Number of surveys and detections for all sites in Madagascar reported as positive for the presence of <i>Batrachochytrium dendrobatidis</i> (<i>Bd</i>) by Bletz et al. (2015a) and the most recent survey results reported by Kolby et al. (2015).

<p>The number of surveys performed at each location is expressed together with the number of those events that resulted in <i>Bd</i> detection, as reported by Bletz et al. (2015a). Cumulative number of amphibians sampled at each site by Bletz et al. (2015a) and number of <i>Bd</i>-positive animals detected (<i>Bd</i>+) is reported, except where samples were pooled for analysis and the number of <i>Bd</i>-positive animals is unknown (unk). The final column ("Feb-March 2014") represents the most recent survey results as reported by Kolby et al. (2015), from sampling at or near sites of previous <i>Bd</i> detection. Areas not surveyed by Kolby et al. (2015) are marked with "N/A"</p><p>Number of surveys and detections for all sites in Madagascar reported as positive for the presence of <i>Batrachochytrium dendrobatidis</i> (<i>Bd</i>) by Bletz et al. (2015a) and the most recent survey results reported by Kolby et al. (2015).</p

Timeline of <i>Batrachochytrium dendrobatidis</i> (<i>Bd</i>) records of detection at affected sites in Madagascar from 2005–2014 as reported by Bletz et al. (2015a) and Kolby et al. (2015).

<p>Surveys with <i>Bd</i> detection marked by "X" and those with only negative results marked by "–". The number of <i>Bd</i>-positive amphibians detected in each survey is presented in parenthesis, except where samples were pooled for analysis and the number of <i>Bd</i>-positive animals remains unknown (unk). Where multiple surveys were performed at a site within the same year, data were combined. The column "2014A" represents surveys reported by Bletz et al. (2015a) and "2014B" represents surveys reported by Kolby et al. (2015)</p><p>Timeline of <i>Batrachochytrium dendrobatidis</i> (<i>Bd</i>) records of detection at affected sites in Madagascar from 2005–2014 as reported by Bletz et al. (2015a) and Kolby et al. (2015).</p

Map of the study region, showing the four main study sites in South-east Queensland as well as the additional survey sites in Queensland and New South Wales, Australia.

<p>Map of the study region, showing the four main study sites in South-east Queensland as well as the additional survey sites in Queensland and New South Wales, Australia.</p

Under the ‘weather linked <i>Bd</i> proliferation hypothesis’, chytridiomycosis dynamics are largely driven by pathogen proliferation (growth) under suitable climatic conditions.

<p>This hypothesis predicts that modelled pathogen growth (GI_W, averaged over the 30 days prior to sampling; GI₃₀) should be positively related to a) disease prevalence in the population (R² = 0.247, F_1,39 = 12.79, p<0.001) (Prediction 3) and c) infection intensity of infected individuals (R² = 0.186, F_1,39 = 8.91, p = 0.005) (Prediction 1) because frogs are ectothermic growth media and disease/transmission dynamics will be dependent on the number of dispersing zoospores. This hypothesis thus also predicts that b) population prevalence should be positively related to infection intensity of infected individuals (R² = 0.284, F_1,39 = 15.49, p<0.001) (Prediction 2).</p

Partial dependence plot from the Random Forest (RF) framework showing the relationship between the probability of <i>Bd</i> infection and GI₃₀ (simulated pathogen growth in the 30 days prior to sampling, from the CLIMEX process-based model) for adult male <i>Litoria pearsoniana.</i>

<p>The response line is a lowess smoother. Data were derived from four field sites across three years of sampling (2006–2009). In a model containing the 30 day growth index (GI₃₀), body length (SUL) and Rain (mm on the day of sampling), positive PCR results for <i>Bd</i> infection were correctly predicted in ∼72% of cases (see results).</p

Variable importance plot from the Random Forest (RF) framework for predicting infection status in adult male <i>Litoria pearsoniana</i> captured at four study sites across three years of study (2006–2009).

<p>To assess importance of each variable: after growing the kth tree, the values of the target variable among all out-of-bag (OOB) cases are randomly permuted and the OOB cases are run down the tree. The decrease in the number of votes for the correct class due to permuting is averaged over the forest. RH.minT₃₀ is the average maximum relative humidity in the 30 days prior to sampling, Rain when sampling is the amount of rain (in mm) on the day swabs were taken in the field, T.max₃₀ is the average maximum temperature in the 30 days prior to sampling, Year is the year in which samples were taken, Body size is measured as snout-urostyle length (SUL; measured in mm).</p

Partial dependence plot from the Random Forest (RF) framework showing the relationship between probability of <i>Bd</i> infection and month of the active season for adult male <i>L. pearsoniana</i>.

<p>This seasonal pattern of infection is now considered typical in forest frogs in subtropical south-east Queensland (see e.g., Kriger and Hero 2007).</p

Partial dependence plots from the Random Forest (RF) framework showing the relationship between probability of <i>Bd</i> infection in adult male <i>Litoria pearsoniana</i> and each of the continuous variables included in the pruned RF model.

<p>Sampling was conducted across four sites and three years of study (2006–2009). Response lines are lowess smoothers.</p

Pre- and post-immune stimulation hematologic values for uninfected and <i>Batrachochytrium dendrobatidis</i>-infected <i>Litoria caerulea</i>.

d<p>BW, body weight.</p>a<p>Paired-samples <i>t</i>-tests between days 0 and 7 within each group (uninfected and infected).</p>b<p>Independent-samples <i>t</i>-tests between the two groups for the <i>change</i> in each variable from day 0 to day 7.</p><p>Pre- and post-immune stimulation hematologic values for uninfected and <i>Batrachochytrium dendrobatidis</i>-infected <i>Litoria caerulea</i>.</p

Recapture probability and State change probability.

<p>Conditional Arnason-Schwarz model in which outcome probabilities are (S) survival, (p) recapture, (Ψ) state change, and the variables that can influence the outcomes are (g) site, (t) time in weeks, (to) state at previous capture, (f) state at capture. Panels (a) and (b) represent the two-disease-state model, <i>Bd</i> positive (Bd+) and <i>Bd</i> negative (Bd-), in which the best model was S(g)p(g*t)Ψ(to*f*t). Panels (c) and (d) represent the three-disease-state model, <i>Bd</i> negative (Bd-), low infection intensity of >350ZE (Low) and high infection intensity of >350ZE (High), in which the best model was S(g*f)p(g*to+t)Ψ(to*f). (a) Recapture probability per week in a two-disease-state model. Factors included in the best model for recapture probability were site and week, Error bars indicate 95% confidence interval. (b) Probability of changing state per week in a two-disease-state model. Factors included in the best model for state change probability were week, infection state at current capture, and infection state at previous capture, and error bars indicate 95% confidence interval. (c) Recapture probability per week in a three-disease-state model. Factors included in the best model for recapture probability were site, state of infection and week. Error bars indicate standard error, and only one error bar included for figure clarity. (d) Probability of changing state in a three-disease-state model, error bars indicate 95% confidence interval. Sites are Oglives Dam (OD) and Sponar’s Creek (SC).</p