The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (https://www.nhm.ac.uk/our-science/our-work/biodiversity/predicts.html)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.

Summary: Land‐use change is the main driver of global biodiversity loss, but its relative impact on species turnover (β‐diversity) across multiple spatial scales remains unclear. Plant communities in fragmented rain forests can undergo declines (floristic homogenization) or increases (floristic differentiation) in β‐diversity. We tested these alternative hypotheses analysing a large vegetation data base from a hierarchically nested sampling design (450 plots in 45 forest patches in 3 landscapes with different deforestation levels) at Los Tuxtlas rain forest, Mexico. Differences in β‐diversity across spatial scales (i.e. among plots, among patches, and among landscapes) were analysed using multiplicative diversity decompositions of Hill numbers. Plant β‐diversity among plots within forest patches decreased in landscapes with higher deforestation levels, leading to floristic homogenization within patches. This homogenization process can be explained by the loss of rare and shade‐tolerant plant species, and the recruitment and dominance of disturbance‐adapted species, and can limit the accumulation of species (γ‐diversity) in landscapes with higher deforestation. Nevertheless, the landscape with the highest deforestation level showed the highest floristic differentiation among patches. This landscape showed the greatest isolation distances between patches; a landscape spatial pattern that can limit the interchange of seeds (and species) between patches. Because the study patches are undergoing secondary succession following disturbances (e.g. logging, edge effects), different disturbance regimes and increased distance among patches could lead to higher β‐diversity. Synthesis. These findings indicate that patterns of floristic homogenization and differentiation depend on the landscape configuration and on the spatial scale of analysis. At the landscape scale, our results suggest that, in accordance with non‐equilibrium dynamics and the landscape‐divergence hypothesis, patches located in landscapes with different forest cover and different connectivity can experience contrasting successional pathways due to increasing levels of compositional differentiation between patches. These novel findings add further uncertainties to the maintenance of biodiversity in severely deforested tropical landscapes and have key ecological implications for biodiversity conservation planning.

Ruiz-Mallén, I., C. Schunko, E. Corbera, M. Rös, and V. Reyes-García. 2015. Meanings, drivers, and motivations for community-based conservation in Latin America. Ecology and Society 20(3):33. https://doi.org/10.5751/ES-07733-200333

The repeated use of sleeping sites by frugivorous vertebrates promotes the deposition and aggregation of copious amounts of seeds in these sites. This spatially contagious pattern of seed deposition has key implications for seed dispersal, particularly because such patterns can persist through recruitment. Assessing the seed rain patterns in sleeping sites thus represents a fundamental step in understanding the spatial structure and regeneration of plant assemblages. We evaluated the seed rain produced by spider monkeys (Ateles geoffroyi) in latrines located beneath 60 sleeping trees in two continuous forest sites (CFS) and three forest fragments (FF) in the Lacandona rainforest, Mexico. We tested for differences among latrines, among sites, and between forest conditions in the abundance, diversity (α-, β- and, γ-components) and evenness of seed assemblages. We recorded 45,919 seeds ≥5 mm (in length) from 68 species. The abundance of seeds was 1.7 times higher in FF than in CFS, particularly because of the dominance of a few plant species. As a consequence, community evenness tended to be lower within FF. β-diversity of common and dominant species was two times greater among FF than between CFS. Although mean α-diversity per latrine did not differ among sites, the greater β-diversity among latrines in CFS increased γ-diversity in these sites, particularly when considering common and dominant species. Our results support the hypothesis that fruit scarcity in FF can ‘force’ spider monkeys to deplete the available fruit patches more intensively than in CFS. This feeding strategy can limit the effectiveness of spider monkeys as seed dispersers in FF, because (i) it can limit the number of seed dispersers visiting such fruit patches; (ii) it increases seed dispersal limitation; and (iii) it can contribute to the floristic homogenization (i.e., reduced β-diversity among latrines) in fragmented landscapes.

Detecting individual trees and quantifying their biomass is crucial for carbon accounting procedures at the stand, landscape, and national levels. A significant challenge for many organizations is the amount of effort necessary to document carbon storage levels, especially in terms of human labor. To advance towards the goal of efficiently assessing the carbon content of forest, we evaluate methods to detect trees from high-resolution images taken from unoccupied aerial systems (UAS). In the process, we introduce the Digital Elevated Vegetation Model (DEVM), a representation that combines multispectral images, digital surface models, and digital terrain models. We show that the DEVM facilitates the development of refined synthetic data to detect individual trees using deep learning-based approaches. We carried out experiments in two tree fields located in different countries. Simultaneously, we perform comparisons among an array of classical and deep learning-based methods highlighting the precision and reliability of the DEVM.