created in Google Earth Pro version 7.1.8 (Google Inc. 2017). For the patchlandscape-scale approach, we estimated forest cover, matrix permeability, patch density and Euclidean mean distance to the nearest fragment (Table 1) in radii from the center of the focal fragment (Arroyo-Rodríguez and Fahrig 2014) of each of the 32 independent patch-landscape sampling units.
We quanti ed the types of land cover in each landscape using satellite images with 30-m spatial resolution made available by the Brazilian Annual Land Use and Land Cover Mapping Project (MapBiomas, collection 4). We classi ed the land cover types following MapBiomas: forest formation (including dense, open and mixed ombrophilous forests, semideciduous and deciduous seasonal forests, and secondary forest), planted forest of commercial tree species, grasslands, farming (including annual and perennial crops and pasture), wetlands, water (rivers and lakes) and urban infrastructure (urban areas with a predominance of non-vegetated surfaces, including buildings and roads and other transportation infrastructure). The mapping of the MapBiomas Project has an accuracy of 85.8% for the Atlantic Forest biome. We used ArcGis 10.3 (Esri 2014) for the GIS processing and Fragstats (McGarigal et al. 2012) to calculate the landscape metrics described below.
The proportion of the patch-landscape covered by forest is the main metric of habitat availability for arboreal primates such as howler monkeys. A larger forest cover may promote a lower richness of directsoil parasites because howlers will be less likely to descend to the ground to cross non-forest matrix elements. It may also promote a lower richness of direct-arboreal parasites because howlers will be able to use larger home ranges, thereby reducing the risk of reinfection (see Bicca-Marques and Calegaro-Marques 2016).
The type of matrix in uences the effectiveness of fragment isolation via its permeability to species dispersal (Metzger and Décamps 1997). A permeable matrix that allows howlers to move between forest fragments increases their risk of infection with direct-soil parasites. We classi ed the permeability of land cover types in a gradient from low (weight 1) to high  Table A1).
Patch density is a measure of the fragmentation of the patch-landscape. A highly fragmented patchlandscape may re ect a greater presence of people and domestic animals in the landscape and inside the target forest fragment. This presence increases the howlers' risk of contact with generalist direct-soil parasites shared with these hosts.
The mean Euclidean distance to the nearest forest fragment is a measure of between-fragment isolation in the patch-landscape. The higher the isolation between fragments in a given patch-landscape, the longer the distance that howlers have to cross in the matrix to move between habitat patches.
Consequently, the higher the risk of infection with direct-soil parasites.
We identi ed the spatial scale with the greatest explanatory power (scale of effect) of the categories of parasite richness in the analysis at the patch-landscape-scale (Jackson and Fahrig 2012). We built buffers with radii of 250, 500, 750 and 1,000 m from the center of the target forest fragment of each patch-landscape (Fig. 2). We used 250 m as the smallest radius because the likelihood of successful howler dispersal through a non-forest matrix between discrete habitat patches decreases signi cantly at distances longer than 200 m (Mandujano and Estrada 2005). We calculated the effect of each patchlandscape metric (Table 1) for each category of parasite richness inside each buffer. The 750-m buffer, for instance, showed the greatest effect of forest cover on overall parasite richness. Then, we generated an equation containing all patch-landscape metrics and their scales with greatest effects to model their potential as predictors of parasite richness. The equation for the modelling of overall parasite richness was: where is the forest cover inside the 750-m buffer, is the patch density inside the 1,000-m buffer, is the mean Euclidean distance to the nearest forest fragment inside the 1,000-m and is the permeability of the matrix inside the 1,000-m buffer.
We used the variance in ation factor (VIF) to check for multicollinearity between variables at the patchlandscape-scale. We excluded matrix permeability from all equations because it was strongly collinear with forest cover (VIF>4; Supplementary Material Table A2) in all models. The remaining three metrics were not colinear (all VIF<4; Supplementary Material Table A3). Therefore, we modelled the effect of forest cover, patch density and mean Euclidean distance to the nearest forest fragment on the four categories of parasite richness.

