Very short delays prior to escape from potential predators may function efficiently as adaptive risk-assessment periods

Periods between predator detection and an escape response (escape delays) by prey upon attack by a predator often arise because animals trade-off the benefits such a delay gives for assessing risk accurately with the costs of not escaping as quickly as possible. We tested whether freezing behaviour (complete immobility in a previously foraging bird) observed in chaffinches before escaping from an approaching potential threat functions as a period of risk-assessment, and whether information on predator identity is gained even when time available is very short. We flew either a model of a sparrowhawk (predator) or a woodpigeon (no threat) at single chaffinches. Escape delays were significantly shorter with the hawk, except when a model first appeared close to the chaffinch. Chaffinches were significantly more vigilant when they resumed feeding after exposure to the sparrowhawk compared to the woodpigeon showing that they were able to distinguish between threats, and this applied even when time available for assessment was short (an average of 0.29 s). Our results show freezing in chaffinches functions as an effective economic risk assessment period, and that threat information is gained even when very short periods of time are available during an attack.

Estimation of milk production from smallholder dairy cattle in the coastal lowlands of Kenya

A study was carried out on 92 smallholder farms in Kwale district in Coast Province of Kenya to estimate the milk yield. The effect of concentrate feed supplementation on milk yield was also evaluated. Data were collected during a one-year observational longitudinal study. Analysis was done for 371 observations following 63 calving events. The mean annual milk offtake was estimated at 2021 kg/cow. Forty-nine (77.8%) of the lactating cows were supplemented with concentrate feeds at varying rates of 0.5-3.0 kg/cow per day. Supplementary feeding of lactating cows led to a significantly higher mean daily milk yield compared to non-supplemented cows throughout the year (p<0.05). The mean annual milk offtake from supplemented cows (2195 kg/cow) was 18.6% more than offtake from non-supplemented cows, a difference that was statistically significant (p<0.05). Therefore, supplementary feeding of commercial feed concentrates was a rational management practice. It was also concluded that milk production from smallholder dairy cows in the coastal lowlands of Kenya was comparable to that from similar production systems but lower than national targets.

Seroprevalence estimation and risk factors for A. marginale on smallholder dairy farms in Tanzania

A cross-sectional serological survey of A. marginale was conducted on 200 randomly selected smallholder farms in each of the Tanga and Iringa Regions of Tanzania between January and April 1999. Sera, from dairy cattle of all ages, sexes and breeds were tested for antibodies against A. marginale using an indirect enzyme-linked immunosorbent assay. Antibodies to A. marginale were present in cattle throughout the study areas and the overall prevalence was 20% for Tanga and 37% for Iringa. The forces of infection based on the age seroprevalence profile were estimated at 8 for Tanga and 15 for Iringa per 100 cattle years-risk, respectively. In both regions, seroprevalence increased with age (β = 0.01 and 0.017 per year of age, p < 0.005, in Tanga and Iringa, respectively). Older animals in Iringa were significantly and negatively associated with decreased seropositivity (β = −0.002, p = 0.0029). Further results of logistic regression models reveal that geographic location of animals in Tanga was associated with seropositivity (odds ratio (OR) = 2.94, p = 0.005, for Tanga Rural and OR = 2.38, p = 0.066, for Muheza). Animals acquired as a gift in Iringa had higher odds for seropositivity than brought-in cattle (OR = 2.44, p = 0.005). Our study has identified and quantified some key risk factors that can guide planners devising disease control strategies.

Cross-sectional estimation of Babesia bovis antibody prevalence in cattle in two contrasting dairying areas in Tanzania

The crude prevalence of antibodies to Babesia bovis infection in cattle was estimated by serology using indirect ELISA during the period January to April, 1999. Sera were obtained from 1395 dairy cattle (of all ages, sexes and breeds) on smallholder farms, the majority being kept under a zero grazing regime. The crude prevalence of antibodies to Babesia bovis was 6 % for Tanga and 12 % for Iringa. The forces of infection based on the age sero-prevalence profile, were estimated at six for Iringa and four for Tanga per 100 cattle years-risk, respectively. Using random effect logistic regression as the analytical method, the factors (variables) of age, source of animals and geographic location were hypothesised to be associated with sero-positivity of Babesia bovis in the two regions.

Seroprevalence estimation and risk factors for A-marginale on smallholder dairy farms in Tanzania

A cross-sectional serological survey of A. marginale was conducted on 200 randomly selected smallholder farms in each of the Tanga and Iringa Regions of Tanzania between January and April 1999. Sera, from dairy cattle of all ages, sexes and breeds were tested for antibodies against A. marginale using an indirect enzyme-linked immunosorbent assay. Antibodies to A. marginale were present in cattle throughout the study areas and the overall prevalence was 20% for Tanga and 37% for Iringa. The forces of infection based on the age seroprevalence profile were estimated at 8 for Tanga and 15 for Iringa per 100 cattle years-risk, respectively. In both regions, seroprevalence increased with age (beta = 0.01 and 0.017 per year of age, p < 0.005, in Tanga and Iringa, respectively). Older animals in Iringa were significantly and negatively associated with decreased seropositivity (beta = -0.002, p = 0.0029). Further results of logistic regression models reveal that geographic location of animals in Tanga was associated with seropositivity (odds ratio (OR) = 2.94, p = 0.005, for Tanga Rural and OR = 2.38, p = 0.066, for Muheza). Animals acquired as a gift in Iringa had higher odds for seropositivity than brought-in cattle (OR = 2.44, p = 0.005). Our study has identified and quantified some key risk factors that can guide planners devising disease control strategies.

