Summary1. Acoustic monitoring can be an efficient, cheap, non-invasive alternative to physical trapping of individuals. Spatially explicit capture-recapture (SECR) methods have been proposed to estimate calling animal abundance and density from data collected by a fixed array of microphones. However, these methods make some assumptions that are unlikely to hold in many situations, and the consequences of violating these are yet to be investigated. 2. We generalize existing acoustic SECR methodology, enabling these methods to be used in a much wider variety of situations. We incorporate time-of-arrival (TOA) data collected by the microphone array, increasing the precision of calling animal density estimates. We use our method to estimate calling male density of the Cape Peninsula Moss Frog Arthroleptella lightfooti. 3. Our method gives rise to an estimator of calling animal density that has negligible bias, and 95% confidence intervals with appropriate coverage. We show that using TOA information can substantially improve estimate precision. 4. Our analysis of the A. lightfooti data provides the first statistically rigorous estimate of calling male density for an anuran population using a microphone array. This method fills a methodological gap in the monitoring of frog populations and is applicable to acoustic monitoring of other species that call or vocalize.

A fundamental problem in wildlife ecology and management is estimation of population size or density. The two dominant methods in this area are capture-recapture (CR) and distance sampling (DS), each with its own largely separate literature. We develop a class of models that synthesizes them. It accommodates a spectrum of models ranging from nonspatial CR models (with no information on animal locations) through to DS and mark-recapture distance sampling (MRDS) models, in which animal locations are observed without error. Between these lie spatially explicit capture-recapture (SECR) models that include only capture locations, and a variety of models with less location data than are typical of DS surveys but more than are normally used on SECR surveys. In addition to unifying CR and DS models, the class provides a means of improving inference from SECR models by adding supplementary location data, and a means of incorporating measurement error into DS and MRDS models. We illustrate their utility by comparing inference on acoustic surveys of gibbons and frogs using only capture locations, using estimated angles (gibbons) and combinations of received signal strength and time-of-arrival data (frogs), and on a visual MRDS survey of whales, comparing estimates with exact and estimated distances. Supplementary materials for this article are available online.

Summary1. Global amphibian declines have resulted in a vital need for monitoring programmes that follow population trends. Monitoring using advertisement calls is ideal as choruses are undisturbed during data collection. However, methods currently employed by managers frequently rely on trained observers and/or do not provide density data on which to base trends. 2. This study explores the utility of monitoring using acoustic spatially explicit capturerecapture (aSCR) with time of arrival (ToA) and signal strength (SS) as a quantitative monitoring technique to measure call density of a threatened but visually cryptic anuran, the Cape peninsula moss frog Arthroleptella lightfooti. 3. The relationships between temporal and climatic variables (date, rainfall, temperature) and A. lightfooti call density at three study sites on the Cape peninsula, South Africa, were examined. Acoustic data, collected from an array of six microphones over 4 months during the winter breeding season, provided a time series of call density estimates. 4. Model selection indicated that call density was primarily associated with seasonality fitted as a quadratic function. Call density peaked mid-breeding season. At the main study site, the lowest recorded mean call density (0Á160 calls m À2 min À1 ) occurred in May and reached its peak mid-July (1Á259 calls m À2 min À1 ). The sites differed in call density, but also the effective sampling area. 5. Synthesis and applications. The monitoring technique, acoustic spatially explicit capturerecapture (aSCR), quantitatively estimates call density of calling animals without disturbing them or their environment. In addition, time of arrival (ToA) and signal strength (SS) data significantly add to the accuracy of call localization, which in turn increases precision of call density estimates without the need for specialist field staff. This technique appears ideally suited to aid the monitoring of visually cryptic, acoustically active species.

Some animal species are hard to see but easy to hear. Standard visual methods for estimating population density for such species are often ineffective or inefficient, but methods based on passive acoustics show more promise. We develop spatially explicit capture-recapture (SECR) methods for territorial vocalising species, in which humans act as an acoustic detector array. We use SECR and estimated bearing data from a single-occasion acoustic survey of a gibbon population in northeastern Cambodia to estimate the density of calling groups. The properties of the estimator are assessed using a simulation study, in which a variety of survey designs are also investigated. We then present a new form of the SECR likelihood for multi-occasion data which accounts for the stochastic availability of animals. In the context of gibbon surveys this allows model-based estimation of the proportion of groups that produce territorial vocalisations on a given day, thereby enabling the density of groups, instead of the density of calling groups, to be estimated. We illustrate the performance of this new estimator by simulation. We show that it is possible to estimate density reliably from human acoustic detections of visually cryptic species using SECR methods. For gibbon surveys we also show that incorporating observers’ estimates of bearings to detected groups substantially improves estimator performance. Using the new form of the SECR likelihood we demonstrate that estimates of availability, in addition to population density and detection function parameters, can be obtained from multi-occasion data, and that the detection function parameters are not confounded with the availability parameter. This acoustic SECR method provides a means of obtaining reliable density estimates for territorial vocalising species. It is also efficient in terms of data requirements since since it only requires routine survey data. We anticipate that the low-tech field requirements will make this method an attractive option in many situations where populations can be surveyed acoustically by humans.

The issue of medication safety is highly significant when anti-cancer therapy is used as a treatment modality due to the high potential for harm from these agents and the disease context in which they are being used. These guidelines provide recommendations on the safe prescribing, dispensing and administration of chemotherapy and related agents used in the treatment of cancer. The guidelines represent a multidisciplinary collaboration to standardise the complex process of providing chemotherapy for cancer and to enhance patient safety. These are consensus guidelines based on the best available evidence and expert opinion of professionals working in cancer care. The aim of these guidelines is to assist in the prevention of medication errors and to improve patient safety with respect to the treatment of cancer. This guidance is intended for a multi-disciplinary audience and will have most relevance for medical, nursing and pharmacy staff involved in the complex processes of delivering chemotherapy and associated treatment. The scope of the guidelines includes; all patients and age groups receiving chemotherapy and targeted therapy for the treatment of cancer and cancer therapy administered by any route in both the hospital and home setting. These guidelines should be seen as point of reference for practitioners providing cancer chemotherapy services.