Diversity of beetle species and functional traits along gradients of deadwood suggests weak environmental filtering

Background: Gradients in local environmental characteristics may favour the abundance of species with particular traits, while other species decline, or favour species with different traits at the same time, without an increase in average species abundances. Therefore, we asked: do variations in species and traits differ along gradients of deadwood variables? Do species abundance and trait occurrence change with species richness within or between functional groups? Thus, we analysed the beetle assemblages of five forest sites located in Italy, along the Apennines mountains.Methods: From 2012 to 2018 we sampled beetles and five deadwood types in 193 plots to characterise the deadwood gradient: standing dead trees, snags, dead downed trees, coarse woody debris, and stumps. We modelled beetle species relative abundances and trophic traits occurrences against the deadwood variables using joint species distribution models.Results: Out of 462 species, only 77 showed significant responses to at least one deadwood type, with a weak mean response across species. Trophic groups showed mostly negative responses to deadwood variables. Species abundance increased with species richness among sites only for phytophagous and saproxylophagous. Trait occurrence did not increase with species richness among sites, except for phytophagous and saproxylophagous.However, trait occurrence changed significantly with species richness of several trophic groups within some sites.We found that increases in species richness do not result in decreases in species abundance of a given trophic group, but rather null or positive relationships were found suggesting low interspecific competition.Conclusions: Our findings suggest that in Mediterranean mountain forests there is still room for increasing the level of naturalness, at least for what concerns deadwood management. On one side, our findings suggest that competition for deadwood substrates is still low, on the other side they indicate that increasing deadwood volume and types to improve overall beetle richness may increase also beetle abundances.

Nonlinear versus linearised model on stand density model fitting and stand density index calculation: analysis of coefficients estimation via simulation

The stand density index,one of the most important metrics for managing site occupancy,is generally calculated from empirical data by means of a coefficient derived from the“self-thinning rule”or stand density model.I undertook an exploratory analysis of model fitting based on simulated data.I discuss the use of the logarithmic transformation(i.e.,linearisation)of the relationship between the total number of trees per hectare(N)and the quadratic mean diameter of the stand(QMD).I compare the classic method used by Reineke(J Agric Res 46:627–638,1933),i.e.,linear OLS model fitting after logarithmic transformation of data,with the“pure”powerlaw model,which represents the native mathematical structure of this relationship.I evaluated the results according to the correlation between N and QMD.Linear OLS and nonlinear fitting agreed in the estimation of coefficients only for highly correlated(between-1 and-0.85)or poorly correlated([-0.39)datasets.At average correlation values(i.e.,between-0.75 and-0.4),it is probable that for practical applications,the differences were relevant,especially concerning the key coefficient for Reineke’s stand density index calculation.This introduced a non-negligible bias in SDI calculation.The linearised log–log model always estimated a lower slope term than did the exponent of the nonlinear function except at the extremes of the correlation range.While the logarithmic transformation is mathematically correct and always equivalent to a nonlinear model in case of prediction of the dependent variable,the difference detected in my studies between the two methods(i.e.,coefficient estimation)was directly related to the correlation between N and QMD in each simulated/disturbed dataset.In general,given the power law as the“natural”structure of the N versus QMD relationship,the nonlinear model is preferred,with a logarithmic transformation used only in the case of violation of parametric assumptions(e.g.data distributed non-normally).