Global trends and factors associated with the illegal killing of elephants: a hierarchical bayesian analysis of carcass encounter data

Elephant poaching and the ivory trade remain high on the agenda at meetings of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Well-informed debates require robust estimates of trends, the spatial distribution of poaching, and drivers of poaching. We present an analysis of trends and drivers of an indicator of elephant poaching of all elephant species. The site-based monitoring system known as Monitoring the Illegal Killing of Elephants (MIKE), set up by the 10th Conference of the Parties of CITES in 1997, produces carcass encounter data reported mainly by anti-poaching patrols. Data analyzed were site by year totals of 6,337 carcasses from 66 sites in Africa and Asia from 2002–2009. Analysis of these observational data is a serious challenge to traditional statistical methods because of the opportunistic and non-random nature of patrols, and the heterogeneity across sites. Adopting a Bayesian hierarchical modeling approach, we used the proportion of carcasses that were illegally killed (PIKE) as a poaching index, to estimate the trend and the effects of site- and country-level factors associated with poaching. Important drivers of illegal killing that emerged at country level were poor governance and low levels of human development, and at site level, forest cover and area of the site in regions where human population density is low. After a drop from 2002, PIKE remained fairly constant from 2003 until 2006, after which it increased until 2008. The results for 2009 indicate a decline. Sites with PIKE ranging from the lowest to the highest were identified. The results of the analysis provide a sound information base for scientific evidence-based decision making in the CITES process.

Resistance as a factor in environmental exposure of anticoagulant rodenticides: a modelling approach

Anticoagulant rodenticide (AR) resistance in Norway rat populations has been a problem for fifty years, however its impact on non-target species, particularly predatory and scavenging animals has received little attention. Field trials were conducted on farms in Germany and England where resistance to anticoagulant rodenticides had been confirmed. Resistance is conferred by different mutations of the VKORC1 gene in each of these regions: tyrosine139cysteine in Germany and leucine120glutamine in England. A modelling approach was used to study the transference of the anticoagulants into the environment during treatments for Norway rat control. Baiting with brodifacoum resulted in lower levels of AR entering the food chain via the rats and lower numbers of live rats carrying residues during and after the trials due to its lower application rate and efficacy against resistant rats. Bromadiolone and difenacoum resulted in markedly higher levels of AR uptake into the rat population and larger numbers of live rats carrying residues during the trials and for long periods after the baiting period. Neither bromadiolone nor difenacoum provided full control on any of the treated farms. In resistant areas where ineffective compounds are used there is the potential for higher levels of AR exposure to non-target animals, particularly predators of rats and scavengers of rat carcasses. Thus, resistance influences the total amount of AR available to non-targets and should be considered when dealing with rat infestations, as resistance-breakers may present a lower risk to wildlife.

Skilloscopy: Bayesian modeling of decision makers' skill

This paper proposes and demonstrates an approach, Skilloscopy, to the assessment of decision makers. In an increasingly sophisticated, connected and information-rich world, decision making is becoming both more important and more difficult. At the same time, modelling decision-making on computers is becoming more feasible and of interest, partly because the information-input to those decisions is increasingly on record. The aims of Skilloscopy are to rate and rank decision makers in a domain relative to each other: the aims do not include an analysis of why a decision is wrong or suboptimal, nor the modelling of the underlying cognitive process of making the decisions. In the proposed method a decision-maker is characterised by a probability distribution of their competence in choosing among quantifiable alternatives. This probability distribution is derived by classic Bayesian inference from a combination of prior belief and the evidence of the decisions. Thus, decision-makers’ skills may be better compared, rated and ranked. The proposed method is applied and evaluated in the gamedomain of Chess. A large set of games by players across a broad range of the World Chess Federation (FIDE) Elo ratings has been used to infer the distribution of players’ rating directly from the moves they play rather than from game outcomes. Demonstration applications address questions frequently asked by the Chess community regarding the stability of the Elo rating scale, the comparison of players of different eras and/or leagues, and controversial incidents possibly involving fraud. The method of Skilloscopy may be applied in any decision domain where the value of the decision-options can be quantified.

