A covariate adjustment for zero-truncated approaches to estimating the size of hidden and elusive populations

In this paper we consider the estimation of population size from onesource capture–recapture data, that is, a list in which individuals can potentially be found repeatedly and where the question is how many individuals are missed by the list. As a typical example, we provide data from a drug user study in Bangkok from 2001 where the list consists of drug users who repeatedly contact treatment institutions. Drug users with 1, 2, 3, . . . contacts occur, but drug users with zero contacts are not present, requiring the size of this group to be estimated. Statistically, these data can be considered as stemming from a zero-truncated count distribution.We revisit an estimator for the population size suggested by Zelterman that is known to be robust under potential unobserved heterogeneity. We demonstrate that the Zelterman estimator can be viewed as a maximum likelihood estimator for a locally truncated Poisson likelihood which is equivalent to a binomial likelihood. This result allows the extension of the Zelterman estimator by means of logistic regression to include observed heterogeneity in the form of covariates. We also review an estimator proposed by Chao and explain why we are not able to obtain similar results for this estimator. The Zelterman estimator is applied in two case studies, the first a drug user study from Bangkok, the second an illegal immigrant study in the Netherlands. Our results suggest the new estimator should be used, in particular, if substantial unobserved heterogeneity is present.

Effect of growth time on the surface and adhesion properties of Lactobacillus rhamnosus GG

Aims: To investigate the changes in the surface properties of Lactobacillus rhamnosus GG during growth, and relate them with the ability of the Lactobacillus cells to adhere to Caco-2 cells. Methods and Results: Lactobacillus rhamnosus GG was grown in complex medium, and cell samples taken at four time points and freeze dried. Untreated and trypsin treated freeze dried samples were analysed for their composition using SDS-PAGE analysis and Fourier transform infrared spectroscopy (FTIR), hydrophobicity and zeta potential, and for their ability to adhere to Caco-2 cells. The results suggested that in the case of early exponential phase samples (4 and 8 h), the net surface properties, i.e. hydrophobicity and charge, were determined to a large extent by anionic hydrophilic components, whereas in the case of stationary phase samples (13 and 26 h), hydrophobic proteins seemed to play the biggest role. Considerable differences were also observed between the ability of the different samples to adhere to Caco-2 cells; maximum adhesion was observed for the early stationary phase sample (13 h). The results suggested that the adhesion to Caco-2 cells was influenced by both proteins and non-proteinaceous compounds present on the surface of the Lactobacillus cells. Conclusion: The surface properties of Lact. rhamnosus GG changed during growth, which in return affected the ability of the Lactobacillus cells to adhere to Caco-2 cells. Significance and Impact of the Study: The levels of adhesion of Lactobacillus cells to Caco-2 cells were influenced by the growth time and reflected changes on the bacterial surface. This study provides critical information on the physicochemical factors that influence bacterial adhesion to intestinal cells.

Influence of agricultural management, sward structure and food resources on grassland field use by birds in lowland England.

Agricultural management of grassland in lowland Britain has changed fundamentally in the last 50 years, resulting in spatial and structural uniformity within the pastoral landscape. The full extent to which these changes may have reduced the suitability of grassland as foraging habitat for birds is unknown. This study investigated the mechanisms by which these changes have impacted on birds and their food supplies. We quantified field use by birds in summer and winter in two grassland areas of lowland England (Devon and Buckinghamshire) over 3 years, relating bird occurrence to the management, sward structure and seed and invertebrate food resources of individual fields. Management intensity was defined in terms of annual nitrogen input. There was no consistent effect of management intensity on total seed head production, although those of grasses generally increased with inputs while forbs were rare throughout. Relationships between management intensity and abundance of soil and epigeal invertebrates were complex. Soil beetle larvae were consistently lower in abundance, and surface-active beetle larvae counts consistently higher, in intensively managed fields. Foliar invertebrates showed more consistent negatively relationships with management intensity. Most bird species occurred at low densities. There were consistent relationships across regions and years between the occurrence of birds and measures of field management. In winter, there was a tendency towards higher occupancy of intensively managed fields by species feeding on soil invertebrates. In summer, there were few such relationships, although many species avoided fields with tall swards. Use of fields by birds was generally not related to measures of seed or invertebrate food abundance. While granivorous species were perhaps too rare to detect a relationship, in insectivores the strong negative relationships (in summer) with sward height suggested that access to food may be the critical factor. While it appears that intensification of grassland management has been deleterious to the summer food resources of insectivorous birds that use insects living within the grass sward, intensification may have been beneficial to several species in winter through the enhancement of soil invertebrates. Synthesis and applications. We suggest that attempts to restore habitat quality for birds in grassland landscapes need to create a range of management intensities and sward structures at the field and farm scales. A greater understanding of methods to enhance prey accessibility, as well as abundance, for insectivorous birds is required.

