Why I voted as I did in our controversial ecological ideas poll

Recently we polled readers about their views on various controversial ideas in ecology. Here’s how I voted, with links to key references explaining my thought process. I share my thinking in the hopes of encouraging others to share their thinking as well. We’ll all learn something that way. And hopefully it’ll be fun (though I recognize that disagreeing with other people about science on the internet isn’t everyone’s cup of tea, which is fine.)

Recall that the poll asked respondents to indicate on a 5 point scale whether each idea was definitely false (1), definitely true (5), or somewhere in between. Respondents were asked not to provide an opinion on any idea about which they knew nothing, and to indicate their level of expertise on each idea (expert, know something, know a bit, know nothing). Ideas are listed in rough descending order of how controversial they turned out to be among poll respondents.

Species interactions typically are stronger and more specialized in the tropics: I know a bit, and picked 2 (probably false). Jeff Ollerton and Angela Moles’ guest post and subsequent paper and references therein make me dubious that this idea “typically” holds. Also, the discussion around this idea both on our blog and in the peer-reviewed literature (Anstett et al. 2016) suggests to me an idea that’s pretty vague. There seems to be significant disagreement as to exactly what’s being claimed and why we might expect it to be true, theoretically. Which leaves many “researcher degrees of freedom” to decide exactly how to operationalize and test the idea. I don’t think vague ideas pan out very often in ecology (or any other field, such as social psychology). They can prompt a lot valuable research, in part because vague but interesting-sounding ideas attract lots of people to work on them. But the outcomes of that research don’t tend to include the discovery of clear-cut, general empirical patterns, or well-developed theories that can be tightly linked to data. My “prior” is that any claim that’s so vague that experts disagree how to operationalize and test it will probably not turn out to be “typically” true. But I’m sure there are exceptions. And maybe my impression of the vagueness of this idea just reflects my comparative ignorance. Looking forward to your comments, as always.

Local biodiversity is declining in most or all localities: I’m an expert, and I picked 1 (definitely false). Vellend et al. 2013 and Dornelas et al. 2014 compiled the available data, which show that this idea is false. I am aware of the criticisms these papers have received. But as Vellend et al. (2016) show, those criticisms are based on a mixture of technical mistakes, and debatable arguments that seem unlikely to overturn the key results. I think the available data on this question are sufficient to constitute a severe test that this idea failed to pass. (Philosophical aside: Further data might of course change that conclusion–or might not! But that’s always true, about every question in science (and life). We could be wrong, and our conclusions might change in future. I don’t think that fact means that scientists should never, ever characterize any idea as “definitely” true or false. If asked “What’s the speed of light”, you should answer “186,000 miles per second”, not “Well, our current best estimate is 186,000 miles per second, but we could be wrong andfuture data might change that estimate.” Routine, ritualistic expressions of doubt about every statement don’t serve any purpose. So I respectfully disagree with any poll respondents who refused to pick 1 or 5 for any of the questions on the grounds that scientists should never say that any scientific claim is “definitely” true or false. But I’m not sure there were any such respondents, so maybe this aside is pointless.)

Species’ poleward geographic range limits typically are set by abiotic factors, not species interactions: I know something, and I picked 2 (probably false). Louthan et al. (2015) say that this idea “has not been clearly defined or broadly tested”. And see the excellent review of the limited evidence from transplant experiments in Hargreaves et al. (2014). Also, abiotic factors and species interactions probably aren’t best thought of as alternatives here, because they’ll interact in various ways. On their own those considerations would have led me to pick “2” or “3”. I went with “2” because the underlying logic of this hypothesis has never quite made sense to me. The basic idea, as I understand it (and perhaps I’m totally off-base here, since I’m not an expert!), is that “harsh” environments such as at northern latitudes, densities of competitors will be low. If competitor densities are low, interspecific competition won’t much reduce a species’ per-capita growth rate when rare. Therefore a species’ northern range limit will be found wherever it’s able to tolerate the local abiotic conditions just well enough to break even demographically. Leave aside the potential empirical problems with this, such as that an environment that’s harsh for one species might be benign for its competitors, or the potential role of dispersal in setting range limits. This idea doesn’t work even on its own terms. In a harsh environment, a species’ per-capita growth rate when rare will be low even in the absence of interspecific competition (that’s presumably what “harsh” means). Which means that interspecific competition doesn’t need to much reduce the per-capita growth rate when rare to push it into negative territory. So even if interspecific competition doesn’t much reduce your per-capita growth rate when rare in harsh environments, well, it only needs to reduce your per-capita growth rate when rare by a little bit for you to get competitively excluded. The intuitively-appealing but logically-flawed idea that anything that tends to hold down species’ densities–“harsh” environments, high density-independent mortality rates, frequent and/or intense disturbances–thereby prevents competitive exclusion is one of the common mistaken ideas in ecology (Chesson and Huntly 1997 (great paper), Violle et al. 2010, Fox 2012, and see here).

