Historical charcoal burning and coppicing suppressed beech and increased forest vegetation heterogeneity
TL;DR: In this paper, an approach combining a vegetation resurvey and charcoal kiln anthracology was proposed to uncover hidden links between current biological processes and the historical human legacies, with consequent important implications for nature protection and management.
Abstract: QUESTIONS: Long‐term legacies of historical human activities in temperate forests are increasingly recognised as an important driver of vegetation diversity and composition. To uncover centuries‐old legacies, novel approaches are, however, needed. Here, we combine anthracology of historical charcoal kilns and long‐term vegetation resurveys. We asked whether the historical coppicing oriented on charcoal production affected tree‐species composition and how the forest understorey vegetation changed after the coppicing was abandoned. LOCATION: Temperate broadleaved forests in the Slovak Karst National Park, central Europe. METHODS: To explore the historical forest structure and long‐term changes in tree composition, we sampled charcoal remains from 28 historical kilns, identified the burnt tree taxa and estimated the original diameter of the burnt wood. To analyse the vegetation changes over the last four decades, we resurveyed plant composition of 60 quasi‐permanent plots established in 1975. RESULTS: Historical charcoal burning was associated with coppicing, which decreased Fagus sylvatica dominance and favoured Quercus spp. in the tree layer. Several decades after the abandonment of coppicing, we observed the decline of Quercus spp. and spread of shade‐casting tree species with nutrient‐rich litter. This probably triggered the identified demise of light‐demanding species, the spread of nitrophytes and taxonomic homogenisation of the forest understorey. CONCLUSIONS: The shift from historical coppicing to current high‐forest management was likely a main driver of the observed taxonomic homogenisation and decline of light‐demanding plants, as in other European lowland forests. Long‐term data from charcoal kilns showed, however, that closed‐canopy forests dominated by beech were historically more common and observed changes in vegetation thus represent a natural process. Findings also suggest that coppicing taking place over centuries enhanced diversity of forest understorey vegetation. Our novel approach combining a vegetation resurvey and charcoal kiln anthracology thus uncovered otherwise hidden links between current biological processes and the historical human legacies, with consequent important implications for nature protection and management.
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TL;DR: In this article, the authors proposed a generalized description of soil stratigraphy on RCHs, based on average layer thicknesses and their dependence on the sites slope inclinations, and proposed a model with two idealized RCH shapes with slope controlled properties that allow for an easy computation of site diameters and elemental stocks.
Abstract: Relict charcoal hearths (RCHs) are anthropogenic geomorphic features with an average diameter of 12 m found in many forests of Central Europe and in the eastern USA wherever pre-coal iron production took place or other industries demanded the production of charcoal. To expand the knowledge about their geoarchaeological significance and their legacy effect on soil properties and forest ecosystems, we propose a method for a generalized description of soil stratigraphy on RCHs. We studied 154 soil profiles at 52 RCH sites alongside two 1 km transects in Litchfield County, Connecticut, USA. The sites can be classified based on the slope inclination, with sites on 4° mostly are built as levelled and multilayered platforms. The latter have two or more charcoal rich technogenic Auh-layers separated by intermediate Auh-layers mostly consisting of mineral substrate. Based on average layer thicknesses and their dependence on the sites slope inclinations, we propose a model with two idealized RCH shapes with slope controlled properties that allow for an easy computation of site diameters and elemental stocks. With ongoing advances in remote sensing of RCH sites, our proposed model can help to further understand the effects of historic land use on a landscape scale.
10 citations
TL;DR: In this paper , the authors review three coupled legacies linked by charcoal hearths (RCHs): landscape-scale geomorphic effect, unique soil fingerprint, and an evolving novel ecosystem.
8 citations
TL;DR: In this article, the authors performed a multi-proxy analysis of 7990 charcoal fragments from 28 charcoal kilns to assess the long-term changes in forest composition and structure, but also in harvesting practices and related silvicultural systems.
