Sexual segregation and flexible mating patterns in temperate bats

Angell et al. 2013 Sexual segregation and flexible mating patterns in temperate bats. PloS One. 8(1): e54194.  

Figure 3 from the paper. Posterior distributions for paternity probabilities at the group level. Posterior distributions for the probabilities that fathers (at the group level) came from roosts in the (blue) upper-elevation, (yellow) mid-elevation and (green) low-elevation, and from (red) swarming sites. For (A) low-elevation offspring (the inset graph shows the Wharfedale roost posterior distributions in greater detail), and (B) mid-elevation offspring. 


Following the last two discussions, this week’s paper was selected on the basis that it used non-lethal DNA collection techniques to determine how intra-specific niche separation influences mating patterns.

Matthew Guy

A large number of temperate bat species, including Myotis daubentonii, display sexual segregation along altitudinal gradients. In these species, mating usually occurs during autumn swarming events. However, at the upper limit of the female range, Senior et al. (2005), found evidence of summer mating within roosts where dominant males are tolerated by females. Using the same population of M. daubentonii, this paper extends this work to identify if this is the dominant mating strategy throughout the altitudinal range of the species and, if not, can the differences in mating strategy be explained by foraging habitat quality?

DNA was extracted from wing punches and a novel Bayesian approach was used to assign the probability of parentage of juveniles from low altitudinal roosts to males from different roosting sites and swarming sites. During our discussion, nobody had a lot of experience with the genetic methods used and we found the results section difficult to read. However, the figures clearly demonstrate that the probability that these juveniles are fathered by males from anywhere other than swarming sites is very low. We thought that this was a really nice example of how figures can be used to give a clear overall impression of complex data, especially for the lay person. This result was in contrast to that found by Senior et al. at mid-elevation roosts suggesting a flexible mating strategy over an altitudinal gradient.

Foraging habitat quality was assessed using bat activity, weight and temperature, all of which declined significantly with altitude. The paper surmises that by excluding males from roosts, pregnant and lactating females can reduce intra-specific competition for the high-quality foraging grounds. However, the carrying capacity at intermediate sites is lower and so supports fewer females. In these areas, the thermoregulatory benefits provided by males in the roost outweigh the costs incurred by additional competition. The paper pulls these results together qualitatively, stating that the mating strategy is adapted to the social structure, which, in turn has evolved in response to environmental conditions at a given altitude. However, we felt that an analysis of prevalent mating strategy (i.e. probability juveniles were fathered at swarming events) within individual roosts and local foraging habitat quality together would address the second part of the research question more directly.

Over all, we felt that the paper was well written and, in combination with the Senior et al. paper results, presented an interesting behavioural response. However, the scope of the paper is fairly limited, largely due to a combination of studying a single species and developing ideas of a previous single study. One potential way to widen the papers appeal could have been to incorporate a discussion on how the novel genetic technique developed in this study could be applied to other species populations.

The paper ends by posing the question: Is this flexible mating behaviour capable of dealing with changes in prey distribution and roost microclimate predicted by climate change? Our discussions came to the conclusion that climate change could cause a decrease in the success rate of mating during autumn swarming events, potentially reducing gene flow. An increase in temperature would drive prey species upstream, where the higher proportions of more turbulent water would reduce the quantity and quality of the forging grounds. This could lead to a reduction in females within local nursery roosts making them more reliant on males for roost thermoregulation, and hence, an increase in the prevalence of summer mating. We thought that actually addressing the question, at least to some extent, in the discussion would have made for interesting conclusion and again potentially widen the papers appeal.

Mammal predator and prey species richness are strongly linked at macroscales

Sandom et al.. Ecology 94: 1112-1122. DOI:10.1890/12-1342.1. Mammal predator and prey species richness are strongly linked at macroscales

mammal_map

Mammals, mammals, everywhere, where there’s plenty of prey to eat. Taken from Sandom et al