Steady state and dynamic modelling of nitrogen in the River Kennet: impacts of land use change since the 1930s

Steady state and dynamic models have been developed and applied to the River Kennet system. Annual nitrogen exports from the land surface to the river have been estimated based on land use from the 1930s and the 1990s. Long term modelled trends indicate that there has been a large increase in nitrogen transport into the river system driven by increased fertiliser application associated with increased cereal production, increased population and increased livestock levels. The dynamic model INCA Integrated Nitrogen in Catchments. has been applied to simulate the day-to-day transport of N from the terrestrial ecosystem to the riverine environment. This process-based model generates spatial and temporal data and reproduces the observed instream concentrations. Applying the model to current land use and 1930s land use indicates that there has been a major shift in the short term dynamics since the 1930s, with increased river and groundwater concentrations caused by both non-point source pollution from agriculture and point source discharges. �

The Natural History of Slapton Ley Nature Reserve XVIII: nitrogen and phosphorus losses from the catchment - an export coefficient modelling approach

Nitrogen and phosphorus losses from the catchment of Slapton Ley, a small coastal lake in SW England, were calculated using an adaptation of a model developed by Jorgensen (1980). A detailed survey of the catchment revealed that its land use is dominated by both permanent and temporary grassland (respectively 38 and 32% of its total area), and that the remainder is made up of the cultivation of cereals and field vegetables, and market gardening. Livestock numbers in the catchment constitute ca. 6600 head of cattle, 10,000 sheep, 590 pigs, 1700 poultry and 58 horses. The permanent human population of the area is ca. 2000, served by two small gravity-fed sewage treatment works (STWs). Inputs to, and losses from, farmland in the catchment were computed using Jorgensen’s model, and coefficients derived from the data of Cooke (1976), Gostick (1982), Rast and Lee (1983) and Vollenweider (1968). Allowing for outputs from STWs, the total annual external load of N and P upon Slapton Ley is 160 t (35 kg ha-1) a-1 N, and 4.8 t (1.05 kg ha-1) a-1 P. Accordingly to Vollenweider (1968, 1975), such loadings exceed OECD permissible level by a factor of ca. 50 in the case of N, and ca. 5 in that of P. In order to reduce nutrient loads, attention would need to be paid to both STW and agricultural sources.

The structure of paranoia in the general population

Background: Psychotic phenomena appear to form a continuum with normal experience and beliefs, and may build on common emotional interpersonal concerns. Aims: We tested predictions that paranoid ideation is exponentially distributed and hierarchically arranged in the general population, and that persecutory ideas build on more common cognitions of mistrust, interpersonal sensitivity and ideas of reference. Method: Items were chosen from the Structured Clinical Interview for DSM-IV Axis II Disorders (SCID-II) questionnaire and the Psychosis Screening Questionnaire in the second British National Survey of Psychiatric Morbidity (n = 8580), to test a putative hierarchy of paranoid development using confirmatory factor analysis, latent class analysis and factor mixture modelling analysis. Results: Different types of paranoid ideation ranged in frequency from less than 2% to nearly 30%. Total scores on these items followed an almost perfect exponential distribution (r = 0.99). Our four a priori first-order factors were corroborated (interpersonal sensitivity; mistrust; ideas of reference; ideas of persecution). These mapped onto four classes of individual respondents: a rare, severe, persecutory class with high endorsement of all item factors, including persecutory ideation; a quasi-normal class with infrequent endorsement of interpersonal sensitivity, mistrust and ideas of reference, and no ideas of persecution; and two intermediate classes, characterised respectively by relatively high endorsement of items relating to mistrust and to ideas of reference. Conclusions: The paranoia continuum has implications for the aetiology, mechanisms and treatment of psychotic disorders, while confirming the lack of a clear distinction from normal experiences and processes.

