Tag Archives: progress in ecology

On Broad Issues in Ecology

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Any young ecologist wishing to get a grasp on the most important ecological questions of the century could find no better place to start than the thoughtful compilations of Bill Sutherland and his colleagues in the U.K. (Sutherland et al. 2006; Sutherland et al. 2010; Sutherland et al. 2013). In general none of these questions by itself could be the focus of a thesis which by definition must deal with something concrete in a 2-3 year time frame, but they can serve as an overarching goal for a life in science. In all of these exercises an attempt was made to canvass dozens to hundreds of ecologists mainly from Britain but including many from other parts of the world to suggest and then cull down questions into a feasible framework.

This whole approach is most useful, but the authors recognize there are some limitations on exercises of this type. A particularly crucial limitation is:

“…there was a tendency to pose broad questions rather than the more focussed question we were aiming for. There is a tension between posing broad unanswerable questions and those so narrow that they cease to be perceived as fundamental.” (Sutherland et al. 2013, p 60).

I want to focus here on the problems of decomposing broad unanswerable questions in ecology to guide our ecological research in the future. I will discuss here only two of the population ecology questions.

Begin with question 13 on page 61 of Sutherland et al. (2013):

13. How do species and population traits and landscape configuration interact to determine realized dispersal distances?

To translate this into a project we have first to decide on a species to study and specific populations of that species. This opens Pandora’s Box because there are many thousands of species and we have to pick. We do not pick the species at random, yet we wish to develop a general answer to this question, so right away we are lost in how to translate detailed species and area specific data on movements into a general conclusion. So just for illustration suppose we pick a convenient mammal like the red squirrel of North America. It is territorial and diurnal and can be fitted with GPS collars so that movements can be readily measured, so in a sense it would be considered an ideal species to study to answer question 13, even though it is not a random choice. It ranges from Alaska to Labrador down to Arizona and North Carolina. There are a variety of landscapes throughout this geographic range, some highly altered by humans, some not. I do not know how many intensive studies of red squirrels are being or have been carried out. I would wager that the entire NSF (or NSERC, or ARC) budget could be spent to set up a series of studies of duration 5-20 years to gather these data throughout the range of this species. Clearly this will never be done, and we can only hope that the results of a few specific studies in non-randomly chosen areas over shorter time periods will answer question 13 for this one species.

Landscape configuration alone boggles my mind. It is in many areas an historical artifact of fire or human occupation and land use, and yet we need principles to generalize about it. We can model it and pretend that our models mimic reality without the availability of an experimental test. Is this the ecology of the future?

Another way to answer question 13 is to use tiny organisms like insects that we can replicate readily in small areas at minimal cost. Such studies are useful but again I am not sure they will provide a general answer to question 13. These studies can provide insights about specific insects in specific communities and with a good number of such studies on a variety of systems perhaps we would be in a position to achieve some generality. Otherwise we could be accused of “stamp collecting”.

Question 14 (Sutherland et al. 2013 page 61) has similar problems to question 13 but is more tractable I think.

14 What is the heritability/genetic basis of dispersal and movement behaviour?

This is a simpler question, given modern genetics, and can be answered for a particular species in a particular ecosystem. It is restrictive, if it is a field study, in allowing only those species that do not disperse beyond the detection range of the equipment used, and in requiring long-term genetic paternity data to estimate heritability. The methods are available but have so far been used on few species in very specific areas (e.g. superb fairy wrens in a Botanical Garden, Double et al. 2005). It is an important question to ask and answer but again the generality of the results at the present time have to be assumed rather than measured by replicated studies.

The bottom line is that questions like these two have been with ecologists for some years now and have been answered in some detail only in a few vertebrate species in very specific locations. How we generalize these results is an open question even with modern technology.

Double, M.C., Peakall, R., Beck, N.R., and Cockburn, A. 2005. Dispersal, philopatry, and infidelity: dissecting local genetic structure in superb fairy-wrens (Malurus cyaneus). Evolution 59(3): 625-635.

Sutherland, W.J. et al. 2006. The identification of 100 ecological questions of high policy relevance in the UK. Journal of Applied Ecology 43(4): 617-627. doi:10.1111/j.1365-2664.2006.01188.x.

Sutherland, W.J.et al. 2010. A horizon scan of global conservation issues for 2010. Trends in Ecology & Evolution 25(1): 1-7.

Sutherland, W.J. 2013. Identification of 100 fundamental ecological questions. Journal of Ecology 101(1): 58-67. doi:10.1111/1365-2745.12025.