The intermediate disturbance hypothesis (empirical version): species diversity typically peaks at intermediate disturbance frequency or intensity: I’m an expert, and I picked 1 (definitely false). Mackey and Currie (2001) reviewed the empirical literature on the IDH. According to their results this idea is false; peak diversity at intermediate disturbance isn’t “typical”. Even controlled experimental manipulations of disturbance often fail to find a humped diversity-disturbance relationship (reviewed in Fox 2012). The meta-analysis of Svensson et al. (2012) does find that, when you measure diversity as species richness and fit a quadratic regression to the diversity-disturbance relationship, most published studies estimate a negative quadratic term. Trouble is, quadratic regression has recently been shown to be a biased test that almost certainly generates many false positive humps. Concave-down bivariate relationships tend to be misdiagnosed as humped by quadratic regression.

The intermediate disturbance hypothesis (one theoretical version): disturbances promote diversity by interrupting competitive exclusion and preventing the community from achieving equilibrium: I’m an expert, and I picked 1 (definitely false). See Fox (2012) and Chesson and Huntly (1997) for explanation of why this idea is definitely false. Note that there are of course “fluctuation-dependent” coexistence mechanisms by which periodic disturbances can generate stable coexistence that would not occur in the absence of those disturbances (see the series of posts starting here). Perhaps fluctuation-dependent coexistence mechanisms were what some poll respondents had in mind when they characterized this idea as probably or definitely true? In my view, summarizing fluctuation-dependent coexistence mechanisms by saying that disturbances or other environmental fluctuations “interrupt” competitive exclusion is a bad way to summarize them.

Biodiversity is among the most important determinants of ecosystem function: I know a bit, and I picked 3. I confess I was lazy on this one. I only vaguely recalled the relevant review papers and couldn’t be bothered to look them up, so I picked “3”. I’ve since gone back and looked up Hooper et al. (2012), Tilman et al. (2012), and Duffy et al. (2017). On that basis, I’d now say I know something and might lean towards “4” (probably true).

Habitat fragmentation per se (as distinct from habitat loss) typically reduces biodiversity: I know a bit, and I picked 2 (probably false). And honestly, you could probably talk me into “1” (definitely false). Fahrig (2017) reviewed the relevant literature and found that 3/4 of significant effects of habitat fragmentation per se on biodiversity are positive. Which you wouldn’t know if you only read the abstracts of published studies. Fahrig (2017) found that authors writing on this topic systematically skew their findings in their abstracts, often not bothering to mention positive effects of habitat fragmentation on biodiversity even when the only significant effects they found were positive.

Interspecific competition can be reliably inferred from information about functional trait similarity: I’m an expert, and I picked 1 (definitely false). Yes, I know that there are famous cases like Darwin’s finches, in which there’s a clear, interpretable mechanistic link between key phenotypic traits used in feeding, and the occurrence of interspecific competition (in Darwin’s finches, the key traits are beak size and shape). Threespine stickleback in postglacial lakes in British Columbia are another classic example. And there are others. But such examples are rare. Most attempts to infer interspecific competition from “functional traits”, or even to use functional traits to explain the outcome of interspecific competition that’s been demonstrated via other means, fail. For instance, Kraft et al. (2015). I speculate that that’s in part because many “functional traits” get measured because they’re convenient to measure, not because they have a tight mechanistic connection with competitive outcomes.

Species diversity typically peaks at intermediate productivity: I’m an expert, and I picked 1 (definitely false). My reasoning here is more or less the same as for the empirical version of the IDH. Not enough empirical studies find humped diversity-productivity relationships in enough systems for a humped relationship to be considered “typical” (Mittelbach et al. 2001), and many putatively-humped relationships probably are false positives because quadratic regression is a biased test for humps.

Species richness on continents is dominated by ecological limits rather than evolutionary limits: I know something, and I picked 3 (not sure). I learned about this controversy from close reading of the papers arising from the Oxford-style debate at the ASN meeting a few years ago. I thought that debate was very interesting. I partially agreed and partially disagreed with both sides, while also thinking the question might not be sufficiently well-posed to be resolvable.

Higher biodiversity will buffer ecosystem function against climate change: I know a bit, and I picked 3 (not sure). This is another one on which I was lazy and didn’t bother to remind myself of what the relevant studies actually found.

Predator functional responses typically are ratio-dependent: I’m an expert, and I picked 1 (definitely false). First of all, it’s important to remember that ratio-dependence is a very special case of functional responses that depend on both predator and prey densities. Published measurements or estimates of functional response shape often find that per-predator, per-prey feeding rate depends on both predator and prey density in some fashion, but they rarely (never mind “typically”) find ratio dependence (DeLong and Vasseur 2011, Schenk et al. 2005, Zimmerman et al. 2014, Nowak et al. 2017). Second, other lines of empirical evidence for ratio-dependent functional responses are very weak and indirect. The fact that ratio-dependent predator-prey models can be fit to (e.g.) data on changes in trophic level biomasses along productivity gradients is not evidence for ratio-dependent functional responses, because all sorts of other mechanisms having nothing to do with functional response shape can explain those data. Third, the theoretical arguments for ratio-dependent models strike me as…idiosyncratic, and I agree with Peter Abrams’ criticisms of them.

