DataSheet1_The Certainty of Uncertainty: Potential Sources of Bias and Imprecision in Disease Ecology Studies.docx

<p>Wildlife diseases have important implications for wildlife and human health, the preservation of biodiversity and the resilience of ecosystems. However, understanding disease dynamics and the impacts of pathogens in wild populations is challenging because these complex systems can rarely, if ever, be observed without error. Uncertainty in disease ecology studies is commonly defined in terms of either heterogeneity in detectability (due to variation in the probability of encountering, capturing, or detecting individuals in their natural habitat) or uncertainty in disease state assignment (due to misclassification errors or incomplete information). In reality, however, uncertainty in disease ecology studies extends beyond these components of observation error and can arise from multiple varied processes, each of which can lead to bias and a lack of precision in parameter estimates. Here, we present an inventory of the sources of potential uncertainty in studies that attempt to quantify disease-relevant parameters from wild populations (e.g., prevalence, incidence, transmission rates, force of infection, risk of infection, persistence times, and disease-induced impacts). We show that uncertainty can arise via processes pertaining to aspects of the disease system, the study design, the methods used to study the system, and the state of knowledge of the system, and that uncertainties generated via one process can propagate through to others because of interactions between the numerous biological, methodological and environmental factors at play. We show that many of these sources of uncertainty may not be immediately apparent to researchers (for example, unidentified crypticity among vectors, hosts or pathogens, a mismatch between the temporal scale of sampling and disease dynamics, demographic or social misclassification), and thus have received comparatively little consideration in the literature to date. Finally, we discuss the type of bias or imprecision introduced by these varied sources of uncertainty and briefly present appropriate sampling and analytical methods to account for, or minimise, their influence on estimates of disease-relevant parameters. This review should assist researchers and practitioners to navigate the pitfalls of uncertainty in wildlife disease ecology studies.</p

Capture histories used to model pair fidelity and survival in E_surge

Data include capture histories (column 'H:') for a) female blue and great tits of Wytham woods; b) male blue and great tits of Wytham woods; c) female great tits of Wytham and Bagley woods; d) male great tits of Wytham and Bagley woods. For each dataset, the capture history is followed by the column 'S:' (denotes number of individuals with the capture history), the column '$COV:Mgp' (covariate coding for age, either Juvenile or Adult), and the column '$COV:Sp; or ;$COV:Pop; (coding for Species: G - great tit, B -Blue tit; or population Bag - Bagley wood, Wyth = Wytham woods). These capture histories were used to model pair fidelity and survival in program E_Surge as described in the Supplementary material of the Culina et al. 2015. Capture histories consist of 6 different codes that describe the 'event' that happen for a particular bird in a particular season. The original data on breeding pairs come from the long-term monitored populations in these woods

individual sequence dataset

Mhc class I allele data of 618 great tits. Alleles are identified with their Genbank accession number

Additional file 2: Figure S1. of Associations between perceived institutional support, job enjoyment, and intentions to work in the United Kingdom: national questionnaire survey of first year doctors

Percentage of doctors responding to each of the 12 attitude statements on a 5-point scale: Strongly Agree (SA), Agree (A), Neither agree nor disagree (N), Disagree (D), Strongly Disagree (SD). See Methods/Table 1 for full wording of the attitude statements. (TIFF 2294 kb

reproductive performance dataset

The data used for reproductive performance analyse

Allele vs. supertype information

Genbank accession numbers of the 755 functional Mhc class I alleles and supertype informatio

raw 454 sequence data

Sequence data generated from bidirectional 454 pyrosequencing. 454 pyrosequencing was performed on 1532 genomic DNA samples from 1492 great tits, collected between 2006–2010 in Wytham and Bagley Woods

MARK input dataset

The data used for mark-recapture analysi

MARK input file 5bv2

MARK input file used to conduct the multistate capture-mark-recapture analysis. In this dataset the mice with unknown infection states were assigned to the uninfected state. This text file contains the mouse ear tag identification number (ear.tag) and the capture history for each individual mouse (ch5). The capture history has 21 occasions and four states: A, B, C, and D. The definitions of the four states are as follows: A = susceptible juvenile B = infected juvenile C = susceptible adult D = infected adult The eight columns after the capture history column refer to the eight combinations of the four different areas: Control Area (cont), Mallard Road (mal), Nauyaug Point (nau), New Area (new) and the two sexes: female (f), male (m). Thus the eight columns refer to the following groups: cont.f = Control Area females cont.m = Control Area males mal.m = Mallard Road females mal.f = Mallard Road females nau.f = Nauyaug Point females nau.m = Nauyaug Point males new.f = New Area females new.m = New Area male

The Lyme Disease Pathogen Has No Effect on the Survival of Its Rodent Reservoir Host

<div><p>Zoonotic pathogens that cause devastating morbidity and mortality in humans may be relatively harmless in their natural reservoir hosts. The tick-borne bacterium <i>Borrelia burgdorferi</i> causes Lyme disease in humans but few studies have investigated whether this pathogen reduces the fitness of its reservoir hosts under natural conditions. We analyzed four years of capture-mark-recapture (CMR) data on a population of white-footed mice, <i>Peromyscus leucopus</i>, to test whether <i>B</i>. <i>burgdorferi</i> and its tick vector affect the survival of this important reservoir host. We used a multi-state CMR approach to model mouse survival and mouse infection rates as a function of a variety of ecologically relevant explanatory factors. We found no effect of <i>B</i>. <i>burgdorferi</i> infection or tick burden on the survival of <i>P</i>. <i>leucopus</i>. Our estimates of the probability of infection varied by an order of magnitude (0.051 to 0.535) and were consistent with our understanding of Lyme disease in the Northeastern United States. <i>B</i>. <i>burgdorferi</i> establishes a chronic avirulent infection in their rodent reservoir hosts because this pathogen depends on rodent mobility to achieve transmission to its sedentary tick vector. The estimates of <i>B</i>. <i>burgdorferi</i> infection risk will facilitate future theoretical studies on the epidemiology of Lyme disease.</p></div