Forest diversity plays a key role in determining the stand carbon stocks of Mexican forests

Arasa-Gisbert, R., Vayreda, J., Román-Cuesta, R.M., Villela, S.A., Mayorga, R., Retana, J. (2018) Forest diversity plays a key role in determining the stand carbon stocks of Mexican forests. Forest Ecology and Management. 415-416: 160-171.
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Doi: 10.1016/j.foreco.2018.02.023

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Calibrating the severity of forest defoliation by pine processionary moth with landsat and UAV imagery

Otsu K., Pla M., Vayreda J., Brotons L. (2018) Calibrating the severity of forest defoliation by pine processionary moth with landsat and UAV imagery. Sensors (Switzerland). 18: 0-0.
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Doi: 10.3390/s18103278

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The pine processionary moth (Thaumetopoea pityocampa Dennis and Schiff.), one of the major defoliating insects in Mediterranean forests, has become an increasing threat to the forest health of the region over the past two decades. After a recent outbreak of T. pityocampa in Catalonia, Spain, we attempted to estimate the damage severity by capturing the maximum defoliation period over winter between pre-outbreak and post-outbreak images. The difference in vegetation index (dVI) derived from Landsat 8 was used as the change detection indicator and was further calibrated with Unmanned Aerial Vehicle (UAV) imagery. Regression models between predicted dVIs and observed defoliation degrees by UAV were compared among five selected dVIs for the coefficient of determination. Our results found the highest R-squared value (0.815) using Moisture Stress Index (MSI), with an overall accuracy of 72%, as a promising approach for estimating the severity of defoliation in affected areas where ground-truth data is limited. We concluded with the high potential of using UAVs as an alternative method to obtain ground-truth data for cost-effectively monitoring forest health. In future studies, combining UAV images with satellite data may be considered to validate model predictions of the forest condition for developing ecosystem service tools. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

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Assessing the distribution of forest ecosystem services in a highly populated Mediterranean region

Roces-Díaz J.V., Vayreda J., Banqué-Casanovas M., Cusó M., Anton M., Bonet J.A., Brotons L., De Cáceres M., Herrando S., Martínez de Aragón J., de-Miguel S., Martínez-Vilalta J. (2018) Assessing the distribution of forest ecosystem services in a highly populated Mediterranean region. Ecological Indicators. 93: 986-997.
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Doi: 10.1016/j.ecolind.2018.05.076

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Forest ecosystems provide a wide range of goods and services to society and host high levels of biodiversity. Nevertheless, forest ecosystem services (ES) are often quantified and assessed using simplified methodologies (e.g., proxy methods based exclusively on Land Use Land Cover maps) that introduce substantial uncertainty in the analysis by ignoring, for instance, the species composition and spatial configuration of the ecosystems studied. In this work we defined and calculated a set of 12 indicators of several ES for the forests of the highly populated region of Catalonia (North-eastern Iberian Peninsula). The indicators combined different sources of information such as forest surveys, ecological model predictions and official statistics, but also included additional land cover information. All ES indicators were aggregated at the municipality level to compare their values and distribution patterns. We assessed spatial trade-offs and synergies among ES, as well as their relationships with a set of socioeconomic, climatic and biodiversity variables using correlation analyses and mixed-effects models. The results suggest a clustering of provisioning and regulating ES in mountainous zones towards the North of the study area. These two types of services showed a high degree of spatial similarity and presented high positive correlations. In contrast, cultural ES showed a more scattered pattern, which included lower elevation areas in the South of the study region. Climatic conditions were the main determinants of the spatial variability in the supply of the different ES, with most indicators being positively associated with precipitation and negatively associated with temperature. In addition, biodiversity (particularly woody species richness) showed positive relations with most of these ES, while socioeconomic variables (such as population density and the percentage employment in agriculture) showed negative associations with most of them. The combination of information from different data sources (including primary data) allowed for a detailed analysis of forest ES, likely removing some of the problems derived from approaches based only on proxy methods. In addition, the use of municipalities as study unit makes results directly relevant to management and planning strategies operating at this scale (e.g., forest management and planning). © 2018 Elsevier Ltd

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The spatial level of analysis affects the patterns of forest ecosystem services supply and their relationships

Roces-Díaz, J.V., Vayreda, J., Banqué-Casanovas, M., Díaz-Varela, E., Bonet, J.A., Brotons, L., de-Miguel, S., Herrando, S., Martínez-Vilalta, J. (2018) The spatial level of analysis affects the patterns of forest ecosystem services supply and their relationships. Science of the Total Environment. 626: 1270-1283.
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Doi: 10.1016/j.scitotenv.2018.01.150

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Species-specific, pan-European diameter increment models based on data of 2.3 million trees

Schelhaas M.-J., Hengeveld G.M., Heidema N., Thürig E., Rohner B., Vacchiano G., Vayreda J., Redmond J., Socha J., Fridman J., Tomter S., Polley H., Barreiro S., Nabuurs G.-J. (2018) Species-specific, pan-European diameter increment models based on data of 2.3 million trees. Forest Ecosystems. 5: 0-0.
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Doi: 10.1186/s40663-018-0133-3

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Background: Over the last decades, many forest simulators have been developed for the forests of individual European countries. The underlying growth models are usually based on national datasets of varying size, obtained from National Forest Inventories or from long-term research plots. Many of these models include country- and location-specific predictors, such as site quality indices that may aggregate climate, soil properties and topography effects. Consequently, it is not sensible to compare such models among countries, and it is often impossible to apply models outside the region or country they were developed for. However, there is a clear need for more generically applicable but still locally accurate and climate sensitive simulators at the European scale, which requires the development of models that are applicable across the European continent. The purpose of this study is to develop tree diameter increment models that are applicable at the European scale, but still locally accurate. We compiled and used a dataset of diameter increment observations of over 2.3 million trees from 10 National Forest Inventories in Europe and a set of 99 potential explanatory variables covering forest structure, weather, climate, soil and nutrient deposition. Results: Diameter increment models are presented for 20 species/species groups. Selection of explanatory variables was done using a combination of forward and backward selection methods. The explained variance ranged from 10% to 53% depending on the species. Variables related to forest structure (basal area of the stand and relative size of the tree) contributed most to the explained variance, but environmental variables were important to account for spatial patterns. The type of environmental variables included differed greatly among species. Conclusions: The presented diameter increment models are the first of their kind that are applicable at the European scale. This is an important step towards the development of a new generation of forest development simulators that can be applied at the European scale, but that are sensitive to variations in growing conditions and applicable to a wider range of management systems than before. This allows European scale but detailed analyses concerning topics like CO2 sequestration, wood mobilisation, long term impact of management, etc. © 2018, The Author(s).

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