Yield models for predicting aboveground ectomycorrhizal fungal productivity in Pinus sylvestris and Pinus pinaster stands of northern Spain

Sánchez-González, Mariola; de-Miguel, Sergio; Martín‐Pinto, Pablo; Martínez‐Peña, Fernando; Pasalodos-Tato, María; Rueda, Juan; Aragón, Juán Martínez de; Cañellas, Isabel; Bonet, J. A.

doi:10.1186/s40663-019-0211-1

Cited by 18 publications

(8 citation statements)

References 42 publications

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“…This particular method is commonly utilized as the best way to estimate variance parameters since it is unbiased and better suited to unbalanced survey data. [15][16][17][18] Sites and plots were specified as fixed factors whereas the ecotrophic groups were considered as random factors and their effects were calculated through the covariate model. The response variables were (a) species incidence for each taxon and (b) yield of sporophores for each taxon.…”

Section: Statistical Analysesmentioning

confidence: 99%

“…Where REML estimate showed significant effects of forest management in sacred and control plots or significant interactions with the examined diversity levels, ANOVA contrasts (General ANOVA) were used in order to test whether differences among groups were statistically significant. 19,18 A simple paired t-test was also applied for the comparison of the totals (among the different ecotrophic groups) for the different forest management types (sacred or control). The facilities in Genstat v12 (VSN International Ltd, Hemel Hempsted-Hertfordshire, UK) were used for the statistical analysis.…”

Section: Statistical Analysesmentioning

confidence: 99%

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Fungal diversity in sacred groves vs. managed forests in Epirus, NW Greece

Diamandis¹,

Topalidou²,

Avtzis³

et al. 2021

JMEN

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Fungal diversity and yield based on sporophore production was assessed in sacred groves of Epirus NW Greece and was compared to nearby forests. Sacred groves are woodland surrounding old chapels or monasteries which have been left undisturbed and hold oldgrowth trees. In eight sites, plots were set in sacred groves and also in managed coppice forests (as controls). Sampling was conducted in 2013, 2014, 2015. The 208 fungal taxa recorded were classified as ectomycorrhizal (ECM), saprotrophic and xylotrophic. Sacred groves were found to hold greater species richness and higher yield but when fungal groups were compared individually, yield of ECM fungi was higher in control sites. On the basis of the current research it was found that: 1) Sacred groves hold more fungal diversity of xylotrophic and saprotrophic fungi. 2) ECM fungi are more productive in younger, managed forests. 3) Even with clearcut logging on a 30-yr rotation, fungal diversity remains high. These results should be considered when managing old-growth woods for fungal conservation.

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Section: Statistical Analysesmentioning

confidence: 99%

Section: Statistical Analysesmentioning

confidence: 99%

Fungal diversity in sacred groves vs. managed forests in Epirus, NW Greece

Diamandis¹,

Topalidou²,

Avtzis³

et al. 2021

JMEN

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“…The large number of variables involved and their presumed interactions may often yield a misconception that fungal productivity is highly stochastic or very difficult to predict. Previous research to estimate mushroom productivity over large scales has been mainly based on mixed-effects modeling (de-Miguel et al 2014;Sánchez-González et al 2019). Despite being a valid approach, it may have certain limitations that are worth assessing in comparison with alternative methods that remain unexplored.…”

Section: Introductionmentioning

confidence: 99%

Performance of statistical and machine learning-based methods for predicting biogeographical patterns of fungal productivity in forest ecosystems

