The common vampire bat Desmodus rotundus (Chiroptera: Phyllostomidae) and the transmission of the rabies virus to livestock: A contact network approach and recommendations for surveillance and control

Journal Pre-proof The common vampire bat Desmodus rotundus (Chiroptera: Phyllostomidae) and the transmission of the rabies virus to livestock: a contact network approach and recommendations for surveillance and control Felipe Rocha, Ricardo Augusto Dias

PII:

S0167-5877(19)30598-7

DOI:

https://doi.org/10.1016/j.prevetmed.2019.104809

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PREVET 104809

To appear in:

Preventive Veterinary Medicine

Received Date:

23 August 2019

Revised Date:

21 October 2019

Accepted Date:

22 October 2019

Please cite this article as: Rocha F, Dias RA, The common vampire bat Desmodus rotundus (Chiroptera: Phyllostomidae) and the transmission of the rabies virus to livestock: a contact network approach and recommendations for surveillance and control, Preventive Veterinary Medicine (2019), doi: https://doi.org/10.1016/j.prevetmed.2019.104809

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The common vampire bat Desmodus rotundus (Chiroptera: Phyllostomidae) and the transmission of the rabies virus to livestock: a contact network approach and recommendations for surveillance and control

Felipe Rochaa,b, Ricardo Augusto Diasa*

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Laboratory of Epidemiology and Biostatistics, School of Veterinary Medicine, University of São

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Paulo, Av. Prof. Dr. Orlando Marques Paiva, 87, 05508-270, São Paulo, SP, Brazil Pan American Center for Foot-and-mouth Disease and Veterinary Public Health (PANAFTOSA),

Pan American Health Association/World Health Organization, Av. Gov. Leonel de Moura Brizola,

Corresponding author. Tel.: +55(11)3091-7700. E-mail address: [email protected]

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*

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7778, 25045-002, Duque de Caxias, RJ, Brazil

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A bimodal network connecting vampire bat roosts and farms was used to represent the rabies virus circulation in bats and livestock Livestock rabies outbreaks occurred in the farm communities, with possible introductions from neighboring areas It was possible to predict the roost occupation type by Desmodus rotundus males and females It was possible to predict the livestock rabies outbreaks using the network and physical parameters

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Highlights

Abstract

The importance of the common vampire bat Desmodus rotundus for the transmission of the rabies virus does not lie solely in its ability to transmit this disease to other mammals, but also in its capacity to adapt to environmental and climatic changes, granting them a wide geographical distribution. Control of this disease is currently based on culling of the vampire bat and vaccination of the

livestock. A transmission model incorporating geographic and behavioral determinants of the vampire bat was proposed to direct and optimize the epidemiological surveillance and control of livestock rabies. This model was built using a bimodal network connecting 260 vampire bat roosts among themselves (roost-roost-network) and with 1,557 farms (roost-farm network) in eastern Sao Paulo State, Brazil. These roosts were grouped in 9 communities, some very interconnected, and some not and the farms were grouped in 14 communities. From 2013 to 2017, 44 livestock rabies outbreaks occurred in the area, circulating among the farm communities during the entire period, with possible introductions from neighboring areas. Based on the network and environment parameters, it

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was possible to reasonably predict both the roosts’ occupation type (harem, bachelor, overnight and empty) and livestock rabies outbreak occurrence. The network approach brings light to the importance of phylogenetic studies of bats and rabies virus. Finally, the understanding of the

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interactions between bats and their feeding sources, influenced by the environment, allows to

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establish more precise surveillance and control measures and, ultimately, with a lower cost-benefit

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ratio of these actions.

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Keywords: rabies, livestock, vampire bat, Desmodus rotundus, network, surveillance.

Introduction

The common vampire bat Desmodus rotundus (E. Geoffroy, 1810) is the main responsible for

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the maintenance and transmission of the rabies virus (RABV) to livestock in rural areas of Latin America (Calisher et al., 2016). The importance of this bat lies not only on its ability to transmit the RABV due to its feeding and social habits, but on its adaptation traits, granting them a wide geographic distribution from Northern Mexico to Northern Argentina (Hayes and Piaggio, 2018). The D. rotundus belongs to the Family Phyllostomidae and order Chiroptera (Neuweiller, 2000), feeding exclusively on mammals' blood (Greenhall, 1988). It often forms small colonies of

few individuals, but eventually thousands of vampire bats can be observed in a wide range of natural and artificial roosts (Mialhe, 2013). The social structure of the vampire bat is complex and developed (Wilkinson, 1985; Kunz and Fenton, 2003). A single colony can occupy several different diurnal roosts, the main one containing the adult and juvenile females defended by few top-rank males and satellite roosts occupied by young and adult bachelors, who often challenge the top-rank males for the control of the harem (Crichton and Krutzsch, 2000). The vampire bats can fly up to 10 km to night foraging (Medina

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et al., 2007), but this distance is determined by the number of individuals in the roost, since individuals may fly further from the closest feeding sources to minimize competition with conspecifics (Kunz and Fenton, 2003; Rocha et al., upcoming 2019). In order to minimize the energy loss during foraging and browsing, overnight roosts can be used do complete digestion, protection

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from predators and social interactions (Kunz and Fenton, 2003).

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In the wild, the vampire bat preys upon large wild mammals of the rain forest. After the sixteenth century, deforestation, livestock intensification and urbanization decreased their wild preys,

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but increased the availability of domestic preys and artificial roosts (Johnson et al., 2014). This artificial high abundance of the vampire bat results from their preference to prey upon domestic rather

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than wild mammals. Domestic livestock are a more predictable feeding source than wild animals, and these animals are constantly attacked by vampire bats once in the area (Voight and Kelm, 2006; Mialhe, 2014). However, feeding behavior upon domestic animals may result in transmission of the RABV, which in turn is a dead end for the virus, since it is not transmitted among domestic livestock.

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Most of the Latin American countries implemented surveillance and control measures based

on empirical and scientific knowledge of the RABV transmission to reduce the impacts of the livestock rabies. The livestock rabies surveillance is based on roost monitoring, involving bat captures and sampling of D. rotundus to RABV diagnosis, and on the surveillance of vampire bat attacks upon livestock. Control measures may include vaccination of livestock and control of vampire bat populations, based on the administration of a warfarin paste in the back of the captured vampire bats

so that during social grooming, conspecifics ingest the paste and indistinctly die of hemorrhage (MAPA, 2009; Rocha et al., upcoming 2019). However, if not executed properly, the colonies disruption caused by the indiscriminate culling may increase disputes for resources, such as females and roosts, and consequently, increase the infectious contacts between the bats (Streicker et al., 2012; Blackwood et al., 2013). In Brazil, attempts to incorporate qualitative risk models (Dias et al., 2011; Braga et al., 2014) were shown to be susceptible to failure to obtain basic surveillance information, due to the

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heterogeneity of the local veterinary services. Livestock rabies incidence reports are compiled from these surveillance systems and few observational studies have been made (Kunz and Fenton, 2003). Moreover, no emphasis has been given to the ecology of the vampire bat and transmission of the RABV.

