Monday, January 25, 2016

Like it or not, intense trophy hunting causes evolution of small horns in mountain sheep

[ This post is by Marco Festa-Bianchet at Université de Sherbrooke, I am just putting it up. –B. ]

In most of Canada, mountain sheep (Ovis canadensis and O. dalli) rams are hunted based on a minimum horn curl: it is illegal to shoot rams with small horns.  There is no quota for residents of the province or territory; the harvest is only limited by the availability of ‘legal’ rams, like the unfortunate 4-year-old in the picture.  Selection against ‘legal’ rams is very strong, as the harvest rate is likely between 30% and 60%. Horn size affects reproductive success, but mostly for mature rams who can serially defend estrous ewes.  A ram with rapidly-growing horns may become a highly successful breeder if it survives to 7 years, but it may be legal at 4 years, and the hunting season is before the rut.  In other words, the selective pressure of the hunt sets in 2–3 years before the fitness benefits from sexual selection.  Like many morphological traits in mammals, heritability accounts for about a third of variance in horn size.  In Alberta, the 4/5-curl rule illustrated in the picture has been in place for about 50 years in nearly all provincial lands where hunting is allowed – a wide geographical area. If one wanted to select for small horns, one could do a lot worse.


Ram 642 was shot at Sheep River, Alberta, at 4 years of age.  The plexiglass illustrates the field rule to assess the definition of ‘4/5-curl’: the horn tip intercepts an imaginary line from the base of the horn to the front of the eye when viewed from the side.  Few 4-year-old rams have horns this large, and large horns do little to improve the reproductive success of rams younger than about 7 years.  If ram 642 had grown one less cm of horn, it would have been illegal to kill him.  Photo from Alberta Fish & Wildlife.

We first tested the hypothesis that hunting could drive evolution in mountain sheep in a paper in Nature in 2003, based on the deep pedigree of the Ram Mountain population, monitored since 1972.  We showed that breeding values for ram horn length had declined over about 5 generations.  The genetically correlated trait of ram body mass, not a direct target of artificial selection, also declined.  Ours was the first suggestion that selective hunting could lead to evolutionary change, and it attracted substantial interest.  Unfortunately, it was also used to promote an anti-hunting agenda, leading to much heaping of poison over our research by several pro-hunting groups and individuals.  Some media coverage implied that any trophy hunting would lead to an evolutionary response.  That is unlikely.  In bighorn sheep, the combination of age-specific effects of horn size on male mating success, unlimited harvests, and strong heritability of horn size create conditions favoring an evolutionary response to artificial selection; those conditions may or may not exist in other species.  For example, in chamois and mountain goats an evolutionary response to trophy hunting is unlikely, partly because the links between age, horn size, and reproductive success are much weaker than in bighorn sheep.  It is important to consider the possible evolutionary implications of harvest policies, but not all selective harvests will lead to genetic changes.


Bighorn sheep rams in the trap at Ram Mountain in 2014.  All these rams fit the legal definition of 4/5-curl.  Photo by Gabriel Pigeon.

The initial narrative opposing our results maintained that selection favoring small-horned rams only occurred at Ram Mountain, a small (20–100 breeding females) isolated population with a 40% harvest rate for legal rams and little possibility of genetic rescue.  To test this narrative, we worked with wildlife managers in Alberta and British Columbia to analyze data on harvested rams.  A clear pattern emerged that ram horns were getting smaller in places where selective harvest was intense: Alberta, central BC, and the Peace region of northern BC for Stone’s sheep, a different species.  No declines were detected in the Rocky Mountains of BC, where a more restrictive definition of ‘legal’ ram decreased the artificial selective pressure and, crucially, allowed some large-horned rams to survive to the age at which large horns increase fitness.  In Northern BC, there was no decline in horn size of Stone’s rams shot in the Skeena region, where harvest pressure is lower than in the Peace.  Horn size of mountain sheep is declining over much of Canada, but only in areas under intense selective harvesting.  That answered the first criticism.

The second criticism was more substantial.  In 2006, Eric Postma pointed out that the statistical technique used by several papers (including ours) to estimate breeding values did not sufficiently account for possible temporal changes in the environment.  He suggested that tests of changes in breeding value should include year of measurement as a random variable, something that we had not done.  Three years later, Jarrod Hadfield and coauthors pointed out additional problems, including possible biases due to genetic drift and other sources of error, and suggested a Bayesian analysis to avoid these biases.  These criticisms were valid, but our paper had included the annual mass of yearling females to account for some environmental variability.  Still, doubts persisted. Our opponents rejoiced, gleefully pointed to these apparently fatal flaws, and argued that management of mountain sheep in Canada was just fine.

