Estimating wildlife disease dynamics in complex systems using an Approximate Bayesian Computation framework

The microscopic five of the big five: Managing zoonotic diseases within and beyond African wildlife protected areas

African protected areas strive to conserve the continent's great biodiversity with a targeted focus on the flagship 'Big Five' megafauna. Though often not considered, this biodiversity protection also extends to the lesser-known microbes and parasites that are maintained in these diverse ecosystems, often in a silent and endemically stable state. Climate and anthropogenic change, and associated diversity loss, however, are altering these dynamics leading to shifts in ecological interactions and pathogen spill over into new niches and hosts. As many African protected areas are bordered by game and livestock farms, as well as villages, they provide an ideal study system to assess infection dynamics at the human-livestock-wildlife interface. Here we review five zoonotic, multi-host diseases (bovine tuberculosis, brucellosis, Rift Valley fever, schistosomiasis and cryptosporidiosis)-the 'Microscopic Five'-and discuss the biotic and abiotic drivers of parasite transmission using the iconic Kruger National Park, South Africa, as a case study. We identify knowledge gaps regarding the impact of the 'Microscopic Five' on wildlife within parks and highlight the need for more empirical data, particularly for neglected (schistosomiasis) and newly emerging (cryptosporidiosis) diseases, as well as zoonotic disease risk from the rising bush meat trade and game farm industry. As protected areas strive to become further embedded in the socio-economic systems that surround them, providing benefits to local communities, One Health approaches can help maintain the ecological integrity of ecosystems, while protecting local communities and economies from the negative impacts of disease.

From theory to practice in pattern-oriented modelling: identifying and using empirical patterns in predictive models

To robustly predict the effects of disturbance and ecosystem changes on species, it is necessary to produce structurally realistic models with high predictive power and flexibility. To ensure that these models reflect the natural conditions necessary for reliable prediction, models must be informed and tested using relevant empirical observations. Pattern-oriented modelling (POM) offers a systematic framework for employing empirical patterns throughout the modelling process and has been coupled with complex systems modelling, such as in agent-based models (ABMs). However, while the production of ABMs has been rising rapidly, the explicit use of POM has not increased. Challenges with identifying patterns and an absence of specific guidelines on how to implement empirical observations may limit the accessibility of POM and lead to the production of models which lack a systematic consideration of reality. This review serves to provide guidance on how to identify and apply patterns following a POM approach in ABMs (POM-ABMs), specifically addressing: where in the ecological hierarchy can we find patterns; what kinds of patterns are useful; how should simulations and observations be compared; and when in the modelling cycle are patterns used? The guidance and examples provided herein are intended to encourage the application of POM and inspire efficient identification and implementation of patterns for both new and experienced modellers alike. Additionally, by generalising patterns found especially useful for POM-ABM development, these guidelines provide practical help for the identification of data gaps and guide the collection of observations useful for the development and verification of predictive models. Improving the accessibility and explicitness of POM could facilitate the production of robust and structurally realistic models in the ecological community, contributing to the advancement of predictive ecology at large.

African Lions and Zoonotic Diseases: Implications for Commercial Lion Farms in South Africa

Simple Summary

In South Africa, thousands of African lions are bred on farms for commercial purposes, such as tourism, trophy hunting, and traditional medicine. Lions on farms often have direct contact with people, such as farm workers and tourists. Such close contact between wild animals and humans creates opportunities for the spread of zoonotic diseases (diseases that can be passed between animals and people). To help understand the health risks associated with lion farms, our study compiled a list of pathogens (bacteria, viruses, parasites, and fungi) known to affect African lions. We reviewed 148 scientific papers and identified a total of 63 pathogens recorded in both wild and captive lions, most of which were parasites (35, 56%), followed by viruses (17, 27%) and bacteria (11, 17%). This included pathogens that can be passed from lions to other animals and to humans. We also found a total of 83 diseases and clinical symptoms associated with these pathogens. Given that pathogens and their associated infectious diseases can cause harm to both animals and public health, we recommend that the lion farming industry in South Africa takes action to prevent and manage potential disease outbreaks.

Abstract

African lions (Panthera leo) are bred in captivity on commercial farms across South Africa and often have close contact with farm staff, tourists, and other industry workers. As transmission of zoonotic diseases occurs through close proximity between wildlife and humans, these commercial captive breeding operations pose a potential risk to thousands of captive lions and to public health. An understanding of pathogens known to affect lions is needed to effectively assess the risk of disease emergence and transmission within the industry. Here, we conduct a systematic search of the academic literature, identifying 148 peer-reviewed studies, to summarize the range of pathogens and parasites known to affect African lions. A total of 63 pathogenic organisms were recorded, belonging to 35 genera across 30 taxonomic families. Over half were parasites (35, 56%), followed by viruses (17, 27%) and bacteria (11, 17%). A number of novel pathogens representing unidentified and undescribed species were also reported. Among the pathogenic inventory are species that can be transmitted from lions to other species, including humans. In addition, 83 clinical symptoms and diseases associated with these pathogens were identified. Given the risks posed by infectious diseases, this research highlights the potential public health risks associated with the captive breeding industry. We recommend that relevant authorities take imminent action to help prevent and manage the risks posed by zoonotic pathogens on lion farms.

Characterization of potential superspreader farms for bovine tuberculosis: A review

Background

Variation in host attributes that influence their contact rates and infectiousness can lead some individuals to make disproportionate contributions to the spread of infections. Understanding the roles of such ‘superspreaders’ can be crucial in deciding where to direct disease surveillance and controls to greatest effect. In the epidemiology of bovine tuberculosis (bTB) in Great Britain, it has been suggested that a minority of cattle farms or herds might make disproportionate contributions to the spread of Mycobacterium bovis, and hence might be considered ‘superspreader farms’.

