Management of UK priority invasive alien plants: a systematic review protocol

Funder: The David and Claudia Harding FoundationAbstract: Background: Invasive alien plant species often have negative environmental and social impacts, such as loss of biodiversity and alteration of ecosystem services. As a result, managing the introduction, establishment, and abundance of invasive species is a major priority. To do this effectively, we need evidence on the effect of management interventions (such as using herbicide or cutting to control invasive plants). This evidence should not only include the effects of these management interventions on invasive alien species, but also on native species and other non-target outcomes such as ecosystem services. Such evidence would allow for comparison of the trade-offs between different management interventions. In the planned review we aim to assess how management interventions to control nine priority invasive alien plants species in England and Wales affect environmental outcomes. These species are: Japanese knotweed (Fallopia japonica) Nuttall’s waterweed (Elodea nuttallii), Chilean rhubarb (Gunnera tinctoria), Giant hogweed (Heracleum mantegazzianum), Floating pennywort (Hydrocotyle ranunculoides), Himalayan balsam (Impatiens glandulifera), Curly waterweed (Lagarosiphon major), American skunk cabbage (Lysichiton americanus), Parrot’s feather (Myriophyllum aquaticum). Methods: Searches will be in English and use bibliographic databases (Scopus, Web of Science Core Collection, Open Access Theses and Dissertations, and Conservation Evidence) and internet searches (Google Scholar), as well as specialist databases. Our methodology will only use the names of each species (scientific names and common names, including synonyms) as our search string (we will not use qualifiers, such as “AND invasive”). This will give low specificity but will increase the likelihood of capturing all relevant information. We will use predefined criteria for study inclusion and data extraction. We will screen publications in two stages: (1) using titles and abstracts and (2) using full texts. Consistency of inclusion will be checked by two people screening a random sample of 10% of titles and abstracts. This dual-screening will be subject to kappa analysis and any disagreements resolved through discussion. We will use critical appraisal to assess study validity by identifying studies that are potentially prone to bias

Dynamic meta-analysis: a method of using global evidence for local decision making

Background Meta-analysis is often used to make generalisations across all available evidence at the global scale. But how can these global generalisations be used for evidence-based decision making at the local scale, if the global evidence is not perceived to be relevant to local decisions? We show how an interactive method of meta-analysis—dynamic meta-analysis—can be used to assess the local relevance of global evidence. Results We developed Metadataset (www.metadataset.com) as a proof-of-concept for dynamic meta-analysis. Using Metadataset, we show how evidence can be filtered and weighted, and results can be recalculated, using dynamic methods of subgroup analysis, meta-regression, and recalibration. With an example from agroecology, we show how dynamic meta-analysis could lead to different conclusions for different subsets of the global evidence. Dynamic meta-analysis could also lead to a rebalancing of power and responsibility in evidence synthesis, since evidence users would be able to make decisions that are typically made by systematic reviewers—decisions about which studies to include (e.g. critical appraisal) and how to handle missing or poorly reported data (e.g. sensitivity analysis). Conclusions In this study, we show how dynamic meta-analysis can meet an important challenge in evidence-based decision making—the challenge of using global evidence for local decisions. We suggest that dynamic meta-analysis can be used for subject-wide evidence synthesis in several scientific disciplines, including agroecology and conservation biology. Future studies should develop standardised classification systems for the metadata that are used to filter and weight the evidence. Future studies should also develop standardised software packages, so that researchers can efficiently publish dynamic versions of their meta-analyses and keep them up-to-date as living systematic reviews. Metadataset is a proof-of-concept for this type of software, and it is open source. Future studies should improve the user experience, scale the software architecture, agree on standards for data and metadata storage and processing, and develop protocols for responsible evidence use

A systematic map of cassava farming practices and their agricultural and environmental impacts using new ontologies: Agri‐ontologies 1.0

Cassava is consumed by 800 million people and is a staple crop in Africa. Its production may increase under climate change due to its high drought tolerance. We produced a systematic map of scientific studies about cassava farming practices, with the aim of identifying knowledge gaps and clusters. Our secondary aim was to develop a classification system for [1] farming interventions and [2] agricultural, economic and environmental outcomes. Standardised classification systems facilitate data reuse, including for evidence synthesis, and promote research efficiency. Following our published protocol, we searched eight publication databases using the search string ‘cassava OR mandioca OR manihot OR manioc OR yuca’ in December 2017. We screened 36,580 records and included publications that measured the impact of cassava farming practices on agricultural, economic or environmental outcomes, including yield, soil, water, wildlife and labour. We classified the resultant 1599 publications by interventions, outcomes, location, study year and study design. We assessed coding consistency using Kappa scores. We found regional knowledge clusters (Nigeria, Columbia and Brazil accounted for 45.5% of country occurrences) and gaps (e.g. the Democratic Republic of Congo). There were knowledge clusters for interventions testing cultivar type, fertiliser use and diversifying crop rotations and outcomes related to crop production (e.g. yield/biomass). We found knowledge gaps for environmental interventions and outcomes (e.g. 5% of studies measured pollutants or wildlife). In terms of study design, reporting standards were poor (e.g. 24% of studies did not report start dates), average study duration was 2 years, and average publication delays were 4 years. The Kappa scores indicated that we successfully developed consistent ontologies (named Agri-ontologies 1.0). The map and ontologies are available online: https://www.metadataset.com/. This systematic map of cassava farming practices can direct researchers and funders to knowledge gaps that need addressing, and reviewers to knowledge clusters for synthesis. Better research practices should be promoted within cassava research, as poor reporting standards, short study durations and long publication delays result in an ineffective research environment. This systematic map provides an evidence base for cassava production and the ontologies (Agri-ontologies 1.0) can be applied to other systems to facilitate more efficient and effective synthesis

