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Wetlands, as the most essential ecosystem, are degraded throughout the world. Wetlands in Zhenlai county, with the Momoge National Nature Reserve, which was included on the Ramsar list, have degraded by nearly 30%. Wetland degradation is a long-term continuous process with annual or interannual changes in water area, water level, or vegetation presence and growth. Therefore, it requires sufficiently frequent and high-spatial-resolution data to represent its dynamics. This study mapped yearly land-use maps with 30-m resolution from 1985 to 2018 using Landsat data in Google Earth Engine (GEE) to explore the wetland degradation process and mapped 12-day interval land-use maps with 15-m resolution using the Sentinel-1B and Sentinel-2 data in GEE and other assistant platforms to study the characteristics of wetland dynamics in 2018. Four sets of maps were generated using Sentinel-1B (S1), Sentinel-2 (S2), the combination of Sentinel-1B and Sentinel-2 (S12), and S12 with multitemporal remote sensing (S12'). All of the classifications were performed in the Random Forest Classification (RFC) method using remote sensing indicators. The results indicate that S12' was the most accurate. Then, the impact of the historic land-use degradation process on current wetland change dynamics was discussed. Stable, degradation, and restoration periods were identified according to the annual changes in wetlands. The degraded, stable, restored, and vulnerable zones were assessed based on the transformation characteristics among wetlands and other land-use types. The impact of historical land-use trajectories on wetland change characteristics nowadays is diverse in land-use types and distributions, and the ecological environment quality is the comprehensive result of the effect of historical land-use trajectories and the amount of rainfall and receding water from paddy fields. This study offers a new method to map high-spatiotemporal-resolution land-use (S12') and addresses the relationship between historic wetland change characteristics and its status quo. The findings are also applicable to wetland research in other regions. This study could provide more detailed scientific guidance for wetland managers by quickly detecting wetland changes at a finer spatiotemporal resolution. [ABSTRACT FROM AUTHOR]

Simple Summary: Estimation of population abundance or density is necessary for managing deer populations. However, there is no estimation of Siberian roe deer (Capreolus pygargus) in the Lesser Xing'an Mountains, northeast China where the density of roe deer is much lower than that of European or North American populations. We used fecal-DNA capture-mark-recapture to estimate the abundance and density in Liangshui National Nature Reserve. We collected 422 fecal pellet groups during two sampling periods in 2016, identified them to be 77 individuals by the DNA microsatellite technique and estimated the abundance of roe deer to be 87 deer (80–112, 95% CI) using the Program CAPTURE. Density was estimated to be 2.9 deer/km2 (2.7–3.7, 95% CI). Our study estimated the roe deer population abundance by a feces-based capture-mark-recapture approach in northeast China, successfully demonstrating the applicability of this feces sampling method in monitoring deer populations in this area. It also contributes to the development of low-density deer population ecology and management. It is necessary to estimate the population abundance of deer for managing their populations. However, most estimates are from high-density populations inhabiting the forests of North America or Europe; there is currently a lack of necessary knowledge regarding low-density deer populations in different forest habitats. In this article, we used fecal DNA based on the capture-mark-recapture method to estimate the population abundance of Siberian roe deer (Capreolus pygargus) in Liangshui National Nature Reserve in the Lesser Xing'an Mountains, northeast China, where the deer population was found to be of a low density by limited studies. We used a robust survey design to collect 422 fecal pellet groups in 2016 and extracted DNA from those samples, generating 265 different genotypes; we thus identified 77 deer individuals based on six microsatellite markers (Roe1, Roe8, Roe9, BM757, MB25 and OarFCB304). With capture and recapture records of these 77 individuals, the abundance of roe deer was estimated to be 87 deer (80–112, 95% CI) using the Program CAPTURE. Using an effective sampling area which resulted from the mean maximum recapture distance (MMRD), we converted the population abundance to a density of 2.9 deer/km2 (2.7–3.7, 95% CI). Our study estimated the roe deer population abundance by a feces-based capture-mark-recapture approach in northeast China, successfully demonstrating the applicability of non-invasive genetic sampling in monitoring populations of deer in this area, which contributes to the development of low-density deer population ecology and management. [ABSTRACT FROM AUTHOR]

Black carbon (BC) from incomplete combustion of biomass and fossil fuel is widespread in sediments and soils because of its high stability in nature and is considered an important component of the global carbon sink. However, knowledge of BC stocks and influencing factors in forest ecosystems is currently limited. We investigated soil BC contents in burned boreal forests of the Great Khingan Mountains, northeast China. We collected soil samples from 14 sites with different fire severities, slope positions and aspects. The samples were analyzed by the chemo-thermal oxidation method to obtain their BC concentrations. The BC concentrations of the studied soils ranged from 0.03 to 36.91 mg C g−1, with a mean of 1.44 ± 0.11 mg C g−1. BC concentrations gradually decline with depth, and that was significantly less in the 20–30 cm layer compared to all shallower layers. Forests burned by moderate-severity fires had the highest soil BC, the shady aspect had higher soil BC than the sunny aspect. Our results provide some basic data for evaluating the soil BC sink in boreal forests, which is a useful amendment to current carbon budget and carbon cycle in boreal forest ecosystems. [ABSTRACT FROM AUTHOR]