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  • 31 October 2019

Vampire bats make ‘friends’ — and keep them close

A common vampire bat wearing a lightweight sensor that allows researchers to track its social contacts. Credit: Sherri and Brock Fenton

Much like many primates, vampire bats can form strong bonds with each other and often maintain these friendships even after being uprooted.

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Nature 575 , 11 (2019)

doi: https://doi.org/10.1038/d41586-019-03286-w

Curr. Biol. (2019)

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Social foraging in vampire bats is predicted by long-term cooperative relationships

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected] (SPR); [email protected] (GGC)

Affiliations Department of Evolution, Ecology and Organismal Biology, The Ohio State University, Columbus, Ohio, United States of America, Smithsonian Tropical Research Institute, Balboa, Ancón, Panamá, Museum für Naturkunde, Leibniz-Institute for Evolution and Biodiversity Science, Berlin, Germany

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Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliations Department of Evolution, Ecology and Organismal Biology, The Ohio State University, Columbus, Ohio, United States of America, Smithsonian Tropical Research Institute, Balboa, Ancón, Panamá

  • Simon P. Ripperger, 
  • Gerald G. Carter

PLOS

  • Published: September 23, 2021
  • https://doi.org/10.1371/journal.pbio.3001366
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Fig 1

Stable social bonds in group-living animals can provide greater access to food. A striking example is that female vampire bats often regurgitate blood to socially bonded kin and nonkin that failed in their nightly hunt. Food-sharing relationships form via preferred associations and social grooming within roosts. However, it remains unclear whether these cooperative relationships extend beyond the roost. To evaluate if long-term cooperative relationships in vampire bats play a role in foraging, we tested if foraging encounters measured by proximity sensors could be explained by wild roosting proximity, kinship, or rates of co-feeding, social grooming, and food sharing during 21 months in captivity. We assessed evidence for 6 hypothetical scenarios of social foraging, ranging from individual to collective hunting. We found that closely bonded female vampire bats departed their roost separately, but often reunited far outside the roost. Repeating foraging encounters were predicted by within-roost association and histories of cooperation in captivity, even when accounting for kinship. Foraging bats demonstrated both affiliative and competitive interactions with different social calls linked to each interaction type. We suggest that social foraging could have implications for social evolution if “local” within-roost cooperation and “global” outside-roost competition enhances fitness interdependence between frequent roostmates.

Citation: Ripperger SP, Carter GG (2021) Social foraging in vampire bats is predicted by long-term cooperative relationships. PLoS Biol 19(9): e3001366. https://doi.org/10.1371/journal.pbio.3001366

Academic Editor: Catherine Hobaiter, University of St Andrews, UNITED KINGDOM

Received: April 20, 2021; Accepted: July 16, 2021; Published: September 23, 2021

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Data Availability: All relevant data and R-code underlying are available from figshare. Ripperger, Simon; Carter, Gerald (2021): Data and R code for "Social foraging in vampire bats is predicted by long-term cooperative relationships": https://doi.org/10.6084/m9.figshare.14529588.v2 .

Funding: This study was funded by grants of the National Science Foundation (Integrative Organismal Systems #2015928; GGC; https://www.nsf.org/ ), of the Deutsche Forschungsgemeinschaft ( https://www.dfg.de/ ) within the research unit FOR-1508, a Smithsonian Scholarly Studies Awards grant (GGC, SPR; https://www.si.edu/ ), and a National Geographic Society Research Grant WW-057R-17 (GGC; https://www.nationalgeographic.com/ ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: GPS, Global Positioning System; ICC, intraclass correlation coefficient; IR, infrared; LMM, linear mixed-effect model; MRQAP, multiple regression quadratic assignment procedure with double semi-partialling; NSF, National Science Foundation; PHS, Public Health Service; QAP, quadratic assignment procedure

Introduction

Socializing and foraging are 2 key determinants of reproduction and survival that can influence each other in several interesting ways. Preferred social relationships can drive foraging decisions (e.g., great tits [ 1 ]). Conversely, shared foraging behaviors might shape how relationships form (e.g., bottlenose dolphins [ 2 ]). Social relationships can determine access to food because closely affiliated individuals can peacefully co-feed at a food patch, hunt together [ 3 ], cooperatively defend food patches (e.g., [ 4 – 6 ]), or even give food to less successful foragers (e.g., chimpanzees [ 7 ]). Access to food is therefore one benefit of long-term cooperative relationships, i.e., stable preferred associations that involve cooperative investments such as grooming and food sharing. For example, grooming in chacma baboons promotes tolerance during foraging [ 8 ], and vervet monkeys strategically groom individuals that control access to food due to social dominance [ 9 ] or an experimentally manipulated ability to access food [ 10 ]. A particularly clear nonprimate example of cooperative relationships providing food occurs in common vampire bats where females regurgitate ingested blood to socially bonded kin and nonkin that failed to feed that night [ 11 , 12 ].

Food-sharing relationships in vampire bats form as preferred associates escalate social grooming [ 13 ]. These preferred associations and cooperative interactions occur within the day roost. However, little is known about if or how cooperative relationships extend beyond the roost. For example, foraging with socially bonded roostmates might increase efficiency in searching for prey or feeding from wounds, but it remains unclear if or how vampire bats perform social hunting. Several authors provide anecdotal evidence for groups of females apparently flying together, adult females departing roosts in groups of 2 to 6, and groups arriving together at a pasture or approaching and circling prey [ 14 – 17 ]. There are also observations of up to 4 individuals feeding simultaneously from different wounds on the same cow [ 14 ] or pairs feeding on the same wound [ 14 , 16 ]. Wilkinson [ 16 ] described evidence that mother–daughter pairs co-forage and share wounds, but found no evidence that frequent roostmates forage together.

Social foraging can take many forms, from mere aggregations of individuals attracted to a common resource to coordinated foraging groups with differentiated roles. Socially hunting species can be placed on a spectrum of resource sharing from individual foragers competing to group-level sharing [ 3 ]. The form of social foraging and the scale of competition over resources outside the roost can have implications for the evolution of food-sharing relationships. Several evolutionary models of vampire bat food sharing as multilevel selection view them as foraging individually then sharing food at the group level [ 18 – 20 ], but this view contrasts with evidence that food-sharing relationships within groups are reciprocal and highly differentiated [ 11 , 21 ]. An alternative possibility is that individualized relationships drive both within-roost resource sharing and social hunting. This hypothesis is not mutually exclusive with group hunting, because even if individuals forage in groups, specific pairs could be more likely to compete or share a wound or host [ 14 – 16 , 22 ].

Here, we assessed the relative evidence for a range of hypothetical scenarios that vary in degree of coordination of social foraging among socially bonded bats ( Fig 1 ). In the simplest case, preferred roostmates might not coordinate their behavior outside the roost. If instead bats optimize individual foraging efficiency by preferentially departing, following, or foraging with their preferred roostmates, then within-roost networks should predict co-departures or foraging encounters. Alternatively, to maximize their collective search area, bats might prefer to forage with bats outside their network of cooperative relationships and actually avoid foraging with their frequent roostmates. If so, within-roost and outside-roost networks should be negatively correlated. Finally, if entire roosting groups forage together, then we expect similarly dense and highly correlated within-roost and outside-roost networks.

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For the same roosting association networks, each scenario predicts different outcomes for how preferred within-roost relationships correlate with co-departures or encounters during foraging. Preferred roostmates (shown as pair of light brown and dark brown bats) might either (A) not coordinate their behavior outside the roost, (B) coordinate only their departures, (C) depart independently and reunite during foraging, (D) coordinate departures and foraging, or (E) avoid foraging together. Alternatively, the bats could (F) depart and forage as a large group.

https://doi.org/10.1371/journal.pbio.3001366.g001

To evaluate evidence for these scenarios, we tested whether nightly foraging departures and encounters were predicted by kinship, roosting associations based on 2 levels of proximity (during the previous day or over the whole study), and rates of social grooming, food sharing, and co-feeding in captivity. To document roosting associations and foraging encounters, we analyzed social encounter data from proximity sensors placed on 50 free-ranging common vampire bats. As additional predictors for 23 of these bats, we used published long-term rates of social grooming and food sharing [ 23 ] and co-feeding rates from when these bats were captive. Using simultaneous ultrasonic recording and infrared (IR) video, we also describe a distinct new type of vampire bat call only observed during hunting interactions. Multiple lines of evidence show that cooperative relationships in vampire bats extend outside the roost. More generally, our findings illustrate how social relationships and networks can both extend and vary across contexts.

Subjects were common vampire bats ( Desmodus rotundus ) including 27 wild-caught adult females that were tagged and released and 23 previously captive females (17 adults and their 6 subadult captive-born daughters) that had spent the past 21 months in captivity and were then tagged and released back into their wild roost tree (see [ 13 , 23 ]). See S1 Text for details.

We assumed that known mother–daughter pairs had a kinship of 0.5. To estimate kinship for all other pairs, we genotyped bats at 17 polymorphic microsatellite loci (DNA isolated via a salt–chloroform procedure from 3- to 4-mm biopsy punch stored in 80 or 95% ethanol), then used the Wang estimator in the R package “related.” See S1 Text for details.

Past cooperative interaction rates in previously captive bats

To measure cooperative relationships in the previously captive bats, we used previously published rates of social grooming and food sharing from experimental fasting trials [ 13 ]. See S1 Text for details. To assess tolerance while feeding, we also analyzed observations of co-feeding among the same captive vampire bats. Social interactions were observed at blood spout feeders while the bats were in captivity, including 1,300 competitive interactions and 277 cases of co-feeding where 2 bats were observed feeding from the same blood spout at the same time (from 1,050 hours of observation from 70 nights). In 201 of these cases, both bats were clearly identified. We used these to construct a co-feeding network of the number of dyadic co-feeding events (range = 0 to 6) for each pair.

To test correlations between the captive co-feeding network and networks of food sharing or social grooming, we used Mantel tests. To test the same correlation while controlling for overlap in individual feeding times, we also used a custom double permutation test [ 24 ]. This procedure calculates an adjusted co-feeding rate for each pair as the difference between the observed co-feeding rate and the median expected co-feeding rate from 5,000 permutations of the co-feeding bat identities, permuted among the bats seen within each hour. To test for preferred captive co-feeding partners, we also used the same within-hour permutations to test if social differentiation in co-feeding (the coefficient of variation in co-feeding rates) was greater than expected from the null model.

Association rates in the wild using proximity sensors

We placed custom-made proximity sensors on all 50 female common vampire bats (sensor mass: 1.8 g; 4.5% to 6.9% of each bat’s pre-feeding mass) that automatically documented dyadic associations among all 50 tagged bats when those come within the reception range of 5 to 10 m [ 23 , 25 ]. To log encounters, each proximity sensor broadcasted a signal every 2 seconds to update the duration of each encounter. We used 1 second as the duration of encounters that were shorter than 2 successive signals (i.e., encounters shorter than 2 seconds). The maximum signal strength of each encounter is used as an estimate for a minimum proximity between 2 tagged bats during the encounter by comparing the signal intensity to a calibration curve [ 23 , 25 , 26 ].

We collected association data on the free-ranging bats at Tolé, Panama (8° 12′ 03″ N 81° 43′ 46″ W), a rural area that is mainly composed of cattle pastures for meat production. Around 200 to 250 common vampire bats roosted inside a hollow tree on a cattle pasture that was about 15 ha in size. To create a stable food patch during part of our study, we corralled approximately 100 heads of cattle at a distance of approximately 300 m from the roost from 6 PM until 6 AM between the evening of September 21 until the morning of September 26, 2017 (days 1 to 5 in our study). Before and after that time period, the cattle were ranging freely. A neighboring, much larger pasture west of the roost had about 1,500 heads of cattle within a distance of 1 to 2 km (Fig A in S1 Text ).

To construct networks of roosting association rates during each daytime period within the roost, we relied on roosting association data that had been used in a previous study [ 23 ]. Based on the same 2 thresholds of signal strength as before, we defined 2 categories of proximity: “associations” (within approximately 50 cm) and “close contacts” (within approximately 2 cm). Roosting network edges were rates of within-roost association or close contact, i.e., the total time 2 bats spent in association per unit of time. See S1 Text for details.

To help localize bats, we used base stations that can detect tagged bats at distances of about 150 m. We placed these stations at the roost and at 5 other locations in the surrounding cattle pastures to help localize individuals and encounters as inside or outside the roost. To identify departures from the roost, we found the points in time where each bat lost connection from the roost base station and almost all of the many tagged bats in the colony within communication range (i.e., a sudden drop in associations from many bats down to 0 to 3 bats; see [ 25 ]). Some departing bats also contacted base stations on the cattle pasture (Fig A in S1 Text ). We used the same kind of data to infer the return times to the roost for each bat and night.

Of the 629 dyadic encounters that occurred 1 minute after leaving the roost and 1 minute before arriving at the roost, we excluded 43 encounters from further analysis, because a proximity sensor contacted the roost base station, suggesting that those encounters occurred while bats were roosting at the entrance or on the outside of the roost tree. The remaining 586 encounters occurred farther away, outside the communication range of the roost base station, and we refer to these as “foraging encounters.”

Observing interactions of foraging vampire bats

At Tolé, we only observed 2 occasions where 2 bats stopped at the monitored cattle pasture and were associated (for 3.5 and 4.6 minutes). When releasing the corralled cattle in the morning, we observed bite marks. However, to avoid changing their behavior, we did not get close enough to the cattle at night to record audio or video of bats interacting. To collect direct observations on foraging behavior, we therefore recorded simultaneous audio and video of bat foraging behavior at a different farm near La Chorrera, Panama (8° 52′ 42″ N 79° 52′ 05″ W) using an IR spotlight, IR-sensitive video camera (Sony AX53 4K camcorder), and an Avisoft condenser microphone (CM16, frequency range 1 to 200 kHz) and digitizer (Avisoft USG 116 Hbm, 1,000 kHz sampling rate, 16-bit resolution) connected to a notebook computer. One observer (SPR) moved with a herd of about 20 grazing cattle without visible light, only using the viewfinder of the IR camera. To compare social calls made during foraging with calls from inside a roost, we used the same recording equipment to record social calls from a roost only a few hundred meters from the foraging site at La Chorrera.