Testing symmetry and homogeneity in the almost ideal demand system with co-integrated data using fully modified estimation and the bootstrap

Conventional seemingly unrelated estimation of the almost ideal demand system is shown to lead to small sample bias and distortions in the size of a Wald test for symmetry and homogeneity when the data are co-integrated. A fully modified estimator is developed in an attempt to remedy these problems. It is shown that this estimator reduces the small sample bias but fails to eliminate the size distortion.. Bootstrapping is shown to be ineffective as a method of removing small sample bias in both the conventional and fully modified estimators. Bootstrapping is effective, however, as a method of removing. size distortion and performs equally well in this respect with both estimators.

Vegetation dynamics in abandoned crop fields on a Mediterranean island: Development of succession model and estimation of disturbance thresholds

This case study on the Sifnos island, Greece, assesses the main factors controlling vegetation succession following crop abandonment and describes the vegetation dynamics of maquis and phrygana formations in relation to alternative theories of secondary succession. Field survey data were collected and analysed at community as well as species level. The results show that vegetation succession on abandoned crop fields is determined by the combined effects of grazing intensity, soil and geological characteristics and time. The analysis determines the quantitative grazing thresholds that modify the successional pathway. Light grazing leads to dominance by maquis vegetation while overgrazing leads to phryganic vegetation. The proposed model shows that vegetation succession following crop abandonment is a complex multi-factor process where the final or the stable stage of the process is not predefined but depends on the factors affecting succession. An example of the use of succession models and disturbance thresholds as a policy assessment tool is presented by evaluating the likely vegetation impacts of the recent reform of the Common Agricultural Policy on Sifnos island over a 20-30-year time horizon. (c) 2006 Elsevier B.V. All rights reserved.

An adaptive approach to implementing bivariate group sequential clinical trial designs

In clinical trials, situations often arise where more than one response from each patient is of interest; and it is required that any decision to stop the study be based upon some or all of these measures simultaneously. Theory for the design of sequential experiments with simultaneous bivariate responses is described by Jennison and Turnbull (Jennison, C., Turnbull, B. W. (1993). Group sequential tests for bivariate response: interim analyses of clinical trials with both efficacy and safety endpoints. Biometrics 49:741-752) and Cook and Farewell (Cook, R. J., Farewell, V. T. (1994). Guidelines for monitoring efficacy and toxicity responses in clinical trials. Biometrics 50:1146-1152) in the context of one efficacy and one safety response. These expositions are in terms of normally distributed data with known covariance. The methods proposed require specification of the correlation, ρ between test statistics monitored as part of the sequential test. It can be difficult to quantify ρ and previous authors have suggested simply taking the lowest plausible value, as this will guarantee power. This paper begins with an illustration of the effect that inappropriate specification of ρ can have on the preservation of trial error rates. It is shown that both the type I error and the power can be adversely affected. As a possible solution to this problem, formulas are provided for the calculation of correlation from data collected as part of the trial. An adaptive approach is proposed and evaluated that makes use of these formulas and an example is provided to illustrate the method. Attention is restricted to the bivariate case for ease of computation, although the formulas derived are applicable in the general multivariate case.

An adaptive group sequential design for phase II/III clinical trials that select a single treatment from several

There is increasing interest in combining Phases II and III of clinical development into a single trial in which one of a small number of competing experimental treatments is ultimately selected and where a valid comparison is made between this treatment and the control treatment. Such a trial usually proceeds in stages, with the least promising experimental treatments dropped as soon as possible. In this paper we present a highly flexible design that uses adaptive group sequential methodology to monitor an order statistic. By using this approach, it is possible to design a trial which can have any number of stages, begins with any number of experimental treatments, and permits any number of these to continue at any stage. The test statistic used is based upon efficient scores, so the method can be easily applied to binary, ordinal, failure time, or normally distributed outcomes. The method is illustrated with an example, and simulations are conducted to investigate its type I error rate and power under a range of scenarios.

A practical comparison of group-sequential and adaptive designs

Sequential methods provide a formal framework by which clinical trial data can be monitored as they accumulate. The results from interim analyses can be used either to modify the design of the remainder of the trial or to stop the trial as soon as sufficient evidence of either the presence or absence of a treatment effect is available. The circumstances under which the trial will be stopped with a claim of superiority for the experimental treatment, must, however, be determined in advance so as to control the overall type I error rate. One approach to calculating the stopping rule is the group-sequential method. A relatively recent alternative to group-sequential approaches is the adaptive design method. This latter approach provides considerable flexibility in changes to the design of a clinical trial at an interim point. However, a criticism is that the method by which evidence from different parts of the trial is combined means that a final comparison of treatments is not based on a sufficient statistic for the treatment difference, suggesting that the method may lack power. The aim of this paper is to compare two adaptive design approaches with the group-sequential approach. We first compare the form of the stopping boundaries obtained using the different methods. We then focus on a comparison of the power of the different trials when they are designed so as to be as similar as possible. We conclude that all methods acceptably control type I error rate and power when the sample size is modified based on a variance estimate, provided no interim analysis is so small that the asymptotic properties of the test statistic no longer hold. In the latter case, the group-sequential approach is to be preferred. Provided that asymptotic assumptions hold, the adaptive design approaches control the type I error rate even if the sample size is adjusted on the basis of an estimate of the treatment effect, showing that the adaptive designs allow more modifications than the group-sequential method.

Variance estimation with highly stratified sampling designs with unequal probabilities

It is common practice to design a survey with a large number of strata. However, in this case the usual techniques for variance estimation can be inaccurate. This paper proposes a variance estimator for estimators of totals. The method proposed can be implemented with standard statistical packages without any specific programming, as it involves simple techniques of estimation, such as regression fitting.