Damien Caillaud

Gorilla susceptibility to Ebola virus: The cost of sociality

Since 1994, there have been nine human Ebola-Zaire virus (EBOV) outbreaks in eastern Gabon and northwestern Congo [1–3]. A majority of them originated from the handling of ape carcasses found by local hunters [4]. The impact of Ebola-Zaire virus on great ape density is suspected to be high [2,5,6], but neither the demographic consequences of outbreaks nor the way the virus spreads within an ape population are well known. The large population of western lowland gorillas, Gorilla gorilla gorilla, monitored since 2001 at the Lokoue clearing, Odzala-Kokoua National Park, Congo, was affected in 2004, providing us with the opportunity to address both questions using an original statistical approach mixing capture–recapture and epidemiological models.

Network Models: An Underutilized Tool in Wildlife Epidemiology?

Although the approach of contact network epidemiology has been increasing in popularity for studying transmission of infectious diseases in human populations, it has generally been an underutilized approach for investigating disease outbreaks in wildlife populations. In this paper we explore the differences between the type of data that can be collected on human and wildlife populations, provide an update on recent advances that have been made in wildlife epidemiology by using a network approach, and discuss why networks might have been underutilized and why networks could and should be used more in the future. We conclude with ideas for future directions and a call for field biologists and network modelers to engage in more cross-disciplinary collaboration.

Social network analysis of wild chimpanzees provides insights for predicting infectious disease risk

1. Heterogeneity in host association patterns can alter pathogen transmission and strategies for control. Great apes are highly social and endangered animals that have experienced substantial population declines from directly transmitted pathogens; as such, network approaches to quantify contact heterogeneity could be crucially important for predicting infection probability and outbreak size following pathogen introduction, especially owing to challenges in collecting real-time infection data for endangered wildlife. 2. We present here the first study using network analysis to quantify contact heterogeneity in wild apes, with applications for predicting community-wide infectious disease risk. Specifically, within a wild chimpanzee community, we ask how associations between individuals vary over time, and we identify traits of highly connected individuals that might contribute disproportionately to pathogen spread. 3. We used field observations of behavioural encounters in a habituated wild chimpanzee community in Kibale National Park, Uganda to construct monthly party level (i.e. subgroup) and close-contact (i.e. ≤ 5 m) association networks over a 9-month period. 4. Network analysis revealed that networks were highly dynamic over time. In particular, oestrous events significantly increased pairwise party associations, suggesting that community-wide disease outbreaks should be more likely to occur when many females are in oestrus. 5. Bayesian models and permutation tests identified traits of chimpanzees that were highly connected within the network. Individuals with large families (i.e. mothers and their juveniles) that range in the core of the community territory and to a lesser extent high-ranking males were central to association networks, and thus represent the most important individuals to target for disease intervention strategies. 6. Overall, we show striking temporal variation in network structure and traits that predict association patterns in a wild chimpanzee community. These empirically-derived networks can inform dynamic models of pathogen transmission and have practical applications for infectious disease management of endangered wildlife species.

Evidence for a host role in thermotolerance divergence between populations of the mustard hill coral (Porites astreoides) from different reef environments

Studying the mechanisms that enable coral populations to inhabit spatially varying thermal environments can help evaluate how they will respond in time to the effects of global climate change and elucidate the evolutionary forces that enable or constrain adaptation. Inshore reefs in the Florida Keys experience higher temperatures than offshore reefs for prolonged periods during the summer. We conducted a common garden experiment with heat stress as our selective agent to test for local thermal adaptation in corals from inshore and offshore reefs. We show that inshore corals are more tolerant of a 6‐week temperature stress than offshore corals. Compared with inshore corals, offshore corals in the 31 °C treatment showed significantly elevated bleaching levels concomitant with a tendency towards reduced growth. In addition, dinoflagellate symbionts (Symbiodinium sp.) of offshore corals exhibited reduced photosynthetic efficiency. We did not detect differences in the frequencies of major (>5%) haplotypes comprising Symbiodinium communities hosted by inshore and offshore corals, nor did we observe frequency shifts (‘shuffling’) in response to thermal stress. Instead, coral host populations showed significant genetic divergence between inshore and offshore reefs, suggesting that in Porites astreoides, the coral host might play a prominent role in holobiont thermotolerance. Our results demonstrate that coral populations inhabiting reefs <10‐km apart can exhibit substantial differences in their physiological response to thermal stress, which could impact their population dynamics under climate change.

