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  • Author or Editor: G. Bacaro x
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There are many different metrics for expressing the dissimilarity between two samples or plots. The large majority of these dissimilarity measures attempt to summarize different aspects of plot-to-plot dissimilarity based either on species presences and absences within plots or on species abundances. Here, we propose a new parametric measure of plot-to-plot functional dissimilarity that incorporates information about the degree of functional dissimilarity between species. This measure is obtained from the combination of Hurlbert’s ‘expected species diversity’ with one dissimilarity coefficient of the ‘Bray-Curtis family’ and calculates the expected functional dissimilarity between the species in plot A and their functional nearest neighbors in plot B if m individuals are chosen randomly with replacement from each of the two plots. Due to its parametric nature, the proposed measure has a different sensitivity to the presence of rare and abundant species within plots as a function of the selected parameter value.

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Plant communities are generally spatially structured. Therefore, in order to enhance the interpretation of distance-dependent community patterns, spatially explicit measures of β-diversity are needed that, besides simple species turnover, are able to account for the rate at which biological similarity decays with increasing distance. We show that a multivariate semivariogram computed from species presence and absence data can be considered as a space-dependent alternative to existing definitions of β-diversity. To illustrate how the proposed method works, we used a classical data set from a second-growth piedmont hardwood forest.

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Ponds contribute substantially to the maintenance of regional biodiversity. Despite a growing body of literature on biotic-abiotic relationships in ponds, only few generalizations have been made. The difficulty in identifying the main drivers of pond biodiversity has been typically attributed to the heterogeneity of the local and regional conditions characterizing ponds. However, little is known on how the use of different analytical approaches and community response variables affects the results of analysis of community patterns in ponds. Here, we used a range of methods to model the response of water beetle and plant community data (species richness and composition) to a set of 12 environmental and management variables in 45 farmland ponds. The strength of biotic-abiotic relationships and the contribution of each variable to the overall explained variance in the reduced models varied substantially, for both plants and beetles, depending on the method used to analyze the data. Models of species richness included a lower number of variables and explained a larger amount of variation compared to models of species composition, reflecting the higher complexity characterizing multispecies response matrices. Only two variables were never selected by any of the model, indicative of the heterogeneity characterizing pond ecosystems, while some models failed to select important variables. Based on our findings, we recommend the use of multiple modeling approaches when attempting to identify the principal determinants of biodiversity for each response variable, including at least a non-parametric approach, as well as the use of both species richness and composition as the response variables. The results of this modeling exercise are discussed in relation to their practical use in the formulation of conservation strategies.

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Litter mass represents a key factor in the process of carbon sequestration. Pine plantations are known to accumulate high amounts of litter, which may act as real carbon sink only if it persists for long time. Thus, predicting litter mass by means of robust and straightforward models which convey information from several ecological predictors become crucial in this framework. The aim of this paper was to test for relationships between environmental predictors and pine litter mass (total branch, needle and cone) by Generalized Linear Models, exploring the contribution of different environmental variables in describing patterns of pine litter mass. Different predictors accounting for seasonality, spatial and geomorphological variability, pine stand properties, remotely sensed derived biomass were taken into account. Considering total litter mass, observed vs. predicted values showed a statistically highly significant relation (p < 0.001) by retaining four variables: elevation, latitude, stand age and season. Similar results were achieved for the needle litter mass, which represented anyway the largest fraction of the litter. Regarding branch litter mass, only stand age appeared to be a significant variable. For cone litter mass, no variables were statistically significant in explaining its variance. Potential ecological background processes responsible for the correlations between variables are discussed.

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A multi-stage cluster sampling is proposed for quantifying and monitoring plant species richness at multiple spatial grains over large spatial extents. An unbiased estimator of average species richness at different grains and a conservative estimator of its sampling variance are obtained in a complete design-based framework, i.e., avoiding any assumption about the ecological community under study. An application to the Nature Reserve “ Lago di Montepulciano ” demonstrates that the proposed strategy may accomplish practical advantages and quite satisfactory levels of accuracy.

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Rarefaction has long represented a powerful tool for detecting species richness and its variation across spatial scales. Some authors recently reintroduced the mathematical expression for calculating sample-based rarefaction curves. While some of them did not claim any advances, others presented this formula as a new analytical solution. We provide evidence about formulations of the sample-based rarefaction formula older than those recently proposed in ecological literature.