On Adaptive Management

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I was fortunate to be on the sidelines at UBC in the 1970s when Carl Walters, Ray Hilborn, and Buzz Holling developed and refined the ideas of adaptive management. Working mostly in a fisheries context in which management is both possible and essential, they developed a new paradigm of how to proceed in the management of natural resources to reduce or avoid the mistakes of the past (Walters & Hilborn 1978). Somehow it was one of those times in science where everything worked because these three ecologists were a near perfect fit to one another, full of new ideas and inspired guesses about how to put their ideas into action. Many other scientists joined in, and Holling (1978) put this collaboration together in a book that can still be downloaded from the website of the International Institute for Applied Systems Analysis (IASA) in Vienna:
(http://www.iiasa.ac.at/publication/more_XB-78-103.php

Adaptive management became the new paradigm, now taken up with gusto by many natural resources and conservation agencies (Westgate, Likens & Lindenmayer 2013). Adaptive management can be carried out in two different ways. Passive adaptive management involves having a model of the system being managed and manipulating it in a series of ways that improve the model fit over time. Active adaptive management takes several different models and uses different management manipulations to decide which model best describes how the system operates. Both approaches intend to reduce the uncertainty about how the system works so as to define the limits of management options.

The message was (as they argued) nothing more than common sense, to learn by doing. But common sense is uncommonly used, as we see too often even in the 21st century. Adaptive management became very popular in the 1990s, but while many took up the banner of adaptive management, relatively few cases have been successfully completed (Walters 2007; Westgate, Likens & Lindenmayer 2013). There are many different reasons for this (discussed well in these two papers), not the least of which is the communication gap between research scientists and resource managers. Research scientists typically wish to test an ecological hypothesis by a management manipulation, but the resource manager may not be able to use this particular management manipulation in practice because it costs too much. To be useful in the real world any management experiment needs to have careful, long-term monitoring to map its outcome, and management agencies do not often have the opportunity to carry out extensive monitoring. The underlying cause then is mainly financial, and resource agencies rarely have an adequate budget to cover the important wildlife and fisheries issues they are supposed to manage.

If anything, reading this ‘old’ literature should remind ecologists that the problems discussed are inherent in management and will not go away as we move into the era of climate change. Let me stop with a few of the guideposts from Holling’s book:

Treat assessment as an ongoing process…
Remember that uncertainties are inherent…
Involve decision makers early in the analysis…
Establish a degree of belief for each of your alternative models…
Avoid facile and narcotic compression of indicators such as cost/benefit ratios that are generally inappropriate for environmental problems….

And probably remind yourself that there can be wisdom in the elders….

The take-home message for me in re-reading these older papers on adaptive management is that it is similar to the problem we have with models in ecology. We can produce simple models or in this case solutions to management problems on paper, but getting them to work properly in the real world where social viewpoints, political power, and scientific information collide is extremely difficult. This is no reason to stop doing the best science and to try to weld it into management agencies. But it is easier said than done.

Holling, C.S. (1978) Adaptive Environmental Assessment and Management. John Wiley and Sons, Chichester, UK.

Walters, C.J. (2007) Is adaptive management helping to solve fisheries problems? Ambio, 36, 304-307.

Walters, C.J. & Hilborn, R. (1978) Ecological optimization and adaptive management. Annual Review of Ecology and Systematics, 9, 157-188.

Westgate, M.J., Likens, G.E. & Lindenmayer, D.B. (2013) Adaptive management of biological systems: A review. Biological Conservation, 158, 128-139.

On Repeatability in Ecology

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One of the elementary lessons of statistics is that every measurement must be repeatable so that differences or changes in some ecological variable can be interpreted with respect to some ecological or environmental mechanism. So if we count 40 elephants in one year and count 80 in the following year, we know that population abundance has changed and we do not have to consider the possibility that the repeatability of our counting method is so poor that 40 and 80 could refer to the same population size. Both precision and bias come into the discussion at this point. Much of the elaboration of ecological methods involves the attempt to improve the precision of methods such as those for estimating abundance or species richness. There is less discussion of the problem of bias.

The repeatability that is most crucial in forging a solid science is that associated with experiments. We should not simply do an important experiment in a single place and then assume the results apply world-wide. Of course we do this, but we should always remember that this is a gigantic leap of faith. Ecologists are often not willing to repeat critical experiments, in contrast to scientists in chemistry or molecular biology. Part of this reluctance is understandable because the costs associated with many important field experiments is large and funding committees must then judge whether to repeat the old or fund the new. But if we do not repeat the old, we never can discover the limits to our hypotheses or generalizations. Given a limited amount of money, experimental designs often limit the potential generality of the conclusions. Should you have 2 or 4 or 6 replicates? Should you have more replicates and fewer treatment sites or levels of manipulation? When we can, we try one way and then another to see if we get similar results.

