Long-term change in drivers of forest cover expansion : an analysis for Switzerland (1850-2000)
TL;DR: In this paper, the authors quantified the spatiotemporal dynamics in drivers of forest gain in Switzerland and found that both biophysical and socioeconomic variables co-drive forest gain.
Abstract: The spatial distribution of forests in Europe represents the legacy of centuries of human land use decisions. Due to the limited availability of historical data, most studies on forest cover change focus only on analyzing recent decades, thereby overlooking the important long-term context. However, the latter is essential to improve our understanding of present landscape patterns. This study quantifies the spatiotemporal dynamics in drivers of forest gain in Switzerland. Specifically, we model forest gain in a long-term study covering 150 years (1850–2000) split into periods of similar length (∼30 years). This makes it possible to identify non-linear dynamics and whether drivers have changed over time. The rates of forest change are quantified based on analyzing historical maps and contemporary forest inventory data. Generalized additive models (GAMs) are fitted to examine the variation in the relative importance of socioeconomic and biophysical explanatory variables. Our results suggest that both biophysical and socioeconomic variables co-drive forest gain. Biophysical variables (such as temperature and slope) were identified as the major drivers explaining variations in forest gain. The most important socioeconomic driver was the change in the percentage of people employed per economic sector, although its effect came with a substantial time lag. Changes in employment per sector for the periods 1920–1941 and 1941–1980 were relevant for forest gain between 1980 and 2000. The identified time lag effect emphasizes the added value of long-term studies, since legacies may persist for decades, adding further complexity to contemporary land change processes. These findings are relevant to many temperate ecosystems that are experiencing increases in forest cover. Such insights can improve both future forest change predictions as well as the development of policies for sustainable landscape management.
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01 Jan 2003
TL;DR: In this article, three periods of landscape dynamics are considered: the traditional landscapes before the important changes that started in the 18th century, the landscapes of the revolutions age of the 19th to 20th century and the post-modern new landscapes.
Abstract: Landscapes change because they are the expression of the dynamic interaction between natural and cultural forces in the environment. Cultural landscapes are the result of consecutive reorganization of the land in order to adapt its use and spatial structure better to the changing societal demands. Particularly in Europe, history has recorded many successive and even devastating landscape changes, which have left barely any relics today. Today, the changes are seen as a menace, as a negative evolution because they cause a loss of diversity, coherence and identity, which were characteristic for the traditional cultural landscapes that are rapidly vanishing. This growing concern is also expressed in the European Landscape Convention, which will be used as a start for the analysis in this article. Three periods of landscape dynamics are considered: the traditional landscapes before the important changes that started in the 18th century, the landscapes of the revolutions age of the 19th to 20th century, and the post-modern new landscapes. The combined effect of the driving forces such as accessibility, urbanization, globalization and the impact of calamities have been different in each of the periods and affected the nature and pace of the changes as well as the perception people have had about the landscape. Values change accordingly and so does the way of using and shaping the landscape. It is argued that this changing perception also influences what kind and aspects of landscapes are studied, protected and managed. Diversity and identity of cultural landscapes are central in the discussion. It is shown that coherence between small composing elements in a broader spatial context is important for the legibility of the landscape and that the ability to tell the (his)story of a place strongly enhances the identity and the overall value. This offers criteria for inventorying and assessing landscapes, which is needed to define future management and development. Although the general trends of future development of the European landscapes are rather well known, planning and managing future landscape remains difficult and extremely uncertain. The processes and management in past traditional landscapes and the manifold relations people have towards the perceivable environment and the symbolic meaning it generates, offer valuable knowledge for more sustainable planning and management for future landscapes.
147 citations
University of Lausanne1, University of Wollongong2, Swiss Federal Institute for Forest, Snow and Landscape Research3, University of British Columbia4, University of Vienna5, Environment Agency6, University of Maryland, Baltimore County7, Martin Luther University of Halle-Wittenberg8, University of Geneva9, Yale University10, École Polytechnique Fédérale de Lausanne11, University of Antwerp12, University of Picardie Jules Verne13, University of Helsinki14, University of Montpellier15, University of Zurich16, VU University Amsterdam17, University at Buffalo18, Joint Nature Conservation Committee19, University of Bern20
TL;DR: In this paper, a survey of the literature on remote sensing data products available to ecological modelers interested in improving predictions of species range dynamics under global change is presented, focusing on the key biophysical processes underlying the distribution of species in the Anthropocene including climate variability, changes in land cover, and disturbances.
