Spatial prediction of Lactarius deliciosus and Lactarius salmonicolor mushroom distribution with logistic regression models in the Kızılcasu Planning Unit, Turkey

Abstract

Integration of non-wood forest products (NWFPs) into forest management planning has become an increasingly important issue in forestry over the last decade. Among NWFPs, mushrooms are valued due to their medicinal, commercial, high nutritional and recreational importance. Commercial mushroom harvesting also provides important income to local dwellers and contributes to the economic value of regional forests. Sustainable management of these products at the regional scale requires information on their locations in diverse forest settings and the ability to predict and map their spatial distributions over the landscape. This study focuses on modeling the spatial distribution of commercially harvested Lactarius deliciosus and L. salmonicolor mushrooms in the Kızılcasu Forest Planning Unit, Turkey. The best models were developed based on topographic, climatic and stand characteristics, separately through logistic regression analysis using SPSS™. The best topographic model provided better classification success (69.3 %) than the best climatic (65.4 %) and stand (65 %) models. However, the overall best model, with 73 % overall classification success, used a mix of several variables. The best models were integrated into an Arc/Info GIS program to create spatial distribution maps of L. deliciosus and L. salmonicolor in the planning area. Our approach may be useful to predict the occurrence and distribution of other NWFPs and provide a valuable tool for designing silvicultural prescriptions and preparing multiple-use forest management plans.

Sustaining the Joint Production of Timber and Lactarius Mushroom: A Case Study of a Forest Management Planning Unit in Northwestern Turkey

“…The DSS includes a number of logistic models to predict spatial distribution of each kind of mushroom species. In this study, the following equation was used to predict the spatial distribution of related mushroom [14,41] (Equation (1)). In the mushroom integrated ETÇAP DSS, 0.5 was used as p threshold value; hence, a stand whose p threshold value is equal or greater than 0.5 was assumed to have mushroom otherwise no mushroom exists:…”

“…The prediction of spatial distribution of L. deliciosus and L. salmonicolor mushroom in each period was determined by logistic regression models [14] in the mushroom integrated ETÇAP DSS module. The DSS includes a number of logistic models to predict spatial distribution of each kind of mushroom species.…”

Lactarius deliciosus Fr. soil extraradical mycelium correlates with stand fruitbody productivity and is increased by forest thinning

“…Recent modeling efforts (Bonet et al, 2008(Bonet et al, , 2010(Bonet et al, , 2012Martínez-Peña et al, 2012b;Kucuker and Baskent, 2015;Tahvanainen et al, 2016) help us identify significant factors affecting mushroom yields. Despite the advances of the modeling process, high variability of mushroom yields, affected mainly by weather conditions (Martínez-Peña et al, 2012a, 2012b restricts the use of sporocarp yields as a good indicator for observing the effect of changes in fungal patterns occurring in the ecosystems.…”

The availability and supply of marketed mushrooms in Eastern Finland

“…The current forest planning systems are timber production oriented, but there are some decision support systems (DSSs) developed for NWFPs too (Pukkala 2002). So far, yield models are formulated, for example, for the production of berries (Miina et al 2009;Turtiainen et al 2013) and stone pine cones (Calama et al 2008), but also for mushrooms, such as wild mushrooms in pine stands in Spain (Bonet et al 2008;Bonet et al 2010), L. group deliciosus and B. edulis in pine stands in Spain (Martínez-Peña et al 2012), mycorrhizal mushrooms and B. edulis in Cistus ladanifer scrublands in Spain (Hernández-Rodríguez et al 2015) and L. deliciosus and L. salmonicolor in Turkey (Kucuker and Baskent 2015). The models can be included in the simulators to produce management schedules and information on the future yields and profitability of the co-production of timber and NWFPs.…”