Here, we present results from the most comprehensive compilation of Holocene peat soil properties with associated carbon and nitrogen accumulation rates for northern peatlands. Our database consists of 268 peat cores from 215 sites located north of 45°N. It encompasses regions within which peat carbon data have only recently become available, such as the West Siberia Lowlands, the Hudson Bay Lowlands, Kamchatka in Far East Russia, and the Tibetan Plateau. For all northern peatlands, carbon content in organic matter was estimated at 42 ± 3% (standard deviation) for Sphagnum peat, 51 ± 2% for non- Sphagnum peat, and at 49 ± 2% overall. Dry bulk density averaged 0.12 ± 0.07 g/cm3, organic matter bulk density averaged 0.11 ± 0.05 g/cm3, and total carbon content in peat averaged 47 ± 6%. In general, large differences were found between Sphagnum and non- Sphagnum peat types in terms of peat properties. Time-weighted peat carbon accumulation rates averaged 23 ± 2 (standard error of mean) g C/m2/yr during the Holocene on the basis of 151 peat cores from 127 sites, with the highest rates of carbon accumulation (25–28 g C/m2/yr) recorded during the early Holocene when the climate was warmer than the present. Furthermore, we estimate the northern peatland carbon and nitrogen pools at 436 and 10 gigatons, respectively. The database is publicly available at https://peatlands.lehigh.edu .
Summary1. Including predictors in species distribution models at inappropriate spatial scales can decrease the variance explained, add residual spatial autocorrelation (RSA) and lead to the wrong conclusions. Some studies have measured predictors within different buffer sizes (scales) around sample locations, regressed each predictor against the response at each scale and selected the scale with the best model fit as the appropriate scale for this predictor. However, a predictor can influence a species at several scales or show several scales with good model fit due to a bias caused by RSA. This makes the evaluation of all scales with good model fit necessary. With potentially several scales per predictor and multiple predictors to evaluate, the number of predictors can be large relative to the number of data points, potentially impeding variable selection with traditional statistical techniques, such as logistic regression. 2. We trialled a variable selection process using the random forest algorithm, which allows the simultaneous evaluation of several scales of multiple predictors. Using simulated responses, we compared the performance of models resulting from this approach with models using the known predictors at arbitrary and at the known spatial scales. We also apply the proposed approach to a real data set of curlew (Numenius arquata). 3. AIC, AUC and Naglekerke's pseudo R 2 of the models resulting from the proposed variable selection were often very similar to the models with the known predictors at known spatial scales. Only two of nine models required the addition of spatial eigenvectors to account for RSA. Arbitrary scale models always required the addition of spatial eigenvectors. 75% (50-100%) of the known predictors were selected at scales similar to the known scale (within 3 km). In the curlew model, predictors at large, medium and small spatial scales were selected, suggesting that for appropriate landscape-scale models multiple scales need to be evaluated. 4. The proposed approach selected several of the correct predictors at appropriate spatial scales out of 544 possible predictors. Thus, it facilitates the evaluation of multiple spatial scales of multiple predictors against each other in landscape-scale models.
Summary1. High-resolution vegetation maps are a valuable resource for conservation, land management and research. In Great Britain, the National Vegetation Classification (NVC) is widely used to describe vegetation communities. NVC maps are typically produced from ground surveys which are prohibitively expensive for large areas. An approach to produce NVC maps more cost-effectively for large areas would be valuable. 2. Creation of vegetation community maps from aerial or satellite images has often had limited success as the clusters separable by spectral reflectance frequently do not correspond well to vegetation community classes. Such maps have also been produced by exploring correlations between community occurrence and environmental variables. The latter approach can have limitations where anthropogenic activities have altered the distribution of vegetation communities. We combined these two approaches and classified 24 common NVC classes of the Yorkshire Dales and an additional class 'wood' consisting of trees and bushes at a resolution of 5 m from mostly remotely sensed variables with the algorithm random forest. 3. Classification accuracy was highest when environmental variables at low and high resolution (50 and 5-10 m, respectively) were added to aerial image information aggregated to a resolution of 5 m. Low-resolution environmental variables are likely to be correlated with the dominant vegetation surrounding a location and thus could represent critical area requirements or local species pool effects, while high-resolution environmental variables represent the environmental conditions at the location. 4. Overall classification accuracy was 87-92%. The median producer's and user's class accuracies were 95% (58-100%) and 92% (67-100%), respectively. 5. Synthesis and applications. The classification accuracies achieved in this study, the number of classes differentiated, their level of detail and the resolution were high compared with those of other studies. This approach could allow the production of good-quality NVC maps for large areas. In contrast to existing maps of broad land cover types, such maps would provide more detailed vegetation community data for applications like the monitoring of vegetation in a changing climate, the study of animal-habitat relationships, conservation management or land use planning.
In a recent discussion of research priorities for palaeoecology, it was suggested that palaeoecological data can be applied and used to inform nature conservation practice.The present study exemplifies this approach and was conducted on a degraded blanket mire in Yorkshire, UK, in collaboration with a field-based moorland restoration agency. High-resolution, multiproxy palaeoecological analyses on a peat core from Mossdale Moor reconstructed mid to late-Holocene vegetation changes. Humification, pollen, plant macrofossil and charcoal analyses carried out throughout the peat profile show marked changes in species composition and indicate their potential causes.Results suggest that human clearance in the Mesolithic-Neolithic transition may have initiated peat growth at Mossdale Moor, making this landscape 'semi-natural' in its origin. Further human-induced changes are identified at 1300 cal. years BP, most likely clearance by fire, and between 20-0 cm depth where a substantial charcoal increase is interpreted as recent (<400 years) management practices using burning to encourage browse on the moor. The long-term ecological history of the moor, derived using palaeoecological techniques, will be used to inform conservation practice and to help set feasible targets for restoration and conservation at Mossdale Moor.
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