A looming issue now is climate change which means that the ecosystem studied in 1980 is possibly rather different than the one you now study in 2014, or the place someone manipulated in 1970 is not the same community you manipulated this year. The worst case scenario would be to find out that you have to do the same experiment every ten years to check if the whole response system has changed. Impossible with current funding levels. How can we develop a robust set of generalizations or ‘theories’ in ecology if the world is changing so that the food webs we so carefully described have now broken down? I am not sure what the answers are to these difficult questions.

And then you pile evolution into this mix and wonder if organisms can change like Donelson et al.’s (2012) tropical reef fish, so that climate changes might be less significant than we currently think, at least for some species. The frustration that ecologists now face over these issues with respect to ecosystem management boils over in many verbal discussions like those on “novel ecosystems” (Hobbs et al. 2014, Aronson et al. 2014) that can be viewed as critical decisions about how to think about environmental change or a discussion about angels on pinheads.

Underlying all of this is the global issue of repeatability, and whether our current perceptions of how to manage ecosystems is sufficiently reliable to sidestep the adaptive management scenarios that seem so useful in theory (Conroy et al. 2011) but are at present rare in practice (Keith et al. 2011). The need for action in conservation biology seems to trump the need for repeatability to test the generalizations on which we base our management recommendations. This need is apparent in all our sciences that affect humans directly. In agriculture we release new varieties of crops with minimal long term studies of their effects on the ecosystem, or we introduce new methods such as no till agriculture without adequate studies of its impacts on soil structure and pest species. This kind of hubris does guarantee long term employment in mitigating adverse consequences, but is perhaps not an optimal way to proceed in environmental management. We cannot follow the Hippocratic Oath in applied ecology because all our management actions create winners and losers, and ‘harm’ then becomes an opinion about how we designate ‘winners’ and ‘losers’. Using social science is one way out of this dilemma, but history gives sparse support for the idea of ‘expert’ opinion for good environmental action.

Aronson, J., Murcia, C., Kattan, G.H., Moreno-Mateos, D., Dixon, K. & Simberloff, D. (2014) The road to confusion is paved with novel ecosystem labels: a reply to Hobbs et al. Trends in Ecology & Evolution, 29, 646-647.

Conroy, M.J., Runge, M.C., Nichols, J.D., Stodola, K.W. & Cooper, R.J. (2011) Conservation in the face of climate change: The roles of alternative models, monitoring, and adaptation in confronting and reducing uncertainty. Biological Conservation, 144, 1204-1213.

Donelson, J.M., Munday, P.L., McCormick, M.I. & Pitcher, C.R. (2012) Rapid transgenerational acclimation of a tropical reef fish to climate change. Nature Climate Change, 2, 30-32.

Hobbs, R.J., Higgs, E.S. & Harris, J.A. (2014) Novel ecosystems: concept or inconvenient reality? A response to Murcia et al. Trends in Ecology & Evolution, 29, 645-646.

Keith, D.A., Martin, T.G., McDonald-Madden, E. & Walters, C. (2011) Uncertainty and adaptive management for biodiversity conservation. Biological Conservation, 144, 1175-1178.

Is Ecology like Economics?

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One statement in Thomas Piketty’s book on economics struck me as a possible description of ecology’s development. On page 32 he states:

“To put it bluntly, the discipline of economics has yet to get over its childish passion for mathematics and for purely theoretical and often highly ideological speculation at the expense of historical research and collaboration with the other social sciences. Economists are all too often preoccupied with petty mathematical problems of interest only to themselves. This obsession with mathematics is an easy way of acquiring the appearance of scientificity without having to answer the far more complex questions posed by the world we live in.”

If this is at least a partially correct summary of ecology’s history, we could argue that finally in the last 20 years ecology has begun to analyze the far more complex questions posed by the ecological world. But it does so with a background of oversimplified models, whether verbal or mathematical, that we are continually trying to fit our data into. Square pegs into round holes.

Part of this problem arises from the hierarchy of science in which physics and in particular mathematics are ranked as the ideals of science to which we should all strive. It is another verbal model of the science world constructed after the fact with little attention to the details of how physics and the other hard sciences have actually progressed over the past three centuries.

Sciences also rank high in the public mind when they provide humans with more gadgets and better cars and airplanes, so that technology and science are always confused. Physics led to engineering which led to all our modern gadgets and progress. Biology has assisted medicine in continually improving human health, and natural history has enriched our lives by raising our appreciation of biodiversity. But ecology has provided a less clearly articulated vision for humans with a new list of commandments that seem to inhibit economic ‘progress’. Much of what we find in conservation biology and wildlife management simply states the obvious that humans have made a terrible mess of life on Earth – extinctions, overharvesting, pollution of lakes and the ocean, and invasive weeds among other things. In some sense ecologists are like the priests of old, warning us that God or some spiritual force will punish us if we violate some commandments or regulations. In our case it is the Earth that suffers from poorly thought out human alterations, and, in a nutshell, CO2 is the new god that will indeed guarantee that the end is near. No one really wants to hear or believe this, if we accept the polls taken in North America.

