José Antonio Bonet

Empirical models for predicting the production of wild mushrooms in Scots pine (Pinus sylvestris L.) forests in the Central Pyrenees

Mushroom picking has become a widespread autumn recreational activity in the Central Pyrenees and other regions of Spain. Predictive models that relate mushroom production or fungal species richness with forest stand and site characteristics are not available. This study used mushroom production data from 24 Scots pine plots over 3 years to develop a predictive model that could facilitate forest management decisions when comparing silvicultural options in terms of mushroom production. Mixed modelling was used to model the dependence of mushroom production on stand and site factors. The results showed that productions were greatest when stand basal area was approximately 20 m2 ha−1. Increasing elevation and northern aspect increased total mushroom production as well as the production of edible and marketed mushrooms. Increasing slope decreased productions. Marketed Lactarius spp., the most important group collected in the region, showed similar relationships. The annual variation in mushroom production correlated with autumn rainfall. Mushroom species richness was highest when the total production was highest.RésuméLa cueillette de champignons est devenue une activité de loisir très répandue dans les Pyrénées centrales ainsi que dans d’autres régions d’Espagne. Aucun modèle prédictif de production ou de richesse en espèces en fonction des caractéristiques des peuplements et des stations n’est disponible actuellement. La présente étude s’est basée sur des données de récolte de champignons de 24 placettes de pin sylvestre suivies pendant 3 ans pour développer un modèle prédictif pouvant servir de modèle d’aide à la décision pour des opérations de gestion forestière. Un modèle mixte a été mis en œuvre pour analyser les relations entre facteurs stationnels et de peuplement. Les résultats montrent que la production était maximale quand la surface terrière était de l’ordre de 20 m2 ha−1. La production totale ainsi que celle de champignons comestibles et commercialisables augmentaient avec l’altitude et dans les pentes orientées au nord. Le groupe des Lactaires (Lactarius spp.) le plus important champignon commercialisable de la région, présentait des réponses similaires. La variabilité interannuelle de production était étroitement corrélée à celle des pluies automnales. La richesse en espèce était étroitement corrélée à la production totale.

Mushroom Production as an Alternative for Rural Development in a Forested Mountainous Area

Wild mushrooms are recognized as important non-wood forest products in mountainous ecosystems, but their real potential for generating rural economies has not been fully evaluated due to the difficulties in obtaining reliable productivity data, minimizing their true potential as contributor to rural economies. Mushroom yield models based on large data series from Pinus forest ecosystems in the region of Catalonia (Spain), combined with data from the Spanish National Forest Inventory allow us to estimate the potential mushroom productivity by forest ecosystems. The results of 24,500 tons/yr of mushrooms of which 16,300 tons are classified as edible and 7,900 tons are commonly marketed demonstrate the importance of mushroom productions in Catalonian pine forests, mostly located in mountainous areas where the development of agricultural activities is limited. Economic mushroom value is estimated at 48 million € for the edible mushroom and 32 million € for those corresponding to marketable yields, confirming the potential of this non-wood forest product. These production results and corresponding economic values provide a basis for the incorporation of wild mushrooms as significant non-wood forest products in the development of forest policies in mountainous areas.

Even-aged or uneven-aged modelling approach? A case for Pinus brutia

ContextThe past management of Pinus brutia forests in Lebanon has led to diverse stand structures that cannot be easily classified as even-aged (EA) or uneven-aged (UA). Most stands are between these stand types, and they may be called as “semi-even-aged”. This is a very common characteristic throughout the Mediterranean conifer forests and makes the choice between the EA and UA approaches problematic, in both management and modelling. However, previous research has devoted little attention to the performance of growth and yield models when applied to transitional stand structures.AimsThe aim of this study was to find the best modelling approach and to recommend equations for simulating the dynamics of the semi-even-aged P. brutia stands of Lebanon on an individual-tree basis.MethodsFifty sample plots were measured in Lebanon. Individual-tree growth models were fitted to the whole dataset using either UA or EA modelling approach. Models were also fitted using two sub-samples containing the most EA and the most UA plots. The performance and accuracy of the two modelling approaches were evaluated in all three datasets.ResultsThe article provides the first complete growth model for uneven-aged P. brutia stands. The EA sub-models presented better statistical fitting. However, the UA sub-models enabled more accurate predictions of wood production and were almost as good as the EA sub-models when predicting stand dynamics of the EA plots. The EA approach provided poor predictions, and the errors were high when it was applied to UA stands.ConclusionsIn structurally complex stands, the UA modelling approach is to be preferred since it predicts the whole stand dynamics more accurately and enables simulations of a broader range of silvicultural treatments.