6 citations
TL;DR: In this paper , the authors define which silvicultural systems and forest operations can have an influence on forest tree biodiversity by summarising the findings of nearly 60 papers published in the last ten years (2013-2022).
Abstract: Abstract Purpose of Review Biodiversity is one of the most important features of forest ecosystems. One of the goals of Sustainable Forest Management is to reduce biodiversity disturbance, which can occur as a consequence of timber harvesting. The aim of this review was to define which silvicultural systems and forest operations can have an influence on forest tree biodiversity by summarising the findings of nearly 60 papers published in the last ten years (2013–2022). Recent Findings In natural forest ecosystems characterised by a high level of structural complexity, such as uneven-aged tropical forests, selective logging and retention forestry are, in general, suitable forms of intervention that have a limited impact on tree biodiversity. Forest operations, in particular, should be of low intensity and try to simulate as much as possible small-scale natural disturbances. Thinning has proved to be a valid treatment for managing tree biodiversity. However, it is important to shape the magnitude of thinnings according to the management aims. Limited removal is recommended in interventions for maintaining the current structure, and more extensive removal is appropriate in cases when a change in species composition is expected, e.g. in the conversion of planted coniferous stands to uneven-aged mixed or broadleaved stands. In addition, coppicing is suitable for maintaining tree biodiversity due to its effectiveness in fostering the presence of light-demanding tree species. Findings show that it is important to establish the right rotation age, considering that an excessively short period between coppicing interventions can be detrimental to functional biodiversity. Skid trails and landing sites represent suitable areas for the initial establishment of natural regeneration. However, generally, the level of biodiversity on these sites declines with time as a consequence of soil compaction, thus highlighting the importance of the forest infrastructure network planning. Summary In uneven-aged tropical forests, selective logging and retention forestry are the most suitable options for maintaining tree biodiversity. Thinning and coppicing help to manage biodiversity, whilst intensive thinning helps to change species composition. Skid trails and landing sites can support natural regeneration. Recommendations and management options were developed, as well as possible future research directions. The authors recommend that future studies should investigate how much tree biodiversity depends on different levels of harvesting technology applied within the same silvicultural treatment.
4 citations
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TL;DR: The origins, challenges and solutions of NIH Image and ImageJ software are discussed, and how their history can serve to advise and inform other software projects.
Abstract: For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
44,587 citations
TL;DR: In this article, a non-parametric method for multivariate analysis of variance, based on sums of squared distances, is proposed. But it is not suitable for most ecological multivariate data sets.
Abstract: Hypothesis-testing methods for multivariate data are needed to make rigorous probability statements about the effects of factors and their interactions in experiments. Analysis of variance is particularly powerful for the analysis of univariate data. The traditional multivariate analogues, however, are too stringent in their assumptions for most ecological multivariate data sets. Non-parametric methods, based on permutation tests, are preferable. This paper describes a new non-parametric method for multivariate analysis of variance, after McArdle and Anderson (in press). It is given here, with several applications in ecology, to provide an alternative and perhaps more intuitive formulation for ANOVA (based on sums of squared distances) to complement the description pro- vided by McArdle and Anderson (in press) for the analysis of any linear model. It is an improvement on previous non-parametric methods because it allows a direct additive partitioning of variation for complex models. It does this while maintaining the flexibility and lack of formal assumptions of other non-parametric methods. The test- statistic is a multivariate analogue to Fisher's F-ratio and is calculated directly from any symmetric distance or dissimilarity matrix. P-values are then obtained using permutations. Some examples of the method are given for tests involving several factors, including factorial and hierarchical (nested) designs and tests of interactions.
12,328 citations
TL;DR: In this article, a Laplace approximation is used to obtain an approximate restricted maximum likelihood (REML) or marginal likelihood (ML) for smoothing parameter selection in semiparametric regression.