Acoustic analysis of calls in foraging bats

We used Avisoft SASLab Pro (Raimund Specht, Avisoft Bioacoustics, Glienicke/Nordbahn, Germany; version 5.2.13) to measure acoustic parameters of the social call types. Start and end of calls were determined manually, based on the oscillogram. Subsequently, 5 acoustic parameters were measured automatically: 1 temporal (duration) and 4 spectral parameters (peak frequency at maximum amplitude, minimum and maximum frequency, and bandwidth). Acoustic parameter extraction was restricted to the fundamental frequency. Spectrograms were created using a Hamming window with 1024-point fast Fourier transform and 93.75% overlap (resulting in a 977 Hz frequency resolution and a time resolution of 0.064 ms). To estimate the frequency curvatures of the different call types, we measured the spectral parameters at 11 different locations distributed evenly over the fundamental frequency of each call. To compare call structure from different contexts (roosting versus foraging and antagonistic versus affiliative behavior) in multivariate space, we plotted the first 2 principal components after entering these measures into a principal component analyses with varimax rotation (using the “foreign” package in R).

Statistical analysis of foraging behavior

To estimate foraging bouts, we calculated the periods when each bat was distant from the roost tree ( S1 Fig ). Then, to test whether the previously captive bats and never-captive control bats differed in their departure times and foraging bout durations, we fit linear mixed-effect models (LMMs) with type of bat and day as fixed effects and bat as a random intercept. We calculated p -values using Satterthwaite degrees of freedom method with the R package lmerTest. To compare consistency of onsets and durations, we measured the unadjusted repeatability (intraclass correlation coefficient or ICC) for each type of bat. To count how often tagged bats departed together, we counted and inspected cases where bats departed the roost within 1 minute of each other.

Preferred associations during foraging

To test if repeated foraging encounters occurred among the same bats more than expected by chance, we used a permutation test that compared observed and expected social differentiation while controlling for overlap in foraging times. For social differentiation, we used the coefficient of variation in co-foraging rates, which increases when some pairs have more repeated encounters than others and decreases when all pairs have similar encounter rates. We first used a simple and conservative measure of co-foraging: counting the presence or absence of an encounter during each hour outside the roost over 9 days. These counts varied from 0 to 15. If 2 bats met twice in the same hour, this is still one encounter. We used this method because all bats were sampled evenly within each night and most foraging encounters were brief (median = 1 second). These present versus absent observations in each hour were swapped to randomize the data. Specifically, we permuted one bat in every dyad to a random possible partner that was also outside the roost during that same day and hour. By randomizing the data this way 5,000 times, we generated a null distribution of social differentiation values expected by chance.

Predictors of social foraging

To test predictors of social foraging, we constructed foraging encounter networks where edges were based on either duration of total encounter time outside the roost (seconds) or number of days with foraging encounters (0 to 9 days). The latter response variable is far more conservative because it only counts repeats across different days. We included the following predictors: kinship, within-roost association rate, within-roost close contact rate, social-grooming rate, and food-sharing rate. We also tested the effect of dyad type (i.e., both bats previously captive, both bats never captive, one bat previously captive, or both bats captive-born juveniles). We did not use number of nights with foraging encounters as a response for tests that only included the previously captive bats, because 9 of these bats (including all captive-born bats) left the roost during the study period [ 23 ].

To test the effect of predictor networks on a response network, we used regression quadratic assignment procedure (QAP) for single predictors or multiple regression quadratic assignment procedure with double semi-partialling (MRQAP) for 2 predictors (using the “asnipe” R package [ 27 ]). To create null models, we used constrained (within-day) node label permutations. This approach is necessary for preserving the daily and nightly network structure (e.g., distribution of edges and edge weights) and for controlling for the presence or absence of bats in the roost each day. To control for foraging bout overlap in each pair, we included that measure as a covariate. We also used QAP to test whether the within-roosting association on each day predicted the subsequent foraging network that night. We then bootstrapped the mean of the slopes across the 8 days to test for an overall paired day–night effect.

Consistency of individual social traits

To test whether bats that are more socially connected within the roost are also more connected in foraging networks, we tested if the nodes’ degree centrality was correlated between roosting and foraging networks. We measured degree centrality independently within each day or night network when the bat was present and then took the mean for each bat. Bats with no encounters in that day or night were considered missing for that day (i.e., not counted as zero degree). We fit general linear mixed effect models with foraging network centrality as the response variable, roosting network centrality (either association and close contact) as fixed effect, and bat as random intercept. p -Values were calculated from 5,000 permutations of the bat’s foraging centralities within each night (i.e., constrained node label permutations (within night) control for the fact that foraging and roosting network centralities could be correlated simply by some bats being present at the site longer). Throughout the results, we use “p-null” to indicate p -values that come from a null model where permutations were constrained within day. All data and R code are available on Figshare [ 28 ].

Ethics statement

Our protocols adhered to the following guidelines: (1) The US Government Principles for the Utilization and Care of Vertebrate Animals Used in Testing, Research, and Training, developed by the Interagency Research Animal Committee and adopted in 1985 by the Office of Science and Technology Policy; (2) The Animal Welfare Act, 7 United States Code (USC) §2131 et. seq., and the regulations promulgated thereunder by the US Department of Agriculture (USDA); and (3) Public Health Service (PHS) Policy on Humane Care and the Use of Laboratory Animals, August 2002, for all PHS- or National Science Foundation (NSF)-supported activities involving vertebrate animals. All experiments were approved by the Smithsonian Tropical Research Institute Animal Care and Use Committee (#2015-0915-2018-A9 and #2017-0102-2020) and by the Panamanian Ministry of the Environment (#SE/A-76-16 and #SE/AH-2-17).

Sampled bats did not depart together

The never-captive control bats departed from the roost 8.3 hours after sunset and returned 2.5 hours later, on average ( S1 Fig ). The previously captive bats foraged earlier and less predictably (see below). We observed only 5 cases where 2 bats departed within 5 seconds of each other, and none of these cases was followed by a foraging encounter. For the cases where pairs did have a foraging encounter, the shortest differences in departure times were 8, 21, and 28 seconds.

Previous captivity influenced departures and foraging

Compared to the never-captive control bats, the previously captive bats departed the roost on average 1.6 hours earlier (LMM, t = −4.55, p < 0.0001), but they did not forage consistently longer (t = 1.29, p = 0.2; S6 Data ). The captive-born bats departed 2 hours earlier (t = −3.15, p = 0.002) and also did not forage longer (t = −0.41, df = 47.8, p = 0.7) than control bats. All these models control for departure times being on average 14 minutes later each day (t = 6.6, p < 0.0001; S6 Data ), perhaps due to moonset times being approximately 40 to 45 minutes later each day during the study period. The total duration of foraging encounters did not clearly differ between types of pairs ( S3 Data , left), but pairs of control bats had significantly more nights with foraging encounters ( S3 Data , right) compared to other types of pairs, possibly due to control bats having more consistent foraging times. Departure times were more consistent across days within each control bat (ICC = 0.58) compared to within each previously captive bat (ICC = 0.21) or captive-born bat (ICC = 0). The duration of the longest foraging bout was also more consistent in wild control bats (ICC = 0.54) than in the previously captive bats (ICC = 0.35) or captive-born bats (ICC = 0.15).

Preferred associations in foraging encounter networks

Foraging encounters were orders of magnitude shorter in duration than within-roost encounters; their median duration was 1 second, and they never exceeded 30 minutes ( S2 Data ). Of 151 pairs with a foraging encounter, 45 did this repeatedly across 9 nights. Pairs of bats varied in the number of hours in which they reunited, and this variation was greater than expected from our null model that simulated random encounters among bats that were outside the roost in the same hour (observed social differentiation = 4.36; p-null < 0.001; 95% of expected values: −2.2 to 2.4). Most of these foraging encounters occurred at locations outside our sampled areas, but 10 encounters (involving 8 pairs of bats) occurred near the other base stations on the surrounding cattle pastures (Fig A in S1 Text ), and only 3 foraging encounters (among 3 pairs) occurred at the corral that we created as a stable food patch about 300 m from the roost (2 encounters on days 1 and 3 while the cattle were present and 1 encounter on day 7).

Kinship predicts foraging encounters

Kinship predicted the number of nights with foraging encounters (QAP, β = 15.4, n = 46 bats, p-null < 0.0001) and foraging encounter time (β = 15.4, n = 47 bats, p-null = 0.022) even when controlling for bout overlap (MRQAP, β = 0.10, p = 0.002; Fig 2 ). The median duration of a foraging encounter for close kin (kinship >0.1) was 9 seconds, compared to 1 second for nonkin (kinship <0.1).

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Foraging encounter rates were predicted by roosting associations, kinship, and previous long-term rates of social grooming and food sharing in captivity. To facilitate visual comparisons, we applied the Fruchterman–Reingold algorithm to a fully connected unweighted network and used this layout to fix the spatial coordinates of nodes across networks (except for the sparse night-by-night foraging networks), we scaled edge strength in each network, and we removed nodes without edges. In the kinship network, only edges with kinship estimates >0.24 are shown, and bats without kin in the group are not plotted. In the paired night–day networks of association in the wild, we only detected a clear correlation between day and night networks on day 4 (Table A in S1 Text ). Numerical values underlying this figure are available in S1 Data .

https://doi.org/10.1371/journal.pbio.3001366.g002

Within-roost association rates predicted foraging encounters

Bats that spent more time near each other within the tree during the day also spent more time together outside the roost during the night (associations: QAP, β = 29.5, p-null < 0.001; close contact: QAP, β = 24.7, p-null = 0.002) even when controlling for the foraging bout overlap (associations: MRQAP, β = 0.092, p = 0.003; close contact associations: MRQAP, β = 0.078, p = 0.015). Pairs with greater within-roost association rates also had foraging encounters on more nights (associations: QAP, β = 0.07, p-null < 0.001; close contact association: QAP, β = 0.04, p-null = 0.021; Fig 2 ).

When we tested the effect of each pair’s daytime roosting proximity on their foraging encounters on that subsequent night, we found a clear effect within only 1 of the 8 days (Table A in S1 Text ), but the overall effect size across days was greater than 0 (associations: mean β = 0.026, 95% CI = 0.004 to 0.051; close contact associations: mean β = 0.018, 95% CI = 0.003 to 0.04).

Roosting degree centrality predicted foraging degree centrality

Bats that associated with more partners within the roost also associated with more partners at night outside the roost (associations: β = 0.034, n = 48 bats, one-tailed p-null = 0.008; close contact: β = 0.055, one-tailed p-null = 0.078; S4 Data ). p -Values are one-tailed because the center of the expected β values from the null model was not 0 ( S4 Data ).

Cooperative relationships in captivity predict foraging encounters in the field

In the previously captive bats, kinship and cooperative relationship were independent predictors of social foraging. Foraging encounter time was predicted by food sharing (β = 38.7, n = 22, p-null = 0.015; MRQAP controlling for bout overlap: β = 0.20, p = 0.014), by food sharing when controlling for kinship (MRQAP, sharing: β = 0.16, p = 0.022; kinship: β = 0.14, p = 0.049), and by social grooming (QAP, β = 26.5, n = 22, p-null = 0.032), but the effect of social grooming was unclear when controlling for bout overlap (MRQAP, β = 0.12, p-null = 0.063).

Co-feeding among familiar captive bats was not limited to cooperative relationships

In contrast to the evidence for social differentiation in the field, we detected only weak evidence for preferred associations during co-feeding in captivity (social differentiation = 2.10, p-null = 0.047 when controlling for hour, p-null = 0.041 when not controlling for hour), and we found no correlation between captive co-feeding and social grooming, food sharing, or social foraging time in the wild (see Table B in S1 Text ).

Behavioral interactions during foraging

To sample bat interactions during foraging encounters, we recorded IR video and ultrasonic audio of 14 interactions between foraging vampire bats (Tables B and C in S1 Text ). Social calls during foraging had 3 general spectral shapes ( Fig 3 , S5 Data ). “Downward sweeping calls” are also recorded often in roosts (Fig E in S1 Text ) and are produced by socially isolated vampire bats in captivity [ 29 , 30 ]. “Buzz calls” were noisy without clear tonal structure and occurred during antagonistic interactions. We observed “n-shaped calls” produced by bats interacting while near cattle ( Fig 3 ). To our knowledge, this call type is distinct from others ( S5 Data ) and has never been seen in wild roosts, from confrontations at the feeders in captivity [ 31 ], or from individually isolated bats in captivity [ 29 ].

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Behavioral context was derived from synchronized video, and we identified the calling bat when mouth movements were visible. Calls include (a) echolocation calls (biosonar), (b) undulated down sweep, which was only observed in one recording where 2 bats where flying near a cow, (c) down sweep calls, (d) n-shaped calls, and (e) buzz calls recorded while 2 bats engaged in antagonistic behavior on a single cow. Inter-call intervals were modified for the figure, except for the call sequence in panel (e), a sequence recorded from 2 aggressively interacting bats.

https://doi.org/10.1371/journal.pbio.3001366.g003

Long-term cooperative relationships predicted repeated foraging encounters

All the female vampire bats we tagged departed the roost separately, but they often reunited far from the roost during foraging bouts ( Fig 1 ). The rates of these foraging encounters were consistently higher than expected in specific pairs. These frequent encounters were predicted by roosting associations, kinship, and the history of social grooming and food sharing in captivity, even when accounting for kinship. Previous experiments with female vampire bats suggest that these measures—roosting proximity, social grooming, and food sharing—reflect an underlying cooperative relationship [ 11 – 13 , 16 , 23 ]. Here, we knew the cooperation histories among the previously captive bats and that these individuals had no interactions with the control bats for at least the previous 21 months. We could therefore infer that relationships typically defined by associations and cooperative interactions within roosts, also extend beyond the roost and may provide benefits during foraging (see “Implications for cooperation”). In addition to consistent social relationships across context (from captivity to roosting to foraging), we found evidence that bats that encountered more associates in the roost during the days also encountered more associates while foraging during the nights, suggesting consistent individual variation in social traits. Taken together, these observations of social foraging cannot be fully explained by nonsocial factors such as shared site preferences.

