Use of monthly collected milk yields for the detection of the emergence of the 2007 French BTV epizootic

Two culicoides-borne diseases, Bluetongue (BTV) and Schmallenberg, have emerged in the European cattle population since 2006. Other diseases transmitted by these vectors could emerge. This justifies the development of syndromic surveillance programs whereby one or several indicators would be routinely monitored for the early detection of emerging diseases. The aim of this study was to evaluate milk yield from milk recording in dairy cattle as an indicator to be included in an emerging disease surveillance system. It was hypothesized that emergences would result in episodes of low milk production clustered in space and time. The 2007 BTV epizootic in France was used as a case study. Because it had already emerged in neighbouring countries, the disease emergence was expected and notification was mandatory. Herd-test-day milk productions were predicted for the entire country for 2006 and 2007 from herd historical data using linear mixed models. The differences between observed and predicted milk productions were averaged per week and per municipality and used as input for a space-time prospective scan statistic. Log likelihood ratios (LLR) associated with clusters were used to define alarms. The threshold chosen was a trade-off between detection timeliness and the number of false alarms per week. The first four BTV notifications occurred on the 12th (two notifications), 13th and 27th of July 2007. The 12th of July was considered to be the date of emergence. Alarms occurring before the 1st of March 2007 were considered to be false alarms. Using an LLR of 50, there were an average of 1.7 false alarms per week and the BTV emergence was detected seven weeks after emergence. Using an LLR of 100, there were an average of 0.8 false alarms per week and the BTV emergence was detected 9 weeks after emergence. Detection may have been delayed because of a discontinuation of milk recording between mid-July and mid-August. The first cluster with an LLR > 100 located in the emergence area was further investigated. A difference between observed and predicted production of >1 kg/cow/day was observed around the time of emergence. However, a difference of equal magnitude was observed during the year preceding the outbreak. Milk production predicted from herd history alone did not allow the detection of the 2007 BTV emergence in France. Further research should be conducted on improving the prediction of test-day milk yield and on combining it with other indicators based on routinely collected data.     

Detecting dominant landscape objects through multiple scales: An integration of object-specific methods and watershed segmentation

Complex systems, such as landscapes, are composed of different critical levels of organization where interactions are stronger within levels than among levels, and where each level operates at relatively distinct time and spatial scales. To detect significant features occurring at specific levels of organization in a landscape, two steps are required. First, a multiscale dataset must be generated from which these features can emerge. Second, a procedure must be developed to delineate individual image-objects and identify them as they change through scale. In this paper, we introduce a framework for the automatic definition of multiscale landscape features using object-specific techniques and marker-controlled watershed segmentation. By applying this framework to a high-resolution satellite scene, image-objects of varying size and shape can be delineated and studied individually at their characteristic scale of expression. This framework involves three main steps: 1) multiscale dataset generation using an object-specific analysis and upscaling technique, 2) marker-controlled watershed transformation to automatically delineate individual image-objects as they evolve through scale, and 3) landscape feature identification to assess the significance of these image-objects in terms of meaningful landscape features. This study was conducted on an agro-forested region in southwest Quebec, Canada, using IKONOS satellite data. Results show that image-objects tend to persist within one or two scale domains, and then suddenly disappear at the next, while new image-objects emerge at coarser scale domains. We suggest that these patterns are associated to sudden shifts in the entire image structure at certain scale domains, which may correspond to critical landscape thresholds.This revised version was published online in May 2005 with corrections to the Cover Date.     

Does the Honeybee (Hymenoptera: Apidae) Reduce the Blooming Period of Canola?

