Hi All! Happy spring break and happy St. Patty's Day!
Does anyone have the ability to access the full article below? Can we access it through Fordham? It appears pretty relevant to class and reflects a few conversations that have gone on. I've had no luck tracking down who the two elusive researchers from UCAL Davis are, so I can't find the original paper (if there is one?)
Enjoy the rest of break!
http://www.sciencemag.org/cgi/content/short/327/5971/1318
An interview with the author starts on pg 6 - http://www.sciencemag.org/cgi/data/327/5971/1395-b/DC1/1
Thursday, March 18, 2010
Wednesday, March 10, 2010
Studying for the Midterm
How’s the studying going? Hopefully well! Earlier today there was a productive study session in LH 370. Here’s what we learned:
Be cautious, however, if your study partner tries to eat one of the text books.
It’s always a good idea to consult the text books!
Be cautious, however, if your study partner tries to eat one of the text books.
Those zero growth isoclines get a bit trippy if you stare at them long enough… carrying capacity over alpha is greater than what?
After too long, we all find ourselves passing out on the desk using Peter Morin’s Community Ecology text as a pillow, don’t we? The solution – espresso shots and chocolate covered coffee beans!
Happy Studying!
Tuesday, March 9, 2010
Meta-post
Thanks for all of your wonderful posts and comments! I hope this is useful and fun and not too overwhelming!
Regarding Thursday night exam- I was not able to get a computer lab so we will be meeting in the regular room and writing using pencils and pens!
Some general comments regarding issues that we have touched on but been too time-strapped to explore further:
What is equilibrium? One definition is that it is "the point at which forces cancel each other". To a community ecologist, the "forces" might be competition, predation, mutualisms etc. (biotic factors), which is, of course, a gross oversimplification of nature--- Comm. ecologists may integrate disturbances, as we saw with the Moretti paper and its investigation of fire. An example of a "theoretical equilibrium" is the abundances of two competing species that results in crossing of their zero-growth isoclines-- all other things (prey populations, parasatism, climate, floods, fires, etc.) being equal (never the case), the "community" would be at equilibrium. Also, the community might be locally stable if a small increase in either results in a move back towards the center. Generally however, more studies are debunking the idea of the "balance of nature" or any sort of stable system. Fluctuations and chaotic change in population numbers seems to be more the rule than the exception. I think this topic will continue to be discussed as we move forward in the class.
Species richness and other measures of diversity- Which is the best to use? A benefit of observed species richness is that it is easy to understand and convey. Diversity indices provide numbers that are only interpretable relative to each other. Estimators, while supposedly being more robust, take you one or more steps from your empirically observed data. As you keep fiddling with different estimators, indices, functional descriptions, etc. you can run into the problem of too many analyses (inflating type I error), and ultimate confusion! Best idea is to really read up on the literature beforehand and develop a compelling a priori dependent variable for your a priori hypotheses. Email scientists that have published these papers and ask them for ideas for good measures or diversity to use.
Lemaitre et al 2009- Did they just die of the cold? The authors state that the primary source of mortality was hypothermia and they also state that "the effect of temperature was additive to other variables influencing survival and did not interact with the presence of bot fly (Appendix 1, ESM; Table 1)." I did not see the interaction data in Table 1 and could not access the appendix to evaluate this claim (without seeing mean temp for bot fly + vs. -). They give this data for confinement time in the discussion.
Invasive species? Are they all that bad? Short answer: if they compete with you, no. If they eat you, yes. From Sax and Gaines 2008 (not a required reading):
"Among the 204 vertebrate species with listed causes of extinction, some form of predation (including human hunting, carnivory, and infectious disease) is cited as the sole factor responsible for species extinctions in 69 (33.8%) of extinctions, predation together with other
contributing factors is cited for 85 (41.7%) of extinctions, and competition together with other factors is listed for 18 (8.8%) of extinctions. In no case is competition listed as the sole cause of species extinction."
More evidence for the importance of predation relative to competition?
