A 'Landscape physiology' approach for assessing bee health highlights the benefits of floral landscape enrichment and semi-natural habitats

Abstract

Understanding how anthropogenic landscape alteration affects populations of ecologically- and economically-important insect pollinators has never been more pressing. In this context, the assessment of landscape quality typically relies on spatial distribution studies, but, whether habitat-restoration techniques actually improve the health of targeted pollinator populations remains obscure. This gap could be filled by a comprehensive understanding of how gradients of landscape quality influence pollinator physiology. We therefore used this approach for honey bees (Apis mellifera) to test whether landscape patterns can shape bee health. We focused on the pre-wintering period since abnormally high winter colony losses have often been observed. By exposing colonies to different landscapes, enriched in melliferous catch crops and surrounded by semi-natural habitats, we found that bee physiology (i.e. fat body mass and level of vitellogenin) was significantly improved by the presence of flowering catch crops. Catch crop presence was associated with a significant increase in pollen diet diversity. The influence of semi-natural habitats on bee health was even stronger. Vitellogenin level was in turn significantly linked to higher overwintering survival. Therefore, our experimental study, combining landscape ecology and bee physiology, offers an exciting proof-of-concept for directly identifying stressful or suitable landscapes and promoting efficient pollinator conservation.

Figure 1. Path model revealing the ecophysiological basis of honey bee colony overwintering survival.

Significance level is indicated next to each link. All links stand for positive effects, except Varroa infestation level that negatively affects overwinter survival. When at least two links reach the same box, their thickness is proportional to their effect coefficient (standardized on their respective range for direct comparison). Total explained variance (r²) is indicated in the box for each response variable in the causal chain. Landscape influence on bee health is highlighted in grey.

Figure 2. Representation of the path model links showing the influence of Varroa infestation and vitellogenin levels on overwintering survival.

Survival probability was influenced positively by vitellogenin level (a) and negatively by Varroa infestation level (b). The continuous lines show model predictions. For the binary response variable (colony overwintering survival), data are represented as mean ± SE after being pooled into groups of consistent sizes. Tick marks show the position of raw data along the horizontal axis.

Figure 3. Relationship between fat body content and vitellogenin levels.

Figure 4. Representation of the path model links showing the influence of landscape quality variables on bee physiological traits.

Melliferous catch crop and semi-natural habitats positively influenced the fat body (a) and vitellogenin levels (b). Fat body and vitellogenin values were averaged per apiary and plotted as a function of landscape metrics. Trends are depicted by the regression planes. A slight horizontal jitter was applied to separate overlying apiary data with equal flowering catch crop treatments.