Collinearity refers to the non independence of predictor variables, usually in a regression‐type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial scales and through time. Across disciplines, different approaches to addressing collinearity problems have been developed, ranging from clustering of predictors, threshold‐based pre‐selection, through latent variable methods, to shrinkage and regularisation. Using simulated data with five predictor‐response relationships of increasing complexity and eight levels of collinearity we compared ways to address collinearity with standard multiple regression and machine‐learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset with strong collinearity by evaluating its performance on a test dataset without any collinearity. We found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold‐based pre‐selection. Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold‐based pre‐selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the ‘folk lore’‐thresholds of correlation coefficients between predictor variables of |r| >0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre‐analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them.

Bee pollinators are currently recorded with many different sampling methods. However, the relative performances of these methods have not been systematically evaluated and compared. In response to the strong need to record ongoing shifts in pollinator diversity and abundance, global and regional pollinator initiatives must adopt standardized sampling protocols when developing large‐scale and long‐term monitoring schemes. We systematically evaluated the performance of six sampling methods (observation plots, pan traps, standardized and variable transect walks, trap nests with reed internodes or paper tubes) that are commonly used across a wide range of geographical regions in Europe and in two habitat types (agricultural and seminatural). We focused on bees since they represent the most important pollinator group worldwide. Several characteristics of the methods were considered in order to evaluate their performance in assessing bee diversity: sample coverage, observed species richness, species richness estimators, collector biases (identified by subunit‐based rarefaction curves), species composition of the samples, and the indication of overall bee species richness (estimated from combined total samples). The most efficient method in all geographical regions, in both the agricultural and seminatural habitats, was the pan trap method. It had the highest sample coverage, collected the highest number of species, showed negligible collector bias, detected similar species as the transect methods, and was the best indicator of overall bee species richness. The transect methods were also relatively efficient, but they had a significant collector bias. The observation plots showed poor performance. As trap nests are restricted to cavity‐nesting bee species, they had a naturally low sample coverage. However, both trap nest types detected additional species that were not recorded by any of the other methods. For large‐scale and long‐term monitoring schemes with surveyors with different experience levels, we recommend pan traps as the most efficient, unbiased, and cost‐effective method for sampling bee diversity. Trap nests with reed internodes could be used as a complementary sampling method to maximize the numbers of collected species. Transect walks are the principal method for detailed studies focusing on plant–pollinator associations. Moreover, they can be used in monitoring schemes after training the surveyors to standardize their collection skills.

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The sunflower crop provides an important honey flow for beekeepers. In France, beekeepers observed a decrease in honey yield from this crop these past years compared to the 1980s–1990s. They suspect the new cultivars to be less productive in nectar compared to the older ones, but no data is available to support this, and it is known that climate conditions have a strong impact on nectar secretion. This study aimed to explore the effect of abiotic environmental conditions on nectar secretion in sunflower, as well the range of variation of this secretion in a sample of current cultivars. Thirty-four current sunflower hybrid cultivars were sampled in test plots for their nectar secretion under varying conditions of temperature, air humidity and soil moisture. Air humidity controlled the sugar concentration of nectar, and thus its volume. To study nectar secretion independently from this effect, analyses subsequently focused on nectar sugar mass per floret. The nectar sugar mass increased with temperature up to an optimum of 32 °C, while the variation range of soil water tension was not sufficient to detect an effect on nectar sugar mass. This varied by up to 100% among the 34 cultivars (from 101 to 216 μg sugar per staminate floret in average), with a similar range to those reported in the literature for older cultivars. Likewise, oleic cultivars, a new type introduced since the early 2000s, were found to secrete the same amounts of nectar as linoleic cultivars, an older conventional type. The more self-fertile cultivars also showed no reduction in nectar secretion. Finally, we tested the method that measures the nectar gross secretion rate in one hybrid, and we observed that this hybrid secreted in average 28 μg sugar per hour per staminate floret. The potential benefits of this method were discussed.

Establishment of a bee collection (Hymenoptera, Apoidea) as part of a biodiversity study. In the framework of the European ALARM project, we used sampling methods to assess pollinator diversity (bees and syrphid flies) and abondance, and therefore started reference collections. Identification required killing, pinning, and adequate preparation of the insects sampled. We present a set of guidelines for the preparation of bee specimens and a protocol for processing specimens caught in liquid media. We also provide recommendations to fill out labels for collection purposes. Finally, we suggest some techniques that can help keeping a well-curated bee collection.

The seed supply chain is one of most sophisticated elements of the agricultural value chain with long lead times, fragmented structure and high levels of uncertainty. Since the seed industry has received less attention in research compared with other sectors in the agriculture industry, it has enormous potential for improvement due to the lack of comprehensive mathematical optimization applications, increasing competition within the industry and decreasing spare arable land worldwide. All of the existing optimization applications in the seed supply chain have concerned land allocation at the farm level as well as regional level processing and distribution after harvesting. This research closes the gap between farm level planning and regional level distribution through optimization of seed production planning at a regional level, taking account of a number of complex constraints and practical preferences. Compared to a "business as usual" approach, the proposed application can save up to 16% of the total cost as well as 9% land usage and effectively mitigate major risks in the planning phase. The method is evaluated using Syngenta's industrial case studies.