Overall and income specific effect on prevalence of overweight and obesity of 20% sugar sweetened drink tax in UK: econometric and comparative risk assessment modelling study

Objective To model the overall and income specific effect of a 20% tax on sugar sweetened drinks on the prevalence of overweight and obesity in the UK. Design Econometric and comparative risk assessment modelling study. Setting United Kingdom. Population Adults aged 16 and over. Intervention A 20% tax on sugar sweetened drinks. Main outcome measures The primary outcomes were the overall and income specific changes in the number and percentage of overweight (body mass index ≥25) and obese (≥30) adults in the UK following the implementation of the tax. Secondary outcomes were the effect by age group (16-29, 30-49, and ≥50 years) and by UK constituent country. The revenue generated from the tax and the income specific changes in weekly expenditure on drinks were also estimated. Results A 20% tax on sugar sweetened drinks was estimated to reduce the number of obese adults in the UK by 1.3% (95% credible interval 0.8% to 1.7%) or 180 000 (110 000 to 247 000) people and the number who are overweight by 0.9% (0.6% to 1.1%) or 285 000 (201 000 to 364 000) people. The predicted reductions in prevalence of obesity for income thirds 1 (lowest income), 2, and 3 (highest income) were 1.3% (0.3% to 2.0%), 0.9% (0.1% to 1.6%), and 2.1% (1.3% to 2.9%). The effect on obesity declined with age. Predicted annual revenue was £276m (£272m to £279m), with estimated increases in total expenditure on drinks for income thirds 1, 2, and 3 of 2.1% (1.4% to 3.0%), 1.7% (1.2% to 2.2%), and 0.8% (0.4% to 1.2%). Conclusions A 20% tax on sugar sweetened drinks would lead to a reduction in the prevalence of obesity in the UK of 1.3% (around 180 000 people). The greatest effects may occur in young people, with no significant differences between income groups. Both effects warrant further exploration. Taxation of sugar sweetened drinks is a promising population measure to target population obesity, particularly among younger adults.

A generalised Bayesian instrumental variable approach under student t-distributed errors with application

Bayesian analysis is given of an instrumental variable model that allows for heteroscedasticity in both the structural equation and the instrument equation. Specifically, the approach for dealing with heteroscedastic errors in Geweke (1993) is extended to the Bayesian instrumental variable estimator outlined in Rossi et al. (2005). Heteroscedasticity is treated by modelling the variance for each error using a hierarchical prior that is Gamma distributed. The computation is carried out by using a Markov chain Monte Carlo sampling algorithm with an augmented draw for the heteroscedastic case. An example using real data illustrates the approach and shows that ignoring heteroscedasticity in the instrument equation when it exists may lead to biased estimates.

Assessing the impact on chronic disease of incorporating the societal cost of greenhouse gases into the price of food: an econometric and comparative risk assessment modelling study

Objectives To model the impact on chronic disease of a tax on UK food and drink that internalises the wider costs to society of greenhouse gas (GHG) emissions and to estimate the potential revenue. Design An econometric and comparative risk assessment modelling study. Setting The UK. Participants The UK adult population. Interventions Two tax scenarios are modelled: (A) a tax of £2.72/tonne carbon dioxide equivalents (tCO2e)/100 g product applied to all food and drink groups with above average GHG emissions. (B) As with scenario (A) but food groups with emissions below average are subsidised to create a tax neutral scenario. Outcome measures Primary outcomes are change in UK population mortality from chronic diseases following the implementation of each taxation strategy, the change in the UK GHG emissions and the predicted revenue. Secondary outcomes are the changes to the micronutrient composition of the UK diet. Results Scenario (A) results in 7770 (95% credible intervals 7150 to 8390) deaths averted and a reduction in GHG emissions of 18 683 (14 665to 22 889) ktCO2e/year. Estimated annual revenue is £2.02 (£1.98 to £2.06) billion. Scenario (B) results in 2685 (1966 to 3402) extra deaths and a reduction in GHG emissions of 15 228 (11 245to 19 492) ktCO2e/year. Conclusions Incorporating the societal cost of GHG into the price of foods could save 7770 lives in the UK each year, reduce food-related GHG emissions and generate substantial tax revenue. The revenue neutral scenario (B) demonstrates that sustainability and health goals are not always aligned. Future work should focus on investigating the health impact by population subgroup and on designing fiscal strategies to promote both sustainable and healthy diets.