The third criticism was rather odd.  A paper in PNAS used the correlation between a ram’s mass at mating and his offsprings’ mass at weaning to measure inheritance.  The paper then developed an Integral Projection Model for an imaginary hunt based on ram mass, and having presented no data on horns, concluded that changes in horn size were entirely ‘demographic’.  Of course, given that the ram–lamb mass correlation was zero, that conclusion was supported by the simulation. The paper was quickly refuted.

The fourth criticism was that the decline in ram horn size in Alberta was caused by a decrease in sport harvest of female sheep.  That criticism was based on the assumption that bighorn sheep are like white-tailed deer and should thus show very strong density-dependence.  It failed to explain why ram horn size did not decline in British Columbia where ram harvests were less intense, despite the absence of any female harvest, or why the largest rams shot in Alberta appear to be coming out of the National Parks, where there is no female harvest.

The fifth criticism was that artificial selection could not occur because emigration of unselected rams from the National Parks ensures a genetic rescue.  That viewpoint is encouraging, because it admits that artificial selection could take place. The evidence undermines the case here: rams shot near the National Parks have larger horns than those shot far from the parks, but over time they also show an increasing age at harvest and decreasing horn size, trends indicative of slower growth rate.

The sixth criticism was that trophy book records suggest that the horns of bighorn rams shot in North America did not become smaller over time.  That is akin to monitoring the average speed of humans by considering only Olympic sprinters. Unfortunately, as we recently showed, a left-truncated sample cannot detect a declining trend, and record books only list horns attaining a minimum ‘score’.  Harvested rams are also a biased sample, as it is illegal to shoot small-horned rams.  Declining trends in the population are thus underestimated by analyses of harvested rams and completely missed by record books.

Most of these criticisms were easily refuted except for #2.  That required the reanalysis presented in our new paper in Evolutionary Applications.  A Bayesian analysis of horn size at Ram Mountain, accounting for statistical criticisms and including the possible effects of genetic drift, confirmed that while subject to intense trophy hunting, rams evolved genetically smaller horns. Because harvest intensity declined tenfold from 1996 and the trophy hunt was stopped in 2011, we also looked at what happened when the artificial selective pressure was removed.  As expected, the decline in breeding value stopped, but was not reversed; because artificial selection was much more intense than natural selection, it will take a long time to reverse the decline in horn breeding values.  Finally, we found that traits genetically correlated with horn length but that are not the direct target of artificial selection (horn base circumference in rams, horn length in ewes) show similar trends, while horn base circumference in ewes, which is not genetically correlated with horn length in rams, does not.


The Ram Mountain trap in May 2013.  We had to shovel snow to see sheep going into the trap!

To sum up.  At Ram Mountain, analyses of horn length combined with a deep pedigree showed that intense selective hunting led to an evolutionary change.  Long-term monitoring of rams shot in Alberta and BC suggests that where selective hunting pressure is high, horn growth has slowed.  There is no evidence of phenotypic rescue from protected areas, despite ram migrations, and there is no evidence that the decline in horn size is due to increasing population density.  We have not yet explored in detail the effects of climate change, but preliminary analyses suggest that it should have a positive effect on horn growth, as reported for ibex in the Alps.  So, what to do? There are two obvious ways to counter the artificial selective pressure: (1) lower harvest intensity, and (2) close the hunt earlier, so that rams that move out of National Parks looking for breeding opportunities can spread unselected genes instead of getting shot.

This is a contentious issue with strong consequences for policy.  Therefore, we were very conservative in our approach, accounting for all serious critiques of the analysis by Coltman et al. in 2003.  We confirm the initial conclusion: killing large-horned rams drives rapid evolution towards smaller horns. Wildlife biologists in Alberta have proposed changes in sheep-hunting regulations to reduce the artificial selective pressure.  Whether that science-based proposal will be politically acceptable remains to be seen.

Saturday, January 16, 2016

A Narcissist Index (n-index) for academics


We all know the h-index, the number of papers by a given author that have been cited at least that many times. For instance, if you have 52 papers cited at least 52 times but the 53rd most-cited paper is only cited 52 (rather than 53 times), your h-index is 52. This index is widely used to assess the performance of academics and has sometimes been praised but much more widely vilified.

Source
Other indices have been suggested, some seriously and some just for fun. For instance, the Kardashian Index (k-index) is the number of twitter followers divided by the number of citations you have accumulated. If, for example, you have 1594 twitter followers and 8564 total citations, your k-index is 0.19.

Source
While at this week’s American Society of Naturalist’s meeting (Asilomar, CA), I happened to join a conversation that – through a series of unplanned segues – led to the suggestions for a new index by which to assess scientists – the Narcissist Index (n-index).