130 citations
TL;DR: In this paper, the authors combined an old paper-based US civil war map and modern aerial photos to derive land-use history and landscape dynamics at fine scales for a region near Chancellorsville, USA, from 1867 to 2014.
41 citations
TL;DR: In this paper, the authors determine the level and types of landscape changes and make preliminary study on natural and socio-economic factors on changes in forest landscapes within the protected area, Śleza Landscape Park, and its buffer zone using long-term analyses covering a period of 140 years.
Abstract: Changes in forest landscapes have been connected with human activity for centuries and can be considered one of the main driving forces of change from a global perspective. The spatial distribution of forests changes along with the geopolitical situation, demographic changes, intensification of agriculture, urbanization, or changes in land use policy. However, due to the limited availability of historical data, the driving forces of changes in forest landscapes are most often considered in relation to recent decades, without taking long-term analyses into account. The aim of this paper is to determine the level and types of landscape changes and make preliminary study on natural and socio-economic factors on changes in forest landscapes within the protected area, Śleza Landscape Park, and its buffer zone using long-term analyses covering a period of 140 years (1883–2013). A comparison of historical and current maps and demographic data related to three consecutive periods of time as well as natural and location factors by using the ArcGIS software allows the selected driving forces of forest landscape transformations to be analyzed. We took into account natural factors such as the elevation, slope, and exposure of the hillside and socio-economic drivers like population changes, distances to centers of municipalities, main roads, and built-up areas.
41 citations
TL;DR: In this paper, a prioritization approach based on scenarios maximising both the provision of ecosystem services and the conservation of biodiversity hotspots is proposed, where different weighting scenarios for the α diversity in four taxonomic groups and 10 mapped ecosystem services were used to simulate varying priorities of policymakers in a mountain region.
Abstract: As anthropogenic degradation of biodiversity and ecosystems increases, so does the potential threat to the supply of ecosystem services, a key contribution of nature to people. Biodiversity has often been used in spatial conservation planning and has been regarded as one among multiple services delivered by ecosystems. Hence, biodiversity conservation planning should be integrated in a framework of prioritizing services in order to inform decision-making. Here, we propose a prioritization approach based on scenarios maximising both the provision of ecosystem services and the conservation of biodiversity hotspots. Different weighting scenarios for the α-diversity in four taxonomic groups and 10 mapped ecosystem services were used to simulate varying priorities of policymakers in a mountain region. Our results illustrate how increasing priorities to ecosystem services can be disadvantageous to biodiversity. Moreover, the analysis to identify priority areas that best compromise the conservation of α-diversity and ecosystem services are predominantly not located within the current protected area network. Our analyses stress the need for an appropriate weighting of biodiversity within decision making that seek to integrate multiple ecosystem services. Our study paves the way toward further integration of multiple biodiversity groups and components, ecosystem services and various socio-economic scenarios, ultimately fuelling the development of more informed, evidence-based spatial planning decisions for conservation.
34 citations
References
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Book•
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01 Jan 2007
TL;DR: The R Book is the first comprehensive reference manual for the R language, including practical guidance and full coverage of the graphics facilities, and introduces the advantages of the R environment, detailing its applications in a wide range of disciplines.
Abstract: The high-level language of R is recognized as one of the most powerful and flexible statistical software environments, and is rapidly becoming the standard setting for quantitative analysis, statistics and graphics. R provides free access to unrivalled coverage and cutting-edge applications, enabling the user to apply numerous statistical methods ranging from simple regression to time series or multivariate analysis. Building on the success of the authors bestselling Statistics: An Introduction using R, The R Book is packed with worked examples, providing an all inclusive guide to R, ideal for novice and more accomplished users alike. The book assumes no background in statistics or computing and introduces the advantages of the R environment, detailing its applications in a wide range of disciplines. Provides the first comprehensive reference manual for the R language, including practical guidance and full coverage of the graphics facilities. Introduces all the statistical models covered by R, beginning with simple classical tests such as chi-square and t-test. Proceeds to examine more advance methods, from regression and analysis of variance, through to generalized linear models, generalized mixed models, time series, spatial statistics, multivariate statistics and much more. The R Book is aimed at undergraduates, postgraduates and professionals in science, engineering and medicine. It is also ideal for students and professionals in statistics, economics, geography and the social sciences.