Objectives and Methods

We review the literature to identify the characteristics of farms that have the potential to contribute to exceptional values in the three main components of the farm reproductive number ‐ R_f: contact rate, infectiousness and duration of infectiousness, and therefore might characterize potential superspreader farms for bovine tuberculosis in Great Britain.

Results

Farms exhibit marked heterogeneity in contact rates arising from between‐farm trading of cattle. A minority of farms act as trading hubs that greatly augment connections within cattle trading networks. Herd infectiousness might be increased by high within‐herd transmission or the presence of supershedding individuals, or infectiousness might be prolonged due to undetected infections or by repeated local transmission, via wildlife or fomites.

Conclusions

Targeting control methods on putative superspreader farms might yield disproportionate benefits in controlling endemic bovine tuberculosis in Great Britain. However, real‐time identification of any such farms, and integration of controls with industry practices, present analytical, operational and policy challenges.

Calibrating an individual-based movement model to predict functional connectivity for little owls

Dispersal is crucial for population viability and thus a popular target for conservation measures. However, the ability of individuals to move between habitat patches is notoriously difficult to estimate. One solution is to quantify functional connectivity via realistic individual-based movement models. Such simulation models, however, are difficult to build and even more difficult to parameterize. Here, we use the example of natal little owl (Athene noctua) dispersal to develop a new analysis chain for the calibration of individual-based dispersal models using a hybrid of statistical parameter estimation and Approximate Bayesian Computation (ABC). Specifically, we use locations of 126 radio-tracked juveniles to first estimate habitat utilization by generalized additive models (GAMs) and the biased random bridges (BRB) method. We then include the estimated parameters in a spatially explicit individual-based model (IBM) of little owl dispersal and calibrate further movement parameters using ABC. To derive efficient summary statistics, we use a new dimension reduction method based on random forest (RF) regression. Finally, we use the calibrated IBM to predict the dispersal potential of little owls from local populations in southwestern Germany to suitable habitat patches in northern Switzerland. We show that pre-calibrating habitat preference parameters while inferring movement behavioral parameters via ABC is a computationally efficient solution to obtain a plausible IBM parameterization. We also find that dimension reduction via RF regression outperforms the widely used least squares regression, which we applied as a benchmark approach. Estimated movement parameters for the individuals reveal plausible inter-individual and inter-sexual differences in movement behavior during natal dispersal. In agreement with a sex-biased dispersal distance in little owls, females show longer individual flights and higher directional persistence. Simulations from the fitted model indicate that a (re)colonization of northern Switzerland is generally possible, albeit restricted. We conclude that the presented analysis chain is a sensible work-flow to assess dispersal connectivity across species and ecosystems. It embraces species- and individual-specific behavioral responses to the landscape and allows likelihood-based calibration, despite an irregular sampling design. Our study highlights existing, yet narrow dispersal corridors, which may require enhancements to facilitate a recolonization of little owl habitat patches in northern Switzerland.

Genetic insights into dispersal distance and disperser fitness of African lions (Panthera leo) from the latitudinal extremes of the Kruger National Park, South Africa

Background

Female lions generally do not disperse far beyond their natal range, while males can disperse distances of over 200 km. However, in bush-like ecosystems dispersal distances less than 25 km are reported. Here, we investigate dispersal in lions sampled from the northern and southern extremes of Kruger National Park, a bush-like ecosystem in South Africa where bovine tuberculosis prevalence ranges from low to high across a north-south gradient.

Results

A total of 109 individuals sampled from 1998 to 2004 were typed using 11 microsatellite markers, and mitochondrial RS-3 gene sequences were generated for 28 of these individuals. Considerable north-south genetic differentiation was observed in both datasets. Dispersal was male-biased and generally further than 25 km, with long-distance male gene flow (75–200 km, detected for two individuals) confirming that male lions can travel large distances, even in bush-like ecosystems. In contrast, females generally did not disperse further than 20 km, with two distinctive RS-3 gene clusters for northern and southern females indicating no or rare long-distance female dispersal. However, dispersal rate for the predominantly non-territorial females from southern Kruger (fraction dispersers ≥0.68) was higher than previously reported. Of relevance was the below-average body condition of dispersers and their low presence in prides, suggesting low fitness.

Conclusions

Large genetic differences between the two sampling localities, and low relatedness among males and high dispersal rates among females in the south, suggestive of unstable territory structure and high pride turnover, have potential implications for spread of diseases and the management of the Kruger lion population.

Electronic supplementary material

The online version of this article (10.1186/s12863-018-0607-x) contains supplementary material, which is available to authorized users.

Taking error into account when fitting models using Approximate Bayesian Computation

Stochastic computer simulations are often the only practical way of answering questions relating to ecological management. However, due to their complexity, such models are difficult to calibrate and evaluate. Approximate Bayesian Computation (ABC) offers an increasingly popular approach to this problem, widely applied across a variety of fields. However, ensuring the accuracy of ABC's estimates has been difficult. Here, we obtain more accurate estimates by incorporating estimation of error into the ABC protocol. We show how this can be done where the data consist of repeated measures of the same quantity and errors may be assumed to be normally distributed and independent. We then derive the correct acceptance probabilities for a probabilistic ABC algorithm, and update the coverage test with which accuracy is assessed. We apply this method, which we call error-calibrated ABC, to a toy example and a realistic 14-parameter simulation model of earthworms that is used in environmental risk assessment. A comparison with exact methods and the diagnostic coverage test show that our approach improves estimation of parameter values and their credible intervals for both models.