The geographical and environmental determinants of genetic diversity for four alpine conifers of the European Alps

Climate is one of the most important drivers of local adaptation in forest tree species. Standing levels of genetic diversity and structure within and among natural populations of forest trees are determined by the interplay between climatic heterogeneity and the balance between selection and gene flow. To investigate this interplay, single nucleotide polymorphisms (SNPs) were genotyped in 24 to 37 populations from four subalpine conifers, Abies alba Mill., Larix decidua Mill., Pinus cembra L. and Pinus mugo Turra, across their natural ranges in the Italian Alps and Apennines. Patterns of population structure were apparent using a Bayesian clustering program, STRUCTURE, which identified three to five genetic groups per species. Geographical correlates with these patterns, however, were only apparent for P. cembra. Multivariate environmental variables [i.e. principal components (PCs)] were subsequently tested for association with SNPs using a Bayesian generalized linear mixed model. The majority of the SNPs, ranging from six in L. decidua to 18 in P. mugo, were associated with PC1, corresponding to winter precipitation and seasonal minimum temperature. In A. alba, four SNPs were associated with PC2, corresponding to the seasonal minimum temperature. Functional annotation of those genes with the orthologs in Arabidopsis revealed several genes involved in abiotic stress response. This study provides a detailed assessment of population structure and its association with environment and geography in four coniferous species in the Italian mountains.     

Multi-scale sampling and statistical linear estimators to assess land use status and change

Question: Multi-temporal analysis of remotely sensed imagery has proven to be a powerful tool for assessment and monitoring of landscape diversity. Here the feasibility of assessing land-use diversity and land-use change was tested at multiple scales and over time by means of statistical linear estimators based on a probabilistic sampling design. Location: The study area (the district of Asciano, Tuscany, Italy) is characterized by erosional forms typical of Pliocene claystone (i.e. calanchi and biancane) that have been subject to the phenomenon of biancane reworking over the past 50 years, mainly owing to the expansion of intensive agriculture. Methods: Cells at two different scales (50 m × 50 m and 10 m × 10 m) were classified by two operators according to a multilevel legend, using 1954 and 2000 aerial photographs. Inter-operator agreement and accuracy were tested by Cohen's K coefficient. Total land cover estimation for each class was carried out using a multistage estimator, while the variance was estimated by means of the Wolter estimator. Field-based information on plant species composition was recorded in order to test for a relationship between land use and plant community composition by anova and indicator species analysis. Results: Agreement between photointerpreters and accuracy were significantly higher than those expected by chance, proving that the approach proposed is reproducible, as long as proper quality assurance methods are used. Our data show that, at the two scales considered (50 m × 50 m and 10 m × 10 m), crops have increased against woodlands and semi-natural areas, the latter showing the highest and significantly different mean species richness. Meanwhile, an increase in the coverage of trees and shrubs was found within the semi-natural areas, probably as a result of secondary succession occurring on typical landscape elements such as biancane. Conclusions: Inferential statistics made it possible to acquire quantitative information on the abundance of land cover classes, allowing formal multi-temporal and multi-scale analysis. Sampling design-based statistical linear estimators were found to be a powerful tool for assessing landscape trends considering both time expenditure and other costs. They make it possible to maintain the same scale of analysis over time series data and to detect both coarse- and fine-grained changes in spatial patterns.     

High resolution satellite imagery for tropical biodiversity studies: the devil is in the detail

While high resolution satellite remote sensing has been hailed as a very useful source of data for biodiversity assessment and monitoring, applications have been more developed in temperate areas. The biodiverse tropics offer a challenge of an altogether different magnitude for hyperspatial and hyperspectral remote sensing. This paper examines issues related to hyperspatial and hyperspectral remotely sensed imagery, which constitutes one of the most potentially powerful yet underutilized sources of for tropical research on biodiversity. Hyperspatial data with their increased pixel resolution are possibly best suited at facilitating the accurate location of features such as tree canopies, but less suited to the identification of aspects such as species identity, particularly when spatial resolution becomes too fine and pixels are smaller than the size of the object (e.g., tree canopy) being identified. Hyperspectral data on the other hand, with their high spectral resolution, can be used to record information pertaining to a range of critical plant properties related to species identity, and can be very effective used for discriminating tree species in tropical forests, despite the greater complexity of such environments. There remains a glaring gap in the easy availability of hyperspectral and hyperspatial satellite data in the tropics due to reasons of cost, data coverage, and security restrictions. Stimulating discussion on the applications of this powerful, but underutilized tool by ecologists, is the first step in promoting a more extensive use of such data for ecological studies in tropical biodiversity rich areas.     