The conversation started on a totally different topic – how to convince more authors to submit to The American Naturalist. The first question posed to the group was “How many Am Nat papers would you give up to have one Nature or Science paper?” and progressed to questions like “What difference in number of citations for a paper would convince you to publish in Am Nat rather than Science or Nature.” The basic point was to evaluate perceptions about how journal prestige as opposed to actual paper impact (citations) influence how people choose journals.

At this point, the conversation switched to citation rates and what influences them, which led inevitably to the topic of self-citation. For instance, one might inflate their citations simply by citing themselves. Or, even if they don’t do this, it is just generally frowned upon as Narcissistic – or, I suppose, an indication that you are just not widely read.

Although it is typical to criticize self-citation, some have argued that it is merely “an inevitable outcome of a cohesive and sustained research program.” I won’t go into the details here – you can read the paper with this alternative viewpoint – but the whole discussion led someone to say “What we really need to do is figure out what proportion of a person’s citations are self-citations and from this we can calculate a Narcissist Index – the n-index.”


Out came Web of Science (Google Scholar does not track self-citations) and I knew in seconds that I had 829 self-citations out of 8540 total citations for an n-index of 0.1. Hmmm, that sounded high to my friends and colleagues, but we needed some frame of reference. So we started calculating the n-index for everyone in the conversation and for some of our friends (and fathers and mentors), most of whom were at the meeting.

We quickly realized that several factors could influence (or be influenced by) the n-index, including the h-index, total number of papers, “scientific age”, and total number of citations. Back to Web of Science for a bit more data. At this point it became clear that Rowan Barrett was by far the greatest outlier with the lowest proportion of self-citations including in relation to all of these other variables – so he was been deleted from the analyses that follow.

The strongest correlation was with scientific age – longer-established researchers (based on their first paper in Web of Science) have a lower n-index (R2 = 9.2%). Perhaps young people have had less time for their work to become well known – or perhaps self-citation was simply lower in “the good old days” – or maybe the literature is so vast now that it is hard to stand-out and so be cited by others.


A similar correlation was evident with total citations – the n-index is lower for people with more total citations (R2 = 8.9%). At one level, this association might suggest that self-citation is not a very effective way of increasing your total citations – instead it acts against you. However, it seems much more likely that the association occurs simply because a person can only cite themselves a limited number of times, whereas other people can cite them much more frequently.


These two variables (total citations and scientific age) are obviously correlated, and this was certainly the case even in our tiny non-random data set (R2 = 61.2%). So, if one really cared about such things, I guess the n-index could be corrected for its various correlates. Indeed, the h-index is often adjusted for scientific age. Interestingly, both the above correlations (n-index on scientific age and n-index on total citations) appear much lower than the correlation between h-index and scientific age (59%) in the same data set, which I guess proves that you really can succeed in narcissism (or research program cohesion) at any age.

So how did I fare? I am sad to say that I do not have the highest n-index in the pool of 19 people we examined – I am only 3rd. Nor do I have the highest residual of n-index regressed on scientific age – only 4th. Nor do I have the highest residual of n-index regressed on total citations – only 3rd.

Perhaps I am not as good as I thought I was.

Bah - just check out my awesome papers (Hendry et al. 1995, 1996, 1997, 1998, 1999, 2000a, 2000b, 2000c, 2000d, 2000e, 2001, 2002, 2003 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020).



Notes and disclaimers:
  1. Dan Bolnick came up with the name "n-index"
  2. This analysis was done completely in fun and is not meant to single out anyone one way or the other (except for Rowan). Moreover, none of us consider this to be a useful index in any way; merely something that entertained us briefly while drinking beer.
  3. Previous authors have calculated and discussed the n-index or something like it. However, we had more fun ignoring this literature and exploring the ideas ourselves.
  4. The data presented here will not be accurate. First, they are a non-random selection of the people who happened to be standing around supplemented by our friends, people who write papers about the merits of self-citation, and more senior folks who we could use to better populate the range of parameter space. The small number of people included in the analysis are simply an indication of how long my computer battery lasted. Second, because Web of Science does not have a unique identifier for people, at least one person in the analysis is a combination of several people. However, we did confirm that at least all of the highly cited papers were for the people in question. Third, we searched only for last name plus the initials. If someone left an initial off some of their papers, we would miss it. We would also miss papers if someone changed their name. Fourth, no formal stats were conducted – nor do I recommend wasting your time to do so.
Finally:

I recently (April 9, 2016) produced a word cloud for my entire forthcoming book Eco-Evolutionary Dynamics. The most common words were "et" and "al", which I guess is not surprising.
Removing those uninteresting words, I generated the word cloud below.
I put it out on twitter and Menno Schilthuizen immediately noted the "relative modest" appearance of the word "Hendry." I hadn't even noticed my name in the cloud but, looking more closely, I realized that it is the only obvious name in the entire thing, which suggests a new index of narcissism: the appearance of your own name in a word cloud of your work.