P-values irrelevant? I think so but keep following the increasing literature on this debate. All the techniques you learn in stats (regression, anova, ancova, etc) can be assessed in an information theory framework so are completely relevant to your development as ecologists. The "Pitfalls" article mentions that you should either use p-values or information theory but not both-- this seems a little dogmatic. Some reviewers will be more familiar with p-values and others with AIC--- if it you have space you may want to provide both. Just keep your inferences locked to one paradigm.
Regarding Thursday night exam- I was not able to get a computer lab so we will be meeting in the regular room and writing using pencils and pens!
Some general comments regarding issues that we have touched on but been too time-strapped to explore further:
What is equilibrium? One definition is that it is "the point at which forces cancel each other". To a community ecologist, the "forces" might be competition, predation, mutualisms etc. (biotic factors), which is, of course, a gross oversimplification of nature--- Comm. ecologists may integrate disturbances, as we saw with the Moretti paper and its investigation of fire. An example of a "theoretical equilibrium" is the abundances of two competing species that results in crossing of their zero-growth isoclines-- all other things (prey populations, parasatism, climate, floods, fires, etc.) being equal (never the case), the "community" would be at equilibrium. Also, the community might be locally stable if a small increase in either results in a move back towards the center. Generally however, more studies are debunking the idea of the "balance of nature" or any sort of stable system. Fluctuations and chaotic change in population numbers seems to be more the rule than the exception. I think this topic will continue to be discussed as we move forward in the class.
Species richness and other measures of diversity- Which is the best to use? A benefit of observed species richness is that it is easy to understand and convey. Diversity indices provide numbers that are only interpretable relative to each other. Estimators, while supposedly being more robust, take you one or more steps from your empirically observed data. As you keep fiddling with different estimators, indices, functional descriptions, etc. you can run into the problem of too many analyses (inflating type I error), and ultimate confusion! Best idea is to really read up on the literature beforehand and develop a compelling a priori dependent variable for your a priori hypotheses. Email scientists that have published these papers and ask them for ideas for good measures or diversity to use.
Lemaitre et al 2009- Did they just die of the cold? The authors state that the primary source of mortality was hypothermia and they also state that "the effect of temperature was additive to other variables influencing survival and did not interact with the presence of bot fly (Appendix 1, ESM; Table 1)." I did not see the interaction data in Table 1 and could not access the appendix to evaluate this claim (without seeing mean temp for bot fly + vs. -). They give this data for confinement time in the discussion.
Invasive species? Are they all that bad? Short answer: if they compete with you, no. If they eat you, yes. From Sax and Gaines 2008 (not a required reading):
"Among the 204 vertebrate species with listed causes of extinction, some form of predation (including human hunting, carnivory, and infectious disease) is cited as the sole factor responsible for species extinctions in 69 (33.8%) of extinctions, predation together with other
contributing factors is cited for 85 (41.7%) of extinctions, and competition together with other factors is listed for 18 (8.8%) of extinctions. In no case is competition listed as the sole cause of species extinction."
More evidence for the importance of predation relative to competition?
P-values irrelevant? I think so but keep following the increasing literature on this debate. All the techniques you learn in stats (regression, anova, ancova, etc) can be assessed in an information theory framework so are completely relevant to your development as ecologists. The "Pitfalls" article mentions that you should either use p-values or information theory but not both-- this seems a little dogmatic. Some reviewers will be more familiar with p-values and others with AIC--- if it you have space you may want to provide both. Just keep your inferences locked to one paradigm.
Thursday, March 4, 2010
Leamaitre et al. Bot fly parasitism of the red-backed vole.
While most investigations that identify factors that control population dynamics tend to focus on predation and competition, there is little empirical evidence of the role that parasites play on host populations. This study aims to assess the role of parasitism of red-backed vole by bot flies in boreal forests in Quebec, Canada.
