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Anticipating species distributions in space and time is necessary for effective biodiversity conservation and for prioritising management interventions. This is especially true when considering invasive species. In such a case, anticipating their spread is important to effectively plan management actions. However, considering uncertainty in the output of species distribution models is critical for correctly interpreting results and avoiding inappropriate decision-making. In particular, when dealing with species inventories, the bias resulting from sampling effort may lead to an over- or under-estimation of the local density of occurrences of a species. In this paper we propose an innovative method to i) map sampling effort bias using cartogram models and ii) explicitly consider such uncertainty in the modeling procedure under a Bayesian framework, which allows the integration of multilevel input data with prior information to improve the anticipation species distributions.

Networks of protected areas are fundamental for biodiversity conservation, but many factors determine their conservation efficiency. In particular, on top of other human-driven disturbances, invasions by non-native species can cause habitat and biodiversity loss. Jointly understanding what drives patterns of plant diversity and of non-native species in protected areas is therefore a priority. We tested whether the richness and composition of native and non-native plant species within a network of protected areas follow similar patterns across spatial scales. Specifically, we addressed three questions: (a) what is the degree of congruence in species richness between native and non-native species? (b) do changes in the composition of non-native species across ecological gradients reflect a similar turnover of native species along the same gradients ? (c) what are the main environmental and human disturbance drivers controlling species richness in these two groups of species? Species richness and composition of native and non-native plant species were compared at two spatial scales: the plot scale (10m × 10m) and the Protected Area scale (PA). In addition, we fit Generalized Linear Models to identify the most important drivers of native and non-native species richness at each scale, focusing on environmental conditions (climate, topography) and on the main sources of human disturbance in the area (land use and roads). We found a significant positive correlation between the turnover of native and non-native species composition at both plot and PA scales, whereas their species richness was only correlated at the larger PA scale. The lack of congruence between the richness of native and non-native species at the plot scale was likely driven by differential responses to fine scale environmental factors, with non-natives favoring drier climates and milder slopes (climate and slope). In addition, more non-native species were found closer to road-ways in the reserve network. In contrast, the congruence in the richness of native and non-native species at the broader PA scale was mainly driven by the common influence of PA area, but also by similar responses of the two groups of species to climatic heterogeneity. Thus, our study highlights the strong spatial dependence of the relationship between native and non-native species richness and of their responses to environmental variation. Taken together, our results suggest that within the study region the introduction and establishment of non-native species would be more likely in warmer and dryer areas, with high native species richness at large spatial scale but intermediate levels of anthropogenic disturbances and mild slope inclinations and elevation at fine scale. Such an exhaustive understanding of the factors that influence the spread of non-native species, especially in networks of protected areas is crucial to inform conservation managers on how to control or curb non-native species.

Changes in land use are among the forces shaping Earth’s surface. In many industrialized areas, the loss of a traditional state of dynamic equilibrium between traditional management and natural dynamics is followed by abandonment to regeneration processes. This can reduce ecological complexity at the landscape scale and negatively affect biodiversity patterns. In this study, we investigate the relation between land use change and plant species diversity in the network of protected areas (PAs) of the province of Siena (Tuscany, Central Italy). This is an area characterized by long-lasting human activities and highly renowned cultural landscapes. We used remotely sensed, mapping and ground based plant compositional data, to investigate the present pattern of plant species diversity, the changes of landscape structure and changes in forest habitats. Most of the plant diversity present in this network of PAs is due to broad scale gradients due to ecological diversity but also to human management. Most of the area is currently covered by forests and analysis of a historical sequence of spatial data reveals that this is largely a consequence of the abandonment of traditional management during the last decades. Finally, focusing on forest succession as a consequence of land use change, we demonstrate that species richness significantly declines with increasing age of forest stands. Taken together, our results confirm that the recent trends of rural abandonment are leading to homogenization and biodiversity loss in traditional landscapes of Mediterranean Europe. We discuss implications for policy, and suggest that PA management in cultural and historical landscapes should pay increasing attention traditional anthropic practices.