Although some foraging encounters may have occurred before or after foraging, most of these encounters were likely to have occurred during foraging for several reasons. First, foraging encounters were brief, whereas associations among nonmoving bats should be much longer in duration ( S2 Data ). Second, foraging is likely to take up a substantial amount of the limited time outside the roost (mean = 2.4 hours). After commuting, searching, and selecting a host, a vampire bat can take up to 30 minutes to select a wound site, 10 to 40 minutes to prepare the wound site, and 9 to 40 minutes to feed [ 14 , 32 ]. Third, we used IR video to observe several interactions on or near cattle that were consistent with the short durations of foraging encounters in the proximity data (e.g., S1 , S2 and S4 Videos). Fourth, foraging encounters among close female kin had a median duration of 9 seconds and were longer than among nonkin (median duration of 1 second), which is consistent with observations that affiliative interactions last longer.

No clear evidence for highly coordinated collective movements

For animals with fluid social structures (e.g., high fission–fusion dynamics), it is important to clarify the ambiguous meaning of a “social group,” and, similarly, one must distinguish between different possible forms of “social foraging” [ 3 ]. In bats, the relative degree of social coordination during foraging can be difficult to assess and compare due to differing limitations in the observational methods and the lack of knowledge of differentiated social relationships within the colony. In this study, we took advantage of well-described within-roost relationships to assess evidence for several alternative scenarios of foraging behavior ( Fig 1 ). Kinship and rates of association and cooperation led to longer and more frequent foraging encounters, but we did not observe highly coordinated joint departures or collective movements ( Fig 1 ). This fluid pattern, of not moving in coordinated stable groups yet repeatedly encountering preferred associates during foraging, is also reflected in co-roosting networks where individuals form roosting groups that frequently change composition, yet maintain preferred relationships over time [ 16 ]. Given the many unsampled bats inside the same tree (approximately 200), it is possible that bats departed with other unobserved roostmates, but we did not see departures of large groups (while catching bats outside the roost) nor did we see evidence for coordination between roosting and departing in the tagged bats.

The ways that specific bats reunited with preferred associates therefore remain unknown, but the downward sweeping calls that we recorded in foraging bats ( Fig 3 ) are similar to individually variable contact calls that vampire bats use to find and recognize preferred partners [ 29 ]. The role of calls, in particular a possibly foraging-specific call type (“n-shaped call” in Fig 3 ), warrants further investigation. In several other bat species, there is abundant evidence for socially influenced foraging based on eavesdropping on echolocation calls (e.g., [ 33 – 37 ]). The omnivorous greater spear-nosed bat in Trinidad appears to coordinate group foraging based on a group-specific contact call [ 38 ], and, in the fish-eating greater bulldog bat, female roostmates appear to depart individually, then assemble into small groups outside the roost to forage together, possibly coordinating their movements with calls [ 39 ].

Affiliative and competitive interactions

Given the difficulty of making a bite compared to the ease of drinking from an open wound, some individual vampire bats appear to exploit the bites already made by others and fights can occur over open wounds or hosts [ 14 , 16 , 22 , 39 , 40 ], but it remains unclear how often these competitive interactions occur among familiar versus unfamiliar vampire bats. In our study, we observed foraging vampire bats engaging in both affiliative and competitive interactions (see Table C in S1 Text and S1 – S5 Videos), and the competitive interactions were far more aggressive than what we observed among familiar captive bats feeding from an accessible and unlimited source of blood. This observation and our results above are consistent with the hypothesis that aggressive competitive interactions are more likely between less familiar bats.

The fluid nature of foraging encounters has potential implications for social dominance. Dominance hierarchies should be common when animals move together in groups, because the same frequent groupmates will also be primary competitors for first access to food [ 8 , 9 ]. Dominance hierarchies among familiar female vampire bats, which do not always travel or forage together, are indeed less clear and linear than among female mammals that do travel and forage in more stable groups [ 41 ]. Furthermore, blood from an open wound is not as limited of a resource as a discrete food item, so competition over food might be relatively low among familiar vampire bats that tolerate each other (as observed in captivity) and even share food, compared to unfamiliar conspecifics that might “steal” a wound.

Foraging behavior and social preferences may create a feedback loop. Social relationships can guide foraging decisions and help individuals gain access to defendable food [ 1 , 9 ]. For instance, experimental manipulation of social structure in zebra finches can impact how individuals forage together [ 42 ]. Conversely, decisions about where to forage may influence the formation of social bonds. For instance, dolphins that share individual preferences for foraging sites or behaviors are more likely to associate in other contexts [ 2 , 43 ]. In vampire bats, stable isotope analyses suggest that individuals within the same colony have individualized foraging preferences; they repeatedly target different kinds of prey such as cattle versus sea lions [ 44 ]. As in dolphins, shared foraging preferences might similarly help drive social associations in vampire bats.

Implications for cooperation

Vampire bats might benefit from foraging with socially tolerant partners (rather than alone or with random strangers) by acquiring social information on where to feed or by gaining access to open wounds. A single open wound can sequentially feed several bats, and allowing a close social partner to sequentially feed on the same open wound could be less costly to a successful forager than regurgitating blood to that individual later at the roost. Put differently, socially bonded bats could benefit from each other’s foraging success, creating interdependence [ 45 ]. The presence of a socially bonded partner might even allow for joint defense of food against third parties, as seen in ravens [ 46 , 47 ].

Such forms of social foraging in vampire bats may have implications for the spatial scale of competition—a key factor shaping social evolution in humans [ 48 ] and other group-living animals [ 49 ]. In female vampire bats, cooperation occurs “locally” with specific frequent roostmates, and competition over food might occur more “globally” with members of the much larger population. If so, a more “global” scale of competition could reduce conflict and increase interdependence among highly associated females. To test this idea, it would be useful to determine if sampled groups of vampire bats consistently feed on the same or different prey individuals and if vampire bats are more likely to approach or avoid the social calls of foraging bats that are frequent roostmates versus unfamiliar conspecifics.

Implications for describing social structure

A major advantage of proximity sensors is the ability to continuously track associations among multiple individual bats both inside and outside their roost, which allows for the construction of dynamic and multilayer networks. Studies on social foraging and other social behaviors in bats and other small highly mobile vertebrates have historically been limited by the available tracking technology [ 25 ]. Radiotelemetry has poor spatial resolution and continuously tracking many individuals is difficult. Current Global Positioning System (GPS) tags for bats have rather short runtimes, and the tags need to be recovered to download the data. Onboard ultrasound recorders (e.g., [ 34 ]) do not reveal the identity of encountered individuals. A major downside to proximity sensors was that many foraging encounters occurred at unknown locations. However, placing proximity sensors or antennas at more locations and on the livestock would allow a better reconstruction of foraging behavior. A combination of biologging approaches can also help to overcome existing challenges (e.g., [ 50 , 51 ]). Rapid standardized high-throughput methods for measuring social network structure, such as social proximity sensors, allow for social networks to be mapped quickly across multiple populations and species, enabling comparative studies investigating evolutionary and ecological drivers of social complexity across species.

Supporting information

S1 text. supporting information methods and results..

https://doi.org/10.1371/journal.pbio.3001366.s001

S1 Fig. Time of foraging bouts by bat and day.

https://doi.org/10.1371/journal.pbio.3001366.s002

S1 Data. Data for Fig 2 .

https://doi.org/10.1371/journal.pbio.3001366.s003

S2 Data. Data for Fig B in S1 Text .

https://doi.org/10.1371/journal.pbio.3001366.s004

S3 Data. Data for Fig C in S1 Text .

https://doi.org/10.1371/journal.pbio.3001366.s005

S4 Data. R-script for creating Fig D in S1 Text .

https://doi.org/10.1371/journal.pbio.3001366.s006

S5 Data. Data for Fig F in S1 Text .

https://doi.org/10.1371/journal.pbio.3001366.s007

S6 Data. Data for S1 Fig .

https://doi.org/10.1371/journal.pbio.3001366.s008

S1 Video. Three cows are grazing within few meters distance.

Each of the 3 cows has a vampire attached to its neck. Two of the bat individuals seem to be vocalizing in the direction of the other individuals (seconds 1 to 5 and 23 to 24).

https://doi.org/10.1371/journal.pbio.3001366.s009

S2 Video. A vampire bat seems to be making a bite on the neck of a cow.

A second vampire bat joins and both engage in fight and fly away.

https://doi.org/10.1371/journal.pbio.3001366.s010

S3 Video. One bat is drinking from an open wound on the neck of a cow.

The feet of a second bat hanging on the opposite site of the neck are visible. The first bat moves around, and both bats make body contact. The first bat gets hit by the ear of the cow, then both bats start pushing each other from one side of the cow neck to the other side, and a social call is audible (second 28; likely a “z”-call).

https://doi.org/10.1371/journal.pbio.3001366.s011

S4 Video. Two bats feed from different wounds on the same cow.

The cow starts walking toward a second cow. One bat flies up and returns. When the first cow gets pushed by the second cow, the bats fly away.

https://doi.org/10.1371/journal.pbio.3001366.s012

S5 Video. Two bats feed from different wounds on the same cow, and one bat vocalizes but not in the direction of the other bat.

https://doi.org/10.1371/journal.pbio.3001366.s013

Acknowledgments

We thank O. Castrellón and C. de León for permission to conduct fieldwork on their properties and D. Josic, J. Berrío-Martínez, V. Flores, M. Le Chevallier, B. Cassens, N. Duda, R. Crisp, M. Nowak, and G. Cohen for their assistance during field work. We are grateful to M. Knörnschild and A. Fernandez for supporting the collection and analysis of acoustic data, I. Waurick for her valuable assistance and expertise during molecular lab work, R. Crisp for observations of co-feeding, and I. Razik and E. Siebert for creating the line drawings in Fig 1 (I.R.: cattle and tree; E.S.: bats). We thank D. Dechmann, J. Kohles, A. Fernandez, and J. Wilkinson for providing valuable feedback on earlier versions of this manuscript.

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A two-step metagenomics approach for prey identification from the blood meals of common vampire bats (Desmodus rotundus)

ABSTRACTThe feeding behaviour of the sanguivorous common vampire bat (Desmodus rotundus) facilitates the transmission of pathogens that can impact both human and animal health. To formulate effective strategies in controlling the spread of diseases, there is a need to obtain information on which animals they feed on. One DNA-based approach, shotgun sequencing, can be used to obtain such information. Even though it is costly, shotgun sequencing can be used to simultaneously retrieve prey and vampire bat mitochondrial DNA for population studies within one round of sequencing. However, due to the challenges of analysing shotgun sequenced metagenomic data such as false negatives/positives and typically low proportion of reads mapped to diet items, shotgun sequencing has not been used for the identification of prey from common vampire bat blood meals. To overcome these challenges and generate longer mitochondrial contigs which could be useful for prey population studies, we shotgun sequenced common vampire bat blood meal samples (n=8) and utilised a two-step metagenomic approach based on combining existing bioinformatic workflows (alignment and de novo mtDNA assembly) to identify prey. Further, we validated our results to detections made through metabarcoding. We accurately identified the common vampire bats’ prey in seven out of eight samples without any false positives. We also generated prey mitochondrial contig lengths between 138bp to 3231bp (mean=985bp, SD=981bp). As we develop more computationally efficient bioinformatics pipelines and reduce sequencing costs, we can expect an uptake in metagenomics dietary studies in the near future.

Habituation of common vampire bats to biologgers

Rapid advancements in biologging technology have led to unprecedented insights into animal behaviour, but testing the effects of biologgers on tagged animals is necessary for both scientific and ethical reasons. Here, we measured how quickly 13 wild-caught and captively isolated common vampire bats ( Desmodus rotundus ) habituated to mock proximity sensors glued to their dorsal fur. To assess habituation, we scored video-recorded behaviours every minute from 18.00 to 06.00 for 3 days, then compared the rates of grooming directed to the sensor tag versus to their own body. During the first hour, the mean tag-grooming rate declined dramatically from 53% of sampled time (95% CI = 36–65%, n = 6) to 16% (8–24%, n = 9), and down to 4% by hour 5 (1–6%, n = 13), while grooming of the bat's own body did not decline. When tags are firmly attached, isolated individual vampire bats mostly habituate within an hour of tag attachment. In two cases, however, tags became loose before falling off causing the bats to dishabituate. For tags glued to fur, behavioural data are likely to be impacted immediately after the tag is attached and when it is loose before it falls off.

Long-term maintenance of multidrug-resistant Escherichia coli carried by vampire bats and shared with livestock in Peru

Vampire bats may share gut microbes, social convergence of gut microbiomes in vampire bats.

The ‘social microbiome’ can fundamentally shape the costs and benefits of group-living, but understanding social transmission of microbes in free-living animals is challenging due to confounding effects of kinship and shared environments (e.g. highly associated individuals often share the same spaces, food and water). Here, we report evidence for convergence towards a social microbiome among introduced common vampire bats, Desmodus rotundus , a highly social species in which adults feed only on blood, and engage in both mouth-to-body allogrooming and mouth-to-mouth regurgitated food sharing. Shotgun sequencing of samples from six zoos in the USA, 15 wild-caught bats from a colony in Belize and 31 bats from three colonies in Panama showed that faecal microbiomes were more similar within colonies than between colonies. To assess microbial transmission, we created an experimentally merged group of the Panama bats from the three distant sites by housing these bats together for four months. In this merged colony, we found evidence that dyadic gut microbiome similarity increased with both clustering and oral contact, leading to microbiome convergence among introduced bats. Our findings demonstrate that social interactions shape microbiome similarity even when controlling for past social history, kinship, environment and diet.

Morphological adaptations during development of the kidneys in Vampire bats

Gene losses in the common vampire bat illuminate molecular adaptations to blood feeding.