Top-down effects typically are stronger and more important than bottom-up effects: I’m an expert, and I picked 3 (not sure). Basically, I picked “3” for reasons 5 and 6 on this list. I’m not sure this question actually makes sense as stated, though it could be modified to make sense. And on theoretical grounds, we should expect the strength of top-down effects to depend on basal resource enrichment, and the strength of bottom-up effects to depend on food web structure (e.g., Oksanen et al. 1981, Abrams 1993, Leibold 1996).

Species’ equatorward geographic range limits typically are set by species interactions, not abiotic factors: I know something, and I picked 4 (probably true).
My reasoning on this one is analogous to my reasoning for poleward geographic range limits, discussed above.

The dilution effect: host biodiversity generally reduces disease prevalence or infection risk: I know a bit, and I picked 2 (probably false). My impression from the occasional papers I’ve read, occasional comments from others on this blog, and chatting with an expert (our own Meghan Duffy) is that the dilution effect occurs in some systems but not others. So based on what little I know, I think it’s probably false to say that host biodiversity “generally” reduces disease prevalence or infection risk.

Ecological networks (e.g., food webs, plant-pollinator networks) have the structures they do because other structures would be unstable: I’m an expert, and I picked 1 (definitely false). From reading the literature, the experience of a former student of mine trying to document an alpine plant-pollinator network (Olito and Fox 2014), and speaking with experts who are using new methods to better sample “who pollinates whom”, I am dubious of the empirical evidence base on this. That’s not a criticism of the many outstanding empirical researchers who work on this; it’s just a really hard thing to sample. I suspect published plant-pollinator interaction networks badly undersample rare interactions, and I’m sure I’m not alone in that suspicion. I also think that pollinator choices about which plants to pollinate often are quite variable and flexible in space and time, at many scales (the same could be said of many predators and their choice of prey). So I worry that theoretical models trying to explain observed plant-pollinator network structures on the grounds that other structures would be unstable are in a similar position to food web theory back in the late ’70s and ’80s, when the available comparative data on food web structure weren’t good enough to theorize usefully about. I also think that persistence of plants and their pollinators, and predators and their prey, depends on various factors besides interaction network structure. Just because network structure A is less likely to be stable than network structure B, all else being equal, in a theoretical model in which other persistence-affecting mechanisms are ignored, doesn’t necessarily mean that structure A is unlikely to be observed, or that structure A is less likely to be observed than structure B (see here for related discussion). Finally, theory provides mixed predictions on whether stable interaction networks have more realistic (or even different) structures than unstable ones (e.g., Kristensen 2008). The answer seems to depend on details of model assumptions, including but not limited the chosen measure of stability.

The enemy release hypothesis: invasive species often spread and become abundant relative to natives because enemies attack the invasives less: I know nothing and so expressed no opinion.

Hubbell’s neutral model: community dynamics typically are dominated by drift, because all individuals of all species typically are demographically equivalent: I’m an expert, and I picked 1 (definitely false). As I think is widely recognized at this point, the fact that neutral models can fit empirical species-abundance distributions is no evidence for communities actually being neutrally stable, so that drift dominates community dynamics. The shape of the species-abundance distribution is not informative about the processes that generated it (e.g., McGill 2003, and see this old post). And there are of course all sorts of other lines of evidence showing as conclusively as anything can ever be shown in ecology that community dynamics are not “typically” dominated by drift (although drift does sometimes matter; Gilbert and Levine 2017). Just off the top of my head (because if I tried to be comprehensive we’d be here all week): Yenni et al. (2017), Usinowicz et al. (2017), Kraft et al. (2015), Harpole and Tilman (2006), Siepielski et al. (2010), Silvertown et al. (2006), the various lines of evidence reviewed in Siepielski and McPeek (2010), Hille Ris Lambers et al. (2012) and Vellend (2016)…

Interspecific competition and facilitation can be reliably inferred from co-occurrence matrices or correlation matrices of species’ abundances: I’m an expert, and I picked 1 (definitely false). See here for my reasoning. And see Sander et al. (2017), Barner et al. (2018) and Freilich et al. (2018), finding that a wide range of methods based on co-occurrence or correlational data completely fail to infer species interactions that are known to occur in a well-studied natural system (the rocky intertidal). Any method for inferring species interactions from observational data that fails when applied to data from the rocky intertidal–a system famous for its intense species interactions!–is unreliable.

Interspecific competition can be reliably inferred from information about phylogenetic relatedness: I’m an expert, and I picked 1 (definitely false). See Mayfield and Levine (2010) and other lines of evidence, briefly reviewed here. 