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By using radio-telemetry, Rocha et al (upcoming 2019), evaluated some geographical and

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behavioral determinants of the D. rotunudus foraging pattern. Along with previous studies and incorporating network analysis, it would be possible to direct and optimize the epidemiological

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surveillance actions and control of rabies transmission in the future. The concept of networks is often used to verify how the movement of individuals influence

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the introduction and spread of diseases, the risks, and the control strategies (Munro and Gregory, 2009). A network is a set of nodes connected by edges, the nodes being the representation of individuals or epidemiological units and the edges the interactions or connections between the nodes. These networks can also encompass communities, which are sets of nodes related to each other in a

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meaningful way, sharing characteristics (Newman, 2006; Olesen et al., 2007). This analysis could be used to elucidate how D. rotundus bats colonies, farms, and the surrounding environment are related, connected and interact with each other and, above all, to bring light to vampire bat and roost ecology in the field of rabies epidemiology. The aim of the present work is to build and describe contact networks between D. rotundus roosts and between vampire bat roosts and farms. To achieve that, the determinants have been

assigned to these networks in order to explore the spread and maintenance of rabies virus in these environments and communities.

Material and Methods

Study design and data The study was conducted in the the Eastern São Paulo State, Brazil, at the Paraíba do Sul river

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valley, which stretches from west to east, up to the Rio de Janeiro State border. The area is 7,000 km2 wide, delimited to the north by the mountain range of Serra da Mantiqueira, reaching elevations above 2,000 m above the sea level and to the south by another mountain range called Serra do Mar, with

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elevations up to 2,000 m, where several protected areas are located, including the Bocaina National

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Park, located southeast of the study area. Despite the conurbation of cities connecting Sao Paulo and Rio de Janeiro along the Paraíba do Sul river, the area is mainly rural.

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The area was chosen due to previous works made in the region (Dias et al., 2011; Rocha et al., upcoming 2019) and for being one of the most studied and assessed areas by the local veterinary

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service, especially the recording and monitoring of vampire bat roosts. The area experienced a large livestock rabies epidemic in 2013, which triggered intense control measures based on culling of the vampire bat practiced until 2015 (Rocha et al., upcoming 2019). Since then, no D. rotundus control has been made, but the area is still considered endemic. The vaccination of livestock against rabies is

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not compulsory since 2008.

The present work aims at building a contact network model between vampire bat roosts and

feeding sites, ie, farms, in order to subsidize the tracing of the rabies circulation in the study area. To achieve that, two contact networks were built, one connecting each vampire bat roost to farms (roostfarm network), and the other connecting the roosts (roost-roost network). The first network was related to foraging and the second, to reproduction, since the vampire bats interact to compete for

females and for the best roosts (Wilkinson, 1985; Kunz and Fenton, 2003; Carter and Leffer, 2015; Wilkinson et al., 2016). Even though the two networks were distinct, they shared common vertices, the vampire bat roosts. For that reason, the two networks were considered as a bipartite network (Zhou et al., 2007, Newman MEJ, 2002). The roosts-farms network was directed while the roostroost network was non-directed (Grisi-Filho et al., 2013). The geographic coordinates of the vampire bat roosts and farms, which were updated in 2015, were obtained at the Coordenadoria de Defesa Agropecuária (CDA), the Sao Paulo State animal

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health service. The vampire bat roosts were recorded during active surveillance activities from 2013 and 2017, being visited yearly by CDA staff. At these occasions, the number of captured bats, roost type and its occupation were recorded. The roost occupation was classified as harem (occupied mostly by females and pups), bachelor (occupied by young males), overnight (used as temporary resting stop

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during foraging and digestion), or empty. The records obtained at CDA were also completed during

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field campaigns performed in 2017 and 2018.

The number of times the roosts were classified in each occupation type and the average of the

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maximum number of captured bats were compared by t-test using the t.test( ) function and the proportions of the roost types and occupation were compared by prop.test( ) function of stats package

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of R (R, 2017). Moreover, the correlation between numeric variables was made in cor( ) function of stats package of R (R, 2017).

Finally, the records of livestock rabies outbreaks from 2013 to 2017 were obtained at the surveillance system of the Sao Paulo State Livestock Rabies Control Program (PECRH), held by

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CDA, being used to validate the model. Moreover, the vampire bat attacks on livestock, which started to be recorded in 2017, were also kindly made available by CDA.

Networks construction

Before the roost-farm network construction, the vampire bats foraging area for each roost, called catchment area hereafter, was defined in order to determine which farms could be used as feeding sources. To achieve that, the following assumptions were considered: 1. Maximum foraging distance of 10 km (Medina et al., 2007; MAPA, 2009; Benavides et al., 2016); 2. Up to five bats from a given roost could feed in each livestock herd per night. The feeding period of a vampire bat may last up to three hours and a single vampire bat from a given roost feeds on

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a herd at a time (Rocha et al., upcoming 2019), since vampire bats tend to avoid competition for food sources (Gillam and Fenton, 2016; Rocha et al., upcoming 2019);

3. Vampire bats systematically attack the same animals in a herd, selecting them according to the species, age, and docility (Voigt and Kelm, 2006; Mialhe, 2014; Bobrowiec et al., 2015);

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4. Vampire bats tend to forage in herds located below the elevation of their daily roost (Rocha et

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al., upcoming 2019);

5. A friction coefficient, ie, the difficulty to reach a prey and back to the roost, is given by the slope

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between the roost and the herd.

The elevations of the vampire bat roosts and farms were obtained from an ASTER digital

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elevation model (DEM) obtained at NASA (https://asterweb.jpl.nasa.gov/gdem.asp). Using the elevation of each roost, isoclines were generated from the DEM, in the “contour” plugin of QGIS 2.18.7 (QGIS, 2017). The isoclines generated polygons, that were edited, at a distance of 10 km from the correspondent root, generating their catchment areas. After that, all farms within the catchment

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area were associated with the corresponding roost, generating an adjacency matrix, that was later used to build the roost-farm network. If a given farm was included in catchment areas of different roosts, associations were made with the corresponding roosts, meaning that this particular farm could be attacked by vampire bats from multiple roosts. The roost-roost network was built in a different way. When comparing the elevations of the roosts, it was observed that the harem roosts close to the bachelor roosts were located at both higher

and lower elevations. Therefore, the approach used to build the roost-farms network, ie, that feeding sites would be at equal or lower elevations compared to the correspondent roosts would not be reasonable. In order to generate the roost-roost network, a 10 km buffer from each roost was build and all roosts within the buffer were linked, generating a second adjacency matrix. The construction of both networks was made using the adjacency matrixes in Gephi 0.9.2 (Gephi, 2017) and represented in thematic maps made in QGIS 2.18.7 (QGIS, 2017).

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Accessibility of the feeding sites This parameter refers to the difficulty at which each feeding site is reached by the vampire bats from a given roost. The calculation was based in the two-step floating catchment area model, proposed by Radke and Mu (2000) and modified by Polo et al. (2015). The first step of the model is

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the calculation of the Gaussian competition weight (Wij) between the roosts i and farms j:

1

𝑊𝑖𝑗 =

𝑁𝑖𝑗

(1)

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𝑗∈{𝑑𝑖𝑗 <10𝑘𝑚}

where dij is the distance between the roost i and a farm j within its catchment area and Nij is

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the number of farms in the catchment area of the roost i. Gaussian competition weight is an approximation of the demand for a given farm affected by the proximity of neighboring farms. Therefore, for a given roost i, the accessibility will be increased whereas the demand of the farms within its catchment area will be reduced. The Wij will be 1 if a

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single farm is present within its catchment area and decreases as the number of alternative farms increase.