Feeding exclusively on blood, vampire bats represent the only obligate sanguivorous lineage among mammals. To uncover genomic changes associated with adaptations to this unique dietary specialization, we generated a new haplotype-resolved reference-quality genome of the common vampire bat (Desmodus rotundus) and screened 26 bat species for genes that were specifically lost in the vampire bat lineage. We discovered previously-unknown gene losses that relate to metabolic and physiological changes, such as reduced insulin secretion (FFAR1, SLC30A8), limited glycogen stores (PPP1R3E), and a distinct gastric physiology (CTSE). Other gene losses likely reflect the biased nutrient composition (ERN2, CTRL) and distinct pathogen diversity of blood (RNASE7). Interestingly, the loss of REP15 likely helped vampire bats to adapt to high dietary iron levels by enhancing iron excretion and the loss of the 24S-hydroxycholesterol metabolizing enzyme CYP39A1 could contribute to their exceptional cognitive abilities. Finally, losses of key cone phototransduction genes (PDE6H, PDE6C) suggest that these strictly-nocturnal bats completely lack cone-based vision. These findings enhance our understanding of vampire bat biology and the genomic underpinnings of adaptations to sanguivory.

ZOONOSIS CONTROL POLICY IN THE STATE OF RIO GRANDE DO SUL (PART 1)

Background: The scientist, graduated in veterinary medicine, coordinator of the Herbivorous Rabies Control Program, Wilson Hoffmeister Júnior, was interviewed. The Inspector of the Secretariat of Agriculture, Livestock, and Rural Development (SEAPDR) of the State of Rio Grande do Sul, which develops one of the work fronts of sanitary defense. Objective: to analyze the work of prevention and control of rabies in the state of Rio Grande do Sul (Brazil). Methods: the interview was formulated using the Herbivorous Rabies Control Program (PNCRH-RS) as an information base. Results and Discussion: The PNCRH-RS is a public policy program that has operated for decades in the state of Rio Grande do Sul, and it has contributed to the elimination of certain types of rabies in the state. In addition to keeping rabies transmitted by vampire bats under control, preventing or reducing economic losses, and ensuring the health and quality of the herd in the state of Rio Grande do Sul. Conclusions: the uninterrupted continuity of the PNCRH-RS guaranteed the economic viability of rural producers, increased their profitability, and ensured animal health and public health in the state of Rio Grande do Sul.

Serological Surveillance of Rabies in Free-Range and Captive Common Vampire Bats Desmodus rotundus

The control of vampire bat rabies (VBR) in Brazil is based on the culling of Desmodus rotundus and the surveillance of outbreaks caused by D. rotundus in cattle and humans in addition to vaccination of susceptible livestock. The detection of anti-rabies antibodies in vampire bats indicates exposure to the rabies virus, and several studies have reported an increase of these antibodies following experimental infection. However, the dynamics of anti-rabies antibodies in natural populations of D. rotundus remains poorly understood. In this study, we took advantage of recent outbreaks of VBR among livestock in the Sao Paulo region of Brazil to test whether seroprevalence in D. rotundus reflects the incidence of rabies in nearby livestock populations. Sixty-four D. rotundus were captured during and after outbreaks from roost located in municipalities belonging to three regions with different incidences of rabies in herbivores. Sixteen seropositive bats were then kept in captivity for up to 120 days, and their antibodies and virus levels were quantified at different time points using the rapid fluorescent focus inhibition test (RFFIT). Antibody titers were associated with the occurrence of ongoing outbreak, with a higher proportion of bats showing titer &gt;0.5 IU/ml in the region with a recent outbreak. However, low titers were still detected in bats from regions reporting the last outbreak of rabies at least 3 years prior to sampling. This study suggests that serological surveillance of rabies in vampire bats can be used as a tool to evaluate risk of outbreaks in at risk populations of cattle and human.

Social foraging in vampire bats is predicted by long-term cooperative relationships

Stable social bonds in group-living animals can provide greater access to food. A striking example is that female vampire bats often regurgitate blood to socially bonded kin and nonkin that failed in their nightly hunt. Food-sharing relationships form via preferred associations and social grooming within roosts. However, it remains unclear whether these cooperative relationships extend beyond the roost. To evaluate if long-term cooperative relationships in vampire bats play a role in foraging, we tested if foraging encounters measured by proximity sensors could be explained by wild roosting proximity, kinship, or rates of co-feeding, social grooming, and food sharing during 21 months in captivity. We assessed evidence for 6 hypothetical scenarios of social foraging, ranging from individual to collective hunting. We found that closely bonded female vampire bats departed their roost separately, but often reunited far outside the roost. Repeating foraging encounters were predicted by within-roost association and histories of cooperation in captivity, even when accounting for kinship. Foraging bats demonstrated both affiliative and competitive interactions with different social calls linked to each interaction type. We suggest that social foraging could have implications for social evolution if “local” within-roost cooperation and “global” outside-roost competition enhances fitness interdependence between frequent roostmates.

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  • Copy URL https://www.pbs.org/newshour/science/how-did-vampire-bats-get-a-taste-for-blood-scientists-have-drawn-the-answer

How did vampire bats get a taste for blood? Scientists have drawn the answer

WASHINGTON (AP) — Scientists have figured out why vampire bats are the only mammals that can survive on a diet of just blood.

They compared the genome of common vampire bats to 26 other bat species and identified 13 genes that are missing or no longer work in vampire bats. Over the years, those gene tweaks helped them adapt to a blood diet rich in iron and protein but with minimal fats or carbohydrates, the researchers reported Friday in the journal Science Advances.

READ MORE: Florida’s starving manatees fed 55 tons of lettuce after pollution killed seagrass

The bats live in South and Central America and are basically “living Draculas,” said co-author Michael Hiller of Germany’s Max Planck Institute. About 3 inches (8 centimeters) long with a wingspan of 7 inches (18 centimeters), the bats bite and than lap up blood from livestock or other animals at night.

Most mammals couldn’t survive on a low-calorie liquid diet of blood. Only three vampire species of the 1,400 kinds of bats can do that — the others eat mostly insects, fruit, nectar, pollen or meat, such as small frogs and fish.

“Blood is a terrible food source,” said Hannah Kim Frank, a bat researcher at Tulane University, who was not involved in the study. “It’s totally bizarre and amazing that vampire bats can survive on blood — they are really weird, even among bats.”

Some other creatures also have a taste for blood, including mosquitoes, bedbugs, leeches and fleas.

The latest work expands upon research by another team that pinpointed three of the 13 gene losses.

“The new paper shows how different vampire bats are from even other closely related bats, which eat nectar and fruit,” said Kate Langwig, a bat researcher at Virginia Tech, who had no role in the study.

With such a low-calorie diet, vampire bats can’t go long without a meal. In a pinch, well-fed ones will regurgitate their food to share with a starving neighbor. They seem to keep track of who has helped them in the past, said Hiller, noting that vampire bats have complex social relationships.

“It’s not a kin thing,” said Tulane’s Frank. “They just notice and remember: You’re a good sharer, I will reward you.”

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Inferring the disruption of rabies circulation in vampire bat populations using a betaherpesvirus-vectored transmissible vaccine

Megan e. griffiths.

a Medical Research Council–University of Glasgow Centre for Virus Research, Glasgow G61 1QH, United Kingdom

Diana K. Meza

b School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow G61 1QH, United Kingdom

Daniel T. Haydon

Daniel g. streicker, associated data.

The aligned sequencing read files used for model fitting are available from the Sequence Read Archive (bioproject ID: PRJNA732673 ) ( 60 ). Sample data for each of the 36 datasets, as well as code used to fit each transmission model, and run simulations are available at https://doi.org/10.6084/m9.figshare.20764960.v2 ( 61 ).

Significance

Spillover of wildlife viruses causes global health and economic burdens and remains largely unpreventable. Vaccines that disrupt virus transmission within wildlife reservoirs might prevent spillover but face the unresolved challenge of delivering vaccines to remote and reclusive wildlife populations. Exploiting benign viruses as self-spreading vaccines offers a possible solution. A betaherpesvirus found in vampire bats is a potential candidate vector for a transmissible vaccine targeting vampire bat rabies, an important source of rabies in Latin America, but the dynamics of its transmission in natural bat populations remain unknown. Using epidemiological models and field-derived viral genomic data, we simulate how a future betaherpesvirus-based vaccine might spread. We demonstrate its capacity for high vaccine coverage and long-term prevention of rabies outbreaks.

Transmissible vaccines are an emerging biotechnology that hold prospects to eliminate pathogens from wildlife populations. Such vaccines would genetically modify naturally occurring, nonpathogenic viruses (“viral vectors”) to express pathogen antigens while retaining their capacity to transmit. The epidemiology of candidate viral vectors within the target wildlife population has been notoriously challenging to resolve but underpins the selection of effective vectors prior to major investments in vaccine development. Here, we used spatiotemporally replicated deep sequencing to parameterize competing epidemiological mechanistic models of Desmodus rotundus betaherpesvirus (DrBHV), a proposed vector for a transmissible vaccine targeting vampire bat-transmitted rabies. Using 36 strain- and location-specific time series of prevalence collected over 6 y, we found that lifelong infections with cycles of latency and reactivation, combined with a high R 0 (6.9; CI: 4.39 to 7.85), are necessary to explain patterns of DrBHV infection observed in wild bats. These epidemiological properties suggest that DrBHV may be suited to vector a lifelong, self-boosting, and transmissible vaccine. Simulations showed that inoculating a single bat with a DrBHV-vectored rabies vaccine could immunize >80% of a bat population, reducing the size, frequency, and duration of rabies outbreaks by 50 to 95%. Gradual loss of infectious vaccine from vaccinated individuals is expected but can be countered by inoculating larger but practically achievable proportions of bat populations. Parameterizing epidemiological models using accessible genomic data brings transmissible vaccines one step closer to implementation.

Vaccinating wildlife reservoirs of infection can protect vulnerable species and prevent spillover of pathogens which threaten human health and economic livelihoods ( 1 , 2 ). Successful reservoir vaccination strategies must immunize sufficient proportions of host populations to alter epidemiological dynamics. For example, large-scale distributions of vaccine-laden baits have controlled or eliminated carnivore rabies in parts of the Americas and Europe ( 2 , 3 ). Unfortunately, scalable vaccination remains challenging for wild animals which live in inaccessible or unknown locations or have diets that are incompatible with bait delivery systems ( 4 ). Although “transferable” vaccines that spread from treated to untreated individuals by direct contact (i.e., one generation of transfer only) are under development, the deliberately constrained spatial and temporal reach of such vaccines would necessitate sustained vaccination campaigns ( 5 ). Vaccines that disseminate autonomously through an infectious process (“transmissible” vaccines) have the theoretical capability to overcome these challenges ( 6 – 9 ). Transmissible vaccines are proposed to genetically modify benign, unattenuated, and host-specific viruses which already circulate within the reservoir host population to express an immunogenic transgene from the target pathogen ( 7 ). Ideally, this recombinant viral vaccine would retain the characteristics of the wild-type vector, allowing it to spread through the reservoir host population once released to a small number of individuals.

Despite their theoretical promise, management applications of transmissible vaccines have yet to materialize. This in part reflects appropriately conservative attitudes toward the prospect of releasing replication-competent recombinant viruses into natural populations ( 10 ). Therefore, anticipating the outcomes of vaccine releases requires knowledge of the biological mechanisms that govern the transmission of the vector virus in its natural host ( 11 , 12 ). Even for zoonotic viruses with a wealth of preexisting data, conclusively identifying maintenance mechanisms within wildlife reservoirs is extremely challenging due to the large number of confounding factors in natural systems. Uncertainty remains even after integrating data from long-term monitoring studies, experimental infections, and/or public heath surveillance ( 13 – 16 ). The challenge is considerably greater for candidate transmissible vaccine vectors which tend to lack historical data that could inform population dynamics within their natural host species. Indeed, several theoretical modeling studies have shown potential benefits and challenges of vaccine transmission, but none to date has used data from a candidate vector in a target host population to inform how a specific vector–pathogen–host system might respond to transmissible vaccine release ( 6 – 8 , 17 , 18 ). Given that closely related viruses, or even the same virus in a different host species, can have different within- and between-host dynamics [e.g., pathogenesis, prevalence, replication rate, and transmission ( 19 )], studying the target host and prospective viral vector in tandem is crucial to avoid ineffective vaccine releases or misdirected investment in intrinsically inappropriate vaccine platforms.

Vampire bat-transmitted rabies virus (VBRV) is a Lyssavirus that causes significant human health and agricultural burdens in much of Latin America ( 20 , 21 ). VBRV management currently includes culling bats using poisons, pre-exposure and postexposure vaccination of humans ( 22 ), and pre-exposure vaccination of livestock; however, these measures are costly and have failed to curtail increasing rabies outbreaks in some countries or its spread to new frontiers ( 23 ). Given the difficulties in preventing rabies outbreaks, new biotechnologies such as transmissible vaccines could help tackle rabies at the source. We recently identified a candidate transmissible vaccine vector, Desmodus rotundus betaherpesvirus (DrBHV), that circulates at high prevalence in apparently healthy Peruvian vampire bats and appears to be restricted to infect common vampire bats and perhaps closely related bat species ( 24 , 25 ). Betaherpesviruses (BHVs) are considered leading candidates for transmissible vaccine vectors due to their low pathogenicity in healthy hosts, high host specificity, and capacity to express foreign transgenes ( 7 , 26 ). We further showed that most bats harbor multiple DrBHV strains and that resampled bats frequently acquire additional strains throughout their lifetime (i.e., capacity for “superinfection”). If preserved in a vaccine, the lack of protective cross-immunity implied by superinfection would enable immunization of bats with established wild-type DrBHV infections ( 24 ). DrBHV therefore meets the core transmissible vaccine prerequisites of host specificity, low virulence, capacity for high prevalence, and superinfection. However, the within-host mechanisms that underlie DrBHV population dynamics, coupled with bat population ecology and behavior that will define its immunological and epidemiological properties as a vaccine, remain uncharacterized.