Population growth typically is density-dependent: I’m an expert, and I picked 5 (definitely true). Time series analyses almost invariably estimate density dependence, though it’s often often too weak for the 95% c.i. to exclude zero, given the typical length of ecological time series and typical levels of sampling error (Ziebarth et al. 2010, Knape and deValpine 2012). The preponderance of experiments that have looked for intraspecific competition find it. And if population growth weren’t typically density-dependent, species typically would either increase to impossibly high abundances or else go extinct, in much less time than the typical species lifetime as estimated from the fossil record (millions of years).

Earth is currently experiencing a 6th mass extinction: I know a bit, and I picked 2 (probably false). Paleontologist Doug Erwin studies mass extinctions, and his arguments against the claim that we’re in the midst of a 6th mass extinction seem reasonable to me.

17 thoughts on “Why I voted as I did in our controversial ecological ideas poll

  1. I largely agreed with your assessments. Rarely did I find myself more than 1 away from your ranking. A few additions/comments:

    Local diversity – there have been 3 or 4 more papers using timeseries in addition to the Vellend & Dornelas papers that all get the same result. The only ones that don’t get the same result have used space for time (e.g. PREDICTS) and they show only mild declines.

    IDH empirical – I am a little more open to the idea that there may be some consistent empirical pattern in an identifiable (e.g. single scale) subset of the data.

    BIodiveristy is among the most important determinates of ecosystem function. Depends on what function of course, but if we use NPP or standing biomass as proxy for others as many studies do, my sense is biodiversity comes after climate, nutrients and species composition. Not sure I would call 4th “among most important”

    On global diversity I tip a bit more to ecological limits (Currie’s work with high correlations with climate has been highly repeatable and there are climate links even into paleo time). I haven’t seen any equally repeatable work determining which aspects of diversification drive standing richness. But I do think it is a bit of a false dichotomy.

    Habitat fragmentation- I might be tempted to go to 1. Long before Fahrig’s important work I dug into the literature to teach this subject and I was shocked at how many times richness went up with fragmentation (edge effects usually). They might not have been the species people wanted but richness was up.

    Ratio dependence – I don’t follow this field as closely as you, but I find ratio dependence a far more intuitive view so I hold out some hope its true.

    6th mass extinction – I would go with a 1. Classic case of ecologists feeling the need to exaggerate. We’re doing something bad to extinction rates. Isn’t that enough? Apparently not. We have to claim that humans are as bad as the worst things that have ever happened to the earth (all of which are really extreme). The evidence is weak on many levels. Two key points. We are comparing observed extinction rates that are based almost entirely in islands with fossil extinction rates which are almost always about widespread species. 75% of species going extinct (definition of a mass extinction) is really a lot – even if you make the giant leap of extrapolating recent extinction rates onto the mainlands you are going to have to wait around for many thousands of years to see 75%. And nobody has a clue how humans will be treating the planet 100 years from now let alone 40,000.

    • “I am a little more open to the idea that there may be some consistent empirical pattern in an identifiable (e.g. single scale) subset of the data.”

      The issue of scale-dependence is potentially relevant to the diversity-productivity idea too. I’m not so sure myself, but it’s possible.

      “I find ratio dependence a far more intuitive view so I hold out some hope its true.”

      You’re fired. Clear out your desk. Leave your ID and key to the blog with me.

      Just kidding, obviously. 🙂 In seriousness, if you find ratio-dependence (as opposed to some other form of predator-dependence) intuitively appealing, I guess I’d say read Peter Abrams’ papers and see if you still have the same intuitions afterwards. In particular, if you think that functional responses are strictly ratio dependent then you’re saying that predators can never starve, no matter how rare prey are, so long as predators are even rarer. That’s pretty much the opposite of “intuitively appealing” for me!

      • I thought that one would get a rise out of you! In fairness, I am not unaware of the state of the field and teach accordingly.

        It probably says something that it just might be the question I am least expert on. So I’ve never had to do the serious work to commit to an informed opinion. Just dabbled in it from afar combined with noticing what does and doesn’t make sense to undergrads when I teach it. Which just might be a big part of the formula for how zombie ideas get going.

  2. Interestingly, easily the most-clicked link in this post is Doug Erwin’s criticism of the idea that Earth is in the midst of a 6th mass extinction. Though perhaps I shouldn’t be surprised. That’s the least-controversial idea on the list (most poll respondents think it’s probably or definitely true), and probably also the idea that the largest number of readers are familiar with and care a lot about. Plausibly, readers are most likely to click a link that (i) concerns an issue they know and care about, and (ii) contradicts their confidently-held views.

  3. Hi Jeremy, just because it seemed like fun (and, I think, you were hoping we would do this) I’ve taken my own run at this. A couple of caveats, I didn’t do any reading of the literature when I filled out the survey so, I wasn’t bringing nearly the rigor you were to your decision-making. I rarely had specific papers in my head when I was making my decisions – it was more my general impression of the literature that I’ve run across over the last 20 years or so. That means that some ( most?) of my reasoning might not be as well supported as I would have wanted. So, if I written something that seems to you to be patently false, it probably is…but not deliberately so – it’s just my faulty recollection of the literature. Also, I wasn’t prepared to put a 1 or a 5 unless I had also decided I was an expert and I didn’t think I was an expert very often.

