Why do house-hunting ants recruit in both directions?

To perform tasks, organisms often use multiple procedures. Explaining the breadth of such behavioural repertoires is not always straightforward. During house hunting, colonies of Temnothorax albipennis ants use a range of behaviours to organise their emigrations. In particular, the ants use tandem running to recruit naïve ants to potential nest sites. Initially, they use forward tandem runs (FTRs) in which one leader takes a single follower along the route from the old nest to the new one. Later, they use reverse tandem runs (RTRs) in the opposite direction. Tandem runs are used to teach active ants the route between the nests, so that they can be involved quickly in nest evaluation and subsequent recruitment. When a quorum of decision-makers at the new nest is reached, they switch to carrying nestmates. This is three times faster than tandem running. As a rule, having more FTRs early should thus mean faster emigrations, thereby reducing the colony’s vulnerability. So why do ants use RTRs, which are both slow and late? It would seem quicker and simpler for the ants to use more FTRs (and higher quorums) to have enough knowledgeable ants to do all the carrying. In this study, we present the first testable theoretical explanation for the role of RTRs. We set out to find the theoretically fastest emigration strategy for a set of emigration conditions. We conclude that RTRs can have a positive effect on emigration speed if FTRs are limited. In these cases, low quorums together with lots of reverse tandem running give the fastest emigration

An update of the Worldwide Integrated Assessment (WIA) on systemic insecticides. Part 2: impacts on organisms and ecosystems

New information on the lethal and sublethal effects of neonicotinoids and fipronil on organisms is presented in this review, complementing the previous WIA in 2015. The high toxicity of these systemic insecticides to invertebrates has been confirmed and expanded to include more species and compounds. Most of the recent research has focused on bees and the sublethal and ecological impacts these insecticides have on pollinators. Toxic effects on other invertebrate taxa also covered predatory and parasitoid natural enemies and aquatic arthropods. Little, while not much new information has been gathered on soil organisms. The impact on marine coastal ecosystems is still largely uncharted. The chronic lethality of neonicotinoids to insects and crustaceans, and the strengthened evidence that these chemicals also impair the immune system and reproduction, highlights the dangers of this particular insecticidal classneonicotinoids and fipronil. , withContinued large scale – mostly prophylactic – use of these persistent organochlorine pesticides has the potential to greatly decreasecompletely eliminate populations of arthropods in both terrestrial and aquatic environments. Sublethal effects on fish, reptiles, frogs, birds and mammals are also reported, showing a better understanding of the mechanisms of toxicity of these insecticides in vertebrates, and their deleterious impacts on growth, reproduction and neurobehaviour of most of the species tested. This review concludes with a summary of impacts on the ecosystem services and functioning, particularly on pollination, soil biota and aquatic invertebrate communities, thus reinforcing the previous WIA conclusions (van der Sluijs et al. 2015)

A farewell to the sum of Akaike weights: The benefits of alternative metrics for variable importance estimations in model selection

Galipaud M, Gillingham MAF, Dechaume-Moncharmont F-X. A farewell to the sum of Akaike weights: The benefits of alternative metrics for variable importance estimations in model selection. METHODS IN ECOLOGY AND EVOLUTION. 2017;8(12):1668-1678.1. In a previous article, we advocated against using the sum of Akaike weights (SW) as a metric to distinguish between genuine and spurious variables in Information Theoretic (IT) statistical analyses. A recent article (Giam & Olden, Methods in Ecology and Evolution, 2016, 7, 388) criticises our finding and instead argues in favour of SW. It points out that (1) we performed a biased data-generation procedure and (2) we erroneously evaluated SW on its capacity to estimate the proportion of variance in the data explained by a variable. We here respond to these points. 2. Giam and Olden's first concern is unfounded. When using the data-generating code they proposed, SW remains very imprecise. To respond to their second concern, we first list the meanings taken by a variable's importance in the context of IT. Although, SW is presented as an estimate of variable relative importance in methodological textbooks (i.e. a variable's rank in importance or its relative contribution to the variance in the data), it is also used as a metric of variable absolute importance (i.e. a variable's absolute effect size or its statistical significance). We then compare SW to alternative metrics on its ability to estimate variable absolute or relative importance. 3. SW values have low repeatability across analyses. As a result, based on SW, it is hard to distinguish between variables with weak and large effects. For estimations of variable absolute importance, experimenters should prefer model-averaged parameter estimates and/or compare nested models based on evidence ratios. Sum of Akaike weights is also a poor metric of variable relative importance. We showed that correct variable ranking in importance was generally more frequent when using model-averaged standardised parameter estimates, than when using SW. 4. To avoid recurrent errors in ecology and evolution, we therefore warn against the use of SW for estimations of variable absolute and relative importance and we propose that experimenters should instead use model-averaged standardised parameter estimates for statistical inferences

Males do not always switch females when presented with a better reproductive option

Galipaud M, Bollache L, Oughadou A, Dechaume-Moncharmont F-X. Males do not always switch females when presented with a better reproductive option. Behavioral Ecology. 2015;26(2):359-366

Ecologists overestimate the importance of predictor variables in model averaging: a plea for cautious interpretations.

9 pagesInternational audienceInformation-theory procedures are powerful tools for multimodel inference and are now standard methods in ecology. When performing model averaging on a given set of models, the importance of a predictor variable is commonly estimated by summing the weights of models where the variable appears, the so-called sum of weights (SW). However, SWs have received little methodological attention and are frequently misinterpreted. We assessed the reliability of SW by performing model selection and averaging on simulated data sets including variables strongly and weakly correlated to the response variable and a variable unrelated to the response. Our aim was to investigate how useful SWs are to inform about the relative importance of predictor variables. SW can take a wide range of possible values, even for predictor variables unrelated to the response. As a consequence, SW with intermediate values cannot be confidently interpreted as denoting importance for the considered predictor variable. Increasing sample size using an alternative information criterion for model selection or using only a subset of candidate models for model averaging did not qualitatively change our results: a variable of a given effect size can take a wide range of SW values. Contrary to what is assumed in many ecological studies, it seems hazardous to define a threshold for SW above which a variable is considered as having a statistical effect on the response and SW is not a measure of effect size. Although we did not consider every possible condition of analysis, it is likely that in most situations, SW is a poor estimate of variable's importance