These are just my favourite quotes from talks on day two of ESA:
(All from great, but anonymous, speakers)
After showing the results of a spatial statistics test: "...But still I was worried because that would be using statistics to prove something and that feels wrong."
On being asked how the speaker quantified earthworm abundance: "I used a non-invasive electroshock technique".
(I'm sure this is normal procedure, it just sounds hilarious to the uninformed).
Wednesday, August 7, 2013
Tuesday, August 6, 2013
ESA day 2: The shampoo salesman and new questions.
Day two started off on a high note with Bernhard Schmid's talk on evolution in biodiversity-ecosystem function (BEF) experiments. He is one of the originators of the Jena biodiveristy experiment, for years they have been maintaining plant species in monocultures and in polycultures to assess how much more ecosystem function is produced by multi-species assemblages over single species monocultures. However, it occurred to Schmid that species in these two contexts face different pressures, which may have resulted in evolutionary changes. In monocultures, species face high intraspecific densities and thus competition is severe, as is negative indirect effects like pathogen sharing and herbivory rates. Within polycultures, intrraspecific interactions may involve niche differences, with opportunities for character divergence to further stabilize coexistence. He reported on an experiment that took seeds and cuttings from monoculture and polyculture populations and grew then in monoculture or polyculture. He showed that individuals originating from monoculture did better in monoculture and species originating from polyculture did better in polyculture. The implications are fascinating. If the rate of evolutionary change in performance are equivalent between monocultures and polycultures, the BEF relationships should remain constant. However, if the rates of change are greater for polyculture populations the BEF relationship should get stronger over time. Conversely, BEF relationships should became weaker if higher evolutionary change in monoculture.
It was hard to top this talk, but there were several other impressive talks as well. Jacob Vander Laan used a country-wide dataset on aquatic insect diversity across the USA and showed that at larger scales, beta-diversity decreases with connectivity, but is seemingly unaffected by environmental heterogeneity.
Restoration is community assembly with management goals and Emily Grman gave an interesting talk on assessing the success of prairie restoration by accounting for management activities, landscape, historical and local abiotic factors. She showed that management activities were the most important, with species-rich sowings result in rich communities, even though many of the species are not those in the sown mixture. Sowing a high diversity of grasses did not increase diversity, but high diversity of forbes did. Other factors like landscape influences and local factors were not important.
Will Pearse examined plant diversity patterns and homogenization across six large urban centres. He showed that there has been little taxonomic homogenization, but substantial phylogenetic and moderate functional trait homogenization. Beyond the interesting questions about how urban centres may cause biotic homogenization is the new tools that Pearse created for these analyses, and that are available online. As a self described 'shampoo salesman', he created a general tool called Phylogenerator that creates a pipeline that makes estimating trees form sequence data more efficient -definitely a tool that ecologists should be using. He further created a way to quantify complex leaf shapes and has a tool available for that, called Stalkless.
All in all , this was a good day, one that has stimulated new questions and approaches. These talks got me thinking about some of my data and experiments and how I can extend them to new questions.
Monday, August 5, 2013
ESA 2013 Day 1: Temporal variation, roller derby, and topics in between
With day 1 over, ESA 2013 was off to an excellent start. Minneapolis seems like a very friendly place, and I enjoyed perhaps the most chatty bus ride I've ever experienced. As always, I failed to determine the best point on the trade off plot between cherry-picking certain talks based on topic, speaker and friends, and staying put in a session with an interesting topic. Nonetheless I managed to see some really good talks.
Among them, I saw Lauren Shoemaker in the Theoretical Ecology section, who illustrated how to model the four metacommunity paradigms (I.e. species sorting, mass effects, neutral, and patch dynamics) with the Chessonian framework of equalizing and stabilizing forces. She illustrated how both deterministic and stochastic models could replicate dynamics from the four paradigms. This suggests that rather than the usual description of the neutral paradigm as stochastic and the mass effect and species sorting paradigms as niche-based and therefore deterministic, the terms niche and deterministic and neutral and stochastic should not be synonymous. Rather, in the Chessonian framework, fitness differences drive neutral-type dynamics and spatial niches structure the species sorting and mass effects paradigms. More importantly, the results show how the paradigms are just a few sets of points on the much broader set of parameter values that could describe metacommunity dynamics.