Land-cover change, a major driver of the distribution and functioning of ecosystems, is characterized by a high diversity of patterns of change across space and time. Thus, a large amount of information is necessary to analyse change and develop plans for proper management of natural resources. In this work we tested MaxEnt algorithm in a completely remote land-cover classification and change analysis. In order to provide an empirical example, we selected south-eastern Italian Alps, manly Trentino-South Tyrol, as test region. We classified two Landsat images (1976 and 2001) in order to forecast probability of occurrence for unsampled locations and to determine the best subset of predictors (spectral bands). A difference map for each land cover class, representing the difference between 1976 and 2001 probability of occurrence values, was built. In order to better address the patterns of change analysis, we put together difference maps and topographic variables. The latter are considered, at least in the study area, as the main environmental drivers of land-use change, in connection with climate change. Our results indicate that the selected algorithm, applied to land cover classes, can provide reliable data, especially when referring to classes with homogeneous texture properties and surface reflectance. The performed models had satisfactory predictive performance, showing relatively clear patterns of difference between the two considered time steps. The development of a methodology that, in the absence of field data, allow to obtain data on land use change dynamics, is of extreme importance for land planning and management.

Anticipating species distributions in space and time is necessary for effective biodiversity conservation and for prioritising management interventions. This is especially true when considering invasive species. In such a case, anticipating their spread is important to effectively plan management actions. However, considering uncertainty in the output of species distribution models is critical for correctly interpreting results and avoiding inappropriate decision-making. In particular, when dealing with species inventories, the bias resulting from sampling effort may lead to an over- or under-estimation of the local density of occurrences of a species. In this paper we propose an innovative method to i) map sampling effort bias using cartogram models and ii) explicitly consider such uncertainty in the modeling procedure under a Bayesian framework, which allows the integration of multilevel input data with prior information to improve the anticipation species distributions.

The identification of shape and size of sampling units maximising species richness has major implications in conservation planning and monitoring actions. In this study we tested the effect of three sampling unit shapes (rectangles, squared, and randomly shaped sampling units) on the number of recorded plant species. We used a large dataset gathered in the network of protected areas of the Siena Province, Italy, as part of a joint project with the Italian National Forest Inventory. This dataset is composed of plant species occurrence data recorded from 604 plots (10m x10m), each divided in a grid of 16 contiguous subplot units (2.5 m x 2.5 m). Furthermore, we evaluated the effect of plot orientation along the main environmental gradient, related to slope, to understand how the selection of plot orientation (when elongated plots are used) influences the number of species collected. In total, 1041 plant species were recorded from the study plots. Species richness was significantly higher using randomly shaped sampling units compared to rectangular and squared units. Although rectangular units captured a significantly larger number of species than squared ones, plot orientation along the main environmental gradient (terrain slope) did not show a systematic effect on the number of recorded species. The decision of whether or not using rectangular versus squared plots should therefore depend upon the objectives of the survey and the characteristics of the area to be surveyed, with squared plots being more suitable for assessing species composition of more homogeneous vegetation units while rectangular plots being more suited for recording more species in the pooled sample of a large area. If the aim of a survey is to evaluate the biodiversity and conservation value of a ecosystem/community, sampling protocols that maximise the number of recorded species given the same sampling effort should be used. Furthermore, considerations on the orientation of rectangular plots should be carefully made to enhance reproducibility of collected data and correctly interpret the results of a survey. This information is crucial to develop cost-effective surveys and monitoring programs aimed at maximising the number of collected species.

The identification of shape and size of sampling units that maximises the number of plant species recorded in multiscale sampling designs has major implications in conservation planning and monitoring actions. In this paper we tested the effect of three sampling shapes (rectangles, squared, and randomly shaped sampling units) on the number of recorded species. We used a large dataset derived from the network of protected areas in the Siena Province, Italy. This dataset is composed of plant species occurrence data recorded from 604 plots (10 m × 10 m), each divided in a grid of 16 contiguous subplot units (2.5 m × 2.5 m). Moreover, we evaluated the effect of plot orientation along the main environmental gradient, to examine how the selection of plot orientation (when elongated plots are used) influences the number of species collected. In total, 1041 plant species were recorded from the study plots. A significantly higher species richness was recorded by the random arrangement of 4 subplots within each plot in comparison to the ‘rectangle’ and ‘square’ shapes. Although the rectangular shape captured a significant larger number of species than squared ones, plot orientation along the main environmental gradient did not show a systematic effect on the number of recorded species. We concluded that the choice of whether or not using elongated (rectangular) versus squared plots should dependent upon the objectives of the specific survey with squared plots being more suitable for assessing species composition of more homogeneous vegetation units and rectangular plots being more suited for recording more species in the pooled sample of a large area.