The second step is the calculation of the Gaussian impedance (Gij) for each link of the

network: −𝛽(

𝐺𝑖𝑗 =

𝑒

𝑑𝑖𝑗 2 ) 𝑑0 −𝑒−𝛽

1−𝑒−𝛽

(2)

where ?? is the impedance, which represents the difficulty of a bat reach a feeding source, ie, the slope, given by the difference of elevations between the roost and the farm (in meters), divided by the Euclidean distance between these vertices. The third step is the calculation of the farm-roost ratio, ie, the offer (attendance capacity) and demand (vampire bat population to be fed per night) ratio (Rij): 𝑅𝑗 = ∑

𝑆𝑗

(3)

𝑖∈{𝑑𝑖𝑗 <10𝑘𝑚} (𝑃𝑖 𝐺𝑖𝑗 𝑊𝑖𝑗 )

where Sj is the capacity of the farm j to feed vampire bats and Pi is the population of vampire

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bats potentially attended in the catchment area of the farm i, ie, the maximum number of bats from the same roost using the farm j per night, fixed in 5, multiplied by the number of roosts connected with this farm.

𝐴𝑖 =

∑

𝑖∈{𝑑𝑖𝑗 <10𝑘𝑚}

𝑊𝑖𝑗 𝑅𝑖 𝐺(𝑑𝑖𝑗,𝑑

0

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farms j within the catchment area of each roost i:

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The forth and last step is the calculation of the accessibility to farms (Ai), ie, the search of all

)

(4)

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Carrying capacity of the roosts

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The calculations were made in the QGIS 2.18.7 (QGIS, 2017).

This parameter represents the potential number of vampire bats present at each roost. Since each farm may feed up to five bats from a given roost, the carrying capacity (Ki) is given by: ∑

𝑗∈{𝑑𝑖𝑗 <10𝑘𝑚}

𝑃𝑗 𝐺𝑖𝑗 𝑊𝑖𝑗

(5)

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𝐾𝑖 =

where Pj is he maximum number of bats in a roost i using a farm j per night, ie, five. This

parameter is similar to the Rj denominator, however referring to the roosts.

Detection of communities To determine the farm communities, given by their connected roosts, the accessibility was used as weight of each link. As for the vertices, no weight was considered, since the herd size plays

no role in feeding site selection (Rocha et al., upcoming 2019) and the predation of the same animals maximizes the foraging success (Voigt and Kelm, 2006; Bobrowiec et al., 2015). This network was given by one-way links between roosts and farms, ie, the possibility of vampire bats access to farms is given by the in-degree (possibility of vampire bat access, Kin,ij) (Newman, 2002): 𝑛

𝐾𝑖𝑛,𝑖𝑗 = ∑ 𝑎𝑖𝑗

(6)

𝑗=1

where aij is the number of links between a given roost and the farms within its catchment area. The Louvain method was used to detect the communities (Blondel et al., 2008), with the resolution

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fixed in 1. This calculation was made in Gephi 0.9.2 (Gephi, 2017). Finally, Voronoi polygons for each farm were built and grouped according to the correspondent community, originating a continuous delimitation of the farm communities, given by

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roosts. This procedure was made in QGIS 2.18.7 (QGIS, 2017).

The roost-roost network was given by two-way links between the roosts. Although the same

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weight was attributed to each link, some roosts showed a higher number of connections with other

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roosts, and therefore a higher in-degree. The in-degree (Kin,ii), the out-degree (Kout,ii), and the mean degree (K) of each vertice (roost) were calculated as (Newman, 2002): 𝑛

𝐾𝑖𝑛,𝑖𝑖 = ∑ 𝑎𝑖𝑖 𝑛

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𝑖=1

(7)

𝐾𝑜𝑢𝑡,𝑖𝑖 = ∑ 𝑎𝑖𝑖 𝑖=1

(8)

𝐾 2

(9)

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[𝐾𝑖𝑛,𝑖𝑖 ] = [𝐾𝑜𝑢𝑡,𝑖𝑖 ] =

where aii is the number of links between a given roost and the other roosts within its 10 km

buffer. The communities were determined in the same way as the roost-farm network, using Gephi 0.9.2 (Gephi, 2017). Finally, to describe the influence of the roosts in the network, the eigenvector centrality (??i) was calculated in Gephi 0.9.2 (Gephi, 2017). To achieve this, the mean degree (K) was assigned to

each roost and the eigenvector centrality (??i) of each vertice was based on the K of its neighbors (Farkas et al., 2002).

Roost ecology model First, the number of livestock rabies outbreaks and the roost network parameters - potential carrying capacity (Ki), mean degree (K), accessibility (Ai), eigenvector centrality (??i), and elevation

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(in meters) - were organized in tables showing roost types by occupation. Their average values were compared by the t-test using the t.test( ) function of stats package of R (R, 2017). The same way, the herd size, elevation, connected roosts, potential number of vampire bats using the farms, bat attack incidence, and outbreaks were organized by farm communities.

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A multinomial logistic regression model was used to verify the association of the estimated

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network parameters with the roost occupation type, used as dependent variable. A hierarchization of the roost occupation type was considered as follow: (a) if a roost was recorded as harem at least once

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during the study period, it has been classified as “harem”; (b) if a roost has been recorded as bachelor at least once and never as harem, it has been classified as “bachelor”; (c) if a roost has never been

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recorded as a daily roost, but as overnight roost at least once, it has been classified as “overnight”; and finally, (d) if the roost has never been occupied by vampire bats, it has been classified as “empty”. The last category was used as baseline.

The independent variables were: number of times the roost was classified in each occupation

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type, maximum number of captured bats, potential carrying capacity (Ki), mean degree (K), roostroost community, accessibility (Ai), eigenvector centrality (??i), and elevation of the roosts. These variables were not submitted to any transformation or categorization and correlation between the numeric variables was tested prior to the regression analysis. The analysis was made in R (R, 2017), using the vglm( ) function of the VGAM package. The significance level was 0.05.

RABV circulation model A binomial logistic regression model was used to verify the association of the network parameters with livestock rabies outbreaks in the roost catchment areas, used as dependent variable. The same variables of the multinomial model were used as independent variables, including the roost occupation (harem, bachelor, overnight and empty). The analysis was made in R (R, 2017) using the glm( ) function of the ISLR package. The significance level was 0.05. Finally, the livestock rabies outbreaks were located in the thematic map of the farm

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communities. After that the roost communities associated with the outbreaks were traced, generating yearly chord graphs. Assuming that prior to the transmission of the RABV to livestock, which was recorded in the farm communities, it circulated among the vampire bat population, the chord graphs allowed to infer from which roost community or neighboring areas the virus may have originated

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Results

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A total of 260 roosts and 1,684 farms were recorded in the study area (Fig 1). The farms and roosts were evenly distributed through the study area, except in a large area in the east, near the Rio de Janeiro State border, which comprises the Bocaina National Park. From the 260 roosts in the study area, 115 (44.2%) were recorded as harem at least once, 60