Abstract: Summary. Recent work by Reiss and Ogden provides a theoretical basis for sometimes preferring restricted maximum likelihood (REML) to generalized cross-validation (GCV) for smoothing parameter selection in semiparametric regression. However, existing REML or marginal likelihood (ML) based methods for semiparametric generalized linear models (GLMs) use iterative REML or ML estimation of the smoothing parameters of working linear approximations to the GLM. Such indirect schemes need not converge and fail to do so in a non-negligible proportion of practical analyses. By contrast, very reliable prediction error criteria smoothing parameter selection methods are available, based on direct optimization of GCV, or related criteria, for the GLM itself. Since such methods directly optimize properly defined functions of the smoothing parameters, they have much more reliable convergence properties. The paper develops the first such method for REML or ML estimation of smoothing parameters. A Laplace approximation is used to obtain an approximate REML or ML for any GLM, which is suitable for efficient direct optimization. This REML or ML criterion requires that Newton–Raphson iteration, rather than Fisher scoring, be used for GLM fitting, and a computationally stable approach to this is proposed. The REML or ML criterion itself is optimized by a Newton method, with the derivatives required obtained by a mixture of implicit differentiation and direct methods. The method will cope with numerical rank deficiency in the fitted model and in fact provides a slight improvement in numerical robustness on the earlier method of Wood for prediction error criteria based smoothness selection. Simulation results suggest that the new REML and ML methods offer some improvement in mean-square error performance relative to GCV or Akaike's information criterion in most cases, without the small number of severe undersmoothing failures to which Akaike's information criterion and GCV are prone. This is achieved at the same computational cost as GCV or Akaike's information criterion. The new approach also eliminates the convergence failures of previous REML- or ML-based approaches for penalized GLMs and usually has lower computational cost than these alternatives. Example applications are presented in adaptive smoothing, scalar on function regression and generalized additive model selection.
4,846 citations
TL;DR: A unified framework for recursive partitioning is proposed which embeds tree-structured regression models into a well defined theory of conditional inference procedures and it is shown that the predicted accuracy of trees with early stopping is equivalent to the prediction accuracy of pruned trees with unbiased variable selection.
Abstract: Recursive binary partitioning is a popular tool for regression analysis. Two fundamental problems of exhaustive search procedures usually applied to fit such models have been known for a long time: overfitting and a selection bias towards covariates with many possible splits or missing values. While pruning procedures are able to solve the overfitting problem, the variable selection bias still seriously affects the interpretability of tree-structured regression models. For some special cases unbiased procedures have been suggested, however lacking a common theoretical foundation. We propose a unified framework for recursive partitioning which embeds tree-structured regression models into a well defined theory of conditional inference procedures. Stopping criteria based on multiple test procedures are implemented and it is shown that the predictive performance of the resulting trees is as good as the performance of established exhaustive search procedures. It turns out that the partitions and therefore the...
3,246 citations
TL;DR: This work presents permutation tests to assess the statistical significance of species-site group associations and bootstrap methods for obtaining confidence intervals, which includes several new indices.
Abstract: Ecologists often face the task of studying the association between single species and one or several groups of sites representing habitat types, community types, or other categories. Besides characterizing the ecological preference of the species, the strength of the association usually presents a lot of interest for conservation biology, landscape mapping and management, and natural reserve design, among other applications. The indices most frequently employed to assess these relationships are the phi coefficient of association and the indicator value index (IndVal). We compare these two approaches by putting them into a broader framework of related measures, which includes several new indices. We present permutation tests to assess the statistical significance of species-site group associations and bootstrap methods for obtaining confidence intervals. Correlation measures, such as the phi coefficient, are more context-dependent than indicator values but allow focusing on the preference of the species. In contrast, the two components of an indicator value index directly assess the value of the species as a bioindicator because they can be interpreted as its positive predictive value and sensitivity. Ecologists should select the most appropriate index of association strength according to their objective and then compute confidence intervals to determine the precision of the estimate.
2,428 citations