The red-backed vole was chosen for this study as it is the most abundant small mammal and primary host of bot flies, in eastern Canada. Density-dependent impacts of bot fly parasitism on host survival and reproduction would ultimately be needed if this parasitism regulated vole populations.
In order to determine the potential impact that bot flies have on regulating populations of red-backed voles, this study:
- Assessed the impact of the parasites on:
- Probability of vole survival under stressful conditions, and whether this was density dependent.
- Reproductive activity in females
- Indentified main factors driving the relationship between:
- individual risk of infection and
- abundance of bot flies in red-backed voles, host abundance, two host life history traits (sex and body conditions) and combination of three habitats variables
- Impact of bot fly prevalence on growth rate of red-backed vole population between mid-July and mid-August
The probability of vole survival in live traps decreased with bot fly infection. The authors state that this indicates that infection by bot flies on red-backed voles is a recent coevolution since strong host-parasite coevolution should not reduce host survival (as this would impact parasite survival). However some authors argue that the reduced host survival is not so much due to coevolution hypothesis as it is an artifact of mark-recapture studies. Resident voles would be more highly infested (due to bot egg placement near burrow openings) and more resident voles would be recaptured than transient voles which would inflate survival estimates of highly infected resident voles.
Probability of infection and abundance of bot flies in red-backed voles, were both negatively correlated with vole abundance. The authors concluded three possible reasons for this.
1. A one year time lag between population cycles of host and parasite since the bot fly requires two years from egg deposition to adult emergence.
2. When vole populations are at capacity, a larger proportion is transient and these transient voles have lower probability of infection.
3. Dilution effect of all hosts could occur when vole population is high.
Sex and body condition of the host and habitat variability were not significant predictors of infection probability and abundance of bot flies.
Bot fly infection prevalence was linked to a decrease in short term growth rate of vole pop over summer. This study indicates that bot flies can impact host population growth, during peak reproductive season of the host.
Bot flies have the potential to reduce survival of red-backed voles, an effect that may apply to large portions of populations. This study supports the idea that parasites may play a role in regulating population dynamics of hosts, although bot flies may be more of a limiting factor than a regulating one.
Another key point from discussion in class: since most mortality in live traps was due to hypothermia, the conclusion from this would be; don’t freeze the voles in your experiment, it might screw with your data.
Hey All,
Here is the link to the NPR radiolab episode we were talking about in class. Its pretty entertaining! Enjoy!
http://www.wnyc.org/flashplayer/player.html#/play/%2Fstream%2Fxspf%2F133384
ps. the bit about the hook worms curing allergies starts at approximately 30 min, the explanation of crazy cat ladies follows shortly after
Here is the link to the NPR radiolab episode we were talking about in class. Its pretty entertaining! Enjoy!
http://www.wnyc.org/flashplayer/player.html#/play/%2Fstream%2Fxspf%2F133384
ps. the bit about the hook worms curing allergies starts at approximately 30 min, the explanation of crazy cat ladies follows shortly after
Johnson and Omland: Why everything you know about P-values is stupid
Johnson and Omland argue for a new model of evaluating statistical tests instead of the traditional P-value tests against the null hypothesis. While we are used to tests with a null hypothesis, it is typically biologically irrelevant and its rejection is only indirect support for another alternative hypothesis. Furthermore, rejection happens at an arbitrary threshold and can be ineffective at evaluating incredibly complex and dynamic systems that biologists routinely study in nature.
And out of the wasteland of useless null hypotheses, “model selection” emerges as a saving light! Since it is able to evaluate and select a best-fit model out of a number of candidate models the researcher has proposed, it can evaluate complex situations and multiple influences. After using a simple X2 test or a parametric bootstrap procedure to fit the global model to the data, individual models can be evaluated by Least Squares to determine their effectiveness, and the Schwarz and Akaike Information Criterion can synthesize several models simultaneously into a new “best set” of available models.