Since landscape attributes show different patterns at different spatial extents, it is fundamental to identify how the relation between landscape structure and plant species diversity at local scale varies with scale. Then, it is fundamental to assess the appropriate extent at which landscape factors affect plant species richness at the local scale. To investigate this relation, data on plant species richness of forest communities at plot scale were extracted from a large data set and landscape metrics were calculated around the same plots for a range of extents (250–3000 m). Then, multiple regression models and variance partitioning techniques were applied to assess the amount of variance explained by the landscape metrics on plant species richness for a range of extents. In general, we found that increasing extent of the surrounding landscape analyzed, improved the strength of relationship between the landscape metrics and the properties of plant communities at plot scale. The medium-large extent was most informative as it combined a decent total variance explained with high variance explained by the pure fractions of complexity, fragmentation and disturbance and the minimum of collinearity. In conclusion, we found that it is possible and beneficial to identify a specific extent, where the redundancy in the predictor variables is minimized and the explanatory power of the pure fractions (or single groups) maximized, when examining landscape structure effects on local plant species richness.

Despite a considerable effort of scientific community and a huge amount of literature, the capacity to assess and monitor biodiversity at coarser spatial scales in short time periods is still limited. Thus assessing indicator or surrogate information from existing data sets, such as forest inventories, is a challenge for biodiversity management and monitoring. We used two forest data sets (woody plants and all vascular plants) to test whether the diversity of woody plant species can be used as predictor of the diversity of all vascular plant species. Our study was performed in the forests of Liguria, Italy. In order to take into account several levels of community organisation, we calculated different measures of species diversity at different levels of sampling hierarchy for both data sets (alpha, beta and gamma diversity). We used ordinary linear regression to test the predictive power of the diversity measures obtained by the occurrences of woody plant species with respect to those obtained by all vascular plant species. Our results suggest that beta diversity and gamma diversity of woody species can be used to predict the beta diversity and the gamma diversity of all vascular plant species, at different levels of sampling hierarchy, while the alpha diversity of woody species cannot be used to predict the alpha diversity of all vascular plant species. These results point out the importance to consider measures based non only on species richness and to interpret the relation between species richness of woody plants and species richness of all vascular plants taking into account the scale dependence of this relations. Thus, our work demonstrates the feasibility of using data on woody plant species as recorded by forest inventories to predict the diversity patterns of all plant species in forest ecosystems.

The most pervasive threats to biological diversity are directly or indirectly linked to the road networks. For this reason, over the last few decades, interest in the study of the ecological characteristics of the edges associated with roads has increased. This work aims to investigate the effect of roads as a humaninduced disturbance on the plant diversity in two managed Mediterranean forest sites, focusing on the responses of plants species richness, evenness, composition and taxonomic diversity. A stratified random sampling was performed in two protected areas located in Tuscany, Central Italy. The species richness, composition and abundance were measured in 53$20 � 20 m plots. Ordinary Least Square and quantile regressions were used to study the effect of the roads on species richness, evenness and taxonomic distinctness, and redundancy analysis was used to examine the species composition. Generalized linear models in conjunction with an Information Criterion-based approach to model selection were used to test the role of road distance in the structure of forest plant biodiversity. Our findings indicated a clear relationship between road distance and different plant biodiversity facets, which showed its maximum effect in the first 0e20 m forest-to-road segment and a mitigation after the 200 m threshold. Furthermore, the presence and abundance of many key forest species, such as Fagus sylvatica and Abies alba, were influenced more by the road distance than by other environmental gradients. The few remnants of core forest habitats in the Mediterranean basin highlight the need to recognize that road construction and maintenance have several ecological implications and accordingly require long-term monitoring programs.

Effective conservation of biodiversity in the face of increasing human impacts and global environmental changes requires accurate measurement of key trends and alternative management actions at landscape scales. Past ecological conditions are certainly important key factors in determining the present species diversity patterns and the inclusion of such factors (e.g. by historical cartographic data) can dramatically improve the predictive power of ecological models. In this paper we applied a retrogressive approach with the aim of simulating secondary forest regrowth effects on plant species diversity using present field data and historic land-use maps. The field data from an extensive sample were here used to model the temporal species richness change among the forest areas in the last 60 years. In order to rebuild the past species pool matrix using present field data and historical land use map, we applied a nearest neighbour selection using spatial query. Species-based rarefaction curves were derived for the two dates (1954 and 2010); the two datasets have been interpolated using inverse distance weighted algorithm, obtaining two maps showing the distribution of plant species richness for the two dates. The results showed that the cessation of human pressure on semi-natural areas and the consequent forest recovery, resulted in a decrease of vascular plant as a woodland flora replaces the open habitats flora. This study also showed that secondary forest regrowth and its effect on plant species diversity may be revealed by a retrogressive analysis, which represent a valid support in case of high uncertainty or absence of historical data.