So the bottom line for ecologists should be to concentrate on the complex questions posed by the biological world, and try first to understand the problems and second to suggest some way to solve them. Much easier said than done, as we can see from the current economic mess in what might be a sister science.

Piketty, T. 2014. Capital in the Twenty-First Century. Belknap Press, Harvard University, Boston. 696 pp. ISBN 9780674430006

Back to p-Values

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Alas ecology has slipped lower on the totem-pole of serious sciences by an article that has captured the attention of the media:

Low-Décarie, E., Chivers, C., and Granados, M. 2014. Rising complexity and falling explanatory power in ecology. Frontiers in Ecology and the Environment 12(7): 412-418. doi: 10.1890/130230.

There is much that is positive in this paper, so you should read it if only to decide whether or not to use it in a graduate seminar in statistics or in ecology. Much of what is concluded is certainly true, that there are more p-values in papers now than there were some years ago. The question then comes down to what these kinds of statistics mean and how this would justify a conclusion captured by the media that explanatory power in ecology is declining over time, and the bottom line of what to do about falling p-values. Since as far as I can see most statisticians today seem to believe that p-values are meaningless (e.g. Ioannidis 2005), one wonders what the value of showing this trend is. A second item that most statisticians agree about is that R2 values are a poor measure of anything other than the items in a particular data set. Any ecological paper that contains data to be analysed and reported summarizes many tests providing p-values and R2 values of which only some are reported. It would be interesting to do a comparison with what is recognized as a mature science (like physics or genetics) by asking whether the past revolutions in understanding and prediction power in those sciences corresponded with increasing numbers of p-values or R2 values.

To ask these questions is to ask what is the metric of scientific progress? At the present time we confuse progress with some indicators that may have little to do with scientific advancement. As journal editors we race to increase their impact factor which is interpreted as a measure of importance. For appointments to university positions we ask how many citations a person has and how many papers they have produced. We confuse scientific value with some numbers which ironically might have a very low R2 value as predictors of potential progress in a science. These numbers make sense as metrics to tell publication houses how influential their journals are, or to tell Department Heads how fantastic their job choices are, but we fool ourselves if we accept them as indicators of value to science.

If you wish to judge scientific progress you might wish to look at books that have gathered together the most important papers of the time, and examine a sequence of these from the 1950s to the present time. What is striking is that papers that seemed critically important in the 1960s or 1970s are now thought to be concerned with relatively uninteresting side issues, and conversely papers that were ignored earlier are now thought to be critical to understanding. A list of these changes might be a useful accessory to anyone asking about how to judge importance or progress in a science.

A final comment would be to look at the reasons why a relatively mature science like geology has completely failed to be able to predict earthquakes in advance and even to specify the locations of some earthquakes (Steina et al. 2012; Uyeda 2013). Progress in understanding does not of necessity dictate progress in prediction. And we ought to be wary of confusing progress with p-and R2 values.

Ioannidis, J.P.A. 2005. Why most published research findings are false. PLoS Medicine 2(8): e124.

Steina, S., Gellerb, R.J., and Liuc, M. 2012. Why earthquake hazard maps often fail and what to do about it. Tectonophysics 562-563: 1-24. doi: 10.1016/j.tecto.2012.06.047.

Uyeda, S. 2013. On earthquake prediction in Japan. Proceedings of the Japan Academy, Series B 89(9): 391-400. doi: 10.2183/pjab.89.391.

The Snowshoe Hare 10-year Cycle – A Cautionary Tale

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We have been working on the ten-year cycle of snowshoe hares (Lepus americanus) in the southwest Yukon since 1975 trying to answer the simple question of what causes these cyclic fluctuations. I think that we now understand the causes of the cyclic dynamics, which is not to say all things are known but the broad picture is complete. But some misunderstanding persists, hence this one page summary. Some biology first.

The snowshoe hare cycle has been known from Canada lynx fur return data for more than 100 years, and of course known to First Nations people much before that. Hares are herbivores of small trees and shrubs, they reproduce at age 1 and rarely live more than 1-2 years. They have 2-4 litters in a summer, with litter size around 4-6. Juvenile losses are high and at best populations increase about three-to-four-fold per year. Almost everything eats them – lynx, coyotes, great-horned owls, goshawks, a long list of predators on the young. Reproduction collapses with rising density and females reduce their output from 4 litters to 2 in the peak and decline phase.

The obvious driving factors when Lloyd Keith and his students began working on the hare cycle in Alberta in the 1960s were winter food shortage and predation. When there is a high hare peak, damage to shrubs and small trees is obvious. But it was quite clear in Keith’s studies that the decline phase continued well after the vegetation recovered, and so he postulated a two-factor explanation, winter food shortage followed by high predation losses. He looked for disease and parasite problems in hares but found nothing.