    Species interactions typically are stronger and more specialized in the tropics: I know a bit, and picked 2 (probably false).

    Similar rationale as Jeremy although with little specific reading of the literature – these kind of proposed large-scale patterns about something that has intuitive but superficial appeal usually turn out to be, at best, weakly true.

    Local biodiversity is declining in most or all localities: I know some, and I picked 1 or 2.

    Most of the literature with a few exceptions shows that species richness is increasing or staying flat locally.

    Species’ poleward geographic range limits typically are set by abiotic factors, not species interactions: I know a bit, and I picked 4 (probably true).

    There is relatively little evidence that competitive interactions drive community composition at large scales. For example, competitive exclusion by invading species is rarely documented – most documented effects of invasive species are caused by predation (also a biotic interaction, which is why I wasn’t ready to put a 5…I’m not sure what the evidence is that that predation/disease/parasitism define range limits but that would be more plausible to me).

    The intermediate disturbance hypothesis (empirical version): species diversity typically peaks at intermediate disturbance frequency or intensity: I know a bit, and I picked 2 (probably false).

    Based on the Mackey/Currie paper and Jeremy’s convincing arguments in this blog.

    The intermediate disturbance hypothesis (one theoretical version): disturbances promote diversity by interrupting competitive exclusion and preventing the community from achieving equilibrium: I know a bit, and I picked 2 (probably false).

    Based on Peter Chesson and Jeremy’s work.
    .

    Biodiversity is among the most important determinants of ecosystem function: I know some, and I picked 2.

    This is a tricky one because there are so many endpoints when we talk about ecosystem functions – productivity, stability, nutrient cycling – and there are several confounding variables that can lead to relationships between species richness and some function even if species richness isn’t the driver. In addition, few studies explicitly compare the importance of diversity relative to other potential determinants so we would have to use some other index (R2) to make inferences about the relative importance of species richness. In the end, I decided that there isn’t a single function where I’ve been convinced by the data that that species richness is more than ‘a determinant’.

    Habitat fragmentation per se (as distinct from habitat loss) typically reduces biodiversity: I’m an expert, and I picked 1.

    I think for most of us who work or have worked in this area, Lenore Fahrig’s research pulling the body of work on this question together, has put it to bed.

    Interspecific competition can be reliably inferred from information about functional trait similarity: I’m know a bit and I picked 3 (maybe).

    I think it’s unlikely that functional trait similarity alone could reliably imply interspecific competition but I believe that it could be an important line of evidence leading to or away from that conclusion.

    Species diversity typically peaks at intermediate productivity: I know a bit, and I think I picked 2 (probably false).

    Currie and Mackay

    Species richness on continents is dominated by ecological limits rather than evolutionary limits:

    I knew nothing).

    Higher biodiversity will buffer ecosystem function against climate change: I know a bit, and I picked 3 (not sure).

    I wasn’t able to pull to mind specific papers but my arguments are the same as above – it may be true for some functions and not true for others

    Predator functional responses typically are ratio-dependent:

    I know nothing.

    Top-down effects typically are stronger and more important than bottom-up effects: I know some, and I picked 2 (probably false).

    It seems to me that almost all of our research (not to mention just having our eyes open in the world) tell us that when you increase resource availability, organisms and populations grow faster. It’s why we water and put fertilizers on crops. I don’t think the evidence is as consistently true for top-down effects…that if you increase predators you always get a decline in organismal or population growth. Now, if increasing one resource (say, nitrogen in aquatic systems) results in a decrease of another resource (say, oxygen) then things get a little more complicated. In the end, I’m not absolutely sure on this.

    Species’ equatorward geographic range limits typically are set by species interactions, not abiotic factors: I know a bit, and I picked 2 (probably false).

    Same as above

    The dilution effect: host biodiversity generally reduces disease prevalence or infection risk:

    I know nothing.

    Ecological networks (e.g., food webs, plant-pollinator networks) have the structures they do because other structures would be unstable: I know nothing.

    The enemy release hypothesis: invasive species often spread and become abundant relative to natives because enemies attack the invasives less: I know a bit and said 2 (probably false).

    My memory of the literature was that the results were generally inconclusive. I had a quick look at a review paper before writing this and I don’t think my impression was too far off – perhaps evidence of a small effect.

    Hubbell’s neutral model: community dynamics typically are dominated by drift, because all individuals of all species typically are demographically equivalent: I know some, and I picked 2 (probably false).