et al. 2021

Self Cite

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Background The prediction of biogeographical patterns from a large number of driving factors with complex interactions, correlations and non-linear dependences require advanced analytical methods and modeling tools. This study compares different statistical and machine learning-based models for predicting fungal productivity biogeographical patterns as a case study for the thorough assessment of the performance of alternative modeling approaches to provide accurate and ecologically-consistent predictions. Methods We evaluated and compared the performance of two statistical modeling techniques, namely, generalized linear mixed models and geographically weighted regression, and four techniques based on different machine learning algorithms, namely, random forest, extreme gradient boosting, support vector machine and artificial neural network to predict fungal productivity. Model evaluation was conducted using a systematic methodology combining random, spatial and environmental blocking together with the assessment of the ecological consistency of spatially-explicit model predictions according to scientific knowledge. Results Fungal productivity predictions were sensitive to the modeling approach and the number of predictors used. Moreover, the importance assigned to different predictors varied between machine learning modeling approaches. Decision tree-based models increased prediction accuracy by more than 10% compared to other machine learning approaches, and by more than 20% compared to statistical models, and resulted in higher ecological consistence of the predicted biogeographical patterns of fungal productivity. Conclusions Decision tree-based models were the best approach for prediction both in sampling-like environments as well as in extrapolation beyond the spatial and climatic range of the modeling data. In this study, we show that proper variable selection is crucial to create robust models for extrapolation in biophysically differentiated areas. This allows for reducing the dimensions of the ecosystem space described by the predictors of the models, resulting in higher similarity between the modeling data and the environmental conditions over the whole study area. When dealing with spatial-temporal data in the analysis of biogeographical patterns, environmental blocking is postulated as a highly informative technique to be used in cross-validation to assess the prediction error over larger scales.

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“…However, we need to understand the role of root biomass production into soil pro le, and how it can contribute to soil quality. Thus, we hypothesized that (i) plant species with high root biomass production over a temporal scale may improve soil quality by promoting some physical and chemical properties as described by Laurindo et al (2021); and (ii) soil quality index will follow certain soil-plant patterns and, therefore, the variability among plots in relation to plant, soil physical, and soil chemical properties was also studied as proposed by Sánchez-González et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Predictive physico-chemical model for soil quality index in a long-term green manure farming system at tropical conditions, North-eastern Brazil

Nascimento¹,

Souza²,

Silva³

et al. 2021

Preprint

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Soil quality index shed light on soil health and its capacity to sustain high primary production. It also can assist decision-making in farming systems by integrating this valuable product into soil management planning. However, the currently existing models are based on rather local data, and thus, there is a lack of predictive tools to monitor soil quality on farming systems at tropical conditions. We characterized soil physico-chemical properties, plant biomass production under a 6-year experiment in a sandy soil from Tropical ecosystem, using ten treatments: Brachiaria decumbens, Canavalia ensiformis, Crotalaria juncea, Crotalaria ochroleuca, Crotalaria spectabilis, Lablab purpureus, Mucuna pruriens, Neonotonia wightii, Pennisetum glaucum, and Stilozobium aterrimum. We found that most of the soil physico-chemical properties were correlated with each other by Pearson’s correlation analysis. On the other hand, RDA illustrated that shoot dry biomass was related to soil C stock, K+, macro- and microporosity. Soil pH, Al3+, Ca2+, Mg2+, K+, Olsen’s P, Na+, soil C stock, bulk density, microporosity, macroporosity, and permanent wilting point were the main factors driving primary production in our long-term study. Our findings suggest that: 1) a consecutive green manure practice without any input of fertilizers after 6 years changed positively by increasing soil fertility (e.g., Ca2+, Mg2+, K+ and Olsen’s P), and improving plant growth and soil quality in tropical savanna climate conditions; and 2) the 33 multivariate predictive models may provide a deeper view about the benefits of using plant species as green manure by creating positive plant-soil feedback thus promoting soil quality.

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Yield models for predicting aboveground ectomycorrhizal fungal productivity in Pinus sylvestris and Pinus pinaster stands of northern Spain

Cited by 18 publications

References 42 publications

Fungal diversity in sacred groves vs. managed forests in Epirus, NW Greece

Fungal diversity in sacred groves vs. managed forests in Epirus, NW Greece

Performance of statistical and machine learning-based methods for predicting biogeographical patterns of fungal productivity in forest ecosystems

Predictive physico-chemical model for soil quality index in a long-term green manure farming system at tropical conditions, North-eastern Brazil

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