Johnson and Omland highlight model selection’s successes in biological fields. In ecology, model selection is extremely useful in modeling population cycles, and abundance and survival probabilities. By modeling different survival and encounter probabilities, researchers can separate the probability of marked animal death against the probability of the marked animal surviving but not being recaptured. In addition, model selection is extremely useful for phylogenetic trees, where the order of each mutation is treated as a separate model and evaluated against one another for best-fit. Lastly, they mention that it can also be used for identifying selective pressures and adaptation in the wild.
In their second to last section, they highlight fields that should have converted to the wonders of model selection, but are stuck in the heathen ways of null hypothesis tests. In particular, they cite statistical phylogeography as being able to use current genetic distributions to recreate possible historical populations. In ecosystem science, model selection would be able to evaluate complex trophic systems and multiple food-web models.
Johnson and Omland do acknowledge a few pitfalls about using model selection, but give very little time addressing these issues (apparently leaving it to Anderson and Burnham). First, they note that the conclusions can only be as the multiple models the researcher develops before testing the data. However, I see the eagerness to jump onto the model-selection bandwagon can start from poor initial hypotheses and relying solely on post-hoc conclusions. Secondly, they state that the predictions must actually be biologically plausible and not “merely” statistical significant. If these are not plausible but are “statistically significant” this should indicate either a flaw with this modeling or an unknown factor that is biologically plausible. And lastly, they state that it might not be useful in all situations and null hypothesis testing should be preferred.
…wait what? I thought you were telling us that null-hypothesis tests were terrible and model selection infinitely better? Perhaps they have a little realistic sense about this model after all.
These models do seem like they could be extremely useful, but I would exercise extreme caution about making too many post-hoc conclusions based on the data. And while these models are evaluated as best-fit, there is no objective decision for when the best is good ENOUGH. All models could be relatively terrible at evaluating the natural situation. I would see Anderson and Burnham’s paper for more discussion about the dangers of these tests.
And out of the wasteland of useless null hypotheses, “model selection” emerges as a saving light! Since it is able to evaluate and select a best-fit model out of a number of candidate models the researcher has proposed, it can evaluate complex situations and multiple influences. After using a simple X2 test or a parametric bootstrap procedure to fit the global model to the data, individual models can be evaluated by Least Squares to determine their effectiveness, and the Schwarz and Akaike Information Criterion can synthesize several models simultaneously into a new “best set” of available models.
Johnson and Omland highlight model selection’s successes in biological fields. In ecology, model selection is extremely useful in modeling population cycles, and abundance and survival probabilities. By modeling different survival and encounter probabilities, researchers can separate the probability of marked animal death against the probability of the marked animal surviving but not being recaptured. In addition, model selection is extremely useful for phylogenetic trees, where the order of each mutation is treated as a separate model and evaluated against one another for best-fit. Lastly, they mention that it can also be used for identifying selective pressures and adaptation in the wild.
In their second to last section, they highlight fields that should have converted to the wonders of model selection, but are stuck in the heathen ways of null hypothesis tests. In particular, they cite statistical phylogeography as being able to use current genetic distributions to recreate possible historical populations. In ecosystem science, model selection would be able to evaluate complex trophic systems and multiple food-web models.
Johnson and Omland do acknowledge a few pitfalls about using model selection, but give very little time addressing these issues (apparently leaving it to Anderson and Burnham). First, they note that the conclusions can only be as the multiple models the researcher develops before testing the data. However, I see the eagerness to jump onto the model-selection bandwagon can start from poor initial hypotheses and relying solely on post-hoc conclusions. Secondly, they state that the predictions must actually be biologically plausible and not “merely” statistical significant. If these are not plausible but are “statistically significant” this should indicate either a flaw with this modeling or an unknown factor that is biologically plausible. And lastly, they state that it might not be useful in all situations and null hypothesis testing should be preferred.
…wait what? I thought you were telling us that null-hypothesis tests were terrible and model selection infinitely better? Perhaps they have a little realistic sense about this model after all.