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(23.1%) as bachelor roosts at least once, 56 (21.5%) as overnight roosts at least once, and 29 roosts (11.2%) were never occupied during the monitoring period (Table 1, Fig 1). These roosts frequently changed between harem, bachelor, and overnight, with the assigned occupation being observed a little more than once in the study period (Table 1). No difference on the average number of times the roosts were recorded between the occupation types (harem, bachelor, overnight, and empty) was observed. Even though the empty roosts were most often classified as such, they did not reach the maximum

value of four times, because this particular roost type was not as frequently visited as the other types. Harem and overnight showed higher average number of outbreaks recorded on their catchment areas. The most frequent roost types were abandoned houses, caves, manholes and tunnels (Table 1). When comparing the roost types, lower outbreak averages were observed in the catchment areas associated with tunnels and bridges. The roost catchment areas were highly overlapped in the north and south of the study area, accompanying the river and stream valleys (Fig 2). The roost-farm network connected all the 260

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roosts to 1,557 farms (92.5%) by 7,355 links. Each roost was connected in average with 28.3 farms (ranging from 1 to 139), while each farm was connected in average with 4.4 roosts (ranging from 1 to 18). Fourteen roost-farm communities were obtained (Fig 3-A). The roost-roost network resulted in 1,922 links, with each roost connected in average with 7.5 roosts (ranging from 1 to 24). The roosts

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were grouped in nine communities (Fig 3-B). Some roost communities were poorly (communities 1

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and 8) or not connected (community 5) with the others. Moreover, the roost communities 1-4 were connected with communities 6-9 by a single roost (Fig 3-B).

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Over the years, a large number of D. rotundus were captured in the study area. A total of 863 vampire bats were captured in 2013, and 708 in 2014. In 2015, no captures were made and in 2016,

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only 72 were captured. Considering only the maximum number of captured bats per roost throughout the study period, the average was higher in harem than bachelor roosts, even when considering the roost types (Table 2). Nevertheless, the maximum number of captured bats was not correlated with the potential carrying capacity (Ki) in harem and bachelor roosts (r = 0.079). In turn, the mean Ki of

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harem, bachelor, and empty roosts were higher than overnight roosts (Table 2), even though the empty roosts were never recorded as occupied by vampire bats. The potential carrying capacity of the roosts was high throughout the study area, excepting the roost community 1 (Fig 1 - Supplementary Material). Considering harem and bachelor roosts, the potential number of vampire bats in the study region was 140,000, or 20 vampire bats/km2.

The mean degree (K) of harem, bachelor, and overnight roosts was higher than of the empty roosts. This has been observed in most of the roost types, excepting abandoned houses, where the K of overnight roosts was higher than other occupation types (Table 2). No difference in average accessibility (Ai) was observed between the roost occupations (Table 2), and a high heterogeneity of the accessibility of roosts to farms was observed (Fig 2 - Supplementary Material). The eigenvector centrality (??i) of the overnight roosts was higher than of the harem and bachelor roosts, which in

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turn are higher than of empty roosts (Table 2). The highest eigenvector centrality (??i) values were observed in the roosts located both in the north and south of the study area (Fig 3 - Supplementary Material). Finally, the elevations of the overnight and empty roosts tend to be higher than of harem roosts, which in turn are higher than of bachelor roosts (Table 2).

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As for the roost communities' parameters, the proportion of the harem was higher than of

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other occupation types in all communities (higher in community 2), excepting community 8, in which the empty roosts were more frequent (Table 3). Despite also being similar between communities, the

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carrying capacity (Ki) was higher in communities 6 and 8 and lower in 1, and the mean degree (K), higher in communities 3 and 9, and lower in 5, 4, and 8 (Table 3). The average elevations of the

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communities 1-6 were lower than of the 7-9, and the mean number of outbreaks recorded in the catchment areas was lower in communities 1, 7, and 9, and higher in 3 and 4 (Table 3). Considering the farm communities' parameters , the average herd sizes were similar between the communities; the elevations lower in the communities 1-8 and higher in 9-14; and the average number of connected

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roosts was higher in community 12 and lower in 1, 4, and 11 (Table 4). Moreover, the potential number of vampire bats using the farms was similar between the communities, but lower in community 1; and the bat attack incidence was also similar among communities, reaching 1.5% of the herd, or 2,250 animals in the study area. As for the multinomial logistic regression model, the accessibility of roosts to farms (Ai) was excluded, since it was highly correlated with the potential carrying capacity (Ki) (r = 0.84). The

coefficients of this model were shown in Table 5. A unit increase in the eigenvector centrality (??i) was associated with the increase in the odds of being bachelor, harem or overnight roosts versus empty roost in the amount of 15.35 (standard error = 4.68; p = 0.0010), 12.02 (standard error = 4.58; p = 0.0086), and 15.75 (standard error = 15.75; p = 0.00079), respectively. A unit increase in the mean degree (K) was associated with the decrease in the odds of being bachelor or overnight roosts versus empty roost in the amount of 0.28 (standard error = 0.11; p = 0.0098) and 0.30 (standard error = 0.11; p = 0.0079), respectively. A unit increase of the elevation was associated with the decrease in

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the odds of being bachelor roost versus empty roost in the amount of 0.00059 (standard error = 0.001; p = 0.0098). Finally, a unit increase in the potential carrying capacity (Ki) was associated with the increase in the odds of being harem roosts versus empty roost in the amount of 0.00078 (standard

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error = 0.00035; p = 0.028). The precision of the model was low (47.6%), but most of the correct predictions were due to harem roosts (72% of total precision). For this roost occupation, 78.7% of the

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predicted results were correct.

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A total of 44 livestock rabies outbreaks were recorded in the study period. As for the binomial logistic regression model, the eigenvector centrality (??i), elevation and mean degree (Ki) were

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associated with the presence of livestock rabies outbreaks in the catchment area, with odds ratios of 3.32 (standard error = 1.38; p = 0.016), 0.0020 (standard error = 0.00080; p = 0.012), and 0.00053 (standard error = 0.00019; p = 0.0046), respectively. However, the precision of this model was low (45%) (Table 6).

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The possible tracing of the livestock rabies epidemic was made using the chord graph (Fig 5),

and the livestock rabies outbreaks records represented over the farm communities (Fig 4). At the beginning of the study period, three RABV introductions from outside of the study area may have occurred. The first, from Minas Gerais State (north of the study area), in the roost community 2, resulted in three outbreaks in the farm community 2. Another, from Rio de Janeiro State (east of the study area) in the roost communities 4 and 5, resulted in five outbreaks in the farm community 6.

Finally, an introduction from Sao Paulo State (west of the study area) in roost community 9, resulted in five outbreaks in the farm community 12 that lasted until 2014, when the roost community 7 may also have infected this farm community. Concurrently, the RABV was already circulating in the center of the study area, in roost community 7, causing four outbreaks in farm communities 8, four in the community 10, and one in community 13, by the end of 2013. Both farm communities 8 and 13 remained infected until 2015, but in 2014, they started to be infected by roost communities 3 and 9, respectively. The introduction

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of the RABV in the roost community 3 in 2014 was probably from roost communities 2, 4 or 7, or Minas Gerais or Rio de Janeiro and in the roost community 9, for instance, from roost communities 6 or 7, or Sao Paulo State. Until the end of 2015, two additional outbreaks were recorded in farm community 8 and 9 in farm community 13.