Testing the winter food limitation would appear to be simple but is fraught with problems. Everyone believes that food is an ultimate limiting factor, so that it must be involved in the cyclic dynamics. We began testing food limitation in the mid-1970s and found that one could add natural food or artificial food (rabbit chow) and apparently have no effect on cyclic dynamics. Hares came to the food grids so the density increased by immigration, but the decline started at the same time and at the same rate as on control grids. So what is the role of food?

Our next attempt was to do a factorial experiment adding food, reducing predation, and doing both together. The details are important, replication was never enough for the manipulated treatments, we did it only for 10 years rather than 20 or 30. What we found was that there was an interaction between food addition and mammal predator exclusion so that the combined treatment increased to a much higher density than any single treatment. But this result came with a puzzle. What is the role of food? Hares showed no evidence of malnutrition in the peak or decline, fed hares did not increase their reproductive output. What produced the strong interaction between food addition and predator reduction?

The next breakthrough came when Rudy Boonstra suggested that predator-caused stress might underlie these strange dynamics. Because we could now measure stress with faecal cortisol measures we could test for stress directly in free-ranging hares. The surprise was that this idea worked and Michael Sheriff capped off the stress hypothesis by showing that not only does predator-induced stress reduce reproductive rates, but the stress effect is inherited maternally in the next generation.

The bottom line: the whole dynamics of the snowshoe hare cycle are predator-induced. All the changes in mortality and reproduction are direct and indirect effects of predators chasing and eating hares. The experimental food/predator interaction was mechanistically wrong in targeting food as a major limiting factor.

This of course does not mean that food is irrelevant as an important factor to study in hare cycles. In particular very high peak populations damage shrubs and small trees and we do not yet have the details of how this works out in time. Secondary chemicals are certainly involved here.

Why does all this matter? Two points. First, the hare cycle is often trumpeted as an example of a tri-trophic interaction of food – hares – predators, when in fact it seems to be a simple predator-prey system, as Lotka suggested in 1925. Models of the hare cycle have proliferated over time, and there are far more models of the cycle in existence than there are long-term field studies or field experiments. It is possible to model the hare cycle as a predator-prey oscillation, as a food plant-hare oscillation, as a parasite-hare interaction, as a cosmic particle – hare oscillation, as an intrinsic social – maternal effects interaction, and I have probably missed some other combinations of delayed-density dependent factors that have been discussed. That one can produce a formal mathematical model of the hare cycle does not mean that the chosen factor is the correct one.

The other point I would leave you with is the large amount of field work needed to sort out the mechanisms driving the population dynamics of hares. Ecology is not simple. This enigma of the ten-year cycle has always been a classic example in ecology and perhaps it is now solved. Or perhaps not?

Boonstra, R., D. Hik, G. R. Singleton, and A. Tinnikov. 1998. The impact of predator-induced stress on the snowshoe hare cycle. Ecological Monographs 68:371-394.

Boutin, S., C. J. Krebs, R. Boonstra, M. R. T. Dale, S. J. Hannon, K. Martin, A. R. E. Sinclair, J. N. M. Smith, R. Turkington, M. Blower, A. Byrom, F. I. Doyle, C. Doyle, D. Hik, L. Hofer, A. Hubbs, T. Karels, D. L. Murray, V. Nams, M. O’Donoghue, C. Rohner, and S. Schweiger. 1995. Population changes of the vertebrate community during a snowshoe hare cycle in Canada’s boreal forest. Oikos 74:69-80.

Keith, L. B., and L. A. Windberg. 1978. A demographic analysis of the snowshoe hare cycle. Wildlife Monographs 58:1-70.

Keith, L. B. 1990. Dynamics of snowshoe hare populations. Current Mammalogy 4:119-195.

Krebs, C. J., S. Boutin, R. Boonstra, A. R. E. Sinclair, J. N. M. Smith, M. R. T. Dale, K. Martin, and R. Turkington. 1995. Impact of food and predation on the snowshoe hare cycle. Science 269:1112-1115.

Krebs, C. J., S. Boutin, and R. Boonstra, editors. 2001. Ecosystem Dynamics of the Boreal Forest: the Kluane Project. Oxford University Press, New York.

Sheriff, M. J., C. J. Krebs, and R. Boonstra. 2009. The sensitive hare: sublethal effects of predator stress on reproduction in snowshoe hares. Journal of Animal Ecology 78:1249-1258.

Yan, C., N. C. Stenseth, C. J. Krebs, and Z. Zhang. 2013. Linking climate change to population cycles of hares and lynx. Global Change Biology 19:3263-3271.

