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


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?


Phylogenetic trait-based analyses of ecological networks

Rafferty, N.E., and Ives, A.I. Ecology (in press). DOI:10.1890/12-1948.1. Phylogenetic trait-based analyses of ecological networks

By Alvesgaspar (Own work) [GFDL or CC-BY-SA-3.0], via Wikimedia Commons

An animal and a plant, or a plant and an animal. They’re all in a phylogeny, somewhere, right? By Alvesgaspar (Own work) [GFDL or CC-BY-SA-3.0], via Wikimedia Commons

Will Pearse

Will Pearse

There are relatively few studies of ecological interaction networks that use phylogenetic information, and so this study, which no only does so but also suggests a new method for doing it, is great.

Anthony Ives is constantly developing new methods that are useful to eco-phylogeneticists and ecologists in general, and this paper is an excellent example. Phylogenetic linear mixed models are a way of simultaneously examining trait and phylogenetic information, and strike me as an extension of Ives’ earlier work with Matt Helmus. I love these methods; they allow us to answer questions we’re actually interested in (what determines species interactions) using phylogenetic and trait data. They don’t just look at a property of our data (like phylogenetic dispersion) and then force us to infer from that other properties of the system. My only criticism (which is better communicated by Nate Swenson) is we should go further – why collect phylogenetic information if we only use it to validate trait data? I, probably unfairly, want to shift the question to ‘what determines how species interactions evolve‘.

I think the interaction network literature is filled with cases of people trying to find unifying, abstract aspects of structure – motifs. I wonder if this is because the phrase ‘interaction network’ can cover so many kinds of interactions (pollinators, herbivores, competitors, etc.) in so many different taxa. Maybe eco-phylogeneticists can help, and there are over-arching phylogenetic patterns that can unify all these different systems and approaches. Every organisms on Earth fits into the Tree of Life somewhere, and that makes every single interaction network study, be it of bats or bell-flowers, comparable in some way. Which can only be a good thing!

Lynsey McInnes

Lynsey McInnes

I was really excited about this paper when I chose it. Traits, phylogenies, my pet interest ecological networks! I have the feeling the paper is really good, but I have to say I struggled with it. I think I dove too quickly into the deep end. Maybe I should have gotten more comfortable with the network literature or the traits on phylogenies literature, but I still haven’t learnt to do things like that…

Across biological sub-fields, everybody knows that species’ interactions matter, but, at least in the fields I have most experience of, namely macroecology and macroevolution, explicitly incorporating the effect of species’ interactions (or making them the point of an analysis) is only a very recent development. I think this means that any advance is a good one (and I feel like we’ve mentioned this a number of times before on PEGE).  So, in that respect, this paper is neat. It takes a well-studied, tractable set of plant-pollinator interactions and attempts to parse out the reasons underlying the different communities: do traits or relatedness underlie them? The authors make the case that methods such as theirs can help predict how guilds of species will fare under climate change as  it allows them to anticipate whether phenological mismatch will take hold or whether, in the pool of the other guild, species exist that can match advancing phenologies. Admirable.

I guess the missing link as I see it can be surmised from the title of the paper – ecological networks. What about evolutionary responses? Is it necessary to consider that mismatches might be ameliorated by co-evolutionary responses to environmental change? Is the timescale too short? Will there be less pressure for evolutionary change if, for the plants for example within the pollinator pool there are species ready to pollinate them following their flowering advance. Perhaps teasing apart this additional potential response will be a feature of a subsequent paper from these guys.

I also wonder how space affects these patterns. How do patterns and expectations change as one investigates multiple populations across a larger region? How does local adaptation affect responses? What about variation in the available plants/pollinators across space? What about gene flow among populations? How might one go about incorporating traits, phylogeny and interactions with intraspecific variation and space? Is that too much to consider at once?

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