Lynsey McInnes

Lynsey McInnes

Will picked this paper out of a choice of five I gave him as he wanted to diversify away from community phylogenetics. For me, I liked the safe return to pure macroecology and I liked the similarity in approach to a recent paper I published looking at whether global diversity patterns of vertebrates reflect those of monocots? (Blatant self-promotion). I also used structural equation modelling in that paper (requested by a reviewer, I will admit) but remain dubious over their power to extract relative strengths of direct and indirect effects (more on this in a second).So, Sandom et al. look to see whether there is evidence of top down or bottom up effects of predator richness on prey richness (and vice versa) using mammals as their study taxon. They find a significant effect of prey richness on predators but little evidence for an effect in the other direction. This is a nice result and I was quite surprised by the strength of the signal. Like Jetz et al.’s, my own paper and a few others, at these macro-scales there has only been limited evidence for biotic effects on diversity patterns – collinear diversity apparently being mostly the result of similar responses to environmental gradients.  And therein lies the problem – when can we ever know we have included the right variables in our model to cover the myriad environmental effects such that the variable ‘prey richness’ is not just filling into for some omitted environmental variable? This is macrocological madness strike 2 (strike 1 was when ‘latitude’ always came out as a significant predictor of richness gradients as it stood in for some immeasurable combination of abiotic variables). OK, while there was no mechanistic explanation for ‘latitude’ explaining richness patterns and there are strong mechanistic explanations for an expected association among trophic levels, I am still dubious…I have another couple of methodological worries, although I totally admit I don’t see any easy fix for them. My first worry is that there are so few predator species (125) vs. prey (3966) meaning that grid cell richness has a much lower range and maximum for predators (max: 19) than prey (186). I feel uncomfortable treating these groupings as equivalent, presumably the spatial autocorrelation among grid cells in terms of species present must extend over longer distances for predators than prey? Maybe this is not strictly an issue with the questions being asked here and may only be a helpful explanatory reason why there was not much evidence for a top down control of predator richness on prey richness? I also had a similar problem with my own data on monocots vs. vertebrates (there are a magnitude more monocot species than vertebrates…).

Two more queries: why such big grid cells? Why not 100km x 100km like most other macroecological studies on mammals so far? Why PCA climatic variables rather than make informed choices on the variables you want to include (I don’t think PCA is strictly a verb, sorry). This is, admittedly, a pet-hate of mine, but it just strikes me as an unnecessary extra step to remove the reader from the data. In this instance, I also wonder how each axis changed from region to region in the region-specific SEMs?

Finally, will the diet database be made openly accessible? I really hope so, it sounds like a great resource for a bunch of further questions. For instance I would really have just liked to have seen more ‘basic’ analyses of the data: average range sizes for the two groups? Body sizes?

That was a really grumbley post and very unfair given each and every point could have been applied to my own work. I suppose one is always harshest on what one knows best…I found this paper interesting to read, thoughtful on the different relationships possible and a great attempt to incorporate biotic effects into macroecological studies. More please!


Will Pearse

Will Pearse

This is an extremely far-reaching paper; the authors link predator and prey diversity at the macro-scale, and show that there are more predator species where there are more prey species.

My initial nit-picking niggle was wondering what exactly we can infer about such an inherently local-scale process as predation at the macro-scale – what relevance do predators and prey hundreds of kilometers away from one-another have? However, I think this link has got to be real – the authors control for spatial auto-correlation, they control for habitat (in the best way we can), and they still find this relationship. In fact, they find differences among predators and preys in their dependence on environmental conditions, which makes me think they’re picking up something real.

Maybe predators specialise on particular prey species, and so prey diversity begets predator diversity. We kind of know that to be the case, although we also know some species are generalists. An alternative, that I find more interesting, is that maybe (at a coarse scale) having more prey species makes a system more robust, and so able to support a wider variety of predators. Indeed, if prey species are more dependent on environmental factors than predators (as this paper finds), maybe having a wider variety of prey species gives the predators a more reliable food supply, and so a greater diversity of species can be supported. This is essentially a species-level re-working of the insurance hypothesis in the ecosystem function literature.

Which, essentially, brings me right back to the nit-picking question I had to begin with. Why is it that every explanation I think of for these patterns involves local-scale patterns, when this is a global-scale pattern? Can my thinking scale up to two degree grid cells? I’d now like to see how we can relate these coarser grid cell patterns to what’s going on inside the grid cells. What happens inside a grid cell that has an unusually high diversity of predators or prey?

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