Data analyses
We used generalized linear mixed models (GLMMs) to assess the relationship between habitat patch (forest fragment) size or patch-landscape metrics and the four categories of parasite richness. We checked the normality, homoscedasticity and autocorrelation of residuals to validate the models. We built the models with the Gaussian family because these assumptions were met. Moreover, we used the logit family to build binomial models with binary variables. We used fragment size and howler group size as xed factors and season of fecal sample collection as random factor in the global model of the analysis at the patch-scale. Similarly, we used group size and the three patch-landscape metrics as xed factors and season of fecal sample collection as random factor in the global model of the analysis at the patchlandscape-scale. We included the season of fecal sample collection because it may in uence the dynamics of parasitic infections due to seasonal uctuations in climatic conditions ). We used the function dredge of the MuMln package of R (Barton 2016) to assess the in uence of all predictor combinations on the four categories of parasite richness.
We used the Akaike Information Criterion (AIC) to select the model(s) with the greatest explanatory power of the predictor effects on parasite richness. Speci cally, we used the AICc as recommended for small samples (Burnham and Anderson 2003). Although the model with the lowest AICc has the best adjustment, all models with ΔAICc<6 are equally parsimonious (Richards 2015). We considered that a given patch-landscape metric explains the parasite richness of howler monkeys if it is included in the best model or in many parsimonious models (Richards 2011) and if its relationship with parasite richness is signi cant. We run all analyses in R 3.5.1 (R Core Team 2018) using the lme4, car and MuMln packages

Data availability
All associated data will be available in a data repository when the paper is published.
Fragment size showed an inverse relationship with overall parasite richness in the patch-scale approach ( Table 3, Fig. 3a). However, the signi cance of this relationship disappears with the exclusion of the two largest fragments (>250 ha) and their negative samples (parameter=-0.008, SE=0.008, p=0.275). The richness of direct-soil parasites showed a weak inverse relationship with fragment size (Table 3, Fig. 3b). This relationship also weakens substantially with the exclusion of the two largest fragments and their samples (parameter=-0.001, SE=0.003, p=0.592). Group size showed a direct relationship only with the occurrence of indirect-HI parasites (Fig. 3c).
All models of overall parasite richness in the patch-landscape-scale approach showed ΔAICc<6 and low weight (maximum=0.293, minimum=0.036; Supplementary Material Table A4). The model with the lowest ΔAICc included forest cover (inverse relationship with richness) and group size (weak direct relationship with richness; Table 4, Figs. 4a and 4b). The mean Euclidean distance to the nearest forest fragment also showed a direct relationship with overall parasite richness in the third model (Table 4, Fig. 4c).
No patch-landscape metric showed a signi cant relationship with the richness of direct-soil parasites in the 14 models with ΔAICc<6. The model with the lowest ΔAICc only included the mean Euclidean distance to the nearest forest fragment (Table 4, Supplementary Material Table A5). The relationship between this metric and richness category is stronger when group size is entered as a random factor in the modelling (parameter=0.014, SE=0.007, p=0.048; Fig. 4d).
Forest cover was the only metric with a signi cant (inverse) relationship with the occurrence of directarboreal parasites (Fig. 4e). It was also the unique variable included in the model with lowest ΔAICc from the 13 models with ΔAICc<6, whose weights ranged from 0.287 to 0.017 (Table 4, Supplementary  Material Table A6).
Finally, patch density showed a direct relationship with the occurrence of indirect-IH parasites ( Table 4). The model with lowest ΔAICc only included patch density (Fig. 4f). Group size also showed a direct relationship with the occurrence of B. studeri (Fig. 4g). However, all models had low weight (maximum=0.171, minimum=0.012; Table 4, Supplementary Material Table A7).

Discussion
The 10 gastrointestinal parasite taxa that we found in the fecal samples, most with direct-soil cycles, were shared with humans and domestic animals at least at the genus level (Kowalewski and Gillespie 2009) and included the rst record of Balantidium sp. for the brown howler monkey. This basal eukaryote had been reported for Alouattacaraya, A. pigra and A. seniculus (Solórzano-García and Pérez-Ponce de Léon 2018). The overall parasite richness of our sample was similar to that recorded in studies that sampled 15+ groups of Alouatta spp. ). An alternative non-mutually exclusive explanation for these relationships is that small fragment size and low forest cover are associated with low food availability.
