Intervention Now to Eliminate Repeat Unintended Pregnancy in Teenagers (INTERUPT): a systematic review of intervention effectiveness and cost-effectiveness, and qualitative and realist synthesis of implementation factors and user engagement.

BACKGROUND: Unintended repeat conceptions can result in emotional, psychological and educational harm to young women, often with enduring implications for their life chances. This study aimed to identify which young women are at the greatest risk of repeat unintended pregnancies; which interventions are effective and cost-effective; and what are the barriers to and facilitators for the uptake of these interventions. METHODS: We conducted a mixed-methods systematic review which included meta-analysis, framework synthesis and application of realist principles, with stakeholder input and service user feedback to address this. We searched 20 electronic databases, including MEDLINE, Excerpta Medica database, Applied Social Sciences Index and Abstracts and Research Papers in Economics, to cover a broad range of health, social science, health economics and grey literature sources. Searches were conducted between May 2013 and June 2014 and updated in August 2015. RESULTS: Twelve randomised controlled trials (RCTs), two quasi-RCTs, 10 qualitative studies and 53 other quantitative studies were identified. The RCTs evaluated psychosocial interventions and an emergency contraception programme. The primary outcome was repeat conception rate: the event rate was 132 of 308 (43%) in the intervention group versus 140 of 289 (48%) for the control group, with a non-significant risk ratio (RR) of 0.92 [95% confidence interval (CI) 0.78-1.08]. Four studies reported subsequent birth rates: 29 of 237 (12%) events for the intervention arm versus 46 out of 224 (21%) for the control arm, with an RR of 0.60 (95% CI 0.39-0.93). Many repeat conceptions occurred in the context of poverty, low expectations and aspirations and negligible opportunities. Qualitative and realist evidence highlighted the importance of context, motivation, future planning and giving young women a central and active role in the development of new interventions. CONCLUSIONS: Little or no evidence for the effectiveness or cost-effectiveness of any of the interventions to reduce repeat pregnancy in young women was found. Qualitative and realist evidence helped to explain gaps in intervention design that should be addressed. More theory-based, rigorously evaluated programmes need to be developed to reduce unintended repeat pregnancy in young women. TRIAL REGISTRATION: PROSPERO, CRD42012003168 . Cochrane registration number: i = fertility/0068

Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation

Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land–climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation: (1) size variation at the organ and plant level and (2) leaf economics balancing leaf persistence against plant growth potential. The orthogonality of these two axes suggests they are differently influenced by environmental drivers. We find that these axes persist in a global dataset of 17 traits across more than 20,000 species. We find a dominant joint effect of climate and soil on trait variation. Additional independent climate effects are also observed across most traits, whereas independent soil effects are almost exclusively observed for economics traits. Variation in size traits correlates well with a latitudinal gradient related to water or energy limitation. In contrast, variation in economics traits is better explained by interactions of climate with soil fertility. These findings have the potential to improve our understanding of biodiversity patterns and our predictions of climate change impacts on biogeochemical cycles

Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation

Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land–climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation: (1) size variation at the organ and plant level and (2) leaf economics balancing leaf persistence against plant growth potential. The orthogonality of these two axes suggests they are differently influenced by environmental drivers. We find that these axes persist in a global dataset of 17 traits across more than 20,000 species. We find a dominant joint effect of climate and soil on trait variation. Additional independent climate effects are also observed across most traits, whereas independent soil effects are almost exclusively observed for economics traits. Variation in size traits correlates well with a latitudinal gradient related to water or energy limitation. In contrast, varia- tion in economics traits is better explained by interactions of climate with soil fertility. These findings have the potential to improve our understanding of biodiversity patterns and our predictions of climate change impacts on biogeochemical cycles.Environmental Biolog

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Resource limitation, tolerance,and the future of ecological plant classification

Throughout the evolutionary history of plants, drought, shade, and scarcity of nutrients have structured ecosystems and communities globally. Humans have begun to drastically alter the prevalence of these environmental factors with untold consequences for plant communities and ecosystems worldwide. Given limitations in using organ-level traits to predict ecological performance of species, recent advances using tolerances of low resource availability as plant functional traits are revealing the often hidden roles these factors have in structuring communities and are becoming central to classifying plants ecologically. For example, measuring the physiological drought tolerance of plants has increased the predictability of differences among species in their ability to survive drought as well as the distribution of species within and among ecosystems. Quantifying the shade tolerance of species has improved our understanding of local and regional species diversity and how species have sorted within and among regions. As the stresses on ecosystems continue to shift, coordinated studies of whole-plant growth centered on tolerance of low resource availability will be central in predicting future ecosystem functioning and biodiversity. This will require efforts that quantify tolerances for large numbers of species and develop bioinformatic and other techniques for comparing large number of species

Leaf shape and size tracks habitat transitions across forest-grassland boundaries in the grass family (Poaceae)

Grass leaf shape is a strong indicator of their habitat with linear leaves predominating in open areas and ovate leaves distinguishing forest-associated grasses. This pattern among extant species suggests that ancestral shifts between forest and open habitats may have coincided with changes in leaf shape or size. We tested relationships between habitat, climate, photosynthetic pathway and leaf shape and size in a phylogenetic framework to evaluate drivers of leaf shape and size variation over the evolutionary history of the family. We also estimated the ancestral habitat of Poaceae and tested whether forest margins served as transitional zones for shifts between forests and grasslands. We found that grass leaf shape is converging towards different shape optima in the forest understory, forest margins and open habitats. Leaf size also varies with habitat. Grasses have smaller leaves in open and drier areas, and in areas with high solar irradiance. Direct transitions between linear and ovate leaves are rare as are direct shifts between forest and open habitats. The most likely ancestral habitat of the family was the forest understory and forest margins along with an intermediate leaf shape served as important transitional habitat and morphology respectively for subsequent shifts across forest-grassland biome boundaries.Timothy J. Gallaher, Dean C. Adams, Lakshmi Attigala, Sean V. Burke, Joseph M. Craine, Melvin R. Duvall, Phillip C. Klahs, Emma Sherratt, William P. Wysocki, and Lynn G. Clar

A methodology to derive global maps of leaf traits using remote sensing and climate data

This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then aggregated to Plant Functional Types (PFTs). Next, the spatial abundance of PFTs at MODIS resolution (500 m) is calculated using Landsat data (30 m). Based on these PFT abundances, representative trait values are calculated for MODIS pixels with nearby trait data. Finally, different regression algorithms are applied to globally predict trait estimates from these MODIS pixels using remote sensing and climate data. The methods were compared in terms of precision, robustness and efficiency. The best model (random forests regression) shows good precision (normalized RMSE <= 20%) and goodness of fit (averaged Pearson's correlation R = 0.78) in any considered trait. Along with the estimated global maps of leaf traits, we provide associated uncertainty estimates derived from the regression models. The process chain is modular, and can easily accommodate new traits, data streams (traits databases and remote sensing data), and methods. The machine learning techniques applied allow attribution of information gain to data input and thus provide the opportunity to understand trait-environment relationships at the plant and ecosystem scales. The new data products the gap-filled trait matrix, a global map of PFT abundance per MODIS gridcells and the high-resolution global leaf trait maps are complementary to existing large-scale observations of the land surface and we therefore anticipate substantial contributions to advances in quantifying, understanding and prediction of the Earth system