Global dataset of soil organic carbon in tidal marshes.

Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha-1 in the top 30 cm and 231 ± 134 Mg SOC ha-1 in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies.

Global mangrove soil organic carbon stocks dataset at 30 m resolution for the year 2020 based on spatiotemporal predictive machine learning.

This dataset presents global soil organic carbon stocks in mangrove forests at 30 m resolution, predicted for 2020. We used spatiotemporal ensemble machine learning to produce predictions of soil organic carbon content and bulk density (BD) to 1 m soil depth, which were then aggregated to calculate soil organic carbon stocks. This was done by using training data points of both SOC (%) and BD in mangroves from a global dataset and from recently published studies, and globally consistent predictive covariate layers. A total of 10,331 soil samples were validated to have SOC (%) measurements and were used for predictive soil mapping. We used time-series remote sensing data specific to time periods when the training data were sampled, as well as long-term (static) layers to train an ensemble of machine learning model. Ensemble models were used to improve performance, robustness and unbiasedness as opposed to just using one learner. In addition, we performed spatial cross-validation by using spatial blocking of training data points to assess model performance. We predicted SOC stocks for the 2020 time period and applied them to a 2020 mangrove extent map, presenting both mean predictions and prediction intervals to represent the uncertainty around our predictions. Predictions are available for download under CC-BY license from 10.5281/zenodo.7729491 and also as Cloud-Optimized GeoTIFFs (global mosaics).

Contrasting drivers of belowground nitrogen cycling in a montane grassland exposed to a multifactorial global change experiment with elevated CO2 , warming, and drought.

Depolymerization of high-molecular weight organic nitrogen (N) represents the major bottleneck of soil N cycling and yet is poorly understood compared to the subsequent inorganic N processes. Given the importance of organic N cycling and the rise of global change, we investigated the responses of soil protein depolymerization and microbial amino acid consumption to increased temperature, elevated atmospheric CO2 , and drought. The study was conducted in a global change facility in a managed montane grassland in Austria, where elevated CO2 (eCO2 ) and elevated temperature (eT) were stimulated for 4 years, and were combined with a drought event. Gross protein depolymerization and microbial amino acid consumption rates (alongside with gross organic N mineralization and nitrification) were measured using 15 N isotope pool dilution techniques. Whereas eCO2  showed no individual effect, eT had distinct effects which were modulated by season, with a negative effect of eT on soil organic N process rates in spring, neutral effects in summer, and positive effects in fall. We attribute this to a combination of changes in substrate availability and seasonal temperature changes. Drought led to a doubling of organic N process rates, which returned to rates found under ambient conditions within 3 months after rewetting. Notably, we observed a shift in the control of soil protein depolymerization, from plant substrate controls under continuous environmental change drivers (eT and eCO2 ) to controls via microbial turnover and soil organic N availability under the pulse disturbance (drought). To the best of our knowledge, this is the first study which analyzed the individual versus combined effects of multiple global change factors and of seasonality on soil organic N processes and thereby strongly contributes to our understanding of terrestrial N cycling in a future world.

Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system.

While rodents frequently host ectoparasites that can vector zoonotic diseases, often little is known about their ectoparasite communities, even in places where hosts frequently interact with humans. Yosemite National Park is an area of high human-wildlife interaction and high potential zoonotic disease transfer. Nonetheless, relatively few studies have surveyed the flea communities on mammalian hosts in this area, and even fewer have characterized the environmental and host factors that predict infestation. We focused on two species, the alpine chipmunk (Tamias alpinus) and the lodgepole chipmunk (T. speciosus), which inhabit Yosemite and surrounding areas and can host fleas that vector plague. Because these hosts are exhibiting differential responses to environmental change, it is valuable to establish baselines for their flea communities before further changes occur. We surveyed fleas on these chipmunk hosts during three years (2013-2015), including in the year of a plague epizootic (2015), and documented significant inter-host differences in flea communities and changes across years. Flea abundance was associated with host traits including sex and fecal glucocorticoid metabolite levels. The average number of fleas per individual and the proportion of individuals carrying fleas increased across years for T. speciosus but not for T. alpinus. To better understand these patterns, we constructed models to identify environmental predictors of flea abundance for the two most common flea species, Ceratophyllus ciliatus mononis and Eumolpianus eumolpi. Results showed host-dependent differences in environmental predictors of flea abundance for E. eumolpi and C. ciliatus mononis, with notable ties to ambient temperature variation and elevation. These results provide insight into factors affecting flea abundance on two chipmunk species, which may be linked to changing climate and possible future plague epizootics.