The aim of this study was to determine if and how the honeybee reduces the blooming period of canola (Brassica napus). Colonies were installed in different fields of canola in the Chaudiere‐Appalaches (two replicates) and Quebec City (one replicate) regions to create a density gradient of zero to three colonies per hectare. To calculate the blooming period under these conditions, 10 labelled plants were caged in the field, out of the reach of pollinators, and 10 others were labelled in the field and exposed to foragers. The number of flowers found on each plant was recorded daily. This study also aims at demonstrating that the canola plant will produce new flowers as long as it does not reach its maximum carrying capacity, thereby extending the blooming period. To simulate the fall of unfertilized flowers, a predetermined number of flowers from 20 randomly selected plants in one of the replicates were cut off every day. The plant, when it reaches its maximum carrying capacity, stops producing new flowers. With three colonies per hectare, the blooming period was reduced by 3.8 days, or 17 % compared with in the absence of pollinators. Because of the efficient pollen transfer to the stigma, the honeybees do not only cause the flower to live for a shorter period of time, but they also bring about a decrease in the number of flowers produced by the plant, thereby reducing the duration of blooming period.     

Modelling diurnal and seasonal patterns of maize pollen emission in relation to meteorological factors

Modelling the risk of cross-pollination between maize crops can help to define efficient isolation methods to reduce the risk of gene flow between these crops. However, the use of such models over the pollen emission season is limited by poor estimations of pollen emission. In this study, we present a model that predicts hourly pollen emission fluxes over the pollen season and that accounts for effects of both meteorological conditions and crop variety. It consists of two sub-models that predict: (i) the seasonal pattern on a daily basis and (ii) the normalized diurnal pattern on an hourly basis.To assess the variability of pollen emission, ten field experiments were carried out in July and August over four years using three crop varieties. The model was built from (i) a parameterisation of the measured diurnal and seasonal patterns of pollen emission followed by (ii) a quantification of the relationships between parameter values and meteorological conditions. Total production of pollen was fairly constant for a given variety over years and sowing dates, while patterns of emission varied with meteorological conditions. The pollen emission season was longer when temperature was low and humidity was high. In most cases, the diurnal pattern was unimodal: the onset and the peak of emission were delayed when relative humidity was high or wind speed was low. In some cases, a second peak of emission occurred during the afternoon. This was observed more frequently when temperatures were high.     

A multiscale framework for landscape analysis: Object-specific analysis and upscaling

Landscapes are complex systems that require a multiscale approach to fully understand, manage, and predict their behavior. Remote sensing technologies represent the primary data source for landscape analysis, but suffer from the modifiable areal unit problem (MAUP). To reduce the effects of MAUP when using remote sensing data for multiscale analysis we present a novel analytical and upscaling framework based on the spatial influence of the dominant objects composing a scene. By considering landscapes as hierarchical in nature, we theorize how a multiscale extension of this object-specific framework may assist in automatically defining critical landscape thresholds, domains of scale, ecotone boundaries, and the grain and extent at which scale-dependent ecological models could be developed and applied through scale.     

Spatial pattern of coniferous and deciduous forest patches in an Eastern North America agricultural landscape: the influence of land use and physical attributes

In agricultural landscapes, most studies have investigated the influence of the spatial pattern of forest patches on other ecological phenomena and processes, such as animal movement and biodiversity. However, few have focused on explaining the spatial pattern of the forest patches themselves. Understanding how these patterns relate to the processes that generate them is fundamental in developing a sound theory of landscape ecology, and in devising rational management strategies. In this paper, the pattern of the overall forest patches, as well as the pattern of deciduous and coniferous patches in an agricultural landscape of Southern Quebec, Canada, were analyzed and related to landscape physical attributes and land use, using remote sensing, geographic information systems and statistical methods. Results show that the role of landscape physical attributes on forest patch pattern has been modified by land use. In the study area, coniferous or deciduous patches are not associated with a specific surface deposit. In addition, physical attributes explain only a small proportion of the abundance of conifers on past abandoned land compared with land-use factors. Physical attributes only indirectly influence the forest pattern because they strongly influence the land-use practices. Our results reveal a conifer recovery process with the abandonment of agricultural land. On past abandoned land, conifers expand with increasing stand age, mostly by invasion from neighboring coniferous patches. Spatially, coniferous patches are usually located on the margins of the overall forest patches, and they are connected to non-forest land-use types such as crop and pasture, the latter being the most important. By showing the importance of some coniferous forest types that did not exist in the precolonial forest, a new perspective emerges when landscape, especially, land-use dynamics are taken into account.