Friday, January 1, 2016

RESOLUTION – I will get a faculty position

I have already written two posts about “How to Get a Faculty Position” – the first on getting the interview and the second on succeedingduring the interview. In both cases, I noted that I would write three posts on the topic, with the last one being something like “Guaranteeing Success.” Sounds like an appropriate New Year’s resolution to me.

We can start from the premise that you have just failed in one – or several – or many – attempts to get interviews or to get job offers after the interview. This is an easy starting premise as nearly everyone will experience that state. In my first year on the market, I applied for 7 jobs and had 1 interview and no offers. In my second year on the market, I applied for 7 jobs again and had 1 interview again and no offers again. At this point, I might well have quit and gone on to something different, as many people apparently do. In my third year, I applied for 46 jobs, had 19 requests to interview, went for 9 interviews, and had 3 offers. I accepted the third offer – McGill University – and cancelled the other 10 interviews. 

Clearly I had a big shift in approach from my first two years to my third. This shift – and my experiences since – have led me to the big conclusion that you won’t hear from most others:

Anyone with a decent record can get a faculty position!

Don’t believe me? Well, perhaps it is because I need to add two major qualifiers to this assertion. If you adopt these qualifiers, which I will now outline, you will get a job.

1. Don’t be picky.

Everyone has preferences for where they want to work – an Ivy League school (Harvard!) or a particular country (Switzerland!) or geographical region (New England!) or city (Montreal!) – but restricting yourself like means that your chances go way down. In reality, you should not restrict yourself in this manner because, thinking globally, there are hundreds to thousands of relevant (to you) faculty jobs around the world each year. Importantly, many of those positions are in less desirable places or at less prestigious institutions, which means that competition for them will be lower and your chances correspondingly higher – particularly if you apply for a lot of them. 

SOURCE

Now, I know what you are thinking – “Yuk, I don’t want a job at the University of Southwestern Northern Nunavut.” Neither do I, but here is the important thing to remember: You are not marrying your institution – you are dating it. If you don’t like it, you can leave – at any time and for any reason. The key is to get yourself into a job where you can prove your abilities as an independent researcher, get your start-up money, get a grant, do some teaching, and so on. That can happen at nearly any university in nearly any state or country. After proving your abilities as a faculty member you can – if you want – start applying strategically again. This sequence is extremely common – many faculty members start at seemingly less desirable places and then move in five or so years to somewhere they really want to be. On the flip side, many people find that they actually do like the university or place where they start and end up having long, exciting, productive careers at places they never would have targeted at the outset. Moreover, I have found that nearly everywhere in the world has interesting aspects to commend it – nature, art, music, architecture, scenery, night life, peace and quiet, whatever – why not experience them for a while or forever.


Of course, a key point here is to make sure that you get a first position that will enable you – should you desire – to later move up your own personal preferred food chain. For instance, if you ultimately want a research position, then you had better get your first faculty position at a research institution.

2. Don’t give up.

We are continually bombarded with stats on how many people give up on their dreams of being a professor because they can’t find a job or for other reasons, such as a particular life style that is, whether real or imagined, incompatible with academia. These people are typically those that don’t follow the point above. (Note that I am not being judgmental here.) Thus, the people who aren’t picky and don’t give up are the ones that will ultimately succeed. Yes, it might take years. Yes, you might be underpaid during that period. Yes, you might have to move a lot. But, also, yes, you will eventually get a job. Thus, if you goal really is a faculty position – and that is your primary goal – then you will get one if you are not picky and you don’t give up. If you don’t want to wait it out, if you want a better paid position, if you want to live in a particular place, if whatever … great, go for it. But if a faculty position is what matters most, then don’t be picky and don’t give up.

SOURCE
However, note that you do have to have a decent record. That is, if you are interested in a research position, you do have to be publishing research papers – not tons, but some, and you need to do so recently – you can’t have long gaps in your publication record (see the earlier posts). If you are interested in a teaching position, then you need to be gaining good teaching experience. And so on.

Resolution

If you really want an academic position (or any position for that matter), make it your New Year’s resolution to get one by not being picky and not giving up. Of course, you might not achieve our resolution for several New Years to come – but you will eventually.

Happy New Year.

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My earlier "How To" posts are here