On Journal Referees

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I have been an editor of enough ecological journals to know the problems of referees first hand. The start is to try to find a referee for a particular paper. I would guess now that more than two-thirds of scientists asked to referee a potential paper say they do not have time. This leads to another question of why no one has any time to do anything, but that is a digression. If one is fortunate you get 2 or 3 good referees.

The next problem comes when the reviews of the paper come back. Besides dealing with the timing of return of the reviews, there are four rules which ought to be enforced on all referees. First, review the potential paper as it is. Do not write a review saying this is what you should have written- that is not your job. Second, if the paper is good enough, be positive in making suggestions for improvement. If it is not good enough in your opinion, try to say so politely and suggest alternate journals. Perhaps the authors are aiming for a journal that is too prestigious. Third, do not say in so many words that the author should cite the following 4 papers of mine…. And fourth, do not make ad hominem attacks on the authors. If you do not like people from Texas, this is not the place to take it out on the particular authors who happen to live there.

Given the reviews, the managing editor for the paper ought to make a judgment. Some reviews do not follow the four rules above. A good editor discards these and puts a black mark on the file of that particular reviewer. I would not submit a referee’s review to the authors if it violated any of the above 4 rules. I have known and respected editors who operated this way in the past.

The difficulty now is that ecological journals are overrun. This is driven in part by the desire to maximize the number of papers one publishes in order to get a job, and in part by journals not wanting to publish longer papers. Journals do not either have the funding or the desire to grow in relation to the number of users. This typically means that papers are sent out for reviews with a note attached saying that we have to reject 80% or so of papers regardless of how good they are, a rather depressing order from above. When this level of automatic rejection is reached, the editor in chief has the power to reject any kinds of papers not in favour at the moment. I like models so let’s publish lots of model papers. Or I like data so let’s publish only a few model papers.

One reason journals are overrun is that many of the papers published in our best ecology journals are discussions of what we ought to be doing. They may be well written but they add nothing to the wisdom of our age if they simply repeat what has been in standard textbooks for the last 30 years. In days gone by, many of these papers I think might have been given as a review seminar, possibly at a meeting, but no one would have thought that they were worthy of publication. Clearly the editors of some of our journals think it is more important to talk about what to do rather than to do it.

I think without any empirical data that the quality of reviews of manuscripts has deteriorated as the number of papers published has increased. I often have to translate reviews for young scientists who are devastated by some casual remark in a review. “Forget that nonsense, deal with this point as it is important, ignore this insult to your supervisor, go have a nice glass of red wine and relax, ……”. One learns how to deal with poor reviews.

I have been reading Bertram Murray’s book “What Were They Thinking? Is Population Ecology a Science?” (2011), unfortunately published after he died in 2010. It is a long diatribe about reviews of some of his papers and it would be instructive for any young ecologist to read it. You can appreciate why Murray had trouble with some editors just from the subtitle of his book, “Is Population Ecology a Science?” It illustrates very well that even established ecologists have difficulty dealing with reviews they think are not fair. In defense of Murray, he was able to get many of his papers published, and he cites these in this book. One will not come away from this reading with much respect for ornithology journals.

I think if you can get one good, thoughtful review of your manuscript you should be delighted. And if you are rejected from your favourite journal, try another one. The walls of academia could be papered with letters of rejection for our most eminent ecologists, so you are in the company of good people.

Meanwhile if you are asked to referee a paper, do a good job and try to obey the four rules. Truth and justice do not always win out in any endeavour if you are trying to get a paper published. At least if you are a referee you can try to avoid these issues.

Barto, E. Kathryn, and Matthias C. Rillig. 2012. “Dissemination biases in ecology: effect sizes matter more than quality.” Oikos 121 (2):228-235. doi: 10.1111/j.1600-0706.2011.19401.x.

Ioannidis, John P. A. 2005. “Why most published research findings are false.” PLoS Medicine 2 (8):e124.

Medawar, P.B. 1963. “Is the scientific paper a fraud?” In The Threat and the Glory, edited by P.B. Medawar, 228-233. New York: Harper Collins.

Merrill, E. 2014. “Should we be publishing more null results?” Journal of Wildlife Management 78 (4):569-570. doi: 10.1002/jwmg.715.

Murray, Bertram G., Jr. 2011. What Were They Thinking? Is Population Ecology a Science? Infinity Publishing. 310 pp. ISBN 9780741463937

 

Some Reflections on Evo-Eco

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Some ecologists study evolutionary processes and we call them evolutionary ecologists. They have their own journals and are a thriving field of science. Other ecologists study populations, communities, and ecosystems in ecological time and do not in general concern themselves with evolutionary changes.The question is should they? Evo-Eco is a search for evolutionary changes that have a decisive impact on observable ecological changes like that of a collapsing bird population.