Here, we use deep sequencing data from longitudinally sampled vampire bats to evaluate a range of possible transmission models for DrBHV. These include conventional models of BHV maintenance via lifelong infection cycles of latency and reactivation ( 27 – 29 ). Given our prior observation that detections of DrBHV vary within individuals through time ( 24 ), we also consider models which allow viral clearance and infection stage-specific variation in detectability. We applied a maximum likelihood framework to 36 strain-specific time series of prevalence derived from five ecozones (seven administrative regions) of Peru between 2013 and 2018 ( 24 ) ( Fig. 1 ) and use the best-supported transmission model to evaluate the scope of expected vaccine transmission ( 30 ). Using a previously developed model of VBRV transmission, we next quantified the potential impact of a transmissible DrBHV-vectored vaccine on VBRV outbreaks ( 16 ). Finally, since the most likely long-term evolutionary outcome of any transmissible vaccine release is a return to the nonvaccine wild type, we assess the impact of reversion on long- and short-term vaccination strategies against VBRV ( 31 ).

An external file that holds a picture, illustration, etc.
Object name is pnas.2216667120fig01.jpg

Genomic detections of DrBHV from longitudinally collected field samples. ( A ) Map of Peru showing the regions from which saliva samples (N = 127) were collected. Colors show the ecozones within which samples were grouped. ( B ) Relative sample sizes collected from each of the five ecozones from 2013 to 2015 colored by the ecozone. ( C ) Positive (above the x axis) and negative (below the x axis) samples by deep sequencing divided by each strain of DrBHV (1 to 11; rows) and location of sample collection (columns) over time (2013 to 2018). Blank graphs indicate strains that were never detected in samples from the respective ecozone during the study period. The Inset in C shows the axis labels for all other plots.

Inferring Model Structure from Spatially Replicated Strain-Specific Time Series.

We explored eight competing models of DrBHV transmission within vampire bat colonies, representing hypotheses based on the transmission of other betaherpesviruses ( 27 , 32 ) and earlier findings about DrBHV ( 24 ) ( Fig. 2 A ). Each model was built upon the basic susceptible-infected model framework ( 33 , 34 ), where individuals are born susceptible (S) and enter the infected class (I H ) at rate β . We assumed a) that DrBHV causes no morbidity or mortality in infected bats and b) that there is no significant competition between cocirculating DrBHV strains (although we relax this assumption in later models). We also included mechanisms within each model whereby previously DrBHV-positive individuals could become negative at later observation dates based on such observations from longitudinally sampled individuals ( 24 , 25 ).

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Model fitting selects the Susceptible-Infected-Latent model of transmission. ( A ) Schematic diagram of each of the models (0 to VII) tested against the data. Demographic processes are omitted for clarity. Unfilled states represent uninfected bats, while filled states represent infected bats. Dark gray fill shows that this state is considered detectable by sequencing in all models; light gray fill shows that DrBHV is detectable by sequencing only in models marked ‘b’ in panel B . ( B ) Log likelihood and ΔAIC score (with zero associated with the best-fitting model highlighted in red) for the MLE for each model tested. Underlined letters in the model structure show which states of infection were considered detectable by sequencing. Log-likelihood values marked ‘NA’ (not applicable) show models in which no parameter sets met the requirements for detection of infection given by longitudinal sample data.

In model I Susceptible-Infected-Susceptible (SIS), individuals recover from infection at rate γ and become susceptible once more. Models II and III extend model I to include a “recovered” or “immune” compartment (M H ), which infected individuals transition to at rate γ . Individuals either remain immune to DrBHV until death (model II, Susceptible-Infected-Recovered (SIR)) or lose immunity at rate χ and return to the susceptible compartment (model III, Susceptible-Infected-Recovered-Susceptible (SIRS)). Model IV (Susceptible-Infected-Latent (SIL)) extends the framework to include lifelong cycles of active (I H ) and latent (L H ) infections as seen in other betaherpesviruses ( 32 ), where only actively infected bats can transmit. The remaining models extend upon model IV to incorporate the clearance of infection with no immunity (model V), lifelong immunity (model VI), and temporary immunity (model VII). For each model that includes a latent period (models IV to VII), we explore scenario a) in which only active infections are detectable by sequencing and b) in which both active and latent infections are detectable. We assume that detection via deep sequencing is binary, such that 100% of active infections are detected, and either 0 or 100% of latent infections are detectable. We relax these assumptions in a simple SI model (model 0) and test a range of detection rates, such that this model is able to produce loss of strain observations in individuals over time. Full details and ordinary differential equations (ODEs) for each model are provided in the Materials and Methods section and SI Appendix .

Given the lack of experimental infection studies for DrBHV, all model parameters were estimated. Specifically, we used likelihood-based inference methods to fit stochastic compartmental models (described above) to 36 datasets derived from Illumina deep sequencing, each describing the prevalence of a given strain in a given location through time ( Fig. 1 ). We first explore parameter combinations to obtain maximum likelihood estimates (MLEs) for each transmission model and then use the AIC (Akaike information criterion) values derived from these MLEs to identify the best-fitting model of transmission. Parameter ranges for this model fitting were defined based on betaherpesviruses studied in other species. We further constrained the range of each parameter using a separate dataset from longitudinally sampled individual vampire bats (N = 14 bats and 29 samples; SI Appendix , Tables S2 and S3 ) ( 24 ). Specifically, by repeatedly simulating the change in infection state over time of individuals previously observed to be positive for DrBHV infection, we produced the proportion of expected observation losses (i.e., the proportion of strains that were no longer detectable due to either clearance or latency) for each parameter set. We then compared these predictions to the longitudinal dataset and excluded parameter combinations that were unable to produce the observed pattern of strain loss. Once the best-fitting model was identified, a full grid search was carried out to produce likelihood profiles for each parameter.

Model IV (lifelong infection with cycles of latency and reactivation, SIL) was the best-fitting model, with none other of models I to VII tested plausibly explaining the data (ΔAIC >18 for all alternative models, Fig. 2 B ). Model IV has three parameters for which MLEs were obtained; β (transmissibility) = 1.10 (95% CI: 0.78 to 1.25), γ (1/infectious period) = 2.00 (95% CI: 2.00 to 2.47), and ω (1/latent period) = 6.00 (95% CI: 4.97 to 6.00) ( SI Appendix , Fig. S1 ). These parameters translate to an average infectious period of 6 mo and an average latent period of 2 mo, suggesting a bat is on average actively shedding virus for 3 times as long as DrBHV is latent. The corresponding basic reproduction number (R 0 ), calculated for the lifetime of the bat ( Eq. 1 ), was 6.9 (95% CI: 4.39 to 7.86). We also explored the possibility that acute primary infections have enhanced infectiousness relative to later reactivated infections by expanding model IV to include two infectious classes (model IV-i, SI Appendix , Fig. S2 ). Model IV-i performed comparably to model IV (ΔAIC < 2) and had a similar R 0 estimate (R 0 = ~6.1, β 1 = 2.65, and β 2 = 0.88). While the acute stage of active infection was shorter than that in model IV (γ 1 = 9.5), subsequent infectious periods remained 3 to 4 times longer than the average latent period (γ 2 = 1.85 and ω = 7.68), suggesting that the addition of an acute infectious period did not fundamentally alter the most likely within-host kinetics of DrBHV infection. Model 0 also performed similarly to models IV and IV-i (ΔAIC < 2), with a transmission rate of β = 0.55. However, model 0 was only plausible with detection rates from 50 to 70% (MLE detection rate = 53%). Given that the overall strain-specific prevalence has been previously observed at up to 65% (>90% local prevalence), a detection rate this low would suggest that >100% of bats were actually infected. Furthermore, since bats that show strain loss are still positive for other strains of DrBHV, it is likely that missed detections represent low viral loads in a particular strain, which effectively converges to model IV. Given these observations, the AIC of model IV lower than that of model 0 despite its increased complexity and the similarity of model IV to other herpesvirus transmission, we focused subsequent analyses on model IV. Among the less competitive models, those including latency were consistency ranked above those with viral clearance, and models in which only active infections could be detected by deep sequencing performed better than those in which latent infections were also detectable ( Fig. 2 B ). Together, these results show that while the basic transmission biology of DrBHV is analogous to that of human and murine cytomegaloviruses (i.e., lifelong infection with intermittent reactivation) ( 32 ), the ratio between active and latent infection times in vampire bats seems to differ from these systems, in which latent periods are typically longer than active infection ( 35 ). The lifelong recrudescence with long active infection periods in a potential DrBHV-vectored vaccine might allow vaccinated individuals to regularly boost their own immunity due to repeated reexposure to the vaccine insert, as well as facilitating continued transmission of the vaccine to new generations.

Long- and Short-Term Dynamics of DrBHV Transmission.

Under the SIL model (model IV), DrBHV reaches a steady state of population prevalence (hereafter, “equilibrium prevalence”), reflecting the potential coverage of a DrBHV-vectored transmissible vaccine under optimal conditions (hereafter, “equilibrium coverage”). However, given that the genetic manipulation required for vaccine development may decrease transmissibility ( 36 ), we calculated the equilibrium coverage assuming R 0 values ranging from 0 to 8 (the upper limit of the estimated R 0 CI). A vaccine transmitting at the MLE R 0 value of DrBHV (6.9) is predicted to reach 84% equilibrium coverage, with 63% of bats actively infected and 21% latently infected at any point in time. Within the MLE Cis for R 0 , equilibrium coverage reaches between 78 and 88% ( Fig. 3 A ). Values of R 0 below the lower confidence bound still produce significant equilibrium coverage. For example, a DrBHV-vectored vaccine with an R 0  = 2 (less than half of the estimated 95% CI lower limit) is predicted to vaccinate 50% of a bat colony at equilibrium.

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DrBHV at equilibrium: Gradual spread of DrBHV into large fractions of bat populations. ( A ) The equilibrium prevalence of a DrBHV-vectored vaccine with R 0 from 0-MLE, with total, active, and latent infections. The dashed line shows the MLE R 0 value and the shaded area, the CI down to the lower bound around this value. ( B ) Example stochastic simulation of DrBHV spread through a fully susceptible population at the MLE R 0 , with total prevalence in black reaching equilibrium around 12 y, while fluctuations continue between the active and latent states. ( C ) The equilibrium prevalence of a DrBHV-vectored vaccine (MLE and MLE lower CI R 0 ) in a population already infected with wild-type DrBHV with cross-immunity from 0 to 1. ( D ) The time in years taken to reach equilibrium (within 10% of final equilibrium coverage) for the MLE and MLE lower CI R 0 and cross-immunity 0 to 1 when 1 bat is inoculated at t = 0.

While the equilibrium prevalence values and R 0 predicted for DrBHV are high, the SIL model assumes that transmission is distributed across all infectious periods occurring throughout the lifetime of the bat [on average, 8.36 y ( 37 ), yielding a relatively low transmission rate during each infectious phase (β = 1.1). Consequently, DrBHV prevalence increases slowly after the initial introduction, with ca. 12 y required to reach equilibrium coverage after the release of a single inoculated bat ( Fig. 3 B and D ). The length of this waiting time does not change appreciably within the 95% CI of R 0 , so would be largely unaffected by moderate reductions in R 0 from genetic manipulation.

While field data in vampire bats are inconsistent with competition or cross-immunity between different DrBHV strains ( 24 ), same-strain reinfection might be hampered by existing immunity. Such same-strain cross-immunity might arise if a vaccine was released into a population in which the progenitor strain already circulated or if a vaccine reverted to a transmissible wild type. Therefore, we investigate the impact of varying levels of cross-immunity (0 to 100%) between the vaccine and the progenitor wild-type strain circulating at equilibrium prevalence. With complete cross-immunity, the vaccine is unable to invade. However, cross-immunity <=50% only reduces equilibrium coverage to ~79% (a 5% reduction) at the MLE R 0 ( Fig. 3 C ). The level of cross-immunity has a greater relative impact on the rate of vaccine transmission, with 50% cross-immunity more than doubling the waiting time to equilibrium (ca. 27 y; Fig. 3 D ). A sensitivity analysis of DrBHV transmission parameters and the level of cross-immunity ( SI Appendix , Table S4 ) show that model projections are most sensitive to the rate of transmission, closely followed by cross-immunity, and support our previous results that equilibrium prevalence is less sensitive than the rate of transmission to changes in cross-immunity. Vaccine transmission appears to be robust to moderate levels of cross-immunity to wild-type DrBHV that could theoretically impede vaccine spread, but increased levels of initial vaccine application may be required to offset slower vaccine invasion.

Rabies Dynamics in a Vaccinated Population.

Having resolved the natural transmission biology of DrBHV, we next simulated its application as a transmissible rabies vaccine using a range of possible R 0 values for VBRV (0.6 to 2) ( 5 , 16 ). We simulate outbreaks in a population with vaccine coverage from 0 to 84% (equilibrium coverage) and vaccine efficacy (E) of 70 to 100% protection against developing lethal and infectious rabies upon exposure ( 38 ). At the equilibrium vaccine coverage produced by the MLE parameters and 100% vaccine efficacy, we observe a 94% reduction in outbreak size (averaged across all VBRV R 0 ), a 75% reduction in outbreak frequency, and a 70% reduction in outbreak duration ( Fig. 4 ). With lower vaccine efficacy (E = 0.7), reductions in outbreak metrics are lower but still substantial, with equilibrium vaccine coverage reducing outbreak size by 82% and outbreak frequency and duration by approximately half (47% and 50%, respectively). Less efficient vaccine transmission caused by reductions in transmissibility due to genetic manipulation (DrBHV R 0 = 2 and equilibrium coverage = 50%) or by increased levels of cross-immunity (90% cross-immunity and equilibrium coverage = ~50%) still reduced rabies outbreak size, frequency, and duration by approximately 60 to 75%, 28 to 45%, and 30 to 40%, respectively (averaged across all VBRV R 0 and vaccine efficacy 70 to 100%) ( Fig. 4 ). These results show that lower coverage, arising either because DrBHV has not yet reached equilibrium or because of reduced vaccine transmission, should still benefit rabies control.