Mushroom biomass and diversity are driven by different spatio-temporal scales along Mediterranean elevation gradients

Mushrooms are important non-wood-forest-products in many Mediterranean ecosystems, being highly vulnerable to climate change. However, the ecological scales of variation of mushroom productivity and diversity, and climate dependence has been usually overlooked due to a lack of available data. We determined the spatio-temporal variability of epigeous sporocarps and the climatic factors driving their fruiting to plan future sustainable management of wild mushrooms production. We collected fruiting bodies in Pinus sylvestris stands along an elevation gradient for 8 consecutive years. Overall, sporocarp biomass was mainly dependent on inter-annual variations, whereas richness was more spatial-scale dependent. Elevation was not significant, but there were clear elevational differences in biomass and richness patterns between ectomycorrhizal and saprotrophic guilds. The main driver of variation was late-summer-early-autumn precipitation. Thus, different scale processes (inter-annual vs. spatial-scale) drive sporocarp biomass and diversity patterns; temporal effects for biomass and ectomycorrhizal fungi vs. spatial scale for diversity and saprotrophic fungi. The significant role of precipitation across fungal guilds and spatio-temporal scales indicates that it is a limiting resource controlling sporocarp production and diversity in Mediterranean regions. The high spatial and temporal variability of mushrooms emphasize the need for long-term datasets of multiple spatial points to effectively characterize fungal fruiting patterns.

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

The implementation of the Ecosystem Services (ES) framework (including supply and demand) should be based on accurate spatial assessments to make it useful for land planning or environmental management. Despite the inherent dependence of ES assessments on the spatial resolution at which they are conducted, the studies analyzing these effects on ES supply and their relationships are still scarce. To study the influence of the spatial level of analysis on ES patterns and on the relationships among different ES, we selected seven indicators representing ES supply and three variables that describe forest cover and biodiversity for Catalonia (NE Iberian Peninsula). These indicators were estimated at three different scales: local, municipality and county. Our results showed differences in the ES patterns among the levels of analysis. The higher levels (municipality/county) removed part of the local heterogeneity of the patterns observed at the local scale, particularly for ES indicators characterized by a finely grained, scattered distribution. The relationships between ES indicators were generally similar at the three levels. However, some negative relationships (potential trade-offs) that were detected at the local level changed to positive (and significant) relationships at municipality and county. Spatial autocorrelation showed similarities between patterns at local and municipality levels, but differences with county level. We conclude that the use of high-resolution spatial data is preferable whenever available, in particular when identifying hotspots or trade-offs/synergies is of primary interest. When the main objective is describing broad patterns of ES, intermediate levels (e.g., municipality) are also adequate, as they conserve many of the properties of assessments conducted at finer scales, allowing the integration of data sources and, usually, being more directly relevant for policy-making. In conclusion, our results warn against the uncritical use of coarse (aggregated) spatial ES data and indicators in strategies for land use planning and forest conservation.

Mushroom Emergence Detected by Combining Spore Trapping with Molecular Techniques

ABSTRACT Obtaining reliable and representative mushroom production data requires time-consuming sampling schemes. In this paper, we assessed a simple methodology to detect mushroom emergence by trapping the fungal spores of the fruiting body community in plots where mushroom production was determined weekly. We compared the performance of filter paper traps with that of funnel traps and combined these spore trapping methods with species-specific quantitative real-time PCR and Illumina MiSeq to determine the spore abundance. Significantly more MiSeq proportional reads were generated for both ectomycorrhizal and saprotrophic fungal species using filter traps than were obtained using funnel traps. The spores of 37 fungal species that produced fruiting bodies in the study plots were identified. Spore community composition changed considerably over time due to the emergence of ephemeral fruiting bodies and rapid spore deposition (lasting from 1 to 2 weeks), which occurred in the absence of rainfall events. For many species, the emergence of epigeous fruiting bodies was followed by a peak in the relative abundance of their airborne spores. There were significant positive relationships between fruiting body yields and spore abundance in time for five of seven fungal species. There was no relationship between fruiting body yields and their spore abundance at plot level, indicating that some of the spores captured in each plot were arriving from the surrounding areas. Differences in fungal detection capacity by spore trapping may indicate different dispersal ability between fungal species. Further research can help to identify the spore rain patterns for most common fungal species. IMPORTANCE Mushroom monitoring represents a serious challenge in economic and logistical terms because sampling approaches demand extensive field work at both the spatial and temporal scales. In addition, the identification of fungal taxa depends on the expertise of experienced fungal taxonomists. Similarly, the study of fungal dispersal has been constrained by technological limitations, especially because the morphological identification of spores is a challenging and time-consuming task. Here, we demonstrate that spores from ectomycorrhizal and saprotrophic fungal species can be identified using simple spore traps together with either MiSeq fungus-specific amplicon sequencing or species-specific quantitative real-time PCR. In addition, the proposed methodology can be used to characterize the airborne fungal community and to detect mushroom emergence in forest ecosystems.