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Background There is little published guidance as to the sample size required for a pilot or feasibility trial despite the fact that a sample size justification is a key element in the design of a trial. A sample size justification should give the minimum number of participants needed in order to meet the objectives of the trial. This paper seeks to describe the target sample sizes set for pilot and feasibility randomised controlled trials, currently running within the United Kingdom. Methods Data were gathered from the United Kingdom Clinical Research Network (UKCRN) database using the search terms ‘pilot’ and ‘feasibility’. From this search 513 studies were assessed for eligibility of which 79 met the inclusion criteria. Where the data summary on the UKCRN Database was incomplete, data were also gathered from: the International Standardised Randomised Controlled Trial Number (ISRCTN) register; the clinicaltrials.gov website and the website of the funders. For 62 of the trials, it was necessary to contact members of the research team by email to ensure completeness. Results Of the 79 trials analysed, 50 (63.3%) were labelled as pilot trials, 25 (31.6%) feasibility and 14 were described as both pilot and feasibility trials. The majority had two arms (n = 68, 86.1%) and the two most common endpoints were continuous (n = 45, 57.0%) and dichotomous (n = 31, 39.2%). Pilot trials were found to have a smaller sample size per arm (median = 30, range = 8 to 114 participants) than feasibility trials (median = 36, range = 10 to 300 participants). By type of endpoint, across feasibility and pilot trials, the median sample size per arm was 36 (range = 10 to 300 participants) for trials with a dichotomous endpoint and 30 (range = 8 to 114 participants) for trials with a continuous endpoint. Publicly funded pilot trials appear to be larger than industry funded pilot trials: median sample sizes of 33 (range = 15 to 114 participants) and 25 (range = 8 to 100 participants) respectively. Conclusion All studies should have a sample size justification. Not all studies however need to have a sample size calculation. For pilot and feasibility trials, while a sample size justification is important, a formal sample size calculation may not be appropriate. The results in this paper describe the observed sample sizes in feasibility and pilot randomised controlled trials on the UKCRN Database.

The consideration of information on social values in conjunction with biological data is critical for achieving both socially acceptable and scientifically defensible conservation planning outcomes. However, the influence of social values on spatial conservation priorities has received limited attention and is poorly understood. We present an approach that incorporates quantitative data on social values for conservation and social preferences for development into spatial conservation planning. We undertook a public participation GIS survey to spatially represent social values and development preferences and used species distribution models for 7 threatened fauna species to represent biological values. These spatially explicit data were simultaneously included in the conservation planning software Zonation to examine how conservation priorities changed with the inclusion of social data. Integrating spatially explicit information about social values and development preferences with biological data produced prioritizations that differed spatially from the solution based on only biological data. However, the integrated solutions protected a similar proportion of the species’ distributions, indicating that Zonation effectively combined the biological and social data to produce socially feasible conservation solutions of approximately equivalent biological value. We were able to identify areas of the landscape where synergies and conflicts between different value sets are likely to occur. Identification of these synergies and conflicts will allow decision makers to target communication strategies to specific areas and ensure effective community engagement and positive conservation outcomes. Integración de Valores Biológicos y Sociales al Priorizar Sitios para la Conservación de la Biodiversidad

Habitat modification and introduced mammalian predators are linked to global species extinctions and declines, but their relative influences can be uncertain, often making conservation management difficult. Using landscape-scale models, we quantified the relative impacts of habitat modification and mammalian predation on the range contraction of a threatened New Zealand riverine duck. We combined 38 years of whio ( Hymenolaimus malacorhynchos ) observations with national-scale environmental data to predict relative likelihood of occurrence (RLO) under two scenarios using bootstrapped boosted regression trees (BRT). Our models used training data from contemporary environments to predict the potential contemporary whio distribution across New Zealand riverscapes in the absence of introduced mammalian predators. Then, using estimates of environments prior to human arrival, we used the same models to hindcast potential pre-human whio distribution prior to widespread land clearance. Comparing RLO differences between potential pre-human, potential contemporary and observed contemporary distributions allowed us to assess the relative impacts of the two main drivers of decline; habitat modification and mammalian predation. Whio have undergone widespread catastrophic declines most likely linked to mammalian predation, with smaller declines due to habitat modification (range contractions of 95% and 37%, respectively). We also identified areas of potential contemporary habitat outside their current range that would be suitable for whio conservation if mammalian predator control could be implemented. Our approach presents a practical technique for estimating the relative importance of global change drivers in species declines and extinctions, as well as providing valuable information to improve conservation planning.

