Nontidal wetlands are estimated to contribute significantly to the soil carbon pool across the globe. However, our understanding of the occurrence and variability of carbon storage between wetland types and across regions represents a major impediment to the ability of nations to include wetlands in greenhouse gas inventories and carbon offset initiatives. We performed a large‐scale survey of nontidal wetland soil carbon stocks and accretion rates from the state of Victoria in south‐eastern Australia—a region spanning 237,000 km2 and containing >35,000 temperate, alpine, and semi‐arid wetlands. From an analysis of >1,600 samples across 103 wetlands, we found that alpine wetlands had the highest carbon stocks (290 ± 180 Mg Corg ha−1), while permanent open freshwater wetlands and saline wetlands had the lowest carbon stocks (110 ± 120 and 60 ± 50 Mg Corg ha−1, respectively). Permanent open freshwater sites sequestered on average three times more carbon per year over the last century than shallow freshwater marshes (2.50 ± 0.44 and 0.79 ± 0.45 Mg Corg ha−1 year−1, respectively). Using this data, we estimate that wetlands in Victoria have a soil carbon stock in the upper 1 m of 68 million tons of Corg, with an annual soil carbon sequestration rate of 3 million tons of CO2 eq. year−1—equivalent to the annual emissions of about 3% of the state's population. Since European settlement (~1834), drainage and loss of 260,530 ha of wetlands may have released between 20 and 75 million tons CO2 equivalents (based on 27%–90% of soil carbon converted to CO2). Overall, we show that despite substantial spatial variability within wetland types, some wetland types differ in their carbon stocks and sequestration rates. The duration of water inundation, plant community composition, and allochthonous carbon inputs likely play an important role in influencing variation in carbon storage.

Background: Buruli ulcer (BU) is a neglected tropical disease caused by infection of subcutaneous tissue with Mycobacterium ulcerans. BU is commonly reported across rural regions of Central and West Africa but has been increasing dramatically in temperate southeast Australia around the major metropolitan city of Melbourne, with most disease transmission occurring in the summer months. Previous research has shown that Australian native possums are reservoirs of M. ulcerans and that they shed the bacteria in their fecal material (excreta). Field surveys show that locales where possums harbor M. ulcerans overlap with human cases of BU, raising the possibility of using possum excreta surveys to predict the risk of disease occurrence in humans.Methods: We thus established a highly structured 12-month possum excreta surveillance program across an area of 350 km2 in the Mornington Peninsula area 70 km south of Melbourne, Australia. The primary objective of our study was to assess using statistical modelling if M. ulcerans surveillance of possum excreta provided useful information for predicting future human BU case locations.Results: Over two sampling campaigns in summer and winter, we collected 2282 possum excreta specimens of which 11% were PCR positive for M. ulcerans-specific DNA. Using the spatial scanning statistical tool SaTScan, we observed non-random, co-correlated clustering of both M. ulcerans positive possum excreta and human BU cases. We next trained a statistical model with the Mornington Peninsula excreta survey data to predict the future likelihood of human BU cases occurring in the region. By observing where human BU cases subsequently occurred, we show that the excreta model performance was superior to a null model trained using the previous year's human BU case incidence data (AUC 0.66 vs 0.55). We then used data unseen by the excreta-informed model from a new survey of 661 possum excreta specimens in Geelong, a geographically separate BU endemic area to the southwest of Melbourne, to prospectively predict the location of human BU cases in that region. As for the Mornington Peninsula, the excreta-based BU prediction model outperformed the null model (AUC 0.75 vs 0.50) and pinpointed specific locations in Geelong where interventions could be deployed to interrupt disease spread.Conclusions: This study highlights the One Health nature of BU by confirming a quantitative relationship between possum excreta shedding of M. ulcerans and humans developing BU. The excreta survey-informed modeling we have described will be a powerful tool for efficient targeting of public health responses to stop BU.Funding: This research was supported by the National Health and Medical Research Council of Australia and the Victorian Government Department of Health (GNT1152807 and GNT1196396).

Contributor Roles Taxonomy (CRediT) has recently changed how author contributions are acknowledged. To extend and complement CRediT, we propose MeRIT, a new way of writing the Methods section using the author’s initials to further clarify contributor roles for reproducibility and replicability.