It must be funny for Peter Chesson to follow up a talk in which his name is used as an adjective. After the talk on the Chessonian framework, he spoke about the fact that environment is fluid and non-stationary, yet models of communities have almost always treated it as being at equilibrium. Since it is not, ideally models of community dynamics would begin to incorporate environmental variation, and ask questions more relevant to non-equilibrium systems. For example: when is long-term persistence expected, given this non-stationarity and can communities in a non-stationary system still be stable? He showed that including environmental fluidity into models doesn't mean that communities are necessarily unstable, for example, when spatial and temporal trends of environmental variation match, communities may be stationary.
In another of many good talks about temporal variation (seemingly a popular topic of late), Colin Kremer showed that altering the basic characteristics of abiotic temporal variation (amplitude, means, periodicity) changed the amount of diversity present as communities evolved over time. Temporal variation isn't a simple concept anymore than spatial variability is - it has different characteristics with different effects on ecological dynamics and needs to be considered in greater depth.
My biggest disappointment was that I had a time conflict and couldn't attend a talk titled "Significant changes in the skin microbiome mediated by the sport of roller derby". No doubt I would have learned a lot.
Tuesday, July 9, 2013
Monday, July 1, 2013
Carnival of Evolution is up!
The latest Carnival of Evolution (#61 if you are keeping track) is up and running at Teaching Biology. It is the Crustie Lovin' Edition.
Friday, June 28, 2013
MacArthur's words still resonate 40 years on
I recently received an old library copy of “Geographical Ecology: Patterns in the Distribution of Species” by Robert MacArthur (1972). It’s the last book that MacArthur wrote before his early death to cancer. It is an ambitious book that connects repeated ecological patterns to mechanisms as broad as the earth’s rotations (producing climate as we experience it) and as focused as organismal behaviour.
But honestly, the thing that has struck me most so far as I read is the timelessness and wisdom in MacArthur's introduction. Issues ranging from focusing on questions versus systems, the value of repeated patterns, complexity, and what generality really means, aren't at all new.
“To do science is to search for repeated patterns, not simply to accumulate facts, and to do the science of geographical ecology is to search for patterns of plant and animal life that can be put on a map. The person best equipped to do this is the naturalist who loves to note changes in bird life up a mountainside, or changes in plant life from mainland to island, or changes in butterflies from temperature to tropics. But not all naturalists want to do science; many take refuge in nature’s complexity as a justification to oppose any search for patterns. This book is addressed to those who do wish to do science. Doing science is not such a barrier to feeling or such a dehumanizing influence as is often made out. It does not take the beauty from nature. The only rules of scientific method are honest observations and accurate logic. To be great it must also be guided by a judgment, almost an instinct, for what is worth studying. No one should feel that honest and accuracy guided by imagination have any power to take away nature’s beauty.
Science should be general in its principles. A well-known ecologist remarked that any pattern visible in my birds but not in his Paramecium would not be interesting, because, I presume, he felt it would not be general. The theme running through this book is that the structure of the environment, the morphology of the species, the economics of species behaviour, and the dynamics of population changes are the four essential ingredients of all interesting biogeographic patterns. Any good generalization will be likely to build in all these ingredients, and a bird pattern would only be expected to look like that of a Paramecium if birds and Paramecium had the same morphology, economics, and dynamics, and found themselves in environments of the same structure.”
--Robert MacArthur
But honestly, the thing that has struck me most so far as I read is the timelessness and wisdom in MacArthur's introduction. Issues ranging from focusing on questions versus systems, the value of repeated patterns, complexity, and what generality really means, aren't at all new.
“To do science is to search for repeated patterns, not simply to accumulate facts, and to do the science of geographical ecology is to search for patterns of plant and animal life that can be put on a map. The person best equipped to do this is the naturalist who loves to note changes in bird life up a mountainside, or changes in plant life from mainland to island, or changes in butterflies from temperature to tropics. But not all naturalists want to do science; many take refuge in nature’s complexity as a justification to oppose any search for patterns. This book is addressed to those who do wish to do science. Doing science is not such a barrier to feeling or such a dehumanizing influence as is often made out. It does not take the beauty from nature. The only rules of scientific method are honest observations and accurate logic. To be great it must also be guided by a judgment, almost an instinct, for what is worth studying. No one should feel that honest and accuracy guided by imagination have any power to take away nature’s beauty.