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Moreover, in 2014 the introduction of the RABV in the roost community 6, probably from

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roost communities 7 or 9, or Sao Paulo State, resulted in one outbreak in the farm community 7 and one in farm community 9, which remained infected until 2015, when two additional outbreaks were

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recorded. In 2015, a new introduction from Minas Gerais State in roost community 2 (the same way it happened in 2013) resulted in one outbreak in farm community 2. In 2016, an introduction of the

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virus in roost community 1, probably from roost community 2, or Sao Paulo or Minas Gerais, caused one outbreak in farm community 1 and another, in 2017. Another reintroduction of the virus in the roost community 4 from roost community 3 or Rio de Janeiro, caused one outbreak in farm community 6 in 2016. Finally, reintroductions of the virus in roost communities 6 or 9, probably from

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Sao Paulo, resulted in one outbreak in the farm community 9. No livestock rabies outbreaks were recorded in farm communities 3, 4, 5, and 11 during the study period. These results indicate the transmission of the RABV between vampire bats from the roost

communities 6, 7, and 9 during the study period, resulting in a constant circulation of the RABV. This result is corroborated by the cluster of high eigenvector centrality values (Fig 3 - Supplementary Material) in this area. A less intense interaction of vampire bats from the roost communities 1-4 was

expected even with the existence of a second cluster of high eigenvector centrality values in this area. These two clusters are poorly connected by a single roost. Finally, no livestock rabies outbreaks were associated with the roost community 8.

Discussion

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Bat roost ecology has always been neglected as a research field, rarely approached by researchers (Kunz and Lumsden, 2003). Moreover, the construction and description of a network of vampire bat roosts and farms was presented for the first time. This has been achieved by compiling basic ecological characteristics of the Desmodus rotundus (Wilkinson, 1985; Greenhall, 1988; Kunz

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and Fenton, 2003; Voigt and Kelm, 2006; Medina et al., 2007; MAPA, 2009; Mialhe, 2014;

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Bobrowiec et al., 2015; Carter and Leffer, 2015; Benavides et al., 2016; Gillam and Fenton, 2016; Wilkinson et al., 2016; Delpietro et al., 2017; Rocha et al., 2019).

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A frequent change between roost occupation types (harem, bachelor and overnight) was observed, making it a challenge to determine the predictors for its occupancy by D. rotundus (Mendes

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et al., 2017). Harem and overnight roosts were more associated with livestock outbreaks. The force of infection proportioned by these roosts may be a consequence of (a) higher number of individuals in the harems; and (b) higher centrality in the network and social contact between individuals of different colonies on the overnight roosts (Kunz and Fenton, 2003). Despite showing support capacity

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as high as harems, the bachelor roosts had five times lower numbers of captured bats, consistent with observations in Argentina, as a result of female philopatry and immature male dispersal to a number of different roosts (Delpietro et al., 2017). The distinctive characteristics between the occupation types were the support capacity (higher for harem and bachelor), eigenvector centrality (lower for harem and bachelor), and elevation, which was lower for bachelor, intermediate for harem, and higher for overnight roosts. Empty roosts for instance, despite sharing characteristics with the others

occupation types, were always recorded as empty if the mean degree and the eigenvector centrality were low. Tunnels and bridges were less associated with rabies, probably because they were located far from the farms. The multinomial logistic regression showed that with increased eigenvector centrality, mean degree and potential carrying capacity were associated with the roost occupation types, being considered reasonable predictors. Even though the mean degree has been associated with livestock rabies outbreaks in the catchment areas as a protective factor, the eigenvector centrality was

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associated as a risk factor. This can be explained by the friendship paradox (Feld, 1991; Amaku et al., 2014), which states that the neighbouring vertices will always have more connections than a given vertice. Although there are roosts connected with few others, the neighbouring roosts will always have more connections, explaining why the RABV infection risk will be always higher in neighboring

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roosts. Therefore, the network configuration is a key factor for livestock rabies occurrence. The

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binomial logistic regression in turn, was not as precise, but eigenvector centrality, elevation and mean degree were good predictors of livestock rabies outbreaks.

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The high overlapping of bat roost catchment areas resulted in several roosts connected to each farm, compatible with the high incidence of bat attacks and ease of RABV transmission to livestock.

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Considering that the livestock herd in the farms connected with bat roosts was 150,000, and the incidence of bat attacks was recorded at 1.5% in 2017, a total of 2,250 animals were bitten, more likely frequently (Langoni et al., 2008). Considering that on average 4.4 roost were associated with each farm, the number of bats feeding upon livestock could reach 9,900 bats, ie, 7.1% of the current

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carrying capacity. Its impossible to determine the impact of the intense culling until 2015, nor the amount of population growth since then, even though there are signs of population recovery (Rocha et al., upcoming 2019). Another noteworthy point is that the carrying capacity estimation did not considered the roost size, ie, the estimation was not modulated by physical restrictions of the roosts, which were not visited. If so, like proposed by Delpietro et al. (2017) the precision of this estimation would be increased. Another point of concern is the estimation of alternative preys in the region, such

as the invasive feral pig. This specie was recorded in the study area in 2014 (Pedrosa et al., 2015) and has been associated with vampire bats (Galetti et al., 2016). Indistinct culling affects only adults, since juveniles rarely groom adults and are less likely exposed to the anticoagulant paste (Streicker et al., 2012). This situation may increase the bat rabies prevalence by increasing the recruitment of susceptibles or dispersal. If the culling is widespread, immigration of bats from neighboring colonies would increase the probability of perpetuation of the RABV circulation in the treated area (Streicker et al., 2012), which was probably the case of the study

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area. Even with a presumably lower density of vampire bats during the study period, the livestock rabies was still present, as a consequence of a low correlation between vampire bat density and RABV spillover to livestock. The infectious contacts among bats may be more dependent on the prevalence than on the bat population density, i.e., more frequency-dependent than density-dependent (Streicker

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et al., 2012).

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In rural areas such as the study region, the high density of livestock offers the vampire bat an abundant feeding source, supporting a higher population distributed in a high number of roosts if

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compared to sylvatic environments. As the abundance of vampire bats and roosts increase, the number of roost connections and infectious contacts among vampire bats also increase. For that reason, the

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continuous circulation of the RABV is more likely to occur (Streicker et al., 2012) than the currently accepted idea of slowly spread of the virus from colony to colony, unable to reinfect until a threshold number of susceptible reestablishes (Kessels et al., 2017). As shown in the chord graphs of the RABV spread in the roost and farm communities, despite a slight decrease of the outbreaks by the end of the

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study period, the frequent appearance of livestock outbreaks was incompatible with densitydependance of the vampire bat population. The local circulation of the RABV and confinement of the spillover in some locations may

actually be related with the configuration of the networks of roosts and farms. In the study area, two distinct clusters of roost communities, one up north and other to the south of the study area were connected by a single roost, which roughly determined two clusters of farm communities as well.

When we focus the roost community, no pattern explained the occurrence of livestock rabies outbreaks in the associated catchment areas. Excepting the community 8, in which empty roosts were more frequent, the most frequent occupation type was harem. The carrying capacity, mean degree, and accessibility were also similar. Both clusters presented a high eigenvector centrality core and communities 1-6 were located at lower elevations than communities 7-9. The farm communities were even more homogenous, but even so livestock rabies was not reported in all of them. The RABV may have circulated in farm communities 1-5 and 7-8, not contemporarily with the farm communities 9-

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14. The farm community 6 was connected with roost communities of the neighboring State of Rio de Janeiro.