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Even if the establishment of nature reserves is to date a reality and the increase of protected areas is going to grow year after year, monitoring programs aiming to assess the effectiveness of the established protected areas for biodiversity conservation are still needed. That is the case for the Natura 2000 network in Europe, for which monitoring methods and programs are not yet well-established. A probabilistic sampling procedure is proposed and tested for quantifying and monitoring plant species diversity within a local network of protected areas, namely the Natura 2000 network in the Siena Province, Italy. On the basis of a sampling strategy of one 100 m 2 plot randomly located in each 1 km × 1 km cell, four Sites of Community Importance (SCIs) were investigated in 2005. The gradients in species composition at the plot scale were largely related to elevation and forest cover. The species richness values of the four SCIs were compared by means of sample-based rarefaction curves. Then, additive partitioning of species richness was applied to determine the most important spatial components in determining the total species richness of the network. Compositional differences among the plots within each SCI were the most responsible of the total species richness. These methodologies can be adopted for assessing plant species richness within a large region or within a reserve network and, if combined with additive partitioning, they can be used as a set of large scale indicators of species diversity.

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In Europe, epiphytic lichens are incorporated in forest diversity monitoring projects in which sampling at the tree level is carried out on 4 grids on the 4 cardinal points (N, S, E, W) of the trunk. Our results, based on the analysis of a dataset referring to six forest sites in NE-Italy and including 264 trees, indicate that a lichen assessment based on sampling at the tree level less than four cardinal points might be effective in estimating species richness across different forest types, showing very high rates of species capture. Similar results were achieved if the reduction of sampling effort is applied to the number of trees sampled within each area. This effect can be explained taking into account the redundant information collected on the same tree. In the framework of forest monitoring programs, the main perspective of our results is related to the possibility of investing saved resources for improving lichen inventories by including in the surveys currently neglected microhabitats. Further studies would be welcome to identify an optimal balance between sampling effort and information gathered, as economic resources are often a constraint to activate and maintain large-scale and long-term monitoring projects.

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The land mollusc faunas of three forest areas of Tuscany (central Italy) were sampled to test the effect of geographical and environmental factors on the structure of biodiversity. A total of 60 sites were surveyed in the years 2009-2011, recording species richness and abundance of snails in 400 m2 plots randomly selected in beech and oak woods. Sampling strategy relied on a combination of visual search and litter analysis. Environmental variables (topsoil pH and altitude) and UTM coordinates were recorded to detect relationships with species richness and number of individuals per plot. Abundance data were analyzed using non-metric multidimensional scaling and canonical correspondence analysis; faunal similarity within and between areas was computed by the Bray Curtis index and snail assemblages of the two forest types were compared. A total of 55 species were recorded, with low values of local richness and abundance per site compared to other forest sites in central and northern Europe. Total richness was similar in the three areas, but composition and local richness varied significantly between them. Geographical factors explained the highest percentage of variance, while habitat type, altitude and pH only accounted for a minor part. Internal similarity was greater than between-area similarity in two out of three areas. Beech forests had richer and more heterogeneous faunas, but lower levels of abundance than oak woods. The results are discussed in terms of historical biogeography and local environmental conditions, and compared with those from similar surveys across Europe.

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Community Ecology
Authors: S. Ongaro, S. Martellos, G. Bacaro, A. De Agostini, A. Cogoni and P. Cortis

The Mediterranean is one of the major biodiversity hotspots of the world. It has been identified as the “core” of the speciation process for many groups of organisms. It hosts an impressive number of species, many of which are classified as endangered taxa. Climate change in such a diverse context could heavily influence community composition, reducing ecosystems resistance and resilience. This study aims at depicting the distribution of nine orchid species in the island of Sardinia (Italy), and at forecasting their future distribution in consequence of climate change. The models were produced by following an “ensemble” approach. We analysed present and future (2070) niche for the nine species, using Land Use and Soil Type, as well as 8 bioclimatic variables as predictors, selected because of their influence on the fitness of these orchids. Climate change in the next years, at Mediterranean latitudes, is predicted to results mainly in an increase of temperature and a decrease of precipitation. In 2070, the general trend for almost all modelled taxa is the widening of the suitable areas. However, not always the newly gained areas have high probability of presence. A correct interpretation of environmental changes is needed for developing effective conservation strategies.

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