Neither genetic nor observational data alone are sufficient for understanding sex-biased dispersal in a social-group-living species

Complex sex‐biased dispersal patterns often characterize social‐group‐living species and may ultimately drive patterns of cooperation and competition within and among groups. This study investigates whether observational data or genetic data alone can elucidate the potentially complex dispersal patterns of social‐group‐living black and white colobus monkeys (Colobus guereza, ‘guerezas’), or whether combining both data types provides novel insights. We employed long‐term observation of eight neighbouring guereza groups in Kibale National Park, Uganda, as well as microsatellite genotyping of these and two other neighbouring groups. We created a statistical model to examine the observational data and used dyadic relatedness values within and among groups to analyse the genetic data. Analyses of observational and genetic data both supported the conclusion that males typically disperse from their natal groups and often transfer into nearby groups and probably beyond. Both data types also supported the conclusion that females are more philopatric than males but provided somewhat conflicting evidence about the extent of female philopatry. Observational data suggested that female dispersal is rare or nonexistent and transfers into neighbouring groups do not occur, but genetic data revealed numerous pairs of closely related adult females among neighbouring groups. Only by combining both data types were we able to understand the complexity of sex‐biased dispersal patterns in guerezas and the processes that could explain our seemingly conflicting results. We suggest that the data are compatible with a scenario of group dissolution prior to the start of this study, followed by female transfers into different neighbouring groups.

Females Shape the Genetic Structure of a Gorilla Population

Dispersal, one of the key life-history features of a species, influences gene flow and, consequently, the genetic structuring of populations. Landscape characteristics such as rivers, mountains, or habitat fragmentation affect dispersal and result in broad-scale genetic structuring of various mammalian species [1-5]. However, less attention has been paid to studying how dispersal is influenced by finer-scale microgeographic variation in a continuous habitat. Here we investigate the genetic structure of a closed population of approximately 300 endangered mountain gorillas living in multiple groups in a small (331 km(2)) forest in southwestern Uganda. In a species in which both sexes routinely disperse, population genetic structure in females was influenced by distance, altitude, and plant community composition, whereas males were not geographically structured. The effect of distance fits the observed tendency of females to transfer to neighboring groups, whereas the effects of altitude and vegetation reflect the changing species composition of locally available food resources. These results suggest that individual dietary preferences are important even in a highly mobile species living amid abundant food, and we propose that preference for natal habitats will influence dispersal decisions in many other vertebrate taxa.

Network-based vaccination improves prospects for disease control in wild chimpanzees

Many endangered wildlife populations are vulnerable to infectious diseases for which vaccines exist; yet, pragmatic considerations often preclude large-scale vaccination efforts. These barriers could be reduced by focusing on individuals with the highest contact rates. However, the question then becomes whether targeted vaccination is sufficient to prevent large outbreaks. To evaluate the efficacy of targeted wildlife vaccinations, we simulate pathogen transmission and control on monthly association networks informed by behavioural data from a wild chimpanzee community (Kanyawara N = 37, Kibale National Park, Uganda). Despite considerable variation across monthly networks, our simulations indicate that targeting the most connected individuals can prevent large outbreaks with up to 35% fewer vaccines than random vaccination. Transmission heterogeneities might be attributed to biological differences among individuals (e.g. sex, age, dominance and family size). Thus, we also evaluate the effectiveness of a trait-based vaccination strategy, as trait data are often easier to collect than interaction data. Our simulations indicate that a trait-based strategy can prevent large outbreaks with up to 18% fewer vaccines than random vaccination, demonstrating that individual traits can serve as effective estimates of connectivity. Overall, these results suggest that fine-scale behavioural data can help optimize pathogen control efforts for endangered wildlife.