Science should be general in its principles. A well-known ecologist remarked that any pattern visible in my birds but not in his Paramecium would not be interesting, because, I presume, he felt it would not be general. The theme running through this book is that the structure of the environment, the morphology of the species, the economics of species behaviour, and the dynamics of population changes are the four essential ingredients of all interesting biogeographic patterns. Any good generalization will be likely to build in all these ingredients, and a bird pattern would only be expected to look like that of a Paramecium if birds and Paramecium had the same morphology, economics, and dynamics, and found themselves in environments of the same structure.”
--Robert MacArthur
It's interesting that an introduction written in 1972 is so relevant that it could have been written today. The pessimistic view is that ecology is just iterating through the same problems and solutions, or progress is slow. Or maybe classic books remain as classics because their authors understood and explored the issues at the core of the science and had the benefit of being there in the formative years. It's fun to see that when MacArthur thanks particularly four friends who influenced his work most, he means G. Evelyn Hutchinson, E.O. Wilson, Richard Levins, and Jared Diamond. I suppose any book influenced by the combination of all these scientists and written by MacArthur will always have something interesting to say.
Wednesday, June 26, 2013
Evidence for the evolution of limiting similarity in diving beetle communities
From Scheffer and van Nes (2006): Evenly spaced clusters of species along a niche axis (x-axis) evolved in response to competition. |
The follow-up paper -Vergnon et al. (2013)- tests for the pattern predicted in Scheffer and van Nes (2006) using communities of subterranean diving beetles (Coleoptera, Dytiscidae) in Australia. These species have evolved for over 5 million years in isolated aquifers. If limiting similarity structured beetle communities, the authors predicted that there should be regularity in the spacing of species along a niche axis. If competitive interactions determine species' positions on the niche axis, then their absolute positions on the niche axis could vary between communities so long as their relative positions are evenly spaced. If, in contrast, niches are driven by environmental conditions, species in different communities/aquifers should have similar absolute positions along the niche axis.
The authors used a nice combination of statistics, modelling and observational data (34 communities of beetles representing 75 total species) to test for these predicted patterns. They used beetle size as the measure of niche position, since size is often an indicator of niche position and food availability and identity. For almost all aquifers, co-occurring beetles were significantly different in size. Further, species in different aquifers classified as occurring in the same size classes (small, medium, large), had different absolute sizes (i.e. the largest beetle in one 2-species aquifer was not similar in size to the largest beetle in another 2-species aquifer).
Although the absolute size of species differed between aquifers, the ratio of sizes (regularity of spacing on the niche axis) was highly consistent. Further, simulations of evolution of body size due to competition were capable of reproducing the observed size structure of the diving beetles.
This paper does a nice job of integrating theory and data, and combining pattern and process. The focus is on testing contrasting predictions, and the authors use complementary approaches to test statistically for the presence of patterns and to demonstrate with simulations the relationship between the evolution of limiting similarity and the observed pattern. The evidence is suggestive that limiting similarity and not pre-existing environmental niches explains the size structure of communities of competing diving beetles. There are still questions about how far these inferences can be extended. For example, do we expect that predefined environmental niches are really the same across aquifers? How important is competition in these communities - at the moment, the authors only have minimal evidence of gut content overlap from a single aquifer. Further the low diversity of aquifer communities (~1-5 diving beetle species) means that the prediction of clusters of multiple similar species made in the original Scheffer and van Nes paper can't be tested. But the fact that aquifer diving beetle communities have low diversity and are very simplistic is beneficial for the authors. Patterns in diverse communities where multiple processes (predation, migration, etc) are important may be too complex to show clear evidence in observational data. Simple systems (including microcosms) are a good place to find evidence that a process of interest actually occurs. Whether or not that process is important across many systems is of course a more difficult question to answer.