    I didn’t go all the way to definitely false because, although I believe it’s false, I think the ‘drift’ signal is pretty strong in some, if not most, most communities. Chesson’s work suggests that long-term community composition will be driven by limiting similarity AND limiting dissimilarity. If we interpret similarity in fitness as a measure of niche overlap (which is a bit of stretch but not to the breaking point, in my opinion) then small differences in niche overlap will contribute to species co-existence. Of course, large differences in niche overlap decrease the importance of fitness differences so there are tradeoffs that are hard to sort out. In addition, the fact that most species co-vary positively suggests that their similarities are more important than their differences.

    Interspecific competition and facilitation can be reliably inferred from co-occurrence matrices or correlation matrices of species’ abundances: I’m an expert, and I picked 2 (probably false).

    Same story as above, I agree that correlation matrices alone can’t imply interspecific competition and facilitation but they can (1) be an important line of evidence, and (2) exclude some plausible mechanisms driving community composition.

    Interspecific competition can be reliably inferred from information about phylogenetic relatedness:

    I know some, and I picked 2 (probably false). See above

    Population growth typically is density-dependent: I’m an expert, and I picked 2 (probably false).

    If the threshold for ‘typically’ is that (1) there is often some kind of negative relationship between Nt-1 and Nt or Nt/Nt-1 and Nt-1 or (2) in the limit, populations must be bounded, then this one is hard to argue against. But I don’t think these are reasonable thresholds. However, if we define ‘typically’ as ‘in most years for most populations, the density in year t will respond negatively to densities in previous years, then I think most of the evidence (and definitely, the work we’re doing) suggests density dependence is not an important driver.

    Earth is currently experiencing a 6th mass extinction: I know a bit, and I picked 2 (probably false).

    Most of the estimates suggesting we’re experiencing a 6th mass extinction are based on projections of what is going to go extinct over the next XXX years and we’ve never been very good at those kinds of projections. If we look at the number of actual documented extinctions there have been in the last 100 years they are (at least, to me, given my preconceptions) surprisingly low. This strikes me as the kind of hyperbole folks concerned about conservation use when they think there isn’t enough public interest in an important conservation issue. But the distance between “important conservation concern” and “6th mass extinction” is pretty large.

    • Thanks for taking the time to share this Jeff!

      Re: density dependence, I suggest having another look at Ziebarth et al.; they address the issue you raise (I think; perhaps I’ve slightly misunderstood your point). It’s actually when density-dependence is *strong* that we expect most year-to-year variation in density to be explained by year-to-year fluctuations in abiotic environmental variables.

  4. OK, I’m a little late to the party on this one, but the issue around mass extinction or not seems to be largely definitional. That is, how “mass” does an extinction have to be before we can truly call it a mass extinction? Peter Brannen’s definition requires the wholesale unravelling of ecosystems. Fair enough. But I suspect that to the reading public, politicians and even many scientists, this subtle distinction may get lost in the mix. I can almost read the headline “Eminent scientist says Earth is not in the middle of a mass extinction”. Policy makers and much of the press will draw the conclusion that there’s nothing to worry about.

    Meantime, a very large proportion of the larger, more charismatic creatures – that is those most people could name, have seen their ranges contract dramatically, with, probably, more to come in the future. An ecologist might argue that your tigers, elephants, snow leaopards, cheetahs, orangutans, right whales etc are merely the tip of the biodiversity iceberg. But to a non-scientist who, say, watches Attenborough Docs, these are the creatures with which they are familiar, and which matter to them.

    If Brannen is correct – and I guess from a geological standpoint he probably is – then we are not in the grip of a mass extinction (numbers), but I think it might be legitimate to say we are entering a mass extinction (kilograms).

    • What if the eminent scientist says (as Doug Erwin more or less does) “We’re not in the middle of a mass extinction, but extinction risk is still worth worrying about”?

      • Which is pretty much what he did say. However, perhaps the order in which he said it probably matters. I know this is me playing amateur psychologist, but I have been involved in political processes as a candidate and an activist. What people will here is the first part of his statement – “there is no mass extinction” and the rest will get filtered out or forgotten or wilfully ignored (except by a few critical readers). He could have reversed the order of his statement and said soemthing like “the IUCN Red data list confirm that thousands of species are at risk of extinction, but a mass extinction, in the geological sense, is not happening right now.” But then maybe people woudl have just heard the first part of that statement?

      • Andrew, but in a way this highlights the problem. The problem is not Doug Erwin telling the truth, the problem was scientists who ran around saying something that was not scientifically supported (6th major mass extinction) because they thought it was a good idea. That has now created a situation where Doug (or myself or lots of other people who afraid to say anything) is supposedly harming environmentalism for telling the scientific truth. We shouldn’t be putting scientists in that position.

        To be perfectly honest I don’t think the 6th mass extinction idea was even that effective outside of the already convinced (it was ecologists making up a story that sounds scary to ecologists). And now it has created a muck where scientists are telling other scientists you cannot say the scientific truth in public, which makes science look really bad and indeed makes scientists look not very trustable.

        I’m a big fan of scientists getting more involved and advocating (https://dynamicecology.wordpress.com/2018/04/16/science-advocacy-and-honesty/) but we cannot leave our scientific principles behind when we do it.

