Statistics for Spatial Data.

Linear models

Changes to forest production drivers (light, water, temperature, and site nutrient) over the last 55 years have been documented in peer-reviewed literature. The main objective of this paper is to review documented evidence of the impacts of climate change trends on forest productivity since the middle of the 20th century. We first present a concise overview of the climate controls of forest production, provide evidence of how the main controls have changed in the last 55 years, followed by a core section outlining our findings of observed and documented impacts on forest productivity and a brief discussion of the complications of interpreting trends in net primary production (NPP). At finer spatial scales, a trend is difficult to decipher, but globally, based on both satellite and ground-based data, climatic changes seemed to have a generally positive impact on forest productivity when water was not limiting. Of the 49 papers reporting forest production levels we reviewed, 37 showed a positive growth trend, five a negative trend, three reported both a positive and a negative trend for different time periods, one reported a positive and no trend for different geographic areas, and two reported no trend. Forests occupy 52% of the Earth’s land surface and tend to occupy more temperature and radiation-limited environments. Less than 7% of forests are in strongly water-limited systems. The combined and interacting effects of temperature, radiation, and precipitation changes with the positive effect of CO2, the negative effects of O3 and other pollutants, and the presently positive effects of N will not be elucidated with experimental manipulation of one or a few factors at a time. Assessments of the greening of the biosphere depend on both accurate measurements of rates (net ecosystem exchange, NPP), how much is stored at the ecosystem level (net ecosystem production) and quantification of disturbances rates on final net biome production.

/pdf/impacts-of-climate-change-on-natural-forest-productivity-49rtnvdj6k.pdf

Impacts of climate change on natural forest productivity – evidence since the middle of the 20th century

Responses of forest trees to single and multiple environmental stresses from seedlings to mature plants: Past stress history, stress interactions, tolerance and acclimation

How can we manage farmlands, forests, and rangelands to respond to the triple challenge of the Anthropocene-biodiversity loss, climate change, and unsustainable land use? When managed by using biodiversity-based techniques such as agroforestry, silvopasture, diversified farming, and ecosystem-based forest management, these socioeconomic systems can help maintain biodiversity and provide habitat connectivity, thereby complementing protected areas and providing greater resilience to climate change. Simultaneously, the use of these management techniques can improve yields and profitability more sustainably, enhancing livelihoods and food security. This approach to "working lands conservation" can create landscapes that work for nature and people. However, many socioeconomic challenges impede the uptake of biodiversity-based land management practices. Although improving voluntary incentives, market instruments, environmental regulations, and governance is essential to support working lands conservation, it is community action, social movements, and broad coalitions among citizens, businesses, nonprofits, and government agencies that have the power to transform how we manage land and protect the environment.

Landscapes that work for biodiversity and people

An individual-tree heightdiameter model was developed for stone pine (Pinus pinea L.) in Spain. Five biparametric nonlinear equations were fitted and evaluated based on a data set consisting of 86...

Interregional nonlinear height-diameter model with random coefficients for stone pine in Spain

.................................................................................................................. 12 PRESENTACIÓN............................................................................................................. 13 INTRODUCCIÓN............................................................................................................. 15 Sobre el ciclo del carbono y los bosques .............................................................. 15 El carbono en los ecosistemas forestales ............................................................. 18 Papel de los bosques frente al cambio climático................................................... 20 Producción del bosque y desarrollo social ................................................. 20 Gestión de bosques para la mitigación del cambio climático ....................... 21 METODOLOGIA ............................................................................................................. 25 Toma de datos ................................................................................................... 26 Determinación de la materia seca ....................................................................... 29 Obtención de valores modulares de biomasa........................................................ 30 Cálculo de incrementos anuales de biomasa ........................................................ 36 Cálculo del CO2 acumulado ................................................................................. 37 Aplicación de los valores modulares a los datos del Segundo Inventario Forestal Nacional ................................................................................................................ 38 Balances de CO2................................................................................................. 38 Algunas consideraciones a la metodología general ............................................... 38 PRODUCCIÓN DE BIOMASA Y FIJACIÓN DE CO2 POR LAS PRINCIPALES ESPECIES FORESTALES ESPAÑOLAS ........................................................................................................... 43 Frondosas.......................................................................................................... 45 Alnus glutinosa ........................................................................................ 45 Betula spp. .............................................................................................. 51 Castanea sativa ....................................................................................... 57 Ceratonia siliqua ...................................................................................... 65 Eucalyptus spp. ....................................................................................... 69 Fagus sylvatica ........................................................................................ 77 Fraxinus spp. ........................................................................................... 83 Olea europaea ......................................................................................... 91 Populus x euramericana ........................................................................... 97 Quercus canariensis................................................................................. 105 Quercus faginea....................................................................................... 111 Quercus ilex ............................................................................................ 119 Quercus pyrenaica ................................................................................... 127 Quercus robur/Q. petraea ......................................................................... 135 Quercus suber ......................................................................................... 141 Otras frondosas ....................................................................................... 147 Coníferas ........................................................................................................... 153 Abies alba ............................................................................................... 153 Abies pinsapo .......................................................................................... 159 Juniperus oxycedrus ................................................................................ 163 Juniperus phoenicea ................................................................................ 169 Juniperus thurifera .................................................................................. 173 Pinus canariensis ..................................................................................... 179 Pinus halepensis ...................................................................................... 183 Pinus nigra .............................................................................................. 191 Pinus pinaster.......................................................................................... 199 Pinus pinea.............................................................................................. 207 Pinus radiata ........................................................................................... 213 Pinus sylvestris........................................................................................ 219 Pinus uncinata ......................................................................................... 227 Otras coníferas ........................................................................................ 233 Laurisilvas y Fayal-Brezal ................................................................................... 239 Erica arborea ........................................................................................... 239 Ilex canariensis........................................................................................ 243 Laurus azorica ......................................................................................... 247 Myrica faya ............................................................................................. 251 Otras laurisilvas ....................................................................................... 255 BALANCE NACIONAL DE BIOMASA Y CO2......................................................................... 259 Resultados por grupos de especies ..................................................................... 259 Frondosas ............................................................................................... 259 Coníferas................................................................................................. 260 Laurisilva y Fayal-Brezal........................................................................... 261 Comparación entre grupos de especies ..................................................... 262 Balance general de biomasa y CO2 para todas las especies conjuntamente ............ 264 BIBLIOGRAFÍA............................................................................................................... 264 8 Producción de biomasa y fijación de CO2 por los bosques españoles