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Increased vaccine coverage reduces the size, frequency, and duration of rabies virus outbreaks. ( A ) Outbreak size (i.e., total number of bats that died from rabies virus over the course of the outbreak), ( B ) frequency (i.e., percentage of simulations in which the introduced bat infected at least one other individual), and ( C ) duration of the outbreak in days (i.e., length of time from the introduction of the first rabid bat until there are no more rabid or exposed bats remaining in the population) for varying degrees of rabies virus R 0 . Points show the mean values for A and C calculated from 1,000 simulations in which a single rabid bat is introduced into a colony prevaccinated to the level indicated by the x axis. Both pathogen and vaccine transmission take place during the simulations. In each simulation, vaccine efficacy of 1 and 0.7 is tested, with E = 0.7 shown with reduced opacity. ( D – F ) The percent reduction in each outbreak metric compared to an outbreak in an unvaccinated population. The dotted lines show the percent reduction achieved averaged across all VBRV R 0 values at the vaccine coverage achieved by the DrBHV MLE R 0 at equilibrium. The red dashed line and shaded area in each panel indicate the vaccine coverage and 95% CI expected for a DrBHV-vectored vaccine with the MLE R 0 value.

Equilibrium and near-equilibrium coverage could be achieved more quickly by increasing the initial proportion of bat populations inoculated ( Fig. 5 A ). At the MLE DrBHV parameters and with no cross-immunity, approximately 30% vaccine coverage is achievable in <2 y after inoculating 10% of the bat population, or <9 mo after 20% inoculation, and can halve the mean size of rabies outbreaks at both 100% and 70% vaccine efficacy ( Fig. 5 A ). Higher coverage (~65 to 84%) is needed to produce equivalent reductions in outbreak frequency and duration depending on vaccine efficacy, but this level of coverage is still reached in <5 y and can be accelerated, if necessary, by increasing inoculation effort. Increasing the level of cross-immunity to wild-type virus in the population considerably slows vaccine spread, with approximately a threefold increase in the time taken to reach 30% vaccine coverage for the same effort at 50% cross-immunity. At even higher levels of cross-immunity (80%), the utility of this strategy as a short-term control approach is reduced, but it still holds benefit as a long-term control measure in nonrabies-endemic areas.

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The effect of increased inoculation on rabies outbreak size over time. ( A ) The time taken to reach different levels of population coverage (up to equilibrium) with the MLE R 0 when an increasing percentage of the population is inoculated at t = 0. Results shown for varying levels of cross-immunity with precirculating wild-type DrBHV (0%, 50%, and 80%). The dotted line shows the vaccine coverage (~30%) required to reduce the mean size of a rabies outbreak by 50% for 70 to 100% vaccine efficacy. This value was averaged across all three rabies R 0 values. ( B ) The additional percent reduction in outbreak size for each 5% increase in population inoculated at t = 0. Each column shows the mean percent reduction of rabies virus outbreak size at R 0 = 2 and DrBHV R 0 at the MLE. A single rabid bat was introduced to the colony at the above time points after vaccination. All values which fall below the 5% threshold, beneath which a 5% increase in vaccination effort produces less than a 5% reduction in outbreak size, are shown in gray scale, with 0% indicated in white.

To identify the optimal deployment strategy for an area, considering both the initial inoculation effort and expected waiting time until rabies introduction, we measured the additional reduction in rabies outbreak size for each 5% increase in the initial inoculation effort. Inoculating higher proportions of the population has diminishing returns for the resulting reduction in rabies virus outbreak size, with the cutoff of additional inoculation effort beyond which an additional 5% inoculation produces <5% decrease in outbreak size depending on the timescale at which rabies enters the population postinoculation with vaccine. We considered scenarios in which a rabies outbreak occurred 0, 1, 2, and 4 y postvaccine introduction. Without prior vaccine transmission (i.e., introducing rabies immediately after inoculation), the cutoff of additional inoculation benefit occurs at ~55% initial vaccination effort ( Fig. 5 B ). When vaccine is allowed to transmit, the cutoff occurs at ~42% inoculation effort after 1 y, ~27% after 2 y, and ~12% after 4 y of vaccine transmission. At all time points, the greatest benefit (reduction in outbreak size relative to the initial vaccination effort) occurs when <20% of bats are inoculated. Previous mark–recapture studies suggest that <10% of vampire bat colonies are typically captured in a single night ( 23 ), so it is encouraging that the vast majority of outbreak reduction can take place at this realistic level of vaccine application. Given that the greatest benefit of transmission is observed at the later time points (with this pattern becoming even more extreme when introducing cross-immunity), vaccination effort must be decided in combination with rabies risk factors, including the local incidence of rabies in vampire bats, and the spillover potential in the relevant time frames.

Effect of Vaccine Loss on Vaccine Coverage and Rabies Transmission.

A major hurdle in the successful application of transmissible vaccines is that vaccines may be gradually lost from individuals via several mechanisms. First, transgene loss or silencing could return the vaccine to its wild-type state, producing reverted virions that, without the fitness cost of an unneeded gene, outcompete the vaccine ( 17 , 36 ). Such competition could also arise with wild-type strains of DrBHV that already circulate in the population. Alternatively, vaccine loss could simply occur irrespective of competition if vaccine strains go extinct due to low fitness or heightened recognition by the host immune system arising from genetic manipulation. We investigate how vaccine loss from individual hosts regardless of the underlying mechanism (hereafter “host reversion”) affects the viability of DrBHV-vectored transmissible vaccine releases for rabies control.

Specifically, we modified the DrBHV transmission model to include host reversion from the actively vaccine infected state, into carrying only the empty viral vector, effectively returning to wild-type DrBHV (I HW ). We assume that reverted bats no longer transmit a rabies-immunizing version of DrBHV but can transmit the wild-type version. Additionally, we assume that there is varying adaptive cross-immunity from infection, such that reverted bats can be later reinfected with the vaccine at a reduced rate mediated by the proportionality constant ρ as in the two-strain competition model introduced earlier ( Fig. 6 A ). First, we calculated whether the vaccine could reach a nonzero steady state in the population, with DrBHV R 0 varying from 1 to 8. At the MLE R 0 , the vaccine persists at equilibrium at <0.9 reversions per bat per year. On average, this rate corresponds to the loss of all vaccinated bats within 1 y after vaccine infection. With more frequent reversion, the vaccine is eventually lost from the bat population ( Fig. 6 B ). Second, we evaluated the impact of cross-immunity between the vaccine and the reverted vector at the MLE R 0 ( Fig. 6 B ) and show that an increased level of cross-immunity decreases the rate of reversion that can be tolerated in order for the vaccine to sustain circulation. At 50% cross-immunity, the vaccine is only able to reach a positive equilibrium state with <0.5 reversions per bat per year.

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The effects of host reversion on long- and short-term vaccine coverage. ( A ) Schematic of the transmission model of DrBHV with reversion of a vaccinated host to a reverted host carrying the wild-type strain (I HW ) at rate η. Gray states indicate bats immune to VBRV. ( B ) The combinations of vaccine basic reproduction number (1 to 8) and rate of host reversion (0 to 2) that allow the vaccine to reach a positive equilibrium state in the host population ( Left ). The dashed line indicates the maximum rate of reversion for which equilibrium coverage exists at the MLE R 0 . The combinations of cross-immunity to the reverted vector and rate of host reversion at the MLE R 0 of DrBHV ( Right ).

To investigate the extent of increased inoculation that would be required to counteract the short-term impact of host reversion, we simulated 4 y of vaccine transmission after inoculating 0 to 100% of the population, considering varying levels of cross-immunity (0%, 50%, and 80%) and host reversion (0 to 10 host reversions per bat per year, such that 10 reversions per year equate to reversion after an average of 1.2 mo after the initial infection) ( 17 ). At both 1 and 4 y after inoculation, reversion diminishes vaccine transmissibility, with the greatest effect observed at the longer time point ( SI Appendix , Fig. S3 ). This means that reversion has potentially the greatest impact on the long-term success of vaccination campaigns compared to short term. At >5 reversions per bat per year, impacts of reversion plateau and the transmissible vaccine approaches the effective coverage of a conventional vaccine. At around 2 reversions per bat per year, the initial inoculation effort required to reduce rabies outbreak size by half at the 4-y mark is 30% compared to <10% without reversion. This relatively modest increase, similar across all levels of cross-immunity, shows that some levels of reversion can be tolerated and offset by increasing inoculation, although maximizing vaccine stability and minimizing fitness costs will be key to producing effective transmissible vaccines that can produce longer-term benefits with lower effort.

Transmissible vaccines present an enticing opportunity to stymie the transmission of reemerging zoonoses within their reservoirs, thereby limiting spillover into human and livestock populations. Betaherpesviruses are leading candidates for transmissible vaccine vectors ( 11 , 26 , 28 , 29 , 39 – 41 ), but practical applications have in part been precluded by insufficient understanding of how betaherpesvirus-derived vaccines might spread within natural populations of important reservoir host species. By combining longitudinal field sampling data with deep sequencing and mathematical models, we have identified the most likely mechanism and parameters underpinning DrBHV wild-type transmission which will govern its application as a vector of a transmissible vaccine targeting vampire bat rabies. In addition, we have shown that this level of transmission would be expected to significantly reduce rabies transmission and, by extension, help prevent spillover events even in the face of cross-immunity and vaccine reversion.

An unresolved challenge for the development of transmissible vaccines has been the need to understand the dynamical outcomes of spread prior to release into the wild or indeed costly investments in vaccine development and testing. In the absence of in vivo systems, which are rarely available for wild hosts, time series field data paired with compartmental models provide some ability to discern the mechanisms of long-term viral maintenance ( 30 ). However, such approaches are often unable to distinguish between all competing hypotheses of viral transmission dynamics ( 16 ). We show how a data-rich approach which uses spatially replicated, longitudinal, genomics can provide greater resolution, revealing the within- and between-host mechanisms that enable long-term maintenance of viruses in wild populations. We show that DrBHV undergoes lifelong cycles of latency and reactivation similar to other betaherpesviruses ( 32 , 42 , 43 ). Excitingly, this capacity for recrudescence might generate a self-boosting vaccine that transmits sporadically throughout the lifetime of infected hosts, for example, enabling immunization of future birth cohorts without additional deployment effort. We also found that the relative lengths of the active and latent periods were reversed for DrBHV relative to other betaherpesviruses, with DrBHV estimated to spend more time in the active state than in latency. The reasons for this altered within-host biology relative to other betaherpesviruses are unknown but may reflect differences in bat immunity relative to other host taxa or processes that are hidden by the simplicity of the models investigated such as heterogeneity between hosts ( 44 , 45 ). Regardless, extended periods of active infections would favor immune boosting against rabies due to the prolonged expression of the genetically inserted antigen, for this system, rabies glycoprotein ( 46 ). Our model selection also implies that DrBHV is undetectable during the latent stage of infection using our current sequencing methods and sample type. This finding suggests the need for care in future experiments focused on vaccine loss and indicates that prevalence is likely to be underestimated in field studies. Importantly, our conclusions naturally integrate this uncertainty through explicitly modeling latency and detectability, illustrating the value of mechanistic models to interpret field data.

A main advantage of transmissible vaccines over conventional vaccines is the theoretical ability to reach equilibrium coverage in the population, enabling long-term protection from a single vaccine release. Although we show that DrBHV could be expected to have a major impact on rabies at equilibrium ( Fig. 4 ), low infectiousness spread across the lifetime of a host means vaccine equilibrium is achieved slowly (>12 y) and would be further hampered by both cross-immunity to preexisting wild-type DrBHV in the population and reversion, ( Figs. 3  and 6 ). An important caveat to this latter conclusion is that we model the full reversion of individual hosts, i.e., the loss of all antigen-bearing virions within an individual, which at this rate of reversion (η = 1) corresponds to the loss of vaccine from all vaccinated bats each year. Unless the antigenic cargo of the vaccine causes severe detriment to the replication of the vector virus, full vaccine loss in an individual in this time frame seems unlikely. While inserted sequences are lost from virions in cell culture during passage ( 31 , 47 ), this process may be slower at the level of a whole organism given the possibility of viral compartmentalization within different tissues, protective effects of latency, and differences in selection pressures. Maintaining vaccine fitness and reducing any increased host immunity due to the presence of the vaccine antigen by selection of an appropriate region for antigen insertion into the vector genome will be important to consider. Nevertheless, what constitutes a “realistic” rate of reversion is undetermined, and our results highlight the need for long-term in vivo studies to identify the host and viral factors that contribute to reversion rates ( 17 ).

Although equilibrium maintenance of vaccine transmission is robust to partial cross-immunity, the combined effects of cross-immunity and reversion compromise equilibrium vaccine coverage. Importantly, inoculating higher proportions of bat populations alleviates both issues and might be accomplished via active or passive vaccine deployment strategies. First, high nightly capture rates would enable direct inoculation of large fractions of bat populations with relatively low effort ( 5 ). Second, if oral vaccine infection can occur, this would open prospects to use topically applied vaccine-laden transferable gels which spread between bats by allogrooming. This strategy is already used to poison vampire bats for population control ( 48 , 49 ) and experimental application of topical gels containing a fluorescent biomarker to 17 to 50% of bats exposed and 57.5 to 89.9% of untreated bats in a field setting ( 5 ). Transferable deployment of a transmissible vaccine featuring some degree of reversion may be desirable to achieve high initial coverage and extend vaccine protection while safeguarding against indefinite vaccine circulation, which has been speculated to introduce unknown risks surrounding potential mutations and host range changes over longer periods of time ( 10 ).

Our simulations show the potential for DrBHV-vectored vaccines to become an effective and low-cost avenue to reduce the human health and economic burden of VBRV. Importantly, our projections may underestimate the true efficacy of rabies control that might be achievable for several reasons. First, we model a conservative epidemiological scenario focusing on bat colonies that are previously unexposed to rabies. This context is relevant for areas experiencing viral invasions; however, most vaccination will target rabies-endemic bat populations, where substantial levels of protective immunity to VBRV from immunizing rabies exposures would augment vaccine-induced immunity ( 16 , 50 ). Second, the relatively low R 0 of VBRV and its dependence on transmission between colonies for long-term maintenance means that it naturally exists on an extinction threshold ( 16 , 51 ). Therefore, vaccination of key populations could not only reduce rabies outbreak metrics within that vaccinated bat colony but also trigger VBRV extinction at the intercolony level. The spatial dynamics of VBRV also implies that optimal vaccination strategies will be context dependent. For example, our results show that if an outbreak is not expected to occur within the next 4 y (e.g., areas that have slow rates of rabies invasion), inoculating >15% of bats is unnecessary, assuming low levels of cross-immunity. Ultimately, spatially explicit models of rabies transmission will need to be developed to identify bat populations that could be strategically targeted and to understand the intercolony outcomes of transmissible vaccine releases ( 50 ). Additionally, potentially important complexities of DrBHV transmission that we were unable to evaluate here, including age structuring ( 42 ), vertical transmission of DrBHV ( 52 , 53 ), and individual heterogeneity should be investigated in captive bats. Finally, the model-based conclusions of this study should be experimentally confirmed once a DrBHV-vectored rabies vaccine becomes available. Our sensitivity analysis points to the vaccine transmission rate, level of cross-immunity, and vaccine efficacy as priorities for experimental interrogation. Finally, numerous safety and efficacy checks (e.g., host specificity and lack of clinical disease and lack of T cell exhaustion) must also be carried out and evaluated within appropriate regulatory frameworks prior to vaccine releases to any natural population.

