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Motivation: The intrinsic characteristics of islands make them a unique study system for the investigation of ecological and evolutionary dynamics. The Mediterranean Basin, an island‐rich biodiversity hotspot, still lacks a comprehensive spatial database for these geographic features. This study presents the first comprehensive spatial database of all Mediterranean islands larger than 0.01 km2, aiding ecological investigations and interdisciplinary research. Main types of variable contained: The MEDIS spatial database offers detailed information on 39 geographic, climatic, ecological and land‐use variables, including island area, perimeter, isolation metrics, climatic space, terrain data, land cover, palaeogeography, road networks and geological information, providing a multifaceted view of each island's characteristics. Spatial location and grain: The study encompasses 2217 islands in the Mediterranean Basin larger than 0.01 km2. The spatial grain of the datasets on which the selected variables are based varies from 10 m (ESA WorldCover) to 1 km (CHELSA‐BIOCLIM+). Time period and grain: The spatial database incorporates data from various sources, each with its own timeframe, such as the Global Shoreline Vector from 2014 Landsat imagery and the WorldCover dataset from 2021. Historical data like the Paleocoastlines GIS dataset offer insights into island configurations during the Last Glacial Maximum. Major taxa and level of measurement: While not focusing on specific taxa, the study lays the foundation for comprehensive research on Mediterranean islands, facilitating comparisons and investigations into the distribution of native, endemic or alien species. The level of measurement is extensive, encompassing a wide range of variables and providing polygonal features rather than centroids' coordinates. [ABSTRACT FROM AUTHOR]

Small islands represent a common feature in the Mediterranean and host a significant fraction of its biodiversity. However, the distribution of plant species richness across spatial scales—from local communities (alpha) to whole islands (gamma)—is largely unknown, and so is the influence of environmental, geographical, and topographical factors. By building upon classic biogeographic theory, we used the species–area relationship and about 4500 vegetation plots in 54 Central Mediterranean small islands to identify hotspots of plant species richness and the underlying spatial determinants across scales. To do so, we fitted and averaged eight species–area models on gamma and alpha richness against island area and plot size, respectively. Based on positive deviations from the fitted curves, we identified 12 islands as cross‐scale hotspots. These islands encompassed around 70% of species and habitat richness, as well as almost 50% of the rarest species in the data set, while occupying less than 40% of the total island surface. By fitting generalized linear mixed models, we found that gamma richness was mainly explained by island area and was weakly related to mean annual temperature (positively) and annual precipitation (negatively). As for alpha richness, after accounting for the idiosyncratic effect of habitats and islands, plot size and gamma richness remained the only significant predictors, showing a positive relationship. This work contributes to the understanding of the patterns and drivers of plant diversity in Central Mediterranean small islands and outlines a useful methodology for the prioritization of conservation efforts. [ABSTRACT FROM AUTHOR]

Unsustainable exploitation of tropical forest resources is raising worldwide concern. In Papua New Guinea (PNG) timber harvesting has been identified as a major contributor to deforestation and forest degradation but its impact on biodiversity is still poorly understood. In this study we investigated the effect of selective logging on tree taxonomic composition, structure and diversity of PNG forests. We used data from 101 one-hectare permanent sample plots (PSPs) belonging to two vegetation types: low altitude forests on plains and fans (type P) and low altitude forests on uplands (type H). We used multivariate techniques to test for significant differences in species composition between plots of different vegetation types and disturbance regimes, identifying the tree taxa to which these differences could be ascribed. ANOVA was used to test for differences between logged-over and unlogged forest PSPs with respect to biodiversity (richness, Shannon's diversity, Pielou's evenness) and stand structure (stem density, basal area--BA). Temporal trends of forest features were analyzed using linear regression. Significant differences in taxonomic composition were found between logged-over and unlogged plots of the H type (p = 0.04). No differences were found in richness, diversity and evenness between logged-over and unlogged forest plots, while stem density was higher in the latter (421 ± 153 stems ha-1). Greater BA was found in unlogged forests (30.28 ± 4.45 m² ha-1) of the H type when compared to the logged-over stands (15.52 ± 4.04 m² ha-1). We detected positive trends in richness (0.55 ± 0.19 taxa ha-1 yr-1) and diversity after logging. Furthermore, H type forest exhibited positive trends in stem density (9 ± 1 stems ha-1 yr-1) and BA (0.42 ± 0.06 m² ha-1 yr-1) with elapsed time since harvesting. Our analysis highlights some significant effects of logging activities on biodiversity and structure of PNG forests. Additionally, forests exhibited a significant recovery with respect to richness, diversity and stand structure. These preliminary results will be compared with data collected by the forthcoming National Forest Inventory in order to assess and monitor the effects of human activities and ecological factors on PNG forest biodiversity and develop appropriate conservation measures and sustainable management strategies. [ABSTRACT FROM AUTHOR]

National forest inventories (NFIs) have to be properly designed in order to yield statistically representative data and make best use of limited resources. A key element to accomplish this is the knowledge of the local variability in the sampled forest stand features. In this study, we estimated the variability of tree taxonomic richness and carbon (C) stock in logged-over and unlogged lowland tropical forests of Papua New Guinea, to define the optimal plot and sample size needed to estimate these characteristics in the context of the forthcoming implementation of the new NFI. We used data from 133 one-hectare permanent sample plots to calculate the coefficient of variation (CV) of C stock and taxonomic richness at different simulated plot sizes. CV was seen to rapidly decrease with increasing plot size up to 0.2-0.3 ha for both features following an inverse-exponential trend. Optimum plot size ranged between 0.08 and 0.32 ha, with 75-164 plots needed to achieve an estimation within 5 per cent of the true mean (95 per cent confidence), depending on the stand feature and the previous silvicultural treatment. We concluded that the establishment of a network of 319 permanent sample plots between 0.2 and 0.3 ha in size would represent an efficient sampling scheme in lowland forests for the new NFI. [ABSTRACT FROM AUTHOR]