Producción de biomasa y fijación de CO2 por los bosques españoles

Quantifying the carbon balance in forests is one of the main challenges in forest management. Forest carbon stocks are usually estimated indirectly through biomass equations applied to forest inventories, frequently considering different tree biomass components. The aim of this study is to develop systems of equations for predicting tree biomass components for the main forest softwood species in Spain: Abies alba Mill., A. pinsapo Boiss., Juniperus thurifera L., Pinus canariensis Sweet ex Spreng., P. halepensis Mill., P. nigra Arn., P. pinaster Ait., P. pinea L., P. sylvestris L., P. uncinata Mill. For each species, a system of additive biomass models was fitted using seemingly unrelated regression. Diameter at the breast height and total height were used as independent variables. Diameter appears in all component models, while tree height was included in the stem component model of all species and in some branch component equations. Total height was included in order to improve biomass estimations at different sites. These biomass models were compared to previously available equations in order to test their accuracy and it was found that they yielded better fitting statistics in all cases. Moreover, the models fulfil the additivity property. We also developed root:shoot ratios in order to determine the partitioning into aboveground and belowground biomass. A number of differences were found between species, with a minimum of 0.183 for A. alba and a maximum of 0.385 for P. uncinata. The mean value for the softwood species studied was 0.265. Since the Spanish National Forest Inventory (NFI) records species, tree diameter and height of sample trees, these biomass models and ratios can be used to accurately estimate carbon stocks from NFI data.

https://dialnet.unirioja.es/descarga/articulo/3642216.pdf

New models for estimating the carbon sink capacity of Spanish softwood species

To estimate forest carbon pools from forest inventories it is necessary to have biomass models or biomass expansion factors. In this study, tree biomass models were developed for the main hardwood forest species in Spain: Alnus glutinosa , Castanea sativa , Ceratonia siliqua , Eucalyptus globulus , Fagus sylvatica , Fraxinus angustifolia , Olea europaea var. sylvestris , Populus x euramericana , Quercus canariensis , Quercus faginea , Quercus ilex , Quercus pyrenaica and Quercus suber . Different tree biomass components were considered: stem with bark, branches of different sizes, above and belowground biomass. For each species, a system of equations was fitted using seemingly unrelated regression, fulfilling the additivity property between biomass components. Diameter and total height were explored as independent variables. All models included tree diameter whereas for the majority of species, total height was only considered in the stem biomass models and in some of the branch models. The comparison of the new biomass models with previous models fitted separately for each tree component indicated an improvement in the accuracy of the models. A mean reduction of 20% in the root mean square error and a mean increase in the model efficiency of 7% in comparison with recently published models. So, the fitted models allow estimating more accurately the biomass stock in hardwood species from the Spanish National Forest Inventory data.

https://dialnet.unirioja.es/descarga/articulo/4854717.pdf

Biomass models to estimate carbon stocks for hardwood tree species

Cet article presente la reponse de taillis de chene tauzins (Quercus pyrenaica Willd.) a des eclaircies par le bas de differentes intensites. Au total, quatre traitements ont ete appliques: eclaircie faible, moyenne et forte en enlevant respectivement 25, 35 et 50 % de la surface terriere et un traitement sans eclaircie (controle). Les resultats ont mis en evidence des ecarts significatifs en ce qui concerne le diametre et la biomasse de l'arbre moyen et l'accroissement courant du diametre aux trois dates d'inventaires pratiques (1994, 1998, 2002): a chacun de ces inventaires, des valeurs plus importantes ont ete obtenues pour l'arbre moyen lorsque l'intensite d'eclaircie etait la plus forte. Aucun ecart significatif entre traitements n' a ete observe en ce qui concerne la production totale (surface terriere et biomasse). La structure du peuplement est plus reguliere dans les parcelles eclaircies, reduisant par la-meme les risques d'incendies et augmentant la valeur paysagere et recreative des peuplements ainsi que leur aptitude au sylvo-pastoralisme.

/pdf/growth-response-to-thinning-in-quercus-pyrenaica-willd-1zklrb3181.pdf

Gregorio Montero

Papers

Interregional nonlinear height-diameter model with random coefficients for stone pine in Spain

Producción de biomasa y fijación de CO2 por los bosques españoles

New models for estimating the carbon sink capacity of Spanish softwood species

Biomass models to estimate carbon stocks for hardwood tree species

Growth response to thinning in Quercus pyrenaica Willd. coppice stands in Spanish central mountain