The network approach would be highly supported by the understanding of the phylogenetic structure of both vampire bat and RABV populations. Few studies addressed the population genetic

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structure of the vampire bat, mostly in Amazon (Huguin et al., 2018) and Mexico (Romero-Nava et

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al., 2013; Huguin et al., 2018) due to the concerns of its the expansion of the United States (Piaggio et al., 2017). Even though the D. rotundus has a wide geographical distribution and apparently no

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physical barriers for dispersal and gene flow are identifiable in its distribution range, its population is subdivided by ecodomains in east (Amazonian) and west (Atlantic Rain Forest) South American,

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detected by mitocondrial markers (Martins et al., 2009). The Atlantic Rain Forest lineage for instance is subdivided in Northern and Southern phylogenetic groups, with latitudinal division similar to parsimonial analysis of endemicity of amphibians, reptiles, birds, and invertebrates (Martins et al., 2009). The study area is located at the geographic transition of the Northern and Southern Atlantic

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Rain Forest phylogenetic groups, as exemplified by the roost community 5. The RABV evolutionary history, in turn, is largely unclear (Fischer et al., 2018), mostly

because of sampling bias, since most of the data are originated from passive surveillance systems. Although several methods for antigenic characterization of the Lyssaviruses have been developed, are only focused at assigning a specimen to a particular viral type, being all that is required to RABV surveillance. The virus samples isolated from the vampire bats and livestock are often classified as

variant 3 (AgV 3) (Jackson, 2013). Although genetic methods are potentially more discriminatory, few studies (Heinemann et al., 2002) that were made in the study area, demonstrated that all strains isolated from livestock belonged to the D. rotundus variant (AgV 3) and no difference between the strains isolated from vampire bats were observed. Along with a proper collection of field data, the network approach allows the implementation of pragmatic surveillance and control of the emergent threat posed by vampire bat rabies. Therefore, the recommendations for a livestock rabies surveillance and control program the present work can

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provide are: (a) to avoid disturbances in the roosts, which may cause dispersion (Streicker et al., 2012; Delpietro et al., 2017);

(b) record and systematic monitoring of presence vampire bats, with minimal interference

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sampling (eg. saliva) for rabies molecular diagnosis;

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restricted to the capture and determination of the roost occupation and appropriate non-destructive

(c) vaccination of livestock in roost catchment areas where vampire bat aggression is

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increasing or is a rabies positive bat has been found;

(d) catchment areas construction in locations where bat attacks are reported, to facilitate the

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identification of the bat roost location and management. After locating a vampire bat roost, the construction of the catchment area could also allow the identification of other farms at risk, where the surveillance of bat attacks should be strengthened.

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Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance support Code 001.

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Figures captions Figure 1

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Figure 2

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Figure 3

Figure 4

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Figure 5

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Table 1. Number of Desmodus rotundus roost types by occupation, times each roost was recorded in that given occupation type, and livestock rabies outbreaks per catchment areas in Guaratinguetá, Brazil, from 2013 to 2017. Harem Roost type N

Bachelor

Aver age T (SD)

Aver age Out. (SD)

N

Overnight

Aver age T (SD)

Aver age Out. (SD)

N

Empty

Aver age T (SD)

Aver age Out. (SD)

N

Total

Aver age T (SD)

Aver age Out. (SD)

N

Aver age T (SD)

Aver age Out. (SD)

28

1.4a (0.6)

1.6a (1.7)

28

1.4a (0.6)

1.0a (1.4)

31

1.1a (0.5)

1.0a (1.2)

4

2.0a (1.1)

0.5a (1.0)

91

1.3 (0.6)

1.2 (1.5)

Cave

42

1.4a (0.6)

1.5a (1.6)

14

1.1a (0.4)

1.2ab (1.2)

13

1.1a (0.5)

1.5ab (1.7)

19

2.3a (0.9)

0.3b (0.7)

88

1.5 (0.8)

1.2 (1.5)

Manhole

20

1.3a (0.5)

0.8a (1.1)

12

1.7a (0.6)

0.7a (1.5)

3

0.7a (0.6)

1.0a (1.7)

3

3.0 -

-

38

1.5 (0.7)

0.7 (1.2)

Tunnel

9

1.4a (1.0)

0.3 (0.5)

1

1.0 -

-

2

1.5a (0.7)

-

2

2.5a (0.7)

1.0 -

14

1.6 (0.9)

0.4 (0.5)

Abandoned building

2

1.0 -

0.5a (0.7)

2

1.0 -

0.5a (0.7)

4

1 (0.8)

1.5a (1.9)

-

-

-

8

1.0 (0.5)

1.0 (1.4)

Corral

7

1.0 -

0.4 (0.5)

1

1.0 -

-

0

-

-

-

-

-

8

1.0 -

0.4 (0.5)

Bridge

3

1.3 (0.6)

0.3 (0.6)

1

1.0 -

-

2

1.0 -

-

-

-

-

6

1.2 (0.4)

0.2 (0.4)

Basement

2

1.0 (0)

0.5 (0.7)

1

2.0 -

Coal kiln

1

2.0 -

1.0 -

-

-

Water tank

-

-

-

-

Tree hollow

1

1.0 -

-

115

1.4a (0.6)

1.2a (1.4)

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re 0

-

-

-

-

-

3

1.3 (0.6)

0.3 (0.6)

-

0

-

-

1

2.0 -

-

2

2.0 -

0.5 (0.7)

-

-

1

1.0 -

2.0 -

-

-

-

1

1.0 -

2.0 -

-

-

-

-

-

-

-

-

-

1

1.0 -

-

60

1.4a (0.5)

0.9ab (1.3)

56

1.1a (0.5)

1.1a (1.4)

29

2.3a (0.9)

0.3b (0.7)

260

1.4 (0.7)

1.0 (1.4)

lP

-

ur na

Total

ro of

Abandoned house

Acronyms: N = number of roosts; T = number of times the roosts were classified in this category (out

Jo

of four times); Out. = number of livestock rabies outbreaks recorded in the roosts catchment areas from 2013 to 2017; SD = standard deviation. Idents: different letters = significant difference (p < 0.05) between roost classifications (harem, bachelor, overnight, empty) in the line; same letters = no difference in the line.

Table 2. Average of the maximum number of Desmodus rotundus captured by roost type and roost parameters (potential carrying capacity, mean degree,

Mean MCB (SD)

Mean Ki (SD)

Mean K (SD)

Mean ??i (SD)

Mean Ai (SD)

Overnight

Mean Mean Mean Mean Ki Mean Ai MCB K ??i (SD) (SD) (SD) (SD) (SD)

Mean E (SD)

Mean E (SD)

-p

Roost type

Bachelor

ro

Harem

of

accessibility, eigenvector centrality and elevation) according to the roost occupation in Guaratinguetá, Brazil, from 2013 to 2017.