Effects of plant pathogens on population dynamics and community composition in grassland ecosystems: two case studies

Grassland ecosystems comprise a major portion of the earth’s terrestrial surface, ranging from high-input cultivated monocultures or simple species mixtures to relatively unmanaged but dynamic systems. Plant pathogens are a component of these systems with their impact dependent on many interacting factors, including grassland species population dynamics and community composition, the topics covered in this paper. Plant pathogens are affected by these interactions and also act reciprocally by modifying their nature. We review these features of disease in grasslands and then introduce the 150-year long-term Park Grass Experiment (PGE) at Rothamsted Research in the UK. We then consider in detail two plant-pathogen systems present in the PGE, Tragopogon pratensis-Puccinia hysterium and Holcus lanata-Puccinia coronata. These two systems have very different life history characteristics: the first, a biennial member of the Asteraceae infected by its host-specific, systemic rust; the second, a perennial grass infected by a host-non-specific rust. We illustrate how observational, experimental and modelling studies can contribute to a better understanding of population dynamics, competitive interactions and evolutionary outcomes. With Tragopogon pratensis-Puccinia hysterium, characterised as an “outbreak” species in the PGE, we show that pathogen-induced mortality is unlikely to be involved in host population regulation; and that the presence of even a short-lived seed-bank can affect the qualitative outcomes of the host-pathogen dynamics. With Holcus lanata-Puccinia coronata, we show how nutrient conditions can affect adaptation in terms of host defence mechanisms, and that co-existence of competing species affected by a common generalist pathogen is unlikely.

Forecasting with Bayesian multivariate vintage-based VARs

We consider the forecasting of macroeconomic variables that are subject to revisions, using Bayesian vintage-based vector autoregressions. The prior incorporates the belief that, after the first few data releases, subsequent ones are likely to consist of revisions that are largely unpredictable. The Bayesian approach allows the joint modelling of the data revisions of more than one variable, while keeping the concomitant increase in parameter estimation uncertainty manageable. Our model provides markedly more accurate forecasts of post-revision values of inflation than do other models in the literature.

Effects of noise and by-catch on a Danish harbour porpoise population

Ships and wind turbines generate noise, which can have a negative impact on marine mammal populations by scaring animals away. Effective modelling of how this affects the populations has to take account of the location and timing of disturbances. Here we construct an individual-based model of harbour porpoises in the Inner Danish Waters. Individuals have their own energy budgets constructed using established principles of physiological ecology. Data are lacking on the spatial distribution of food which is instead inferred from knowledge of time-varying porpoise distributions. The model produces plausible patterns of population dynamics and matches well the age distribution of porpoises caught in by-catch. It estimates the effect of existing wind farms as a 10% reduction in population size when food recovers fast (after two days). Proposed new wind farms and ships do not result in further population declines. The population is however sensitive to variations in mortality resulting from by-catch and to the speed at which food recovers after being depleted. If food recovers slowly the effect of wind turbines becomes negligible, whereas ships are estimated to have a significant negative impact on the population. Annual by-catch rates ≥10% lead to monotonously decreasing populations and to extinction, and even the estimated by-catch rate from the adjacent area (approximately 4.1%) has a strong impact on the population. This suggests that conservation efforts should be more focused on reducing by-catch in commercial gillnet fisheries than on limiting the amount of anthropogenic noise. Individual-based models are unique in their ability to take account of the location and timing of disturbances and to show their likely effects on populations. The models also identify deficiencies in the existing database and can be used to set priorities for future field research.