Background In an evaluation of a new health technology, a pilot trial may be undertaken prior to a trial that makes a definitive assessment of benefit. The objective of pilot studies is to provide sufficient evidence that a larger definitive trial can be undertaken and, at times, to provide a preliminary assessment of benefit. Methods We describe significance thresholds, confidence intervals and surrogate markers in the context of pilot studies and how Bayesian methods can be used in pilot trials. We use a worked example to illustrate the issues raised. Results We show how significance levels other than the traditional 5% should be considered to provide preliminary evidence for efficacy and how estimation and confidence intervals should be the focus to provide an estimated range of possible treatment effects. We also illustrate how Bayesian methods could also assist in the early assessment of a health technology. Conclusions We recommend that in pilot trials the focus should be on descriptive statistics and estimation, using confidence intervals, rather than formal hypothesis testing and that confidence intervals other than 95% confidence intervals, such as 85% or 75%, be used for the estimation. The confidence interval should then be interpreted with regards to the minimum clinically important difference. We also recommend that Bayesian methods be used to assist in the interpretation of pilot trials. Surrogate endpoints can also be used in pilot trials but they must reliably predict the overall effect on the clinical outcome.

A pilot study can be an important step in the assessment of an intervention by providing information to design the future definitive trial. Pilot studies can be used to estimate the recruitment and retention rates and population variance and to provide preliminary evidence of efficacy potential. However, estimation is poor because pilot studies are small, so sensitivity analyses for the main trial's sample size calculations should be undertaken. We demonstrate how to carry out easy-to-perform sensitivity analysis for designing trials based on pilot data using an example. Furthermore, we introduce rules of thumb for the size of the pilot study so that the overall sample size, for both pilot and main trials, is minimized. The example illustrates how sample size estimates for the main trial can alter dramatically by plausibly varying assumptions. Required sample size for 90% power varied from 392 to 692 depending on assumptions. Some scenarios were not feasible based on the pilot study recruitment and retention rates. Pilot studies can be used to help design the main trial, but caution should be exercised. We recommend the use of sensitivity analyses to assess the robustness of the design assumptions for a main trial.

Biodiversity impact assessments under threatened species legislation often focus on individual development proposals at a single location, usually for a single species, leading to inadequate assessments of multiple impacts that accumulate over large spatial scales for multiple species. Regulations requiring ad‐hoc assessments can lead to “death by a thousand cuts,” where biodiversity is degraded by many small impacts that individually do not appear to threaten species’ persistence. Spatial prioritization methods can improve the efficiency of decision‐making by explicitly considering cumulative impacts of multiple proposed developments on multiple species over large spatial scales. We present an assessment approach and a unique case study in which spatial prioritization tools were used to support strategic assessment of a large development plan in Western Australia. The application of the approach helped identify relatively minor alterations to development plans that resulted in reductions in biodiversity impacts and informed expansion of the protected area network. Using these tools to assess trade‐offs between conservation and development will help identify planning footprints that minimize biodiversity losses.