From Vergnon et al. (2013): regularity of spacing between competing diving beetles (measured as the body size ratio). |
This paper does a nice job of integrating theory and data, and combining pattern and process. The focus is on testing contrasting predictions, and the authors use complementary approaches to test statistically for the presence of patterns and to demonstrate with simulations the relationship between the evolution of limiting similarity and the observed pattern. The evidence is suggestive that limiting similarity and not pre-existing environmental niches explains the size structure of communities of competing diving beetles. There are still questions about how far these inferences can be extended. For example, do we expect that predefined environmental niches are really the same across aquifers? How important is competition in these communities - at the moment, the authors only have minimal evidence of gut content overlap from a single aquifer. Further the low diversity of aquifer communities (~1-5 diving beetle species) means that the prediction of clusters of multiple similar species made in the original Scheffer and van Nes paper can't be tested. But the fact that aquifer diving beetle communities have low diversity and are very simplistic is beneficial for the authors. Patterns in diverse communities where multiple processes (predation, migration, etc) are important may be too complex to show clear evidence in observational data. Simple systems (including microcosms) are a good place to find evidence that a process of interest actually occurs. Whether or not that process is important across many systems is of course a more difficult question to answer.
Friday, June 21, 2013
Movement patterns in populations of early academics
Sometimes of the perks of academic life are also the most difficult parts – frequent travel opportunities mean you are also frequently away from friends and family (and spend too much time in airports). The nature of the university job market provides global opportunities for work, but also means that in reality opportunities and circumstances can constrain you to places you wouldn't have chosen otherwise. Your friends will cover the world, but you will rarely be in the same room together. The apprenticeship-like nature of early academic positions means that you will move, probably many times, before you find a permanent position (if you do).
I have a friend who grew up with diplomat parents, which meant her family moved to a new place in the world every few years. The result was that she often felt like she didn’t have a strong connection to any one place or group of people. Academia isn’t quite so extreme, but you can understand why after moving to one place for undergrad, another for a Masters and/or PhD, one or two more for postdocs, your interactions and place in the world can feel rather impermanent. It also means that, for better or worse, your social circle includes other academics, and they are also shifting from place to place. When I tell non-academic friends and family (who mostly have settled in a single place) about upcoming moves, they are often more excited than I am about the opportunity to pick up and go. No doubt this is a grass-is-always-greener situation, but I often think that the most notable and difficult aspect of academic mobility is that you end up saying goodbye a lot.
I wonder whether some of the academic ambivalence expressed is aggravated by this early, necessary transience. Certainly there is lots of evidence that residential mobility (i.e. moving) relates to higher mortality and lowered health indicators, though some studies suggest that this effect may be more true for introverts than extroverts (presumably because extroverts form new friendships more easily). Academics share this phenomenon with groups like military families and third culture kids. The commonality is that, with every move it becomes harder to define home as a particular place – it is more like an intangible connection to multiple places and people. And maybe that's not so terrible - a good friend who was raised by an academic suggested that the key is to redefine your life and friendships as being global rather than local. And eventually professors settle down (I can think of a few people who have been at one university for 30+ years). But in the interim there is always the not-insignificant tension between the costs and benefits of uprooting yourself every few years, and the slow loss of individuals who are not capable of this mobility, from the academic pipeline.
I have a friend who grew up with diplomat parents, which meant her family moved to a new place in the world every few years. The result was that she often felt like she didn’t have a strong connection to any one place or group of people. Academia isn’t quite so extreme, but you can understand why after moving to one place for undergrad, another for a Masters and/or PhD, one or two more for postdocs, your interactions and place in the world can feel rather impermanent. It also means that, for better or worse, your social circle includes other academics, and they are also shifting from place to place. When I tell non-academic friends and family (who mostly have settled in a single place) about upcoming moves, they are often more excited than I am about the opportunity to pick up and go. No doubt this is a grass-is-always-greener situation, but I often think that the most notable and difficult aspect of academic mobility is that you end up saying goodbye a lot.