Experimental verification of suction sampler capture efficiency in grasslands of differing vegetation height and structure

1. Suction sampling is a popular method for the collection of quantitative data on grassland invertebrate populations, although there have been no detailed studies into the effectiveness of the method. 2. We investigate the effect of effort (duration and number of suction samples) and sward height on the efficiency of suction sampling of grassland beetle, true bug, planthopper and spider Populations. We also compare Suction sampling with an absolute sampling method based on the destructive removal of turfs. 3. Sampling for durations of 16 seconds was sufficient to collect 90% of all individuals and species of grassland beetles, with less time required for the true bugs, spiders and planthoppers. The number of samples required to collect 90% of the species was more variable, although in general 55 sub-samples was sufficient for all groups, except the true bugs. Increasing sward height had a negative effect on the capture efficiency of suction sampling. 4. The assemblage structure of beetles, planthoppers and spiders was independent of the sampling method (suction or absolute) used. 5. Synthesis and applications. In contrast to other sampling methods used in grassland habitats (e.g. sweep netting or pitfall trapping), suction sampling is an effective quantitative tool for the measurement of invertebrate diversity and assemblage structure providing sward height is included as a covariate. The effective sampling of beetles, true bugs, planthoppers and spiders altogether requires a minimum sampling effort of 110 sub-samples of duration of 16 seconds. Such sampling intensities can be adjusted depending on the taxa sampled, and we provide information to minimize sampling problems associated with this versatile technique. Suction sampling should remain an important component in the toolbox of experimental techniques used during both experimental and management sampling regimes within agroecosystems, grasslands or other low-lying vegetation types.

A cross-taxonomic index for quantifying the health of farmland biodiversity

P>1. The development of sustainable, multi-functional agricultural systems involves reconciling the needs of agricultural production with the objectives for environmental protection, including biodiversity conservation. However, the definition of sustainability remains ambiguous and it has proven difficult to identify suitable indicators for monitoring progress towards, and the successful achievement of, sustainability. 2. In this study, we show that a trait-based approach can be used to assess the detrimental impacts of agricultural change to a broad range of taxonomic groupings and derive a standardised index of farmland biodiversity health, built around an objective of achieving stable or increasing populations in all species associated with agricultural landscapes. 3. To demonstrate its application, we assess the health of UK farmland biodiversity relative to this goal. Our results suggest that the populations of two-thirds of 333 plant and animal species assessed are unsustainable under current UK agricultural practices. 4. We then explore the potential benefits of an agri-environment scheme, Entry Level Stewardship (ELS), to farmland biodiversity in the UK under differing levels of risk mitigation delivery. We show that ELS has the potential to make a significant contribution to progress towards sustainability targets but that this potential is severely restricted by current patterns of scheme deployment. 5.Synthesis and applications: We have developed a cross-taxonomic sustainability index which can be used to assess both the current health of farmland biodiversity and the impacts of future agricultural changes relative to quantitative biodiversity targets. Although biodiversity conservation is just one of a number of factors that must be considered when defining sustainability, we believe our cross-taxonomic index has the potential to be a valuable tool for guiding the development of sustainable agricultural systems.

Toward a combined seasonal weather and crop productivity forecasting system: Determination of the working spatial scale

A methodology is presented for the development of a combined seasonal weather and crop productivity forecasting system. The first stage of the methodology is the determination of the spatial scale(s) on which the system could operate; this determination has been made for the case of groundnut production in India. Rainfall is a dominant climatic determinant of groundnut yield in India. The relationship between yield and rainfall has been explored using data from 1966 to 1995. On the all-India scale, seasonal rainfall explains 52% of the variance in yield. On the subdivisional scale, correlations vary between variance r(2) = 0.62 (significance level p < 10(-4)) and a negative correlation with r(2) = 0.1 (p = 0.13). The spatial structure of the relationship between rainfall and groundnut yield has been explored using empirical orthogonal function (EOF) analysis. A coherent, large-scale pattern emerges for both rainfall and yield. On the subdivisional scale (similar to 300 km), the first principal component (PC) of rainfall is correlated well with the first PC of yield (r(2) = 0.53, p < 10(-4)), demonstrating that the large-scale patterns picked out by the EOFs are related. The physical significance of this result is demonstrated. Use of larger averaging areas for the EOF analysis resulted in lower and (over time) less robust correlations. Because of this loss of detail when using larger spatial scales, the subdivisional scale is suggested as an upper limit on the spatial scale for the proposed forecasting system. Further, district-level EOFs of the yield data demonstrate the validity of upscaling these data to the subdivisional scale. Similar patterns have been produced using data on both of these scales, and the first PCs are very highly correlated (r(2) = 0.96). Hence, a working spatial scale has been identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modeling work for the case of groundnut production in India. Last, the change in correlation between yield and seasonal rainfall during the study period has been examined using seasonal totals and monthly EOFs. A further link between yield and subseasonal variability is demonstrated via analysis of dynamical data.