6,975 citations
"Long-term change in drivers of fore..." refers background in this paper
...The advantage of a GAM is its non-parametric, flexible response function that is used to describe the relationship between explanatory variables and the response variable, which maximizes the quality of prediction (Crawley 2007)....
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DOI•
[...]
29 Sep 2022
TL;DR: The R Book is the first comprehensive reference manual for the R language, including practical guidance and full coverage of the graphics facilities, and introduces the advantages of the R environment, detailing its applications in a wide range of disciplines.
Abstract: The high-level language of R is recognized as one of the most powerful and flexible statistical software environments, and is rapidly becoming the standard setting for quantitative analysis, statistics and graphics. R provides free access to unrivalled coverage and cutting-edge applications, enabling the user to apply numerous statistical methods ranging from simple regression to time series or multivariate analysis. Building on the success of the authors bestselling Statistics: An Introduction using R, The R Book is packed with worked examples, providing an all inclusive guide to R, ideal for novice and more accomplished users alike. The book assumes no background in statistics or computing and introduces the advantages of the R environment, detailing its applications in a wide range of disciplines. Provides the first comprehensive reference manual for the R language, including practical guidance and full coverage of the graphics facilities. Introduces all the statistical models covered by R, beginning with simple classical tests such as chi-square and t-test. Proceeds to examine more advance methods, from regression and analysis of variance, through to generalized linear models, generalized mixed models, time series, spatial statistics, multivariate statistics and much more. The R Book is aimed at undergraduates, postgraduates and professionals in science, engineering and medicine. It is also ideal for students and professionals in statistics, economics, geography and the social sciences.
6,732 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
"Long-term change in drivers of fore..." refers methods in this paper
...GAM models were fitted in R version 3.2.2 using the package Bmgcv^ (Wood 2011)....
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TL;DR: In this article, the authors present a generalized linear model for categorical data, which is based on the Logit model, and use it to fit Logistic Regression models.
Abstract: Preface. 1. Introduction: Distributions and Inference for Categorical Data. 1.1 Categorical Response Data. 1.2 Distributions for Categorical Data. 1.3 Statistical Inference for Categorical Data. 1.4 Statistical Inference for Binomial Parameters. 1.5 Statistical Inference for Multinomial Parameters. Notes. Problems. 2. Describing Contingency Tables. 2.1 Probability Structure for Contingency Tables. 2.2 Comparing Two Proportions. 2.3 Partial Association in Stratified 2 x 2 Tables. 2.4 Extensions for I x J Tables. Notes. Problems. 3. Inference for Contingency Tables. 3.1 Confidence Intervals for Association Parameters. 3.2 Testing Independence in Two Way Contingency Tables. 3.3 Following Up Chi Squared Tests. 3.4 Two Way Tables with Ordered Classifications. 3.5 Small Sample Tests of Independence. 3.6 Small Sample Confidence Intervals for 2 x 2 Tables . 3.7 Extensions for Multiway Tables and Nontabulated Responses. Notes. Problems. 4. Introduction to Generalized Linear Models. 4.1 Generalized Linear Model. 4.2 Generalized Linear Models for Binary Data. 4.3 Generalized Linear Models for Counts. 4.4 Moments and Likelihood for Generalized Linear Models . 4.5 Inference for Generalized Linear Models. 4.6 Fitting Generalized Linear Models. 4.7 Quasi likelihood and Generalized Linear Models . 4.8 Generalized Additive Models . Notes. Problems. 5. Logistic Regression. 5.1 Interpreting Parameters in Logistic Regression. 5.2 Inference for Logistic Regression. 5.3 Logit Models with Categorical Predictors. 