      • I absolutely agree, though I do not know what the answer to public and political misinterpretation is. My point was that the truth has to be communicated with care, and even then people will filter it through (what Herman Daly called) their pre-analytic premises. Look, I have enough difficulty getting my students to differentiate hypothesis from theory and to stop talking about “proving” a hypothesis!

        I guess that there is also the issue that Brannen’s definition may be only one such because plenty of scientists are writing about mass extinction in a less restrictive sense (https://www.nature.com/articles/nature09678 and https://www.theguardian.com/environment/2017/jul/10/earths-sixth-mass-extinction-event-already-underway-scientists-warn and https://www.theguardian.com/commentisfree/2017/jul/11/sixth-mass-extinction-habitats-destroy-population).

      • @Andrew – sounds like we’re mostly in agreement. My only point is that Doug (or I) would only have to worry about in what order we said things because some scientists said we were in the middle of a 6th mass extinction.

        Otherwise we could all be presenting a near universal consensus that there are elevated rates of extinction with much of the present day flora and fauna at risk.

        Its almost a tragedy of the commons problem. There are various academic rewards for being more provocative and exaggerating then others. And the same in the journalism and outreach domains. But then it creates a moral hazard for those who just want to tell the truth as it is strongly scientifically supported. And it ultimately weakens the public perception of scientists.

        Climate scientists face this same issue. You can talk about degrees of warming (which is extremely well justified) or you can talk about killer storms (which is a scientific stretch). In my opinion climate scientists were getting further when they stuck to the first story. But they are now sucked into the mire too.

        RE the definition of mass extinction I am trained in, it is 75% of the species going extinct (which often has to be extrapolated from lower levels of genera or family extinctions observable in the fossil record) in a few million years (2 million if you need that precision which often paleontologists don’t). That’s where the notion of a mass extinction just becomes really untenable. We’re at a few percent now, and even if everything at risk goes extinct it doesn’t rise to more than 5-20%. You pretty much have to assume that humans will hammer the planet at an ever acceleratingly worse rate putting more and more currently large-ranged abundant species into risk and ultimate extinction for 10,000 years to get 75%. I admit that is a possible scenario, but it is a giant leap to claim that we know that is what is happening. And its not a biological claim.

        But we probably will lose most of our megafauna and endemic specialists on a time scale of hundreds of years.

  5. Hey Jeremy, I had a chance to go through that paper you recommended and I won’t pretend I’m absolutely sure of this but here’s my take on what their result implies –

    When population regulation is very strong, populations cannot fluctuate very much and so, if they fluctuate a lot it has to be due to drivers other than density dependent population regulation. Thus, when they are strongly regulated we will see a strong correlation between those other drivers and population changes.
    So, if populations fluctuate a lot even though they are strongly regulated what does that mean? The only way I can see this working is that environmental variation changes the equilibrium population size. So, under perfect population regulation, populations can only fluctuate if the equilibrium population changes from year-to-year. Thus, populations can be strongly regulated and we still won’t see a strong correlation between Nt and Nt-1.
    But I think this misses a key point related to a recent post by Mark Vellend – ‘importance’. The average person talking about density dependence is talking either about whether density dependence can explain fine grain fluctuations or long-term boundedness. If fine-grain fluctuations are decided by fine-grain changes in equilibrium size due to environmental variability or long-term boundaries are decided by equilibrium size boundaries set by environmental variability then I think it’s reasonable to talk about environmental variability being more important than density dependent population regulation.

    Now, I can see somebody arguing that the fine-grain fluctuations would be larger and the long-term boundaries might be wider if there was no density dependent population regulation and they would be right…but that seems like a pretty hard nut to crack. The problem is that if we see no evidence that Nt is affected by Nt-1 it’s either because population regulation is extremely strong or extremely weak. If we see weak evidence that Nt is affected by Nt-1 then it is either because population regulation is quite strong or quite weak. And so on. We would have to be able to compare observed fluctuations or boundaries to hypothetical fluctuations or boundaries if there was no population regulation and I’m not sure how we would tweezer that apart with observational data. Maybe if we had the key environmental variables.

    And I’m trying to sort out the logic. Here’s the thought experiment – if we could almost perfectly predict population fluctuations based on a set of environmental variables (with small amounts of variability due to demographic stochasticity) and we had two years of identical environmental conditions but slightly different population sizes and we found no relationship between Nt and Nt-1 after controlling for the environmental effects, could we conclude there was no density dependent regulation?

    • I hope you don’t mind me jumping in here, but this is an interesting discussion! Jeff, regarding the connections to whether environmental variability or density dependence is “more important” in the sense of Mark Vellend’s post, I think being very precise about what question you’re asking is crucial. The Ziebarth result shows that density dependence can be quite strong even if environmental variability, not previous densities, predicts year-to-year abundance fluctuations. So questions about the strength of density dependence and what predicts year-to-year abundance fluctuations seem not to inform each other in the same way as many ecologists have long regarded. That said, I’m intrigued by the thought experiment you proposed and I think in concept it’s promising that if we could very precisely predict and control for environmental effects we could use the relationship between Nt and Nt-1 to evaluate evidence for density dependent regulation. I don’t think I completely follow the details of the set up, but that’s probably irrelevant.