Effects of cover crops on multiple ecosystem services: Ten meta-analyses of data from arable farmland in California and the Mediterranean

Cover crops are considered to be beneficial for multiple ecosystem services, and they have been widely promoted through the Common Agricultural Policy (CAP) in the EU and Farm Bill Conservation Title Programs, such as the Environmental Quality Incentives Program (EQIP), in the USA. However, it can be difficult to decide whether the beneficial effects of cover crops on some ecosystem services are likely to outweigh their harmful effects on other services, and thus to decide whether they should be promoted by agricultural policy in specific situations. We used meta-analysis to quantify the effects of cover crops on five ecosystem services (food production, climate regulation, soil and water regulation, and weed control) in arable farmland in California and the Mediterranean, based on 326 experiments reported in 57 publications. In plots with cover crops, there as 13% less water, 9% more organic matter and 41% more microbial biomass in the soil, 27% fewer weeds, and 15% higher carbon dioxide emissions (but also more carbon stored in soil organic matter), compared to control plots with bare soils or winter fallows. Cash crop yields were 16% higher in plots that had legumes as cover crops (compared to controls) but 7% lower in plots that had non-legumes as cover crops. Soil nitrogen content was 41% lower, and nitrate leaching was 53% lower, in plots that had non-legume cover crops (compared to controls) but not significantly different in plots that had legumes. We did not find enough data to quantify the effects of cover crops on biodiversity conservation, pollination, or pest regulation. These gaps in the evidence need to be closed if cover crops continue to be widely promoted. We suggest that this novel combination of multiple meta-analyses for multiple ecosystem services could be used to support multi-criteria decision making about agri-environmental policy

Evidence synthesis as the basis for decision analysis: a method of selecting the best agricultural practices for multiple ecosystem services

Agricultural management practices have impacts not only on crops and livestock, but also on soil, water, wildlife, and ecosystem services. Agricultural research provides evidence about these impacts, but it is unclear how this evidence should be used to make decisions. Two methods are widely used in decision making: evidence synthesis and decision analysis. However, a system of evidence-based decision making that integrates these two methods has not yet been established. Moreover, the standard methods of evidence synthesis have a narrow focus (e.g., the effects of one management practice), but the standard methods of decision analysis have a wide focus (e.g., the comparative effectiveness of multiple management practices). Thus, there is a mismatch between the outputs from evidence synthesis and the inputs that are needed for decision analysis. We show how evidence for a wide range of agricultural practices can be reviewed and summarized simultaneously (“subject-wide evidence synthesis”), and how this evidence can be assessed by experts and used for decision making (“multiple-criteria decision analysis”). We show how these methods could be used by The Nature Conservancy (TNC) in California to select the best management practices for multiple ecosystem services in Mediterranean-type farmland and rangeland, based on a subject-wide evidence synthesis that was published by Conservation Evidence (www.conservationevidence.com). This method of “evidence-based decision analysis” could be used at different scales, from the local scale (farmers deciding which practices to adopt) to the national or international scale (policy makers deciding which practices to support through agricultural subsidies or other payments for ecosystem services). We discuss the strengths and weaknesses of this method, and we suggest some general principles for improving evidence synthesis as the basis for multi-criteria decision analysis

The adaptive capacity of maize-based conservation agriculture systems to climate stress in tropical and subtropical environments: A meta-regression of yields

Conservation agriculture is widely promoted across sub-Saharan Africa as a sustainable farming practice that enhances adaptive capacity to climate change. The interactions between climate stress, management, and soil are critical to understanding the adaptive capacity of conservation agriculture. Yet conservation agriculture syntheses to date have largely neglected climate, especially the effects of extreme heat. For the sub-tropics and tropics, we use meta-regression, in combination with global soil and climate datasets, to test four hypotheses: (1) that relative yield performance of conservation agriculture improves with increasing drought and temperature stress; (2) that the effects of moisture and temperature stress exposure interact; (3) that the effects of moisture and temperature stress are modified by soil texture; and (4) that crop diversification, fertilizer application rate, or the time since no-till implementation will enhance conservation agriculture performance under climate stress. Our results support the hypothesis that the relative maize yield performance of conservation agriculture improves with increasing drought severity or exposure to high temperatures. Further, there is an interaction of moisture and heat stress on conservation agriculture performance and their combined effect is both non-additive and modified by soil clay content, supporting our second and third hypotheses. Finally, we found only limited support for our fourth hypothesis as (1) increasing nitrogen application rates did not improve the relative performance of conservation agriculture under high heat stress; (2) crop diversification did not notably improve conservation agriculture performance, but did increase its stability with heat stress; and (3) a statistically robust effect of the time since no-till implementation was not evident. Our meta-regression supports the narrative that conservation agriculture enhances the adaptive capacity of maize production in sub-Saharan Africa under drought and/or heat stress. However, in very wet seasons and on clay-rich soils, conservation agriculture yields less compared to conventional practices