Mean Ki (SD)

Mean K (SD)

Empty Mean ??i (SD)

Mean Ai (SD)

Mean Mean Mean Mean Ki Mean Ai E K ??i (SD) (SD) (SD) (SD) (SD)

Mean E (SD)

18.6a (15.0)

863a (1,062)

16.1a,b (5.4)

1,389a (2,691)

0.34a (0.29)

857a (180)

5.4b (3.0)

797a (1,586)

15.0a 2,479a,b (6.6) (10,552)

0.32a (0.29)

770a (192)

328b (568)

19.0b (6.4)

346b,c (1,018)

0.37a (0.33)

847a (189)

432a,b (537)

12.0a (5.2)

312a,c (471)

0.14a (0.16)

842a (192)

Cave

26.4a (19.9)

1,128a (1,540)

14.0a (5.2)

2,597a (5,859)

0.22a (0.20)

837a (189)

3.9b (2.5)

947a (1,216)

11.2a (6.8)

1,433a (2,145)

0.15a,b (0.20)

846a (193)

791a (1,137)

12.7a (5.7)

1,750a (4,429)

0.20a,b (0.22)

991b (169)

1,151a (1,929)

11.3a (4.2)

2,071a (4,536)

0.10b (0.20)

919a,b (256)

Manhole

36.4a (31.6)

440a (734)

14.3a (5.9)

700a (1,973)

0.21a (0.22)

722a (205)

5.7b (3.7)

209a (414)

15.3a (5.1)

211a (580)

0.29a (0.20)

595a (132)

959a (849)

11.5a (9.2)

870a (1,005)

0.16a (0.21)

892a (548)

242a (203)

12.0a (3.4)

163a (173)

0.08a (0.05)

576a (42)

Tunnel

41.3 (35.2)

1,340a (2,559)

11.8a 6,591a (7.1) (18,598)

0.17a (0.19)

694a (287)

6.0 -

2 -

22.0 -

0.3 -

0.51 -

492 -

66a (93)

14.5a (6.4)

41 -

0.27a (0.29)

531a (83)

2a (3)

10.0a (4.2)

0.2a (0.2)

0.11a (0.13)

597a (223)

19.5a (3.5)

21a (30)

16.0a (1.4)

7a (9)

0.19a (0.10)

601a (16)

4.5a (2.1)

85a (109)

18.0a (1.4)

29a (41)

0.34a (0.11)

609a (40)

386a (616)

17.5a (7.0)

457a (809)

0.47a (0.34)

878a (153)

-

-

-

-

-

Corral

17.7 (25.6)

681 (372)

16.6 (4.0)

658 (505)

0.20 (0.17)

795 (184)

5.0 -

292 -

9.0 -

96 -

0.11 -

517 -

-

-

-

-

-

-

-

-

-

-

Bridge

41.7 (42.5)

0 -

19.0a (7.9)

0 -

0.44a (0.33)

607a (186)

9.0 -

2 -

22.0 -

0.1 -

0.44 -

542 -

5 (6)

19a (7.1)

0.7 (1)

0.33a (0.31)

555a (89)

-

-

-

-

-

Basement

21.5 (7.8)

27 (27)

12.5 (4.9)

25 (35)

0.16 (0.12)

588 (76)

9.0 -

0 -

10.0 -

0 -

0.08 -

512 -

-

-

-

-

-

-

-

-

-

-

Coal kiln

10 -

3,379 -

16.0 -

2,023 -

0.17 -

1,232 -

-

-

-

-

-

-

-

-

-

-

-

854 -

16.0 -

1,245 -

0.15 -

982 -

Water tank

Tree hollow

Total

al P

ur n

Jo

Abandoned building

re

Abandoned house

-

-

-

-

-

-

-

-

-

-

-

-

-

19.0 -

0.01 -

0.28 -

746 -

-

-

-

-

-

15.0 -

266 -

8.0 -

154 -

0.02 -

537 -

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

26.8a (24.1)

882a (1,370)

14.7a (5.6)

2,134a (6,679)

0.25a (0.23)

794a (209)

5.1b (3.0)

642a,b (1,270)

14.4a (6.4)

1,538a (7,340)

0.28a,b (0.25)

731b (199)

442b (751)

15.2a (6.3)

678a (2,288)

0.33b (0.30)

861c (223)

829a,b (1,579)

11.6b (3.9)

1,388a (3,654)

0.11c (0.08)

850a,c (256)

Acronyms: MCB = maximum number of captured vampire bats (only in harem and bachelor roosts); Ki = potential carrying capacity of the roosts; SD

of

= standard deviation; K = degree; Ai = roost accessibility to farms; ??i = eigenvector centrality of roosts; E = elevation (m); SD = standard deviation.

ro

Idents: different letters = significant difference (p < 0.05) between roost classifications (harem, bachelor, overnight, empty) in the line; same letters = no

Jo

ur n

al P

re

-p

difference in the line.

Table 3. Desomdus rotundus roosts counts, occupation, maximum number of captured bats, potential carrying capacity, degree, accessibility, eigenvector

Roost occupation Harem (%)

Bachelor (%)

Overnight (%)

22

8abcd (36.4)

5abc (22.7)

6acd (27.3)

2

31

19ab (61.3)

6ac (19.3)

2bc (6.5)

3

42

19abcd (45.2)

18bc (42.9)

5abc (11.9)

4

18

8abcd (44.4)

7abc (38.9)

1abc (5.6)

5

13

6abcd (46.1)

6

32

18abd (56.2)

7

46

20abcd (43.5)

8

9

9

47 260

al P

Mean Ki (SD)

Mean K (SD)

Mean Ai (SD)

Mean ??i (SD)

Mean E (SD)

Mean Out. (SD)

3abc (13.6)

13.9abcfef (19.7)

12a (27)

17.0ad (2.6)

347abc (860)

0.256a (0.036)

770a (137)

1.6ad (0.9)

4abc (12.9)

15.8abcde (19.7)

826bce (787)

13.0bd (2.3)

980abcdg (1.2E6)

0.144b (0.040)

764a (183)

0.7bcde (0.9)

-

21.0abc (25.7)

554bce (1,261)

17.2a (5.2)

1.3E+6abcdfg (4.8E+6)

0.337c (0.163)

576bcd (112)

0.3bce (1.0)

2abc (11.1)

12.2abcdf (13.2)

951bcd (1,029)

7.5c (1.8)

938abcdfg (1.4E+6)

0.025d (0.014)

645bc (155)

0.3bc (0.6)

2abcd (15.4)

2abc (15.4)

7.8abcdef (10.7)

358bce (441)

5.5e (2.0)

1.5E+5cdefg (2.3E+5)

0.009e (0.003)

572bd (99)

1.3abde (1.1)

3a (9.4)

8acd (25.0)

3abc (9.4)

16.0abcdef (29.9)

1,721cd (2,102)

12.7bd (3.7)

6.9E+6de (1.4E+7)

0.122b (0.052)

854a (250)

0.7bde (0.9)

9ac (19.6)

9abc (19.5)

8ab (17.4)

10.6acdef (16.7)

632bce (1,023)

14.8bd (5.4)

9.3E+5cdfg (2.0E+6)

0.270ac (0.238)

893e (130)

1.5ad (1.7)

ur n

3abc (23.1)

1cd (11.1)

1abc (11.1)

2abcd (22.2)

5d (55.6)

2.4adef (5.2)

1,375bcd (2,581)

7.5c (0.9)

2.7E+6cdef (6.2E+6)

0.018d (0.004)

1,090e (137)

-

16bcd (34.0)

8ac (17.0)

21ad (44.7)

2ac (4.3)

7.8cdef (14.1)

448be (705)

18.7a (6.2)

4.7E+5bcdg (1.0E+6)

0.559f (0.301)

963f (159)

1.7ad (1.7)

115a (44.2)

60b (23.1)

56b (21.5)

29b (11.2)

13.0 (20.3)

725 (1,272)

14.5 (5.8)

1.6E+6 (5.9E+6)

0.259 (0.249)

798 (217)

1 (1.4)

Jo

Total

Empty (%)

re

1

Mean MCB (SD)

ro

N

-p

Roost community

of

centrality and elevation by roost community in Guaratinguetá, Brazil, from 2013 to 2017.