      • I have to confess that I’m stumbling in the dark on this one, trying to sort out what I think publicly hoping that folks like you commenting will help me work through it. And, you certainly get the point of the thought experiment…if there was anything else in the details, I missed it too.

        So, the equation used in the Ziebarth paper shows that if the AR coefficients =0 and the MA coefficients =0, the population size will be a constant (i.e. never deviate from the long-term mean). If the AR coefficients = 0 and the MA coefficients don’t equal zero (and the MA component is about environmental stochasticity), it implies that the equilibrium population size is constantly changing in response to the environment and that the population can hit that new equilibrium instantly (because the AR coefficients =0). If we knew exactly how equilibrium population size changed with the environment we could perfectly predict both fine-grain fluctuations and long-term boundaries using the environmental variables. The role that strong population regulation plays is to allow the population to instantaneously respond to changes in the environment.

        What do we say about a process (population regulation) that, at its strongest, will leave no signal except that historical population sizes won’t matter? This seems darn near metaphysical to me. At a minimum, it seems like a paradox. It implies that if population regulation is as strong as it can be (i.e. equilibrium population sizes are reached instantaneously) there will never be any evidence of density dependence…and I would conclude that these populations are not density dependent. Does that mean that strongly regulated and density dependent mean two very different things? So, populations can be very strongly regulated but both their fine-grain fluctuations and long-term boundaries are very, very weakly density dependent. In this case, by ‘weakly density-dependent’ I mean that almost none of the fine-grain fluctuations or long-term boundaries are caused by a population being forced away from and then returning to the equilibrium population size. In the limit, of perfectly regulated populations, they would be absolutely density independent (i.e. density would have no effect on fluctuations or boundaries and you could, in theory, perfectly predict population fluctuations or boundaries while knowing nothing about the history of population sizes). It seems to me that, for density-dependence to be a useful concept, we have to acknowledge that it means something very different than population regulation.

        Back to the Ziebarth paper, I’m not convinced that showing evidence that a population is strongly regulated says anything definitive about whether it’s density dependent because the more strongly regulated a population is, the less its history matters and, I think, density dependence is about the effect that historical population sizes have on future population sizes. The paradox is that historical population sizes don’t matter when regulation is very strong or when it’s very weak – it’s in the middle that history matters.

        I have to go back and read the literature – I suspect I’m stumbling along well-trod ground.

      • “density dependence is about the effect that historical population sizes have on future population sizes.”

        Not to be pedantic (and apologies for not having more time to engage right now), but that’s not correct. Density-dependence means that per-capita growth rates depend on current or past population sizes. And it’s not true in general to say that density-dependence means that current population sizes will depend on past population sizes–indeed, in many cases that’s almost the opposite of correct. Think for instance of the simplest model of density-dependent population growth: the continuous time logistic. In that model, the effect of density-dependence in the long run is precisely to erase history. Population size goes to K no matter what the initial conditions were. So that, if all you know is current population size K, you have no information about past population densities.

        Re: population regulation vs. density dependence: population regulation as Ziebarth et al. define it can’t happen without density dependence.

        It might help your intuitions to code up a simple model with known density dependence and then run it (or its population dynamical output) through the Ziebarth et al. analysis.

  6. Hi Jeremy, I should have said that density dependence is about the effect that historical population sizes have on future population growth rates…but, I’m pretty sure that wouldn’t bridge the gap between how I presented this and how you think about it. And don’t get me wrong, I don’t have any answers here – I’m trying to work through some facts that are difficult to reconcile.
    I don’t think my main problem is that my intuition about the model is faulty (although I could be wrong about this). The strength of density dependent regulation in the Ziebarth model measures the speed at which a population returns to equilibrium after being perturbed (I think) and ,at the limit, when the AR coefficients = 0, the population returns to equilibrium instantaneously and thus, historical population sizes have no effect on future growth rates. (If I’ve got that wrong then my intuition may be the problem.) We could, in theory, predict population fluctuations and boundaries perfectly without any historical knowledge about population size even though density dependent regulation is as strong as it can be. My problem is that I think this result can’t co-exist with what some people mean when they say a population is density dependent. When some scientists, managers, and just regular folks talk about populations being density dependent, they mean “Historical population sizes have a detectable effect on future growth rates.”

    Definition 1 of density dependent population: a populations that is density-dependent regulated.
    Definition 2 of density dependent population: a population where historical population size has a detectable effect on future growth rates.

    One response to that is to say “I’m sorry, definition 2 is not what density dependent regulation implies and therefore not what density dependent means.” And I’m fine with that but then we need a name for the definition 2 concept because it is one that people think about and has, at a minimum, applied implications.

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