By integrating longitudinal field data studies, genomic techniques, and modeling, we have inferred the transmission biology of a newly discovered virus in a wild reservoir without relying on experimental infections or borrowing data from better characterized host–virus systems. Our data-driven models provide the most biologically realistic projection of transmissible vaccine dynamics in a target system to date and demonstrate the feasibility of employing DrBHV to combat rabies in vampire bats and thereby prevent spillover to other species. No transmissible vaccine has been deployed in wildlife to prevent transmission to humans or domestic animals. Our results set an encouraging benchmark for the data required to optimize the deployment transmissible vaccines to combat zoonoses.

Materials and Methods

Model selection and parameter estimation from field data..

To evaluate and parameterize possible transmission models for DrBHV, we created strain- and location-specific time series of infection prevalence using sequences published by Griffiths et al. ( 24 ). This study deep sequenced saliva samples collected from wild vampire bats between 2013 and 2018 from eight departments of Peru and identified 11 circulating strains of DrBHV. We took a subset of these data ( Fig. 1 ) from seven departments (grouped into five regions) which were sampled over multiple years. We used time series with 4 y of prevalence data for each of the 11 strains in each region, totaling 36 datasets with which to fit each transmission model (0-VII, SI Appendix , Eqs. S1 – S28 and Table S1 ).

Additionally, we used data from 20 longitudinally sampled individual bats, resampled over the course of 3 mo to 4 y, to identify bounds on the amount of apparent strain loss that we would expect to observe over time. Prior to model fitting, we narrowed down the parameter space by simulating individual-level infection state over time for each parameter set ( SI Appendix , Table S2 ) and model combination. We recorded whether an individual infected at t = 0 would be recorded as positive or negative for the same virus strain when resampled 3 mo, 6 mo, 1 y, and 2 y after the initial DrBHV detection at t = 0. Using a custom script in R ( Data, Materials, and Software Availability ), we simulated infection states for 1,000 individuals and recorded the average proportion not detectable at time point two. If this proportion fell outside the 95% CIs from the data ( SI Appendix , Fig. S4 and Table S3 ), the parameter set was deemed implausible and discarded, while parameters producing proportions within the CIs were retained.

We used pfilter from the R package pomp ( 54 ) to parameterize and compare stochastic versions of models I to VII (full differential equations for each model in SI Appendix equations) with the field data. We performed a search within the parameter space for each model ( SI Appendix , Table S1 ) and calculated the log likelihood at each point using 1,000 repetitions of the particle filtering process. This method requires a “process” model which describes the hypothesized transmission dynamics of DrBHV and an “observation” model that relates to the collected field data, i.e., for this true number of cases, what will we observe given a certain sample size. The process model was provided by our transmission models ( Eqs. 2 – 4 and SI Appendix , Eqs. S1 – S28 ). For the observation model, we assumed that our field data follow a binomial distribution, so that the number of DrBHV-positive samples detected by sequencing k is given by k ~ B i n ( n , p )  , where n is the number of samples collected at this time point, and p is the proportion of true positives given by the process model for a given parameter set ( 16 ). In all models marked a) ( Fig. 2 B ), we assumed that only active infections (I H ) can be detected by sequencing, so p  = I h /N, whereas in models marked b), we assume that latent infections (L H ) are also detectable, so p = I h +L h /N.

For each parameter set within a model ( SI Appendix , Table S2 ), the log likelihood was calculated for each of the 36 datasets and summed. The highest (least negative) summed log likelihood represents the MLE for a particular model. Model comparisons used the AIC. For the top model, we perform a more detailed grid search over the parameters β , γ , and ω to create a likelihood profile for each. We used the 95% CIs from the profiles of each parameter to calculate the CI for DrBHV R 0 . In this model, R 0 is calculated as the total number of new infections arising from an infected individual in a fully susceptible population over the total time that the individual is infected, i.e., given lifelong infection, the number of new bats that one infected bat will vaccinate in its lifetime. DrBHV R 0 was calculated from the ordinary differential equations (ODEs) for this model using the next-generation matrix ( SI Appendix , Eqs. S58 – S60 ) as follows:

Deterministic Modeling of DrBHV Transmission.

To evaluate the transmission dynamics of DrBHV, we implemented a deterministic version of model IV using the package deSolve in R ( 55 ). Individuals are born into the susceptible class at rate b and die at a constant rate d from each class (not disease related). We assumed throughout that b  =  d , such that in the absence of rabies, the population size remains constant. Actively infected individuals infect susceptible at rate β , while latency and reactivation occur at rates γ and ω, respectively. The ODEs for the deterministic model are as follows:

We also implemented a two-strain model of DrBHV transmission ( SI Appendix , Fig. S5 and Eqs. S29 – S37 ), in which the bat population is already infected with the wild-type DrBHV vaccine progenitor (I W ). Individuals that are already infected with this wild type can also become infected with the vaccine upon an infectious contact with a vaccinated individual at probability ρ, where 100% cross-immunity is given by ρ = 0 and no superinfection is able to take place ( 56 , 57 ).

Equilibrium Vaccine Coverage Calculations.

To explore the potential long-term impact of a DrBHV-vectored transmissible vaccine, we derived a set of steady-state equations for model IV based on the ODEs for this model. We calculated the number of actively (I H ) and latently (L H ) infected individuals in terms of the transmission parameters for a colony size K .

Stochastic Modeling of Rabies Transmission.

We model the transmission of rabies virus using a stochastic framework implemented in the R package adaptivetau ( 58 ). Bats are born susceptible (S) to rabies virus. Upon exposure to a rabid bat (R), bats enter the exposed (E R ) class at rate θ before leaving at rate ν . At this point, bats either become temporarily immune (M R ) to rabies virus with a probability of 0.9, λ , or become rabid (R) with a probability of 0.1, δ ( 16 ). Rabid bats die due to rabies at rate τ ( 16 , 38 , 59 ), whereas immune bats will return to the susceptible class at rate ϕ . We model the simultaneous transmission of both the vaccine and the pathogen, and the system of ODEs that represent this model can be found in SI Appendix ( SI Appendix , Eqs. S38 – S44 for vaccine-only model and 45 to 57 for DrBHV two-strain model).

For each simulation including rabies virus, we modeled the introduction of a single rabid bat and allow the simulation to run for 2 y. We used 1,000 simulations of the stochastic model for each parameter set/scenario and recorded the mean outbreak size (total number of deaths from rabies virus over the simulation period minus the introduced bat), the frequency at which outbreaks occur (the percentage of simulations in which the introduced bat successfully infected one or more other bats), and the mean outbreak duration (the length of time from the introduction of the first rabid bat until there are no more rabid or exposed bats remaining in the population). Rabies was modeled with R 0 values of 0.6, 1, and 2, and the R 0 equation derived previously by Blackwood et al. ( 16 ).

Vaccine Reversion.

Additionally, we introduced vaccine reversion, by which actively infected individuals lose all virus containing the vaccine insert, with individuals becoming carriers of only the empty vector at rate η ( SI Appendix , Eqs. S23 – S29 ). We assumed that only actively infected bats undergo vaccine reversion as reversion requires virus replication. We tested varying levels of cross-immunity between the vaccine and the reverted, effectively wild type, vector which transmits independently of the remaining vaccine strains.

Sensitivity Analysis.

We analyzed the proportional sensitivity of three model projections (equilibrium coverage, time to 50% coverage, and rabies outbreak size) to uncertainty in DrBHV model parameters and to cross-immunity and vaccine efficacy using the method of differences. The full results of this analysis can be found in SI Appendix , Table S4 .

Supplementary Material

Appendix 01 (PDF)

Acknowledgments

M.E.G. was supported by a Medical Research Council scholarship via the MRC-CVR PhD program (MC_UU_12014/12) ( https://mrc.ukri.org/ ). D.K.M. was supported by the Human Frontier Science Program (RGP0013/2018) ( https://www.hfsp.org/ ) and the Mexican National Council for Science and Technology (CONACYT, 334795/472296) ( https://www.conacyt.mx/ ). D.G.S. was supported by a Wellcome Trust Senior Research Fellowship (217221/Z/19/Z) ( https://wellcome.org/ ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This research was funded in part by the Wellcome Trust [grant number 217221/Z/19/Z]. Portions of the paper were developed from the thesis of M.E.G.

Author contributions

M.E.G. and D.G.S. designed research; M.E.G. performed research; D.K.M. and D.T.H. contributed new reagents/analytic tools; M.E.G. analyzed data; and M.E.G., D.T.H., and D.G.S. wrote the paper.

Competing interests

The authors declare no competing interest.

This article is a PNAS Direct Submission. O.N.B. is a guest editor invited by the Editorial Board.

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How Bats Evolved Into Vampires & What It Takes To Live On Blood

How Bats Evolved Into Vampires & What It Takes To Live On Blood

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  • / Apr 20, 2018

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This could sound like something from a horror-fantasy movie script - Viral, bacterial and mammalian genes have combined to create an animal that needs to feed on blood!

However, seen from another perspective, this is the fascinating tale of how evolution has acted on a species of the bat so that it can exploit one of the most niche food sources ever.

Now, a new research paper assesses the genome of the vampire bat, as a whole. The authors of this article have also uncovered valuable insights on how the bats' gut microbiome protects the animals from the (significant) downside of their diet.

How Vampire Bats Feed

Sanguinovores are rare bat species that are now so well-adapted to drinking blood that it is all they eat. These creatures hail from the Desmodontinae subfamily in the mammalian order of Chiroptera.

The three Desmodontinae species of the bat share highly specialized traits such as the ability to detect potential 'donors' under various conditions. These mammals can even pinpoint bare skin so they can identify where to bite.

A whole-genome study of one of the vampire bat species, Desmodus rotundus , suggests that the contents of their guts have, over time, become highly adapted to their unusual diet. This adaptation is as much to confer protection on the bats from their diet as to help them digest it.

The Downsides of Vampirism

Dependency on a blood-rich diet could also have its downsides.

Blood is surprisingly low in many vitamins, fats, and carbohydrates, at least, from the bat's perspective. In addition, the blood of many animals is likely to contain potential pathogenic viruses or bacteria.

The vampire bats may cope with this by cultivating a gut microbiome or a community of 'friendly' bacteria. It appears that this microbiome found in Desmodus rotundus is also present to address vitamin deficiencies and compensate for the negligible fat content in their diet. Therefore, it seems that the bats have specifically evolved to live symbiotically with their gut bacteria.

Common vampire bat, D. rotundus, feeding on animal blood (Source: Public Domain)

Common vampire bat, D. rotundus, feeding on animal blood (Source: Public Domain )

In cases such as these, the combined genomes of animal and bacterial species can be termed as hologenome. The hologenome also consists of beneficial genetic traits picked up from other species in the course of evolutionary history.

The Desmodus rotundus hologenome was assessed by sequencing and collating it, in remarkably fine detail. Researchers divided the entire sequence into fragments of 1000 kilobases or less, each. They subsequently reduced it to smaller fragments of up to 8.8 kilobases. This process was performed in order to isolate and identify individual genes or specific groups of genes.

Plant Genes in Sanguinovores?

Many of the genes corresponded to completely different species besides Desmodus rotundus . This observation was made, by experts, in the course of a comparative genomic analysis, where genes found in the sequences were matched with known genes associated with various species.

This study resulted in findings that D. rotundus had incorporated genes from other bat types in the past, which included an insectivore, Pteronotus parnellii , a fruit-eater ( Pteropus vampyrus ), and the greater false vampire bat, Megaderma lyra .

The analysis also found genes and genetic motifs found in plants and ruminants. On the other hand, that kind of genetic information is found in all kinds of species, including humans.

The D. rotundus genome had picked up and incorporated a number of viral genes particular to the Chiroptera order. They are known as endogenous viral elements. These elements could have been present to confer an evolutionary immunity to the stand-alone viruses in question.

The elements found in the vampire bat genome were particularly diverse compared to non-bat mammals and included those of the Parvoviridae and Bornaviridae species.

Additionally, the genes were also in line with those associated with other bats. However, bats may also incorporate retroviral elements into their DNA, but they were found to be remarkably diminished in the D. rotundus genome.

Vampire Genes

D. rotundus also exhibited specific genetic adaptations from their sanguivore (or blood-eating) lifestyle.

For example, 'custom' splicing of the TRPV1 gene was associated with the bat's ability to sense thermal impulses. The gene TAS2R3 , involved in the perception of bitter tastes, had also been selectively emphasized during D. rotundus' evolution.

The vampire bat's gut metagenome, which represented the microbiome, equated to nearly 90 gigabases of genetic data. This may have corresponded to just over 30 individual bacteria types, some of which were shared by the metagenomes of carnivorous or insectivorous bats. However, other parts of the metagenome were found to be unique to D. rotundus .

The vampire bat is called so because it survives by drinking the blood of other animals living in its native environment, Latin America. The name, in fact, refers to three separate bat species. One of them, D. rotundus , has been the subject of a 'holistic' genetic analysis.

This study has underpinned the importance of the bat's gut microbiome to its ability to derive benefits from its evolutionary niche. It now appears that D. rotundus also requires a unique profile of gut bacteria to do so.

In addition, the bat's genome has undergone extensive variations to sense its prey and drink blood.