There are two schools of thought. The first is that evo-eco is very important and the changes that ecologists are trying to understand are partly caused by ecological mechanisms like predation and competition but are also associated with genetic changes that affect survival and reproduction. Consequently an ecologist studying the declining bird population should study both genetics and ecology. The second school of thought is that evo-eco is rarely of any importance in causing ecological changes, so that we can more or less ignore genetics if we wish to understand why this bird population is disappearing.

A practical problem immediately rears its head. To be safe we should all follow evo-eco in case genetics is involved in dynamics. But given the number of problems that ecologists face, the number of scientists available to analyse them, and the research dollars available it is rare to have the time, energy or money to take the comprehensive route. Conservation ecologists are perhaps the most tightly squeezed of all ecologists because they have no time to spare. Environmental managers request answers about what to do, and the immediate causes of conservation problems are (as everyone knows) habitat loss, introduced pests and diseases, and pollution.

The consequence of all this is that the two schools of thought drift apart. I cannot foresee any easy way to solve this issue. Progress in evolutionary ecology is often very slow and knowing the past rarely gives us much insight into predicting the human-affected future. Progress in conventional ecology is faster but our understanding is based on short-term studies of unknown generality for future events. Both schools of thought race along with mathematical models that may or may not tell us anything about the real world, but are conceptually elegant and in a pinch might be called progress if we had time to test them adequately.

The most useful evo-eco approach has been to look at human-caused selection via fishing for large sized fish or hunting for Dall sheep with the largest horns. The overuse of antibiotics for human sickness and as prophylactics for our farm animals is another classic case in which to understand the ecological dynamics we need to know the evolutionary changes that we humans have caused. These are clear cases in which genetic insights can teach us very much.

I end with a story from my past. In the 1950s, nearly 70 years ago now, Dennis Chitty working at Oxford on population fluctuations in small grassland rodents considered that he could reject most of the conventional explanations for animal population changes, and he suggested that individuals might change in quality with population density. This change he thought might involve genetic selection for traits that were favourable only in high density populations that reappeared every 3-4 years. So in some strange sense he was one of the earliest evo-eco ecologists. The result was that he was nearly laughed out of Oxford by the geneticists in control. The great evolutionary geneticist E.B. Ford told Chitty he was completely mad to think that short term selection was possible on a scale to impact population dynamics. Genetic changes took dozens to hundreds of years at the best of time. There were of course in the 1950s only the most primitive of genetic methods available for mammals that all look the same in their coat colour, and the idea that changes in animal behaviour involving territoriality might cause genetic shifts on a short-term period gradually lost favour. Few now think that Chitty was right in being evo-eco, but in some sense he was ahead of his time in thinking that natural selection might operate quickly in field populations. Given the many physiological and behavioural changes that can occur phenotypically in mammals, most subsequent work on grassland rodents has become buried in mechanisms that do not change because of genetic selection.

When we try to sort out whether to be concerned about evo-eco, we must strike a compromise between what the exact question is that we are trying to investigate, and how we can best construct a decision tree that can operate in real time with results that are useful for the research question. Not every ecological problem can be solved by sequencing the study organism.

Chitty, D. 1960. Population processes in the vole and their relevance to general theory. Canadian Journal of Zoology 38:99-113.

On Important Questions in Ecology

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There is a most interesting paper that you should read about the important questions in ecology:

Sutherland, W.J. et al. (2013) Identification of 100 fundamental ecological questions. Journal of Ecology, 101, 58-67.

This paper represents the views of 388 ecologists who culled through all of the 754 questions submitted and vetted in a two day workshop in London in April 2012. There are many thesis topics highlighted in this list and it gives a good overview of what many ecologists think is important. But there are some problems with this approach that you might wish to consider after you read this paper.

We can begin with a relatively trivial point. The title indicates that it will discuss ‘fundamental’ questions in ecology but the Summary changes this to ‘important’ questions. To be sure the authors recognize that what we now think is ‘important’ may be judged in the future to be less than important, so in a sense they recognize this problem. ‘Important’ is not an operational word in science, and consequently it is always a focus for endless argument. But let us not get involved with semantics and look at the actual 100 questions.

As I read the paper I was reminded of the discussion in Peters (1991, p. 13) who had the audacity to point out that academic ecologists thrived on unanswerable questions. In particular Peters (1991) focused on ‘why’ questions as being high on the list of unanswerable ones, and it is good to see that there are only 2 questions out of 100 that have a ‘why’ in them. Most of the questions posed are ‘how’ questions (about 65 instances) and ‘what’ questions (about 52 instances).

In framing questions in any science there is a fine line in the continuum of very broad questions that define an agenda and at the other extreme to very specific questions about one species or community. With very broad questions there will never be a clear point at which we can say that we have answered that question so we can move on. With very specific questions we can answer them experimentally and move on. So where do we cut the cake of questions? Most of these 100 questions are very broad and so they both illuminate and frustrate me because they cannot be answered without making them more specific.