Simulation of crop yields using ERA-40: Limits to skill and nonstationarity in weather-yield relationships

Reanalysis data provide an excellent test bed for impacts prediction systems. because they represent an upper limit on the skill of climate models. Indian groundnut (Arachis hypogaea L.) yields have been simulated using the General Large-Area Model (GLAM) for annual crops and the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40). The ability of ERA-40 to represent the Indian summer monsoon has been examined. The ability of GLAM. when driven with daily ERA-40 data, to model both observed yields and observed relationships between subseasonal weather and yield has been assessed. Mean yields "were simulated well across much of India. Correlations between observed and modeled yields, where these are significant. are comparable to correlations between observed yields and ERA-40 rainfall. Uncertainties due to the input planting window, crop duration, and weather data have been examined. A reduction in the root-mean-square error of simulated yields was achieved by applying bias correction techniques to the precipitation. The stability of the relationship between weather and yield over time has been examined. Weather-yield correlations vary on decadal time scales. and this has direct implications for the accuracy of yield simulations. Analysis of the skewness of both detrended yields and precipitation suggest that nonclimatic factors are partly responsible for this nonstationarity. Evidence from other studies, including data on cereal and pulse yields, indicates that this result is not particular to groundnut yield. The detection and modeling of nonstationary weather-yield relationships emerges from this study as an important part of the process of understanding and predicting the impacts of climate variability and change on crop yields.

Integrating socio-economics and ecology: a taxonomy of quantitative methods and a review of their use in agro-ecology

Answering many of the critical questions in conservation, development and environmental management requires integrating the social and natural sciences. However, understanding the array of available quantitative methods and their associated terminology presents a major barrier to successful collaboration. We provide an overview of quantitative socio-economic methods that distils their complexity into a simple taxonomy. We outline how each has been used in conjunction with ecological models to address questions relating to the management of socio-ecological systems. We review the application of social and ecological quantitative concepts to agro-ecology and classify the approaches used to integrate the two disciplines. Our review included all published integrated models from 2003 to 2008 in 27 journals that publish agricultural modelling research. Although our focus is on agro-ecology, many of the results are broadly applicable to other fields involving an interaction between human activities and ecology. We found 36 papers that integrated social and ecological concepts in a quantitative model. Four different approaches to integration were used, depending on the scale at which human welfare was quantified. Most models viewed humans as pure profit maximizers, both when calculating welfare and predicting behaviour. Synthesis and applications. We reached two main conclusions based on our taxonomy and review. The first is that quantitative methods that extend predictions of behaviour and measurements of welfare beyond a simple market value basis are underutilized by integrated models. The second is that the accuracy of prediction for integrated models remains largely unquantified. Addressing both problems requires researchers to reach a common understanding of modelling goals and data requirements during the early stages of a project.

Genotypic and environmental influences on the performance of wheat root systems

Genetic and environmental factors interact to determine the growth and activity of crop root systems. This paper examines the effects of agronomic management and genotype on wheat root systems in the UK and Australia, and suggests ways in which root limitations to crop performance might be alleviated. In a field study in the UK which examined late-season growth and activity, fungicide maintained the size of the root system during early grain-filling, and there were significant differences between cultivars in root distribution with depth below 0.3 m. Shamrock had a longer root system below 0.3 m than varieties such as Hereward and Consort. Fungicide significantly increased root growth at 0.1-0.2 m in one season. In Australia, a wheat line selected for high shoot vigour had associated root vigour during early seedling growth but the effect on root growth did not persist. The results provide examples of genotypic differences in wheat root growth under field conditions which interact with agronomic management in ways which can be exploited to benefit growth and yield in diverse environments.

Bayes estimates of distance-to-market: Transactions costs, cooperatives and milk-market development in the Ethiopian highlands

Rationalizing non-participation as a resource deficiency in the household, this paper identifies strategies for milk-market development in the Ethiopian highlands. The additional amounts of covariates required for Positive marketable surplus -'distances-to market'-are computed from a model in which production and sales are correlated; sales are left-censored at some Unobserved thresholds production efficiencies are heterogeneous: and the data are in the form of a panel. Incorporating these features into the modeling exercise ant because they are fundamental to the data-generating environment. There are four reasons. First, because production and sales decisions are enacted within the same household, both decisions are affected by the same exogenous shocks, and production and sales are therefore likely to be correlated. Second. because selling, involves time and time is arguably the most important resource available to a subsistence household, the minimum Sales amount is not zero but, rather, some unobserved threshold that lies beyond zero. Third. the Potential existence of heterogeneous abilities in management, ones that lie latent from the econometrician's perspective, suggest that production efficiencies should be permitted to vary across households. Fourth, we observe a single set of households during multiple visits in a single production year. The results convey clearly that institutional and production) innovations alone are insufficient to encourage participation. Market-precipitating innovation requires complementary inputs, especially improvements in human capital and reductions in risk. Copyright (c) 20 08 John Wiley & Sons, Ltd.