Acronyms: N = number of roosts; MCB = maximum number of captured vampire bats (only in harem and bachelor roosts); Ki = potential carrying capacity of the roosts; K = degree; Ai = roost accessibility to farms; ??i = eigenvector centrality of roosts; E = elevation (m); Out. = number of livestock rabies outbreaks recorded in the roosts catchment areas from 2013 to 2017; SD = standard deviation. Idents: different letters = significant difference (p < 0.05) between roost communities (1 - 9) in the column; same letters = no difference in the column.

Table 4. Livestock farms counts, herd size, elevation, number of connected roosts, potential number of vampire bats using each farm, vampire bar aggression incidence and livestock rabies outbreaks per farm communities in Guaratinguetá, Brazil, from 2013 to 2017.

Farm community

Farms

Average number of connected roosts per farm (SD)

Average elevation (m) (SD)

Average herd size (SD)

Average potential number of bats using each farm (SD)

Average bat aggression incidence* (SD)

Livestock outbreaks

53

173.2a (208.6)

558.0ad (40)

2.6a (2.8)

69.4ae (61.8)

0.003adij (0.008)

3

2

206

95.3a (126.3)

636.3bcd (139.5)

5.6b (3.3)

107.2bcdefiklm (48.4)

0.025bcek (0.113)

4

3

137

84.8a (123.1)

586.2bcd (104.6)

5.4b (3.5)

94.8bcekm (47.6)

0.018bcdefghijk (0.050)

-

4

46

117.1a (187.5)

572.3abcd (62.5)

2.9ab (1.6)

88.0abcekm (46.1)

0.004acdfghij (0.011)

-

5

76

118.7a (228.2)

562.3abcd (95.3)

4.4b (2.1)

114.4bdefiklm (48.3)

0.017bcefghijk (0.049)

-

6

123

161.6a (328.5)

525.8abcd (80.3)

3.4b (1.6)

76.8abcdem (47.4)

0.023bcefghijk (0.085)

6

7

113

96.1a (111.5)

642.3bef (112.8)

5.1b (2.0)

120.3bdfkm (76.3)

0.006acdefghij (0.019)

1

8

134

104.4a (139.0)

607.1bce (44.0)

3.9b (1.9)

156.7gijklm (88.6)

0.009defghijk (0.030)

6

9

169

82.1a (117.7)

893.7fgj (52.1)

5.1b (2.2)

219.2hijklm (125.1)

0.002acdefghj (0.013)

5

10

144

109.5a (130.5)

917.5hik (78.5)

3.6b (2.0)

104.7bdfghiklm (66.1)

0.006acdefgij (0.020)

5

12 13

Unclassified Total

-p

re

lP

79

55.4a (63.0)

1,059.4hijk (109.1)

3.0ab (2.1)

156.6ghjklm (98.0)

0.004acdefgh (0.016)

-

93

66.7a (103.2)

829.2fgijk (58.8)

8.5c (4.7)

99.6bcdfghijklm (37.2)

0.035bcegjk (0.152)

5

134

59.0a (60.2)

955.4hijk (95.2)

5.2b (4.2)

111.0bdghijklm (61.3)

0.022bcegjk (0.056)

9

50

36.6a (42.9)

1,190.3hijk (91.1)

4.1b (2.7)

86.6bcdefghijklm (40.5)

0.022bcegjk (0.040)

-

127

57.4a (79.9)

-

-

-

0.016bcefghijk (0.050)

-

1,684

93.1 (152.8)

745.1 (207.1)

4.7 (3.1)

111.9 (85.4)

0.015 (0.066)

44

Jo

14

ur na

11

ro of

1

Acronyms: SD = standard deviation. Idents: different letter = significant difference (p < 0.05) between roost communities (1 - 9) in the column; same letters = no difference in the column. *Only recorded in 2017.

Table 5. Multinomial regression analysis model of roost occupation. Roost occupation category1

Standard error

Bachelor

Intercept Times in this category Mean degree (K) Eigenvector centrality (??i) Elevation Carrying capacity (Ki)

4.13 0.059 - 0.28 15.35 - 0.0034 0.00059

1.49 0.24 0.11 4.68 0.001 0.00038

2.78 0.24 - 2.58 3.28 - 2.58 1.56

0.0055 0.81 0.0098 0.0010 0.0098 0.12

Harem

Intercept Times in this category Mean degree (K) Eigenvector centrality (??i) Elevation Carrying capacity (Ki)

2.48 0.024 - 0.15 12.02 -0.0018 0.00078

1.41 0.23 0.10 4.58 0.0011 0.00035

1.75 0.10 - 1.49 2.63 - 1.53 2.20

0.079 0.92 0.14 0.0086 0.12 0.028

Overnight

Intercept Times in this category Mean degree (K) Eigenvector centrality (??i) Elevation Carrying capacity (Ki)

1.61 - 0.092 - 0.30 15.75 0.00047 0.00014

1.01 - 0.34 - 2.66 3.35 0.37 0.36

0.31 0.74 0.0079 0.00079 0.71 0.72

z

p

ro of

-p 1.59 0.27 0.11 4.69 0.0013 0.00040

re

lP

1

Odds ratio

Independent variables

The baseline category was “empty”.

Residual deviance = 569.49 in 720 degrees of freedom. Log-log-likelihood = -284.25 in 720 degrees

Jo

ur na

of freedom. No Hauck-Donner effect found in any of the estimates.

Table 6. Binomial regression analysis model of livestock rabies outbreaks in the catchment area of bat roosts and independent variables. Independent variables

Standard error

z

p

- 2.12 - 1.01 0.35 - 0.29 - 0.05 - 0.05 3.32 0.0020 0.00053

0.92 0.62 0.36 0.43 0.15 0.06 1.38 0.00080 0.00019

- 2.29 - 1.63 0.97 - 0.66 - 0.33 - 0.90 2.40 2.52 2.83

0.022 0.10 0.33 0.51 0.74 0.37 0.016 0.012 0.0046

ro of

Intercept Roost occupation - Empty Roost occupation - Harem Roost occupation - Overnight Times in the occupation category Mean degree (K) Eigenvector centrality (??i) Elevation Carrying capacity (Ki)

Odds ratio

Null deviance = 338.68 in 245 degrees of freedom. Residual deviance = 291.07 in 237 degrees of

Jo

ur na

lP

re

-p

freedom. AIC = 309.07. Number of Fischer scoring iterations = 4.