Let me go over just one example. Question 11 What are the evolutionary and ecological mechanisms that govern species’ range margins? First, we might note that this question goes back at least 138 years to Alfred Wallace (1876, The Geographical Distribution of Animals), and has been repeated in many ecology textbooks ever since. There are few organisms for which it has been answered and very much speculation about it. At the moment the ecological mechanism in favour is ‘climate’. This is a question that can be answered ecologically only for particular species, and cannot be answered in real (human) time for the evolutionary mechanisms. Consequently it is an area rife for correlational ecology whose conclusions could possibly be tested in a hundred years if not longer. All of these problems should not stand in the way of doing studies on range margins, and there are many hundreds of papers that attest to this conclusion. My question is when will we know that we have answered this question, and my answer is never. We can in some cases use paleoecology to get at these issues, and then extrapolate that the future will be like the past, a most dubious assumption. My concern is that if we have not answered this question in 138 years, what is the hope that we will answer it now?

It is good to be optimistic about the future development of ecological science. Perhaps I have picked a poor example from the list of 100 questions, and my concern is that in this case at least this is not a question that I would suggest to a new PhD student. Still I am glad to have this list set out so clearly and perhaps the next step would be to write a synthesis paper on each of the 100 topics and discuss how much progress has been made on that particular issue, and what exactly we might do to answer the question more rapidly. How can we avoid in ecology what Cox (2007) called a “yawning abyss of vacuous generalities”?

Cox, D. R. (2007) Applied statistics: A review. Annals of Applied Statistics, 1, 1-16.

Peters, R. H. (1991) A Critique for Ecology, Cambridge University Press, Cambridge, England.

Sutherland, W. J., Freckleton, R. P., Godfray, H. C. J., Beissinger, S. R., Benton, T., Cameron, D. D., Carmel, Y., Coomes, D. A., Coulson, T., Emmerson, M. C., Hails, R. S., Hays, G. C., Hodgson, D. J., Hutchings, M. J., Johnson, D., Jones, J. P. G., Keeling, M. J., Kokko, H., Kunin, W. E. & Lambin, X. (2013) Identification of 100 fundamental ecological questions. Journal of Ecology, 101, 58-67.

On House Mouse Outbreaks in Australia

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It occurred to me after some recent discussions that the problem of house mouse outbreaks in Australia is almost a paradigm for modern ecological science. A brief synopsis. At irregular intervals house mice (an introduced pest) reach high densities in the wheat growing areas of eastern and southern Australia, and cause serious damage to wheat, barley, oats, and sunflower crops. There are two approaches to this applied problem.

The ecological approach is to understand why these outbreaks occur and why for many years (2-9 years) between outbreaks, hardly a mouse can be found. This approach has been highly successful led by a series of excellent Australian ecologists over the last 40 years. The key limitation is food, combined with social interactions, and the food supply is driven by rain at critical times of the year to provide seeds for the mice. There are no competitors for house mice, and there are a few insignificant predators, overwhelmed by the mouse’s high reproductive rate. These ecological facts are clearly known, and the job now is to build the best predictive models to help the farmers anticipate when the outbreak is coming. There are still important ecological questions to be studied, to be sure, but the broad outline of the ecological play is well described.

The management approach is much simpler because farmers can control house mice with poison, primarily zinc phosphide, and for them the question is when to poison, and secondarily (over time and with more research) can we develop better poisons so there are few non-target problems. Poisoning costs time and money so good farmers wish to minimize these costs.

The long-term issues get lost in this situation, a model of the way the world operates now with ecological and environmental problems. Questions about sustainability multiply in any system dependent on poisons for a solution. Will the target organisms become resistant so the poison does not work? Many examples exist of this already. Are there any long-term problems with soil organisms, or non-target species? No research yet on these issues, and perhaps they are more serious with herbicide applications in agriculture. And while predators do not control house mice during outbreaks, they do eat many of them and this food pulse may have implications for the wider ecosystem. We focus on farming and forget the wider ecosystem which has no dollars attached to it.

Ecologists recognize that these issues are not the farmers’ fault, but we raise the question of who worries about the long-term future of this system, and the answers to these long-term questions. The government is rushing to get out of long-term ecological and agricultural research and we leave problems that do not have immediacy.

Consequently we become short-sighted as a society. Long-term research becomes 1-3 years and not the 50-100 years that ecologists would support. And consequently applied ecologists bounce from one problem to the next under the paradigm that, no matter what we do, science will come up with a technological fix. There should be a better way. To go back to our house mice, we might ask (for example) if we implement no-till agriculture, what will be the consequences for house mouse survival and future outbreaks? The practical minister of agriculture will respond that we have no time or money for such research, so we lurch along, managing the world in an ad-hoc manner. There should be a better way. But meanwhile we must follow the money.