Buruli ulcer (BU) is a neglected tropical disease caused by infection of subcutaneous tissue with Mycobacterium ulcerans. BU is commonly reported across rural regions of Central and West Africa but has been increasing dramatically in temperate southeast Australia around the major metropolitan city of Melbourne. Previous research has shown that Australian native possums are reservoirs of M. ulcerans and that they shed the bacteria in their fecal material (excreta). Field surveys show that locales where possums harbor M. ulcerans overlap with human cases of BU, raising the possibility of using possum excreta surveys to predict the risk of disease occurrence in humans. We thus established a highly structured 12-month possum excreta surveillance program across an area of 350 km2 in the Mornington Peninsula area 70 km south of Melbourne, Australia. The primary objective of our study was to assess if M. ulcerans surveillance of possum excreta provided useful information for predicting future human BU case locations. Over two sampling campaigns in summer and winter, we collected 2282 possum excreta specimens of which 11% were PCR positive for M. ulcerans-specific DNA. Using the spatial scanning statistical tool SatScan, we observed non-random, co-correlated clustering of both M. ulcerans positive possum excreta and human BU cases. We next trained a statistical model with the Mornington Peninsula excreta survey data to predict the future likelihood of human BU cases occurring in the region. By observing where human BU cases subsequently occurred, we show that the excreta model performance was superior to a null model trained using the previous year's human BU case incidence data (AUC 0.66 vs 0.55). We then used data unseen by the excreta-informed model from a new survey of 661 possum excreta specimens in Geelong, a geographically separate BU endemic area to the southwest of Melbourne, to prospectively predict the location of human BU cases in that region. As for the Mornington Peninsula, the excreta-based BU prediction model outperformed the null model (AUC 0.75 vs 0.50) and pinpointed specific locations in Geelong where interventions could be deployed to interrupt disease spread. This study highlights the One Health nature of BU by confirming a quantitative relationship between possum excreta shedding of M. ulcerans and humans developing BU. The excreta survey-informed modeling we have described will be a powerful tool for efficient targeting of public health responses to stop BU.

Ecological models used to forecast range change (range change models; RCM) have recently diversified to account for a greater number of ecological and observational processes in pursuit of more accurate and realistic predictions. Theory suggests that process-explicit RCMs should generate more robust forecasts, particularly under novel environmental conditions. RCMs accounting for processes are generally more complex and data hungry, and so, require extra effort to build. Thus, it is necessary to understand when the effort of building a more realistic model is likely to generate more reliable forecasts. Here, we review the literature to explore whether process-explicit models have been tested through benchmarking their temporal predictive performance (i.e. their predictive performance when transferred in time) and model transferability (i.e. their ability to keep their predictive performance when transferred to generate predictions into a different time) against simpler models, and highlight the gaps between the rapid development of process-explicit RCMs and the testing of their potential improvements. We found that, out of five ecological processes (dispersal, demography, physiology, evolution, species interactions) and two observational processes (sampling bias, imperfect detection) that may influence reliability of forecasts, only the effects of dispersal, demography and imperfect detection have been benchmarked using temporally-independent datasets. Only nine out of twenty-nine process-explicit model types have been tested to assess whether accounting for processes improves temporal predictive performance. We found no benchmarks assessing model transferability. We discuss potential reasons for the lack of empirical validation of process-explicit models. Considering these findings, we propose an expanded research agenda to properly test the performance of process-explicit RCMs, and highlight some opportunities to fill the gaps by suggesting models to be benchmarked using existing historical datasets.

Code review increases reliability and improves reproducibility of research. As such, code review is an inevitable step in software development and is common in subjects such as computer science. However, despite its importance, code review is noticeably lacking in ecology and evolutionary biology. This is problematic as it facilitates the propagation of coding errors and a reduction in reproducibility of published results. To address this, we provide a detailed commentary on how to effectively review code, how to set up your project to enable this form of review and detail its possible implementation at several stages throughout the research process. This guide serves as a primer for code review, and adoption of the principles and advice here will go a long way in promoting more open, reliable, and transparent ecology and evolutionary biology.

Plant cell wall biomass is composed of a range of different types of carbon-based compounds. The proportions of the primary carbon types affect how cell walls decompose, an important ecosystem process because their decay contributes to soil carbon. Traditionally, these components are estimated using wet chemistry methods that can be costly and degrade the environment. Thermogravimetric analysis is an alternative method, already used by biofuel researchers, that involves pyrolysing dry, ground plant litter and estimating contribution of carbon components from a resulting mass decay curve. Because carbon types break down relatively independently, we can apply a mixture model to the multi-peaked rate of mass loss curve to identify mass loss of each carbon component. The mixchar package conducts this peak separation analysis in an open-source and reproducible way using R. mixchar has been tested over a range of plant litter types, composed primarily of the fiber components: hemicellulose, cellulose, and lignin.

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