Progress in observing and modelling the urban boundary layer

The urban boundary layer (UBL) is the part of the atmosphere in which most of the planet’s population now lives, and is one of the most complex and least understood microclimates. Given potential climate change impacts and the requirement to develop cities sustainably, the need for sound modelling and observational tools becomes pressing. This review paper considers progress made in studies of the UBL in terms of a conceptual framework spanning microscale to mesoscale determinants of UBL structure and evolution. Considerable progress in observing and modelling the urban surface energy balance has been made. The urban roughness sub-layer is an important region requiring attention as assumptions about atmospheric turbulence break down in this layer and it may dominate coupling of the surface to the UBL due to its considerable depth. The upper 90% of the UBL (mixed and residual layers) remains under-researched but new remote sensing methods and high resolution modelling tools now permit rapid progress. Surface heterogeneity dominates from neighbourhood to regional scales and should be more strongly considered in future studies. Specific research priorities include humidity within the UBL, high-rise urban canopies and the development of long-term, spatially extensive measurement networks coupled strongly to model development.

Neural population models and cortical field theory: overview

The term neural population models (NPMs) is used here as catchall for a wide range of approaches that have been variously called neural mass models, mean field models, neural field models, bulk models, and so forth. All NPMs attempt to describe the collective action of neural assemblies directly. Some NPMs treat the densely populated tissue of cortex as an excitable medium, leading to spatially continuous cortical field theories (CFTs). An indirect approach would start by modelling individual cells and then would explain the collective action of a group of cells by coupling many individual models together. In contrast, NPMs employ collective state variables, typically defined as averages over the group of cells, in order to describe the population activity directly in a single model. The strength and the weakness of his approach are hence one and the same: simplification by bulk. Is this justified and indeed useful, or does it lead to oversimplification which fails to capture the pheno ...

Effects of agricultural management practices on earthworm populations and crop yield: validation and application of a mechanistic modelling approach

There is little consensus on how agriculture will meet future food demands sustainably. Soils and their biota play a crucial role by mediating ecosystem services that support agricultural productivity. However, a multitude of site-specific environmental factors and management practices interact to affect the ability of soil biota to perform vital functions, confounding the interpretation of results from experimental approaches. Insights can be gained through models, which integrate the physiological, biological and ecological mechanisms underpinning soil functions. We present a powerful modelling approach for predicting how agricultural management practices (pesticide applications and tillage) affect soil functioning through earthworm populations. By combining energy budgets and individual-based simulation models, and integrating key behavioural and ecological drivers, we accurately predict population responses to pesticide applications in different climatic conditions. We use the model to analyse the ecological consequences of different weed management practices. Our results demonstrate that an important link between agricultural management (herbicide applications and zero, reduced and conventional tillage) and earthworms is the maintenance of soil organic matter (SOM). We show how zero and reduced tillage practices can increase crop yields while preserving natural ecosystem functions. This demonstrates how management practices which aim to sustain agricultural productivity should account for their effects on earthworm populations, as their proliferation stimulates agricultural productivity. Synthesis and applications. Our results indicate that conventional tillage practices have longer term effects on soil biota than pesticide control, if the pesticide has a short dissipation time. The risk of earthworm populations becoming exposed to toxic pesticides will be reduced under dry soil conditions. Similarly, an increase in soil organic matter could increase the recovery rate of earthworm populations. However, effects are not necessarily additive and the impact of different management practices on earthworms depends on their timing and the prevailing environmental conditions. Our model can be used to determine which combinations of crop management practices and climatic conditions pose least overall risk to earthworm populations. Linking our model mechanistically to crop yield models would aid the optimization of crop management systems by exploring the trade-off between different ecosystem services.