5.4 Multiple Logistic Regression. 5.5 Fitting Logistic Regression Models. Notes. Problems. 6. Building and Applying Logistic Regression Models. 6.1 Strategies in Model Selection. 6.2 Logistic Regression Diagnostics. 6.3 Inference About Conditional Associations in 2 x 2 x K Tables. 6.4 Using Models to Improve Inferential Power. 6.5 Sample Size and Power Considerations . 6.6 Probit and Complementary Log Log Models . 6.7 Conditional Logistic Regression and Exact Distributions . Notes. Problems. 7. Logit Models for Multinomial Responses. 7.1 Nominal Responses: Baseline Category Logit Models. 7.2 Ordinal Responses: Cumulative Logit Models. 7.3 Ordinal Responses: Cumulative Link Models. 7.4 Alternative Models for Ordinal Responses . 7.5 Testing Conditional Independence in I x J x K Tables . 7.6 Discrete Choice Multinomial Logit Models . Notes. Problems. 8. Loglinear Models for Contingency Tables. 8.1 Loglinear Models for Two Way Tables. 8.2 Loglinear Models for Independence and Interaction in Three Way Tables. 8.3 Inference for Loglinear Models. 8.4 Loglinear Models for Higher Dimensions. 8.5 The Loglinear Logit Model Connection. 8.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions . 8.7 Loglinear Model Fitting: Iterative Methods and their Application . Notes. Problems. 9. Building and Extending Loglinear/Logit Models. 9.1 Association Graphs and Collapsibility. 9.2 Model Selection and Comparison. 9.3 Diagnostics for Checking Models. 9.4 Modeling Ordinal Associations. 9.5 Association Models . 9.6 Association Models, Correlation Models, and Correspondence Analysis . 9.7 Poisson Regression for Rates. 9.8 Empty Cells and Sparseness in Modeling Contingency Tables. Notes. Problems. 10. Models for Matched Pairs. 10.1 Comparing Dependent Proportions. 10.2 Conditional Logistic Regression for Binary Matched Pairs. 10.3 Marginal Models for Square Contingency Tables. 10.4 Symmetry, Quasi symmetry, and Quasiindependence. 10.5 Measuring Agreement Between Observers. 10.6 Bradley Terry Model for Paired Preferences. 10.7 Marginal Models and Quasi symmetry Models for Matched Sets . Notes. Problems. 11. Analyzing Repeated Categorical Response Data. 11.1 Comparing Marginal Distributions: Multiple Responses. 11.2 Marginal Modeling: Maximum Likelihood Approach. 11.3 Marginal Modeling: Generalized Estimating Equations Approach. 11.4 Quasi likelihood and Its GEE Multivariate Extension: Details . 11.5 Markov Chains: Transitional Modeling. Notes. Problems. 12. Random Effects: Generalized Linear Mixed Models for Categorical Responses. 12.1 Random Effects Modeling of Clustered Categorical Data. 12.2 Binary Responses: Logistic Normal Model. 12.3 Examples of Random Effects Models for Binary Data. 12.4 Random Effects Models for Multinomial Data. 12.5 Multivariate Random Effects Models for Binary Data. 12.6 GLMM Fitting, Inference, and Prediction. Notes. Problems. 13. Other Mixture Models for Categorical Data . 13.1 Latent Class Models. 13.2 Nonparametric Random Effects Models. 13.3 Beta Binomial Models. 13.4 Negative Binomial Regression. 13.5 Poisson Regression with Random Effects. Notes. Problems. 14. Asymptotic Theory for Parametric Models. 14.1 Delta Method. 14.2 Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities. 14.3 Asymptotic Distributions of Residuals and Goodnessof Fit Statistics. 14.4 Asymptotic Distributions for Logit/Loglinear Models. Notes. Problems. 15. Alternative Estimation Theory for Parametric Models. 15.1 Weighted Least Squares for Categorical Data. 15.2 Bayesian Inference for Categorical Data. 15.3 Other Methods of Estimation. Notes. Problems. 16. Historical Tour of Categorical Data Analysis . 16.1 Pearson Yule Association Controversy. 16.2 R. A. Fisher s Contributions. 16.3 Logistic Regression. 16.4 Multiway Contingency Tables and Loglinear Models. 16.5 Recent and Future? Developments. Appendix A. Using Computer Software to Analyze Categorical Data. A.1 Software for Categorical Data Analysis. A.2 Examples of SAS Code by Chapter. Appendix B. Chi Squared Distribution Values. References. Examples Index. Author Index. Subject Index. Sections marked with an asterisk are less important for an overview.
4,650 citations