The effect of positional error on fine scale species distribution models increases for specialist species

Species occurrences inherently include positional error. Such error can be problematic for species distribution models (SDMs), especially those based on fine-resolution environmental data. It has been suggested that there could be a link between the influence of positional error and the width of the species ecological niche. Although positional errors in species occurrence data may imply serious limitations, especially for modelling species with narrow ecological niche, it has never been thoroughly explored. We used a virtual species approach to assess the effects of the positional error on fine-scale SDMs for species with environmental niches of different widths. We simulated three virtual species with varying niche breadth, from specialist to generalist. The true distribution of these virtual species was then altered by introducing different levels of positional error (from 5 to 500 m). We built generalized linear models and MaxEnt models using the distribution of the three virtual species (unaltered and altered) and a combination of environmental data at 5 m resolution. The models’ performance and niche overlap were compared to assess the effect of positional error with varying niche breadth in the geographical and environmental space. The positional error negatively impacted performance and niche overlap metrics. The amplitude of the influence of positional error depended on the species niche, with models for specialist species being more affected than those for generalist species. The positional error had the same effect on both modelling techniques. Finally, increasing sample size did not mitigate the negative influence of positional error. We showed that fine-scale SDMs are considerably affected by positional error, even when such error is low. Therefore, where new surveys are undertaken, we recommend paying attention to data collection techniques to minimize the positional error in occurrence data and thus to avoid its negative effect on SDMs, especially when studying specialist species.     

Determinants of plant community composition of remnant biancane badlands: a hierarchical approach to quantify species‐environment relationships

Question: Which environmental variables best explain patterns in the vegetation of biancane badlands? What is the role of spatial scales in structuring the vegetation of biancane badlands within the agricultural matrix? Location: Five biancane badlands in Central Italy (Tuscany). Methods: An object‐oriented approach on high‐resolution multispectral images was used to classify physiognomic vegetation types in five biancane badlands. Within each badland, data on vascular plant species abundance were collected using a stratified random design. Variation partitioning based on partial redundancy analysis was used to evaluate the contribution of three sets of environmental predictors, recorded at the spatial scales of plot, patch and biancane badland in explaining patterns in plant community composition. Results: Environmental variables included in the final model – electrical conductivity and carbon/nitrogen ratio (plot scale), shape index (patch scale) and area (biancane badland scale) – accounted for 15.5% of the total variation in plant community composition. Soil characteristics measured at the plot level explained the majority of variation. In the smallest badlands, Bromus erectus perennial grasslands were absent, while annual grasslands, linked with harsh soil conditions (i.e. high soil salinity), were not affected by either the surface area of biancane badlands or by the soil nitrogen availability. Conclusions: The identification of the major predictors of patterns in remnant vegetation requires conducting investigations at multiple spatial scale. Management strategies should operate at different spatial scale, preventing any further reduction in the size of existing badlands and relying on habitat‐ instead of area‐focused conservation practices.     

Benefits of hyperspectral remote sensing for tracking plant invasions

Aim We aim to report what hyperspectral remote sensing can offer for invasion ecologists and review recent progress made in plant invasion research using hyperspectral remote sensing. Location United States. Methods We review the utility of hyperspectral remote sensing for detecting, mapping and predicting the spatial spread of invasive species. We cover a range of topics including the trade‐off between spatial and spectral resolutions and classification accuracy, the benefits of using time series to incorporate phenology in mapping species distribution, the potential of biochemical and physiological properties in hyperspectral spectral reflectance for tracking ecosystem changes caused by invasions, and the capacity of hyperspectral data as a valuable input for quantitative models developed for assessing the future spread of invasive species. Results Hyperspectral remote sensing holds great promise for invasion research. Spectral information provided by hyperspectral sensors can detect invaders at the species level across a range of community and ecosystem types. Furthermore, hyperspectral data can be used to assess habitat suitability and model the future spread of invasive species, thus providing timely information for invasion risk analysis. Main conclusions Our review suggests that hyperspectral remote sensing can effectively provide a baseline of invasive species distributions for future monitoring and control efforts. Furthermore, information on the spatial distribution of invasive species can help land managers to make long‐term constructive conservation plans for protecting and maintaining natural ecosystems.     

Role of African protected areas in maintaining connectivity for large mammals

The African protected area (PA) network has the potential to act as a set of functionally interconnected patches that conserve meta-populations of mammal species, but individual PAs are vulnerable to habitat change which may disrupt connectivity and increase extinction risk. Individual PAs have different roles in maintaining connectivity, depending on their size and location. We measured their contribution to network connectivity (irreplaceability) for carnivores and ungulates and combined it with a measure of vulnerability based on a 30-year trend in remotely sensed vegetation cover (Normalized Difference Vegetation Index). Highly irreplaceable PAs occurred mainly in southern and eastern Africa. Vegetation cover change was generally faster outside than inside PAs and particularly so in southern Africa. The extent of change increased with the distance from PAs. About 5% of highly irreplaceable PAs experienced a faster vegetation cover loss than their surroundings, thus requiring particular conservation attention. Our analysis identified PAs at risk whose isolation would disrupt the connectivity of the PA network for large mammals. This is an example of how ecological spatial modelling can be combined with large-scale remote sensing data to investigate how land cover change may affect ecological processes and species conservation.