Modelling ingrowth in Mediterranean pine forests: a case study from Scots pine (Pinus sylvestris L.) and Mediterranean Maritime pine (Pinus pinaster Ait.) stands in Spain

Abstract

National Forest Inventories (NFI) are a basic tool for forestry planning at the National level. A new two-step system for predicting ingrowth compatible with NFI data is presented in order to improve long-term estimation of stand condition. In growth and yield models, an ingrowth submodel is a key feature for long-term estimation. An accurate projection of ingrowth is needed to avoid model projection bias and inaccuracy. A two-step approach was used, which consisted of (I) estimating the probability of ingrowth occurrence on a sample plot and (II) quantifying the ingrowth in terms of basal area. Logistic regression was used for step I, while linear regression was used for step II. A good performance of the joint ingrowth model for Scots pine (Pinus sylvestris L.) and Mediterranean Maritime pine (Pinus pinaster Ait. Ssp mesogeensis) stands was observed. Logistic model include quadratic mean diameter as independent variables for both species while basal area is only included for Mediterranean Maritime. Quadratic mean diameter is the only independent variable in linear model for both species. The presented two-step modeling methodology for ingrowth is applicable to data from National Forest Inventories with concentric plots.Key words: concentric plot, Upgrowth, Recruitment, National Forest Inventory. ResumenModelo de masa incorporada en pinares mediterráneos: Un caso de estudio en rodales de pino silvestre (Pinus sylvestris L.) y pino negral (Pinus pinister Ait.) en España Los Inventarios Forestales Nacionales (IFN) son un instrumento básico para la planificación forestal a nivel nacional. Con el objeto de predecir la masa incorporada, se presenta un nuevo sistema bietápico compatible con los datos del IFN para de esta forma mejorar las estimaciones a largo plazo. En los modelos de crecimiento y producción, un modelo de masa incorporada es clave para la proyección a largo plazo dado que se precisa una adecuada estimación de la masa incorporada para evitar sesgos e imprecisiones. Se utilizó un método bietápico basado en (I) estimar la probabilidad de presencia de masa incorporada en la parcela y (II) cuantificar la masa incorporada en área basimétrica. Para el paso I se utilizó la regresión logística mientras que para el paso II se utilizó regresión lineal. Se observó un buen comportamiento del modelo conjunto tanto para pino silvestre (Pinus sylvestris L.) como para pino negral (Pinus pinaster Ait. Ssp mesogeensis). El modelo logístico incluye el diámetro medio cuadrático como variable independiente para ambas especies mientras que el área basimétrica solo es significativa en el caso del pino negral. En el modelo lineal, el diámetro medio cuadrático es significativo para las dos especies. El método bietápico presentado para estimar la masa incorporada es aplicable a los datos de Inventarios Forestales Nacionales basados en parcelas concéntricas.

Mediterranean Pine Forests: Management Effects on Carbon Stocks

“…A two step regression approach (Woollons, 1998;Alvarez-González et al, 2004;Bravo et al, 2008) was used to model the presence of CWD in pine plantations. In the first step, a logistic model was fitted to predict the probability of CWD presence, and in the second step, linear models were used to predict the basal area of snags and the volume of logs.…”

Modelling coarse woody debris in Pinus spp. plantations. A case study in Northern Spain

“…Periodically, dead and decayed trees have been harvested, maintaining only an average of from 1 to 10 snags per 5 hectares, in accordance with the forestry management guidelines in the Castilla y Leon region (Northern Spain). To model different binary events in forest research, a logistic equation has been used alone (Bravo-Oviedo et al, 2006) or combined with a linear model in a two-step approach (Álvarez et al, 2004;Bravo et al, 2008). The equations obtained in this study allowed us to model CWD presence and quantify snags and logs in Mediterranean Pinus spp.…”

“…Sci. 67 (2010) by Woollons (1998), has been used successfully in cases such as the probability of mortality or survival (Bravo and Montero, 2001;Woollons, 1998), or ingrowth (Bravo et al, 2008). This method is similar to one frequently applied in Decision Theory (Hamilton and Brickell, 1983).…”

Two-stage ingrowth models for four major tree species in Alberta

“…Various stand-level variables were evaluated as potential predictors for models 5 and 6. Stand density (number of stems per ha), stand basal area and mean stand diameter (arithmetic or quadratic mean diameter) were considered the most important predictors in ingrowth modelling (Hann 1980;Vanclay 1989;Qin 1998;Muhairwe 2003;Lexerod 2005;Lexerød and Eid 2005;Bravo et al 2008), but tree species composition and inter-tree competition may also be significant (Shifley et al 1993;Qin 1998;Trasobares et al 2004;Lexerod 2005). Other predictors used for ingrowth modelling included site quality (Hann 1980;Vanclay 1989;Qin 1998;Lexerod 2005) and stand density index (Muhairwe 2003).…”

“…Ingrowth can be quantified as the number, basal area or volume of trees or seedlings that reach or exceed a specific measurement threshold over a certain period of time (Beers 1962;Lexerød and Eid 2005;Bravo et al 2008). It is the link between the regeneration of a tree species and the creation of a mature stand.…”