This condition could increase the probability of howlers of using the matrix to move between habitat patches in their search for food, thereby exposing them to direct-soil parasites. This outcome is compatible with the fact that the mean Euclidean distance to the nearest fragment was a good predictor of parasite richness.
The analyses of the relationships of the occurrence of T.minutus (the only howler parasite with a directarboreal cycle in this study) at the patch-and patch-landscape-scales produced contrasting results. While fragment size did not predict the occurrence of this pinworm at the patch-scale, forest cover showed a negative relationship with its occurrence at the patch-landscape-scale. The latter could be explained by the howlers' habit of rubbing the perianal region on tree trunks after defecation (Hirano et al. 2008). This habit increases the release of pregnant female pinworms in the substrate, thereby increasing the risk of infection and reinfection of group members via ingestion of eggs, particularly in smaller home ranges. If reinfection increases parasitic load, it shall increase the likelihood of nding eggs in howlers' feces. However, given that the model's weight was low, that patch size did not predict this pinworm's occurrence at the patch-scale and that necropsies of 36 howlers from the same region showed a prevalence of 100% with this nematode (Jesus et al. submitted), it is more likely that this relationship with forest cover is spurious.
The interpretation of the positive relationship between the occurrence of the only parasite with indirect-IH cycle, B. studeri, and patch density and group size focuses on the ecology of howlers and the oribatid mite intermediate hosts (Denegri 1993 On the other hand, given that the heterogeneity of the landscape for forest living species, such as howlers, increases with increasing density of forest fragments, the relationship between patch density and B. studeri occurrence is compatible with the hypothesis that the intermediate host mites are terrestrial and coprophagous. This hypothesis assumes that howler group size and relative density are inversely related to habitat patch size (Peres 1997) and that larger groups produce larger fecal clusters on the forest oor as a consequence of their behavior of defecating synchronously in "latrines" after resting periods (Gilbert 1997; Kowalewski and Zunino 2005; Pouvelle et al. 2009). Larger fecal clusters are likely to be more attractive to coprophagous mites. Given that howlers descend more often to the forest oor to cross canopy gaps or to move between habitat patches in smaller and more disturbed forest fragments (Prates and Bicca-Marques 2008; Bicca-Marques et al. 2020), the contact with mites might occur on the ground. However, the ingestion of mites is likely to occur intentionally during self-or allogrooming or unintentionally during feeding, both in the canopy, as howlers do not groom on the ground and rarely feed in this environment.
The low predictive power of forest cover, patch density and mean Euclidean distance to the nearest fragment suggest that these patch-landscape metrics are weak modulators of the interactions between howler monkeys and their parasites. Two non-mutually exclusive hypotheses may explain these ndings. First, these metrics are inadequate to represent the in uence of humans and domestic animals as potential sources of parasites shared with howlers. Second, howlers use the matrix only rarely. It is likely that the density of humans and domestic animals, their parasite richness and prevalence and their use of forest fragments are stronger modulators of the contamination of the environment with the infective stages of parasites with direct-soil cycles and of howlers' infection. The identi cation of humans and domestic animals as the likely sources of the infection of howlers (A. pigra, Vitazkova andWade 2006, 2007;Vitazkova 2009) and gorillas (Gorillaberingei, Graczyk et al. 2002) with Giardia spp. supports the importance of evaluating the in uence of these re ned metrics on parasite-wildlife interaction in anthropic landscapes. It is also necessary to identify and study the biology of intermediate hosts, such as oribatid mites, to uncover the relationship between the landscape and the interaction of parasites like Bertiella spp. with arboreal primate hosts.
In sum, we did not nd strong evidence that the potential use of the anthropogenic matrix and of other elements of the patch-landscape by the howler monkeys has increased their interaction with parasites. It is more likely that howler groups limit their activities to the fragment interior and to a narrow strip of the        Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.

Figure 2
Scheme of a patch-landscape with the 250-, 500-, 750-and 1,000-m radii from the centroid (the center of the focal forest fragment where the fecal samples were collected) and the classi cation of the types of land cover