Landscape-scale drivers of pollinator communities may depend on land-use configuration

Research into pollinators in managed landscapes has recently combined approaches of pollination ecology and landscape ecology, because key stressors are likely to interact across wide areas. While laboratory and field experiments are valuable for furthering understanding, studies are required to investigate the interacting drivers of pollinator health and diversity across a broader range of landscapes and a wider array of taxa. Here, we use a network of 96 study landscapes in six topographically diverse regions of Britain, to test the combined importance of honeybee density, insecticide loadings, floral resource availability and habitat diversity to pollinator communities. We also explore the interactions between these drivers and the cover and proximity of semi-natural habitat. We found that among our four drivers, only honeybee density was positively related to wild pollinator abundance and diversity, and the positive association between abundance and floral resources depended on insecticide loadings and habitat diversity. By contrast, our exploratory models including habitat composition metrics revealed a complex suite of interactive effects. These results demonstrate that improving pollinator community composition and health is unlikely to be achieved with general resource enhancements only. Rather, local land-use context should be considered in fine-tuning pollinator management and conservation. This article is part of the theme issue 'Natural processes influencing pollinator health: from chemistry to landscapes'.

Camera traps and activity signs to estimate wild boar density and derive abundance indices

BACKGROUND

Populations of wild boar and feral pigs are increasing worldwide, in parallel with their significant environmental and economic impact. Reliable methods of monitoring trends and estimating abundance are needed to measure the effects of interventions on population size. The main aims of this study, carried out in five English woodlands were: (i) to compare wild boar abundance indices obtained from camera trap surveys and from activity signs; and (ii) to assess the precision of density estimates in relation to different densities of camera traps. For each woodland, we calculated a passive activity index (PAI) based on camera trap surveys, rooting activity and wild boar trails on transects, and estimated absolute densities based on camera trap surveys.

RESULTS

PAIs obtained using different methods showed similar patterns. We found significant between-year differences in abundance of wild boar using PAIs based on camera trap surveys and on trails on transects, but not on signs of rooting on transects. The density of wild boar from camera trap surveys varied between 0.7 and 7 animals/km² . Increasing the density of camera traps above nine per km² did not increase the precision of the estimate of wild boar density.

CONCLUSION

PAIs based on number of wild boar trails and on camera trap data appear to be more sensitive to changes in population size than PAIs based on signs of rooting. For wild boar densities similar to those recorded in this study, nine camera traps per km² are sufficient to estimate the mean density of wild boar. © 2017 Crown copyright. Pest Management Science © 2017 Society of Chemical Industry.

An improved regulatory sampling method for mapping and representing plant disease from a limited number of samples

A key challenge for plant pathologists is to develop efficient methods to describe spatial patterns of disease spread accurately from a limited number of samples. Knowledge of disease spread is essential for informing and justifying plant disease management measures. A mechanistic modelling approach is adopted for disease mapping which is based on disease dispersal gradients and consideration of host pattern. The method is extended to provide measures of uncertainty for the estimates of disease at each host location. In addition, improvements have been made to increase computational efficiency by better initialising the disease status of unsampled hosts and speeding up the optimisation process of the model parameters. These improvements facilitate the practical use of the method by providing information on: (a) mechanisms of pathogen dispersal, (b) distance and pattern of disease spread, and (c) prediction of infection probabilities for unsampled hosts. Two data sets of disease observations, Huanglongbing (HLB) of citrus and strawberry powdery mildew, were used to evaluate the performance of the new method for disease mapping. The result showed that our method gave better estimates of precision for unsampled hosts, compared to both the original method and spatial interpolation. This enables decision makers to understand the spatial aspects of disease processes, and thus formulate regulatory actions accordingly to enhance disease control.

Indicator organisms for assessing sanitization during composting of plant wastes

The potential for using plant pathogens and seeds as indicator organisms for assessing sanitization of plant wastes during composting was tested in bench-scale flask and large-scale systems. Plasmodiophora brassicae was unsuitable due to high temperature tolerance in dry to moist composts, and detection of viable inoculum post-composting using bioassay plants not corresponding with that using TaqMan® PCR, possibly due to preservation of nucleic acids at elevated temperatures. Several other plant pathogens (Sclerotinia sclerotiorum, Microdochium nivale, Phytophthora cinnamomi and Phytophthora nicotianae) were unsuitable due their low temperature tolerance. Fusarium oxysporum f.sp. cepae and f.sp. radicis-lycopersici chlamydospores and tomato seeds were suitable indicators due to their moderate temperature tolerance and ease of viability testing post-composting. Abutilon seeds were more tolerant than tomato seeds of compost temperatures ≥52°C but more prone to degradation at lower temperatures and therefore less suitable as indicators. Relationships between compost temperature during exposures of 2-10 days and subsequent viability of the above chlamydospores or seeds enabled the sanitizing effect of composting processes to be predicted within 2-6 days. Plant waste type (woody or vegetable) had a small but significant effect on the relationship for tomato seeds but not for F. oxysporum chlamydospores.

Integrating natural and social science perspectives on plant disease risk, management and policy formulation

Plant diseases threaten both food security and the botanical diversity of natural ecosystems. Substantial research effort is focused on pathogen detection and control, with detailed risk management available for many plant diseases. Risk can be assessed using analytical techniques that account for disease pressure both spatially and temporally. We suggest that such technical assessments of disease risk may not provide an adequate guide to the strategies undertaken by growers and government to manage plant disease. Instead, risk-management strategies need to account more fully for intuitive and normative responses that act to balance conflicting interests between stakeholder organizations concerned with plant diseases within the managed and natural environments. Modes of effective engagement between policy makers and stakeholders are explored in the paper, together with an assessment of such engagement in two case studies of contemporary non-indigenous diseases in one food and in one non-food sector. Finally, a model is proposed for greater integration of stakeholders in policy decisions.

Modeling of Relationships Between Weather and Septoria tritici Epidemics on Winter Wheat: A Critical Approach

ABSTRACT Two models for predicting Septoria tritici on winter wheat (cv. Riband) were developed using a program based on an iterative search of correlations between disease severity and weather. Data from four consecutive cropping seasons (1993/94 until 1996/97) at nine sites throughout England were used. A qualitative model predicted the presence or absence of Septoria tritici (at a 5% severity threshold within the top three leaf layers) using winter temperature (January/February) and wind speed to about the first node detectable growth stage. For sites above the disease threshold, a quantitative model predicted severity of Septoria tritici using rainfall during stem elongation. A test statistic was derived to test the validity of the iterative search used to obtain both models. This statistic was used in combination with bootstrap analyses in which the search program was rerun using weather data from previous years, therefore uncorrelated with the disease data, to investigate how likely correlations such as the ones found in our models would have been in the absence of genuine relationships.