Apoptosis of neurons in rats with experimental allergic encephalomyelitis

NPP2, also known as phosphodiesterase-I alpha/autotaxin, is a type-II membrane protein that belongs to the nucleotide pyrophosphatase/phosphodiesterase family (NPP). We have recently demonstrated that NPP2 is expressed and released by differentiating oligodendrocytes during the critical stages of CNS myelination. The structural domains of this secreted macromolecule suggest a functional role in the regulation of oligodendrocyte adhesion. Here, we present data that demonstrates that NPP2 interferes with the ability of oligodendroglial cells to adhere to known CNS adhesion molecules present during the onset of myelination, such as fibronection, vitronectin, and merosin (laminin2). Responses to NPP2 appear to be regulated by a different mechanism depending on the developmental stage of the oligodendrocyte. Although the exact mechanisms for NPP2 mediated counter-adhesion are unknown, our studies have implicated that an active signalling mechanism involving heterotrimeric G proteins is responsible for adhesion modulation. These studies clearly define a role of NPP2 as a matricellular protein modulating oligodendrocyte adhesion and suggest that NPP2 function may represent the first step of oligodendrocyte remodelling when differentiating oligodendrocytes are actively involved in the formation of the myelin sheath.     

Impact of alien species on dune systems: a multifaceted approach

We applied a multifaceted approach, in terms of taxonomic, phylogenetic and functional diversity, to study at fine scale how three plant communities occurring in a Mediterranean dune have been affected by the encroachment of alien species. We sampled 81 sites in a Site of Community Importance in Central Italy. Past and present land use/cover data have been derived using GIS and remote sensing tools. Information on plants phylogenesis and functional traits has been gathered from several databases. Ecological variables have been collected. GLMs in conjunction with an Information Based approach were used to model species composition, richness and phylogenetic diversity. Multivariate analysis has been used to study functional diversity. The results outlined how total species richness is related to recent land transformations and to a set of environmental factors. The analyses of functional and phylogenetic diversity support the idea that alien species significantly affect the functional and phylogenetic characteristics of the native plant communities. Habitat filtering seems to be predominant in not-invaded plots, whereas limiting similarity/niche differentiation is predominant in driving community assembly of invaded communities. The attained scenario depicts the spread of a reduced group of alien species phylogenetically and functionally well-differentiated, able to reduce the abundance of native species, not to exclude them though. Ultimately, the multifaceted approach assisted in understanding the community assembly of dune vegetation, and to discern the relative impact of alien species on native plant communities. Such approach represents a crucial step to achieve an efficient management of dune habitats, as useful tool to monitor and to effectively protect their biodiversity and functioning.     

Sampling strategy matters to accurately estimate response curves' parameters in species distribution models

Aim

Assessing how different sampling strategies affect the accuracy and precision of species response curves estimated by parametric species distribution models.

Major Taxa Studied

Virtual plant species.

Location

Abruzzo (Italy).

Time Period

Timeless (simulated data).

Methods

We simulated the occurrence of two virtual species with different ecology (generalist vs specialist) and distribution extent. We sampled their occurrence following different sampling strategies: random, stratified, systematic, topographic, uniform within the environmental space (hereafter, uniform) and close to roads. For each sampling design and species, we ran 500 simulations at increasing sampling efforts (total: 42,000 replicates). For each replicate, we fitted a binomial generalised linear model, extracted model coefficients for precipitation and temperature, and compared them with true coefficients from the known species' equation. We evaluated the quality of the estimated response curves by computing bias, variance and root mean squared error (RMSE). Additionally, we (i) assessed the impact of missing covariates on the performance of the sampling approaches and (ii) evaluated the effect of incompletely sampling the environmental space on the uniform approach.

Results

For the generalist species, we found the lowest RMSE when uniformly sampling the environmental space, while sampling occurrence data close to roads provided the worst performance. For the specialist species, all sampling designs showed comparable outcomes. Excluding important predictors similarly affected all sampling strategies. Sampling limited portions of the environmental space reduced the performance of the uniform approach, regardless of the portion surveyed.

Main Conclusions

Our results suggest that a proper estimate of the species response curve can be obtained when the choice of the sampling strategy is guided by the species' ecology. Overall, uniformly sampling the environmental space seems more efficient for species with wide environmental tolerances. The advantage of seeking the most appropriate sampling strategy vanishes when modelling species with narrow realised niches.     

Using satellite imagery to assess plant species richness: The role of multispectral systems

Question: The use of variations in the spectral responses of remotely sensed images was recently proposed as an indicator of plant species richness (Spectral Variation Hypothesis, SVH). In this paper we addressed the issue of the potential use of multispectral sensors by testing the hypothesis that only some of the bands recorded in a remotely sensed image contain information related to the variation in species richness. Location: Montepulciano Lake, central Italy. Methods: We assessed how data compression techniques, such as Principal Component Analysis (PCA), influence the relationship between spectral heterogeneity and species richness and evaluated which spectral interval is the most adequate for predicting species richness by means of linear regression analysis. Results: The original multispectral data set and the first two non-standardized principal components can both be used as predictors of plant species richness (R²∼ 0.48; p < 0.001), confirming that PCA is an effective tool for compressing multi-spectral data without loss of information. Using single spectral bands, the near infrared band explained 41% of variance in species richness (p < 0.01), while the visible wavelengths had much lower prediction powers. Conclusions: The potential of satellite data for estimating species richness is likely to be due to the near infrared bands, rather than to the visible bands, which share highly redundant information. Since optimal band selection for image processing is a crucial task and it will assume increasing importance with the growing availability of hyperspectral data, in this paper we suggest a ‘near infrared way’for assessing species richness directly from remotely sensed data.     

Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward

Ecosystem structure, especially vertical vegetation structure, is one of the six essential biodiversity variable classes and is an important aspect of habitat heterogeneity, affecting species distributions and diversity by providing shelter, foraging, and nesting sites. Point clouds from airborne laser scanning (ALS) can be used to derive such detailed information on vegetation structure. However, public agencies usually only provide digital elevation models, which do not provide information on vertical vegetation structure. Calculating vertical structure variables from ALS point clouds requires extensive data processing and remote sensing skills that most ecologists do not have. However, such information on vegetation structure is extremely valuable for many analyses of habitat use and species distribution. We here propose 10 variables that should be easily accessible to researchers and stakeholders through national data portals. In addition, we argue for a consistent selection of variables and their systematic testing, which would allow for continuous improvement of such a list to keep it up-to-date with the latest evidence. This initiative is particularly needed not only to advance ecological and biodiversity research by providing valuable open datasets but also to guide potential users in the face of increasing availability of global vegetation structure products.     

Fuzzy species distribution models: a way to represent plant communities spatially

Fuzzy set theory has generally been applied to smooth classification cut‐offs, with an unavoidable loss of information. In this commentary, I rely on both advantages and disadvantages of the methods proposed in Duff et al., in this issue of the Journal of Vegetation Science, to map the variability over space of vegetation classes based on fuzzy sets and species distribution models.     

Seeing the unseen by remote sensing: satellite imagery applied to species distribution modelling

Remotely‐sensed proxies have been acknowledged as powerful tools for estimating species’ spatial distributions, whatever the taxonomic group being considered. Jiang et al. (2013) provide a robust example of seeing the unseen by remote sensing, predicting the distribution of epiphyllous liverworts from SPOT Vegetation remotely‐sensed data.