These models do seem like they could be extremely useful, but I would exercise extreme caution about making too many post-hoc conclusions based on the data. And while these models are evaluated as best-fit, there is no objective decision for when the best is good ENOUGH. All models could be relatively terrible at evaluating the natural situation. I would see Anderson and Burnham’s paper for more discussion about the dangers of these tests.
Moretti et. al. 2009: Taxonomical vs. functional responses of bee communities to fire in two contrasting climatic regions
Moretti (et al 2009) examined bee communities in response to fire as a model of animal function trait response to environmental change. The study took place in two different regions; a temperate climate on a mountainside in Switzerland, and a Mediterranean climate in Israel. The climate in Switzerland was wet/warm in the summer, and dry/cool in the winter, while Israel was hot /dry in the summer, and cool/wet in the winter. Both areas are prone to fire but they occur more frequently, of higher intensity/extent, and during the summer in the Mediterranean climate, while in the temperate climate they are of low-medium intensity and quick spreading, furthermore they mainly occur in the winter (coinciding with vegetation dormancy).
Bees were collected at 21 sites in each of the study areas. These sites were located in a range of habitats in different succession stages following fire (e.g. from recently experienced fire, to no fires for 20-30 years, to unburnt areas). Bees were identified and functional traits were described. Functional traits, listed in Table 2, are such things as nesting specialization (in the mud, in wood, snail shells etc.), dispersion (measured as ITD, inter-tegula distance, i.e., distance between wings), or other characteristics of an organism which has demonstrable links to the organism’s functions. Functional traits were calculated into mT, an average for a given trait weighted by species abundance, and a measure of functional diversity (FD). *this may be a rough definition, but I understand functional diversity as the diversity or range of things that an organism, or an organisms traits, do in a community*.
The hypotheses of this study were: “(i) fire affects both species and functional composition of bees at both taxonomic and functional levels, that is, through extinctions, immigrations, or dominance redistribution within the community; (ii) the response of bee communities to fire is smaller in the Mediterranean region because of the longer history of fire in arid conditions”.
Following fire disturbance, a greater number of species were present than pre-fire conditions in both climates. However, this diversity of species decreased quickly following fire, likely because habitats immediately following fire support the highest floral diversity compared to later successional stages.
Functional traits selected by fire were similar in both countries, but some traits were identified which were affected differently between the two regions (Table 4). This differentiation in trait responses highlights the effects of the regional climates and fire regime on the functional components within the regional species pool. This can be further explained by looking at Figure 2, which displays the variance of mean traits and functional diversity in each climate. Overall, mean traits and functional diversity change more dramatically in Switzerland than in Israel (Figure 1). Changes in functional diversity in Israel due to fire were not significant, while they were Switzerland (albeit, only be 21.9%).
What this tells us, is that the responses of bee traits to fires are not exclusively a result of the fire regime in the area, rather, the climate of the region as a whole, including landscape structure. The climate and landscape influence the diversity of traits present. In Israel, the landscape is highly heterogeneous as a result of land use, vegetation type, and fire, resulting in higher heterogeneity in species and trait assemblage. Switzerland, on the other hand, dominated by quickly establishing chestnut trees, is less heterogeneous, and therefore, there is less heterogeneity in bee species and trait assemblage. So, despite results showing a significant shift in species composition with fire in both regions, the functional composition of the bees present in the Mediterranean remained stable, likely as a result of the regions characteristics and functions and higher diversity of traits in the system.
Below is a link to a presentation by Moretti. It discusses some of his other work, but the 2009 paper is presented beginning at slide 51. I recommend looking through the entire presentation as it’s pretty interesting/helpful. Also, note page 58, it has a figure of the species response to fire in both the Mediterranean and temperate climates (I think this would have been good to show in the paper, but I guess if he had to cut a figure, this would be the best one).
I’ll update this if any issues/questions arise in class. See ya in a few hours!
http://www.eurac.edu/NR/rdonlyres/836134A5-3468-4888-979C-C32A14370A06/0/Moretti_Marco.pdf
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