We developed a method to predict the susceptibility of New Zealand estuaries to eutrophication. This method predicts macroalgae and phytoplankton responses to potential nutrient concentrations and flushing times, obtained nationally from simple dilution models, a GIS land-use model and physical estuary properties. Macroalgal response was based on an empirically derived relationship between potential nitrogen concentrations and an established macroalgal index (EQR) and phytoplankton response using an analytical growth model. Intertidal area was used to determine which primary producer was likely to lead to eutrophic conditions within estuaries. We calculated the eutrophication susceptibility of 399 New Zealand estuaries and assigned them to susceptibility bands A (lowest expected impact) to D (highest expected impact). Twenty-seven percent of New Zealand estuaries have high or very high eutrophication susceptibilities (band C or D), mostly (63 % of band C and D) due to macroalgae. The physical properties of estuaries strongly influence susceptibility to macroalgae or phytoplankton blooms, and estuaries with similar physical properties cluster spatially around New Zealand’s coasts. As a result, regional patterns in susceptibility are apparent due to a combination of estuary types and land use patterns. The few areas in New Zealand with consistently low estuary eutrophication susceptibilities are either undeveloped or have estuaries with short flushing times, low intertidal area and/or minimal tidal influx. Estuaries with conditions favourable for macroalgae are most at risk. Our approach provides estuaryintegrated susceptibility scores likely to be of use as a regional or national screening tool to prioritise more in-depth estuary assessments, to evaluate likely responses to altered nutrient loading regimes and assist in developing management strategies for estuaries.

Inter-individual differences in demographic traits of iteroparous species can arise through learning and maturation, as well as from permanent differences in individual ‘quality’ and sex-specific constraints. As the ability to acquire energy determines the resources an individual can allocate to reproduction and self-maintenance, foraging behavior is a key trait to study to better understand the mechanisms underlying these differences. So far, most seabird studies have focused on the effect of maturation and learning processes on foraging performance, while only a few have included measures of individual quality. Here, we investigated the effects of age, breeding experience, sex, and individual breeding quality on the foraging behavior and location of 83 known-age Adélie penguins at Cape Bird, Ross Sea, Antarctica. Over a 2 yr period, we showed that (1) high-quality birds dived deeper than lower quality ones, apparently catching a higher number of prey per dive and targeting different foraging locations; (2) females performed longer foraging trips and a higher number of dives compared to males; (3) there were no significant agerelated differences in foraging behavior; and (4) breeding experience had a weak influence on foraging behavior. We suggest that high-quality individuals have higher physiological ability, enabling them to dive deeper and forage more effectively. Further inquiry should focus on determining the physiological differences among penguins of different quality.

Quantifies the relative impacts of habitat modification and mammalian predation on the range contraction of whio (Hymenolaimus malacorhynchos), a threatened New Zealand riverine duck. Source: National Library of New Zealand Te Puna Matauranga o Aotearoa, licensed by the Department of Internal Affairs for re-use under the Creative Commons Attribution 3.0 New Zealand Licence.

A common land and water management task is to determine where and by how much source loadings need to change to meet water quality limits in receiving environments. This paper addresses the problem of quantifying changes in loading when limits are specified in many locations in a large and spatially heterogeneous catchment, accounting for cumulative downstream impacts. Current approaches to this problem tend to use either scenario analysis or optimization, which suffer from difficulties of generating scenarios that meet the limits, or high complexity of optimization approaches. In contrast, we present a novel method in which simple catchment models, load limits, upstream/downstream spatial relationships and spatial allocation rules are combined to arrive at source load changes. The process iteratively establishes the critical location (river segment or lake) where the limits are most constraining, and then adjusts sources upstream of the critical location to meet the limit at that location. The method is demonstrated with application to New Zealand (268,000 km2) for nutrients and the microbial indicator E. coli, which was conducted to support policy development regarding water quality limits. The model provided useful insights, such as a source load excess (the need for source load reduction) even after mitigation measures are introduced in order to comply with E. coli limits. On the other hand, there was headroom (ability to increase source loading) for nutrients. The method enables assessment of the necessary source load reductions to achieve water quality limits over broad areas such as large catchments or whole regions.