Modeling the spatial distribution and fruiting pattern of a key tree species in a neotropical forest: Methodology and potential applications

Background The movement patterns of wild animals depend crucially on the spatial and temporal availability of resources in their habitat. To date, most attempts to model this relationship were forced to rely on simplified assumptions about the spatiotemporal distribution of food resources. Here we demonstrate how advances in statistics permit the combination of sparse ground sampling with remote sensing imagery to generate biological relevant, spatially and temporally explicit distributions of food resources. We illustrate our procedure by creating a detailed simulation model of fruit production patterns for Dipteryx oleifera, a keystone tree species, on Barro Colorado Island (BCI), Panama. Methodology and Principal Findings Aerial photographs providing GPS positions for large, canopy trees, the complete census of a 50-ha and 25-ha area, diameter at breast height data from haphazardly sampled trees and long-term phenology data from six trees were used to fit 1) a point process model of tree spatial distribution and 2) a generalized linear mixed-effect model of temporal variation of fruit production. The fitted parameters from these models are then used to create a stochastic simulation model which incorporates spatio-temporal variations of D. oleifera fruit availability on BCI. Conclusions and Significance We present a framework that can provide a statistical characterization of the habitat that can be included in agent-based models of animal movements. When environmental heterogeneity cannot be exhaustively mapped, this approach can be a powerful alternative. The results of our model on the spatio-temporal variation in D. oleifera fruit availability will be used to understand behavioral and movement patterns of several species on BCI.

Recovery Potential of a Western Lowland Gorilla Population following a Major Ebola Outbreak: Results from a Ten Year Study

Investigating the recovery capacity of wildlife populations following demographic crashes is of great interest to ecologists and conservationists. Opportunities to study these aspects are rare due to the difficulty of monitoring populations both before and after a demographic crash. Ebola outbreaks in central Africa have killed up to 95% of the individuals in affected western lowland gorilla (Gorilla gorilla gorilla) populations. Assessing whether and how fast affected populations recover is essential for the conservation of this critically endangered taxon. The gorilla population visiting Lokoué forest clearing, Odzala-Kokoua National Park, Republic of the Congo, has been monitored before, two years after and six years after Ebola affected it in 2004. This allowed us to describe Ebolas short-term and long-term impacts on the structure of the population. The size of the population, which included around 380 gorillas before the Ebola outbreak, dropped to less than 40 individuals after the outbreak. It then remained stable for six years after the outbreak. However, the demographic structure of this small population has significantly changed. Although several solitary males have disappeared, the immigration of adult females, the formation of new breeding groups, and several birth events suggest that the population is showing potential to recover. During the outbreak, surviving adult and subadult females joined old solitary silverbacks. Those females were subsequently observed joining young silverbacks, forming new breeding groups where they later gave birth. Interestingly, some females were observed joining silverbacks that were unlikely to have sired their infant, but no infanticide was observed. The consequences of the Ebola outbreak on the population structure were different two years and six years after the outbreak. Therefore, our results could be used as demographic indicators to detect and date outbreaks that have happened in other, non-monitored gorilla populations.

Epidemiological effects of group size variation in social species

Contact patterns in group-structured populations determine the course of infectious disease outbreaks. Network-based models have revealed important connections between group-level contact patterns and the dynamics of epidemics, but these models typically ignore heterogeneities in within-group composition. Here, we analyse a flexible mathematical model of disease transmission in a hierarchically structured wildlife population, and find that increased variation in group size reduces the epidemic threshold, making social animal populations susceptible to a broader range of pathogens. Variation in group size also increases the likelihood of an epidemic for mildly transmissible diseases, but can reduce the likelihood and expected size of an epidemic for highly transmissible diseases. Further, we introduce the concept of epidemiological effective group size, which we define to be the group size of a hypothetical population containing groups of identical size that has the same epidemic threshold as an observed population. Using data from the Serengeti Lion Project, we find that pride-living Serengeti lions are epidemiologically comparable to a homogeneous population with up to 20 per cent larger prides.