I wonder whether some of the academic ambivalence expressed is aggravated by this early, necessary transience. Certainly there is lots of evidence that residential mobility (i.e. moving) relates to higher mortality and lowered health indicators, though some studies suggest that this effect may be more true for introverts than extroverts (presumably because extroverts form new friendships more easily). Academics share this phenomenon with groups like military families and third culture kids. The commonality is that, with every move it becomes harder to define home as a particular place – it is more like an intangible connection to multiple places and people. And maybe that's not so terrible - a good friend who was raised by an academic suggested that the key is to redefine your life and friendships as being global rather than local. And eventually professors settle down (I can think of a few people who have been at one university for 30+ years). But in the interim there is always the not-insignificant tension between the costs and benefits of uprooting yourself every few years, and the slow loss of individuals who are not capable of this mobility, from the academic pipeline.
Felsenstein for SMBE president :)
A highly entertaining, somewhat relevant update to my post about Joe Felsenstein's 'dishonour roll'. Felsenstein is running for President of the Society for Molecular Biology and Evolution, and his personal statement is a must-read career retrospective. If you don't at least crack a smile, you might be taking science a bit too seriously...
For example: "[Felsenstein] has been President of the Society for the Study of Evolution, and imagines that he could be President of the SMBE, even though he has not yet learned the names of all 20 amino acids."
Honestly, I think it gives more insight than most bios into the person and their work.
For example: "[Felsenstein] has been President of the Society for the Study of Evolution, and imagines that he could be President of the SMBE, even though he has not yet learned the names of all 20 amino acids."
Honestly, I think it gives more insight than most bios into the person and their work.
Monday, June 17, 2013
Another round in Diamond vs. Simberloff: revisiting the checkerboard pattern debate
Edward F. Connor, Michael D. Collins, and Daniel Simberloff. 2013. "The Chequered History of Checkerboard Distributions." Ecology. http://dx.doi.org/10.1890/12-1471.1.
One of the most vociferous recent debates in community ecology started in the 1970s between Jared Diamond and Dan Simberloff (and colleagues) regarding whether 'checkerboard patterns' of bird distributions provided evidence for interspecific competition. This was an early and particularly heated example of the pattern versus process debate that continues in various forms today. Diamond (1975) proposed that the distribution of birds in the Bismark Archipelago, and particularly the fact that some pairs of bird species did not co-occur on the same islands (producing a checkerboard pattern), was evidence that competition between species limited their distributions. The issue with using this checkerboard pattern as evidence of competition, which Connor and Simberloff (1979) subsequently pointed out, was that a null model was necessary to determine whether it was actually different from random patterns of apparent non-independence between species pairs. Further, other mechanisms (different habitat requirements, speciation, dispersal limitations) could also produce non-independence between species pairs. The original debate may have died down, but the methodology for null models of communities suggested by Connor and Simberloff has greatly influenced modern ecological methods, and continues to be debated and modified to this day.
The original null model of bird distributions in the Bismark Archipelago involved a binary community matrix (rows represent islands, columns represent species) with 0s and 1s representing species presences or absences. Hence, all the 1s in a row represent the species present on the island. The original null model approach involved randomly shuffling the 0s and 1s, maintaining island richness (row sums) and species range sizes (column sums). The authors of a new paper in Ecology admit that the original null models didn’t accurately capture what Diamond meant by a "checkerboard pattern". This is interesting in part because two of the authors (E.F. Connor and Dan Simberloff) lead the debate against Diamond and introduced the binary matrix approach for generating null expectations. So there is a little bit of a ‘mea culpa’ here. The authors note that earlier null models captured patterns of non-overlap between species' distributions but didn’t differentiate between non-overlap between species with overlapping ranges compared to non-overlap between species which simply occurred on sets of geographically distant islands (referred to here as 'regional allopatry'). The original binary matrix approach didn’t consider spatial proximity of species ranges.
With this fact in mind, the authors re-analyzed checkerboard patterns in the Bismark Archipelago, but in such a way as to control for regional allopatry. True checkerboarding was defined as: “a congeneric or within-guild pair with exclusive distribution, co-occurrence in at least one island group, and geographic ranges that overlap more or significantly more than expected under an hypothesis of pairwise independence”. This definition appears closer to Jared Diamond's original definition and so a null model that captures this is probably a better test of the original hypothesis. The authors looked at the overlap of convex hulls defining species’ ranges and when randomizing the binary matrix, added the further restriction that species could occur only within the island groups where they were actually found (instead of being randomly shuffled through any island, as before).