The paper was recently published in the journal, Nature Ecology & Evolution , and represents the importance of the association between genes and biological traits.

Top Image: Desmodus rotundus, a common type of vampire bat (Source: Public Domain )

M. L. Zepeda Mendoza, et al. (2018) Hologenomic adaptations underlying the evolution of sanguivory in the common vampire bat. Nature Ecology & Evolution. 2 :(4). pp.659-668.

S. R. Bordenstein, et al. (2015) Host Biology in Light of the Microbiome: Ten Principles of Holobionts and Hologenomes. PLoS Biol. 13 :(8). pp.e1002226.

P. Aiewsakun, et al. (2015) Endogenous viruses: Connecting recent and ancient viral evolution. Virology. 479-480 : pp.26-37.

M. Escalera-Zamudio, et al. (2015) A novel endogenous betaretrovirus in the common vampire bat (Desmodus rotundus) suggests multiple independent infection and cross-species transmission events. J Virol. 89 :(9). pp.5180-5184.

Brandslet S, 2018, Vampire bats’ bloody teamwork , https://geminiresearchnews.com/2018/04/vampire-bats-bloody-teamwork/ , (accessed on 11 Apr 2018)

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vampire bat research paper

Vampire bat immunity and infection risk respond to livestock rearing

vampire bat

The availability of livestock as a food source for vampire bats influences their immune response and infection by bacterial pathogens, according to a new paper in Philosophical Transactions of the Royal Society B. Because cattle ranching is common in areas where the bats live, the findings have implications for human as well as animal health.

The study explores how the availability of livestock as a food source influences bat immunity, infection risk and reproduction, from microscopic to landscape scales. It was led by University of Georgia Odum School of Ecology doctoral student Daniel Becker and co-authored by an international team of researchers, including Odum School faculty Sonia Altizer and Daniel Streicker. The paper is part of a special theme issue, edited by Becker, Altizer and colleagues, about the complex ways that human-provided food alters infectious diseases in wildlife.

“In this study we examined several ways that food provided by people would affect infection in vampire bats,” said Becker, who is now a postdoctoral researcher at Montana State University. “If we want to understand how supplemental feeding or land use changes affect disease risk, it’s important to quantify different processes like population size, immunity and feeding behavior.”

UGA researcher Daniel Becker with researchers and local residents in the Peruvian Amazon

Whether intentional—as with backyard bird feeders—or inadvertent—as when agriculture moves into areas where wildlife live—access to human-provided sources of food can affect both wildlife and public health.

In some cases, easy access to plentiful food means wildlife benefit from improved nutrition and reduced stress, allowing them to better fight off infection. In other cases, infection rates can rise, such as when supplemental food increases wildlife reproduction rates; the resulting larger population sizes can help some diseases to spread. Easily accessible food sources can also bring together animal species that would not otherwise interact, allowing pathogens to cross over from one species to another—including to humans.

Earlier work on this topic by Becker and UGA ecologist Richard Hall used computer simulation models to show that pathogen spread depended strongly on how supplemental feeding influenced host immunity. In the new research, Becker and his co-authors explored this idea in a real-world animal system. They conducted a four-year field study of vampire bat colonies in Peru and Belize at sites ranging from relatively undisturbed forest, with little animal prey for vampire bats, to high-intensity cattle ranching that provided ample vampire bat food.

They looked at whether immune response differed because of the availability of livestock as a food source, and whether these changes predicted infection risk by two kinds of bacteria common in bats, Bartonella and hemoplasmas.

Becker and his colleagues collected hair and blood samples from hundreds of bats and recorded each bat’s age, sex and reproductive state, tagging each one with a unique identifier before releasing it back into the wild.

Stable isotope analysis of the bat hair samples showed that bats in all areas fed on livestock—even in heavily forested habitat where a few subsistence farmers might be raising just a pig or some chickens, Becker said. “They’re going to seek them out because it’s just such an easy food source,” he said.

But livestock abundance was associated with significant differences in bat reproduction, immunity and rates of infection.

Areas with more livestock had more reproductive bats and more male bats. Previous studies have shown that more male bats are born when maternal condition is good—which might be expected where there is plenty of food available—and because male bats from other areas may immigrate to such locations.

“The vampire bats in low- and high-livestock habitats have very different immune profiles,” said Becker. “Vampire bats in the high-livestock sites really showed immune data skewed toward innate immunity, but vampire bats in low-livestock areas are investing more in adaptive immune response.”

Innate immunity consists of cells that are always ready to fight infection. Adaptive immunity is activated when pathogens breach innate immune defenses.

Bats from areas with more livestock also had lower rates of pathogen infection, especially in the case of Bartonella, which is likely transmitted by insects like bat flies or fleas.

“You might not expect transmission of an insect-transmitted pathogen to be affected by provisioning, because it’s not really clear how having more food would influence bat-fly-bat contact,” said Becker. “So in that case, if bats are better able to clear infection, this could explain why bats in livestock-dense areas have lower infection risk.”

Because hemoplasmas are thought to be directly transmitted from bat to bat, denser colonies—a result of increased immigration and reproduction in areas with more livestock—could raise contact rates enough to partially overcome bats’ enhanced innate immunity, he said.

The results of the study have implications for animal and human health. Because adaptive immunity can play a greater role than innate immunity in fighting viruses, bats in high-livestock areas might be more susceptible to viruses like rabies and influenza that can be transmitted to domesticated animals and humans. And activities that bring bats into proximity with livestock and people increase the risk of cross-species transmission of pathogens.

Becker said that it was important to note that his team’s data suggests that there’s a difference between subsistence farming and large-scale cattle ranching.

“We’re definitely not trying to suggest that people in these regions should get rid of all their chickens, or small numbers of other livestock,” he said. “It’s when you start clearing vast tracts of forest that’s probably a big driver of what’s going on here, because then you’re depleting the bats’ natural food and replacing it with this new food source, and that’s having all these individual and population level consequences for the bats.”

Besides Becker, Altizer and Streicker, who is also on the faculty of the University of Glasgow, the study’s other co-authors are Gábor Czirják of the Leibniz Institute for Zoo and Wildlife Research; Dmitriy Volokhov and Vladimir Chizhikov of the U.S. Food and Drug Administration; Alexandra Bentz and Kristen Navara of the UGA department of poultry science; Jorge Carrera of the Universidad Nacional de Piura; Melinda Camus of the UGA College of Veterinary Medicine; Brock Fenton of the University of Western Ontario; Nancy Simmons of the American Museum of Natural History; Sergio Recuenco of the Universidad Nacional Mayor de San Marcos; and Amy Gilbert of the U.S. Department of Agriculture Animal and Plant Health Inspection Service Wildlife Services.

Support for the research was provided by the National Science Foundation, the ARCS Foundation, Sigma Xi, Animal Behavior Society, Bat Conservation International, American Society of Mammalogists, UGA Odum School of Ecology, UGA Graduate School, UGA Latin American and Caribbean Studies Institute, UGA Biomedical and Health Sciences Institute, Explorer’s Club, Leibniz Institute for Zoo and Wildlife Research, UGA Global Programs International Travel Award, American Museum of Natural History Taxonomic Mammalogy Fund, USDA National Rabies Management Program and a Sir Henry Dale Fellowship, jointly funded by the Wellcome Trust and Royal Society.

Besides Becker, editors of the theme issue are Hall and Altizer of the University of Georgia, Kristian Forbes of the University of Helsinki and Raina Plowright of Montana State University.

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  1. A database of common vampire bat reports

    Abstract The common vampire bat ( Desmodus rotundus) is a sanguivorous (i.e., blood-eating) bat species distributed in the Americas from northern Mexico southwards to central Chile and...

  2. Effects of culling vampire bats on the spatial spread and ...

    Vol 9, Issue 10 DOI: 10.1126/sciadv.add7437 Abstract Controlling pathogen circulation in wildlife reservoirs is notoriously challenging. In Latin America, vampire bats have been culled for decades in hopes of mitigating lethal rabies infections in humans and livestock. Whether culls reduce or exacerbate rabies transmission remains controversial.

  3. Gene losses in the common vampire bat illuminate molecular ...

    Vampire bats are the only mammals that feed exclusively on blood. To uncover genomic changes associated with this dietary adaptation, we generated a haplotype-resolved genome of the common vampire bat and screened 27 bat species for genes that were specifically lost in the vampire bat lineage.

  4. Vampire Bat Rabies: Ecology, Epidemiology and Control

    Vampire Bat Rabies: Ecology, Epidemiology and Control License CC BY 3.0 Authors: Nicholas Johnson Animal and Plant Health Agency Nidia Arechiga-Ceballos Instituto de Diagnóstico y Referencia...

  5. Development of New Food-Sharing Relationships in Vampire Bats

    Over 424 days, new food sharing developed in 10.8% of the 996 potential relationships among all bats ( Figure S1 ), 14.5% of 608 potential relationships among females, 15.6% of 243 potential relationships among wild-caught adult females, and 9.1% of 748 potential relationships between an adult female and a captive-born bat (7 females, 6 males ...

  6. A database of common vampire bat reports

    Abstract The common vampire bat ( Desmodus rotundus) is a sanguivorous (i.e., blood-eating) bat species distributed in the Americas from northern Mexico southwards to central Chile and Argentina.

  7. (PDF) A database of common vampire bat reports

    PDF | The common vampire bat ( Desmodus rotundus ) is a sanguivorous (i.e., blood-eating) bat species distributed in the Americas from northern Mexico... | Find, read and cite all the research you ...

  8. Inferring the disruption of rabies circulation in vampire bat ...

    Exploiting benign viruses as self-spreading vaccines offers a possible solution. A betaherpesvirus found in vampire bats is a potential candidate vector for a transmissible vaccine targeting vampire bat rabies, an important source of rabies in Latin America, but the dynamics of its transmission in natural bat populations remain unknown.

  9. Vampire bats make 'friends'

    To document this behaviour, Gerald Carter at the Ohio State University in Columbus, Simon Ripperger at the Leibniz Institute for Evolution and Biodiversity Science in Berlin and their colleagues...

  10. Social foraging in vampire bats is predicted by long-term ...

    A striking example is that female vampire bats often regurgitate blood to socially bonded kin and nonkin that failed in their nightly hunt. Food-sharing relationships form via preferred associations and social grooming within roosts. However, it remains unclear whether these cooperative relationships extend beyond the roost.

  11. Vampire Bats that Cooperate in the Lab Maintain Their Social Networks

    Vampire bats could move within the tree or leave the tree for a different roost. To identify which individuals moved to a different roost, we extracted the nightly arrivals and departures of tagged bats from the tree on each day. ... A.K., and R.K.) within the research unit FOR-1508, a Smithsonian Scholarly Studies Awards grant (R.A.P., G.G.C ...

  12. PDF The Evolution of Sanguivory in Vampire Bats: Origins and

    2 Smithsonian Tropical Research Institute, Apartado 0843-03092, Balboa, Ancón, Republic of ... Three decades ago, Dr. Fenton wrote a paper proposing that the vampire bats may have evolved from a wound-feeding insectivorous ancestor (Fenton 1992). That hypothesis, based on observations of other animals, was novel

  13. Vampire Bat Rabies: Ecology, Epidemiology and Control

    Vampire bats are only found in Latin America and their unique method of obtaining nutrition, blood-feeding or haematophagy, has only evolved in the New World. The adaptations that enable blood-feeding also make the vampire bat highly effective at transmitting rabies virus. ... Feature papers represent the most advanced research with significant ...

  14. Vampire bats go with the flow

    Commonly called vampire bats, these nocturnal flying mammals have a body length of roughly 3 in and a wingspan up to 15 in. ... Research résumé. One of vampire bats' unique feeding adaptations has become an important focus of biomedical research: the anticoagulant protein in their saliva that prevents blood from clotting so that the bats can ...

  15. Vampire Venom: Vasodilatory Mechanisms of Vampire Bat ( Desmodus ...

    Animals that specialise in blood feeding have particular challenges in obtaining their meal, whereby they impair blood hemostasis by promoting anticoagulation and vasodilation in order to facilitate feeding. These convergent selection pressures have been studied in a number of lineages, ranging from fleas to leeches. However, the vampire bat (Desmondus rotundus) is unstudied in regards to ...

  16. vampire bats Latest Research Papers

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  17. PDF VAMPIRE BATS

    Exhibit Dates: May 2014 - January 2015 VAMPIRE BATS - The Good, the Bad, and the Amazing Vampire bats are sanguivores, organisms that feed upon the blood of other animals. They are the only mammals that feed exclusively on blood. Despite horror-movie depictions, vampire bats very rarely bite humans to feed on their blood.

  18. How did vampire bats get a taste for blood? Scientists have drawn ...

    The latest work expands upon research by another team that pinpointed three of the 13 gene losses. "The new paper shows how different vampire bats are from even other closely related bats, which ...

  19. Inferring the disruption of rabies circulation in vampire bat

    Vampire bat-transmitted rabies virus ... M.E.G. was supported by a Medical Research Council scholarship via the MRC-CVR PhD program (MC_UU_12014/12) ... and M.E.G., D.T.H., and D.G.S. wrote the paper. Competing interests . The authors declare no competing interest. Footnotes. This article is a PNAS Direct Submission. O.N.B. is a guest editor ...

  20. How Bats Evolved Into Vampires & What It Takes To Live On Blood

    In addition, the bat's genome has undergone extensive variations to sense its prey and drink blood. The paper was recently published in the journal, Nature Ecology & Evolution, and represents the importance of the association between genes and biological traits. Top Image: Desmodus rotundus, a common type of vampire bat (Source: Public Domain)

  21. Vampire bat immunity and infection risk respond to ...

    Vampire bat immunity and infection risk respond to livestock rearing By Beth Gavrilles The availability of livestock as a food source for vampire bats influences their immune response and infection by bacterial pathogens, according to a new paper in Philosophical Transactions of the Royal Society B.

  22. Vampire Bat Research Paper

    Vampire Bat Research Paper Decent Essays 634 Words 3 Pages Open Document Firstly, Vampire bats and their survival are important to the food chain and are a secondary pollinator leading to increases in plant based food production of which helps to prevent famine in a particular area where they are common.