Ten-year assessment of the 100 priority questions for global biodiversity conservation

In 2008, a group of conservation scientists compiled a list of 100 priority questions for the conservation of the world's biodiversity ?Sutherland et al. (2009) Conservation Biology, 23, 557?567?. However, now almost a decade later, no one has yet published a study gauging how much progress has been made in addressing these 100 high?priority questions in the peer?reviewed literature. Here we take a first step toward re?examining the 100 questions and identify key knowledge gaps that still remain. Through a combination of a questionnaire and a literature review, we evaluated each of the 100 questions on the basis of two criteria: relevance and effort. We defined highly?relevant questions as those which ? if answered ? would have the greatest impact on global biodiversity conservation, while effort was quantified based on the number of review publications addressing a particular question, which we used as a proxy for research effort. Using this approach we identified a set of questions that, despite being perceived as highly relevant, have been the focus of relatively few review publications over the past ten years. These questions covered a broad range of topics but predominantly tackled three major themes: the conservation and management of freshwater ecosystems, the role of societal structures in shaping interactions between people and the environment, and the impacts of conservation interventions. We see these questions as important knowledge gaps that have so far received insufficient attention and may need to be prioritised in future research

Dataset supporting: "A systematic map of cassava farming practices and their agricultural and environmental impacts using new ontologies: Agri-ontologies 1.0"

These data support a systematic map of scientific studies about cassava farming practices, which was made with the aim of identifying knowledge gaps and clusters. A secondary aim of the study was to develop a hierarchical classification system for [1] farming interventions, and [2] agricultural, economic, and environmental outcomes. This standardized classification system for agricultural metadata can facilitate dataset reuse and promote research efficiency across syntheses. Following our published protocol [2], we searched eight publication databases/repositories using the search string “cassava OR mandioca OR manihot OR manioc OR yuca” in December 2017. We screened 36,580 records at title and abstract and then at full text stage, and included publications that measured the impact of cassava farming practices on agricultural or environmental outcomes, including: yield, soil, water, wildlife, pests, pollutants, profits, and labour. We classified the resultant 1,599 publications by interventions, outcomes, study location, study years, and study design. We assessed coding consistency using Kappa scores. This map is available online via an interactive database: https://www.metadataset.com/ (registration required). The Kappa scores indicated that we successfully developed a consistent intervention and outcome ontology that can be applied to other systems

Simple study designs in ecology produce inaccurate estimates of biodiversity responses

Monitoring the impacts of anthropogenic threats and interventions to mitigate these threats is key to understanding how to best conserve biodiversity. Ecologists use many different study designs to monitor such impacts. Simpler designs lacking controls (e.g. Before–After (BA) and After) or pre-impact data (e.g. Control–Impact (CI)) are considered to be less robust than more complex designs (e.g. Before–After Control-Impact (BACI) or Randomized Controlled Trials (RCTs)). However, we lack quantitative estimates of how much less accurate simpler study designs are in ecology. Understanding this could help prioritize research and weight studies by their design's accuracy in meta-analysis and evidence assessment. We compared how accurately five study designs estimated the true effect of a simulated environmental impact that caused a step-change response in a population's density. We derived empirical estimates of several simulation parameters from 47 ecological datasets to ensure our simulations were realistic. We measured design performance by determining the percentage of simulations where: (a) the true effect fell within the 95% Confidence Intervals of effect size estimates, and (b) each design correctly estimated the true effect's direction and magnitude. We also considered how sample size affected their performance. We demonstrated that BACI designs performed: 1.3–1.8 times better than RCTs; 2.9–4.2 times versus BA; 3.2–4.6 times versus CI; and 7.1–10.1 times versus After designs (depending on sample size), when correctly estimating true effect's direction and magnitude to within ±30%. Although BACI designs suffered from low power at small sample sizes, they outperformed other designs for almost all performance measures. Increasing sample size improved BACI design accuracy, but only increased the precision of simpler designs around biased estimates. Synthesis and applications. We suggest that more investment in more robust designs is needed in ecology since inferences from simpler designs, even with large sample sizes may be misleading. Facilitating this requires longer-term funding and stronger research–practice partnerships. We also propose ‘accuracy weights’ and demonstrate how they can weight studies in three recent meta-analyses by accounting for study design and sample size. We hope these help decision-makers and meta-analysts better account for study design when assessing evidence