Even with these clarified and more precise null models, the results remain consistent. True checkerboarding appears to rarely occur compared to chance. Of course, this doesn't mean that competition is not important, but “Rather, in echoing what we said many years ago, one can only conclude that, if they do compete, competition does not strongly affect their patterns of distribution among islands.” More generally, the endurance of this particular debate says a lot about the longstanding tension in ecology over the value and wealth of information captured by ecological patterns, and the limitations and caveats that come with such data. There is also a subtle message about the limitations of null models: they are often treated as a magic wand for dealing with observed patterns, but null models are limited by our own understanding (or ignorance) of the processes at play and our interpretation of their meaning.
One of the most vociferous recent debates in community ecology started in the 1970s between Jared Diamond and Dan Simberloff (and colleagues) regarding whether 'checkerboard patterns' of bird distributions provided evidence for interspecific competition. This was an early and particularly heated example of the pattern versus process debate that continues in various forms today. Diamond (1975) proposed that the distribution of birds in the Bismark Archipelago, and particularly the fact that some pairs of bird species did not co-occur on the same islands (producing a checkerboard pattern), was evidence that competition between species limited their distributions. The issue with using this checkerboard pattern as evidence of competition, which Connor and Simberloff (1979) subsequently pointed out, was that a null model was necessary to determine whether it was actually different from random patterns of apparent non-independence between species pairs. Further, other mechanisms (different habitat requirements, speciation, dispersal limitations) could also produce non-independence between species pairs. The original debate may have died down, but the methodology for null models of communities suggested by Connor and Simberloff has greatly influenced modern ecological methods, and continues to be debated and modified to this day.
The original null model of bird distributions in the Bismark Archipelago involved a binary community matrix (rows represent islands, columns represent species) with 0s and 1s representing species presences or absences. Hence, all the 1s in a row represent the species present on the island. The original null model approach involved randomly shuffling the 0s and 1s, maintaining island richness (row sums) and species range sizes (column sums). The authors of a new paper in Ecology admit that the original null models didn’t accurately capture what Diamond meant by a "checkerboard pattern". This is interesting in part because two of the authors (E.F. Connor and Dan Simberloff) lead the debate against Diamond and introduced the binary matrix approach for generating null expectations. So there is a little bit of a ‘mea culpa’ here. The authors note that earlier null models captured patterns of non-overlap between species' distributions but didn’t differentiate between non-overlap between species with overlapping ranges compared to non-overlap between species which simply occurred on sets of geographically distant islands (referred to here as 'regional allopatry'). The original binary matrix approach didn’t consider spatial proximity of species ranges.
With this fact in mind, the authors re-analyzed checkerboard patterns in the Bismark Archipelago, but in such a way as to control for regional allopatry. True checkerboarding was defined as: “a congeneric or within-guild pair with exclusive distribution, co-occurrence in at least one island group, and geographic ranges that overlap more or significantly more than expected under an hypothesis of pairwise independence”. This definition appears closer to Jared Diamond's original definition and so a null model that captures this is probably a better test of the original hypothesis. The authors looked at the overlap of convex hulls defining species’ ranges and when randomizing the binary matrix, added the further restriction that species could occur only within the island groups where they were actually found (instead of being randomly shuffled through any island, as before).
Even with these clarified and more precise null models, the results remain consistent. True checkerboarding appears to rarely occur compared to chance. Of course, this doesn't mean that competition is not important, but “Rather, in echoing what we said many years ago, one can only conclude that, if they do compete, competition does not strongly affect their patterns of distribution among islands.” More generally, the endurance of this particular debate says a lot about the longstanding tension in ecology over the value and wealth of information captured by ecological patterns, and the limitations and caveats that come with such data. There is also a subtle message about the limitations of null models: they are often treated as a magic wand for dealing with observed patterns, but null models are limited by our own understanding (or ignorance) of the processes at play and our interpretation of their meaning.
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