The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts

Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species’ threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project – and avert – future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups – including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems – www.predicts.org.uk).We make site-level summary data available alongside this article. The full database will be publicly available in 2015

Below the canopy: global trends in forest vertebrate populations and their drivers

Global forest assessments use forest area as an indicator of biodiversity status, which may mask below-canopy pressures driving forest biodiversity loss and 'empty forest' syndrome. The status of forest biodiversity is important not only for species conservation but also because species loss can have consequences for forest health and carbon storage. We aimed to develop a global indicator of forest specialist vertebrate populations to improve assessments of forest biodiversity status. Using the Living Planet Index methodology, we developed a weighted composite Forest Specialist Index for the period 1970-2014. We then investigated potential correlates of forest vertebrate population change. We analysed the relationship between the average rate of change of forest vertebrate populations and satellite-derived tree cover trends, as well as other pressures. On average, forest vertebrate populations declined by 53% between 1970 and 2014. We found little evidence of a consistent global effect of tree cover change on forest vertebrate populations, but a significant negative effect of exploitation threat on forest specialists. In conclusion, we found that the forest area is a poor indicator of forest biodiversity status. For forest biodiversity to recover, conservation management needs to be informed by monitoring all threats to vertebrates, including those below the canopy

Relating characteristics of global biodiversity targets to reported progress

To inform governmental discussions on the nature of a revised Strategic Plan for Biodiversity of the Convention on Biological Diversity (CBD), we reviewed the relevant literature and assessed the framing of the 20 Aichi Biodiversity Targets in the current strategic plan. We asked international experts from nongovernmental organizations, academia, government agencies, international organizations, research institutes, and the CBD to score the Aichi Targets and their constituent elements against a set of specific, measurable, ambitious, realistic, unambiguous, scalable, and comprehensive criteria (SMART based, excluding time bound because all targets are bound to 2015 or 2020). We then investigated the relationship between these expert scores and reported progress toward the target elements by using the findings from 2 global progress assessments (Global Biodiversity Outlook and the Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services). We analyzed the data with ordinal logistic regressions. We found significant positive relationships (p < 0.05) between progress and the extent to which the target elements were perceived to be measurable, realistic, unambiguous, and scalable. There was some evidence of a relationship between progress and specificity of the target elements, but no relationship between progress and ambition. We are the first to show associations between progress and the extent to which the Aichi Targets meet certain SMART criteria. As negotiations around the post‐2020 biodiversity framework proceed, decision makers should strive to ensure that new or revised targets are effectively structured and clearly worded to allow the translation of targets into actionable policies that can be successfully implemented nationally, regionally, and globally

The present and future effects of land use on ecological assemblages in tropical grasslands and savannas in Africa

The world is currently experiencing a period of rapid, human-driven biodiversity loss. Over the past decade, numerous metrics for biodiversity have been used to create indicators to track change in biodiversity. However, our ability to predict future changes has been limited. In this study, we use two very different models to predict the status and possible futures for the composition and diversity of ecological assemblages in African tropical grasslands and savannas under land-use change. We show that ecological assemblages are affected more by land use in African grasslands and savannas than in other biomes. We estimate that average losses of assemblage composition and diversity are already between 9.7 and 42.0%, depending on the model and measure used. If current socio-economic trajectories continue (‘business-as-usual’), the likely associated land-use changes are predicted to lead to a further 5.6–12.3% loss of assemblage composition and diversity. In contrast, a scenario that assumes more efficient use of agricultural areas (thus requiring a smaller total area) could be associated with a partial reversal ‒ of as much as 3.2% ‒ of past losses. While the agriculture that causes the majority of land-use change is an important source of economic growth, projections of the effects of land use on ecological assemblages can allow for more informed decisions

Land use and soil characteristics affect soil organisms differently from above-ground assemblages

Background: Land-use is a major driver of changes in biodiversity worldwide, but studies have overwhelmingly focused on above-ground taxa: the effects on soil biodiversity are less well known, despite the importance of soil organisms in ecosystem functioning. We modelled data from a global biodiversity database to compare how the abundance of soil-dwelling and above-ground organisms responded to land use and soil properties. Results: We found that land use affects overall abundance differently in soil and above-ground assemblages. The abundance of soil organisms was markedly lower in cropland and plantation habitats than in primary vegetation and pasture. Soil properties influenced the abundance of soil biota in ways that differed among land uses, suggesting they shape both abundance and its response to land use. Conclusions: Our results caution against assuming models or indicators derived from above-ground data can apply to soil assemblages and highlight the potential value of incorporating soil properties into biodiversity models

