Response to ‘The European nitrogen cycle: response to Schulze et al,Global Change Biology (2010) 16, pp. 1451–1469’

Winniwarter and colleagues present alternative estimates for several of the nitrogen (N) fluxes provided by Schulze and colleagues. They reason that numeric discrepancies between largely dependent estimates and lack of detail in Schulze's estimates urges caution in interpreting these numbers. In this reply we provide methodological details enhancing the transparency of Schulze's estimates and argue that convergence between land‐ and atmosphere‐based estimates should be reached before individual estimates can be rejected. Only for the nitrous oxide and NO_x fluxes a balance between atmosphere and land‐based estimates has been reached. Convergence between independent estimates has not been reached yet for NO‐, NH₃‐ and N‐deposition estimates. As stated by Schulze and colleagues these N‐fluxes remain potentially biased and therefore come with a large uncertainty, irrespective of the reported precision.     

Open science and meta‐analysis allow for rapid advances in ecology: A response to Menegotto et al. (2019)

Menegotto and colleagues’ (2019) commentary on our paper (Kinlock et al., 2018) does not negate our findings, but by recategorizing and reanalysing a portion of our data set, advances our knowledge of the latitudinal diversity gradients (LDGs) in marine ecosystems, particularly emphasizing different findings for benthic LDGs as a result of the recategorization of the data. Furthermore, we see the contribution by Menegotto et al. (2019) as highlighting the importance of scientific transparency; we believe that this insight into the nature of LDGs in marine systems would have been delayed, if not unobtainable, had we not provided fully transparent methods and complete data in our paper.     

The eco‐evolutionary consequences of interspecific phenological asynchrony – a theoretical perspective

The timing of biological events (phenology) is an important aspect of both a species’ life cycle and how it interacts with other species and its environment. Patterns of phenological change have been given much scientific attention, particularly recently in relation to climate change. For pairs of interacting species, if their rates of phenological change differ, then this may lead to asynchrony between them and disruption of their ecological interactions. However it is often difficult to interpret differential rates of phenological change and to predict their ecological and evolutionary consequences. We review theoretical results regarding this topic, with special emphasis on those arising from life history theory, evolutionary game theory and population dynamic models. Much ecological research on phenological change builds upon the concept of match/mismatch, so we start by putting forward a simple but general model that captures essential elements of this concept. We then systematically compare the predictions of this baseline model with expectations from theory in which additional ecological mechanisms and features of species life cycles are taken into account. We discuss the ways in which the fitness consequences of interspecific phenological asynchrony may be weak, strong, or idiosyncratic. We discuss theory showing that synchrony is not necessarily an expected evolutionary outcome, and how population densities are not necessarily maximized by adaptation, and the implications of these findings. By bringing together theoretical developments regarding the eco‐evolutionary consequences of phenological asynchrony, we provide an overview of available alternative hypotheses for interpreting empirical patterns as well as the starting point for the next generation of theory in this field.     

Using n‐dimensional hypervolumes for species distribution modelling: A response to Qiao et al. ()

Hypervolume approaches are used to quantify functional diversity and quantify environmental niches for species distribution modelling. Recently, Qiao et al. ( 2016 ) criticized our geometrical kernel density estimation (KDE) method for measuring hypervolumes. They used a simulation analysis to argue that the method yields high error rates and makes biased estimates of fundamental niches. Here, we show that (a) KDE output depends in useful ways on dataset size and bias, (b) other species distribution modelling methods make equally stringent but different assumptions about dataset bias, (c) simulation results presented by Qiao et al. ( 2016 ) were incorrect, with revised analyses showing performance comparable to other methods, and (d) hypervolume methods are more general than KDE and have other benefits for niche modelling. As a result, our KDE method remains a promising tool for species distribution modelling.     

Internet addiction and sleep quality: a response to Jahan et al. (2019)

Sleep and Biological Rhythms -     

High dose refuge strategies and genetically modified crops – reply to Tabashnik et al.

Cultivating non‐toxic conventional crops (refuges) in the proximity to transgenic crops that produce Bacillus thuringienesis (Bt) toxins is widely recommended to delay pest adaptation to these toxins. Using a spatially structured model of resistance evolution, Vacher and co‐workers (Vacher, C., Bourguet, D., Rousset, F., Chevillon, C. & Hochberg, M.E. 2003. J. Evol. Biol. 16 : 378–387.) show that the percentage of refuge fields required for the sustainable control of pests can be reduced through intermediate levels of refuge field aggregation and by lowering the toxin dose produced by Bt plants. Tabashnik, B.E., Gould, F. & Carrière, Y. (2004 J. Evol. Biol doi: 10.1111/j1420–9101.2004.00695.x) call into question the results of Vacher et al. (2003) concerning the effect of toxin dose. They argue that these results arise from invalid assumptions about larval concentration–mortality responses for the insect considered, the cotton pest Heliothis virescens. We show here that the models presented by Vacher et al. (2003) and Tabashnik et al. (2004) both show inaccuracies in their definitions of genotypic fitness. The level of dominance estimated by Tabashnik et al. (2004) from larval mortality rates data is irrelevant to resistance evolution, and the fitness cost of resistance evolution, and the fitness cost of resistance is inaccurately integrated into their framework. Neverthless, the comments of Tabashnik et al. (2004) are very helpful in elucidating the definitions of genotypic fitness used in Vacher et al. (2003) and in pointing out the essential factors in predicting the evolution of insect resistance to Bt transgenic crops, namely, accurate estimations of the fitness cost of resistance, of the dominance level of this cost, and of the variations in the dominance level of the advantage conferred by the resistance with Bt toxin dose.