No abstract

Insect populations are prone to respond to global changes through shifts in phenology, distribution and abundance. However, global changes cover several factors such as climate and land-use, the relative importance of these being largely unknown. Here, we aim at disentangling the effects of climate, land-use, and geographical drivers on aphid abundance and phenology in France, at a regional scale and over the last 40 years. We used aerial data obtained from suction traps between 1978 and 2015 on five aphid species varying in their degree of specialization to legumes, along with climate, legume crop area and geographical data. Effects of environmental and geographical variables on aphid annual abundance and spring migration dates were analyzed using generalized linear mixed models. We found that within the last four decades, aphids have advanced their spring migration by a month, mostly due to the increase in temperature early in the year, and their abundance decreased by half on average, presumably in response to a combination of factors. The influence of legume crop area decreased with the degree of specialization of the aphid species to such crops. The effect of geographical variation was high even when controlling for environmental variables, suggesting that many other spatially structured processes act on aphid population characteristics. Multifactorial analyses helped to partition the effects of different global change drivers. Climate and land-use changes have strong effects on aphid populations, with important implications for future agriculture. Additionally, trait-based response variation could have major consequences at the community scale.

1. Investigations in nutritional ecology often require the identification of animal feeding patterns in natural conditions (what, where, and when do animals eat). Thus, methods are needed to trace not only individual resource uptake but also the relative use of different resources in a population or community.2. Recent biochemical developments allow predicting the use of sugar-rich resources from insects in the field. Individual feeding status (feeding history, food sources) is inferred by comparing insect sugar profiles with those of individuals fed on controlled diets. Individual assignations are then used to predict the relative consumption of different resources at the population or community level. As both steps may generate error, accurate prediction rules are needed. However, research from other domains (e.g., protein-marking studies) suggests that classical decision rules used for such tasks may sometimes induce bias.3. This study evaluated the performance of these rules and compared them to alternative methods on simulated, realistic datasets. It tested different methods for individual classification but also introduced methods for prevalence estimation, whose specific purpose is to estimate the relative frequency of different classes.4. Alternative methods substantially outperformed the traditional algorithms to predict insect individual feeding status and population class distribution (relative frequency of insects with different feeding status). This study provided a simple decision tool to choose a method according to dataset size, variance, and biochemical method used.5. Alternative methods should increase prediction confidence in future studies. Such approaches should easily be generalized to a wider range of systems.