De Cáceres M., Coll L., Legendre P., Allen R.B., Wiser S.K., Fortin M.-J., Condit R., Hubbell S. (2019) Trajectory analysis in community ecology. Ecological Monographs. : 0-0.EnlaceDoi: 10.1002/ecm.1350
Ecologists have long been interested in how communities change over time. Addressing questions about community dynamics requires ways of representing and comparing the variety of dynamics observed across space. Until now, most analytical frameworks have been based on the comparison of synchronous observations across sites and between repeated surveys. An alternative perspective considers community dynamics as trajectories in a chosen space of community resemblance and utilizes trajectories as objects to be analyzed and compared using their geometry. While methods that take this second perspective exist, for example to test for particular trajectory shapes, there is a need for formal analytical frameworks that fully develop the potential of this approach. By adapting concepts and procedures used for the analysis of spatial trajectories, we present a framework for describing and comparing community trajectories. A key element of our contribution is the means to assess the geometric resemblance between trajectories, which allows users to describe, quantify, and analyze variation in community dynamics. We illustrate the behavior of our framework using simulated data and two spatiotemporal community data sets differing in the community properties of interest (species composition vs. size distribution of individuals). We conclude by evaluating the advantages and limitations of our community trajectory analysis framework, highlighting its broad domain of application and anticipating potential extensions. © 2019 by the Ecological Society of America
De Cáceres M., Martín-Alcón S., González-Olabarria J.R., Coll L. (2019) A general method for the classification of forest stands using species composition and vertical and horizontal structure. Annals of Forest Science. 76: 0-0.EnlaceDoi: 10.1007/s13595-019-0824-0
Key message: We present a novel approach to define pure- and mixed-forest typologies from the comparison of pairs of forest plots in terms of species identity, diameter, and height of their trees. Context: Forest typologies are useful for many purposes, including forest mapping, assessing habitat quality, studying forest dynamics, or defining sustainable management strategies. Quantitative typologies meant for forestry applications normally focus on horizontal and vertical structure of forest plots as main classification criteria, with species composition often playing a secondary role. The selection of relevant variables is often idiosyncratic and influenced by a priori expectations of the forest types to be distinguished. Aims: We present a general framework to define forest typologies where the dissimilarity between forest stands is assessed using coefficients that integrate the information of species composition with the univariate distribution of tree diameters or heights or the bivariate distribution of tree diameters and heights. Methods: We illustrate our proposal with the classification of forest inventory plots in Catalonia (NE Spain), comparing the results obtained using the bivariate distribution of diameters and heights to those obtained using either tree heights or tree diameters only. Results: The number of subtypes obtained using the tree diameter distribution for the calculation of dissimilarity was often the same as those obtained from the tree height distribution or to those using the bivariate distribution. However, classifications obtained using the three approaches were often different in terms of forest plot membership. Conclusion: The proposed classification framework is particularly suited to define forest typologies from forest inventory data and allows taking advantage of the bivariate distribution of diameters and heights if both variables are measured. It can provide support to the development of typologies in situations where fine-scale variability of topographic, climatic, and legacy management factors leads to fine-scale variation in forest structure and composition, including uneven-aged and mixed stands. © 2019, INRA and Springer-Verlag France SAS, part of Springer Nature.
Morán-Ordóñez A., Roces-Díaz J.V., Otsu K., Ameztegui A., Coll L., Lefevre F., Retana J., Brotons L. (2019) The use of scenarios and models to evaluate the future of nature values and ecosystem services in Mediterranean forests. Regional Environmental Change. 19: 415-428.EnlaceDoi: 10.1007/s10113-018-1408-5
Science and society are increasingly interested in predicting the effects of global change and socio-economic development on natural systems, to ensure maintenance of both ecosystems and human well-being. The Intergovernmental Platform on Biodiversity and Ecosystem Services has identified the combination of ecological modelling and scenario forecasting as key to improving our understanding of those effects, by evaluating the relationships and feedbacks between direct and indirect drivers of change, biodiversity, and ecosystem services. Using as case study the forests of the Mediterranean basin (complex socio-ecological systems of high social and conservation value), we reviewed the literature to assess (1) what are the modelling approaches most commonly used to predict the condition and trends of biodiversity and ecosystem services under future scenarios of global change, (2) what are the drivers of change considered in future scenarios and at what scales, and (3) what are the nature and ecosystem service indicators most commonly evaluated. Our review shows that forecasting studies make relatively little use of modelling approaches accounting for actual ecological processes and feedbacks between different socio-ecological sectors; predictions are generally made on the basis of a single (mainly climate) or a few drivers of change. In general, there is a bias in the set of nature and ecosystem service indicators assessed. In particular, cultural services and human well-being are greatly underrepresented in the literature. We argue that these shortfalls hamper our capacity to make the best use of predictive tools to inform decision-making in the context of global change. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
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