The PREDICTS database: A global database of how local terrestrial biodiversity responds to human impacts

© 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species' threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project - and avert - future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups - including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems - www.predicts.org.uk). We make site-level summary data available alongside this article. The full database will be publicly available in 2015. The collation of biodiversity datasets with broad taxonomic and biogeographic extents is necessary to understand historical declines and to project - and hopefully avert - future declines. We describe a newly collated database of more than 1.6 million biodiversity measurements from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world

The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-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

Reconciling biodiversity indicators to guide understanding and action

Many metrics can be used to capture trends in biodiversity and, in turn, these metrics inform biodiversity indicators. Sampling biases, genuine differences between metrics, or both, can often cause indicators to appear to be in conflict. This lack of congruence confuses policy makers and the general public, hindering effective responses to the biodiversity crisis. We show how different and seemingly inconsistent metrics of biodiversity can, in fact, emerge from the same scenario of biodiversity change. We develop a simple, evidence-based narrative of biodiversity change and implement it in a simulation model. The model demonstrates how, for example, species richness can remain stable in a given landscape, whereas other measures (e.g. compositional similarity) can be in sharp decline. We suggest that linking biodiversity metrics in a simple model will support more robust indicator development, enable stronger predictions of biodiversity change, and provide policy-relevant advice at a range of scales

Fast, scalable, and automated identification of articles for biodiversity and macroecological datasets

Aim: Understanding broad‐scale ecological patterns and processes is necessary if we are to mitigate the consequences of anthropogenically driven biodiversity degradation. However, such analyses require large datasets and current data collation methods can be slow, involving extensive human input. Given rapid and ever‐increasing rates of scientific publication, manually identifying data sources among hundreds of thousands of articles is a significant challenge, which can create a bottleneck in the generation of ecological databases. / Innovation: Here, we demonstrate the use of general, text‐classification approaches to identify relevant biodiversity articles. We apply this to two freely available example databases, the Living Planet Database and the database of the PREDICTS (Projecting Responses of Ecological Diversity in Changing Terrestrial Systems) project, both of which underpin important biodiversity indicators. We assess machine‐learning classifiers based on logistic regression (LR) and convolutional neural networks, and identify aspects of the text‐processing workflow that influence classification performance. / Main conclusions: Our best classifiers can distinguish relevant from non‐relevant articles with over 90% accuracy. Using readily available abstracts and titles or abstracts alone produces significantly better results than using titles alone. LR and neural network models performed similarly. Crucially, we show that deploying such models on real‐world search results can significantly increase the rate at which potentially relevant papers are recovered compared to a current manual protocol. Furthermore, our results indicate that, given a modest initial sample of 100 relevant papers, high‐performing classifiers could be generated quickly through iteratively updating the training texts based on targeted literature searches. These findings clearly demonstrate the usefulness of text‐mining methods for constructing and enhancing ecological datasets, and wider application of these techniques has the potential to benefit large‐scale analyses more broadly. We provide source code and examples that can be used to create new classifiers for other datasets

Modelling and projecting the response of local assemblage composition to land use change across Colombia

Understanding the impact of land use change within assemblages is fundamental to mitigation policies at local and regional scale. Here, we aim to quantify how site-level terrestrial assemblages are responding to land use change in Colombia a mega-diverse country and to project future biodiversity under different scenarios of land use change associated with climate change policies. Location: Colombia (northern South America). Methods: We collated original biodiversity data from 17 publications (285 sites) that examined how human impact affects terrestrial biodiversity in Colombia. From each site we estimated compositional intactness (i.e. compositional similarity to undisturbed sites). We fitted generalized linear mixed-effects models to estimate how these measures of local biodiversity vary across land use habitats. Using space-for-time substitution, we applied our estimates to hindcast biodiversity changes since 1500 and project future changes under climate change policies of the four representative concentration pathways (RCPs). Results: Assemblages in urban, cropland and pasture sites were compositionally very different from those in primary vegetation. We infer that average compositional intactness has been reduced by 18% across Colombia to date, with strong regional variation. The best RCP scenario for future biodiversity is GCAM-RCP4.5, a path that favours the expansion of secondary forests under a strong carbon market; while the worst is MESSAGE-RCP8.5, ‘the business-as-usual’ scenario. Main conclusions: Land use change has driven an increasing change in the composition of ecological assemblages in Colombia. By 2095, the implementation of carbon markets policy of climate change from GCAM-RCP4.5 could mitigate these changes in community composition. In contrast, the business-as-usual scenario MESSAGE-RCP8.5 predicts a steep community change placing the quality of ecosystems at risk