Carolyn: I’ve been working on trying to identify some structures we found associated with the semitransparent patches in Ceraphronoidea. We spoke to Missy Hazen about what they might be, and she suggested that they might be related to membrane recycling. I’ve been researching membrane recycling since then, and after taking a look back through the Microscopic Anatomy of Arthropods, I think I have finally figured out what the structures we’re seeing are. My guess it that they are lamellar bodies. There’s a good picture in Microscopic Anatomy volume 2, page 665, but I also found the following three papers that all discuss lamellar bodies. They are membrane-bound structures with excess membrane folds that are produced when fat bodies or vacuoles are broken down, and they are involved in organelle recycling, as well as storage and secretion. It seems like there are even lamellar bodies associated with photoreceptors, which is fascinating because we think the tissues underneath the semitransparent patches in Ceraphronoidea might contain photoreceptors.
- McDermid, Heather, and Michael Locke (1983) Tyrosine storage vacuoles in insect fat body. Tissue and Cell 15 (1): 137–158 DOI: 10.1016/0040-8166(83)90039-3
- Vigneron, Aurélien, Florent Masson, Agnès Vallier, Séverine Balmand, Marjolaine Rey, Carole Vincent-Monégat, Emre Aksoy, Etienne Aubailly-Giraud, Anna Zaidman-Rémy, and Abdelaziz Heddi. (2014) Insects recycle endosymbionts when the benefit is over. Current Biology 24 (19): 2267–73. DOI: 10.1016/j.cub.2014.07.065
- White, Richard H. (1968) The effect of light and light deprivation upon the ultrastructure of the larval mosquito eye. III. Multivesicular bodies and protein uptake. Journal of Experimental Zoology 169 (3): 261–277 DOI: 10.1002/jez.1401690302
Emily: I read a study published by Fourcade et al. (2014), in which they examine the efficacy of MAXENT models at handling biased locality data. Acknowledging that this is not unusual of species data, the researchers used real and virtual datasets with a variety of biases. They found that AUC (area under the curve), a measure of data fit to the model of species distributions, is not a good indicator of how well the model fits the data. The AUC values were found to be pretty high even when data had strong bias. Fourcade et al. suggest that it is best to ensure that the sample size is large enough to balance out data biases. Systematic sampling, in which a subset of the locality records are picked from the total, can help break up the spatial bias in records. MAXENT already removes records that are in the same grid cells, but this helps remove data that appears to be almost stacked geographically. When I look at data coming from our collection and others, I often think about just how similar the localities must be when they are collected extremely close to one another. However, it is hard to determine exactly how to correct this spatial bias-further georeferencing research and collecting efforts will help!
István: My weekly readings are listed in the Know your Insect course syllabus! Needless to say, it’s been an intense and incredible set of discussions. Watch for dedicated blog posts about each discovery.
Andy: I’m in the throes of ENT 432, so most of my weekly reads are dominated by the topics at hand. This week it is Odonata (e.g., Gorb 1999, Mischiati et al. 2015) and Ephemeroptera, as well as hypotheses concerning the origin of insect wings (Engel et al. 2013).